Analysis of electromagnetic interactions in antenna arrays through equivalent dipole models
Bibliographic record
Abstract
In this paper, we propose a new method for the study of mutual coupling in general antenna array problems with arbitrary size. This method is based on the idea of obtaining equivalent dipole models to replace a complicated radiation problem by a discrete distribution of few infinitesimal dipoles radiating in free space or semi-half free space [1]. The basic idea is to avoid solving the actual boundary-value problem and search instead for equivalent source representation for the antenna at hand. The price, of course, is that the obtained dipole model is not unique, but with the acceptable sense that it is valid only in the region exterior to some small domain around the antenna. It was observed by the authors that when mutual coupling between the elements is strong, the equivalent current distribution obtained for the single element fails to predict correctly the near-field behavior. However, in the present work a suitable hypothesis pertinent to the nature of mutual coupling in antenna arrays is advanced to modify the original method in order to predict correctly the new field due to strong mutual coupling. The new method is based on modeling mutual coupling as a multiple scattering effect taking place between the antenna element and the nearby right and left elements where we assume for simplicity a linear array configuration. It turns out that this hypothesis predicts correctly, within the original method error, the interaction. Moreover, only one dipole model, which takes into effect the first-neighborhood interactions, can be used to predict the correct near field for arbitrary large arrays.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".