bluuebunny/crossref_metadata_2025 huggingface dataset:
A Locally Advanced Endometrioid Adenocarcinoma Arising from Vaginal Endometriosis: Management and Review of the Literature
Authors: Costanza Mariangela , Herrera Fernanda , Hastir Delfyne , Mathevet Patrice , Sarivalasis Apostolos
Published: September 2021

Endometrioid adenocarcinoma associated with endometriosis at extrauterine or extraovarian localization is a rare entity. Often presenting with local spread without nodal and distant metastasis, this entity has no specific staging system nor treatment guidelines. In the case of nodal and distant spread, the treatment decision requires personalization. In this article, we present the diagnosis and surgical and systemic treatment of a 56-year-old woman diagnosed with an endometriosis-associated advanced endometrioid adenocarcinoma of the vagina with nodal involvement. Following an extensive review of the scarce data reported to guide the treatment choices in this rare setting, we proposed a multidisciplinary treatment with laparoscopic surgical cytoreduction, four cycles of adjuvant chemotherapy with carboplatin and paclitaxel, and radiotherapy with brachytherapy. Due to an anaphylactic reaction on the first administration, paclitaxel was replaced with nab-paclitaxel. Despite many negative prognostic factors, the patient is free from relapse after 48 months. We report the case of a locally advanced endometrioid adenocarcinoma associated with endometriosis of the vagina, with pelvic nodal spread, and the relevant literature review of similar cases.
GIS-Based Forest Fire Risk Model: A Case Study in Laoshan National Forest Park, Nanjing
Authors: Zhao Pengcheng , Zhang Fuquan , Lin Haifeng , Xu Shuwen
Published: September 2021

Fire risk prediction is significant for fire prevention and fire resource allocation. Fire risk maps are effective methods for quantifying regional fire risk. Laoshan National Forest Park has many precious natural resources and tourist attractions, but there is no fire risk assessment model. This paper aims to construct the forest fire risk map for Nanjing Laoshan National Forest Park. The forest fire risk model is constructed by factors (altitude, aspect, topographic wetness index, slope, distance to roads and populated areas, normalized difference vegetation index, and temperature) which have a great influence on the probability of inducing fire in Laoshan. Since the importance of factors in different study areas is inconsistent, it is necessary to calculate the significance of each factor of Laoshan. After the significance calculation is completed, the fire risk model of Laoshan can be obtained. Then, the fire risk map can be plotted based on the model. This fire risk map can clarify the fire risk level of each part of the study area, with 16.97% extremely low risk, 48.32% low risk, 17.35% moderate risk, 12.74% high risk and 4.62% extremely high risk, and it is compared with the data of MODIS fire anomaly point. The result shows that the accuracy of the risk map is 76.65%.
CW Direct Detection Lidar with a Large Dynamic Range of Wind Speed Sensing in a Remote and Spatially Confined Volume
Authors: Meng Lichun , Pedersen Christian , Rodrigo Peter John
Published: September 2021

A novel continuous-wave (CW) direct detection lidar (DDL) is demonstrated to be capable of wind speed measurement 40 m away with an update rate of 4 Hz using a fiber-based scanning Fabry–Perot interferometer as an optical frequency discriminator. The proposed CW DDL has a large dynamic wind speed range with no sign ambiguity and its sensitivity is assessed by comparing its performance with that of a CW coherent detection lidar (CDL) in a side-by-side wind measurement. A theoretical model of the spatial weighting function of the fiber-based CW DDL is also presented and validated experimentally. This work shows that the CW DDL has a spatially confined measurement volume with a Lorentzian axial profile similar to that of a CW CDL. The proposed DDL has potential use in various applications in which requirements such as high-speed wind sensing and directional discrimination are not met by state-of-the-art Doppler wind lidar systems.
Smoke Detection from Himawari-8 Satellite Data over Kalimantan Island Using Multilayer Perceptrons
Authors: Mo Yuhao , Yang Xin , Tang Hong , Li Zhigang
Published: September 2021

The smoke from biomass burning on Kalimantan Island has caused severe environmental problems in Southeast Asia’s primary burning regions and surrounding regions due to the overspread haze. To monitor the biomass burning aerosol plumes on Kalimantan Island, the high-temporal-resolution Himawari-8 satellite data were used in this study. However, studies are limited on smoke detection using satellite remote sensing for Kalimantan Island because of the difficulty caused by frequently occurring clouds and the lack of prior knowledge on applying traditional threshold methods. In this study, we used the multilayer perceptron (MLP) method to identify smoke over Kalimantan Island in August 2015, one of the most severe fire seasons. To prepare sufficient supervision information, a pixel-level labeled dataset was established based on the Himawari-8 data. Based on the labeled dataset, three MLP approaches and two sampling methods were applied to create training samples. A comparison between the detection results for the MLP approaches and classification tree analysis (i.e., CTA) showed that MLP is superior to CTA. The visualization results also showed that the detected smoke areas included those mixed with clouds. Some detected smoke is difficult to identify by the human eye, suggesting that the explanatory dataset built for this study is sufficiently comprehensive. Therefore, the pixel-level labeled dataset and MLP are suitable for regions that are frequently cloud-covered.
Response of Vegetation Photosynthetic Phenology to Urbanization in Dongting Lake Basin, China
Authors: Li Peng , Sun Mai , Liu Yuxin , Ren Peixin , Peng Changhui , Zhou Xiaolu , Tang Jiayi
Published: September 2021

Urbanization can induce environmental changes such as the urban heat island effect, which in turn influence the terrestrial ecosystem. However, the effect of urbanization on the phenology of subtropical vegetation remains relatively unexplored. This study analyzed the changing trend of vegetation photosynthetic phenology in Dongting Lake basin, China, and its response to urbanization using nighttime light and chlorophyll fluorescence datasets. Our results indicated the start of the growing season (SOS) of vegetation in the study area was significantly advanced by 0.70 days per year, whereas the end of the growing season (EOS) was delayed by 0.24 days per year during 2000–2017. We found that urbanization promoted the SOS advance and EOS delay. With increasing urbanization intensity, the sensitivity of SOS to urbanization firstly increased then decreased, while the sensitivity of EOS to urbanization decreased with urbanization intensity. The climate sensitivity of vegetation phenology varied with urbanization intensity; urbanization induced an earlier SOS by increasing preseason minimum temperatures and a later EOS by increasing preseason precipitation. These findings improve our understanding of the vegetation phenology response to urbanization in subtropical regions and highlight the need to integrate human activities into future vegetation phenology models.
Mapping Forest Vertical Structure in Sogwang-ri Forest from Full-Waveform Lidar Point Clouds Using Deep Neural Network
Authors: Park Sung-Hwan , Jung Hyung-Sup , Lee Sunmin , Kim Eun-Sook
Published: September 2021

The role of forests is increasing because of rapid land use changes worldwide that have implications on ecosystems and the carbon cycle. Therefore, it is necessary to obtain accurate information about forests and build forest inventories. However, it is difficult to assess the internal structure of the forest through 2D remote sensing techniques and fieldwork. In this aspect, we proposed a method for estimating the vertical structure of forests based on full-waveform light detection and ranging (FW LiDAR) data in this study. Voxel-based tree point density maps were generated by estimating the number of canopy height points in each voxel grid from the raster digital terrain model (DTM) and canopy height points after pre-processing the LiDAR point clouds. We applied an unsupervised classification algorithm to the voxel-based tree point density maps and identified seven classes by profile pattern analysis for the forest vertical types. The classification accuracy was found to be 72.73% from the validation from 11 field investigation sites, which was additionally confirmed through comparative analysis with aerial images. Based on this pre-classification reference map, which is assumed to be ground truths, the deep neural network (DNN) model was finally applied to perform the final classification. As a result of accuracy assessment, it showed accuracy of 92.72% with a good performance. These results demonstrate the potential of vertical structure estimation for extensive forests using FW LiDAR data and that the distinction between one-storied and two-storied forests can be clearly represented. This technique is expected to contribute to efficient and effective management of forests based on accurate information derived from the proposed method.
Retrieving High-Resolution Aerosol Optical Depth from GF-4 PMS Imagery in Eastern China
Authors: Sun Zhendong , Wei Jing , Zhang Ning , He Yulong , Sun Yu , Liu Xirong , Yu Huiyong , Sun Lin
Published: September 2021

Gaofen 4 (GF-4) is a geostationary satellite, with a panchromatic and multispectral sensor (PMS) onboard, and has great potential in observing atmospheric aerosols. In this study, we developed an aerosol optical depth (AOD) retrieval algorithm for the GF-4 satellite. AOD retrieval was realized based on the pre-calculated surface reflectance database and 6S radiative transfer model. We customized the unique aerosol type according to the long time series aerosol parameters provided by the Aerosol Robotic Network (AERONET) site. The solar zenith angle, relative azimuth angle, and satellite zenith angle of the GF-4 panchromatic multispectral sensor image were calculated pixel-by-pixel. Our 1 km AOD retrievals were validated against AERONET Version 3 measurements and compared with MOD04 C6 AOD products at different resolutions. The results showed that our GF-4 AOD algorithm had a good robustness in both bright urban areas and dark rural areas. A total of 71.33% of the AOD retrievals fell within the expected errors of ±(0.05% + 20%); root-mean-square error (RMSE) and mean absolute error (MAE) were 0.922 and 0.122, respectively. The accuracy of GF-4 AOD in rural areas was slightly higher than that in urban areas. In comparison with MOD04 products, the accuracy of GF-4 AOD was much higher than that of MOD04 3 km and 10 km dark target AOD, but slightly worse than that of MOD04 10 km deep blue AOD. For different values of land surface reflectance (LSR), the accuracy of GF-4 AOD gradually deteriorated with an increase in the LSR. These results have theoretical and practical significance for aerosol research and can improve retrieval algorithms using the GF-4 satellite.
Monitoring of Wheat Powdery Mildew under Different Nitrogen Input Levels Using Hyperspectral Remote Sensing
Authors: Liu Wei , Sun Chaofei , Zhao Yanan , Xu Fei , Song Yuli , Fan Jieru , Zhou Yilin , Xu Xiangming
Published: September 2021

Both wheat powdery mildew severities and nitrogen input levels can lead to changes in spectral reflectance, but they have been rarely studied simultaneously for their effect on spectral reflectance. To determine the effects and influences of different nitrogen input levels on monitoring wheat powdery mildew and estimating yield by near-ground hyperspectral remote sensing, Canopy hyperspectral reflectance data acquired at Feekes growth stage (GS) 10.5.3, 10.5.4, and 11.1 were used to monitor wheat powdery mildew and estimate grain yield under different nitrogen input levels during the 2016–2017, 2017–2018, 2018–2019 and 2019–2020 seasons. The relationships of powdery mildew and grain yield with vegetation indices (VIs) derived from spectral reflectance data across the visible (VIS) and near-infrared (NIR) regions of the spectrum were studied. The relationships of canopy spectral reflectance or first derivative spectral reflectance with powdery mildew did not differ under different nitrogen input levels. However, the dynamics of VIs differed in their sensitivities to nitrogen input levels, disease severity, grain yield, The area of the red edge peak (Σdr680–760 nm) was a better overall predictor for both disease severity and grain yield through linear regression models. The slope parameter estimates did not differ between the two nitrogen input levels at each GSs. Hyperspectral indices can be used to monitor wheat powdery mildew and estimate grain yield under different nitrogen input levels, but such models are dependent on GS and year, further research is needed to consider how to incorporate the growth stage and year-to-year variation into future applications.
Geometric Evolution of the Chongce Glacier during 1970–2020, Detected by Multi-Source Satellite Observations
Authors: Sun Yongling , Liu Lin , Pei Yuanyuan , Wang Kai
Published: September 2021

Glacier surge, which causes a quick movement of ice mass from high to low elevation, is closely associated to the glacial hazards of debris flows and glacial lake outburst floods. Over the West Kunlun Shan, surge events have been detected for some glaciers, however, the characteristics (e.g., the active phase) of the identified surge-type glaciers are not fully understood due to the paucity of long-term observations of glacier changes. In this study, we investigated the geometric evolution of the Chongce Glacier (a surge-type glacier) over the past five decades. Glacier elevation changes were observed by comparing topographic data from different times. Surface velocity and terminus position were derived using a cross-correlation algorithm and band ratio method, respectively. A decreasing rate of glacier surface thinning was found for the Chongce Glacier during the studied period. Glacier elevation changes of −0.46 ± 0.12, −0.12 ± 0.05, and 0.27 ± 0.11 m yr−1 were estimated for the periods of 1970–2000, 2000–2012, and 2012–2018, respectively. Moreover, this glacier experienced obvious surface lowering over the terminus zone and clear surface thickening over the upper zone during 1970–2000, and the opposite during 2000–2018. Surface velocity of the Chongce Glacier was less than 300 m yr−1 in 1990–1993, and then quickly increased to more than 1000 m yr−1 between 1994 and 1998, and dropped to less than 50 m yr−1 in 1999–2020. Over the past five decades, the Chongce Glacier generally experienced a slight retreat, except for a terminus advance from 1995 to 1999. According to the spatial pattern of glacier elevation changes in 1970–2000 and the long-term changes of glacier velocity and terminus position, the recent surge event at the Chongce Glacier likely initiated in winter 1993 and terminated in winter 1998. Furthermore, the start date, end date, and duration of the active phase indicate that the detected surge event was likely triggered by a thermal mechanism.
A High Latitude Model for the E Layer Dominated Ionosphere
Authors: Kamal Sumon , Jakowski Norbert , Hoque Mohammed Mainul , Wickert Jens
Published: September 2021

Under certain conditions, the ionization of the E layer can dominate over that of the F2 layer. This phenomenon is called the E layer dominated ionosphere (ELDI) and occurs mainly in the auroral regions. In the present work, we model the variation of the ELDI for the Northern and Southern Hemispheres. Our proposed Neustrelitz ELDI Event Model (NEEM) is an empirical, climatological model that describes ELDI characteristics by means of four submodels for selected model observables, considering the dependencies on appropriate model drivers. The observables include the occurrence probability of ELDI events and typical E layer parameters that are important to describe the propagation medium for High Frequency (HF) radio waves. The model drivers are the geomagnetic latitude, local time, day of year, solar activity and the convection electric field. During our investigation, we found clear trends for the model observables depending on the drivers, which can be well represented by parametric functions. In this regard, the submodel NEEM-N characterizes the peak electron density NmE of the E layer, while the submodels NEEM-H and NEEM-W describe the corresponding peak height hmE and the vertical width wvE of the E layer electron density profile, respectively. Furthermore, the submodel NEEM-P specifies the ELDI occurrence probability %ELDI. The dataset underlying our studies contains more than two million vertical electron density profiles covering a period of almost 13 years. These profiles were derived from ionospheric GPS radio occultation observations on board the six COSMIC/FORMOSAT-3 satellites (Constellation Observing System for Meteorology, Ionosphere and Climate/Formosa Satellite Mission 3). We divided the dataset into a modeling dataset for determining the model coefficients and a test dataset for subsequent model validation. The normalized root mean square deviation (NRMS) between the original and the predicted model observables yields similar values across both datasets and both hemispheres. For NEEM-N, we obtain an NRMS varying between 36.1% and 47.1% and for NEEM-H, between 6.1% and 6.3%. In the case of NEEM-W, the NRMS varies between 38.5% and 41.1%, while it varies between 56.5% and 60.3% for NEEM-P. In summary, the proposed NEEM utilizes primary relationships with geophysical and solar wind observables, which are useful for describing ELDI occurrences and the associated changes of the E layer properties. In this manner, the NEEM paves the way for future prediction of the ELDI and of its characteristics in technical applications, especially from the fields of telecommunications and navigation.
Mapping Canopy Heights in Dense Tropical Forests Using Low-Cost UAV-Derived Photogrammetric Point Clouds and Machine Learning Approaches
Authors: Zhang He , Bauters Marijn , Boeckx Pascal , Van Oost Kristof
Published: September 2021

Tropical forests are a key component of the global carbon cycle and climate change mitigation. Field- or LiDAR-based approaches enable reliable measurements of the structure and above-ground biomass (AGB) of tropical forests. Data derived from digital aerial photogrammetry (DAP) on the unmanned aerial vehicle (UAV) platform offer several advantages over field- and LiDAR-based approaches in terms of scale and efficiency, and DAP has been presented as a viable and economical alternative in boreal or deciduous forests. However, detecting with DAP the ground in dense tropical forests, which is required for the estimation of canopy height, is currently considered highly challenging. To address this issue, we present a generally applicable method that is based on machine learning methods to identify the forest floor in DAP-derived point clouds of dense tropical forests. We capitalize on the DAP-derived high-resolution vertical forest structure to inform ground detection. We conducted UAV-DAP surveys combined with field inventories in the tropical forest of the Congo Basin. Using airborne LiDAR (ALS) for ground truthing, we present a canopy height model (CHM) generation workflow that constitutes the detection, classification and interpolation of ground points using a combination of local minima filters, supervised machine learning algorithms and TIN densification for classifying ground points using spectral and geometrical features from the UAV-based 3D data. We demonstrate that our DAP-based method provides estimates of tree heights that are identical to LiDAR-based approaches (conservatively estimated NSE = 0.88, RMSE = 1.6 m). An external validation shows that our method is capable of providing accurate and precise estimates of tree heights and AGB in dense tropical forests (DAP vs. field inventories of old forest: r2 = 0.913, RMSE = 31.93 Mg ha−1). Overall, this study demonstrates that the application of cheap and easily deployable UAV-DAP platforms can be deployed without expert knowledge to generate biophysical information and advance the study and monitoring of dense tropical forests.
Temporal and Spatial Autocorrelation as Determinants of Regional AOD-PM2.5 Model Performance in the Middle East
Authors: Chau Khang , Franklin Meredith , Lee Huikyo , Garay Michael , Kalashnikova Olga
Published: September 2021

Exposure to fine particulate matter (PM2.5) air pollution has been shown in numerous studies to be associated with detrimental health effects. However, the ability to conduct epidemiological assessments can be limited due to challenges in generating reliable PM2.5 estimates, particularly in parts of the world such as the Middle East where measurements are scarce and extreme meteorological events such as sandstorms are frequent. In order to supplement exposure modeling efforts under such conditions, satellite-retrieved aerosol optical depth (AOD) has proven to be useful due to its global coverage. By using AODs from the Multiangle Implementation of Atmospheric Correction (MAIAC) of the MODerate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging Spectroradiometer (MISR) combined with meteorological and assimilated aerosol information from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), we constructed machine learning models to predict PM2.5 in the area surrounding the Persian Gulf, including Kuwait, Bahrain, and the United Arab Emirates (U.A.E). Our models showed regional differences in predictive performance, with better results in the U.A.E. (median test R2 = 0.66) than Kuwait (median test R2 = 0.51). Variable importance also differed by region, where satellite-retrieved AOD variables were more important for predicting PM2.5 in Kuwait than in the U.A.E. Divergent trends in the temporal and spatial autocorrelations of PM2.5 and AOD in the two regions offered possible explanations for differences in predictive performance and variable importance. In a test of model transferability, we found that models trained in one region and applied to another did not predict PM2.5 well, even if the transferred model had better performance. Overall the results of our study suggest that models developed over large geographic areas could generate PM2.5 estimates with greater uncertainty than could be obtained by taking a regional modeling approach. Furthermore, development of methods to better incorporate spatial and temporal autocorrelations in machine learning models warrants further examination.
Dynamic Divide Grouping Non-Orthogonal Multiple Access in Terrestrial-Satellite Integrated Network
Authors: Yan Yanjun , Xu Huihui , Zhang Ning , Han Guangjie , Liu Mingliu
Published: September 2021

Non-orthogonal multiple access (NOMA) has been extensively studied to improve the performance of the Terrestrial-Satellite Integrated Network (TSIN) on account of the shortage of frequency band resources. In this paper, the terrestrial network and satellite network synergistically provide complete coverage for ground users, and based on the architecture, we first formulate a constrained optimization problem to maximize the sum rate of the TSIN under the limited spectrum resources. As the terrestrial networks and the satellite network will cause interference to each other, we first investigate the capacity performance of the terrestrial networks and the satellite networks separately, in which the optimal power control factor expression is derived. Then, by constructing the relationship model between user elevation angle, beam angle and distance, we develop a dynamic group pairing schemes to ensure the effective pairing of NOMA users. Based on the user pairing, to obtain the optimal resource allocation, a joint optimization algorithm of power allocation, beam channel and base station channel resource is proposed. Finally, simulation results are provided to evaluate the user paring scheme as well as the total system performance, in comparison with the existing works.
Use of Multiple Low Cost Carbon Dioxide Sensors to Measure Exhaled Breath Distribution with Face Mask Type and Wearing Behaviour
Authors: Salman Naveed , Khan Muhammad Waqas , Lim Michael , Khan Amir , Kemp Andrew H. , Noakes Catherine J.
Published: September 2021

The use of cloth face coverings and face masks has become widespread in light of the COVID-19 pandemic. This paper presents a method of using low cost wirelessly connected carbon dioxide (CO2) sensors to measure the effects of properly and improperly worn face masks on the concentration distribution of exhaled breath around the face. Four types of face masks are used in two indoor environment scenarios. CO2 as a proxy for exhaled breath is being measured with the Sensirion SCD30 CO2 sensor, and data are being transferred wirelessly to a base station. The exhaled CO2 is measured in four directions at various distances from the head of the subject, and interpolated to create spatial heat maps of CO2 concentration. Statistical analysis using the Friedman’s analysis of variance (ANOVA) test is carried out to determine the validity of the null hypotheses (i.e., distribution of the CO2 is same) between different experiment conditions. Results suggest CO2 concentrations vary little with the type of mask used; however, improper use of the face mask results in statistically different CO2 spatial distribution of concentration. The use of low cost sensors with a visual interpolation tool could provide an effective method of demonstrating the importance of proper mask wearing to the public.
Measuring Vehicle Profile Size: Lidar-Based System and K-Frame-Based Methodology
Authors: Zhang Qiang , Wang Zihao , Shao Jianwen , Weng Libo , Gao Fei
Published: September 2021

At present, light curtain is a widely-used method to measure the vehicle profile size. However, it is sensitive to temperature, humidity, dust and other weather factors. In this paper, a lidar-based system with a K-frame-based algorithm is proposed for measuring vehicle profile size. The system is composed of left lidar, right lidar, front lidar, control box and industry controlling computer. Within the system, a K-frame-based methodology is investigated, which include several probable algorithm combinations. Three groups of experiments are conducted. An optimal algorithm combination, A16, is determined through the first group experiments. In the second group experiments, various types of vehicles are chosen to verify the generality and repeatability of the proposed system and methodology. The third group experiments are implemented to compare with vision-based methods and other lidar-based methods. The experimental results show that the proposed K-frame-based methodology is far more accurate than the comparative methods.
JSCC-Cast: A Joint Source Channel Coding Video Encoding and Transmission System with Limited Digital Metadata
Authors: Balsa Jose , Fresnedo Óscar , García-Naya José A. , Domínguez-Bolaño Tomás , Castedo Luis
Published: September 2021

This work considers the design and practical implementation of JSCC-Cast, a comprehensive analog video encoding and transmission system requiring a reduced amount of digital metadata. Suitable applications for JSCC-Cast are multicast transmissions over time-varying channels and Internet of Things wireless connectivity of end devices having severe constraints on their computational capabilities. The proposed system exhibits a similar image quality compared to existing analog and hybrid encoding alternatives such as Softcast. Its design is based on the use of linear transforms that exploit the spatial and temporal redundancy and the analog encoding of the transformed coefficients with different protection levels depending on their relevance. JSCC-Cast is compared to Softcast, which is considered the benchmark for analog and hybrid video coding, and with an all-digital H.265-based encoder. The results show that, depending on the scenario and considering image quality metrics such as the structural similarity index measure, the peak signal-to-noise ratio, and the perceived quality of the video, JSCC-Cast exhibits a performance close to that of Softcast but with less metadata and not requiring a feedback channel in order to track channel variations. Moreover, in some circumstances, the JSCC-Cast obtains a perceived quality for the frames comparable to those displayed by the digital one.
A Method for Medical Data Analysis Using the LogNNet for Clinical Decision Support Systems and Edge Computing in Healthcare
Authors: Velichko Andrei
Published: September 2021

Edge computing is a fast-growing and much needed technology in healthcare. The problem of implementing artificial intelligence on edge devices is the complexity and high resource intensity of the most known neural network data analysis methods and algorithms. The difficulty of implementing these methods on low-power microcontrollers with small memory size calls for the development of new effective algorithms for neural networks. This study presents a new method for analyzing medical data based on the LogNNet neural network, which uses chaotic mappings to transform input information. The method effectively solves classification problems and calculates risk factors for the presence of a disease in a patient according to a set of medical health indicators. The efficiency of LogNNet in assessing perinatal risk is illustrated on cardiotocogram data obtained from the UC Irvine machine learning repository. The classification accuracy reaches ~91% with the~3–10 kB of RAM used on the Arduino microcontroller. Using the LogNNet network trained on a publicly available database of the Israeli Ministry of Health, a service concept for COVID-19 express testing is provided. A classification accuracy of ~95% is achieved, and~0.6 kB of RAM is used. In all examples, the model is tested using standard classification quality metrics: precision, recall, and F1-measure. The LogNNet architecture allows the implementation of artificial intelligence on medical peripherals of the Internet of Things with low RAM resources and can be used in clinical decision support systems.
Eventual Convergence of the Reputation-Based Algorithm in IoT Sensor Networks
Authors: Lebiedź Jacek , Cofta Piotr , Orłowski Cezary
Published: September 2021

Uncertainty in dense heterogeneous IoT sensor networks can be decreased by applying reputation-inspired algorithms, such as the EWMA (Exponentially Weighted Moving Average) algorithm, which is widely used in social networks. Despite its popularity, the eventual convergence of this algorithm for the purpose of IoT networks has not been widely studied, and results of simulations are often taken in lieu of the more rigorous proof. Therefore the question remains, whether under stable conditions, in realistic situations found in IoT networks, this algorithm indeed converges. This paper demonstrates proof of the eventual convergence of the EWMA algorithm. The proof consists of two steps: it models the sensor network as the UOG (Uniform Opinion Graph) that enables the analytical approach to the problem, and then offers the mathematical proof of eventual convergence, using formalizations identified in the previous step. The paper demonstrates that the EWMA algorithm converges under all realistic conditions.
Wireless Manipulation Mechanism and Analysis for Actively Assistive Pinch Movements
Authors: Ji Dong-Min , Jung Won-Suk , Kim Sung-Hoon
Published: September 2021

Pinching motions are important for holding and retaining objects with precision. Therefore, training exercises for the thumb and index finger are extremely important in the field of hand rehabilitation. Considering the need for training convenience, we developed a device and a driving system to assist pinching motions actively via a lightweight, simple, and wireless mechanism driven by the magnetic forces and torques generated by magnets attached to the tip of these two fingers. This device provides accurate pinching motions through the linking structures connecting the two magnets. The fabricated device has minimal mechanical elements with an ultralightweight of 57.2 g. The magnetic field, the intensity of which is based on the time variant, generates a pinching motion between the thumb and index finger, thus rendering it possible to achieve repetitive training. To verify the generation of an active pinching motion, we fabricated a finger model using a 3D printer and a rubber sheet and observed the active motions generated by the newly developed device. We also verified the performance of the proposed mechanism and driving method via various experiments and magnetic simulations. The proposed mechanism represents an important breakthrough for patients requiring hand rehabilitation and wearable assistive motion devices.
Towards Fully Autonomous UAVs: A Survey
Authors: Elmokadem Taha , Savkin Andrey V.
Published: September 2021

Unmanned Aerial Vehicles have undergone rapid developments in recent decades. This has made them very popular for various military and civilian applications allowing us to reach places that were previously hard to reach in addition to saving time and lives. A highly desirable direction when developing unmanned aerial vehicles is towards achieving fully autonomous missions and performing their dedicated tasks with minimum human interaction. Thus, this paper provides a survey of some of the recent developments in the field of unmanned aerial vehicles related to safe autonomous navigation, which is a very critical component in the whole system. A great part of this paper focus on advanced methods capable of producing three-dimensional avoidance maneuvers and safe trajectories. Research challenges related to unmanned aerial vehicle development are also highlighted.
Cybersecurity in Power Grids: Challenges and Opportunities
Authors: Krause Tim , Ernst Raphael , Klaer Benedikt , Hacker Immanuel , Henze Martin
Published: September 2021

Increasing volatilities within power transmission and distribution force power grid operators to amplify their use of communication infrastructure to monitor and control their grid. The resulting increase in communication creates a larger attack surface for malicious actors. Indeed, cyber attacks on power grids have already succeeded in causing temporary, large-scale blackouts in the recent past. In this paper, we analyze the communication infrastructure of power grids to derive resulting fundamental challenges of power grids with respect to cybersecurity. Based on these challenges, we identify a broad set of resulting attack vectors and attack scenarios that threaten the security of power grids. To address these challenges, we propose to rely on a defense-in-depth strategy, which encompasses measures for (i) device and application security, (ii) network security, and (iii) physical security, as well as (iv) policies, procedures, and awareness. For each of these categories, we distill and discuss a comprehensive set of state-of-the art approaches, as well as identify further opportunities to strengthen cybersecurity in interconnected power grids.
A Cost-Effective CNN-LSTM-Based Solution for Predicting Faulty Remote Water Meter Reading Devices in AMI Systems
Authors: Lee Jaeseung , Choi Woojin , Kim Jibum
Published: September 2021

Automatic meter infrastructure (AMI) systems using remote metering are being widely used to utilize water resources efficiently and minimize non-revenue water. We propose a convolutional neural network-long short-term memory network (CNN-LSTM)-based solution that can predict faulty remote water meter reading (RWMR) devices by analyzing approximately 2,850,000 AMI data collected from 2762 customers over 360 days in a small-sized city in South Korea. The AMI data used in this study is a challenging, highly unbalanced real-world dataset with limited features. First, we perform extensive preprocessing steps and extract meaningful features for handling this challenging dataset with limited features. Next, we select important features that have a higher influence on the classifier using a recursive feature elimination method. Finally, we apply the CNN-LSTM model for predicting faulty RWMR devices. We also propose an efficient training method for ML models to learn the unbalanced real-world AMI dataset. A cost-effective threshold for evaluating the performance of ML models is proposed by considering the mispredictions of ML models as well as the cost. Our experimental results show that an F-measure of 0.82 and MCC of 0.83 are obtained when the CNN-LSTM model is used for prediction.
Detection of Health-Related Events and Behaviours from Wearable Sensor Lifestyle Data Using Symbolic Intelligence: A Proof-of-Concept Application in the Care of Multiple Sclerosis
Authors: Stavropoulos Thanos G. , Meditskos Georgios , Lazarou Ioulietta , Mpaltadoros Lampros , Papagiannopoulos Sotirios , Tsolaki Magda , Kompatsiaris Ioannis
Published: September 2021

In this paper, we demonstrate the potential of a knowledge-driven framework to improve the efficiency and effectiveness of care through remote and intelligent assessment. More specifically, we present a rule-based approach to detect health related problems from wearable lifestyle sensor data that add clinical value to take informed decisions on follow-up and intervention. We use OWL 2 ontologies as the underlying knowledge representation formalism for modelling contextual information and high-level concepts and relations among them. The conceptual model of our framework is defined on top of existing modelling standards, such as SOSA and WADM, promoting the creation of interoperable knowledge graphs. On top of the symbolic knowledge graphs, we define a rule-based framework for infusing expert knowledge in the form of SHACL constraints and rules to recognise patterns, anomalies and situations of interest based on the predefined and stored rules and conditions. A dashboard visualizes both sensor data and detected events to facilitate clinical supervision and decision making. Preliminary results on the performance and scalability are presented, while a focus group of clinicians involved in an exploratory research study revealed their preferences and perspectives to shape future clinical research using the framework.
Respiratory Activity during Exercise: A Feasibility Study on Transition Point Estimation Using Impedance Pneumography
Authors: Młyńczak Marcel , Krysztofiak Hubert
Published: September 2021

The current diagnostic procedures for assessing physiological response to exercise comprise blood lactates measurements, ergospirometry, and electrocardiography. The first is not continuous, the second requires specialized equipment distorting natural breathing, and the last is indirect. Therefore, we decided to perform the feasibility study with impedance pneumography as an alternative technique. We attempted to determine points in respiratory-related signals, acquired during stress test conditions, that suggest a transition similar to the gas exchange threshold. In addition, we analyzed whether or not respiratory activity reaches steady states during graded exercise. Forty-four students (35 females), practicing sports on different levels, performed a graded exercise test until exhaustion on cycloergometer. Eventually, the results from 34 of them were used. The data were acquired with Pneumonitor 2. The signals demonstrated that the steady state phenomenon is not as evident as for heart rate. The results indicated respiratory rate approaches show the transition point at the earliest (more than 6 min before the end of the exercise test on average), and the tidal volume ones at the latest (less than 5 min). A combination gave intermediate findings. The results showed the impedance pneumography appears reasonable for the transition point estimation, but this should be further studied with the reference.
A Cipher Based on Prefix Codes
Authors: Grošek Otokar , Hromada Viliam , Horák Peter
Published: September 2021

A prefix code, a P-code, is a code where no codeword is a prefix of another codeword. In this paper, a symmetric cipher based on prefix codes is proposed. The simplicity of the design makes this cipher usable for Internet of Things applications. Our goal is to investigate the security of this cipher. A detailed analysis of the fundamental properties of P-codes shows that the keyspace of the cipher is too large to mount a brute-force attack. Specifically, in this regard we will find bounds on the number of minimal P-codes containing a binary word given in advance. Furthermore, the statistical attack is difficult to mount on such cryptosystem due to the attacker’s lack of information about the actual words used in the substitution mapping. The results of a statistical analysis of possible keys are also presented. It turns out that the distribution of the number of minimal P-codes over all binary words of a fixed length is Gaussian.
Pulse Rate Variability Analysis Using Remote Photoplethysmography Signals
Authors: Yu Su-Gyeong , Kim So-Eui , Kim Na Hye , Suh Kun Ha , Lee Eui Chul
Published: September 2021

Pulse rate variability (PRV) refers to the change in the interval between pulses in the blood volume pulse (BVP) signal acquired using photoplethysmography (PPG). PRV is an indicator of the health status of an individual’s autonomic nervous system. A representative method for measuring BVP is contact PPG (CPPG). CPPG may cause discomfort to a user, because the sensor is attached to the finger for measurements. In contrast, noncontact remote PPG (RPPG) extracts BVP signals from face data using a camera without the need for a sensor. However, because the existing RPPG is a technology that extracts a single pulse rate rather than a continuous BVP signal, it is difficult to extract additional health status indicators. Therefore, in this study, PRV analysis is performed using lab-based RPPG technology that can yield continuous BVP signals. In addition, we intended to confirm that the analysis of PRV via RPPG can be performed with the same quality as analysis via CPPG. The experimental results confirmed that the temporal and frequency parameters of PRV extracted from RPPG and CPPG were similar. In terms of correlation, the PRVs of RPPG and CPPG yielded correlation coefficients between 0.98 and 1.0.
Detecting Climate Driven Changes in Chlorophyll-a Using High Frequency Monitoring: The Impact of the 2019 European Heatwave in Three Contrasting Aquatic Systems
Authors: Free Gary , Bresciani Mariano , Pinardi Monica , Giardino Claudia , Alikas Krista , Kangro Kersti , Rõõm Eva-Ingrid , Vaičiūtė Diana , Bučas Martynas , Tiškus Edvinas , Hommersom Annelies , Laanen Marnix , Peters Steef
Published: September 2021

The frequency of heatwave events in Europe is increasing as a result of climate change. This can have implications for the water quality and ecological functioning of aquatic systems. We deployed three spectroradiometer WISPstations at three sites in Europe (Italy, Estonia, and Lithuania/Russia) to measure chlorophyll-a at high frequency. A heatwave in July 2019 occurred with record daily maximum temperatures over 40 °C in parts of Europe. The effects of the resulting storm that ended the heatwave were more discernable than the heatwave itself. Following the storm, chlorophyll-a concentrations increased markedly in two of the lakes and remained high for the duration of the summer while at one site concentrations increased linearly. Heatwaves and subsequent storms appeared to play an important role in structuring the phenology of the primary producers, with wider implications for lake functioning. Chlorophyll-a peaked in early September, after which a wind event dissipated concentrations until calmer conditions returned. Synoptic coordinated high frequency monitoring needs to be advanced in Europe as part of water management policy and to improve knowledge on the implications of climate change. Lakes, as dynamic ecosystems with fast moving species-succession, provide a prism to observe the scale of future change.
The Data Sensor Hub (DaSH): A Physical Computing System to Support Middle School Inquiry Science Instruction
Authors: Gendreau Chakarov Alexandra , Biddy Quentin , Hennessy Elliott Colin , Recker Mimi
Published: September 2021

This article describes a sensor-based physical computing system, called the Data Sensor Hub (DaSH), which enables students to process, analyze, and display data streams collected using a variety of sensors. The system is built around the portable and affordable BBC micro:bit microcontroller (expanded with the gator:bit), which students program using a visual, cloud-based programming environment intended for novices. Students connect a variety of sensors (measuring temperature, humidity, carbon dioxide, sound, acceleration, magnetism, etc.) and write programs to analyze and visualize the collected sensor data streams. The article also describes two instructional units intended for middle grade science classes that use this sensor-based system. These inquiry-oriented units engage students in designing the system to collect data from the world around them to investigate scientific phenomena of interest. The units are designed to help students develop the ability to meaningfully integrate computing as they engage in place-based learning activities while using tools that more closely approximate the practices of contemporary scientists as well as other STEM workers. Finally, the article articulates how the DaSH and units have elicited different kinds of teacher practices using student drawn modeling activities, facilitating debugging practices, and developing place-based science practices.
Use of Pressure-Measuring Insoles to Characterize Gait Parameters in Simulated Reduced-Gravity Conditions
Authors: Ison Christian , Neilsen Connor , DeBerardinis Jessica , Trabia Mohamed B. , Dufek Janet S.
Published: September 2021

Prior researchers have observed the effect of simulated reduced-gravity exercise. However, the extent to which lower-body positive-pressure treadmill (LBPPT) walking alters kinematic gait characteristics is not well understood. The purpose of the study was to investigate the effect of LBPPT walking on selected gait parameters in simulated reduced-gravity conditions. Twenty-nine college-aged volunteers participated in this cross-sectional study. Participants wore pressure-measuring insoles (Medilogic GmBH, Schönefeld, Germany) and completed three 3.5-min walking trials on the LBPPT (AlterG, Inc., Fremont, CA, USA) at 100% (normal gravity) as well as reduced-gravity conditions of 40% and 20% body weight (BW). The resulting insole data were analyzed to calculate center of pressure (COP) variables: COP path length and width and stance time. The results showed that 100% BW condition was significantly different from both the 40% and 20% BW conditions, p < 0.05. There were no significant differences observed between the 40% and 20% BW conditions for COP path length and width. Conversely, stance time significantly differed between the 40% and 20% BW conditions. The findings of this study may prove beneficial for clinicians as they develop rehabilitation strategies to effectively unload the individual’s body weight to perform safe exercises.