High-Dynamic-Range Chipless Microwave Resonator-Based Strain Sensor
Bibliographic record
Abstract
Microwave split-ring resonators are utilized as sensors in a wide variety of applications due to their remarkable features, such as extremely low cost, high sensitivity, and relatively high quality factor. In this article, another application is enabled according to a recently demonstrated chipless tag-reader structure providing the possibility of simplifying the sensor structure from a “multilayer structure” consisting of a dielectric substrate sandwiched between two metallic layers to a single-layer structure formed from a conductive material. This capability is specifically important for strain sensing applications as it brings the possibility of utilizing low stiff conductive materials instead of copper (which is the primary material used in microwave application) while keeping the reader structure with high-quality microwave application-specified substrates intact. With the explained approach in this work, a low tensile silver-aluminum silicone elastomer conductive material is considered for the tag providing a very high tensile dynamic range. According to the whole sensing system structure, as high elongation range of as 20% and the high sensitivity in the range of 25 MHz/1% of strain is achieved. Multiple simulations and experimental results support the idea of the novel microwave strain sensor proposed in this work.
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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) | 0.001 | 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".