dataesr/scientific-paragraphs-categorization huggingface dataset:
HOTVOLC: Geostationary-data-driven operational service HOTVOLC is a Web-GIS (Geographic Information System) volcano monitoring system (Fig. 1 ) using SEVIRI (Spinning Enhanced Visible and Infrared Imager) sensor on-board METEOSAT geostationary satellite (https:// hotvo lc. opgc. fr) and developed at the OPGC (Observatoire de Physique du Globe de Clermont-Ferrand) in 2009 after the installation of the first receiving station. The spectral bands of the SEVIRI sensor allow the HOT-VOLC system to simultaneously characterize volcanic ash, sulfur dioxide, and lava flow emissions. It is designed for the real-time monitoring of ~ 50 active volcanoes and provides high value-added products at the frequency of one image every 15 min with a pixel resolution of 3 × 3 km at nadir. HOTVOLC is open-access and data can be downloaded from the entire database covering the period 2010-2021.
Satellite products are delivered in the form of (i) geo-referenced images (geotiff ) tiled on a background map, and (ii) time series (csv) associated with interactive data visualization technologies. HOTVOLC is part of the SNOV and is labelled by the CNRS since 2012. Within this framework we ensure real-time monitoring of French volcanic targets, as for Piton de la Fournaise effusive eruptions (e. g. ,  Peltier et al. , 2021; Thivet et al. , 2020). Also, we provide timely information on other volcanic targets whose products may affect French territories such as the Icelandic 2010 Eyjafjallajökull eruption (e. g. ,  Bonadonna et al. , 2011; Labazuy et al. , 2012) , whose volcanic ash plumes reached the French airspace. Since 2018, HOTVOLC falls under the official function of Meteo-France  (Gouhier et al.
2 ). The system currently monitors over 70 volcanoes worldwide, but the number is regularly increasing as its flexible design allows for rapid addition of new volcanoes in response to volcanic unrest in any part of the globe. In this study we will only present Sentinel-2 data from MOUNTS, here used to derive information on lava flow field emplacement. Sentinel products are automatically downloaded from the Copernicus Open Access Hub as soon as they are available (typically 2-12 h from sensing for Sentinel-2 L1C products), and immediately processed and published on the MOUNTS website (typically 0. 5-3 h after availability online). Sentinel-2 images are acquired from two polar-orbiting satellites (Sentinel-2A and -2B, launched in 2015 and 2017 respectively), and placed 180° from each other in the same sun-synchronous orbit.
The resulting DEM, with 2 × 2 m Ground Sampling Distance (GSD) was co-registered to the IslandsDEMv0 using the co-registration method by  Nuth & Kaab (2011) , implemented in python by D. Shean (https:// github. com/ dshean/ demco reg,  Shean et al. , 2016) , and the corresponding orthoimage, with 0. 5 × 0. 5 m GSD, was shifted horizontally by the same vector. Lava outlines were manually digitized from the co-registered Pléiades orthoimage. The difference in elevation between each Pléiades DEM and the IslandsDEM resulted in a lava thickness map, which was subsequently cropped using the lava outlines. This allowed the calculation of the lava volumes, by estimating an average thickness value multiplied by the area of the lavas. The uncertainties in the lava volume were estimated using the Normalized Mean Absolute Deviation (NMAD,  Höhle and Höhle, 2009)  in the unchanged areas around the eruption site, conservatively assuming this value as the uncertainty in the average lava thickness.
, 2022). The flights collected vertical aerial photographs at a 3-6 s interval 2000-6000 feet above ground, ensuring an overlap of 70% to 90% between photographs along a flightline. These photographs were processed using MicMac  (Pierrot Deseilligny et al. , 2011 , Rupnik et al. , 2017) , in a semi-automated workflow following the steps described by  Belart et al. , (2019). The photogrammetric processing required the digitization of Ground Control Points (GCPs). The GCPs were extracted from an orthomosaic and a DEM obtained from a previous photogrammetric survey done on the 7th of March, with similar level of detail as the photogrammetric surveys carried out during the eruption. The data was processed into a DEM of 1x1 m GSD and an orthomosaic of 0. 2x0. 2 m GSD, which allowed retrieval of areal coverage, lava volume, lava thickness and effusion rate in a similar manner as described in Sect.
Sentinel-2 hot spots detection The Sentinel-2 MSI satellite images, are analyzed by applying a recent volcano-dedicated, automated and global hot spot detection algorithm based on fixed ratios in the shortwave infrared (SWIR), with a contextual threshold derived from a statistical distribution of anomalous pixel clusters  (Massimetti et al. , 2020). The aim of the algorithm is to precisely detect the hottest portion, directly related to the ascent of magmatic fluids subaerially exposed, and to quantify the number of thermally active pixels, or in other words the radiant emitting hot area of a lava body. Indeed, SWIR signals (1. 1 to 3. 0 μm) record almost purely thermal emissions produced by hot emitting surfaces  (Harris, 2013; Blackett, 2017). Sentinel-2 dataset (Level 1C Product) is analyzed considering the SWIR top-of-atmosphere (TOA) reflectances in the ρ12 (2.
Meteosat-SEVIRI As a geostationary platform, the MSG-SEVIRI satellite allows rapid detection of lava hot spots as well as the estimation of quantitative parameters such as lava volume and lava effusion rates. This operational effort is currently being carried out by the HOTVOLC web-service, especially for Icelandic targets where volcanic eruptions are frequent. Therefore, results presented here directly come from data of the HOTVOLC platform, i. e. , in crisis response mode, and no offline processing has been carried out for this particular case. This fills the main objective of the paper, that is, to show how satellite data can assist rapid decision making and response with online data using operational routines. In Fig. 8 , we show a time series of the lava Volume Flow Rate (VFR in m 3 /s) for the first 10 days of the eruption, associated with the cumulative lava volume over the same period.
The absence of prior usage for hazard monitoring is probably linked to non-systematic availability of these datasets. For the Fagradalsfjall eruption, Pléiades acquisitions were available, during the first ten days of the event, thanks to a special tasking request made to Airbus DS by CNES after the CIEST 2 activation. We benefited from a favorable situation where the eruptive event had been anticipated and weather conditions during this period were quite good. The systematic acquisitions over the eruption site lasted for 10 days but additional stereo Pléiades images have been acquired subsequently (28 th of April and 2 nd of July) by the Icelandic Volcanoes Supersite project supported by the Committee on Earth Observing Satellites or by commercial requests. Both the subsequent reanalysis of the results produced initially in an operational way and the comparison with area, thickness, volume, and effusion rates derived from airborne surveys validate the near-real time estimations obtained in "response mode" and rapidly provided to local authorities for crisis management.
In addition, Pléiades images have the potential to provide useful complementary information on the state of the volcanic plume (elevation and velocity). For the response mode, we relied on local processing chains, quickly adjusting off-theshelf tools. Indeed, operational monitoring platforms for volcanic activity like MOUNTS or HOTVOLC usually takes advantage of systematic and freely distributed satellite acquisitions. In this study, by comparing the lava flow area and effusion rate estimations derived from Pléiades images with, respectively, the area and effusion rates obtained from respectively Sentinel-2 data and from MSG-SEVIRI data, we confirmed the potential of these open-access platforms to quantitatively provide robust real-time information for effusive eruption monitoring (see Table  3  for a summary of results obtained by various independent methods).
Acknowledgements HOTVOLC data processing performed by MG and YG was supported by  French CNRS-INSU  through the  National Observation Service in Volcanolgy (SNOV) , and the  French Space Agency named Centre National d'Études Spatiales (CNES). Pléiades images have been acquired thanks to the  CNES  via  CIEST 2 (© CNES 2021 , Distribution Airbus DS), CIEST 2 is part of  ForM@Ter  (https:// en. polet erres olide. fr/) and supported by  ISDeform National Observation Service. VP was supported by the  CNES  project  MagmaTrack. We also thank the handling editor for helpful corrections.
Liu et al. [23]  analyzed the possibility of improving the structure of heat pipes implanted in walls, for a better heat transfer performance. Li et al. [24]  and  Zhang et al. [25]  investigated the dynamic heat transfer performance of the wall implanted with heat pipes during heating season. According to their report, the suggested method can save 21. 61% of energy, and the heat flow ratio to the inside of the wall is 4:1. In the same manner, the wall with heat pipes implanted was analyzed during the winter season by Sun et al. [26]. The performance of a proposed heat pipe-assisted solar wall was investigated by  Michael et al. [27]  through simulation and experiments. The heat pipe model was calculated using electrical resistances, and to simulate hourly performance, MatLab codes were used.
Numerical Approach to the Global System To choose an optimum design for the experimental stand, the global sy signed in 3D using Autodesk Inventor software and subjected to numerica using Autodesk CFD software. The geometry of the global system presente was reproduced at a real scale for the modules built in the experimental mo  A constant temperature of 15 • C was applied to the exterior air volume, which reproduced the initial conditions of laboratory indoor air. Adiabatic conditions were imposed for the insulated components, leading to a heat flux of ϕ = 0 W/ m 2 applied on the polystyrene surfaces. Various boundary conditions of heat fluxes, from 100 to 250 W/m 2 , were applied on the absorber plate and heat pipe evaporator surfaces. Due to the software's limitation with regard to simulating the heat pipe multiphase change when the evaporation-condensation process occurs, a new material with a very high and constant thermal conductivity was created and assigned to the heat pipe inner volume.
From the software database, materials were assigned: glass for the glass; wood for the wood shell of the solar heat absorber and for the room; and copper for the heat pipes. The geometry was discretized in 300,000 elements using the mesh generator of the software. Based on automatic local curvature, the meshing of the solid and fluid zones was generated at a medium size while the contact between them was generated at a smaller size for better capture of the heat transfer. Assuming a laminar incompressible flow at hydrostatic pressure, the heat transfer mechanisms by radiation, convection, and conduction were proposed to be resolved during the simulation.
Charging Cycle All 4 concrete samples were subjected to a 10 h charging cycle and a 14 h discharging cycle. The data logger recorded the temperatures for all 11 sensors every 60 s. Tables  1 2 3 4 present the average temperature at the surface of the concrete blocks recorded by the sensors at specific moments, after one hour, five hours, and at the end of the charging cycle (10 h). The discharging cycle begins once the solar simulator is turned off, after 10 h. The cycle lasts for 14 h, thus completing a 24 h cycle. The following figures present the temperature variation of all 8 sensors placed on the external face of the concrete wall, for all of the samples tested. Figure  4  presents the variation of temperature recorded by the sensors for the control sample. ER REVIEW 6 of 14 In the first hour, the heat pipes increased the average temperature of concrete sample CW01 by 1 °C in all 4 tests.
We propose a theoretical study of two realistic estimators of conditional distribution functions and conditional quantiles using random forests. The estimation process uses the bootstrap samples generated from the original dataset when constructing the forest. Bootstrap samples are reused to define the first estimator, while the second requires only the original sample, once the forest has been built. We prove that both proposed estimators of the conditional distribution functions are consistent uniformly a. s. To the best of our knowledge, it is the first proof of a. s. consistency and including the bootstrap part. The consistency result holds for a large class of functions, including additive models and products.
, X d }. Random forests are also related to some local averaging algorithms such as nearest neighbors methods  [20, 3]  or kernel estimators  [30]. More precisely, thanks to  [20] , the random forest method can be seen as an adaptive neighborhood regression procedure and therefore the prediction (estimation of the conditional mean) can be formulated as a weighted average of the observed response variables. Based on that approach, we develop a Weighted Conditional Empirical Cumulative Distribution Function (W C ECDF) approximating the Conditional Cumulative Distribution Function (C CDF). Then, α-quantile estimates are obtained by using W C ECDF instead of C CDF. [23]  defined a W C ECDF with weights using the original dataset whereas we allow to construct the weights using the bootstrap samples, as it is done practically in regression random forests.
We prove the almost sure consistency of these estimators. The main hypothesis are: (1) the convergence to 0 of the variation of the conditional distribution function on leaves; (2) a control of the size of the leaves, implying that the trees are not fully developed. In Section 5, we prove that the first hypothesis is satisfied for a large class of models, including additive models and product functions. Moreover, the first hypothesis is also satisfied for some modified version of the CART algorithm, such as in  [23]. The second hypothesis may be seen as a stopping rule in the algorithm, so that it should be controlled by practitioners. An implementation of both algorithms is made available within a Julia package called ConditionalDistributionForest  [14]  as well as a python package named qosa-indices,  [13].
The C CDF can be seen as a regression function. Several authors such as  [7, 4, 35, 32, 25, 34, 16]  have established asymptotic properties of particular variants and simplifications of the original Breiman's random forest algorithm. Facing some theoretical issues with the bootstrap, most studies replace it by subsampling, assuming that each tree is grown with s n < n observations randomly chosen without replacement from the original dataset. Most of the time, in order to ensure the convergence of the simplified model, the subsampling rate s n /n is assumed to tend to zero at some prescribed rate, assumption that excludes the bootstrap mode. Besides, the consistency is generally showed by assuming that the number of trees goes to infinity which is not fully relevant in practice. Under some conditions,  [28]  showed that if the infinite random forest regression estimator is L 2 consistent then so does the finite random forest regression estimator when the number of trees goes to infinity in a controlled way.
This consistency result holds for a large class of functions called ♠-class which contains additive functions, product functions e. g. So that our result is much broader than previous ones. Also, a sub-product of the consistency proof is an asymptotic proximity result between empirical and theoretical trees (see Proposition 5. 4), which is interesting in itself for further theoretical studies on random forests. The paper is organized as follows. Breiman's random forest algorithm is detailed in Section 2 and notations are stated. The random forest estimations of C CDF based both on bootstrap samples and the original dataset are introduced in Section 3 as a natural generalization of regression random forests. The main consistency results are presented in Section 4 and a deep discussion on the variation of the conditional distribution function on leaves is proposed in Section 5.
Breiman's random forest The aim of this section is to present Breiman's random forest algorithm as well as notations used throughout this paper. Random forest is a generic term to name an aggregation scheme of decision trees allowing to deal with both supervised classification and regression tasks. We are only concerned with the regression task. The general framework is the nonparametric regression estimation where an input random vector X ∈ X = d i=1 [u i , v i ] ⊂ R d is observed and a response Y ∈ R is predicted by estimating the regression function m(x) = E [ Y | X = x]. We assume that we are given a training sample D n = X j , Y j j=1,. ,n of independent random variables distributed as the prototype pair (X, Y ) which is a (d + 1)-dimensional random vector. The purpose is to use the dataset D n to construct an estimator m n : X → R of the function m.
This criterion is implemented in the RandomForestRegressor class of the python package Scikit-Learn  [26]  or in the build forest function of the Julia  [2]  package DecisionTree. Building several different trees from a single dataset requires to randomize the tree building process. [6]  proposed to inject some randomness both in the dataset and in the tree construction. First of all, prior to the construction of each tree, a resampling step is done by bootstrapping  [12]  from the original dataset, that is, by choosing uniformly at random n times from n observations with replacement. Only these bootstrap observations are taken into account in the tree building. Accordingly, the min samples leaf hyperparameter introduced previously refers, in the random forest method, to the minimum number of bootstrap observations contained in each leaf of a tree.
Conditional Distribution Forests We aim to estimate F (y|x) = P(Y y|X = x). Two estimators may be defined. One uses the bootstrap samples both in the forest construction and in the estimation. The other uses the original sample in the estimation part. Once the distribution function has been estimated, the conditional quantiles may be estimated straightforwardly. 3. 1. Bootstrap samples based estimator. First of all, let us define the random variable B j Θ 1 , D n as the number of times that the observation X j , Y j has been drawn from the original dataset for the -th tree construction. Thanks to it, the conditional mean estimator in Equation (2. 3) may be rewritten as m b k,n (x; Θ 1 ,. , Θ k , D n ) = n j=1 1 k k =1 B j Θ 1 , D n 1 {X j ∈An(x;Θ ,Dn)} N b n (x; Θ , D n ) Y j = n j=1 w b n,j (x; Θ 1 ,.
, n : F b k,n ( Y p | X = x; Θ 1 ,. , Θ k , D n ) α. Let us turn now to the estimator using the original sample. w o n,j (x; Θ 1 ,. , Θ k , D n ) = 1 k k =1 1 {X j ∈An(x;Θ ,Dn)} N o n (x; Θ , D n ) , where N o n (x; Θ , D n ) is the number of points of D n that fall into A n (x; Θ , D n ). As previously, the weights w o n,j (x; Θ 1 ,. , Θ k , D n ) are nonnegative random variables as functions of Θ 1 ,. , Θ k , D n and their sum over j = 1,. , n equals 1. It was proposed in  [23]  to estimate the C CDF with (3. 6) F o k,n ( y| X = x; Θ 1 ,. , Θ k , D n ) = n j=1 w o n,j (x; Θ 1 ,. , Θ k , D n ) 1 {Y j y}. The conditional quantiles are then estimated by plugging F o k,n ( y| X = x; Θ 1 ,. , Θ k , D n ) instead of F ( y| X = x) as before. A python library named qosa-indices has also been developed to perform the numerical estimations of conditional distributions and quantiles for both methods.
It is available at  [13]  and uses Scikit-Learn, Numpy, Numba. Both approaches are also implemented in a Julia package based on the library DecisionTree and that is available at  [14]. It has to be noted that a package called quantregForest has been made available in R  [27]  and can be found at  [24]. The estimation method currently implemented in quantregForest is different from the method described in  [23]. As a matter of fact, for a new observation x and the -th tree, one element of D n = X j , Y j j=1,. ,n falling into in the leaf node A n (x; Θ , D n ) is chosen at random. This gives, k values of Y and allows to estimate the conditional distribution function with the classical Empirical Cumulative Distribution Function associated with the empirical measure.
s. -→ n→∞ F ( y| X = x). Now, thanks to Dini's second theorem, let us sketch how to obtain the almost sure uniform convergence relative to y of the estimator. Note that Y j y = U j F Y |X=x (y) under Assumption 4. 3 with U j = F Y |X=x Y j , j = 1,. , n which are i. i. d random variables. Then, (6. 2) is equivalent to ∀x ∈ X , ∀s ∈ [0, 1] , n j=1 w b j (x) 1 {Uj s} a. s. -→ n→∞ s. As in the proof of Glivenko-Cantelli's Theorem, using that s → n j=1 w b j (x) 1 {Uj (ω) s} is increasing and Dini's second theorem, we get the uniform convergence almost everywhere, which concludes the proof of the theorem. We now turn to the proofs of Proposition 6. 1 and Lemma 6. 2. To that aim, the following lemma, based on Vapnik-Chervonenkis classes  [33]  is a key tool. Lemma 6. 3. Consider D n and D n , two independent datasets of independent n samples of (X, Y ).
Conclusion This article proposes two conditional distribution functions and conditional quantiles approximations based on random forests. The former is a natural generalisation of the random forest estimator of the regression function making use of the bootstrap samples, while the latter is based on a variant using only the original dataset. The consistency of the bootstrap samples based estimator is shown under realistic assumptions and constitutes the major contribution of this paper. Indeed, this is the first consistency result handling the bootstrap component in a random forest method whereas it is usually replaced by subsampling. As for the second estimator, the consistency proof established in  [23]  for a simplified random forest model is extended to a realistic one by taking into account all the randomness used in the procedure.
5 /250 for each tree. These experiments are replicated s = 500 times. Figure  1  below shows the C CDF approximations computed with the estimator using the original dataset on the left-hand side and those of the estimator calculated with the bootstrap samples on the right-hand side. On each graph, the orange plain line is the true C CDF, while the blue ones represent the 95% quantiles of the replications. Figure  1  therefore allows to display and compare approximated curves visually. From a quantitative perspective, the quality of the estimators is measured at each point x using the following average Kolmogorov-Smirnov distance KS(x) = 1 s s j=1 KS j (x). According to the numerical results displayed in Figure  1 , estimators perform well for points that are well represented in the training sample but, the performance decreases for extreme points.
1 to 0. 9, the distribution of the estimators of the conditional quantiles computed with the original dataset on the left-hand side and with the bootstrap samples on the right-hand side. The estimates have been calculated with the following setting: a sample of size n = 10 4 , a forest grown with n trees = 500 and the minimum number of samples required to be at a leaf node set to min samples leaf = √ n • log(n) 1. 5 /250. In order to assess the quality of the estimators at these points, the , where qα j (x) is the estimator on the j's sample, j = 1,. , s. Based on the graphs obtained in Figure  2 , it seems difficult to say if one estimator is better than the other. It appears that the performance of the two estimators differs a bit depending on the observation x. In order to get global measures of both estimators, we define an averaged version of the previous indicators computed with p = 5 × 10.