Embeddable wireless strain sensor based on resonant rf cavities
Bibliographic record
Abstract
In this article we describe a type of sensor to monitor strain. The strain sensor is a passive device that can be embedded or attached to a structure and then remotely interrogated though a wireless interface. Such a system has the advantage of requiring no permanent physical connection, either electrical or optical, to an interrogation system. The sensor is a conducting coaxial electromagnetic cavity that is embedded in or bonded to the structure in which strain is to be measured. The cavity will exhibit resonance for electrical wavelengths two times the cavity length. Changes in the structure’s dimensions will be reflected in changes in the dimensions of the cavity, and will result in a shift of the resonant frequency of the cavity. The sensor incorporates an antenna so that the resonant frequency of the cavity can be determined by remote interrogation. The acquired resonant frequency is then used to calculate the strain in the structure. The sensor presented in this article operates at a frequency of approximately 2.45 GHz, and exhibits a shift in resonance of 2.45 kHz per microstrain (με). We have demonstrated a strain sensitivity of less than 1με with a bandwidth of 25 Hz. This class of embeddable sensor is expected to have the greatest application in monitoring the health of, and assessing damage in, civil structures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".