dataesr/scientific-paragraphs-categorization huggingface dataset:
Ufo framework Tofu is based on the open source ufo framework  (Vogelgesang et al. , 2012) , which (1) provides a large library of image processing algorithm implementations, (2) connects them into a workflow (a directed acyclic graph), and (3) executes these workflows on a broad range of computer systems. The computer programs 2 of 12 Tomas ˇFaragoet al. tofu framework is written in C in a cross-platform and bindingfriendly way, so that it can be used on different operating systems (Linux, Mac, Windows) and accessed from different programming languages (e. g. Python). It uses OpenCL for hardware-agnostic parallelization which allows for efficient execution on both CPUs and GPUs from various vendors, including NVIDIA, AMD and Intel. From the user perspective, there is a detailed description on how to install the prerequisites and the software itself on different operating systems and processor architectures in the manual (https://ufocore.
readthedocs. io/en/latest/install/index. html). Moreover, there are various Docker images available for download on Docker Hub (https://hub. docker. com/r/tfarago/ufo-kit), which allows users to skip the installation step on Linux. The framework can read raw, tif, hdf5 and edf file types and write raw, tif, hdf5 and jpg. Table  1  summarizes the components of the software stack behind tofu. Currently, there are over 90 image processing algorithms implemented in ufo-filters; here we will shortly describe the ones we implemented for obtaining high-quality 3D reconstruction. 2D Phase retrieval algorithms for near-field Fresnel diffraction images (also known as propagation-based phase contrast images) have been implemented, including the transport-of-intensity method  (Paganin et al. , 2002)  and various contrast transfer function approaches  (Moosmann et al.
Some metric, e. g. the standard deviation, can then be applied to such output to find the correct parameter value. Based on the specified geometry, an optimized back projection OpenCL kernel code with a minimal number of mathematical operations is generated at run-time, which leads to optimal reconstruction speed (e. g. coordinate transformations required for the tilted rotation axis in the case of laminography may be omitted in the case of tomography). On the top of that, only projection regions necessary for the specified reconstructed volume are read from the input data in order to minimize I/O. Ring artifact removal is based on two algorithms, one for removing narrow and one for removing broad rings. Narrow rings, often stemming from noise, are removed by suppressing stripes which are close to being vertical in a sinogram (a row in a sinogram represents a projection under a certain angle), implemented by filtering the 2D Fourier transform of the sinogram.
User interfaces In this section, we will describe user interfaces (UIs) for working with the ufo framework. Our primary focus will be the two GUIs tofu flow and tofu ez, which enable user-friendly creation of image processing workflows and batch processing of tomographic data sets. We will also briefly describe the CLIs; Table  2  contains the complete list of the user interfaces. With any of the interfaces, the 3D reconstruction produces quantitatively the following output: (i) In the case of monochromatic absorption input data, the voxel values are unitless and correspond to Áx , where Áx is the pixel size of the detector and the linear attenuation coefficient. (ii) In the case of phase retrieval applied on projections (and within the approximation limits of the respective retrieval algorithm), the voxels correspond to either the unitless phase shift À 2Áx/ or, in the case of the transportof-intensity method and specification of the part of the refractive index, the Áx in meters.
(iii) After ring removal, the results can no longer be quantitatively interpreted. Algorithms from the ufo framework in the flow scene are represented as graphical nodes. Several nodes can be combined into one composite node in order to remove clutter. Algorithm parameters can be set directly inside nodes using standard input widgets from the Qt (https://www. qt. io) library. Moreover, parameters can be linked between nodes, i. e. changing one node's parameter automatically updates another one's, which minimizes the amount of required manual adjustments. Execution of a flow in the scene, including scheduling and utilization of all GPUs in the system, is by default left to ufo-core. In the case of the memory-demanding 3D reconstruction, tofu flow creates several batches and executes them in sequence if more data than what fits into the GPU memory needs to be reconstructed.
Tofu ez Writing custom scripts for batch processing of data allows one to tailor the reconstruction workflow perfectly for each particular case. However, not every research group can afford to have a person with a computer science background or an image processing specialist. To address this problem we have developed a user-friendly interface tofu ez which can be used to reconstruct data by scientists without substantial knowledge of the Linux command line or Python scripting skills. Tofu ez (Fig. 4 ) simplifies the usage of ufo-launch and tofu by exposing all important parameters in a PyQT5-based interface and automatically formatting a suitable list of commands depending on the user input. Typical applications of tofu ez include: (i) Optimization of reconstruction parameters. (ii) Single-click reconstruction of freshly acquired data during the experiment.
Those CT sets whose names are not yet in the output directory will be reconstructed. If requested, tofu ez will automatically estimate the center of rotation parameter, which is the only unknown variable in the input of the filtered backprojection algorithm in parallel beam geometry. This information is used during the second pass, when the program creates an array of ufo-launch and tofu commands according to defined parameters and then executes them sequentially. The commands can also be printed on the screen. Fig. 5  shows how tofu ez creates workflows in different situations. Metadata can be loaded from a configuration file and all parameters that could be used to re-run the reconstruction are saved automatically along with the reconstructed data. Operations belonging to the following 12 categories can be chained together to form a workflow which can be applied to multiple data sets in the input directory automatically: (i) Horizontal stitching of half acquisition mode data (see explanation in the paragraph below the list).
Command line interfaces The generic CLI program ufo-launch comes with ufo-core and not tofu, but we will shortly describe it for completeness. A workflow can be created by connecting multiple algorithms with exclamation mark, similar to chaining commands on the Linux command line using the pipe symbol. For instance, the following command reads images from a disk, bins and flips them in the left-right fashion and writes the results to the disk: CLI-based sub-commands in tofu (see Table  2 ) contain predefined, parametrizable image processing flows. For instance, the following command performs flat field correction, fixes possible extreme values, computes the absorptivity and performs parallel beam tomographic reconstruction with the rotation axis in pixel 951, returning 200 slices around the vertical projection center with a spacing of 1 row: The following command performs phase retrieval based on the transport-of-intensity approach: Experienced users can write scripts of any complexity or integrate workflows into custom software by using ufo-launch or one of the tofu CLI sub-commands.
Performance With tofu, the overall throughput of systems with two or more GPUs can easily become limited by disk I/O performance instead of 3D reconstruction. Fig. 10  provides the reconstruction performance for different types of geometries and systems. A performance comparison with respect to some other reconstruction software is shown in Table  3. Throughout this section, the term data set size defines both the input and output sizes, i. e. for data set size N, the input are N projections of size N 2 and the output is a volume of size N 3. The aim of the first benchmark was to show how fast our filtered back projection implementation is for different input sizes and reconstruction geometries on various systems. We used parallel beam tomography, parallel beam laminography, cone beam tomography and cone beam laminography geometries and data set sizes 1024, 2048 and 4096.
Reconstruction performance was measured on a notebook with an Intel i9-10885H processor, 32 GB of RAM and an NVIDIA RTX 2070 Super graphics card with 8 GB of RAM. The second system was a workstation with Intel i7-7820X processor, 32 GB of RAM and two NVIDIA RTX 4000 graphics cards, each with 8 GB RAM. The most powerful system was a server with two Intel Xeon Silver 4114 processors, 256 GB RAM and four GeForce RTX 2080 Ti graphics cards, each with 11 GB RAM. We measured the wall time of tofu reco from invocation to return and used the so-called dryrun mode, where no data is read or written to the disk. Only the creation of empty OpenCL buffers, filtered back projection and downloading of the reconstructed volume from graphics memory to main memory was performed. In this, as well as in the second benchmark below, no flat field correction or other pre-and post-processing steps were applied and the input and ouput data were in single-precision floating-point arithmetic.
Conclusion We presented tofu, a set of versatile high-level user interfaces for tomographic image processing including a command-line interface for efficient scripting and two graphical user interfaces for user-friendliness. It includes many pre-and postprocessing algorithms, such as phase retrieval and ringremoval and supports parallel beam, cone beam, tomographic and laminographic geometries. Thus it can reconstruct data acquired with different types of light sources, setups and complicated geometries, as we have demonstrated by several use cases. The modularity makes this an ideal tool for method development platforms, where data processing workflows need to be easily adjustable. The image processing code can run on GPUs, therefore the package is not only user-friendly but also fast and, as the performance measurements show, users can obtain reconstructions in short times even by using ordinary, off-the-shelf hardware.
3. 1. Tofu flowTofu flow is a GUI (Fig. 2) for visual composition of image processing workflows. Its main advantage is quick and flexible flow creation with embedded visualization of results. This combination makes the program interactive, which is very desirable when it comes to optimization of image processing parameters or education and training of people who are not yet familiar with tomographic image processing. It is written in Python 3 and PyQT5 (https://riverbankcomputing. com/ software/pyqt); the flow scene is based on qtpynodeeditor (https://github. com/klauer/qtpynodeeditor).
Acknowledgements SG would like to acknowledge  J. Stobbs ,  A. Webb ,  N. Zhu ,  A. Panahifar  for participating in testing and their feedback;  T. Bond  for help with the stitching tools;  R. Wagner  for writing the Ansible script for the automatic deployment of the ufo framework and tofu on RHEL/CentOS7 computers;  J. Forshaw ,  S. Escario , and  B. Tutolo  from  University of Calgary  for letting us to use one of their samples for demonstration purposes;  Christian Schlepuetz  from the  Swiss Light Source  for sharing the source code of their flat-field correction algorithm. We acknowledge the  KIT  light source, the  Canadian Light Source (CLS)  and the  Institut Laue-Langevin (ILL)  for provision of instruments needed for the experiments used in this paper. We would like to thank the  Institute for Beam Physics and Technology (IBPT)  for the operation of the storage ring, the  Karlsruhe Research Accelerator (KARA).
2b ). Here, the elliptical shape of the output beam remains unchanged up to ∼0. 75 mW. However, at higher powers, the polarization state has a tendency to become increasingly circularly polarized. Using the data of the rotation angle from Fig. 2  (LEP state) and the value of the Verdet constant for benzene at 700 nm (265. 4° T -1 m -1 )  30  fitted from the available literature datasets (Supplementary Fig. 2a ), Eq. (  1 ) enables to calculate the magnitude of the static magnetic flux density present in the liquid. Fig. 3  shows that even under moderate average laser input powers, the static magnetic flux density in benzene reaches magnitudes in the range of 1-75 T. For example, with a 0. 5 mW input beam in the LEP state, the measured rotation angle is ∼18. 6°, which according to Eq. (  1 ) results in a peak magnetic flux density of ∼35 T.
Here, the input power range was extended because chloroform has an even weaker optical Kerr constant than benzene  31. Recalling that the rotation angle in the linear regime is of the form = E , where E = F , G, H. is the slope (see Eq. (  25 ) in Methods section), then from the comparison between the slopes of a reference solvent and another solvent at a given wavelength λ, the unknown Verdet constant of the latter is determined by I0J K = I0J 78L ⁄ M. ⁄ 78L K = E I0J E 78L ⁄ M. ⁄ 78L K , (3) where 78L K and I0J K are the Verdet constants at the wavelength λ of the reference and unknown solvent, respectively. Considering without loss of generality only the LEP state, one finds from the linear fit of the rotation angle of benzene (Fig. 2 ) that E 78L ≈ 37. agreement with values at 700 nm obtained from fitting of literature datasets (Table  1 ).
It is important to note that, under strong applied or optically-induced magnetic fields, higher-order MOEs and IMOEs occurs, such as the CME and ICME, which are quadratic with the magnetic field and correspond to magnetic linear birefringence (MLB) contributions. However, the current determination of the Verdet constant is done in the low power regime where the slope at the origin depends linearly on the Verdet constant. To further validate the method and for comparison purpose, it was applied to two other solvents, toluene and water. The plots of rotation angle for these solvents as function of the laser input power in the LEP state are given in Supplementary Fig. 5. The Verdet constants of these solvents calculated with the external reference method are found in Table  1. Table  1. Comparison between Verdet constants of diamagnetic liquids calculated with the external reference method and those obtained from fitting of available literature datasets (Supplementary Fig.
Acknowledgements This research has been carried out with financial support from the  Région Aquitaine  (CRA:  2015-1R10205-00004858 ) and the  French State , managed by the  French National Research Agency (ANR)  in the framework of the project " NLOChiramat"  ( ANR-21-CE06-0018-03 ) and of "the  Investments for the future "  Programme IdEx Bordeaux LAPHIA  ( ANR-10-IDEX-03-02 ).
Context. Characterization of directly imaged exoplanets is one of the most eagerly anticipated science functions of the James Webb Space Telescope. MIRI, the mid-IR instrument, has the capability to provide unique spatially resolved photometric data points in a spectral range never before achieved for such objects. Aims. We aim to present the very first on-sky contrast measurements of the MIRI coronagraphs. In addition to a classical Lyot coronagraph at the longest wavelength, this observing mode implements the concept of the four-quadrant phase mask for the very first time in a space telescope. Methods. We observed single stars together with a series of reference stars to measure raw contrasts as they are delivered on the detector, as well as reference-subtracted contrasts. Results. The MIRI coronagraphs achieve raw contrasts better than 10 -3 at the smallest angular separations (within 1 ) and about 10 -5 farther out (beyond 5 ∼ 6 ).
Measured contrasts The data were processed with the dedicated JWST pipeline up to stage 1, which involved detector-level corrections (calwebb_detector1; see JWST pipeline documentation) and produced level 2a count-rate data, but without flat field correction (not available at the time of commissioning), nor background subtraction. Then we used our own specific procedures for subtracting the background and reference star images. There was no further treatment to remove the glow stick effect other than background subtraction. Contrasts were measured azimuthally as a function of the angular separations first in the raw coronagraphic image (azimuthal mean), and then with the subtraction of a reference image (azimuthal standard deviation). We warn the reader that the contrast curves presented here are meant for comparison with simulated data and to demonstrate the capacity of the MIRI coronagraphs, and it would be overly optimistic to use them to assess the detectability of exoplanets, which would require accounting for the coronagraph's field transmission as well as the contrast definition proposed in  Mawet et al.
Acknowledgements. The work presented is the effort of the entire MIRI team and the enthusiasm within the MIRI partnership is a significant factor in its success. We would like to thank  Scott Rhorbach  for his invaluable assistance and modelling to resolve the cause of the glowsticks issue, and the JWST and MIRI commissioning teams for their support in the execution of MIRI commissioning. The following National and International Funding Agencies funded and supported the  MIRI development: NASA ;  ESA ;  Belgian Science Policy Office ;  Centre Nationale d'Etudes Spatiales (CNES) ;  Danish National Space Centre; Deutsches Zentrum fur Luft-und Raumfahrt (DLR) ;  Enterprise Ireland; Ministerio De Economiá y Competividad ;  Netherlands Research School for Astronomy (NOVA) ;  Netherlands Organisation for Scientific Research (NWO) ;  Science and Technology Facilities Council ; Swiss Space Office; Swedish National Space Board; and  UK Space Agency.
Within the framework of the CIEST 2 (Cellule d'Intervention d'Expertise Scientifique et Technique new generation) and thanks to the support of CNES, the French space agency, the first phase of the Fagradalsfjall eruption was exceptionally well covered by high resolution optical satellite data, through daily acquisitions of Pléiades images in stereo mode. In this study, we show how Pléiades data provided real-time information useful for the operational monitoring of the ongoing eruption. An estimation of the volume of lava emitted as well as the corresponding effusion rate could be derived and delivered to the civil protection less than 6 h after the data acquisition. This information is complementary to and consistent with estimates obtained through the HOTVOLC service using SEVIRI (Spinning Enhanced Visible and Infrared Imager) sensor on-board Meteosat Second Generation (MGS) geostationary satellites, operated by the European Space Agency (ESA), characterized by a lower spatial resolution and a higher temporal one.
Satellites have already been extensively used to produce digital elevation models (DEMs) in volcanic areas and infer the volume of eruptive deposits by comparing the differences between a DEM obtained after the emplacement of deposits with a pre-eruptive DEM. While most studies are based on TanDEM-X bistatic radar data (e. g. Albino 2015 , Bato 2016 , Albino 2020) , some use highresolution Pléiades optical data acquired in stereo mode  (Bagnardi et al 2016; Carrara et al 2019). For the October 2010 effusive eruption of Piton de la Fournaise, Réunion Island,  Bato et al, (2016)  made a direct comparison of mean effusion rates derived by DEMs differentiation and by thermal anomalies quantification from MODIS data and demonstrated a fairly good agreement between the two independent dataset. While the growth rate of domes has been estimated from Pléiades imagery  (Pinel et al.
, 2019; Massimetti et al. , 2020). Finally, the coherence of radar data can also be used in real time to derive the evolution of the surface covered by the lava  (Ebmeier et al. , 2012; Kubanek et al. , 2015; Valade et al. , 2019; Richter and Froger 2020). In order to promote the use of satellite data for hazards studies and mitigation, two French initiatives have been undertaken. (i) The Technical-Scientific Intervention and expertise unit (CIEST 2 -Cellule d'Intervention d'Expertise Scientifique et Technique) new generation, was created in 2019 following the expression of interest of about 30 French scientists. The objective is to extend and facilitate the acquisition and use of very high optical images from Pléiades acquired under the International Charter "Space and Major Disasters", for the understanding and study of geological hazards.
The CIEST 2 initiative is now placed in the framework of the solid Earth national data and services pole Form@ter. (ii) In parallel, HOTVOLC is a geostationary satellite-data-driven service dedicated to the real-time monitoring of active volcanoes, allowing lava hot spots, ash and SO 2 clouds products to be detected and tracked at an acquisition rate of one image every 15 min  (Gouhier et al. , 2016; 2020). HOTVOLC uses Meteosat-SEVIRI infrared images and is part of the National Observation Service for Volcanology (SNOV -Service National des Observations en Volcanologie) operated by the CNRS (Centre National de la Recherche Scientifique). Its mission is to ensure continuous and permanent monitoring of French volcanoes, as well as volcanic targets (Italy, Iceland, Lesser Antilles, etc. ) whose products may affect French territories.
In this context, the recent Icelandic eruption of Mt. Fagradalsfjall in the Reykjanes Peninsula, which started on March 19, 2021 offers a very good opportunity to demonstrate the ability of the CIEST 2 and HOTVOLC initiatives to provide a rapid and concerted response to gather crucial information useful for making informed decisions. The Fagradalsfjall eruption was closely monitored with remote sensing data through the CIEST 2 , HOTVOLC and MOUNTS initiatives during the first 10 days of the eruption, and through the entire eruption using a large amount of airborne data  (Pedersen et al. , 2022). The eruption is a long-term basaltic effusive eruption that initiated as a fissure eruption on 19 March 2021 within an enclosed valley, accompanied by small lava fountains which ended on 18 september.
In this paper, we present the two French initiatives CIEST 2 and HOT-VOLC with associated methodology, and discuss their capabilities and limitations, as well as the major interest of coupling these two approaches. We also present the potential contribution of Sentinel-2 data for the estimation of lava surface from the operational platforms MOUNTS. Then, we describe the results obtained from Pléiades and Meteosat data. This comprises, in particular, the estimation of lava flows volume and volcanic plume elevation from Pléiades DEMs, as well as the comparison between average and instantaneous lava discharge rates using Pléiades and Meteosat images, respectively. We also provide airborne data at very high spatial resolution, hereafter used as a validation of satellite-based products. Operational initiatives for a rapid response using CNES/ ESA spatial resources CIEST 2 : Technical-Scientific Intervention and expertise unit CIEST 2 is a French initiative aiming at fostering cooperation of the geophysical community around the use of satellite imagery for geohazards monitoring and understanding.