dataesr/scientific-paragraphs-categorization huggingface dataset:
The ICE3 one-moment microphysical scheme  (Pinty and Jabouille, 1998)  is used to compute the evolution of five hydrometeor species (rain, snow, graupel, cloud ice and cloud liquid water). Radiative fluxes are computed with the  Fouquart and Bonnel (1980)  https://doi. org/10. 5194/acp-21-11857-2021 Atmos. Chem. Phys. , 21, 11857-11887, 2021 scheme for short-wave radiation and the RRTM (Rapid Radiative Transfer Model,  Mlawer et al. , 1997)  scheme for long-wave radiation. The surface exchanges are computed by the SURFace EXternalisé (SURFEX) surface model  (Masson et al. , 2013)  considering four different surface types: land, towns, sea and inland waters (lakes and rivers). Output fluxes are weightaveraged inside each grid box according to the fraction of each respective tile defined with physiographic data from the ECOCLIMAP database  (Masson et al.
The wave coupling in AW is done with six cores of eight processes for WW3 and one core to manage the SST field with a toy model. It increases the total CPU time by 13. 8 % and the time elapsed by 57 % with respect to AYSSTatl. Finally, AOW shows both increases in elapsed time (and integrated elapsed time -IET) and in total a CPU cost consistent with the addition of the two couplings. Code and data availability. Although the operational AROME code cannot be obtained, the modified sources for cy41 are available on demand to the authors for the partners of the ACCORD consortium and will be included in the cycle 48 Météo-France official release. The source codes of the other components are available online. -WaveWatchIII was used in version 5. -The SURFEX open-source version (Open-SURFEX), including the interface with OASIS from v8_0, is available at http://www.
The surface weather station data and the chains of thermistors on the Lion and Azur Météo-France moored buoys are available in the MIS-TRALS/HyMeX database (https://mistrals. sedoo. fr/, last access: 5 August 2021) (MISTRALS, 2021) after subscription. Oceanographic buoy data and the PSY4V3R1 daily analyses of Mercator Ocean International are available through the Copernicus Marine Environment Monitoring Service (CMEMS) portal (https://marine. copernicus. eu/, last access: 5 August 2021) (CMEMS, 2021) after user registration. Author contributions. All the authors (CS, CLB, and MNB) contributed to the conceptualization and methodology of the study as well as drafting, reviewing and editing the article. MNB developed the WASP parameterization and managed its integration into the SURFEX code. The configuration NWMED72 of both the NEMO and WaveWatchIII models was developed and coupled to AROME by CS and CLB.
16, which is distributed under an open-source style license through a password-protected distribution site at https://polar. ncep. noaa. gov/waves/wavewatch/ (last access: 5 August 2021) (NOAA/NCEP, 2021a). Since version 6. 07, WaveWatchIII is distributed using GitHub (https://github. com/NOAA-EMC/ WW3, last access: 5 August 2021) (NOAA/NCEP, 2021b) without any username and password required to access the software package. -NEMO is available at https://www. nemo-ocean. eu/ (last access: 5 August 2021) (NEMO, 2021) after user registration on the NEMO website. The version used is NEMO_v3. 6_STABLE for Mediterranean configurations (see https://sourcesup. renater. fr/wiki/morcemed/nemconfig, last access: 5 August 2021, Institut Pierre Simon Laplace, 2021 and Appendix).
This chapter represents an initial effort to identify needed improvements in current costing methods and to provide guidance for future cost studies, especially for advanced (pre-commercial) technologies. Continuing efforts also are needed to further develop the models, databases, and guidelines that support applications of the hybrid costing method. This includes methodological improvements and quantitative guidelines to improve the development of FOAK cost estimates and reduce the uncertainties in projected NOAK costs for advanced technologies. Despite current limitations, however, the hybrid modelling approach can enhance prevailing assessments of advanced technologies by providing new insights regarding the time frame and requirements for their cost-competitiveness with existing or other advanced technology concepts.
Thus, the reported value of BEC for an advanced technology -which is the building block for total cost estimatesis arguably the most poorly documented and least transparent component of an estimated hypothetical NOAK cost. Thus, NOAK cost studies should always discuss the methods and data sources used to obtain BEC estimates. To avoid ambiguity about the cost basis, good practice also demands that the cost year be clearly (and repeatedly) reported, as well as the location to which the BEC estimate applies. In this regard, the IEAGHG recently published a set of location-specific cost factors that give the relative costs of materials and labour in twelve geographic regions worldwide (see Table  B -1 in Appendix B)  [37]. Cost variations within a region also should be considered using other sources of country-specific data (for example, for U.
S. power plants the IECM computer modelling tool adjusts the BEC for six geographic regions based on U. S. data for materials and labour costs)  [38]. In addition, information on the current cost of advanced technology components should be provided to give context to the BEC estimates, and help readers assess the degree of progress required to achieve the assumed NOAK cost values. Finally, to add a degree of conservatism and credibility to hypothetical NOAK cost estimates, we recommend that estimates of the future Bare Erected Cost of a mature technology take into consideration the actual current state of that technology. This is because the future BEC of a technology currently at a low TRL value is inherently more uncertain and speculative than that of a technology at a higher TRL. NOAK studies should therefore report whether (or how) such considerations are reflected in the BEC estimate.
This guidance also emphasizes that for LCOE calculations, the assumed CF should be the levelised value over the life of the plant. This weighs initial years of operation more heavily than later years because of discounting (see, e. g. ,  [8] ). Historical data on the capacity factor of mature coal-fired and gas-fired power plants over their operating life offer one credible benchmark for numerical assumptions about the future CF of a new NOAK plant. Although time series data on the CF individual generating units over their full operating life are not readily available, one case study of a large coal-fired unit reported a 25-year average capacity factor of 77% from 1981-2005, with a levelised CF of 71% due to the characteristically lower CF during the first years of operation  [8]. The U. S.
Thus, in view of the historical data noted above, an assumption that the levelised capacity factor over the life of a plant is equal to its availability is a bounding case that yields the theoretical minimum value of LCOE for the technology analysed. Based on historical data, the actual expected CF would be lower and the estimated LCOE higher. This distinction between expected and bounding values of CF and LCOE should be clearly acknowledged in NOAK cost study results that employ that assumption. Failure to highlight this distinction can easily lead to misunderstandings that would bias comparative cost results and technology rankings based on LCOE.
Those factors are shown in the last column of Table  B -1 in Appendix B. Pending further data acquisition and analysis efforts to quantify this factor, the guidance in Table  1 -5 is suggested by the authors of this chapter to ensure that this potential cost adder is not overlooked in FOAK cost estimates. The suggested value of this factor, informed by the studies of Greig, et al. [40]  and IEAGHG  [37] , is greatest for the first commercial installation. The system contingency cost then decreases for subsequent projects, assuming that any systems-related problems found at a FOAK installation will result in design changes (and associated cost increases) to eliminate the problem in subsequent replications. To the extent that initial design changes are not wholly successful, some risk for subsequent early-mover projects may remain, pending demonstration that the reliability problem has indeed been resolved successfully.
Variable O&M cost items Analogous to an LCOE calculation for a power plant, this calculation would combine the estimated capital costs (for buildings and equipment), O&M costs (for feedstock materials, labour, utilities, etc. ), and financing cost to estimate a cost (or price) per unit of product at the production scale needed for the FOAK facility. Such an estimate, if feasible, would provide a reasonable bound for purposes of a FOAK cost estimate. All other VOM costs can be estimated following the standard cost guidelines cited earlier. Note that the NETL methodology  [16]  considers the maintenance costs for planned and unplanned equipment outages to also be variable cost items. Here too, prudence would suggest that these estimated costs also will be higher for FOAK installations where reliability data are lacking.
Uncertainty analysis also is important in calculating aspirational NOAK cost goals since these also depend heavily on assumptions and judgments that may vary considerably. This chapter represents an initial effort to identify needed improvements in current costing methods and to provide guidance for future cost studies, especially for advanced (pre-commercial) technologies. Continuing efforts also are needed to further develop the models, databases, and guidelines that support applications of the hybrid costing method. This includes methodological improvements and quantitative guidelines to improve the development of FOAK cost estimates and reduce the uncertainties in projected NOAK costs for advanced technologies. Despite current limitations, however, the hybrid modelling approach can enhance prevailing assessments of advanced technologies by providing new insights regarding the time frame and requirements for their cost-competitiveness with existing or other advanced technology concepts.
For this reason, it is recommended to start with a tool that provides the best technical guidance, e. g. , through the inclusion of interactive procedures or step-by-step checklists. In addition, it is recommended to choose a tool that offers active discussion forums among the user community to help the user with methodological or model implementation issues. Examples include UQLab and FOQUS. Further guidance on the selection of uncertainty analysis tools can be sought for instance through online databases like swMATH. Table  3 -8 summarizes the above considerations. A sampling technique to identify the posterior probability distribution of a parameter once the prior probability distribution has been determined. 119.
Acknowledgements Support for the first chapter of this white paper was provided, in part, by a subcontract to  Carnegie Mellon University  from  KeyLogic Systems, Inc. under Contract No. DE-FE0025912  from the  U. S. Department of Energy's National Energy Technology Laboratory. The authors would also like to thank  Rahul Anantharaman ,  Chao Fu ,  Jabir Ali Ouassou ,  Mari Voldsund ,  Chris Greig  and  Ann Aalberg  for their helpful comments and inputs. Acknowledgements. 2 Executive summary. 3.
Engineered nanomaterials (ENMs) are intentionally designed and produced by humans to revolutionize the manufacturing sector, such as electronic goods, paints, tires, clothes, cosmetic products, and biomedicine. With the spread of these ENMs in our daily lives, scientific research have generated a huge amount of data related to their potential impacts on human and environment health. To date, these data are gathered in databases mainly focused on the (eco)toxicity and occupational exposure to ENMs. These databases are therefore not suitable to build well-informed environmental exposure scenarios covering the life cycle of ENMs. In this paper, we report the construction of one of the first centralized mesocosm database management system for environmental nanosafety (called MESOCOSM) containing experimental data collected from mesocosm experiments suited for understanding and quantifying both the environmental hazard and exposure.
The database, which is publicly available through https://aliayadi. github. io/ MESOCOSM-database/, contains 5200 entities covering tens of unique experiments investigating Ag, CeO 2 , CuO, T iO 2 -based ENMs as well as nano-enabled products. These entities are divided into different groups i. e. physicochemical properties of ENMS, environmental, exposure and hazard endpoints, and other general information about the mesocosm testing, resulting in more than forty parameters in the database. The MESOCOSM database is equipped with a powerful application, consisting of a graphical user interface (GUI), allowing users to manage and search data using complex queries without relying on programmers. MESOCOSM aims to predict and explain ENMs behavior and fate in different ecosystems as well as their potential impacts on the environment at different stages of the nanoproducts lifecycle.
However, the experimental data produced were stored in non-standardized forms limiting the accessibility, interoperability, and reusability of the generated data for further applications. To meet this need, the development of a centralized mesocosm database management system for environmental nanosafety that will allow to collect, standardize, and store all the exposure and hazard experimental data obtained in mesocosms was required  [5, 9]. Consequently, the mesocosm database management system for environmental nanosafety (MESOCOSM) presented in this paper, will be one of the first free access database systems providing and managing robust environmental exposure and hazard data. The proposed system aims to achieve a highly organized relational database along with an appropriate software application, consisting of a graphical user interface (GUI), that facilitate processing and access to that data.
The MESOCOSM database provides 5200 unique experimental data on tens of unique mesocosm experiments based on Ag, CeO 2 , CuO, T iO 2 -based ENMs as well as nano-enabled products  [11, 12, 13]  with different shape, size, surface coatings and matrix. These data were collected from mesocosm testing to address environmental nanosafety throughout the lifecycle of nanoproducts, from the early stages of production to the end of life. MESOCOSM will be an added-value to model and forecast the behavior and fate of ENMs in the environment, as well as to assess their potential environmental risks.
Overview of nanomaterial-related databases Over the last decades, experimental studies on (eco)toxicologically, behavior and fate of ENMs have significantly increased. This evolution comes along an increase in the number of databases (non-exhaustively listed below), knowledge base  [14, 15] , web-based libraries  [16] , or also registry repositories  [17, 18, 19]  with the financial support of the EU and US international organizations. An overview of ENM-related databases is provided in Table  1. Several of these databases are not specific to ENMs. Among others, the National Toxicology Program database (NTP) contain modern toxicology and molecular biology data to identify substances (ENMs and others) in the environment that may affect human health  [20]. The ECETOC Aquatic Toxicity database (EAT) is an ecotoxicological database regarding the toxicity of diverse substances to aquatic species in fresh and saline waters  [21].
The EAT database gives access to the Human Exposure Assessment Tools Database (heatDB) which collects all publicly available sources of human exposure data. The National Institute for Occupational Safety and Health database (NIOSH) provide databases focusing on occupational toxicology information (ENMs and others)  [22]. One last example, is the Ecotoxicological Risk Assessment database (ERA) that includes the concentration ranges for various human-use drug residues, their potential effects for aquatic organisms, and the temporal variations of the ecotoxicological risks  [23]. Of course, with the enhanced research and development activities around Nanosciences and Nanotechnologies, ENM-specific databases were developed. Most of them focus on toxicity and/or ecotoxicity data. For instance, the NanoE-Tox is a database on the nanoecotoxicity of eight ENMs that gathers data from more than 220 scientific articles  [24].
The Nano Environmental Health and Safety database (NanoEHS) concerns the physico-chemical properties of ENMs, their biocompatibility and potential toxicity to human health  [25]. The NanoPUZZLES dataset gathers toxicity and cytotoxicity data. NanoEHS, NanoPUZZLES are used to predict the relationships among the structure, properties, molecular interactions, and toxicity of ENMs  [26, 27]. The NanoMILE database aims to understand the mechanisms of ENMs interactions with living systems and the environment  [28]. Both NanoPUZZLES and NanoMILE used the ISA-TAB-Nano template as an ENMs data exchange standard  [29]. Other examples are the Nano Exposure and Contextual Information database (NECID) that collects exposure measurements of nano objects and their agglomerates and aggregates  [30]  in an occupational exposure perspective, or also the GUIDEnano database about the human health risks of nano-enabled products all along the product life cycle  [31].
Finally, one of the larger European database is eNanoMapper that integrates generic nanosafety data from the scientific literature. eNanoMapper includes functionalities for data protection and sharing, data quality assurance, and search interfaces for different usages  [32]. eNanoMapper is part of the computational infrastructure for toxicological data management of ENMs. Several of these ENM-databases are domain specific and are addressed to specific communities. For instance, the NanoMiner database is designed for toxicogenomic researchers and contains more than 400 human transcriptome samples exposed to various types of ENMs  [18]. The CaNanoLab database is intended for the biomedical community who wants to use of nanotechnologies in biomedicine  [33]. The NanoToxDB database provides information to biologists on ENMs toxicity to a specific aquatic invertebrate, the Daphnia magna  [34].
The InterNano database is dedicated to the nanomanufacturing community  [35]. It shares standard nanomanufacturing processes and information on the properties of ENMs across sectors  [36]  to facilitate the commercial development of nanotechnology. Regarding the environmental exposure to ENMs, only two databases specifically address this domain: the NanoInformatics Knowledge Commons database (NIKC) and the ECOTOXicology knowledgebase (ECO-TOX). The NIKC, developed by the Center for Environmental Implications of NanoTechnology (CEINT), provides a critical mass of experimental data generated in the CEINT center and related to environmental exposure and hazard data. The ECOTOX is an open access repository including single chemical toxicity data for aquatic life, terrestrial plants, and wildlife  [37].
Developed by the Center for Computational Toxicology and Exposure (CCTE) and the Great Lakes Toxicology Ecology Division (GLTED) which are both components of the USEPA Office of Research and Development, the ECOTOX database incorporates three independent databases of ecotoxicity data from various literature sources the AQUIRE, PHYTOTOX, and TERRETOX focusing on aquatic life, terrestrial plants, and terrestrial wildlife, respectively. Finally, few of these databases act as networking tools as the NanoWerk Nanomaterial database which provides information about nano conferences, nano events, nano companies, laboratories, and nanotechnology applications  [38] , as well as the nanoHub database which provides information about available resources (from video presentations to publications to simulation tools)  [39].
Most of these databases were based on data extracted from published literature. Interestingly, few of them combine the database with modeling tools to go further in the assessment and forecasting of the risks for human and environmental health. As an example, the Nanomaterial-Biological Interactions Knowledge Base (NBI Knowledge Base) is designed to interpret nanomaterial exposure effects in biological systems  [40]. It acts as a depository for data but is also equipped with data mining tools to predict the biological interactions of ENMs. The Online Chemical Modeling Environment database (OCHEM) provides experimental measurements and properties associated to chemical compounds  [41]. It also incorporates a modeling framework to create a predictive model of biological and physicochemical properties of chemical compounds  [42].
One last example is the Modeling Assays Platform for hazard ranking of engineered nanoparticles database (MOD-ENP-TOX) and the Nanomaterials Experiment-based Predictor of Environmental Risk and Toxicity (NanoExPert) database that contains information about nanoparticles properties and their interaction with biological systems  [23, 43, 44]. Both of them act as tools to predict the toxicity and ecotoxicity of ENMs. This literature survey of the existing databases on ENMs highlights that most of them focused either on the properties of ENMs, their hazards (toxicity, ecotoxicity, bioavailability) or the occupational exposure. However, there is an obvious lack of databases about environmental exposure to ENMs. Gathering data about the exposure and hazard of ENMs within ecosystems and taking into account different lifecycle stages of ENMs-based products, the MESOCOSM database will fill this gap.
MESOCOSM will be integrated with the NIKC  [50]  and the eNanoMapper  [32]  databases. It will also be integrated into the search engine of the international NanoInformaTIX database. NanoInformaTIX is a comprehensive database integrating several relevant databases from EU/US projects supporting various aspects of ENMs nanosafety (materials, hazard, environmental release, occupational exposure, omics, bio-nano interactions, pharmacokinetics,. ), to improve predictive (eco)toxicity assessment of ENMs during their whole life cycle. By itself, the development of the MESOCOSM database will be an added-value in the field and its integration with other nano-database will provide the needed tool for sustainable innovation pathways of nanotechnologies. In particular, the quality of the data provided by the MESOCOSM database may support manufacturers and researchers to consider the behavior of ENMs under different environmental conditions and during their entire life-cycle in order to provide a complete view of their potential risks on the environment.
MESOCOSM database challenges The database is available to all on GitHub repository. Scientists and industries can visualize the totality or a part of the dataset, download the SQL database file and manipulate it with any database management system (Oracle, Postgres, MySQL, DB2,. ), remotely interact with the database via an application program interface (API), or also download it with its application (graphical user interface) for local usage (on the local computer or server). As described in Figure  2 , the application provides dialog boxes displayed on the frontend layer to interact with users, allowing either (i) to notify the user that the input (insertion or modification) has been successfully done, (ii) to confront the user with a choice, or also (iii) to ask the user to fill some missing fields, and (iv) to ask the user to fulfill a request form or select search criteria to retrieve information or refine the request, respectively.