Reviewerly/exHarmony huggingface dataset:
3D Radiative Transfer through Cumulus Clouds, Comparing Modeling to Observations

&amp;lt;p&amp;gt;Clouds modify surface solar irradiance fields by scattering and absorbing radiation. The spatial distribution of surface irradiance drives horizontal heterogeneity in the surface heat and moisture fluxes, which may affect the further development of boundary layer clouds. Additionally, spatial variability of solar irradiance affects the electricity production by solar energy. However, accurately capturing the spatial distribution of surface solar irradiance in cloud-resolving models is not straightforward. Radiative transfer in the atmosphere is generally solved using 1D two-stream approximations, which are computationally efficient but neglect the horizontal energy transport by radiation. Alternatively, realistic surface solar irradiance fields can be computed using Monte Carlo ray tracing methods, which are highly accurate but computationally expensive and therefore often only used for a limited number of cloud fields. Moreover, ray traced irradiance fields are only as accurate as the input cloud fields and therefore still require validation by observations, which are generally only available as single point measurements at a relatively coarse temporal resolution. In this study, we make use of a dense spatial grid of radiation measurements that was set-up during the FESSTVaL measurement campaign in Germany, and aim to provide a unique validation of the spatial variability in surface irradiance fields produced by a suite of radiative transfer approximations. First, we run large-eddy simulations of three different days during the campaign, two days with shallow and one day with deep convection, to generate high-resolution cloud fields that can be validated using LIDAR observations. Radiative fluxes are then computed for each cloud field using the two-stream approximation, Monte Carlo ray tracing, and a number of approximations for three dimensional radiative transfer such as tilted columns and the TenStream solver. The comparison of these various radiative transfer techniques against observations may contribute to a better understanding of the spatial variability of surface solar irradiance under cumulus clouds. Moreover, this can give a better insight into which radiative transfer approximations can capture the surface irradiance variability to sufficient accuracy for solar energy applications.&amp;lt;/p&amp;gt;
DEPARTMENT OF ORIENTAL MANUSCRIPTS: A BRIEF HISTORY AND THE MOST IMPORTANT RESULTS OF THE ACTIVITY

The prerequisites and history of the creation of the Department of Oriental Manuscripts of the Order of the Badge of Honour Institute of History, Language and Literature of the Ufa Federal Research Centre of the Russian Academy of Sciences (IHLL UFRC RAS) are considered. The Institute, as is known, owns the Fund of Manuscripts named after G.B. Khusainov, containing a rich written heritage of great scientific and cultural significance. Some of them were written over 200 years ago. The fate of some of them is mysterious and unusual, and, undoubtedly, they are witnesses of certain events in our history and are a valuable primary source for studying the history and spiritual culture of the Bashkir people. The priority topics of scientific research of the department are identified and described, the relevance and significance of the development of which is due to the need to introduce into scientific circulation and popularize Arabic written monuments in Arabic, Persian and Turkic languages, available in the collections of the UFRC RAS, and above all, in the Fund of Manuscripts named after G.B. Khusainov. The results of the purposeful work of the Department of Oriental Manuscripts on the study and systematization of early printed books and manuscripts of the above-mentioned Fund, the introduction of new documents into scientific circulation are shown. Separately, the work of field expeditions of recent years, which were carried out with the financial support of grants from various scientific foundations, is singled out and analyzed. Archaeographic and complex expeditions have been and remain the main source of replenishment of the fund of manuscripts and early printed books with new documents.
Overview of Blockchain and Cloud Service Integration

Blockchain has unlimited possibilities to break through many application areas. Cloud service is an on-demand service paradigm that facilitates the availability of shared resources for data storage and computing. Last several years, the combination of blockchain and cloud services has attracted much attention to ensure efficiency, openness, safety and even provide better cloud services in the form of novel service models. In order to develop the all potential of blockchain and cloud integration, it is significance that have a clear comprehension of current work in this area. None of the current overviews covers blockchain cloud integration from a service-oriented perspective. This article aims to outline the service orientation of blockchain integration to fill this vacancy. This overview explores different service models that integrate blockchain. For each service model, the existing work is summarized and comparatively analyzed, and a clear and concise view is provided in each sort.
Combination of satellite and ground measurements of hourly surface solar radiation data in Germany

&amp;lt;p&amp;gt;As part of a restructuring of the solar radiation measurement network, the German Meteorological Service (DWD) is pursuing the goal of expanding its high-quality surface measurements using pyranometers (to 42 stations) while discontinuing measurements with other instruments. At the same time, surface solar radiation products from satellite data are progressively improving in quality and can be used to compensate for the reduction of ground measurements and to increase the spatial coverage of solar radiation information over Germany. For this purpose, the project DUETT aims at a merging of solar radiation data from the 42 pyranometer stations and near real-time data based on measurements from METEOSAT-SEVIRI (with a spatial resolution of about 5 km). As products, hourly data of the global horizontal irradiance (GHI) and the sunshine duration (SDU) will be provided on a 2 x 2 km grid for Germany with a time delay of 15 minutes after each full hour.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Merging is performed in three main steps: 1. Time aggregation to derive hourly data from instantaneous 15-minute measurements of the satellite; 2. Determination of deviations between hourly satellite and ground measurements; 3. Interpolation of the deviations to target grid of 2 km resolution using Ordinary Kriging. These three basic steps involve an 'optical flow' technique to generate intermediate images of the satellite data, a correction algorithm for the clear-sky satellite data to consider the impact of varying water vapour columns on surface radiation and a regression approach to reduce systematic deviations between satellite and station data. We present the latest version of the merging procedure for both radiation parameters GHI and SDU. The steps of the algorithms are illustrated, strategies for correcting systematic errors in the satellite data are shown and open issues are discussed. Furthermore, validation results based on independent validation data and based on cross validation will be presented.&amp;lt;/p&amp;gt;
A computationally efficient parameterization for 3D radiative effects at the surface in large-eddy simulations

&amp;lt;p&amp;gt;The amount of solar radiation that reaches the Earth&amp;amp;#8217;s surface is strongly influenced by clouds and aerosols. This results in a complex pattern of radiation at the surface with cloud shadows and regions with enhanced solar radiation. Cloud enhancements can cause surface irradiances up to 25% higher than under clear sky conditions. Insight into these peaks is valuable to operators of the electricity grid, as these peaks can cause grid voltages to exceed the safety limits. Furthermore, the radiation at the surface impacts the surface fluxes, boundary layer turbulence and clouds. Therefore, proper modelling of surface radiation is important for predictions of solar energy production and for detailed weather forecasts. Current 1D calculations of radiative transfer cannot capture the complex pattern of surface radiation, whereas 3D calculations are very costly. In this study, we developed a simple method to account for the 3D radiative effects at the surface in LES. The horizontal spreading of the diffuse radiation is accounted for by applying a spatial filter to the surface diffuse radiation field. With this filter, we add only little extra complexity to the existing 1D calculation. Therefore, our method is computationally efficient. The filtering of the diffuse radiation is applied to the results of a Large Eddy Simulation for a summer day in Cabauw, the Netherlands, on which shallow cumulus clouds formed during the day. The results of the LES simulation are compared to detailed high-quality observations (1Hz). Without the filtering, the cloud enhancements are not captured at all, and the probability distribution of global radiation is unimodal, whereas the observed distribution is bimodal. After filtering, our results closely match with the observations. The probability distribution of global radiation is now bimodal and cloud enhancements are simulated. We found that small changes in the filter width do not strongly influence the results. Furthermore, we showed that the width of the filter can be parameterized as a linear function of one variable related to the cloud field, e.g. the maximum cloud size or the cloud base height. Hence, this work presents a proof-of-concept for our method to come to more realistic surface irradiances by filtering diffuse radiation at the surface.&amp;lt;/p&amp;gt;
Surface Solar Radiation based on Satellite Observations &amp;#8211; the new SARAH-3 Climate Data Record

&amp;lt;p&amp;gt;The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) generates and distributes high quality long-term climate data records (CDR) of energy and water cycle parameters, which are freely available.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In 2022, a new version of the &amp;amp;#8220;Surface Solar Radiation data set &amp;amp;#8211; Heliosat&amp;amp;#8221; will be released: SARAH-3. As the previous editions, the SARAH-3 climate data record is based on satellite observations from the first and second METEOSAT generations and provides various surface radiation parameters, including global radiation, direct radiation, sunshine duration, photosynthetic active radiation and others. SARAH-3 covers the time period 1983 to 2020 and offers 30-minute instantaneous data as well as daily and monthly means on a regular 0.05&amp;amp;#176; x 0.05&amp;amp;#176; lon/ lat grid. Compared to previous versions, the data quality of SARAH-3 has been substantially improved over snow-covered surfaces, e.g, in the Alpine region, by the use of an internally derived snow mask. SARAH-3 will be accompanied by a near-realtime data processing, which enables operational applications, e.g., climate monitoring&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this presentation, an overview of the SARAH climate data record and example applications will be given. A focus will be on the SARAH-3 developments and validation with surface observations. Further, SARAH-3 will be used for the analysis of climate variability and potential trends in Europe during the last decades. The data record reveals that there is a positive trend of surface solar radiation in Europe during the last decades, which is superimposed by decadal and regional variability. The most significant brightening during the last decades is estimated for spring.&amp;lt;/p&amp;gt;
Towards the combination of in-situ and satellite-based solar radiation data in near-realtime &amp;#8211; the project DUETT at DWD

&amp;lt;p&amp;gt;As the production of the current solar radiation sensor systems ceased and changes due to automation of the measuring network were necessary, a new concept of solar radiation measurements in Germany was established by Deutscher Wetterdienst (DWD). Aiming at an efficient network with high-quality data, it was decided to enhance the coverage of high-quality pyranometers that measure global and diffuse solar radiation, herewith also allowing the calculation of sunshine duration via the pyranometric method. The use of low to moderate quality instruments to measure solar radiation and sunshine duration will be discontinued in the future. To compensate for the reduction of the number of in-situ measurements and with solar radiation products from satellite data continuously improving in quality, the integration of both data sources was planned within the project DUETT at DWD. &amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The project DUETT focuses on the development of merging algorithms for the parameters global radiation and sunshine duration and therefore uses data from the (future) 42 pyranometer stations as well as near-real-time satellite data based on measurements of the METEOSAT SEVIRI instrument with a latency of only a few minutes. Preoperational, merged DUETT products are processed on an hourly basis with a spatial resolution of 2x2 km for Germany and a time delay of 15 minutes after each full (synoptic) hour for both parameters. Here, validation results of the latest version of the merging procedure, based on independent in-situ data, are presented for both parameters for a validation period of one full year; possible future improvements are also discussed.&amp;lt;/p&amp;gt;
Video Objects Detection Using Deep Convolutional Neural Networks

Deep Convolutional Neural Network (CNN) technology has attracted the interest of researchers in computer vision to discover objects in photos and videos and recognize faces and their characteristics because it has effective performance. This paper describes the main features of CNN's most accurate deep learning and its applications. The YOLOv3 model was used to accurately classify and predict objects in each frame of a video by embedding the object box and extracting features using the ImageAI library. This library supports pre-trained models on the coco dataset to detect pedestrians and objects using training data (video about Kirkuk University). It is possible to use the importance of this study to monitor the traffic or search for something tracked by video surveillance. In addition to detecting pedestrians in the video, visible objects, and faces. The result compares this video detection method and is evaluated using performance metrics such as a map. It achieved the best video processing performance with an accuracy of 99.52% and very high accuracy for video object detection (98.97% people, 99.12% cars, 100% bicycle, 100% clock), which proved to be higher performance compared to other detection models.
Voice Assistance for Dumb People Based on Hand Gestures

It is extremely difficult for people who are deaf to communicate their message to normal people. Communication becomes extremely difficult in case of an emergency, while travelling or among unfamiliar people because most people are not trained in hand sign language. We propose a voice aid system that allows deaf persons to communicate with regular people by using hand motions and gestures. This system uses motion and flex sensors, as well as a speaker unit. The circuitry that runs this system is fuelled by batteries. The system includes various pre-programmed messages such as "need help," "where is the washroom," and other common phrases that enable deaf individuals communicate basic information. The technology analyses a person's hand motions for different variants. It also has a trigger sensor to provide the impression that the person wants to activate the device and say something.
Fine-tuning machine confidence with human relevance for video discovery

research-article Open Access Share on Fine-tuning machine confidence with human relevance for video discovery Authors: Oana Inel University of Zurich University of ZurichView Profile , Lora Aroyo Google GoogleView Profile Authors Info & Claims InteractionsVolume 29Issue 4July - August 2022 pp 78–82https://doi.org/10.1145/3539596Online:29 June 2022Publication History 0citation336DownloadsMetricsTotal Citations0Total Downloads336Last 12 Months336Last 6 weeks23 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Financial Risk Evaluation of Digital Currency Based on CART Algorithm Blockchain

In recent years, digital currencies based on blockchain technology have brought about a boom in the research of encrypted digital currencies with the design concept of peer-to-peer trading and decentralization, but due to the lack of supervision and obvious speculative characteristics of the digital currency market, the sharp fluctuations in the market can easily trigger investor sentiment fluctuations, which in turn will lead to social instability and even financial system risks. In this paper, through the analysis of the financial risk source factors of blockchain digital currency, the evaluation index is established, the risk evaluation index system is constructed, and then the CART classification algorithm is used to analyze it and evaluate and test the model. The characteristic factor structure obtained by the CART algorithm in this paper better explains the financial risk characteristics of blockchain digital currency and gives relatively reliable identification results. The results show that the CART decision tree classification method is effective and has a high accuracy rate, which classifies the financial risks of blockchain digital currency, and the method has excellent adaptability and matchability for the classification of risk problems.
The Effect of Loss and Optimization Functions on Bitcoin Rate Prediction in LSTM

In recent years, Bitcoin cryptocurrency has become a growing trend in the world. For this reason, researchers from many fields are examining various artificial intelligence models to predict Bitcoin rates. In particular, Deep Learning algorithms have been shown to outperform traditional models in predicting cryptocurrency rates. However, very few studies have examined the effect of parameters used in deep learning algorithms on the algorithm. Optimization and loss functions are very important, which affect the algorithm's ability to make a successful prediction. In this study, Long-Short Term Memory, a deep learning algorithm, is used to predict daily Bitcoin prices and the effect of optimization/loss functions on the accuracy rate is evaluated. Experimental results showed that the Long-Short Term Memory model made the best predictions as a result of working with the Adam optimization function and the Mean Square Error loss function.
Design and Analysis of P&amp;O algorithm based MPPT solar system in PSIM

Conventional solar panels suffer with non-identical losses (resistive losses, diode leakage, loss of material properties etc.). To overcome these unwanted phenomena, a solar maximum power point tracking (MPPT) algorithm has been introduced. In this paper, the algorithms used for tracking the sun (or the solar maximum power point tracking algorithm) has been introduced &amp; a brief description of the basic &amp; easiest algorithm i.e., the P&amp;O Algorithm using PSIM software has been provided to give a clear view about the concept &amp; implementation of MPPT. Keywords: Photovoltaic, MPPT, P&amp;O, PSIM
Uni-Dimensional Autoencoder Reinforced Multilayer Perceptron Network for Individual Behavior Detection

In recent years, due to the increasing number of public security incidents, the field of individual behavior detection has made great progress. Among them, MLP method is representative, but its defects are also very obvious. this paper proposes an autoencoder fusion MLP based novel network structure for behavior detection, which can significantly improve the recognition accuracy. The proposed network extracts the color-based features from the video, outputs and compressed the features as a one-dimensional vector with autoencoder, and finally input the parameters into the fully connected layer for the classification of abnormal behaviors. The proposed network achieved the accuracy of 67% on the UCF-Crime data set, and significantly enhanced the accuracy on simple data sets. The experimental results indicate the autoencoder achieves promising performance on individual behavior recognition and potentially on the crowd behaviors.
Dangerous Behavior Recognition Based on Pose Estimation and Action Analysis

In order to identify dangerous behaviors such as fighting in public area, proposed a deep learning model that combines human post estimation and action analysis for behavior recognition. The model takes video frame sequence as input, first make use of HRNet as backbone network to detect human body joints and generate human pose frame sequence, and then, make use of recurrent neural network for action analysis from the pose frame sequence, thus determine whether there is dangerous behavior in this video. In the first part, optimization was made on HRNet so as to reduce its parameter and improve the accuracy of joint location. In the second part, introduced Cubic LSTM as a comprehensive model for action recognition, which analysis the motion of human joints from both temporal sequence and spatial sequence, thus achieve better inference score. Experiments show that the recognition accuracy of the proposed method can reach 92.8%. In the last part, a dangerous behavior recognition system is developed based on this model, which use a monitoring host to analysis the captured video frames from cameras, and identify dangerous behavior from them automatically, thus serve for public security tasks.
Clustering based ACO and ABC algorithms for the shadow detection and removal

Shadow detection and removal is challenging due to different factors like image context, light conditions, complexity etc. [1-3].So, accruing, detection, convergence and removal in terms of computer vision is a challenging task [4-6].There are several algorithms were explored in the same direction for the proper detection and removal.Clustering, classification and nature inspired algorithms were used mostly [7, 8].Main clustering algorithms are kmeans, fuzzy c-means (FCM) and hierarchical clustering.These algorithms are beneficial in key grouping and shadow pointing [9-12].Ant colony optimization (ACO), particle swarm optimization (PSO), teaching learning-based optimization (TLBO), cuckoo search, artificial bee colony (ABC), etc. are the main nature inspired algorithms [13,14].
Organisational design of a university’s social mission: a functional approach

In the article author considers the theory of organisational design in relation to the study of new adaptive properties of university education, altered especially by the conditions of COVID‑19. The analysed concerns content of methodological approaches and principles of organisational design of functional structure of university education. Author considers the role of the goal-setting and strategic management stage of the university in modern conditions and compares the system-functional and system-targeted approaches to the organisational design of the university mission. The functional content of the strategy allowing full realisation of competitive advantages of universities in the global market of educational services is considered.