RACER Coumpound Helicopter: Operational Wireless FTI Data Transfer from ROTOR's up to Fuselage
Bibliographic record
Abstract
Acquiring helicopter rotor data is always a very sensitive point that requires at least "effort and special attention". This data acquisition is generally managed by a "physical link" (slip ring for example) whereas wireless products are now present everywhere with a technology more than promising. The objective of this paper is to show how the wireless technology was developed within the framework of RACER COUMPOUND HELICOPTER to monitor the three rotors in accordance with CS29 regulations for the mechanical assembly, DO160 rules for environmental constraints and IRIG 106 standard for Flight Test Instrumentation domain notwithstanding that this wireless acquisition means will be used on a daily basis to monitor the data from the three rotors of the RACER. The paper provides an overview of this project, supported by the CEE (Horizon 2020/CS2), and from the initial requirement up to the operational results obtained during the flight test campaigns carried out on the H175. Finally, this paper, based on this use case ( RACER oriented) , aims also to open the perspective of using such a wireless application on helicopters in service in the frame of health monitoring system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".