Learning Outcomes:

1. Select TiO2 anatase nanoparticle size distribution and surface area to maximize activity while minimizing aggregation.

2. Specify dopants/co-catalysts or surface modifications to shift activation spectrum and enhance charge separation under space-relevant illumination.

3. Choose mesh and hydrogel materials compatible with vacuum, radiation, atomic oxygen, and thermal cycling; justify with datasheets and literature.

4. Design protective coatings or binders to retain nanoparticles and sustain performance over mission duration.

5. Compile a materials down-select report including outgassing limits (NASA/ASTM E595) and ECSS cleanliness compliance.

6. Define coupon tests to screen durability under UV, AO, and thermal cycling and establish pass/fail criteria.

Learning Outcomes:

1. Compute required irradiance and radiant exposure to achieve bandgap activation for the chosen catalyst formulation.

2. Quantify spectral overlap between emitters and catalyst absorption, adjusting dopant levels or adding plasmonic enhancements as needed.

3. Size emitters, drivers, and optics (collimators, diffusers, reflectors) to achieve target irradiance uniformity over the mesh/hydrogel plate.

4. Perform Monte Carlo ray tracing to estimate optical throughput, scattering, and non-uniformity and set alignment tolerances.

5. Allocate optical and electrical safety factors to meet performance across temperature and degradation at EOL.

6. Produce an optical BOM and risk register with mitigation strategies for emitter aging and contamination.

Learning Outcomes:

1. Derive photon flux at the catalyst surface and map to electron–hole pair generation considering recombination losses.

2. Link photon flux to reaction rate constants using Langmuir–Hinshelwood or appropriate kinetics and calibrate parameters.

3. Couple mass transfer (diffusion/advection) with surface kinetics to predict per-pass degradation and residence time requirements.

4. Achieve target degradation per pass with ≥25% margin and document parameter sensitivities via Sobol or Morris methods.

5. Validate model predictions against first-order experiments and reconcile discrepancies with updated parameters.

6. Publish a photon-to-reaction budget with uncertainty bounds and recommendations for design margins.

Learning Outcomes:

1. Parameterize hydrogel swelling, porosity, and diffusion coefficients to model reagent transport under vacuum/microgravity.

2. Design capillary-driven wicking across mesh layers and compute residence time distributions without pumps.

3. Verify no-boil-off operation using thermal/vacuum analyses and define containment features to prevent leakage or FOD.

4. Prototype mesh–hydrogel stacks and measure flow/retention to validate models within ±10%.

5. Establish cleaning/servicing procedures to maintain permeability and prevent fouling across reuse cycles.

6. Document fluidics design rules and test protocols for engineering verification.

Learning Outcomes:

1. Define contamination control plans meeting NASA outgassing and ECSS cleanliness classes for reactor materials and assembly.

2. Specify nanoparticle handling, filtration, and disposal procedures for TiO2 and soot/hydrocarbon surrogates with appropriate PPE and controls.

3. Design benchtop efficacy tests (spectrophotometry or GC–MS) using standardized surrogates and safe handling protocols.

Learning Outcomes:

1. Down-select emitters (UV-A/UV-C LEDs, lasers) based on spectral overlap, radiance, efficiency, and radiation tolerance.

2. Design constant-current drivers with dimming and pulse-width modulation to control radiant exposure accurately.

3. Characterize emitter IV/thermal behavior and derive safe operating areas across mission temperatures.

4. Implement lifetime acceleration tests (HTOL/temperature-humidity-bias surrogates) and estimate lumen/radiant depreciation.

5. Integrate current/temperature telemetry and protections (OTP/OCP) into drivers and verify trip thresholds.

6. Document an emitter-driver hardware specification with test results and EOL performance projections.

Learning Outcomes:

1. Assemble an optical bench with collimators/diffusers/reflectors to achieve target irradiance uniformity over the reaction plate.

2. Map spatial irradiance using calibrated sensors and compute uniformity metrics (min/avg/max and RMS).

3. Tune optics alignment and diffusers to meet uniformity and throughput targets with quantified tolerances.

4. Design and validate baffles, apertures, and coatings to suppress stray light and back-reflections into sensors.

5. Correlate ray-trace predictions with measured maps and update model parameters within ±10%.

6. Publish an optical alignment and maintenance procedure for integration and reuse cycles.

Learning Outcomes:

1. Develop fabrication workflows for coating/binding TiO2 on mesh while ensuring adhesion and porosity targets.

2. Specify hydrogel preparation, swelling control, and thickness tolerances to achieve designed residence times.

3. Implement QA checks (thickness, porosity, surface area) using SEM, BET, or surrogate metrology and record lot histories.

4. Design fixtures and assembly jigs that maintain alignment and compression without damaging hydrogels.

5. Establish cleanliness controls and bakeout steps to meet outgassing/cleanliness requirements.

6. Release a controlled traveler and inspection checklist for repeatable assembly and traceability.

Learning Outcomes:

1. Plan and execute process qualification runs with statistically significant samples and acceptance criteria.

2. Conduct environmental stresses (thermal cycling, vacuum exposure, AO simulation, UV soak) and track performance drift.

3. Model degradation kinetics for catalyst activity and hydrogel properties to predict EOL margins.

4. Define refurbishment intervals and service procedures based on measured degradation trends.

5. Establish SPC control charts for critical parameters and implement corrective actions for out-of-control signals.

6. Compile a process qualification report linking fabrication parameters to performance and reliability outcomes.

Learning Outcomes:

1. Measure sensor QE versus wavelength using calibrated sources and record lens transmission and vignetting profiles.

2. Calibrate focus and MTF across the field using slanted-edge targets and compute effective resolution.

3. Generate exposure and gain response curves and linearization parameters to standardize radiometric output.

4. Implement dark-frame subtraction and flat-field correction to remove non-uniformity and fixed-pattern noise.

5. Estimate radiance uncertainty ≤2% through error propagation of calibration measurements and document procedures.

6. Publish a calibration report with traceability, scripts, and raw data for reproducibility.

Learning Outcomes:

1. Model read noise, shot noise, dark current, and thermal noise to predict SNR under varying illumination and temperature.

2. Compute detection thresholds using Neyman–Pearson criteria and set operating points for desired false-alarm rates.

3. Validate SNR models against laboratory image sequences with controlled radiance levels and exposure times.

4. Quantify vignetting and stray light impacts on detection performance and recommend optical baffles/filters.

5. Integrate calibration constants into the detection pipeline and verify consistent radiometric units across stages.

6. Deliver a detection threshold specification tied to mission KPIs and link budget.

Learning Outcomes:

1. Implement spectral/background suppression and morphological operators to isolate soot/hydrocarbon signatures from stars and hot pixels.

2. Curate datasets with hard negatives (glints, hot pixels, sensor artifacts) and label quality controls for training and validation.

3. Train and evaluate classical and deep classifiers using cross-validation and report AUROC, precision–recall, and confusion matrices.

4. Tune hyperparameters and decision thresholds to achieve target false-positive rate ≤3% at ≥92% recall.

5. Package the pipeline as reusable Python modules with unit tests and performance benchmarks on the target hardware.

6. Document failure cases and propose data or algorithmic remedies to reduce residual false positives.

Learning Outcomes:

1. Generate synthetic datasets via Blender/Mitsuba with star fields, stray light, and sensor noise consistent with calibration.

2. Apply domain randomization and domain adaptation (e.g., CycleGAN, AdaBN) to bridge synthetic-to-real gaps.

3. Demonstrate ≥15% AUROC improvement over real-only training on a held-out real test set and justify the approach statistically.

4. Automate dataset versioning, metadata, and provenance for reproducibility and incremental learning.

5. Evaluate simulation fidelity through ablation studies and update parameters to match measured radiometry.

6. Publish a synthetic data playbook and integration guide for ongoing model updates.

Learning Outcomes:

1. Fuse imagery with IMU/attitude timestamps to stabilize detections and reduce jitter-induced false positives.

2. Implement a multi-target tracker (e.g., GNN/Kalman/JPDA) and quantify ≥30% false-positive reduction versus vision-only baselines.

3. Optimize inference using quantization-aware training and TensorFlow Lite to meet ≤50 ms per frame at ≤3 W compute budget.

Learning Outcomes:

1. Establish labeling guidelines and perform inter-annotator agreement analyses (Cohen’s kappa) to ensure dataset quality.

2. Audit class imbalance, background bias, and spurious correlations and implement sampling/augmentation remedies.

3. Design robust evaluation protocols with stratified splits and stress tests covering glints, hot pixels, and star clusters.

4. Compute calibration curves and expected calibration error (ECE) to assess classifier confidence quality.

5. Instrument error analysis dashboards to track false positives/negatives by condition and propose targeted fixes.

6. Publish a dataset card and model card documenting limitations, ethics, and intended use.

Learning Outcomes:

1. Implement dataset and model versioning with reproducible pipelines and provenance metadata.

2. Automate continuous training and evaluation with quality gates tied to AUROC and power/latency budgets.

3. Deploy TensorFlow Lite models with A/B testing on the Raspberry Pi and collect edge telemetry for drift detection.

4. Integrate watchdogs and confidence-based deferral to safe modes when model uncertainty exceeds thresholds.

5. Schedule periodic on-device recalibration and dataset refresh workflows synchronized with operations windows.

6. Produce MLOps SOPs and rollback procedures for safe iterative improvements.

Learning Outcomes:

1. Select and mount bandpass/longpass filters to maximize soot signature contrast while minimizing star/glint leakage.

2. Design apertures and baffles to suppress off-axis stray light and validate via measured PSF/veiling glare tests.

3. Quantify filter transmission, angle sensitivity, and temperature effects and update the detection link budget.

4. Integrate mechanical shutters or electronic rolling-shutter strategies to mitigate smear in dynamic scenes.

5. Correlate laboratory measurements with optical models and refine acceptance limits for stray-light tolerance.

6. Release a filter/baffle ICD and maintenance plan for contamination control.

Learning Outcomes:

1. Implement auto-exposure and gain scheduling that maintains SNR targets without saturating under varying backgrounds.

2. Characterize saturation, blooming, and hot-pixel behavior and set protective thresholds and timeouts.

3. Measure end-to-end detection performance versus exposure/gain settings and optimize for AUROC at fixed false-positive rate.

4. Integrate thermal monitoring to avoid sensor overheating and throttle frame rate or exposure duration as needed.

5. Validate exposure control on motion and brightness ramps and document stability and hysteresis properties.

6. Publish exposure-control configurations and health limits as part of the sensing subsystem ICD.

Learning Outcomes:

1. Estimate inertia properties from CAD/boresight configurations and validate via simple pendulum or spin tests where feasible.

2. Size reaction wheels for torque and momentum storage to meet slew/pointing requirements with ≥20% authority margin.

3. Select cold-gas thruster impulse bit and configuration to achieve required translational/rotational maneuvers.

4. Compute actuator duty cycles and thermal loads across mission modes and verify mass/volume constraints.

5. Allocate control authority between wheels and thrusters for desaturation and contingency operations.

6. Produce an actuator sizing report with sensitivity analyses and design margins.

Learning Outcomes:

1. Design PID/LQR controllers with integral action for pointing and slewing under gravity-gradient, drag, and solar pressure disturbances.

2. Simulate closed-loop responses to meet settling time, overshoot, and steady-state error allocations.

3. Conduct Monte Carlo runs over inertia/actuator uncertainties and identify robust gain sets and schedules.

4. Implement rate limiting, anti-windup, and saturation handling to prevent integrator runaway and chatter.

5. Verify pointing stability with injected sensor noise and actuator delays to ensure robustness to implementation realities.

6. Deliver controller tuning tables and validation scripts for integration.

Learning Outcomes:

1. Implement an EKF with gyro, accelerometer, and camera aiding, including quaternion or multiplicative error representations.

2. Model sensor biases, process/measurement noise, and discretization to meet covariance and stability requirements.

3. Calibrate gyro bias stability and update filter parameters to maintain steady-state error allocations.

4. Validate estimation accuracy using truth-model simulations and quantify covariance consistency (NEES/NIS tests).

5. Integrate estimator outputs with controller timing and verify closed-loop performance under IMU dropouts.

6. Publish an estimation performance report with statistical confidence intervals.

Learning Outcomes:

1. Plan momentum management schedules and thruster desaturation events to maintain wheel speeds within operational limits.

2. Compute propellant budgets and actuator duty cycles over mission timelines including contingency scenarios.

3. Design FDIR logic and safe modes (e.g., B-dot or equivalent) that guarantee loss-of-control avoidance under credible faults.

4. Demonstrate zero loss-of-control in ≥95% Monte Carlo runs with injected sensor/actuator faults and delays.

5. Correlate software models with hardware-in-the-loop results or air-bearing table tests for pointing and slew accuracy.

6. Deliver a verified GNC package with procedures for on-orbit tuning and safing.

Learning Outcomes:

1. Assemble a 6-DOF simulation in MATLAB/Simulink or Basilisk with orbit propagators, environmental torques, and actuator models.

2. Inject realistic sensor models (bias, noise, latency) and verify numerical stability and timestep selection.

3. Validate disturbance models (gravity-gradient, solar pressure, magnetic) against reference data and sensitivity bounds.

4. Parameterize maneuver scripts (slews, approach, station-keeping) and measure performance metrics automatically.

5. Design experiment matrices to cover corner cases in inertia, mass properties, and environmental extremes.

6. Publish a validation report confirming simulation credibility for design decisions.

Learning Outcomes:

1. Execute Monte Carlo scenario campaigns across mission phases and compute distributions for pointing error and settling time.

2. Quantify actuator duty cycles, desaturation frequency, and thermal loads over representative timelines.

3. Evaluate controller robustness to star-tracker/camera dropouts and IMU drift and propose mitigation strategies.

4. Optimize gain schedules or mode transitions to reduce energy use while preserving pointing performance.

5. Establish acceptance thresholds and margins for GNC KPIs and trace them to system requirements.

6. Deliver scenario analysis dashboards and recommendations for design updates.

Learning Outcomes:

1. Design a HIL bench with real-time plant models for actuators and sensors synchronized to controller hardware.

2. Integrate timing, latency, and jitter measurements to ensure closed-loop fidelity under real-time constraints.

3. Develop fault injection tools for sensor bias jumps, packet loss, and actuator saturation with repeatable scripts.

4. Calibrate plant models against benchtop actuator/sensor data and quantify model error limits.

5. Verify closed-loop stability and performance on the HIL bench under nominal and degraded conditions.

6. Document HIL procedures, acceptance criteria, and regression test suites.

Learning Outcomes:

1. Plan and execute air-bearing table tests to validate pointing, slew, and disturbance rejection performance.

2. Correlate air-bearing results with simulation/HIL outputs and reconcile discrepancies with updated models.

3. Inject FDIR scenarios (wheel stall, thruster misfire, sensor blackout) and measure recovery times and safety compliance.

4. Validate safe-mode entry/exit logic and momentum management under constrained actuation.

5. Quantify pass/fail rates and produce evidence of ≥95% zero loss-of-control across Monte Carlo test sets.

6. Publish a GNC verification report with correlated results and FDIR coverage.

Learning Outcomes:

1. Define operating modes (search, clean, transit, safe, return) and enumerate loads with duty cycles and priorities.

2. Build a mode-based power budget including conversion/harness losses and uncertainty factors; maintain ≥25% margin per mode.

3. Quantify dynamic transients and inrush for emitters, compute hardware, and communications and implement mitigations.

4. Automate budget calculations and visualize margins over mission timelines for design reviews.

5. Validate the budget against benchtop load measurements and update assumptions with evidence.

6. Publish a controlled power budget linked to requirements and ICDs.

Learning Outcomes:

1. Model orbital illumination and eclipses to determine available energy over mission operations.

2. Size solar arrays and MPPT to meet energy needs with degradation and temperature effects at EOL.

3. Select battery chemistry and capacity to achieve depth-of-discharge ≤80% at EOL and estimate cycle life under mission profiles.

4. Compute charge/discharge efficiencies and thermal impacts to ensure safe operating area compliance.

5. Perform sensitivity analyses on degradation rates and thermal conditions to set design margins.

6. Deliver a sizing report with BOM, test plans, and risk mitigations.

Learning Outcomes:

1. Implement MPPT, load shedding, and load scheduling policies on the embedded controller to respect mode priorities.

2. Design deterministic mode-transition logic with hysteresis, timers, and guards to prevent oscillations and brownouts.

3. Integrate telemetry hooks for energy per gram remediated and use data to refine scheduling strategies.

4. Verify mode transitions on HIL rigs and demonstrate predictable behavior under fault injections and eclipses.

5. Document power control ICDs (telemetry, commands, safety interlocks) and verify completeness.

6. Publish test results and update margins based on measured controller performance.

Learning Outcomes:

1. Model environmental heat fluxes (albedo, IR, solar) and internal loads from emitters/electronics to predict component temperatures.

2. Design heat paths, radiators, and insulation to keep components within allowable ranges with ≥5 C worst-case margin.

3. Select thermal interface materials and fasteners to ensure mechanical and thermal reliability across cycling.

4. Plan and execute TVAC and thermal balance tests and correlate models with ≤10% error.

5. Implement thermal safing and throttling strategies for emitters and compute nodes under off-nominal conditions.

6. Publish a thermal control report with correlated models and updated margins.

Learning Outcomes:

1. Implement safe charging/discharging protections (OCP, OVP, OTP, short-circuit) and verify compliance via test.

2. Design for EMI/EMC with filtering, grounding, and harnessing consistent with standards and validate via pre-compliance scans.

3. Estimate TID/SEE/SEU rates using orbit radiation models and allocate component derating or shielding as required.

4. Select radiation-tolerant components for critical paths and document trade-offs with availability and cost.

5. Conduct fault-injection tests to assess SEU resilience and log detected events with recovery actions.

6. Deliver an environmental qualification plan covering EMI/EMC and radiation with acceptance criteria.

Learning Outcomes:

1. Design DC/DC chains, converters, and harnessing with quantified conversion losses and thermal dissipation.

2. Implement protections (OCP/OVP/UVLO/OTP, soft-start, inrush limiting) and verify trip curves against specifications.

3. Dimension fuses, eFuses, and current-limiters and assess selectivity for fault isolation.

4. Validate grounding, bonding, and shielding strategies to minimize common-mode noise and ground loops.

5. Conduct benchtop load-step tests and record stability margins and transient response.

6. Release a hardware design dossier with schematics, BoM, and compliance evidence.

Learning Outcomes:

1. Instrument current/voltage/temperature sensors on critical rails and calibrate measurement accuracy within specified tolerances.

2. Implement energy accounting to compute energy per gram remediated and publish KPIs to telemetry.

3. Develop anomaly detection for power system health (e.g., SOC drift, unexpected load growth) with alert thresholds.

4. Correlate telemetry with ground truth from lab tests and update calibration constants and filters.

5. Integrate power health into autonomy decision-making for load shedding and mode transitions.

6. Publish telemetry/command ICDs and dashboards for operations and analysis.

Learning Outcomes:

1. Plan TVAC test matrices covering hot/cold cases and operational modes and define acceptance limits.

2. Instrument high-value nodes with thermocouples/RTDs and validate sensor accuracy and placement.

3. Correlate thermal models with TVAC data to ≤10% error and update material properties or boundary conditions accordingly.

4. Recompute worst-case margins and revise thermal design parameters or radiators as needed.

5. Document nonconformances and corrective actions with updated analysis evidence.

6. Publish a correlated thermal model package and updated margin report.

Learning Outcomes:

1. Parametrize radiator geometry, emissivity, and view factors and optimize for mass and performance within packaging constraints.

2. Assess degradation of optical properties (emissivity/absorptivity) due to contamination and radiation and allocate end-of-life margins.

3. Implement thermal throttling policies for emitters/compute under off-nominal heat loads and verify safe recovery behavior.

4. Validate MLI seams, vents, and purge strategies to avoid trapping volatiles and ensure cleanliness.

5. Quantify thermal cycle fatigue risks for interfaces and fasteners and select mitigations.

6. Release radiator drawings, test data, and maintenance recommendations for reuse cycles.

Learning Outcomes:

1. Define the autonomy state machine (deploy, detect, approach, clean, verify, return) with entry/exit guards, interlocks, and explicit timeouts.

2. Model critical properties (deadlock-freedom, liveness, mutual exclusion) and verify via model checking (TLA+ or Spin).

3. Specify watchdogs, heartbeats, and recovery paths for sensor/actuator timeouts and degraded modes.

4. Allocate responsibilities between onboard compute and microcontrollers and document timing contracts.

5. Create safety cases linking hazards to state invariants and mitigations within the state machine.

6. Publish verified statecharts and property proofs as part of the design baseline.

Learning Outcomes:

1. Implement ROS 2 nodes and micro-ROS firmware to interface sensors, actuators, and power controllers with appropriate DDS QoS profiles.

2. Enforce clock synchronization (NTP/PTP) and validate timestamp coherence across processors and sensors.

3. Establish heartbeat supervision and node lifecycle management to detect and recover from hangs or crashes.

4. Measure end-to-end latency and jitter per topic and ensure compliance with autonomy timing budgets.

5. Integrate parameter servers and diagnostics to support on-orbit reconfiguration and health monitoring.

6. Produce interface tests and ICD updates validated on real hardware.

Learning Outcomes:

1. Establish bandwidth and latency budgets for telemetry and command links and verify against ground station capabilities.

2. Design store-and-forward data packaging and compression for on-orbit logging and downlink windows.

3. Implement anomaly triage workflows with prioritized telemetry, event codes, and snapshot buffers.

4. Validate link robustness with packet loss, delay, and corruption tests and quantify recovery procedures.

5. Integrate logging schemas and time bases to support post-mission analysis and digital thread traceability.

6. Publish operations-ready telemetry/command ICDs and procedures.

Learning Outcomes:

1. Develop automated unit, integration, and HIL regression tests with scripted fault injection for sensors and actuators.

2. Achieve ≥90% branch coverage and enforce quality gates in CI before integration milestones.

3. Containerize runtime environments where feasible and implement OTA update workflows with cryptographic signing and rollback safety.

4. Resolve interface defects prior to integration gates and document fixes with traceability to requirements.

5. Conduct end-to-end autonomy dry runs with injected dropouts and saturations, meeting recovery time and safety criteria.

6. Publish CI/CD dashboards, test artifacts, and release notes for design reviews.

Learning Outcomes:

1. Profile CPU, memory, and bus usage across autonomy modes and set real-time priorities for critical threads.

2. Design scheduling policies (rate-monotonic/EDF) to meet latency budgets for perception, control, and communications.

3. Implement resource arbitration for competing tasks (emitters vs. compute vs. comms) with measurable fairness and safety.

4. Measure end-to-end jitter and bound worst-case execution times for safety-critical nodes.

5. Tune QoS profiles and buffer sizes to prevent backpressure and packet loss during high-load events.

6. Publish a real-time performance report with acceptance limits and tuning guidelines.

Learning Outcomes:

1. Implement redundancy and failover patterns (hot/warm standby, majority voting) for critical services.

2. Design degraded operating modes with reduced capabilities that preserve safety and mission continuity.

3. Script watchdog resets, brownout recovery, and state restoration with bounded recovery times.

4. Inject multi-fault scenarios and verify safe transitions to degraded/safe modes without deadlocks.

5. Quantify mean time to recovery (MTTR) and update FDIR thresholds and timers based on evidence.

6. Document fault tolerance strategies and formalize guarantees in safety cases.

Learning Outcomes:

1. Configure DDS security (SROS2) with certificates, permissions, and encrypted topics for critical data paths.

2. Harden Linux and MCU firmware with least-privilege, secure boot, and signed binaries.

3. Scan dependencies for CVEs and implement SBOM tracking with update policies and staging environments.

4. Conduct threat modeling (STRIDE) and implement mitigations for command injection, replay, and spoofing risks.

5. Pen-test telemetry/command links and verify detection/response to intrusion attempts.

6. Publish a cybersecurity plan with controls, monitoring hooks, and incident response procedures.

Learning Outcomes:

1. Design OTA workflows with atomic updates, health checks, and automatic rollback on failure.

2. Validate update procedures under link interruptions and brownouts and measure recovery behavior.

3. Version and verify configuration/state migrations to preserve autonomy invariants across releases.

4. Integrate update logs and test evidence into the digital thread and trace to requirements.

5. Compile safety case evidence demonstrating acceptable risk for OTA-enabled operations.

6. Release an operations playbook for safe updates during mission windows.

Learning Outcomes:

1. Define measurable KPIs (decomposition rate, precision/recall, pointing accuracy, energy per gram remediated, turnaround time) with thresholds and data sources.

2. Map 100% of requirements to verification methods (test, analysis, inspection) and create a verification cross-reference matrix.

3. Allocate acceptance criteria and sampling plans for each verification activity and link to responsible owners and timelines.

4. Build data collection plans and tools to capture evidence during bench, environmental, and field tests.

5. Review verification completeness and risk coverage at TRR/FRR and resolve open actions.

6. Publish an approved verification and validation plan aligned to mission objectives.

Learning Outcomes:

1. Design experiments for photocatalytic efficacy using standardized soot/hydrocarbon surrogates, controls, and replicates.

2. Implement safety controls for UV, nanoparticles, and hydrocarbons, including PPE, enclosures, and waste management.

3. Conduct spectrophotometry or GC–MS measurements and compute degradation rates with uncertainties.

4. Analyze results using appropriate statistics (ANOVA, regression), report effect sizes, and validate model assumptions.

5. Estimate reproducibility and RMSE against models and recommend design adjustments or margins.

6. Document DOE outcomes with traceability to requirements and update the digital thread.

Learning Outcomes:

1. Execute vibration, shock, vacuum, and thermal cycling tests to applicable NASA GEVS/ECSS envelopes with calibrated fixtures.

2. Record test results, anomalies, and nonconformances; perform root-cause analysis and corrective actions.

3. Correlate environmental test data with analytical models and update margins or design parameters accordingly.

4. Verify EMI/EMC, charging/discharging safety, and radiation screening against allocated limits and document evidence.

5. Close verification findings and update FRR packages with objective evidence and waivers as needed.

6. Publish a consolidated environmental test report with requirement traceability.

Learning Outcomes:

1. Develop end-to-end mission ops procedures for deployment, cleaning cycle execution, clear-radius declaration, recovery, and reuse.

2. Create checklists, timing targets, and go/no-go criteria for nominal and off-nominal scenarios.

3. Rehearse operations with timeline simulations and evaluate team readiness using objective performance metrics.

4. Conduct anomaly drills (communications loss, power shortfall, partial actuator failure) and refine recovery playbooks.

5. Integrate telemetry dashboards and scripting for rapid triage and decision support during ops.

6. Deliver a validated operations handbook and training package for flight.

Learning Outcomes:

1. Evaluate environmental, safety, and debris-mitigation compliance and document conformance or required waivers.

2. Produce an ethics and sustainability statement addressing lifecycle, reuse, and end-of-mission disposal considerations.

3. Assemble FRR artifacts mapping 100% of requirements to verification evidence and close remaining actions.

4. Demonstrate operational readiness through end-to-end rehearsals and configuration audits.

5. Quantify residual risks and present acceptance rationale to the review board with supporting data.

6. Achieve FRR approval and publish the flight readiness decision package.

Learning Outcomes:

1. Build repeatable data reduction scripts for bench and environmental tests with version-controlled outputs.

2. Quantify measurement uncertainty using propagation and bootstrap methods and report confidence intervals for KPIs.

3. Construct dashboards for decomposition rate, precision/recall, pointing accuracy, and energy per gram remediated.

4. Validate KPI calculations against independent checks and peer review the analysis pipeline.

5. Generate executive-ready plots and narratives suitable for TRR/FRR decision-making.

6. Publish datasets, scripts, and reports with full traceability to test configurations and requirements.

Learning Outcomes:

1. Conduct configuration audits to verify hardware/software baselines and ICD consistency before reviews.

2. Compile verification evidence packages and resolve open actions with documented objective data.

3. Run mock TRR/FRR sessions and track closure of action items to acceptance thresholds.

4. Coordinate cross-functional sign-offs (SE, GNC, Power/Thermal, Materials, Ops) with clear accountability.

5. Present risk posture and mitigation effectiveness with updated RPNs and acceptance rationale.

6. Secure review board approval and archive final decision gate packages in the digital thread.

Learning Outcomes:

1. Define inspection and refurbishment steps for optics, hydrogels, catalysts, and power systems between missions.

2. Establish requalification test plans (functional/thermal/vacuum) and acceptance criteria for reused hardware.

3. Measure turnaround time and cost per mission and identify bottlenecks and improvement actions.

4. Quantify performance drift across reuse cycles and set thresholds for replacement or overhaul.

5. Document spares strategy, shelf-life management, and logistics for rapid redeployment.

6. Publish a refurbishment and requalification handbook with traceability to requirements.

Learning Outcomes:

1. Perform lifecycle assessment (LCA) considering manufacturing, operations, refurbishment, and end-of-life disposal.

2. Compute environmental impact metrics (energy, materials, waste) and propose reduction strategies.

3. Verify compliance with debris mitigation and planetary protection where applicable and document evidence.

4. Develop end-of-mission disposal or recovery plans aligned with regulatory guidance.

5. Integrate sustainability KPIs into mission decision-making and trade studies.

6. Deliver a sustainability and compliance report for stakeholder review.