An Agile Systems Engineering Analysis of a University CubeSat Project Organization
Bibliographic record
Abstract
Abstract University CubeSat projects become popular in recent decades, and face challenges that include both technical and sociotechnical aspects. However, these teams often lack the infrastructure and resources for having effective systems engineering or project management which are beneficial for addressing these challenges and developing complex systems, such as satellites. In this paper we present the results of an exploratory case study of a university CubeSat team developing an Earth Observation satellite. The Agile Decision Guidance method was applied to pinpoint parts of the project organization that could benefit from agile methods in three specific areas: customer problem space, solution space, and product development space. The results drew attention to areas such as; stakeholder management, knowledge and information management, and the support environment, that could benefit from an agile approach. We outline some of the plans to move forward and how the team responded to the analysis. We also discuss if the method was appropriate for academic small satellite organizations and adaptations of the method made during the assessment.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".