Triangle and Aperiodic Metasurfaces for Bistatic Backscattering Engineering
Bibliographic record
Abstract
Advanced electromagnetic (EM) surfaces are commonly designed to improve wave propagation and scattering. Control of EM wave scattering is one of the important venues for metasurface as an advanced surface, in which the EM surface takes place lower space than the famous metamaterial. At the same time, advanced surfaces get the same EM features as bulk metamaterials. With a new arrangement of metasurfaces and a fully characterized solution, conspicuous bistatic backscattering reduction is achieved over a wide frequency range and incidence angle interval. Closed‐form equivalent electric circuit models are primarily obtained to describe the physical challenges. Triangular and aperiodic metasurfaces are characterized to increase union diffusions of returning field. To evaluate the principal models, numeric and fabrication solutions are performed. Thus, the achieved frequency bandwidth for 10 dB reduction in bistatic backscattering is 80%. Furthermore, this fractional bandwidth goes to 70% in the case of oblique incidence. The main novelty of this research versus the related state of art is the highest 10 dB reduction bandwidth for the bistatic radar‐cross‐section reductions with the new studied coating configurations. Meanwhile, the obtained closed‐form circuit models are suitable for other applications, such as shielding and wave path redirection.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".