Search Results (545)

Our long journey on the road of telecommunications is continuously evolving. We have experienced several technological changes, modernizations, optimizations, and various mergers in the past decades. Virtualization and ‘cloudification’ of legacy telecommunication equipment has made communication networks not only more flexible, but also opened new doors. Brand new types of services have become available thanks to the ongoing fusion of the two domains of telecommunications and IT (Information Technology). This overview paper first discusses the evolution of services with an enhanced focus on mobile networks. Then, the possibilities offered by IT are shown. Finally, some examples are given of how Communication Service Providers and end users can benefit from these recent changes. Full article

As the focus of space exploration has recently shifted from national efforts to private enterprises, interest in the space industry has increased. With the rising number of satellite launches, the risk of collisions between satellites and between satellites and space debris has grown, which can lead not only to property damage but also casualties caused by the debris. To address this issue, various machine learning and deep learning-based methods have been researched to improve the accuracy of satellite orbit prediction and mitigate these risks. However, most studies have applied basic machine learning models to orbit prediction without considering the model size and execution time, even though satellite operations require lightweight models that offer both a strong prediction performance and rapid execution. In this study, we propose a time series forecasting framework, the Lightweight Transformer Encoder (LTE), for satellite orbit prediction. The LTE is a prediction model that modifies the encoder structure of the Transformer model to enhance the accuracy of satellite orbit prediction and reduce the computational resources used. To evaluate its performance, we conducted experiments using about 4.8 million data points collected every minute from January 2016 to December 2018 by the KOMPSAT-3, KOMPSAT-3A, and KOMPSAT-5 satellites, which are part of the Korea Multi-Purpose Satellite (KOMPSAT) series operated by the Korea Aerospace Research Institute (KARI). We compare the performance of our model against various baseline models in terms of prediction error, execution time, and the number of parameters used. Our LTE model demonstrates significant improvements: it reduces the orbit prediction error by 50.61% in the KOMPSAT-3 dataset, 42.40% in the KOMPSAT-3A dataset, and 30.00% in the KOMPSAT-5 dataset compared to the next-best-performing model. Additionally, in the KOMPSAT-3 dataset, it decreases the execution time by 36.86% (from 1731 to 1093 s) and lowers the number of parameters by 2.33% compared to the next-best-performing model. Full article

This study proposes a low-cost, automatic, wide-area real-time water pipeline monitoring model based on Narrowband Internet of Things (NB-IoT) technology, aiming to solve the challenges faced in the context of global water pipeline management. This model focuses on real-time monitoring of pipeline operations to reduce water waste and improve management efficiency, directly contributing to the achievement of the sustainable development goals (SDGs). Water resource management faces several significant global challenges, including water scarcity, inefficient resource utilization, and infrastructure degradation. Traditional water pipeline monitoring systems are often manual, time-consuming, and unable to detect leaks or failures in real time, leading to significant water loss and financial costs. In response to these issues, NB-IoT technology offers a promising solution with its advantages of low power consumption, long-range communication, and cost-effectiveness. The development of an NB-IoT-based smart water pipeline monitoring system is therefore essential for enhancing the efficiency and sustainability of water resource management. Through enabling real-time monitoring and data collection, this system can address critical issues in global water management, reducing waste and supporting the sustainable development goals (SDGs). This model utilizes Low-Power Wide-Area Network (LPWAN) technology, combined with an LTE mobile network and ARM Cortex-M4 microcontroller, to achieve long-distance multi-sensor data collection and monitoring. The research results show that NB-IoT technology can effectively improve water resource management efficiency, reduce water waste, and is of great significance for the digital transformation of infrastructure and the development of smart cities. This technical solution not only supports “Goal 6: Clean Drinking Water and Sanitation” in the United Nations’ sustainable development goals (SDGs) but also promotes the realization of low-cost teaching aids related to engineering education-related information and communication technologies (ICTs). This study demonstrates the key role of ICTs in promoting sustainable development and provides a concrete practical example for smart water resource management. Full article

This article focuses on the study of local truncation errors (LTEs) in the Network Simulation Method (NSM), specifically when using the trapezoidal method and Gear’s methods. The NSM, which represents differential equations through electrical circuit elements, offers advantages in solving nonlinear dynamic systems such as the van der Pol equation. The analysis compares the performance of these numerical methods in terms of their stability and error minimization, with particular emphasis on LTE. By leveraging circuit-based techniques prior to numerical application, the NSM improves convergence. This study evaluates the impact of step size on LTE and highlights the trade-offs between accuracy and computational cost when using the trapezoidal and Gear methods. Full article

The issue of air pollution has recently come to light due to rapid urbanization and population growth globally. Due to its impact on human health, such as causing lung and heart diseases, air quality monitoring is one of the main concerns. Improved air pollution forecasting techniques and systems are needed to minimize the human health impact. Systems that fall under the Internet of Things (IoT) topology have been developed to assess and track numerous air quality metrics. This paper presents a review of IoT systems for air quality measurements, where the emphasis is placed on systems with LTE/4G and LoRa communication capabilities. Firstly, an overview of the IoT monitoring system is provided with recent technologies in the market. A critical review is provided of IoT systems regarding air quality using LTE/4G and LoRa communications systems. Lastly, this paper presents a market analysis of commercial IoT devices in terms of the costs, availability of the device, particulate matter each device can measure, etc. A comparative study of these devices is also presented on LTE/4G and possibly LoRa communications systems. Full article

Smartphone-based location estimation technology is becoming increasingly important across various fields. Accurate location estimation plays a critical role in life-saving efforts during emergency rescue situations, where rapid response is essential. Traditional methods such as GPS often face limitations in indoors or in densely built environments, where signals may be obstructed or reflected, leading to inaccuracies. Similarly, fingerprinting-based methods rely heavily on existing infrastructure and exhibit signal variability, making them less reliable in dynamic, real-world conditions. In this study, we analyzed the strengths and weaknesses of different types of wireless signal data and proposed a new deep learning-based method for location estimation that comprehensively integrates these data sources. The core of our research is the introduction of a ‘matching-map image’ conversion technique that efficiently integrates LTE, WiFi, and BLE signals. These generated matching-map images were applied to a deep learning model, enabling highly accurate and stable location estimates even in challenging emergency rescue situations. In real-world experiments, our method, utilizing multi-source data, achieved a positioning success rate of 85.27%, which meets the US FCC’s E911 standards for location accuracy and reliability across various conditions and environments. This makes the proposed approach particularly well-suited for emergency applications, where both accuracy and speed are critical. Full article

Structural monitoring of quay walls, where various events occur due to unexpected high waves, vessels, and heavy equipment, is essential. However, real-time events cannot be constantly monitored by on-site personnel. To resolve the aforementioned issues, this study proposes an innovative AI-powered, cloud-based wireless sensor system that incorporates a high-sensitivity accelerometer with an ultra-low noise level of 0.003 mg, designed to monitor the low response amplitude of massive quay walls. The sensor can be activated by a scheduled trigger or a long-rangefinder. Vessel detection is performed utilizing the AI-based object detection method, Faster R-CNN, which employs ResNet as the backbone network. The detected anchor box’s position and dimensions are subsequently processed to confirm the presence of a berthing vessel. The collected data are then transmitted wirelessly to a proposed cloud server through LTE communication in real-time. The developed system was installed on a caisson-type quay wall in Korea, where acceleration, tilt, temperature, and camera image data were analyzed to assess its performance for real-time event monitoring. The results demonstrated that the safety of quay walls can be automatically managed by monitoring events during berthing and mooring with the proposed system. Full article

The way we connect with the physical world has completely changed because of the advancement of the Internet of Things (IoT). However, there are several difficulties associated with this change. A significant advancement has been the emergence of intelligent machines that are able to gather data for analysis and decision-making. In terms of IoT security, we are seeing a sharp increase in hacker activities worldwide. Botnets are more common now in many countries, and such attacks are very difficult to counter. In this context, Distributed Denial of Service (DDoS) attacks pose a significant threat to the availability and integrity of online services. In this paper, we developed a predictive model called Markov Detection and Prediction (MDP) using a Continuous-Time Markov Chain (CTMC) to identify and preemptively mitigate DDoS attacks. The MDP model helps in studying, analyzing, and predicting DDoS attacks in Long-Term Evolution for Machine (LTE-M) networks and IoT environments. The results show that using our MDP model, the system is able to differentiate between Authentic, Suspicious, and Malicious traffic. Additionally, we are able to predict the system behavior when facing different DDoS attacks. Full article

This article aims to test, measure and evaluate the quality of voice calls made in a mobile cellular network. A set of drive tests were conducted, during which logs were collected using specialized measurement terminals equipped with a dedicated voice evaluation application. Three different scenarios were considered: the first scenario consisted of a series of mobile-to-mobile calls in a circuit-switched (CS) domain over the GSM network, the second scenario involved similar calls using the VoLTE service in a packet-switched (PS) domain of a 4G network, and the third scenario employed an over-the-top (OTT) media service type via the WhatsApp application in the same PS domain of the 4G network. The measurement results highlight the user experience in each scenario and compare the voice quality evaluated through the Mean Opinion Score (MOS) across the CS and PS domains. The originality of this work consists of in situ measurements performed in Bucharest, Romania, providing detailed, context-specific insights regarding the network performance that can drive local improvements and support policy and investment decisions. Full article

This paper analyzes the quantitative quality parameters of a mobile communication network in a controlled drone logistic use-case scenario. Based on the analysis of standards and recommendations, the values of key performance indicators (KPIs) are set. As the main network-impacting parameters, reference signal received power (RSRP), reference signal received quality (RSRQ), and signal to interference and noise ratio (SINR) were selected. Uplink (UL), downlink (DL), and ping parameters were chosen as the secondary ones, as they indicate the quality of the link depending on primary parameters. The analysis is based on experimental measurements performed using a Latvian mobile operator’s “LMT” JSC infrastructure in a real-life scenario. To evaluate the altitude impact on the selected network parameters, the measurements were performed using a drone as transport for the following altitude values: 40, 60, 90, and 110 m. Network parameter measurements were implemented in automatic mode, allowing switching between LTE4–LTE2 standards, providing the opportunity for more complex analysis. Based on the analysis made, the recommendations for the future mobile networks employed in controlled drone flights should correspond to the following KPI and their values: −100 dBm for RSRP, −16 dB for RSRQ, −5 dB for SINR, 4096 kbps for downlink, 4096 kbps for uplink, and 50 ms for ping. Lastly, recommendations for a network coverage digital twin (DT) model with integrated KPIs are also provided. Full article

Environmental and energy sustainability concerns have catalyzed a global transition toward renewable biofuel alternatives. Among these, biodiesel stands out as a promising substitute for conventional diesel in compression-ignition engines, providing compatibility without requiring modifications to engine design. A comprehensive understanding of biodiesel’s physical properties is crucial for accurately modeling fuel spray, atomization, combustion, and emissions in diesel engines. This study focuses on predicting the physical properties of PODL20 and EB100, including liquid viscosity, density, vapor pressure, latent heat of vaporization, thermal conductivity, gas diffusion coefficients, and surface tension, all integrated into the CONVERGE CFD fuel library for improved combustion simulations. Subsequently, numerical simulations were conducted using the predicted properties of the biodiesels, validated by experimental in-cylinder pressure data. The prediction models demonstrated excellent alignment with the experimental results, confirming their accuracy in simulating spray dynamics, combustion processes, turbulence, ignition, and emissions. Notably, significant improvements in key combustion parameters, such as cylinder pressure and heat release rate, were recorded with the use of biodiesels. Specifically, the heat release rates for PODL20 and EB100 reached 165.74 J/CA and 140.08 J/CA, respectively, compared to 60.2 J/CA for conventional diesel fuel. Furthermore, when evaluating both soot and NO_x emissions, EB100 displayed a more balanced performance, achieving a significant reduction in soot emissions of 34.21% alongside a moderate increase in NO_x emissions of 45.5% compared to diesel fuel. In comparison to PODL20, reductions of 20.4% in soot emissions and 3% in NO_x emissions were also noted. Full article

Radiofrequency electromagnetic fields (RF-EMFs) can penetrate tissues and potentially influence endocrine and brain development. Despite increased mobile phone use among children and adolescents, the long-term effects of RF-EMF exposure on brain and endocrine development remain unclear. This study investigated the effects of long-term evolution band (LTE) EMF exposure on thyroid hormone levels, crucial for metabolism, growth, and development. Four-week-old male mice (C57BL/6) were exposed to LTE EMF (whole-body average specific absorption rate [SAR] 4 W/kg) or a positive control (lead; Pb, 300 ppm in drinking water) for 4 weeks. Subsequently, the mice underwent behavioral tests including open field, marble burying, and nest building. Blood pituitary and thyroid hormone levels, and thyroid hormone-regulating genes within the hypothalamus–pituitary–thyroid (HPT) axis were analyzed. LTE exposure increased T3 levels, while Pb exposure elevated T3 and T4 and decreased ACTH levels. The LTE EMF group showed no gene expression alterations in the thyroid and pituitary glands, but hypothalamic Dio2 and Dio3 expressions were significantly reduced compared to that in the sham-exposed group. Pb exposure altered the hypothalamic mRNA levels of Oatp1c1 and Trh, pituitary mRNA of Trhr, and Tpo and Tg expression in the thyroid. In conclusion, LTE EMF exposure altered hypothalamic Dio2 and Dio3 expression, potentially impacting the HPT axis function. Further research is needed to explore RF-EMF’s impacts on the endocrine system. Full article

We present the results of a self-consistent analysis of the magnetic silicon star BD+00°1659, based on its high-resolution spectra taken from the ESPaDOnS archive (R = 68,000). This narrow-lined star shows the typical high Si abundance and Si ii– iii anomaly, making it an ideal prototype for investigating the vertical distribution of Si and Fe in the stellar atmosphere. The derived abundances, ranging from helium to lanthanides, confirm the star’s classification as a silicon Bp spectral type. Silicon and iron are represented by lines of different ionisation stages (Fe i– iii, Si i– iii), indicating an ionisation imbalance interpreted as evidence of atmospheric stratification. Our stratification analysis reveals that there is a jump in iron and silicon abundances of 1.5 dex at atmospheric layers with an optical depth of $log{\tau }_{5000}$ = −0.85–−1.00. Non-LTE calculations for iron in this stratified atmosphere show minor non-LTE effects. Our results can be applied to studying the impact of stratification on the emergent flux in rapidly rotating Si stars with similar atmospheric parameters and abundance anomalies (for example, MX TrA), where direct stratification analysis is challenging due to line blending. Full article

Protecting sensitive patient data, such as electrocardiogram (ECG) signals, during RF wireless transmission is essential due to the increasing demand for secure telemedicine communications. This paper presents an innovative chaotic-based encryption system designed to enhance the security and integrity of telemedicine data transmission. The proposed system utilizes a multi-scroll chaotic system for ECG signal encryption based on master–slave synchronization. The ECG signal is encrypted by a master system and securely transmitted to a remote location, where it is decrypted by a slave system using an extended state observer. Synchronization between the master and slave is achieved through the Lyapunov criteria, which ensures system stability. The system also supports Orthogonal Frequency Division Multiplexing (OFDM) and adaptive n-quadrature amplitude modulation (n-QAM) schemes to optimize signal discretization. Experimental validations with a custom transceiver scheme confirmed the system’s effectiveness in preventing channel overlap during 2.5 GHz transmissions. Additionally, a commercial RF Power Amplifier (RF-PA) for LTE applications and a development board were integrated to monitor transmission quality. The proposed encryption system ensures robust and efficient RF transmission of ECG data, addressing critical challenges in the wireless communication of sensitive medical information. This approach demonstrates the potential for broader applications in modern telemedicine environments, providing a reliable and efficient solution for the secure transmission of healthcare data. Full article

In the context of 5G networks, this paper investigates microstrip array antennas and mobile terminal MIMO array antennas. It introduces two innovative designs and, based on these, develops and fabricates a mobile terminal antenna. The first of these designs, a 4 × 4 microstrip array antenna operating in the LTE band 42 (3.4–3.6 GHz), is researched and fabricated and an innovative approach, combining embedded and coaxial feeding methods, is proposed and employed. Measurement results indicate a bandwidth of 373 MHz (3.321–3.694 GHz), achieving a relative bandwidth of 10.7%. The antenna exhibits a high gain of 12.7 dBi, with an undistorted radiation pattern, demonstrating excellent directional characteristics. The second of these designs, a “loop-slot” MIMO antenna designed for 5G mobile devices with metal frames, is investigated. By opening slots in the metal frame and integrating them into the antenna’s feeding structure, the decoupling principle is analyzed from the perspective of characteristic mode theory. This design shares resonant modes between the loop and slot antennas, allowing for the overlapping placement of the two antenna units. Experimental results confirm an isolation level exceeding 21 dB, with significantly reduced dimensions. Finally, an eight-unit MIMO antenna is designed and fabricated for 5G mobile devices with metal frames. Continuous optimization of the “loop-slot” module layout and unit spacing leads to a compact and miniaturized antenna structure. Measurement results show an isolation level exceeding 17 dB, radiation efficiency ranging from 65.8% to 73.7%, and an envelope correlation coefficient (ECC) below 0.03. Finally, an analysis of specific absorption rate (SAR) demonstrates excellent MIMO performance in terms of human body radiation exposure. Full article