dataesr/scientific-paragraphs-categorization huggingface dataset:
DFT calculations Periodic DFT calculations were performed using the VASP (Vienna Ab initio Simulation Package) code  [18, 19]. A plane-wave basis set was used with cutoff energies of 400 eV for the relaxation of atomic positions and 1050 eV for cell relaxations within the projector augmented wave (PAW) method of Blöchl  [20] , as adapted by Kresse and Joubert  [21]  and with a Gaussian smearing of 0. 05 eV. As a large supercell was employed in calculations (see the structural model below), the Brillouin zone sampling was restricted to the Γ-point. The semi-local exchange correlation functional PBE was employed  [22]. Since the dispersion interactions, important to describe 129 Xe adsorption in a zeolite, are not correctly described by this method, we have used the TS/HI correction scheme  [23]  which is an accurate variant ot the Tkatchenko-Scheffler dispersion correction improved with iterative Hirshfeld partitioning  [24, 25].
Acknowledgments We acknowledge the support of the  UOP-Honeywell, Label of Excellence for the Centre  for zeolites and nanoporous materials by the  Region of Normandy (CLEAR). This work was granted access to the HPC resources of TGCC under the allocation  2021-A0100810433  by  GENCI-EDARI  project. JR and MB thank  Ioannis Karamanis  ( Université de Lorraine ) for fruitful discussions. JR and MB also acknowledge the financial support of the  COMETE  project  (COnception in silico de Matériaux pour l'EnvironnemenT et l'Energie ) cofounded by the  European Union  under the program " FEDER-FSE Lorraine et Massif des Vosges 2014-2020 ". TB is grateful to the  Slovak Research and Development Agency  under the contract No. VEGA-1/0777/19  and  APVV-20-0127  for the support.
02, H = 0. 08 and H = 0. 15, with 2 different values of λ 2 , namely 0. 02 and 0. 1. We chose T = 2 17 , L = 2 14 and fixed arbitrary σ 2 = 1. For all these parameters we run both GMMM and GMM lnM estimators with Q = 19 and {τ k } k=0,. ,18 = √ 2 k. Our GMM implementations closely follow the one detailed in  [9]  and notably the error covariance is estimated using Newey-West HAC type estimator with a lag L 1/3 and the initialisation is performed using the scaling estimator provided in  [15]. We used the L-BFGS-B minimisation algorithm as provided by scipy. optimize library in Python but we find similar results using alternative methods. In Fig. 4 , are displayed a fit of respectively CM (1, n) and C ln M (1, n) using expressions (44) and (  46 ) with the estimated GMM parameters for a sample of length L = 16384 with H = 0.
Application to realized volatility of asset returns In this section we consider the application of the estimator of former section to characterise the roughness exponent H and the intermittency coefficient λ 2 of realized volatility associated with various assets. Section 4 suggests that the GMM estimator GMM lnM outperforms the other candidate GMMM. This is why we exclusively consider the GMM lnM applied to various empirical daily volatility data. Our study is based on 2 datasets containing respectively stock market indices and individual stock prices:.
Oxford-Man Institute of Quantitative Finance Realized Library (OIQFRL) The Oxford-Man Institute's Realized Library 1 , contains historical records of various estimators of daily realized volatility of several stock indices. This dataset is widely used in various empirical studies and in particular, it was used as a benchmark database in many former studies on rough volatility (see e. g. [15, 9] ). So we apply GMM lnM estimator to analyse the daily volatility time series associated with 24 major stock market indices considered in  [9]. Following this latter work, in the following, we only report obtained results when using bipower variation volatility estimator but we have checked that the same results are obtained when using realized variance estimators at scale 5 min or 10 min. Two GMM lnM estimations are illustrated in Fig.
Yahoo Finance database (YF) We collected historical daily open, high, low close price time-series of 296 individual stocks (from S&P 500 and CAC 40 indices) from Yahoo Finance 2. Stocks were taken from either the S&P 500 index (historical data from 1985-01-01 to 2021-12-31) or the CAC 40 index ((historical data from 2000-01-01 to 2021-12-31). For each stock, we constructed a proxy of the daily volatility using Garman-Klass (GK) estimator described in  [21]. We checked, using Oxford-Man data indices, that such GK estimator provides results that are consistent with the intraday estimators of realized volatility. For all stocks we performed a GMM ln M estimation of H and λ 2. In Fig. 7 , following the exact same structure as Fig. 6 , we illustrated such experiments with two examples, namely Apple and Bank of America realized volatility.
Again we observe that stock volatility fluctuations seem to be long-term correlated. For the selected two stocks, the estimated values of H are respectively 0. 01 and 0. 02. The so-obtained estimations on all the 24 stock indices of the OIQFRL database and the 296 individual stocks of the YF database are summarised in Figures  8  and 9. In Fig. 8 , we have reported the normalised histograms of the estimation H for the Hurst exponents of the stock indices and the individual stocks of the two datasets. We can observe that the two distributions are quite different: while the Hurst exponents of the stock market indices are spread around H 0. 13 with a rather large dispersion (corresponding to a rms of 0. 03), the distribution of H values of individual stocks is mainly peaked around a very small average value H 0.
It is clear from observations, however, that the distributions and properties of AAIW show zonal heterogeneity between basins, with a lighter and fresher AAIW type in the Pacific and denser and saltier AAIW types in the Indian and Atlantic oceans  (Talley et al. 2011) , which suggests that both the spatial and temporal variability of AAIW need to be investigated. The aim of this study is to analyse AAIW properties in one CMIP6 model, the UKESM1-0-LL model from the Met Office Hadley Centre, and to analyse the variability of the salinity minimum in the different basins in order to provide a broader picture of the response of the salinity minimum under future scenarios. The study is structured as follows: Section 2 presents the methods and datasets used. Section 3 describes the definition used to analyse AAIW properties consistently in different scenarios and basins and assesses the link between the salinity minimum and frontal locations.
CMIP6 UKESM1 model The data used in this paper comes from simulations performed with the UK Earth System Model 1 low atmosphere and low ocean resolution (UKESM1-0-LL) from the Met Office Hadley Centre (MOHC). A full description of the model can be found in  Sellar et al. (2019). Briefly, UKESM1-0-LL is an updated version of the CMIP5 HadGEM2-ES model. UKESM1 is built on multiple component models including HadGEM3-C3. 1 for the physical core atmosphere-land-ocean-sea ice model, Joint UK Land Environment Simulator (JULES) for the terrestrial biogeochemistry component, the Model of Ecosystem Dynamics nutrient Utilisation Sequestration and Acidification (MEDUSA) for the ocean biogeochemistry and United Kingdom Chemistry and Aerosol (UKCA) model for the atmospheric composition. HadGEM3-C3. 1 uses the Nucleus for European Modelling of the Ocean (NEMO) model for the ocean and CICE for sea ice  (Williams et al.
Data availability The UKESM1-0-LL  (Tang et al. 2019 ) data was obtained through ESGF/CEDA portal (https:// esgf-index1. ceda. ac. uk/ proje cts/ esgf-ceda/). MIMOC (Schmidtko, Johnson, and Lyman,  n. d. )  data was downloaded from the National Oceanic and Atmospheric Administration (NOAA) portal (https:// www. pmel. noaa. gov/ mimoc/). Data was processed using CDO tools  (Schulzweida 2019 ) and python libraries.
2 ), highlighting the current benefits and difficulties. Our review accounts for the various variables (water elevation, extent, storage, quality) of the surface water components including lakes, rivers, floodplains and wetlands. We analyzed approximately 200 publications. These contributions were selected using the knowledge of the experts that were reunited to make this review and using databases such as Web of Science. We mainly consider published papers in peer-reviewed journals and we do not include reports and other non-research articles and activities such as magazines, newsletters, editorials. Conference papers of notable relevance are also included. The period covered is from ~ 1990 to present. For each variable (Fig. 2 ), we summarized how it is retrieved from remote sensing observations, and present and discuss the advances that have been achieved from this information.
For a detailed and complete description of the characteristics of the different satellite altimetry missions and the estimation of surface water elevation for hydrology, including the various re-trackers used, altimetric waveform description, the specific corrections (geoid gradient for lakes, hooking effect for rivers and small water bodies), the associated errors and biases, the different acquisition modes [Low-Resolution Mode (LRM), SAR, SAR Interferometric (SARIn)], we refer to  Cretaux et al. (2017). Several groups or institutions around the world provided time series of surface water elevation, covering a wide range of water bodies and applications. Table  1  provides the main sources and databases (non-exhaustive) that provide surface water elevation time series over Africa. The in-depth assessment and validation of the water levels derived from the satellite altimeter over lakes, rivers and other inland water bodies were performed worldwide against in situ gauges  (Frappart et al.
Using radar altimetry observations among other data,  Swenson and Wahr (2009)  evidenced a larger impact of human activities (such agriculture practices and the construction of dams) on Lake Victoria, between 2002 and 2008, as compared to other natural lakes in the region. They also showed that only 50% of Lake Victoria's water level variations could be explained by natural causes, while the other half is entirely due to discharges into Lake Kyoga directly downstream. In particular, radar altimetry data revealed that between 2002 and 2008, Lake Victoria's water level decreased about 40% faster than that in Lakes Tanganyika or Malawi. Ahmed and Wiese (2019) also used GRACE data in combination with time series of lake water level changes from the Hydroweb database  (Cretaux et al. 2011)  to determine how much of the Total Water Storage (TWS) variability in East Africa and in the Zambezi basin comes from the lakes, including Lake Malawi.
This is shown in Fig. 4  which displays the maximum amplitude of surface water elevation estimated for all available SV over Africa obtained from the Hydroweb database (see Table  1 ). Note that all available VSs are displayed independently of the altimetry mission. So, it is worth noting that all SVs which are displayed don't necessarily cover the same period of time and don't have the same record length. Here, we used a total of 2157 VSs, including   (2018-present). Moreover, for some VSs, the maximum amplitude is calculated from a time series with a 35-day interval sampling (ENVISAT), while others are estimated from observations with a 27-day (Sentinel-3A/B) or a 10-day (Jason-2/3) interval sampling. However, Fig. 4  clearly shows the high spatial coverage achieved over certain large river basins, but also on secondary size rivers.
Bwangoy et al (2010)  and  Betbeder et al (2014)  employed a combination of SAR L-band and optical imagery to characterize the Cuvette Centrale land cover. They estimated the wetland extent to reach more than 360,000 km 2 , representing about 32% of the total area. Nevertheless, these datasets generally represent large data volumes and suffer from low temporal frequency observations that limit these monitoring to a few samples of a few basins, preventing systematic, long-term assessments of inundation dynamics. This context is changing and SAR observations with a high resolution in the order of 10 m are becoming a very powerful tool to be used for monitoring flood risk and for managing disasters  (Alfieri et al. 2018; Lindersson et al. 2020; Matgen et al. 2020). The case studies over Africa are becoming more relevant such as over Uganda  (Barasa and Wanyama 2020) , Namibia  (Long et al.
4 GHz) microwave signal from Soil Moisture Ocean Salinity (SMOS)  (Fatras et al. 2021). This product has been recently used to map the spatiotemporal variability of water bodies in the Congo River basin at ~ 50 km spatial resolution and weekly temporal resolution from 2010 to 2017, with the ability of the L-Band frequency to retrieve water under dense canopy  (Parrens et al. 2017). They reported that the mean flooded area of the Congo extent was between 2 and 3% of the entire basin. The dataset was also helpful to characterize flood and drought events in the basin during the last 10 years. The development of specific products for Africa remains limited and many studies over this continent rely on the use of products originally designed for global scale applications. These products are generally based on multi-satellite methodologies that combine the complementary strengths of the information provided by passive microwave observations with other types of satellite observations  (Papa et al.
Estimations of surface water extent can also be retrieved using observations in the visible and infrared domain (such as from Landsat, Advanced Very High-Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS) or Sentinel-2A/B to name only the most relevant) which offer moderate to high spatial (~ 5-500 m) and temporal (daily to weekly) resolutions. However, in tropical and subtropical environments, these observations potentially show strong limitations for detecting surface water beneath clouds or dense vegetation. As a consequence, obtaining cloud-free data during the flood season of tropical Africa is a challenge  (Klein et al. 2015; Huang et al. 2018; Mayr et al. 2021). The potential for using the MODIS Terra instrument to monitor changes in flooding was demonstrated over specific regions such as the arid Inner Niger Delta (Bergé-Nguyen and Crétaux 2015).
Small water bodies are very reactive to climate variability and can exhibit complex and sometimes unexpected dynamics, such the paradoxical increase in surface and volume during and after big Sahelian droughts  (Gal et al. 2017). Optical sensors have proved successful to monitor their dynamics in different semiarid regions as for example the Sahel  (Haas et al. 2009 (Haas et al. , 2011;; Gardelle et al. 2010; Gal et al. 2016; Grippa et al. 2017) , Tunisia  (Ogilvie et al. 2018)  and Namibia  (Naidoo et al. 2020). Using Landsat,  Pekel et al (2016)  provided a detailed dataset, the Global Surface Water (GSW) of open inland water worldwide at 30 m resolution, that include the characteristics of the seasonal cycles and trends over the last 30 years. Although open water can be easily detected using the middle infrared bands, the retrieval of water areas is more difficult when aquatic vegetation or trees are present.
Only half of the detected lakes in Fig. 7b  are included in GSW  (Pekel et al. 2016) , mainly due to the fact that this database focuses on open water and misses most of the lakes covered by vegetation. Maximum water area over the 1984-2019 period in this database is equal to only 67,3585 km 2. To conclude this section, it is also worth mentioning the efforts developed to take advantage of the combination of the various observations we described above. For instance, the low resolution but long-term estimates of passive microwave can be combined to optical data, such as those from MODIS with higher spatial resolution, but limited in time, via downscaling methodologies that combine both estimates into long-term, high-resolution products, such as the inundation extent product proposed over the Inner Niger Delta  (Aires et al.
(2016). If the bathymetry of the lake or reservoir is known, this can also be used in combination with water level estimation to infer surface water storage variations. Several databases, generally the ones that also provide water level from altimetry (Sect. 2. 1), now make water storage change estimates in lakes and reservoirs available (Hydroweb and DAHITI for instance), including several water bodies in Africa. Notably,  Tortini et al (2020)  and  Tusker et al. (2019)  built on relationships between elevation and surface area from multiple satellite altimetry missions and surface water extent estimated from Terra/Aqua MODIS or Landsat (such as GSW). They estimate continuous surface water storage changes in large lakes and reservoirs globally for 1992-2019, with many targeted lakes in Africa.
2020 ) combined MODIS-derived surface water extent of the lake and floodplains with water level from altimetry to estimate the variations in surface water storage in the area to be ~ 1. 2 km 3 annually (with a maximum error of 8. 5% on this estimate). In combination with GRACE data, the study further showed that groundwater (estimated with a maximum uncertainty of 23%), which contributes ~ 70% of Lake Chad's annual water storage change, is increasing since the 2000s, mainly because of sustained water supply provided by the two main tributaries of the lake. As a result, over the last two decades, Lake Chad is not shrinking thanks to groundwater and the tropical origin of water supply and is recovering seasonally its surface water extent and volume. As we can see, most of these studies have been performed for large spatial scales and with temporal scales of monthly to seasonal changes and is restricted in general the data to capture only wide rivers, lakes and basin systems.
1 ) lakes, respectively. Robert et al. (2017)  assessed the capability of high (Landsat 7/8) and medium (MODIS) resolution satellite sensors to monitor SPM in extremely turbid waters in the Gourma region (Mali). Sentinel-2 data were also found to provide good results for SPM monitoring in Burkina Faso  (Robert et al. 2021) , over the middle Niger River and over the Lake Chad. All these studies are based on in situ measurements in order to identify the best corresponding band or ratio band to monitor the dynamics of SPM: Depending on the site, this is either the NIR/R ratio or most often the NIR band which seems the best to retrieve SPM values, in particular for high values. However, there is a limit at about 2500 mg/l above which reflectance reaches saturation. An example of the capability of Sentinel-2 to monitor SPM spatiotemporal variability in the Bagré Lake in Burkina Faso is shown in Fig.
2013). Earth Observations have demonstrated their value globally in improving hydrological modeling  (Lettenmaier et al. 2015) , but in Africa, they play an even more crucial role. Almost all hydrological modeling undertaken on the continent now relies on Earth Observation for many of its data needs  (Paris et al. 2022). While the growth in utilizing Earth Observation in hydrological studies only really took off in earnest since 2000, mainly due to the advent of satellite-derived data products (see Sect. 2), it has now become so ubiquitous to use Earth Observation in these studies that many hydrology papers no longer mention Earth Observation in the title  (Lettenmaier et al. 2015). Here, we provide some examples to illustrate the role Earth Observation currently plays in hydrological modeling at different scales and for differing applications over Africa.
We use this to highlight some of the successes of applying these data in hydrological modeling of the continent. One of the significant benefits of using satellite observations in hydrological modeling is the large spatial scale of what can be measured and used, although often at increased computational modeling cost. However, there are very few examples of hydrology modeling specifically for the African continental scale. This may in part be due to regional technical capacity issues, as well as data availability for such studies, but there has also been a tendency for researchers to develop global hydrology models, rather than just at a continental scale. Nevertheless, Earth Observation has contributed to studies specific to the African continent scale. One example of continent scale modeling is the use of daily remotely sensed surface water extent assimilated in the LISFLOOD hydrology model at the continental spatial scale in Africa and South America, with the conclusion that remotely sensed surface water extent improves streamflow simulations, potentially leading to a better forecast of the peak flow in ungauged regions  (Revilla-Romero et al.
2016 ). These models have also been used to explore explicit links between hydrology and malaria transmission across the African continent  (Smith et al. 2020). Unsurprisingly, as observed in Sect. 2, Earth Observation has had much more research focus on hydrology at a large basin scale in Africa including Niger, Nile, Congo, Chad basins. This is perhaps the ideal scale for the use of satellite data in hydrology modeling, where the large scale of the basins makes in situ observations of sufficient detail hard to gather, as well as there being a better performance of hydrology modeling due to the longer timescale responses of such large basins. These large basins are also important transboundary river systems that require multi-country management and Earth Observation has played an important nonpartisan role in a more transparent understanding of the hydrology and water availability of these rivers (Ekeu-wei and Blackburn 2018;  Guzinski et al.
2), Earth Observation is also crucial for primary model build datasets such as land use observations and for model driver datasets such rainfall. These models have used Earth Observation in the Niger Basin to explore a range of issues, such as the use of MODIS and Landsat for vegetation and surface water in groundwater recharge processes  (Leblanc et al. 2007) , and satellite rainfall products such as ECMWF Re-Analysis (ERA)-Interim for runoff estimation  (Oyerinde et al. 2017 ) and the Climate Prediction Center morphing method (CMORPH), the near-real-time legacy product of Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA 3B42RT) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) for flood forecasting  (Casse et al.
Digital elevation models (DEM) derived from radar observations (e. g. , the Shuttle Radar Topography Mission, SRTM) have been used for basin delineation and flow routing to study lake hydrology in the upper Nile Basin  (Bastawesy et al. 2013). Thermal infrared remote sensing data from AVHRR are used to estimate evaporation fluxes from the large Sudd wetland  (Mohamed et al. 2006). Surface water extent observations from Landsat have been used to improve hydrology modeling of the lower Nile  (Gleason et al. 2018). Recently, altimetry-derived surface water storage was assimilated into the World-Wide Water Resources Assessment (W3RA) model using the ensemble Kalman filter (EnKF) for the period of 2003 to 2016 (Khaki and Awange 2020), which improved model outputs, especially the surface water discharge RMSE which is reduced by approximately 33%.
Smaller African basins are also benefiting from modeling with a significant reliance on satellite dataset due to data scarcity, for example for modeling of flood hydrology and the subsequent flood hazard in the Oti River Basin  (Komi et al. 2017) , for water availability modeling in the Rokel-Seli catchment in Sierra Leone  (Masafu et al. 2016)  or for developing a water balance model for Lake Turkuna in East Africa  (Velpuri et al. 2012) , calibrated with a composite series of water level from T/P, Jason-1 and ENVISAT satellite altimetry data.