The performance demands from data-intensive applications, such as multimedia streaming, as well as the growing number of devices connecting to the Internet, will increase the need for higher capacity wireless communication links. The research community has recently explored regions of the spectrum, including the Terahertz band (0.1 THz to 10 THz), that are underutilised for communications. THz frequencies come with a plethora of special challenges, one of which is the very narrow effective beam, thereby requiring a Line of Sight (LoS) between sender and receiver. Researchers have explored the use of reflectors that can redirect beams around blockages. In this paper, we propose a THz signal guidance system where a Digital Twin is used to model, predict and control the signal propagation characteristics of an indoor space. Our approach finds the best THz signal path from the base station to the mobile target via the tunable metamaterial walls, avoiding obstacles as needed, using geometric (ray tracing), path loss and Terahertz Potential Field (THzPF) models. With this knowledge, the digital twin guides the selection of antenna strips at a base station and the reflectors along the signal path. A top-view camera, with advanced image processing, provides context updates (obstacle and mobile target locations) to the digital twin. The image processing system also senses factors like water vapour concentration, and the material composition and surface roughness of obstacles. Such factors affect propagation strength, and the digital twin modifies the beam paths to adapt. Simulation results have shown the efficiency of our control system to maintain a reliable signal connection while minimising the use of antenna and reflector strips. Our system is the first proposal that maximises THz signal-to-noise ratio (SNR) through such a dynamic and robust control system, which integrates image processing of a room with base station configuration.

Optical Inter-Satellite Links (O-ISLs) in Low Earth Orbit (LEO) constellations enable low-latency, high-throughput global communication and are a key enabler of future Non-Terrestrial Networks (NTN) under 5G Advanced, 6G and beyond. This paper presents a simulation framework that integrates realistic orbital propagation from Two-Line Element (TLE) data with physical layer constraints, including Doppler shift, geometric visibility, and hardware limits on Pointing, Acquisition, and Tracking (PAT) terminals, to evaluate dynamic, latency-aware routing. Our results show that Doppler shifts are governed by relative velocity vectors rather than link distance, with most values within±5 GHz, confirming compatibility with current optical transceiver capabilities. Our analysis spans the full spectrum of routing distances to characterize network performance. At the regional scale, a Bangkok–Tokyo route demonstrates consistent latencies of 65–80 ms and bit rates above 6.5 Gbps under a two-PAT limit. Progressing to the global scale, a nearly antipodal route (Bangkok–Los Angeles) reveals a quantifiable trade-off between latency and throughput as a function of maximum ISL distance. These findings validate the feasibility of O-ISL-enabled NTN architectures and provide quantitative guidance for routing strategies and physical layer design in large-scale LEO constellations.

Public transportation is significant in an economical city like Yangon. In Yangon, there are four types of public transport facilities; mostly peoples can choose bus, train, taxi and water bus particularly. Railway system in Yangon city is not very developed. Most of the peoples have to depend on the buses system in the city. The massive amount of using buses in the city as the disadvantages; it can lead to traffic jam and air pollution. In this research article, we proposed an efficient software tool with user-friendly Graphical User Interface (GUI) that can help to upgrade the Yangon city railway system to the intelligent railway system and simulate the Yangon Circular Railway system to a greater extent. In the proposed software, we can easily visualize the train movement, modify the number of stations, the arrival rate, generate the simulation graph, modify the train, stations and speed, etc., to utilize the current resources.