Grating Lobe Reduction in a Phased Array of Limited Scanning
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Bibliographic record
Abstract
Amplitude and phase weighting at the subarray outputs alone causes grating lobes (GLs) in the array factor. A combined approach to disrupt the periodicity in the array is proposed to reduce the GLs. In this approach, three measures are simultaneously used. They are: (1) the optimized amplitude weighting at the subarray ports; (2) using the random subarray; and (3) the random staggering of the rows. The optimization was carried out by using genetic algorithms (GAs). The 24 (along X) times 32 (along Y) phased array was designed to verify the proposed approach. Along Y, the scan range is (-10deg, +10deg) and the subarrays are used. Along X, the scan range is (-45deg, +45deg) without using the subarrays. The comparison was done through array factor. In the whole two dimensional scan space, the simulated results show that the GL is -3.77 dB when using the conventional array, -4.28 dB when using (1) alone, -11 dB when using (2) alone, -14 dB when using (3) alone, -20 dB when using the combined approach proposed in this paper. This approach can be used for a phased array with limited scanning and for the digital beamforming antenna array with adaptive nulling.
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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 | 0.000 |
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