Computational Model of the Influence of an 835 MHz Patch Antenna Distance on Specific Absorption Rate (SAR) and Temperature Change in the Human Head
Bibliographic record
Abstract
Human exposure to mobile phone radio frequency (RF) radiation has caused public concern for human health. Mobile phone RF exposure depends on many different parameters. The aim of this study is to examine the effects of both the RF source distance from a human head and of output power levels on the temperature change and the Specific Absorption Rate (SAR) in the head. The peak spatially-averaged SAR over 1 g of tissue is also identified to compare the study results to Health Canada’s Safety Code 6 exposure limits. The SAR and temperature change in the head are simulated in this study using a Specific Anthropomorphic Mannequin (SAM) head model with heterogeneous dielectric properties and a microstrip patch antenna operating at a frequency of 835 MHz. The microstrip patch antenna distance from the head is varied from 0-15mm and it is operated at three different time-averaged output power levels. The simulation is performed using COMSOL Multiphysics software and is solved with the Finite Element Analysis (FEA) method. The results show that both SAR and temperature change in the head model increase as the distance between the head model and the patch antenna decreases. The peak spatially-averaged SAR over 1 g of tissue is found to triple as the phone moves from 4mm from the head to 0mm from the head. The results from this study indicate that to mitigate possible health risks from RF radiation mobile phones should be kept at least 4mm from the head.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".