A Non-Invasive Phase Sensor for Permittivity and Moisture Estimation Based on Anomalous Dispersion
Bibliographic record
Abstract
The traditional microwave resonance sensors are based on the measurement of the frequency shift and bandwidth of a resonator's amplitude spectrum. Here we propose a novel sensing scheme in which the material properties are estimated by determining the changes in the phase spectrum of an anomalous-phase resonator. In the proposed phase sensing, we exploit the unique double phase reversal which takes place on the edges of the anomalous dispersion region as a signature to detect the resonance. We show that with the phase sensing, a significant reduction in detection errors compared to the traditional sensing can be obtained because of the noise immunity offered by the phase detection and also due to the strong dispersive phase response that reduces the sensor's dependence on the external environment. We also show that the bandwidth determination procedure of the resonance which is needed to characterize the sample losses is significantly simplified. The concept of phase sensing is shown by devising an experimental microstrip open stub resonator whose frequency response lies in the anomalous dispersion region. The dielectric characteristics of the samples placed on the stub are extracted from the resonant frequency and the slope of the phase response. We also demonstrate that the changes in moisture levels can also be detected by utilizing the phase sensing method.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".