Air-to-Ground Transmission and Near Real-Time Visualization of FBG Sensor Data via Cloud Database
Bibliographic record
Abstract
During the last decades, innovative aircraft health management systems have been receiving increasing interest from original equipment manufacturers (OEMs) and aircraft operators. Their implementation could lead to substantial benefits: drastic cuts in turnaround time, operation costs, and life cycle costs (LCCs) as well as sharp increases in system availability, safety, and reliability. An interconnectivity step-up is, hence, needed to guarantee a seamless data transfer. In this article, an integrated open-source solution for reliable data transmission and near real-time graphical visualization is proposed. After a comprehensive calibration and verification campaign performed on a test stand, the overall system has been successfully validated on structural data measured using a network of fiber Bragg gratings (FBGs) mounted on a radio-controlled model aircraft. The result is an effective and robust system able to monitor near real-time critical parameters and health status of structures. With this system, the temperature and displacements of the structure can be displayed on a heat map arranged on a 3-D model and visualized through a computer application on the ground. The proposed methodology can be applied to heterogeneous scenarios, ranging from maintenance planning activities to performance checks, providing an all-in-one solution for flight data management as well as other applications in the structural monitoring domain.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".