An Implanted Spherical Head Model Exposed to Electromagnetic Fields at a Mobile Communication Frequency
Bibliographic record
Abstract
Can cellular phones and personal communication systems base station antennas affect the active or passive implantable medical devices adversely? Concerns over the possible harmful effects of nonionizing irradiaton upon implanted medical devices have been present for many years. Key issues to address are the questions of whether mobile phones have a detrimental effect on implants, and how the interaction of the handset with the body can be minimized in order to both alleviate public fears and improve handset antenna performance and new implant designs. This paper presents a thorough investigation of the scattering of an electromagnetic (EM) wave from a perfectly conducting implant (a cylindrical wire and a very thin cylindrical disk) of electrically small radius (of resonant length), embedded eccentrically into a dielectric spherical head model by a dipole antenna (0.4 wavelength) at 900 MHz. The dyadic Green's function (DGF) for spherical vector wave functions is employed. Analytical expressions for the scattered fields of an implant embedded head model is obtained. Numerical results from analytical expressions are computed for this problem and then compared with the results from the same model using the finite-difference time-domain, EMU-FDTD electromagnetic simulator. Good agreement is observed between the analytical results on the proposed method in comparison with the FDTD method.
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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 | 0.000 |
| 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 | 0.001 | 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".