Design of Antenna Array for Ku-Band Wireless Application
Bibliographic record
Abstract
The antenna is the back bone of communication. In recent time, it needs to communicate in many ways along with different types of data such as voice, video, text etc. A long distance communication satellite needs a seamless transmission. The antenna design for satellite communication is to be array type to avoid the communication failure. This insists to work with array antenna to fulfil the seam less communication through satellite. Further the antenna design depends on the geometry of the patch, the placement of the patch for better gain and bandwidth. In this paper, authors have chosen the circular patch due to its single degree of freedom. The antenna is compact and is of 30X30mm2 where substrate thickness is considered as 1.6mm. The central patch is of rectangular shape with two slots. Slot is made because of better bandwidth. It is cut diagonally at the corner. As a result, the bandwidth is increased to 2.4GHz with a gain of 5.68dB. The substrate is considered to be FR4 Epoxy. The proposed design satisfies the compactness along with the satellite communication band with satisfactory gain. Simulation results compromise with the measured value. A 3X3 array of circular parasitic elements is considered. It is found that the antenna performs well at 14GHz that is meant for Ku band. The radius of the parasitic patch is considered as 4mm to satisfy the less space with good performance.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".