Network Of Stations Research Articles

A Sustainable Solution for Urban Transport Using Photovoltaic Electric Vehicle Charging Stations: A Case Study of the City of Hail in Saudi Arabia

As the global shift toward sustainable transportation gains momentum, the integration of electric vehicles (EVs) becomes imperative, necessitating a robust and environmentally friendly charging infrastructure. Leveraging the abundant solar potential in the region, this study examines the technical, economic, and environmental feasibility of deploying photovoltaic electric vehicle charging stations (PV-EVCSs) in Hail City, Saudi Arabia, as a case study. This study examines factors such as the energy demand, grid integration, and user accessibility, aiming to address the challenges and opportunities presented by the urban fabric. The proposed solar charging station network seeks to catalyze a paradigm shift toward a cleaner and more sustainable transportation ecosystem, embodying a forward-thinking approach to meeting the evolving needs of urban mobility in the 21st century. The analysis encompasses many scenarios, encompassing a range of car battery sizes, charger powers, and car slots per station. Zone 4 is identified as the most crucial area, where seven charging stations are needed to fulfill the expected demand in the absence of any private charging alternatives. The economic evaluation of the 1047.35 kWp PV system reveals an estimated conventional payback time of 11.69 years, accompanied by a return on assets of 10.17%. The system generates accumulated cash flows amounting to SR 7,169,294.62 over 30 years, while the estimated operational and maintenance expenses are predicted to be SR 50,000 per year. The overall investment cost for the solar PV and EV charging stations is SR 4,487,982. This cost is offset by the yearly electricity savings from solar and grid sources, which can reach up to SR 396,465.26 by year 30. This work presents a detailed plan for the future of sustainable transport. It combines technical, environmental, and economic aspects to promote a cleaner and more sustainable urban mobility system.

Optimizing Disaster Response through Efficient Path Planning of Mobile Aerial Base Station with Genetic Algorithm

The use of unmanned aerial vehicles (UAVs), or drones, as mobile aerial base stations (MABSs) in Disaster Response Networks (DRNs) has gained significant interest in addressing coverage gaps of user equipment (UE) and establishing ubiquitous connectivity. In the event of natural disasters, the traditional base station is often destroyed, leading to significant challenges for UEs in establishing communication with emergency services. This study explores the deployment of MABS to provide network service to terrestrial users in a geographical area after a disaster. The UEs are organized into clusters at safe locations or evacuation shelters as part of the communication infrastructure. The main goal is to provide regular wireless communication for geographically dispersed users using Long-Term Evolution (LTE) technology. The MABS traveling at an average speed of 50 km/h visits different cluster centroids determined by the Affinity Propagation Clustering (APC) algorithm. A combination of graph theory and a Genetic Algorithm (GA) was used through mutators with a fitness function to obtain the most efficient flyable paths through an evolution pool of 100 generations. The efficiency of the proposed algorithm was compared with the benchmark fitness function and analyzed using the number of serviced UE performance indicators. System-level simulations were used to evaluate the performance of the proposed new fitness function in terms of the UEs served by the MABS after the MABS deployment, fitness score, service ratio, and path smoothness ratio. The results show that the proposed fitness function improved the overall service of UEs after MABS deployment and the fitness score, service ratio, and path smoothness ratio under a given number of MABS.

Location-routing optimization problem for electric vehicle charging stations in an uncertain transportation network: An adaptive co-evolutionary clustering algorithm

The rapid growth of Electric Vehicles (EVs) has led to issues such as insufficient and unevenly distributed charging stations, posing an increasingly severe challenge. This not only means higher economic and time costs for EV users traveling to charging stations, but also indicates that EVs need to incur additional energy consumption. To address these challenges, this paper first establishes an extended space-time-state network to describe the temporal variability and uncertainty of the traffic environment. Secondly, considering the impact of the energy consumption incurred by EVs traveling to charging stations (CSs) on the layout of CSs, a co-evolutionary optimization model of the location-routing problem is proposed. To solve the model, a two-stage Adaptive Co-evolutionary Clustering Algorithm (ACECA) integrating an adaptive clustering framework and a co-evolutionary mechanism is designed. Finally, the experiment results indicate that there exists a game between a short-term economic investment and long-term benefits of energy conservation and emission reduction. Moreover, ACECA demonstrates stronger solution performance and robustness compared to other algorithms, with the resulting CS location schemes showing superior advantages in energy conservation and in cost saving for users. The research findings can provide theoretical support for the planning of charging station locations for electric vehicles.

Lithological control of the active Mejillones fault from Triassic to Quaternary, northern Chile

Understanding the lithological control of crustal faults near cities is important for assessing the seismic hazard and characterising complex fault systems. We studied the Mejillones Fault (MF), located near one of the strategic ports in northern Chile. The MF is an active geological fault that extends 40 km in the N-S direction and is part of an extensive complex system of structures known as the Atacama Fault System (AFS). We built an S-wave tomography model using seismic ambient noise data that was recorded continuously in a dense network of stations from 2013 to 2015. The tomographic maps exhibited velocity anomalies that correlate with surface geology. We found lower velocities in the western part of the MF than in the eastern zone, with a shear wave velocity (Vs) of about 2.4 km/s, associated to fractured metamorphic rocks (Triassic) and marine sediments (Neogene-Quaternary). Higher velocities (Vs > 3.0 km/s) in the eastern part of the fault are related to igneous rocks from the Upper Jurassic. The difference in velocities between the western and eastern zones of the MF remained persistent in depth. The potential depth of the MF could be >5 km and delimits the boundary between 2 different geological zones. Intermediate Vs (∼2.8 km/s) at −23.25°, observed up to 4 km depth, can be associated with andesitic rocks deposited in an intra-arc basin (Upper Triassic) and accommodated by the MF over the last ∼200 Myr, evidencing the activity of this fault from Triassic to Quaternary.

The Global Historical Climatology Network Monthly Precipitation Dataset, Version 4

The Global Historical Climatology Network (GHCN) monthly precipitation dataset contains historical time series for thousands of land surface stations worldwide. Initially released in 1992 and revised in 1998, the dataset has been employed in a variety of applications over the past three decades, including operational monitoring, applied research, and international assessments. This paper describes the data and methods used to compile the latest edition (version 4), which has three major enhancements. The first enhancement is to the station network, which increased in size by a factor of five due to the inclusion of dozens of new source datasets, most notably GHCN Daily (GHCNd). The second improvement is the application of a rule-based algorithm to compare and merge records representing the same location. The third enhancement is to the quality assurance approach, now consisting of 18 new checks based on GHCNd and other operational systems. Updated monthly, the resulting dataset consists of time series of monthly precipitation totals at more than 120,000 worldwide stations, including more than 33,000 active observing sites.

Fuel flow logistics: an empirical analysis of performance in a network of gas stations using principal component analysis and data envelopment analysis

PurposeThe aim of this study was to evaluate the performance of fuel flow processes in a network of eight gas stations, located in the mesoregion of Alto Paranaíba and Triângulo Mineiro.Design/methodology/approachTwo multi-criteria decision support methods were applied, respectively, of a statistical and mathematical nature, namely, Principal Component Analysis (PCA) and Data Envelopment Analysis (DEA). The research method used was quantitative, with a brief complement of qualitative research, and descriptive in purpose, supported by the inductive method. The data collection stage took place with the support of interviews, with the application of a structured questionnaire, and non-probabilistic sampling, for convenience.FindingsIt was possible to verify that the gas station that stood out the most was station 2 (GS2), which achieved maximum efficiency, a fact that can be justified by the analysis resulting from the application of PCA, as for the product purchase variable (PP), the GS2 is the one that buys the most fuel, and is also the one with the largest storage capacity (C), and the highest volume of product sales (PS), which suggests signs of balance between supply and demand for this station, justifying its prominence.Research limitations/implicationsThe limitations of the study were related to the DEA technique, which requires a number of variables/indicators three times smaller than the number of DMUs considered, and the difficulty in obtaining financial data on the DMUs analyzed. Considering the security and anonymity of the gas station network, it was not possible to use this data.Practical implicationsThe performance assessment of fuel flow processes carried out in this study promotes the efficient use of available resources as well as identifying efficient DMUs that represent benchmarks for improving management processes and performance of inefficient DMUs.Social implicationsFrom a social perspective, this study promotes the improvement of the quality of flow processes and effective management of the fuel supply chain, ensuring the safe storage and transportation of fuels to customer supply. Performance management in this sector moves other sectors of the economy, since an efficient unit represents a balance between supply and demand, and consequently, boosts the regional economy, promoting economic growth of the population. Hiring qualified labor for this purpose also represents one of the implications of the study. From an environmental perspective, optimizing flow processes generates a reduction in greenhouse gas emissions and encourages the formulation of public policies aimed at consolidating sustainable practices.Originality/valuePerformance management applied to the context of the fuel supply chain is a relevant topic that has been little explored in scientific research, with a low level of information detail. This study using the inductive method allows the generalization and replication of this management pattern in other organizations in the sector in order to increase the efficiency of the fuel distribution system, with the perspective of maximizing outputs and reducing input consumption. In this aspect, the study introduces possibilities for advancement in social and environmental perspectives based on the effective management of fuel logistics.

Exploring the interplay between water quality parameters and aquatic fauna in a human-dominated stream network in Iran

Rivers and streams are dynamic linear features that undergo numerous disturbances from nearby human activities. This is particularly evident in Iranian arid regions, where agricultural activities are concentrated along these water bodies to harness their freshwater resources. This study utilized 12 stations in the Maharlu stream network to assess the impact of human activities on its water quality and aquatic fauna. Results indicated that severe nutrient pollution attributed to a high mean nitrate concentration (67.77 ± 79.72 mg/L). By employing PCA analysis, three main principal components were extracted, explaining a variance of 82.61%. PC1 exhibited high positive loadings for dissolved oxygen (DO), while PC2 showed an elevated salt concentration. Midges and non-biting mosquitoes, predominantly from the Chironomidae family, emerged as the dominant taxon, contributing to over 98% of the total species abundance. Furthermore, integrated PCA–CCA analysis revealed downstream shifts in species composition toward more tolerant species, driven by decreasing DO levels and increasing salt concentrations. These shifts in trends found in water quality parameters and species composition were linked to agricultural runoff carrying substantial amounts of fertilizers, pesticides, and organic matter from livestock farming. Consequently, there is an urgent need to implement conservation measures to mitigate their adverse effects.

Predicting Stroke Risk Based on ICD Codes Using Graph-Based Convolutional Neural Networks

In recent years, convolutional neural networks (CNNs) have emerged as highly efficient architectures for image and audio classification tasks, gaining widespread adoption in state-of-the-art methodologies. While CNNs excel in machine learning scenarios where the data representation exhibits a grid structure, they face challenges in generalizing to other data types. For instance, they struggle with data structured on 3D meshes (e.g., measurements from a network of meteorological stations) or data represented by graph structures (e.g., molecular graphs). To address such challenges, the scientific literature proposes novel graph-based convolutional network architectures, extending the classical convolution concept to data structures defined by graphs. In this paper, we use such a deep learning architecture to examine graphs defined using the ICD-10 codes appearing in the medical data of patients who suffered hemorrhagic stroke in Hungary in the period 2006–2012. The purpose of the analysis is to predict the risk of stroke by examining a patient’s ICD graph. Finally, we also compare the effectiveness of this method with classical machine learning classification methods. The results demonstrate that the graph-based method can predict the risk of stroke with an accuracy of over 73%, which is more than 10% higher than the classical methods.

Evaluating the Positional Stability Status of the Druk Corsnet: A Preliminary Study

Abstract— The continuously operating reference station (CORS) that provides a Global Navigation Satellite System (GNSS) enables precise location and height data for various applications such as site selection of important engineering projects and constructions and hazard analysis. Similarly, the most common applications of the CORS network, in Bhutan, are for engineering, cadastral, geodetic, and topographical surveys. In this regard, the consistency and dependability of the data from the CORS station are crucial. Moreover, the ability of such applications heavily depends on precise and reliable data from the CORS network. However, Bhutan has not conducted any studies on the stability, in terms of its position, and height fluctuation of the CORS station. Therefore, the goal of this study is to determine the positional and height variation of the 9 CORS stations that are operational in Bhutan between the years 2016 to 2022. Assessing the stability and dynamic behavior of reference stations in a CORS network is part of position variation analysis. It is feasible to identify and quantify a variety of geodetic occurrences by examining the time series of positional analysis. Analysis of height variation focuses on changes in orthometric heights throughout the CORS network. In our study, all the available GNSS CORS data are processed and the displacement in height and position is computed and compared. The analysis consists of a simple method (mean value subtraction) of time series data, which is computed and compared. The major findings show that the absolute mean standard deviation in the position of the stations was 5.49 cm, 6.36 cm, and 4.24 cm vertical (height), ease, and north horizontal component respectively. The findings highlight that the proposed method of determining the variation in height and location of the CORS stations is fairly practical and will help the user community to guide them in using the CORS network across Bhutan wisely. Similarly, the result of this study will be a relevant source of information to organizations such as the National Land Commission Secretariat (NLCS) as a means to augment the system in the future.

On the Variability in the Temporal Stability Pattern of Soil Moisture Under Mediterranean Conditions

In recent decades, there has been increasing interest in studying the variability in soil water properties and, specifically, the spatiotemporal variability in the soil water content. This is motivated by the notable theoretical and applied research interests in soil moisture dynamics and their implications for many natural processes. This study aimed to study whether there are variations in the spatial pattern of the temporal stability of soil moisture over time and to analyze the possible influences of certain hydroclimatic (soil water content, precipitation, and evapotranspiration) and soil factors (texture, bulk density, and organic matter content) on these variations. This study was conducted within the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS, Spain) under Mediterranean conditions, with daily surface moisture data (0–5 cm depth) obtained from 20 stations for the 2006-2023 period. The results showed differences between the average pattern obtained with the 18-year data series and that obtained with the data series for each year. In more than half of the years studied, the representative station differed from that derived from the average pattern. The mean annual precipitation and summer precipitation characteristics seem to be the main factors influencing the variability in the spatial pattern of the temporal stability of soil moisture.

Learning-driven service caching in MEC networks with bursty data traffic and uncertain delays

Mobile edge computing (MEC) provides extremely low-latency services for mobile users, by attaching computing resources to 5G base stations in an MEC network. Network service providers can cache their services from remote data centers to base stations to serve mobile users within their proximity, thereby reducing service latencies. However, mobile users of network services usually have bursty requests that require immediate processing in the MEC network. The data traffic of such requests is bursty, since mobile users have various hidden features including locations, group tags, and mobility patterns. Furthermore, such bursty data traffic causes uncertain congestion at base stations and thus leads to uncertain processing delays. Considering the limited resources of base stations, network services may not be able to be placed into base stations permanently to handle the bursty data traffic. As such, network services need to be dynamically cached in the MEC network to fully address the bursty data traffic and uncertain processing delays.In this paper, we investigate the problem of dynamic service caching and task offloading in an MEC network, by adopting the online learning technique to harness the challenges brought by bursty data traffic and uncertain processing delays. We first propose an online learning algorithm for the problem with uncertain processing delays, by utilizing the multi-armed bandits technique, and analyze the regret bound of the proposed algorithm. We then propose another online learning algorithm for the problem with bursty data traffic and uncertain processing delays, which adaptively learns the bursty data traffic of requests, based on small samples of mobile users’ hidden features. We also propose a novel architecture of Generative Adversarial Network (GAN) to accurately predict user demands using small samples of mobile users’ hidden features. Based on the proposed GAN model, we then devise an efficient heuristic for the problem with the uncertainties of both bursty data traffic and uncertain delays. We finally evaluate the performance of the proposed algorithms by simulations, using a real data trace. Experimental results show that the performance of the proposed algorithms outperforms existing ones by up to 44% in terms of average delay.

Graph-Based vs. Error State Kalman Filter-Based Fusion of 5G and Inertial Data for MAV Indoor Pose Estimation

5G New Radio Time of Arrival (ToA) data has the potential to revolutionize indoor localization for micro aerial vehicles (MAVs). However, its performance under varying network setups, especially when combined with IMU data for real-time localization, has not been fully explored so far. In this study, we develop an Error State Kalman Filter (ESKF) and a Pose Graph Optimization (PGO) approach to address this gap. We systematically evaluate the performance of the derived approaches for real-time MAV localization in realistic scenarios with 5G base stations in Line-Of-Sight (LOS), demonstrating the potential of 5G technologies in this domain. In order to experimentally test and compare our localization approaches, we augment the EuRoC MAV benchmark dataset for visual-inertial odometry with simulated yet highly realistic 5G ToA measurements. Our experimental results comprehensively assess the impact of varying network setups, including varying base station numbers and network configurations, on ToA-based MAV localization performance. The findings show promising results for seamless and robust localization using 5G ToA measurements, achieving an accuracy of 15 cm throughout the entire trajectory within a graph-based framework with five 5G base stations, and an accuracy of up to 34 cm in the case of ESKF-based localization. Additionally, we measure the run time of both algorithms and show that they are both fast enough for real-time implementation.

Decomposing PM2.5 concentrations in urban environments into meaningful factors 2. Extracting the contribution of traffic-related exhaust emissions

Vehicle-emitted fine particulate matter (PM2.5) has been associated with significant health outcomes and environmental risks. This study estimates the contribution of traffic-related exhaust emissions (TREE) to observed PM2.5 using a novel factorization framework. Specifically, co-measured nitrogen oxides (NOx) concentrations served as a marker of vehicle-tailpipe emissions and were integrated into the optimization of a Non-negative Matrix Factorization (NMF) analysis to guide the factor extraction. The novel TREE-NMF approach was applied to long-term (2012–2019) PM2.5 observations from air quality monitoring (AQM) stations in two urban areas. The extracted TREE factor was evaluated against co-measured black carbon (BC) and PM2.5 species to which the TREE-NMF optimization was blind. The contribution of the TREE factor to the observed PM2.5 concentrations at an AQM station from the first location showed close agreement (R2=0.79) with monitored BC data. In the second location, a comparison of the extracted TREE factor with measurements at a nearby Surface PARTiculate mAtter Network (SPARTAN) station revealed moderate correlations with PM2.5 species commonly associated with fuel combustion, and a good linear regression fit with measured equivalent BC concentrations. The estimated concentrations of the TREE factor at the second location accounted for 7–11 % of the observed PM2.5 in the AQM stations. Moreover, analysis of specific days known to be characterized by little traffic emissions suggested that approximately 60–78 % of the traffic-related PM2.5 concentrations could be attributed to particulate traffic-exhaust emissions. The methodology applied in this study holds great potential in areas with limited monitoring of PM2.5 speciation, in particular BC, and its results could be valuable for both future environmental health research, regional radiative forcing estimates, and promulgation of tailored regulations for traffic-related air pollution abatement.

Digital antenna array based on programmable logic integrated circuits for mobile radio networks

Currently, there is a need to develop new technical solutions for antenna systems for central (base) stations of mobile radio networks, which not only have improved electrical characteristics, but also allow them to adjust from intentional and unintended interference, while providing a reliable communication channel between several correspondents. The purpose of the work is to develop methods for constructing digital antenna arrays with adaptive DN control for VHF mobile radio networks. The presented solution of the ring car allows you to form the DN in the specified directions, to work in multipath mode, as well as the proposed solutions of ring cars can be used both in the center (base) stations of mobile radio communication systems and on mobile objects operating, including in the relay mode.

Small-sized active phased array antenna for base stations of mobile radio networks in limited deployment conditions

Today, most telecommunications structures are oversaturated with antenna technology. There is a need to use high-rise buildings and facilities that are not equipped for communication. However, the use of such objects imposes a number of restrictions on the installation locations of antenna systems. Thus, the problem arises of developing an antenna system that makes it possible to achieve a circular radiation pattern using small-sized antennas. A model of a small-sized UHF AFAR has been developed. A distinctive feature of this AFAR is the use of a ring active phased antenna array with an electrically controlled radiation pattern as an antenna system. Such solutions have not been used in domestic antenna technology, including military radio communications. The antenna complex is proposed to be used for installation on mobile objects, as well as on the upper platforms of towers and masts to provide a circular radiation pattern in places where it is impossible to implement a ring antenna array around the perimeter of the object.

Optimizing NO2 monitoring network using a background map for spatial heterogeneity stratification

Nitrogen dioxide (NO2) is a critical air pollutant affecting health and the environment. However, existing monitoring station networks often fail to adequately capture the regional distribution of NO2, indicating a need for enhanced sampling strategies. This study focuses on Southwest Fujian in China and introduces a high-precision NO2 background map to delineate spatial stratified heterogeneity. Subsequently, the Mean of Surface with Non-homogeneity (MSN) method was employed to optimize the design of the NO2 monitoring network, proposing the addition of 125 new stations. The Bayesian Kriging analysis, utilized to evaluate the optimized network, resulted in a coefficient of determination (R2) of 0.87, a mean absolute percentage error (MAPE) of 9.61%, a mean absolute error (MAE) of 2.75 μg/m3 and a root mean square error (RMSE) of 2.47 μg/m3. The improved accuracy and efficiency of the NO2 monitoring network were validated against the background map. This research underscores the effectiveness of integrating precise mapping techniques with strategic network optimization for superior environmental monitoring outcomes.

The Lofar Global Citizenship Radio Array - “Gloray” (Ska-Low-North)

Abstract This talk describes a proposed transformation of LOFAR, the world’s largest, most sensitive high-resolution radio telescope that operates at low frequencies (<300 MHz), into a multidisciplinary intercontinental facility that will be a unique engine for capacity building and development throughout Europe, North Africa, the Middle East and West Asia. As an astronomical array, GLORAY would be the “Hubble Space Telescope” of low-frequency radio astronomy, having a resolution more than 50 times better than any other presently planned low-frequency facility, including SKA-LOW. The mutually interdependent network of LOFAR antenna stations that presently stretch from Ireland to Latvia would be extended to cover North Africa, the Middle East and West Asia and converted into “innovation hubs”. These would form an intercontinental network for pure research, applied research, capacity building, education and science diplomacy within and between the participating countries.

Distributed Peer-to-Peer Coordination of Hierarchical Three-Phase Energy Transactions Among Electric Vehicle Charging Stations in Constrained Power Distribution and Urban Transportation Networks

Distributed Peer-to-Peer Coordination of Hierarchical Three-Phase Energy Transactions Among Electric Vehicle Charging Stations in Constrained Power Distribution and Urban Transportation Networks

Application of Geoid and Mean Dynamic Topography Models as an Alternative to the Mean Sea Level Approach in Determining Vertical Datum (Case Study: Java Island and Surrounding)

Abstract Indonesia’s archipelagic territory provides constraints for determining a seamless vertical datum throughout the region. The main constraint is how to connect the vertical datum between islands separated by sea. To define the physical height system of land areas, it usually refers to the Mean Sea Level (MSL), which has different values in each area, and the ideal MSL information is not yet available throughout regions with a minimum data duration of one cycle of 18.6 years. To overcome this constraint, in this research, another alternative method is used: applying a vertical datum that refers to a physical geoid reference surface, namely Mean Dynamic Topography (MDT) using Altimetry satellite data. The study area is Java Island and its surroundings, using vertical information from Geodetic Control Point stations, which also function as Tidal stations in the Indonesian Geospatial Reference System network. The range of MDT deviation values obtained from the Satellite Altimetry with Geodetic Ground Control data is ± 2 meters, which is a good value for coastal areas facing the open sea. This shows that the application of the geoid and MDT models can be used as an alternative, seamless MSL approach in the Java Island region and its surroundings.

Constructing a Corrected Stations Network Using Some Geomatic Techniques

Abstract The major objective of this study is to establish a network of Ground Control Points-GCPs which can use it as a reference for any engineering project. Total Station (type: Nikon Nivo 5.C), Optical Level and Garmin Navigator GPS were used to perform traversing. Traversing measurement was achieved by using nine points covered the selected area irregularly. Near Civil Engineering Department at Baghdad University Al-jadiriya, an attempt has been made to assess the accuracy of GPS by comparing the data obtained from the Total Station. The average error of this method is 3.326 m with the highest coefficient of determination (R2) is 0.077 m observed in Northing. While in the Easting the average error is 4.634 m with (R2) 0.099 m observed. The average error 5.513 m with (R2) is 0.071 m is observed in Reduced Level. The accuracy assessments of the interpolation methods (Total Station and Level) have been calculated using the average error 0.240 m with (R2) is 0.735 m is observed in Reduced Level. The GIS 10.3 program can automatically transform the saved data to text format and produce digital plans with attribute.