The effect of gap size on dipole impedance using the induced EMF method
Bibliographic record
Abstract
The dipole is the fundamental elemental antenna. Moreover, the electric dipole and its monopole equivalent on a groundplane are widely used in practice. Despite the long history of dipole research, its complete impedance behaviour remains elusive. In numerical techniques, such as the method of moments, a gap voltage feed can be expected to give a well-defined radiation conductance but a susceptance which is dissimilar to that of a realized antenna, whereas an impressed current feed can give a well-defined radiation resistance, but dissimilar reactance. The reason is that neither of these feeds accurately model the input region of a practical dipole. Two analytic approaches to the dipole impedance are available - the wave structure method and the induced EMF method. The wave structure method does not lend itself to feed detail, but reveals the impact of dipole thickness and length on the impedance of dipoles which is not available from any other approach. It is reliable for short lengths but it remains restricted to an infinitesimal feed gap, i.e., different to a practical dipole antenna. The induced EMF method is accurate for short and impracticably thin antennas. Electromagnetic simulation techniques can be used for practical dipole thicknesses, but no theory is available to benchmark the results of the numerical experiments. The feed modeling remains a long standing problem in terms of accurately matching the complete impedance to physical experimental results. To make a theoretical start on the problem, the induced EMF method with finite feed gap is solved here and the impedance of the thin dipole is presented. The effect of feed gap size for the finite length wire, e.g. the dipole antenna, has not been studied before. From the induced EMF method, the lossless, thin dipole with finite gap turns out to have an extremely wide bandwidth when terminated with 50 or 75 ohms, a new and interesting result in antenna theory.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".