Near-Field Analysis of Electromagnetic Interactions in Antenna Arrays Through Equivalent Dipole Models
Bibliographic record
Abstract
This paper presents an analysis of the near fields of antenna arrays using an alternative approach taking into account field variations due to mutual coupling. The method does not require solving Maxwell's equations under the customary boundary condition, but is based instead on searching for equivalent dipole models that are capable of reproducing the same radiated fields of the actual array (ideally obtained through accurate measurement.) We treat mutual coupling between array elements as essentially a multiple scattering effect and propose a method to calculate the near field of arbitrary linear arrays with significant electromagnetic coupling. It is found that a single (analytical) dipole model obtained under certain circumstances, which are detailed in the paper, can be used to predict correctly the near field radiated by arbitrary-size antenna arrays. The paper ends by developing an application of the equivalent dipole method by expanding the near fields into a sum of propagating and nonpropagating parts. It is shown that the 3-D spatial Fourier transform required in this expansion can be eliminated when the dipole model is used in computing the near field in the domain of the validity of the model.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".