# Artificial Intelligence-Empowered Doppler Weather Profile for Low-Earth-Orbit Satellites
## 2 Methodology
### AI Model Framework for Doppler Weather Profile Analysis  
Selecting the radar mode (which can be selected as either \"uniform\" or \"staggered\") and radar wavelength for both satellites, as discussed earlier, synthetic weather data were generated to evaluate the impact of Doppler as outlined by the authors [29]. The inputs required for the data include power levels, spectrum widths, mean Doppler velocity, noise power, number of samples within a coherent processing interval (CPI), sequence length, pulse repetition frequency, list for the staggered sequence (default setting is (2 or 3), samples factor required for window effects (default setting is 10), and the number of realisations.  
Without specific guidelines, the data were initially split into a training set comprising 75%, a validation set comprising 15%, and a testing set containing 10%. The models were programmed using Python version 3.7 and MATLAB version R2023a for Windows. We have used MATLAB's SatCom Toolbox programming languages and utilised freely available open-source libraries such as numpy, pandas, matplotlib, sci-kit-learn, TensorFlow, and optuna. We conducted model training across various architectures, utilising both the Graphics Processing Unit (GPU) and Central Processing Unit (CPU) to assess and compare  
Figure 4: Carrier frequency offset on the \(X\)-axis with the error in decibels on the \(Y\)-axis. The red trajectory shows theoretical values, and simulated values are represented in green.  
the efficiency of training and prediction times. The models were constructed using the Windows 10 operating system on an Intel i7 Generation 9 processing unit clocked at 3.7 gigahertz, with 16 gigabytes of memory and an Nvidia 1050 Ti GPU, 4.  
Our research involves an in-depth analysis of three unique machine and deep learning architectures through training and assessment using a simulated dataset. We are specifically investigating the effects of different training factors, such as signal-to-noise ratios, quantisation levels, and sequence lengths, on the precision of estimation. Furthermore, we are assessing the ability of each architecture to handle potential receiver scenarios in terms of scalability.  
The research entails the creation of intricate models for LEO constellation satellites, analyzing the connectivity between satellites and ground stations, assessing latency, and developing a hybrid and computationally efficient model integrating Iterative Input Selection-Long Short-Term Memory Network (IIS-LSTM). The primary goal of this model is to accurately forecast the Doppler profile for the weather data of LEO satellites with a high degree of accuracy and calculate the time delay, Doppler and frequency shift between the satellites and the ground stations. Our thorough evaluation takes into account both the Doppler rate and the Doppler shift to assess the influence of each on packet loss.  
The satellite timeframe for this analysis spans from 1 January 2024, starting precisely at 12:00:00 AM Australian Eastern Standard Time (AEST), for 24 h. The simulation operates at a real-time sampling frequency of 1 min. In this analysis, a C band frequency of 5 GHz has been utilised for measurement purposes. The Globalstar terrestrial band offers a bandwidth of 11.5 MHz, while the ISS NAUKA satellite operates at a frequency of 145.8 MHz.