FDTD analysis of the handset antenna and human body interaction
Bibliographic record
Abstract
With the expansion of current use and anticipated further increases in the use of cellular telephones and other personal communication services (PCS), there have been an interest and considerable research effort devoted to interactions between antennas on handsets and the human body. These activities are motivated by two factors: (i) a need to evaluate the deterioration of the antenna performance and to develop better antennas, and (ii) a need to evaluate the rates of RF energy deposition, called specific absorption rates (SAR), in order to evaluate potential health effects and compliance with standards. We address the following issues: (i) to what extent the effect of the human head on the antenna radiation pattern can be simulated by a multilayered tissue sphere, (ii) how important are the quality and resolution of the head model in determination of the head effect on the antenna pattern, total power absorbed in the head (or antenna efficiency in the presence of the head), the peak SAR, and the maximum SAR in 1 g and (iii) how does the distance between the antenna and the head affect the values and location of the peak, and 1 g values of SAR. Additionally, we include the effect of the hand on the parameters investigated and propose a new handset box design that mitigates the effect of the hand on the antenna radiation pattern.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".