Using an Equivalent-Circuit Model to Design Ultra-Wide Band-Stop Frequency-Selective Surface for 5G mm-Wave Applications
Bibliographic record
Abstract
In this work we propose a system of two frequency-selective surface (FSSs) with an ultra-wide band-stop response for 5G millimeter-wave applications. The analyses are based on the equivalent circuit method, which predicts the transmission characteristics for a plane wave with normal and oblique incidence, and the scattering matrix technique, which provides the result of the cascaded structure. The geometries used in the single-layer FSSs are simple to design, and a series of basic equations are described in order to calculate the inductance and capacitance of conducting strips. FSSs prototypes were fabricated and measured in an anechoic chamber. The first FSS is shaped with the four-arms star geometry, which has a resonant frequency at 27.92 GHz for both measured and simulated results. The second FSS is based on the quasi-square geometry, whose resonant frequency for the experimental and numerical results are 35.68 GHz and 35.76 GHz, respectively, for the transverse electric polarization. These two single-layer FSSs present theoretical resonant frequency at 28 GHz (FSS #1) and 35.8 GHz (FSS #2). Cascading of the two FSSs was realized by using an air gap whose effect was analyzed. A gap space in the order of about λ/4 matched with the predicted resonant frequency of the individual structures. Numerical and measured results show excellent agreement with a maximum error of 1.03%. All measured results closely follow those of simulated ones, thus validating the design approach and applications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".