Design of Near-Field Synthesis Arrays Through Global Optimization
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Bibliographic record
Abstract
We provide a study of the relation between the near- and far-fields in antenna systems by working with a concrete design problem. The task of finding an antenna array capable of synthesizing a desired near-field distribution is tackled within the general framework of near-field theories recently proposed in literature. We use a genetic algorithm to search for a set of small antennas by working only with far-field data. It is shown, as predicted theoretically, that such information in the far zone are sufficient for reconstructing the entire near-field in the exterior region. We provide a set of examples demonstrating the procedure and validating the proposals. The methodology can be applied to arbitrary antennas and any desired near-field pattern, and we hope it will help automating design methods in the emerging research area of near-field array synthesis.
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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 | 0.000 |
| 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) | 0.000 | 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