bluuebunny/crossref_metadata_2025 huggingface dataset:
A Simple Statistical Intra-Seasonal Prediction Model for Sea Surface Variables Utilizing Satellite Remote Sensing
Authors: Shao Qi , Zhao Yanling , Li Wei , Han Guijun , Hou Guangchao , Li Chaoliang , Liu Siyuan , Gong Yantian , Liu Hanyu , Qu Ping
Published: February 2022

In this paper, a novel and simple statistical prediction model for sea surface multivariate is developed based on extended empirical orthogonal functions (referred to as the MEEOF model). This simple model embeds the temporal evolution information into the empirical orthogonal function spatial patterns, which effectively captures the spatial distribution of the sea surface variables and their evolution process over time, and can be used to improve the accuracy of intra-seasonal ocean forecasts. At the same time, it considers both the correlation between different spatial and temporal points and the dynamic balance between different sea surface variables. The performance of the MEEOF prediction model has been examined in the South China Sea (SCS) based on remote sensing satellite datasets. Experimental results demonstrate that this model has significant forecasting capability within the forecast window of 15–90 days, compared with the traditional persistence forecasts and optimal climatic normal (OCN) results. Furthermore, this model exhibits good forecast performance throughout the entire forecast window; the final prediction model (referred to as the fusion model) is established by obtaining the weighted average for the MEEOF forecasts and persistence forecast results. Numerical experimental results show that this fusion prediction model consistently outperforms the persistence model, the OCN model, and the linear regression model over the entire forecast window. A case study shows that the propagation of ocean waves and the coordination between different sea surface variables can be well predicted by this simple model, indicating that this fusion model has a potential advantage in intra-seasonal predictions of the ocean.
Eastern Arctic Sea Ice Sensing: First Results from the RADARSAT Constellation Mission Data
Authors: Lyu Hangyu , Huang Weimin , Mahdianpari Masoud
Published: February 2022

Sea ice monitoring plays a vital role in secure navigation and offshore activities. Synthetic aperture radar (SAR) has been widely used as an effective tool for sea ice remote sensing (e.g., ice type classification, concentration and thickness retrieval) for decades because it can collect data by day and night and in almost all weather conditions. The RADARSAT Constellation Mission (RCM) is a new Canadian SAR mission providing several new services and data, with higher spatial coverage and temporal resolution than previous Radarsat missions. As a very deep convolutional neural network, Normalizer-Free ResNet (NFNet) was proposed by DeepMind in early 2021 and achieved a new state-of-the-art accuracy on the ImageNet dataset. In this paper, the RCM data are utilized for sea ice detection and classification using NFNet for the first time. HH, HV and the cross-polarization ratio are extracted from the dual-polarized RCM data with a medium resolution (50 m) for an NFNet-F0 model. Experimental results from Eastern Arctic show that destriping in the HV channel is necessary to improve the quality of sea ice classification. A two-level random forest (RF) classification model is also applied as a conventional technique for comparisons with NFNet. The sea ice concentration estimated based on the classification result from each region was validated with the corresponding polygon of the Canadian weekly regional ice chart. The overall classification accuracy confirms the superior capacity of the NFNet model over the RF model for sea ice monitoring and the sea ice sensing capacity of RCM.
Generating Terrain Data for Geomorphological Analysis by Integrating Topographical Features and Conditional Generative Adversarial Networks
Authors: Li Sijin , Li Ke , Xiong Liyang , Tang Guoan
Published: February 2022

With the development of artificial intelligence techniques for geographical knowledge discovery, simulated terrain generation based on deep-learning algorithms has become one practical way to construct accurate terrain data. However, it is still necessary to discuss whether the simulated topographic data contain the characteristics of specific landforms and can support related geographical studies. Therefore, in this study, a deep learning-based model inspired by previous research is constructed to generate loess landform data. We analyzed the influence of inputting different topographic features on terrain generation and evaluated the similarity between the simulated and reference data. The results show that the deep learning-based model can generate simulated topographic data that include similar elevation and slope probability distributions to the reference data of the loess landform. In addition, the generated results may have inaccurate terrain details, which can be regarded as noise in some cases. This indicates that the selection of input features should be carefully considered. Finally, the simulated data can subsequently support landform and terrain research, especially with intelligence algorithms that require large sets of topographic data.
Dust Radiative Effect Characteristics during a Typical Springtime Dust Storm with Persistent Floating Dust in the Tarim Basin, Northwest China
Authors: Meng Lu , Zhao Tianliang , He Qing , Yang Xinghua , Mamtimin Ali , Wang Minzhong , Pan Honglin , Huo Wen , Yang Fan , Zhou Chenglong
Published: February 2022

A special topography and ultra-high atmospheric boundary layer conditions in the Tarim Basin (TB) lead to the unique spatial–temporal distribution characteristics of dust aerosols. A typical dust storm with persistent floating dust over the TB from 27 April to 1 May 2015 was used to investigate the characteristics of the dust radiative effect using the Weather Research and Forecasting Model with Chemistry (WRF-Chem). Based on reasonable evaluations involving in situ sounding observations, as well as remotely sensed MODIS observations of meteorology, dust aerosols, and the ultra-high atmospheric boundary layer, the simulation characterized the complete characteristics of the dust direct radiative effect (DDRE) during the dust storm outbreak stage and persistent floating dust stage over the TB. During the daytime, the shortwave (SW) radiative effect heated the atmosphere and cooled the land surface (SUR), whereas the longwave (LW) radiative effect had the opposite effect on the TB. Regarding low-level dust, the LW radiative effect was greater than the SW DDRE in the atmosphere, while for high-level dust the situation was reversed. During the nighttime, the LW DDRE at the top of the atmosphere (TOA), at the SUR, and in the atmosphere was less than that during the daytime, when the DDRE at the SUR was the most significant. In contrast to the daytime, the near-surface dust aerosols exerted an LW warming effect in the atmosphere during the nighttime; however, the dust LW radiative effect had a cooling effect from above a 100 m altitude until the top of the dust layer. In contrast, the DDRE heating rate peaked at the top of the dust layer within the TB. The event-averaged net DDRE was 0.53, −5.90, and 6.43 W m−2 at the TOA, at the SUR, and in the atmosphere over the TB, respectively. The dust SW radiative effect was stronger than the dust L4W radiative effect over the TB at the SUR and in the atmosphere. Moreover, the DDRE at the TOA was weaker than that at the SUR. Overall, the study revealed noteworthy radiative effect features of dust aerosols during typical dust storms with persistent floating dust over the TB.
Characterizing Garden Greenspace in a Medieval European City: Added Values of Spatial Resolution and Multi-Temporal Stereo Imagery
Authors: Yan Jingli , Van der Linden Stijn , Tian Yunyu , Van Valckenborgh Jo , Strosse Veerle , Somers Ben
Published: February 2022

Domestic gardens provide residents with immediate access to landscape amenities and numerous ecological provisions. These ecological provisions have been proven to be largely determined by greenspace composition and landscape, but the fragmentation and heterogeneity of garden environments present challenges to greenspace mapping. Here, we first developed a recognition method to create a garden parcel data set in the medieval Leuven city of Belgium, based on the land use layers and agricultural land parcels. Then, we applied multi-sourced satellite imagery to evaluate the added value of spatial resolution, plant phenology and 3D structure in identifying four vegetation types. Finally, we characterized the greenspace landscapes in garden parcels. Compared with single ALOS-2 imagery, SPOT-7 imagery and Pleiades-1A imagery increased the overall accuracy by 4% and 8%, respectively. The accuracy improvement (21%) produced from multi-temporal stereo Pleiades-1A imagery strongly verified the significance of plant phenology and 3D structure in garden mapping. The average greenspace cover in garden parcels was 71% but varied from 56% in urban gardens to 82% in rural gardens. The garden greenspace landscape is fragmented by the artificial structures in urban areas but has a more aggregated size and less complex shapes in rural areas. This study calls for greater attention to be paid to gardens, and for multi-disciplinary studies conducted in collaboration with urban ecologists and landscape designers to maximize the benefits to residents of both immediate landscape amenities and ecological provisions, in the face of global environmental changes and public health risks.
Toward an Optimal Selection of Constraints for Terrestrial Reference Frame (TRF)
Authors: Song Shize , Zhang Zhongkai , Wang Guangli
Published: February 2022

Given that the observations from current space geodetic techniques do not carry all the necessary datum information to realize a Terrestrial Reference System (TRS), and each of the four space geodetic techniques has limits, for instance: Very Long Baseline Interferometry (VLBI) ignores the center of mass and satellite techniques lack the TRS orientation, additional constraints have to be added to the observations. This paper reviews several commonly used constraints, including inner constraints, internal constraints, kinematic constraints, and minimum constraints. Moreover, according to their observation equations and normal equations, the similarities and differences between them are summarized. Finally, we discuss in detail the influence of internal constraints on the scale of VLBI long-term solutions. The results show that there is a strong correlation between the scale parameter and the translation parameter introduced by the combination model at the Institut National de l’Information Géographique et Forestière (IGN), and internal constraints force these two groups of parameters to meet certain conditions, which will lead to the coupling of scale and translation parameters and disturbing the scale information in VLBI observations. The minimum or kinematic constraints are therefore the optimum choices for TRF.
A Transformer-Based Coarse-to-Fine Wide-Swath SAR Image Registration Method under Weak Texture Conditions
Authors: Fan Yibo , Wang Feng , Wang Haipeng
Published: February 2022

As an all-weather and all-day remote sensing image data source, SAR (Synthetic Aperture Radar) images have been widely applied, and their registration accuracy has a direct impact on the downstream task effectiveness. The existing registration algorithms mainly focus on small sub-images, and there is a lack of available accurate matching methods for large-size images. This paper proposes a high-precision, rapid, large-size SAR image dense-matching method. The method mainly includes four steps: down-sampling image pre-registration, sub-image acquisition, dense matching, and the transformation solution. First, the ORB (Oriented FAST and Rotated BRIEF) operator and the GMS (Grid-based Motion Statistics) method are combined to perform rough matching in the semantically rich down-sampled image. In addition, according to the feature point pairs, a group of clustering centers and corresponding images are obtained. Subsequently, a deep learning method based on Transformers is used to register images under weak texture conditions. Finally, the global transformation relationship can be obtained through RANSAC (Random Sample Consensus). Compared with the SOTA algorithm, our method’s correct matching point numbers are increased by more than 2.47 times, and the root mean squared error (RMSE) is reduced by more than 4.16%. The experimental results demonstrate that our proposed method is efficient and accurate, which provides a new idea for SAR image registration.
Electromagnetic Signal Classification Based on Class Exemplar Selection and Multi-Objective Linear Programming
Authors: Zhou Huaji , Bai Jing , Niu Linchun , Xu Jie , Xiao Zhu , Zheng Shilian , Jiao Licheng , Yang Xiaoniu
Published: February 2022

In the increasingly complex electromagnetic environment, a variety of new signal types are appearing; however, existing electromagnetic signal classification (ESC) models cannot handle new signal types. In this context, the emergence of class-incremental learning aims to incrementally update the classification model as new categories emerge. In this paper, an electromagnetic signal classification framework based on class exemplar selection and a multi-objective linear programming classifier (CES-MOLPC) is proposed in order to continuously learn new classes in an incremental manner. Specifically, our approach involves the adaptive selection of class exemplars considering normalized mutual information and a multi-objective linear programming classifier. The former is used to maintain the classification capability of the model for previous categories by selecting key samples, while the latter is used to allow the model to adapt quickly to new categories. Meanwhile, a weighted loss function based on cross-entropy and distillation loss is presented in order to fine-tune the model. We demonstrate the effectiveness of the proposed CES-MOLPC method through extensive experiments on the public RML2016.04c data set and the large-scale real-world ACARS signal data set. The results of the comparative experiments demonstrate that our method can achieve significant improvements over state-of-the-art methods.
Loess Landslide Detection Using Object Detection Algorithms in Northwest China
Authors: Ju Yuanzhen , Xu Qiang , Jin Shichao , Li Weile , Su Yanjun , Dong Xiujun , Guo Qinghua
Published: February 2022

Regional landslide identification is important for the risk management of landslide hazards. The traditional methods of regional landslide identification were mainly conducted by a human being. In previous studies, automatic landslide recognition mainly focused on new landslides distinct from the environment induced by rainfall or earthquake, using the image classification method and semantic segmentation method of deep learning. However, there is a lack of research on the automatic recognition of old loess landslides, which are difficult to distinguish from the environment. Therefore, this study uses the object detection method of deep learning to identify old loess landslides with Google Earth images. At first, a database of loess historical landslide samples was established for deep learning based on Google Earth images. A total of 6111 landslides were interpreted in three landslide areas in Gansu Province, China. Second, three object detection algorithms including the one-stage algorithm RetinaNet and YOLO v3 and the two-stage algorithm Mask R-CNN, were chosen for automatic landslide identification. Mask R-CNN achieved the greatest accuracy, with an AP of 18.9% and F1-score of 55.31%. Among the three landslide areas, the order of identification accuracy from high to low was Site 1, Site 2, and Site 3, with the F1-scores of 62.05%, 61.04% and 50.88%, respectively, which were positively related to their recognition difficulty. The research results proved that the object detection method can be employed for the automatic identification of loess landslides based on Google Earth images.
Design, Fabrication, and Characterisation of a Label-Free Nanosensor for Bioapplications
Authors: García-Ramírez Mario Alberto , González-Reynoso Orfil , Bello-Jiménez Miguel Angel , Vargas-Rodríguez Everado
Published: February 2022

In this paper, we present a hybrid semiconductor structure for biosensing applications that features the co-integration of nanoelectromechanical systems with the well-known metal oxide semiconductor technology. The proposed structure features an MOSFET as a readout element, and a doubly clamped beam that is isolated from the substrate by a thin air gap, as well as by a tunnel oxide layer. The beam structure is functionalised by a thin layer of biotargets, and the main aim is to detect a particular set of biomolecules, such as enzymes, bacteria, viruses, and DNA/RNA chains, among others. In here, a three-dimensional finite element analysis is performed in order to study the behaviour of the functionalised, doubly clamped beam. Preliminary results for the fabrication and characterisation processes show good agreement between the simulated and measured characteristics.
A Multiband Shared Aperture MIMO Antenna for Millimeter-Wave and Sub-6GHz 5G Applications
Authors: Hussain Rifaqat , Abou-Khousa Mohamed , Iqbal Naveed , Algarni Abdullah , Alhuwaimel Saad I. , Zerguine Azzedine , Sharawi Mohammad S.
Published: February 2022

A shared aperture 2-element multiple-input-multiple-output (MIMO) antenna design for 5G standards is presented in this study, one which uses the same radiating structure to cover both the sub-6GHz and millimeter-wave (millimeter-wave) bands. The proposed antenna comprises four concentric pentagonal slots that are uniformly separated from one another. For the sub-6GHz band, the antenna is excited by a single open-end microstrip transmission-line, while a 1 × 8 power divider (PD) connected via a T-junction structure excites the millimeter-wave band. Both the sub-6GHz and mm-wave antennas operate in a MIMO configuration. The proposed antenna design was fabricated on a 120 × 60 mm2 substrate with an edge-to-edge distance of 49 mm. The proposed sub-6GHz antenna covers the following frequency bands: 4–4.5 GHz, 3.1–3.8 GHz, 2.48–2.9 GHz, 1.82–2.14 GHz, and 1.4–1.58 GHz, while the millimeter-wave antenna operates at 28 GHz with at least 500 MHz of bandwidth. A complete antenna analysis is provided via a step-by-step design procedure, an equivalent circuit diagram showing the operation of the shared aperture antenna, and current density analysis at both millimeter-wave and sub-6GHz bands. The proposed antenna design is also characterized in terms of MIMO performance metrics with a good MIMO operation with maximum envelop correlation coefficient value of 0.113. The maximum measured gain and efficiency values obtained were 91% and 8.5 dBi over the entire band of operation. The antenna is backward compatible with 4G bands and also encompasses the sub-6GHz and 28 GHz bands for future 5G wireless communcation systems.
Few-Mode Fiber Characterization System Based on the Spatially and Spectrally Imaging Technique
Authors: Yu Jianxun , Tan Fengze , Yu Changyuan
Published: February 2022

With the widespread use of few-mode fibers, mode characteristics testing becomes essential. In this paper, current few-mode fiber testing techniques are discussed, and the S2 imaging technique is chosen and demonstrated to be capable of few-mode fiber characterization in principle. As a result, the few-mode fiber characterization system with the S2 imaging technique is built and used to obtain accurate mode dispersion of two-mode fibers (a commonly used few-mode fiber) of different lengths. Then, various filters are applied to extract the fundamental and high-order modes to acquire mode coupling components (discrete and distributed mode coupling). The proposed system spectrally characterizes the few-mode fiber by resolving the interference information from the superimposed optical field spatially and has a simple structure and easy operation, which will provide parameter guidance for FMF designing and the FMF sensing experiment optimizing.
Orbital Angular Momentum Mode Sensing Technology Based on Intensity Interrogation
Authors: Huang Churou , Zhu Guoxuan , Bai Zhiyong , Chen Jiayan , Huang Zheng , Liu Rui , Wu Luping , Liu Shen , Fu Cailing , Wang Yiping
Published: February 2022

A novel optical fiber sensing technology based on intensity distribution change in orbital angular momentum (OAM) mode is proposed and implemented herein. The technology utilizes a chiral long-period fiber grating (CLPFG) to directly excite the 1st-order OAM (OAM1) mode. The intensity changes in the coherent superposition state between the fundamental mode and the OAM1 mode at the non-resonant wavelength of the CLPFG is tracked in order to sense the external parameters applied to the grating area. Applying this technology to temperature measurement, the intensity distribution change has a good linear relationship with respect to temperature in the range of 30 °C to 100 °C. When the intensity was denoted by the number of pixels with a gray value of one after binarization of collected images, the sensitivity was 103 px/°C and the corresponding resolution was 0.0097 °C. Meanwhile, theoretical and experimental results show that the sensitivity and resolution can be further improved via changing the area of the collected image. Compared with sensing methods based on spiral interference pattern rotation in previous work, this sensing technology has the advantage of exquisite structure, easy realization, and good stability, thus making it a potential application in practices.
Extending Effective Dynamic Range of Hyperspectral Line Cameras for Short Wave Infrared Imaging
Authors: Shaikh Muhammad Saad , Jaferzadeh Keyvan , Thörnberg Benny
Published: February 2022

In this work, a multi-exposure method is proposed to increase the dynamic range (DR) of hyperspectral imaging using an InGaAs-based short-wave infrared (SWIR) hyperspectral line camera. Spectral signatures of materials were captured for scenarios in which the DR of a scene was greater than the DR of a line camera. To demonstrate the problem and test the proposed multi-exposure method, plastic detection in food waste and polymer sorting were chosen as the test application cases. The DR of the hyperspectral camera and the test samples were calculated experimentally. A multi-exposure method is proposed to create high-dynamic-range (HDR) images of food waste and plastic samples. Using the proposed method, the DR of SWIR imaging was increased from 43 dB to 73 dB, with the lowest allowable signal-to-noise ratio (SNR) set to 20 dB. Principal Component Analysis (PCA) was performed on both HDR and non-HDR image data from each test case to prepare the training and testing data sets. Finally, two support vector machine (SVM) classifiers were trained for each test case to compare the classification performance of the proposed multi-exposure HDR method against the single-exposure non-HDR method. The HDR method was found to outperform the non-HDR method in both test cases, with the classification accuracies of 98% and 90% respectively, for the food waste classification, and with 95% and 35% for the polymer classification.
An Analytical Investigation of Anomaly Detection Methods Based on Sequence to Sequence Model in Satellite Power Subsystem
Authors: Jin Weihua , Zhang Shijie , Sun Bo , Jin Pengli , Li Zhidong
Published: February 2022

The satellite power subsystem is responsible for all power supply in a satellite, and is an important component of it. The system’s performance has a direct impact on the operations of other systems as well as the satellite’s lifespan. Sequence to sequence (seq2seq) learning has recently advanced, gaining even more power in evaluating complicated and large-scale data. The potential of the seq2seq model in detecting anomalies in the satellite power subsystem is investigated in this work. A seq2seq-based scheme is given, with a thorough comparison of different neural-network cell types and levels of data smoothness. Three specific approaches were created to evaluate the seq2seq model performance, taking into account the unsupervised learning mechanism. The findings reveal that a CNN-based seq2seq with attention model under suitable data-smoothing conditions has a better ability to detect anomalies in the satellite power subsystem.
Neural Network Modeling and Dynamic Analysis of Different Types of Engine Mounts for Internal Combustion Engines
Authors: Ferreira Jessimon , Marin Bianca , Lenzi Giane G. , Manuel Calequela J. T. , Balthazar José M. , Lenz Wagner B. , Kossoski Adriano , Tusset Angelo M.
Published: February 2022

This paper presents the results of studies on reducing the amount of vibrations in different frequency ranges generated by a combustion engine through the use of different types of engine mounts. Three different types of engine supports are experimentally and numerically analyzed, namely an elastomeric engine mount, an elastomeric engine mount with a hydraulic component and standard decoupling, and an elastomeric engine mount with a hydraulic component and a modified decoupler—with this engineering design being a novelty in the literature. Experimental tests that considered different excitation frequencies were performed for the three types of engine mounts. Experimental data for stiffness and damping were used to obtain nonlinear mathematical models of the two systems with hydraulic components through the use of an Artificial Neural Network (ANN). For the results, all of the mathematical models presented coefficients of determination, R2, greater than 0.985 for both stiffness and damping, showing an excellent fit for the nonlinear experimental data. Numerical results using a quarter-car suspension model showed a large reduction in vibration amplitudes for the first vibration model when using the hydraulic systems, with values ranging between 48.58% and 66.47%, depending on the tests. The modified system presented smaller amplitudes and smoother behavior when compared to the standard hydraulic model.
Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives
Authors: Fascista Alessio
Published: February 2022

Fighting Earth’s degradation and safeguarding the environment are subjects of topical interest and sources of hot debate in today’s society. According to the United Nations, there is a compelling need to take immediate actions worldwide and to implement large-scale monitoring policies aimed at counteracting the unprecedented levels of air, land, and water pollution. This requires going beyond the legacy technologies currently employed by government authorities and adopting more advanced systems that guarantee a continuous and pervasive monitoring of the environment in all its different aspects. In this paper, we take the research on integrated and large-scale environmental monitoring a step further by providing a comprehensive review that covers transversally all the main applications of wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs), and crowdsensing monitoring technologies. By outlining the available solutions and current limitations, we identify in the cooperation among terrestrial (WSN/crowdsensing) and aerial (UAVs) sensing, coupled with the adoption of advanced signal processing techniques, the major pillars at the basis of future integrated (air, land, and water) and large-scale environmental monitoring systems. This review not only consolidates the progresses achieved in the field of environmental monitoring, but also sheds new lights on potential future research directions and synergies among different research areas.
A New LoRa-like Transceiver Suited for LEO Satellite Communications
Authors: Ben Temim Mohamed Amine , Ferré Guillaume , Tajan Romain
Published: February 2022

LoRa is based on the chirp spread spectrum (CSS) modulation, which has been developed for low power and long-range wireless Internet of Things (IoT) communications. The structure of LoRa signals makes their decoding performance extremely sensitive to synchronization errors. To alleviate this constraint, we propose a modification of the LoRa physical layer, which we refer to as differential CSS (DCSS), associated with an original synchronization algorithm. Based on this modification, we are able to demodulate the received signals without performing a complete frequency synchronization and by tolerating some timing synchronization errors. Hence, our receiver can handle ultra narrow band LoRa-like signals since it has no limitation on the maximum carrier frequency offset, as is actually the case in the deployed LoRa receivers. In addition, in the presence of the Doppler shift varying along the packet duration, DCSS shows better performance than CSS, which makes our proposed receiver a good candidate for communication with a low-Earth orbit (LEO) satellite.
Research on a Partial Aperture Factor Measurement Method for the AGRI Onboard Calibration Assembly
Authors: Si Xiaolong , Li Xiuju , Chen Hongyao , Bao Shiwei , Xu Heyu , Zhang Liming , Huang Wenxin
Published: February 2022

A partial aperture onboard calibration method can solve the onboard calibration problems of some large aperture remote sensors, which is of great significance for the development trend of increasingly large apertures in optical remote sensors. In this paper, the solar diffuser reflectance degradation monitor (SDRDM) in the onboard calibration assembly (CA) of the FengYun-4 (FY-4) advanced geostationary radiance imager (AGRI) was used as the reference radiometer. It was designed for measuring the partial aperture factor (PAF) for the AGRI onboard calibration. First, the linear response count variation relationship between the two was established under the same radiance source input. Then, according to the known bidirectional reflection distribution function (BRDF) of the solar diffuser (SD) in the CA, the relative reflectance ratio coefficient between the AGRI observation direction and the SDRDM observation direction was calculated. On this basis, the response count value of the AGRI and the SDRDM was used to realize the high-precision measurement of the PAF of the AGRI B1~B3 bands by simulating the AGRI onboard calibration measurement under the illumination of a solar simulator in the laboratory. According to the determination process of the relevant parameters of the PAF, the measurement uncertainty of the PAF was analyzed; this uncertainty was greater than 2.04% and provided an important reference for the evaluation of the onboard absolute radiometric calibration uncertainty after launch.
Research on High Sensitivity Oil Debris Detection Sensor Using High Magnetic Permeability Material and Coil Mutual Inductance
Authors: Wang Chengjie , Bai Chenzhao , Yang Zhaoxu , Zhang Hongpeng , Li Wei , Wang Xiaotian , Zheng Yiwen , Ilerioluwa Lebile , Sun Yuqing
Published: February 2022

Metallic contaminants (solid) are generated by friction pair, causing wear of equipment by enters the lubricating system. This poses a great potential threat to the normal operation of such machines. The timely analysis and detection of debris can lead to the avoidance of mechanical failures. Abnormal wear in machinery may produce debris exceeding 10 μm. The traditional inductance detection method has low sensitivity and cannot meet the actual detection requirements. To boost the sensitivity of the inductance sensor, the mutual inductance of coils and the strong magnetic conductivity of permalloy was utilized to design a high sensitivity inductance sensor for the detection of debris in lubricating oil. This design was able to detect 10–15 μm iron particles and 65–70 μm copper particles in the oil. The experimental results illustrate that low-frequency excitation is the best for detecting ferromagnetic particles, while high-frequency excitation has the best effect for detecting non-ferromagnetic particles. This paper demonstrates the significant advantages of coil mutual inductance, and strong magnetic conductivity of permalloy in improving the detection sensitivity of oil debris sensors. This will provide technical support for wear detection in mechanical equipment and fault diagnosis.
Aurora-Trinity: A Super-Light Client for Distributed Ledger Networks Extending the Ethereum Trinity Client
Authors: Benčić Federico Matteo , Podnar Žarko Ivana
Published: February 2022

Light clients for distributed ledger networks can verify blockchain integrity by downloading and analyzing blockchain headers. They are designed to circumvent the high resource requirements, i.e., the large bandwidth and memory requirements that full nodes must meet, which are unsuitable for consumer-grade hardware and resource-constrained devices. Light clients rely on full nodes and trust them implicitly. This leaves them vulnerable to various types of attacks, ranging from accepting maliciously forged data to Eclipse attacks. We introduce Aurora-Trinity, a novel version of light clients that addresses the above-mentioned vulnerability by relying on our original Aurora module, which extends the Ethereum Trinity client. The Aurora module efficiently discovers the presence of malicious or Byzantine nodes in distributed ledger networks with a predefined and acceptable error rate and identifies at least one honest node for persistent or ephemeral communication. The identified honest node is used to detect the latest canonical chain head or to infer the state of an entry in the ledger without downloading the header chain, making the Aurora-Trinity client extremely efficient. It can run on consumer-grade hardware and resource-constrained devices, as the Aurora module consumes about 0.31 MB of RAM and 1 MB of storage at runtime.
A Survey of Crypto Ransomware Attack Detection Methodologies: An Evolving Outlook
Authors: Alqahtani Abdullah , Sheldon Frederick T.
Published: February 2022

Recently, ransomware attacks have been among the major threats that target a wide range of Internet and mobile users throughout the world, especially critical cyber physical systems. Due to its unique characteristics, ransomware has attracted the attention of security professionals and researchers toward achieving safer and higher assurance systems that can effectively detect and prevent such attacks. The state-of-the-art crypto ransomware early detection models rely on specific data acquired during the runtime of an attack’s lifecycle. However, the evasive mechanisms that these attacks employ to avoid detection often nullify the solutions that are currently in place. More effort is needed to keep up with an attacks’ momentum to take the current security defenses to the next level. This survey is devoted to exploring and analyzing the state-of-the-art in ransomware attack detection toward facilitating the research community that endeavors to disrupt this very critical and escalating ransomware problem. The focus is on crypto ransomware as the most prevalent, destructive, and challenging variation. The approaches and open issues pertaining to ransomware detection modeling are reviewed to establish recommendations for future research directions and scope.
Privacy Policies of IoT Devices: Collection and Analysis
Authors: Kuznetsov Mikhail , Novikova Evgenia , Kotenko Igor , Doynikova Elena
Published: February 2022

Currently, personal data collection and processing are widely used while providing digital services within mobile sensing networks for their operation, personalization, and improvement. Personal data are any data that identifiably describe a person. Legislative and regulatory documents adopted in recent years define the key requirements for the processing of personal data. They are based on the principles of lawfulness, fairness, and transparency of personal data processing. Privacy policies are the only legitimate way to provide information on how the personal data of service and device users is collected, processed, and stored. Therefore, the problem of making privacy policies clear and transparent is extremely important as its solution would allow end users to comprehend the risks associated with personal data processing. Currently, a number of approaches for analyzing privacy policies written in natural language have been proposed. Most of them require a large training dataset of privacy policies. In the paper, we examine the existing corpora of privacy policies available for training, discuss their features and conclude on the need for a new dataset of privacy policies for devices and services of the Internet of Things as a part of mobile sensing networks. The authors develop a new technique for collecting and cleaning such privacy policies. The proposed technique differs from existing ones by the usage of e-commerce platforms as a starting point for document search and enables more targeted collection of the URLs to the IoT device manufacturers’ privacy policies. The software tool implementing this technique was used to collect a new corpus of documents in English containing 592 unique privacy policies. The collected corpus contains mainly privacy policies that are developed for the Internet of Things and reflect the latest legislative requirements. The paper also presents the results of the statistical and semantic analysis of the collected privacy policies. These results could be further used by the researchers when elaborating techniques for analysis of the privacy policies written in natural language targeted to enhance their transparency for the end user.
Improving Wearable-Based Activity Recognition Using Image Representations
Authors: Sanchez Guinea Alejandro , Sarabchian Mehran , Mühlhäuser Max
Published: February 2022

Activity recognition based on inertial sensors is an essential task in mobile and ubiquitous computing. To date, the best performing approaches in this task are based on deep learning models. Although the performance of the approaches has been increasingly improving, a number of issues still remain. Specifically, in this paper we focus on the issue of the dependence of today’s state-of-the-art approaches to complex ad hoc deep learning convolutional neural networks (CNNs), recurrent neural networks (RNNs), or a combination of both, which require specialized knowledge and considerable effort for their construction and optimal tuning. To address this issue, in this paper we propose an approach that automatically transforms the inertial sensors time-series data into images that represent in pixel form patterns found over time, allowing even a simple CNN to outperform complex ad hoc deep learning models that combine RNNs and CNNs for activity recognition. We conducted an extensive evaluation considering seven benchmark datasets that are among the most relevant in activity recognition. Our results demonstrate that our approach is able to outperform the state of the art in all cases, based on image representations that are generated through a process that is easy to implement, modify, and extend further, without the need of developing complex deep learning models.
A New Method for Absolute Pose Estimation with Unknown Focal Length and Radial Distortion
Authors: Guo Kai , Ye Hu , Chen Honglin , Gao Xin
Published: February 2022

Estimating the absolute pose of a camera is one of the key steps for computer vision. In some cases, especially when using a wide-angle or zoom lens, the focal length and radial distortion also need to be considered. Therefore, in this paper, an efficient and robust method for a single solution is proposed to estimate the absolute pose for a camera with unknown focal length and radial distortion, using three 2D–3D point correspondences and known camera position. The problem is decomposed into two sub-problems, which makes the estimation simpler and more efficient. The first sub-problem is to estimate the focal length and radial distortion. An important geometric characteristic of radial distortion, that the orientation of the 2D image point with respect to the center of distortion (i.e., principal point in this paper) under radial distortion is unchanged, is used to solve this sub-problem. The focal length and up to four-order radial distortion can be determined with this geometric characteristic, and it can be applied to multiple distortion models. The values with no radial distortion are used as the initial values, which are close to the global optimal solutions. Then, the sub-problem can be efficiently and accurately solved with the initial values. The second sub-problem is to determine the absolute pose with geometric linear constraints. After estimating the focal length and radial distortion, the undistorted image can be obtained, and then the absolute pose can be efficiently determined from the point correspondences and known camera position using the undistorted image. Experimental results indicate this method’s accuracy and numerical stability for pose estimation with unknown focal length and radial distortion in synthetic data and real images.
Use of a Rotating Square Spatial-Frequency Filter to Map the Optical Path Length Variation in Microscopic Biological Samples
Authors: Iglesias Ignacio
Published: February 2022

Gradient images can be obtained using a rotating square mask to filter the angular spectra of the wavefront generated by a complex transmittance object. This method can be applied to measure the three-dimensional structure of microscopic biological samples through the relationship of the phase with the optical path length. This work describes the implementation of a system using an inverted optical microscope and shows the experimental results of phase maps generated by boar sperm cells.
Neurofeedback Therapy for Sensory Over-Responsiveness—A Feasibility Study
Authors: Hamed Ruba , Mizrachi Limor , Granovsky Yelena , Issachar Gil , Yuval-Greenberg Shlomit , Bar-Shalita Tami
Published: February 2022

Background: Difficulty in modulating multisensory input, specifically the sensory over-responsive (SOR) type, is linked to pain hypersensitivity and anxiety, impacting daily function and quality of life in children and adults. Reduced cortical activity recorded under resting state has been reported, suggestive of neuromodulation as a potential therapeutic modality. This feasibility study aimed to explore neurofeedback intervention in SOR. Methods: Healthy women with SOR (n = 10) underwent an experimental feasibility study comprising four measurement time points (T1—baseline; T2—preintervention; T3—postintervention; T4—follow-up). Outcome measures included resting-state EEG recording, in addition to behavioral assessments of life satisfaction, attaining functional goals, pain sensitivity, and anxiety. Intervention targeted the upregulation of alpha oscillatory power over ten sessions. Results: No changes were detected in all measures between T1 and T2. Exploring the changes in brain activity between T2 and T4 revealed power enhancement in delta, theta, beta, and gamma oscillatory bands, detected in the frontal region (p = 0.03–<0.001; Cohen’s d = 0.637–1.126) but not in alpha oscillations. Furthermore, a large effect was found in enhancing life satisfaction and goal attainment (Cohen’s d = 1.18; 1.04, respectively), and reduced pain sensitivity and anxiety trait (Cohen’s d = 0.70). Conclusion: This is the first study demonstrating the feasibility of neurofeedback intervention in SOR.
Insertion Loss and Phase Compensation Using a Circular Slot Via-Hole in a Compact 5G Millimeter Wave (mmWave) Butler Matrix at 28 GHz
Authors: Md Jizat Noorlindawaty , Yusoff Zubaida , Mohd Marzuki Azah Syafiah , Zainudin Norsiha , Yamada Yoshihide
Published: February 2022

Fifth generation (5G) technology aims to provide high peak data rates, increased bandwidth, and supports a 1 millisecond roundtrip latency at millimeter wave (mmWave). However, higher frequency bands in mmWave comes with challenges including poor propagation characteristics and lossy structure. The beamforming Butler matrix (BM) is an alternative design intended to overcome these limitations by controlling the phase and amplitude of the signal, which reduces the path loss and penetration losses. At the mmWave, the wavelength becomes smaller, and the BM planar structure is intricate and faces issues of insertion losses and size due to the complexity. To address these issues, a dual-layer substrate is connected through the via, and the hybrids are arranged side by side. The dual-layer structure circumvents the crossover elements, while the strip line, hybrids, and via-hole are carefully designed on each BM element. The internal design of BM features a compact size and low-profile structure, with dimensions of 23.26 mm × 28.92 mm (2.17 λ0  ×  2.69 λ0), which is ideally suited for the 5G mmWave communication system. The designed BM measured results show return losses, Sii and Sjj, of less than −10 dB, transmission amplitude of −8 ± 2 dB, and an acceptable range of output phase at 28 GHz.
Neuromorphic Architecture Accelerated Automated Seizure Detection in Multi-Channel Scalp EEG
Authors: Ambati Ravi , Raja Shanker , Al-Hameed Majed , John Titus , Arjoune Youness , Shekhar Raj
Published: February 2022

Epileptic focal seizures can be localized in the brain using tracer injections during or immediately after the incidence of a seizure. A real-time automated seizure detection system with minimal latency can help time the injection properly to find the seizure origin accurately. Reliable real-time seizure detection systems have not been clinically reported yet. We developed an anomaly detection-based automated seizure detection system, using scalp-electroencephalogram (EEG) data, which can be trained using a few seizure sessions, and implemented it on commercially available hardware with parallel, neuromorphic architecture—the NeuroStack. We extracted nonlinear, statistical, and discrete wavelet decomposition features, and we developed a graphical user interface and traditional feature selection methods to select the most discriminative features. We investigated Reduced Coulomb Energy (RCE) networks and K-Nearest Neighbors (k-NN) for its several advantages, such as fast learning no local minima problem. We obtained a maximum sensitivity of 91.14%±1.77% and a specificity of 98.77%±0.57% with 5 s epoch duration. The system’s latency was 12 s, which is within most seizure event windows, which last for an average duration of 60 s. Our results showed that the CD feature consumes large computation resources and excluding it can reduce the latency to 3.6 s but at the cost of lower performance 80% sensitivity and 97% specificity. We demonstrated that the proposed methodology achieves a high specificity and an acceptable sensitivity within a short delay. Our results indicated also that individual-based RCE are superior to population-based RCE. The proposed RCE networks has been compared to SVM and ANN as a baseline for comparison as they are the most common machine learning seizure detection methods. SVM and ANN-based systems were trained on the same data as RCE and K-NN with features optimized specifically for them. RCE nets are superior to SVM and ANN. The proposed model also achieves comparable performance to the state-of-the-art deep learning techniques while not requiring a sizeable database, which is often expensive to build. These numbers indicate that the system is viable as a trigger mechanism for tracer injection.