Investigation Into the Effects of the Patch-Type FSS Superstrate on the High-Gain Cavity Resonance Antenna Design
Bibliographic record
Abstract
Results of modeling, design, simulation and fabrication are presented for a high-gain cavity resonance antenna (CRA), employing highly-reflective patch-type superstrates. In order to determine the resonant conditions, the antenna is first analyzed using the transverse equivalent network (TEN) model, as well as the well known simple ray-tracing method. Prior to that, a highly-reflective patch-type frequency selective surface (FSS) is designed in order to be employed as the superstrate layer of the CRA. Next, a 2.5-D full-wave analysis software package, based on the method of moments (ANSOFT Designer v4.0), is utilized to analyze the antenna structure. Using this full-wave analyzer, the input impedance properties of an actual antenna are investigated as well. Then, a 3-D full-wave analyzer, based on the finite element method (ANSOFT HFSS), is used to extract the directivity and radiation patterns of the CRA, taking into account the finiteness of the substrate, superstrate and ground plane. Some previously unaddressed issues, such as the effects of the FSS superstrate on the input impedance characteristics of the probe-fed microstrip patch antenna, acting as the excitation source of the CRA are also studied. The effects of the highly-reflective FSS superstrate size on the CRA directivity, and explicitly its aperture efficiency, are investigated as well. A comparative study is also performed between CRAs with patch-type FSS and high permittivity dielectric superstrates. Measurement results are provided to support the modelings and simulations.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".