Bibliographic record
Abstract
Following a brief review of different types of multiplexer configurations, a systematic design approach has been outlined for the design of manifold-coupled multiplexers. The piecewise approach, optimizing parts of the multiplexer separately in repeated cycles while converging upon an optimal solution, has proved to be very effective for most practical applications. The technique is readily applicable to manifold multiplexers incorporating an arbitrary number of channels, regardless of their bandwidths and channel separations. There are no restrictions on the design and implementation of channel filters onto the manifold; they may be asymmetric, and may incorporate transmission zeros, group delay equalization zeros, or both. The manifold itself is a transmission line, be it a coaxial line or a rectangular waveguide or some other low-loss structure. The costly EM simulation is used economically on manifold junctions and channel filters through the use of space-mapping optimization techniques, where EM-based simulators are used to fine-model each multiplexer channel and coupling matrix representation is used to coarse-model the performance. Fine details such as tuning screws may be included in the design process. This design procedure takes into account the effects of dispersion and spurious modes and, as a result, the overall design and final tuning time can be significantly reduced.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".