dataesr/scientific-paragraphs-categorization huggingface dataset:
While the choice of individual bead types can be made using chemical intuition, still a sizable subset of combined possibilities exists. Importantly, parameterization of bonded and non-bonded parameters should be optimally performed simultaneously since bonded and non-bonded interactions are not independent -they are directly influencing each other via the density of interactions. 12, 13  Recent versions of the Martini force-fields such as Martini 3 rebalanced the density of interactions by introducing an even larger number of possible interaction types, thereby rendering the parameterization of molecules often a non-tractable problem to common users. Automation of coarse-graining is thus critical, especially when constructing large databases of molecules. Automation offers a solution to address the challenge of force-field development, which typically involves collaboration among multiple researchers working on interdependent parameters.
ticle swarm optimization With CGCompiler we present a Python package that streamlines CG molecule parameterization. It employs mixed-variable particle swarm optimization to simultaneously optimize categorical (beadtype) and continuous (bonds, angles, dihedrals,. ) variables. Therefore, CGCompiler is particularly well suited for, but not limited to, parameterization tasks in CG FFs that follow a building block approach. To enable the application of the building block approach also to larger molecular fragments, consisting of more than one CG bead, the method allows for optimization of shared building blocks in different molecules, e. g. the headgroup, linker, or tails of lipids. Molecule parameterization in Martini 3 follows three steps: i) Choice of mapping and bead sizes ii) Assignment of chemical bead types iii) Choice of bonded terms and assignment of bonded parameters.
This is repeated until a termination criterion is fulfilled. Due to noise in the objective function evaluation, the selection of the true best parameters can only be done with a certain probability. Therefore, the set of the best, statistically equal candidate solutions undergoes a screen-to-the-best procedure, which either provides one solution that is significantly better than the rest, or reduces the field of viable candidate solutions further, on which more expensive evaluation simulations would be performed. Figure  1 : Parameterization workflow. i) A set of training systems from which the target properties can be extracted. ii) Target data is acquired from atomistic simulations and experiments. iii) An initial swarm is generated with FF parameters randomly selected from a predefined range of feasible parameters.
Simulation details The Python package is based on evo-MD. 28  All simulations were performed with GROMACS 2020. 4 and 2021. 4 29 and analyzed with in-house Python scripts that are utilizing MDAnalysis,  30, 31  LiPyphilic,  32  SciPy,  33  and pyemd, which is a Python wrapper for Pele and Werman's EMD implementation. 34, 35  Visualization was done with NGLview. 36.
Coarse-grained models All coarse-grained models were simulated using the Martini 3 11 force field. Beta version 14 of the Martini 3 cholesterol parameters was used. 47, 48  Initial configurations of membrane systems were generated with the Python script insane. 49  Details of the employed training systems are listed in Table  2. All systems were energy minimized and equilibrated with the current version of DPSM, that made the Martini 2 model of sphingomyelin compatible with Martini 3. During the particle swarm optimization each system was equilibrated with the candidate FF parameters in two stages, with time steps of 2 fs and 20 fs, respectively. For all coarse-grained production simulations a time step of 20 fs was used. Non-bonded interactions were cut off at 1. 1 nm. For electrostatic interactions the reaction-field method was used with a dielectric constant of 15 and the reaction-field dielectric constant was set to infinity.
Generally, noise-mitigation methods employing OCBA perform the best under various circumstances,  24, 58  but its sequential secondary budget allocation puts constraints on the parallelization of the parameterization algorithm. Still, its integration into the parameterization pipeline should be explored in the future. Together with the general benefits of automation, the here-presented conceptual advantages will further facilitate rigorous CG molecule parameterization. The CGCompiler Python package that comes with our method is tailor-made for parameterization tasks in buildingblock FFs, such as Martini. Also larger building blocks, i. e. , a molecule class with shared regions can be parameterized simultaneously. Our approach is not limited to lipid parameterization, but can be applied to any kind of molecule.
CGCompiler can be easily adapted to the needs of a specific parameterization task. Implementing new observables is not much different from writing Python functions for analyzing MD data. Importantly, our automation platform eases collaborations between individual researchers since a clear overview of the parameterization flow is provided. This also renders force-field reproducibility as well as retrospective force-field corrections, such as corrections to the targets (e. g. , improved atomistic force-fields or simulation settings) or inclusion of additional targets rather straightforward. The here-presented study focuses on method development and the sphingolipid linker parameterization was merely a test case. The parameters of the head group and lipid tails, predefined in our study, are still actively improved/(re)parameterized by the core developers.
Acknowledgement K. S. S. and H. J. R. thank the  NWO Vidi Scheme, The Netherlands , (project number:  723. 016. 005 ) for funding this work. H. J. R thanks the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  for funding this work under  Germany 's Excellence Strategy - EXC 2033 -390677874 -RESOLV. K. S. S. and H. J. R. gratefully acknowledge the computing time granted by the  Resource Allocation Board  and provided on the supercomputer Lise and Emmy at  NHR@ZIB  and  NHR@Göttingen  as part of the NHR infrastructure. The calculations for this research were conducted with computing resources under the project  nip00054. K. S. S. and H. J. R. gratefully acknowledge the  Gauss Centre for Supercomputing  e. V. (www. gauss-centre. eu) for funding this project by providing computing time through the  John von Neumann Institute for Computing (NIC)  on the  GCS Supercomputer JUWELS at Jülich Supercomputing Centre (JSC).
Introduction In order to extract meaningful information from physical systems through X-ray emission or absorption spectra, some aspects of atomic data are required such as line energies and widths, transition rates, mass attenuation coefficients and fluorescence yields, just to name a few. This is true for X-ray emission of bulk, solid materials as well as liquids, gases and plasmas  (Grieken and Markowicz, 2002). Given the wide interest of X-ray techniques in both academia and industry, there are extensive databases with the relevant atomic parameters for elemental quantification of almost every element in the periodic table  (Bambynek et al. , 1972; Krause and Oliver, 1979; Hubbell et al. , 1994; Schoonjans et al. , 2011). These parameters, however, feature high relative uncertainties, especially in the low energy regime, and are mostly confined to neutral elements.
Regarding the calculations made for multiply-stripped hollow neon atoms with double K-shell vacancies by  Karim and Logan, Karim and Logan (1999)  using the Hartree-Fock model, the FY were found to decrease as the electron population in the outer L-shell increases. For the isoelectronic series of Ne, Ar and Kr,  Marques et al. shown that although the K FY almost does not change with the nuclear charge, when compared to the neutral atom's FY, the same is not true for the L-shell, showing very large variations  (Marques et al. , 2020). There are several databases of atomic parameters that are widely used in astrophysics  (Smith et al. , 2001; Foster et al. , 2013; Kaastra and Mewe, 1993; Dere et al. , 1997; Kallman and Palmeri, 2007) , and they also have their own strengths and weaknesses, one of the latter being the fact that the atomic physics approximations used in the calculation of these parameters are much more realistic for isolated atoms in plasmas than in solid state materials.
There are, however, some issues even with the most widely used databases, as shown for example by  Gorczyca et al. (2003). In their work, they audited the fluorescence database by  Kaastra and Mewe (1993) , which is widely used in modeling codes, in particular their scaling laws along isoelectronic sequences. They found serious discrepancies that seem to hinder its application in plasma modeling. The inclusion of different charge states in the simulations is particularly important in the case of plasma and astrophysical studies, as various diagnostics are performed based on the emitted X-ray radiation from the plasma or astrophysical object in study  (Guerra et al. , 2013). Various features can be determined from the X-ray spectra of galaxies, and one of the important elements to measure in the spectra is Fe, particularly the Fe K line energy, width, shape and relative intensity.
This metal presence and abundance can give information about the astronomical system, for example on the presence and composition of the torus around active galactic nuclei  (Fukazawa et al. , 2016). As shown in many recent publications, the precise calculation of these parameters requires state of the art methods such as the Multi-Configuration Dirac-Fock (MCDF) method and for all but the simplest open shell configurations, this task is quite demanding in terms of CPU time and resources  (Cheung et al. , 2021; Guerra et al. , 2018 Guerra et al. , , 2015)). A distributed computation script that enables the use of supercomputers with a near-perfect linear scalability with the number of cores, was developed based on gnu Parallel shell tool  (Tange, 2018)  to compute wavefunctions and transition rates within the MCDF framework.
Acknowledgments This research was funded in part by  FCT (Portugal)  under research center grant  UID/FIS/04559/2020  (LIBPhys). This work was also funded through the project  PTDC/FIS-AQM/31969/2017 , '' Ultrahigh-accuracy X-ray spectroscopy of transition metal oxides and rare earths ''. F. G. acknowledges support from  FCT , Portugal through contract  UI/BD/151000/2021. J. M and J. P. S acknowledge the support of  EMPIR, Germany , under Contract No. 20FUN04  PrimA-LTD. The EMPIR initiative is co-funded by the  European Union 's  Horizon 2020 research and innovation programme  and the  EMPIR , Germany participating States. Part of this work has been carried out under the High Performance Computing Chair -a R&D infrastructure (based at the University of Évora; PI:  M. Avillez ), endorsed by  Hewlett Packard Enterprise (HPE) , and involving a consortium of higher education institutions ( University of Algarve, University of Évora, NOVA University Lisbon, and University of Porto ), research centres (CIAC, CIDE-HUS, CHRC), enterprises ( HPE, ANIET, ASSIMAGRA, Cluster Portugal Mineral Resources, DECSIS, FastCompChem, GeoSense, GEOtek, Health Tech, Starkdata ), and public/private organizations ( Alentejo Tourism-ERT, KIPT Colab ).
At the same time, a database will be developed to collect and quality control new data types like crowdsourced data and other data sources. These datasets should enhance the availability of observational data that are important for nowcasting. The European Weather Cloud (EWC) is one of the hosting options (see Section 3. 3). stations, mobile phones, car sensors and webcams) to NMHS core networks. c. Reinforced efforts to extract more value out of existing capabilities: improving the quality of the OPERA radar Quantitative Precipitation Estimations (QPEs) composite and radial wind products as well as expanding their coverage (see also Section 3. 1. 3); exploring the value of additional products from the Global Navigation Satellite System (GNSS) networks as well as expanding this network towards Eastern Europe and North Africa; developing aerosol products from the Automatic Laser Ceilometer (ALC) network.
The NWCSAF (EUMETSAT, 2021b) provides software to generate a variety of nowcasting-related satellite products in near real time, such as satellite-derived winds, instability indices and humidity, convective cell characterization and tracking, convection initiation detection, turbulence determination, precipitation rate and image extrapolation (Table  1 ). F I G U R E 8 (a) To exploit the enhancements in EUMETSAT's nextgeneration missions for nowcasting, NWCSAF products will be updated and new products developed using the LI and the infrared sounding mission. Table  1  provides a high-level overview of NWCSAF software packages and products planned in the 2022-2027 timeframe, in addition to maintaining the currently operational packages.
Towards 2030 To advance observation-based nowcasting methods, it will be very important to foster the understanding of existing and new observations, for example, of crowdsourced data or satellite-derived cloud, instability, moisture and lightning products (EUMETSAT, 2020a, 2020b), including uncertainty. These datasets should not only be analysed in a statistical way to optimize specific nowcasting verification metrics but should also be investigated to further build our understanding of meteorological processes and the variety of meteorological phenomena relevant in the nowcasting range, for example, via applied research projects at NMHS and with the research community. Development of algorithms should embrace probabilistic approaches, use advances in computing and the development of new approaches adopted from other fields, for example, from ML (e.
RUC (typically hourly) that can be obtained by performing cycled assimilation/forecast steps such as in NOAA's HRRR  (Benjamin et al. , 2016)  is also of great importance. The requested tight deadline for forecast outputs implies short cut-off times (typically 15 min), which makes it difficult to account for observations with later availability such as, for example, most satellite-based radiances. For this reason, data from ground-based sensor, aircraft and Doppler radar are mainly used. Pragmatic choices can be made in order to provide as fast as possible the different elements, such as background or lateral boundary conditions, respectively needed for the DA and for the forecast steps  (Auger et al. , 2016). Non-variational DA techniques can also be used additionally to nudge information such as cloud cover using, for instance, a combination of ceilometers and cloud-top pressure deduced from IR radiances in HRRR, or radar data through latentheating methods (in HRRR again and in the UKV).
Towards 2030 To improve the various nowcasting and seamless methods and tools described in Sections 3. 2. 1-3. 2. 3 towards 2030, we propose a joint undertaking of nowcasting tool developers, to be launched and coordinated within the existing EUMETNET E-NWC programme and the anticipated WMO World Weather Research Programme (WWRP) Research Demonstration Project (RDP) SHOW-HOW. Their objectives should be to improve our understanding of the meteorological processes relevant for the nowcasting/seamless problem, to advance methods of data integration and combination including probabilistic approaches, and to improve the skill of nowcasting tools as well as seamless prediction through pan-European benchmarking and verification. The undertaking should foster the further development of algorithms, using advances in computational approaches and the development of new approaches adopted from other fields, for example, from ML.
| Data and infrastructure With the advent of new-generation satellites and groundbased measurements, observation data rates will increase by orders of magnitude, for example, MTG will generate at least 50 times more data than MSG. To advance nowcasting, it will be important to integrate and synthesize this wealth of data into a few parameters which can be quickly interpreted (see Section 3. 2), requiring (i) standardized data formats such as NetCDF/CF or BUFR, (iii) metadata standards that allow for a description of each measurement and its physical interpretation, (iv) reduced data dissemination rates by using standardized compression methods and (v) nowcasting tools and near real-time data feeds available on centralized clouds, thus bringing users to the data as an alternative to disseminating data to users.
| High-availability data services and emergence of the European Weather Cloud A highly reliable operational data flow is a precondition of safety-critical applications such as using nowcasting techniques for severe weather prediction. With exploding data rates from both the observing system and the model sphere, clear priorities are required in the data services realm to remain affordable and operationally viable. From the space-based observations side in Europe, EUMETSAT will continue to build on EUMETCast Satellite Rebroadcasting System for the delivery of safetycritical application data, in particular MTG data relevant for nowcasting. At the same time it is diversifying its data services portfolio to offer a mix of pull and push data services that complement EUMETCast Satellite for less stringent requirements, for example for applications in research and development, and as exploratory and fallback solutions.
A potential game changer in this regard is the EWC project, jointly developed by ECMWF and EUMETSAT, offering data processing resources close to the meteorological input data. As such, the EWC aims to become the cloud-based platform for meteorological application development and operations in Europe and seeks to enable the digital transformation of the European Meteorological Infrastructure. It is dedicated to support the NMHS of the Member States of both ECMWF and EUMETSAT in fulfilling their official duties to protect life and property from impending meteorological hazards (Figure  11 ). Offering processing and storage capacity for the initiating organizations and their respective Member and Cooperating States, EWC is foreseen to further host activities such as research projects, training and prototyping new products.
There will be near-real-time feeds of operational data from EUMET-SAT and ECMWF, as well as climate data records, allowing users to 'bring' their software into a dedicated environment close to the data, rather than having to rely on dissemination and local capacity. In a pilot phase until 2022, it is planned to host the NWCSAF software and the ESSL Displayer on the EWC. Developers of nowcasting tools should take advantage of the opportunities provided with the Cloud, as a platform to seed the formation of user communities around the development of 'merged' advanced products in support of forecasting, and use of common tools. In the coming years, the EUMETNET Federated Data Coordination Mechanism will be further developed, building on the EWC and enabling standardized access to data, including hourly and sub-hourly observations from surface stations including those not currently routinely exchanged such as rain gauge data, solar radiation, snow depth and wind.
| Code sharing and co-development To share development costs, groups of NMHS and researchers should co-develop nowcasting tools, ideally run on a cloud-based host infrastructure (e. g. , the EWC). This will allow all countries to contribute, including those with small development teams. Open licences such as BSD or MIT should be used for these developments, to ensure sense of ownership and common access. Collaborative software development projects in the nowcasting area, such as pytroll (2021) and pySteps (2021), GeoWeb, and ADAGUC (KNMI, 2021), should be nurtured. Software such as Com-SWIRLS  (Wong et al. , 2016)  and the EUMETSAT Nowcasting SAF software packages (EUMETSAT, 2021b) should be available as openly as possible. As an example, NOAA within the Unified Forecasting System (UFS) framework openly releases pre-operational code and tools for nowcasting and short-term weather prediction applications, as part of its engagement with the weather community  (NOAA, 2021).
| Visualization and display systems Towards 2030, display systems and techniques will have to evolve with an increasing level of sophistication and diversity of nowcasting datasets (objects, fields and probabilistic information), and the needs of nowcasting research. Such systems shall render the results of nowcasting tools (objects, fields and probabilistic information) understandable for forecasters and end users. Several lines of development are important: comprehensive, but at the same time simple, plotting of 4D data cubes combining model and observational data could be a development line, and so is effective display of object-based, EPS and probabilistic forecasts. Powerful web-based meteorological data display systems will plot all data together in a common geographical projection, using next-generation Web Mapping, Feature and Coverage Services.
Towards 2030 • The EWC has become a core piece of meteorological infrastructure for accessing and sharing data, collaborative development of products and tools, and for development and operations. • As identified in Sections 3. 2. 1-3. 2. 3, we recommend a joint undertaking among European NMHS around F I G U R E 1 1 The European Weather Cloud, a joint undertaking by EUMETSAT and ECMWF providing centralized processing and storage capacity, for the benefit of collaborative activities by meteorological services, including development and benchmarking of nowcasting tools (schematic view of the system in its pilot phase). nowcasting, for the (co-) development of nowcasting tools, based on common verification methods, code sharing and use of the EWC as a common cloud infrastructure.
| Forecast verification and quality control Forecast verification has been developed in the last decades (WMO, 2021b). However, many approaches typically used for NWP forecast verification cannot easily be adopted to nowcasting. Verifying nowcasts is mostly relevant in case of high-impact weather events. These are usually rare, localized events, for which there is often a lack of reference data (and sometimes a lack of definition of what is a good warning. ) Validation and verification approaches applied in the nowcasting framework are very diverse. A typical approach to assess the quality of nowcasting a particular severe weather event is a subjective analysis based on available pieces of information and data to make a complete as possible picture. Although very useful, this approach is not applicable for a larger number of events and does not enable the calculation of statistics.
Thorough verification of nowcasts is hampered by the limited availability of observations that could serve as ground truth. While lightning detection networks can be used for the verification of the occurrence of thunderstorms, and radar networks provide useful measurements for the verification of precipitation, there is a lack of observations suitable for the verification of phenomena such as icing, freezing rain, wind gusts, hail, tornadoes, localized heavy precipitation and low clouds. Databases of severe events such as the European Severe Weather Database (ESWD,  Dotzek et al. , 2009)  provide a basis for verifying nowcasts; however, they are often non-publicly maintained by NMHS, often lack references to pertinent observations, and tend to be incomplete in coverage. If no independent ground truth is available, a typical approach is to compare the nowcast with the analysis of the same nowcasting tool, for example, comparing the detected object with the +1 h-nowcast for this object calculated an hour before.