Review of Patch Antennas used in Drone Applications
Bibliographic record
Abstract
Drones are a form of remote-controlled aircraft that can take to the air without the need for a human pilot. An increasing number of people are looking into using drones for a variety of tasks, including but not limited to operations in hazardous areas, environmental monitoring and sensing, aerial spreading of fertilizer and agricultural chemicals, disaster management and transporting goods from one location to another. Drones are receiving a lot of attention for these and other uses. A drone’s position and navigation can only be controlled by the remote pilot via radio frequency (RF) transmission between the drone and the remote pilot. In order to facilitate two-way communications between the drone and its operator, it is necessary to keep a tight eye on the telemetry data and supplementary sensor data being sent and received by the drone in real time. Unmanned flying would not be conceivable without a flexible and dependable communication system. Due to the necessity of complete spatial coverage for drone communication, the antenna radiating in an isotropic pattern presents itself as a promising option for unmanned flying. Therefore, microstrip patch antenna is an excellent choice for drone applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".