dataesr/scientific-paragraphs-categorization huggingface dataset:
Modeling Approach In order to quantify the significance of the brightness asymmetry of the HD 53143 disk, we fit both symmetric and eccentric disk models to the ALMA data. We follow the modeling approach first described in  MacGregor et al. (2013)  and updated for eccentric disks in  MacGregor et al. (2017)  in order to effectively constrain uncertainties and efficiently explore parameter space. Visibilities are sampled from models of the sky brightness using the galario package  (Tazzari et al. 2018 ) and compared directly to the ALMA visibilities within a Markov Chain Monte Carlo (MCMC) framework using the emcee package (Foreman-  Mackey et al. 2013). For the symmetric model, we assume that the disk is radially symmetric and the surface brightness is described by a power law between an inner radius R in and an outer radius R out : I ν ∝ r -0.
5 , where the power-law index describes the temperature profile and the surface density is assumed to be constant as a function of radius. For the eccentric model, we first populate the complex eccentricity space defined by the forced eccentricity and argument of periastron (e f and ω f , respectively) and the proper eccentricity and argument of periastron (e p and ω p , respectively) following  Wyatt et al. (1999). While ω p is assumed to be randomly distributed from 0 to 2π, e f , e p , and ω f are all left as free parameters. We then compute the true anomaly, f, using  the kepler package (Foreman-Mackey et al. 2021), calculate the radial orbital position for each particle, and create a twodimensional image by binning using the desired pixel scale (0. 8 au). By assuming the same r -0. 5 temperature profile as for the symmetric model, we can then determine the flux in each pixel.
Modeling Results Table  2  presents the best-fit model parameters for both the symmetric and eccentric models. The symmetric model is unable to reproduce the data without significant stellar offsets, which makes it clear that an eccentric model is required to describe the HD 53143 debris disk. Figure  2  shows the ALMA 1. 3 mm data (left panel) along with the best-fit symmetric and eccentric models (top and bottom, respectively) displayed at full resolution (left center) and imaged like the ALMA data (right center). The residuals resulting from subtracting the bestfit model from the data are shown on the right. It is clear "by eye" that the eccentric model does a much better job fitting the structure of the outer debris disk. The symmetric model leaves >3σ residuals on the apocenter (northwest) side of the disk.
Inner Dust Emission? Given the significant excess of millimeter emission interior to the outer debris ring, it is clear that there is a source of dust in the inner part of the HD 53143 system. The two 6σ peaks on either side of the star are suggestive of limb brightening from an inclined inner disk. From the image, we can estimate the potential radius and width of this inner ring to be ∼25 au (1 4) and ∼5 au (0 3), respectively, given the separation of the two peaks, with a maximum flux density of ∼80 μJy. We attempted to include this inner component in our eccentric model, but results were limited by the data quality. By fixing the disk flux and radial position, we were able to return a fit on the inclination to be 85 15 5  - + , close to edge on. However, because the current ALMA observations only achieve a resolution of roughly 20 au, this potential inner disk is not well resolved and the geometry is not well constrained.
Comparison to Scattered-light Observations As discussed above, previous HST coronagraphic imaging did not detect flux along the minor axis of the disk  (Kalas et al. 2006; Schneider et al. 2014) , which could suggest that the HD 53143 disk might be face on with resonant clumps of emission. New HST STIS observations from program  GO-16202 (Ren et al. , in prep. ) show that this interpretation is not correct and confirm that the disk is in fact inclined relative to the line of sight. However, while the ALMA data are best fit by a thin (h < 0. 04) disk, the HST data suggest a significant scale height of h ∼ 0. 3. Unlike every other detected inclined debris disk, the HD 53143 disk shows no clear forward-scattering side in any of the previous HST images that match the minor axis of the ALMA observation at visible wavelengths.
Stellar Emission The ALMA observations of HD 53143 were executed over eight nights in 2019 March. Although the sensitivity is not high enough to split the data up further, we are able to fit the flux of the star in each of these eight observations separately. The results are shown in Figure  3. Assuming that HD 53143 is a G9V star (temperature ∼5413 K, radius ∼0. 86 R e ; Fuhrmann & Chini 2015) the expected photospheric flux at 1. 3 mm is 33 μJy using a PHOENIX stellar atmosphere model  (Husser et al. 2013). In observations 2 and 5-8, the best-fit flux for the star is significantly in excess of this value. The excess is most extreme in observation 6, when the star is >3× brighter than we would expect in quiescence. This transient behavior on roughly hour-long timescales is consistent with stellar flaring, which has been observed from several M dwarf stars  (MacGregor et al.
The Joint ALMA Observatory is operated by ESO, AUI/NRAO and NAOJ. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. The TESS data presented in this paper were obtained from the Mikulski Archive for Space Telescopes (MAST) at the Space Telescope Science Institute. The specific observations analyzed can be accessed via https://doi. org/ 10. 17909/t9-nmc8-f686.
In recent years, a wide range of thermal and structural models have been developed in various finite element software programmes to predict the structural and thermal behaviour of steel-concrete composite slabs under fire conditions. In 2018, Piloto et al. [7]  analysed the fire resistance of composite concrete slabs with a profiled steel deck, in this case also with a steel mesh on the top, including reinforcing bars between the ribs. The main objective of this study was to develop two-dimensional numerical models using the Matlab and ANSYS programmes to evaluate the fire resistance of different composite slab configurations according to the insulation criterion. For the development of this research, the fire resistance criterion for insulation (I) was evaluated using numerical and simple calculation methods and then validated using experimental fire tests.
Recently, full-scale 3D numerical models have been developed by Paulo Piloto and his team to evaluate the thermal behaviour of a series of composite slabs with different geometries under the action of different types of standard fires. In  [10] [11] [12] , 3D finite element models were developed to perform thermal analyses based on the insulation criterion (I), and in  [11, 13] , a coupled thermal and mechanical model was applied to perform mechanical analyses based on the load-bearing criterion (R). The thermal models were implemented using Matlab or ANSYS software, and the mechanical model was developed using ANSYS only. In both cases, three-dimensional finite elements were used. In 2021, Bolina et al. [14]  determined the temperature analysis of steel-concrete slabs exposed to a standard fire.
The main objective of this investigation was to develop a comparison of the temperature evolution in the cross-section through different methods: experimental, numerical, and simplified. The authors conducted eight full-scale fire tests, which were used to validate the numerical models developed with the ABAQUS software. The numerical results were compared with the analytical results obtained from Eurocode 1994-1-2  [4]. The authors found a good agreement between the simplified method and the other methods (numerical and experimental) only for the steel deck temperature, but not for the concrete, the reinforcing bars (positive and negative), and the thermal insulation. Therefore, a new approximation was developed to determine the temperature in the rebars, including new factors to evaluate the performance of the thermal insulation.
The composite slabs were subjected to the thermal effects of a standard fire exposure from below. The full-scale tests were simulated with three-dimensional finite elements using the Matlab Partial Differential Equations Toolbox (PDE Toolbox)  [16]. The discrete composite slab models consist of different physical subdomains with different thermal properties corresponding to the components of the slab such as concrete, steel deck, and reinforcing bars. In addition, an air gap, with constant thickness, is included between the steel deck and the bottom surface of the concrete, to simulate the separation effect of both materials (debonding). Effectively, the thermal contact resistance in the joint boundary of layers is an important parameter that is not considered by most of authors  [17]. This air gap model is introduced to simulate a similar thermal contact resistance.
The thermal models are validated with results from experimental tests found in the literature  [7, 8]. The thermal analysis on the steel deck components and on the reinforcing bars is evaluated in every composite slab with different concrete thicknesses h 1. The numerical results are compared with the simplified method from Annex D of Eurocode 1994-1-2. Finally, the results of all simulations developed for the parametric study are used to propose a new formula for the analytical estimation of the temperature of the four slab components. The coefficients of the new proposal are obtained by fitting this proposal to the numerical data using a non-linear least-squares method. It is important to mention that the proposed analytical method avoids unsafe temperature predictions resulting from the use of the simplified method, currently proposed by the standards.
The length of the specimens is 200 mm and does not affect the results. The geometries of the four representatives volumes modelled in Matlab are presented in Figure  3. The multi-domain developed comprises five sub-domains: the steel deck, the air gap, the concrete, the rebars, and the anti-crack mesh. Thus, the materials that compose the physical sub-domains of the slabs are carbon steel (steel deck, rebars, and anti-crack mesh), concrete, and air (air gap to simulate the debonding effect between the steel deck and concrete). In addition, two types of concrete are used in composite slabs: NWC and LWC. Confraplus 60 is a trapezoidal steel deck profile produced by ArcelorMittal. The steel deck is made with S350 steel grade, and the model uses a 1. 25 mm thickness. The geometry is depicted in Figure  2a.
Numerical Solution through Finite Element Method In thermal problems, the finite element mesh is generally used to model solids in which conduction is the predominant heat transfer method, and the radiation and convection are imposed through boundary conditions. To perform a thermal analysis on composite slabs, Equation (  1 ) is discretised by finite elements inside the physical sub-domains, corresponding to different materials. Figure  7  presents the generated mesh, produced by the Matlab PDE Toolbox, for each composite slab. Equation (  1 ) is solved by the finite elements method (FEM), based on the weak-form Galerkin model, using the weighted residual method, thus leading to the energy matrix formulae: C(T) Ṫ + K(T)T = F ( 8 ) where C is the capacitance matrix, Ṫ is the vector of the time derivatives of the nodal temperatures, K is the conductivity matrix, and F is the vector of the thermal loads (for details, see, for instance,  [23] ).
Computational Solution with Matlab The Matlab (R2021a) PDE Toolbox was used to solve the non-linear transient thermal problem. The finite element model of the composite slab, created with Matlab, uses the tetrahedral finite element type. This finite element is defined by four nodes and uses linear interpolating functions. The finite element mesh includes the five sub-domains: concrete, steel deck, rebar, anti-crack mesh, and air gap. Thus, three different materials are used (concrete, steel, and air). Additionally, two different types of concrete are considered: NWC and LWC. The minimum finite element mesh size length changes according to each composite slab profile. Figure  7  depicts the finite element mesh of the models, the element size being selected by a convergence test of the results, which means that the size of the mesh was adjusted until the relative error in nodal temperature calculations reached the maximum value of 10 -3 in the worst scenario.
Computational Times The computational simulations were performed on a Linux system, running in a machine with a 16-core AMD EPYC 7351 CPU and 64 GB of RAM, with a Matlab code purely sequentially. The computational times related to the simulation of the thermal effects, caused by a standard fire over 120 min, are shown in Table  3. These times were obtained in the four slabs with thickness h 1 = 125 mm. The anti-crack mesh is located 19 mm from the top. The space between each bar of the anti-crack mesh is 50 mm, and the diameter of each bar is 4 mm. The results presented in Table  3  show that the computational times of the simulations with NWC are much higher than the times obtained with LWC. This is due to the thermal properties of the LWC, which vary more linearly with temperature than the properties of NWC (see Figure  4 ), creating fewer convergence problems in the numerical integration scheme.
Experimental drag finishing tests The objective of this work is to optimise the drag finishing conditions on Inconel 718 parts produced by SLM. So, cylindrical samples were manufactured (Ø20 × L80 mm) (Fig. 2a ). The surfaces of these parts are not homogeneous and vary from one sample to another. Surfaces were measured by means of a focus variation microscope (magnification x20, vertical and lateral resolutions: 2. 5 and 0. 1 µm). The initial arithmetic mean height of the roughness (Sa~6µm) was quantified by the MountainMaps software after applying a form SL-filter, in accordance with ISO25718. The evolution of Sa was quantified during drag finishing and plotted in Fig. 5 (a) for four orientation angles (): front (~0°), intermediate (~30° and ~60°) and lateral (~90°). One of the objectives of this work is also to determine the abrasive mechanisms (material removal or plastic deformation) involved in drag finishing, depending on the orientation angle () around the sample and on the geometry of abrasive media.
Data collection The women were invited to face-to-face interviews after identification and signing of consent. Clinical data regarding ANC were retrieved from women's health booklets and from the antenatal clinic's register in the health facilities. A structured questionnaire was used to collect data. Six health workers with experience in data collection and speaking a local language conducted faceto-face interviews to obtain demographic characteristics; also, blood pressure was measured at the end of the interview, after approximately 15 min of rest. An electronic blood pressure monitor (Omron M3 Intellisense device) with an adult-sized cuff was used to measure blood pressure with the participant in a sitting position. The initial blood pressure was measured in each arm to determine the arm with the highest blood pressure.
This is done by concatenating FBP scout images at multiple resolutions in the decoder part of a convolutional encoder-decoder (CED). Main Results: This approach is investigated on two different networks, based on Dense Blocks and U-Net to show that a direct mapping can be learned from a sinogram to an image. The results are compared to two post-processing deep learning methods (FBP-ConvNet and DD-Net) and an iterative method that uses a total variation (TV) regularization. Significance: This work presents a novel method that uses information from both sinogram and low-resolution scout images for sparse-view CT image reconstruction. We also generalize this idea by demonstrating results with two different neural networks. This work is in the direction of exploring deep learning across the various stages of the image reconstruction pipeline involving data correction, domain transfer and image improvement.
Typically within this approach the training data consists of sparse-view reconstructions with FBP and the corresponding full-view reconstructed images. The authors in  Jin et al. (2017)  use U-Net with a residual connection for denoising and artefact removal in the sparse-view estimate, while the work in  Zhang et al. (2018)  called DD-Net, uses DenseNet with deconvolution for the same purpose. It is interesting to note that the networks have an encoder-decoder structure, wherein the encoder finds a compact representation of the input domain and the decoder learns to map this representation to the target domain. The dimensions of the input are reduced through the encoder as we go deeper into the layers. On the other hand, each of the decoder layers samples up these feature maps to eventually arrive at the output dimensions.
Another recent work specific to sparse-view CT A c c e p t e d M a n u s c r i p t was proposed by  Wu et al. (2021). Their methodology named DRONE, consists of three modules namely embedding, refinement and awareness. The first module estimates the missing views in the sparse-view sinogram through neural network. An image is then estimated with FBP, which goes through the refinement module for artifact removal and image enhancement. Finally, the awareness module using compresses sensing techniques, establishes data consistency with the measurement data. An alternative approach is using deep learning-based methods to directly map from projection to image space. The challenge in this approach is the management of data and the number of parameters required for learning the mapping. In  Li et al.
Proposed Low Resolution Reconstruction aware CED Model Supervised deep learning-based methods learn the mapping between the measurement y and the corresponding reconstructed image x. In the case of direct deep learning-based A c c e p t e d M a n u s c r i p t image reconstruction this mapping is typically learned via neural networks which can be represented as a function F θ : R n → R m with trainable parameters θ ∈ R np : x = F θ (y). (5) where, x is the predicted image. Most of the works in direct reconstruction for sparse-view CT represent F with a neural network with fully-connected layers. These networks require huge memory and large datasets for training. As an alternative to this, we propose the use of fully convolutional encoder-decoder networks that have lesser trainable parameters and are faster to train.
Dataset The data used in this work is from the Large-Scale CT and PET/CT Dataset for Lung Cancer Diagnosis (Lung-PET-CT-Dx)  (Li et al. 2020 , Clark et al. 2013). Details of the dataset are given in Table  1. The images in this dataset were reconstructed using FBP on full-angular coverage measurement data. We used the ASTRA toolbox, for data processing to create the projection-image pairs  (Van Aarle et al. 2016). A fan-beam geometry with a source to detector distance at 1500 mm and source to the center of the rotation at 1000 mm were considered. The number of detectors was set to 700 and the number of angles was varied to generate different levels of sparsity (n a = 20, 40, 60, 90 and 120). The source was rotated 360 degrees around the object, the angular sampling was adjusted to generate different sparsity configurations.
The noise-free projection data were obtained using the Beer-Lambert law (2) with an input emission intensity of 10 5. The final projection data were obtained by adding Poisson noise (i. e. , (1)) to the noise-free projection data. We finally generated the FBP estimates from the noise-added sparse-projections which were used in training the networks as explained previously. The estimates at different lower resolutions were obtained through nearest-neighbor A c c e p t e d M a n u s c r i p t interpolation of the images at full-resolution (512 × 512). The sinograms which are inputs to the network are resized using the same interpolation technique. Sample images from the dataset used are shown in Figure  3.
Training We implemented the architectures described in the previous section using TensorFlow and Keras  (Abadi et al. 2016 , Chollet et al. 2015). A subset of the dataset consisting of 22,000 2-D CT images was used in this study. We then split the data into 30,000 images for training and 2,000 images for testing. The sinograms and FBP estimates were generated using the ASTRA toolbox as described above. The sinograms were resized to 512 × 512 to ensure symmetry with the images for easier training of the network. The FBP estimates x1 and x2 were resized to the resolutions required for concatenation to the proposed networks. The neural networks were independently trained for each of the sparse-view settings with N a = 20, 40, 60, 90 and 120. The choice of x 1 and x 2 were at 64 × 64 and 128 × 128 resolutions for LRR-CED(D) and 128 × 128 and 256 × 256 resolutions for LRR-CED(U).
Experiments with Real Data The proposed networks were initialized with the weights from the previous study and were then trained on the real data. The real data used in this study was part of the Low Dose CT grand challenge  (McCollough 2016). The data constituted of 10 patients, acquired with flying spot technique and a helical scan. It was a subset of the larger Mayo CT clinic database  (Moen et al. 2021). The data from nine patients constituting of 3,994 2-D slices was used for training and the trained network was tested on another patient data. The three-dimensional (3-D) sinograms obtained from the helical scan were converted into 2-D sinograms through the single slice re-binning method employed in  (Kim et al. 2017). We further resampled the sinograms reducing the number of views to 64.
Concatenation Resolution Selection To select the best possible configuration for concatenation in the proposed approach, we trained the networks with a fixed set of hyper-parameters and different combinations of concatenations. We discuss the results with LRR-CED(D) in this regard. The number of training samples were set to 10,000 for all the experiments. The training data were projections with 90 views, corresponding FBP reconstructed images and the GT. The training was done for 25 epochs. Each of the concatenation setting was evaluated on 5 test patients. The average SSIM for each patient was plotted for each of the experiment setting. In Fig. 12 (a) we have the average SSIM vs Patient plot for single concatenation at a specific resolution. Similarly Fig. 12 (b) consists of plots for double concatenation at two different resolutions.
A set of comparisons among neutrino interaction experiments [MiniBooNE, MINERvA, Tokai-to-Kamioka (T2K), and MicroBooNE] is presented. This gives a broad view of the field of neutrino-nucleus interactions. The emphasis is on charged-current inclusive, quasielastic-like, and pion production experiments. Measurements are compared in new ways. Comparisons of recent data with available event generator codes are made more comprehensively than is regularly found in most previous publications. Generator studies show sensitivities for experimental model dependence. Efficiencies calculated with different generators are presented in a novel way. A comparison of different forward-folding techniques is also presented.