Frequency-domain reconfigurable antenna for COVID-19 tracking
Bibliographic record
Abstract
The COVID -19 outbreak since inception has put the whole world in an unprecedented difficult situation by bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19's spreading across 212 countries globally, an increasing number of infected cases and death tolls rose to 146,841,882, and 3,104,743 (as of April 26, 2021), this remains a real threat to the public health system. This paper presents a novel design for the frequency-domain reconfigurable antenna at Ku and K-bands for satellite-internet of thing (IoT) tracking applications. Four reconfigurable antenna is proposed with the use of four different switch mechanisms. Furthermore, switches are used to change resonance frequency to Ku- and K-bands on the antenna surface with four stages. With the help of the 3D electromagnetic computer simulation technology (CST) studio suite, we model the proposed antenna, perform the simulation with a frequency-domain solver, and validate the results with a time-domain solver with both results obtained in agreement as the proposed reconfigurable antenna operates over a wide frequency range for the satellite-IoT network to track COVID-19 pandemic.
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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) | 0.002 | 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".