Computer-aided software design for spacecraft guidance, navigation and control
Bibliographic record
Abstract
Summary form only given. In the early days of space flight, the attitude and orbit control system (AOCS) was essentially a hard-wired, frozen architecture that could hardly be modified during flight. Then came the microprocessors whose on-board software, hand-coded by humans before flight, could be reprogrammed during flight. In the most recent evolutionary step, flight software can now be automatically generated by another software tool, starting from a high-level graphical representation of the AOCS functions and their interrelations. The presentation will demonstrate some of the limitations but also many of the benefits of using computer-aided software design tools for flight code generation. The impact on the quality of the flight code and on the substantial savings in development and validation time will be illustrated using the particular case of the on-board AOCS software of the PROBA-1 spacecraft. PROBA-1 was launched in October 2001 for a two-year mission and it is still successfully fulfilling its Earth-observation mission today. Flight results will be provided to illustrate the performance of the AOCS software.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.044 | 0.015 |
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".