Related Experiment Video
Updated: Jan 27, 2026
06:43
Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band
Published on: May 2, 2018
7.4K
Parametric simulation dataset of a 2.4 GHz patch antenna with slot for AI-based S11 prediction
Ameni Mersani1,2, Kawther Mekki1, Omrane Necibi1
1University of Tunis El Manar, Faculty of Sciences Tunisia, Microwave Electronics Research Laboratory LR18ES43, 2092 Tunis, Tunisia.
Data in Brief
|January 26, 2026
Summary
This dataset offers over 55,000 microstrip patch antenna simulations near 2.4 GHz. It aids machine learning for antenna design, performance prediction, and optimization in wireless and IoT applications.
Area of Science:
- Electromagnetics and Radio Frequency Engineering
- Computational Intelligence and Machine Learning
Background:
- Microstrip patch antennas are crucial for wireless communication, operating in key frequency bands like 2.4 GHz.
- Efficient antenna design and optimization are vital for the growing Internet of Things (IoT) sector.
- Developing robust machine learning models requires comprehensive datasets for antenna performance prediction.
Purpose of the Study:
- To present a large-scale dataset of simulated microstrip patch antenna performance.
- To provide a resource for benchmarking and developing AI-driven antenna design tools.
- To facilitate advancements in antenna optimization, including impedance matching and bandwidth enhancement.
Main Methods:
- Generated over 55,000 simulation samples using CST Microwave Studio.
- Conducted parameter sweeps and variation studies across diverse geometric configurations.
- Recorded S11 reflection coefficient (return loss) values in decibels for each simulation sample.
Main Results:
- A comprehensive dataset mapping antenna geometry to S11 reflection coefficient is established.
- The dataset covers a wide range of design possibilities for antennas operating near 2.4 GHz.
- The data is suitable for training and validating machine learning models for antenna applications.
Conclusions:
- The dataset is a valuable resource for RF and microwave engineering research, particularly in AI-driven antenna design.
- Future releases will include experimental validation and expanded parameters like impedance for enhanced model robustness.
- This work supports the advancement of automated antenna design and optimization processes.
Related Concept Videos
01:02Long-patch Base Excision Repair
7.9K
Since the discovery of the two BER pathways, there has been a debate about how a cell chooses one pathway over the other and the factors determining this selection. Numerous in vitro experiments have pointed out multiple determinants for the sub-pathway selection. These are:
7.9K
01:15The Antenna Complex
7.7K
Plants and other photosynthetic organisms comprise pigments capable of absorption of direct sunlight. These pigments are present in the reaction center - the main site of photochemical reactions as well as in the antenna complex. Under average light conditions, the rate at which reaction center pigments absorb light is far below the electron transport chain's capacity. As a result, the reaction center alone cannot provide enough energy to drive photosynthesis. The photosynthetic efficiency can...
7.7K
02:27Predicting Molecular Geometry
45.6K
VSEPR Theory for Determination of Electron Pair Geometries
45.6K
01:03Prediction Intervals
3.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
3.3K
01:18Patch Clamp
6.4K
Many fundamental cell functions such as muscle contraction and nerve transmission rely on the electrical signals produced by the movement of positively and negatively charged ions across the cell membrane. One competent method to record current flowing across the whole cell or single ion channel is the patch-clamp technique.
In this method, a glass micropipette containing electrolyte solution is tightly sealed against a small portion of the cell membrane. As a result, a patch of the cell...
In this method, a glass micropipette containing electrolyte solution is tightly sealed against a small portion of the cell membrane. As a result, a patch of the cell...
6.4K
01:07End Point Prediction: Gran Plot
1.2K
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
For potentiometric titration, the Gran plot is created by plotting...
1.2K