Contactless Air-Filled Substrate-Integrated Waveguide (CLAF-SIW) Resonator for Wireless Passive Temperature Sensing
Bibliographic record
Abstract
In this article a wireless passive temperature sensor is designed, fabricated, and tested. The sensor uses a contactless air-filled substrate-integrated waveguide (CLAF-SIW) resonator that consists of an air cavity surrounded by a low-impedance electromagnetic band gap (EBG) structure. The contactless feature of the sensor mitigates the fabrication complexity issue of air-filled SIW (AF-SIW) while it benefits from the high-quality factor of an air-filled resonator. The resonant frequency of the CLAF-SIW resonator is dependent on the permittivity of the substrate and the dimensions of the structure which are both temperature-dependent. Change in temperature results in a variation in the resonant frequency of the sensor. As such, we measure temperature by measuring the resonant frequency of the sensor. The developed CLAF-SIW sensor has a measured unloaded quality factor of 1340 that allows for long-range wireless measurements. The resonant frequency of the sensor is measured using a ringback-based wireless interrogation system. The interrogation system energizes the sensor using radio frequency (RF) pulses and determines the frequency of the ringback signals radiated by the sensor using frequency domain analysis. The sensor has a sensitivity of 300 kHz/$^\circ \text{C}$and an interrogation-to-sensor distance of 80cm.
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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 | 0.001 | 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".