Real-time monitoring systems for Industry 5.0 manufacturing settings need to accommodate a balance of data freshness, safety-critical compliance, and resource optimalization. In this work, we provide a workable UAV coordination framework for industrial applications, combining three aspects: a multi-factor dynamic priority scheme that integrates data freshness metrics with thermal anomaly identification, a drone selection strategy that aims for a balance of space comprehensiveness and task assignments, and a zone-coordination scheme for efficient scalability of conflict resolution. Using a rigorous digital twin simulation model for 23 industrial facilities over a total of seven stages of product manufacturing, we show that our holistic framework outperforms other approaches with an improvement of 34.7
Automatic modulation classification (AMC) is a critical task for cognitive radio and dynamic spectrum access. Its deployment on mobile, power-constrained platforms, such as unmanned aerial vehicles (UAVs), is severely limited by on-device energy consumption. This paper proposes a federated transfer learning (FTL) framework where models are pre-trained on a power-unlimited “ground station” and then fine-tuned with minimal energy on the “UAVs.” We compare the performance, on-device energy cost, and overall efficiency of several transformer-based architectures integrated in the proposed FTL framework against a baseline Deep CNN. We analyze two fine-tuning strategies: a full fine-tuning of the classifier head (FTL-3L) and a lightweight “linear probe” (FTL-1L) that tunes only the final layer. Our results on the RML2016.10b dataset show that while the large transformer model with FTL-3L achieves the highest peak accuracy (93.9 2× that of the Deep CNN. We demonstrate that the small transformer model with FTL-1L provides the optimal trade-off, achieving 93
Achieving energy efficiency is a critical challenge in 5G and Beyond (5GB) cellular networks, particularly in complex architectures such as Heterogeneous Cloud Radio Access Networks (H-CRAN). The dynamic nature of traffic demand, coupled with stringent Quality of Service (QoS) requirements, makes joint resource allocation and energy management a highly challenging optimization problem. In this paper, we investigate the joint control of Advanced Sleep Modes (ASM) and Resource Block (RB) allocation using Deep Reinforcement Learning (DRL). Specifically, we implement and compare two representative DRL algorithms: Deep Q-Network (DQN) and Proximal Policy Optimization (PPO), within a unified H-CRAN system model. Simulation results demonstrate that PPO consistently outperforms DQN in terms of convergence stability and energy efficiency, while satisfying QoS constraints. These results highlight the suitability of policy-gradient-based DRL methods for dynamic energy-aware resource management in future 5G and beyond networks.
The National Institute of Informatics (NII) annually assesses whether Identity Providers (IdPs) participating in the Japanese academic federation GakuNin comply with federation policies. The survey of about 50 items assigns institutions “A” (compliant) or “B” (needs improvement) grades, yet its weighted scoring method and partial manual evaluation limit consistency and trend detection. To address these issues, we applied unsupervised learning methods—K-Means, Gaussian Mixture Model (GMM), and hierarchical clustering—to survey data from FY2022–2024. The approach achieved a Precision of 0.984 or higher and a True Negative Rate (TNR) above 0.77, confirming its effectiveness in distinguishing institutional reliability. Analysis also identified one known evaluation error in the 2022 dataset. The study demonstrates how clustering supports objective trust assessment, reduces human bias, and enhances transparency and reproducibility in evaluating academic authentication federations.
Compression and deduplication are routinely combined in datacenter storage and streaming-service backends, yet the benefits and trade-offs of such pipelines remain unclear. This paper presents a reproducible single-node benchmark of four compression tools and three deduplication tools, evaluated individually and in combination on two contrasting workloads: a large structured CSV dataset and the Linux Kernel source tree. Using containerized experiments, we measure compression ratio, execution time, CPU and RAM usage, and I/O activity to study storage savings, execution order, and workload sensitivity. Results show that combining tools rarely improves space savings over the best standalone method, while execution order matters: deduplication before compression systematically reduces time and resource usage. Deduplication is most effective on redundancy-rich, multi-file data, whereas compressors dominate on flat structured datasets, and high-ratio compressors incur higher CPU/RAM costs. Overall, there is no universal “compress+dedupe” recipe; effective optimization is workload-aware and order-conscious.
