Search Results (550)

Substrates have become essential enabling materials for creating lightweight electronic components, particularly supporting advanced telecommunication technologies. This progress is driven by continuous advancements in novel substrate materials and cutting-edge fabrication techniques, pushing the limits of high-frequency device design. This paper explores both the challenges and breakthroughs in 5G mmWave substrate technology, focusing on recent developments in materials, device fabrication, and integration methods that enhance performance and provide an in-depth analysis of the importance of mmWave technology. This paper also highlights the key concerns in substrate design for researchers and academicians to accelerate the invention and commercialization of substrate designs in areas such as antenna engineering and integrated circuit technologies, as well as addressing key issues like scalability, thermal impact in flexible substrates, and AI-driven RSSI-aware beamforming and its implications. Likewise, since matters related to material losses and substrates’ fabrication constraints are increasingly severe at high frequencies, mmWave substrates need to be looked at; therefore, this paper details, as well, the particular issues related to mmWave propagation and manufacturing design processes for high-frequency devices. Aims at optimizing antenna and system reliability by employing advanced strategies and materials, as well as outlining the existing gaps that need clarification to augment 5G mmWave infrastructure and services. Full article

Short-term load forecasting is a key capability for smart-grid operation, but real smart-meter streams are affected by missing values, communication noise, and non-stationary consumption patterns. This paper studies forecasting using raw smart-meter data collected from domestic consumers in a medium-sized city in southern Spain. In particular, we assess Hierarchical Temporal Memory (HTM), a biologically inspired online sequence learner, against a family of Long Short-Term Memory (LSTM)-based recurrent baselines. HTM offers continual adaptation and avoids a separate training phase, whereas LSTM relies on offline supervised training and may require retraining or fine-tuning under distribution shift. For five-step-ahead forecasting, HTM achieved a test RMSE of 251 kWh (about 15% of average consumption). After hyperparameter optimization, the best tested LSTM configuration achieved a test RMSE of approximately 250 kWh under clean conditions, indicating nearly identical point accuracy between the two approaches. Under synthetic Gaussian-noise injection, however, HTM remained comparatively stable, whereas the optimized LSTM configuration degraded markedly under the tested perturbation protocol. In addition, HTM exhibited a lower runtime in the tested CPU-based implementation. These findings suggest that HTM is a viable online alternative for aggregated smart-meter forecasting, offering competitive accuracy together with a favorable operational profile under the specific evaluation setup considered here. Full article

This paper presents a comprehensive review of existing research on the integration of blockchain technologies with the trust service ecosystem governed by the Electronic Identification, Authentication and Trust Services (eIDAS) Regulation of the European Union (EU). While Public Key Infrastructure (PKI) and electronic signature (ES) systems deployed under eIDAS provide strong cryptographic guarantees, standardized protocols, and cross-border legal recognition, their operational model remains largely centralized, concentrating trust in supervised authorities and service providers. This centralization raises concerns related to transparency, auditability, and resilience that blockchain, with its decentralized consensus and immutable distributed ledgers, has been increasingly explored to address. This review covers the most relevant application domains in which blockchain has been proposed as a complementary layer for Trust Service Providers (TSPs): certificate lifecycle management, remote signature services, signature preservation, signature validation, timestamping, content provenance and authenticity, and the European digital identity (EUDI) Wallet ecosystem. For each domain, this paper analyzes how blockchain can strengthen auditability and distributed trust while preserving the interoperability, legal assurance, and standards compliance required by eIDAS and ETSI (European Telecommunications Standards Institute) frameworks. A quantitative comparison of latency, throughput, and operational costs between blockchain-augmented and traditional architectures is provided, together with a technology maturity classification for each application domain. Finally, the paper identifies current limitations, including scalability, regulatory alignment, privacy constraints, and the absence of production-scale pilot data, and outlines open research challenges for the adoption of blockchain in regulated digital trust services. Full article

This study aims to systematically review how artificial intelligence (AI) is being adopted by small and medium-sized enterprises (SMEs) in Latin America to improve resilience and support sustainable practices in uncertain business environments. Based on the PRISMA protocol, fourteen peer-reviewed studies published between 2020 and 2025 were analyzed. The results identify significant barriers such as lack of adoption, including insufficient technological infrastructure, limited specialized talent, and budgetary constraints. Despite these obstacles, AI is increasingly used to automate processes and enhance predictive decision-making. While most applications remain concentrated in logistics, manufacturing, and telecommunications, their potential to improve efficiency and competitiveness is evident. The review also identifies the absence of robust regulatory frameworks as a critical limitation, particularly regarding ethical issues such as data privacy and algorithmic transparency. Although these aspects are not extensively covered in the reviewed literature, they represent important gaps for future research. In conclusion, the responsible adoption of AI in SMEs can contribute to business performance and sustainability goals, provided that appropriate public policies and sectoral strategies are implemented. Full article

The increasing digitalization of industrial ecosystems under the Industrial Revolution 4.0 has intensified the demand for fast, reliable, and inclusive broadband connectivity. The expansion of 5G technology led by data-driven services addresses the growing demand for high-capacity, low-latency communication through Fixed Wireless Access (FWA) as a cost-effective broadband solution. FWA is a wireless broadband access technology that provides high-speed connectivity to fixed locations using 5G New Radio (NR) infrastructure instead of physical fiber networks, while reducing deployment time and infrastructure investment. This review examines the technical challenges, economic business implications, and comparative performance of 5G FWA relative to other broadband technologies. It also examines the implementation of Enhanced Telecom Operations Map (eTOM) in several telecommunication network functions. The analysis indicates that successful 5G FWA implementation requires not only technical optimization, but also the adaption of standardized, scalable, and AI-driven network management practices. Emphasis is placed on the role of the eTOM as a structured framework for aligning technical, operational, and organizational processes in FWA deployment. This review highlights how eTOM can support readiness assessment, process harmonization, and lifecycle management to ensure consistent and efficient service delivery. This study provides a comprehensive reference for researchers and industry stakeholders in developing sustainable and future-ready 5G FWA networks. Full article

We propose and experimentally validate an automatic urban water leakage detection architecture that leverages dark fiber links already deployed in telecommunication networks in underground conduits in the vicinity of water pipelines. The sensing stage relies on a differential-phase coherent optical time-domain reflectometry interrogator enhanced with optical pulse compression to improve sensitivity. Building on this vibration acquisition stage, automatic leakage detection algorithms are implemented by searching for leak-induced activity in the frequency domain, which is well suited to revealing leakage-related features. After acquiring a baseline calibration to characterize normal-condition vibrations at each sensing position, leakage candidates are identified by comparing distribution-based metrics computed over multiple measurements against the corresponding baseline statistics. Two automatic leakage detection strategies are developed. First, low-complexity feature-based metrics are implemented, enabling continuous monitoring with minimal computational requirements. Second, an autoencoder-based anomaly detection technique is introduced, which also relies on location-specific normal-condition calibration but reduces the dependence on prior knowledge of the expected leakage vibration signatures. A real-world field trial on an urban network demonstrates reliable detection and localization using controlled leak events generated in the field, with measurements performed over a 17 km sensing fiber and an effective spatial resolution of 2.6 m. Benchmarking against a commercial punctual electro-acoustic leak detector yields consistent trends. Overall, the proposed system could complement existing technologies by enabling automated, continuous city-scale monitoring over already deployed dark fiber infrastructure. Full article

The rapid advancement and widespread application of telecommunication technologies have significantly increased human exposure to electromagnetic waves, thereby intensifying the demand for effective electromagnetic shielding materials. Beyond potential health concerns, ensuring the stable performance of highly integrated electronic devices also necessitates protection against electromagnetic interference (EMI). In this study, the effects of processing conditions on the EMI shielding effectiveness (SE) of AZ61 magnesium alloy sheets were systematically investigated. Aging treatment of rolled AZ61 alloy promoted the formation of Mg₁₇Al₁₂ lamellae. Transmission Kikuchi diffraction analysis revealed that plate-like Mg₁₇Al₁₂ precipitates preferentially formed on the (0001) planes of the Mg matrix, contributing to improved EMI shielding. The rolled AZ61 sheet exhibited the highest SE in both the as-rolled state (83.1 dB at 900 MHz) and after aging for 131 h at 250 °C (76.2 dB at 900 MHz). The superior shielding performance of the as-rolled sheet is attributed to its high density of deformation-induced defects such as dislocations and twins, which induce lattice distortions and impede wave propagation. Meanwhile, the enhanced SE from the 131 h-aged condition results from multiple reflections of incident electromagnetic waves facilitated by the matrix–precipitate lamellar microstructure. Full article

Scholarly knowledge graphs integrate bibliographic records from heterogeneous sources and therefore require controlled, auditable deduplication. This paper presents OntoDup, an ontology-driven approach that models entity matching as a governed decision process: Matching outcomes are recorded as reified assertions enriched with governance state, evidence, provenance and operational metadata, while a separate operational view is exposed through policy-driven materialization of consumable identity links. We evaluate OntoDup on the DBLP-ACM and DBLP-Scholar benchmarks under two regimes: (i) a pre-blocked setting using the benchmark candidate lists to compare matching methods under a fixed candidate set, and (ii) an end-to-end setting that generates candidates from the graph with DeepBlocker and applies governed triage and materialization. We report operational precision/recall/$F_1$ computed directly on the graph via SPARQL aggregations, characterize governance workload through state distributions, and quantify inference cost for LLM-based matchers via token and latency metadata attached to assertions. For end-to-end evaluation, we anchor operational links against a full positive reference encoded as idealized validations derived from the benchmark labels, enabling analysis of missed positives in terms of governance status and materialization policy. The experiments show that OntoDup enables evaluation at the level of consumable identity links, review workload, and inference cost, revealing operational trade-offs that are not visible from pairwise matching metrics alone. Full article

Intense technological advancements and market liberalization have intensified competition in the telecommunication industry, challenging established operators to innovate continuously. This study investigates how transformational leadership drives creativity and innovation and, consequently, organizational performance, focusing on its four dimensions: inspirational motivation, idealized influence, individualized consideration, and intellectual stimulation. Data were collected from 314 executives and analyzed using partial least squares structural equation modeling (PLS-SEM). Results indicate that inspirational motivation, individualized consideration, and intellectual stimulation significantly enhance creativity and innovation, whereas idealized influence shows no direct effect. Creativity and innovation further mediate the relationships of inspirational motivation and individualized consideration with organizational performance, highlighting their critical role in translating leadership behaviors into tangible performance outcomes. The findings advance theoretical understanding by clarifying the pathways through which transformational leadership fosters innovation and creative engagement. Practically, the study provides actionable insights for organizational leaders to cultivate a culture of creativity and innovation to sustain competitive advantage, and informs policymakers and regulators in supporting organizational performance and sectoral development. Full article

The rapid pace of digital transformation has increased organizations’ reliance on digital technologies and collaborative systems as key drivers of technological innovation. However, the mechanisms through which digital trust, digital technology, and digital capability shape innovation performance—particularly through knowledge sharing—remain insufficiently explored. This study examined the relationships among these digital enablers and innovation performance, positioning knowledge sharing as a central mediating mechanism grounded in the Knowledge-Based View and Open Innovation Theory. A quantitative research design was adopted, and data were collected through a structured survey of 280 professionals working in IT, software development, telecommunications, and other technology-intensive industries. Convenience sampling was employed, and statistical analyses were conducted using SPSS to assess reliability, validity, and structural relationships. The findings revealed that digital trust, digital technology, and digital capability significantly enhance knowledge-sharing practices, which in turn positively influence innovation performance. Moreover, knowledge sharing was found to play a critical mediating role in translating digital enablers into innovation outcomes. This study contributes to the digital innovation literature by highlighting the importance of digital preparedness and collaborative knowledge practices in fostering technological innovation. The findings also offer practical insights for organizations seeking to strengthen innovation performance by developing digital capabilities, fostering trust, and promoting effective knowledge-sharing cultures in technology-intensive environments. Full article

Artificial intelligence (AI) marketing technologies are reshaping customer engagement in service sectors, yet their performance within integrated digital ecosystems remains poorly understood. Existing research often examines AI tools in isolation, overlooking how the holistic quality of the virtual customer experience (VCE) shapes their impact on consumer decisions, particularly in intangible service contexts such as telecommunications. This study addresses this gap by investigating the influence of four AI technologies—chatbots, dynamic pricing, voice search, and visual search—on purchasing decisions, with VCE tested as a critical moderating mechanism. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) and survey data from 487 telecommunications customers in Saudi Arabia, the findings confirm significant positive direct effects for all four AI tools. Moreover, the VCE significantly amplifies these individual relationships and further strengthens their combined contribution to decision quality, enabling the model to explain 71.2% of the variance in purchasing decisions. The results indicate that competitive advantage in AI-enabled service markets depends not on deploying isolated technologies, but on orchestrating a coherent, high-quality virtual experience ecosystem. By integrating the Technology Acceptance Model (TAM) and Stimulus–Organism–Response (SOR) framework, this study advances the theoretical understanding of how AI and experience design jointly enhance digital decision-making. Practically, it underscores the need for managers to prioritize integrated VCE design to drive sustainable consumption and strengthen customer loyalty in increasingly digital service environments. Full article

Secondary power supplies are an integral part of any complex device that requires power to different circuit nodes. This includes various kinds of telecommunication equipment, the aerospace industry, battery chargers, etc. Secondary power supplies include the most common pulse converters of both the boost, buck, and buck–boost variety, as well as forward, flyback, and push–pull converters. In particular, a galvanic isolation option may be considered for push–pull types. The use of multi–channel secondary power supplies is relevant for the space industry and satellites, where it is necessary to support the operation of many related devices. The efficiency of such devices is high due to their small number of elements and their simplicity of control. PWM (pulse width modulation) controllers can be considered as the last statement. In turn, the presence of radiation-resistant CMOS technology is required in outer space conditions, which is possessed by the PWM controller considered in this paper. Also, high efficiency and small dimensions can be achieved using planar technology. Here, one such secondary power supply, based on the PWM controller 5315EU015 with a power of 10 W, is considered, as well as the proposed design of a planar transformer. A mathematical model obtained from the algebraization of differential equations method, and from the PSIM software v. 22.2 simulation results and experiments is presented. Full article

Integrated optical devices based on lithium niobate (LN) are pivotal in modern navigation systems, telecommunications, and sensing technologies. However, their practical implementation is critically limited by temperature-dependent and long-term operational instability, primarily attributed to the pyroelectric effect inherent in LN. This study addresses this challenge by investigating thermally reduced lithium niobate as a material platform to enhance the stability of integrated optical circuits, with a focus on integrated optical electric field sensors (IOES). We present the fabrication and comprehensive characterization of an IOES based on a Michelson interferometer design. Key performance metrics including optical loss, free spectral range, electro-optical sensitivity, and optical path difference were systematically evaluated. Notably, under normal climatic conditions, the optical path difference of the IOES demonstrated exceptional stability when subjected to an applied voltage ranging from 0 to 5 V, with no observable drift over time. Calibration of the IOES revealed a predominantly linear response, although a third-degree polynomial model provided a more precise fit to the experimental data. The minimum relative error achieved during calibration was 0.47%, underscoring the high accuracy of the device. Our results establish thermally reduced LN as a promising material platform for next-generation integrated optical devices. By mitigating the pyroelectric effect, this approach enables significant improvements in the long-term stability of IOES and other LN-based photonic components. These findings open avenues for the reliable deployment of integrated optical systems in demanding applications where environmental stability is paramount. Full article

Scientific collaboration is increasingly needed to address complex research challenges, yet identifying promising partners in the absence of prior co-authorship remains difficult. We present a decision-support pipeline for discovering researchers who have not previously worked together and whose collaboration is unlikely to emerge without deliberate intervention or institutional incentives. The approach leverages document-level semantic representations to estimate proximity between publications, aggregates these similarities at the author level, and surfaces collaboration opportunities that are not evident from the co-authorship graph. To support interpretation by decision makers, a separate LLM module proposes potential joint research directions, which are subsequently annotated with multi-label fields of study. We evaluate the pipeline through an institutional case study, analyzing 7531 publications from 2009 to 2024 using retrospective, temporally shifted windows. While only a small fraction of suggested pairs materialized spontaneously in subsequent periods, the collaborations that do emerge exhibit strong semantic alignment with the computed recommendations (high cosine similarity) and substantial thematic overlap. These results indicate that semantic proximity can act as an early indicator of latent complementarity between researchers without prior ties, supporting intentional institutional mediation and complementing topology-driven approaches that predict links under passive evolution. Full article

Digitalization of railway traffic dispatching systems is a key step in the modernization of railway telecommunication infrastructure. This paper presents a case study of the migration from legacy analog technology to a software-centric dispatching platform that integrates digital signal processing, optical fiber transmission, and Internet Protocol (IP)-based network architectures, as implemented in the Serbian railway system. The modernization is performed through an iterative, incremental process: existing analog dispatcher equipment and established operating procedures are preserved, while digital dispatching centers, trackside communication nodes, and radio-dispatching services are introduced gradually. This staged evolution enables high-capacity, noise-resilient communication and seamless interconnection between the old and the new subsystems without disrupting railway operations. The adoption of software-based control and integrated digital signal processing provides modular scalability, real-time system supervision, automated diagnostics, and improved maintainability. One of critical services within the new architecture, the Centralized Call Record- and Message-Archiving System (CCRMAS), provides a centralized platform that captures, secures, and retrieves operational railway communication in real time for monitoring, post-incident analysis, and regulatory compliance. The resulting architecture, deployed within Serbian Railways, establishes a scalable and resilient foundation for future automation, interoperability, and integration within intelligent railway traffic-management environments. Thus, the paper extracts a generalizable hybrid migration architecture model and transferable design principles, supported by deployment artifacts and illustrated through migration scenarios, that can be applied to the modernization of other legacy-intensive railway networks. Full article