Millimeter-Wave Broadband Monopulse Radar Antenna for Space Debris Detection
Bibliographic record
Abstract
In today’s world, artificial satellites play critical roles in communication, navigation, weather monitoring, and scientific research. However, the increasing presence of space debris poses a significant threat to their functionality and safety. This debris includes defunct satellites, rocket stages, and fragments from collisions with other objects, all orbiting Earth at high speeds. Such debris particles can collide with operational satellites, potentially causing damage or destruction. Such collisions could result not only in financial losses, but could even have other far-reaching ramifications. To address this challenge, this article introduces a novel thin mm-wave monopulse antenna system designed to detect space debris during satellite missions. This system compares amplitude and phase signals received through both sum and difference beams, enabling effective space debris detection. It consists of a tapered slot antenna integrated with a mm-wave rat-race design on parallel transmission lines, implemented on a PCB with a thickness of 0.17 mm. The proposed rat-race has a quadruple arm structure, which holds a 90° electrical length on each arm, unlike the conventional rat-race. With the proposed rat-race, sum and difference signals are generated over a broad frequency range. Based on the measurement results, the proposed monopulse antenna covers a frequency spectrum of 22–42 GHz.
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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".