Chipless RFID Sensor for Measuring Time-Varying Electric Fields Using a Contactless Air-Filled Substrate-Integrated Waveguide Resonator
Bibliographic record
Abstract
This paper presents a wireless chipless resonator-based sensor for measuring the absolute value of an external time-varying electric field. The sensor is developed using contactless air-filled substrate-integrated waveguide (CLAF-SIW) technology. The sensor employs a low-impedance electromagnetic band gap structure to confine the electric field within the sensor's air cavity. The air cavity is loaded with varactor diodes whose reverse bias voltage is modified by the to-be-measured external electric field. Variation in the external electric field results in a variation of the sensor's resonant frequency. The CLAF-SIW sensor offers a high unloaded quality factor, which is required for a long-distance ringback-based interrogation system. A prototype of the proposed sensor is fabricated and tested. It can measure a time-varying external electric field up to 6.9 kV/m, has a sensitivity of 1.86 (kHz)/(V/m), and can be interrogated from a distance of 80 cm. The feasible maximum bandwidth of the external electric field is 25 kHz. The proposed sensor offers a compact planar multilayer structure that can easily be incorporated with a planar antenna and its size can be reduced by selecting a higher operating frequency without an increase in dielectric loss.
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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) | 0.001 | 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".