bluuebunny/crossref_metadata_2025 huggingface dataset:
Validation of CrIS Radiometric Performance through Its Comparison to ABI
Authors: Wang Zhipeng , Iturbide-Sanchez Flavio , Beierle Peter , Zhang Kun , Tremblay Denis
Published: February 2022

Radiometric intercomparison between satellite remote sensing instruments has become an increasingly common practice to monitor the stability and even the accuracy of their radiance products. The assessment also enables the evaluation of calibration improvements made to these products, as well as the identification and resolution of remaining calibration inadequacies. In this paper, the radiance products of the Cross-track Infrared Sounder (CrIS), an interferometer-based hyperspectral IR sounder in low Earth orbit (LEO), is compared with the level-1b (L1b) radiance products of the infrared (IR) bands of the Advanced Baseline Imager (ABI), an imaging radiometer in geostationary (GEO) orbit. Two CrIS instruments are currently in operation on S-NPP and NOAA-20 satellites, respectively, and two ABI instruments are in operation on GOES-16 and GOES-17 satellites, respectively. Radiometric intercomparisons are performed between each CrIS-ABI pair. An established procedure by GSICS for such GEO-LEO instrument comparison is principally followed to emulate the radiance of ABI IR bands from CrIS spectra of the collocated pixels to be compared with the actual ABI radiance. Results show that the long-term time series of CrIS-ABI radiance bias have been stable within 0.2 K for nearly all ABI IR bands within a spectral range from 3.7 μm to 13.3 μm, except those with known calibration issues. Miscellaneous calibration events that had occurred to either instrument and altered the biases are identified and explained. While the main goal of the work is to support the on-orbit Cal/Val of CrIS, including the future JPSS-2/3/4 CrIS, such observations can also be referenced to further improve the calibration of ABI.
Frequency-Domain Electromagnetic Mapping of an Abandoned Waste Disposal Site: A Case in Sardinia (Italy)
Authors: Deidda Gian Piero , Himi Mahjoub , Barone Ilaria , Cassiani Giorgio , Casas Ponsati Albert
Published: February 2022

For decades, bad practices in municipal and industrial waste management have had negative environmental impacts, generating high health risks for people and the environment. The use of badly designed, not engineered, and not well-operated landfills has, around the world, produced a large number of potentially contaminated sites, for which there are urgent needs to assess the actual risk and to proceed, in case, with reclamation activities. One of these sites, an abandoned waste disposal site located near a Site of Community Importance on the central-eastern coast of Sardinia (Italy), is the subject of the case history described in this work. As a part of a multi-method geophysical characterisation, a frequency-domain electromagnetic (FDEM) mapping survey was carried out with the specific aim of detecting the presence of buried materials (waste) and of delineating the lateral extent of the landfill by identifying the electrical conductivity anomalies produced, for the most part, by the conductive waste fill. Using an EM31 device in the vertical-dipole configuration, at a height of 0.9 m above the ground, both quadrature and in-phase electromagnetic responses were collected over a 7-hectare area with elevation varying between 6 m and 2.8 m above sea level. After removing the measurements identified as data coming from any recognisable surface man-made features within the survey area or near its perimeter, the filtered quadrature response (expressed as apparent conductivity) ranged from 5.5 mS/m to about 188.6 mS/m. All values are beyond the low induction number (LIN) condition and valid for the classical EM31 mapping, thus requiring advanced data processing. To obtain undistorted, meaningful, and interpretable high-resolution maps, measured data have been processed to correct the bias, introduced by the nonlinearity of the device, as a function of height above ground and the topography. The comparative analysis of the apparent conductivity map, obtained by the properly processed EM31 data and some aerial photos that clearly documented the site history, has allowed unequivocal delineation of the landfill extent, in good agreement with the results obtained with other geophysical methods (not described in this paper) and with the ground truthing data provided by three boreholes, which were core-drilled at the end of the study at three locations selected on the basis of the apparent conductivity map.
Marine Oil Pollution in an Area of High Economic Use: Statistical Analyses of SAR Data from the Western Java Sea
Authors: Mohr Veronika , Gade Martin
Published: February 2022

In this paper, we analyze more than 2000 Synthetic Aperture Radar (SAR) images of the Western Java Sea acquired by Sentinel-1 SAR-C and ENVISAT ASAR, with the aim to generate oil pollution statistics for a sea region of high economic use. The spatial distributions show that most oil pollution occurs along the major shipping routes and at oil production sites in that area. The majority of the spills have sizes of less than 1 km2 and an axial ratio smaller than 10. For two sets of SAR images, we compared the results obtained by different operators, who analyzed the same images. While more than 50% of the spills were not found by both operators, the overall spatial patterns derived from their results are the same. Our results indicate that the observed differences are mainly due to lookalikes, which can easily be confused with oil spills, but also due to small oil spills that were overseen by one of the operators. These assumptions are supported by the fact that the percentage of spills jointly found by both operators increased when only oil spills were considered that were found on SAR images acquired at higher mean wind speeds.
Vegetation Monitoring of Protected Areas in Rugged Mountains Using an Improved Shadow-Eliminated Vegetation Index (SEVI)
Authors: Jiang Hong , Yao Maolin , Guo Jia , Zhang Zhaoming , Wu Wenting , Mao Zhengyuan
Published: February 2022

It is significant to study the vegetation of protected areas in rugged mountains where the vegetation grows naturally with minimal eco-society environmental stress from anthropogenic activities. The shadow-eliminated vegetation index (SEVI) was used to monitor the vegetation of protected areas, since it successfully removes topographic shadow effects. In order to auto achieve the best adjustment factor for SEVI calculation from regional area images, we developed a new calculation algorithm using block information entropy (BIE-algorithm). The BIE-algorithm auto-detected typical blocks (subareas) from slope images and achieved the best adjustment factor from a block where the SEVI obtained the highest information entropy in an entire scene. Our obtained regional SEVI result from two scenes of Landsat 8 OLI images using the BIE-algorithm exhibited an overall flat feature with the impression of the relief being drastically removed. It achieved balanced values among three types of samples: Sunny area, self-shadow, and cast shadow, with SEVI means of 0.73, 0.77, and 0.75, respectively, and the corresponding SEVI relative errors of self-shadow and cast shadow were only 4.99% and 1.84%, respectively. The linear regression of SEVI vs. the cosine of the solar incidence angle was nearly horizontal, with an inclination of −0.0207 and a coefficient of determination of 0.0042. The regional SEVI revealed that the vegetation growth level sequence of three protected areas was Wuyishan National Park (SEVI mean of 0.718) > Meihuashan National Nature Reserve (0.672) > Minjiangyuan National Nature Reserve (0.624) > regional background (0.572). The vegetation growth in the protected areas was influenced by the terrain slope and years of establishment of the protected area and by the surrounding buffer zone. The homogeneous distribution of vegetation in a block is influenced by many factors, such as the actual vegetation types, block size, and shape, which need consideration when the proposed BIE-algorithm is used.
Interannual Relationship between Haze Days in December–January and Satellite-Based Leaf Area Index in August–September over Central North China
Authors: Ji Liuqing , Fan Ke
Published: February 2022

Haze pollution in central North China has become a hot topic in recent decades due to its serious environmental and health effects. In this work, the interannual relationship between haze days in December–January (DJ_HD) and leaf area index in August–September (AS_LAI) over central North China, along with the possible physical mechanisms involved, are investigated. The relationship varies in different periods, being significant during 1982–2000 (P1) but insignificant during 2001–2014 (P2). During P1, there is an in-phase relationship between AS_LAI and surface evaporation, and an out-of-phase relationship between AS_LAI and surface albedo in August–September. The surface evaporation and albedo anomalies persist to October–November and are associated with lower top-layer volumetric soil water, upward sensible heat flux and downward latent heat flux anomalies in October–November, which act as the bridge in the relationship between AS_LAI and DJ_HD. Both the volumetric soil water and heat fluxes anomalies persist to December–January and correspond to atmospheric circulations similar to the weakened East Asian winter monsoon pattern, which is the dominant system for winter haze events. Thus, the ventilation conditions in December–January are favorable for the accumulation of haze particles. However, during P2, the relationships are not significant between AS_LAI and volumetric soil water or surface soil temperature during October–January. Meanwhile, the East Asian winter monsoon is likely strengthened and tends to be more significantly affected by factors including Arctic sea ice, Arctic Oscillation, etc. Therefore, the effects of AS_LAI on the monsoon may become insignificant and, in turn, the relationship between AS_LAI and DJ_HD becomes insignificant during P2.
On-Orbit Radiometric Performance of GF-7 Satellite Multispectral Imagery
Authors: Tang Hongzhao , Xie Junfeng , Tang Xinming , Chen Wei , Li Qi
Published: February 2022

China’s first civilian, sub-meter, high-resolution stereo mapping satellite, GF-7, launched on 3 November 2019. Radiometric characterization of GF-7 multispectral imagery has been performed in this study. A relative radiometric accuracy evaluation of the GF-7 multispectral imagery was performed using several large uniform scenes, and the results showed that the accuracy is better than 2%. The absolute radiometric evaluation of the GF-7 satellite sensor was conducted at the Baotou and Dunhuang calibration sites, using the reflectance-based vicarious approach. The synchronous measurements of surface reflectance and atmospheric parameters were collected as the input for the radiative transfer model. The official radiometrically calibrated coefficient of the GF-7 multispectral imagery was evaluated with the predicted top-of-atmosphere (TOA) radiance from the radiative transfer model. The results indicated that the absolute radiometric accuracy of GF-7 multispectral imagery is better than 5%. In order to monitor the radiometric stability of the GF-7 satellite multispectral sensor, a relative and absolute radiometric accuracy assessment campaign should be performed several times a year.
The Influence of Anisotropic Surface Reflection on Earth’s Outgoing Shortwave Radiance in the Lunar Direction
Authors: Wu Jie , Guo Huadong , Ding Yixing , Shang Haolu , Li Tong , Li Lei , Lv Mingyang
Published: February 2022

The variation in the radiation budget at Earth’s top of the atmosphere (TOA) represents the most fundamental metric defining the status of global climate change. The accurate estimation of Earth’s shortwave radiant exitance is of critical importance to study Earth’s radiation budget (ERB) at TOA. Measuring Earth’s outgoing shortwave radiance (OSR) is a key point to estimate Earth’s shortwave radiant exitance. Compared with space-borne satellite systems, Moon-based sensors (MS) could provide large-scale, continuous, and long-term data for Earth radiation observations, bringing a new perspective on ERB. However, the factors affecting the estimation of Earth’s OSR in the lunar direction have not yet been fully explored, for example, anisotropic surface reflection and the effects of clouds and aerosols on radiation budget. In this work, we only focused on the influence of anisotropic surface reflection. To evaluate the extent of this influence, we constructed a model to estimate Earth’s OSR in the lunar direction (EOSRiLD), integrating the variables of anisotropic surface reflection (scene types, solar zenith angles, viewing zenith angles, and relative azimuth angles) and radiant flux in Moon-viewed sunlit regions. Then, we discussed it over three time periods (Earth’s rotation, revolution period, and synodic month cycle) and analyzed the impact of three variables (area of the Moon-viewed sunlit region, scene types, and incident-viewing angular bins) on anisotropic EOSRiLD. Our results indicate that EOSRiLD based on the assumptions of anisotropic and isotropic reflection is different but they all show the same monthly cycle change, which is related to the area of the Moon-viewed sunlit region. At the beginning and end of the lunar month, the differences between anisotropy and isotropy are greatest in each cycle; when it is close to the first half of each cycle, there is a small difference peak. Both anisotropy and isotropy are caused by the relative azimuth angles between the Sun and Moon. In conclusion, even if the Moon-based platform has a wider scope than space-borne satellites, the difference is still large between anisotropy and isotropy. Therefore, we still need to consider the anisotropic surface reflection based on the Moon-based observation.
Comprehensive Study on the Tropospheric Wet Delay and Horizontal Gradients during a Severe Weather Event
Authors: Graffigna Victoria , Hernández-Pajares Manuel , Azpilicueta Francisco , Gende Mauricio
Published: February 2022

GNSS meteorology is today one of the most growing technologies to monitor severe weather events. In this paper, we present the usage of 160 GPS reference stations over the period of 14 days to monitor and track Hurricane Harvey, which struck Texas in August 2017. We estimate the Zenith Wet Delay (ZWD) and the tropospheric gradients with 30 s interval using TOMION v2 software and carry out the processing in Precise Point Positioning (PPP) mode. We study the relationship of these parameters with atmospheric variables extracted from Tropical Rainfall Measuring Mission (TRMM) satellite mission and climate reanalysis model ERA5. This research finds that the ZWD shows patterns related to the rainfall rate and to the location of the hurricane. We also find that the tropospheric gradients are correlated with water vapor gradients before and after the hurricane, and with the wind and the pressure gradients only after the hurricane. This study also shows a new finding regarding the spectral distribution of the gradients, with a clear diurnal period present, which is also found on the ZWD itself. This kind of study approaches the GNSS meteorology to the increasing requirements of meteorologist in terms of monitoring severe weather events.
A Method for Obtaining a DEM with Curved Abscissa from MLS Data for Linear Infrastructure Survey Design
Authors: Barbarella Maurizio , Di Benedetto Alessandro , Fiani Margherita
Published: February 2022

The sudden deterioration of the condition of linear infrastructure networks makes road management a complex task. Knowledge of the surface condition of the pavement is a requirement in order to estimate the causes of instabilities, select the appropriate action and identify all those sections that require urgent intervention. The mobile laser scanning (MLS) technique allows for a fast and safe diagnosis, thus making it possible to plan an early intervention program quickly and cost-effectively. This paper describes a methodology implemented with a twofold purpose: (i) the optimal definition, during the design phase, of the input parameters of the MLS survey (velocity of the vehicle and acquisition rate), defined through the study of the relationship between these parameters and the density of the scanned points and, therefore, with the resolution that allows the analysis of a certain type of pavement distress; (ii) the creation of a Digital Elevation Model with a curved abscissa (DEMc), specific for the analysis of road pavements. The field surveys made and the procedure developed allowed the velocity of the MLS to be associated with the resolution of the DEMc, and thus its capability to highlight distresses at different levels of severity. The creation of the road model is semiautomatic; the height value of each single node of the grid is estimated through spatial interpolation algorithms. Starting from experimental data, a few charts were created that relate the density of the point cloud to the variation of the acquisition rate, together with the minimum resolution. Depending on the type of distress analyzed, it is possible to infer the values to be respected of the parameters. In this way, it should be possible to draw up a few guidelines about MLS surveys addressing linear infrastructures focused on the optimization of the survey design, so as to identify strategies that can maximize benefits with the same available budget.
Snow Virga above the Swiss Plateau Observed by a Micro Rain Radar
Authors: Beynon Ruben , Hocke Klemens
Published: February 2022

Studies of snow virga precipitation are rare. In this study, we investigated data from a vertically pointing Doppler Micro Rain Radar (MRR) in Bern, Switzerland, from 2008 to 2013 for snow virga precipitation events. The MRR data were reprocessed using the radar data processing algorithm of Garcia-Benardi et al., which allows the reliable determination of the snow virga precipitation rate. We focus on a long-lasting snow virga event from 17 March 2013, supported by atmospheric reanalysis data and atmospheric back trajectories. The snow virga was associated with a wind shear carrying moist air and snow precipitation in the upper air layers and dry air in the lower air layers. The lowest altitudes reached by the precipitation varied between 300 m and 1500 m above the ground over the course of the event. The duration of the snow virga was 22 h. In disagreement with the MRR observations, ERA5 reanalysis indicated drizzle at the ground over a time segment of 4 h during the snow virga event.
Associations between Street-View Perceptions and Housing Prices: Subjective vs. Objective Measures Using Computer Vision and Machine Learning Techniques
Authors: Xu Xiang , Qiu Waishan , Li Wenjing , Liu Xun , Zhang Ziye , Li Xiaojiang , Luo Dan
Published: February 2022

This study investigated the extent to which subjectively and objectively measured street-level perceptions complement or conflict with each other in explaining property value. Street-scene perceptions can be subjectively assessed from self-reported survey questions, or objectively quantified from land use data or pixel ratios of physical features extracted from street-view imagery. Prior studies mainly relied on objective indicators to describe perceptions and found that a better street environment is associated with a price premium. While very few studies have addressed the impact of subjectively-assessed perceptions. We hypothesized that human perceptions have a subtle relationship to physical features that cannot be comprehensively captured with objective indicators. Subjective measures could be more effective to describe human perceptions, thus might explain more housing price variations. To test the hypothesis, we both subjectively and objectively measured six pairwise eye-level perceptions (i.e., Greenness, Walkability, Safety, Imageability, Enclosure, and Complexity). We then investigated their coherence and divergence for each perception respectively. Moreover, we revealed their similar or opposite effects in explaining house prices in Shanghai using the hedonic price model (HPM). Our intention was not to make causal statements. Instead, we set to address the coherent and conflicting effects of the two measures in explaining people’s behaviors and preferences. Our method is high-throughput by extending classical urban design measurement protocols with current artificial intelligence (AI) frameworks for urban-scene understanding. First, we found the percentage increases in housing prices attributable to street-view perceptions were significant for both subjective and objective measures. While subjective scores explained more variance over objective scores. Second, the two measures exhibited opposite signs in explaining house prices for Greenness and Imageability perceptions. Our results indicated that objective measures which simply extract or recombine individual streetscape pixels cannot fully capture human perceptions. For perceptual qualities that were not familiar to the average person (e.g., Imageability), a subjective framework exhibits better performance. Conversely, for perceptions whose connotation are self-evident (e.g., Greenness), objective measures could outperform the subjective counterparts. This study demonstrates a more holistic understanding for street-scene perceptions and their relations to property values. It also sheds light on future studies where the coherence and divergence of the two measures could be further stressed.
An Adaptive Surrogate-Assisted Endmember Extraction Framework Based on Intelligent Optimization Algorithms for Hyperspectral Remote Sensing Images
Authors: Wang Zhao , Li Jianzhao , Liu Yiting , Xie Fei , Li Peng
Published: February 2022

As the foremost step of spectral unmixing, endmember extraction has been one of the most challenging techniques in the spectral unmixing processing due to the mixing of pixels and the complexity of hyperspectral remote sensing images. The existing geometrial-based endmember extraction algorithms have achieved the ideal results, but most of these algorithms perform poorly when they do not meet the assumption of simplex structure. Recently, many intelligent optimization algorithms have been employed to solve the problem of endmember extraction. Although they achieved the better performance than the geometrial-based algorithms in different complex scenarios, they also suffer from the time-consuming problem. In order to alleviate the above problems, balance the two key indicators of accuracy and running time, an adaptive surrogate-assisted endmember extraction (ASAEE) framework based on intelligent optimization algorithms is proposed for hyperspectral remote sensing images in this paper. In the proposed framework, the surrogate-assisted model is established to reduce the expensive time cost of the intelligent algorithms by fitting the fully constrained evaluation value with the low-cost estimated value. In more detail, three commonly used intelligent algorithms, namely genetic algorithm, particle swarm optimization algorithm and differential evolution algorithm, are specifically designed into the ASAEE framework to verify the effectiveness and robustness. In addition, an adaptive weight surrogate-assisted model selection strategy is proposed, which can automatically adjust the weights of different surrogate models according to the characteristics of different intelligent algorithms. Experimental results on three data sets (including two simulated data sets and one real data set) show the effectiveness and the excellent performance of the proposed ASAEE framework.
Blue-Sky Albedo Reduction and Associated Influencing Factors of Stable Land Cover Types in the Middle-High Latitudes of the Northern Hemisphere during 1982–2015
Authors: Yuan Saisai , Wang Yeqiao , Zhang Hongyan , Zhao Jianjun , Guo Xiaoyi , Xiong Tao , Li Hui , Zhao Hang
Published: February 2022

Land surface albedo (LSA) directly affects the radiation balance and the surface heat budget. LSA is a key variable for local and global climate research. The complexity of LSA variations and the driving factors highlight the importance of continuous spatial and temporal monitoring. Snow, vegetation and soil are the main underlying surface factors affecting LSA dynamics. In this study, we combined Global Land Surface Satellite (GLASS) products and ERA5 reanalysis products to analyze the spatiotemporal variation and drivers of annual mean blue-sky albedo for stable land cover types in the middle-high latitudes of the Northern Hemisphere (30~90°N) from 1982 to 2015. Snow cover (SC) exhibited a decreasing trend in 99.59% of all pixels (23.73% significant), with a rate of −0.0813. Soil moisture (SM) exhibited a decreasing trend in 85.66% of all pixels (22.27% significant), with a rate of −0.0002. The leaf area index (LAI) exhibited a greening trend in 74.38% of all pixels (25.23% significant), with a rate of 0.0014. Blue-sky albedo exhibited a decreasing trend in 98.97% of all pixels (65.12% significant), with a rate of −0.0008 (OLS slope). Approximately 98.16% of all pixels (57.01% significant) exhibited a positive correlation between blue-sky albedo and SC. Approximately 47.78% and 67.38% of all pixels (17.13% and 25.3% significant, respectively) exhibited a negative correlation between blue-sky albedo and SM and LAI, respectively. Approximately 10.31%, 20.81% and 68.88% of the pixel blue-sky albedo reduction was mainly controlled by SC, SM and LAI, respectively. The decrease in blue-sky albedo north of 40°N was mainly caused by the decrease in SC. The decrease in blue-sky albedo south of 40°N was mainly caused by SM reduction and vegetation greening. The decrease in blue-sky albedo in the western Tibetan Plateau was caused by vegetation greening, SM increase and SC reduction. The results have important scientific significance for the study of surface processes and global climate change.
Multitask Learning-Based for SAR Image Superpixel Generation
Authors: Liu Jiafei , Wang Qingsong , Cheng Jianda , Xiang Deliang , Jing Wenbo
Published: February 2022

Most of the existing synthetic aperture radar (SAR) image superpixel generation methods are designed based on the raw SAR images or artificially designed features. However, such methods have the following limitations: (1) SAR images are severely affected by speckle noise, resulting in unstable pixel distance estimation. (2) Artificially designed features cannot be well-adapted to complex SAR image scenes, such as the building regions. Aiming to overcome these shortcomings, we propose a multitask learning-based superpixel generation network (ML-SGN) for SAR images. ML-SGN firstly utilizes a multitask feature extractor to extract deep features, and constructs a high-dimensional feature space containing intensity information, deep semantic informantion, and spatial information. Then, we define an effective pixel distance measure based on the high-dimensional feature space. In addition, we design a differentiable soft assignment operation instead of the non-differentiable nearest neighbor operation, so that the differentiable Simple Linear Iterative Clustering (SLIC) and multitask feature extractor can be combined into an end-to-end superpixel generation network. Comprehensive evaluations are performed on two real SAR images with different bands, which demonstrate that our proposed method outperforms other state-of-the-art methods.
Modeling Analysis of SM2 Construction Attacks in the Open Secure Sockets Layer Based on Petri Net
Authors: Deng Xi , Zhang Liumei , Wang Yichuan , Jiang Fanzhi
Published: February 2022

The detection and defense of malicious attacks are critical to the proper functioning of network security. Due to the diversity and rapid updates of the attack methods used by attackers, traditional defense mechanisms have been challenged. In this context, a more effective method to predict vulnerabilities in network systems is considered an urgent need to protect network security. In this paper, we propose a formal modeling and analysis approach based on Petri net vulnerability exploitation. We used the Common Vulnerabilities and Exposures (CVE)-2021-3711 vulnerability source code to build a model. A patch model was built to address the problems of this model. Finally, the time injected by the actual attacker and the time simulated by the software were calculated separately. The results showed that the simulation time was shorter than the actual attack time, and ultra-real-time simulation could be achieved. By modeling the network system with this method, the model can be found to arrive at an illegitimate state according to the structure of Petri nets themselves and thus discover unknown vulnerabilities. This method provides a reference method for exploring unknown vulnerabilities.
Assessment of Thigh Angular Velocity by an Activity Monitor to Describe Sit-to-Stand Performance
Authors: Klenk Jochen , Ba Alassane , Sczuka Kim S. , Daub Urban , Lindemann Ulrich
Published: February 2022

The assessment of sit-to-stand (STS) performance is highly relevant, especially in older persons, but testing STS performance in the laboratory does not necessarily reflect STS performance in daily life. Therefore, the aim was to validate a wearable sensor-based measure to be used under unsupervised daily life conditions. Since thigh orientation from horizontal to vertical is characteristic for STS movement, peak angular velocity (PAV) of the thigh was chosen as the outcome variable. A total of 20 younger and older healthy persons and geriatric patients (mean age: 55.5 ± 20.8 years; 55% women) with a wide range of STS performance were instructed to stand up from a chair at their usual pace. STS performance was measured by an activity monitor, force plates, and an opto-electronic system. The association between PAV measured by the thigh-worn activity monitor and PAV measured by the opto-electronic system (gold standard) was r = 0.74. The association between PAV measured by the thigh-worn activity monitor and peak power measured by force plate and opto-electronic system was r = 0.76. The Intra-Class Coefficient (ICC) of agreement between the 2 trials was ICC(A,1) = 0.76. In this sample of persons with a wide range of physical performance, PAV as measured by a thigh-worn acceleration sensor was a valid and reliable measure of STS performance.
An Enhanced Intrusion Detection Model Based on Improved kNN in WSNs
Authors: Liu Gaoyuan , Zhao Huiqi , Fan Fang , Liu Gang , Xu Qiang , Nazir Shah
Published: February 2022

Aiming at the intrusion detection problem of the wireless sensor network (WSN), considering the combined characteristics of the wireless sensor network, we consider setting up a corresponding intrusion detection system on the edge side through edge computing. An intrusion detection system (IDS), as a proactive network security protection technology, provides an effective defense system for the WSN. In this paper, we propose a WSN intelligent intrusion detection model, through the introduction of the k-Nearest Neighbor algorithm (kNN) in machine learning and the introduction of the arithmetic optimization algorithm (AOA) in evolutionary calculation, to form an edge intelligence framework that specifically performs the intrusion detection when the WSN encounters a DoS attack. In order to enhance the accuracy of the model, we use a parallel strategy to enhance the communication between the populations and use the Lévy flight strategy to adjust the optimization. The proposed PL-AOA algorithm performs well in the benchmark function test and effectively guarantees the improvement of the kNN classifier. We use Matlab2018b to conduct simulation experiments based on the WSN-DS data set and our model achieves 99% ACC, with a nearly 10% improvement compared with the original kNN when performing DoS intrusion detection. The experimental results show that the proposed intrusion detection model has good effects and practical application significance.
Attention-Based Deep Recurrent Neural Network to Forecast the Temperature Behavior of an Electric Arc Furnace Side-Wall
Authors: Godoy-Rojas Diego F. , Leon-Medina Jersson X. , Rueda Bernardo , Vargas Whilmar , Romero Juan , Pedraza Cesar , Pozo Francesc , Tibaduiza Diego A.
Published: February 2022

Structural health monitoring (SHM) in an electric arc furnace is performed in several ways. It depends on the kind of element or variable to monitor. For instance, the lining of these furnaces is made of refractory materials that can be worn out over time. Therefore, monitoring the temperatures on the walls and the cooling elements of the furnace is essential for correct structural monitoring. In this work, a multivariate time series temperature prediction was performed through a deep learning approach. To take advantage of data from the last 5 years while not neglecting the initial parts of the sequence in the oldest years, an attention mechanism was used to model time series forecasting using deep learning. The attention mechanism was built on the foundation of the encoder–decoder approach in neural networks. Thus, with the use of an attention mechanism, the long-term dependency of the temperature predictions in a furnace was improved. A warm-up period in the training process of the neural network was implemented. The results of the attention-based mechanism were compared with the use of recurrent neural network architectures to deal with time series data, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The results of the Average Root Mean Square Error (ARMSE) obtained with the attention-based mechanism were the lowest. Finally, a variable importance study was performed to identify the best variables to train the model.
A Linear Technique for Artifacts Correction and Compensation in Phase Interferometric Angle of Arrival Estimation
Authors: Florio Antonello , Avitabile Gianfranco , Coviello Giuseppe
Published: February 2022

Radio localization and radio positioning are relevant research fields for many telecommunications technologies. Usually, the solutions proposed by the literature rely on adaptive techniques related to some parameters that can be extracted from the received signal in cooperative device tracking. In this paper, we explore the artifacts that may be introduced into Angle-of-Arrival estimation based on phase interferometry, and we introduce a simple technique to mitigate their impact. Details of the mathematical discussion are presented and the approach is experimentally validated. The experimental results are compared with raw data to demonstrate the effectiveness of the proposed technique.
A Low-Power Distributed Visual Sensor Network for Real-Time Barcode Localization and Identification
Authors: Hendrikx Leander , Zhong Rui , Cornelis Bruno , Munteanu Adrian
Published: February 2022

A novel low-power distributed Visual Sensor Network (VSN) system is proposed, which performs real-time collaborative barcode localization, tracking, and robust identification. Due to a dynamic triggering mechanism and efficient transmission protocols, communication is organized amongst the nodes themselves rather than being orchestrated by a single sink node, achieving lower congestion and significantly reducing the vulnerability of the overall system. Specifically, early detection of the moving barcode is achieved through a dynamic triggering mechanism. A hierarchical transmission protocol is designed, within which different communication protocols are used, depending on the type of data exchanged among nodes. Real-Time Transport Protocol (RTP) is employed for video communication, while the Transmission Control Protocol (TCP) and Long Range (LoRa) protocol are used for passing messages amongst the nodes in the VSN. Through an extensive experimental evaluation, we demonstrate that the proposed distributed VSN brings substantial advantages in terms of accuracy, power savings, and time complexity compared to an equivalent system performing centralized processing.
Separation Sciences in Poland
Authors: Buszewski Bogusław
Published: February 2022

This paper describes the most significant achievements related to the creation and development of separation methods. Tswiett Nerst’s achievements and the role of such centers as Lublin, Warsaw, Gdańsk, Poznań, Łódź, Silesia, Kraków and Toruń are presented, as well as the role of leaders who created theories and methodological studies on chromatography and related techniques. The role of sample preparation methods is emphasized based on the same or similar theoretical foundations. The directions of developing these techniques in Poland today and in the future are presented.
Place-Making through Media: How Media Environments Make a Difference for Long-Term Care Residents’ Agency
Authors: Wagner Sarah
Published: February 2022

This paper explores the unique relationships care home residents have with communication media. Drawing on findings from an ethnographic case study at a long-term care site in British Columbia, Canada, I describe how care home residents’ everyday media practices are intertwined with their negotiations of longstanding attachments and new living spaces. The research draws connections between the spatiotemporal contexts of media use and residents’ experiences of social agency. Long-term care residents in this research were challenged to engage with the wider community, maintain friendships, or stay current with events and politics because their preferred ways of using communication media were not possible in long-term care. The communication inequalities experienced by care home residents were not simply about their lack of access to media or content but about their inability to find continuity with their established media habits in terms of time and place. While most research about communication media in care homes has been intervention oriented, this research suggests that long-term care service and funding policies require greater attention to create flexible, diverse, and supportive media environments.
Multivariate Threshold Regression Models with Cure Rates: Identification and Estimation in the Presence of the Esscher Property
Authors: Lee Mei-Ling Ting , Whitmore George A.
Published: February 2022

The first hitting time of a boundary or threshold by the sample path of a stochastic process is the central concept of threshold regression models for survival data analysis. Regression functions for the process and threshold parameters in these models are multivariate combinations of explanatory variates. The stochastic process under investigation may be a univariate stochastic process or a multivariate stochastic process. The stochastic processes of interest to us in this report are those that possess stationary independent increments (i.e., Lévy processes) as well as the Esscher property. The Esscher transform is a transformation of probability density functions that has applications in actuarial science, financial engineering, and other fields. Lévy processes with this property are often encountered in practical applications. Frequently, these applications also involve a ‘cure rate’ fraction because some individuals are susceptible to failure and others not. Cure rates may arise endogenously from the model alone or exogenously from mixing of distinct statistical populations in the data set. We show, using both theoretical analysis and case demonstrations, that model estimates derived from typical survival data may not be able to distinguish between individuals in the cure rate fraction who are not susceptible to failure and those who may be susceptible to failure but escape the fate by chance. The ambiguity is aggravated by right censoring of survival times and by minor misspecifications of the model. Slightly incorrect specifications for regression functions or for the stochastic process can lead to problems with model identification and estimation. In this situation, additional guidance for estimating the fraction of non-susceptibles must come from subject matter expertise or from data types other than survival times, censored or otherwise. The identifiability issue is confronted directly in threshold regression but is also present when applying other kinds of models commonly used for survival data analysis. Other methods, however, usually do not provide a framework for recognizing or dealing with the issue and so the issue is often unintentionally ignored. The theoretical foundations of this work are set out, which presents new and somewhat surprising results for the first hitting time distributions of Lévy processes that have the Esscher property.
Advances in Research on Technological, Pedagogical, Didactical, and Social Competencies of Preservice TCFL Teachers
Authors: Oubibi Mohamed , Zhao Wei , Wang Yue , Zhou Yueliang , Jiang Qiang , Li Yue , Xu Xiaoqing , Qiao Lifang
Published: February 2022

With the development of China, learning the Chinese language has received more and more attention from international students. Teaching Chinese as a foreign language (TCFL) is crucial for spreading Chinese culture and promoting cultural exchanges. In recent years it has become the focus of attention of many scholars, and the heterogeneous cultural backgrounds and diverse language needs of learners determine that TCFL should promote the development of students’ intercultural communication competence in language practice with the use of educational technology. In this study, 82 preservice teachers as an experiment group and 64 preservice teachers as a control group participated in this study; this research aims to improve the quality and level of TCFL, using innovative teaching tools as a means to target preservice TCFL teachers and create a teaching–learning design strategy based on the smart teaching tools Moso Teach (MT) and Rain Classroom (RC). The findings from this research show the importance of the use of smart teaching tools and the improvement of preservice teachers during the experiment compared to preservice teachers with traditional methods. This paper answers the following questions: (1) How to improve preservice TCFL teachers’ competencies by integrating smart teaching tools into their learning process? (2) How to integrate technological, pedagogical, didactic, and social competencies into preservice teachers’ daily teaching CFL?
Does ESG Impact Really Enhance Portfolio Profitability?
Authors: Cesarone Francesco , Martino Manuel Luis , Carleo Alessandra
Published: February 2022

Over the last few decades, growing attention to the topic of social responsibility has affected financial markets and institutional authorities. Indeed, recent environmental, social, and financial crises have inevitably led regulators and investors to take into account the sustainable investing issue; however, the question of how Environmental, Social, and Governance (ESG) criteria impact financial portfolio performances is still open. In this work, we examine a multi-objective optimization model for portfolio selection, where we add to the classical Mean-Variance analysis a third non-financial goal represented by the ESG scores. The resulting optimization problem, formulated as a convex quadratic programming, consists of minimizing the portfolio variance with parametric lower bounds on the levels of the portfolio expected return and ESG. We provide here an extensive empirical analysis on five datasets involving real-world capital market indexes from major stock markets. Our empirical findings typically reveal the presence of two behavioral patterns for the 16 Mean-Variance-ESG portfolios analyzed. Indeed, over the last fifteen years we can distinguish two non-overlapping time windows on which the inclusion of portfolio ESG targets leads to different regimes in terms of portfolio profitability. Furthermore, on the most recent time window, we observe that, for the US markets, imposing a high ESG target tends to select portfolios that show better financial performances than other strategies, whereas for the European markets the ESG constraint does not seem to improve the portfolio profitability.
Application of Physics-Informed Machine Learning Techniques for Power Grid Parameter Estimation
Authors: Lakshminarayana Subhash , Sthapit Saurav , Maple Carsten
Published: February 2022

Power grid parameter estimation involves the estimation of unknown parameters, such as the inertia and damping coefficients, from the observed dynamics. In this work, we present physics-informed machine learning algorithms for the power system parameter estimation problem. First, we propose a novel algorithm to solve the parameter estimation based on the Sparse Identification of Nonlinear Dynamics (SINDy) approach, which uses sparse regression to infer the parameters that best describe the observed data. We then compare its performance against another benchmark algorithm, namely, the physics-informed neural networks (PINN) approach applied to parameter estimation. We perform extensive simulations on IEEE bus systems to examine the performance of the aforementioned algorithms. Our results show that the SINDy algorithm outperforms the PINN algorithm in estimating the power grid parameters over a wide range of system parameters (including high and low inertia systems) and power grid architectures. Particularly, in case of the slow dynamics system, the proposed SINDy algorithms outperforms the PINN algorithm, which struggles to accurately determine the parameters. Moreover, it is extremely efficient computationally and so takes significantly less time than the PINN algorithm, thus making it suitable for real-time parameter estimation. Furthermore, we present an extension of the SINDy algorithm to a scenario where the operator does not have the exact knowledge of the underlying system model. We also present a decentralised implementation of the SINDy algorithm which only requires limited information exchange between the neighbouring nodes of a power grid.
The Emergence of Different Local Resilience Arrangements Regarding Extreme Weather Events in Small Municipalities—A Case Study from the Wielkopolska Region, Poland
Authors: Choryński Adam , Pińskwar Iwona , Graczyk Dariusz , Krzyżaniak Michał
Published: February 2022

Compared with other parts of the world, Poland is a relatively safe country in terms of natural disasters. Nevertheless, extreme weather events have become a significant threat in recent years, especially for local communities. These are exposed to intense rainfall, heavy wind, and heatwaves, as are larger towns. However, small municipalities have different economic, social, and human potential for undertaking preventive actions regarding meteorological extremes. In this paper, we are looking at what activities local communities from the Wielkopolska region in Poland undertake to cope with extreme weather events—specifically, heavy rainfall and heatwaves. We analyze the municipalities that are most and least exposed to extremes, based on meteorological data. These are further compared with local resilience measures in the event of extreme meteorological events through the risk management analysis of selected municipalities. The emergence of two approaches regarding extreme weather events has been observed. First, local arrangements consist of different resilience types. Both of the identified approaches are concentrated around rescue activities, representing recovery resilience. They differ in the second component of resilience: municipalities that have suffered more from weather extremes manifest more resistance resilience, whereas those communities where fewer meteorological events took place demonstrate more creativity-type resilience.