bluuebunny/crossref_metadata_2025 huggingface dataset:
Retrieval of Stratospheric Ozone Profiles from Limb Scattering Measurements of the Backward Limb Spectrometer on Chinese Space Laboratory Tiangong-2: Preliminary Results
Authors: Liu Song , Zong Xuemei , Qiao Congcong , Lyu Daren , Zhang Wenxing , Zhang Jinqiang , Liu Hailei , Duan Minzheng
Published: September 2022

The Backward Limb Spectrometer (BLS) onboard the Tiangong-2 (TG-2) space laboratory, the first spaceborne limb sounding instrument of China, was successfully launched on 15 September 2016, and its measurements of scattered photons of sunlight along the limb line-of-sight (LOS) in the 290–1000 nm range could be used to derive the vertical distribution of stratospheric ozone with high vertical resolution. Ozone profiles with a vertical resolution of one km in 10–40 km and 30–50 km were retrieved by the triplet and pair methods, respectively, and the ozone profiles retrieved by the BLS were compared with the ozone sounding data over four sounding stations. Meanwhile, the Ozone Mapping and Profiler Suite Limb Profiler (OMPS/LP) version 2.5 (v2.5) stratospheric ozone profile product was also introduced for comparison. The retrieval results showed a good agreement with the ozone profiles of ozone sounding and the OMPS/LP v2.5 product, and the differences were basically within 25% above 20 km, while relatively larger differences occasionally occurred below 20 km. The case studies over four sites worldwide demonstrate that the BLS is capable of measuring stratospheric ozone profiles with high vertical resolution.
Detection of Waste Plastics in the Environment: Application of Copernicus Earth Observation Data
Authors: Lavender Samantha
Published: September 2022

The detection of waste plastics in the marine and terrestrial environment using satellite Earth Observation data offers the possibility of large-scale mapping and reducing on-the-ground manual investigation. In addition, costs are kept to a minimum by utilizing free-to-access Copernicus data. A Machine Learning-based classifier was developed to run on Sentinel-1 and -2 data. In support of the training and validation, a dataset was created with terrestrial and aquatic cases by manually digitizing varying landcover classes alongside plastics under the sub-categories of greenhouses, plastic, tyres and waste sites. The trained classifier, including an Artificial Neural Network and post-processing decision tree, was verified using five locations encompassing these different forms of plastic. Although exact matchups are challenging to digitize, the performance has generated high accuracy statistics, and the resulting land cover classifications have been used to map the occurrence of plastic waste in aquatic and terrestrial environments.
Comparison of Ground Point Filtering Algorithms for High-Density Point Clouds Collected by Terrestrial LiDAR
Authors: Bailey Gene , Li Yingkui , McKinney Nathan , Yoder Daniel , Wright Wesley , Herrero Hannah
Published: September 2022

Terrestrial LiDAR (light detection and ranging) has been used to quantify micro-topographic changes using high-density 3D point clouds in which extracting the ground surface is susceptible to off-terrain (OT) points. Various filtering algorithms are available in classifying ground and OT points, but additional research is needed to choose and implement a suitable algorithm for a given surface. This paper assesses the performance of three filtering algorithms in classifying terrestrial LiDAR point clouds: a cloth simulation filter (CSF), a modified slope-based filter (MSBF), and a random forest (RF) classifier, based on a typical use-case in quantifying soil erosion and surface denudation. A hillslope plot was scanned before and after removing vegetation to generate a test dataset of ground and OT points. Each algorithm was then tested against this dataset with various parameters/settings to obtain the highest performance. CSF produced the best classification with a Kappa value of 0.86, but its performance is highly influenced by the ‘time-step’ parameter. MSBF had the highest precision of 0.94 for ground point classification but the highest Kappa value of only 0.62. RF produced balanced classifications with the highest Kappa value of 0.75. This work provides valuable information in optimizing the parameters of the filtering algorithms to improve their performance in detecting micro-topographic changes.
Fuzzy Logic Modeling of Land Degradation in a Loess Plateau Watershed, China
Authors: Lu Ang , Tian Peng , Mu Xingmin , Zhao Guangju , Feng Qingyu , Guo Jianying , Xu Wenlong
Published: September 2022

Various land degradation processes have led to land productivity reduction, food insecurity and ecosystem destruction. The Loess Plateau (LP) suffered from severe land degradation, such as vegetation degradation, soil erosion and desertification. This study assessed land degradation changes by considering different land degradation types including vegetation degradation, soil erosion, aridity, loss of soil organic carbon and desertification in the Huangfuchuan watershed of the northern LP. A comprehensive land degradation index (LDI) was developed by combining different degradation processes using the fuzzy logic modeling method. Our results showed significant land use transitions from bare land and sandy area to grass land and forest land from 1990 to 2018, which were consistent with an obvious increase in vegetation cover from 31.24% to 40.72%. The soil erosion rate predicted by the RUSLE model decreased by 51.95% during 1990–2018. The basin-average LDI decreased from 0.68 in 1990 to 0.51 in 2018, suggesting the great success of land degradation prevention in a fragile ecological environment region on the LP during the past decades. This study proposed an integrated framework for land degradation assessment in the high erodible area. The results can provide good references for the improvement of ecological environment in the future.
The Effects of Anthropogenic Pressure on Rivers: A Case Study in the Metropolitan City of Reggio Calabria
Authors: Foti Giandomenico , Bombino Giuseppe , D’Agostino Daniela , Barbaro Giuseppe
Published: September 2022

In the second half of the twentieth century, after the end of the Second World War, a considerable anthropogenic pressure was observed in most of the Mediterranean territories. This process has caused the expansion of existing settlements and the construction of numerous new towns, often located very close to rivers. A frequent consequence of this process is the transformation of several rivers through planform changes, narrowing, channelization and culverting to recover spaces where inhabited centers expanded, and the construction of structures interacting with rivers. This issue is very important in territories such as the Metropolitan City of Reggio Calabria, in southern Italy, which is an interesting case study due to the considerable anthropogenic pressures observed in the last 70 years. The main goal of this paper is to evaluate the effects of anthropogenic pressure in the last 70 years on some rivers of the Metropolitan City of Reggio Calabria in terms of the following issues: planform changes, channelization, culverting, and the presence of structures and infrastructures interacting with rivers. The specific goals of this paper are the quantification of the effects of anthropogenic pressure on the rivers of the study area analyzing sixteen parameters, the identification of possible conditions of hydraulic hazard through the analysis of past events, and the proposal of structural and non-structural mitigation interventions. In many rivers of the study area, the significant effects of anthropogenic pressure are visible through rivers that pass above highways, barred rivers, rivers replaced by roads and numerous crossing roads with a missing levee.
Accurately Stable Q-Compensated Reverse-Time Migration Scheme for Heterogeneous Viscoelastic Media
Authors: Wang Ning , Shi Ying , Zhou Hui
Published: September 2022

The development of multi-component seismic acquisition technology creates new possibilities for the high-precision imaging of complex media. Compared to the scalar acoustic wave equation, the elastic wave equation takes the information of P-waves, S-waves, and converted waves into account simultaneously, enabling accurate description of actual seismic propagation. However, inherent attenuation is one of the important factors that restricts multi-component high-precision migration imaging. Its influence is mainly reflected in the following three ways: first, the attenuation of the amplitude energy makes the deep structure display unclear; second, phase distortion introduces errors to the positioning of underground structures; and third, the loss of high frequency components reduces imaging resolution. Therefore, it is crucial to fully consider the absorption and attenuation characteristics of the real Earth during seismic modeling and imaging. This paper aims to develop an accurate attenuation compensation reverse-time migration scheme for complex heterogeneous viscoelastic media. We first utilize a novel viscoelastic wave equation with decoupled fractional Laplacians to depict the Earth’s attenuation behavior. Then, an adaptive stable attenuation compensation operator is developed to realize high-precision attenuation compensation imaging. Several synthetic and field data analyses verify the effectiveness of the proposed method.
Sensitivity Analysis of 1,3-Butadiene Monitoring Based on Space-Based Detection in the Infrared Band
Authors: Bai Jingyu , Bai Lu , Li Jinlu , Wang Yankun , Xie Jinyu , Zhang Danmeng , Guo Lixin
Published: September 2022

The monitoring of the Volatile Organic Compounds (VOCs) in the atmosphere is of great significance for reducing chemical pollution, warning of fires, and improving air quality. Among the VOCs, 1,3-butadiene is essential to monitor as a carcinogenic environmental pollutant. The space-based detection of 1,3-butadiene was made possible by recently development of infrared detection satellites and advances in spectroscopic techniques. In this work, based on the demand for space-based infrared detection of 1,3-butadiene, a modeling method of Earth background radiance characteristics coupled with remote sensing data and physical model is proposed, which can effectively simulate the Earth background radiance field distribution under any atmospheric conditions. Specifically, infrared spectral radiance of 1,3-butadiene was simulated from absorption cross-section data. Further, combined with the radiative transfer model and atmospheric profiles of satellite sensor data, such as temperature, pressure, and H2O and O3 mixing ratio, the atmospheric transmittance and atmospheric background radiance are simulated. Finally, infrared space-based detection of 1,3-butadiene is simulated by coupling Earth background radiance and the detectability is analyzed and discussed by using signal-to-clutter ratio (SCR). The results show that 1,3-butadiene has an absorption effect in space-based infrared detection. The detectability of the narrow band 9.8–10.0 μm and 10.9–11.1 μm is better than that of the wide band 9.0–12.0 μm, and 10.9–11.1 μm is a better detection band for 1,3-butadiene than other bands. This paper provides a method for the space-based infrared detection of 1,3-butadiene. It also provides a reference for selecting the appropriate band for the detector.
A Review of Marine Gravity Field Recovery from Satellite Altimetry
Authors: Li Zhen , Guo Jinyun , Ji Bing , Wan Xiaoyun , Zhang Shengjun
Published: September 2022

Marine gravity field recovery relies heavily on satellite altimetry. Thanks to the evolution of altimetry missions and the improvements in altimeter data processing methods, the marine gravity field model has been prominently enhanced in accuracy and resolution. However, high-accuracy and high-resolution gravity field recovery from satellite altimeter data remains particularly challenging. We provide an overview of advances in satellite altimetry for marine gravity field recovery, focusing on the impact factors and available models of altimetric gravity field construction. Firstly, the evolution of altimetry missions and the contribution to gravity field recovery are reviewed, from the existing altimetry missions to the future altimetry missions. Secondly, because the methods of altimeter data processing are of great significance when obtaining high-quality sea surface height observations, these improved methods are summarized and analyzed, especially for coastal altimetry. In addition, the problems to be resolved in altimeter data processing are highlighted. Thirdly, the characteristics of gravity recovery methods are analyzed, including the inverse Stokes formula, the inverse Vening Meinesz formula, Laplace’s equation, and least squares collocation. Furthermore, the latest global marine gravity field models are introduced, including the use of altimeter data and processing methods. The performance of the available global gravity field model is also evaluated by shipboard gravity measurements. The root mean square of difference between the available global marine gravity model and shipboard gravity from the National Centers for Environmental Information is approximately 5.10 mGal in the low-middle latitude regions, which is better than the result in high-latitude regions. In coastal areas, the accuracy of models still needs to be further improved, particularly within 40 km from the coastline. Meanwhile, the SDUST2021GRA model derived from the Shandong University of Science and Technology team also exhibited an exciting performance. Finally, the future challenges for marine gravity field recovery from satellite altimetry are discussed.
Assessment of Sentinel-2-MSI Atmospheric Correction Processors and In Situ Spectrometry Waters Quality Algorithms
Authors: Sòria-Perpinyà Xavier , Delegido Jesús , Urrego Esther Patricia , Ruíz-Verdú Antonio , Soria Juan Miguel , Vicente Eduardo , Moreno José
Published: September 2022

The validation of algorithms developed from in situ reflectance to estimate water quality variables has the challenge of atmospheric correction (AC) when applied to satellite images. Estimating water quality variables from satellite images requires an accurate estimation of remote sensing reflectances (Rrs) which vary according to the AC applied. Validation processes for both Rrs and water quality algorithms were carried out, relating the in situ Rrs (convoluted to Sentinel-2-MSI spectral response function) with the satellite Rrs coming from different ACs (C2RCC, C2X, C2XC, and Polymer), and also relating the in situ water quality variable data with estimated water quality variable values, applying the water quality algorithms to the Rrs obtained for each AC. Regarding the Rrs validation results, the best ACs tested in this work were C2XC and Polymer. Regarding the water quality algorithm validation, the best results were also obtained using C2XC and Polymer Rrs. The results demonstrate the usefulness of the water quality algorithms developed from in situ reflectances since they are not specific to an AC and can be used with any processor.
High-Acceleration Vibration Calibration System Based on Phase-Locked Resonance Control
Authors: Cheng Ran , Liu Zhihua , Zhai Guodong , Lv Qi , Yang Ming , Cai Chenguang
Published: September 2022

In order to ensure the measurement accuracy of high-acceleration vibration sensors used in engineering applications, it is necessary to calibrate their key performance parameters at high acceleration. The high-acceleration vibration calibration system produces high-acceleration vibration by utilizing the resonance amplification principle; however, the resonance frequency of the resonant beam changes with increasing amplitude, affected by the influences of nonlinear and other factors. In this study, a phase-locked resonance tracking control method based on the phase resonance principle is proposed to accurately and quickly track the resonance frequency of the resonant beam, which can improve the accuracy and stability of resonance control. The resonant beam is able to produce stable vibration with an amplitude exceeding 7500 m/s2 by phase-locking and tracking the resonant frequency. A calibration system built with this method can provide stable vibration with an amplitude of 500–10,000 m/s2 in the range of 80–4000 Hz. Comparison experiments with the commonly used amplitude iteration amplification method demonstrate that the proposed method can give an acceleration stability control index of less than 0.5% and a resonance tracking time of less than 0.1 s.
Ocean Wind Observation Based on the Mean Square Slope Using a Self-Developed Miniature Wave Buoy
Authors: Zhong Yao-Zhao , Chien Hwa , Chang Huan-Meng , Cheng Hao-Yuan
Published: September 2022

Real-time, continuous, and long-term marine monitoring data benefits ocean research. This study developed a low-cost, multi-parameter, miniature wave buoy. High spatial and temporal resolution of sea surface parameters, including wind, waves, and current, can be obtained at low cost through the deployment of numerous buoys, thus forming an observation array. Tested in the laboratory water tank, the relative error of water surface slope measurement of the buoy was approximately 5.6% when the slope angle was less than 15°. For frequencies between 0.1 and 1.0 Hz, the measurement of slope spectrum was almost identical to that of the wave gauge. The buoy underestimated the slope spectrum between 1.0–1.56 Hz. A good relationship (r2 = 0.75) was obtained between wind speed at 10 m above sea surface (U10) and the low-pass-filtered mean square slope (LPMSS). After incorporating the wave age into the U10 inversion process, the root mean square error (RMSE) and BIAS were reduced to 1.15 m/s and 0.02 m/s, respectively. The 2D distribution of buoy-measured slope components was used to detect the wind direction, with an RMSE of 23.7°. The spectral tail slope steepened with increasing wind speed at low wind speeds (<7 m/s). A technical flow chart of the miniature wave buoy is proposed to observe the sea surface parameters. This miniature buoy will play an essential complementary role in the growing demand for sea state monitoring, especially in nearshore oceans.
Design of a Multi-Sensor System for Exploring the Relation between Finger Spasticity and Voluntary Movement in Patients with Stroke
Authors: Lin Bor-Shing , Lee I-Jung , Hsiao Pei-Chi , Yang Shu-Yu , Chen Chen-Yu , Lee Si-Huei , Huang Yu-Fang , Yen Mao-Hsu , Hu Yu Hen
Published: September 2022

A novel wearable multi-sensor data glove system is developed to explore the relation between finger spasticity and voluntary movement in patients with stroke. Many stroke patients suffer from finger spasticity, which is detrimental to their manual dexterity. Diagnosing and assessing the degrees of spasticity require neurological testing performed by trained professionals to estimate finger spasticity scores via the modified Ashworth scale (MAS). The proposed system offers an objective, quantitative solution to assess the finger spasticity of patients with stroke and complements the manual neurological test. In this work, the hardware and software components of this system are described. By requiring patients to perform five designated tasks, biomechanical measurements including linear and angular speed, acceleration, and pressure at every finger joint and upper limb are recorded, making up more than 1000 features for each task. We conducted a preliminary clinical test with 14 subjects using this system. Statistical analysis is performed on the acquired measurements to identify a small subset of features that are most likely to discriminate a healthy patient from patients suffering from finger spasticity. This encouraging result validates the feasibility of this proposed system to quantitatively and objectively assess finger spasticity.
Battery-Less NFC Potentiostat for Electrochemical Point-of-Care Sensors Based on COTS Components
Authors: Lazaro Antonio , Villarino Ramon , Lazaro Marc , Canellas Nicolau , Prieto-Simon Beatriz , Girbau David
Published: September 2022

This work studies the feasibility of using a battery-less Near-Field Communication (NFC) potentiostat for the next generation of electrochemical point-of-care sensors. A design based on an NFC microchip, a microcontroller, and a custom potentiostat based on an operational amplifier is presented. A proof-of-concept prototype has been designed and used to quantify glucose concentration using commercial glucose test strips from chronoamperometry measurements. The device is harvested and the sensor is read using a mobile phone. The prototype uses an antenna loop covered with ferrite sheets to ensure stable operation of the electronics when the mobile phone is used as reader. The use of ferrite reduces the detuning caused by the proximity of the metal parts of the mobile phone. A comparison with a commercial glucometer device is provided. Results obtained using a commercial glucometer and those provided by the proposed potentiostat show an excellent agreement.
Physics-Based Simulation of Soft-Body Deformation Using RGB-D Data
Authors: Kang Daeun , Moon Jaeseok , Yang Saeyoung , Kwon Taesoo , Kim Yejin
Published: September 2022

Providing real-time interaction in an immersive environment has drawn considerable attention in the virtual training fields. Physics-based simulations are suitable for such environments; however, they require the definition and adjustment of coefficients that determine material properties, making the methods more complex and time-consuming. In this paper, we introduce a novel approach to simulating the soft-body deformation of an observed object. Using an off-the-shelf RGB-D sensor, the proposed approach tracks an object’s movement and simulates its deformation in an iterative manner. Polygonal models with different resolutions are used to improve the simulation speed and visual quality. During the simulation process, a low-resolution model is used for surface deformation using neighboring feature points detected from the sensor, and a volumetric model is added for internal force estimation. To visualize the observed model in detail, the deformed and low-resolution model is mapped to a high-resolution model using mean value coordinate interpolation. To handle topological deformations, such as cutting or tearing, a part intersected by a cutting tool is recognized by the sensor and responds to external forces. As shown in the experimental results, our approach generates convincing deformations of observed objects in real time.
Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers
Authors: Hassan Ch. Anwar ul , Iqbal Jawaid , Irfan Rizwana , Hussain Saddam , Algarni Abeer D. , Bukhari Syed Sabir Hussain , Alturki Nazik , Ullah Syed Sajid
Published: September 2022

Coronary heart disease is one of the major causes of deaths around the globe. Predicating a heart disease is one of the most challenging tasks in the field of clinical data analysis. Machine learning (ML) is useful in diagnostic assistance in terms of decision making and prediction on the basis of the data produced by healthcare sector globally. We have also perceived ML techniques employed in the medical field of disease prediction. In this regard, numerous research studies have been shown on heart disease prediction using an ML classifier. In this paper, we used eleven ML classifiers to identify key features, which improved the predictability of heart disease. To introduce the prediction model, various feature combinations and well-known classification algorithms were used. We achieved 95% accuracy with gradient boosted trees and multilayer perceptron in the heart disease prediction model. The Random Forest gives a better performance level in heart disease prediction, with an accuracy level of 96%.
Precision Seeding Compensation and Positioning Based on Multisensors
Authors: Sun Jiaze , Zhang Yan , Zhang Yuting , Li Peize , Teng Guifa
Published: September 2022

The current multi-row planter always leads to uneven seeding spacing between rows while seeding in curve paths, which causes uneven growth, a cost increase of production and management, and reduced yield. With the development of smart farming technology, a curve seeding compensation and precise positioning model is proposed in the paper to calculate the real-time speed and position of each seeding unit based on the information from multisensors, such as GNSS and IMU, and to predict the next seeding position to achieve uniform seeding on the curve and improve the unit yield of crops. MATLAB Simulink simulation experiments show that the seeding pass rate of the model is 99.97% when the positioning accuracy is ±0.01 m and the traction speed is 1 m/s, and the seeding pass rate of the five-row seeder is as high as 99.81% when the traction speed is 3 m/s, which verifies the effectiveness and practicality of the model.
Dynamic Compensation Method for Humidity Sensors Based on Temperature and Humidity Decoupling
Authors: Yang Wenxuan , Li Wenchang , Lu Huaxiang , Liu Jian , Zhang Tianyi
Published: September 2022

Currently, integrated humidity sensors with fast-response time are widely needed. The most commonly used polyimide capacitive humidity sensor has a long response time, which is difficult to meet the need for a fast response. Most studies focusing on technology and materials have a high cost and are difficult to ensure compatability with the CMOS process. The dynamic compensation method can shorten the response time by only adding digital circuits or software processing. However, conventional compensation technology is not suitable for humidity sensors due to temperature coupling. This paper proposes a new dynamic compensation method for humidity sensors based on the decoupling of temperature factors by analyzing the coupling relationship between sensor dynamic characteristics and temperature. Simulations and experiments were used to verify the proposed method. The experimental results show that the proposed method reduces the humidity response time of the sensor by 85.6%. The proposed method can effectively shorten the response time of humidity sensors.
Temperature and Humidity Sensitivity of Polymer Optical Fibre Sensors Tuned by Pre-Strain
Authors: Pospori Andreas , Ioannou Andreas , Kalli Kyriacos
Published: September 2022

Polymer optical fibre Bragg grating (POFBG) sensors are of high interest due to their enhanced fracture toughness, flexibility in bending, and sensitivity in stress and pressure monitoring applications compared to silica-based sensors. The POFBG sensors can also detect humidity due to the hydrophilic nature of some polymers. However, multi-parameter sensing can cause cross-sensitivity issues in certain applications if the temperature and humidity measurements are not adequately compensated. In this work, we demonstrate the possibility of selectively tuning sensors’ temperature and humidity sensitivities to the desired level by applying a certain amount of fibre pre-strain. The temperature sensitivity of POFBG sensors fabricated in perfluoropolymers (CYTOP) can be selectively tuned from positive to negative values, having the option for insensitivity in specific temperature ranges depending on the amount of the applied pre-strain. The humidity sensitivity of sensors can also be changed from positive values to insensitivity. The importance of thermal annealing treatment of POFBG sensors for improved repeatability in temperature measurements is also reported. An array of 4 multiplexed POFBGs was fabricated, and each sensor was pre-strained accordingly to demonstrate the possibility of having targeted temperature and humidity sensitivities along the same fibre.
Extrinsic Calibration of Multiple 3D LiDAR Sensors by the Use of Planar Objects
Authors: Lee Hyunsuk , Chung Woojin
Published: September 2022

Three-dimensional light detection and ranging (LiDAR) sensors have received much attention in the field of autonomous navigation owing to their accurate, robust, and rich geometric information. Autonomous vehicles are typically equipped with multiple 3D LiDARs because there are many commercially available low-cost 3D LiDARs. Extrinsic calibration of multiple LiDAR sensors is essential in order to obtain consistent geometric information. This paper presents a systematic procedure for the extrinsic calibration of multiple 3D LiDAR sensors using plane objects. At least three independent planes are required within the common field of view of the LiDAR sensors. The planes satisfying the condition can easily be found on objects such as the ground, walls, or columns in indoor and outdoor environments. Therefore, the proposed method does not require environmental modifications such as using artificial calibration objects. Multiple LiDARs typically have different viewpoints to reduce blind spots. This situation increases the difficulty of the extrinsic calibration using conventional registration algorithms. We suggest a plane registration method for cases in which correspondences are not known. The entire calibration process can easily be automated using the proposed registration technique. The presented experimental results clearly show that the proposed method generates more accurate extrinsic parameters than conventional point cloud registration methods.
IoT Off-Grid, Data Collection from a Machine Learning Classification Using UAV
Authors: Goulart Ademir , Pinto Alex Sandro Roschildt , Boava Adão , Branco Kalinka R. L. J. Castelo
Published: September 2022

IoT encompasses various objects, technologies, communication standards, sensors, actuators in powered environments, and networked communication. The concept adopted here, IoT off-grid, considers an environment without commercial electricity and commercial internet. Managing various utilities with IoT and collecting the relevant information from this environment is the purpose of this project. It uses machine learning to select relevant data. These data are collected safely using a drone that travels through the off-grid stations. A systematic literature mapping is presented, identifying the state of the art. The result is a software architecture proposal with configurations in the drone and off-grid stations that contemplate data collection from the IoT off-grid environment. The results are also presented with different selection algorithms used in machine learning and final execution in the prototype.
FedMSA: A Model Selection and Adaptation System for Federated Learning
Authors: Sun Rui , Li Yinhao , Shah Tejal , Sham Ringo W. H. , Szydlo Tomasz , Qian Bin , Thakker Dhaval , Ranjan Rajiv
Published: September 2022

Federated Learning (FL) enables multiple clients to train a shared model collaboratively without sharing any personal data. However, selecting a model and adapting it quickly to meet user expectations in a large-scale FL application with heterogeneous devices is challenging. In this paper, we propose a model selection and adaptation system for Federated Learning (FedMSA), which includes a hardware-aware model selection algorithm that trades-off model training efficiency and model performance base on FL developers’ expectation. Meanwhile, considering the expected model should be achieved by dynamic model adaptation, FedMSA supports full automation in building and deployment of the FL task to different hardware at scale. Experiments on benchmark and real-world datasets demonstrate the effectiveness of the model selection algorithm of FedMSA in real devices (e.g., Raspberry Pi and Jetson nano).
Adaptive Load Forecasting Methodology Based on Generalized Additive Model with Automatic Variable Selection
Authors: Krstonijević Sovjetka
Published: September 2022

For decentralized energy management in a smart grid, there is a need for electric load forecasting at different places in the grid hierarchy and for different levels of aggregation. Load forecasting functionality relies on the load time series prediction model, which provides accurate forecasts. Complex and heterogeneous multi-source load time series in a smart grid require flexible modeling approaches to meet the accuracy demand. This work proposes an adaptive load forecasting methodology based on the generalized additive model (GAM) with the big data estimation method. It is based on a set of GAM terms, constructed for a specific multi-source load forecasting application in the grid and a procedure that dynamically selects the most relevant terms and generates forecasts for particular load time series. Data from publicly available New York Independent System Operator (NYISO) databases are used for testing. The 24-hour-ahead forecasting results for eleven New York City zones, of different sizes and types, indicate the applicability of the proposed methodology.
Optimal Mapping of Spiking Neural Network to Neuromorphic Hardware for Edge-AI
Authors: Xiao Chao , Chen Jihua , Wang Lei
Published: September 2022

Neuromorphic hardware, the new generation of non-von Neumann computing system, implements spiking neurons and synapses to spiking neural network (SNN)-based applications. The energy-efficient property makes the neuromorphic hardware suitable for power-constrained environments where sensors and edge nodes of the internet of things (IoT) work. The mapping of SNNs onto neuromorphic hardware is challenging because a non-optimized mapping may result in a high network-on-chip (NoC) latency and energy consumption. In this paper, we propose NeuMap, a simple and fast toolchain, to map SNNs onto the multicore neuromorphic hardware. NeuMap first obtains the communication patterns of an SNN by calculation that simplifies the mapping process. Then, NeuMap exploits localized connections, divides the adjacent layers into a sub-network, and partitions each sub-network into multiple clusters while meeting the hardware resource constraints. Finally, we employ a meta-heuristics algorithm to search for the best cluster-to-core mapping scheme in the reduced searching space. We conduct experiments using six realistic SNN-based applications to evaluate NeuMap and two prior works (SpiNeMap and SNEAP). The experimental results show that, compared to SpiNeMap and SNEAP, NeuMap reduces the average energy consumption by 84% and 17% and has 55% and 12% lower spike latency, respectively.
Exploitation of SnO2/Polypyrrole Interface for Detection of Ammonia Vapors Using Conductometric and Optical Techniques: A Theoretical and Experimental Analysis
Authors: Gahlot Ajay Pratap Singh , Paliwal Ayushi , Kapoor Avinashi
Published: September 2022

This study describes the construction of a lab-built Surface Plasmon Resonance (SPR) system for gas sensing applications employing a highly sensitive and trustworthy optical approach. The nanocomposite thin film of tin oxide (SnO2) and Polypyrrole (PPy) were prepared for sensing highly toxic gas, i.e., ammonia (NH3) gas. The gas sensor was validated by both optical and conductometric techniques of gas sensing. The optical SPR gas sensor is based on the change in refractive index at the SnO2/Polypyrrole (PPy) interface with gas adsorption (NH3). The thickness of SnO2 and Polypyrrole thin films was optimised using theoretical calculations for a sharp SPR reflectance curve. The manuscript also offers theoretical SPR curves for different PPy and SnO2 layer thicknesses. To support the theoretical conclusions, the effects of NH3 gas on the prism/Au/SnO2/Polypyrrole system were also investigated experimentally. In comparison to other research described in the literature, it was observed that the constructed sensor’s sensitivity was higher. The obtained results demonstrate the utility of the SPR setup in the investigation of the interactions of adhered gas molecules with dielectrics and gas sensing. For conductometric gas sensing studies, the film having optimised thicknesses for sharp SPR reflectance curves was separately prepared on Interdigitated Electrodes. At a low working temperature of roughly 150 °C, the sensing response of the constructed film was observed and found to be maximal (60).
Application of Deep Convolutional Neural Network for Automatic Detection of Digital Optical Fiber Repeater
Authors: Tian Xingkang , Wu Fan , Zhang Cong , Fan Wenhao , Liu Yuanan
Published: September 2022

The digital optical fiber repeater (DOFR) is an important infrastructure in the LTE networks, which solve the problem of poor regional signal quality. Various types of conventional measurement data from the LTE network cannot indicate whether a working DOFR is present in the cell. Currently, the detection of DOFRs relies solely on maintenance engineers for field detection. Manual detection methods are not timely or efficient, because of the large number and wide geographical distribution of DOFRs. Implementing automatic detection of DOFR can reduce the maintenance cost for mobile network operators. We treat the DOFR detection problem as a classification problem and employ a deep convolutional neural network (DCNN) to tackle it. The measurement report (MR) we used in this paper are tabular data, which is not an ideal input for DCNN. We propose a novel MR representation method that takes the overall MR data of a cell as a sample rather than a single record in the table, and represents the MR data as a pseudo-image matrix (PIM). The PIM will be used as the input for training DCNN, and the trained DCNN will be used to perform DOFR detection tasks. We conducted a series of experiments on real MR data, and the classification accuracy can achieve 93%. The proposed AI-based method can effectively detect the DOFR in a cell.
Research on the Application of Visual Recognition in the Engine Room of Intelligent Ships
Authors: Shang Di , Zhang Jundong , Zhou Kunxin , Wang Tianjian , Qi Jiahao
Published: September 2022

In the engine room of intelligent ships, visual recognition is an essential technical precondition for automatic inspection. At present, the problems of visual recognition in marine engine rooms include missing detection, low accuracy, slow speed, and imperfect datasets. For these problems, this paper proposes a marine engine room equipment recognition model based on the improved You Only Look Once v5 (YOLOv5) algorithm. The channel pruning method based on batch normalization (BN) layer weight value is used to improve the recognition speed. The complete intersection over union (CIoU) loss function and hard-swish activation function are used to enhance detection accuracy. Meanwhile, soft-NMS is used as the non-maximum suppression (NMS) method to reduce the false rate and missed detection rate. Then, the main equipment in the marine engine room (MEMER) dataset is built. Finally, comparative experiments and ablation experiments are carried out on the MEMER dataset to verify the strategy’s efficacy on the model performance boost. Specifically, this model can accurately detect 100.00% of diesel engines, 95.91% of pumps, 94.29% of coolers, 98.54% of oil separators, 64.21% of meters, 60.23% of reservoirs, and 75.32% of valves in the actual marine engine room.
An Efficient and Secure Energy Trading Approach with Machine Learning Technique and Consortium Blockchain
Authors: Ashfaq Tehreem , Khalid Muhammad Irfan , Ali Gauhar , Affendi Mohammad El , Iqbal Jawaid , Hussain Saddam , Ullah Syed Sajid , Yahaya Adamu Sani , Khalid Rabiya , Mateen Abdul
Published: September 2022

In this paper, a secure energy trading mechanism based on blockchain technology is proposed. The proposed model deals with energy trading problems such as insecure energy trading and inefficient charging mechanisms for electric vehicles (EVs) in a vehicular energy network (VEN). EVs face two major problems: finding an optimal charging station and calculating the exact amount of energy required to reach the selected charging station. Moreover, in traditional trading approaches, centralized parties are involved in energy trading, which leads to various issues such as increased computational cost, increased computational delay, data tempering and a single point of failure. Furthermore, EVs face various energy challenges, such as imbalanced load supply and fluctuations in voltage level. Therefore, a demand-response (DR) pricing strategy enables EV users to flatten load curves and efficiently adjust electricity usage. In this work, communication between EVs and aggregators is efficiently performed through blockchain. Moreover, a branching concept is involved in the proposed system, which divides EV data into two different branches: a Fraud Chain (F-chain) and an Integrity Chain (I-chain). The proposed branching mechanism helps solve the storage problem and reduces computational time. Moreover, an attacker model is designed to check the robustness of the proposed system against double-spending and replay attacks. Security analysis of the proposed smart contract is also given in this paper. Simulation results show that the proposed work efficiently reduces the charging cost and time in a VEN.
Adaptive Discount Factor for Deep Reinforcement Learning in Continuing Tasks with Uncertainty
Authors: Kim MyeongSeop , Kim Jung-Su , Choi Myoung-Su , Park Jae-Han
Published: September 2022

Reinforcement learning (RL) trains an agent by maximizing the sum of a discounted reward. Since the discount factor has a critical effect on the learning performance of the RL agent, it is important to choose the discount factor properly. When uncertainties are involved in the training, the learning performance with a constant discount factor can be limited. For the purpose of obtaining acceptable learning performance consistently, this paper proposes an adaptive rule for the discount factor based on the advantage function. Additionally, how to use the advantage function in both on-policy and off-policy algorithms is presented. To demonstrate the performance of the proposed adaptive rule, it is applied to PPO (Proximal Policy Optimization) for Tetris in order to validate the on-policy case, and to SAC (Soft Actor-Critic) for the motion planning of a robot manipulator to validate the off-policy case. In both cases, the proposed method results in a better or similar performance compared with cases using the best constant discount factors found by exhaustive search. Hence, the proposed adaptive discount factor automatically finds a discount factor that leads to comparable training performance, and that can be applied to representative deep reinforcement learning problems.
Examination of Identity Negotiation, Sexual Health Behavior, and Healthcare-Seeking Behavior of Transgender Sex Workers in India
Authors: Dasgupta Satarupa
Published: September 2022

The current study, which expects to fill in the gap in research on transgender sex workers in India, examines the sexual identity negotiation, risk perception and condom compliance, sexual health screening and testing behavior, contextual barriers to healthcare seeking, and barriers to community mobilization among this population. The study was conducted in the red-light districts of Kolkata and rural subdivisions of West Bengal, and Eastern India. Transgender sex workers comprise 15% of the sex workers’ populace in India, yet they are an understudied and underserved group in the commercial sex sector. It is anticipated that the study will help to formulate future programmatic interventions that can cater more effectively to the health needs of Indian transgender commercial sex workers, contribute to the HIV/STI risk reduction among this group, and reduce barriers to attaining health.
Correction: Bibri, S.E.; Allam, Z. The Metaverse as a Virtual Form of Data-Driven Smart Urbanism: On Post-Pandemic Governance through the Prism of the Logic of Surveillance Capitalism. Smart Cities 2022, 5, 715–727
Authors: Bibri Simon Elias , Allam Zaheer
Published: September 2022

The authors wish to make the following corrections to their paper [...]
Social Support and Self-Efficacy on Turnover Intentions: The Mediating Role of Conflict and Commitment
Authors: Mondo Marina , Pileri Jessica , Carta Federica , De Simone Silvia
Published: September 2022

Turnover intentions are a phenomenon that affects the life of organizations and causes highly negative consequences. Based on previous studies, it is possible to consider antecedents to turnover in terms of both individual and social perceived resources, which previous research does not usually examine simultaneously. The aim of this study was to explore the role of both resources (individual and social) on turnover intentions. Thus, we hypothesized that perceived social support and self-efficacy have an impact on turnover intentions and that this relationship is mediated by interpersonal conflict and Affective Commitment. A total of 392 Italian employees completed a self-report questionnaire. A structural equation model was tested. The results showed that interpersonal conflict and Affective Commitment fully mediated the relationship between social support, self-efficacy and turnover intentions. Practical implications are discussed.
Projecting Experience of Technology-Based MSMEs in Indonesia: Role of Absorptive Capacity Matter in Strategic Alliances and Organizational Performance Relationship
Authors: Kustiningsih Nanik , Tjahjadi Bambang , Soewarno Noorlailie
Published: September 2022

This paper investigates absorptive capacity’s significance in mediating strategic alliance and organizational performance using the partial least square structural equation model (PLS-SEM) Warp PLS 7.0. The online and offline questionnaires were distributed to 308 Indonesian MSME owners/managers. The results suggest that strategic alliance directly influences the organizational performance of MSME’s technology-based in Indonesia. Further analysis attests that absorptive capacity partially mediates strategic alliance and organizational performance relationships; this research is specific to MSMEs in Indonesia, and thus generalization to other countries needs further verification. MSME’s owners or managers can use the knowledge on the relationship between the strategic alliance and organizational performance, including absorptive capacity in mediating the relationship, for policymaking and manifesting strategic alliances through collaboration with partners to achieve companies’ common goals is also highly recommended.
Correction: Pérez Vega et al. A Systematic Review for Establishing Relevant Environmental Parameters for Urban Lighting: Translating Research into Practice. Sustainability 2022, 14, 1107
Authors: Pérez Vega Catherine , Zielinska-Dabkowska Karolina M. , Schroer Sibylle , Jechow Andreas , Hölker Franz
Published: September 2022

In the original publication [...]
Numerical Study of the Effect of Flap Geometry in a Multi-Slot Ducted Wind Turbine
Authors: García Auyanet Antonio , Verdin Patrick G.
Published: September 2022

One possible way to harness wind more efficiently in low-wind urban areas is to place wind turbines inside a duct. A known issue of such approach is due to the flow separation that can occur at the diffuser walls. This can be avoided using a channelled structure consisting of a duct and a flap, also known as a multi-slot system. The present work describes the effects of a flap geometry on the turbine performance, through computational fluid dynamics (CFD). Four flaps based on airfoils, with different thicknesses and cambers, were evaluated. It was found that thinner and more cambered flaps produce higher wind turbine performance, showing power augmentations up to 2.5 compared to a bare turbine. A comparison between the multi-slot design and a single-piece duct of the same geometry was also performed, showing that the multi-slot design is more efficient if the flow is maintained attached to the flap.