The T-Sat1 Nanosatellite Design and Implementation Through a Team of Teams
Bibliographic record
Abstract
It is very challenging to design complex machines and systems that operate in very difficult remote locations, under largely unknown or uncertain conditions. Specifications for such systems must be extremely detailed and extensive, with input from professionals who have designed such systems before, and who gained considerable experience from their operations. Since much of the operating environment is not known in advance, cognitive informatics and computing should play a critical role in such design and operation. This paper describes such a complex system, the T-Sat1 nanosatellite, including its characteristics, its mission, subsystems, as well as the development of specifications, protocols for verification, testing, launch, early operating procedures, and concepts for nominal operations. Particular attention is given to the formation and maintenance of a team of teams, with a multitude of their interactions. The design teams must focus on the satellite subsystems, assembly, integration and testing. The teams of advisors (from academia, aerospace and other industries, business, military, government, and other organizations such as the radio community) must focus on optimal assistance provided to the corresponding design teams.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".