dataesr/scientific-paragraphs-categorization huggingface dataset:
Methodology and software design The purpose of this section is to outline the design of e4clim. To show its full potential, this description remains voluntarily abstract, while the next Section 3 gives the concrete implementation of the climate-aware mean-variance analysis. Summarizing some of the methodology's main objectives (Sect. 1), the e4clim software is a flexible tool allowing for the evaluation of energy mixes, taking into account the variability of the demand and production, from both mature and emerging technologies, on a broad range of time scales. An energy mix is defined here as a set of georeferenced capacities (e. g. at the scale of bidding zones or states) per energy source and may be prescribed, e. g. taking the actual mix of a given area, or optimized, e. g. with the mean-variance analysis detailed in the next Section 3.
Dependence on the climate data The robustness of the mean-variance analysis presented in Section 3 depends on the quality of the energy estimates from the climate data. The latter is in turn impacted by climate-model biases. With the e4clim software, it is possible to use different climatedata sources to test the sensitivity of the results to biases stemming from the climate data. To illustrate this point, we compare the results of Section 4 obtained with the daily CORDEX data with intraday parameterizations (Appendix A) with results obtained from hourly simulations from the MERRA-2 reanalysis (Sect. A. 2. 2), over the same period , for which intraday parameterizations are not needed. Divergence in the results may thus stem both from differences in the climate data and from these parameterizations.
Interannual to decadal variability To assess the impact of interannual climate variability (as found in the CORDEX data) on energy mixes, we repeat the mean-variance analysis successively using data blocks of one year, from 1989 to 2012, rather than the full 1989-2012 block. In other words, each of the 23 optimal frontiers are optimized for the climatic conditions of a given year and low-frequency climate variability results in different optimal mixes. The mean-risk ratio for the unconstrained global frontier can be used as observable of these changes. It is found to average to 1. 46 with 95% of its realizations belonging to the centered interval [1. 43, 1. 49]. Thus, even though the average of the yearly mean-risk ratio is close to the one of 1. 43 obtained in Section 4. 1 using the full record, interannual climate variability in the CORDEX data is responsible for year-to-year variations of the mean-risk ratio of up to 4%.
Intraday variability A large fraction of the PV, demand and wind variance is contained in the intraday range. Yet, climate data is not always available at an hourly sampling. This is for instance the case of the CORDEX data used here, for which intraday parameterizations are added to the energy models (Sect. A. 3). To test the impact of ignoring such fluctuations on the optimal mixes, we represent in Figure  8  optimal frontiers (left) and the PVwind distribution of the maximum-mean-risk-ratio mix (right) obtained directly from the daily CORDEX data without intraday parameterizations. It is clear that the risk is underestimated by a factor two or more compared to the risk obtained using hourly data (c. f. Fig. 4  and 5 ). This can be understood from the fact that, while the mean capacity factors remain unchanged, the variance in the modeled daily PV and wind capacity factors is dramatically underestimated (see Appendix C).
Conclusion This work is aimed at developing an integrated modelling framework dedicated to the assessment and elaboration of optimal energy mixes taking into account flexibility needs associated with high shares of VRE. It relies on a methodology extending mean-variance analysis to resolve low-frequency climate variability and allow new technologies to be integrated. This methodology is implemented as an extensible open-source Python software, e4clim. Its potential is demonstrated with an application to a recommissioning of the 2015 Italian PV-wind mix. This application is, however, but one example of possible implementations in the e4clim system. The software's flow is divided in three steps: (i) energy time series are first estimated from climate data and fitted to observations; (ii) a VRE mix is then prescribed or optimized by, e.
g. , mean-variance analysis; (iii) the mix properties are finally analysed. The first step relies on climate data to take production and demand-variability on a broad range of time scales into account and to allow for the integration of new technologies for which no or little observations are available. The current version of e4clim is also adapted to consider optimal strategies in a warming climate using 21st century regional projections provided by the CORDEX program (not shown here). Using climate data from multiple independent sources is then recommended to estimate errors stemming from these sources. Different optimal scenarios are derived in the second step, ranging from maximizing the total renewable energy penetration to minimizing the total risk, and so, flexibility requirements to meet the demand.
This research was also supported by the ANR project FOREWER (ANR-14-414 CE05-0028) and the HyMeX project (HYdrological cycle in The Mediterranean EXperiment) through the working group Renewable Energy, funded by INSU-MISTRALS program. The e4clim open-source software is available at https://gitlab. in2p3. fr/alexis. tantet/e4clim and its documentation at https://alexis. tantet. pages. in2p3. fr/ e4clim/. It includes a reference guide and an example corresponding to the application presented here. Here, we give more information on data sources, models and validation results for the application of the e4clim software to the Italian PV-wind mix recommissioning (see Sect. 4).
A. 1 Energy data: GME and GSE databases Time series of the hourly Italian regional electricity demand and of the yearly regional renewable capacity factors are used to design the demand and generation models. See the "demand" and "generation data" blocks at the top of Figure  1. These variables are extracted from two publicly available databases provided respectively by the market operator GME 2  and the energy operator GSE 3. For this reason, we first briefly comment on the structure of the Italian electricity market and next describe the databases we use. The Italian power market consists of 7 foreign virtual zones, 6 regional sub-markets, or bidding zones, and 5 poles of limited production. The 20 administrative regions composing the Italian territory are aggregated in the 6 bidding zones (Fig.
3a ): Northern Italy (NORD), Central-Northern Italy (CNOR), Central-Southern Italy (CSUD), Southern Italy (SUD), Sardinia (SARD) and Sicily (SICI). Each zone has its own generation mix determined by historical and geographic reasons and characterized by a given level of efficiency. For instance, the Northern regions have larger hydroelectric production due to the proximity to the Alps. Inter-zonal transmission capacities are not equally distributed either. The Italian power exchange, which is managed by the GME 4  is composed of a spot market, a forward market and a platform for the physical delivery of contracts concluded on the financial derivatives segment of the Italian Stock Exchange. The spot market is composed of three sub-markets: the day-ahead, the intraday and the ancillary services The GME database encompasses hourly bids and offers in the wholesale electricity market from 2004 to 2017; the offers are identified by supplier's technology.
A. 2 Climate data The mean-variance optimization problem (Appendix B) relies on electricity demand and PV and wind capacity-factor time-series. Observed time series are only a few years long, too short to resolve low-frequency climate variability. The models described in Section A. 3 are thus used to predict these energy time-series from climate data. See the "climate data" block at the top of Figure  1. In this study, one particular CORDEX regional simulation is mainly used, that we refer to as the CORDEX data. This choice is motivated by the fact that, contrary to reanalysis products, CORDEX projections for the 21st century are also available, which could be used to apply the e4clim software to assess the impact of climate change on energy mixes. Another climate dataset, the MERRA-2 reanalysis, is also used (i) to parametrize intraday wind-fluctuations not resolved by the CORDEX data (Sect.
, 2014 , Chiriaco et al. , 2014] , heavy precipitation  [Lebeaupin Brossier et al. , 2013 , 2015 , Berthou et al. , 2014 , 2015 , 2016 ] and offshore wind energy potential assessment  [Omrani et al. , 2017]  in a configuration coupled or not with a regional ocean model for the Mediterranean Sea  [Drobinski et al. , 2012]. The simulation, that we refer to as the CORDEX data, is available on the HyMex/MED-CORDEX database 6.
A. 2. 2 MERRA-2 reanalysis The MERRA-2 dataset, used in Appendix C, is a state-of-the-art reanalysis providing, among other products, hourly time series of atmospheric variables from 1980 to present day. As a reanalysis it combines observation data (from NASA's GMAO) with the NASA's GEOS modeling and analysis system. See  Gelaro et al. [2017]  for a full description, and  Fujiwara et al. [2017] , for a comparison of various reanalyses. The MERRA-2 product presents the advantage over the CORDEX data of being provided at an hourly sampling, of containing 50m (in addition to 10m) wind data and of overlapping with both the GSE and GME data. However, we have chosen to use the CORDEX data for the application to Italy, in order to be able to extend this study to climate change scenarios using CORDEX projections in future work.
The vector of shape parameters and the correlation matrix are assumed to be constant and are estimated from the MERRA-2 10m-wind data (Sect. A. 2. 2). The validity of our parameterization thus relies on the following assumptions: (i) at each grid point, intraday fluctuations are identically Weibull-distributed and independent; (ii) the shape parameters and the correlation matrix are independent of time; (iii) these parameters are the same for the MERRA-2 and the CORDEX datasets, respectively. Hourly realizations to be fed to the transfer function are then obtained by randomly drawing samples from the multivariate Weibull distribution. In order to estimate the parameters and to draw samples, we use a change of variable from a Weibull to a normal distribution, as described in  Villanueva et al.
A. 3. 2 PV model We simulate the PV production for arrays at each gridpoint composed of multi-crystalline silicon PV cells. The crystalline silicon PV cell occupies about 90% of the PV market, among which multi-crystalline PV cells have the highest share at 53% and monocrystalline PV cells have a 33% share  [Hosenuzzaman et al. , 2015]. Each module has a nominal power of 250 W for an area of 1. 675 m 2 , resulting in a reference efficiency of about 15% 9. The real efficiency of the cell is, however, dependent on its temperature, which is itself dependent on the air temperature and the wind from the CORDEX data and on the global tilted irradiance (see below). This dependence is modeled using the thermal model described in  Duffie and Beckman [2013, Chap. 23]   10. Solar radiation from CORDEX is partitioned into direct, diffuse and reflected components  [Duffie and Beckman, 2013, Chap.
C Evaluation against observations The robustness of the results presented in Section 4. 1 is tested here. We evaluate the capacity of the demand and capacity factor models to reproduce the spectral characteristics of observations. For that purpose, we compare in Table  2  the percentage of the variance of the Italian average of the electricity demand (top), PV capacity factor (middle) and wind capacity factor (bottom) explained by periods greater than a year, less or equal than a year and greater than a day and less or equal than a day. These estimates are (i) directly from observed time series; (ii) from the models applied to the daily CORDEX data; (iii) and from the models applied to the MERRA-2 data with 10m and 50m wind 16. Because the demand observations from GME, the CORDEX and the MERRA-2 data overlap over the 2005-2012 period, it is possible to select a common period over which to compare the demand variance.
However, this is not the case of capacity factor observations, as the GSE and the CORDEX data do not overlap (the GSE is selected over the 2013-2017 period, see Sect. A. 1). Then, the common period of 2013-2018 is selected for the GSE and the MERRA-2 capacity factors, but the 1989-2012 period is used for the CORDEX capacity factors. We can see that, regardless of the climate data, the demand model does not resolve the little interannual variability of the observed demand. This is to be expected if this variability is due to socio-economial factors rather than interannual climate variability. In fact, the variance from year-to-year unresolved factors is modeled by the Bayesian model as intraday random perturbations, so that part of the interannual variance is transferred to the intraday variance.
23 and 1. 43, respectively. We can thus see that large differences exist between the observations and the MERRA-2 frontiers, which translate into qualitatively different capacity distributions. These results suggest that, while using climate data to perform the mean-risk analysis allows one to study the impact of climate variability on mixes, care should be taken when interpreting the optimal capacity distributions in absolute terms. The fact that biases exist between the results obtained from the different climate datasets shows that, while some of the differences with observations may be due to the energy models, some of them originate from the climate data. As suggested in Section 4. 3, we recommend analysing results using multiple, relatively independent, climate data sources to estimate biases stemming from the climate data.
| Mass spectrometry experiments The present experimental data were recorded with a crossed electronmolecular beam set-up combined with a quadrupole mass spectrometer. It was described in detail elsewhere. 32 Briefly, electrons were emitted from a hairpin filament of the ion source and subsequently formed into an energetically and spatially focused beam by a hemispherical electron monochromator (HEM). A Faraday cup at the end of the HEM allows for checking stable electron beam conditions. The energy resolution for the present data was around 100 meV as determined from the full-width-half-maximum (FWHM) of the wellknown SF 6 -/SF 6 resonance. 33 The molecular beam originates from an oven filled with His that was mounted perpendicularly to the HEM. It was heated at abovementioned conditions to evaporate the His.
As a result, the parameters of such function, especially the AE, can be extracted. For this purpose, a software tool was developed based on a previous version  37  written in Python,  38  using the SciPy,  39  NumPy,  40  and Matplotlib 41 libraries. The cross-section function is fitted by means of the Chi-Square (χ 2 ) method and minimising the squared sum of the difference of data and fit for each point. The data points are weighted according to their respective uncertainties. The required input parameters are the fitting range around the estimated AE and the resolution ρ. The number of iterations depends on the number of data points of the according measurement and is higher for larger data sets. With each iteration of the fitting procedure, the fitting range is narrowed and centred around the determined AE value, until either the set number of iterations is reached or alternatively the change of the fitting range falls below a beforehand specified value.
The present ratio of [His-CO 2 ] + and [His-COOH] + is only ~20% at the sample temperature of 160°C and thus just a factor of ~3 above the isotope ratio of 6. 6% due to C-13, N-15, or H-2 in [His-COOH] +. The absence may be ascribed to the different nature of the ionising particle-electrons in the present work against photons in Wilson et al  31  -or the subsequent degradation of [His-CO 2 ] due to the elevated temperature in the present experiment. We favour the latter explanation since the electron ionisation mass spectrum of histamine available at the NIST database  14  shows an abundant peak at m/z 30 This assignment is further supported by the constancy of the ion signal at m/z 111 when the sample temperature is increased (see Figure  1C ). This means that the increase of the [His-COOH] + isotope and the decrease of the background signal, both relative to the signal of the parent ion, keep their balance with increasing temperature.
The mass spectrum at ~70 eV and electron ionisation close to thresholdThe mass spectrum shown in Figure1Areveals that the parent ion His˙+ at m/z 155 is a minor species among the other ions observed. Therefore, one may conclude that strong fragmentation may occur upon ionisation of His by electron interactions. The most abundant peaks above m/z 32 can be found in the present spectrum at m/z 110 ([His-COOH] + ), m/z 82 ([C 4 N 2 H 6 ]˙+), m/z 81 ([C 4 N 2 H 5 ] + ), and m/z 44 (CO 2 + /[NH 3 CHCH 2 ]˙+). Major peaks at low masses, m/z 18, 28, and 32, can be assigned to ionisation of residual water and air. The electron ionisation mass spectrum (at 75 eV) reported in the Spectral Database for Organic Compounds 52  is in qualitative agreement with the present spectrum, since the same abundant ions-with m/z 82 and m/z 81 as the most abundant species-are observed.
C α -C β bond cleavage showed to have free reaction energies which are lower than the experimental values, namely, 8. 99 eV for τ His and 8. 93 eV for π His and can be excluded since no threshold at these energies is discernible in the present experimental data. Since the formation of 4-methylimidazole by thermal decomposition was suggested in Wilson et al, 31 we also investigated if the ion yield at m/z 81 was formed by hydrogen loss from 4-methylimidazole, reaction (7b). The mass spectrum of 4-methylimidazole in the NIST database suggests this process, since the ratio of m/z 81 to m/z 82 is 0. 75. We calculated the corresponding AEs, obtained a value at 10. 5 eV considering that the ion yield at m/z 81 is formed by the loss of an H atom from the CH 3 group of 4-methylimidazole and the AE is 12.
ACKNOWLEDGEMENTS This work was supported by  FWF, Vienna ,  P30332. R. M. and P. L. V. acknowledge the  Portuguese National Funding Agency FCT-MCTES  through  PD/BD/114452/2016  and research grants  UID/FIS/00068/ 2019  and  PTDC/FIS-AQM/31281/2017. This work was also supported by  Radiation Biology and Biophysics Doctoral Training Programme (RaBBiT ,  PD/00193/2012 );  UID/Multi/04378/2013  ( UCIBIO ). L. F. is thankful for the support from the  University of Innsbruck  via grant  P7440-035-011 , the  Institut de Physique Nucleáire de Lyon (Institut de Physique des 2 Infinis de Lyon ), and to the  LABEX Lyon Institute of Origins  ( ANR-10-LABX-0066 ) of the  Université de Lyon  for its financial support within the  programme "Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) of the French government operated by the  National Research Agency (ANR).
Funding information Agence Nationale de la Recherche , Grant/ Award Numbers:  ANR-10-LABX-0066  and  ANR-11-IDEX-0007 ;  Austrian Science Fund , Grant/Award Number:  P30332 ;  Ministério da Ciência, Tecnologia e Ensino Superior , Grant/ Award Number:  P7440-035-011 ;  University of Innsbruck ; Investissements d'Avenir;  LABEX Lyon Institute of Origins ;  Institut de Physique Nucleáire de Lyon ;  Radiation Biology and Biophysics Doctoral Training Programme , Grant/ Award Number:  PD/00193/2012 ;  Portuguese National Funding Agency FCT-MCTES , Grant/ Award Numbers:  PTDC/FIS-AQM/31281/ 2017 ,  UID/FIS/00068/2019  and  PD/BD/ 114452/2016 ;  FWF ,  Vienna, Grant/Award Number: P30332 -------------------------------------.
4 | RESULTS AND DISCUSSION 4. 1 | 41 The mass spectrum at ~70 eV and electron ionisation close to thresholdThe mass spectrum shown in Figure1Areveals that the parent ion His˙+ at m/z 155 is a minor species among the other ions observed. Therefore, one may conclude that strong fragmentation may occur upon ionisation of His by electron interactions. The most abundant peaks above m/z 32 can be found in the present spectrum at m/z 110 ([His-COOH] + ), m/z 82 ([C 4 N 2 H 6 ]˙+), m/z 81 ([C 4 N 2 H 5 ] + ), and m/z 44 (CO 2 + /[NH 3 CHCH 2 ]˙+). Major peaks at low masses, m/z 18, 28, and 32, can be assigned to ionisation of residual water and air. The electron ionisation mass spectrum (at 75 eV) reported in the Spectral Database for Organic Compounds 52  is in qualitative agreement with the present spectrum, since the same abundant ions-with m/z 82 and m/z 81 as the most abundant species-are observed.
Active automata learning allows to learn software in the form of an automaton representing its behavior. The algorithm SL * , as implemented in RALib, is one of few algorithms today that allows learning automata with data parameters. In this paper we investigate the suitability of SL * to learn software in an industrial environment. For this purpose we learned a number of industrial systems, with and without data. Our conclusion is that SL * appears to be very suitable for learning systems of limited size with data parameters in an industrial environment. However, as it stands, SL * is not scalable enough to deal with more complex systems. Moreover, having more data theories available will increase practical usability.
M. Falk and P. F. Brostean, available at bitbucket. org/learnlib/ralib/). In contrast with the finite state machines that L * infers, SL * infers register automata, a type of extended finite state machine (EFSM) which holds registers and transition guards that compare registers with data parameters. The research question this paper is concerned with is how suitable SL * is for learning software behavior including data parameters through active automata learning in an industrial environment. In particular we want to know what its shortcomings are and how complex the systems are that it can cope with. For this purpose we learn a number of systems at ASML, which is a company in Veldhoven, The Netherlands, making wafer scanners. Wafer scanners repeatedly project images on silicium wafers to produce integrated circuits at a nano meter scale.
The challenge is to project each image exactly on top of each other. These scanners consist of highly advanced hardware controlled by 50Mline of code. ASML wants to replace parts of the existing code base by model based software. Therefore, ASML explores whether such models can be learned automatically from the code. We symbolically learned both standalone components, and combinations of them. We compare this with learning the system without data parameters by instantiating data parameters with a few concrete values. Modulo some effort to adapt the tools to the industrial environment and struggling with implementations errors, we can conclude that SL * is suitable for learning software behavior with data parameters in an industrial environment for systems with limited complexity. For more complex systems learned partial results may also provide useful insights into the behavior of the system and potentially indicate errors in the implementation.
e. , the natural numbers with only equality, or R, {=, <, >} , i. e. , the real numbers with an ordering. So far, besides SL * there is only one other major method, namely Tomte  [1, 2] , that can deal with data. Tomte uses a similar technique but a different framework architecture where a separate mapper component maps abstract data to concrete values. This makes the learning algorithm independent from handling the data. In SL * data is completely integrated into the learning algorithm. This is why we chose to use SL * in our investigation. Outline. We first provide some preliminaries in Section 2 after which we summarize how SL * works in Section 3. Section 4 reports on the suitability of the adapted version of SL * in an industrial environment after which Section 5 follows with a discussion and conclusion.