How to Set Up a CubeSat Project – Preliminary Survey Results
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
CubeSats have been developed by many different institutions since they were introduced by California Polytechnic State University and Stanford University in 1999. A number of papers give lessons learned for individual satellites, some from a technical perspective and other from an educational point of view. However, there is no existing overview of how CubeSat projects are generally set up. The aim of this paper is to fill this gap, in order to offer those wishing to start a CubeSat programme some ideas of where to start, what equipment is needed and some lessons learned in terms of management. This information was gathered via a survey which was publicised via conferences, mailing lists and LinkedIn groups.<br/>At time of writing, 40 groups have completed the survey, including universities, agencies and companies. The respondents came from the US, Europe, Canada, Taiwan, Korea, China, Africa and South America. The majority of the groups were building 1U or 3U CubeSats with Technology Demonstrator or Science Experiment payloads. The groups were asked a series of questions relating to the characteristics of their projects, including the duration of the project, costs and what they spent their money on - including which components they built themselves and which they bought from suppliers. <br/>The groups were asked what first steps they took in setting up their programme and what equipment and facilities were necessary. They were also asked about how they managed and scheduled the project across multiple cohorts of students. This was identified as problematic by many groups and a variety of ideas and solutions were proposed. Lessons learned covered many aspects of the project with some common themes emerging: planning, learning from other groups, student continuity, documentation, integrating the project within the curriculum, mentoring, software development, simplicity and testing. The groups were asked for their advice to future programme leaders and this is summarised in the paper.<br/>
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it