dataesr/scientific-paragraphs-categorization huggingface dataset:
Results of the case study The interaction between the coffee tree and the CBB models is constructed around the attraction of the pest on the type of fruits (from the CBB point of view). A specific detail concerns the way fruit maturity is considered in both human and fruit models. Among the datasets, 2 trees were chosen (named C52 and C22), exhibiting dissimilar fruiting dynamics (Fig. 8 , bars). Tree C52 produced ripe fruits for nearly the entire year, whereas tree C22 produced fruits for only the first one-third of the monitoring period. --------------------  The proportion of infested fruits among harvested fruits for the 2 trees is the result of a MIMIC simulation in which an initial number of CBB was introduced. MIMIC simulated the attack dynamic accurately. There was one significant peak in tree C22 and 2 less significant ones.
Performances and coding key figures The MIMIC code is compact, with reduced complexity. The complexity is linear as a function of the number of events. Table  5  shows its performance in the study case. Most of the simulation time is spent on initialization, which consists of interacting with the user layer. The rest of the run time is split between the MIMIC's kernel components and the external models. Initialization clearly consumes the majority of simulation time. This is because Julia is executing a code for the first time. On the first run, Julia simulates a virtual machine to compile the algorithms. Hence,the second run requires less memory (3,720 GB with 28. 9 s for the first run compared to a 727 MB with 3. 81 s for the next one).
Choosing Julia, user-friendly meta programming Julia is a fast, dynamic, easy to use, and open-source programming language; it is accessible to the modeling community. Its syntax bears a resemblance to the common program languages used by modelers (Python, R, and MATLAB). In addition, the UIM does not need to be written as a complex algorithm, as shown on our example. When using dynamic programming (by requesting user-MIMIC parser to produce the pseudo-models), Julia's speed is advantageous because MIMIC's input (UC and UIM) is preprocessed and an optimized code is generated. The parser in MIMIC will take most of the time and resources at the first run of the program. However, after it, the simulations are swift (Table  5 ) and users can update the simulation parameters without losing performance.
Conclusion The literature on the limitations of model coupling (plants and P&D) reveals that feedback is not taken into consideration and that it is challenging to find an appropriate framework without modifying the structures and logic of the models involved. In this paper, the proposed framework was developed to address this issue and to provide a platform to couple prebuilt models. This framework is depicted by the tool MIMIC. MIMIC provides the ability to couple models in a variety of ways, ranging from direct data transmission between models to more complex interaction principles that require a thirdparty tool to add a new element and modify the outputs of the interacting models. MIMIC's primary benefit is that it gives users the tools and freedom to construct their own interactions. Given that all the coupled models and components of MIMIC are represented as pseudo-models, the implementation with Julia's high-speed metaprogramming enables rapid interaction outcomes.
The implementation of MIMIC in Julia provides a flexible and straightforward algorithmic environment for users with limited coding experience and a good trade-off with other modelingoriented programming languages (R, Python, MATLAB, etc. ). The case study presented in this paper illustrates the MIMIC framework, coupling a coffee tree fructification model and a CBB model. As a result, the proposed approach emphasizes the challenge on coupling plant growth and P&Ds interactions' models.
Acknowledgment The authors want to address special thanks to  T. Arsouze  ( Cirad-Amap ) for help and advice on software development and to  B. Dufour  for help with the case study and for providing the validation data. Funding: This work is part of the  EU (European Union)   Desira Robust  project, supported by  EU   FOOD/2021/ 427-759/ DESIRA. It is also supported by a  CIRAD Bios department  PhD grant and  French National Research Agency  under the  Investments for the Future Program , referred as  ANR-16-CONV-0004/ # DigitAg  programs. H. E. M. T. and M. J. have been supported by the  MaCS4Plants (Mathematics and Computer Sciences For Plants) CIRAD  network, initiated from the  AGAP Institute  and AMAP joint research units. Author contributions: H. E. M. T. : Conceptualization, implementation, and writing.
1. 1 Sequential coupling or loose coupling: Models are completely decoupled or models exchange data through inputs/ outputs (I/O). 2. Shared coupling: (a) GUI (graphical user interface): Models share a common GUI or (b) Data: Models share the full I/O database. 3. Embedded or integrated: One model is fully contained within the other (usually as a subroutine) or model codes are merged into a single coherent model. 4. Framework coupling: Use of a global modeling framework, where the models are coupled using a third-party tool commonly called "Coupler, " based on a combination of the previous methods.
The prediction of power fluxes and plasma-wall interactions impacted by MHD processes during ITER operation [disruption, Edge Localized Modes (ELMs), 3D magnetic fields applied for ELM control, etc. ] requires models that include an accurate description of the MHD processes themselves, as well as of the edge plasma and plasma-wall interaction processes. In this paper, we report progress on improving the edge plasma physics models in the nonlinear extended MHD code JOREK, which has capabilities to simulate the MHD response of the plasma to the applied external 3D fields, disruptions and ELMs. The extended MHD model includes E Â B drifts, diamagnetic drifts, and neoclassical flows. These drifts can have large influences, on e. g. , divertor asymmetries. Realistic divertor conditions are important for impurity sputtering, transport, and their effect on the plasma.
In this work, we implemented kinetic and fluid neutral physics modules, investigated the influence of poloidal flows under divertor conditions in the ITER PFPO-1 (1. 8T/5MA) H-mode plasma scenario, and compared the divertor plasma conditions and heat flux to the wall for both the fluid and kinetic neutral model (in JOREK) to the well-established 2D boundary plasma simulation code suite SOLPS-ITER. As an application of the newly developed model, we investigated time-dependent divertor solutions and the transition from attached to partially detached plasmas. We present the formation of a high-field-side high-densityregion and how it is driven by poloidal E Â B drifts.
I. INTRODUCTION Simulation of transient phenomena driven by MHD in tokamak plasmas and their control have so far mostly focused on the MHD processes while the associated plasma-edge and plasma-wall interaction aspects of these processes have been addressed with specialized edge plasma codes in open-loop. 2, 3  This consists in taking the magnetic geometry as given by the MHD codes as fixed in time and using the edge-plasma codes to model the plasma in these conditions. This has two drawbacks: one is that transient modeling of edge-plasma and plasma wall interactions driven by MHD processes cannot be accurately done and the second is that the varying behavior of the edge plasma and plasma-wall interactions does not feedback on the MHD. The work presented here aims to address these two drawbacks by improving the edge plasma physics description in the non-linear extended MHD code JOREK, which has demonstrated capabilities to model the MHD and core plasma behavior during Edge Localized Mode (ELM) suppression by external 3D fields,  4, 5  ELMs 6,7 and disruptions  8  to cite few important examples.
JOREK is a fully implicit 3D non-linear extended MHD code for realistic tokamak X-point plasmas. 1, 9, 10  The JOREK code has typically been developed and used for studying MHD instabilities such as ELMs and disruptions. 6, 11, 12  The present version of JOREK includes some divertor physics in terms of sheath boundary conditions (Machone velocity and sheath transmission factors) and a simple neutral fluid model (reflection of outgoing ions as neutrals, ionization, recombination, and radiation). This model has been extensively applied for simulations of ELMs  12  and ELM control by RMPs  5  and by pellet triggering. 13  The aim of this paper is to describe, benchmark, and apply an improved physics model for the neutrals in the JOREK code. Previously, a kinetic particle framework  14  has been added to the JOREK fluid MHD models.
A. Continuity equation For mass sources and losses due to ionization and recombination, we use effective rate coefficients from the OpenADAS database. 40  The reactions rates are then C rec n ¼ n e n i S rec ; C ion ði;eÞ ¼ n n n e S ion ; (12) where S ðrec;ionÞ ðm 3 =sÞ are the recombination and ionization rate coefficients (i. e. , hrvi) and n n=i=e ðm À3 Þ are the number densities of neutrals, ions and electrons. C ðm À3 =sÞ is the number density source. Note that the rate coefficient depends on the electron density and temperature S ¼ Sðn e ; T e Þ. The ionization and recombination reactions involve one neutral, one electron, and one ion and, thus, it holds that C ion i ¼ C ion e ¼ C ion n and C rec n ¼ C rec i ¼ C rec e. Let us write just the partial time derivative of the continuity equation and the source terms of interest.
D. Assumptions and overview To use the model in JOREK, we can reduce the equations and remove some terms by implementing assumptions made in JOREK. We reduce the ion and electron continuity equation by regarding only one plasma fluid. Let the center-of-mass density be q m i n i þ m e n e , and n e ¼ n i in the absence of impurities. We then assume the limit of m e ! 0. Then, q ¼ m i n i and m i =m n ¼ 1. This further results in the electron momentum equations being zero and all terms with electron mass disappearing. Furthermore, note that in Eq. (A6), there is only a loss term for T e , and nothing for the energy to go to. This is the energy lost due to radiation. In the ADAS database the Continuum and line power driven by recombination is combined with Bremsstrahlung of dominant ions (PRB).
B. Fluid and kinetic boundary interaction Plasma-wall interaction is essential for a high-recycling plasma, modeling detachment and sputtering of the wall material. A general sputtering module has already been implemented  14  based on the Eckstein sputtering coefficient. 44  Both the fluid and kinetic particles can reflect and sputter on the wall. For recycling (i. e. , plasma hitting the boundary and being re-introduced in the domain as a neutral), the TRIM-code database  45  is used to determine the probability of fast reflection or thermal desorption based on the electron temperature and wall material. For the plasma fluid, the boundary values are known and we can integrate the total reflected or sputtered flux on the boundary, N refl=sputter a ¼ ð ð YðE0ðTÞ; hÞC plasma dA: A fixed number of superparticles with combined weight N refl=sputter a is sampled where the location is weighted by the yield times plasma flux.
Momentum equation To write the momentum conservation to the JOREK form for any species, we can expand the derivative with the product rule and write the equation as m a n a @v a @t i;r ¼ @m a n a v a @t À m a v a @n a @t : (A2) The second term in the right-hand side of Eq. (A2) makes sure that the equation is still in a conservative form; and that the code will still have good conservation properties. Writing Eq. (  14 ) in the form of Eq. (A2) gives us the following result: m i n i @v i @t i;r ¼ ÀC rec n m i v i À m i v i ðÀC rec n Þ þC ion i m i v n À m i v i ðC ion i Þ ¼ C ion i m i ðv n À v i Þ; m e n e @v e @t i;r ¼ ÀC rec n m e v e À m e v e ðÀC rec n Þ þC ion i m e v n À m e v e ðC ion i Þ ¼ C ion i m e ðv n À v e Þ; m n n n @v n @t i;r ¼ C rec n ðm i v i þ m e v e Þ À m n v n ðC rec n Þ ÀC ion i m n v n À m n v n ðÀC ion n Þ ¼ C rec n ðm i v i þ m e v e À m n v n Þ: (A3) Note that the recombination terms for the ions and electrons disappear, and the ionization term disappears for the neutrals.
ACKNOWLEDGMENTS This work was part of the research programme (No. 19KE01 ) of the  Foundation for Nederlandse Wetenschappelijk Onderzoek Instituten (NWO -I), which was part of the NWO and has been carried out within the framework of the  EUROfusion Consortium , funded by the  European Union  via the  Euratom Research and Training Programme  (Grant Agreement No. 101052200 -EUROfusion ) and by the  ITER Organization  under Implementing Agreement No. 43-2174  with the  Eindhoven University of Technology (TU/e)  and with  TU/e  work order No. 107640. ITER is the  Nuclear Facility , INB No. 174. Computations were performed using computer clusters Marconi-Fusion and the  ITER Scientific Data and Computing Center (SDCC). The views and opinions expressed herein do not necessarily reflect those of the European Commission nor the  ITER Organization  nor NWO-I.
Single integrated units are typically preferred and easier to accommodate. Meanwhile, the construction of Argonaut has been approved. With a total mass of delivered cargo of 1500 kg, Argonaut will be compatible with different mission types: cargo, science rover, sample return stage, scientific/technology demonstration packages, in-situ resources, production equipment, power generation equipment, etc. Future missions in lunar orbit or at the Moon surface will be defined based on the scientific priorities identified for the program and will be selected from the ongoing and future AOs. The following investigations have been proposed in the Fundamental Physics and Astrophysics White Papers: 1   • Einstein's Equivalence Principle tests through lunar laser ranging experiments to the Moon. • Tests of de-coherence models in quantum mechanics with entangled photons distributed on the Earth-to-Moon baseline.
Delivery of the first MoonLIGHT-MPAc-ADC proto flight model (PFM1) is currently foreseen by the end of 2023. In parallel and following the PFM1 delivery, a second PFM will be built by ESA and INFN for new opportunities and studies for new applications, like GW detection on the Moon (like this proposal), a Geophysical package for ESA's Argonaut, Lunar Geophysical Network (LGN) activities. The current CP11 reflector system is required to have an accuracy of equal or less than 3 degrees, although its design can be customized to be capable of 1 degree. Potential sources of uncertainty and mitigation strategies for the reflector. The heritage that is being acquired through the 2024 CP11 mission (delivery of the PFM1 to IM-3 foreseen in August 2023) represents a significant risk reduction in reflector performance.
Acknowledgements We thank the  International Space Science Institute (ISSI) in Bern  for hosting this group of authors on October 5 -6, 2022, during the Forum on Lunar Gravitational-wave Detection. We want to thank  Michela Mapelli  for her contribution to the ISSI Forum on black-hole binary populations. University Paris Cité co-authors (S. K, P. L, S. de R. , T. K. , J. M. ) acknowledge the financial support of  LabEx UnivEarthS  ( ANR-10-LABX-0023  and  ANR-18-IDEX-0001 ). SDA and MM wish to thank  ESA  for the support to MPAc under the  ESA-INFN  contract n. 4000133721/21/NL/CR  and wish to thank  ASI  for the support under the.
Background The rapid development of omics acquisition techniques has induced the production of a large volume of heterogeneous and multi-level omics datasets measured on the same individuals. Information of biological interest is obtained using predictive and exploratory statistical methods performed on one omic level or, more widely from the so-called integration methods. The latter methods, which have been increasingly developed in the past few years, aim at providing an understandable representation of the combined information provided by several omic levels or at extracting information about interlevel crosstalk. Some of these methods are already available under the form of packaged software or libraries, and especially as R packages (like STATegRa https:// doi. org/ doi: † Élise Maigné and Céline Noirot have been contributed equally to this work.
10. 18129/ B9. bioc. STATe gRa, mixOmics  [1] , MOFA  [2, 3] , mixKernel  [4, 5] , among others; see https:// github. com/ mikel ove/ aweso me-multi-omics for a more complete list or  [6] [7] [8] ). However, the use of these packages still requires to learn a programming language and to have access to sufficient statistical knowledge to choose method parameters and interpret outputs. Hence, several web based applications have been deployed to perform (sometimes advanced) analyses on omics data, like SHAMAN https:// shaman. paste ur. fr/  [9] , dedicated to differential analysis of metagenomic data, or iDEP http:// gelab. org/ idep/  [10] , GENAVi https:// junkd nalab. shiny apps. io/ GENAVi/  [11] , IDEAmex http:// www. uusmb. unam. mx/ ideam ex/  [12] , DIANE https:// diane. bpmp. inrae. fr  [13] , all providing complete analysis pipelines for RNAseq or other count data, mostly oriented toward differential analysis.
Code organization ASTERICS frontend is developed in Vue. js https:// vuejs. org/ and uses the CSS framework Bulma https:// bulma. io/. This allows a flexible and modular organization of the different screens and analyses, as well as an easy management of persistant sessions and multiple user sessions. The backend is controlled with flask https:// flask. palle tspro jects. com/, pyRserve https:// pypi. org/ proje ct/ pyRse rve/ and Rserve https:// www. rforge. net/ Rserve/ to launch analyses performed with R scripts. R package versions are controlled using renv https:// CRAN. R-proje ct. org/ packa ge= renv to ensure stability and reproducibility of the analyses. An R wrapper runs the function corresponding to the analysis chosen by the user, converts the interactive output plots and the output tables in JSON to serve the interface and maintain a database of datasets and analyses.
Availability and deployment ASTERICS is distributed under GNU General Public License Version 3 and its source code are available at https:// forge mia. inra. fr/ aster ics/ aster ics, including recipes for building self-contained docker images. In addition, an open issue repository is available for users wanting to report bugs or suggest improvements https:// forge mia. inra. fr/ aster ics/ aster ics-issues. ASTERICS is available online at https:// aster ics. miat. inrae. fr and does not require any user account creation. It can also be deployed for local usage or on servers using the 3 docker images provided with the project repository https:// forge mia. inra. fr/ aster ics/ aster ics/ conta iner_ regis try 1 , following the installation instructions provided in the README file of the project repository.
Workspaces, including data, user email and performed analyses, are deleted after 30 days of inactivity. Upon connection, users can also automatically load an example dataset extracted from The Cancer Genome Atlas (TCGA, https:// www. cancer. gov/ tcga) and related to breast cancer as already studied in  [24]. In addition, other data derived from this initial example are available at https:// doi. org/ 10. 15454/ YNMQUY, including information on cancer type and datasets with missing values obtained from the complete dataset with different missing value mechanisms. The complete description of the dataset extractions is also provided in this repository. Analyses available in ASTERICS are organized into three main menus: 1. Edit contains three submenus to (a) perform simple dataset manipulations (e.
g. , selecting rows or columns based on manual selection or on a condition, changing a variable type, etc. ); (b) explore, clean or impute missing values. Missing values can be either imputed (single PCA imputation as implemented in missMDA or k-NN imputation as implemented in VIM) or rows/columns containing more missing values than a user chosen threshold can be removed from the dataset; (c) perform different types of normalization (from a basic scaling to unit variance to more sophisticated normalizations like the TMM and TMMwsp normalizations of edgeR for RNA-seq data or the correction of an explicit batch effect as implemented in the "ComBat" method of sva  [25] ); 2. Explore contains five submenus to perform simple exploratory analyses of the datasets, including: (a) basic descriptive statistics and tests (numerical and graphical summaries for one, two or all the variables, complex plots including up to five variables or correlation plot between all variables); (b) PCA (with plots for individuals that can be interactively combined with additional metadata variables and plots for variables that can be filtered based on a given correlation threshold) and its extension to mixed data  [26] ; (c) heatmap or dotplot (for which rows and/or columns can be organized by a hierarchical clustering and that can also be combined with metadata describing individuals); (d) clustering (based on a fast version of hierarchical clustering  [16]  or on k-means).
Outputs of this analysis include a heatmap and a PCA individual plot based on the initial data where the clusters are displayed with colors; the PCA plot can also be interactively combined with external information to compare the clustering with known groups of individuals; (e) self-organizing maps  [27] , a method combining clustering and dimension reduction, that offers various graphics usable to obtain a summarized representation of the typology of the individuals in a dataset; 3. Integrate contains all analyses that are based on data coming from at least two different datasets, in a generic sense (these datasets can be omics datasets or other types of data, including metadata). This includes both analyses designed to explore relationships between at least two datasets containing, each, multiple variables (PLS and MFA) or analyses designed to compare all numeric variables in a given dataset with respect to the levels of a categorical variable from another dataset (PLS-DA and differential analysis).
Integrate contains 4 submenus to perform data integration with (a) PLS  [28] , a method unraveling relationships between two numeric datasets acquired on the same matched individuals. Similarly to PCA, it creates composite variables (e. g. , linear combinations of the original variables) in each dataset, so as to maximize the covariance between these composite variables; (b) PLS-DA  [29] , a method, based on PLS, integrating a numeric dataset with a categorical variable. The categorical variable is used as an information on classes (or conditions) on individuals and the method creates composite variables from the numeric dataset so that they separate at best the individuals according to their classes. As for differential analysis, this helps highlight the variables the most involved in the difference between conditions; (c) MFA  [30] , a generalization of the PCA for cases where individuals are described by several groups of variables, including categorical ones.
The method weights each group of variables in a data-driven fashion so as to balance the contribution of each group in the global PCA; (d) differential analysis of all the numeric variables in a given dataset with respect to the level of an external categorical variable. In this analysis, statistical tests are performed on all the variables of a numeric dataset to compare their means with respect to the levels of a categorical variable (thus describing conditions on individuals). The differential analysis is automatically adjusted to the type of data provided by the user: for generic or continuous data, a normality test can be performed to choose between 1-way ANOVA or Kruskal-Wallis test. For count data, the test defaults to negative binomial GLM tests as implemented in edgeR  [20]. Also, posthoc tests, comparing all pairs of the factor levels can be performed when the global test is found positive.