dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2019) , that can improve the calibration and validation processes of remotely sensed products. Several satellite products are available for the remote sensing of the cryosphere, and for this study we considered products obtained by optical sensors characterized by different spatial resolutions: high (<100 m); intermediate (100 m-1 km); and low (>1 km). The integration between those prod-ucts and ground-based imagery will be tested in order to improve the dataset concerning snow cover over a decade. As part of iCUPE, we aim to develop a semiautomatic procedure focused on preparing a snow product useful for monitoring spatial and spectral variations of the snow cover. The first year was concerned with data collection and the analysis of terrestrial images that will be complementary to Sentinel multispectral images (Fig.
21 ). We selected a study area located close to Ny-Ålesund (Svalbard, Norway) where the Italian Arctic station represents an important scientific facility and experimental activities are appropriately supported. The site is characterized by intense international research collaborations, and different data sources can be included as input for the integration between remotely sensed data and terrestrial images collected by webcams. We created an overview of the algorithms focused on processing multispectral images and snow index calculations as well as the automatic geo-rectification of ground-based photography and the routine estimation of fractional snow cover. From this perspective, we carried out a first field campaign aiming to survey the available webcams at the study site and deployed a new webcam on the CNR Climate Change Tower.
This site has proved to be the most suitable for the collection of ancillary data necessary for a correct interpretation of both satellite and terrestrial images. This CNR infrastructure is equipped with different meteorological sensors as well as instruments focused on detecting the radiative properties of the atmosphere and the snow-covered surface. The core of this activity was the development of a procedure suitable to collect remotely sensed data and match these images to FSC estimations obtained using terrestrial photography. The resampling of snow-non-snow pixels, provided by webcams (Fig. 22a  and c ) using grids associated with satellite imagery, provided a continuous dataset of FSC to NDSI relations (Fig. 22b  and d ) that can enhance the recognition of different snow cover types at the considered site.
With a revisit time of 11 d, we can limit the drainage duration with this no better than 11 d; however, the combination between optical (Sentinel-2) and TerraSAR-X data allowed us to constrain the duration of one particular drainage event to be shorter than 1 d  (Neckel et al. , 2020). With the Copernicus Sentinel program, continuation of satellite missions is assured, which makes the development of methods to derive time series of lake drainage events based on these sensors a desirable approach for obtaining long time series in the future, as the formation, filling, and drainage of supraglacial lakes will increase in the future under warming. Radar remote sensing is most attractive for observing polar areas, as it is independent of clouds and daylight. While the Copernicus program does not contain a bistatic radar mission required for lake volume estimates, Sentinel-1 is a Cband dual polarimetric mission, for which we explore the potential to retrieve quantities for the supraglacial hydrological cycle.
Integrating examples This section presents a suite of integrative examples developed during the iCUPE project, which utilized the comprehensive observation capacities and combined technologies in order to address satellite data validation tasks based on in situ observations, the assessment of aerosol emissions from flaring in the Arctic in a combination of aircraft observations, satellite retrievals, and modeling tools. We also present work towards the development of proxy variables from the comprehensive observations and performed a value tree analysis for the Arctic observation network as a whole.
Satellite data validation SMEAR II  (Hari and Kulmala, 2005)  in Hyytiälä is the ground-validation site for the current NASA Global Precipitation Mission (GPM;  Skofronick-Jackson et al. , 2018)  and upcoming ESA EarthCARE satellites  (Illingworth et al. , 2015). The satellite calibration and validation activities can be divided into two main approaches, i. e. , validation and verification of the assumptions and parameterizations used in the retrieval algorithms and direct validation of satellite observations. Within iCUPE, the main focus of our satellite validation activities was the microphysical assumptions and parameterizations used in ice cloud and snowfall retrievals. Given the climate and comprehensive surface-based observations of precipitating ice particles, our observations allowed us to constrain retrievals and refine these parameterizations.
Anthropogenic emissions from oil-gas extraction over northern Russia: a combined approach using aircraft, satellite data, and modeling Aircraft data collected for the first time over northern Russian oil-and gas-producing regions were used to examine the validity of available emission inventories. The data were col-lected in October 2014 and July 2017 as part of the French-Russian YAK-AEROSIB program  (Paris et al. , 2010; Antokhina et al. , 2018; Fig. 25). A combination of different approaches was applied, involving joint analysis of aircraft and satellite data together with source-receptor modeling using FLEXPART/FLEXPART-WRF and regional WRF-Chem modeling  (Marelle et al. , 2017 (Marelle et al. , , 2018) )  to investigate discrepancies in both black carbon (BC) and methane (CH 4 ) anthropogenic emissions over Russia, where very few data are available to date.
, 2020). The data are being further analyzed using daily VIIRS (Visible Infrared Imaging Radiometer Suite) satellite night-light data (provided courtesy of Earth Observation Group, Payne Institute for Public Policy, https://eogdata. mines. edu/download_ viirs_fire. html, last access: 2 July 2020) to examine the sensitivity of modeled BC to daily variability in flaring emissions. Preliminary results suggest that the simulated BC is highly sensitive to significant observed variability. Methane emissions from oil and gas activities are similarly poorly known, and their spatial and temporal distribution is related to that of BC (e. g. ,  Gvakharia et al. , 2017). Inventories are affected by (1) highly variable emission factors, (2) highly variable scheduled or unscheduled operations leading to the release of methane, and (3) the uncertain spatial distribution of regional totals (Fig.
, 2014) , forest and desert maps (Advanced Very High Resolution Radiometer -AVHRR database,  Sertel et al. , 2010) , and gas flaring emission maps (ECLIPSEv54 database -Evaluating the CLimate and air quality ImPacts of Short-livEd pollutants; described in  Klimont et al. , 2017). An example of a lidar aerosol vertical profile is shown in Fig. 27a  for a mixture of gas flaring emissions from the Ob Valley below 3 km (Fig. 27e ) and a dust layer transported from Kazakhstan above 3 km (Fig. 27c ) during a time period with no forest fires in Siberia. Aerosol-scattering ratios at 808 nm increased by a factor of 3 due to this event, while the corresponding AOD reached 0. 3, in good agreement with the Tomsk AERONET sun photometer observations and the MODIS hot spot near TOMSK (Fig. 27b ). Elevated CO columns seen by the Infrared Atmospheric Sounding Interferometer (IASI) above the Ob Valley and Tomsk also support gas flaring emissions during this period.
Deriving proxy variables from in situ and satellite data In iCUPE, we developed proxies for atmospheric variables from the in situ data gathered at Finnish measurement sites. In practice, this means that we derive formulas that describe the proxy variables as functions of more commonly measured parameters. Previously, proxies have been derived mainly for concentrations of gas-phase molecules, such as https://doi. org/10. 5194/acp-20-8551-2020 Atmos. Chem. Phys. , 20, 8551-8592, 2020 sulfuric acid  (Petäjä et al. , 2009; Mikkonen et al. , 2011)  and monoterpenes  (Kontkanen et al. , 2016). In iCUPE, we are developing proxies for condensation sink (CS), boundary layer height (BLH), and ecosystem-level gross primary production (GPP). The aim is to produce proxies that rely on variables that can also be retrieved from satellite data in order to produce proxies with good spatial coverage (e.
iCUPE data flow One of the main outcomes of iCUPE involves new data products developed based on the comprehensive use of in situ and satellite remote sensing. The new iCUPE data products, which will fill, e. g. , the observational gap for the key variables persisting organic pollutants (POPs In total, iCUPE will provide more than 20 datasets as products usable for researchers, decision and policy makers, stakeholders, and end-user communities. These will be a valuable and important contribution from the iCUPE project activities in piloting open access to Arctic data. All these datasets will be publicly available for different applications and services. Focusing on the Arctic region territories, the planned datasets will include, e. g. , novel data on anthropogenic contaminants in snow and ice cores, organic contaminants in the air-snow-water, and concentrations of different chemical species and aerosols, as well as their characteristics including vertical profiles.
Within iCUPE we will also develop specific datasets focusing on selected geographical areas in northern latitudes. The data provision is open, and Russian iCUPE collaborators have also provided data from the Russian Arctic, such as atmospheric mercury measurements at Amderma station, elemental and organic carbon over the northwestern coast of the Kandalaksha Bay in the White Sea, and microclimatic features and urban heat island intensity in cities of the Arctic region. Each of the datasets is promoted through "teasers" (https://www. atm. helsinki. fi/icupe/index. php/datasets/ submitted-datasets, last access: 29 June 2020), which provide the metadata information pertinent to the datasets. The majority of archived datasets (as products) are directly linked (and downloadable), and corresponding metadata are included.
The University of Helsinki will take responsibility for long-term storage, accessibility, and maintenance. The raw data will be hosted and maintained by the dataset providers. As soon as more iCUPE data products are available, we will explore different platforms to disseminate the iCUPE datasets. To facilitate and standardize access to data, cloud-based online platforms, known as Data and Information Access Services (DIAS), provide storage, centralized access, and handling for data and processing tools. DIAS platforms (CREODIAS -creodias. eu; SOBLOOsobloo. eu; MUNDI -https://mundiwebservices. com, last access: 29 June 2020; ONDA -https://www. onda-dias. eu/ cms, last access: 29 June 2020; WEkEO -https://www. wekeo. eu, last access: 29 June 2020) allow users to explore, process, and download Copernicus data and information as well as to have the ability to process and combine data from other sources.
The SAON portal expands the visibility of iCUPE data to the circumpolar context and PEEX to Russian and Chinese researchers and other end-user communities. For selected topical datasets, some will be included in the University of Helsinki smart-SMEAR platform  (Junninen et al. , 2009)  and disseminated via metadata catalogs to a wider audience. As a whole we need to utilize the full capacity of the multi-platform approach (in situ, satellites, and models) to address the fate of pollutants in the Arctic. This will provide tools to perform targeted reductions but can also provide new insights into the life cycle of pollutants in the Arctic environment.
Earth observation (EO) inputs like SYNOP station measurements of the physical atmosphere and, from other stations, ocean variables, were linked to key products, out-comes, and/or services such as numerical weather prediction. Groups, in this case the weather service, were also connected to the key objectives of the assessment framework (Fig. 30 ), such as weather or environmental information services. We weighted the analysis by annual costs for operation. Representative yearly unit costs of EO inputs and modeling components were estimated by station experts or estimated based on European Union projects or Copernicus program tenders. For observational capacity and its monetary value, we utilized the WMO OSCAR database for satellite and surface observation systems (https://www. wmo-sat. info/oscar/, last access: 29 June 2020) north of 60 • N.
This provided us with the volume of stations in different station and mission categories in the Arctic (Table  2 ). Based on our analysis, the total yearly value of this observation system, including EO inputs and modeling, is over EUR 204 million. Compared to the observing system estimated costs in the area 30 to 60 • N, this is only about one-fifth. Another way to scale Arctic needs is to look at the distribution of stations globally for operational networks: the WMO No. iCUPE products as datasets on the following 1. Emerging organic contaminants in air from the Arctic 2. Ground-based measurements for particle number, black carbon mass, and ozone concentration 3. Black carbon and aerosol absorption of Arctic research infrastructures 4. Anthropogenic contaminants in snow from polar regions 5.
Anthropogenic contaminants in ice cores 6. Emerging organic contaminants in snow from the Arctic 7. Emerging organic contaminants in water from the Arctic 8. Snow spectral reflectance 9. Aerosol vertical profiles from ground-based and satellite observations in Finland and Russia 10. Arctic atmospheric mercury observations: updated GMOS database 11. Blueprint for novel proxy variables integrating in situ and satellite data 12. Arctic parameters based on ground-based remote sensing and airborne platforms 13. Precipitation in the high latitudes 14. Novel optical remote sensing products on snow, vegetation, and gas flaring mapping in selected sites 15. Arctic atmospheric mercury isotope observations 16. Time series of lake size changes in northeast Greenland 17. Aerosol reanalysis for SMEAR II 18.
Organic aerosols in the Arctic based on source apportionment No. Datasets resulting from collaboration with PEEX Russian partners 20. Mercury measurements at Amderma station in the Russian Arctic 21. Elemental and organic carbon over the northwestern coast of the Kandalaksha Bay in the White Sea 22. Microclimatic features and urban heat island intensity in cities of the Arctic region 23. Atmospheric composition at Fonovaya Observatory, west Siberia has organized the integrated ground-observing system WI-GOS stations on land; cosponsoring is mainly with the International Maritime Organization (IMO) for marine platforms, and nonaffiliated institutions include research organizations distributing information in near-real time. The Arctic slice compared to northern midlatitudes has about one-sixth of the stations with about one-third of the aerial coverage.
Spectral information stored in the narrow wavelengths of the visible spectrum is the most promising for differentiating spectrally similar vegetation communities and for delivering data on photosynthetic activity and biomass. The lack of ground-validated datasets and an overall scarcity of dense, high-quality image time series remain a challenge. The increasing availability of hyperspectral data will face similar challenges, and given the limited number of hyperspectral remote sensing studies in the Arctic, highquality, ground-validated data are required to accurately interpret these dense and complex datasets. The rise of more advanced classification methods such as machine-learning techniques is highly promising for Arctic vegetation mapping using advanced remote sensing platforms. An increased effort to develop Arctic-specific algorithms is needed.
Within iCUPE, the benefits of combining in situ and satellite remote sensing with multi-scale modeling in cryospheric, terrestrial, and marine regimes as well as the atmospheric https://doi. org/10. 5194/acp-20-8551-2020 Atmos. Chem. Phys. , 20, 8551-8592, 2020 domain are clear. Such integrative activities need to be expanded and continued in order to provide verified environmental data and services in the changing Arctic. Data availability. The data presented in this paper are available through the authors upon request. The iCUPE data products are available through the iCUPE web page: https://www. atm. helsinki. fi/icupe/index. php/datasets (iCUPE, 2020). Author contributions. TP had the initial idea. All authors contributed to specific sections of the paper and commented on the paper. TP, EMD, KT, JS, and HKL formed the editorial team consolidating the text.
Acknowledgements. We thank  Jacques Pelon  for assistance with research conducted at LATMOS and IGE. The  Royal Danish Air Force  is acknowledged for providing free transport of equipment to Station Nord, and the staff at  Station Nord  is especially acknowledged for excellent support. For UAV measurements in Ny-Ålesund we thank the whole AWIPEV team and Kingsbay crew for support during preparation and for hosting us. Janne Levula  and the staff of the  Hyytiälä Station for Measuring Ecosystem-Atmosphere Relations II (SMEAR II ), as well as  Nicki Hickmon  and  Michael Ritche , are acknowledged for their work during the  BAECC  project. Financial support. This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 689443  via project iCUPE (Integrative and Comprehensive Understanding on Polar Environments) and through  ACTRIS2  ( 654109 ).
Part of this study was supported by the  Danish Environmental Protection Agency  and the  Danish Energy Agency by means of DANCEA funds for Environmental Support  to the Arctic Region. Part of this study was funded by the  German Science Foundation (Deutsche Forschungsgemeinschaft, DFG)  as project numbers  LA 2907/5-3 ,  BA 1988/14-3 , and  WI 1449/22-3. Julia Schmale  holds the Ingvar Kamprad Chair, sponsored by  Ferring Pharmaceuticals. Open-access funding provided by  Helsinki University Library. Review statement. This paper was edited by Imre SALMA and reviewed by two anonymous referees.
Study Population This is a cross-sectional analysis of the baseline data of the Paris Prospective Study 3 (PPS 3), an ongoing community-based prospective study, registered on the WHO international clinical trial registry platform (NCT00741728). This study aims to explore novel markers of heart variability and arterial ageing for the risk of sudden cardiac death and other cardiovascular disease phenotypes in initially healthy subjects. 12 The PPS3 protocol was approved by the Ethics Committee of the Cochin Hospital (Paris) and every volunteer signed an informed consent. The standard medical examination at the IPC included a complete clinical examination including measurement of height, weight and blood pressure, coupled with standard biological tests after an overnight fast. A self-administered questionnaire provided information related to professional activity, lifestyle (tobacco and alcohol consumption, physical activity, diet), personal and family medical history (including history of CVD), current health status and medication consumption.
Then, linear regression analysis was run to compare the mean difference in the number of metrics at ideal level between subjects with ≥5 vs. those with <5 FTUs, adjusting for the same covariates as above. All the analyses were two-sided and p values <0. 05 indicated statistical significance (except for the interaction analysis for which a p value of <0. 10 was chosen). Statistical analyses were performed using SAS version 9. 4 (Cary, North Carolina, USA). The present observational study followed the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for cross-sectional studies (supplemental file).
The growth of local and transnational initiatives represents something significant. According to Keohane and Victor, we are currently witnessing a 'Cambrian explosion' in transnational institutions, standards, financing arrangements, and programs governing climate change  (Keohane and Victor, 2011). Bulkeley et al. reveal  the flowering of local initiative by presenting and analysing extensive databases of sub-national and non-state organizations involved in transnational climate initiatives and their manifestation across the globe  (Bulkeley et al. , 2012; Castan Broto and Bulkeley, 2013). The overall picture that emerges portrays a new institutional environment for climate governance, which is rich, highly complex and marked by forms of 'polycentric' governance with multiple governing actors at different scales operating independently to craft new norms and rules without central coordination  (Abbott, 2012; Chan et al.
ANIPPO environment was developed in Second life, a free 3D virtual world where users can create, connect, and chat with others from around the world using voice and text. The problems that are proposed in ANIPPO are formulated and modeled in this 3D virtual environment. Pacurar (2018)  shows that the process of identifying the student with his avatar, helps him to build mental representations. Christou et al. (2006, p. 169)  underline that the interest of using 3D geometry software offers a "semiotic perspective about mathematics as a meaning-making endeavor". Similarly, the research results show the importance of representations in the teaching and learning of mathematics  (Goldin 1998). The ANIPPO environment will allow to work in different representation registers  (Duval 2006 ): -the register of the ANIPPO virtual world where different treatments are possible: the movement of an avatar in the environment by keyboard commands, the visual control on the screen of a virtual movement of an avatar, visual exploration on the screen of a virtual place (for example by moving the avatar around a virtual solid or inside a virtual solid), the register of natural language written in the windows provided to respond to the posed problems, or in chats between pupils, in which for example pupils can exchange information by referring to references in the virtual space, -the register of oral language during exchanges between pupils.
Duval invites us to distinguish between intra-register processing (such as moving inside the ANIPPO environment, or even counting small cubes by calculating numbers written in figures) and inter-register conversion processing (like the description in natural language on how to move in the ANIPPO environment or the description in natural language of an object located in the ANIPPO virtual space): "Mathematical comprehension begins when coordination of registers starts up. […] Mathematical thinking processes depend on a cognitive synergy of registers of representation"  (Duval 2006, p. 126). In our research, students use these representations and exchanges in the case of problem-solving activities that occupy a central place in the French curriculum  (Artigue et al. 2007 ). These activities relate to open problems: "Open problems are there defined as problems with a short text, which induces neither a method nor a solution; their solving does not reduce to the direct application of known results or tools; they situate in a context that is familiar enough to the students to make the problem meaningful, and allow them to engage in trials and conjectures "(Ibidem p.