High-Frequency Radars: Beamforming Calibrations Using Ships as Reflectors*
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
Abstract Linear array antennas and beamforming techniques offer some advantages compared to direction finding using squared arrays. The azimuthal resolution depends on the number of antenna elements and their spacing. Assuming an ideal beam pattern and no amplitude taper across the aperture, 16 antennas in a linear array spaced at half the electromagnetic wavelength theoretically provide a beam resolution of 3.5° normal to the array, and up to twice that when the beam is steered within an azimuthal range of 60° from the direction normal to the array. However, miscalibrated phases among antenna elements, cables, and receivers (e.g., caused by service activities without recalibration) can cause errors in the beam-steering direction and distortions of the beam pattern, resulting in unreliable ocean surface current and wave estimations. The present work uses opportunistic ship echoes randomly received by oceanographic high-frequency radars to correct an unusual case of severe phase differences between receiver channels, leading to a dramatic improvement of the surface current patterns. The method proposed allows for simplified calibrations of phases to account for hardware-related changes without the need to conduct the regular calibration procedure and can be applied during postprocessing of datasets acquired with insufficient calibration.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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