# Artificial Intelligence-Empowered Doppler Weather Profile for Low-Earth-Orbit Satellites
## 5 Limitations and Future Research Work  
Despite progress in the field, there are several obstacles and outstanding concerns regarding the effective deployment of these technologies on a widespread basis. In circumstances involving vast areas, remote or inaccessible locations, or hazardous environments, the capability to enable reliable communication without depending on current infrastructure offers a substantial benefit of low-power and long-range technologies. It is imperative to develop a comprehensive strategy for future LEO connectivity that provides a detailed outline of both the system and technology components involved in space networking and communications. Additional enhancements may be incorporated into the existing study analysis outlined within this document. Implementing AI techniques for satellite communication parameters optimisation is key to developing efficient low-power, long-range network infrastructures for the future. It is also important to consider comprehensive network management strategies and the seamless integration of these networks into existing IoT ecosystems in this rapidly changing environment. By taking advantage of the robust signal reception of LEO satellite signals and leveraging the radio environment-reshaping capability of RISs, the integration of these two cutting-edge technologies paints a compelling picture of a future where localisation services exceed the current limitations. Given the intricate computational demands associated with beamforming designs for reconfigurable intelligent surface (RIS) units, it is prudent to explore future studies about energy-efficient methodologies that incorporate artificial intelligence (AI) augmentation. Analyzing LoRa Doppler with noise and sensitivity presents a promising opportunity for future exploration. It is essential to recognise the lack of a specific approach for addressing the Doppler shift and rate, requiring additional research. A noteworthy consideration in the deployment of micro Doppler radar systems is the implementation of a federated learning strategy. Current research efforts, including those conducted by the authors [35; 36], are actively exploring this area. It is proposed that a unified convolutional neural network (CNN) model could be employed across all distributed radar units (referred to as clients). This model would be trained utilizing a federated learning approach, leveraging a wireless backhaul connection to the central server in future investigations. Another future direction can be developing solutions for a challenging situation when satellites are moving in opposite trajectories, resulting in the highest achievable Doppler shift. This scenario has the potential to create difficulties in sustaining consistent satellite communication, potentially resulting in data loss or system malfunction. Therefore, future research needs to focus on developing strategies to mitigate this Doppler shift and guarantee reliable communication. E.S. : Conceptualisation; Data curation; Formal analysis; Investigation; Methodology; Project administration; Resources; Software; Validation; Visualisation; Writing--original draft; review & editing. R.C.D. : Resources; Supervision; Article Submission. C.P.D. : Proofreading, Writing--review and editing. B.D.C. : Proofreading, Supervision. All authors have read and agreed to the published version of the manuscript. This research was conducted by the first author from the University of Southern Queensland for National Intelligence Postdoctoral Grant (project number NIIFG-2022-009) and funded by the Australian Government. Dr [PERSON] and Prof. [PERSON] acknowledge the support and contribution of the Office of National Intelligence for the project. The study did not require any ethical approval. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The authors thank the MDPI editor, the Sensors team and all reviewers for their suggestions. The authors declare no conflicts of interest.