From e4645be8e24a5bbe2d469f5b32546cbb86f823b4 Mon Sep 17 00:00:00 2001 From: Rui Hirokawa Date: Mon, 13 Oct 2025 20:32:51 +0900 Subject: [PATCH 1/6] - corrected STEC correction in MADOCA-PPP. - added log for cssr_mdc. --- src/cssrlib/cssr_mdc.py | 60 ++++++++++++++++++++++++++++++++++++++--- src/cssrlib/cssrlib.py | 4 +-- src/cssrlib/pppssr.py | 1 + 3 files changed, 59 insertions(+), 6 deletions(-) diff --git a/src/cssrlib/cssr_mdc.py b/src/cssrlib/cssr_mdc.py index 41b92b1..30c1b11 100644 --- a/src/cssrlib/cssr_mdc.py +++ b/src/cssrlib/cssr_mdc.py @@ -10,7 +10,7 @@ import numpy as np import bitstruct as bs from cssrlib.cssrlib import cssr, sCSSRTYPE, sCSSR, local_corr, sCType -from cssrlib.gnss import gpst2time, uGNSS, prn2sat, rCST +from cssrlib.gnss import gpst2time, time2str, uGNSS, prn2sat, rCST, sat2id class areaInfo(): @@ -46,6 +46,8 @@ def __init__(self, foutname=None): self.ci = {} self.area = -1 + self.reg = -1 + self.alrt = 0 self.narea_t = {1: 8, 2: 16, 3: 5, 4: 1, 5: 8} self.MAXNET = np.sum(list(self.narea_t.values())) @@ -108,9 +110,10 @@ def get_stec(self, dlat=0.0, dlon=0.0): # if p.inet_ref != self.iodssr: # return 0.0 stec = np.zeros(p.nsat_n) + v = np.array([1, dlat, dlon, dlat*dlon, dlat**2, dlon**2]) for i, sat in enumerate(p.sat_n): - stec[i] = [1, dlat, dlon, dlat*dlon, dlat**2, dlon**2]@p.ci[sat] + stec[i] = v@p.ci[sat] return stec @@ -121,6 +124,9 @@ def decode_mdc_stec_area(self, buff, i=0): self.tow0 = tow//3600*3600 reg, alrt, len_, narea = bs.unpack_from('u8u1u16u5', buff, i) i += 30 + self.reg = reg + self.alrt = alrt + if reg not in self.pnt: self.pnt[reg] = {} @@ -130,14 +136,14 @@ def decode_mdc_stec_area(self, buff, i=0): if sid == 0: # rectangle shape latr, lonr, lats, lons = bs.unpack_from('s11u12u8u8', buff, i) - if self.monlevel > 2: + if self.monlevel >= 2: print(f"{reg} {area:2d} {sid} {latr*0.1:5.1f} " f"{lonr*0.1:5.1f} {lats*0.1:3.1f} {lons*0.1:3.1f}") self.pnt[reg][area] = areaInfo( sid, latr*0.1, lonr*0.1, lats*0.1, lons*0.1) else: # circle range latr, lonr, rng = bs.unpack_from('s15u16u8', buff, i) - if self.monlevel > 2: + if self.monlevel >= 2: print(f"{reg} {area:2d} {sid} {latr*0.01:6.2f} " f"{lonr*0.01:6.2f} {rng*10}") self.pnt[reg][area] = areaInfo( @@ -168,6 +174,9 @@ def decode_mdc_stec_corr(self, buff, i=0): reg, area, stype_ = bs.unpack_from('u8u5u2', buff, i) i += 15 + self.reg = reg + self.area = area + nsat = bs.unpack_from('u5u5u5u5u5', buff, i) i += 25 # gps, glo, gal, bds, qzss @@ -224,6 +233,48 @@ def decode_mdc_stec_corr(self, buff, i=0): return i + def out_log(self): + # if self.msgtype not in (1, 2): + # return super(cssr_mdc, self).out_log() + + sz_t = [1, 3, 4, 6] + + if self.time == -1: + return + + self.fh.write("{:4d}\t{:s}\n".format(self.msgtype, + time2str(self.time))) + + if self.msgtype == 1: + + self.fh.write(f"Reg\tArea\tsid\t" + f"latr\tlonr\tlats\tlons\n") + + for area in self.pnt[self.reg].keys(): + p = self.pnt[self.reg][area] + self.fh.write(f"{self.reg}\t{area:2d}\t{p.sid}\t" + f"{p.latr:3.1f}\t{p.lonr:4.1f}\t") + + if p.sid == 0: + self.fh.write(f"{p.lats:3.1f}\t{p.lons:3.1f}\n") + else: + self.fh.write(f"{p.rng:3.1f}\n") + + elif self.msgtype == 2: + inet = self.get_inet(self.reg, self.area) + + self.fh.write(f"Reg:{self.reg}\tArea:{self.area:2d}\n") + self.fh.write("Sat,stype,c00,c01,c10,c11,c20,c02\n") + + for sat in self.lc[inet].sat_n: + stype = self.lc[inet].stype[sat] + ci = self.lc[inet].ci[sat] + self.fh.write(f"{sat2id(sat)}\t{stype}") + for k in range(sz_t[stype]): + self.fh.write(f"\t{ci[k]:6.2f}") + self.fh.write("\n") + self.fh.flush() + def decode_cssr(self, msg, i=0): """decode Compact SSR message with MADOCA-PPP extension """ df = {'msgtype': 4073} @@ -233,6 +284,7 @@ def decode_cssr(self, msg, i=0): if df['msgtype'] not in [1, 2, 4073]: return -1 + self.msgtype = df['msgtype'] self.subtype = df['subtype'] if df['msgtype'] == 4073: # Compact SSR if self.subtype == sCSSR.MASK: diff --git a/src/cssrlib/cssrlib.py b/src/cssrlib/cssrlib.py index c49e538..dc1e4e6 100644 --- a/src/cssrlib/cssrlib.py +++ b/src/cssrlib/cssrlib.py @@ -282,7 +282,7 @@ def __init__(self, foutname=None): self.sid = -1 self.cstat = 0 self.local_pbias = True # for QZS CLAS - self.buff = bytearray(250*5) + self.buff = bytearray(250*10) self.buff_p = None self.sinfo = bytearray(160) self.grid = None @@ -1316,7 +1316,7 @@ def decode_l6msg(self, msg, ofst): if l6head['sid'] == 1: self.fcnt = 0 self.buff_p = copy.copy(self.buff) - self.buff = bytearray(250*5) + self.buff = bytearray(250*10) if l6head['vendor'] == 5: # CLAS facility = facility_t[l6head['pt']][l6head['facility']] diff --git a/src/cssrlib/pppssr.py b/src/cssrlib/pppssr.py index 12ac42c..4be1f81 100644 --- a/src/cssrlib/pppssr.py +++ b/src/cssrlib/pppssr.py @@ -497,6 +497,7 @@ def zdres(self, obs, cs, bsx, rs, vs, dts, rr, rtype=1): if self.nav.trop_opt == 2 or self.nav.iono_opt == 2: # from cssr inet = cs.find_grid_index(pos) dlat, dlon = cs.get_dpos(pos) + cs.inet = inet else: inet = -1 From d93a94d61d744d36b3dbbfb5fd57ed7ee141e9ee Mon Sep 17 00:00:00 2001 From: Rui Hirokawa Date: Mon, 13 Oct 2025 23:58:41 +0900 Subject: [PATCH 2/6] - fixed jisx0402, timestamp. --- src/cssrlib/ewss.py | 60 +++++++++++++++++++++++++-------------------- 1 file changed, 33 insertions(+), 27 deletions(-) diff --git a/src/cssrlib/ewss.py b/src/cssrlib/ewss.py index 44bd917..d914a99 100644 --- a/src/cssrlib/ewss.py +++ b/src/cssrlib/ewss.py @@ -5,10 +5,10 @@ Specification Issue 1.0, 2024 [2] Quasi-Zenith Satellite System Interface Specification - DCX Service (IS-QZSS-DCX-001), 2024 + DCX Service (IS-QZSS-DCX-003), March, 2025 [3] Quasi-Zenith Satellite System Interface Specification - DC Report Service (IS-QZSS-DCR-013), 2024 + DC Report Service (IS-QZSS-DCR-014), April, 2025 @author Rui Hirokawa @@ -16,7 +16,7 @@ import bitstruct as bs from cssrlib.gnss import time2str, epoch2time, time2epoch, timeadd, \ - time2gpst, gpst2time + time2gpst, gpst2time, utc2gpst from enum import IntEnum import json import numpy as np @@ -691,9 +691,14 @@ def decode_jma_marine(self, msg, i): def decode(self, msg, i): """ decode DC-report messages """ + self.rc, self.dc = bs.unpack_from('u3u4', msg, i) i += 7 + if self.dc not in self.dc_m_t.keys() or \ + self.rc not in self.rc_m_t.keys(): + return -1 + if self.monlevel > 0: print(f"[DCR] {self.rc_m_t[self.rc]} {self.dc_m_t[self.dc]}") @@ -739,7 +744,7 @@ def __init__(self, bdir='../data/ewss/camf/', year=0): self.pref_t = json.load(fh) # JIS X0402 - self.mc_t = pd.read_csv(bdir+'000323625.csv', encoding='sjis') + self.mc_t = pd.read_csv(bdir+'000323625.csv', encoding='utf-8') self.bdir = bdir self.city_t = None @@ -1070,10 +1075,6 @@ def decode_ext(self, msg, i): # for Japan if self.pid == 1: # L-Alert - if self.city_t is None: - with open(self.bdir+'City_list.json', 'r', - encoding='utf-8') as fh: - self.city_t = json.load(fh) # EX1 target area code # EX2 Evacuate Direction Type @@ -1092,18 +1093,26 @@ def decode_ext(self, msg, i): ex5, ex6, ex7, vn = bs.unpack_from('u5u5u7u6', msg, i) i += 23 - pref_code = '{:02d}'.format(ex1//1000) - city_code = '{:03d}'.format(ex1 % 1000) + v = self.mc_t[self.mc_t['tiiki-code'] == ex1] if ex1 == 0: return i - name = '' - for city in self.city_t['cities']: - if city['pref_code'] == pref_code and \ - city['city_code'] == city_code: - name = city['name'] - break + if len(v) > 0: + pref = v['ken-name'].item() + + if v['sityouson-name1'].isna().item(): + if v['sityouson-name2'].isna().item(): + if v['sityouson-name3'].isna().item(): + city = None + else: + city = v['sityouson-name3'].item() + else: + city = v['sityouson-name2'].item() + else: + city = v['sityouson-name1'].item() + + name = pref + city if len(name) == 0: return i @@ -1205,28 +1214,25 @@ def decode(self, msg, i): self.severity = self.severity_t[sev] # 3.3 Hazard Chronology (beginning of hazard) + wn, tow, dur = bs.unpack_from('u1u14u2', msg, i) i += 17 # wn: 0:current week, 1: next week # tow: 1:mon 00:00, 2:mon 00:01, 3:mon 00:02 # .. sun 23:59 - dow_ = (tow-1) // 1440 - min_ = (tow-1) % 1440 - hour_ = min_ // 60 - min_ -= hour_*60 - - week, tow = time2gpst(self.time) + week, _ = time2gpst(self.time) week += wn - tow_ = dow_*86400+hour_*3600+min_*60 - # harzrd onset (UTC) - self.th = gpst2time(week, tow_) + tow = (tow-1)*60+86400 # sec from sun + if tow >= 604800: + tow -= 604800 + week += 1 + # harzrd onset (UTC) + self.th = gpst2time(week, tow) self.epoch = time2epoch(self.th) - # self.epoch = [wn, dow_, hour_, min_] - # duration (A8) : 0:unknown,1:<6h,2:6h<=d<12h,3:12h<=d<24h self.duration = self.duration_t[dur] From 9c56c7c6163884a91d5708ab08c7b1521a6416a2 Mon Sep 17 00:00:00 2001 From: h-shiono Date: Wed, 15 Oct 2025 13:57:54 +0900 Subject: [PATCH 3/6] support .gz files in decode_nav, decode_obsh --- src/cssrlib/rinex.py | 1009 +++++++++++++++++----------------- src/cssrlib/test/test_eph.py | 105 ++-- src/cssrlib/test/test_rnx.py | 88 +-- 3 files changed, 636 insertions(+), 566 deletions(-) diff --git a/src/cssrlib/rinex.py b/src/cssrlib/rinex.py index 4fe90cb..0e1f72f 100644 --- a/src/cssrlib/rinex.py +++ b/src/cssrlib/rinex.py @@ -6,11 +6,12 @@ """ import numpy as np +from pathlib import Path from cssrlib.gnss import uGNSS, uTYP, rSigRnx from cssrlib.gnss import bdt2gpst, time2bdt from cssrlib.gnss import gpst2time, bdt2time, epoch2time, timediff, gtime_t from cssrlib.gnss import prn2sat, char2sys, timeget, utc2gpst, time2epoch -from cssrlib.gnss import Eph, Geph, Obs, sat2id, sat2prn, gpst2bdt, time2gpst +from cssrlib.gnss import Eph, Geph, Obs, Nav, sat2id, sat2prn, gpst2bdt, time2gpst from cssrlib.gnss import timeadd, id2sat, gpst2utc, Seph, STOParam, EOPParam from cssrlib.gnss import IONParam @@ -150,7 +151,19 @@ def decode_time(self, s, ofst=0, slen=2): t = epoch2time([year, month, day, hour, minute, sec]) return t - def decode_nav(self, navfile, nav, append=False): + def decode_nav(self, navfile: str, nav: Nav, append: bool = False) -> Nav: + """Wrapper for decode_nav with Path support""" + + navfile: Path = Path(navfile) + if navfile.suffix.lower() in ['.gz', '.z']: + import gzip + with gzip.open(navfile, 'rt') as fnav: + return self._decode_nav(fnav, nav, append) + else: + with open(navfile, 'rt') as fnav: + return self._decode_nav(fnav, nav, append) + + def _decode_nav(self, fnav, nav, append=False): """ Decode RINEX Navigation message from file @@ -163,573 +176,573 @@ def decode_nav(self, navfile, nav, append=False): nav.geph = [] nav.seph = [] - with open(navfile, 'rt') as fnav: - for line in fnav: - if line[60:73] == 'END OF HEADER': - break - elif line[60:80] == 'RINEX VERSION / TYPE': - self.ver = float(line[4:10]) - if self.ver < 3.02: - return -1 - elif line[60:76] == 'IONOSPHERIC CORR': - if line[0:4] == 'GPSA' or line[0:4] == 'QZSA': - for k in range(4): - nav.ion[0, k] = self.flt(line[5+k*12:5+(k+1)*12]) - if line[0:4] == 'GPSB' or line[0:4] == 'QZSB': - for k in range(4): - nav.ion[1, k] = self.flt(line[5+k*12:5+(k+1)*12]) - elif line[60:72] == 'LEAP SECONDS': - nav.leaps = int(line[:6]) - for line in fnav: + for line in fnav: + if line[60:73] == 'END OF HEADER': + break + elif line[60:80] == 'RINEX VERSION / TYPE': + self.ver = float(line[4:10]) + if self.ver < 3.02: + return -1 + elif line[60:76] == 'IONOSPHERIC CORR': + if line[0:4] == 'GPSA' or line[0:4] == 'QZSA': + for k in range(4): + nav.ion[0, k] = self.flt(line[5+k*12:5+(k+1)*12]) + if line[0:4] == 'GPSB' or line[0:4] == 'QZSB': + for k in range(4): + nav.ion[1, k] = self.flt(line[5+k*12:5+(k+1)*12]) + elif line[60:72] == 'LEAP SECONDS': + nav.leaps = int(line[:6]) - if self.ver >= 4.0: + for line in fnav: - if line[0:5] == '> STO': # system time offset (TBD) + if self.ver >= 4.0: - sys = char2sys(line[6]) - itype = line[10:14] + if line[0:5] == '> STO': # system time offset (TBD) - if sys not in nav.sto_prm: - nav.sto_prm[sys] = {} + sys = char2sys(line[6]) + itype = line[10:14] - if itype not in self.itype_t: - fnav.readline() - fnav.readline() - continue + if sys not in nav.sto_prm: + nav.sto_prm[sys] = {} - im = self.itype_t[itype] + if itype not in self.itype_t: + fnav.readline() + fnav.readline() + continue - if im not in nav.sto_prm[sys]: - nav.sto_prm[sys][im] = STOParam() + im = self.itype_t[itype] + + if im not in nav.sto_prm[sys]: + nav.sto_prm[sys][im] = STOParam() + + line = fnav.readline() + nav.sto_prm[sys][im].t_ot = self.decode_time(line, 4) + mode = line[24:28] + if mode[0:2] in self.ofst_src and \ + mode[2:4] in self.ofst_src: + nav.sto_prm[sys][im].prm[0] = \ + self.ofst_src[mode[0:2]] + nav.sto_prm[sys][im].prm[1] = \ + self.ofst_src[mode[2:4]] + + line = fnav.readline() + nav.sto_prm[sys][im].t_t = self.flt(line, 0) + for k in range(3): + nav.sto_prm[sys][im].a[k] = self.flt(line, k+1) + continue - line = fnav.readline() - nav.sto_prm[sys][im].t_ot = self.decode_time(line, 4) - mode = line[24:28] - if mode[0:2] in self.ofst_src and \ - mode[2:4] in self.ofst_src: - nav.sto_prm[sys][im].prm[0] = \ - self.ofst_src[mode[0:2]] - nav.sto_prm[sys][im].prm[1] = \ - self.ofst_src[mode[2:4]] + elif line[0:5] == '> EOP': # earth orientation param + sys = char2sys(line[6]) + itype = line[10:14] - line = fnav.readline() - nav.sto_prm[sys][im].t_t = self.flt(line, 0) - for k in range(3): - nav.sto_prm[sys][im].a[k] = self.flt(line, k+1) - continue + if sys not in nav.eop_prm: + nav.eop_prm[sys] = {} - elif line[0:5] == '> EOP': # earth orientation param - sys = char2sys(line[6]) - itype = line[10:14] + if itype not in self.itype_t: + fnav.readline() + fnav.readline() + fnav.readline() + continue - if sys not in nav.eop_prm: - nav.eop_prm[sys] = {} + im = self.itype_t[itype] + + if im not in nav.eop_prm[sys]: + nav.eop_prm[sys][im] = EOPParam() + + line = fnav.readline() + nav.eop_prm[sys][im].t_eop = self.decode_time(line, 4) + for k in range(3): + nav.eop_prm[sys][im].prm[k] = self.flt(line, k+1) + line = fnav.readline() + for k in range(3): + nav.eop_prm[sys][im].prm[k+3] = self.flt(line, k+1) + line = fnav.readline() + nav.eop_prm[sys][im].t_t = self.flt(line, 0) + for k in range(3): + nav.eop_prm[sys][im].prm[k+6] = self.flt(line, k+1) + continue - if itype not in self.itype_t: - fnav.readline() - fnav.readline() - fnav.readline() - continue + elif line[0:5] == '> ION': # iono (TBD) + sys = char2sys(line[6]) + itype = line[10:14] + stype = '' if len(line) < 20 else line[15:19] - im = self.itype_t[itype] + if sys not in nav.ion_prm: + nav.ion_prm[sys] = {} - if im not in nav.eop_prm[sys]: - nav.eop_prm[sys][im] = EOPParam() + im = self.itype_t[itype] + nav.ion_prm[sys][im] = IONParam() + line = fnav.readline() + nav.ion_prm[sys][im].t_tm = self.decode_time(line, 4) + if sys == uGNSS.GAL and itype == 'IFNV': # Nequick-G + for k in range(3): # ai0, ai1, ai2 + nav.ion_prm[sys][im].prm[k] = \ + self.flt(line, k+1) line = fnav.readline() - nav.eop_prm[sys][im].t_eop = self.decode_time(line, 4) - for k in range(3): - nav.eop_prm[sys][im].prm[k] = self.flt(line, k+1) - line = fnav.readline() + # disturbance flags + nav.ion_prm[sys][im].prm[3] = \ + int(self.flt(line, 0)) + elif sys == uGNSS.BDS and itype == 'CNVX': # BDGIM for k in range(3): - nav.eop_prm[sys][im].prm[k+3] = self.flt(line, k+1) + nav.ion_prm[sys][im].prm[k] = \ + self.flt(line, k+1) line = fnav.readline() - nav.eop_prm[sys][im].t_t = self.flt(line, 0) - for k in range(3): - nav.eop_prm[sys][im].prm[k+6] = self.flt(line, k+1) - continue - - elif line[0:5] == '> ION': # iono (TBD) - sys = char2sys(line[6]) - itype = line[10:14] - stype = '' if len(line) < 20 else line[15:19] - - if sys not in nav.ion_prm: - nav.ion_prm[sys] = {} - - im = self.itype_t[itype] - nav.ion_prm[sys][im] = IONParam() - + for k in range(4): + nav.ion_prm[sys][im].prm[k+3] = \ + self.flt(line, k) line = fnav.readline() - nav.ion_prm[sys][im].t_tm = self.decode_time(line, 4) - if sys == uGNSS.GAL and itype == 'IFNV': # Nequick-G - for k in range(3): # ai0, ai1, ai2 - nav.ion_prm[sys][im].prm[k] = \ - self.flt(line, k+1) + for k in range(2): + nav.ion_prm[sys][im].prm[k+7] = \ + self.flt(line, k) + elif sys == uGNSS.IRN and itype == 'L1NV': # L1NAV + if stype == 'KLOB': # + iodk = self.flt(line, 1) line = fnav.readline() - # disturbance flags - nav.ion_prm[sys][im].prm[3] = \ - int(self.flt(line, 0)) - elif sys == uGNSS.BDS and itype == 'CNVX': # BDGIM - for k in range(3): + for k in range(4): nav.ion_prm[sys][im].prm[k] = \ - self.flt(line, k+1) + self.flt(line, k) line = fnav.readline() for k in range(4): - nav.ion_prm[sys][im].prm[k+3] = \ + nav.ion_prm[sys][im].prm[k+4] = \ self.flt(line, k) + nav.ion_prm[sys][im].iod = iodk line = fnav.readline() - for k in range(2): - nav.ion_prm[sys][im].prm[k+7] = \ + nav.ion_prm[sys][im].region = np.zeros(4) + for k in range(4): + nav.ion_prm[sys][im].region[k] = \ self.flt(line, k) - elif sys == uGNSS.IRN and itype == 'L1NV': # L1NAV - if stype == 'KLOB': # - iodk = self.flt(line, 1) + elif stype == 'NEQN': + nav.ion_prm[sys][im].iod = self.flt(line, 1) + prm = np.zeros((3, 8)) + for j in range(3): line = fnav.readline() - for k in range(4): - nav.ion_prm[sys][im].prm[k] = \ - self.flt(line, k) - line = fnav.readline() - for k in range(4): - nav.ion_prm[sys][im].prm[k+4] = \ - self.flt(line, k) - nav.ion_prm[sys][im].iod = iodk + for k in range(4): # a0, a1, a2, idf + prm[j, k] = self.flt(line, k) line = fnav.readline() - nav.ion_prm[sys][im].region = np.zeros(4) + # lon_min, lon_max, mopid_min, mopid_max for k in range(4): - nav.ion_prm[sys][im].region[k] = \ - self.flt(line, k) - elif stype == 'NEQN': - nav.ion_prm[sys][im].iod = self.flt(line, 1) - prm = np.zeros((3, 8)) - for j in range(3): - line = fnav.readline() - for k in range(4): # a0, a1, a2, idf - prm[j, k] = self.flt(line, k) - line = fnav.readline() - # lon_min, lon_max, mopid_min, mopid_max - for k in range(4): - prm[j, k+4] = self.flt(line, k) - nav.ion_prm[sys][im].prm = prm - - elif sys == uGNSS.GLO and itype == 'LXOC': - c_A = self.flt(line, 1) - c_F10_7 = self.flt(line, 2) - c_Ap = self.flt(line, 3) - nav.ion_prm[sys][im].prm[0:3] = \ - [c_A, c_F10_7, c_Ap] - - else: # Klobuchar (LNAV, D1D2, CNVX) - nav.ion_prm[sys][im].prm = np.zeros(9) - - for k in range(3): - nav.ion_prm[sys][im].prm[k] = \ - self.flt(line, k+1) - line = fnav.readline() - for k in range(4): - nav.ion_prm[sys][im].prm[k+3] = \ - self.flt(line, k) - line = fnav.readline() - nav.ion_prm[sys][im].prm[7] = self.flt(line, 0) - if len(line) >= 42: - nav.ion_prm[sys][im].prm[8] = \ - int(self.flt(line, 1)) - continue + prm[j, k+4] = self.flt(line, k) + nav.ion_prm[sys][im].prm = prm + + elif sys == uGNSS.GLO and itype == 'LXOC': + c_A = self.flt(line, 1) + c_F10_7 = self.flt(line, 2) + c_Ap = self.flt(line, 3) + nav.ion_prm[sys][im].prm[0:3] = \ + [c_A, c_F10_7, c_Ap] - elif line[0:5] == '> EPH': - sys = char2sys(line[6]) - self.mode_nav = 0 # LNAV, D1/D2, INAV - m = line[10:14] - if m == 'CNAV' or m == 'CNV1' or m == 'FNAV': - self.mode_nav = 1 - elif m == 'CNV2' or m == 'L1NV': - self.mode_nav = 2 - elif m == 'CNV3': - self.mode_nav = 3 - elif m == 'FDMA': - self.mode_nav = 0 - elif m == 'L1OC': - self.mode_nav = 1 - elif m == 'L3OC': - self.mode_nav = 3 - elif m == 'SBAS': - self.mode_nav = 0 + else: # Klobuchar (LNAV, D1D2, CNVX) + nav.ion_prm[sys][im].prm = np.zeros(9) + + for k in range(3): + nav.ion_prm[sys][im].prm[k] = \ + self.flt(line, k+1) line = fnav.readline() + for k in range(4): + nav.ion_prm[sys][im].prm[k+3] = \ + self.flt(line, k) + line = fnav.readline() + nav.ion_prm[sys][im].prm[7] = self.flt(line, 0) + if len(line) >= 42: + nav.ion_prm[sys][im].prm[8] = \ + int(self.flt(line, 1)) + continue + + elif line[0:5] == '> EPH': + sys = char2sys(line[6]) + self.mode_nav = 0 # LNAV, D1/D2, INAV + m = line[10:14] + if m == 'CNAV' or m == 'CNV1' or m == 'FNAV': + self.mode_nav = 1 + elif m == 'CNV2' or m == 'L1NV': + self.mode_nav = 2 + elif m == 'CNV3': + self.mode_nav = 3 + elif m == 'FDMA': + self.mode_nav = 0 + elif m == 'L1OC': + self.mode_nav = 1 + elif m == 'L3OC': + self.mode_nav = 3 + elif m == 'SBAS': + self.mode_nav = 0 + line = fnav.readline() + + elif self.ver >= 3.0: # RINEX 3.0.x + self.mode_nav = 0 + + # Process ephemeris information + # + sys = char2sys(line[0]) - elif self.ver >= 3.0: # RINEX 3.0.x - self.mode_nav = 0 + # Skip undesired constellations + # + if sys == uGNSS.GLO: + prn = int(line[1:3]) + sat = prn2sat(sys, prn) + geph = Geph(sat) + + pos = np.zeros(3) + vel = np.zeros(3) + acc = np.zeros(3) + + geph.mode = self.mode_nav + toc = self.decode_time(line, 4) + week, tocs = time2gpst(toc) + toc = gpst2time(week, + np.floor((tocs+450.0)/900.0)*900.0) + dow = int(tocs//86400.0) + + geph.taun = -self.flt(line, 1) + geph.gamn = self.flt(line, 2) + if self.mode_nav == 0: # FDMA + t0 = self.flt(line, 3) + else: # L1OC, L3OC + bet_ = self.flt(line, 3) # clock drift rate - # Process ephemeris information - # - sys = char2sys(line[0]) + line = fnav.readline() # line #1 + pos[0] = self.flt(line, 0)*1e3 + vel[0] = self.flt(line, 1)*1e3 + acc[0] = self.flt(line, 2)*1e3 + geph.svh = int(self.flt(line, 3)) - # Skip undesired constellations + line = fnav.readline() # line #2 + pos[1] = self.flt(line, 0)*1e3 + vel[1] = self.flt(line, 1)*1e3 + acc[1] = self.flt(line, 2)*1e3 + + if self.mode_nav == 0: # FDMA + geph.frq = int(self.flt(line, 3)) + + if geph.frq > 128: + geph.frq -= 256 + else: # L1OC + dvalid = int(self.flt(line, 3)) + + line = fnav.readline() # line #3 + pos[2] = self.flt(line, 0)*1e3 + vel[2] = self.flt(line, 1)*1e3 + acc[2] = self.flt(line, 2)*1e3 + + geph.pos = pos + geph.vel = vel + geph.acc = acc + + if self.mode_nav == 0: # FDMA + geph.age = int(self.flt(line, 3)) + elif self.mode_nav == 1: # L1OC + tgd_L2OCp = self.flt(line, 3) # tgd_L2OCp + elif self.mode_nav == 3: # L3OC + isc_L3OCp = self.flt(line, 3) # isc_L3OCp + + # Use GLONASS line #4 only from RINEX v3.05 onwards # - if sys == uGNSS.GLO: - prn = int(line[1:3]) - sat = prn2sat(sys, prn) - geph = Geph(sat) - - pos = np.zeros(3) - vel = np.zeros(3) - acc = np.zeros(3) - - geph.mode = self.mode_nav - toc = self.decode_time(line, 4) - week, tocs = time2gpst(toc) - toc = gpst2time(week, - np.floor((tocs+450.0)/900.0)*900.0) - dow = int(tocs//86400.0) - - geph.taun = -self.flt(line, 1) - geph.gamn = self.flt(line, 2) + if self.ver >= 3.05: + + line = fnav.readline() # line #4 + if self.mode_nav == 0: # FDMA - t0 = self.flt(line, 3) - else: # L1OC, L3OC - bet_ = self.flt(line, 3) # clock drift rate + # b7-8: M, b6: P4, b5: P3, b4: P2, b2-3: P1, b0-1: P + geph.status = int(self.flt(line, 0)) + geph.dtaun = -self.flt(line, 1) + geph.urai = int(self.flt(line, 2)) + if len(line) >= 80: + geph.svh = int(self.flt(line, 3)) + else: # L1OC,L3OC + sattype = int(self.flt(line, 0)) + src = int(self.flt(line, 1)) + aode_ee = int(self.flt(line, 2)) + aode_et = int(self.flt(line, 3)) + + line = fnav.readline() # line #5 + P2 = int(self.flt(line, 0)) # attitude flag + t0 = self.flt(line, 1) # sec of day, UTC(SU) + tau1 = self.flt(line, 2) + tau2 = self.flt(line, 3) + + line = fnav.readline() # line #6 + yaw = self.flt(line, 0) + sgn = int(self.flt(line, 1)) + win = self.flt(line, 2) + dw = self.flt(line, 3) + + line = fnav.readline() # line #7 + wmax = self.flt(line, 0) + dxpc = self.flt(line, 1) + dypc = self.flt(line, 2) + dzpc = self.flt(line, 3) - line = fnav.readline() # line #1 - pos[0] = self.flt(line, 0)*1e3 - vel[0] = self.flt(line, 1)*1e3 - acc[0] = self.flt(line, 2)*1e3 - geph.svh = int(self.flt(line, 3)) + line = fnav.readline() # line #8 + urai_orb = int(self.flt(line, 0)) + urai_clk = int(self.flt(line, 1)) + tot = self.flt(line, 2) - line = fnav.readline() # line #2 - pos[1] = self.flt(line, 0)*1e3 - vel[1] = self.flt(line, 1)*1e3 - acc[1] = self.flt(line, 2)*1e3 + tod = t0 % 86400.0 + tof = gpst2time(week, tod + dow*86400.0) + tof = self.adjday(tof, toc) - if self.mode_nav == 0: # FDMA - geph.frq = int(self.flt(line, 3)) + geph.toe = utc2gpst(toc) + geph.tof = utc2gpst(tof) - if geph.frq > 128: - geph.frq -= 256 - else: # L1OC - dvalid = int(self.flt(line, 3)) + # iode = Tb(7bit) + geph.iode = int(((tocs+10800.0) % 86400)/900.0+0.5) - line = fnav.readline() # line #3 - pos[2] = self.flt(line, 0)*1e3 - vel[2] = self.flt(line, 1)*1e3 - acc[2] = self.flt(line, 2)*1e3 + nav.geph.append(geph) + continue - geph.pos = pos - geph.vel = vel - geph.acc = acc + elif sys == uGNSS.SBS: + prn = int(line[1:3])+100 + sat = prn2sat(sys, prn) + seph = Seph(sat) - if self.mode_nav == 0: # FDMA - geph.age = int(self.flt(line, 3)) - elif self.mode_nav == 1: # L1OC - tgd_L2OCp = self.flt(line, 3) # tgd_L2OCp - elif self.mode_nav == 3: # L3OC - isc_L3OCp = self.flt(line, 3) # isc_L3OCp + pos = np.zeros(3) + vel = np.zeros(3) + acc = np.zeros(3) - # Use GLONASS line #4 only from RINEX v3.05 onwards - # - if self.ver >= 3.05: - - line = fnav.readline() # line #4 - - if self.mode_nav == 0: # FDMA - # b7-8: M, b6: P4, b5: P3, b4: P2, b2-3: P1, b0-1: P - geph.status = int(self.flt(line, 0)) - geph.dtaun = -self.flt(line, 1) - geph.urai = int(self.flt(line, 2)) - if len(line) >= 80: - geph.svh = int(self.flt(line, 3)) - else: # L1OC,L3OC - sattype = int(self.flt(line, 0)) - src = int(self.flt(line, 1)) - aode_ee = int(self.flt(line, 2)) - aode_et = int(self.flt(line, 3)) - - line = fnav.readline() # line #5 - P2 = int(self.flt(line, 0)) # attitude flag - t0 = self.flt(line, 1) # sec of day, UTC(SU) - tau1 = self.flt(line, 2) - tau2 = self.flt(line, 3) - - line = fnav.readline() # line #6 - yaw = self.flt(line, 0) - sgn = int(self.flt(line, 1)) - win = self.flt(line, 2) - dw = self.flt(line, 3) - - line = fnav.readline() # line #7 - wmax = self.flt(line, 0) - dxpc = self.flt(line, 1) - dypc = self.flt(line, 2) - dzpc = self.flt(line, 3) - - line = fnav.readline() # line #8 - urai_orb = int(self.flt(line, 0)) - urai_clk = int(self.flt(line, 1)) - tot = self.flt(line, 2) - - tod = t0 % 86400.0 - tof = gpst2time(week, tod + dow*86400.0) - tof = self.adjday(tof, toc) - - geph.toe = utc2gpst(toc) - geph.tof = utc2gpst(tof) - - # iode = Tb(7bit) - geph.iode = int(((tocs+10800.0) % 86400)/900.0+0.5) - - nav.geph.append(geph) - continue + seph.toc = self.decode_time(line, 4) + seph.af0 = self.flt(line, 1) + seph.af1 = self.flt(line, 2) + seph.tot = self.flt(line, 3) - elif sys == uGNSS.SBS: - prn = int(line[1:3])+100 - sat = prn2sat(sys, prn) - seph = Seph(sat) - - pos = np.zeros(3) - vel = np.zeros(3) - acc = np.zeros(3) - - seph.toc = self.decode_time(line, 4) - seph.af0 = self.flt(line, 1) - seph.af1 = self.flt(line, 2) - seph.tot = self.flt(line, 3) - - line = fnav.readline() # line #1 - pos[0] = self.flt(line, 0)*1e3 - vel[0] = self.flt(line, 1)*1e3 - acc[0] = self.flt(line, 2)*1e3 - seph.svh = int(self.flt(line, 3)) - - line = fnav.readline() # line #2 - pos[1] = self.flt(line, 0)*1e3 - vel[1] = self.flt(line, 1)*1e3 - acc[1] = self.flt(line, 2)*1e3 - seph.sva = self.flt(line, 3) - - line = fnav.readline() # line #3 - pos[2] = self.flt(line, 0)*1e3 - vel[2] = self.flt(line, 1)*1e3 - acc[2] = self.flt(line, 2)*1e3 - seph.iodn = int(self.flt(line, 3)) - - seph.pos = pos - seph.vel = vel - seph.acc = acc - - nav.seph.append(seph) - continue + line = fnav.readline() # line #1 + pos[0] = self.flt(line, 0)*1e3 + vel[0] = self.flt(line, 1)*1e3 + acc[0] = self.flt(line, 2)*1e3 + seph.svh = int(self.flt(line, 3)) - elif sys not in (uGNSS.GPS, uGNSS.GAL, uGNSS.QZS, uGNSS.BDS, - uGNSS.IRN): - continue + line = fnav.readline() # line #2 + pos[1] = self.flt(line, 0)*1e3 + vel[1] = self.flt(line, 1)*1e3 + acc[1] = self.flt(line, 2)*1e3 + seph.sva = self.flt(line, 3) - prn = int(line[1:3]) - if sys == uGNSS.QZS: - prn += 192 - sat = prn2sat(sys, prn) - eph = Eph(sat) + line = fnav.readline() # line #3 + pos[2] = self.flt(line, 0)*1e3 + vel[2] = self.flt(line, 1)*1e3 + acc[2] = self.flt(line, 2)*1e3 + seph.iodn = int(self.flt(line, 3)) - eph.urai = np.zeros(4, dtype=int) - eph.sisai = np.zeros(4, dtype=int) - eph.isc = np.zeros(6) + seph.pos = pos + seph.vel = vel + seph.acc = acc - eph.mode = self.mode_nav + nav.seph.append(seph) + continue - eph.toc = self.decode_time(line, 4) - eph.af0 = self.flt(line, 1) - eph.af1 = self.flt(line, 2) - eph.af2 = self.flt(line, 3) + elif sys not in (uGNSS.GPS, uGNSS.GAL, uGNSS.QZS, uGNSS.BDS, + uGNSS.IRN): + continue - line = fnav.readline() # line #1 + prn = int(line[1:3]) + if sys == uGNSS.QZS: + prn += 192 + sat = prn2sat(sys, prn) + eph = Eph(sat) - if sys == uGNSS.GAL or \ - (sys == uGNSS.IRN and self.mode_nav == 0): - eph.iode = int(self.flt(line, 0)) - eph.iodc = eph.iode - else: - if self.mode_nav > 0: - eph.Adot = self.flt(line, 0) - else: - eph.iode = int(self.flt(line, 0)) + eph.urai = np.zeros(4, dtype=int) + eph.sisai = np.zeros(4, dtype=int) + eph.isc = np.zeros(6) - eph.crs = self.flt(line, 1) - eph.deln = self.flt(line, 2) - eph.M0 = self.flt(line, 3) + eph.mode = self.mode_nav - line = fnav.readline() # line #2 - eph.cuc = self.flt(line, 0) - eph.e = self.flt(line, 1) - eph.cus = self.flt(line, 2) - sqrtA = self.flt(line, 3) - eph.A = sqrtA**2 + eph.toc = self.decode_time(line, 4) + eph.af0 = self.flt(line, 1) + eph.af1 = self.flt(line, 2) + eph.af2 = self.flt(line, 3) - line = fnav.readline() # line #3 - if sys == uGNSS.IRN and self.mode_nav == 2: + line = fnav.readline() # line #1 + + if sys == uGNSS.GAL or \ + (sys == uGNSS.IRN and self.mode_nav == 0): + eph.iode = int(self.flt(line, 0)) + eph.iodc = eph.iode + else: + if self.mode_nav > 0: + eph.Adot = self.flt(line, 0) + else: eph.iode = int(self.flt(line, 0)) - eph.iode = eph.iodc + + eph.crs = self.flt(line, 1) + eph.deln = self.flt(line, 2) + eph.M0 = self.flt(line, 3) + + line = fnav.readline() # line #2 + eph.cuc = self.flt(line, 0) + eph.e = self.flt(line, 1) + eph.cus = self.flt(line, 2) + sqrtA = self.flt(line, 3) + eph.A = sqrtA**2 + + line = fnav.readline() # line #3 + if sys == uGNSS.IRN and self.mode_nav == 2: + eph.iode = int(self.flt(line, 0)) + eph.iode = eph.iodc + else: + if (sys == uGNSS.GPS or sys == uGNSS.QZS) and \ + self.mode_nav > 0: # CNAV, CNAV/2 + eph.tops = self.flt(line, 0) + eph.week, eph.toes = time2gpst(eph.toc) else: - if (sys == uGNSS.GPS or sys == uGNSS.QZS) and \ - self.mode_nav > 0: # CNAV, CNAV/2 - eph.tops = self.flt(line, 0) - eph.week, eph.toes = time2gpst(eph.toc) - else: - eph.toes = self.flt(line, 0) - eph.cic = self.flt(line, 1) - eph.OMG0 = self.flt(line, 2) - eph.cis = self.flt(line, 3) + eph.toes = self.flt(line, 0) + eph.cic = self.flt(line, 1) + eph.OMG0 = self.flt(line, 2) + eph.cis = self.flt(line, 3) - line = fnav.readline() # line #4 - eph.i0 = self.flt(line, 0) - eph.crc = self.flt(line, 1) - eph.omg = self.flt(line, 2) - eph.OMGd = self.flt(line, 3) + line = fnav.readline() # line #4 + eph.i0 = self.flt(line, 0) + eph.crc = self.flt(line, 1) + eph.omg = self.flt(line, 2) + eph.OMGd = self.flt(line, 3) - line = fnav.readline() # line #5 - eph.idot = self.flt(line, 0) + line = fnav.readline() # line #5 + eph.idot = self.flt(line, 0) - if sys == uGNSS.GAL or self.mode_nav == 0: - eph.code = int(self.flt(line, 1)) # source for GAL - eph.week = int(self.flt(line, 2)) + if sys == uGNSS.GAL or self.mode_nav == 0: + eph.code = int(self.flt(line, 1)) # source for GAL + eph.week = int(self.flt(line, 2)) - if sys == uGNSS.GAL and self.ver < 4.0: - eph.mode = 1 if eph.code & 0x2 else 0 + if sys == uGNSS.GAL and self.ver < 4.0: + eph.mode = 1 if eph.code & 0x2 else 0 - elif sys == uGNSS.IRN and self.mode_nav == 0: - eph.week = int(self.flt(line, 2)) + elif sys == uGNSS.IRN and self.mode_nav == 0: + eph.week = int(self.flt(line, 2)) + else: + eph.delnd = self.flt(line, 1) + if sys == uGNSS.BDS: + eph.sattype = int(self.flt(line, 2)) + eph.tops = int(self.flt(line, 3)) + elif sys == uGNSS.IRN and self.mode_nav == 2: + eph.integ = int(self.flt(line, 3)) # rsf + else: # CNAV, CNAV/2 + eph.urai = [0, 0, 0, 0] + eph.urai[0] = int(self.flt(line, 2)) + eph.urai[1] = int(self.flt(line, 3)) + + line = fnav.readline() # line #6 + if sys == uGNSS.BDS and self.mode_nav > 0: + eph.sisai[0] = int(self.flt(line, 0)) # oe + eph.sisai[1] = int(self.flt(line, 1)) # ocb + eph.sisai[2] = int(self.flt(line, 2)) # oc1 + eph.sisai[3] = int(self.flt(line, 3)) # oc2 + elif sys == uGNSS.IRN: + if self.mode_nav == 0: + eph.sva = self.flt(line, 0) + else: # L1NV + eph.urai = self.flt(line, 0) + eph.svh = int(self.flt(line, 1)) + if self.mode_nav == 2 and eph.integ == 1: + eph.tgd = int(self.flt(line, 3)) else: - eph.delnd = self.flt(line, 1) - if sys == uGNSS.BDS: - eph.sattype = int(self.flt(line, 2)) - eph.tops = int(self.flt(line, 3)) - elif sys == uGNSS.IRN and self.mode_nav == 2: - eph.integ = int(self.flt(line, 3)) # rsf - else: # CNAV, CNAV/2 - eph.urai = [0, 0, 0, 0] - eph.urai[0] = int(self.flt(line, 2)) - eph.urai[1] = int(self.flt(line, 3)) - - line = fnav.readline() # line #6 - if sys == uGNSS.BDS and self.mode_nav > 0: - eph.sisai[0] = int(self.flt(line, 0)) # oe - eph.sisai[1] = int(self.flt(line, 1)) # ocb - eph.sisai[2] = int(self.flt(line, 2)) # oc1 - eph.sisai[3] = int(self.flt(line, 3)) # oc2 - elif sys == uGNSS.IRN: + eph.tgd = int(self.flt(line, 2)) + else: + eph.sva = int(self.flt(line, 0)) + eph.svh = int(self.flt(line, 1)) + eph.tgd = float(self.flt(line, 2)) + if sys == uGNSS.GPS or sys == uGNSS.QZS: if self.mode_nav == 0: - eph.sva = self.flt(line, 0) - else: # L1NV - eph.urai = self.flt(line, 0) - eph.svh = int(self.flt(line, 1)) - if self.mode_nav == 2 and eph.integ == 1: - eph.tgd = int(self.flt(line, 3)) + eph.iodc = int(self.flt(line, 3)) else: - eph.tgd = int(self.flt(line, 2)) - else: - eph.sva = int(self.flt(line, 0)) - eph.svh = int(self.flt(line, 1)) - eph.tgd = float(self.flt(line, 2)) - if sys == uGNSS.GPS or sys == uGNSS.QZS: - if self.mode_nav == 0: - eph.iodc = int(self.flt(line, 3)) - else: - eph.urai[2] = int(self.flt(line, 3)) # URAI_NED2 - eph.urai[3] = eph.sva # URAI_ED - elif sys == uGNSS.GAL: - tgd_b = float(self.flt(line, 3)) - if (eph.code >> 9) & 1: # E5b,E1 - eph.tgd_b = eph.tgd - eph.tgd = tgd_b - else: # E5a,E1 - eph.tgd_b = tgd_b - elif sys == uGNSS.BDS: - eph.tgd_b = float(self.flt(line, 3)) # tgd2 B2/B3 - - if sys == uGNSS.QZS: - eph.code = eph.svh & 0x11 # L1C/A:0x01 or L1C/B:0x10 - eph.svh = eph.svh & 0xEE # mask L1C/A, L1C/B health - - if self.mode_nav < 3: - line = fnav.readline() # line #7 - if sys == uGNSS.BDS: - if self.mode_nav == 0: # D1/D2 - tot = self.flt(line, 0) - eph.iodc = int(self.flt(line, 1)) - else: # CNAV-1,2,3 - if self.mode_nav == 1: # CNAV-1 - eph.isc[0] = float(self.flt(line, 0)) # B1Cd - elif self.mode_nav == 2: # CNAV-2 - eph.isc[1] = float(self.flt(line, 1)) # B2ad - - eph.tgd = float(self.flt(line, 2)) # tgd_B1Cp - eph.tgd_b = float(self.flt(line, 3)) # tgd_B2ap - - elif sys == uGNSS.IRN: - if self.mode_nav > 0: - if eph.integ == 0: # rsf - eph.isc[5] = float(self.flt(line, 0)) # S - eph.isc[4] = float(self.flt(line, 1)) # L1D - else: - eph.isc[5] = float(self.flt(line, 2)) # L1P - eph.isc[4] = float(self.flt(line, 3)) # L1D - - line = fnav.readline() # line #8 + eph.urai[2] = int(self.flt(line, 3)) # URAI_NED2 + eph.urai[3] = eph.sva # URAI_ED + elif sys == uGNSS.GAL: + tgd_b = float(self.flt(line, 3)) + if (eph.code >> 9) & 1: # E5b,E1 + eph.tgd_b = eph.tgd + eph.tgd = tgd_b + else: # E5a,E1 + eph.tgd_b = tgd_b + elif sys == uGNSS.BDS: + eph.tgd_b = float(self.flt(line, 3)) # tgd2 B2/B3 - tot = self.flt(line, 0) + if sys == uGNSS.QZS: + eph.code = eph.svh & 0x11 # L1C/A:0x01 or L1C/B:0x10 + eph.svh = eph.svh & 0xEE # mask L1C/A, L1C/B health - elif sys == uGNSS.GAL: + if self.mode_nav < 3: + line = fnav.readline() # line #7 + if sys == uGNSS.BDS: + if self.mode_nav == 0: # D1/D2 tot = self.flt(line, 0) + eph.iodc = int(self.flt(line, 1)) + else: # CNAV-1,2,3 + if self.mode_nav == 1: # CNAV-1 + eph.isc[0] = float(self.flt(line, 0)) # B1Cd + elif self.mode_nav == 2: # CNAV-2 + eph.isc[1] = float(self.flt(line, 1)) # B2ad - elif sys in (uGNSS.GPS, uGNSS.QZS): - if self.mode_nav > 0: # CNAV, CNAV/2 - eph.isc[0] = self.flt(line, 0) # ISC_L1CA - eph.isc[1] = self.flt(line, 1) # ISC_L2C - eph.isc[2] = self.flt(line, 2) # ISC_L5I5 - eph.isc[3] = self.flt(line, 3) # ISC_L5Q5 - else: # LNAV - tot = self.flt(line, 0) - if len(line) >= 42: - eph.fit = int(self.flt(line, 1)) - - if sys in (uGNSS.GPS, uGNSS.QZS): - if self.mode_nav > 0: # CNAV, CNAV/2 - line = fnav.readline() # line #8 - if self.mode_nav == 2: # CNAV/2 - eph.isc[4] = self.flt(line, 0) # ISC_L1Cd - eph.isc[5] = self.flt(line, 1) # ISC_L1Cp + eph.tgd = float(self.flt(line, 2)) # tgd_B1Cp + eph.tgd_b = float(self.flt(line, 3)) # tgd_B2ap - line = fnav.readline() # line #9 + elif sys == uGNSS.IRN: + if self.mode_nav > 0: + if eph.integ == 0: # rsf + eph.isc[5] = float(self.flt(line, 0)) # S + eph.isc[4] = float(self.flt(line, 1)) # L1D + else: + eph.isc[5] = float(self.flt(line, 2)) # L1P + eph.isc[4] = float(self.flt(line, 3)) # L1D - tot = int(self.flt(line, 0)) - eph.wn_op = int(self.flt(line, 1)) - if len(line) >= 61: # optional - eph.integ = int(self.flt(line, 2)) + line = fnav.readline() # line #8 - elif sys == uGNSS.BDS and self.mode_nav > 0: # CNAV-1,2,3 - line = fnav.readline() # line #8 - eph.sismai = int(self.flt(line, 0)) - eph.svh = int(self.flt(line, 1)) - eph.integ = int(self.flt(line, 2)) - if self.mode_nav < 3: # CNAV-1,2 - eph.iodc = int(self.flt(line, 3)) - else: # CNAV-3 - eph.tgd_b = float(self.flt(line, 3)) # tgd_B2bI + tot = self.flt(line, 0) - line = fnav.readline() # line #9 + elif sys == uGNSS.GAL: tot = self.flt(line, 0) - if self.mode_nav < 3: # CNAV-1,2 - eph.iode = int(self.flt(line, 3)) - if sys == uGNSS.BDS: - if self.mode_nav > 0: - eph.week, _ = time2bdt(eph.toc) - eph.toc = bdt2gpst(eph.toc) - eph.toe = bdt2gpst(bdt2time(eph.week, eph.toes)) - eph.tot = bdt2gpst(bdt2time(eph.week, tot)) - else: - eph.toe = gpst2time(eph.week, eph.toes) - eph.tot = gpst2time(eph.week, tot) + elif sys in (uGNSS.GPS, uGNSS.QZS): + if self.mode_nav > 0: # CNAV, CNAV/2 + eph.isc[0] = self.flt(line, 0) # ISC_L1CA + eph.isc[1] = self.flt(line, 1) # ISC_L2C + eph.isc[2] = self.flt(line, 2) # ISC_L5I5 + eph.isc[3] = self.flt(line, 3) # ISC_L5Q5 + else: # LNAV + tot = self.flt(line, 0) + if len(line) >= 42: + eph.fit = int(self.flt(line, 1)) + + if sys in (uGNSS.GPS, uGNSS.QZS): + if self.mode_nav > 0: # CNAV, CNAV/2 + line = fnav.readline() # line #8 + if self.mode_nav == 2: # CNAV/2 + eph.isc[4] = self.flt(line, 0) # ISC_L1Cd + eph.isc[5] = self.flt(line, 1) # ISC_L1Cp + + line = fnav.readline() # line #9 + + tot = int(self.flt(line, 0)) + eph.wn_op = int(self.flt(line, 1)) + if len(line) >= 61: # optional + eph.integ = int(self.flt(line, 2)) + + elif sys == uGNSS.BDS and self.mode_nav > 0: # CNAV-1,2,3 + line = fnav.readline() # line #8 + eph.sismai = int(self.flt(line, 0)) + eph.svh = int(self.flt(line, 1)) + eph.integ = int(self.flt(line, 2)) + if self.mode_nav < 3: # CNAV-1,2 + eph.iodc = int(self.flt(line, 3)) + else: # CNAV-3 + eph.tgd_b = float(self.flt(line, 3)) # tgd_B2bI + + line = fnav.readline() # line #9 + tot = self.flt(line, 0) + if self.mode_nav < 3: # CNAV-1,2 + eph.iode = int(self.flt(line, 3)) + + if sys == uGNSS.BDS: + if self.mode_nav > 0: + eph.week, _ = time2bdt(eph.toc) + eph.toc = bdt2gpst(eph.toc) + eph.toe = bdt2gpst(bdt2time(eph.week, eph.toes)) + eph.tot = bdt2gpst(bdt2time(eph.week, tot)) + else: + eph.toe = gpst2time(eph.week, eph.toes) + eph.tot = gpst2time(eph.week, tot) - nav.eph.append(eph) + nav.eph.append(eph) return nav @@ -784,10 +797,20 @@ def decode_clk(self, clkfile, nav): return nav + def decode_obsh(self, obsfile: str) -> int: + """Wrapper of decode RINEX Observation header from file""" + + obsfile: Path = Path(obsfile) + if obsfile.suffix.lower() in ['.gz', '.z']: + import gzip + self.fobs = gzip.open(obsfile, 'rt', encoding='utf-8', errors='ignore') + else: + self.fobs = open(obsfile, 'rt') + return self._decode_obsh() + # TODO: decode GLONASS FCN lines - def decode_obsh(self, obsfile): + def _decode_obsh(self): """ decode RINEX Observation header from file """ - self.fobs = open(obsfile, 'rt') for line in self.fobs: if line[60:73] == 'END OF HEADER': break diff --git a/src/cssrlib/test/test_eph.py b/src/cssrlib/test/test_eph.py index 40fbacc..74a0200 100644 --- a/src/cssrlib/test/test_eph.py +++ b/src/cssrlib/test/test_eph.py @@ -6,45 +6,70 @@ timeadd, ecef2pos from cssrlib.ephemeris import findeph, eph2pos -navfile = '../data/30340780.21q' -nav = Nav() -dec = rnxdec() -nav = dec.decode_nav(navfile, nav) - -n = 24*3600//300 -t0 = epoch2time([2021, 3, 19, 0, 0, 0]) - -flg_plot = True - -if True: - t = t0 - sat = prn2sat(uGNSS.QZS, 194) - eph = findeph(nav.eph, t, sat) - rs, vs, dts = eph2pos(t, eph, True) - -if flg_plot: - lon0 = 135 - plt.figure(figsize=(6, 6)) - ax = plt.axes(projection=ccrs.Orthographic(central_longitude=lon0, - central_latitude=0)) - ax.coastlines(resolution='50m') - ax.gridlines() - ax.stock_img() - pos = np.zeros((n, 3)) - - for k in range(uGNSS.MAXSAT): - sat = k+1 - sys, prn = sat2prn(sat) - if sys != uGNSS.QZS: - continue - for i in range(n): - t = timeadd(t0, i*300) - if eph is None: + +def test_eph(navfile: str) -> None: + nav = Nav() + dec = rnxdec() + nav = dec.decode_nav(navfile, nav) + + n = 24*3600//300 + t0 = epoch2time([2021, 3, 19, 0, 0, 0]) + + flg_plot = True + + if True: + t = t0 + sat = prn2sat(uGNSS.QZS, 194) + eph = findeph(nav.eph, t, sat) + rs, vs, dts = eph2pos(t, eph, True) + + if flg_plot: + lon0 = 135 + plt.figure(figsize=(6, 6)) + ax = plt.axes(projection=ccrs.Orthographic(central_longitude=lon0, + central_latitude=0)) + ax.coastlines(resolution='50m') + ax.gridlines() + ax.stock_img() + pos = np.zeros((n, 3)) + + for k in range(uGNSS.MAXSAT): + sat = k+1 + sys, prn = sat2prn(sat) + if sys != uGNSS.QZS: continue - rs, dts = eph2pos(t, eph) - pos[i, :] = ecef2pos(rs) - pos[i, 0] = np.rad2deg(pos[i, 0]) - pos[i, 1] = np.rad2deg(pos[i, 1]) + for i in range(n): + t = timeadd(t0, i*300) + if eph is None: + continue + rs, dts = eph2pos(t, eph) + pos[i, :] = ecef2pos(rs) + pos[i, 0] = np.rad2deg(pos[i, 0]) + pos[i, 1] = np.rad2deg(pos[i, 1]) + + plt.plot(pos[:, 1], pos[:, 0], 'm-', transform=ccrs.Geodetic()) + plt.show() + + +if __name__ == "__main__": + # Uncompressed RINEX navigation file + print("Test with uncompressed RINEX navigation file") + navfile = '../data/30340780.21q' + test_eph(navfile) + + # Test with compressed RINEX navigation file + print("Test with compressed RINEX navigation file") + + # Compress navfile with gzip beforehand + navfile_ga = navfile + '.gz' + import gzip + with open(navfile, 'rb') as f_in: + with gzip.open(navfile_ga, 'wb') as f_out: + f_out.writelines(f_in) + + # Run test + test_eph(navfile_ga) - plt.plot(pos[:, 1], pos[:, 0], 'm-', transform=ccrs.Geodetic()) - plt.show() + # Clean up + import os + os.remove(navfile_ga) \ No newline at end of file diff --git a/src/cssrlib/test/test_rnx.py b/src/cssrlib/test/test_rnx.py index 2a46c01..8cbb732 100644 --- a/src/cssrlib/test/test_rnx.py +++ b/src/cssrlib/test/test_rnx.py @@ -8,52 +8,74 @@ from cssrlib.gnss import uTYP, rSigRnx from cssrlib.gnss import sat2id, sat2prn -obsfile = '../data/SEPT078M1.21O' +def test_rnx(obsfile: str) -> None: + sigs = [rSigRnx("GC1C"), rSigRnx("GC2W"), + rSigRnx("GL1C"), rSigRnx("GL2W"), + rSigRnx("GS1C"), rSigRnx("GS2W"), + rSigRnx("EC1X"), rSigRnx("EC5X"), + rSigRnx("EL1X"), rSigRnx("EL5X"), + rSigRnx("ES1X"), rSigRnx("ES5X"), + rSigRnx("JC1C"), rSigRnx("JC2S"), + rSigRnx("JL1C"), rSigRnx("JL2S"), + rSigRnx("JS1C"), rSigRnx("JS2S")] -sigs = [rSigRnx("GC1C"), rSigRnx("GC2W"), - rSigRnx("GL1C"), rSigRnx("GL2W"), - rSigRnx("GS1C"), rSigRnx("GS2W"), - rSigRnx("EC1X"), rSigRnx("EC5X"), - rSigRnx("EL1X"), rSigRnx("EL5X"), - rSigRnx("ES1X"), rSigRnx("ES5X"), - rSigRnx("JC1C"), rSigRnx("JC2S"), - rSigRnx("JL1C"), rSigRnx("JL2S"), - rSigRnx("JS1C"), rSigRnx("JS2S")] + dec = rnxdec() + dec.setSignals(sigs) -dec = rnxdec() -dec.setSignals(sigs) + nep = 2 + if dec.decode_obsh(obsfile) >= 0: -nep = 2 -if dec.decode_obsh(obsfile) >= 0: + dec.autoSubstituteSignals() - dec.autoSubstituteSignals() + for ne in range(nep): - for ne in range(nep): + obs = dec.decode_obs() - obs = dec.decode_obs() + print("{:%Y-%m-%d %T}".format(datetime.utcfromtimestamp(obs.t.time))) - print("{:%Y-%m-%d %T}".format(datetime.utcfromtimestamp(obs.t.time))) + for i, sat in enumerate(obs.sat): - for i, sat in enumerate(obs.sat): + txt = "{} ".format(sat2id(sat)) - txt = "{} ".format(sat2id(sat)) + sys, _ = sat2prn(sat) + sigs = obs.sig[sys][uTYP.C] + for j, sig in enumerate(sigs): + txt += "{} {:13.3f} ".format(sig.str(), obs.P[i, j]) - sys, _ = sat2prn(sat) - sigs = obs.sig[sys][uTYP.C] - for j, sig in enumerate(sigs): - txt += "{} {:13.3f} ".format(sig.str(), obs.P[i, j]) + sigs = obs.sig[sys][uTYP.L] + for j, sig in enumerate(sigs): + txt += "{} {:13.3f} ".format(sig.str(), obs.L[i, j]) - sigs = obs.sig[sys][uTYP.L] - for j, sig in enumerate(sigs): - txt += "{} {:13.3f} ".format(sig.str(), obs.L[i, j]) + sigs = obs.sig[sys][uTYP.S] + for j, sig in enumerate(sigs): + txt += "{} {:7.3f} ".format(sig.str(), obs.S[i, j]) - sigs = obs.sig[sys][uTYP.S] - for j, sig in enumerate(sigs): - txt += "{} {:7.3f} ".format(sig.str(), obs.S[i, j]) + print(txt) - print(txt) + print() - print() + dec.fobs.close() - dec.fobs.close() +if __name__ == "__main__": + # Uncompressed RINEX observation file + print("Test with uncompressed RINEX observation file") + obsfile = '../data/SEPT078M1.21O' + test_rnx(obsfile) + + # Test with compressed RINEX observation file + print("Test with compressed RINEX observation file") + + # Compress obsfile with gzip beforehand + obsfile_gz = obsfile + '.gz' + import gzip + with open(obsfile, 'rb') as f_in: + with gzip.open(obsfile_gz, 'wb') as f_out: + f_out.writelines(f_in) + + # Run test + test_rnx(obsfile_gz) + + # Clean up + import os + os.remove(obsfile_gz) From e11896df3b582f7b33193ba46edd1568507ea52b Mon Sep 17 00:00:00 2001 From: Rui Hirokawa Date: Thu, 16 Oct 2025 23:16:39 +0900 Subject: [PATCH 4/6] - updated variables. --- src/cssrlib/ewss.py | 279 +++++++++++++++++++++++++++++++++++--------- 1 file changed, 224 insertions(+), 55 deletions(-) diff --git a/src/cssrlib/ewss.py b/src/cssrlib/ewss.py index d914a99..5e9e65f 100644 --- a/src/cssrlib/ewss.py +++ b/src/cssrlib/ewss.py @@ -16,7 +16,7 @@ import bitstruct as bs from cssrlib.gnss import time2str, epoch2time, time2epoch, timeadd, \ - time2gpst, gpst2time, utc2gpst + time2gpst, gpst2time, utc2gpst, gtime_t from enum import IntEnum import json import numpy as np @@ -180,6 +180,7 @@ def __init__(self, bdir='../data/ewss/', year=0): self.msg_path = bdir self.monlevel = 0 self.year = year + self.time = gtime_t() def decode(self, msg, i): None @@ -252,6 +253,10 @@ def __init__(self, bdir='../data/ewss/jma/', year=0): self.lv_t = self.load_msg('tab4_1_2_44.txt') self.reg_fl_t = self.load_msg('tab4_1_2_45.txt') + # Typhoon + self.sr_t = self.load_msg('tab4_1_2_48.txt') + self.ic_t = self.load_msg('tab4_1_2_49.txt') + # for Marine self.dw_m_t = self.load_msg('tab4_1_2_52.txt') self.reg_m_t = self.load_msg('tab4_1_2_53.txt') @@ -259,13 +264,16 @@ def __init__(self, bdir='../data/ewss/jma/', year=0): self.pidx = 0 self.msg = bytearray(512) - self.vn = -1 + self.te = gtime_t() + self.pos = np.array([]) + self.params = None + self.vn = -1 # version def decode_jma_earthquake(self, msg, i): """ JMA Earthquake """ month, day, hour, minute = bs.unpack_from('u4u5u5u6', msg, i) i += 20 - itype, _ = bs.unpack_from('u2u4', msg, i) + self.it, _ = bs.unpack_from('u2u4', msg, i) i += 6 lgL1, lgU1 = bs.unpack_from('u3u3', msg, i) @@ -306,7 +314,7 @@ def decode_jma_earthquake(self, msg, i): i += 12 if self.monlevel > 0: - print(f"Earthquake ({self.itype_t[itype]}) " + + print(f"Earthquake ({self.itype_t[self.it]}) " + f"{month:2d}/{day:02d} {hour:2d}:{minute:02d}") for k in range(3): @@ -323,13 +331,23 @@ def decode_jma_earthquake(self, msg, i): s_ += self.pre_t[reg]+" " print(s_) + ep = time2epoch(self.time) + self.year = ep[0] + self.month = ep[1] + self.ta = epoch2time([self.year, month, day, hour, minute, 0.0]) + self.tb = epoch2time([self.year, month, d1, h1, m1, 0.0]) + + self.params = [] + self.params.append(reg_) + self.params.append([de, ma, ep, L1, U1]) + return i def decode_jma_hypocenter(self, msg, i): """ JMA Epicenter """ month, day, hour, minute = bs.unpack_from('u4u5u5u6', msg, i) i += 20 - itype, _ = bs.unpack_from('u2u10', msg, i) + self.it, _ = bs.unpack_from('u2u10', msg, i) i += 12 co_ = [] @@ -361,7 +379,7 @@ def decode_jma_hypocenter(self, msg, i): i += 12 if self.monlevel > 0: - print(f"Epicenter ({self.itype_t[itype]})" + + print(f"Epicenter ({self.itype_t[self.it]})" + f" {month:2d}/{day:02d} {hour:2d}:{minute:02d}") for k in range(3): @@ -372,13 +390,24 @@ def decode_jma_hypocenter(self, msg, i): print(f"depth={de}km mag={ma:.1f}") print(f"epicenter:{self.ep_t[ep]} lat {lat:.4f} lon {lon:.4f}") + ep = time2epoch(self.time) + self.year = ep[0] + self.month = ep[1] + self.ta = epoch2time([self.year, month, day, hour, minute, 0.0]) + + self.pos = [lat, lon] + + self.params = [] + self.params.append(co_) + self.params.append([de, ma, ep]) + return i def decode_jma_seismic_intencity(self, msg, i): """ JMA Seismic Intensity """ month, day, hour, minute = bs.unpack_from('u4u5u5u6', msg, i) i += 20 - itype, _ = bs.unpack_from('u2u10', msg, i) + self.it, _ = bs.unpack_from('u2u10', msg, i) i += 12 d1, h1, m1 = bs.unpack_from('u5u5u6', msg, i) @@ -400,20 +429,30 @@ def decode_jma_seismic_intencity(self, msg, i): i += 12 if self.monlevel > 0: - print(f"Intensity ({self.itype_t[itype]})" + + print(f"Intensity ({self.itype_t[self.it]})" + f" {month:2d}/{day:02d} {hour:2d}:{minute:02d}") print(f"occurance time: {month}/{d1} {h1:2d}:{m1:02d}") for k, reg in enumerate(reg_): print(f"{self.mag_i_t[es_[k]]} {self.pre_i_t[reg]}") + ep = time2epoch(self.time) + self.year = ep[0] + self.month = ep[1] + self.ta = epoch2time([self.year, month, day, hour, minute, 0.0]) + self.tb = epoch2time([self.year, month, d1, h1, m1, 0.0]) + + self.params = [] + self.params.append(es_) + self.params.append(reg_) + return i def decode_jma_nankai_earthquake(self, msg, i): """ JMA Nankai Earthquake """ month, day, hour, minute = bs.unpack_from('u4u5u5u6', msg, i) i += 20 - itype, _, is_ = bs.unpack_from('u2u10u4', msg, i) + self.it, _, is_ = bs.unpack_from('u2u10u4', msg, i) i += 16 txt = bytearray(18) @@ -431,27 +470,40 @@ def decode_jma_nankai_earthquake(self, msg, i): i += 12 if self.monlevel > 0: - print(f"Nankai Trough Earthquake ({self.itype_t[itype]}) " + + print(f"Nankai Trough Earthquake ({self.itype_t[self.it]}) " + f"{self.isc_t[is_]} " + f"{month:2d}/{day:02d} {hour:2d}:{minute:02d} {pn}/{pm}") if self.pidx == (1 << pm)-1: print(self.msg.decode('utf-8')) + + ep = time2epoch(self.time) + self.year = ep[0] + self.month = ep[1] + self.ta = epoch2time([self.year, month, day, hour, minute, 0.0]) + + self.params = [] + self.params.append(txt) + self.params.append([pn, pm]) + return i def decode_jma_tsunami(self, msg, i): """ JMA Tsunami """ month, day, hour, minute = bs.unpack_from('u4u5u5u6', msg, i) i += 20 - itype, _ = bs.unpack_from('u2u10', msg, i) + self.it, _ = bs.unpack_from('u2u10', msg, i) i += 12 if self.monlevel > 0: - print(f"Tsunami ({self.itype_t[itype]}) " + + print(f"Tsunami ({self.itype_t[self.it]}) " + f"{month:2d}/{day:02d} {hour:2d}:{minute:02d}") + co_ = [] + for k in range(3): co = bs.unpack_from('u9', msg, i)[0] i += 9 + co_.append(co) if self.monlevel > 0: if co > 0 and co in self.co_t: @@ -464,6 +516,16 @@ def decode_jma_tsunami(self, msg, i): if dw > 0 and dw in self.dw_t: print(f"{self.dw_w_t[dw]}") + ep = time2epoch(self.time) + self.year = ep[0] + self.month = ep[1] + self.ta = epoch2time([self.year, month, day, hour, minute, 0.0]) + + self.tb = [] + self.params = [] + self.params.append(co_) + self.params.append([dw]) + for k in range(5): d, h, m = bs.unpack_from('u1u5u6', msg, i) i += 12 @@ -473,44 +535,61 @@ def decode_jma_tsunami(self, msg, i): if d == 0 and h == 0 and m == 0 and th == 0 and reg == 0: continue + tb = epoch2time([self.year, month, d, h, m, 0.0]) + self.tb.append(tb) + + self.params.append([th, reg]) + if self.monlevel > 0: print(f"{d} {h:2d}:{m:02d} " + f"{self.th_w_t[th]} {self.reg_w_t[reg]}") self.vn = bs.unpack_from('u6', msg, i)[0] i += 12 - return i def decode_jma_nw_pacific_tsunami(self, msg, i): """ JMA North Pacific Tsunami """ month, day, hour, minute = bs.unpack_from('u4u5u5u6', msg, i) i += 20 - itype, _, tp = bs.unpack_from('u2u10u3', msg, i) + self.it, _, tp = bs.unpack_from('u2u10u3', msg, i) i += 15 + self.tb = [] + self.params = [] + self.params.append(tp) + for k in range(5): d, h, m = bs.unpack_from('u1u5u6', msg, i) i += 12 th, reg = bs.unpack_from('u9u7', msg, i) i += 16 + tb = epoch2time([self.year, month, d, h, m, 0.0]) + self.tb.append(tb) + self.params.append([th, reg]) + i += 18 self.vn = bs.unpack_from('u6', msg, i)[0] i += 12 if self.monlevel > 0: print( - f"NP Tsunami ({itype}) {month:2d}/{day:02d} " + + f"NP Tsunami ({self.it}) {month:2d}/{day:02d} " + f"{hour:2d}:{minute:02d}") + ep = time2epoch(self.time) + self.year = ep[0] + self.month = ep[1] + self.ta = epoch2time([self.year, month, day, hour, minute, 0.0]) + return i def decode_jma_volcano(self, msg, i): """ JMA Volcano """ month, day, hour, minute = bs.unpack_from('u4u5u5u6', msg, i) i += 20 - itype, _, du = bs.unpack_from('u2u7u3', msg, i) + self.it, _, du = bs.unpack_from('u2u7u3', msg, i) i += 12 d1, h1, m1 = bs.unpack_from('u5u5u6', msg, i) @@ -529,7 +608,7 @@ def decode_jma_volcano(self, msg, i): i += 12 if self.monlevel > 0: - print(f"Volcano ({self.itype_t[itype]}) " + + print(f"Volcano ({self.itype_t[self.it]}) " + f"{month:2d}/{day:02d} {hour:2d}:{minute:02d}") print(f"du={du} activity time: {month}/{d1} {h1:2d}:{m1:02d}") print(f"{self.dw_v_t[dw]} {self.vo_t[vo]}") @@ -537,13 +616,22 @@ def decode_jma_volcano(self, msg, i): for reg in reg_: print(self.reg_v_t[reg]) + ep = time2epoch(self.time) + self.year = ep[0] + self.month = ep[1] + self.ta = epoch2time([self.year, month, day, hour, minute, 0.0]) + self.tb = epoch2time([self.year, month, d1, h1, m1, 0.0]) + + self.params = [du, dw, vo] + self.params.append(reg_) + return i def decode_jma_ash_fall(self, msg, i): """ JMA Ash Fall """ month, day, hour, minute = bs.unpack_from('u4u5u5u6', msg, i) i += 20 - itype = bs.unpack_from('u2', msg, i)[0] + self.it = bs.unpack_from('u2', msg, i)[0] i += 12 d1, h1, m1, dw1, vo = bs.unpack_from('u5u5u6u2u12', msg, i) @@ -555,19 +643,27 @@ def decode_jma_ash_fall(self, msg, i): 2: "Ash Fall Forecast (Detailed)"} if self.monlevel > 0: - print(f"Ash Fall ({self.itype_t[itype]}) " + + print(f"Ash Fall ({self.itype_t[self.it]}) " + f"{month:2d}/{day:02d} {hour:2d}:{minute:02d}") print(f" {month}/{d1} {h1:2d}:{m1:02d} {self.dw1_t[dw1]} {vo_}") + self.params = [dw1, vo] for k in range(4): ho, dw2, reg = bs.unpack_from('u3u3u23', msg, i) i += 29 if reg > 0: + self.params.append([ho, dw2, reg]) reg_ = self.reg_v_t[reg] dw_ = self.dw_t[dw2] if self.monlevel > 0: print(f"{ho}h {dw_} {reg_}") + ep = time2epoch(self.time) + self.year = ep[0] + self.month = ep[1] + self.ta = epoch2time([self.year, month, day, hour, minute, 0.0]) + self.tb = epoch2time([self.year, month, d1, h1, m1, 0.0]) + i += 15 self.vn = bs.unpack_from('u6', msg, i)[0] i += 12 @@ -578,24 +674,32 @@ def decode_jma_weather(self, msg, i): """ JMA Weather """ month, day, hour, minute = bs.unpack_from('u4u5u5u6', msg, i) i += 20 - itype, _, ar = bs.unpack_from('u2u10u3', msg, i) + # ar: warning state + self.it, _, ar = bs.unpack_from('u2u10u3', msg, i) i += 15 - self.ar_t = {1: "issue", 2: "cansellation"} if self.monlevel > 0: - print(f"Weather ({self.itype_t[itype]}) " + + print(f"Weather ({self.itype_t[self.it]}) " + f"{month:2d}/{day:02d} {hour:2d}:{minute:02d} " + f"{self.ar_t[ar]}") + self.params = [ar] for k in range(6): ww, pl = bs.unpack_from('u5u19', msg, i) i += 24 if ww == 0 or pl == 0: continue + + self.params.append([ww, pl]) if self.monlevel > 0: print(f"{self.ww_t[ww]} {self.reg_ww_t[pl]}") + ep = time2epoch(self.time) + self.year = ep[0] + self.month = ep[1] + self.ta = epoch2time([self.year, month, day, hour, minute, 0.0]) + i += 14 # spare 2 self.vn = bs.unpack_from('u6', msg, i)[0] i += 12 @@ -605,36 +709,55 @@ def decode_jma_flood(self, msg, i): """ JMA Flood """ month, day, hour, minute = bs.unpack_from('u4u5u5u6', msg, i) i += 20 - itype, _ = bs.unpack_from('u2u10', msg, i) + self.it, _ = bs.unpack_from('u2u10', msg, i) i += 12 if self.monlevel > 0: - print(f"Flood ({self.itype_t[itype]}) " + + print(f"Flood ({self.itype_t[self.it]}) " + f"{month:2d}/{day:02d} {hour:2d}:{minute:02d}") + self.params = [] for k in range(3): lv, reg = bs.unpack_from('u4u40', msg, i) i += 44 if lv == 0 and reg == 0: continue + + self.params.append([lv, reg]) + if self.monlevel > 0: print(f"{self.lv_t[lv]} {self.reg_fl_t[reg]}") i += 29 + + ep = time2epoch(self.time) + self.year = ep[0] + self.month = ep[1] + self.ta = epoch2time([self.year, month, day, hour, minute, 0.0]) + self.vn = bs.unpack_from('u6', msg, i)[0] i += 12 return i def decode_jma_typhoon(self, msg, i): """ JMA Tyhoon """ + + # month, day, hour, minute: Report time (UTC) + # itype: information type + # d1, h1, m1: reference time + # dt: Type of Reference Time month, day, hour, minute = bs.unpack_from('u4u5u5u6', msg, i) i += 20 - itype, _, d1, h1, m1, dt = bs.unpack_from('u2u10u5u5u6u3', msg, i) + self.it, _, d1, h1, m1, dt = bs.unpack_from('u2u10u5u5u6u3', msg, i) i += 31+8 self.dt_tp_t = {1: 'Analysis', 2: 'Estimate', 3: 'Forecast'} + # du: elapsed time + # tn: typhoon number + # sr: scale category + # ic: intensity category du, tn, sr, ic = bs.unpack_from('u7u7u4u4', msg, i) i += 22 @@ -650,15 +773,28 @@ def decode_jma_typhoon(self, msg, i): if s == 1: lon = -lon + # pr: central pressure + # w1: Maximum wind speed + # w2: Maximum wind gust speed pr, w1, w2 = bs.unpack_from('u11u7u7', msg, i) i += 71 if self.monlevel > 0: - print(f"Typhoon ({self.itype_t[itype]}) #{tn} ({sr}/{ic}) " + + print(f"Typhoon ({self.itype_t[self.it]}) #{tn} ({sr}/{ic}) " + f"{month:2d}/{day:02d} {hour:2d}:{minute:02d} " + f"{month}/{d1} {h1:2d}:{m1:02d} {self.dt_tp_t[dt]} {du}h " + f"lat={lat:.4f} lon={lon:.4f} pr={pr} w1={w1} w2={w2}") + ep = time2epoch(self.time) + self.year = ep[0] + self.month = month + + self.ta = epoch2time([self.year, month, day, hour, minute, 0.0]) + self.tb = epoch2time([self.year, month, d1, h1, m1, 0.0]) + + self.pos = np.array([lat, lon]) + self.params = [dt, du, tn, sr, ic, pr, w1, w2] + self.vn = bs.unpack_from('u6', msg, i)[0] i += 12 return i @@ -667,20 +803,28 @@ def decode_jma_marine(self, msg, i): """ JMA Marine """ month, day, hour, minute = bs.unpack_from('u4u5u5u6', msg, i) i += 20 - itype = bs.unpack_from('u2', msg, i)[0] + self.it = bs.unpack_from('u2', msg, i)[0] i += 12 + ep = time2epoch(self.time) + self.year = ep[0] + self.month = month + self.ta = epoch2time([self.year, month, day, hour, minute, 0.0]) + if self.monlevel > 0: - print(f"Manine ({self.itype_t[itype]}) " + + print(f"Manine ({self.itype_t[self.it]}) " + f"{month:2d}/{day:02d} {hour:2d}:{minute:02d}") + # dw: warning code #1-8 + # maring forecast region #1-8 + self.params = [] for k in range(8): dw, reg = bs.unpack_from('u5u14', msg, i) i += 19 if dw == 0 and reg == 0: continue - + self.params.append([dw, reg]) if self.monlevel > 0: print(f"{self.dw_m_t[dw]} {self.reg_m_t[reg]}") @@ -689,6 +833,35 @@ def decode_jma_marine(self, msg, i): i += 12 return i + def gen_msg(self, dc): + """ generate message """ + msg = "" + + if dc == 12: # typhoon + msg = f"Typhoon ({self.itype_t[self.it]})\n" + msg += f" report time: {time2str(self.ta)}\n" + msg += f" typhoon #{self.params[2]}\n" + msg += f" reference time: {time2str(self.tb)}\n" + msg += f" reference type: {self.dt_tp_t[self.params[0]]}\n" + msg += f" elapsed time: {self.params[1]}h\n" + if self.params[3] > 0: + msg += f" magnitute: {self.sr_t[self.params[3]]}\n" + if self.params[4] > 0: + msg += f" intensity: {self.ic_t[self.params[4]]}\n" + msg += f" lat/lon: {self.pos[0]:.4f}/{self.pos[1]:.4f}\n" + msg += f" central pressure: {self.params[5]} hPa\n" + msg += f" maximum wind: {self.params[6]} m/s\n" + msg += f" maximum instant wind: {self.params[7]} m/s\n" + + if dc == 14: # marine + msg = f"Marine ({self.itype_t[self.it]})\n" + msg += f" report time: {time2str(self.ta)}\n" + for dw, reg in self.params: + msg += f" code: {self.dw_m_t[dw]}\t" + msg += f" region: {self.reg_m_t[reg]}\n" + + return msg + def decode(self, msg, i): """ decode DC-report messages """ @@ -739,12 +912,12 @@ def __init__(self, bdir='../data/ewss/camf/', year=0): self.hazard_t = self.load_msg('hazard.txt') # JIS X0401 - with open(bdir+'jisx0401-en.json', 'r', - encoding='utf-8') as fh: + with open(bdir+'jisx0401-en.json', 'r', encoding='utf-8') as fh: self.pref_t = json.load(fh) # JIS X0402 - self.mc_t = pd.read_csv(bdir+'000323625.csv', encoding='utf-8') + self.mc_t = pd.read_csv( + bdir+'000323625.csv', encoding='utf-8') self.bdir = bdir self.city_t = None @@ -755,7 +928,8 @@ def __init__(self, bdir='../data/ewss/camf/', year=0): self.region_t = self.load_msg('region.txt') - self.severity_t = ('unknown', 'moderate', 'severe', 'extreme') + self.severity_t = ('unknown', 'moderate', + 'severe', 'extreme') self.duration_t = ('unknown', 'duration<6h', '6h 0: - pref = v['ken-name'].item() - - if v['sityouson-name1'].isna().item(): - if v['sityouson-name2'].isna().item(): - if v['sityouson-name3'].isna().item(): - city = None - else: - city = v['sityouson-name3'].item() - else: - city = v['sityouson-name2'].item() - else: - city = v['sityouson-name1'].item() + name = v['ken-name'].item() - name = pref + city + if v['sityouson-name1'].isna().item() == False: + name += v['sityouson-name1'].item() + if v['sityouson-name2'].isna().item() == False: + name += v['sityouson-name2'].item() + if v['sityouson-name3'].isna().item() == False: + name += v['sityouson-name3'].item() if len(name) == 0: return i @@ -1198,7 +1368,7 @@ def decode(self, msg, i): # pid=2 (FDMA) or pid=3 (Related Ministries) -> J-Alert # pid=4 (Municipality) -> municipality-transmitted info - # 3.2 Hazard + # 3.2 Hazard (A4, A5) hcat, sev = bs.unpack_from('u7u2', msg, i) i += 9 @@ -1213,8 +1383,7 @@ def decode(self, msg, i): # sev: 0:unknown,1:moderate,2:severe,3:extreme self.severity = self.severity_t[sev] - # 3.3 Hazard Chronology (beginning of hazard) - + # 3.3 Hazard Chronology (beginning of hazard) A6, A7, A8 wn, tow, dur = bs.unpack_from('u1u14u2', msg, i) i += 17 @@ -1275,7 +1444,7 @@ def decode(self, msg, i): # list-B(5): Tab. 4.2-15 self.ver += 1 - # 3.6 Target Area + # 3.6 Target Area A12, A13, A14, A15, A16 lati, loni, smai, smii, azi = bs.unpack_from('u16u17u5u5u6', msg, i) i += 49 @@ -1286,7 +1455,7 @@ def decode(self, msg, i): self.Lm = self.r_t[smii] # semi-minor axis [km] self.az = -90.0+180.0/64*azi - # 3.7 Main Subject for Specific Settings + # 3.7 Main Subject for Specific Settings A17 subj = bs.unpack_from('u2', msg, i)[0] i += 2 @@ -1491,7 +1660,7 @@ def decode(self, msg, i): i += 15 if self.monlevel > 0: - print(f"[DCX] {time2str(self.th)} hcat={hcat} " + + print(f"[DCX] {time2str(self.th)} hcat={hcat} subj={subj} " + f"pid={self.pid} {self.severity} {self.hazard} " + f"inst={inst} info={info} {s} ") From 0510098c39bb24d38e7f978060b76815ff05ecbe Mon Sep 17 00:00:00 2001 From: Rui Hirokawa Date: Sat, 1 Nov 2025 18:34:55 +0900 Subject: [PATCH 5/6] updated RTCM SC134 MT54-11 message. --- src/cssrlib/cssrlib.py | 1 + src/cssrlib/rtcm.py | 96 +++++++++++++++++++++++------------------- 2 files changed, 53 insertions(+), 44 deletions(-) diff --git a/src/cssrlib/cssrlib.py b/src/cssrlib/cssrlib.py index dc1e4e6..2a53dfe 100644 --- a/src/cssrlib/cssrlib.py +++ b/src/cssrlib/cssrlib.py @@ -1347,6 +1347,7 @@ def __init__(self): self.len = 0 self.dlen = 0 self.msgtype = 0 + self.iodssr = 0 def encode_mask(self, v, bitlen, ofst=1): """ encode n-bit mask with offset """ diff --git a/src/cssrlib/rtcm.py b/src/cssrlib/rtcm.py index 6bcca62..4cbc730 100644 --- a/src/cssrlib/rtcm.py +++ b/src/cssrlib/rtcm.py @@ -57,9 +57,15 @@ class sRTCM(IntEnum): class Integrity(): """ class for integrity information in SC-134 """ - pid = 0 # provider id DFi027 (0-4095) + pid = 0 # augmentation provider id DFi027 (0-4095) vp = 0 # validity period DFi065 (0-15) uri = 0 # update rate interval DFi067 (b16) + + # placeholder for RTCM SSR + pidssr = 0 # SSR provider ID + sidssr = 0 # SSR solution type + iodssr = 0 # SSR iod + tow = 0 iod_sys = {} # issue of GNSS satellite mask DFi010 (b2) sts = {} # constellation integrity status DFi029 (b16) @@ -166,11 +172,12 @@ class Integrity(): def __init__(self): self.sys_r_tbl = {self.sys_tbl[s]: s for s in self.sys_tbl.keys()} - None + self.vp_r_tbl = {s: k for k, s in enumerate(self.vp_tbl)} class rtcm(cssr): """ class to decode RTCM3 messages """ + def __init__(self, foutname=None): super().__init__(foutname) self.len = 0 @@ -180,6 +187,9 @@ def __init__(self, foutname=None): self.lock = {} self.mask_pbias = False + self.pid = 0 # SSR Provider ID + self.sid = 0 # SSR Solution Type + self.nrtk_r = {} self.msm_t = { @@ -529,6 +539,9 @@ def decode_head(self, msg, i, sys): else: nsat = 0 + self.pid = pid + self.sid = sid + v = {'iodssr': iodssr, 'nsat': nsat} return i, v @@ -2522,36 +2535,34 @@ def decode_integrity_mmap(self, msg, i): def decode_integrity_ssr(self, msg, i): """ RTCM SC-134 SSR integrity message (MT11,12,13) """ - pid, tow, mask_sys = bs.unpack_from('u12u30u16', msg, i) - i += 58 - tow *= 1e-3 + self.integ.pid = bs.unpack_from('u12', msg, i)[0] + i += 12 - # update rate interval DFi067 + # SSR provider ID, solution type, iod + self.pid, self.sid, self.iodssr = bs.unpack_from('u16u4u4', msg, i) + i += 24 # mask_sys:: DFi013 0:GPS,1:GLO,2:GAL,3:BDS,4:QZS,5:IRN + tow, mask_sys = bs.unpack_from('u30u16', msg, i) + i += 46 - if self.msgtype == 11: - vp, uri = bs.unpack_from('u4u16', msg, i) - i += 20 - # Validity Period DFi065 (0-15) - self.integ.vp = self.integ.vp_tbl[vp] - self.integ.uri = uri*0.1 # update rate interval DFi067 (0.1) + self.integ.tow = tow*1e-3 + self.integ.mask_sys = mask_sys + + vp, uri = bs.unpack_from('u4u16', msg, i) + i += 20 + self.integ.vp = self.integ.vp_tbl[vp] # Validity Period DFi065 (0-15) + self.integ.uri = uri*0.1 # update rate interval DFi067 (0.1) sys_t, nsys = self.decode_mask(mask_sys, 16, ofst=0) iod_sys = {} - for k in range(nsys): - sys = self.integ.sys_tbl[sys_t[k]] - iod_sys[sys] = bs.unpack_from('u2', msg, i)[0] - i += 2 - flag_t = {} - nid = {} for sys_ in sys_t: - nid_, mask_sat = bs.unpack_from('u8u64', msg, i) - i += 72 + sys = self.integ.sys_tbl[sys_] + mask_sat, iod_sys[sys] = bs.unpack_from('u64u2', msg, i) + i += 66 svid_t, nsat = self.decode_mask(mask_sat, 64) sys = self.integ.sys_tbl[sys_] - nid[sys] = nid_ flag_t[sys] = {} for svid in svid_t: ofst = 192 if sys == uGNSS.QZS else 0 @@ -2560,11 +2571,7 @@ def decode_integrity_ssr(self, msg, i): flag_t[sys][sat] = bs.unpack_from('u2', msg, i)[0] i += 2 - self.integ.mask_sys = mask_sys - self.integ.pid = pid - self.integ.tow = tow self.integ.iod_sys = iod_sys - self.integ.nid = nid self.integ.flag = flag_t def decode(self, msg, subtype=None): @@ -2702,6 +2709,9 @@ def __init__(self): super().__init__() self.integ = Integrity() + self.pid = 0 # SSR Provider ID + self.sid = 0 # SSR Solution Type + def set_sync(self, msg, k): msg[k] = 0xd3 @@ -2732,27 +2742,27 @@ def encode_integrity_ssr(self, msg, i): for sys in sys_t: gnss_t.append(self.integ.sys_r_tbl[sys]) + # mask_sys:: DFi013 0:GPS,1:GLO,2:GAL,3:BDS,4:QZS,5:IRN mask_sys = self.encode_mask(gnss_t, 16, ofst=0) - bs.pack_into('u12', msg, i, self.integ.pid) # provider id DFi027 + # augmentation service provider id DFi027 + bs.pack_into('u12', msg, i, self.integ.pid) i += 12 - bs.pack_into('u30', msg, i, self.integ.tow) # tow DFi008 + + # SSR provider id, soluition type, iod + bs.pack_into('u16u4u4', msg, i, self.pid, self.sid, self.iodssr) + i += 24 + + bs.pack_into('u30', msg, i, self.integ.tow*1e3) # tow DFi008 i += 30 bs.pack_into('u16', msg, i, mask_sys) # GNSS constellation mask DFi013 i += 16 - # mask_sys:: DFi013 0:GPS,1:GLO,2:GAL,3:BDS,4:QZS,5:IRN - - if self.msgtype == 11: - # validity period DFi065 - # update rate interval DFi067 - bs.pack_into('u4u16', msg, i, self.integ.vp, self.integ.uri) - i += 20 - - for sys in sys_t: - # issue of GNSS satellite mask DFi010 - bs.pack_into('u2', msg, i, self.integ.iod_sys[sys]) - i += 2 + # validity period DFi065 + # update rate interval DFi067 + vp = self.integ.vp_r_tbl[self.integ.vp] + bs.pack_into('u4u16', msg, i, vp, self.integ.uri*10) + i += 20 for sys in sys_t: flag = self.integ.flag[sys] @@ -2763,12 +2773,10 @@ def encode_integrity_ssr(self, msg, i): ofst = 192 if sys == uGNSS.QZS else 0 svid_t.append(prn-ofst) - mask_sat = self.encode_mask(svid_t, 64) - nid_ = self.integ.nid[sys] - # network id DFi071 # GNSS satellite mask DFi009 - bs.pack_into('u8u64', msg, i, nid_, mask_sat) - i += 72 + mask_sat = self.encode_mask(svid_t, 64) + bs.pack_into('u64u2', msg, i, mask_sat, self.integ.iod_sys[sys]) + i += 66 for f in flag.values(): # integrity flag DFi068 From a9909ce5e05573f6fcddc6cf82dff4046a36e8e4 Mon Sep 17 00:00:00 2001 From: Rui Hirokawa Date: Sun, 2 Nov 2025 15:18:08 +0900 Subject: [PATCH 6/6] - added notebook for ppp, auth, basic, ewss. --- tutorials/auth.ipynb | 5334 +++++++++++++++++++++++++++++++++++++++ tutorials/basic.ipynb | 1091 ++++++++ tutorials/cssrlib.ipynb | 3596 +------------------------- tutorials/ewss.ipynb | 509 ++++ tutorials/ppp.ipynb | 2644 +++++++++++++++++++ 5 files changed, 9592 insertions(+), 3582 deletions(-) create mode 100644 tutorials/auth.ipynb create mode 100644 tutorials/basic.ipynb create mode 100644 tutorials/ewss.ipynb create mode 100644 tutorials/ppp.ipynb diff --git a/tutorials/auth.ipynb b/tutorials/auth.ipynb new file mode 100644 index 0000000..1ab4004 --- /dev/null +++ b/tutorials/auth.ipynb @@ -0,0 +1,5334 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f3c9133e", + "metadata": {}, + "source": [ + "# Authentication Demonstration" + ] + }, + { + "cell_type": "markdown", + "id": "5d0e0f68", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "691bfaee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "c:\\work\\gps\\cssrlib\\tutorials\\cssrlib-data\\samples\n" + ] + } + ], + "source": [ + "%cd cssrlib-data/samples" + ] + }, + { + "cell_type": "markdown", + "id": "5d59c2dc", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "f10a0a4c", + "metadata": {}, + "source": [ + "## Example 1: Galileo OSNMA Demonstration " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "72e7f554", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import cssrlib.osnma as om\n", + "from sys import exit as sys_exit\n", + "from binascii import unhexlify, hexlify\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "98ee0559", + "metadata": {}, + "outputs": [], + "source": [ + "tofst = -2 # time offset to synchronize tow\n", + "mt_file = 'OSNMA_MerkleTree_20240115100000_newPKID_1.xml'\n", + "\n", + "if not os.path.exists('../data/pubkey/osnma/'+mt_file):\n", + " print('please install OSNMA_MerkleTree*.xml from EUSPA.')\n", + " sys_exit(0)\n", + "\n", + "nma = om.osnma(mt_file)\n", + "\n", + "nma.flg_slowmac = False" + ] + }, + { + "cell_type": "markdown", + "id": "e1f11eb5", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "cad2d819", + "metadata": {}, + "outputs": [], + "source": [ + "year = 2025\n", + "doy = 233\n", + "session = 'h'\n", + "\n", + "file_galinav = f'../data/doy{year}-{doy:03d}/{doy:03d}{session}_galinav.txt'\n", + "\n", + "dtype_ = [('tow', 'i8'), ('wn', 'i8'), ('prn', 'i8'),\n", + " ('mt', 'i8'), ('k', 'i8'), ('nma', 'S10'),\n", + " ('wt', 'i8'), ('nav', 'S32')]\n", + "\n", + "dtype_ = [('wn', 'int'), ('tow', 'float'), ('prn', 'int'),\n", + " ('type', 'int'), ('len', 'int'), ('nav', 'S512')]\n", + "\n", + "v = np.genfromtxt(file_galinav, dtype=dtype_)" + ] + }, + { + "cell_type": "markdown", + "id": "ede5b159", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d6cf512a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "decode_hk succeeded 2380/370919 prn=21 gst_tow=370890 did=5\n", + "root-key verified 2380/370919 prn=21 gst_tow=370890\n", + "Key chain verified 2380/370919 prn=21 gst_tow=370890\n", + "MAC Look-up Table verified on 2380/370919 prn=21 gst_tow=370860\n", + "MACSEQ Verified on 2380/370919 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=108 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=23 adkd= 0 iodnav=103 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=27 adkd= 0 iodnav=104 tag verified\n", + "MAC Look-up Table verified on 2380/370919 prn=10 gst_tow=370860\n", + "MACSEQ Verified on 2380/370919 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=108 tag verified\n", + "# 2 prn_d=12 adkd= 0 iodnav=108 tag verified\n", + "# 3 prn_d=10 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=10 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/370919 prn=33 gst_tow=370860\n", + "MACSEQ Verified on 2380/370919 prn=33\n", + "# 1 prn_d=33 adkd= 0 iodnav=108 tag verified\n", + "# 2 prn_d=23 adkd= 0 iodnav=103 tag verified\n", + "# 3 prn_d=33 adkd= 4 tag verified\n", + "4 prn_d=31 adkd=0 navmsg is not available.\n", + "5 prn_d=33 adkd=12 slow-MAC is skipped\n", + "6 prn_d=26 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/370919 prn=4 gst_tow=370860\n", + "MACSEQ Verified on 2380/370919 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=108 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "# 4 prn_d=12 adkd= 0 iodnav=108 tag verified\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/370949 prn=19 gst_tow=370920\n", + "MAC Look-up Table verified on 2380/370949 prn=19 gst_tow=370890\n", + "MACSEQ Verified on 2380/370949 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=12 adkd= 0 iodnav=108 tag verified\n", + "# 3 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=19 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=27 adkd= 0 iodnav=104 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/370949 prn=21 gst_tow=370890\n", + "MACSEQ Verified on 2380/370949 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=26 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=23 adkd= 0 iodnav=103 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/370949 prn=10 gst_tow=370890\n", + "MACSEQ Verified on 2380/370949 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=23 adkd= 0 iodnav=103 tag verified\n", + "# 3 prn_d=12 adkd= 0 iodnav=108 tag verified\n", + "4 prn_d=10 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=12 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/370949 prn=33 gst_tow=370890\n", + "MACSEQ Verified on 2380/370949 prn=33\n", + "# 1 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=12 adkd= 0 iodnav=108 tag verified\n", + "# 3 prn_d=23 adkd= 0 iodnav=103 tag verified\n", + "4 prn_d=33 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=23 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/370949 prn=4 gst_tow=370890\n", + "MACSEQ Verified on 2380/370949 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=16 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=12 adkd= 0 iodnav=108 tag verified\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/370979 prn=19 gst_tow=370950\n", + "MAC Look-up Table verified on 2380/370979 prn=19 gst_tow=370920\n", + "MACSEQ Verified on 2380/370979 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=19 adkd= 4 tag verified\n", + "# 4 prn_d=27 adkd= 0 iodnav=104 tag verified\n", + "5 prn_d=19 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "MAC Look-up Table verified on 2380/370979 prn=21 gst_tow=370920\n", + "MACSEQ Verified on 2380/370979 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=23 adkd= 0 iodnav=103 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=27 adkd= 0 iodnav=104 tag verified\n", + "MAC Look-up Table verified on 2380/370979 prn=10 gst_tow=370920\n", + "MACSEQ Verified on 2380/370979 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=12 adkd= 0 iodnav=108 tag verified\n", + "# 3 prn_d=10 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=10 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/370979 prn=4 gst_tow=370920\n", + "MACSEQ Verified on 2380/370979 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "# 4 prn_d=12 adkd= 0 iodnav=108 tag verified\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/370979 prn=33 gst_tow=370920\n", + "MACSEQ Verified on 2380/370979 prn=33\n", + "# 1 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=23 adkd= 0 iodnav=103 tag verified\n", + "# 3 prn_d=33 adkd= 4 tag verified\n", + "4 prn_d=31 adkd=0 navmsg is not available.\n", + "5 prn_d=33 adkd=12 slow-MAC is skipped\n", + "6 prn_d=26 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/371009 prn=19 gst_tow=370980\n", + "MAC Look-up Table verified on 2380/371009 prn=19 gst_tow=370950\n", + "MACSEQ Verified on 2380/371009 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=12 adkd= 0 iodnav=108 tag verified\n", + "# 3 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=19 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=27 adkd= 0 iodnav=104 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "decode_hk succeeded 2380/371009 prn=21 gst_tow=370980 did=5\n", + "MAC Look-up Table verified on 2380/371009 prn=21 gst_tow=370950\n", + "MACSEQ Verified on 2380/371009 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=26 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=23 adkd= 0 iodnav=103 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371009 prn=10 gst_tow=370950\n", + "MACSEQ Verified on 2380/371009 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=23 adkd= 0 iodnav=103 tag verified\n", + "# 3 prn_d=12 adkd= 0 iodnav=108 tag verified\n", + "4 prn_d=10 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=12 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371009 prn=33 gst_tow=370950\n", + "MACSEQ Verified on 2380/371009 prn=33\n", + "# 1 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=12 adkd= 0 iodnav=108 tag verified\n", + "# 3 prn_d=23 adkd= 0 iodnav=103 tag verified\n", + "4 prn_d=33 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=23 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371009 prn=4 gst_tow=370950\n", + "MACSEQ Verified on 2380/371009 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=16 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=12 adkd= 0 iodnav=108 tag verified\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/371039 prn=19 gst_tow=371010\n", + "MAC Look-up Table verified on 2380/371039 prn=19 gst_tow=370980\n", + "MACSEQ Verified on 2380/371039 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=19 adkd= 4 tag verified\n", + "# 4 prn_d=27 adkd= 0 iodnav=104 tag verified\n", + "5 prn_d=19 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "MAC Look-up Table verified on 2380/371039 prn=21 gst_tow=370980\n", + "MACSEQ Verified on 2380/371039 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=23 adkd= 0 iodnav=103 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=27 adkd= 0 iodnav=104 tag verified\n", + "MAC Look-up Table verified on 2380/371039 prn=10 gst_tow=370980\n", + "MACSEQ Verified on 2380/371039 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=12 adkd= 0 iodnav=108 tag verified\n", + "# 3 prn_d=10 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=10 adkd=12 slow-MAC is skipped\n", + "6 prn_d=6 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371039 prn=33 gst_tow=370980\n", + "MACSEQ Verified on 2380/371039 prn=33\n", + "# 1 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=23 adkd= 0 iodnav=103 tag verified\n", + "# 3 prn_d=33 adkd= 4 tag verified\n", + "4 prn_d=31 adkd=0 navmsg is not available.\n", + "5 prn_d=33 adkd=12 slow-MAC is skipped\n", + "6 prn_d=26 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371039 prn=4 gst_tow=370980\n", + "MACSEQ Verified on 2380/371039 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=12 adkd= 0 iodnav=108 tag verified\n", + "Key chain verified 2380/371069 prn=19 gst_tow=371040\n", + "MAC Look-up Table verified on 2380/371069 prn=19 gst_tow=371010\n", + "MACSEQ Verified on 2380/371069 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=12 adkd= 0 iodnav=108 tag verified\n", + "# 3 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=19 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=27 adkd= 0 iodnav=104 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371069 prn=21 gst_tow=371010\n", + "MACSEQ Verified on 2380/371069 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=26 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=23 adkd= 0 iodnav=103 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371069 prn=10 gst_tow=371010\n", + "MACSEQ Verified on 2380/371069 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=9 adkd=0 navmsg is not available.\n", + "# 3 prn_d=12 adkd= 0 iodnav=108 tag verified\n", + "4 prn_d=10 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=12 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371069 prn=33 gst_tow=371010\n", + "MACSEQ Verified on 2380/371069 prn=33\n", + "# 1 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=12 adkd= 0 iodnav=108 tag verified\n", + "# 3 prn_d=23 adkd= 0 iodnav=103 tag verified\n", + "4 prn_d=33 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=23 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371069 prn=4 gst_tow=371010\n", + "MACSEQ Verified on 2380/371069 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=9 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/371099 prn=19 gst_tow=371070\n", + "MAC Look-up Table verified on 2380/371099 prn=19 gst_tow=371040\n", + "MACSEQ Verified on 2380/371099 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=19 adkd= 4 tag verified\n", + "# 4 prn_d=27 adkd= 0 iodnav=104 tag verified\n", + "5 prn_d=19 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "decode_hk succeeded 2380/371099 prn=21 gst_tow=371070 did=5\n", + "MAC Look-up Table verified on 2380/371099 prn=21 gst_tow=371040\n", + "MACSEQ Verified on 2380/371099 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=23 adkd= 0 iodnav=103 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=27 adkd= 0 iodnav=104 tag verified\n", + "MAC Look-up Table verified on 2380/371099 prn=10 gst_tow=371040\n", + "MACSEQ Verified on 2380/371099 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=12 adkd= 0 iodnav=108 tag verified\n", + "# 3 prn_d=10 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=10 adkd=12 slow-MAC is skipped\n", + "6 prn_d=6 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371099 prn=33 gst_tow=371040\n", + "MACSEQ Verified on 2380/371099 prn=33\n", + "# 1 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=23 adkd= 0 iodnav=103 tag verified\n", + "# 3 prn_d=33 adkd= 4 tag verified\n", + "4 prn_d=31 adkd=0 navmsg is not available.\n", + "5 prn_d=33 adkd=12 slow-MAC is skipped\n", + "6 prn_d=26 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371099 prn=4 gst_tow=371040\n", + "MACSEQ Verified on 2380/371099 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=12 adkd= 0 iodnav=108 tag verified\n", + "Key chain verified 2380/371129 prn=19 gst_tow=371100\n", + "MAC Look-up Table verified on 2380/371129 prn=19 gst_tow=371070\n", + "MACSEQ Verified on 2380/371129 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=12 adkd= 0 iodnav=108 tag verified\n", + "# 3 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=19 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=27 adkd= 0 iodnav=104 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371129 prn=21 gst_tow=371070\n", + "MACSEQ Verified on 2380/371129 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=26 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=23 adkd= 0 iodnav=103 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371129 prn=10 gst_tow=371070\n", + "MACSEQ Verified on 2380/371129 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=9 adkd=0 navmsg is not available.\n", + "# 3 prn_d=12 adkd= 0 iodnav=108 tag verified\n", + "4 prn_d=10 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=12 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371129 prn=4 gst_tow=371070\n", + "MACSEQ Verified on 2380/371129 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=9 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/371159 prn=19 gst_tow=371130\n", + "MAC Look-up Table verified on 2380/371159 prn=19 gst_tow=371100\n", + "MACSEQ Verified on 2380/371159 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=19 adkd= 4 tag verified\n", + "# 4 prn_d=27 adkd= 0 iodnav=104 tag verified\n", + "5 prn_d=19 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "MAC Look-up Table verified on 2380/371159 prn=21 gst_tow=371100\n", + "MACSEQ Verified on 2380/371159 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=23 adkd= 0 iodnav=103 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=27 adkd= 0 iodnav=104 tag verified\n", + "MAC Look-up Table not verified on 2380/371159 prn=12 gst_tow=371100\n", + "MAC Look-up Table verified on 2380/371159 prn=10 gst_tow=371100\n", + "MACSEQ Verified on 2380/371159 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=12 adkd= 0 iodnav=108 tag verified\n", + "# 3 prn_d=10 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=10 adkd=12 slow-MAC is skipped\n", + "6 prn_d=6 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371159 prn=4 gst_tow=371100\n", + "MACSEQ Verified on 2380/371159 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=12 adkd= 0 iodnav=108 tag verified\n", + "Key chain verified 2380/371189 prn=19 gst_tow=371160\n", + "MAC Look-up Table verified on 2380/371189 prn=19 gst_tow=371130\n", + "MACSEQ Verified on 2380/371189 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=12 adkd= 0 iodnav=108 tag verified\n", + "# 3 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=19 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=27 adkd= 0 iodnav=104 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "decode_hk succeeded 2380/371189 prn=21 gst_tow=371160 did=5\n", + "MAC Look-up Table verified on 2380/371189 prn=21 gst_tow=371130\n", + "MACSEQ Verified on 2380/371189 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=23 adkd= 0 iodnav=103 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371189 prn=12 gst_tow=371130\n", + "MACSEQ Verified on 2380/371189 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=108 tag verified\n", + "2 prn_d=9 adkd=0 navmsg is not available.\n", + "3 prn_d=6 adkd=0 navmsg is not available.\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=6 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371189 prn=10 gst_tow=371130\n", + "MACSEQ Verified on 2380/371189 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=9 adkd=0 navmsg is not available.\n", + "# 3 prn_d=12 adkd= 0 iodnav=108 tag verified\n", + "4 prn_d=10 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=12 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371189 prn=4 gst_tow=371130\n", + "MACSEQ Verified on 2380/371189 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=9 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/371219 prn=19 gst_tow=371190\n", + "MAC Look-up Table verified on 2380/371219 prn=19 gst_tow=371160\n", + "MACSEQ Verified on 2380/371219 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=19 adkd= 4 tag verified\n", + "# 4 prn_d=27 adkd= 0 iodnav=104 tag verified\n", + "5 prn_d=19 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "MAC Look-up Table verified on 2380/371219 prn=21 gst_tow=371160\n", + "MACSEQ Verified on 2380/371219 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=23 adkd= 0 iodnav=103 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=27 adkd= 0 iodnav=104 tag verified\n", + "MAC Look-up Table verified on 2380/371219 prn=12 gst_tow=371160\n", + "MACSEQ Verified on 2380/371219 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "MAC Look-up Table verified on 2380/371219 prn=10 gst_tow=371160\n", + "MACSEQ Verified on 2380/371219 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=10 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=10 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371219 prn=4 gst_tow=371160\n", + "MACSEQ Verified on 2380/371219 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/371249 prn=19 gst_tow=371220\n", + "MAC Look-up Table verified on 2380/371249 prn=19 gst_tow=371190\n", + "MACSEQ Verified on 2380/371249 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=30 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=19 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=27 adkd= 0 iodnav=104 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371249 prn=21 gst_tow=371190\n", + "MACSEQ Verified on 2380/371249 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=23 adkd= 0 iodnav=103 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371249 prn=12 gst_tow=371190\n", + "MACSEQ Verified on 2380/371249 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=9 adkd=0 navmsg is not available.\n", + "3 prn_d=6 adkd=0 navmsg is not available.\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=6 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371249 prn=10 gst_tow=371190\n", + "MACSEQ Verified on 2380/371249 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=10 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371249 prn=4 gst_tow=371190\n", + "MACSEQ Verified on 2380/371249 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/371279 prn=19 gst_tow=371250\n", + "MAC Look-up Table verified on 2380/371279 prn=19 gst_tow=371220\n", + "MACSEQ Verified on 2380/371279 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=19 adkd= 4 tag verified\n", + "# 4 prn_d=27 adkd= 0 iodnav=104 tag verified\n", + "5 prn_d=19 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "MAC Look-up Table verified on 2380/371279 prn=21 gst_tow=371220\n", + "MACSEQ Verified on 2380/371279 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=23 adkd= 0 iodnav=103 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=27 adkd= 0 iodnav=104 tag verified\n", + "MAC Look-up Table not verified on 2380/371279 prn=27 gst_tow=371220\n", + "MAC Look-up Table verified on 2380/371279 prn=12 gst_tow=371220\n", + "MACSEQ Verified on 2380/371279 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "MAC Look-up Table verified on 2380/371279 prn=10 gst_tow=371220\n", + "MACSEQ Verified on 2380/371279 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=10 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=10 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table not verified on 2380/371279 prn=23 gst_tow=371220\n", + "MAC Look-up Table verified on 2380/371279 prn=4 gst_tow=371220\n", + "MACSEQ Verified on 2380/371279 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/371309 prn=19 gst_tow=371280\n", + "MAC Look-up Table verified on 2380/371309 prn=19 gst_tow=371250\n", + "MACSEQ Verified on 2380/371309 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=30 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=19 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=27 adkd= 0 iodnav=104 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "decode_hk succeeded 2380/371309 prn=21 gst_tow=371280 did=5\n", + "MAC Look-up Table verified on 2380/371309 prn=21 gst_tow=371250\n", + "MACSEQ Verified on 2380/371309 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=23 adkd= 0 iodnav=103 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371309 prn=27 gst_tow=371250\n", + "MACSEQ Verified on 2380/371309 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=104 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=27 adkd=12 slow-MAC is skipped\n", + "5 prn_d=30 adkd=0 navmsg is not available.\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371309 prn=12 gst_tow=371250\n", + "MACSEQ Verified on 2380/371309 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=9 adkd=0 navmsg is not available.\n", + "3 prn_d=6 adkd=0 navmsg is not available.\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=6 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371309 prn=10 gst_tow=371250\n", + "MACSEQ Verified on 2380/371309 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=10 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371309 prn=23 gst_tow=371250\n", + "MACSEQ Verified on 2380/371309 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=103 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371309 prn=4 gst_tow=371250\n", + "MACSEQ Verified on 2380/371309 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/371339 prn=19 gst_tow=371310\n", + "MAC Look-up Table verified on 2380/371339 prn=19 gst_tow=371280\n", + "MACSEQ Verified on 2380/371339 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=19 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=19 adkd=12 slow-MAC is skipped\n", + "6 prn_d=30 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371339 prn=21 gst_tow=371280\n", + "MACSEQ Verified on 2380/371339 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "4 prn_d=7 adkd=0 navmsg is not available.\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "MAC Look-up Table not verified on 2380/371339 prn=29 gst_tow=371280\n", + "MAC Look-up Table verified on 2380/371339 prn=27 gst_tow=371280\n", + "MACSEQ Verified on 2380/371339 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=27 adkd= 4 tag verified\n", + "4 prn_d=30 adkd=0 navmsg is not available.\n", + "5 prn_d=27 adkd=12 slow-MAC is skipped\n", + "6 prn_d=7 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371339 prn=12 gst_tow=371280\n", + "MACSEQ Verified on 2380/371339 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "MAC Look-up Table verified on 2380/371339 prn=10 gst_tow=371280\n", + "MACSEQ Verified on 2380/371339 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=10 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=10 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371339 prn=23 gst_tow=371280\n", + "MACSEQ Verified on 2380/371339 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "4 prn_d=31 adkd=0 navmsg is not available.\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "MAC Look-up Table verified on 2380/371339 prn=4 gst_tow=371280\n", + "MACSEQ Verified on 2380/371339 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/371369 prn=19 gst_tow=371340\n", + "MAC Look-up Table verified on 2380/371369 prn=19 gst_tow=371310\n", + "MACSEQ Verified on 2380/371369 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=19 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371369 prn=21 gst_tow=371310\n", + "MACSEQ Verified on 2380/371369 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=31 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "5 prn_d=7 adkd=0 navmsg is not available.\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371369 prn=29 gst_tow=371310\n", + "MACSEQ Verified on 2380/371369 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=29 adkd=12 slow-MAC is skipped\n", + "5 prn_d=30 adkd=0 navmsg is not available.\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371369 prn=27 gst_tow=371310\n", + "MACSEQ Verified on 2380/371369 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=27 adkd=12 slow-MAC is skipped\n", + "5 prn_d=30 adkd=0 navmsg is not available.\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371369 prn=12 gst_tow=371310\n", + "MACSEQ Verified on 2380/371369 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=9 adkd=0 navmsg is not available.\n", + "3 prn_d=6 adkd=0 navmsg is not available.\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=6 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371369 prn=10 gst_tow=371310\n", + "MACSEQ Verified on 2380/371369 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=10 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "decode_hk succeeded 2380/371369 prn=23 gst_tow=371340 did=5\n", + "MAC Look-up Table verified on 2380/371369 prn=23 gst_tow=371310\n", + "MACSEQ Verified on 2380/371369 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371369 prn=4 gst_tow=371310\n", + "MACSEQ Verified on 2380/371369 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/371399 prn=19 gst_tow=371370\n", + "MAC Look-up Table verified on 2380/371399 prn=19 gst_tow=371340\n", + "MACSEQ Verified on 2380/371399 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=19 adkd= 4 tag verified\n", + "4 prn_d=30 adkd=0 navmsg is not available.\n", + "5 prn_d=19 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371399 prn=29 gst_tow=371340\n", + "MACSEQ Verified on 2380/371399 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 4 tag verified\n", + "4 prn_d=30 adkd=0 navmsg is not available.\n", + "5 prn_d=29 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371399 prn=21 gst_tow=371340\n", + "MACSEQ Verified on 2380/371399 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "6 prn_d=31 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371399 prn=27 gst_tow=371340\n", + "MACSEQ Verified on 2380/371399 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=30 adkd=0 navmsg is not available.\n", + "# 3 prn_d=27 adkd= 4 tag verified\n", + "4 prn_d=7 adkd=0 navmsg is not available.\n", + "5 prn_d=27 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371399 prn=12 gst_tow=371340\n", + "MACSEQ Verified on 2380/371399 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "MAC Look-up Table verified on 2380/371399 prn=10 gst_tow=371340\n", + "MACSEQ Verified on 2380/371399 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=10 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=10 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371399 prn=23 gst_tow=371340\n", + "MACSEQ Verified on 2380/371399 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "4 prn_d=31 adkd=0 navmsg is not available.\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "6 prn_d=7 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371399 prn=4 gst_tow=371340\n", + "MACSEQ Verified on 2380/371399 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/371429 prn=19 gst_tow=371400\n", + "MAC Look-up Table verified on 2380/371429 prn=19 gst_tow=371370\n", + "MACSEQ Verified on 2380/371429 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=19 adkd=12 slow-MAC is skipped\n", + "5 prn_d=30 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371429 prn=21 gst_tow=371370\n", + "MACSEQ Verified on 2380/371429 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=30 adkd=0 navmsg is not available.\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371429 prn=29 gst_tow=371370\n", + "MACSEQ Verified on 2380/371429 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=29 adkd=12 slow-MAC is skipped\n", + "5 prn_d=30 adkd=0 navmsg is not available.\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371429 prn=27 gst_tow=371370\n", + "MACSEQ Verified on 2380/371429 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=15 adkd=0 navmsg is not available.\n", + "3 prn_d=30 adkd=0 navmsg is not available.\n", + "4 prn_d=27 adkd=12 slow-MAC is skipped\n", + "5 prn_d=7 adkd=0 navmsg is not available.\n", + "6 prn_d=30 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371429 prn=12 gst_tow=371370\n", + "MACSEQ Verified on 2380/371429 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=31 adkd=0 navmsg is not available.\n", + "3 prn_d=6 adkd=0 navmsg is not available.\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=6 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371429 prn=10 gst_tow=371370\n", + "MACSEQ Verified on 2380/371429 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=10 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371429 prn=23 gst_tow=371370\n", + "MACSEQ Verified on 2380/371429 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=24 adkd=0 navmsg is not available.\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371429 prn=4 gst_tow=371370\n", + "MACSEQ Verified on 2380/371429 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/371459 prn=19 gst_tow=371430\n", + "MAC Look-up Table verified on 2380/371459 prn=19 gst_tow=371400\n", + "MACSEQ Verified on 2380/371459 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=19 adkd= 4 tag verified\n", + "4 prn_d=30 adkd=0 navmsg is not available.\n", + "5 prn_d=19 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371459 prn=21 gst_tow=371400\n", + "MACSEQ Verified on 2380/371459 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "6 prn_d=31 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371459 prn=29 gst_tow=371400\n", + "MACSEQ Verified on 2380/371459 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 4 tag verified\n", + "4 prn_d=30 adkd=0 navmsg is not available.\n", + "5 prn_d=29 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371459 prn=27 gst_tow=371400\n", + "MACSEQ Verified on 2380/371459 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=30 adkd=0 navmsg is not available.\n", + "# 3 prn_d=27 adkd= 4 tag verified\n", + "4 prn_d=7 adkd=0 navmsg is not available.\n", + "5 prn_d=27 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371459 prn=12 gst_tow=371400\n", + "MACSEQ Verified on 2380/371459 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "decode_hk succeeded 2380/371459 prn=23 gst_tow=371430 did=5\n", + "MAC Look-up Table verified on 2380/371459 prn=23 gst_tow=371400\n", + "MACSEQ Verified on 2380/371459 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "4 prn_d=31 adkd=0 navmsg is not available.\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "6 prn_d=7 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371459 prn=10 gst_tow=371400\n", + "MACSEQ Verified on 2380/371459 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=10 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=10 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371459 prn=4 gst_tow=371400\n", + "MACSEQ Verified on 2380/371459 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/371489 prn=19 gst_tow=371460\n", + "MAC Look-up Table verified on 2380/371489 prn=19 gst_tow=371430\n", + "MACSEQ Verified on 2380/371489 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=19 adkd=12 slow-MAC is skipped\n", + "5 prn_d=30 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371489 prn=21 gst_tow=371430\n", + "MACSEQ Verified on 2380/371489 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=30 adkd=0 navmsg is not available.\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371489 prn=29 gst_tow=371430\n", + "MACSEQ Verified on 2380/371489 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=29 adkd=12 slow-MAC is skipped\n", + "5 prn_d=30 adkd=0 navmsg is not available.\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371489 prn=27 gst_tow=371430\n", + "MACSEQ Verified on 2380/371489 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=15 adkd=0 navmsg is not available.\n", + "3 prn_d=30 adkd=0 navmsg is not available.\n", + "4 prn_d=27 adkd=12 slow-MAC is skipped\n", + "5 prn_d=7 adkd=0 navmsg is not available.\n", + "6 prn_d=30 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371489 prn=12 gst_tow=371430\n", + "MACSEQ Verified on 2380/371489 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=31 adkd=0 navmsg is not available.\n", + "3 prn_d=6 adkd=0 navmsg is not available.\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=6 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371489 prn=23 gst_tow=371430\n", + "MACSEQ Verified on 2380/371489 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=24 adkd=0 navmsg is not available.\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371489 prn=10 gst_tow=371430\n", + "MACSEQ Verified on 2380/371489 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=10 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371489 prn=4 gst_tow=371430\n", + "MACSEQ Verified on 2380/371489 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/371519 prn=19 gst_tow=371490\n", + "MAC Look-up Table verified on 2380/371519 prn=19 gst_tow=371460\n", + "MACSEQ Verified on 2380/371519 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=19 adkd= 4 tag verified\n", + "4 prn_d=30 adkd=0 navmsg is not available.\n", + "5 prn_d=19 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371519 prn=21 gst_tow=371460\n", + "MACSEQ Verified on 2380/371519 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "6 prn_d=31 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371519 prn=29 gst_tow=371460\n", + "MACSEQ Verified on 2380/371519 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 4 tag verified\n", + "4 prn_d=30 adkd=0 navmsg is not available.\n", + "5 prn_d=29 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371519 prn=27 gst_tow=371460\n", + "MACSEQ Verified on 2380/371519 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=30 adkd=0 navmsg is not available.\n", + "# 3 prn_d=27 adkd= 4 tag verified\n", + "4 prn_d=7 adkd=0 navmsg is not available.\n", + "5 prn_d=27 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371519 prn=12 gst_tow=371460\n", + "MACSEQ Verified on 2380/371519 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=109 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "decode_hk succeeded 2380/371519 prn=23 gst_tow=371490 did=5\n", + "MAC Look-up Table verified on 2380/371519 prn=23 gst_tow=371460\n", + "MACSEQ Verified on 2380/371519 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "4 prn_d=31 adkd=0 navmsg is not available.\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "6 prn_d=7 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371519 prn=10 gst_tow=371460\n", + "MACSEQ Verified on 2380/371519 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=10 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=10 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371519 prn=4 gst_tow=371460\n", + "MACSEQ Verified on 2380/371519 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=109 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/371549 prn=19 gst_tow=371520\n", + "MAC Look-up Table verified on 2380/371549 prn=19 gst_tow=371490\n", + "MACSEQ Verified on 2380/371549 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=19 adkd=12 slow-MAC is skipped\n", + "5 prn_d=30 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371549 prn=21 gst_tow=371490\n", + "MACSEQ Verified on 2380/371549 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=30 adkd=0 navmsg is not available.\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371549 prn=29 gst_tow=371490\n", + "MACSEQ Verified on 2380/371549 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=29 adkd=12 slow-MAC is skipped\n", + "5 prn_d=30 adkd=0 navmsg is not available.\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371549 prn=27 gst_tow=371490\n", + "MACSEQ Verified on 2380/371549 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=15 adkd=0 navmsg is not available.\n", + "3 prn_d=30 adkd=0 navmsg is not available.\n", + "4 prn_d=27 adkd=12 slow-MAC is skipped\n", + "5 prn_d=7 adkd=0 navmsg is not available.\n", + "6 prn_d=30 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371549 prn=12 gst_tow=371490\n", + "MACSEQ Verified on 2380/371549 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=31 adkd=0 navmsg is not available.\n", + "3 prn_d=6 adkd=0 navmsg is not available.\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=6 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371549 prn=10 gst_tow=371490\n", + "MACSEQ Verified on 2380/371549 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=10 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371549 prn=23 gst_tow=371490\n", + "MACSEQ Verified on 2380/371549 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=24 adkd=0 navmsg is not available.\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371549 prn=4 gst_tow=371490\n", + "MACSEQ Verified on 2380/371549 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/371579 prn=19 gst_tow=371550\n", + "MAC Look-up Table verified on 2380/371579 prn=19 gst_tow=371520\n", + "MACSEQ Verified on 2380/371579 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=19 adkd= 4 tag verified\n", + "4 prn_d=36 adkd=0 navmsg is not available.\n", + "5 prn_d=19 adkd=12 slow-MAC is skipped\n", + "6 prn_d=30 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371579 prn=21 gst_tow=371520\n", + "MACSEQ Verified on 2380/371579 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "6 prn_d=31 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371579 prn=29 gst_tow=371520\n", + "MACSEQ Verified on 2380/371579 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 4 tag verified\n", + "4 prn_d=30 adkd=0 navmsg is not available.\n", + "5 prn_d=29 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371579 prn=27 gst_tow=371520\n", + "MACSEQ Verified on 2380/371579 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=30 adkd=0 navmsg is not available.\n", + "# 3 prn_d=27 adkd= 4 tag verified\n", + "4 prn_d=7 adkd=0 navmsg is not available.\n", + "5 prn_d=27 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371579 prn=12 gst_tow=371520\n", + "MACSEQ Verified on 2380/371579 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "MAC Look-up Table verified on 2380/371579 prn=10 gst_tow=371520\n", + "MACSEQ Verified on 2380/371579 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=10 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "decode_hk succeeded 2380/371579 prn=23 gst_tow=371550 did=5\n", + "MAC Look-up Table verified on 2380/371579 prn=23 gst_tow=371520\n", + "MACSEQ Verified on 2380/371579 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "4 prn_d=31 adkd=0 navmsg is not available.\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "6 prn_d=7 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371579 prn=4 gst_tow=371520\n", + "MACSEQ Verified on 2380/371579 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/371609 prn=19 gst_tow=371580\n", + "MAC Look-up Table verified on 2380/371609 prn=19 gst_tow=371550\n", + "MACSEQ Verified on 2380/371609 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=19 adkd=12 slow-MAC is skipped\n", + "5 prn_d=36 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371609 prn=21 gst_tow=371550\n", + "MACSEQ Verified on 2380/371609 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=30 adkd=0 navmsg is not available.\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371609 prn=29 gst_tow=371550\n", + "MACSEQ Verified on 2380/371609 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=15 adkd=0 navmsg is not available.\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=29 adkd=12 slow-MAC is skipped\n", + "5 prn_d=30 adkd=0 navmsg is not available.\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371609 prn=27 gst_tow=371550\n", + "MACSEQ Verified on 2380/371609 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=15 adkd=0 navmsg is not available.\n", + "3 prn_d=30 adkd=0 navmsg is not available.\n", + "4 prn_d=27 adkd=12 slow-MAC is skipped\n", + "5 prn_d=7 adkd=0 navmsg is not available.\n", + "6 prn_d=30 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371609 prn=12 gst_tow=371550\n", + "MACSEQ Verified on 2380/371609 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=31 adkd=0 navmsg is not available.\n", + "3 prn_d=6 adkd=0 navmsg is not available.\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=6 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371609 prn=10 gst_tow=371550\n", + "MACSEQ Verified on 2380/371609 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "3 prn_d=6 adkd=0 navmsg is not available.\n", + "4 prn_d=10 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=6 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371609 prn=23 gst_tow=371550\n", + "MACSEQ Verified on 2380/371609 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=24 adkd=0 navmsg is not available.\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371609 prn=4 gst_tow=371550\n", + "MACSEQ Verified on 2380/371609 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/371639 prn=19 gst_tow=371610\n", + "MAC Look-up Table verified on 2380/371639 prn=19 gst_tow=371580\n", + "MACSEQ Verified on 2380/371639 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=19 adkd= 4 tag verified\n", + "4 prn_d=36 adkd=0 navmsg is not available.\n", + "5 prn_d=19 adkd=12 slow-MAC is skipped\n", + "6 prn_d=30 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371639 prn=21 gst_tow=371580\n", + "MACSEQ Verified on 2380/371639 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "6 prn_d=31 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371639 prn=29 gst_tow=371580\n", + "MACSEQ Verified on 2380/371639 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 4 tag verified\n", + "4 prn_d=30 adkd=0 navmsg is not available.\n", + "5 prn_d=29 adkd=12 slow-MAC is skipped\n", + "6 prn_d=15 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371639 prn=27 gst_tow=371580\n", + "MACSEQ Verified on 2380/371639 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=30 adkd=0 navmsg is not available.\n", + "# 3 prn_d=27 adkd= 4 tag verified\n", + "4 prn_d=7 adkd=0 navmsg is not available.\n", + "5 prn_d=27 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371639 prn=12 gst_tow=371580\n", + "MACSEQ Verified on 2380/371639 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "MAC Look-up Table verified on 2380/371639 prn=23 gst_tow=371580\n", + "MACSEQ Verified on 2380/371639 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "4 prn_d=31 adkd=0 navmsg is not available.\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "6 prn_d=7 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371639 prn=10 gst_tow=371580\n", + "MACSEQ Verified on 2380/371639 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=10 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371639 prn=4 gst_tow=371580\n", + "MACSEQ Verified on 2380/371639 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/371669 prn=19 gst_tow=371640\n", + "MAC Look-up Table verified on 2380/371669 prn=19 gst_tow=371610\n", + "MACSEQ Verified on 2380/371669 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=19 adkd=12 slow-MAC is skipped\n", + "5 prn_d=36 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371669 prn=21 gst_tow=371610\n", + "MACSEQ Verified on 2380/371669 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=30 adkd=0 navmsg is not available.\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371669 prn=29 gst_tow=371610\n", + "MACSEQ Verified on 2380/371669 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=29 adkd=12 slow-MAC is skipped\n", + "5 prn_d=30 adkd=0 navmsg is not available.\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371669 prn=27 gst_tow=371610\n", + "MACSEQ Verified on 2380/371669 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=15 adkd=0 navmsg is not available.\n", + "3 prn_d=30 adkd=0 navmsg is not available.\n", + "4 prn_d=27 adkd=12 slow-MAC is skipped\n", + "5 prn_d=7 adkd=0 navmsg is not available.\n", + "6 prn_d=30 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371669 prn=12 gst_tow=371610\n", + "MACSEQ Verified on 2380/371669 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=31 adkd=0 navmsg is not available.\n", + "3 prn_d=6 adkd=0 navmsg is not available.\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=6 adkd=12 slow-MAC is skipped\n", + "decode_hk succeeded 2380/371669 prn=23 gst_tow=371640 did=5\n", + "MAC Look-up Table verified on 2380/371669 prn=23 gst_tow=371610\n", + "MACSEQ Verified on 2380/371669 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=24 adkd=0 navmsg is not available.\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371669 prn=10 gst_tow=371610\n", + "MACSEQ Verified on 2380/371669 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "3 prn_d=6 adkd=0 navmsg is not available.\n", + "4 prn_d=10 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=6 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371669 prn=4 gst_tow=371610\n", + "MACSEQ Verified on 2380/371669 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/371699 prn=19 gst_tow=371670\n", + "MAC Look-up Table verified on 2380/371699 prn=19 gst_tow=371640\n", + "MACSEQ Verified on 2380/371699 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=19 adkd= 4 tag verified\n", + "4 prn_d=36 adkd=0 navmsg is not available.\n", + "5 prn_d=19 adkd=12 slow-MAC is skipped\n", + "6 prn_d=30 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371699 prn=21 gst_tow=371640\n", + "MACSEQ Verified on 2380/371699 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "6 prn_d=31 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371699 prn=29 gst_tow=371640\n", + "MACSEQ Verified on 2380/371699 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 4 tag verified\n", + "4 prn_d=30 adkd=0 navmsg is not available.\n", + "5 prn_d=29 adkd=12 slow-MAC is skipped\n", + "6 prn_d=15 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371699 prn=27 gst_tow=371640\n", + "MACSEQ Verified on 2380/371699 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=30 adkd=0 navmsg is not available.\n", + "# 3 prn_d=27 adkd= 4 tag verified\n", + "4 prn_d=7 adkd=0 navmsg is not available.\n", + "5 prn_d=27 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371699 prn=12 gst_tow=371640\n", + "MACSEQ Verified on 2380/371699 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "MAC Look-up Table verified on 2380/371699 prn=10 gst_tow=371640\n", + "MACSEQ Verified on 2380/371699 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=10 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371699 prn=23 gst_tow=371640\n", + "MACSEQ Verified on 2380/371699 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "4 prn_d=31 adkd=0 navmsg is not available.\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "6 prn_d=7 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371699 prn=4 gst_tow=371640\n", + "MACSEQ Verified on 2380/371699 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/371729 prn=19 gst_tow=371700\n", + "MAC Look-up Table verified on 2380/371729 prn=19 gst_tow=371670\n", + "MACSEQ Verified on 2380/371729 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=19 adkd=12 slow-MAC is skipped\n", + "5 prn_d=36 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371729 prn=21 gst_tow=371670\n", + "MACSEQ Verified on 2380/371729 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=30 adkd=0 navmsg is not available.\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371729 prn=29 gst_tow=371670\n", + "MACSEQ Verified on 2380/371729 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=29 adkd=12 slow-MAC is skipped\n", + "5 prn_d=30 adkd=0 navmsg is not available.\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371729 prn=27 gst_tow=371670\n", + "MACSEQ Verified on 2380/371729 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=15 adkd=0 navmsg is not available.\n", + "3 prn_d=30 adkd=0 navmsg is not available.\n", + "4 prn_d=27 adkd=12 slow-MAC is skipped\n", + "5 prn_d=7 adkd=0 navmsg is not available.\n", + "6 prn_d=30 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371729 prn=12 gst_tow=371670\n", + "MACSEQ Verified on 2380/371729 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=31 adkd=0 navmsg is not available.\n", + "3 prn_d=6 adkd=0 navmsg is not available.\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=6 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371729 prn=10 gst_tow=371670\n", + "MACSEQ Verified on 2380/371729 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "3 prn_d=6 adkd=0 navmsg is not available.\n", + "4 prn_d=10 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=6 adkd=12 slow-MAC is skipped\n", + "decode_hk succeeded 2380/371729 prn=23 gst_tow=371700 did=5\n", + "MAC Look-up Table verified on 2380/371729 prn=23 gst_tow=371670\n", + "MACSEQ Verified on 2380/371729 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=24 adkd=0 navmsg is not available.\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371729 prn=4 gst_tow=371670\n", + "MACSEQ Verified on 2380/371729 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/371759 prn=19 gst_tow=371730\n", + "MAC Look-up Table verified on 2380/371759 prn=19 gst_tow=371700\n", + "MACSEQ Verified on 2380/371759 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=19 adkd= 4 tag verified\n", + "4 prn_d=36 adkd=0 navmsg is not available.\n", + "5 prn_d=19 adkd=12 slow-MAC is skipped\n", + "6 prn_d=30 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371759 prn=21 gst_tow=371700\n", + "MACSEQ Verified on 2380/371759 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "6 prn_d=31 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371759 prn=29 gst_tow=371700\n", + "MACSEQ Verified on 2380/371759 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 4 tag verified\n", + "4 prn_d=30 adkd=0 navmsg is not available.\n", + "5 prn_d=29 adkd=12 slow-MAC is skipped\n", + "6 prn_d=15 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371759 prn=27 gst_tow=371700\n", + "MACSEQ Verified on 2380/371759 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=30 adkd=0 navmsg is not available.\n", + "# 3 prn_d=27 adkd= 4 tag verified\n", + "4 prn_d=7 adkd=0 navmsg is not available.\n", + "5 prn_d=27 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371759 prn=12 gst_tow=371700\n", + "MACSEQ Verified on 2380/371759 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "MAC Look-up Table verified on 2380/371759 prn=23 gst_tow=371700\n", + "MACSEQ Verified on 2380/371759 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "4 prn_d=31 adkd=0 navmsg is not available.\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "6 prn_d=7 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371759 prn=10 gst_tow=371700\n", + "MACSEQ Verified on 2380/371759 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=10 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371759 prn=4 gst_tow=371700\n", + "MACSEQ Verified on 2380/371759 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/371789 prn=19 gst_tow=371760\n", + "MAC Look-up Table verified on 2380/371789 prn=19 gst_tow=371730\n", + "MACSEQ Verified on 2380/371789 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=19 adkd=12 slow-MAC is skipped\n", + "5 prn_d=36 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371789 prn=21 gst_tow=371730\n", + "MACSEQ Verified on 2380/371789 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=30 adkd=0 navmsg is not available.\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371789 prn=29 gst_tow=371730\n", + "MACSEQ Verified on 2380/371789 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=29 adkd=12 slow-MAC is skipped\n", + "5 prn_d=30 adkd=0 navmsg is not available.\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371789 prn=27 gst_tow=371730\n", + "MACSEQ Verified on 2380/371789 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=15 adkd=0 navmsg is not available.\n", + "3 prn_d=30 adkd=0 navmsg is not available.\n", + "4 prn_d=27 adkd=12 slow-MAC is skipped\n", + "5 prn_d=7 adkd=0 navmsg is not available.\n", + "6 prn_d=30 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371789 prn=12 gst_tow=371730\n", + "MACSEQ Verified on 2380/371789 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=31 adkd=0 navmsg is not available.\n", + "3 prn_d=6 adkd=0 navmsg is not available.\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=6 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371789 prn=23 gst_tow=371730\n", + "MACSEQ Verified on 2380/371789 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=24 adkd=0 navmsg is not available.\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371789 prn=10 gst_tow=371730\n", + "MACSEQ Verified on 2380/371789 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "3 prn_d=6 adkd=0 navmsg is not available.\n", + "4 prn_d=10 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=6 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371789 prn=4 gst_tow=371730\n", + "MACSEQ Verified on 2380/371789 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/371819 prn=19 gst_tow=371790\n", + "MAC Look-up Table verified on 2380/371819 prn=19 gst_tow=371760\n", + "MACSEQ Verified on 2380/371819 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=19 adkd= 4 tag verified\n", + "4 prn_d=36 adkd=0 navmsg is not available.\n", + "5 prn_d=19 adkd=12 slow-MAC is skipped\n", + "6 prn_d=30 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371819 prn=21 gst_tow=371760\n", + "MACSEQ Verified on 2380/371819 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "6 prn_d=31 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371819 prn=29 gst_tow=371760\n", + "MACSEQ Verified on 2380/371819 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 4 tag verified\n", + "4 prn_d=30 adkd=0 navmsg is not available.\n", + "5 prn_d=29 adkd=12 slow-MAC is skipped\n", + "6 prn_d=15 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371819 prn=27 gst_tow=371760\n", + "MACSEQ Verified on 2380/371819 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=30 adkd=0 navmsg is not available.\n", + "# 3 prn_d=27 adkd= 4 tag verified\n", + "4 prn_d=7 adkd=0 navmsg is not available.\n", + "5 prn_d=27 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371819 prn=12 gst_tow=371760\n", + "MACSEQ Verified on 2380/371819 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "decode_hk succeeded 2380/371819 prn=23 gst_tow=371790 did=5\n", + "MAC Look-up Table verified on 2380/371819 prn=23 gst_tow=371760\n", + "MACSEQ Verified on 2380/371819 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "4 prn_d=31 adkd=0 navmsg is not available.\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "6 prn_d=7 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371819 prn=10 gst_tow=371760\n", + "MACSEQ Verified on 2380/371819 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=10 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/371819 prn=4 gst_tow=371760\n", + "MACSEQ Verified on 2380/371819 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/371849 prn=19 gst_tow=371820\n", + "MAC Look-up Table verified on 2380/371849 prn=19 gst_tow=371790\n", + "MACSEQ Verified on 2380/371849 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=19 adkd=12 slow-MAC is skipped\n", + "5 prn_d=36 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371849 prn=21 gst_tow=371790\n", + "MACSEQ Verified on 2380/371849 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=30 adkd=0 navmsg is not available.\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371849 prn=29 gst_tow=371790\n", + "MACSEQ Verified on 2380/371849 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=29 adkd=12 slow-MAC is skipped\n", + "5 prn_d=30 adkd=0 navmsg is not available.\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371849 prn=27 gst_tow=371790\n", + "MACSEQ Verified on 2380/371849 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=15 adkd=0 navmsg is not available.\n", + "3 prn_d=30 adkd=0 navmsg is not available.\n", + "4 prn_d=27 adkd=12 slow-MAC is skipped\n", + "5 prn_d=7 adkd=0 navmsg is not available.\n", + "6 prn_d=30 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371849 prn=12 gst_tow=371790\n", + "MACSEQ Verified on 2380/371849 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=31 adkd=0 navmsg is not available.\n", + "3 prn_d=6 adkd=0 navmsg is not available.\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=6 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371849 prn=23 gst_tow=371790\n", + "MACSEQ Verified on 2380/371849 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=110 tag verified\n", + "2 prn_d=24 adkd=0 navmsg is not available.\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371849 prn=4 gst_tow=371790\n", + "MACSEQ Verified on 2380/371849 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/371849 prn=10 gst_tow=371790\n", + "MACSEQ Verified on 2380/371849 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=110 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "3 prn_d=6 adkd=0 navmsg is not available.\n", + "4 prn_d=10 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=6 adkd=12 slow-MAC is skipped\n", + "Key chain not verified 2380/371939 prn=19 gst_tow=371910\n", + "decode_hk succeeded 2380/371939 prn=29 gst_tow=371910 did=5\n", + "Key chain verified 2380/371939 prn=29 gst_tow=371910\n", + "MAC Look-up Table verified on 2380/371939 prn=29 gst_tow=371880\n", + "MACSEQ not verified on 2380/371939 prn=29\n", + "MAC Look-up Table verified on 2380/371939 prn=27 gst_tow=371880\n", + "MACSEQ not verified on 2380/371939 prn=27\n", + "MAC Look-up Table verified on 2380/371939 prn=23 gst_tow=371880\n", + "MACSEQ not verified on 2380/371939 prn=23\n", + "MAC Look-up Table verified on 2380/371939 prn=12 gst_tow=371880\n", + "MACSEQ not verified on 2380/371939 prn=12\n", + "MAC Look-up Table verified on 2380/371939 prn=10 gst_tow=371880\n", + "MACSEQ not verified on 2380/371939 prn=10\n", + "MAC Look-up Table verified on 2380/371939 prn=4 gst_tow=371880\n", + "MACSEQ not verified on 2380/371939 prn=4\n", + "MAC Look-up Table verified on 2380/371939 prn=21 gst_tow=371880\n", + "MACSEQ not verified on 2380/371939 prn=21\n", + "Key chain not verified 2380/372119 prn=19 gst_tow=372090\n", + "Key chain verified 2380/372119 prn=21 gst_tow=372090\n", + "MAC Look-up Table not verified on 2380/372119 prn=21 gst_tow=372060\n", + "MAC Look-up Table not verified on 2380/372119 prn=29 gst_tow=372060\n", + "MAC Look-up Table not verified on 2380/372119 prn=27 gst_tow=372060\n", + "MAC Look-up Table not verified on 2380/372119 prn=23 gst_tow=372060\n", + "MAC Look-up Table not verified on 2380/372119 prn=12 gst_tow=372060\n", + "MAC Look-up Table not verified on 2380/372119 prn=10 gst_tow=372060\n", + "MAC Look-up Table not verified on 2380/372119 prn=4 gst_tow=372060\n", + "Key chain not verified 2380/372179 prn=19 gst_tow=372150\n", + "Key chain verified 2380/372179 prn=29 gst_tow=372150\n", + "MAC Look-up Table not verified on 2380/372179 prn=29 gst_tow=372120\n", + "MAC Look-up Table not verified on 2380/372179 prn=27 gst_tow=372120\n", + "MAC Look-up Table not verified on 2380/372179 prn=23 gst_tow=372120\n", + "MAC Look-up Table not verified on 2380/372179 prn=12 gst_tow=372120\n", + "MAC Look-up Table not verified on 2380/372179 prn=4 gst_tow=372120\n", + "MAC Look-up Table not verified on 2380/372179 prn=10 gst_tow=372120\n", + "MAC Look-up Table not verified on 2380/372179 prn=21 gst_tow=372120\n", + "Key chain verified 2380/372209 prn=19 gst_tow=372180\n", + "MAC Look-up Table verified on 2380/372209 prn=19 gst_tow=372150\n", + "MACSEQ Verified on 2380/372209 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=19 adkd=12 slow-MAC is skipped\n", + "5 prn_d=36 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "decode_hk succeeded 2380/372209 prn=29 gst_tow=372180 did=5\n", + "MAC Look-up Table verified on 2380/372209 prn=29 gst_tow=372150\n", + "MACSEQ Verified on 2380/372209 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=29 adkd=12 slow-MAC is skipped\n", + "5 prn_d=30 adkd=0 navmsg is not available.\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372209 prn=27 gst_tow=372150\n", + "MACSEQ Verified on 2380/372209 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=15 adkd=0 navmsg is not available.\n", + "3 prn_d=30 adkd=0 navmsg is not available.\n", + "4 prn_d=27 adkd=12 slow-MAC is skipped\n", + "5 prn_d=7 adkd=0 navmsg is not available.\n", + "6 prn_d=30 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372209 prn=23 gst_tow=372150\n", + "MACSEQ Verified on 2380/372209 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=24 adkd=0 navmsg is not available.\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372209 prn=4 gst_tow=372150\n", + "MACSEQ Verified on 2380/372209 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372209 prn=12 gst_tow=372150\n", + "MACSEQ Verified on 2380/372209 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=31 adkd=0 navmsg is not available.\n", + "3 prn_d=6 adkd=0 navmsg is not available.\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=6 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372209 prn=10 gst_tow=372150\n", + "MACSEQ Verified on 2380/372209 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "3 prn_d=6 adkd=0 navmsg is not available.\n", + "4 prn_d=10 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=6 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372209 prn=21 gst_tow=372150\n", + "MACSEQ Verified on 2380/372209 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=30 adkd=0 navmsg is not available.\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/372239 prn=19 gst_tow=372210\n", + "MAC Look-up Table verified on 2380/372239 prn=19 gst_tow=372180\n", + "MACSEQ Verified on 2380/372239 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=19 adkd= 4 tag verified\n", + "4 prn_d=36 adkd=0 navmsg is not available.\n", + "5 prn_d=19 adkd=12 slow-MAC is skipped\n", + "6 prn_d=30 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/372239 prn=29 gst_tow=372180\n", + "MACSEQ Verified on 2380/372239 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 4 tag verified\n", + "4 prn_d=30 adkd=0 navmsg is not available.\n", + "5 prn_d=29 adkd=12 slow-MAC is skipped\n", + "6 prn_d=15 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/372239 prn=27 gst_tow=372180\n", + "MACSEQ Verified on 2380/372239 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=30 adkd=0 navmsg is not available.\n", + "# 3 prn_d=27 adkd= 4 tag verified\n", + "4 prn_d=7 adkd=0 navmsg is not available.\n", + "5 prn_d=27 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/372239 prn=23 gst_tow=372180\n", + "MACSEQ Verified on 2380/372239 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "4 prn_d=31 adkd=0 navmsg is not available.\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "6 prn_d=7 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/372239 prn=4 gst_tow=372180\n", + "MACSEQ Verified on 2380/372239 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/372239 prn=12 gst_tow=372180\n", + "MACSEQ Verified on 2380/372239 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "MAC Look-up Table verified on 2380/372239 prn=10 gst_tow=372180\n", + "MACSEQ Verified on 2380/372239 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=10 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/372239 prn=21 gst_tow=372180\n", + "MACSEQ Verified on 2380/372239 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "6 prn_d=31 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/372269 prn=19 gst_tow=372240\n", + "MAC Look-up Table verified on 2380/372269 prn=19 gst_tow=372210\n", + "MACSEQ Verified on 2380/372269 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=6 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=19 adkd=12 slow-MAC is skipped\n", + "5 prn_d=36 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372269 prn=21 gst_tow=372210\n", + "MACSEQ Verified on 2380/372269 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=30 adkd=0 navmsg is not available.\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372269 prn=29 gst_tow=372210\n", + "MACSEQ Verified on 2380/372269 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=29 adkd=12 slow-MAC is skipped\n", + "5 prn_d=30 adkd=0 navmsg is not available.\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372269 prn=27 gst_tow=372210\n", + "MACSEQ Verified on 2380/372269 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=15 adkd=0 navmsg is not available.\n", + "3 prn_d=30 adkd=0 navmsg is not available.\n", + "4 prn_d=27 adkd=12 slow-MAC is skipped\n", + "5 prn_d=7 adkd=0 navmsg is not available.\n", + "6 prn_d=30 adkd=12 slow-MAC is skipped\n", + "decode_hk succeeded 2380/372269 prn=23 gst_tow=372240 did=5\n", + "MAC Look-up Table verified on 2380/372269 prn=23 gst_tow=372210\n", + "MACSEQ Verified on 2380/372269 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=24 adkd=0 navmsg is not available.\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372269 prn=12 gst_tow=372210\n", + "MACSEQ Verified on 2380/372269 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=31 adkd=0 navmsg is not available.\n", + "3 prn_d=6 adkd=0 navmsg is not available.\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=6 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372269 prn=10 gst_tow=372210\n", + "MACSEQ Verified on 2380/372269 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "3 prn_d=6 adkd=0 navmsg is not available.\n", + "4 prn_d=10 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=6 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/372299 prn=19 gst_tow=372270\n", + "MAC Look-up Table verified on 2380/372299 prn=19 gst_tow=372240\n", + "MACSEQ Verified on 2380/372299 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d= 4 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=19 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=19 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/372299 prn=29 gst_tow=372240\n", + "MACSEQ Verified on 2380/372299 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 4 tag verified\n", + "4 prn_d=30 adkd=0 navmsg is not available.\n", + "5 prn_d=29 adkd=12 slow-MAC is skipped\n", + "6 prn_d=15 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/372299 prn=27 gst_tow=372240\n", + "MACSEQ Verified on 2380/372299 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=30 adkd=0 navmsg is not available.\n", + "# 3 prn_d=27 adkd= 4 tag verified\n", + "4 prn_d=7 adkd=0 navmsg is not available.\n", + "5 prn_d=27 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/372299 prn=23 gst_tow=372240\n", + "MACSEQ Verified on 2380/372299 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "4 prn_d=31 adkd=0 navmsg is not available.\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "6 prn_d=7 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/372299 prn=12 gst_tow=372240\n", + "MACSEQ Verified on 2380/372299 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d= 4 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "MAC Look-up Table verified on 2380/372299 prn=10 gst_tow=372240\n", + "MACSEQ Verified on 2380/372299 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d= 4 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=10 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "MAC Look-up Table verified on 2380/372299 prn=21 gst_tow=372240\n", + "MACSEQ Verified on 2380/372299 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d= 4 adkd= 0 iodnav=111 tag verified\n", + "Key chain verified 2380/372329 prn=19 gst_tow=372300\n", + "MAC Look-up Table verified on 2380/372329 prn=19 gst_tow=372270\n", + "MACSEQ Verified on 2380/372329 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=30 adkd=0 navmsg is not available.\n", + "# 3 prn_d= 4 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=19 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=4 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372329 prn=21 gst_tow=372270\n", + "MACSEQ Verified on 2380/372329 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=31 adkd=0 navmsg is not available.\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372329 prn=29 gst_tow=372270\n", + "MACSEQ Verified on 2380/372329 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d= 4 adkd= 0 iodnav=111 tag verified\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=29 adkd=12 slow-MAC is skipped\n", + "5 prn_d=30 adkd=0 navmsg is not available.\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372329 prn=27 gst_tow=372270\n", + "MACSEQ Verified on 2380/372329 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=15 adkd=0 navmsg is not available.\n", + "3 prn_d=30 adkd=0 navmsg is not available.\n", + "4 prn_d=27 adkd=12 slow-MAC is skipped\n", + "5 prn_d=7 adkd=0 navmsg is not available.\n", + "6 prn_d=30 adkd=12 slow-MAC is skipped\n", + "decode_hk succeeded 2380/372329 prn=23 gst_tow=372300 did=5\n", + "MAC Look-up Table verified on 2380/372329 prn=23 gst_tow=372270\n", + "MACSEQ Verified on 2380/372329 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d= 4 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372329 prn=12 gst_tow=372270\n", + "MACSEQ Verified on 2380/372329 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d= 4 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=4 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372329 prn=10 gst_tow=372270\n", + "MACSEQ Verified on 2380/372329 prn=10\n", + "# 1 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "# 3 prn_d= 4 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=10 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=4 adkd=12 slow-MAC is skipped\n", + "Key chain not verified 2380/372419 prn=19 gst_tow=372390\n", + "Key chain verified 2380/372419 prn=29 gst_tow=372390\n", + "MAC Look-up Table verified on 2380/372419 prn=29 gst_tow=372360\n", + "MACSEQ not verified on 2380/372419 prn=29\n", + "MAC Look-up Table verified on 2380/372419 prn=27 gst_tow=372360\n", + "MACSEQ not verified on 2380/372419 prn=27\n", + "MAC Look-up Table verified on 2380/372419 prn=23 gst_tow=372360\n", + "MACSEQ not verified on 2380/372419 prn=23\n", + "MAC Look-up Table verified on 2380/372419 prn=12 gst_tow=372360\n", + "MACSEQ not verified on 2380/372419 prn=12\n", + "MAC Look-up Table verified on 2380/372419 prn=21 gst_tow=372360\n", + "MACSEQ not verified on 2380/372419 prn=21\n", + "Key chain verified 2380/372449 prn=19 gst_tow=372420\n", + "MAC Look-up Table verified on 2380/372449 prn=19 gst_tow=372390\n", + "MACSEQ Verified on 2380/372449 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "# 3 prn_d= 4 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=19 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=4 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372449 prn=21 gst_tow=372390\n", + "MACSEQ Verified on 2380/372449 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372449 prn=29 gst_tow=372390\n", + "MACSEQ Verified on 2380/372449 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d= 4 adkd= 0 iodnav=111 tag verified\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=29 adkd=12 slow-MAC is skipped\n", + "5 prn_d=30 adkd=0 navmsg is not available.\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372449 prn=27 gst_tow=372390\n", + "MACSEQ Verified on 2380/372449 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=15 adkd=0 navmsg is not available.\n", + "3 prn_d=30 adkd=0 navmsg is not available.\n", + "4 prn_d=27 adkd=12 slow-MAC is skipped\n", + "5 prn_d=7 adkd=0 navmsg is not available.\n", + "6 prn_d=30 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372449 prn=23 gst_tow=372390\n", + "MACSEQ Verified on 2380/372449 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372449 prn=12 gst_tow=372390\n", + "MACSEQ Verified on 2380/372449 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d= 4 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/372479 prn=19 gst_tow=372450\n", + "MAC Look-up Table verified on 2380/372479 prn=19 gst_tow=372420\n", + "MACSEQ Verified on 2380/372479 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d= 4 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=19 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=19 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/372479 prn=29 gst_tow=372420\n", + "MACSEQ Verified on 2380/372479 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 4 tag verified\n", + "4 prn_d=30 adkd=0 navmsg is not available.\n", + "5 prn_d=29 adkd=12 slow-MAC is skipped\n", + "6 prn_d=15 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/372479 prn=27 gst_tow=372420\n", + "MACSEQ Verified on 2380/372479 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=30 adkd=0 navmsg is not available.\n", + "# 3 prn_d=27 adkd= 4 tag verified\n", + "4 prn_d=7 adkd=0 navmsg is not available.\n", + "5 prn_d=27 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/372479 prn=23 gst_tow=372420\n", + "MACSEQ Verified on 2380/372479 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "4 prn_d=31 adkd=0 navmsg is not available.\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/372479 prn=12 gst_tow=372420\n", + "MACSEQ Verified on 2380/372479 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d= 4 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "6 prn_d=6 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/372479 prn=21 gst_tow=372420\n", + "MACSEQ Verified on 2380/372479 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d= 4 adkd= 0 iodnav=111 tag verified\n", + "Key chain verified 2380/372509 prn=19 gst_tow=372480\n", + "MAC Look-up Table verified on 2380/372509 prn=19 gst_tow=372450\n", + "MACSEQ Verified on 2380/372509 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "# 3 prn_d= 4 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=19 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=4 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372509 prn=29 gst_tow=372450\n", + "MACSEQ Verified on 2380/372509 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d= 4 adkd= 0 iodnav=111 tag verified\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=29 adkd=12 slow-MAC is skipped\n", + "5 prn_d=30 adkd=0 navmsg is not available.\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372509 prn=27 gst_tow=372450\n", + "MACSEQ Verified on 2380/372509 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=15 adkd=0 navmsg is not available.\n", + "3 prn_d=30 adkd=0 navmsg is not available.\n", + "4 prn_d=27 adkd=12 slow-MAC is skipped\n", + "5 prn_d=7 adkd=0 navmsg is not available.\n", + "6 prn_d=30 adkd=12 slow-MAC is skipped\n", + "decode_hk succeeded 2380/372509 prn=23 gst_tow=372480 did=5\n", + "MAC Look-up Table verified on 2380/372509 prn=23 gst_tow=372450\n", + "MACSEQ Verified on 2380/372509 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372509 prn=12 gst_tow=372450\n", + "MACSEQ Verified on 2380/372509 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d= 4 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372509 prn=21 gst_tow=372450\n", + "MACSEQ Verified on 2380/372509 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/372539 prn=19 gst_tow=372510\n", + "MAC Look-up Table verified on 2380/372539 prn=19 gst_tow=372480\n", + "MACSEQ Verified on 2380/372539 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d= 4 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=19 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=19 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/372539 prn=29 gst_tow=372480\n", + "MACSEQ Verified on 2380/372539 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 4 tag verified\n", + "4 prn_d=30 adkd=0 navmsg is not available.\n", + "5 prn_d=29 adkd=12 slow-MAC is skipped\n", + "6 prn_d=15 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/372539 prn=27 gst_tow=372480\n", + "MACSEQ Verified on 2380/372539 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=30 adkd=0 navmsg is not available.\n", + "# 3 prn_d=27 adkd= 4 tag verified\n", + "4 prn_d=7 adkd=0 navmsg is not available.\n", + "5 prn_d=27 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/372539 prn=23 gst_tow=372480\n", + "MACSEQ Verified on 2380/372539 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "4 prn_d=31 adkd=0 navmsg is not available.\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table not verified on 2380/372539 prn=4 gst_tow=372480\n", + "MAC Look-up Table verified on 2380/372539 prn=12 gst_tow=372480\n", + "MACSEQ Verified on 2380/372539 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d= 4 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "6 prn_d=6 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/372539 prn=21 gst_tow=372480\n", + "MACSEQ Verified on 2380/372539 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d= 4 adkd= 0 iodnav=111 tag verified\n", + "Key chain verified 2380/372569 prn=19 gst_tow=372540\n", + "MAC Look-up Table verified on 2380/372569 prn=19 gst_tow=372510\n", + "MACSEQ Verified on 2380/372569 prn=19\n", + "# 1 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "# 3 prn_d= 4 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=19 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=4 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372569 prn=29 gst_tow=372510\n", + "MACSEQ Verified on 2380/372569 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d= 4 adkd= 0 iodnav=111 tag verified\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=29 adkd=12 slow-MAC is skipped\n", + "5 prn_d=30 adkd=0 navmsg is not available.\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372569 prn=27 gst_tow=372510\n", + "MACSEQ Verified on 2380/372569 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=15 adkd=0 navmsg is not available.\n", + "3 prn_d=30 adkd=0 navmsg is not available.\n", + "4 prn_d=27 adkd=12 slow-MAC is skipped\n", + "5 prn_d=7 adkd=0 navmsg is not available.\n", + "6 prn_d=30 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372569 prn=23 gst_tow=372510\n", + "MACSEQ Verified on 2380/372569 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372569 prn=4 gst_tow=372510\n", + "MACSEQ Verified on 2380/372569 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=111 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372569 prn=12 gst_tow=372510\n", + "MACSEQ Verified on 2380/372569 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d= 4 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372569 prn=21 gst_tow=372510\n", + "MACSEQ Verified on 2380/372569 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=111 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "3 prn_d=7 adkd=0 navmsg is not available.\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=7 adkd=12 slow-MAC is skipped\n", + "Key chain not verified 2380/372629 prn=29 gst_tow=372600\n", + "Key chain verified 2380/372629 prn=27 gst_tow=372600\n", + "MAC Look-up Table not verified on 2380/372629 prn=27 gst_tow=372570\n", + "MAC Look-up Table not verified on 2380/372629 prn=23 gst_tow=372570\n", + "MAC Look-up Table not verified on 2380/372629 prn=4 gst_tow=372570\n", + "MAC Look-up Table not verified on 2380/372629 prn=12 gst_tow=372570\n", + "MAC Look-up Table not verified on 2380/372629 prn=21 gst_tow=372570\n", + "Key chain not verified 2380/372749 prn=29 gst_tow=372720\n", + "Key chain verified 2380/372749 prn=23 gst_tow=372720\n", + "MAC Look-up Table not verified on 2380/372749 prn=23 gst_tow=372690\n", + "MAC Look-up Table not verified on 2380/372749 prn=27 gst_tow=372690\n", + "MAC Look-up Table not verified on 2380/372749 prn=4 gst_tow=372690\n", + "MAC Look-up Table not verified on 2380/372749 prn=12 gst_tow=372690\n", + "MAC Look-up Table not verified on 2380/372749 prn=21 gst_tow=372690\n", + "Key chain verified 2380/372779 prn=29 gst_tow=372750\n", + "MAC Look-up Table verified on 2380/372779 prn=29 gst_tow=372720\n", + "MACSEQ Verified on 2380/372779 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=29 adkd= 4 tag verified\n", + "4 prn_d=7 adkd=0 navmsg is not available.\n", + "5 prn_d=29 adkd=12 slow-MAC is skipped\n", + "6 prn_d=30 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/372779 prn=23 gst_tow=372720\n", + "MACSEQ Verified on 2380/372779 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "4 prn_d=31 adkd=0 navmsg is not available.\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/372779 prn=27 gst_tow=372720\n", + "MACSEQ Verified on 2380/372779 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=27 adkd= 4 tag verified\n", + "4 prn_d=30 adkd=0 navmsg is not available.\n", + "5 prn_d=27 adkd=12 slow-MAC is skipped\n", + "6 prn_d=7 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/372779 prn=4 gst_tow=372720\n", + "MACSEQ Verified on 2380/372779 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "# 4 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=6 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/372779 prn=12 gst_tow=372720\n", + "MACSEQ Verified on 2380/372779 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "MAC Look-up Table verified on 2380/372779 prn=21 gst_tow=372720\n", + "MACSEQ Verified on 2380/372779 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "4 prn_d=7 adkd=0 navmsg is not available.\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "Key chain verified 2380/372809 prn=29 gst_tow=372780\n", + "MAC Look-up Table verified on 2380/372809 prn=29 gst_tow=372750\n", + "MACSEQ Verified on 2380/372809 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "2 prn_d=15 adkd=0 navmsg is not available.\n", + "# 3 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=29 adkd=12 slow-MAC is skipped\n", + "5 prn_d=7 adkd=0 navmsg is not available.\n", + "6 prn_d=19 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372809 prn=23 gst_tow=372750\n", + "MACSEQ Verified on 2380/372809 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372809 prn=27 gst_tow=372750\n", + "MACSEQ Verified on 2380/372809 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "# 3 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=27 adkd=12 slow-MAC is skipped\n", + "5 prn_d=30 adkd=0 navmsg is not available.\n", + "6 prn_d=19 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372809 prn=4 gst_tow=372750\n", + "MACSEQ Verified on 2380/372809 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=112 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372809 prn=12 gst_tow=372750\n", + "MACSEQ Verified on 2380/372809 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372809 prn=21 gst_tow=372750\n", + "MACSEQ Verified on 2380/372809 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "5 prn_d=7 adkd=0 navmsg is not available.\n", + "6 prn_d=19 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/372839 prn=29 gst_tow=372810\n", + "MAC Look-up Table verified on 2380/372839 prn=29 gst_tow=372780\n", + "MACSEQ Verified on 2380/372839 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=29 adkd= 4 tag verified\n", + "4 prn_d=7 adkd=0 navmsg is not available.\n", + "5 prn_d=29 adkd=12 slow-MAC is skipped\n", + "6 prn_d=30 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/372839 prn=27 gst_tow=372780\n", + "MACSEQ Verified on 2380/372839 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=27 adkd= 4 tag verified\n", + "4 prn_d=30 adkd=0 navmsg is not available.\n", + "5 prn_d=27 adkd=12 slow-MAC is skipped\n", + "6 prn_d=7 adkd=0 navmsg is not available.\n", + "decode_hk succeeded 2380/372839 prn=23 gst_tow=372810 did=5\n", + "MAC Look-up Table verified on 2380/372839 prn=23 gst_tow=372780\n", + "MACSEQ Verified on 2380/372839 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "4 prn_d=31 adkd=0 navmsg is not available.\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/372839 prn=4 gst_tow=372780\n", + "MACSEQ Verified on 2380/372839 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "# 4 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=6 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/372839 prn=12 gst_tow=372780\n", + "MACSEQ Verified on 2380/372839 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "MAC Look-up Table verified on 2380/372839 prn=21 gst_tow=372780\n", + "MACSEQ Verified on 2380/372839 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "4 prn_d=7 adkd=0 navmsg is not available.\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "Key chain verified 2380/372869 prn=29 gst_tow=372840\n", + "MAC Look-up Table verified on 2380/372869 prn=29 gst_tow=372810\n", + "MACSEQ Verified on 2380/372869 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "2 prn_d=15 adkd=0 navmsg is not available.\n", + "# 3 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=29 adkd=12 slow-MAC is skipped\n", + "5 prn_d=7 adkd=0 navmsg is not available.\n", + "6 prn_d=19 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372869 prn=23 gst_tow=372810\n", + "MACSEQ Verified on 2380/372869 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372869 prn=27 gst_tow=372810\n", + "MACSEQ Verified on 2380/372869 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "# 3 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=27 adkd=12 slow-MAC is skipped\n", + "5 prn_d=30 adkd=0 navmsg is not available.\n", + "6 prn_d=19 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372869 prn=4 gst_tow=372810\n", + "MACSEQ Verified on 2380/372869 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=112 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372869 prn=21 gst_tow=372810\n", + "MACSEQ Verified on 2380/372869 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "5 prn_d=7 adkd=0 navmsg is not available.\n", + "6 prn_d=19 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372869 prn=12 gst_tow=372810\n", + "MACSEQ Verified on 2380/372869 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/372899 prn=29 gst_tow=372870\n", + "MAC Look-up Table verified on 2380/372899 prn=29 gst_tow=372840\n", + "MACSEQ Verified on 2380/372899 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=29 adkd= 4 tag verified\n", + "4 prn_d=7 adkd=0 navmsg is not available.\n", + "5 prn_d=29 adkd=12 slow-MAC is skipped\n", + "6 prn_d=30 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/372899 prn=27 gst_tow=372840\n", + "MACSEQ Verified on 2380/372899 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=27 adkd= 4 tag verified\n", + "4 prn_d=30 adkd=0 navmsg is not available.\n", + "5 prn_d=27 adkd=12 slow-MAC is skipped\n", + "6 prn_d=7 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/372899 prn=23 gst_tow=372840\n", + "MACSEQ Verified on 2380/372899 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "4 prn_d=31 adkd=0 navmsg is not available.\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/372899 prn=4 gst_tow=372840\n", + "MACSEQ Verified on 2380/372899 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "# 4 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=6 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/372899 prn=12 gst_tow=372840\n", + "MACSEQ Verified on 2380/372899 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "MAC Look-up Table verified on 2380/372899 prn=21 gst_tow=372840\n", + "MACSEQ Verified on 2380/372899 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "4 prn_d=7 adkd=0 navmsg is not available.\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "Key chain verified 2380/372929 prn=29 gst_tow=372900\n", + "MAC Look-up Table verified on 2380/372929 prn=29 gst_tow=372870\n", + "MACSEQ Verified on 2380/372929 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "2 prn_d=15 adkd=0 navmsg is not available.\n", + "# 3 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=29 adkd=12 slow-MAC is skipped\n", + "5 prn_d=7 adkd=0 navmsg is not available.\n", + "6 prn_d=19 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372929 prn=23 gst_tow=372870\n", + "MACSEQ Verified on 2380/372929 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372929 prn=27 gst_tow=372870\n", + "MACSEQ Verified on 2380/372929 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "# 3 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=27 adkd=12 slow-MAC is skipped\n", + "5 prn_d=30 adkd=0 navmsg is not available.\n", + "6 prn_d=19 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372929 prn=4 gst_tow=372870\n", + "MACSEQ Verified on 2380/372929 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=112 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372929 prn=21 gst_tow=372870\n", + "MACSEQ Verified on 2380/372929 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "5 prn_d=7 adkd=0 navmsg is not available.\n", + "6 prn_d=19 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372929 prn=12 gst_tow=372870\n", + "MACSEQ Verified on 2380/372929 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/372959 prn=29 gst_tow=372930\n", + "MAC Look-up Table verified on 2380/372959 prn=29 gst_tow=372900\n", + "MACSEQ Verified on 2380/372959 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=29 adkd= 4 tag verified\n", + "4 prn_d=7 adkd=0 navmsg is not available.\n", + "5 prn_d=29 adkd=12 slow-MAC is skipped\n", + "6 prn_d=30 adkd=0 navmsg is not available.\n", + "decode_hk succeeded 2380/372959 prn=23 gst_tow=372930 did=5\n", + "MAC Look-up Table verified on 2380/372959 prn=23 gst_tow=372900\n", + "MACSEQ Verified on 2380/372959 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "4 prn_d=31 adkd=0 navmsg is not available.\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/372959 prn=27 gst_tow=372900\n", + "MACSEQ Verified on 2380/372959 prn=27\n", + "# 1 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=27 adkd= 4 tag verified\n", + "4 prn_d=30 adkd=0 navmsg is not available.\n", + "5 prn_d=27 adkd=12 slow-MAC is skipped\n", + "6 prn_d=7 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/372959 prn=4 gst_tow=372900\n", + "MACSEQ Verified on 2380/372959 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/372959 prn=12 gst_tow=372900\n", + "MACSEQ Verified on 2380/372959 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "4 prn_d=6 adkd=0 navmsg is not available.\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "MAC Look-up Table verified on 2380/372959 prn=21 gst_tow=372900\n", + "MACSEQ Verified on 2380/372959 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "4 prn_d=7 adkd=0 navmsg is not available.\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "Key chain verified 2380/372989 prn=29 gst_tow=372960\n", + "MAC Look-up Table verified on 2380/372989 prn=29 gst_tow=372930\n", + "MACSEQ Verified on 2380/372989 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "2 prn_d=15 adkd=0 navmsg is not available.\n", + "# 3 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=29 adkd=12 slow-MAC is skipped\n", + "5 prn_d=7 adkd=0 navmsg is not available.\n", + "6 prn_d=19 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372989 prn=23 gst_tow=372930\n", + "MACSEQ Verified on 2380/372989 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372989 prn=4 gst_tow=372930\n", + "MACSEQ Verified on 2380/372989 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=112 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372989 prn=21 gst_tow=372930\n", + "MACSEQ Verified on 2380/372989 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "5 prn_d=7 adkd=0 navmsg is not available.\n", + "6 prn_d=19 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/372989 prn=12 gst_tow=372930\n", + "MACSEQ Verified on 2380/372989 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "5 prn_d=6 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/373019 prn=29 gst_tow=372990\n", + "MAC Look-up Table verified on 2380/373019 prn=29 gst_tow=372960\n", + "MACSEQ Verified on 2380/373019 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "# 3 prn_d=29 adkd= 4 tag verified\n", + "# 4 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=29 adkd=12 slow-MAC is skipped\n", + "6 prn_d=7 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373019 prn=23 gst_tow=372960\n", + "MACSEQ Verified on 2380/373019 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "4 prn_d=31 adkd=0 navmsg is not available.\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/373019 prn=4 gst_tow=372960\n", + "MACSEQ Verified on 2380/373019 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "# 4 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373019 prn=21 gst_tow=372960\n", + "MACSEQ Verified on 2380/373019 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "6 prn_d=7 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373019 prn=12 gst_tow=372960\n", + "MACSEQ Verified on 2380/373019 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "Key chain verified 2380/373049 prn=29 gst_tow=373020\n", + "MAC Look-up Table verified on 2380/373049 prn=29 gst_tow=372990\n", + "MACSEQ Verified on 2380/373049 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "2 prn_d=30 adkd=0 navmsg is not available.\n", + "# 3 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "4 prn_d=29 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=27 adkd=12 slow-MAC is skipped\n", + "decode_hk succeeded 2380/373049 prn=23 gst_tow=373020 did=5\n", + "MAC Look-up Table verified on 2380/373049 prn=23 gst_tow=372990\n", + "MACSEQ Verified on 2380/373049 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373049 prn=4 gst_tow=372990\n", + "MACSEQ Verified on 2380/373049 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373049 prn=21 gst_tow=372990\n", + "MACSEQ Verified on 2380/373049 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "6 prn_d=19 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373049 prn=12 gst_tow=372990\n", + "MACSEQ Verified on 2380/373049 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=112 tag verified\n", + "2 prn_d=31 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/373079 prn=29 gst_tow=373050\n", + "MAC Look-up Table verified on 2380/373079 prn=29 gst_tow=373020\n", + "MACSEQ Verified on 2380/373079 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "# 3 prn_d=29 adkd= 4 tag verified\n", + "# 4 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=29 adkd=12 slow-MAC is skipped\n", + "6 prn_d=7 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373079 prn=23 gst_tow=373020\n", + "MACSEQ Verified on 2380/373079 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "4 prn_d=31 adkd=0 navmsg is not available.\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/373079 prn=4 gst_tow=373020\n", + "MACSEQ Verified on 2380/373079 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "# 4 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373079 prn=21 gst_tow=373020\n", + "MACSEQ Verified on 2380/373079 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "6 prn_d=7 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373079 prn=12 gst_tow=373020\n", + "MACSEQ Verified on 2380/373079 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "Key chain verified 2380/373109 prn=29 gst_tow=373080\n", + "MAC Look-up Table verified on 2380/373109 prn=29 gst_tow=373050\n", + "MACSEQ Verified on 2380/373109 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "2 prn_d=30 adkd=0 navmsg is not available.\n", + "# 3 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "4 prn_d=29 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=27 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373109 prn=23 gst_tow=373050\n", + "MACSEQ Verified on 2380/373109 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373109 prn=4 gst_tow=373050\n", + "MACSEQ Verified on 2380/373109 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373109 prn=21 gst_tow=373050\n", + "MACSEQ Verified on 2380/373109 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "6 prn_d=19 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373109 prn=12 gst_tow=373050\n", + "MACSEQ Verified on 2380/373109 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=112 tag verified\n", + "2 prn_d=31 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/373139 prn=29 gst_tow=373110\n", + "MAC Look-up Table verified on 2380/373139 prn=29 gst_tow=373080\n", + "MACSEQ Verified on 2380/373139 prn=29\n", + "# 1 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "# 3 prn_d=29 adkd= 4 tag verified\n", + "# 4 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=29 adkd=12 slow-MAC is skipped\n", + "6 prn_d=7 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373139 prn=23 gst_tow=373080\n", + "MACSEQ Verified on 2380/373139 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "4 prn_d=31 adkd=0 navmsg is not available.\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/373139 prn=4 gst_tow=373080\n", + "MACSEQ Verified on 2380/373139 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "# 4 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table not verified on 2380/373139 prn=6 gst_tow=373080\n", + "MAC Look-up Table verified on 2380/373139 prn=21 gst_tow=373080\n", + "MACSEQ Verified on 2380/373139 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "6 prn_d=7 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373139 prn=12 gst_tow=373080\n", + "MACSEQ Verified on 2380/373139 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "decode_hk succeeded 2380/373169 prn=23 gst_tow=373140 did=5\n", + "Key chain verified 2380/373169 prn=23 gst_tow=373140\n", + "MAC Look-up Table verified on 2380/373169 prn=23 gst_tow=373110\n", + "MACSEQ Verified on 2380/373169 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373169 prn=4 gst_tow=373110\n", + "MACSEQ Verified on 2380/373169 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373169 prn=6 gst_tow=373110\n", + "MACSEQ Verified on 2380/373169 prn=6\n", + "1 prn_d=6 adkd=0 navmsg is not available.\n", + "2 prn_d=5 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=6 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373169 prn=21 gst_tow=373110\n", + "MACSEQ Verified on 2380/373169 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "6 prn_d=19 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373169 prn=12 gst_tow=373110\n", + "MACSEQ Verified on 2380/373169 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=112 tag verified\n", + "2 prn_d=31 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/373199 prn=23 gst_tow=373170\n", + "MAC Look-up Table verified on 2380/373199 prn=23 gst_tow=373140\n", + "MACSEQ Verified on 2380/373199 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "4 prn_d=31 adkd=0 navmsg is not available.\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "MAC Look-up Table verified on 2380/373199 prn=4 gst_tow=373140\n", + "MACSEQ Verified on 2380/373199 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "# 4 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373199 prn=6 gst_tow=373140\n", + "MACSEQ Verified on 2380/373199 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 6 adkd= 4 tag verified\n", + "# 4 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=6 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373199 prn=21 gst_tow=373140\n", + "MACSEQ Verified on 2380/373199 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "MAC Look-up Table verified on 2380/373199 prn=12 gst_tow=373140\n", + "MACSEQ Verified on 2380/373199 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "Key chain verified 2380/373229 prn=23 gst_tow=373200\n", + "MAC Look-up Table verified on 2380/373229 prn=23 gst_tow=373170\n", + "MACSEQ Verified on 2380/373229 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373229 prn=4 gst_tow=373170\n", + "MACSEQ Verified on 2380/373229 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373229 prn=6 gst_tow=373170\n", + "MACSEQ Verified on 2380/373229 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=112 tag verified\n", + "2 prn_d=5 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=6 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373229 prn=12 gst_tow=373170\n", + "MACSEQ Verified on 2380/373229 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=112 tag verified\n", + "2 prn_d=31 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373229 prn=21 gst_tow=373170\n", + "MACSEQ Verified on 2380/373229 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=112 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/373259 prn=23 gst_tow=373230\n", + "MAC Look-up Table verified on 2380/373259 prn=23 gst_tow=373200\n", + "MACSEQ Verified on 2380/373259 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "4 prn_d=31 adkd=0 navmsg is not available.\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "MAC Look-up Table verified on 2380/373259 prn=4 gst_tow=373200\n", + "MACSEQ Verified on 2380/373259 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "# 4 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373259 prn=21 gst_tow=373200\n", + "MACSEQ Verified on 2380/373259 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "MAC Look-up Table verified on 2380/373259 prn=6 gst_tow=373200\n", + "MACSEQ Verified on 2380/373259 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 6 adkd= 4 tag verified\n", + "4 prn_d=9 adkd=0 navmsg is not available.\n", + "5 prn_d=6 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/373259 prn=12 gst_tow=373200\n", + "MACSEQ Verified on 2380/373259 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "decode_hk succeeded 2380/373289 prn=23 gst_tow=373260 did=5\n", + "Key chain verified 2380/373289 prn=23 gst_tow=373260\n", + "MAC Look-up Table verified on 2380/373289 prn=23 gst_tow=373230\n", + "MACSEQ Verified on 2380/373289 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373289 prn=4 gst_tow=373230\n", + "MACSEQ Verified on 2380/373289 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=112 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373289 prn=21 gst_tow=373230\n", + "MACSEQ Verified on 2380/373289 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=112 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373289 prn=6 gst_tow=373230\n", + "MACSEQ Verified on 2380/373289 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=112 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=6 adkd=12 slow-MAC is skipped\n", + "5 prn_d=9 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373289 prn=12 gst_tow=373230\n", + "MACSEQ Verified on 2380/373289 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=112 tag verified\n", + "2 prn_d=31 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/373319 prn=23 gst_tow=373290\n", + "MAC Look-up Table verified on 2380/373319 prn=23 gst_tow=373260\n", + "MACSEQ Verified on 2380/373319 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "4 prn_d=31 adkd=0 navmsg is not available.\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "MAC Look-up Table verified on 2380/373319 prn=4 gst_tow=373260\n", + "MACSEQ Verified on 2380/373319 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "# 4 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373319 prn=21 gst_tow=373260\n", + "MACSEQ Verified on 2380/373319 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "MAC Look-up Table verified on 2380/373319 prn=6 gst_tow=373260\n", + "MACSEQ Verified on 2380/373319 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 6 adkd= 4 tag verified\n", + "4 prn_d=9 adkd=0 navmsg is not available.\n", + "5 prn_d=6 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/373319 prn=12 gst_tow=373260\n", + "MACSEQ Verified on 2380/373319 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=112 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "Key chain verified 2380/373349 prn=23 gst_tow=373320\n", + "MAC Look-up Table verified on 2380/373349 prn=23 gst_tow=373290\n", + "MACSEQ Verified on 2380/373349 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "5 prn_d=31 adkd=0 navmsg is not available.\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373349 prn=4 gst_tow=373290\n", + "MACSEQ Verified on 2380/373349 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373349 prn=6 gst_tow=373290\n", + "MACSEQ Verified on 2380/373349 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=6 adkd=12 slow-MAC is skipped\n", + "5 prn_d=9 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373349 prn=12 gst_tow=373290\n", + "MACSEQ Verified on 2380/373349 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=31 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/373379 prn=23 gst_tow=373350\n", + "MAC Look-up Table verified on 2380/373379 prn=23 gst_tow=373320\n", + "MACSEQ Verified on 2380/373379 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "# 4 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/373379 prn=4 gst_tow=373320\n", + "MACSEQ Verified on 2380/373379 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "# 4 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table not verified on 2380/373379 prn=21 gst_tow=373320\n", + "MAC Look-up Table not verified on 2380/373379 prn=11 gst_tow=373320\n", + "MAC Look-up Table verified on 2380/373379 prn=6 gst_tow=373320\n", + "MACSEQ Verified on 2380/373379 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 3 prn_d= 6 adkd= 4 tag verified\n", + "4 prn_d=9 adkd=0 navmsg is not available.\n", + "5 prn_d=6 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/373379 prn=12 gst_tow=373320\n", + "MACSEQ Verified on 2380/373379 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "decode_hk succeeded 2380/373409 prn=23 gst_tow=373380 did=5\n", + "Key chain verified 2380/373409 prn=23 gst_tow=373380\n", + "MAC Look-up Table verified on 2380/373409 prn=23 gst_tow=373350\n", + "MACSEQ Verified on 2380/373409 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373409 prn=4 gst_tow=373350\n", + "MACSEQ Verified on 2380/373409 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373409 prn=21 gst_tow=373350\n", + "MACSEQ not verified on 2380/373409 prn=21\n", + "MAC Look-up Table verified on 2380/373409 prn=11 gst_tow=373350\n", + "MACSEQ Verified on 2380/373409 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=11 adkd=12 slow-MAC is skipped\n", + "5 prn_d=9 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373409 prn=6 gst_tow=373350\n", + "MACSEQ Verified on 2380/373409 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=36 adkd=0 navmsg is not available.\n", + "# 3 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "4 prn_d=6 adkd=12 slow-MAC is skipped\n", + "5 prn_d=9 adkd=0 navmsg is not available.\n", + "6 prn_d=11 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373409 prn=12 gst_tow=373350\n", + "MACSEQ Verified on 2380/373409 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=9 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=11 adkd= 0 iodnav=106 tag verified\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/373439 prn=21 gst_tow=373410\n", + "MAC Look-up Table verified on 2380/373439 prn=21 gst_tow=373380\n", + "MACSEQ Verified on 2380/373439 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "MAC Look-up Table verified on 2380/373439 prn=23 gst_tow=373380\n", + "MACSEQ Verified on 2380/373439 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "# 4 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/373439 prn=4 gst_tow=373380\n", + "MACSEQ Verified on 2380/373439 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=9 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373439 prn=11 gst_tow=373380\n", + "MACSEQ Verified on 2380/373439 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=11 adkd= 4 tag verified\n", + "4 prn_d=9 adkd=0 navmsg is not available.\n", + "5 prn_d=11 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373439 prn=6 gst_tow=373380\n", + "MACSEQ Verified on 2380/373439 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=9 adkd=0 navmsg is not available.\n", + "# 3 prn_d= 6 adkd= 4 tag verified\n", + "# 4 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=6 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373439 prn=12 gst_tow=373380\n", + "MACSEQ Verified on 2380/373439 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/373469 prn=23 gst_tow=373440\n", + "MAC Look-up Table verified on 2380/373469 prn=23 gst_tow=373410\n", + "MACSEQ Verified on 2380/373469 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373469 prn=4 gst_tow=373410\n", + "MACSEQ Verified on 2380/373469 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=9 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373469 prn=21 gst_tow=373410\n", + "MACSEQ Verified on 2380/373469 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373469 prn=11 gst_tow=373410\n", + "MACSEQ Verified on 2380/373469 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=11 adkd=12 slow-MAC is skipped\n", + "5 prn_d=9 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373469 prn=6 gst_tow=373410\n", + "MACSEQ Verified on 2380/373469 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=5 adkd=0 navmsg is not available.\n", + "3 prn_d=9 adkd=0 navmsg is not available.\n", + "4 prn_d=6 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=9 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373469 prn=12 gst_tow=373410\n", + "MACSEQ Verified on 2380/373469 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=24 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "decode_hk succeeded 2380/373499 prn=23 gst_tow=373470 did=5\n", + "Key chain verified 2380/373499 prn=23 gst_tow=373470\n", + "MAC Look-up Table verified on 2380/373499 prn=23 gst_tow=373440\n", + "MACSEQ Verified on 2380/373499 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "# 4 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/373499 prn=4 gst_tow=373440\n", + "MACSEQ Verified on 2380/373499 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=9 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373499 prn=21 gst_tow=373440\n", + "MACSEQ Verified on 2380/373499 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "MAC Look-up Table verified on 2380/373499 prn=11 gst_tow=373440\n", + "MACSEQ Verified on 2380/373499 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=11 adkd= 4 tag verified\n", + "4 prn_d=9 adkd=0 navmsg is not available.\n", + "5 prn_d=11 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373499 prn=6 gst_tow=373440\n", + "MACSEQ Verified on 2380/373499 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=9 adkd=0 navmsg is not available.\n", + "# 3 prn_d= 6 adkd= 4 tag verified\n", + "# 4 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=6 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373499 prn=12 gst_tow=373440\n", + "MACSEQ Verified on 2380/373499 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/373529 prn=23 gst_tow=373500\n", + "MAC Look-up Table verified on 2380/373529 prn=23 gst_tow=373470\n", + "MACSEQ Verified on 2380/373529 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373529 prn=4 gst_tow=373470\n", + "MACSEQ Verified on 2380/373529 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=9 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373529 prn=21 gst_tow=373470\n", + "MACSEQ Verified on 2380/373529 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373529 prn=11 gst_tow=373470\n", + "MACSEQ Verified on 2380/373529 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=11 adkd=12 slow-MAC is skipped\n", + "5 prn_d=9 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373529 prn=6 gst_tow=373470\n", + "MACSEQ Verified on 2380/373529 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=5 adkd=0 navmsg is not available.\n", + "3 prn_d=9 adkd=0 navmsg is not available.\n", + "4 prn_d=6 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=9 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373529 prn=12 gst_tow=373470\n", + "MACSEQ Verified on 2380/373529 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=24 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/373559 prn=23 gst_tow=373530\n", + "MAC Look-up Table verified on 2380/373559 prn=23 gst_tow=373500\n", + "MACSEQ Verified on 2380/373559 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "# 4 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/373559 prn=4 gst_tow=373500\n", + "MACSEQ Verified on 2380/373559 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=9 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373559 prn=21 gst_tow=373500\n", + "MACSEQ Verified on 2380/373559 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "MAC Look-up Table verified on 2380/373559 prn=11 gst_tow=373500\n", + "MACSEQ Verified on 2380/373559 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=11 adkd= 4 tag verified\n", + "4 prn_d=9 adkd=0 navmsg is not available.\n", + "5 prn_d=11 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373559 prn=12 gst_tow=373500\n", + "MACSEQ Verified on 2380/373559 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373559 prn=6 gst_tow=373500\n", + "MACSEQ Verified on 2380/373559 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=9 adkd=0 navmsg is not available.\n", + "# 3 prn_d= 6 adkd= 4 tag verified\n", + "# 4 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=6 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/373589 prn=21 gst_tow=373560\n", + "MAC Look-up Table verified on 2380/373589 prn=21 gst_tow=373530\n", + "MACSEQ Verified on 2380/373589 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "decode_hk succeeded 2380/373589 prn=23 gst_tow=373560 did=5\n", + "MAC Look-up Table verified on 2380/373589 prn=23 gst_tow=373530\n", + "MACSEQ Verified on 2380/373589 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373589 prn=4 gst_tow=373530\n", + "MACSEQ Verified on 2380/373589 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=9 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373589 prn=11 gst_tow=373530\n", + "MACSEQ Verified on 2380/373589 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=11 adkd=12 slow-MAC is skipped\n", + "5 prn_d=9 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373589 prn=12 gst_tow=373530\n", + "MACSEQ Verified on 2380/373589 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=24 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373589 prn=6 gst_tow=373530\n", + "MACSEQ Verified on 2380/373589 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=5 adkd=0 navmsg is not available.\n", + "3 prn_d=9 adkd=0 navmsg is not available.\n", + "4 prn_d=6 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=9 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/373619 prn=21 gst_tow=373590\n", + "MAC Look-up Table verified on 2380/373619 prn=21 gst_tow=373560\n", + "MACSEQ Verified on 2380/373619 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "MAC Look-up Table verified on 2380/373619 prn=23 gst_tow=373560\n", + "MACSEQ Verified on 2380/373619 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "# 4 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/373619 prn=4 gst_tow=373560\n", + "MACSEQ Verified on 2380/373619 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=9 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373619 prn=11 gst_tow=373560\n", + "MACSEQ Verified on 2380/373619 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=11 adkd= 4 tag verified\n", + "4 prn_d=9 adkd=0 navmsg is not available.\n", + "5 prn_d=11 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373619 prn=12 gst_tow=373560\n", + "MACSEQ Verified on 2380/373619 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373619 prn=6 gst_tow=373560\n", + "MACSEQ Verified on 2380/373619 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=9 adkd=0 navmsg is not available.\n", + "# 3 prn_d= 6 adkd= 4 tag verified\n", + "# 4 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=6 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/373649 prn=21 gst_tow=373620\n", + "MAC Look-up Table verified on 2380/373649 prn=21 gst_tow=373590\n", + "MACSEQ Verified on 2380/373649 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373649 prn=23 gst_tow=373590\n", + "MACSEQ Verified on 2380/373649 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373649 prn=4 gst_tow=373590\n", + "MACSEQ Verified on 2380/373649 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=9 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373649 prn=11 gst_tow=373590\n", + "MACSEQ Verified on 2380/373649 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=11 adkd=12 slow-MAC is skipped\n", + "5 prn_d=9 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373649 prn=12 gst_tow=373590\n", + "MACSEQ Verified on 2380/373649 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=24 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373649 prn=6 gst_tow=373590\n", + "MACSEQ Verified on 2380/373649 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=5 adkd=0 navmsg is not available.\n", + "3 prn_d=9 adkd=0 navmsg is not available.\n", + "4 prn_d=6 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=9 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/373679 prn=21 gst_tow=373650\n", + "MAC Look-up Table verified on 2380/373679 prn=21 gst_tow=373620\n", + "MACSEQ Verified on 2380/373679 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "decode_hk succeeded 2380/373679 prn=23 gst_tow=373650 did=5\n", + "MAC Look-up Table verified on 2380/373679 prn=23 gst_tow=373620\n", + "MACSEQ Verified on 2380/373679 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "# 4 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/373679 prn=4 gst_tow=373620\n", + "MACSEQ Verified on 2380/373679 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=36 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373679 prn=11 gst_tow=373620\n", + "MACSEQ Verified on 2380/373679 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=11 adkd= 4 tag verified\n", + "4 prn_d=9 adkd=0 navmsg is not available.\n", + "5 prn_d=11 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373679 prn=12 gst_tow=373620\n", + "MACSEQ Verified on 2380/373679 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373679 prn=6 gst_tow=373620\n", + "MACSEQ Verified on 2380/373679 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=9 adkd=0 navmsg is not available.\n", + "# 3 prn_d= 6 adkd= 4 tag verified\n", + "# 4 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=6 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/373709 prn=21 gst_tow=373680\n", + "MAC Look-up Table verified on 2380/373709 prn=21 gst_tow=373650\n", + "MACSEQ Verified on 2380/373709 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373709 prn=23 gst_tow=373650\n", + "MACSEQ Verified on 2380/373709 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373709 prn=4 gst_tow=373650\n", + "MACSEQ Verified on 2380/373709 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=36 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373709 prn=11 gst_tow=373650\n", + "MACSEQ Verified on 2380/373709 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=11 adkd=12 slow-MAC is skipped\n", + "5 prn_d=9 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373709 prn=12 gst_tow=373650\n", + "MACSEQ Verified on 2380/373709 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=24 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373709 prn=6 gst_tow=373650\n", + "MACSEQ Verified on 2380/373709 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=5 adkd=0 navmsg is not available.\n", + "3 prn_d=9 adkd=0 navmsg is not available.\n", + "4 prn_d=6 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=9 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/373739 prn=21 gst_tow=373710\n", + "MAC Look-up Table verified on 2380/373739 prn=21 gst_tow=373680\n", + "MACSEQ Verified on 2380/373739 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "MAC Look-up Table verified on 2380/373739 prn=23 gst_tow=373680\n", + "MACSEQ Verified on 2380/373739 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "# 4 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/373739 prn=4 gst_tow=373680\n", + "MACSEQ Verified on 2380/373739 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=36 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373739 prn=11 gst_tow=373680\n", + "MACSEQ Verified on 2380/373739 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=11 adkd= 4 tag verified\n", + "4 prn_d=9 adkd=0 navmsg is not available.\n", + "5 prn_d=11 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373739 prn=12 gst_tow=373680\n", + "MACSEQ Verified on 2380/373739 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373739 prn=6 gst_tow=373680\n", + "MACSEQ Verified on 2380/373739 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=9 adkd=0 navmsg is not available.\n", + "# 3 prn_d= 6 adkd= 4 tag verified\n", + "# 4 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=6 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/373769 prn=21 gst_tow=373740\n", + "MAC Look-up Table verified on 2380/373769 prn=21 gst_tow=373710\n", + "MACSEQ Verified on 2380/373769 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "decode_hk succeeded 2380/373769 prn=23 gst_tow=373740 did=5\n", + "MAC Look-up Table verified on 2380/373769 prn=23 gst_tow=373710\n", + "MACSEQ Verified on 2380/373769 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373769 prn=4 gst_tow=373710\n", + "MACSEQ Verified on 2380/373769 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=36 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373769 prn=11 gst_tow=373710\n", + "MACSEQ Verified on 2380/373769 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=11 adkd=12 slow-MAC is skipped\n", + "5 prn_d=9 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373769 prn=12 gst_tow=373710\n", + "MACSEQ Verified on 2380/373769 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=24 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373769 prn=6 gst_tow=373710\n", + "MACSEQ Verified on 2380/373769 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=5 adkd=0 navmsg is not available.\n", + "3 prn_d=9 adkd=0 navmsg is not available.\n", + "4 prn_d=6 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=9 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/373799 prn=21 gst_tow=373770\n", + "MAC Look-up Table verified on 2380/373799 prn=21 gst_tow=373740\n", + "MACSEQ Verified on 2380/373799 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "MAC Look-up Table verified on 2380/373799 prn=23 gst_tow=373740\n", + "MACSEQ Verified on 2380/373799 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "# 4 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/373799 prn=4 gst_tow=373740\n", + "MACSEQ Verified on 2380/373799 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=36 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373799 prn=11 gst_tow=373740\n", + "MACSEQ Verified on 2380/373799 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=11 adkd= 4 tag verified\n", + "4 prn_d=9 adkd=0 navmsg is not available.\n", + "5 prn_d=11 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373799 prn=12 gst_tow=373740\n", + "MACSEQ Verified on 2380/373799 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373799 prn=6 gst_tow=373740\n", + "MACSEQ Verified on 2380/373799 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=9 adkd=0 navmsg is not available.\n", + "# 3 prn_d= 6 adkd= 4 tag verified\n", + "# 4 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=6 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/373829 prn=21 gst_tow=373800\n", + "MAC Look-up Table verified on 2380/373829 prn=21 gst_tow=373770\n", + "MACSEQ Verified on 2380/373829 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373829 prn=23 gst_tow=373770\n", + "MACSEQ Verified on 2380/373829 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373829 prn=4 gst_tow=373770\n", + "MACSEQ Verified on 2380/373829 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=36 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373829 prn=11 gst_tow=373770\n", + "MACSEQ Verified on 2380/373829 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=11 adkd=12 slow-MAC is skipped\n", + "5 prn_d=9 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373829 prn=12 gst_tow=373770\n", + "MACSEQ Verified on 2380/373829 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=24 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373829 prn=6 gst_tow=373770\n", + "MACSEQ Verified on 2380/373829 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=5 adkd=0 navmsg is not available.\n", + "3 prn_d=9 adkd=0 navmsg is not available.\n", + "4 prn_d=6 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=9 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/373859 prn=21 gst_tow=373830\n", + "MAC Look-up Table verified on 2380/373859 prn=21 gst_tow=373800\n", + "MACSEQ Verified on 2380/373859 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "decode_hk succeeded 2380/373859 prn=23 gst_tow=373830 did=5\n", + "MAC Look-up Table verified on 2380/373859 prn=23 gst_tow=373800\n", + "MACSEQ Verified on 2380/373859 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "# 4 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/373859 prn=4 gst_tow=373800\n", + "MACSEQ Verified on 2380/373859 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=36 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373859 prn=11 gst_tow=373800\n", + "MACSEQ Verified on 2380/373859 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=11 adkd= 4 tag verified\n", + "4 prn_d=9 adkd=0 navmsg is not available.\n", + "5 prn_d=11 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373859 prn=12 gst_tow=373800\n", + "MACSEQ Verified on 2380/373859 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373859 prn=6 gst_tow=373800\n", + "MACSEQ Verified on 2380/373859 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=9 adkd=0 navmsg is not available.\n", + "# 3 prn_d= 6 adkd= 4 tag verified\n", + "# 4 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=6 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/373889 prn=21 gst_tow=373860\n", + "MAC Look-up Table verified on 2380/373889 prn=21 gst_tow=373830\n", + "MACSEQ Verified on 2380/373889 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373889 prn=23 gst_tow=373830\n", + "MACSEQ Verified on 2380/373889 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373889 prn=4 gst_tow=373830\n", + "MACSEQ Verified on 2380/373889 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=36 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373889 prn=11 gst_tow=373830\n", + "MACSEQ Verified on 2380/373889 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=11 adkd=12 slow-MAC is skipped\n", + "5 prn_d=9 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373889 prn=12 gst_tow=373830\n", + "MACSEQ Verified on 2380/373889 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=24 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373889 prn=6 gst_tow=373830\n", + "MACSEQ Verified on 2380/373889 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=5 adkd=0 navmsg is not available.\n", + "3 prn_d=9 adkd=0 navmsg is not available.\n", + "4 prn_d=6 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=9 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/373919 prn=21 gst_tow=373890\n", + "MAC Look-up Table verified on 2380/373919 prn=21 gst_tow=373860\n", + "MACSEQ Verified on 2380/373919 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "MAC Look-up Table verified on 2380/373919 prn=23 gst_tow=373860\n", + "MACSEQ Verified on 2380/373919 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "# 4 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/373919 prn=4 gst_tow=373860\n", + "MACSEQ Verified on 2380/373919 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=36 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373919 prn=11 gst_tow=373860\n", + "MACSEQ Verified on 2380/373919 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=11 adkd= 4 tag verified\n", + "4 prn_d=9 adkd=0 navmsg is not available.\n", + "5 prn_d=11 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373919 prn=12 gst_tow=373860\n", + "MACSEQ Verified on 2380/373919 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=113 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373919 prn=6 gst_tow=373860\n", + "MACSEQ Verified on 2380/373919 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=113 tag verified\n", + "2 prn_d=9 adkd=0 navmsg is not available.\n", + "# 3 prn_d= 6 adkd= 4 tag verified\n", + "# 4 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=6 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/373949 prn=21 gst_tow=373920\n", + "MAC Look-up Table verified on 2380/373949 prn=21 gst_tow=373890\n", + "MACSEQ Verified on 2380/373949 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=114 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "decode_hk succeeded 2380/373949 prn=23 gst_tow=373920 did=5\n", + "MAC Look-up Table verified on 2380/373949 prn=23 gst_tow=373890\n", + "MACSEQ Verified on 2380/373949 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373949 prn=4 gst_tow=373890\n", + "MACSEQ Verified on 2380/373949 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=36 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373949 prn=11 gst_tow=373890\n", + "MACSEQ Verified on 2380/373949 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=11 adkd=12 slow-MAC is skipped\n", + "5 prn_d=9 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373949 prn=12 gst_tow=373890\n", + "MACSEQ Verified on 2380/373949 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=114 tag verified\n", + "2 prn_d=24 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/373949 prn=6 gst_tow=373890\n", + "MACSEQ Verified on 2380/373949 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=114 tag verified\n", + "2 prn_d=5 adkd=0 navmsg is not available.\n", + "3 prn_d=9 adkd=0 navmsg is not available.\n", + "4 prn_d=6 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=9 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/373979 prn=21 gst_tow=373950\n", + "MAC Look-up Table verified on 2380/373979 prn=21 gst_tow=373920\n", + "MACSEQ Verified on 2380/373979 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "MAC Look-up Table verified on 2380/373979 prn=23 gst_tow=373920\n", + "MACSEQ Verified on 2380/373979 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "# 4 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/373979 prn=4 gst_tow=373920\n", + "MACSEQ Verified on 2380/373979 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=36 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373979 prn=11 gst_tow=373920\n", + "MACSEQ Verified on 2380/373979 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=11 adkd= 4 tag verified\n", + "4 prn_d=9 adkd=0 navmsg is not available.\n", + "5 prn_d=11 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373979 prn=12 gst_tow=373920\n", + "MACSEQ Verified on 2380/373979 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/373979 prn=6 gst_tow=373920\n", + "MACSEQ Verified on 2380/373979 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=114 tag verified\n", + "2 prn_d=9 adkd=0 navmsg is not available.\n", + "# 3 prn_d= 6 adkd= 4 tag verified\n", + "# 4 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=6 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/374009 prn=21 gst_tow=373980\n", + "MAC Look-up Table verified on 2380/374009 prn=21 gst_tow=373950\n", + "MACSEQ Verified on 2380/374009 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=114 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374009 prn=23 gst_tow=373950\n", + "MACSEQ Verified on 2380/374009 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374009 prn=4 gst_tow=373950\n", + "MACSEQ Verified on 2380/374009 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=36 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374009 prn=11 gst_tow=373950\n", + "MACSEQ Verified on 2380/374009 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=11 adkd=12 slow-MAC is skipped\n", + "5 prn_d=9 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374009 prn=12 gst_tow=373950\n", + "MACSEQ Verified on 2380/374009 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=114 tag verified\n", + "2 prn_d=24 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374009 prn=6 gst_tow=373950\n", + "MACSEQ Verified on 2380/374009 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=114 tag verified\n", + "2 prn_d=5 adkd=0 navmsg is not available.\n", + "3 prn_d=9 adkd=0 navmsg is not available.\n", + "4 prn_d=6 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=9 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/374039 prn=21 gst_tow=374010\n", + "MAC Look-up Table verified on 2380/374039 prn=21 gst_tow=373980\n", + "MACSEQ Verified on 2380/374039 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "decode_hk succeeded 2380/374039 prn=23 gst_tow=374010 did=5\n", + "MAC Look-up Table verified on 2380/374039 prn=23 gst_tow=373980\n", + "MACSEQ Verified on 2380/374039 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "# 4 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/374039 prn=4 gst_tow=373980\n", + "MACSEQ Verified on 2380/374039 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=36 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/374039 prn=11 gst_tow=373980\n", + "MACSEQ Verified on 2380/374039 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=11 adkd= 4 tag verified\n", + "4 prn_d=9 adkd=0 navmsg is not available.\n", + "5 prn_d=11 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/374039 prn=12 gst_tow=373980\n", + "MACSEQ Verified on 2380/374039 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/374039 prn=6 gst_tow=373980\n", + "MACSEQ Verified on 2380/374039 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=114 tag verified\n", + "2 prn_d=9 adkd=0 navmsg is not available.\n", + "# 3 prn_d= 6 adkd= 4 tag verified\n", + "# 4 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=6 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/374069 prn=21 gst_tow=374040\n", + "MAC Look-up Table verified on 2380/374069 prn=21 gst_tow=374010\n", + "MACSEQ Verified on 2380/374069 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=114 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374069 prn=23 gst_tow=374010\n", + "MACSEQ Verified on 2380/374069 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374069 prn=4 gst_tow=374010\n", + "MACSEQ Verified on 2380/374069 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=36 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374069 prn=11 gst_tow=374010\n", + "MACSEQ Verified on 2380/374069 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=11 adkd=12 slow-MAC is skipped\n", + "5 prn_d=9 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374069 prn=12 gst_tow=374010\n", + "MACSEQ Verified on 2380/374069 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=114 tag verified\n", + "2 prn_d=24 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374069 prn=6 gst_tow=374010\n", + "MACSEQ Verified on 2380/374069 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=114 tag verified\n", + "2 prn_d=5 adkd=0 navmsg is not available.\n", + "3 prn_d=9 adkd=0 navmsg is not available.\n", + "4 prn_d=6 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=9 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/374099 prn=21 gst_tow=374070\n", + "MAC Look-up Table verified on 2380/374099 prn=21 gst_tow=374040\n", + "MACSEQ Verified on 2380/374099 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "MAC Look-up Table verified on 2380/374099 prn=23 gst_tow=374040\n", + "MACSEQ Verified on 2380/374099 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "# 4 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/374099 prn=4 gst_tow=374040\n", + "MACSEQ Verified on 2380/374099 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=36 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/374099 prn=11 gst_tow=374040\n", + "MACSEQ Verified on 2380/374099 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=11 adkd= 4 tag verified\n", + "4 prn_d=9 adkd=0 navmsg is not available.\n", + "5 prn_d=11 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/374099 prn=12 gst_tow=374040\n", + "MACSEQ Verified on 2380/374099 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/374099 prn=6 gst_tow=374040\n", + "MACSEQ Verified on 2380/374099 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=114 tag verified\n", + "2 prn_d=9 adkd=0 navmsg is not available.\n", + "# 3 prn_d= 6 adkd= 4 tag verified\n", + "# 4 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=6 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/374129 prn=21 gst_tow=374100\n", + "MAC Look-up Table verified on 2380/374129 prn=21 gst_tow=374070\n", + "MACSEQ Verified on 2380/374129 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=114 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "decode_hk succeeded 2380/374129 prn=23 gst_tow=374100 did=5\n", + "MAC Look-up Table verified on 2380/374129 prn=23 gst_tow=374070\n", + "MACSEQ Verified on 2380/374129 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374129 prn=4 gst_tow=374070\n", + "MACSEQ Verified on 2380/374129 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=36 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374129 prn=11 gst_tow=374070\n", + "MACSEQ Verified on 2380/374129 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=11 adkd=12 slow-MAC is skipped\n", + "5 prn_d=9 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374129 prn=12 gst_tow=374070\n", + "MACSEQ Verified on 2380/374129 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=114 tag verified\n", + "2 prn_d=24 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374129 prn=6 gst_tow=374070\n", + "MACSEQ Verified on 2380/374129 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "3 prn_d=9 adkd=0 navmsg is not available.\n", + "4 prn_d=6 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=9 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/374159 prn=21 gst_tow=374130\n", + "MAC Look-up Table verified on 2380/374159 prn=21 gst_tow=374100\n", + "MACSEQ Verified on 2380/374159 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "MAC Look-up Table verified on 2380/374159 prn=23 gst_tow=374100\n", + "MACSEQ Verified on 2380/374159 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "# 4 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/374159 prn=4 gst_tow=374100\n", + "MACSEQ Verified on 2380/374159 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=36 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/374159 prn=11 gst_tow=374100\n", + "MACSEQ Verified on 2380/374159 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=11 adkd= 4 tag verified\n", + "4 prn_d=9 adkd=0 navmsg is not available.\n", + "5 prn_d=11 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/374159 prn=12 gst_tow=374100\n", + "MACSEQ Verified on 2380/374159 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/374159 prn=6 gst_tow=374100\n", + "MACSEQ Verified on 2380/374159 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=114 tag verified\n", + "2 prn_d=9 adkd=0 navmsg is not available.\n", + "# 3 prn_d= 6 adkd= 4 tag verified\n", + "# 4 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=6 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/374189 prn=21 gst_tow=374160\n", + "MAC Look-up Table verified on 2380/374189 prn=21 gst_tow=374130\n", + "MACSEQ Verified on 2380/374189 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=114 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=29 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374189 prn=23 gst_tow=374130\n", + "MACSEQ Verified on 2380/374189 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374189 prn=4 gst_tow=374130\n", + "MACSEQ Verified on 2380/374189 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=36 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374189 prn=11 gst_tow=374130\n", + "MACSEQ Verified on 2380/374189 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=11 adkd=12 slow-MAC is skipped\n", + "5 prn_d=9 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374189 prn=12 gst_tow=374130\n", + "MACSEQ Verified on 2380/374189 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=114 tag verified\n", + "2 prn_d=24 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374189 prn=6 gst_tow=374130\n", + "MACSEQ Verified on 2380/374189 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "3 prn_d=9 adkd=0 navmsg is not available.\n", + "4 prn_d=6 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=9 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/374219 prn=21 gst_tow=374190\n", + "MAC Look-up Table verified on 2380/374219 prn=21 gst_tow=374160\n", + "MACSEQ Verified on 2380/374219 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "decode_hk succeeded 2380/374219 prn=23 gst_tow=374190 did=5\n", + "MAC Look-up Table verified on 2380/374219 prn=23 gst_tow=374160\n", + "MACSEQ Verified on 2380/374219 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "# 4 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/374219 prn=4 gst_tow=374160\n", + "MACSEQ Verified on 2380/374219 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=36 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/374219 prn=11 gst_tow=374160\n", + "MACSEQ Verified on 2380/374219 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=11 adkd= 4 tag verified\n", + "4 prn_d=9 adkd=0 navmsg is not available.\n", + "5 prn_d=11 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/374219 prn=12 gst_tow=374160\n", + "MACSEQ Verified on 2380/374219 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/374219 prn=6 gst_tow=374160\n", + "MACSEQ Verified on 2380/374219 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=114 tag verified\n", + "2 prn_d=9 adkd=0 navmsg is not available.\n", + "# 3 prn_d= 6 adkd= 4 tag verified\n", + "# 4 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=6 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/374249 prn=21 gst_tow=374220\n", + "MAC Look-up Table verified on 2380/374249 prn=21 gst_tow=374190\n", + "MACSEQ Verified on 2380/374249 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=114 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "6 prn_d=19 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374249 prn=23 gst_tow=374190\n", + "MACSEQ Verified on 2380/374249 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374249 prn=4 gst_tow=374190\n", + "MACSEQ Verified on 2380/374249 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=36 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374249 prn=11 gst_tow=374190\n", + "MACSEQ Verified on 2380/374249 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=11 adkd=12 slow-MAC is skipped\n", + "5 prn_d=9 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374249 prn=12 gst_tow=374190\n", + "MACSEQ Verified on 2380/374249 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=114 tag verified\n", + "2 prn_d=24 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374249 prn=6 gst_tow=374190\n", + "MACSEQ Verified on 2380/374249 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "3 prn_d=9 adkd=0 navmsg is not available.\n", + "4 prn_d=6 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=9 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/374279 prn=21 gst_tow=374250\n", + "MAC Look-up Table verified on 2380/374279 prn=21 gst_tow=374220\n", + "MACSEQ Verified on 2380/374279 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "MAC Look-up Table verified on 2380/374279 prn=23 gst_tow=374220\n", + "MACSEQ Verified on 2380/374279 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "# 4 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/374279 prn=4 gst_tow=374220\n", + "MACSEQ Verified on 2380/374279 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=36 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/374279 prn=11 gst_tow=374220\n", + "MACSEQ Verified on 2380/374279 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=11 adkd= 4 tag verified\n", + "4 prn_d=9 adkd=0 navmsg is not available.\n", + "5 prn_d=11 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/374279 prn=12 gst_tow=374220\n", + "MACSEQ Verified on 2380/374279 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/374279 prn=6 gst_tow=374220\n", + "MACSEQ Verified on 2380/374279 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=114 tag verified\n", + "2 prn_d=9 adkd=0 navmsg is not available.\n", + "# 3 prn_d= 6 adkd= 4 tag verified\n", + "# 4 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=6 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/374309 prn=21 gst_tow=374280\n", + "MAC Look-up Table verified on 2380/374309 prn=21 gst_tow=374250\n", + "MACSEQ Verified on 2380/374309 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=114 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "6 prn_d=19 adkd=12 slow-MAC is skipped\n", + "decode_hk succeeded 2380/374309 prn=23 gst_tow=374280 did=5\n", + "MAC Look-up Table verified on 2380/374309 prn=23 gst_tow=374250\n", + "MACSEQ Verified on 2380/374309 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374309 prn=4 gst_tow=374250\n", + "MACSEQ Verified on 2380/374309 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=36 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374309 prn=11 gst_tow=374250\n", + "MACSEQ Verified on 2380/374309 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=11 adkd=12 slow-MAC is skipped\n", + "5 prn_d=9 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374309 prn=12 gst_tow=374250\n", + "MACSEQ Verified on 2380/374309 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=114 tag verified\n", + "2 prn_d=24 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374309 prn=6 gst_tow=374250\n", + "MACSEQ Verified on 2380/374309 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "3 prn_d=9 adkd=0 navmsg is not available.\n", + "4 prn_d=6 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=9 adkd=12 slow-MAC is skipped\n", + "Key chain verified 2380/374339 prn=21 gst_tow=374310\n", + "MAC Look-up Table verified on 2380/374339 prn=21 gst_tow=374280\n", + "MACSEQ Verified on 2380/374339 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=21 adkd= 4 tag verified\n", + "# 4 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "5 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=27 adkd= 0 iodnav=112 tag verified\n", + "MAC Look-up Table verified on 2380/374339 prn=23 gst_tow=374280\n", + "MACSEQ Verified on 2380/374339 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "# 3 prn_d=23 adkd= 4 tag verified\n", + "# 4 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "5 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 6 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "MAC Look-up Table verified on 2380/374339 prn=4 gst_tow=374280\n", + "MACSEQ Verified on 2380/374339 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d= 4 adkd= 4 tag verified\n", + "4 prn_d=36 adkd=0 navmsg is not available.\n", + "5 prn_d=4 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/374339 prn=11 gst_tow=374280\n", + "MACSEQ Verified on 2380/374339 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=11 adkd= 4 tag verified\n", + "4 prn_d=9 adkd=0 navmsg is not available.\n", + "5 prn_d=11 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/374339 prn=12 gst_tow=374280\n", + "MACSEQ Verified on 2380/374339 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=12 adkd= 4 tag verified\n", + "# 4 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "5 prn_d=12 adkd=12 slow-MAC is skipped\n", + "6 prn_d=9 adkd=0 navmsg is not available.\n", + "MAC Look-up Table verified on 2380/374339 prn=6 gst_tow=374280\n", + "MACSEQ Verified on 2380/374339 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=114 tag verified\n", + "2 prn_d=9 adkd=0 navmsg is not available.\n", + "# 3 prn_d= 6 adkd= 4 tag verified\n", + "# 4 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "5 prn_d=6 adkd=12 slow-MAC is skipped\n", + "6 prn_d=36 adkd=0 navmsg is not available.\n", + "Key chain verified 2380/374369 prn=21 gst_tow=374340\n", + "MAC Look-up Table verified on 2380/374369 prn=21 gst_tow=374310\n", + "MACSEQ Verified on 2380/374369 prn=21\n", + "# 1 prn_d=21 adkd= 0 iodnav=114 tag verified\n", + "2 prn_d=7 adkd=0 navmsg is not available.\n", + "# 3 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=21 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "6 prn_d=19 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374369 prn=23 gst_tow=374310\n", + "MACSEQ Verified on 2380/374369 prn=23\n", + "# 1 prn_d=23 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "4 prn_d=23 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=29 adkd= 0 iodnav=112 tag verified\n", + "6 prn_d=33 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374369 prn=4 gst_tow=374310\n", + "MACSEQ Verified on 2380/374369 prn=4\n", + "# 1 prn_d= 4 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=4 adkd=12 slow-MAC is skipped\n", + "5 prn_d=36 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374369 prn=11 gst_tow=374310\n", + "MACSEQ Verified on 2380/374369 prn=11\n", + "# 1 prn_d=11 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=11 adkd=12 slow-MAC is skipped\n", + "5 prn_d=9 adkd=0 navmsg is not available.\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374369 prn=12 gst_tow=374310\n", + "MACSEQ Verified on 2380/374369 prn=12\n", + "# 1 prn_d=12 adkd= 0 iodnav=114 tag verified\n", + "2 prn_d=24 adkd=0 navmsg is not available.\n", + "# 3 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "4 prn_d=12 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=33 adkd= 0 iodnav=109 tag verified\n", + "6 prn_d=10 adkd=12 slow-MAC is skipped\n", + "MAC Look-up Table verified on 2380/374369 prn=6 gst_tow=374310\n", + "MACSEQ Verified on 2380/374369 prn=6\n", + "# 1 prn_d= 6 adkd= 0 iodnav=114 tag verified\n", + "# 2 prn_d=19 adkd= 0 iodnav=111 tag verified\n", + "3 prn_d=9 adkd=0 navmsg is not available.\n", + "4 prn_d=6 adkd=12 slow-MAC is skipped\n", + "# 5 prn_d=10 adkd= 0 iodnav=111 tag verified\n", + "6 prn_d=9 adkd=12 slow-MAC is skipped\n" + ] + } + ], + "source": [ + "i = 0\n", + "\n", + "v = v[v['type'] == 0] # E1 only\n", + "tow = np.unique(v['tow'])\n", + "ntow = len(tow)\n", + "nsat = np.zeros((ntow, 3), dtype=int)\n", + "vstatus = np.zeros(ntow, dtype=int)\n", + "\n", + "# nep = 90\n", + "# nep = 180\n", + "nep = 300\n", + "nep = 1799\n", + "\n", + "for i, t in enumerate(tow[0:nep]):\n", + " vi = v[v['tow'] == t]\n", + " for vn in vi:\n", + " tow_ = int(vn['tow'])+tofst\n", + " prn = int(vn['prn'])\n", + " nma.prn_a = prn\n", + " msg = unhexlify(vn['nav']) # I/NAV (120bit+120bit)\n", + " nav, nma_b = nma.load_gal_inav(msg)\n", + " nma.save_gal_inav(nav, prn, tow_)\n", + " if nma_b[0] != 0: # for connected satellite\n", + " nma.decode(nma_b, int(vn['wn']), tow_, prn)\n", + " nsat[i, 1] += 1\n", + "\n", + " nsat[i, 0] = len(vi)\n", + " nsat[i, 2] = nma.nsat # authenticated sat\n", + " vstatus[i] = nma.status" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f164146a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if True:\n", + " fsz = 11\n", + " tmax = 300\n", + "\n", + " fig, ax = plt.subplots()\n", + " plt.plot(tow-tow[0], nsat[:, 0], '.-', label='tracked')\n", + " plt.plot(tow-tow[0], nsat[:, 1], '.-', label='connected')\n", + " plt.plot(tow-tow[0], nsat[:, 2], '.-', label='authenticated')\n", + " plt.grid()\n", + " plt.legend()\n", + " plt.xlim([0, tmax])\n", + " ax.set_xticks(np.arange(0, 300, 30))\n", + " plt.ylabel('number of satellites', fontsize=fsz)\n", + " plt.xlabel('time [s]', fontsize=fsz)\n", + " plt.savefig('osnma-{0:d}-nsat-{1:d}.png'.format(doy, tmax))\n", + " plt.show()\n", + "\n", + " y = np.ones(ntow)\n", + " lbl_t = ['rootkey-loaded', 'rootkey-verified', 'keychain-verified',\n", + " 'utc-verified', 'auth-position']\n", + " fig, ax = plt.subplots()\n", + " for k in range(5):\n", + " idx = np.where(vstatus & (1 << k))\n", + " plt.plot(tow[idx]-tow[0], y[idx]*(k+1), '.', label=lbl_t[k])\n", + " plt.grid()\n", + " ax.set_yticks(np.arange(0, 6))\n", + " ax.set_xticks(np.arange(0, 300, 30))\n", + " plt.legend()\n", + " plt.ylim([0, 6])\n", + " plt.xlim([0, tmax])\n", + " plt.ylabel('status', fontsize=fsz)\n", + " plt.xlabel('time [s]', fontsize=fsz)\n", + " plt.savefig('osnma-{0:d}-status.png'.format(doy))\n", + " plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "1d89aa5f", + "metadata": {}, + "source": [ + "## Example 2: QZSS QZNMA Demonstration\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20a28e12", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from sys import exit as sys_exit\n", + "from binascii import unhexlify\n", + "import numpy as np\n", + "from cssrlib.gnss import prn2sat, uGNSS\n", + "from cssrlib.qznma import qznma, uNavId\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "f2d1b3b7", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "168382c0", + "metadata": {}, + "outputs": [], + "source": [ + "if not os.path.exists('../data/pubkey/qznma/002.der'):\n", + " print('please install public key file from QSS.')\n", + " sys_exit(0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eda16091", + "metadata": {}, + "outputs": [], + "source": [ + "dtype = [('wn', 'int'), ('tow', 'float'), ('prn', 'int'),\n", + " ('type', 'int'), ('len', 'int'), ('nav', 'S512')]\n", + "msg_nav_t = {uNavId.GPS_LNAV: 'LNAV', uNavId.GPS_CNAV: 'CNAV',\n", + " uNavId.GPS_CNAV2: 'CNAV2',\n", + " uNavId.GAL_FNAV: 'F/NAV', uNavId.GAL_INAV: 'I/NAV'}\n", + "\n", + "# prn_ref = -1\n", + "prn_ref = 199\n", + "navmode = uNavId.GPS_LNAV # 1:LNAV, 2:CNAV, 3:CNAV2\n", + "year = 2025\n", + "doy = 233\n", + "session = 'h'\n", + "flg_gnss = True\n", + "\n", + "qz = qznma()\n", + "qz.monlevel = 1\n", + "\n", + "bdir = f'../data/doy{year}-{doy:03d}/'\n", + "\n", + "if navmode == uNavId.GPS_LNAV:\n", + " navfile = bdir+f'{doy:03d}{session}_qzslnav.txt'\n", + "elif navmode == uNavId.GPS_CNAV:\n", + " navfile = bdir+f'{doy:03d}{session}_qzscnav.txt'\n", + "elif navmode == uNavId.GPS_CNAV2:\n", + " navfile = bdir+f'{doy:03d}{session}_qzscnav2.txt'\n", + "\n", + "v = np.genfromtxt(navfile, dtype=dtype)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a4de4b9e", + "metadata": {}, + "outputs": [], + "source": [ + "if navmode == uNavId.GPS_CNAV:\n", + " v = v[v['type'] == 26] # L5 CNAV only\n", + "\n", + "if flg_gnss:\n", + " navfile_n = bdir+f'{doy:03d}{session}_qzsl6.txt'\n", + " navfile_gpslnav = bdir+f'{doy:03d}{session}_gpslnav.txt'\n", + " navfile_gpscnav = bdir+f'{doy:03d}{session}_gpscnav.txt'\n", + " # navfile_gpscnav2 = bdir+'{doy:03d}{session}_gpscnav2.txt'\n", + " navfile_galinav = bdir+f'{doy:03d}{session}_galinav.txt'\n", + " navfile_galfnav = bdir+f'{doy:03d}{session}_galfnav.txt'\n", + "\n", + " # load navigation message\n", + " qz.load_navmsg_lnav(navfile_gpslnav)\n", + " qz.load_navmsg_cnav(navfile_gpscnav)\n", + " qz.load_navmsg_inav(navfile_galinav)\n", + " qz.load_navmsg_fnav(navfile_galfnav)\n", + "\n", + " vn = np.genfromtxt(navfile_n, dtype=dtype)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cd4e671d", + "metadata": {}, + "outputs": [], + "source": [ + "# tow_ = np.unique(v['tow'])\n", + "tow_ = np.arange(v['tow'][0], v['tow'][-1])\n", + "nep = len(tow_)\n", + "# nep = 1200\n", + "\n", + "nsat = np.zeros((nep, 4), dtype=int)\n", + "vstatus = np.zeros(nep, dtype=int)\n", + "\n", + "for k in range(nep):\n", + " vi_ = v[v['tow'] == tow_[k]]\n", + "\n", + " for vi in vi_:\n", + " msg = unhexlify(vi['nav'])\n", + " sat = prn2sat(uGNSS.QZS, vi['prn'])\n", + " qz.decode(tow_[k], msg, None, sat, navmode)\n", + "\n", + " if flg_gnss:\n", + " vin_ = vn[(vn['tow'] == tow_[k]) & (vn['type'] == 1)]\n", + " if prn_ref > 0:\n", + " vin_ = vin_[vin_['prn'] == prn_ref]\n", + "\n", + " for vin in vin_:\n", + " msg_n = unhexlify(vin['nav'])\n", + " qz.decode(tow_[k], None, msg_n, sat, navmode)\n", + "\n", + " nsat[k, 0] = qz.count_tracked_sat(tow_[k])\n", + " nsat[k, 1:] = np.array([qz.nsat[d] for d in qz.nsat])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "290616a3", + "metadata": {}, + "outputs": [], + "source": [ + "if True:\n", + " fsz = 11\n", + "\n", + " tmax = 300\n", + " t = tow_-tow_[0]\n", + "\n", + " fig, ax = plt.subplots()\n", + " # plt.plot(tow_-tow_[0], nsat[:, 0], label='tracked')\n", + " plt.plot(t, nsat[:, 1], '.-', label='GPS ' +\n", + " msg_nav_t[qz.navmode[uGNSS.GPS]])\n", + " plt.plot(t, nsat[:, 2], '.-', label='GAL ' +\n", + " msg_nav_t[qz.navmode[uGNSS.GAL]])\n", + " plt.plot(t, nsat[:, 3], '.-', label='QZS '+msg_nav_t[navmode])\n", + " plt.grid()\n", + " plt.legend()\n", + " plt.xlim([0, tmax])\n", + " # ax.set_xticks(np.arange(0, 300, 30))\n", + " plt.ylabel('number of satellites', fontsize=fsz)\n", + " plt.xlabel('time [s]', fontsize=fsz)\n", + " plt.savefig('qznma-{0:d}-nsat-{1:d}.png'.format(doy, tmax))\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b85617fd", + "metadata": {}, + "source": [ + "## Reference\n", + "\n", + "[^1] Galileo Open Service Navigation Message Authentication (OSNMA)\n", + " Signal-in-Space Interface Control Document (SIS ICD), October, 2023.\n", + "\n", + "[^2] Galileo Open Service Navigation Message Authentication (OSNMA)\n", + " Receiver Guidelines Issue 1.3, January, 2024.\n", + "\n", + "[^3] Quasi-Zenith Satellite System Interface Specification\n", + " Signal Authentication Service (IS-QZSS-SAS-001),\n", + " March, 2024" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/basic.ipynb b/tutorials/basic.ipynb new file mode 100644 index 0000000..32f9ecf --- /dev/null +++ b/tutorials/basic.ipynb @@ -0,0 +1,1091 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a4123944", + "metadata": { + "id": "a4123944" + }, + "source": [ + "# Demonstration of basic functions " + ] + }, + { + "cell_type": "markdown", + "id": "f4191fe5", + "metadata": { + "id": "f4191fe5" + }, + "source": [ + "\n", + "## Examples\n", + "\n", + "This tutorial provides examples to show the basic features of CSSRlib for Standalone, RTK.\n", + "\n", + "- Visualizing orbit of QZSS satellite\n", + "- Showing skyplot\n", + "- Standalone positioning\n", + "- RTK positioning\n", + "\n", + "Note that despite the static setup\n", + "of the antenna, all data sets are processed assuming a non-stationary antenna. A motion model has not been used for the\n", + "receiver position. Instead, a sufficiently large amount of process noise has been added to the variance of the predicted position\n", + "states.\n", + "\n", + "Click on the arrows in the left margin to open or close an example" + ] + }, + { + "cell_type": "markdown", + "id": "b85900d9", + "metadata": { + "id": "b85900d9" + }, + "source": [ + "## Example 1: Visualizing the Orbit of a Satellite\n", + "\n", + "In this first example, we will introduce the basic features of CSSRlib to calculate and visualize a QZSS satellite orbit." + ] + }, + { + "cell_type": "markdown", + "id": "bfe3a51f", + "metadata": { + "id": "bfe3a51f" + }, + "source": [ + "First, we will load the required Python modules such as numpy, matplotlib, and cartopy. Then we will load modules from CSSRlib." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a214e8d3", + "metadata": { + "id": "a214e8d3", + "scrolled": true + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import cartopy.crs as ccrs\n", + "import numpy as np\n", + "\n", + "from cssrlib.rinex import rnxdec\n", + "from cssrlib.gnss import Nav, epoch2time, prn2sat, uGNSS, sat2prn,\\\n", + " timeadd, ecef2pos\n", + "from cssrlib.ephemeris import findeph, eph2pos" + ] + }, + { + "cell_type": "markdown", + "id": "51e1bdf4", + "metadata": { + "id": "51e1bdf4" + }, + "source": [ + "In CSSRlib, the epoch is defined as Python Class `gtime_t` as in RTKlib. The snippet defines epoch \"19/3/2021 0:00:00\", shows the internal variable of `gtime_t`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7e7fe465", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7e7fe465", + "outputId": "f11ace96-58a0-4b45-d08b-c66111d8bafe" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1616112000, 0)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t0 = epoch2time([2021, 3, 19, 0, 0, 0]) # year, month, day, hour, minute, sec\n", + "t0.time,t0.sec" + ] + }, + { + "cell_type": "markdown", + "id": "d4685e83", + "metadata": { + "id": "d4685e83" + }, + "source": [ + "Then, the ephemeris in RINEX format is loaded using RINEX decoder in CSSRlib." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "121d290e", + "metadata": { + "id": "121d290e" + }, + "outputs": [], + "source": [ + "dec = rnxdec()\n", + "nav = dec.decode_nav( 'cssrlib-data/data/doy2021-078/30340780.21q', Nav()) # load RINEX navigation file" + ] + }, + { + "cell_type": "markdown", + "id": "155442bb", + "metadata": { + "id": "155442bb" + }, + "source": [ + "The satellites are identified by the system (GPS, Galileo, QZS, ...) and by PRN number. They should be converted into the internal satellite numbers using prn2sat. This snippet shows the satellite number for QZSS with PRN 194 (QZS-2):" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8a855d63", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8a855d63", + "outputId": "4a472f36-a361-4e98-99f4-266d6565cf44" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "70" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sat = prn2sat(uGNSS.QZS, 194)\n", + "sat" + ] + }, + { + "cell_type": "markdown", + "id": "100629b9", + "metadata": { + "id": "100629b9" + }, + "source": [ + "The orbit position of a GNSS satellite can be calculated using ephemeris defined in the RINEX navigation file. The valid ephemeris for a specific epoch can be searched for using `findeph`. Position and velocity in ECEF and clock bias are calculated by eph2pos. The position in geodetic cordinates can be calculated by ecef2pos:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "821f0d38", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "821f0d38", + "outputId": "56b690f1-ef2b-4666-afb1-7cef7090f210" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rs= [-27695465.67390223 25576355.5431149 23733383.2869802 ] vs= [ 618.82498925 -271.15881898 1281.49729409] dts= 3.4869334074196084e-06\n", + "pos= [5.62300943e-01 2.39595272e+00 3.81752334e+07]\n" + ] + } + ], + "source": [ + "t = t0\n", + "eph = findeph(nav.eph, t, sat)\n", + "rs, vs, dts = eph2pos(t, eph, True)\n", + "print('rs=',rs, 'vs=', vs,'dts=', dts)\n", + "pos = ecef2pos(rs)\n", + "print('pos=',pos)" + ] + }, + { + "cell_type": "markdown", + "id": "9ca6e0b2", + "metadata": { + "id": "9ca6e0b2" + }, + "source": [ + "The orbit of QZS-2 for 1 day (24 hours) is calculated and plotted using Cartopy:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "fc82fdfc", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 400 + }, + "id": "fc82fdfc", + "outputId": "1df8431b-4097-4ec1-cc91-e64677021dd4", + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "lon0 = 135\n", + "plt.figure(figsize=(4, 4))\n", + "ax = plt.axes(projection=ccrs.Orthographic(central_longitude=lon0,\n", + " central_latitude=0))\n", + "ax.coastlines(resolution='50m')\n", + "ax.gridlines()\n", + "ax.stock_img()\n", + "\n", + "n = 24*3600//300\n", + "pos = np.zeros((n, 3))\n", + "r2d = 180/np.pi\n", + "\n", + "for k in range(uGNSS.MAXSAT):\n", + " sat = k+1\n", + " sys, prn = sat2prn(sat)\n", + " if sys != uGNSS.QZS: # skip non QZSS\n", + " continue\n", + " for i in range(n):\n", + " t = timeadd(t0, i*300)\n", + " if eph is None:\n", + " continue\n", + " rs, dts = eph2pos(t, eph)\n", + " pos[i, :] = ecef2pos(rs)\n", + "\n", + " plt.plot(pos[:, 1]*r2d, pos[:, 0]*r2d, 'm-', transform=ccrs.Geodetic())\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d93de7b9", + "metadata": { + "id": "d93de7b9" + }, + "source": [ + "## Example 2: Showing a Skyplot\n", + "\n", + "For the visualization of measurements, CSSRlib supports the skyplots of satellite orbit paths at specific locations. At first, the required modules are loaded." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "acae2d99", + "metadata": { + "id": "acae2d99" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from cssrlib.gnss import Nav, ecef2pos, geodist, satazel, timediff, uGNSS, rSigRnx\n", + "from cssrlib.ephemeris import findeph, eph2pos\n", + "from cssrlib.plot import skyplot, plot_elv\n", + "from cssrlib.rinex import rnxdec" + ] + }, + { + "cell_type": "markdown", + "id": "212a514f", + "metadata": { + "id": "212a514f" + }, + "source": [ + "Then, an instance of a RINEX decoder is generated. In this example, the observation and navigation files measured by a Septentrio Mosaic-X5 receiver are used. We also need to specify which satellite signals will be used." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "7936c023", + "metadata": { + "id": "7936c023" + }, + "outputs": [], + "source": [ + "navfile = 'cssrlib-data/data/doy2021-078/SEPT078M.21P'\n", + "obsfile = 'cssrlib-data/data/doy2021-078/SEPT078M.21O'\n", + "\n", + "dec = rnxdec()\n", + "nav = dec.decode_nav(navfile,Nav())\n", + "\n", + "sigs = [rSigRnx(\"GC1C\"), rSigRnx(\"EC1C\"), rSigRnx(\"JC1C\")]\n", + "dec.setSignals(sigs)" + ] + }, + { + "cell_type": "markdown", + "id": "33661340", + "metadata": { + "id": "33661340" + }, + "source": [ + "Next, the orbit of tracked satellites is calculated for 15 minutes of epoch." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "52f2754b", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "52f2754b", + "outputId": "1df58f43-e83a-4bfc-a073-04cd3cf22e29" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch=899/900" + ] + } + ], + "source": [ + "nep = 15*60 # 15 minutes\n", + "elv = np.ones((nep, uGNSS.MAXSAT))*np.nan\n", + "azm = np.ones((nep, uGNSS.MAXSAT))*np.nan\n", + "t = np.zeros(nep)*np.nan\n", + "\n", + "if dec.decode_obsh(obsfile) >= 0:\n", + " rr = dec.pos\n", + " pos = ecef2pos(rr)\n", + " for ne in range(nep):\n", + " print('\\repoch=%d/%d' % (ne, nep), end='')\n", + " obs = dec.decode_obs()\n", + " if ne == 0:\n", + " t0 = obs.t\n", + " t[ne] = timediff(obs.t, t0)\n", + " for k, sat in enumerate(obs.sat):\n", + " eph = findeph(nav.eph, obs.t, sat)\n", + " if eph is None:\n", + " continue\n", + " rs, dts = eph2pos(obs.t, eph)\n", + " r, e = geodist(rs, rr)\n", + " azm[ne, sat-1], elv[ne, sat-1] = satazel(pos, e)\n", + " dec.fobs.close()" + ] + }, + { + "cell_type": "markdown", + "id": "9173173b", + "metadata": { + "id": "9173173b" + }, + "source": [ + "The elevation and azimuth angles of each satellite are recorded, and the skyplot is generated as below:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "73f6d5a9", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 459 + }, + "id": "73f6d5a9", + "outputId": "a861001d-5c1d-4a0f-817a-af96be58bbd8" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plot_elv(t, elv)\n", + "nsat = skyplot(azm, elv)" + ] + }, + { + "cell_type": "markdown", + "id": "5a058567", + "metadata": { + "id": "5a058567" + }, + "source": [ + "## Example 3: Standalone positioning\n", + "\n", + "This section introduces standalone GNSS positioning using RINEX observations from a Septentrio Mosaic-X5 receiver." + ] + }, + { + "cell_type": "markdown", + "id": "HlMNxLjFD1FK", + "metadata": { + "id": "HlMNxLjFD1FK" + }, + "source": [ + "First, the required Python modules are loaded." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "0b6966d0", + "metadata": { + "id": "0b6966d0" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from cssrlib.rinex import rnxdec\n", + "from cssrlib.gnss import ecef2pos, timediff, dops, ecef2enu, rSigRnx, Nav\n", + "from cssrlib.pntpos import stdpos" + ] + }, + { + "cell_type": "markdown", + "id": "81f53fb6", + "metadata": { + "id": "81f53fb6" + }, + "source": [ + "Then the rover position is defined for reference." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "4395530a", + "metadata": { + "id": "4395530a" + }, + "outputs": [], + "source": [ + "xyz_ref = [-3962108.6726, 3381309.4719, 3668678.6264]\n", + "pos_ref = ecef2pos(xyz_ref)" + ] + }, + { + "cell_type": "markdown", + "id": "c9e2c786", + "metadata": { + "id": "c9e2c786" + }, + "source": [ + "RINEX navigation and observation files are defined, an instance of RINEX decoder is generated, and satellite signals are specified." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "3a6534d6", + "metadata": { + "id": "3a6534d6" + }, + "outputs": [], + "source": [ + "navfile = 'cssrlib-data/data/doy2023-223/NAV223.23p'\n", + "obsfile = 'cssrlib-data/data/doy2023-223/SEPT223Y.23O' # PolaRX5\n", + "dec = rnxdec()\n", + "nav = dec.decode_nav(navfile, Nav(nf=1))\n", + "nav.pmode = 0\n", + "\n", + "sigs = [rSigRnx(\"GC1C\"), rSigRnx(\"EC1C\"), rSigRnx(\"JC1C\"),\n", + " rSigRnx(\"GL1C\"), rSigRnx(\"EL1C\"), rSigRnx(\"JL1C\"),\n", + " rSigRnx(\"GS1C\"), rSigRnx(\"ES1C\"), rSigRnx(\"JS1C\")]\n", + "dec.setSignals(sigs)\n", + "dec.autoSubstituteSignals()" + ] + }, + { + "cell_type": "markdown", + "id": "c05a83bf", + "metadata": { + "id": "c05a83bf" + }, + "source": [ + "The variables for position, DOPs, and number of satellite are defined for 6 minutes epoch." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "b9c7f872", + "metadata": { + "id": "b9c7f872" + }, + "outputs": [], + "source": [ + "nep = 6*60 # 6 minutes\n", + "t = np.zeros(nep)\n", + "enu = np.zeros((nep, 3))\n", + "dop = np.zeros((nep, 4))\n", + "nsat = np.zeros(nep, dtype=int)\n", + "\n", + "std = stdpos(nav, dec.pos, 'test_stdpos.log')\n", + "sol = np.zeros((nep, nav.nx))" + ] + }, + { + "cell_type": "markdown", + "id": "e48d2d16", + "metadata": { + "id": "e48d2d16" + }, + "source": [ + "The standalone GNSS positioning by `std.process()` is conducted for 6 minutes." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "63fee89c", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "63fee89c", + "outputId": "1d5536c5-dd69-4e7f-a031-2ce77de64aab" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch=359/360" + ] + } + ], + "source": [ + "if dec.decode_obsh(obsfile) >= 0:\n", + " nav.x[0:3] = dec.pos\n", + " for ne in range(nep):\n", + " print('\\repoch=%d/%d' % (ne, nep), end='')\n", + " obs = dec.decode_obs()\n", + " if ne == 0:\n", + " t0 = nav.t = obs.t\n", + " t[ne] = timediff(obs.t, t0)\n", + " # nav, az, el = pntpos(obs, nav)\n", + " std.process(obs, cs=None)\n", + " sol[ne, :] = nav.x\n", + " dop[ne, :] = std.dop\n", + " enu[ne, :] = ecef2enu(pos_ref, sol[ne, 0:3]-xyz_ref)\n", + " #nsat[ne] = len(el)\n", + " dec.fobs.close()" + ] + }, + { + "cell_type": "markdown", + "id": "b1d924c7", + "metadata": { + "id": "b1d924c7" + }, + "source": [ + "The ENU position relative to the reference position is plotted." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "c6b68b55", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 393 + }, + "id": "c6b68b55", + "outputId": "a88918ff-43b7-4f6e-9f7e-e2d7207f2305" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plt_enu(t, enu, dmax=0.4):\n", + " plt.figure(figsize=(5,4))\n", + " plt.plot(t, enu)\n", + " plt.ylabel('pos err[m]')\n", + " plt.xlabel('time[s]')\n", + " plt.legend(['east', 'north', 'up'])\n", + " plt.grid()\n", + " plt.axis([0, nep, -dmax, dmax])\n", + " plt.show()\n", + "\n", + "plt_enu(t, enu, 10)" + ] + }, + { + "cell_type": "markdown", + "id": "d2f49f64", + "metadata": { + "id": "d2f49f64" + }, + "source": [ + "To measure the geometry for positioning, DOPs (PDOP, HDOP, VDOP) are plotted:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "5c2d1644", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 393 + }, + "id": "5c2d1644", + "outputId": "cad2e346-925c-48a2-efc8-f0acc09f1ddd" + }, + "outputs": [ + { + "data": { + "image/png": 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Xp549e9LKlSuN87766iuqWLEide7cWRIXlwJ54rFkZ8yYIYnM1Icffkhvvvkm9e/fX5Z57733pFTI65riQXiff/55GdF+wYIFMnQfD+buKigRAgC4UKBPoJTMnIGrIu/cuSNVldZGd+d9O+Lll1+mmJgY+uSTT6Rqc8WKFZLUeNunTp2iESNGmC0fGRkpJUiWlpZGly9fpvbt25stw695RHvL9VQ+Pj7UqlUr2b6rIBECALgQ3xOzt3qyOIkw1ydXtm8tETqqT58+cl/xu+++kxHmeRDe2bNnk9agahQAAKzixivPPfeclARXrVol1ZtPPPGEvMfVmAcOmJds9+/fb/w9ODhYGtv8+OOPZsvwa8sqVNP1cnNz6fDhw7J9V0GJEAAACq0e/ctf/iINXP72t78Z548ePZqGDBki1Zhc3cnJkpcxbSzDjWAmT55MdevWlXuDS5YsoWPHjsmyprjBDTee4eTHJc5bt24ZW6a6AhIhAADY9Mwzz1CFChXozJkzNHDgQOP86OhoOnfuHE2YMEG6TXBjl5EjR9KmTZuMy7z++uuUmppKb7zxBl27dk1Kgt98841Zi1G1GwZPnCTr1asny3CjHFdBIgQAAJv4XiM3erFm4sSJMpnilqGm63KJkKfCWKtmdSXcIwQAAF1DIgQAAF1D1SgAAJSIWrVqOfTYN2dBiRAAAHQNiRAAwAXcoeSjFcoj/iyRCAEAnMjX11d+ZmZmlvShaEbm/c9S/Wxdfo9w165d9MEHH0jP/+TkZFq/fj3169fP5vI7duyQB7Ra4nWrVq1q1qGSt3vlyhWKiIigjz/+mFq3bu3o4QEAuBVvb28qV66c9KNjQUFB8pg1Zz1iLTs7W/r1PYpHrLkLNa67d+9KbPxZ8mfKn22JJMKMjAxJVNzrnx+9Yy/ujMmP3FFVrlzZ+PuaNWto7NixtHDhQmrTpo08mbx79+6yjulyAACeSL3oV5OhM6sMOVkEBgY6LdmWBMu4OAmaFqRcngh5WA6eHMUJjQ/eGh5/ip9wPnToUHnNCZEf8spjV7311lsO7wsAwJ3wH+/Q0FD5O5iTk+O0/fC2udbuqaeeemTVhu5Ajevpp5+WZPioSoIu7z7Bz5nLysqS0Yd5MEd1aA4u7nI1a2xsrHFZLtJHRUXRvn37rG6Lt8OTiof7UD8sZ37JXE2NRUsxMcTlWRDXo/Wo/4gXGH0iN1f24cz9uJoaF+cG/p0nW4pzPp2eCPkqiEt4/GBWTl6ff/45derUSR6nw08xv379OuXl5VGVKlXM1uPXp0+ftrrNmTNn0tSpUwvM53GwuP5da7Zs2UJahLg8C+LyHFqMyd64itMoyemJkIft4EnVrl07eVArP2H8yy+/LNY2ufTI9xRNS4Th4eHSKCckJIS0gq9s+MR37dpVc9UciMtzIC7PocWYHI1LrSF0+yfLcGvQPXv2yO/8hHEuwl+9etVsGX5t62Yoj5TMkyX+gLR08lWIy7MgLs+ixbi0GJO9cRUn7hJpX8tDbXCVKfPz86OWLVvS1q1bje9z/S+/joyMLInDAwAAHXG4RJienk6JiYnG1+fPn5fExuNV1ahRQ6otL126RMuXL5f3uStE7dq1qUmTJtL/g+8Rbtu2jTZv3mzcBldzDh48WO4jcmmR1+FuGmorUgAAALdJhIcOHTLrIK/eq+NEtnTpUukon5SUZHyfW4XyoIycHLkhS/PmzemHH34w2wYP8JiSkkJxcXHSoZ5bmCYkJBRoQAMAAFDiiZBbfBb2nDdOhqZ49GKeijJq1CiZAAAAXEk7z+ABAAAoBiRCAADQNSRCAADQNSRCAADQNSRCAADQNSRCAADQNSRCAADQNSRCAADQNSRCAADQNSRCAADQNSRCAADQNSRCAADQNSRCAADQNSRCAADQNSRCAADQNSRCAADQNSRCAADQNSRCAADQNSRCAADQNSRCAADQNSRCAADQNSRCAADQNSRCAADQNSRCAADQNSRCAADQNSRCAADQNSRCAADQNYcT4a5du6hPnz4UFhZGBoOBNmzYUOjyX3/9NXXt2pUqVapEwcHBFBkZSZs2bTJbZsqUKbIt06lhw4aORwMAAODsRJiRkUERERE0f/58uxMnJ8L4+Hg6fPgwde7cWRLp0aNHzZZr0qQJJScnG6c9e/Y4emgAAAAO83F0hZ49e8pkrzlz5pi9fuedd2jjxo3073//mx5//PEHB+LjQ1WrVnX0cAAAAFybCB9Wfn4+3blzhypUqGA2/+zZs1LdGhAQINWnM2fOpBo1aljdRlZWlkyqtLQ0+ZmTkyOTVqixaCkmhrg8C+LyHFqMydG4ihO7QVEUxeG11JUNBlq/fj3169fP7nXef/99evfdd+n06dNUuXJlmff9999Teno6PfbYY1ItOnXqVLp06RKdOHGCypQpU2AbfE+Rl7G0cuVKCgoKKm44AADg4TIzM2ngwIGUmpoq7VLcLhFyooqJiZGq0aioKJvL3b59m2rWrEmzZs2iYcOG2VUiDA8PlyQaEhJCWsFXNlu2bJF7rL6+vqQViMuzIC7PocWYHI2L80HFihUdSoQuqxpdvXo1DR8+nNatW1doEmTlypWjBg0aUGJiotX3/f39ZbLEH5CWTr4KcXkWxOVZtBiXFmOyN67ixO2SfoSrVq2ioUOHys/evXsXuTxXk547d45CQ0NdcXgAAKBjDpcIOUmZltTOnz9Px44dk8Yv3LglNjZW7u8tX77cWB06ePBgmjt3LrVp04auXLki8wMDA6ls2bLy+7hx46RLBVeHXr58mSZPnkze3t40YMCARxcpAADAoygRHjp0SLo9qF0fxo4dK7/HxcXJa75Pl5SUZFx+0aJFlJubS6+99pqU8NRp9OjRxmUuXrwoSY8by7z00ktyn2///v3SCR8AAMCtSoSdOnWiwtrXLF261Oz1jh077Lp/CAAAUBLwrFEAANA1JEIAANA1JEIAANA1lz9izZmmfXuKgspwB0qDvDYY1N/U363NV38rbJkHy8n/77/By6qrGwpZn18UfRwFjyU/L5/OXDLQxd3npRWtug/LfatxmO/DZL7JMaovbG3Lcv6DWM0/A+MyBuvHUtg+8vLy6OcbBvI+eVWeMWuwcx/qG2axFRKH7fNk/fO3/r2xfe7MfpKB8vJy6VIG0Zkrd4x9mR7FPozzLT4b6981299za/uQ5Yv4/HNz8ig7j+heTh7lkVeh/5ZsfZ8B3NlDPVnGXfCTBLgrRviYteTlj0esAbibIi92Clx0PZhvua550le3b+Nix+yCwmIZIrp37x4FBAaQl9ny5hek6k+bF1TG/5kfo/ULJ8uLhQfbtbxwtnaRYdymlQtw/sl/zm/cuC5PVvH2+rPCr+AFlLXPx2C14KAeo+k5sjwPptu0dgFq7Xya7tNaHJafsaLk04ULF6h2rVpmhQLLcyBHkJ1Jsf1auueTZVzhtafr3C8REnF2V1O8wv8Zf3/wRmHLmM4ns/n3130w+/7vD9b/c579+yDT+fe3xfLy8+mPPy5S9erVyWDwMs43OSTZj/k+zPf9YBXr+7CMg6zON+65wHyTj9OuffAv+Uo+3bx5i8qXL2/8F2JrH/zC8nM2/m7lWAqP78G2rH2GtvZh65wW2Iei0L2sLPLz87//R6mQfVg9R/Z9b/489qLPkzux9u/PYgkqGQZKzXnwuEZt8KLfUm+S9njRzuQHXfNsCTI4fj41lQiHd6yluWeNxscnUa9eTTX1uKQ/44qnXr1aazSuTm4VlyRIB5ItWczPzs6hzZs3U9du3aQq2yzZ2rzIs74P86RfxLoWx2PPPswuKou4SOP+zTzuafv2Hf6Mq4gLjCL3YbKctVitHb/VCxvFVnyKzQtwdR7HdPToMYpo0YK8vb0sYrZ2MfhgfcvPyvSixRhDYRfeVj4/y3nGz6mIONQLYPW9vLx8Sjx3jurWqUsGr/uFAsvPUt1nViZNJh0nQgAoyLTa8P4ch9b3NSjk701U2t/HrRL8o7hwuVCKqElYsGbi4pi8Lx6lXhGhmonJeJGZc5Z6datv10O3HU2EaDUKAAC6hkQIAAC6hkQIAAC6hkQIAAC6hkQIAAC6hkQIAAC6hkQIAAC6hkQIAAC6hkQIAAC6hkQIAAC6hkQIAAC6hkQIAAC6hkQIAAC6hkQIAAC6hkQIAAC6hkQIAAC6hkQIAAC6hkQIAAC6hkQIAAC6hkQIAAC65nAi3LVrF/Xp04fCwsLIYDDQhg0bilxnx44d9MQTT5C/vz/Vq1ePli5dWmCZ+fPnU61atSggIIDatGlDBw8edPTQAAAAnJ8IMzIyKCIiQhKXPc6fP0+9e/emzp0707Fjx2jMmDE0fPhw2rRpk3GZNWvW0NixY2ny5Ml05MgR2X737t3p2rVrjh4eAACAQ3wcW5yoZ8+eMtlr4cKFVLt2bfroo4/kdaNGjWjPnj00e/ZsSXZs1qxZFBMTQ0OHDjWu891339HixYvprbfecvQQAQAAnJcIHbVv3z6Kiooym8cJkEuGLDs7mw4fPkyxsbHG9728vGQdXtearKwsmVRpaWnyMycnRyatUGPRUkwMcXkWxOU5tBiTo3EVJ3anJ8IrV65QlSpVzObxa05ed+/epVu3blFeXp7VZU6fPm11mzNnzqSpU6cWmL99+3YKCgoirdmyZQtpEeLyLIjLc2gxJnvjyszMJLdLhM7ApUe+p6jipBoeHi73IUNCQkgr+MqGT3zXrl3J19eXtAJxeRbE5Tm0GJOjcak1hG6VCKtWrUpXr141m8evg4ODKTAwkLy9vWWytgyvaw23PuXJEn9AWjr5KsTlWRCXZ9FiXFqMyd64ihO30/sRRkZG0tatW83mcWbn+czPz49atmxptkx+fr68VpcBAABwFocTYXp6unSD4EntHsG/JyUlGastBw0aZFx+xIgR9J///IcmTJgg9/w++eQTWrt2Lf3jH/8wLsPVnJ999hktW7aMTp06RSNHjpRuGmorUgAAAGdxuGr00KFDci9Opd6rGzx4sHSUT05ONiZFxl0nuCsEJ765c+dS9erV6fPPPzd2nWDR0dGUkpJCcXFx0rimRYsWlJCQUKABDQAAQIknwk6dOpGiKDbft/bUGF7n6NGjhW531KhRMgEAALiSQSksq3kIbiVUtmxZarygMXkHesuj35iBzH/ef2HzvQLr3X9tytp7BbZlsL282f7s2A/3lwzwD7C6foFYCjkmh2Oxtj91GUdjsVxeeXDObO3H3lgsP/PCjsnuWIr5/eF/StevX6dKlSqRl8HL4WMqbiyWn3mxY7HxmfM9+0uXLkltDvfxdeSYrJ3Xos6n6bq2tm1tG6afn+n7trah5Ct09uxZql+/vjTY+3MLRW+jqH8vjsTl0DYs4rYWF5+r4z8fl6dz+fj4FPnZmW3DYHE8lsfs4Gdj9zZsHJPpNriL3f79+ymybaSxMYytuO5m3KW2tdtSamqqNMrUXSJstKCRJEIAANCnwJxA+inmJ4cSoUf2I7Rl4182UvmQ8vK7mt8VLn7cZ22erfeMP02uE4zrGX8UfM/qfm2sZ23bpu/l5ubK4+g6dOggV3fF2UdhsVkeQ5HH5sDnV9h6HBc/VL1169ZyJV7UfgvdRyGxFfi87VnPbPWiP2/T483Ny6Wff/6ZIppHkLePd8F9WLnmtPe7UGA9O86pXd9zO5bluLihW8OGDcnL2+uh9mHte2q6nK3Po8htWHm/0G0oipSefv/9d6pRs4aUdIuzjcI+e2tx8Q/Tc1bkNmzEbWsbXMq9lnKNKlaqKLUSxdmGI5+/1XPnwDbMlilkG/wzPSNdHpgipflCPju/XD9ylKYSYYXAChQSqK0O9YneidSgfANN9QniuG753qLI0AfVHFqJy+uUF/Wq00tzccVfiKdejTUYV0o89XpSO3FJTPHx1KuzdmIyi6tX0XFJDSGVJUdgPEIAANA1JEIAANA1JEIAANA1JEIAANA1JEIAANA1JEIAANA1JEIAANA1JEIAANA1JEIAANA1JEIAANA1JEIAANA1JEIAANA1JEIAANA1JEIAANA1JEIAANA1JEIAANA1JEIAANA1JEIAANA1JEIAANA1JEIAANA1JEIAANA1JEIAANA1JEIAANA1JEIAANC1YiXC+fPnU61atSggIIDatGlDBw8etLlsp06dyGAwFJh69+5tXGbIkCEF3u/Ro0fxIgIAAHCADzlozZo1NHbsWFq4cKEkwTlz5lD37t3pzJkzVLly5QLLf/3115SdnW18fePGDYqIiKAXX3zRbDlOfEuWLDG+9vf3d/TQAAAAnJ8IZ82aRTExMTR06FB5zQnxu+++o8WLF9Nbb71VYPkKFSqYvV69ejUFBQUVSISc+KpWrWrXMWRlZcmkSktLk585OTkyaYUai5ZiYojLsyAuz6HFmByNqzixGxRFUexdmEt2nMS++uor6tevn3H+4MGD6fbt27Rx48Yit9GsWTOKjIykRYsWmVWNbtiwgfz8/Kh8+fL0zDPP0IwZMygkJMTqNqZMmUJTp04tMH/lypVyfAAAoE+ZmZk0cOBASk1NpeDg4EefCC9fvkzVqlWjvXv3SjJTTZgwgXbu3EkHDhwodH2+l8jVqbxc69atC5QSa9euTefOnaOJEydS6dKlad++feTt7W1XiTA8PJySk5NtJk9PxFc2W7Zsoa5du5Kvry9pBeLyLIjLc2gxJkfj4nxQsWJFhxKhw1WjD+OLL76QEqFpEmT9+/c3/s7vN2/enOrWrUs7duygLl26FNgOV6Nau4fIH5CWTr4KcXkWxOVZtBiXFmOyN67ixO1Qq1HOslxCu3r1qtl8fl3U/b2MjAwp+Q0bNqzI/dSpU0f2lZiY6MjhAQAAODcR8j28li1b0tatW43z8vPz5bVpVak169atk+rMv/3tb0Xu5+LFi9K6NDQ01JHDAwAAcH4/Qu468dlnn9GyZcvo1KlTNHLkSCntqa1IBw0aRLGxsVarRbmBjeU9vPT0dBo/fjzt37+fLly4IEm1b9++VK9ePemWAQAA4EwO3yOMjo6mlJQUiouLoytXrlCLFi0oISGBqlSpIu8nJSWRl5d5fuU+hnv27KHNmzcX2B5XtR4/flwSK7c8DQsLo27dutH06dMd7ktouHSYKKMskcGgzrn/w/I1FfG+q15T4e/n5lFgVgpR6h9EPj4lfzyP6nVeLhmUXKL8XKI8g+3lLY8LAMAJHGo16q64lVDZsmUp9a0yFOyPP57aVVIXLvbtXyEDZWdnkZ9/wP13XLt/+y50HN8G/4lIvZNGZYPLylOfHk1MJX+xxrd1rqWkyINAvLy83etisZjfifx8hZL++IPCa9QgbymQeM75KOx1Xn4+nT17luo3aEDefK4K+TzSsvKpbLc33bfVqLMp5WoSBdwvjRrz+/2fxnSv2Hjf2a8d3z//lp+XS17e3nzF8nD71wTL2MxnlzT+tyh1GLl3SEs4rnL8y13SFP5LIU38/nweh2ZiqsW/3CBN8SaihvzLFTsWVso4vH1NJcLc4TuINNSPMDcnh+Lj46lXr16Prim04uzEX/QyD/oERZGvVPlaHFuJXaxYJljH1ue4du/eTR07dvgzrhK4eLJY8ZF8Jrl5eXTw4AFq/WRr8jH26/XMc2T6muP65fjP1KxZc/Lx9nLy8bjiM1EoLy+ffvvtDDWQkpOXx58joShSIkxK+p1qcEnXWBq0sXw2xz2PdJsIwQ7ucP/NJ4dyfEoRBZbnTj+kGTk5dCfwPFHlxpqKS8nJoZTTd0mp+4zm4kq6VI6atuilmbjyc3Lot7R4qtehF3lrJCY1ruPx8VS9px1xySM3HUuEGIYJAAB0DYkQAAB0DYkQAAB0DYkQAAB0DYkQAAB0DYkQAAB0DYkQAAB0DYkQAAB0DYkQAAB0DYkQAAB0DYkQAAB0DYkQAAB0DYkQAAB0DYkQAAB0DYkQAAB0DYkQAAB0DYkQAAB0DYkQAAB0DYkQAAB0DYkQAAB0DYkQAAB0DYkQAAB0DYkQAAB0DYkQAAB0rViJcP78+VSrVi0KCAigNm3a0MGDB20uu3TpUjIYDGYTr2dKURSKi4uj0NBQCgwMpKioKDp79mxxDg0AAMC5iXDNmjU0duxYmjx5Mh05coQiIiKoe/fudO3aNZvrBAcHU3JysnH6/fffzd5///33ad68ebRw4UI6cOAAlSpVSrZ57949Rw8PAADAuYlw1qxZFBMTQ0OHDqXGjRtL8goKCqLFixfbXIdLgVWrVjVOVapUMSsNzpkzh95++23q27cvNW/enJYvX06XL1+mDRs2OHp4AAAADvFxZOHs7Gw6fPgwxcbGGud5eXlJVea+fftsrpeenk41a9ak/Px8euKJJ+idd96hJk2ayHvnz5+nK1euyDZUZcuWlSpX3mb//v0LbC8rK0smVVpamvzMycmRSSvUWLQUE0NcngVxeQ4txuRoXMWJ3aFEeP36dcrLyzMr0TF+ffr0aavrPPbYY1Ja5JJeamoqffjhh9SuXTs6efIkVa9eXZKgug3LbarvWZo5cyZNnTq1wPzt27dL6VRrtmzZQlqEuDwL4vIcWozJ3rgyMzPJqYmwOCIjI2VScRJs1KgRffrppzR9+vRibZNLpHyf0rREGB4eTp07d6aQkBDSCr6y4RPftWtX8vX1Ja1AXJ4FcXkOLcbkaFxqDaHTEmHFihXJ29ubrl69ajafX/O9P3twEI8//jglJibKa3U93ga3GjXdZosWLaxuw9/fXyZr29bSyVchLs+CuDyLFuPSYkz2xlWcuB1qLOPn50ctW7akrVu3GufxfT9+bVrqKwxXrf7yyy/GpFe7dm1Jhqbb5IzOrUft3SYAAEBxOVw1ylWSgwcPplatWlHr1q2lxWdGRoa0ImWDBg2iatWqyX08Nm3aNGrbti3Vq1ePbt++TR988IF0nxg+fLixRemYMWNoxowZVL9+fUmMkyZNorCwMOrXr1+xAwMAAHBKIoyOjqaUlBTpAM+NWbj6MiEhwdjYJSkpSVqSqm7duiXdLXjZ8uXLS4ly79690vVCNWHCBEmmr7zyiiTLDh06yDYtO94DAAA8asVqLDNq1CiZrNmxY4fZ69mzZ8tUGC4VcsmRJwAAAFfCs0YBAEDXkAgBAEDXkAgBAEDXkAgBAEDXkAgBAEDXkAgBAEDXkAgBAEDXkAgBAEDXkAgBAEDXkAgBAEDXkAgBAEDXkAgBAEDXkAgBAEDXkAgBAEDXkAgBAEDXkAgBAEDXkAgBAEDXkAgBAEDXkAgBAEDXkAgBAEDXkAgBAEDXkAgBAEDXkAgBAEDXkAgBAEDXkAgBAEDXkAgBAEDXkAgBAEDXipUI58+fT7Vq1aKAgABq06YNHTx40Oayn332GXXs2JHKly8vU1RUVIHlhwwZQgaDwWzq0aNHcQ4NAADAuYlwzZo1NHbsWJo8eTIdOXKEIiIiqHv37nTt2jWry+/YsYMGDBhA27dvp3379lF4eDh169aNLl26ZLYcJ77k5GTjtGrVKkcPDQAAwPmJcNasWRQTE0NDhw6lxo0b08KFCykoKIgWL15sdfkVK1bQq6++Si1atKCGDRvS559/Tvn5+bR161az5fz9/alq1arGiUuPAAAAzubjyMLZ2dl0+PBhio2NNc7z8vKS6k4u7dkjMzOTcnJyqEKFCgVKjpUrV5YE+Mwzz9CMGTMoJCTE6jaysrJkUqWlpclP3i5PWqHGoqWYGOLyLIjLc2gxJkfjKk7sBkVRFHsXvnz5MlWrVo327t1LkZGRxvkTJkygnTt30oEDB4rcBpcON23aRCdPnpR7jGz16tVSqqxduzadO3eOJk6cSKVLl5bk6u3tXWAbU6ZMoalTpxaYv3LlStkOAADoU2ZmJg0cOJBSU1MpODj40ZcIH9a7774rSY9Lf2oSZP379zf+3qxZM2revDnVrVtXluvSpUuB7XCJlO9TmpYI+d5j586dbZYiPRFf2WzZsoW6du1Kvr6+pBWIy7MgLs+hxZgcjUutIXSEQ4mwYsWKUkK7evWq2Xx+zff1CvPhhx9KIvzhhx8k0RWmTp06sq/ExESriZDvJ/JkiT8gLZ18FeLyLIjLs2gxLi3GZG9cxYnbocYyfn5+1LJlS7OGLmrDF9OqUkvvv/8+TZ8+nRISEqhVq1ZF7ufixYt048YNCg0NdeTwAAAAnN9qlKskuW/gsmXL6NSpUzRy5EjKyMiQVqRs0KBBZo1p3nvvPZo0aZK0KuW+h1euXJEpPT1d3uef48ePp/3799OFCxckqfbt25fq1asn3TIAAACcyeF7hNHR0ZSSkkJxcXGS0LhbBJf0qlSpIu8nJSVJS1LVggULpLXpCy+8YLYd7ofIjV64qvX48eOSWG/fvk1hYWHSz5BLkNaqPwEAAB6lYjWWGTVqlEzWcAMXU1zKK0xgYKC0IgUAACgJeNYoAADoGhIhAADoGhIhAADoGhIhAADoGhIhAADoGhIhAADoGhIhAADoGhIhAADoGhIhAADoGhIhAADoGhIhAADoGhIhAADoGhIhAADoGhIhAADoGhIhAADoGhIhAADoGhIhAADoGhIhAADoGhIhAADoGhIhAADoGhIhAADoGhIhAADoGhIhAADoGhIhAADoGhIhAADoGhIhAADoGhIhAADoGhIhAADoWrES4fz586lWrVoUEBBAbdq0oYMHDxa6/Lp166hhw4ayfLNmzSg+Pt7sfUVRKC4ujkJDQykwMJCioqLo7NmzxTk0AAAA5ybCNWvW0NixY2ny5Ml05MgRioiIoO7du9O1a9esLr93714aMGAADRs2jI4ePUr9+vWT6cSJE8Zl3n//fZo3bx4tXLiQDhw4QKVKlZJt3rt3z9HDAwAAcG4inDVrFsXExNDQoUOpcePGkryCgoJo8eLFVpefO3cu9ejRg8aPH0+NGjWi6dOn0xNPPEH/+te/jKXBOXPm0Ntvv019+/al5s2b0/Lly+ny5cu0YcMGRw8PAADAIT6OLJydnU2HDx+m2NhY4zwvLy+pyty3b5/VdXg+lyBNcWlPTXLnz5+nK1euyDZUZcuWlSpXXrd///4FtpmVlSWTKjU1VX7evHmTtCQnJ4cyMzPpxo0b5OvrS1qBuDwL4vIcWozJ0bju3LljLGQ5JRFev36d8vLyqEqVKmbz+fXp06etrsNJztryPF99X51naxlLM2fOpKlTpxaY36BBA0fCAQAAjeKEyIWqR54I3QWXSE1Lmbdv36aaNWtSUlKS3YF7grS0NAoPD6c//viDgoODSSsQl2dBXJ5DizE5GheXBDkJhoWFkb0cSoQVK1Ykb29vunr1qtl8fl21alWr6/D8wpZXf/I8bjVqukyLFi2sbtPf318mS5wEtXTyVRwT4vIciMuzaDEuLcbkSFyOFogcaizj5+dHLVu2pK1btxrn5efny+vIyEir6/B80+XZli1bjMvXrl1bkqHpMpz9ufWorW0CAAA8Kg5XjXKV5ODBg6lVq1bUunVrafGZkZEhrUjZoEGDqFq1anIfj40ePZqefvpp+uijj6h37960evVqOnToEC1atEjeNxgMNGbMGJoxYwbVr19fEuOkSZOkWMvdLAAAANwqEUZHR1NKSop0gOfGLFx9mZCQYGzswvfpuCWpql27drRy5UrpHjFx4kRJdtxitGnTpsZlJkyYIMn0lVdekft9HTp0kG1yB3x7cDUp92u0Vl3qyRCXZ0FcnkWLcWkxJlfEZVAcaWMKAACgMXjWKAAA6BoSIQAA6BoSIQAA6BoSIQAA6JomEqGjw0K5mylTpkg3EtOJh61S8Sgcr732GoWEhFDp0qXp+eefL/CQgpK2a9cu6tOnj3R74eO3fGC6PUNt8bNiX375ZekwW65cORmxJD09ndw5riFDhhQ4d/yQeXePi7s3Pfnkk1SmTBmqXLmydFU6c+aM2TL2fO+4lTh3i+IH7/N2+OH6ubm55K4xderUqcD5GjFihNvGxBYsWCCDEaidybl/9ffff++x58neuFx6rhQPt3r1asXPz09ZvHixcvLkSSUmJkYpV66ccvXqVcVTTJ48WWnSpImSnJxsnFJSUozvjxgxQgkPD1e2bt2qHDp0SGnbtq3Srl07xZ3Ex8cr//znP5Wvv/6aWyEr69evN3v/3XffVcqWLats2LBB+fnnn5W//vWvSu3atZW7d+8al+nRo4cSERGh7N+/X9m9e7dSr149ZcCAAYo7xzV48GA5btNzd/PmTbNl3DGu7t27K0uWLFFOnDihHDt2TOnVq5dSo0YNJT093e7vXW5urtK0aVMlKipKOXr0qHxWFStWVGJjY902pqefflr+Rpier9TUVLeNiX3zzTfKd999p/z222/KmTNnlIkTJyq+vr4SpyeeJ3vjcuW58vhE2Lp1a+W1114zvs7Ly1PCwsKUmTNnKp6UCPkPpTW3b9+WL8e6deuM806dOiV/lPft26e4I8uEkZ+fr1StWlX54IMPzOLy9/dXVq1aJa9//fVXWe+nn34yLvP9998rBoNBuXTpkuIObCXCvn372lzHE+Ji165dk+PcuXOn3d87/sPj5eWlXLlyxbjMggULlODgYCUrK0txt5jUP66jR4+2uY67x6QqX7688vnnn2viPFmLy9XnyqOrRtVhoUyHcCpqWCh3xdWEXP1Wp04dqUbjIj/j+HgIEtMYudq0Ro0aHhNjUUNtMf7J1Yb8xCIVL8/nkx+358527Ngh1TKPPfYYjRw5UoaKUXlKXOpQZhUqVLD7e8c/mzVrZjZyDA+xxo9IPHnyJLlbTKoVK1bIc5P5oR78AH8e3kfl7jHx6D/8dC5+AAlXJWrhPFmLy9XnyiNHn3iYYaHcESeEpUuXyh/S5ORkGWKqY8eOdOLECUkg/IxX/mNq7zBV7saeobb4JycTUz4+PvJHzJ3j5PuBzz33nDwa8Ny5c/L0pJ49e8o/Un5AvSfExc8L5scctm/f3vjEJ3u+d7aGWFPfc7eY2MCBA2WkGr7oPH78OL355ptyH/Hrr79265h++eUXSRB8P5DvA65fv14GRj927JhHn6dfbMTl6nPl0YlQK/gPp4pvHnNi5C/A2rVrpWEJuC/TgaP56pTPX926daWU2KVLF/IE3NCCL7r27NlDWmErJn6Mo+n54sZbfJ74IobPm7vii2ROelzK/eqrr+R5zzt37iRP95iNuDgZuvJceXTVaHGGhfIEfHXHgwwnJiZKHFwFzM9g9dQYTYfaKmw4rmvXrpm9z62/uMWlp8TJuGqbv5d87jwhrlGjRtG3335L27dvp+rVqxvn2/O9szXEmvqeu8VkDV90MtPz5Y4xcamvXr16MvoPt46NiIiguXPnevR5KiwuV58rj06ExRkWyhNw03q+6uErII7P19fXLEauHuB7iJ4Soz1DbfFP/sfM9zxU27Ztk/Op/gPwBBcvXpR7hOrYmu4aF7f94YTBVVF8PHyOTNnzveOfXLVlmuh5iDVuCq9Wb7lTTNZwaYSZni93iskW/v5kZWV55HmyJy6XnytFA90nuPXh0qVLpYXeK6+8It0nTFsSubs33nhD2bFjh3L+/Hnlxx9/lObA3AyYW72pzaO5Gfi2bdukeXRkZKRM7uTOnTvShJkn/lrNmjVLfv/999+N3Sf4vGzcuFE5fvy4tLS01n3i8ccfVw4cOKDs2bNHqV+/fol3MygsLn5v3Lhx0jqPz90PP/ygPPHEE3Lc9+7dc+u4Ro4cKd1Z+Htn2jw9MzPTuExR3zu1+Xq3bt2ku0JCQoJSqVKlEmuWX1RMiYmJyrRp0yQWPl/8XaxTp47y1FNPuW1M7K233pKWr3zM/G+HX3Or482bN3vkebInLlefK49PhOzjjz+WLwL3J+TuFNxfy5NER0croaGhcvzVqlWT1/xFUHGyePXVV6VpcVBQkPLss8/KP3B3sn37dkkUlhN3L1C7UEyaNEmpUqWKXLh06dJF+g6ZunHjhiSI0qVLSxPooUOHSrJx17j4Dyz/I+R/fNyEvWbNmtLvyfIizB3jshYTT9wPz5Hv3YULF5SePXsqgYGBcvHGF3U5OTluGVNSUpL8Ia1QoYJ8B7k/5/jx4836prlbTOzvf/+7fLf47wN/1/jfjpoEPfE82ROXq88VhmECAABd8+h7hAAAAA8LiRAAAHQNiRAAAHQNiRAAAHQNiRAAAHQNiRAAAHQNiRAAAHQNiRAAAHQNiRCghPFIFQaDocCDkx81HuqL98MTD1FkjyFDhhjX2bBhg1OPD6CkIBECuFinTp3MElG7du1kHEoerNjZ+IHEvK/p06fbtTyPBMDLA2gZxiMEcINRVFw1HA6X7BzZFydnVyRogJKEEiGAC3FVIw88yiUttcpRrbJUq0b5NY9JyWPq8cClQUFB9MILL1BmZiYtW7aMatWqReXLl6fXX3+d8vLyjNvm4WvGjRtH1apVo1KlSskwT1ztWpRPPvmE6tevTwEBATLCN+8LQE9QIgRwIU6Av/32GzVt2pSmTZsm806ePFlgOU568+bNo9WrV9OdO3foueeeo2effVYSZHx8PP3nP/+h559/ntq3b0/R0dGyDo/F9+uvv8o6YWFhMi5fjx49ZMw2TnTWHDp0SBLql19+KVW0PGDw7t27nfwpALgXJEIAF+JqRq4K5VKeWkV5+vTpAsvl5OTQggULqG7duvKaS2mcrHgE7tKlS8vAo507d5ZR2DkR8kCsS5YskZ+cBBmXDhMSEmT+O++8Y/V4eHkuPf7lL3+hMmXKUM2aNenxxx936mcA4G6QCAHcECdKNQkyrrLkKlFOgqbz1NG5udTH1aQNGjQw2w5Xl4aEhNjcT9euXSX51alTR0qPPHHJk/cPoBdIhABuyNfX1+w130O0Ni8/P19+T09PJ29vbzp8+LD8NGWaPC1xKfDIkSNyL3Hz5s0UFxdHU6ZMoZ9++kmqYQH0AIkQwMW4atS0kcujwNWZvE0uIXbs2NGhdX18fCgqKkqmyZMnSwLctm2b3JcE0AMkQgAX4yrOAwcO0IULF6S0ppbqHgZXib788ss0aNAg+uijjyQxpqSk0NatW6l58+bUu3dvq+txy1RuePPUU09JS1RuiMPHw61VAfQC3ScAXIwbsXD1JTd4qVSpkjRYeRS4UQwnwjfeeEMSWb9+/aSKs0aNGjbX4dLf119/Tc888ww1atSIFi5cSKtWraImTZo8kmMC8AQGRVGUkj4IAHA+7p/IT7QpzqPc+H4kd8fg5AqgNSgRAuhIamqqVMe++eabdi0/YsSIQhvbAGgBSoQAOsEd87kfololWrFixSLX4cY3aWlp8ntoaKj0OQTQGiRCAADQNVSNAgCAriERAgCAriERAgCAriERAgCAriERAgCAriERAgCAriERAgCAriERAgAA6dn/AxTlVtpsxAMYAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plt_dop(t, dop):\n", + " nep = len(t)\n", + " plt.figure(figsize=(5,4))\n", + " plt.plot(t, dop)\n", + " plt.legend(['pdop', 'hdop', 'vdop'])\n", + " plt.grid()\n", + " plt.axis([0, nep, 0, 2])\n", + " plt.xlabel('time[s]')\n", + " plt.show()\n", + "\n", + "plt_dop(t,dop[:,1:])" + ] + }, + { + "cell_type": "markdown", + "id": "3b8b422e", + "metadata": { + "id": "3b8b422e" + }, + "source": [ + "## Example 4: RTK positioning\n", + "\n", + "This section demonstrates static RTK positioning under open-sky conditions using a Septentrio Mosaic-X5 rover and a Trimble Net-R9 base. The results are obtained using dual-frequency pseudorange and carrier-phase observations of GPS, Galileo and QZSS. " + ] + }, + { + "cell_type": "markdown", + "id": "QxWZFIuDDrJL", + "metadata": { + "id": "QxWZFIuDDrJL" + }, + "source": [ + "First, load the required Python modules." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "efb004df", + "metadata": { + "id": "efb004df" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import sys\n", + "\n", + "from cssrlib.rinex import rnxdec, sync_obs\n", + "from cssrlib.gnss import rSigRnx, Nav, time2str, ecef2pos, ecef2enu, epoch2time, time2doy, timediff\n", + "from cssrlib.peph import atxdec, searchpcv\n", + "from cssrlib.rtk import rtkpos\n", + "from cssrlib.plot import plot_enu" + ] + }, + { + "cell_type": "markdown", + "id": "8d7dc263", + "metadata": { + "id": "8d7dc263" + }, + "source": [ + "Define the reference position of the rover for evaulation." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2f4925cd", + "metadata": { + "id": "2f4925cd" + }, + "outputs": [], + "source": [ + "xyz_ref = [-3962108.6836, 3381309.5672, 3668678.6720]\n", + "pos_ref = ecef2pos(xyz_ref)" + ] + }, + { + "cell_type": "markdown", + "id": "Y3W-iGU2zAgE", + "metadata": { + "id": "Y3W-iGU2zAgE" + }, + "source": [ + "Specify the satellite signals to be processed for base and rover. We choose L1C/A, L2P(Y) for GPS, E1, E5a for Galileo, L1C/A, L2C(L) for QZSS." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "_wHtdNenzCBI", + "metadata": { + "id": "_wHtdNenzCBI" + }, + "outputs": [], + "source": [ + "sigs = [rSigRnx(\"GC1C\"), rSigRnx(\"GC2W\"),\n", + " rSigRnx(\"EC1C\"), rSigRnx(\"EC5Q\"),\n", + " rSigRnx(\"JC1C\"), rSigRnx(\"JC2L\"),\n", + " rSigRnx(\"GL1C\"), rSigRnx(\"GL2W\"),\n", + " rSigRnx(\"EL1C\"), rSigRnx(\"EL5Q\"),\n", + " rSigRnx(\"JL1C\"), rSigRnx(\"JL2L\"),\n", + " rSigRnx(\"GS1C\"), rSigRnx(\"GS2W\"),\n", + " rSigRnx(\"ES1C\"), rSigRnx(\"ES5Q\"),\n", + " rSigRnx(\"JS1C\"), rSigRnx(\"JS2L\")]" + ] + }, + { + "cell_type": "markdown", + "id": "ba9c559c", + "metadata": { + "id": "ba9c559c" + }, + "source": [ + "Load the measurement and ephemeris data for the rover (Septentrio Mosaic-X5)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "27b26fcf", + "metadata": { + "id": "27b26fcf" + }, + "outputs": [], + "source": [ + "# Start epoch, number of epochs\n", + "ep = [2025, 8, 21, 7, 0, 0] # year, month, day, hour, min, sec\n", + "\n", + "time = epoch2time(ep)\n", + "year = ep[0]\n", + "doy = int(time2doy(time))\n", + "let = chr(ord('a')+ep[3])\n", + "bdir = f'cssrlib-data/data/doy{ep[0]:04d}-{doy:03d}/'\n", + "navfile = bdir+f'{doy:03d}{let}_rnx.nav'\n", + "obsfile = bdir+f'{doy:03d}{let}_rnx.obs'\n", + "\n", + "# rover\n", + "dec = rnxdec()\n", + "dec.setSignals(sigs)\n", + "nav = Nav()\n", + "dec.decode_nav(navfile, nav)\n", + "dec.decode_obsh(obsfile)\n", + "dec.autoSubstituteSignals()" + ] + }, + { + "cell_type": "markdown", + "id": "6521c242", + "metadata": { + "id": "6521c242" + }, + "source": [ + "For RTK positioning, the observation file for the base station also needs to be specified, as well as the position of the base station." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "85530a51", + "metadata": { + "id": "85530a51" + }, + "outputs": [], + "source": [ + "# base station\n", + "station_id = '3034' # GSI 3034 fujisawa\n", + "nav.rb = [-3959400.6242, 3385704.4927, 3667523.1257] # GSI 3034 fujisawa\n", + "\n", + "basefile = bdir+f'{station_id}{doy}{let}.{year%100:02d}o'\n", + "decb = rnxdec()\n", + "decb.setSignals(sigs)\n", + "decb.decode_obsh(basefile)\n", + "decb.autoSubstituteSignals()" + ] + }, + { + "cell_type": "markdown", + "id": "-U6kZhcR0tjZ", + "metadata": { + "id": "-U6kZhcR0tjZ" + }, + "source": [ + "Load the antenna data for the satellites and receivers" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2biX0yEj02cX", + "metadata": { + "id": "2biX0yEj02cX" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Rover] Receiver: SEPT MOSAICX5 Antenna: JAVRINGANT_DM JVDM\n", + "[Base] Receiver: TRIMBLE ALLOY Antenna: TRM159900.00 GSI6\n" + ] + } + ], + "source": [ + "atxfile = bdir + '../antex/igs20.atx'\n", + "ngsantfile = bdir + '../GSI_PCV.TXT'\n", + "\n", + "atx = atxdec()\n", + "atx.readpcv(atxfile)\n", + "atx.readngspcv(ngsantfile)\n", + "\n", + "# Set PCO/PCV information\n", + "nav.rcv_ant = searchpcv(atx.pcvr, dec.ant, dec.ts)\n", + "nav.rcv_ant_b = searchpcv(atx.pcvr, decb.ant, dec.ts)\n", + "\n", + "# Get equipment information\n", + "print(f\"[Rover] Receiver: {dec.rcv} Antenna: {dec.ant}\")\n", + "print(f\"[Base] Receiver: {decb.rcv} Antenna: {decb.ant}\")" + ] + }, + { + "cell_type": "markdown", + "id": "18286a6a", + "metadata": { + "id": "18286a6a" + }, + "source": [ + "Initialize the variables for position and the RTK configuration parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "de72b4fc", + "metadata": { + "id": "de72b4fc" + }, + "outputs": [], + "source": [ + "rtk = rtkpos(nav, dec.pos, 'test_rtk.log')\n", + "rr = dec.pos" + ] + }, + { + "cell_type": "markdown", + "id": "f8bca1dc", + "metadata": { + "id": "f8bca1dc" + }, + "source": [ + "Run RTK positioning for 3 minutes (epochs) using `rtk.process()`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7cb2c994", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7cb2c994", + "outputId": "4e255d85-df3a-46f4-ef1f-d45b8e6acd6b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 2025-08-21 07:03:00 ENU 0.0007 0.0058 -0.0656, 2D 0.0059, mode 4" + ] + } + ], + "source": [ + "nep = 3 * 60 # 3 minutes\n", + "t = np.zeros(nep)\n", + "enu = np.zeros((nep, 3))\n", + "smode = np.zeros(nep, dtype=int)\n", + "\n", + "for ne in range(nep):\n", + " obs, obsb = sync_obs(dec, decb)\n", + " if ne == 0:\n", + " t0 = nav.t = obs.t\n", + " rtk.process(obs, obsb=obsb)\n", + " t[ne] = timediff(nav.t, t0)\n", + " sol = nav.xa[0:3] if nav.smode == 4 else nav.x[0:3]\n", + " enu[ne, :] = ecef2enu(pos_ref, sol-xyz_ref)\n", + " smode[ne] = nav.smode\n", + " # Log to standard output\n", + " sys.stdout.write('\\r {} ENU {:7.4f} {:7.4f} {:7.4f}, 2D {:6.4f}, mode {:1d}'\n", + " .format(time2str(obs.t),\n", + " enu[ne, 0], enu[ne, 1], enu[ne, 2],\n", + " np.sqrt(enu[ne, 0]**2+enu[ne, 1]**2),\n", + " smode[ne]))\n", + "\n", + "dec.fobs.close()\n", + "decb.fobs.close()" + ] + }, + { + "cell_type": "markdown", + "id": "54c2a23a", + "metadata": { + "id": "54c2a23a" + }, + "source": [ + "Plot the position relative to the reference position." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "92fbb0cd", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 393 + }, + "id": "92fbb0cd", + "outputId": "96f60208-34dc-4bc2-e528-eba39c405454" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from cssrlib.plot import plot_enu\n", + "\n", + "plot_enu(t, enu, smode)" + ] + }, + { + "cell_type": "markdown", + "id": "06d4a4ac", + "metadata": { + "id": "06d4a4ac" + }, + "source": [ + "## Reference" + ] + }, + { + "cell_type": "markdown", + "id": "7d78ff97", + "metadata": { + "id": "7d78ff97" + }, + "source": [ + "- [^1] T. Takasu, “RTKLIB: Open Source Program Package for RTK-GPS,” FOSS4G 2009 Tokyo, Japan, 2009.\n", + "- [^2] Hirokawa, R., Hauschild, A., Everett, T. (2023). Python Toolkit for Open PPP/PPP-RTK Services. In *Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2023)*\n", + "- [^3] Hirokawa, R., Hauschild, A. (2025). CSSRlib: Python Toolkit for High-Accuracy, Secure, and Resilient Positioning Services. In *Proceedings of the 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2025)*" + ] + }, + { + "cell_type": "markdown", + "id": "53eb0684", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [ + "b85900d9", + "d93de7b9", + "5a058567", + "3b8b422e", + "1c4b06ee", + "rKUv0nEl8vDm", + "8993fc13", + "hPjJOF2T8uxG", + "nf1Rwd3u8vXK" + ], + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/cssrlib.ipynb b/tutorials/cssrlib.ipynb index bc426bd..c6fdc77 100644 --- a/tutorials/cssrlib.ipynb +++ b/tutorials/cssrlib.ipynb @@ -7,7 +7,7 @@ "id": "a4123944" }, "source": [ - "# Introduction to the high accuracy positioning with open PPP/PPP-RTK services" + "# Introduction to CSSRlib" ] }, { @@ -17,9 +17,9 @@ "id": "ee5d7f23" }, "source": [ - "## Introduction to CSSRlib\n", + "## Key concepts of CSSRlib\n", "\n", - "*CSSRLIB* is a open toolkit in Python for high accuracy GNSS positioning. It supports SSR (State-Space Representation) based potitioning such as PPP (Precise Point Positioning) or PPP-RTK (Realtime Kinematic), and also supports RTK. The goal of the CSSRlib toolkit is to provide an easy-to-understand open implementation to learn PPP/PPP-RTK positioning provided by satellite-based open PPP/PPP-RTK services such as QZSS CLAS, Galileo HAS, and BeiDou 3 PPP. It also supports ground based open service by IGS. The code is based on RTKlib.\n", + "*CSSRLIB* is a open toolkit in Python for high accuracy GNSS positioning. It supports SSR (State-Space Representation) based potitioning such as PPP (Precise Point Positioning) or PPP-RTK (Realtime Kinematic), and also supports RTK. The goal of the CSSRlib toolkit is to provide an easy-to-understand open implementation to learn PPP/PPP-RTK positioning provided by satellite-based open PPP/PPP-RTK services such as QZSS CLAS/MADOCA-PPP, Galileo HAS, BDS 3 PPP, and PPP via SouthPAN. It also supports ground based open service by IGS and RTCM 3. The code is based on RTKlib.\n", "\n", "It supports the following open format:\n", "\n", @@ -175,7 +175,11 @@ "- `rtk`: RTK positioning\n", "- `rawnav`: decoder for raw navigation message\n", "- `mlambda`: Ambiguity resolution using modified LAMBDA method\n", - "- `sbas`: SBAS (L1/L5) message decoder\n" + "- `sbas`: SBAS (L1/L5) message decoder\n", + "- `ewss` : Emergency Warning Satellite System decoder\n", + "- `osnma` : Galileo Open Service Navigation Message Authentication (OSNMA) decoder\n", + "- `qznma` : QZSS Navigation Message Authentication (QZNMA) decoder\n", + "- `dgps` : Differential GNSS correction decoder for QZSS SLAS" ] }, { @@ -1462,3591 +1466,19 @@ }, { "cell_type": "markdown", - "id": "f4191fe5", - "metadata": { - "id": "f4191fe5" - }, - "source": [ - "\n", - "## Examples\n", - "\n", - "This tutorial provides examples to show the basic features of CSSRlib for Standalone, RTK, PPP-RTK, and PPP positioning using correction data from open PPP/PPP-RTK services. The following examples are included:\n", - "\n", - "- Visualizing orbit of QZSS satellite\n", - "- Showing skyplot\n", - "- Standalone positioning\n", - "- RTK positioning\n", - "- PPP-RTK positioning (QZSS-CLAS)\n", - "- PPP positioning (BeiDou)\n", - "- PPP positioning (Galileo HAS)\n", - "- PPP positioning (IGS)\n", - "- PPP positioning (MADOCA-PPP)\n", - "- PPP positioning (PPP via SouthPAN)\n", - "- PPP positioning (JPL GDGPS via RTCM)\n", - "\n", - "Note that despite the static setup\n", - "of the antenna, all data sets are processed assuming a non-stationary antenna. A motion model has not been used for the\n", - "receiver position. Instead, a sufficiently large amount of process noise has been added to the variance of the predicted position\n", - "states.\n", - "\n", - "Click on the arrows in the left margin to open or close an example" - ] - }, - { - "cell_type": "markdown", - "id": "b85900d9", - "metadata": { - "id": "b85900d9" - }, - "source": [ - "## Example 1: Visualizing the Orbit of a Satellite\n", - "\n", - "In this first example, we will introduce the basic features of CSSRlib to calculate and visualize a QZSS satellite orbit." - ] - }, - { - "cell_type": "markdown", - "id": "bfe3a51f", - "metadata": { - "id": "bfe3a51f" - }, - "source": [ - "First, we will load the required Python modules such as numpy, matplotlib, and cartopy. Then we will load modules from CSSRlib." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "a214e8d3", - "metadata": { - "id": "a214e8d3", - "scrolled": true - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import cartopy.crs as ccrs\n", - "import numpy as np\n", - "\n", - "from cssrlib.rinex import rnxdec\n", - "from cssrlib.gnss import Nav, epoch2time, prn2sat, uGNSS, sat2prn,\\\n", - " timeadd, ecef2pos\n", - "from cssrlib.ephemeris import findeph, eph2pos" - ] - }, - { - "cell_type": "markdown", - "id": "51e1bdf4", - "metadata": { - "id": "51e1bdf4" - }, - "source": [ - "In CSSRlib, the epoch is defined as Python Class `gtime_t` as in RTKlib. The snippet defines epoch \"19/3/2021 0:00:00\", shows the internal variable of `gtime_t`." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "7e7fe465", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "7e7fe465", - "outputId": "f11ace96-58a0-4b45-d08b-c66111d8bafe" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(1616112000, 0)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t0 = epoch2time([2021, 3, 19, 0, 0, 0]) # year, month, day, hour, minute, sec\n", - "t0.time,t0.sec" - ] - }, - { - "cell_type": "markdown", - "id": "d4685e83", - "metadata": { - "id": "d4685e83" - }, - "source": [ - "Then, the ephemeris in RINEX format is loaded using RINEX decoder in CSSRlib." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "121d290e", - "metadata": { - "id": "121d290e" - }, - "outputs": [], - "source": [ - "dec = rnxdec()\n", - "nav = dec.decode_nav( 'cssrlib-data/data/doy2021-078/30340780.21q', Nav()) # load RINEX navigation file" - ] - }, - { - "cell_type": "markdown", - "id": "155442bb", - "metadata": { - "id": "155442bb" - }, - "source": [ - "The satellites are identified by the system (GPS, Galileo, QZS, ...) and by PRN number. They should be converted into the internal satellite numbers using prn2sat. This snippet shows the satellite number for QZSS with PRN 194 (QZS-2):" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "8a855d63", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "8a855d63", - "outputId": "4a472f36-a361-4e98-99f4-266d6565cf44" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "70" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sat = prn2sat(uGNSS.QZS, 194)\n", - "sat" - ] - }, - { - "cell_type": "markdown", - "id": "100629b9", - "metadata": { - "id": "100629b9" - }, - "source": [ - "The orbit position of a GNSS satellite can be calculated using ephemeris defined in the RINEX navigation file. The valid ephemeris for a specific epoch can be searched for using `findeph`. Position and velocity in ECEF and clock bias are calculated by eph2pos. The position in geodetic cordinates can be calculated by ecef2pos:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "821f0d38", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "821f0d38", - "outputId": "56b690f1-ef2b-4666-afb1-7cef7090f210" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rs= [-27695465.67390223 25576355.5431149 23733383.2869802 ] vs= [ 618.82498925 -271.15881898 1281.49729409] dts= 3.4869334074196084e-06\n", - "pos= [5.62300943e-01 2.39595272e+00 3.81752334e+07]\n" - ] - } - ], - "source": [ - "t = t0\n", - "eph = findeph(nav.eph, t, sat)\n", - "rs, vs, dts = eph2pos(t, eph, True)\n", - "print('rs=',rs, 'vs=', vs,'dts=', dts)\n", - "pos = ecef2pos(rs)\n", - "print('pos=',pos)" - ] - }, - { - "cell_type": "markdown", - "id": "9ca6e0b2", - "metadata": { - "id": "9ca6e0b2" - }, - "source": [ - "The orbit of QZS-2 for 1 day (24 hours) is calculated and plotted using Cartopy:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "fc82fdfc", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 400 - }, - "id": "fc82fdfc", - "outputId": "1df8431b-4097-4ec1-cc91-e64677021dd4", - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "lon0 = 135\n", - "plt.figure(figsize=(4, 4))\n", - "ax = plt.axes(projection=ccrs.Orthographic(central_longitude=lon0,\n", - " central_latitude=0))\n", - "ax.coastlines(resolution='50m')\n", - "ax.gridlines()\n", - "ax.stock_img()\n", - "\n", - "n = 24*3600//300\n", - "pos = np.zeros((n, 3))\n", - "r2d = 180/np.pi\n", - "\n", - "for k in range(uGNSS.MAXSAT):\n", - " sat = k+1\n", - " sys, prn = sat2prn(sat)\n", - " if sys != uGNSS.QZS: # skip non QZSS\n", - " continue\n", - " for i in range(n):\n", - " t = timeadd(t0, i*300)\n", - " if eph is None:\n", - " continue\n", - " rs, dts = eph2pos(t, eph)\n", - " pos[i, :] = ecef2pos(rs)\n", - "\n", - " plt.plot(pos[:, 1]*r2d, pos[:, 0]*r2d, 'm-', transform=ccrs.Geodetic())\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "d93de7b9", - "metadata": { - "id": "d93de7b9" - }, - "source": [ - "## Example 2: Showing a Skyplot\n", - "\n", - "For the visualization of measurements, CSSRlib supports the skyplots of satellite orbit paths at specific locations. At first, the required modules are loaded." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "acae2d99", - "metadata": { - "id": "acae2d99" - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "from cssrlib.gnss import Nav, ecef2pos, geodist, satazel, timediff, uGNSS, rSigRnx\n", - "from cssrlib.ephemeris import findeph, eph2pos\n", - "from cssrlib.plot import skyplot, plot_elv\n", - "from cssrlib.rinex import rnxdec" - ] - }, - { - "cell_type": "markdown", - "id": "212a514f", - "metadata": { - "id": "212a514f" - }, - "source": [ - "Then, an instance of a RINEX decoder is generated. In this example, the observation and navigation files measured by a Septentrio Mosaic-X5 receiver are used. We also need to specify which satellite signals will be used." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "7936c023", - "metadata": { - "id": "7936c023" - }, - "outputs": [], - "source": [ - "navfile = 'cssrlib-data/data/doy2021-078/SEPT078M.21P'\n", - "obsfile = 'cssrlib-data/data/doy2021-078/SEPT078M.21O'\n", - "\n", - "dec = rnxdec()\n", - "nav = dec.decode_nav(navfile,Nav())\n", - "\n", - "sigs = [rSigRnx(\"GC1C\"), rSigRnx(\"EC1C\"), rSigRnx(\"JC1C\")]\n", - "dec.setSignals(sigs)" - ] - }, - { - "cell_type": "markdown", - "id": "33661340", - "metadata": { - "id": "33661340" - }, - "source": [ - "Next, the orbit of tracked satellites is calculated for 15 minutes of epoch." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "52f2754b", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "52f2754b", - "outputId": "1df58f43-e83a-4bfc-a073-04cd3cf22e29" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "epoch=899/900" - ] - } - ], - "source": [ - "nep = 15*60 # 15 minutes\n", - "elv = np.ones((nep, uGNSS.MAXSAT))*np.nan\n", - "azm = np.ones((nep, uGNSS.MAXSAT))*np.nan\n", - "t = np.zeros(nep)*np.nan\n", - "\n", - "if dec.decode_obsh(obsfile) >= 0:\n", - " rr = dec.pos\n", - " pos = ecef2pos(rr)\n", - " for ne in range(nep):\n", - " print('\\repoch=%d/%d' % (ne, nep), end='')\n", - " obs = dec.decode_obs()\n", - " if ne == 0:\n", - " t0 = obs.t\n", - " t[ne] = timediff(obs.t, t0)\n", - " for k, sat in enumerate(obs.sat):\n", - " eph = findeph(nav.eph, obs.t, sat)\n", - " if eph is None:\n", - " continue\n", - " rs, dts = eph2pos(obs.t, eph)\n", - " r, e = geodist(rs, rr)\n", - " azm[ne, sat-1], elv[ne, sat-1] = satazel(pos, e)\n", - " dec.fobs.close()" - ] - }, - { - "cell_type": "markdown", - "id": "9173173b", - "metadata": { - "id": "9173173b" - }, - "source": [ - "The elevation and azimuth angles of each satellite are recorded, and the skyplot is generated as below:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "73f6d5a9", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 459 - }, - "id": "73f6d5a9", - "outputId": "a861001d-5c1d-4a0f-817a-af96be58bbd8" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#plot_elv(t, elv)\n", - "nsat = skyplot(azm, elv)" - ] - }, - { - "cell_type": "markdown", - "id": "5a058567", - "metadata": { - "id": "5a058567" - }, - "source": [ - "## Example 3: Standalone positioning\n", - "\n", - "This section introduces standalone GNSS positioning using RINEX observations from a Septentrio Mosaic-X5 receiver." - ] - }, - { - "cell_type": "markdown", - "id": "HlMNxLjFD1FK", - "metadata": { - "id": "HlMNxLjFD1FK" - }, - "source": [ - "First, the required Python modules are loaded." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "0b6966d0", - "metadata": { - "id": "0b6966d0" - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from cssrlib.rinex import rnxdec\n", - "from cssrlib.gnss import ecef2pos, timediff, dops, ecef2enu, rSigRnx, Nav\n", - "from cssrlib.pntpos import stdpos" - ] - }, - { - "cell_type": "markdown", - "id": "81f53fb6", - "metadata": { - "id": "81f53fb6" - }, - "source": [ - "Then the rover position is defined for reference." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "4395530a", - "metadata": { - "id": "4395530a" - }, - "outputs": [], - "source": [ - "xyz_ref = [-3962108.6726, 3381309.4719, 3668678.6264]\n", - "pos_ref = ecef2pos(xyz_ref)" - ] - }, - { - "cell_type": "markdown", - "id": "c9e2c786", - "metadata": { - "id": "c9e2c786" - }, - "source": [ - "RINEX navigation and observation files are defined, an instance of RINEX decoder is generated, and satellite signals are specified." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "3a6534d6", - "metadata": { - "id": "3a6534d6" - }, - "outputs": [], - "source": [ - "navfile = 'cssrlib-data/data/doy2023-223/NAV223.23p'\n", - "obsfile = 'cssrlib-data/data/doy2023-223/SEPT223Y.23O' # PolaRX5\n", - "dec = rnxdec()\n", - "nav = dec.decode_nav(navfile, Nav(nf=1))\n", - "nav.pmode = 0\n", - "\n", - "sigs = [rSigRnx(\"GC1C\"), rSigRnx(\"EC1C\"), rSigRnx(\"JC1C\"),\n", - " rSigRnx(\"GL1C\"), rSigRnx(\"EL1C\"), rSigRnx(\"JL1C\"),\n", - " rSigRnx(\"GS1C\"), rSigRnx(\"ES1C\"), rSigRnx(\"JS1C\")]\n", - "dec.setSignals(sigs)\n", - "dec.autoSubstituteSignals()" - ] - }, - { - "cell_type": "markdown", - "id": "c05a83bf", - "metadata": { - "id": "c05a83bf" - }, - "source": [ - "The variables for position, DOPs, and number of satellite are defined for 6 minutes epoch." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "b9c7f872", - "metadata": { - "id": "b9c7f872" - }, - "outputs": [], - "source": [ - "nep = 6*60 # 6 minutes\n", - "t = np.zeros(nep)\n", - "enu = np.zeros((nep, 3))\n", - "dop = np.zeros((nep, 4))\n", - "nsat = np.zeros(nep, dtype=int)\n", - "\n", - "std = stdpos(nav, dec.pos, 'test_stdpos.log')\n", - "sol = np.zeros((nep, nav.nx))" - ] - }, - { - "cell_type": "markdown", - "id": "e48d2d16", - "metadata": { - "id": "e48d2d16" - }, - "source": [ - "The standalone GNSS positioning by `std.process()` is conducted for 6 minutes." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "63fee89c", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "63fee89c", - "outputId": "1d5536c5-dd69-4e7f-a031-2ce77de64aab" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "epoch=359/360" - ] - } - ], - "source": [ - "if dec.decode_obsh(obsfile) >= 0:\n", - " nav.x[0:3] = dec.pos\n", - " for ne in range(nep):\n", - " print('\\repoch=%d/%d' % (ne, nep), end='')\n", - " obs = dec.decode_obs()\n", - " if ne == 0:\n", - " t0 = nav.t = obs.t\n", - " t[ne] = timediff(obs.t, t0)\n", - " # nav, az, el = pntpos(obs, nav)\n", - " std.process(obs, cs=None)\n", - " sol[ne, :] = nav.x\n", - " dop[ne, :] = std.dop\n", - " enu[ne, :] = ecef2enu(pos_ref, sol[ne, 0:3]-xyz_ref)\n", - " #nsat[ne] = len(el)\n", - " dec.fobs.close()" - ] - }, - { - "cell_type": "markdown", - "id": "b1d924c7", - "metadata": { - "id": "b1d924c7" - }, - "source": [ - "The ENU position relative to the reference position is plotted." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "c6b68b55", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 393 - }, - "id": "c6b68b55", - "outputId": "a88918ff-43b7-4f6e-9f7e-e2d7207f2305" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plt_enu(t, enu, dmax=0.4):\n", - " plt.figure(figsize=(5,4))\n", - " plt.plot(t, enu)\n", - " plt.ylabel('pos err[m]')\n", - " plt.xlabel('time[s]')\n", - " plt.legend(['east', 'north', 'up'])\n", - " plt.grid()\n", - " plt.axis([0, nep, -dmax, dmax])\n", - " plt.show()\n", - "\n", - "plt_enu(t, enu, 10)" - ] - }, - { - "cell_type": "markdown", - "id": "d2f49f64", - "metadata": { - "id": "d2f49f64" - }, - "source": [ - "To measure the geometry for positioning, DOPs (PDOP, HDOP, VDOP) are plotted:" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "5c2d1644", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 393 - }, - "id": "5c2d1644", - "outputId": "cad2e346-925c-48a2-efc8-f0acc09f1ddd" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plt_dop(t, dop):\n", - " nep = len(t)\n", - " plt.figure(figsize=(5,4))\n", - " plt.plot(t, dop)\n", - " plt.legend(['pdop', 'hdop', 'vdop'])\n", - " plt.grid()\n", - " plt.axis([0, nep, 0, 2])\n", - " plt.xlabel('time[s]')\n", - " plt.show()\n", - "\n", - "plt_dop(t,dop[:,1:])" - ] - }, - { - "cell_type": "markdown", - "id": "3b8b422e", - "metadata": { - "id": "3b8b422e" - }, - "source": [ - "## Example 4: RTK positioning\n", - "\n", - "This section demonstrates static RTK positioning under open-sky conditions using a Septentrio Mosaic-X5 rover and a Trimble Net-R9 base. The results are obtained using dual-frequency pseudorange and carrier-phase observations of GPS, Galileo and QZSS. " - ] - }, - { - "cell_type": "markdown", - "id": "QxWZFIuDDrJL", - "metadata": { - "id": "QxWZFIuDDrJL" - }, - "source": [ - "First, load the required Python modules." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "efb004df", - "metadata": { - "id": "efb004df" - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import sys\n", - "\n", - "from cssrlib.rinex import rnxdec, sync_obs\n", - "from cssrlib.gnss import rSigRnx, Nav, time2str, ecef2pos, ecef2enu, epoch2time, time2doy, timediff\n", - "from cssrlib.peph import atxdec, searchpcv\n", - "from cssrlib.rtk import rtkpos\n", - "from cssrlib.plot import plot_enu" - ] - }, - { - "cell_type": "markdown", - "id": "8d7dc263", - "metadata": { - "id": "8d7dc263" - }, - "source": [ - "Define the reference position of the rover for evaulation." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "2f4925cd", - "metadata": { - "id": "2f4925cd" - }, - "outputs": [], - "source": [ - "xyz_ref = [-3962108.6836, 3381309.5672, 3668678.6720]\n", - "pos_ref = ecef2pos(xyz_ref)" - ] - }, - { - "cell_type": "markdown", - "id": "Y3W-iGU2zAgE", - "metadata": { - "id": "Y3W-iGU2zAgE" - }, - "source": [ - "Specify the satellite signals to be processed for base and rover. We choose L1C/A, L2P(Y) for GPS, E1, E5a for Galileo, L1C/A, L2C(L) for QZSS." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "_wHtdNenzCBI", - "metadata": { - "id": "_wHtdNenzCBI" - }, - "outputs": [], - "source": [ - "sigs = [rSigRnx(\"GC1C\"), rSigRnx(\"GC2W\"),\n", - " rSigRnx(\"EC1C\"), rSigRnx(\"EC5Q\"),\n", - " rSigRnx(\"JC1C\"), rSigRnx(\"JC2L\"),\n", - " rSigRnx(\"GL1C\"), rSigRnx(\"GL2W\"),\n", - " rSigRnx(\"EL1C\"), rSigRnx(\"EL5Q\"),\n", - " rSigRnx(\"JL1C\"), rSigRnx(\"JL2L\"),\n", - " rSigRnx(\"GS1C\"), rSigRnx(\"GS2W\"),\n", - " rSigRnx(\"ES1C\"), rSigRnx(\"ES5Q\"),\n", - " rSigRnx(\"JS1C\"), rSigRnx(\"JS2L\")]" - ] - }, - { - "cell_type": "markdown", - "id": "ba9c559c", - "metadata": { - "id": "ba9c559c" - }, - "source": [ - "Load the measurement and ephemeris data for the rover (Septentrio Mosaic-X5)." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "27b26fcf", - "metadata": { - "id": "27b26fcf" - }, - "outputs": [], - "source": [ - "# Start epoch, number of epochs\n", - "ep = [2025, 8, 21, 7, 0, 0] # year, month, day, hour, min, sec\n", - "\n", - "time = epoch2time(ep)\n", - "year = ep[0]\n", - "doy = int(time2doy(time))\n", - "let = chr(ord('a')+ep[3])\n", - "bdir = f'cssrlib-data/data/doy{ep[0]:04d}-{doy:03d}/'\n", - "navfile = bdir+f'{doy:03d}{let}_rnx.nav'\n", - "obsfile = bdir+f'{doy:03d}{let}_rnx.obs'\n", - "\n", - "# rover\n", - "dec = rnxdec()\n", - "dec.setSignals(sigs)\n", - "nav = Nav()\n", - "dec.decode_nav(navfile, nav)\n", - "dec.decode_obsh(obsfile)\n", - "dec.autoSubstituteSignals()" - ] - }, - { - "cell_type": "markdown", - "id": "6521c242", - "metadata": { - "id": "6521c242" - }, - "source": [ - "For RTK positioning, the observation file for the base station also needs to be specified, as well as the position of the base station." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "85530a51", - "metadata": { - "id": "85530a51" - }, - "outputs": [], - "source": [ - "# base station\n", - "station_id = '3034' # GSI 3034 fujisawa\n", - "nav.rb = [-3959400.6242, 3385704.4927, 3667523.1257] # GSI 3034 fujisawa\n", - "\n", - "basefile = bdir+f'{station_id}{doy}{let}.{year%100:02d}o'\n", - "decb = rnxdec()\n", - "decb.setSignals(sigs)\n", - "decb.decode_obsh(basefile)\n", - "decb.autoSubstituteSignals()" - ] - }, - { - "cell_type": "markdown", - "id": "-U6kZhcR0tjZ", - "metadata": { - "id": "-U6kZhcR0tjZ" - }, - "source": [ - "Load the antenna data for the satellites and receivers" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "2biX0yEj02cX", - "metadata": { - "id": "2biX0yEj02cX" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Rover] Receiver: SEPT MOSAICX5 Antenna: JAVRINGANT_DM JVDM\n", - "[Base] Receiver: TRIMBLE ALLOY Antenna: TRM159900.00 GSI6\n" - ] - } - ], - "source": [ - "atxfile = bdir + '../antex/igs20.atx'\n", - "ngsantfile = bdir + '../GSI_PCV.TXT'\n", - "\n", - "atx = atxdec()\n", - "atx.readpcv(atxfile)\n", - "atx.readngspcv(ngsantfile)\n", - "\n", - "# Set PCO/PCV information\n", - "nav.rcv_ant = searchpcv(atx.pcvr, dec.ant, dec.ts)\n", - "nav.rcv_ant_b = searchpcv(atx.pcvr, decb.ant, dec.ts)\n", - "\n", - "# Get equipment information\n", - "print(f\"[Rover] Receiver: {dec.rcv} Antenna: {dec.ant}\")\n", - "print(f\"[Base] Receiver: {decb.rcv} Antenna: {decb.ant}\")" - ] - }, - { - "cell_type": "markdown", - "id": "18286a6a", - "metadata": { - "id": "18286a6a" - }, - "source": [ - "Initialize the variables for position and the RTK configuration parameters." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "de72b4fc", - "metadata": { - "id": "de72b4fc" - }, - "outputs": [], - "source": [ - "rtk = rtkpos(nav, dec.pos, 'test_rtk.log')\n", - "rr = dec.pos" - ] - }, - { - "cell_type": "markdown", - "id": "f8bca1dc", - "metadata": { - "id": "f8bca1dc" - }, - "source": [ - "Run RTK positioning for 3 minutes (epochs) using `rtk.process()`." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "7cb2c994", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "7cb2c994", - "outputId": "4e255d85-df3a-46f4-ef1f-d45b8e6acd6b" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 2025-08-21 07:03:00 ENU 0.0007 0.0058 -0.0656, 2D 0.0059, mode 4" - ] - } - ], - "source": [ - "nep = 3 * 60 # 3 minutes\n", - "t = np.zeros(nep)\n", - "enu = np.zeros((nep, 3))\n", - "smode = np.zeros(nep, dtype=int)\n", - "\n", - "for ne in range(nep):\n", - " obs, obsb = sync_obs(dec, decb)\n", - " if ne == 0:\n", - " t0 = nav.t = obs.t\n", - " rtk.process(obs, obsb=obsb)\n", - " t[ne] = timediff(nav.t, t0)\n", - " sol = nav.xa[0:3] if nav.smode == 4 else nav.x[0:3]\n", - " enu[ne, :] = ecef2enu(pos_ref, sol-xyz_ref)\n", - " smode[ne] = nav.smode\n", - " # Log to standard output\n", - " sys.stdout.write('\\r {} ENU {:7.4f} {:7.4f} {:7.4f}, 2D {:6.4f}, mode {:1d}'\n", - " .format(time2str(obs.t),\n", - " enu[ne, 0], enu[ne, 1], enu[ne, 2],\n", - " np.sqrt(enu[ne, 0]**2+enu[ne, 1]**2),\n", - " smode[ne]))\n", - "\n", - "dec.fobs.close()\n", - "decb.fobs.close()" - ] - }, - { - "cell_type": "markdown", - "id": "54c2a23a", - "metadata": { - "id": "54c2a23a" - }, - "source": [ - "Plot the position relative to the reference position." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "92fbb0cd", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 393 - }, - "id": "92fbb0cd", - "outputId": "96f60208-34dc-4bc2-e528-eba39c405454" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from cssrlib.plot import plot_enu\n", - "\n", - "plot_enu(t, enu, smode)" - ] - }, - { - "cell_type": "markdown", - "id": "1c4b06ee", - "metadata": { - "id": "1c4b06ee" - }, - "source": [ - "## Example 5: PPP-RTK positioning (QZSS CLAS)\n", - "\n", - "This section demonstrates PPP-RTK positioning using uncombined receiver observations and recorded QZSS\n", - "L6 binary messages." - ] - }, - { - "cell_type": "markdown", - "id": "lnzfElSPNdSa", - "metadata": { - "id": "lnzfElSPNdSa" - }, - "source": [ - "First, load the required Python modules." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "2c71bec7", - "metadata": { - "id": "2c71bec7" - }, - "outputs": [], - "source": [ - "from copy import deepcopy\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from sys import stdout\n", - "from binascii import unhexlify\n", - "\n", - "from cssrlib.cssrlib import cssr\n", - "from cssrlib.gnss import ecef2pos, Nav, time2gpst, timediff, time2str, time2doy\n", - "from cssrlib.gnss import rSigRnx, sys2str, epoch2time\n", - "from cssrlib.peph import atxdec, searchpcv\n", - "from cssrlib.ppprtk import ppprtkpos\n", - "from cssrlib.rinex import rnxdec" - ] - }, - { - "cell_type": "markdown", - "id": "a0d40a99", - "metadata": { - "id": "a0d40a99" - }, - "source": [ - "Define the RINEX observation, navigation, and antenna files, and starting epoch" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "8abf712f", - "metadata": { - "id": "8abf712f" - }, - "outputs": [], - "source": [ - "# Start epoch, number of epochs\n", - "ep = [2025, 8, 21, 7, 0, 0] # year, month, day, hour, min, sec\n", - "\n", - "time = epoch2time(ep)\n", - "year = ep[0]\n", - "doy = int(time2doy(time))\n", - "let = chr(ord('a')+ep[3])\n", - "bdir = f'cssrlib-data/data/doy{ep[0]:04d}-{doy:03d}/'\n", - "navfile = bdir+f'{doy:03d}{let}_rnx.nav'\n", - "obsfile = bdir+f'{doy:03d}{let}_rnx.obs'\n", - "\n", - "atxfile = 'cssrlib-data/data/antex/igs20.atx'\n", - "\n", - "nav = Nav()\n", - "nav = dec.decode_nav(navfile, nav)\n", - "\n", - "atx = atxdec()\n", - "atx.readpcv(atxfile)" - ] - }, - { - "cell_type": "markdown", - "id": "c803c03a", - "metadata": { - "id": "c803c03a" - }, - "source": [ - "For reference, specify the rover position." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "d60ec1b6", - "metadata": { - "id": "d60ec1b6" - }, - "outputs": [], - "source": [ - "xyz_ref = [-3962108.7007, 3381309.5532, 3668678.6648]\n", - "pos_ref = ecef2pos(xyz_ref)" - ] - }, - { - "cell_type": "markdown", - "id": "ad8ce40c", - "metadata": { - "id": "ad8ce40c" - }, - "source": [ - "QZSS CLAS correction messages are encoded in Compact SSR format. In this example, the recorded L6 data is used. Also load the grid position of QZSS CLAS as defined in IS-QZSS-L6\n", - "." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "03836a0a", - "metadata": { - "id": "03836a0a" - }, - "outputs": [], - "source": [ - "file_l6 = bdir + f'{doy:03d}{let}_qzsl6.txt'\n", - "prn_ref = 199 # QZSS PRN\n", - "l6_ch = 0 # 0:L6D, 1:L6E\n", - "\n", - "griddef = 'cssrlib-data/data/clas_grid.def'\n", - "cs = cssr()\n", - "cs.monlevel = 1\n", - "time = epoch2time(ep)\n", - "cs.week = time2gpst(time)[0]\n", - "cs.read_griddef(griddef)" - ] - }, - { - "cell_type": "markdown", - "id": "owr8qtA63hn4", - "metadata": { - "id": "owr8qtA63hn4" - }, - "source": [ - "Specify signals to be processed. We choose L1C/A+L2P(Y) for GPS, E1+E5a for Galileo, L1C+L2C(L) for QZSS." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "Loa7UDk43iBF", - "metadata": { - "id": "Loa7UDk43iBF" - }, - "outputs": [], - "source": [ - "sigs = [rSigRnx(\"GC1C\"), rSigRnx(\"GC2W\"),\n", - " rSigRnx(\"EC1C\"), rSigRnx(\"EC5Q\"),\n", - " rSigRnx(\"JC1C\"), rSigRnx(\"JC2L\"),\n", - " rSigRnx(\"GL1C\"), rSigRnx(\"GL2W\"),\n", - " rSigRnx(\"EL1C\"), rSigRnx(\"EL5Q\"),\n", - " rSigRnx(\"JL1C\"), rSigRnx(\"JL2L\"),\n", - " rSigRnx(\"GS1C\"), rSigRnx(\"GS2W\"),\n", - " rSigRnx(\"ES1C\"), rSigRnx(\"ES5Q\"),\n", - " rSigRnx(\"JS1C\"), rSigRnx(\"JS2L\")]\n", - "\n", - "rnx = rnxdec()\n", - "rnx.setSignals(sigs)" - ] - }, - { - "cell_type": "markdown", - "id": "gu9YXwY56gwE", - "metadata": { - "id": "gu9YXwY56gwE" - }, - "source": [ - "Initialize position and antenna PCO/PCVs" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "f4ea3b73", - "metadata": { - "id": "f4ea3b73" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Rover] Receiver: SEPT MOSAICX5 Antenna: JAVRINGANT_DM JVDM\n" - ] - } - ], - "source": [ - "if rnx.decode_obsh(obsfile) >= 0:\n", - " # Auto-substitute signals\n", - " rnx.autoSubstituteSignals()\n", - "\n", - " # Initialize position\n", - " ppprtk = ppprtkpos(nav, rnx.pos, 'test_ppprtk.log')\n", - "\n", - " # Set PCO/PCV information\n", - " nav.rcv_ant = searchpcv(atx.pcvr, rnx.ant, rnx.ts)\n", - " # Get equipment information\n", - " print(f\"[Rover] Receiver: {dec.rcv} Antenna: {dec.ant}\")" - ] - }, - { - "cell_type": "markdown", - "id": "R_V2QknK68gO", - "metadata": { - "id": "R_V2QknK68gO" - }, - "source": [ - "Print available signals and selected signals" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "voS1RIxb68wl", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "voS1RIxb68wl", - "outputId": "878b5434-0e0e-418b-b657-3294d4601a50" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Available signals\n", - "GPS C1C L1C D1C S1C C2W L2W D2W S2W C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", - "GLONASS C1C L1C D1C S1C C2C L2C D2C S2C C2P L2P D2P S2P C3Q L3Q D3Q S3Q\n", - "GALILEO C1C L1C D1C S1C C5Q L5Q D5Q S5Q C7Q L7Q D7Q S7Q C8Q L8Q D8Q S8Q C6C L6C D6C S6C\n", - "BEIDOU C1P L1P D1P S1P C2I L2I D2I S2I C5P L5P D5P S5P C6I L6I D6I S6I C7D L7D D7D S7D C7I L7I D7I S7I\n", - "QZSS C1C L1C D1C S1C C1E L1E D1E S1E C1L L1L D1L S1L C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", - "IRNSS C1P L1P D1P S1P C5A L5A D5A S5A\n", - "\n", - "Selected signals\n", - "GPS C1C C2W L1C L2W S1C S2W \n", - "GALILEO C1C C5Q L1C L5Q S1C S5Q \n", - "QZSS C1C C2L L1C L2L S1C S2L \n" - ] - } - ], - "source": [ - "print(\"Available signals\")\n", - "for sys, sigs in rnx.sig_map.items():\n", - " txt = \"{:7s} {}\".format(sys2str(sys),\n", - " ' '.join([sig.str() for sig in sigs.values()]))\n", - " print(txt)\n", - "\n", - "print(\"\\nSelected signals\")\n", - "for sys, tmp in rnx.sig_tab.items():\n", - " txt = \"{:7s} \".format(sys2str(sys))\n", - " for _, sigs in tmp.items():\n", - " txt += \"{} \".format(' '.join([sig.str() for sig in sigs]))\n", - " print(txt)\n" - ] - }, - { - "cell_type": "markdown", - "id": "0lkUmvxU7Xs8", - "metadata": { - "id": "0lkUmvxU7Xs8" - }, - "source": [ - "Get grid location and open L6 file" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "i8sLKFEc7YFX", - "metadata": { - "id": "i8sLKFEc7YFX" - }, - "outputs": [], - "source": [ - "pos = ecef2pos(rnx.pos)\n", - "inet = cs.find_grid_index(pos)\n", - "\n", - "dtype = [('wn', 'int'), ('tow', 'int'), ('prn', 'int'),\n", - " ('type', 'int'), ('len', 'int'), ('nav', 'S500')]\n", - "v = np.genfromtxt(file_l6, dtype=dtype)" - ] - }, - { - "cell_type": "markdown", - "id": "4061cecc", - "metadata": { - "id": "4061cecc" - }, - "source": [ - "Set runtime to 3 minutes and initialize variables" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "defdd418", - "metadata": { - "id": "defdd418" - }, - "outputs": [], - "source": [ - "nep = 3*60 # 3 minutes, increase this for longer run\n", - "\n", - "t = np.zeros(nep)\n", - "tc = np.zeros(nep)\n", - "enu = np.ones((nep, 3))*np.nan\n", - "sol = np.zeros((nep, 4))\n", - "dop = np.zeros((nep, 4))\n", - "smode = np.zeros(nep, dtype=int)" - ] - }, - { - "cell_type": "markdown", - "id": "90dc765a", - "metadata": { - "id": "90dc765a" - }, - "source": [ - "Run PPP-RTK positioning using `ppprtkpos()` for 3 minutes. QZSS L6 messages are encoded in sub-frames (5 second per sub-frame), for the simple implementation, the recorded data is decoded every 5 seconds." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "wi0RcbzX6u4t", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "wi0RcbzX6u4t", - "outputId": "5d592183-ca13-4ea2-a880-96d422cccf40" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 2025-08-21 07:03:01 ENU 0.017 0.002 -0.086, 2D 0.017, mode 4" - ] - } - ], - "source": [ - "# Skip epoch until start time\n", - "obs = rnx.decode_obs()\n", - "while time > obs.t and obs.t.time != 0:\n", - " obs = rnx.decode_obs()\n", - "\n", - "for ne in range(nep):\n", - " week, tow = time2gpst(obs.t)\n", - "\n", - " vi = v[(v['tow'] == tow) & (v['type'] == l6_ch)\n", - " & (v['prn'] == prn_ref)]\n", - " if len(vi) > 0:\n", - " cs.decode_l6msg(unhexlify(vi['nav'][0]), 0)\n", - " if cs.fcnt == 5: # end of sub-frame\n", - " cs.decode_cssr(bytes(cs.buff), 0)\n", - "\n", - " if ne == 0:\n", - " nav.t = deepcopy(obs.t)\n", - " t0 = deepcopy(obs.t)\n", - " t0.time = t0.time//30*30\n", - " cs.time = obs.t\n", - " nav.time_p = t0\n", - "\n", - " cstat = cs.chk_stat()\n", - " if cstat:\n", - " ppprtk.process(obs, cs=cs)\n", - "\n", - " t[ne] = timediff(nav.t, t0) / 60\n", - "\n", - " sol = nav.xa[0:3] if nav.smode == 4 else nav.x[0:3]\n", - " enu[ne, :] = ecef2enu(pos_ref, sol-xyz_ref)\n", - " smode[ne] = nav.smode\n", - "\n", - " # Log to standard output\n", - " stdout.write('\\r {} ENU {:7.3f} {:7.3f} {:7.3f}, 2D {:6.3f}, mode {:1d}'\n", - " .format(time2str(obs.t),\n", - " enu[ne, 0], enu[ne, 1], enu[ne, 2],\n", - " np.sqrt(enu[ne, 0]**2+enu[ne, 1]**2),\n", - " smode[ne]))\n", - "\n", - " # Get new epoch, exit after last epoch\n", - " obs = rnx.decode_obs()\n", - " if obs.t.time == 0:\n", - " break\n", - "\n", - "rnx.fobs.close()\n" - ] - }, - { - "cell_type": "markdown", - "id": "sZlaW82rOoVK", - "metadata": { - "id": "sZlaW82rOoVK" - }, - "source": [ - "Plot the solution position relative to the reference position.\n", - "\n", - "A complete set of QZSS CLAS corrections is available every 30 seconds. An ambiguity-fixed solution, as indicated by the green dots in the plot, is available just after the QZSS CLAS correction messages have been received. As expected,\n", - "the positioning errors are at centimeter level." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "11df8601", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 778 - }, - "id": "11df8601", - "outputId": "afd73534-13a6-4451-f5ff-c1b56ddb330e" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from cssrlib.plot import plot_enu\n", - "\n", - "plot_enu(t, enu, smode)" - ] - }, - { - "cell_type": "markdown", - "id": "6EC-s_PGXwlG", - "metadata": { - "id": "6EC-s_PGXwlG" - }, - "source": [ - "Plot the horizontal errors." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "ce83ece4", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 547 - }, - "id": "ce83ece4", - "outputId": "481782dd-bb3f-4363-f5b2-595522597883" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_enu(t, enu, smode, figtype=2)" - ] - }, - { - "cell_type": "markdown", - "id": "rKUv0nEl8vDm", - "metadata": { - "id": "rKUv0nEl8vDm" - }, - "source": [ - "\n", - "## Example 6: PPP-AR positioning (IGS)\n", - "\n", - "This section demonstrates PPP positioning using IGS offline products from the International GNSS Service (IGS) and a Septentrio PolaRx5 receiver. In this case, the orbit and clock offset information is obtained from\n", - "SP3 files and, optionally, Clock-RINEX files can be used. Pseudorange and carrier-phase observable specific biases (OSBs) are\n", - "loaded from Bias-SINEX files." - ] - }, - { - "cell_type": "markdown", - "id": "_ltFQVZeoWr9", - "metadata": { - "id": "_ltFQVZeoWr9" - }, - "source": [ - "First, load the required Python modules." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "iP6Q1ifMKzDG", - "metadata": { - "id": "iP6Q1ifMKzDG" - }, - "outputs": [], - "source": [ - "from copy import deepcopy\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.dates as md\n", - "import numpy as np\n", - "from sys import stdout\n", - "\n", - "from cssrlib.gnss import time2doy, time2str, timediff, epoch2time, ecef2enu, ecef2pos\n", - "from cssrlib.gnss import Nav, rSigRnx, sys2str\n", - "from cssrlib.peph import atxdec, searchpcv, peph, biasdec\n", - "from cssrlib.pppssr import pppos\n", - "from cssrlib.rinex import rnxdec" - ] - }, - { - "cell_type": "markdown", - "id": "GITYAIKuKzDR", - "metadata": { - "id": "GITYAIKuKzDR" - }, - "source": [ - "Define the input data and parameters for this example. For signals, we choosed L1C/A+L2P(Y) for GPS, E1+E5a for Galileo." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "B8Rm_x8yKzDR", - "metadata": { - "id": "B8Rm_x8yKzDR" - }, - "outputs": [], - "source": [ - "# Start epoch, number of epochs\n", - "ep = [2025, 8, 21, 7, 0, 0]\n", - "\n", - "time = epoch2time(ep)\n", - "year = ep[0]\n", - "doy = int(time2doy(time))\n", - "let = chr(ord('a')+ep[3])\n", - "bdir = f'cssrlib-data/data/doy{ep[0]:04d}-{doy:03d}/'\n", - "navfile = bdir+f'{doy:03d}{let}_rnx.nav'\n", - "obsfile = bdir+f'{doy:03d}{let}_rnx.obs'\n", - "\n", - "ac = 'COD0OPSFIN'\n", - "\n", - "orbfile = bdir+f'../igs/{ac}_{year:4d}{doy:03d}0000_01D_05M_ORB.SP3'\n", - "clkfile = bdir+f'../igs/{ac}_{year:4d}{doy:03d}0000_01D_30S_CLK.CLK'\n", - "bsxfile = bdir+f'../igs/{ac}_{year:4d}{doy:03d}0000_01D_01D_OSB.BIA'\n", - "\n", - "# Set user reference position\n", - "xyz_ref = [-3962108.6836, 3381309.5672, 3668678.6720]\n", - "pos_ref = ecef2pos(xyz_ref)\n", - "\n", - "# Define signals to be processed\n", - "sigs = [rSigRnx(\"GC1C\"), rSigRnx(\"GC2W\"),\n", - " rSigRnx(\"GL1C\"), rSigRnx(\"GL2W\"),\n", - " rSigRnx(\"GS1C\"), rSigRnx(\"GS2W\"),\n", - " rSigRnx(\"EC1C\"), rSigRnx(\"EC5Q\"),\n", - " rSigRnx(\"EL1C\"), rSigRnx(\"EL5Q\"),\n", - " rSigRnx(\"ES1C\"), rSigRnx(\"ES5Q\")]\n", - "\n", - "atxfile = bdir+'../antex/I20.ATX'" - ] - }, - { - "cell_type": "markdown", - "id": "wPNb6p1rKzDR", - "metadata": { - "id": "wPNb6p1rKzDR" - }, - "source": [ - "Load and parse the input data" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "_1PZV8NaKzDS", - "metadata": { - "id": "_1PZV8NaKzDS" - }, - "outputs": [], - "source": [ - "rnx = rnxdec()\n", - "rnx.setSignals(sigs)\n", - "\n", - "nav = Nav()\n", - "orb = peph()\n", - "\n", - "nav.pmode = 0 # Positioning mode: 0:static, 1:kinematic\n", - "\n", - "# Decode RINEX NAV data\n", - "nav = rnx.decode_nav(navfile, nav)\n", - "\n", - "# Load precise orbits and clock offsets\n", - "nav = orb.parse_sp3(orbfile, nav)\n", - "nav = rnx.decode_clk(clkfile, nav)\n", - "\n", - "# Load code and phase biases from Bias-SINEX\n", - "bsx = biasdec()\n", - "bsx.parse(bsxfile)\n", - "\n", - "# Load ANTEX data for satellites and stations\n", - "atx = atxdec()\n", - "atx.readpcv(atxfile)\n", - "\n", - "nav.monlevel = 0 # Logging level\n", - "\n", - "# Load RINEX OBS file header\n", - "if rnx.decode_obsh(obsfile) >= 0:\n", - "\n", - " # Auto-substitute signals\n", - " rnx.autoSubstituteSignals()\n", - "\n", - " # Initialize position\n", - " ppp = pppos(nav, rnx.pos, 'test_pppigs.log')\n", - " nav.ephopt = 4 # IGS\n", - " nav.armode = 3 # 1: continuous, 3: fix-and-hold\n", - " nav.parmode = 1 # 1: normal, 2: partial ambiguity resolution\n", - "\n", - " # change default settings\n", - " nav.elmin = np.deg2rad(10.0) # min sat elevation\n", - " nav.thresar = 2.0 # ambiguity resolution threshold\n", - "\n", - " # Set PCO/PCV information\n", - " nav.sat_ant = atx.pcvs\n", - " nav.rcv_ant = searchpcv(atx.pcvr, rnx.ant, rnx.ts)" - ] - }, - { - "cell_type": "markdown", - "id": "WLp7TAJdKzDS", - "metadata": { - "id": "WLp7TAJdKzDS" - }, - "source": [ - "Print the available and selected satellite signals" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "Ry_M-9k0KzDS", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Ry_M-9k0KzDS", - "outputId": "e238d2df-a79e-48a5-f2ac-bf3d1c3c0970" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Available signals\n", - "GPS C1C L1C D1C S1C C2W L2W D2W S2W C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", - "GLONASS C1C L1C D1C S1C C2C L2C D2C S2C C2P L2P D2P S2P C3Q L3Q D3Q S3Q\n", - "GALILEO C1C L1C D1C S1C C5Q L5Q D5Q S5Q C7Q L7Q D7Q S7Q C8Q L8Q D8Q S8Q C6C L6C D6C S6C\n", - "BEIDOU C1P L1P D1P S1P C2I L2I D2I S2I C5P L5P D5P S5P C6I L6I D6I S6I C7D L7D D7D S7D C7I L7I D7I S7I\n", - "QZSS C1C L1C D1C S1C C1E L1E D1E S1E C1L L1L D1L S1L C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", - "IRNSS C1P L1P D1P S1P C5A L5A D5A S5A\n", - "\n", - "Selected signals\n", - "GPS C1C C2W L1C L2W S1C S2W \n", - "GALILEO C1C C5Q L1C L5Q S1C S5Q \n" - ] - } - ], - "source": [ - "print(\"Available signals\")\n", - "for sys, sigs in rnx.sig_map.items():\n", - " txt = \"{:7s} {}\".format(sys2str(sys),\n", - " ' '.join([sig.str() for sig in sigs.values()]))\n", - " print(txt)\n", - "\n", - "print(\"\\nSelected signals\")\n", - "for sys, tmp in rnx.sig_tab.items():\n", - " txt = \"{:7s} \".format(sys2str(sys))\n", - " for _, sigs in tmp.items():\n", - " txt += \"{} \".format(' '.join([sig.str() for sig in sigs]))\n", - " print(txt)" - ] - }, - { - "cell_type": "markdown", - "id": "o9w39_PgKzDS", - "metadata": { - "id": "o9w39_PgKzDS" - }, - "source": [ - "Run solution for 15 minutes (ok to abort before finished)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "F6yvNFBMKzDS", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "F6yvNFBMKzDS", - "outputId": "6a0f5543-c685-40f8-e55b-0cfb0b50694a" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 2025-08-21 07:15:01 ENU -0.197 0.098 -0.267, 2D 0.220, mode 5" - ] - } - ], - "source": [ - "# increase to run longer, set to 10 minutes to see ambiguity resolution\n", - "nep = 15 * 60 # 15 minutes\n", - "\n", - "# Intialize data structures for results\n", - "t = np.zeros(nep)\n", - "tc = np.zeros(nep)\n", - "enu = np.ones((nep, 3))*np.nan\n", - "sol = np.zeros((nep, 4))\n", - "dop = np.zeros((nep, 4))\n", - "ztd = np.zeros((nep, 1))\n", - "smode = np.zeros(nep, dtype=int)\n", - "\n", - "# Skip epochs until start time\n", - "obs = rnx.decode_obs()\n", - "while time > obs.t and obs.t.time != 0:\n", - " obs = rnx.decode_obs()\n", - "\n", - "# Loop over number of epoch from file start\n", - "for ne in range(nep):\n", - "\n", - " # Set initial epoch\n", - " if ne == 0:\n", - " nav.t = deepcopy(obs.t)\n", - " t0 = deepcopy(obs.t)\n", - "\n", - " # Call PPP module with IGS products\n", - " ppp.process(obs, orb=orb, bsx=bsx)\n", - "\n", - " # Save output\n", - " t[ne] = timediff(nav.t, t0) / 86400.0\n", - "\n", - " sol = nav.xa[0:3] if nav.smode == 4 else nav.x[0:3]\n", - " enu[ne, :] = ecef2enu(pos_ref, sol-xyz_ref)\n", - "\n", - " ztd[ne] = nav.xa[ppp.IT(nav.na)] if nav.smode == 4 else nav.x[ppp.IT(nav.na)]\n", - " smode[ne] = nav.smode\n", - "\n", - " # Log to standard output\n", - " stdout.write('\\r {} ENU {:7.3f} {:7.3f} {:7.3f}, 2D {:6.3f}, mode {:1d}'\n", - " .format(time2str(obs.t),\n", - " enu[ne, 0], enu[ne, 1], enu[ne, 2],\n", - " np.sqrt(enu[ne, 0]**2+enu[ne, 1]**2),\n", - " smode[ne]))\n", - "\n", - " # Get new epoch, exit after last epoch\n", - " obs = rnx.decode_obs()\n", - " if obs.t.time == 0:\n", - " break\n", - "\n", - "rnx.fobs.close()\n" - ] - }, - { - "cell_type": "markdown", - "id": "UIN1k3sPKzDT", - "metadata": { - "id": "UIN1k3sPKzDT" - }, - "source": [ - "Plot results.\n", - "\n", - "The default run length is set to 15 minutes to keep the tutorial moving." - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "iXeH8x2HKzDT", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 795 - }, - "id": "iXeH8x2HKzDT", - "outputId": "216de780-8018-45c5-8747-54bd99bf4d61" - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from cssrlib.plot import plot_enu\n", - "\n", - "plot_enu(t, enu, smode, ztd)" - ] - }, - { - "cell_type": "markdown", - "id": "8993fc13", - "metadata": { - "id": "8993fc13" - }, - "source": [ - "## Example 7: PPP positioning (BeiDou PPP)\n", - "\n", - "This section demonstrates PPP-RTK positioning using BeiDou B1C and B2a pilot observations for BeiDou-3 B2b PPP and GPS L1 C/A and L2 P(Y) with a Septentrio PolaRx5 receiver." - ] - }, - { - "cell_type": "markdown", - "id": "ul3qp8h3oHin", - "metadata": { - "id": "ul3qp8h3oHin" - }, - "source": [ - "First, load the required Python modules." - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "IGih7GGt_9z6", - "metadata": { - "id": "IGih7GGt_9z6" - }, - "outputs": [], - "source": [ - "from binascii import unhexlify\n", - "from copy import deepcopy\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.dates as md\n", - "import numpy as np\n", - "from sys import stdout\n", - "\n", - "from cssrlib.gnss import ecef2pos, ecef2enu, Nav, rSigRnx, sys2str\n", - "from cssrlib.gnss import time2gpst, time2doy, time2str, timediff, epoch2time\n", - "from cssrlib.peph import atxdec, searchpcv, peph\n", - "from cssrlib.cssr_bds import cssr_bds\n", - "from cssrlib.pppssr import pppos\n", - "from cssrlib.rinex import rnxdec" - ] - }, - { - "cell_type": "markdown", - "id": "eGLKroylAeUU", - "metadata": { - "id": "eGLKroylAeUU" - }, - "source": [ - "Define the input data and parameters for this example. For signals, we choose L1C/A+L2P(Y) for GPS, B1C(P)+B2a(P) for BDS." - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "hYbK60gqAfWG", - "metadata": { - "id": "hYbK60gqAfWG" - }, - "outputs": [], - "source": [ - "ep = [2025, 8, 21, 7, 0, 0] # start epoch\n", - "\n", - "time = epoch2time(ep)\n", - "year = ep[0]\n", - "doy = int(time2doy(time))\n", - "\n", - "let = chr(ord('a')+ep[3])\n", - "bdir = f'cssrlib-data/data/doy{ep[0]:04d}-{doy:03d}/'\n", - "navfile = bdir+f'{doy:03d}{let}_rnx.nav'\n", - "obsfile = bdir+f'{doy:03d}{let}_rnx.obs'\n", - "atxfile = bdir+'../antex/igs20.atx'\n", - "\n", - "file_bds = bdir+f'{doy:03d}{let}_bdsb2b.txt'\n", - "prn_ref = 59 # satellite PRN to receive BDS PPP collection\n", - "\n", - "dtype = [('wn', 'int'), ('tow', 'int'), ('prn', 'int'),\n", - " ('type', 'int'), ('len', 'int'), ('nav', 'S124')]\n", - "\n", - "xyz_ref = [-3962108.6836, 3381309.5672, 3668678.6720]\n", - "pos_ref = ecef2pos(xyz_ref)\n", - "\n", - "# Define signals to be processed\n", - "sigs = [rSigRnx(\"GC1C\"), rSigRnx(\"GC2W\"),\n", - " rSigRnx(\"GL1C\"), rSigRnx(\"GL2W\"),\n", - " rSigRnx(\"GS1C\"), rSigRnx(\"GS2W\"),\n", - " rSigRnx(\"CC1P\"), rSigRnx(\"CC5P\"),\n", - " rSigRnx(\"CL1P\"), rSigRnx(\"CL5P\"),\n", - " rSigRnx(\"CS1P\"), rSigRnx(\"CS5P\")]\n", - "\n", - "time = epoch2time(ep)\n", - "year = ep[0]\n", - "doy = int(time2doy(time))" - ] - }, - { - "cell_type": "markdown", - "id": "PrqsDGtJAdTY", - "metadata": { - "id": "PrqsDGtJAdTY" - }, - "source": [ - "Load and parse the input data" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "E56Qi-3cENFV", - "metadata": { - "id": "E56Qi-3cENFV" - }, - "outputs": [], - "source": [ - "rnx = rnxdec()\n", - "rnx.setSignals(sigs)\n", - "\n", - "nav = Nav()\n", - "orb = peph()\n", - "\n", - "nav.pmode = 0 # Positioning mode: 0:static, 1:kinematic\n", - "\n", - "# Decode RINEX NAV data\n", - "nav = rnx.decode_nav(navfile, nav)\n", - "\n", - "# Load PPP corrections\n", - "v = np.genfromtxt(file_bds, dtype=dtype)\n", - "\n", - "cs = cssr_bds()\n", - "cs.monlevel = 0\n", - "\n", - "# Load ANTEX data for satellites and stations\n", - "atx = atxdec()\n", - "atx.readpcv(atxfile)\n", - "\n", - "nav.monlevel = 0 # Logging level\n", - "\n", - "# Load RINEX OBS file header\n", - "if rnx.decode_obsh(obsfile) >= 0:\n", - "\n", - " # Auto-substitute signals\n", - " rnx.autoSubstituteSignals()\n", - "\n", - " # Initialize position\n", - " ppp = pppos(nav, rnx.pos, 'test_pppbds.log')\n", - "\n", - " # Set PCO/PCV information\n", - " nav.sat_ant = atx.pcvs\n", - " nav.rcv_ant = searchpcv(atx.pcvr, rnx.ant, rnx.ts)" - ] - }, - { - "cell_type": "markdown", - "id": "tghWoxc9EhfE", - "metadata": { - "id": "tghWoxc9EhfE" - }, - "source": [ - "Print the available and selected satellite signals" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "DEDeJQscFYG9", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "DEDeJQscFYG9", - "outputId": "863b7757-f882-49df-8121-aac2cf756a27" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Available signals\n", - "GPS C1C L1C D1C S1C C2W L2W D2W S2W C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", - "GLONASS C1C L1C D1C S1C C2C L2C D2C S2C C2P L2P D2P S2P C3Q L3Q D3Q S3Q\n", - "GALILEO C1C L1C D1C S1C C5Q L5Q D5Q S5Q C7Q L7Q D7Q S7Q C8Q L8Q D8Q S8Q C6C L6C D6C S6C\n", - "BEIDOU C1P L1P D1P S1P C2I L2I D2I S2I C5P L5P D5P S5P C6I L6I D6I S6I C7D L7D D7D S7D C7I L7I D7I S7I\n", - "QZSS C1C L1C D1C S1C C1E L1E D1E S1E C1L L1L D1L S1L C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", - "IRNSS C1P L1P D1P S1P C5A L5A D5A S5A\n", - "\n", - "Selected signals\n", - "GPS C1C C2W L1C L2W S1C S2W \n", - "BEIDOU C1P C5P L1P L5P S1P S5P \n" - ] - } - ], - "source": [ - "print(\"Available signals\")\n", - "for sys, sigs in rnx.sig_map.items():\n", - " txt = \"{:7s} {}\".format(sys2str(sys),\n", - " ' '.join([sig.str() for sig in sigs.values()]))\n", - " print(txt)\n", - "\n", - "print(\"\\nSelected signals\")\n", - "for sys, tmp in rnx.sig_tab.items():\n", - " txt = \"{:7s} \".format(sys2str(sys))\n", - " for _, sigs in tmp.items():\n", - " txt += \"{} \".format(' '.join([sig.str() for sig in sigs]))\n", - " print(txt)" - ] - }, - { - "cell_type": "markdown", - "id": "AmIWL6FBEsJW", - "metadata": { - "id": "AmIWL6FBEsJW" - }, - "source": [ - "Process data for 15 minutes (ok to abort before finished)" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "GMsOCMUPEtvP", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "GMsOCMUPEtvP", - "outputId": "7bf96376-2da3-44ec-c90b-2ba5ed733e5a" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 2025-08-21 07:00:27 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=3\n", - " 2025-08-21 07:15:00 ENU -0.127 -0.045 0.302, 2D 0.135, mode 5" - ] - } - ], - "source": [ - "nep = 15 * 60 # increase this to run longer\n", - "\n", - "# Intialize data structures for results\n", - "t = np.zeros(nep)\n", - "tc = np.zeros(nep)\n", - "enu = np.ones((nep, 3))*np.nan\n", - "sol = np.zeros((nep, 4))\n", - "dop = np.zeros((nep, 4))\n", - "ztd = np.zeros((nep, 1))\n", - "smode = np.zeros(nep, dtype=int)\n", - "\n", - "# Skip epochs until start time\n", - "obs = rnx.decode_obs()\n", - "while time > obs.t and obs.t.time != 0:\n", - " obs = rnx.decode_obs()\n", - "\n", - "# Loop over number of epoch from file start\n", - "for ne in range(nep):\n", - " week, tow = time2gpst(obs.t)\n", - " cs.week = week\n", - " cs.tow0 = tow // 86400 * 86400\n", - "\n", - " # Set intial epoch\n", - " if ne == 0:\n", - " nav.t = deepcopy(obs.t)\n", - " t0 = deepcopy(obs.t)\n", - " t0.time = t0.time // 30 * 30\n", - " nav.time_p = t0\n", - "\n", - " vi = v[(v['tow'] == tow) & (v['prn'] == prn_ref)]\n", - " if len(vi) > 0:\n", - " buff = unhexlify(vi['nav'][0])\n", - " cs.decode_cssr(buff, 0)\n", - "\n", - " # Call PPP module with BDS-PPP corrections\n", - " if (cs.lc[0].cstat & 0xf) == 0xf:\n", - " ppp.process(obs, cs=cs)\n", - "\n", - " # Save output\n", - " t[ne] = timediff(nav.t, t0) / 86400.0\n", - "\n", - " sol = nav.xa[0:3] if nav.smode == 4 else nav.x[0:3]\n", - " enu[ne, :] = ecef2enu(pos_ref, sol-xyz_ref)\n", - "\n", - " ztd[ne] = nav.xa[ppp.IT(nav.na)] if nav.smode == 4 else nav.x[ppp.IT(nav.na)]\n", - " smode[ne] = nav.smode\n", - "\n", - " # Log to standard output\n", - " stdout.write('\\r {} ENU {:7.3f} {:7.3f} {:7.3f}, 2D {:6.3f}, mode {:1d}'\n", - " .format(time2str(obs.t),\n", - " enu[ne, 0], enu[ne, 1], enu[ne, 2],\n", - " np.sqrt(enu[ne, 0]**2+enu[ne, 1]**2),\n", - " smode[ne]))\n", - "\n", - " # Get new epoch, exit after last epoch\n", - " obs = rnx.decode_obs()\n", - " if obs.t.time == 0:\n", - " break\n", - "\n", - "rnx.fobs.close()" - ] - }, - { - "cell_type": "markdown", - "id": "-QkxrW1_GVYU", - "metadata": { - "id": "-QkxrW1_GVYU" - }, - "source": [ - "Plot results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ttuzs72KHeFs", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 795 - }, - "id": "ttuzs72KHeFs", - "outputId": "797c28c9-5aa2-491c-a68a-fc3b38bbf64d" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from cssrlib.plot import plot_enu\n", - "\n", - "plot_enu(t, enu, smode, ztd)" - ] - }, - { - "cell_type": "markdown", - "id": "hPjJOF2T8uxG", - "metadata": { - "id": "hPjJOF2T8uxG" - }, - "source": [ - "\n", - "## Example 8: PPP positioning (Galileo HAS)\n", - "\n", - "This section demonstrates PPP positioning using Galileo HAS corrections with a Septentrio PolaRx5 receiver." - ] - }, - { - "cell_type": "markdown", - "id": "3CU6La60p2gQ", - "metadata": { - "id": "3CU6La60p2gQ" - }, - "source": [ - "First, load the required Python modules." - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "tQzCZh9--Ias", - "metadata": { - "id": "tQzCZh9--Ias" - }, - "outputs": [], - "source": [ - "from copy import deepcopy\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.dates as md\n", - "import numpy as np\n", - "from binascii import unhexlify\n", - "import bitstruct as bs\n", - "from sys import stdout\n", - "\n", - "from cssrlib.gnss import ecef2pos, ecef2enu, Nav, rSigRnx, sys2str\n", - "from cssrlib.gnss import time2gpst, time2doy, time2str, timediff, epoch2time\n", - "from cssrlib.peph import atxdec, searchpcv\n", - "from cssrlib.cssr_has import cssr_has, cnav_msg\n", - "from cssrlib.pppssr import pppos\n", - "from cssrlib.rinex import rnxdec" - ] - }, - { - "cell_type": "markdown", - "id": "EpWHwoJa-Ias", - "metadata": { - "id": "EpWHwoJa-Ias" - }, - "source": [ - "Define the input data and parameters for this example" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "p9pQ2yeo-Iat", - "metadata": { - "id": "p9pQ2yeo-Iat" - }, - "outputs": [], - "source": [ - "# Start epoch and number of epochs\n", - "ep = [2025, 8, 21, 7, 0, 0] # start epoch\n", - "\n", - "time = epoch2time(ep)\n", - "year = ep[0]\n", - "doy = int(time2doy(time))\n", - "\n", - "let = chr(ord('a')+ep[3])\n", - "bdir = f'cssrlib-data/data/doy{ep[0]:04d}-{doy:03d}/'\n", - "navfile = bdir+f'{doy:03d}{let}_rnx.nav'\n", - "obsfile = bdir+f'{doy:03d}{let}_rnx.obs'\n", - "atxfile = bdir+'../antex/igs20.atx'\n", - "\n", - "# Specify Galile HAS corrections files\n", - "file_has = bdir+f'{doy:03d}{let}_gale6.txt'\n", - "\n", - "dtype = [('wn', 'int'), ('tow', 'int'), ('prn', 'int'),\n", - " ('type', 'int'), ('len', 'int'), ('nav', 'S124')]\n", - "\n", - "# Set user reference position\n", - "xyz_ref = [-3962108.6836, 3381309.5672, 3668678.6720]\n", - "pos_ref = ecef2pos(xyz_ref)\n", - "\n", - "# Define signals to be processed\n", - "sigs = [rSigRnx(\"GC1C\"), rSigRnx(\"GC2L\"),\n", - " rSigRnx(\"GL1C\"), rSigRnx(\"GL2L\"),\n", - " rSigRnx(\"GS1C\"), rSigRnx(\"GS2L\"),\n", - " rSigRnx(\"EC1C\"), rSigRnx(\"EC5Q\"),\n", - " rSigRnx(\"EL1C\"), rSigRnx(\"EL5Q\"),\n", - " rSigRnx(\"ES1C\"), rSigRnx(\"ES5Q\")]" - ] - }, - { - "cell_type": "markdown", - "id": "6d24e8d2", - "metadata": {}, - "source": [ - "For decoding Galileo CNAV pages, Galileo CNAV message parser is defined. And, a table for Reed-Solomon decoding has loaded." - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "bdf460f9", - "metadata": {}, - "outputs": [], - "source": [ - "# Table for Reed-Solomon decoding\n", - "file_gm = bdir+\"../../samples/Galileo-HAS-SIS-ICD_1.0_Annex_B_Reed_Solomon_Generator_Matrix.txt\"\n", - "# Galileo CNAV message parser\n", - "cnav = cnav_msg()\n", - "cnav.load_gmat(file_gm)" - ] - }, - { - "cell_type": "markdown", - "id": "3ORyJKZI-Iat", - "metadata": { - "id": "3ORyJKZI-Iat" - }, - "source": [ - "Load and parse the input data" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "G3uGHYHh-Iat", - "metadata": { - "id": "G3uGHYHh-Iat" - }, - "outputs": [], - "source": [ - "rnx = rnxdec()\n", - "rnx.setSignals(sigs)\n", - "\n", - "nav = Nav()\n", - "nav.pmode = 0 # Positioning mode: 0:static, 1:kinematic\n", - "\n", - "# Decode RINEX NAV data\n", - "nav = rnx.decode_nav(navfile, nav)\n", - "\n", - "# Load PPP corrections\n", - "v = np.genfromtxt(file_has, dtype=dtype)\n", - "gMat = np.genfromtxt(file_gm, dtype=\"u1\", delimiter=\",\")\n", - "\n", - "# Load ANTEX data for satellites and stations\n", - "atx = atxdec()\n", - "atx.readpcv(atxfile)\n", - "\n", - "cs = cssr_has()\n", - "# cs.monlevel = 2\n", - "nav.monlevel = 0 # Logging level\n", - "\n", - "# Load RINEX OBS file header\n", - "if rnx.decode_obsh(obsfile) >= 0:\n", - "\n", - " # Auto-substitute signals\n", - " rnx.autoSubstituteSignals()\n", - "\n", - " # Initialize position\n", - " ppp = pppos(nav, rnx.pos, 'test_ppphas.log')\n", - "\n", - " # Modify default config parameters\n", - " nav.elmin = np.deg2rad(5.0) # min sat el\n", - "\n", - " # Set PCO/PCV information\n", - " nav.sat_ant = atx.pcvs\n", - " nav.rcv_ant = searchpcv(atx.pcvr, rnx.ant, rnx.ts)" - ] - }, - { - "cell_type": "markdown", - "id": "4rL8k8Vi-Iau", - "metadata": { - "id": "4rL8k8Vi-Iau" - }, - "source": [ - "Print the available satellite and selected signals. We choose L1C/A+L2P(Y) for GPS, E1+E5b for Galileo." - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "n_Oxkxn--Iau", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "n_Oxkxn--Iau", - "outputId": "f2fdaca6-da70-4f10-fb65-fbe8d8e2f659" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Available signals\n", - "GPS C1C L1C D1C S1C C2W L2W D2W S2W C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", - "GLONASS C1C L1C D1C S1C C2C L2C D2C S2C C2P L2P D2P S2P C3Q L3Q D3Q S3Q\n", - "GALILEO C1C L1C D1C S1C C5Q L5Q D5Q S5Q C7Q L7Q D7Q S7Q C8Q L8Q D8Q S8Q C6C L6C D6C S6C\n", - "BEIDOU C1P L1P D1P S1P C2I L2I D2I S2I C5P L5P D5P S5P C6I L6I D6I S6I C7D L7D D7D S7D C7I L7I D7I S7I\n", - "QZSS C1C L1C D1C S1C C1E L1E D1E S1E C1L L1L D1L S1L C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", - "IRNSS C1P L1P D1P S1P C5A L5A D5A S5A\n", - "\n", - "Selected signals\n", - "GPS C1C C2L L1C L2L S1C S2L \n", - "GALILEO C1C C5Q L1C L5Q S1C S5Q \n" - ] - } - ], - "source": [ - "print(\"Available signals\")\n", - "for sys, sigs in rnx.sig_map.items():\n", - " txt = \"{:7s} {}\".format(sys2str(sys),\n", - " ' '.join([sig.str() for sig in sigs.values()]))\n", - " print(txt)\n", - "\n", - "print(\"\\nSelected signals\")\n", - "for sys, tmp in rnx.sig_tab.items():\n", - " txt = \"{:7s} \".format(sys2str(sys))\n", - " for _, sigs in tmp.items():\n", - " txt += \"{} \".format(' '.join([sig.str() for sig in sigs]))\n", - " print(txt)" - ] - }, - { - "cell_type": "markdown", - "id": "ZRBRDQtw-Iau", - "metadata": { - "id": "ZRBRDQtw-Iau" - }, - "source": [ - "Process data for 15 minutes (ok to abort before finished)" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "uVeqUA6A-Iav", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "uVeqUA6A-Iav", - "outputId": "8e1440b6-0917-4ef5-8fc9-0925b6df09d3" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 2025-08-21 07:15:00 ENU 0.035 0.224 -0.582, 2D 0.226, mode 5 nsat 19/16/16\n" - ] - } - ], - "source": [ - "nep = 15 * 60 # increase this to run longer\n", - "\n", - "# Intialize data structures for results\n", - "t = np.zeros(nep)\n", - "enu = np.ones((nep, 3))*np.nan\n", - "sol = np.zeros((nep, 4))\n", - "ztd = np.zeros((nep, 1))\n", - "smode = np.zeros(nep, dtype=int)\n", - "nsat = np.zeros((nep, 3), dtype=int)\n", - "\n", - "# Skip epochs until start time\n", - "obs = rnx.decode_obs()\n", - "while time > obs.t and obs.t.time != 0:\n", - " obs = rnx.decode_obs()\n", - "\n", - "# Loop over number of epoch from file start\n", - "for ne in range(nep):\n", - " week, tow = time2gpst(obs.t)\n", - " cs.week = week\n", - " cs.tow0 = tow // 3600 * 3600\n", - "\n", - " # Set initial epoch\n", - " if ne == 0:\n", - " nav.t = deepcopy(obs.t)\n", - " t0 = deepcopy(obs.t)\n", - " t0.time = t0.time // 30 * 30\n", - " nav.time_p = t0\n", - "\n", - " vi = v[v['tow'] == tow]\n", - "\n", - " HASmsg = cnav.decode_cnav(tow, vi) # decode CNAV pages\n", - " if HASmsg is not None:\n", - " cs.msgtype = cnav.msgtype\n", - " cs.decode_cssr(HASmsg) # decode HAS messages\n", - "\n", - " # Call PPP module with HAS corrections\n", - " if (cs.lc[0].cstat & 0xf) == 0xf:\n", - " ppp.process(obs, cs=cs)\n", - "\n", - " # Save output\n", - " t[ne] = timediff(nav.t, t0) / 86400.0\n", - "\n", - " sol = nav.xa[0:3] if nav.smode == 4 else nav.x[0:3]\n", - " enu[ne, :] = ecef2enu(pos_ref, sol-xyz_ref)\n", - "\n", - " ztd[ne] = nav.xa[ppp.IT(nav.na)] if nav.smode == 4 else nav.x[ppp.IT(nav.na)]\n", - " smode[ne] = nav.smode\n", - " nsat[ne, :] = nav.nsat\n", - "\n", - " nav.fout.write(\"{} {:14.4f} {:14.4f} {:14.4f} \"\n", - " \"ENU {:7.3f} {:7.3f} {:7.3f}, 2D {:6.3f}, mode {:1d}\\n\"\n", - " .format(time2str(obs.t),\n", - " sol[0], sol[1], sol[2],\n", - " enu[ne, 0], enu[ne, 1], enu[ne, 2],\n", - " np.sqrt(enu[ne, 0]**2+enu[ne, 1]**2),\n", - " smode[ne]))\n", - "\n", - " stdout.write('\\r {} ENU {:7.3f} {:7.3f} {:7.3f}, 2D {:6.3f}, mode {:1d} nsat {:2d}/{:2d}/{:2d}'\n", - " .format(time2str(obs.t),\n", - " enu[ne, 0], enu[ne, 1], enu[ne, 2],\n", - " np.sqrt(enu[ne, 0]**2+enu[ne, 1]**2),\n", - " smode[ne],\n", - " nsat[ne, 0], nsat[ne, 1], nsat[ne, 2]))\n", - "\n", - " # Get new epoch, exit after last epoch\n", - " obs = rnx.decode_obs()\n", - " if obs.t.time == 0:\n", - " break\n", - "\n", - "stdout.write('\\n')\n", - "rnx.fobs.close()\n" - ] - }, - { - "cell_type": "markdown", - "id": "yXJXvBP5-Iav", - "metadata": { - "id": "yXJXvBP5-Iav" - }, - "source": [ - "Plot results" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "jBUBDuDH-Iax", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 795 - }, - "id": "jBUBDuDH-Iax", - "outputId": "d9fbe8c3-9ae9-48f5-cd48-325f07d63ab9" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from cssrlib.plot import plot_enu\n", - "\n", - "plot_enu(t, enu, smode, ztd)" - ] - }, - { - "cell_type": "markdown", - "id": "OdegzV6Wpwb3", - "metadata": { - "id": "OdegzV6Wpwb3" - }, - "source": [] - }, - { - "cell_type": "markdown", - "id": "nf1Rwd3u8vXK", - "metadata": { - "id": "nf1Rwd3u8vXK" - }, - "source": [ - "\n", - "## Example 9: PPP positioning (MADOCA-PPP)\n", - "\n", - "This section demonstrates PPP positioning using QZSS MADOCA-PPP corrections with a Septentrio mosaic-X5 receiver." - ] - }, - { - "cell_type": "markdown", - "id": "to8XjhfTtpCu", - "metadata": { - "id": "to8XjhfTtpCu" - }, - "source": [ - "First, load the required Python modules." - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "wsXq8ow7Kt6-", - "metadata": { - "id": "wsXq8ow7Kt6-" - }, - "outputs": [], - "source": [ - "from copy import deepcopy\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from binascii import unhexlify\n", - "from sys import stdout\n", - "\n", - "from cssrlib.gnss import ecef2pos, ecef2enu, Nav, rSigRnx, sys2str\n", - "from cssrlib.gnss import time2gpst, time2doy, time2str, timediff, epoch2time\n", - "from cssrlib.peph import atxdec, searchpcv\n", - "from cssrlib.cssr_mdc import cssr_mdc\n", - "from cssrlib.pppssr import pppos\n", - "from cssrlib.rinex import rnxdec" - ] - }, - { - "cell_type": "markdown", - "id": "sIWw2QoZKt7I", - "metadata": { - "id": "sIWw2QoZKt7I" - }, - "source": [ - "Define the input data and parameters for this example" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "yzPWKG1oKt7I", - "metadata": { - "id": "yzPWKG1oKt7I" - }, - "outputs": [], - "source": [ - "# Start epoch and number of epochs\n", - "ep = [2025, 8, 21, 7, 0, 0]\n", - "\n", - "time = epoch2time(ep)\n", - "year = ep[0]\n", - "doy = int(time2doy(time))\n", - "\n", - "let = chr(ord('a')+ep[3])\n", - "bdir = f'cssrlib-data/data/doy{ep[0]:04d}-{doy:03d}/'\n", - "navfile = bdir+f'{doy:03d}{let}_rnx.nav'\n", - "obsfile = bdir+f'{doy:03d}{let}_rnx.obs'\n", - "atxfile = bdir+'../antex/igs20.atx'\n", - "\n", - "# Specify L6 corrections files\n", - "file_l6 = bdir+f'{doy:03d}{let}_qzsl6.txt'\n", - "prn_ref = 199 # QZSS PRN\n", - "l6_ch = 1 # 0:L6D, 1:L6E\n", - "dtype = [('wn', 'int'), ('tow', 'int'), ('prn', 'int'),\n", - " ('type', 'int'), ('len', 'int'), ('nav', 'S500')]\n", - "\n", - "# Set user reference position\n", - "xyz_ref = [-3962108.6836, 3381309.5672, 3668678.6720]\n", - "pos_ref = ecef2pos(xyz_ref)\n", - "\n", - "# Define signals to be processed\n", - "sigs = [rSigRnx(\"GC1C\"), rSigRnx(\"GC2W\"),\n", - " rSigRnx(\"GL1C\"), rSigRnx(\"GL2W\"),\n", - " rSigRnx(\"GS1C\"), rSigRnx(\"GS2W\"),\n", - " rSigRnx(\"EC1C\"), rSigRnx(\"EC5Q\"),\n", - " rSigRnx(\"EL1C\"), rSigRnx(\"EL5Q\"),\n", - " rSigRnx(\"ES1C\"), rSigRnx(\"ES5Q\"),\n", - " rSigRnx(\"JC1C\"), rSigRnx(\"JC2L\"),\n", - " rSigRnx(\"JL1C\"), rSigRnx(\"JL2L\"),\n", - " rSigRnx(\"JS1C\"), rSigRnx(\"JS2L\")]\n" - ] - }, - { - "cell_type": "markdown", - "id": "-CjlD8OGKt7I", - "metadata": { - "id": "-CjlD8OGKt7I" - }, - "source": [ - "Load and parse the input data" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "Z-oB0iDWKt7I", - "metadata": { - "id": "Z-oB0iDWKt7I" - }, - "outputs": [], - "source": [ - "rnx = rnxdec()\n", - "rnx.setSignals(sigs)\n", - "\n", - "nav = Nav()\n", - "nav.pmode = 0 # Positioning mode: 0:static, 1:kinematic\n", - "\n", - "# Decode RINEX NAV data\n", - "nav = rnx.decode_nav(navfile, nav)\n", - "\n", - "# Load PPP corrections\n", - "v = np.genfromtxt(file_l6, dtype=dtype)\n", - "\n", - "cs = cssr_mdc('madoca_cssr.log')\n", - "cs.monlevel = 0\n", - "\n", - "# Load ANTEX data for satellites and stations\n", - "atx = atxdec()\n", - "atx.readpcv(atxfile)\n", - "\n", - "nav.monlevel = 0 # Logging level\n", - "\n", - "# Load RINEX OBS file header\n", - "if rnx.decode_obsh(obsfile) >= 0:\n", - "\n", - " # Auto-substitute signals\n", - " rnx.autoSubstituteSignals()\n", - "\n", - " # Initialize position\n", - " rr = rnx.pos\n", - " pos = ecef2pos(rr)\n", - " ppp = pppos(nav, rnx.pos, 'test_pppmdc.log')\n", - " nav.elmin = np.deg2rad(5.0)\n", - " nav.glo_ch = rnx.glo_ch\n", - "\n", - " # Set PCO/PCV information\n", - " nav.sat_ant = atx.pcvs\n", - " nav.rcv_ant = searchpcv(atx.pcvr, rnx.ant, rnx.ts)" - ] - }, - { - "cell_type": "markdown", - "id": "jHYIRQokKt7J", - "metadata": { - "id": "jHYIRQokKt7J" - }, - "source": [ - "Print the available satellite signals" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "NqhVIdPgKt7J", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "NqhVIdPgKt7J", - "outputId": "2ea50f20-ffed-4da0-f7c7-83d0de604e57" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Available signals\n", - "GPS C1C L1C D1C S1C C2W L2W D2W S2W C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", - "GLONASS C1C L1C D1C S1C C2C L2C D2C S2C C2P L2P D2P S2P C3Q L3Q D3Q S3Q\n", - "GALILEO C1C L1C D1C S1C C5Q L5Q D5Q S5Q C7Q L7Q D7Q S7Q C8Q L8Q D8Q S8Q C6C L6C D6C S6C\n", - "BEIDOU C1P L1P D1P S1P C2I L2I D2I S2I C5P L5P D5P S5P C6I L6I D6I S6I C7D L7D D7D S7D C7I L7I D7I S7I\n", - "QZSS C1C L1C D1C S1C C1E L1E D1E S1E C1L L1L D1L S1L C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", - "IRNSS C1P L1P D1P S1P C5A L5A D5A S5A\n", - "\n", - "Selected signals\n", - "GPS C1C C2W L1C L2W S1C S2W \n", - "GALILEO C1C C5Q L1C L5Q S1C S5Q \n", - "QZSS C1C C2L L1C L2L S1C S2L \n" - ] - } - ], - "source": [ - "print(\"Available signals\")\n", - "for sys, sigs in rnx.sig_map.items():\n", - " txt = \"{:7s} {}\".format(sys2str(sys),\n", - " ' '.join([sig.str() for sig in sigs.values()]))\n", - " print(txt)\n", - "\n", - "print(\"\\nSelected signals\")\n", - "for sys, tmp in rnx.sig_tab.items():\n", - " txt = \"{:7s} \".format(sys2str(sys))\n", - " for _, sigs in tmp.items():\n", - " txt += \"{} \".format(' '.join([sig.str() for sig in sigs]))\n", - " print(txt)" - ] - }, - { - "cell_type": "markdown", - "id": "zDhWQUZAKt7J", - "metadata": { - "id": "zDhWQUZAKt7J" - }, - "source": [ - "Process data for 15 minutes (ok to abort before finished)" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "tjGh4fpZKt7J", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "tjGh4fpZKt7J", - "outputId": "fc918566-9605-46ef-858c-658c7426fc8e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 2025-08-21 07:15:00 ENU -0.191 0.079 -0.037, 2D 0.207, mode 5" - ] - } - ], - "source": [ - "nep = 15 * 60 # increase this to run longer\n", - "\n", - "# Intialize data structures for results\n", - "t = np.zeros(nep)\n", - "enu = np.ones((nep, 3))*np.nan\n", - "sol = np.zeros((nep, 4))\n", - "ztd = np.zeros((nep, 1))\n", - "smode = np.zeros(nep, dtype=int)\n", - "\n", - "# Skip epochs until start time\n", - "obs = rnx.decode_obs()\n", - "while time > obs.t and obs.t.time != 0:\n", - " obs = rnx.decode_obs()\n", - "\n", - "# Loop over number of epoch from file start\n", - "for ne in range(nep):\n", - " week, tow = time2gpst(obs.t)\n", - " cs.week = week\n", - " cs.tow0 = tow // 3600*3600\n", - "\n", - " # Set initial epoch\n", - " if ne == 0:\n", - " nav.t = deepcopy(obs.t)\n", - " t0 = deepcopy(obs.t)\n", - " t0.time = t0.time//30*30\n", - " nav.time_p = t0\n", - "\n", - " vi = v[(v['tow'] == tow) & (v['type'] == l6_ch)\n", - " & (v['prn'] == prn_ref)]\n", - " if len(vi) > 0:\n", - " msg = unhexlify(vi['nav'][0])\n", - " cs.decode_l6msg(msg, 0)\n", - " if cs.fcnt == 5: # end of sub-frame\n", - " cs.decode_cssr(cs.buff, 0)\n", - "\n", - " # Call PPP module\n", - " if (cs.lc[0].cstat & 0xf) == 0xf:\n", - " ppp.process(obs, cs=cs)\n", - "\n", - " # Save output\n", - " t[ne] = timediff(nav.t, t0) / 86400.0\n", - " sol = nav.xa[0:3] if nav.smode == 4 else nav.x[0:3]\n", - " enu[ne, :] = ecef2enu(pos_ref, sol-xyz_ref)\n", - " ztd[ne] = nav.xa[ppp.IT(nav.na)] if nav.smode == 4 else nav.x[ppp.IT(nav.na)]\n", - " smode[ne] = nav.smode\n", - "\n", - " # Log to standard output\n", - " stdout.write('\\r {} ENU {:7.3f} {:7.3f} {:7.3f}, 2D {:6.3f}, mode {:1d}'\n", - " .format(time2str(obs.t),\n", - " enu[ne, 0], enu[ne, 1], enu[ne, 2],\n", - " np.sqrt(enu[ne, 0]**2+enu[ne, 1]**2),\n", - " smode[ne]))\n", - "\n", - " # Get new epoch, exit after last epoch\n", - " obs = rnx.decode_obs()\n", - " if obs.t.time == 0:\n", - " break" - ] - }, - { - "cell_type": "markdown", - "id": "JvZIeMofKt7K", - "metadata": { - "id": "JvZIeMofKt7K" - }, - "source": [ - "Plot results" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "PHo6f5f1Kt7K", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 795 - }, - "id": "PHo6f5f1Kt7K", - "outputId": "831cb237-f64e-4564-bfa1-ac19b2ae4451" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from cssrlib.plot import plot_enu\n", - "\n", - "plot_enu(t, enu, smode, ztd)" - ] - }, - { - "cell_type": "markdown", - "id": "140a3d36", - "metadata": {}, - "source": [ - "## Example 10: PPP positioning (PPP via SouthPAN)\n", - "\n", - "This section demonstrates PPP positioning using PPP via SouthPAN (PVS) corrections with a Septentrio mosaic-X5 receiver." - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "4ef1b043", - "metadata": {}, - "outputs": [], - "source": [ - "from copy import deepcopy\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from binascii import unhexlify\n", - "from sys import stdout\n", - "\n", - "from cssrlib.gnss import ecef2pos, ecef2enu, Nav, rSigRnx, sys2str\n", - "from cssrlib.gnss import time2gpst, time2doy, time2str, timediff, epoch2time\n", - "from cssrlib.peph import atxdec, searchpcv\n", - "from cssrlib.cssr_pvs import cssr_pvs\n", - "from cssrlib.pppssr import pppos\n", - "from cssrlib.rinex import rnxdec" - ] - }, - { - "cell_type": "markdown", - "id": "fda6108f", - "metadata": {}, - "source": [ - "PVS correction data can be obtained from L5 SBAS correction from PRN 122. " - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "847a774c", - "metadata": {}, - "outputs": [], - "source": [ - "# Start epoch and number of epochs\n", - "ep = [2025, 8, 21, 7, 0, 0]\n", - "\n", - "time = epoch2time(ep)\n", - "year = ep[0]\n", - "doy = int(time2doy(time))\n", - "\n", - "let = chr(ord('a')+ep[3])\n", - "bdir = f'cssrlib-data/data/doy{ep[0]:04d}-{doy:03d}/'\n", - "navfile = bdir+f'{doy:03d}{let}_rnx.nav'\n", - "obsfile = bdir+f'{doy:03d}{let}_rnx.obs'\n", - "atxfile = bdir+'../antex/igs20.atx'\n", - "\n", - "# Specify SBAS corrections files\n", - "file_sbas = bdir+f'{doy:03d}{let}_sbas.txt'\n", - "prn_ref = 122 # satellite PRN for PRN122\n", - "sbas_type = 1 # L1: 0, L5: 1\n", - "\n", - "dtype = [('wn', 'int'), ('tow', 'float'), ('prn', 'int'),\n", - " ('type', 'int'), ('marker', 'S2'), ('nav', 'S124')]\n", - "\n", - "# Set user reference position\n", - "xyz_ref = [-3962108.6836, 3381309.5672, 3668678.6720]\n", - "pos_ref = ecef2pos(xyz_ref)\n", - "\n", - "# Define signals to be processed\n", - "sigs = [rSigRnx(\"GC1C\"), rSigRnx(\"GC5Q\"),\n", - " rSigRnx(\"GL1C\"), rSigRnx(\"GL5Q\"),\n", - " rSigRnx(\"GS1C\"), rSigRnx(\"GS5Q\"),\n", - " rSigRnx(\"EC1C\"), rSigRnx(\"EC5Q\"),\n", - " rSigRnx(\"EL1C\"), rSigRnx(\"EL5Q\"),\n", - " rSigRnx(\"ES1C\"), rSigRnx(\"ES5Q\")]\n" - ] - }, - { - "cell_type": "markdown", - "id": "8d18ea4c", - "metadata": {}, - "source": [ - "Antenna PCO/PCV correction parameters are loaded. " - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "6117e773", - "metadata": {}, - "outputs": [], - "source": [ - "rnx = rnxdec()\n", - "rnx.setSignals(sigs)\n", - "\n", - "nav = Nav()\n", - "nav.pmode = 0 # Positioning mode: 0:static, 1:kinematic\n", - "\n", - "# Decode RINEX NAV data\n", - "nav = rnx.decode_nav(navfile, nav)\n", - "\n", - "# Load PPP corrections\n", - "v = np.genfromtxt(file_sbas, dtype=dtype)\n", - "\n", - "cs = cssr_pvs()\n", - "cs.monlevel = 0\n", - "\n", - "# Load ANTEX data for satellites and stations\n", - "atx = atxdec()\n", - "atx.readpcv(atxfile)\n", - "\n", - "nav.monlevel = 0 # Logging level\n", - "\n", - "# Load RINEX OBS file header\n", - "if rnx.decode_obsh(obsfile) >= 0:\n", - "\n", - " # Auto-substitute signals\n", - " rnx.autoSubstituteSignals()\n", - "\n", - " # Initialize position\n", - " rr = rnx.pos\n", - " pos = ecef2pos(rr)\n", - " ppp = pppos(nav, rnx.pos, 'test_ppppvs.log')\n", - " nav.elmin = np.deg2rad(5.0)\n", - " nav.glo_ch = rnx.glo_ch\n", - "\n", - " # Set PCO/PCV information\n", - " nav.sat_ant = atx.pcvs\n", - " nav.rcv_ant = searchpcv(atx.pcvr, rnx.ant, rnx.ts)" - ] - }, - { - "cell_type": "markdown", - "id": "be76051d", - "metadata": {}, - "source": [ - "For signals, L1C/A+L5 for GPS, E1+E5a for Galileo are used." - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "id": "f39f72ca", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Available signals\n", - "GPS C1C L1C D1C S1C C2W L2W D2W S2W C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", - "GLONASS C1C L1C D1C S1C C2C L2C D2C S2C C2P L2P D2P S2P C3Q L3Q D3Q S3Q\n", - "GALILEO C1C L1C D1C S1C C5Q L5Q D5Q S5Q C7Q L7Q D7Q S7Q C8Q L8Q D8Q S8Q C6C L6C D6C S6C\n", - "BEIDOU C1P L1P D1P S1P C2I L2I D2I S2I C5P L5P D5P S5P C6I L6I D6I S6I C7D L7D D7D S7D C7I L7I D7I S7I\n", - "QZSS C1C L1C D1C S1C C1E L1E D1E S1E C1L L1L D1L S1L C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", - "IRNSS C1P L1P D1P S1P C5A L5A D5A S5A\n", - "\n", - "Selected signals\n", - "GPS C1C C5Q L1C L5Q S1C S5Q \n", - "GALILEO C1C C5Q L1C L5Q S1C S5Q \n" - ] - } - ], - "source": [ - "print(\"Available signals\")\n", - "for sys, sigs in rnx.sig_map.items():\n", - " txt = \"{:7s} {}\".format(sys2str(sys),\n", - " ' '.join([sig.str() for sig in sigs.values()]))\n", - " print(txt)\n", - "\n", - "print(\"\\nSelected signals\")\n", - "for sys, tmp in rnx.sig_tab.items():\n", - " txt = \"{:7s} \".format(sys2str(sys))\n", - " for _, sigs in tmp.items():\n", - " txt += \"{} \".format(' '.join([sig.str() for sig in sigs]))\n", - " print(txt)" - ] - }, - { - "cell_type": "markdown", - "id": "44cde2fb", - "metadata": {}, - "source": [ - "Process data for 15 minutes (ok to abort before finished)" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "id": "cf2603db", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " too few satellites < 6: nsat=0\n", - " 2025-08-21 07:00:01 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=1\n", - " 2025-08-21 07:00:02 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=1\n", - " 2025-08-21 07:00:03 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=1\n", - " 2025-08-21 07:00:04 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=2\n", - " 2025-08-21 07:00:05 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=3\n", - " 2025-08-21 07:00:06 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=3\n", - " 2025-08-21 07:00:07 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=3\n", - " 2025-08-21 07:00:08 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=3\n", - " 2025-08-21 07:00:09 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=4\n", - " 2025-08-21 07:00:10 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=4\n", - " 2025-08-21 07:00:11 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=5\n", - " 2025-08-21 07:00:12 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=5\n", - " 2025-08-21 07:00:13 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=5\n", - " 2025-08-21 07:00:14 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: ns=5\n", - " 2025-08-21 07:00:15 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: ns=5\n", - " 2025-08-21 07:00:16 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: ns=5\n", - " 2025-08-21 07:00:17 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: ns=5\n", - " 2025-08-21 07:15:00 ENU 0.131 -0.009 -0.368, 2D 0.131, mode 5" - ] - } - ], - "source": [ - "nep = 15 * 60 # increase this to run longer\n", - "\n", - "# Intialize data structures for results\n", - "t = np.zeros(nep)\n", - "enu = np.ones((nep, 3))*np.nan\n", - "sol = np.zeros((nep, 4))\n", - "ztd = np.zeros((nep, 1))\n", - "smode = np.zeros(nep, dtype=int)\n", - "\n", - "# Skip epochs until start time\n", - "obs = rnx.decode_obs()\n", - "while time > obs.t and obs.t.time != 0:\n", - " obs = rnx.decode_obs()\n", - "\n", - "# Loop over number of epoch from file start\n", - "for ne in range(nep):\n", - " week, tow = time2gpst(obs.t)\n", - " cs.week = week\n", - " cs.tow0 = tow//86400*86400\n", - " cs.time0 = obs.t\n", - "\n", - " # Set initial epoch\n", - " if ne == 0:\n", - " nav.t = deepcopy(obs.t)\n", - " t0 = deepcopy(obs.t)\n", - " t0.time = t0.time//30*30\n", - " nav.time_p = t0\n", - "\n", - " vi = v[(v['tow'] == tow) & (v['prn'] == prn_ref)\n", - " & (v['type'] > 30)]\n", - " if len(vi) > 0:\n", - " msg = unhexlify(vi['nav'][0])\n", - " cs.decode_cssr(msg, 0)\n", - "\n", - " cs.check_validity(obs.t)\n", - "\n", - " # Call PPP module\n", - " if (cs.lc[0].cstat & 0x6) == 0x6:\n", - " ppp.process(obs, cs=cs)\n", - "\n", - " # Save output\n", - " t[ne] = timediff(nav.t, t0) / 86400.0\n", - " sol = nav.xa[0:3] if nav.smode == 4 else nav.x[0:3]\n", - " enu[ne, :] = ecef2enu(pos_ref, sol-xyz_ref)\n", - " ztd[ne] = nav.xa[ppp.IT(nav.na)] if nav.smode == 4 else nav.x[ppp.IT(nav.na)]\n", - " smode[ne] = nav.smode\n", - "\n", - " # Log to standard output\n", - " stdout.write('\\r {} ENU {:7.3f} {:7.3f} {:7.3f}, 2D {:6.3f}, mode {:1d}'\n", - " .format(time2str(obs.t),\n", - " enu[ne, 0], enu[ne, 1], enu[ne, 2],\n", - " np.sqrt(enu[ne, 0]**2+enu[ne, 1]**2),\n", - " smode[ne]))\n", - "\n", - " # Get new epoch, exit after last epoch\n", - " obs = rnx.decode_obs()\n", - " if obs.t.time == 0:\n", - " break" - ] - }, - { - "cell_type": "markdown", - "id": "f9675705", - "metadata": {}, - "source": [ - "Plot results" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "5e119a44", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from cssrlib.plot import plot_enu\n", - "\n", - "plot_enu(t, enu, smode, ztd)" - ] - }, - { - "cell_type": "markdown", - "id": "c0d4d20c", - "metadata": {}, - "source": [ - "## Example 11: PPP positioning (RTCM correction from JPL GDGPS)\n", - "\n", - "This section demonstrates PPP positioning using PPP via JPL GDGPS (GPSHAS) corrections with a Septentrio mosaic-X5 receiver." - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "516c04a4", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from copy import deepcopy\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.dates as md\n", - "import numpy as np\n", - "from sys import exit as sys_exit\n", - "from sys import stdout\n", - "\n", - "from cssrlib.gnss import ecef2pos, ecef2enu, Nav, rSigRnx, sys2str\n", - "from cssrlib.gnss import time2gpst, time2doy, time2str, timediff, epoch2time\n", - "from cssrlib.cssrlib import sCSSRTYPE, sCType\n", - "from cssrlib.peph import atxdec, searchpcv\n", - "from cssrlib.rtcm import rtcm\n", - "from cssrlib.pppssr import pppos\n", - "from cssrlib.rinex import rnxdec\n" - ] - }, - { - "cell_type": "markdown", - "id": "ff6345bb", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": 81, - "id": "101bb120", - "metadata": {}, - "outputs": [], - "source": [ - "# Start epoch and number of epochs\n", - "ep = [2025, 8, 21, 7, 0, 0] # start epoch\n", - "\n", - "time = epoch2time(ep)\n", - "year = ep[0]\n", - "doy = int(time2doy(time))\n", - "\n", - "let = chr(ord('a')+ep[3])\n", - "bdir = f'cssrlib-data/data/doy{ep[0]:04d}-{doy:03d}/'\n", - "navfile = bdir+f'{doy:03d}{let}_rnx.nav'\n", - "obsfile = bdir+f'{doy:03d}{let}_rnx.obs'\n", - "atxfile = bdir+'../antex/igs20.atx'\n", - "\n", - "# Specify JPL GDGPS corrections file in RTCM format\n", - "file_rtcm = bdir+f'jpl{doy:03d}{let}.rtcm3'\n", - "\n", - "cs_mask = 1 << sCType.CLOCK | 1 << sCType.ORBIT\n", - "\n", - "# Set user reference position\n", - "xyz_ref = [-3962108.6836, 3381309.5672, 3668678.6720]\n", - "pos_ref = ecef2pos(xyz_ref)\n", - "\n", - "# Define signals to be processed\n", - "sigs = [rSigRnx(\"GC1C\"), rSigRnx(\"GC2W\"),\n", - " rSigRnx(\"GL1C\"), rSigRnx(\"GL2W\"),\n", - " rSigRnx(\"GS1C\"), rSigRnx(\"GS2W\"),\n", - " rSigRnx(\"EC1C\"), rSigRnx(\"EC7Q\"),\n", - " rSigRnx(\"EL1C\"), rSigRnx(\"EL7Q\"),\n", - " rSigRnx(\"ES1C\"), rSigRnx(\"ES7Q\")]\n" - ] - }, - { - "cell_type": "markdown", - "id": "73e491d9", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": 82, - "id": "2667f49c", - "metadata": {}, - "outputs": [], - "source": [ - "rnx = rnxdec()\n", - "rnx.setSignals(sigs)\n", - "\n", - "nav = Nav()\n", - "nav.pmode = 0 # Positioning mode: 0:static, 1:kinematic\n", - "\n", - "# Decode RINEX NAV data\n", - "nav = rnx.decode_nav(navfile, nav)\n", - "\n", - "# Load PPP corrections\n", - "v = np.genfromtxt(file_sbas, dtype=dtype)\n", - "\n", - "cs = rtcm()\n", - "cs.monlevel = 0\n", - "cs.cssrmode = sCSSRTYPE.RTCM3_SSR\n", - "cs.inet = 0\n", - "# mask phase-bias for JPL GDGPS\n", - "cs.mask_pbias = True\n", - "\n", - "# Load ANTEX data for satellites and stations\n", - "atx = atxdec()\n", - "atx.readpcv(atxfile)\n", - "\n", - "nav.monlevel = 0 # Logging level\n", - "\n", - "if True:\n", - " fc = open(file_rtcm, 'rb')\n", - " if not fc:\n", - " print(\"RTCM message file cannot open.\")\n", - "\n", - " blen = os.path.getsize(file_rtcm)\n", - " msg = fc.read(blen)\n", - " maxlen = len(msg)-5\n", - " fc.close()\n", - "\n", - "# Load RINEX OBS file header\n", - "if rnx.decode_obsh(obsfile) >= 0:\n", - "\n", - " # Auto-substitute signals\n", - " rnx.autoSubstituteSignals()\n", - "\n", - " # Initialize position\n", - " rr = rnx.pos\n", - " pos = ecef2pos(rr)\n", - " ppp = pppos(nav, rnx.pos, 'test_ppprtcm.log')\n", - " nav.elmin = np.deg2rad(5.0)\n", - " \n", - " # Set PCO/PCV information\n", - " nav.sat_ant = atx.pcvs\n", - " nav.rcv_ant = searchpcv(atx.pcvr, rnx.ant, rnx.ts)" - ] - }, - { - "cell_type": "markdown", - "id": "7c8972f3", - "metadata": {}, - "source": [ - "For signals, L1C/A+L2P(Y) for GPS, E1+E5b for Galileo are used." - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "id": "d93c948e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Available signals\n", - "GPS C1C L1C D1C S1C C2W L2W D2W S2W C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", - "GLONASS C1C L1C D1C S1C C2C L2C D2C S2C C2P L2P D2P S2P C3Q L3Q D3Q S3Q\n", - "GALILEO C1C L1C D1C S1C C5Q L5Q D5Q S5Q C7Q L7Q D7Q S7Q C8Q L8Q D8Q S8Q C6C L6C D6C S6C\n", - "BEIDOU C1P L1P D1P S1P C2I L2I D2I S2I C5P L5P D5P S5P C6I L6I D6I S6I C7D L7D D7D S7D C7I L7I D7I S7I\n", - "QZSS C1C L1C D1C S1C C1E L1E D1E S1E C1L L1L D1L S1L C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", - "IRNSS C1P L1P D1P S1P C5A L5A D5A S5A\n", - "\n", - "Selected signals\n", - "GPS C1C C2W L1C L2W S1C S2W \n", - "GALILEO C1C C7Q L1C L7Q S1C S7Q \n" - ] - } - ], - "source": [ - "print(\"Available signals\")\n", - "for sys, sigs in rnx.sig_map.items():\n", - " txt = \"{:7s} {}\".format(sys2str(sys),\n", - " ' '.join([sig.str() for sig in sigs.values()]))\n", - " print(txt)\n", - "\n", - "print(\"\\nSelected signals\")\n", - "for sys, tmp in rnx.sig_tab.items():\n", - " txt = \"{:7s} \".format(sys2str(sys))\n", - " for _, sigs in tmp.items():\n", - " txt += \"{} \".format(' '.join([sig.str() for sig in sigs]))\n", - " print(txt)" - ] - }, - { - "cell_type": "markdown", - "id": "de32c42f", + "id": "dc735380", "metadata": {}, "source": [ - "Process data for 15 minutes (ok to abort before finished)" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "id": "e6fabd9b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 2025-08-21 07:15:00 ENU -0.013 0.114 -0.323, 2D 0.115, mode 5" - ] - } - ], - "source": [ - "nep = 15 * 60 # increase this to run longer\n", - "\n", - "# Intialize data structures for results\n", - "t = np.zeros(nep)\n", - "enu = np.ones((nep, 3))*np.nan\n", - "sol = np.zeros((nep, 4))\n", - "ztd = np.zeros((nep, 1))\n", - "smode = np.zeros(nep, dtype=int)\n", - "\n", - "# Skip epochs until start time\n", - "obs = rnx.decode_obs()\n", - "while time > obs.t and obs.t.time != 0:\n", - " obs = rnx.decode_obs()\n", + "## Demonstration and examples\n", "\n", - "k = 0\n", - "# Loop over number of epoch from file start\n", - "for ne in range(nep):\n", - " week, tow = time2gpst(obs.t)\n", - " cs.week = week\n", - " cs.tow0 = tow//3600*3600\n", - " \n", - " # Set initial epoch\n", - " if ne == 0:\n", - " nav.t = deepcopy(obs.t)\n", - " t0 = deepcopy(obs.t)\n", - " t0.time = t0.time//30*30\n", - " nav.time_p = t0\n", + "[1. Basic functions of CSSRlib](./basic.ipynb)\n", "\n", - " while True:\n", - " stat = cs.sync(msg, k)\n", - " if stat is False:\n", - " k += 1\n", - " continue\n", - " if not cs.checksum(msg, k, maxlen):\n", - " k += 1\n", - " continue\n", + "[2. PPP/PPP-RTK Demonstration](./ppp.ipynb)\n", "\n", - " tc = cs.decode_time(msg[k:k+cs.len+3])\n", - " if (tc is not False) and timediff(tc, obs.t) > 0:\n", - " break\n", - "\n", - " _, _, eph, geph, seph = cs.decode(msg[k:k+cs.len+3])\n", - " k += cs.dlen\n", - "\n", - " if cs.msgtype in cs.eph_t.values():\n", - " nav.eph.append(eph)\n", - "\n", - " # Call PPP module\n", - " if (cs.lc[0].cstat & cs_mask) == cs_mask:\n", - " ppp.process(obs, cs=cs)\n", - "\n", - " # Save output\n", - " t[ne] = timediff(nav.t, t0) / 86400.0\n", - " sol = nav.xa[0:3] if nav.smode == 4 else nav.x[0:3]\n", - " enu[ne, :] = ecef2enu(pos_ref, sol-xyz_ref)\n", - " ztd[ne] = nav.xa[ppp.IT(nav.na)] if nav.smode == 4 else nav.x[ppp.IT(nav.na)]\n", - " smode[ne] = nav.smode\n", - "\n", - " # Log to standard output\n", - " stdout.write('\\r {} ENU {:7.3f} {:7.3f} {:7.3f}, 2D {:6.3f}, mode {:1d}'\n", - " .format(time2str(obs.t),\n", - " enu[ne, 0], enu[ne, 1], enu[ne, 2],\n", - " np.sqrt(enu[ne, 0]**2+enu[ne, 1]**2),\n", - " smode[ne]))\n", - "\n", - " # Get new epoch, exit after last epoch\n", - " obs = rnx.decode_obs()\n", - " if obs.t.time == 0:\n", - " break" - ] - }, - { - "cell_type": "markdown", - "id": "b873361d", - "metadata": {}, - "source": [ - "Plot result" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "id": "b6920ead", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from cssrlib.plot import plot_enu\n", + "[3. Emergency Warning Satellite Service (EWSS) Demonstration](./ewss.ipynb)\n", "\n", - "plot_enu(t, enu, smode, ztd)" - ] - }, - { - "cell_type": "markdown", - "id": "06d4a4ac", - "metadata": { - "id": "06d4a4ac" - }, - "source": [ - "## Reference" - ] - }, - { - "cell_type": "markdown", - "id": "7d78ff97", - "metadata": { - "id": "7d78ff97" - }, - "source": [ - "- [^1] T. Takasu, “RTKLIB: Open Source Program Package for RTK-GPS,” FOSS4G 2009 Tokyo, Japan, 2009.\n", - "- [^2] Hirokawa, R., Hauschild, A., Everett, T. (2023). Python Toolkit for Open PPP/PPP-RTK Services. In *Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2023)*\n", - "- [^3] Hirokawa, R., Hauschild, A. (2025). CSSRlib: Python Toolkit for High-Accuracy, Secure, and Resilient Positioning Services. In *Proceedings of the 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2025)*" + "[4. Authentication Demonstration](./auth.ipynb)\n" ] - }, - { - "cell_type": "markdown", - "id": "53eb0684", - "metadata": {}, - "source": [] } ], "metadata": { diff --git a/tutorials/ewss.ipynb b/tutorials/ewss.ipynb new file mode 100644 index 0000000..e8e1700 --- /dev/null +++ b/tutorials/ewss.ipynb @@ -0,0 +1,509 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f3c9133e", + "metadata": {}, + "source": [ + "# Emergency Warning Satellite Service (EWSS) Demonstration\n" + ] + }, + { + "cell_type": "markdown", + "id": "5d0e0f68", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "691bfaee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "c:\\work\\gps\\cssrlib\\tutorials\\cssrlib-data\\samples\n" + ] + } + ], + "source": [ + "%cd cssrlib-data/samples" + ] + }, + { + "cell_type": "markdown", + "id": "5d59c2dc", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "72e7f554", + "metadata": {}, + "outputs": [], + "source": [ + "from binascii import unhexlify\n", + "import numpy as np\n", + "import bitstruct as bs\n", + "from cssrlib.gnss import time2doy, epoch2time, gpst2time\n", + "from cssrlib.ewss import jmaDec, camfDec\n", + "import cartopy.crs as ccrs\n", + "import cartopy.io.img_tiles as cimgt\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "98ee0559", + "metadata": {}, + "outputs": [], + "source": [ + "ep = [2025, 8, 21, 7, 0, 0]\n", + "time = epoch2time(ep)\n", + "year = ep[0]\n", + "doy = int(time2doy(time))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "cad2d819", + "metadata": {}, + "outputs": [], + "source": [ + "cs = jmaDec()\n", + "cs.monlevel = 0\n", + "cs.time = time\n", + "\n", + "csx = camfDec()\n", + "csx.monlevel = 2\n", + "csx.time = time" + ] + }, + { + "cell_type": "markdown", + "id": "ede5b159", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d6cf512a", + "metadata": {}, + "outputs": [], + "source": [ + "file_sbas = '../data/doy2025-233/233h_sbas.txt'\n", + "dtype = [('wn', 'int'), ('tow', 'float'), ('prn', 'int'),\n", + " ('type', 'int'), ('marker', 'S2'), ('nav', 'S124')]\n", + "v = np.genfromtxt(file_sbas, dtype=dtype)\n", + "tow = np.unique(v['tow'])" + ] + }, + { + "cell_type": "markdown", + "id": "3af35836", + "metadata": {}, + "source": [ + "## Parsing DCR" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "63ca7225", + "metadata": {}, + "outputs": [], + "source": [ + "def parse_dcr(vi_dcr, cs, params=[], pos=None):\n", + " if len(vi_dcr) == 0:\n", + " return params, pos\n", + " for vi_ in vi_dcr:\n", + " cs.time = gpst2time(vi_['wn'], vi_['tow'])\n", + " dcr = unhexlify(vi_['nav'])\n", + " cs.decode(dcr, 14)\n", + " print(cs.gen_msg(cs.dc))\n", + "\n", + " if cs.dc == 10: # DCR Typhoon\n", + " pos = cs.pos if pos is None else np.vstack([pos, cs.pos])\n", + " params.append(cs.params)\n", + "\n", + " return params, pos\n" + ] + }, + { + "cell_type": "markdown", + "id": "982c128c", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "590d0337", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Typhoon (Issue)\n", + " report time: 2025-08-21 06:50:00\n", + " typhoon #12\n", + " reference time: 2025-08-21 12:00:00\n", + " reference type: Forecast\n", + " elapsed time: 6h\n", + " lat/lon: 31.5833/130.1667\n", + " central pressure: 1002 hPa\n", + " maximum wind: 18 m/s\n", + " maximum instant wind: 25 m/s\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Typhoon (Issue)\n", + " report time: 2025-08-21 06:50:00\n", + " typhoon #12\n", + " reference time: 2025-08-21 15:00:00\n", + " reference type: Forecast\n", + " elapsed time: 9h\n", + " lat/lon: 31.5833/130.4167\n", + " central pressure: 1004 hPa\n", + " maximum wind: 18 m/s\n", + " maximum instant wind: 25 m/s\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Typhoon (Issue)\n", + " report time: 2025-08-21 06:50:00\n", + " typhoon #12\n", + " reference time: 2025-08-21 18:00:00\n", + " reference type: Forecast\n", + " elapsed time: 12h\n", + " lat/lon: 31.5833/130.6667\n", + " central pressure: 1004 hPa\n", + " maximum wind: 18 m/s\n", + " maximum instant wind: 25 m/s\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Typhoon (Issue)\n", + " report time: 2025-08-21 06:50:00\n", + " typhoon #12\n", + " reference time: 2025-08-21 21:00:00\n", + " reference type: Forecast\n", + " elapsed time: 15h\n", + " lat/lon: 31.8333/131.0000\n", + " central pressure: 1004 hPa\n", + " maximum wind: 18 m/s\n", + " maximum instant wind: 25 m/s\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Typhoon (Issue)\n", + " report time: 2025-08-21 06:50:00\n", + " typhoon #12\n", + " reference time: 2025-08-22 00:00:00\n", + " reference type: Forecast\n", + " elapsed time: 18h\n", + " lat/lon: 32.0000/131.4167\n", + " central pressure: 1004 hPa\n", + " maximum wind: 18 m/s\n", + " maximum instant wind: 25 m/s\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Typhoon (Issue)\n", + " report time: 2025-08-21 06:50:00\n", + " typhoon #12\n", + " reference time: 2025-08-22 03:00:00\n", + " reference type: Forecast\n", + " elapsed time: 21h\n", + " lat/lon: 32.0833/131.5833\n", + " central pressure: 1006 hPa\n", + " maximum wind: 18 m/s\n", + " maximum instant wind: 25 m/s\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Typhoon (Issue)\n", + " report time: 2025-08-21 06:50:00\n", + " typhoon #12\n", + " reference time: 2025-08-22 06:00:00\n", + " reference type: Forecast\n", + " elapsed time: 24h\n", + " lat/lon: 32.3333/132.0000\n", + " central pressure: 1006 hPa\n", + " maximum wind: 0 m/s\n", + " maximum instant wind: 0 m/s\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Marine (Issue)\n", + " report time: 2025-08-21 02:35:00\n", + " code: 海上強風警報\t region: 鹿児島海域\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Marine (Issue)\n", + " report time: 2025-08-21 02:35:00\n", + " code: 海上濃霧警報\t region: サハリン東方海上\n", + " code: 海上濃霧警報\t region: サハリン西方海上\n", + " code: 海上濃霧警報\t region: 網走沖\n", + " code: 海上濃霧警報\t region: 宗谷海峡\n", + " code: 海上濃霧警報\t region: 北海道西方海上\n", + " code: 海上濃霧警報\t region: 北海道東方海上\n", + " code: 海上濃霧警報\t region: 釧路沖\n", + " code: 海上濃霧警報\t region: 三陸沖西部\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Marine (Issue)\n", + " report time: 2025-08-21 02:35:00\n", + " code: 海上濃霧警報\t region: 関東海域北部\n", + " code: 海上濃霧警報\t region: 沿海州南部沖\n", + " code: 海上濃霧警報\t region: 日本海北西部\n", + " code: 海上風警報\t region: 奄美海域\n", + " code: 海上強風警報\t region: 北海道東方海上\n", + " code: 海上強風警報\t region: 長崎西海上\n", + " code: 海上強風警報\t region: 女島南西海上\n", + " code: 海上強風警報\t region: 日向灘\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Typhoon (Issue)\n", + " report time: 2025-08-21 06:50:00\n", + " typhoon #12\n", + " reference time: 2025-08-21 06:00:00\n", + " reference type: Analysis\n", + " elapsed time: 0h\n", + " lat/lon: 31.5833/130.0000\n", + " central pressure: 1002 hPa\n", + " maximum wind: 18 m/s\n", + " maximum instant wind: 25 m/s\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Typhoon (Issue)\n", + " report time: 2025-08-21 06:50:00\n", + " typhoon #12\n", + " reference time: 2025-08-21 07:00:00\n", + " reference type: Estimate\n", + " elapsed time: 1h\n", + " lat/lon: 31.5833/130.0833\n", + " central pressure: 1002 hPa\n", + " maximum wind: 18 m/s\n", + " maximum instant wind: 25 m/s\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Typhoon (Issue)\n", + " report time: 2025-08-21 06:50:00\n", + " typhoon #12\n", + " reference time: 2025-08-21 09:00:00\n", + " reference type: Forecast\n", + " elapsed time: 3h\n", + " lat/lon: 31.5833/130.0833\n", + " central pressure: 1002 hPa\n", + " maximum wind: 18 m/s\n", + " maximum instant wind: 25 m/s\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Typhoon (Issue)\n", + " report time: 2025-08-21 06:50:00\n", + " typhoon #12\n", + " reference time: 2025-08-21 12:00:00\n", + " reference type: Forecast\n", + " elapsed time: 6h\n", + " lat/lon: 31.5833/130.1667\n", + " central pressure: 1002 hPa\n", + " maximum wind: 18 m/s\n", + " maximum instant wind: 25 m/s\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Typhoon (Issue)\n", + " report time: 2025-08-21 06:50:00\n", + " typhoon #12\n", + " reference time: 2025-08-21 15:00:00\n", + " reference type: Forecast\n", + " elapsed time: 9h\n", + " lat/lon: 31.5833/130.4167\n", + " central pressure: 1004 hPa\n", + " maximum wind: 18 m/s\n", + " maximum instant wind: 25 m/s\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "params = []\n", + "pos = None\n", + "\n", + "prn_ref = 199\n", + "nep = 300\n", + "\n", + "for ne in range(nep):\n", + " vi = v[(v['tow'] == tow[ne]) & (v['prn'] == prn_ref)]\n", + "\n", + " vi_dcr = vi[vi['type'] == 43] # DCR\n", + " params, pos = parse_dcr(vi_dcr, cs, params, pos)" + ] + }, + { + "cell_type": "markdown", + "id": "ea642908", + "metadata": {}, + "source": [ + "## Parsing CAMF in DCX" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "e4be73d3", + "metadata": {}, + "outputs": [], + "source": [ + "def parse_dcx(vi_dcx, csx):\n", + " if len(vi_dcx) == 0:\n", + " return\n", + " for vi_ in vi_dcx:\n", + " csx.time = gpst2time(vi_['wn'], vi_['tow'])\n", + " buff = unhexlify(vi_['nav'])\n", + "\n", + " sdmt, sdm = bs.unpack_from('u1u9', buff, 14)\n", + " # print(f\"sdmt={sdmt} sdm={sdm:0b}\")\n", + " csx.decode(buff, 24) # decode CAMF message\n", + " csx.decode_ext(buff, 24+122) # decode extended message" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "be86cb37", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[DCX-ext] pid=1 2025-08-21 07:00:59 熊本県上益城郡益城町 Leave the additional target area range. -90.0 45.0 0.216 0.216 -90.0 1\n", + "[DCX-ext] pid=1 2025-08-21 07:01:03 熊本県上益城郡益城町 Leave the additional target area range. -90.0 45.0 0.216 0.216 -90.0 1\n", + "[DCX] 2025-08-21 07:00:00 hcat=82 subj=0 pid=1 moderate MET - Tropical cyclone (typhoon) inst=0 info=0 (not specified) (not specified) \n", + "[DCX-ext] pid=1 2025-08-21 07:01:11 鹿児島県南九州市 Leave the additional target area range. -90.0 45.0 0.216 0.216 -90.0 1\n", + "[DCX] 2025-08-21 07:00:00 hcat=82 subj=0 pid=1 moderate MET - Tropical cyclone (typhoon) inst=0 info=0 (not specified) (not specified) \n", + "[DCX-ext] pid=1 2025-08-21 07:01:15 鹿児島県南九州市 Leave the additional target area range. -90.0 45.0 0.216 0.216 -90.0 1\n", + "[DCX] 2025-08-21 07:01:00 hcat=82 subj=0 pid=1 severe MET - Tropical cyclone (typhoon) inst=0 info=0 (not specified) (not specified) \n", + "[DCX-ext] pid=1 2025-08-21 07:02:15 鹿児島県南九州市 Leave the additional target area range. -90.0 45.0 0.216 0.216 -90.0 1\n", + "[DCX] 2025-08-21 07:01:00 hcat=82 subj=0 pid=1 severe MET - Tropical cyclone (typhoon) inst=0 info=0 (not specified) (not specified) \n", + "[DCX-ext] pid=1 2025-08-21 07:02:19 鹿児島県南九州市 Leave the additional target area range. -90.0 45.0 0.216 0.216 -90.0 1\n" + ] + } + ], + "source": [ + "for ne in range(nep):\n", + " vi = v[(v['tow'] == tow[ne]) & (v['prn'] == prn_ref)]\n", + " vi_dcx = vi[vi['type'] == 44] # DCX\n", + " parse_dcx(vi_dcx, csx)" + ] + }, + { + "cell_type": "markdown", + "id": "4183e2c7", + "metadata": {}, + "source": [ + "## Plot result" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "028526f8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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BvFkr2leTCiJIi/OUf6cyX5WkFBTDeNmA66mOv5fizulUThmkkrCGsms3MMcnkZ+fUJKw4xhkYJTIoj2ZljwUObqWYegyZZ8e/v4XJ8t1hpGnvnQpDXN46tQCxmHC5rsfYN68TfT0y5TaDfLWDtJp8+QdJBc5S4vHzXTapxt0rYxoJyfvadzd3uDym+/yzW99i53NO8Aq3/tXf8DFZ57h7sYGL3zjW2zVlxlJjXjv5fk4l2AWs/i7G3lOddDlbJYyGiZkzQTT1RkbMYPDIzpHHWzHZtgbUKtXcTyHucV5okqO28iIOhBuq9W5GCovZhiC/nkAVvpgTIoMxUdgwpl4/G1lx2zgNjyS3r3PsVo4aznD3ZRolOFUdFVNTGGlnmMSJilWKogoXZHeZKG1dJNyqUSW5xiZThbqVOYMdtIRxtAi2M2orBYzAUWCcxI1E5jyGuRxDVNDl7aZDePQwB8EGJnDiEBVBJKsjguR/PHXQ+VPqaYSHdu21H38MKGv+4wjAe9IUjKO4atBWsw4plVaLIgmaZHV40nrLldoJ69W8FIEjRVkMZonm33nxPr8SUJgM8mkJunYUomg0Ae8/8nW3RFz5a5KEkfjmvpS8LeHFuMiy0hvrJX6oPuMwgzbillutvjRoE+l3qDi51hlKVpjVbLJRU5HDnFg0momNJZb1Jcg8HWWyqtce+8D3vjZT3HqDVbnWwRByLA/UPcLU/BnU+pZzOLhEGTOwKdyMMJva+hnYuxW0f5xMxNjvkllvk51qUl+FKANhFlm0O33aM4vMnJinMXJUpgLRzYn2pWZI9grGYabq3nBI7kVUnE4k2riodMqjpklqRpAm47FcK+n1Bj83TFzleV7HAfpGi3kdHYi2hcttYtPZCZJiaO3Nzh98Ry9d++ysrqM1nYxPAst0Rn1UobjlHJFY67ucPvta7SfX0UbhByeSwj0CEefto50glwrFufJDn4aYy0kyqA8X6O3eURjrU2GwGsnQ+qPCrkOUlUNLMZawtDuEoQ5OIlqIaVayl7cU7MHM7PQBjZGVSDIueruS+tJiIknl9o0T6nWPXrbGWZbSrz4+PoLc/sTTxI7ZoPgoEzNClhuHrLVbxOfHFBL2ykVUkvOICirKmG1fsDuoEmkstZ9tEZqSaggdoPcZb9bpRT2yS+9wI87I9yVU6Sazn6/xVzQx3RjhmNHYZb7eASahev3KFc1VYmUxik//ZMf8vXf+xbjgc/OzW3u/OwKz7z4IqNB/0N3MrOYxa90TBLE4qiHsZpgLGXok06EzBP2jSHOGUctjIO8j9v0sBYXkBGq7GyHCrx/YvHXwKqAWcpIxxDeld10hpbpuKsZZuWBh88mUh6lB5jPE2TS0foelbka8ThSw2677GB5DqV6teA52AXPQe3qyznhfsooEbRScbByYvLWj9/k24vL/PlPfkbpi58nvBXhVstEY5/+YEi7Ncc7b9zm/Pmz/OKnb3J674hmu4Gx2FabU0EDuRRrnTyXhFT9TFBL8uSlpRRP2j26ruNUXHoHAVa1jufEij7wYCUxFRySKCoEo+BGNDSCUaaOKUlJ3UKuUSqJQmYQGYxy5qsW+chhFIRFEhZoU+l+qQ45RpqGjEc55oGBtqSpSkSqLKmkPvEkMT/exnZbjGs1Rv0qSfrwg4xjhzudJTwzYqV+oCoOTcvY7M5PZhhFSCIpmyHlPMLLMxqewdDLcKOQlbkFjozi1Lq6h2VHlDKNXRqEqaWyppVlBGOdknoj6uiOzsvf/jIdN8UMpN2l0Xj+ZQFJs7xxnWs//5ki/sxiFrO4FzLe9eIx84MBzoUAs35vsCsLmj4hggk5q/gZDMMIzZygcB4Ywp4MWaQlWRjnM/RcJx4Vw+MkF70nHcuTIapWwExloT/x8VRonK1D7IpL89Q8hmUofkQ0DhViSpH48kLaQxbP6X1lXiAL7eg2uEsZceKD53Lh4nl++J0fML+wgO8HlKtVfvHm25w7vcbZC2fYvH2XSrVMv9NldW1VIStHoxFVWuq4Al8t1OM01aqa/ky+1PnKIh6aRaLTNZxalWi3j52NhU5XsMyULlSukogkBU+zjxOFQj+dWN0Nhbia6IAIIVwSx6ZG84yJEVskdopnW2RWThxnjKPp+d0f0upKqyHeMxq9Wwntkkuj7ajH9/UPIRH+VZNE45ROHoRUy10GQil/DIdeqgupIqSqsM2YNJP+YH7f0Ebuu6PVVFuqrY95xi3jvVBmnEa8tWtydOLGvbBMaFp4TkDgF6gqTw/xWgGaIdOunH45Yu92SO/IZLlWp/VKkw+SnG6Qs7hykaeWThHoJtmTw4NnMYu/8+HZIYvhEcZifl+C+DCZDJGpUNyCE5pL0g5KwkTt8tXCn0mC0TB1g9hIEVpYVsrI9jWivYTSskP3zj71NWFR6GpYm0/goWmcMtzrUl0Ufbj7taEcIUWdEBJU/x5aBaJSBr96jjevMT5IuPvHtzm9XGUwp/P6F16n3x3imC62oysCoB+MufTyM6zfuMOl155je2ubWq1OpmcqSUh0tckMdLIrf1QcixpOhAJl7++YJo21BYZHfUbDIaWFqrqtIaqAssmdsKAfF6auK5HUzEgLnkRbIxs4sOlQW7QwS7l6HCHoSZsrsVKIzIeIieqf8jCi5zRnYie2QkYNMp+elHmfPLrJYdQxSEKXdKKj8rgIE4tbR0u4ZqxaTckU/XTi9OX5JFJOOTlOSWM76ZL1LA4nFcdCpcM4ctWAO0otdQ9Tz7CilJo7xjT1412EwhWPoTc3x5Fu0s4HVOweg8hmNzbYlcH3kyXOWcziQ6L4cD6ZNNrf5phoKmc5eZhhnX9YAO9RoW7iyXA1Jx+YUE1IwpjRdhfLLuCV1UpZkVZl1y+w9ljLCHtjKksNjHbG8IMA161Smqvh98bE3QS7aREHkRIG9RoVvGbloQRxMuQ1EPir7qWTWWUB/9QEzlpJcY2c+J2YcByijarsbR3Q7XTVfZ9+8Sn29g5Iw5hbN25Rm2uyW/UJLpUYhxNSnRWo26YnKiVtwk0Is7iA1z5wXXQzVxpJSqBPzSE0yrUKJSpKdmo6LJe/p+J705nGPeGPIkToz8lsAnus1jjdt1g6U+LwdoQ9qcC66Yh+Oi6G/vLcT5AB5XjSvlJyHsKc10wS38Ja1FRy6KUjsgky6hNNEjLFd0oGW4eOgqt9eGhkucFYcL8fEYdhme8fDilX+mSZwVibU2VuxfFpeEO2+3MMQk+1rWrOiHl9hOYm6KmtyDFZkjPYjQhHOrkLuadzMK5j6pWPRRqZxSw+KsqmxlfaDu/0YnaEJPRLF7LRSqk7IyrOmKSTkJkP8A2eJISwdYL13F6ep1Iqs3Frg0TNDnQaS218X2Q7bLySx+Cwj+lZLH9hAXO3hH9kU583cKoWI2tIjCjGir5QMSR+4LTVYijJoW6W1KI3ClO1aYxki5inhJrs9IuWjmBh5lafIjVTvHqL+aaD68mipaNVHOpWm63NTfau36E9GlOeW1FzhtxK1eBYhvKal6gV8ljATy2YhqqOZHEfKQnuItkWycFW1VPBoSvaSwfbBzTm6qqCmcJWp7MAdZ55RjwoYLRKQkOgtgV2mHgslZGuEEt5KSa2hZEtCM9MyZz0U/9eqkqlJ5VRMRyVyORxusno3mA90BgOQ/S1kETIaMAiolX+SZPpBKE1l5PdiMjnnlSF+6NupBGjs524rPVd+rZTzB3QGIQl5stdPCtkGHpqiOMnDkd2xqhXYnE0Zr4ZMogCNrfKjOYbZLYUtsVjPly9zGIWf72IM42WZfFr8yb/+u7ol46MKaKXp5s7mEmkeANGkuM9Tnr6MTHlFom3gxkYHG3vsHb+tPrdytlVJcUhMwTRb7Ims0VZ1J05kzExrmExd9ojHLp0tmLyuklpzqGf+WjmvbnltDpQyBSRyAgEsmpji0p0BkakgxsptI8iMeQao2FMuC+EWw1vBc6fbyu+gt5JlYSGJjOWXsbe9jbnzp/jrbfe4exLl7id9YrlVB6uFhekNJmVmDm5tJEml0hmEDJLUJSuKQtA1mdLJw91tAfW3epcjdzRGefhcVUi7R65jyCviDQSMyHzJm0tlXtFhE+HajYRIExUt6QbjfBjnZ3EJ9PvFwwUiKuQEmVz7em2qkSkIdNJhRQhelMRYVwo4oqgavGaPNmM9mOtolImpUaEaX643d3HD41IM1inTRJOYbU5o1AMKlAwWqkqLCPhaFzFj23maj0sa8BQnniQEroeuSNl3ixm8elFlOX8m60BniGqmr9MRMycijakmo3JdiPGfU1BVu3mI/wbPuwox54PAs1MifoZ9dW2gmHKMWQgWpbP4mRHfyxJLX1418HMctVH3096uCWbyimT7p0IyxGI4gl12OnMIdGOF0DB/5dMG1u6Azl4NZP9/ZhhLy7mEYkMxUWJNYYKOKuaQiC10hLrN24UyUvTmVuIaTba7O7v8Lkvf47t61twbg3sg3sbXzE3qiSFVAYPoIUmi/1DLacH1twkTpSmVfPM3H2cMiX0N1FzzUIDvXTCkGjyWsn1VaxpRyCtBWpKZiWJmvXK8PtBGLFAjDUGqa+uubC15WuY+cSiobWfK68dpQI7UZENJgP4TzRJhFlE3vGIkyeX933yKF7gkyGDbyknPSsq2k0nfifVhWGmCuEgkLry3ojE0hgnniLrzWIWn1aEmUaY/TIliGLdcfaHuPUAs1rwCR40wnnikHU7SRkdDtAyA48ylmeekK6Yym6cfPipf1sRsngLGUxWoHxNo7MhLGbxmCiMiFQ7KwezmmGIVYDIXGimYlBXLBcbk/5+ohjR1eVChkN8IXp7IoFhUDuvYRo6vWSkWtILr15QyUa3hJuVMzxIqcydZVzOqD63CPmIsdAIhP4mrZ68qB50U8PITIWaUpp/kiQeMZFSBkpqiH+vw2JaJo35xuOlvSfX6ZFvI5HfKKdkI7muGbpTPKYs9NGejrPywAxJqrrMJCVW5MFC+A8l3bG11ycLMsrnClkUqTakRb8/fjIUz8daTYdbGYfrHqN6QQ3/tCPNNfzExtAL28CT9qVK2sMvM1hP6a07RIb0Awu29yxmMYv7w9RSGuK5cgrKLQtHtV4e/xmWlkpJQTQfxtMPt7qkfqyQRpXlGrbtkfQNkoFO1NMIexrxYKIhNKkoZGGWBXYa6neTL+mlS8coOTTIe4L9N5XgnpzdXFLCW49YoIZz02ctrJFuDMn3I27/5C2ygy0W3TKVYU73rVu0DTj1dAnn1pjgjR0WozLJrR56kOPv9oiPfA6ublGp6eytXyXaCkl2E/xfbFHXPJriBXE7pKq7VHSXsuHg4agWjnw9KuQ8DTtX1s3TPCjPOUgiOvtHj38rqvbSo18DlTuMHL2S4PvSapdja6rdL6jb+KioCKZhGxqL1NXMRhJp0WYqZMKjbk5lTVdGR1LptdIy5XUd48j+5JNEP66oslAJST1Wi3zSWPtrW/oUg++uX6EXlB+C21atMdU0orxkwnyF8VyDvrwhRGRwFrOYxYlPUs6ZPMTcXmdFb2BcHdAe2LTyMjXdo0WZtl6hrU2+9Ar2ekD1SGNRq6nbSCRBTDwIaNYbzDVbNOt11b7xyjpGSUxuUrXzdxpCxssJxkUSkPaKantM2htqbRMzn741YVlr6K6mdrcyD1BfMgfwUuJxyNU3r5Dt+lz52RX8XZ9b79/iYOcQ3UjZu7uNvh/x1nd+ysLyKh+8+RaLcZkPfvY+rUods5+x8cFtjrb2uHnlGt39I66/d408Tnjn7V/Q2bnD0c1N3v7LX1DKHO78/Brv/egtmpQLBFJcCOdJshS+yKNiOrAW97hkfM/fIdZTnIqn0F+PDGGfCyJJKqfHL4OqzZ72bDVQzyMDeykn6WlEB0UiljN9WWuxopVVcpcYpj4OBot6nUpWwvId5blR2vL43j/7Q974zp/zoz/+Hz+FJFFuYZxxFIPwcVGyQsWylif2ycS96uFkCAT3MKpx5DfoZoW67ONu+8sSgt6yjfiE5OQsZvHXjDynPA6ZC0OuvXeFeL3P2z98k3h7wK0/eYvozV2u/eHPiN49oPPTO8TvH9B7Y4PLP32H4cYR7//RTxldP1A70nAYqDZPuV4uhquyuEsriJSx6AtZY4aMGeWhMtkZJgmjLCiYw/dORy28eWgUMtxChutaZEO98K+eeoLa0kou/HUOdve5dfk6URKKRkiBrHQybt68zeHhIW7VI9c1Kqfrqp1imSYvvfoi16/fIIoj0jTl+pWrbG7cve+jVa1W2N3ZYXd3i2ajiXdksXnnrmJ5j7e6qtLKJo0JOa5AXx+MY3OgQlGwmJ9P3OtkZiEwXuVn8WGf+3ucuYd/J0PxaojZCI+vlx5ZlOs2aV/H6di8mLUoJxZ6mrGmN9RrJWirubyKv5MTdaEdV1me99AGRziuzT/4p79PufoA9f2TmEnIwrwbTC1EHr0YyxxhSp77NEMIdsfEkV96yY2cqjOmpvkKbbKZt5QkyS9zwpvFZxDTFe++5n8BTTW0DI8I98in3DlAX2ly5twZJTnRajfpHnUVcW1rQ1ovFXqdLsPBkFF/qBLAyuoKnaOugqPub+7SPHcOp+ox3B1iUKFUk5UtJ8lSJZl9/PAKAZaSi1NlWfr3D4vayW5Y+SbIt/K3SIFbDoPDDL2ZqdZUsKUpSY+Ga3Dh2Utc+vLzJN/5uTIPE+6FZVt87oufo3fUIxrF1Gt13v/uz5R0uaCG7ty8UxDmxj6VcoW1Vy5wuL6r7lcqeQqiW2vUaLWKyf2gNyAYdTl99hytxSaH6+Jtc4HEickSCEwFtH3oJZBUINBdM9fJYl1BWJXYntiV5rmS55DZjV0qSIb3v35QNUpU7XvLsCSWYRqo5DqN47tJC6qUUkXnUtJkVIo53E15Z88vZjLonF+rUXEthqOcy/sd9e9m1eXpxTaO3ebN+IhmqcJFa55mpf5EbzMtP9nYekz0+33q9Tr/5U9v4lYK5uCHx/SQs0XuSUKqh7o1opWPMOoR/sBjJ27gJ580imwWf3ciRxseoJsW2LXjuUMpi6iZIS1bIzmK2Hj/BqdWPLWYXbhwTu2sXdtBF5mb/T0a7Sb+cEypXqHT6xHFoVpwDw6PmJ+f5+Bgj8pik24zIUkzsoFOb/eA5VNryo1N+t8nUT5qOREbTWmhqGHrA8uL8KYErSQVwYmY02rsXk6prul0+yPlouas5NQtFyc3CcRGNbNI9Zy6OD5oBvsHIcE4U4NpvZZi5RlZSVds6bm0rNBFQztB8w1G3hgr1xXjWcaWkZ5iqjaPyHoUKq6OYTHuZ4wHKQsrNkM9JIoFhioqhynDLHis5aea3WQWVmiTlSNlxCSK1TIkHh70VNvJci2VnIrbCwLCZMmpY9v3N3SE36DY0CeWTxeDNcpUsRQV4PqwwmqcKvKfJNXVRs44TLi87mO6onoBp9o1bNNg72ikXD5fe3aVLPH5r//ZP+f3fvcf8N/9q3/N/+2/+r/S6/Wo1Yr30KPiUyISzJLDk0fOfKlLJQknQl4alpmQRrPZyizuhQAyKnZAlBoEiU3LHVBODrHNEoY3VhBQ5QVRSmjYJWpGiVu7EbmT0h/20FoOURazv7NPGEecOXdWkbI2bm+o96DR6+K+sEyoG+wKE7dRZztK0J+q09VFvC5RSBvRXfJOr9LPTOZFetswSVMZSgshb6LYmoppjrCli8JGvhfUkshuaLGF7iUT1voJtzpRUnBguJ5hzxtkC5FCFg3yQOyLVCtnSEA1s1iizKCbs7flk58L0d3JoHey5a7lJcZWgujxZUMDw8uVJLjMT8T/YUSo2kcjLSGKUsVF8CoGtrTFStDbFeG+pMhvBqSBPA/hJTx+Py2/i7RIQaRJ7+k8yX/VhQadjQNqwjgX8T9pUVF0XEI9VOrU4oMjRxH2eDceqirvJAt7HlclCAHkVjSbcZyw1C7jGClHgzF9eQ/kOkvtCudW6qoCFH5kGGUc7KzT37nKn229x7d+/Tf5n/2j/5jLV67we//wd1WS+Kj4lNlmuZpNCMMzTOwJ+/mENgyF70QxbH7yxDJFMP3yQV2L8Xs+ITXJd/Ih8uIYY2KTmA1M/MAj/Xjjoln8HQ7p/q9WBzRKXaJUY7vbpm0MMFoeR5ubzC0sHiueKtcyw1SLlO7AytfWCJMxS7U2vQ+6DHp9wjAimB8xHo5UzzzPMvzRWC04igMgH0cjUX3+VAhlojA6FgJZoIy7nEqNkhaR+iameHeONWIrIC/HZJmwnW21sz9uUkgHRoauRoJh5DStCiXDUVDNfjJSC2MiXgiZxtx5i7CUMhJI6QMhTm/n0xobmz7bYYx1PiZ1H5bhHqWBSgYlsUzWhGwcKakMQfZM2ziywxdVV8exiGNNqY1Lh1e0nzLVPisUX5Mkwxcm9pN4QsvnV8+KsYrkB1noZa6RZsooSa61hLCro0Bsn+WxirumWXzM4n5U+3yTMXcZsaLXeE5vUDVS8mzITj5mw/DpxCFJDz7fPEulZN9DWEmlEY9ZXlliY3OL0B8xHo85c+Y03eHoid5/nzolWWQ15so9pcG0M2gp5dh7arAatinyGvnDUuKPjVxxJGT2EaW/fAtpq9THNhIliSCwtl63il2J1PBKQq8lVGt9loOUu735WVU2C7Upss2R2v3K22RR72PZYi6f0lhr3a/AD+zFfYx9l1EYUHJyvFJJmeZcW99QO3gZ5K5vbFBxSpimVUhfJ48AmhgZApYRAT3dLaxDa8sNoZSRK3tQjUxmDyUdTRYtdQJSVRQ6a3qWqAWRka0kfcRdUndzSrqtYLWm7mJbBrtxl4EfEusGfTdQc47j5yPKqeIjnRjMR2VubgZkjRzWQjKpnh7x/pDZwVEypMcYK3ZxbE0t/lPJjmlMzXqkAxQNRLF2Yncq8hnK+0IjDUTqJ/hQw6CHfi46SiccFATdFI1C1XKS1lY4Brsco4s46fQ2ZPhZSJwkSiVWZgwn6xZJIG1cLujz9AOdNTPkgBEf5F1iNe/XsMYO9TWxBLwX0op6+tkX+dPv/gBz8Qz7Ycz3v/8XGLbDxYsX/3YkCT9yVAknNqUi2Lc3aJJOJMblJR5H9z+pJ4lR9Mvbqy/bgUoQKmKN+Vr3PuPyaYkurPJZ/KqHkLDE2z0h8B1sX3bbYFXDY7LZYKtD8/zCfYQtVUV4BVR9WmEo/5ZajX6vV7RAShWlk9TtFaJ3sjCdDIF/hqm0fCYyFcoes0gkxdqa4xu+Qhol8hkXsxw5B10r0I9DndzTlDCdLouvSIsrWkDRZpqer/gxiHvbcD/FaKTEDxLPchjfEM8YsQ+NWDztsFsaPnKIfN+Vk9FIoGEJU1s3lCObsjHVDSKZRE9CHkoUVQW4JG0l05PWj0kS5SqxJaIHdTInPMChLJLYhASo3JfTQs5DVFknIddNDJuUOu4EUutOVGbVtUfH1XXmNQ9Rd7IznTjK6VoB+5mPlZqsphVqiUM395WvdruS8cZwiH+gY0UWeqTTLlVwLIMwSjnojukOQgbjiDjJqC1cwnNNgkEf3RTf6x4/f+MnfxuShMYodpW/hLSUZOf8MI9B+5WZdwjiS6TT1Ycs1AvKf6P4fhppprE3bNIZV3+pn+ss/vpRMkOWa4cFLFqsPYUsWi8WranlZ221TW+jQ3mhhuXe+zj7po+I9+S+Dl5KV/NpfeUsYg0voRjRwjBOI3Usac10stHx4h2nAlud7pChv9WhPFeAVuRdaWsWo9gX2A+4RSvmeHH3YrKBhTb0sOspsT5p1ajdca52+YtWQx1HCHbhKFW2oWJIdDJBKD/trQLuWjol1UDEjsj5TS07PyS0WHr/No5T8BsKyK7MbbRHW6JaUzKchtvKGO5muO2c1I0mTOoiKR/PGqYJYpIY7x1seu73ukaRQIdLjuiEF8P8sYGeii4Uk7ZYwY5u+Sb5OEcsikpWhj2w6ImjppFwaPqEXkzVtOnnBploUR2V+NrqPMMopVVxeP/WIXd2+mzvD5lrlFiZr6jEcPnWEU+dabHQ9Fhfz3jPdVg9e5YbN27+7agklChf/Mu78/8kwzWje/wReROJWfmDEixKS76wjfxr8xFn8UsdMstzjLjYTMhOXYnd3Vt8lBObY1BZrN5nqDVVQRVOgaBphMUc6DmBEShghAqxxBQ/ZPEgsDOGXlFhFBadBc7fNgvJa9m1O7USWZwq+1BHmMeTZKLJEmIInHViqiPS45HIVoMtcFCZvU122jJ0laaUJAaZDYjwXpgkhLsallhrThfjSYhndtrXqDxVVERSgYhT3of6ZaOp45JaiuAXTWQqpL1V0dwT0tziWy1z0sIBTn4iknSJr+FWdcb7GSM/UpXF9NHksR80LpPn1rDK6hhisayOLTeR10qev/AxlpvKcrWyUFQTQhRMe5Ik5CLJ8DpHCzX10tTKOtvGmN08oOTKbCkUqT6GMkjXNYJ+zmg3pOq5PHOmgSWzFSnUpDWWptzdHfDypQUqJUs91siPVdtRLzU5iAWNJjbOGW//4m1G4+GTvQ+f6Faz+AQip+qKS9XkQyAf+mBiTXgidD2n4Q2UkKF4bcziVzTyXLkv5iOx9UwLkpn/8MdVFoLxwQC7WsatTVq3OUQ7hb6P7FzFP1otWvI12YAUiqoGejMl7AvO3VGJCHksQTtN3NMU0zhLlYtc784+pWpZ+SYLmkktmHIfUWi1MzWgTYaopGTUE6LcQFPN/YlGney4J/LYh/EAAQId3UkxMgOrnt5LUFlGFuhKpbZ0QdpM9z/fB2OafCTmzKqqisZWSqQFqs0kIdWELOTSRiuSm46RFK4OaaIRjiR/psRhTiLXo5wTdXKMhjx+8ZgPJgipAtasJhfMOaQIeSfepI+vEGWMTcUFkcJFCR/alqoo9MRk0WnQrlZJ7YQwLoyKrJJGmGdcS0bs5MoQ9j5Gt4j1iVNdsJuxslDHcTz6w1idv9z2+p0j+qOIr76ySsWb2KymEXd3e6w1TY4in3/f1VkdpVQbNV7+2qukHvw//+//1Ue+FWdJ4jOJYtgui/+JH9335n9w5jKTF/kVjjynJOql9gCtOnF/mzivPXzTHKvsYjr3S2yLo5lROYH8kV3tZGerblMuUIVCutOr8mZMC9+CoYlWKVpasqiqnbsI8vXGNM4uqAQhfgTHMwzZ7AxMpbeUDDOScYbRTIhEWyjMlNyGXstUfz/SYtWTz3xdVTrjUYC5nFKaKwa3sqLK48WDjEAkJE6BMWlCTNtgj0wSIlinIKrFvyeaGNi5i2GK84IkWVTCKAtPQs0JIIoKtVXTMKi0DSUDLq2wLI3RRyb6WCfaBXu+4GLc3wqDalRhxVrANDLFcF7QPPq5T82wWXWraJFO6KUMiOnWLbJDAz8cMWqMKbtm4WttThKQOHVmIzr5vd29qqrGBrW8Thwm0DdxdY2rN/ssV0IcW1czCNc2eeHiHFfvdHDtIgnmQmhMRnQHI146VyEzYmqGiT5f56W//zp9L6Y3mUd9VMySxGcUSWrSGdeYK3cLoqxSjXzUhx7lozGbR/xqJ4jV0oEaUE+BDOr98ghYaBoJ6misbEOnoSw0HQj2clzxfRFy9APS0mmqF7IRE1lq9dBmjJ5bha6SDMZFpjsyMd1cIXRk6OvqloKYSm9f8RxEotqNSbZMJdXtnc3R5FRSjaxjkQkX4dAg03Ky1MCoSVLKMKoQbSVY85kqcHIx3dE1tXuP71p4Zwppa3VeJwbDD1+vIsEoWLk80Vhn5OfoUtkImnfgEVu+IsOJvLa0zrRcx0osSjURw4uI8gxHc1VSjGUiIPm2nCCuDMmhQXTLwFxJlNr05CVSw2nRZhLo6sFAp+xBVXdo6CWe1ts0zBKdUcqa6TAOY3aGvkqGb0e32dnYxDln0jDLKlFIC8zDZjMtxABtaZWFJt2jgFZUpVZ3SeKUfhYx1ODMuTleaHsYE4itJJNBMlbud1IBqrZfGqj2k7SuVcGnwavlhKt0OXBlXvXk6j+zJPGZhEacmeyP6sSpwUKliy5iZhP535MhY7Fghmz61U4QlXsJ4vhX42Jwff/Nc3p3j6ifaimTn2nIwlFahdFNjf6mhncuwxak7H2gIU2pK08jTSbtI/GaFme2KUIoC+gfdJg7u0DZkAq3WGRLlk0/GitEULxhY89luHNF26cQN02hpKGHJqVTqZqPqNmKbqglPe3JLGNq3VN4P4d3Qdsr0byQkjSK2d20/SS/f1ByezoDMQyTquFS1TzSsQ6ViIN0oFRPc1MQVoIfKuCgmZZhZLZCFE2uljpjkfKQNlWgR2qGIegxo5ZSEY2kA4vRnqYqLDmGvBbi8z22x2zoe6yYNaLIJottzpjLaKbJoeg/RSlHewO12x/ubrC5OWTudJtsMWcQ+3iH4qXtYM0Jk1pKphwndNA3PZquw/lmk/lKjaN+yOXNHo2aR6lVouKZSgZ9Gj4xP4s3SDKLHr5K4vUsxZOEgc4oSKmXLS64GfvK027ymj9hO3uWJD7DkCG++HXP593CuP2h309bTbOX5VcrcspmgJ0VtqIPJohim5wVyqj3AWk0RdJ60B9ZQtjK5XM5/auiQSHy2wJhSgrCmHSeZEetFbBVCWNC9JLfCxRVhtSH1w+onq5TKdfxjKJSEXZ1yXJIsoS4o6mdtjWfYrULGG2S5UoOQ1pHZqajNU+gg1wx8bHIE1P5QEjiEiVYgYQ2EpeNOz6Xnq4ybI0YThVjZXCh3V9NiOeDXAepIGpWSdmZKlG9SHr/GtFEXK+odFD+E5XcxXI0BJAlCFjHKzwjpsquCjYs13Pi6iachaJCybGrOYMdnaQrVZlZbO4kmWqwnY84JODV0hqLeoUwSQnjhJKtUS+X2O8FaGnEf/Pf/rf8g3/0u9R8U5EQ5Ry21m+rWcXiqWX6+R6ryy2O1g9olh0qXoqewBtvvk17foW6NUQLeuidEuOejm+tcPXqNUWKy6pFq2yQRvw0vKmqNkmDIuPRaJjc2Qu4tGpwYPnKojXLNMVLC+Mnox/MVqPPmBQlXBFTUvkj9PuEICg8kl8+JvksHsVvUB4AHwljzpn3eopwithfykL+4F2m359AzEibPU9ypXEkEgyPPLKAiwwNeyFRCygiy62OLwqA2b1Zx4O6c2lGMPCprbQxRApiUvGqhdRE8SeGnVhVHN65FGWzMDHqkTAMA0ILpyQdq3vyG5loGXk5ybaBUc8LRBMxjaSMvu9yWiSw58ckJDiaoUyJpmim+9tNsvMvjilIKZkjiEKrFdm4rklPMtCJGJojwiClEnhKy8irFscSnt8U7SStJlFzlfaP8CnEJ1oQWLk8H0ejtKQz3DJV28my7vl7SwhRMdI1rmf7dPORatXVcotlf4HeoEgSvW6P29dukY5CNje20MXuNE752Zu/4Pd+/3f5wfd/wP/8n/xjfvLnf65ekEtPXVKJ9dqN6yy22ty6s057fo5Mt9jf3uLv/cav8+577/Ln3/8Lvv2f/ScFWVAUQSaqDpIobzJQsNdy5PCD20eMTZFjaXBjXOLuyITg8Ik+uLMk8ZlFrhaCujtSi4EM/E4uBlFiKoa16PLM4pc75GWdL/c4GNWUJ8pHhUiwCG9Gdx6GRKvjKbxmQdLCKG4z2u+j2TrVxccLs8VdcGvS4pHhswwqJsQvoxhoF2ZAhVPZyZ16GIRKnM5p6iCo2RQGml+gsgU5NcxIjwy8Mym5oKUmrZspl0CLC52kSPgd01DMZp14z1A3t1onWkd2TlUgm3nOgdYrdvip9PszlXBkFiLnI/7UdaOkFnQRFpw63E3760kSEyTBwwYIMlrRIo5GKE0rKZcsW1PHlqpBQXwnFOlpG6pQTpLElxTHzg30VkIa5lgPvEZyDm9H6+o46poGGkdbDqk7VM+xVbZZnF/gt379t9ne2uTK4AMFZ62USliWrQT2bMvmcO9QqdzWazXVFttY36Bar9LvDyjX6py99JSyLt25u8HtW7cwXY/x/iGhPHJayJYbxVncOzlDw1sVR6eI/DashnO8FxcIM/8JScmzJPEZhViwtkq9R/5umiB8Vf7NCHS/7CEf0YOReBs82Ws5ClyySl8pkT42Jk5lwtKXRbW0WKW/3XmkNaZaqCKID3XqZwv8vWblKhGd3JHLQu6atmpVnFxYFdx18win6mKIpEUkPagCPSSw7fSuhb0aFYif4wRRuKylIw3LKCqiB88/6eiKQ+GcKtBRBRUoJ84S+qMxi80Kju7Tiwq1VfGF8AxLJQupHOaU5pOrXNceFAhU4KipZtIjXxQhIUSETkocG2gjsV11lBWzpNBYdva2MK6lj39P3TZPNRLxNS3FGIlGtKuRtVPVzpvGtIJSD5OAcdfhleUluoc73Fy/i/fMs3zrN36DvW6CW5unPTdPyXO4cO4s3/i1r7Ozs8vp1VNcuXmNFz//MlmUsDPus/r8Cxxs7nDhqRcRvnql3VZP8gu/njPfqvPuO9d45UtfVNDj9NDgpfkVEivkRrJ3XCG5RolN8TXXc8yaxs9vHNCpV6W/yJPGLEl8JpFTd4eFx0YyMVwRfPmEYb05SxB/x0J74gQhrsQrdhfTu595/9ARZefvZAqiqmA7dobl2optXV9rHi/+kiCiIwh3NAXdTIVpJUlDlFiVHM4920tLt47tRe9LNBrMXVzC747wmmLurJP1DOJRRtrVMVqC9DnJ9tSKKicSzSHIKnEx4JXjyp+JoqlAZRdXPMqWphzyLM1mFPe4uxni2RanF+s0NIdBGqg1XSmrpgmRVDWGpUQB5ac1XeYw0EsKeRuF8JHPlFP4YQscVd13UgWoLGlmKtlFtvhqF97aqRaqGYQj0iCRzFIgMRP1+H43RRNDpEhHLyfKaU/PLPRSitV1KS/qyvdBkpUemYR+SpykRPs6Vl/jRt7nxtX3eeHpC9y+vUHJsUmGW6xvbqmh8zNPXeL86QUl6b2+cZeLr72E/nxTzXPufP99zGdeYj1voK++wJXJZZ52BlefS+k6Y6rzXyKSVl0woBpUWDtbBb1KmCZsJEeqctxMjiYyKKKjV2Z9vkSUiCf4/Z2MD3+PzuJTD1F8rUmbSd50gYHmFTh0eeH2h03GswriVzLMPGPZ6lGuFfDFkzH9/D5YWygOg0i6DE28WgVN5DdOhGxqhUjXuKSTiX7Q5H2Wi3KycAI0IccVfX5VQaSTltNkrpHGKVmSMtjukcQxdsVR94t2hRyW45xN0GNLyUcospgMNeQ9LSdcj4tkIbMT437JCunb65nNit4k2E3Y9AMl7hmEsLMXc+lUmZ0jH+qJQlEtJA3e3t5SycaUGYLoDQ1S/H6q5jBjJ8SrWdQqDr3MVz35Mh5tu6QW7mk1IFVJqOyei/M9GTLHkIXdGrgMDwLFAVFJU2Yvto7myHNIyAYa2YG48OWU6xbJgYHhOrTx8CKDO+sDcpFEiXL0Ucalcy2qpwyc9tPcvdXnqy8+zR//0R9z1OkUz8eylbfHe9fucOrMKa7euk792fMcbmwzPjcHr7/MUeAQhI+qLHPudBvYZrn4Nssxs1CJJsrQ/MrRiPXAYaw1KbtjnFpQvJlijZZd5WpooqU5q7UD1sdPZkcwSxKfQSg59NQkjnRFqsOSnRuEiUnHF/D1rMX0qxaeofGSPlA8BndcoVcWhnKiesWC95/PKqoNs20I/beI6c5PKgrNFPkLA7/j45SdY4ST9MyNkth/3g+f06SnLqjTXFeIHtmZB0lIKt4PJ8T9/KMBdsWjcaZN2A/o3uhRMhewFjPM+lTdLi7aXrJDjwz0zECriNT1hFwnpDynUDOdhgyCW4slblztk5ZCGismq9oCW3s5TXeMaZTZS2I6v9hgdXWZO5u38U6XCLOIZKAx0mMMO8apmlQdh1JYJ/Php9/9My799qvgaVhb4iExwlitqd1zxfAwEoe0GTFCkoCprq98SQJL+hrhfo7h+ThnxXMC1aaTkGG8PFPXkHZcgi3Xr+uQH1oKLhsK4S/N2dge4w9jmg0Hs55SOq2TtH2GIhI4b/H83FMEeYVSuUR32CeLE8aBTz9OufLe2zinWqqVNhim3LiyQdh4Vh138qo94p2jKRVtP54u8DmbVon58YCf3NjhZt2hi3iS25jjCq3RSPnTOGZCp2rhJ4oZo1psi9WCl/FRMYPRfAYRZwaD0MN2ImVwMo1ZavjbE1J+lywZzj7CTe2JQrmDPPF9HV1j5913cPop629ex7nlE/18l/pmRu+Hdxhd3yfqiozLYyLRFFbfLrlq96/mEGlG1AmxHY1oHN4nYS2VQhZrjP1IEa7kNMX7wbUc5eR2DDcVoplnK1QRsYMVN7DnUAmiYDxPkpVUCgMTEkNpJCmms4TMTcSHeTKwn4rgJXlC2bXJzQxWY5ZKNRpll0AQPj/9IdtHd8jqIX/yB39EMBySZz71cYK3O+SZxQb20SGaMISPDIa39tm6dR0pcn7yg7/k4KfrlDYy9HEqZhIMbuyz++YtgltHdK/cptLNOUsb470etY2EVatFOShjHLq0ztqU18R2tGiLGW7xJRe+oZfp/2idJb1G8u4hy0FOgy5LjZgzZyu0qy6tssPrLyzyrc+d5oULbcxmqoyS5rUqYZ5yJ98hTTpcu3VbcT0ODg85ODjk1o0b6hr7g5wwzMj9qKj4jhFxT7o6aPTjMt6ZhphZMJZKTr1AomCrs6dX2cya3Irn+cmRTjjZO+wOpiKiHx2zSuIzCMeImKt00UR7xy36prLTkQpDRNzEbWwWf3MhM8i50pj5SkfxVG532ydmCrl6jUTCQtoN8iGeuhhIYqnZY2JRW82hXeqzN2oqg63HRl6wnBtmRhSMObizo8x/DvYPuHXtJofb+5y/dJ6du1tUrHloPwKBIouJiPNVYyXHMdzp4bUqdG93YaxjPqsx2h3ROCP+zRM5CUNMiDK0SDrw4CchnuWoPv8oDRWiSaCvhitOc9KzMgjXU6iMMcr2Q8NxlRPqBXpJuYCetNsWDoEMh/VEJZ45q6akrzfvDFk55bJljzjM+riBhx/GqmXyw//wF7Tfnudgf1/ZJV+/fJU7f7TOxUuX2Lu6yWDos7u9zTd/85v80f/wB3zuc68JnIkoiggHI/4//+Jf8sUvfUHpTH3nT77Ll7/6Ff7ln/4LvvWbv84P//TP+a1v/z2uv3Od69c+4Hf+yT+mE6F6+KWyqfgQ/XSsZgz3uc/lcO3dD1hbW+W9n72D97JOr9un0+tz6eA82zv7ivV851qfwXPP8sGVK5z+/NOcPb1ESytR0V320z43zQ5PvfaytA4YBj6aXvh0j4cD/uI/fA8/CHnje9+l2m5SYLY+Xsj78b2hSzmIiE9aoZ54zaTNdfxekD1GZhJEk5bVR8QsSXwGIaWeyu2GaMXbSjteMNiJoEEekk6fxWcdl/QxpYNN5rQWqaljliMOI5eynlIyha06JIxc7GFIYJXpYzBKdPpZwlL9QO3aun6FI7+muC4PhqNHlKXNaGSknSHGrTu05xtUn75E49IS7VqTTqfLwvIiS/MLvPvWu5w7c4ZyrUIk6p8PxmQnL7MAp+rgVG3iUY6jz1N+XnbCOW7uqspCcRbUupCTWwl5bBIkMYahK2ipsIujJGJ8NFJS46IDFe0ZZD2zYFEvVh8tH1PoZh/PPO5jQkuV44lwnUbLqLIWzHNrs8eFlQpaA0r5EkEWsX00olFycCyTZ595ldr8Ch+8/8GEZS3oJovnXv48f/pHf8C3fue3uf7BZXrdI1565SVu377D6soK5VKZF195jR/88IcqQch8pVTyeOmrr/HWL97k9V/7AlcvX2Eo3gvBWB3X1HLWLlaVQJ/4RsiTqOllPIGjJmOiKKZ30KM1X+b8hXO88Rc/YX5+jiiMyNKUp556ms7hAUEQKH+O5sIcoyhWirv9/Q7p6VO8n2zRy4rBejfSMC68pBjQTz37OWJNpyyVKxq93MTJcsq6+Fhn9OJHzyGmL3v+2BaUkAtFTv3RzaEsFXtkYYp//CV/liQ+9RDTmGLHJTs5U8pBmVtL/1PtSmfxNx3paMDbf/EGL7z4PI1andH1n3N2eYWjo0PGYUTj9BoH6xs4ps3q6irD/W3a5SrZmZcU8EAqkd3RlK/w4Ic4p+aNmK901Wq6f3eLU60ao26P1aUFbvzoPQVbac41ef61cwRHQ1aDUzReWmNgx4/U11HrcSlVMhf0LZI0YbydUj4j7moa0TAiHkeFntMJ+e0skYF1rlpPUij5aYitW7StGuaiQQmbo8MI3TeondI5OuiSaw3y0FZy4AJHFWb0kekXiqqaTlVzlRSEDIubWYm+EaoWVmREjJOQ/MhiL/BZO1vG90IaWYlmUmbvaJ8f/MUP+NqXPkepVMIuN2jNt2m1mri2jWM7lLwSWTXhmdef5Qff+VP88Zj/6B/+R3z333+XUqnM4eEhtVoVKXy8UhnbstT1KpXLCqort0nHqONLEvYDX3l1C9S4ezcmTWLMCekh8jNKdQPXLXPrL9/idLPB3evrvPLqq8RJhG06mJmmuBi25bAw12IwGOCbNrrrstLw0Oyc+mpbXYutpHMM0Y0ynQ3lWX/yvTHdHBb9n+6kYpW5yaNCeDdTaPW9wq34l4g0ipmTDA/MKCaZsOdPhiE66H/F0PKTAu6PCSn/6vU6/+VPb+JWnqyPNYtp5KzV9xW6ST5Ugs0WBEjqxAzyhBsHqyfsXGfxWYe0kJ6Lt3H6HX7+k5/x4rPPst/rMOoOOHvxHHsb29RaDba3tzl/+iyOY5PaGt1Oj+GlLzAII55aqPN2L6Zi+4rrojwgZBEObSrZkFrSw0gTtfNPk4Tu3V2eevY0VS3mzs07LC0v0ppr4S+J5s9Q9fcViesjzl2RvIY5wxtQPgdWDcL+mCRM8NqV48GxtHyUF4Io1/mWkvcQ5rYgk6qmR/ruIfPPn+bwvbuU2otodkrY7VOqVwj0hHKpoqQenEzj8o/e5umvvKRYyYJYCg9GaC1H+hdc/8n7nHnpIl5mE9ThTq/PUn+eZ0+3Scwx+yOf3TsxgyDl5rvf53d+8xv85Cc/4Xe+/W0O+xFnVxvsd45IU5s7Ox30WoyxVCQvfS8kElXbmo030kiGARfnhNEcUvIstg+GdMIUYy4tRPpKgsCK0T0bvZ+RjWz63Q6NZY+BLwCxjJW1irIJlSrI83OMbk7fj9hYv0lvOKLvNXlxoUFnf0+1Gc9cPM/O5l2G3R69Xp/nXnyB9cUL7EcpNTtBM4bMuxqvem2uJpscTZjfUmVu9ec+dM4gzp1Nb8D2oP3IjYa4E0qCDxNLEXJFd2kYyoAaVmoHSvLnYLeEPQ4ZtesnWk333kVyjJM20cFwwP/l8+cVmU9cCx8Xs0riU43iBZIedc6YI9+j7o4hM8lFNVw3lMja1M51Fp99SDUXe23sUpOXfqNN0zbJtkxeevlF9rf38ColLpw/x/zivGoFCRP4oHPIqYsX+fnln3OmYpHH89Sr88xXjpQeUtbL8Q+gkhuUSglmSfWHMNqqYU/12WVMy+Dg5zvs7uyqxBFEIaWFZTqBS9UZfySGXQ2DxzljJamt4ToWqXhP5+A1ygVZbXIQUTKS6kF96xUViHAKZOcr79C763cJxwHd/Q5zYcT1K9dYWV7GXl3h3Xfe5plLTzH2xwoau3FrnVqzruQrtu9usTA3R/e9gUp0/cMet9+6Rtn1qD69irZnEmkJ7904wA8DwjjHT3KaC1VWFtv88Mc/FK4zu12fw90tunt3+Isf/pjnXv4CeDrtpTYbP77CweYe49GY9tI8l772IkHVZN6rEIjSqQZ3D3pcHQzJFxPVaiu8qwMUyIcAq2ZSc2z0RYeRCPMlgboW3UxTDG5BfNm2iTMHvSyktrzCcFe0m07juCl1YaObBkF9jv61q9zd2KDdbrN+2GWnkSr0k8WIo0GN7SHcskY4Rg3PTdWmoRtM5GM/JEQA9PGabaK1ZB13JaQqFRFQSRKy8MeppRJR7GiUu0M14xGmtYTMPVulvtoMVW2fg3GNnl9+TNvq0TFLEp9S6FpKu9wny3TlIyEvqAxFxXhIaBG2MDhHJq4ZT94AM6zTpxvFwLjAH5281hrXYhvbSKm4VQJriP1Cm7FjYRyKX3DErdu3eeWLr/GzH76hetumZeKWPapmxHDokzZTlmQGKP4D45xMpKOXxGc6VoqpiuglnIKg4BDoVrHR8xyXtdOrSuRN+tl6rjOObAWTtoz0/rM/UfDLrEFQcuM7Gu5qjl3WsUReQgjZo0jtTdxG6b77yV9xR2Q5ipaTktWWzucwpV6tsXThjFhK0Ov0lBZUuVxWon8iESHPuT8YkEYxlWqFYOQT+AFxEBZmQpL6LItavcbK2iqHuwcku3B4YxvaMfVmk9g2qK+2OEggGowpLz/PipWyuthmd/sOP/jJj7l06SLNVotMG2AYNo5u8uaPf87K8hKVSpkrb7/Pyotn8T1TWa1eGUK8ayhIsHk2wRDV1/yeKODxkFb+aKnSZpKUKeguEa8YpD5j1XIzOaSo3mTBV3wJt4yRWArpq1drSmZcKB+O4zA/P6/uX/E8BUIR2fV+UCq02aod4sRkd1xH8xcn77WP/mynmc7huPaRt50r9zG09LiKkMpA1KUVoVHPSSwTI4pJvXsGVA13SFlg91JNmIc8XfKwsxJvRDM/ib/REJG+flBWmV88q10rwjTkxS2xP6xzurGLoYtb3UhVEx1/5mn9aYd8WPzExk8clqpH+JGjVHmbxoiGPsKSRd1NFfxfFpZev48/9tXCINIQ49FIIX8M26Tb6bLyjRfp+QOqc/KBlUU9xanm5PMicGSQ9j0l9maVZKg52cWPzELtN4toP71CmxUCMQhKNQI9fSx2vfAIgGC7MO3JMw1vCcxKoRXkpymaq1NZrBGNo2KhlMdUsMpcyTZkQx0aMdnQINrS0GyDvAynPvcUW4eHLL1wjjk95VSc4JU9JR9+qvmUqjKWqmco+wZOq0zqxyQOeIGOaVlkRs4gC5k/s4weZiwtneLG1Ts8dbrGO3dus33uLJGmESkOggblMrVyCSOJ+aCbUB5GLJw+S2VugdQfcfZLz7CX9dVCLLvifm+gErMkLm2YUOnX6G5HWKmNvuIrU6OTa+uDNqOSJhMnpDKuEJb9gi43oS9L28xPC3KE2kCkkI5luO8yb8Po7lVGY/HPgIVRF0fXWT57jtwwsRyHlqkxSFCfbwnZsYepUjz80N369OzkNGTTn+ZPxtLfGzaUa2Wh8fYA4kwIko6DIbpXE1SBbRQESkmA06vh2V2es8vMZxb/u498xFkl8SlEgZeX/0v/sIBD5mqnUXF8xpGrEDBB5ODmRRXRC2QbOqskPt3QOAqKeZrspKv2WCWGKHNw3OiYBT9t83iZxbg/UlDFbq/H3sYOp86dxntmHk+zufrnb5Hv2bir3gOSFoW/cy4Lv5eptoA0I7w8vI9Ckfoa+05fkeJUFJvcY+G9aeFwfGwxKdsQkldO6YyOlhTSLso4SFRaRbtIhqNOznCnT+vC/PRpK32luA+lc6mqEhIvZjTeo3l6XuksXTvaQsqbzXSEWcoEZkOa+hPWsUaQh9TMOj/88Y955tXnufHeNc69/BRHt3bwSh7DwZBnX36Oq9cuc/v6bZ595gWq1To/eOstRnOL2LmMcu9/f/cF32/aGBaUTJ2WY3P3yvt87guvcPlPfsZrX/08y9U2lmEyGo1Uopbn+O4P3+Y//kf/iHQJ7FrM21qgUD33Lr+mpMyFSCekuam6qxd51NIye4OE2DDodas4ZoRjxUSxVHKCBMsYBCX6uUeIST+OqTzXoNdr0tKG7Hdj6s+uMcZmN6wxzjTGYVHxScdAWkvTHf5HxUUzwRz3GNXmqB1u0W2vCsVDdYoECCEEu3yaTAQyP6nYNGUpK1pwMhCXoXVxn+GUgKfB+fqYW9jkmcmvN0vMeWu8G99VQoTy3tqJuwrZdkqf8ST+RkJ2Eqv1ffWGESSChKO0wcWsPKPiDNkfNkgTnUg31W2kETKLzyKmC25ONrQw3RC3Ob4vOUwj0lK8cgnbthkPxwpWqSU6Y2H+Hsbook66IHo9Jrn9sHqrIp1NbGuPI9YLza6JGJ46FZHwnjDwT8ZUT0kljFgShJgE6Xgr2cQTopgtyHlIF0V9L8tlYtI411ZmRLWVpjIjkqQo0E+J7kYHV0hgF9v4e32wXQzPRI+dgqk9SVInd+JeowJ+ThwXiqsCNZW2lXxvZw4jf0xMRuiHmIaFPxxiVFbg1W8ost/DlqPFIFZaJQIHH+9tU/Zcbt26wfnnzrGzs8ef/8Gf8dWvf4Vnnn+G9955j7VTq6wtrvLW22/jOg5OOeQduoXt6YkQeYqz+iLjKGGcxIgyiSik7ucOt7WMwdhjEJUZ6xZaDEaYkU2kQ3SZ80jbT8upOWP6ocedXo1RaKnXzhoLqzwBb0CY5PSje9X/x5HWUZz1owNu/eV3+cKv/yZ/+f0/5Uu/9uvcvXuXZr2q2nlOrYbr2mxv7yrdJ7dcUcnYtgx6fTFUMmgvLtHvdDAcl4PTz3IUZ1TFkGhg8M3TtrJRXbZ1xeZu2SW26KnXr2Z6XDQXSETi9wliNpP4hMMSNIjtq9JRIG22GVO2AjWL2O63GcdN9QERPadQtPpFskPZUs4qic8mcspGqPq6pDr5uFi4czH1OfESjAlZ+drThD5UrQzf1Mk0k3E2IjMtmp87w8iJ0OJCj0vsMR8r8z19ZOE2yKRVfiay3XI/I1cS2TzgUjhdWOOeqLlqmNUcpy0J4p5An/K8zk8AIoU1nBQS2NUl2aDkBfM6y8mlDXJkY9o6dll28AZeo0HQGyr8vF2eOsXdz3lQ563lDMsphmWyu7urJCZ6Zsjy2gqNVoPBaKgkRE6dOU3Vq2FaDpsiw102FDy12BOf2O2DGs5Li7UisuXlGsM44fQzF9ntHPD888+pBfMgGdJNxlx89Tm8zGXnYI+Fc8scOh2FHOo8sMjJcZfSeS7vaexmGe2SwWIl4SBwudqzJx4U8jyn7wQhjMs3E4jwhKlsGbH6DNtmpHhMnh0yCG0WXYtGRSdSirMnN3Yf/7MrSXh+foFb77xNW4b/u1usX7tK0Gry9Kuv8fYv3qJkW5QbTY62t7E9l/Xb65xaW2Xl/Hl03eD25ffY2t2n3mpRaS3j+CG/99wpttZTmuMUz414a7NDf5RQruoYdQtPM1nT2uwdBRx1Hq1K/WDMILCfcBh6wsW5TZK0YOhahkAZNVU9ROKrawdFOyoyKHk+nbA602/6DEOghqere0pcT+kbiSqvtGlkE10unNumodaUUFo49xLAyQFyYTg/uc1jksR9xxLJbXkc915CUINWQRuV7r//FL00uqVRuQDGMcxd+guQDmWQaqBV7zcpEjlwRWYrSRvk3rlGnYIaLbIRx1WCHKcvzGgNvSrkt8efv6OZOLdD7IYMtHX6dkQ5MApf5UzDr4G+b2OONLYqLkllgGsFHI5q7I8aDyykk3GucDak2Z/rmN0xy9ohpUupsiKdp8Z6dqB0kkTs1d7xcFchcAJVyUyFA4tq696Ru4cNtuKGys6yEfCsmGH0+F2+3KbujdTsUFpFIp8jt1yodpUffRblyHx3tOcQzS/xjWUTB4d/vROQqTnUdJbw8RLFRSujbeTEUaTWA2FEixxJrVwmqdQIDg9YLttcefNN1U669NILIDyPcpPcdlkwM5JhnxAdW8+48s77lByHc+cusnkkw3mN2rLNnndEu1xiNWlz92CAlRpULYe9jo+WBXzzi8/OILCffRRvFtc6abgihkNdNbQu5REEOkFoE2YOvWQ2j/gsQ+Q1ZB4kdqFqnbFzsBK1gMpQGVEKPdl+esBm9qHWidokfzigsPB30NDkq3I/akkdU2QsJoli2u5JQxjf1iidLuYQ0i465j0MLQyZoVgPu9gVEvRSIZnHLndplJPuGZRPa8q+UgbSx7pOXo42dlHQpg+JMEsY1MUeaYwp9GCR9rCSCf9Cx78L/u2EuOXQXt3HnlQlAtZ4VEiFLW1ZQ3y6u1X2XY9wz8AeJwxKIpBXuMKpKsTK6ccBiSn+2kJEVTrixe+kGhwZxImlWj5d+TxNvB4Ul+C+BCGEtUy1ACUZSEiVsFg9LMT8rIhRKNeCohsQ5QS3DXzPZTRfxtfgB70hTb24rqu1QwU9nR7rySPneqxzQy0RJ+5bLmQy8mGG5rTY1TTmnnuFtVqZzdu36O1u03r2JUy3zHf+vWhcDXjmmWf4yuuvsW5bbGzuERlzLC3NMxiG+N2E9Mii46aMjUNsz8BzdA62fdYWyrx/fYZu+huIHNeMit3RiVDSOZp4SozIBgZ38jkCkZlMZAehP5FE9Cw+uSSxOWyz6hxREeewqVeDtH0EfTQQfGqxMCk3uMeZ2EzjQxApU8lqOUKwleMsC0v5QUQK5PJY4YR9LzOIBPxbgl7KsaqFEN+0ZaSSlJUdzzGOF3s0BRkVdL+qkKRqmaKiZDGSIbdH4ZEw9Y+QGYlwdlJJYPpDLa/7TxSSOCLLQso16cVPoLVJjr+t4W9ZHLRqtE+FagAsMQhLCtk3uVDFNZH5SK4p4qGrR2oy26r36I9tojgnuGUoUyK3pivyqQxbk6EMlS0sQwyUMrXrVslJ1xUxVSuLb7PB7lGN9D4r15OfpuLfslmT6n66sMvndfoecCeD7HSQkexFJEOTvrTC7CqnmnuEcag4D520OI54wQwlqXzsdtNkHvWY35p6otaFg1jn0KiqucrRxh0q1RqHV97h3Fe/RRJGijG+fv06Lz39DIunnsNuhpimwWLd5fRClWbNYT3rMIhEWDTHCi06WxFxnLG+E/P06elr8+Exm0l8wiGciAeTxDTqeZWwG7Mc9TFa86wnxsRcpQjpjp7RIzSBW9oerTzmamJKLlEhPVVLoITCoP2kT/xXJjRSUccMGrgyaJ2Y/Rwnipp4LEzMZroOuif4oQ/ZZctsQWSbH7CjnaJ5CoSKRpYU/tCPPSuZi8iCZ+SM1jPMZobVKhb06YL4KAc69SXaP6arJMCPj2fKcF4j6kHmaxiVewq1KknEpjLRUY9rROoCFBpMjz9Hr10m2FdDhqLrJaqzBzrDDZPDdp286VFxe8cJQljGgv+XG0tbZ6l2pBBFUlFLS6y/W6fkBQVz3LHozrcwjvq4N3KqCyXOLze5wTY7BwErS1WG+oBE/C+E66HcjfTCcU9BfXNMU6qkB4mpudLNOu2J78SYqjdkd9A6/q2cpzCdxdtCeA7N4QHDQYmd0gK5Z5KYFuakypJKxVecmkTB28NUTJs++VmiJKtGqQC4CDqyi4HtCQ9DwzUtKqMOtnBcNI1ypcKN9U1yZw7Pc3hqrUGzYtAdFYZPc3aZYRzSO0yoiEhllnFOyIxzVfzx/V7gj4tZkviE43EJQkUqTNwjuju7vPTa57CPOgo141VrJIHPzvYOZcdS5K0XX32VPPQ5NRgTiauY9EnPnGV//Q766lmu5e6s2vgrh0agmRz0Gyw5B4VY3km/BiNXzN2mXsKtahwkfQWlfDDULl4xySYL58nfndATFby/kLEelSiOZxx2hh6aZGMNPYHSgqY0mWRBlEG0cnebnqD8JWAHSVCyOBomsbSRlEuAkJVtAishvmthtWIyL8OYCH6q4yQ6Wmrem2ck0uIw0eofvvWQZ9Tf65EnVdwFS81jwj2N7lyDtF5W731B9fVE7HBcVe2ee7tmTSUIsfF1SNjuznOku9QY0aqIz4JGWvHYsy2S7oD5XGM76WL3q+imxdlSm6v4isEtM5XEtxkPSviOyWjoKCj5wzyDXCnzigLzKbtOIw1YH3uKcTw9L7EOlupSgMrqGaYZ/VZNSYcLodHMxWtb5253Xt1O5ijSrvo0Yevj2KWUhCxXDzkcNTiVaySnFzFXz7Hzs58xOtrmhS+8RGevw/Coh2XmWK7FqUWpHlySKMARQFYSc7Tvk41yGp5DuW0pVz8jzgiExCu+IE8QsyTxCYZCM500f5+GrAOpTieIKC/NUU/EZGSEf7hHf33M0pkzBN1DBkc9yq0Gy8vLbFy7giXwwFiGdIYiEvk3r3C0vYcThJgXXuaRgpGzeLLQxMjGJvVNjPID1qFiiTAw1fWtVA2lUSQ2mo+KYpE/oZX9qNde7ewnTOBHLSyqzw7ztkt6EKB5LqXIoueE5Na0bVVEomVKHjwOhbVfOL9NheSkHeVpDqMru8wvL+HUNcJGwp4xOHG+oKcWWulE5TPdfMsQX/E27h/On/x39eIig+0hVlene6tHTZ9j0TXYMzTiTGOrN3dCfK4wV5Lqt5/kqoJQ7R0zo1EeSjYmUUZAutJGUkNo20RbqBAvdOhoPn17BSoVNvwe0VhXJDY5371ui67mierGYxdr+Ty2yz01h9hMxA8cBsGCgpzLFRVouhBZpf00vTY2GSUjYKE5OE4G2/32sWSG3KYflj5VNKK0moQ01yoNWGnsERoWpxeW6PjQeP01vHKXvdEQwhWee+EiFxuLaKmOH6Zsb+/w7vvv0R+Oee65FzFdj+/98f/Ib/3+tyGw+N73vqM2HYvLS7QvLT7R+cySxCcYUoZK2fpg9Dc7xDcDegurXGzU8Wqh8PsxhPSzUObu+h3Orq1iDcY4nofbnGNkmZi6jLWE1epgWSZ+9wjLdvAmA65Z/DVDLzRzjBM7KrXmCzS1HKPrMeOuQSwLvLCWpB0lN5pu6CficI8aSyjUkgzCpd9fjYv7RAKjNQqBvSnscmIlKn9iI2P35gaRH/By5SXufv8yKyvLbO7uKKa38ANOnz/H1cuXOX/hAnfvrONYNq2FlhoeD4YD/AzWb9ymfnuH06dPkXsePMiZkhOezA2mT0ec5LK+pdpdMpvJDOGCPDAvEyE8N8E955H2M6qtCq8sLXL56iarVo1RqclWZqrkM73nUuqTdw44nDuFltYoazKXSVVL5FnfZ8/V2ctEMGXKVYAFb8xAG3HkV9n1BREYUiofqVbSYbeNlaf0tWLA/JFSF6M6jdKg8ARJzYnAneysByxWBfKVK5irSObI7QPbonQwxtR99AWUN738/GDUmMwPpS33WcDVNVWNqWtSOeIOBzxXOoWeuXjmAr3tnINqhw0vxEltlrSKmpHEScKffefP+frXvsryqRo3b91lfmGeu9dv8dRLz6hL9vKLL9If9DCDJ3sesyTxicajd5ODjSNeO/8y371yg8riAsZci8RzOFt7lsHhAWuug+64PPvVr/N+ZBY+uLVl9WFLJlRLtQNsncY8LxIIQmya2qLP4q8awogfaS4u/gngfLFrV7MB3Weh7lANPAI/JUtleAyak6oh7+P69yrRyHxBDZgh3jdgKMimgmAnw/FM5gdejCGemXIHPyENctrtFY66mxwNesR+xI0r1zl78SxBGCjy2ubNdUaDIR+8c5mLT1/i2pVrykMhjWMlg/35L32Ro71D1s6scbi7T3V++aHzU9anvnnPa115TaTorcIjQpKbPkEw3XtOJ0xrsoykpxHbPVIjom+V6Ny4xZx3lzWvrJzuXEPnzvoGukhub25x8UJAqhu4BzpbG7eVnIcxv4B7rnLskGalKfUopN4scW2k0RlX1GsxihzuHC0Vbmvo0ryj5AZKZkVaMwJdPaluenzOaEoPSaoBaRNJMpCWVMFYjohTg65fVb9Xvi4CDBCBhEYK25Ispa7Q1NziSSQzPq1EIarCernPIB9zwSirz/7wMMY8U6iRXUt2uIPOmj5PGiY89/xzHBwdMvAj3n/3sjrSjavXee7l55Rc+mA84M033+Lpp55+orOYJYlPMKSH+SiHiPZzy/z4g/dYe+51op0dttc3KFUrjIVlev0GURgyt7jI0twqWxOa/0fHLEF8EiFa/NNQjZagEONTCTpP6WQDNFusQqPCe0fQQYGOJja0kgSmEPkJqU0NkgOduCO8C5HmEEKcjrOWF3MBvYC8ilZTNnLRaym9D7YoHcFO2OPF156lvugotE5jqcV8q02lXFfaUUmcEAYRGAZnT63x81+8RbVRUaqv0kIQvaX13buce/6CUmotVcuEJYMB43vvGkkKZk6S6NgPvZ2KybWC5I6kJZWo85WFSGCzwvKVhBZ3C1mR1lPzjKKxqnYFJyZkuw/efVs9xksvvkAchQp9c/P2bZ66eJ69vT0sXWM89JlfadE56FBbXWYUeZQs6cEfYYRwtdPgUL/fn0O0iqRVdba1rfr0Ugm4Vqgk+NOKTmdcUwlBXk9JGFXHp+qM1OBXEoTs/qcCevJ8tvtSfeUn5iY5S5WOIvmNtw06lRrjUXny8J/MZ02OUjM1BUQRGwtpwX3YFKhhCmJNJ4gq5KU+sTKigc4gxHFMNOlFTqrgiIw7xj4X2nO8+srrtJs1tDjjxVdeRjtbJtrsEzoZF19/js27u7z6zS8QjO69Lz4sZkniEwx5Mz5qd+nUSiy+8hSdoUejq2EJzb5UptxsYdp3ycRRK0uxxgNso67QS7P49KMY8j6AT1Es5uKfsnkepwVE8pg7IUNuMfwZmioZqAGy9PGFj+CnRIeQBYXBlEiIhhu6QkgZ1QnsVEJub+ZUNJP6uEFvcxuvatEsaXgVC61viqAs5778DPHWgN2dbdW2kjbjeKFG6YJFojus+meon5qnXw7VYiMVpha7RHpO3xqiVTUSP3sItZQkRsFfOPGzKQdDJRGpksTDemgpGHCaZPjbBrqsbMI/7GlYLrR3TTavXmMc5Fx65hK1RpNyvUmSJFTn5mkNRjTPnOblL32RJM3wKq6qICoLi5SqNRpaSpTbqn00V+7hyAzG1jGTCIXXfVDADlQy6AZlBWGVmYIgk8p2wbwWKWyZI8jMYKUm1qIpUWKz2Z+foKzuHUle9anc0fQnU/KrlcTU2iY6uhKB/KT8XiqmzpnDO8q7QUsTupU2oSTlNCNWkiAmdharQb2RpazEQ6woYr/hFbXu5Hz3O2PaNY9eliLKVVOXQEF+Xd7q8fTcPO3FKiORjrcTdvp9jHmNDodoqwaltRUO8hjfKUQJPypmSeITiwl79DEhrNMKFpGU060WnuuSjEcsLi2qvnQYBOxtbaGdKvSeZvHpR9vOWfRM+kpAbbKnE6RSomjEal4hHz6ZNT0YapctCULWzY6t+vginyEJwV3LESVuiaQL0a5O0k8xaycShTx+7HJwJ+HXvvwFsrCn5Bl+8fb7ai5lVG0O39kgDiIq5+aUMN/uexvYF8ukesSOFqE/XWF/HEKcquQliQtxKYs19Eo2MRYqBOGmIQnDSPL7WN+P0laS+wocWCorgbnqXordzkh2TJzFnErd5Y23xzQikZ7JKbkmnaMjet0ulUqVaDhEkzmJH3DY61O+8AzxqkuYowbBssC/PDckTru0RxauFqnH2jtq0dFLj9i8axMF36L+kYF3EhlKMNNUngqmShACSxWegUTf99gbtZ9Iin+qilCxR+SJsLCHKpePFA/ikwk5gztXP4Ak4ht//3e4/cMfYGQJIz+gVKmomZPMFGQd2dzdh/l5LJlRls5jlQzEnTxJMjr9gNTMaZ1yOdKiQrhvIg8jboBHYlGQx8qz+8DtK32wWHYdMi/yUpIJCVOp7D5BzJLEJxjC0nwsyze2EXkc++x5OqJn3+/juC6l5dMkmk4pGCvRsejk9mYWn0IU17dipTxbC/Esg1RzlLdAAWm9R1yRXWTFKQQAT0ZRURRVhSKiVWI1Y8hiHW8NBZ+chinr3WpOvK+TDsBekl26yFFrrN+KeP3CKpZpcOMoZys44LDbZ8G20bOUsQi9WTZO2RWojmop2UZ0rFgkGrAyG1HzBWEji/6UfK+kPky1Q1VaUdNnPn1rSWtMKqZHXZ37ZEcEtC8S5MJszonvmopvYS+Io53O11++xM2NCp9fqzIMfN6/dVNpCqWxuNk5Sk5i8/YdFpaX6A6G7Bh11SIapRklb8Bu0i1Mn+wm/tCj4o2xmwHmqIC0PhwPLvRFc1dMd9TrFU8XdJvbnWUFb320n8P0Od77uSzMimUdamiBsLdNdvzWJ86D8LwStUqb3ds3SZOIzuERzbk5Gs0We1ubLK6tsbe+TrVWZzjoU9I0GplDo+Ng2GO6uc5gPGbxtIfl6NSzMnYQIy/1yBsjMAzXFcRYAYcW5JuYS6nW6FQCRmRYPsbKP0sSn1DIm+xR8FdFFMoMNRjbjTJ2ZZIpL5zZLPDpk02qRklp2c/i0w3pVZesgOWShi3IJlNTA+ljpJIQ6cSHPJekL9Idj0/aakGVBTfTiPa04wRwXwjnwgHjbEZ8oBPdMdUcIrxtIkyX2zuH7AUB2umAZtlj8F6P8V3ZhWtKHts0dG795IpqBwl5TZncnTylyYxDJYW6kOImi7uYHIl3RShVkaG8LNRQXvgQ3qN1phRxLy2Ie/fWRsEDQ3SgUT6XYqoZs/TVU7wkpbW6wnt5yhIJNz74gKdfeJEwDKiUHFzPVXOMUCDbahF3GMf28bJ9NK6pobCaN1SPFOqrSWHQ9egk8aQhMv33TVxOPks1d5AZhXiJqH1BnlOKBzSiQxKZJ7U8dn2BvT7oS/3XC2kjn3/pVWrVCvXhERUxdtI17HIFwxbdpTN0egOWzl2QniDB3jbe3AKbwzHa5ia3xiOcxlksw+ZC02UvzzCGFlVBQtY0jF0dpyLy4Tr9dMwgvSeAONEuVK1SfWyhP0Ie5nExSxKfUMjATPDY0vsU5mYhmVB8MKSMfUCo46Hx9qx++CxCFogRDW+IrXuQmvSyEePshJy3tFkqiUoUhnW/sF8izpAxmBMEsvTmk554LyTYNQ1T0EuPgTzJ7s2az4j1nPiG7PfAuiRto4D4SKNUyoUqxsXfehlf+syhWJ8WQ1VHxpKykGm2cmS777jyPhO2t+wwTjK+J4lCk3MV0T9JfEnRHhNXOyU7crKaEAs2SZTG/a5uEmqtEXVZkRIXK83EIrc0hnrI20GqOARSbAj4otFqsXP3Llmzyc7WpmqjjIaidzTNnsXgWHgAhSR3wGJjV1UYEgIzlcpAPkN/FeG8D3/tfQUTFfvPkm6y3vEYdwNKQx/D1plbrXJYy9kLW/dZiZYNjVUjYz3RCU7MC0uGxoKlsRFm9803Hhdy33c1j2f39/jR9/6EZmtOva9eePF59g+PGPR6Cur8+le/Rqff49333+fZVxyeWZjnZx8M2Tocqc3N119dRM9jgv5YGU2ZTsGWHxxmNM4Y7CVd5bA3DWGFKNDDOMCpeKRGirzlTwojfljMksQnFFLc7Qxaale0MEFJiM6LSABMpQlm8TcfksglRkFEZKTEaXSPq6C+JoPlie+DqjL0SUIQKGs5IegWLSlDqpEFMCQ5yAD8IdmMYnFVx5tGotEoeyysWAzbPuE448gKFc+hJnZvWs7ICJXv+TA5oUorQ3TCD38bTchwx8TsaYIT6Y2uXbSkRMCQCYdD9R9OsLglP1SK+0saM1KdaJiTdGNMTzLHhNuR6xxGetH6ygxiWfzMOgu/9vfZyzXKS2cZGwbu0gWFvRFM0Ug3yKOTbZ5c8RTmvZFyAZwS3CRW63v0gsqxH8snFZJ0hMckMv01y+QbDZO/2A7pztVpehb9y2+wfPFpvFgqDdDLFfLAp2U7XP3h93n6ldfxvQr6sK/8HSx/SCWMOHQXGEy1cz4i5FZWlrIn3uZSGYpPRL/HeDgiSVLCwCcZDjnY3lZkzpvXb3B+bZlzT71Kvtnj9edW1YA6yBPMqq4kSaSqE7KnmEN1TQHBZGRxShIJSVRQdBGGZxMMfHXp3XJZgRLUxuEJYpYkPqGQRDCl6otmTbvUO0HAmcXflhAYpGcFSvoisgVFU2gfaRPj+OOQHf+RpuCrIk2ter1nE0SX8XgLJgusyHI/eN/pTWQNFgkMZ7Iwp2B2TeqeQXbYZTFv0h8OcXSXeuTyiz/7KS+/8iJriw1CPVN+Ci3bJtIyRBT6UXHsYW2mxHsi3Z2iOTlWrWBxq527b6DX4nsuePKXDN4nz3NqgSaw3eh2sfOc06qUDIvhKMITYtmpSB1PCeFZIXGacpCK+VDhpd1LMnrHFLppT+zke//hRTRMhNUuntJivtVQnyGZAShr2Y9h4vNkoSmorZxHU2YfwuzWhzTdnIFVbBD63T69H30fp1xTRELhevhRzF6/r4Al3cvvsDQ/x7vvX6bdqCk9pYHjwvmFJz4LKUREEqZaqzK/sEiv16U0v8xhd6h+Px6N8Z0SUZZx4dIlut0e+0ch1+8OOH+qjWUZlMoRtxjQSSOVWGTuICKQofBljnzV4oxGAZbrYNommaWrNqjXrOAfDXDKXqFRdpy0PzxmSeITixPleSa0elFYnFUPf7tCFgqXvUGLRaMnIEj1EsmCcLIKUK2lIxk0a7inUjVkTgaZShDqdtMdevLhL/HJ/r+S/941OVNpkiURP/vZL6g1agrRojD8N3LCoc/O+jbNsEVfkG9rK8wHOpvWGNcTgyqdOE+VHecwDLGVXlFObyOhbpVwZNefFvOEoe5TOys9aoORFiNqFiXdVQtKmMV4knwm1p7D2EeXmUbHwloKWShVeVZv0x+EbIv+0aLJfrV/rPWjFNaNhDTN0ade2n+F10KqBZk9KIvX4/5/rpBKkkA+jZAW0Wm7RCc9Ynfcp5KWcNavkMytUbIsdENj5dQpBdXduPYBYZyQi6+5aajXsNvpYAsKKUpYObdMt39P8uRJQtmKeBXOPfOcYtlfWl0lDkNKlTK6aarq4naYo5+6iD3q0HSqvHejw1OnGlw81SC0Q34R7yBegNMI9/pkovgnAIztIfPPzuNWS4Xul2moOVOhAaZjr7bp3TmgNF8jDIvE9FExSxKfSsySw9/e0FQb0LZSWlEPXdpKJ14ulSD6GpVeFasJPb2LWUPxH1Tbf/KJUc2RVCczHwMjFOM7WVzyTCUT6etXRmUiM2OxVeLZpy6wsrLIBzduE0SFLWij0cAteWzc3qBRbXL7rR02jZy5pWWWKwZ3d26xdGaFzmaHsjNPd/M67cVVrGqZvbff5ekXX8Mg5+q7b3PqwqtEd44YjQ9wXpqnMXa4+ZPLeJ7HpS8+z52fX2Pt5fPcfesmp58/z+13rzMaDjjffIZg54irZqIEBj0TxgdjqkadA/G7OOHgpxuZat1Ef8VlRMAcouf04OtTVBGfYIgda57j5gmnjJj6yKFuLjP2I+4cXOdLi3V+fO0tWp//Cku5gBU0Du0qLzYbSpFZJNJ7VpnDKCV3XBbHIzzXYWRYmPMZ4ccUUdsU26IXvoytafh5zpaAyGpLRLnoXOXsJDmRYdJvVWC3R8Mec+FUmdyOuZbuHScI4YGOe0PMiqMSQHd4QKnRLn6ni91tkdiOEWuTlmrjzByxHxN0ZmS6Wczi0WuG8FbiGm4QU7Hv7abUbl8Qr3sOcZhzc1ejtKtTPpWp5BB3hY0si43N+cYcd+MuPWGtaiIWZ+O5Jr4p0tc5/g6qElFeEbLZ9k01AJ1bqZB7Os+/8DxBFPDqF19TQ0q3VkLry2QrZ645TxjlDPrXsXSTp1frlEol3nrjDdav3uXiqy9hVxLWN+6oqmD+1BdYv3sXty5VSsytjQ2e/fXf5J0/+zn9bpfXTp/nztV3uXD+gvIg8EKDrfUt1p4/x9btTdbmVhl0+1x67nnyQUDvoMtheqAInrLDHXYHLM3ZSj5dEdQFZisVhl3BN0xu/a1F5eWYZLS0ITWBh9oxupVxx9SZy2roMj9cWOL2+lVePndG+XR/8O57SrG3euFpIs/G7w/Y293BbS+wU1+hG6hhhVCc7/Op+DjhZ7n6+tAzz3P2BhHtwQjjdMp7+hajOFLMavV7uX+UqSrBLbmIw/nC84sMP4B4lGFXxLmwmJ25uk1vN1E2yYLmE8kYO7NYSFae6HxnlcQsfgWjYNw28yaWHjHOIuJ+AfOU37WqJrfHGt2FJklgkK6PMYKU9CDHmBeJ5Yhf7OyQ6akaWnuGhRs7HG4rXFLRXoo1KpcyPNtRvtPJdY1nlyuMXIufDgJe8Pd46ydvUq1XsT2H5599juvXrit14LLn8fyzz7K3U5D5RobDdpDywivP86Mf/5RkrcXwgw0llXH72g3Of+5L1Oo1hbOX9kKpViHyfdbv3MaxLMKjI7IgQYty3nvrPWyEhwGO8AjE8c6Ugb6JXXNkXA37OcHIV8cvTcUkpcs2gdUqc6IcPM3mnGtwZ/SQgd/fgshpGQkLZg+9OizEGSfruXLnM2POLbQYVMu8cOkMZdfi5++8Q1e1lWSjsEmysMjRwR77O7voe/usfXWB7kN+FZ/GqecYW9tUez2c5QxjsURHOUdNf53T3TrEq5VozDfVzEyqVuHNmAuQbtmcOVdRTojJKGfsZ4RDg1Ptsnrd3UZOX5KV+WQe17MkMYtfyZAN1SnbYcFY5kayz/puiLuaUnUduld0enWFZ2Xo1Rh7VYwso3nQpxQOcJugtWQvreOkjvqiEuO1MzLZYUp7WAxx9FzJjOddi5W6ScWVXWRIpunKvWBhcUHt1mWBDoKID67f4PTqCv2+fHhzfuOb31Tn+sNuyM8GIS+5NvNrp+n6NbShzwu/8dsMtjYxxwMWl5dVosgw6RweEncPef5LX2au3STeP+D1117mx3/5UzUDabfanD17jr/8zo9YWFzErNQwLHjnxz/lwguXqNSqtM8sKW+FfJxg6jqWbaFpoWo3KX0rN2EzOcJXVhqFJtITXXc9VfDWRwnyfWKR51TzkEuVgGrNZEfACff/mmCQE1tjlhtlBkHKIICnL10iqu1xuH4XGnN0sYijmPNPXVKJ4ug+aY+PdUInhvkf/Zz1KKHlJ1Sf0Uk0n/EQBrtd2ueXVBspCWOciosuXhqjUFW44kMikO7Ul0Vd4/Zdv0CMaQZWNaO6kijoq+iCdrKUyMgYMEsSs5jFY0PayG8OEn6nbHIhaaGVIxYbDoO9mKtEpOLaokL8GsQ2U6MzX6NU8klHBuGuw5yRcapZxXFNru4eoumusiiNhVSpZJhzojhF7xksnGpgWXA4ijkIdPZ9gzjNsXSD5cVFev0+pVoDp9bELYdYNZs4S9hOIiJ7D9OocjVtUbk4RyfMaD/7OltRRuNSnVtWRvD8lxlNZTfW4LrY5S5ZDMyI1bWnWbLK/M7vfJuB5bPPELc9z8WX59BNQ0GBX/ntL6oB9mE+xNKaxJNl1cSjTA1fdrLyI5nh9C2wRSI9Z5xLo+PJF0tjkiRkQft48TBL+nGzh3rus9DoMCzFDB9V4kQ6YzvkltslSAOWzBIHQcZhmlNZmCPyI/qNBerjHovnL7Gzva1aecPCAu9Dz1L8M5bNXN22bchmRJZqlELvdZEqv38E9lC0TY364Yh6q0zeNghtSwEbqnqZw+s76I5RbEAyG8HcaZEYIWlkUYa9klF2TNyKEChzoizCVvyUnGGWKWc+S1BdAsPVDJLH+I8/GLNKYha/siGi06ZRIhb4Y29IfCXj7sGQ0VLrYR9PTSNGiEpt7FKG7pT4Ss2iYRqUXYNSy2D9qM+d2x1yW8OVD3MzpXcQsdQq4esZndTibV88nnOsSo2585dw7ZSdnSPmFpZ46qWqws3vbm3yTphw1O+TO101oRQCm59nx3j8zaD4gEdRhptFjKJ7bRBJa0IYy80ee0GZt/o5p0sjXnc0bqU7+GaMlmu4mjiViQVnznp8oKocpXL+IeuguirSdvJNcicijsyPLc9+4kiPiJMPfo9eLkQ4QT0JD+nRd8upJBFtT5LtuJBpf+Ah1Px2Yk2blBMO84QOAYGWccorc8fvs33U5bC2RN8XBYU64pKar9TU3/4TcCHals7Wv/vXfOHXf4Px3S2azYaSeH/v/cs888oXyF2XdDikVC4xFOE9w0B3PfWe0NOUdu6RlQ1Mzaa7k+FrEWlgKvCD4dfJBzlO20Pkrax6ji4aXEKku5HjzslgOlNzC6G0yOsaTWQc5N/Kul0ThFxGmInr4QwCO4tZfGjUrcI1bbMfElYDMtOg7zok9uMkHTTlczyeeAi96ed8xcsoY9DWSlTaNnYl47ATMhrESgHWcgysBgS+xltRQjcptraXtRKOXULTEtLFea5JH9wrEFT60kVGPbWiUc1LjGL3PivQkyFw6/vRQDlz5b5SVZUQL2a574afMUoi6rqBV8142llUVpYygr6V7KtFZMFqMEoDRipZ5FiInLjsVA3GuSjNTkxZFdoyJRIr0EzktJ+0bfTRtyvc5PrsDQqVAmFly3NplXps9hYenyQ0jcWShlsfw2MRZ5qaqSjTp8mpyHO6mffV4hk5EclGQlAS7wuX4GMNWnJ1SEfXmKvXuPXWzyl7JUJ/rCC0pDHj6+9Sq9W5qngWDSqtNqH4gIzHVBs1bly/ifXqN+mNEko1nUSeR2BRXdSxSjn9oxh7sQRGquYSU+i2Qt2JhL3kRaXmniso7dQ0qpi0FWZJgspLs+L+J3W6PvQ1+TiXYRaz+LsUIgUtn5O9oyGRnrC5D515qSL4kP7x1LMZVlwDLc/YOvQVIqZddXjOWWGnPCZaCOhkY0wl3uhh2xYb/ej4/r74natNXtEOeHQYdIMHbeUePp8HDXGCxFLJQbgMktREwn6xeqSkMPpZm/NRgFF18co2qZnSzUaKLyEtiLZZpZQ5alG7mFfxRIPEcNnN+lxPdvGzSKhb4h7B0aiuxCmPyYVPnCw+PEkUwh2agtcu1o6oOSO1wImkhuhuSVJ8WHgv53zZ5TC1CM1HEA9lZx0ayiXwmMXOvURxNe+SCkptKad61ydpempu9CQhZ3I68xneucarLz3PtljHei53NjaoVloKKr2w2FbquJ39DuWSbBBsTq2uqDbU3du3GHV7OLaJ7UCpkmGvpkqTL7qb4S4UO38BQwj6Thf2u7zmsaV8rKOj4mfT5yWJw5QB/OT8HE1IizKs1/GzRFWRpvimP+HrNUsSs/gVjBxP15i3CvtQaQdFeynuaoWnWznbcZ+NQU2xYyXWLKgEfTbtOoOJSI8rfg+eQcMSBJLPKNIY+DGuZVA1bdzMpjwA3a2yE6bcUQY3n8VzK5zU5Ev0xCSBiBmWzAKElxDnGh+kBnk85qWhju0VkiRSPcRxyoJVp21WaGk25TQhz3X6Q/FYcDmtLXMjPmAYZYSRyyB3kc54NQtITJ1+LozmScr7ENs+IXmLyvqjbiMtJbEclXCsiIoos+bapCIyWKh02RQf7QcSo1QcOikrXp1b2d5kV28SC/LneLm85xXyYMht5D7nKhXatkbgwFsFIf/DKweRPK94BG9/wAtrS9x+/11efPEFdT3bawtkcYbrOmrmZBkWF06fU4lBPCPENiDPU9yXXsFPEgxRE67kpEauFvVwoBEHGX2BRsvzDW1Gow61eXHpK6pO/7aBs5IpTk9hgyvjIoPwhFpooFpLmRp6qwpErnMiRMonG8TPksQs/g5E/tCCIRpNtq4pFJMwi4XBO23ZrDkGz5YM/uxf/nOsb/8uf/rv/pDPv/46e2+8yeprL+IfHZCuvihsOLUUeMMOP/n//Td84T//XzNMlEA3iSiNphmRmbOX+dwauDxXdpU/dbPicOfOHf6Hf/vv+MJ/8k/YEOXfPKFm6bhaziCF4aeaMYpefrM0UFLnU7OeqWBeisGVkcuumVEfBpRbmSpo5DqJZPq8XqUiNplpRG8Y0XF9BqT8/KCEn8+RoitznhWzg2NHWOWILDKoDMpKhmY3F5TV9DxOvkw5lTRk3u3TT0p0NOnF3w8plUQwvTSiBrvZmy+k9kVUMNeUo9yjrEQdPadk6VSMEncymcpIUpbXaiJbIv+bZieReD9pxzoJT7O4qDfYrEMQRzxfLnF1lBBJa2byXOT/Mp9YdBIs76hQFLbnsS6e5d13L/Pi88/z05+9yV0RN6yW+dzrr3HjvVt09o+UIm61XuPShfPcWb+pnPpOnVnDvdDmqD/CqCQKxirie6mvF1IwzZy8b2LO5cpnBDtErxT9TmkvGeJRYmkYAiSYSN1P5xAyok7FSVASkFlcZ9WemrwWcn2eJGZJYha/5JHjaAmukk5IKbkprXLMwcilbtlU7IxBNuAgkB1qTX1ApBqwNdjb3uEP/+C/p16rUju9iH/1Ct/7k+9x89ZN/qf/uMp3/vQ7arj3jd/6TSWd0P/L79Cs1rhx6yZf+PXf5N/9d98jDgM+/7v/kBt/8K9pfPEL/OhP/wO25fD51z/H+5cvM/oX/y+++LWvce3KFT7/977Nj/7tH3Lut3+Pa0oy/tMNkbuYtpzEze3koi2L3lGS08kt1joVqq2+alf0U58trUsZhyCAeq3EjWSPjukrxFcciWdDTtnzqVQLSQpZZw0zoe711CKcHmrspSIedT/qSJJ3qzTAa44wkghfHPhi4yOropNxUp31ZFR1k2Ey5E5ydJwYokzwuVOPEIGI6uA+3EQ6mTB03cY2M4w85zUHPucl/OU448bYVpajT1dszno6TV1jW/fYSkJ6/pBFs8KFp19Vgof9/kDxTKShub2xjWFZCgm2u7/D4uo8qbTUDFhcWlDaTAtaE0kPicDTUmlh6hhS4TkJmiGYVQPDFdfBlDiIUZ59kxJHgSDK0jYtNMhEenzihaVEI+M0VsNxlRzyItXVrJKqTHb1e1LiHxazJDGLv+GYfECPC/iPd1/PSDjv9ai4GVkibFMNbQTVOKWVxVihwx7zmJnGcxWDa6NYeQv/oh9x2Omyvr7B7/+n/wifkM2tbb729a+heyUMr8Lc3Bzvv/MOvf0d5ZUg7YH/9L/4P/DzN3/Oj//DH7MwP8fPLl/m5VD6yrd5+tJFhiOfp587T2qYrJ09z+vf+Cbvv/ET9nb3eDUK2bh9izOJeBV/2klC/BJKyjNh+v0jr6Cmsx01SPZ1GvWBGkjvxz2Ckc182WM3P2SQy2Ii1p5C6CqG5I4R3dctKrw1it16vTKg2y8Vch3iy5FHLHtHSrE0t3Ol2STy+QVX4q/wzLRMJb8CKVW8c4xswJYp2in3V2gKNSQLpG+o5ybnLO0Y2W2rMfzEQ0MOIz4Meq7h2AbDIMYiU9aq52yLz5USyqaBZ8s4HwZjk2zfweyXCNA4EHMmXaNRtbEdUdw1OHfxLPWFOns7e+q6nL94juFwyHA84uDgULHoRdDPyHQ1u9Jzk7yckGoCZSpmJbLZj9MMM8sZHvXUY0jrqGTaBHGsBtZlyyQkZRz6GI6j9KeSLFOJSRlVCVdCE+iseGZbioGt9JyMqY/4h8csSczibzBy2qU+JTtQ7ZB7C9qTx6k8oYFJHBSkr+qcpnZlTS2jbVmYXYvDUcqrNZvENHi+LIkiYT+IqdXr/Of/y3/Kv/n//itOL60VYn+2QTgece2dXxCGIeVKhTzLmF9aolqrsXfnFs+88jn+u3/+z/gv/k//Z372xpv0drcIfB/LtnnulVd548c/UguNcBA0wyRLU0zLpLe7TTB+Mr2cTy4+OvHK8HknazA69JhzeiToXA1tnNhntXGkKgBZXK2JLaiEtO4e9s5OVEvFtIXp3Gc/qCvtqsVyl7iksTtoEgUiSji908ffFDhGzFylxzhyVJIQ3dhFx2DOCsn0QnBc/gj/2NQMckMn8QufDJFUUec5Qfco69m0mMkw3Z1rMY7tsNeNaFY0Dv2MtpdTSiOiEEbDnK2DMbe2e0R6jFc1qDg6/ThEq6R0rQHPfuUFTnXO0Gg3Cka7YypWveXZmIYw2z1OP32OrG3R6sdqMbfq4kkhM6DCUW4a8aGOKe1AKYTihMpio7j+IoQYS+PQUrpNaRwpNJMM3+V5qOcl7z9FHNVU5VDRREyykKKVx0lm7aZZ/DKEbcYK0SKOZQ+H1BbTnvL9BjSGlrNcSgl6Oiu1OdoVQ+nS+H7K7nhARkqs69w58pnTNfYPIvY6I3q7V0mSgOZTr/CVr30Dp9Ti2/+T3yeMAr7+ta9SWT2Hc+Um5557gf5f/iVnn32O5vIpvvV7/5DFcxfp373NxfNnePr5F9irtPna7/4e1y9f5pWvfR13aZXDm7d56qVXWX72JdJKSzmLXXr9C1iOy4133+GVr34NBWF52MTwby4mfeq+7jAK5wuzPV0jCk0GQYmaOyqSxDH5Sjt2jpu2aqaOdmHqsj9sUrZDVqwDXCci1A3udpc+EV8Vz47Ue6LrV2h6Q86VM77kLnN7XGZTG+FtJQSDMdXnFhhf3sf1yviCUHBTKnkJehFjNRcvpCxMVRcINBSauqOSjmum7PZDbmz2aFRcXCfB0GIsSTKZz8BPsddSSks5JRlyJ+LbIOx6k1E85sDOMcsWO/mAIE0ol220+YYiHkql0s366BWdJA8xWtJuE0/qoqqZVmRSMagrLcWYkrMHu+yqa6ycAzOBuaakicEoCpWnhHIz1E3lLVEMsYs/wzgiSVMiK8HRLGxpiaU+B/GMcT2LX4LY6bdVkniU74ZrRqzUDtUC1h1WCWMb00rwLDhdNmhpCdcDk8SxKBk6Yz/FH2ecKjcINY03ukIyC1ipOgz9iCT02d/d5hvf+Br/5o++y5f/3jeJ4yGLKy16e11euPQs3x+m8PV/wL4J559+mt6Z53hL1D8rK+wCLy4scbS1wdd/7evY4sHguSz81u+zH2f8JMuxv/Zt5fzwE2lznHmeLfl79dnCbOZb51RuuK1IWX81cbhPNTSN9IQ3hrRjtvttVeG1vIGy5y3gqQX/Ql6fptdTngaatEsMg8NBQ91evgT5c6ayo6qJCeTpr3uCaidcJCiNOS/ikuOq4686Vbpaj+vvXebnf/FT/jf/x/8t/4//93/Nr/3mr9EYt9Xg2Kg3+bf//b/lf/G/+s+4fPk65y+c4/DgkAtPXeDujQ2iss5lhtiOy/6t91ldO0U02GNns6c0rMb+mEvPvvj/Z+8/nOy4rzxP9JM+83pXvlAoeIAAPSmpJUrt1WZ6ZnrM7tsXG7v73J/x/pMXG/FiYiNebGzsxOzM7HTP9LRa3bIURVK0AGEL5e31Lv2L88t7CwUQpEC1yKbYOGo0gapr8ubN/J3fOedrONq6Tm42T/7UnBo0p7os7qhrzTNt5Qoniq5qNqIL3DlQHBV/NFbnWCRORCXYNEV+cLIJkgHzKKCz06a6UsdvjZRpkOV5DHY0urfWmbmwoB6bpAkjqSIsnchOGXVT7ErmXZ2N2DVc01QttSARSXfhaeiIFmVSCuikkapCntQu+Wm76SsVj7JV/2HipErNpzhE48nOM0kJHzuMTGm4ASUru9BdM6HouNgiSifOaWKbicZKJcGPIgYjncPOWMlg9PpjlRQKUUrcG3N/4LM8m1c+wnutIf/23/8F/+zP/pigGvL+D99lqbPIWz97k9Nz83jtEQvFOtagy0/+9m/5xv/1DKKAtxlpzFkaN3/2Ds9cOs+P//bv+P2VM/z1D3/A7D9dZji54URq4+RnyP6T/fdh9P6XLEGcqNweVG2CjjHUrl2G4KdruwqCKnwLaRm1RkVVCe51hfimY+iJetwDw43M311eU34nbnt/3xDEkyQoeYdVvcK8oLBC0BODOavMHU2jWq3y5t+8TrVWxdIsdu/usHb3LpcvXKBaqbB58z679zapukXe/OkbLNRnufv+TX7R76PpOq+8/DL9XocffO+64jZceeEZ3vrZW0obq1Is0u42+fkbr/NPz/9fGHlJNteYDI4tXacfZ3wY9XOZDUwqBJkPyL+P7WFPXALjzojQD6itNBi2++pXhZkSg8MeWlJg9vyy0gKbPlfMhCT0BRiugemBIY68Aiqw7GxoP/kORMbDxSXKC6xWjsHC1lx8/6ksxz+ySPFMX8EexWBe8OayO38wFv5iombpLAyOaBbqVHyRlfBYj02mG1QhHMsxFU2d+aMNxrMrSoNIfl0XgVEtYTfScNKIejJG7zromkGSCzDtmPPaLB/tHNELAnKWRk3TOPRTDgcRtmVS9ExKRZswiukGQ8aaT87JsdseoXs6/+Jf/Cs+uH1Aohe5//rP+fCt9znzp6c4Ojjg3/z//lde/u6f4qx/xC9++mPufvQRxv/6b5g5tUL+m98lt3OPG7c+Yn9jjX6vx4//y19ku7SvSEhFV3BGdEb5EyCCKfJHNsUpFa9LayRmQbYaPG93Gg8Mgx45FTK3uN/KMP3JryyO9+hrTqGcCWkSctAcq42GZ+ksu1Vy2Fx95hn+7m9/wO/+4e8qJds7H91UBk+SACQKhTyVaoU7N29jWSZ3b91RCCSpFgT2u3MgiCRdVUiWZbGwuKASjpfzuH9/nf6gpxZqucYGQXBsSCSvpVBEIk2VRCopTMMyTXKuzSiWGcSDzyPP83sixpeQqxbQTZ18rXhcEeRnSoxjTYlAjrpH6jVDP8TKOcR+iFvO4S7CeEujfCF7YSHeqZaTLhMaHTMy0fJiaxph56XKEAfDRJlRPUk8rSS+EpGqvvG83cZ0Q0ruUPV/5eaVdsFYvJO/oBDnrzf/z3/Hd//1f8tP/vovuPbMFV5dPk3U6bCzf0C+UlWuX36/y9HuNktJxHKxqFBGFc9Rs4FnSkXCKKDy3DxjQqxUo5+kSqDs7b0t7Mim0DDIJTr5sYZrQf3ULK6TiZcddX02jw5Izw1xxZ5SJo6xxmI+x6pWpNkLuXW/zamzL/E1DGzDoFwt89v//b/kTlglKC3wwh8UKYkI3stf5+Zbb1A3daL2EfVGA8dxaB011aLRbDb5qoRcL1I1PAjxyRDPjZEaEsuwUyxAw+N5hPTVP3nx/yS46q8jhAy5kitTKZps77d48423VTXz3HMvUcw5XH7xKtVyGT8IqazUGY3GXFy9RGNxlvJCnVo84Mrp5/G7Ea3tQ179nSu8/9Z7zJyeo7zcYOejDc6/eElpLJm1PC/9/tfVAi0tovXb93nmG8/juHkMJQGSLeiqXafkMkRDyVCzg2nFIJqGovTbOeqSb+SJgggn76rqw++MyM1KEpMEk1UfKsGop2pYxcybpLRcV78f9UcYicW4NSARX+xhQDLyyA0tgt5IVRFepaC4E8lIV1Ih6jDk9RXBEppbTXxBUT1BPDVg/opE2R1gatlWTkp7MXx3TJFQ+OLbGtLLvfGzn+JYNu2jQwaHB2zfuYkRRwTNA8b7u9z94H2aOzvc++gGw3aLw+1NBr0u1XKJzuEh1995lziKlAhdxcgxo4sWvkazNaJX79J2O8R5n/m6y0LVUvDK6ScdBzGhDFk98a6WUlzDLEDO9PBDeOFCg7OnKuyuf8TrP/2JQi+Zlk1KgdzNXzBvw91fvM3shctKRVRIeWeTIVdf/TphEPDyH/4xtdlZXvz27+B6jzqr/SbHgzbT9F/i1Z63x6oq3WjPs9+vn2ghTmcrX3zMOSYFzSBOUlrdLlEacdhqYjs5fv7mm2rBfv/dD3nvvfcpXJql8NwCt7c3SfMGo7pG/oVFdtOYvUMb7ewco7pOdbFB/tIsh/kx9kuzDFcdDuoh9zjiqBHRqyfsl8Z4L80TXyxwKMSGQD8e3O/f2qYzGCioqTFOlOdDZ/2Q4VGP5t19xoGvwBVRGDPY66oZxOHtXdxGEcPKZgqymMt9I1a1iiAubSovJRloSuRPHpOvFzEdk8rpGcycjVsqkPgpfhDjNIrkGyVa11tEuw7myEHPCYwja1O1dptsbu4SOZnk+JPE00riNz6ym1RIRwVvjBbp2I6ucNHdWDyPjS/2aFI4feEC3XaLlfPnsXWdtfV1GtU6XrWutI5azRb12Tnq1QpeoQihePwWmVtcIBgMlK6MlPjj+x32N3a4fPECzy0tsh33+IV1iOHIRS+M0QzrLrutTuirkl5IWzsHPYrzKSNNoICQBkqKCFu8ONOUmxst+v6YfMHjlVdeUUPGKIx48z/9RwgDVkTGII4Jl8/j9FrsbGzQ/bf/Gy+/8jKlmVl2DY+F5VNEYtQjSKWvZKSq9RRPqtEHm41pAkkouiM1b5AZwRfb1kxp2BrDkc+H945IYh0nN8tyaZnNjQ2+973vc2lrh29/+1u89fbbuBshQbeH48cK9jl6a4NcaZ4wTIjsgFwD9uMuxtUKPW2MWC/JblwQQYZUAwpMJN7gmbGPTFn8IMC2LDTfJiTk8O4OtTNz6lrc/2iH0nIFp5pXsFSpCqQtJG0mwxUfbR3rdENdu5XT9eMKQhKCvKZwJsZRhkiS58ruPxU5rUiG3VlloioNhYIyiLYN5s7kcargC+JprDN/YYluv0m+XlaQcDl+CTvvYBuZlti4+9S+9B9FiAjafOkI24jxdBEjAleIMwKBO04QXxySZi+IGc2cgZkzNCfvbNRW2NU0BNSjjqK+yqytU+0fMpDdX77Asy8vUXBgd22g+AWvfOtVPnzzXVbqgiY6YlQ/RXMbrhRLHGgDeoREMhCUXrkGZS1UvelkHCojHzencbQ2Ucg0U3Rb46PdliJ2DasD0oUAOgXm56oYgcErr/2xgj7Wzi6w24toXHyJrRDqxRpX/+jPVKUR1+uUT13mziim9vXf406qcf4P/5QN1WT+h9lR//0jY0JPJTum/576PhwNhaX+8YaDPKboDFWbU5LJfq9KOJ1NfAGxPop5rmarWZdhW1Rrc3QGPpYTMxgMOX36NB98cJ3vvPYdWq0uO2s7lKpVNbfa3Wti5CtKMNBdymQt5EKSBKCnEyntMGMq24aJH0cKUYShKyHE0XisDJk0W8O2NIK2Qd6bw0gcRdgrrdZUZXBSZVUt0lMcwyQhKAb0BNIqXAfZ7os0iFjW+oS4ho1r2nT8rDqRM5vXHEX8lLZW7CcEezqua2HVYdTPRP8KeWEMJiT9iN5gQH+3rWYbSqPrsEP51AyeZeOUnoyX9LSS+A0PGQ4K+zQvGGhcdbNPCU4VN+JUqcN6N0OjfBEhiaAp9lcfi4cX0YKp0Ts6ZDQcMh4Zyov4+q1b7O3u0mq36XW7vPqdr7P2wTpmboEb95vMzsiFHXN/os+pEqHc36l4Gac0u0MGw4gwSulsxtjzKUZOdlvZ++viBx047Ho9hTRK66GCA4qfzigB88ijO7K5ntgICEXiMEw5KmdewKkoLUxiX1yL1GcSSMlvaoLIFvvl8oGqRGUgLSGLv8BbD4dlVUl80gB5t1tTsFh5vAy911uzX1B7U1PD3I3dLuWCw8XTDfrDgHfvHTLqb/PcCy8wN7/A9tY2P/zRj3n+uefo9bskQjTrGQyaPYzzBu5ceiyMNz3sCQ9bDaPVTIEMCvyAvwDDrli7hqRhLB6wtJp7lE/PqOtIobwsQ1ULkkjEPlaSjiQBxf4WqsUEDSWRSl2iG9Ril+YH25QW5hkP+5w+M4N+6HPYPGL2fJVeLlabv+TmEXOzc7Qn6q+2lZIUU4JuSi6nQSEmlGyTpnj1gvps0pYadQaKzJefryrSo29Jq+6pdtM/ihAST2ecx9SGeKqN+XDV4B+jSr5cuHwlZKxptFstbNumIXBFx2Om3qDT7uKaLruHIUdOjlMLsForc0CXO1Fb3cjSTrMNh6N+xM5BQBimmIZ4HljkPZvZ5SprxsExDl0iNVL2tHYmgHZ8KlI0G8ycRmnsYCUxji6Syg/Lgn9VQ5RiZYE3dQEUZyNYuZ46PKz19LhQPhXtWXUNliRRiD2m6CN9ASGSGZ3OmJcvzale/LvrB4T1ESuN80TjEVvr99EMTSGR/GhMmAZElvTgI1IjwW2YDCNhDQZKAkPSQTphJysZDzWwFRFH4YaIppOuBPGMIFUJotAo0948JJ3RKJ6qKtmN1A0xAofU19UcwLEtJY8h7SlVHaSR8q1QTGdVGWQQ476fJ793RNAZEnhD3v75Wzw/fFbxKu6u3edqdI3ezg6Ui+zv7FE56FMqFimV8hh1h/3iSN0TiWaQ12zEHDeUhDQl3smcoygbyAyG65hSrRgPoa8+LZ5WEr/hIbs4UaJsj/KUc766raUPr5oHOiwU+liGr+SVp/DBL0PI5jtNYirVKuV6nTBJVDtA5ggzjbpSvMxXTWqzJkdaL2sDCDNC06gZBdzUZthM+PlBF2M24oVClRmrQs83GI4jqo7FXtRhMGEnZKW/NrHhzGQmFGZdcpVUY32bubpH1UlUC+9HPXFs++qHtIjWWnOPkBmf3ERoimCSttQXGWaaknM1wjjmnfv7tCptJZGxtdnj+d96Dt22uBPKJkGc2uDq+W8xUn+DVWzGIj4yEMiRqZz2xOZTrkHl0CpyHqSqvx9E2TWnVFQj4YHECpUkCai8XFevp+S3J0gmLReDb2AKBN1I1eLt6uYxj2IQjhUiSTZGUnEkUUheXsurEeRH3F+7z+Vrl2kftQmCgPX760p6vFqq0WodsnbvHr+1PMf+4R5W12F2dkWpvMrCL0lH5g9yPadpJg8uszalJaWLqqxGwXZUghiKLa4fPNm5/ly/yafxuYewkH0zVNLMUj7Go8x9Sv2fIBgskUqwJjLZX55ohin505cxR332nDy2pjE/G3EhCjl76RlKS0WlkdNtp8pechD38EwHUxzDPENJWBv5FJbGGK7OwNJpaJYaKOYcAz8YkOjRA9mIRPrN5kN6QyLotmqU6B1GCs4qZ25tBL9AkE7pPxLSpJgWPenmQSTY4we8iEde54sKYbrPhAH9QchP723gz44wC1nil0X4jUFXCTpGgjs9cXQiWSEcgj5+xvsQG9qxRdixGIlbnSWeC9BPA6X+K20ZianDW3OnRa6WxylmvXxJHMLDkJhqQUlVhR0Rjk10K9utjWXBFll5P1CD8KKbwzazY5HnOVYP6WSK98XZqxeVKvHR9iGlUpGLz11RkuHt5n0uXr7IhWcuEyURumvT6/fx7exKzdpk2UxFIK7jyFcwW7nuhXgnA3FPF/vXkO5woFBhevpkIpNPk8RveIwim1FPBniJKvkLJV+hdfb7ZfodlzA1Jjf1lytErvvDoVzeObmLVLEvi3tw2GdxucD9nZ4q1a3lQJXyuh5SwsVLHfxxQqlmUszZDCKbUIuVCnR7GJJ3EsZBRN4Ns2R5YqeXjjU0WwTdsmMo6Q5nKfJBt6e8h+WO3jwaktQz05svZwirN6bs9hXj+YusDuUaE2tREer7h4uUmttlsNGjZ8U4p0SeIpsrCPZobibH7Xs+9nyCU3swa5ArTdBJSt9OxP+yhqdCN4npSCoe4SNxlxII0YQpLdLb5gMHN8e1sRzxBnk4J05tRNXjZOYwFi9pTW1CpiJ6iiUtZE/bVZu5XjjOQB2GoRQDYs/FeW6ejnhWHw3Y3duh1RFIr0iXS9sq5N79+4SCvnvtMpFRIK8V6Se+kgRX/hlyv6Djh4GqLo5Z2ZquqpneeMRo5CuOhS2mu0rd95fH0yTxGx/Z1SqVQnNUwLEOFSTxQHknfHlmEJ8WssxdzZvMDIbsDsesbQaYJR1jMVCoEqVGIDedIRLVOqkbE6Q+pBaXzHl26SohtVY0oubYWLrIPGvksegj4msT2OYj+nKqB58IbFCn7FrKPvLUwOfZ+ojXBzK4lRbKl+8cihSGSJmoXesXWPB4lrQz/2ErLFuPyI37xFGKU9LIObZa9LP+fkK/NKKQmkSHOkYtVjMGmUHJhsGYLKbGBK0laUJamMhA25MKQMS4NDWzUO0jO1v4lbxGlBCrnbns2DMxPYVKmpj5HIfAcWMboyhyMgI9fZAk5LFiH6qqDuFEZL9BGzmkbkRgpRipxuHekSL/VWoV9rb38GQDY+jsbu1TKuZVxZGYqRL1O5a/maClutEog9jq1oR3kc0+hmrQbuHELoYDdg5GgyfjSXy5ehBP4+8Vo9BWgnn7/epvSIKQATR8u2pzThb51pDVsyVGaUwyP6Z/Yqcj5boM/2S32Iz66s9W1OSQPkXNVZ92qPtsj/sYdnZjXqCk5JGnN7rgzfEziWtZNBaTAt2xwdJMieHQz3T7dciNQpa8UCF/vnyhKdazeDJ8nozmx8UgcNnt1fgHizSlyhArF1KveoR3Nuj+eJ3lbgHvns8cRexdn7NuiXLQ4/SoyuiNHWaCArWODR92WBoXOfz+bYZvbNOIctlwd1IJTKbVD+kraakMyXXa9/ZxG4VMTl6q0kd0mY5DXsaN6I8HaqYg1Uh26FMf8IwQl80wdNFpx3AFTptiaBo53VKbotNnVnEcVzH6JS2JlIhsCqZzBLE3lUr7ZM6W1lcYKIjVsQmRDMiTgUHas9XnNKsxej4m0mOFunqSeJokvjIhksUOzVHpWMb5yxGfxMqVmyLhlWrMZVdMV1JyNR3fSEiKIUOpFB6JQTzmMOwSCvVUkkIScDvcYz/pqv6zEOZGRsBNvUeopeQ1i6tGA0cAsomwr1PSOEuenuyqRg4lN2GhHONaARs7bboDn7V1n+e1GeZt40ty7k7++Yc8mmkp9gVvQBT8NKWcjikXuoqLIPa0m/fvc/b0afLFPLffv0mwP+Du+7eUheyd2zdJ/Yg3f/Qmw1tt7vz0BrfevaEIZINml3OnV9Em3+/xQh9r6AaKn6DADVK5xJpiTRdP1R+0rtIJRPZkm2nCf5CzJMJ5WmRjWTIw1pQkh0iETx+j3i/KjJD0wMK1dbVpyemZrIxumxweHTAKx5QrWetT1GqLxaKqMI7fT7aC079PkpC0tNT8TdPRQouka2WzllKALm53uhRMhiLsPZTcPiWetpuexme9Yz/TIiEeBCI82FU2lNpDA9D5Ygs/qXNjELNsGQz7EXEg0EFDQHwPvY7cbH1p9p78mbSUdBdPswkG0nMOSKyIARFj0fc381Q1i2tpwkfmEcMgIRQZhY7FQMr1XMiBqJOSUJ8r4uUM3rt9yNmVErduH/LycoFh7NB+MombX1NMmLFGpPyph4FLyRqSjE1aaU4pgT5+cPwVDeHApAkzZpdKuQtWRBKkiAKNIHf6vQF5KgRByP3rdzIXuElorka9UWN97a7a1YsOk8R47DMajYjwjhOEMukJTTQzUbv0qcfGKAnRSo66/lRrSarQCZLpoUVWdf6kuoggttFCAzMfYxlZMpGBsUJKBaZavMVnW+ZjpniiTJQRpWUq84ny2SrOxTp+FHLuypzyvBhFAYPtlLnYZKhn94aC5UZi22scVyyKoS3VcmCr19fLGblUVS2TkDmJsLmldfYk8TRJPI3H3ZnUTY2cKFbK/tEw2EsEjZE5f50T68o44Y7mnuCRCVNXerAPGv8rVkrR7xOVQ4WwmjqJya5dWLrj0OJ236eoRXRaA07NWYrcJmxQgSsKnE/d7NO+cJL1ntXuTIjbRpFVo47r2xz5Q/W4xEgYxj6RGmRkl3dNL3F5aLI/jFnP7auZhrzAhxyp35eNvLpphfF9ftmlNxqy3DDZ2GzzzEyZ1zWH+AshI6ZYcULD6FG0hxhGTMAIS4vQqjElMQRwxVSpqhKFnNOvbLKY7ORzUchsoYVXHqrZgZIv7+mE42zR3tneoTHfoFavcuFb1/DyebVQSkKQRbQ+U8dxPZZLSxw2D9XvgjBkc2OLlcWLmU2odHAGDrYjC7qMJbLKRWLYEoXWGNOzM87BtGKYtHSm6CYJ1UYSJFQpQvdFGkdjHISMfR8CE0fLKa9qqZTFg0Ku53Ci1CyvOZ6Q27piiCoVryby8n31M0kQ4a5O8Zov4wkV03aXHIMcm2lYpGMDw0rRctGEFij374MEIY+XZBWMgydWMNbSJ6g5ut0u5XKZ//cbd3FFa+dpfOXj7OiIH/6b/w8Lp06r/mjh5W+xG0JeLu73f0672aT+23+k4HYiEeCnGrNeH8FsxEOPWDfwWnu885/+Pd/4f/6/6ATS59VpWC3aicta31VJZSnqYW2PVAn8+68ssr7fo1BwcXIjPkiPVFUgMb0ZMnVMjZLmcEVfxklsOr0xveFISRMcaWMOe0NeLVSpejX644ShHysJcYEOvq9t0p8YwBd0l7zhKhFBSTtmR2c2p3N0JP7XAbMViw93R9yp1RmLUuDnuiBn7bcls0Wh1M8KCoFsiXfytM0hLei+qXwZ/NRiM6x9qbgvv5aYagzFCUVtRKXaxfEUP/54tzxa08mVDEqRg91Aid3Jut5LfdzAVjbchnhZGCmGXD7SEko0/J0EU1xqhS0vC7AZ001GEMnOW0fzZNedtXDiMKZ194D8QhnDFktWUy22/ijjIjk5QcPxkGqrY2SucLJLZ2iTaELE09EjEzenEzsRQfSgQp62rR6S6ZhyMqbJaDIbab+fYlo6hYvZwn+cnBTPx0CXDUOoK56GI9B34UhM3OpOVj1SMUkFJm2pUX/I/3Dm9+l0OpRKn8xzeVpJPI3HRhQEuLk8r/2zP6dcyPNf//f/jW/8yZ/x4//471lZnCMe9Nn/i3/LK6+8xNu/eIcXf/t3+OA/f59zV87xo+/9BNvL8Y3XXiP0x+x9/3usnj3L+to9fnbvLgvnLlJ66Tt05AY28kThkLkZF92wcSyH4SBiLu+wmhb4kPZxJz67eTSlvOkNPdKcjnBND7sBlbyFNpBuRIyZWuy3UoLxWMlGz1VctdC2OiMqriCesiRh67K7y26BKEgJnZgdK0Gf06nkHTY3xxlpqt0hqNYmdc3nE0aSskiHQnVw7MX8qEGD6hgUI0wiDD+k0bE5SgpEsv39KoRwBpRn+ZBiaYCVD7BUhycjzckCh0BV5aHViK2bEYVZyKWWYkbLwthVXs+Z+q9Sp8j6QOqy6Y9TnLaGXX6Ai1ULqPw91kj6MrvSiKS10+pQWZpDt0ReQ4ORDlaqXOWmNqGCpor0rP8vi/BwPFaoK13aTZGOadsZm7sQE8vMbHIFTVnQ6vPIbFlmCLLbV/uoBxuRTPgPRlsCq9XQyw98udUGQrgYqYEmEGABa+QEyQdB+vF5hZrJTV5bfQZdywb0TxBPB9dP4xNje32dH/6H/4ODe7fZuHdPma2r/8Yxf/Wf/k91oY3dPDsba5hJh537G/z4r37Ab/2Tf66Gdpu3b3D93Xe5+cEHlFbP4ToulVqD62+9iStSAUKgG4cUPYv+QBQ9D9k9GrC5P2C3o6P1cjihrYZ8gtIQJqvspNNAw6nGVAvSG4ZG2SWIDSzTplb1qMzqlOoR81WHYk5uauiPQlJhW59Y6gUhtRu2OYp6dH2f2JYGV8pB0mPNOWK4MsQv+Ni9IYVWF5HGcWXwJ854v9brRkTKfQr1Xnazf0oomSFFlEyo11rUTGlHfBlRWJ8xOcQR83qH1coujfkWTsnHMFJVYU4XPNkkqMUynxKJgKkUlbrAOwPVZ1eDZEkOcq1MYrqTlj/uLAR7GkkkkNZMIG9KghNuhO6mGIVE6X0VaiU828VNXTzbQncTEl9gtFlFIe8Xy658nJB0TTUgToc22tjBtAyMSgT5kNTOYNyhzCqmCeIEJFZCqpBM5M/AMkxMTQbLRgbZHUG8Y6rKKd8QgUGDspXHilzsooZbS9GLkboephXn9PWnoZLK5J8qGWkooqDIjzxJPK0k/tHFx2cHnxSr5y+w8i/+e7qGwARNOrs7jEeZyt21559je2ODl4IxwdinuXOg5LVzuRyDo13lDeGdmmPp1CkM02S4fpfX/+7v+Obv/q6SFpBNTS4JGXYGXDvXoNkdc+VMne4gYOewx0xFZyv0CUaCBDHVME7zYvVH9mSBn7Lf7RGmDpWiQ04WV03Dc03aqcXNpENOK1NJTLqDMUE6puZpbD2yoAZppJivosMcxyYBJr1Y+tCQmin2kqbWnMp6nyXH4OJqXbmHjcOAvTDgra0jKo0F9oNEzWym0uUyqxFVZ8/Q6Ykd3yeEdKSXnJA8o1+aIE6GQmyaKSVvQHOYJ0p/M29lPU1o0KdS6mLmBXacMaPHSahamTJenrZi1AJrJkSHBsFRSm5VWjIZF0FZg8aJIsUZuRN6XRl7TrWT9FjOUcxgLUVLNeIhOAvgzCToJ54Tj0KCwZB8w1UbAuEYJKLT7ekkfQc5DCUrEwt3J8HKoeTC1f0UmuDKMSeqJaT4FJK8JjMtNTdLJ7O1zO7v+Ofyp3SAapH1B0MaZ2bZDkfMNkpcvlhSw5M4NfnJj95g7utn1XP7t46wL5Xoxw9QT4+Ges84OU5KKmlMKpUnid/MK+tp/L2i7A7pjnOf2s92yhWuffNbNKOEozDh6//kn3Lno+u89J3fZvHiJQor55VFo2XFfP2br9LcOeR3fu93WDq3wo++9yNefuEaK9eukngVGsun6O3t8Lt/9Ecc7u/x4kuv4mxucPf1H5E2FpgpvUC1lMM0dDzHVIJt4zTgnt4mycUZ6FJuyoGpBnNmbDFr5qkXNYahDMLlBkjojlKcMCAnO04t5V50yKmhRsG1SWyTOAw/JtiqFhFBPIn0RzJmcBLyPmXaLog9Juzf69JuDbm8WmV5tqA8DcoVj+WGSzOMeX9tg/p8jdYg4j4es0aCsX2XzuI5hrHMHGAhDegatvL6kHRyORfzvGPQ8l1uaSIY8dnsUG0rpKj5tNR3+Zs1xNbSlIbeo15vkSJJOkUzTUV+k1264iSckJNRPXuRwB6ZuKcFGaSpxx17R4uQnpj3mLHyXpCNhfTpJ1hWteMuVES311IqvwM/YHALdEvDFjVY2dWnmvJIdxoZaW0UBcf+1FpiKhdDO58Qa0LECxUCyVfqsJMSTxLDiUVf0E3S1pz6U0x5Fg/FZAiu6xpbdzfY+HCNxcUlZstzHLz+IZXVM4xbJj/8yeucP3ued954i9PtNi+8+iLr24c0LhQ/OTko/aYQc9Imm849PimhPC6eJomvbDxqav8gOuPcI4JuH497mod5+hp+lGnDbM2fx1o4j2b67Hp9ktTFMgJiT+eZC1/D6UN765A0CHn5+WvqYuwNQuZWV9k/OKSUyzFfKdAolcjlirz/3tv8j3/+x/zlX32PW+tNLq7KjZ7i2qJOKTd3jnNWlevJ4YODykdqyDhjWMzJTUtKq+fjmWHWehApZU1E/GTHlXA4GDNvDVUP1tHypIYPYetja2kqAz957U+4bxRqZSbkuVKFrXsxOwcddg87LFRScoUa+62R2NTw4V//Ba+99m3sOKbW6ZPLe9y/t8ZFU1OIkkG3y1G7xer5i4Tr65xZOUVlaLCWxBirhWMJh89UTTgJNadLf+QQ/ibdzkKM0wbUa23FX5EBr6oYlLzEpHI4Aduc/js41DFLKXY1+7IUkEFKvUh4AaZKBtEwzWQ2nAitEB4T2JQ0hqYr0pr4NegOeKvg74NVmbS0FKpIw3SsTJQvfUCGE8SRnheIdagkMKY7cln0p/190QeTz3bMvZgwvqWNOZ1FnFygpaIoCsx6R0hwOukQfvT9H/I//d/+77z9g5+wuHxKuSf+/A2D1177JoNBF8exKZaKvP+zt9k+3Kf+u2c+5i9+/PqSEJTk+YNKRhH+nhD+KvEbdFU9jc8S4gcwX2qy06njx/ZnFnQTXwgx9ZmGtFJ8UhbyXWzB7aeyS4oYpw7l2OSnf/dj3n37Ha5cucKtW7eVguZ3vvMd1u+vKcvPvVRj07UZ9HuqDP+t177Df/mv/5Vytc6lq8tsbLep7XVYmS/TqHgcdsasLMxwGI7ZT/qTm0t2hJn0slTvQj7NOxqGwF3lZ3FEP0ko60U1pBTpgnWnw1B6/prLjJHDSTJjppMh3AP1s085LQXNYt5zaJxNubk9UjfZm3daXLlS5VzNVAPLa89c5edv/4JzZ1bQfZ97a3dY39zk6qVzHB0dsbGxwdmzZ8h7LloU0trfY2TqhHHE0pnzCiX2WUMhbQpjGsGQnaj4QL3wSx0pRcbMVlqKlzAhGTxYPMVI6BG5i2w4DFFLJ78ibR1NJXeFStInLxHKgDtUQ12tGKifS1JQxDFlTqXhpzGDNHNqU60gJ1HPE2qEqLZu3dmgcKr+MR5ELAJ9rYiBXFdGjF3RMPMTJv/kcWpBlkpnYCnZcAHmxYGhGM7Tz3V8BkRAUMZJPQPGHvVIZ7GR526+xL/61/+aIBjiea7yVZEUmi8UODpsKjkPz8uxNDfP7VbvoRnHoyGpQYQ71Lk8qTUliVI2Lk9YeT5NEl/REAy9mMKIxs/DMTWaFPex5DNLOxwNyjSHJfX807VdRtLL11NefO45bly/ycgPlPS3zC7cnK3QQY7tcG9tTRmwzMzNEBLw0nPP8PzVSwqyc6sX8uolh/du7Cvr0kLOZqPXUxf5slHlIJHK5QFEcFcbZtIaoY2djzFwKaUWeRf8gYaWTzCHLn6tpwTQitLKSEfEaUj4yM2kduOClxS0iPHJO3k7NRhpCbvOiPppk9aGGMrk+bu//hs25kqcP3eOldOrzMwtqgH50eEh9cuXePXVlxWRqVEqYq6usrq4iJFEzFZKnFpeYmNji0qpzGKaY4uuuqk/U8gQ3dB5puBw0Fbr0pc8xCAqYS7fxnAmWP4p1HPap5+EuMDJd64Y9oLGkaIg1dFiAS9IaypBK08qBXnCFOCgi4OUeDgYeBP0mnKbE4MpVa1kt8FU5VU0mtKhVJMJTqOgvJ+l+jzmIQw0oq4GPZ3cWVGHhdEm5FczHxKFTpKrVfX4UxIrYXRUwjYCDJlPSCKT5CHtNF1T4oLiKKftWVxYFEOwEes792nuJaycvUB1fpkwGHJqbo4Pb9zk9//gjykUCty6eZPV06usrJylXi+zODOrbFcPJwoEJ0PuHUkfIjVzLDsyOacy3K8bBe5rJ1y0PiWeJomvaKSpsDwf77+cs3xVCXh6SBDYjDTRu38SuQXthORHqiwrvdhgUNB4/623uXXrFq7n0e201a6t3etkxu6OyfPPXaM/HFPI59je2VUtGk+1lhKcoc9Ob8SV1RrbRwNm4gTPlJuryzDtZ0PkCStW4cpT2IlGoMuwLmGDPvXUYVUryjSXw90AYz4i0GQGkdCOBni6owhM4iPwsXMVaWog/uAHU2jkgwX7QBtxlI4nbQON+orHmbLN/v1I+QVfv7PNysppOt19yuU6l648y7DfVuSG23fv8uILz+PnCixZgp2HMysN+r7F8tIyphGom/1XDWlmHBpNTGYFHMuXnSBX13vkC5Fa0x+CgJ6IapKj9+Gu2ikvXl5imPikI5N8RcetJrSNjGT2UFgptpbZj8o7yXcvMx5p4500nzqJ9pEwPBhugLWUoIc5ju7tUFkxiDs2qalh+iaWp1G9ZBI7ItyXop2C4Rp4S2CV9WO+hDIoskLysz2ltSSLtTP2GG2ljJoRdkFaTgZ+M+ZMo8C9zQ69/oBQQBhRRH3B58b1D9jdXOfU0hIzs3OQ+Pzd377OxUuX+dnPf4rnFlhafYYoCjEXxfnu4xI2ShV2KtWBrjYfYn3qv3/IqQunufuLWyxfWXqir+1pkvhHGAXbpyICX75OjjG+rbMf5OiLbafacTxJGSpG9EXO5gboesRg5PPqKy8rp7nQ93Fdl631LWzH4rDdUigmWehPnVpSCJZhGHDYFqvRbPB4ZrGsrChNU1fm9tdWc+q27gghKdWU9o2gpKZKnkrawJVLPzveQ82nOY5Zsgucqrjc8AckjuzwNEZJoBaZ0JDXfMxO/WQbQC1kmnK+E+TTyZguNPLffYb4oz5LF0+p4244dQ631zg4OlC6QsPuIbbrsb6+zocffKBYvt9+7ZvsdYd0h0KqSjm1WGVH67Ab9ekRPLSQfdaQHrsgajIM6JcxZAGPmTP6zNQGiu0WRNlOfTovmEYBC2NziJ3k8YdDRtebvPf+e5w9u8rc8hxH99oY5/MqOUrrUdpJUgMqdVRlNToR2lPotQfJ/3i+MEGgqaoj1nDz0h5KGO2ZDDttyjNz6GMbY9Yg1G2cKMapRUoDSRHfJBm4Cc6yJBeDnAyoS5na6vF7ibfLQIOOTX8UK7mYlUtF8gVLDeh30hHdcUC9bnPlxQpdquhyqFqZ3OZ9ytUyG9vb/PZ3f5sP336fo8MD0ovnqFVr9Hp99jWD/sjHva3hnNfQiylVIzsniiA4CUm+q2YVP/YZjWPu39ukaOXYXt8i8Z9MBfZpkviNiekC8vfpOWcm97FvUrADzHyEUUM5ZVXCHgd9k/WoTPpL+9rZ0ryQjDEGIWtNn4vPP8vNd989RoKIDr4pi7pucObCOU4vLOJZLq+//ZYiILWqHVZnGuRjV900OTe7FOfqee7dvcNHH21ydnWFXKKxVC8zPOxSnatztLlPbb5Ba79JoZwj8jSigY/uuoRBysbmGsb8ArP1IunYZTAYKrbsuKwR3G7jnql87NPI4FQWC1GJzVpaMBr66u4QuOtDjz0+Nyl+ZOBc9jiINEq9mGarpfrG0l46tTBHuZiXqSaprjM7v8jGXh/djCkVLVqtEe8c7uHPZ9XJw6/9Gb/VFPyxTfBrSBC2LjvwVElsh7I7foLniJKvZ2gK6vtx9eEMQFH2hjS0Hl4hULIXorV0LGMxCTU4HscUj0xGPdBlvpCESiJ+bmFWbTgO9g7oxSFmVCdnhQpmLJWD6C0JtHQcRZkHxCfcJ8LxmRLLhGsgp97Ja4R6po5q9EzcWibMl2Fnx5kYn6ChQjEjygbWYh5kFwyMVRiuSzUK9kQgV2YN0o7SbfBqIbkFg0CPGeoD3KDE4baPbRlEWoxxVmcjPVKbH0d3aTV9Wrs7HO0eqPvn5od3KDTm2Nn/PrfXtylVKkSaTqdUYM8r4HSHzN+OcasxdXF5LDjsGC32aAl4WJ2HcBgxn7pcv7+lztHNDz+i3+nSL2ZaVr8sniaJL33IopXimQGj0PmYh/XjHi+aMWoOO7nRT+5PF3IDZjUftyg3SvYz3dAw7JRSMsYZ5hjHj7apJotYkuIEY6w4wIvG6FpEXHdoLNqU0gal2jcI5GduZvauCfVTbsycrhzyGlGDi69eJXRSmkmfTjjiMotUtNzxLk9uvru3r3P2/AU00+XDN97lpa+/wIfv3aacL/KLH71FoVDkwguXuH97jfJCjYP1PVaWz3Dv+nWWz5/iZz97m2999zVuvX+H3Z19rnz3RYx2yg//4vu89s9+D33ZeHjXLlIjPoxaPQzHYNgc4JRyavWN/ABLILSh6ONoWAVhhuv0DzqMOiZOziY5srCWAs7/wTVqH25TnJuhaJdZv3mLSq3K0tIipXqBaCFhL+mzL97JXkz3ZopXTDG8CfLl7xGhcEn+nkNrWXCvxV0M2yHqtkhnlxT/Y7JcPkDIiCKrEkaEXqJTly3w9j1ai+cYxRnZUEE9Jx7s1VyfsjsiGhn0xP1h0l6aynNPqwmRwmi/fp9x4pGrLuB4FguNRWID8n6JlZUVdvb2WJpvMLahmQYMxJ/hhJLr1Lf55DD5ZKg2opapsFqWhm/6RIZJGMU07+/RODWrYKtSlRy/TmpgI14QgXp+znVxDZNBHKAJ8e4cDO9pBIfgzCf4TXDmULBaaQZKy8vCwDmyae+PWZ11aY1EcNDEMmwWtApVeaFY511/h1K5Qrc/pD47y4c3b/O1P/4n5Isldd7azSOVYJU3nq7jl/JsC3opgrW9AGdrSEn8MURqJheQMuTG2gabhsP5i+eY//NT6Ach49BnOH46k/gKRKoQRAvFIyWKt9meVSJ5n8ZvkB3gM3GPYiHHuNWkX1+kFUyx2zBjmSxaBulgTFQyTpTg0BUf6ccMss04oTwcYPtD3NI4Y6WKJp4j9gwR++mQjpicVEU180TbxJnIHXStTCqBFsasxX4yULs5IfXtx32qbp5WMiRKQyp4Ck++ublBvT6rbl4BH035DJ1Wh0G3zwu//woHO/sPSEpmih/4VIWAtL6FP/C5d/sexWoZy7PZfOcW84sLrL17izPLL9IVrKHAHfWUYDgiPfJkgolTdbBzovqZwRXTWJRXM90kCUkkTjFHrloiZ+WIj1KshYggF7OnBcxcXWSoBfTTJsVrS7S3E66+tsju3IDgRBtAGL7uAvibOt7plFQAaPoDKYXPGknyy8mRvyyEQzLSTZqv/5DZuVmc0QBtc5P5+Tk6gyGVSgXXMLh15w7u3CzlWo3bd++RFHLcuXWbU+0m89WakvCuVyt0eh3VWtRrJQa6QavVZuV3L9NDyI3xsWhjVk1kciu9QUCoi/Jqg/JShd0Ptqk/s8xq/QK9o2ZmL9rq0NsbEl3Mq82IzL+mbcJj2QpBSD2SeKdGQcoPwjcJ3IwDIXpKY5FsWZnBdEWEcmLkFcUK+aSHOlE+UsNphbJS/f6ptpKG5egUzmgE+zrBQYRT1DCLE7nuFAq+idvxONyN8WyTte2AmYrL2YU6A03AFFIJReR1h5Wix2FjhhnLQY9C6rkcnfV7/Naf/yu0IOBgYw23aBIe7/A0QtnQTGKcpnTksJIiRhCSDnrkuru0Ep9Gd0xrZ4tOq0ulWlaVx5PE00riSxsZ+mgm3zm+9QXSutVuKIG3Twp57OH6Ght7W7z8rdcY33yHwqBPFPisrJxibX0dt15Tw+PGbEPtKIpenpnFBexQU8J8Dx9GSn7Qp0ALZzXBzMO4PcTQbdIk6wWnuoYfBXT323iNQiZ4Zhr4vTG2WVBYdVnLd5DhrGjaTxrG0tZJQt6LNmghA+qUaprHLeW4+OxL6FaeemOOt998l3joM1Mosry0yNUXrnL3jeucPrvK++9+qHb79vOXaSzMcOMH76kEYVZcXvnmK2yvb5OuDWjvtzhz5Rz3btzB7YnarNhQpoSjiPF4RGHZVuV+6hsY+RO95YnxkNyTsmjkZ0TfP/MCkJa2NhtjFh5oAO1rveMvYuwGJIuwec9U7QBpPxx/T5oQuMTfIlXaPEq3x07Jn5L212df7BWM9zPKuD8a8uwdI68Wt+rpc6y99wsO9vaolgoc7Oywu36fy5cvKVKiQDOlSopEquXOLovLy0q9t7mzTfvoCDP0OX/tojLB2VrbRDN1pdh6dnCNVOZMdjZYzg43O5/y19LpC0pob2Wpxt4H64oz8LXga+TOnaPf7/P2m79Q7P+Z5RkK570MGKGZqp0Tye5fUG+GcCF0kvYYoyj8kek1ly3qCqhgplgKBKEr6XC/OyRXFea6IP4SYrE69XX0xFYkPLkOLM2kMXBxLJthd8CMoPjGY3o7LUpLddrVlKIAOZpdSvU6+jBFAESC4C44Jo3TLk7BZjeMcHWLnWSfg1hsdsWbPcfRQJzlXPQLLzCOBXadEKGxp2u0whQ9D279lDpP3U9g8U95ECJnG8vn8xw6xW+qlmBXJGj2D7Fth2F/wMJM/Ymui6dJ4kscsuyIn7BYk4q/gESgpAU+PUQ/3jItRt0u/U6bvc1NauUipWJOmaPv7x8yMzuLYRkMjppqMddti1KuTM32OEpEA+fB+yR7uwTLHYzAZdjyVStGEkF/r4NTzhEOfdyCh1lw1C6us9WkuFDB7/iI4KZZTnFLnhqKP0TqAVqpNHDJ7CNV+2LMpT94gV7is3c3oFZb5sq5ZYLEImdq/Ms//UNGdoy2kCN2NC6UrqIHDnumT/mFZWod0df32Iq6lFfKLCwVMXs21779dbpmyPPnZwnNGC2ISPWY7t4ujQsLCg6pktunoINEMj1j8Rpq8GqUEunpfSI1Qe1cRQ+olhDsaziLk8XqxO+dRorTkIoFhvc1wh7YHx+bfGExTFIuP/MsLcvDW1hiKV9k5tw5CitnGQ9H6MUiRT9Wi6BUHdW5ec6dWcUrlVWvvDboUKkWqddqVOpVJa3h5StqNlSt19lb3yRuDmlpIdUzZ8FJhUOmjIRwZBOikw7ytJ2A2vIML736MsV6hXTBIz46YmFxQfXqhe4i+kbiby7QVtXeTAW5J/Oerrq2x6MhrgPtjSN1ParFfLFOd71DabHG4HCIXbAZiDbXoiS8THFXNWhF3kNMcIuRattKFSEEup1bm2o+JhyiV//gG7z5X3/M6oVVPvqbX/Dqb7/GD/76e5w+v0LrvR2VlDzLURume60ekR9RXKkxnLcUMtCIMr2lzshlJAZBCnn34PrI8FsPm02NHpUM+CWh5jyup14hrOQYffC2QiAW62US78lmWE+TxJc2RO5Xvp7sonhStzl59MzKKqXnnic52KAy0yBXLjNXy+NW88zONVhszDAMxniOXMA69XqDza1N8pZL4/7bNMcp6bNfV2V83tColmp4M5YSPnMq+eP3Ki/XFdPVkhJdhoWT8r52Zlb915rJKbnkJI3o73cxHJNctaBEyrJBo/gKxwraOL015OdSccjOLSxq7G9HzFTnyLsxnb6PbcoxicxzqJRBNU+nHY9JR5JrBDsvC05fiQAO+rFq4eD1VCtDZJQHCnMprZWIwW6f6mp2rIrdKxh8GWI/EscaPOJwFxoZXFYeJ/83NMGMPjVRmEUNfzfF3zAwZ2N0Ue1UDPETqB4Rs2uAv5clFlMUEB+6GjK8aHqSjNXTiAdSgeh0g9yvRa5TfJlveA2iUYpRXcGoaWwJs9LMQUkW/RRz9Vm25fvVYy5V6xx9+A5pp0vh2cs4gya6leOtt99TEvNLp1boj8Z0Ox3V7ltvN7GSIVXdo1ZxaNu9DGVkpMTjFP9IDJegPvDY3llj9dvPsP/hBvFGJp9++txp9dl7/kABApSm0oQV75lSHUI0DjCcHIX5LNvWz82rtbdxflFtBiqnZ5T+EqaDmbMpCrop0tB9Rx2Las2KpaiCRadqVjIKAgxdVGJj3nr9Lf7wX/4Ro9QnikNmry5z76O7JMMhXt5l7tlVCi2Hjz74gHduvKuujZVLZ46/PQHkus6QzfbMI8oHnx8RUl7ZS2Mqlboa3gcjH121KH95PE0SX5IQvL9CHinXsWmklF2Bjmp0/QeL86eF7HBu2BW0YcLZUzZ9v8I4zFGu9Okd9bFNg/c/vM7q2dPUqlWlTvn+u++zsLyAblpYpJRErtk1uOgZVNOUvVGOTlfK8FD15pUEQTox/zFS9EQGzxNVzqLIFUuPOOMf6DlRt7RVr1eM5HubTaqLdaWtJBFHiVrH1adVhvMx3e0WnlQr3ZDUrym575IzVhf6wId2N+G8U8EWRI2fcqN8cOzudXw+5f8dcx9Efz9FDL3E5F4ZvPRGalYhO06JrC8uPaXjrsRxTB3J0oGhSFHSqlCfV2YawtbtWIqDIdIeqlX8EKQ2Y/oWTuk47Rz+Qaz0frTZIBOim3opS2+7mBKPUoW/L5xP0WWSLLwW3aFsiJRKZnTf7Y8JhYylg15OGLQd+rJl/rUsMpriAUjIf8Ip6/gE+1haMlWvR9EdkY80Zk4vsrO1QymMeP/6bZqHTVqtpiJG1mo11XISqY3dnT0al54lKW+ztFRhy2wpa0/lPS4w3l1UgrTnI5p2gPfsAtu08a7WGeom9nKdsai9JgZ24tFJBmrBm8psjwQGLOot9RJHd3fVfMqwBKJ24rsUnSchaKS6uibVuZcpRGihl0Sa5WFZ7SRKiMdyVmzM1JYpIX/y53/CvdvrnDtzVikbr79xG0dY9CWXYBRw7yc3cE0bu2hx9ZvPqfcYtoeqwo8TS3UH2iOZp/z950hPGnKGb0UGhatfI9hvUSwHbOpPxs15miS+JDFNBmJwPw1Lj9Tguj168LMniVCkKdyBKk0F698JE94/KnDuYJ/24ZFibsZJSDlfoNfpKxmN7Z0d/sm//lNWCyssxVUcPSGXiNLpgHEI9UWX1LY5iGVGMpFXnsbknyK+97GYMJlV2WubGK5F+6B9fHM7+bza1UmCkFlHMPSprDRYMRuUTJet9IhqwSCV10lTCi64ls5+V2NnBLadUPNcOrqfKYY+5nyoRV8UVgMT3ZREHBF0R8dVxLHuvnhgjw20AFJJBhMWsIJAiky5CAHqMkeYfE7ZdU4Yv8IMk4pIJSbJTeIOJhvkyMAa5sjnTcyyQWNBwx8mtPZ8+u0B9tKD3rIsTO6cIG+gf0cYvakainqhS28/IRgl+EODSCSlG6maD6WJwTAqKR+ELxKCXbDHVGyf0eaYpFAkdmySUYTj5YmifarVijoNXrHER7duK+l4YeHXtT7NpYA7+kGWhY6d3jTiICO26TnoG0NVpWm5lH46Yqj7x4x7VXmIcJ8zMe15BNkkSUGq3KmZz0O+CgotmCVYbQK/FXRftqHPzHzkcaawvQUW3bel4Mg+vgnzz62oFuOzF2do4XPpO6/id9vUnltmvT/gzMWX0HMj3OWC2ql3Ao+xyNjXNCrugNiMKQRDWuqe/mIlVJpRypF8jkqFZneAtv4YQuJj4mmS+JKEuJJlYMMHw0dBGh0Oyp+ZJ9HId5S39Hp79rictTRB/wjyI6JQLGC7DoVGic29HfXK8wvzdL2EPSPA3u9RC8S1a8xG0mdm0WPkoMprtWN+9A0VCkgWSnmvbPeuHiM2obLbnj5MpLzrhUzqIIyI/Zi4q2FWE5q39yidquNWcmrnPacX6Poh/qAJ6WK20hseYz8kTDRqBcHDW0SJhtk2WPBCfC/gBu3HSltMEUrTXXt5KdPneegx8k8R+hsZ6k8qC76sX5I4JGnY8u8MM68eK9LlU/8aU3risfIWUC0rIcEaKW6Ypz7nKFtMeZ4/TPEKkhw91m6FpCLxfOIuVOdoKSU40BhtplAyODqKqc5buLMGW0cenbiA1k8piqmCkdJP3M+5VZFQ9gbqGhWvckeLsIYp1rjOoH2X3dpF8nM1mpt3uHDpErV6nTsffUQ+n88G37U6leXTdHc2cMVjQSrmE9+Rgr9K20xysKRlOR+SoIW8JhLfJwb+U7isEmVUPhDCbckImYr1rOCfGdpp3BlQUECDB8Y7MjNTpkBOlgyOE4lUDqGuNhEKZSFJSOYSJZEEeVBZ7tHLnqNPjiOwMBZ0GqZBVTO4t90htwA9rc84sWkGNkWnhyk6Z4aQKC3GoZtttP4BIhtsa/jlAv5jWquPi6dJ4ksEdc0M7k9GljRkaF3zuhwOy0+ktSTaSrKUP+h3ppStBE+PGRg286dWsWyDVlPMXXKsnjvN7NyCKvd7RzpmZYRW86mYFnkLBtoQOzUzg/hPCrmRZBGVasLLloDMEvKRNtC0D2+ZmLrN/kfb2L5Fbr503PoxlHAbdPoRsTXD3V2f8WhAkAZESnJbU2xTmU/IyjLyY+pFg/may6lCgTUmCKMTeHklmSCVwsBkNOigyWyj/jjLxpTUCbOqoCXaUymaFx1XBlMi4aORiQ+mSpJBhSiGRgY5MayZFh56gmGJvLWIycVEWoQRZGS+h5RBZRZUtujta5iJxcyqg2Hpyjip5aUcTVqPzVhEr2MygPPnZ6k6V2xSdvuZpIbTJRw47MVl9lOP2tnn2fITTN1h6eyzCn7pFGaZPX1ZdXn2NAPBwN0OUs6vLtPW2g+18kQyoq67BH3YdGWWlC3UU02tNJye90xtNfs+s9aeJO90aKmWZqJHJ2RbZKZjM+6N1bBYZmbH14C0SScqqLGycMvMfzQnk6IXtze1MZBjEJl6/eOM/GMlV1ECGBvYpomtW2zcH2PNm2CI7I2upG6KqkOg0x4VGQQugfBZHqPM/A+VLJ4kniaJL0HILCJvj9SF9HBkC1zd9XmmaPJ2oNEU0/ZPDYEWfjzZdEONK1cX0c+t0vI96jmxW09IFuD8uRc4vBfR1fvkzidiDszIixgLQ2ciFiZ+uU9igiMDY3XpKXzo4++FKaY96dnUzy8RW6OsP6xrRB2wDj1uRF229zoYcYeLp4rUGnmFahF5i3FkU/E1lRRsy6TT7XFvd8TNzYizSw6eM2I4XaxPzhVEcTTUcOMi+qd08NSuUuYrJZEgn5KyePLw9az6EHexkjglJARJSCseUNRzOJFNmz56NSbY1bEXsgQmO2g5zbpUZE0HK06pLTqqlRQQMsh3aCQ6w7bJKHSVmq+wvj+fyJjS9XwXzxzT7TsUFf9AYy8oM0S29yl9GWqrgTfcEybdxyK7hqw0YhS1lTWo2lDI14HO81qdSmrx834bsyy59YE0t8L3+YaSw5DvQXFV1BehjJ3RczGp/E64OBMhu+lz5Y+TdxVyT+YS8p2qBCKJRSpFgbcqm9MUxjrJWNpXCbp85+otJj1U7QQc+lGVXoHfllKKqcfOTZ+mbmF7KaYIPA5LHPRlcH6yZvhNUOj9eDxNEl+CcE1fVQtZJfHgQpJdnDBW4yjHOaPAO4ywNRnOiQTwBEqq4BwpqZntXI6frybHGataXmOcpGwkHeTebvsV4uEYT/fRtJi+NmaYaHg52VHJpFX6IrryKlAt4CcsjVULQJX/smpkssmftLjKTELMYQxXw7BkIYSwCeV2hedOz3L93iHfeuEUP399jZwNv3jnXb71za+rqqHgxMxXbJVcxROsVsrc6baaAYetAIIc5sIQOycJwcRMdLqmzyjxGXf7jPbHVM7XHpIGz2k2huz+pIukW6rbMDbHShPpSUN97slsQisFpFrEkaC7ZAWdaD61kh5O5BLoofJFEHe0wS2RnZYhfoqruei+xWicMLMsnIUUr6wxSDRGY1MlyI9XnL/uSJXUvGuG9HyX3X6RU6UmFIYMxy5jX1BEn23Bk68q0nV6rYpy07OKgUqeollVwVJsdiM0skV/2nEV06bCpN0ngAPxiBCUw8TLWaGFRF1gZKBbDponSSyrPIRrYjku/m5EJNWCMt3JCIt6zc9IdxOjIqXOiqMG1+p6F/VXIXsa08rl+LSo6/ZkRTzSx/T2IwYdi96pElG/QM/3lQqzyPt9FeJpkvgShMwPJGIFSZtKeUPJHWJoKXVPliqLeT3CO9jDregMhmB6wthKSPox/oLLAQK4T9GShNJ+W14Q44yF40XsdutsdWbUezSsntodCkpHmL+y2ZMKQHx81SZKesEDM+sJn+jH/rKQZJTI89QwUkOvfrzsOZZLQMeRwW/XYDTWcHyHJa/IzHyOu5tt5qsWOTvmRz/+EffX1/juH36Xv/rrv+PVl1/krbevc3h0hOd6uJ7DqaVF2p2eGsCLNo1lOzSa87QGfQa9Ppef/QYDewQzqYJHVlcW0MxshysUrjIuZiclaImmjoUZDJW3sljT1OZKBHFEJx/+0moqSxCGQnjJ+iC99+gR1JWsraMwRLMm58FOcWY1nFmIBhCu6wwsj5XlPPVZ0TkKFeHww6ZHc1yabAQ+z0iVSnDeHqvdcD8Ez0xV5XI0LCvG/+Mbbp8eoiu13WvghRFl78HA9HbapaeFuEWD+L4By8LCzkANCtwgSUETn2tNVXaqJSQgBkkmE6a9XglJx8KvEICDiN1NhBkDg8KZAs37B+QqebxKLpufTYfZgoYe6RCZx3OhaZV7TGhMZb6ePGh1TQqZqYyKv6spkb/9aplYfFs0ga5/WUUWf7V4miS+BNH3PTY7M4SxyWyhpaoKU48p2SmebihXsx/u9tgfGuRDkQpIaMwaRP0UqwHGksn+nSELbo/AjzGDANceZ+lmQ2dUsUkdGAQethGSkwQh/dYTRYf03NXVMNXml7nCwCQxQsWPOIkQkXicfIRgyw0xcUk0UhngPtLLlWpBkDtxUyc3sMmnNrMli3KjRBhpbB8O2NrrsVQ3qeZS9jpjDg8OFRpFJCGuX7/OM5cvcOPGTcLA5w//8A/467/5PqZuKAa5DC977T7PP3+On77+MwqFPJ12h2vPXIP9hO6gTfV8Q7l/yY7TiU3OJWX0sUlz1OODdz9QLYnZuqCeUtXeam83WV+/z/P/zbdpafLETwk5d5JoJfF+wrnKdrPZzMIX070Dm+KMTdU2OGqPuWsVCIt5dsVQaU8nZ0obLqTjG7/UTfDXFVKd7g9K+GLrLGRBNJqqFfr3S1CyBZHqeFrBKQADoog6hKrGcDfG7hlobgYHloVYnbvchAuhxgDTFDWZLUx3+nZEaqdoIuokqCSpanNCsouprFRJwoTefof8TOHB9zGyFAJJU8PpTCJk6timtKUmN4M+qQhUy+r44LMEId+hfkEnFp7K5Mi+qO/pi4qnSeJLEDJD6Pl5is5AIZOm17CjO5nwXN9hTXYprsbCoi2OQgp6Wao5al4QElO4GJP0jzhtztD1fHqmoDcgOIxI1nzcSoFxKYdjhqo6ORlqgzRZ2KYXuurV5iP8e8IUzkTosgc/eJi6yU+U3xJqhyXVh+ykpWdjip0kJC0dzdfIaRYXqyXmZm16I9mhatzd6uFYJqsLZUzN5+hgm//8+i1OX3iGc2fP8k//2T/lL/7yL6mUy7zx8zfVTlOQM5VqlX5/wAfXP2JWFGXz2dyiVC6rYaLALs9duMjMfIPDQpP+ZpHxboImTNcDg3zi8fbOLt1ujyuXF1m9ck6pvgZNMUwS6YIhvW4vG2RKP/6Ru0WtT5L4pG8un1W6HW62w62lOaL2WAkCduwMvnkc0jtvOoT7CfPnPDo7MVZocdrLcVsIWtJCSaEXWPRUMfZ4X5DPI+QwuzJgjVNcxSb/dVQu4gzoM19oYToRuhjxTJBFcv2YisuQ+XQI0i0+Ej/pBOtYWigjLipWtZeqYbW0SVPlasfDCKVkAphQdrTZ85Rek6ujDyefR0GWhTipZfamE1KzIlQ+bkXUJrIrU+MrNMbbmiI+5q8kDDQLfums8Dc3niaJL1FkPfYHMYx9Bt0Cm35VSR5UvAGFErRvwPh2Srg6UmgZ6XUbYuPpiDlOU2niKHx/ktBr7VC5vEBlvctu4hH4FoktO6Is1EIvSEol3fmg1aX+pqfYs5kIneDyrXKa7eROLBxyY8mNPjUDEvRIXreouA6HO6lyxpq1CixVTIo50cuRfZnD/f0Rm/tDGhWHyytlCp6ku4TeMOEHP32b5599ltu31/nv/of/Cddz+e4fz6lezbB7RLVeI/RDquUC/+1/8y+RLl2hViYRsb72ALde5J/P/0t68Ujp/dzPdZSYmxGNCA5MvIbGabtE3sxzdO8GK3NleoM+Z8+eJtJDvDkLPW/hd8WmNFMtHVsGHZGNnnxu8ZsQJEzgp2hOhFGImdNyNHSboZbS2+mycf0uum4oxdpWs43near9tTA7x+7eITP1GvYoIO8azFklDCGJBSN+Sb3ya42pU5uoPcj+dxQnWMLX+LUliCw8LVDWN7ojvU3ZQBikekQamvgDT7WVCoshw90EbzVmvK6jubFqy2Xchqw9pKoLL1SJAoU+mpA6U02xqaWNaeTkZxMi46QtJEk+HAUklQzhpF5MErsw20/cdzIAn/pbPO7zpyKfsgnRkYbmJAySPPvDJxPK+02Np0niS+Hz8PEkoVozQxNdYbKzn/UCj8NwRG4lINoWwlVC7ozo1j+4oE/CVOXGqJyZxbQ13OKYnD9kfsYgr5tKy14MStRzmhp2fYLR123F7j0Iu8Ragl1C+QAH+9ImMrBnUrTixPRddldqjGKqobPdsVTSsDWHIDVZKbqUS3nqJdEO99nbP6TZ6lCoLrJzFHBxpcRsWdJVrPypO8OAds+nVq1z6/Y9jPwi+VyB/+Xf/M9cu3aN5194gbnzy7z5wzfY2tljbn6Wd95+l7nFeb7xna+zvbHD9sYmz778HJvrWxS+vsRAAcaGatHIL5QUYsvQLUZWTMFKsF2X/YMjnvvtl/ngrXcUg3Z3Z5dXvvkN1u7eZjQaKctVfz9Gv1hQ58zTLBzNRnRB3ar4aot0s8l5rYgrEEs07sVD/LGvpKB/+p9/qHSf4iji2rNX+fn3X2dmYZ7b21t4lqUE1+YaM3TGMjjNet+ftEDLT6umJPmUQaoJjYJRojGOxZ7zya812ZVLtaDoAUqRN0sM+Yl/xq8zQUgcpQW6gxz1oKdanq1hEVsPBYrBSLPJm2OWnTaWmWCaobrOogMD99SJDzU9JGVYnZHiFMAiyJRZ9URanPK7yUxiMgCPxsJHSRXDflp1yPxI7i2Ra0kFCXdcKE/UZE9EOlVKTlKCZtZmkkQmPte6M2KcDNT85qsaT5PE31OltegMGYf2J1qFfpbXG0+HgnFK2rcILdHNqRJPwNoydN7sVDlXH5K7kBAcaQxuQ/4C6ArH//DF3dttk2+U1M/dmkZtvUf7MI9ZrHDG9UEP2AwHqmdiFqFCHms/whFWc9FSvs5mJ2FYSYlPJTjbGtF2QlTwCEoBDc0kNHV21wMaRoELc65iRMtw1bV1DMujI3BDsY+MI376s7eVt4JmNhkc3qfjLXPjg4zMV2802Njc4nde+wZ/9iffpdUP2D3s8/67b3Ph/HnW1tfIn6lhVcrc29qguXdEp9tVHI8oThTidmt9k92dHQzDZP7Uwschi9JHjrt4ixZtxsievfziaRo9l3FiMArG2K6NH/qKld056tAfDhRpz/BkoF1gQctRD13FYu+I0GAm6sCAkBtpm0VylMV9wLFwXIHjepQrJdIoVsKK21u7VGtVtnY2WTm9Qr/ZodPt0O+1+fD6RwSU0WqlT9xzLFsp3R//Fb1Oh+e+9W1GRwdU6jWlEtqeOaXACh2puD5BCE6RzBTXJOvxm+KR8Ygz3K8/pEbU8HWNvais0HixSkau+pwyKD9VbZETx8LDCEs0sioRQTtVrUpBfqlXmc7FJiquitg44eLIbyxBIynl1Ew2RcQs40AImWO8ch63nPmWqG9s5KpKRdqhU+VY+Z2aP0x5EI+cN01Ifz2RfNJwG2JTqtEZ5Sbzmq9uPE0SvyLxbbbQVtwGGTAfDiqM+4Ib/9VuNPFVKDkDKoL6EC2gscgRRPSDIqGi6k5fd7LHmVzEdj1zVBveQvk72LOpMmafRnGhStAbKw6CUYQzF0x6d8bsajIEdSmaOUWKTk71MQ2d6F6H9ZtbWJbJ+e9cZXDY5b/827/iD/8f/5R4nHDzrXeV3EItXcANyvzw+/8nz7z4As+cXWWxKCgg0esfoUzmlO6+xTgIOIpDhM/0zNVnuHN3jaNWS+3Qf/L6z7hw4Rz7u/uK/7C7I7Jx2fEbusbKXJ7Nu3DtuRd54//7P/Oi+1vqZl1cWWLUG7C/t4/vj/jm779Gu9Oh3e7wg+/9gKvPX6M6W8d9ZJ0UOeh8fXJDa0KKjvBzEZrhU3IcxUQXQTqZRUibuT7foDAuML+wyP5Bm+C2zX6kERYytc4eIblVqaayNzpgxKHsjVOD3KzDqe9coe6VcbAYrrdwvRzPPHeFw70DLj97hUhQQ52BmqG88fr7XLt4mhs/eR/OnX/stSTLqrFzX2khXf297/LuX/0nqpUya9c/xA9DLly5ws2PbnL2a9/kfn1FKfA+2lYSXSZxn5MK4vNNDI8JTSN+5D11sTQtNtF1YWKbWKlJGqXq/OcqhuJSyPFKhawqX9Wq0hVj2tEsbGkLGr6Sn1HKJIppPVSyK0bOVu2m0nxV3TmCV1NmQor4mIAbKaFHmWNJKK7FxG/i44euqf+KJalVzzgtk4+EqfwgvlqIppPxNEn8CiHDZcVAnUjUe5b/oI//K4RUI4tlgbpIU9gAGabJDM6XAeYJZIyYmdtjZPlV/5ZEIYmhDIMbcuNIknjwurI7EhlvO++gWwZDZ8S51Qqjj4aEWh5KFme9IteTkbo5l+fr7NzYwHFt8o7HRx++y8rKMs3bu0peeGl5MTNe0YXf0Kc6U2Nr4z5fe+UiXjpGuLV7LdmZ+bSHMPQHdFuH9JqbePkSq8sNXMdiYWGBjc1Nzqyu0mp3FCR0tlzitL6iWi9TUTyBo84unOKv/uov+e6f/zHvfv/nvPxPvqVufvH5FWVRz3MY+mMK5SKu6/CHf/KH1GcaBP4Ye+JXcMy6ljoteoznsSl+3yluMcduexcr72DlHOWToTsezV6H8kKBeAFORznsyMQPUuqRxa31PuZpk0DL+BTyiuJE5qdDxep2Uo/Qh7W799Qx/eTHP8WyLBqNBusbm1RrFba2tpk7s8zfvvceq1dOc9BsMSyViO1J++QkxDgcUSnmcXM5VZ3t7+1x7uJl1Y8XbwMZ/m7e+ADztdMPxPkmxyW2pEVLhBozSOmXIaQaH4ZZFR6bEUnOoNcM0F0QK+xCwWR4t8f8yhx9M3OG0yzx8ggpBg56NyItu3SMsTLg6R92mSmVMb0aAydSoI4pS1uc+7KKIbtvk56JVnhk+P041J42SRBBpqtlNx48ruAMOVUNud+c/1QzsN/keJokfoU4GpRxjAjPGqsL47Avgni6Iqz9KiE3f1YEy4Wc7U4iIYCNTyq/puqCXCgfKptJXTMy3L6wcbd0zHqMO/fo62oUF6v0dztKpptqnmYccW7exa35zLs6R0lAOhTtHJ3d3cyI6M6tO1w6vMjh3iELKwts3d7ktd/5Nq9/8FOqtTLD0YhgLyBX9Djab9JvDkhsl3fudhSiqJCzOT1nk+get8ImDiU6YtIyc03tYKMg4OvfepW9nQNu3rpDrVLF0nS+8dvfUbs6UQxVrWUd7t+7xR/98R8xmkswyg6dO3s0ZmrMLc3TWJpTu8NKpazcxV79s2+z8/49Vp4/z+HmnnQ1VJIQ9dn+dksNtKU/7UwlGqZ4XyNlMA5xL5YItQh7WOfeaIi2OktqhiROytDUCNIx1+2AnG0y5+apjF3sts5YpMqFERxD1Mt2mzLglyJwoz+gYMU4eYfm0RG5Qk5JkhweHrKztUW9XiWOI7xVl3ntspL2mDU7jI8GdJM8vpcjzLmUoyGn7ZDVS8v857ffZNj+j7z47DXW1tZYnq3z9rvvEQSBIoZ5ros/2VXLmMtUsuxQy/Wp5Eest+eUmOSXIQT2vderqYpcoN8z5SZGT2TVTXphXzIH+/d3GO536Qz6NBo1PMvjxs2bLC8skggpU9No9loEfojj2JxaWeHG9etc+aNXaAmUe+IgN1UeVmzyUoQuLOtIjCmyAbiuSEEneBTHM6AC3XjIqBlhePoDpN9k4/blOJOfXzxNEp8x5IJYNCB34FCuV8mnGsuWTre3z2Z5nk6UqKGgrxKG9kTSB6Lp4g/mKB+sKRy2cabI+lh0XrIWlvSPpaKwjVgliGroMd5oY57L029Kq8pDX/YZp8FDMsfHOkBzJbU4Bp2QimdglXRu2l3W0x6+FkNeeMsRxrkS1iY8u/wSQVnna3/6HaKqxkonphdpnHvpGYJwQGNlTr1eVNQ5P04JQ5P31/qszjkU8w5BpNEdBFi2Rqm2hGU5XL02y49/9EM816bb62caasL5SCKah/v0e11efvGFh86O8AbOXHyRIDY4p5ncXoxpLXoiscaFf/Gqaj9I9SK+yWFq0qNH7pVFttIu6apLPJGEEK8LU1UHNk7RzRBdYosqrQWFh4fUjRgNBVufEntj1S7P2nqZZOG0xS/v2U19+lqgWlH9UJA2KXFPU/1zw0XNd6Q3lIQpcdmnUMrR7+rMLs0xvzjP2q175PI5zpw7o5BaOAY9c4x9OibY00iHOoXlmILeITkYM96z0I5ukeZstHNX+Gf/8r9jOOhQLZdYvnCNouswt3hKbTZE+XekG7wzEK/jAM+WOYtUpkKy1OjGIufxZVrWsmMRTTKZufmuQz7fIRiPKM3mGDTbygzIKJhKRfbWjdtcuHBBfS97W9vkC0U1bD5z9gzdVldBlve2donCCL83JLUNlTh18Rk5cT+KKKAmt5cgnKZHcuK+ORmOZpGPchxtDsidyfgbJ53jRX5DjMG+qvE0SXymSCmZGtpHH+DU63R2NonyebWIr9+9x/lrqbI+bPb7NBfP0lQdiE++IeU3C6UmRWeMeWfIBz99W/X8n3GeJ3e4zwXPx6nVad69RWH5NPYoIlxrYi3M81//8m95+Xe+zmL9DL3uHtyJmD0/Q6yLKuhY9XCnF7K0iBI/IWwn5Eqakp8QL+xhmuHVp3C/vu6TP1OmGw1p0QTXwBYPBbFL3OtSOitQ2TwH9KjmHeZ9i2YScP9wwHLdY7Hu8OH6QA2SHQPOLmnMFm382UWZp7IwP8dRs4WXz+MHoWJKr927S61SodPpKBKSH04N6OGgPWJhJs+djTZ62uCi1uBG1GRbmRI9ngk+SEV+9eFQsEl0ulst8jNF1Xo7ThAnTH9UyFBUiIWf8LVNfZllD9BxRiR5neiujVFJyZ0Jj82Epi8g1YUkw8bpeYbDIVY5x8y5JcoC2Y1iSk5BGTF1RU5dFJEWE8W8Hm9pGHrKUj7P+eUaf/39Gxw1O7z8okfO87ixe5/W/g5nn3mGWsHm9sEWt27f5pkXrvL2D3/CpT9+hbY+mIyzss8mwnyH/cx+9csYsmXa65Qof/AOC16OxMlRm62xtLhIbqbAaniWaBSpdl1RVEzFHte1qdfreOUcvfkhg2Csqotyu4GbL9Pvj9FSE9xEyb8Lp0UpCWiR8oqQTcY0HjeLSCdtytEulMoeC7OOEmY8lJJxEqLw+lWOp0niiSObB5REIykMqK2usPb6Dq39PXI5jzs3b7Kyelr1ytdv32Z+bo4mn+YDIYtzQs4eKVmOuYUy1+NYLUCuZrN7/QZba2tcu/YM2weHPJP4vPXWm1i2Tau2reCtmzfuMSg02dreVYSkr2nfoDDfoJjLMSYk8CNiYfb6Cf4YctUUV7FYDRGiYMiDJCHQ14ZZVIupaDkJIslx4Mq4yt3tIfYwxLsvOvwmtp0yGqdspj5hrLFcdVluOOy1Y4Z+ypn5gtp5B5F4P8S4Us5L/zbnsrUbUCxXVRaQ3V6uUKQ6O6MQXNLyCcUDI4zxw4RWb0y56KkW1C9uN6mXc9S8KodRgO/6agFUHg8TWWmRGHlc4hCby2KtSOcwVp4WUyG4jzGh5TWy6eenXwkTXR95X2spxFqYGC5NiVonQ4d2MqQnBkOllDZtWIJW1KIiGwxLmU/gyGtGOcUAPl2Nccsm1chk98Bn+2jM2UsvKQ6eqaX8H//u3/HBh9cVqfDKvXt842uv8h/+w3/kueef5f7WBrsb2zR+sUbu1QXGSQbvFF2oZr9E8A96y8v3I3Bb8fR41JEt04sq6316/YBmHFLSZugdtdlq9yjtl1heXObmzZs4rquuz3qtrgAQP3vz5ximyfwzZ+gX5YvQoVFmpI3QStm1J3IpaddSIn5KOkXcAaX99Eh76dHQBZklcmDDhPJ52UPEDOUHkw1D38/R808MAr+C8TRJPGEI8qgebmKODSqnF1j/+VssLS9z+8YN6jOzfOO111QvXSCFtZlZCraDLQvYFD43YWxOESeOEZLEukJGLea7yo3t6rPPsHbnPm/95KeqtJYLWFjFneGYtdt3FOrmwtWzzDWq7DcPKZaKypA9n8spV7kkiImb0NmO0BZjYi1CFwlkLyIULSdsddHLjk3czrpJKBtnxclwDZs40Bj1Q3zZ/uZFtsKg1YoYjSMc8ZM+k8NJDMZBRM4RobmshM85CnlJd5RQKlrUKqJ6lCLo2v0uCg7r2hrFSo2Lz1RJsJiteuQck9/+9rfxXAvfD3BsM2sFpbaSxW62dRaqJvO1OntHQ+7ttDFqIUFjnNmGyj3uTBAvskMUwTZBrTxyv4t+kvgziw3ldLf4icieR1zyxJJVVVonWhGZuY2QFTPS1qfYYmfgBjciGpg4nolpaXiGjSP8CDG2mb5XAA0nwk0cZoIynmkqqOXKfI6ffbjHUXvIfKPAfsfn97/7R7S7XQbDAX/yJ3+kyHf/5H/8c/763/5nxRHJF3MsP3+OvbSXvXacMh4W6CZicfoPV0UIwMOzAqX/JAlB1xPllyLzEQFvLJUPSUYxxuIF6pd0qsUK3V7E1sYWtUYV13HY3d5R38mr33iF/nhIs5sh5eI4YVXmggLWyIlw5SOVpheRCuJ2Ag9Mh+JXbqCVg8dXEHJjCAs8NglG0pWK6aQ+E2HkjDMRxnTHOTVX+bJWZ7+OeJoknjDELGS4E6JFEaOLS5yqzmO1m8yvruKdOY+9taGM0ovFImcvXeLmRzc4X6qQRBFz8/Pcv7dGvlwmKpSJHZem4WP7CQeDIr0ooLjksvf6EYXGPC++9ALvvvEGq+fPUVxawe0NuHDpMs/GEZt3bjG/eoZDP4ZgzAt/9Lvsf/gecRhSrs5y89Z7wi+idHaOSBdF1mzVy8+UGR71aAlUsG9S9qt0k5Ttva5a6EN9hKEFJG6ojHEExeEHCTMLFs+crfHe3iHv622eN2vUnKyvPx5CSW2iNLqjmGbXZ265wH/pGrh2yjPFSElbx4lLZxhxY6QznxMsusPadp+cZ7MyV1Rm9bZtkijOhsg35xkNhwocoMUDdLuo/J+1/JjNG2Py5RRDwVBPhLQS+qLlP1nlFdnqwb0rswhHJDI2DhVyKdcoPpQoFETUhKhro6dDwrb4F/vojq7aEpqpq2RaqFcyIb+JZ7a8vsA0p22Jk73q45AFy4vRA49c4KmBtu5oiikuC3gYZkPvUBYjzcfREkaJx9BPOOoMiaKYetkRwXYMzeR7f/O3nD13hlq5wr//D3/B1/7kd2h12wqBdurMCjc/vMnIiUgmjm/BMMdBr6Y4LSdjKif5RdnfSDIw9YhWIBVrSjXXUwNrAZyJba/a2cuGqpSSlLOiTnwhBLkVxVJdBsomVGxRBcklMlCOJfIpA9ycp8AK7tXlzPvq8UeQJQiZR0ysY5XjoPxVsODyPNnYKd+RDHoeWdDLdxlvx7JvOj5hsinaGyzQP6HZ9FWNp0niCUNgeqPqZXURX+qFJMN9WsMR1dlZdm7fZHh4yNHBgSp7n3vxBXbW1/H9W2rXWXdM9u7cZHZuDtu2KV56ht3EolRtszgK6ccubw09jGdfY8nR+Oj9N1g5e46RKGcWZ1n89jL9Ox+QhCHnLl1gt91De/YbFNav8+bffp9qziUOAg46+1hSsfgBZ+M623qXfjJSUE5ZBN3U4t5uh/u/uMHytVOYuoW3KOqvypAaXVof0kJJUqJRSrwe4dgRG/s+lbpDU+9yxJgS4giXbUolwQRRSneYMF8xaeR0/ubIYDjQ8BONZwsBczI0zTkUEo+4uUVj1uXCzCw7hwPevXXA2eUSSzNFNNMlDfsqWbT6obKyXKdPJ+xykA6IjFgli+BQw118UJ1NvQRUr1lUQoW3ICuM3Pgn5MplAc/PluluNZVDnsRJTR7xJx50OqTyvS4s4DoF1ctWzxSdIT9ivCtzIi/zszakiNDJC15TVSwJw+SBdMc0Mge1lNAcSQMbO3GIfIhDIWSKnEpKmktwdFHQ1VRFlDRHrG0PsPIxpy8UWMhVee/GNp3+iHNnlnnx6jkGRAwFaTOK6G21+a0//W1uXb/JuecuMRLXpkij5pc5isucymscpgZt+eImy9oF1UtPGYkHOSm3Pz7O+bWGyJxnfCJRwnUw/ZjT1T2lUKy+nxMDdfmudulyaq7CzNwcnCpy5Po898rzCrElKsD1Wo3hYMjZy+cwdBO3VGAUJkrM8lFxyel3rf4t4A6RHZGNhXAuxJK2bz1wHJTrR2TypUoFBY9OBoaq1AWYoEh3hsjKC5T4qzuwnsbTJPHEoZGathpc6zsb2JUyvh6wtb5Bo1Ji8+hIDSZFesEulNSuTi7u/d1dcsWS6qsXSyXyxSIbgucOHIZh5rE89YEQY7bo9od8+ObP1S5wdn6e2dlZCsuL/Me//MvJaxTVzud3/6zIf/yb76nEMztT4979NV545SXeGSc0RTF2LaFWrrKUL7DhdZR0RjAsMRjFVC7Ns9/uEaYtcmmJcG+EXbCV73VxoaJWkLCl4R/pvJm2OLPiEUhGkGGtVBvCDhcJaRvlFDfwYaVhMkws/rqn05/YNt/qmGr46ot3QJIwn4e1uKqkKxZMOL1QUqX+ztFQyUFbIjehhtcJnUHAeHGY+TlMVl25ya1ayuiO+K2Kl31Kf79DOPDJz5RUhYAVTVpDWUsBkW2WZDF5vswkigs1Nfs59kSeQGJFZ0mY5rkVkVgQITld7VbVgiI+yDj44WG2NEQ6piEyJNqD10anYHhK16kZ9R9ifMvvRVPrwO+o1p4tkOQJSV9ABlItzFsVBcO8tTFk3A/Jn4ORE3JTG9BLfAqrGv6OVDQN/CjkptnGP+dx97CHdXGW7XwP+5V5ha4b9QXNZNLZ3MH1Yg7v3eHq5SscdnuUSkWcfJHWnVsKJnxuboaubqNp4mj4ecZEBFJLWK7s41qBUoVdquxnTVAtUVLfapGeIMlark/uYp1RLuagtad8z3P1AvpijaNRSGC4uPMNNWMTqEY8CJXI38dagFItTDZL09ahsLbF00R9aEkKjx6t9qAVmApnSVzybBTJVY5d5EUGUxjcVzieJonPGLLDqZ1aJux06MUp+bPnceQq0jSqjYZaaExBzpiWMoEvjEZqqFybX6QyM0u31ZzsmB4myknIzC1oHrKyekZ5A8s8olytUF49y+lz57BME8PQMXMF1WMPg4Bqrca//9//HVeuXmFgxOwtnUYvhoyGI5pbPr4kLUcnEKtGQoqrOkY9wfQtwlGZVLT0dYO0Z6GlEWFHNPYh2NHJXYqVEueROWR5VFGL5FDsMmW2EmnIOtcbQSWv0Y/hv7QNNlStP5FMBrYHIrsQoWsRG30Pv9Uj54YkRRPdLKh5hQwyozDAHyd0xymnZvIYhnAvJtnmRMjuXa5atYxMeCnV1Vl6Oy210w9GPk7Rw6vklQCc0vbpWpmNqpDRolDxRqpnZrLvc+J2JjvR8WCA5Wa7efURZMgpdqzyOvKwxKC8Wsfvj5TcgzieqZbeCRM+GZILCEBaUFJZKJVTAQqYttrdizeBn4YEyccRMcIGbq2PifUE63ysKoXp+rOdtNTnrp/y0DcN7giCrizegqkSNhyPUxxZ0OTMyDELC9hOiEKdvBEQ+j67tz5SQoZjU2f+7AX2N9a5ffcuf/bn/1x5b/BYO9dff8h1L6rHokgs504ShbSAwm5KsG7izj1IVR1GpHWD4UEXt16lO4xo3d9S7VNBMbkznjqfx+ZQ1sSf2siuHVsz8GPx93gYFv7QTOoxeIOsusyuj9H9TMxPEs/hoMrRsELeGT8yfP/qxtMk8RmjF6W8kTjMuQ5Dt8Kd0Kas27hf+x3aw7FyjHs3suhffpm+m8Osn+ItrcDg0ssMlW1jgbbiP3w89sUH+NI1fv6//S9ce+FFNu/fZ+n0aULZEIuRULnM9sYGK7PzJLNLNGZnGfT7WKahEkYUR7jWkFauiN8ooJkdtKiFc+Ryvl5g5Pq0zJ4aVitHOEGJnPB6OPqgiT6so7Vd6kUHY8ZH/MMcDEqJTSkw6eshLT9VG7Whr6mZxN3A4PWezoFa9x6+3dqByVsHRSVPLgRBzWxwHZ0lX2PZhJxrEYQpCzXpPQfMKsjSWFUVwyMw5x5BnshG0wV/R8OaiTNfbA1FGpTw0rxqP3W3jvBqRUzHUm0F5TEQaQz3h5TmZrLe87TFIYlEoK/RAP8wxrYERjRJUKIPJCx4GVKL17XkjoJLHGYJycvnxASWQRxkInOacBFGKkEoMqOVU0lCPkEonBRT1jFTVRt+EqrnKja0n3J/vY9VBnsmIXxEEXgaR7JoljSCQYJTzs6LWYDxfY1U5CJETiTJs/vOR7RHAYWzV4kP9zi1eopSNc/9W/e4cPYc99c3Wb1wgfmLV1TrsFCrf2GzCYnmsKiGviJxU7B96nYb/zaYCzHOzINFXKoz+XvYHuNVAizXQcvnCYdjnJL3cW6DWOjKdz3O2qhBkvmtHwvbPIEvyjSyKkLD0A2cKxF9CipBSItJPGAmr8hXPZ4mic8c0muHjmti6mOSoU1LSmTRw8imuGzL+iLbbGXmk8sMgMVcSDak0mj/hLtRzcRsh1df+7ZqbeUHQzZ398gvr1JpzBB7eWYvXiG2bbWbXz5zhvryKRZOn6KxuEAvNYlSnShN+XAYcKbaoZqDXGqTVGIlgBZPjuXjb56QW/RwCrLzNtQwMZfYBHqkoLKRIG98W33epGDwpp9wwdO4NYbXe8ZDOkGPni/ZOU7J6Knl0CJlK/aZjw1yjlhXphz0EvKOQ9GVHZzP3IzJ/ds6elVUbrOZQH+/rSoEmQdEBw6joxDT8xgH4MhgW6EfpTLRVIIYHnWxS54yafKqedlzK3MaI5+1No5PuhsTdDy8aoH+XpNw4GLoTsaEl+QgGVEG4scufWK5ajJqDZSPciR8k8n/5Nw/0EpKVZJVsuLKFtVWLHlRypVKw9KNDIM/iujcCXBmI4y6MIM/fRELpO9+op2iW2BVU8abOs68SI/oDNpjSjmLgtvHcE0Knkm/0+L5V6+xc38va1vmimilGh8mViYI+CsqBvx9CHQ5K6Di9dDVZt/AbTyYITmaqc5PmESUztQZ7w/JSQKZeQA5PfZ4OAlCkK2acocTqG3GuJaF/tFjUBuDiUPg9LUerTTipoFZFD8VjU6zfGIG8dVPDtN4miR+xej5niLCSX9VDOl/XbGp53DPvYDqDq08Qxf46RC8a99gEAs8NHvc+jCl+MzX2U3AKy2yLm2MCKIwK99FgqFhuIRbKbGbqj61wF8NTZLIw5aa4+6IJBA0TELUzuYCiZniGJaSo5Ybdcvqc02r4kbyb4t2kvKfWhmeJ9sLP/lNI49+vW1h6z6XRYvK0RXCRyyMj7Qhd5ImY1H2nEuUjHli9dSxOUWB+pokSYCzHJPsDHEaZaKOyKZD8eKD+YDIQpeWsoFob6dN/7BH0B9RPT3zsaMRhJH4esu6IBaX8vqaoEWFWSsVhww2pYXz6DPjDD02rQektRGliZo9SEhFI8P2rMLQlYSInP+TIcVFby3BnQe7+qAP/kmnVH4XdbPZzPFraFPRuZTxhs6GP8AvzzJKQ8ojkUEZMrd4ms21TZo7XX72k58p0UK0HZ791rf5sD9VCPjiI4h1DPGVEDSaK4v2g9+pcynndjIodkou4SDEKtnHng9T8yJlFztJGMQGUranXvb9THkxDycD0YcRG9SpttPDfAkFZNjTCVs6uYsR49Bj+PdWev7NjKdJ4leKyUWW/vp3FKL/NH7MZl+ql5Mh90VLiRPJ71Lmi00qWkJrVCSITZYKIwqjAkf+CKoGvZ5GsfR4wKO4sWmaiet6DN4XM5gxC9fczOsgNBmnIe10zK20ixuWYJxyXjdoCrxSnYLPeg40NTC9HyT0Eh07CakUMn9gXzMYK1XNBLOSMLwPWiVVu3zVWhJSXkMGrCn2+eymNXMpg/vyt6yP/CiSRYbxYjgjn7O1fkhhpqxEDzMVRU0homw7VCgXGX6Pu0O6203KKslkC7ZCTvWEuRurnbt6X89W8NhIJB800XaaDEFlBxpnO1g13/iEPYQsRL39iMSOcFWCeBjd8zhfA/HtSIZgrZz8uVwE0pgx8KQ9VwhxtZxqlaT3dXzxUwhhHOrE0ZhCsUixVmf9zm2lnqsblWxoPJmVfZEhfAlNEp9ImwjP5USYyjloalkq3tU2nev7pKYM3jOZcTlnJ5OASuyxiVbNkFvTquBRuHMSpBgCRpgw6I/P9QnPinBXx15K8HHZ6Va+ZHImX1w8TRK/QsigdSbfVnotX4YLR44nF4XYSUTeDdjya4wimyCI6fqwMbLRYp2VtE0gA9zs3lMh947ccLLbHt1OSf0CxasJQ3OkPBCmJkZpqLEz9qnV21ymTCGOON2JuR1IG+dXCY27PYsoF9EIEnKezCYSuvsj0s0j5s8uMPZgYMaK1FY0i8rgKB/bxD2fsGKoHXsvHWe+3uIw9pgjme44pbKQPyKBMWoP6O+JcmxdVQqGzBombQdF5gsjinOycE5MlaT/I1DaSUUhAn6GKcnFYOyLL0dMbkYEmzI47nRHK4qsBcvD0s3j6kK9xyTxhKOU3kFI7uJjzs5jnNF03yS4pzO76uBZQlgM1Fwj9IXYB3o5nHCXs+PQLI1c3WT3doePPvyI/nDE6TMrXLl2hXRhlbOmzk2noqRQ8tZYqcMKTPXzSxQnv5/sPUa+x2DPRItj8qsPP1paqlESqwpYDf4TmbHUjk24Tp6r6TlXaq5WpKo2dc6mPtmPPF5acghX4pE5xclk4awkHO0VaCfVr6zC65PE0yTxK0TOGlPJ9ZQ88D9MbzKTNpBWl9zYXhJimyG6mOCMDeYLTYaRS6vn0y24NINs66sHJc7EORw9Zj+M0HM9cCQJiPNDhSgSmYENiraohJrqpjwO2bXZCW1txAfiWGdaNGrQ2jc5FGD6r3QeNA59GYpn8BLb0rjxwQ2effU53v35O7Q7baIoUTIM5nZVqb7urt9RntRLK0vcu3uPi3/4EjtBH82e8CaSbGcodqqPNY4RA6ZSTokdyuDZrpofcxcMRRF2CvQROKb0rpVsh7R0ZP4grSQNz8ojdASRY4/7QvDLniLQWpmLlMw8JcPLpB1k4U6FRxEcq/e2N0KsRSHqnTwjQjgTv4/pSDs7Kjdw6d2DQtlg3EkZtiMaiznytq5aWj23P8X3ZLIhmqbahWnDZuWl54lLEYueSZjo5DSP3rBD/co8vr5NRRD/QuALHbY6M5/rAHs2f8Q4ytP13QwRJhpjPYPcNVEHeHi2IEx5leSl8kwyyZrBdgd7JofmCeQ4W7inXBlJjgrWLCTFro5ennBcHpXdEPdA0WBTUNuHUU8P/i7Vo7TDzH/UCULiaZL4FSJn+4xDBz+yH1Snj55YaXl+LndbSsEeK6MWuYA32zPkzDG67IYn17w9TgR/wU43T7s21Y/SOAgseqJoqWu045TcyGM+3ySJx0S7JrlTEYXKvGKJ99r949109vSMvSsxSKVFkBnJnyoN6LUs/I8NBp8shrHGQazTbHYwbVfJXMRjkSxJFIvWlmF2vszu5gaabSr11lq9xv7eAe1mm0ZgYfgF9gpihAm+2JNqOpe0CtvpkN5jHOpluC2D6X5rH80t091tUV+de8DOzjkKypwNmhOCkw53U7ikmSmLCkxezxscfrjHzKkVlajxYsWFqBi5B4Q/UY9NRvRFVlwGqqOUUNPIVR5oQcn7VYy8GnTLLnoch4yigGSs0bufMHcqh5MTyLIQvGJ218bUFx1KdQsnLbIbto/9oB3DVFWgnw4YBRqWUocNVOtG3i/SXNZHOSzHEH6fajVNfR0+z7DMmNbIVKKYIsXhakNSRzY4j3eDU3+klReF6u+GsP2DBD33yGxHGSllVYP6tpTMsDBDs03DQxXFpOiU5D1NypK4py0+mYOomURTIxA5gEfil6t7fbXiaZL4FVzp4sSkkZq4jCk6NoMUuppFVU+45WsUZfEct9jwanREDnvy7OktULYMFgi4F5tqniD7Hbnk52ydkSCnlNx4xjN4bKur0FJyBs1hSZF6Km4/W7zkRfIyvdbwb5l0c0WShwalmowTGKsOkkbPcEiCGqWDI2YqFsVlh3Y8IEl0hUE//tSqjJ/spifyE6I0K1BOL6cx2xuxEed/hdlEdk46B1u83xqT8xxefOF57q3f58qLV2mPe6piKRh5Ti0usLa7yekLZwhGIbONBo1ajYZX5Ginxav1Bo6mcUvrMdIilsiJGSu/4DAzGzrRvhFUi+HoVFaFzJhSPdWgs9OkNF/J5iXDgEZ9noLpKeOenuEz1kaqZST/k3zZkGvAyLNrRAR6TOWZM+Rtj54fMB7GFEr2g7lDMqYTDY49xdXPVTLM5E9Ex6lk5KS3hmtYiHpGsxORBCZFbPVdNBY08jlLbQxsG8RMzZ+duOeoIW9WEapzOp2PTMKqQbivYxbkuhIr2ZR+P8ceFRhnngjZ1f35zyQ6oxJhkmkdtcd5tMhlLh0+NE+ahmwI1PGLx8jkZ3bNJs1Ewz6lPZeil2PCbh6DEM2JSIU3MZFYEniszNvMdZ9oFOBYJqVKGd0wsXSddhIQ2z6Rb+GVXSXqqfgcQE04kKHPju4qrbZWJEAFvtLxNEk8caTU8x21IDvkOXr9Fq7tctDvq4rh8rPPko5HpKOAvGWydv0Dnv3aN4i0lINmW7FcW0dHijU9Uy6BP1SLirC0Tccm9gOC1iGnFxZo9kfohTK3neoJ1EmqTFlEJG2nW1dtJvkj/sCSME4ep2Du+4lH5D6ej/EgNAaJw7g8R7jf5Py+iduwGOuh8osYJeLvbGAXXLWYnYQLysIbpKGSkqh6Adu93MesKZ/knM46Po7MFaJAyYZUK1U64wF7nSPMU0XM0OaDn73H7MwcbsEliEOOWk1FJsx7Hv2ezziKyclxovEMZQaaCCZkJjMPsZ6nQ97JnymcVfwmpAUl0tPDVp/ls6cU+UpY06JfJW0PEVdS7Z8UFmOLi5Hshh2GukZHg+bhEXE5Il/MYxuG0pFKCkLPSD6WICQMO7Oc1fomXsmlu5FQjGN+/u5PsRyLlXPnSHTZkGRKdd/7q5+wuLzIN37/Wxyu7/HWj9/k5e/+Afe32owLOmOpEh49u5PPbhZSwkMIeym6+GWkDoPQzYhnx8nhwTnKfvZ5hCgNTPkFk/eRnX9oEx+EOLOJIhROuQ9SXSn48InrSsik3Y0W5fMPUGqPmzkoeZnSkGjoYkjCHBtKm8vwEhJp0WoWN999j/MXznPvxhq6oXH2zBm6ozG7B4eEYahUE56xE+7dusXcbIODg0PsWoWjoxZzMzPKF6V0+QXuKoG/r258tT/drzGU1LU9VizRnD6ivrLCxr37SoajWK1x//qHSodnMPbp6Tob9+8zNzenFtl7739ApVRkdvkU4TDk9todCq5NL4gYd9uUqnXFzk79Pn5zH8G/Dna3qT73TQVxnUbJHRLG+kPDRdlZigqlfbzXgrCt4dsOZW+gjld+L5BdSSof3ylqRIZBs1xi516LlXKOwOwqmQuBeHZ32wrFI5r8CmwyQYMYgiIRqQlSvFyM14vp/wo6NlYcMTuzyHzZoV7J89FHN/n522/TGw548dUXlaTJnVt3laHM/PyCWvDEXnUwGKo+daDLTa+z2TYouSk5G4qWwI0SDhir9oEsNFpoYI1cwjRSzGfFwJ6eAZlTFD3VhpFkIb8fiueohCBgFHIoWzadVGclEjmO7CdFLDqElOYq+IMxepjl0f8AAKSASURBVOQrFFWuYMo+AF+zcRxPudqpZHPMzBY/Z420r3G4F1CbdzGGQxoLDS4/f4Uff+9HnLlwhpHvs/ryJc7v7bF67YqqBO9ev6M0woKoS5wP2buVIIWIKP4qGK20TMRnSEsVaVJCLyXEYzDzJoNuidjxOK+gpzpbka52wwIWK0RjjgxXtUt7icaOwmJ/jiESNQsVtK0WlZKvnPmUoOKkMpAkq3gpppGhxaSVVjvp2PjJITMHMz984NI4zuGFNgdRR8muX37mGe7dWWM4HPDy115i4+66uraFpDocDAh8n6PtLaXntd3vcfbqVXrNI2rVCs2DA+aWluj02+A1+CrH0yTxhCG3ijCGw94YP04IBwK9M1R/e35uVl08ErLYi8znWYXMSBn3+mqhE7a0XHzqteKYUs4l6fUZtI5wxad5OFA6TGI1OTgSpdJCxpU4EUeDqbbOg/2ep8t84OHHidf1rNXCsYSdHKleViV0lG2lqG0+GnIDJa5NX6qFUEe3xMc66wWXF2v0NlsUy3U0JQmeqKGoLJKysx4mPjlxRjV8hrFBMtFNetLYbsfMeaay8UyiMefPnWNrd5daEjJ3aZmD29ssn16m3+4p7as4DlUqUheutMEcjaWGTac/ZKleY+zHDPsBMooReZRFs6agoFKQWVWNXhop3aPHxXQHq5BJqYaMnIdKIjSzAZUEcTo2kb1wz9e5/uFtXnnxMv3Ipm9FhK0ummYTF1Ge11bexByA6eeo5SVRiP9fjK1ZjDoxncMxVFIa521MGdqmBt12l/ZBCy+XUz1yec9qbKr5uVRzwhy8f+8++VKB7Y/WKH9jhdE4UDOF8U7KoJkR/wR9FY+EVQ/WTPYZiHX8gcdBVOKCMaTzxg+pNeb42ulztPp9ZmOL9Vs3qS+tUHJM9r0qO2rS8/mFpYtmWQ6qCf7mLsVzJhUrRzsdKukSaelM5wkyA1MxSogVou2XL18nqwyvYLH7zgZBZ8BmyaZmVqg3Kpw5v0qhOsPqeTube7g5pTw79OXeyuxRC9UaXn1G6bL1uh3mqnXa7Tb+3BlRu/lKx9Mk8UvjwQo8DFy4M4B4xPKla1RzOfKVMloQUnv5slJp9Qc6d3E5c+EK1qiPmaZK514vlKDTpN864uzFi+QrVbhxncWFeez5ZTVQvuen5IRUtAi9VKP1EDt62jl+EDl7zKJ3pIxrTsoOOLWUdCRKl5m6pTCUpU1Vcoa0RoXHLuKSECJNp3ckRkC2kpZIouz5+VKdVATW7u1TOdXAsKwM0jkZKo4JmK2O6LZMOgrp9KShkRTK9A2UN3alWuKd997n7MWzysT+/ju3WXr2DEbFZXTQpXi2RmftgP3tA4LIxwx9xYfKz9gctge0Oy1myw59YK+dMpvLkwghMA4ZFxQtK6sQPiWHTYelc4nN+cjkozDBtGEm1CgnOoITE9Om2/e32ds/YGdvkdd/8lMuXbpIe28PzTjg1HcuE2iRMqix8gaMTYKBjpuTRUhaUyOOtseUzxrkGxaWIWimFKuRY+bUHJubW1x46VkiP6Red1Q75uL5Cxg1h+F4zGv//PdwZgoM11ukhkXgZatU/kzWTlNCdjKclnnYwGB0V0fPJ6rKkHnYkZ5SFDHEakMZaHXX7/LRjY+Iz56m025xb+0+r3796wwTG5zPL0nImn/ObzG+f5vZlSV0u0rZt1h7+zqnv3Z5ooslCdtQu3ppO6VWSj5vYKQWA4KPtfEe/i4Fup3i72vooUHHitnfbNIolWnv9CnP55R9brsl/tiRYqFvrm+RL5aUJHl7ZgXNH5GPU25XlwkEXmvm0OrVrG1ZWmL8yVIDX5l4miQ+MaZ79pSaQEUFyJKEOGcqFEyL1toaN957X+34rj7/vHipMx76NG/s4S4sYzUaDPZ3aR4ecvrCRdpBxK0f/hDbtpTO0iu//Tv8/I03lJtdurFF9cVvsB+cXL1++cWXt3x0uVpFMuKkvJHABvOZ7LdyWwv1iYzDJ7+mLiqtno4WZtpCSjE5sNCK8vpyQ1vMnVui2+zQ77dxK3kGR13Kyw0MU8d2EpbzfYKxzkhE+J+4mkjpRjHDwMALYq5dfYahFvBhtE3pxSV26GLMGpTmFxnpCdUriwyqeca9hMqCheRtqWgWl3PcXx8xW7YpOBo5W0TkRMI8IJcHaR6dHOZ+6hElKbWJ1tOp2KaSZM8Lo4itwxaN2QXu3dvEsS3WN3fI5wvs7exhuTbBeEwBh46Wie8pnokTY8UWw45JrxkyHESUF50MqtmxSA0TQ9jSWoK7tIS9nNA0AzwxkopzdEcj6gtF3kkP8EXRVn3mDuZpkygZPswY1jWFDFMEMSGLF0W5NKsiirZLtRmg33qT0bkLSsHXHwwZ62NyubySsa9Wa+TKVYLREKP4+bZR5JLU+11cU8f0A4ajNqPDlPV79xWxUzxF5hZmuX9/iyQWl0XI51zlLSEmQ6e/dYmmOzhmqctAOumaymRLIhno+NuaQofZOZ2261M4v8xRt825SxdpfnibXK6A7ebZ2trmzNnfo/fBdWqNBuVSCX3UYXdjnf3dbeYupMQzS5+7nPqXMZ4miU8IWeLKXl+5aM0U2ipZpL6O7hpYukuky80+IJfPs7O9jTf7snKOM+RGFfhiqrG3t680kXZ294jPVPHHI8bDgbIgTfpdtdVpzMwSJikbIiXwKbuix0Uoz3kc8vTk+iykYpHWSHQGsqJ+wuJdSWMaBVf59xY0C70dYBo2TWuEP0H1SNhFV+kgqYql6ClHvTTvMfYHBLGHk89PksSThkZXuMKGruTBwyRh6I6VCdFeKuddo6A7lHQbLzXYE0hnbaRYztvrEUnDpDJjId4viRmrrr+vi0CeJlrTuAWNwyhSeVS0gOasCvthRymHPhoPSHA6ZiIwUhhEOkGSYOspv/jwLkauwcbhPX7/d1/Dcz3aQ59Kp0GlVqXdbKqqYNzSCHyDSI9JxGxIxAIVtheGA5/inIWeTzHshJyhYUvvPdKIRaJC2OOJkOJEkiKgSYvxQFj1I0Q28OSxBicIesLwlhCpcZUgJkxkSXTpIPuv5+X44IM3een8GX6wu8+3Ll2mZEqh47EX68wbEUWRXx+O6OaqrAvULsxmAp9HyDGWCnmCJOTmjZsUCt7ERW+sAB2iZLy9vcdoOObqc1fVv+/fvoNp2+TyHv17Pu1ck9JSmaSjMbhtqOokNTSCIMFzDZycphR1tXwwuVd0ClpdtbmaR21Wz12k3WrT7XZJgoB8qYRWm2Xt3m2c0+cxc3kc8dt28ww/2+35lYmnSeITQvZmXYHoKe8EW0FfRWhO1h5ZMC1b2jAl5sScJp/HEnTSwa6CKwZhRCHw1XCxPjND6/CA09GQfn2GIPDVLkmkm+tzcxTLFfYPDj4DmiSD4QqiSdpfsSVicZ++Q26OyrjiW/0pN/u1Wo7R/YGCVtqHOrffX6NQKjDzwkpmYDNBjERmrJzBFG1Ch5nlIpqhMw4jru+X6Yw/O7HOT4V7EVMrCykqROvFlMcFrNTCjHWc1KDuSXtNY6Cw8GCWUoxczOAgwL+b4BZ0fNtgw4oVoiybIYGVFy8AIb+hrEOVn4xuZfLRJ8/qCcauLK6HcYztaxhJwEYzpWinDCOLvbvrzM3P8IOfvqW8KbycTChSut0O77zzHjnPY3FxgXMvX+NwPGIwSDkajjD0kEouz8LZnEoQvuhP6bayMJXFUlpaitY3KQrFTyQkPraYlcnyRCfwwTFPkG9TwplrWuJarpjWk0uF8X6qpOGFO1IqJ7x45TTv3lnn0rXLvP/THygF4Uq9zsLps6zdXycOfDX8qK+ewqu7FEa5CSLp158opH3TKhTx/DbP/d6LHO776LrD/LMvqk1DXUiC7Q7lqxW8co5xOGZmts544BMbITfW7rGQ1Lj+XzaYWTqLs5TgzAjTfKrNMvmOZaZ0PJs4MaPwHPaPxNZXoypWwPc+4vS58xwd7YOX53risFydpZzE3HIqShftH2M8TRIPxcdAhAodtNVt4Oohy94BqQcBIfMXZnmp+vvEpqfM6XtBTOjmhImlXmYkSIxTZ2kJCma5RFe3KF6+Kih7nHBMszZHYWZVEdOU0c5nEFgruwOqXo/11hzdfoGq1VFtpcciUCeVxCDwFBLq8ZES6xq2JR7TsHl3g/lzyzS39rn3X9/FF5VT0UCSXVXeo1gocPvGLS5eucjN27dZvrBKutjAV1u1z76YxLrOXi9g1oipmxqNnEGs5YmSIpalc38U0wtHDGNfYUedxFZ6UjJzKSxLFtTZuTni9HMFAi3bRedTh7ETKnSM5aB249LSUb/THQaxyLo/sjWMNaKehj9IudEPue2K3pPGgm6RRjqLq8s0zswTWwW6732g+tlB6NOo17l7d03Jtuc8Vy3mdsFQelSl1EILfYI0ItbEmU7H0WTToUSwT3ifx2ohlwqnEw0fPjbZHk/NeE62FSeJWz3EMBTSbEqmEy2k0cb/n73/DpIkTdP8sJ9rDx2RsjKzsrTortZ61I5aOdjTd8DdHo4E7CAIA2kw/EEaADsYDUZCGI0E/ziDkeAZjSSMZjgANJ7g7d6K25ud2d1RPa116aqs1JmhhWt32vt5RFaW6J7unp7p6u542rKzMjLC3SMy4nu/VzzPoynWcPEYRHsZlcSgfnKJ/vESzshkO40pFEpkSURJi9m+eVVlRG6hRHOvR6801q76BUGOPHB7WGdMNiJPuTCOUrHGhaOWwd7bPyEOPBzHUhNtM4sLXLt4TY2Tn3z0DOmerlSSC0sWxQu5plYeDO6d4LvjvBkM4oDT33qKvUxjbzjLzLHzSt79nUSnVLcZ6LaSsO9aBeorpxSn5YuKaZC4CyVRJZVFPyhQcTyWqvt0vQp7wxr7gwazVlvtpt/yhuylsyR+TjJLZQi7IjIdOWT3ii3krBzXZCNviRa9rHM1NY4owgNrMtHvzJB8aEaORntUUaWjILHYoYrZS6jOil7sbRzwAzINPdFoJYUPPOZlL+F4FjFXtNgbakqyIpZGfBBw6uGzam78+ntXcXwfbzhSCoOuIzPoOuvXtomLp/Cl8fExNpxypTd1m6wXccyMqZcLFFzxXNBoRynrQcQLtRKbw5ReEFOmQkExoSPMsq68F6yiTO4kSrZCvUbDAMPJ11eB6aAsQ2WKSHbvZcOlm+T1fKnaJCOIdgzitoa1ElM+mwsfCrqZeAmMpTVSjVqgKyOp0POxyiW1ky9XypQrJXrdHuVqKX/9lQbkbd9rGb8dJgEF01YBQhYlCb7DNKCXjJSgXU7Yu83vUIvexHNZNgJjVv3hADHBRMZDHhvsiC9Flr8F5f0ZwV7F50YWMNJiahVN9ViEvT4YjKBWYXFliUyMlWYW6PihyiB+MaoBtyGbl7LdIcGh6A4YDJ0DaUPfD6iUq9y4do1Hn3hM8VL2dvfRTZtBO2Tu6KOEZ7ewXOteX4kPQgqie7lX7KnXplhIuNSZG/NFJCu3VOYmaEYZzS9oBjHBNEjcAdlx55oyjhEpPkKW6VTcoQoSarZ+YCiTdpkSioToJPgQH6TDd7kdD2Th+eibNdGS8cSJaPzQYexSTXv39CekgWlnDksljebgg0dTt/sjsutv0bVM6gsnMJMRJ0+cIHN0lT24iUGlXiUZhmRpinXiNE61SmVlFa06x8XDAkQfA+thwhYGe+g8PvKwBiGlYoE94OlCgCOObxWRstDHMhcabuaiDy02e33Ors7SbHosHxG1TliXTodME4x366aVEQ2hbOmM9IxkoOG1M7V4ZqGmSolGI1EG9+7SodFJYVfHNh1TspLcMlXE/o4eW2Xu2BLDZhtvMGRucQ635KoGcKVRVcJ7MuE0TP0DkUT1NyF3phN5DtFxyhPInEF8IKZ4t1S40hEa+3eLt/YHvI4SdKK+sLk1Cqu3rTqFf7ARDg8GWsXn4smvPKsW2KTVUjpfsycvMBx57PshpfkjvxTpiUHoYvfmMETqRj5v49u1TGRZimpzsrSyQhSEDEe+ygIKxRKhH1Cfs2jhjDWYPjzEREpzhbSX/1ywR5jSx7rPePgU0yBxH6gWtSKulRxPlWpESE9gmTHYieJLhB+pOfuLhEY/dZkdOdjl4I6Sk9TWpexy1K5wWU8Z3dG6uFssBIZRpLbbdhhTWZhla+MmM48fVWWdyz95m7OPnGNvr8XS/AJeocJPYoPm4kO5WudEo//neB5yeZdDaKY2XyrHEA5VmacgAqvxiJKwl2UKSNl/Ctvb4Fi5ys7aiJppkxRSBp1YMWutkhLQuH10KTMZOguJw3oaMrwFzqxFUovQRbhP+i2ibnJQssixlNicSwyaqc67opQqWaY5pHDc5uJwj+KcmOC4aFqBAhX6+z0G1gBCXxHFJlnEBCKa6KvgkL9eov/liFz25K9yoHV+6JWR4CT2mbIpuZ9/8+SxkpVckyEFDXf5zh6GJdyNLQ29lGDYmrqOYEbKiz6ccNnxY/aSGbJSlUSIeVLh+iVECTEe2g0bErXuwGaYcWHpKGm3xVC84+0C2wsnOPu8Sdxt0e60qZ+w1YjrPQJ+PwNq2utQNU/pN4n73Sf4vD5PmGYSitCjzCYPPGsli5ByU63QV01iYSofvAWlNKwaYePd3gOAWNfp+iXmS7c5AAc6RUaCLlpFB7JkYwZullGU7oowtnUdHJfBo18hM0OerEasv3mFF3/0Y74efZULD53nD994h7lyldbOPgv1BrU5jV5Lpm4m0y+3CX52luFKvTvTqJg6fpKxLWZHqkTysz7MIoeR8UddkyfLJk9VTPbEC4KUSknq9/kiq/o+MqFkZJxaqbG+0+f4co2d/T7VYsaJUpnNTo/KbI1+5NPseYy2YwZWgh/lBESjbRPrmjqOcEKkQ+wuSqZyu7/TNCJ2MzgSm/Rkht8OlBSJZFNC0otKIqIn6Uh+/7Rm4rclSIhMt0S3eyFN6bwcda/P+aRHca/UxHicObnt33w3onHF0T4pzXA5RnwQdISvZs5kBGsGxVOpyohiPSQZ6uhGgmv20TSZNMp5Lp+SB9EBhEi6XlnErh0hEP0rYM9LKNdXqMytMFfu0NNaSgI/LxF9BKisTAyH8veulLwOqgJT3INpkFCa8ylHym2aw5pKeWdKPbb7M9xoLakexUQnSWkkDQyisq7+LbugBwWDqMBcrOd+zocQpDHdaEjD8ojSMn5mHQTCZaONmG2Fvmj12/iWjWdWWLNHLKzM8cwzT7G8tIxjWpQKRWy7oCa2ygWXGT3l+UbKn+6JUqocMWcl14k4UwppVKSZqlEybbKBwdZA45UgU0IZH9aUyNZjylamRBSDRFd+E7Y5UnaW0kwX9rL4SdRKDq919/B0A3PY4vf/6R/yne/8BjdurfOVF14g6KfE3ZjlepGqazD0Qo7UywwGQ6jXGPgjxcJ1Z4pqzJl+RJLGyh9bvCy2KwalyCQZ+CzN19h/e51hd8DpLz2BPgzwMOiX82Ah2ZvbKBMOfLz2QPFJ1DjquKehnp1kLXGqXh9lujN+1j/Td1l6E4Ec6U6ox6UQ7mg4kkEICe3QxNbkmGJxKoEkCXNLWBk3FbOpflhkb9ggTB6U7DjHvhq/vRN+GjBT2CM2AhUbcmmn939P3c/eVBAlFr5XYhg5aoP1IPjCPKh4cFa5TwVjZ7fYouNVWKi02RvUKdkelp6oD428gSaIYpPYsNhqjxmXDww0fM1i2CtRnumPG5v53l52vb1in7LvU8hiNvw5MtPggm2oXXBkRLhFn2qok0QG7WGDuYGh/JsfffwFrl19h3qtzjPPPs2JE8fZ399TFqJSBj5vwE4xYyOQEcuM+SxkZTZR00QTjAiIMo0LC1VmO/AvB+KZnV/zz8J7fsiMActmwI24yILM8GeBChAy/SMN3zfDdRY7c1SKFs2dLkeqcO36Df7BP/h/8Dt/99/j//nf/b/5zl/5Dj/449/l4QsX+P6ffJ9qrUIYhirgffWbX2Pj1jrNvX01fy+6PGvXbtJstVleWWJ3d4/f+bt/m//7P/7/KcG3erWm+DGtVpv5ygx/+md/RhxHfONv/CbGyrjib8iQm1htBgx2usroSDwxRHZcaIo9b6gW6HLVUWU6QxOP8EQptKq3pPLSvuv1kcQ1MNDuyiKUnWkfvM1cyM+Ut6uW8ymUt0YmwSj31pD7StlJLGqt+fxcTb9GM63kY6ITQ6QH1L9ZrupIpUvJzhltWaSjufcGkrx8JPyWWCnJTrgvapBA/K6Fy5IWWe/dHiyZ4v3xBQ0Sec9Beg1STmoNq6q8JD8fqTTpeuVD46K3PzDS2LKshNT7xUsqf1TIRmgrbLC8C8WZoWp0lrQCpaTAQPPQCwn1YsapvhCODGoySeMc4Z1kIxdRc0V+ImXF6tEI5jCOnufSWhezeJS9npDojnJ1s8/C8QustWI2mkMWGzbfqpmMCqna9cUVGaXN2It6atGTCaKGVsHRNYpZzOmZEmtBwsVE+jziJfHBkbYV2PzzAEq6lB8ini3lss+ubjNKxZcB+rFPZ2efLx9bVY8puyHtdktxFYTbEPg+aZowGPTVv0UHSoQDX3/pNR554lH2tnbVlJZTKLB+85aS7f7qC89y9cZNTp04yaXLV7n53nVFgPzO3/lL/Hf/x3/At3/t2/S6Xd5++22+/Ju/oljo116/xMPHnlO9BuVNYEBky7irRX+nT/V4UZVwPD9SchmlUkF5YgeBz6g1wBKFWz1VFq2mMqq46+87+d8hJd4JJDuw5hOG7T1Mf4b22h4zxxcUj0MtjoeyFEM4Gn2Z8JKl1MAYaZhuooijthmrDPpB3VXn4r15KVDxCNVl3hvUpLSqpsTGLoHqXtltQyKBpfs4mmxAUrIoUhufzTSX7j98Pm38XR62YmU4WkYrM1kwMq4G8rfO73PMlaCUsT0WXLut3fzZxxc0SOSGJ/IlKNuekt/u+UX1c/793g+Kr4zQf7m8/KoItUk1QQTqpBIiekr3vaemJn/WaTDfsqiIU51eYz8ScbKUWddUweBYzaLdCqg3itimyx49NsMOaQyF2EG72aLnWITDPsdONxg6KVuvvsfsiWM0ZAfe2efmrZscPXGKtzdj5ve7SiqhfnyW1v6Aq+9cYu7EEViysPdjep0dymfmuXT1FsdXj1HcvsI3jp1ia3uLa/WjDD4wUOTTX31lMJZxdRCzWopJ9dyjQ4167mk0TJvL124yGIU03CoPX3iEv/rX/jpvvfUGpXKJ1376Ks39FpomwoXiSJazkUXJNo4TXnvtNZ569inWrt9Q4oWJ7KoNA8vUsSyTuUadl9td3vj+KzRmZzFti/VbGxw/fox3X3qT0dDjkRcex08jzFRn7+YOjaU5xRyuzVSxTZP9tX2qC7PU3CJl11Lnbm+1caslrIpkNhFxGimSpuBeN7X335eI8F+WhtSOzqoS2fzZZfp7XcX8F+8F6Y3Iz9WlBvFIzw2jZNVzMqpzPSpmT/FkpNz6oAYIganHyoFRQc3IGnlG797uIeVMdJkAyzMnxTo/ZEuauxfmPLuiqTE7aPHKn/wxz3/968wVSgwzMWEylCyJU6vjt/ZxZ+fx9napaKmS6Xji8SfQB0PcVAYPREY+46hRII5i3FZPEWjFHCmIY95J3Y/EgXoQ8YUNEpOpJXn7iPhdo9hXgSIvL93/gyLZhozs/bJg6xqNG2+TxhELJ8/SvXkT5+Sj6k3ZE2c5Q2SwZXQy3+nIW3GU6CRGg30ydlOYtSLmNXjtn/yIL//1b9NrttncbJKxSrVks7vrM5Imnlj+lk0uvvYubuE6lWoZc0UE7SzefP0VvrxYZDib8fqrr/LcV56j2+ly5uFjfP8f/ZFa2L554td468U3WFxaJO2GLByd5Z3XXqa12+Krp36d1999l7fffo9Tp04TDkdsXr2C/qww4T4kNI3dzKHQ0SnWlVOO+rCbQ5Oi3mVz/Zaagjl/4iv8tb/x17lw5iSupVGpVHn34nv8m3/3f87s0gLngvOUqxWs2ZLyyTi72uDY02fo7DT5zd/5S9SrVbKCwWOnl2k4BQrH5xTv4TtH/jLbt7b4xt/5DiYWZ4KYE4+dxrlyVfk/FM7NMRKCXwrzqwuKR1EoziqCXMEpECdiIhThJSHD/S6FSgnDsbEtSwUuo2CRRBrN6zvMnlw88Oa+w3JTxB/v89aUpns0GGGLAvF4qq26UD9koCESKkWVcWTbLoWTeaNdRkHT0CC1UnYHjQ+Ubfm0YegJK7U91cOZQKtFynNc9ZGcXK31sPnQRKJEXg91Wzr2LJfhEzOj7yds149Qn1/ArjVYe+sNFSB6o5ESsZxZXuLdl17m9MkTbG/vYB09ipYmrL35mvq7NdttpfgsCs/XbwWsHDvG4OZVSl4Pp1Sm2+tjn3lqGiQ+qxBp5cPaqreNe+4nsnd7YH3yuF8G5Exba2u8/aMf8Nf/9b9De/0mM1HA2rWrPPHVr3HxtVfpDsV34VkuvvOeKnU89sQTvP3iTzh66rSSMb958V1Wn3+a7Y1tXv/9n3BsRd7osNce8ub6LslyQGFedlxQTkyKxSK1RpV3336XU0+d49ZbN5hfmOfaG5f5ysIShmnR2mmzvrbGnJCuOm0VaJOuz+NPP85771xUqf78qSU2b24oSfFkd0gUR2xs7PLE449TKny8vYlMoO15NY6WAiw7pWK4pEYBzWsTJXEu12EYbN66Rb/dotfvURCZjKUlTq3Os6cHpJHDT773Y4796qO0xOPBTUlrGsbSPAEZO2oWM4Mi7BNhlQ32si7ZIpQWV+hrEY7oOp07ys20h3ZygchKiYXk59ps3dxg6dTK7Z6AcDJk0qtSxBt4YnHEzNGFnCEdj3sHIgSomOEWs0tzishoiYeHio35TljV3807d6SKLxKCv+dRPOuoDGeyQN4ml4kuVEJ/s0uZVaKqS+o5FKKAwDC5Fc2RtI27JOgfPNQcTw2RqIJTqjMKCwSJScUZYoquS6iRKQvU249RgVZEvkKDJJQAK2q4uVd258YVCsY+9skLpAWXVEaok1xNeWZ+nq2NDZp7e+r9I6Kc8j2MIo6srOD1xI9Cpy5iiOUypXKZvY11ep0OvudRrdW5ceMGmvPL21D+IvE5yiQylRHI3HmeMt+52EsQEBc32S0lWW7co2qNGniRrbKIuyHm8KaRqGN+mnjuK1/h+3/0B5w5c5pw0McfjXjre98ltR3mFhZVLTqJQm5eu8pircKg28WIPLU4RGHM26++wcW336VUKnF0ZZluq8vzZx+hfXNEKCqhUj9v6fR2RWWzxCNPP8aNazeo6QWu9D2eeeEZ3n5TFv8CqWaxsbWHbhdZ39zm5BPPqg/d+rVNJbkso6GRHzDc7HDq/DnKtTJ7a9vMNmZ44asv8NqLr/GNr3xVEeXudZ/+GZC/leZQH8xxsm4zX6jgH0uI4lnWtvs8dGYO0Wb44Q9/QLlYVKWqleUlJaxYcm2+9+Mf0hsOePmHL/HlbhfbdXjoW0/RdLz7VpFl8RblWPnuapbqLyiIrqIr9T9pigoZT1f9hc52j/mji+oQeT1c6hpmLqkuDPWiq56D6BKpmrmqk+e+JJaS5kDZaeLkO19LN1X5K0hi4mGCWb/tMz7B6Bbo9QRT3JYOJx6TpxHqpH3pCS2S1m1GJZd+VKBijkhinVjJAz+4wWGCMJHSkoYXWuyN5hiFzoG+2mptD30kL3V8wCGR558MbdqDGoPUUZLjKsjua0R7OzyUNWi327hnb3Dma48RDCJKT31Z2ZcuJ0OOHTvKoFTnaBRSin3mlpaUqZjeWGAv1liK+gycEqPMoCmk1bkVlTHMLZ2mVShQnFkm1YX0+tlXBfycBImMemHAkUpLTSodTp2lqm3oGYuVNmVnpETxNrtzqjktTet6oa+8ou/n2iYBQiad5Jif1gfJdhyOHj+mBPRkoRGJAs0wqTXqbG7vcq3dxj5/mu3NTUzTUh+cWqPBtctXxnLRluTqPPbk40oKur3bUgKDaRpT1WVnrqtyQ7YlczcajzzzVXaSAV/7N36LzC3wxHd+hcsjDedLK7wc6bgvfEt5/IaWS1uDnowhamDbMcXZJjNWgjFjKG/t2cXTmIUMKxMXu4w2MSe+8QTtvkn1iRfY+Mh6OBqPVk2eKWb0hqlyXasUbdakMTyzwjAx6Yq8RhDSDkIWjizS7XbVpNGobnDmmQv8yT/+I27dvMWvaAZnnn6YnxX/ZUom/55Ljkxq27aT4XuGkqXWi4lyv0tDi7BrYMskEwmanXsrT7yaVS9E8UVE62uUu6UZjrrN1RzsTBabGbbXtxV7W84UhRGj7pCoH+JSYijZUb1ENArU+6C/EVA+ayqZ8gNujKyQgZRhDDBTOq1NHGceczal15cGuk7Hvz+H40HFSG3k5ukFhTs2gaPIZXdQ50iplUvi6/nUVtw32e7P0RNtlklWlRPwSRtHePndLWbqBquWyM73uPnmOs6FpzGymJde/KGafjv18AWOrizRbHf50Z/9mcoU/tK/+jfotnrcvHmdUxceIXWLXNUrKsDnb5CC8g3/3Cytn6dnIou5oacU7UD9O5fXSCkZAa4RqWa03C7NahHI6/hlNe7aGdXQ1RT7ZA+ZjwJKAJH7SjZx+3e/XIiE+NEXfoWBbrC4dIoiCfNRRBoGHKlXCL/7R9y4eo36mUd58swTZKbJ2ZkK87euU6o1FKGqHcNCycU0dQwtJh36zM/N873v/QDXNul2LAytgSP9jQWHH8QGKQUKoyFBL8GPDXxJ2dUrcGBHLwSMO661GaXMdT1mTsXq1ZSAJjpARigctUgtYKpRHGncuhGxMTtLXPro6fgxx8CxbFzLV818WcIHXsz54w0WZooEoYjWFZTCp0hAHz12lNm5eRIjo7PfoVwq89VvfI39vSYn/IBMJonGf9yClmeXMp0kfO2CbmPK1nRCUDzM4BYpJUw1Zil3EaVc3+9TKUGxUcDGwfMsQhH1M4RAl6LrUtYRj+kkb3K6DkGW4OiO2vlKsLANjblGQ5WRtm9uUFysYtgGheMzyrzIluBhp9hFRx0zmy8jOnyHA4QKEommpCdEQqS60lC+Cl4kj3nQE4cMS4+I0js3Zmlm0Dk0jn4YbT830tKH0CgOiGOdnbSGJ9K693myumVjP/48M/UdStgMOi0GvR7zwQArCtTGTKbCwuFAcX/kdRYNM5lw88IIJ/TY295So89z3/gtos+5+N/nJkhIZiBjrZqoWvs6C8UOrhaihZqSPYgwD/oOc+UuBTtgrz/P6XhIsLOJuXKKpiiMivx16hFaFoFWYN+XtPbTeRPI5/mqWqAnkH+LlneBdR9WvvwtZr70Ta4YRfrjKaHw8jXe+pd/qOqkM7NzPPlbv83LP/g+M/U6l995k52NTf7mv/Y3VADp9PtUZ5fwswzzWMAmIzr9IkQOveijOMyJralBHIgPds7yTYYwuprhnkiUCJ9M4Giehb5tUqhKo/zjrVRdNcmTYNkukXgh6yJUJyOlLmkW4JoZTz3zlCp7+WHI9ta28iqo12u8+cNXSaKYa1eu8uRTT/Li7/05v/Y7v41fjVS0kSazfDmi6yE4lDncD45hKmFHmUryeyP0kklW1NWUk2XkirpqrkbPJ6mUM14aEKWJkpR3hbmtuhRjsT+1wKc44hoHHD1/TAkCim2nZCNiRZqNTElrMCw1wEpSz1QAKB7Py2NSV1dcAFd8JCyySGNwXRh0dXYv76HNFcjcD+cR/WlAXu3lWpPOaIZu8GHfg1oeKAzoyGSiENPFxPpnwAtLxE5Aeb6m1G+jco2CobP9p9/nS1/7Gnv7TWLLVjal0qsT7xjxi/F9nzPnz2M5Dk6viePOfuab01+AIDFWYvUN9CSjXu0piQXJPLWGrE6gj3e+cr+JH4MIhvbefIPHHn+MKO6j3dxQ0wt7rSYnz5xVMsRLs0dI51d/xqjmLx8iRX5FLMoE42srGxpaEqvpndnZOWYWFjCTmCvvvINt2cRRoG7f3NrihRe+zOZeh7Weh300Lx/tdBof/4JkJxxnhJ3xdOJWTtjybhgY5QxtaFIqFTm/OsMPr7dIxNbzY5ykE41dxzDxMo2L+yO6/QHnljSRemMUabRbLZZXlun0unz5S8/zxltvUT0xx7f/7b+I1osp/bMyX/0bv6aIbO1eymhPx13Q0Rq5L8OH2mkrHSUZTxajpEiVfyoyYqqJOm0eKFU2JRIfY3tYEfQTQp3wJKQZmgl/RHdU010dUhrRSaJ2sdKnkFco0ITZf1teQ0QmRehPkyltI8WoZUQ3ldxsvpmRv4OuKz5BavuEt2wKjVm6RclOGp+JwX1DS2gUWnQDUVX+aJuJRMqDH+qtpdH3y3QDHb+1h1lvEOxuYa6s8OQ3vsVA/nbFEp7vMXf6PNXlo+pvk1RqzDfmad9awzh+hkBuu9uM/nOGz0mQyNA1aTCKbWcsGy1VETkMpRQpcULPP0xxEEoeqT4zUrdeu3oF23EpirJpuYw/HHDkyBG2t7cpLEqQ4AHDvR+eU5nH66/8lNFgSOD57O5sK9ltcdwxTQPTLKqFUVJnqhlhMabY1xi5Jpudufv2ZT4Usoyq5lM9HuNdy+nexVUJEgmmKLc2NWYfNlkuFri1m7JWqZCah/SwPtxJ1P03/Zi3HJtSEqqyRCNJqMza2EberJTg8Rf/4l9mu9nj2aceox1kfOnMHGt0GA1TSgWLF37n2zRN8XlL1BSTXYLwpkXi5hpHP+OpqqwoGRro5YTeRofifEUFiPFlMowDItmUjMQ0ycAqZeQiImNbUfEndwqKfS0BQrIEy7LU7cICV4u8NLLzR6jDTm6TxV8vkyvCuhlRWynQ5yxtoceZRj5VJWOxmxZWVUObtei3ZDz2QwbATxHyeYzi0lgw8hcLP9V4nRLUSiqxlVNeHoBZXMwFGEuL3BjLculmQ80gxDJJJQvn4mkCZVX3gL+gnwA+J0FCsoME/QNSPiVnIDLfsoPY30fbCmglTR55+DGaGzdZfeRxhsMhhCGr54+qJuKtrW2Kx86w88AFiPuj7ZS48MxzhP0uF995W00qrZ48iVv4iWIUywRUpVSiWC5xLdmnkw5JDYP9XuPjN+fFglWPOTkzZGSmaCc19NjAnMnn1t2KzkK9SMUoqImebfFftnM3tg+NMKbU6yk5hjlNZ+jZeE6BsxXhi+i4bhFNS5Ua7ShN2Nob4VYTMitlFITcGo7UiKlWTPCMGCHMS4lH7ExlZ085pbAKg/WMyrEUy5WpHxmHzEtB+fMUddWxoc9Qx6qIM+AIt1FUzGal8p1pZCLhHur4Ylnq+Ajh2e44ZBXZxOSGSKZlqEkZ0dWKwgxXhgvGjW3xuojTRPU6pM+hTIRUKpz3HfLyV6Y2Q0nPIOlluKduCwMqmY4gIdm2MRyDoGxT0AJMIyZ6gLTG3h8aO4PaWHDvF78ATzprB0uHtHPuF6CyO2+7730+p/gsvGs+FGSRM8yciXs/SKMxqybKpJ4tm8zWKfT71GQqpODiDXo0GrNqWuHaG6+SJTFzx0/yVrGhVEw/C9DTlGGvqxQ0n/zV36SzucFWp89jf+vfVHXxZ8oab7z4IvUTczTTEUmaKPvT4eCjW45Odvazesyj8wG+ESsTetFt0uqxWtRkgV2wagcWnXL/o7bOlduiqR/iNBnldofFlR6mbIYznWgUUxloXBuYDDyfkys1PMNgO/a4vjWgVDVw50y2iOmbKWkxHKui3j6mNCSDLCROU0wRLZyxKCYyLRShLWrETkYQRWihiZ7qSAs7NRIyca8b5dat3baHVtCVy5ywf9USLgKLQvIav0ZJnOHJ+OaOkZezbOkbpIy0SGUVYRjjlEwiYiLxqdYy4iRRvQvhWBxIS0hwkexrHAzkfokMBJgaujPmZMQQdSHet7FEFFF4G8M6ThA9QNL2PxvhWI15igcDn4MgkSmSjcj9duMKVbd/X30bgaFp1K0Sc49WaO+1OFpbpZTq3Hh1RxmbaMUSu+0eu1ubapJBc7cp15bwPiNBYiPMKF54Xg0e7cpc/sPLtGRM0JcFPeVEyeXJ557norlOFA3UwtL2qx9L/dNKU845AUdmUqXI6fUypQ8lw0K6ZqisQbIHCRAyHmplJmGQse5LIP/Zb7tcSgFMkbZwhjgzE4JYxsgJqc26PKrP4IcGr17ZJaoVWbvl4ZQ0aov58aWY6DgGZipy6LfdAuSvGYrptSrv5BNHsijXyi7VeYsoytgfJCQy2FQISQ0RiIvU9JM8uhW26e6KT3WfWn1eeV5gZthl5SlHNN7RywIfBRFORUcva6QDW1mF4uT3k/8s1yDUotwdL06IlXhhpqS8ZdpJoFRkDUNN+Ej7ewKzIJsjCLY0omHOdzEcnaBaZt8qq7FRwYPMpJ7iwcdnPkiI7pJIaqx3FhgOilQqIm53b31IOVoZjlqwZFdWWajRSUYYkaMmQmSuv9Vs4VgyuuipUcHO7g6LD6XKHe2zABEb6x3ymzwsoCc2njVDYzPo0GWUO5hlBdpqXv6jLyAFPeHofIIhO9nQphRb9JKIiuNQNYpqV2zEplqgy2GJm/2It7yUnrz++s8IDnGCO/AoRiNKBZ/SMdlh33m/LgFtPGYdUe11uHypi+nozC6JP7YQ1USZVlce1bEn5hG3jy/PXaQaFGTXmojwHhScCNsw8fGJq55Sup2cV5rRqgSVyfBXQqFcwM0cjIIamiUbGYTChxA57/E5hFWtF4UYl0vT6Y1I2ZDqUpIyhCEuGko553+i1STCiOrfUm6RbEzP76MMiiIL1wpul0WCjLRvQEmjdDIv73ndInvhDEkqnIxMeaG3vcoDrck0xYONz3iQyD/FYhYk2i5aPVJz4ffcS4zPwwL7slilDg81fBxDp67VCXc8tUvzhgMM36NcLCsNFscRT+iMvvhZftCq9plAhkPEpW6ToDRUM+iDqKbk0WV09eMg91jOFx7LFu2hhEwUNcfrqjfUeVfKHrJQWwlNKRMpw+kP1v43hz61YZv6YoDdyNDHAwh3i90J+1n8rrPA4+RymfXXOhgzFrstLw8OOlQKwg+ZyF8fuvbxoitcgmxgKsIbbkjPiumH4pCXv4dMWaCluTy2FZXsqLvepDZTQ1cpk4guCiM6UpnEoXpWfg75cfx2VJIPQqYrjZvOSUYsEzSaeyBap34vRTplRjSexpMgorrlGUEiCrHCjcynmMIuaA7YKwm6oZGOJChJcJaRZDkGNEe5npM6liIF5lpfU0zxhQgSMsZqmyHNUY2lahNXF9LWvZ8A2UXt9GfGzVkYpRa2nvcxCnaJ7PGvKJKkfDjL4qTWWFHqBw4Je8r56rMNpUdj9PAqfcVQXe/Of/xJpjGGicHNHZ3lRopuJ0RZTNEST/CMXuzTDhy6ppVbiB7E7Q84X5JQ6vSpGx2q51N00XGWP8L4z6n6hIlGmuiKj7EZuui2zolaqLK+WlFX46JmJWHBLJMkGV0vwO/HZE6+qE7UumQ8WvENpE9RiXLhPCnIjXf8E+Q8h9s/iwGRW3CpV2oEfYiLIgMjxJxxO3mYB1xFYrNEY0gUQvPHTjICGYlN7ZSoV8AsSM5iqSAxySTUfcYN8knEnXhel8xhfhzpSyRQSQvUF3UMy8TyNZpGSKWg0Wh32Hbn2AxSJRIp5S9L05izNGa6O2xWjzCI84D+eZ7vn4IvepDIexGL5TYtLwGZOLGTXCon//VtpmwmOjgxAfkETz+4zfQd3KXBvBcfzkQ+D8boGSXNp1weqVbz3rD+cwcIgdh+XowKrG/FnKgMKDipYiALtpoma+L29aFOkfceSv0e1aRD9QxoiUna1QkiW7GEy4auNHVGmUUqTXDTwC4kaJaMi8a5l3dBdI5E9shQu+pAC4mKHoatU/LKDBNfOIiYvs0giCjXhYsgftkZBd2hqOWSJp1kqHoBUpbMeQ23S5cqqxB5FLlmU0NLcz6D2vVbQuzLI1E6MMjcUElEKAXSMXFQsgk5tmGlpKWUZFCkoCVqe6+0hYwxUz0b9yMOMb3lnJIVqU5LoBFvGBQtix/93h/wK7/5DdrtjmJjC2nvvdcvMbO8ypdOn2XnxjVmllfobG8zW69y7Z23Ofdorn7c6g1oLp9mf5paTPEB+Exvk0eRw1Z/VsltFEo+nl9g2CoRtl2CVgF/v0jStZQA25FqS5HovlCQ8k2SMmv10ZyEflBU2lWfTBNTFjaNgWHSjA2Cvsnejsn6tsuaXyVWTVftQ0gwJBwp7lMcjRhGJVq7VfpRka20zlo2y45ew040rJ1t3PUrPKyPKFx6ma9VMgZvvcoo0vmf/vEf4NoO5rBD88UbFDWbwRtbGHsRtdTi6p/8mEWziHZ1QPfNG6wUSuz92SW2v3uJ1aCG/+o2P/nv/4Qb//JtTiazjF7a4tYfvk29qVPSXdWw1jMN17IVIVAazFqiUTRzboN6NeTpmikJEanjkwZ67ginUoFDr5qSABftpwhnxsOoRHlWIxNm46x1Uqo6kL4e+18IRK4j2TB5dLHOomNx7OQJLr51ieZOk+bmHt29jjLRqZUKvP3H/5ydq1e4+L0/pmwZbF+7zJV33sbvtOlsbXD99VcoR0poaIopPo9BQohIYjtazktJmsWOXuVmNM+1cJHryQI3knn2qaC7Y0brF2a0Li+blPWA44U9ClWPQVBUrmOfeHFB0+hpDjeMEldlYU8rHzJAyBRUxmp9n2NWxvGTS/RX5mnadexCqAKQ9JoEke/x0p9+j+5+k0Gpwc7Ghiqsb25uKg2kq9euM+rvcem9d+g22zipwc3LN6lENqLEcvPaTfz1JlfeeJd2s8ng1g5uqcDSyRUuv/YeC08cp1qrcupLDyt58fZuk8e+9ATv/fgtiqIhFCYUdRtDyl2+9A+kl5AxSAPCOB9lVSOo4+/S8VYBYpxByO3q+Y5d4g69dOiSgVRD9EpM3NZIdiyczMYWBdhDRjnqOHFGsm5STTwuvvwmV29uM3NkiZOPP0WlVsMyXfUen11YoLm7x6PPPEdtZoZHn3ue1v4+lXqDZ7/6NaJURAdjqo2GGo2eYorPablpAk2N+MkOORdilNT89u6tHVTIcttnJRH+RYGU4ur6kJHv0uk08GMpp/xiCErDcEJv/2jHll6R4c8yb6X4jYBqW8c0PSW+KGWmwxClosgbcs7JuKbrmLIYq9o9zC/M8fbbbyrBPMe26azv02o2efulN3j+219j8cgib73yJoVSURkkSdlHvBd0SyaSYvpaoEZhAyMmSXLva9OyVADyhx6t3T0qCw2GvSGzR2aI9JTICIl8KXnlu/04lmmlnPEs5c1x3zkvTd1tIHQfZFqKM6tjbJTI1qC+ZGAUoNPzSeLcp9rrxMykNp2dTY6vrvLezXWOHT1PZNpK0HGm0cD3A3xh1EujW6Srv/xN3vVTnJnj7MoorfSMVD9Pw84ydtQ03LQvMcXnOkgctg66FyKLLFLgk/t9MZAHzuFE2yn+RT/3j3nsDDpDn2vzTVrDCpnmqHHmG80l5Sc+Oa7luDz39a/imtDf3ebRCw/zx9/7M46trirF19XV42qXvnx8SXkEtFptvvKr31LGMW6mc/z0ccWWXlpaZL/Von56kfU/eZnm+i7nv/o4/TRiZm5G7aqtRgHHcXjlT17k4acvkJVt6oUFJRluO7lKrEw7xbp4nRu4rqka3qkwupU1pvAjJEXPx2DTMVnvMJQ0x7iUNNFkEk5FaqbMHtXx9nT2b0SkWaLY2XZRgpboRGk0yjb2hcfo3LrB6tHT3Lp2her8EXa3d6lWa1x67918WCBDBYvS/AnanwNPgyk+PXxOgsTPwhclOPyynnOmFkHpd5hKvVQj1IWfcHss9kNdoZRbjFQRIaWZrgL6sHpHgJAldN2pce58mUeKGTWR8Tt+jMcee1Q1aXdbQwqzZ9HKKcMFsOaXFO9h6PjMie2kNqLy3FGSNGUggnrHl9hK+5z4zcfVtNT+MCZNImpPLdHLfCzN5OS3HiXojtjrtbFiXfEuHF1Mh0wl8mahE2aRalor4iB5FjEpK6lsVgQmbelL3PmcVUlKaBtj72VlUDQOGoqIZ0bUFgpU513V+A70IGeGZylJmLB5M+Hpao3SY2e4tB2ymWRsrK3hui6m6yruheu4KhPy9z4rDJ8pHmR8QYLEFB8Ph/OzsWZQBjVtxIw1wCxFGHY+5tnvl9lIGuOS1oc/+l5UQW9XiA1NjTTfyw6WiaGYhhNT1iUjzAOILKmBP+TNy9u0NbFgFRnuMTfBFflf7pFTEc9j5RInU0zZKDd5E234WKdrjNRuPiAmSHxGvQGVuZqacJJSjyzSri7XmM9wSWD0Q8kq8mmnXLn1UDlTS4lF4K8c3lZoHfclJBj6kXhiBGoMd/I4CRTdZKSyFJluEha4SHQcvBJmRrzs8+JWyqligVFqEgQho+FQ2WZuXL+uuB3iRStyI5MsZYopfh5Mg8QU7wuZPJordun6JdUjKKUBdXuYs9qdsdvaeD0vJR7WsPq+ujvaeHEWBwUZR/aysaid8lWIFPN5iJRz7gwQs07ME7MR85aLoY2nhQRpxK3tLgMjpnZOI1ZSLHd5QE829uMGck4mM5QkhxLFEOkMEnTPxbIy+iMfu+gSipasEiG87Schki4H3AURGc6khxGRBTo4idLBUvIZ49FVmXISe1BhYuu29DnylEKxvYWXoTSaTCW8dzj5SkONXhTlpkGlREltHLyGcvxCRroa8M67KY1ijeWnns+9RMIAveCycFxj2ywh3ngr/S63PiOSMlM8uJgGiSneF1L2kfJPPfGYt/sUKyMlYHe/ilIq00iZrshbyxZsRyLFPN49xwn1Th8tSzlWMynZKW9GLkPNYL63T3k2VRLdNwcLeGNF1Ak6ocmVjs7SjJ1LXkwY9F7KlZaHeTrk/bTr1KipZhKOHfWEHSDKq5IxyC9jT9jJDpqV4UUhmpSPdI3BZk+ZFCkunxLTGxPrxhwGKTmpIGNEENoqu1LHT1Ji8YaIJJgJ6ztBGXmHNllR+haxkglRDHgzOGC7q2A2EZuNheynqyCceSaU7gwiqpchWlJOxiAI2DNcEjOiR00FFnmq2dhzQzNrZIdkWqaY4uNgGiSm+ABoBJmpZNZLM4OcC3AflHSHql3meqKx4qa0v/+HPPXt36Df6ytdftPzaMy7jDp9SpnJ3o0tnl49I3t4nCMuiRHhRym2A+GwzU6xoWxXJx7l3Ujnpu8zb3lstBKubXrKlW7UGOE6snDfJdsxuXoJBIcE8Q5E98IITaxVA4dKw8TTQszMpGDnYn/1Ro1SuYQvchtKEUMsR0W7SSdKMmUxmt+WomvSlxEJ+lwvShRiXcNRaq/CJhTVWL2YkolKbGqgVeSKREZDpGTSXBZEsgb1aRwbC4lTnnIPSlUmQvF21qakwLsaXi+ldGbAcmOkZKvjzqLq7RzGNDxM8UlgGiSm+GCote79R4dleaubZWbSGtesDLu5qfgP0Y3L7Ny4qQJLrVak5De4du0aRcvhyrVr/Gq9SqfbxbEstrd2KFVrNBo93nv1dY598zdoWzV1/LKd8PxCgJUZ9GOHYZAvsF7oK7nuXC02b/pOAoX8LLwGsRGd6DBNAoTAwMKKXIxGSmKkSgZcMg75kibyTndbufupJnDmKOkNcZETrpu8EjICm0YZSZiqMlqSZgRegJkUKVomZiVVzW7lW12StodJ4MbKrCjt2tj1XCxQyYKYGVp5TPKMtHGgyK9VGNjC6s56lmJ048aEexrZpsPikkPc8FRZK07Fg+LzoA4wxYOIaZCY4gOQYWYp1eLgfYelVAEoSll7+woL5RrD3S0Wjp1Scutzs1XKtbKavNlb38ayTEXiWlldZjQY0e10VaN1dn6O3b022VgbKY1jlIIK0AsNXt5zWXXhuJsp0bpWoYNe10lHebYgC+rhprFcanBXgJiMo2qZji4cjGqMZugqMIgAt0D9XtcpN6oHxLjhaKiyBLdYICD3wpaMwfcizLCAhkkSpJhZmULFIBXipqapwDOK/Pz6NJ1MRlylwe3mGYf0MAzxwZ4wWpWb4m1C3sHzcVI0CRCRTrBh4AYOq8sVrtllFkc+TWvEzqAMqRDwxl4ZalIqh2QZ07bEFD8PpkFiiveHTDIZI+xS8IGTrRfXrrCSluhdfY9HnnoGLw6onVxQI6rFUpFOq8P87Cwjz6O2OENTG+bHjpbUlNBgVKF60pS5UJbjiK7hHkiVqrFYX+PhasB+L+N6v4V9KlHWs2Fbk/41upWL3k0g464yc3SY4SwEuizV0HxbsZtlSCp3nhsL5onw3tiMaNDpq96EBBFZwR3XzfsS46VXAo2TFSnMagSIdLempNiFKyH3kONMOOfybz+ObvMixDciFMKdTupZymHuoB8x9qGYrPAHz8nIvSbEwz2bibj81lvMPfEsu2+8pTSaakafOPBxqzU1zru9ucnC4hHFGaE2w1WnMVV+neJjYxokpnhfOMTMVkWP+v3vI2ua3Shz6dIaFWeGJOrTXFvjzfVNzj98nvnFBf7w9/9IBQsZJX3qL32VLb2XL4g2DNslNgKHRBZ0VXp3DnlJjs8BvL0FW+0W+rEg5x4YYJUMwu0E+4iI4wmzeSJhfnsq6WDhHZehDDe/n0he5D7Sce46p6aO8tvq8w0iP8QSX3Dxnh6T5NRxVKCx1MRUKDpN4lthGHk5a8x5SJMMMR5V5xQWtmHkJScZl5XSkkxESVdcMoQ0fw1VPBg3x5Uo4KGgJ8eWMpNeTjGaDmno0774Ou29PeXVLqQ73ws4snKUnfU1jhw/yf6Vi3hxTPfaVdyv/KbqpUwxxcfBNEhMcX9kGbPmANOVXfB9fh3qpCKNnclYqcXJ55/m4doi+1s7dLt9tja3WT1xDNPJZTBkoRyNRgRWqiZuVH8gNGlGNZLxiOr9LyPDCGPMbgf9eIBVGfs5xxpWXXSdMrxb+Q69dFJq/PdvYqsR1FhXvQ1hT0tAsnTxms4X+EnWkaQxQRYxaPeoFWaUoqy6XfgKsa4MlmQ0VXoHih8hpaRxvyMnymUk0oR3LfWzPG+JV+JZPembZMItUUFEamoZWd+EgmRHmnL2u/vy5bn5LR9/b4NKZYmTJ45ROLfC3rvraFISi2KMkcf25gZLy8tcu3SJRq2iAl+1XlcyHFNM8XExDRJT3P+NkaWUS8P7/k4WLQkSupg8jZHoGTeTkFJ9jkqlxkMXHmbx2LJaXI+fPM7ajTWVWRRGNlhj0psli7ts698vSGSYUcj8VpPy2QCreigAxBpaIcGuZFgzMLw2PubkkYeIZJEfYYhgXmSSFRNMLGVJGgtXIYtzt0KZECJhEHtkBtgLJdrNFqV5KeFIYNPQYwu9Nh4vVefKWdZJNJ4+Sk0yKX+Zoco0VADKmXTK11ogWYI8RoKSEqkX4p+dkXnj7CLR0cbTTBNIBjTqdLgwv0yz2eLc80/w9vUrOJah+tzF46cIgxBr2FFs63avT+Whx4nssurKjMKpLMcUHx/TIDHF/d8YZjImfX3wCzT5vZRSOr7P7H6AfXSVxqLO5uUec8szHH36SVZOnmZUbXB5ZFKo5J7N/tAmfF/PDhk1jZgvtZR3tFW9Pb2kIOS5SFMCe1L3F0/oya/Vzn68EMdRotRTFR+iGqtehThryKKtSkXicZ3liqvCrpaugpD2hOsni/iw3ScJYqqlBdXUTkfJwSKeRRqZcCICuXPeczBtkc+QKJOoMpAcRBrUKjhIyUd4EKGMvRoqc8AVzkVC5sboElSk73CIF6h+DnUap+Z57/VNFpeP8NpWi2CQUJ2Z4dI773DCcmnt7CiRQdsyKT/6LO/EJrGYnU8xxc+JaZCY4j7IKBKgG/d6hSvIYmxIAf62L5MsbMMkoFWuoOsh+ihmMHOSdzyxWSjjWmV6vo5NzDHTwgsd9oMq8X2lqjMcI2Kltkt2I0Wfzc2k7oCekclMqtqOSxAwiUcJVnn86/FxRSBPTRGN8mtWPANhXEuTetyD8IVNISOnsvsfM6NlQS/P1/LykRcxU3EZDWJS0ZiKI+UvgS9KshlZLVIjr8KbUIHH0smGlspI9MRUsucy5qrHeRAQQqIEB+FB3DHNJM3xMJcJmdA7xOVOq0UUqjpp+xgtq0QQJvhbWwwGfXzfU0ZI0jtRgdG06IcR8T0v2BRTfDxM30lT3APhOVQcWd3v3+xUO3ZxYRvrOU0wWxIJj4St3qySAZ8g1gz88Y+hZnBrME8ks/0Tr+l7zp+yWG6ht2LiSMeevzOLOJgAGp/aNHXMuZSkp2GWpE+gjWl4eVAQ8Q0vNSllt9nLwkHIn8u4ZCTSrWleDpLjWqaJa1h4cUh/r09UrGKXwIgsxSyPwgynKgqFkImTnpExSIK8wa3FGNI78Uy0Ut6cTrt5aiLlJa0Q37YolQkrMa4e27xKA17s1UNN6lbj9vtYDSSTc8QWJVCDAOVyhW6nk2cwEiQkuQqF4j3FFJ8cpkFiiruQMVPsUTR+hmOZBInDjmtj6Yo4ETXXuxf/jIIlftCykOl4miViSO9z4IyaM8TYDYhHGvZx4TPc2WdQ5SRPFte8xKTIcw2d7rVUqjyq5CKCfGqsVRjWgXAmbNJIdJREY+nOY00W7EmAkGzA1U0iyTikIlQt5M33xMYb5s1qp5IzogUSYEQJQzgfwszOpTwydGlwj7Ms1b9JNDJPykyTKSwNLTQOpDgEdilTMiFZMlaxGkc15aIqWlByieUa9vGz9DWdpdkFsmIZXzcVqc9sbhPYxYMR4g+L3/p7/wFnv/8vuPyNX+cP/ou//5EeO8XnG9MgMcU9kAX9kJrFPchr5vnOd5JtqHHSxGJ/JEzpOwOA/FRzR0rUrucXCWPrDjnwOw8O3g6Uehrlx2L0sffQZFFPgiznOkh1pjh2HJSMQbyuCxB1NPTZ6CB+SQnJLtgYUYAmLnOHMpGJtPfBiOx4QZZMRDoFIqCh9vJxht+OSI2EUt1B98fHGZPv5HG2KU560mYwFCP7juc/eZryWkmZKRhPM8VaPgAg/uDFlEhL1BSwtC4mvIr8PBnBVj5Fldka16UXY1Ruu7N78nTygKQXFxQb/KPgP3r0CEaSqGM9/Y/+IU/80/+J/8Nb2x/pGFN8fvHFsWqb4kNDsoH3g1pPIz2vnUuTNtXzDCHN8CPZzd77lpKHbPcbDAOX1fouy7V9VdK6z9Exw4B62EGbEd2ke7kOemRRrEmNf6xvdAj2bEbc0gn7qIkjWVwlA1D9Bg1F7pss2Dm7Ol+ID7O1JYuQRrgIAU6eb6HUQCvpDL22Cki+FignOdMwc3E/SQqkMW1mpIam+iEHC/z42g+egxqdhbRjKd0mo5RQcA1lh6r6H6qhfru8NuFIBPswSAsERSk23X7md2vfftRWtWQQkwBx0G5KEnX7FFOoz9z0ZZjiTmiMIjcfTQ1ETkKau2MVVNEWkttkJytMYaA5rObZQWSwO5x5nxczX4LKzkiVovYG9Tt6FhOI4N3yTAvLFFvQ8SPHO+pJ/V7esSKPobSWhFRwCLpM1y7HBLc0kkHuMS0LeS6mJ4FvnPVI32DMWZgQ15Sc+FjMTz3nwCAdGWohT+0Ep5w/vjccKXHBIYHShhINpkgXv2tfTXjdw54eq8MqnsU4q5DRXSk/mY2YgmkqRra43R28VJPJJimrBeBtgB8ZdOZnSe9yuft5ISWmu/8S8vPZ7//xJ3qeKT67mAaJKe6B+EdsRXUGfpG4bxN3HNK+RdqzcmmLYl7Xl4U+SGyaoxob/SWC2D60yt3r79ALSmx25xgp/+r7qLbKVFMhn2RKBrd/f7j0Ijw4J7GxDVPtuA8jZz2LYlOKKHtMzH4mrm+iY6Sa1Eqi466eypiBbWCqcpZiVut5X8F2hNuQKUJcGAVkhgj4JUrcTwKDL6OzY/OKSY/j8DUpsp5ceCYlI3WrooY40tCPQjVtdfsB40a1lO964F3XiX2T7sIC2d02d58ApAdxd04nP1/+xq994uea4rOJaZCY4j7Q6AYlmmmZtlFUjnP9zEUTsTlZxCf+CpmOIXV0KTklefPA1GPmSl2WKk3lNHf4mIOgyCAsvq+1qug4bfVnSWwbPTCVQuo9V5bqpGJ5mgZKwG/CiZisdDJ+Kk1xCRITW9D84ONdvMhkmEY+6jr+lS2+FkaJ80aD5aCmVFx1CYRCdDMygiRi4/qGItZZIgMut8ukVhrjx6HSVbqjt3GYHDjpL+gafmId+FKo3ksiGUT+HO52kYuHEO/pypY1WbCIXfsjWcN+WEiTOhFZkduXq36eNq+nmGDauJ7iHsi+e6HSplHoKRnqguUThbnpzyRAjEJXlaVklPUwJEDMFLvEiU5zJHajHxb5BNRiuQ1BRDYjpjn33kt6AuIsd8fE01hKVRZ+3chU70GUWXXn0Njs2PPuoHwlzXTNYYkSM9jYqU7PS7HThL5m08+keZ/R22mrfkNlsY4ho6qpRkkX97rcd7pg2GriSBzvVMAS+e+xS1GYxLimg59Fyh/bFR2oTFzBAzUJJaS9XuRh6gaWIZ4UuX6U/NdrpliLEd6GRtgo/ELNIaRJnU83/bHKIKYBYorDmAaJKe5CRsH21RisNmY9y1eWhmi2kNBQVqYbXeE6TMoftxdj24zGAaJOmLyPZdw9kPPEzBT72FrAqGmQmfe2YNVZxJdBHNsKsRrDPRD0U0queamnsASzwypaMaU4nmiKJHDYCSPNI0pjTmlVVimpxnNrlNAno2xZDP0QbeSwpLvsjYYk/ZTqiSqGbRBHMVXdZvd7lzjx3DmIUzbeucHDzz7K/toOpUqFKIsI/YBivURSdNh48SrLXz6D0Y65+tq7DPoDnvj2s8Qljc0fXGb5+TNixk24NVCBrlat0tnvcGJpkY4WElkRvl7MXed+gZDA8Ae/0DNM8VnFtNw0xR2QpWh2HCDGo/y32wvSNI5t1Ve4PcJ6qHegdIJcbnaWVZ/iA+VjD8ExI443tlVjW0ZYMxEBFMmLuwnfisSHsvZMpaE8vH0NE3c68aIuVgy6N9eo9006b26QXu+x9aN3WPIKlG5EHO9VmfHLNLsh71zbUiNBXqtNr9vnz/7sz3B6Q4y2h5H61I7Ooo1cxW+QqSUJFtcuXaX13gabb9/gxsXrRKOQ9bV19JrF2z95gyuvXyQrm/Ru7nPl7UskeyPihsG5R88pX4mkqJPselx/9yqjtTaxF/Lyn7/I9bevYswXWb95i3ndxW4WmZtdIEqme7kpPj1M331T3AFdT9VC78c2jUI/7yuEuiJ4CRFuszdHoDKEewOAxJH9Ye4o9+ECRF5DkXNocYy/B/GWoXoOhVVxZrv/Y6RxrFdFFM9Qo6RqvLWQsuwUOWoURRCcP+ve4N2fvqn+HYYRBgav/+QVlo4s8tbFN8jSGE03OHlslZdeeoVev8/C4oLiSOzc2Mp3T8tFQjtnZLtZFQY2etGgVquytb6t5DaqtcrBM+m7MdW5GrbrEBsZV1+/SLla5uabV3nkV5/hjZd+wtPfeI7Y0Ln+2mVKlRLX3rzCc19/jl6vT22mgS0cjFSjl0SM4iifqhJexX2mwaaY4peBaZCY4g5IE3rSS3DNkIo1yj2YnYQgcvCjuwOE1ODzir/IaUgg+bAZhKmnqn9hJRHBFVOVs9xzsWo6S4C4W/L7oEmtegtZ3kSXLxmv9UsUmw7v3LzC3GyNhbl5Zo+vsHtrXTGw22GXcqnK7s4uFx4+zw9++CO+8fVf4crlK5RkobcMBqMRs/OzNOZn8QYjnKOztBkx6gxJRdk1KGAkFnMLc9TrdWzXptVsY1om1VpVjebOriwoKyPT19VxTj9xlmuvXiLbHLGztYPx0ts889xT2IbBc7/xVa69fgknNXjhay/gFAp01naYnW3Q7YZUGjYbsZT4pgFiik8PWnb3WMV90Ov1qNVq/Kc/vYZbzndOU3y+IcpHJ2e2ccRK1M5INI2uV2Zn0FDlIQkIwpyWhX6p0mF/VMI2Y1ojeX/87EWtbGg8W0rZ9tYI9vOxWvfoncznu3G/t+oBSxoNa21Eoaezs7vHQ488Q6FksFAuoPrNGHi+j+eHmIbB1vYmjzx0mq1wwIYxUP2FCZdCVdYyja7uKS5E8+o2tZU5jKCIU8+o6m5egZMMK9aICxF6mtHJPMqZS+ZbDDGo6BqeNqRWtFWGogyJFB8kZ0/3Mp+K5o5V002CbY3ZmQJu0eDmuwOOnijzJ72UpmJ4TwPFFJ8s/EGf//y5U3S7XarV9x8ymWYSU9wXEghMI2Y4KrDnVYkSg2TcqJYy1FLJJ8tsdFIcAwZhyr4KEB8OS2GI1x7SqFbZrwywFt8/OExwN4tZTRFNGNTChF60aW1vUG3USLOYF3/yOs9cOMHi4gLXbtzg3fcuEsUxjz78MBceOsFO1Oai1lHTUpPu3B2BSGQ2kpT6sXniIMLb76OVCzTNQX5feTlSUXwVI6EQOcytnolWFJGMlCANsLSEvYGnMp5UmM0yvSQcjPFzaaZ9xaFoJBV6vYzKUkTs5VyPPcOnGxamAWKKTxXTIDHFfSE9if1BXZU6vOg2SU4GNct6TEGxnW9PIBkTfYoPASGhef0RxZPQdHpYUUbmm1C8rdJ6x3jrxLOi76npJbdWzEX57hIJ1ByDyvPHMTOdUS8jCDw2dpvUZuap16rMzDQIZKEuW7yR7tLMPLWYH77syZTUwc/6mJWta6RiPXr4fDLpaiW5ZPlAbFc1pRRbKQSkWjQmzom2kqG8L8QD28hyJvjh85V1l6Ju0yUgjjKGnQCnbtARQcKxFnvF1JVXUyt6f+ENuU8sr+3UqnSKTxDTIDHF+0BTgULGXw+jaPsUVF9AJ8xi9aUE8bLSWJz7/vLfB8gyCmmIm/lsMhR+s3oXallCNjLgLlc2JaCh/Kd1xVfIkpj+ZpvKcuO+mYewqqVf4SIZw0O0ui16vTajkUdppkZJg5ub67hLS8oD4p7LEzvQVh+n7JLGKXbJVRmUricEfgc3KOReGuOmu2JvG7FqaEsZyzFi5WftpyL4J0FPQxNin1iU6iIdfuf5JAxUzSIGGpU5k852RMUy6azfpNUZ8PSjT7FhFZkNB8SdJuXF40oAUPWAFIsc5C9U1KDR2UZzi9y0qwhFRHT+mh8QVKaY4sNgGiSmeF/IFNOdk0yaYkzviGCe0yUx8wASp1ArdDGNgO3+++k35ZBdenDlEvFZF9s2Du3IZdlLyXpjSzgrPZD/EFJbGicqODROLuTn9CNMVyab7uNH4VlKNqNfsCkWjtHVR9izZeKWeC1kLKweoTvRcRpv94UMJxmOH0WKlCe7/XDgq/NHsUaqu1glcZS7MwQqc6PJDboYD8FQFGfF0GisEWWYmmJw03WhGKgyWR5KNRpmGfHFE4iDXpJkdIOI2WPzbLX6VAwIf/JdokaNzc0tnrBN1jc3sUydbruD5bicPnGMS+9eZFh0aXV7nDx9WjXo3WKR7NzTtD6iKuwUUxzGlCcxxQdOOt2t6ipLeVvUSO9aeAw9IfwQ8/xWEHFkrsLcQkGJ9E2gOBl2BuUArRKSxTHVoIQ+0mlf3icOY6pHZ1Xpx3Rthvt9goGv5LrvVqgNidmujhgUPbbbfXYaAevlPqNjJqNjFt0FiJKx78NEaTWO8PyA9s09Vc4ybYvyYk1lE5atkfSbmI6dy33Hh7SkJmS+cQkpV3JNVJPaMIQBPn79hK1uJ1ihwxGrzpI9w5LdoDTWQh91Uvx+ytySw8AL8EjRLZu9VltlT5trN7l5/RpxHOKPhqzfuKF6HNVKmebmJoE3ottuceLUadYuX2RnY4PE8ygeUridYoqPg2kmMcUH4P5lI0MTn4V8UZY1UngVorkkkuE5xoY5B0WZA2lUSr0ejdMG0bv7HDm+zICAkux4LR09zmjvtLBKDtHukFopItITGqvLua/0pEdhaFSXG/Q2W2rXL0FDeTMIE1tY2EVxgoC0FBEmOuZIxyjfLmMlcYzXGVBslA/8IMQf2usMqa3O3n72mnbQ/6jM1WHkIIZxuNJvuK0oe3dPQ4JDPiN1qBkvJkSVmHpUZjWs4NkhfSl3ieW1n9Leilg4YRPvtIl6feylY5w9U0QvlEhXjjJTKfHs136FLI5YWlri+LFVHLeAXSoz9Hwip8hMvUaz0+PEl7/BoNnEKpW5Pi03TfFzYhokpvjIiDOdwHMoSNPWyBT7WljBMvVUdjzFm9DFuS412ejNqN9XxZaz73GkCG5ZZ6/VYbZU580XX+LY6ipHVpe4evGamjTyAx/bsYlmZyk+tYwXBUqWQqnDJslBI1nY0F7TY7TlU5mfQSuJ5edtHTxVxhJ5j0PZkEwpSelKFm8JCmmYYFcLaopJfSAc615uRiw1JBu9GikfiYMTZIcyiHGwmCjOHnwXOp+mM6O5WDIWW7QJRtAwS4yMAVGSsr8WUl8ysQoarVGfStFgtNfi1o2bzFy+zIX9XdqPPcrNpWXCIMIyDeYWFnj33XdZXFklNS0q5x/jDS9DOH830gxjrqICZTxtYk/xc2IaJKb4yIgzg824wXIXCmUPc6QpM6GS7R3YiUoZZUlzWU1SNjyNSrtH2B3RqMQEg0wt9O12myNHl7Fsi/Vb6wRBwNEzx/F9H3/oEfohxbFRTyF2OGo5bBpDRmIRmuZWoI5lYR3xGHT3cTKbQbNPoVbEqRZzkb/UUURAv+8RewHhMKC6MkOhUM4XcjFL6o1URlI9Ur/3yQqRbZhLhyvdvvFU1SSoTMpJE8VZ+d1ERHCSbYi43zmtTinTEMHY1NUYBmIBqNHeiLCLOqV63p+Ze/goG5d8TNPg+f/z/4mHf/zDA/H12te/ye/+rb+tRAAdx2Fvd48TZ87SancYxuAfkiifxoYpPilMg8QUHwMaQWqySYOFXu5pXRedI1MkMTQc3cLVLNzQgI6Ps90Hy6ZiBfjDARsv7fLMC89g+tDpdjiyssRaa5uzpaoaBR1aseoZCEGubQVqmGg1KNMYmdSLNq/5HcJwzLg2M+E3U9CKtK5uUz+5oLSQ5GuwNcQIG7Ru7VE7OgOZreS+DzgW4xFXKTsJJgKBh53qZHXWxWrUGduDjn8n3yeBYvKzPHZy24Gpka5TMFx29ISV2MSVoKNpSlCwuS4y4zB3PG/Ai/HQdrfLyCug/9k/4+vjAJG/4nD2T7/H0a9/nd3jJ9RxJVAUCgXMkUfnbp2rKab4hDANElP8XIFinQamzP97LnPiAV2SXTa09gNu3IrxhglnjjU4d6zBcNDj+z/4IXapRLvVo6ibigV94/oNqscXaDU7XN/YZ/n0MeKGyY7WzUs6crqSjMiamL7BqazGerXLKHcVUkUdaTA3Ti1iWBZ2MW8GV+ZdkrZB7YQw9W6Xkg6T8u6ejrpnWiq7t5yU33An8U5lDiIVcsggydYtZs2KyiRkDY8npA9hce/7WBHMn7Cl166ONxyFdG4mRJUKF65fv69j3CPtNtEzzzKztMLp+WXVTFeudz9bOGGKKT4WpkFiip8DSiuWKNO4FRZYSAPipk97PZR1kCN1l9WzDaplB8fScRoNvv3Nb7OT9mnv7XL18hq9TldpIS2uLPHST99ic30Ty3GwC0tkEwM7Da7R4bniPPt7uqrJP1GY5e24QyeIINYZ3mxTOToLgYFZTZgXoT8nYyuIKIkHt50SkuQucLLeG1ruAUGm2B2HntKdMIToZxCKfIaRYWkhSRLnzemJWdDYAW8SAyZ+FTNmSQUI4UCUUx1nHHC8IKbVFYXZBrNK9STFC2M2riXU5ma4lNlsfeev8Pj/7f9yl0oWrP3GX8J47EleTXUGekpHeiLHH8KbjrlO8QvCAxIkMiwjJpWRy48gEDfFg4K8SetrGd5WiFPRmT/qUAps0iSiN4S5msUoGbFtd7k19CjFKafOnWJnc0ftgke9AUdXj1Iul9UibGs6Wd9SU0GZNME1jXYGBdsgjKHfSTFNC80JFAGv/nCN1pUtGifmqQ5q1NMyOjHtbMR8t0CxYJLoMZkKDhltK1EmQuJRvR/31XdZ3IuajS0fiwxCLWEURDSyErGRsp3EmGac8xzG8hpSXlJciXHGM0dZZQ3SN2hoJXwtxk01FhIJFflCv9uNcKsWyjZbCHZpyMVrI+Zm61x1dIIBvHz+KZb+yt/k8X/yPx487o2/8jd55dyTYvJ9wHZfC6cZxBRfgCAhS4xrRvixRSISB1N89pBBbz8hawacWq1gOhqhHTFIRwyGEdfDhAEREaLcqqFZBteu3yCOI0zNxKkVqccNrHKB2soMqZ2hO2J2pKnxVlPTcVOdsi12n8Lh0CgXM1qTMoupUTvZwBRv6M19CkdLlF2DNXk7ieyHHjNftUg1Fy8KYRgq9rNj6syZVTrxQDnF1UYWw1stBr0+Zx+/wHarhR506LdanH7sOLFnY2ouRmYeZBHSaLfLLn53xGBrW5WAThw7RhSM0BZsRlpGS08QpauBlnFzEFM7auNHGW09IEwSAtPill5gc3C7z/G7/9V/w8t/+9/i6Cs/Yf3pF9h67KlP7+87xRcWD0SQkNZjPxDv4yk+q9C9kMLQp3y0SKpn7MU9JdkhO/Q0NdCEpa3kLKTMnxEs25QaJ9BLiRoNvZn1sEuW5JRsZ6PbDAt5jJR50KmImF6sUXKgPUqoeSUKrhDPIpUFWK7NcSpUl2xiv8vbe5mSCXcLOjtewOZaSJaNcCrgLFlqFNUtS6PdZNHKJ5v0NOJ3/+C7/Nq/8lu8/dM3cFyb9k4TwzHZ+NElldHsbO+wsLiozlkqFXnnnXc5c/o0+60mtVIJzTR5e7dH/UiDmYUVlWK0DMXcII5T5Wtdf/cN3O++yOCrz5E8+TQLBYfQNlgP7pTRkMAwDQ5T8EUPElN89nHMNag7OqWjOu20T5jeFsOTEdQsMECmkcaVRI+ATMTu0pRQy+8rAeX9EJAw0CMKWT4J1CgatEYpVmrg6bf1paQU5McGr6010U/qnJ2ZJzIzSppNlAgrPKa5E1PahfqijdfPsB2NUa9De7/FsfllfuXrX6W1s6vkv8MwxLZtIullAIPBQIUvCQ7S35DpLMuy1M97+3tIrensuRPcuLmRl6FkBDbVkJa5kOe6nZAn//f/CUf/yT89KCNt/+W/xvp/+fdJp83nKR5ATIPEFJ8I3ErC7mafooyA3lUx1PKS/UFj96DhO57q+TAtKBHuu+j3WTULGDgUNUNlFCM/ISvc5rf1tYSi9Bfmha0ccIs+ejxQwUMa1JLFZA2N7k0N2zVwKzpJkHHrlausLh6h2Wxx6umHiTo+SVEmeAfMGxX8UGS/TQJbjJVS9i5tUDuxwIxrcDSUqa4ip06t0iiUMQ2No4uLeGaqgtJsIv54UiKL2f3eiwcBQr02wJF/+o9477f+Nfq/+SUJP9N35BQPFKYNgCk+EawFGaGowQo7+W7IxI+ZkjYdZdQjUOJ3Eiw+xHy/2mD7BiMt5JLV5u2szZCYjinlIxkpPahO0UxGXO330JxcBDA3+sn9HSZSGSKX1Fi26GxFrL/jqWtYWT7C7t4e1fI8mR9z89o1xbWYLzd4+Ucvsb2+xR//4R/j90e0iyHFRxbo1RKa9ohWOWRNa9OphCRWSKJHOGZMjZSFRMuVbjNw+hrH33zjvqOtx6+/Tbd1pxT5R/8QT5vYU3zymAaJKT4RjGKHdlwhkXbCXVDBQOQxihFpz1R9BcVREIvSu5bMu39WASIYv02dvF7fIeDFbI83siaxGyr+RH5MiD0IvRR7JiMJYxUo7rkemcSqjKidh7lVh+ZmQHFxlUd+/TkqcyUuv3FRKc+GrRGd67vs7ezR2+uwfuMWcRjhpSEtzUNPTepRjXJQVrIht7I+7+kDbpkxW2ZCW8tU03ooLn4a7HYCGt/6xj1Lufw8evo5li2TRzSPJwyfJTtPs8YsjUOvz2FNXpizNC6ELfV91YKHjVB6+FNM8YlhWm6a4hOCRuAWCHtdrJqQyu78bZalaK7oOekqKzBkTsHI0H2LiiM2qMKbNpQpzyAI8LWASEZWxWNCnNzKtw2JlNjreARUSlmU5H5K2IlgV8NcECY2lOaq+N0hliujtIdUWyXuZJFqrFdqBRZrBdq3fHprGqszDidOn+DWrTVmVubYvr6pnO3K1QrnzpwhHoSYwxp6rFG0HUxLQw4/GEjXJKPt+Opj1YmhtR1SqBg0FmyyMCNMoPSVZ4n/9t/C/O//h4OexA/PnsN85AJrP/wuoTfk3PnznDl+ivlilWw0wLBMfD+gWKnS3d9Tz6E6O8ew06bmVPjJv/h9nvvtv0whDmi1mlhL56eaTVN8Yph6XE/xiUGPExbb2zQuhCpLOIzb7GSNrG2RxTpWOWVOaviJiVUUq1DwBilOQScIEoZxQKhFxEJNNu8NPIchGUxwy8Cop9jzae7nIBLgw4D+RovKyiy9zTazpxYO9JUmqBslqnqRUS9htJuyuliR9jdpzVSLuB3oiquRhjEt22cv6Sv+hozMinSIlmkq6EjZylsTMyCN+oqlfl/UHMJhRncnwohTvnq+geu4hC+/Rveff5/W+ZP8w8tXOPPIo2xurFOuVCCJWD22yub2HkfmZ8EwuXL5Ms9/5Wusr6+jZan6/XvvvEsaBVy5dJm5I8t89Zvf4OL1m/hPf/MOHacppvh5PK6n5aYpPjGkhk5ouvftS0zkKpQAYCNCnw+UsVDqQRqmpFFG4kOhJBpIYDs6NavIjFbB6Zbyfkb0/lEi2AfrSIK9kB0os8r57JLD7Nkl7JLNzKkFwoGngsVB8xzoJSNiEopVHaOU8O7VfdZ6Hv3YYz/q0LM8OvqI61aL3bR30N2I0pjOWszuWxH+boa/i2qC28cSfDtgaI/o2QNK8zpHHy4ivLeXLvdYE8mSk4+x9jv/BvGTT7K0usqbr73G+QuPYJqmmsI68dAFXnvpJY6dPcPrr76mguvexi1ee/FFlo6u8OarL6trHw0GNGZmFHmv39xXPZgppvgkMS03TfHJQRrX4u2gFt/3X9Anm/jUSghsH31UpLuvxJCI7AAztlUZZ+IfUXDFDjQgG5hQi8bSF3cdVIaljHtvn5gCCZRDXK2IbhkkUYJh5enOpLG9futV3rv0pyxUV3jvnS2c66K5lOIaDolh8MjT/6q6pDCxcMyINIB4kFE6lQepLMoonhZuxyFf7iyimwwVYa84Z7JaqJElCS/92fcp2jpf+fILPPvNb/HCt79NvVZnpz8kimMasw2+/q1vUVpc5dd/6zfpyrEbDZ6eOUIHmwvf/HX2Nrc58ewLaIbIjBdJR0NWTzpcmo7STvEJYhokpvgEMZ5Y+giVjkHmUajYFDSL2Ijpm0OKRkTFrKCLNEeWMUhD9Dgik7JPz1L9CfGxOOhRKKE+nVR8FO6+osm1KO+HPGgkQazY0ZWlxoHI3yD06LR7FEyRPV+lP9riaOWkeoz4Qby1+yp1o8gwgTA2VZBIpApWAbOoYazeKfZ3GKM0VIHCT2JqFReHjGrRUvyKzVaXq5cu0uv1lctecWaOh558gtd/8hMePneWf/n//f/geyOe+vXv8JYzRyT6VMAN+d/qDOuTtnYszn6u+vc0RkzxSWIaJL5QyDD1RMmgRKn86T/ZMRg5msNQlYvuB2lMS1nnziuSRTSgZJoEZqAKObKolrMYsekZpgGteJDf2U7RrJRsaKqm94ScJ5NNsqs3indmMGqxDHUl0Cc9BK0iKym4jRL60KS32aG8WFXlKQlEXiz6YTkPou918EsjbNNGU2J+uQ+2k1WJzG4eDIPbnhH389q+/Rwz/DQijjLW9CZ1ipw4fx7RLxxJP8Mb0e92c9a4aRLub/HKS69yfXaW4WCQP0d/hF6YKDa933mmmOKTx7Qn8YVBhmOGnJrZ4czsPiUrHI9Ypko3SxfHtZ/z+EXbZ7YsYo35ft5ER1q7AguTea2megzibS2/czSLAg5O4CqHtgC5JqgYrvKjkFJNMxbnhclCPM4GZNJJbvAMdZy4rWGU85FadSXZbb/rLNLRKrljnfxbbk+jhMFun9J8Ja/h+4YqZWmuzr63x0/XvoeXBry38zpvbvyU1zdexLFzz4mCbjJv22SeRrgDztwHvypCwFswa8wZNdzUVdLje60RKQW2ejqLtQoPP3xe9RZGwwErZ86R1RdJ0lRpQomPdRiEbG9s/Jx/nymm+HiYZhJfICyWhsx1YnpbHZ465bJm2mrRW0w6rDszeGJsoHhfwhA+WJoV21mUqB0NPNnAH/ouK7fKTLKMo9UI68YQ124QOwn10CEyUjbNLslAJzAz1W8opRaGTB+pRT1Di3RKsyJdUaEXj3K+AymDxD+4isNQO+tCghFYzKYVgsEQY8W/0+tB+heZpgKEurkYKyc7QslCoDJbw9BNFThIdLRaxJHqI5xrJ8z9+A32v/QceyceoTAqsnDUVeUciVBFPaW7YxA0DZyjMYZ77+s8ySnqWoGqWVIXHAnZ0EvZuxZStx3m6wVOLNaIwoDXX3mVmbk5DNPkxnvv8Nj8AoHvUywW1cFKlQp6tYE3tZub4lPANEh8gZAmBTbeeY+5eo1SN0K/9APcap331m+xcvwESbvLTKNGY2GJ/Y019ppttaN1XJdTx45xTUY1jx9n/eZNHn3oPFcvXqJRr9IWT4jZGQahz60b13j66We48ZM1Tp88SX22wpm5Gpt6SN/tUTNL6CkMk0DV+qUMZPsFdM3CyETcO1FktWHo3+nzcL9A4UR4o5g01DDHDLIJ+U5c6w6PzeaBQjSkoPVek8axuZyDIfcZczCe/l/+Fxz7h793wF9Y+1u/zat/778k6FmUKrnUt4zBdtoelbMQG/cS//RM4yG9QUHTqWLRSWEvDNldC1StaGHV4eHyLJZmq8e000RJnrt1GUEUxnpEXKzw6//Ov6+0nMppxMWf/Ihi7T7WqlNM8UvANEh8YSC+ykUeOX6G1s42F9+5SJKG7G5usLA4r5qo/mhEOxaOg0lzd4fl46fodjoEcvv2JqN+D7/XViWqjSuX6HXapLHPzPwi4XBAmkYsLS+yv7dH5Hls3rpFmJykPChy9sgcW0aX3binSlBVs0jJcBTDOg40JaC3l3SVhafggwLEBKLIEZcCXMfCH+ZN5EkPQquPM4i7A4uVYi8aGA3xpL59jtrLbx8EiPzVgmP/w+8x/Hf/ba7WzuPYGk7JwOymVBsGvnEnkzv1NOKOgevIRJRObGps+CEdL2LHi/NeTDnGK0Az7rJgzSKMilacUf32X6In3hSigaXB6770jPImdMkoUv/qb7AjPivTTGKKTwHTIPEFgpQreoZGoBkcefhhenu3qNYaBCGYhSLVI8s0Fo+wu77GyrmHKTVmmJmbJQ1DdFJMt8CR5WV02+HYiRPU1teZn5sBw8LUEoa9Hp1Bh8X5OcqlIvW5BfzhiLWra/SaNZ748hP0Uk8xnUWee9KvSI2UJIsPAsRHgZDcZldstjdcrEpEZCfKf+KDhAO1+4z/zPzo9ftqKlV/+gruX7lAOEo53ihwaxhwdrbMFWI8VYNCkeiEyJfqMdEg5oYubnMGvqicL0ToK0Kky/A2MzpJQg+P2WjEqlbFI2U7tvCVj8q9FzxMMoYHuc0UU/zyMQ0SXxhkVAsdmiWDlv0ou2ZA/dwpNvwiQZyXPji6yPUswzrZYAc4YetoV1+l22mrWsrZhx5StfLm7p7yVFg8cgS3WMkbxtEIw9I5+dApdrd2GfoeJTNlv++TSZYSBFRxuWAt83p4S7nBLZo1NTGkJRqhqMd+DMjUkOl6uAUTs2eS1oYkljQl7r+wyiST8CNG+zJymuHWipiuRetLT9wTV+Tn4ZefpVDWiXdSiqFOEGnMFIuczjIuhV0CMybq5M304lJ+Pj/2SMXXWk+JxVVPIATD8ZiIZEl7aZ99+qSmzqJRZDNtEKkmzVR4aYoHC9Mg8QWCuLzteWVCmegJLfqhNfnNHfeLxzvtbpQS7u5i6Dpvvvoqs3NzapZ/Z3tbNVW3JJNYXMTUY1r7e/iez7A/UGOh+7tN5cNQWlimvW/xpScfUsccZeJNl6pTyuhr1ShiFTX6vfQOye/JEp+3xd9/Fy2/66ce1fkS/WsabtlW57A0Gbe9V1VVmtuFmTJpLCzvmMiPlQVp5+kLqgchJabJuVv/+l/Hf+YJikHGru9xeb3JyI+5tRey1wkYRTqBLSq0Gu6p21mQ6EZFSJ/j8DhuhiamSeltRrh6VlZKqThiJU7ZGM0QpZO/yRRTPBiYBokvBPJFds+rjX/+cLtVkf+RpvXm2pqSfrAdh9GgT31mhvb+Pg8//ria8deKxQMClxjteJ7PyvEVglGAo2k4RpWm4bGT9FmPWwfH97OINBmq8pNnCktNJ7NTVYqqtA0Kis+Q0SuHahxWEInb3V1sAcVZkP9iiHoGNdvh0coia0mT9eT2+SbQDV194ZgEw1DJfydxyp/+x/8hT/+7f5f6T18jfeE5ik89zTFlfapjztusbXSoli0sI+H4UgWr0mYvTFW5ybVNyppLL/OIklhdj7wWk0BhuJry1HYik9DJR33VX0IqY3aKG8TYUq6aBokpHjBMg8TnHLqWMFPo0/YrJOndfOSfjXK5wtLKCqPRCMdxlGSEGte0bHAKDLstrMWjzFgpze0dFUiqp+dxLIdrL11UWkpRL+Gt/ibWXRpiMsXkjbkRmYWaNpJdeBmbN777E57+0jOEo4BOu8PCwjy7u7scO3OU0QzsZzIqm8t2xAON5m7EwnKJuaJNMS1S0RxOmfP0M59ueh/98jGckq2+5FijQUD74fMYzz/LYmygxxnXtn38OGFnZ4unz5+kP+zTarY5snQE1yhwxBHL1QwnM1l/8QqrL5zF00OSJEHXTLQ0IzGzXDVWRmmbAcliUZXJughfQqjgEKQmXiz0uimmeLAwDRKfa2SULGExayQi0f0R0Uth9uEnqUjtPgrZsyw4YpHECW6W8UaisdRY4t1RylyxRPn4BQzHZ+i0sAiZfXqVdi9UXhLmz7AwVxtuJ0WPhYMwUKOgl15/l1q9RqfTYWVliW6rx8Z7a/zKlx/ntW7Gxr6PJlIdZkZxNaZRMVmlgifmDaLjmka0Wh5JpGHNiMf2B2dQzmyRdquF4zoUMof9rZCiY4MWsbO+xiUTtra3KJVK9Ho9NNdW3t2dXp80itjfb1JyXAI/YBiIk53BKPBYmJ+j3e1SsF2SKCZ9L5UKFUe+cpqWPlLlqnZYJp32I6Z4ADENEp875HUfQ0sp2MJFSGiO8hn8j4pRkvG2N/nJFqNpCA7X+TOueVKLz1gf5Kwy07NYrhmUbQ+xufZu6rinMpUh/ExoGa5ui9oG3/rLv0ZrvaV24pValWKhgFWwKFVr/PkPXmF9b0D5yAWwdFI7JmlqlOwiqW2jq2ZxRqc3ImjmTnT+uo6znKrruJ+ERu6bXcXVdHq7HbyuQ6PWwNBidpoey/NzNJtNdN1UGlGWY/Huuxd56qkn2NrZob27j+f7DAdDRp7PzMIMG1dv8fCTF7j8zmWWj6+ws7lDOPIp18tYjk0qr2UBklhnmDr3US2cYopPH9Mg8TmCNpbeqDgjqu5Q1c6bw+ov5cwTxKnBrc4CFcdjcdSnYmZUdYdYDxim/gcfRdOoxQ6Wq9Fr98i0lNkLy/SvNnn3nUssLx/DLVTYa3bQCy7b8zXsSokwCil0fF7ab9MoDqiWHI4vlIj6uQ+EXs77Fd5NDb2YYZQyVfo6HCxkHFeIfkZFxzAc+v0MPRlw9foNPD/iuWeeUuHXNEz8KATLINM0jpxeURmA9tBprHJBZQhmBp1ej8e++SxuscDj81X2dvc5d+Ec1y9fZ/WhE/hpjOemYwtXHaWK8tGrgVNM8QvHNEh8jiAB4sTMttJhCiOLtfbCWMjvlwkRvdPo+SWykc5S1FceC2LE87Mg7nT9jT0uvfau2mlLD2RmdpbXXnmDlaNHeOf1V/it3/6LvHy5zO7iClaxRCCrsuEQzNj00pSO5mG0PW7s9ihbOoU5neF2SulMpvSdlLx3UycZZBgFCRig29JwF2pb7mWxc2uXuOcwagUMOwOKRZcgivj+n/6p6tGI1Pijj1zANi2uX7zO5Xcv89xvf5VNu6dY5Ep6vGbgGSM0zRPVQ7SySxRazJlnaC1GpJpGnIQqe+iNSkSicz7FFA8gpkHicwPJIhJMPZ9K6gUlRpGUgD69Eka/6GDK5NDNgIJoJ7kfXFGRBnCcpZi2xdLyEeI4xnRMDENXzWLdNNnodomPnsAu39Xk0DRSw6BJCaSxnaaqj7JSCtH6AWmgITJKuGCVMqI+6rZwT8NspKRGxvbIJw5T0Ktk5ojErPHkV2ZZLpZ5+9ItLl2+yrmHzrFQW2AUBly6clnpLZVKBcWBEGlzcaOLZRLLEAb1xKM6t2/ds4YMR0odBN3U8lFY4XNkWk7rmGKKBxBTFdjPEebc0ZhVIDIXDwAxS9cZLFSUxlF4w0ZXrOIPRmOmjmWLp7NHGObjqcItKBRLFMtFbMMglvHV94V2EDBGrsvOoIFZ1/FuaMSjXK68ZNnUZ2yKczq6lRFuilos1BYsls8WOHa+jFkMKFQNUAZIBlt7O8zMCTs9wHUd1aj2vADDMJUXBIZkECl+EirehXwJ7pQS13IZ9fEEr7ot09CN9BNQ4Z1iil8MppnE5wIZli5OayLykCkGddeXbfOnj9iwKJ8rom8E+FcstKUAs3KbUHYYImgX2w4Pff05Ui0kSROKxSqPPf0ctYWy6mt0hzGFUCcsuB/orZBDY6C5LDs17KMdhtc1jKJJWs2NhvrbOXHQWc5wF01cSzKBnPDmFkoYVkR7aKDNDGicWGTR91QJrDBbwalWOPPQOXRNxAVNgoFPWs7U6Oth7sZhroT8naS0JQ19+Z5fovRLNFKZPjt4ST7Y2W+KKX6ZmAaJzwGEhLVUbSrms8AxIuURMYo+/Tq3LHfXRy4nljLKXZ399xK0Rkx53qBkOsqtzQtDNTGU+Bk9r6sWTplIOn6iipHY4lTB0eoMP331ZWbOrjDfX+PI0uNc91PCNEPijSXZwzjQGJpGkGaIMGzNMgnfucaJZ47jnU5Jb3SxmaXnh1ROxUQ9k8xJcr5FlqggEQYp1dkSoR+y3wnY7nQgjjj264+oxnUv9dn3utROHhXZV46eX2QgK79IrJtjS9Sx1/Th5rgElIbt4EcR80mRUNjWcjVlm85Aggmc0nx2dIeqkmPX2Ray3n1CxjTvmOKXhWmQ+BxA3OYKlsyn5v2IllfBiyZb1U8bGqPI5kaicXa2ie1nOJpLNXSYo0KqZ2w6TYaZpyaOdDtVK2K4YVDcKyk5jNMrdcU5+Bf/7A85fvoU1SNHWRq2Kb/1JssPXcDv9+hsbbJy4jh+ELK/tcWFJ5+ldf0yM7UaL73+Bl5nn4cev8DW/j4LpkZ3Y51CpUSvPeCxLz/OzVeusKvtc/rZC4yimELFIRiO8AZdaVGQzdnsNUfoxQQtdFSmsM0mbk0aLeIcZ0DfUkHjwCq1lEuQCxwMappDzXK5mYx44/d+xJPPP0VnZ5dafZbH3YzY89h4+w2OnD7LQrVEKnLj3RGl+QX8dotyrcKg0yUzLdZK8wymqrBT/BIwDRKfA4xih53BDFV3QGtUZRAUpLDBgwPZ2RsMwgxnNSW5laFbGvWGRZ8AgxTR5Du86y4taVx7q8OFE3PUipJNZOzv77O9t8ff/Y9/nfbadYZ7O7x5/QphHPPo009z7aWf0Oz0eOjJp7j5vT9gd79J4dFHsAydpSOL/PCP/lT1bLZubXDi/En+9F/+KecePsfaS++x32px5dJlZhePYK6Uc30pvcjcikN9xWU/7tEfjBhue2Kwil0zSMOIyNQxixapGaKLIGLupJQr0YoTnpFi6TrnowZ6YBBqMYXYolKpcP3dq0rgMPAC2r1LnD53niQKCNtNtnY38QMfu1Inam/T6vTY8UYq+riVKrNPS5D49P6iU/xicCBVL5JgD4jw74O0kkzxsaHR9sqsdRbpB8VPJEAsvfkqz/+//q/q+wSP/uN/yN/49/+O+j7BV//+f8W//Re+qr5/EMRjIZDsRiQqjgXsBl1evHmL9H/zn/H803+VU//RP4CuiZWZebM3lv6AmPDErG9ucXVjn+rMLH/9X/+f8dof/z43L77Lyd11nvnedzm6eYvQH47r/xB6nvLHEL2pi+++o24/emxFWYFKIFI/r65Qa9SZmZ1Rch+bGxuqIS1M71CLlCte1wuxq5YqQVWMAlbJonaySv1kleJMifKROoNm7nctn27xxsZIyfRE+XGLzEjWt4i6BuvpiEALuPjeezijAfV6nVMPP0avN8J2i7iFAp12m1q9wfziEWzXpVAqsbi0rJwBlfmTbTMzN6+e1xSfn6Cgq3Jp/l0gseFBCRACLZuMX3wARIKgVqvxn/70Gm5ZnF2m+DzjL/wn/yse/yf/44Ea6ht/5W+y+vJPaNy6cXBbe/UExeYezui220FQLPFfv3zjfY6aMacPWJhpopmZEgT8C6vfwBx6B4+PiyX+p+99T/EXlq0GpazA66++zImVOu+2fE6uLtE4vkR66xYP/+f/Oxr/6J8cPPa9r3yFt/7e/5aF1ePc2tzi+COPEK7fpFS3ibWM+mKd9kYHy5LRU4PKbIWd7T1KlRJZlHDr6gZF1+X0+TPsun2VSXTWY2YaRaxKxn7Uy7OLw88oywh6PnbZUT0IJRo4biBIIMrvk99Xgtfum2s8UzrFRXHv+5Uvs7nnUcxiirUaoW6CNyD2RxjVWXzTYS9MiMUnXEuJo0RlRLm7t8ZWlE3LTZ9RaEoRIQ8Kaj+UVyh/6fAHff7z507R7XapVt+fdDstN01xByRzmAQIgXyXnyf/nnyXgHH3bRIwJKP4wX/wn9znVdWINANd5LJJOP9f/rcHAWLyeHM05On/4b8l+F//Z2xf90jdiCNHZrixvYfXWGTQ77P2h6+j/+BHfGUcICaPfeiHP+SdH/+IW4ZN6aEnuPSTPyfstSlWXM786uNcZQ93roClmRiFlHbaJliCHkOyOCOemUM7qjQ7qBkFhmlAwdGVwquXBvcECHVemWxyTVo39pg5OZ+Xy+7jhjdB7fgcP379HU4dO8ONtT3i4ZDIcbh+/QZHj63y3ptvKsl1KTvx8NNcFNcicbRT0EV/fBwSH6Bt5hQfGhIYlHxYlv9VhZLzWcC03DTFHVh9+cf3dWj7sLc9/C/++fu+opaeUTRt5RGx8rt/ct/Hr/7+91hyK9iOwZGGyxOPPMyTz7/A7Olz3Lz4HhvX1zl569Z9H7ty/Qp729ukShzwNQaDPq//9FU02arJZ9NNiMKMJJj4OWSqROTvaGjllMAKuZG2GCbixKcjKiKGIUGjRM0oHjjp3fEBMg1K81UVIA5/3Q9OtcjKr5xjuFhmEGc09/Zo7uww8ANGYaQykUKxqJ5MJ/qMrCBTfKisQb6khCSBIZQg8RmK89MgMcUduPXMl+7Zp95v7/p+t7376//K+7/ZMumW5AIdG3/hW/d9/MZ3vsXF9SZnVxscP1KjUnIpl0QTKScHSjZxffXYfR+7tnqMomNS0kMMA0xT59iJYwyutihpLkEWEhsRkR4TZLEqFyUDjcwHey7vK4gR0k7YZdNv0xwM6Lt9wiyibpQ4YtVZMGu42m1jIL8zJLlD9PD9oZTNY429UVllVfMLiywsLSmehZy8WCwRRVF+v2m28LkoJwkkIMjXZygu3IFpkJjiDmw99pTqQUze0JOehPQgDt8mP0sP4vBt8vP9S035PWxp6I4fcenv/XvE5cIdj5efX/8P/x1Ve5+r5JIiwmd4L8ibwqWCQ2N2lvRLX+HVJ5++47Ebv/Ud0hee5WtfeYqFbMTikQVae00KxQL7e3vEJPk1xgke/sEiHOxo2EvpWCIjP5iiwWliUJ3hJSHdeKRus3STgm5TNG77PjiVApoKYIeeqRxDDJRExjy9HRyygZDuXHppAbs+g1co09Zs5k6exp6dZ/XZL/H0r/0WpcUlkruOOcVnC0mWZw6f1cBwGNOexBT34Hf/q/+Gl//2v8XRV37C+tMvqMAhkKmm83/8+1z8te/w1l/9HXWb9CCkxCQZxPsHiHx3VS6KVehYs4iM37v1fc79F/8tK7/3PTZ++5sqcOijDD/OCWSjMOR6qPHuMOa4oxGUy7QMk0K5zJ/8O/8LdgYDjt64xrUTp6j/tX+VrX/xu/R6P+DsuXPUZmq4RRdTNyjPVImkuSDXYaVkkY7m5BaqIqWh5yrneYwQRVY9d3YwXY1kX6e+UlIlMvV4KScdLnYJiS/JrUolRzIyjXCQq8+qu/lj7oS0K+yUqGuSaBqXM4esfCRXlpX4FMpXik4JrVQiHJfIpvjsIePzhel00xS/FAgb/NG5Jo3EJXID2snw/m/IVKO4UeP0TJ2RofMnA+jFkoVozNu5emGmaxhRiCY1JWCQ6VTMLiulQd4otgxlcyq7fzsWraeUAUE+aRTmybMEiSTK8Nc1iqsyc6ip3b9mZyqQqJHWOCPdsigbRRaPOyq4SYmql4wOrj+JEvzukPpcnSe0OeJRxpo5pGWJc974SSVSlFYRiX67wno8e0j6b4rPIozx988yVWU63TTFA4WyFdOwbTQ/l+R+Pxi6xsPHZ9jcGHKjWKIXSwNXU/IbG/7hj6Rx6BOaUHB8OoafB4lDPd+RMdZRymRZ1hUzWqvmftniMZElGqnoMxl5lmHb0ng2VeZhmTqzJ0p0bqQMujGVunXgzS3H83uektowLDO3ME2FTJfRsC1aihw4vggxmBjvME0jUY30aZD4bEKXXsO4nPR5yxjeD9Ny0xS/cAjLebHoK3KcnmlEH/DpUguppbO4UOGlzocXutNkp36/46kAAXGYYMoab95WZ433dSxHR6/m8hkl3WHGrKhykoy8qgYkOnE9xu5aFBqW8ssWW9RJ07q+OqcyjCK2CjqmqdGR2tH9oIPthFhBQjBtB36mYIynmyU4fLgxhc8Ppo3rKX7hKJohdSdUHzIhtnlZrjN1P0iPYGPUpRPG+B9hTjC9S4bc0Sz1NSkJpUk+7qoyhlQj6oqfhI61kKoAIf2Eqoy5arrKDkQrSgKEjMx6zZTFRol+S2O3FSsfiiSMcevlAzXbZa1IHGckVkpX+bxy32b2oF9S7n1TfDZgCxta/3RJb582ppnEFL9gZCyUAo7qLuXEYjuLD5rI94OIp97yOhTkfmn9Q55DIxraUNBUwzjXvUlvq7FqGYboHmQo2Y9Zp8DOpoc1n2AaBhXDUUHC1nJJEHmoN0jo78f4XkKxZLLBgLf9Ar24RnVo4wRdTGNIVtSwTY1aYuPFGXvakPh+Gq0pDPtFNtM62ZiNPcWDnTnIQEEiweELTlmZBokpfqFwzYSVQsxiVmQ0SsgMkfYej5seyFZkZJGG2viLKJ6dMEj66GlZ2Gof6jytpAy7UKkOcYs+kSbZQz7BJCQ1+behm1TNguI56H5EGhpoPcjqFqajE+gp/jCh14yxHZ1iw1Rfa4lDGOrsRbK4G+xlJrpdxkpS7N2IqhZyvRyh2zFbnq/c7+7h00mpyYwxouwLV674rAUHGZIQqfngCx4cJpgGiSk+PsSwJ0kIZMroYFG8bbBjaRlfL8NJrYCdaeyKMmopI04PGfMkEG7paiG35rJ8wqgcK7e2ohYQiqrqB3mejpHoOntZhaBlc0TbxyxEqhR0eFzVSS2KtqnKTXPLriQYyi41iFP6o4Q0SnEdk2PHysoYqO1FrOsu696EPDc+lvKu0AhMnQCTPi6J1mHO6ZN5OnhGPs1k56WsCaxSgDOKiacciAcOUgDUx5mD91miQ/8SMA0SU3wMZMrDohRFLFZaNEc1ero7tkzNfy/y3ytuBzOyaSe6WlBLrkMzGyj3vAmCbQ1DxGFdGUHV0Ip5EznToWCEdJK7vKw/CJpGz3RI+7OsBC0MOZaTqmAk/tOF1MGxTOL1HnOlEkEUUjxSI5FokWbqumTySh8YSGzaL7jgG6zYGZ0Ehu+7eGjseVVsO6Bc8NSYreJiSKAYixlK8Bt2S3iH2NpTfPrQx8FBiG8fNFDxRcY0SEzx8d44WkrDHmCWIhbsFoWgyP6oqtRMnTRm3upRMDxuFvrUdZf5UZVSFZrJ6I7jCFfBLBpohUQaBgc7b/leKPpY3YTI+AhvU01jmLmEtk1NJqlkjkjTWTBq2J5FJS3ww9d+pKS3v/Gr3+Dl776E69i02x2lm1Qtl+n2+5RLJbRihWNZwtXra6yeu8Cl0uL7OsIlqc5GZ57TcxtY4nQnpL2RCSWZs4V2t8ZOUiP7EFnRFL94KO2xcXB4kGS5H0RMO2hTfAxo+KoJXSUeWWhuQsUZcdzd57i5z7HqDuXZPno5VmWXTuZzzWjxZrKlxDGkMZzKbr1nqR6EIq8dChATuAWfiuF/ZA6r3LsTVWhYRapRmVpcYs6rwjDDa0Vq0sl2XPZ39/GCgMuXrih+RqlYYH19nYUj8wRRxBsv/ZQsS5UPRXtjPVfw/IDXRJOM5PaPqv+S9i3ivk07K00DxAMERaeZBocPhWkmMcXHhIaf2jTDKgtBSwUKw0koyHirWHdODFTkgxhpxFqi+BKq9CKENrGic1PMgqymd9buD6DDXKlLHGn0Qik7fdhduEbHt2mVQ2Zih34z4Xp/jaC1g2E7fOVLz1OYqbK/ucPq8jJnT56kUHRV4FhdXWVzd49jZ8/T8yNYPEZlGOHOH2H/Z5xVRls7wwqmmSj7WDFZajgDCk6QK89OF6VPHRP/kWlP+sNjGiSm+Dmg0Y5LuMOISjhCL0WKhzCBCghCeZZ7SjlpIlEhGDd1ddmeZ5OP7l1Hl99bCYGMt35ECKN5y9NYmEmpiStekDJMdYwkIwhDXvvzHzEajjh+4jiVapm3335HiQHKc5p99FE2ejGFh5/kpchBP/YIqSjG/sxFXmN3dOfYrmQXBdenZo9o+u9v7DLFLwfTOP3RMQ0SU/xckCmfzahOHZvZQR+74osN8+SXeRlm0oyWoBHoKmBMMgdjNucqvJ80tm6lOFlMoH3Ut6pGP7Toe0OKmknt/CKlpRor5TnefvUtbl6/iWmZqpRUsG1u3LypehKlUomZKGIt1RgmBtFEqO8jnPcwhpHLjfYSSTrtRUzx2cS0JzHFzwkpIum0oxLNqKK8qRXGYnqaczsg5P2HOxfdomtTtd07PBrufIdmVJ3hx9oCepHD+gCGBY9UB7NRoGn5RKRYtq0a1jMzDRWkyuUKezv7aLpOUhmxOLfD8foWy3qHQiomROnH3Idqqgz1SfiOTzHFp4FpJjGFQt3UaOgZO4nGESOjlWpKffWUJWUhjauhBIP7w9JjZgp9GrZPIyvihjByEhzdZl/v5RLcY0+Fw0FDpC8czVTHd7Byv4cs10WaQO5bLPhYYUL0Ud+u4xMPMo+CcCR0R+lHye2u62AYOo7rstdssri0yMjzqDZqGCLUZ2YYRky90KHqDYgCEe0r0Y7KH6E3MsUUn31Mg8QUCnNpwK0/+j2e/ct/lVf+2T/hmV//DVqjAcawp5zTnp+dA6cIcUSv26VcqTAYDNT31t42x1bnGe73cDLY2Fjj9ENnCUYx+qxJbEWqeS2QnkUeHCyljTSx+pTvLnk2cXegMJ2IebPHVtS4Y0JIZBPKpq6IUK3oMPsih3A1Ck5ITS/ipSGJTCrpKaefe5jTzz2URy5R7TB19uMeC185reQ5evjjUlmuHpvZPoZ4XASV6btlii8cpkFiCoUsTcnShPY7b5KEAfHOJjfeeItiwWJ7a5cvfeVLCOutvbtDfzCkePIkV19/nS99+QVaF9/jeKnA5VfewTEMLl++xPLiEZq9NnptES1NFYNZLyUUNUcFifv5QL9foJAeR73Rgy5sB3XScdPjuJkyu3+DoR/QOHGBWBZ0EQSX+fckwTE1lgoF/Is7VBcbOIZF4MTcsPeoGgUMXUeKT/3IUwGkqNuqQT1KAuVMl8SJChJpZNDyZ+hmH2XCaoopPh+YBokpFKQWf+ahh1i7fo2V48fodbq4BVe5tC2vHiMKQvbbe2hJwsz8PP3BgEKhwMbamnrsSLKOsQnQyXOnaTU7OEUHtws3b25SeWKeglFA8of7BYi7A4WYBh3ODYS9PFPvY/YMNvwKidT4o5Ct61fY3m1ytNVSntZHT5yiubXF9t4ei8ePYjfK3Lhyi/Wrtzh96jTpvMWoESsva5HsmDTMJasRq9IguS3noRRepYHt12ln5fE9J9c0DRZTfDEwDRJTKLQNh4Wzj7F65tF8J47GfBhi2ZZakMVfoRKGqvQUazphCqtnH+WEETFIdUpZiLV8gjk9xXWhs99lpl7jpy++zJnVo1y8eovq46fVuQ4c2w6Q33A4eJiaQSgGDYdQNG3OzhdY2Yl5ozfCKpqIc8Ps3DxxEtPt9HAKJUzlV53R3NklCEeMRh7tfpcTx49JtexggT88USXPT85pmxZxJn0TDV3UWnWo2CLa1yTJDIahq76mmOKLgmmQmEKhGWU072gFyAJqgcch6pF9h1/j+YLNSz/+AY3ZWW5du85jzzzJG5sbFAsub735Ns9+6Vmi+SX+4K2rrD50il5TehCi6zRhJ4/35mLoIh4Ojmg4yS25X/TdAcVLA3wjYHDpIiezDGOxyumnnqbVbGNXqpi2Q7/V5MjCjPLBNksl+oM9Hv/q0xgFS2pYhA2DAYP7/tXj8ZM73CeR8xt6Ss3t0/Eq+NFH52xMMcVnGdMgMcXHhpDL2vv7rB4/rvwb5o8scemtd7jZXcNxi1y/dgvzK7+O1TjODhn7YYKuyTCoBAoR3UvViKsEBLER1fqw4gwplxMMV0pXEX5s0xxWsY2Y2VKP9ajJTjagHDgsmjVurG8T+R6lJOXo6gq32m22tzZxCwWyYZfSk3PsGSIGEqsS0h0lLDQ1XSV2pALpP9wODvm/0xDiWGO7O88gLE3LTFN84TANElN8bDhpxNLRoziOQ7/XU4u1YRoqs5CF1hSPasWwzhvKcWbeSzU47D+ki4a/zpLXoySrcyEXzusFJdU9qBaGGHrE4nPHWRrMUCjMcO3tdxVTOmw2cQyd0BtRKldYX7vJyUdPkno6sRurkda7Ty5XJWUmuVn+nU3EfGKDOBHxQdBig6ZWnQaIKb6wmAaJKT42bqYWTzz0CLHv8eQ3vs12d8jyN3+LYZQwFw3p9/s0xRnuIwoHrqd1jhm7uBnUnRiz2iPRAlwjzwaS0OCWF1HXc0tUbzhSO//mvoZuWoRBwNz8HK2wR7VYJlP+DodIfWPIY6RRLQeRoKYa1ZnGaFBkK6oTCwNPlzgm01TTRvUUX0xMg8QUHxuDOOUVvQLFCqnIHsnoaj9vNt/QilAuEX5kqU2NKDPYH9RZpkWJEvOOiVaQRbxA5GX0U5+OERMaJgtLS3iep3SY9GKZuZkZOs194tBTBsXSe9Z0g7RnoLkpmR0fTCmpktI4QEwgvfJ+4iozIdXBn2KKLzimQWKKnwve+wSBcEKz/ljQiGwd3ITQ8ChpJknXUOUoy9Wwq4hLBFc8qB9/OHeKy2AIjDQNY/YobjZirt5Rntd6SQKPiPzFRJ4426doRVGlHVO6BYmmeBGJbzHEmWYOU0wxxjRITPFAomAGGFaqJMZ34pjFhsEoTljzbIbDAvt+QflCRGIslKVEYkkh5vXiT5yknHAGxFqMLQEm0LBNnbgUK1fRRDSlhhbioipNcxm1tU1DBYqtsIauOaq5PpWTnmKKqcDfFA8oVLPaX8C86FPrugyudBiENu6tDrN9DT2zeYgRs5de5lxvk6OuwenuJvOXX+UxbcRq2SR4s0l0tc9MXEDb6rBqzGJfHXE8q1DrRywIhW6rxWKjQHZzj2LkUrNLrKy/xxl7Kio9xRSCqTTlFA8kotRkOCrx2g9f4Y0/+S5dexbfK3HlpRdpvfsmp4sGWy/+OaeOH0PvdXhC99l66zUWlpfZffXHlEYJW9c3SPs+RTTeeuVNvL0e7736Dn5nwObNdTo7LX78x39OY2Tzgz/6c3pCAAz6vPvijzB2bqnMZIopvuiYBokpHmi02i2GgwGa42B3miRRxPbNGxxJfeIoRM9Sbl67itfvYRdd9LkjeCOPWrXCytEVdrZ3VWukvd/i2luX8P3g4NjCmmg0Gvzouz9gbmGeomHSv3aJmdkZbr3zBnPSF5liii84pj2JKR5caBpnzp7j+Pnz9C69paaQTj7+FF6vQ9TaZ/HoMS6/965a7o1qgyxOufWD77J6apV2r8fO1g5hFCgC3YmzJzn3K0/gewGGYeI4NpZlcfbCQ1y+eIVTZ89Rc3S2O21OPPksm++9w3wasDUWHJxiii8qtGxCLf0A9Ho9arUa/+lPr+GWp3LJU/xyUDN1FsyMfqZT13Nfi/9/e3fW3EZ6nXH833ujsZEgCIAUV0kzomb1jMcuJ85VKrlILnKdKn+dfKekkovEKdc4tide4lUbOdxAkCAJYm/09qa6qZnIjmFrRgtF4vyqUJJAUARRZD94l3Pe4xC8dOOTnp4cpyiGQxLL5jyBLXuENvaJPD1bjM6nb4EsRWgmWdPAgfIp4hLpGkFo0514qMTJOuCmgZQuesdxQjOCugk+Oof+s9V+Qtwc/qDPP33rNt1ul1Jp+tG6MpIQb6xulNDNyi5iWtk9l+9nzp95zBEuhdin4V7Q0SK0anpRv6x/8NMxRLo3VhlcqIAwVOwFGoOgkLX7SKvA/6/k+/ffKw2efl0hZp2EhLi20lYdFa/HoneBNrqso0hvX9bGKZ1+eqzqqJS194iVTpykPaFkRVqI5yUhIa4t04gouUO0UENz48tmtQkk2bSRTneSpz2YJ0nbzEowCPG1SEiIayuMTfY7NTa9Eyx3ApFGr1vMzqIOIjsbOVxOKQkhvi4JCXGNpS290+ZLaRm1Rm9QoBlUsgOTZOQgxMshISGuNcuMiCKD8YVLT3clIIR4ySQkxLVeuF7MX9ANc5yHJRk8CPEKSEiIa8syInQ9YZyeOf2Hh0UIIV4KCQlxbQXpwvVF/enuJSHEqyAhIa6x9BwJCQghXiXpYCaEEGIqCQkhhBBTSUgIIYSYSkJCCCHEVBISQgghppKQEEIIMZWEhBBCiKkkJIQQQkwlITHzFKYeUXYG2Eb4/05om23qmZsQs0kqrmecqcesz7dwzDA70vOgUyNIrKt+Wm8AhaEn5KwJg0nuqp+MEFdGQmLGJemRnrGBMqLsEB9pc3HJ0BJuldpZiI4CV14XMbMkJGZcGgr73Rq2HjFJrJd+MdRIsIyYMA2ia3MY0OXZ2QVnnB1qlE7D+ZFz1U9KiCshaxIzTyNWBuPYyUYVL+8irjC0mOXiGbcXmix4vWvySitcM2AxP0TXNCxdo+ENs/vTVyZvaOiawtK+3i/P5asraxzi+pCRhHhlPGuSvRM/H5Wy9Y7rYrUQUD70mVtdROuExJpJ7JoYQLn1hPDWbcrBCN/2aIYwShSuBoFKAwTMpzn7RYhMkjQwwdJ16vGYvmnRj/Xs84axBIZ4s0lIiFdEox/kstuz910lXUueTqdNfx7pKCFvh/zPpz9l6/4W4TigVJ4j13mEZdns7mxTOmoyv1RHG4+wO3021lY4brZYnq8w8X3iKMhGIL3hOPs/awsVfN/HROOsc8rde2/zeHuP6so62/OrWYgI8aaSkBCv0Ju0/qCo5bv0A4dh4P3xR0QhcfsAp+CysrqKphu022domkm/06FcnqNYLtNYWuasfUwQBKxt3Ka58wQz53HRaqLSKSrbptPtsnn/HeI45uDJIwzHJdQ15ubnGfT6+KMRhVEfswKT1/5aCPH8JCTEzOxWKrpDhuH0aS9/5yF/XXdp/myH73zyTQI7YX1tDd3OkRtEuElA3tDp2AXurqxgJzGxUlSXGhx0+kzcPCqK0eOAOcclsu1s+aG20CCMwmwUo2wHTVfU5pfwc0VGMt0k3nASEmImxErn80492+b7RylF3jT53cPf0di4xUmrxU9+9BmGYfLuBx9Qrt/i8PCcz8/aVFbWiOZK/PePf8zh/j7vffghwdsf8nicpCdvg+5CWpeY3jLpzijncmD1xX32PMSv6ZsX4gVISIgZoRHGU4oElcIZDViZN6l88g1yrsPhD55gmTZ2Lke7fUpx9Q6VpWUcYvzOGYnn0u92GQ0GDHo9SkkkmwXFjSQhIWaeHscsjjq4dzUmVoSmdAbDIVvvbpEoxeHBEYP2MXuPH1OtVpi//wFR55h8ocCH3/wmumHg62kAydBA3DxSJyFmWzrNNBqSXw5QrkZMQqwSao06saGRWDa1pWVUHOEtVBmGCf3Pn1Can6dYWaCyfhurUMJLvpxbEuJGkZGEuLFKOhR1BZrGaaxN3WpaCIaYeePLzViTJKT8Xo0iNrujIlZicxAp2oUEQ7t80JOhwrj7EZ/HCe5SlSjLCNnKKm4eCQlxY5X6bVqf/gfvfvA+K8tr9G0HAv9yl5Gbg/EIzcuT9wwqWgW/M8EsmSTjCE/P8eCzX9J4/2P0wRlzXhEvCLPAwSugRkMCJ0cvnZqSHUriBpOQEDfXQoPa8jJLd7f40b/9M57rEIQRSaLQdY1+r0dlvszAzjFq5djf32V9Y5Pm4QFra+v0Oh38T/+TublyVhjXHwyydYh6dYHHT3Yo1pew7n1CIAMIcYPJmoS4sfRwgu24nLVPiOMkmw1q3LpFuVzKitxqS0uYtkMURfQ651kRnGWa5PMeF90ud96+jZfPk35qFg6NBmsbm3QvLtB0HcO2ZYJJ3HgykhA31kS3WHjnGxwpg6Vv/xX5Qj6ror5lKLS02llXDMcBx81D7r9zl+FwSHmuQn+8RimfYzQYs762wTkOZ6EClTAxTIq1Vax+j8QrPF2LEOLmkpAQN9ZxqDjO2vKlNW7lrP9F2nzv26rPT/71X4ijiG6vxz9+73scHp/TPTuh9V+f8d1/+Bt+8P1PGfZ8oihm/q/+ll1lPzPwTrv1lZ8plhPi5pLpJjFz0nXm5uFlnyVd17k4P6d70eU3v35AdbFG0h3jD8aMhqOsP1M1GmPLb4qYUfKjL2ZO4PusrK+T8/Jomka5UqK+ssry8jIr761hFC28fCFbt7Btm95JC/2NalYoxOsj001ipkQKLuYb3P/oY9qnZ3jlcno+H6bSKZSKOOh04pBctc5apYERhegLNWnnLWaWhISYObt+QmFxk7i6yb3wHOUP6Q117m7con8R8FOnQtSoECmFp2sESqVr1kLMJAkJMXPS630vUuSJmYsGjCchQWAQBoqHZpWhMtLDv7PH9qVQTsw4WZMQM0lDsR53sZKYziAibwd0zDxtI39ZVS2EyEhIiBeQvtu+nuXGC25AtTTJdjopzcVzLbqJQSIBIcTvkekm8bWl50GX3SFBbGa3KDZR12AXkGNErJWGxKbOaKhBT6F06Di565p5QrwyEhLia0sb5cWJzspcO4uGcWjTn3gMJrksNN6sM64v24IbmuJ2eYBrhhiaQWg4uOsJcc4j77tMAu1PrkOUTQ1DAz9Bjh4VM0FCQrwAjd7EwxkFOGZIyRmRt8dMPJuj7gKj0PlKQaFrCZYRpddywsREKe2lBk05CagEXUpOgqbpeGGOn/3kh3zn777Lr3/4S24vr/LW/AL7E8XEcrHjANM0IYou17FVwi1LRxv16AYx4/IiUZxg6hCEMb7t4gRjuobLSdrGQ4gbQEJCvLD2YI6SO8xCIm11YRtQK1xw0K0SJc/7I6aYyw2oFTrZv/zIpud72fRVOjK5PJv6xQIjSEcBnQHjY4NCzWUyDGgfn9B+3GTnwWPylstod5dxGFGvVmi1zyAMmIRhduZcoVDgJJ/LTqtDN+C4RWtvN2sIqFsOxYLH/v4hja13OK/dyWoyhLjuJCTEC7q8cA+DHIOJg0FMbzxHZ1wiSSf6//AHLj2OwdAI06mfNADihHlLI2/o6LaPkR4SBHiWT9EOyGHTi/uMxxVaI5dJ8vWDYqIs1GYO/yBAGWNUL+HuWxv8/LOfs7Rczyqs05BbWVujd3ZMEEwYp+3EF2uUKxWKpRKHO9tUalWSKOb09JRJEOC6Lqt3Vjl48hjDNPFyOfR0ATwNEyGuOQkJ8VKkaxPN3iKapv7ku/4lW6d0+AhvvoJnKprOPMWgj9U5onx3kVgrkSRPK9diRfc3R2y8t0bi91jJuxxNFJpSTAwdK04IdZPDSfJc680JGmNcShWfwYM2WrXIux9/xL3793Fcl1CzMaMg6+m0VF+gcdGjUCyimRa6YWZ/1hYr2bc2CiJKq5ssBQFaepiRm2Olvkrn/JxeqUIo003ihpCQEC+J9lxTS91YEZ4cc7a3zb37W+z9+/e5d2eTB08e8n74Xvau/Py0Q7PVZHV1le1HTzg+bFGtLtDr/Zb22TmFYoGNtXUeP97BW1rBXn//+dtmaBF6JSGpG+TLZfa293j42wcUyyXubW1xuL/PebtN8+iYt7fu8f63PmFvd5+j3V1WNm8TRyE7jx6xtLxEhE7h/of8YgyMLjdGabkqSrrDihtE6iTEazWKFU6pzFvv3Wd3ZxtDC+mcnmQfU3FCq3XMZOxT8PJ0zy94a+ttcq7LaDDMTpNbvlXnrTubnDQPSUiYjEbPvWs1LaCz0rMkDJ3CnQpJFXzfzw4bCiYBk9GYXrfPeecia+w3Ggy4uOgxHAwZj8eYlkkwHGQn2tWXb+HmcjQT4/eqRWSCSdw0MpIQr5eWUNnYJC7FLDtrLAwXWVxsZDuE4iQmOtJYWmpweNqiOreAk3OorNWyKmhXN1GJIrF1nEYpW3TW51dpP+fUjqYlOKaf/T1dLkg0lXWBHfZ7FMtz2YK0bhos1hu0jo5wvTyBV8xCbX4poZefQ52fkfO8LFjQdCbStkPccBIS4rWKEp1fJB7lcZ9b9bQBd57R0CeXmPglnardoHfapXS7xlhT+HFA7CgSXRGcjFAWnNohmqtBscTRAJIsJP78gna6kD4OHUx9lK2btP0qK3WXj2u3iMYjItNk7f2P6LUOGUcxZr4Ibh4tmFBcXuWhUWTrzjvcufchKhqDaWZNAIW4ySQkxGuXLkvHSr/cSpoknO2dYw0VR8ct1ldXs1HD3i8esHF7g+3tbUrFUlpEwd7uHh///XdJl6m1p9fmSfRVfoQ1en4e24g47FYJI4eLXC5bz/A8jTBR2XSRtTYHq+/SBpqDmPLKPYYJBLHil4mVnUhXNj2obTKUfa7ihpOQEFciiMysYjtnWPS7I6oLdZpHLfrdXhYI6VbU5ucH3N7YzGoPXNemUatjFhySZHC5xVS/bAfyVeonun4+qwpPv3bqIrzcSTV+dtroD6aQ0urqL3zxkQsJBzEjJCTElZjEVlYwV3AS3tq8jWVZrK+vsLhUIy2FsPYc6o0aZjnHQr3KRfsMwzCJLA0juTy3OlbpjqqnZ1g/N+3LgBBC/Hmyu0lcibTlRlqpbUQmv/rVr3n08AkX3T4TC8ZJSLdzATmTk8KE4/MTtrd32N8/QO+FWdFbajhxn9ZkCCFeFQkJcUU0hoFLb+RysHfI7s4B+/tNrACaj/ao1hZpHhwRq8ttsWN/zEm7nc4L4RhW1tvpZDD/5jURFOKGkZAQVys0qVartE+a+P4ATdfYeucex0fHJH5ISXPJWS6D3ig7QvT08xaWZnPSn3+6HiGEeJXkt0xcIQ3HD8kXPGr1RYrFIgY6Dx48YvWTtwgvxmjbPVzDYf3OBpZpYXkuhwMvW4CWUYQQr56EhLhCiuPcHI2/+Es2c4PsnrGuU53f4Cwe4BRdClaRwoaHlYQ4yuV0UuRk4EpACPGaSEiIK6RxNIHWxKIWa1mr8fRMiewjpkVIwrEaEMUGw8DjfFTMdkTJCEKI10dCQlw5hc5xf572oIyhX4aEpcfknTHjwMmqpNPiOwkHIV4/CQnxhkjrFwyS+LLuIYwtRmE6rSSEuEqyu0kIIcRUEhJCCCGmkpAQQggxlYSEEEKIqSQkhBBCTCUhIYQQYioJCSGEEFNJSAghhJhKQkIIIcRUEhJCCCGmkpAQQggxlYSEEEKIqSQkhBBCTCUhIYQQ4uW0Cp8M+l/l4eIa0FB4ls8wa8utXfXTEUK8Js97PdeUUurPPcj3fTY3N2m1Wi/juQkhhHgDNBoNdnZ2cF33xULii6AIguBlPj8hhBBXyLbtPxkQXykkhBBCzB5ZuBZCCDGVhIQQQoipJCSEEEJMJSEhhBBiKgkJIYQQU0lICCGEmEpCQgghBNP8L0PviT4v37VxAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "extent = [128, 134, 30, 35] # kyusyu, Japan\n", + "\n", + "fig = plt.figure()\n", + "\n", + "stamen_terrain = cimgt.GoogleTiles()\n", + "ax = plt.axes(projection=stamen_terrain.crs)\n", + "ax.set_extent(extent)\n", + "ax.add_image(stamen_terrain, 8)\n", + "\n", + "if pos is not None:\n", + " sm = plt.plot(pos[:, 1], pos[:, 0], 'r.', transform=ccrs.PlateCarree())\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/ppp.ipynb b/tutorials/ppp.ipynb new file mode 100644 index 0000000..496099e --- /dev/null +++ b/tutorials/ppp.ipynb @@ -0,0 +1,2644 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a4123944", + "metadata": { + "id": "a4123944" + }, + "source": [ + "# PPP/PPP-RTK Demonstration" + ] + }, + { + "cell_type": "markdown", + "id": "f4191fe5", + "metadata": { + "id": "f4191fe5" + }, + "source": [ + "\n", + "## Examples\n", + "\n", + "This tutorial provides examples to show the basic features of CSSRlib for PPP-RTK, and PPP positioning using correction data from open PPP/PPP-RTK services. The following examples are included:\n", + "\n", + "- PPP-RTK positioning (QZSS-CLAS)\n", + "- PPP positioning (BeiDou)\n", + "- PPP positioning (Galileo HAS)\n", + "- PPP positioning (IGS)\n", + "- PPP positioning (MADOCA-PPP)\n", + "- PPP positioning (PPP via SouthPAN)\n", + "- PPP positioning (JPL GDGPS via RTCM)\n", + "\n", + "Note that despite the static setup\n", + "of the antenna, all data sets are processed assuming a non-stationary antenna. A motion model has not been used for the\n", + "receiver position. Instead, a sufficiently large amount of process noise has been added to the variance of the predicted position\n", + "states.\n", + "\n", + "Click on the arrows in the left margin to open or close an example" + ] + }, + { + "cell_type": "markdown", + "id": "1c4b06ee", + "metadata": { + "id": "1c4b06ee" + }, + "source": [ + "## Example 1: PPP-RTK positioning (QZSS CLAS)\n", + "\n", + "This section demonstrates PPP-RTK positioning using uncombined receiver observations and recorded QZSS\n", + "L6 binary messages." + ] + }, + { + "cell_type": "markdown", + "id": "lnzfElSPNdSa", + "metadata": { + "id": "lnzfElSPNdSa" + }, + "source": [ + "First, load the required Python modules." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "2c71bec7", + "metadata": { + "id": "2c71bec7" + }, + "outputs": [], + "source": [ + "from copy import deepcopy\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from sys import stdout\n", + "from binascii import unhexlify\n", + "\n", + "from cssrlib.cssrlib import cssr\n", + "from cssrlib.gnss import ecef2pos, Nav, time2gpst, timediff, time2str, time2doy\n", + "from cssrlib.gnss import rSigRnx, sys2str, epoch2time\n", + "from cssrlib.peph import atxdec, searchpcv\n", + "from cssrlib.ppprtk import ppprtkpos\n", + "from cssrlib.rinex import rnxdec" + ] + }, + { + "cell_type": "markdown", + "id": "a0d40a99", + "metadata": { + "id": "a0d40a99" + }, + "source": [ + "Define the RINEX observation, navigation, and antenna files, and starting epoch" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "8abf712f", + "metadata": { + "id": "8abf712f" + }, + "outputs": [], + "source": [ + "# Start epoch, number of epochs\n", + "ep = [2025, 8, 21, 7, 0, 0] # year, month, day, hour, min, sec\n", + "\n", + "time = epoch2time(ep)\n", + "year = ep[0]\n", + "doy = int(time2doy(time))\n", + "let = chr(ord('a')+ep[3])\n", + "bdir = f'cssrlib-data/data/doy{ep[0]:04d}-{doy:03d}/'\n", + "navfile = bdir+f'{doy:03d}{let}_rnx.nav'\n", + "obsfile = bdir+f'{doy:03d}{let}_rnx.obs'\n", + "\n", + "atxfile = 'cssrlib-data/data/antex/igs20.atx'\n", + "\n", + "nav = Nav()\n", + "nav = dec.decode_nav(navfile, nav)\n", + "\n", + "atx = atxdec()\n", + "atx.readpcv(atxfile)" + ] + }, + { + "cell_type": "markdown", + "id": "c803c03a", + "metadata": { + "id": "c803c03a" + }, + "source": [ + "For reference, specify the rover position." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "d60ec1b6", + "metadata": { + "id": "d60ec1b6" + }, + "outputs": [], + "source": [ + "xyz_ref = [-3962108.7007, 3381309.5532, 3668678.6648]\n", + "pos_ref = ecef2pos(xyz_ref)" + ] + }, + { + "cell_type": "markdown", + "id": "ad8ce40c", + "metadata": { + "id": "ad8ce40c" + }, + "source": [ + "QZSS CLAS correction messages are encoded in Compact SSR format. In this example, the recorded L6 data is used. Also load the grid position of QZSS CLAS as defined in IS-QZSS-L6\n", + "." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "03836a0a", + "metadata": { + "id": "03836a0a" + }, + "outputs": [], + "source": [ + "file_l6 = bdir + f'{doy:03d}{let}_qzsl6.txt'\n", + "prn_ref = 199 # QZSS PRN\n", + "l6_ch = 0 # 0:L6D, 1:L6E\n", + "\n", + "griddef = 'cssrlib-data/data/clas_grid.def'\n", + "cs = cssr()\n", + "cs.monlevel = 1\n", + "time = epoch2time(ep)\n", + "cs.week = time2gpst(time)[0]\n", + "cs.read_griddef(griddef)" + ] + }, + { + "cell_type": "markdown", + "id": "owr8qtA63hn4", + "metadata": { + "id": "owr8qtA63hn4" + }, + "source": [ + "Specify signals to be processed. We choose L1C/A+L2P(Y) for GPS, E1+E5a for Galileo, L1C+L2C(L) for QZSS." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "Loa7UDk43iBF", + "metadata": { + "id": "Loa7UDk43iBF" + }, + "outputs": [], + "source": [ + "sigs = [rSigRnx(\"GC1C\"), rSigRnx(\"GC2W\"),\n", + " rSigRnx(\"EC1C\"), rSigRnx(\"EC5Q\"),\n", + " rSigRnx(\"JC1C\"), rSigRnx(\"JC2L\"),\n", + " rSigRnx(\"GL1C\"), rSigRnx(\"GL2W\"),\n", + " rSigRnx(\"EL1C\"), rSigRnx(\"EL5Q\"),\n", + " rSigRnx(\"JL1C\"), rSigRnx(\"JL2L\"),\n", + " rSigRnx(\"GS1C\"), rSigRnx(\"GS2W\"),\n", + " rSigRnx(\"ES1C\"), rSigRnx(\"ES5Q\"),\n", + " rSigRnx(\"JS1C\"), rSigRnx(\"JS2L\")]\n", + "\n", + "rnx = rnxdec()\n", + "rnx.setSignals(sigs)" + ] + }, + { + "cell_type": "markdown", + "id": "gu9YXwY56gwE", + "metadata": { + "id": "gu9YXwY56gwE" + }, + "source": [ + "Initialize position and antenna PCO/PCVs" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f4ea3b73", + "metadata": { + "id": "f4ea3b73" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Rover] Receiver: SEPT MOSAICX5 Antenna: JAVRINGANT_DM JVDM\n" + ] + } + ], + "source": [ + "if rnx.decode_obsh(obsfile) >= 0:\n", + " # Auto-substitute signals\n", + " rnx.autoSubstituteSignals()\n", + "\n", + " # Initialize position\n", + " ppprtk = ppprtkpos(nav, rnx.pos, 'test_ppprtk.log')\n", + "\n", + " # Set PCO/PCV information\n", + " nav.rcv_ant = searchpcv(atx.pcvr, rnx.ant, rnx.ts)\n", + " # Get equipment information\n", + " print(f\"[Rover] Receiver: {dec.rcv} Antenna: {dec.ant}\")" + ] + }, + { + "cell_type": "markdown", + "id": "R_V2QknK68gO", + "metadata": { + "id": "R_V2QknK68gO" + }, + "source": [ + "Print available signals and selected signals" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "voS1RIxb68wl", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "voS1RIxb68wl", + "outputId": "878b5434-0e0e-418b-b657-3294d4601a50" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available signals\n", + "GPS C1C L1C D1C S1C C2W L2W D2W S2W C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", + "GLONASS C1C L1C D1C S1C C2C L2C D2C S2C C2P L2P D2P S2P C3Q L3Q D3Q S3Q\n", + "GALILEO C1C L1C D1C S1C C5Q L5Q D5Q S5Q C7Q L7Q D7Q S7Q C8Q L8Q D8Q S8Q C6C L6C D6C S6C\n", + "BEIDOU C1P L1P D1P S1P C2I L2I D2I S2I C5P L5P D5P S5P C6I L6I D6I S6I C7D L7D D7D S7D C7I L7I D7I S7I\n", + "QZSS C1C L1C D1C S1C C1E L1E D1E S1E C1L L1L D1L S1L C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", + "IRNSS C1P L1P D1P S1P C5A L5A D5A S5A\n", + "\n", + "Selected signals\n", + "GPS C1C C2W L1C L2W S1C S2W \n", + "GALILEO C1C C5Q L1C L5Q S1C S5Q \n", + "QZSS C1C C2L L1C L2L S1C S2L \n" + ] + } + ], + "source": [ + "print(\"Available signals\")\n", + "for sys, sigs in rnx.sig_map.items():\n", + " txt = \"{:7s} {}\".format(sys2str(sys),\n", + " ' '.join([sig.str() for sig in sigs.values()]))\n", + " print(txt)\n", + "\n", + "print(\"\\nSelected signals\")\n", + "for sys, tmp in rnx.sig_tab.items():\n", + " txt = \"{:7s} \".format(sys2str(sys))\n", + " for _, sigs in tmp.items():\n", + " txt += \"{} \".format(' '.join([sig.str() for sig in sigs]))\n", + " print(txt)\n" + ] + }, + { + "cell_type": "markdown", + "id": "0lkUmvxU7Xs8", + "metadata": { + "id": "0lkUmvxU7Xs8" + }, + "source": [ + "Get grid location and open L6 file" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "i8sLKFEc7YFX", + "metadata": { + "id": "i8sLKFEc7YFX" + }, + "outputs": [], + "source": [ + "pos = ecef2pos(rnx.pos)\n", + "inet = cs.find_grid_index(pos)\n", + "\n", + "dtype = [('wn', 'int'), ('tow', 'int'), ('prn', 'int'),\n", + " ('type', 'int'), ('len', 'int'), ('nav', 'S500')]\n", + "v = np.genfromtxt(file_l6, dtype=dtype)" + ] + }, + { + "cell_type": "markdown", + "id": "4061cecc", + "metadata": { + "id": "4061cecc" + }, + "source": [ + "Set runtime to 3 minutes and initialize variables" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "defdd418", + "metadata": { + "id": "defdd418" + }, + "outputs": [], + "source": [ + "nep = 3*60 # 3 minutes, increase this for longer run\n", + "\n", + "t = np.zeros(nep)\n", + "tc = np.zeros(nep)\n", + "enu = np.ones((nep, 3))*np.nan\n", + "sol = np.zeros((nep, 4))\n", + "dop = np.zeros((nep, 4))\n", + "smode = np.zeros(nep, dtype=int)" + ] + }, + { + "cell_type": "markdown", + "id": "90dc765a", + "metadata": { + "id": "90dc765a" + }, + "source": [ + "Run PPP-RTK positioning using `ppprtkpos()` for 3 minutes. QZSS L6 messages are encoded in sub-frames (5 second per sub-frame), for the simple implementation, the recorded data is decoded every 5 seconds." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "wi0RcbzX6u4t", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wi0RcbzX6u4t", + "outputId": "5d592183-ca13-4ea2-a880-96d422cccf40" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 2025-08-21 07:03:01 ENU 0.017 0.002 -0.086, 2D 0.017, mode 4" + ] + } + ], + "source": [ + "# Skip epoch until start time\n", + "obs = rnx.decode_obs()\n", + "while time > obs.t and obs.t.time != 0:\n", + " obs = rnx.decode_obs()\n", + "\n", + "for ne in range(nep):\n", + " week, tow = time2gpst(obs.t)\n", + "\n", + " vi = v[(v['tow'] == tow) & (v['type'] == l6_ch)\n", + " & (v['prn'] == prn_ref)]\n", + " if len(vi) > 0:\n", + " cs.decode_l6msg(unhexlify(vi['nav'][0]), 0)\n", + " if cs.fcnt == 5: # end of sub-frame\n", + " cs.decode_cssr(bytes(cs.buff), 0)\n", + "\n", + " if ne == 0:\n", + " nav.t = deepcopy(obs.t)\n", + " t0 = deepcopy(obs.t)\n", + " t0.time = t0.time//30*30\n", + " cs.time = obs.t\n", + " nav.time_p = t0\n", + "\n", + " cstat = cs.chk_stat()\n", + " if cstat:\n", + " ppprtk.process(obs, cs=cs)\n", + "\n", + " t[ne] = timediff(nav.t, t0) / 60\n", + "\n", + " sol = nav.xa[0:3] if nav.smode == 4 else nav.x[0:3]\n", + " enu[ne, :] = ecef2enu(pos_ref, sol-xyz_ref)\n", + " smode[ne] = nav.smode\n", + "\n", + " # Log to standard output\n", + " stdout.write('\\r {} ENU {:7.3f} {:7.3f} {:7.3f}, 2D {:6.3f}, mode {:1d}'\n", + " .format(time2str(obs.t),\n", + " enu[ne, 0], enu[ne, 1], enu[ne, 2],\n", + " np.sqrt(enu[ne, 0]**2+enu[ne, 1]**2),\n", + " smode[ne]))\n", + "\n", + " # Get new epoch, exit after last epoch\n", + " obs = rnx.decode_obs()\n", + " if obs.t.time == 0:\n", + " break\n", + "\n", + "rnx.fobs.close()\n" + ] + }, + { + "cell_type": "markdown", + "id": "sZlaW82rOoVK", + "metadata": { + "id": "sZlaW82rOoVK" + }, + "source": [ + "Plot the solution position relative to the reference position.\n", + "\n", + "A complete set of QZSS CLAS corrections is available every 30 seconds. An ambiguity-fixed solution, as indicated by the green dots in the plot, is available just after the QZSS CLAS correction messages have been received. As expected,\n", + "the positioning errors are at centimeter level." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "11df8601", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 778 + }, + "id": "11df8601", + "outputId": "afd73534-13a6-4451-f5ff-c1b56ddb330e" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from cssrlib.plot import plot_enu\n", + "\n", + "plot_enu(t, enu, smode)" + ] + }, + { + "cell_type": "markdown", + "id": "6EC-s_PGXwlG", + "metadata": { + "id": "6EC-s_PGXwlG" + }, + "source": [ + "Plot the horizontal errors." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "ce83ece4", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 547 + }, + "id": "ce83ece4", + "outputId": "481782dd-bb3f-4363-f5b2-595522597883" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_enu(t, enu, smode, figtype=2)" + ] + }, + { + "cell_type": "markdown", + "id": "rKUv0nEl8vDm", + "metadata": { + "id": "rKUv0nEl8vDm" + }, + "source": [ + "\n", + "## Example 2: PPP-AR positioning (IGS)\n", + "\n", + "This section demonstrates PPP positioning using IGS offline products from the International GNSS Service (IGS) and a Septentrio PolaRx5 receiver. In this case, the orbit and clock offset information is obtained from\n", + "SP3 files and, optionally, Clock-RINEX files can be used. Pseudorange and carrier-phase observable specific biases (OSBs) are\n", + "loaded from Bias-SINEX files." + ] + }, + { + "cell_type": "markdown", + "id": "_ltFQVZeoWr9", + "metadata": { + "id": "_ltFQVZeoWr9" + }, + "source": [ + "First, load the required Python modules." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "iP6Q1ifMKzDG", + "metadata": { + "id": "iP6Q1ifMKzDG" + }, + "outputs": [], + "source": [ + "from copy import deepcopy\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as md\n", + "import numpy as np\n", + "from sys import stdout\n", + "\n", + "from cssrlib.gnss import time2doy, time2str, timediff, epoch2time, ecef2enu, ecef2pos\n", + "from cssrlib.gnss import Nav, rSigRnx, sys2str\n", + "from cssrlib.peph import atxdec, searchpcv, peph, biasdec\n", + "from cssrlib.pppssr import pppos\n", + "from cssrlib.rinex import rnxdec" + ] + }, + { + "cell_type": "markdown", + "id": "GITYAIKuKzDR", + "metadata": { + "id": "GITYAIKuKzDR" + }, + "source": [ + "Define the input data and parameters for this example. For signals, we choosed L1C/A+L2P(Y) for GPS, E1+E5a for Galileo." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "B8Rm_x8yKzDR", + "metadata": { + "id": "B8Rm_x8yKzDR" + }, + "outputs": [], + "source": [ + "# Start epoch, number of epochs\n", + "ep = [2025, 8, 21, 7, 0, 0]\n", + "\n", + "time = epoch2time(ep)\n", + "year = ep[0]\n", + "doy = int(time2doy(time))\n", + "let = chr(ord('a')+ep[3])\n", + "bdir = f'cssrlib-data/data/doy{ep[0]:04d}-{doy:03d}/'\n", + "navfile = bdir+f'{doy:03d}{let}_rnx.nav'\n", + "obsfile = bdir+f'{doy:03d}{let}_rnx.obs'\n", + "\n", + "ac = 'COD0OPSFIN'\n", + "\n", + "orbfile = bdir+f'../igs/{ac}_{year:4d}{doy:03d}0000_01D_05M_ORB.SP3'\n", + "clkfile = bdir+f'../igs/{ac}_{year:4d}{doy:03d}0000_01D_30S_CLK.CLK'\n", + "bsxfile = bdir+f'../igs/{ac}_{year:4d}{doy:03d}0000_01D_01D_OSB.BIA'\n", + "\n", + "# Set user reference position\n", + "xyz_ref = [-3962108.6836, 3381309.5672, 3668678.6720]\n", + "pos_ref = ecef2pos(xyz_ref)\n", + "\n", + "# Define signals to be processed\n", + "sigs = [rSigRnx(\"GC1C\"), rSigRnx(\"GC2W\"),\n", + " rSigRnx(\"GL1C\"), rSigRnx(\"GL2W\"),\n", + " rSigRnx(\"GS1C\"), rSigRnx(\"GS2W\"),\n", + " rSigRnx(\"EC1C\"), rSigRnx(\"EC5Q\"),\n", + " rSigRnx(\"EL1C\"), rSigRnx(\"EL5Q\"),\n", + " rSigRnx(\"ES1C\"), rSigRnx(\"ES5Q\")]\n", + "\n", + "atxfile = bdir+'../antex/I20.ATX'" + ] + }, + { + "cell_type": "markdown", + "id": "wPNb6p1rKzDR", + "metadata": { + "id": "wPNb6p1rKzDR" + }, + "source": [ + "Load and parse the input data" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "_1PZV8NaKzDS", + "metadata": { + "id": "_1PZV8NaKzDS" + }, + "outputs": [], + "source": [ + "rnx = rnxdec()\n", + "rnx.setSignals(sigs)\n", + "\n", + "nav = Nav()\n", + "orb = peph()\n", + "\n", + "nav.pmode = 0 # Positioning mode: 0:static, 1:kinematic\n", + "\n", + "# Decode RINEX NAV data\n", + "nav = rnx.decode_nav(navfile, nav)\n", + "\n", + "# Load precise orbits and clock offsets\n", + "nav = orb.parse_sp3(orbfile, nav)\n", + "nav = rnx.decode_clk(clkfile, nav)\n", + "\n", + "# Load code and phase biases from Bias-SINEX\n", + "bsx = biasdec()\n", + "bsx.parse(bsxfile)\n", + "\n", + "# Load ANTEX data for satellites and stations\n", + "atx = atxdec()\n", + "atx.readpcv(atxfile)\n", + "\n", + "nav.monlevel = 0 # Logging level\n", + "\n", + "# Load RINEX OBS file header\n", + "if rnx.decode_obsh(obsfile) >= 0:\n", + "\n", + " # Auto-substitute signals\n", + " rnx.autoSubstituteSignals()\n", + "\n", + " # Initialize position\n", + " ppp = pppos(nav, rnx.pos, 'test_pppigs.log')\n", + " nav.ephopt = 4 # IGS\n", + " nav.armode = 3 # 1: continuous, 3: fix-and-hold\n", + " nav.parmode = 1 # 1: normal, 2: partial ambiguity resolution\n", + "\n", + " # change default settings\n", + " nav.elmin = np.deg2rad(10.0) # min sat elevation\n", + " nav.thresar = 2.0 # ambiguity resolution threshold\n", + "\n", + " # Set PCO/PCV information\n", + " nav.sat_ant = atx.pcvs\n", + " nav.rcv_ant = searchpcv(atx.pcvr, rnx.ant, rnx.ts)" + ] + }, + { + "cell_type": "markdown", + "id": "WLp7TAJdKzDS", + "metadata": { + "id": "WLp7TAJdKzDS" + }, + "source": [ + "Print the available and selected satellite signals" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "Ry_M-9k0KzDS", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Ry_M-9k0KzDS", + "outputId": "e238d2df-a79e-48a5-f2ac-bf3d1c3c0970" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available signals\n", + "GPS C1C L1C D1C S1C C2W L2W D2W S2W C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", + "GLONASS C1C L1C D1C S1C C2C L2C D2C S2C C2P L2P D2P S2P C3Q L3Q D3Q S3Q\n", + "GALILEO C1C L1C D1C S1C C5Q L5Q D5Q S5Q C7Q L7Q D7Q S7Q C8Q L8Q D8Q S8Q C6C L6C D6C S6C\n", + "BEIDOU C1P L1P D1P S1P C2I L2I D2I S2I C5P L5P D5P S5P C6I L6I D6I S6I C7D L7D D7D S7D C7I L7I D7I S7I\n", + "QZSS C1C L1C D1C S1C C1E L1E D1E S1E C1L L1L D1L S1L C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", + "IRNSS C1P L1P D1P S1P C5A L5A D5A S5A\n", + "\n", + "Selected signals\n", + "GPS C1C C2W L1C L2W S1C S2W \n", + "GALILEO C1C C5Q L1C L5Q S1C S5Q \n" + ] + } + ], + "source": [ + "print(\"Available signals\")\n", + "for sys, sigs in rnx.sig_map.items():\n", + " txt = \"{:7s} {}\".format(sys2str(sys),\n", + " ' '.join([sig.str() for sig in sigs.values()]))\n", + " print(txt)\n", + "\n", + "print(\"\\nSelected signals\")\n", + "for sys, tmp in rnx.sig_tab.items():\n", + " txt = \"{:7s} \".format(sys2str(sys))\n", + " for _, sigs in tmp.items():\n", + " txt += \"{} \".format(' '.join([sig.str() for sig in sigs]))\n", + " print(txt)" + ] + }, + { + "cell_type": "markdown", + "id": "o9w39_PgKzDS", + "metadata": { + "id": "o9w39_PgKzDS" + }, + "source": [ + "Run solution for 15 minutes (ok to abort before finished)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "F6yvNFBMKzDS", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "F6yvNFBMKzDS", + "outputId": "6a0f5543-c685-40f8-e55b-0cfb0b50694a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 2025-08-21 07:15:01 ENU -0.197 0.098 -0.267, 2D 0.220, mode 5" + ] + } + ], + "source": [ + "# increase to run longer, set to 10 minutes to see ambiguity resolution\n", + "nep = 15 * 60 # 15 minutes\n", + "\n", + "# Intialize data structures for results\n", + "t = np.zeros(nep)\n", + "tc = np.zeros(nep)\n", + "enu = np.ones((nep, 3))*np.nan\n", + "sol = np.zeros((nep, 4))\n", + "dop = np.zeros((nep, 4))\n", + "ztd = np.zeros((nep, 1))\n", + "smode = np.zeros(nep, dtype=int)\n", + "\n", + "# Skip epochs until start time\n", + "obs = rnx.decode_obs()\n", + "while time > obs.t and obs.t.time != 0:\n", + " obs = rnx.decode_obs()\n", + "\n", + "# Loop over number of epoch from file start\n", + "for ne in range(nep):\n", + "\n", + " # Set initial epoch\n", + " if ne == 0:\n", + " nav.t = deepcopy(obs.t)\n", + " t0 = deepcopy(obs.t)\n", + "\n", + " # Call PPP module with IGS products\n", + " ppp.process(obs, orb=orb, bsx=bsx)\n", + "\n", + " # Save output\n", + " t[ne] = timediff(nav.t, t0) / 86400.0\n", + "\n", + " sol = nav.xa[0:3] if nav.smode == 4 else nav.x[0:3]\n", + " enu[ne, :] = ecef2enu(pos_ref, sol-xyz_ref)\n", + "\n", + " ztd[ne] = nav.xa[ppp.IT(nav.na)] if nav.smode == 4 else nav.x[ppp.IT(nav.na)]\n", + " smode[ne] = nav.smode\n", + "\n", + " # Log to standard output\n", + " stdout.write('\\r {} ENU {:7.3f} {:7.3f} {:7.3f}, 2D {:6.3f}, mode {:1d}'\n", + " .format(time2str(obs.t),\n", + " enu[ne, 0], enu[ne, 1], enu[ne, 2],\n", + " np.sqrt(enu[ne, 0]**2+enu[ne, 1]**2),\n", + " smode[ne]))\n", + "\n", + " # Get new epoch, exit after last epoch\n", + " obs = rnx.decode_obs()\n", + " if obs.t.time == 0:\n", + " break\n", + "\n", + "rnx.fobs.close()\n" + ] + }, + { + "cell_type": "markdown", + "id": "UIN1k3sPKzDT", + "metadata": { + "id": "UIN1k3sPKzDT" + }, + "source": [ + "Plot results.\n", + "\n", + "The default run length is set to 15 minutes to keep the tutorial moving." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "iXeH8x2HKzDT", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 795 + }, + "id": "iXeH8x2HKzDT", + "outputId": "216de780-8018-45c5-8747-54bd99bf4d61" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from cssrlib.plot import plot_enu\n", + "\n", + "plot_enu(t, enu, smode, ztd)" + ] + }, + { + "cell_type": "markdown", + "id": "8993fc13", + "metadata": { + "id": "8993fc13" + }, + "source": [ + "## Example 3: PPP positioning (BDS PPP)\n", + "\n", + "This section demonstrates PPP-RTK positioning using BDS B1C and B2a pilot observations for BDS-3 B2b PPP and GPS L1 C/A and L2 P(Y) with a Septentrio mosaic-X5 receiver." + ] + }, + { + "cell_type": "markdown", + "id": "ul3qp8h3oHin", + "metadata": { + "id": "ul3qp8h3oHin" + }, + "source": [ + "First, load the required Python modules." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "IGih7GGt_9z6", + "metadata": { + "id": "IGih7GGt_9z6" + }, + "outputs": [], + "source": [ + "from binascii import unhexlify\n", + "from copy import deepcopy\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as md\n", + "import numpy as np\n", + "from sys import stdout\n", + "\n", + "from cssrlib.gnss import ecef2pos, ecef2enu, Nav, rSigRnx, sys2str\n", + "from cssrlib.gnss import time2gpst, time2doy, time2str, timediff, epoch2time\n", + "from cssrlib.peph import atxdec, searchpcv, peph\n", + "from cssrlib.cssr_bds import cssr_bds\n", + "from cssrlib.pppssr import pppos\n", + "from cssrlib.rinex import rnxdec" + ] + }, + { + "cell_type": "markdown", + "id": "eGLKroylAeUU", + "metadata": { + "id": "eGLKroylAeUU" + }, + "source": [ + "Define the input data and parameters for this example. For signals, we choose L1C/A+L2P(Y) for GPS, B1C(P)+B2a(P) for BDS." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "hYbK60gqAfWG", + "metadata": { + "id": "hYbK60gqAfWG" + }, + "outputs": [], + "source": [ + "ep = [2025, 8, 21, 7, 0, 0] # start epoch\n", + "\n", + "time = epoch2time(ep)\n", + "year = ep[0]\n", + "doy = int(time2doy(time))\n", + "\n", + "let = chr(ord('a')+ep[3])\n", + "bdir = f'cssrlib-data/data/doy{ep[0]:04d}-{doy:03d}/'\n", + "navfile = bdir+f'{doy:03d}{let}_rnx.nav'\n", + "obsfile = bdir+f'{doy:03d}{let}_rnx.obs'\n", + "atxfile = bdir+'../antex/igs20.atx'\n", + "\n", + "file_bds = bdir+f'{doy:03d}{let}_bdsb2b.txt'\n", + "prn_ref = 59 # satellite PRN to receive BDS PPP collection\n", + "\n", + "dtype = [('wn', 'int'), ('tow', 'int'), ('prn', 'int'),\n", + " ('type', 'int'), ('len', 'int'), ('nav', 'S124')]\n", + "\n", + "xyz_ref = [-3962108.6836, 3381309.5672, 3668678.6720]\n", + "pos_ref = ecef2pos(xyz_ref)\n", + "\n", + "# Define signals to be processed\n", + "sigs = [rSigRnx(\"GC1C\"), rSigRnx(\"GC2W\"),\n", + " rSigRnx(\"GL1C\"), rSigRnx(\"GL2W\"),\n", + " rSigRnx(\"GS1C\"), rSigRnx(\"GS2W\"),\n", + " rSigRnx(\"CC1P\"), rSigRnx(\"CC5P\"),\n", + " rSigRnx(\"CL1P\"), rSigRnx(\"CL5P\"),\n", + " rSigRnx(\"CS1P\"), rSigRnx(\"CS5P\")]\n", + "\n", + "time = epoch2time(ep)\n", + "year = ep[0]\n", + "doy = int(time2doy(time))" + ] + }, + { + "cell_type": "markdown", + "id": "PrqsDGtJAdTY", + "metadata": { + "id": "PrqsDGtJAdTY" + }, + "source": [ + "Load and parse the input data" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "E56Qi-3cENFV", + "metadata": { + "id": "E56Qi-3cENFV" + }, + "outputs": [], + "source": [ + "rnx = rnxdec()\n", + "rnx.setSignals(sigs)\n", + "\n", + "nav = Nav()\n", + "orb = peph()\n", + "\n", + "nav.pmode = 0 # Positioning mode: 0:static, 1:kinematic\n", + "\n", + "# Decode RINEX NAV data\n", + "nav = rnx.decode_nav(navfile, nav)\n", + "\n", + "# Load PPP corrections\n", + "v = np.genfromtxt(file_bds, dtype=dtype)\n", + "\n", + "cs = cssr_bds()\n", + "cs.monlevel = 0\n", + "\n", + "# Load ANTEX data for satellites and stations\n", + "atx = atxdec()\n", + "atx.readpcv(atxfile)\n", + "\n", + "nav.monlevel = 0 # Logging level\n", + "\n", + "# Load RINEX OBS file header\n", + "if rnx.decode_obsh(obsfile) >= 0:\n", + "\n", + " # Auto-substitute signals\n", + " rnx.autoSubstituteSignals()\n", + "\n", + " # Initialize position\n", + " ppp = pppos(nav, rnx.pos, 'test_pppbds.log')\n", + "\n", + " # Set PCO/PCV information\n", + " nav.sat_ant = atx.pcvs\n", + " nav.rcv_ant = searchpcv(atx.pcvr, rnx.ant, rnx.ts)" + ] + }, + { + "cell_type": "markdown", + "id": "tghWoxc9EhfE", + "metadata": { + "id": "tghWoxc9EhfE" + }, + "source": [ + "Print the available and selected satellite signals" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "DEDeJQscFYG9", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DEDeJQscFYG9", + "outputId": "863b7757-f882-49df-8121-aac2cf756a27" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available signals\n", + "GPS C1C L1C D1C S1C C2W L2W D2W S2W C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", + "GLONASS C1C L1C D1C S1C C2C L2C D2C S2C C2P L2P D2P S2P C3Q L3Q D3Q S3Q\n", + "GALILEO C1C L1C D1C S1C C5Q L5Q D5Q S5Q C7Q L7Q D7Q S7Q C8Q L8Q D8Q S8Q C6C L6C D6C S6C\n", + "BEIDOU C1P L1P D1P S1P C2I L2I D2I S2I C5P L5P D5P S5P C6I L6I D6I S6I C7D L7D D7D S7D C7I L7I D7I S7I\n", + "QZSS C1C L1C D1C S1C C1E L1E D1E S1E C1L L1L D1L S1L C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", + "IRNSS C1P L1P D1P S1P C5A L5A D5A S5A\n", + "\n", + "Selected signals\n", + "GPS C1C C2W L1C L2W S1C S2W \n", + "BEIDOU C1P C5P L1P L5P S1P S5P \n" + ] + } + ], + "source": [ + "print(\"Available signals\")\n", + "for sys, sigs in rnx.sig_map.items():\n", + " txt = \"{:7s} {}\".format(sys2str(sys),\n", + " ' '.join([sig.str() for sig in sigs.values()]))\n", + " print(txt)\n", + "\n", + "print(\"\\nSelected signals\")\n", + "for sys, tmp in rnx.sig_tab.items():\n", + " txt = \"{:7s} \".format(sys2str(sys))\n", + " for _, sigs in tmp.items():\n", + " txt += \"{} \".format(' '.join([sig.str() for sig in sigs]))\n", + " print(txt)" + ] + }, + { + "cell_type": "markdown", + "id": "AmIWL6FBEsJW", + "metadata": { + "id": "AmIWL6FBEsJW" + }, + "source": [ + "Process data for 15 minutes (ok to abort before finished)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "GMsOCMUPEtvP", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GMsOCMUPEtvP", + "outputId": "7bf96376-2da3-44ec-c90b-2ba5ed733e5a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 2025-08-21 07:00:27 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=3\n", + " 2025-08-21 07:15:00 ENU -0.127 -0.045 0.302, 2D 0.135, mode 5" + ] + } + ], + "source": [ + "nep = 15 * 60 # increase this to run longer\n", + "\n", + "# Intialize data structures for results\n", + "t = np.zeros(nep)\n", + "tc = np.zeros(nep)\n", + "enu = np.ones((nep, 3))*np.nan\n", + "sol = np.zeros((nep, 4))\n", + "dop = np.zeros((nep, 4))\n", + "ztd = np.zeros((nep, 1))\n", + "smode = np.zeros(nep, dtype=int)\n", + "\n", + "# Skip epochs until start time\n", + "obs = rnx.decode_obs()\n", + "while time > obs.t and obs.t.time != 0:\n", + " obs = rnx.decode_obs()\n", + "\n", + "# Loop over number of epoch from file start\n", + "for ne in range(nep):\n", + " week, tow = time2gpst(obs.t)\n", + " cs.week = week\n", + " cs.tow0 = tow // 86400 * 86400\n", + "\n", + " # Set intial epoch\n", + " if ne == 0:\n", + " nav.t = deepcopy(obs.t)\n", + " t0 = deepcopy(obs.t)\n", + " t0.time = t0.time // 30 * 30\n", + " nav.time_p = t0\n", + "\n", + " vi = v[(v['tow'] == tow) & (v['prn'] == prn_ref)]\n", + " if len(vi) > 0:\n", + " buff = unhexlify(vi['nav'][0])\n", + " cs.decode_cssr(buff, 0)\n", + "\n", + " # Call PPP module with BDS-PPP corrections\n", + " if (cs.lc[0].cstat & 0xf) == 0xf:\n", + " ppp.process(obs, cs=cs)\n", + "\n", + " # Save output\n", + " t[ne] = timediff(nav.t, t0) / 86400.0\n", + "\n", + " sol = nav.xa[0:3] if nav.smode == 4 else nav.x[0:3]\n", + " enu[ne, :] = ecef2enu(pos_ref, sol-xyz_ref)\n", + "\n", + " ztd[ne] = nav.xa[ppp.IT(nav.na)] if nav.smode == 4 else nav.x[ppp.IT(nav.na)]\n", + " smode[ne] = nav.smode\n", + "\n", + " # Log to standard output\n", + " stdout.write('\\r {} ENU {:7.3f} {:7.3f} {:7.3f}, 2D {:6.3f}, mode {:1d}'\n", + " .format(time2str(obs.t),\n", + " enu[ne, 0], enu[ne, 1], enu[ne, 2],\n", + " np.sqrt(enu[ne, 0]**2+enu[ne, 1]**2),\n", + " smode[ne]))\n", + "\n", + " # Get new epoch, exit after last epoch\n", + " obs = rnx.decode_obs()\n", + " if obs.t.time == 0:\n", + " break\n", + "\n", + "rnx.fobs.close()" + ] + }, + { + "cell_type": "markdown", + "id": "-QkxrW1_GVYU", + "metadata": { + "id": "-QkxrW1_GVYU" + }, + "source": [ + "Plot results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ttuzs72KHeFs", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 795 + }, + "id": "ttuzs72KHeFs", + "outputId": "797c28c9-5aa2-491c-a68a-fc3b38bbf64d" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from cssrlib.plot import plot_enu\n", + "\n", + "plot_enu(t, enu, smode, ztd)" + ] + }, + { + "cell_type": "markdown", + "id": "hPjJOF2T8uxG", + "metadata": { + "id": "hPjJOF2T8uxG" + }, + "source": [ + "\n", + "## Example 4: PPP positioning (Galileo HAS)\n", + "\n", + "This section demonstrates PPP positioning using Galileo HAS corrections with a Septentrio mosaic-X5 receiver." + ] + }, + { + "cell_type": "markdown", + "id": "3CU6La60p2gQ", + "metadata": { + "id": "3CU6La60p2gQ" + }, + "source": [ + "First, load the required Python modules." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "tQzCZh9--Ias", + "metadata": { + "id": "tQzCZh9--Ias" + }, + "outputs": [], + "source": [ + "from copy import deepcopy\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as md\n", + "import numpy as np\n", + "from binascii import unhexlify\n", + "import bitstruct as bs\n", + "from sys import stdout\n", + "\n", + "from cssrlib.gnss import ecef2pos, ecef2enu, Nav, rSigRnx, sys2str\n", + "from cssrlib.gnss import time2gpst, time2doy, time2str, timediff, epoch2time\n", + "from cssrlib.peph import atxdec, searchpcv\n", + "from cssrlib.cssr_has import cssr_has, cnav_msg\n", + "from cssrlib.pppssr import pppos\n", + "from cssrlib.rinex import rnxdec" + ] + }, + { + "cell_type": "markdown", + "id": "EpWHwoJa-Ias", + "metadata": { + "id": "EpWHwoJa-Ias" + }, + "source": [ + "Define the input data and parameters for this example" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "p9pQ2yeo-Iat", + "metadata": { + "id": "p9pQ2yeo-Iat" + }, + "outputs": [], + "source": [ + "# Start epoch and number of epochs\n", + "ep = [2025, 8, 21, 7, 0, 0] # start epoch\n", + "\n", + "time = epoch2time(ep)\n", + "year = ep[0]\n", + "doy = int(time2doy(time))\n", + "\n", + "let = chr(ord('a')+ep[3])\n", + "bdir = f'cssrlib-data/data/doy{ep[0]:04d}-{doy:03d}/'\n", + "navfile = bdir+f'{doy:03d}{let}_rnx.nav'\n", + "obsfile = bdir+f'{doy:03d}{let}_rnx.obs'\n", + "atxfile = bdir+'../antex/igs20.atx'\n", + "\n", + "# Specify Galile HAS corrections files\n", + "file_has = bdir+f'{doy:03d}{let}_gale6.txt'\n", + "\n", + "dtype = [('wn', 'int'), ('tow', 'int'), ('prn', 'int'),\n", + " ('type', 'int'), ('len', 'int'), ('nav', 'S124')]\n", + "\n", + "# Set user reference position\n", + "xyz_ref = [-3962108.6836, 3381309.5672, 3668678.6720]\n", + "pos_ref = ecef2pos(xyz_ref)\n", + "\n", + "# Define signals to be processed\n", + "sigs = [rSigRnx(\"GC1C\"), rSigRnx(\"GC2L\"),\n", + " rSigRnx(\"GL1C\"), rSigRnx(\"GL2L\"),\n", + " rSigRnx(\"GS1C\"), rSigRnx(\"GS2L\"),\n", + " rSigRnx(\"EC1C\"), rSigRnx(\"EC5Q\"),\n", + " rSigRnx(\"EL1C\"), rSigRnx(\"EL5Q\"),\n", + " rSigRnx(\"ES1C\"), rSigRnx(\"ES5Q\")]" + ] + }, + { + "cell_type": "markdown", + "id": "6d24e8d2", + "metadata": {}, + "source": [ + "For decoding Galileo CNAV pages, Galileo CNAV message parser is defined. And, a table for Reed-Solomon decoding has loaded." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "bdf460f9", + "metadata": {}, + "outputs": [], + "source": [ + "# Table for Reed-Solomon decoding\n", + "file_gm = bdir+\"../../samples/Galileo-HAS-SIS-ICD_1.0_Annex_B_Reed_Solomon_Generator_Matrix.txt\"\n", + "# Galileo CNAV message parser\n", + "cnav = cnav_msg()\n", + "cnav.load_gmat(file_gm)" + ] + }, + { + "cell_type": "markdown", + "id": "3ORyJKZI-Iat", + "metadata": { + "id": "3ORyJKZI-Iat" + }, + "source": [ + "Load and parse the input data" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "G3uGHYHh-Iat", + "metadata": { + "id": "G3uGHYHh-Iat" + }, + "outputs": [], + "source": [ + "rnx = rnxdec()\n", + "rnx.setSignals(sigs)\n", + "\n", + "nav = Nav()\n", + "nav.pmode = 0 # Positioning mode: 0:static, 1:kinematic\n", + "\n", + "# Decode RINEX NAV data\n", + "nav = rnx.decode_nav(navfile, nav)\n", + "\n", + "# Load PPP corrections\n", + "v = np.genfromtxt(file_has, dtype=dtype)\n", + "gMat = np.genfromtxt(file_gm, dtype=\"u1\", delimiter=\",\")\n", + "\n", + "# Load ANTEX data for satellites and stations\n", + "atx = atxdec()\n", + "atx.readpcv(atxfile)\n", + "\n", + "cs = cssr_has()\n", + "# cs.monlevel = 2\n", + "nav.monlevel = 0 # Logging level\n", + "\n", + "# Load RINEX OBS file header\n", + "if rnx.decode_obsh(obsfile) >= 0:\n", + "\n", + " # Auto-substitute signals\n", + " rnx.autoSubstituteSignals()\n", + "\n", + " # Initialize position\n", + " ppp = pppos(nav, rnx.pos, 'test_ppphas.log')\n", + "\n", + " # Modify default config parameters\n", + " nav.elmin = np.deg2rad(5.0) # min sat el\n", + "\n", + " # Set PCO/PCV information\n", + " nav.sat_ant = atx.pcvs\n", + " nav.rcv_ant = searchpcv(atx.pcvr, rnx.ant, rnx.ts)" + ] + }, + { + "cell_type": "markdown", + "id": "4rL8k8Vi-Iau", + "metadata": { + "id": "4rL8k8Vi-Iau" + }, + "source": [ + "Print the available satellite and selected signals. We choose L1C/A+L2P(Y) for GPS, E1+E5b for Galileo." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "n_Oxkxn--Iau", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "n_Oxkxn--Iau", + "outputId": "f2fdaca6-da70-4f10-fb65-fbe8d8e2f659" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available signals\n", + "GPS C1C L1C D1C S1C C2W L2W D2W S2W C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", + "GLONASS C1C L1C D1C S1C C2C L2C D2C S2C C2P L2P D2P S2P C3Q L3Q D3Q S3Q\n", + "GALILEO C1C L1C D1C S1C C5Q L5Q D5Q S5Q C7Q L7Q D7Q S7Q C8Q L8Q D8Q S8Q C6C L6C D6C S6C\n", + "BEIDOU C1P L1P D1P S1P C2I L2I D2I S2I C5P L5P D5P S5P C6I L6I D6I S6I C7D L7D D7D S7D C7I L7I D7I S7I\n", + "QZSS C1C L1C D1C S1C C1E L1E D1E S1E C1L L1L D1L S1L C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", + "IRNSS C1P L1P D1P S1P C5A L5A D5A S5A\n", + "\n", + "Selected signals\n", + "GPS C1C C2L L1C L2L S1C S2L \n", + "GALILEO C1C C5Q L1C L5Q S1C S5Q \n" + ] + } + ], + "source": [ + "print(\"Available signals\")\n", + "for sys, sigs in rnx.sig_map.items():\n", + " txt = \"{:7s} {}\".format(sys2str(sys),\n", + " ' '.join([sig.str() for sig in sigs.values()]))\n", + " print(txt)\n", + "\n", + "print(\"\\nSelected signals\")\n", + "for sys, tmp in rnx.sig_tab.items():\n", + " txt = \"{:7s} \".format(sys2str(sys))\n", + " for _, sigs in tmp.items():\n", + " txt += \"{} \".format(' '.join([sig.str() for sig in sigs]))\n", + " print(txt)" + ] + }, + { + "cell_type": "markdown", + "id": "ZRBRDQtw-Iau", + "metadata": { + "id": "ZRBRDQtw-Iau" + }, + "source": [ + "Process data for 15 minutes (ok to abort before finished)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "uVeqUA6A-Iav", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "uVeqUA6A-Iav", + "outputId": "8e1440b6-0917-4ef5-8fc9-0925b6df09d3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 2025-08-21 07:15:00 ENU 0.035 0.224 -0.582, 2D 0.226, mode 5 nsat 19/16/16\n" + ] + } + ], + "source": [ + "nep = 15 * 60 # increase this to run longer\n", + "\n", + "# Intialize data structures for results\n", + "t = np.zeros(nep)\n", + "enu = np.ones((nep, 3))*np.nan\n", + "sol = np.zeros((nep, 4))\n", + "ztd = np.zeros((nep, 1))\n", + "smode = np.zeros(nep, dtype=int)\n", + "nsat = np.zeros((nep, 3), dtype=int)\n", + "\n", + "# Skip epochs until start time\n", + "obs = rnx.decode_obs()\n", + "while time > obs.t and obs.t.time != 0:\n", + " obs = rnx.decode_obs()\n", + "\n", + "# Loop over number of epoch from file start\n", + "for ne in range(nep):\n", + " week, tow = time2gpst(obs.t)\n", + " cs.week = week\n", + " cs.tow0 = tow // 3600 * 3600\n", + "\n", + " # Set initial epoch\n", + " if ne == 0:\n", + " nav.t = deepcopy(obs.t)\n", + " t0 = deepcopy(obs.t)\n", + " t0.time = t0.time // 30 * 30\n", + " nav.time_p = t0\n", + "\n", + " vi = v[v['tow'] == tow]\n", + "\n", + " HASmsg = cnav.decode_cnav(tow, vi) # decode CNAV pages\n", + " if HASmsg is not None:\n", + " cs.msgtype = cnav.msgtype\n", + " cs.decode_cssr(HASmsg) # decode HAS messages\n", + "\n", + " # Call PPP module with HAS corrections\n", + " if (cs.lc[0].cstat & 0xf) == 0xf:\n", + " ppp.process(obs, cs=cs)\n", + "\n", + " # Save output\n", + " t[ne] = timediff(nav.t, t0) / 86400.0\n", + "\n", + " sol = nav.xa[0:3] if nav.smode == 4 else nav.x[0:3]\n", + " enu[ne, :] = ecef2enu(pos_ref, sol-xyz_ref)\n", + "\n", + " ztd[ne] = nav.xa[ppp.IT(nav.na)] if nav.smode == 4 else nav.x[ppp.IT(nav.na)]\n", + " smode[ne] = nav.smode\n", + " nsat[ne, :] = nav.nsat\n", + "\n", + " nav.fout.write(\"{} {:14.4f} {:14.4f} {:14.4f} \"\n", + " \"ENU {:7.3f} {:7.3f} {:7.3f}, 2D {:6.3f}, mode {:1d}\\n\"\n", + " .format(time2str(obs.t),\n", + " sol[0], sol[1], sol[2],\n", + " enu[ne, 0], enu[ne, 1], enu[ne, 2],\n", + " np.sqrt(enu[ne, 0]**2+enu[ne, 1]**2),\n", + " smode[ne]))\n", + "\n", + " stdout.write('\\r {} ENU {:7.3f} {:7.3f} {:7.3f}, 2D {:6.3f}, mode {:1d} nsat {:2d}/{:2d}/{:2d}'\n", + " .format(time2str(obs.t),\n", + " enu[ne, 0], enu[ne, 1], enu[ne, 2],\n", + " np.sqrt(enu[ne, 0]**2+enu[ne, 1]**2),\n", + " smode[ne],\n", + " nsat[ne, 0], nsat[ne, 1], nsat[ne, 2]))\n", + "\n", + " # Get new epoch, exit after last epoch\n", + " obs = rnx.decode_obs()\n", + " if obs.t.time == 0:\n", + " break\n", + "\n", + "stdout.write('\\n')\n", + "rnx.fobs.close()\n" + ] + }, + { + "cell_type": "markdown", + "id": "yXJXvBP5-Iav", + "metadata": { + "id": "yXJXvBP5-Iav" + }, + "source": [ + "Plot results" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "jBUBDuDH-Iax", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 795 + }, + "id": "jBUBDuDH-Iax", + "outputId": "d9fbe8c3-9ae9-48f5-cd48-325f07d63ab9" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from cssrlib.plot import plot_enu\n", + "\n", + "plot_enu(t, enu, smode, ztd)" + ] + }, + { + "cell_type": "markdown", + "id": "OdegzV6Wpwb3", + "metadata": { + "id": "OdegzV6Wpwb3" + }, + "source": [] + }, + { + "cell_type": "markdown", + "id": "nf1Rwd3u8vXK", + "metadata": { + "id": "nf1Rwd3u8vXK" + }, + "source": [ + "\n", + "## Example 5: PPP positioning (MADOCA-PPP)\n", + "\n", + "This section demonstrates PPP positioning using QZSS MADOCA-PPP corrections from Javad DELTA-3S receiver and obserbations with a Septentrio mosaic-X5 receiver." + ] + }, + { + "cell_type": "markdown", + "id": "to8XjhfTtpCu", + "metadata": { + "id": "to8XjhfTtpCu" + }, + "source": [ + "First, load the required Python modules." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "wsXq8ow7Kt6-", + "metadata": { + "id": "wsXq8ow7Kt6-" + }, + "outputs": [], + "source": [ + "from copy import deepcopy\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from binascii import unhexlify\n", + "from sys import stdout\n", + "\n", + "from cssrlib.gnss import ecef2pos, ecef2enu, Nav, rSigRnx, sys2str\n", + "from cssrlib.gnss import time2gpst, time2doy, time2str, timediff, epoch2time\n", + "from cssrlib.peph import atxdec, searchpcv\n", + "from cssrlib.cssr_mdc import cssr_mdc\n", + "from cssrlib.pppssr import pppos\n", + "from cssrlib.rinex import rnxdec" + ] + }, + { + "cell_type": "markdown", + "id": "sIWw2QoZKt7I", + "metadata": { + "id": "sIWw2QoZKt7I" + }, + "source": [ + "Define the input data and parameters for this example" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "yzPWKG1oKt7I", + "metadata": { + "id": "yzPWKG1oKt7I" + }, + "outputs": [], + "source": [ + "# Start epoch and number of epochs\n", + "ep = [2025, 8, 21, 7, 0, 0]\n", + "\n", + "time = epoch2time(ep)\n", + "year = ep[0]\n", + "doy = int(time2doy(time))\n", + "\n", + "let = chr(ord('a')+ep[3])\n", + "bdir = f'cssrlib-data/data/doy{ep[0]:04d}-{doy:03d}/'\n", + "navfile = bdir+f'{doy:03d}{let}_rnx.nav'\n", + "obsfile = bdir+f'{doy:03d}{let}_rnx.obs'\n", + "atxfile = bdir+'../antex/igs20.atx'\n", + "\n", + "# Specify L6 corrections files\n", + "file_l6 = bdir+f'{doy:03d}{let}_qzsl6.txt'\n", + "prn_ref = 199 # QZSS PRN\n", + "l6_ch = 1 # 0:L6D, 1:L6E\n", + "dtype = [('wn', 'int'), ('tow', 'int'), ('prn', 'int'),\n", + " ('type', 'int'), ('len', 'int'), ('nav', 'S500')]\n", + "\n", + "# Set user reference position\n", + "xyz_ref = [-3962108.6836, 3381309.5672, 3668678.6720]\n", + "pos_ref = ecef2pos(xyz_ref)\n", + "\n", + "# Define signals to be processed\n", + "sigs = [rSigRnx(\"GC1C\"), rSigRnx(\"GC2W\"),\n", + " rSigRnx(\"GL1C\"), rSigRnx(\"GL2W\"),\n", + " rSigRnx(\"GS1C\"), rSigRnx(\"GS2W\"),\n", + " rSigRnx(\"EC1C\"), rSigRnx(\"EC5Q\"),\n", + " rSigRnx(\"EL1C\"), rSigRnx(\"EL5Q\"),\n", + " rSigRnx(\"ES1C\"), rSigRnx(\"ES5Q\"),\n", + " rSigRnx(\"JC1C\"), rSigRnx(\"JC2L\"),\n", + " rSigRnx(\"JL1C\"), rSigRnx(\"JL2L\"),\n", + " rSigRnx(\"JS1C\"), rSigRnx(\"JS2L\")]\n" + ] + }, + { + "cell_type": "markdown", + "id": "-CjlD8OGKt7I", + "metadata": { + "id": "-CjlD8OGKt7I" + }, + "source": [ + "Load and parse the input data" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "Z-oB0iDWKt7I", + "metadata": { + "id": "Z-oB0iDWKt7I" + }, + "outputs": [], + "source": [ + "rnx = rnxdec()\n", + "rnx.setSignals(sigs)\n", + "\n", + "nav = Nav()\n", + "nav.pmode = 0 # Positioning mode: 0:static, 1:kinematic\n", + "\n", + "# Decode RINEX NAV data\n", + "nav = rnx.decode_nav(navfile, nav)\n", + "\n", + "# Load PPP corrections\n", + "v = np.genfromtxt(file_l6, dtype=dtype)\n", + "\n", + "cs = cssr_mdc('madoca_cssr.log')\n", + "cs.monlevel = 0\n", + "\n", + "# Load ANTEX data for satellites and stations\n", + "atx = atxdec()\n", + "atx.readpcv(atxfile)\n", + "\n", + "nav.monlevel = 0 # Logging level\n", + "\n", + "# Load RINEX OBS file header\n", + "if rnx.decode_obsh(obsfile) >= 0:\n", + "\n", + " # Auto-substitute signals\n", + " rnx.autoSubstituteSignals()\n", + "\n", + " # Initialize position\n", + " rr = rnx.pos\n", + " pos = ecef2pos(rr)\n", + " ppp = pppos(nav, rnx.pos, 'test_pppmdc.log')\n", + " nav.elmin = np.deg2rad(5.0)\n", + " nav.glo_ch = rnx.glo_ch\n", + "\n", + " # Set PCO/PCV information\n", + " nav.sat_ant = atx.pcvs\n", + " nav.rcv_ant = searchpcv(atx.pcvr, rnx.ant, rnx.ts)" + ] + }, + { + "cell_type": "markdown", + "id": "jHYIRQokKt7J", + "metadata": { + "id": "jHYIRQokKt7J" + }, + "source": [ + "Print the available satellite signals" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "NqhVIdPgKt7J", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NqhVIdPgKt7J", + "outputId": "2ea50f20-ffed-4da0-f7c7-83d0de604e57" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available signals\n", + "GPS C1C L1C D1C S1C C2W L2W D2W S2W C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", + "GLONASS C1C L1C D1C S1C C2C L2C D2C S2C C2P L2P D2P S2P C3Q L3Q D3Q S3Q\n", + "GALILEO C1C L1C D1C S1C C5Q L5Q D5Q S5Q C7Q L7Q D7Q S7Q C8Q L8Q D8Q S8Q C6C L6C D6C S6C\n", + "BEIDOU C1P L1P D1P S1P C2I L2I D2I S2I C5P L5P D5P S5P C6I L6I D6I S6I C7D L7D D7D S7D C7I L7I D7I S7I\n", + "QZSS C1C L1C D1C S1C C1E L1E D1E S1E C1L L1L D1L S1L C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", + "IRNSS C1P L1P D1P S1P C5A L5A D5A S5A\n", + "\n", + "Selected signals\n", + "GPS C1C C2W L1C L2W S1C S2W \n", + "GALILEO C1C C5Q L1C L5Q S1C S5Q \n", + "QZSS C1C C2L L1C L2L S1C S2L \n" + ] + } + ], + "source": [ + "print(\"Available signals\")\n", + "for sys, sigs in rnx.sig_map.items():\n", + " txt = \"{:7s} {}\".format(sys2str(sys),\n", + " ' '.join([sig.str() for sig in sigs.values()]))\n", + " print(txt)\n", + "\n", + "print(\"\\nSelected signals\")\n", + "for sys, tmp in rnx.sig_tab.items():\n", + " txt = \"{:7s} \".format(sys2str(sys))\n", + " for _, sigs in tmp.items():\n", + " txt += \"{} \".format(' '.join([sig.str() for sig in sigs]))\n", + " print(txt)" + ] + }, + { + "cell_type": "markdown", + "id": "zDhWQUZAKt7J", + "metadata": { + "id": "zDhWQUZAKt7J" + }, + "source": [ + "Process data for 15 minutes (ok to abort before finished)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "tjGh4fpZKt7J", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tjGh4fpZKt7J", + "outputId": "fc918566-9605-46ef-858c-658c7426fc8e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 2025-08-21 07:15:00 ENU -0.191 0.079 -0.037, 2D 0.207, mode 5" + ] + } + ], + "source": [ + "nep = 15 * 60 # increase this to run longer\n", + "\n", + "# Intialize data structures for results\n", + "t = np.zeros(nep)\n", + "enu = np.ones((nep, 3))*np.nan\n", + "sol = np.zeros((nep, 4))\n", + "ztd = np.zeros((nep, 1))\n", + "smode = np.zeros(nep, dtype=int)\n", + "\n", + "# Skip epochs until start time\n", + "obs = rnx.decode_obs()\n", + "while time > obs.t and obs.t.time != 0:\n", + " obs = rnx.decode_obs()\n", + "\n", + "# Loop over number of epoch from file start\n", + "for ne in range(nep):\n", + " week, tow = time2gpst(obs.t)\n", + " cs.week = week\n", + " cs.tow0 = tow // 3600*3600\n", + "\n", + " # Set initial epoch\n", + " if ne == 0:\n", + " nav.t = deepcopy(obs.t)\n", + " t0 = deepcopy(obs.t)\n", + " t0.time = t0.time//30*30\n", + " nav.time_p = t0\n", + "\n", + " vi = v[(v['tow'] == tow) & (v['type'] == l6_ch)\n", + " & (v['prn'] == prn_ref)]\n", + " if len(vi) > 0:\n", + " msg = unhexlify(vi['nav'][0])\n", + " cs.decode_l6msg(msg, 0)\n", + " if cs.fcnt == 5: # end of sub-frame\n", + " cs.decode_cssr(cs.buff, 0)\n", + "\n", + " # Call PPP module\n", + " if (cs.lc[0].cstat & 0xf) == 0xf:\n", + " ppp.process(obs, cs=cs)\n", + "\n", + " # Save output\n", + " t[ne] = timediff(nav.t, t0) / 86400.0\n", + " sol = nav.xa[0:3] if nav.smode == 4 else nav.x[0:3]\n", + " enu[ne, :] = ecef2enu(pos_ref, sol-xyz_ref)\n", + " ztd[ne] = nav.xa[ppp.IT(nav.na)] if nav.smode == 4 else nav.x[ppp.IT(nav.na)]\n", + " smode[ne] = nav.smode\n", + "\n", + " # Log to standard output\n", + " stdout.write('\\r {} ENU {:7.3f} {:7.3f} {:7.3f}, 2D {:6.3f}, mode {:1d}'\n", + " .format(time2str(obs.t),\n", + " enu[ne, 0], enu[ne, 1], enu[ne, 2],\n", + " np.sqrt(enu[ne, 0]**2+enu[ne, 1]**2),\n", + " smode[ne]))\n", + "\n", + " # Get new epoch, exit after last epoch\n", + " obs = rnx.decode_obs()\n", + " if obs.t.time == 0:\n", + " break" + ] + }, + { + "cell_type": "markdown", + "id": "JvZIeMofKt7K", + "metadata": { + "id": "JvZIeMofKt7K" + }, + "source": [ + "Plot results" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "PHo6f5f1Kt7K", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 795 + }, + "id": "PHo6f5f1Kt7K", + "outputId": "831cb237-f64e-4564-bfa1-ac19b2ae4451" + }, + "outputs": [ + { + "data": { + 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from cssrlib.plot import plot_enu\n", + "\n", + "plot_enu(t, enu, smode, ztd)" + ] + }, + { + "cell_type": "markdown", + "id": "140a3d36", + "metadata": {}, + "source": [ + "## Example 6: PPP positioning (PPP via SouthPAN)\n", + "\n", + "This section demonstrates PPP positioning using PPP via SouthPAN (PVS) corrections with a Septentrio mosaic-X5 receiver." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "4ef1b043", + "metadata": {}, + "outputs": [], + "source": [ + "from copy import deepcopy\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from binascii import unhexlify\n", + "from sys import stdout\n", + "\n", + "from cssrlib.gnss import ecef2pos, ecef2enu, Nav, rSigRnx, sys2str\n", + "from cssrlib.gnss import time2gpst, time2doy, time2str, timediff, epoch2time\n", + "from cssrlib.peph import atxdec, searchpcv\n", + "from cssrlib.cssr_pvs import cssr_pvs\n", + "from cssrlib.pppssr import pppos\n", + "from cssrlib.rinex import rnxdec" + ] + }, + { + "cell_type": "markdown", + "id": "fda6108f", + "metadata": {}, + "source": [ + "PVS correction data can be obtained from L5 SBAS correction from PRN 122. " + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "847a774c", + "metadata": {}, + "outputs": [], + "source": [ + "# Start epoch and number of epochs\n", + "ep = [2025, 8, 21, 7, 0, 0]\n", + "\n", + "time = epoch2time(ep)\n", + "year = ep[0]\n", + "doy = int(time2doy(time))\n", + "\n", + "let = chr(ord('a')+ep[3])\n", + "bdir = f'cssrlib-data/data/doy{ep[0]:04d}-{doy:03d}/'\n", + "navfile = bdir+f'{doy:03d}{let}_rnx.nav'\n", + "obsfile = bdir+f'{doy:03d}{let}_rnx.obs'\n", + "atxfile = bdir+'../antex/igs20.atx'\n", + "\n", + "# Specify SBAS corrections files\n", + "file_sbas = bdir+f'{doy:03d}{let}_sbas.txt'\n", + "prn_ref = 122 # satellite PRN for PRN122\n", + "sbas_type = 1 # L1: 0, L5: 1\n", + "\n", + "dtype = [('wn', 'int'), ('tow', 'float'), ('prn', 'int'),\n", + " ('type', 'int'), ('marker', 'S2'), ('nav', 'S124')]\n", + "\n", + "# Set user reference position\n", + "xyz_ref = [-3962108.6836, 3381309.5672, 3668678.6720]\n", + "pos_ref = ecef2pos(xyz_ref)\n", + "\n", + "# Define signals to be processed\n", + "sigs = [rSigRnx(\"GC1C\"), rSigRnx(\"GC5Q\"),\n", + " rSigRnx(\"GL1C\"), rSigRnx(\"GL5Q\"),\n", + " rSigRnx(\"GS1C\"), rSigRnx(\"GS5Q\"),\n", + " rSigRnx(\"EC1C\"), rSigRnx(\"EC5Q\"),\n", + " rSigRnx(\"EL1C\"), rSigRnx(\"EL5Q\"),\n", + " rSigRnx(\"ES1C\"), rSigRnx(\"ES5Q\")]\n" + ] + }, + { + "cell_type": "markdown", + "id": "8d18ea4c", + "metadata": {}, + "source": [ + "Antenna PCO/PCV correction parameters are loaded. " + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "6117e773", + "metadata": {}, + "outputs": [], + "source": [ + "rnx = rnxdec()\n", + "rnx.setSignals(sigs)\n", + "\n", + "nav = Nav()\n", + "nav.pmode = 0 # Positioning mode: 0:static, 1:kinematic\n", + "\n", + "# Decode RINEX NAV data\n", + "nav = rnx.decode_nav(navfile, nav)\n", + "\n", + "# Load PPP corrections\n", + "v = np.genfromtxt(file_sbas, dtype=dtype)\n", + "\n", + "cs = cssr_pvs()\n", + "cs.monlevel = 0\n", + "\n", + "# Load ANTEX data for satellites and stations\n", + "atx = atxdec()\n", + "atx.readpcv(atxfile)\n", + "\n", + "nav.monlevel = 0 # Logging level\n", + "\n", + "# Load RINEX OBS file header\n", + "if rnx.decode_obsh(obsfile) >= 0:\n", + "\n", + " # Auto-substitute signals\n", + " rnx.autoSubstituteSignals()\n", + "\n", + " # Initialize position\n", + " rr = rnx.pos\n", + " pos = ecef2pos(rr)\n", + " ppp = pppos(nav, rnx.pos, 'test_ppppvs.log')\n", + " nav.elmin = np.deg2rad(5.0)\n", + " nav.glo_ch = rnx.glo_ch\n", + "\n", + " # Set PCO/PCV information\n", + " nav.sat_ant = atx.pcvs\n", + " nav.rcv_ant = searchpcv(atx.pcvr, rnx.ant, rnx.ts)" + ] + }, + { + "cell_type": "markdown", + "id": "be76051d", + "metadata": {}, + "source": [ + "For signals, L1C/A+L5 for GPS, E1+E5a for Galileo are used." + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "f39f72ca", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available signals\n", + "GPS C1C L1C D1C S1C C2W L2W D2W S2W C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", + "GLONASS C1C L1C D1C S1C C2C L2C D2C S2C C2P L2P D2P S2P C3Q L3Q D3Q S3Q\n", + "GALILEO C1C L1C D1C S1C C5Q L5Q D5Q S5Q C7Q L7Q D7Q S7Q C8Q L8Q D8Q S8Q C6C L6C D6C S6C\n", + "BEIDOU C1P L1P D1P S1P C2I L2I D2I S2I C5P L5P D5P S5P C6I L6I D6I S6I C7D L7D D7D S7D C7I L7I D7I S7I\n", + "QZSS C1C L1C D1C S1C C1E L1E D1E S1E C1L L1L D1L S1L C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", + "IRNSS C1P L1P D1P S1P C5A L5A D5A S5A\n", + "\n", + "Selected signals\n", + "GPS C1C C5Q L1C L5Q S1C S5Q \n", + "GALILEO C1C C5Q L1C L5Q S1C S5Q \n" + ] + } + ], + "source": [ + "print(\"Available signals\")\n", + "for sys, sigs in rnx.sig_map.items():\n", + " txt = \"{:7s} {}\".format(sys2str(sys),\n", + " ' '.join([sig.str() for sig in sigs.values()]))\n", + " print(txt)\n", + "\n", + "print(\"\\nSelected signals\")\n", + "for sys, tmp in rnx.sig_tab.items():\n", + " txt = \"{:7s} \".format(sys2str(sys))\n", + " for _, sigs in tmp.items():\n", + " txt += \"{} \".format(' '.join([sig.str() for sig in sigs]))\n", + " print(txt)" + ] + }, + { + "cell_type": "markdown", + "id": "44cde2fb", + "metadata": {}, + "source": [ + "Process data for 15 minutes (ok to abort before finished)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "cf2603db", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " too few satellites < 6: nsat=0\n", + " 2025-08-21 07:00:01 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=1\n", + " 2025-08-21 07:00:02 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=1\n", + " 2025-08-21 07:00:03 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=1\n", + " 2025-08-21 07:00:04 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=2\n", + " 2025-08-21 07:00:05 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=3\n", + " 2025-08-21 07:00:06 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=3\n", + " 2025-08-21 07:00:07 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=3\n", + " 2025-08-21 07:00:08 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=3\n", + " 2025-08-21 07:00:09 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=4\n", + " 2025-08-21 07:00:10 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=4\n", + " 2025-08-21 07:00:11 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=5\n", + " 2025-08-21 07:00:12 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=5\n", + " 2025-08-21 07:00:13 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: nsat=5\n", + " 2025-08-21 07:00:14 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: ns=5\n", + " 2025-08-21 07:00:15 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: ns=5\n", + " 2025-08-21 07:00:16 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: ns=5\n", + " 2025-08-21 07:00:17 ENU 0.280 0.554 0.748, 2D 0.621, mode 0 too few satellites < 6: ns=5\n", + " 2025-08-21 07:15:00 ENU 0.131 -0.009 -0.368, 2D 0.131, mode 5" + ] + } + ], + "source": [ + "nep = 15 * 60 # increase this to run longer\n", + "\n", + "# Intialize data structures for results\n", + "t = np.zeros(nep)\n", + "enu = np.ones((nep, 3))*np.nan\n", + "sol = np.zeros((nep, 4))\n", + "ztd = np.zeros((nep, 1))\n", + "smode = np.zeros(nep, dtype=int)\n", + "\n", + "# Skip epochs until start time\n", + "obs = rnx.decode_obs()\n", + "while time > obs.t and obs.t.time != 0:\n", + " obs = rnx.decode_obs()\n", + "\n", + "# Loop over number of epoch from file start\n", + "for ne in range(nep):\n", + " week, tow = time2gpst(obs.t)\n", + " cs.week = week\n", + " cs.tow0 = tow//86400*86400\n", + " cs.time0 = obs.t\n", + "\n", + " # Set initial epoch\n", + " if ne == 0:\n", + " nav.t = deepcopy(obs.t)\n", + " t0 = deepcopy(obs.t)\n", + " t0.time = t0.time//30*30\n", + " nav.time_p = t0\n", + "\n", + " vi = v[(v['tow'] == tow) & (v['prn'] == prn_ref)\n", + " & (v['type'] > 30)]\n", + " if len(vi) > 0:\n", + " msg = unhexlify(vi['nav'][0])\n", + " cs.decode_cssr(msg, 0)\n", + "\n", + " cs.check_validity(obs.t)\n", + "\n", + " # Call PPP module\n", + " if (cs.lc[0].cstat & 0x6) == 0x6:\n", + " ppp.process(obs, cs=cs)\n", + "\n", + " # Save output\n", + " t[ne] = timediff(nav.t, t0) / 86400.0\n", + " sol = nav.xa[0:3] if nav.smode == 4 else nav.x[0:3]\n", + " enu[ne, :] = ecef2enu(pos_ref, sol-xyz_ref)\n", + " ztd[ne] = nav.xa[ppp.IT(nav.na)] if nav.smode == 4 else nav.x[ppp.IT(nav.na)]\n", + " smode[ne] = nav.smode\n", + "\n", + " # Log to standard output\n", + " stdout.write('\\r {} ENU {:7.3f} {:7.3f} {:7.3f}, 2D {:6.3f}, mode {:1d}'\n", + " .format(time2str(obs.t),\n", + " enu[ne, 0], enu[ne, 1], enu[ne, 2],\n", + " np.sqrt(enu[ne, 0]**2+enu[ne, 1]**2),\n", + " smode[ne]))\n", + "\n", + " # Get new epoch, exit after last epoch\n", + " obs = rnx.decode_obs()\n", + " if obs.t.time == 0:\n", + " break" + ] + }, + { + "cell_type": "markdown", + "id": "f9675705", + "metadata": {}, + "source": [ + "Plot results" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "5e119a44", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from cssrlib.plot import plot_enu\n", + "\n", + "plot_enu(t, enu, smode, ztd)" + ] + }, + { + "cell_type": "markdown", + "id": "c0d4d20c", + "metadata": {}, + "source": [ + "## Example 7: PPP positioning (RTCM correction from JPL GDGPS)\n", + "\n", + "This section demonstrates PPP positioning using PPP via JPL GDGPS (GPSHAS) corrections with a Septentrio mosaic-X5 receiver." + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "516c04a4", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from copy import deepcopy\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as md\n", + "import numpy as np\n", + "from sys import exit as sys_exit\n", + "from sys import stdout\n", + "\n", + "from cssrlib.gnss import ecef2pos, ecef2enu, Nav, rSigRnx, sys2str\n", + "from cssrlib.gnss import time2gpst, time2doy, time2str, timediff, epoch2time\n", + "from cssrlib.cssrlib import sCSSRTYPE, sCType\n", + "from cssrlib.peph import atxdec, searchpcv\n", + "from cssrlib.rtcm import rtcm\n", + "from cssrlib.pppssr import pppos\n", + "from cssrlib.rinex import rnxdec\n" + ] + }, + { + "cell_type": "markdown", + "id": "ff6345bb", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "101bb120", + "metadata": {}, + "outputs": [], + "source": [ + "# Start epoch and number of epochs\n", + "ep = [2025, 8, 21, 7, 0, 0] # start epoch\n", + "\n", + "time = epoch2time(ep)\n", + "year = ep[0]\n", + "doy = int(time2doy(time))\n", + "\n", + "let = chr(ord('a')+ep[3])\n", + "bdir = f'cssrlib-data/data/doy{ep[0]:04d}-{doy:03d}/'\n", + "navfile = bdir+f'{doy:03d}{let}_rnx.nav'\n", + "obsfile = bdir+f'{doy:03d}{let}_rnx.obs'\n", + "atxfile = bdir+'../antex/igs20.atx'\n", + "\n", + "# Specify JPL GDGPS corrections file in RTCM format\n", + "file_rtcm = bdir+f'jpl{doy:03d}{let}.rtcm3'\n", + "\n", + "cs_mask = 1 << sCType.CLOCK | 1 << sCType.ORBIT\n", + "\n", + "# Set user reference position\n", + "xyz_ref = [-3962108.6836, 3381309.5672, 3668678.6720]\n", + "pos_ref = ecef2pos(xyz_ref)\n", + "\n", + "# Define signals to be processed\n", + "sigs = [rSigRnx(\"GC1C\"), rSigRnx(\"GC2W\"),\n", + " rSigRnx(\"GL1C\"), rSigRnx(\"GL2W\"),\n", + " rSigRnx(\"GS1C\"), rSigRnx(\"GS2W\"),\n", + " rSigRnx(\"EC1C\"), rSigRnx(\"EC7Q\"),\n", + " rSigRnx(\"EL1C\"), rSigRnx(\"EL7Q\"),\n", + " rSigRnx(\"ES1C\"), rSigRnx(\"ES7Q\")]\n" + ] + }, + { + "cell_type": "markdown", + "id": "73e491d9", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "2667f49c", + "metadata": {}, + "outputs": [], + "source": [ + "rnx = rnxdec()\n", + "rnx.setSignals(sigs)\n", + "\n", + "nav = Nav()\n", + "nav.pmode = 0 # Positioning mode: 0:static, 1:kinematic\n", + "\n", + "# Decode RINEX NAV data\n", + "nav = rnx.decode_nav(navfile, nav)\n", + "\n", + "# Load PPP corrections\n", + "v = np.genfromtxt(file_sbas, dtype=dtype)\n", + "\n", + "cs = rtcm()\n", + "cs.monlevel = 0\n", + "cs.cssrmode = sCSSRTYPE.RTCM3_SSR\n", + "cs.inet = 0\n", + "# mask phase-bias for JPL GDGPS\n", + "cs.mask_pbias = True\n", + "\n", + "# Load ANTEX data for satellites and stations\n", + "atx = atxdec()\n", + "atx.readpcv(atxfile)\n", + "\n", + "nav.monlevel = 0 # Logging level\n", + "\n", + "if True:\n", + " fc = open(file_rtcm, 'rb')\n", + " if not fc:\n", + " print(\"RTCM message file cannot open.\")\n", + "\n", + " blen = os.path.getsize(file_rtcm)\n", + " msg = fc.read(blen)\n", + " maxlen = len(msg)-5\n", + " fc.close()\n", + "\n", + "# Load RINEX OBS file header\n", + "if rnx.decode_obsh(obsfile) >= 0:\n", + "\n", + " # Auto-substitute signals\n", + " rnx.autoSubstituteSignals()\n", + "\n", + " # Initialize position\n", + " rr = rnx.pos\n", + " pos = ecef2pos(rr)\n", + " ppp = pppos(nav, rnx.pos, 'test_ppprtcm.log')\n", + " nav.elmin = np.deg2rad(5.0)\n", + " \n", + " # Set PCO/PCV information\n", + " nav.sat_ant = atx.pcvs\n", + " nav.rcv_ant = searchpcv(atx.pcvr, rnx.ant, rnx.ts)" + ] + }, + { + "cell_type": "markdown", + "id": "7c8972f3", + "metadata": {}, + "source": [ + "For signals, L1C/A+L2P(Y) for GPS, E1+E5b for Galileo are used." + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "d93c948e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available signals\n", + "GPS C1C L1C D1C S1C C2W L2W D2W S2W C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", + "GLONASS C1C L1C D1C S1C C2C L2C D2C S2C C2P L2P D2P S2P C3Q L3Q D3Q S3Q\n", + "GALILEO C1C L1C D1C S1C C5Q L5Q D5Q S5Q C7Q L7Q D7Q S7Q C8Q L8Q D8Q S8Q C6C L6C D6C S6C\n", + "BEIDOU C1P L1P D1P S1P C2I L2I D2I S2I C5P L5P D5P S5P C6I L6I D6I S6I C7D L7D D7D S7D C7I L7I D7I S7I\n", + "QZSS C1C L1C D1C S1C C1E L1E D1E S1E C1L L1L D1L S1L C2L L2L D2L S2L C5Q L5Q D5Q S5Q\n", + "IRNSS C1P L1P D1P S1P C5A L5A D5A S5A\n", + "\n", + "Selected signals\n", + "GPS C1C C2W L1C L2W S1C S2W \n", + "GALILEO C1C C7Q L1C L7Q S1C S7Q \n" + ] + } + ], + "source": [ + "print(\"Available signals\")\n", + "for sys, sigs in rnx.sig_map.items():\n", + " txt = \"{:7s} {}\".format(sys2str(sys),\n", + " ' '.join([sig.str() for sig in sigs.values()]))\n", + " print(txt)\n", + "\n", + "print(\"\\nSelected signals\")\n", + "for sys, tmp in rnx.sig_tab.items():\n", + " txt = \"{:7s} \".format(sys2str(sys))\n", + " for _, sigs in tmp.items():\n", + " txt += \"{} \".format(' '.join([sig.str() for sig in sigs]))\n", + " print(txt)" + ] + }, + { + "cell_type": "markdown", + "id": "de32c42f", + "metadata": {}, + "source": [ + "Process data for 15 minutes (ok to abort before finished)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "e6fabd9b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 2025-08-21 07:15:00 ENU -0.013 0.114 -0.323, 2D 0.115, mode 5" + ] + } + ], + "source": [ + "nep = 15 * 60 # increase this to run longer\n", + "\n", + "# Intialize data structures for results\n", + "t = np.zeros(nep)\n", + "enu = np.ones((nep, 3))*np.nan\n", + "sol = np.zeros((nep, 4))\n", + "ztd = np.zeros((nep, 1))\n", + "smode = np.zeros(nep, dtype=int)\n", + "\n", + "# Skip epochs until start time\n", + "obs = rnx.decode_obs()\n", + "while time > obs.t and obs.t.time != 0:\n", + " obs = rnx.decode_obs()\n", + "\n", + "k = 0\n", + "# Loop over number of epoch from file start\n", + "for ne in range(nep):\n", + " week, tow = time2gpst(obs.t)\n", + " cs.week = week\n", + " cs.tow0 = tow//3600*3600\n", + " \n", + " # Set initial epoch\n", + " if ne == 0:\n", + " nav.t = deepcopy(obs.t)\n", + " t0 = deepcopy(obs.t)\n", + " t0.time = t0.time//30*30\n", + " nav.time_p = t0\n", + "\n", + " while True:\n", + " stat = cs.sync(msg, k)\n", + " if stat is False:\n", + " k += 1\n", + " continue\n", + " if not cs.checksum(msg, k, maxlen):\n", + " k += 1\n", + " continue\n", + "\n", + " tc = cs.decode_time(msg[k:k+cs.len+3])\n", + " if (tc is not False) and timediff(tc, obs.t) > 0:\n", + " break\n", + "\n", + " _, _, eph, geph, seph = cs.decode(msg[k:k+cs.len+3])\n", + " k += cs.dlen\n", + "\n", + " if cs.msgtype in cs.eph_t.values():\n", + " nav.eph.append(eph)\n", + "\n", + " # Call PPP module\n", + " if (cs.lc[0].cstat & cs_mask) == cs_mask:\n", + " ppp.process(obs, cs=cs)\n", + "\n", + " # Save output\n", + " t[ne] = timediff(nav.t, t0) / 86400.0\n", + " sol = nav.xa[0:3] if nav.smode == 4 else nav.x[0:3]\n", + " enu[ne, :] = ecef2enu(pos_ref, sol-xyz_ref)\n", + " ztd[ne] = nav.xa[ppp.IT(nav.na)] if nav.smode == 4 else nav.x[ppp.IT(nav.na)]\n", + " smode[ne] = nav.smode\n", + "\n", + " # Log to standard output\n", + " stdout.write('\\r {} ENU {:7.3f} {:7.3f} {:7.3f}, 2D {:6.3f}, mode {:1d}'\n", + " .format(time2str(obs.t),\n", + " enu[ne, 0], enu[ne, 1], enu[ne, 2],\n", + " np.sqrt(enu[ne, 0]**2+enu[ne, 1]**2),\n", + " smode[ne]))\n", + "\n", + " # Get new epoch, exit after last epoch\n", + " obs = rnx.decode_obs()\n", + " if obs.t.time == 0:\n", + " break" + ] + }, + { + "cell_type": "markdown", + "id": "b873361d", + "metadata": {}, + "source": [ + "Plot result" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "b6920ead", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from cssrlib.plot import plot_enu\n", + "\n", + "plot_enu(t, enu, smode, ztd)" + ] + }, + { + "cell_type": "markdown", + "id": "06d4a4ac", + "metadata": { + "id": "06d4a4ac" + }, + "source": [ + "## Reference" + ] + }, + { + "cell_type": "markdown", + "id": "7d78ff97", + "metadata": { + "id": "7d78ff97" + }, + "source": [ + "- [^1] T. Takasu, “RTKLIB: Open Source Program Package for RTK-GPS,” FOSS4G 2009 Tokyo, Japan, 2009.\n", + "- [^2] Hirokawa, R., Hauschild, A., Everett, T. (2023). Python Toolkit for Open PPP/PPP-RTK Services. In *Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2023)*\n", + "- [^3] Hirokawa, R., Hauschild, A. (2025). CSSRlib: Python Toolkit for High-Accuracy, Secure, and Resilient Positioning Services. In *Proceedings of the 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2025)*" + ] + }, + { + "cell_type": "markdown", + "id": "53eb0684", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [ + "b85900d9", + "d93de7b9", + "5a058567", + "3b8b422e", + "1c4b06ee", + "rKUv0nEl8vDm", + "8993fc13", + "hPjJOF2T8uxG", + "nf1Rwd3u8vXK" + ], + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}