Dual-Band Frequency Selective Surface Sensor for Detection and Mapping of Coating Wear
Bibliographic record
Abstract
Coating degradation on aircraft surfaces poses a critical challenge to structural integrity and operational safety, requiring precise detection of damages over large surface areas. Current non-destructive testing methods either lack the sensitivity to detect early-stage damage or are limited by slow, area-wise inspections that affect faster inspections. This work presents a multi-resonant frequency selective surface (FSS) sensor capable of localizing coating wear by correlating resonant frequency shifts with changes in coating thickness. The FSS consists of two regions with 10x10 arrays of nested square patch resonators, designed to resonate at 4.5 GHz and 5 GHz, respectively. Each region responds independently, with minimal interference, enabling precise identification of localized wear. The developed FSS is tested by the erosive wear of a 225 µm PVC coating layer by layer, resulting in resonant frequency shifts of up to 198 MHz in Region A and 231 MHz in Region B, with an average shift of 70 MHz per 75 µm of wear. With its high sensitivity, and precise localization capabilities, the developed FSS sensor system promises non-contact structural health monitoring across aerospace, automotive, and civil infrastructure applications.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".