Near-Field Shielding Analysis of Single-Sided Flexible Metasurface Stopband TE: Comparative Approach
Bibliographic record
Abstract
Near-field (NF) studies comprising four common 10 GHz stopband frequency selective surfaces (FSSs) of single-sided, single-layer metasurfaces are conducted. The studies are to determine the NF efficiency of the metasurfaces for electromagnetic shielding applications. First, the FSSs' shielding performance in both far-field (FF) and NF zones is considered using the finite element method (FEM) based on the cell shielding effectiveness (SE). Analytical derivations of the FF- and NF-SE are presented, incorporating the equivalent circuit model and infinitesimal electric/magnetic source model, respectively. A nonlinear regression learning approach is utilized to predict an acceptable NF model. In the comparison study, the edge diffraction effects due to the finite structures are taken into consideration for 1-D and 2-D diffractions. The NF-SE performance of conformal structures is analyzed based on the radii of curvature. When the structure is bent, the movement of the desired transmission zero in the NF region intensely degrades the NF-SE, which may lead to a highly unstable shielding performance. Moreover, data sets are extracted for efficient usage of the structures under test by introducing two useful reconfigurable parameters, distance and radius. For verification, the FEM results are compared with the method of moment (MOM) results. The developed scenarios create a reliable criterion for further advancements of the flexible shielding surfaces from the NF point of view.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".