<title>Generation of Bessel limited-diffraction beams with hexagonal sparse arrays</title>
Bibliographic record
Abstract
The transducer driving function for Bessel beam has circular symmetry and can be generated by annular or 2-D arrays. In 2-D array, the elements are divided into a number of rings. Taking advantage of the circular symmetry, it was shown that arranging the elements in a hexagonal pattern instead of ordinary rectangular pattern could produce almost the same field pattern with 14% less elements. Our aim here is to eliminate some of the elements of the hexagonal array and obtain a hexagonal sparse array while maintaining the quality of the generated field. In our proposed method, starting from the outer most ring, a specific number of the elements of the ring are randomly selected and turned off. The field pattern of the resulting sparse arrays is simulated and compared to the field of the array with all of its elements active. If the relative mean square error is lower than a specific threshold value, more elements of the ring are turned off. This procedure is then repeated for the next ring until reaching the central ring. Our simulations for hexagonal sparse arrays show that for an error threshold of 4%, an acceptable Bessel beam can be generated only with 22% of the transducer elements used in the original hexagonal arrays. Generated beam still shows its non-diffracting property over a limited distance.
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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) | 0.000 | 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) | 0.004 | 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".