Wideband Relative Permittivity Characterization of Thin Low Permittivity Textile Materials Based on Ridge Gap Waveguides
Bibliographic record
Abstract
Medical applications and wearable antennas need accurate electrical characterization of textile material. Many techniques have been presented before to obtain the dielectric constant of any material and some of these techniques are applied on textile materials. Due to the mechanical characteristics of these materials, such as the small thickness and the flexibility, many presented techniques are not suitable to be deployed. In this paper, the ridge gap waveguide (RGW) structure is used to measure the relative permittivity of textile materials. The RGW is one of the state-of-the-art guiding structures. Its quasi-TEM characteristics simplify the procedure of predicting the unknown permittivity. The second feature of RGW is the small height, which is more suitable for the standard dimensions of the textile material samples. Moreover, the wide bandwidth of the RGW, which is about 2.5:1, enables performing the calculations over the guide operating bandwidth. This property of the RGWs leads to characterize the textile material with a single setup and utilize these results in a wide range of applications. A mathematical algorithm is presented to find the dielectric constant; then, it is applied in the case of many materials to determine the accuracy of the proposed algorithm. The extracted values of the relative permittivity are compared with the expected values, in the case of materials with well-known electrical characteristics, and this step shows very high accuracy with a percentage error less than 2%.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".