This paper develops a novel communication method for an ultra-high-speed train that runs in an evacuated tube. The proposed method significantly reduces the number of needed base stations to provide adequate coverage and data rates. Moreover, the time connectivity for each base station was enhanced considerably. The proposed method can provide improvements in terms of transmitted power and received power, either fixed or variable; this method offers a fixed or variant data rate. Moreover, the paper studies the effects of the divergence angle on transmitted and received power. Additionally, the proposed communication procedure might produce a system with a fixed data rate, such as 1.25 Gbps. It can also create a design with adaptive divergence angles (that can be altered dynamically) depending on the train distance to the base station. The results show that this method is promising for working for an ultra-high-speed train that runs in an evacuated tube. It can reduce the base stations number from 500 to less than 10 base stations with respect to the data rate and power consumption. Furthermore, a new handover method is proposed and addressed in this work.

Quantum Key Distribution (QKD) is a pivotal technology in the quest for secure communication, harnessing the power of quantum mechanics to ensure robust data protection. However, scaling QKD to meet the demands of high-speed, real-world applications remains a significant challenge. Traditional key rate determination methods, dependent on complex mathematical models, often fall short in efficiency and scalability. In this paper, we propose an approach that involves integrating machine learning (ML) techniques with the Cascade error correction protocol to enhance the scalability and efficiency of QKD systems. Our ML-based approach utilizes an autoencoder framework to predict the Quantum Bit Error Rate (QBER) and final key length with over 99\% accuracy. This method significantly reduces error correction time, maintaining a consistently low computation time even with large input sizes, such as data rates up to 156 Mbps. In contrast, traditional methods exhibit exponentially increasing computation times as input sizes grow, highlighting the superior scalability of our ML-based solution. Through comprehensive simulations, we demonstrate that our method not only accelerates the error correction process but also optimizes resource utilization, making it more cost-effective and practical for real-world deployment. The Cascade protocol's integration further enhances system security by dynamically adjusting error correction based on real-time QBER observations, providing robust protection against potential eavesdropping. Our research establishes a new benchmark for scalable, high-throughput QKD systems, proving that machine learning can significantly advance the field of quantum cryptography. This work continues the evolution towards truly scalable quantum communication.

Useful quantum-key supply from intermittent satellite contacts depends on finite-block security, authentication, relay inventories, and application demand, not optical transmittance alone. We simulate a trusted high-altitude-platform (HAP) relay between a ground station and a 500 km low-Earth-orbit satellite using three-intensity decoy-state BB84, finite-key estimation, authenticated key inventories, and a 200-pass service model. The comparator is the satellite-to-ground (S→G) downlink; a groundto-satellite uplink is not simulated, so the margins apply only to this downlink. With a 0.20 m HAP receive radius, the relay yields 13.175 Mbit/pass, 15.65% of the 84.185 Mbit/pass direct yield. With a 0.50 m HAP radius, the relay yield rises to 116.981 Mbit/pass, 38.77% above the 84.298 Mbit/pass direct yield. The unchanged direct-link estimates vary by under 0.2% from independent Monte Carlo sampling. The advantage remains 29.57% under a common contact window and 28.42% with equal elevation masks. Protocol tuning raises the 0.20 m relay yield by 36.74% without closing its gap to the direct link. For workloads derived from the 0.20 m relay supply, a 30 Mbit buffer produced no starved schedules for the 0.50 m relay in 10,000 simulations per demand. Receive-aperture collection is the main modeled condition for relay advantage under the stated assumptions.