Miracle-dz/UniFAR-FTUs huggingface dataset:
Powering Milliwatts to Megawatts

Switched-mode power converters are used to interface equipments and consumer electronic devices over a wide range of power levels. The switch technology of any power converter is a signature of its power rating. The article discusses different power electronic switch technologies used in modern power supply implementation and evaluates their impact in the system design. From the design prospective, power loss is more important than efficiency. It can be established that as power level of converter goes up, it is indispensable to have higher efficiency due to thermal design considerations. A trend between the switching frequency and its efficiency with respect to the power rating of the converter has been inculcated in the article.
InversionNet: An Efficient and Accurate Data-Driven Full Waveform Inversion

Full-waveform inversion problems are usually formulated as optimization problems, where the forward-wave propagation operator <inline-formula><tex-math notation="LaTeX">$f$</tex-math></inline-formula> maps the subsurface velocity structures to seismic signals. The existing computational methods for solving full-waveform inversion are not only computationally expensive, but also yields low-resolution results because of the ill-posedness and cycle skipping issues of full-waveform inversion. To resolve those issues, we employ machine-learning techniques to solve the full-waveform inversion. Specifically, we focus on applying convolutional neural network (CNN) to directly derive the inversion operator <inline-formula><tex-math notation="LaTeX">$f^{-1}$</tex-math></inline-formula> so that the velocity structure can be obtained without knowing the forward operator <inline-formula><tex-math notation="LaTeX">$f$</tex-math></inline-formula>. We build a convolutional neural network with an encoder-decoder structure to model the correspondence from seismic data to subsurface velocity structures. Furthermore, we employ the conditional random field (CRF) on top of the CNN to generate structural predictions by modeling the interactions between different locations on the velocity model. To evaluate the performance of our inversion technique, we compare it to both existing physics-driven methods and other data-driven method. Our numerical examples using synthetic seismic reflection data show that the propose CNN-CRF model significantly improve the accuracy of the velocity inversion while the computational time is reduced.
Retrieving Land Surface Temperature from Satellite Imagery with a Novel Combined Strategy

Land surface temperature (LST) is a key parameter for land cover analysis and for many fields of study, for example, in agriculture, due to its relationship with the state of the crop in the evaluation of natural phenomena such as volcanic eruptions and geothermal areas, in desertification studies, or in the estimation of several variables of environmental interest such as evapotranspiration. The computation of LST from satellite imagery is possible due to the advances in thermal infrared technology and its implementation in artificial satellites. For example, Landsat 8 incorporates Operational Land Imager(OLI) and Thermal InfraRed Sensor(TIRS)sensors the images from which, in combination with data from other satellite platforms (such as Terra and Aqua) provide all the information needed for the computation of LST. Different methodologies have been developed for the computation of LST from satellite images, such as single-channel and split-window methodologies. In this paper, two existing single-channel methodologies are evaluated through their application to images from Landsat 8, with the aim at determining the optimal atmospheric conditions for their application, instead of searching for the best methodology for all cases. This evaluation results in the development of a new adaptive strategy for the computation of LST consisting of a conditional process that uses the environmental conditions to determine the most suitable computation method.
Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation

Land Surface Temperature (LST) is an important parameter for many scientific disciplines since it affects the interaction between the land and the atmosphere. Many LST retrieval algorithms based on remotely sensed images have been introduced so far, where the Land Surface Emissivity (LSE) is one of the main factors affecting the accuracy of the LST estimation. The aim of this study is to evaluate the performance of LST retrieval methods using different LSE models and data of old and current Landsat missions. Mono Window Algorithm (MWA), Radiative Transfer Equation (RTE) method, Single Channel Algorithm (SCA) and Split Window Algorithm (SWA) were assessed as LST retrieval methods processing data of Landsat missions (Landsat 5, 7 and 8) over rural pixels. Considering the LSE models introduced in the literature, different Normalized Difference Vegetation Index (NDVI)-based LSE models were investigated in this study. Specifically, three LSE models were considered for the LST estimation from Landsat 5 Thematic Mapper (TM) and seven Enhanced Thematic Mapper Plus (ETM+), and six for Landsat 8. For the accurate evaluation of the estimated LST, in-situ LST data were obtained from the Surface Radiation Budget Network (SURFRAD) stations. In total, forty-five daytime Landsat images; fifteen images for each Landsat mission, acquired in the Spring-Summer-Autumn period in the mid-latitude region in the Northern Hemisphere were acquired over five SURFRAD rural sites. After determining the best LSE model for the study case, firstly, the LST retrieval accuracy was evaluated considering the sensor type: when using Landsat 5 TM, 7 ETM+, and 8 Operational Land Imager (OLI), and Thermal Infrared Sensor (TIRS) data separately, RTE, MWA, and MWA presented the best results, respectively. Then, the performance was evaluated independently of the sensor types. In this case, all LST methods provided satisfying results, with MWA having a slightly better accuracy with a Root Mean Square Error (RMSE) equals to 2.39 K and a lower bias error. In addition, the spatio-temporal and seasonal analyses indicated that RTE and SCA presented similar results regardless of the season, while MWA differed from RTE and SCA for all seasons, especially in summer. To efficiently perform this work, an ArcGIS toolbox, including all the methods and models analyzed here, was implemented and provided as a user facility for the LST retrieval from Landsat data.
Exploring the antecedents of mobile payment service usage

The number of smartphone users has increased with the maturity of mobile networks, which has not only led to a new lifestyle but has also facilitated the development of mobile application services. Smartphones are regarded as essential communication devices. Currently, diverse groups of people are considering using mobile payment services. Thus, the motives for using mobile payment as well as individual motives for continuing usage are of great research interest. The purpose of this paper is to examine the behavioral motivations underlying individual intentions to continue using mobile payment. ,To explore the factors affecting the intention to use mobile payment services, this study constructed a theoretical framework based on cost-benefit theory that also considers social influences to form an integrated research model that explains the intentions of individuals to use mobile payment services. Online questionnaires were used to evaluate individuals with experience using mobile payment services. A total of 302 questionnaires were collected. Structural equation modeling was employed to assess the relationships among factors included in the research model. ,Perceived value, social norms and social self-image played crucial roles in the intention to use mobile payment services. Furthermore, perceived benefits (relative advantage and service compatibility) and perceived costs (security risks and perceived fees) determined users’ perceived value. Social self-image positively affected users’ perceived value; in the context of a mobile-oriented information system, the ability of a mobile payment service to satisfy a user’s demands with respect to social self-image influenced the user’s perceived value of using such services. ,This study contributes to a theoretical understanding of factors that explain users’ intention to use mobile payment services.
Integration of DInSAR and SBAS Techniques to Determine Mining-Related Deformations Using Sentinel-1 Data: The Case Study of Rydułtowy Mine in Poland

Underground coal exploitation often results in land-surface subsidence, the rate of which depends on geological characteristics, the mechanical properties of the rocks, and the applied extraction technology. Since mining-related subsidence is characterized by “fast” displacement and high nonlinearity, monitoring this process by using Interferometric Synthetic Aperture Radar (InSAR) is very challenging. The Small BAseline Subset (SBAS) approach needs to predefine an a priori deformation model to properly estimate an interferometric component related to displacements. As a consequence, there is a lack of distributed scatterers (DS) when the selected a priori deformation model deviates from the real deformation. The conventional differential SAR interferometry (DInSAR) approach does not have this limitation, since it does not need any deformation model. However, the accuracy of this technique is limited by factors related to spatial and temporal decorrelation, signal delays due to the atmospheric artifacts, and orbital or topographic errors. Therefore, this study presents the integration of DInSAR and SBAS techniques in order to leverage the advantages and overcome the disadvantages of both methods and to retrieve the complete deformation pattern over the investigated study area. The obtained results were evaluated internally and externally with leveling data. Results indicated that the Kriging-based integration method of DInSAR and SBAS can be effectively applied to monitor mining-related subsidence. The root-mean-square Error (RMSE) between modeled and measured deformation by InSAR was found to be 11 and 13 mm for vertical and horizontal displacements, respectively. Moreover, DInSAR technique as a cost-effective and complementary method to conventional geodetic techniques can be applied for effective monitoring fast mining subsidence. The minimum and maximum RMSE between DInSAR displacement and specific leveling profiles were found to be 0.9 and 3.2 cm, respectively. Since the SBAS processing failed in subsidence estimation in the area of maximum deformation rate, the deformation estimates outside the maximum rate could only be compared. In these areas, the good agreement between SBAS and DInSAR indicates that the SBAS technique could be reliable for monitoring the residual subsidence that surrounds the subsidence trough. Using the proposed approach, we detected subsidence of up to −1 m and planar displacements (east–west) of up to 0.24 m.
Detection of Methane Emission from a Local Source Using GOSAT Target Observations

Emissions of atmospheric methane (CH4), which greatly contributes to radiative forcing, have larger uncertainties than those for carbon dioxide (CO2). The Thermal And Near-infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observing SATellite (GOSAT) launched in 2009 has demonstrated global grid observations of the total column density of CO2 and CH4 from space, and thus reduced uncertainty in the global flux estimation. In this paper, we present a case study on local CH4 emission detection from a single-point source using an available series of GOSAT data. By modifying the grid observation pattern, the pointing mechanism of TANSO-FTS targets a natural gas leak point at Aliso Canyon in Southern California, where the clear-sky frequency is high. To enhance local emission estimates, we retrieved CO2 and CH4 partial column-averaged dry-air mole fractions of the lower troposphere (XCO2 (LT) and XCH4 (LT)) by simultaneous use of both sunlight reflected from Earth’s surface and thermal emissions from the atmosphere. The time-series data of Aliso Canyon showed a large enhancement that decreased with time after its initial blowout, compared with reference point data and filtered with wind direction simulated by the Weather Research and Forecasting (WRF) model.
Computational Resource Abuse in Web Applications

Internet browsers include Application Programming Interfaces (APIs) to support Web applications that require complex functionality, e.g., to let end users watch videos, make phone calls, and play video games. Meanwhile, many Web applications employ the browser APIs to rely on the user's hardware to execute intensive computation, access the Graphics Processing Unit (GPU), use persistent storage, and establish network connections. However, providing access to the system's computational resources, i.e., processing, storage, and networking, through the browser creates an opportunity for attackers to abuse resources. Principally, the problem occurs when an attacker compromises a Web site and includes malicious code to abuse its visitor's computational resources. For example, an attacker can abuse the user's system networking capabilities to perform a Denial of Service (DoS) attack against third parties. What is more, computational resource abuse has not received widespread attention from the Web security community because most of the current specifications are focused on content and session properties such as isolation, confidentiality, and integrity. Our primary goal is to study computational resource abuse and to advance the state of the art by providing a general attacker model, multiple case studies, a thorough analysis of available security mechanisms, and a new detection mechanism. To this end, we implemented and evaluated three scenarios where attackers use multiple browser APIs to abuse networking, local storage, and computation. Further, depending on the scenario, an attacker can use browsers to perform Denial of Service against third-party Web sites, create a network of browsers to store and distribute arbitrary data, or use browsers to establish anonymous connections similarly to The Onion Router (Tor). Our analysis also includes a real-life resource abuse case found in the wild, i.e., CryptoJacking, where thousands of Web sites forced their visitors to perform crypto-currency mining without their consent. In the general case, attacks presented in this thesis share the attacker model and two key characteristics: 1) the browser's end user remains oblivious to the attack, and 2) an attacker has to invest little resources in comparison to the resources he obtains. In addition to the attack's analysis, we present how existing, and upcoming, security enforcement mechanisms from Web security can hinder an attacker and their drawbacks. Moreover, we propose a novel detection approach based on browser API usage patterns. Finally, we evaluate the accuracy of our detection model, after training it with the real-life crypto-mining scenario, through a large scale analysis of the most popular Web sites.
SISTER: Spectral-Image Similarity-Based Tensor With Enhanced-Sparsity Reconstruction for Sparse-View Multi-Energy CT

Multi-energy computed tomography (MCT) has a great potential in material decomposition, tissue characterization, lesion detection, and other applications. However, the severe noise that exists within projections makes it difficult to obtain high-quality MCT images. To overcome this limitation, we propose a method termed Spectral-Image Similarity-based Tensor with Enhanced-sparsity Reconstruction (SISTER) method. SISTER utilizes the non-local feature similarity in the spatial-spectral domain by clustering similar spatial-spectral patches within non-local window to a 4th-order tensor group. Compared with the image gradient L0-norm with tensor dictionary learning (L0TDL) method, by adopting tensor decomposition rather than tensor dictionary learning, SISTER overcomes the instability of tensor dictionary. Besides, in our SISTER method the weight coefficients update strategy is also optimized. Both numerical simulation and preclinical dataset were performed to evaluate and validate the performance of SISTER. Qualitative and quantitative results show that the proposed method can lead to a promising improvement of edge preservation, finer feature recovery, and noise suppression.
A Middle Miocene (~14 Ma) vertebrate assemblage from Palasava, Rapar Taluka, Kutch (Kachchh) District, Gujarat State, western India

ABSTRACT The present article aims to record a recently discovered middle Miocene vertebrate assemblage (including marine and terrestrial mammals) from Palasava, Kutch (Kachchh) District, Gujarat State, western India. The faunal assemblage comprising fishes, chelonians, crocodiles, snakes, birds and mammals is significant in terms of palaeoecology, palaeoenvironment and biostratigraphy of the region and yields evidence concerning its biogeographic affinities linked to Neogene Eustatic history. Palasava fauna together with previously known Neogene (particularly Miocene) vertebrate assemblages from the Kutch region indicates the presence of a riverine-estuarine system(s) linked to the sea, i.e. the depositional centres were quite close to the sea. Furthermore, the Palasava vertebrate assemblage (recorded herein) points towards a prevalence of warm, humid/wet, tropical to sub-tropical environmental conditions. The terrestrial mammalian assemblage from Palasava represents at least seven families with six of the terrestrial mammalian taxa identified herein to the genus level, namely: Sanitherium, Sivameryx, Brachypotherium, Zygolophodon, Gomphotherium, and Deinotherium. The overall assemblage (including Zygolophodon and Deinotherium) is indicative of a Middle Miocene, 14 ± 2 Ma (Langhian to Serravallian) age for the sedimentary succession at Palasava. Furthermore, a ~ 14 Ma age for the Palasava fauna indicates a correlation to regression cycle R5 in the Tagus Valley succession of Portugal.
A Full-Waveform Airborne Laser Scanning Metric Extraction Tool for Forest Structure Modelling. Do Scan Angle and Radiometric Correction Matter?

In the last decade, full-waveform airborne laser scanning (ALSFW) has proven to be a promising tool for forestry applications. Compared to traditional discrete airborne laser scanning (ALSD), it is capable of registering the complete signal going through the different vertical layers of the vegetation, allowing for a better characterization of the forest structure. However, there is a lack of ALSFW software tools for taking greater advantage of these data. Additionally, most of the existing software tools do not include radiometric correction, which is essential for the use of ALSFW data, since extracted metrics depend on radiometric values. This paper describes and presents a software tool named WoLFeX for clipping, radiometrically correcting, voxelizing the waves, and extracting object-oriented metrics from ALSFW data. Moreover, extracted metrics can be used as input for generating either classification or regression models for forestry, ecology, and fire sciences applications. An example application of WoLFeX was carried out to test the influence of the relative radiometric correction and the acquisition scan angle (1) on the ALSFW metric return waveform energy (RWE) values, and (2) on the estimation of three forest fuel variables (CFL: canopy fuel load, CH: canopy height, and CBH: canopy base height). Results show that radiometric differences in RWE values computed from different scan angle intervals (0◦–5◦ and 15◦–20◦) were reduced, but not removed, when the relative radiometric correction was applied. Additionally, the estimation of height variables (i.e., CH and CBH) was not strongly influenced by the relative radiometric correction, while the model obtained for CFL improved from R2 = 0.62 up to R2 = 0.79 after applying the correction. These results show the significance of the relative radiometric correction for reducing radiometric differences measured from different scan angles and for modelling some stand-level forest fuel variables.
Characterization of Northern Hemisphere snow water equivalent datasets, 1981–2010

Five daily, gridded Northern Hemisphere snow water equival ent (SWE) datasets are analyzed over the 1981–2010 period in order to quantify the spatial and tempor al consistency of satellite retrievals, land surface assimilation systems, physical snow models, and reanalyse s. While the climatologies of total Northern Hemisphere snow water mass (SWM) vary among the datasets by as muc h as 50%, their interannual variability and daily anomalies are comparable, showing moderate to good te mporal correlations (between 0.60and 0.85) on both interannual and intraseasonal time scales. Wintert im trends of total Northern Hemisphere SWM are consistently negative over the 1981–2010 period among t he five datasets but vary in strength by a factor of two to three. Examining spatial patterns of SWE indicates that the datasets are most consistent with one another over boreal forest regions compared to Arctic and al pine regions. Additionally, the datasets derived using relatively recent reanalyses are strongly correlate d with one another and show better correlations with the satellite product (GlobSnow) than do those using older r eanalyses. Finally, a comparison of eight reanalysis datasets over the 2001–2010 period shows that land surf ace model differences control the majority of spread in the climatological value of SWM, while meteorolog ical forcing differences control the majority of the spread in temporal correlations of SWM anomalies.
Government Governance of Smart Cities in China

China, as one of the most modern countries in Asia, has played a vital role in developing domestics smart cities since the government has been mainly promoting the urbanization strategy for years in order to achieve the highest urbanization growth rate as well as the vast space for development in the future. Before carrying out the plan of smart cities, the government has conducted a lot of researchs on how to construct the domestics smart cities. Thus, based on the practical situation in China, the government decided to develop diverse strategies in different cities. It is no doubt that the general development is successful while the problems are obvious as well. Therefore, the issues that lack the cybersecurity, the funding gap, as well as the lack of overall planning, should be concerned.
Quality Assurance and Safety of Crops & Foods

Effect of two blanching methods (blanching in steam or in hot water), on antioxidant properties of daylily flowers were determined with antioxidant assays, including 1-diphenyl-2-picrylhydrazyl free radical scavenging, superoxide anion scavenging activity, reducing power and nitric oxide radical-scavenging. Antioxidant compounds, such as phenolic composition, ascorbic acid and β-carotene were also analysed. Results showed that the phenolic compounds, β-carotene, ascorbic acid and antioxidant activities in daylily flowers were affected significantly by blanching pretreatments. Blanching enhanced antioxidant activities and (+)-catechin content, and resulted in great reduction of β-carotene, ascorbic acid, but steam blanching had the higher antioxidant activity and related compound contents than those of hot water blanching. A significant positive correlation between antioxidant activity and (+)-catechin content was observed in this work. Overall, steam blanching would be a better choice for daylily flower pre-treatment than blanching in hot water.