The Sound the Air Makes: High-Performance Tunable Filters Based on Air-Cavity Resonators
Bibliographic record
Abstract
Tunable filters have a wide range of applications from software-defined radio to reconfigurable satellite payloads. They are a key building block for any flexible transceivers. A variety of tunable filter technologies can be found in the literature. Examples include: planar tunable filters employing solid-state or microelectromechanical systems (MEMS) varactors [1]-[6], and ferroelectric variable capacitor tuned coaxial filters [7]. The choice of technology is driven by the application. In this article, we focus on applications requiring high performance, including low loss, high-power handling capability, and high stability, mainly for communications satellites or wireless base stations. These requirements immediately rule out any low-quality factor (Q) technologies. For instance, besides low Q, planar-type tunable filters typically suffer from poor selectivity and transmission-response variation over the tuning range. Technologies based on substrate-integrated-waveguide (SIW) offer better Q than microstrip circuits and advantage in packaging [8]-[10]. However, in most cases, their Q is comparable to strip-line circuits with the same volume. Air-cavity resonators, on the other hand, offer high-Q in the range of thousands to tens of thousands and high-power handling and are therefore one of the obvious choices. The addition of each requirement, such as power, selectivity, vibration, and temperature stability, further limits available choices. We are not aware of an existing technology that satisfies all these requirements. The search for a viable solution for the targeted high-end applications is indeed a difficult journey, with years of experience accumulation from past good and bad designs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".