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@@ -23,7 +23,7 @@ Differentiable SGP4.
 
 ## Info
 
-This repository contains the code discussed in [this paper](https://arxiv.org/abs/2402.04830). $\partial \textrm{SGP4}$ is a differentiable version of SGP4 implemented using PyTorch. By making SGP4 differentiable, $\partial \textrm{SGP4}$ facilitates various space-related applications, including spacecraft orbit determination, covariance transformation, state transition matrix computation, and covariance propagation. Additionally, $\partial \textrm{SGP4}$'s PyTorch implementation allows for parallel orbital propagation across batches of Two-Line Element Sets (TLEs), leveraging the computational power of CPUs, GPUs, and advanced hardware for distributed prediction of satellite positions at future times. Furthermore, $\partial \textrm{SGP4}$'s differentiability enables integration with modern machine learning techniques. Thus, we propose a novel orbital propagation paradigm, ML-$\partial \textrm{SGP4}$, where neural networks are integrated into the orbital propagator. Through stochastic gradient descent, this combined model's inputs, outputs, and parameters can be iteratively refined, surpassing SGP4's precision while maintaining computational speed. This empowers satellite operators and researchers to train the model using specific ephemeris or high-precision numerical propagation data, advancing orbital prediction capabilities compared to the standard SGP4.
+This repository contains the code discussed in [this paper](https://arxiv.org/abs/2402.04830). $\partial \textrm{SGP4}$ is a differentiable version of SGP4 implemented using PyTorch. By making SGP4 differentiable, $\partial \textrm{SGP4}$ facilitates various space-related applications, including spacecraft orbit determination, covariance transformation, state transition matrix computation, and covariance propagation. Additionally, $\partial \textrm{SGP4}$'s PyTorch implementation allows for parallel orbital propagation across batches of Two-Line Element Sets (TLEs), leveraging the computational power of CPUs, GPUs, and advanced hardware for distributed prediction of satellite positions at future times. Furthermore, $\partial \textrm{SGP4}$'s differentiability enables integration with modern machine learning techniques. Thus, we propose a novel orbital propagation paradigm, $\textrm{ML}-\partial \textrm{SGP4}$, where neural networks are integrated into the orbital propagator. Through stochastic gradient descent, this combined model's inputs, outputs, and parameters can be iteratively refined, surpassing SGP4's precision while maintaining computational speed. This empowers satellite operators and researchers to train the model using specific ephemeris or high-precision numerical propagation data, advancing orbital prediction capabilities compared to the standard SGP4.
 
 ## Goals