Space Environment Modelling and Torque-Optimal Guidance for CubeSat Applications
Bibliographic record
Abstract
CubeSats and nano-satellites provide flexible low-cost platforms for the academic and scientific communities to conduct cutting-edge research in the harsh environment of space.The mission life of nano-satellites is often limited by the attitude actuators, and it is therefore beneficial to reduce torque and angular momentum usage during reorientation maneuvers.In this capacity, a computationally lightweight torque-optimal guidance algorithm was formulated, solved using pseudospectral methods, and validated in a MATLAB-Simulink environment.A low-computation atmospheric density model, developed in support of this research, was extensively validated via performance assessment of passive CubeSat aerostabilization.Results indicate that this torque-optimal guidance algorithm demonstrates substantial improvements in performance and pointing accuracy over an Eigenaxis controller for similar maneuvers, with low to moderate computational overhead.In doing so, it presents a significant advancement towards the development of intelligent GN&C systems for small satellites.iii I n m em o r yo fC o l um b i aa n dC h a l l e n g e r .
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".