Maximizing the Gap Height in Gap-Waveguides with Helical Wires Operating at the Vicinity of Resonance
Bibliographic record
Abstract
Gap-waveguides made of bed-of-nails are revisited with special interest given to the maximum practically achievable gap height. In the cases where the gap height is to be maximized, it is important to have excitation techniques that are also conforming to the same gap height. To achieve this, various forms of gap-waveguides are investigated. The most suitable form is found to be the groove-gap-waveguide with top/bottom excitation. This type is called here separated-gap-waveguide (SGW). The maximum theoretical gap height between the top surface of the nails and the cover plate is a quarter wavelength. However, this is achieved at the expense of usable bandwidth. A practical experiment is presented to validate this feature. To increase this theoretical limit, the SGW made of bed-of-helical wires (springs) is investigated. In this case, the frequency bandgap is found to be in the vicinity of the first resonance of the helical wires. The benefits of using bed-of-springs instead of bed-of-nails are exposed in this article. The theoretical analysis needed to justify the use of the springs despite their difficult implementation is discussed. The benefits include minimizing the height of the bed structure itself while adding the saved height to the gap height. Furthermore, the use of bed-of-springs allows the frequency of operation to be much lower compared to the use of bed-of-nails. A comparison between the two structures is presented based on experimental results.
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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 | 0.000 | 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 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".