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Metamaterial-Antennas

Metamaterial-Antennas

active ARFF Database: Open Database, Contents: Original Authors Visibility: public Uploaded 24-03-2022 by Dustin Carrion
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  • Computer Systems Machine Learning
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Context Microstrip Antennas are low-profile antennas applied in high-performance aircraft, spacecraft, satellite and missile applications, where size, weight, performance, ease of installation and aerodynamic profile are constraints. Civilian applications includes mobile communication and wireless connection. Metamaterials are specific dispositions of unit cells with peculiar electrical properties. These materials present properties that doesnt exist on nature. Because his dispositions, electrical permissivity and magnetic permeability are negatives. This produces negative refraction, which makes the eletromagnetic radiation behave diferent from expected. Inspiration In order to optimize antenna projetcs, why not apply ML?

13 features

Wmnumeric2 unique values
0 missing
W0mnumeric4 unique values
0 missing
dmnumeric4 unique values
0 missing
tmnumeric2 unique values
0 missing
rowsnumeric3 unique values
0 missing
Xanumeric6 unique values
0 missing
Yanumeric4 unique values
0 missing
gainnumeric572 unique values
0 missing
vswrnumeric569 unique values
0 missing
bandwidthnumeric183 unique values
63 missing
snumeric572 unique values
0 missing
prnumeric569 unique values
0 missing
p0numeric568 unique values
0 missing

19 properties

572
Number of instances (rows) of the dataset.
13
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
63
Number of missing values in the dataset.
63
Number of instances with at least one value missing.
13
Number of numeric attributes.
0
Number of nominal attributes.
0.02
Number of attributes divided by the number of instances.
100
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
11.01
Percentage of instances having missing values.
Average class difference between consecutive instances.
0.85
Percentage of missing values.

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