ICEBEAR‐3D: A Low Elevation Imaging Radar Using a Non‐Uniform Coplanar Receiver Array for E Region Observations
Bibliographic record
Abstract
Abstract The Ionospheric Continuous‐wave E region Bistatic Experimental Auroral Radar (ICEBEAR) has been reconfigured using a phase error minimization and stochastic antenna location perturbation technique. The resulting 45‐baseline sparse non‐uniform coplanar T‐shaped array, ICEBEAR‐3D, is used for aperture synthesis radar imaging of low elevation targets. The reconfigured receiver antenna array now has a field of view ±45° azimuth and 0°–45° elevation at 0.1° angular resolution. Within this field of view no aliasing occurs. Radar targets are imaged using the Suppressed Spherical Wave Harmonic Transform (Suppressed‐SWHT) technique. This imaging method uses precalculated constant coefficient matrices to solve the integral transform from visibility to brightness through direct matrix multiplication. The method then suppresses image artefacts (dirty beam) due to undersampling by combining brightness maps of differing harmonic order. Measuring elevation angles of targets at low elevations with radar interferometers has been a long standing problem. ICEBEAR‐3D elucidates the underlying misinterpretations of the conventional geometry for vertical interferometry especially for low elevation angles. The proper phase reference vertical interferometry geometry is given which allows radar interferometers to unambiguously measure elevation angles from zenith to horizon without special calibration. The receiver antenna array reconfiguration, Suppressed‐SWHT imaging technique, and proper geometry for vertical interferometry are validated by showing agreement of the meteor trail altitude distribution with numerous data sets from other radars.
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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) | 0.001 | 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".