Squint-free beamforming in series-fed antenna arrays using synthesized non-foster elements
Bibliographic record
Abstract
A method for squint-free arbitrary-angle broadband beamforming in series-fed antenna arrays is introduced. This method originates from the idea of loading a feedline with non-Foster elements. This type of loading reduces the per unit length inductance and capacitance of the transmission line and a fast-wave non-dispersive propagation, required for squint-free beamforming in series-fed antenna arrays, is obtained. Importantly, the challenges associated with traditional implementations of non-Foster reactive elements (e.g. stability) are circumvented by introducing a new methodology using negative-group-delay (NGD) networks. This method is established by showing that non-Foster reactive elements and NGD networks influence propagating waves in a similar manner. Subsequently, a series feeding network for linear antenna arrays is designed by loading a host transmission line with loss-compensated NGD networks. In summary, this paper introduces a new method for synthesizing stable non-Foster reactances, using NGD networks, which is utilized to present a solution to the beam squinting problem in series-fed antenna arrays.
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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) | 0.001 | 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) | 0.002 | 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".