dataesr/scientific-paragraphs-categorization huggingface dataset:
Stacking method We used the eFEDS image and exposure maps at 0. 2-0. 6 keV and 0. 6-2. 3 keV provided in EDR. We computed the count rate of the X-ray photons in the eFEDS field and stacked the countrate map at the position of the filaments. We did not use the provided eFEDS background maps in our analysis because our analysis pipeline includes a background-subtraction step. We checked that subtracting the background maps from the image maps did not change our results. Then, we followed the stacking method in T20x, which is illustrated in Fig. 2. (i) In the left panel of Fig. 2 , the black dots represent the extremities of the segments, defining the filament by DisPerSE. The black straight line connecting these extremities constitutes the filament spine. We used data within 20 Mpc from the filament spine.
We repeated this null test 1000 times and computed the average and standard deviation of the 1000 null-test set of profiles. The result shows that the average of the null profiles is consistent with zero with no discernible structure, as shown in Fig. 3. This result suggests that our estimator is not biased by the foreground and background X-ray emissions. In addition, the standard deviation of the null profiles is consistent with the uncertainty from the bootstrap estimate (gray area in Fig. 3 ), and the ensemble of the null profiles can be used to estimate the uncertainty and significance of our measured signal. We hence estimated the S/N of our measured X-ray profile as S /N = χ 2 data -χ 2 null , (2) where χ 2 data = i, j x data (R i ) T (C -1 i j ) x data (R j ) (3) χ 2 null = i, j x null (R i ) T (C -1 i j ) x null (R j ), (4) where x data (R i ) is the X-ray count-rate at the R i bin from the data X-ray profile, x null (R i ) is the value at the R i bin from the null tests, and C i j is the covariance matrix of the data X-ray profile, estimated from bootstrap resampling.
Spectral energy distribution To perform our X-ray spectral analysis, we produced new image maps with narrower energy bands using the eROSITA Science Analysis Software System (eSASS) developed by the eROSITA collaboration. The eSASS includes a collection of tasks, scripts, and libraries to create eROSITA calibrated science data products and to perform various interactive data analysis tasks  (Brunner et al. 2022; Predehl et al. 2021 ). As processed in  Brunner et al. (2022) , using the evtool command in the eSASS software, we merged the four event files into one, applied filters of FLAG = 0xc00fff30 (selecting good events from the nominal field of view, excluding bad pixels), PATTERN ≤ 15 (including single, double, triple, and quadruple events), and FLAREGTI (background flare is filtered out), extracted the selected events in specific energy ranges, and created image maps with a resolution of 4 arcsec per pixel.
A161, page 4 of 10 We did not correct the data for the temporary malfunctioning of the camera electronics. Instead, we masked the associated area that included these data. We created image maps in the energy range of 0. 4-0. 5, 0. 5-0. 6, 0. 6-0. 8, 0. 8-1. 0, 1. 0-1. 3, and 1. 3-2. 3 keV. These energy bins were determined to increase the number of energy bands and to avoid that the total number of X-ray photons was low at each energy band. We excluded the X-ray events below 0. 4 keV and above 2. 3 keV because the S/N at these energy bands was consistent with zero, and adding these data did not change our results. From these new image maps, we recomputed the count-rate maps. Before we proceeded with the stacking analysis for the new count-rate maps, we additionally masked the overlap area of filaments at different redshifts in 0.
Model fitting As with T20x, we modeled the X-ray SED measurement using the Python X-ray spectral analysis package (PyXspec) interface with the X-ray spectral analysis package (XSPEC;  Arnaud 1996). The ancillary response file (ARF) and response matrix file (RMF) were extracted using the srctool command in the eSASS software. We considered the X-ray emission based on the Astrophysical Plasma Emission Code (APEC) model  (Smith et al. 2001)  with two free parameters: the normalization of surface brightness and temperature. We assumed the gas metallicity to be ∼0. 1 of the solar value  (Anders & Grevesse 1989)  based on the filament analysis with the IllustrisTNG simulations (priv. comm. with Galárraga-Espinosa et al. 2021). For the photoelectric absorption, we used the phabs model in XSPEC, and the neutral atomic hydrogen (HI) column density in the eFEDS region was estimated to be ∼3 × 10 20 cm -2 with the HI4PI map (HI4PI Collaboration 2016).
5 Mpc from the filament core with a temperature of (0. 8-2. 0) × 10 6 K (∼0. 05-2 keV), depending on the filament length, and converges to ∼0. 9 × 10 6 K (∼0. 01 keV) at 20 Mpc from the filament core. Thus, the gas temperature gradient is predicted in hydrodynamic simulations, but our estimated gas temperature is higher than the predictions from hydrodynamic simulations. The reasons for this difference is still unclear because the uncertainties of our temperature measurements are relatively large, but future SRG/eROSITA data will provide an all-sky X-ray measurement that will be helpful in understanding the physical properties of gas in filaments in more detail.
Thus, possible explanations for the thermal emission are either low-mass halos with masses below ∼3 × 10 13 M or diffuse gas in the filaments. To distinguish between these contributions, the gas distribution around the low-mass halos needs to be understood; however, it is not well constrained so far. For example, the gas fraction in low-mass halos does not agree among different hydrodynamic simulations (e. g. ,  Chisari et al. 2018; Schneider et al. 2019). The X-ray signal may also be caused by unresolved clumps in the vicinity of the accretion regions of hot halos  (Reiprich et al. 2021 ) and/or unresolved accreting sources in low-mass star-forming galaxies. To interpret the nature of the thermal emission measure in stacked filaments, SRG/eROSITA will provide an all-sky X-ray measurement, and the data will help us to understand the gas distribution around low-mass halos.
Acknowledgements. The authors thank an anonymous referee for the useful comments and suggestions. This research has been supported by the funding for the  Baryon Picture of the Cosmos (ByoPiC)  project from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  grant agreement  ERC-2015-AdG 695561. The authors acknowledge fruitful discussions with the members of the  ByoPiC  project (https: //byopic. eu/team). This work was supported by  World Premier International Research Center Initiative (WPI) ,  MEXT, Japan. Kavli IPMU  was established by  World Premier International Research Center Initiative (WPI) ,  MEXT, Japan. Kavli IPMU  is supported by  World Premier International Research Center Initiative (WPI) ,  MEXT, Japan. This work is based on data from eROSITA, the soft X-ray instrument aboard SRG, a joint  Russian-German  science mission supported by the  Russian Space Agency (Roskosmos) , in the interests of the  Russian Academy of Sciences  represented by its  Space Research Institute (IKI) , and the  Deutsches Zentrum für Luft-und Raumfahrt (DLR).
g. Sequence Type, serovar) in the foods considered by the risk assessment. Another criterion to constitute the dataset is to consider the distribution of phenotypes for the trait of interest. The set of strains may be chosen in order to have a balanced distribution of phenotypes. This could improve the statistical power of the applied method to identify the markers. This criterion based on the balance of phenotypes may be in contradiction with the criterion of the representativeness of strains isolated from a food sector. Several sets of strains can thus be considered for different objectives: marker determination, validation (for these two sets, both genotypes and phenotypes will be available), and prediction based on the Genome Wide Association Study (GWAS) results. For this last set of strains, it is not necessary to have the phenotypes, only the genomes of strains representative of the food sector are necessary.
An important challenge using GWAS is to take into account multiple statistical testing. To avoid high falsepositive rates, a test correction must be performed considering the large number of tests being performed and to distinguish the significant results from those that will be observed by chance. Additional factors negatively affecting the performance of bacterial GWAS  [20••]  are linkage disequilibrium (i. e. the nonrandom association of genetic alleles) and bacterial-population structure (i. e. the presence of subpopulations that present large differences in the prevalence of both the allelic and phenotype frequencies). Feeding GWAS with a recombination-aware phylogenetic tree of the observed dataset helps to account for the population structure. Nowadays, a multiplicity of methods and complete pipelines, for example, Scoary or pyseer, to conduct microbial GWAS, are freely accessible.
Machine-learning methods By definition, machine-learning methods rely on computer systems that are able to learn and adapt, by using algorithms and statistical methods to analyze and draw inferences from patterns in data. Numerous algorithms have been trained to recognize patterns in bacterial genomic data  [22]. The general process of the identification of genomic markers is presented in Figure  1b. The dataset is split in a training and a testing dataset. The training is usually carried out with different algorithms that are then sorted based on their performance on the testing dataset  [23]. After sufficient repetitions and selection of the algorithm, the trained machine can take as input the genome features of other strains to predict their phenotype. The genomic features used in machine-learning methods are the same as applied in GWAS to predict bacterial phenotypes.
The first software used in bacterial GWAS or for phylogenetic methods concentrates on qualitative phenotypes. Dividing the continuous phenotypes of strains into well-defined categories is often tricky, even in a priori discriminatory conditions close to growth limits, due to minor differences between strains and experimental uncertainty  [44]. In such a situation, a solution is to carry out the analysis on the most extreme phenotypes and to proceed with the identification of markers excluding strains that present phenotypes around the median, for example, by excluding values between plus or minus one standard deviation around the median  [42]. If the whole dataset is kept, hierarchical clustering could help to objectively define the phenotypic groups  [40]. Software able to overcome issues are now available for taking into account the continuous nature of some phenotypes  [18, 45].
Funding This work was supported by funding from the  European Union 's  Horizon 2020 Research and Innovation programme  under grant agreement No 773830:  One Health European Joint Programme  (project  CARE: Cross-sectoral framework for quality Assurance Resources for countries in the European Union ). The research leading to this article has also received funding from the  French National Research Agency  ( Opticold Project ,  ANR-15-CE21-0011-02 ).
CODVerif is an approach that aims to verify the data being inserted in a data store continuously. CODVerif leverages the combination of ontology and workflow technologies in order to define workflows that are specific to the domain of "monitoring data insertion. " These domain-specific workflows are constrained on two dimensions: (1) They use a set of workflow elements that are specific to the "monitoring data insertion" domain. (2) The logic that these workflows support is predefined by relying on a set of common data insertion scenarios. Nevertheless, CODVerif is flexible enough to allow users to define continuous data verification workflows with higher complexity logic thanks to workflow operators that can be applied on the "monitoring data insertion domain"-specific workflows. To illustrate the applicability of CODVerif, the authors deploy it in a customer relationship management (CRM) application and show how CODVerif is used to support users to verify the data they populate in the CRM.
INTRODUCTION Service-oriented architecture applications (SOAs) define a set of services from a service provider. Service consumers could invoke these services by providing the right inputs to obtain the expected outputs. For many applications, the input/output data does not need to be defined rigorously (e. g. , class attributes representing informal descriptions). However, for many other applications, it is necessary to define SOA input/output data very rigorously (e. g. , an attribute that aims to capture a URL (Uniform Resource Location) needs to be well-defined), otherwise, problems would appear at run-time when the data will be used. The rigor comes from the specificity of the application that the SOA supports. This application happens for certain cyber-physical systems (CPSs) applications where the input should uphold a standardized specification strictly, and users cannot deviate from the constraints defined by that standard when defining inputs for SOA services.
A typical example is the STK SOA data definition standard  (Osorio et al. , 2006)  and its application to CPSs (https://bit. ly/3K0JOJS). In STK, data must be defined rigorously following the constraints defined by the standard. Otherwise, the data would create problems at run-time when CPS on-board services are invoked. Moreover, besides the standard, SOA data might operate the CPS; thus, the data must uphold a certain naming convention  (Paniagua et al. , 2019). Therefore, as a good practice, additional rules might be applied even though the standard left the door open and did not put constraints on certain data aspects. Managing the definition of SOA services' input and output data could be done in a data store  (Fischer et al. , 2018). Nevertheless, because of the complexity of this data and the number of constraints associated with its definition, it could be challenging for users to ensure the coherency of the SOA data they define.
Furthermore, there are two approaches to address the problem of enforcing SOA data coherency in a data store, namely: (i) a-posteriori SOA data verification approach and (ii) continuous SOA data verification approach (CODVerif). In the a-posteriori verification approach, users define their SOA input/output data but check the data's coherency at the end of the data definition phase. The advantage of this approach is that users would not be constrained by a lot of rules related to data coherency when inserting data. Furthermore, this approach is simple and can even rely on third party software to perform the verification once the SOA data is deemed ready (For STK standard, third-party software (https://bit. ly/3NyToG0) can check STK SOA data coherency). Nevertheless, the drawbacks of the a-posteriori verification approach are two-fold: • Users will figure out the errors when the data definition phase is almost done (van der  Aalst & Pesic, 2006).
This means that users could affect the delivery of their data because they would repeat defining data to resolve the problems raised. • Users would face many errors in many objects in the data store. These problems often need to be addressed sequentially and manually. With the CODVerif approach, each time users populate a piece of data, one or many Monitoring Data Insertion processes or Workflows (MDIWs) are launched. An MDIW checks whether the piece of information populated is coherent, following the rules imposed by the standard or even by naming conventions if they would be applicable. If the MDIW detects that a certain object has not been wellpopulated in the data store, it will raise the warnings or create actions that will remain pending until the problems raised are resolved. The advantage of this approach is that users can continuously address the problems related to data coherency.
CODVerif Overview In this paper, we propose the CODVerif framework to enforce SOA data coherency when this data is managed in data stores. The general architecture of CODVerif is depicted in Figure  1. In the bottom part of the CODVerif architecture, we have a data store and a workflow engine. The store for SOA services data could go from an OpenOffice Calc sheet to an object-oriented or relational database defining objects and their attributes. The workflow engine could run workflows modeled with business process management notation (BPMN)  (ObjectManagementGroup, 2022)  or other notations. Above these two components, CODVerif provides the capability of defining monitoring data insertion workflows (MDIWs). These workflow models are specific to the "monitoring data insertion" domain. Furthermore, these workflow models use specific elements defined in an ontology  (Yu, 2014).
We call this domain-specific ontology Monitoring Data Insertion Workflow ontology (MDIWO, which is available here https://bit. ly/3JXDaDU). MDIWs models capture the specific activities and events that must be considered ensuring that we satisfy the rules imposed by the target data definition standard or the rules imposed by an adopted naming convention for populating data in the data store. With the configuration of CODVerif that we described until now, users will probably be overwhelmed if we let them define MDIWs that aim to verify their SOA data. Knowing that defining workflow models can be challenging  (Dumas et al. , 2013 ) and as we are targeting a specific domain, that is "monitoring data insertion" domain, CODVerif addresses this challenge by developing a set of generic monitoring data insertion workflows (GMDIWs).
In summary, CODVerif architecture defines GMDIWs by relying on MDIWO to enforce data coherency rules imposed by SOA standards and the potential naming conventions. The CODVerif architecture described until now and depicted in the bottom part of Figure  1  is abstract. Nevertheless, CODVerif users can use this abstract layer to create an instance of the CODVerif architecture for a specific use-case of the "monitoring data insertion" domain. Examples of use cases could be the verification of STK data in a data store or customer data definition in customer management systems (CRM)  (Shaalan, 2020). If we consider CRM data as depicted in the top part of Figure  1 , the instantiation of the CODVerif architecture for this specific use-case consists in using the set of GMDIWs to attach to relevant objects or attributes in the CRM data store one or multiple instances of GMDIWs (hereafter, workflow instances).
Running Example We have initially prepared the study in this paper to address STK standard data for CPSs SOA-based applications. However, this standard is related to CPS and needs a complex background before elaborating on an easy-to-follow example. Thus, to illustrate our CODVerif approach, we will apply it to the definition of customers' data in a CRM called  Salesforce (www. salesforce. com). This use case is relevant because the definition of CRM data is often constrained by complex naming conventions  (Acker et al. , 2011). The same approach could be applied to STK-based data and other SOA data standards. This paper is organized as follows. Next, we propose the conceptual framework that supports the CODVerif architecture of Figure  1. In this section, we detail the different components of CODVerif by providing their formal definitions and by proving some important properties of CODVerif.
With these two constraints, we propose the following workflow operations to instantiate and extend CODVerif: • Combine the monitoring data insertion scenarios (GMDIW) with the domain ontology (MDIWO) via the instantiation operation to create concrete monitoring data insertion workflows, • Automated creation of MDIW that aims to keep the data store content coherent even after the removal of data (the inverse of populating data) via the negation operation, • Create complex MDIWs via the composition operation. We depict the cODVerif core elements cited above in Figure  2. In the next subsequent sections, we elaborate on these CODVerif core elements.
MDIWO leverages populating data activities that act on references and attributes with their different types. Furthermore, MDIWO defines disjoint relationships between concepts to facilitate identifying a concept and its inverse. A typical example of a disjoint relationship between two message events is reference_defined_Receive_MESSAGE and reference_cleared_Receive_MESSAGE, which are opposites. MDIWO supports CODVerif in specifying workflow models related to different scenarios related to populating data. Furthermore, MDIWO aims to support the automated generation of workflow models that undo what populating data workflows did. Finally, MDIWO aims to support the instantiation of monitoring data insertion workflows for specific use cases. Thus, MDIWO defines four concepts:.
Concepts Related to Conditions Associated with Workflow Flows Conditions could be associated with flows when defining XOR and OR gateways. Therefore, MDIWO defines a hierarchy of concepts to capture conditions related to populating data in a data store and their inverse. Every ontology can define concepts and individuals  (Yu, 2014). Individuals are realizations of the concepts for a specific use case (e. g. , populating data for a CRM or populating data for STK). At this stage, we do not need to define any individual in MDIWO because individuals will be specific to the use case to be addressed.
Composition of MDIW It is possible to associate an object in a data store with multiple MDIWs to check multiple coherency rules for that particular object. Hence, we need to define the composition of different MDIWs. Indeed, the composition of MDIWs consists of applying multiple MDIW on the same attribute that will be populated. The composition of MDIWs is a first-order logic formula involving different MDIWs. The composition operator of MDIWs should satisfy the following requirements: 1. It should be possible to compose an MDIW and its negation ØMDIW , 2. It should be possible to apply the negation on a composite MDIW Composite MDIW -, and the resultant ¬ -Composite MDIW should be applicable. To compose MDIWs, we rely on first-order logic operators because CODVerif aims to enforce multiple coherency rules simultaneously.
GENERIC MONITORING DATA INSERTION wORKFLOwS (GMDIw) Different scenarios exist for populating data in a data store. It is necessary to identify these scenarios to develop the appropriate GMDIWs that will monitor and guide users when populating data. To develop the scenarios of populating data, we have relied on two inputs: 1. The experience gained when populating data for different purposes while considering naming conventions, 2. The study of the data coherency rules is defined by different SOA standards, including STK. We have identified 8 GMDIWs related to data insertion. These GMDIWs do not aim to define a comprehensive list of data insertion scenarios, but we believe they offer important insights. In Table  2 , we present the rationale behind each GMDIW along with its negation ØGMDIW which can be calculated automatically thanks to the negation operation defined above.
CODVERIF PROTOTyPE We have developed a three-profile liferay-based portal  (Yuan, 2012)  to implement the CODVerif framework, as illustrated in Figure  4. The first profile is the CODVerif-Admin (top part of Figure  4 ). Users having this profile aim to perform the following activities required for CODVerif: • Define and extend the CODVerif ontology (MDIWO). • Define the data model corresponding to a specific domain whose data needs to be verified (e. g. , CRM or STK).