Waveguide sensor with metamaterial structure for determination of dielectric properties
Bibliographic record
Abstract
Microwave sensor (MWS) compared with classical sensor, offers many advantage such as rapid and nondestructive measurement. At microwave (MW) frequencies, dielectric properties of materials depend on frequency, moisture content, bulk density and temperature. MW waveguide sensors can measure properties of materials based on MW interaction with matter, and provide information about dielectric properties of investigated dielectric material, characterized with complex permittivity. The paper presents a new approach for determination of the dielectric properties of dielectric material by embedding a metamaterial (MM) structure over the aperture of waveguide sensor in order to increase the sensing properties of classical waveguide sensor. The optimal design of MM structure for waveguide sensor tuning in MW X-band is obtained. In this new approach the MM function in two ways: like a tool for increasing the sensibility of classical waveguide sensor and the tool sensitive to the dielectric properties of investigated material through the adjusted resonance frequency of designed MM units. The numerical simulation of 2D MM structure properties and experimental results for dielectric properties of dielectric materials are carried out.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".