How to Set Up a CubeSat Project – Preliminary Survey Results
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Notice bibliographique
Résumé
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/>
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Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
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Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
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