Update README.md

README.md

@@ -9,4 +9,203 @@ tags:
 - DeepLearning
 - ArrayAnalysis
 pretty_name: 'Radiation Patterns '
----
+---
+📡 RadPat-50K: A Large-Scale Benchmark for Antenna Array Radiation Patterns
+RadPat-50K is a large-scale synthetic dataset of 50,000 radiation patterns generated for Uniform Linear Arrays (ULA).
+Each sample includes polar plots and rectangular plots of the array factor, along with metadata for design and performance parameters.
+
+
+## Dataset Details
+📊 Dataset Highlights
+•	Size: 50,000 radiation patterns
+•	Array Elements (N): 4, 8, 12, 16, 24, 32, 48, 64
+•	Element Spacing (λ): 0.25, 0.5, 0.75, 1.0
+•	Steering Angles (°): –60, –45, –30, –15, 0, 15, 30, 45, 60
+•	Weighting Schemes: 
+o	Uniform
+o	Binomial
+o	Cosine
+o	Kaiser
+o	Hamming
+o	Hann
+o	Blackman
+o	Exponential
+•	Variations (for diversity):
+o	⚡ Amplitude noise
+o	📐 Phase noise
+o	🎯 Steering jitter
+________________________________________
+
+
+🔬 Applications
+This dataset is well-suited for research in machine learning, deep learning, and signal processing, including:
+•	📑 Antenna pattern classification
+•	🎛️ Beamforming analysis
+•	🚨 Grating lobe detection
+•	🏗️ Data-driven array design
+________________________________________
+🧾 Metadata per Record
+Each radiation pattern entry includes:
+•	Antenna parameters: number of elements, element spacing, steering angle, weighting scheme
+•	Noise parameters: amplitude noise, phase noise, steering jitter
+🧾 Performance Metrics
+•	📡 Directivity
+•	📏 Half-Power Beamwidth (HPBW)
+•	📐 Main Lobe Angle
+
+________________________________________
+
+
+
+
+### Dataset Description
+
+📂 Dataset Description
+The RadPat-50K dataset is organized into three primary files for ease of experimentation:
+•	📁 Rect_50K.zip → Contains 50,000 rectangular plot images of antenna array radiation patterns.
+•	📁 Polar_50K.zip → Contains 50,000 polar plot images of antenna array radiation patterns.
+•	📑 Metadata_50K.csv → A structured metadata file providing detailed information for each sample.
+📊 Metadata Contents
+Each record in Metadata_50K.csv includes:
+•	id → Unique identifier for each sample
+•	N → Number of array elements
+•	spacing_wavelengths → Element spacing in wavelengths
+•	weights → Applied weighting scheme
+•	steering_deg_nominal → Nominal steering angle (in degrees)
+•	steering_deg_noisy → Steering angle with noise (in degrees)
+•	amp_noise_std → Standard deviation of amplitude noise
+•	phase_noise_std → Standard deviation of phase noise
+•	grating_lobe → Presence of grating lobe (True / False)
+•	image_rect → File name of the corresponding rectangular plot
+•	image_polar → File name of the corresponding polar plot
+•	D_peak_dBi → Peak directivity (in dBi)
+•	HPBW_deg → Half-Power Beamwidth (in degrees)
+•	main_lobe_angle_deg → Main lobe angle (in degrees)
+
+⚡ Quick Experimentation Subset (RadPat-10K)
+For rapid prototyping and experimentation, we provide a 10K subset of the full dataset.
+This subset maintains the same structure, variations, and metadata fields as the full 50K dataset, but with fewer samples for faster loading and reduced storage needs.
+📂 Files Included
+•	Rect_10K.zip → 10,000 rectangular plot images
+•	Polar_10K.zip → 10,000 polar plot images
+•	Metadata_10K.csv → Metadata corresponding to the 10K subset
+🔎 Use Cases
+•	Ideal for notebook demonstrations (Kaggle, Colab, Jupyter)
+•	Quick experimentation with classification, regression, or beamforming analysis
+•	Reduces compute/storage requirements while preserving dataset diversity
+To ensure transparency, accessibility, and wider reach, the RadPat-50K dataset is hosted across multiple trusted public repositories:
+•	📊 Kaggle – for seamless integration with notebooks and ML workflows
+•	🤗 Hugging Face Datasets – for direct use in deep learning pipelines
+•	📡 IEEE Dataport – for the research and engineering community
+•	📑 Mendeley Data – for academic visibility and long-term preservation
+This multi-platform availability ensures that researchers, engineers, and practitioners can easily access, reproduce, and build upon the dataset.
+
+
+
+
+- **Curated by:** Dr.Bhuma Chandra Mohan
+- **Funded by : ** Bapatla Engineering College
+  
+
+### Dataset Sources [optional]
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Repository:** https://huggingface.co/datasets/chandrabhuma/RadPat-50K
+
+## Uses
+
+🤖 Benchmark Potential
+•	Training and evaluating classification & regres
+RadPat-50K provides a standardized benchmark for:sion models
+•	Developing robust antenna array designs under noise conditions
+•	Advancing AI-driven RF and antenna research
+1. Classification Benchmarks
+•	Task A: Weighting Scheme Classification
+o	Input: Radiation pattern image
+o	Output: Weighting scheme label (uniform, cosine, blackman, etc.)
+•	Task B: Number of Elements Classification
+o	Input: Radiation pattern image
+o	Output: Class label (N = 4, 8, 12, 16, 24, 32, 48, 64)
+•	Task C: Spacing Classification
+o	Input: Radiation pattern image
+o	Output: Class label (d = 0.25λ, 0.5λ, 0.75λ, 1.0λ)
+•	Task D: Joint Classification
+o	Input: Radiation pattern image
+o	Output: Multi-task prediction (N, spacing, weighting, steering angle category).
+________________________________________
+2. Regression Benchmarks
+•	Task E: Directivity Prediction
+o	Input: Radiation pattern image
+o	Output: Directivity (linear or dB).
+•	Task F: HPBW Prediction
+o	Input: Radiation pattern image
+o	Output: Half Power Beamwidth in degrees.
+________________________________________
+3. Multi-Label / Structured Prediction
+•	Task G: Parameter Recovery
+o	Input: Radiation pattern image
+o	Output: A set of antenna parameters (N, spacing, weighting scheme, steering angle).
+________________________________________
+4. Vision-Language Benchmarks (VQA-style)
+•	Task H: Antenna Q&A
+o	Input: (Image + Question)
+o	Example Qs:
+	"What is the main lobe direction?"
+	"Which weighting scheme is applied?"
+	"How many array elements are used?"
+o	Output: Answer (text).
+•	Task I: Captioning
+o	Input: Radiation pattern image
+o	Output: Caption like
+"16-element array, 0.5λ spacing, uniform weighting, steered to 30° with gain ≈ 12 dB and HPBW ≈ 14°."
+ 
+
+
+
+## Dataset Structure
+
+### Curation Rationale
+
+There is no dataset on Antenna Radiation Patterns Analysis and Synthesis using Machine Learning and Deep Learning techniques.
+This is the first large scale dataset consisting of 50K radiation patterns in both polar and rectangular format.
+
+
+
+
+#### Data Collection and Processing
+
+The RadPat-50K dataset can be regenerated from first principles using standard array factor formulations. The following procedure was used:
+1.	Environment Setup
+Install Python (≥3.9) along with the required packages: numpy, scipy, matplotlib, and pandas.
+2.	Array Configuration
+A uniform linear array (ULA) was considered with varying numbers of elements and inter-element spacings. The number of elements was selected from
+N = {4, 8, 12, 16, 24, 32, 48, 64}.
+Inter-element spacings were chosen from values such as 0.25λ, 0.5λ, 0.75λ, and 1λ.
+Steering angles were selected from −60°, −45°, −30°, −15°, 0°, 15°, 30°, 45°, and 60°.
+3.	Weighting Schemes
+Multiple excitation tapers were applied to control sidelobe levels and beamwidth. The following weighting functions were used: uniform, binomial, cosine, Kaiser, Hamming, Hann, Blackman, and exponential.
+4.	Pattern Computation
+For each configuration, the array factor was computed and radiation patterns were generated in both rectangular (dB vs. angle) and polar coordinates. Derived antenna parameters such as gain, directivity, and half-power beamwidth (HPBW) were extracted. Sidelobe levels (SLL) were not included.
+5.	Variations for Diversity
+To simulate practical conditions, additional variations were introduced:
+o	⚡ Amplitude noise
+o	📐 Phase noise
+o	🎯 Steering jitter
+For these variations, only CSV metadata files were generated (no images).
+6.	Data Generation
+A complete sweep over all combinations of array elements × steering angle × element spacing × weighting scheme was performed. The resulting radiation patterns were stored as JPG images (750×750 resolution), while the corresponding numerical attributes were saved in CSV files.
+
+
+
+#### Who are the source data producers?
+
+1.  Dr.Bhuma Chandra Mohan, Professor, ECE Dept, Bapatla Engineering College, Bapatla.
+2.  Dr.Pallaviram Sure, Professor, Department of Electronics and Communications Engineering, Ramaiah University of Applied Sciences
+3.	Dr.P.Surendra Kumar, Associate Professor, ECE Dept, Bapatla Engineering College, Bapatla
+4.	Dr.J.Chandrasekhar Rao, Associate Professor, ECE Dept, Bapatla Engineering	 College, Bapatla
+5.	Dr.U.Srinivasa Rao, Associate Professor, ECE Dept, Bapatla Engineering College, Bapatla. 
+
+## Dataset Card Contact
+
+For its usage or for any queries...contact: chandrabhuma@gmail.com