Journal of ICT Research and Applications

Intelligent Approaches to Managing Communication Channels in Hybrid Terrestrial-Satellite Networks with LEO Segment

2026-04-30T21:44:26+07:00

The integration of terrestrial 5G/6G networks with Low Earth Orbit (LEO) satellite systems is creating a class of hybrid Satellite–Terrestrial Integrated Networks/Non-Terrestrial Networks (STIN/NTN) for which channel management in the face of highly dynamic topologies and traffic heterogeneity is becoming a key challenge. This paper examines intelligent approaches to radio resource allocation in such networks based on deep reinforcement learning (DRL) and multi-agent algorithms. A conceptual architecture for a channel management system in a hybrid network with a LEO segment is proposed, including a centralized Software-Defined Networking/Artificial Intelligence (SDN/AI) coordinator and distributed DRL agents at the satellite and ground base station levels. To analytically interpret the gains from intelligent management, an aggregated M/M/c/c teletraffic model is introduced, using the concept of an “equivalent number of channels,” which allows for linking DRL resource allocation with classical assessments of blocking probability and throughput. The obtained numerical results show that increasing the effective system capacity by 20% due to intelligent inter-segment load redistribution leads to a 30–40% reduction in blocking and a 10–18% increase in throughput in the high-load region, which is consistent with the results of detailed DRL studies for LEO networks presented in the modern literature.

Post-Disaster Resilient Communication through Infrastructure-Free Networks

2026-04-30T21:46:12+07:00

Traditional communication systems are often disrupted during disasters, leaving communities disconnected and interrupting the exchange of essential information. In these circumstances, a reliable communication approach is critical for coordinating relief and recovery activities. This work presents a Resilient Communication Model (RCM), an infrastructure-free solution designed to enhance post-disaster communication. The model relies on dynamic, self-organizing links between nearby devices, allowing victims and rescuers to communicate effectively. Due to its lightweight and flexible design, the system is particularly well-suited for basic devices, which are more likely to remain functional in crisis conditions. Simulation studies and comparative evaluations with other algorithms demonstrate that the model minimizes packet loss ratios (PLR), delay, and enhances communication stability, packet delivery ratio (PDR) and throughput during network disruptions. Practical experiments across diverse scenarios further confirm the model’s feasibility and resilience. Overall, the findings demonstrate that the model can reliably function as a fallback communication system when standard networks are unavailable.

Improving JavaScript Learning Engagement with Gamification Techniques

2026-05-18T10:44:02+07:00

JavaScript is highly demanded in modern development, but traditional learning methods often fail to engage students. To address this, this study designed and developed a responsive, gamified web-based JavaScript learning application using the ReactJS library. The application integrates gamification elements such as dashboards, learning modules, challenges, points, levels, ranks, and achievements to enhance learning engagement. The application’s usability and acceptance were evaluated using the Technology Acceptance Model (TAM) through a survey involving 35 participants. The evaluation yielded a Perceived Usefulness score of 90.00% and a Perceived Ease of Use score of 89.71%, resulting in a high overall average score of 89.85%. These results indicate that respondents strongly agree that the application is highly useful for escalating learning motivation and is easy to use. While the TAM results confirm high user acceptance and demonstrate that gamification effectively eases the JavaScript learning process, our future research will incorporate objective cognitive assessments to definitively measure its absolute pedagogical effectiveness.

Performance Evaluation of Nanoscale Single- and Dual-Gate MESFETs Using COMSOL Multiphysics

2026-05-25T10:26:34+07:00

In this work, a comparative numerical simulation of single-gate and double-gate depletion mode GaAs MESFETs in the nanometer domain using COMSOL Multiphysics was carried out. The objective was to evaluate the significance of multi-gate structures based on electrostatic control capability in terms of performance improvement in Schottky gate MESFETs. Four models were considered, including a traditional single-gate model, an analogous double-gate model, and their corresponding reduced scale models. Comparisons were made in terms of the concentration of electrons and output response depending on the value of the gate bias voltage. There were almost no differences between the two models when scaled at the same dimensions. The reduced dual-gate device exhibited lower leakage current and superior channel control compared to its single-gate counterpart, particularly with higher drain bias applied. This suggests that while the dual-gate design may not be advantageous in all dimensions, it proves more effective when MESFET structures are aggressively scaled, indicating that dual-gate configurations are preferable for nanoscale GaAs MESFETs operating in regimes where electrostatic degradation is significant.

Komodo-7B with Hybrid Retrieval and Q-LoRA for Indonesian Population Administration Question Answering

2026-06-05T17:16:44+07:00

Fast, responsive, and informative public services are societal demands that must be fulfilled by government agencies, among which the Department of Population and Civil Registration of Surabaya City. To enhance service quality, this study developed a Large Language Model (LLM)-based Question Answering (QA) system to address public inquiries regarding Identity Card (ID) and Family Card (FC) services. The proposed system utilizes the Komodo-7B model, which was customized using Quantized Low-Rank Adaptation (Q-LoRA) fine-tuning and integrated with a Retrieval-Augmented Generation (RAG) approach to improve the accuracy and relevance of the generated responses. The training process leveraged a real-world complaint dataset from Disdukcapil alongside the open-source MS MARCO dataset. Furthermore, the RAG implementation employs sentence vectorization via SentenceTransformer and cosine similarity-based context retrieval. System performance was evaluated using ROUGE and METEOR metrics across four scenarios: Komodo-7B Base, RAG Komodo-7B Base, Fine-Tuned Komodo-7B, and RAG Fine-Tuned Komodo-7B. The results show that the RAG Fine-Tuned Komodo-7B configuration delivered the best performance, achieving F1-Scores of 0.3554 for ROUGE-1, 0.3096 for ROUGE-L, and 0.2886 for METEOR.