Field and Dispersion Engineering in Cylindrical Metallic Waveguides Using Cascaded Metasurfaces
Bibliographic record
Abstract
Metasurfaces (MTSs), the two-dimensional counterparts of metamaterials, have gained significant attention in recent years for their ability to control and manipulate various electromagnetic (EM) functionalities. Leveraging the unique properties of metasurfaces opens extensive possibilities for dispersion engineering and wave manipulation. One notable application is their use in modifying the propagation characteristics of cylindrical waveguides enclosed by perfect electric conductors (PECs), which inherently support a discrete spectrum of modes (C. J. M. Barker, N. De Zanche, and A. K. Iyer, IEEE Trans. Microw. Theory Tech., 71(8), 3392, 2023). Metasurfaces generally exhibit electric, magnetic, or magnetoelectric responses. Among these, bianisotropic surfaces represent a class of metasurfaces that offers complete control over wave propagation. The bianisotropic transition condition can be realized using cascaded patterned metallic sheets. However, maintaining the local periodicity approximation requires that these layers be positioned extremely close to one another. A rigorous method for analyzing cascaded layers involves using network parameters, such as ABCD matrices or wave matrices. By applying the ABCD matrix method, the overall response of the cascaded layers can be calculated by multiplying the matrices of individual layers (C.-W. Lin and A. Grbic, IEEE Trans. Antennas Propag., 69(10), 6546, 2021). This approach accurately models wave propagation between layers, relaxing the constraints on small separation distances and enabling more flexible designs and easier fabrication.
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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 | 0.001 | 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".