Innovative Mission Concepts and Ground Segment Software Based on Existing SFL Spacecraft
Bibliographic record
Abstract
Recent science and technology advancements of micro and nanosatellites have opened possibilities for new space missions that would otherwise be impossible to be carried out by small spacecraft. The Space Flight Laboratory, located at the University of Toronto Institute for Aerospace Studies, is one of the worldwide leaders in small satellite development. The Space Flight Laboratory’s capabilities and technology are considered in two concept studies addressed in this thesis. In one study, the adaptation of the Generic Nanosatellite Bus for a high-precision astronomical calibration mission is explored. The bus expands the capabilities of low-cost nanosatellites by accommodating large and complex payloads. In another study, the NEMO-AM platform is adapted to a mission intended for quantum physics experiments from space, based on the demanding payload requirements. These two studies confirm the capabilities of the Space Fight Lab in undertaking innovative space missions at a fraction of the cost. Lastly, the design and development of a multi-mission telemetry data management system is described. This system is designed and built from the ground up, and has been put into operational use at the Space Flight Laboratory.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".