An Open-Source CubeSat Electrical Power Supply
Bibliographic record
Abstract
The reliability of the electrical power system (EPS) in satellites is a major determinant of mission success. In the case of cost-constrained CubeSat satellites, the available commercial EPS options are relatively expensive and limited in their capabilities. This article describes a feature-rich and fault-tolerant EPS for CubeSats that is being distributed under the open-source model, which is well-suited for supporting the modest budgets and diverse mission objectives of both academic as well as industrial satellite designers. Adoption of the open source model promotes further development of the initial design. The proposed EPS provides capabilities that match or surpass the features and fault-tolerance of the commercial EPS alternatives. It provides four independent channels that provide optimized power extraction from the solar panels using maximum power point tracking. The fault-tolerance capabilities of the EPS include the use of a dual-processor microcontroller with ECC memory and 18 power channels (plus 2 internal) with over-current sensing and automatic channel shut-down, with the ability to automatically adjust the current threshold over time to account for radiation-induced aging effects. The article details the capabilities and fault-tolerance features of the EPS, and then presents the results of detailed testing and characterization experiments on a completed implementation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".