bluuebunny/crossref_metadata_2025 huggingface dataset:
A Remote Sensing Method for Crop Mapping Based on Multiscale Neighborhood Feature Extraction
Authors: Wu Yongchuang , Wu Yanlan , Wang Biao , Yang Hui
Published: December 2022

Obtaining accurate and timely crop mapping is essential for refined agricultural refinement and food security. Due to the spectral similarity between different crops, the influence of image resolution, the boundary blur and spatial inconsistency that often occur in remotely sensed crop mapping, remotely sensed crop mapping still faces great challenges. In this article, we propose to extend a neighborhood window centered on the target pixel to enhance the receptive field of our model and extract the spatial and spectral features of different neighborhood sizes through a multiscale network. In addition, we also designed a coordinate convolutional module and a convolutional block attention module to further enhance the spatial information and spectral features in the neighborhoods. Our experimental results show that this method allowed us to obtain accuracy scores of 0.9481, 0.9115, 0.9307 and 0.8729 for OA, kappa coefficient, F1 score and IOU, respectively, which were better than those obtained using other methods (Resnet-18, MLP and RFC). The comparison of the experimental results obtained from different neighborhood window sizes shows that the spatial inconsistency and boundary blurring in crop mapping could be effectively reduced by extending the neighborhood windows. It was also shown in the ablation experiments that the coordinate convolutional and convolutional block attention modules played active roles in the network. Therefore, the method proposed in this article could provide reliable technical support for remotely sensed crop mapping.
Riparian Plant Evapotranspiration and Consumptive Use for Selected Areas of the Little Colorado River Watershed on the Navajo Nation
Authors: Nagler Pamela L. , Barreto-Muñoz Armando , Sall Ibrahima , Lurtz Matthew R. , Didan Kamel
Published: December 2022

Estimates of riparian vegetation water use are important for hydromorphological assessment, partitioning within human and natural environments, and informing environmental policy decisions. The objectives of this study were to calculate the actual evapotranspiration (ETa) (mm/day and mm/year) and derive riparian vegetation annual consumptive use (CU) in acre-feet (AF) for select riparian areas of the Little Colorado River watershed within the Navajo Nation, in northeastern Arizona, USA. This was accomplished by first estimating the riparian land cover area for trees and shrubs using a 2019 summer scene from National Agricultural Imagery Program (NAIP) (1 m resolution), and then fusing the riparian delineation with Landsat-8 OLI (30-m) to estimate ETa for 2014–2020. We used indirect remote sensing methods based on gridded weather data, Daymet (1 km) and PRISM (4 km), and Landsat measurements of vegetation activity using the two-band Enhanced Vegetation Index (EVI2). Estimates of potential ET were calculated using Blaney-Criddle. Riparian ETa was quantified using the Nagler ET(EVI2) approach. Using both vector and raster estimates of tree, shrub, and total riparian area, we produced the first CU measurements for this region. Our best estimate of annual CU is 36,983 AF with a range between 31,648–41,585 AF and refines earlier projections of 25,387–46,397 AF.
An Algorithm to Detect Endangered Cultural Heritage by Agricultural Expansion in Drylands at a Global Scale
Authors: Conesa Francesc C. , Orengo Hector A. , Lobo Agustín , Petrie Cameron A.
Published: December 2022

This article presents AgriExp, a remote-based workflow for the rapid mapping and monitoring of archaeological and cultural heritage locations endangered by new agricultural expansion and encroachment. Our approach is powered by the cloud-computing data cataloguing and processing capabilities of Google Earth Engine and it uses all the available scenes from the Sentinel-2 image collection to map index-based multi-aggregate yearly vegetation changes. A user-defined index threshold maps the first per-pixel occurrence of an abrupt vegetation change and returns an updated and classified multi-temporal image aggregate in almost-real-time. The algorithm requires an input vector table such as data gazetteers or heritage inventories, and it performs buffer zonal statistics for each site to return a series of spatial indicators of potential site disturbance. It also returns time series charts for the evaluation and validation of the local to regional vegetation trends and the seasonal phenology. Additionally, we used multi-temporal MODIS, Sentinel-2 and high-resolution Planet imagery for further photo-interpretation of critically endangered sites. AgriExp was first tested in the arid region of the Cholistan Desert in eastern Pakistan. Here, hundreds of archaeological mound surfaces are threatened by the accelerated transformation of barren lands into new irrigated agricultural lands. We have provided the algorithm code with the article to ensure that AgriExp can be exported and implemented with little computational cost by academics and heritage practitioners alike to monitor critically endangered archaeological and cultural landscapes elsewhere.
Snowmelt Runoff in the Yarlung Zangbo River Basin and Runoff Change in the Future
Authors: Ji Haoyu , Peng Dingzhi , Gu Yu , Luo Xiaoyu , Pang Bo , Zhu Zhongfan
Published: December 2022

Comprehending the impacts of climate change on regional hydrology and future projections of water supplies is of great value to manage the water resources in the Yarlung Zangbo River Basin (YZRB). However, large uncertainties from both input data and the model itself exert obstacles to accurate projections. In this work, a hydrological modeling framework was established over the YZRB linking the Variable Infiltration Capacity (VIC) with an empirical formulation, called the degree-day glacier-melt scheme (VIC–Glacier). The model performance was evaluated through three aspects, including streamflow, snow cover area, and glacier area. Nine GCM models and three emission scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) in CMIP6 were chosen to drive the calibrated VIC–Glacier model. The results showed that both precipitation and temperature resulted in an increase of around 25% and 13%, respectively, in multi-year average runoff from June to September, under SSP5-8.5 and SSP1-2.6. The precipitation runoff was projected to increase, as compensation for the decrease of glacier runoff and snow runoff by the end of the 21st century. An apparent increasing trend in the runoff was expected over the YZRB before 2050 and after the year 2060 under SSP 5-8.5, with a steeply decreasing trend from 2050 to 2060, and a negligible decreasing trend under SSP1-2.6 from 2020 to 2060, in contrast to an increasing trend from 2060 to 2100.
Land Cover Mapping Using Sentinel-1 Time-Series Data and Machine-Learning Classifiers in Agricultural Sub-Saharan Landscape
Authors: Dahhani Sara , Raji Mohamed , Hakdaoui Mustapha , Lhissou Rachid
Published: December 2022

This paper shows the efficiency of machine learning for improving land use/cover classification from synthetic aperture radar (SAR) satellite imagery as a tool that can be used in some sub-Saharan countries that experience frequent clouds. Indeed, we aimed to map the land use and land cover, especially in agricultural areas, using SAR C-band Sentinel-1 (S-1) time-series data over our study area, located in the Kaffrine region of Senegal. We assessed the performance and the processing time of three machine-learning classifiers applied on two inputs. In fact, we applied the random forest (RF), K-D tree K-nearest neighbor (KDtKNN), and maximum likelihood (MLL) classifiers using two separate inputs, namely a set of monthly S-1 time-series data acquired during 2020 and the principal components (PCs) of the time-series dataset. In addition, the RF and KDtKNN classifiers were processed using different tree numbers for RF (10, 15, 50, and 100) and different neighbor numbers for KDtKNN (5, 10, and 15). The retrieved land cover classes included water, shrubs and scrubs, trees, bare soil, built-up areas, and cropland. The RF classification using the S-1 time-series data gave the best performance in terms of accuracy (overall accuracy = 0.84, kappa = 0.73) with 50 trees. However, the processing time was relatively slower compared to KDtKNN, which also gave a good accuracy (overall accuracy = 0.82, kappa = 0.68). Our results were compared to the FROM-GLC, ESRI, and ESA world cover maps and showed significant improvements in some land use and land cover classes.
Fast Target Localization in FMCW-MIMO Radar with Low SNR and Snapshot via Multi-DeepNet
Authors: Su Yunye , Lan Xiang , Shi Jinmei , Sun Lu , Wang Xianpeng
Published: December 2022

Frequency modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radars are widely applied in target localization. However, during the process, the estimation accuracy decreases sharply without considerable signal-to-noise ratio (SNR) and sufficient snapshot number. It is therefore necessary to consider estimation schemes that are valid under low signal-to-noise ratio (SNR) and snapshot. In this paper, a fast target localization framework based on multiple deep neural networks named Multi-DeepNet is proposed. In the scheme, multiple interoperating deep networks are employed to achieve accurate target localization in harsh environments. Firstly, we designed a coarse estimate using deep learning to determine the interval where the angle is located. Then, multiple neural networks are designed to realize accurate estimation. After that, the range estimation is determined. Finally, angles and ranges are matched by comparing the Frobenius norm. Simulations and experiments are conducted to verify the efficiency and accuracy of the proposed framework.
Evaluation of Partitioned Evaporation and Transpiration Estimates within the DisALEXI Modeling Framework over Irrigated Crops in California
Authors: Knipper Kyle , Anderson Martha , Bambach Nicolas , Kustas William , Gao Feng , Zahn Einara , Hain Christopher , McElrone Andrew , Belfiore Oscar Rosario , Castro Sebastian , Alsina Maria Mar , Saa Sebastian
Published: December 2022

Accurate characterization of evapotranspiration (ET) is imperative in water-limited cropping systems such as California vineyards and almond orchards. Satellite-based ET modeling techniques, including the atmosphere–land exchange inverse model (ALEXI) and associated flux disaggregation technique (DisALEXI), have proven reliable in determining field scale ET. However, validation efforts typically focus on ET and omit an evaluation of partitioned evaporation (E) and transpiration (T). ALEXI/DisALEXI is based on the two-source energy balance (TSEB) model, making it uniquely qualified to derive E and T individually. The current study evaluated E and T estimates derived using two formulations of DisALEXI; one based on Priestley-Taylor (DisALEXI-PT) and the other on Penman-Monteith (DisALEXI-PM). The modeled values were validated against partitioned fluxes derived from the conditional eddy covariance (CEC) approach using EC flux towers in three wine grape vineyards and three almond orchards for the year 2021. Modeled estimates were derived using Landsat 8 Collection 2 thermal infrared and surface reflectance imagery as well as Harmonized Landsat and Sentinel-2 surface reflectance datasets as input into DisALEXI. The results indicated that the modeled total ET fluxes were similar between the two methods, but the partitioned values diverged, with DisALEXI-PT overestimating E and slightly underestimating T when compared to CEC estimates. Conversely, DisALEXI-PM agreed better with CEC-derived E and overestimated T estimates under non-advective conditions. Compared to one another, DisALEXI-PM estimated canopy temperatures ~5 °C cooler and soil temperatures ~5 °C warmer than DisALEXI-PT, causing differences in E and T of −2.6 mm day−1 and +2.6 mm day−1, respectively. The evaluation of the iterative process required for DisALEXI indicates DisALEXI-PM ET values converge on ALEXI ET with proportionate adjustments to E and T, while DisALEXI-PT convergence is driven by adjustments to E. The analysis presented here can potentially drive improvements in the modeling framework to provide specific soil and canopy consumptive water use information in unique canopy structures, allowing for improved irrigation and water use efficiencies in these water-limited systems.
Recent Advances in Light-Induced Thermoelastic Spectroscopy for Gas Sensing: A Review
Authors: Pan Yufeng , Zhao Jinbiao , Lu Ping , Sima Chaotan , Liu Deming
Published: December 2022

Light-induced thermoelastic spectroscopy (LITES) is a promising optical approach for gas sensing, which uses a quartz tuning fork (QTF) as a photothermal detector, instead of a commercial photodetector. Since the QTF has the advantages of low cost, small size, high resonance frequency, high-quality factor (Q-factor), and a wide spectral response range, and the LITES sensor has received extensive attention and obtained great development. This review paper summarizes and discusses the advances of the QTF-based, state-of-the-art LITES gas sensing technique in recent years and presents the development prospects of LITES sensor in the future.
npphen: An R-Package for Detecting and Mapping Extreme Vegetation Anomalies Based on Remotely Sensed Phenological Variability
Authors: Chávez Roberto O. , Estay Sergio A. , Lastra José A. , Riquelme Carlos G. , Olea Matías , Aguayo Javiera , Decuyper Mathieu
Published: December 2022

Monitoring vegetation disturbances using long remote sensing time series is crucial to support environmental management, biodiversity conservation, and adaptation strategies to climate change from global to local scales. However, it is difficult to assess whether a remotely detected vegetation disturbance is critical or not, since available operational remote sensing methods deliver only maps of the vegetation anomalies but not maps of how “uncommon” or “extreme” the detected anomalies are based on the available records of the reference period. In this technical note, we present a new release of the probabilistic method and its implementation, the npphen R package, designed to detect not only vegetation anomalies from remotely sensed vegetation indices, but also to quantify the position of the anomalous observations within the historical frequency distribution of the phenological annual records. This version of the R package includes two new key functions to detect and map extreme vegetation anomalies: ExtremeAnom and ExtremeAnoMap. The npphen package allows remote sensing users to detect vegetation changes for a wide range of ecosystems, taking advantage of the flexibility of kernel density estimations to account for any shape of annual phenology and its variability through time. It provides a uniform statistical framework to study all types of vegetation dynamics, contributing to global monitoring efforts such as the GEO-BON Essential Biodiversity Variables.
Motion Optimization and Control of Single and Multiple Autonomous Aerial, Land, and Marine Robots
Authors: Ghabcheloo Reza , Pascoal António
Published: December 2022

Fast-paced developments in the fields of aerial, land, and marine robotics are steadily paving the way for a wide spectrum of scientific and commercial applications of autonomous vehicles with far-reaching societal implications [...]
Can Microsaccades Be Used for Biometrics?
Authors: Alexiev Kiril , Vakarelski Teodor
Published: December 2022

Human eyes are in constant motion. Even when we fix our gaze on a certain point, our eyes continue to move. When looking at a point, scientists have distinguished three different fixational eye movements (FEM)—microsaccades, drift and tremor. The main goal of this paper is to investigate one of these FEMs—microsaccades—as a source of information for biometric analysis. The paper argues why microsaccades are preferred for biometric analysis over the other two fixational eye movements. The process of microsaccades’ extraction is described. Thirteen parameters are defined for microsaccade analysis, and their derivation is given. A gradient algorithm was used to solve the biometric problem. An assessment of the weights of the different pairs of parameters in solving the biometric task was made.
An Improved African Vulture Optimization Algorithm for Dual-Resource Constrained Multi-Objective Flexible Job Shop Scheduling Problems
Authors: He Zhou , Tang Biao , Luan Fei
Published: December 2022

According to the characteristics of flexible job shop scheduling problems, a dual-resource constrained flexible job shop scheduling problem (DRCFJSP) model with machine and worker constraints is constructed such that the makespan and total delay are minimized. An improved African vulture optimization algorithm (IAVOA) is developed to solve the presented problem. A three-segment representation is proposed to code the problem, including the operation sequence, machine allocation, and worker selection. In addition, the African vulture optimization algorithm (AVOA) is improved in three aspects: First, in order to enhance the quality of the initial population, three types of rules are employed in population initialization. Second, a memory bank is constructed to retain the optimal individuals in each iteration to increase the calculation precision. Finally, a neighborhood search operation is designed for individuals with certain conditions such that the makespan and total delay are further optimized. The simulation results indicate that the qualities of the solutions obtained by the developed approach are superior to those of the existing approaches.
Identity-Preserved Human Posture Detection in Infrared Thermal Images: A Benchmark
Authors: Guo Yongping , Chen Ying , Deng Jianzhi , Li Shuiwang , Zhou Hui
Published: December 2022

Human pose estimation has a variety of real-life applications, including human action recognition, AI-powered personal trainers, robotics, motion capture and augmented reality, gaming, and video surveillance. However, most current human pose estimation systems are based on RGB images, which do not seriously take into account personal privacy. Although identity-preserved algorithms are very desirable when human pose estimation is applied to scenarios where personal privacy does matter, developing human pose estimation algorithms based on identity-preserved modalities, such as thermal images concerned here, is very challenging due to the limited amount of training data currently available and the fact that infrared thermal images, unlike RGB images, lack rich texture cues which makes annotating training data itself impractical. In this paper, we formulate a new task with privacy protection that lies between human detection and human pose estimation by introducing a benchmark for IPHPDT (i.e., Identity-Preserved Human Posture Detection in Thermal images). This task has a threefold novel purpose: the first is to establish an identity-preserved task with thermal images; the second is to achieve more information other than the location of persons as provided by human detection for more advanced computer vision applications; the third is to avoid difficulties in collecting well-annotated data for human pose estimation in thermal images. The presented IPHPDT dataset contains four types of human postures, consisting of 75,000 images well-annotated with axis-aligned bounding boxes and postures of the persons. Based on this well-annotated IPHPDT dataset and three state-of-the-art algorithms, i.e., YOLOF (short for You Only Look One-level Feature), YOLOX (short for Exceeding YOLO Series in 2021) and TOOD (short for Task-aligned One-stage Object Detection), we establish three baseline detectors, called IPH-YOLOF, IPH-YOLOX, and IPH-TOOD. In the experiments, three baseline detectors are used to recognize four infrared human postures, and the mean average precision can reach 70.4%. The results show that the three baseline detectors can effectively perform accurate posture detection on the IPHPDT dataset. By releasing IPHPDT, we expect to encourage more future studies into human posture detection in infrared thermal images and draw more attention to this challenging task.
An Efficient and Intelligent Detection Method for Fabric Defects based on Improved YOLOv5
Authors: Lin Guijuan , Liu Keyu , Xia Xuke , Yan Ruopeng
Published: December 2022

Limited by computing resources of embedded devices, there are problems in the field of fabric defect detection, including small defect size, extremely unbalanced aspect ratio of defect size, and slow detection speed. To address these problems, a sliding window multihead self-attention mechanism is proposed for the detection of small targets, and the Swin Transformer module is introduced to replace the main module in the original YOLOv5 algorithm. First, to reduce the distance between several scales, the weighted bidirectional feature network is employed on embedded devices. In addition, it is helpful to improve the perception of small-target faults by incorporating a detection layer to achieve four-scale detection. At last, to improve the learning of positive sample instances and lower the missed detection rate, the generalized focal loss function is finally implemented on YOLOv5. Experimental results show that the accuracy of the improved algorithm on the fabric dataset reaches 85.6%, and the mAP is increased by 4.2% to 76.5%, which meets the requirements for real-time detection on embedded devices.
Feature Selection Method Using Multi-Agent Reinforcement Learning Based on Guide Agents
Authors: Kim Minwoo , Bae Jinhee , Wang Bohyun , Ko Hansol , Lim Joon S.
Published: December 2022

In this study, we propose a method to automatically find features from a dataset that are effective for classification or prediction, using a new method called multi-agent reinforcement learning and a guide agent. Each feature of the dataset has one of the main and guide agents, and these agents decide whether to select a feature. Main agents select the optimal features, and guide agents present the criteria for judging the main agents’ actions. After obtaining the main and guide rewards for the features selected by the agents, the main agent that behaves differently from the guide agent updates their Q-values by calculating the learning reward delivered to the main agents. The behavior comparison helps the main agent decide whether its own behavior is correct, without using other algorithms. After performing this process for each episode, the features are finally selected. The feature selection method proposed in this study uses multiple agents, reducing the number of actions each agent can perform and finding optimal features effectively and quickly. Finally, comparative experimental results on multiple datasets show that the proposed method can select effective features for classification and increase classification accuracy.
Langmuir-Blodgett Films of Arachidic and Stearic Acids as Sensitive Coatings for Chloroform HF SAW Sensors
Authors: Gorbachev Ilya , Smirnov Andrey , Ivanov George , Avramov Ivan , Datsuk Elizaveta , Venelinov Tony , Bogdanova Evgenija , Anisimkin Vladimir , Kolesov Vladimir , Kuznetsova Iren
Published: December 2022

Properties of the Langmuir-Blodgett (LB) films of arachidic and stearic acids, versus the amount of the films’ monolayers were studied and applied for chloroform vapor detection with acoustoelectric high-frequency SAW sensors, based on an AT quartz two-port Rayleigh type SAW resonator (414 MHz) and ST-X quartz SAW delay line (157.5 MHz). Using both devices, it was confirmed that the film with 17 monolayers of stearic acid deposited on the surface of the SAW delay line at a surface pressure of 30 mN/m in the solid phase has the best sensitivity towards chloroform vapors, compared with the same films with other numbers of monolayers. For the SAW resonator sensing using slightly longer arachidic acid molecules, the optimum performance was reached with 17 LB film layers due to a sharper decrease in the Q-factor with mass loading. To understand the background of the result, Atomic Force Microscopy (AFM) in intermittent contact mode was used to study the morphology of the films, depending on the number of monolayers. The presence of the advanced morphology of the film surface with a maximal average roughness (9.3 nm) and surface area (29.7 µm2) was found only for 17-monolayer film. The effects of the chloroform vapors on the amplitude and the phase of the acoustic signal for both SAW devices at 20 °C were measured and compared with those for toluene and ethanol vapors; the largest responses were detected for chloroform vapor. For the film with an optimal number of monolayers, the largest amplitude response was measured for the resonator-based device. Conversely, the largest change in the acoustic phase produced by chloroform adsorption was measured for delay-line configuration. Finally, it was established that the gas responses for both devices coated with the LB films are completely restored 60 s after chamber cleaning with dry air.
Responsiveness of Daily Life Gait Quality Characteristics over One Year in Older Adults Who Experienced a Fall or Engaged in Balance Exercise
Authors: Schootemeijer Sabine , Weijer Roel H. A. , Hoozemans Marco J. M. , Delbaere Kim , Pijnappels Mirjam , van Schooten Kimberley S.
Published: December 2022

Gait quality characteristics obtained from daily-life accelerometry are clinically relevant for fall risk in older adults but it is unknown whether these characteristics are responsive to changes in gait quality. We aimed to test whether accelerometry-based daily-life gait quality characteristics are reliable and responsive to changes over one year in older adults who experienced a fall or an exercise intervention. One-week trunk acceleration data were collected from 522 participants (65–97 years), at baseline and after one year. We calculated median values of walking speed, regularity (sample entropy), stability (logarithmic rate of divergence per stride), and a gait quality composite score, across all 10-s gait epochs derived from one-week gait episodes. Intraclass correlation coefficients (ICC) and limits of agreement (LOA) were determined for 198 participants who did not fall nor participated in an exercise intervention during follow-up. For responsiveness to change, we determined the number of participants who fell (n = 209) or participated in an exercise intervention (n = 115) that showed a change beyond the LOA. ICCs for agreement between baseline and follow-up exceeded 0.70 for all gait quality characteristics except for vertical gait stability (ICC = 0.69, 95% CI [0.62, 0.75]) and walking speed (ICC = 0.68, 95% CI [0.62, 0.74]). Only walking speed, vertical and mediolateral gait stability changed significantly in the exercisers over one year but effect sizes were below 0.2. The characteristic associated with most fallers beyond the LOA was mediolateral sample entropy (4.8% of fallers). For the exercisers, this was gait stability in three directions and the gait quality composite score (2.6% of exercisers). The gait quality characteristics obtained by median values over one week of trunk accelerometry were not responsive to presumed changes in gait quality after a fall or an exercise intervention in older people. This is likely due to large (within subjects) differences in gait behaviour that participants show in daily life.
RAPT: A Robust Attack Path Tracing Algorithm to Mitigate SYN-Flood DDoS Cyberattacks
Authors: AlArnaout Zakwan , Mostafa Nour , Alabed Samer , Aly Wael Hosny Fouad , Shdefat Ahmed
Published: December 2022

In the recent past, Distributed Denial of Service (DDoS) attacks have become more abundant and present one of the most serious security threats. In a DDoS attack, the attacker controls a botnet of daemons residing in vulnerable hosts that send a significant amount of traffic to flood the victim or the network infrastructure. In this paper, a common type of DDoS attacks known as “TCP SYN-Flood” is studied. This type of attack uses spoofed Internet Protocol (IP) addresses for SYN packets by exploiting the weakness in Transmission Control Protocol (TCP) 3-Way handshake used by the TCP/IP suite of protocols, which make the web servers unreachable for legitimate users or even worse, it might lead to server crash. In this paper, a resilient, efficient, lightweight, and robust IP traceback algorithm is proposed using an IP tracing packet for each attack path. The proposed algorithm suggests that edge routers—where the attack starts from—observe the traffic pattern passing through, and if the observed traffic carries the signature of TCP SYN-Flood DDoS attack and a high percentage of it is destined to a particular web server(s), it starts the tracing process by generating an IP trace packet, which accompanies the attack path recording the routers’ IP addresses on the path between the attacker/daemon and the victim, which can extract the path and react properly upon receiving it by discarding any SYN packets originating from that attacker/daemon. To our knowledge, this is the first research that efficiently traces these kinds of attacks while they are running. The proposed solution has low computation and message overhead, efficient detection and tracing time, and converges in near optimal time. The results are validated using extensive simulation runs.
Assessment of EMF Human Exposure Levels Due to Wearable Antennas at 5G Frequency Band
Authors: Gallucci Silvia , Bonato Marta , Benini Martina , Chiaramello Emma , Fiocchi Serena , Tognola Gabriella , Parazzini Marta
Published: December 2022

(1) Background: This work aims to assess human exposure to EMF due to two different wearable antennas tuned to two 5G bands. (2) Methods: The first one was centered in the lower 5G band, around f = 3.5 GHz, whereas the second one was tuned to the upper 5G band, at 26.5 GHz. Both antennas were positioned on the trunk of four simulated human models. The exposure assessment was performed by electromagnetic numerical simulations. Exposure levels were assessed by quantifying the specific absorption rate averaged on 10 g of tissue (SAR10g) and the absorbed power density (Sab), depending on the frequency of the wearable antenna. (3) Results: the higher exposure values that resulted were always mainly concentrated in a superficial area just below the antenna itself. In addition, these resulting distributions were narrowed around their peak values and tended to flatten toward lower values in farther anatomical body regions. All the exposure levels complied with ICNIRP guidelines when considering realistic input power. (4) Conclusions: This work highlights the importance of performing an exposure assessment when the antenna is placed on the human wearer, considering the growth of wearable technology and its wide variety of application, particularly regarding future 5G networks.
Development of A Multi-Spectral Pyrometry Sensor for High-Speed Transient Surface-Temperature Measurements in Combustion-Relevant Harsh Environments
Authors: Neupane Sneha , Jatana Gurneesh Singh , Lutz Timothy P. , Partridge William P.
Published: December 2022

Accurate and high-speed transient surface-temperature measurements of combustion devices including internal combustion (IC) engines, gas turbines, etc., provide validation targets and boundary conditions for computational fluid dynamics models, and are broadly relevant to technology advancements such as performance improvement and emissions reduction. Development and demonstration of a multi-infrared-channel pyrometry-based optical instrument for high-speed surface-temperature measurement is described. The measurement principle is based on multi-spectral radiation thermometry (MRT) and uses surface thermal radiation at four discrete spectral regions and a corresponding emissivity model to obtain surface temperature via non-linear least squares (NLLS) optimization. Rules of thumb for specifying the spectral regions and considerations to avoid interference with common combustion products are developed; the impact of these along with linear and non-linear MRT analysis are assessed as a function of temperature and signal-to-noise ratio. A multi-start method to determine the MRT-solution global optimum is described and demonstrated. The resulting multi-channel transient pyrometry instrument is described along with practical considerations including optical-alignment drift, matching intra-channel transient response, and solution-confidence indicators. The instrument demonstrated excellent >97% accuracy and >99% 2-sigma precision over the 400–800 °C range, with ~20 µs (50 kHz, equivalent to 0.2 cad at 2000 RPM IC-engine operation) transient response in the bench validation.
Photoluminescent Sensor of Scarification Efficiency of Fodder Plants’ Seeds
Authors: Belyakov Mikhail V.
Published: December 2022

Optoelectronic sensors open up new possibilities for predicting the yield for their possible correction, including increasing the seed germination of forage plants. The luminescent properties of unscarified and scarified seeds of various germination galega, clover and alfalfa are compared. The dependence of germination on the photoluminescence flux is approximated by linear equations with a determination coefficient R2 = 0.932–0.999. A technological process for analyzing the scarification quality of forage seed plants is proposed, including sample preparation, photoluminescence excitation and registration, amplification of the received electrical signal and determination of germination based on calibration equations. This is followed by a decision on sowing, or re-scarification. The scheme of the scarification quality control device has been developed for which the LED, as well as the radiation receiver and other elements, has been selected according to the energy efficiency criterion. Mechanical scarification of the forage plants’ seed surfaces has a significant effect on their photoluminescent properties. The flux increases by 1.5–1.7 times for galega, 2.0–3.0 times for clover and 2.3–3.9 times for alfalfa. Linear approximation of the flux dependence on germination with a high coefficient of determination allows us to obtain reliable linear calibration equations. Preliminary mock-up laboratory tests allow us to talk about the developed method’s effectiveness and device.
Design and Optimization of High-Power and Low-Frequency Broadband Transducer with Giant Magnetostrictive Material
Authors: Yang Long , Wang Wenjie , Zhao Xu , Li Haojun , Xiang Yue
Published: December 2022

The applications of sensors in the aerospace industry are mostly concentrated in the middle and high frequencies, and low-frequency sensors often face the problems of low power and short working bandwidth. A lightweight, thin, high-power, low-frequency broadband transducer based on giant magnetostrictive material is designed. The design and optimization processes of the core components are introduced and analyzed emphatically. The finite element simulation results are validated by the PSV-100 laser vibration meter. Three basic configurations of the work panel are proposed, and the optimal configuration is determined by modal, acoustic, and vibration coupling analyses. Compared with the original configuration, it is found that the lowest resonant frequency of the optimal configuration is reduced by 24.6% and the highest resonant frequency within 2000 Hz is 1744.9 Hz, which is 54.2% higher than that of the original configuration. This greatly improves the vibration power and operating frequency range of the transducer. Then, the honeycomb structure is innovatively applied to the work panel, and it is verified that the honeycomb structure has a great effect on the vibration performance of the work panel. By optimizing the size of the honeycomb structure, it is determined that the honeycomb structure can improve the vibration power of the work panel to its maximum value when the distance between the half-opposite sides of the hexagon is H = 3.5 mm. It can reduce the resonant frequency of the work panel; the lowest resonant frequency is reduced by 12.8%. At the same time, the application of a honeycomb panel structure can reduce the weight of the transducer.
Research on Self-Noise Characteristics of Nine Types of Seismometers Obtained by PDF Representation Using Continuous Seismic Data from the Malingshan Seismic Station, China
Authors: Wang Kaiming , Li Wenyi , Zhao Lijun , Yu Daxin , Wei Shaogang
Published: December 2022

The self-noise level of a seismometer can determine the performance of the seismic instrument and limit the ability to use seismic data to solve geoscience problems. Accurately measuring and simultaneously comparing the self-noise models from different types of seismometers has long been a challenging task due to the constraints of observation conditions. In this paper, the self-noise power spectral density (PSD) values of nine types of seismometers are calculated using four months of continuous seismic waveforms from Malingshan seismic station, China, and nine self-noise models are obtained based on the probability density function (PDF) representation. For the seismometer STS-2.5, the self-noise levels on the horizontal channels (E–W and N–S) are significantly higher than that on the vertical channel (U–D) in the microseism band (0.1 Hz to 1 Hz), which is a computing bias caused by the misalignment between the sensors in the horizontal direction, while the remarkably elevated noise on the horizontal channels at the low frequencies (<0.6 Hz) may originate from the local variation of atmospheric pressure. As for the very broadband seismometers Trillium-Horizon-120 and Trillium-120PA, and the ultra-broadband seismometers Trillium-Horizon-360 and CMG-3T-360, there is a trade-off between the microseism band range and low-frequency range in the PSD curves of the vertical channel. When the level of self-noise in the microseism band is high, the self-noise at low frequencies is relatively low. Although compared with the other very broadband seismometers, the self-noise level of the vertical component of the STS-2.5 is 3 dB to 4 dB lower at frequencies less than 1 Hz, the self-noise level of the STS-2.5 at high frequencies (>2 Hz) is slightly higher than others. From our observations, we conclude that the nine seismometers cannot reach the lowest noise level in all frequency bands within the working range.
Polarization-Sensitive Patterning of Azopolymer Thin Films Using Multiple Structured Laser Beams
Authors: Porfirev Alexey P. , Khonina Svetlana N. , Ivliev Nikolay A. , Fomchenkov Sergey A. , Porfirev Denis P. , Karpeev Sergey V.
Published: December 2022

The polarization sensitivity of azopolymers is well known. Therefore, these materials are actively used in many applications of photonics. Recently, the unique possibilities of processing such materials using a structured laser beam were demonstrated, which revealed the key role of the distribution of polarization and the longitudinal component of light in determining the shape of the nano- and microstructures formed on the surfaces of thin azopolymer films. Here, we present numerical and experimental results demonstrating the high polarization sensitivity of thin azopolymer films to the local polarization state of an illuminating structured laser beam consisting of a set of light spots. To form such arrays of spots with a controlled distribution of polarization, different polarization states of laser beams, both homogeneous and locally inhomogeneous, were used. The results obtained show the possibility of implementing a parallel non-uniform patterning of thin azopolymer films depending on the polarization distribution of the illuminating laser beam. We believe that the demonstrated results will not only make it possible to implement the simultaneous detection of local polarization states of complex-shaped light fields but will also be used for the high-performance fabrication of diffractive optical elements and metasurfaces.
Soil Deformation after Water Drop Impact—A Review of the Measurement Methods
Authors: Mazur Rafał , Ryżak Magdalena , Sochan Agata , Beczek Michał , Polakowski Cezary , Bieganowski Andrzej
Published: December 2022

Water erosion is an unfavorable phenomenon causing soil degradation. One of the factors causing water erosion is heavy or prolonged rainfall, the first effect of which is the deformation of the soil surface and the formation of microcraters. This paper presents an overview of research methods allowing the study of microcraters as well as the process of their formation. A tabular summary of work on the measurements of various quantities describing the craters is presented. The said quantities are divided into three groups: (i) static quantities, (ii) dynamic quantities, and (iii) dimensionless parameters. The most important measurement methods used to study crater properties, such as (i) basic manual measurement methods, (ii) photography, (iii) high-speed imaging, (iv) profilometers, (v) 3D surface modelling, and (vi) computed tomography (CT) and its possibilities and limitations are discussed. The main challenges and prospects of research on soil surface deformation are also presented.
A Comprehensive Predictive-Learning Framework for Optimal Scheduling and Control of Smart Home Appliances Based on User and Appliance Classification
Authors: Shafqat Wafa , Lee Kyu-Tae , Kim Do-Hyeun
Published: December 2022

Energy consumption is increasing daily, and with that comes a continuous increase in energy costs. Predicting future energy consumption and building an effective energy management system for smart homes has become essential for many industrialists to solve the problem of energy wastage. Machine learning has shown significant outcomes in the field of energy management systems. This paper presents a comprehensive predictive-learning based framework for smart home energy management systems. We propose five modules: classification, prediction, optimization, scheduling, and controllers. In the classification module, we classify the category of users and appliances by using k-means clustering and support vector machine based classification. We predict the future energy consumption and energy cost for each user category using long-term memory in the prediction module. We define objective functions for optimization and use grey wolf optimization and particle swarm optimization for scheduling appliances. For each case, we give priority to user preferences and indoor and outdoor environmental conditions. We define control rules to control the usage of appliances according to the schedule while prioritizing user preferences and minimizing energy consumption and cost. We perform experiments to evaluate the performance of our proposed methodology, and the results show that our proposed approach significantly reduces energy cost while providing an optimized solution for energy consumption that prioritizes user preferences and considers both indoor and outdoor environmental factors.
Understanding How CNNs Recognize Facial Expressions: A Case Study with LIME and CEM
Authors: del Castillo Torres Guillermo , Roig-Maimó Maria Francesca , Mascaró-Oliver Miquel , Amengual-Alcover Esperança , Mas-Sansó Ramon
Published: December 2022

Recognizing facial expressions has been a persistent goal in the scientific community. Since the rise of artificial intelligence, convolutional neural networks (CNN) have become popular to recognize facial expressions, as images can be directly used as input. Current CNN models can achieve high recognition rates, but they give no clue about their reasoning process. Explainable artificial intelligence (XAI) has been developed as a means to help to interpret the results obtained by machine learning models. When dealing with images, one of the most-used XAI techniques is LIME. LIME highlights the areas of the image that contribute to a classification. As an alternative to LIME, the CEM method appeared, providing explanations in a way that is natural for human classification: besides highlighting what is sufficient to justify a classification, it also identifies what should be absent to maintain it and to distinguish it from another classification. This study presents the results of comparing LIME and CEM applied over complex images such as facial expression images. While CEM could be used to explain the results on images described with a reduced number of features, LIME would be the method of choice when dealing with images described with a huge number of features.
In Vitro and In Silico Screening Analysis of Artabotrys sumatranus Leaf and Twig Extracts for α-Glucosidase Inhibition Activity and Its Relationship with Antioxidant Activity
Authors: Rosa Dela , Elya Berna , Hanafi Muhammad , Khatib Alfi , Surya Muhammad Imam
Published: December 2022

Artabotrys sumatranus is one of the Artabotrys species, which lives in Sumatera, Java, and Borneo in Indonesia. No research has been found related to its activity. The objective of this study was to explore the potential of A. sumatranus leaf and twig extracts as the source of an anti-diabetic agent through the α-glucosidase inhibition mechanism, as well as the relationship between the antioxidant and the α-glucosidase inhibition activities in these extracts. Ethanol extracts from leaf and twig A. sumatranus were subjected to several assays: total phenolic content, total flavonoid content, antioxidant activity using DPPH (2,2-diphenyl-1-picrylhydrazyl), radical scavenging activity, and FRAP (ferric reducing antioxidant power) analysis, as well as α-glucosidase inhibition. Later, GC-MS (gas chromatography-mass spectrometer) and LC-MS/MS (liquid chromatography-mass spectrometer/mass spectrometer) analysis were conducted to identify the compounds inside the extracts. The identified compounds were tested for potential α-glucosidase inhibition activity using a molecular docking simulation. As a result, the A. sumatranus leaf extract showed more potential than the twig extract as α-glucosidase inhibitor and antioxidant agent. In addition, from the comparison between the measured quantities, it can be deduced that most of the α-glucosidase active compounds in the A. sumatranus are also antioxidant agents. Several active compounds with a high affinity to α-glucosidase inhibition were identified using the molecular docking simulation. It was concluded that A. sumatranus twig and leaf extracts seem to be potential sources of α-glucosidase inhibitors.
Study of Different Chiral Columns for the Enantiomeric Separation of Azoles Using Supercritical Fluid Chromatography
Authors: Toribio Laura , Magdaleno Irene , Martín-Gómez Beatriz , Martín María T. , Valverde Silvia , Ares Ana M.
Published: December 2022

The enantiomeric separation of antifungal compounds is an arduous task in pharmaceutical and biomedical fields due to the different properties that each diastereoisomer presents. The enantioseparation of a group of fungicides (sulconazole, bifonazole, triadimefon and triadimenol) using supercritical fluid chromatography was achieved in this work. For this goal, four different chiral columns based on polysaccharide derivatives, as well as the effect of different chromatographic parameters such as temperature, type and percentage of organic modifier (methanol, ethanol and isopropanol), were thoroughly investigated. The inversion of the elution order of enantiomers as a result of a change in the stationary phase or organic modifier was also evaluated by employing a circular dichroism detector. The best separation conditions, in terms of the enantioresolution and analysis time, were obtained with the Lux® Cellulose-2 column using isopropanol as the organic modifier.