Thermal analysis, design, and implementation of an L‐band high‐power stepped impedance low‐pass filter
Bibliographic record
Abstract
Summary This paper presents high power, thermal analyses, and implementation of a stepped impedance high‐power low‐pass filter (LPF). A comprehensive model and analysis have been developed for the design and simulation of the LPF. In this analysis, power handling capacity and breakdown‐voltage are discussed, and the effects of critical points are considered. The attenuation due to conductor and dielectric losses is also studied. The novelty of our approach lies in employing theoretical analysis to estimate the power‐dissipation of the filter based on the proposed equivalent circuit. An accurate method is also introduced to calculate attenuation in the filter's elements. Thermal analysis to obtain accurate temperature profiles is done for the first time based on the electro‐thermal simulation. Consequently, an effective cooling method is used to spread heat across the entire filter. Finally, the filter was implemented and tested to operate at L‐band with handling 8 kW peak and 800 W average power. The insertion‐loss is less than 0.27 dB, the stop‐band attenuation is more than 60 dB, and the return‐loss is better than 15.7 dB. The filter is capable of tolerating produced heat without any destructive effects at a maximum temperature of about 200°C above ambient. The theoretical analysis and experimental results show that the LPF is suitable for high‐power microwave applications.
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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 | 0.001 | 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".