Low-Profile Substrate Integrated Choke Rings for GNSS Multipath Mitigation
Bibliographic record
Abstract
In this article, a novel technique for the miniaturization of corrugated structures is presented, and multifolded corrugations (MFCs) are introduced. The miniaturization is achieved by implementing multiple folds or slits inside corrugations. The proposed miniaturization technique is modeled using the modal expansion method. An equivalent circuit is extracted, which precisely models the surface impedance of the proposed MFCs. A time-efficient design procedure is presented based on the equivalent circuit model, and a dual-band double-folded substrate integrated choke ring (DFSICR) structure is designed to suppress the propagation of surface waves over the main global navigation satellite system (GNSS) frequency bands: L1 (1573–1587 MHz) and L2 (1215–1240 MHz). The DFSICR demonstrates significant multipath mitigation capabilities, close to that of a classic choke ring, while it eliminates the drawbacks of a conventional choke ring, such as large size, heaviness, costly fabrication, and inability to integrate with printed circuit board (PCB) structures. An example design shows miniaturization of 85% in height and 38% in diameter compared to the conventional choke ring. A prototype is fabricated using FR4 laminates, which demonstrates a weight reduction of more than 90% compared to the classic choke ring. The measurement results confirm the validity of the proposed technique.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".