bluuebunny/crossref_metadata_2025 huggingface dataset:
Wavelet Transform-Based Inertial Neural Network for Spatial Positioning Using Inertial Measurement Units
Authors: Tang Yong , Gong Jianhua , Li Yi , Zhang Guoyong , Yang Banghui , Yang Zhiyuan
Published: June 2024

As the demand for spatial positioning continues to grow, positioning methods based on inertial measurement units (IMUs) are emerging as a promising research topic due to their low cost and robustness against environmental interference. These methods are particularly well suited for global navigation satellite system (GNSS)-denied environments and challenging visual scenarios. While existing algorithms for position estimation using IMUs have demonstrated some effectiveness, there is still significant room for improvement in terms of estimation accuracy. Current approaches primarily treat IMU data as simple time series, neglecting the frequency-domain characteristics of IMU signals. This paper emphasizes the importance of frequency-domain information in IMU signals and proposes a novel neural network, WINNet (Wavelet Inertial Neural Network), which integrates time- and frequency-domain signals using a wavelet transform for spatial positioning with inertial sensors. Additionally, we collected ground-truth data using a LiDAR setup and combined it with the TLIO dataset to form a new IMU spatial positioning dataset. The experimental results demonstrate that our proposed method outperforms the current state-of-the-art inertial neural network algorithms in terms of the ATE, RTE, and drift error metrics overall.
STs-NeRF: Novel View Synthesis of Space Targets Based on Improved Neural Radiance Fields
Authors: Ma Kaidi , Liu Peixun , Sun Haijiang , Teng Jiawei
Published: June 2024

Since Neural Radiation Field (NeRF) was first proposed, a large number of studies dedicated to them have emerged. These fields achieved very good results in their respective contexts, but they are not sufficiently practical for our project. If we want to obtain novel images of satellites photographed in space by another satellite, we must face problems like inaccurate camera focal lengths and poor image texture. There are also some small structures on satellites that NeRF-like algorithms cannot render well. In these cases, the NeRF’s performance cannot sufficiently meet the project’s needs. In fact, the images rendered by the NeRF will have many incomplete structures, while the MipNeRF will blur the edges of the structures on the satellite and obtain unrealistic colors. In response to these problems, we proposed STs-NeRF, which improves the quality of the new perspective images through an encoding module and a new network structure. We found a method for calculating poses that are suitable for our dataset and that enhance the network’s input learning effect by recoding the sampling points and viewing directions through a dynamic encoding (DE) module. Then, we input them into our layer-by-layer normalized multi-layer perceptron (LLNMLP). By simultaneously inputting points and directions into the network, we avoid the mutual influence between light rays, and through layer-by-layer normalization, we ease the model’s overfitting from a training perspective. Since real images should not be made public, we created a synthetic dataset and conducted a series of experiments. The experiments showed that our method achieves the best results in reconstructing captured satellite images, compared with the NeRF, the MipNeRF, the NeuS and the NeRF2Mesh, and improves the Peak Signal-to-Noise Ratio (PSNR) by 19%. We have also tested on public datasets, and our NeRF can still render acceptable images on datasets with better textures.
Methods for Designating Protective Zones of Historical and Cultural Purpose Using Non-Invasive Methods—Two Case Studies for Ukraine and Poland
Authors: Chetverikov Borys , Babiy Luybov , Oryński Szymon , Różycki Sebastian
Published: June 2024

Technologies and scientific progress are of major importance in monitoring cultural heritage (CH). Studies of CH preservation play a crucial role in understanding human history, inheriting splendid nations’ cultures, and safeguarding cultural diversity. The scientific community and the countries must lead a battle to maintain and preserve CH. The possibilities in the search for underground objects (using non-invasive methods) that have been forgotten are presented. This article presents the results of research conducted in the area of the Citadel in Lviv (Ukraine) and an execution site of the Second World War (Kazimierz Biskupi, Poland). This research makes it possible to highlight the problem of researching to find and adequately commemorate objects and events that are essential elements of the CH of Ukraine and Poland. The proposed non-invasive methods allow the designation of protection zones of historical and cultural significance.
Automated Flood Prediction along Railway Tracks Using Remotely Sensed Data and Traditional Flood Models
Authors: Zakaria Abdul-Rashid , Oommen Thomas , Lautala Pasi
Published: June 2024

Ground hazards are a significant problem in the global economy, costing millions of dollars in damage each year. Railroad tracks are vulnerable to ground hazards like flooding since they traverse multiple terrains with complex environmental factors and diverse human developments. Traditionally, flood-hazard assessments are generated using models like the Hydrological Engineering Center–River Analysis System (HEC-RAS). However, these maps are typically created for design flood events (10, 50, 100, 500 years) and are not available for any specific storm event, as they are not designed for individual flood predictions. Remotely sensed methods, on the other hand, offer precise flood extents only during the flooding, which means the actual flood extents cannot be determined beforehand. Railroad agencies need daily flood extent maps before rainfall events to manage and plan for the parts of the railroad network that will be impacted during each rainfall event. A new approach would involve using traditional flood-modeling layers and remotely sensed flood model outputs such as flood maps created using the Google Earth Engine. These new approaches will use machine-learning tools in flood prediction and extent mapping. This new approach will allow for determining the extent of flood for each rainfall event on a daily basis using rainfall forecast; therefore, flooding extents will be modeled before the actual flood, allowing railroad managers to plan for flood events pre-emptively. Two approaches were used: support vector machines and deep neural networks. Both methods were fine-tuned using grid-search cross-validation; the deep neural network model was chosen as the best model since it was computationally less expensive in training the model and had fewer type II errors or false negatives, which were the priorities for the flood modeling and would be suitable for developing the automated system for the entire railway corridor. The best deep neural network was then deployed and used to assess the extent of flooding for two floods in 2020 and 2022. The results indicate that the model accurately approximates the actual flooding extent and can predict flooding on a daily temporal basis using rainfall forecasts.
Life Cycle Mining Deformation Monitoring and Analysis Using Sentinel-1 and Radarsat-2 InSAR Time Series
Authors: Ma Zhi , Yang Xiaoqing , Xie Lei , Dong Wei
Published: June 2024

The life cycle of mining results in various patterns of surface deformation as it progresses through development, production, and reclamation. Therefore, the spatial–temporal patterns of ground deformation provide a crucial indicator to understand the mining activities, related geohazards, and environmental restoration. This study investigates the decadal deformation (2012–2022) of three coal mines during different stages of mines’ life cycles in Henan, China, using radar interferometry with Radarsat-2 and Sentinel-1 data. The results reveal multiple deformation patterns across different areas: the Changcun mine area changed from ground subsidence to uplift following the termination of exploitation in 2016; the Xiadian mine area has been continuously developing over the past decade, resulting in a cumulative subsidence of 55.6 mm; and the Liyuan mine area exhibits surface rebound at a rate of 7.9 mm/year since its closure in 2007. We also probe the mining geometry of the production process by using a rectangular model. This study highlights the significance of long-term InSAR observations and deformation modeling in elucidating the mining operation dynamics of small mining zones in their production, transition, and post-closure periods, thereby facilitating the management of small-scale mining.
Modeling Potential Habitats of Macrophytes in Small Lakes: A GIS and Remote Sensing-Based Approach
Authors: Robran Bastian , Kroth Frederike , Kuhwald Katja , Schneider Thomas , Oppelt Natascha
Published: June 2024

Macrophytes, which are foundational to freshwater ecosystems, face significant threats due to habitat degradation globally. Habitat suitability models are vital tools used to investigate the relationship between macrophytes and their environment. This study addresses a critical gap by developing a Geographic information system-based HSM tailored for small lakes, which are often overlooked in ecological studies. We included various abiotic predictors to model the potential macrophyte habitat for several small lakes in southern Bavaria (Germany). Key factors such as the distance to groundwater inflow, depth, availability of photosynthetically active radiation (PAR), and littoral slope were identified as significant predictors of macrophyte occurrence. Notably, the HSM integrates remote sensing-based data to derive PAR availability at the growing depths of the macrophytes using Sentinel-2 MSI data. Integration of an MSI-based time series of PAR availability enabled the introduction of a temporal component allowing monitoring and predicting changes in macrophyte habitats over time. The modeled habitat suitability score correlates highly (R = 0.908) with macrophyte occurrence. We see great promise in using habitat modeling for macrophytes as a tool for water management; in particular, the use of Sentinel-2 MSI data for habitat suitability modeling holds promise for advancing water management. By demonstrating the efficacy of GIS- and remote sensing-based HSM, we pave the way for future applications of this innovative approach in ecological conservation and resource management.
DriveLLaVA: Human-Level Behavior Decisions via Vision Language Model
Authors: Zhao Rui , Yuan Qirui , Li Jinyu , Fan Yuze , Li Yun , Gao Fei
Published: June 2024

Human-level driving is the ultimate goal of autonomous driving. As the top-level decision-making aspect of autonomous driving, behavior decision establishes short-term driving behavior strategies by evaluating road structures, adhering to traffic rules, and analyzing the intentions of other traffic participants. Existing behavior decisions are primarily implemented based on rule-based methods, exhibiting insufficient generalization capabilities when faced with new and unseen driving scenarios. In this paper, we propose a novel behavior decision method that leverages the inherent generalization and commonsense reasoning abilities of visual language models (VLMs) to learn and simulate the behavior decision process in human driving. We constructed a novel instruction-following dataset containing a large number of image–text instructions paired with corresponding driving behavior labels, to support the learning of the Drive Large Language and Vision Assistant (DriveLLaVA) and enhance the transparency and interpretability of the entire decision process. DriveLLaVA is fine-tuned on this dataset using the Low-Rank Adaptation (LoRA) approach, which efficiently optimizes the model parameter count and significantly reduces training costs. We conducted extensive experiments on a large-scale instruction-following dataset, and compared with state-of-the-art methods, DriveLLaVA demonstrated excellent behavior decision performance. DriveLLaVA is capable of handling various complex driving scenarios, showing strong robustness and generalization abilities.
Design, Fabrication, and Evaluation of 3D Biopotential Electrodes and Intelligent Garment System for Sports Monitoring
Authors: Shen Deyao , Wang Jianping , Koncar Vladan , Goyal Krittika , Tao Xuyuan
Published: June 2024

This study presents the development and evaluation of an innovative intelligent garment system, incorporating 3D knitted silver biopotential electrodes, designed for long-term sports monitoring. By integrating advanced textile engineering with wearable monitoring technologies, we introduce a novel approach to real-time physiological signal acquisition, focusing on enhancing athletic performance analysis and fatigue detection. Utilizing low-resistance silver fibers, our electrodes demonstrate significantly reduced skin-to-electrode impedance, facilitating improved signal quality and reliability, especially during physical activities. The garment system, embedded with these electrodes, offers a non-invasive, comfortable solution for continuous ECG and EMG monitoring, addressing the limitations of traditional Ag/AgCl electrodes, such as skin irritation and signal degradation over time. Through various experimentation, including impedance measurements and biosignal acquisition during cycling activities, we validate the system’s effectiveness in capturing high-quality physiological data. Our findings illustrate the electrodes’ superior performance in both dry and wet conditions. This study not only advances the field of intelligent garments and biopotential monitoring, but also provides valuable insights for the application of intelligent sports wearables in the future.
A Multi-Task Joint Learning Model Based on Transformer and Customized Gate Control for Predicting Remaining Useful Life and Health Status of Tools
Authors: Hou Chunming , Zheng Liaomo
Published: June 2024

Previous studies have primarily focused on predicting the remaining useful life (RUL) of tools as an independent process. However, the RUL of a tool is closely related to its wear stage. In light of this, a multi-task joint learning model based on a transformer encoder and customized gate control (TECGC) is proposed for simultaneous prediction of tool RUL and tool wear stages. Specifically, the transformer encoder is employed as the backbone of the TECGC model for extracting shared features from the original data. The customized gate control (CGC) is utilized to extract task-specific features relevant to tool RUL prediction and tool wear stage and shared features. Finally, by integrating these components, the tool RUL and the tool wear stage can be predicted simultaneously by the TECGC model. In addition, a dynamic adaptive multi-task learning loss function is proposed for the model’s training to enhance its calculation efficiency. This approach avoids unsatisfactory prediction performance of the model caused by unreasonable selection of trade-off parameters of the loss function. The effectiveness of the TECGC model is evaluated using the PHM2010 dataset. The results demonstrate its capability to accurately predict tool RUL and tool wear stages.
Method for Underground Mining Shaft Sensor Data Collection
Authors: Adamek Artur , Będkowski Janusz , Kamiński Paweł , Pasek Rafał , Pełka Michał , Zawiślak Jan
Published: June 2024

The motivation behind this research is the lack of an underground mining shaft data set in the literature in the form of open access. For this reason, our data set can be used for many research purposes such as shaft inspection, 3D measurements, simultaneous localization and mapping, artificial intelligence, etc. The data collection method incorporates rotated Velodyne VLP-16, Velodyne Ultra Puck VLP-32c, Livox Tele-15, IMU Xsens MTi-30 and Faro Focus 3D. The ground truth data were acquired with a geodetic survey including 15 ground control points and 6 Faro Focus 3D terrestrial laser scanner stations of a total 273,784,932 of 3D measurement points. This data set provides an end-user case study of realistic applications in mobile mapping technology. The goal of this research was to fill the gap in the underground mining data set domain. The result is the first open-access data set for an underground mining shaft (shaft depth −300 m).
Vision-Based UAV Detection and Localization to Indoor Positioning System
Authors: Choutri Kheireddine , Lagha Mohand , Meshoul Souham , Shaiba Hadil , Chegrani Akram , Yahiaoui Mohamed
Published: June 2024

In recent years, the technological landscape has undergone a profound metamorphosis catalyzed by the widespread integration of drones across diverse sectors. Essential to the drone manufacturing process is comprehensive testing, typically conducted in controlled laboratory settings to uphold safety and privacy standards. However, a formidable challenge emerges due to the inherent limitations of GPS signals within indoor environments, posing a threat to the accuracy of drone positioning. This limitation not only jeopardizes testing validity but also introduces instability and inaccuracies, compromising the assessment of drone performance. Given the pivotal role of precise GPS-derived data in drone autopilots, addressing this indoor-based GPS constraint is imperative to ensure the reliability and resilience of unmanned aerial vehicles (UAVs). This paper delves into the implementation of an Indoor Positioning System (IPS) leveraging computer vision. The proposed system endeavors to detect and localize UAVs within indoor environments through an enhanced vision-based triangulation approach. A comparative analysis with alternative positioning methodologies is undertaken to ascertain the efficacy of the proposed system. The results obtained showcase the efficiency and precision of the designed system in detecting and localizing various types of UAVs, underscoring its potential to advance the field of indoor drone navigation and testing.
Malware Detection for Internet of Things Using One-Class Classification
Authors: Shi Tongxin , McCann Roy A. , Huang Ying , Wang Wei , Kong Jun
Published: June 2024

The increasing usage of interconnected devices within the Internet of Things (IoT) and Industrial IoT (IIoT) has significantly enhanced efficiency and utility in both personal and industrial settings but also heightened cybersecurity vulnerabilities, particularly through IoT malware. This paper explores the use of one-class classification, a method of unsupervised learning, which is especially suitable for unlabeled data, dynamic environments, and malware detection, which is a form of anomaly detection. We introduce the TF-IDF method for transforming nominal features into numerical formats that avoid information loss and manage dimensionality effectively, which is crucial for enhancing pattern recognition when combined with n-grams. Furthermore, we compare the performance of multi-class vs. one-class classification models, including Isolation Forest and deep autoencoder, that are trained with both benign and malicious NetFlow samples vs. trained exclusively on benign NetFlow samples. We achieve 100% recall with precision rates above 80% and 90% across various test datasets using one-class classification. These models show the adaptability of unsupervised learning, especially one-class classification, to the evolving malware threats in the IoT domain, offering insights into enhancing IoT security frameworks and suggesting directions for future research in this critical area.
Diagnosis of Rotor Component Shedding in Rotating Machinery: A Data-Driven Approach
Authors: Zhang Sikai , Lin Qizhe , Lin Jiayao
Published: June 2024

The potential for rotor component shedding in rotating machinery poses significant risks, necessitating the development of an early and precise fault diagnosis technique to prevent catastrophic failures and reduce maintenance costs. This study introduces a data-driven approach to detect rotor component shedding at its inception, thereby enhancing operational safety and minimizing downtime. Utilizing frequency analysis, this research identifies harmonic amplitudes within rotor vibration data as key indicators of impending faults. The methodology employs principal component analysis (PCA) to orthogonalize and reduce the dimensionality of vibration data from rotor sensors, followed by k-fold cross-validation to select a subset of significant features, ensuring the detection algorithm’s robustness and generalizability. These features are then integrated into a linear discriminant analysis (LDA) model, which serves as the diagnostic engine to predict the probability of rotor component shedding. The efficacy of the approach is demonstrated through its application to 16 industrial compressors and turbines, proving its value in providing timely fault warnings and enhancing operational reliability.
An Inertial-Based Wearable System for Monitoring Vital Signs during Sleep
Authors: Kontaxis Spyridon , Kanellos Foivos , Ntanis Adamantios , Kostikis Nicholas , Konitsiotis Spyridon , Rigas George
Published: June 2024

This study explores the feasibility of a wearable system to monitor vital signs during sleep. The system incorporates five inertial measurement units (IMUs) located on the waist, the arms, and the legs. To evaluate the performance of a novel framework, twenty-three participants underwent a sleep study, and vital signs, including respiratory rate (RR) and heart rate (HR), were monitored via polysomnography (PSG). The dataset comprises individuals with varying severity of sleep-disordered breathing (SDB). Using a single IMU sensor positioned at the waist, strong correlations of more than 0.95 with the PSG-derived vital signs were obtained. Low inter-participant mean absolute errors of about 0.66 breaths/min and 1.32 beats/min were achieved, for RR and HR, respectively. The percentage of data available for analysis, representing the time coverage, was 98.3% for RR estimation and 78.3% for HR estimation. Nevertheless, the fusion of data from IMUs positioned at the arms and legs enhanced the inter-participant time coverage of HR estimation by over 15%. These findings imply that the proposed methodology can be used for vital sign monitoring during sleep, paving the way for a comprehensive understanding of sleep quality in individuals with SDB.
Neural Colour Correction for Indoor 3D Reconstruction Using RGB-D Data
Authors: Madeira Tiago , Oliveira Miguel , Dias Paulo
Published: June 2024

With the rise in popularity of different human-centred applications using 3D reconstruction data, the problem of generating photo-realistic models has become an important task. In a multiview acquisition system, particularly for large indoor scenes, the acquisition conditions will differ along the environment, causing colour differences between captures and unappealing visual artefacts in the produced models. We propose a novel neural-based approach to colour correction for indoor 3D reconstruction. It is a lightweight and efficient approach that can be used to harmonize colour from sparse captures over complex indoor scenes. Our approach uses a fully connected deep neural network to learn an implicit representation of the colour in 3D space, while capturing camera-dependent effects. We then leverage this continuous function as reference data to estimate the required transformations to regenerate pixels in each capture. Experiments to evaluate the proposed method on several scenes of the MP3D dataset show that it outperforms other relevant state-of-the-art approaches.
LBRT: Local-Information-Refined Transformer for Image Copy–Move Forgery Detection
Authors: Liang Peng , Li Ziyuan , Tu Hang , Zhao Huimin
Published: June 2024

The current deep learning methods for copy–move forgery detection (CMFD) are mostly based on deep convolutional neural networks, which frequently discard a large amount of detail information throughout convolutional feature extraction and have poor long-range information extraction capabilities. The Transformer structure is adept at modeling global context information, but the patch-wise self-attention calculation still neglects the extraction of details in local regions that have been tampered with. A local-information-refined dual-branch network, LBRT (Local Branch Refinement Transformer), is designed in this study. It performs Transformer encoding on the global patches segmented from the image and local patches re-segmented from the global patches using a global modeling branch and a local refinement branch, respectively. The self-attention features from both branches are precisely fused, and the fused feature map is then up-sampled and decoded. Therefore, LBRT considers both global semantic information modeling and local detail information refinement. The experimental results show that LBRT outperforms several state-of-the-art CMFD methods on the USCISI dataset, CASIA CMFD dataset, and DEFACTO CMFD dataset.
A Surface Acoustic Wave-Based PM 1.0 Fine Dust Detection System Using Full Digital Time-Interleaved Counters
Authors: Kim Chang-Hyeon , Yang Ki-Hoon , Song Yeon-Seob , Yoo Sang-Sun , Pu Younggun , Kim Il-Hwan , Chung Seok-Whan , Choi Kwang-Wook , Park Jun-Eun , Lee Kang-Yoon
Published: June 2024

This paper proposed a fine dust detection system using time-interleaved counters in which surface acoustic wave (SAW) sensors changed the resonance point characteristic. When fine dust was applied to the SAW sensor, the resonance point decreased. The SAW oscillator made of the SAW sensor and radio frequency (RF) amplifier generated an oscillation frequency that was the same as the resonance frequency. The oscillation frequency was transferred to digital data by a 20-bit asynchronous counter. This system has two channels: a sensing channel and a reference channel. Each channel has a SAW oscillator and a 20-bit asynchronous counter. The difference of the two channel counter results is the frequency difference. Through this, it is possible to know whether fine dust adheres to the SAW sensor. The proposed circuit achieved 0.95 ppm frequency resolution when it was operated at a frequency of 460 MHz. This circuit was implemented in a TSMC 130 nm CMOS process.
PMUT-Based System for Continuous Monitoring of Bolted Joints Preload
Authors: Sanvito Stefano , Passoni Marco , Giusti Domenico , Terenzi Marco , Prelini Carlo , La Mura Monica , Savoia Alessandro Stuart
Published: June 2024

In this paper, we present a bolt preload monitoring system, including the system architecture and algorithms. We show how Finite Element Method (FEM) simulations aided the design and how we processed signals to achieve experimental validation. The preload is measured using a Piezoelectric Micromachined Ultrasonic Transducer (PMUT) in pulse-echo mode, by detecting the Change in Time-of-Flight (CTOF) of the acoustic wave generated by the PMUT, between no-load and load conditions. We performed FEM simulations to analyze the wave propagation inside the bolt and understand the effect of different configurations and parameters, such as transducer bandwidth, transducer position (head/tip), presence or absence of threads, as well as the frequency of the acoustic waves. In order to couple the PMUT to the bolt, a novel assembly process involving the deposition of an elastomeric acoustic impedance matching layer was developed. We achieved, for the first time with PMUTs, an experimental measure of bolt preload from the CTOF, with a good signal-to-noise ratio. Due to its low cost and small size, this system has great potential for use in the field for continuous monitoring throughout the operative life of the bolt.
Snow-CLOCs: Camera-LiDAR Object Candidate Fusion for 3D Object Detection in Snowy Conditions
Authors: Fan Xiangsuo , Xiao Dachuan , Li Qi , Gong Rui
Published: June 2024

Although existing 3D object-detection methods have achieved promising results on conventional datasets, it is still challenging to detect objects in data collected under adverse weather conditions. Data distortion from LiDAR and cameras in such conditions leads to poor performance of traditional single-sensor detection methods. Multi-modal data-fusion methods struggle with data distortion and low alignment accuracy, making accurate target detection difficult. To address this, we propose a multi-modal object-detection algorithm, Snow-CLOCs, specifically for snowy conditions. In image detection, we improved the YOLOv5 algorithm by integrating the InceptionNeXt network to enhance feature extraction and using the Wise-IoU algorithm to reduce dependency on high-quality data. For LiDAR point-cloud detection, we built upon the SECOND algorithm and employed the DROR filter to remove noise, enhancing detection accuracy. We combined the detection results from the camera and LiDAR into a unified detection set, represented using a sparse tensor, and extracted features through a 2D convolutional neural network to achieve object detection and localization. Snow-CLOCs achieved a detection accuracy of 86.61% for vehicle detection in snowy conditions.
Single-Shot Direct Transmission Terahertz Imaging Based on Intense Broadband Terahertz Radiation
Authors: Yue Zhang , Peng Xiaoyu , Li Guangyuan , Zhou Yilei , Pu Yezi , Zhang Yuhui
Published: June 2024

There are numerous applications of terahertz (THz) imaging in many fields. However, current THz imaging is generally based on scanning technique due to the limited intensity of the THz sources. Thus, it takes a long time to obtain a frame image of the target and cannot meet the requirement of fast THz imaging. Here, we demonstrate a single-shot direct THz imaging strategy based on a broadband intense THz source with a frequency range of 0.1~23 THz and a THz camera with a frequency response range of 1~7 THz. This THz source was generated from the laser–plasma interaction, with its central frequency at ~12 THz. The frame rate of this imaging system was 8.5 frames per second. The imaging resolution reached 146.2 μm. With this imaging system, a single-shot THz image for a target object with a size of more than 7 cm was routinely obtained, showing a potential application for fast THz imaging. Furthermore, we proposed and tested an image enhancement algorithm based on an improved dark channel prior (DCP) theory and multi-scale retinex (MSR) theory to optimize the image brightness, contrast, entropy and peak signal-to-noise ratio (PSNR).
A Within-Subject Multimodal NIRS-EEG Classifier for Infant Data
Authors: Gemignani Jessica , Gervain Judit
Published: June 2024

Functional Near Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) are commonly employed neuroimaging methods in developmental neuroscience. Since they offer complementary strengths and their simultaneous recording is relatively easy, combining them is highly desirable. However, to date, very few infant studies have been conducted with NIRS-EEG, partly because analyzing and interpreting multimodal data is challenging. In this work, we propose a framework to carry out a multivariate pattern analysis that uses an NIRS-EEG feature matrix, obtained by selecting EEG trials presented within larger NIRS blocks, and combining the corresponding features. Importantly, this classifier is intended to be sensitive enough to apply to individual-level, and not group-level data. We tested the classifier on NIRS-EEG data acquired from five newborn infants who were listening to human speech and monkey vocalizations. We evaluated how accurately the model classified stimuli when applied to EEG data alone, NIRS data alone, or combined NIRS-EEG data. For three out of five infants, the classifier achieved high and statistically significant accuracy when using features from the NIRS data alone, but even higher accuracy when using combined EEG and NIRS data, particularly from both hemoglobin components. For the other two infants, accuracies were lower overall, but for one of them the highest accuracy was still achieved when using combined EEG and NIRS data with both hemoglobin components. We discuss how classification based on joint NIRS-EEG data could be modified to fit the needs of different experimental paradigms and needs.
DTR-SHIELD: Mutual Synchronization for Protecting against DoS Attacks on the SHIELD Protocol with AES-CTR Mode
Authors: Lee Sang-su , Moon Jong-sik , Choi Yong-je , Kim Daewon , Lee Seungkwang
Published: June 2024

To enhance security in the semiconductor industry’s globalized production, the Defense Advanced Research Projects Agency (DARPA) proposed an authentication protocol under the Supply Chain Hardware Integrity for Electronics Defense (SHIELD) program. This protocol integrates a secure hardware root-of-trust, known as a dielet, into integrated circuits (ICs). The SHIELD protocol, combined with the Advanced Encryption Standard (AES) in counter mode, named CTR-SHIELD, targets try-and-check attacks. However, CTR-SHIELD is vulnerable to desynchronization attacks on its counter blocks. To counteract this, we introduce the DTR-SHIELD protocol, where DTR stands for double counters. DTR-SHIELD addresses the desynchronization issue by altering the counter incrementation process, which previously solely relied on truncated serial IDs. Our protocol adds a new AES encryption step and requires the dielet to transmit an additional 100 bits, ensuring more robust security through active server involvement and message verification.
The Coal Phase-Out in Germany and Its Regional Impact on Economic Worries
Authors: Baron Daniel , Bartl Walter
Published: June 2024

Market-driven closures of coal mines have typically been associated with negative economic consequences for the affected regions. In Germany, structural policy directed towards ameliorating the negative effects of hard coal decline in the Ruhr area lagged behind the onset of decline, caused major political conflicts, and has been variously criticized for its reactive character that failed to generate a positive image of the future. The phase-out of lignite coal in Germany, in contrast, is policy-driven and accompanied from the outset by compensatory and investment policies that strive to facilitate regional structural change proactively. Against this backdrop, we investigate how the policies of lignite coal phase-out initiated in 2018 and the accompanying public discourse affect the economic worries of individuals in the remaining three lignite coal mining areas in Germany. We focus on the period 2016–2021, using a longitudinal multilevel design based on regional and individual data from the German Socio-Economic Panel (GSOEP). Overall, probabilities of strong economic worries decrease throughout the observed period. Results show that subjective economic worries do not significantly differ between residents of lignite mining regions and those in the rest of the country. Further robustness analyses confirm that the coal phase-out policy has not increased economic uncertainties in the populations of German lignite mining regions. Based on our results, we discuss the need for future research on the effects of policy designs of clean energy transitions on subjective perceptions of the future.
Intergenerational Judo: Synthesising Evidence- and Eminence-Based Knowledge on Judo across Ages
Authors: Ciaccioni Simone , Perazzetti Andrea , Magnanini Angela , Kozsla Tibor , Capranica Laura , Doupona Mojca
Published: June 2024

In alignment with European policies regarding intergenerational sports, this study seeks to integrate existing evidence- (i.e., umbrella review) and eminence- (i.e., international validated educational courses for coaches) based knowledge on judo training across the lifespan. For evidence knowledge, searches of the Google Scholar, ISI-WoS, PubMed, and Scopus databases (PROSPERO ID: CRD42024535420) for studies focused on training youth and older judo practitioners, with no time restriction up to April 2024, were conducted. This resulted in 16 systematic reviews meeting the inclusion criteria, with an “excellent” (n = 6), “good” (n = 7), and “fair” (n = 3) quality according to the NIH tool. With a large sample size (n = 372,655), the reviews focused on wellbeing (n = 9; bone status, injuries, and psychophysical effects) and performance (n = 7; athlete success, combat time, rapid weight loss, and the Special Judo Fitness Test), examining athletic levels (novices to Olympics), sex, and age (children to older adults). For eminence knowledge, the International Judo Federation Academy courses encompassed the “Undergraduate Certificate as Judo Instructor” (UCJI), which provides a comprehensive structure for teaching judo, including techniques, moral code, roles, responsibilities, refereeing, safety, and age-specific belt examination requirements and progression, and the “Educating Judo Coaches for Older Practitioners” (EdJCO) curricula, which provide theoretical and applied modules (e.g., ageing, health, and organisation) to train older judo practitioners. The findings were summarised in a framework, highlighting the most relevant aspects of intergenerational judo programmes (i.e., aims, key characteristics, coaches’ roles, barriers, and facilitators). To develop a comprehensive educational intergenerational judo methodology, future research should examine coaches’ and practitioners’ opinions on intergenerational judo activities carried out in different national contexts.
Body Mass Index Trends for the Top Five Finishers in Men’s Grand Tour and Monument Cycling Events from 1994–2023: Implications for Athletes and Sporting Stakeholders
Authors: Smith Alexander , Wyler Helen , van Wijnkoop Moritz , Colangelo Jill , Liebrenz Michael , Buadze Anna
Published: June 2024

Weight-related issues can be prevalent in elite-level sports, especially in men’s road cycling, where riders may exhibit harmful behaviours, with potentially adverse outcomes for mental and physical health. This study investigated Body Mass Index (BMI) values amongst the top five finishers in the three Grand Tours and the five Monuments races between 1994 and 2023 to assess longitudinal patterns. Publicly available height and weight figures were sourced from ProCyclingStats and BMI scores were calculated for n = 154 and n = 255 individual athletes for the Grand Tours and Monuments, respectively. Two analyses were conducted with correlations and ANOVAs: the first included the BMIs of all top-five finishes and the second focussed on the BMIs of new top-five entrants. The results from both analyses revealed consistent mean BMI decreases over the years and larger effect sizes were apparent in the Grand Tours compared to the Monuments. Although lower BMIs are associated with certain performance advantages, these declining trajectories suggest a need for enhanced awareness in the cycling community and possible regulatory measures and educational programmes to promote the sustainable wellbeing of riders. This may be particularly pertinent given the wider evidence of unhealthy weight-related attitudes and behaviours throughout the sport.
Effects of Ischemic Preconditioning on Sport-Specific Performance in Highly Trained Taekwondo Athletes
Authors: Albuquerque Maicon Rodrigues , Flôr Cristiano Arruda Gomes , Ribeiro Amanda Isadora Santos , Mesquita Paulo Henrique Caldeira , Franchini Emerson , Laurentino Gilberto Candido
Published: June 2024

Ischemic preconditioning (IPC), which involves episodes of blood flow restriction followed by reperfusion, has uncertain effects on athletes. Additionally, employing sports-specific tests that are highly familiar to athletes can enhance methodological rigor in determining IPC’s effects on taekwondo performance. This study aimed to investigate IPC’s influence on taekwondo athletes’ performance through two studies. To induce occlusion in both studies, the cuff was inflated to an individualized occlusion pressure established for each athlete’s lower limb, with four cycles of occlusion lasting five minutes each, alternated with five-minute reperfusion intervals. Both traditional frequentist statistics and Bayesian analysis were employed. In the first study, eleven high-level athletes were subjected to either IPC or a placebo (SHAM) procedure on both legs, followed by performing countermovement jumps (CMJs) and a specific taekwondo endurance test. However, no significant differences were observed in taekwondo endurance performance or CMJ between the IPC and SHAM conditions. The second study involved fourteen elite athletes who underwent the same IPC or SHAM conditions, performing CMJ and three bouts of the Multiple Frequency Speed of Kick test (FSKTmult) in three blocks, each separated by approximately thirty minutes. Again, the results indicated no significant differences in FSKTmult measures or CMJ performance between the two conditions. In conclusion, IPC did not significantly affect neuromuscular (in both studies), endurance (in the first study), or anaerobic (in the second study) performance in these taekwondo athletes.
Defining Sustainable Placemaking in Spatial Planning: Lessons from a South African Case Study
Authors: Strydom Wessel , Puren Karen , Drewes Ernst
Published: June 2024

This study explores the concept of sustainable placemaking from a spatial planning perspective, focusing on Marabastad, a township in South Africa. Drawing on the New Urban Agenda and Sustainable Development Goals, this research seeks to understand how placemaking can contribute to the creation of liveable, resilient, and sustainable human settlements. Through a review of the placemaking literature and a case study, this study examines the evolution of placemaking towards sustainability and its application in diverse cultural, geographic, and socio-economic contexts. The findings reveal the importance of creating and enhancing places that are environmentally responsible, socially inclusive, economically viable, and culturally meaningful. The Marabastad case study illustrates the challenges and possibilities of sustainable placemaking in a multicultural and urbanising community. Economic interventions (e.g., tourism promotion and community markets), social interventions (e.g., education and community events), and environmental interventions (e.g., infrastructure upgrades and preservation of cultural sites) are proposed to address the complex challenges faced by Marabastad. This study emphasises the role of spatial planners in advancing theories and practises of sustainable placemaking, highlighting the need for context-specific approaches to promote inclusive and resilient urban environments.
Promotion of Sustainable Products: Can Sustainability Labels Promote User Selection of Environmentally Friendly Products?
Authors: Houf Lex , Szymkowiak Andrea , Shepherd Lynsay A.
Published: June 2024

Sustainable development is growing in importance in today’s climate crisis. With the percentage of sales via digital channels increasing annually and consumers becoming aware of the environmental impact of their choices, a huge opportunity presents itself for promoting sustainable goods online if designers can find an effective way to raise awareness in consumers. Using a simulated e-commerce site (webshop), we investigated whether the presence or absence of sustainability labels displayed next to product images influenced users’ product selections. There was a significant association between the presentation of sustainability labels and the number of selected sustainable products. Overall, participants were familiar with sustainability labels and indicated willingness to pay ‘extra’ for sustainable products, while there was more variation in the way they felt that sustainability labels influenced their product choices. The findings highlight the complexities of factors influencing purchasing decisions and the need for more design-inspired research in this area. Whilst user interface design may be an effective means to influence sustainable product choices, design should also enable consumers to make informed product choices, while still providing a ‘fair’ e-commerce environment.
Assessing the Susceptibility of the Xiangka Debris Flow Using Analytic Hierarchy Process, Fuzzy Comprehensive Evaluation Method, and Cloud Model
Authors: Li Yan , Wang Jianguo , Ju Keping , Wei Shengyun , Wang Zhinan , Hu Jian
Published: June 2024

The seasonal Xiangka debris flow, breaking out frequently in Xinghai County, Qinghai Province, poses a serious threat to resident safety, has significant potential economic impacts, and inflicts severe damage on the geological environment, vegetation, and land resources in the area. Therefore, a susceptibility assessment is crucial. Utilizing data from field investigations, meteorology, and remote sensing, this study devised an assessment system using 10 evaluation factors with pronounced regional characteristics as susceptibility indices. Based on data processing using ArcGIS 10.7 and MATLAB R2016B, this study assessed the susceptibility of the Xiangka debris flow using AHP, the fuzzy comprehensive evaluation method, and a cloud model. The analysis results show that, based on AHP, the primary index affecting the occurrence of Xiangka debris flow is mainly source factor (0.447). The secondary indices are mainly the length ratio of the mud sand supply section (0.219), fractional vegetation cover (FVC, 0.208), and watershed area (0.192). Combined with the actual characteristics, it can be seen that the formation conditions of the Xiangka debris flow primarily encompass the following: sources such as slope erosion and accumulation at gully exits, challenging topography and terrain conducive to the accumulation of water and solid materials, and water source aspects like surface runoff from intense rainfall. Based on the fuzzy mathematical method—fuzzy coordinate method—cloud model, it is concluded that the degree of susceptibility is mild-to-moderate. The combination of these methods provides a new idea for the evaluation of debris flow susceptibility. This study can provide a theoretical basis for the layout of treatment engineering and geological disaster prevention in this area and promote the sustainable development of the ecological environment.
Investigating User-Centric Factors Influencing Smartwatch Adoption and User Experience in the Philippines
Authors: Gumasing Ma. Janice J. , Carrillo Gilliane Zoe Dennis V. , De Guzman Mickhael Andrei A. , Suñga Cara Althea R. , Tan Siegfred Yvan B. , Mascariola Mellicynt M. , Ong Ardvin Kester S.
Published: June 2024

Smartwatches enable users to easily monitor their health, self-quantify, and track various activities. However, manufacturers and researchers in the field of smartwatches must explore and improve perceived usability to enhance the user experience of consumers and increase the device’s adoption rate. Therefore, this study investigates the factors influencing the adoption of smartwatches among Filipinos, focusing on usability and demographic influences. This is performed by utilizing the UTAUT2 model to examine key factors. External variables are explored, including perceived usability and privacy. To analyze the data acquired, partial least squares structural equation modeling (PLS-SEM) was conducted. The results indicated that performance expectancy, effort expectancy, social influence, hedonic motivation, price value, habit, and behavioral intention significantly influence smartwatch adoption. Habit emerged as positively affecting intention to use and usage behavior. However, facilitating conditions were found not to be significant in influencing intention to use and usage behavior, and privacy was perceived as having an insignificant relationship with the intention to use smartwatches. These findings offer theoretical and practical implications for enhancing smartwatch design and usability, addressing the diverse needs of users, and expanding inclusivity in the market.