Microwave Sensor Based on Microstrip Line Photonic Band Gap (PBG) Structure
Bibliographic record
Abstract
A hybrid integration of planar microstrip line and Photonic Band Gap (PBG) structure is proposed for liquid material complex permittivity characterization. The periodic non-through holes drilled in the substrate to generate the bandgap protect also the microstrip from direct contact with liquid under test (LUT). Based on this configuration, two versions are developed to address the measurement constraint. The first sensor exploits the frequency shift of the bandgap center due to the change in the permittivity of the LUT filled in the holes. The second sensor exploits the behavior of the bandgap as a reflector to construct a resonant structure sensitive to the variation in LUT permittivity. The dimensions of the planar structures are optimized to achieve high precision and discrimination capability. The different empirical expressions describing the complex permittivity with the measured parameters for the two sensors were carried out. For experimental validation, prototypes are used to characterize different commercial oils. The frequency shift related to a change of 1 in LUT permittivity corresponds to 300 MHz around 8.3 GHz. The resonant-mode sensor spans a permittivity range from 1 to 9 with a precision better than 6.6 %. The proposed low-cost sensors are simple and reusable, satisfying the requirement of industrial applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".