dataesr/scientific-paragraphs-categorization huggingface dataset:
It combines assumptions and concentration measurements of environmental agents in air, water, soil, and food, with data on inhalation and ingestion rates, to produce an estimate of intake of the environmental agent. To describe the ingestion pathway, it was necessary to define the body weight, the food behavior, the water consumption, the soil quantity ingested, and the auto-consumption factor of the considered population. In the context of environmental health inequalities cartography, the exposure scenarios needed to allow the comparison among the different defined groups. To describe more realistic exposure scenarios, subgroups in terms of age were defined (children 0-17 of age and adults of age 17-70). These two age-class exposure variables could easily be determined from the French national studies, and corresponded to a compromise between the calculation time and age class variability relevance.
Receptor parameters were built to account for inter-and infra-regional disparities in the population ingestion comportment. The consumption rates of the different food categories were derived from the French database CIBLEX (French exposure variables database), which is a compilation of different available exposure factors, at a regional scale. To assess the exposure dose, predicted concentrations of the substance of interest, from local exposure media and national data for commercial foodstuffs, were integrated in the model. The pathways of exposure considered here were: The estimation of concentrations in the homegrown products was done by combining air, soil, and transfer models. The exposure associated with the consumption of products, outside the study zone, were also included. The model's equations were adapted, considering the local and chronic nature of exposure.
• Estimating the mean multi-annual concentration by municipality, considering the network complexity. Left censored data have been a ubiquitous problem in environmental sciences. Different approaches have been proposed, the most simplistic one being the substitution of under the detection limit measurement with a constant. However, this kind of method has been proven to introduce great bias in the estimation, as well as under-estimating the variance when the percentage of left-censored data was well over 5%  [4]. An alternative was the use of multiple imputation. The idea was to replace each missing value (non-detected) with a set of plausible values that represent the uncertainty about the right value to impute, instead of filling in a single value for each missing value. These multiple imputed datasets were then analyzed using the standard procedures for complete data and combining the results from these analyses.
Usually in environmental science, a model-based imputation is suggested  [27]. Model-based imputation consists of generating random observations below the detection limit, from a distribution estimated from the detected sample values, and analyzing them as complete cases. Such a method would not work with these data, since there were numerous facilities for which most or all the measurements were beneath the detection limit, making it unreliable to fit a distribution for each facility. Honaker  [28] , has proposed a bootstrap-based expectation-maximization (EM) algorithm to perform the multiple imputation. This method consists of creating multiple "complete" datasets, that could then be analyzed as complete cases. The algorithm treats time series-cross sectional data, by using the Expectation-Minimization (EM) algorithm on bootstrap samples of the original data, to draw values from the complete data parameters.
In this manner, the correlation between these substances could serve for the simulation of the concentration measurements under the detection limit. Once the imputed datasets were retained, the estimation of the concentrations for each water distribution unit was carried out as the multi-annual weighted mean. The weights were calculated by the segments of the influence method  [16]. According to this method, a median was traced between two neighbor events, then a weight was assigned to each realization. This weight was equal to the number of days between the two included segments, divided by the total number of days between the starting date (1 January 2000), and the finish date (31 December 2012). Finally, the results were spatialized by georeferencing the concentrations, for each municipality, to the 3 × 3 km grid of reference.
As it is not possible to directly test its performance, we applied the imputation method on a more complete pollutant dataset (water arsenic concentration correlated with selenium pollutant). The new dataset for the simulation was chosen to correspond to different pollutants of the same water information system. We artificially substituted the known concentrations with different DL thresholds (3 µg•L -1 and 5 µg•L -1 resulting, respectively, in 82% and 88% of the measurements under the detection limit). We examined the 'bias' using Absolute Mean Error (AME) and Root-Squared Mean Error (RMSE) statistics to estimate how close the imputed values were to the actual observed values. To demonstrate the properties of the method, we compared statistics from those obtained for the naive methods for handling data of this nature, including a simple substitution with DL/ √ 2 and DL/2.
Kriging is then used to interpolate the transformed values. The interpolated values were calculated as the probability of exceeding the detection limit. This probability variable was then used as a predictor in residual kriging. The second predictor that could be considered for the residual kriging, was the distance between the locations and the pre-identified polluted sites. The variable was constructed using the BASOL database. First, a buffer was built around the polluted sites. Then the distance between the polluted site and the monitoring locations that fell inside the buffer, was calculated. Different buffer radii was tested to find the optimal inverse distance decay function, using the Akaike criterion  [34]. Once the auxiliary variables were constructed, residual kriging was then employed to interpolate the concentration measurements.
Water B[a]P concentrations under the detection limit were estimated through the multiple imputation method. The strong correlations between substances (85-90%), provided a solid foundation to apply an imputation method for the cross-sectional time series (A Program for Missing Data II: AMELIA II R Package)  [28]. For each water distribution unit, the high correlations between the concentrations of the substances and the time trends were also considered for the estimation of the missing values. A minimum of five imputed sets was usually sufficient, however, due to the high percentage of values under the detection limit, eight imputed sets were obtained instead. An example of the concentrations for B[a]P, before and after the imputations for the two units, can be seen in Figure  2. The variation observed among the imputed datasets depended on the observed data before the imputation.
For treatment facilities that had all their monitored values under the detection limit, only correlation between the substances was considered for the imputation. Once the imputed datasets were produced, the multi-year weighted concentration for each treatment facility was calculated and so was the concentration for each municipality, weighted by the population served by each facility in each municipality. Finally, the concentrations were georeferenced on the 3 × 3 km grid to be included in the spatial model. The spatialized results for the municipalities are shown in Figure  3. Table  2  shows the estimated parameters to compare the difference between the true and the estimated values obtained from our imputation and the naive methods, for the arsenic correlated with selenium pollutant. For the imputation method, the variation that was observed could be partially explained due to the auxiliary variable selected and their weak correlation.
However, comparing with the methods of replacing the values under the detection limit, either by DL = 2 or DL = / √ 2, the imputation method had the smallest absolute mean error. These simulations demonstrate that the imputation method exhibited a better performance in terms of reduced bias. The initial topsoil concentration data are presented in Figure  4. The probability of a measurement to be above the detection limit (to be used as a covariate in the model) was calculated by the indicator kriging (Figure  5a ). The second covariate was the distance from the polluted sites (Figure  5b ). According to the Akaike criterion, the best buffer to consider is 3 km. Along with those two variables, fourteen soil properties were tested to be included in the random forest model. The optimal model was selected to be the one that minimized the Out-Of-the-Bag error.
Discussion The assessment of health risk associated with exposure to environmental hazards and the analysis of the spatial variation of risks, require, both, accurate and detailed population and environmental data, as well as clearly defined exposure assessments. Efforts were made to select complete, accurate, and up-to-date datasets, to include in the process of exposure assessment. In environmental modeling and the evaluation of environmental health inequalities, data from different sources were usually considered-emission sources, environmental quality, exposure, and population. Data were selected according to availability of harmonized databases and their geographical and temporal representativeness. Depending on the case, data that did not exactly meet the objectives of the study, could be adapted with appropriate processing, and constituted a robust base to assess the human health risk.
To overcome the limitations imposed by the quality of the available databases, statistical and geostatistical processing techniques were employed, addressing the issues that arose from each environmental compartment, separately. Due to its hydrophobic nature, B[a]P was found in water in small concentrations, therefore, the exact measurement could not always be reported. Reconstructing these concentrations was not trivial, especially when the rate of observations under the detection limit was high. An imputation method was selected to treat those values under the detection limit to avoid distortion when estimating the contribution of the drinking water as an exposure pathway towards the total exposure. The imputation that was performed, considering the relationship between the B[a]P and other PAH substances, as well as the temporal correlation, allowed for capturing a more realistic spatial distribution, compared to a simple substitution method that would result in a smooth, uniform result.
As a result, a refined spatial distribution of the B[a]P might also have reflected the pollution origins, and the variance between the imputed datasets provided a quantification of the associated uncertainty. Environmental data were usually collected from monitoring locations, and therefore, were samples and did not provide sufficient spatial representativeness or exhaustive characterization of the environmental media. In many cases, the use of geostatistical methods that could consider several auxiliary/secondary variables were employed to address this issue. Combining complementary databases-such as the emissions, population, and altitude, in the case of air and the physio-chemical properties and distance from the polluted sites, for the case of soil-allowed for capturing the phenomena, more exhaustively.
After calibrating the model, the random-forest residual kriging was shown to outperform linear regression. Random forest regression was also known to be more expensive, cost-wise, however given the size of the database and the improvement of the result, the difference was negligible. Starting with the available sparse spatial monitoring databases and with the appropriate processing, we assembled a refined spatial database to serve as an input in the spatial exposure model. Benzo[a]pyrene, which was the focus of this study, was a good indicator of the rest PAH substances, due to the strong correlation between them and the fact that it was found in higher concentrations than the rest. In this approach, an upstream environmental compartment was used as an input for the model, to predict the other, or was used as a secondary information to increase the data resolution.
Conclusions Spatial identification of environmental health inequalities, their determinants, and susceptible populations were studied through the integration and processing of spatial data to construct exposure indicators: • GIS provided geoprocessing tools to assembled databases from different sources; • developed statistic methods improved the spatial and temporal data representativeness; • the exposure multimedia linked the environmental and exposure compartments; • the exposure assessment framework provided a methodological framework to combine and integrate all exposure pathways. The data processing and spatial predictions, unavoidably, introduced uncertainty in the model. Quantifying this uncertainty was important and should have accompanied the results, to avoid potential misclassification of areas and populations at risk of overexposure.
An exhaustive description of exposure required the integration of data from different environmental compartments and supports. However, limitations due to data availability or quality usually arose. The use of these databases was not initially assembled for the objective of estimating exposure, which might have led to biases in the estimations. Common problems included limited number of observations, spatial and temporal heterogeneity, lack of meta-data, and measurements under the detection limit. In this work, to address the issues that arise from the poor quality available data of the B[a]P, in the three environmental compartments, statistical and geoprocessing techniques were employed. Specifically, temporal correlation and relations between the variables were employed in an effort to make up for the under the detection limit values.
Moreover, complementary databases were used to construct interesting auxiliary variables to improve the spatial data representativeness. The refined spatial databases assembled then provided a more robust base to assess the human exposure. Our results led us to identify the pollutant sources, determinants of exposure, and potential hotspot areas. In this way, areas and population in risk of overexposure could be identified early, allowing the public authorities to implement policies to manage the phenomena. Exposure indicators and data processing algorithms will be integrated in the French coordinated integrated environment and the health platform PLAINE, to map and analyze the environmental health inequalities, at the national scale, for other substances (such as pesticides).
Acknowledgments: RMQS soil sampling and physico-chemical analyses were supported by the  GIS Sol , which is a scientific group of interest on soils involving the  French Ministry for ecology and sustainable development and Ministry of agriculture , the  French National forest inventory (IFN) ,  ADEME (Agence de  l'environnement et de la maîtrise de l'énergie, which is a French government agency concerned with environmental protection and energy management),  IRD  ( Institut de recherche pour le développement , which is a French public research organization dedicated to southern countries) and  INRA (Institut national de la recherche agronomique , which is a French public research organization dedicated to agriculture). Claudy Jolivet  is thanked for his strong involvement in the RMQS monitoring network.
Although there were several severe accident experiment campaigns  [8] [9] [10] [11] [12]  which produced valuable databases  [13] , there remain substantial uncertainties that need to be reduced further  [14]. In particular, there is scarcity of experiments with prototypic reactor components and materials. As a result, significant discrepancies are often observed in key quantities predicted by different codes such as hydrogen generation, coolability of the core during reflooding, vessel failure, and molten corium-concrete interaction. Attempts to identify research priorities needed to advance the severe accident knowledge and code validation are reported in  [15, 16]. To address the gap in the experimental database and in modelling and simulation, many multinational efforts were launched to guide research and development efforts.
Acknowledgments: The authors want to thank especially  E. Dudas  for very insightful discussions. This research has been supported by the  (Indo-French) CE-FIPRA/IFCPAR  Project No. 5404-2. Support from  CNRS LIA-THEP  and the  INFRE-HEPNET of CE-FIPRA/IFCPAR  is also acknowledged. G. B. acknowledges support of the J. C. Bose National Fellowship  from the  Department of Science and Technology, Government of India (SERB  Grant No. SB/S2/JCB-062/2016 ). M. D. acknowledges support from the  Brazilian Ph. D. program "Ciências sem Fronteiras" -CNPQ  Process No. 202055/2015-9. This work was also supported by the  France-US PICS  no. 06482 , PICS MicroDark. M. D. and Y. M. acknowledge partial support from the  European Union   Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie :  RISE Invis-iblesPlus  (Grant No  690575 ) and the  ITN Elusives  (Grant No  674896 ).
, 2011). Using these data and geostatistical technics, we simulated SOCS maps at the ICOS ecosystem station for two dates. Using these results, we realized 100 virtual samplings of increasing number of cores. Using this method, we investigated the minimum detectable changes in SOCS, according to compact strata probability sampling, assuming future re-sampling of a various set of cores and a constant spatial variance in time (e. g. same variogram for the two sampling rounds). As a correlation between sampling times is expected, but unknown, we did the simulations for two extreme values of this correlation, 0. 2 and 0. 8.
, 2008; Morvan et al. , 2008)  rather than pedogenic horizons,e. g. to avoid subjectivity and to allow comparison between sites. Moreover, there may be different successions of horizons within a single site, hampering the formulations of averages. Another rationale is that it is better to have fixed increments for data assimilation in models that will be run for all ICOS ecosystem stations. Finally, the depth increments we recommend are consistent with the increments adopted by the international GlobalSoilMap initiative  (Arrouays et al. , 2014). Organic surface layers (O horizons) are characterized by a much higher turn-over rate of C than mineral layers. Therefore it is mandatory to sample them separately. Moreover, they are also characterized by a higher shortrange heterogeneity than mineral layers.
Soil description The first core or pit of each subplot should be described and recorded by a soil scientist or an adequately trained staff. The following data must be reported: -Soil type following WRB classification  (FAO, 2014; IUSS Working Group WRB. , 2015) : (e. g. , Albic Stagnic Luvisol (Cutanic, Endoclayic)) (http://www. fao. org/3/ i3794en/I3794en. pdf) -Horizons description: each horizon of the profile must be described using the following variables: -Upper and lower vertical limits of the horizon -Color of the horizon using the Munsell color code chart -Soil water state, estimated according to 5 classes (dry, moist, wet, wet-satiated, saturated; e. g. NRCS-USDA, 2012) -Texture of the horizon using field finger test -The presence of stones and their nature -Diagnostic horizon name (if applicable) according to the FAO WRB (FAO, 2014, http://www.
The researchers analyzed the safety factors affecting the tower crane externally on the whole but failed to study or analyze the structural forces. By building Tower Crane (TC)option, supporting design flow, and related databases, Sohn Hyo Won  [2]  proposed a method for optimizing economic feasibility under promotion and stability conditions. Mara and T. G. [4]  conducted wind tunnel tests to examine the effects of tower crane solid ratio and location on the overall wind load of building crane system. Their study provided a reference for the safety properties at tower crane support connections under wind loads, but they failed to consider the influence of wind loads on stresses and deformations at support connections. Li Yunling  [5]  claimed the necessity of dynamic analysis on tower cranes under seismic action and pinpointed the key factors based on the damage characteristics of tower cranes.
Comparison of model calculations with test results The load-displacement data were extracted from the ABAQUS numerical simulations and compared with the experimental data to mutually verify the correctness and reliability of the data. The three specimens were crosscompared, as shown in Figs. 10 11 12. As shown in Fig. 10 , the specimen is a reinforced embedded part with no pegs on the end slab. The test and simulation values of ultimate capacity differed by 10. 6%, which is acceptable. The bearing capacity is always controlled by the strength of shear wall concrete because of the same damage pattern between the two. However, the two exhibited a large difference in the displacement curve. In the early loading stage, the elastic modulus of concrete defined in the simulation calculation can rather truly reflect its actual force state under the test loads because the concrete is still in the elastic stage without crack generation and the structural stiffness does not undergo significant change.
The possible damage patterns of the embedded parts include the cross-sectional area of the tensile strength of the anchor plate, weld connection of the anchor plate, overall punching damage considering the group anchorage effect, and bending damage of narrow limbs outside the wall, among others. In this study, the force performance of the attached to the wall joints of high-performance jib crane was investigated. Through comparative analysis, the reliability of the static pullout test data and the rationality of the finite element models were mutually validated. The research results were further extended on the basis of the validated finite element models, which are of referential significance to the analysis of the force performance of such joints and of the working mechanism for the enhancement of the seismic resistance of the joints.
Emerging infectious diseases Frederic Bartumeus (Blanes Advanced Study Centre, Girona, Spain) described the organisation of a Platform for the Integrated Control of Arbovirosis in CATalonia (PICAT): Mosquito Alert 2. 0  [8]. Traditional methods for tracking disease-carrying mosquitoes are hitting budget constraints because the scale over which they must be implemented is growing exponentially. Citizen science offers a set of innovative tools for public health management, allowing sustained and flexible data collection while facilitating public participation in problem solving  [9, 10]. There is increasing evidence that combining citizen scientist data with other sources of information significantly improves our knowledge in a given area  [11]. In particular, once we adjust for sampling bias, vector data obtained through the Mosquito Alert citizen science programme has almost the same quality and predictive power as that obtained through traditional surveillance.