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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".