Efficient Evaluation of Gradient Transmit-Arrays Through an Equivalent Dispersive Dielectric Description
Bibliographic record
Abstract
The growing popularity of transmit-arrays (TAs) for various antenna applications is calling for effective analysis and optimization methods. TAs are, usually, electrically large, comprising thousands of unit-cells formed by subwavelength metallic scatterers. Full-wave optimization cycles needed to meet stringent specifications in terms of gain, cross-polarization, bandwidth, scan-loss, etc., may be impaired by unrealistically required computational time and memory resources. To overcome this, we propose a modified homogenization method that, unlike other approaches, captures the internal reflections in the unit-cells and its resonances for each polarization, thus, correctly describing unit-cells' frequency response in the band of interest. We define equivalent dispersive anisotropic media for gradient TAs. These surrogate models enable fast analysis and optimization of TAs without compromising the accuracy. As an example, we analyze a TA composed of phase rotation (PR) unit-cells. PR unit-cells present wideband low axial ratio for a TA but challenge the validation of existing homogenization methods. Detailed general description of the method is provided so that it can be applied to other unit-cells and avoid training time and resources required for machine learning-based methods. Using the surrogate cells, the full-wave analysis time and memory of the TA reduces 13 and 4 times, respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".