Pixelated Checkerboard Metasurface for Ultra-Wideband Radar Cross Section Reduction
Bibliographic record
Abstract
In this paper we designed and fabricated a metasurface working as a radar cross section (RCS) reducer over an ultra wide band of frequency from 3.8 to 10.7 GHz. The designed metasurface is a chessboard-like surface made of alternating tiles, with each tile composed of identical unit cells. We develop a novel, simple, highly robust and fully automated approach for designing the unit cells. First, a topology optimization algorithm is used to engineer the shape of the two unit cells. The area of each unit cell is pixelated. A particle swarm optimization algorithm is applied wherein each pixel corresponds to a bit having a binary value of 1 or 0 indicating metallization or no metallization. With the objective of reducing the RCS over a specified frequency range, the optimization algorithm is then linked to a full wave three-dimensional electromagnetic simulator. To validate the design procedure, a surface was designed, fabricated and experimentally tested showing significantly enhanced performance than previous works. Additionally, angular analysis is also presented showing good stability and wide-angle behavior of the designed RCS reducer. The automated design procedure has a wide range of applications and can be easily extended to design surfaces for antennas, energy harvesters, noise mitigation in electronic circuit boards amongst others.
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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) | 0.001 | 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".