Update README.md

📡 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 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
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🧾 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
🤖 Benchmark Potential
RadPat-50K provides a standardized benchmark for:
• Training and evaluating classification & regression 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).
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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.
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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).
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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 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, Zenodo, Mendeley, IEEE Dataport, etc.,
This multi-platform availability ensures that researchers, engineers, and practitioners can easily access, reproduce, and build upon the dataset.

README.md

@@ -1,3 +1,12 @@
----
-license: cc-by-sa-4.0
----
+---
+license: cc-by-sa-4.0
+task_categories:
+- image-classification
+language:
+- en
+tags:
+- Antenna
+- DeepLearning
+- ArrayAnalysis
+pretty_name: 'Radiation Patterns '
+---