bluuebunny/crossref_metadata_2025 huggingface dataset:
SAMLDroid: A Static Taint Analysis and Machine Learning Combined High-Accuracy Method for Identifying Android Apps with Location Privacy Leakage Risks
Authors: Hu Guangwu , Zhang Bin , Xiao Xi , Zhang Weizhe , Liao Long , Zhou Ying , Yan Xia
Published: November 2021

Insecure applications (apps) are increasingly used to steal users’ location information for illegal purposes, which has aroused great concern in recent years. Although the existing methods, i.e., static and dynamic taint analysis, have shown great merit for identifying such apps, which mainly rely on statically analyzing source code or dynamically monitoring the location data flow, identification accuracy is still under research, since the analysis results contain a certain false positive or true negative rate. In order to improve the accuracy and reduce the misjudging rate in the process of vetting suspicious apps, this paper proposes SAMLDroid, a combined method of static code analysis and machine learning for identifying Android apps with location privacy leakage, which can effectively improve the identification rate compared with existing methods. SAMLDroid first uses static analysis to scrutinize source code to investigate apps with location acquiring intentions. Then it exploits a well-trained classifier and integrates an app’s multiple features to dynamically analyze the pattern and deliver the final verdict about the app’s property. Finally, it is proved by conducting experiments, that the accuracy rate of SAMLDroid is up to 98.4%, which is nearly 20% higher than Apparecium.
A Nighttime Vehicle Detection Method with Attentive GAN for Accurate Classification and Regression
Authors: Liu Yan , Qiu Tiantian , Wang Jingwen , Qi Wenting
Published: November 2021

Vehicle detection plays a vital role in the design of Automatic Driving System (ADS), which has achieved remarkable improvements in recent years. However, vehicle detection in night scenes still has considerable challenges for the reason that the vehicle features are not obvious and are easily affected by complex road lighting or lights from vehicles. In this paper, a high-accuracy vehicle detection algorithm is proposed to detect vehicles in night scenes. Firstly, an improved Generative Adversarial Network (GAN), named Attentive GAN, is used to enhance the vehicle features of nighttime images. Then, with the purpose of achieving a higher detection accuracy, a multiple local regression is employed in the regression branch, which predicts multiple bounding box offsets. An improved Region of Interest (RoI) pooling method is used to get distinguishing features in a classification branch based on Faster Region-based Convolutional Neural Network (R-CNN). Cross entropy loss is introduced to improve the accuracy of classification branch. The proposed method is examined with the proposed dataset, which is composed of the selected nighttime images from BDD-100k dataset (Berkeley Diverse Driving Database, including 100,000 images). Compared with a series of state-of-the-art detectors, the experiments demonstrate that the proposed algorithm can effectively contribute to vehicle detection accuracy in nighttime.
Examining the Research on Business Information-Entropy Correlation in the Accounting Process of Organizations
Authors: Abad-Segura Emilio , González-Zamar Mariana-Daniela , Squillante Massimo
Published: November 2021

Open business organizations, where information flows, is shared, and exchanged, are more prepared to adapt and survive chaos, uncertainty, and entropy, so they will be more predisposed to change management. The aim of this study is to analyze research trends at the international level on business information–entropy correlation in the accounting process of organizations. Mathematical and statistical techniques were applied to 980 articles during the period 1974–2020, obtaining results on the scientific productivity of the driving agents of this topic: authors, research institutions, countries/territories, and journals. Five lines of research were identified during the period analyzed, which mainly study information theory, maximum entropy, information entropy, decision-making, and enthalpy. Future research should focus on analyzing the evolution of this topic, which forms new thematic axes related to bitcoin market efficiency, business hierarchy information, business model evaluation systems, catastrophic economic collapse, corporate diversification, CSR reports affecting accounting conservatism, economic income accounting, and information loss. Currently, the research presents an upward trend, which allows a growing interest in the subject to be deduced in the academic and scientific community worldwide.
(Semi-)Automatically Parsing Private Protocols for In-Vehicle ECU Communications
Authors: Chen Tongtong , Li Xiangxue
Published: November 2021

In-vehicle electronic control unit (ECU) communications generally count on private protocols (defined by the manufacturers) under controller area network (CAN) specifications. Parsing the private protocols for a particular vehicle model would be of great significance in testing the vehicle’s resistance to various attacks, as well as in designing efficient intrusion detection and prevention systems (IDPS) for the vehicle. This paper proposes a suite of methods for parsing ECU private protocols on in-vehicle CAN network. These methods include an algorithm for parsing discrete variables (encoded in a discrete manner, e.g., gear state), an algorithm for parsing continuous variables (encoded in a continuous manner, e.g., vehicle speed), and a parsing method based on upper-layer protocols (e.g., OBD and UDS). Extensive verifications have been performed on five different brands of automobiles (including an electric vehicle) to demonstrate the universality and the correctness of these parsing algorithms. Some parsing tips and experiences are also presented. Our continuous-variables parsing algorithm could run in a semi-automatic manner and the parsing algorithm from upper-layer protocols could execute in a completely automatic manner. One might view the results obtained by our parsing algorithms as an important indicator of penetration testing on in-vehicle CAN network.
Entropy Measures for Data Analysis II: Theory, Algorithms and Applications
Authors: Keller Karsten
Published: November 2021

Entropies and entropy-like quantities are playing an increasing role in modern non-linear data analysis [...]
Personal Interest Attention Graph Neural Networks for Session-Based Recommendation
Authors: Zhang Xiangde , Zhou Yuan , Wang Jianping , Lu Xiaojun
Published: November 2021

Session-based recommendations aim to predict a user’s next click based on the user’s current and historical sessions, which can be applied to shopping websites and APPs. Existing session-based recommendation methods cannot accurately capture the complex transitions between items. In addition, some approaches compress sessions into a fixed representation vector without taking into account the user’s interest preferences at the current moment, thus limiting the accuracy of recommendations. Considering the diversity of items and users’ interests, a personalized interest attention graph neural network (PIA-GNN) is proposed for session-based recommendation. This approach utilizes personalized graph convolutional networks (PGNN) to capture complex transitions between items, invoking an interest-aware mechanism to activate users’ interest in different items adaptively. In addition, a self-attention layer is used to capture long-term dependencies between items when capturing users’ long-term preferences. In this paper, the cross-entropy loss is used as the objective function to train our model. We conduct rich experiments on two real datasets, and the results show that PIA-GNN outperforms existing personalized session-aware recommendation methods.
The Impact of Industrialization, Trade Openness, Financial Development, and Energy Consumption on Economic Growth in Indonesia
Authors: Elfaki Khalid Eltayeb , Handoyo Rossanto Dwi , Ibrahim Kabiru Hannafi
Published: November 2021

This study aimed to scrutinize the impact of financial development, energy consumption, industrialization, and trade openness on economic growth in Indonesia over the period 1984–2018. To do so, the study employed the autoregressive distributed lag (ARDL) model to estimate the long-run and short-run nexus among the variables. Furthermore, fully modified ordinary least squares (FMOLS), dynamic least squares (DOLS), and canonical cointegrating regression (CCR) were used for a more robust examination of the empirical findings. The result of cointegration confirms the presence of cointegration among the variables. Findings from the ARDL indicate that industrialization, energy consumption, and financial development (measured by domestic credit) positively influence economic growth in the long run. However, financial development (measured by money supply) and trade openness demonstrate a negative effect on economic growth. The positive nexus among industrialization, financial development, energy consumption, and economic growth explains that these variables were stimulating growth in Indonesia. The error correction term indicates a 68% annual adjustment from any deviation in the previous period’s long-run equilibrium economic growth. These findings provide a strong testimony that industrialization and financial development are key to sustained long-run economic growth in Indonesia.
Architecting an Advanced Maturity Model for Business Processes in the Gig Economy: A Platform-Based Project Standardization
Authors: Gandhi Arfive , Sucahyo Yudho Giri
Published: November 2021

The business continuity of the gig economy is strongly driven by the operator’s ability to manage the maturity of business processes. Moreover, projects in the gig economy are risky due to the lack of monitoring and involvement of actors’ profiles. When business processes become mature as the target, platform-based project results can satisfy actors’ expectations. To reach targeted maturity, operators need to standardize their business processes. This standardization is actualized in a maturity model as a benchmark and guideline tool. It exposes how mature the current business processes are and the required improvements. This research aims to construct a maturity model systematically and comprehensively to encourage operators in the gig economy (as the model user) to improve the products and services delivered. This research has constructed a new maturity model for business processes using the maturity model development phases initiated by de Bruin et al. It explores the gig economy ecosystem in Indonesia. This research initiates the maturity model by collecting 48 factors in the gig economy. It continues by composing 13 determinant candidates as representations of the factors. After an empirical test involving 200 people (consisting of gig worker, client, and operator) and two iterations of mixed-method validation involving 16 experts, this research generates ten determinants classified into three dimensions: actors, platforms, and transactions. The maturity level of each determinant is measured to indicate its position toward digital business continuity.
Mapping the Moderating Role of Logistics Performance of Logistics Infrastructure on Economic Growth in Developing Countries
Authors: Khadim Zunaira , Batool Irem , Akbar Ahsan , Poulova Petra , Akbar Minahs
Published: November 2021

Logistics performance is an important determinant of economic growth. The present study investigates the moderating role of logistics performance of the logistic infrastructure on economic growth in developing countries. We employ the World Bank computed LPI index in the year 2010, 2012, 2014, 2016 and 2018 to measure the logistic performance. The current research includes the 50 developing economies, and a panel data set comprising of total 300 observations is collected. The study used the conventional Cobb–Douglas production function with labor, capital stock as main drivers of economic growth. The study found that the labor and capital endowments have significantly different impacts in terms of elasticity coefficients for developing countries with different logistics performance levels. It implies that logistics performance, i.e., the efficient performance of logistic infrastructure, plays a moderator role in economic growth in developing economies.
Educational Significance of Nanoscience–Nanotechnology: Primary School Teachers’ and Students’ Voices after a Training Program
Authors: Spyrtou Anna , Manou Leonidas , Peikos Giorgos
Published: November 2021

Most of the modern technological applications we use in our daily life originate from the progress of Nanoscience–Nanotechnology (NST). The projection, showing the great impact that these advances are going to have on society, exhorts science education researchers to incorporate the modern field into educational contexts. Among the several issues that have to be dealt with, NST’s educational significance comes to the fore. This pilot study aims to examine whether both nano-trained primary school teachers and nano-trained students acknowledge the need for the inclusion of the NST content to school contexts. Fourteen primary school teachers and ten students, after their participation in an NST training course, were interviewed in order to provide their justifications. The results show that the vast majority of the participants acknowledge the educational significance of NST. The main arguments were associated with career possibilities, the relevance of the content to everyday life, and the need for nanoliteracy attainment. The results of this study can be used by NST education researchers in order to formulate a NST content structure for primary school nanoeducation.
Digital University Teaching and Learning in Management—The Gini from the COVID-19 Bottle and Its Empirical Representations in Germany
Authors: Witt Tobias , Klumpp Matthias , Beyer Beatriz
Published: November 2021

Digitalization of teaching, learning, and assessment in higher education has gained increasing attention in research in the recent years. While previous research investigated issues of effectiveness, course attendance, and course evaluation from a long-term perspective, the current COVID-19 pandemic forced higher education institutions to digitalize teaching, learning, and assessment in a very short time. In this context, we investigate the effects of the digitalization of three courses from operations research and management science in the summer term 2020, namely two large lectures and tutorials for undergraduate, and a seminar for graduate students. To that end, student performance, course and exam attendance rates, and course evaluations are compared to the setting of the same courses in the previous year 2019 with a traditional, non-digitalized setting. Next to the quantitative data, qualitative statements from the course evaluations and students’ expectations expressed during the term are investigated. Findings indicate that the lecturers’ understanding of learning behavior has to develop further as interaction is required in any format, on-site or digital. Absenteeism and procrastination are important risk areas especially in digital management education. Instruments would have to be adapted to digital settings, but with care and relating to course specifics (including digital evaluation). Digital education does not make learning per se easier or harder, but we observed that the students’ understanding and performance gap increased in digital teaching times. As an outlook, we propose the longitudinal investigation of the ongoing digitalization during the COVID-19 pandemic, and going beyond, investigate opportunities of the current crisis situation for implementing the long-term transition to digital education in higher institution institutions.
Influence of COVID-19 Epidemic on Dark Web Contents
Authors: Razaque Abdul , Valiyev Bakhytzhan , Alotaibi Bandar , Alotaibi Munif , Amanzholova Saule , Alotaibi Aziz
Published: November 2021

The Dark Web is known as a place triggering a variety of criminal activities. Anonymization techniques enable illegal operations, leading to the loss of confidential information and its further use as bait, a trade product or even a crime tool. Despite technical progress, there is still not enough awareness of the Dark Web and its secret activity. In this study, we introduced the Dark Web Enhanced Analysis (DWEA) in order to analyze and gather information about the content accessed on the Dark Net based on data characteristics. The research was performed to identify how the Dark Web has been influenced by recent global events, such as the COVID-19 epidemic. The research included the usage of a crawler, which scans the network and collects data for further analysis with machine learning. The result of this work determines the influence of the COVID-19 epidemic on the Dark Net.
Connected Objects Geo-Localization Based on SS-RSRP of 5G Networks
Authors: Bannour Ahmed , Harbaoui Ahmed , Alsolami Fawaz
Published: November 2021

The Global Positioning System (GPS) is not the only way to solve connected objects’ geo-localization problems; it is also possible to use the mobile network infrastructure to geo-locate objects connected to the network, using antennas and signals designed for voice and data transfer, such as the 5th generation network. 5G is considered as a least expensive solution because there is no specific equipment to set up. As long as the object is in an area covered by the network, it connects to the nearest 5G Micro-Cell (MC). Through exchange of signals with the MC node we can locate the object. Currently, this location is very fast with less than 5 s but not very precise because it depends on the number of MC antennas of the operator in question and their distance. This paper presents a novel technique to geo-locate connected object in a covered 5G area. We exploit the 5G SS-RSRP used for signal quality measurement, to estimate the distance between two Connected Objects (COs) in move and in a dense urban area. The overall goal is to present a new concept laying on the 5G SS-RSRP signalling. The proposed solution takes into consideration the Deterministic and the Stochastic effect of the received signals which is not treated by the previous works. The accuracy is optimum even after approaching to the distance of one meter which is not reached in previous works too. Our method can also be deployed in the upcoming 5G network since it relies on 5G signals itself. This work and that of Wang are both based on RSRP and give comparable theoretical complexities therefore comparable theoretical execution times as well. However, to obtain a reliable learning Wang requires a huge amount of data which makes it difficult to get a real time aspect of this algorithm. The use of RSRP and the elimination of the learning phase will give more chance to our work to achieve desired performances. Numerical results show the appropriateness of the proposed algorithms and good location accuracy of around one meter. The Cramer Rao Lower Bound derivations shows the robustness of the proposed estimator and consolidate the work.
Methodology of Adaptive Instantaneous Overcurrent Protection Setting
Authors: Jimenez Saul , Vázquez Ernesto , Gonzalez-Longatt Francisco
Published: November 2021

This paper proposed a methodology of adaptive instantaneous overcurrent protection (AIOCP) setting that ensures that the protection coverage remains unchanged regardless of the operating condition of the electrical network. The methodology calculates the protection setting parameters based on the real-time estimation of the Thevenin equivalent circuit (TEC). The estimation algorithm uses the positive-sequence voltage and current values and a system of non-linear equations, which is solved iteratively by the Gauss–Newton method. The proposed methodology calculates the IOCP settings in real time by using the real-time estimation of the TEC; therefore, any change in the electric network conditions is represented in the TEC, and the calculated setting keeps the desired protection coverage constant. Simulation results demonstrate that the proposed AIOCP can keep constant the protection coverage overcoming the classical problems of classical IOCP: sub-allocation and over-coverage.
An Arrow-Curve Path Planning Model for Mobile Beacon Node Aided Localization in Air Pollution Monitoring System IoT
Authors: Ahmed Enas M. , Hady Anar A. , El-Kader Sherine M. Abd , Khalil Abeer Twakol , Mohamed Wael A.
Published: November 2021

In wireless sensor networks, it is crucial to support the collected data of sensor nodes with position information. One of the promising ways to acquire the position of unknown nodes is using a mobile anchor node that traverses throughout the network, stops at determined points, and sends its position to aid in obtaining the location of other unknown nodes. The main challenge in using mobile anchor nodes lies in designing the path model with the highest localization accuracy, shortest path length, full coverage area, and minimal power consumption. In this paper, a path model named the Arrow-Curve path model is proposed for mobile node aided localization. The proposed path model can effectively localize all the static unknown sensor positions in the network field with high positioning accuracy and low power consumption while pledging full coverage area. The sensor network is implemented using MATLAB simulation and MCU node in both static unknown nodes and the mobile anchor node. The realtime environment guarantees a realistic environmental model with reliable results. The path model is implemented in realtime in indoor and outdoor environments and compared to the H-Curve path model using a trilateration technique. The results show that the suggested path model achieves better results compared to H-Curve model. The proposed path model achieves an average position error less than that of H-Curve by 10.6% in a simulation environment, 5% in an outdoor realtime environment, and 9% in an indoor realtime environment, and it decreases power consumption by 62.65% in the simulation environment, 50% in the outdoor realtime environment, and 75% in the realtime environment in indoor compared to H-Curve.
Ferroelectrics Based on HfO2 Film
Authors: Song Chong-Myeong , Kwon Hyuk-Jun
Published: November 2021

The discovery of ferroelectricity in HfO2 thin film, which is compatible with the CMOS process, has revived interest in ferroelectric memory devices. HfO2 has been found to exhibit high ferroelectricity at a few nanometers thickness, and studies have rapidly progressed in the past decade. Ferroelectricity can be induced in HfO2 by various deposition methods and heat treatment processes. By combining ferroelectric materials with field-effect transistors, devices that combine logic and memory functions can be implemented. Ferroelectric HfO2-based devices show high potential, but there are some challenges to overcome in endurance and characterization. In this paper, we discuss the fabrication and characteristics of ferroelectric HfO2 film and various applications, including negative capacitance (NC)), Ferroelectric random-access memory (FeRAM), Ferroelectric tunnel junction (FTJ), and Ferroelectric Field-effect Transistor (FeFET).
Advanced Double Layered Multi-Agent Systems Based on A3C in Real-Time Path Planning
Authors: Lee Dajeong , Kim Junoh , Cho Kyungeun , Sung Yunsick
Published: November 2021

In this paper, we propose an advanced double layered multi-agent system to reduce learning time, expressing a state space using a 2D grid. This system is based on asynchronous advantage actor-critic systems (A3C) and reduces the state space that agents need to consider by hierarchically expressing a 2D grid space and determining actions. Specifically, the state space is expressed in the upper and lower layers. Based on the learning results using A3C in the lower layer, the upper layer makes decisions without additional learning, and accordingly, the total learning time can be reduced. Our method was verified experimentally using a virtual autonomous surface vehicle simulator. It reduced the learning time required to reach a 90% goal achievement rate by 7.1% compared to the conventional double layered A3C. In addition, the goal achievement by the proposed method was 18.86% higher than that of the traditional double layered A3C over 20,000 learning episodes.
2.45 GHz Band Quadrature Microwave Frequency Discriminators with Integrated Correlators Based on Power Dividers and Rat-Race Hybrids
Authors: Rutkowski Adam , Stadnik Hubert
Published: November 2021

Instantaneous frequency measurement devices are useful for conducting extremely fast measurements of the current frequency value of microwave signals, even if their duration is extremely short. This paper presents the principle of determination of temporary values of the microwave signal phase and frequency using interferometer techniques, based on passive microwave components. Additionally, the structures and results of measurements of two novel versions of integrated microwave correlators for microwave frequency discriminators, made on a single printed circuit board, are shown. Three Wilkinson-type, single-stage power dividers, and two rat-race hybrids create the developed correlators. The developed devices were designed to work over a wide frequency range, i.e., of 1.6–3.1 GHz, and can be used to monitor Wi-Fi devices as well as pulse and CW radar systems operating in the S band. They can also be applied in passive radars and active Doppler radars. The view of the printed circuits boards and results of measurements are presented. Recommendations for improving the accuracy of measurement are proposed.
Precision Fibre Angle Inspection for Carbon Fibre Composite Structures Using Polarisation Vision
Authors: Atkinson Gary A. , O’Hara Nash Sean , Smith Lyndon N.
Published: November 2021

This paper evaluates the precision of polarisation imaging technology for the inspection of carbon fibre composite components. Specifically, it assesses the feasibility of the technology for fibre orientation measurements based on the premise that light is polarised by reflection from such anisotropically conductive surfaces. A recently commercialised Sony IMX250MZR sensor is used for data capture by using various lighting conditions. The paper shows that it is possible to obtain sub-degree accuracy for cured and dry woven and unidirectional materials in ideal conditions, which comprised dark field illumination. Indeed, in ideal conditions, the average relative angles can be measured to an accuracy of 0.1–0.2°. The results also demonstrate a precision of the order 1° for more general illumination, such as dome illumination and ambient lighting, for certain material type/lens combinations. However, it is also shown that the precision varies considerably depending on illumination, lens choice and material type, with some results having errors above 2°. Finally, a feasibility study into the inspection of three-dimensional components suggests that only limited application is possible for non-planar regions without further research. Nevertheless, the observed phenomena for such components are, at least, qualitatively understood based on physics theory.
Comparison of Deep Neural Network Models and Effectiveness of EMG Signal Feature Value for Estimating Dorsiflexion
Authors: Zaffir Muhammad Akmal Bin Mohammed , Nuwantha Praveen , Arase Daiki , Sakurai Keiko , Tamura Hiroki
Published: November 2021

Robotic ankle–foot orthoses (AFO) are often used for gait rehabilitation. Our research focuses on the design and development of a robotic AFO with minimum number of sensor inputs. However, this leads to degradation of gait estimation accuracy. To prevent degradation of accuracy, we compared a few neural network models in order to determine the best network when only two input channels are being used. Further, the EMG signal feature value of average rate of change was used as input. LSTM showed the highest accuracy. However, MLP with a small number of hidden layers showed results similar to LSTM. Moreover, the accuracy for all models, with the exception of LSTM for one subject (SD), increased with the addition of feature value (average rate of change) as input. In conclusion, time-series networks work best with a small number of sensor inputs. However, depending on the optimizer being used, even a simple network can outrun a deep learning network. Furthermore, our results show that applying EMG signal feature value as an input tends to increase the estimation accuracy of the network.
No-Reference Video Quality Assessment Based on Benford’s Law and Perceptual Features
Authors: Varga Domonkos
Published: November 2021

No-reference video quality assessment (NR-VQA) has piqued the scientific community’s interest throughout the last few decades, owing to its importance in human-centered interfaces. The goal of NR-VQA is to predict the perceptual quality of digital videos without any information about their distortion-free counterparts. Over the past few decades, NR-VQA has become a very popular research topic due to the spread of multimedia content and video databases. For successful video quality evaluation, creating an effective video representation from the original video is a crucial step. In this paper, we propose a powerful feature vector for NR-VQA inspired by Benford’s law. Specifically, it is demonstrated that first-digit distributions extracted from different transform domains of the video volume data are quality-aware features and can be effectively mapped onto perceptual quality scores. Extensive experiments were carried out on two large, authentically distorted VQA benchmark databases.
Multi-Criteria Analysis of a Developed Prefabricated Footing System on Reactive Soil Foundation
Authors: Teodosio Bertrand , Bonacci Francesco , Seo Seongwon , Baduge Kasun Shanaka Kristombu , Mendis Priyan
Published: November 2021

The need for advancements in residential construction and the hazard induced by the shrink–swell reactive soil movement prompted the development of the prefabricated footing system of this study, which was assessed and compared to a conventional waffle raft using a multi-criteria analysis. The assessment evaluates the structural performance, cost efficiency, and sustainability using finite element modelling, life cycle cost analysis, and life cycle assessment, respectively. The structural performance of the developed prefabricated system was found to have reduced the deformation and cracking by approximately 40%. However, the cost, GHG emission, and embodied energy were higher in the prefabricated footing system due to the greater required amount of concrete and steel than that of the waffle raft. The cost difference between the two systems can be reduced to as low as 6% when prefabricated systems were installed in a highly reactive sites with large floor areas. The life cycle assessment further observed that the prefabricated footing systems consume up to 21% more energy and up to 18% more GHG emissions. These can significantly be compensated by reusing the developed prefabricated footing system, decreasing the GHG emission and energy consumption by 75–77% and 55–59% with respect to that of the waffle raft.
Comparing Different Levels of Technical Systems for a Modular Safety Approval—Why the State of the Art Does Not Dispense with System Tests Yet
Authors: Klamann Björn , Winner Hermann
Published: November 2021

While systems in the automotive industry have become increasingly complex, the related processes require comprehensive testing to be carried out at lower levels of a system. Nevertheless, the final safety validation is still required to be carried out at the system level by automotive standards like ISO 26262. Using its guidelines for the development of automated vehicles and applying them for field operation tests has been proven to be economically unfeasible. The concept of a modular safety approval provides the opportunity to reduce the testing effort after updates and for a broader set of vehicle variants. In this paper, we present insufficiencies that occur on lower levels of hierarchy compared to the system level. Using a completely new approach, we show that errors arise due to faulty decomposition processes wherein, e.g., functions, test scenarios, risks, or requirements of a system are decomposed to the module level. Thus, we identify three main categories of errors: insufficiently functional architectures, performing the wrong tests, and performing the right tests wrongly. We provide more detailed errors and present examples from the research project UNICARagil. Finally, these findings are taken to define rules for the development and testing of modules to dispense with system tests.
A GIS-Based Multicriteria Assessment for Identification of Positive Energy Districts Boundary in Cities
Authors: Alpagut Beril , Lopez Romo Arantza , Hernández Patxi , Tabanoğlu Oya , Hermoso Martinez Nekane
Published: November 2021

Discussions regarding the definition of Positive Energy Districts and the concept of a boundary are still being actively held. Even though there are certain initiatives working on the boundary limitations for PEDs, there is no methodology or tool developed for selecting peculiar spaces for future PED implementations. The paper focuses on a flexible GIS-based Multicriteria assessment method that identifies the most suitable areas to reach an annual positive non-renewable energy balance. For that purpose, a GIS-based tool is developed to indicate the boundary from an energy perspective harmonized with urban design and land-use planning. The method emphasizes evaluation through economic, social, political, legal, environmental, and technical criteria, and the results present the suitability of areas at macro and micro scales. The current study outlines macro-scale analyses in six European cities that represent Follower Cities under the MAKING-CITY H2020 project. Further research will be conducted for micro-scale analyses and the outcomes will pursue a technology selection process.
Mechanical Wear Contact between the Wheel and Rail on a Turnout with Variable Stiffness
Authors: Kisilowski Jerzy , Kowalik Rafał
Published: November 2021

The operation and maintenance of railroad turnouts for rail vehicle traffic moving at speeds from 200 km/h to 350 km/h significantly differ from the processes of track operation without turnouts, curves, and crossings. Intensive wear of the railroad turnout components (switch blade, retaining rods, rails, and cross-brace) occurs. The movement of a rail vehicle on a switch causes high-dynamic impact, including vertical, normal, and lateral forces. This causes intensive rail and wheel wear. This paper presents the wear of rails and of the needle in a railroad turnout on a straight track. Geometrical irregularities of the track and the generation of vertical and normal forces occurring at the point of contact of the wheel with turnout elements are additionally considered in this study. To analyse the causes of rail wear in turnouts, selected technical–operational parameters were assumed, such as the type of rail vehicle, the type of turnout, and the maximum allowable axle load. The wear process of turnout elements (along its length) and wheel wear is presented. An important element, considering the occurrence of large vertical and normal forces affecting wear and tear, was the adoption of variable track stiffness along the switch. This stiffness is assumed according to the results of measurements on the real track. The wear processes were determined by using the work of Kalker and Chudzikiewicz as a basis. This paper presents results from simulation studies of wear and wear coefficients for different speeds. Wear results were compared with nominal rail and wheel shapes. Finally, conclusions from the tests are formulated.
Factors of Renewable Energy Consumption in the European Countries—The Bayesian Averaging Classical Estimates Approach
Authors: Khan Atif Maqbool , Kwiatkowski Jacek , Osińska Magdalena , Błażejowski Marcin
Published: November 2021

The paper aims to identify the most likely factors that determine the demand for energy consumption from renewable sources (renewable energy consumption—REC) in European countries. Although in Europe, a high environmental awareness is omnipresent, countries differ in scope and share of REC due to historical energetic policies and dependencies, investments into renewable and traditional energetic sectors, R&D development, structural changes required by energetic policy change, and many other factors. The study refers to a set of macroeconomic, institutional, and social factors affecting energetic renewable policy and REC in selected European countries in two points of time: i.e., before and after the Paris Agreement. The Bayesian Average Classical Estimates (BACE) is applied to indicate the most likely factors affecting REC in 2015 and 2018. The comparison of the results reveals that the Gross Domestic Product (GDP) level, nuclear and hydro energy consumption were the determinants significant in both analyzed years. Furthermore, it became clear that in 2015, the REC depended strongly on the energy consumption structure, while in 2018, the foreign direct investment and trade openness played their role in increasing renewable energy consumption. The direction of changes is gradual and positive. It complies with the Sustainable Development Goals (SDGs).
Optimal Control Method of Variable Air Volume Terminal Unit System
Authors: Kim Hyo-Jun , Cho Young-Hum
Published: November 2021

This study reviewed the existing studies on the types of variable air volume (VAV) terminal units, control and operation methods, prediction models, and sensor calibration methods. As a result of analyzing the existing research trends on the system type, characteristics, and control method of VAV terminal units studies such as theoretical verification and energy simulation were conducted to improve the existing control methods, reset the set value using a mathematical model, and add a monitoring sensor for the application of control methods. The mathematical model used in the study of VAV terminal unit control methods was used to derive set values for minimum air volume, supply temperature, ventilation requirements, and indoor comfort. The mathematical model has a limitation in collecting input information for professional knowledge and model development, and development of a building environment prediction model using a black box model is being studied. The VAV terminal unit system uses a sensor to control the device, and when an error occurs in the sensor, indoor comfort problems and energy waste occur. To solve this problem, sensor calibration techniques have been developed using statistical models, mathematical models, and Bayesian statistical models. The possibility of developing a method for calibrating the variable air volume terminal unit sensor using the prediction model was confirmed. In conclusion, the VAV terminal unit system is one of the most energy efficient systems. The energy saving potential of current VAV systems can still be improved through control methods, the use of predictive models, and sensor calibration methods.
Optimized Extreme Learning Machine-Based Main Bearing Temperature Monitoring Considering Ambient Conditions’ Effects
Authors: Hou Zhengnan , Lv Xiaoxiao , Zhuang Shengxian
Published: November 2021

Wind Turbines (WTs) are exposed to harsh conditions and can experience extreme weather, such as blizzards and cold waves, which can directly affect temperature monitoring. This paper analyzes the effects of ambient conditions on WT monitoring. To reduce these effects, a novel WT monitoring method is also proposed in this paper. Compared with existing methods, the proposed method has two advantages: (1) the changes in ambient conditions are added to the input of the WT model; (2) an Extreme Learning Machine (ELM) optimized by Genetic Algorithm (GA) is applied to construct the WT model. Using Supervisory Control and Data Acquisition (SCADA), compared with the method that does not consider the changes in ambient conditions, the proposed method can reduce the number of false alarms and provide an earlier alarm when a failure does occur.
Voltage Profile Analysis on a Grid with Power Injection from a Wind Farm
Authors: Serem Noah , Munda Josiah , Letting Lawrence
Published: November 2021

Wind power penetration into the grid is increasing throughout the world due to centralized power generation constraints such as shortage of fossil fuel, need to reduce gas emissions, long transmission losses and need for more supply of electrical power. Connection of wind power into the grid results in power quality issues such as voltage profile changes and harmonics. This necessitates coming up with correction measures in order to meet power quality standards. This paper deals with the analysis of the effects of injecting wind power to the grid on voltage profile. Branch participation factors are used to analyze the sections of the grid where effects on voltage profile are highest due to wind power penetration. Reactive power-voltage sensitivity analysis is used to determine the buses that are more sensitive to the changes brought by the wind power injection. Two cases were considered for the injection of the wind power: IEEE 14 test bus system and IEEE 39 test bus system.
Volatility and Dispersion of Hourly Electricity Contracts on the German Continuous Intraday Market
Authors: Baule Rainer , Naumann Michael
Published: November 2021

Intraday electricity trading on the continuous intraday market of EPEX SPOT is particularly well suited for the rebalancing of energy production. We analyzed the volatility and dispersion of individual hourly contracts, taking into account the particularities of the market, due to which the standard volatility measure from financial time series cannot be applied. We used and analyzed five measures for price fluctuations, which turned out to be similarly well suited for electricity contracts, with small differences. We then identified fundamental drivers of price fluctuations: the relative share of wind in the overall mix increased dispersion. In addition, price dispersion was positively correlated with the traded volume as well as the absolute difference between the day-ahead auction price and the volume-weighted intraday price. We furthermore analyzed the timely structure of price fluctuations to identify forecast indicators for a contract’s peak trading hour before maturity, finding that trading-related variables are more important to forecast price fluctuations than fundamental factors. With lagged realizations and additional external drivers, forecast regressions reached an adjusted R2 of 0.479 for volatility and around 0.3 for the dispersion measures.
Visual Exploration of Energy Use in EU 28: Dynamics, Patterns, Policies
Authors: Halkos George , Tsilika Kyriaki
Published: November 2021

The paper places emphasis on primary energy resources, their covariation, and their correlation with socioeconomic factors and aims to provide a systematic analysis of their development over time. The analysis uses evidence from European Union (EU) country-level data and is based on visual analytics techniques. Different results from the same territories show that energy consumption does not always reflect or is due to climatological or meteorological conditions. Extensive use of visualization is adopted as a means of contributing to the understanding of energy use, some involved problems and concepts, and energy consumption trends over time. We present an approach that addresses the informatics challenges based on the integration of visualization software, data integration, and cluster analysis. Our cross-sectional energy review advocates that EU energy leaders are moving towards a low-carbon economy. The correlations of energy variables with economic and pollution effects are stronger in greater levels of energy use, which means that energy use has an obvious impact on economic growth and the environment. Visual and automated methods employed for the analysis, reveal the direction, the strength, and the nature of the dependence structure, in clusters covering the range of energy use in EU 28 countries.
The Way of Usage and Location in a Big City Agglomeration as Impact Factors of the Nurseries Indoor Air Quality
Authors: Basińska Małgorzata , Ratajczak Katarzyna , Michałkiewicz Michał , Fuć Paweł , Siedlecki Maciej
Published: November 2021

The quality of outdoor air in many places, especially in urbanized areas, is bad; therefore, a series of studies aimed at assessing the impact of outdoor air parameters on indoor air quality was conducted. Four nurseries (three buildings) located in different parts of Poznań, Poland were selected as research sites. During the research period, the concentrations of CO2, PM2.5, and PM10 inside and outside the buildings over time were analyzed. Measurements outside the buildings were supplemented with an analysis of traffic near nurseries and an assessment of pollutant emissions from vehicles of various categories. Measurements were conducted during the COVID-19 pandemic in a weekly series during winter (December)—(W), spring (April)—(S), and at the end of spring (June)—(LS). Testing and analyses indicated poor indoor air quality in three out of four nurseries. To ensure indoor air quality at the proper level, it is necessary to supply rooms with outside air. Ventilating rooms by airing, when the outside air is polluted, reduces the CO2 concentration but worsens the indoor air quality in terms of particulate matter (PM). Seasonality in the concentration of particulate matter in outdoor air and its connection with the outside air temperature was noticed. No relationship between traffic and the values of PM10 and CO2 concentrations inside nurseries was observed.
Injection and Combustion Analysis of Pure Rapeseed Oil Methyl Ester (RME) in a Pump-Line-Nozzle Fuel Injection System
Authors: Caresana Flavio , Bietresato Marco , Renzi Massimiliano
Published: November 2021

This work suggests an interpretation to the quantitatively higher formation of NOx in a compression ignition (CI) engine when fueled with pure biodiesel (B100). A comparative study about the use of rapeseed oil methyl ester (RME) and diesel fuel mixtures on injection timing, in-chamber pressure, heat release rate, and NOx emissions were carried out using a diesel engine equipped with a pump-line-nozzle injection system. Such engines are still widely adopted mainly in agriculture, as the fleet of agricultural machinery is particularly old (often over 20 years) and the use of biofuels can reduce the environmental footprint of the sector. This work aims to supply some general explanations and figures useful to interpret the phenomena occurring within the fuel line and in the combustion process when using biodiesel, as well as in engines with different construction characteristics and fueling systems. Given the contradictory results available in the literature, the so-called “biodiesel NOx effect” cannot be explained solely by the different physical properties of biodiesel (in particular, a higher bulk modulus). Experimental results show that, with the same pump settings, the start of injection with the RME is slightly advanced while the injection pressure values remain almost the same. With the RME, the pressure in the injection line increases faster due to its greater bulk modulus but the pressure rise starts from a lower residual pressure. The start of combustion takes place earlier, the heat release during the premixed phase is steeper, and a higher peak is reached. The NOx emissions with the RME are at least 9% higher when compared to mineral diesel fuel. The greater amount of the RME injected per cycle compensates for its minor lower heating value, and the brake torque at full load is similar to the two analyzed fuels. Finally, a variation of the pump line timing is evaluated in order to assess the effect of the delay and the advance of the injection on the performance of the engine and on the emissions. A viable and simple solution in the variation of the injection strategy is suggested to counterbalance the biodiesel NOx effect.