Unrestricted Wideband Prediction for Antenna Radiation Efficiency Using Wheeler Caps
Bibliographic record
Abstract
The radiation efficiency prediction using the Wheeler cap is introduced using a semianalytic approach based on the adaptive fitting quality factor method. The procedure details, highlighting the benefits of using the proposed method, are presented, especially in the presence of cavity modes. Also, a procedure is proposed to eliminate the effect of cavity modes for low-frequency antenna measurements. Several studies over the suitability of the proposed procedure are presented to justify the usage of the quality-factor-based method beyond its previous limited usages. The reproduction of results is ensured by considering sufficient data based on an asymptotic prediction of the cavity modes' frequency spacing. Moreover, full-wave simulation results are presented to verify the proposed method and its immunity to the effects of arbitrary Wheeler caps' cavity modes in a controlled environment, anticipating possible sources of errors. Also, the same simulated responses are used through other circuit model-independent radiation efficiency prediction procedures to evaluate the performance of the proposed method. Furthermore, the measurement results are presented for various antennas to verify the proposed method in comparison with the full-wave analysis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".