Faris Abdul Kareem Khazal

Background: Satellite systems which orbit in Low Earth Orbit (LEO) are one of the innovative solutions to enable the connectivity in segregated geophysical parts of the world which are left out of the digital society, as they provide almost instantaneous, high-speed connections. Despite progress made there are barriers to deployment including lack of scalability, high costs, traffic management, and environmental vulnerability. Objective: The aim of the present study is enhancing the overall throughput and concurrently – steaking the LEO satellite networks reliable operation in various applications with usage of the effective traffic control and adaptive routing techniques meanwhile taking into account the costs and other factors. Methods: Quantitative and qualitative research was used in this study in which both theoretical and simulations of LEO satellite networks were used. The traffic engineering was done using Software-defined networking (SDN), whereas bio-inspired routing, including bee colony optimization algorithms, were evaluated for adaptive routing. Parameters like latency, throughout, packet loss, and costs that are exhibited to change with conditions like atmospheric interferences were also considered. Results: The results proved that the current latency can be cut by up to 60%, packet lost by up to 90%, with operating expenses slashed beyond 85% and resource utilization beyond 85%. Improved routing techniques improved transmission reliability over dynamic network loads; simulations have validated the environmental suitability of LEO networks. Conclusion: The article offers a coherent framework for the appropriate design of LEO satellite networks and discusses their ability to address the digital divide and guarantee economic, effective, and highly accessible computing access globally.

  Background: Quantum computing has brought in all new paradigm for computational processing providing unparallel ability for data analysis. Considering worldwide data production is expected to exceed 180 trillion zettabytes by 2025 the utilization of the conventional computing framework hampers the real-time processing of data. People consider quantum computing, which uses principles of quantum mechanics to solve problems 100 and 1,000 times faster than classical computing.   Objective: The article looks at quantum computing and its relevance to real time data analytics to determine its relevance, hence its impact, by the year 2025. It is worthwhile to emphasize the comparison of quantum algorithms with traditional approaches to dealing with extensive, data-centered workloads in various fields.   Methods: A comparison was made on quantum versus classical computing algorithms based on criteria such as, the flow rate, precision, and flexibility. Data sets provided by the finance stream, including real-time stock analysis, supply chain and logistics, genomic sequencing from the healthcare domain were used. Over 10 million simulation experiments were performed to gain trends and insights into the operational problems for quantum simulation.   Results: The study establishes differences in the efficiencies of these two approaches, with quantum algorithms speeding up particular tasks as much as a hundred times higher than classical algorithms and almost 15% of the error rate being decreased if quantum error correction modes were used. In scalability tests it was shown that quantum systems could process data sets larger than 10 terabytes with little slowdown, compared to a classical system, which reduced efficiency by as much as 30%. However, in present day quantum hardware, processing the capability is limited and problems arise with regards the error correction protocol.   Conclusion: Quantum computing, on the other hand, has an unconventional prospect of real-time data analytics to operate at high efficiency and big scale on data-bound concerns. However, much progress is required in the way of bettering coherence times and reducing exacting error rates, crucial advances for total realization of quantum potentialities by 2025