Design of Compact Huygens’ Metasurface Pairs With Multiple Reflections for Arbitrary Wave Transformations
Bibliographic record
Abstract
Huygens' metasurfaces (HMSs) have demonstrated a remarkable potential to perform wave transformations within a subwavelength region. In particular, omega-bianisotropic HMSs have allowed for the passive implementation of any wave transformation that conserves real power locally. Previous reports have also shown that HMS pairs are capable of realizing transformations that break the local power conservation requirement by redistributing the total power, while the wave propagates between the two metasurfaces. However, the required separation distance overshadows the low-profile characteristics of the individual metasurfaces and leads to bulky designs, especially for lower frequencies. In this article, we develop a method of designing omega-bianisotropic HMS pairs, relying on a point-matching process of the real power at the two metasurfaces. We highlight the versatility of our method by presenting two variations of the configuration, depending on whether the electromagnetic source is located within or outside the metasurface pair. Based on the examples of a cylindrical-wave to plane-wave transformation and a beam expander, we examine the impact of multiple reflections, as a way to overcome the size limitations and design compact structures. Moreover, we explore possible beamforming applications through an example of a Taylor-pattern antenna with a single feed-point between the two metasurfaces.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".