bluuebunny/crossref_metadata_2025 huggingface dataset:
Variability and Formation Mechanism of Polynyas in Eastern Prydz Bay, Antarctica
Authors: Hou Saisai , Shi Jiuxin
Published: December 2021

Based on satellite remote sensing, several polynyas have been found in Prydz Bay, East Antarctica. Compared with the Mackenzie Bay Polynya, the only polynya in the west, the polynyas in eastern Prydz Bay have a larger area and higher ice production, but have never been studied individually. In this study, four recurrent polynyas were identified in eastern Prydz Bay from sea ice concentration data during 2002–2011. Their areas generally exhibit synchronous temporal variations and have good correlation with wind speed, which indicates that they are primarily wind-driven polynyas that need at least one stationary ice barrier to block the inflow of drifting sea ice. The components of the ice barriers of these four polynyas were identified through comparison of satellite remote sensing visible images and synthetic aperture radar images. All types of fast ice, including landfast ice, offshore fast ice and ice fingers serving as ice barriers for these polynyas are anchored by an assemblage of small icebergs and have an approximately year-round period of variations that also regulates the variability of polynyas. The movement and grounding of giant icebergs near the polynyas significantly affects the development of the polynyas. The results of this study illustrate the important impact of icebergs on Antarctic wind-driven polynyas and the formation of dense shelf water.
ACFNet: A Feature Fusion Network for Glacial Lake Extraction Based on Optical and Synthetic Aperture Radar Images
Authors: Wang Jinxiao , Chen Fang , Zhang Meimei , Yu Bo
Published: December 2021

Glacial lake extraction is essential for studying the response of glacial lakes to climate change and assessing the risks of glacial lake outburst floods. Most methods for glacial lake extraction are based on either optical images or synthetic aperture radar (SAR) images. Although deep learning methods can extract features of optical and SAR images well, efficiently fusing two modality features for glacial lake extraction with high accuracy is challenging. In this study, to make full use of the spectral characteristics of optical images and the geometric characteristics of SAR images, we propose an atrous convolution fusion network (ACFNet) to extract glacial lakes based on Landsat 8 optical images and Sentinel-1 SAR images. ACFNet adequately fuses high-level features of optical and SAR data in different receptive fields using atrous convolution. Compared with four fusion models in which data fusion occurs at the input, encoder, decoder, and output stages, two classical semantic segmentation models (SegNet and DeepLabV3+), and a recently proposed model based on U-Net, our model achieves the best results with an intersection-over-union of 0.8278. The experiments show that fully extracting the characteristics of optical and SAR data and appropriately fusing them are vital steps in a network’s performance of glacial lake extraction.
Upscaling Remote Sensing Inversion Model of Wheat Field Cultivated Land Quality in the Huang-Huai-Hai Agricultural Region, China
Authors: Li Yinshuai , Chang Chunyan , Wang Zhuoran , Qi Guanghui , Dong Chao , Zhao Gengxing
Published: December 2021

It is an objective demand for sustainable agricultural development to realize fast and accurate cultivated land quality assessment. In this paper, Tengzhou city (county-scale hilly area: scale A), Shanghe county (county-scale plain area: scale B), and Huang-Huai-Hai region (including large-scale hilly and plain area: scale C and D) were taken as research areas. Through the conversion of evaluation systems, the inversion models at the county-scale were constructed. Then, the image scale conversion was carried out based on the numerical regression method, and the upscaling inversion was realized. The results showed that: (1) the conversion models of evaluation systems (CMES) are Y = 1.021x − 4.989 (CMESA−B), Y = 0.801x + 16.925 (CMESA−C), and Y = 0.959x + 3.458 (CMESC−D); (2) the booting stage is the best inversion phase; (3) the back propagation neural network model based on the combination index group (CI-BPNN) is the best inversion model, with the R2 are 0.723 (modeling set) and 0.722 (verification set). CI-BPNN and CI-BPNN-CMESA−B models are suitable for the hilly and plain areas at the county-scale, and the level area ratio difference is less than 4.87%. Furthermore, (4) the reflectance conversion model of short-wave infrared 2 is cubic, and the rest are quadratic. CI-BPNN-CMESA−C and CI-BPNN-CMESA−C-CMESC−D models realized upscaling inversion in the hilly and plain areas, with the maximum level area ratio difference being 1.60%. Additionally, (5) the wheat field quality has improved steadily since 2001 in the Huang-Huai-Hai region. This study proposes an upscaling inversion method of wheat field quality, which provides a scientific basis for cultivated land management and agricultural production in large areas.
Random Forest Classification of Inundation Following Hurricane Florence (2018) via L-Band Synthetic Aperture Radar and Ancillary Datasets
Authors: Melancon Alexander M. , Molthan Andrew L. , Griffin Robert E. , Mecikalski John R. , Schultz Lori A. , Bell Jordan R.
Published: December 2021

In response to Hurricane Florence of 2018, NASA JPL collected quad-pol L-band SAR data with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument, observing record-setting river stages across North and South Carolina. Fully-polarized SAR images allow for mapping of inundation extent at a high spatial resolution with a unique advantage over optical imaging, stemming from the sensor’s ability to penetrate cloud cover and dense vegetation. This study used random forest classification to generate maps of inundation from L-band UAVSAR imagery processed using the Freeman–Durden decomposition method. An average overall classification accuracy of 87% is achieved with this methodology, with areas of both under- and overprediction for the focus classes of open water and inundated forest. Fuzzy logic operations using hydrologic variables are used to reduce the number of small noise-like features and false detections in areas unlikely to retain water. Following postclassification refinement, estimated flood extents were combined to an event maximum for societal impact assessments. Results from the Hurricane Florence case study are discussed in addition to the limitations of available validation data for accuracy assessments.
Application of Spectral Index-Based Logistic Regression to Detect Inland Water in the South Caucasus
Authors: Worden James , de Beurs Kirsten M. , Koch Jennifer , Owsley Braden C.
Published: December 2021

The Caucasus is a diverse region with many climate zones that range from subtropical lowlands to mountainous alpine areas. The region is marked by irrigated croplands fed by irrigation canals, heavily vegetated wetlands, lakes, and reservoirs. In this study, we demonstrate the development of an improved surface water map based on a global water dataset to get a better understanding of the spatial distribution of small water bodies. First, we used the global water product from the European Commission Joint Research Center (JRC) to generate training data points by stratified random sampling. Next, we applied the optimal probability cut-off logistic regression model to develop surface water datasets for the entire Caucasus region, covering 19 Landsat tiles from May to October 2019. Finally, we used 6745 manually classified points (3261 non-water, 3484 water) to validate both the newly developed water dataset and the JRC global surface water dataset using an estimated proportion of area error matrix to evaluate accuracy. Our approach produced surface water extent maps with higher accuracy (89.2%) and detected 392 km2 more water than the global product (86.7% accuracy). We demonstrate that the newly developed method enables surface water detection of small ponds and lakes, flooded agricultural fields, and narrow irrigation channels, which are particularly important for mosquito-borne diseases.
Transformer-Based Decoder Designs for Semantic Segmentation on Remotely Sensed Images
Authors: Panboonyuen Teerapong , Jitkajornwanich Kulsawasd , Lawawirojwong Siam , Srestasathiern Panu , Vateekul Peerapon
Published: December 2021

Transformers have demonstrated remarkable accomplishments in several natural language processing (NLP) tasks as well as image processing tasks. Herein, we present a deep-learning (DL) model that is capable of improving the semantic segmentation network in two ways. First, utilizing the pre-training Swin Transformer (SwinTF) under Vision Transformer (ViT) as a backbone, the model weights downstream tasks by joining task layers upon the pretrained encoder. Secondly, decoder designs are applied to our DL network with three decoder designs, U-Net, pyramid scene parsing (PSP) network, and feature pyramid network (FPN), to perform pixel-level segmentation. The results are compared with other image labeling state of the art (SOTA) methods, such as global convolutional network (GCN) and ViT. Extensive experiments show that our Swin Transformer (SwinTF) with decoder designs reached a new state of the art on the Thailand Isan Landsat-8 corpus (89.8% F1 score), Thailand North Landsat-8 corpus (63.12% F1 score), and competitive results on ISPRS Vaihingen. Moreover, both our best-proposed methods (SwinTF-PSP and SwinTF-FPN) even outperformed SwinTF with supervised pre-training ViT on the ImageNet-1K in the Thailand, Landsat-8, and ISPRS Vaihingen corpora.
A Multi-Source Data Fusion Decision-Making Method for Disease and Pest Detection of Grape Foliage Based on ShuffleNet V2
Authors: Yang Rui , Lu Xiangyu , Huang Jing , Zhou Jun , Jiao Jie , Liu Yufei , Liu Fei , Su Baofeng , Gu Peiwen
Published: December 2021

Disease and pest detection of grape foliage is essential for grape yield and quality. RGB image (RGBI), multispectral image (MSI), and thermal infrared image (TIRI) are widely used in the health detection of plants. In this study, we collected three types of grape foliage images with six common classes (anthracnose, downy mildew, leafhopper, mites, viral disease, and healthy) in the field. ShuffleNet V2 was used to build up detection models. According to the accuracy of RGBI, MSI, TIRI, and multi-source data concatenation (MDC) models, and a multi-source data fusion (MDF) decision-making method was proposed for improving the detection performance for grape foliage, aiming to enhance the decision-making for RGBI of grape foliage by fusing the MSI and TIRI. The results showed that 40% of the incorrect detection outputs were rectified using the MDF decision-making method. The overall accuracy of MDF model was 96.05%, which had improvements of 2.64%, 13.65%, and 27.79%, compared with the RGBI, MSI, and TIRI models using label smoothing, respectively. In addition, the MDF model was based on the lightweight network with 3.785 M total parameters and 0.362 G multiply-accumulate operations, which could be highly portable and easy to be applied.
SRSDD-v1.0: A High-Resolution SAR Rotation Ship Detection Dataset
Authors: Lei Songlin , Lu Dongdong , Qiu Xiaolan , Ding Chibiao
Published: December 2021

Deep learning has been widely used in the field of SAR ship detection. However, current SAR ship detection still faces many challenges, such as complex scenes, multiple scales, and small targets. In order to promote the solution to the above problems, this article releases a high-resolution SAR ship detection dataset which can be used for rotating frame target detection. The dataset contains six categories of ships. In total, 30 panoramic SAR tiles of the Chinese Gaofen-3 of port areas with a 1-m resolution were cropped to slices, each with 1024 × 1024 pixels. In addition, most of the images in the dataset contain nearshore areas with complex background interference. Eight state-of-the-art rotated detectors and a CFAR-based method were used to evaluate the dataset. Experimental results revealed that the complex background will have a great impact on the performance of detectors.
Fusing Sentinel-1 and -2 to Model GEDI-Derived Vegetation Structure Characteristics in GEE for the Paraguayan Chaco
Authors: Kacic Patrick , Hirner Andreas , Da Ponte Emmanuel
Published: December 2021

Vegetation structure is a key component in assessing habitat quality for wildlife and carbon storage capacity of forests. Studies conducted at global scale demonstrate the increasing pressure of the agricultural frontier on tropical forest, endangering their continuity and biodiversity within. The Paraguayan Chaco has been identified as one of the regions with the highest rate of deforestation in South America. Uninterrupted deforestation activities over the last 30 years have resulted in the loss of 27% of its original cover. The present study focuses on the assessment of vegetation structure characteristics for the complete Paraguayan Chaco by fusing Sentinel-1, -2 and novel spaceborne Light Detection and Ranging (LiDAR) samples from the Global Ecosystem Dynamics Investigation (GEDI). The large study area (240,000 km2) calls for a workflow in the cloud computing environment of Google Earth Engine (GEE) which efficiently processes the multi-temporal and multi-sensor data sets for extrapolation in a tile-based random forest (RF) regression model. GEDI-derived attributes of vegetation structure are available since December 2019, opening novel research perspectives to assess vegetation structure composition in remote areas and at large-scale. Therefore, the combination of global mapping missions, such as Landsat and Sentinel, are predestined to be combined with GEDI data, in order to identify priority areas for nature conservation. Nevertheless, a comprehensive assessment of the vegetation structure of the Paraguayan Chaco has not been conducted yet. For that reason, the present methodology was developed to generate the first high-resolution maps (10 m) of canopy height, total canopy cover, Plant-Area-Index and Foliage-Height-Diversity-Index. The complex ecosystems of the Paraguayan Chaco ranging from arid to humid climates can be described by canopy height values from 1.8 to 17.6 m and canopy covers from sparse to dense (total canopy cover: 0 to 78.1%). Model accuracy according to median R2 amounts to 64.0% for canopy height, 61.4% for total canopy cover, 50.6% for Plant-Area-Index and 48.0% for Foliage-Height-Diversity-Index. The generated maps of vegetation structure should promote environmental-sound land use and conservation strategies in the Paraguayan Chaco, to meet the challenges of expanding agricultural fields and increasing demand of cattle ranching products, which are dominant drivers of tropical forest loss.
Revisited Estimation of Moderate Resolution Sargassum Fractional Coverage Using Decametric Satellite Data (S2-MSI)
Authors: Descloitres Jacques , Minghelli Audrey , Steinmetz François , Chevalier Cristèle , Chami Malik , Berline Léo
Published: December 2021

Since 2011, massive stranding of the brown algae Sargassum has regularly affected the coastal waters of the West Caribbean, Brazil and West Africa, leading to significant environmental and socio-economic impacts. The AFAI algal index (Alternative Floating Algae Index) is often used with remote sensing data in order to estimate the Sargassum coverage, and more precisely the AFAI deviation, which consists of the difference between AFAI and AFAI of the Sargassum-free background. In this study, the AFAI deviation is computed using NASA’s 1 km Terra/MODIS (Moderate-Resolution Imaging Spectroradiometer) and ESA/Copernicus’s 20 m Sentinel-2/MSI (Multi Spectral Instrument) for the same sites and at the same time. Both MODIS and MSI AFAI deviations are compared to confirm the relevance of AFAI deviation technique for two very different spatial resolutions. A high coefficient of determination was found, thus confirming a satisfactory downsampling from 20 m (MSI) to 1 km (MODIS). Then, AFAI deviations are used to estimate the fractional coverage of Sargassum (noted FC). A new linear relationship between the MODIS AFAI deviation and FC is established using the dense Sargassum aggregations observed by MSI data. The AFAI deviation is proportional to FC with a factor of proportionality close to 0.08. Finally, it is shown that the factor is dependent on the Sargassum spectral reflectance, submersion or physiological state.
Multi-Cloud Resource Management Techniques for Cyber-Physical Systems
Authors: Bucur Vlad , Miclea Liviu-Cristian
Published: December 2021

Information technology is based on data management between various sources. Software projects, as varied as simple applications or as complex as self-driving cars, are heavily reliant on the amounts, and types, of data ingested by one or more interconnected systems. Data is not only consumed but is transformed or mutated which requires copious amounts of computing resources. One of the most exciting areas of cyber-physical systems, autonomous vehicles, makes heavy use of deep learning and AI to mimic the highly complex actions of a human driver. Attempting to map human behavior (a large and abstract concept) requires large amounts of data, used by AIs to increase their knowledge and better attempt to solve complex problems. This paper outlines a full-fledged solution for managing resources in a multi-cloud environment. The purpose of this API is to accommodate ever-increasing resource requirements by leveraging the multi-cloud and using commercially available tools to scale resources and make systems more resilient while remaining as cloud agnostic as possible. To that effect, the work herein will consist of an architectural breakdown of the resource management API, a low-level description of the implementation and an experiment aimed at proving the feasibility, and applicability of the systems described.
The Impact of Load Style Variation on Gait Recognition Based on sEMG Images Using a Convolutional Neural Network
Authors: Zhang Xianfu , Hu Yuping , Luo Ruimin , Li Chao , Tang Zhichuan
Published: December 2021

Surface electromyogram (sEMG) signals are widely employed as a neural control source for lower-limb exoskeletons, in which gait recognition based on sEMG is particularly important. Many scholars have taken measures to improve the accuracy of gait recognition, but several real-time limitations affect its applicability, of which variation in the load styles is obvious. The purposes of this study are to (1) investigate the impact of different load styles on gait recognition; (2) study whether good gait recognition performance can be obtained when a convolutional neural network (CNN) is used to deal with the sEMG image from sparse multichannel sEMG (SMC-sEMG); and (3) explore whether the control system of the lower-limb exoskeleton trained by sEMG from part of the load styles still works efficiently in a real-time environment where multiload styles are required. In addition, we discuss an effective method to improve gait recognition at the levels of the load styles. In our experiment, fifteen able-bodied male graduate students with load (20% of body weight) and using three load styles (SBP = backpack, SCS = cross shoulder, SSS = straight shoulder) were asked to walk uniformly on a treadmill. Each subject performed 50 continuous gait cycles under three speeds (V3 = 3 km/h, V5 = 5 km/h, and V7 = 7 km/h). A CNN was employed to deal with sEMG images from sEMG signals for gait recognition, and back propagation neural networks (BPNNs) and support vector machines (SVMs) were used for comparison by dealing with the same sEMG signal. The results indicated that (1) different load styles had remarkable impact on the gait recognition at three speeds under three load styles (p < 0.001); (2) the performance of gait recognition from the CNN was better than that from the SVM and BPNN at each speed (84.83%, 81.63%, and 83.76% at V3; 93.40%, 88.48%, and 92.36% at V5; and 90.1%, 86.32%, and 85.42% at V7, respectively); and (3) when all the data from three load styles were pooled as testing sets at each speed, more load styles were included in the training set, better performance was obtained, and the statistical analysis suggested that the kinds of load styles included in training set had a significant effect on gait recognition (p = 0.002), from which it can be concluded that the control system of a lower-limb exoskeleton trained by sEMG using only some load styles is not sufficient in a real-time environment.
Orthorectification of Skin Nevi Images by Means of 3D Model of the Human Body
Authors: Szczypiński Piotr M. , Sprawka Katarzyna
Published: December 2021

Melanoma is the most lethal form of skin cancer, and develops from mutation of pigment-producing cells. As it becomes malignant, it usually grows in size, changes proportions, and develops an irregular border. We introduce a system for early detection of such changes, which enables whole-body screening, especially useful in patients with atypical mole syndrome. The paper proposes a procedure to build a 3D model of the patient, relate the high-resolution skin images with the model, and orthorectify these images to enable detection of size and shape changes in nevi. The novelty is in the application of image encoding indices and barycentric coordinates of the mesh triangles. The proposed procedure was validated with a set of markers of a specified geometry. The markers were attached to the body of a volunteer and analyzed by the system. The results of quantitative comparison of original and corrected images confirm that the orthorectification allows for more accurate estimation of size and proportions of skin nevi.
Comparison of Cheese Aroma Intensity Measured Using an Electronic Nose (E-Nose) Non-Destructively with the Aroma Intensity Scores of a Sensory Evaluation: A Pilot Study
Authors: Fujioka Kouki
Published: December 2021

Cheese aroma is known to affect consumer preference. One of the methods to measure cheese aroma is the use of an electronic nose (e-nose), which has been used to classify cheese types, production areas, and cheese ages. However, few studies have directly compared the aroma intensity scores derived from sensory evaluations with the values of metal oxide semiconductor sensors that can easily measure the aroma intensity. This pilot study aimed to investigate the relationship between sensory evaluation scores and e-nose values with respect to cheese aroma. Five types of processed cheese (two types of normal processed cheese, one type containing aged cheese, and two types containing blue cheese), and one type of natural cheese were used as samples. The sensor values obtained using the electronic nose, which measured sample aroma non-destructively, and five sensory evaluation scores related to aroma (aroma intensity before intake, during mastication, and after swallowing; taste intensity during mastication; and remaining flavor after swallowing (lasting flavor)) determined by six panelists, were compared. The e-nose values of many of the tested cheese types were significantly different, whereas the sensory scores of the one or two types of processed cheese containing blue cheese and those of the natural cheese were significantly different. Significant correlations were observed between the means of e-nose values and the medians of aroma intensity scores derived from the sensory evaluation testing before intake, during mastication, and after swallowing. In particular, the aroma intensity score during mastication was found to have a linear relationship with the e-nose values (Pearson’s R = 0.983). In conclusion, the e-nose values correlated with the sensory scores with respect to cheese aroma intensity and could be helpful in predicting them.
Biomechanical Characteristics of Vertical Jumping of Preschool Children in China Based on Motion Capture and Simulation Modeling
Authors: Zhao Panchao , Ji Zhongqiu , Wen Ruixiang , Li Jiahui , Liang Xiao , Jiang Guiping
Published: December 2021

Vertical jumping is one of the basic motor skills, and it is an essential part of many sports. The main purpose of this paper is to investigate characteristics of vertical jumping of children. This paper uses a motion capture system, three-dimensional platforms, and a simulation modeling system to analyze the kinematics and dynamics performance of children’s vertical jumping. The compression time increases from 3 to 4 years old, and flight height and time increases with age and stage gradually. In the compression phase and pushing phase, the hip and knee joint play a major role; in the landing phase, the knee and ankle joint play a major role. Muscle forces are mainly affected by age, and the three types of muscle force had two different trends. The muscle force of the shank and thigh increased with age, and the pelvic girdle muscles showed an “low–high–low” trend. The regression model suggests that the force of GMiP and the hip angular velocity have a great influence on jumping ability. Therefore, if we want to improve the jumping ability of preschool children, we should pay more attention to hip exercises. We should integrate the hip exercises into interesting games, which are more in line with their physical and mental health.
Comparative Study of γ- and e-Radiation-Induced Effects on FBGs Using Different Femtosecond Laser Inscription Methods
Authors: Theodosiou Antreas , Leal-Junior Arnaldo , Marques Carlos , Frizera Anselmo , Fernandes Antonio J. S. , Stancalie Andrei , Ioannou Andreas , Ighigeanu Daniel , Mihalcea Razvan , Negut Constantin Daniel , Kalli Kyriacos
Published: December 2021

This work presents an extensive, comparative study of the gamma and electron radiation effects on the behaviour of femtosecond laser-inscribed fibre Bragg gratings (FBGs) using the point-by-point and plane-by-plane inscription methods. The FBGs were inscribed in standard telecommunication single mode silica fibre (SMF28) and exposed to a total accumulated radiation dose of 15 kGy for both gamma and electron radiation. The gratings’ spectra were measured and analysed before and after the exposure to radiation, with complementary material characterisation using Fourier transform infrared (FTIR) spectroscopy. Changes in the response of the FBGs’ temperature coefficients were analysed on exposure to the different types of radiation, and we consider which of the two inscription methods result in gratings that are more robust in such harsh environments. Moreover, we used the FTIR spectroscopy to locate which chemical bonds are responsible for the changes on temperature coefficients and which are related with the optical characteristics of the FBGs.
A Coupled Piezoelectric Sensor for MMG-Based Human-Machine Interfaces
Authors: Szumilas Mateusz , Władziński Michał , Wildner Krzysztof
Published: December 2021

Mechanomyography (MMG) is a technique of recording muscles activity that may be considered a suitable choice for human–machine interfaces (HMI). The design of sensors used for MMG and their spatial distribution are among the deciding factors behind their successful implementation to HMI. We present a new design of a MMG sensor, which consists of two coupled piezoelectric discs in a single housing. The sensor’s functionality was verified in two experimental setups related to typical MMG applications: an estimation of the force/MMG relationship under static conditions and a neural network-based gesture classification. The results showed exponential relationships between acquired MMG and exerted force (for up to 60% of the maximal voluntary contraction) alongside good classification accuracy (94.3%) of eight hand motions based on MMG from a single-site acquisition at the forearm. The simplification of the MMG-based HMI interface in terms of spatial arrangement is rendered possible with the designed sensor.
Real-Time 3D Object Detection and SLAM Fusion in a Low-Cost LiDAR Test Vehicle Setup
Authors: Fernandes Duarte , Afonso Tiago , Girão Pedro , Gonzalez Dibet , Silva António , Névoa Rafael , Novais Paulo , Monteiro João , Melo-Pinto Pedro
Published: December 2021

Recently released research about deep learning applications related to perception for autonomous driving focuses heavily on the usage of LiDAR point cloud data as input for the neural networks, highlighting the importance of LiDAR technology in the field of Autonomous Driving (AD). In this sense, a great percentage of the vehicle platforms used to create the datasets released for the development of these neural networks, as well as some AD commercial solutions available on the market, heavily invest in an array of sensors, including a large number of sensors as well as several sensor modalities. However, these costs create a barrier to entry for low-cost solutions for the performance of critical perception tasks such as Object Detection and SLAM. This paper explores current vehicle platforms and proposes a low-cost, LiDAR-based test vehicle platform capable of running critical perception tasks (Object Detection and SLAM) in real time. Additionally, we propose the creation of a deep learning-based inference model for Object Detection deployed in a resource-constrained device, as well as a graph-based SLAM implementation, providing important considerations, explored while taking into account the real-time processing requirement and presenting relevant results demonstrating the usability of the developed work in the context of the proposed low-cost platform.
Can Formal Security Verification Really Be Optional? Scrutinizing the Security of IMD Authentication Protocols
Authors: Duguma Daniel Gerbi , You Ilsun , Gebremariam Yonas Engida , Kim Jiyoon
Published: December 2021

The need for continuous monitoring of physiological information of critical organs of the human body, combined with the ever-growing field of electronics and sensor technologies and the vast opportunities brought by 5G connectivity, have made implantable medical devices (IMDs) the most necessitated devices in the health arena. IMDs are very sensitive since they are implanted in the human body, and the patients depend on them for the proper functioning of their vital organs. Simultaneously, they are intrinsically vulnerable to several attacks mainly due to their resource limitations and the wireless channel utilized for data transmission. Hence, failing to secure them would put the patient’s life in jeopardy and damage the reputations of the manufacturers. To date, various researchers have proposed different countermeasures to keep the confidentiality, integrity, and availability of IMD systems with privacy and safety specifications. Despite the appreciated efforts made by the research community, there are issues with these proposed solutions. Principally, there are at least three critical problems. (1) Inadequate essential capabilities (such as emergency authentication, key update mechanism, anonymity, and adaptability); (2) heavy computational and communication overheads; and (3) lack of rigorous formal security verification. Motivated by this, we have thoroughly analyzed the current IMD authentication protocols by utilizing two formal approaches: the Burrows–Abadi–Needham logic (BAN logic) and the Automated Validation of Internet Security Protocols and Applications (AVISPA). In addition, we compared these schemes against their security strengths, computational overheads, latency, and other vital features, such as emergency authentications, key update mechanisms, and adaptabilities.
WildGait: Learning Gait Representations from Raw Surveillance Streams
Authors: Cosma Adrian , Radoi Ion Emilian
Published: December 2021

The use of gait for person identification has important advantages such as being non-invasive, unobtrusive, not requiring cooperation and being less likely to be obscured compared to other biometrics. Existing methods for gait recognition require cooperative gait scenarios, in which a single person is walking multiple times in a straight line in front of a camera. We address the challenges of real-world scenarios in which camera feeds capture multiple people, who in most cases pass in front of the camera only once. We address privacy concerns by using only motion information of walking individuals, with no identifiable appearance-based information. As such, we propose a self-supervised learning framework, WildGait, which consists of pre-training a Spatio-Temporal Graph Convolutional Network on a large number of automatically annotated skeleton sequences obtained from raw, real-world surveillance streams to learn useful gait signatures. We collected and compiled the largest pretraining dataset to date of anonymized walking skeletons called Uncooperative Wild Gait, containing over 38k tracklets of anonymized walking 2D skeletons. We make the dataset available to the research community. Our results surpass the current state-of-the-art pose-based gait recognition solutions. Our proposed method is reliable in training gait recognition methods in unconstrained environments, especially in settings with scarce amounts of annotated data.
Recognizing Full-Body Exercise Execution Errors Using the Teslasuit
Authors: Caserman Polona , Krug Clemens , Göbel Stefan
Published: December 2021

Regular physical exercise is essential for overall health; however, it is also crucial to mitigate the probability of injuries due to incorrect exercise executions. Existing health or fitness applications often neglect accurate full-body motion recognition and focus on a single body part. Furthermore, they often detect only specific errors or provide feedback first after the execution. This lack raises the necessity for the automated detection of full-body execution errors in real-time to assist users in correcting motor skills. To address this challenge, we propose a method for movement assessment using a full-body haptic motion capture suit. We train probabilistic movement models using the data of 10 inertial sensors to detect exercise execution errors. Additionally, we provide haptic feedback, employing transcutaneous electrical nerve stimulation immediately, as soon as an error occurs, to correct the movements. The results based on a dataset collected from 15 subjects show that our approach can detect severe movement execution errors directly during the workout and provide haptic feedback at respective body locations. These results suggest that a haptic full-body motion capture suit, such as the Teslasuit, is promising for movement assessment and can give appropriate haptic feedback to the users so that they can improve their movements.
Assessment of Air Quality Perception and Its Effects on Users’ Thermal Comfort in Office Buildings
Authors: Oliveira Candi Citadini de , Rupp Ricardo Forgiarini , Ghisi Enedir
Published: December 2021

Since people spend most of their time in indoor environments, the objective of this work was to study indoor air quality and its effects on users’ thermal comfort. Based on previous data from a building with a central air-conditioning system and two mixed-mode buildings located in the humid subtropical climate of Florianópolis, southern Brazil, statistical analyses were performed. Each user’s subjective answer obtained through a questionnaire was combined with the corresponding environmental conditions measured by instruments. Results showed that improvement in air quality was associated with the reduction of air temperature and humidity ratio. Also, there was a significant influence of thermal and humidity sensation on air quality satisfaction. Users felt more satisfied or neutral with air quality for being in thermal comfort, and not because of the CO2 concentration—which means that air quality perception is influenced by factors other than CO2. This study recommends implementing an air exchange device in split air-conditioners with air recirculation commonly used in mixed-mode buildings in Brazil. It is important to provide suitable indoor ventilation to reduce pollutant concentration, ensure good air quality and prevent respiratory diseases.
The Contribution of Socio-Demographic Factors to Walking Behavior Considering Destination Types; Case Study: Temuco, Chile
Authors: Paydar Mohammad , Fard Asal Kamani
Published: December 2021

The improvement of walking contributes to maintain the minimum rate of physical activity and therefore the residents’ public health. This is while the amount of walking in daily transport of inhabitants has significantly decreased during the last decades in Temuco, Chile. In addition, the impact of socio-demographic as well as social factors on the improvement of walking behavior has been paid less attention in previous studies. This study aims to examine the contribution of socio-demographic factors and the active family environment to walking behavior as well as walking level based on three types of destinations in Temuco. The results of “Encuesta Origin Destino” (EOD) in Temuco were used to examine the objectives. It was found that a high percentage of people who walk in Temuco belong to low-income families. Moreover, most of the people who walk, do not have a driver’s license nor private cars in their household. Certain socio-demographic factors such as age, gender, driver’s license, private cars, and access to TV were found to have associations with overall walking behavior as well as walking level depending on three different destination types. Furthermore, the correlation was found between walking behavior and active family environment. These findings were discussed and their implications were addressed as well.
Gender Equality, Human Development, and PISA Results over Time
Authors: Campbell Janine Anne , McIntyre Joseph , Kucirkova Natalia
Published: December 2021

Gender equality through the empowerment, representation, and provision of equal opportunities for all, regardless of gender, is increasingly recognised as a driver of social outcomes and a fundamental human right. This study explores the longitudinal (2006–2018) relationship between gender equality, human development, and education results as measured by PISA. Gender equality and human development are consistently correlated with PISA scores at each time point; however, when controlling for starting values and country effects only change in gender equality positively predicts change in PISA scores (F = 22.6, p < 0.001, R2 = 0.58). Research and policy implications for the longitudinal interpretation of the relationship of PISA results with system-level factors as well as the relationship between gender equality and education are discussed in this paper. In consideration of the impact of COVID-19 on education and gender equality, the findings from this study support continued political effort towards gender-equal human development in a post-COVID-19 world.
Development of SnO2 Composites as Electron Transport Layer in Unencapsulated CH3NH3PbI3 Solar Cells
Authors: Sannino Gennaro V. , De Maria Antonella , La Ferrara Vera , Rametta Gabriella , Mercaldo Lucia V. , Addonizio Maria Luisa , Lancellotti Laura , Pecoraro Adriana , Muñoz-García Ana B. , Pavone Michele , Delli Veneri Paola
Published: December 2021

Improving morphological and electronic properties of the electron transport layer (ETL) is a critical issue to fabricate highly efficient perovskite solar cells. Tin dioxide is used as an ETL for its peculiarities such as low-temperature solution-process and high electron mobility and several handlings have been tested to increase its performances. Herein, SnO2:ZnO and SnO2:In2O3 composites are studied as ETL in planar n-i-p CH3NH3PbI3 solar cells fabricated in ambient air, starting from glass/ITO substrates. Morphological, electrical and optical properties of zinc- and indium-oxide nanoparticles (NPs) are investigated. First-principle calculations are also reported and help to further explain the experimental evidences. Photovoltaic performances of full devices show an improvement in efficiency for SnO2:In2O3–based solar cells with respect to pristine SnO2, probably due to a suppression of interfacial charge recombination between ITO/ETL and ETL/perovskite. Moreover, a better homogeneity of SnO2:In2O3 deposition with respect to SnO2:ZnO composites, conducts an increase in perovskite grain size and, consequently, the device performances.
Development of Sustainable Energy Use with Attention to Fruitful Policy
Authors: Razmjoo Armin , Rezaei Mostafa , Mirjalili Seyedali , Majidi Nezhad Meysam , Piras Giuseppe
Published: December 2021

There are different energy approaches around the world to the development of sustainable energy systems. In this regard, the role of governments, local governments, and people in the development and use of sustainable energy is remarkable. This research, concerning the present epistemic and normative differences, aims to investigate the societal debate on citizen inclusion, local and national attempts to develop clear procedures and guidelines in the transition to sustainable energy use in different countries. Existing theories, subjectivities, and policy implications for different countries are first carefully analyzed. Based on theories, evidence, and policy implications, the behavioural insights for sustainable energy use are then examined. The results show that national governments should never ignore the psychology and behaviour of people, especially in terms of economic behaviour, performance applicable and knowledge of local governments and people in sustainable energy development. Channels of communication between local, people, and national governments, can make a robust shared network and implement simple policies such as increasing their authority. They can also encourage and build capacity through the training, support, trust and knowledge capacity of local governments and people to move toward sustainable energy development. Therefore, focusing on government and maintaining national authority should be departed from any approaches that local government and the public should be constrained as minor actors in sustainable energy governance networks. This work demonstrates that local governments can develop sustainable energy. Moreover, national governments can overcome issues and further control sustainable energy public policy goals under difficult national political conditions.
Spatio-Temporal Fish Catch Assessments Using Fishing Vessel Trajectories and Coastal Fish Landing Data from around Jeju Island
Authors: Owiredu Solomon Amoah , Kim Kwang-Il
Published: December 2021

While pressure on marine ecosystems leading to declines in global fish catches have been attributed to excessive fishing and to unregulated and unreported fishing, existing management practices have yet to fully address these declines. Estimation of spatial and temporal distribution of fisheries resources and the extent of fishing impacts on marine ecosystems using vessel trajectories has become central in recent studies. This study proposed the use of trajectories of 771 Korean coastal and offshore fishing vessels and one-year fish landing data to estimate variations in commercial fish species, vessel, and fishing gear activity distributions in the waters around Jeju island. A set of standards were applied to identify individual fishing tracks of major gears and uniformly distributed catch to fishing segments of trajectories to produce spatio-temporal distributions of catch, fishing activities, and vessel reliance on fishing grounds at a fine spatial scale. The method identified reference points that can inform management at local and regional scales. We discuss the opportunities of combining larger datasets collected over a longer period and applying predictive modeling techniques in making extensive assessments, including climate change impacts on fishing activities that can inform resource management and marine spatial planning.
Approaching Healthy City Ontology: First-Level Classes Definition Using BFO
Authors: Pietra Caterina , De Lotto Roberto , Bahshwan Rakan
Published: December 2021

In recent decades, the concept of the healthy city (HC) has become more and more relevant in many fields, such as city administration and scientific environment, and has become a commonly understood concept in the general public. Due to the breakneck growth of people living in urban contexts, the subsequent necessity to guarantee good urban conditions for all kinds of citizens, and the general deterioration of the hearth environment caused by human activities (concentrated in urban settlements), this issue is increasing in its relevance. In this paper, the authors discuss the concept of the HC from an ontological point of view to organize the highly complex system of elements and the mutual relations that constitute the idea of HC. The main goals of an HC are quite intuitive, but the number of components that define and manage it is vast and related to different disciplines: sustainability, urban management, urban planning, and health and social studies. With the presented research, the authors intend to start an organizational definition of the HC using basic formal ontology (BFO). Considering the definition of HC, the authors focus on the ontology process and the different typologies of ontological structures. Then, the authors describe a first-level scheme of HC ontology and, finally, discuss possible applications of the presented study and next research steps.
Evaluation of Outlier Filtering Algorithms for Accurate Travel Time Measurement Incorporating Lane-Splitting Situations
Authors: Asqool Obada , Koting Suhana , Saifizul Ahmad
Published: December 2021

Malaysia has a high percentage of motorcycles. Due to lane-splitting, travel times of motorcycles are less than passenger cars at congestion. Because of this, collecting travel times using the media access control (MAC) address is not straightforward. Many outlier filtering algorithms for travel time datasets have not been evaluated for their capability to filter lane-splitting observations. This study aims to identify the best travel time filtering algorithms for the data containing lane-splitting observations and how to use the best algorithm. Two stages were adopted to achieve the objective of the study. The first stage validates the performance of the previous algorithms, and the second stage checks the sensitivity of the algorithm parameters for different days. The analysis uses the travel time data for three routes in Kuala Lumpur collected by Wi-Fi detectors in May 2018. The results show that the Jang algorithm has the best performance for two of the three routes, and the TransGuide algorithm is the best algorithm for one route. However, the parameters of Jang and TransGuide algorithms are sensitive for different days, and the parameters require daily calibration to obtain acceptable results. Using proper calibration of the algorithm parameters, the Jang and TransGuide algorithms produced the most accurate filtered travel time datasets compared to other algorithms