Harvesting Energy From Aviation Data Lines: Implementation and Experimental Results
Bibliographic record
Abstract
Wiring is one of the main challenges in aircrafts. The avionics industry is exploring new schemes to minimize the number of power and data cables to build lighter, more reliable and fuel-efficient aircrafts. In this paper, we describe a novel integrated power harvesting interface to procure power required for avionic sensors. The implemented power harvesting approach is based on a modified power over Ethernet scheme in which the power in the ARINC 825 field (data) bus during its idle times serves as the source for the power conversion chain. A transistor-level design is carried out in CMOSP 0.35 μm (AMS) 3.3 V/5 V technology and the system performance is investigated under various conditions to improve its efficiency. From the experimental tests, an overall efficiency of 60 % was achieved and the harvesting device provided an output power of 10.08 mW for feeding sensors. Reported experimental results proved that the proposed power recovery scheme could serve as a power recovery unit to supply embedded sensors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".