Update README.md

README.md

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 # FedJam Dataset
 
-**Multimodal Dataset for Federated Jamming Detection**
 
-The **FedJam dataset** is a multimodal dataset designed for **jamming detection and classification in wireless networks**, pairing **time–frequency spectrogram images** with **cross-layer network KPI time series**. The dataset is designed to support **multimodal learning**, **federated learning**, and **robust classification under heterogeneous data distributions**.
+The **FedJam dataset** is a **multimodal dataset** designed for jamming detection and classification in wireless networks, pairing **time–frequency spectrogram images** with **cross-layer network KPI time series**. The dataset is designed to support **multimodal learning**, **federated learning**, and **robust classification under heterogeneous data distributions**.
 
 Each sample contains **aligned vision and time-series modalities**, enabling joint modeling of physical-layer signal characteristics and network-layer performance indicators.
 
@@ -62,7 +61,6 @@ Each dataset sample contains:
   - `1`: Jamming type A  
   - `2`: Jamming type B  
   - `3`: Jamming type C  
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 ## Loading the Dataset
@@ -96,5 +94,4 @@ If you use this dataset, please cite:
 I. Panitsas, I. Ofeidis, and L. Tassiulas,
 “FedJam: Multimodal Federated Learning Framework for Jamming Detection,”
 arXiv:2508.09369 [cs.NI], 2025. doi:10.48550/arXiv.2508.09369.
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