dataesr/scientific-paragraphs-categorization huggingface dataset:
More studies using these electronic batteries should be conducted to assess their sensitivity and specificity and to assess relationship with standard neuropsychological tests. With the self-administration of tests, feedbacks to patients are necessary with a suitable manner. This approach also needs follow-up screening of cognitive difficulties. The disadvantages to these batteries also include the lack of familiarization for some patients with electronic tools, especially older patients. Even so, these electronic cognitive batteries (Table  2 ) could be used for large database implementation. They present the advantage of having little verbal material, thus reducing the impact of the practice in longitudinal studies with repeated assessment and tests could more easily be proposed in a random order e) Cognitive assessment performed by phone, based on tests with known psychometric properties, could be an alternative to electronic tests performed without a neuropsychologist, even if cautions should be taken to reduce the risk of cheating by taking notes.
Acknowledgments: The authors gratefully acknowledge the financial support from the  Multi-Scale Fluid-Solid Interactions in Architected and Natural Materials (MUSE)  Project, an  Energy Frontier Research Center  funded by the  U. S. Department of Energy, Office of Science, Basic Energy Sciences  under Award # DESC0019285. The authors also acknowledge the  Center for High Performance Computing (CHPC) at the University of Utah  and the research group of  Professor Milind Deo  at the  University of Utah  for accessing their computing facilities. T. V. would like to thank  Mr. Bang He  from the  University of Utah  for useful discussion regarding the manuscript.
de Longueville et al. [10]  identified two main causes to this lack of information on the effect of dust on the population living in the countries bordering the Sahara: (i) The absence of systematic particulate air pollution data (e. g. ,  [22] ); and (ii) poor-quality public health data that could be used to adequately support impact studies (e. g. ,  [23] ). The absence of systematic particulate air pollution data is still a reality when focusing on Africa: In the 2016 update of the WHO Global Urban Ambient Air Pollution Database (http://www. who. int/phe/health_topics/outdoorair/databases/cities/en/), which reports annual means of PM 2. 5 and PM 10 concentrations measured throughout the world (2975 locations), measurements for only 52 cities located in 13 African countries are reported, including seven cities in Morocco and four cities in Tunisia.
[46]  in Figure  2 ), which suggests that southern Tunisia is not a dust source or a pathway for Saharan dust transport as important as the Sahel is. [45] ; black = this study; blue = the 2016 WHO GUAAP Database; orange = Kaly et al. [46]. Details on the location, type, and data coverage is given for each station in Table  A2  (Appendix B). Rural and regional background stations are identified using bold italics font.
Indeed, Moulin et al. [31]  reported that April was a period of intense dust uplift over North Africa because of the Sharav cyclones  [47] , and that the major source regions for the Mediterranean were: (i) South Tunisia around 30° N for the western central basin  [26] , and Libyan desert just below 30° N for the eastern central basin  [48]. More recently, Fiedler et al. [49]  showed that above 20° N, up to 80% of desert dust emissions was due to atmospheric depressions whose frequency over the north of Africa is the highest between March and May, and then between December and February. [45] ; black = this study; blue = the 2016 WHO GUAAP Database; orange = Kaly et al. [46]. Details on the location, type, and data coverage is given for each station in Table  A2  (Appendix B). Rural and regional background stations are identified using bold italics font.
Acknowledgments: The authors want to thank (1) Houcine Khatteli, Director of the Institut des Régions Arides (IRA) of Médenine, for the constant support of IRA in all research related to wind erosion, and (2) the Chemistry-Aerosol Mediterranean Experiment (ChArMEx) reaserch program (http://charmex. lsce. ipsl. fr) that allowed the installation of the sunphotometer. The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and READY website (http://www. ready. noaa. gov) used in this publication. MODIS data used in this study were downloaded from the Giovanni online data system, developed and maintained by the NASA GES DISC. We also acknowledge the MODIS mission scientists and associated NASA personnel for the production of the data used in this research effort.
g. ,  [33] ). 72 h backward trajectories starting at 12:00UTC on both days were computed, ending at the ground level (0 m) at El Fjé, using the ensemble model (i. e. , 27 backward trajectories were computed for all identified days) forced by the GDAS (Global Data Assimilation System) meteorological database at 0. 5 •. In complement to the air mass trajectories, we used the AOD at 550 nm derived from the measurements of the Moderate Resolution Imaging Spectroradiometer (MODIS) on-board the Aqua satellite: 1 • × 1 • daily maps of the merged Dark Target/Deep Blue product available in the collection 6 MODIS aerosol products over land and ocean  [58]  were used to determine the most probable origin of mineral dust observed during both days. To do so, AOD maps were produced for each of the days composing the 72 h backward trajectories computed for 12 February and 13 May 2014, and the most probable origin of mineral dust corresponds to the location where the air-mass trajectory crosses near the surface a region with a high AOD.
The origin of the air-masses was determined using the online version of the HYbrid Single Particle Lagrangian Integrated Trajectory model (HYSPLIT;  [53, 54] ). This method is indeed widely used to determine the origin of African dust contributions to the total aerosol load (e. g. ,  [55] [56] [57] ) or the origin of mineral dust deposits (e. g. ,  [33] ). 72 h backward trajectories starting at 12:00UTC on both days were computed, ending at the ground level (0 m) at El Fjé, using the ensemble model (i. e. , 27 backward trajectories were computed for all identified days) forced by the GDAS (Global Data Assimilation System) meteorological database at 0. 5°.
[Subject: students]; MA7: Upload the finished materials to the site (Wordpress); MA8: create and print quick response (QR) codes [Subject: students]; MA9: Students present their work [Subject: students]. Infrastructure: The infrastructure mentioned in the scenario involves: at least 15 computers with internet access; mobile telephones with cameras and QR code scanner; digital photo cameras; software: Animaps, Wordpress, Panorama, QR code generator. Functionalities/affordances of infrastructure: Web-search: Find information around a subject using relevant keywords, offer access to various types of information regarding the topic of interest and sort the information found from the most relevant to the least relevant. Other infrastructure. Similar result: take interviews, use information from an accredited source.
Synergies between clear-sky thermal IR data with MW for cloudy sky have recently been proposed with promising results. For example, Andre et al. [44]  combined Moderate Resolution Imaging Spectroradiometer (MODIS) and SSM/I brightness temperatures over the circumpolar Arctic and reported root mean squared differences (RMSDs) of 2 K when comparing their retrievals to five experimental sites of the EU-PAGE21 project (https://www. page21. eu/). Since MODIS and SSM/I do not have the same overpass times, they were synchronized by fitting a diurnal cycle model derived using ERA-Interim data  [45]. Duan et al. [21]  proposed a retrieval scheme using AMSR-E together with MODIS (both of which are onboard Aqua), and showed performances of about 4 K for cloudy scenes, when compared to 4 in situ stations over the Heihe River Basin in China.
In order to reduce the uncertainty in merged MW and TIR LST, Zhang et al. [46]  developed a pixel-based method that fully utilizes the spatial interrelationships between neighboring temperatures and merges the two LST types in the temporal and spatial domains. By applying their approach to AMSR-E/AMSR2 and MODIS LST over a study area in China, they generated an 11-year data record of 1-km daily all-weather LST, which had an accuracy of 1. 29-2. 71 K compared to in situ LST obtained over diverse land surfaces  [46]. The third methodology to derive LST estimates under cloudy conditions, i. e. surface EB-based methods, is somewhat less popular as it tends to be computationally more demanding and requires additional ancillary data, which makes it harder to implement operationally. Jin and Dickinson  [47]  proposed an algorithm using LST information from neighboring clear-sky pixels together with sensible, latent and longwave fluxes, which were parameterized as a function of skin temperature.
The method was applied to data collected during the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) and the Boreal Ecosystem-Atmosphere Study (BOREAS) (https://www. gewex. org/data-sets-international-satellite-land-surface-climatology-project-islscp/). The estimation of the fluxes required meteorological information, which was obtained from nearby meteorological weather stations. Once the fluxes were determined, skin temperature was estimated by solving the surface EB equation. This method assumes that cloudy skin temperature can be estimated by adjusting a corresponding clear-sky value to the different radiative and flux inputs induced by the cloud. The method was extended to MSG/SEVIRI retrievals by Lu et al. [48] , taking advantage of the high temporal sampling of the sensor.
The approach is applied to MSG/SEVIRI observations, thereby laying the ground for the first all-weather LST product to be generated by the LSA-SAF. Section 2 describes the LSA-SAF all-weather LST, the AMSR-E LST and the in situ LST datasets and provides a brief description of the algorithms used to derive them. In Section 3 an inter-comparison between the different datasets used in this work is performed, namely between IR LST and EB model skin temperature in clear sky situations (Section 3. 1), IR/EB model surface temperature datasets, and in situ (3 stations; focus on the representation of the diurnal cycle, see Section 3. 2). Furthermore, the proposed all-weather IR/energy-balance LST is compared with the MW-based LST product in terms of spatial coherence and dependence on land cover (shown in Section 3.
Methodology and Datasets 2. 1. Satellite Application Facility on Land Surface Analysis (LSA-SAF) All-Weather Land Surface Temperature (LST) The product proposed here consists of half-hourly retrievals of LST for every MSG/SEVIRI land pixel, regardless of cloud coverage. The product will be made available through the LSA-SAF portal (http://lsa-saf. eumetsat. int/) in near real time and will be distributed in HDF5 (Hierarchical Data Format) format, similarly to the remaining LSA-SAF products. A product quality flag will provide information about the cloud cover and other relevant information impacting the retrieval (depending if the pixel was considered cloudy or clear sky), such as quality of the emissivity estimates, high total water vapour amounts or satellite viewing angles. For the entire year of 2010, a set of all-weather LST was generated for the MSG/SEVIRI pixels used in the colocations with in situ stations.
For the purpose of spatial comparisons, full-disk retrievals for 10 days in January and 10 days in July 2010 were produced, encompassing Europe, Africa and part of South America (Figure  1 ). This particular period was chosen for validation and inter-comparison purposes, because the corresponding in situ and MW datasets were already available. The wide area being covered ensures representativeness of the environmental conditions within those months, while the choice of months allows investigating differences between dry and rainy seasons. The all-weather LST dataset consists of a combination of LSA-SAF standard LST product derived from thermal IR brightness temperatures, and cloudy sky estimates obtained with the surface energy balance (EB) model. The former is made available by the LSA-SAF at the temporal and spatial resolution of MSG/SEVIRI (15 min; 3-km sample distance at nadir) from 2004 onwards.
g. bare soil, several classes of low and high vegetation, water, snow, rocks and urban surfaces. The surface energy balance is solved for each tile i separately: 𝑅𝑛 = 𝐻 + 𝐿𝐸 + 𝐺 , (3) where  is net radiation,  is sensible heat,  is latent heat and  is ground heat flux for the i-th tile of within a pixel. All these terms are parameterized as a function of several other variables and the reader is referred to the product ATBD for the specific details. The fraction of each tile within a MSG/SEVIRI pixel and its respective parameters are determined from the ECOCLIMAP-II database  [75, 76]. Net radiation ( ) is determined for each tile from the shortwave and longwave fluxes at the surface: Each MSG/SEVIRI pixel is considered to be composed of homogeneous tiles corresponding to different surface types, e.
g. bare soil, several classes of low and high vegetation, water, snow, rocks and urban surfaces. The surface energy balance is solved for each tile i separately: 𝑅 , = (1 -𝛼)𝑆 ↓ + 𝜖 𝐿 ↓ -𝜎𝑇 , , (4) Rn i = H i + LE i + G i , (3) where Rn i is net radiation, H i is sensible heat, LE i is latent heat and G i is ground heat flux for the i-th tile of within a pixel. All these terms are parameterized as a function of several other variables and the reader is referred to the product ATBD for the specific details. The fraction of each tile within a MSG/SEVIRI pixel and its respective parameters are determined from the ECOCLIMAP-II database  [75, 76]. Net radiation (Rn i ) is determined for each tile from the shortwave and longwave fluxes at the surface: R n,i = (1 -α)S ↓ + L ↓ -σT 4 sk,i , (4) where α is (pixel) surface albedo, is (pixel) broad-band infrared emissivity, S ↓ and L ↓ are the (pixel) downward shortwave and longwave fluxes at the surface, respectively, σ is the Stefan-Boltzmann constant and T sk,i is the skin temperature of the i-th tile.
Near-surface meteorological fields such as 2 m air temperature and specific humidity, surface pressure and wind speed at 10 m are retrieved from the latest available ECMWF operational forecasts (up to 24 hours) and resampled to the MSG/SEVIRI grid. The ECMWF data are available hourly and the set of fields closest in time to SEVIRI observations is used. The forecasts (current operational model has a horizontal resolution of about 9 km) are bi-linearly interpolated to the SEVIRI geostationary projection; air temperature is further corrected using a constant lapse rate to account for differences between model and pixel topography. The surface energy balance is solved under all-sky conditions and, therefore, skin temperature is also produced for all weather conditions. There are very few situations for which the model does not converge after a sufficiently large number of iterations (it stops after 100 iterations).
Microwave-Based Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) LST An alternative approach to obtain LST under all-sky conditions consists in the use of microwave observations, as these are nearly unaffected by clouds. MW LST data used here corresponds to the dataset developed by  Jimenez et al. [25] , where a neural-network approach is applied to brightness temperatures measured by the AMSR-E instrument on-board Aqua satellite at 18. 7, 23. 8, 36. 5 and 89. 0 GHz. The effective mean resolution of the product is 12 km (footprint size of the 36. 5 GHz channel), but information from the channels with larger footprints also affects the retrieval. The AMSR-E LST is produced twice-daily for almost all weather conditions at equator crossing times of 1:30 AM/PM. For this dataset, several limitations have been identified by  Jimenez et al.
The main advantage of the AMSR-E dataset compared to the MODIS counterpart is its much larger frequency of successful retrievals: the authors showed that for frequently cloudy pixels, the increase in coverage in AMSR-E compared to MODIS exceeds 250%. Based on comparisons made against a training database used to calibrate the neural network algorithm, Jiménez et al. [25]  reported an expected precision of 2. 8 K or better for almost 75% of the global land surface. A comparison with in situ stations spread across a wide range of biomes and climates showed a RMSD of around 4 K, which is larger than that reported for MODIS (around 2. 4 K), but with nearly 3 times more sampling due to its ability to measure LST even under cloudy conditions. For the purpose of comparison with MSG/SEVIRI-based all-weather LST, we consider LST derived from AMSR-E [25] for the whole year of 2010.
In Situ LST The LST estimates from the LSA-SAF MSG/SEVIRI (level-2) product are routinely validated against dedicated in situ stations, equipped with upward and downward looking longwave radiometers, the "KT15. 85 IIP" produced by Heitronics GmbH, Wiesbaden, Germany. This type of instrument measures thermal infrared radiance in the 9. 6 -11. 5 µm interval with a 0. 03 • C resolution and ±0. 3 • C accuracy over the corresponding temperature range. In this work, data from three permanent LST validation stations operated by Karlsruhe Institute of Technology (KIT) are used. These are located within the MSG/SEVIRI disk (Figure  3 ): Évora (Mediterranean, Portugal), Kalahari (Steppe, Namibia) and Gobabeb (Desert, Namibia). This study considers all data collected by stations dedicated to LST validation for this period over the MSG/SEVIRI disk.
These fractions are then used in combination with the available measurements of tree canopy, sunlit ground and air temperatures (used as proxy for shaded ground temperature) to estimate the LST as would be retrieved by the satellite. However, it must be stressed that while MSG/SEVIRI IR LSTs are retrieved directly from the measured thermal emissions at a given viewing geometry, EB estimates correspond to an average of tile skin temperature, which assumes tile fractions based on the ECOCLIMAP-II database and disregards any shadowing effects. Therefore, the correction method developed by Ermida et al. [18]  is only applied to the in situ clear sky values used for the validation of clear sky LST values retrieved via the split-windows algorithm. The validation of EB skin temperature makes use of in situ observations upscaled using known fractions of tree canopies/open ground around the station.
These fractions are then used in combination with the available measurements of tree canopy, sunlit ground and air temperatures (used as proxy for shaded ground temperature) to estimate the LST as would be retrieved by the satellite. However, it must be stressed that while MSG/SEVIRI IR LSTs are retrieved directly from the measured thermal emissions at a given viewing geometry, EB estimates correspond to an average of tile skin temperature, which assumes tile fractions based on the ECOCLIMAP-II database and disregards any shadowing effects. Therefore, the correction method developed by Ermida et al. [18]  is only applied to the in situ clear  A summary of all the datasets used in this study is provided in Table  1. A summary of all the datasets used in this study is provided in Table  1.
Statistical Metrics In this study, for the comparison with in situ data, we use robust statistics recommended by the Committee on Earth Observation Satellites Working Group (CEOS) on Calibration and Validation-Land Product Validation (LPV) Subgroup for validation of Land Surface Temperature Products  [82, 87]. The median error, µ, which is a measure of the accuracy is given by: µ = median(LST sat -T s ), (9) where LST sat stands for satellite LST and T S represents in situ LST. The median of the absolute residuals, σ, reports on precision and is given by: σ = median LST sat -µ , (10) Finally, an estimate of total uncertainty is given by root-mean-square-difference, RMSD: RMSD = (LST sat -T s ) 2 N , (11) where N represents the sample size. For the comparison of satellite datasets, the traditional average (bias) and standard deviation (STD) of LST differences are used instead of the robust metrics.
Time Series To illustrate the general behavior of the different products, Figure  6  provides time-series of the various LST datasets for Evora (in situ with and without directional correction, IR split-window LST, all-weather EB model T sk and AMSR-E LST), for a spring (23 March-7 April 2010) and summer (7-22 August 2010) period. The area surrounding Evora station experiences strong seasonal (and inter-annual) vegetation variability as the understory grows and desiccates. This introduces some emissivity variability as well as significant temperature contrasts between surface elements, particularly during summer. A correction that accounts for directional effects was introduced in order to improve comparisons with MSG/SEVIRI level 2 clear-sky LST estimates  [18, 90]. The adjusted time series (shown in green) show differences to the original in situ estimate up to 2 K, with lower LST values around the daily maximum for the periods shown.
4, and global summary statistics are shown in Table  2. All datasets are sampled in 30 min frequency for the matchups. Colocations are only possible when all datasets are available. If any of the inputs for the IR LST and EB models are unavailable at the time of the processing, or are of low quality, all-weather LST cannot be obtained. In situ stations also contain missing data. The IR LST follows the in situ curve quite closely, with some negative differences for partly cloudy days (these differences are quantified below). The EB model skin temperature seems to 'overshoot' at the daily maximum by a few degrees, whereas at nighttime it gets cooler than in situ observations for some days (e. g. , 2-3 Apr, 12-13 Aug), and warmer for others (e. g. , 9-10 Aug). Regarding the AMSR-E all-weather LST product, at nighttime in August there is a clear overestimation of in situ LSTs.
Each plot of Figure  7  contains the summary statistics described in Section 2. 4, and global summary statistics are shown in Table  2. All datasets are sampled in 30 min frequency for the matchups. Colocations are only possible when all datasets are available. If any of the inputs for the IR LST and EB models are unavailable at the time of the processing, or are of low quality, all-weather LST cannot be obtained. In situ stations also contain missing data. The comparison of clear sky EB model  and IR LST statistics complements the results shown in Figure  4 , and shows that the IR LST outperforms the EB model  for all stations, both in terms of accuracy, precision and uncertainty, supporting its use for clear sky cases in the all-weather LST product. The clear sky  RMSD may be up to 2 K higher than that obtained for the IR LST, with larger differences for Kalahari and Gobabeb stations.
In Situ Comparisons The full year of 2010 LST retrievals from AMSR-E LST was compared with both the IR LST + EB model T Sk All-Weather LST product and with in situ LST data from the aforementioned stations. The AMSR-E estimates used here were already validated against in situ [25] and independent measurements over clear sky pixels  [26]. The purpose of this section is to assess the quality of the new all-weather LST product by comparing it to the performance of an alternative product. Data from the 3 sources were carefully collocated in order to ensure similar sampling between MSG/in situ and AMSR-E/in situ comparisons. For all AMSR-E swaths in 2010, the closest pixel to the station was determined and only considered if its distance was less than 5 km. Then the in situ value closest in time (i.