Survey of the Fixed-Geometry Wheeler Cap Method: The Myth of a Simple Antenna Efficiency Measurement Technique
Bibliographic record
Abstract
Rigorous experimental estimation of antenna efficiency is challenging and so the simplicity of the fixed-geometry Wheeler cap method has made it an attractive low-cost option. Although experimental results have revealed multiple issues, the method continues to be applied and attracts much research attention well over a half century since it was published. Wheeler’s original proposal was for electrically small antennas, but the method has been expanded and applied to wideband, multiport, and millimeter wave antennas. Ongoing publications have pointed out successes and shortfalls but have seldom been conclusive as to a range of validity for the method. Modern simulation offers insight that cannot be easily found through physical experimentation and is used here to investigate the artifacts of experimental results. This paper offers a survey of the extraordinary research efforts on the Wheeler method and presents new results showing its applicable range to be extremely limited. The antenna must be low-loss and electrically small enough to avoid antenna characteristic modes – in addition to the Wheeler cap being small enough to exclude cavity modes. However, the antenna must also be electrically large enough so that its radiation resistance is sufficient to avert instrumentation measurement errors. Given these constraints and the many potential sources of errors, the method should be considered impracticable for general antennas.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".