Analysis of electromagnetic interactions in antenna arrays through equivalent dipole models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it