Input impedance of a multilayer insulated monopole antenna
Bibliographic record
Abstract
An efficient modal expansion method was developed for the analysis of the sleeve monopole antenna fed by a coaxial line [Shen and McPhie, 1995]. This method is employed to calculate the current distribution and input impedance of a multilayer insulated monopole antenna fed by a coaxial transmission line. The modal expansion analysis is facilitated by the introduction of a perfectly matched boundary (PMB), which is the combination of an electrical wall and a magnetic wall. The resulting guided-wave structure is then divided into several sub-regions; the electromagnetic fields in these subregions are expressed by the summation of their modal functions weighted by some unknown expansion coefficients. These coefficients, which lead to the current distribution and input impedance of the antenna, are found by enforcing the boundary and continuity conditions at conducting surfaces and regional interfaces. An efficient recursive algorithm is presented to implement the analysis of an arbitrary multilayer insulated monopole antenna. The modal expansion method presented not only takes the effect of the coaxial feed line into account, but is also valid for the cases of a thick monopole antenna and a monopole immersed in a multilayer dielectric cylinder of arbitrary permittivity. Numerical results for the input impedance of a dielectric-coated monopole antenna and an air-insulated monopole are compared with experimental ones available in the literature.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".