The design and development of a propulsion system for the CanX-2 and CanX-4/-5 nanosatellite missions
Bibliographic record
Abstract
At the University of Toronto's Space Flight Laboratory, two satellites, CanX-4 and CanX-5, are currently in development for the purpose of demonstrating autonomous formation flying using nanosatellite buses. The use of a nanosatellite bus offers a cost-effective means to investigate the possibility of future satellite missions involving multiple spacecraft that together act as a single mission spacecraft for coordinated observations, in situ measurements, or virtual instrumentation. In order for formation flying to take place, a novel cold-gas propulsion system called the Canadian Nanosatellite Advanced Propulsion System (CNAPS), is also being developed to provide the thrust needed for formation maintenance and augmentation. The technologies and design schemes used in CNAPS stem from the Nanosatellite Propulsion System (NANOPS), which was built as a technology demonstrator on the CanX-2 nanosatellite. While NANOPS is a key payload on CanX-2, several other key enabling technologies are being tested in an attempt to mitigate risk and improve the reliability of the critical components intended for the CanX-4/-5 mission such as a GPS receiver and antenna, attitude control system, and CMOS imaging system. The details of the design and testing as well as recommendations for further development for both NANOPS and CNAPS are discussed.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".