Data set for Augmented Reality-Enhanced Microwave-based Wireless Monitoring System for Smart Coating Applications
Bibliographic record
Abstract
The critical impact of coating wear on structural stability and safety has initiated extensive research in the development of real-time coating health monitoring systems for aircraft, naval vessels, and infrastructure. Among the various developed systems, microwave-based systems have garnered significant interest for their real-time and remote operability but struggle with poor localization capabilities over large surfaces. This work presents and investigates the wear detection and localization capabilities of a smart coating system that leverages embedded microwave-based passive split ring resonator (SRR) sensors. The system performance is validated by observing its resonant response during the erosion of the coating through both mechanical and chemical erosion. Furthermore, the work examines the potential of the developed SRR array to localize the region of coating damage through AI-based post-processing techniques, enabling real-time wear localization over extensive surfaces including aircraft and pipelines. Additionally, a cloud-based augmented reality system is integrated for out-of-sight monitoring of coating wear, thereby enhancing visual identification, ease of system data interpretation and faster decision-making, promising their robust application in harsh industrial environments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.026 |
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".