bluuebunny/crossref_metadata_2025 huggingface dataset:
Unsupervised Stereo Matching with Surface Normal Assistance for Indoor Depth Estimation
Authors: Fan Xiule , Amiri Ali Jahani , Fidan Baris , Jeon Soo
Published: December 2023

To obtain more accurate depth information with stereo cameras, various learning-based stereo-matching algorithms have been developed recently. These algorithms, however, are significantly affected by textureless regions in indoor applications. To address this problem, we propose a new deep-neural-network-based data-driven stereo-matching scheme that utilizes the surface normal. The proposed scheme includes a neural network and a two-stage training strategy. The neural network involves a feature-extraction module, a normal-estimation branch, and a disparity-estimation branch. The training processes of the feature-extraction module and the normal-estimation branch are supervised while the training of the disparity-estimation branch is performed unsupervised. Experimental results indicate that the proposed scheme is capable of estimating the surface normal accurately in textureless regions, leading to improvement in the disparity-estimation accuracy and stereo-matching quality in indoor applications involving such textureless regions.
Monitoring of Lake Ice Phenology Changes in Bosten Lake Based on Bayesian Change Detection Algorithm and Passive Microwave Remote Sensing (PMRS) Data
Authors: Kuluwan Yimuran , Rusuli Yusufujiang , Ainiwaer Mireguli
Published: December 2023

Lake ice phenology (LIP), hiding information about lake energy and material exchange, serves as an important indicator of climate change. Utilizing an efficient technique to swiftly extract lake ice information is crucial in the field of lake ice research. The Bayesian ensemble change detection (BECD) algorithm stands out as a powerful tool, requiring no threshold compared to other algorithms and, instead, utilizing the probability of abrupt changes to detect positions. This method is predominantly employed by automatically extracting change points from time series data, showcasing its efficiency and accuracy, especially in revealing phenological and seasonal characteristics. This paper focuses on Bosten Lake (BL) and employs PMRS data in conjunction with the Bayesian change detection algorithm. It introduces an automated method for extracting LIP information based on the Bayesian change detection algorithm. In this study, the BECD algorithm was employed to extract lake ice phenology information from passive microwave remote sensing data on Bosten Lake. The reliability of the passive microwave remote sensing data was further investigated through cross-validation with MOD10A1 data. Additionally, the Mann–Kendall non-parametric test was applied to analyze the trends in lake ice phenology changes in Bosten Lake. Spatial variations were examined using MOD09GQ data. The results indicate: (1) The Bayesian change detection algorithm (BCDA), in conjunction with PMRS data, offers a high level of accuracy and reliability in extracting the lake ice freezing and thawing processes. It accurately captures the phenological parameters of BL’s ice. (2) The average start date of lake ice freezing is in mid-December, lasting for about three months, and the start date of ice thawing is usually in mid-March. The freezing duration (FD) of lake ice is relatively short, shortening each year, while the thawing speed is faster. The stability of the lake ice complete ice cover duration is poor, averaging 84 days. (3) The dynamic evolution of BL ice is rapid and regionally distinct, with the lake center, southwest, and southeast regions being the earliest areas for ice formation and thawing, while the northwest coastal and Huang Shui Gou areas experience later ice formation. (4) Since 1978, BL’s ice has exhibited noticeable trends: the onset of freezing, the commencement of thawing, complete thawing, and full freezing have progressively advanced in regard to dates. The periods of full ice coverage, ice presence, thawing, and freezing have all shown a tendency toward shorter durations. This study introduces an innovative method for LIP extraction, opening up new prospects for the study of lake ecosystem and strategy formulation, which is worthy of further exploration and application in other lakes and regions.
Generating ICS Anomaly Data Reflecting Cyber-Attack Based on Systematic Sampling and Linear Regression
Authors: Lee Ju Hyeon , Ji Il Hwan , Jeon Seung Ho , Seo Jung Taek
Published: December 2023

Cyber threats to industrial control systems (ICSs) have increased as information and communications technology (ICT) has been incorporated. In response to these cyber threats, we are implementing a range of security equipment and specialized training programs. Anomaly data stemming from cyber-attacks are crucial for effectively testing security equipment and conducting cyber training exercises. However, securing anomaly data in an ICS environment requires a lot of effort. For this reason, we propose a method for generating anomaly data that reflects cyber-attack characteristics. This method uses systematic sampling and linear regression models in an ICS environment to generate anomaly data reflecting cyber-attack characteristics based on benign data. The method uses statistical analysis to identify features indicative of cyber-attack characteristics and alters their values from benign data through systematic sampling. The transformed data are then used to train a linear regression model. The linear regression model can predict features because it has learned the linear relationships between data features. This experiment used ICS_PCAPS data generated based on Modbus, frequently used in ICS. In this experiment, more than 50,000 new anomaly data pieces were generated. As a result of using some of the new anomaly data generated as training data for the existing model, no significant performance degradation occurred. Additionally, comparing some of the new anomaly data with the original benign and attack data using kernel density estimation confirmed that the new anomaly data pattern was changing from benign data to attack data. In this way, anomaly data that partially reflect the pattern of the attack data were created. The proposed method generates anomaly data like cyber-attack data quickly and logically, free from the constraints of cost, time, and original cyber-attack data required in existing research.
Access Control, Key Management, and Trust for Emerging Wireless Body Area Networks
Authors: Salehi Shahraki Ahmad , Lauer Hagen , Grobler Marthie , Sakzad Amin , Rudolph Carsten
Published: December 2023

Wireless Body Area Networks (WBANs) are an emerging industrial technology for monitoring physiological data. These networks employ medical wearable and implanted biomedical sensors aimed at improving quality of life by providing body-oriented services through a variety of industrial sensing gadgets. The sensors collect vital data from the body and forward this information to other nodes for further services using short-range wireless communication technology. In this paper, we provide a multi-aspect review of recent advancements made in this field pertaining to cross-domain security, privacy, and trust issues. The aim is to present an overall review of WBAN research and projects based on applications, devices, and communication architecture. We examine current issues and challenges with WBAN communications and technologies, with the aim of providing insights for a future vision of remote healthcare systems. We specifically address the potential and shortcomings of various Wireless Body Area Network (WBAN) architectures and communication schemes that are proposed to maintain security, privacy, and trust within digital healthcare systems. Although current solutions and schemes aim to provide some level of security, several serious challenges remain that need to be understood and addressed. Our aim is to suggest future research directions for establishing best practices in protecting healthcare data. This includes monitoring, access control, key management, and trust management. The distinguishing feature of this survey is the combination of our review with a critical perspective on the future of WBANs.
Surface Passivation by Quantum Exclusion: On the Quantum Efficiency and Stability of Delta-Doped CCDs and CMOS Image Sensors in Space
Authors: Hoenk Michael E. , Jewell April D. , Kyne Gillian , Hennessy John , Jones Todd , Shapiro Charles , Bush Nathan , Nikzad Shouleh , Morris David , Lawrie Katherine , Skottfelt Jesper
Published: December 2023

Radiation-induced damage and instabilities in back-illuminated silicon detectors have proved to be challenging in multiple NASA and commercial applications. In this paper, we develop a model of detector quantum efficiency (QE) as a function of Si–SiO2 interface and oxide trap densities to analyze the performance of silicon detectors and explore the requirements for stable, radiation-hardened surface passivation. By analyzing QE data acquired before, during, and after, exposure to damaging UV radiation, we explore the physical and chemical mechanisms underlying UV-induced surface damage, variable surface charge, QE, and stability in ion-implanted and delta-doped detectors. Delta-doped CCD and CMOS image sensors are shown to be uniquely hardened against surface damage caused by ionizing radiation, enabling the stability and photometric accuracy required by NASA for exoplanet science and time domain astronomy.
Investigation of the Influence of Infrared Illumination on the Pulse Shapes of Output Signals of CdZnTe Detectors
Authors: Ivanov Victor , Fjodorovs Viktors , Hinoverovs Sergejs , Loutchanski Anatoli , Ogorodniks Vadims , Vidinejevs Sergejs
Published: December 2023

The spectrometric characteristics of CdZnTe detectors are largely determined by the nonuniformity of the material and the influence of the negative polarization effects associated with the formation of space charges in the sensitive volume of the detector. They change the electric field distribution in the detector and affect the efficiency of the charge carrier collection. An analysis of the waveforms of the output pulses was used to investigate the uniformity of the charge collection and electric field distribution in the detectors when irradiated by the alpha particles. The influence of infrared (IR) illumination on these parameters was evaluated. IR illumination had no positive effect on the planar detector but greatly improved the charge collection in quasi-hemispherical detectors in the peripheral (corner) regions. The output pulse amplitude increased, and the rise time notably decreased. Polarization that occurred predominantly in the corner regions at low temperatures (from −30 °C to −20 °C) was eliminated using IR illumination.
A New Insight on the “S” Shape Pattern of Soft Faults in Time-Domain Reflectometry
Authors: Loete Florent
Published: December 2023

This paper proposes a new interpretation of the commonly observed, very specific, time-domain response associated with a soft defect in an electrical line under test using time-domain reflectometry. The reflectometry reveals the nature of a defect by analyzing the reflections undergone by an injected pulse at the impedance discontinuities present on the line. The faulty section considered in this work is modeled as a local modification of the characteristic impedance. Using the developed model, we explain how, depending on the physical and electrical characteristics of the faulty section, the associated signature yields a very specific “S”-shaped pattern. The influence of the probing signal is also investigated. Finally, it is shown that the amplitude of the reflected signal cannot be interpreted straightforwardly as a mirror of the severity of the defect and that consequently, small echoes can mask more significant defects.
Accurate Monocular SLAM Initialization via Structural Line Tracking
Authors: Gu Tianlun , Zhang Jianwei , Liu Yanli
Published: December 2023

In this paper, we present a novel monocular simultaneous localization and mapping (SLAM) initialization algorithm that relies on structural features by tracking structural lines. This approach addresses the limitations of the traditional method, which can fail to account for a lack of features or their uneven distribution. Our proposed method utilizes a sliding window approach to guarantee the quality and stability of the initial pose estimation. We incorporate multiple geometric constraints, orthogonal dominant directions, and coplanar structural lines to construct an efficient pose optimization strategy. Experimental evaluations conducted on both the collected chessboard datasets and real scene datasets show that our approach provides superior results in terms of accuracy and real-time performance compared to the well-tuned baseline methods. Notably, our algorithm achieves these improvements while being computationally lightweight, without the need for matrix decomposition.
A Capacitive Ice-Sensor Based on Graphene Nano-Platelets Strips
Authors: Sibilia Sarah , Tari Luca , Bertocchi Francesco , Chiodini Sergio , Maffucci Antonio
Published: December 2023

This paper investigates the possibility of realizing ice sensors based on the electrical response of thin strips made from pressed graphene nano-platelets. The novelty of this work resides in the use of the same graphene strips that can act as heating elements via the Joule effect, thus opening the route for a combined device able to both detect and remove ice. A planar capacitive sensor is designed and fabricated, in which the graphene strip acts as one of the armatures. The sensing principle is based on the high sensitivity of the planar capacitor to the change in electrical permittivity in the presence of ice, as shown in the experimental case study discussed here, can also be interpreted by means of a simple circuit and electromagnetic model. The properties of the sensor are analyzed, and the frequency range for its use as an ice detector has been established.
A Novel Hierarchical Security Solution for Controller-Area-Network-Based 3D Printing in a Post-Quantum World
Authors: Cultice Tyler , Clark Joseph , Yang Wu , Thapliyal Himanshu
Published: December 2023

As the popularity of 3D printing or additive manufacturing (AM) continues to increase for use in commercial and defense supply chains, the requirement for reliable, robust protection from adversaries has become more important than ever. Three-dimensional printing security focuses on protecting both the individual Industrial Internet of Things (I-IoT) AM devices and the networks that connect hundreds of these machines together. Additionally, rapid improvements in quantum computing demonstrate a vital need for robust security in a post-quantum future for critical AM manufacturing, especially for applications in, for example, the medical and defense industries. In this paper, we discuss the attack surface of adversarial data manipulation on the physical inter-device communication bus, Controller Area Network (CAN). We propose a novel, hierarchical tree solution for a secure, post-quantum-supported security framework for CAN-based AM devices. Through using subnet hopping between isolated CAN buses, our framework maintains the ability to use legacy or third-party devices in a plug-and-play fashion while securing and minimizing the attack surface of hardware Trojans or other adversaries. The results of the physical implementation of our framework demonstrate 25% and 90% improvement in message costs for authentication compared to existing lightweight and post-quantum CAN security solutions, respectively. Additionally, we performed timing benchmarks on the normal communication (hopping) and authentication schemes of our framework.
Carbon Paste Electrodes Surface-Modified with Surfactants: Principles of Surface Interactions at the Interface between Two Immiscible Liquid Phases
Authors: Švancara Ivan , Sýs Milan
Published: December 2023

Carbon paste electrodes ex-situ modified with different surfactants were studied using cyclic voltammetry with two model redox couples, namely hexaammineruthenium (II)/(III) and hexacyanoferrate (II)/(III), in 0.1 mol L−1 acetate buffer (pH 4), 0.1 mol L−1 phosphate buffer (pH 7), and 0.1 mol L−1 ammonia buffer (pH 9) at a scan rate ranging from 50 to 500 mV s−1. Distinct effects of pH, ionic strength, and the composition of supporting media, as well as of the amount of surfactant and its accumulation at the electrode surface, could be observed and found reflected in changes of double-layer capacitance and electrode kinetics. It has been proved that, at the two-phase interface, the presence of surfactants results in elctrostatic interactions that dominate in the transfer of model substances, possibly accompanied also by the effect of erosion at the carbon paste surface. The individual findings depend on the configurations investigated, which are also illustrated on numerous schemes of the actual microstructure at the respective electrode surface. Finally, principal observations and results are highlighted and discussed with respect to the future development and possible applications of sensors based on surfactant-modified composited electrodes.
High-Performance SAW Resonator with Spurious Mode Suppression Using Hexagonal Weighted Electrode Structure
Authors: Liu Yulong , Wang Hongliang , Zhang Feng , Gou Luhao , Zhang Shengkuo , Cao Gang , Zhang Pengcheng
Published: December 2023

Surface acoustic wave resonators are widely applied in electronics, communication, and other engineering fields. However, the spurious modes generally present in resonators can cause deterioration in device performance. Therefore, this paper proposes a hexagonal weighted structure to suppress them. With the construction of a finite element resonator model, the parameters of the interdigital transducer (IDT) and the area of the dummy finger weighting are determined. The spurious waves are confined within the dummy finger area, whereas the main mode is less affected by this structure. To verify the suppression effect of the simulation, resonators with conventional and hexagonal weighted structures are fabricated using the micro-electromechanical systems (MEMS) process. After the S-parameter test of the prepared resonators, the hexagonal weighted resonators achieve a high level of spurious mode suppression. Their properties are superior to those of the conventional structure, with a higher Q value (10,406), a higher minimum return loss (25.7 dB), and a lower ratio of peak sidelobe (19%). This work provides a feasible solution for the design of SAW resonators to suppress spurious modes.
Joint Task Offloading and Resource Allocation for Intelligent Reflecting Surface-Aided Integrated Sensing and Communication Systems Using Deep Reinforcement Learning Algorithm
Authors: Yang Liu , Wei Yifei , Wang Xiaojun
Published: December 2023

This paper investigates an intelligent reflecting surface (IRS)-aided integrated sensing and communication (ISAC) framework to cope with the problem of spectrum scarcity and poor wireless environment. The main goal of the proposed framework in this work is to optimize the overall performance of the system, including sensing, communication, and computational offloading. We aim to achieve the trade-off between system performance and overhead by optimizing spectrum and computing resource allocation. On the one hand, the joint design of transmit beamforming and phase shift matrices can enhance the radar sensing quality and increase the communication data rate. On the other hand, task offloading and computation resource allocation optimize energy consumption and delay. Due to the coupled and high dimension optimization variables, the optimization problem is non-convex and NP-hard. Meanwhile, given the dynamic wireless channel condition, we formulate the optimization design as a Markov decision process. To tackle this complex optimization problem, we proposed two innovative deep reinforcement learning (DRL)-based schemes. Specifically, a deep deterministic policy gradient (DDPG) method is proposed to address the continuous high-dimensional action space, and the prioritized experience replay is adopted to speed up the convergence process. Then, a twin delayed DDPG algorithm is designed based on this DRL framework. Numerical results confirm the effectiveness of proposed schemes compared with the benchmark methods.
Application of Machine Learning for Calibrating Gas Sensors for Methane Emissions Monitoring
Authors: Andrews Ballard , Chakrabarti Aditi , Dauphin Mathieu , Speck Andrew
Published: December 2023

Methane leaks are a significant component of greenhouse gas emissions and a global problem for the oil and gas industry. Emissions occur from a wide variety of sites with no discernable patterns, requiring methodologies to frequently monitor these releases throughout the entire production chain. To cost-effectively monitor widely dispersed well pads, we developed a methane point instrument to be deployed at facilities and connected to a cloud-based interpretation platform that provides real-time continuous monitoring in all weather conditions. The methane sensor is calibrated with machine learning methods of Gaussian process regression and the results are compared with artificial neural networks. A machine learning approach incorporates environmental effects into the sensor response and achieves the accuracies required for methane emissions monitoring with a small number of parameters. The sensors achieve an accuracy of 1 part per million methane (ppm) and can detect leaks at rates of less than 0.6 kg/h.
Particle Tracking and Micromixing Performance Characterization with a Mobile Device
Authors: Naula Duchi Edisson A. , Betancourt Cervantes Héctor Andrés , Yañez Espinosa Christian Rodrigo , Rodríguez Ciro A. , Garza-Castañon Luis E. , Martínez López J. Israel
Published: December 2023

Strategies to stir and mix reagents in microfluid devices have evolved concomitantly with advancements in manufacturing techniques and sensing. While there is a large array of reported designs to combine and homogenize liquids, most of the characterization has been focused on setups with two inlets and one outlet. While this configuration is helpful to directly evaluate the effects of features and parameters on the mixing degree, it does not portray the conditions for experiments that involve more than two substances required to be subsequently combined. In this work, we present a mixing characterization methodology based on particle tracking as an alternative to the most common approach to measure homogeneity using the standard deviation of pixel intensities from a grayscale image. The proposed algorithm is implemented on a free and open-source mobile application (MIQUOD) for Android devices, numerically tested on COMSOL Multiphysics, and experimentally tested on a bidimensional split and recombine micromixer and a three-dimensional micromixer with sinusoidal grooves for different Reynolds numbers and geometrical features for samples with fluids seeded with red, blue, and green microparticles. The application uses concentration field data and particle track data to evaluate up to eleven performance metrics. Furthermore, with the insights from the experimental and numerical data, a mixing index for particles (mp) is proposed to characterize mixing performance for scenarios with multiple input reagents.
Application of p and n-Type Silicon Nanowires as Human Respiratory Sensing Device
Authors: Fakhri Elham , Sultan Muhammad Taha , Manolescu Andrei , Ingvarsson Snorri , Svavarsson Halldor Gudfinnur
Published: December 2023

Accurate and fast breath monitoring is of great importance for various healthcare applications, for example, medical diagnoses, studying sleep apnea, and early detection of physiological disorders. Devices meant for such applications tend to be uncomfortable for the subject (patient) and pricey. Therefore, there is a need for a cost-effective, lightweight, small-dimensional, and non-invasive device whose presence does not interfere with the observed signals. This paper reports on the fabrication of a highly sensitive human respiratory sensor based on silicon nanowires (SiNWs) fabricated by a top-down method of metal-assisted chemical-etching (MACE). Besides other important factors, reducing the final cost of the sensor is of paramount importance. One of the factors that increases the final price of the sensors is using gold (Au) electrodes. Herein, we investigate the sensor’s response using aluminum (Al) electrodes as a cost-effective alternative, considering the fact that the electrode’s work function is crucial in electronic device design, impacting device electronic properties and electron transport efficiency at the electrode–semiconductor interface. Therefore a comparison is made between SiNWs breath sensors made from both p-type and n-type silicon to investigate the effect of the dopant and electrode type on the SiNWs respiratory sensing functionality. A distinct directional variation was observed in the sample’s response with Au and Al electrodes. Finally, performing a qualitative study revealed that the electrical resistance across the SiNWs renders greater sensitivity to breath than to dry air pressure. No definitive research demonstrating the mechanism behind these effects exists, thus prompting our study to investigate the underlying process.
Cyber-WISE: A Cyber-Physical Deep Wireless Indoor Positioning System and Digital Twin Approach
Authors: Karakusak Muhammed Zahid , Kivrak Hasan , Watson Simon , Ozdemir Mehmet Kemal
Published: December 2023

In recent decades, there have been significant research efforts focusing on wireless indoor localization systems, with fingerprinting techniques based on received signal strength leading the way. The majority of the suggested approaches require challenging and laborious Wi-Fi site surveys to construct a radio map, which is then utilized to match radio signatures with particular locations. In this paper, a novel next-generation cyber-physical wireless indoor positioning system is presented that addresses the challenges of fingerprinting techniques associated with data collection. The proposed approach not only facilitates an interactive digital representation that fosters informed decision-making through a digital twin interface but also ensures adaptability to new scenarios, scalability, and suitability for large environments and evolving conditions during the process of constructing the radio map. Additionally, it reduces the labor cost and laborious data collection process while helping to increase the efficiency of fingerprint-based positioning methods through accurate ground-truth data collection. This is also convenient for working in remote environments to improve human safety in locations where human access is limited or hazardous and to address issues related to radio map obsolescence. The feasibility of the cyber-physical system design is successfully verified and evaluated with real-world experiments in which a ground robot is utilized to obtain a radio map autonomously in real-time in a challenging environment through an informed decision process. With the proposed setup, the results demonstrate the success of RSSI-based indoor positioning using deep learning models, including MLP, LSTM Model 1, and LSTM Model 2, achieving an average localization error of ≤2.16 m in individual areas. Specifically, LSTM Model 2 achieves an average localization error as low as 1.55 m and 1.97 m with 83.33% and 81.05% of the errors within 2 m for individual and combined areas, respectively. These outcomes demonstrate that the proposed cyber-physical wireless indoor positioning approach, which is based on the application of dynamic Wi-Fi RSS surveying through human feedback using autonomous mobile robots, effectively leverages the precision of deep learning models, resulting in localization performance comparable to the literature. Furthermore, they highlight its potential for suitability for deployment in real-world scenarios and practical applicability.
UAV Photogrammetry for Estimating Stand Parameters of an Old Japanese Larch Plantation Using Different Filtering Methods at Two Flight Altitudes
Authors: Karthigesu Jeyavanan , Owari Toshiaki , Tsuyuki Satoshi , Hiroshima Takuya
Published: December 2023

Old plantations are iconic sites, and estimating stand parameters is crucial for valuation and management. This study aimed to estimate stand parameters of a 115-year-old Japanese larch (Larix kaempferi (Lamb.) Carrière) plantation at the University of Tokyo Hokkaido Forest (UTHF) in central Hokkaido, northern Japan, using unmanned aerial vehicle (UAV) photogrammetry. High-resolution RGB imagery was collected using a DJI Matrice 300 real-time kinematic (RTK) at altitudes of 80 and 120 m. Structure from motion (SfM) technology was applied to generate 3D point clouds and orthomosaics. We used different filtering methods, search radii, and window sizes for individual tree detection (ITD), and tree height (TH) and crown area (CA) were estimated from a canopy height model (CHM). Additionally, a freely available shiny R package (SRP) and manually digitalized CA were used. A multiple linear regression (MLR) model was used to estimate the diameter at breast height (DBH), stem volume (V), and carbon stock (CST). Higher accuracy was obtained for ITD (F-score: 0.8–0.87) and TH (R2: 0.76–0.77; RMSE: 1.45–1.55 m) than for other stand parameters. Overall, the flying altitude of the UAV and selected filtering methods influenced the success of stand parameter estimation in old-aged plantations, with the UAV at 80 m generating more accurate results for ITD, CA, and DBH, while the UAV at 120 m produced higher accuracy for TH, V, and CST with Gaussian and mean filtering.
Dynamic-Distance-Based Thresholding for UAV-Based Face Verification Algorithms
Authors: Diez-Tomillo Julio , Alcaraz-Calero Jose Maria , Wang Qi
Published: December 2023

Face verification, crucial for identity authentication and access control in our digital society, faces significant challenges when comparing images taken in diverse environments, which vary in terms of distance, angle, and lighting conditions. These disparities often lead to decreased accuracy due to significant resolution changes. This paper introduces an adaptive face verification solution tailored for diverse conditions, particularly focusing on Unmanned Aerial Vehicle (UAV)-based public safety applications. Our approach features an innovative adaptive verification threshold algorithm and an optimised operation pipeline, specifically designed to accommodate varying distances between the UAV and the human subject. The proposed solution is implemented based on a UAV platform and empirically compared with several state-of-the-art solutions. Empirical results have shown that an improvement of 15% in accuracy can be achieved.
Towards Minimizing the LiDAR Sim-to-Real Domain Shift: Object-Level Local Domain Adaptation for 3D Point Clouds of Autonomous Vehicles
Authors: Huch Sebastian , Lienkamp Markus
Published: December 2023

Perception algorithms for autonomous vehicles demand large, labeled datasets. Real-world data acquisition and annotation costs are high, making synthetic data from simulation a cost-effective option. However, training on one source domain and testing on a target domain can cause a domain shift attributed to local structure differences, resulting in a decrease in the model’s performance. We propose a novel domain adaptation approach to address this challenge and to minimize the domain shift between simulated and real-world LiDAR data. Our approach adapts 3D point clouds on the object level by learning the local characteristics of the target domain. A key feature involves downsampling to ensure domain invariance of the input data. The network comprises a state-of-the-art point completion network combined with a discriminator to guide training in an adversarial manner. We quantify the reduction in domain shift by training object detectors with the source, target, and adapted datasets. Our method successfully reduces the sim-to-real domain shift in a distribution-aligned dataset by almost 50%, from 8.63% to 4.36% 3D average precision. It is trained exclusively using target data, making it scalable and applicable to adapt point clouds from any source domain.
Comprehensive Thermodynamic Performance Evaluation of Various Gas Liquefaction Cycles for Cryogenic Energy Storage
Authors: Kılıç Muhsin , Altun Ayse Fidan
Published: December 2023

This paper conducts comparative thermodynamic analysis and performance evaluations of various gas liquefaction configurations. The four most common liquefaction systems (Linde–Hampson, Kapitza, Heylandt, and Claude) were considered. The isothermal and multi-stage isentropic compression processes were evaluated and compared as actual compression processes. Thermodynamic evaluation is based on the energy required to compress a unit mass of gas, the liquefied air mass flow rate, and the exergetic efficiency. The modeling results show that three-stage compression cycles retain lower energy requirements. Increasing the compression stage from one to two for all the processes decreases the energy requirement by 34 to 38%. Changing the compression stage number from two to three reduces the energy requirement by 13%. The compression pressure and expander flow rate ratio significantly affect the liquefied air mass flow rate. Hence, a parametric analysis was conducted to obtain the best operating conditions for each considered cycle. Depending on the compression pressure, the optimum expander flow rate values of the Claude, Kapitza, and Heylandt cycles change from 0.65 to 0.5, 0.65 to 0.55, and 0.35 to 0.30, respectively. For the optimum cases, the Claude, Kapitza, and Heylandt cycles result in liquid yields that are about 2.5, 2.2, and 1.6 times higher than that of the Linde–Hampson cycle. The Claude cycle is the best operating cycle for all the considered performance metrics. Moreover, the performances of the Linde–Hampson and Claude cycles are investigated for various gases. Under the same operating conditions, the results show that better performance parameters are obtained with the gases that have relatively high normal boiling temperatures.
An Empirical Study on the Impact of Digital Economy Innovation Development on the Export Quality of Chinese Electromechanical Products
Authors: Wang Qingnian , Chen Shiyi , Wang Yunpei
Published: December 2023

This study measures the export quality of electromechanical products using data from China’s electromechanical products from 2011 to 2021, empirically investigates the effects of the level of innovation and the growth of the digital economy (DE) on the export quality of electromechanical products, and assesses potential industry heterogeneity. With an erratic upward trend, it is observed that the export quality of Chinese electromechanical products has suffered twice. China’s DE has demonstrated a rising trend in innovation and growth year over year. The creation and growth of the DE throughout the study period had an inverted “U” dynamic effect on the export quality of electromechanical goods, which was initially boosted and later decreased. It is possible to raise the export quality of Chinese mechanical and electrical products by expanding firm size and raising human resource levels. The export quality of Chinese mechanical and electrical products is not much impacted by innovation, R&D capacity, or degree of openness. The inverted “U”-shaped dynamic impacts of the DE’s creation and development on the export quality of electromechanical products are still present after endogeneity analysis and robustness testing, which initially promote and then suppress the rate. The inverted “U”-shaped relationship between DE innovation development and the quality of electromechanical product exports of the manufacture of computers and other electronic equipment, as well as general equipment manufacturing, is still significant, according to the results of sub-industry tests. The study presented in this article offers policy recommendations for facilitating the innovative growth of the DE and raising the standard of electromechanical exports from China.
The Role of Environmental Management Systems and Energy Management Systems in the Adoption of Energy Recuperation Technologies in Central European Manufacturing Companies
Authors: Šebo Juraj , Prester Jasna , Šebová Miriam
Published: December 2023

The diffusion of technologies within an economic system is an intricate process, influenced by a variety of factors, including governmental policies, the characteristics of adopting companies, and the technologies that can be adopted. This study aimed to investigate the relationship between the implementation of environmental management systems (EMSs), such as ISO 14001, or energy management systems (EnMSs), such as ISO 50001, and the adoption of energy recuperation technologies (ERTs), which are a subset of energy efficient technologies (EETs). To achieve this, our research leveraged data from the 2018 European Manufacturing Survey, specifically a subsample of 798 companies across five European countries: Croatia, Slovenia, Austria, Slovakia, and Lithuania. Due to the investigation of relationships and the type of variables used, we employed a two-step ordinary least squares (OLS) regression analysis. Our analysis uncovered that the current utilization of EMSs and EnMSs within companies is significantly linked to the current use of ERTs. However, upon further examination of the implementation timeline, it became improbable that EMSs or EnMSs have a substantial impact on enhancing the adoption of these technologies in the short term. Moreover, our results show that technological intensity and product complexity does not play a determining role in the adoption of ERTs, but they did show that larger companies tend to invest more in ERTs, which is in line with the findings of previous studies.
An In-Depth Analysis of Physical, Chemical, and Microplastic Parameters of Landfill Fine Fraction for Biocover Construction
Authors: Sholokhova Anastasiia , Pitak Inna , Denafas Gintaras , Kalpokaitė-Dičkuvienė Regina , Praspaliauskas Marius , Burlakovs Juris
Published: December 2023

Landfills pose global challenges, notably in terms of greenhouse gas (GHG) emissions, pollution release, and extensive land occupation. The transformative practice of landfill mining has redefined these sites as valuable resource reservoirs. The fine fraction (FF), often constituting the majority of excavated waste, is currently underutilized but holds the potential for biocover construction to mitigate methane emissions. This study comprehensively analyzes the FF from the Kuršenai landfill, collecting samples from various depths, reaching up to 10.5 m. The most suitable layers for biocover construction were determined based on basic physical and chemical parameters, along with the concentration of heavy metals and microplastics. The findings unveil significant parameter variations across different depths. Moderate–high correlations (ranging from 0.5 to 0.84) between several parameters were observed. The layer at a depth of 4.5–6 m emerged as the most suitable for biocover construction. However, this layer is characterized by elevated microplastic concentrations (30,208 ± 273 particles/kg), posing a challenge for its use in biocovers as microplastics can be released into the environment during FF extraction and biocover construction. Additionally, microplastics become finer with depth, increasing the associated risks. Therefore, a balanced approach considering material properties and pollution concentrations is vital for sustainable waste management practices.
A Novel Offsite Construction Method for Social Housing in Emerging Economies for Low Cost and Reduced Environmental Impact
Authors: Tapia Danilo , González Marcelo , Vera Sergio , Aguilar Carlos
Published: December 2023

Offsite construction methods have shown many advantages over traditional construction techniques, especially related to efficiency and productivity during the construction phase. Nevertheless, offsite construction generally involves oversizing the internal structure of the modules due to the internal stresses produced during transport and lifting operations, producing an increase in material usage, direct cost, and carbon footprint. In developing countries, the direct cost of social housing is the most important factor determining the feasibility of construction. For this reason, oversizing the internal structure of the modules can play an important role in the adoption of a modern construction technique such as offsite construction systems. In order to solve this issue, a temporary reusable stiffener structure is proposed to allow an economical offsite construction system using a lightweight steel framing structure used in traditional methods. The reusable structure was designed using a finite element method, and the direct cost and carbon footprint of the structure were evaluated. The results show that the proposed construction strategy allows for a low cost and reduced environmental impact due to a lower usage of materials in the modules and the possibility of a circular economy approach to the reusable structure.
Limited Response of Curve Safety Level to Friction Factor and Superelevation Variation under Repeated Traffic Loads
Authors: Xu Jinliang , Jia Miao , Gao Chao , Lv Wenzhen
Published: December 2023

Although road horizontal curves are high-risk sections for accidents, current road safety assessments often neglect the dynamic evolution of superelevation and the friction factor. The connotation for road safety level was clarified by examining the significance of road factors in traffic safety through the systemic characteristics of roads. Among these characteristics, curve safety level is determined by the ratio of the supply and demand of the lateral friction factor. On the basis of international standards and specifications, this study clarified the design supply and demand of friction factors for curve by considering the distribution of tangential and lateral friction factors. Expanding on the steady-state bicycle model while accounting for road geometric parameters and vehicle operation characteristics, the lateral friction factor demanded for vehicles was quantified. Meanwhile, the characteristics of the friction factor supplied and the superelevation variation were analyzed by using the road service life as a variable, along with their influence on the actual supply of the friction factor and the curve safety level. The results of the analysis indicate a rapid decrease in curve safety level during the first two years of road utilization, followed by a slower declining trend, with a significant 27% reduction in curve safety level by the end of the second year. Furthermore, the decline in the curve safety level is mainly attributed to variations in the road surface friction factor, whereas the influence of superelevation variation on the curve safety level is restricted. In the absence of maintenance interventions, the curve safety level will decrease by over 30% after three years of operation. Controlling operational speed is one of the effective measures for ensuring traffic safety. Meanwhile, the impact of the friction factor and the superelevation variation on the curve safety level accumulates over time, thus causing drivers to have difficulty perceiving these alterations. Therefore, dynamic safety evaluations that account for the fluctuation in the friction factor and superelevation induced by repetitive vehicle loading must be undertaken.
Does ChatGPT Play a Double-Edged Sword Role in the Field of Higher Education? An In-Depth Exploration of the Factors Affecting Student Performance
Authors: Chen Jiangjie , Zhuo Ziqing , Lin Jiacheng
Published: December 2023

The application of generative artificial intelligence in the field of education has been receiving increasing attention, with the performance of chatbot ChatGPT being particularly prominent. This study aims to explore in depth the performance impact on higher education students utilizing ChatGPT. To this end, we conducted a survey on 448 university students and employed the partial-least squares (PLS) method of structural equation modeling for data analysis. The results indicate that all eight hypothetical paths posited in this study were supported, and surprisingly, the hypothesis that technology characteristics have a direct effect on performance impact was supported. Moreover, the study found that overall quality is a crucial factor determining performance impact. Overall quality indirectly affects performance impact through task-technology fit, technology characteristics, and compatibility, among which the mediating effect of compatibility is most significant, followed by technology characteristics. This study offers practical recommendations for students on the proper use of ChatGPT during the learning process and assists developers in enhancing the services of the ChatGPT system.
“Lines Demarked”: A Way to Foster Occupational Health in Police Officers
Authors: Carvalho Vânia Sofia , Chambel Maria José , Marta Beatriz
Published: December 2023

Police officers are part of a demanding professional activity with a high risk of occupational health and work–family conflict, and this is a topic of relevance to understanding occupational health sustainability. Based on this, this study developed and tested a mediation model that accounts for the work–family conflict (WFC) in the relationship between work–family boundary segmentation and well-being (i.e., burnout and engagement). A sample of 291 police officers from Portugal was used, and the hypotheses were tested by using structural equation modeling methods implemented with Mplus. The results indicated that over nonstandard work schedules and unpredictable working hours, family–work segmentation was negatively related to WFC, and work–family segmentation was negatively related to family-to-work conflict. Moreover, WFC fully mediated the relationship between segmentation and engagement but only partially mediated the relationship between segmentation and burnout. Conversely, family-to-work conflict fully mediated the relationship between segmentation and burnout but was not related to engagement. Such results suggest that the occupational health of these professionals is interdependent on their opportunity to enact the segmentation strategy to manage the boundary between work and family domains. In terms of its practical implications, this study sheds light on the environmental conditions of police officers that can foster and sustain their well-being.
Creating Strategies to Mitigate the Adverse Effects of Overtourism in Rural Destinations: Experience from the Czech Republic
Authors: Drápela Emil
Published: December 2023

Overtourism is appearing more and more often in large world-famous cities and in many rural regions, where the infrastructure capacity is insufficient for large numbers of visitors. In rural communities, this creates resistance to tourism, traffic congestion, or damage to nature. Therefore, destinations experiencing tourism growth must have a strategy to prevent and limit the harmful effects of overtourism. The article presents a methodology that first identifies manifestations of overtourism in a destination and then uses a standardized procedure to evaluate the usability of different types of strategies in a selected destination. This procedure should lead to the creation of a comprehensive plan guaranteeing the sustainable development of tourism in the destination. The use of the methodology is explained at 12 rural locations in the Czech Republic, which were selected concerning the diversity of manifestations of overtourism.