SpacecraftAttitudeSlew.m

%[text] # Symbolic Attitude Dynamics for Spacecraft Slew Maneuver Planning
%[text] Derive closed-form slew time equations, rate profiles, and pointing sensitivity from Euler's rotational dynamics. Using closed-form parametric analysis answers the "how long does it take to turn?" question without running a simulation for every scenario.
%[text] **Workflow:** Euler's equations from first principles → eigenaxis reduction to scalar ODE → `dsolve` for exact bang-bang and rate-limited slew profiles → closed-form slew time as a symbolic function of inertia, torque, rate limit, and angle → `matlabFunction` code generation → sensitivity analysis for inertia growth and actuator degradation → pointing budget with `vpa` → mission timeline integration
%[text] Requires: Symbolic Math Toolbox™, Aerospace Toolbox™ (for quaternion)
%%
%[text] ## 1. Euler's Equations of Rotational Motion
%[text] For a rigid spacecraft with body-fixed principal axes, the rotational dynamics are governed by Euler's equations. The angular momentum $\\vec{H} = \\mathbf{I}\\vec{\\omega}$ evolves under applied torque $\\vec{\\tau}$:
%[text] $ \\mathbf{I}\\dot{\\vec{\\omega}} + \\vec{\\omega} \\times (\\mathbf{I}\\vec{\\omega}) = \\vec{\\tau} $
%[text] where $\\mathbf{I} = \\mathrm{diag}(I\_x, I\_y, I\_z)$ is the principal moment of inertia tensor. Expanding in component form:
%[text] $ I\_x \\dot{\\omega}\_x + (I\_z - I\_y)\\omega\_y \\omega\_z = \\tau\_x $
%[text] $ I\_y \\dot{\\omega}\_y + (I\_x - I\_z)\\omega\_z \\omega\_x = \\tau\_y $
%[text] $ I\_z \\dot{\\omega}\_z + (I\_y - I\_x)\\omega\_x \\omega\_y = \\tau\_z $
%[text] The cross-coupling terms $(I\_z - I\_y)\\omega\_y\\omega\_z$ etc. are the gyroscopic torques that make 3D rotational dynamics nonlinear. For single-axis slews, these terms vanish.
syms Ix Iy Iz positive
syms wx wy wz real
syms taux tauy tauz real
I_tensor = diag([Ix, Iy, Iz]);
omega_vec = [wx; wy; wz];
tau_vec = [taux; tauy; tauz];
euler_eqs = I_tensor * sym('alpha', [3 1]) + cross(omega_vec, I_tensor * omega_vec) - tau_vec;
euler_eqs_explicit = [
    Ix * sym('alpha_x') + (Iz - Iy)*wy*wz - taux;
    Iy * sym('alpha_y') + (Ix - Iz)*wz*wx - tauy;
    Iz * sym('alpha_z') + (Iy - Ix)*wx*wy - tauz];
disp('Euler''s equations (principal axes, tau = I*alpha + omega x H):') %[output:1d35c264]
disp(euler_eqs_explicit) %[output:617fdfb4]
%%
%[text] ## 2. Eigenaxis Slew - Reduction to Scalar Dynamics
%[text] For a rotation purely about one principal axis (the "eigenaxis"), the cross-coupling terms vanish because the other two angular velocity components are zero. This is the standard maneuver mode for reaction-wheel-based ADCS: the flight software computes the eigenaxis (shortest-path rotation axis) and commands torque along it.
%[text] With rotation about a single axis with moment of inertia $I$, the dynamics reduce to:
%[text] $ I \\ddot{\\theta} = \\tau(t) $
%[text] This is a simple double-integrator: torque produces angular acceleration, which integrates to rate, which integrates to angle. The slew planning problem becomes: given constraints on maximum torque $\\tau\_{\\max}$ and maximum angular rate $\\omega\_{\\max}$, what is the minimum time to rotate through angle $\\Theta$?
syms I_ax positive
syms t
syms theta(t)
Dtheta = diff(theta, t);
D2theta = diff(theta, t, 2);
syms tau_applied
eigenaxis_eom = I_ax * D2theta == tau_applied;
disp('Eigenaxis equation of motion:') %[output:7f9c21a8]
disp(eigenaxis_eom) %[output:51f0ca3c]
%%
%[text] ## 3. Time-Optimal (Bang-Bang) Slew Profile
%[text] When the slew is limited only by available torque $\\tau\_{\\max}$ (no rate constraint), the time-optimal solution is a **bang-bang** profile: full positive torque for the first half of the slew, then full negative torque to decelerate to rest. The angular acceleration is $\\pm \\alpha\_{\\max} = \\tau\_{\\max}/I$.
%[text] **Phase 1** (accelerate, $0 \\le t \\le t\_s/2$): $\\ddot{\\theta} = +\\alpha\_{\\max}$
%[text] **Phase 2** (decelerate, $t\_s/2 \\le t \\le t\_s$): $\\ddot{\\theta} = -\\alpha\_{\\max}$
%[text] Solve each phase with `dsolve` to get the exact angle and rate profiles.
syms alpha_max positive
syms Theta positive
syms ts positive
syms t_var
syms th1(t_var)
Dth1 = diff(th1, t_var);
sol_phase1 = dsolve(diff(th1, t_var, 2) == alpha_max, ...
    th1(0) == 0, Dth1(0) == 0);
disp('Phase 1 (accelerate) — angle vs time:') %[output:7860a39b]
theta_p1 = simplify(sol_phase1);
omega_p1 = simplify(diff(sol_phase1, t_var));
disp(theta_p1) %[output:4c6714f5]
disp('Phase 1 — rate vs time:') %[output:872a8b06]
disp(omega_p1) %[output:7ed9941d]
%%
%[text] ### Closed-Form Bang-Bang Slew Time
%[text] At the midpoint $t = t\_s/2$, the spacecraft has rotated half the total angle $\\Theta/2$ and reached peak rate $\\omega\_{\\mathrm{peak}}$. Solving $\\Theta/2 = \\frac{1}{2}\\alpha\_{\\max}(t\_s/2)^2$ for $t\_s$:
syms t_slew_bb positive
half_angle_eq = alpha_max/2 * (t_slew_bb/2)^2 == Theta/2;
t_slew_bb_sol = solve(half_angle_eq, t_slew_bb);
disp('Bang-bang slew time (torque-limited):') %[output:06813a9a]
disp(t_slew_bb_sol) %[output:49714da8]
omega_peak_bb = simplify(alpha_max * t_slew_bb_sol / 2);
disp('Peak angular rate during bang-bang slew:') %[output:6aee3c77]
disp(omega_peak_bb) %[output:9296e0e2]
%%
%[text] ### Express in Terms of Physical Parameters
%[text] Substituting $\\alpha\_{\\max} = \\tau\_{\\max}/I$ gives slew time and peak rate as functions of the spacecraft's physical properties:
syms tau_max positive
t_bb_physical = simplify(subs(t_slew_bb_sol, alpha_max, tau_max/I_ax));
omega_peak_physical = simplify(subs(omega_peak_bb, alpha_max, tau_max/I_ax));
disp('Slew time t_s = f(Theta, I, tau_max):') %[output:403d7f8c]
disp(t_bb_physical) %[output:3a051330]
disp('Peak rate omega_peak = f(Theta, I, tau_max):') %[output:4c599a04]
disp(omega_peak_physical) %[output:7300d6f4]
%%
%[text] ## 4. Rate-Limited (Trapezoidal) Slew Profile
%[text] Spacecraft have a maximum angular rate $\\omega\_{\\max}$ imposed by star tracker acquisition limits, structural flex excitation, or reaction wheel momentum saturation. When the bang-bang peak rate exceeds $\\omega\_{\\max}$, the profile becomes **trapezoidal**: accelerate at $\\alpha\_{\\max}$ until hitting the rate limit, coast at $\\omega\_{\\max}$, then decelerate.
%[text] **Phase 1** (accelerate): $0 \\to \\omega\_{\\max}$ in time $t\_a = \\omega\_{\\max}/\\alpha\_{\\max}$
%[text] **Phase 2** (coast): hold $\\omega\_{\\max}$ for time $t\_c$
%[text] **Phase 3** (decelerate): $\\omega\_{\\max} \\to 0$ in time $t\_a$ (symmetric)
%[text] The total angle constraint $\\Theta = \\alpha\_{\\max} t\_a^2 + \\omega\_{\\max} t\_c$ determines the coast time.
syms omega_max positive
syms t_accel t_coast
t_accel_expr = omega_max / alpha_max;
theta_accel = alpha_max / 2 * t_accel_expr^2;
theta_coast_eq = Theta == 2*theta_accel + omega_max * t_coast;
t_coast_sol = solve(theta_coast_eq, t_coast);
disp('Acceleration time t_a:') %[output:698eb76f]
disp(t_accel_expr) %[output:16a02e39]
disp('Coast time t_c:') %[output:7aa08495]
disp(simplify(t_coast_sol)) %[output:9ceeb4da]
t_slew_trap = simplify(2*t_accel_expr + t_coast_sol);
disp('Total trapezoidal slew time:') %[output:40c0e4e6]
disp(t_slew_trap) %[output:2b00c1d3]
%%
%[text] ### Unified Slew Time - Regime Selection
%[text] The trapezoidal profile applies only when $\\omega\_{\\mathrm{peak,bb}} \> \\omega\_{\\max}$. Otherwise the bang-bang profile completes before the rate limit is reached. The crossover angle $\\Theta^\*$ where the regimes switch is:
%[text] $ \\Theta^\* = \\omega\_{\\max}^2 / \\alpha\_{\\max} $
%[text] Below $\\Theta^\*$ the slew is torque-limited (bang-bang); above it, rate-limited (trapezoidal).
Theta_crossover = simplify(omega_max^2 / alpha_max);
disp('Crossover angle (bang-bang ↔ trapezoidal):') %[output:1be92d34]
disp(Theta_crossover) %[output:0d54efdb]
t_slew_unified = piecewise(Theta <= Theta_crossover, ...
    subs(t_slew_bb_sol, alpha_max, alpha_max), ...
    Theta > Theta_crossover, t_slew_trap);
t_slew_unified = simplify(t_slew_unified);
disp('Unified slew time expression:') %[output:99801056]
disp(t_slew_unified) %[output:9a1558b8]
%%
%[text] ## 5. Parametric Slew Time
%[text] The unified slew time is now a closed-form symbolic function of four parameters: slew angle $\\Theta$, moment of inertia $I$, maximum torque $\\tau\_{\\max}$, and maximum rate $\\omega\_{\\max}$. This parametric form lets mission planners explore the entire design space without re-running anything.
t_slew_full = subs(t_slew_unified, alpha_max, tau_max/I_ax);
t_slew_full = simplify(t_slew_full);
disp('Slew time t_s(Theta, I, tau_max, omega_max):') %[output:41524589]
disp(t_slew_full) %[output:8db7f28d]
Theta_cross_phys = simplify(subs(Theta_crossover, alpha_max, tau_max/I_ax));
disp('Regime crossover angle (physical parameters):') %[output:67f4ae8d]
disp(Theta_cross_phys) %[output:78fab988]
%%
%[text] ## 6. Reference Mission: Agile Earth-Observing Satellite
%[text] Evaluate for a high-resolution agile imaging satellite performing rapid cross-track retargeting. This class of spacecraft (e.g., Pleiades Neo, WorldView Legion) requires fast slews to maximize daily imaging capacity.
%[text:table]
%[text] | Parameter | Value | Rationale |
%[text] | --- | --- | --- |
%[text] | Bus mass | 500 kg | Typical agile EO platform |
%[text] | Pitch inertia | 120 kg·m² | Pitch axis (cross-track slew) |
%[text] | Reaction wheel torque | 0\.3 N·m | 4-wheel pyramid, single-axis component |
%[text] | Max angular rate | 3\.0 deg/s | Star tracker tracking limit |
%[text] | Slew angle | 30 deg (nominal), up to 45 deg (off-nadir) |
%[text:table]
I_val = 120;
tau_max_val = 0.3;
omega_max_val = deg2rad(3.0);
Theta_nom = deg2rad(30);
alpha_max_val = tau_max_val / I_val;
Theta_cross_val = omega_max_val^2 / alpha_max_val;
fprintf('=== Reference Mission: Agile EO Satellite ===\n') %[output:0e070fd9]
fprintf('Pitch inertia:       %.0f kg·m²\n', I_val) %[output:2327ab8d]
fprintf('Available torque:    %.2f N·m\n', tau_max_val) %[output:3f65aacf]
fprintf('Max angular accel:   %.4f deg/s²\n', rad2deg(alpha_max_val)) %[output:93cef9b0]
fprintf('Max angular rate:    %.1f deg/s\n', rad2deg(omega_max_val)) %[output:26a06cfd]
fprintf('Crossover angle:     %.2f deg\n', rad2deg(Theta_cross_val)) %[output:629ff994]
if Theta_nom > Theta_cross_val
    regime_str = 'RATE-LIMITED';
else
    regime_str = 'TORQUE-LIMITED';
end
fprintf('Nominal slew:        %.0f deg  →  %s regime\n', rad2deg(Theta_nom), regime_str) %[output:5b6d9667]
%%
%[text] ### Evaluate Slew Times
%[text] Compute exact slew times for typical imaging scenarios using the closed-form expressions.
slew_angles_deg = [5, 10, 15, 20, 30, 45];
slew_angles_rad = deg2rad(slew_angles_deg);
t_slew_vals = zeros(size(slew_angles_rad));
for k = 1:length(slew_angles_rad)
    ang = slew_angles_rad(k);
    if ang <= Theta_cross_val
        t_slew_vals(k) = 2*sqrt(ang / alpha_max_val);
    else
        t_a = omega_max_val / alpha_max_val;
        t_c = (ang - omega_max_val^2/alpha_max_val) / omega_max_val;
        t_slew_vals(k) = 2*t_a + t_c;
    end
end
fprintf('\n=== Slew Times (closed-form) ===\n') %[output:76e83fab]
fprintf('  Angle [deg]   Time [s]   Regime\n') %[output:83216c19]
for k = 1:length(slew_angles_deg) %[output:group:71dd9007]
    if slew_angles_rad(k) > Theta_cross_val
        regime = 'rate-lim';
    else
        regime = 'torque-lim';
    end
    fprintf('  %5.0f°        %6.2f      %s\n', slew_angles_deg(k), t_slew_vals(k), regime) %[output:6551f020]
end %[output:group:71dd9007]
%%
%[text] ## 7. Code Generation: Deployable Slew Planner
%[text] Convert the symbolic slew time expressions into optimized MATLAB® functions via `matlabFunction`. The piecewise slew time has two regimes (bang-bang and trapezoidal), so generate each branch separately and combine them in a vectorized wrapper that uses logical indexing.
t_bb_expr = subs(t_slew_bb_sol, alpha_max, tau_max/I_ax);
t_trap_expr = subs(t_slew_trap, alpha_max, tau_max/I_ax);
Theta_cross_expr = I_ax * omega_max^2 / tau_max;
matlabFunction(t_bb_expr, t_trap_expr, Theta_cross_expr, ...
    'File', 'slewTimeKernels', ...
    'Vars', {Theta, I_ax, tau_max, omega_max}, ...
    'Outputs', {'t_bb', 't_trap', 'Theta_star'});
slewTimeCode = [...
    "function t_slew = slewTime(Theta, I, tau_max, omega_max)"
    "% SLEWTIME  Closed-form slew time (vectorized over Theta)."
    "%   t_slew = slewTime(Theta, I, tau_max, omega_max)"
    "%   Handles both bang-bang and rate-limited regimes."
    "[t_bb, t_trap, Theta_star] = slewTimeKernels(Theta, I, tau_max, omega_max);"
    "t_slew = t_bb;"
    "mask = Theta > Theta_star;"
    "t_slew(mask) = t_trap(mask);"
    "end"];
writelines(slewTimeCode, 'slewTime.m');
fprintf('Generated: slewTimeKernels.m (symbolic core)\n') %[output:023b8a16]
fprintf('Generated: slewTime.m (vectorized wrapper)\n') %[output:10b9cc8d]
fprintf('  Signature: t_slew = slewTime(Theta, I, tau_max, omega_max)\n') %[output:43e885fc]
fprintf('  Accepts vectorized Theta for batch evaluation\n') %[output:18a021ae]
%%
%[text] ### Generate Slew Profile Functions
%[text] For detailed profile visualization and downstream ADCS simulation, generate functions that return the angular rate profile (trapezoidal or triangular) and the angle-vs-time trajectory.
syms t_eval positive
omega_profile_trap = piecewise(...
    t_eval <= omega_max/alpha_max, alpha_max * t_eval, ...
    t_eval <= omega_max/alpha_max + (Theta - omega_max^2/alpha_max)/omega_max, omega_max, ...
    symtrue, omega_max - alpha_max * (t_eval - omega_max/alpha_max - (Theta - omega_max^2/alpha_max)/omega_max));
omega_profile_bb = piecewise(...
    t_eval <= sqrt(Theta/alpha_max), alpha_max * t_eval, ...
    symtrue, alpha_max * (2*sqrt(Theta/alpha_max) - t_eval));
matlabFunction(omega_profile_trap, ...
    'File', 'slewRateProfileTrap', ...
    'Vars', {t_eval, Theta, alpha_max, omega_max}, ...
    'Outputs', {'omega'});
matlabFunction(omega_profile_bb, ...
    'File', 'slewRateProfileBB', ...
    'Vars', {t_eval, alpha_max, Theta}, ...
    'Outputs', {'omega'});
fprintf('Generated: slewRateProfileTrap.m\n') %[output:39b6274a]
fprintf('Generated: slewRateProfileBB.m\n') %[output:68720af9]
%%
%[text] ### Visualize the Slew Profile for the 30° Reference Maneuver
%[text] The profile shape depends on whether the maneuver is torque-limited (bang-bang/triangular) or rate-limited (trapezoidal). At 30° with a crossover of ~63°, this maneuver is torque-limited: the spacecraft never reaches the rate limit.
if Theta_nom < Theta_cross_val
    % Bang-bang (triangular) regime
    t_half_val = sqrt(Theta_nom / alpha_max_val);
    t_total_val = 2 * t_half_val;
    omega_peak_val = alpha_max_val * t_half_val;
    t_profile = linspace(0, t_total_val, 500);
    omega_profile = zeros(size(t_profile));
    theta_profile = zeros(size(t_profile));
    for k = 1:length(t_profile)
        tk = t_profile(k);
        if tk <= t_half_val
            omega_profile(k) = alpha_max_val * tk;
            theta_profile(k) = 0.5 * alpha_max_val * tk^2;
        else
            dt = tk - t_half_val;
            omega_profile(k) = omega_peak_val - alpha_max_val * dt;
            theta_profile(k) = 0.5*alpha_max_val*t_half_val^2 + ...
                omega_peak_val*dt - 0.5*alpha_max_val*dt^2;
        end
    end
    profile_label = 'Bang-Bang (triangular)';
else
    % Trapezoidal regime
    t_a_val = omega_max_val / alpha_max_val;
    t_c_val = (Theta_nom - omega_max_val^2/alpha_max_val) / omega_max_val;
    t_total_val = 2*t_a_val + t_c_val;
    omega_peak_val = omega_max_val;
    t_profile = linspace(0, t_total_val, 500);
    omega_profile = zeros(size(t_profile));
    theta_profile = zeros(size(t_profile));
    for k = 1:length(t_profile)
        tk = t_profile(k);
        if tk <= t_a_val
            omega_profile(k) = alpha_max_val * tk;
            theta_profile(k) = 0.5 * alpha_max_val * tk^2;
        elseif tk <= t_a_val + t_c_val
            omega_profile(k) = omega_max_val;
            theta_profile(k) = 0.5*alpha_max_val*t_a_val^2 + omega_max_val*(tk - t_a_val);
        else
            dt = tk - t_a_val - t_c_val;
            omega_profile(k) = omega_max_val - alpha_max_val * dt;
            theta_profile(k) = 0.5*alpha_max_val*t_a_val^2 + omega_max_val*t_c_val + ...
                omega_max_val*dt - 0.5*alpha_max_val*dt^2;
        end
    end
    profile_label = 'Trapezoidal (rate-limited)';
end
fprintf('Profile regime:      %s\n', profile_label) %[output:2acb83fe]
fprintf('Peak angular rate:   %.2f deg/s (limit: %.1f deg/s)\n', ... %[output:group:5653d2d8] %[output:835adeeb]
    rad2deg(omega_peak_val), rad2deg(omega_max_val)) %[output:group:5653d2d8] %[output:835adeeb]
fprintf('Slew time:           %.2f s\n', t_total_val) %[output:1048d81a]
tiledlayout(2,1) %[output:7029d38e]
nexttile %[output:7029d38e]
plot(t_profile, rad2deg(omega_profile), 'b-', 'LineWidth', 2) %[output:7029d38e]
hold on %[output:7029d38e]
yline(rad2deg(omega_max_val), 'r--', '\omega_{max} (limit)', 'LineWidth', 1.5) %[output:7029d38e]
hold off %[output:7029d38e]
ylabel('Angular rate [deg/s]') %[output:7029d38e]
title(sprintf('30° Slew — %s (t_s = %.1f s)', profile_label, t_total_val)) %[output:7029d38e]
grid on %[output:7029d38e]
nexttile %[output:7029d38e]
plot(t_profile, rad2deg(theta_profile), 'b-', 'LineWidth', 2) %[output:7029d38e]
hold on %[output:7029d38e]
yline(30, 'r--', '30° target', 'LineWidth', 1.5) %[output:7029d38e]
hold off %[output:7029d38e]
xlabel('Time [s]') %[output:7029d38e]
ylabel('Angle [deg]') %[output:7029d38e]
grid on %[output:7029d38e]
%%
%[text] ### Aerospace Toolbox Integration: Quaternion Attitude Propagation
%[text] The symbolically-derived angle profile feeds directly into Aerospace Toolbox quaternion objects for 3D attitude representation. This bridges the gap between the 1D eigenaxis planning (symbolic) and full 3D attitude simulation (numeric). The eigenaxis for a cross-track slew is the pitch axis (body Y).
slew_axis = [0 1 0];  % pitch axis (body-Y) for cross-track imaging slew
n_pts = 50;
t_quat = linspace(0, t_total_val, n_pts);
theta_quat = interp1(t_profile, theta_profile, t_quat);
rotvecs = theta_quat(:) * slew_axis;  % N×3 rotation vectors (angle * axis)
q_slew = quaternion(rotvecs, 'rotvec');
% Propagate a body-fixed vector (e.g., sensor boresight along body-Z)
boresight_body = [0 0 1];
boresight_inertial = rotatepoint(q_slew, boresight_body);
plot3(boresight_inertial(:,1), boresight_inertial(:,2), boresight_inertial(:,3), ... %[output:1ed25d5e]
    'b-', 'LineWidth', 2) %[output:1ed25d5e]
hold on %[output:1ed25d5e]
plot3(boresight_inertial(1,1), boresight_inertial(1,2), boresight_inertial(1,3), ... %[output:1ed25d5e]
    'go', 'MarkerSize', 10, 'MarkerFaceColor', 'g') %[output:1ed25d5e]
plot3(boresight_inertial(end,1), boresight_inertial(end,2), boresight_inertial(end,3), ... %[output:1ed25d5e]
    'r^', 'MarkerSize', 10, 'MarkerFaceColor', 'r') %[output:1ed25d5e]
hold off %[output:1ed25d5e]
xlabel('X'); ylabel('Y'); zlabel('Z') %[output:1ed25d5e]
title('Sensor Boresight Trace During 30° Slew') %[output:1ed25d5e]
legend('Boresight path', 'Start', 'End', 'Location', 'best') %[output:1ed25d5e]
grid on %[output:1ed25d5e]
axis equal %[output:1ed25d5e]
view(135, 25) %[output:1ed25d5e]
%%
%[text] ## 8. Sensitivity Analysis: Inertia Growth & Actuator Degradation
%[text] Over mission life, spacecraft inertia changes (fuel depletion, solar array deployment, appendage thermal distortion) and reaction wheel torque degrades (bearing friction, driver aging). The symbolic slew time gives exact partial derivatives — no finite-difference approximation.
%[text] ### Inertia Sensitivity
%[text] How does slew time change per unit increase in inertia?
dt_dI = diff(t_slew_full, I_ax);
dt_dI = simplify(dt_dI);
disp('∂t_slew/∂I (sensitivity to inertia):') %[output:061f7316]
disp(dt_dI) %[output:9eac0858]
%%
%[text] ### Torque Sensitivity
%[text] How does slew time change if available torque decreases (wheel degradation)?
dt_dtau = diff(t_slew_full, tau_max);
dt_dtau = simplify(dt_dtau);
disp('∂t_slew/∂tau_max (sensitivity to available torque):') %[output:4e7b6619]
disp(dt_dtau) %[output:6b6aef0b]
%%
%[text] ### Rate Limit Sensitivity
dt_domega = diff(t_slew_full, omega_max);
dt_domega = simplify(dt_domega);
disp('∂t_slew/∂omega_max (sensitivity to rate limit):') %[output:6dc1a528]
disp(dt_domega) %[output:377325a2]
%%
%[text] ### Generate Deployable Sensitivity Functions
matlabFunction(dt_dI, dt_dtau, dt_domega, ...
    'File', 'slewTimeSensitivity', ...
    'Vars', {Theta, I_ax, tau_max, omega_max}, ...
    'Outputs', {'dts_dI', 'dts_dtau', 'dts_domega'});
fprintf('Generated: slewTimeSensitivity.m\n') %[output:29e40214]
%%
%[text] ### Evaluate Sensitivity at Reference Conditions
%[text] Quantify the impact of realistic degradation scenarios: +10% inertia growth (fuel redistribution) and -15% torque loss (wheel aging at year 5).
[dts_dI_val, dts_dtau_val, dts_domega_val] = slewTimeSensitivity(...
    Theta_nom, I_val, tau_max_val, omega_max_val);
fprintf('\n=== Sensitivity at 30° Slew ===\n') %[output:82c9cb8e]
fprintf('∂t/∂I:         %+.4f s per kg·m²\n', dts_dI_val) %[output:6711ea47]
fprintf('∂t/∂tau_max:   %+.4f s per N·m\n', dts_dtau_val) %[output:9c9659eb]
fprintf('∂t/∂omega_max: %+.4f s per rad/s\n', dts_domega_val) %[output:5babffc5]
delta_I = 0.10 * I_val;
delta_tau = -0.15 * tau_max_val;
dt_inertia_growth = dts_dI_val * delta_I;
dt_torque_loss = dts_dtau_val * delta_tau;
fprintf('\n=== Mission-Life Degradation Impact (30° slew) ===\n') %[output:84a82310]
fprintf('+10%% inertia:   %+.2f s  (%.1f%% increase)\n', ... %[output:group:483904d5] %[output:4b780009]
    dt_inertia_growth, 100*dt_inertia_growth/t_total_val) %[output:group:483904d5] %[output:4b780009]
fprintf('-15%% torque:    %+.2f s  (%.1f%% increase)\n', ... %[output:group:37b64722] %[output:6a9936f8]
    dt_torque_loss, 100*dt_torque_loss/t_total_val) %[output:group:37b64722] %[output:6a9936f8]
fprintf('Combined:       %+.2f s  (%.1f%% increase)\n', ... %[output:group:9f72345d] %[output:112e4154]
    dt_inertia_growth + dt_torque_loss, ... %[output:112e4154]
    100*(dt_inertia_growth + dt_torque_loss)/t_total_val) %[output:group:9f72345d] %[output:112e4154]
%%
%[text] ### Validation via Variable-Precision Arithmetic
%[text] The symbolic derivative is exact by construction, but we can verify it independently: evaluate the slew time expression at $I$ and $I + \\delta$ using 32-digit arithmetic, then compare the finite-difference quotient against the analytic sensitivity. This is a critical step in certification workflows.
delta_sym = sym(1)/1000;  % small perturbation (exact rational)
I_sym = sym(I_val);
tau_sym = sym(tau_max_val);
omega_sym = sym(omega_max_val);
Theta_sym = sym(Theta_nom);
t_at_I = vpa(subs(t_slew_full, [Theta, I_ax, tau_max, omega_max], ...
    [Theta_sym, I_sym, tau_sym, omega_sym]), 32);
t_at_I_plus = vpa(subs(t_slew_full, [Theta, I_ax, tau_max, omega_max], ...
    [Theta_sym, I_sym + delta_sym, tau_sym, omega_sym]), 32);
fd_sensitivity = (t_at_I_plus - t_at_I) / delta_sym;
analytic_sensitivity = vpa(subs(dt_dI, [Theta, I_ax, tau_max, omega_max], ...
    [Theta_sym, I_sym, tau_sym, omega_sym]), 32);
fprintf('\n=== VPA Verification (32-digit) ===\n') %[output:0b56d028]
fprintf('Analytic ∂t/∂I:       %s\n', char(analytic_sensitivity)) %[output:7868d38d]
fprintf('Finite-diff (δI=0.001): %s\n', char(fd_sensitivity)) %[output:6a50275a]
fprintf('Agreement to %.0f digits\n', ... %[output:group:6f2e92aa] %[output:1ae55ec2]
    -log10(double(abs(analytic_sensitivity - fd_sensitivity)/analytic_sensitivity))) %[output:group:6f2e92aa] %[output:1ae55ec2]
%%
%[text] ### Sensitivity Across Slew Angles
%[text] The sensitivity structure changes at the regime crossover. Plotting it reveals where inertia growth hurts most.
Theta_sweep = linspace(deg2rad(1), deg2rad(60), 300);
dts_dI_sweep = zeros(size(Theta_sweep));
dts_dtau_sweep = zeros(size(Theta_sweep));
for k = 1:length(Theta_sweep)
    [dts_dI_sweep(k), dts_dtau_sweep(k), ~] = slewTimeSensitivity(...
        Theta_sweep(k), I_val, tau_max_val, omega_max_val);
end
clf %[output:3d549916]
yyaxis left %[output:3d549916]
plot(rad2deg(Theta_sweep), dts_dI_sweep, 'b-', 'LineWidth', 1.5) %[output:3d549916]
ylabel('\partial t_s / \partial I  [s / kg \cdot m^2]') %[output:3d549916]
yyaxis right %[output:3d549916]
plot(rad2deg(Theta_sweep), dts_dtau_sweep, 'r-', 'LineWidth', 1.5) %[output:3d549916]
ylabel('\partial t_s / \partial \tau_{max}  [s / N \cdot m]') %[output:3d549916]
xlabel('Slew angle [deg]') %[output:3d549916]
xline(rad2deg(Theta_cross_val), 'k--', 'Regime crossover', 'LineWidth', 1.5) %[output:3d549916]
title('Slew Time Sensitivity vs. Maneuver Size') %[output:3d549916]
legend('Inertia sensitivity', 'Torque sensitivity', 'Location', 'best') %[output:3d549916]
grid on %[output:3d549916]
%%
%[text] ## 9. Pointing Budget Decomposition
%[text] High-resolution imaging requires sub-arcsecond pointing stability after the slew settles. The total pointing error is the RSS of independent contributors. Each term is a closed-form symbolic expression, enabling exact allocation without requiring Monte Carlo simulation.
%[text] Error sources for a typical agile EO satellite:
%[text] - Star tracker noise: $\\sigma\_{\\mathrm{ST}}$
%[text] - Reaction wheel jitter (imbalance-induced): $\\sigma\_{\\mathrm{RW}}$
%[text] - Structural flex (appendage modes): $\\sigma\_{\\mathrm{flex}}$
%[text] - Thermal distortion (gradient across bus): $\\sigma\_{\\mathrm{therm}}$ \
syms sigma_ST sigma_RW sigma_flex sigma_therm positive
sigma_total = sqrt(sigma_ST^2 + sigma_RW^2 + sigma_flex^2 + sigma_therm^2);
disp('Total pointing error (RSS):') %[output:7f2d0325]
disp(sigma_total) %[output:91efc409]
%%
%[text] ### Sensitivity of Pointing Budget to Each Source
%[text] The partial derivative $\\partial\\sigma\_{\\mathrm{total}}/\\partial\\sigma\_i$ tells the pointing engineer which error source offers the best return on investment for improvement.
dsig_dST = simplify(diff(sigma_total, sigma_ST));
dsig_dRW = simplify(diff(sigma_total, sigma_RW));
dsig_dFlex = simplify(diff(sigma_total, sigma_flex));
dsig_dTherm = simplify(diff(sigma_total, sigma_therm));
disp('∂σ_total/∂σ_ST:') %[output:1f25dab6]
disp(dsig_dST) %[output:7e797a91]
%%
%[text] ### Evaluate Pointing Budget
%[text] Substitute realistic error allocations for a high-resolution agile EO satellite (0.5 m GSD from 500 km LEO requires total pointing error below ~1 arcsec).
sigma_ST_val = 0.3;      % arcsec — high-end star tracker
sigma_RW_val = 0.15;     % arcsec — balanced reaction wheels
sigma_flex_val = 0.2;    % arcsec — stiff CFRP bus
sigma_therm_val = 0.1;   % arcsec — thermal control
sigma_total_val = double(subs(sigma_total, ...
    [sigma_ST, sigma_RW, sigma_flex, sigma_therm], ...
    [sigma_ST_val, sigma_RW_val, sigma_flex_val, sigma_therm_val]));
fprintf('=== Pointing Budget ===\n') %[output:870dcdde]
fprintf('Star tracker:       %.2f arcsec\n', sigma_ST_val) %[output:868ceb39]
fprintf('Wheel jitter:       %.2f arcsec\n', sigma_RW_val) %[output:71753915]
fprintf('Structural flex:    %.2f arcsec\n', sigma_flex_val) %[output:357a7b27]
fprintf('Thermal distortion: %.2f arcsec\n', sigma_therm_val) %[output:54617ded]
fprintf('Total (RSS):        %.4f arcsec\n', sigma_total_val) %[output:78b0c845]
%%
%[text] ### Variance Contribution Breakdown
%[text] Identify the dominant error by analyzing each source's fractional contribution to the total variance.
variances = [sigma_ST_val^2, sigma_RW_val^2, sigma_flex_val^2, sigma_therm_val^2];
total_var = sum(variances);
pct_contributions = 100 * variances / total_var;
clf %[output:22202c5e]
barh(pct_contributions) %[output:22202c5e]
set(gca, 'YTick', 1:4, 'YTickLabel', ... %[output:22202c5e]
    {'Star Tracker', 'Wheel Jitter', 'Struct. Flex', 'Thermal'}) %[output:22202c5e]
xlabel('% of total pointing variance') %[output:22202c5e]
title('Pointing Budget — Variance Decomposition') %[output:22202c5e]
grid on %[output:22202c5e]
fprintf('\nVariance contributions:\n') %[output:7772e9fa]
fprintf('  Star tracker:    %5.1f%%n', pct_contributions(1)) %[output:4a59f503]
fprintf('  Wheel jitter:    %5.1f%%n', pct_contributions(2)) %[output:97f4a711]
fprintf('  Structural flex: %5.1f%%n', pct_contributions(3)) %[output:9a781b8d]
fprintf('  Thermal:         %5.1f%%n', pct_contributions(4)) %[output:7ff91f3e]
%%
%[text] ## 10. Multi-Target Scheduling
%[text] The ultimate payoff: given a sequence of imaging targets, the generated slew planner computes all inter-target slew times in a single vectorized call. This feeds directly into scheduling optimizers that maximize daily imaging capacity.
%[text] Scenario: 8 imaging targets in one orbital pass, at various off-nadir angles along the ground track.
target_angles_deg = [0, 15, -20, 35, -10, 25, -30, 5];
n_targets = length(target_angles_deg);
slew_angles_pass = abs(diff(target_angles_deg));
slew_angles_pass_rad = deg2rad(slew_angles_pass);
t_slews_pass = slewTime(slew_angles_pass_rad, I_val, tau_max_val, omega_max_val);
settle_time = 2.0;
image_time = 8.0;
t_slew_total = sum(t_slews_pass);
t_settle_total = settle_time * (n_targets - 1);
t_image_total = image_time * n_targets;
t_pass_total = t_slew_total + t_settle_total + t_image_total;
fprintf('=== Multi-Target Pass Timeline ===\n') %[output:7ef6692b]
fprintf('Targets: %d  |  Orbit pass window: ~600 s\n', n_targets) %[output:56ce576e]
fprintf('\nTarget schedule (off-nadir angles):\n') %[output:7d0c35e1]
fprintf('  ') %[output:333f75be]
fprintf('%+d° → ', target_angles_deg(1:end-1)) %[output:42640187]
fprintf('%+d°\n', target_angles_deg(end)) %[output:701cd70d]
fprintf('\nInter-target slews:\n') %[output:140edeef]
fprintf('  Slew     Angle    Time\n') %[output:10f571d6]
for k = 1:length(slew_angles_pass) %[output:group:857ceee9]
    fprintf('  %d→%d     %4.0f°   %5.1f s\n', k, k+1, slew_angles_pass(k), t_slews_pass(k)) %[output:397491c0]
end %[output:group:857ceee9]
fprintf('\n  Total slew time:    %5.1f s\n', t_slew_total) %[output:0f325f43]
fprintf('  Total settle time:  %5.1f s  (%.1f s × %d)\n', t_settle_total, settle_time, n_targets-1) %[output:97bee2e7]
fprintf('  Total image time:   %5.1f s  (%.1f s × %d)\n', t_image_total, image_time, n_targets) %[output:88e83878]
fprintf('  ─────────────────────────\n') %[output:8aeddb67]
fprintf('  Pass total:         %5.1f s\n', t_pass_total) %[output:36253c07]
%%
%[text] ### Visualize the Timeline
%[text] Build a Gantt-style timeline showing how slew, settle, and imaging segments fill the orbital pass window.
n_segments = 3*n_targets - 2;  % image + (slew + settle) for each transition
seg_starts = zeros(1, n_segments);
seg_durations = zeros(1, n_segments);
seg_types = zeros(1, n_segments);  % 1=slew, 2=settle, 3=image
t_cumulative = 0;
seg_idx = 0;
for k = 1:n_targets
    if k > 1
        seg_idx = seg_idx + 1;
        seg_starts(seg_idx) = t_cumulative;
        seg_durations(seg_idx) = t_slews_pass(k-1);
        seg_types(seg_idx) = 1;
        t_cumulative = t_cumulative + t_slews_pass(k-1);
        seg_idx = seg_idx + 1;
        seg_starts(seg_idx) = t_cumulative;
        seg_durations(seg_idx) = settle_time;
        seg_types(seg_idx) = 2;
        t_cumulative = t_cumulative + settle_time;
    end
    seg_idx = seg_idx + 1;
    seg_starts(seg_idx) = t_cumulative;
    seg_durations(seg_idx) = image_time;
    seg_types(seg_idx) = 3;
    t_cumulative = t_cumulative + image_time;
end
colors_tl = [0.85 0.33 0.10; 0.93 0.69 0.13; 0.00 0.45 0.74];
clf %[output:7c320700]
hold on %[output:7c320700]
for s = 1:seg_idx
    patch([seg_starts(s), seg_starts(s)+seg_durations(s), ... %[output:7c320700]
           seg_starts(s)+seg_durations(s), seg_starts(s)], ... %[output:7c320700]
          [0.1, 0.1, 0.9, 0.9], colors_tl(seg_types(s),:), ... %[output:7c320700]
          'FaceAlpha', 0.85, 'EdgeColor', 'w', 'LineWidth', 0.5, ... %[output:7c320700]
          'HandleVisibility', 'off') %[output:7c320700]
end
patch(nan, nan, colors_tl(1,:), 'DisplayName', 'Slew') %[output:7c320700]
patch(nan, nan, colors_tl(2,:), 'DisplayName', 'Settle') %[output:7c320700]
patch(nan, nan, colors_tl(3,:), 'DisplayName', 'Image') %[output:7c320700]
hold off %[output:7c320700]
xlabel('Time [s]') %[output:7c320700]
title('Orbital Pass Timeline — 8 Imaging Targets') %[output:7c320700]
legend('Location', 'northoutside', 'Orientation', 'horizontal') %[output:7c320700]
set(gca, 'YTick', [], 'YLim', [0 1]) %[output:7c320700]
xlim([0, t_pass_total]) %[output:7c320700]
%%
%[text] ### Impact of Degradation on Mission Capacity
%[text] Re-evaluate the pass timeline under end-of-life conditions (inertia +10%, torque -15%) to quantify how many targets the spacecraft can still service within the pass window.
I_eol = I_val * 1.10;
tau_eol = tau_max_val * 0.85;
t_slews_eol = slewTime(slew_angles_pass_rad, I_eol, tau_eol, omega_max_val);
t_pass_eol = sum(t_slews_eol) + t_settle_total + t_image_total;
fprintf('\n=== End-of-Life Timeline Impact ===\n') %[output:67eca67d]
fprintf('BOL pass total:  %.1f s\n', t_pass_total) %[output:5b1aab9d]
fprintf('EOL pass total:  %.1f s  (+%.1f s, +%.1f%%)\n', ... %[output:group:3b45b1cd] %[output:14853dff]
    t_pass_eol, t_pass_eol - t_pass_total, ... %[output:14853dff]
    100*(t_pass_eol - t_pass_total)/t_pass_total) %[output:group:3b45b1cd] %[output:14853dff]
pass_window = 600;
if t_pass_eol > pass_window %[output:group:200550d1]
    t_cumulative_eol = image_time;
    max_targets_eol = 1;
    for k = 1:length(slew_angles_pass)
        t_cumulative_eol = t_cumulative_eol + t_slews_eol(k) + settle_time + image_time;
        if t_cumulative_eol > pass_window
            break;
        end
        max_targets_eol = max_targets_eol + 1;
    end
    fprintf('Pass window:     %.0f s\n', pass_window)
    fprintf('EOL capacity:    %d of %d targets (%.0f%% utilization)\n', ...
        max_targets_eol, n_targets, 100*max_targets_eol/n_targets)
else
    fprintf('EOL: all %d targets still fit within %.0f s window\n', n_targets, pass_window) %[output:6501c037]
end %[output:group:200550d1]
%%
%[text] ### Parametric Exploration of Slew Time Trade Space 
%[text] The symbolic slew planner enables instant parametric sweeps. Here we show slew time vs. angle for multiple torque levels, capturing the full actuator sizing trade.
Theta_sweep_fine = linspace(deg2rad(0.5), deg2rad(60), 500);
tau_levels = [0.15, 0.20, 0.30, 0.50];
clf %[output:40522523]
hold on %[output:40522523]
for k = 1:length(tau_levels)
    t_sweep_k = slewTime(Theta_sweep_fine, I_val, tau_levels(k), omega_max_val);
    plot(rad2deg(Theta_sweep_fine), t_sweep_k, 'LineWidth', 2, ... %[output:40522523]
        'DisplayName', sprintf('%.2f N\\cdotm', tau_levels(k))) %[output:40522523]
end
xline(rad2deg(Theta_cross_val), 'k--', 'HandleVisibility', 'off') %[output:40522523]
hold off %[output:40522523]
xlabel('Slew angle [deg]') %[output:40522523]
ylabel('Slew time [s]') %[output:40522523]
title('Slew Time Trade Space — Actuator Sizing (\tau_{max})') %[output:40522523]
legend('Location', 'northwest') %[output:40522523]
grid on %[output:40522523]

%[appendix]{"version":"1.0"}
%---
%[metadata:view]
%   data: {"layout":"inline"}
%---
%[output:1d35c264]
%   data: {"dataType":"text","outputData":{"text":"Euler's equations (principal axes, tau = I*alpha + omega x H):\n","truncated":false}}
%---
%[output:617fdfb4]
%   data: {"dataType":"symbolic","outputData":{"name":"","value":"\\left\\lbrack \\begin{array}{c}\n\\mathrm{Ix}\\,\\alpha_x -\\mathrm{taux}-\\mathrm{wy}\\,\\mathrm{wz}\\,{\\left(\\mathrm{Iy}-\\mathrm{Iz}\\right)}\\\\\n\\mathrm{Iy}\\,\\alpha_y -\\mathrm{tauy}+\\mathrm{wx}\\,\\mathrm{wz}\\,{\\left(\\mathrm{Ix}-\\mathrm{Iz}\\right)}\\\\\n\\mathrm{Iz}\\,\\alpha_z -\\mathrm{tauz}-\\mathrm{wx}\\,\\mathrm{wy}\\,{\\left(\\mathrm{Ix}-\\mathrm{Iy}\\right)}\n\\end{array}\\right\\rbrack"}}
%---
%[output:7f9c21a8]
%   data: {"dataType":"text","outputData":{"text":"Eigenaxis equation of motion:\n","truncated":false}}
%---
%[output:51f0ca3c]
%   data: {"dataType":"symbolic","outputData":{"name":"","value":"I_{\\textrm{ax}} \\,\\frac{\\partial^2 }{\\partial t^2 }\\;\\theta \\left(t\\right)=\\tau_{\\textrm{applied}}"}}
%---
%[output:7860a39b]
%   data: {"dataType":"text","outputData":{"text":"Phase 1 (accelerate) — angle vs time:\n","truncated":false}}
%---
%[output:4c6714f5]
%   data: {"dataType":"symbolic","outputData":{"name":"","value":"\\frac{\\alpha_{\\max } \\,{t_{\\textrm{var}} }^2 }{2}"}}
%---
%[output:872a8b06]
%   data: {"dataType":"text","outputData":{"text":"Phase 1 — rate vs time:\n","truncated":false}}
%---
%[output:7ed9941d]
%   data: {"dataType":"symbolic","outputData":{"name":"","value":"\\alpha_{\\max } \\,t_{\\textrm{var}}"}}
%---
%[output:06813a9a]
%   data: {"dataType":"text","outputData":{"text":"Bang-bang slew time (torque-limited):\n","truncated":false}}
%---
%[output:49714da8]
%   data: {"dataType":"symbolic","outputData":{"name":"","value":"\\frac{2\\,\\sqrt{\\Theta }}{\\sqrt{\\alpha_{\\max } }}"}}
%---
%[output:6aee3c77]
%   data: {"dataType":"text","outputData":{"text":"Peak angular rate during bang-bang slew:\n","truncated":false}}
%---
%[output:9296e0e2]
%   data: {"dataType":"symbolic","outputData":{"name":"","value":"\\sqrt{\\Theta }\\,\\sqrt{\\alpha_{\\max } }"}}
%---
%[output:403d7f8c]
%   data: {"dataType":"text","outputData":{"text":"Slew time t_s = f(Theta, I, tau_max):\n","truncated":false}}
%---
%[output:3a051330]
%   data: {"dataType":"symbolic","outputData":{"name":"","value":"\\frac{2\\,\\sqrt{I_{\\textrm{ax}} }\\,\\sqrt{\\Theta }}{\\sqrt{\\tau_{\\max } }}"}}
%---
%[output:4c599a04]
%   data: {"dataType":"text","outputData":{"text":"Peak rate omega_peak = f(Theta, I, tau_max):\n","truncated":false}}
%---
%[output:7300d6f4]
%   data: {"dataType":"symbolic","outputData":{"name":"","value":"\\frac{\\sqrt{\\Theta }\\,\\sqrt{\\tau_{\\max } }}{\\sqrt{I_{\\textrm{ax}} }}"}}
%---
%[output:698eb76f]
%   data: {"dataType":"text","outputData":{"text":"Acceleration time t_a:\n","truncated":false}}
%---
%[output:16a02e39]
%   data: {"dataType":"symbolic","outputData":{"name":"","value":"\\frac{\\omega_{\\max } }{\\alpha_{\\max } }"}}
%---
%[output:7aa08495]
%   data: {"dataType":"text","outputData":{"text":"Coast time t_c:\n","truncated":false}}
%---
%[output:9ceeb4da]
%   data: {"dataType":"symbolic","outputData":{"name":"","value":"\\frac{\\Theta -\\frac{{\\omega_{\\max } }^2 }{\\alpha_{\\max } }}{\\omega_{\\max } }"}}
%---
%[output:40c0e4e6]
%   data: {"dataType":"text","outputData":{"text":"Total trapezoidal slew time:\n","truncated":false}}
%---
%[output:2b00c1d3]
%   data: {"dataType":"symbolic","outputData":{"name":"","value":"\\frac{{\\omega_{\\max } }^2 +\\Theta \\,\\alpha_{\\max } }{\\alpha_{\\max } \\,\\omega_{\\max } }"}}
%---
%[output:1be92d34]
%   data: {"dataType":"text","outputData":{"text":"Crossover angle (bang-bang ↔ trapezoidal):\n","truncated":false}}
%---
%[output:0d54efdb]
%   data: {"dataType":"symbolic","outputData":{"name":"","value":"\\frac{{\\omega_{\\max } }^2 }{\\alpha_{\\max } }"}}
%---
%[output:99801056]
%   data: {"dataType":"text","outputData":{"text":"Unified slew time expression:\n","truncated":false}}
%---
%[output:9a1558b8]
%   data: {"dataType":"symbolic","outputData":{"name":"","value":"\\left\\lbrace \\begin{array}{cl}\n\\frac{2\\,\\sqrt{\\Theta }}{\\sqrt{\\alpha_{\\max } }} & \\;\\textrm{if}\\;\\;\\Theta \\,\\alpha_{\\max } \\le {\\omega_{\\max } }^2 \\\\\n\\frac{{\\omega_{\\max } }^2 +\\Theta \\,\\alpha_{\\max } }{\\alpha_{\\max } \\,\\omega_{\\max } } & \\;\\textrm{if}\\;\\;{\\omega_{\\max } }^2 <\\Theta \\,\\alpha_{\\max } \n\\end{array}\\right."}}
%---
%[output:41524589]
%   data: {"dataType":"text","outputData":{"text":"Slew time t_s(Theta, I, tau_max, omega_max):\n","truncated":false}}
%---
%[output:8db7f28d]
%   data: {"dataType":"symbolic","outputData":{"name":"","value":"\\left\\lbrace \\begin{array}{cl}\n\\frac{2\\,\\sqrt{I_{\\textrm{ax}} }\\,\\sqrt{\\Theta }}{\\sqrt{\\tau_{\\max } }} & \\;\\textrm{if}\\;\\;\\Theta \\,\\tau_{\\max } \\le I_{\\textrm{ax}} \\,{\\omega_{\\max } }^2 \\\\\n\\frac{I_{\\textrm{ax}} \\,{\\omega_{\\max } }^2 +\\Theta \\,\\tau_{\\max } }{\\omega_{\\max } \\,\\tau_{\\max } } & \\;\\textrm{if}\\;\\;I_{\\textrm{ax}} \\,{\\omega_{\\max } }^2 <\\Theta \\,\\tau_{\\max } \n\\end{array}\\right."}}
%---
%[output:67f4ae8d]
%   data: {"dataType":"text","outputData":{"text":"Regime crossover angle (physical parameters):\n","truncated":false}}
%---
%[output:78fab988]
%   data: {"dataType":"symbolic","outputData":{"name":"","value":"\\frac{I_{\\textrm{ax}} \\,{\\omega_{\\max } }^2 }{\\tau_{\\max } }"}}
%---
%[output:0e070fd9]
%   data: {"dataType":"text","outputData":{"text":"=== Reference Mission: Agile EO Satellite ===\n","truncated":false}}
%---
%[output:2327ab8d]
%   data: {"dataType":"text","outputData":{"text":"Pitch inertia:       120 kg·m²\n","truncated":false}}
%---
%[output:3f65aacf]
%   data: {"dataType":"text","outputData":{"text":"Available torque:    0.30 N·m\n","truncated":false}}
%---
%[output:93cef9b0]
%   data: {"dataType":"text","outputData":{"text":"Max angular accel:   0.1432 deg\/s²\n","truncated":false}}
%---
%[output:26a06cfd]
%   data: {"dataType":"text","outputData":{"text":"Max angular rate:    3.0 deg\/s\n","truncated":false}}
%---
%[output:629ff994]
%   data: {"dataType":"text","outputData":{"text":"Crossover angle:     62.83 deg\n","truncated":false}}
%---
%[output:5b6d9667]
%   data: {"dataType":"text","outputData":{"text":"Nominal slew:        30 deg  →  TORQUE-LIMITED regime\n","truncated":false}}
%---
%[output:76e83fab]
%   data: {"dataType":"text","outputData":{"text":"\n=== Slew Times (closed-form) ===\n","truncated":false}}
%---
%[output:83216c19]
%   data: {"dataType":"text","outputData":{"text":"  Angle [deg]   Time [s]   Regime\n","truncated":false}}
%---
%[output:6551f020]
%   data: {"dataType":"text","outputData":{"text":"      5°         11.82      torque-lim\n     10°         16.71      torque-lim\n     15°         20.47      torque-lim\n     20°         23.63      torque-lim\n     30°         28.94      torque-lim\n     45°         35.45      torque-lim\n","truncated":false}}
%---
%[output:023b8a16]
%   data: {"dataType":"text","outputData":{"text":"Generated: slewTimeKernels.m (symbolic core)\n","truncated":false}}
%---
%[output:10b9cc8d]
%   data: {"dataType":"text","outputData":{"text":"Generated: slewTime.m (vectorized wrapper)\n","truncated":false}}
%---
%[output:43e885fc]
%   data: {"dataType":"text","outputData":{"text":"  Signature: t_slew = slewTime(Theta, I, tau_max, omega_max)\n","truncated":false}}
%---
%[output:18a021ae]
%   data: {"dataType":"text","outputData":{"text":"  Accepts vectorized Theta for batch evaluation\n","truncated":false}}
%---
%[output:39b6274a]
%   data: {"dataType":"text","outputData":{"text":"Generated: slewRateProfileTrap.m\n","truncated":false}}
%---
%[output:68720af9]
%   data: {"dataType":"text","outputData":{"text":"Generated: slewRateProfileBB.m\n","truncated":false}}
%---
%[output:2acb83fe]
%   data: {"dataType":"text","outputData":{"text":"Profile regime:      Bang-Bang (triangular)\n","truncated":false}}
%---
%[output:835adeeb]
%   data: {"dataType":"text","outputData":{"text":"Peak angular rate:   2.07 deg\/s (limit: 3.0 deg\/s)\n","truncated":false}}
%---
%[output:1048d81a]
%   data: {"dataType":"text","outputData":{"text":"Slew time:           28.94 s\n","truncated":false}}
%---
%[output:7029d38e]
%   data: 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","height":337,"width":560}}
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","height":337,"width":560}}
%---
%[output:061f7316]
%   data: {"dataType":"text","outputData":{"text":"∂t_slew\/∂I (sensitivity to inertia):\n","truncated":false}}
%---
%[output:9eac0858]
%   data: {"dataType":"symbolic","outputData":{"name":"","value":"\\left\\lbrace \\begin{array}{cl}\n\\frac{\\sqrt{\\Theta }}{\\sqrt{I_{\\textrm{ax}} }\\,\\sqrt{\\tau_{\\max } }} & \\;\\textrm{if}\\;\\;\\Theta \\,\\tau_{\\max } <I_{\\textrm{ax}} \\,{\\omega_{\\max } }^2 \\\\\n\\frac{\\omega_{\\max } }{\\tau_{\\max } } & \\;\\textrm{if}\\;\\;I_{\\textrm{ax}} \\,{\\omega_{\\max } }^2 <\\Theta \\,\\tau_{\\max } \n\\end{array}\\right."}}
%---
%[output:4e7b6619]
%   data: {"dataType":"text","outputData":{"text":"∂t_slew\/∂tau_max (sensitivity to available torque):\n","truncated":false}}
%---
%[output:6b6aef0b]
%   data: {"dataType":"symbolic","outputData":{"name":"","value":"\\left\\lbrace \\begin{array}{cl}\n-\\frac{\\sqrt{I_{\\textrm{ax}} }\\,\\sqrt{\\Theta }}{{\\tau_{\\max } }^{3\/2} } & \\;\\textrm{if}\\;\\;\\Theta \\,\\tau_{\\max } <I_{\\textrm{ax}} \\,{\\omega_{\\max } }^2 \\\\\n-\\frac{I_{\\textrm{ax}} \\,\\omega_{\\max } }{{\\tau_{\\max } }^2 } & \\;\\textrm{if}\\;\\;I_{\\textrm{ax}} \\,{\\omega_{\\max } }^2 <\\Theta \\,\\tau_{\\max } \n\\end{array}\\right."}}
%---
%[output:6dc1a528]
%   data: {"dataType":"text","outputData":{"text":"∂t_slew\/∂omega_max (sensitivity to rate limit):\n","truncated":false}}
%---
%[output:377325a2]
%   data: {"dataType":"symbolic","outputData":{"name":"","value":"\\left\\lbrace \\begin{array}{cl}\n0 & \\;\\textrm{if}\\;\\;\\Theta \\,\\tau_{\\max } <I_{\\textrm{ax}} \\,{\\omega_{\\max } }^2 \\\\\n-\\frac{\\Theta \\,\\tau_{\\max } -I_{\\textrm{ax}} \\,{\\omega_{\\max } }^2 }{{\\omega_{\\max } }^2 \\,\\tau_{\\max } } & \\;\\textrm{if}\\;\\;I_{\\textrm{ax}} \\,{\\omega_{\\max } }^2 <\\Theta \\,\\tau_{\\max } \n\\end{array}\\right."}}
%---
%[output:29e40214]
%   data: {"dataType":"text","outputData":{"text":"Generated: slewTimeSensitivity.m\n","truncated":false}}
%---
%[output:82c9cb8e]
%   data: {"dataType":"text","outputData":{"text":"\n=== Sensitivity at 30° Slew ===\n","truncated":false}}
%---
%[output:6711ea47]
%   data: {"dataType":"text","outputData":{"text":"∂t\/∂I:         +0.1206 s per kg·m²\n","truncated":false}}
%---
%[output:9c9659eb]
%   data: {"dataType":"text","outputData":{"text":"∂t\/∂tau_max:   -48.2401 s per N·m\n","truncated":false}}
%---
%[output:5babffc5]
%   data: {"dataType":"text","outputData":{"text":"∂t\/∂omega_max: +0.0000 s per rad\/s\n","truncated":false}}
%---
%[output:84a82310]
%   data: {"dataType":"text","outputData":{"text":"\n=== Mission-Life Degradation Impact (30° slew) ===\n","truncated":false}}
%---
%[output:4b780009]
%   data: {"dataType":"text","outputData":{"text":"+10% inertia:   +1.45 s  (5.0% increase)\n","truncated":false}}
%---
%[output:6a9936f8]
%   data: {"dataType":"text","outputData":{"text":"-15% torque:    +2.17 s  (7.5% increase)\n","truncated":false}}
%---
%[output:112e4154]
%   data: {"dataType":"text","outputData":{"text":"Combined:       +3.62 s  (12.5% increase)\n","truncated":false}}
%---
%[output:0b56d028]
%   data: {"dataType":"text","outputData":{"text":"\n=== VPA Verification (32-digit) ===\n","truncated":false}}
%---
%[output:7868d38d]
%   data: {"dataType":"text","outputData":{"text":"Analytic ∂t\/∂I:       0.12060020909304460989383577121513\n","truncated":false}}
%---
%[output:6a50275a]
%   data: {"dataType":"text","outputData":{"text":"Finite-diff (δI=0.001): 0.12059995784365587074682401000842\n","truncated":false}}
%---
%[output:1ae55ec2]
%   data: {"dataType":"text","outputData":{"text":"Agreement to 6 digits\n","truncated":false}}
%---
%[output:3d549916]
%   data: 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","height":337,"width":560}}
%---
%[output:7f2d0325]
%   data: {"dataType":"text","outputData":{"text":"Total pointing error (RSS):\n","truncated":false}}
%---
%[output:91efc409]
%   data: {"dataType":"symbolic","outputData":{"name":"","value":"\\sqrt{{\\sigma_{\\textrm{ST}} }^2 +{\\sigma_{\\textrm{RW}} }^2 +{\\sigma_{\\textrm{flex}} }^2 +{\\sigma_{\\textrm{therm}} }^2 }"}}
%---
%[output:1f25dab6]
%   data: {"dataType":"text","outputData":{"text":"∂σ_total\/∂σ_ST:\n","truncated":false}}
%---
%[output:7e797a91]
%   data: {"dataType":"symbolic","outputData":{"name":"","value":"\\frac{\\sigma_{\\textrm{ST}} }{\\sqrt{{\\sigma_{\\textrm{ST}} }^2 +{\\sigma_{\\textrm{RW}} }^2 +{\\sigma_{\\textrm{flex}} }^2 +{\\sigma_{\\textrm{therm}} }^2 }}"}}
%---
%[output:870dcdde]
%   data: {"dataType":"text","outputData":{"text":"=== Pointing Budget ===\n","truncated":false}}
%---
%[output:868ceb39]
%   data: {"dataType":"text","outputData":{"text":"Star tracker:       0.30 arcsec\n","truncated":false}}
%---
%[output:71753915]
%   data: {"dataType":"text","outputData":{"text":"Wheel jitter:       0.15 arcsec\n","truncated":false}}
%---
%[output:357a7b27]
%   data: {"dataType":"text","outputData":{"text":"Structural flex:    0.20 arcsec\n","truncated":false}}
%---
%[output:54617ded]
%   data: {"dataType":"text","outputData":{"text":"Thermal distortion: 0.10 arcsec\n","truncated":false}}
%---
%[output:78b0c845]
%   data: {"dataType":"text","outputData":{"text":"Total (RSS):        0.4031 arcsec\n","truncated":false}}
%---
%[output:22202c5e]
%   data: 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","height":337,"width":560}}
%---
%[output:7772e9fa]
%   data: {"dataType":"text","outputData":{"text":"\nVariance contributions:\n","truncated":false}}
%---
%[output:4a59f503]
%   data: {"dataType":"text","outputData":{"text":"  Star tracker:     55.4%n","truncated":false}}
%---
%[output:97f4a711]
%   data: {"dataType":"text","outputData":{"text":"  Wheel jitter:     13.8%n","truncated":false}}
%---
%[output:9a781b8d]
%   data: {"dataType":"text","outputData":{"text":"  Structural flex:  24.6%n","truncated":false}}
%---
%[output:7ff91f3e]
%   data: {"dataType":"text","outputData":{"text":"  Thermal:           6.2%n","truncated":false}}
%---
%[output:7ef6692b]
%   data: {"dataType":"text","outputData":{"text":"=== Multi-Target Pass Timeline ===\n","truncated":false}}
%---
%[output:56ce576e]
%   data: {"dataType":"text","outputData":{"text":"Targets: 8  |  Orbit pass window: ~600 s\n","truncated":false}}
%---
%[output:7d0c35e1]
%   data: {"dataType":"text","outputData":{"text":"\nTarget schedule (off-nadir angles):\n","truncated":false}}
%---
%[output:333f75be]
%   data: {"dataType":"text","outputData":{"text":"  ","truncated":false}}
%---
%[output:42640187]
%   data: {"dataType":"text","outputData":{"text":"+0° → +15° → -20° → +35° → -10° → +25° → -30° → ","truncated":false}}
%---
%[output:701cd70d]
%   data: {"dataType":"text","outputData":{"text":"+5°\n","truncated":false}}
%---
%[output:140edeef]
%   data: {"dataType":"text","outputData":{"text":"\nInter-target slews:\n","truncated":false}}
%---
%[output:10f571d6]
%   data: {"dataType":"text","outputData":{"text":"  Slew     Angle    Time\n","truncated":false}}
%---
%[output:397491c0]
%   data: {"dataType":"text","outputData":{"text":"  1→2       15°    20.5 s\n  2→3       35°    31.3 s\n  3→4       55°    39.2 s\n  4→5       45°    35.4 s\n  5→6       35°    31.3 s\n  6→7       55°    39.2 s\n  7→8       35°    31.3 s\n","truncated":false}}
%---
%[output:0f325f43]
%   data: {"dataType":"text","outputData":{"text":"\n  Total slew time:    228.1 s\n","truncated":false}}
%---
%[output:97bee2e7]
%   data: {"dataType":"text","outputData":{"text":"  Total settle time:   14.0 s  (2.0 s × 7)\n","truncated":false}}
%---
%[output:88e83878]
%   data: {"dataType":"text","outputData":{"text":"  Total image time:    64.0 s  (8.0 s × 8)\n","truncated":false}}
%---
%[output:8aeddb67]
%   data: {"dataType":"text","outputData":{"text":"  ─────────────────────────\n","truncated":false}}
%---
%[output:36253c07]
%   data: {"dataType":"text","outputData":{"text":"  Pass total:         306.1 s\n","truncated":false}}
%---
%[output:7c320700]
%   data: 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cyG3gzIPb4YuYHiWKKe2ZR3wl1futb32ptx7EefPDB+JwM\/cktG\/o7bcD2HBe7tGwSU64Y3XLLLfFK4ZIlS+KVBeJgGesGPSb74Jq2py8Zx0zTsgceeCCOFdo7zHjB6utf\/3rYv39\/qqrnFGf80kbEQD9iiWlaN2y9qb7stF9bstt+97vfjVctuYLz8Y9\/PN6mw41tmN56663h8OHDvIyFuruNi\/fffz9u0\/6Dq0G0MbucduJNfdnlztdXwASmvn1baMv4VHv22WfPiiF7++NLX\/pSaz1XbLhMTWKULu1zQm5tMDPDZW625eV1110Xb2PwyY0TGvtTUh2c0A4dOsSmAxdOirwhUXij402AEzEJGVeZBjkO23MJnlhSIe5sENlbVWzz8MMPZ1fnMo8jFsRCTFSKHcdjymuuLPGGx20oXqe4aDd99fTTT8c3Y9Z1LW0rUh1MWcWVDq540J+8gdFvxEBhnmNR2HaShba2199pWfs27a9pV4qddTgRP4V53pwZd\/3Gy759+8Lu3btjok8fUSdXLwZNYFL\/0sfMEwvtGbde6mkv\/dqS3Z5YiIlbjH\/4wx\/iBxZc8KGNXEHhdzPt02tccEUFb+pke+a5bXnqqafyMjB+qDMVfj+JNa70R+0FTGBq38XFNJATOCfy9qNfdtllJ9xuYj0nN05EzHe6bcPyG9J7RAAAEABJREFUQQpvxJwcU+IxyD79tiE2bm9lP6V3O06KnZMzVzpIgtova19zzTXxkNlEiU\/NcWHOP7j9RUwpmSSJIRHjMFxRYkohuSDJYD7FxRsGyQZ9SF+ybpCCF\/5sy5TXzFN4c2XKGxY2FOZZltYxT+FTNIkp2wxb2I\/9qScVjDkW7eLNlcI8y1iXtus3pT20i+2SYRrTc+bMCeeccw6rTijdxguujJWrr746pPFFTJQTKujyIvUvfcx82mzcelM9nabd2pLdlliIiWWpX1esWNH6ved3HUfWU9I29EXqa+az65jPFvqAOtgu7cPVzew2ztdfwASm\/n08kRamEwifIPnE134QHnrl5JxO7ml9Oumn1zlM4xUC3rS+8pWvhEceeSSkN6dR6uYTXvo0xzT7iY43xX7H4UoL+6UrHBiQzKSTK88HsZ7jpPhIGki8eHNIyyYx5YoYycwk6k518gbOG3l6XZZpepNMb668wTJPfGkd86OUbmN6kPEyyvHSPt2Om9bnOR2mLdOIi6ss\/G5yVYdEhraSzLR\/YGC5pb4CJjD17duJtowTCMkJb9DtXzPmEy3PTxBAuuLAfLfCJ31OPqznTTz73AnL2kv7CZJ9qYN4iCt7RaF933FeD3OclMhwa4ArH+3PAqVEJp2AuyWC48Q76L4kHCQebE9iRYKVCrcB0tUB1o9TUr\/xCTzVn6Z4ZeumH3mDSuuHmbIf+2frS\/OMLcYYhfm0fBLTfuOFZJKxwRU+bpUQA+OBwvyoZRL19mtLt1hTn2dvRaa60j5pm27jgmRz\/vz5afMTpvQz\/c34YH\/OR3x4OmEjX9RWwARm3K5t8P5cZeDTDycNrjKkS7lcUYCFEwpv1Mz3KyQ87M+VHU7g1Mv+vfbjRMgnrlNOOSVuxmvq4BI8CU1cmOOPdKLtdpz0KZUYUsEFH5IrEgWutKR1TFOs3R6GzjH8rlXxBsEfw2MD+o64UiFe3uxZN26hP+nX5JeOQR+mN\/Bxj9Ft\/3RsxhZjjMI88bCu237jLO83Xnj4nX5nfDBO8LjjjjvCggULxjlsfKg+73r7taVbwDhjzO8jY5028gwLiVvaB3+2GWRcMD4Zk4899lj85hX1pcL+1Jtuk6b6ndZXwASmvn078ZalTz+cgNoPxif5QR9M5eTFNzdSHbx+5plnWvfM0\/I0TSdFXnPyP++88+LDfLymEA\/HZz59jZv5cQvJGHWnepjPHgePn\/3sZ\/EbLmkbppy4+ZYRiQInb\/ZjeSq0l1tfrE\/Lpj2lbakt6djEzbK84sKHfqW96Ri84fBNqLyu8qR626edjk0cxMO69u3zeI1ptq+Zx5O6GZe48hpnlmHxve99b+wEZhL19msL8Xcq2GKMNeuZPvHEE4HbmbymtG\/DMiyy4yJ7JZfntb761a8G7Ng2W3gIetJjKXs858cTGHdvE5hxBd0\/kKhwCTdbOOFlabhFwHqmaTknLi7\/Ur72ta+1\/ogWr1nHdkx5TWE+u4z6KBwrGwPzLGMd8+zDlNcs53V76bc+bZ+2oy7mqS\/Nsw1vHiQpLEuF1yxnPYX90jqm2baxvluh\/WxLYT5tl+ojFpal18mabdmHwjzbsC3HZlteU9IyllOycVMXy5iyLW8S3F5K27S\/ZhuOxTEpzGeXUReFOtiXdZMuxEAsHJfCPMs6HZeYiC1t0\/6afbCgHqa8po\/xYD+2Zxm+bENhPhkzz1UnrhZwJYg3Y\/Y77bTT4jeT2JdCfMRAYZ5l7Et91MVrCjGwjOkg9bJPr0L8xEN7aBfbpuNyHOY5fprPrmc5ryncTiZB42FwbqfSjrfeeit+M4n1qdA21lEfhWMTQ1pPnSynsB3bZ5exnMKytI\/T+guYwNS\/j22hAgqUUCDdQiI0bo1wK4RbSVxV5BYQ61k3bGE\/9me\/POulvmFLulpKm2gbbSQm6uG2KokI88UUj1p1AROYqveg8SugQCUFuLLBrUNuq2QbwBULrsiwPrt80Hn2m0S9gx4\/ux0JCn+pm1tC2eXcTuMqTnaZ8woMK2ACM6yY2yuggAI5CFAFb\/DcEuH2RyrZ2zZsM0qZVL2jxMKtIG4JpfYxNXkZRdJ92gVMYNpFfK2AAgoooIACpRcwgSl9FxmgApMQsE4FFFCg2gImMH36j3+X5NFHHx34H1jrU52rFVBAAQUUUCAHAROYPogmMH2ARlztbgoooIACCowjYAIzjp77KqCAAgoooEAhAg1NYAqx9qAKKKCAAgookJOACUxOkFajgAIKKKBA7QVK1EATmBJ1hqEooIACCiigwGACJjCDObmVAgoooEDxAkagQEvABKZF4YwCCiiggAIKVEXABKYqPWWcCihQvIARKKBAaQRMYErTFQaigAIKKKCAAoMKmMAMKuV2ChQvYAQKKKCAAjMCJjAzEE4UUEABBRRQoDoCJjDV6aviIzUCBRRQQAEFSiJgAlOSjjAMBRRQQAEFFBhcoEoJzOCtcksFFFBAAQUUqLWACUytu9fGKaCAAgooUE8BE5h69qutUkABBRRQoNYCJjC17l4bp4ACChQvYAQKTELABGYSqtapgAIKKKCAAhMVMIGZKK+VK6BA8QJGoIACdRQwgaljr9omBRRQQAEFai5gAlPzDrZ5xQsYgQIKKKBA\/gImMAOYbtq0KSxYsGCALd1EAQUUUEABBaYhYALTR3np0qXhs3+2OBz845nhl799r2c58O6\/xNp+\/\/9+FaZVPnjnrXjMD\/5pR\/jj798KxHBinJ1jZjt2nFaceRyHeGkjbWWeNgzaVpzyiKFsddAuLHChMD+ICduwLfuXrU3DxkMbaHvdxwXtjH0287vOPP04SGFb9h\/Wtgrb067YvhmXYc4L7FeFNqYYiXeUsc5+dSwmMAP06uv\/+H644ft\/H\/7q6dd6lse27o+1vfXY\/wjTKof\/bkM85vt\/\/63wrwdfiPP94kzr2fjw331\/arGOa8KJijcp2krseKe29Jpygv\/gH9+sTDuHcvqbb0AR\/mXvD8M\/v7Y2zv\/Pv\/3\/PcdpsuJEf\/SVv628Cyf3P\/7zocC4YHzQ36mNvaabd\/0ueg3lPcXf7Vlxpb7e94Oh+5qG1qGvZ5kc74\/DI54D+T3B5XDFzoGc5xnrxD7MOZDt61YmnsDUDcz2KKCAAgoooEDxAiYwxfeBESiggAIKKNAu4Os+AiYwfYBcrYACCiiggALlEzCBKV+fGJECCihQvIARKFByAROYkneQ4SmggAIKKKDAbAETmNkmLlFAgeIFjEABBRToKWAC05PHlQoooIACCihQRgETmDL2ijEVL2AECiiggAKlFjCBKXX3GJwCCiiggAIKdBIwgemkUvwyI1BAAQUUUECBHgImMD1wXKWAAgoooIAC5RTonMCUM1ajUkABBRRQQAEFooAJTGTwhwIKKKCAAuMLWMP0BExgpmftkRRQQAEFFFAgJwETmJwgrUYBBRQoXsAIFGiOgAlMc\/raliqggAIKKFAbAROY2nSlDVGgeAEjUEABBaYlYAIzLWmPo4ACCiiggAK5CZjA5EZpRcULGIECCiigQFMETGCa0tO2UwEFFFBAgRoJmMDk2JlWpYACCiiggALTETCBmY6zR1FAAQUUUECBzgIjLTWBGYnNnRRQQAEFFFCgSAETmCL1PbYCCiigQPECRlBJAROYSnabQSuggAIKKNBsAROYZve\/rVdAgeIFjEABBUYQMIEZAc1dFFBAAQUUUKBYAROYYv09ugLFCxiBAgooUEEBE5gKdpohK6CAAgoo0HQBE5imj4Di228ECiiggAIKDC1gAjM0mTsooIACCiigQNECJjBF94DHV0ABBRRQQIGhBUxghiZzBwUUUEABBRQoWsAEpuge8PgKKKCAAgooMLSACczQZO6ggAIKKFC8gBE0XcAEpukjwPYroIACCihQQQETmAp2miEroEDxAkaggALFCpjAFOvv0RVQQAEFFFBgBAETmBHQ3EWB4gWMQAEFFGi2gAlMs\/vf1iuggAIKKFBJAROYSnZb8UEbgQIKKKCAAkUKmMAUqe+xFVBAAQUUUGAkgYomMCO11Z0UUEABBRRQoCYCJjA16UiboYACCiigQF+BGm1gAlOjzrQpCiiggAIKNEXABKYpPW07FVBAgeIFjECB3ARMYHKjtCIFFFBAAQUUmJaACcy0pD2OAgoUL2AECihQGwETmNp0pQ1RQAEFFFCgOQImMM3pa1tavIARKKCAAgrkJGACkxOk1SiggAIKKKDA9ARMYKZnXfyRjEABBRRQQIGaCJjA1KQjbYYCCiiggAJNEphmAtMkV9uqgAIKKKCAAhMUMIGZIK5VK6CAAgooML6ANXQSMIHppOIyBRRQQAEFFCi1gAlMqbvH4BRQQIHiBYxAgTIKmMCUsVeMSQEFFFBAAQV6CpjA9ORxpQIKFC9gBAoooMBsAROY2SYuUUABBRRQQIGSC5jAlLyDDK94ASNQQAEFFCifgAlM+frEiBRQQAEFFFCgj4AJTB+g4lcbgQIKKKCAAgq0C5jAtIv4WgEFFFBAAQVKL9A3gSl9CwxQAQUUUEABBRonYALTuC63wQoooIACUxDwEBMWMIGZMLDVK6CAAgoooED+AiYw+ZtaowIKKFC8gBEoUHMBE5iad7DNU0ABBRRQoI4CJjB17FXbpEDxAkaggAIKTFTABGaivFaugAIKKKCAApMQMIGZhKp1Fi9gBAoooIACtRYwgal199o4BRRQQAEF6ilgAjOZfrVWBRRQQAEFFJiggAnMBHGtWgEFFFBAAQWGERh8WxOYwa3cUgEFFFBAAQVKImACU5KOMAwFFFBAgeIFjKA6AiYw1ekrI1VAAQUUUECBGQETmBkIJwoooEDxAkaggAKDCpjADCrldgoooIACCihQGgETmNJ0hYEoULyAESiggAJVETCBqUpPGacCCiiggAIKtARMYFoUzhQvYAQKKKCAAgoMJmACM5iTWymggAIKKKBAiQRMYDKd4awCCiiggAIKVEPABKYa\/WSUCiiggAIKlFWgkLhMYAph96AKKKCAAgooMI6ACcw4eu6rgAIKKFC8gBE0UsAEppHdbqMVUEABBRSotoAJTLX7z+gVUKB4ASNQQIECBExgCkD3kAoooIACCigwnoAJzHh+7q1A8QJGoIACCjRQwASmgZ1ukxVQQAEFFKi6gAlM1Xuw+PiNQAEFFFBAgakLmMBMndwDKqCAAgoooMC4AtVPYMYVcH8FFFBAAQUUqJyACUzlusyAFVBAAQUUGF+g6jWYwFS9B41fAQUUUECBBgqYwDSw022yAgooULyAESgwnoAJzHh+7q2AAgoooIACBQiYwBSA7iEVUKB4ASNQQIFqC5jAVLv\/jF4BBRRQQIFGCpjANLLbbXTxAkaggAIKKDCOgAnMOHruq4ACCiiggAKFCJjAFMJe\/EGNQAEFFFBAgSoLmMBUufeMXQEFFFBAgYYKFJTANFTbZiuggAIKKKBALsWb2S4AABAASURBVAImMLkwWokCCiiggAJTEPAQLQETmBaFMwoooIACCihQFQETmKr0lHEqoIACxQsYgQKlETCBKU1XGIgCCiiggAIKDCpgAjOolNspoEDxAkaggAIKzAiYwMxAOFFAAQUUUECB6giYwFSnr4y0eAEjUEABBRQoiYAJTEk6wjAUUEABBRRQYHABE5jBrYrf0ggUUEABBRRQIAqYwEQGfyiggAIKKKBAlQSGSWCq1C5jVUABBRRQQIEaC5jA1LhzbZoCCiigQBkEjGESAiYwk1C1TgUUUEABBRSYqIAJzER5rVwBBRQoXsAIFKijgAlMHXvVNimggAIKKFBzAROYmnewzVOgeAEjUEABBfIXMIHJ39QaFVBAAQUUUGDCAiYwEwa2+uIFjEABBRRQoH4CJjD161NbpIACCiigQO0FTGAm3sUeQAEFFFBAAQXyFjCByVvU+hRQQAEFFFBgfIE+NZjA9AFytQIKKKCAAgqUT8AEpnx9YkQKKKCAAsULGEHJBUxgSt5BhqeAAgoooIACswVMYGabuEQBBRQoXsAIFFCgp4AJTE8eVyqggAIKKKBAGQVMYMrYK8akQPECRqCAAgqUWsAEptTdY3AKKKCAAgoo0EnABKaTisuKFzACBRRQQAEFegiYwPTAcZUCCiiggAIKlFPABKZzv7hUAQUUUEABBUosYAJT4s4xNAUUUEABBaolML1oTWCmZ+2RFFBAAQUUUCAnAROYnCCtRgEFFFCgeAEjaI6ACUxz+tqWKqCAAgooUBsBE5jadKUNUUCB4gWMQAEFpiVgAjMtaY+jgAIKKKCAArkJmMDkRmlFChQvYAQKKKBAUwRMYJrS07ZTAQUUUECBGgmYwNSoM4tvihEooIACCigwHQETmOk4exQFFFBAAQUUyFGgVglMji5WpYACCiiggAIlFjCBKXHnGJoCCiiggAJTEKjkIUxgKtltBq2AAgoooECzBUxgmt3\/tl4BBRQoXsAIFBhBwARmBDR3UUABBRRQQIFiBUxgivX36AooULyAESigQAUFTGAq2GmGrIACCiigQNMFTGCaPgJsf\/ECRqCAAgooMLSACczQZO6ggAIKKKCAAkULmMAU3QPFH98IFFBAAQUUqJyACUzlusyAFVBAAQUUUKD4BMY+UEABBRRQQAEFhhQwgRkSzM0VUEABBRQog0DTYzCBafoIsP0KKKCAAgpUUMAEpoKdZsgKKKBA8QJGoECxAiYwxfp7dAUUUEABBRQYQcAEZgQ0d1FAgeIFjEABBZotYALT7P639QoooIACClRSwASmkt1m0MULGIECCiigQJECJjBF6ntsBRRQQAEFFBhJwARmJLbidzICBRRQQAEFmixgAtPk3rftCiiggAIKVFRgxASmoq01bAUUUEABBRSohYAJTC260UYooIACClRCwCBzEzCByY3SihRQQAEFFFBgWgImMNOS9jgKKKBA8QJGoEBtBExgatOVNkQBBRRQQIHmCJjANKevbakCxQsYgQIKKJCTgAlMTpBWo4ACCiiggALTEzCBmZ61RypewAgUUEABBWoiYAJTk460GQoooIACCjRJwARmmr3tsRRQQAEFFFAgFwETmFwYrUQBBRRQQAEFJiXQqV4TmE4qbcuu\/PO54em\/WhJ+tOLCnuUbyxbEPT\/zjb8J0ypn\/+fV8Zin\/cf\/HU6Z\/xfhnLNO7RljasP\/+svPx\/0+teqvpxbruCYf++Rnwsf+9OJAWwke79SeXtMvnndm+MSfXVKZdg7j9Kmv\/TUU4dSF\/zWcev6NcZ6+7eWR1jFWTr\/8LyvvQt+mcXHyn8wL9HdqY6\/pdYs\/HRhTw3gXuW3q6z+58K6h+5qBUYe+7uQ\/6jmQ8wcuVTsHcp4f9hzI7zptrVsxgenTo9u2bQuU\/7Twk\/HEyMmxW0mDhBPqtAonYJrACfzkT3yG2b5xpvjZmP2nFeu4xyFe2khbmcc7taXXlO3Yftzjl3V\/2pZ16WWRXcd+Ver\/bv60IbbleHKLA\/2dbWe3ebZjv271lnE58dLG9DvQrW3ty9kPpzK2adyYaFds30z\/M9\/e\/m6v2Zb9+8dwSfwQVPR2xJvtf8Zwt7Zll7Md+9atmMAM0KMrV66MScwAm7qJAgoooIACCkxBwARmCsgeQgEFFBhHwH0VUGC2gAnMbBOXKKCAAgoooEDJBUxgSt5BhqdA8QJGoIACCpRPwASmfH1iRAoooIACCijQR8AEpg+Qq4sXMAIFFFBAAQXaBUxg2kV8rYACCiiggAKlFzCB6dtFbqCAAgoooIACZRMwgSlbjxiPAgoooIACdRCYcBtMYCYMbPUKKKCAAgookL+ACUz+ptaogAIKKFC8gBHUXMAEpuYdbPMUUEABBRSoo4AJTB171TYpoEDxAkaggAITFTCBmSivlSuggAIKKKDAJARMYCahap0KFC9gBAoooECtBUxgat29Nk4BBRRQQIF6CpjA1LNfi2+VESiggAIKKDBBAROYCeLWuer9+\/eHRx99NDCtcztHaRsm2nSW06azC0u1QaFz0aazC0ubbFPXBIZ+tUxQoMm\/NP1YtekupI023QW6r3HcaNNJwASmk4rLFFBAAQUUaKRAdRptAjNgX73yyith27ZtlhmDAwcORDldZo8JbWabpN8dbbRJY2GYqeNmvHHDFax4wq7ZDxOYPh26YMGCcPnll8fnPVauXBksHxnceeedUY5nPTT5yCQ5aHOiR3Jhqo02jINepdM6x8144+bZZ5+N5+u6\/TCB6dOjJDAPPfRQ2LRpk0UDx4BjwDHgGKjcGLj++uv7vNNVc7UJzAD9RhKzdOnSYNHAMVD3MWD7HOP1GwO8hw3wVle5TUxgKtdlBqyAAgoooIACJjCOAQVKJGAoCiiggAKDCZjADObkVgoooIACCihQIgETmBJ1RvGhGIECCiiggALVEDCBqUY\/GaUCCiiggAIKZARKlcBk4irF7Jo1a8LChQtjeemll0oRUxFBHD16NP79m2TBFJsUy9tvvx2uvPLK6MSU12ldnae7du0K1157bWhvLzYYUdrHDftcfPHF0Yq\/d4FtHY1oZ7sNTowPXFJ56qmnWs1nnzrb0NbUbqbtY6Op46aXS9PHDGOEsULhd4PfkdYvzPGZpo6Z402P\/5vARIbZPxg427dvj395d\/369eGBBx6Y9UY1e696Ljl27Fg4cuRIeO6558LevXtjefjhh1uNXbt2bbjsssvicqa8bq2s6QwnklWrVoX33nvvhBb2GjckKw8++GC45ZZbws6dOwP\/bdy4kUmtSjebQ4cOhTPPPDP+TqVxdPPNN8e2192GcfH000+32s455Y477ghYAcD6buebOtvQ7l4uTR4zjI3777+\/dd7lvHHbbbe13oewy4yZE96j6jxm+H1JxQQmSbRNn3\/++fimPHfu3Pj3X84555zWm07bprV\/yUmERs6bN4\/JCYVPSPwSXXPNNXH5jTfeGH7zm9+0fsniwpr94BPj8uXLw9VXXz2rZb3Gzb59+wJ\/0nvZsmXh9NNPD6tXrw4vv\/xy4GQzq6KKLuhlwzg644wzwpw5c2a1ru42V111VdiyZUvgfELjlyxZEs4+++yACa+bOm76ueDT1DGzePHieH5gyhjhvPHhhx82fsxgkYoJTJLITHlDOXjwYFi0aFFmaQh79uw54XVTXnASab\/SkNrOOn6pssnNu+++2\/olS9vVafqFL3whJrMpaUtt6zdusDrppJNC1op\/44UrXKmOqk+72dCuXr8\/TbDBoFOpxbjp1LAcljlm\/h1x69atgT9Id\/7558cPPb3eo5ry+2QC8+\/jY9ZcSmD4tDx\/\/vxZ65uygJPIG2+8Ea9EcS+W5xi48pLaf9ZZZ7XelHlz5nVaV8cpf6mUMdGtbb3GDVfy0hUIrLil0q2eKi7vZcM44h\/w4+oD46j9GaC622T7c\/PmzfElFnHm+I8mj5vjzY\/\/t7s4ZkK8zcjzL9ya56pt9tzT9DFjAhN\/bfzRS4CTCG9MPLfBsws853L77bfHTwG99nOdAkkgXWW47rrr4rNSjCXW3XfffUzGLZXan2cXeDO6++67W7eUKtWACQXb7uKY+QiaW0g7duyIz8Lw3BROH63xpwlMjzHAGzer0y8S800sPLDLP2aZMn+ec+FZDp5bwCN7y4hLl7xmeVNLr3GTvWWEVbdbc3WzY+wwhhhLtI3XfJrk+al0Na8JNrz53HTTTYGHeHn+A4tUmjxuOrkwRhwzaXSEQCLDc3c8L5WWNnnMYGACg0Jb4Ren0y2jdLmubfNGvuQ2EbdAKMxnEXjN8uyyWs\/PNK7fuMGk\/ZZR9rbJTDWNmqT2N8GGN2k+QfNtvmzy0vRx082l2y9Ck8ZM1iB9kOZ9qOljJrmYwCSJtikPaPL1Pj4dct+eT4fZ+9Vtm9f2Jb80PKvAt0toJK\/5KvAFF1wQL3\/zrQrmf\/CDH7A6MOU1y+OChv3oNW54+I5vVHCfH8cNGzaEK664In4jqe5M\/B7x7BRvVrSV1\/xpgtT+utvwlViSl3Xr1sVP0hhkS1PHTS8XxkjTxwy\/HxgxVngf2r17d+DbSLxu6pih7amYwCSJtimfkHjWg2c\/uORb4vvVbZHn+5JMn8vdfN2XBy9TEpd9duGuu+4K3ApgPVNe5xtFdWrrNW6w\/Pa3vx2efPLJkBz5WzLVad3okZLQPvPMM\/FvVTBO+L3i9yv9HZi62\/ANEv6WEucS2p9K+mDQ1HHTy6XpY4ZbRvfcc0\/gTzYwXhg72QS4qWMmexYygclqtM1zv56HVikMlrbVjXnJmwv3onGgMM+yBMCJZsuWLfHhTKa8TuvqPGVMdGpvr3HDSYkH8jo51smqkw3jAi\/aTsEp2+Y625Co0eb2wvJkgEdaj19azrSuNrQ\/tTk7ZTntbvKYof2Mg6wLr1meShPHTGo70\/ETGGqxKKCAAgoooIACUxQwgZkitodSQAEFFFAgCTgdT8AEZjw\/91ZAAQUUUECBAgRMYApA95AKKKBA8QJGoEC1BUxgqt1\/Rq+AAgoooEAjBUxgGtntNlqBwQT4uy18hbNb4d9o+c53vhP4W0H8bZvBav1oq1F\/rlmzJhAPx05\/I6NbXcNs260OlyugQDkFTGDK2S9GpUApBPjaZvoaJ\/9+EX+\/hb\/zk5bxlfB77703tH+1ftLB828qcWy+XtzrWHzNlL98y1+H7rWd6xRQoHoCJjDV6zMjzkXAShRQQAEFqixgAlPl3jN2BUogwF+TTbeQ0p9\/5y+Gcosne6uH7XhNSdun8NOtHtaxX79bQ2k\/pty6oj72pQy7P3VYFFCgegImMAX1mYdVoM4CP\/nJT8KLL74Y\/zrz1VdfHf8cOu3l1hP\/pgv\/ttjGjRtDSj7SOtaT\/PBPLAyaxPDPWsyfPz8ei\/1vueWWwL\/XRd3Ua1FAgXoKmMDUs19tlQKFCqxYsSL+Y58EwT86d+655waeW+H1nDlzAv+iMPNF7Pl2AAABw0lEQVT79u0L\/BtBPFfDawrP3ZD08A+D8rpXIUk5ePDgCZvwZ+in\/UzOCQH4QgEFJinQqtsEpkXhjAIK5CWwaNGigao6dOhQ2L17d+DhYG7\/pLJ58+aB9uff5Fq9enVge\/b19tFAbG6kQC0ETGBq0Y02QoFqCuzZsydwdYbbStz+yRa+QTRIq7hiw358S+qiiy6Kt6tMZAaRc5uRBNypNAImMKXpCgNRoHkCXKk5fPhw4ErMuK3nagy3jlIis3Xr1nGrdH8FFCixgAlMiTvH0BSouwC3jrhqkn3oNn2TiW8t9Ws\/z8DwDSS+xZS25bma\/fv3h2XLlqVFdZraFgUUmBEwgZmBcKKAAtMX4KrJ+vXr44GXLFkS\/8IuSQ0PAfMwblzR4wf7P\/LII2H79u1xX56DWb58ebjnnntCvz9y16NaVymgQAUETGAq0EmGqEAZBEgWNm3aFNoTC15z64b1c+fODVu2bAk8l5JiZp5lrGMZ27E9+2Vf8xxLKmkd6\/sV6qX+tC9TjtlvP9croEC1BUxgqt1\/Rq+AAgoooEAjBUxgGtntlW20gSsQBfja9CDfNOLZGG4pvfvuu3E\/fyigQH0E\/g0AAP\/\/8cY5SAAAAAZJREFUAwDJcLL3HPwJNwAAAABJRU5ErkJggg==","height":337,"width":560}}
%---
%[output:67eca67d]
%   data: {"dataType":"text","outputData":{"text":"\n=== End-of-Life Timeline Impact ===\n","truncated":false}}
%---
%[output:5b1aab9d]
%   data: {"dataType":"text","outputData":{"text":"BOL pass total:  306.1 s\n","truncated":false}}
%---
%[output:14853dff]
%   data: {"dataType":"text","outputData":{"text":"EOL pass total:  337.5 s  (+31.4 s, +10.3%)\n","truncated":false}}
%---
%[output:6501c037]
%   data: {"dataType":"text","outputData":{"text":"EOL: all 8 targets still fit within 600 s window\n","truncated":false}}
%---
%[output:40522523]
%   data: 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","height":337,"width":560}}
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