Fiber Bragg Grating Sensors ice detection: Methodologies and performance
Bibliographic record
Abstract
New Researches have been done recently in order to create and adapt new systems to the recent Appendix O FAR 25 Aeronautical aircraft icing regulations. One important issue are the Supercooled Large Droplets (SLD) that are an important hazard in many cases. Novel sensor technologies for aircraft are being developed in order to assess the ice severity and to discriminate between large size droplets and small size droplets ice accretions. The European SENS4ICE project gives founding for the research and development of several aeronautical ice detection systems that can discriminate between Appendix C and Appendix O conditions. In the present paper, the strengths and weaknesses of the Fiber Optic ice Detector (FOD) developed by INTA are discussed using a test matrix from the Canadian National Research Council (NRC). The FOD has presented good performance at the NRC ice wind tunnel tests in the ice detection time, detecting ice in most cases earlier than required by the actual standard (ED-103). Its lightness, low intrusiveness and small size, makes the FOD ideal for aeronautical applications. It measures indirectly the icing cloud characteristics, such as the liquid water content (LWC) and the ice accretion rate, so only an approximate severity assessment could be done.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".