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Optimizing a 6 GHz RF Exposure System for in Vivo Experiments: A Comparative Study of Open-Ended Waveguide and Horn Antennas

2025· article· W4417132380 on OpenAlexaff
Abdelelah M. Alzahed, Shea E. Gordon-McIntosh, Theodore Egube, Alp Özgün, Emily West, Gregory W. McGarr

Bibliographic record

Venuenot available
Typearticle
Language
FieldBiochemistry, Genetics and Molecular Biology
TopicElectromagnetic Fields and Biological Effects
Canadian institutionsHealth Canada
Fundersnot available
KeywordsHorn antennaSpecific absorption rateFrench hornAntenna (radio)WaveguideDirectional antennaBeam (structure)Near and far field

Abstract

fetched live from OpenAlex

This paper comprehensively investigates the optimization of localized radiofrequency electromagnetic field exposure configurations for human experiments with open-ended waveguide and horn antennas operating at 6 GHz. By using a simulation-based approach to vary the antenna-to-tissue distance and analyzing key performance metrics including the peak specific absorption rate (SAR) and beam spot, we aimed to optimize the experimental setup for safe exposures on the forearm while maintaining optimal performance levels. When comparing the two antennas across a range of antenna-to-tissue distances, the horn exhibited a higher SAR and a more focused beam spot compared to the open-ended waveguide. Specifically, at a distance of 50 mm, the spatially averaged peak SAR in 10 g (pSAR) on the skin surface was$5.5 \mathrm{W} / \text{kg}$and$3.4 \mathrm{W} / \text{kg}$for the horn and open-ended waveguide, respectively. These findings underscore the importance of antenna selection and placement when optimizing RF exposure configurations for human experiments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.026
GPT teacher head0.321
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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