dataesr/scientific-paragraphs-categorization huggingface dataset:
Questionnaire design Data for this study was collected by means of a self-administered questionnaire (see in appendix). The questionnaire was entitled: "Are you prepared for the evacuation of the Front de Seine towers?". It was designed to gather data on household intentions regarding an autonomous evacuation (that is to evacuate or to remain at home) and the availability of evacuation destinations as well as modes of self-travel in the case of major flooding of the River Seine. Even at the international level, there were only a few surveys on preparation for evacuation and decision making in the event of flooding with slow kinetics  (Fujiki, 2017). Becerra et al. (2013)  asserted, however, that when a hazard is weak, vulnerability is also weakened. Often, the existing surveys deal with the case of hurricanes, tsunamis or earthquakes (fast kinetics).
Characterization of the deposited NPs through SEM The samples prepared for investigating the optical H 2 sensing properties of the synthesized particles were obtained by deposition of particles on the glass substrate following the procedure reported above. The topography of the samples has been investigated by SEM as a function of different densities of core-shell nanoparticles. SEM images were obtained on a Zeiss GeminiSEM 500 working at 3 keV with the secondary electron emission with limiting vertical resolutions from 1 nm to 20 nm and total yield detection modes to contrast the topographic from the chemical information (Fig. 2 ). The SEM images are analyzed by software "ImageJ," which allows us to determine the average size of core-shell nanoparticles and the density in the form of a histogram by the binarization and thresholding of SEM images.
This study shows very well that the optical sensors require a precise composition of the core-shell system and a good control of the particles' deposition on the optical device. For the simulation study, we design an FDTD (Finite Differential Time Domain) model based on our experimental parameters. The FDTD calculations are based on the resolution of the Maxwell equations in the time domain. The model leads to more information (field exaltations, transmission, and so on) necessary to study the wave propagation in different materials for different nanostructured systems. 15 The results are then compared with the experimental results obtained by the optical spectrum analyzer measurements. The calculations are performed using FDTD Lumerical software and were focused on the light absorption properties of the system.
ACKNOWLEDGMENTS The authors would like to thank:  S. Roques  and  J. Bartringer  at  ICube/MaCEPV ,  Strasbourg. J. Faerber  ( SEM Images ) at  IPCMS, Strasbourg , is acknowledged for his contributions. Nicolas Javahiraly  and  Arnaud Brioude  acknowledge support of the  ANR  ( Nhyledect Project ) from the  Agence Nationale de la Recherche (ANR).
C. Required laser power The dimensioning of such an experiment has been achieved by numerical simulations with COMSOL Multiphysics Software (Finite Elements Method). Depending on the required spatial gradients and temporal dynamics, the laser beam and temporal profile can be adjusted to reach the required conditions. With a typical thickness of the disk sample in the range of 0. 5-1mm, few hundreds Watts of laser power for the transient annular laser beam heating are required to reach temperature close to 2000°C in few tens of milliseconds (Example Fig. 2 ). Fig. 2. Simulation of radial temperature distribution at the surface of a UO2 disk submitted to 300W annular laser beam heating during 100ms (in the configuration shown on Fig. 1 ). The initial tempeature in this example is 900°C.
1541 nm) in θ-2θ configuration (step size of 0. 02 • ), equipped with a fast counting Bruker LynxEye detector, with Silicon strip technology. Rietveld refinement was performed by MAUD (material analysis using diffraction) software  [54]. XRD data were analyzed to obtain structural parameters: experimental (a exp. ) lattice parameters, X-ray density (ρ xrd ) and mean grain diameter (<D xrd >). The distribution of cations on tetrahedral and octahedral sites in the studied samples was determined by analyzing the XRD pattern, employing the Bertaut method  [55] , as was also reported in earlier reports  [56] [57] [58]. XRD intensity depends on the atomic position in the spinel unit cell whereas the XRD peak position relies on the size and the shape of the unit cell. Bertaut's method utilizes the following pair of reflections: (400), (422) and (220), (400), according to the expression: I obs hkl I obs h k l = I cal hkl I cal h k l (1) where I hkl obs and I hkl cal are respectively the observed and estimated intensities for the reflection (hkl).
3 L kg -1 , initial volume = 7 L kg -1 , recirculation three times without evaporation) the energy demand should decrease to 23 MJ kg -1. Further environmental benefits and drawbacks of the optimized two-stage process for birch bark conversion into biofuel were evaluated by LCA. 27 LCA is a tool that quantifies the potential environmental impacts of products throughout their entire life cycle from raw material extraction to the end of life. The applied methodology follows the four-phases framework standardized by ISO 14040  [ISO]. Further details and the data and assumptions used in the analysis are given in ESI section 5. † It should be noted that the technology should not be implemented at areas where it could compete with people who rely on birch bark for living, for example the Sami people in north of Scandinavia.
This kind of procedure is called pixel-wise classification, a scenario where generally two main variables are required, the pixels as samples to be classified and the distinctive features (like spectral response or neighborhood information) as characteristics to be classified upon. Moreover, the samples-to-features relationship can be described in a matrix form. Naturally a HSI acquires the spectral features of the scenes, mapping each pixel position, represented by a couple of positive integers (x, y), to a vector of positive real values that correspond to the material's spectral response measured by the sensor, resulting in a structure that is often represented as a data cube of third-order¹. When the pixels are rearranged in lexicographic order as samples, i. e. the first two modes merge into one, the spectral mode can then represent the set of features, making it a direct way to classify a HSI.
However, due to the high spectral resolution of HSI, pixels are considered to be in a very high dimensional feature space, which calls for dimensionality reduction. One conventional way to solve the problem of dimensionality is through Principal Component Analysis (PCA)  [22] , which works by reducing the number of features to that of the most significant principal components, then passing the corresponding second-order block of data to the classifier  [31]. In fact, real HSI acquisition may contain mixed pixels, presenting challenges in acquiring the exact spectral features of the pixels and limiting the classification accuracy. As a result, the classification map may look noisy. Moreover, due to the advancement in optical sensor technology, spatial information surrounding the pixels is becoming more relevant especially when it comes to identifying relatively small objects.
A HSI is a third-order array, which can also be called a three-way array, and it is of dimensions I 1 × I 2 × J where I 1 and I 2 represent the number of horizontal and vertical pixel positions respectively and J represents the number of spectral bands. One issue regarding the mentioned methods is that, even though they prove to improve significantly on HSI feature classification with respect to classification of only spectral features, they either tend to treat the different kinds of features as one entity such as stacking spectral and spatial features as one, or they provide separate treatment of the features but don't take into account exploring the natural high-order properties and relationships among the modes of the data blocks that could be obtained. In other words, such data blocks can show interesting relationships between the modes, including those among the features themselves, and these relationships are lost.
-Provides the matrix with low-dimensional feature space (i. e. low-rank decomposition). -Conserves the information found in the tensor. This goal was explored with preliminary results that can be found in ISMM 2019  [23]  and IGARSS 2019  [24]  contributions, and the following is an extension of those two. [23]  and  [24]  include preliminary results using AMD and EMAP as morphological settings applied on one dataset. The current version includes more detailed analysis, better modeled results, more morphological settings, an additional dataset, and further discussions regarding the possible use of unconstrained CPD and the potential unmixing based on different types of features. First, we start by creating high-order tensors from HSI using spatial transformations derived from MM, then we jointly handle the different variety of features by means of nonnegative CPD.
This is to say that CPD directly produces a matrix that represents the mode of pixels in what we would call a "compact matrix form", which we choose to provide to the classifier, without the need to "matricize" the tensor by rearranging the feature modes. We also take advantage of other properties of this decomposition. For example, CPD enjoys uniqueness under mild conditions, it represents the pixels in a relatively low dimensional feature space and accounts to the data structure as a whole despite its complexity, and it is flexible with constraints (we mainly mention the nonnegativity constraint on the factors which is an important feature since it potentially allows physical interpretation of the decomposed data  [38] ). Experiments are conducted on two real HSI datasets. For each dataset, we show some experiments done over tensors of order three, having one spectral mode and another spatial one, and other experiments done over tensors of order four, having one spectral mode and two spatial ones.
m are called the residuals because they contain the residues obtained from the differences between each two successive morphological transformations. In the case of HSI, the structure and the residuals are of order three, and at the end they are stacked along the fourth mode to create a fourth-order block of data. In our approach, we choose to build the morphological decomposition using the set {S, R - i , R + i } i=1. m instead of {S, R i } i=1. m to preserve the nonnegativity of the data, which will be relevant for physical interpretation in the future. Thus, for the sake of consistency, we refer to it as Positive-AMD (PAMD). As such, the obtained fourth-order hyperspectral data set is denoted by T and arranged in such an order: [S, R - 1 , R + 1 ,. , R - m , R + m ], which is represented by an I 1 × I 2 × J × K array, and K = 2m + 1 is the number of terms along the morphological decomposition.
Experiments In this section, we talk about the experiments conducted in this work. First, we give a description of the data sets that we use. Then we present the experimental set-ups and their corresponding results for further discussion. In order to demonstrate our method, we use two real HSI datasets as shown in section 6. 1. In the results sections, we demonstrate the results on Pavia University first, then we show those of DFC. For each HSI, we show three different kinds of morphological applications: -EMP: Based on morphological operators. The decomposed tensor is three-way. The spatial way is the stacking of the transformations as they are. -PAMD: Based on morphological operators. The decomposed tensor is three-way. The spatial way is a form of a successive differential stacking of the transformations.
Pavia University The first HSI, known as Pavia University, was taken over the University of Pavia and acquired by the ROSIS sensor. The image has a spatial size of 610 × 340 pixels with a geometric resolution of 1. 3 meters, and consists of 103 spectral bands. The groundtruth image is included in the data set and it consists of nine classes: trees, asphalt, bitumen, gravel, metal sheets, shadows, self-blocking bricks, meadows, and bare soil. Additionally, 40002 pixels are available as test set, and 3921 pixels are available as training set. The training set is fixed. Figure  7  shows the HSI in false colors (by choosing the bands 58, 34, and 17 as Red, Green and Blue components) and the training set and ground-truth used for classification.
Data Fusion Contest (DFC) image for IEEE GRSS 2013 The second HSI was acquired over the University of Houston campus and the neighboring urban area by the ITRES-CASI 1500 sensor. The image has a spatial size of 349 × 1905 pixels with a geometric resolution of 2. 5m, and consists of 144 spectral bands. The groundtruth image is included in the data set and it consists of 15 classes: grass healthy, grass stressed, grass synthetic, tree, soil, water, residential, commercial, road, highway, railway, parking lot 1, parking lot 2, tennis court, and running track. Additionally, 12197 pixels are available as test set, and 2832 pixels are available as training set. The training set is fixed.
We notice that, practically, compressing or decomposing Y can be computationally very demanding, so we tend to reduce the spectral dimension in the original HSI, I. In order to do that and conserve both the nonnegativity and the information in I, we use Nonnegative Matrix Factorization (NMF) on its first matrix unfolding, call it I, where one mode represents pixels arranged in lexicographic order, and the other represents spectral bands. NMF decomposes I into two other matrices with nonnegative entries, e. g. I = W H T , where W represents pixel information, and the number of columns in W and H is defined by the rank of the NMF, call it R NMF , which is usually relatively small. W is then chosen and rearranged into a data cube, W, with reduced spectral dimension, to be used in order to form Y.
Suppose that we note by R NMF as the size of the reduced spectra, then in the case of Pavia HSI, Y is of dimensions 207400 × R NMF × 9 × 4. Now, following what we do with EMP and PAMD, Y is decomposed using compressed nonnegative CPD. The steps are visualized in a flowchart as seen in figure  8. In a similar manner, in the case of TPCA, PCA of I was computed as means of reducing the dimension of the spectral mode before forming the EMAP on the principal components. After that, Y is formed. The PCA and EMAP part was done in  [14]. For NMF, we choose a value 40 of the rank which corresponds to a reconstruction error of 0. 65% compared to the original HSI. For PCA, we choose four principal components which explains the data by 99. 16%. For CPD, we choose one value of the rank, which is R = 40.
EMAP Here, we used the same four types of attributes, where the choice of the threshold values was also based on observed changes between transformations: -Area of the region; λa = {300, 2000, 6000, 9000} -Diagonal of the bounding box; λ d = {20, 45, 75, 150} -Standard deviation; λs = {20, 30, 40, 50} -Moment of inertia; λ i = {0. 2, 0. 3, 0. 4, 0. 5} Consequently, we obtain a tensor Y ∈ R 664845×144×9×4. Following the same steps taken in the EMAP part of Pavia's HSI, and considering the larger block of data in the case of DFC, NMF is performed with a rank of 40, chosen with a reconstruction error of 0. 48%. As for the PCA part of the original image, five principal components were chosen, explaining the original HSI with 99. 87%. As for the CPD, we choose a rank of 40. For TPCA, three principal components were chosen for the thirdmode tensor unfolding explaining the HSI by 99.
Unconstrained CPD The emphasis in this work was on Nonnegative CPD, which is more intuitive when it comes to physical interpretation of the data. However, we think that classifying the data is still possible without the nonnegativity constraints. The advantage in this case is that the decomposition without constraints is faster than that with constraints. Considering that CPD without constraints is another way of rewriting the data in a tensor decomposition format, it was worth showing some of the results even though they might have no physical interpretation. In this case, the original tensor is still compressed and the compressed version becomes the input to AO-ADMM. Since there are no constraints on the uncompressed factors of the decomposition, AO-ADMM becomes pretty much the same as computing Alternating Least Squares.
Conclusion In this paper, CPD was proposed as a technique in the framework of spectral-spatial classification of hyperspectral data with spatial information being added using Mathematical Morphology as an application. In general, CPD provides an intuitive approach to deal with such multi-modal feature data where the results in the decomposition can be used for pixel-wise classification in a low dimensional feature space without loss of information. The focus here was on nonnegative CPD (with compression of the data), which further provides a better understanding of the results that are distributed over the decomposed factors, and where each factor   describes one of the modes of the tensor in an interpretable way. Experiments on two datasets and three different morphological settings were carried out to explore the classification aspect of the proposed technique, compared to results obtained through TPCA as a base algorithm in the framework of tensor modeling.
, 2010). They also stressed that sometimes, the theoretical calendar of agricultural production can differ from the real state of production (due to meteorological conditions for instance). To better cope with such uncertainties, realistic characterisations of the affected zones and the states of their agricultural and animal production at the time of the accident are needed. In the preparedness phase, it seems necessary to better consider the seasonality and characteristics of agricultural and animal productions. This would imply actions to: -study in depth the characteristics of agricultural areas to select and model countermeasures (identify sensitive and less sensitive areas, etc. ); -create a knowledge database of land uses, agricultural and husbandry productions and feedstuff calendars; -on this basis, establish a metafile with the countermeasures adapted to the type of soil and crops and protective actions associated with animal productions; -develop a process in order to be able to quickly acquire the real development state of agricultural productions during the crisis management.
In the preparedness phase, this recommendation would imply actions such as: -to establish/reinforce "reference" status of the territory: reference levels (background levels maps), cancer registry, local data (population ages, location of public-access buildings, etc. ), endangered species, protected areas, socioeconomic issues, population food dietary, etc. ; -to set up a metafile aggregating all the data of the territory: create databases that are not yet available (cancer registry, epidemiological data, agricultural productions database, etc. ); -to develop dedicated tool for automatic update of databases; -to prepare, with the stakeholder network survey, ques tionnaires to be given to the affected population following the accident, to adapt health, social and financial supports; -to identify actors to be involved for specific issues following the accident (e.
Acknowledgement. The work described in this paper was conducted within the  CONFIDENCE project  which was part of the  CONCERT  project. This project has received funding from the  Euratom  research and training programme 2014 2018 under grant agreement No  662287. Disclaimer (Art. 29. 5 GA). This publication reflects only the author's view. Responsibility for the information and views expressed therein lies entirely with the authors. The European Commission is not responsible for any use that may be made of the information it contains.
ωi is the vector containing the readout layer weights obtained from linear regression, and b T i is the vector containing the target output of the task Ti. Exploiting the two LP modes for each regression stems from nonlinear mixing the two input data streams in the VCSEL dynamics so that the two polarization modes contains part of the information of both processed tasks. For the training of the reservoir, we use 20 000 samples, i. e. , sliding block of five consecutive distorted bits. Since we record optical power of LPx,y modes for the 492 nodes, the size of S is 20 000 × 984. The performance of the reservoir is tested on 5380 samples and measured using the bit error rate (BER). As already stated, for each value of the feedback strength, there is a corresponding optimal injection power for the reservoir computer.
3). We define R CO,max as the radius at which the total number of detected pixels within a given annulus falls below 10. Varying this threshold by a factor of 2 changes R CO,max by only 4 % on average. The distribution of R CO,max /R 25 for our sample, shown in the left panel of Figure  2 , has a range of 0. 26-1. 2, and median of 0. 72. Our data thus probe out to ∼3. 6 CO scale lengths  (following Schruba et al. 2011) , thereby characterizing ∼90% of the total CO emission  (Schruba et al. 2011; Puschnig et al. 2020). Moreover, our maps typically probe beyond radii where the rotation curve flattens out (which happens on average within 0. 1 R 25 , as discussed in Section 4. 4. 1). For a subset of galaxies, however, the CO emission traces only the part of the galaxy where rotational velocities rise steeply.
Considerations for Modeling CO velocity fields Our implementation is tailored to three features of our data. First, our observations are confined to the central part of the galactic disk, where a flat circular disk is a good approximation to the galactic geometry. Second, the CO emission is clumpy, requiring a method that is robust to missing data. Finally, the molecular gas emission highlights spiral arms and bars where noncircular motions can be significant. We address these considerations by using a Bayesian framework, sampling a probability density function describing the data using a Markov Chain Monte Carlo (MCMC) sampler. Most existing techniques to determine the kinematic properties of galaxies build on the basic tilted-ring model of the line-of-sight velocity field first introduced by  Rogstad et al.
The algorithm performs this radial decomposition in concentric, annular bins generated from the orientation parameters, φ and i. The radial bin width along the kinematic major axis is chosen to correspond to the observed FWHM of the circularized ALMA beam. Even at the highest spatial resolutions of our data set, maps contain sufficient numbers of lines-of-sight per radial bin to yield statistically well-constrained least-squares solutions. The velocity field model extends out to the edge of the observed velocity field so that, in practice, the leastsquares fit (Equation  9 ) occurs across all detected pixels in the PHANGS first moment map. For each new set of orientation parameters sampled in the full parameter space probed with MCMC, a unique set of radial bins is defined. Depending on the azimuthal coverage of the outermost bins (i.
e. , bar and/or spiral structure). When the motions in the gas associated with these features are left unfitted, they mimic spatial variations in disk orientation that manifest as wellknown variations in tilted-ring determinations of φ and i with radius in a galaxy, as we also show below. Therefore, we characterize the uncertainties in our analysis using a jackknifing technique, which sub-samples the data that is incorporated into the analysis for a given galaxy. We first divide each observed velocity field into four radial bins using ellipses that are oriented based on the photometric values. Each radial bin corresponds to an annulus of the same width (i. e. , R CO,max /4). We then create three jackknife realizations of each velocity field by eliminating two of the four radial bins for a given realization.