Miracle-dz/UniFAR-FTUs huggingface dataset:
SPGD algorithm optimization based on Adam optimizer

The SPGD (stochastic parallel gradient descent) is a control algorithm widely used in WFSless (wavefront sensorless) AO (adaptive optics) system.The gain is commonly set to a fixed value in the traditional SPGD algorithm.With the increase of the number of DM (deformable mirror) actuators, the optimization space of the algorithm becomes larger, which can easily lead to the slow convergence speed of the algorithm and the rise of the probability of falling into the local optima. Adam(Adaptive Moment Estimation) optimizer is an optimized stochastic gradient descent algorithm commonly used in deep learning with the advantage of achieving adaptive gain. Wavefront aberrations under different turbulence strength as correction objects, WFSless AO systems are built with 32, 61, 97 and 127 elements DM as wavefront corrector respectively. Results show that the optimized algorithm can converge faster than basic SPGD and the probability of falling into local optima decreases. The system’s convergence speed is increased by about 30%. The probability of falling into local optima is decreased by 29.8% , 30.3% , 32.6% and 35.9% respectively under D/r0=5 and by 28.8% , 30.5% , 33.3% and 34.5% respectively under D/r0=15. The advantages of the optimized algorithm are more obvious as the number of DM actuators increases. Above results provide a theoretical basis for the practical application of the SPGD algorithm based on Adam optimization.
Verification of the isotopic atmospheric general circulation model for a monitoring station in Labytnangi

To validate the atmospheric general circulation models ECHAM5-wiso and ECHAM6-wiso, supplemented with isotopic modules, a simulation was performed in the nudging mode to known values of temperature, pressure, speed and wind direction obtained from a retrospective analysis. The simulation results were compared with the data on the isotopic composition (δHDO and δH218O) of water vapor and precipitation at the monitoring station in Labytnangi (66.660°N, 66.409°E).
Research of the influence of the street canyon geometry on the turbulent flow structure

The work is devoted to the application of the developed microscale mathematical model for the study of turbulent flows and the transport of passive gaseous impurities in street canyon. Parametric calculations aimed at studying the structure of the flow and the nature of the spread of polluting emissions in a street canyon, depending on the geometric dimensions of the canyon, have been carried out.
Research status of laser active imaging detection

Laser active imaging uses a pulsed laser to illuminate the target. Through imaging detection of the target's reflected light, more detailed information of the target can be obtained. It can be used in severe weather conditions, at night and in the light of fire or smoke to monitor the target and attitude measurement. This article introduces the working principle and research status of four active laser imaging methods, including commonly used point scanning imaging, continuous light imaging, fringe tube imaging and APD imaging.
Processing of lidar data on trace atmospheric gases

A software package has been developed for processing data of the lidar gas analysis of the atmosphere. The lidar echoes and retrieval results are visualized in different modes in the modules for logging lidar sensing data and retrieving trace atmospheric gas concentration profiles. Lidar sensing data are processed and methane concentration profiles are retrieved. A methane concentration profile retrieved from the lidar data recorded in Tomsk is presented as an example.
Remote sensing image scene classification using deep combinative feature learning

Scene classification shows pivotal role in remote sensing image researches. Since challenges of large similarity between classes, high diversity in each class and huge variations in background, spatial resolution, translation, etc., remote sensing image scene classification still urgently need development. In this paper, we propose a novel method named deep combinative feature learning (DCFL) to extract low-level texture and high-level semantic information from different network layers. First, feature encoder VGGNet-16 is fine-tuned for subsequent multi-scale feature extraction. And two shallow convolutional (Conv) layers are selected for convolutional feature summing maps (CFSM), from which we extract uniform LBP with rotation invariance to excavate detailed texture. Deep semantic features from fully-connected (FC) layer concatenated with shallow detailed features constitute deep combinative features, which are thrown into support vector machine (SVM) classifier for final classification. Extensive experiments are carried out and results prove the comparable advantages and effectiveness of the proposed DCFL contrasting with different state-of-art methods.
Radiative and temperature effects of experimental cloud-radiation interaction scheme of COSMO model

We analyzed macrophysical characteristics of clouds and their radiative effects using COSMO mesoscale model and four German CLOUDNET sites. The study was performed for selected days with optically thick cloudiness, which relates to the period from April to October 2018. The short-term forecasts with only 3-hour time interval permit to evaluate only cloud microphysics effect on global irradiance at ground. The comparisons of liquid cloud water content (QC) and ice cloud content (QI) between measurements and modelling are discussed. We showed that the CLOUDRAD experimental cloud radiation interaction scheme provides a better agreement in global irradiance with observations than that from the default scheme.
Multi-year changes in the mesoscale waves according to the data of drift and radio-meteor measurements at Collm, Germany

The intensity of mesoscale wind speed variations with periods 1.7 – 5.6 hr was determined by digital filtering of the measurements at mesosphere and lower thermosphere region, which had been performed at the Collm Observatory of Leipzig University (51.3° N, 13.0° E) using the ionospheric drift method in the years 1983 – 2007 and with a meteor radar in 2004 – 2018. This made it possible to obtain the continuous series of measurements for the interval of 1983 - 2018. Interannual changes at 88 km altitude are studied. The annual mean zonal wind was directed to the east in 1983 – 2018 and grew from 3 to 10 m/s. The meridional southward wind reached a minimum of 2 m/s around 2002 and a maximum of 10 m/s in 2012. The intensity of mesoscale waves varied within the limits of up to 10%.
A method for measuring the width of welding seam based on machine vision

Aiming at the problems of low measurement efficiency and large measurement error when manually measuring the width of the welding seam using the welding seam ruler, a method for measuring the width of the weld seam based on machine vision was proposed in this paper. Firstly, the hardware of the measuring device was designed. The hardware included a measurement platform, an image acquisition and control module, a computer. One motor guide, one stepper motor, an industrial camera and a lighting source were installed on the measurement platform. Secondly, based on Matlab + OpenCV, the measurement software was developed. The software included five functions: controlling the stepper motor and the lighting source, receiving weld seam images, preprocessing the images, segmenting images, correcting images and calculating the width of the weld seam. The image segmentation was implemented by GrabCut algorithm, which could extract the weld area very well. The calculation of the weld width was based on the imaging size and resolution of the industrial camera and the pixels positions on the upper and lower edges of the weld area. Finally, a measurement experiment was carried out on the width of the weld. According to the measurement results, the measurement accuracy of the width of the welding seam reached to 0.1 mm and the measurement speed reached to 30.7 mm / s. The results showed that measuring the width of the welding seam based on machine vision could replace manual measurement and be applied to industrial automation.
Adaptive optical system for correction of laser beam going through turbulent atmosphere

One of the main problems in tasks of laser beam propagation though Earth’s atmosphere is decrease the efficiency of the optic-electronic systems operation due to atmospheric turbulence influence that leads to laser beam’s wavefront distortions. Use of fast adaptive optical system are suggested to solve this problem. It allows to compensate the wavefront distortions, which upper bound of the spectrum is up to 150 Hz, in real time. Owing to the fact that adaptive optical system is discrete (it’s defined by digital camera included in the system), the sampling rate shall be at least 1500 Hz (frames per second).
Remote sensing object detection based on YOLO and embedded systems

Remote sensing image object detection is the primary task in the field of intelligent processing and has important practical application value. However, the current intelligent processing method of remote sensing images is difficult to meet the real-time requirements. In order to improve the effectiveness, real-time on-board processing of the collected images has become an important direction. This article compares several commonly used deep learning object detection algorithms, selects YOLO v3 for in-depth research and optimization, and transplants the debugged algorithm to the Cambrian 1H8 embedded edge intelligent computing platform for performance testing. Experiments show that the algorithm has high accuracy and the running speed basically meets the real-time requirements. It can be used to study and test the real-time processing performance of remote sensing images.
Clustering and visualization of long-term data on thunderstorms for the North Caucasus

For the organization of lightning protection measures, data on the nature of the distribution of lightning discharges, their number and current value over the territory are required. To this end, lightning direction finding systems are being developed and implemented in many countries around the world. Lightning direction finding technology significantly reduces the risk of lightning strikes, and also allows you to take into account thunderstorm activity in the design and placement of buildings and structures. It is aimed at minimizing serious disruptions in the power industry, mass accidents and damage to power lines, preventing the failure of sensitive electronics and communication networks, and so on, which, in turn, determines its high economic efficiency. This article discusses issues related to clustering and visual representation of long-term data on thunderstorm processes in the North Caucasus in 2009-2019. Information about lightning discharges was obtained on the basis OF ls8000 data from Vaisala using a network of lightning recorders at the geophysical situation monitoring center. The article presents a picture of the spatial and temporal distribution of thunderstorm clusters for the North Caucasus. This makes it possible to trace the dynamics of thunderstorm centers, the spatial position of thunderstorms, the characteristics of discharges per unit area and time, to determine the characteristic development trends, and to identify the main factors that affect the obtained characteristics of the spatial and temporal distributions of lightning activity.
Modeling of injection of aluminum plasma jet to the upper atmosphere at the initial stage of active geophysical rocket experiment

The problem of modeling the injection of an aluminum jet of a plasma generator into the upper atmosphere during an Active Geophysical experiment is considered. The high-speed jet is a calibrated source of exposure. The gas-dynamic parameters of the jet are calculated. It is shown that in a rapidly expanding jet a violation of thermodynamic equilibrium occurs, causing to "freezing" of the ionic composition. The plasma jet interacts with the atmosphere and the geomagnetic field, causing the glow, the formation of a "diamagnetic cavity", and the generation of electromagnetic waves.
A multiview autostereoscopic display system using holographic projection

A multiview autostereoscopic display system using holographic projection is proposed, which solves the problem of reconstruction damage existing in the traditional display when the image is incompletely loaded. The elemental image (EI) captured from the 3D scene is encoded, and a single reconstructed scene is obtained by adding the phases of the blazed grating and the converging spherical wave. An imaging lens is introduced to control the position and size of the reconstructed scene, so that the reconstructed scene can be matched with the lenticular lens to generate parallax images. The effectiveness of this method was verified by optical experiments. Experimental results show that the system can generate parallax images, and compared with the traditional multiview autostereoscopic display, it can reproduce the complete scene when the image is incompletely loaded, which improves the stability of the imaging system.
Modeling the absorption properties of organic carbon in biomass burning smoke in Siberia using remote sensing data

A regional chemistry-transport model and remote sensing observations are used to study the absorption properties of organic carbon in biomass burning (BB) aerosol emitted from wildfires in Siberia. Using multi-wavelength retrievals of the absorption and extinction aerosol optical depths (AAOD and AOD) at Siberian sites of AERONET, we obtained an empirical relationship enabling the estimation of the contribution of brown carbon (BrC) to the BB aerosol absorption in a near-UV spectral region by using available satellite observations of AAOD and AOD. Application of this relationship to constrain our simulations of a large-scale episode of transport of BB plumes from Siberia towards Europe allowed us to parameterize an imaginary part of the refractive index of organic carbon in Siberian BB aerosol as a function of the BB aerosol photochemical age as well as to estimate the efficient mass fraction of BrC in the smoke plumes.
High precision monoscopic fringe reflectometry based on a reference plane

Fringe reflectometry is a favorite method for 3D measurement of specular surfaces with the advantages of simple structure, low cost and high precision. The traditional reflectometry method is based on the telecentric optical path assumption. When the surface has a large diameter or curvature, this assumption is hard to be guaranteed. In addition, the non-sinusoidal distortion of fringes will also introduce significant phase errors and degrade the measurement accuracy. To avoid these problems, a high precision monoscopic fringe reflection measurement model is proposed based on a reference plane. The relative phase change between the measured surface and the reference plane is used to get the accurate corresponding screen points for each point on camera imaging plane. With these correspondence point pairs, the normal vectors and the 3D geometry can be obtained. To validate the effectiveness of the proposed method, a concave spherical mirror was measured. The peak to valley value of the residue error can reach 0. 784μm which demonstrates high accuracy of the method.
Image denoising via adaptive dictionary learning based on single-pixel imaging

Based on the principle of compressed sensing, a single-pixel imaging system (single-pixel camera) was built in our laboratory, and our single-pixel camera can successfully acquire the picture of imaging scene of a distant building. However, the process of single-pixel imaging is easily disturbed by noise. In order to reduce the interference of noise in the imaging results as much as possible, we apply the method of adaptive dictionary learning to denoise the reconstructed image in our single-pixel camera. The block-coordinate decent idea and K-SVD algorithm are used to complete the dictionary learning by training on the noisy reconstructed image in the method of block-by-block scanning, which doesn’t require the extra corpus of image patches, so the method of dictionary learning is self-adaptive to the imaging picture that need to be denoised. The overcomplete DWT (discrete wavelet transform) dictionary is applied as the known initial dictionary that will be trained in the adaptive dictionary learning. The effectiveness of the adaptive dictionary learning has been demonstrated by the simulation experiment and the practical imaging experiment, the method of adaptive dictionary learning based on the initialization of overcomplete DWT dictionary effectively reduces the imaging noise and improves the image reconstruction accuracy and imaging quality.
SAMA-VTOL: A new unmanned aircraft system for remotely sensed data collection

In recent years, unmanned aircraft systems (UASs) are frequently used in many different applications of photogrammetry such as building damage monitoring, archaeological mapping and vegetation monitoring. In this paper, a new vertical take-off and landing fixed-wing UAS is proposed to robust photogrammetry missions, called SAMA-VTOL. In this study, the capability of SAMA-VTOL is investigated for generating orthophoto. The major stages are including designing, building and experimental scenario. First, a brief description of design and build is introduced. Next, an experiment was done to generate accurate orthophoto with minimum ground control points requirements. The processing step, which includes automatic aerial triangulation with camera calibration and model generation. In this regard, the Pix4Dmapper software was used to orientate the images, produce point clouds, creating digital surface model and generating orthophoto mosaic. Experimental results based on the test area covering 26.3 hectares indicate that our SAMA-VTOL performs well in the orthophoto mosaic task.
Construction of forecasts in environmental protection as a solution to the continuation problem

We discuss the issues of solving a wide range of environmental forecasting and design problems, which are formulated as mathematical continuation problems. Our approach's main idea is that we improve the quality of state function recovery by applying sensitivity theory methods, solving inverse problems, and data assimilation problems. This approach is demonstrated for the urban air quality problem in the example of the city of Novosibirsk.
Must-Read Family Medicine Research—Glucosamine/Chondroitin Supplements and Mortality, Telomere Length and the Doctor-Patient Relationship, Reducing Opioid Use, and More

This issue of the Journal contains some exceptional research articles. A few are truly “must-reads,” including a fascinating look at the relationship between having a usual source of care and telomere length. Glucosamine/chrondroitin supplementation seems to be helpful for more than just arthritis pain. There is a very practical advice on keeping patients discharged from the emergency department out of the hospital and on reducing patient requests for inappropriate antibiotics. This issue also features 5 articles addressing how family physicians can combat the opioid epidemic. Three articles highlight research on diabetes and another 3 on breast cancer. Payment reform, dermoscopy, and telemedicine are among many other topics covered.
A Lightweight Defeasible Description Logic in Depth - Quantification in Rational Reasoning and Beyond

Description Logics (DLs) are increasingly successful knowledge representation formalisms, useful for any application requiring implicit derivation of knowledge from explicitly known facts. A prominent example domain benefiting from these formalisms since the 1990s is the biomedical field. This area contributes an intangible amount of facts and relations between lowand high-level concepts such as the constitution of cells or interactions between studied illnesses, their symptoms and remedies. DLs are well-suited for handling large formal knowledge repositories and computing inferable coherences throughout such data, relying on their well-founded first-order semantics. In particular, DLs of reduced expressivity have proven a tremendous worth for handling large ontologies due to their computational tractability. In spite of these assets and prevailing influence, classical DLs are not well-suited to adequately model some of the most intuitive forms of reasoning. The capability for abductive reasoning is imperative for any field subjected to incomplete knowledge and the motivation to complete it with typical expectations. When such default expectations receive contradicting evidence, an abductive formalism is able to retract previously drawn, conflicting conclusions. Common examples often include human reasoning or a default characterisation of properties in biology, such as the normal arrangement of organs in the human body. Treatment of such defeasible knowledge must be aware of exceptional cases—such as a human suffering from the congenital condition situs inversus—and therefore accommodate for the ability to retract defeasible conclusions in a non-monotonic fashion. Specifically tailored non-monotonic semantics have been continuously investigated for DLs in the past 30 years. A particularly promising approach, is rooted in the research by Kraus, Lehmann and Magidor for preferential (propositional) logics and Rational Closure (RC). The biggest advantages of RC are its well-behaviour in terms of formal inference postulates and the efficient computation of defeasible entailments, by relying on a tractable reduction to classical reasoning in the underlying formalism. A major contribution of this work is a reorganisation of the core of this reasoning method, into an abstract framework formalisation. This framework is then easily instantiated to provide the reduction method for RC in DLs as well as more advanced closure operators, such as Relevant or Lexicographic Closure. In spite of their practical aptitude, we discovered that all reduction approaches fail to provide any defeasible conclusions for elements that only occur in the relational neighbourhood of the inspected elements. More explicitly, a distinguishing advantage of DLs over propositional logic is the capability to model binary relations and describe aspects of a related concept in terms of existential and universal quantification. Previous approaches to RC (and
Development of Analytical Techniques for Liquid Propellant Surveillance

Abstract : The U.S. Army has made a commitment to develop liquid gun propellants as a potential replacement of solid propellants used in the 105-mm and 155-mm guns. The stability and therefore integrity of these propellers, after being stored for extended periods of time, is necessary in order to meet ballistic requirements. The long-term storage of liquid propellants is required to determine whether there is any instability; its cause; its cause; and, if necessary, its prevention. At this time failsafe criteria is not available for liquid propellants under long-term storage conditions. A methodology is being developed to establish this criteria. Analytical methods have been developed to provide the means to monitor these long-term storage studies. Previously developed techniques for this program were reviewed and those which were applicable for this program were investigated. Keywords: Ion chromatography, Hydroxylammonium nitrate, Mass spectrometry, Nitric acid, Triethanolammonium nitrate, Transition metals, Titration.