Design and thermal analysis for a novel EMCCD camera payload in a 1U CubeSat form factor
Bibliographic record
Abstract
Nüvü Camēras, a Canadian company that designs a range of CCD and EMCCD cameras and controllers, recently began development on a miniaturized EMCCD controller for a CubeSat form factor. The detector for this payload requires near-cryogenic temperatures, approximately 188K, for performance operation. A temperature requirement of that magnitude for a CubeSat form factor is challenging given the low thermal mass, volume, surface area, and power availability for heat storage, dissipation and control systems that would typically be available for larger form factor spacecraft. The goal of this project is to design and per- form thermal analysis for the Nüvü Camēras CubeSat EMCCD Controller that allows for cold-biased active temperature control of both the controller electronics and detector. The EMCCD controller had an operational temperature range of −35◦C to +60◦C while the detector had a performance range of −110◦C to −85◦C with a desire to maintain a resolu- tion of ±0.25◦C. To meet these requirements, a system was designed within 3D modeling software Autodesk Inventor and imported into Thermal Desktop for thermal analysis and iteration. Models were updated based on thermal analysis results, adjusted by hand, and then tested again until a passive cooling and active heating system that met the require- ments was achieved. The final control system was shown to be capable of cooling from 20◦C (293.15K) to −85◦C (188.15K) and beyond given a Sun Synchronous orbit at 600km with attitude control and operational requirements. It was also shown to be capable of heating up, using resistive heaters on key components, beyond the thermal inertia of the system and environment, indicating viable control on orbit. In the future a PID control method can be implemented, and its use is being investigated by Nüvü Camēras for achieving the desired resolution of ±0.25◦C in the future.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".