Modeling antenna close to the human body
Bibliographic record
Abstract
Evaluation of antenna performance needs to be addressed in complex environments rather than in free space. There are many situations when such a need arises. We give examples of evaluation when a human body is in the close proximity of an antenna. In this case, it is also important to evaluate the power absorption in the body in terms of the specific absorption rate (SAR). Two antennas, which show robust performance either on a cellular telephone or on a laptop computer are evaluated. They both operate in two frequency bands allocated for wireless communication. The antenna impedance matching, radiation patterns, and SAR in the user's head are computed using the FDTD method. Work currently in progress is aimed at evaluation of the SAR when the whole human body is in the near radiation field of a directional antenna. In this case, the exposure cannot be effectively be approximated by a limited number of plane waves, and modeling of the antenna together with the human body leads to a very large computational problem, The solution that is being investigated is modification to the FDTD that allows for exclusion of the antenna from the computational space and representation of its radiation characteristic on the Huygens space in FDTD.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".