dataesr/scientific-paragraphs-categorization huggingface dataset:
indicated above, six key parameters regulating transpiration and photosynthesis in the basic sub-model (eqs. (C. 2) and (C. 3)) could not be estimated from plant trait databases. With the aim make transpiration, gross photosynthesis and growth predictions obtained by the basic sub-model as similar as possible to those produced by the advanced sub-model, we conducted a meta-modelling exercise in which the results of simulations with the advanced sub-model were used to provide estimates for the six parameters of the basic sub-model. The meta-modelling exercise was conducted separately for each of twelve prioritized species on the basis of their importance in the study area. These were Pinus halepensis Mill. (Aleppo pine), Pinus nigra spp. salzmannii J. F. Arnold (black pine), Pinus sylvestris L.
(ver. 2. 8. 1) requires taxon-specific estimates for 117 different parameters (including qualitative variables and allometric coefficients) to be run. The different procedures employed for parameter estimation are summarized in Table2. a and are detailed in the following subsections. TableB1includes the strategy used to estimate taxon values for each parameter. The final taxon parameter estimates are given in TableB3. a) Estimation procedures Num. Params. % Forest Inventory Data (section 4. 1) 12 10. 3% Plant trait databases (section 4. 2) 49 41. 9% Allometry databases (section 4. 2) 19 16. 2% Meta-modelling exercise (section 4. 3) 6 5. 1% Calibration exercise (section 4. 4) 3 2. 6% None (always requiring imputation) 28 23. 9% b) Imputation procedures (section 4. 5) Num. Params. % Quantitative trait relationship 17 14.
The third set of goals was to characterize indigenous goats of African smallholders. This set of goals included identifying goat populations to characterize, collecting samples, extracting DNA, generating genotype and sequence data, and conducting analyses to identify signatures of selection. We eventually concluded that the GoatSNP50 performed well when genotyping African goats, and it became clear that it was unnecessary to designing a new SNP-chip. Instead, the AGIN partnered with the IGGC to contribute design efforts to upcoming versions of the chip. The ultimate objective of this goal was to identify population-based signatures of selection with genotypic data along with data from targeted resequencing of these adapted populations. These signatures could then be traced with data from low-density SNP panels across generations to ensure that those areas previously impacted by selection would be maintained while introgressing loci elsewhere in the genome.
Working groups coordinated by leading scientists have been identified and several have completed their efforts  (Bertolini et al. , 2018; Colli et al. , 2018; Stella et al. , 2018). An agreement was initiated to formalize a partnership with the AdaptMap Consortium and is intended to facilitate goat data sharing globally and encourage coordination and collaboration in characterizing the extensive variety of represented goat populations. The ultimate objective of AdaptMap is to enhance genetic improvement by understanding the adaptation of goats to diverse constraints. The efforts will result in a wide sampling of existing genetic diversity representing Africa as well as related non-African populations. This increased sampling will potentially increase power to detect signatures of selection, in addition to extending the training on phenotyping goats using the African Goat Improvement Network image collection protocol (AGIN-ICP).
VarGoats project The VarGoats project has as a long-term goal to sequence over 1,000 goat genomes. The scientific objective is to identify variants in goat genomes associated with domestication and adaptation. Currently, the project has described a dataset of 1,159 goats, including over 250 individuals collected by AGIN  (Denoyelle et al. , 2021). The VarGoats website is located at www. goatgenome. org/vargoats. html. The VarGoats project was made possible by a call for large scale DNA sequencing projects by France Genomique. The data has been made available to VarGoats participants and data analysis is being performed in working groups (~60 international scientific participants), most of them already created in the AdaptMap program.
Consequently, FAO supports the work of groups like AGIN to facilitate phenotypic and genomic characterization activities. The FAO also supports data collection and sharing through the Domestic Animal Diversity Information System (DAD-IS), a global database of AnGR to provide a data repository and a resource for sharing that data online. The FAO objective for this work is to achieve sustainable management of land, water, and genetic resources and improved responses to global environmental challenges affecting food and agriculture. With the assistance from donors, FAO has supported direct funding of AnGR characterization such as AGIN as part of its effort to achieve this outcome. This collaboration is a 3-way partnership, with FAO conducting field sample collection and compiling phenotypic data and pedigrees, USDA-ARS is providing equipment and guidance for sampling as well as DNA extraction and genotyping, and the AdaptMap consortium is providing data analysis and interpretation.
Acknowledgments USDA-ARS  provided tremendous support for the  African Goat Improvement Network  through over ten years of base project funding and over a dozen formal agreements, support of students, post-docs, and visiting scientists, as well as providing logistical support for the AGIN meetings. The  US Agency for International Development (USAID)  provided funding established by the  Norman Borlaug Commemorative Research Initiative , a collaborative research effort between  USDA-ARS  and  USAID. The  goat improvement project  and  AGIN  was funded by the  Feed the Future program  in  USAID  and championed by several  USAID  leaders, including  Max Rothschild ,  Lindsay Parish ,  Elaine Grings , and  Saharah Moon Chapotin. The  USDA-ARS Office of International Research Engagement and Cooperation  ( OIRECformerly Office of International Research Programs (OIRP) ) is the main contact for ARS international engagement and provides leadership and coordination for the agency's international relationships.
The Heroku containers, called Dynos, are virtualized Linux containers to run programs in isolation. -Region: Applications on Heroku may be deployed in various geographic regions. -Buildpack: Buildpacks convert the deployed code to an executable slug on a Dyno. -Add-ons: Add-ons are additional services that can be attached to a Heroku application to provide extra functionalities such as data storage, monitoring, analytics, and data processing. Figure  2  presents the Heroku cloud with services and constraints between these services. In addition to the mandatory components, optional functionalities, such as Heroku add-ons, are available. To help the developer, they are maintained by either a third-party provider or by Heroku. Add-ons are installed onto applications by using the Heroku service API interface.
Furthermore, other constraints must be taken into account. For example, in Heroku, they express regional availability of services, interservice dependencies, as well as architectural constraints. As a result, developers are expected to be Heroku experts in order to manage and control applications in a safe way while taking into account all the constraints on the context, in which the application is hosted. Research Question -The main challenge is to allow the reconfiguration of software systems in a safe manner while avoiding additional overhead at run-time, the paper aims to address the following research question (RQ): RQ How to enforce domain constraints during dynamic reconfiguration at low cost? 3 Background.
Feature Models Introduced for product lines, feature models are used for representing the commonality and variability of features and of relationships among them  [6]. A feature f could be a software artefact such as a part of code, a component, or a requirement. In Fig. 2  for the he Heroku cloud, features, graphically represented by rectangles, are organized in a tree-like hierarchy with multiple levels of increasing detail. To express the variability of the system, feature models provide 1) a decomposition in sub-features, where a subfeature may be mandatory (black circle), or optional (unfilled circle), 2) XOR-group or an OR-group. In a XOR-group, exactly one feature is selected, while in an OR-group, one or more features are selected, whenever the parent feature is selected. In addition, the combination of the optional and mandatory features is seen as an AND-group.
JavaBIP Component-based Approach A component is a software object, that encapsulates certain behaviours of a software element. The concept of component is broad and may be used for component-based software systems, microservices, service-oriented applications, and so on. For the coordination of concurrent components, we make use of JavaBIP  [7] , which is an opensource Java implementation of the BIP (Behaviour-Interaction-Priority) framework  [4]. Given a set of components and a set of their ports, the component behaviour is defined by a finite state machine (FSM) with transitions labelled by ports. JavaBIP allows two types of ports: enforceable and spontaneous. Enforceable ports represent actions controlled by the JavaBIP engine. They can be synchronised, i. e. executed together atomically.
FeCo4Reco Implementation and Experiments On the implementation Our model transformation process has been implemented using the ATLAS Transformation Language (ATL)  [18]. ATL is a domain-specific language for specifying model-to-model transformations. Starting from a source model that conforms to a source meta-model, it allows the developer to produce a target model that conforms to a target meta-model  [26]. In our approach, the generated model specification conforms to the JavaBIP meta-model  [24]. The generated XML file is parsed using the DOM library in Java to generate the JavaBIP specification and the glue coordination. The reader can find our implementation on the Zenodo platform  [1]. Figure  6  shows the FeCo4Reco architecture that enables stages 3 and 4 (Sect. 1). The run-time CBVM consists of BIP Specs and the coordination glue.
However, dealing with liveness temporal properties remains a future work direction. The novelty of our approach lies in the generation of a correct-by-construction runtime CBVM that enforces domain constraints for dynamic reconfiguration while leveraging SPL tools to capture domain variability. Unlike many works using SPL techniques and tools, our approach goes beyond employing static variability models at run-time, e. g. [14] , and avoids the overhead of computing or validating new target configurations explicitly. Furthermore, our approach differs from other by-construction reconfiguration techniques, such as  [29] , which require computing the global LTS to derive the reconfiguration paths. Instead, we encode the feature model constraints into a JavaBIP run-time CBVM. In turn, the JavaBIP engine relies on Binary Decision Diagrams (BDDs)  [3]  to efficiently encode the various operational and coordination constraints of the system and to compute the possible interactions from the current CBVM state  [7, 17].
In particular, permanent constraints, which encode information about the behavior, glue, and data wires of the components, are encoded only once at the initialization of the Jav-aBIP system. The only constraints that are recomputed at each cycle are the temporary ones that encode the current states of the components. This allows us to strike the right balance between, on one hand, precomputing the global reconfiguration LTS-which involves a very long computation at the initialisation phase and potentially requires a huge amount of memory to store the result-and, on the other hand, verifying all the constraints at run-time-which eliminates the initialisation phase and the memory overhead at the cost of a run time overhead orders of magnitude larger than that observed with our approach.
As a future work direction, we will explore stronger heuristics to enforce Assumption 1. Furthermore, we intend to generalise our approach to constraints among features formulated in terms of arbitrary Boolean formulas. As shown in  [8] , that will require extending JavaBIP with priority models. To streamline the user experience and minimise the necessity to wait for user input, we also plan to extend the feature model with default values. Additionally, we aim to incorporate temporal constraints over features, e. g. excluding the possibility of downgrading the database plan  [29]  and plan to investigate the analytical treatment of the space complexity of the BDDs used in our approach.
⋆  S. Bliudze  was partially supported by  ANR Investissments d'avenir  (grant number  ANR-16-IDEX-0004 ULNE ) ⋆⋆ O. Kouchnarenko was supported by the  EIPHI Graduate School  (grant number  ANR-17-EURE-0002 ). This work was partially carried out while she was on a research leave at  Inria Lille.
A Python package for the analysis of dark-field X-ray microscopy (DFXM) and rocking curve imaging (RCI) data is presented. DFXM is a non-destructive diffraction imaging technique that provides three-dimensional maps of lattice strain and orientation. The darfix package enables fast processing and visualization of these data, providing the user with the essential tools to extract information from the acquired images in a fast and intuitive manner. These data processing and visualization tools can be either imported as library components or accessed through a graphical user interface as an Orange add-on. In the latter case, the different analysis modules can be easily chained to define computational workflows. Operations on larger-than-memory image sets are supported through the implementation of online versions of the data processing algorithms, effectively trading performance for feasibility when the computing resources are limited.
, 2018) , the austenitic transformation in shape memory alloys  (Bucsek et al. , 2019) , recovery in metals  (Mavrikakis et al. , 2019; Ahl et al. , 2020) , embedded particles in steel  (Hlushko et al. , 2020) , visualization of dislocation structures  (Jakobsen et al. , 2019; Dresselhaus-Marais et al. , 2021a)  and the structure of biominerals  (Cook et al. , 2018; Schoeppler et al. , 2022). DFXM is conceptually similar to dark-field electron microscopy in transmission electron microscopy, which is used to selectively image strain and orientation across materials science, physics, geoscience and numerous other fields  (Williams & Carter, 1996; Nellist & Pennycook, 2000; Morones et al. , 2005). While the darfix Python package has been specifically designed as a tool to facilitate the data treatment of DFXM data in particular, it can be used in a larger scope for imaging and diffraction data obtained from other techniques.
Scan types The 2D images recorded as a function of the different rotation angles form the main data sets of DFXM. darfix facilitates the treatment of the individual raw images and the systematic analysis of scans. The following recurring scan types are commonly used in DFXM: 2. 1. 1. Rocking curve imaging. Rocking curve imaging is an extension of classical diffraction topography. The diffraction topograph is measured as a function of the 'rocking' angle, (see Fig. 1 ). Rocking curve imaging is typically performed without the magnifying objective lens, using the near-field camera. In this case the resolution in the 2 and transverse () directions is relatively low  (Tran Thi et al. , 2017). It can, however, also be carried out in DFXM mode. 2. 1. 2. Mosaicity scans. Mosaicity scans can be seen as a generalization of rocking curve imaging, taking advantage of the improved resolution in the transverse () direction due to the limited angular acceptance of the objective lens  (Poulsen et al.
The darfix codebase darfix is a Python library that provides a set of computer vision techniques for the analysis of dark-field X-ray microscopy data. It is split into two modules: (1) a back-end that contains the core processing utilities, algorithm implementations and abstractions, and (2) a graphical user interface (GUI). The typical user would interact with the core module through the GUI, but it is possible and straightforward to access the core methods directly by importing that part of the library. The central code object is Dataset, which encapsulates the properties of a stack of images (multiple rotation angles can vary within a single stack of images, each axis adds a dimension to the image stack). Operations on darfix (e. g. blind source separation, hot pixel removal, shift correction) are implemented as isolated tasks acting on a dataset and returning a dataset.
This allows computational workflows to be defined by chaining several operations together. Furthermore, as datasets are commonly of the order of several hundred gigabytes, the software provides online versions for many of the data processing algorithms: the output is built incrementally as more data are brought in. The modules can then be processed sequentially in chunks if memory usage is limited. This is particularly relevant for dataset sizes larger than the available memory. If the option 'use data from disk' is selected when loading the data, these online versions will be automatically used throughout the workflow. darfix is not offered as a stand-alone application, but includes an Orange (Dems ˇar & Zupan, 2013) add-on for the definition of workflows. Orange is a platform for performing data analysis and visualization, which allows for the creation of workflows using the darfix GUI.
A typical workflow comprises tasks such as raw data selection, background removal, region-of-interest selection, scan variable identification, scan processing (e. g. rocking curve fitting), display of the results, etc. (see Fig. 3 ). All the tasks in darfix are independent (in the GUI as well as in the back-end part), so that for every task there is a different dedicated widget. These widgets are based on Qt  (The Qt Company, 2021)  and silx  (Vincent et al. , 2021). They can be linked through the workflow created in Orange. In addition, workflows in Orange can be called from the command line with different input datasets. The algorithms that were used in the original workflow will be then reproduced without the need of the GUI. Moreover, a workflow defined from the GUI can be saved with its settings and reused using ewoks  (De Nolf et al.
, 2022). This allows users to launch the same workflow (algorithms and settings) but with a different input data set. This is particularly useful when the same workflow has to be carried out for a series of datasets, e. g. the same analysis has to be applied to every layer in a 3D volume map. darfix contains a series of image processing methods for the analysis of DFXM scans. These methods can be organized into three groups: the selection of the data, the pre-processing algorithms, and the analysis and visualization of the results. To provide a complete overview of the current modules and their functionalities, a workflow exported from the GUI is represented in Fig. 2. The following sections provide an overview of the used techniques. A simplified version of a darfix workflow, focused on data pre-processing, is presented in Fig.
File inputs: data selection Currently, darfix accepts the ESRF data format (. edf) of the ID06 detectors, and is able to automatically characterize the relevant instrument angle settings by analyzing the input files' metadata. HDF5  (The HDF Group, 1997 -2022; Collette, 2013)  is accepted, but only without dimension definition as the analysis of metadata is still under development. Output data can be saved from the GUI in different formats: EDF, HDF5 [NeXus data format  (Ko ¨nnecke et al. , 2015) ], CSV, TIFF, NumPy, ASCII and PNG. In the analysis there is usually the possibility to export specific data in a HDF5 file, which can then be used for any required postprocessing. Alternatively, when working without the GUI, NumPy arrays can be extracted from the Dataset object for further processing.
Modified data are automatically saved onto disk under a folder chosen by the user. By default, only the output dataset of the most recent task of the workflow is saved, but there exists the possibility to copy the output of every task by appending a Data copy widget to it. Exporting data in a format compatible with 3D visualization and processing software, e. g. Paraview  (Ahrens et al. , 2005; Ayachit, 2015) , is planned for a future release. 3. 2. Pre-processing 3. 2. 1. Noise removal. Noise in the data is practically unavoidable, and it can either come from the lens, the environment or the diffraction of the sample. The first step of the pre-analysis, after defining the dimensions of a dataset (or second if we apply a region of interest to the data) is to detect and remove this noise from the sequence of images.
darfix provides the following tools for noise removal. Background subtraction. This is a widely used approach that calculates the foreground mask of an image by subtracting it from a background model containing the static part of an image sequence. In darfix, the background is an image that can either be calculated using the pixel-wise mean or the median of a set of data images (for example, dark images from the scan). This background image is then subtracted from each of the original images to obtain the foreground mask. While both mean and median subtractions can be used depending on the input dataset, the pixel-wise mean is less robust to outliers. On the other hand, the pixel-wise median is more computationally expensive when data are not in memory. In that last case, the user has two options: (i) Select chunks of a certain shape so that the median is computed consecutively in all of them.
Although this method Figure  2  darfix GUI where a full workflow is shown. The three main modules -selection of data, pre-processing, and analysis and visualization -are indicated by brackets. A description of each widget's functionalities is given in the lablels. obtains a median of all the images, it is still time consuming as the input/output operations slow the process considerably. (ii) Only use a subgroup of the stack of images to calculate the median: the user inputs a step k value so that the algorithm selects the images with index i, i + k, i + 2k,. Hot pixel removal. Sometimes, after performing background subtraction on the images, isolated groups of pixels appear at some or all of the images in the stack. These pixels are called 'hot' because they have higher intensity than the other pixels around them, and they are usually not part of the object we want to analyze but noise that needs to be treated.
As the hot pixels are independent from one image to the other, this technique can be applied sequentially to the stack: (i) Apply a median filter to every image of the stack and subtract it from the original image. (ii) The hot pixels are identified as the ones with higher value than the standard deviation of this subtraction. (iii) The hot pixels intensity values are replaced by the values of their corresponding pixels in the image with the median filter applied. Threshold removal. Pixels with values lower than specified will be set to 0. The use of any of these noise removal tools is optional. 3. 2. 2. Image registration. Alignment errors in the experimental hardware frequently cause a shift of the image as a function of rotation angle. darfix contains a module for the detection and correction of such shifts.