Bibliographic record
Abstract
Tunable dielectric resonator filters can potentially address wireless and satellite applications that require very high Q values (4,000 and up) with a limited tuning range (less than 15%). Such high Q requirements cannot be met by any other known non-superconductor tunable filter technology at the present time. The intent of this paper is to provide newcomers and end users with the current status and prospective of using dielectric resonators for tunable filters. It is an enabling technology for high-Q tunable filter applications. A key challenge, however, is to increase the tuning range without degrading the Q value. While several techniques have been reported to demonstrate the feasibility of tuning dielectric resonators, the tunable dielectric resonator filter technology is still in its infancy. Very limited research effort has been dedicated to explore the potential for improving the tuning range. Most of the work reported thus far has focused on the use of TE01deltamodes and standard shape resonators demonstrating a narrow tuning range. We believe that the tuning range can be increased while maintaining reasonably high Q values by exploring the use of other modes and by the use of non-standard-shape dielectric resonators.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".