dataesr/scientific-paragraphs-categorization huggingface dataset:
Impact simulations are now capable of modeling the tendency of rocks and granular materials to change volume during shear failure, that is, dilatancy. Results of impact simulations using this model are consistent with porosity measurements and the gravity anomalies of simple craters  (Collins, 2014). However, limited porosity data have hindered comparisons with complex craters. Furthermore, while the importance of fracturing in reducing the density of para-autochthonous impactites has been widely acknowledged, detailed analysis of the microporosity and microfracturing of shocked para-autochthonous rocks (e. g. ,  Winkler et al. , 2018)  is limited, particularly for naturally shocked rocks. Here, the Chicxulub impact structure is used as a case study to understand the production of porosity during complex crater formation.
1029/2019JE005929 ture (Figure  S1c ). The expedition recovered core between 505. 42 and 1334. 69 mbsf (meters below sea floor) including, from 750. 25-1334. 69 mbsf, a sequence of uplifted granitic target rocks, with occasional sheet intrusions of preimpact igneous rocks and impact-related breccias and melt rocks. A schematic lithological column and location map is shown in Figures  S1a  and S1b , respectively. Physical property data from the expedition  (Christeson et al. , 2018; Morgan et al. , 2017)  have shown that the shocked target rocks within the Chicxulub peak ring have porosities of approximately 10%, which is a likely cause of the gravity minimum associated with the peak ring. However, the microstructural cause of this porosity is uncertain and it is not known how porosity is more widely distributed across the crater.
Here, we assess the cause of the unusually low densities and high porosities of the uplifted granitic rocks of the Chicxulub peak ring. These results help constrain cratering-related deformation mechanisms and the timing of porosity generation. Observational results are obtained by petrophysical and petrographic techniques, with additional data from remote geophysical surveys. The observational data are then compared to the results of numerical impact simulations, which can predict porosity distribution and microfracture orientations.
Data Sets 2. 1. Geophysical and Petrophysical Data 2. 1. 1. Gravity Data Numerous gravity surveys have been acquired at the site of the Chicxulub structure. Data used here (Figure  S1b ) were provided by Alan Hildebrand and Mark Pilkington (see  Pilkington et al. , 1994) , supplemented by data acquired during the 2005 seismic surveying of the structure (see  Gulick et al. , 2008). Comparison between the results of numerical impact simulations and gravity data requires the determination of an average gravity profile across the structure, without any regional gravity effects. Following  Vermeesch et al. (2009) , data were initially transformed to a cartesian coordinate system with an origin at the crater center (21. 31 ( ) 2 The gravity data were then corrected to account for the regional variation of the Bouguer gravity field across the site.
Expedition Data IODP-ICDP Expedition 364 collected density data from cores using two different methods. First, cores were analyzed with a Geotek Multi-Sensor Core Logger (MSCL), which, alongside other physical property measurements, measured bulk density by gamma ray attenuation. These measurements were acquired at 2-cm intervals where, due to the machine setup, the individual resolution was 0. 5 cm (see  Morgan et al. , 2017, for details). The second set of density data derives from He-pycnometry measurements (Quantachrome pentapycnometer) of discrete samples taken from the core. Samples had a diameter of 2 cm and an approximate volume of 6 cm 3. Samples were taken approximately once per core section (approximately every 1. 2 m), and a total of 719 samples were measured. He-pycnometry, in addition to allowing determination of bulk density, also allows the determination of grain density and porosity (see  Morgan et al.
SEM Imaging and Analysis To understand the microstructural cause of the porosity within the granitic basement rocks, 10 thin sections were prepared from samples of the recovered granitic target rocks at different depths within Hole M0077A. Backscattered electron images of the thin sections were obtained using a Zeiss Ultra Plus field emission scanning electron microscope (SEM) with the large-area imaging software and hardware package, ZEISS Atlas 5, at the Natural History Museum, London. Individual images were obtained with an accelerating voltage of 20 kV using the backscattered electron detector, a working distance of 8. 0 mm, and a resolution of 748. 9 nm per pixel. The Atlas 5 software was then used to stitch the individual images from a section to produce a single composite image. The total areas of each stitched image are shown in Table  S1.
10. 1029/2019JE005929 Image processing was carried out in order to analyze and quantify porosity within the thin sections. Processing was achieved using the ImageJ software package  (Schindelin et al. , 2012). The first stage required to analyze porosity is image thresholding. The images were deliberately acquired such that there was a significant contrast of grayscale values between pores (black), and mineral phases (gray to white). ImageJ provides several algorithms that automatically thresholds images into objects (pores) and background (solid material), resulting in an image consisting of only binary values, black for pores and white for background. Here, the default ImageJ thresholding algorithm was used, and the result then manually checked and, if required, adjusted. Once pores were thresholded into a binary image file, porosity estimates could be made by assuming the application of the Delesse principle that the volume fraction of pores can be estimated by calculating the area fraction of pores in a 2-D image.
The iSALE Shock Physics Code We simulated the formation of the Chicxulub impact structure using the iSALE shock physics code. iSALE is a multirheology, multimaterial code based on the SALE hydrocode  (Amsden et al. , 1980). The original code has been modified to include an elastoplastic constitutive model, fragmentation models  (Melosh et al. , 1992) , various equations of state  (Ivanov et al. , 1997) , a porous compaction model  (Wünnemann et al. , 2006) , and a dilatancy model  (Collins, 2014). iSALE and its precursor codes, SALES-2  (Collins et al. , 2002)  and SALE-B  (Ivanov, 2005) , have been used to simulate the formation of the Chicxulub impact structure in previous studies  (Collins et al. , 2002 (Collins et al. , , 2008;; Ivanov, 2005; Morgan et al. , 2016; Rae et al. , 2019). Here, we use the model parameters presented by  Rae et al.
( 5 )  max is a material-specific value, where large values indicate a large tendency to dilate upon shear deformation. For impact cratering simulations, and large-scale numerical simulations of rock masses,  Collins (2014)  suggests that  max values range between 0. 045 and 0. 180, where  max = 0. 045, corresponding to a Low Geological Strength Index (GSI) material, provides the best fit to the gravity and porosity data of simple craters and small complex craters. Here, that value is used to test its applicability to larger complex craters. From a simulation with this dilatancy model, it is possible to calculate the resultant gravity anomaly associated with the simulated crater including the effects of density reduction due to shear-induced dilatancy and the filling of the impact crater with postimpact sedimentary rocks.
Data Sources The OpenCancer 9 organization gathers people working on cancer prediction issues. Their aim is to provide tools aimed at helping health authorities to take public policy decisions in terms of cancer prevention. OpenCancer has linked and merged data from the World Health Organization (WHO)  10  , World Bank (WB)  11  , the International Labour Organization (ILO)  12  and the Food and Agriculture Organization (FAO)  13  of the United Nations to build a large data set covering 38 countries and many regions within these countries between 1970 and 2002. For this application, OpenCancer provided a first version of the database restricted to the WHO, WB and FAO sources. Each row is characterized by a 5-tuple (cancer type, country, gender, ethnicity, age group) and 5 495 features. For this application the single cancer type, which has been considered, is the colon cancer.
Data Pre-processing Data Cleaning The first version of this sub-database suffers from a vast number of missing cells due to the lack of information in the available repositories. To fix this issue prior to running any learning model, OpenCancer had discarded every feature exhibiting a missing data ratio higher than 50 % and imputed other missing data with a standard mean strategy. The resulting database-merged from both FAO and WB, including the incidence provided from WHO-includes 3 365 risk factors (features) and 45 888 records. Each record, seen as a population, is identified via a 6-tuple defined as {Sex, Age group, Country, Region, Ethnicity, Year}. To measure the occurrence of the colon cancer disease in the population, the number of new cases of the disease within a population occurring over 1970 and 2002 is used, referring to the incidence measure.
Feature Selection Once the consistent database is ready for use, a feature selection step is performed. To deal with the large amount of the epidemiological data, a distributed version of rough set theory for feature selection  [5] , named Sp-RST, is used. Sp-RST is based on a parallel programming design that allows to tackle big data sets over a cluster of machines independently from the underlying hardware and/or software. To select the most important risk factors from the input consistent database, and for the purpose of colon cancer incidence prediction, Sp-RST proceeds as follows: Problem formalization Technically, the epidemiological database is first stored in an associated Distributed File System (DFS) that is accessible from any computer of the used cluster. To operate on the given DFS in a parallel way, a Resilient Distributed Data set (RDD) is created.
We may formalize the latter as a given information table defined as T RDD , where the universe U = {x 1 ,. , x N } is the set of data items reflecting the population and is identified as a 6-tuple defined as {Sex, Age group, Country, Region, Ethnicity, Year}. The conditional attribute set C = {c 1 ,. , c V } contains every single feature of the T RDD information table, and presents the risk factors. The decision attribute D of our learning problem corresponds to the class (label) of each T RDD sample. It has continuous values d and refers to the incidence of the colon cancer. The condition attribute feature D is defined as follows: D = {Typology 1 ,. , Typology I }. The conditional attribute set C presents the pool from where the most convenient risk factors will be selected. Feature selection process For feature selection, the given T RDD information table is partitioned first into data blocks based on splits from the conditional attribute set C.
Hence, T RDD = m i=1 (C r )T RDD (i) , where r ∈ {1,. , V }. Each T RDD (i) is constructed based on r random features selected from C, where ∀T RDD (i) : {c r } = m i=1 T RDD (i). Within a distributed implementation, Sp-RST is applied to every single T RDD (i) so that at the end all the intermediate results will be gathered from the different m partitions. Specifically, Sp-RST will first compute the indiscernibility relation for the decision class defined as IN D(D) : IN D(d i ). More precisely, Sp-RST will calculate the indiscernibility relation for every decision class d i by gathering the same T RDD data items, which are defined in the universe U = {x 1 ,. , x N } and which belong to the same class d i. This task is independent from the m generated partitions and, as the result, depends on the data items class and not on the features.
In fact, at this stage Sp-RST removes in each computation level the unnecessary features that may affect negatively the performance of any learning algorithm. Finally, Sp-RST keeps the set of combinations having the minimum number of features by satisfying the full reduct constraints discussed in Section 3: γ(C r , AllComb (Cr) ) = γ(C r ) while there is no AllComb (Cr) ⊂ AllComb (Cr) such that γ(C r , AllComb (Cr) ) = γ(C r , AllComb (Cr) ). Each combination satisfying this condition is considered as a viable minimum reduct set. The attributes of the reduct set describe all concepts in the original training data set T RDD (i). The output of each partition is either a single reduct RED i (D) (C r ) or a family of reducts RED F i (D) (C r ). Based on the RST preliminaries previously mentioned in Section 3, any reduct of RED F i (D) (C r ) can be used to represent the T RDD (i) information table.
Consequently, if Sp-RST generates only one reduct, for a specific T RDD (i) block, then the output of this feature selection phase is the set of the RED i (D) (C r ) features. These features reflect the most informative ones among the C r attributes resulting a new reduced T RDD (i) , T RDD (i) (RED), which preserves nearly the same data quality as its corresponding T RDD (i) (C r ) that is based on the whole feature set C r. On the other hand, if Sp-RST generates a family of reducts then the algorithm randomly selects one reduct among RED F i (D) (C r ) to represent the corresponding T RDD (i). This random choice is justified by the same priority of all the reducts in RED F i (D) (C r ). In other words, any reduct included in RED F i (D) (C r ) can be used to replace the T RDD (i) (C r ) features.
At this stage, each i data block has its output RED i (D) (C r ) referring to the selected features. However, since each T RDD (i) is based on distinct features and with respect to T RDD = m i=1 (C r )T RDD (i) a union of the selected feature sets is required to represent the initial T RDD ; defined as Reduct m = m i=1 RED i (D) (C r ). In order to ensure the performance of Sp-RST while avoiding considerable information loss, the algorithm runs over N iterations on the T RDD m data blocks and thus generates N Reduct m. Hence, at the end an intersection of all the obtained Reduct m is needed; defined as Reduct = N n=1 Reduct m. By removing irrelevant and redundant features, Sp-RST can reduce the dimensionality of the data from T RDD (C) to T RDD (Reduct). More precisely, Sp-RST was able to reduce the considered epidemiological database from 3 364 risk factors to only around 840 features.
Experimental plan, testbed and tools Our experiments are performed on the High Performance Computing Wales platform (HPC Wales), which provides a distributed computing facility. Under this testbed, we used 12 dual-core Intel Westmere Xeon X5650 2. 67 GHz CPUs and 36GB of memory to test the performance of Sp-RST, which is implemented in Scala 2. 11 within Spark 2. 1. 1. The main aim of our experimentation is to demonstrate that RST is relevant to real world big data applications as it can offer insights into the selected risk factors, speed up the learning process, ensure the performance of the colon cancer incidence prediction model without huge information loss, and simplify the learned model for epidemiologists.
Parameters settings As previously mentioned, we use the Random Forest Regression implementation provided in the Spark framework with the following parameters: maxDepth=5, numTrees=20, featureSubsetStrategy='all' and impurity='variance'. The algorithm automatically identifies categorical features and indexes them. The database is split into training and test sets where 30% of the database is held out for testing. Meanwhile, for the Sp-RST settings, we set the number of partitions to 841 partitions; generating 4 features per partition (based on preliminary experiments). We run the settings on 8 nodes on HPC Wales. For the purpose of this study we set the number of iterations of Sp-RST to 10.
Categories of Selected Risk Factors Recall that Sp-RST runs over 10 iterations and that at its last algorithmic stage an intersection of the generated reducts at each iteration is made. However, for the considered data set, this intersection is empty. Hence, we have modified the algorithm to return all 10 different reducts to be presented to the epidemiologists. In the following, we present two different types of results: averages accumulated over the 10 reduced datasets and separate numbers for each of the iterations performed. Both parts of the data set (FAO, World Bank) contain 9 different categories of risk factors as shown in Table  1. Here, we list the number of different factors in each category, the average number of factors selected over the 10 iterations of Sp-RST and the corresponding percentages.
From Table  1 , and based on the WB database, we notice that there is only a small variation in the distribution of the selected risk factors. Exceptions are the gender and poverty risk factors, which are not selected by the algorithm. The same comments can be made for the FAO database where the food security risk factor does not appear in any of the selected feature sets. Epidemiologists confirm that these results are quite expected. This demonstrates that our method is able to select the most interesting features to keep-the key risk factors. We depict the ratios of each category within the set of selected risk factors for each database (FAO & WB) in Figure  1 , and for an overall view, the distribution of the categories of the combined data sets is presented in Figure  2. Based on these figures, epidemiologists confirmed again that the selected risk factors are expected to appear in each of their corresponding databases (though potentially with a different overall distribution).
The categories of the selected risk factors in the FAO data set, in the WB database and for the combined database, separately for each of the 10 iterations are presented in Figure  3 , Figure  4  and Figure  5 , respectively. Based on Figure  3 , Figure  4  and Figure  5 , we can see that the risk factors partially overlap within the 10 iterations. This might be interesting from an epidemiological point of view as it can influence the consideration of other pos- sible risk factors, which appear with different distributions. Indeed, the overlap between the selected risk factors from one iteration to another may call the attention of the epidemiologist in cases where a firm decision is taken with respect to a specific risk factor. These results are considered to be very important for the epidemiologists as they help them in the decision making process.
The data set obtained is much easier to interpret and still yields comparable regression results. The process of regression is much faster on the reduced data set. As discussed earlier, we are currently only considering a subset of the Open-Cancer data set. We plan to expand our study to the complete set in future work. Moreover, we will work more closely with epidemiologist to further improve our method, both with respect to interpretation of the results and precision of the regression model. One important aspect in this context will be the consideration of missing values in the original data set.
received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sk lodowska-Curie  grant agreement No  702527. This work was based on a first version of a database provided by the OpenCancer organization, part of Épidemium-a data challenge oriented and community -based open science program. Additional thanks go to the  Épidemium group ,  Roche ,  La Paillasse  and to the Supercomputing Wales project, which is part-funded by the  European Regional Development Fund  via the  Welsh Government. Zaineb Chelly Dagdia  et al.
In this context, an experimental study of a concrete block system is conducted to test the thermal response of this configuration by the application of cyclic solicitations. The comprehension of this integrated constructive solution essentially passes by the possession of validated numerical tools. For this, two models have been developed. The first model using the electrical-thermal analogy, based on an RC equivalence, is distinguished by its relative simplicity and weak time computing demand. Each of these two factors, R and C summarise the system properties and has a direct influence on the building transient simulations over a year. The second one is based on direct numerical simulation DNS of the energy and fluid flow equations using commercial code COMSOL Multiphysics. Such DNS is time consuming and could not be used to simulate over a year but aims only to validate the first RC circuit approach.
It is based on the combination of a thermal resistance R and a thermal capacity C for each studied thermal element. The RC model is distinguished by its speed, which is an important factor in simulations related to the building where the heat exchanges require substantial time intervals where calculations generally become very heavy  [38] [39] [40] [41] [42] [43]. In addition to the phenomena often modelled, namely conduction and convection, a modelling of the phase change of paraffin with integrated ventilation system will be presented in this study. The features of the Simscape library integrated in the Matlab/Simulink environment are used. The method of resolution, thus adopted makes it possible to work on a circuit constituted of variable resistances and capacitances. This article is part of the studies on the direct integration of a phase change material into a concrete block  [44, 45].
System modelling The RC circuit modelling, proposed in this work, is based on the use of the Matlab Simscape tool. Its library proposes several models for the direct integration of electrical, thermal, mechanical and hydraulic phenomena which allow to quickly creating physical system models in the Simulink environment. The electrical model enables the creation of the circuit from the physical connections to obtain block diagrams. In the sense of electrical and thermal analogy, every part of our physical model will be replaced by an electrical circuit of resistances and capacitances, as well as for imposed boundary conditions. Before arriving at the equivalent diagram representing the studied element, simple models were used to translate the behaviour of a single block of PCM. Thereafter, the other elements were added progressively; the concrete parts as well as the air tube.
On the numerical section, an RC network model has been developed, it proposes a simplified approach to models the thermal heat transfer by using the Simscape tool under Matlab, it allows both to get closer to the performance of CFD models in taking into account the evolution of the physical phenomena and it offers a great speed of the execution of the simulations, a parameter required in the calculations related to the building. Similarly, the model proposes a new approach to modelling the phase change by using grey boxes with their resistances and varying capacitances. The finite element model is proposed as a reference for comparing the accuracy of the RC model. The analysis of the obtained results has confirmed the validity of the RC model for predicting the temperature evolutions of the system surface.
The immense data volume gathered from multiple imaging modalities in existing clinical datasets can greatly facilitate exploratory radiomic analysis. The heterogeneous composition of head and neck cancers can also be captured in a non-invasive procedure, which can serve as an essential adjunct to clinical decision-making. The suffix "omics" characterizes different sources of large data volumes whereby this suffix is preceded by the defining name of the original materials of data collection. This leads, for example, to the description of the "genomics" in the context of genetic expression analysis, and protein analyses become "proteomics" or "metabolomics" when the metabolome is analyzed. In analogy, the newly defined "radiomics" field finds its way into clinical research where a large amount of data from radiological imaging become available for analysis  [3].