The design, implementation and testing of the thermal control system of the CanX-2 nanosatellite, and, The preliminary design of the attitude determination and control system for the generic nanosatellite bus
Bibliographic record
Abstract
The University of Toronto's Space Flight Laboratory operates the Canadian Advanced Nanospace eXperiment program in order to develop low-cost nanosatellites for education and research. Building of the laboratory's expertise in microsatellite system design, this unique program trains highly skilled space system engineers for the Canadian public and private sectors while producing low-cost, quick-to-launch satellite platforms for the scientific and engineering communities. Major projects completed through this program include the CanX-2 nanosatellite, which stands to be one of the most advanced in its class through sophistication and capability. Master's students at the Space Flight Laboratory channel their various backgrounds into highly qualified areas of expertise by developing key subsystems for CanX missions. This thesis will describe the development of the passive thermal control subsystem for CanX-2, and the preliminary design of the attitude determination and control subsystem for the CanX-3 astronomy mission and the CanX-4 CanX-5 formation flight mission.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".