dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion This paper introduces the Solar-MACH tool to the scientific community. Solar-MACH automatically obtains and visualizes the constellation of the heliospheric spacecraft fleet for a given time and provides a first estimation of the magnetic connectivity of all observers. Here, we illustrated the general workflow of the tool, presented the corresponding Python package solarmach and its PFSS extension, and described the important web version of it provided through the streamlit library. While the tool is intended to have a very simple interface, it is important to point out that for the magnetic backmapping several assumptions are made in the background. Those results should therefore only be interpreted by users with sufficient knowledge of the topic. The development process of Solar-MACH started at the University of Kiel, Germany, and further discussion took place within the ESA Heliophysics Archives USer (HAUS) group.
Now, the main development continues within the SERPENTINE EU Horizon 2020 project, which has been presented in the beginning of this paper. It is important to note that the project is, and remains, fully open source (under the BSD 3-Clause License) and very much encourages everyone to contribute, either by actual code or by any other kind of feedback. The software is completely developed in Python and uses established open-source Python libraries such as pfsspy, streamlit, and sunpy. The development takes place continuously in public GitHub repositories (separated for the Python package solarmach and the web application), which are also the main entry point for any kind of participation. We encourage users to give feedback on bugs or possible new features by submitting GitHub issues. This should also be the starting point for code contributions.
But of course we are also open to feedback through direct communication. All code versions released on GitHub are automatically archived at Zenodo with a DOI, so that each version (or the latest) can be cited independently. Solar-MACH is already used widely in the scientific community (e. g. ,  Badman et al. , 2022; Hu et al. , 2022; Mierla et al. , 2022; Papaioannou et al. , 2022; Rodríguez-García et al. , 2022) , but the development is still ongoing with the introduction of additional functionalities. Some important future plans have already been mentioned in this paper, such as fully integrating the PFSS extension into Solar-MACH. Further ideas are listed in the GitHub repository's issues section and contain, among others, using dynamically obtained solar wind speeds for the HMF lines, adding trajectories of spacecraft, or introducing features related to CME analyses, such as providing a reference cone instead of an arrow.
Funding This study has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 101004159  (SERPENTINE). JG, CP, RV, and DP acknowledge the support of  Academy of Finland (FORESAIL , grants  312357 ,  336809 , and  336807 ). ND acknowledges the support of  Academy of Finland (SHOCKSEE , grant  346902 ). JvF thanks the  German Space Agency (Deutsches Zentrum für Luft-und Raumfahrt  e. V. , DLR) for their support of his work on the Solar Orbiter EPD team at the  University of Kiel  under grant  50OT2002. AK acknowledges financial support from  NASA 's  NNN06AA01C  ( SO -SIS  Phase-E) contract. LR-G acknowledges the financial support by the  Spanish Ministerio de Ciencia, Innovación y Universidades FEDER/MCIU/AEI  Projects  ESP2017-88436-R  and  PID2019-104863RB-I00/AEI/10.
Data availability statement The source code used in this study can be found online: • Python package code in GitHub repository solarmach, archived at https://doi. org/10. 5281/zenodo. 7311178 • Streamlit code in GitHub repository Solar-MACH, archived at https://doi. org/10. 5281/zenodo. 7311215 • Extended example Jupyter Notebook in GitHub repository serpentine, archived at https://zenodo. org/record/7104866.
As the flame arrives at the obstacles, it is strongly wrinkled by the vortical, turbulent structures present in the flow. This creates a strong increase of the flame surface and consumption rate as the flame transitions to the turbulent regime. Figure  1  shows results from the simulations of  Vermorel et al. (2017)  that illustrate the creation of vortical structures in the wake of the obstacles that are in the path of a propagating flame front, together with the subsequent FVI leading to wrinkling, transition to turbulence and strong FA. This establishes a positive feedback loop of acceleration that is the main driver of the overpressure generation. Therefore, it is essential to correctly capture the flame, the vortices and their interaction to recover the correct overpressure. Several experimental studies have explored deflagrations in confined and obstructed geometries and provide a validation database for LES: for example the semi-confined, obstructed explosion chamber from the University of Sydney  (Kent et al.
linear least-squares interpolation was also implemented using the vertices in the immediate neighbourhood of the vertex edge stencil. Note that the results shown in this work are obtained only using the Barycentric interpolation. Since the AMR methodology imposes a target resolution depending on the mask (see Fig. 2 ), mesh resolution remains mostly constant (either ∆ coarse x or ∆ fine x ) thus ensuring very low interpolation errors. The kalpaTARU library is linked to the AVBP solver using a plain C language application programming interface (API). All input data like mesh, metric and solution fields are shared as memory pointers avoiding redundant data transfer and storage. After adaptation, adapted local mesh data is copied back into AVBP arrays directly.
Firstly, the experimental database includes different operating points in variable geometries, thus enabling the generic nature of the AMR approach developed here to be assessed and validated. Secondly, previous works have already shown the ability of LES to reproduce the physics of such phenomena  (Vermorel et al. , 2017; Volpiani et al. , 2017)  using static and homogeneous meshes, thus constituting a solid basis of comparison for the evaluation of AMR results. For this work, three configurations are retained, as summarised in Table  7. Two different geometries are used. The first geometry (noted A) corresponds to a chamber with 1 grid of obstacles followed by a central obstacle (Fig. 21b top ). The second (noted B) corresponds to a chamber with 3 grids of obstacles followed by a central obstacle (Fig.
Acknowledgements The authors thank  TotalEnergies, GRTgaz and Air Liquide  for their financial support in the framework of the  LEFEX  project and  ANRT  for the funding through  CIFRE-2021-1379. This work was performed using HPC resources from  GENCI-IDRIS  (Grant  2023-A0132B10157 ). The authors thank  INRIA (MMG3D)  for providing their developments, as well as  V. Moureau  and  A. Misdariis  for their efforts and their assistance.
Expanding the solution space In applying models, local stakeholders and researchers make assumptions about future conditions (e. g. , rate of deforestation, growth in imports or exports). Poor choices, misleading historical data, and unexpected future events, can lead to unrealistic or inaccurate assumptions. Navarro  Garcia et al. (2022)  use scenario discovery to systematically explore the effect of different parameter ranges on model outputs. Through coupling the FABLE Calculator with a Monte Carlo simulation software, the authors were able to systematically explore the effect of a range of each parameter's values on model outputs and differentiate thousands of possible pathways for food and land-use systems. They distinguish 'resilient' pathways as those that incorporate a diversified portfolio of solutions working together to hit multiple sustainability targets, and more risky pathways that rely on small sets of solutions or individual levers (e.
Future development priorities The existing tools and methodological advances presented in this special feature already go a long way to ensuring that FABLE outputs are policy relevant and have practical utility. The value of FABLE's approach could be further strengthened by closing several key data and methodological gaps. These include distinguishing effects of agroecologically managed farmland (e. g. , agroforestry, zero synthetic inputs, maintenance of natural infrastructures in and around fields, improved rice management) from other farming systems, separating extensive and intensive pasture, better representing forest production systems, and incorporating climate adaptation and resilience assumptions into the scenarios. Data availability is a key constraint to further developing the capacity of FABLE models, and national and global monitoring efforts should continue to try and close data gaps.
Cross-country collaboration and consistency The FABLE consortium itself is one of the world's largest multidisciplinary collaborations focused on tackling the challenge of achieving a sustainable future in the existing national policy contexts. Researchers involved in the FABLE Consortium are specialists in every aspect of the food system and regularly share information, guidance, and resources on research, stakeholder engagement strategies, and methodological advances with the entire group. This gives each country team easy access to the latest data and science on FABLE-related topics and a go-to person to ask about topics that lie outside their area of expertise. The consortium members work together to agree on global targets, develop methodologies to apply consistently across countries to quantify impacts of food and land-use scenarios, and iteratively adjust assumptions to ensure global trade balance.
Acknowledgements This work was supported by  Norway's International Climate and Forest Initiative , the  UK Engineering and Physical Sciences Research Council  (PAH, award number  EP/R01860X/1 ), and the  UK Natural Environment Research Council (PAH , Award Nos. NE/ T003952/2  and  NE/R016429/1  as part of the  UK-SCAPE programme delivering National Capability ). The authors would like to thank the  Food and Land Use Coalition (FOLU)  and the  World Resources Institute  for their support to the  FABLE Secretariat.
Introduction Artificial intelligence is revolutionising many areas of scientific research, helping scientists to examine large amounts of data, that are needed to address important open questions  1. Neuroscience is a prominent example of a field that would greatly benefit from automated analysis tools for large-scale comparative investigations. In particular, animals, from insects to humans, show a rich diversity of behavioural profiles (or personalities) that may be underpinned by anatomical and cognitive variability  2. Identifying natural variations in brain morphologies and performances between individuals can therefore help to understand the evolution of species and their potential for resilience in the widespread context of biodiversity loss  3. However, such an approach currently requires extensive manual effort in order to obtain and analyse large and high-quality brain datasets, which typically limits investigations to a few individuals and to model species for which brain atlases are available (e.
However, image segmentation and post-processing steps still require manual assistance  [18] [19] [20] [21]  , which is time-consuming (several hours or days to reconstruct a structure) and therefore not compatible with analyses of large datasets. Here, we demonstrate how deep learning can considerably speed up 3D image analyses for large-scale comparative brain studies in non-model species. We illustrate our approach using the semi-automated  22, 23  and automated segmentation methods integrated into the recently developed online platform Biomedisa  24  (https://biomedisa. org) to analyse micro-CT scan data from bee brains (Fig. 1 ). Despite its small size (<1 mm 3 ), the bee brain supports a puzzlingly rich behavioural repertoire that presents extensive inter-individual variability  25. In social bees, such as honey bees and bumblebees, this variability is central to the division of labour that supports complex social organisations, and some of it has been associated with maturation of specific brain areas due to development and foraging experience  26, 27.
individuals). To fill this knowledge gap, we collected volumetric data on brains of 110 honey bees and 77 bumblebees and quantified variations in brain area volumes. For each species we used semiautomated segmentation to annotate the training data for a deep neural network, which was then used for subsequent automatic segmentation of the full dataset. This reduced data processing time by approximately up to 98% compared to a conventional manual segmentation approach. Our approach revealed size variations across brain samples as well as strong anatomical asymmetries, supporting behavioural observations.
2 ). ALs and OLs are involved in olfactory and visual processing, respectively, while the CX and MBs play important roles in locomotor behaviour, learning and memory, respectively  34. The volumetric analysis of the different neuropils required their isolation from the CT scans by segmentation (Figs. 1, 2 ). For better automatic segmentation (Fig. 3 ), each dataset was manually cropped to the area of the neuropils (Fig. 2d ) using AVIZO 2019. 1, resulting in an average size of 451 ×273×167 voxels. For automatic segmentation, we used the deep neural network interface from the online platform Biomedisa. To train a deep neural network, Biomedisa needs a set of fully segmented volumetric images. To create the first three datasets, labels were assigned manually to the six neuropils in every 10th slice and every 5th slice in the interval containing CX within the 3D volume using AVIZO 2019.
1 (Thermo Fisher Scientific, Waltham, USA). For better performance compared to purely morphological interpolation (see "Methods" and Fig. 3 ), Biomedisa's semi-automatic smart interpolation was used to segment the remaining volume between the pre-segmented slices. Before interpolation, the image data was slightly smoothed using Biomedisa's denoise function (see "Methods"). Subsequently, outliers (i. e. unconnected voxels or islands) were removed and segmentation errors were corrected manually by an expert using AVIZO 2019. 1. Additional training data was then iteratively created using neural networks. Starting from three semi-automatically segmented images, we trained neural networks on 3, 7, 12, 18, and 26 images by adding manually corrected segmentation results of the last trained network to the training data after each step (Fig.
To test the suitability of our approach to brain datasets from other species, we also performed micro-CT scanning of 77 bumblebees (Bombus terrestris) from 4 commercially-available colonies (colony A: 18 bumblebees, colony B: 20 bumblebees, colony C: 21 bumblebees, colony D: 18 bumblebees). Based on the evaluation for an increasing number of training images (see "Methods", Fig. 3  and Supplementary Table  1 ), we decided to semi-automatically segment 13 training images (which we considered to be a good balance between manual effort and accuracy) using Biomedisa to train a neural network on the bumblebee dataset. The trained network was then used to automatically segment the remaining 64 bumblebee CT scans. Overall, AVIZO 2019. 1 was used for pre-segmentation, correction of segmentation results and measurement of absolute neuropil volumes calculated with the voxel count function.
Biomedisa was used for smart interpolation to create the initial training data, training of deep neural networks and subsequent automatic segmentation. Since Biomedisa supports the AMIRA Mesh File format, deployed by AVIZO and AMIRA, data can be easily transferred between the Biomedisa online platform and AVIZO 2019. 1. In addition to AMIRA meshes (AM), Biomedisa supports many other common data formats (such as DICOM, NifTI, TIFF, NRRD, MHA, MHD) and can also be used in combination with other segmentation tools. For both datasets, this automatic segmentation considerably reduced the time and effort required for image analysis compared to purely manual segmentation. For each 3D image, it took about 5 to 10 minutes for the whole procedure: importing the data into AVIZO 2019. 1, cropping to the area of the neuropils, exporting the data, uploading it to Biomedisa, performing automatic segmentation, downloading and importing the segmentation result into AVIZO 2019.
1, correcting the segmentation result manually, and measuring volumes. In contrast, a purely manual or semi-automatic approach would have taken several hours for each CT scan (approx. 5 to 6 h using conventional manual segmentation and 2 to 3 h using Biomedisa's semiautomatic segmentation). Depending on the quality of the automated segmentation result, either no correction was required (Supplementary Fig. 1a ) or manual correction typically took 1 to 2 minutes (Supplementary Fig. 1b ), rarely longer if the result was significantly flawed (Supplementary Fig. 1c ). Typical artefacts resulting from the automatic segmentation are outliers (Supplementary Fig. 1b ) which can be easily removed either with Biomedisa's cleaning function or with AVIZO 2019. 1. Overall, with this approach, the manual effort for segmenting the full honey bee dataset (15 to 27 h) was about 95 to 98% less than purely manual segmentation (550 to 660 h) and about 87 to 93% less for the bumblebee dataset (31 to 50 h compared to 385 to 462 h).
Brain volumes varied significantly among honey bees. To validate our approach, we compared our results with previously published data, focusing on the volumes of the six neuropils considered (Fig. 4 ) in honey bees. Division of labour in honey bee colonies is primarily based on age differences among individuals  36. Since workers vary little in body size  37  , it was expected that their brain volume would also show only small variation. Our results are broadly consistent with previous studies based on smaller datasets  27, [31] [32] [33] [38] [39] [40] [41] [42] [43] [44]  and using different quantitative approaches (CT scan data  43, 44  , or stereo  27, 31, 32, 40  , confocal  33, 38, 39  , and nuclear magnetic resonance 39 microscopy). Wh n data do not match, we expect our measures to be more accurate than those in previous studies since they were obtained from thinner slices (5.
2a ). When assessing correlations between absolute volumes across neuropils, we found strong correlations between OL, LO and ME volumes (Supplementary Table  2 ), as previously reported  32. In our dataset, however, AL volume is positively correlated with all other neuropils. We also found positive correlations for MB, CX and OTH with all other neuropils, except between MB and CX, which were not correlated. We next compared the relative neuropil volumes in order to search for relevant allometric relationships (Supplementary Table  2 ). Again, we found strong correlations between the relative volumes of OL, LO and ME, consistent with previous studies  32. In addition, the relative volume of AL correlates positively with those of OL, ME, and LO, while previous work on honey bees  32  and other bee species  46  suggested a negative correlation between AL and OL as a possible trade-off between visual and olfactory processing.
Automatic cropping. As an alternative to manual cropping, a neural network can be trained with Biomedisa to automatically crop the image data to the region of interest before segmentation. Here the DenseNet121 69 pretrained on the ImageNet database is used to decide whether a slice of the volume contains the object or not. Applying the network to all three axes creates a bounding box covering the honey bee brain. Adding a buffer of 25 slices to all sides after cropping ensures that the entire object is within the bounding box. The network is trained in two steps. First, the head of the pre-trained network is removed and replaced with a global average pooling layer, a dropout layer with a probability of 0. 3 to avoid overfitting, and a final dense layer with sigmoid activation function. The weights of the base model are kept fixed while the head is optimised with binary cross-entropy, Adam optimiser with a learning rate of 0.
Acknowledgements Three-dimensional data acquisitions were performed using the micro-CT facilities of the  MRI  platform member of the national infrastructure  France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ,  «Investments  for the future»), and of the  Labex CEMEB  ( ANR-10-LABX-0004 ), and  NUMEV  ( ANR-10-LABX-0020 ). We further acknowledge the support by the projects ASTOR ( 05K2013 ) and  NOVA  ( 05K2016 ) funded by the  German Federal Ministry of Education and Research (BMBF) ,  Informatics for Life  funded by the  Klaus Tschira Foundation , the state of  Baden-Württemberg  through bwHPC, the  Ministry of Science, Research and the Arts Baden-Württemberg (MWK)  through the data storage service SDS@hd, and the  German Research Foundation  (DFG;  INST 35/1314-1 FUGG  and  INST 35/1134-1 FUGG ).
CM was funded by a PhD fellowship from the  French Ministry of Higher Education, Research and Innovation. ML was funded by the  Agence Nationale de la Recherche  ( 3DNaviBee ANR-19-CE37-0024 ), the  Agence de la Transition Ecologique  (project  LOTAPIS ), and the  European Commission  ( FEDER ECONECT MP0021763 ,  ERC Cog BEE-.
Advanced data assimilation (DA) methods, widely used in geophysical and climate studies to merge observations with numerical models, can improve the state estimates and consequent forecasts. We interface the deterministic Ensemble Kalman filter (DEnKF) to the Lagrangian sea ice model, neXtSIM. The ensemble is generated by perturbing the atmospheric and oceanic forcing throughout the simulations and randomly initialized ice cohesion. Our ensemble-DA system assimilates sea ice concentration (SIC) from the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) and sea ice thickness (SIT) from the merged CryoSat-2 and SMOS datasets (CS2SMOS). Because neXtSIM is computationally solved on a time-dependent evolving mesh, it is a challenging application for ensemble DA. As a solution, we perform the DEnKF analysis on a fixed reference mesh, where model variables are interpolated before the DA and then back to each member's mesh after the DA.
Observations We assimilate the CS2SMOS SIT observations and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) OSI-SAF SIC observations. In the interest of studying the feasibility of the DEnKF with neXtSIM, and because of the lack of proper independent pan-Arctic observations for validation, we primarily use the same observations products that are assimilated. As a complement, we shall also use the OSI-SAF SID as an independent validation dataset.
CS2SMOS sea ice thickness for assimilation The CS2SMOS provides a daily SIT product that merges the weekly Cryosat-2 altimeter data with the daily Soil Moisture and Ocean Salinity (SMOS) radiometer data. The Cryosat-2 SIT is retrieved from the freeboard (the ice/snow height above sea level) measured by altimetry satellites, which can only detect thick ice. In contrast, the SMOS SIT is more reliable for thin ice. It is derived from microwave brightness temperatures in the L-band. The two datasets are projected on the Equal-Area Scalable Earth 2 (EASE2) 25 km grid  (Brodzik et al. , 2012 ) by optimal interpolation. The CS2SMOS data provide mapping errors as its uncertainty estimates, including the uncertainty of ice thicker than 1 m from Cryosat-2 and the uncertainty of ice thinner than 1 m from SMOS.