Payload Software Interface Development and Testing for the NorSat-TD Microsatellite Mission
Bibliographic record
Abstract
Smaller nano- and microsatellites have become ideal platforms for validating new space technologies before their widespread use. The Space Flight Laboratory (SFL) at the University of Toronto Institute for Aerospace Studies (UTIAS) has developed several satellite platforms featuring heritage subsystem designs that can be adapted to accommodate a variety of novel payloads. Software interfaces provide an important final link between payloads and other subsystems and help to support all payload functions. This thesis presents the contributions of the author in developing and testing payload software interfaces for the NorSat-TD microsatellite mission which launched in April 2023. Ground and on-board computer software interfaces were created to support all required functions for two of the mission’s novel payloads. In addition, a generic test script was created to streamline the development of automated tests for new payloads. All payload tests were created from this template and used throughout development of the spacecraft. Furthermore, all of NorSat-TD's payloads and their software interfaces are currently functioning as intended and working to meet their on-orbit mission objectives.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.009 | 0.004 |
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".