Update README.md

README.md

@@ -103,12 +103,24 @@ Application: Used for improving the accuracy of GNSS/GPS positioning by predicti
 
 **Technical**
 
--Synthetic Generation: The data is algorithmically generated, likely using a simplified physics-based simulation or a Generative Adversarial Network (GAN) to ensure it mirrors real-world radar and satellite observations.
--Multivariate Structure: It typically contains variables representing:
--Spatial Coordinates: Latitude, longitude, and altitude.
--Temporal Data: Timestamps reflecting diurnal (day/night) cycles.
--Physical Parameters: Electron density, magnetic field orientation, and solar flux indices (e.g., F10.7 index).
--Format: Distributed as a tabular dataset (often in .csv or .parquet formats) to be compatible with common machine learning frameworks like PyTorch or TensorFlow.
+-Synthetic Generation:
+The data is algorithmically generated, likely using a simplified physics-based simulation or a Generative Adversarial Network (GAN) to ensure it mirrors 
+real-world radar and satellite observations.
+
+-Multivariate Structure:
+It typically contains variables representing:
+
+-Spatial Coordinates: 
+Latitude, longitude, and altitude.
+
+-Temporal Data:
+Timestamps reflecting diurnal (day/night) cycles.
+
+-Physical Parameters: 
+Electron density, magnetic field orientation, and solar flux indices (e.g., F10.7 index).
+
+-Format:
+Distributed as a tabular dataset (often in .csv or .parquet formats) to be compatible with common machine learning frameworks like PyTorch or TensorFlow.