dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2022). It is striking to see that several flavonoid-related structures were predicted for this network, by Mass2SMILES. The structure, the number of aromatic hydroxyl groups (Mass2SMILES: 3/ MSNovelist: 5) and the number of benzene rings (Mass2SMILES: 2/ MSNovelist: 3) predicted by MSNovelist does not correspond with the ones predicted by Mass2SMILES for this feature 377. Nonetheless, both Mass2SMILES and MSNovelist converge on a flavonoid like structure for this feature. Molecular networks with structures as depicted in Figure  3  may hence be combined in future studies with Mass2SMILES or MSNovelist predictions to further assist in the annotation of unknowns as well as to give insights into the compound class and dominating functional groups. One drawback of Mass2SMILES is that it relies on the cddd package  (Winter et al.
, 2019)  which runs on relatively old Python (3. 6) and tensorflow (1. 10) versions, future models should be built on a pretrained SMILES transformer model such as ChemBERTa-2  (Ahmad et al. , 2022)  or a transformer model that has been trained on a random to canonical SMILES translation task such as is the cddd model, but with recurrent neural networks. The major limitation we see to further improve the accuracy of Mass2SMILES, however, is the lack of comprehensive publicly available annotated MS/MS data to better cover the extremely structurally diverse chemical space of natural products. We expect that including high quality MS/MS data from databases such as METLIN or Mzcloud would greatly increase the performance of the overall model. In addition, future progress in molecular dynamics calculations such as QCxMS  (Koopman and Grimme, 2021)   increasing availability of large-scale annotated MS/MS data, the use of large language models such as LLaMA  (Touvron et al.
Conclusions Mass2SMILES is a novel deep learning-based approach for the annotation of MS/MS spectra with SMILES, which in addition also predicts the number of several functional groups present in a molecule. It is also able to predict the adduct type and gives an estimation of the molecular formula. This software can easily be applied to large metabolomics datasets and may represent an alternative to computationally intensive database searches. We demonstrate the capabilities of Mass2SMILES on the CASMI 2022 dataset, as well as on a previously reported large scale metabolomics dataset. We expect that this tool will aid the metabolomics community in further illuminating the large amount of dark matter present in current experiments. (Elser et al. , 2022). First, predicted SMILES were converted into molecular formulas and then compared with the predictions from SIRIUS, if consistent, SMILES were further converted with ClassyFire into chemical classes.
Acknowledgments The authors would like to acknowledge the  High-Performance Computing Center of the University of Strasbourg  for supporting this work by providing scientific support and access to computing resources, notably funded by the  Equipex Equip@Meso  project ( Programme Investissements d'Avenir ) and the  CPER Alsacalcul/Big Data. In addition, we would like to acknowledge access to computing resources at the  Institute of Molecular Biology of Plants (IBMP) ,  CNRS | University of Strasbourg (IBMP). Funding: D. E. , and E. G. were funded by the  CNRS. D. E. and E. G. were supported by a  IdEx (Investissement d'Avenir)  grants from the  University of Strasbourg , with a  IdEX PhD fellowship  to D. E and a  IdEx Grant Recherche Exploratoire  to E. G.
and the NIST 2020 HRMS database was purchased by the Institute of Molecular Biology of Plants, CNRS | University of Strasbourg (IBMP). The NIST database was preprocessed with a script from MassFormer (Young et al. , 2021) to correct corrupted. mol files. For the NIST and the BMDMS-NP (Lee et al. , 2020) (which is contained within GNPS) the composite spectra were calculated to account for the acquisition of these spectral at several collision energies (see also Data and code availability). Early access to mFam Consortium Staging Database was kindly provided by Chimmiri Anusha, Steffen Neumann and Gerd Balcke. The training data was preprocessed with.
Gupta et al. (1998)  recognized that parameter estimation for any hydrological model is inherently a multi-objective problem. Observations in addition to streamflow provide information on the behaviour of specific fluxes or storages and constrain 115 parameters better than just streamflow observations. In the context of multi-objective calibration,  Efstratiadis and Koutsoyiannis (2010)  recommended a 1:5 or 1:6 ratio between the number of objectives and the number of calibration parameters to optimize parameter identifiability and to facilitate the search algorithm to find a robust solution of the given optimization problem. Developing criteria based on different 120 features of the same observations could potentially increase the ratio of objectives to the number of parameters. However, this approach is not favoured, as the overall information content within any observation dataset is inherently limited.
, 2022; Meyer Oliveira et al. , 2021; Huang et al. , 2020; Nijzink et al. , 2018; López López et al. , 2017). To the best of our knowledge, only a few studies have attempted to simultaneously use all three variables on the right-hand side of the water balance equation to condition a hydrological model  (Yang et al. , 2022; Dembélé et al. , 2020; Livneh and Lettenmaier, 2012). While the study by  Huang et al. (2020)  employed streamflow data for 155 bias correction of the ET dataset, they utilized the bias-corrected ET and TWSA for parameterization of a hydrological model, aiming to establish a streamflow-independent calibration scheme. Similarly, in a study by  Nijzink et al. (2018) , in-situ streamflow observations were utilized to benchmark the performance of five hydrological models across 27 very small European catchments (area < 1600 km2).
Total water storage anomalies (TWSA) The TWSA dataset is based on Level-2 data (Spherical Harmonic Coefficient, SHC) of the GRACE and GRACE Follow-On of TU Graz monthly solutions  (Mayer-Gürr et al. , 2018)  up 315 to degree and order 96, and by applying the anisotropic DDK3 filter  (Kusche et al. , 2009)  to correct the degree-related correlated noise. Further corrections were necessary to eliminate errors related to low-degree effects such as glacial isostatic adjustment  (Gerdener et al. , 2020). Mirza, 2003)  who reported that uncertainty of streamflow could reach 20% in those stations 340 due to the method of velocity measurement from non-anchored boats and inaccurate measurement of depths of current meters.
We deployed a master-slave parallel implementation of Borg-MOEA and ran the algorithm on 401 nodes of a cluster machine operating under Scientific Linux 7 environment. Related to this configuration, the initial population size 400 was used which was equal to the number of slave processes. 𝑁𝑁𝑆𝑆𝑁𝑁 = 1 - ∑ �𝑠𝑠𝑖𝑖𝑚𝑚 (𝑡𝑡) -𝑜𝑜𝑜𝑜𝑠𝑠 (𝑡𝑡) � 2 𝑇𝑇 𝑡𝑡=1 ∑ �𝑠𝑠𝑖𝑖𝑚𝑚 (𝑡𝑡) -𝜇𝜇 obs � 2 𝑇𝑇 𝑡𝑡=1 = 1 - 𝑀𝑀𝑀𝑀𝑀𝑀 𝜎𝜎 𝑜𝑜𝑜𝑜𝑜𝑜 2 (2).
The "compromise" solution or parameter set is said to have the "best" overall performance among the non-dominated solutions, and it is determined by finding the solution with the lowest Euclidian distance (ED, Eq. (  3 )) in the objective space from the point of 485 theoretical best values of the objectives, known as the "utopia" point. Separate compromise solutions were determined for each replication of an experiment, and an "overall compromise solution", which is the result of merging all solutions from the eight replications, was also determined. 𝑁𝑁𝐸𝐸 = �∑ (𝑈𝑈 𝑖𝑖 -𝑂𝑂 𝑖𝑖 ) 2 𝑛𝑛 𝑖𝑖=1 (3) 490 where n is the number of objectives, and Ui and Oi represent respectively the best value of the i th objective and the i th object of a solution parameter set. The observation datasets available for this study cover different periods (Table  3 ).
Uncertainty estimation To account for the uncertainty of the observation data in the calibration results in its entirety, 510 we would need to repeat the calibration multiple times with alternative realizations of the observation time series. This is not feasible given its high computational demand. Alternatively,  Werth and Güntner (2010)  defined an error ellipse around the compromise solution after the regular calibration against the original observation time series. For defining the length of one axis of the ellipse, they generated 5000 perturbed observation time series of the observable 515 according to its assumed error characteristics and calculated the performance indices of the simulated time series for each of them. The axis length was then determined by the standard deviation of the performance indices.
Acknowledgments. This study was enabled by the financial support of the  German Research Foundation  for the research unit "Understanding the global freshwater system by combining geodetic and remote sensing information with  modelling using a calibration/data assimilation approach (GlobalCDA) ". The authors thank  Felix Theodor Portmann  for first analyses and discussions,  Kerstin Schulze ,  University of Bonn, Germany  for processing the GRACE TWSA 1135 data, and  Muhammad Masood  of  Bangladesh Water Development Board (BWDB), Dhaka, Bangladesh  for providing streamflow observations.
Code availability: The WaterGAP code is accessible at 1120 https://github. com/HydrologyFrankfurt/WaterGAP2 Data availability. All optimal parameter sets obtained for the two basins together with the resulting performance metrics are compiled in an Excel file included in the supplement. The supplement (MS Excel file) is available online at URL??? Supplement. The supplement related to this article (MS Word file) is available online at URL??? 1125.
305 used the ensemble mean as monthly ET observation. The standard deviation (σ with N = 14) of the mean is also provided in the dataset as an estimate of the monthly observation error; we https://doi. org/10. 5194/egusphere-2023-2324 Preprint. Discussion started: 7 November 2023 c Author(s) 2023. CC BY 4. 0 License. used 2-σ range as the uncertainty of the ET observations in our analysis. The errors are provided in absolute terms with the unit of mm water equivalent for each month, which equivalenting relative terms corresponds to 25% in the Ganges basin and 24% in the Brahmaputra basin. 310.
Introduction The Whirlpool galaxy (or Messier 51, M 51) is one of the bestknown extragalactic objects in the sky for professional and amateur astronomers. Despite its proximity and many observations conducted by generations of astronomers, the distance to M 51 remains uncertain compared to other well-studied galaxies, with little agreement between the various methods (see e. g. McQuinn et al. 2016 ). This uncertainty is a limiting factor for studies that use the distance as an input, such The Cepheid catalogue shown in Table  B. 1 is available at the CDS via anonymous ftp to cdsarc. cds. unistra. fr  (130. 79. 128. 5)  or via https://cdsarc. cds. unistra. fr/viz-bin/cat/J/A+A/ 678/A44 The data produced in this work, such as the final M 51 Cepheid catalogue and the flux calibrated spectral time series of SN 2005cs are available at the GitHub page of the author (https://github.
Data To obtain a Cepheid distance to M 51, we made use of the catalogue and data presented in  Conroy et al. (2018, hereafter C18) , which were derived based on 34 epochs taken between October 2016 and September 2017 by HST during Cycle 24, using the Advanced Camera for Surveys (ACS). The photometric data published in C18 were derived using the DOLPHOT software package  (Dolphin 2000). Although this is a different software from the one that is regularly used for the reduction in Cepheid distance studies (namely DAOPHOT; e. g. Riess et al. 2022) , the steps taken for the brightness and error budget estimation (e. g. the point-spread-function (PSF) model estimation or the artificial star tests) match the procedure described in the latest Cepheid works (e. g. Yuan et al. 2022). The applied photometric techniques were also tested before in the context of the Panchromatic Hubble Andromeda Treasury (PHAT) survey  (Dalcanton et al.
We emphasise that the number of available epochs in this catalogue is significantly higher than what is usually available for extragalactic Cepheids. This makes C18 an exceptionally rich dataset. To derive the supernova distance, we applied an augmented version of EPM to the data of SN 2005cs. SN 2005cs is a type IIP supernova (SN IIP). It was discovered on 2005 June 28 by  Kloehr et al. (2005). It was followed thoroughly both photometrically and spectroscopically (e. g. by  Pastorello et al. 2006 Pastorello et al. , 2009)). We adopted the photometric data obtained by the Katzman Automatic Imaging Telescope (KAIT;  Filippenko et al. 2001)  owing to its good coverage and quality, along with early photometric observations from amateur astronomers as collected by  Pastorello et al. (2009). We included spectroscopic observations from multiple sources that were taken in the epoch range required for our EPM approach, as described in Sect.
We chose to build this step on principal component analysis (PCA;  Pearson 1901 ). The PCA is a commonly used tool for reducing the dimensionality of data, hence allowing for a fast and cheap classification and comparison of dataset elements (see e. g. Dobos et al. 2012; Bhardwaj et al. 2016; Seli et al. 2022). As a reference sample, we adopted the Cepheid set from  Yoachim et al. (2009) , who applied a PCA to obtain a reliable template set of Cepheid light curves. This set consists of Milky Way and LMC V-and I-band light curves that were covered well enough in phase to allow a detailed analysis of the light-curve shapes. Because the  Yoachim et al. (2009)  sample of Cepheids was measured in the Bessell system  Bessell (1979) , we converted the M 51 measurements from the ACS system before the light-curve fitting based on the relations described in  Sirianni et al.
To implement this method, we made use of the Python package pyGAM  (Servén et al. 2018). While the Fourier method would also perform perfectly for Cepheid light curves, it may not handle other types of variables, such as eclipsing binaries, with similar precision. Furthermore, the number of the Fourier components has to be varied from one star to the next in order to avoid overfitting. This can lead to biases when all the light-curve models of different complexities are inspected together, however. Here, GAM models provide a viable alternative because they yield smooth light-curve models whose complexity is automatically set by the data quality. This property makes the technique favourable for generic light curves. To fit the light curves using GAMs, we first calculated their phase curves using the C18 periods.
Fitting the period-luminosity relation In order to measure the distance to M 51 based on the newly found Cepheids, we chose NGC 4258 as the anchor galaxy. It is the only maser host with observed Cepheid variables. We , their brightnesses in the HST bands, and their metallicities were adopted from  Yuan et al. (2022). The data published in that paper were also obtained using ACS, hence no additional zero-point corrections were necessary. To estimate the reference PL relation fit parameters that were later used for the distance estimation of M 51, we fit Eq. (  4 ) to the NGC 4258 data set. To fit the model, we made use of the UltraNest 10 package  (Buchner 2021) , which allows for Bayesian inference on complex, arbitrarily defined likelihoods based on the nested-sampling Monte Carlo algorithm MLFriends  (Buchner 2016 (Buchner , 2019)).
Fit of the fundamental mode alone In the version with the fundamental mode alone, we applied a period cut of 10 days to separate the fundamental-mode Cepheids from their overtone counterparts. The period limit for this cut was motivated by the photometric incompleteness because fundamental-mode Cepheids with shorter periods were too faint and were absent from our sample. Hence, the majority of the removed stars were overtone Cepheids. To fit the PL relation to the filtered dataset, we applied the outlier-rejection method presented in  Kodric et al. (2015 Kodric et al. ( , 2018)). This method works iteratively, and it is based on the median absolute deviation (MAD) of the data points. Throughout this iterative procedure, the MAD of the dataset is calculated at each step, and then the data point with the greatest deviation is discarded as long as it is at least κ times away from the model value (where κ is a tuning parameter).
40 + 0. 09 mag and a distance estimate of D = 7. 59 ± 0. 30 Mpc for M 51. It is worth comparing the fitted observed scatter values: We obtained σ phot. ,M 51 = 0. 20 mag for M 51 and σ phot. ,N4258 = 0. 16 mag for NGC 4258. A similar result of σ phot. ,M 101 = 0. 21 mag can be obtained for M 101 based on the data from  Hoffmann et al. (2016). It is important to note that for the comparison NGC 4258 and M 101 values we also used F160W data, which generally reduce the scatter of the Wesenheit magnitudes. The similarity of these values shows the precision of the C18 data and the high quality of the Cepheid sample well.
2008; Vinkó et al. 2012). The latest independent EPM analysis by  Vinkó et al. (2012)  yielded a distance of 8. 4 ± 0. 7 Mpc based on photospheric velocity measurements using model spectra generated by SYNOW  (Parrent et al. 2010). We repeated the analysis based on tailored EPM of SN 2005cs using the spectral emulator introduced in  Vogl et al. (2020). The main goal of reanalysing the data of SN 2005cs was twofold: On the one hand, we aimed to investigate how the improvements of the spectral fitting method affect the outcome of the analysis, and on the other hand, we wished to compare this updated result to the independently obtained Cepheid distance. We stress that we did not aim to calibrate the SN II method based on this Cepheid distance because the EPM does not require any such calibration.
Interpolated light curves To measure the distance to SN 2005cs, we require knowledge of the absolute and observed magnitudes at matching epochs, which are then compared through Eqs. (  5 ) and (  6 ). Determining the former requires the modelling of spectral observations, but photometry is rarely available simultaneously with spectral epochs. We calculated the observed magnitudes at these epochs by reconstructing the light curves using Gaussian processes (GPs). For the implementation, we used the george 13  Python package  (Ambikasaran et al. 2015). GPs present an excellent way to interpolate light curves because they provide a nonparametric way of fitting while taking into account the uncertainties in the data. As a result, we obtained smooth and continuous light-curve fits (see Fig. 13 ) that were used to estimate the brightness in the spectral epochs.
This corresponds to µ = 29. 20 ± 0. 20 mag. When we tighten the t 0 prior used for the EPM regression based on the light curve, the final fit yields a distance of D = 7. 34 ± 0. 39 Mpc (i. e. µ = 29. 33 ± 0. 11 mag). We point out that the resulting t 0 estimate of the conservative version is in tension with the amateur photometric data because SN 2005cs was already visible in images taken earlier. Hence, we strongly favour the approach that uses the early light-curve fit. We also note that including the tighter constraints reduces the EPM distance uncertainties by almost a factor of two. This highlights the importance of having an informed prior on the time of explosion for a precise distance estimate.
Acknowledgements. The authors thank the anonymous referee for the constructive comments and remarks that helped improve the paper. We also thank  Jason Spyromilio  for the comments he provided while writing the manuscript, and  Zoi Spetsieri  for carrying out a check on stellar association bias for the Cepheid sample. The research was completed with the extensive use of Python, along with the numpy (Harris et al. 2020), scipy (Virtanen et al. 2020) and astropy (Astropy Collaboration 2018) modules. This research made use of Tardis, a community-developed software package for spectral synthesis in supernovae (Kerzendorf & Sim 2014;Kerzendorf et al. 2022). The development of Tardis received support from the  Google Summer of Code initiative  and from  ESA 's  Summer of Code in Space program. Tardis makes extensive use of Astropy and PyNE.
C. V. and W. H. were supported for part of this work by the  Excellence Cluster ORIGINS , which is funded by the  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation)  under Germany's  Excellence Strategy - EXC-2094-390783311. R. I. A. acknowledges support from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (Grant Agreement No. 947660 ). R. I. A. further acknowledges funding by the  Swiss National Science Foundation  through an  Eccellenza Professorial Fellowship  (award  PCEFP2_194638 ). S. T. acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  ( LENSNOVA : grant agreement No  771776 ). S. B. acknowledges support from the  Alexander von Humboldt foundation  and thanks  Sherry Suyu  and her group at the  Technische Universität München (TUM)  for their hospitality.
In addition, it was assumed that the trajectories for methane abatement were followed by the different countries simultaneously. A deviation by individual countries may lead to carbon leakage and give them an economic advantage that must be countered through the inclusion of upstream methane emissions in the European carbon border adjustment mechanism, as outlined previously. Finally, while we analyse the role of the two main GHGs responsible of global warming as of today (CO 2 and methane), in a future with nearly a fourth of final energy consumption satisfied by hydrogen, hydrogen leakage can also cause significant global warming effect (with 30 times higher potency than CO 2 over 20 years)  49. In our analysis we use empirical data to assess the methane footprint of natural gas, and the technology data to assess CO 2 emissions.
Moreover, it includes the competition between different hydrogen transport routes (gaseous hydrogen via pipelines or liquified hydrogen and ammonia via shipping) to assess the least-cost hydrogen import routes. Via methane emissions calculation module, the modelling framework also accounts for the methane footprint of natural gas and low-carbon hydrogen, both produced in Europe and imported from neighbouring regions Europe. The MIRET-EU model MIRET-EU is a dynamic energy system optimisation model that represents European energy system in a detailed manner from primary energy sources until the final energy in its consumed form. It optimises the investments in different energy supply, transformation, transport and storage through the period 2010 to 2050 to estimate least-cost capacity investment pathways based on the TIMES generator.
The modelling framework of the MIRET-EU model follows the evolution of the TIMES framework successively in the RES2020's Pan European TIMES model  51  and the JRC-EU-TIMES  52  , MIRET-FR  53  and TIAM-IFPEN 18,54-57 models with additional technologyspecific constraints in the transport sector, refineries and bioenergy conversion technologies, hydrogen infrastructure, power sector and industrial processes. MIRET-EU studies the evolution of the European energy system in the period between 2010 and 2050 with 10-year optimisation time steps. As a part of this optimisation concerns past dates, the 2010-2020 period is calibrated and fine-tuned using the latest constraints (COVID-19 pandemic, Russia-Ukraine war, etc. ) and data from the most wellknown energy databases such as the JRC-IDEES (The EU's Joint Research Centre's Integrated Database of the European Energy System), POTEn-CIA (Policy Oriented Tool for Energy and Climate Change Impact Assessment), EUROSTAT, and other databases published by international organisation such as IEA, IRENA, and the World Bank.