dataesr/scientific-paragraphs-categorization huggingface dataset:
9 Conclusions and recommendations for future evolutionWe have presented the first validation of a new SYNERGY global aerosol product, derived from the data from the OLCI and SLSTR sensors aboard the Sentinel-3A and Sentinel-3B satellites. Combined, the two satellites provide close to daily global coverage and provide aerosol measurements with a latency of 2-3 d. In this study we have compared the aerosol product with ground-based photometer data from four networks: AERONET, SKYNET, SURFRAD, and MAN, as well as with MODIS combined Dark Target and Deep Blue algorithms. The aim of this study was to provide global characterisation of the current aerosol retrieval and to guide future algorithm development. Over ocean, the performance of SYNERGY-retrieved AOD is good and consistent with the reference MAN dataset (rms ∼ 0.
hold up when focusing on group and personal leadership, especially for groups and individuals that are marginalized or excluded. Data were collected in three different ways. Desk research was done to identify various projects by and for young people in water cooperation (Table  1 ); an online survey was developed and shared to collect information on cases where young people act in the context of water cooperation and to identify barriers to their participation. The survey was distributed through social media channels (Facebook and Twitter), as well as targeted emails to academic and institutional networks in 2018. Lastly, a workshop using action research and research-creation methods was followed by in-depth interviews. A grounded theory approach, of applying constant comparative methods between data, codes and categories to discover patterns and construct theory, was used to identify and refine the roles  (Charmaz, 2014).
Acknowledgements The authors have drawn on their experiences with different youth water associations and networks, which they would like to thank and acknowledge: the  Water Youth Network, World Youth Parliament for Water ,  Mediterranean Youth  for  Water Network ,  European Youth Parliament for Water , and the  Scheldt Youth Parliament. The action research and coordination would not have been possible without  Dr. Amel M. Azab  of the  Nile Basin Capacity Building Network (NBCBN) , nor without the young leaders who joined the water diplomacy workshop and continue creating new pathways for cooperation. The authors are also grateful for the constructive feedback received from the reviewers and from the editors, in particular to  Ronnie Seagren  for her read-through and edits.
They count transparency, accountability, fairness, and privacy as core values and, as can be seen in Table  1 , add several other values. Whereas the Organisation for Economic Co-operation and Development document is seemingly a collection of general and nonbinding guidelines rather than a list of key values that should constitute an ethical compass, ACM's detailed and developed code is a combination of values, rules, and norms, while it specifies key values and basic rules. In any case, none the listed codes deal with the uniqueness of these studies when they are linked to medical research based on an online archive. The various ethics committees in the medical system have not established proper rules in relation to research based on data from the internet and have effectively placed the responsibility on the internet companies.
Acknowledgments: We thank the anonymous reviewer and  J. A. Cartwright  for valuable comments and the editorial board ( K. Hodges  and  M. Martinez ) for editorial handling of this manuscript. Hayabusa2 was developed and built by  Japan Aerospace Exploration Agency (JAXA) , with contributions from the  German Aerospace Center  and the  Centre National d'Études Spatiales (CNES) , and in collaboration with  NASA  and other universities, institutes, and companies in Japan. The Hayabusa2 sampler system was developed by  JAXA ,  The University of Tokyo, Hokkaido University, Kyushu University, Japan Agency for Marine-Earth Science and Technology , and other universities, institutes, and companies in Japan. Funding: R. O. was supported by  JSPS KAKENHI  grant numbers  JP19H01959  and  JP20H05846.
B. M. , E. F. , M. W. B. , and D. B. were supported by the  European Research Council  ( Photonis Advanced Grant  no. 695618  and  VOLATILIS Starting Grant 715028 ) and by the  CNES. Work by  D. K. , M. E. I. R. , and H. B. has been carried out within the framework of the  NCCR PlanetS  and the  Ambizione program  supported by the  Swiss NSF  (grants  200020_182649 ,  51NF40-182901 , and  PZ00P2_193331 ). J. G. , S. A. C. , T. L. , and L. F. were funded by  STFC  grants numbers  ST/R000751/1  and  ST/V000675/1. K. Has. and A. I. were supported by  JSPS KAKENHI  grant number  JP20H00190.
Acknowledgments We gratefully acknowledge discussions with  Adam Chapman ,  Juan Hernandez  and  Shan-Ming Ruan. We are also grateful to our many collaborators in papers on the subject of complexity in the past few years. The work of SC is supported by the  Israel Science Foundation  (grant No. 1417/21 ). SC acknowledges the support of  Carole  and  Marcus Weinstein  through the  BGU Presidential Faculty Recruitment Fund.
, 2016). At Mars,  (Bertucci et al. , 2004)  proposed that certain low-frequency wave signatures in the NASA/Mars Global Surveyor (MGS) dataset could be consistent with MM behaviour although this could not be conclusively demonstrated because of insufficient instrumentation (no ion measurements). The same year,  Espley et al. (2004)  used MGS magnetometer data to argue for the presence of MM structures. More recently, using time-frequency analysis of magnetometer and plasma data from the NASA/Mars Atmosphere and Volatile EvolutioN (MAVEN) mission,  Ruhunusiri et al. (2015)  created maps of the low-frequency wave content of Mars' plasma environment for the first year of operations, using the hierarchical identification scheme of  Song et al. (1994)  based on transport ratios. Whereas slow mode waves (Alfvén waves and quasi-parallel slow waves) were statistically found to dominate both in the solar wind and in the magnetosheath region, waves consistent with mirror modes were on average confined on the dayside to the region closest to the magnetic pile-up boundary (MPB) and extending on the nightside in the magnetotail.
Because (i) (proton) plasma bulk speeds in the magnetosheath at Mars and Venus are of similar magnitude, reaching a large fraction of the upstream solar wind speed  (Halekas, Brain, et al. , 2017; Bader et al. , 2019) , and (ii) MAVEN has a relatively slower orbital speed of 0. 6× 10 -3 R P /s with R p the planet's radius, i. e. , bow shock crossings at about 2 km/s, compared to VEX' 1. 5×10 -3 R P /s, MM structures are expected to appear in the MAVEN data on at least similar timescales as for VEX' detections. To our knowledge, we present in this study the first unambiguous observation of quasilinear mirror mode structures at Mars. This event took place during the early part of the MAVEN mission on 25 December 2014 around 11:30 UT, in the receding phase of solar cycle 24. MMs were found wedged against the MPB behind a quasi-perpendicular bow shock crossing -a classical empirical picture reminiscent of Earth-bound observations.
SWIA has thus two three-dimensional (3D) scanning modes, one coarse (360 • ×90 • , ∆φ = ∆θ = 22. 5 • in anode/deflection angles, energy 25 eV/q to 25 keV/q  (Halekas, Ruhunusiri, et al. , 2017) , suitable for magnetosphere and pickup ions), one fine (45 • × 45 • , ∆φ = 4. 5 • , ∆θ = 3. 75 • , 10% energy windows, suited to solar wind ions), both with 48 energy steps. Additionally, two separate 3D data packet telemetry modes are used on board which result in the Survey (lower cadence, maximum availability) and Archive (higher cadence but lower availability) modes. The combination of the scanning and telemetry modes results in four different data products, labelled in the following SWIFA (SWIA Fine Archive), SWIFS (Fine Survey), SWICA (Coarse Archive) and SWICS (Coarse Survey). The last two modes are thus those with lowest angular resolution and are usually selected in the magnetosheath.
Densities presented in the following are calculated from these onboard energy fluxes by integrating over the 64 energy bins, assuming isotropy over SWEA's blind spots. With its temporal resolution of ∼ 2 s, SWEA complements SWIA for total plasma density measurements when structures lasting less than about 8 s (2 × ∆t SWIA ) are identified in magnetometer datasets, which is often the case for MMs. Because of quasi-neutrality, the electron density N e must theoretically be equal to the ion density, i. e. , N e ∼ N i. The absolute determination of the electron density depends on different experimental parameters (such as the product used) and on the accuracy in the determination of the spacecraft potential. The electron density from the SPEC product has the better available time resolution but its absolute value can differ from the moments computed on the ground from the 3D velocity distribution.
, 1986) , past observations at Earth and Venus, and tweaked to the MAVEN Mars dataset to capture at least once most structures seen in a test interval. Criterion 5 ( B bg ≥ 11 nT), inspired by a similar one from  Dimmock et al. (2015)  for observations at Earth and used in Simon  Wedlund et al. (2021)  with some degree of success to isolate solar wind areas in MAVEN B-field data, aims at removing potential outliers outside of typical magnetosheath B-field levels in an interval around the MM candidate. Additionally, non-Gaussian statistics may help with the classification of MM candidates  (Osmane et al. , 2015). The propensity of the signal distribution in a given time interval (containing, for instance, m successive measurements of B) towards peaks or dips can be measured by its skewness, expressed as Skew = 1 m m j=1 |B j | -B bg 3 /σ 3 , where σ is the standard deviation of the module |B j | of B at each time step j.
The detected candidate MM all present a clear B -N i anticorrelation as demonstrated with the relative B-field and density variations ∆B/B and ∆N i /N i. The gain in temporal resolution (4 s) from the onboard datasets allows the ion density variations to follow even more closely those of the magnetic field throughout the interval. A supplementary indicator is given for the first MM detection (dip) around 11:26:45 UT by SWEA's ∆N e /N e at a resolution of 2 s, which allows this 8-9 s-long structure to be probed with almost 4 electron density data points. The B -N i and B -N e anticorrelations for this event are striking, and represent the clearest and most unambiguous MM signature detected to date at Mars. The consistency found between onboard and manually calculated moments, as well as the electron density data for this event, gives confidence for the rest of the interval.
The revised set of criteria is given in Table  1 , yielding 7 'dip' structures and 3 'peak' structures, with 1 potential outlier, totalling 33 s out of a 240 -s interval. Some of these peak structures appear to be nothing more than counterparts of dip struc- Robust detections across an entire orbit may be difficult as small adjustments in angles and eigenvalue ratios can change the results, increasing or decreasing the number of potential candidates. The difficulty lies in the aforementioned mixed presence of peaks and dips in the study interval considered. Automatic detection applied to larger datasets may in this way benefit from considering smaller intervals of a few minutes on which to calculate the background field levels, depending on the skewness of the B-field distribution in that interval  (Génot, Budnik, Hellinger, et al.
pendicular components of relevant physical quantities such as B MFA ,  P MFA  (see Appendix B) or the mirror mode criterion MMIC of Eq. (1) were also similar, with only occasional spikes when using B linked to the temporal resolution of SWIA (see Fig. B1). In the main study, the low-pass filtered background field, noted B bg , was used for simplicity and to keep consistent with the MM detection algorithm. -24-manuscript submitted to JGR:  Space Physics Acknowledgments C. Simon Wedlund ,  M. Volwerk  and  C. Möstl  thank the  Austrian Science Fund (FWF) :  P32035-N36 ,  P31659-N27 ,  P31521-N27. The calibrated MAVEN/MAG, SWIA and SWEA datasets are available from the  NASA Planetary Data System (PDS). Throughout the manuscript all inverse tangents were calculated using the atan2 function in order to resolve all four angular quadrants.
Data Since we are interested in investigating the radial evolution of solar wind turbulence, we used measurements provided by PSP and the BepiColombo spacecraft when they were radially aligned and orbiting near 0. 17 and 0. 6 au, respectively. Since we cannot evaluate the Elsässer variables, due to the lack of solar wind speed measurements, we used magnetic field measurements provided by the magnetometers on board both spacecraft. The PSP magnetic field observations are taken from the FIELDS Fluxgate Magnetometer (MAG;  Bale et al. 2016)  and are averaged to a 1 s cadence from their native four samples per cycle cadence (see also  Alberti et al. 2020; Chen et al. 2020). The data were freely retrieved from the Space Physics Data Facility Coordinated Data Analysis Web interface. 6 The BepiColombo magnetic field data were obtained from the MPO-MAG team  (Glassmeier et al.
Discussion and Conclusions In this work, we have investigated the evolution of the solar wind magnetic field scaling properties between the PSP and BepiColombo orbits during their first radial alignment when they sampled the same plasma parcel. We found that the second-order structure function, closely related to the PSD behavior, shows a steeper scaling at BepiColombo's orbit, being the scaling exponent ζ(2) ∼ 2/3, than at PSP's one, being ζ(2) ∼ 1/2. This result is in agreement with previous studies (e. g. ,  Alberti et al. 2020; Chen et al. 2020) , and can be related to the increasingly Alfvénic nature of the near-Sun solar wind, reflected in a shallower spectrum  (Telloni et al. 2021). By evaluating the highorder scaling exponents ζ(q), up to the statistically accessible orders (i. e. ,  q N log p max 10 ( ), N p being the number of data points; Dudok de Wit 2004), we have reported the evidence for the nonlinear convex behavior of ζ(q) as a function of q, usually related to the phenomenon of intermittency (e.
Wind stresses and ocean currents in physical space Outputs of wind stress and ocean current from the coupled simulation are decomposed into different components based on temporal and spatial scales, with this decomposition based on the time and spatial variability of winds and currents. Figure  1a  and b show a snapshot of wind stresses and ocean currents in the global ocean. Videos of these two key variables are available in https://doi. org/10. 5281/zenodo. 6478679 (Torres, 2022b). The wind stress variations have large scales, O(1000 km) (Fig. 1a ), resulting from atmospheric weather patterns that propagate rapidly, for example, going from South Africa to South America within 6-10 d (Fig. 1a ). Embedded within these large-scale patterns are smaller scale patterns (as small as 100 km), some of them propagating with the large-scale ones, others being quasi-stationary (see Box 1 in Fig.
These characteristics are consistent with previous studies  (Watanabe and Hibiya, 2002; Zhai, 2013;  https://doi. org/10. 5194/gmd-15-8041-2022 Geosci. Model Dev. , 15, 8041-8058, 2022 ) and zonally integrated during the four seasons. Yu et al. , 2018). Also, the emergence of hurricanes in summer and fall, particularly in the Northern Hemisphere, have a strong signature on the wind work (see for example Fig. 5c , and also videos in https://doi. org/10. 5281/zenodo. 6478679 (Torres, 2022b). The spectra of ocean current and wind stress for different seasons (first and second columns of Fig. 6a , respectively) in the Kuroshio Extension reveal how the different time and spatial scales associated with the wind work components vary seasonally. The effective spatial scales considered in these spectra are smaller than 1000 km and the time scales are smaller than 3 months.
Thus, a spectral analysis shows that a phase shift of 3-12 h between wind stress and ocean currents, reduces this component by a factor of up to 10. The high frequency component also has a strong seasonality because of winter atmospheric storms. The low-frequency component (corresponding to wind stress and ocean currents with time scales between 3 d and 3 months) has been analyzed following the approach of  Rai et al. (2021) , which consists of defining a critical length scale  that splits this component into a negative small-scale part and a positive large-scale part. The positive part exhibits a strong seasonality at mid-latitudes, not present in the negative part. In addition, the magnitude of the positive part is 10 times larger than the negative part. These characteristics of the wind work, not found when using geostrophic currents diagnosed from altimetry datasets, point to the importance of low-frequency ageostrophic current contribution to the wind work  (Rai et al.
and at movies (https://doi. org/10. 5281/zenodo. 6478679, Torres, 2022b, in particular see the movie GEOS_ECCO_TAUSPEED. mp4) indicates that this positive wind work comes from the signature of hurricanes with a size smaller than L c during summer and fall. However, this positive wind work has a much smaller magnitude than the negative wind work observed at mid-latitudes (Fig. 7e). Comparison of Figs. 9a-dand 8a-d reveals that τ lf> u o lf> (the wind work corresponding to scales > L c ) differs greatly from τ lf<. u o lf< , not only in terms of sign but also in terms of magnitude and seasonality. These figures indicate that the magnitude of τ lf> u o lf> is 10 times larger than τ lf<. u o lf< and is closer to, although smaller by a factor 2, than the total timedependent wind work (Figs. 5e and 9e).
Acknowledgements. This research was carried out in part at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration (NASA)  and funded through the  internal Research and Technology Development program. Hector S. Torres ,  Jinbo Wang ,  Alexander Wineteer ,  Ernesto Rodriguez ,  Dimitris Menemenlis ,  Hong Zhang , and  Dragana Perkovic-Martin  were supported by the  NASA Physical Oceanography (PO)  and Modeling, Analysis, and  Prediction (MAP)  programs. Andrea Molod ,  Christopher N. Hill , and  Ehud Strobach  also received funding from  NASA MAP. Patrice Klein  acknowledges support from the  SWOT Science Team , the  NASA   S-Mode  project, and the  QuikSCAT mission. Andrew F. Thompson  and  Mar Flexas  were supported by the  NASA S-MODE  project, award number  80NSSC19K1004 , and PDRDF funding from  NASA 's  Jet Propulsion Laboratory.
Code availability. The exact version of the model used to produce the results used in this paper is archived on Zenodo (https://doi. org/10. 5281/zenodo. 6686083, Torres, 2022a), as are input data and scripts to run the model and produce the plots. Data availability. The coupled ocean-atmosphere simulation can be found at: https://portal. nccs. nasa. gov/datashare/ G5NR/DYAMONDv2/GEOS_6km_Atmosphere-MITgcm_4km_ Ocean-Coupled/GEOSgcm_output/ (NASA, 2022). In particular, the dataset contained in the folder geosgcm_surf/ was used in this study. The variables used in this study are U (east-west velocity component), V (north-south velocity component), oceTAUX (east-west wind stress component), and oceTAUY (north-south wind stress component). Video supplement. Videos at https://doi. org/10. 5281/zenodo.
The authors acknowledge the  Nuclear Spectroscopy group of the University of Jyväskylä  for the loan of the two CLOVER detectors from MARA focal plane and their help with the acquisition system. CloudVeneto [50] is acknowledged for the use of computing and storage facilities. The authors acknowledge support from the  European Union Seventh Framework Programme  through ENSAR, Contract No. 262010. This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 771036  ( ERC CoG MAIDEN ). R. P. D. G. has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sk lodowska-Curie  grant agreement No  844829. A. K. acknowledges the support from the  Academy of Finland  under Grant No.
275389  and  312544 , and T. E. under Grant No. 295207  and  306980. R. T. acknowledges " P2IO LabEx  ( ANR-10-LABX-0038 )" in the framework " Investissements d'Avenir"  ( ANR-11-IDEX-0003-01 ) managed by the  Agence Nationale de la Recherche (ANR), France.
Acknowledgements I thank the editor,  Mathias Kifmann , and an anonymous reviewer for their careful reading and useful suggestions and my co-authors of related papers  Matthew Adler ,  Maddalena Ferranna ,  Daniel Herrera-Araujo ,  Christoph Rheinberger , and  Nicolas Treich  for many helpful discussions. I acknowledge funding from the  U. S. National Science Foundation  (award number  1824492 ) and the  French National Research Agency (ANR)  under the  Investments for the Future program (Investissements d'Avenir , grant  ANR-17-EURE-0010 ).
, 2000; Seager et al. , 1963). In the context of climate change, 8 the earth's surface temperature could locally increase from 12°C to 20°C. The effect of this increase in temperature on the main properties and partitioning coefficients of oil and LNAPL compounds were calculated based on the available data of the literature and gathered in Tab. 1. The oil viscosity decreases up to 41% over this temperature range, increasing the LNAPL mobility (Tab. 1). On the contrary, the variation of the LNAPL density, soil-water partition coefficients, and LNAPL/water interfacial tensions appear to be negligible over this range of temperatures  (Gaito et al. , 2012; Imhoff et al. , 1997; Sleep and Ma, 1997). Ten  Hulscher and Cornelissen (1996)  revealed that a decrease by 2-3% of the equilibrium sorption constant can be expected under this range of temperatures for most organic pollutants, probably favoring their desorption and remobilization into the air and water and making them more available for degrading microorganisms  (McAlexander and Sihota, 2019; Yadav and Hassanizadeh, 2011).
Acknowledgments This work was supported by the  French PIA  project " Lorraine Université d'Excellence" , reference  ANR-15-IDEX-04-LUE , and the  BRGM  (the  French Geological Survey ). The authors would like to thank  Lorraine University  and the BRGM. This work is included in the scientific program of the  GISFI Science of the Total Environment 806  (2022) 150412 https://doi. org/10. 1016/j. scitotenv. 2021. 15041220 research consortium dedicated to the knowledge and the development of remediation technologies for degraded and polluted lands (Groupement d'Intérêt Scientifique sur les Friches Industrielleshttp://www. gisfi. univ-lorraine. fr).
Introduction Remote sensing, from visible (VIS), through thermal infrared (TIR), up to microwaves (MW), represents a key tool to study and monitor our planet, especially when field measurements are hard to produce because of environmental coverage (e. g. , oceans) and/or technical and operational issues (e. g. , inaccessibility, or seasonal or annual variations). Remote sensing multispectral data are critical to monitor the results of human activities, e. g. , polluting gases or industry-made products, and natural hazards, e. g. , hurricanes, floods, fires, earthquakes, volcanic eruptions, sandstorms, soil erosions, and landslides  [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] , whereas the availability of more frequent views over the same area allows for a better management of natural hazards and disaster e.