Band-Reconfigurable Tunable Bandpass Filters Based on Mode-Switching Concept
Bibliographic record
Abstract
This article presents an approach to the development of band-reconfigurable tunable bandpass filters (BPFs) using a mode-switching concept. The key to realize this feature is that the first higher-order mode TE201 can be switched to the diagonal modes TE201 and TE102 using tuning elements in a square waveguide cavity. A pair of tuning elements are deployed along the diagonal line of a cavity while the other pair are arranged at its anti-diagonal line. If the two pairs of tuning elements have the same depths in the cavity, the first higher-order mode TE201 can be formed, and its resonant frequency can be tuned effectively. If the tuning elements have different depths, the TE201 mode can be switched to the diagonal modes TE201 and TE102, whose resonant frequencies can be tuned individually. Accordingly, the corresponding tunable BPF can be reconfigured between single- and dual-band states. Moreover, a flexible number of transmission poles can also be implemented in the dual-band state. For experimental verifications, two band-reconfigurable tunable BPFs are designed, fabricated, and measured in Ku-band. The first design has 2 and 2+2 poles in the single- and dual-band states, respectively, whereas the second design introduces one transmission zero (TZ), which has 3 and 3+2 poles in the single- and dual-band states, respectively. Regardless of single- and dual-band responses, each band can be frequency-tuned.
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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) | 0.001 | 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".