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title: README
emoji: 🌍
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colorTo: gray
sdk: static
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# 🚀 RFInject: Synthetic RF Interference Injection for Sentinel-1 SAR L0 Data
## 📌 Motivation
- **Radio Frequency Interference (\gls{RFI})** is a **major source of performance degradation** in modern **Synthetic Aperture Radar (\gls{SAR})** missions.
- The **Copernicus Sentinel-1 constellation** is significantly affected, with numerous studies reporting its **detrimental impact**.
- However, the **lack of standardized and reproducible datasets** has so far **limited systematic benchmarking** of RFI detection and mitigation strategies.
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## 🛠️ What RFInject Brings
**RFInject** introduces a **methodology for controlled synthetic RFI injection** into clean Sentinel-1 L0 raw bursts, enabling:
- ✅ **Reproducible benchmarking** of mitigation algorithms
- ✅ **Realistic simulation** while retaining authentic system properties
- ✅ **Full parameter control** over RFI characteristics
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## 📐 Methodology Highlights
The framework is based on a **parametric signal model**:
- 🎯 **Synthetic RFI generation** by superimposing **modulated chirp trains** onto authentic Sentinel-1 radar echoes.
- 🧠 **Spectral and statistical fidelity** ensured to reflect real operational systems.
- 📊 **Metadata-rich parameter sets** controlling:
- 📡 Waveform diversity
- 🌍 Spatial extent
- ⚡ Power scaling
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## 📂 Dataset Features
- **Clean Sentinel-1 L0 bursts** → contaminated with **controlled synthetic RFI**
- **Fully reproducible** contamination scenarios
- **Rich metadata** for systematic testing across **different algorithms** and **experimental setups**
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## 🎯 Impact and Applications
The dataset empowers researchers to:
- 🕵️♂️ **Detect** RFI more reliably
- 🛡️ **Mitigate** its impact effectively
- 🤖 Develop **learning-based solutions** for robust **RFI-resilient SAR processing pipelines**
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