# Artificial Intelligence-Empowered Doppler Weather Profile for Low-Earth-Orbit Satellites
## 3 Results and Discussion  
Our study involved conducting thorough simulations to analyse the computational complexity, scalability, and mean squared error performance of deep learning architectures in comparison to traditional signal processing techniques. The findings show that training with quantised data obtained from signals with signal-to-noise ratios (SNRs) between 0 and 30 dB significantly boosts the performance of deep learning estimators across the entire range of SNRs examined. We effectively detected and tracked the trajectories of two LEO satellite constellations using ground-based radar systems and conducted calculations for latency and Doppler in a satellite setting utilising LoRa modulation and analyzing suitable resolutions. Our initial assignment entailed the creation and upkeep of a comprehensive inventory of celestial bodies circling Earth, a vital component in space surveillance. The process involved the detection and categorisation of newly discovered objects, the inclusion in the inventory, the maintenance of accurate orbital information for existing objects, the continuous monitoring of orbit alterations over time, and the forecasting of potential reentries into the Earth's atmosphere. These tasks are essential to ensuring the safety of operations in outer space and mitigating the risk of collisions with other satellites or established debris. In investigating the LoRa ground SatCom link budget and scalability, while considering several channel conditions and user densities that mirror realistic application scenarios, it has been determined that LoRa modulation can establish a connection with an LEO. This can happen with a huge percentage of success of more than 97% at a distance of roughly 2300 km. Additionally, it has been found that the network can accommodate extensive connectivity. The mid-850 MHz band is less preferable than the 500 MHz band due to increased susceptibility to the Doppler effect. The significance of the lower band lies in its widespread coverage in various global regions and its proximity to less than 930 MHz band, which are crucial for coverage in countries of the northern hemisphere. This underscores the importance of the study conducted as ISS NAUKA has a downlink frequency of 631 MHz and Globalstar 500 MHz. The analysis also suggests that higher Spreading Factors, such as SF12, are more susceptible to a Doppler shift when compared to lower spreading factors in a mobile environment. As the bandwidth B decreases in Figure 10, the LoRa modulation becomes increasingly susceptible to static Doppler effects. This susceptibility is illustrated in Figure 10. The legend's colour coding effectively differentiates between correct reception with the black dots and red, green, and yellow dots indicating Doppler rate, shift, or a combination. Upon observation, it is noted that there is a loss of data packets when the elevation angle (E) is equal to or less than 40 degrees, regardless of the spreading factor (SF) utilised. Unlike SF8, which functions effectively for E angles greater than 40 degrees, dynamic Doppler affects both SF 12 and 10, which are characterised by fluctuations in frequency during the reception of data packets due to the changing Doppler shift rate. Our findings suggest that static Doppler interference poses challenges at lower altitudes, whereas dynamic Doppler interference proves more effective as the satellite approaches the target at higher altitudes. This phenomenon is linked to a reduction in the satellite's coverage range, leading to a gap in communication. As a result, the window of reliable Doppler connection decreases, potentially impeding the establishment of device communication. Our analysis includes assessments for modulation techniques commonly used in Internet of Things (IoT) standards, such as long-range (LoRa) modulation. It is worth mentioning that higher elevation angles result in a higher Doppler rate (absolute value).