A Micro Copper Mesh-Based Optically Transparent Triple-Band Frequency Selective Surface
Bibliographic record
Abstract
Frequency selective surfaces (FSSs) are low-profile structures consisting of periodic elements that can manipulate the behavior of electromagnetic waves. In various fields, such as vehicular and indoor communication and personal electronic devices, their use as spatial filters is limited primarily due to safety and aesthetic reasons. A possible solution in this regard is using transparent conducting oxides or graphene on glass. However, the transmission performance of these filters is relatively poor as they inherently suffer from higher degrees of ohmic loss as compared to their opaque counterparts. Thus, we propose a suitable alternative that offers lower transmission loss with similar a transparency profile (76.2%) using copper mesh instead. The present letter discusses the design of an optically transparent triple-band bandpass filter that passes electromagnetic waves only at 2.4, 3.7, and 5.7 GHz and provides a minimum attenuation of 35 dB at transmission zeros. Investigations have also been carried out on the proposed structure under variations in both polarization ($\alpha _{1}$) and incidence ($\alpha _{2}$) angles. The designed FSS has wide angular stability and is fourfold symmetric. Thus, its filtering performance is largely unaffected under variations in polarization angle. A prototype of the proposed filter has been fabricated and experimentally tested for validating the simulation results, and a good agreement between them has been achieved.
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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 | 0.001 | 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".