bluuebunny/crossref_metadata_2025 huggingface dataset:
All-Dielectric Dual-Band Metamaterial Absorber Based on Ring Nanocavity in Visible Region for Sensing Applications
Authors: Liu Fei , Zou Meiling , Feng Zhenjie , Ni Bo , Ye Baisong , Wang Yunji
Published: January 2023

In this study, an all-dielectric metamaterial absorber consisting of a ring nanocavity array, a spacer layer, and a metallic substrate is designed and investigated. The simulation results show that the two perfect absorption peaks (99.91% and 99.96%) are achieved at 539 nm and 673 nm. The two resonance modes caused by the different electric and magnetic field distributions of the ring nanocavity structure lead to different absorption and sensing properties. In addition, the influence of the structural parameters, such as the width of the nanocavity, on the sensing characteristics was studied and is presented here. A high sensitivity and narrow band result in a huge figure of merit when the proposed absorber is operated as a refractive index sensor.
Biopolymeric Fibrous Aerogels: The Sustainable Alternative for Water Remediation
Authors: Romero-Montero Alejandra , Valencia-Bermúdez José Luis , Rosas-Meléndez Samuel A. , Núñez-Tapia Israel , Piña-Barba María Cristina , Leyva-Gómez Gerardo , Del Prado-Audelo María Luisa
Published: January 2023

The increment in water pollution due to the massive development in the industrial sector is a worldwide concern due to its impact on the environment and human health. Therefore, the development of new and sustainable alternatives for water remediation is needed. In this context, aerogels present high porosity, low density, and a remarkable adsorption capacity, making them candidates for remediation applications demonstrating high efficiency in removing pollutants from the air, soil, and water. Specifically, polymer-based aerogels could be modified in their high surface area to integrate functional groups, decrease their hydrophilicity, or increase their lipophilicity, among other variations, expanding and enhancing their efficiency as adsorbents for the removal of various pollutants in water. The aerogels based on natural polymers such as cellulose, chitosan, or alginate processed by different techniques presented high adsorption capacities, efficacy in oil/water separation and dye removal, and excellent recyclability after several cycles. Although there are different reviews based on aerogels, this work gives an overview of just the natural biopolymers employed to elaborate aerogels as an eco-friendly and renewable alternative. In addition, here we show the synthesis methods and applications in water cleaning from pollutants such as dyes, oil, and pharmaceuticals, providing novel information for the future development of biopolymeric-based aerogel.
Synthesis and Characterization of Orange Peel Modified Hydrogels as Efficient Adsorbents for Methylene Blue (MB)
Authors: Al-Mhyawi Saedah R. , Abdel-Tawab Nader Abdel-Hamed , El Nashar Rasha M.
Published: January 2023

In recent years, due to the developments in the textile industry, water contaminated with synthetic dyes such as methylene blue (MB) has become an environmental threat based on the possible impacts in terms of chemical and biochemical demand, which leads to disturbance in aquatic plants photosynthesis, besides their possible toxicity and carcinogenicity for humans. In this work, an adsorbent hydrogel is prepared via free radical polymerization comprising acrylic acid (PAA) as a monomer and orange peel (OP) as a natural modifier rich in OH and COOH present in its cellulose and pectin content. The resulting hydrogels were optimized in terms of the content of OP and the number of cross-linkers and characterized morphologically using Scanning electron microscopy. Furthermore, BET analysis was used to follow the variation in the porosity and in terms of the surface area of the modified hydrogel. The adsorption behavior was found to follow pseudo-second-order as a kinetic model, and Langmuir, Freundlich, and Temkin isotherm models. The combination of OP and PAA has sharply enhanced the adsorption percent of the hydrogel to reach 84% at the first 10 min of incubation with an adsorption capacity of more than 1.93 gm/gm. Due to its low value of pHPZc, the desorption of MB was efficiently performed at pH 2 using HCl, and the desorbed OP-PAA were found to be reusable up to ten times without a decrease in their efficiency. Accordingly, OP-PAA hydrogel represents a promising efficient, cost-effective, and environmentally friendly adsorbent for MB as a model cationic dye that can be applied for the treatment of contaminated waters.
High Efficiency Uranium(VI) Removal from Wastewater by Strong Alkaline Ion Exchange Fiber: Effect and Characteristic
Authors: Zhou Shiping , Dong Faqin , Qin Yilin
Published: January 2023

In this study, we analyzed the removal efficiency of uranium(U(VI)) in wastewater at relatively low concentrations using strong alkaline ion exchange fiber (SAIEF). Static tests showed that the strong alkali fibers can purify U(VI) containing wastewater in a concentration range of 20–100 mg L−1 with an optimal pH of 10.5 and contact time of 15–30 min. Adsorption and desorption cycling tests indicated that, adsorbed uranium is easily desorbed by 0.1 mol L−1 HCl, and the fiber still maintained the original adsorption efficiency after eight cycles. According to dynamic penetration test results, the SAIEF saturation adsorption capacity was 423.9 mg g−1, and the effluent concentration of uranium through two series columns was less than 0.05 mg L−1, reaching the national standard for non-receiving water (GB23727-2009) SEM-EDS and FTIR analysis revealed that the functional group of SAIEF is CH2N+(CH3)3Cl−. Addotionally, the major forms of fiber exchange adsorption are (UO2)2CO3(OH)3−, UO2(CO)34− and UO2(OH)3−. The results indicate that the SAIEF is an excellent material for uranium removal.
Memory-Improving Activity of the Flower Extract from Chrysanthemum boreale (Makino) Maskino in Scopolamine-Treated Rodents
Authors: Lee Seungeun , Jang Gwiyeong , Jung Jiwook , Park Saetbyeol , Lee Jeonghoon , Lee Yunji , Lee Jihye , Ji Yunjeong , Choi Jehun , Kim Geumsoog
Published: January 2023

One of the factors related to the pathogenesis of Alzheimer’s disease, a disease characterized by gradual cognitive and memory impairment, is an inflammatory process induced by the amyloid-β-mediated activation of microglia. In the present study, an extract of the Chrysanthemum boreale (Makino) Makino (CB) flower, which has inhibitory effects on inflammation and the production of phosphorylated tau in cells, was investigated for its ameliorative effect on memory dysfunction in scopolamine-treated Alzheimer’s disease models. The CB-extract-diet-administered groups, which were treated chronically with scopolamine (intraperitoneal), showed increased spontaneous alterations (12.5–15.5% increase) in the Y-maze test and latency to escape (3.7–6.7-fold increase) in the passive avoidance test, compared to the negative control (NC) group. Rats administered the CB extract also showed a higher tendency (66–86% increase) of hippocampal brain-derived neurotrophic factor expression than NC rats. Moreover, the ratio of phosphorylated extracellular signal-regulated kinase/extracellular signal-regulated kinase in the CB-extract-administered group was lower (48.0–52.2%) than that (100%) in the NC group. In the Morris water maze test conducted on the fifth day, the free-swimming times of the CB-extract-administered mice that were also treated with scopolamine for a short time (5 d) increased (51.7–56.1%) significantly compared to those of the NC mice. Finally, high-performance liquid chromatography analysis confirmed that isochlorogenic acid A, linarin, and chlorogenic acid are the major phenolic components of the CB extract. These results suggest that the extract of CB flowers might be useful as a functional material with memory-enhancing effects.
Impact of Self-Fabricated Graphene–Metal Oxide Composite Anodes on Metal Degradation and Energy Generation via a Microbial Fuel Cell
Authors: Ahmad Akil , Alshammari Mohammed B. , Ibrahim Mohamad Nasir Mohamad
Published: January 2023

Microbial fuel cells (MFCs) are thought to be ecologically friendly, despite electron transport and generation challenges. In order to address this, the efficiency of MFCs was investigated using two different anode electrodes made from biomass: graphene oxide (GO) and graphene oxide-metal oxide (GO-MO) (GO-ZnO). After 18 days of operation, the maximum power density for GO was 0.69 mW/m2, whereas the maximum power density for GO-ZnO was 1.05 mW/m2. Furthermore, the ability of MFCs to transform the soluble metal ions (Cd2+, Cr3+, Pb2+, and Ni2+) into an insoluble state was investigated, which is a secondary use of MFCs with significant benefits. In the soluble state of metal ion transformation into an insoluble state, the rate of GO-ZnO was higher (92.71%) than that of GO (81.20%). The outcomes of material, analytical, and biological tests undertaken to validate the efficiency of anodes are presented. It has been shown that using innovative materials as electrodes in MFCs is a potential method for improving electron transport. Furthermore, as an organic substrate, food waste seems to be a viable alternative to more traditional options. In light of these discoveries, we investigate various unanswered issues and possibilities for MFCs. Organic substrate evaluation trials were also included in the present results to demonstrate that organic waste may be a reliable source of MFC performance. This article also has a thorough discussion of food waste oxidation, as well as challenges and future recommendations.
An Adaptive Routing Algorithm for Inter-Satellite Networks Based on the Combination of Multipath Transmission and Q-Learning
Authors: Shi Yuanji , Yuan Zhiwei , Zhu Xiaorong , Zhu Hongbo
Published: January 2023

In a satellite network, the inter-satellite link can facilitate the information transmission and exchange between satellites, and the packet routing of the inter-satellite link is the key development direction of satellite communication systems. Aiming at the complex topology and dynamic change in LEO satellite networks, the traditional single shortest path algorithm can no longer meet the optimal path requirement. Therefore, this paper proposes a multi-path routing algorithm based on an improved breadth-first search. First, according to the inter-satellite network topology information, the improved breadth-first search algorithm is used to obtain all the front hop node information of the destination node. Second, all the shortest paths are obtained by backtracking the path through the front hop node. Finally, according to the inter-satellite network, the bandwidth capacity of the traffic and nodes determines the optimal path from multiple shortest paths. However, due to the high dynamics of low-orbit satellite networks, the topology changes rapidly, and the global topology of the network is often not available. At this time, in order to enhance the adaptability of the algorithm, this paper proposes an inter-satellite network dynamic routing algorithm based on reinforcement learning. Verified by simulation experiments, the proposed algorithm can improve the throughput of the inter-satellite network, reduce the time delay, and the packet loss rate.
Investigating the Quality of UAV-Based Images for the Thermographic Analysis of Buildings
Authors: Mayer Zoe , Epperlein Andres , Vollmer Elena , Volk Rebekka , Schultmann Frank
Published: January 2023

Thermography for building audits is commonly carried out by means of terrestrial recording processes with static cameras. The implementation of drones to automatically acquire images from various perspectives can speed up and facilitate the procedure but requires higher recording distances, utilizes changing recording angles and has to contend with the effects of movement during image capture. This study investigates the influence of different drone settings on the quality of thermographic images for building audits in comparison to ground-based acquisition. To this end, several buildings are photographically captured via unmanned aerial vehicle and classical terrestrial means to generate a dataset of 968 images in total. These are analyzed and compared according to five quality criteria that are explicitly chosen for this study to establish best-practice rules for thermal image acquisition. We discover that flight speeds of up to 5 m/s have no visible effects on the image quality. The combination of smaller distances (22 m above a building) and a 45° camera angle are found to allow for both the qualitative and quantitative analysis of rooftops as well as a qualitative screening of building façades. Greater distances of 42 m between camera and building may expedite the acquisition procedure for larger-scaled district coverage but cannot be relied upon for thermal analyses beyond qualitative studies.
Sudden Track Turning of Typhoon Prapiroon (2012) Enhanced the Upper Ocean Response
Authors: Zhang Yihan , Liu Yuhao , Guan Shoude , Wang Qian , Zhao Wei , Tian Jiwei
Published: January 2023

Due to the change in environmental steering flow influenced by the surrounding synoptic systems, typhoon tracks often manifest sudden turnings, potentially prolonging the residence time of typhoon wind forcing and, thus, exerting a remarkable upper ocean response. Typhoon Prapiroon (2012) in the western North Pacific, had a very complex track and underwent two sudden-turning stages over its lifespan. On the basis of satellite and Argo float observations, this paper studies the surface and subsurface ocean environmental responses to Prapiroon. The observations show that the oceanic responses during the two sudden-turning stages of Prapiroon were much more remarkable than those in the straight-moving stage, including significant sea surface temperature (SST) cooling (~7 °C), sea surface chlorophyll-a (Chl-a) concentration increase (>0.30 mg m−3), and sea surface height anomaly (SSHA) reduction (<−50 cm), compared with those in the straight-moving stage, with SST cooling weaker than 3 °C, Chl-a concentration increase less than 0.05 mg m−3, and SSHA reduction less than −10 cm. By employing the three-dimensional Price–Weller–Pinkel (3DPWP) model to conduct a series of sensitivity experiments, we separate the contribution of the typhoon track’s sudden turnings to the upper ocean response and find that the relative contributions of the two sudden turnings to SST cooling (sea surface salinity salinification) reached 38.4% (23.5%) and 46.8% (28.0%), respectively. In addition, the model experiments further show that the sudden turning could also induce stronger upwelling in the subsurface ocean. Our results demonstrate that typhoon track sudden turning could result in more kinetic energy input into the upper ocean, enhancing the physical and biological responses in the upper ocean.
Synthetic Aperture Radar (SAR) Meets Deep Learning
Authors: Zhang Tianwen , Zeng Tianjiao , Zhang Xiaoling
Published: January 2023

Synthetic aperture radar (SAR) is an important active microwave imaging sensor [...]
Low-Rank Constrained Attention-Enhanced Multiple Spatial–Spectral Feature Fusion for Small Sample Hyperspectral Image Classification
Authors: Feng Fan , Zhang Yongsheng , Zhang Jin , Liu Bing
Published: January 2023

Hyperspectral images contain rich features in both spectral and spatial domains, which bring opportunities for accurate recognition of similar materials and promote various fine-grained remote sensing applications. Although deep learning models have been extensively investigated in the field of hyperspectral image classification (HSIC) tasks, classification performance is still limited under small sample conditions, and this has been a longstanding problem. The features extracted by complex network structures with large model size are redundant to some extent and prone to overfitting. This paper proposes a low-rank constrained attention-enhanced multiple feature fusion network (LAMFN). Firstly, factor analysis is used to extract very few components that can describe the original data using covariance information to perform spectral feature preprocessing. Then, a lightweight attention-enhanced 3D convolution module is used for deep feature extraction, and the position-sensitive information is supplemented using a 2D coordinate attention. The above widely varying spatial–spectral feature groups are fused through a simple composite residual structure. Finally, low-rank second-order pooling is adopted to enhance the convolutional feature selectivity and achieve classification. Extensive experiments were conducted on four representative hyperspectral datasets with different spatial–spectral characteristics, namely Indian Pines (IP), Pavia Center (PC), Houston (HU), and WHU-HongHu (WHU). The contrast methods include several advanced models proposed recently, including residual CNNs, attention-based CNNs, and transformer-based models. Using only five samples per class for training, LAMFN achieved overall accuracies of 78.15%, 97.18%, 81.35%, and 87.93% on the above datasets, which has an improvement of 0.82%, 1.12%, 1.67%, and 0.89% compared to the second-best model. The running time of LAMFN is moderate. For example, the training time of LAMFN on the WHU dataset was 29.1 s, and the contrast models ranged from 3.0 s to 341.4 s. In addition, ablation experiments and comparisons with some advanced semi-supervised learning methods further validated the effectiveness of the proposed model designs.
Deformation Detection of Mining Tunnel Based on Automatic Target Recognition
Authors: Ji Changqi , Sun Haili , Zhong Ruofei , Sun Mingze , Li Jincheng , Lu Yue
Published: January 2023

Mining tunnels have irregular and diverse cross-sectional shapes. Structural deformation detection using mobile laser measurement has some problems, such as the inconvenient positioning of the deformation, difficulties in unifying the multiphase data, and difficulties in solving the section parameters. To address these problems, this paper proposes a mining tunnel deformation detection method based on automatic target recognition. Firstly, a mobile tunnel laser detection scheme combined with the target layout is designed. Secondly, a preview image of the tunnel lining is generated using the mobile laser point cloud data, and the index relationship between the image and point cloud is established. The target recognition accuracy of the You Only Look Once version 4 (YOLOv4) model is optimized by integrating the prediction confidence threshold, target spatial position, and target gray scale rule. Based on target recognition and positioning, the chord length and vault net height of the mining tunnel are calculated using gross error elimination and curve fitting. Finally, the engineering application of the model and algorithm is realized using ML.NET. The research method was verified using the field measurement data of the mining tunnel. The target recognition accuracy reached 100%, and the repeated deviations of the chord length and net height of the arch crown were 1.7 mm and 1.4 mm, respectively, which established the effectiveness and high accuracy of the research method.
Tri-CNN: A Three Branch Model for Hyperspectral Image Classification
Authors: Alkhatib Mohammed Q. , Al-Saad Mina , Aburaed Nour , Almansoori Saeed , Zabalza Jaime , Marshall Stephen , Al-Ahmad Hussain
Published: January 2023

Hyperspectral Image (HSI) classification methods that use Deep Learning (DL) have proven to be effective in recent years. In particular, Convolutional Neural Networks (CNNs) have demonstrated extremely powerful performance in such tasks. However, the lack of training samples is one of the main contributors to low classification performance. Traditional CNN-based techniques under-utilize the inter-band correlations of HSI because they primarily use 2D-CNNs for feature extraction. Contrariwise, 3D-CNNs extract both spectral and spatial information using the same operation. While this overcomes the limitation of 2D-CNNs, it may lead to insufficient extraction of features. In order to overcome this issue, we propose an HSI classification approach named Tri-CNN which is based on a multi-scale 3D-CNN and three-branch feature fusion. We first extract HSI features using 3D-CNN at various scales. The three different features are then flattened and concatenated. To obtain the classification results, the fused features then traverse a number of fully connected layers and eventually a softmax layer. Experimental results are conducted on three datasets, Pavia University (PU), Salinas scene (SA) and GulfPort (GP) datasets, respectively. Classification results indicate that our proposed methodology shows remarkable performance in terms of the Overall Accuracy (OA), Average Accuracy (AA), and Kappa metrics when compared against existing methods.
Spatiotemporal Variation Characteristics and Dynamic Persistence Analysis of Carbon Sources/Sinks in the Yellow River Basin
Authors: Zhang Kun , Zhu Changming , Ma Xiaodong , Zhang Xin , Yang Dehu , Shao Yakui
Published: January 2023

Net ecosystem productivity (NEP) is an important indicator for estimating regional carbon sources/sinks. The study focuses on a comprehensive computational simulation and spatiotemporal variation study of the NEP in the Yellow River basin from 2000 to 2020 using NPP data products from MODIS combined with a quantitative NEP estimation model followed by a comprehensive analysis of the spatiotemporal variation characteristics and dynamic procession persistence analysis based on meteorological data and land use data. The results show that: (1) The total NEP in the Yellow River basin had an overall increasing trend from 2000 to 2020, with a Theil–Sen trend from −23.37 to 43.66 gCm−2a−1 and a mean increase of 4.64 gCm−2a−1 (p < 0.01, 2-tailed). (2) Most areas of the Yellow River basin are carbon sink areas, and the annual average NEP per unit area was 208.56 gCm−2a−1 from 2000 to 2020. There were, however, substantial spatial and temporal variations in the NEP. Most of the carbon source area was located in the Kubuqi Desert and its surroundings. (3) Changes in land use patterns were the main cause of changes in regional NEP. During the 2000–2020 period, 1154.24 t of NEP were added, mainly due to changes in land use, e.g., the conversion of farmland to forests and grasslands. (4) The future development in 83.43% of the area is uncertain according to the Hurst index dynamic persistence analysis. In conclusion, although the carbon−sink capacity of the terrestrial ecosystem in the Yellow River basin is increasing and the regional carbon sink potential is increasing in the future, the future development of new energy resources has regional uncertainties, and the stability of the basin ecosystem needs to be enhanced.
Calibration Procedure of a Multi-Camera System: Process Uncertainty Budget
Authors: Leizea Ibai , Herrera Imanol , Puerto Pablo
Published: January 2023

The Automated six Degrees of Freedom (DoF) definition of industrial components has become an added value in production processes as long as the required accuracy is guaranteed. This is where multi-camera systems are finding their niche in the market. These systems provide, among other things, the ease of automating tracking processes without human intervention and knowledge about vision and/or metrology. In addition, the cost of integrating a new sensor into the complete system is negligible compared to other multi-tracker systems. The increase in information from different points of view in multi-camera systems raises the accuracy, based on the premise that the more points of view, the lower the level of uncertainty. This work is devoted to the calibration procedures of multi-camera systems, which is decisive to achieve high performance, with a particular focus on the uncertainty budget. Moreover, an evaluation methodology has been carried out, which is key to determining the level of accuracy of the measurement system.
Model-Based Damage Localization Using the Particle Swarm Optimization Algorithm and Dynamic Time Wrapping for Pattern Recreation
Authors: Zacharakis Ilias , Giagopoulos Dimitrios
Published: January 2023

Vibration-based damage detection methods are a subcategory of Structural Health Monitoring (SHM) methods that rely on the fact that structural damage will affect the dynamic characteristic of a structure. The presented methodology uses Finite Element Models coupled with a metaheuristic optimization algorithm in order to locate the damage in a structure. The search domains of the optimization algorithm are the variables that control a parametric area, which is inserted into the FE model. During the optimization procedure, this area changes location, stiffness, and mass to simulate the effect of the physical damage. The final output is a damaged FE model which can approximate the dynamic response of the damaged structure and indicate the damaged area. For the current implementation of this Damage Detection Framework, the Particle Swarm Optimization algorithm is used. As an effective metric of the comparison between the FE model and the experimental structure, Transmittance Functions (TF) are used that require output only acceleration signals. As with most model-based methods, a common concern is the modeling error and how this can be surpassed. For this reason, the Dynamic Time Wrapping (DTW) algorithm is applied. When damage occurs in a structure it creates some differences between the Transmittance Functions (TF) of the healthy and the damaged state. With the use of DTW, the damaged pattern is recreated around the TF of the FE model, while creating the same differences and, thus, minimizing the modeling error. The effectiveness of the proposed methodology is tested on a small truss structure that consists of Carbon-Fiber Reinforced Polymer (CFRP) filament wound beams and aluminum connectors, where four cases are examined with the damage to be located on the composite material.
GIS-Based Assessment of Hybrid Pumped Hydro Storage as a Potential Solution for the Clean Energy Transition: The Case of the Kardia Lignite Mine, Western Greece
Authors: Krassakis Pavlos , Karavias Andreas , Zygouri Evangelia , Roumpos Christos , Louloudis Georgios , Pyrgaki Konstantina , Koukouzas Nikolaos , Kempka Thomas , Karapanos Dimitris
Published: January 2023

Planned decommissioning of coal-fired plants in Europe requires innovative technical and economic strategies to support coal regions on their path towards a climate-resilient future. The repurposing of open pit mines into hybrid pumped hydro power storage (HPHS) of excess energy from the electric grid, and renewable sources will contribute to the EU Green Deal, increase the economic value, stabilize the regional job market and contribute to the EU energy supply security. This study aims to present a preliminary phase of a geospatial workflow used to evaluate land suitability by implementing a multi-criteria decision making (MCDM) technique with an advanced geographic information system (GIS) in the context of an interdisciplinary feasibility study on HPHS in the Kardia lignite open pit mine (Western Macedonia, Greece). The introduced geospatial analysis is based on the utilization of the constraints and ranking criteria within the boundaries of the abandoned mine regarding specific topographic and proximity criteria. The applied criteria were selected from the literature, while for their weights, the experts’ judgement was introduced by implementing the analytic hierarchy process (AHP), in the framework of the ATLANTIS research program. According to the results, seven regions were recognized as suitable, with a potential energy storage capacity from 1.09 to 5.16 GWh. Particularly, the present study’s results reveal that 9.27% (212,884 m2) of the area had a very low suitability, 15.83% (363,599 m2) had a low suitability, 23.99% (550,998 m2) had a moderate suitability, 24.99% (573,813 m2) had a high suitability, and 25.92% (595,125 m2) had a very high suitability for the construction of the upper reservoir. The proposed semi-automatic geospatial workflow introduces an innovative tool that can be applied to open pit mines globally to identify the optimum design for an HPHS system depending on the existing lower reservoir.
Heart Rate Estimation from Incomplete Electrocardiography Signals
Authors: Song Yawei , Chen Jia , Zhang Rongxin
Published: January 2023

As one of the most remarkable indicators of physiological health, heart rate (HR) has become an unfailing investigation for researchers. Unlike many existing methods, this article proposes an approach to implement short-time HR estimation from electrocardiography in time series missing patterns. Benefiting from the rapid development of deep learning, we adopted a bidirectional long short-term memory model (Bi-LSTM) and temporal convolution network (TCN) to recover complete heartbeat signals from those with durations are less than one cardiac cycle, and the estimated HR from recovered segment combining the input and the predicted output. We also compared the performance of Bi-LSTM and TCN in PhysioNet dataset. Validating the method over a resting heart rate range of 60–120 bpm in the database without significant arrhythmias and a corresponding range of 30–150 bpm in the database with arrhythmias, we found that networks provide an estimated approach for incomplete signals in a fixed format. These results are consistent with real heartbeats in the normal heartbeat dataset (γ > 0.7, RMSE < 10) and in the arrhythmia database (γ > 0.6, RMSE < 30), verifying that HR could be estimated by models in advance. We also discussed the short-time limits for the predictive model. It could be used for physiological purposes such as mobile sensing in time-constrained scenarios, and providing useful insights for better time series analyses in missing data patterns.
A Survey on Ground Segmentation Methods for Automotive LiDAR Sensors
Authors: Gomes Tiago , Matias Diogo , Campos André , Cunha Luís , Roriz Ricardo
Published: January 2023

In the near future, autonomous vehicles with full self-driving features will populate our public roads. However, fully autonomous cars will require robust perception systems to safely navigate the environment, which includes cameras, RADAR devices, and Light Detection and Ranging (LiDAR) sensors. LiDAR is currently a key sensor for the future of autonomous driving since it can read the vehicle’s vicinity and provide a real-time 3D visualization of the surroundings through a point cloud representation. These features can assist the autonomous vehicle in several tasks, such as object identification and obstacle avoidance, accurate speed and distance measurements, road navigation, and more. However, it is crucial to detect the ground plane and road limits to safely navigate the environment, which requires extracting information from the point cloud to accurately detect common road boundaries. This article presents a survey of existing methods used to detect and extract ground points from LiDAR point clouds. It summarizes the already extensive literature and proposes a comprehensive taxonomy to help understand the current ground segmentation methods that can be used in automotive LiDAR sensors.
Study of Pressure Distribution in Floor Tiles with Printed P(VDF:TrFE) Sensors for Smart Surface Applications
Authors: Alvarez Rueda Asier , Schäffner Philipp , Petritz Andreas , Groten Jonas , Tschepp Andreas , Petersen Frank , Zirkl Martin , Stadlober Barbara
Published: January 2023

Pressure sensors integrated in surfaces, such as the floor, can enable movement, event, and object detection with relatively little effort and without raising privacy concerns, such as video surveillance. Usually, this requires a distributed array of sensor pixels, whose design must be optimized according to the expected use case to reduce implementation costs while providing sufficient sensitivity. In this work, we present an unobtrusive smart floor concept based on floor tiles equipped with a printed piezoelectric sensor matrix. The sensor element adds less than 130 µm in thickness to the floor tile and offers a pressure sensitivity of 36 pC/N for a 1 cm2 pixel size. A floor model was established to simulate how the localized pressure excitation acting on the floor spreads into the sensor layer, where the error is only 1.5%. The model is valuable for optimizing the pixel density and arrangement for event and object detection while considering the smart floor implementation in buildings. Finally, a demonstration, including wireless connection to the computer, is presented, showing the viability of the tile to detect finger touch or movement of a metallic rod.
Exploration of Effective Time-Velocity Distribution for Doppler-Radar-Based Personal Gait Identification Using Deep Learning
Authors: Shioiri Keitaro , Saho Kenshi
Published: January 2023

Personal identification based on radar gait measurement is an important application of biometric technology because it enables remote and continuous identification of people, irrespective of the lighting conditions and subjects’ outfits. This study explores an effective time-velocity distribution and its relevant parameters for Doppler-radar-based personal gait identification using deep learning. Most conventional studies on radar-based gait identification used a short-time Fourier transform (STFT), which is a general method to obtain time-velocity distribution for motion recognition using Doppler radar. However, the length of the window function that controls the time and velocity resolutions of the time-velocity image was empirically selected, and several other methods for calculating high-resolution time-velocity distributions were not considered. In this study, we compared four types of representative time-velocity distributions calculated from the Doppler-radar-received signals: STFT, wavelet transform, Wigner–Ville distribution, and smoothed pseudo-Wigner–Ville distribution. In addition, the identification accuracies of various parameter settings were also investigated. We observed that the optimally tuned STFT outperformed other high-resolution distributions, and a short length of the window function in the STFT process led to a reasonable accuracy; the best identification accuracy was 99% for the identification of twenty-five test subjects. These results indicate that STFT is the optimal time-velocity distribution for gait-based personal identification using the Doppler radar, although the time and velocity resolutions of the other methods were better than those of the STFT.
Design and In Situ Validation of Low-Cost and Easy to Apply Anti-Biofouling Techniques for Oceanographic Continuous Monitoring with Optical Instruments
Authors: Matos Tiago , Pinto Vânia , Sousa Paulo , Martins Marcos , Fernández Emilio , Henriques Renato , Gonçalves Luis Miguel
Published: January 2023

Biofouling is the major factor that limits long-term monitoring studies with automated optical instruments. Protection of the sensing areas, surfaces, and structural housing of the sensors must be considered to deliver reliable data without the need for cleaning or maintenance. In this work, we present the design and field validation of different techniques for biofouling protection based on different housing materials, biocides, and transparent coatings. Six optical turbidity probes were built using polylactic acid (PLA), acrylonitrile butadiene styrene (ABS), PLA with copper filament, ABS coated with PDMS, ABS coated with epoxy and ABS assembled with a system for in situ chlorine production. The probes were deployed in the sea for 48 days and their anti-biofouling efficiency was evaluated using the results of the field experiment, visual inspections, and calibration signal loss after the tests. The PLA and ABS were used as samplers without fouling protection. The probe with chlorine production outperformed the other techniques, providing reliable data during the in situ experiment. The copper probe had lower performance but still retarded the biological growth. The techniques based on transparent coatings, epoxy, and PDMS did not prevent biofilm formation and suffered mostly from micro-biofouling.
The Effect of Glycerol-Based Suspensions on the Characteristics of Resonators Excited by a Longitudinal Electric Field
Authors: Semyonov Alexander , Zaitsev Boris , Teplykh Andrey , Borodina Irina
Published: January 2023

This study examines the effect of suspensions based on pure glycerol and diamond powder with different concentrations on the characteristics of resonators with a longitudinal electric field. We used two disk resonators made of the quartz and langasite plates with round electrodes on both sides of the plate and resonant frequencies of 4.4 and 4.1 MHz, operating in shear and longitudinal acoustic modes, respectively. Each resonator was mounted on the bottom of a 30 mL liquid container. During the experiments, the container was filled with the suspension under study in such a way that the resonator was completely immersed in the suspension, and the frequency dependences of the real and imaginary parts of the electrical impedance of the resonator were measured. As a result, the shear modulus of the elasticity and shear coefficient of the viscosity of the studied suspensions were determined. The material constants of the suspensions were found by fitting the theoretical frequency dependences of the real and imaginary parts of the electrical impedance of the resonator to the experimentally measured ones, which was calculated using Mason’s equivalent circuit. As a result, the dependencies of the density, shear modulus of elasticity, shear viscosity coefficient, and velocity of the shear acoustic wave on the volume concentration of the diamond particles were constructed.
An Insight into the Machine-Learning-Based Fileless Malware Detection
Authors: Khalid Osama , Ullah Subhan , Ahmad Tahir , Saeed Saqib , Alabbad Dina A. , Aslam Mudassar , Buriro Attaullah , Ahmad Rizwan
Published: January 2023

In recent years, massive development in the malware industry changed the entire landscape for malware development. Therefore, cybercriminals became more sophisticated by advancing their development techniques from file-based to fileless malware. As file-based malware depends on files to spread itself, on the other hand, fileless malware does not require a traditional file system and uses benign processes to carry out its malicious intent. Therefore, it evades conventional detection techniques and remains stealthy. This paper briefly explains fileless malware, its life cycle, and its infection chain. Moreover, it proposes a detection technique based on feature analysis using machine learning for fileless malware detection. The virtual machine acquired the memory dumps upon executing the malicious and non-malicious samples. Then the necessary features are extracted using the Volatility memory forensics tool, which is then analyzed using machine learning classification algorithms. After that, the best algorithm is selected based on the k-fold cross-validation score. Experimental evaluation has shown that Random Forest outperforms other machine learning classifiers (Decision Tree, Support Vector Machine, Logistic Regression, K-Nearest Neighbor, XGBoost, and Gradient Boosting). It achieved an overall accuracy of 93.33% with a True Positive Rate (TPR) of 87.5% at zeroFalse Positive Rate (FPR) for fileless malware collected from five widely used datasets (VirusShare, AnyRun, PolySwarm, HatchingTriage, and JoESadbox).
Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile Crowdsourcing
Authors: Bruns Ralf , Dötterl Jeremias , Dunkel Jürgen , Ossowski Sascha
Published: January 2023

Mobile crowdsourcing refers to systems where the completion of tasks necessarily requires physical movement of crowdworkers in an on-demand workforce. Evidence suggests that in such systems, tasks often get assigned to crowdworkers who struggle to complete those tasks successfully, resulting in high failure rates and low service quality. A promising solution to ensure higher quality of service is to continuously adapt the assignment and respond to failure-causing events by transferring tasks to better-suited workers who use different routes or vehicles. However, implementing task transfers in mobile crowdsourcing is difficult because workers are autonomous and may reject transfer requests. Moreover, task outcomes are uncertain and need to be predicted. In this paper, we propose different mechanisms to achieve outcome prediction and task coordination in mobile crowdsourcing. First, we analyze different data stream learning approaches for the prediction of task outcomes. Second, based on the suggested prediction model, we propose and evaluate two different approaches for task coordination with different degrees of autonomy: an opportunistic approach for crowdshipping with collaborative, but non-autonomous workers, and a market-based model with autonomous workers for crowdsensing.
Remote Photoplethysmography with a High-Speed Camera Reveals Temporal and Amplitude Differences between Glabrous and Non-Glabrous Skin
Authors: Cao Meiyun , Burton Timothy , Saiko Gennadi , Douplik Alexandre
Published: January 2023

Photoplethysmography (PPG) is a noninvasive optical technology with applications including vital sign extraction and patient monitoring. However, its current use is primarily limited to heart rate and oxygenation monitoring. This study aims to demonstrate the utility of PPG for physiological investigations. In particular, we sought to demonstrate the utility of simultaneous data acquisition from several regions of tissue using remote/contactless PPG (rPPG). Specifically, using a high-speed scientific-grade camera, we collected rPPG from the hands (palmar/dorsal) of 22 healthy volunteers. Data collected through the red and green channels of the RGB CMOS sensor were analyzed. We found a statistically significant difference in the amplitude of the glabrous skin signal over the non-glabrous skin signal (1.41 ± 0.85 in the red channel and 2.27 ± 0.88 in the green channel). In addition, we found a statistically significant lead of the red channel over the green channel, which is consistent between glabrous (17.13 ± 10.69 ms) and non-glabrous (19.31 ± 12.66 ms) skin. We also found a statistically significant lead time (32.69 ± 55.26 ms in the red channel and 40.56 ± 26.97 ms in the green channel) of the glabrous PPG signal over the non-glabrous, which cannot be explained by bilateral variability. These results demonstrate the utility of rPPG imaging as a tool for fundamental physiological studies and can be used to inform the development of PPG-based devices.
Global–Local Facial Fusion Based GAN Generated Fake Face Detection
Authors: Xue Ziyu , Jiang Xiuhua , Liu Qingtong , Wei Zhaoshan
Published: January 2023

Media content forgery is widely spread over the Internet and has raised severe societal concerns. With the development of deep learning, new technologies such as generative adversarial networks (GANs) and media forgery technology have already been utilized for politicians and celebrity forgery, which has a terrible impact on society. Existing GAN-generated face detection approaches rely on detecting image artifacts and the generated traces. However, these methods are model-specific, and the performance is deteriorated when faced with more complicated methods. What’s more, it is challenging to identify forgery images with perturbations such as JPEG compression, gamma correction, and other disturbances. In this paper, we propose a global–local facial fusion network, namely GLFNet, to fully exploit the local physiological and global receptive features. Specifically, GLFNet consists of two branches, i.e., the local region detection branch and the global detection branch. The former branch detects the forged traces from the facial parts, such as the iris and pupils. The latter branch adopts a residual connection to distinguish real images from fake ones. GLFNet obtains forged traces through various ways by combining physiological characteristics with deep learning. The method is stable with physiological properties when learning the deep learning features. As a result, it is more robust than the single-class detection methods. Experimental results on two benchmarks have demonstrated superiority and generalization compared with other methods.
Survey on Exact kNN Queries over High-Dimensional Data Space
Authors: Ukey Nimish , Yang Zhengyi , Li Binghao , Zhang Guangjian , Hu Yiheng , Zhang Wenjie
Published: January 2023

k nearest neighbours (kNN) queries are fundamental in many applications, ranging from data mining, recommendation system and Internet of Things, to Industry 4.0 framework applications. In mining, specifically, it can be used for the classification of human activities, iterative closest point registration and pattern recognition and has also been helpful for intrusion detection systems and fault detection. Due to the importance of kNN queries, many algorithms have been proposed in the literature, for both static and dynamic data. In this paper, we focus on exact kNN queries and present a comprehensive survey of exact kNN queries. In particular, we study two fundamental types of exact kNN queries: the kNN Search queries and the kNN Join queries. Our survey focuses on exact approaches over high-dimensional data space, which covers 20 kNN Search methods and 9 kNN Join methods. To the best of our knowledge, this is the first work of a comprehensive survey of exact kNN queries over high-dimensional datasets. We specifically categorise the algorithms based on indexing strategies, data and space partitioning strategies, clustering techniques and the computing paradigm. We provide useful insights for the evolution of approaches based on the various categorisation factors, as well as the possibility of further expansion. Lastly, we discuss some open challenges and future research directions.
Measures and Policies for Reducing PM Exceedances through the Use of Air Quality Modeling: The Case of Thessaloniki, Greece
Authors: Progiou Athena , Liora Natalia , Sebos Ioannis , Chatzimichail Christina , Melas Dimitrios
Published: January 2023

The main aim of this study is the identification of the most appropriate measures and policies to combat particulate air pollution in the city of Thessaloniki, Greece through a methodology including the calculation of emissions by sector and the application of an air quality modeling system. The identification of the current air quality situation showed that the exceedances of the mean PM10 daily levels occur during the cold winter months with residential heating being the major contributor representing a 73% share of total PM10 emissions. Moreover, the effect of the biomass consumption for heating purposes on PM10 concentrations has been verified and quantified by implementing a regression model identifying that a PM10 emissions reduction by 45% would result in the elimination of the exceedances of the mean PM10 daily values. Based on the above, a set of cost-effective measures and policies were defined and their impact on PM10 emissions was estimated. Finally, the air quality modeling system was applied for the mitigation scenarios selected, leading to a significant reduction of 67% in the number of exceedances observed and ensuring compliance with the limit of 35 exceedances of the daily value limit per year pursuant to European Legislation.