dataesr/scientific-paragraphs-categorization huggingface dataset:
Our approach uses an expert system, made up of inference rules, to automate the proposition injection and to save time by allowing to reuse the rules on several IoT systems. We represent inference rules with this format: When conditions on facts Then actions on facts (format taken by the Drools inference engine ("Red-Hat-Software", 2020)). The facts, which belong to the knowledge base of the expert system, are here the transitions of → L(c1). To ensure that the transition completion is performed in a finite time and in a deterministic way, the inference rules have to meet these hypotheses: • (finite complexity): a rule can only be applied a limited number of times on the same knowledge base, • (soundness): the inference rules are Modus Ponens (simple implications that lead to sound facts if the original facts are true).
Two cameras can check for updates and install them. This usecase aims at focusing on vulnerabilities and security measures related to the update mechanisms. For every use-case, we collected a first log that includes traces of "normal behaviours" and generated first models with CkTail. We identified the testable components, on which we applied a set of penetration testing tools. During this testing stage, we collected a second larger log. Testing was only possible on the components of UC1. We manually analysed the source code of the software (when available) and the logs to list the ENISA security measures that have been implemented and those that are not applied. Then, we generated LTSs along with dependency graphs. We used the 11 property types formulating the security measures provided by the ENISA organisation related to communications.
In  (Ge et al. , 2017) , security models are devised from data collected from an IoT system. A security analysis is then performed to find potential attack scenarios, evaluate security metrics and assess the effectiveness of different defense strategies. Other works introduce MbT security testing methods. Some of them are said to be active, i. e. test cases are generated by hands or from a given specification, and are later used to experiment IoT systems  (Gutiérrez-Madro ñal et al. , 2019; Ahmad et al. , 2016; Tappler et al. , 2017; Matheu Garcia et al. , 2019). Others are called passive, i. e. they are based upon monitoring tools, which security issues by checking rule satisfiability in the the long run  (Siby et al. , 2017; Chaabouni et al. , 2019; Maksymyuk et al. , 2017). The tool IoTSAT  (Mohsin et al.
Multivariate analysis A principal component analysis (PCA) was used to describe the main tendencies in the data set. The input data used for the PCA were first normalised (centred and reduced). PCA turns original variables into new variables i. e. principal components (PCs), which are orthogonal and not inter-correlated. The PCs were ordered in such a way that the first components represent the largest fraction of the total data variance. A loading plot and a score plot were generated that represent respectively the correlations between the variables and the similarities/ dissimilarities between samples  (Parinet et al. , 2010). The PCA was carried out using the FactoMineR package and Rstudio 1. 2 software.
Univariate analysis The Shapiro-Wilk test was carried out to check data distribution. According to the previous results, a Friedman test, Nemenyi (post-hoc Friedman) test, paired-Wilcoxon test or a one-way ANOVA test and paired t-test were carried out in order to observe any impact of cooking processes on CLD content in beef. The Rstudio 1. 2 software was used for univariate analysis.
, 2019). Before being processed by Haloseeker 1. 0, HRMS centroid spectra and their corresponding data have to be converted into a universal format (. MzXML) using the Proteowizard software (http://proteowizard. sourceforge. net). Haloseeker 1. 0 extracts features (i. e. a pair made up of an m/z value and chromatographic retention time) that belong to the same compound and clusters them. It also offers an intuitive interface and H/Cl mass defect graph in order to find metabolites and halogenated compounds. In our case, the following conditions were used: polarity of the ionisation = negative; m/z tolerance = 10 ppm; peak width = 5 to 60 s; prefilter step = 0; prefilter level = 0; sntresh = 0; Rt tolerance = 1 s; m/z tolerance = 0. 5 mDa; Rt min = 1. 5 min; pairing & ratio filter = F1; slope filter = all or 100% Cl.
In silico annotation Original equipment manufacturers such as Sciex offer with the HRMS itself diverse software to help with the identification/annotation step. The Formula Finder algorithm, which is part of the MasterView software (Sciex), tries to predict the possible chemical formula based on the MS and MS/MS spectrum using the precursor ion's mass accuracy, isotopic pattern and MS/MS fragmentation pattern. Nevertheless, a high formula finder score does not always mean that the identified compound is actually the compound in the sample. To set the algorithm, prior to annotation the user should determine the range of numbers for C, H, N, O and Cl (for example from 0 to 10 for C in the raw formula). In this study, the following parameters were set: H (1-50), C (1-30), O (0-10), Cl (0-10).
All muscles vs all offal The same approach as above was used for the two datasets highlighted by the PCA: "All muscles" (n = 20) and "All offal" (n = 10) (Table  2 ). For the four cooking processes, the median CLD concentrations in all muscles were lower than those measured in uncooked matrices (Fig. 2b, Table 2  "all muscles"). The lowest median value of 2. 8 ng CLD. g -1 f. m. was obtained with the microwave process, which reduced the CLD concentration in cooked meat to below the new MRL (20 ng CLD. g -1 f. m. ). The data were heterogeneously dispersed, being highest for the grill and oven cooking processes and lowest for the pan process (Fig. 2b ). It has to be noticed the fact that the median CLD concentrations measured for oven and pan cooking processes are below the new MRL (20 ng CLD.
Isotopic profile and mass defect approach with non-target screening The uncooked liver, microwaved liver and experimental blank (sample not containing the analyte of interest, but having followed the whole analysis process) were processed using the isotopic profile and mass defect approach through the Haloseeker software. The objective was to use this blind/non-target approach to look for organochlorine compounds in the samples being studied to detect any CLD metabolites and microwaved by-products. For the uncooked liver, either all the features were kept (Fig. 5a  and Table  SD-6 ) or only chlorine compounds were retained (100% Cl) (Fig. 5b  and Table  SD-6 ). A list of features belonging to CLD   (390. 7, 392. 7 and 394. 7  Da at 19 min) with the same in-source fragmentation as CLD and the loss of 80 Da.
In silico annotation comparison The Haloseeker and MasterView (Formula Finder) annotation algorithms' ability to propose a pertinent raw formula with a relevant score was compared. The features of CLD or 5b-hydro-CLD and their corresponding standards were used as references for uncooked and microwaved livers as it was certain that these compounds were present in the studied samples. To produce the annotation, two common lists of features (m/z -Rt) with a chromatographic retention time over 1. 5 min originally obtained by the Haloseeker software from the uncooked and microwaved liver were used by both software programs (Table  SD-6 ). Moreover, only chlorinated compounds were selected for the annotation. All the results are displayed in Table SD-6 and Fig. 5b  and d. In the uncooked liver, where the concentrations of CLD and 5bhydro-CLD are the highest, the annotation and selection algorithm used by Haloseeker was not able to find a raw formula for the features belonging to the CLD clusters (Fig.
Acknowledgements This work was supported by the  ANR   AlimOmic project , grant  ANR-JCJC-2019  of the  French Agence Nationale de la Recherche. The authors would like warmly to thank  Julien Vacher  from  DAAF972  for providing the naturally contaminated matrices. The authors would also like to thank  Pierre-Loic Saaidi  from  CEA-Genopôle  for the 5b-hydro-CLD standard.
We present a novel approach to simulate and reconstruct annual glacier-wide surface mass balance (SMB) series based on a deep artificial neural network (ANN; i. e. deep learning). This method has been included as the SMB component of an open-source regional glacier evolution model. While most glacier models tend to incorporate more and more physical processes, here we take an alternative approach by creating a parameterized model based on data science. Annual glacier-wide SMBs can be simulated from topo-climatic predictors using either deep learning or Lasso (least absolute shrinkage and selection operator; regularized multilinear regression), whereas the glacier geometry is updated using a glacier-specific parameterization. We compare and cross-validate our nonlinear deep learning SMB model against other standard linear statistical methods on a dataset of 32 French Alpine glaciers.
Deep learning is found to outperform linear methods, with improved explained variance (up to + 64 % in space and +108 % in time) and accuracy (up to +47 % in space and +58 % in time), resulting in an estimated r 2 of 0. 77 and a root-mean-square error (RMSE) of 0. 51 m w. e. Substantial nonlinear structures are captured by deep learning, with around 35 % of nonlinear behaviour in the temporal dimension. For the glacier geometry evolution, the main uncertainties come from the ice thickness data used to initialize the model. These results should encourage the use of deep learning in glacier modelling as a powerful nonlinear tool, capable of capturing the nonlinearities of the climate and glacier systems, that can serve to reconstruct or simulate SMB time series for individual glaciers in a whole region for past and future climates.
g. Jouvet et al. , 2009; Réveillet et al. , 2015). At the same time, the use of these different approaches strongly depends on available data, whose spatial and temporal resolutions have an important impact on the results' quality and uncertainties (e. g. Réveillet et al. , 2018). Parameterized glacier dynamics models and empirical and statistical SMB models require a reference or training dataset to calibrate the relationships, which can then be used for projections with the hypothesis that relationships remain stationary in time. On the contrary, process-based and specially physics-based glacier dynamics and SMB models have the advantage of representing physical processes, but they require larger datasets at higher spatial and temporal resolutions with a consequently higher computational cost  (Réveillet et al.
, 2018). For SMB modelling, meteorological reanalyses provide an attractive alternative to sparse point observations, although their spatial resolution and suitability to complex high-mountain topography are often not good enough for high-resolution physics-based glacio-hydrological applications. However, parameterized models are much more flexible, equally dealing with fewer and coarser meteorological data as well as the state-of-the-art reanalyses, which allows them to work at resolutions much closer to the glaciers' scale and to reduce uncertainties. The current resolution of climate projections is still too low to adequately drive most glacier physical processes, but the ever-growing datasets of historical data are paving the way for the training of parameterized machine learning models.
, 2001). Nonetheless, more recent developments in the field of machine learning and optimization enabled the use of deeper network structures than the threelayer ANN of  Steiner et al. (2005). These deeper ANNs, which remain unexploited in glaciology, allow us to capture more nonlinear structures in the data even for relatively small datasets  (Ingrassia and Morlini, 2005; Olson et al. , 2018). Here, we present a parameterized regional open-source glacier model: the ALpine Parameterized Glacier Model (ALPGM; Bolibar, 2019). While most glacier evolution models tend to incorporate more and more physical processes in SMB or ice dynamics (e. g. Maussion et al. , 2019; Zekollari et al. , 2019) , ALPGM takes an alternative approach based on data science for SMB modelling and parameterizations for glacier dynamics simulation.
, 2009) , while the initial ice thickness distribution of glaciers is taken from  Farinotti et al. (2019) , for which we performed a sensitivity analysis based on field observations. In the next section, we present an overview of the proposed glacier evolution model framework with a detailed description of the two components used to simulate the annual glacier-wide SMB and the glacier geometry update. Then, a case study using French Alpine glaciers is presented, which enables us to illustrate an example of application of the proposed framework, including a rich dataset, the parameterized functions, and the results and their performance. In the end, several aspects regarding machine and deep learning modelling in glaciology are discussed, from which we make some recommendations and draw the final conclusions.
Model overview and workflow ALPGM is an open-source glacier model coded in Python. The source code of the model is accessible in the project repository (see Code and data availability). It is structured in multiple files which execute specific separate tasks. The model can be divided into two main components: (1) the Glacier-wide SMB Simulation and (2) the Glacier Geometry Update. The Glacier-wide SMB Simulation component is based on machine learning, taking both meteorological and topographical variables as inputs. The Glacier Geome-try Update component generates the glacier-specific parameterized functions and modifies annually the geometry of the glacier (e. g. ice thickness distribution, glacier outline) based on the glacier-wide SMB models generated by the Glacierwide SMB simulation component.
Then, the SMB model or models are trained with the full topoclimatic dataset. These models are stored in intermediate files, allowing the user to skip this step for future runs. 3. Performances of the SMB models can be evaluated with a leave-one-glacier-out (LOGO) or a leave-one-year-out (LOYO) cross validation. This step can be skipped when using already-established models. Basic statistical performance metrics are given for each glacier and model as well as plots with the simulated cumulative glacierwide SMBs compared to their reference values with uncertainties for each of the glaciers from the training dataset. 4. The Glacier Geometry Update component starts with the generation of the glacier-specific parameterized functions, using a raster containing the difference of the two preselected digital elevation models (DEMs) covering the study area for two separate dates as well as the glacier contours.
These parameterized functions are then stored in individual files to be used in the final simulations. 5. Once all previous steps have been run, the glacier evolution simulations are launched. For each glacier, the initial ice thickness and DEM rasters and the glacier geometry update function are retrieved. Then, in a loop, for every glacier and year, the topographical data are computed from these raster files. The climate predictors at the glacier's current centroid are retrieved from the climate data (e. g. reanalysis or projections), and with all this data the input topo-climatic data for the www. the-cryosphere. net/14/565/2020/ The Cryosphere, 14, 565-584, 2020 glacier-wide SMB model are assembled. Afterwards, the glacier-wide SMB for this glacier and year is simulated, which, combined with the glacier-specific geometry update function, allows us to update the glacier's ice thickness and DEM rasters.
For the machine learning training, the relevant predictors must be selected, so we perform a sensitivity study of the annual glacier-wide SMB to topographical and climatic variables over the study training period. This can be performed with individual linear regressions between each variable and glacier-wide SMB data. After identification of the topographical and climatic variables that can potentially explain annual glacier-wide SMB variability for the region of interest, a training dataset is built. An effective way of expanding the training dataset in order to dig deeper into the available data is to combine the climatic and topographical input variables  (Weisberg, 2014). Such combinations can be expressed following Eq. (  1 ): SMB g,y = f ( ˆ , Ĉ) + ε g,y , (1) where is a vector of the selected topographical predictors, Ĉ is a vector with the selected climatic features and ε g,y is the residual error for each annual glacier-wide SMB value, SMB g,y.
Once the training dataset is created, different algorithms f (two linear and one nonlinear, for the case of this study) can be chosen to create the SMB model: (1) OLS (ordinary-leastsquare) all-possible multiple linear regressions, (2) Lasso (least absolute shrinkage and selection operator;  Tibshirani, 1996)  and (3) a deep ANN. ALPGM uses some of the most popular machine learning Python libraries: statsmodels  (Seabold and Perktold, 2010) , scikit-learn  (Pedregosa et al. , 2011)  and Keras  (Chollet, 2015)  with a TensorFlow backend. The overall workflow of the machine learning glacier-wide SMB model production in ALPGM is summarized in Fig. 2.
( , 2008)) , where the reader can find a thorough introduction to the use of ANNs in glaciology. ANNs gained recent interest thanks to improvements of optimization algorithms allowing the training deep neural networks, which lead to better representation of complex data patterns. As their learnt parameters are difficult to interpret, ANNs are adequate tools when the quality of predictions prevails over the interpretability of the model (the latter likely involving causal inference, sensitivity testing or modelling of ancillary variables). This is precisely the case in our study context www. the-cryosphere. net/14/565/2020/ The Cryosphere, 14, 565-584, 2020 here, where abundant knowledge about glacier physics further helps in choosing adequate variables as input to deep learning. Their ability to model complex functions of the input parameters makes them particularly suitable for modelling complex nonlinear systems such as the climate system  (Houghton et al.
The main consequence of regularization is generalization, for which the produced model is capable of better adapting to different configurations of the input data. The hyperparameters used to configure the ANN are determined using cross validation in order to find the bestperforming combination of number of units, hidden layers, activation function, learning rate and regularization method. Due to the relatively small size of our dataset, we encountered the best performances with a quite small deep ANN, with a total of six layers (four hidden layers) with a (N, 40, 20, 10, 5, 1) architecture (Fig. 3 ), where N is the number of selected features. Since the ANN already performs all the possible combinations between features (predictors), we use a reduced version of the training matrix from Eq.
Setting the sample weights as the inverse of the probability density function during the ANN training can partly compensate for the imbalance of a dataset. This boosts the performance of the model for the extreme values at the cost of sacrificing some performance on more average values, which can be seen as a r 2 -RMSE trade-off (see Figs. 6 and 9 from the case study). The correct setting of the sample weights allows the modeller to adapt the ANN to each dataset and application.
Glacier-wide surface mass balance An annual glacier-wide SMB dataset, reconstructed using remote-sensing based on changes in glacier volume and the snow line altitude, is used  (Rabatel et al. , 2016). This dataset is constituted by annual glacier-wide SMB values for 30 glaciers in the French Alps (Fig. 4 ) for 31 years, between 1984 and 2014. The great variety in topographical characteristics of the glaciers included in the dataset, with good coverage of the three main clusters or groups of glaciers in the French Alps (Fig. 4 ), makes them an ideal training dataset for the model. Each of the clusters represents a different setup of glaciers with different contrasting latitudes (Écrins and Mont-Blanc), longitudes (Écrins and Vanoise), glacier size (smaller glaciers in Écrins and Vanoise vs.
larger ones in Mont-Blanc) and climatic characteristics, with a Mediterranean influence towards the south of the study region. For more details regarding this dataset refer to  Rabatel et al. (2016). Data from the Mer de Glace, Saint-Sorlin, Sarennes and Argentière glaciers are also used, coming from field observations from the GLACIOCLIM observatory. For some of these glaciers, glacier-wide SMB values have been available since 1949, although only values from 1959 onwards were used to match the meteorological reanalysis. This makes a total of 32 glaciers (Argentière and Saint-Sorlin glaciers belonging to the two datasets), representing 1048 annual glacier-wide SMB values (taking into account some gaps in the dataset).
Glacier ice thickness Glacier ice thickness data come from  Farinotti et al. (2019) , hereafter F19, based on the Randolph Glacier Inventory v6. 0 (RGI;  Consortium, 2017). The ice thickness values represent the latest consensus estimate, averaging an ensemble of different methods based on the principles of ice flow dynamics to invert the ice thickness from surface characteristics. We also have ice thickness data acquired by diverse field methods (seismic, ground-penetrating radar or hotwater drilling;  Rabatel et al. , 2018)  for four glaciers of the GLACIOCLIM observatory. We compared these in situ thickness data with the simulated ice thicknesses from F19 (refer to Supplement for detailed information). Although differences can be found (locally up to 100 % in the worst cases), no systematic biases were found with respect to glacier local slope or glacier altitude; therefore, no systematic correction was applied to the dataset.
Climate data In our French Alps case study, ALPGM is forced with daily mean near-surface (2 m) temperatures, daily cumulative snowfall and rain. The SAFRAN dataset is used to provide these data close to the glaciers' centroids. SAFRAN meteorological data  (Durand et al. , 2009)  are a reanalysis of weather data including observations from different networks and specific to the French mountain regions (Alps, Pyrenees and Corsica). Instead of being structured as a grid, data are provided at the scale of massifs, which are in turn divided into altitude bands of 300 m and into five different aspects (north, south, east, west and flat).
Causal analysis By running the Lasso algorithm on the dataset based on Eqs. (  2 ) and (3), we obtain the contribution of each predictor in order to explain the annual glacier-wide SMB variance. Regarding the climatic variables, accumulation-related predictors (winter snowfall; summer snowfall; and several winter, spring and even summer months) appear to be the most important predictors. Ablation-related predictors also seem to be relevant, mainly with CPDD and summer-and shoulder-season months (Fig. 5 ). Interestingly, meteorological conditions in the transition months are crucial for the annual glacier-wide SMB in the French Alps. (1) October temperature is determinant for the transition between the ablation and the accumulation season, favouring a lengthening of melting when temperature remains positive or conversely allowing snowfalls that protect the ice and contribute to the accumulation when temperatures are negative.