dataesr/scientific-paragraphs-categorization huggingface dataset:
Determination of ABS and PP Model Properties The behavior of PP and ABS can be well described through this modelling. The Young's modulus E, the Poisson's ratio ν and the yield stress R 0 were determined from stress/strain data. The GTN model has been implemented using the Matlab software. The porosity evolution parameters q 1 and q 2 are arbitrarily fixed to 1, adjustments of these values would necessitate some local data (as porosity distribution)  [35]. The GTN model is therefore reduced to the native Gurson model. The remaining material parameters were identified by using a mean square method on stress/strain and volumetric strain/strain data between experiments and numeric data at each timestep of the simulation. Parameters present in Table  3  lead to a good accordance between experiments and modelling for both stress (Figure  14 ) and volumetric strain (Figure  15 ).
Prediction of ABS/PP Blend Properties For the sake of simplicity, a plane stress assumption is made for the numerical computations even if this strong hypothesis is far from reality. The interface between matrix and fillers is assumed to be perfect (displacement continuity) and phase properties are described through the set of parameters of Table  3. This assumption of perfect interface will be discussed in next section as the compatibility of ABS and PP is quite questionable. We assume that the properties of ABS and PP in the blend are similar to those of the neat ABS and PP. From the particle size distribution shown in Figure  10 , two kinds of microstructures were generated via DIGIMAT software (for both ABS/PP and ABS/PP/PP-g-MA blends). Mesh microstructures based on triangular elements and boundary conditions are presented in Figure  16.
The RVE (representative volume element) sizes were chosen to stabilize the macroscopic stress/strain responses. As local behavior is of interest for this study, the chosen mesh refinement leads to stabilize local blend response. We assume the structure symmetry. A prescribed displacement (corresponding to 10% of strain) was applied on the top of the sample. The non-linear numerical simulation was carried out using the FE software Zebulon, which was developed at Mines Paristech  [38]. This RVE model is thus useful to predict the mechanical properties of ABS/PP composites. The size of the PP nodules is much lower when a compatibilizer is added: this induces both a reduction in the RVE size and interparticular nodules distances for the ABS/PP/PP-g-MA blend (see Figure  16 ). We can deduce from these results that interface areas between ABS and PP are increased since nodules are smaller.
3, x FOR PEER REVIEW 14 of 20 We assume that the properties of ABS and PP in the blend are similar to those of the neat ABS and PP. From the particle size distribution shown in Figure  10 , two kinds of microstructures were generated via DIGIMAT software (for both ABS/PP and ABS/PP/PP-g-MA blends). Mesh microstructures based on triangular elements and boundary conditions are presented in Figure  16. The RVE (representative volume element) sizes were chosen to stabilize the macroscopic stress/strain responses. As local behavior is of interest for this study, the chosen mesh refinement leads to stabilize local blend response. We assume the structure symmetry. A prescribed displacement (corresponding to 10% of strain) was applied on the top of the sample. The non-linear numerical simulation was carried out using the FE software Zebulon, which was developed at Mines Paristech  [39].
However their analysis was restricted to a 10 year period as the database was discontinued. Satellite data have also been used to quantify the aviation impact on cirrus. Tesche et al  [8]  found an increase in cirrus optical depth in satellite lidar observations in portions of the satellite track that were crossed by aircraft in comparison to neighboring control conditions. Stordal et al  [9]  correlated trends in cirrus cover with aircraft density data and obtained a noisy signal from which they estimate a regional increase in cloud cover by 1%-2% attributable to aircraft, and estimated a radiative forcing of 30 mW m -2 in the period 1992-1999 vs 1984-1991. A common problem to trend analyses is that multiple influences on cirrus, including global warming and natural decadal variability, may also act as confounding factors.
Telemonitoring of BP has been mentioned in the guidelines as well, offering a couple of advantages (for instance improvement of adherence). During the Covid-19 pandemic, it has been widely used for obvious practical reasons, recommended for cardiovascular diseases in particular  25  , and expanded to teleconsultations or, in a broader sense, to "delivering healthcare remotely 26 ". A potential limitation of our study is the fact that the numbers given by the EC may represent more an estimate than an extraction of data from large hospital databases, and as such have to be taken with some caution. However, we strongly believe that the trends shown in our survey are robust. Moreover, we do not have access to intermediate (i. e. BP measurements) or cardiovascular outcome data within this survey.
, 2010)  at different temporal scales. Data mining methods are able to analyze large datasets and may be a good alternative to traditional statistical methods  (Giraudel and Lek, 2001). These methods can produce readable results, thereby facilitating interactions between data miners and experts  (Džeroski et al. , 1997). Two categories of data mining methods exist: supervised and unsupervised methods. Supervised methods require a learning dataset to build a specific model adapted to a given issue, including variables and expected results. When the model is built, it has to be tested on a second dataset. After this validation stage, it can be applied to a third dataset to predict results expected in similar conditions. Unsupervised methods do not require a learning dataset and make it possible to explore and identify unexpected rules in a dataset.
The dataset used Available data were collected in 1,781 sampling sites (Fig. 1 ) in the framework of the French network created to assess the ecological status of waterbodies, management unit, according to the WFD. Fieldwork was undertaken by regional environmental agencies on the French national river network between 2007 and 2013. Table  1  lists the volume of the data we used. The 23,071,909 physico-chemical results concerned 1,201 parameters. Among them, 2% were major parameters (parameters with concentrations in milligram per liter, e. g. pH, nitrogen) and 98% were minor parameters, including micro-pollutants (parameters with concentrations in microgram per liter, e. g. copper, atrazine). analyzes of the major elements were conducted 12 times a year and analyzes of minor elements four or six times a year.
Data preprocessing We discretized chemical and biological data using the five levels represented by colors, which symbolize the different statuses of the WFD; i. e. blue: high, green: good, yellow: moderate, orange: poor, and red: bad. For the physico-chemical data, we used two sets of thresholds: the first was created in France before the application of the WFD, hereafter referred to as SEQ  (MEDD and AE, 2003 ) and a second one upon application of the WFD, hereafter called the WFD guide  (MEEM, 2012). We performed physico-chemical discretization following a three-step   For each biological index, the thresholds from MEEM (2012) were applied. In most cases, about two-thirds of the results correspond to the status required by the WFD guide in class 1, high status, or in class 2, good status and one third of degraded quality (Class 3, moderate status; class 4: poor status; class 5: bad status) except for IPR (52% in classes 1 and 2 and 48% in classes 3, 4 and 5).
A pattern extracting tool named "PRESTOR" To explore data resulting from monitoring the quality status of rivers, sequential pattern mining, an unsupervised data mining tool able to treat large volumes of data and to account for the temporal aspect of events has been used. This method first introduced by  Agrawal and Srikant (1995)  is a temporal extension of association rules  (Agrawal and Srikant, 1994). It was then developed for and used in engineering software  (Ren et al. , 2009) , medicine  (Sallaberry et al. , 2011) , or marketing  (George and Binu, 2012). Sequential pattern mining allows to reveal frequent temporal regularities (called sequential patterns) in a sequence database, each pattern being associated to the set of sequences that contain it. Unfortunately, the number of extracted patterns can be high and they provide redundant information, making their interpretation difficult.
In order to reduce this formation redundancy and to limit the number of patterns and hence the volume of the result,  Fabrègue et al. (2013)  proposed to use closed partially ordered patterns (CPO-patterns). Actually, a CPO-pattern is a summary of a set of sequential patterns shared by a set of sequences  (Casas-Garriga, 2005). CPO-patterns can be used in all kinds of sequential databases and have three main advantages: (1) they provide more detailed information on order among elements; (2) they are depicted by a directed acyclic graph, which is easy to understand; (3) they summarize sequential pattern sets. PRESTOR (temporal PRESsure categories patterns extracTOR) is a tool designed for the analysis of a big temporal database. It was implemented in C++ allowing the best computing performances (along with intel TBB for parallel programming and BOOST and QT to handle different kinds of interfaces).
PRESTOR is a command line software, most of the configuration is done in a "config. ini" file. It can fetch data directly from the database or from well-formatted files. Several configuration options are provided (described below) to select specific data subsets. The result consists in pictures and a few files providing different kinds of statistical values to help understanding the results. Frequent Closed Partially Ordered Patterns The main algorithm at the core of PRESTOR generates frequent closed partially ordered patterns (CPO-patterns) as proposed by  Fabrègue et al. (2013). A sequential pattern is a succession of physico-chemical events that can be checked by the samples preceding a biological status assessment at a sampling site on one or more occasions. Formally, it is a sequence of itemsets.
An item is a value to be recognised and an itemset is a set of values assessed at the same point in time (values from the same sample). For example, having at the same time a high value for the alteration PHOS (noted High PHOS) and a bad value for the alteration MPOR (noted Bad MPOR) is an itemset we denote (High PHOS, Bad MPOR). <(High TEMP),(High PHOS, Bad MPOR) > is then a sequential pattern checked at all stations where the itemset (High TEMP) is found before the itemset (High PHOS, Bad MPOR). Let us consider thetoy database shown in Table  4 , presenting the history of 3 stations. In this paper, we focus on the biological indices (e. g. IBGN) we want to characterize using the preceding samples. To this end, we build sequences of itemsets from this database following two simple rules: the sequence must end on a biological sample, called the generating context, and the duration in months between the first and the last samples is bounded by a value (here 6 months) defined by the operator.
All the paths in the graph are checked by referring to all the supporting sequences. Implementing the method proposed by  Fabrègue et al. (2013) , PRESTOR extracts all the frequent CPO-patterns for a given sequence database. The frequency of a pattern is the ratio of the size of its support to the size of the sequence database. In some extracts, CPO-patterns extracted in several contexts may be identical. Each has a generating context, e. g. High IBGN, High IBMR. For this set of identical CPOpatterns, the context of the CPO-pattern with the highest frequency is called the dominant context. The frequency of a pattern in its dominant context is denoted by f max. Hereafter, we refer to CPO-patterns simply as patterns. Choice of parameters (region, time, reference thresholds, etc. ) The operator can modify several parameters to obtain different kinds of results.
A filter can be applied to limit the analysis to a subset of the database restricted to a given region or a period of time. It is also possible to choose between the two quality reference norms for discretizing the physico-chemical values (SEQ or the WFD guide). The operator has to define a frequency threshold below which the patterns are not extracted. The algorithm itself can also be parameterized to consider different time lengths (in months) for the input sequences. One can also limit the values considered to remove irrelevant values. One example of an irrelevant value is High TEMP because it appears in more than 90% of the sequences in the database. This generates a significant number of parasite nodes which provide no information. Note that only the High TEMP is filtered but Bad TEMP is kept as such because this lowest general value may provide some information.
Results In our conditions (Computer INTEL COR I7-4790 3. 6 Go and 16 Go RAM), extractions take from few seconds to few minutes, depending on the size of the sequence dataset and the configuration parameters. The number of patterns increases with an increase in time-length and with a decrease in the minimum frequency or in the number of sampling sites, but without constant correlations. The number of patterns obtained using the WFD thresholds guide is always higher than with SEQ thresholds in the same configuration.
Batch of patterns in a specific configuration The results were obtained with the following configuration: [area: France, period: 2007-2013, table of thresholds: SEQ, time-length: 24 months, minimum frequency: 0. 6]. We first eliminated 4 pressure categories for which a single status dominates over 80%: TEMP, ACID and EPRV in high status and HAP in moderate status, to avoid patterns with repetitive and no discriminating items. A first batch of patterns was generated for a minimal frequency of 0. 96. As 90% of the patterns obtained contained only five pressure categories in a single status, high or good (good PAES, high MINE, good MOOX, good AZOT and good PHOS), hence masking the impact of other parameters, we also eliminated these pressure categories in these statuses from the dataset. A second batch of patterns was generated without these pressure categories, for a minimum frequency of 0.
6. The distribution of the alteration categories in the different statuses foundin the patterns differed from that in the dataset. The distribution of pressure categories for major parameters, was comparable: (8-9%: from 90,681 results for NITR to 102,006 results for TEMP compared to the total number of physico-chemical results: 1,146,544), while nitrogen was dominant in the patterns (30% = 953/3,124). The micro-pollutants were less abundant in the dataset (from 3% -count: 36,158 -for PCB to 6% -count: 66,523 -for PEST). Conversely PEST was abundant in the patterns (29%). The distribution of sequences per index varied from 2,416 (8%) for IBMR to 8,404 (26%) for IBD, while 67 (8% of) patterns had I2M2 as context and 433 (i. e. 53%) had IBMR as context. Five main pressure categories were found in the 809 patterns (Fig.
In the bad IBMR context, patterns 623 and 564 (Fig. 6 ) represented multi-stress conditions: moderate MOOX, and moderate PAES for the first, with added bad PEST for the second. (Pressure categories: PHOSphosphorous -; NITR nitrates -; PESTpesticides -; MPORother organic hydrocarbons -; MOOXoxidizable organic matter -; MPMIheavy metals). Fabrègue et al. (2014)  were the first to show the promising potential of patterns to describe sequential data concerning quality in river ecosystems. We continued this work using a bigger dataset (here 1,146,544 physico-chemical data and 24,593 biological data) and by multiplying the possible configurations. The PRESTOR tool we created was specifically applied to extract patterns based on different criteria (time-length, minimum frequency, etc. ) directly from hydro-ecosystem databases.
A promising tool to reveal temporal sequences preceding a biological status To proceed from a potentially useful to an operational method using PRESTOR, we proposed a double process before and after extraction to control the number of patterns extracted and to identify the most relevant ones. Analysing of the 809 patterns we extracted is irrelevant for an expert. Some are nested in others, for example, a pattern of three successive "poor PESTs" included in another one of four successive "poor PESTs" (Fig. 6 ). After extraction, the three metrics (complexity (C), scarcity (S) and emergence (E)) help selecting the most relevant patterns among hundreds or even thousand ones. The process generated more patterns for the smaller sub-datasets. This bias is shared by other methods as well as by data mining and statistical methods, which is why the pre and post-processing are recommended particularly for environmental data mining  (Gibert et al.
, 2008). Here, there can be many patterns in a given context for different reasons: 1) few sequences were available: in our case, IBMR represented 8% of sequences and extracted 53% of patterns, because the threshold of minimum frequency is easily reached; 2) data covered all the range of values: dataset for IBMR were more heterogeneous than those for IBD, which represented the maximum number of sequences (27%) and extracted only 18% of patterns; 3) data were numerous but homogeneous, as were.
4  Durance and Ormerod (2007)  in streams in Wales between 1981 and 2005 or to the forecasts of 1-9 • C announced by  Webb (1996)  for 2050. But the mechanisms which control freshwater temperature behavior in the context of global climate change are complex, in particular because temperature increases have been observed in winter while summer temperatures have remained relatively stable  (Webb et al. , 2008). This could explain why, in the French dataset, temperatures remained stable in high status, and underlines the need to change the way we define this pressure category. In Europe and North America, acidification of freshwater decreased throughout the 1980s and 1990s thanks to the reduction of atmospheric emissions of sulphur dioxide  (Stoddard et al. , 1999). Therefore, pressure due to acidification is now localized rather than widespread, which is probably why it is difficult to observe this pressure in a study conducted at national scale.
The quality class for the effect of eutrophication (EPRV), in the SEQ (op. cit), is generally defined for water combining basic pH and oversaturated in oxygen. In our dataset, this pressure category was mainly in high quality based on this definition, which is why it was not included in the analysis of patterns. Again, HAP are ubiquitous contaminants to a moderate degree in our study area, as also shown by  Motelay-Massei et al. (2004)  in the soils of the Seine River Basin. • C to Table 5 (continued ) 1. 7 • C increase observed by The five following type of pressure categories are selected according to the patterns extracted from the complete dataset with a time length of two years. The second type pressure categories was measured in different statuses but considered as dominant in only one status.
There were macropollutants in high or good quality statuses: mineralisation (MINE) in high status, suspended matter (MES), oxidisable organic matter (MOOX), nitrogen excluding nitrate (AZOT) and phosphorus matter (PHOS) in good status. They were removed from the pattern extractions, but they can be considered as regularly measured in each site. The third type includes pressure categories that were absent or rare in the patterns. Polychloro-biphenyls (PCB) did not appear in any patterns. This pressure category was the one with the lowest number of measurements in the dataset (completeness rate: 33% versus at least or more than 50% for the other micro-pollutants). Teil et al. (2004)  estimated that the pressure exerted by PCB in the Seine River Basin to be beyond the global national estimation of industrial inputs, but we did not observe it at the national scale.
Pressure categories ACID and MINE in other classes than the high one accounted for less than 4% of all appearances of pressures. The fourth type represents pressure categories that were dominant in patternsbut poorly discriminant, regardless of the biological status: pesticides (PEST) was only found in poor or bad statuses in the patterns we extracted. This pressure category was the one in which the number of measurements of micro-pollutants was the highest in the dataset (completeness rate: 61%). According to the Millenium Ecosystem Assessment Programme (  2005 ), pesticides are one of the major stressors in freshwater ecosystems. The patterns extracted revealed the spatial and temporal dominance of this pressure category in the French dataset. After nitrates, it was the second most dominant category in all the patterns extracted, but was found in all biological statuses from high to bad.
A specific biological response to a specific pressure category? Except for heavy metals (MPMI), none of the pressure categories was specific to one biological index in the patterns extracted from the French dataset we used. Many authors who used large national indices and several biological indices reported that all biological groups responded firstly to macro-pollutants in general  (Dahm et al. , 2013; Marzin et al. , 2012) , particularly to organic matter and nutrients  (Villeneuve et al. , 2015). Haury et al. (2006)  showed that the IBMR was sensitive to trophic disruption but also to heavy organic pollution. Nutrients did not appear to be specific to patterns extracted for IBMR or IBD indices. However high and good IBMRs (generating contexts) were associated with High PHOS and MOOX or good NITR.
PHOS was also the dominant pressure category for patterns extracted for IBD index (dominant context). Nevertheless, excess nutrients are known to have indirect effects on aquatic organisms, especially on macroinvertebrates  (Dolédec et al. , 2006; Lemm and Feld, 2017). The pressure acidification (ACID) was well represented (18%) in the patterns extracted for the IBD index, whereas  Larras et al. (2017)  found a weak correlation between this pressure and diatoms, but their approach using life history traits differs substantially from our approach. Fishes are known to have lower responses to physico-chemical pressures  (Dahm et al. , 2013; Marzin et al. , 2012; Villeneuve et al. , 2015). Indeed, the patterns extracted for IPR contained the lowest number of pressures (6/10). Few authors who used large datasets tested biological responses to all micro-pollutant categories, except  Larras et al.
(2017)  and  Mondy and Usseglio-Polatera (2013)  respectively for diatoms and macroinvertebrates using the same French dataset. These authors found good biological responses for pesticides but poor responses for polycyclic aromatic hydrocarbons (PAH), heavy metals (MPMI) and other organic hydrocarbons (MPOR), whereas the last pressure category was widely represented in the patterns we extracted in our study.
5  and pattern 442, Fig. 6 ). Studies of the change effect in pressure categories over time are scarce or limited to general trends in the case of degradation such as the hydraulic management of large rivers (e. g. Fruget et al. , 2001; Trémolières, 1994)  or more recently in the case of restoration (e. g. Meyer et al. , 2013; Staentzel et al. , 2017). However databases storing temporal monitoring data on rivers do exist. Most studies used data on each site on each sampling occasion as not accounting for time information to increase the gradient of measurements before timeless treatments, because the methods used were not able to incorporate the temporal dimension  (D'heygere et al. , 2006; Dahm et al. , 2013; Larras et al. , 2017; Marzin et al. , 2012; Mondy and Usseglio-Polatera, 2013; Villeneuve et al.