Reduction of Electromagnetic Field Distortion in Microwave Sensors in Contact With Skin Tissues
Bibliographic record
Abstract
The electrically resistive epidermis, the outermost layer of skin tissues, can substantially distort the electromagnetic (EM) field of microwave sensors, limiting the depth of penetration into the body. To address this issue, a geometrically modified microstrip transmission line ($\mu $TL) is structured, designed to minimize EM field distortion upon contact with skin tissues. This novel structured$\mu $TL (S$\mu $TL) enhances the performance of conventional$\mu $TLs (C$\mu $TLs) and microwave C$\mu $TL resonators. The S$\mu $TL’s design involves inverting the ratio of the C$\mu $TL length and the ground region underneath it, creating a vertical inversion that introduces a consistent phase shift positively correlated with the inversion ratio. This added phase shift alters the reflected phase of the C$\mu $TL. Furthermore, the polarization direction of the EM field in the S$\mu $TL changes. This research study explores the application of the S$\mu $TL within a C$\mu $TL resonator sensor, demonstrating that the structured design allows for greater control over the intensity of EM field interactions with the surrounding medium. By facilitating stronger EM field interactions, the S$\mu $TL sensor exhibits improved shape factor (SF) and sensitivity. Experimentation confirms that the EM field of the S$\mu $TL-based sensor penetrates deeper into turkey skin tissue compared to that of the conventional C$\mu $TL sensors, highlighting its potential for more effective EM field penetration and performance in sensor 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 | 0.001 |
| 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 | 0.001 | 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".