dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2017a)  as well as scales of 0. 5 • used in climate studies  (Taylor et al. , 2012; Jacob et al. , 2014). Input runoff at 4 km for the German dataset and at 24 km for the European dataset is rescaled internally in mRM to the desired routing resolution that is provided in a configuration file. The mesoscale hydrologic model mHM coupled to mRM using the regionalised Muskingum-Cunge (rMC) and the adaptive time step (aTS) parameterisation were calibrated at all catchments to provide a realistic representation of the hydrologic cycle. Details about model calibration can be found in  Samaniego et al. (2010) ,  Kumar et al. (2013b) ,  Rakovec et al. (2016), and Zink et al. (2017). The calibrations of both mHM and mRM parameters are carried out using the shuffled complex evolution (SCE) algorithm  (Duan et al.
, 1992). The SCE is coupled internally to both models, and SCE parameters (e. g. number of complexes) can be specified by users in a name list. The Kling-Gupta efficiency (KGE) is selected as a metric for evaluating model performance  (Gupta et al. , 2009). The KGE is composed of three measures that relate simulated and observed streamflow. These are the ratio of simulated and observed mean values, the ratio of simulated and observed standard deviations, and the Pearson correlation coefficient. In comparison to the Nash-Sutcliffe efficiency (NSE,  Nash and Sutcliffe, 1970) , KGE provides a more balanced metric that is less sensitive than NSE to high streamflow values. The model calibration and evaluation employs daily values of observed streamflow. The observed values are obtained from the Global Runoff Data Centre (GRDC) for the period from 1950 to 2010.
, 2012). Some large basins in the European dataset show up to 0. 03 higher KGE values using = 0 compared to = 0. 5. Note that the numerical diffusion of this finite difference solver (Eq. 4) depends linearly on  (Cunge, 1969 ). An value of 0 corresponds to full numeric diffusion, whereas a value of 0. 5 corresponds to no diffusion. The numerical diffusion is often chosen to correspond to the actual physical diffusion of the river by adjusting the value of  (Todini, 2007; Beighley et al. , 2009). The aTS is using a space-weighting factor = 0 because this value provides slightly better estimates than a value of 0. 5, but the impact of this factor is overall negligible. The density function of KGE is skewed when comparing the performances between optimised γ values at each gaug-ing station and resolution with a constant value of 15 for all stations (Fig.
3b ). A value of 15 is chosen because it provides the best compromise solution of the obtained optimised values (Fig. 3d ). It can be expected that optimised parameters give higher performances than a fixed value. The performance increments with optimised parameters were, however, less than 0. 01 in more than 37 % of the basins while only about 42 % of the basins exhibited KGE values of 0. 01 to 0. 05. This means that performance increments with optimised γ values were below 0. 05 in 79 % of the basins (Fig. 3b ). Overall, the KGE values for the European and German dataset are very high with only 4 % of the basins exhibiting a KGE value less than 0. 6 (Fig. 3c ). The median KGE values are 0. 89 and 0. 94 for the European and German dataset, respectively. KGE values are, however, highly dependent on the hydrologic model used (i.
e. mHM) and the quality of the input data. The hydrologic model determines the partitioning of precipitation into evapotranspiration and runoff as well as the temporal dynamics of generated runoff. It thus affects all three components of the KGE: the bias, the variance, and the correlation. The routing model, on the other hand, is not able to change the long-term water balance, and is thus not affecting the bias term of the KGE. The routing model is, however, able to change the dynamics of simulated streamflow and thus greatly affects the variance term of the KGE. The distribution of the optimised parameter values is very different for the German and the European datasets with median values of 4 and 21, respectively (Fig. 3d ). These differences originate from the resolution of the underlying digital elevation model (DEM) and hence the slopes used in Eq.
(  5 ). The slope data for the German dataset are available at a 100 m resolution, while it is at 500 m resolution for the European dataset. The slopes will be larger and more variable at 100 m resolution compared to 500 m resolution. This implies that lower slope values (European dataset) are associated with higher γ values, and higher slope values (German dataset) are associated with lower γ values, which results in similar celerities for the two datasets. This highlights that the parameter values obtained are highly dependent on the underlying dataset, which has been identified as a major source of hydrologic modelling uncertainty  (Livneh et al. , 2015). Hydrographs for two German river basins that exhibit KGEs of 0. 05 and 0. 11, respectively, are shown in Fig. 3e  and f. These KGE values were among the highest of all basins and model resolutions considered in this study.
Temporal and spatial scalability The aTS scheme is first run with different temporally aggregated inputs and second on different spatial resolutions to demonstrate its scalability across time and space. The adaptive time step procedure of the aTS allows the user to choose different input time steps. This might be the case if input runoff is provided as an aggregate over a specific period, for example as daily runoff. The aTS aggregates or disaggregates any given temporal resolution to the internal time step constrained by a Courant number of 1 (Eq. 6). Similar performances are achieved with the aTS using either daily or hourly inputs across all basins in the German and European dataset at every spatial resolution (Fig. 4 ). This is achieved by aggregation and disaggregation to the internal time step, but it is also affected by the fact that we compare against the observed daily discharge.
Sub-daily differences are thus averaged out before comparison. Observed hourly discharge would contain information about sub-daily variability that could not be obtained from daily inputs and, thus, hourly inputs might perform better in this case. However, observed discharge is mainly available on a daily resolution. Evidently, aggregated input provides less variable runoff to the routing scheme, leading to less variable river discharge. Aggregation does, however, not change the absolute values (bias). The KGE values therefore appear due to changes in streamflow variability, which should be reduced with aggregated runoff values. Subtle differences exist between the KGE values for the German and European dataset. The median KGE values are almost zero for the European basins (Fig. 4b ) with very low standard deviations.
Median KGE values for the German dataset are in contrast slightly negative around -0. 005 (Fig. 4a ). The differences between the German and European dataset come mainly from the spatial resolution at which gridded runoff inputs for mRM were generated. Forcing for mRM was provided at 4 km resolution for the German dataset, which is the lowest resolution of the meteorological input  (Zink et al. , 2017). The input runoff for mRM has been generated at a 24 km resolution for the European dataset, which corresponds to the resolution of the meteorological E-OBS dataset  (Haylock et al. , 2008). Runoff data at the 4 km scale exhibit much higher spatial variability compared to the coarser 24 km runoff. The higher spatial variability of the German dataset is substantially reduced when using daily runoff compared to hourly runoff, which generates the small mismatch between using hourly and daily inputs for the German dataset (Fig.
4a ). The equalisation of variability from averaging is less pronounced in the less variable runoff fields of the European dataset. For the spatial scaling, KGE values relative to the finest possible model resolutions (1 km for German and 3 km for European dataset) are reported (Fig. 5 ). In other words, the reference values (observations) in the calculations of KGE are replaced by simulated streamflow obtained with optimised parameters at the highest resolution. Perfect spatial scaling is hence indicated by a relative KGE value of 1. Figure  5  shows the probability density functions of the relative KGE values estimated over all basins for each model resolution. The optimised parameter obtained at the highest spatial resolution for each basin is transferred for the aTS and the rMC parameterisation to the model runs at the coarser spatial resolutions.
Results shown in Fig. 5  clearly demonstrate a remarkable spatial scalability of the aTS in comparison to the original rMC parameterisation. The lowest median relative KGE of 0. 977, which represents a change of less than 3 %, is observed at the coarsest resolution of 48 km for the European dataset. The overall lowest relative KGE is 0. 85 for the aTS  and 0. 22 for the rMC scheme. The aTS scheme shows an improved scalability because it considers the between-grid heterogeneity of celerities through the parameterisation based on terrain slope (Eq. 5) and the numerical stability criteria (Eq. 6). The spatial scalability of the aTS is higher for the German dataset compared to the European dataset. This can be attributed to the spatial resolution of the slope data used in the parameterisation of celerity (Eq.
5), which is available at 100 m resolution in the German dataset compared to 500 m in the European dataset. The representation of river slopes is thus more realistic in the German dataset. Notably, a similar spatial scalability is found for both the aTS and rMC parameterisation if default parameters are used (not shown). The adaptive time step scheme (aTS) shows, in summary, remarkable temporal and spatial scalability in comparison to its predecessor. The adaptive time step allows for aggregated or disaggregated input (generated runoff) from any given temporal resolution.
Comparison of adaptive time step routing with regionalised Muskingum-Cunge parameterisation The adaptive time step scheme (aTS) is the successor of the regionalised Muskingum-Cunge (rMC) routing implemented in mHM. A detailed analysis of the differences in model performances between the two routing parameterisations is presented here for the German and European dataset and selected spatial resolutions (Fig. 6 ). The performances are comparable across the German and European dataset (Fig. 6 ) if the aTS and rMC are calibrated individually on each basin and at each resolution. However, the cumulative distribution functions (CDFS) of KGE values are skewed towards positive values indicating, in general, higher performance for the aTS than rMC (Fig. 6a-j , solid blue line in right panels). This improvement is slightly higher for the German dataset compared to the European, which can be attributed to the higher spatial resolution of the slope data in the former.
7a  for 12 km and b for 50 km). REMO was nested into ERA-Interim reanalysis in the EURO-CORDEX project, which therefore permits the comparison with observed streamflow. The Danube basin is part of the European dataset used for the evaluation in previous sections. The same setup was used for routing gridded runoff fields of the REMO model, and only the number indicating the routing resolution had to be changed in the mRM configuration file. There are striking differences between the observed and simulated streamflow (Fig. 7 ). This is due to the fact that REMO uses a simple runoff generation consisting of direct surface runoff and soil drainage. There is no groundwater description in REMO so that the only water storage is in the river itself, and the baseflow is much too low. This becomes evident as most runoff generated by REMO is surface runoff.
ufz. de/mhm (last access: 21 June 2019). In detail, the software code is available through a public Git repository hosted at the Helmholtz Centre for Environmental Research -UFZ with the URL https: //git. ufz. de/mhm/mrm/ (last access: 21 June 2019;  Thober et al. , 2019). The software version used for this paper can also be identified by the Git tag "mRMv1. 0". The manual of mHM contains a chapter on the installation and user guide of mRM (chap. 9), and the full mHM manual is also contained in the mRM Git repository. Input and output data of mRM are also included in the Git repository to test successful installation (see manual on how to run the test basin). Data availability. The data used within the European dataset are described in  Rakovec et al. (2016) , and the data used within the German dataset are described in  Zink et al.
We acknowledge the E-OBS dataset from the EU-FP6 project ENSEMBLES (http:// ensembles-eu. metoffice. com, last access: 21 June 2019) and the data providers in the ECA&D project (http://www. ecad. eu, last access: 21 June 2019). We also acknowledge our data providers: the European Environment Agency, the Harmonized World Soil Database, the Global Runoff Data Centre, German Meteorological Service (DWD), the Joint Research Centre of the European Commission, the Federal Institute for Geosciences and Natural Resources (BGR), the Federal Agency for Cartography and Geodesy (BKG), and the European Water Archive (EWA). We thank the editor, Jeffrey Neal, for handling our paper and Thomas Riddick and one anonymous reviewer for their constructive comments that helped to substantially improve our model and paper.
Financial support. This research has been supported by the Bundesministerium für Bildung und Forschung (grant no. 01LS1611A) and the Agence Nationale de la Recherche (grant no. ANR-11-LABX-0002-01). The article processing charges for this open-access publication were covered by a Research Centre of the Helmholtz Association Review statement. This paper was edited by Jeffrey Neal and reviewed by Thomas Riddick and one anonymous referee.
We implemented "siland" in the adaptable and user-friendly R eponym package. It performs landscape analysis on georeferenced point observations (described in a Geographic Information System shapefile format) and allows for effects tests, effects maps and models comparison. We illustrated its use on a real dataset by the study of a crop pest (codling moth densities). Numerous studies demonstrate that the distribution of species richness and abundance depend on both local and landscape variables  [1] [2] [3]. However, studying the relationships between landscape and species distributions remains challenging because the shape and the scale of landscape effects are unknown  4  and can be missed if assessed at an incorrect scale  5. The studied data usually contains georeferenced observations at point sites, named response variables hereafter, and the description of several landscape spatial variables, named landscape variables hereafter.
Huais  15  proposed a very convenient R function "multifit" to select scales but with some limitation (it is not distance weighted, requires the choice of a set of scales), while calling for further developments of such a method to generalise this type of automated analysis. At present the analysis of landscape effects on population parameters (e. g. abundance or occurrence) therefore remains complex and the results obtained with the methods mentioned above are not easily comparable. Here we present a general method for landscape effect analysis. We implemented this method in "siland", a package for the R statistical computing environment. Based on a sequential maximum likelihood estimation procedure, our method improves current methods by providing the joint estimation of the effects of landscape variables as well as the spatial scales of these effects and by allowing comparisons between two spatial effects models.
Case study. We illustrated the abilities of siland on an example previously described and analyzed in Ricci  17  : the study of codling moth densities, an insect pest specialized on apple orchards in the Basse Durance Valley in southeastern France (see Fig. 1 ). The datasets can be extracted from the package using the commands data(dataCmoth) and data(landCmoth). The complete analysis script and outputs are available in Supplementary Information file 1 (SI 1). dataCmoth is a data frame with two columns named X and Y containing the observation locations, a column Cmoth, containing the response variable of the study (the mean number of codling moths in the orchards), and a column trait, describing a local variable (the number of insecticide treatments applied in the orchard). landCmoth is a sf object (from package sf  18  ) describing the landscape variables: conventional tree orchards (conv), organic tree orchards (org) where conv is equal to 1 if the land use is associated to orchard with conventional practice and 0 otherwise, and so is it for org.
Main functions. The main functions are described in Table  1 , while a full description of package functions is available at https:// cran. r-proje ct. org/ web/ packa ges/ siland/ siland. pdf. The package siland requires two objects containing data. The first object is a data frame composed by two columns named X and Y containing the observation locations, a third column representing the response variable and eventually other columns representing local variables. The second object is a sf object describing landscape variables. It can be obtained directly from shape files of landscape map by using the function st_read() of the package sf (see vignette("siland") for more details). Model estimation is performed using the function Bsiland() for Bsiland method and the function Fsiland() for Fsiland method.
Graphics outputs. The package siland proposes various functions for graphic representation of estimated landscape effects. The function "plotFsiland. land" displays landscape effect map for the estimations of the Fsiland method (Fig. 3 ). The influence of each landscape variable is plotted over all the study area. The global influence of the landscape, i. e. the sum of the effects of all landscape variables can also be plotted. The function plotBsiland. land() displays landscape effect map for the estimation of the Bsiland method (Fig. 2 ). For each landscape variable, the estimated area of effect (buffer of estimated radius) is plotted around each observation locations. The color of the area represents the estimated intensity of the effect i. e. the effect value multiplied by the proportion of the landscape variable in the area of effect.
The figure A is obtained with plotFsiland(resF,landCmoth,data = dataCmoth). Black points represent locations of response variable observations. Yellow and blue squares are the pixels where conventional and organic orchards respectively are present. At the right margin, the light and dark discs represent area of medium influence and significant influence, respectively. Blue and yellow discs represent conventional and the organic orchards, respectively. The figure B is obtained with plotFsiland. sif(resF). The blue and orange lines represent the estimated SIF for the organic and conventional orchards, respectively. The vertical lines represent the SIF mean.
1 ). Note that a Fsiland model with an uniform SIF is similar to Bsiland model. However, in siland package the Bsiland() function calculates the exact percentage of the landscape variable in buffers, when Fsiland() is based on an approximation, i. e. the rasterization of the landscape. As a consequence, the estimation with Fsiland is often faster than with Bsiland but may be erroneous if the definition of the raster is not precise enough.
Conclusion Estimating scales of effect of landscape variables currently remains a great challenge, and consequently so does the estimation of landscape effect. So far no common methodology has emerged. With the siland package, we propose a tool that we believe is useful for all landscape ecologists who wish to investigate this type of question. Here, we have illustrated how with only few siland functions and limited knowledge of R, it is possible to conduct a reproducible and detailed study of multi-scale landscape effect including estimations but also tests and graphs illustrating the results obtained. The "siland" package is very adaptable, it integrates two methods and can handle various types of data (continuous, binary, discrete. ). Applications of siland in a multiyear or multisite framework are already available for the buffers method and presented in the vignette("siland").
Acknowledgements We are grateful to  Claire Lavigne  and  Pierre Franck  for their case study, which illustrates the use of the siland package, and for their useful advice. We are thankful to  Etienne Klein  for his relevant comments. This work was financially supported by the  Metaprogram SMACH (Sustainable Management of Crop Health ; http:// www. smach. inra. fr/) of the France's  National Research Institute for Agriculture, Food and Environment (INRAE).
These results also illustrate the potential gains of introducing such an optimization heuristic algorithm in computational oncology and in particular for characterizing in-silico the optimal combination protocols involving both chemotherapy and immunotherapy. The algorithm we rely on belongs to the family of Monte-Carlo Tree Search (MCTS, henceforth) algorithms. MCTS methods are surveyed in  [3]. This family of algorithms is well-known for their application in a Go playing software. The famous AlphaGo has been very successful in defeating several Go champions in 2016-2017. The applications of MCTS methods to Go software can be found in  [31]. We provide a detailed explanation of our algorithm in Section 3. 2. To the best of our knowledge, MCTS algorithms have not been applied to combinations of chemotherapy and immunotherapy.
The cipher text is computed and transmitted to the user by the UART. A Mixed-Mode Clock Manager (MMCM) Xilinx module is used to generate the clock for the cryptography block. The detailed architectures of PRESENT-128 and GIFT-64-128 are described in Figure  4  and 5 respectively. The Round Constant input is fixed into the crypto block. The existing implementations generally wait for the end of a transmission of the plaintext and the key, to start the ciphering process, which implies one extra latency cycle to the whole process. Our implementation uses the Init input (Figure. 3) as a starting trigger. While the UART is transmitting the data, the Init signal, through the multiplexers, selects the plaintext, the cipher key and the round constant inputs, that are written in their dedicated registers (PTI, KEY and RC).
In our experimental setup, the data acquisition time is linear and requires 1 hour to capture 5000 traces, regardless of the algorithm. Then depending on the cipher used, attack processing time takes from a few seconds to minutes as summarized in Table  7. Note that correlation times are obtained using MATLAB scripts, executed on 2,5 GHz Intel Core i7 processor with 16 Go DDR3. Also, they depend on the number of traces to correlate. Values can be different on another platform but the main idea is the resulting trend. The correlation time of GIFT is around 12 time longer than AES. This gap is the result of the difference between the number of attack round and the complexity of the reverse operations. Because of its bitwise design and its use of key, GIFT needs several formatting operations, as decimal/ binary conversions, which increase the correlation time.
This work was supported in part by the  French Agence Nationale de la Recherche (ANR)  through the Project  IDEFI-FINMINA  under Grant  ANR-11-IDFI-0017 , in part by the  European Union ERDF Funds (European Regional Development Fund) , and in part by the  Occitanie Region Funds  through the Project  GEPETOS VASCO/SECNUM  under Grant  FEDER FSE IEJ 2016-006259.