Meeting the Requirements of Distributed Engine Control via Decentralized, Modular Smart Sensing
Bibliographic record
Abstract
3Wright-Patterson AFB, OH, 45433, USA With the development of reliable smart transducer node devices, it is possible to realize a fully distributed engine control system. Smart nodes should be designed to be fault-tolerant devices as well as be self-contained and modular so that they may be universally applicable to any control system component. Additionally, they should be capable of operating at high temperatures, yet be low-weight and small. In this paper, some concepts are given to address these requirements. To demonstrate the overall system performance, communication resource utilization, and validate fault tolerance, a prototype Smart Sensor Node (SSN) device is evaluated in a high-fidelity hardware-in-the-loop simulator using a CAN bus protocol. To enhance the reliability analyses, a real-time distributed software-inthe loop simulation has been undertaken. The simulator serves as a test bed for validation and verification (V&V) efforts for future distributed engine control components.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".