Equivalent Circuit Modeling for Reflectarrays Using Floquet Modal Expansion
Bibliographic record
Abstract
The starting point in the design of reflectarray antennas is the derivation of the so-called S-curve, which maps changes in unit cell design parameters to the phase of the scattered field from the cell. In general, full-wave simulations are used to derive this curve, though recently a number of analytical techniques have emerged based on equivalent circuit models (ECMs). However, most ECMs are cumbersome to employ, either because they are too specialized or they depend on extraction of component values from supplementary simulations. This paper presents a fully analytical method for predicting the S-curve from dipole-like reflectarray elements based on an ECM derived from a Floquet modal expansion of a planar dipole. The model does not need supplementary simulations that can be used to predict the co-polarized reflection coefficient from a variety of fixed and reconfigurable reflectarray elements. The model is validated against full-wave simulations for several reflectarray element types, including fixed patches, varactor-loaded patches, and patches on tunable substrates, and is shown to be accurate. As such, it could become a highly useful design tool for quickly deriving the S-curve of reflectarray elements during the initial design stages of reflectarrays.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".