Compact low‐pass/band‐pass filters based on quarter‐mode substrate integrated waveguide and a connected network of CSRRs
Bibliographic record
Abstract
In this study, a compact low‐pass filter (LPF) and two compact dual‐wideband band‐pass filters (BPFs) are proposed based on a connected network of complementary split‐ring resonators (CSRRs) and open CSRRs (OCSRRs). The LPF is designed based on microstrip structure, and then the structure is changed to quarter‐mode substrate integrated waveguide for designing the dual‐band BPFs. The 3 dB cutoff frequency of the LPF is 1.84 GHz and its size is 0.094 λ g × 0.094 λ g , where λ is the guide wavelength at 3 dB cutoff frequency. The LPF has a wide stop‐band, up to 16 GHz. The first proposed dual‐wideband BPF has the centre frequencies of 3.85 and 8.03 GHz, with 3 dB fractional bandwidths of 31.69 and 13.7%, respectively. Finally, a sharper dual‐band BPF is obtained by cascading two proposed dual‐wideband BPFs and optimising them for the desirable response. The measured centre frequencies of the second dual‐band BPF are 3.65 and 7.15 GHz with 3 dB fractional bandwidths of 28.21 and 12.52%, respectively. The measurement results of the fabricated filters are in good agreement with the simulation ones. The filters are compared with similar reported filters in the literature, which confirm the compactness of the proposed filters..
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".