bluuebunny/crossref_metadata_2025 huggingface dataset:
Preliminary Assessment and Verification of the Langley Plots Calibration of the Sun Photometer at Mt Foyeding Observatory, Beijing
Authors: Zheng Yu , Che Huizheng , Gui Ke , Xia Xiangao , Zhao Hujia , Li Lei , Zhang Lei , Zhang Xinglu , Zhao Hengheng , Liang Yuanxin , Wang Hong , Wang Yaqiang , Zhang Xiaoye
Published: September 2022

An assessment and verification of the Langley calibration method of the Sun photometer at Mt Foyeding (MFYD) Observatory in Beijing was performed. We explored whether the Langley plot calibration is practicable for this mountainous site by analyzing the aerosol climatology and carrying out a case study. Then, the aerosol optical depth (AOD) results were verified under the reference of AERONET AOD. The results showed that satisfactory atmospheric conditions are present on winter mornings, characterized by a smaller average AOD (~0.09–0.14) and a lower range ratio (~36.97–63.38%) than in the afternoons and over a whole day. The six days selected as the case study all showed stable atmospheric conditions characterized by daily average triplets of <2% for all wavelengths. The residual sum of squares for V0λ at all wavelengths was <0.0002 and the residual standard deviation was <0.2%. A large improvement was found in the linear regression at morning relative to the statistics obtained over the whole day, when the coefficient of determination and residual standard deviation were promoted by 0.22–2.90% and ~2.76–23.32, respectively. The final V0λ value was derived from 31 days of observation and the deviations from the reference V0λ were about −1.69, −1.29, −0.81, −0.42, −0.34, −0.22, −0.63 and −0.36% at 340, 380, 440, 500, 675, 870, 1020 and 1640 nm, respectively. The regression analysis of the AOD validation showed a perfect AOD performance, with 100% of the retrievals lying within the expected error (0.05 ± 10%) from 380 to 1640 nm and 99.99% for the 340 nm band. Good AOD agreement (correlation coefficients > 0.998) and residual standard deviation values ranging from ~0.006 to 0.011 were observed, with the relative mean bias varying from 0.999 to 1.066. The mean biases were concentrated within ±0.02 for the ultraviolet bands and within ±0.01 for the other bands; therefore, the results of this preliminary assessment and verification indicated that the Langley plots method is suitable for photometer calibration at the MFYD Observatory.
Performance of HaiYang-2 Altimetric Data in Marine Gravity Research and a New Global Marine Gravity Model NSOAS22
Authors: Zhang Shengjun , Zhou Runsheng , Jia Yongjun , Jin Taoyong , Kong Xiangxue
Published: September 2022

Haiyang-2 (HY-2) missions have accumulated sea surface height (SSH) observations on a global scale for more than 10 years. Four satellites, HY-2A, HY-2B, HY-2C and HY-2D, provide even but differently distributed data, which play a complementary role in marine gravity studies with other missions. Therefore, this paper evaluates the performances of HY-2 altimetric data in marine gravity modeling from the following four perspectives: SSH accuracy, geoid signal resolution ability, vertical deflections and gravity anomaly. First, the centimeter-magnitude accuracy level of HY-2 data is proved by analyzing SSH discrepancies at crossover points within a certain time limit. Second, the spectral analysis of repetitive along-track data sequences in a time domain shows a geoid resolution range from 18 to 24 km. Taking HY-2 exact repeat missions (ERM), for example, the resolution could be remarkably enhanced by stacking repetitive cycles. Third, validation with an XGM2019 model showed that vertical deflections were reliably computed for all HY-2 missions, but HY-2A performed slightly worse than the other HY-2 missions. Meanwhile, HY-2C and HY-2D with a ~66° orbital inclination obviously had an improved ability to capture east–west signals compared to HY-2A and HY-2B. Finally, we constructed global marine gravity results based on three input datasets, HY-2 dataset only, multi-satellite dataset without HY-2 and multi-satellite dataset with HY-2. Validations were performed using published models and shipborne gravimetric data. The results showed that the HY-2 dataset is capable of improving marine gravity anomaly recoveries and that the accuracy of NSOAS22 with incorporated HY-2 data is comparable to DTU21 and SS V31.1. Furthermore, HY-2 observations should not be the only input dataset to construct a 1’ × 1’ resolution marine gravity model.
A Beacons Selection Method under Random Interference for Indoor Positioning
Authors: Guo Yu , Zheng Jiazhu , Di Shaoning , Xiang Guiqiu , Guo Fei
Published: September 2022

GNSS is still not well applied in indoor environments. This is an important challenge for seamless positioning and navigation. Using other sensors to replace and connect is the mainstream practice at present. No matter what technology is used, the problem of real-time optimal station selection is faced in complex indoor environments. In this paper, we first verified the impact of random interference from walkers on positioning signals in an indoor environment. Based on this phenomenon, we proposed a novel real-time dynamic Beacons selection method (RD) in the field of indoor positioning. First, we introduced a machine learning algorithm for real-time anomaly detection of received signals from different Beacons. Then the Beacon selection is completed based on the real-time anomaly detection results and RSSI. In an indoor scene, we verified the positioning accuracy of three other methods when selecting various numbers of Beacons. Then we used the best selection strategies to compare with the RD method. Experiments showed that the RD method can use the least Beacons to obtain higher accuracy and stable positioning results. This paper provides a new idea for real-time optimal selection of signal sources in a complex indoor environment.
New Tools for Urban Analysis: A SLAM-Based Research in Venice
Authors: Tanduo Beatrice , Martino Andrea , Balletti Caterina , Guerra Francesco
Published: September 2022

This research proposes a detailed analysis of the potential of MMS (Mobile Mapping Systems), supported by SLAM (Simultaneous Localisation And Mapping) algorithms, performed on a multiscale test field in order to make a concrete contribution to the morphological study of cities. These systems, developed with the aim of acquiring a large number of points in a short time, are able to map the surrounding area and automatically localise themselves in real time in relation to a determined reference system. The analysed area, located in Venice, was divided into three different test fields characterised by typical elements potentially comparable to those of other urban realities. The data were acquired using the LiBackPack C50, Kaarta Stencil and Heron Lite systems and compared quantitatively and qualitatively with data obtained from more traditional surveying techniques. Specifically, the data obtained from TLS (Terrestrial Laser Scanning) surveys, supported by topographic measurements, were the most accurate basis on which to evaluate the accuracy and completeness of the three different MMS devices. The standard deviation values were initially analysed in the final 3D global models using the C2C (Cloud to Cloud) and C2M (Cloud to Mesh) distance calculation methods. Subsequently, the geometric differences were investigated through the extraction of horizontal profiles, and two more specific 2D analyses were carried out: the first inspecting the residual parameters calculated after the Helmert transformation from two sets of control points obtained from the profiles, followed by a local strain analysis. The study of the local deformation parameters allowed us to validate the results obtained and to identify the real limits of these survey instruments. The aim was to make a concrete contribution to the formalisation of an operative protocol for the morphological study of the city, exploiting the potential of these technologies to overcome the differences in scale and the gap between outdoor and indoor spaces.
Orthogonal Scattering Model-Based Three-Component Decomposition of Polarimetric SAR Data
Authors: Han Wentao , Fu Haiqiang , Zhu Jianjun , Xie Qinghua , Zhang Shurong
Published: September 2022

New scattering models are constantly emerging to extract the detailed polarization information of ground targets. They contribute to the refined interpretation of Polarimetric Synthetic Aperture Radar (PolSAR) images. However, an increasingly prominent problem lies ahead of model decomposition methods when there are similar scattering models for a decomposition scheme. It is difficult to separate the polarization power of similar scattering mechanisms reasonably and robustly. Therefore, in this paper, we first analyze the necessity of orthogonality between scattering models. Following this, we propose two mutually orthogonal rank-1 scattering models, which can degenerate to mostly current scattering models. The orthogonality and adaptability of scattering models are considered in the model derivation. Simulated PolSAR data, real ALOS-2/PALSAR-2, GF-3, and E-SAR data are selected to validate the proposed method. As shown by the results, first, the proposed method enhances the double-bounce scattering contribution in urban areas and maintains the volume-scattering contribution in vegetation areas because it separates the polarization power of different scattering mechanisms more reasonably. Second, the proposed method is powerful in the robust interpretation of different ground targets, resulting from the orthogonality of scattering models. These two characteristics of orthogonal scattering models are expected to play a positive role in large-scale applications, especially in land-cover classification.
Satellite Fog Detection at Dawn and Dusk Based on the Deep Learning Algorithm under Terrain-Restriction
Authors: Ran Yinze , Ma Huiyun , Liu Zengwei , Wu Xiaojing , Li Yanan , Feng Huihui
Published: September 2022

Fog generally forms at dawn and dusk, which exerts serious impacts on public traffic and human health. Terrain strongly affects fog formation, which provides a useful clue for fog detection from satellite observation. With the aid of the advanced Himawari-8 imager data (H8/AHI), this study develops a deep learning algorithm for fog detection at dawn and dusk under terrain-restriction and enhanced channel domain attention mechanism (DDF-Net). The DDF-Net is based on the traditional U-Net model, with the digital elevation model (DEM) data acting as the auxiliary information to separate fog from the low stratus. Furthermore, the squeeze-and-excitation networks (SE-Net) is integrated to optimize the information extraction for eliminating the influence of solar zenith angles (SZA) on the spectral characteristics over a large region. Results show acceptable accuracy of the DDF-Net. The overall probability of detection (POD) is 84.0% at dawn and 83.7% at dusk. In addition, the terrain-restriction strategy improves the results at the edges of foggy regions and reduces the false alarm rate (FAR) for low stratus. The accuracy is expected to be improved when training at a season or month scale, rather than at a longer temporal scale. Results of our study help to improve the accuracy of fog detection, which could further support the relevant traffic planning or healthy travel.
Resolving the Urban Dilemma of Two Adjacent Rivers through a Dialogue between GIS and Augmented Reality (AR) of Fabrics
Authors: Shih Naai-Jung , Qiu Yu-Huan
Published: September 2022

Keelung Harbor, which is the most important center of sea freight in northern Taiwan, suffers from deteriorating urban development due to limited land supply. A dilemma arose from the Asahikawa River and the Tianliao River fronts, which evolved from cultural landscapes to buried and truncated rivers. This research was aimed at resolving the urban dilemma of the two adjacent rivers through a dialogue between the physical and augmented interaction of fabrics in three scenarios: GIS to AR, AR to GIS, and both. The physical dynamics were used to trace development chronologically by the area and length assessed from historical maps of hydrogeography, architecture, and the railroad. The augmented dynamics involved AR-based simulations and comparisons in terms of skyline overlay, fabric substitution, and fabric disposition. The dynamics involved AR models made by UAV images and 3D drawings. The assessments and simulations determined the key event in Keelung history when the Asahikawa River was leveled up. The dilemma verified from the augmented dynamics facilitated comprehension of the evolvement of the physical dynamics. With the assistance of AR and GIS, we concluded that the specific instance of riverfront reconstruction was an important landmark of meta-relationship.
Species Distribution Models at Regional Scale: Cymodocea nodosa Seagrasses
Authors: Casas Enrique , Martín-García Laura , Hernández-Leal Pedro , Arbelo Manuel
Published: September 2022

Despite their ecological and socio-economic importance, seagrasses are often overlooked in comparison with terrestrial ecosystems. In the Canarian archipelago (Spain), Cymodocea nodosa is the best-established species, sustaining the most important marine ecosystem and providing ecosystem services (ES) of great relevance. Nevertheless, we lack accurate and standardized information regarding the distribution of this species and its ES supply. As a first step, the use of species distribution models is proposed. Various machine learning algorithms and ensemble model techniques were considered along with freely available remote sensing data to assess Cymodocea nodosa’s potential distribution. In a second step, we used InVEST software to estimate the ES provision by this phanerogam on a regional scale, providing spatially explicit monetary assessments and a habitat degradation characterization due to human impacts. The distribution models presented great predictive capabilities and statistical significance, while the ES estimations were in concordance with previous studies. The proposed methodology is presented as a useful tool for environmental management of important communities sensitive to human activities, such as C. nodosa meadows.
Integration of Multi-GNSS PPP-RTK/INS/Vision with a Cascading Kalman Filter for Vehicle Navigation in Urban Areas
Authors: Gu Shengfeng , Dai Chunqi , Mao Feiyu , Fang Wentao
Published: September 2022

Precise point positioning (PPP) has received much attention in recent years for its low cost, high accuracy, and global coverage. Nowadays, PPP with ambiguity resolution and atmospheric augmentation is widely regarded as PPP-RTK (real-time kinematic), which weakens the influence of the long convergence time in PPP and regional service coverage in RTK. However, PPP-RTK cannot work well in urban areas due to limitations of non-line-of-sight (NLOS) conditions. Inertial navigation systems (INS) and vision can realize continuous navigation but suffer from error accumulation. Accordingly, the integration model of multi-GNSS (global navigation satellite system) and PPP-RTK/INS/vision with a cascading Kalman filter and dynamic object removal model was proposed to improve the performance of vehicle navigation in urban areas. Two vehicular tests denoted T01 and T02 were conducted in urban areas to evaluate the navigation performance of the proposed model. T01 was conducted in a relatively open-sky environment and T02 was collected in a GNSS-challenged environment with many obstacles blocking the GNSS signals. The positioning results show that the dynamic object removal model can work well in T02. The results indicate that multi-GNSS PPP-RTK/INS/vision with a cascading Kalman filter can achieve a positioning accuracy of 0.08 m and 0.09 m for T01 in the horizontal and vertical directions and 0.83 m and 0.91 m for T02 in the horizontal and vertical directions, respectively. The accuracy of the velocity and attitude estimations is greatly improved by the introduction of vision.
Sea Level Rise Estimation on the Pacific Coast from Southern California to Vancouver Island
Authors: He Xiaoxing , Montillet Jean-Philippe , Fernandes Rui , Melbourne Timothy I. , Jiang Weiping , Huang Zhengkai
Published: September 2022

Previous studies have estimated the sea level rise (SLR) at various locations on the west coast of the USA and Vancouver Island in Canada. Here, we construct an entire SLR profile from Vancouver Island in the Pacific Northwest to San Diego in Southern California. First, we process global navigation satellite system (GNSS) measurements at 405 stations blanketing the whole coast to generate a profile of vertical land motion (VLM) known to bias century-long tide gauge (TG) measurements recording relative SLR (RSLR). We are then able to estimate the absolute SLR (ASLR) by correcting the SLR with the VLM. Our study emphasizes the relationship between the various tectonic movements (i.e., the Cascadia subduction zone, the San Andreas strike-slip fault system) along the Pacific coast which renders it difficult to accurately estimate the SLR. That is why we precisely model the stochastic noise of both GNSS and tide gauge time series using a combination of various models and information criterions (ICs). We also use the latest altimetry products and sea surface height (SSH) to compare it with ASLR at the same location as the TGs. This study supports previous analysis that the power law + white noise and generalized Gauss–Markov + white noise models are the best stochastic noise models for the GNSS time series. The new coastal profile confirms the large variability of VLM estimates in the Pacific Northwest around the Cascadia subduction zone in agreement with previous studies, and a similar result when the San Andreas fault comes onshore in Central California (San Francisco Bay). Negative RSLR values are mostly located in the Pacific Northwest (Vancouver Island and Olympic Peninsula). We also observe a much bigger variation (about 90–150%) of the ASLR in the Pacific Northwest which is predominantly due to glacial isostatic adjustment (GIA). Moreover, the comparison between the ASLR and the SSH estimates shows similarities in the center of the studied area (South Washington, Oregon planes, and some parts of Southern California) where the tectonic activity does not significantly influence the TG measurements. Finally, the twentieth-century satellite geocentric ocean height rates show a global mean of 1.5 to 1.9 mm/yr. Our estimates based on ASLR and SSH are within this interval.
Trajectory Optimization of Autonomous Surface Vehicles with Outliers for Underwater Target Localization
Authors: Mei Xiaojun , Han Dezhi , Saeed Nasir , Wu Huafeng , Chang Chin-Chen , Han Bin , Ma Teng , Xian Jiangfeng
Published: September 2022

Location awareness is crucial for underwater applications; without it, gathered data would be essentially useless. However, it is impossible to directly determine the location of an underwater target because GPS-reliant methods cannot be utilized in the underwater environment. To this end, the underwater target localization technique has become one of the most critical technologies in underwater applications, wherein GPS-equipped autonomous surface vehicles (ASVs) are typically used to assist with localization. It has been proved that, under the assumption of Gaussian noise, an appropriate geometry among ASVs and the underwater target can enhance localization accuracy. Unfortunately, the conclusion may not hold if outliers arise and the closed-form expression of Cramér–Rao lower bound (CRLB) cannot be established. Eventually, it becomes hard to derive the accepted geometry, particularly for the received signal strength (RSS)-based ranging scenario. Therefore, this work optimizes the trajectory of ASVs with RSS-based ranging and in the presence of outliers to optimally estimate the location of an underwater target. The D-optimality criterion is applied in conjunction with the Monte Carlo method to determine the closed-form expression of the function, which then transforms the problem into an optimized framework. Nevertheless, the framework cannot be solved in the absence of the target location. In this case, the paper presents two methodologies to overcome the issue and achieve the optimum configuration without identifying the target location. (1) A min–max strategy that assumes that the target location drops in an uncertain region for a single or two ASVs is proposed; and (2) a two-phase localization approach (TPLA) that calculates the target location at each time slot for three ASVs is developed. Finally, the optimal trajectories of ASVs are constructed by a series of waypoints based on an analytically tractable measurement model in various conditions.
Identification of the Spring Green-Up Date Derived from Satellite-Based Vegetation Index over a Heterogeneous Ecoregion
Authors: Wu Jianping , Chang Zhongbing , Su Yongxian , Zhang Chaoqun , Wu Xiong , Bi Chongyuan , Liu Liyang , Yang Xueqin , Li Xueyan
Published: September 2022

Multiple methods have been developed to identify the transition threshold from the reconstructed satellite-derived normalized difference vegetation indices (NDVI) time series and to determine the inflection point corresponding to a certain phenology phase (e.g., the spring green-up date (GUD)). We address an issue that large uncertainties might occur in the inflection point identification of spring GUD using the traditional satellite-based methods since different vegetation types exhibit asynchronous phenological phases over a heterogeneous ecoregion. We tentatively developed a Maximum-derivative-based (MDB) method and provided inter-comparisons with two traditional methods to detect the turning points by the reconstructed time-series data of NDVI for identifying the GUD against long-term observations from the sites covered by a mixture of deciduous forest and herbages in the Pan European Phenology network. Results showed that higher annual mean temperature would advance the spring GUD, but the sensitive magnitudes differed depending on the vegetation type. Therefore, the asynchronization of phenological phases among different vegetation types would be more pronounced in the context of global warming. We found that the MDB method outperforms two other traditional methods (the 0.5-threshold-based method and the maximum-ratio-based method) in predicting the GUD of the subsequent-green-up vegetation type when compared with ground observation, especially at sites with observed GUD of herbages earlier than deciduous forest, while the Maximum-ratio-based method showed better performance for identifying GUDs of the foremost-green-up vegetation type. Although the new method improved in our study is not universally applicable on a global scale, our results, however, highlight the limitation of current inflection point identify algorithms in predicting the GUD derived from satellite-based vegetation indices datasets in an ecoregion with heterogeneous vegetation types and asynchronous phenological phases, which makes it helpful for us to better predict plant phenology on an ecoregion-scale under future ongoing climate warming.
Cooling Effects of Urban Vegetation: The Role of Golf Courses
Authors: Nguyen Thu Thi , Eslick Harry , Barber Paul , Harper Richard , Dell Bernard
Published: September 2022

Increased heat in urban environments, from the combined effects of climate change and land use/land cover change, is one of the most severe problems confronting cities and urban residents worldwide, and requires urgent resolution. While large urban green spaces such as parks and nature reserves are widely recognized for their benefits in mitigating urban heat islands (UHIs), the benefit of urban golf courses is less established. This is the first study to combine remote sensing of golf courses with Morphological Spatial Pattern Analysis (MSPA) of vegetation cover. Using ArborCamTM multispectral, high-resolution airborne imagery (0.3 × 0.3 m), this study develops an approach that assesses the role of golf courses in reducing urban land surface temperature (LST) relative to other urban land-uses in Perth, Australia, and identifies factors that influence cooling. The study revealed that urban golf courses had the second lowest LST (around 31 °C) after conservation land (30 °C), compared to industrial, residential, and main road land uses, which ranged from 35 to 37 °C. They thus have a strong capacity for summer urban heat mitigation. Within the golf courses, distance to water bodies and vegetation structure are important factors contributing to cooling effects. Green spaces comprising tall trees (>10 m) and large vegetation patches have strong effects in reducing LST. This suggests that increasing the proportion of large trees, and increasing vegetation connectivity within golf courses and with other local green spaces, can decrease urban LST, thus providing benefits for urban residents. Moreover, as golf courses are useful for biodiversity conservation, planning for new golf course development should embrace the retention of native vegetation and linkages to conservation corridors.
Automated Android Malware Detection Using User Feedback
Authors: Duque João , Mendes Goncalo , Nunes Luís , de Almeida Ana , Serrão Carlos
Published: September 2022

The widespread usage of mobile devices and their seamless adaptation to each user’s needs through useful applications (apps) makes them a prime target for malware developers. Malware is software built to harm the user, e.g., to access sensitive user data, such as banking details, or to hold data hostage and block user access. These apps are distributed in marketplaces that host millions and therefore have their forms of automated malware detection in place to deter malware developers and keep their app store (and reputation) trustworthy. Nevertheless, a non-negligible number of apps can bypass these detectors and remain available in the marketplace for any user to download and install on their device. Current malware detection strategies rely on using static or dynamic app extracted features (or a combination of both) to scale the detection and cover the growing number of apps submitted to the marketplace. In this paper, the main focus is on the apps that bypass the malware detectors and stay in the marketplace long enough to receive user feedback. This paper uses real-world data provided by an app store. The quantitative ratings and potential alert flags assigned to the apps by the users were used as features to train machine learning classifiers that successfully classify malware that evaded previous detection attempts. These results present reasonable accuracy and thus work to help to maintain a user-safe environment.
Assessment of SAR in Road-Users from 5G-V2X Vehicular Connectivity Based on Computational Simulations
Authors: Bonato Marta , Tognola Gabriella , Benini Martina , Gallucci Silvia , Chiaramello Emma , Fiocchi Serena , Parazzini Marta
Published: September 2022

(1) Background: Cooperative Intelligent Transportation Systems (C-ITS) will soon operate using 5G New-Radio (NR) wireless communication, overcoming the limitations of the current V2X (Vehicle-to-Everything) wireless communication technologies and increasing road-safety and driving efficiency. These innovations will also change the RF exposure levels of pedestrians and road-users in general. These people, in fact, will be exposed to additional RF sources coming from nearby cars and from the infrastructure. Therefore, an exposure assessment of people in the proximity of a connected car is necessary and urgent. (2) Methods: Two array antennas for 5G-V2X communication at 3.5 GHz were modelled and mounted on a realistic 3D car model for evaluating the exposure levels of a human model representing people on the road near the car. Computational simulations were conducted using the FDTD solver implemented in the Sim4Life platform; different positions and orientations between the car and the human model were assessed. The analyzed quantities were the Specific Absorption Rate on the whole body (SARwb), averaged over 10 g (SAR10g) in specific tissues, as indicated in the ICNIRP guidelines. (3) Results: the data showed that the highest exposure levels were obtained mostly in the head area of the human model, with the highest peak obtained in the configuration where the main beam of the 5G-V2X antennas was more direct towards the human model. Moreover, in all configurations, the dose absorbed by a pedestrian was well below the ICNIRP guidelines to avoid harmful effects. (4) Conclusions: This work is the first study on human exposure assessment in a 5G-V2X scenario, and it expands the knowledge about the exposure levels for the forthcoming use of 5G in connected vehicles.
Unsupervised Domain Adaptive Corner Detection in Vehicle Plate Images
Authors: Jun Kyungkoo
Published: September 2022

Rectification of vehicle plate images helps to improve the accuracy of license-plate recognition (LPR). It is a perspective-transformation process to project images as if taken from the front geometrically. To obtain the projection matrix, we require the (x, y) coordinates of four corner positions of plates in images. In this paper, we consider the problem of unsupervised domain adaptation for corner detection in plate images. We trained a model with plate images of one country, the source domain, and applied a domain adaptation scheme so that the model is able to work well on the plates of a different country, the target domain. For this study, we created a dataset of 22,096 Korea plate images with corner labels, which are source domain, and 6762 Philippines, which are target domain. To address this problem, we propose a heatmap-based corner-detection model, which outperforms existing scalar-regression methods, and an image classifier for mixed image of source and target images for domain adaptation. The proposed approach achieves better accuracy, which is 19.1% improvement if compared with baseline discriminator-based domain adaptation scheme.
Cloud Data-Driven Intelligent Monitoring System for Interactive Smart Farming
Authors: Dineva Kristina , Atanasova Tatiana
Published: September 2022

Smart farms, as a part of high-tech agriculture, collect a huge amount of data from IoT devices about the conditions of animals, plants, and the environment. These data are most often stored locally and are not used in intelligent monitoring systems to provide opportunities for extracting meaningful knowledge for the farmers. This often leads to a sense of missed transparency, fairness, and accountability, and a lack of motivation for the majority of farmers to invest in sensor-based intelligent systems to support and improve the technological development of their farm and the decision-making process. In this paper, a data-driven intelligent monitoring system in a cloud environment is proposed. The designed architecture enables a comprehensive solution for interaction between data extraction from IoT devices, preprocessing, storage, feature engineering, modelling, and visualization. Streaming data from IoT devices to interactive live reports along with built machine learning (ML) models are included. As a result of the proposed intelligent monitoring system, the collected data and ML modelling outcomes are visualized using a powerful dynamic dashboard. The dashboard allows users to monitor various parameters across the farm and provides an accessible way to view trends, deviations, and patterns in the data. ML models are trained on the collected data and are updated periodically. The data-driven visualization enables farmers to examine, organize, and represent collected farm’s data with the goal of better serving their needs. Performance and durability tests of the system are provided. The proposed solution is a technological bridge with which farmers can easily, affordably, and understandably monitor and track the progress of their farms with easy integration into an existing IoT system.
Development of an Efficiency Platform Based on MQTT for UAV Controlling and DoS Attack Detection
Authors: da Silva Leandro Marcos , Menezes Henrique Bonini de Britto , Luccas Matheus dos Santos , Mailer Christian , Pinto Alex Sandro Roschildt , Boava Adão , Rodrigues Mariana , Ferrão Isadora Garcia , Estrella Júlio Cézar , Branco Kalinka Regina Lucas Jaquie Castelo
Published: September 2022

Several market sectors are attracted by the potential of unmanned aerial vehicles (UAVs), such as delivery, agriculture, and cinema, among others. UAVs are becoming part of Internet of Things (IoT) networks in the development of autonomous and scalable solutions. However, these vehicles are gradually becoming attractive targets for cyberattacks. This study proposes the development of an efficient platform based on the Message Queuing Telemetry Transport (MQTT) protocol for UAV control and Denial-of-Service (DoS) detection embedded in the UAV system. For the efficiency test, latency, network and memory consumption on the platform were measured, in addition to the correlation between payload and delay time. The results of efficiency tests were collected for the three levels of quality of service (QoS). A strong correlation greater than 90% was found between delay and data size for all QoS levels, showing almost a linear proportion. In DoS detection, the best results were a true positive rate (TPR) of 0.97 with 16 features from the AWID2 dataset using LightGBM with Bayesian optimization and data balancing. Unlike other studies, the built platform shows efficiency for UAV control and guarantees security in the communication with the broker and in the Wi-Fi UAV network.
An Adaptive and Robust Control Strategy for Real-Time Hybrid Simulation
Authors: Li Hong-Wei , Wang Fang , Ni Yi-Qing , Wang You-Wu , Xu Zhao-Dong
Published: September 2022

A real-time hybrid simulation (RTHS) is a promising technique to investigate a complicated or large-scale structure by dividing it into numerical and physical substructures and conducting cyber-physical tests on it. The control system design of an RTHS is a challenging topic due to the additional feedback between the physical and numerical substructures, and the complexity of the physical control plant. This paper proposes a novel RTHS control strategy by combining the theories of adaptive control and robust control, where a reformed plant which is highly simplified compared to the physical plant can be used to design the control system without compromising the control performance. The adaptation and robustness features of the control system are realized by the bounded-gain forgetting least-squares estimator and the sliding mode controller, respectively. The control strategy is validated by investigating an RTHS benchmark problem of a nonlinear three-story steel frame The proposed control strategy could simplify the control system design and does not require a precise physical plant; thus, it is an efficient and practical option for an RTHS.
Highly Sensing and Selective Performance Based on Bi-Doped Porous ZnSnO3 Nanospheres for Detection of n-Butanol
Authors: Jiang Lili , Cui Qi , Zhang Ruijia , Zhang Wenqiang
Published: September 2022

In this study, pure zinc stannate (ZnSnO3) and bismuth (Bi)-doped ZnSnO3 composites (Bi-ZnSnO3) were synthesized via the in situ precipitation method, and their microstructures, morphologies, chemical components, sizes, and specific surface areas were characterized, followed by testing their gas sensing properties. The results revealed that Bi-ZnSnO3 showed superior gas sensing properties to n-butanol gas, with an optimal operating temperature of 300 °C, which was 50 °C lower than that of pure ZnSnO3. At this temperature, moreover, the sensitivity of Bi-ZnSnO3 to n-butanol gas at the concentration of 100 ppm reached as high as 1450.65, which was 35.57 times that (41.01) of ammonia gas, 2.93 times that (495.09) of acetone gas, 6.02 times that (241.05) of methanol gas, 2.54 times that (571.48) of formaldehyde gas, and 2.98 times that (486.58) of ethanol gas. Bi-ZnSnO3 had a highly repeatable performance. The total proportion of oxygen vacancies and chemi-adsorbed oxygen in Bi-ZnSnO3 (4 wt%) was 27.72% to 32.68% higher than that of pure ZnSnO3. Therefore, Bi-ZnSnO3 has considerable potential in detecting n-butanol gas by virtue of its excellent gas-sensing properties.
Vulnerabilities of the Open Platform Communication Unified Architecture Protocol in Industrial Internet of Things Operation
Authors: Shin Dong-Hyuk , Kim Ga-Yeong , Euom Ieck-Chae
Published: September 2022

Recently, as new threats from attackers are discovered, the damage and scale of these threats are increasing. Vulnerabilities should be identified early, and countermeasures should be implemented to solve this problem. However, there are limitations to applying the vulnerability discovery framework used in practice. Existing frameworks have limitations in terms of the analysis target. If the analysis target is abstract, it cannot be easily applied to the framework. Therefore, this study proposes a framework for vulnerability discovery and countermeasures that can be applied to any analysis target. The proposed framework includes a structural analysis to discover vulnerabilities from a scenario composition, including analysis targets. In addition, a proof of concept is conducted to derive and verify threats that can actually occur through threat modeling. In this study, the open platform communication integrated architecture used in the industrial control system and industrial Internet of Things environment was selected as an analysis target. We find 30 major threats and four vulnerabilities based on the proposed framework. As a result, the validity of malicious client attacks using certificates and DoS attack scenarios using flooding were validated, and we create countermeasures for these vulnerabilities.
Research on Nonlinear Compensation of the MEMS Gyroscope under Tiny Angular Velocity
Authors: Ren Chunhua , Guo Dongning , Zhang Lu , Wang Tianhe
Published: September 2022

The Micro-Electro-Mechanical System (MEMS) gyroscope has been widely used in various fields, but the output of the MEMS gyroscope has strong nonlinearity, especially in the range of tiny angular velocity. This paper proposes an adaptive Fourier series compensation method (AFCM) based on the steepest descent method and Fourier series residual correction. The proposed method improves the Fourier series fitting method according to the output characteristics of the MEMS gyroscope under tiny angular velocity. Then, the optimal weights are solved by the steepest descent method, and finally the fitting residuals are corrected by Fourier series to further improve the compensation accuracy. In order to verify the effectiveness of the proposed method, the angle velocity component of the earth’s rotation is used as the input of the MEMS gyroscope to obtain the output of the MEMS gyroscope under tiny angular velocities. Experimental characterization resulted in an input angular velocity between −0.0036°/s and 0.0036°/s, compared with the original data, the polynomial compensation method, and the Fourier series compensation method, and the output nonlinearity of the MEMS gyroscope was reduced from 1150.87 ppm, 641.13 ppm, and 250.55 ppm to 68.89 ppm after AFCM compensation, respectively, which verifies the effectiveness and superiority of the proposed method.
Bifurcation Oscillator as an Advanced Sensor for Quantum State Control
Authors: Pashin Dmitrii , Bastrakova Marina , Satanin Arkady , Klenov Nikolay
Published: September 2022

We study bifurcation behavior of a high-quality (high-Q) Josephson oscillator coupled to a superconducting qubit. It is shown that the probability of capture into the state of dynamic equilibrium is sensitive to qubit states. On this basis we present a new measurement method for the superposition state of a qubit due to its influence on transition probabilities between oscillator levels located in the energy region near the classical separatrix. The quantum-mechanical behavior of a bifurcation oscillator is also studied, which makes it possible to understand the mechanism of "entanglement" of oscillator and qubit states during the measurement process. The optimal parameters of the driving current and the state of the oscillator are found for performing one-qubit gates with the required precision, when the influence on the qubit from measurement back-action is minimal. A measurement protocol for state populations of the qubit entangled with the oscillator is presented.
Capped Linex Metric Twin Support Vector Machine for Robust Classification
Authors: Wang Yifan , Yu Guolin , Ma Jun
Published: September 2022

In this paper, a novel robust loss function is designed, namely, capped linear loss function Laε. Simultaneously, we give some ideal and important properties of Laε, such as boundedness, nonconvexity and robustness. Furthermore, a new binary classification learning method is proposed via introducing Laε, which is called the robust twin support vector machine (Linex-TSVM). Linex-TSVM can not only reduce the influence of outliers on Linex-SVM, but also improve the classification performance and robustness of Linex-SVM. Moreover, the effect of outliers on the model can be greatly reduced by introducing two regularization terms to realize the structural risk minimization principle. Finally, a simple and efficient iterative algorithm is designed to solve the non-convex optimization problem Linex-TSVM, and the time complexity of the algorithm is analyzed, which proves that the model satisfies the Bayes rule. Experimental results on multiple datasets demonstrate that the proposed Linex-TSVM can compete with the existing methods in terms of robustness and feasibility.
CETS: Enabling Sustainable IoT with Cooperative Energy Transfer Schedule towards 6G Era
Authors: Kovvali Raja Sravan Kumar , Sundaram Gopikrishnan
Published: September 2022

The large scale of the Internet of Things necessitates using long-lasting physical layer devices for data collection. Deploying large numbers of Wi-Fi-enabled devices is expensive, so the Internet of Everything (IoE) is equipped with multiple communication modules to collect data where Wi-Fi is unavailable. However, because of their extended communication capabilities, IoE devices face energy limitations. As a result, IoE devices must be provided with the necessary energy resources. This paper introduces a novel multi-hop cooperation communication mechanism for Wireless Energy Transfer (WET) in the Wireless Powered-Internet of Everything (WP-IoE). IoE devices are outfitted here with various communication devices such as RF, Bluetooth, and Wi-Fi. This research proposes a two-phase energy transmission schedule to address the energy requirements. For data collection, the first phase provides a distributed tree-based data communication plan. The proposed model’s second phase used the reverse data collection protocol to implement wireless energy transmission. By combining these two phases, an optimized WET framework was created without unmanned aerial vehicles or robots. The experimental findings show that the proposed method in this research increases the average lifetime of the network and has a more significant charge latency and average charge throughput than other models.
A Low Cost IoT Cyber-Physical System for Vehicle and Pedestrian Tracking in a Smart Campus
Authors: Toutouh Jamal , Alba Enrique
Published: September 2022

Human tracking and traffic monitoring systems are required to build advanced intelligent, innovative mobility services. In this study, we introduce an IoT system based on low-cost hardware that has been installed on the campus of the University of Malaga, in Spain. The sensors gather smart wireless devices (Bluetooth and Wi-Fi) anonymous information and environmental noise level around them. This research studies the spatio-temporal behavior of people and noise pollution in the campus as a short-scale Smart City, i.e., a Smart Campus. Applying specific machine learning algorithms, we have analyzed two months of captured data (61 days). The main findings from the analysis show that most university community members move through the campus at similar hours, generating congestion problems. In addition, the campus suffers from acoustic pollution according to regulations; therefore, we conclude that the proposed system is useful for gathering helpful information for the university community members and managers. Thanks to its low cost, it can be easily extended and even used in other similar environments, allowing democratic access to Smart City services as an excellent added value.
Robust Transceiver Design for IRS-Assisted Cascaded MIMO Communication Systems
Authors: Esmaeili Hossein , Kariminezhad Ali , Sezgin Aydin
Published: September 2022

Intelligent reconfigurable surfaces (IRSs) have gained much attention due to their passive behavior that can be a successor to relays in many applications. However, traditional relay systems might still be a perfect choice when reliability and throughput are the main concerns in a communication system. In this work, we use an IRS along with a decode-and-forward relay to provide a possible solution to address one of the main challenges of future wireless networks which is providing reliability. We investigate a robust transceiver design against the residual self-interference (RSI), which maximizes the throughput rate under self-interference channel uncertainty-bound constraints. The yielded problem turns out to be a non-convex optimization problem, where the non-convex objective is optimized over the cone of semidefinite matrices. We propose a novel mathematical method to find a lower bound on the performance of the IRS that can be used as a benchmark. Eventually, we show an important result in which, for the worst-case scenario, IRS can be helpful only if the number of IRS elements are at least as large as the size of the interference channel. Moreover, a novel method based on majorization theory and singular value decomposition (SVD) is proposed to find the best response of the transmitters and relay against worst-case RSI. Furthermore, we propose a multi-level water-filling algorithm to obtain a locally optimal solution iteratively. We show that our algorithm performs better that the state of the art in terms of time complexity as well as robustness. For instance, our numerical results show that the acheivable rate can be increased twofold and almost sixfold, respectively, for the case of small and large antenna array at transceivers.
Epileptic Disorder Detection of Seizures Using EEG Signals
Authors: Alharthi Mariam K. , Moria Kawthar M. , Alghazzawi Daniyal M. , Tayeb Haythum O.
Published: September 2022

Epilepsy is a nervous system disorder. Encephalography (EEG) is a generally utilized clinical approach for recording electrical activity in the brain. Although there are a number of datasets available, most of them are imbalanced due to the presence of fewer epileptic EEG signals compared with non-epileptic EEG signals. This research aims to study the possibility of integrating local EEG signals from an epilepsy center in King Abdulaziz University hospital into the CHB-MIT dataset by applying a new compatibility framework for data integration. The framework comprises multiple functions, which include dominant channel selection followed by the implementation of a novel algorithm for reading XLtek EEG data. The resulting integrated datasets, which contain selective channels, are tested and evaluated using a deep-learning model of 1D-CNN, Bi-LSTM, and attention. The results achieved up to 96.87% accuracy, 96.98% precision, and 96.85% sensitivity, outperforming the other latest systems that have a larger number of EEG channels.