Design and Test Optimizations for Spacecraft Attitude Determination and Control Subsystems
Bibliographic record
Abstract
As the demand for spacecraft grows, spacecraft manufacturers are required to innovate on system design and test procedures in order to meet demand. The University of Toronto Institute of Aerospace Studies (UTIAS) Space Flight Laboratory (SFL) thus requires innovative solutions to optimize the design and test of these spacecraft. This thesis presents four novel solutions to optimize both the Attitude Determination & Control System(s) (ADCS) design of new spacecraft and the test of related hardware. The test of ADCS hardware through software automation methods is explored, utilizing state-of-the-art software techniques to expedite testing procedures for the Hawkeye 360 constellation satellites. On-orbit inertia tensor estimation algorithms are also investigated, providing an alternative to model-based tensor calculations. Additionally, a novel method for repurposing an existing spacecraft inspection camera as a viable Earth Horizon Sensor (EHS) is exhibited. Lastly, automation methods for model-based spacecraft power generation analysis are demonstrated for the NASA StarBurst spacecraft.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".