bluuebunny/crossref_metadata_2025 huggingface dataset:
What Changed in Medina: The Place of Peace and War in the Life of Prophet Muhammad
Authors: Sertkaya Suleyman
Published: February 2023

The Prophet of Islam, Muhammad, is depicted through extremely polar interpretations. Some perceive his life as a source for peace-making societies, whereas others portray him as a “warmonger” or “Prophet of the sword”, and use his examples to justify violence and terrorist attacks. The major incidents referred to in the latter context are the wars and conflicts that occurred after his migration to Medina. These conflicts are also prominent in sīrah narratives of his Medinan life from classical and modern periods. One can argue that there is a significant difference in the way Prophet Muhammad acted in Medina compared to the Meccan period. This is mostly attributed to the power balance, as the Muslims had little power in Mecca, which resulted in them enduring adversities, including verbal insults and physical torture while remaining peaceful and non-violent. In Medina, however, the Muslims obtained relatively more power and behaved differently. The main criticism of the Prophet at this juncture is that he took advantage of this power and became violent; this is the reason all the battles fall in his Medinan life. This article examines the root causes of his behaviour and shift in attitude. It clarifies the Prophet’s goal and agenda at this stage of his life. The article highlights his attitude towards peace and war by holistically analysing the battles and skirmishes that unfolded during the Medinan period. It examines the time spent on war and peace throughout his prophetic mission. In doing so, it enumerates statistical data, such as the number of battlefield casualties and those from expeditions. To attain accurate information in this regard, classical sīrah works and modern research on the battles are referred to as the main resources.
Diversification as a Method of Ensuring the Sustainability of Energy Supply within the Energy Transition
Authors: Gitelman Lazar , Kozhevnikov Mikhail , Visotskaya Yana
Published: February 2023

This article presents a structured approach to the implementation of diversification in the energy sector during the energy transition, accompanied by crisis phenomena in the economy. On the basis of the study of the unique features of the electric power industry, it is proved that diversification is an effective tool ensuring sustainable operation and development in the course of transformation. The specific directions of diversification to achieve technical, environmental and economic results are considered. The authors demonstrate that diversification can manifest itself in different forms: as a way to implement a systematic approach to energy transition management, as a complex set of organizational and technical changes in the electric power industry, and as an institutional mechanism to support innovations. Tools for managing diversification during the energy transition have been identified. In particular, the experience of implementing systems of integrated planning of energy resources is systematized, representing a set of legal and economic procedures that contribute to minimizing public costs for energy services to consumers. The key provisions of the structural maneuver have been formulated. These include specific directions of diversification, consisting of a certain increase in the specific market share of thermal power plants, in the structure of generation capacities of renewable energy sources in order to compensate for their increased production costs, and nuclear power plants under advanced development. The advantages and limitations of the developed principles of diversification in the context of the global energy crisis are discussed.
Different Influences on “Wave Turbopause” Exerted by 6.5 DWs and Gravity Waves
Authors: Ge Wei , Sheng Zheng , Huang Yingying , He Yang , Liao Qixiang , Chang Shujie
Published: February 2023

“Wave turbopause” is defined as the mesospheric altitude level where the temperature fluctuation field indicates a substantial increase in wave amplitude in the vertical direction. It is similar to turbopause in seasonal and latitudinal variations, providing an almost global analysis of turbopause on the basis of satellite measurements rather than localized detection. Previous studies of “wave turbopause” were based on standard deviation of temperature, which is an integrated measure of wave activity. In this study, we distinguish different atmospheric waves and investigate their influences on “wave turbopause”. By comparing the altitude of “wave turbopause” with peak height of amplitude (PHA) for gravity waves and 6.5 days waves (6.5 DWs), whose period is approximately 6.5 days in the mesosphere and lower thermosphere, we find that the seasonal variation in altitude of “wave turbopause” is higher at the winter pole and lower at the summer pole, correlated with PHA for 6.5 DWs but anti-correlated with PHA for gravity waves. We infer that gravity waves reach saturation and break at lower altitudes in the winter when Brunt–Vaisala frequency is also lower between 80 and 100 km altitudes. Finally, the results may imply that seasonal variations of the “wave turbopause” are driven mainly by 6.5 DWs.
Robust Velocity Dealiasing for Weather Radar Based on Convolutional Neural Networks
Authors: Kim Hyeri , Cheong Boonleng
Published: February 2023

Doppler weather radar is an essential tool for monitoring and warning of hazardous weather phenomena. A large aliasing range (ra) is important for surveillance but a high aliasing velocity (va) is also important to obtain storm dynamics unambiguously. However, the ra and va are inversely related to pulse repetition time. This “Doppler dilemma” is more challenging at shorter wavelengths. The proposed algorithm employs a CNN (convolutional neural network), which is widely used in image classification, to tackle the velocity dealiasing issue. Velocity aliasing can be converted to a classification problem. The velocity field and aliased count can be regarded as the input image and the label, respectively. Through a fit-and-adjust process, the best weights and the biases of the model are determined to minimize a cost function. The proposed method is compared against the traditional region-based method. Both methods show similar performance on mostly filled precipitation. For sparsely filled precipitation; however, the CNN demonstrated better performance since the CNN processes the entire scan at once while the region-based method processes only the limited adjacent area.
Coupled Thorens and Soil Conservation Service Models for Soil Erosion Assessment in a Loess Plateau Watershed, China
Authors: Li Changjia , Lu Tong , Wang Shuai , Xu Jiren
Published: February 2023

Assessing soil erosion in China’s severely eroded Loess Plateau is urgently needed but is usually limited by suitable erosion models and long-term field measurements. In this study, we coupled the Thorens and Soil Conservation Service (SCS) models to evaluate runoff and sediment yield during the 1980s and 2010s in the Xiaolihe watershed on the Loess Plateau. Results showed the proposed model framework had a satisfactory performance in modelling spatially distributed runoff and sediment yield. The Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS) and the root mean square error-measured standard deviation ratio (RSR) were 0.93, 4.42% and 0.27 for monthly runoff; and 0.31, 62.31% and 0.82 for monthly sediment yield. The effects of land use changes on runoff and sediment yield were well captured by the SCS and Thorens models. The proposed modelling framework is distributed with a simple structure, requires relatively little data that can be obtained from public datasets, and can be used to predict runoff and sediment yield in other similar ungagged or poorly monitored watersheds. This work has important implications for runoff and erosion assessment in other arid and semi-arid regions, to derive runoff and erosion rates across large areas with scarce field measurements.
Assessing the Performance of Precise Point Positioning (PPP) with the Fully Serviceable Multi-GNSS Constellations: GPS, BDS-3, and Galileo
Authors: Hou Zunyao , Zhou Feng
Published: February 2023

Nowadays, both BDS-3 and Galileo can provide global positioning and navigation services. This contribution carried out a comprehensive analysis and validation of positioning performance in terms of positioning accuracy (RMS) and convergence time, which are derived from BDS-3 and Galileo precise point positioning (PPP) solutions at a global scale. Meanwhile, the comparison with GPS was demonstrated. The performance and geographical distribution of RMS and convergence time for each satellite system were analyzed. GPS outperforms the other two systems on a global scale. Galileo and BDS-3, on the other hand, only perform moderately well in certain latitude zones. The combination of dual systems related to each single system is analyzed. For the dual-system combinations, the combination of systems presents a definite advantage over Galileo and BDS-3, and this advantage is more pronounced for the kinematic PPP. For GPS, the combination with Galileo and BDS-3 has little improvement in positioning performance. For the dual-system combination based on Galileo and BDS-3, the RMS and convergence time can be improved by 50% compared with the single system. The influence of single-system kinematic PPP selection for precise products from different MGEX analysis centers on positioning performance was studied. Among the five precise products, grg products have the best positioning performance for GPS, while cod products have the best positioning performance for Galileo and BDS-3. The difference in RMS and convergence time between 2 cm and 15 min can be caused by different precise product selections.
Spatiotemporal Changes in Supply–Demand Patterns of Carbon Sequestration Services in an Urban Agglomeration under China’s Rapid Urbanization
Authors: Hong Wenhai , Bao Guangdao , Du Yunxia , Guo Yujie , Wang Chengcong , Wang Guodong , Ren Zhibin
Published: February 2023

Quantifying the urban supply and demand of carbon sequestration services is an important prerequisite for achieving global carbon neutrality goals. However, the spatiotemporal patterns for balancing the supply and demand of carbon sequestration services in urban agglomerations remain unclear. In this study, NPP/VIIRS nighttime light data were used to identify the carbon sequestration service demand and were then combined with the carbon sequestration service supply to analyze the spatiotemporal patterns of supply and demand for carbon sequestration services in the Harbin-Changchun urban agglomeration (HCUA) in Northeast China. Our results indicate that both the supply and demand of carbon sequestration services showed increasing trends from 2012 to 2020 in the HCUA. The regions with increasing supply and demand trends were mainly located in the eastern mountainous and western urban areas, respectively. The total supply and demand of carbon sequestration services in the HCUA were 2080.3 Mt·C yr−1 and 433.6 Mt·C yr−1, respectively. Carbon surpluses (supply > demand) were found in most areas (98%), although particularly in the southeastern mountainous region. However, with rapid urbanization, in most cities, the supply–demand ratio decreased from 2012 to 2020, and the proportion of carbon deficit regions showed a continuous increase, which was mainly distributed in newly developed urban areas. The low supply–high demand (L-H) pattern showed significant spatial mismatching for supply and demand in the HCUA. The proportion of regions with the L-H pattern also showed a rapidly increasing trend from 2012 to 2020, indicating a more obvious carbon deficit trend in the future. This study provides important guidelines for formulating effective policies for energy consumption and carbon sequestration to combat global warming under China’s rapid urbanization.
Pineapples’ Detection and Segmentation Based on Faster and Mask R-CNN in UAV Imagery
Authors: Shiu Yi-Shiang , Lee Re-Yang , Chang Yen-Ching
Published: February 2023

Early production warnings are usually labor-intensive, even with remote sensing techniques in highly intensive but fragmented growing areas with various phenological stages. This study used high-resolution unmanned aerial vehicle (UAV) images with a ground sampling distance (GSD) of 3 cm to detect the plant body of pineapples. The detection targets were mature fruits mainly covered with two kinds of sun protection materials—round plastic covers and nets—which could be used to predict the yield in the next two to three months. For round plastic covers (hereafter referred to as wearing a hat), the Faster R-CNN was used to locate and count the number of mature fruits based on input image tiles with a size of 256 × 256 pixels. In the case of intersection-over-union (IoU) > 0.5, the F1-score of the hat wearer detection results was 0.849, the average precision (AP) was 0.739, the precision was 0.990, and the recall was 0.743. We used the Mask R-CNN model for other mature fruits to delineate the fields covered with nets based on input image tiles with a size of 2000 × 2000 pixels and a mean IoU (mIoU) of 0.613. Zonal statistics summed up the area with the number of fields wearing a hat and covered with nets. Then, the thresholding procedure was used to solve the potential issue of farmers’ harvesting in different batches. In pineapple cultivation fields, the zonal results revealed that the overall classification accuracy is 97.46%, and the kappa coefficient is 0.908. The results were expected to demonstrate the critical factors of yield estimation and provide researchers and agricultural administration with similar applications to give early warnings regarding production and adjustments to marketing.
Risk Evaluation of the Sanalona Earthfill Dam Located in Mexico Using Satellite Geodesy Monitoring and Numerical Modeling
Authors: Vázquez-Ontiveros J. René , Ruiz-Armenteros Antonio Miguel , de Lacy M. Clara , Gaxiola-Camacho J. Ramon , Anaya-Díaz Miguel , Vázquez-Becerra G. Esteban
Published: February 2023

Dams are essential structures in the growth of a region due to their ability to store large amounts of water and manage it for different social activities, mainly for human consumption. The study of the structural behavior of dams during their useful life is a fundamental factor for their safety. In terms of structural monitoring, classic terrestrial techniques are usually costly and require much time. Interferometric synthetic aperture radar (InSAR) technology through the persistent scatterer interferometry (PSI) technique has been widely applied to measure millimeter displacements of a dam crest. In this context, this paper presents an investigation about the structural monitoring of the crest of the Sanalona dam in Mexico, applying two geodetic satellite techniques and mathematical modeling to extract the risk of the dam–reservoir system. The applicability of the InSAR technique for monitoring radial displacements in dams is evaluated and compared with both GPS systems and an analytical model based on the finite element method (FEM). The radial displacements of the Sanalona dam follow a seasonal pattern derived from the reservoir level, reaching maximum radial magnitudes close to 13 mm in November when the rainy season ends. GPS recorded and FEM simulated maximum displacements of 7.3 and 6.7 mm, respectively. InSAR derived radial displacements, and the reservoir water level presented a high similarity with a correlation index equal to 0.8. In addition, it was found that the Sanalona dam presents the greatest deformation in the central zone of the crest. On the other hand, based on the reliability analysis, the probability of failure values lower than 8.3 × 10−2 was obtained when the reservoir level was maximum, which means that the radial displacements did not exceed the limit states of the dam–reservoir system in the evaluated period. Finally, the extracted values of the probability of failure demonstrated that the Sanalona dam does not represent a considerable risk to society.
Long-Term Wetland Monitoring Using the Landsat Archive: A Review
Authors: Demarquet Quentin , Rapinel Sébastien , Dufour Simon , Hubert-Moy Laurence
Published: February 2023

Wetlands, which provide multiple functions and ecosystem services, have decreased and been degraded worldwide for several decades due to human activities and climate change. Managers and scientists need tools to characterize and monitor wetland areas, structure, and functions in the long term and at regional and global scales and assess the effects of planning policies on their conservation status. The Landsat earth observation program has collected satellite images since 1972, which makes it the longest global earth observation record with respect to remote sensing. In this review, we describe how Landsat data have been used for long-term (≥20 years) wetland monitoring. A total of 351 articles were analyzed based on 5 topics and 22 attributes that address long-term wetland monitoring and Landsat data analysis issues. Results showed that (1) the open access Landsat archive successfully highlights changes in wetland areas, structure, and functions worldwide; (2) recent progress in artificial intelligence (AI) and machine learning opens new prospects for analyzing the Landsat archive; (3) most unexplored wetlands can be investigated using the Landsat archive; (4) new cloud-computing tools enable dense Landsat times-series to be processed over large areas. We recommend that future studies focus on changes in wetland functions using AI methods along with cloud computing. This review did not include reports and articles that do not mention the use of Landsat imagery.
A KG-Based Integrated UAV Approach for Engineering Semantic Trajectories in the Cultural Heritage Documentation Domain
Authors: Kotis Konstantinos , Angelis Sotiris , Moraitou Efthymia , Kopsachilis Vasilis , Papadopoulou Ermioni-Eirini , Soulakellis Nikolaos , Vaitis Michail
Published: February 2023

Data recordings of the movement of vehicles can be enriched with heterogeneous and multimodal data beyond latitude, longitude, and timestamp and enhanced with complementary segmentations, constituting a semantic trajectory. Semantic Web (SW) technologies have been extensively used for the semantic integration of heterogeneous and multimodal movement-related data, and for the effective modeling of semantic trajectories, in several domains. In this paper, we present an integrated solution for the engineering of cultural heritage semantic trajectories generated from unmanned aerial vehicles (UAVs) and represented as knowledge graphs (KGs). Particularly, this work is motivated by, and evaluated based on, the application domain of UAV missions for documenting regions/points of cultural heritage interest. In this context, this research work extends our previous work on UAV semantic trajectories, contributing (a) an updated methodology for the engineering of semantic trajectories as KGs (STaKG), (b) an implemented toolset for the management of KG-based semantic trajectories, (c) a refined ontology for the representation of knowledge related to UAV semantic trajectories and to cultural heritage documentation, and (d) the application and evaluation of the proposed methodology, the developed toolset, and the ontology within the domain of UAV-based cultural heritage documentation. The evaluation of the integrated UAV solution was achieved by exploiting real datasets collected during three UAV missions to document sites of cultural interest in Lesvos, Greece, i.e., the UNESCO-protected petrified forest of Lesvos Petrified Forest/Geopark, the village of Vrissa, and University Hill.
Mixed-Species Cover Crop Biomass Estimation Using Planet Imagery
Authors: Kharel Tulsi P. , Bhandari Ammar B. , Mubvumba Partson , Tyler Heather L. , Fletcher Reginald S. , Reddy Krishna N.
Published: February 2023

Cover crop biomass is helpful for weed and pest control, soil erosion control, nutrient recycling, and overall soil health and crop productivity improvement. These benefits may vary based on cover crop species and their biomass. There is growing interest in the agricultural sector of using remotely sensed imagery to estimate cover crop biomass. Four small plot study sites located at the United States Department of Agriculture Agricultural Research Service, Crop Production Systems Research Unit farm, Stoneville, MS with different cereals, legumes, and their mixture as fall-seeded cover crops were selected for this analysis. A randomized complete block design with four replications was used at all four study sites. Cover crop biomass and canopy-level hyperspectral data were collected at the end of April, just before cover crop termination. High-resolution (3 m) PlanetScope imagery (Dove satellite constellation with PS2.SD and PSB.SD sensors) was collected throughout the cover crop season from November to April in the 2021 and 2022 study cycles. Results showed that mixed cover crop increased biomass production up to 24% higher compared to single species rye. Reflectance bands (blue, green, red and near infrared) and vegetation indices derived from imagery collected during March were more strongly correlated with biomass (r = 0–0.74) compared to imagery from November (r = 0.01–0.41) and April (r = 0.03–0.57), suggesting that the timing of imagery acquisition is important for biomass estimation. The highest correlation was observed with the near-infrared band (r = 0.74) during March. The R2 for biomass prediction with the random forest model improved from 0.25 to 0.61 when cover crop species/mix information was added along with Planet imagery bands and vegetation indices as biomass predictors. More study with multiple timepoint biomass, hyperspectral, and imagery collection is needed to choose appropriate bands and estimate the biomass of mix cover crop species.
Peculiarities of Resonant Absorption of Electromagnetic Signals in Multilayer Bolometric Sensors
Authors: Bogatskaya Anna V. , Klenov Nikolay V. , Popov Alexander M. , Schegolev Andrey E. , Titovets Pavel A. , Tereshonok Maxim V.
Published: February 2023

We examine the effect of resonant absorption of electromagnetic signals in a silicon semiconductor plasma layer when the dielectric plate is placed behind it both experimentally and numerically. It is shown that such plate acts as a dielectric resonator and can significantly increase the electromagnetic energy absorption in the semiconductor for certain frequencies determined by the dielectric plate parameters. Numerical modelling of the effect is performed under the conditions of conducted experiment. The numerical results are found to be in qualitative agreement with experimental ones. This study confirms the proposed earlier method of increasing the efficiency of bolometric-type detectors of electromagnetic radiation.
Affordable Robotic Mobile Mapping System Based on Lidar with Additional Rotating Planar Reflector
Authors: Będkowski Janusz , Pełka Michał
Published: February 2023

This paper describes an affordable robotic mobile 3D mapping system. It is built with Livox Mid–40 lidar with a conic field of view extended by a custom rotating planar reflector. This 3D sensor is compared with the more expensive Velodyne VLP 16 lidar. It is shown that the proposed sensor reaches satisfactory accuracy and range. Furthermore, it is able to preserve the metric accuracy and non–repetitive scanning pattern of the unmodified sensor. Due to preserving the non–repetitive scan pattern, our system is capable of covering the entire field of view of 38.4 × 360 degrees, which is an added value of conducted research. We show the calibration method, mechanical design, and synchronization details that are necessary to replicate our system. This work extends the applicability of solid–state lidars since the field of view can be reshaped with minimal loss of measurement properties. The solution was part of a system that was evaluated during the 3rd European Robotics Hackathon in the Zwentendorf Nuclear Power Plant. The experimental part of the paper demonstrates that our affordable robotic mobile 3D mapping system is capable of providing 3D maps of a nuclear facility that are comparable to the more expensive solution.
Optimal Resource Allocation for 5G Network Slice Requests Based on Combined PROMETHEE-II and SLE Strategy
Authors: Venkatapathy Sujitha , Srinivasan Thiruvenkadam , Jo Han-Gue , Ra In-Ho
Published: February 2023

The network slicing of physical infrastructure is required for fifth-generation mobile networks to make significant changes in how service providers deliver and defend services in the face of evolving end-user performance requirements. To perform this, a fast and secure slicing technique is employed for node allocation and connection establishment, which necessitates the usage of a large number of domain applications across the network. PROMETHEE-II and SLE algorithms were used in this study’s approach to network design for node allocation and link construction, respectively. The PROMETHEE-II approach takes into account a variety of node characteristics while constructing a node importance rank array (NIRA), including the node capacity, bandwidth of neighboring connections, degree of the node, and proximity centrality among others. The SLE method is proposed to record all possible link configurations for the network slice request (NSR) nodes to guarantee that the shortest path array (SPA) of the NSR has a high acceptance rate. Performance metrics such as the service revenue and acceptance ratio were considered to evaluate the effectiveness of the suggested approach. The effectiveness of network slicing has been further examined under different infrastructure models to determine whether a small-world network structure is beneficial to 5G network. For each scenario, simulations were carried out and the results were compared to previously published findings from other sources.
Detection of Missing Insulator Caps Based on Machine Learning and Morphological Detection
Authors: Zhang Zhaoyun , Chen Hefan , Huang Shihong
Published: February 2023

Missing insulator caps are the key focus of transmission line inspection work. Insulators with a missing cap will experience decreased insulation and mechanical strength and cause transmission line safety accidents. As missing insulator caps often occur in glass and porcelain insulators, this paper proposes a detection method for missing insulator caps in these materials. First, according to the grayscale and color characteristics of these insulators, similar characteristic regions of the insulators are extracted from inspection images, and candidate boxes are generated based on these characteristic regions. Second, the images captured by these boxes are input into the classifier composed of SVM (Support Vector Machine) to identify and locate the insulators. The accuracy, recall and average accuracy of the classifier are all higher than 90%. Finally, this paper proposes a processing method based on the insulator morphology to determine whether an insulator cap is missing. The proposed method can also detect the number of remaining insulators, which can help power supply enterprises to evaluate the degree of insulator damage.
Correlation-Based Anomaly Detection in Industrial Control Systems
Authors: Jadidi Zahra , Pal Shantanu , Hussain Mukhtar , Nguyen Thanh Kien
Published: February 2023

Industrial Control Systems (ICSs) were initially designed to be operated in an isolated network. However, recently, ICSs have been increasingly connected to the Internet to expand their capability, such as remote management. This interconnectivity of ICSs exposes them to cyber-attacks. At the same time, cyber-attacks in ICS networks are different compared to traditional Information Technology (IT) networks. Cyber attacks on ICSs usually involve a sequence of actions and a multitude of devices. However, current anomaly detection systems only focus on local analysis, which misses the correlation between devices and the progress of attacks over time. As a consequence, they lack an effective way to detect attacks at an entire network scale and predict possible future actions of an attack, which is of significant interest to security analysts to identify the weaknesses of their network and prevent similar attacks in the future. To address these two key issues, this paper presents a system-wide anomaly detection solution using recurrent neural networks combined with correlation analysis techniques. The proposed solution has a two-layer analysis. The first layer targets attack detection, and the second layer analyses the detected attack to predict the next possible attack actions. The main contribution of this paper is the proof of the concept implementation using two real-world ICS datasets, SWaT and Power System Attack. Moreover, we show that the proposed solution effectively detects anomalies and attacks on the scale of the entire ICS network.
Precise Measurement and Compensation of the Micro Product of Inertia for Float Assembly in Pendulous Integrating Gyroscopic Accelerometers
Authors: Zhou Xiaojun , Yang Gongliu , Niu Wentao , Tu Yongqiang
Published: February 2023

Nonlinear error has become the most critical factor restricting the measurement accuracy of pendulous integrating gyroscopic accelerometers (PIGA) during their improvement. The key to nonlinear error suppression for PIGA is the precise measurement and compensation of the micro product of inertia (MPOI) of the float assembly. However, the existing equipment and procedure for product of inertia (POI) measurement and compensation do not meet the accuracy requirements for MPOI. To solve this problem, novel equipment and procedures are proposed for the measurement and compensation of MPOI. The principle of the proposed measurement method is to simulate the error produced by MPOI in PIGA by using a single-axis turntable to rotate the float assembly along the eccentric axis to generate a centrifugal moment due to MPOI. The principle of the proposed compensation method is to remove the asymmetric mass to reduce the MPOI to zero. Through experimental validation, it is concluded that: (1) the measurement and compensation accuracy of the proposed method are better than 1 × 10−10 kg·m2 and 3 × 10−10 kg·m2, respectively; (2) the proposed method is validated as the MPOI is reduced from 7.3 × 10−9 kg·m2 to 3 × 10−10 kg·m2 for a real float assembly in PIGA, and the quadratic error of PIGA is reduced from 10−5/g0 to 3 × 10−7/g0.
A High-Linearity Closed-Loop Capacitive Micro-Accelerometer Based on Ring-Diode Capacitance Detection
Authors: Tao Qi , Tang Bin
Published: February 2023

This paper presents an implementation scheme and experimental evaluation of a high-linearity closed-loop capacitive accelerometer based on ring-diode capacitance detection. By deducing the capacitance detection model of the ring-diode considering the influence of the diode, the existing theoretical model error of the ring-diode is corrected and a closed-loop scheme of reusing the detection electrode and the control electrode of the MEMS die is designed to apply this detection scheme to the parallel-plate accelerometer, which only has three independent electrodes. After analyzing the non-linear problems in the existing closed-loop control schemes, a theoretically absolute linear closed-loop control scheme is proposed, and an integrated closed-loop accelerometer is realized by combining the closed-loop diode detection. The experimental results of the ring-diode detection model are in agreement with the theoretical formula. The non-linearity of the accelerometer within ±1 g after the closed-loop is 130 ppm, compared with 1500 ppm when the open-loop is used.
A Hydrogel-Based Electronic Skin for Touch Detection Using Electrical Impedance Tomography
Authors: Zhang Huiyang , Kalra Anubha , Lowe Andrew , Yu Yang , Anand Gautam
Published: February 2023

Recent advancement in wearable and robot-assisted healthcare technology gives rise to the demand for smart interfaces that allow more efficient human-machine interaction. In this paper, a hydrogel-based soft sensor for subtle touch detection is proposed. Adopting the working principle of a biomedical imaging technology known as electrical impedance tomography (EIT), the sensor produces images that display the electrical conductivity distribution of its sensitive region to enable touch detection. The sensor was made from a natural gelatin hydrogel whose electrical conductivity is considerably less than that of human skin. The low conductivity of the sensor enabled a touch-detection mechanism based on a novel short-circuiting approach, which resulted in the reconstructed images being predominantly affected by the electrical contact between the sensor and fingertips, rather than the conventionally used piezoresistive response of the sensing material. The experimental results indicated that the proposed sensor was promising for detecting subtle contacts without the necessity of exerting a noticeable force on the sensor.
VLSI Design Based on Block Truncation Coding for Real-Time Color Image Compression for IoT
Authors: Chen Shih-Lun , Chou He-Sheng , Ke Shih-Yao , Chen Chiung-An , Chen Tsung-Yi , Chan Mei-Ling , Abu Patricia Angela R. , Wang Liang-Hung , Li Kuo-Chen
Published: February 2023

It has always been a major issue for a hospital to acquire real-time information about a patient in emergency situations. Because of this, this research presents a novel high-compression-ratio and real-time-process image compression very-large-scale integration (VLSI) design for image sensors in the Internet of Things (IoT). The design consists of a YEF transform, color sampling, block truncation coding (BTC), threshold optimization, sub-sampling, prediction, quantization, and Golomb–Rice coding. By using machine learning, different BTC parameters are trained to achieve the optimal solution given the parameters. Two optimal reconstruction values and bitmaps for each 4 × 4 block are achieved. An image is divided into 4 × 4 blocks by BTC for numerical conversion and removing inter-pixel redundancy. The sub-sampling, prediction, and quantization steps are performed to reduce redundant information. Finally, the value with a high probability will be coded using Golomb–Rice coding. The proposed algorithm has a higher compression ratio than traditional BTC-based image compression algorithms. Moreover, this research also proposes a real-time image compression chip design based on low-complexity and pipelined architecture by using TSMC 0.18 μm CMOS technology. The operating frequency of the chip can achieve 100 MHz. The core area and the number of logic gates are 598,880 μm2 and 56.3 K, respectively. In addition, this design achieves 50 frames per second, which is suitable for real-time CMOS image sensor compression.
Ambient Hot Box: An Instrument for Thermal Characterization of Building Elements and Constructive Materials
Authors: Carmona Cristian , Muñoz Joan , Alorda-Ladaria Bartomeu
Published: February 2023

In assessing the energy performance of buildings, the thermal performance of the structural components and building materials is crucial. Although reference catalogs are used to determine the thermal properties of construction materials, the use of novel materials or non-homogeneous mixtures, particularly with biomaterials, demands the development of new instruments that are capable of performing rapid, accurate and cost-effective thermal characterization. This study introduces the ambient hot-box, a new tool for measuring the thermal properties of construction components and heterogeneous materials. The paper provides a methodology for measuring a sample’s benchmark and fresh materials using a streamlined hot-box-based instrument. Utilizing samples as a benchmark material, the new instrument is assessed, yielding transmittance values with errors below 4%. The electronic circuits, measurements techniques and instrument implementation are all described.
SpaceART SpaceWire Sniffer for Link Monitoring: A Complete Communication Analysis in a Time-Constrained Test Scenario
Authors: Ciardi Roberto , Vagaggini Simone , Marino Antonino , Nannipieri Pietro , Fanucci Luca
Published: February 2023

The on-board communication standard adopted in current generation space missions of the European Space Agency, and many other agencies as well, is SpaceWire (SpW). In a SpW network, data are exchanged as well-formed packets, whose structure offers low packet overhead and allows developers to tailor their implementation for SpW applications. The development of SpW-compliant devices requires a specific set of test instruments, namely Electrical Ground Support Equipment (EGSE), to verify the correct functionality of SpW units under test. An example of a SpW EGSE is the SpaceART EGSE Emulator, an EGSE for generation and processing of SpW packets for a time-constrained use-case scenario. This EGSE has been developed to address a mission-related SpW communication between a device and an Instrument Control Unit (ICU). It has allowed the generation and processing of specific SpW packets, which cannot be provided by the mostly used general-purpose SpW EGSEs. In this scope, the SpaceART SpW Sniffer, a SpW link analyser for unobtrusive monitoring of SpW link, has been employed to run a comprehensive set of tests and provide further information on the considered scenario. The SpaceART EGSE Emulator-ICU communication has been thoroughly tested through the SpaceART SpW Sniffer, unobtrusively analysing the exchanged data and allowing to assess the compliance with the defined time constraints from an external point of view. The use of the Sniffer has been crucial for testing the on-board communication, providing important support for the success of the mission employing the tested ICU.
On Coverage of Critical Nodes in UAV-Assisted Emergency Networks
Authors: Waheed Maham , Ahmad Rizwan , Ahmed Waqas , Mahtab Alam Muhammad , Magarini Maurizio
Published: February 2023

Unmanned aerial vehicle (UAV)-assisted networks ensure agile and flexible solutions based on the inherent attributes of mobility and altitude adaptation. These features render them suitable for emergency search and rescue operations. Emergency networks (ENs) differ from conventional networks. They often encounter nodes with vital information, i.e., critical nodes (CNs). The efficacy of search and rescue operations highly depends on the eminent coverage of critical nodes to retrieve crucial data. In a UAV-assisted EN, the information delivery from these critical nodes can be ensured through quality-of-service (QoS) guarantees, such as capacity and age of information (AoI). In this work, optimized UAV placement for critical nodes in emergency networks is studied. Two different optimization problems, namely capacity maximization and age of information minimization, are formulated based on the nature of node criticality. Capacity maximization provides general QoS enhancement for critical nodes, whereas AoI is focused on nodes carrying critical information. Simulations carried out in this paper aim to find the optimal placement for each problem based on a two-step approach. At first, the disaster region is partitioned based on CNs’ aggregation. Reinforcement learning (RL) is then applied to observe optimal placement. Finally, network coverage over optimal UAV(s) placement is studied for two scenarios, i.e., network-centric and user-centric. In addition to providing coverage to critical nodes, the proposed scheme also ensures maximum coverage for all on-scene available devices (OSAs).
Multi-Technology Multi-Operator Site Sharing: Compliance Distance Analysis for EMF Exposure
Authors: Elbasheir Mohammed S. , Saeed Rashid A. , Edam Salaheldin
Published: February 2023

In recent years, the development of wireless technologies has led to fast growth in mobile networks, especially with the rise of 5G New Radio (5G NR). A huge number of base stations (BSs) are mandatory to serve the growth of mobile services, which has led to concerns about the increase in electromagnetic field (EMF) radiation exposure levels. To control the overall power emitted by EMF transmitters, international bodies have set maximum exposure limits. This paper investigates the compliance distances (CDs) of shared sites by a group of Mobile Network Operators (MNO) as multi-operators operating with multi-technology and sharing the same tower. The study investigated the CDs of the most two commonly used types of sharing sites, macro and indoor-Based solution sites (IBS). In addition, the study analyzed the power densities and total exposure ratios for the general public and occupational workers in each sharing scenario. The results showed that, compared with a single MNO, the CD increased by 41% in the case of two MNOs, 73% for three MNOs, and 100% for four MNOs. The EMF site sharing scale-up formula was used to estimate the increase in CDs for N number of MNOs assuming that all MNOs use the same site configuration. In addition, the results showed that 5G has the highest contribution to the total exposure ratio (TER) at the CD in the main direction of the antennae.
Unsupervised Drones Swarm Characterization Using RF Signals Analysis and Machine Learning Methods
Authors: Ashush Nerya , Greenberg Shlomo , Manor Erez , Ben-Shimol Yehuda
Published: February 2023

Autonomous unmanned aerial vehicles (UAVs) have attracted increasing academic and industrial attention during the last decade. Using drones have broad benefits in diverse areas, such as civil and military applications, aerial photography and videography, mapping and surveying, agriculture, and disaster management. However, the recent development and innovation in the field of drone (UAV) technology have led to malicious usage of the technology, including the penetration of secure areas (such as airports) and serving terrorist attacks. Autonomous weapon systems might use drone swarms to perform more complex military tasks. Utilizing a large number of drones, simultaneously increases the risk and the reliability of the mission in terms of redundancy, survivability, scalability, and the quality of autonomous performance in a complex environment. This research suggests a new approach for drone swarm characterization and detection using RF signals analysis and various machine learning methods. While most of the existing drone detection and classification methods are typically related to a single drone classification, using supervised approaches, this research work proposes an unsupervised approach for drone swarm characterization. The proposed method utilizes the different radio frequency (RF) signatures of the drone’s transmitters. Various kinds of frequency transform, such as the continuous, discrete, and wavelet scattering transform, have been applied to extract RF features from the radio frequency fingerprint, which have then been used as input for the unsupervised classifier. To reduce the input data dimension, we suggest using unsupervised approaches such as Principal component analysis (PCA), independent component analysis (ICA), uniform manifold approximation and projection (UMAP), and the t-distributed symmetric neighbor embedding (t-SNE) algorithms. The proposed clustering approach is based on common unsupervised methods, including K-means, mean shift, and X-means algorithms. The proposed approach has been evaluated using self-built and common drone swarm datasets. The results demonstrate a classification accuracy of about 95% under additive Gaussian white noise with different levels of SNR.
Helmet-Mounted Real-Time Toxic Gas Monitoring and Prevention System for Workers in Confined Places
Authors: Rajakumar Janani Priyanka Perumpally , Choi Jae-ho
Published: February 2023

Occupational health and safety hazards associated with confined places are mainly caused by exposure to toxic gases and oxygen deficiency. Lack of awareness, inappropriate monitoring, and improper evacuation methods can lead to worker fatalities. Although previous studies have attempted to develop systems to solve this issue, limited research is available on their application in confined places. In this study, a real-time helmet-mounted system was developed to monitor major toxic gases (methane (CH4), hydrogen sulfide (H2S), ammonia (NH3), and carbon monoxide (CO)), oxygen, temperature, and humidity. Workers outside and inside confined spaces receive alerts every second to immediately initiate the rescue operation in the event of a hazard. The test results of a confined environment (wastewater treatment unit) highlighted that concentrations of CH4 and H2S were predominant (13 ppm). Compared to normal atmosphere, CH4 concentration was 122- and 130-fold higher in the landfill and digestion tanks, respectively, while H2S was 36- and 19-fold higher in the primary and secondary clarifiers, respectively. The oxygen content (18.2%) and humidity (33%) were below the minimum required limits. This study will benefit future research to target appropriate toxic gas monitoring and alert workers by studying the existing issues and associated factors in confined places.
Power Efficient Machine Learning Models Deployment on Edge IoT Devices
Authors: Fanariotis Anastasios , Orphanoudakis Theofanis , Kotrotsios Konstantinos , Fotopoulos Vassilis , Keramidas George , Karkazis Panagiotis
Published: February 2023

Computing has undergone a significant transformation over the past two decades, shifting from a machine-based approach to a human-centric, virtually invisible service known as ubiquitous or pervasive computing. This change has been achieved by incorporating small embedded devices into a larger computational system, connected through networking and referred to as edge devices. When these devices are also connected to the Internet, they are generally named Internet-of-Thing (IoT) devices. Developing Machine Learning (ML) algorithms on these types of devices allows them to provide Artificial Intelligence (AI) inference functions such as computer vision, pattern recognition, etc. However, this capability is severely limited by the device’s resource scarcity. Embedded devices have limited computational and power resources available while they must maintain a high degree of autonomy. While there are several published studies that address the computational weakness of these small systems-mostly through optimization and compression of neural networks- they often neglect the power consumption and efficiency implications of these techniques. This study presents power efficiency experimental results from the application of well-known and proven optimization methods using a set of well-known ML models. The results are presented in a meaningful manner considering the “real world” functionality of devices and the provided results are compared with the basic “idle” power consumption of each of the selected systems. Two different systems with completely different architectures and capabilities were used providing us with results that led to interesting conclusions related to the power efficiency of each architecture.