Τhis paper provides a comprehensive overview of sensors commonly used in low-cost, low-power systems, focusing on key concepts such as IoT, Big Data, and smart sensor technologies. It defines the evolving roles of sensors, emphasizing their characteristics, technological advancements, and the transition toward 'smart sensors' with integrated processing capabilities. The article also explores the increasing significance of mini computing devices in educational environments. These devices offer cost-effective and energy-efficient solutions for system monitoring, prototype validation, and real-world application development. By interfacing with wireless sensor networks and IoT systems, mini-computers enable students and researchers to design, test, and deploy sensor-based systems with minimal resource requirements. Furthermore, the paper discusses the use of signal processing techniques to enhance data acquisition and measurement accuracy, making these systems more reliable and suitable for various educational and research applications. This research aims to equip future engineers with the knowledge and tools to integrate cutting-edge sensor networks, IoT, and Big Data technologies into scalable, real-world solutions.

Deepfake technology has advanced swiftly, enabling the rapid production of hyper-realistic synthetic media that pose considerable threats to digital security, privacy, military operations, and information integrity. This paper extensively examines visual intelligence and computer vision methodologies for deepfake detection, covering recent developments in deep learning, adversarial strategies, and feature extraction. It reviews prevalent generation architectures—including GANs, autoencoders, neural rendering, and diffusion models—alongside novel adversarial tactics that enhance realism while evading detection, particularly in military and intelligence contexts. We also investigate visual artifacts and manipulation traces, scrutinizing physical discrepancies, digital fingerprints, and physiological signals as critical detection indicators. The article offers a comprehensive analysis of CNN-based, transformer-based, and frequency-domain detection methods, highlighting their advantages, drawbacks, and practical relevance. Furthermore, we examine assessment measures and generalization challenges, while emphasizing prospective research avenues such as explainable AI, self-supervised learning, and federated learning. This study serves as a significant resource for academics and practitioners combating deepfake disinformation in civilian, military, and hybrid threat environments, providing insights into detection improvements and impending issues in hostile AI.

This paper examines the intersection between digital learning environments and multilingual education policies, with a focus on the linguistic integration of migrant students in Europe. It explores how technology, particularly mobile-assisted learning, artificial intelligence, and immersive tools, can strengthen language acquisition and promote social inclusion. Drawing on European and Greek policy frameworks, the study shows how digital pedagogies operationalize multilingualism as both an educational objective and a social justice priority. Based on a qualitative review of contemporary research and institutional reports, the findings indicate that digitally enhanced learning environments act as catalysts for equity, intercultural dialogue, and active participation when supported by coherent pedagogical design. The paper concludes by outlining policy recommendations for the development of multilingual digital ecosystems that align technological innovation with democratic, inclusive, and human-centred education. Overall, the analysis highlights that technology-mediated multilingualism can effectively reinforce participation, inclusion, and linguistic integration when embedded within robust policy structures and sound pedagogical practice.

Deepfakes, synthetic audiovisual content produced by deep generative models, have escalated into a critical threat across civilian and military domains, enabling identity fraud, disinformation campaigns, and evidence fabrication. In military and high-security contexts specifically, the stakes extend to operational deception, compromised intelligence, and erosion of command trust. This entry explores how modern visual intelligence and computer vision techniques are used to detect deepfakes. It outlines key deepfake generation models, such as GANs, autoencoders, neural rendering, and diffusion systems, while also explaining how adversarial methods enhance realism and challenge existing detectors. The overview highlights visual artifacts, digital patterns, and physiological cues commonly leveraged in detection, and reviews major CNN, transformers, and frequency-based approaches. It also summarizes evaluation practices, and the difficulty of achieving strong generalization. Finally, it identifies emerging directions, including modern intelligence techniques for civilian and military content verification. This survey covers generation architectures (GANs, latent diffusion, neural rendering, video synthesis), the spatial, temporal, frequency-domain, and physiological artifacts they produce, and the detector families that exploit them. We examine evaluation benchmarks and protocols, highlighting cross-generator generalization as the field's central open challenge. Beyond detection, we discuss cryptographic provenance standards, watermarking, and regulatory frameworks (EU AI Act, DSA, GDPR). We conclude that effective deepfake governance requires defense-in-depth integrating forensic detection, verifiable provenance, and institutional accountability.

Cognitive-semantic communication encodes task-relevant meaning rather than raw bits, improving 6G spectral efficiency and reducing latency, yet evidence remains fragmented across methods, evaluations, and deployments. This PRISMA-based review screens 782 records (2019-2025) from seven databases, retaining 73 studies organized into five method families: neural semantic codecs, KG-based pipelines, LLM-assisted codecs, cross-modal encoders, and semantic relays. We examine how goal-oriented semantics, knowledge graphs, and LLMs shape representation, compression, and task quality under rate and latency constraints across UAVs, NTNs, ISAC, Metaverse, and IoT. Reported gains include bandwidth reductions of 20-60% over conventional baselines, task accuracy improvements of 8-23 percentage points over bit-level systems, and cross-modal alignment scores above 0.80; however, these gains derive predominantly from AWGN simulations, and robustness under realistic fading, mobility, and adversarial conditions remains the most critical open problem. Gaps include narrow datasets, inconsistent metric definitions, and unaddressed security, privacy, and standardization concerns. Recommendations include shared multimodal datasets with channel traces, standardized robustness protocols, unified metric cards pairing task outcomes with resource costs, edge-friendly split designs, and integrity checks with privacy audits.

Deepfakes, synthetic audiovisual content produced by deep generative models, have escalated into a critical threat across civilian and military domains, enabling identity fraud, disinformation campaigns, and evidence fabrication. In high-stakes environments, ranging from journalism and finance to healthcare and legal contexts, the consequences extend to severe misinformation, market manipulation, identity fraud, and the erosion of institutional trust. This entry explores how modern visual intelligence and computer-vision techniques are used to detect deepfakes. It outlines key deepfake generation models, such as GANs, autoencoders, neural rendering, and diffusion systems, while also explaining how adversarial methods enhance realism and challenge existing detectors. The overview highlights visual artifacts, digital patterns, and physiological cues commonly leveraged in detection and reviews major CNN, transformer, and frequency-based approaches. It also summarizes evaluation practices and the difficulty of achieving strong generalization. Finally, it identifies emerging directions, including modern intelligence techniques for civilian and military content verification. This survey covers generation architectures (GANs, latent diffusion, neural rendering, video synthesis), the spatial, temporal, frequency-domain, and physiological artifacts they produce, and the detector families that exploit them. We examine evaluation benchmarks and protocols, highlighting cross-generator generalization as the field’s central open challenge. Beyond detection, we discuss cryptographic provenance standards, watermarking, and regulatory frameworks (EU AI Act, DSA, GDPR). We conclude that effective deepfake governance requires defense in depth integrating forensic detection, verifiable provenance, and institutional accountability.