Miracle-dz/UniFAR-FTUs huggingface dataset:
Gradual Boundary Net: A Gradual Boundary Attention Based Deep Learning Framework for Cloud Detection

Cloud detection is a basic and important task in many high level applications of remote sensing technology. Accurate cloud detection is a challenging task. On the one hand, clouds are normally exhibited at different sizes and thicknesses. On the other hand, the boundary between the clouds and their background is usually not sharp. To address the two challenges, we present a deep learning based strategy, i.e., Gradual Boundary Net, which generates a cloud mask for detecting clouds in one cloudy image. The Gradual Boundary Net consists of two stages: (a) coarse location stage and (b) gradual boundary refinement stage. At the coarse location stage, the feature extraction network with four encoders and a cascade partial decoder (CPD) is implemented to obtain the coarse score map for locating the clouds with different sizes and thicknesses roughly. At the gradual boundary refinement stage, the coarse score map is gradually refined by a erasing and fusion strategy with several gradual boundary attention modules (GBAMs). The refined cloud mask is obtained after the two stages. The experimental results validate that our Gradual Boundary Net performs well and achieves outstanding results. The code for implementing the proposed Gradual Boundary Net is available at https://github.com/kang-wu/Gradual-Boundary-Net.
The effect of atmospheric radiative heating by clouds on the Madden‐Julian Oscillation

This article explores how atmospheric radiative heating, due to the presence of clouds, influences the Madden‐Julian Oscillation (MJO) as simulated by four comprehensive atmosphere general circulation models. Simulations in which clouds are transparent to electromagnetic radiation (“clouds‐off”) are compared with control simulations in which clouds are allowed to interact with radiation (“clouds‐on”). Making clouds transparent to radiation leads to robust changes of the mean state: the westerly winds in the equatorial Indo‐Pacific area weaken and the precipitation reveals a shift from single to double Intertropical Convergence Zones. These changes are accompanied by weaker MJOs. Also, the moisture sensitivity of precipitation changes, however not consistently within our group of models. Further analyses show that within the active phase of intraseasonal variability, cloud‐radiative effects amplify the heating profiles compared to clouds‐off. Heating from nonradiative processes is dominated by the parameterized convection, but large‐scale heating associated with cloud microphysical processes acting on the grid‐scale modifies the shape of the heating profile, leading to a top‐heaviness when cloud‐radiative effects are accounted for. The radiative heating due to clouds slows down the phase speed of the MJO. Averaged over the entire MJO life cycle, the column‐integrated radiative heating due to clouds lags the vertically integrated moist static energy by 40°–60° of longitude (equivalently 7–10 days assuming a period of 60 days). All four models studied reveal more pronounced Kelvin waves when clouds are transparent to radiation, suggesting that cloud‐radiative effects on large‐scale heating profiles damp smaller scale, or faster, Kelvin waves and amplify MJO‐like disturbances.
Horizontal flow fields observed in Hinode G-band images - I. Methods

Context. The interaction of plasma motions and magnetic fields is an important mechanism, which drives the solar activity in all its facets. For example, photospheric flows are responsible for the advection of magnetic flux, the redistribution of flux during the decay of sunspots, and the build-up of magnetic shear in flaring active regions. Aims. Systematic studies based on G-band data from the Japanese Hinode mission provide the means to gather statistical properties of horizontal flow fields. This facilitates comparative studies of solar features, e.g., G-band bright points, magnetic knots, pores, and sunspots at various stages of evolution and in distinct magnetic environments, which advances our understanding of the dynamic Sun. Methods. We adapted local correlation tracking (LCT) to measure horizontal flow fields based on G-band images obtained with the Solar Optical Telescope on board Hinode. A total of about 200 time-series with a duration between 1‐16 h and a cadence between 15‐90 s were analyzed. Selecting a high-cadence (Δt = 15 s) as well as a long-duration (ΔT = 16 h) time-series enabled us to optimize and validate the LCT input parameters, which ensures a robust, reliable, uniform, and accurate processing of a huge data volume. Results. The LCT algorithm produces best results for G-band images with a cadence of 60‐90 s. If the cadence is lower, the velocity of slowly moving features will not be reliably detected. If the cadence is higher, the scene on the Sun will have evolved too much to bear any resemblance with the earlier situation. Consequently, in both instances horizontal proper motions are underestimated. The most reliable and yet detailed flow maps are produced using a Gaussian kernel with a size of 2560 km × 2560 km and a full-width-athalf-maximum (FWHM) of 1200 km (corresponding to the size of a typical granule) as sampling window. Conclusions. Horizontal flow maps and graphics for visualizing the properties of photospheric flow fields are typical examples for value-added data products, which can be extracted from solar databases. The results of this study will be made available within the “small projects” section of the German Astrophysical Virtual Observatory (GAVO).
Operation Mode Recognition of Airborne Radar Based on Multi-Feature Fusion RS-ConvNet

Modern warfare has entered the era of information and networking, where electronic warfare (EW) is of vital importance. Operation mode recognition occupies an important po-sition in EW, while the overlapping waveform parameters of airborne radar operation modes make it difficult to accom-plish the recognition task in complex electromagnetic environments' especially under low signal-to-noise ratio (SNR) regions. Analyzing the time-sequential regularity of radar pulse parameters and intermediate frequency (IF) sampling signals, this paper designs a novel representation of operation modes, and proposes a multi-feature residual-and-shrinkage ConvNet (RS-ConvNet) with an attention mechanism to iden-tify multiple air-to-air modes. Simulation results show the proposed method has superior performance under low SNRs.
Land Use Identification of the Metropolitan Area of Guadalajara Using Bicycle Data: an Unsupervised Classification Approach

This paper presents the results from a research project that is focused on land-use, land-mapping and human behavior analysis to evaluate its movement through information sources that contains geo-referenced information. The source used is data provided by MiBici, which is a bicycle sharing platform established in the city of Guadalajara (state of Jalisco) in Mexico. The database includes a combination of different data for every month of recent years, and the access to this information is free and totally available. The methodologies used for this research were agile development techniques for project planning, k-Nearest Neighbors, Decision Trees, and K-Means for the clusterization of different zones. The programming language used for the development was Python and in addition an implementation proposal was performed using the Amazon Web Services platform. The results obtained shows that the different clusters obtained are well defined, the data is classified among them and physically the information have a valuable relationship to the human activity and behavior within the Metropolitan Area of Guadalajara.
General features of Bianchi-I cosmological models in Lovelock gravity

We derived equations of motion corresponding to Bianchi-I cosmological models in the Lovelock gravity. Equations derived in the general case, without any specific ansatz for any number of spatial dimensions and any order of the Lovelock correction. We also analyzed the equations of motion solely taking into account the highest-order correction and described the drastic difference between the cases with odd and even numbers of spatial dimensions. For power-law ansatz we derived conditions for Kasner and generalized Milne regimes for the model considered. Finally, we discuss the possible influence of matter in the form of perfect fluid on the solutions obtained.
Transport de moment cinétique et de champ magnétique par un écoulement tourbillonnaire turbulent : influence de la rotation

L'ecoulement de von Karman est l'ecoulement produit par la rotation de deux disques coaxiaux places en vis-a-vis. Dans la premiere partie de cette these, nous presentons un certain nombre de resultats experimentaux visant a la caracterisation des fluctuations a grande echelle d'un ecoulement d'allongement sensiblement unitaire. Dans certaines circonstances, nous mettons notamment a jour une "bifurcation globale" ainsi qu'un comportement "intermittent" de la structure de l'ecoulement. Nous presentons par ailleurs une relation qui relie les statistiques des fluctuations de l'ecoulement aux valeurs des couples fournis par les moteurs. Enfin, nous ebauchons l'etude de l'influence de la force de Coriolis sur l'ecoulement. La deuxieme partie de cette these presente des resultats numeriques et experimentaux portant sur l'etude des caracteristiques magneto-hydrodynamiques de l'ecoulement, et notamment sur sa capacite a produire un champ magnetique par "effet dynamo". Les resultats experimentaux ont ete obtenus a haut nombre de Reynolds magnetique dans le montage "VKS", qui utilise du sodium liquide.
DISCOVERY OF EXTENDED AND VARIABLE RADIO STRUCTURE FROM THE GAMMA-RAY BINARY SYSTEM PSR B1259−63/LS 2883

PSR B1259−63 is a 48 ms pulsar in a highly eccentric 3.4 year orbit around the young massive star LS 2883. During the periastron passage the system displays transient non-thermal unpulsed emission from radio to very high energy gamma rays. It is one of the three galactic binary systems clearly detected at TeV energies, together with LS 5039 and LS I +61 303. We observed PSR B1259−63 after the 2007 periastron passage with the Australian Long Baseline Array at 2.3 GHz to trace the milliarcsecond (mas) structure of the source at three different epochs. We have discovered extended and variable radio structure. The peak of the radio emission is detected outside the binary system near periastron, at projected distances of 10–20 mas (25–45 AU assuming a distance of 2.3 kpc). The total extent of the emission is ∼50 mas (∼120 AU). This is the first observational evidence that non-accreting pulsars orbiting massive stars can produce variable extended radio emission at AU scales. Similar structures are also seen in LS 5039 and LS I +61 303, in which the nature of the compact object is unknown. The discovery presented here for the young non-accreting pulsar PSR B1259−63 reinforces the link with these two sources and supports the presence of pulsars in these systems as well. A simple kinematical model considering only a spherical stellar wind can approximately trace the extended structures if the binary system orbit has a longitude of the ascending node of Ω ∼ −40° and a magnetization parameter of σ ∼ 0.005.
Deep Learning in Agronomic Forecast

In India, Agriculture plays an imperative role in the development of our country. More than 50% of entirety populace of India is directly or indirectly depends on agriculture and the agronomist are the backbone of the agronomic diligence but the agricultural backdrop facing decades-long tribulations and unpredicted challenges that are so critical to resolve. Some of the major issues that agronomist finding hard is water scarcity, soil erosion, lack of market intellect, poor quality of seeds, usage of fertilizers, inadequate knowledge on preserving soil for further cultivation etc. In addition to this, due to Covid19, still more problems faced by farmers are shortage of agri-inputs, lack of laborers and transportation of farm products. In order to rectify some of the agricultural tribulations, this paper has been primed as an effort to re-evaluate the study on the significance of deep learning techniques in the domain of agro crop yield production and rainfall-temperature forecasting with respect to density. The objective is to build an application that is user-friendly for the agronomist suggesting legitimate crop selection fine points according to the crop year, zone, season, soil nutrient on their farm which hikes the yield of the crops devoid of fertilizers and preserving the soil composition without degradation as well. This work integrates finding soil nutrients such as Nitrogen, Potassium, and Phosphorous. The input parameters considered for predicting the crop are likely the district, the crop year and the previous crop planted. Based on the survey, Artificial Neural Networks(ANN), Convolutional Neural Network(CNN), Decision Tree, Recurrent Neural Network (RNN), Random Forest and Support Vector Machine(SVM) classification models are the best models to predict accurate values and obtained the optimized results.
Ultrasound liver steatosis diagnosis using deep convolutional neural networks

One of the most common liver diseases is nonalcoholic hepatic steatosis. Until now, the standard method used for direct fatty liver quantification in hepatic tissue samples is liver biopsy. However, this method is invasive and involves certain risks for the patient. The goal of this paper is to find a non-invasive, cost-effective and wide available method for hepatic steatosis diagnosis that can replace the standard invasive procedure. The solution proposed is to use ultrasound images and deep convolutional neural networks. We implemented two models of deep convolutional neural networks and used 550 ultrasound images from 55 obese patients (only 17 with healthy liver) to train and test them. Our best model obtained an average accuracy of 87.49%.
Exposure to RF Electromagnetic Fields in the Connected Vehicle: Survey of Existing and Forthcoming Scenarios

Future vehicles will be increasingly connected to enable new applications and improve safety, traffic efficiency and comfort, through the use of several wireless access technologies, ranging from vehicle-to-everything (V2X) connectivity to automotive radar sensing and Internet of Things (IoT) technologies for intra-car wireless sensor networks. These technologies span the radiofrequency (RF) range, from a few hundred MHz as in intra-car network of sensors to hundreds of GHz as in automotive radars used for in-vehicle occupant detection and advanced driver assistance systems. Vehicle occupants and road users in the vicinity of the connected vehicle are thus daily immersed in a multi-source and multi-band electromagnetic field (EMF) generated by such technologies. This paper is the first comprehensive and specific survey about EMF exposure generated by the whole ensemble of connectivity technologies in cars. For each technology we describe the main characteristics, relevant standards, the application domain, and the typical deployment in modern cars. We then extensively characterize the EMF exposure scenarios resulting from such technologies by resuming and comparing the outcomes from past studies on the exposure in the car. Results from past studies suggested that in no case EMF exposure was above the safe limits for the general population. Finally, open challenges for a more realistic characterization of the EMF exposure scenario in the connected car are discussed.
Snow Characterization at Ku-Band with a Bistatic Polarimetric Ground-Based Radar

The Ku-band provides opportunities for investigations of snow morphology through radar observations, since it exhibits a relatively high amount of scattering even from snow layers of limited depth, while maintaining low absorption. Due to technological and practical challenges, the bistatic parameter space of Ku-band radar observations of natural media such as snow, has been relatively unexplored. We present radar measurements of snow cover obtained with KAPRI, a bistatic polarimetric Ku-band radar system. In August 2021 and March 2022, we carried out time series observations of the Aletsch glacier in the Swiss Alps, acquiring a fully-polarimetric interferometric time series of both monostatic and simultaneous bistatic observations of the glacier's accumulation zone. This dataset will serve as a test-bed to investigate new snow parameter inversion methods based on bistatic Ku-band radar data. The bistatic polarimetric measurement configuration, as well as preliminary results of the analysis of radar backscatter, are presented.
Building Extraction from Remote Sensing Images Using Deep Learning and Transfer Learning

This research employs fully convolutional neural networks, followed by the transfer learning method to extract buildings. The model was developed by utilizing layers of down sampling and upsampling. Two convolution layers and a ReLU activation function make up this model. To minimize overfitting the dropout layer is included. The outputs establish that the FCN model adequately predicts the pixels that correspond to buildings, but it also incorrectly predicts many non-building pixels as building pixels. The methodology of transfer learning using U-Net and the pre-trained model is utilized to improve the precision. The segmentation model library is employed, which provides access to about 25 encoders that can be used with the U-Net model. To improve outcomes, we used three alternative encoders. The study ${}^{\prime}\mathrm{s}$ findings show that the model performs better with Inception-V3 and U-Net than the other two encoders. The accuracy of the fully convolutional neural networks model was 89.86 while the accuracy of the Inception-V3 and U-Net architecture was 96.39 percent.
A Study of Dome-C Ice Sheet Parameter Estimation Using 0.5-2 GHz Ultra-Wideband Radiometry

This paper is a preliminary study of estimation of Antarctica Dome-C ice sheet properties using 0.5-2 GHz Ultra Wide Band Software Defined Radiometer (UWBRAD). The estimated ice sheet parameters include the vertical temperature profile and stochastic parameters that describe the density fluctuations within the ice column. The borehole measured temperature profile is used as a prior. The inversion uses regularization to incorporate both the prior borehole measurement and the UWBRAD data in the cost function. A hybrid model that use both cloud and partially-coherent models is chosen for this study as the forward model [1]. Since the partially-coherent model has a long computation time, a neural network is trained to replace the hybrid forward model and this neural network is used in the inversion. A genetic algorithm inversion scheme is used.
ANISOTROPIC FORMATION OF MAGNETIZED CORES IN TURBULENT CLOUDS

In giant molecular clouds (GMCs), shocks driven by converging turbulent flows create high-density, strongly magnetized regions that are locally sheetlike. In previous work, we showed that within these layers, dense filaments and embedded self-gravitating cores form by gathering material along the magnetic field lines. Here, we extend the parameter space of our three-dimensional, turbulent MHD core formation simulations. We confirm the anisotropic core formation model we previously proposed and quantify the dependence of median core properties on the pre-shock inflow velocity and upstream magnetic field strength. Our results suggest that bound core properties are set by the total dynamic pressure (dominated by large-scale turbulence) and thermal sound speed cs in GMCs, independent of magnetic field strength. For models with a Mach number between 5 and 20, the median core masses and radii are comparable to the critical Bonnor–Ebert mass and radius defined using the dynamic pressure for Pext. Our results correspond to M core = 1.2 c s 4 ( G 3 &rgr; 0 v 0 2 ) − 1 / 2 ? > and R core = 0.34 c s 2 ( G &rgr; 0 v 0 2 ) − 1 / 2 ? > for &rgr; 0 ? > and v0, the large-scale mean density and velocity. For our parameter range, the median M core ∼ 0.1 − 1 M ⊙ ? > , but a very high pressure cloud could have lower characteristic core mass. We find cores and filaments form simultaneously, and filament column densities are a factor of ∼2 greater than the surrounding cloud when cores first collapse. We also show that cores identified in our simulations have physical properties comparable to those observed in the Perseus cloud. Superthermal cores in our models are generally also magnetically supercritical, suggesting that the same may be true in observed clouds.
Airborne Microwave Radiometer Observations of East Coast Winter Storms from the Impacts Campaign

The Conical Scanning Millimeter-wave Imaging Radiometer (CoSMIR) is participating as one of the primary remote sensing airborne instruments in the IMPACTS campaign. IMPACTS (Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms) is a 3-year long campaign in Jan-Feb 2020, 2022, and 2023, focusing on studying snowfall and winter storms on the United States East Coast. CoSMIR is a microwave radiometer with frequencies from 50 to 183 GHz that is flying on the high-altitude NASA ER-2 aircraft, complementing the other remote sensing instruments onboard. This presentation will detail CoSMIR's performance in the first two years of the IMPACTS campaign and show initial observations and analysis.
PointFP: A Feature-Preserving Point Cloud Sampling

Many 3D perception applications (e.g., detection, classification, and segmentation) that directly process point cloud have made great progress with the development of 3D sensors in recent years. However, computational costs and storage demands of these applications grow significantly with the increase of point cloud size. Thus, it is necessary to sam-ple the point cloud while preserving semantic features and uniform density. Deep learning-based sampling methods are task-specific and fail to generalize to different tasks. Hence, we propose a task agnostic point cloud simplification method, called point cloud feature-preserving. The proposed method preserves semantic features from the original point cloud by generating representative nodes. The sampling rate could be controlled by adjusting the number of representative nodes. Qualitative and quantitative experimental results on synthetic and real-world remote sensing point cloud datasets demon-strate the effectiveness of the proposed method.
Stochastic scheduling in a multiclass G/G/1 queue

The problem of scheduling customers in a multiclass G/G/1 queue is addressed so as to minimize a weighted sum of the work-loads of the different classes. It is established that the nonidling preemptive fixed priority policy that schedules customers belonging to the class having the maximum weight, minimizes the cost function pathwise at any point in time. This result is based on the application of elementary forward induction arguments and is shown to hold for a very general class of policies. A proof for the optimality of the mu c-rule in the multiclass G/M/1 queue is then obtained as an easy corollary of the first result. <<ETX>>
Experimental Estimation of the State of Health of Lithium-ion Batteries under Different Conditions

As Lithium-ion batteries are used in numerous applications that require energy storage systems, the analysis of battery performance degradation is an increasingly investigated topic. To ensure the safe and optimal operation of battery-powered devices, an evaluation of the state of health of the battery is important. Monitoring the state of charge (SoC) and state of health (SoH) of the battery in a system is a task of the battery management system. During the past years, great efforts have been made to estimate and predict the lifespan of a lithiumion battery considering the calendar ageing. The purpose of the paper is to evaluate how affected are the performances of lithium-ion cells over time, after long-term storage in certain conditions. The battery cells degradation is evaluated by performing a series of tests to determine their capacity and internal resistance.