Solar Array Arcing Mitigation for Polar Low-Earth Orbit Spacecraft
Bibliographic record
Abstract
Electrostatic discharge for polar low-Earth-orbit (LEO) spacecraft is a relatively new and unexplored issue. Discharge mechanisms for LEO spacecraft are significantly different from those encountered in high Earth orbits, and seemingly few designers of new, high-voltage small satellites are aware of the differences between the two environments. Polar-LEO spacecraft encounter both plasma-induced arcing risks (at equatorial latitudes) as well as differential surface charging risks (over auroral zones): two different issues that require very different design techniques to address. There do not appear to be any comprehensive guidelines in the open literature that polar-LEO spacecraft designers can use to avoid the potentially catastrophic risk or arcing in high-voltage satellites. The issue of spacecraft charging and electrostatic discharge (ESD) in the low-Earth orbit environment is discuessed, in the context of satellite power system design. Options for controlling spacecraft charging and for preventing trigger and sustained arcs between high-voltage conductors are presented. These guidlines have been used to size solar panels for the upcoming Canadian Maritime Monitoring and Messaging Microsatellite (M3MSat)-a highly capable mission with relatively high power demand-which is used as a design example. It is concluded that ESD issues for polar LEO spacecraft are both challenging and subtle, and demand careful attention from engineers early in the design process.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".