Low Cost Active Antenna Arrays – Dependence on Array Configuration
Why this work is in the frame
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Bibliographic record
Abstract
Active antenna arrays are very valuable in creating reconfigurable, low-cost, wireless point-to-point networks. These wireless links can be used for a variety of applications from high-speed Internet backbone to multi-media content delivery. The key technical element to a low-cost telecommunication system is to optimize the number of antenna elements and minimize the impact that front end system parameters such as gain, temperature, and noise figure have on the overall circuit. A formulation for general active array optimization is proposed, integrating front end elements such as low noise amplifier, phase shifter, and arbitrary feed networks. A generalized G/T formulation is discussed for active arrays. Tradeoff between module gain/size, feed losses, and amplifier gains/noise figures and their effect on the G/T and SNR of different arrays is shown.
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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 | 0.001 |
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 it