RF Cavity Passive Wireless Sensors With Time-Domain Gating-Based Interrogation for SHM of Civil Structures
Bibliographic record
Abstract
Many existing sensing technologies for application to the monitoring of civil structures have a serious deficiency in that they require some type of wired physical connection to the outside world. This causes significant problems in the installation and long-term use of these sensors. This paper describes a new type of passive wireless sensor that is based on resonant RF cavities, where the resonant frequency is modulated by a measurand. In the case of a strain sensor, the electrical length of the cavity directly modulates its resonant frequency. A probe inside the cavity couples RF signals from the cavity to an externally attached antenna. The sensor can then be interrogated remotely using microwave pulse-echo techniques. Such a system has the advantage of requiring no permanent physical connection between the sensor and the data acquisition system. In this type of sensor, the RF interrogation signal is transmitted to the sensor and then reradiated back to the interrogator from the sensor resulting in a signal strength that decreases with the forth power of distance. This places an upper limit on the distance over which the sensor can be interrogated. Theoretical estimates show that these sensors can be interrogated with sufficient SNR at distances exceeding 10 m for radiated powers of less than 1 mW. We present results for a strain sensor and a displacement sensor that can be interrogated at a distance of 8 m with a strain resolution of less than 10 ppm and displacement resolution of 0.01 mm, respectively.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".