dataesr/scientific-paragraphs-categorization huggingface dataset:
To identify the transmissivity tensor, we needed no less than 5 days of calculation. While the case treated here with a highresolution LBM required only 2 days of calculation. HYDRODISP-LBM/CMA-ES was successfully applied to the real case of the HESB. It gave excellent results in calibration mode. Likewise, the high spatial resolution that we were able to adopt thanks to the high performance of LBM, allowed us to highlight the heterogeneity of HDT. On the other hand, HYDRODISP-LBM/CMA-ES in identification mode seems to give satisfactory results, but the lack of data on the hydro-dispersive aspect of the site, did not allow us to criticize the approach. As perspectives, we are considering new investigations to improve and gain in the robustness of the proposed IOA by carrying out the following actions: 1 Conduct new experiments on the study site on tracking a tracer to build a database which we will use to validate the proposed IOA for identification mode.
To identify the transmissivity tensor, we needed no less than 5 days of calculation. While the case treated here with a high-resolution LBM required only 2 days of calculation. HYDRODISP-LBM/CMA-ES was successfully applied to the real case of the HESB. It gave excellent results in calibration mode. Likewise, the high spatial resolution that we were able to adopt thanks to the high performance of LBM, allowed us to highlight the heterogeneity of HDT. On the other hand, HYDRODISP-LBM/CMA-ES in identification mode seems to give satisfactory results, but the lack of data on the hydro-dispersive aspect of the site, did not allow us to criticize the approach. As perspectives, we are considering new investigations to improve and gain in the robustness of the proposed IOA by carrying out the following actions: 1 Conduct new experiments on the study site on tracking a tracer to build a database which we will use to validate the proposed IOA for identification mode.
Acknowledgments: This study will help us to understand the mechanism of mixing processes of the chalk aquifer in the Oise region especially in the  Hydrogeological Experimental Site of Beauvais  (HESB, northern part of the Paris basin). The identification of HDT will contribute to the knowledge of the hydro-dispersive characteristics of the site in order to enrich the database on the studied site. Likewise, this study will strengthen our expertise on the site and in particular in the event of accidental and catastrophic pollution. The construction of this site has benefited from the support of the  European Regional Development Fund (FEDER) , the  Picardy region (Haut-de-France) , and the  Ministry of Higher Education and Research. This site and its hydrogeological platform are attached to the  AGHYLE Research team "Agroecology, Hydrogeochemistry, Environments and Resources" of the Institut Polytechnique UniLasalle.
Literature review of restoration targets and policies in Brazil and Indonesia The analysis offered in this study is based, first, on a review of existing scientific and peer-reviewed literature which was gathered using different databases and search engines, such as ScienceDirect and Google Scholar. The search included the following keywords: 'restoration' ,  'rehabilitation' , 'reforestation' , 'recover' , 'forest' , 'landscape' , 'land use' , 'tropical' , 'degradation' , 'deforestation' , 'commitment' , 'target' , 'pledge' , 'policies' , 'plan' , 'strategy' , 'implementation' and 'governance' , which  were combined in iterative rounds with the keywords 'Brazil' and 'Indonesia'. Year of publication (cut-off was set in 2014), number of citations, title, and abstract were the main criteria to determine the selected literature.
Second, the study also considered a review of grey literature and institutional reports, such as Brazilian and Indonesian government documentation, their submitted NDCs, legislation, government reports and statistics, publications from NGOs such as the World Resource Institute (WRI) and the International Union for Conservation of Nature (IUCN), and online databases such as Global Forest Watch and Climate Watch, which were selected through snowballing from the peerreviewed literature and following input provided by local experts on the restoration policy landscape of Brazil and Indonesia. The goal was not to provide an exhaustive and detailed review of all Brazilian and Indonesian restoration-related policies, but to identify which are the main policies supporting the implementation of their restoration targets, review their performance in light of the empirical literature, and to discuss the most salient obstacles that both countries face in the implementation of large-scale restoration, and thus in the achievement of their restoration commitments.
One map policy Envisioned as a key instrument to improve spatial planning, and subsequently contribute to the implementation of SF schemes and the ERC, the original One Map initiative was launched by the GoI during 2012 to digitise data and information related to forest areas in one single public portal. Similar to Brazil's SICAR system, the One Map Policy aims at building one single database consolidating all government maps to ensure that all agencies refer to the same land use information. It is intended to facilitate the resolution of conflicts associated with overlapping claims over the same land, keep a record of forest cover change, monitor compliance with forestry law, and detect illegal logging. Inconsistencies in references and standards in the generation of maps by different government agencies issuing licences have resulted in overlapping permits over the same territory.
Improved mapping systems can also help to resolve discrepancies between land-designation and actual land-cover. The Indonesian One Map Policy was explicitly designed to integrate and synchronize divergent geospatial databases with the aim of resolving persistent conflicts related to misalignment between the ownership, legal designation, and the actual standing vegetation on the same land. With unclear land tenure coupled and inaccurate mapping of the landscape, the distinction between productive or protected forests becomes a challenge, and therefore distinguishing whether a particular activity is legal or not may become contested. Also, without clearly defined property boundaries, identifying the landholder is difficult, which in turn poses challenges for targeting incentives for sustainably managing the forest, implementing programmes, collecting taxes and enforcing legislation.
Spatial planning and land tenure regimes will also benefit from improved mapping systems, further strengthening the conditions that support restoration and forest protection. Additional efforts could be put forward in encouraging and facilitating local community participation in mapping activities, favouring accuracy, transparency and inclusion. With robust and technologically sound databases, the costs for monitoring and enforcement would be drastically reduced, liberating budget and staff for other environmental purposes, including scientific research and knowledge transfer. Clearly defined land tenure and secure property rights could possibly enable agricultural intensification, thus reducing incentives to expand plantations by deforesting additional land. In addition, international funding channelled through REDD+ initiatives could help bridge the gap between the ambitious restoration targets and domestic capacity.
Acknowledgments This work is supported by the  RESTORE+  project (www. restoreplus. org/),which is part of the  International Climate Initiative (IKI) , supported by the  Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU)  based on a decision adopted by the  German Bundestag. Thanks to  Ping Yowargana  and  Taryono Darusman  for providing expert advice on the Indonesian restoration policy landscape, and helpful comments and feedback throughout this process. We are equally thankful for feedback from other RESTORE+ Consortium members.
Indeed, the stability of the already saturated hillslopes, prior to the weather event, can only be reduced by changes in ψ air , even if the amplitudes of these changes remain limited to 1 kPa. This occurs everywhere along the hillslope for D = 10 -6 m 2 s -1 , while only the hillslope toe is dominated by ψ air for D = 10 -4 m 2 s -1. For D = 10 -2 m 2 s -1 , changes in ψ rain , up to ∼ 40 kPa, overcome ψ air by more than 1 order of magnitude -except for the very beginning of the event. This suggests that atmospheric effect should be the dominant factor only in the already saturated part of the hillslope, such as close to the toe, where rainfall-induced dynamic pore pressure change is null or low, or at very short timescales, since it is instantaneous. 4 Results -application to natural datasets.
Datasets Taiwan is a mountainous island coming from the convergence between the Eurasian and the Philippines plate. A large portion of the island is composed of steep slopes and mountains, which culminates at 3952 m a. s. l. (above sea level). The reliefs are very steep and composed of sandstone, slate, schist and mudstone  (Lin et al. , 2011; Tsou et al. , 2011). However, a large portion of the surface material is significantly weathered due to the annual precipitation of 2. 5 m. As a region un-   In the model, the recharge is assumed to be equal to the observed rainfall, neglecting evapotranspiration. Atmospheric tides are observed in the atmospheric pressure data, with a diurnal and semidiurnal period and amplitudes of about 0. 03 to 0. 1 kPa, respectively. These tides are removed using notch filters to focus only on typhoons.
In a similar way, a high-pass filter is applied to only keep signals with a period of less than 30 d and remove seasonal components. In the following, we assume that any remaining change in atmospheric pressure is attributable to weather events. A total of 36 major typhoons are identified in the data. Rainfall peak intensity ranges roughly between 0 and 57. 6 mm h -1 , and atmospheric pressure drop reaches -4. 5 kPa. Among the 36 major typhoons, some have led to a strong pressure drop and/or to intense rainfall (Fig. 5a ). For this study, three contrasting typhoons are used to compare atmospheric and rainfall effects: Matsa, Krosa and Morakot. Typhoon Matsa in July 2005 is the event showing the highest peak of rainfall intensity among the dataset. Typhoon Matsa led to several mudslides and floods in Taiwan, but no major landslide.
Typhoon Krosa in October 2007 is associated with the highest atmospheric depression in the dataset. It also re-sulted in minor damage as it passed directly over the island of Taiwan. Typhoon Morakot in August 2009 was devastating and caused more than 10 000 landslides  (Lin et al. , 2011; Lin and Lin, 2015; Hung et al. , 2018; Steer et al. , 2020) , including the Shiaolin landslide which mobilised a volume of 25 × 10 6 m 3 and buried the village of Shiaolin  (Tsou et al. , 2011; Kuo et al. , 2013). We highlight here that Typhoon Morakot was associated with a moderate pressure drop and peak rainfall intensity at the location of the weather station in Taroko, but led to extreme rainfall intensity in southern Taiwan reaching close to 10 mm h -1 (Mihai and Grozavu, 2018). On top of these three events, a theoretical typhoon is tested by taking the arithmetic mean of rainfall and atmospheric pressure of all 36 events in the data (Fig.
Timing of the failure during an extreme weather event Most datasets on landslides occurring during a triggering event are based on comparisons between pre-and post-event satellite images  (Cheng et al. , 2004; Nichol and Wong, 2005; Martha et al. , 2015)  or even Lidar data  (Bernard et al. , 2021) , often acquired days or weeks apart. The timing of landslide occurrence during the event itself remains poorly constrained. This is problematic when trying to attribute landslides to their triggering factor, whether rainfall or atmospheric pressure drop. This results in most landslides being by default attributed to rainfall. At first order, this is a reasonable assumption given that it is the effect leading to the greatest disturbances compared to atmospheric effects. However, this prevents a better understanding of landslide triggering during storms, as ψ rain and ψ air behave differently, with potential implications for landslide hazard.
Acknowledgements. The authors acknowledge the  Taiwan Typhoon and Flood Research Institute, National Applied Research Laboratories , for providing the  Data Bank for Atmospheric & Hydrologic Research service  (https://www. narlabs. org. tw/, last access: 2 November 2017). We also acknowledge support by the  France-Taiwan International Associate Laboratory  " From Deep Earth to Extreme Events ". Financial support. This research has been supported by the  European Research Council ,  H2020   European Research Council (FEA-SIBLe  (grant no. 803721 )). Review statement. This paper was edited by  Mario Parise  and reviewed by three anonymous referees.
work and agreed to its publication. Funding E. M. A. gratefully acknowledges funding by the  U. K. Biotechnology and Biological Sciences Research Council (BBSRC) ; this research was funded by the  BBSRC   Institute Strategic Programme Gut Microbes and Health   BB/R012490/1  and its constituent projects  BBS/E/F/000PR10353  and  BBS/E/F/000PR10356. B. E. D. is supported by the  European Research Council (ERC) Consolidator  grant  865694 :  DiversiPHI  and the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's  Excellence Strategy -EXC 2051 -Project-ID 390713860. This work was supported in part through  Laulima Government Solutions , LLC prime contract with the  US National Institute of Allergy and Infectious Diseases (NIAID)  under Contract No. HHSN272201800013C.
S. Department of Agriculture ,  Hatch  Project, under Accession Number  1021494. E. J. L, D. M. D. and R. C. H were supported by the  National Institute of Allergy and Infectious Diseases of the National Institutes of Health  under Award Number  U24AI162625. Except for  Donald M. Dempsey ,  R. Curtis Hendrickson ,  Richard J. Orton  and  Donald B. Smith , the authors were members of the ICTV Executive Committee during the relevant period.
Acknowledgments L. G. acknowledges financial support from the  National Natural Science Foundation of China  (Awards:  41822605 ,  41871051  and  41727901 ), the  Fundamental Research Funds for Central Universities , the  Strategic Priority Research Program of Chinese Academy of Sciences  ( XDB 41000000 ), and the  National Key R&D Program of China   485  ( 2019YFC1509100 ). B. A. acknowledges funding from the  US NSF   OPP 1446904  and  1542723. J. Cole-Dai  thanks the  US NSF  for funding (Awards  1443663  and  1904142 ). J. S. was supported by the  French National program LEFE (Les Enveloppes Fluides et l'Environnement) ; the  Agence Nationale de la Recherche (ANR)  via contract  ANR-16-CE01-0011-01 EAIIST  ; the  Foundation BNP-Paribas  through its  Climate initiative program  and by the  French Polar Institute (IPEV)  through programs  1177  ( CAPOXI 35-75 ) and  1169  ( EAIIST ).
470 The meteorological data from Amundsen-Scott South Pole Station Meteorological Observations dataset were freely available at http://amrc. ssec. wisc. edu/usap/southpole/. The TCO density and surface ozone concentrations data from the NOAA ozonesonde dataset can be accessed at https://www. esrl. noaa. gov/gmd/dv/data. html. The ice-core ion concentration and isotopes data will be provided upon direct request to the corresponding author. The codes for the numerical simulations and their analysis will be provided upon direct request to the corresponding author.
2. 1 and shown in Fig. 3a. TCO density from 1964 to 2005    was obtained from the NOAA ozonesonde dataset (https://www. esrl. noaa. gov/gmd/dv/data. html) and the austral spring (when the ozone hole occurs in a year) average TCO over this period are plotted in Fig. 3b. Satellite and/or ground-based ozone observations are not available prior to 1964, so in model calculations we just used the averaged 155TCO during 1964TCO during  -1975 (i. e. , when  (i. e. , when  there was no ozone hole) to represent TCO from 1944 to 1963. https://doi. org/10. 5194/acp-2022-417 Preprint. Discussion started: 28 June 2022 c Author(s) 2022. CC BY 4. 0 License. The e-folding depth of actinic flux in snow is mainly determined by snow density, grain size, and snow light absorbing impurities (LAIs, e. g. , dust and black carbon (BC))(Zatko et al.
The calculated 15 εpho 180 varied from -73. 9 to -53. 9 ‰ under different solar zenith angles in the summer half year at the South Pole. Other parameters, for example, the surface meteorological conditions (e. g. , boundary layer height, temperature, pressure, etc. ), and atmospheric chemical properties including ozone and radical (e. g. , OH, RO2 and HO2) concentrations are also needed to calculate the recycling of nitrate in the overlying atmosphere. In the model, the 185 weekly air temperature and surface pressure were obtained from Amundsen-Scott South Pole Station Meteorological Observations dataset (http://amrc. ssec. wisc. edu/usap/southpole/), and the boundary layer height was set as 81 m which is the mean observed value during November and December in 2003 (Neff et al. , 2018). Surface ozone concentrations from 1975 to 2005 were obtained from the NOAA ozonesonde dataset (https://www.
8 increased their solubility. In particular, lactonic forms are expected to be more hydrophobic than acidic forms  21). The relatively hydrophobic lactonic forms may be solubilized into micelles formed by the acidic forms above pH 5. 8. We measured the surface tension of sophorolipid solutions that were adjusted to pH 7 with sodium hydroxide. The static surface tension data are shown in Fig. 3. The surface tension decreased with increasing sophorolipid concentration until it reached a plateau. The shape of the surface tension curve was similar to that observed in typical surfactant systems. Hence, we assumed that the concentration at the start of the plateau region corresponded the critical micelle concentration (cmc) of the sophorolipids. The cmc was determined to be approximately 8 mg/L under the experimental conditions.
(2021). In these three sensitivity experiments, referred to as NoSIP1kmAC, NoSIP250mAC, and NoSIP150mAC, respectively, all SIP processes are turned off and the default constant aerosol number mixing ratio (i. e. , the ratio of the aerosol number concentration and air density = 300 × 10 6 kg -1 ) in the original P3 scheme is used. Huang et al. (2021)  indicated that the simulation with the P3 scheme overestimates the LWC at -10 • C, which enhances the collection of liquid water by ice particles and subsequently increases the mass and size of ice particles but not ice particle number. Ladino et al. (2017)  showed the aerosol concentration decreases with increasing height, and that aerosol concentrations are about 360 and 50 cm -3 within the boundary layer and the free troposphere, respectively, through the vertical profile of aerosols averaged over the entire Ultra-High Sensitivity Aerosol Spectrometer (UH-SAS) dataset during the Cayenne campaign (Fig.
Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-22-2365-2022-supplement. Author contributions. YoH, WW, and GMM designed the study. YoH implemented the parameterization of secondary ice production for the P3 scheme, conducted the experiments and analyses with support from WW, GMM, MX, HM, JM, AVK, and ZQ. MW, CN, AS, AVK, IH, and YaH processed the original observational datasets. YoH wrote the original draft of the paper, and all coauthors contributed to review and editing. Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Science Foundation. Some of the computing for this project was performed at the University of Oklahoma (OU) Supercomputing Center for Education and Research (OSCER). The discussions of HIWC conditions and IKP with Walter Strapp, the discussions of secondary ice productions with Vaughan T. J. Financial support. This research has been supported by the National Science Foundation (grant nos. 1213311 and 1842094). Review statement. This paper was edited by Martina Krämer and reviewed by Emma Järvinen, Minghui Diao, and one anonymous referee.
Phillips, and the discussions of model comparison with Lulin Xue are greatly appreciated. Major North American funding for flight campaigns was provided by the FAA William Hughes Technical Center and Aviation Weather Research Program, the NASA Aeronautics Research Mission Directorate Aviation Safety Program, the Boeing Company, Environment and Climate Change Canada, the NRC of Canada, and Transport Canada. Major European campaign and research funding was provided by (i) the European Commission Seventh Framework Program in research, technological development and demonstration under grant agreement no. ACP2-GA-2012-314314 and (ii) the European Aviation Safety Agency (EASA) Research Program under service contract no. EASA. 2013. FC27. Further funding was provided by the Ice Crystal Consortium.
Acknowledgements. The authors are thankful to Editor  Martina Krämer  and reviewers  Minghui Diao  and  Emma Järvinen  for their comments that contributed to improving the paper. Observational data are provided by  NCAR/EOL  under the sponsorship of the  National Science Foundation  (https://data. eol. ucar. edu/,last access:  26 May 2020 ). The authors are grateful to  NCAR 's.
The functional unit of the research, common to all analyzed RE-to-H2 pathways, is "1 MJ H2 produced and being compressed to 200 bar". As indicated in Fig. 2  below, the life cycle boundaries of cradle-to-grave assessment include: 2) mix of wind electricity production market in France 2019, unified processes to each wind electricity production technologies clarified by the ecoinvent v3. 6 database, i. e. , construction (material extraction, transport, and end-of-life treatment) of wind turbine assembly and wind power plant network connection & auxiliaries; wind electricity production process. 3) facility construction (material extraction, transport, and end-of-life treatment) of the water electrolysis unit (H2 compression unit included). 4) water, energy & chemical consumption (related transport and end-of-life treatment included) during water electrolysis H2 production, purification & compression process.
Thus, special attention should be paid when refining the life cycle boundaries and corresponding life cycle inventory (LCI) parameters. For the feedstock production market in each H2 production pathway, this research follows the protocol and logic shown in Fig. 3  to define the life cycle boundaries, obtain corresponding LCI and parameters data. Thus, building the comparable French national benchmark parametric life cycle model best represents the reality, namely, France's average resource availability and technology market share. As shown in Fig. 2  and Fig. 3 , the French national benchmark parametric LCA models comprise the detailed modeling of the related transportation processes for each RE-to-H2 pathway studied. In consist with methodology in the ecoinvent v3. 6 database, all the transportation processes included in each RE-to-H2 pathway are measured as metric ton*km (t*km hereafter).
Life cycle inventory This section gives a detailed description of LCI for each RE-to-H2 pathway studied, e. g. , the data and hypothesis we collected and harmonized based on the new findings of the current H2 production situation from various data sources and previous research. Corresponding French national benchmark parametric life cycle model, which integrated detailed LCI data of each pathway studied, is shown in Supplementary Materials. The ecoinvent database (version v3. 6) "Allocation, cut-off by classification" is the primary source for the 2019 background LCI used in this research.