bluuebunny/crossref_metadata_2025 huggingface dataset:
Innovation-Based Fault Detection and Exclusion Applied to Ultra-WideBand Augmented Urban GNSS Navigation
Authors: Zabalegui Paul , De Miguel Gorka , Mendizabal Jaizki , Adin Iñigo
Published: December 2022

Due to their ability to provide a worldwide absolute outdoor positioning, Global Navigation Satellite Systems (GNSS) have become a reference technology in terms of navigation technologies. Transportation-related sectors make use of this technology in order to obtain a position, velocity, and time solution for different outdoor tasks and applications. However, the performance of GNSS-based navigation is degraded when employed in urban areas in which satellite visibility is not good enough or nonexistent, as the ranging signals become obstructed or reflected by any of the numerous surrounding objects. For these situations, Ultra-Wideband (UWB) technology is a perfect candidate to complement GNSS as a navigation solution, as its anchor trilateration-based radiofrequency positioning resembles GNSS’s principle. Nevertheless, this fusion is vulnerable to interferences affecting both systems, since multiple signal-degrading error sources can be found in urban environments. Moreover, an inadequate location of the augmenting UWB transmitters can introduce additional errors to the system due to its vulnerability to the multipath effect. Therefore, the misbehavior of an augmentation system could lead to unexpected and critical faults instead of improving the performance of the standalone GNSS. Accordingly, this research work presents the performance improvement caused by the application of Fault Detection and Exclusion methods when applied to a UWB-augmented low-cost GNSS system in urban environments.
Satellite-Observed Spatial and Temporal Sea Surface Temperature Trends of the Baltic Sea between 1982 and 2021
Authors: Jamali Sadegh , Ghorbanian Arsalan , Abdi Abdulhakim M.
Published: December 2022

The Baltic Sea is one of the fastest-warming marginal seas globally, and its temperature rise has adversely affected its physical and biochemical characteristics. In this study, forty years (1982–2021) of sea surface temperature (SST) data from the advanced very high resolution radiometer (AVHRR) were used to investigate spatial and temporal SST variability of the Baltic Sea. To this end, annual maximum and minimum SST stacked series, i.e., time series of stacked layers of satellite data, were generated using high-quality observations acquired at night and were fed to an automatic algorithm to detect linear and non-linear trend patterns. The linear trend pattern was the dominant trend type in both stacked series, while more pixels with non-linear trend patterns were detected when using the annual minimum SST. However, both stacked series showed increases in SST across the Baltic Sea. Annual maximum SST increased by an average of 0.062 ± 0.041 °C per year between 1982 and 2021, while annual minimum SST increased by an average of 0.035 ± 0.017 °C per year over the same period. Averaging annual maximum and minimum trends produces a spatial average of 0.048 ± 0.022 °C rise in SST per year over the last four decades.
An Anchor-Free Detection Algorithm for SAR Ship Targets with Deep Saliency Representation
Authors: Lv Jianming , Chen Jie , Huang Zhixiang , Wan Huiyao , Zhou Chunyan , Wang Daoyuan , Wu Bocai , Sun Long
Published: December 2022

Target detection in synthetic aperture radar (SAR) images has a wide range of applications in military and civilian fields. However, for engineering applications involving edge deployment, it is difficult to find a suitable balance of accuracy and speed for anchor-based SAR image target detection algorithms. Thus, an anchor-free detection algorithm for SAR ship targets with deep saliency representation, called SRDet, is proposed in this paper to improve SAR ship detection performance against complex backgrounds. First, we design a data enhancement method considering semantic relationships. Second, the state-of-the-art anchor-free target detection framework CenterNet2 is used as a benchmark, and a new feature-enhancing lightweight backbone, called LWBackbone, is designed to reduce the number of model parameters while effectively extracting the salient features of SAR targets. Additionally, a new mixed-domain attention mechanism, called CNAM, is proposed to effectively suppress interference from complex land backgrounds and highlight the target area. Finally, we construct a receptive-field-enhanced detection head module, called RFEHead, to improve the multiscale perception performance of the detection head. Experimental results based on three large-scale SAR target detection datasets, SSDD, HRSID and SAR-ship-dataset, show that our algorithm achieves a better balance between ship target detection accuracy and speed and exhibits excellent generalization performance.
Spatial-Temporal Evolution and Influencing Factors Analysis of Ecosystem Services Value: A Case Study in Sunan Canal Basin of Jiangsu Province, Eastern China
Authors: Zhang Xiaoyan , Shen Juqin , Sun Fuhua , Wang Shou
Published: December 2022

The continuing degradation of ecosystem functioning, particularly in areas of fast-growing economies, is a common challenge facing humanity today and a focus of global research on sustainable development. A typical fast-developing economic region in Southeastern China, namely Sunan Canal Basin of Jiangsu Province (SCBJ), was selected for this study. Based on remote sensing monitoring data of land use for five periods of 2000, 2005, 2010, 2015, and 2019, the ecosystem service value (ESV) of SCBJ was measured using the equivalent factor method, and the spatial and temporal evolution of the ESV was analyzed using contribution and spatial statistical methods. Panel quantile regression was employed to explore in depth the segmental effects of the ecosystem service influencing factors and to increase the understanding of ecosystem service influencing mechanisms. Our results showed the following: (1) From 2000 to 2019, the land use structure was stable, and the comprehensive land use dynamic degree was continuously reduced. There were significant differences in the single land use dynamic degree of each land type, especially for built-up land. (2) During the study period, the total ESV increased first and then decreased, with the decreasing regions mainly distributed in the southeast of SCBJ and the urban rapid expansion areas on both sides of the canal. (3) In areas with different levels of ESV, the influencing factors had different impact effects. The mean proximity index (MPI), i.e., land use integrity, had a significant enhancement effect at the low ESV region and a significant dampening effect at the high ESV region. As the quantile points increase, the positive effects of the annual mean temperature (ATE), annual mean precipitation (APR), and net primary productivity (NPP) on ecosystem services gradually increase, which increased the gap between high and low ESV areas, creating a “natural Matthew effect”, while the negative effects of economic density (GDP) and population density (POP) on ecosystem services gradually decreased.
Effects of the Gully Land Consolidation Project on Geohazards on a Typical Watershed on the Loess Plateau of China
Authors: Wang Xiaochen , Xu Qiang , Pu Chuanhao , Li Weile , Zhao Kuanyao , Li Zhigang , Chen Wanlin , Xiu Dehao
Published: December 2022

From 2011 to 2013, a mega project, known as the Gully Land Consolidation Project (GLCP), was implemented in the hilly gully region atop China’s Loess Plateau. However, the GLCP involved large-scale slope excavation and gully backfilling that changed the local geological environment, which led to serious geohazards, such as erosion, soil salinization, and dam failure. In this study, various geohazards caused by the GLCP in the Gutun watershed (GTW) were investigated by combined remote sensing analysis, geophysical exploration, and field surveys, and the relationships between the hazards were also explored. According to the achieved results, increased soil erosion with an average doubling from 2018 to 2020 is widely distributed in the GTW. Furthermore, 195 areas containing clear evidence of salt precipitates were observed in some of the newly created arable lands, especially downstream of the dam. This was mainly attributed to the high water table, evaporation, and soluble salt concentration of the loess. Fifty-nine newly built silt dams, primarily located in the branch channels and at the gully mouth of the Gutun channel, broke in 2020. The osmotic damage and softening caused by the combined effect of the incomplete compaction of the dam body and concentrated heavy rainfall were the main reasons of the dam breaks. The different types of disasters in the GTW after the implementation of the GLCP show a strong spatial relationship that follows the surface water flow path and forms a disaster chain consisting of slope erosion, silt dam breaks, and the soil salinization of near-dam farmlands downstream. This disaster chain amplifies disaster risks and losses. These findings can guide the improvement of the GLCP and inform geohazard mitigation strategies in areas impacted by the GLCP.
Satellite Multi-Sensor Data Analysis of Unusually Strong Polar Lows over the Chukchi and Beaufort Seas in October 2017
Authors: Gurvich Irina , Pichugin Mikhail , Baranyuk Anastasiya
Published: December 2022

Polar lows (PLs) are intense mesoscale weather systems that often cause severe storm winds in the Nordic Seas but were considered as being exceedingly rare in the Pacific Arctic region before sea ice decline. Here, we explore four PLs observed on 18–22 October 2017 in the Chukchi and Beaufort Seas—an area with an exceptionally sparse observation network. The study is based on the combined use of the satellite microwave measurements, as well as infrared imagery, the ERA5, MERRA-2 and NCEP-CFSv2 reanalysis data sets. An unusually strong PLs pair developed near the marginal ice zone during a marine-cold air outbreak in anomalously low sea ice extent conditions. PLs pair moved southward as a mesocyclonic system called the “merry-go-round”, under the upper-level tropospheric vortex with a cold core. Multi-sensor satellite measurements show that, in the mature stage, a PL pair had near-surface wind speeds (W) close to hurricane force—over 30 m/s. Comparison analysis of W distributions within the strongest PL showed that all reanalysis data sets reasonably reproduce the PL median wind speed but underestimate its extreme values by 15–23%. The reanalysis data sets detected only two PLs with horizontal scales of over 220 km. Tracks of identified PLs for all data sets are in good agreement with the ones obtained from satellite images capturing the main features of the mesoscale weather system propagation. For the track of the strongest PL event, ERA5 exhibited the most accordance with satellite observations with a tracking error of 50–60 km.
Cooperative Electromagnetic Data Annotation via Low-Rank Matrix Completion
Authors: Zhang Wei , Yang Jian , Li Qiang , Lin Jingran , Shao Huaizong , Sun Guomin
Published: December 2022

Electromagnetic data annotation is one of the most important steps in many signal processing applications, e.g., radar signal deinterleaving and radar mode analysis. This work considers cooperative electromagnetic data annotation from multiple reconnaissance receivers/platforms. By exploiting the inherent correlation of the electromagnetic signal, as well as the correlation of the observations from multiple receivers, a low-rank matrix recovery formulation is proposed for the cooperative annotation problem. Specifically, considering the measured parameters of the same emitter should be roughly the same at different platforms, the cooperative annotation is modeled as a low-rank matrix recovery problem, which is solved iteratively either by the rank minimization method or the maximum-rank decomposition method. A comparison of the two methods, with the traditional annotation method on both the synthetic and real data, is given. Numerical experiments show that the proposed methods can effectively recover missing annotations and correct annotation errors.
Informativeness of the Long-Term Average Spectral Characteristics of the Bare Soil Surface for the Detection of Soil Cover Degradation with the Neural Network Filtering of Remote Sensing Data
Authors: Rukhovich Dmitry I. , Koroleva Polina V. , Rukhovich Alexey D. , Komissarov Mikhail A.
Published: December 2022

The long-term spectral characteristics of the bare soil surface (BSS) in the BLUE, GREEN, RED, NIR, SWIR1, and SWIR2 Landsat spectral bands are poorly studied. Most often, the RED and NIR spectral bands are used to analyze the spatial heterogeneity of the soil cover; in our opinion, it is outmoded and seems unreasonable. The study of multi-temporal spectral characteristics requires the processing of big remote sensing data based on artificial intelligence in the form of convolutional neural networks. The analysis of BSS belongs to the direct methods of analysis of the soil cover. Soil degradation can be detected by ground methods (field reconnaissance surveys), modeling, or digital methods, and based on the remote sensing data (RSD) analysis. Ground methods are laborious, and modeling gives indirect results. RSD analysis can be based on the principles of calculation of vegetation indices (VIs) and on the BSS identification. The calculation of VIs also provides indirect information about the soil cover through the state of vegetation. BSS analysis is a direct method for analyzing soil cover heterogeneity. In this work, the informativeness of the long-term (37 years) average spectral characteristics of the BLUE, GREEN, RED, NIR, SWIR1 and SWIR2 bands of the Landsat 4–8 satellites for detecting areas of soil degradation with recognition of the BSS using deep machine learning methods was estimated. The objects of study are the spectral characteristics of kastanozems (dark chestnut soils) in the south of Russia in the territory of the Morozovsky district of the Rostov region. Soil degradation in this area is mainly caused by erosion. The following methods were used: retrospective monitoring of soil and land cover, deep machine learning using convolutional neural networks, and cartographic analysis. Six new maps of the average long-term spectral brightness of the BSS have been obtained. The information content of the BSS for six spectral bands has been verified on the basis of ground surveys. The informativeness was determined by the percentage of coincidences of degradation facts identified during the RSD analysis, and those determined in the field. It has been established that the spectral bands line up in the following descending order of information content: RED, NIR, GREEN, BLUE, SWIR1, SWIR2. The accuracy of degradation maps by band was determined as: RED—84.6%, NIR—82.9%, GREEN—78.0%, BLUE—78.0%, SWIR1—75.5%, SWIR2—62.2%.
An Improved Iterative Reweighted STAP Algorithm for Airborne Radar
Authors: Cui Weichen , Wang Tong , Wang Degen , Liu Cheng
Published: December 2022

In recent years, sparse recovery-based space-time adaptive processing (SR-STAP) technique has exhibited excellent performance with insufficient samples. Sparse Bayesian learning algorithms have received considerable attention for their remarkable and reliable performance. Its implementation in large-scale radar systems is however hindered by the overwhelming computational load and slow convergence speed. This paper aims to address these drawbacks by proposing an improved iterative reweighted sparse Bayesian learning algorithm based on expansion-compression variance-components (ExCoV-IIR-MSBL). Firstly, a modified Bayesian probabilistic model for SR-STAP is introduced. Exploiting the intrinsic sparsity prior of the clutter, we divide the space-time coefficients into two parts: the significant part with nontrivial coefficients and the irrelevant part with small or zero coefficients. Meanwhile, we only assign independent hyperparameters to the coefficients in the significant part, while the remaining coefficients share a common hyperparameter. Then the generalized maximum likelihood (GML) criterion is adopted to classify the coefficients, ensuring both accuracy and efficiency. Hence, the parameter space in Bayesian inference will be significantly reduced, and the computational efficiency can be considerably promoted. Both theoretical analysis and numerical experiments validate that the proposed algorithm achieves superior performance with considerably improved computational efficiency in sample shortage scenarios.
Real-Time LEO Satellite Orbits Based on Batch Least-Squares Orbit Determination with Short-Term Orbit Prediction
Authors: Wang Kan , Liu Jiawei , Su Hang , El-Mowafy Ahmed , Yang Xuhai
Published: December 2022

The augmentation of the Global Navigation Satellite System (GNSS) by Low Earth Orbit (LEO) satellites is proposed as an effective method to improve the precision and shorten the convergence time of Precise Point Positioning (PPP). Serving as navigation satellites in the future, LEO satellites need to be provided with their high-accuracy orbits in real-time. This would potentially enable the high-accuracy real-time LEO satellite clock determination, and eventually facilitate the high-accuracy ground-based positioning. Studies have been performed to achieve such real-time orbits using a Kalman filter in both the kinematic and reduced-dynamic modes. Batch Least-Squares (BLS) adjustment delivers more stable orbits in near-real-time, as it performs better phase screening. However, it suffers from longer delays compared to the Kalman filter. With the LEO satellite orbit prediction strategies improved over time, this latency can be bridged by short-term orbit prediction. In this study, using real-time GNSS satellite products, the real-time LEO satellite orbits are obtained based on the batch least-squares adjustment and short-term prediction. LEO ephemeris parameters are generated within specific prediction time windows. Using real data from the 500 km GRACE C satellite and 810 km Sentinel-3B satellite, the near-real-time BLS Precise Orbit Determination (POD) results exhibit good accuracy with an Orbital User Range Error (OURE) of 2–4 cm using different real-time GNSS products. A range of delays of the BLS POD processes are assumed, based on tests performed on different processing machines, leading to various prediction windows, from 3–8 min to 12–17 min that correspond to the real-time usage. The orbital prediction errors are shown to be highly correlated with the orbital height and the prediction time. The computational efficiency thus becomes essential to reduce the prediction errors for a certain LEO satellite. For advanced processing units leading to a prediction window shorter or equal to 6–11 min, one can expect a total real-time orbital error budget of 3–5 cm, provided that an appropriate prediction strategy is applied and high-quality GNSS products are used. For a given fitting interval, the ephemeris fitting errors are generally related to the number of ephemeris parameters and the orbital height. Compared with the prediction errors, the ephemeris fitting errors do not play a significant role in the total error budget when using 22 ephemeris parameters.
Reasons for East Siberia Winter Snow Water Equivalent Increase in the Recent Decades
Authors: Wang Zhibiao , Wu Renguang , Chen Zhang , Huang Gang , Yang Xianke
Published: December 2022

With the rapid warming in the past few decades, the snow water equivalent (SWE) in winter and spring decreased generally over the Northern Hemisphere, but an increasing trend occurred in some areas, especially in east Siberia. In this paper, we analyze the sources and reasons for the SWE increase in east Siberia in winter since 1979 and document projected future SWE changes in this region. The winter SWE changes in east Siberia were not significant over the past four decades until the 2000s, and the SWE increased rapidly thereafter. The SWE increase after the 2000s is mainly contributed by SWE in November, followed by that in winter, and attributed to the increase in snowfall. With the moisture budget diagnosis, we found that the atmospheric dynamic-induced moisture convergence (vertical motion effect and horizontal advection of moisture) are the reasons that contributed to the winter snowfall increase in east Siberia. As east Siberia is cold in winter, even under the high radiative forcing scenario, precipitation in east Siberia will continue to increase and be dominated by snowfall until the 2060s. Thereafter, with the rainfall increase and the accelerated snowmelt due to rising temperature, precipitation will gradually shift to rainfall type and the SWE may turn to decrease.
MSSTNet: A Multi-Scale Spatiotemporal Prediction Neural Network for Precipitation Nowcasting
Authors: Ye Yuankang , Gao Feng , Cheng Wei , Liu Chang , Zhang Shaoqing
Published: December 2022

Convolution-based recurrent neural networks and convolutional neural networks have been used extensively in spatiotemporal prediction. However, these methods tend to concentrate on fixed-scale spatiotemporal state transitions and disregard the complexity of spatiotemporal motion. Through statistical analysis, we found that the distribution of the spatiotemporal sequence and the variety of spatiotemporal motion state transitions exhibit some regularity. In light of these statistics and observations, we propose the Multi-scale Spatiotemporal Neural Network (MSSTNet), an end-to-end neural network based on 3D convolution. It can be separated into three major child modules: a distribution feature extraction module, a multi-scale motion state capture module, and a feature decoding module. Furthermore, the MSST unit is designed to model multi-scale spatial and temporal information in the multi-scale motion state capture module. We first conduct the experiments on the MovingMNIST dataset, which is the most commonly used dataset in the field of spatiotemporal prediction, MSSTNet can achieve state-of-the-art results for this dataset, and ablation experiments demonstrate that the MSST unit has positive significance for spatiotemporal prediction. In addition, this paper applies the model to valuable precipitation nowcasting, due to efficiently capturing the multi-scale information of distribution and motion, the new MSSTNet model can predict the real-world radar echo more accurately.
Plot Quality Aided Plot-to-Track Association in Dense Clutter for Compact High-Frequency Surface Wave Radar
Authors: Sun Weifeng , Li Xiaotong , Ji Yonggang , Dai Yongshou , Huang Weimin
Published: December 2022

Due to high false alarm rate and low positioning accuracy of compact high-frequency surface wave radar in moving vessel detection, false plot-to-track association often occurs during moving vessel tracking, thus leading to track fragmentation and false tracking. In order to address this problem, a plot quality evaluation method is proposed and applied to plot-to-track association. Firstly, the differences in spatial correlation of echo spectrum amplitudes and position among moving vessels, clutters, and noise on a range-Doppler map are analyzed, and a plot quality index integrating multi-directional gradient, local variance, and plot position probability is developed. Then, the plots labeled as low quality are removed to reduce both the negative impact of false alarms on plot-to-track association and the computational burden. Eventually, both plot quality index and kinematic parameters are used to calculate the association cost and determine the plot-track pairs during the plot-to-track association procedure. Experimental results with field data demonstrate that the proposed plot quality index can effectively distinguish moving vessel and other plots. Compared with both the nearest neighbor data association method and the joint probability data association method, the association accuracy of the proposed method is greatly improved and, thus, the tracking continuity is enhanced in dense clutter scenarios.
Soil-Moisture Estimation Based on Multiple-Source Remote-Sensing Images
Authors: Mu Tianhao , Liu Guiwei , Yang Xiguang , Yu Ying
Published: December 2022

Soil moisture plays a significant role in the global hydrological cycle, which is an important component of soil parameterization. Remote sensing is one of the most important methods used to estimate soil moisture. In this study, we developed a new nonlinear Erf-BP neural network method to establish a soil-moisture-content-estimation model with integrated multiple-resource remote-sensing data from high-resolution, hyperspectral and microwave sensors. Next, we compared the result with the single-resource remote-sensing data for SMC (soil-moisture content) estimation models by using the linear-fitting method. The results showed that the soil-moisture estimation model offers better accuracy by using multiple-resource remote-sensing data. Furthermore, the SMC predicted the results by using the new Erf-BP neural network with multiple-resource remote-sensing data and a good overall correlation coefficient of 0.6838. Compared with the linear model’s estimation results, the accuracy of the SMC estimation using the Erf-BP method was increased, and the RMSE decreased from 0.017 g/g to 0.0146 g/g, a decrease of 16.44%. These results also indicate that the improved algorithm of the Erf-BP artificial neural network has better fitting results and precision. This research provides a reference for multiple-resource remote-sensing data for soil-moisture estimation.
Correction: Lin et al. Toward Large-Scale Mapping of Tree Crops with High-Resolution Satellite Imagery and Deep Learning Algorithms: A Case Study of Olive Orchards in Morocco. Remote Sens. 2021, 13, 1740
Authors: Lin Chenxi , Jin Zhenong , Mulla David , Ghosh Rahul , Guan Kaiyu , Kumar Vipin , Cai Yaping
Published: December 2022

In the original article [...]
Unsupervised 3D Reconstruction with Multi-Measure and High-Resolution Loss
Authors: Zheng Yijie , Luo Jianxin , Chen Weiwei , Zhang Yanyan , Sun Haixun , Pan Zhisong
Published: December 2022

Multi-view 3D reconstruction technology based on deep learning is developing rapidly. Unsupervised learning has become a research hotspot because it does not need ground truth labels. The current unsupervised method mainly uses 3DCNN to regularize the cost volume to regression image depth. This approach results in high memory requirements and long computing time. In this paper, we propose an end-to-end unsupervised multi-view 3D reconstruction network framework based on PatchMatch, Unsup_patchmatchnet. It dramatically reduces memory requirements and computing time. We propose a feature point consistency loss function. We incorporate various self-supervised signals such as photometric consistency loss and semantic consistency loss into the loss function. At the same time, we propose a high-resolution loss method. This improves the reconstruction of high-resolution images. The experiment proves that the memory usage of the network is reduced by 80% and the running time is reduced by more than 50% compared with the network using 3DCNN method. The overall error of reconstructed 3D point cloud is only 0.501 mm. It is superior to most current unsupervised multi-view 3D reconstruction networks. Then, we test on different data sets and verify that the network has good generalization.
SDFnT-Based Parameter Estimation for OFDM Radar Systems with Intercarrier Interference
Authors: Wang Jingqi , Wang Pingping , Zhang Ruoyu , Wu Wen
Published: December 2022

The orthogonal frequency division multiplexing (OFDM) radar suffers from severe performance degradation in range-velocity estimation in high mobility scenarios. In this paper, a novel intercarrier interference (ICI)-free parameter estimation method for OFDM radar is proposed. By employing a scale discrete Fresnel transform (SDFnT), the OFDM radar signals are converted to the scale Fresnel domain, and the orthogonality of subcarriers can be recovered with the optimal scale factor. Furthermore, due to the compatibility of the SDFnT and the discrete Fourier Transform (DFT), the proposed method has low computational complexity and high feasibility for OFDM radar implementation. Simulation results show that the proposed SDFnT-based scheme effectively eliminates the ICI effect for single and multiple targets and achieves high accuracy delay-Doppler estimation for OFDM radar systems in circumstances of high velocity and low SNR with consistency and robustness.
Neural Modelling in the Exploration of the Biomethane Potential from Cattle Manure: A Case Study on Herds Structure from Wielkopolskie, Podlaskie, and Mazowieckie Voivodeships in Poland
Authors: Wawrzyniak Agnieszka , Przybylak Andrzej , Sujak Agnieszka , Boniecki Piotr
Published: December 2022

In the presented study, data on the size and structure of cattle herds in Wielkopolskie, Podlaskie, and Mazowieckie voivodeships in 2019 were analyzed and subjected to modelling with the use of artificial intelligence, namely artificial neural networks (ANNs). The potential amount of biogas (m3) from cattle manure and slurry for the analyzed provinces was as follows: for the Mazowieckie Voivodeship, 800,654,186 m3; for the Podlaskie voivodeship, 662,655,274 m3; and for the Wielkopolskie voivodeship, 657,571,373 m3. Neural modelling was applied to find the relationship between the structure of the herds and the amount of generated slurry and manure (biomethane potential), as well as to indicate the most important animal types participating in biogas production. In each of the analyzed cases, the three-layer MLP perceptron with a single hidden layer proved to be the most optimal network structure. Sensitivity analysis of the generated models concerning herd structure showed a significant contribution of dairy cows to the methanogenic potential for both slurry and manure. The amount of slurry produced in the Mazowieckie and Wielkopolskie voivodeships was influenced in turn by heifers (both 6–12 and 12–18 months old) and bulls 12–24 months old, and in the Podlaskie voivodeship by calves and heifers 6–12 months old. As for manure, in addition to cows, bulls 12–24 months old and heifers 12–18 represented the main factor for Mazowieckie and Wielkopolskie voivodeships, and heifers (both 6–12 and 12–18 months old) for Podlaskie voivodeship.
Interference Analysis of 5G NR Base Stations to Fixed Satellite Service Bent-Pipe Transponders in the 6425–7125 MHz Frequency Band
Authors: Pastukh Alexander , Tikhvinskiy Valery , Devyatkin Evgeny , Kostin Artyom
Published: December 2022

Future deployment of 5G NR base stations in the 6425–7125 MHz band raises numerous concerns over the long-term impact on the satellite transponders located in geostationary orbit. To study this impact and understand whether 5G NR may cause adverse effect to the spaceborne receivers, the research which estimated the interference levels to the satellite bent pipe links was done. The study presents the evaluation of aggregate interference from 5G NR base stations located inside the victim satellites’ footprints using Monte-Carlo analysis and calculation of signal-to-noise degradation and bit error rates of the fixed-satellite service (FSS) bent-pipe transponders for each scenario. The results of the study showed the feasibility of co-existence between 5G NR and satellite systems in the 6425–7125 MHz bands, and that no negative impact on the performance of the satellite links is expected.
Precision Magnetic Field Sensing with Dual Multi-Wave Atom Interferometer
Authors: Yan Wenhua , Ren Xudong , Zhou Minkang , Hu Zhongkun
Published: December 2022

Precision magnetic field measurement is widely used for practical applications, fundamental research, and medical purposes, etc. We propose a novel quantum magnetometer based on atoms’ multi-wave (3-wave and 5-wave) Ramsey interference. Our design features high phase sensitivity and can be applied to in situ measurements of the magnetic field inside vacuum chambers. The final state detection is designed to be achieved by Raman’s two-photon transition. The analytical solution for applicable interference fringe is presented. Fringe contrast decay due to atom temperature and magnetic field gradient is simulated to estimate reasonable experimental conditions. Sensitivity functions for phase noise and magnetic field noise in a multi-wave system are derived to estimate the noise level required to reach the expected resolution. The validity of the model, dual-channel features on bias estimation, and the quasi-non-destructive detection feature are discussed.
EEG Authentication System Based on One- and Multi-Class Machine Learning Classifiers
Authors: Hernández-Álvarez Luis , Barbierato Elena , Caputo Stefano , Mucchi Lorenzo , Hernández Encinas Luis
Published: December 2022

In the current Information Age, it is usual to access our personal and professional information, such as bank account data or private documents, in a telematic manner. To ensure the privacy of this information, user authentication systems should be accurately developed. In this work, we focus on biometric authentication, as it depends on the user’s inherent characteristics and, therefore, offers personalized authentication systems. Specifically, we propose an electrocardiogram (EEG)-based user authentication system by employing One-Class and Multi-Class Machine Learning classifiers. In this sense, the main novelty of this article is the introduction of Isolation Forest and Local Outlier Factor classifiers as new tools for user authentication and the investigation of their suitability with EEG data. Additionally, we identify the EEG channels and brainwaves with greater contribution to the authentication and compare them with the traditional dimensionality reduction techniques, Principal Component Analysis, and χ2 statistical test. In our final proposal, we elaborate on a hybrid system resistant to random forgery attacks using an Isolation Forest and a Random Forest classifiers, obtaining a final accuracy of 82.3%, a precision of 91.1% and a recall of 75.3%.
Terahertz Nondestructive Testing with Ultra-Wideband FMCW Radar
Authors: Carré Barnabé , Chopard Adrien , Guillet Jean-Paul , Fauquet Frederic , Mounaix Patrick , Gellie Pierre
Published: December 2022

This paper presents the development, performance, integration, and implementation of a 150 GHz FMCW radar based on a homodyne harmonic mixing scheme for noncontact, nondestructive testing. This system offers high-dynamic-range measurement capabilities up to 100 dB and measurement rates up to 7.62 kHz. Such interesting characteristics make this system attractive for imaging applications or contactless sensing. Numerous samples of different materials and geometries were imaged by taking advantage of the radar’s performance. By taking into account the nonionizing capability of the system, new applicative fields such as food industry and pharmaceutical packaging were explored.
Development of a Paper-Based Analytical Method for the Colorimetric Determination of Calcium in Saliva Samples
Authors: Tarara Maria , Tzanavaras Paraskevas D. , Tsogas George Z.
Published: December 2022

A novel, rapid, and facile method for the colorimetric determination of calcium using micro-analytical paper-based devices (μ-PADs) was developed. The proposed analytical method utilizes the color differences developing, after the addition of calcium, on the surface of the devices because of the complexation reaction of calcium with Methylthymol Blue (MTB) at room temperature, in alkaline pH. The devices were manufactured with chromatographic paper, using wax barriers, and the analytical protocol was easily implemented without the need of any experimental apparatus except for a simple imaging device. The user must regulate the pH, add the solutions on the paper, and measure the color intensity of the formed Ca(II)–MTB complex with a flatbed scanner. The experimental conditions for optimum color development, the possible interfering substances, and the reliability of the paper devices in different preserving conditions were optimized, with satisfactory results. The method exhibited acceptable detection limits (2.9 mg L−1) with sufficiently good precision, which varied from 4.2% (intra-day) to 6.4% (inter-day). Saliva samples from healthy volunteers were successfully analyzed, and the calcium levels were calculated in the range of 30.71 to 84.15 mg L−1.
An Overview of Fog Data Analytics for IoT Applications
Authors: Bhatia Jitendra , Italiya Kiran , Jadeja Kuldeepsinh , Kumhar Malaram , Chauhan Uttam , Tanwar Sudeep , Bhavsar Madhuri , Sharma Ravi , Manea Daniela Lucia , Verdes Marina , Raboaca Maria Simona
Published: December 2022

With the rapid growth in the data and processing over the cloud, it has become easier to access those data. On the other hand, it poses many technical and security challenges to the users of those provisions. Fog computing makes these technical issues manageable to some extent. Fog computing is one of the promising solutions for handling the big data produced by the IoT, which are often security-critical and time-sensitive. Massive IoT data analytics by a fog computing structure is emerging and requires extensive research for more proficient knowledge and smart decisions. Though an advancement in big data analytics is taking place, it does not consider fog data analytics. However, there are many challenges, including heterogeneity, security, accessibility, resource sharing, network communication overhead, the real-time data processing of complex data, etc. This paper explores various research challenges and their solution using the next-generation fog data analytics and IoT networks. We also performed an experimental analysis based on fog computing and cloud architecture. The result shows that fog computing outperforms the cloud in terms of network utilization and latency. Finally, the paper is concluded with future trends.
Self-Relation Attention and Temporal Awareness for Emotion Recognition via Vocal Burst
Authors: Trinh Dang-Linh , Vo Minh-Cong , Kim Soo-Hyung , Yang Hyung-Jeong , Lee Guee-Sang
Published: December 2022

Speech emotion recognition (SER) is one of the most exciting topics many researchers have recently been involved in. Although much research has been conducted recently on this topic, emotion recognition via non-verbal speech (known as the vocal burst) is still sparse. The vocal burst is concise and has meaningless content, which is harder to deal with than verbal speech. Therefore, in this paper, we proposed a self-relation attention and temporal awareness (SRA-TA) module to tackle this problem with vocal bursts, which could capture the dependency in a long-term period and focus on the salient parts of the audio signal as well. Our proposed method contains three main stages. Firstly, the latent features are extracted using a self-supervised learning model from the raw audio signal and its Mel-spectrogram. After the SRA-TA module is utilized to capture the valuable information from latent features, all features are concatenated and fed into ten individual fully-connected layers to predict the scores of 10 emotions. Our proposed method achieves a mean concordance correlation coefficient (CCC) of 0.7295 on the test set, which achieves the first ranking of the high-dimensional emotion task in the 2022 ACII Affective Vocal Burst Workshop & Challenge.
A Wide Dynamic Range Sigma-Delta Modulator for EEG Acquisition Using Randomized DWA and Dynamic-Modulated Scaling-Down Techniques
Authors: Han Yongchun , Liu Wenhao , Zhang Xiangwei , Wang Xiaosong , Liu Xin , Liu Yu
Published: December 2022

This paper proposes a wide dynamic range (DR) and high-resolution discrete-time (DT) 2-order 4-bit sigma-delta modulator with a novel dynamic-modulated scaling-down (DM-SD) technology for non-invasive electroencephalogram (EEG) acquisition. The DM-SD technology can expand the input dynamic range and suppress large input offsets at the same time. The modulator was designed with 180nm CMOS technology with an area of 0.49 mm2. We achieve a 118.1 dB SNDR when the input signal is 437.5 Hz and the signal bandwidth is 1500 Hz. Due to the proposed DM-SD technology, the DR is expanded to 126 dB. The power consumption of the whole modulator is 1.6 mW and a 177.8 dB Schreier figure-of-merit (FoMs) is realized.
An Anomaly Intrusion Detection for High-Density Internet of Things Wireless Communication Network Based Deep Learning Algorithms
Authors: Salman Emad Hmood , Taher Montadar Abas , Hammadi Yousif I. , Mahmood Omar Abdulkareem , Muthanna Ammar , Koucheryavy Andrey
Published: December 2022

Telecommunication networks are growing exponentially due to their significant role in civilization and industry. As a result of this very significant role, diverse applications have been appeared, which require secured links for data transmission. However, Internet-of-Things (IoT) devices are a substantial field that utilizes the wireless communication infrastructure. However, the IoT, besides the diversity of communications, are more vulnerable to attacks due to the physical distribution in real world. Attackers may prevent the services from running or even forward all of the critical data across the network. That is, an Intrusion Detection System (IDS) has to be integrated into the communication networks. In the literature, there are numerous methodologies to implement the IDSs. In this paper, two distinct models are proposed. In the first model, a custom Convolutional Neural Network (CNN) was constructed and combined with Long Short Term Memory (LSTM) deep network layers. The second model was built about the all fully connected layers (dense layers) to construct an Artificial Neural Network (ANN). Thus, the second model, which is a custom of an ANN layers with various dimensions, is proposed. Results were outstanding a compared to the Logistic Regression algorithm (LR), where an accuracy of 97.01% was obtained in the second model and 96.08% in the first model, compared to the LR algorithm, which showed an accuracy of 92.8%.
Intelligent Fault Diagnosis of Industrial Bearings Using Transfer Learning and CNNs Pre-Trained for Audio Classification
Authors: Di Maggio Luigi Gianpio
Published: December 2022

The training of Artificial Intelligence algorithms for machine diagnosis often requires a huge amount of data, which is scarcely available in industry. This work shows that convolutional networks pre-trained for audio classification already contain knowledge for classifying bearing vibrations, since both tasks share the need to extract features from spectrograms. Knowledge transfer is realized through transfer learning to identify localized defects in rolling element bearings. This technique provides a tool to transfer the knowledge embedded in neural networks pre-trained for fulfilling similar tasks to diagnostic scenarios, significantly limiting the amount of data needed for fine-tuning. The VGGish model was fine-tuned for the specific diagnostic task by handling vibration samples. Data were extracted from the test bench for medium-size bearings specially set up in the mechanical engineering laboratories of the Politecnico di Torino. The experiment involved three damage classes. Results show that the model pre-trained using sound spectrograms can be successfully employed for classifying the bearing state through vibration spectrograms. The effectiveness of the model is assessed through comparisons with the existing literature.
Research on Pressure-Flow Characteristics of Pilot Stage in Jet Pipe Servo-Valve
Authors: Kang Shuangqi , Kong Xiangdong , Zhang Jin , Du Ranheng
Published: December 2022

Jet pipe servo-valves are widely used in high-precision servo control systems. However, the accuracy of pressure-flow characteristic equations relevant to its pilot stage needs to be improved. In contrast to the traditional analytical approach using the orifice equation, the article investigates the pressure and flow characteristics of the pilot stage based on the impact jet principle. Taking the pre-stage of a certain type of jet pipe servo-valve as an example, the flow field is simulated using ANASYS software. By comparing the simulation data with the calculation results, the pressure characteristic model is basically consistent with the simulation data, and the relative error is less than 2.3%. The error between the revised flow characteristic model and the simulation result is small, and the maximum deviation is less than 0.0022 L/min. Finally, to verify the applicability of the model to other specifications, the experimental data in the literature are compared with the theoretical calculation results, the maximum relative error of pressure characteristics is 2.19%, and the relative error of flow characteristics is less than 5.34%.
Lower Body Joint Angle Prediction Using Machine Learning and Applied Biomechanical Inverse Dynamics
Authors: Choffin Zachary , Jeong Nathan , Callihan Michael , Sazonov Edward , Jeong Seongcheol
Published: December 2022

Extreme angles in lower body joints may adversely increase the risk of injury to joints. These injuries are common in the workplace and cause persistent pain and significant financial losses to people and companies. The purpose of this study was to predict lower body joint angles from the ankle to the lumbosacral joint (L5S1) by measuring plantar pressures in shoes. Joint angle prediction was aided by a designed footwear sensor consisting of six force-sensing resistors (FSR) and a microcontroller fitted with Bluetooth LE sensors. An Xsens motion capture system was utilized as a ground truth validation measuring 3D joint angles. Thirty-seven human subjects were tested squatting in an IRB-approved study. The Gaussian Process Regression (GPR) linear regression algorithm was used to create a progressive model that predicted the angles of ankle, knee, hip, and L5S1. The footwear sensor showed a promising root mean square error (RMSE) for each joint. The L5S1 angle was predicted to be RMSE of 0.21° for the X-axis and 0.22° for the Y-axis, respectively. This result confirmed that the proposed plantar sensor system had the capability to predict and monitor lower body joint angles for potential injury prevention and training of occupational workers.
Anti-Noise 3D Object Detection of Multimodal Feature Attention Fusion Based on PV-RCNN
Authors: Zhu Yuan , Xu Ruidong , An Hao , Tao Chongben , Lu Ke
Published: December 2022

3D object detection methods based on camera and LiDAR fusion are susceptible to environmental noise. Due to the mismatch of physical characteristics of the two sensors, the feature vectors encoded by the feature layer are in different feature spaces. This leads to the problem of feature information deviation, which has an impact on detection performance. To address this problem, a point-guided feature abstract method is presented to fuse the camera and LiDAR at first. The extracted image features and point cloud features are aggregated to keypoints for enhancing information redundancy. Second, the proposed multimodal feature attention (MFA) mechanism is used to achieve adaptive fusion of point cloud features and image features with information from multiple feature spaces. Finally, a projection-based farthest point sampling (P-FPS) is proposed to downsample the raw point cloud, which can project more keypoints onto the close object and improve the sampling rate of the point-guided image features. The 3D bounding boxes of the object is obtained by the region of interest (ROI) pooling layer and the fully connected layer. The proposed 3D object detection algorithm is evaluated on three different datasets, and the proposed algorithm achieved better detection performance and robustness when the image and point cloud data contain rain noise. The test results on a physical test platform further validate the effectiveness of the algorithm.