Bibliographic record
Abstract
An adaptive predistortion technique has been presented and verified through the design and fabrication of practical filters in both the C and Ku bands. The method allows the realization of microwave filters at a lower cost, lighter mass, smaller volume, and better performance with minimum insertion loss penalties.The concept of lossy filters has been presented from a practical perspective. A simple lossy synthesis technique using any synthesized lossless (nontransversal) filter was shown, which can be used with hyperbolic rotations for loss distribution. Moreover, the limitation on the minimum Q of lossy resonators has been studied using a one-pole filter as a fundamental building block. Lossy four-pole Chebyshev and quasi-elliptic synthesis examples were presented. A four- pole Chebyshev lossy filter in the Ku band has been synthesized, modeled, and fabricated successfully using mixed combline and microstrip technologies. The design has the advantage of having all input-output paths going through more than one resonator, which minimizes unwanted source-to-load coupling, especially at high frequencies. The lossy approach is still at its early stages of development and needs more research and development effort to become as mature as the predistorted filters.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".