Site-dependent numerical safety analysis in microwave skin spectroscopy: comparison of monopole and vivaldi antennas
Bibliographic record
Abstract
Over the past decade, sensors for skin cancer detection, with operation at micro- and millimeter-wave frequency range, have been under investigation. Thus, safety concerns related to radiation exposure have become critical, especially for patients with vulnerable skin. Studies to date fell short in detailed safety assessments. Many evaluations rely on a single-tissue model representing a single anatomical site. Moreover, most studies assess safety solely via specific absorption rate (SAR) but omit the temperature-rise analysis induced by radiation exposure. In this work, we investigate two types of surface-wave-based antennas operating in the microwave band. Multilayer tissue models were constructed to emulate nine major body sites. The key safety metrics, including the SAR distribution and temperature increase, were analyzed through full-wave electromagnetic simulations in Ansys HFSS. The results reveal substantial inter-site variability in the metrics, highlighting the necessity of full-body evaluation prior to determining the overall safety measures for the new diagnostic devices. Furthermore, we derive power limits for surface-wave antennas in accordance with U.S. and Canadian safety standards, and verify their conservativeness via temperature analysis. Our findings provide a basis for a comprehensive framework for radiation safety assessment of wearable devices operating in the microwave band.
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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 | 0.001 | 0.002 |
| 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".