Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article focuses on the design methodologies and performance optimization strategies of dual-band DPA. The state of art in dual-band DPA has been demonstrated with further considerations for optimizing overall performance. Two main optimization strategies are applied: using dual-band phase offset as an all-pass filter and unequal-power division at the input with distinct power division ratio at two frequencies of operation. The performance improvement with these optimization strategies is demonstrated with a case study of a dual-band DPA operating at 1.96 GHz and 3.5 GHz. The use of stub-loaded dispersive structures has been elaborated on in the design of various dual-band components employed in the dual-band DPA architecture. The dual-band DPA architecture, in theory, can be achieved from direct replacement of each single-band component with conventional dual-band components. In practice, however, several optimization strategies are needed to enhance the performance of the designed dual-band DPA. This article elaborated on some of the optimization strategies with appropriate design examples to demonstrate the use fulness of these optimization strategies.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it