bluuebunny/crossref_metadata_2025 huggingface dataset:
Philosophy and Religion in the Political Thought of Alfarabi
Authors: Ali Ishraq
Published: July 2023

Philosophy and religion were the two important sources of knowledge for medieval Arab Muslim polymaths. Owing to the difference between the nature of philosophy and religion, the interplay between philosophy and religion often takes the form of conflict in medieval Muslim thought as exemplified by the Al-Ghazali versus Averroes (Ibn Rusd) polemic. Unlike the Al-Ghazali versus Averroes (Ibn Rushd) polemic, the interplay between philosophy and religion in the political philosophy of Abu Nasr Alfarabi takes the form of harmonious co-existence. Although, for Alfarabi, religion is an inferior form of knowledge as compared to philosophy, the present article will show that philosophy and religion play equally significant roles in Alfarabi’s virtuous city and that in the absence of either philosophy or religion, the political system proposed by Alfarabi cannot exist.
On the Identification of the Riskiest Directional Components from Multivariate Heavy-Tailed Data
Authors: Hägele Miriam , Lehtomaa Jaakko
Published: July 2023

In univariate data, there exist standard procedures for identifying dominating features that produce the largest number of observations. However, in the multivariate setting, the situation is quite different. This paper aims to provide tools and methods for detecting dominating directional components in multivariate data. We study general heavy-tailed multivariate random vectors in dimension d ≥ 2 and present procedures that can be used to explain why the data are heavy-tailed. This is achieved by identifying the set of the riskiest directional components. The results are of particular interest in insurance when setting reinsurance policies, and in finance when hedging a portfolio of multiple assets.
Quantifying Changes in Extent and Velocity of the Hornbreen/Hambergbreen Glacial System (SW, Spitsbergen) Based on Timeseries of Multispectral Satellite Imagery
Authors: Saferna Dawid , Błaszczyk Małgorzata , Grabiec Mariusz , Gądek Bogdan
Published: July 2023

This study focuses on the Hornsund region in Svalbard, where the temperature has risen by 1.14 °C per decade, six times faster than the global average. The accelerating temperature rise in the Arctic has had significant impacts on the Svalbard glaciers, including the Hornbreen–Hambergbreen system (HH system). The HH system connects Sørkapp Land with the rest of Spitsbergen, and its disintegration will lead to the formation of a new island. This study assesses the annual and seasonal changes in the velocity of the HH system and fluctuations of the position of the termini from 1985 to 2021 and their relationship with environmental factors. Furthermore, an assessment was made of the possible date of opening of the Hornsund strait. The study also investigates the impact of the radiometric resolution of satellite images on the quality of the velocity field and the detection of glacier features. Multispectral imagery was used to assess the velocity fields with Glacier Image Velocimetry (v 1.01) software, which uses the feature tracking method. In addition, the Glacier Termini Tracking plugin was used to acquire data on the fluctuating positions of the termini. The long-term mean annual velocity of the Hornbreen was 431 m a−1, while that of Hambergbreen was 141 m a−1. The peak seasonal velocity and fluctuations of the terminus position of Hambergbreen were delayed by approximately one month when compared to Hornbreen. Overall, air and sea surface temperatures influence the velocities and fluctuations of the termini, while precipitation plays a secondary role. If the recession continues, the Hornsund strait may open around 2053. An increase in the quality of velocity maps from 12.7% to 50.2% was found with an increase in radiometric resolution from 8 bit to 16 bit.
Dynamic Vegetation Responses to Climate and Land Use Changes over the Inner Mongolia Reach of the Yellow River Basin, China
Authors: Liu Tingxiang , Zhang Qiang , Li Tiantian , Zhang Kaiwen
Published: July 2023

Ecological protection and high-quality development of the Yellow River Basin (YRB), China, aroused remarkable concerns from China’s Central Government, and has been a major national strategy. The Inner Mongolia reach of the Yellow River Basin (IM-YRB) is a typical dryland with pervasive vegetation restoration through the actions of the ecological projects that have been conducted in recent years. However, how climate changes and human activities, such as land use and land cover (LULC) changes, jointly impact vegetation variations in this region remains poorly understood. Here, using an explainable machine learning technique, we evaluated linkages between the kernel normalized difference vegetation index (kNDVI) and air temperature, precipitation, soil moisture, and LULC changes, and relevant marginal contributions of these four drivers to the observed vegetation changes. The grassland fraction on a pixel level was selected as the quantitative LULC variable due to its key role in regional LULC. We found that interannual kNDVI changes in most areas of this study region were negatively sensitive to temperature, but positively sensitive to precipitation and soil moisture, with area fractions of 71.74%, 96.93%, and 89.33%, respectively. The area fraction of negative kNDVI sensitivity to LULC was roughly equivalent to that of positive kNDVI sensitivity. The contributions of air temperature, precipitation, soil moisture, and LULC to overall kNDVI changes were 21.54%, 33.32%, 32.19%, and 12.95%, respectively. Moisture conditions also play a critical role in vegetation changes, which was reflected by the fluctuating growth of kNDVI as interannual changes in precipitation. Nonetheless, kNDVI changes are also affected by LULC, and LULC became the dominant factor behind the kNDVI anomalies over the grassland restoration regions from barren over the IM-YRB. This research provides theoretical support for dryland vegetation restoration under the influence of climate change.
A Deep Learning-Based Hyperspectral Object Classification Approach via Imbalanced Training Samples Handling
Authors: Islam Md Touhid , Islam Md Rashedul , Uddin Md Palash , Ulhaq Anwaar
Published: July 2023

Object classification in hyperspectral images involves accurately categorizing objects based on their spectral characteristics. However, the high dimensionality of hyperspectral data and class imbalance pose significant challenges to object classification performance. To address these challenges, we propose a framework that incorporates dimensionality reduction and re-sampling as preprocessing steps for a deep learning model. Our framework employs a novel subgroup-based dimensionality reduction technique to extract and select the most informative features with minimal redundancy. Additionally, the data are resampled to achieve class balance across all categories. The reduced and balanced data are then processed through a hybrid CNN model, which combines a 3D learning block and a 2D learning block to extract spectral–spatial features and achieve satisfactory classification accuracy. By adopting this hybrid approach, we simplify the model while improving performance in the presence of noise and limited sample size. We evaluated our proposed model on the Salinas scene, Pavia University, and Kennedy Space Center benchmark hyperspectral datasets, comparing it to state-of-the-art methods. Our object classification technique achieves highly promising results, with overall accuracies of 99.98%, 99.94%, and 99.46% on the three datasets, respectively. This proposed approach offers a compelling solution to overcome the challenges of high dimensionality and class imbalance in hyperspectral object classification.
Geographic-Information-System-Based Risk Assessment of Flooding in Changchun Urban Rail Transit System
Authors: Liu Gexu , Zhang Yichen , Zhang Jiquan , Lang Qiuling , Chen Yanan , Wan Ziyang , Liu Huanan
Published: July 2023

The frequent occurrence of urban flooding in recent years has resulted in significant damage to ground-level infrastructure and poses a substantial threat to the metro system. As the central city’s core transportation network for public transit, this threat can have unpredictable consequences on travel convenience and public safety. Therefore, assessing the risk of urban flooding in the metro system is of utmost importance. This study is the first of its kind to employ comprehensive natural disaster risk assessment theory, establishing an assessment database with 22 indicators. We propose a GIS-based method combined with the analytical hierarchy process (AHP) and an improved entropy weight method to comprehensively evaluate the urban flood risk in Changchun City’s metro systems in China. This study includes a total of nine metro lines, including those that are currently operational as well as those that are in the planning and construction phases, situated in six urban areas of Changchun City. In this study, we utilize the regional risk level within the 500 m buffer zone of the metro lines to represent the flood risk of the metro system. The proposed method assesses the flood risk of Changchun’s rail transit system. The results reveal that over 30% of Changchun’s metro lines are located in high-risk flood areas, mainly concentrated in the densely populated and economically prosperous western part of the central city. To validate the risk assessment, we vectorized the inundation points and overlaid them with the regional flood risk assessment results, achieving a model accuracy of over 90%. As no large-scale flood events have occurred in the Changchun rail transit system, we employed receiver operating characteristic (ROC) curves to verify the accuracy of the flood risk assessment model, resulting in an accuracy rate of 91%. These findings indicate that the present study is highly reliable and can provide decision makers with a scientific basis for mitigating future flood disasters.
Measurement of Total Dissolved Solids and Total Suspended Solids in Water Systems: A Review of the Issues, Conventional, and Remote Sensing Techniques
Authors: Adjovu Godson Ebenezer , Stephen Haroon , James David , Ahmad Sajjad
Published: July 2023

This study provides a comprehensive review of the efforts utilized in the measurement of water quality parameters (WQPs) with a focus on total dissolved solids (TDS) and total suspended solids (TSS). The current method used in the measurement of TDS and TSS includes conventional field and gravimetric approaches. These methods are limited due to the associated cost and labor, and limited spatial coverages. Remote Sensing (RS) applications have, however, been used over the past few decades as an alternative to overcome these limitations. Although they also present underlying atmospheric interferences in images, radiometric and spectral resolution issues. Studies of these WQPs with RS, therefore, require the knowledge and utilization of the best mechanisms. The use of RS for retrieval of TDS, TSS, and their forms has been explored in many studies using images from airborne sensors onboard unmanned aerial vehicles (UAVs) and satellite sensors such as those onboard the Landsat, Sentinel-2, Aqua, and Terra platforms. The images and their spectral properties serve as inputs for deep learning analysis and statistical, and machine learning models. Methods used to retrieve these WQP measurements are dependent on the optical properties of the inland water bodies. While TSS is an optically active parameter, TDS is optically inactive with a low signal–noise ratio. The detection of TDS in the visible, near-infrared, and infrared bands is due to some process that (usually) co-occurs with changes in the TDS that is affecting a WQP that is optically active. This study revealed significant improvements in incorporating RS and conventional approaches in estimating WQPs. The findings reveal that improved spatiotemporal resolution has the potential to effectively detect changes in the WQPs. For effective monitoring of TDS and TSS using RS, we recommend employing atmospheric correction mechanisms to reduce image atmospheric interference, exploration of the fusion of optical and microwave bands, high-resolution hyperspectral images, utilization of ML and deep learning models, calibration and validation using observed data measured from conventional methods. Further studies could focus on the development of new technology and sensors using UAVs and satellite images to produce real-time in situ monitoring of TDS and TSS. The findings presented in this review aid in consolidating understanding and advancement of TDS and TSS measurements in a single repository thereby offering stakeholders, researchers, decision-makers, and regulatory bodies a go-to information resource to enhance their monitoring efforts and mitigation of water quality impairments.
A Graph Memory Neural Network for Sea Surface Temperature Prediction
Authors: Liang Shuchen , Zhao Anming , Qin Mengjiao , Hu Linshu , Wu Sensen , Du Zhenhong , Liu Renyi
Published: July 2023

Sea surface temperature (SST) is a key factor in the marine environment, and its accurate forecasting is important for climatic research, ecological preservation, and economic progression. Existing methods mostly rely on convolutional networks, which encounter difficulties in encoding irregular data. In this paper, allowing for comprehensive encoding of irregular data containing land and islands, we construct a graph structure to represent SST data and propose a graph memory neural network (GMNN). The GMNN includes a graph encoder built upon the iterative graph neural network (GNN) idea to extract spatial relationships within SST data. It not only considers node but also edge information, thereby adequately characterizing spatial correlations. Then, a long short-term memory (LSTM) network is used to capture temporal dynamics in the SST variation process. We choose the data from the Northwest Pacific Ocean to validate GMNN’s effectiveness for SST prediction in different partitions, time scales, and prediction steps. The results show that our model has better performance for both complete and incomplete sea areas compared to other models.
Lake Cyanobacterial Bloom Color Recognition and Spatiotemporal Monitoring with Google Earth Engine and the Forel-Ule Index
Authors: Song Ting , Liu Ge , Zhang Hujun , Yan Fei , Fu Yingbo , Zhang Junyi
Published: July 2023

Cyanobacterial blooms represent a significant environmental problem, threatening aquatic ecosystems worldwide. Caused by the eutrophication of water bodies and global climate change, these blooms have altered freshwater ecosystems worldwide during recent decades. Although cyanobacterial blooms are typically caused by blue-green cyanobacteria, which derive their color from the phycocyanin pigment, other pigmented cyanobacterial blooms have been frequently observed in water bodies. These blooms pose a serious environmental threat to inland waters, endangering global public health and aquatic ecosystems. Therefore, comprehending the mechanism of color variation in cyanobacterial blooms is crucial for revealing the outbreak mechanism and implementing effective prevention and control measures. This study developed a human–machine interactive workflow for extracting cyanobacterial blooms and recognizing their colors based on the Forel-Ule index and Sentinel-2 MultiSpectral Instrument data. Using this workflow, the authors conducted spatiotemporal analysis and statistical analysis of bloom color for cyanobacterial blooms in four typical eutrophic lakes from 2019 to 2022. The findings indicated a declining trend in cyanobacterial blooms across the four studied lakes over the years, among which Hulun Lake experienced an annual increase in cyanobacterial blooms and emerged as the lake with the most severe outbreak of such blooms in 2022. The yellowing status of cyanobacterial blooms varied among the four lakes, with Taihu Lake and Dianchi Lake exhibiting a relatively high proportion of green-yellow and yellow cyanobacterial blooms, followed by Chaohu Lake, whereas Hulun Lake had the lowest occurrence. The workflow developed in this study was implemented in Google Earth Engine and provided an automated, integrated, and rapid monitoring solution for the long-term monitoring and color recognition of cyanobacterial blooms.
Multi-Oriented Enhancement Branch and Context-Aware Module for Few-Shot Oriented Object Detection in Remote Sensing Images
Authors: Su Haozheng , You Yanan , Liu Sixu
Published: July 2023

For oriented object detection, the existing CNN-based methods typically rely on a substantial and diverse dataset, which can be expensive to acquire and demonstrate limited capacity for generalization when faced with new categories that lack annotated samples. In this case, we propose MOCA-Net, a few-shot oriented object detection method with a multi-oriented enhancement branch and context-aware module, utilizing a limited number of annotated samples from novel categories for training. Especially, our method generates multi-oriented and multi-scale positive samples and then inputs them into an RPN and the detection head as a multi-oriented enhancement branch for enhancing the classification and regression capabilities of the detector. And by utilizing the context-aware module, the detector can effectively extract contextual information surrounding the object and incorporate it into RoI features in an adaptive manner, thereby improving its classification capability. As far as we know, our method is the first to attempt this in this field, and comparative experiments conducted on the public remote sensing dataset DOTA for oriented object detection showed that our method is effective.
Patch-Transformer Network: A Wearable-Sensor-Based Fall Detection Method
Authors: Wang Shaobing , Wu Jiang
Published: July 2023

Falls can easily cause major harm to the health of the elderly, and timely detection can avoid further injuries. To detect the occurrence of falls in time, we propose a new method called Patch-Transformer Network (PTN) wearable-sensor-based fall detection algorithm. The neural network includes a convolution layer, a Transformer encoding layer, and a linear classification layer. The convolution layer is used to extract local features and project them into feature matrices. After adding positional coding information, the global features of falls are learned through the multi-head self-attention mechanism in the Transformer encoding layer. Global average pooling (GAP) is used to strengthen the correlation between features and categories. The final classification results are provided by the linear layer. The accuracy of the model obtained on the public available datasets SisFall and UnMib SHAR is 99.86% and 99.14%, respectively. The network model has fewer parameters and lower complexity, with detection times of 0.004 s and 0.001 s on the two datasets. Therefore, our proposed method can timely and accurately detect the occurrence of falls, which is important for protecting the lives of the elderly.
Improving Small-Scale Human Action Recognition Performance Using a 3D Heatmap Volume
Authors: Yuan Lin , He Zhen , Wang Qiang , Xu Leiyang , Ma Xiang
Published: July 2023

In recent years, skeleton-based human action recognition has garnered significant research attention, with proposed recognition or segmentation methods typically validated on large-scale coarse-grained action datasets. However, there remains a lack of research on the recognition of small-scale fine-grained human actions using deep learning methods, which have greater practical significance. To address this gap, we propose a novel approach based on heatmap-based pseudo videos and a unified, general model applicable to all modality datasets. Leveraging anthropometric kinematics as prior information, we extract common human motion features among datasets through an ad hoc pre-trained model. To overcome joint mismatch issues, we partition the human skeleton into five parts, a simple yet effective technique for information sharing. Our approach is evaluated on two datasets, including the public Nursing Activities and our self-built Tai Chi Action dataset. Results from linear evaluation protocol and fine-tuned evaluation demonstrate that our pre-trained model effectively captures common motion features among human actions and achieves steady and precise accuracy across all training settings, while mitigating network overfitting. Notably, our model outperforms state-of-the-art models in recognition accuracy when fusing joint and limb modality features along the channel dimension.
DNS-BC: Fast, Reliable and Secure Domain Name System Caching System Based on a Consortium Blockchain
Authors: Gao Tianfu , Dong Qingkuan
Published: July 2023

The Domain Name System (DNS) is a fundamental component of the internet, responsible for resolving domain names into IP addresses. DNS servers are typically categorized into four types: recursive resolvers, root name servers, Top-Level Domain (TLD) name servers, and authoritative name servers. The latter three types of servers store actual records, while recursive resolvers do not store any real data and are only responsible for querying the other three types of servers and responding to clients. Recursive resolvers typically maintain a caching system to speed up response times, but these caching systems have the drawbacks of a low real-time performance, a poor accuracy, and many security and privacy issues. In this paper, we propose a caching system based on a consortium blockchain, namely DNS-BC, which uses the synchronization mechanism of the consortium blockchain to achieve a high real-time performance, uses the immutable mechanism of the consortium blockchain and our designed credibility management system to achieve up to a 100% accuracy, and has been combined with encrypted transmission protocols to solve common security and privacy issues. At the same time, this caching system can greatly reduce the traffic that name servers need to handle, thereby protecting them from Denial-of-Service (DoS) attacks. To further accelerate the data transmission speed, we have designed a new encrypted DNS protocol called DNS over KCP (DoK). The DoK protocol is based on the KCP protocol, which is a fast and reliable transmission protocol, and its latency can reach one-third of that of TCP when the network environment deteriorates. In our experiments, the transmission time of this protocol is about a quarter of that of the widely used encrypted protocols DNS over TLS (DoT) and DNS over HTTPS (DoH).
Research on Denoising Method for Hydroelectric Unit Vibration Signal Based on ICEEMDAN–PE–SVD
Authors: Zhang Fangqing , Guo Jiang , Yuan Fang , Shi Yongjie , Li Zhaoyang
Published: July 2023

Vibration monitoring and analysis play a crucial role in the fault diagnosis of hydroelectric units. However, accurate extraction and identification of fault features from vibration signals are challenging because of noise interference. To address this issue, this study proposes a novel denoising method for vibration signals based on improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), permutation entropy (PE), and singular value decomposition (SVD). The proposed method is applied for the analysis of hydroelectric unit sway monitoring. Firstly, the ICEEMDAN method is employed to process the signal and obtain several intrinsic mode functions (IMFs), and then the PE values of each IMF are calculated. Subsequently, based on a predefined threshold of PE, appropriate IMFs are selected for reconstruction, achieving the first denoising effect. Then, the SVD is applied to the signal after the first denoising effect, resulting in the SVD spectrum. Finally, according to the principle of the SVD spectrum and the variation in the singular value and its energy value, the signal is reconstructed by choosing the appropriate reconstruction order to achieve the secondary noise reduction effect. In the simulation and case analysis, the method is better than the commonly used wavelet threshold, SVD, CEEMDAN–PE, and ICEEMDAN–PE, with a signal-to-noise ratio (SNR) improvement of 6.9870 dB, 4.6789 dB, 8.9871 dB, and 4.3762 dB, respectively, and where the root-mean-square error (RMSE) is reduced by 0.1426, 0.0824, 0.2093 and 0.0756, respectively, meaning that our method has a better denoising effect and provides a new way for denoising the vibration signal of hydropower units.
Hierarchical Network-Based Tracklets Data Association for Multiple Extended Target Tracking with Intermittent Measurements
Authors: Jiang Kaiyi , Li Yiguo , Ma Tianli , Li Lin
Published: July 2023

The key issue of multiple extended target tracking is to differentiate the origins of the measurements. The association of measurements with the possible origins within the target’s extent is difficult, especially for occlusions or detection blind zones, which cause intermittent measurements. To solve this problem, a hierarchical network-based tracklet data association algorithm (ET-HT) is proposed. At the low association level, a min-cost network flow model based on the divided measurement sets is built to extract the possible tracklets. At the high association level, these tracklets are further associated with the final trajectories. The association is formulated as an integral programming problem for finding the maximum a posterior probability in the network flow model based on the tracklets. Moreover, the state of the extended target is calculated using the in-coordinate interval Kalman smoother. Simulation and experimental results show the superiority of the proposed ET-HT algorithm over JPDA- and RFS-based methods when measurements are intermittently unavailable.
A Ku-Band GaN-on-Si MMIC Power Amplifier with an Asymmetrical Output Combiner
Authors: Pino Javier del , Khemchandani Sunil Lalchand , Mayor-Duarte Daniel , San-Miguel-Montesdeoca Mario , Mateos-Angulo Sergio , de Arriba Francisco , García María
Published: July 2023

In this paper, a microwave monolithic integrated circuit (MMIC) high-power amplifier (HPA) for Ku-band active radar applications based on gallium nitride on silicon (GaN-on-Si) is presented. The design is based on a three-stage architecture and was implemented using the D01GH technology provided by OMMIC foundry. Details on the architecture definition and design process to maximize delivered power are provided along with stability and thermal analyses. To optimize the amplifier performance, an asymmetry was included at the output combiner. Experimental results show that the HPA achieves a 39.5 dBm pulsed-mode output power, a peak linear gain of 23 dB, a drain efficiency of 27%, and good input/output matching in the 16–19 GHz frequency range. The chip area is 5 × 3.5 mm2 and for the measurements was mounted on a custom-made module. These results demonstrate that GaN-on-Si-based Solid-State Power Amplifiers (SSPAs) can be used for the implementation of Ku-band active radars.
ETAM: Next Generation Event Timer for Picosecond-Precision Time and Amplitude Measurements
Authors: Kalinovskis Aleksandrs , Stepanovs Vsevolods , Ancans Armands , Elsts Atis
Published: July 2023

Riga Event Timers have the ability to measure the interval between events with high resolution, on the order of picoseconds. However, they have several drawbacks, such as sensitivity to environmental temperature changes and an inability to capture the amplitude of the events. In this work, we present the ETAM: a next generation Event Timer. Its innovative features include adaptive correction of measurement errors based on an internal temperature sensor, and integrated peak-detector circuit to determine the amplitude of nanosecond-duration pulses. Evaluation shows that the ETAM has high thermal stability with a root mean square error (RMSE) of <3 ps in a temperature range between 0 and +40 °C, and accurate event amplitude measurement capability, with <2.3 mV RMSE in the 100–1000 mV range. These improvements allow the ETAM to be used in satellite laser ranging, optical time-domain reflectometry, and other field applications that require temperature- and amplitude-based time correction in addition to high robustness, performance, and stability.
Monitoring the Air Quality in an HVAC System via an Energy Harvesting Device
Authors: Boragno Corrado , Aiello Orazio , Caviglia Daniele D.
Published: July 2023

The energy consumption of a heating, ventilation, and air conditioning (HVAC) system represents a large amount of the total for a commercial or civic building. In order to optimize the system performance and to increase the comfort of people living or working in a building, it is necessary to monitor the relevant parameters of the circulating air flux. To this end, an array of sensors (i.e., temperature, humidity, and CO2 percentage sensors) is usually deployed along the aeraulic ducts and/or in various rooms. Generally, these sensors are powered by wires or batteries, but both methods have some drawbacks. In this paper, a possible solution to these drawbacks is proposed. It presents a wireless sensor node powered by an Energy Harvesting (EH) device acted on by the air flux itself. The collected data are transmitted to a central unit via a LoRa radio channel. The EH device can be placed in air ducts or close to air outlets.
A Double-Layer Vehicle Speed Prediction Based on BPNN-LSTM for Off-Road Vehicles
Authors: Liu Jichao , Liang Yanyan , Chen Zheng , Li Huaiyi , Zhang Weikang , Sun Junling
Published: July 2023

The accurate prediction of vehicle speed is crucial for the energy management of vehicles. The existing vehicle speed prediction (VSP) methods mainly focus on road vehicles and rarely on off-road vehicles. In this paper, a double-layer VSP method based on backpropagation neural network (BPNN) and long short-term memory (LSTM) for off-road vehicles is proposed. First of all, considering the motion characteristics of off-road vehicles, the VSP problem is established and the relationship between the variables in the problem is carefully analyzed. Then, the double-layer VSP framework is presented, which consists of speed prediction and information update layers. The speed prediction layer established by using LSTM is to predict vehicle speed in the horizon, and the information update layer built by BPNN is to update the prediction information. Finally, with the help of mining truck and loader operation scenarios, the proposed VSP method is compared with the analytical method, BPNN prediction method, and recurrent neural network (RNN) prediction method in terms of speed prediction accuracy. The results show that, under the premise of ensuring the real-time prediction performance, the average prediction error of the proposed BPNN-LSTM prediction method under two operation scenarios reduces by 48.14%, 35.82% and 30.09% compared with the other three methods, respectively. The proposed speed prediction method provides a new solution for predicting the speed of off-road vehicles, effectively improving the speed prediction accuracy.
Low-Cost Orientation Determination System for CubeSat Based Solely on Solar and Magnetic Sensors
Authors: Nurgizat Yerkebulan , Ayazbay Abu-Alim , Galayko Dimitri , Balbayev Gani , Alipbayev Kuanysh
Published: July 2023

CubeSats require accurate determination of their orientation relative to the Sun, Earth, and other celestial bodies to operate successfully and collect scientific data. This paper presents an orientation system based on solar and magnetic sensors that offers a cost-effective and reliable solution for CubeSat navigation. Solar sensors analyze the illumination on each face to measure the satellite’s orientation relative to the Sun, while magnetic sensors determine the Earth’s magnetic field vector in the satellite’s reference frame. By combining the measured data with the known ephemeris of the satellite, the satellite–Sun vector and the magnetic field orientation can be reconstructed. The orientation is expressed using quaternions, representing the rotation from the internal reference system of the satellite to the selected reference system. The proposed system demonstrates the ability to accurately determine the orientation of a CubeSat using only two sensors, making it suitable for installations where more complex and expensive instruments are impractical. Additionally, the paper presents a mathematical model of a low-cost CubeSat orientation system and a hardware implementation of the sensor. The technology, using solar and magnetic sensors, provides a reliable and affordable solution for CubeSat navigation, supporting the increasing sophistication of miniature payloads and enabling accurate satellite positioning in space missions.
Crystal Analyzer Based Multispectral Microtomography Using CCD-Sensor
Authors: Grigoriev Maxim , Zolotov Denis , Ingacheva Anastasia , Buzmakov Alexey , Dyachkova Irina , Asadchikov Victor , Chukalina Marina
Published: July 2023

To solve the problems of spectral tomography, an X-ray optical scheme was proposed, using a crystal analyzer in Laue geometry between the sample and the detector, which allowed for the selection of predetermined pairs of wavelengths from the incident polychromatic radiation to obtain projection images. On a laboratory X-ray microtomography setup, an experiment was carried out for the first time where a mixture of micro-granules of sodium chloride NaCl, silver behenate AgC22H43O2, and lithium niobate LiNbO3 was used as a test sample to identify their spatial arrangement. The elements were chosen based on the presence of absorption edges in two of the elements in the energy range of the polychromatic spectrum of the probing radiation. The method of projection distortion correction was used to preprocess the obtained projections. To interpret the obtained reconstruction results, the segmentation method based on the analysis of joint histograms was used. This allowed us to identify each of the three substances. To compare the results obtained, additional “reference” tomographic measurements were performed: one in polychromatic and two in monochromatic (MoKα-, MoKβ-lines) modes. It took three times less time for the tomographic experiment with the crystal analyzer, while the reconstruction accuracy was comparable to that of the “reference” tomography.
Smartphone Authentication System Using Personal Gaits and a Deep Learning Model
Authors: Choi Jiwoo , Choi Sangil , Kang Taewon
Published: July 2023

In a society centered on hyper-connectivity, information sharing is crucial, but it must be ensured that each piece of information is viewed only by legitimate users; for this purpose, the medium that connects information and users must be able to identify illegal users. In this paper, we propose a smartphone authentication system based on human gait, breaking away from the traditional authentication method of using the smartphone as the medium. After learning human gait features with a convolutional neural network deep learning model, it is mounted on a smartphone to determine whether the user is a legitimate user by walking for 1.8 s while carrying the smartphone. The accuracy, precision, recall, and F1-score were measured as evaluation indicators of the proposed model. These measures all achieved an average of at least 90%. The analysis results show that the proposed system has high reliability. Therefore, this study demonstrates the possibility of using human gait as a new user authentication method. In addition, compared to our previous studies, the gait data collection time for user authentication of the proposed model was reduced from 7 to 1.8 s. This reduction signifies an approximately four-fold performance enhancement through the implementation of filtering techniques and confirms that gait data collected over a short period of time can be used for user authentication.
Sensors for Biomass Monitoring in Vegetated Green Infrastructure: A Review
Authors: Jalilian Farhad , Valeo Caterina , Chu Angus , Bhiladvala Rustom
Published: July 2023

Bioretention cells, or rain gardens, can effectively reduce many contaminants in polluted stormwater through phytoremediation and bioremediation. The vegetated soil structure develops bacterial communities both within the soil and around the vegetation roots that play a significant role in the bioremediative process. Prediction of a bioretention cell’s performance and efficacy is essential to the design process, operation, and maintenance throughout the design life of the cell. One of the key hurdles to these important issues and, therefore, to appropriate designs, is the lack of effective and inexpensive devices for monitoring and quantitatively assessing this bioremediative process in the field. This research reviews the available technologies for biomass monitoring and assesses their potential for quantifying bioremediative processes in rain gardens. The methods are discussed based on accuracy and calibration requirements, potential for use in situ, in real-time, and for characterizing biofilm formation in media that undergoes large fluctuations in nutrient supply. The methods discussed are microscopical, piezoelectric, fiber-optic, thermometric, and electrochemical. Microscopical methods are precluded from field use but would be essential to the calibration and verification of any field-based sensor. Piezoelectric, fiber-optic, thermometric, and some of the electrochemical-based methods reviewed come with limitations by way of support mechanisms or insufficient detection limits. The impedance-based electrochemical method shows the most promise for applications in rain gardens, and it is supported by microscopical methods for calibration and validation.
Application of the Electromagnetic Method to the Spatial Distribution of Subsurface Saline and Fresh Water in the Coastal Mudflat Area of Jiangsu Province
Authors: Zhu Wei , Wang Wenguo , Wang Dayong , Wang Gang , Cui Aiming , Xi Yongzai , Li Fei , Zhang Baowei , Zhang Gege
Published: July 2023

Interfacial zones straddling terrestrial and marine realms, colloquially known as mudflats, epitomize a dynamic nexus between these environments and are fundamental to the coastal ecosystem. The investigation of these regions is paramount for facilitating infrastructural developments including ports, wharfs, cross-sea bridges, and the strategic utilization of freshwater resources sequestered from mainland islands amid ongoing economic progress. Terrestrial realms conventionally employ electromagnetic techniques as efficacious modalities to delineate subterranean geological information, encompassing structural details and water-bearing strata. However, the peculiar topographic and geological nuances of mudflat regions pose substantial challenges for the efficacious application of electromagnetic methodologies. The present paper endeavors to address these challenges by suggesting innovative modifications to the existing instrumentation and evolving novel data acquisition techniques specifically tailored for electromagnetic exploration within mudflat environments. This paper delves into the electrical characteristics of water-bearing layers within mudflats, and ascertains details pertaining to the subterranean structure and the spatial distribution of fresh and saline water resources, through the holistic interpretation of a multitude of profiles.
Compressed Gaussian Estimation under Low Precision Numerical Representation
Authors: Guivant Jose , Narula Karan , Kim Jonghyuk , Li Xuesong , Khan Subhan
Published: July 2023

This paper introduces a novel method for computationally efficient Gaussian estimation of high-dimensional problems such as Simultaneous Localization and Mapping (SLAM) processes and for treating certain Stochastic Partial Differential Equations (SPDEs). The authors have presented the Generalized Compressed Kalman Filter (GCKF) framework to reduce the computational complexity of the filters by partitioning the state vector into local and global and compressing the global state updates. The compressed state update, however, still suffers from high computational costs, making it challenging to implement on embedded processors. We propose a low-precision numerical representation for the global filter, such as 16-bit integer or 32-bit single-precision formats for the global covariance matrix, instead of the expensive double-precision, floating-point representation (64 bits). This truncation can inevitably cause filter instability since the truncated covariance matrix becomes overoptimistic or even turns to be an invalid covariance matrix. We introduce a Minimal Covariance Inflation (MCI) method to make the filter consistent while minimizing the truncation errors. Simulation-based experiments results show significant improvement of the proposed method with a reduction in the processing time with minimal loss of accuracy.
Coupling Analysis of Compound Continuum Robots for Surgery: Another Line of Thought
Authors: Wei Hangxing , Zhang Gang , Wang Shengsong , Zhang Peng , Su Jing , Du Fuxin
Published: July 2023

The compound continuum robot employs both concentric tube components and cable-driven continuum components to achieve its complex motions. Nevertheless, the interaction between these components causes coupling, which inevitably leads to reduced accuracy. Consequently, researchers have been striving to mitigate and compensate for this coupling-induced error in order to enhance the overall performance of the robot. This paper leverages the coupling between the components of the compound continuum robot to accomplish specific surgical procedures. Specifically, the internal concentric tube component is utilized to induce motion in the cable-driven external component, which generates coupled motion under the constraints of the cable. This approach enables the realization of high-precision surgical operations. Specifically, a kinematic model for the proposed robot is established, and an inverse kinematic algorithm is developed. In this inverse kinematic algorithm, the solution of a highly nonlinear system of equations is simplified into the solution of a single nonlinear equation. To demonstrate the effectiveness of the proposed approach, simulations are conducted to evaluate the efficiency of the algorithm. The simulations conducted in this study indicate that the proposed inverse kinematic (IK) algorithm improves computational speed by a significant margin. Specifically, it achieves a speedup of 2.8 × 103 over the Levenberg–Marquardt (LM) method. In addition, experimental results demonstrate that the coupled-motion system achieves high levels of accuracy. Specifically, the repetitive positioning accuracy is measured to be 0.9 mm, and the tracking accuracy is 1.5 mm. This paper is significant for dealing with the coupling of the compound continuum robot.
Fault Detection in Power Distribution Networks Based on Comprehensive-YOLOv5
Authors: Niu Shengsuo , Zhou Xiaosen , Zhou Dasen , Yang Zhiyao , Liang Haiping , Su Haifeng
Published: July 2023

Real-time fault detection in power distribution networks has become a popular issue in current power systems. However, the low power and computational capabilities of edge devices often fail to meet the requirements of real-time detection. To overcome these challenges, this paper proposes a lightweight algorithm, named Comprehensive-YOLOv5, for identifying defects in distribution networks. The proposed method focuses on achieving rapid localization and accurate identification of three common defects: insulator without loop, cable detachment from the insulator, and cable detachment from the spacer. Based on the You Only Look Once version 5 (YOLOv5) algorithm, this paper adopts GhostNet to reconstruct the original backbone of YOLOv5; introduces Bidirectional Feature Pyramid Network (BiFPN) structure to replace Path Aggregation Network (PANet) for feature fusion, which enhances the feature fusion ability; and replaces Generalized Intersection over Union GIOU with Focal Extended Intersection over Union (Focal-EIOU) to optimize the loss function, which improves the mean average precision and speed of the algorithm. The effectiveness of the improved Comprehensive-YOLOv5 algorithm is verified through a “morphological experiment”, while an “algorithm comparison experiment” confirms its superiority over other algorithms. Compared with the original YOLOv5, the Comprehensive-YOLOv5 algorithm improves mean average precision (mAP) from 88.3% to 90.1% and increases Frames per second (FPS) from 20 to 52 frames. This improvement significantly reduces false positives and false negatives in defect detection. Consequently, the proposed algorithm enhances detection speed and improves inspection efficiency, providing a viable solution for real-time detection and deployment at the edge of power distribution networks.
Experimental Investigation on Hover Performance of a Ducted Coaxial-Rotor UAV
Authors: Li Hai , Chen Zaibin , Jia Hongguang
Published: July 2023

This paper presents experimental investigations on aerodynamic performance of a ducted coaxial-rotor system to evaluate its potential application as a small unmanned aerial vehicle (SUAV). Aimed at determining the influence of design parameters (rotor spacing, tip clearance and rotor position within the duct) on hover performance, a variety of systematic measurements for several correlative configurations (single/coaxial rotor with or without a duct) in terms of thrust and torque, as well as power, were conducted in an attempt to identify a better aerodynamic configuration. The experimental results for the coaxial-rotor system indicated that varying rotor spacing affected the thrust-sharing proportion between the two rotors, but this had no significant effect on the propulsive efficiency. The optimal H/R ratio was identified as being 0.40, due to a larger thrust and stronger stability in the case of identical rotation speeds. As for the ducted single-rotor configuration, the tip clearance played a dominant role in improving its thrust performance, especially for smaller gaps (δ≤0.015R), while the rotor position made subordinate contributions. The maximum performance was obtained with the rotor located at the P5 position (0.31Cd from the duct lip), which resulted in an enhancement of approximately 20% in power loading over the isolated single rotor. When the coaxial rotors were surrounded within the duct, the system thrust for a given power degraded with the increasing rotor spacing, which was mainly attributed to the upper rotor suffering from heavier leakage losses. And hence, the ducted coaxial-rotor system with S1 spacing had the best propulsion efficiency and hover performance with a figure of merit of 0.61.
Stripping Enhanced Distillation—A Novel Application in Renewable CO2 to Dimethyl Ether Production and Purification
Authors: Dikić Vladimir , Zubeir Lawien , Sarić Marija , Boon Jurriaan
Published: July 2023

The transition towards a CO2 neutral industry is currently spurring many new developments regarding processes for the conversion of CO2, or CO2-rich streams, into platform molecules such as methanol and dimethyl ether (DME). New processes give rise to new separation challenges, as well as novel opportunities for joint optimization of reaction and separation. In this context, the separation of CO2 and DME can be performed very efficiently using the newly developed concept of stripping enhanced distillation (SED). SED is a distillation process that utilizes an additional stripping component (clearing gas) to promote the separation in the column. SED benefits from the utilization of the feedstock components as a clearing gas that can afterwards be recycled back to the conversion unit with the vapor distillate. Strongly improving the separation performance in the column, this approach also removes the need for external stripping mediums and, in addition, this recycling approach may significantly reduce the demand on the conversion unit upstream of SED. The benefits of using SED are demonstrated for two different processes for DME synthesis: (i) CO2–DME separation after the sorption enhanced DME synthesis (SEDMES) process, using hydrogen as clearing gas, and (ii) CO2–DME separation after direct DME synthesis via dry reforming (DIDR), using methane as a clearing gas. For the different cases, it is shown that, with minimal adaptations, the energy consumption for distillation is reduced by 20–30%, while product losses are minimized at the same time.