Hardware and Software Project Management Best Practices for Small Satellite Systems
Bibliographic record
Abstract
The Command and Data Handling Team for the ORCASat CubeSat project, funded by the Canadian Space Agency, is responsible for delivering a space-ready on-board computer, supporting testing infrastructure, and ground control mission software in a three-year timeline by the launch date in 2021.Members of this team are distributed across two universities and consist of undergraduate students contributing part-time to the project.Co-lead by two individuals, the team has implemented several techniques and practices to handle the challenges that come with managing remote work.We present the methods that have been employed in the management of this team, including meeting format, team communication software, use of version control and task tracking software, and practices for long-term planning.The standardization of a design process methodology, from requirements definition to implementation, is also discussed as it has greatly helped increase the efficiency of the team as a whole.Many of the methods employed in the management of this team were originally based upon well-known software development methodologies, adjusted to meet the needs of combined hardware and software projects.Lessons learned from the management of previous student design team projects were also incorporated into the current management strategy.These techniques are tailored to the rigorous demands of a small spacecraft development program and have contributed to the rapid development of the project and the successes of the team thus far.Employing similar methods would be useful to any other program working under a similar timeline and team composition to that of a student-driven CubeSat development program like ORCASat.
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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 | 0.042 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".