Design and optimization of cost-effective coldproof portable enclosures for polar environment
Bibliographic record
Abstract
Based on the International Electrotechnical Commission standards, the electronic devices in the industrial class (e.g., integrated circuits or batteries) can only operate at the ambient temperature between -40°C and 85°C. For the human-involved regions in Alaska, Northern Canada, and Antarctica, extreme cold condition as low as -55°C might affect sensing electronic devices utilized in the scientific or industrial applications. In this paper, we propose a design and optimization methodology for the self-heating portable enclosures, which can warm up the inner space from -55°C for encasing the low-cost industrial-class electronic devices instead of expensive military-class ones to work reliably within their allowed operating temperature limit. Among the other options, ceramic thermal resistors are selected as the heating elements inside the enclosure. The placement of the thermal resistors is studied with the aid of thermal modelling for the single heating device by using the curve fitting technique to achieve uniform temperature distribution within the enclosure. To maintain the inner temperature above -40°C but with the least power consumption from the thermal resistors, we have developed a control system based on the fuzzy logic controller. For validation, we have utilized COMSOL Multiphysics software and then one prototyped enclosure along with the fuzzy control system. Our experimental measurement exhibits its efficacy compared to the other design options.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".