Configuration of atmospheric radars – antennas, beam patterns, electronics, and calibration
Bibliographic record
Abstract
Introduction In earlier chapters, we have discussed radars in a general sense, and dealt with some of the techniques available to optimize signal detection. We have discussed the conceptual difference between CW and pulsed systems, and concepts like range resolution and sampling strategies. In this chapter, we will take a closer look at the electronics and engineering that is required to develop a radar, and the associated hardware. Key topics will include antennas, transmitters, receivers, and controllers. Some topics from the previous chapter may be repeated, but generally in greater detail. One thing that all radars have in common is a need for a transmit antenna and a receive antenna. These may or may not be located at separate sites. The transmitter transmits radiowaves through a transmitter antenna into the air, and receives echoes from a target, or from multiple targets, with the receiver antenna. When the transmitter and receiver are co-located, the radar is referred to as a “monostatic radar,” while the term “bistatic radar” refers to the case that the transmitter and receiver are physically separated. If two or more receivers which detect echoes from a common target are located at different places, the system is called a “multistatic radar.” The degree of separation can be an important factor as well – if the transmitter and receiver are within maybe a few wavelengths of each other, they may be referred to as either monostatic or bistatic, depending on the application, even though, in the strictest sense, they are bistatic/multistatic. An example is the so-called “spaced antenna method” for measuring winds, in which case there are multiple receiver antennas but the theoretical development is often done in a quasi-monostatic sense. Generally, if the separation between the transmitting and the receiving antennas can be neglected compared with the distance to the target, the system is considered as monostatic, although even then the meaning of “small” and “large” distances depends on the objectives of the experiment. Experiments requiring detailed phase information between receivers may need to be considered multistatic, whereas if no phase information is needed the same configuration might be considered as monostatic, for example.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.015 |
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".