Antenna Design and Analysis Using the Sequential Loading Method
Bibliographic record
Abstract
This article presents a new method for accurately designing and analyzing an antenna or array. The proposed technique operates solely on the transfer admittance matrix of a perfect electrically conducting antenna fabric that is obtained using a full-wave simulator only once. The desired antenna, which must be a subset of the fabric, is then synthesized by progressively loading the fabric and applying a computationally efficient iterative postprocessing technique that transforms the stored admittance matrix into one that corresponds to the desired antenna or array. Given that the admittance matrix of the fabric is used as the basis for all subsequent antennas that can be represented by the fabric, it contains the solutions to an entire class of antennas. The proposed method is demonstrated by applying it to the class of colinear antennas up to five wavelengths long, arranged along a single axis. The method is validated by comparing its results to those obtained using a conventional full-wave simulator. The proposed method is applied first to the case of multiple loads located at arbitrary port locations along the fabric. Then, open-circuited loads are used to decimate the tips of a linear antenna to demonstrate that the method accurately predicts the response of an electrically shorter antenna. Finally, decimation is used to transform the fabric into an array of smaller antennas, where it is shown that mutual coupling effects are accurately predicted.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".