Bibliographic record
Abstract
Planar near-field measurement has proved to be a very appropriate technique to verify the performance of large spaceborne antennas. However it is also often impossible to redo the near-field measurement of the antenna after its integration with the spacecraft due to the size, volume and mass involved. Whenever it is possible to deploy the antenna in the integration room, after its integration with the spacecraft, limited tests might be possible on the antenna such as signature tests of the radiation pattern. What is proposed here is a technique that would allow the full reconstruction of the radiation characteristics of the antenna from a limited near-field measurement data set performed with a portable scanner. This consists of measuring the field on a small scan area which in principle would not be sufficient to allow the reconstruction of the full characteristics of the far-field. However by using the information obtained from a complete near-field measurement of the antenna, before its integration, it is possible to recover the full information about the far-field of the integrated antenna. An experiment was performed on a small linear array antenna to simulate the technique. A full near-field measurement of the antenna was performed and processed, then after displacement of the antenna, a second measurement was done on a smaller scan area. A technique named "near-field measurement deconvolution" is applied to recover the complete far-field characteristics of the antenna from the small scan area.
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How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".