dataesr/scientific-paragraphs-categorization huggingface dataset:
Performance The DYAMOND2 simulation was performed as a series of 1536-node job submissions using the Knights Landing (KNL) nodes of Cori at the National Energy Research Supercomputing Center (NERSC). We found that using 8 MPI processes and 16 OpenMP threads per node provided the optimal balance of memory usage and performance for these 1536-node jobs. The overall throughput for the 40-day simulation, including I/O, was about 4-5 simulated days per day (SDPD). Further details about the performance of this 40day DYAMOND2 simulation can be explored at https://pace. ornl. gov/search/SCREAMv0. SCREAM-DY2. ne1024pg2. 20201127. As shown in Table  2 , the model scales quite well-particularly in the dycore-and can achieve up to 23. 1 SDPD without input or output (IO) on 6144 KNL nodes. The simulation used the Software for Caching Output and Reads for Parallel I/O (SCORPIO) library for reading input data and writing simulation output to the file system.
, 2021; Stevens et al. , 2019). In this section, we provide a brief and broad overview of the tropical cyclones identified in the SCREAM simulation. Figure  18  shows TC tracks during the simulation period from SCREAM, ERA5, and IBTrACS observations  (Knapp et al. , 2010 (Knapp et al. , , 2018)). SCREAM and ERA5 tracks are computed using the TempestExtremes (TE) algorithm and the criteria described in Appendix A1, while IBTrACS are based on expert judgment. Large discrepancies between IBTrACS and reanalysis data sets highlight the importance of using consistent criteria to classify storms. Note as well that the chaotic nature of weather means that storms later in the SCREAM simulation are not expected to match those found in ERA5 or in observations. Within the days of potential predictability (up to 2 weeks), one TC exists in both the SCREAM simulation and ERA5 data (Moderate Tropical Storm Diane).
Although the data for this storm from IBTrACS spans a much shorter period in the storm lifetime than identified by Tempest Extremes from either the reanalysis or the SCREAM simulation, the magnitudes of central pressure and maximum 10-m wind speed are in good agreement between SCREAM, ERA5, and IBTrACS for the period that does overlap. Because Severe Tropical Storm Diane does not fully develop a canonical tropical cyclone structure and exhibits hurricane force winds only for a few hours, we take a more detailed look at a stronger storm in the model which forms on Feb 10 and produces surface wind speeds which classify it as a category 3 hurricane (Figure  19g ). For reference, the storm's maximum intensity (based on minimum surface pressure values) is the median of the five storms tracked in SCREAM (not shown).
Analysis for this paper also revealed several deficiencies which will be fixed in future model versions. First, cloud fraction near the tropopause is corrupted by the use of a relative-humidity based ice cloud fraction scheme tuned for low resolutions (Figure  4 ). Because these spurious clouds had no mass, they had little practical impact on the simulation, but users of SCREAM DYAMOND2 data should be careful to use our post-facto-generated cloud-mask-based cloud fraction for future analysis. Overly strong surface wind speed is a second deficiency (Figure  7 ). Upper level winds are generally reasonable but have unrealistic poleward transport south of Greenland and around Australia (Figure  8 ). Surface temperature at high latitudes is also problematic (Figure  6 ). One potential reason for this is a land initial condition with low snowpack in mountainous regions exacerbated by potentially poor tuning of the lower limit on turbulent mixing in stable conditions and aforementioned biases related to heat transport into polar regions.
Data Availability Statement The SCREAM output described in this paper is publicly available as part of the DYAMOND2 intercomparison as described at https://www. esiwace. eu/services/dyamond. Code for this simulation is available at https://github. com/E3SM-Project/scream/releases/tag/SCREAMv0. The GPM data set used in this study is Version 06B Level 3 half-hourly 0. 1 × 0. 1° final-run gauge-calibrated data as described in G. Huffman  et al. (2019)  and accessible via https://disc. gsfc. nasa. gov/datasets/GPM_3IMERGHH_06/summary. The version of CERES SYN1deg data used here is Edition 4. 1 with a release date of August 22, 2019 and was accessed from: https://ceres-tool. larc. nasa. gov/ord-tool/jsp/SYN1degEd41Selection. jsp. The Himawari-8 visible image used in this paper was supplied by the P-Tree System of the Japan Aerospace Exploration Agency (JAXA; https://www.
Torres et al. (  2017 ) demonstrated the high potential of GRASP retrieval concept for inverting only direct-Sun photometric observations. Espinosa et al. (2017 Espinosa et al. ( , 2019) )  and  Schuster et al. (2019)  used GRASP approach for processing in situ aircraft and laboratory light scattering measurements. Most of these studies benefited from previous GRASP implementations and included certain new elements needed in specific applications. In these regards, in the description below, the paper will focus on new elements developed for interpretation of ground-based active and passive observations, as well as interesting adaptations of previously developed concepts to these applications. In this section, improvements accumulated during GRASP code development and methodological base that are crucial for the presented study are discussed.
Thus, in order to have flexibility for inverting advanced vertical observations the profiles of scattering, extinction, all scattering matrix elements and some of their direct products were included in GRASP interface as possible input/output characteristics. Correspondingly, the GRASP software (https://www. grasp-open. com/products/, last access: 29 March 2021) starting from version 0. 8. 1 allows simulation and inversion of the scattering characteristics for each atmospheric layer shown by Eqs. (  6 ), (  9 ) and (10). It should be noted that in GARRLiC/GRASP the aerosol backscatter and extinction profiles were modelled as a part of the retrieval but remained deeply encapsulated in the calculations. In these regards, at present, various characteristics provided from diverse lidar observations can be modelled and processed by GRASP; i.
5 km). The fits achieved for all COBALD data observed for days presented in Fig. 29  are exceptionally good, with RMSE between measure and fitted backscatter below 0. 02 (Mm • Sr) -1 , zero bias and slope equal to 1. It is worth mentioning that compared to combined retrievals presented in Sect. 3. 1. 1, component approach allows more flexibility in aerosol vertical structure, proposing a more complex model that has almost twice as many parameters describing aerosol vertical distribution as the one used in combined, which results in generally better fits achieved (compare with Fig. 11 ). Thus, the analysed cases overall show encouraging stability, disregarding the data set or approach used, both for reproducing observations and retrieval of aerosol optical properties. Several differences observed are either within the estimated accuracy of the retrieval of 10 Mm -1 , or located in the lower atmosphere, where correct comparison is difficult due to the limitations of lidar observations.
e. without deriving an extinction profile in advance. This demonstrates the capabilities of processing of such type of advanced observations constraining aerosol extinction, without applying additional lidar data preprocessing. This detail could also introduce some of the observed differences in the final retrieval results, e. g. aerosol extinction directly derived from Raman observations demonstrates very low and sometimes negative values above 3500 m. A convincing agreement between Ångström exponent profiles at 355/532 nm estimated from LILAS nighttime observations using different approaches (multi-temporal, component and direct estimation form inelastic observations) could be observed in Fig. 34 , the average difference between the profiles is below 0. 2, with the average difference between GRASP provided profiles below 0.
35 and 16 ). Thus, this section demonstrated a methodology to process single-instrument vertically resolved data. The retrieval was demonstrated on both synthetic and real data to be efficient for achieving reliable retrievals of aerosol properties, allowing consistent and stable retrievals from processing standalone advanced lidar or radiosonde observations. It should be noted that in all demonstrations the aerosol was modelled as an external mixture with predetermined sizes, shapes and compositions. At the same time, it is one of many possibilities of applying GRASP to single-instrument retrievals and other assumptions and constraints can be used. For example, aerosol mixture can only be represented by only one or two components in the case of single-or dualwavelength lidar and many other assumption settings can be used.
2. A multi-temporal approach to simultaneously invert a set of diverse passive and/or active ground-based observations or in situ observations that are not necessarily coincident was demonstrated. For example, records of both day and night passive and active observations can be inverted simultaneously in the frame of the GRASP multi-pixel approach  (Dubovik et al. , 2011)  using a priori constraints on the temporal or/and spatial variability of retrieved parameters. 3. A possibility to process vertically resolved data from a single instrument such as a lidar or backscatter sonde was introduced and demonstrated. The above new functionalities were achieved by relying on the improvements in forward model by extending the capabilities of simulating new types of observations and in the numerical inversion part by adapting the multi-pixel retrieval concept to new types of ground-based and in situ observations.
The new functionalities of GRASP were demonstrated for a synergy processing of the ground-based Sun-photometric and advanced lidar observations including Raman and depolarization data and data of the Sun-photometric and micro- pulse lidar combined with in situ radiosonde backscatter observations collected during the SHADOW and KAUST field campaigns in 2015 and 2016. The multi-temporal approach has shown to be rather efficient for combining noncoincident but close-in-time observations such as diverse day-and nighttime observations during the SHADOW and KAUST field campaigns. As a result, the observations obtained by different techniques can be inverted together and provide a complete set of fully consistent aerosol parameters including detailed size distributions, complex refractive indices, information about particle shape and vertical profiles.
The single-instrument retrievals were relying on the concept considering aerosol as an external mixture of several aerosol components with predetermined sizes, shapes and compositions. This model has been described and demonstrated to be efficient for achieving reliable retrievals of aerosol properties in several situations. Both numerical tests and applications to the data collected during SHADOW and KAUST field campaigns have demonstrated reliable quality of GRASP singleinstrument inversion results. In general, both approaches discussed in this study, i. e. the advanced combination of diverse observations and singleinstrument retrieval using additional constraints in the forward model, help to address significant limitations in processing of single-and multi-instrument observations, allow-ing us to exploit the most of the sensitivities of the instrumentation available.
Generally speaking, both approaches rely on using additional constraints on aerosol columnar properties, either by including the observations with missing sensitivities or by employing direct a priori assumptions. The new functionalities shown were achieved relying on the improvements in forward model by extending the capabilities in simulating new types of observations and in the numerical inversion part by adapting the multi-temporal retrieval concept to new types of ground-based and in situ observations. Since, the inversion and forward model are practically independent modules of GRASP, the number of new retrieval possibilities is not limited by the examples demonstrated here. This altogether makes GRASP very flexible software with exceptional possibilities of data treatment provided by various types of remote sensing and in situ observations that could be applied with very limited efforts to nearly arbitrary combinations of observations available.
Therefore, GRASP offers significantly extended possibilities for processing observations collected during field campaigns obtaining compact records of diverse passive and active observations. AL contributed to realization of the algorithms and different parts of supporting retrieval software. IV and FW contributed to the development of the instruments and their data preparation and analysis. IS contributed to the maintenance of the instruments and data acquisition. SP contributed to the retrieval data preparation and analysis, as well as polishing the article text. AL prepared the manuscript with contributions from all co-authors and conducted the main part of the presented research.
Acknowledgements. We are thankful to the  KAUST Core Lab  and IT department for supporting the data collection and archiving. The authors also pay tribute to the exhausting and, sometimes, sleepless labour of the teams that make the operation of field campaigns possible, notably the teams of AERONET, MPL and  IRD (Institut de Recherche pour le Développement ) in Dakar and  LOA (Laboratoire d'Optique Atmosphérique) in Lille  for SHADOW campaigns and  Jean-Paul Vernier  for the operation of radiosonde flights during KAUST campaigns. Financial support. The research reported in this publication was partially supported by funding from  King Abdullah University of Science and Technology (KAUST)  through the  Competitive Research grant  no. URF/1/2180-01-01  " Combined Radiative and Air Quality Effects of Anthropogenic Air Pollution and Dust over the Arabian Peninsula ".
Code availability. GRASP is an open-source software, available upon registration from https://access-request. grasp-cloud. com/ service/gitlab (last access: 30 March 2021) (GRASP Open repository, 2021). Data availability. Data available upon request. AERONET data used for this study can be downloaded from NASA (2021a) (https: //aeronet. gsfc. nasa. gov/, last access: 30 March 2021). MPL data used for this study can be downloaded from NASA (2021b) https: //mplnet. gsfc. nasa. gov, last access: 30 March 2021). COBALD and LILAS data are available upon request.
Data collection All data are provided by the various government departments, such as, Groundwater quality data collected from Gujarat Water Resources Development Corporation  (GWRDC, n. d. ) , Central Groundwater Board (CGWB, n. d. ), Gujarat, India, and (India-WRIS, n. d. ). The annual and Seasonal fluctuation of groundwater data (Table  1 ) and annual rainfall data (Table  2 ) were downloaded from (India-WRIS, n. d. ). Gujarat Water Resource Development Corporation, Gandhinagar, and Central Groundwater Board, Gujarat, provided 10 years of data about groundwater level and water quality data (Table  3 ) for Surat city, Chorasi, Taluka. Pre-and post-monsoon water level data (Table  4 ) was collected from Gujarat Water Resources Development Corporation  (GWRDC, n. d. ). The following data had collected for the study effect of seawater intrusion on groundwater.
where, • Ck is the dissolved concentration of species k • ; is porosity of the subsurface medium • t is time • xi is the distance along the respective cartesian coordinator axis • Dij is the hydrodynamic dispersion coefficient tensor • vi is the seepage or linear water velocity • qs is the volumetric flow rate per unit volume of aquifer • C s k is the concentration of the source • ∑R n is the chemical reaction term The finite-difference approximation (Equation (  6 )) approach is used to solve the numerical groundwater flow equation  (Salmasi & Azamathulla 2013). The thickness of model layers can change over time. Each cube, referred to as a cell, has its flow equation. In each phase of a MODFLOW stress cycle, the preconditioned conjugate gradient (PCG) solver with an Improved Nonlinear Control package is used to solve the finite difference equations.
2013). In coastal aquifer ecosystems, SWI2 encourages the three-dimensional variable-density groundwater flow and seawater intrusion. This study focuses on the application of SWI2 to a MODFLOW model created in Model Muse. It represents the whole model configuration, dataset operation, boundary condition conceptualization (Table  7 ), and assessment of outcome. We need to collect several hydrogeological surveys, development of datasets, and prove the efficiency of hydraulic testing to provide an entire set of parameters and input data for a valuable computational simulation. The boundary with known flux and boundary with head-based flux are the two forms of groundwater model boundary conditions. A boundary of known flux will appear when modeling water tables in unconfined aquifers with inflow/outflow by lateral contacts between different aquifers.
Seawater intrusion modeling in MODFLOW The results of the seawater intrusion modeling in Model Muse are presented in this section. The outcomes depict the action of the modeled aquifer of the study area. The piezometric levels as well as the salt water-fresh water interface shifts are presented in this section. Simultaneously modeling the aquifer was performed to ensure that the data represented the literature analysis and that it remained accurate in the modeling period. The final results were then imported into Model Muse and adjusted using the trial-and-error method as well as a finer value for conductivity (transmissivity) and river conductance. As a result, the simulation has different effects on these input values depending on their yield. After that, the overall effect of these individual parameters on the salt water-fresh water interface was then determined.
To date, peatland drainage and cultivation emissions have been estimated only using empirical approaches: multiplying emission factors (EFs; carbon emissions per unit area from cultivated peatland) estimated from local field data  (9)  by the area of cultivated peatlands, derived from land-use surveys  (3)  or from the geospatial overlay of a present-day peatland distribution map and an agricultural land-cover map  (10) (11) (12) (13) (14). This method known as Intergovernmental Panel on Climate Change (IPCC) tier 1  (15)  does not take account of EFs decreasing with time after drainage because of the increased recalcitrance of the remaining material  (16). This slow time trend of EFs cannot be characterized accurately from local CO 2 flux monitoring data given the lack of long-term observations.
We show that this number needs to be increased by 18% [calculated as (40/220 Pg C) × 100%] if the budget takes emissions from cultivated northern peatlands into account. From our two extreme conversion scenarios (MIN and MAX), we estimate that a range of 3 to 84 Pg C was emitted by cultivated northern peatlands over 1750-2010. At site scale, observed carbon emissions from cultivated peatlands depend on local context, i. e. , peat type, land-use history, crop type, and management practices such as water table level controls, fertilization, cultivation intensity, and tillage  (32, (48) (49) (50) (51). These local conditions and management history are not known from gridded datasets in the Northern Hemisphere and further cannot be reproduced by a large-scale land surface model with generic parameters, which hinders our capacity to understand the full uncertainty range in cultivated peatland carbon emissions.
Input data Simulations were performed using the ORCHIDEE-PEAT land surface model  (18, 19) , driven by meteorological forcing data, historical land-cover maps, and historical data of atmospheric CO 2 concentrations. For the meteorological forcing, we used CRUNCEP version 8 (http://forge. ipsl. jussieu. fr/orchidee/wiki/Documentation/Forcings) at 2°-by-2° spatial resolution. This dataset covers the years 1901 to 2010. Historical land-cover maps at 0. 25°-by-0. 25° spatial resolution and covering the period from 850 to 2010 were reconstructed by merging the European Space Agency (ESA) Climate Change Initiative (CCI) dataset (www. esa-landcover-cci. org/) with the LUH2v2 dataset (http://luh. umd. edu/). In those maps, vegetation is discretized into 13 plant functional types (PFTs), including one for bare soil type, eight for trees, and one each for C3 natural grass, C4 natural grass, C3 crops, and C4 crops.
Acknowledgments: We would like to thank  L. Elsgaard ,  S. O. Petersen ,  E. M. Veenendaal ,  H. E. Taft , and  D. L. Jones  for providing the information about the history of agricultural used peatland sites. Funding: This work benefited from the  French  state aid managed by the  ANR  under the " Investissements d'avenir " programme with the reference  ANR-16-CONV-0003  and from the  European Research Council  Synergy project SyG-2013-610028 IMBALANCE-P. Simulations were performed using HPC resources from  GENCI-TGCC  (grant  2020-A0070106328 ). The data contributed by A. V. G. -S. and D. J. C were obtained with funds from the  Natural Environment Research Council  ( NERC  standard grant number  NE/I012915/1 ).
Edge Computing enables to perform measurement and cognitive decisions outside a central server by performing data storage, manipulation, and processing on the Internet of Things (IoT) node. Also, Artificial Intelligence (AI) and Machine Learning applications have become a rudimentary procedure in virtually every industrial or preliminary system. Consequently, the Raspberry Pi is adopted, which is a low-cost computing platform that is profitably applied in the field of IoT. As for the software part, among the plethora of Machine Learning (ML) paradigms reported in the literature, we identified Rulex, as a good ML platform, suitable to be implemented on the Raspberry Pi. In this paper, we present the porting of the Rulex ML platform on the board to perform ML forecasts in an IoT setup. Specifically, we explain the porting Rulex's libraries on Windows 32 Bits, Ubuntu 64 Bits, and Raspbian 32 Bits.
Therefore, with the aim of carrying out an in-depth verification of the application possibilities, we propose to perform forecasts on five unrelated datasets from five different applications, having varying sizes in terms of the number of records, skewness, and dimensionality. These include a small Urban Classification dataset, three larger datasets concerning Human Activity detection, a Biomedical dataset related to mental state, and a Vehicle Activity Recognition dataset. The overall accuracies for the forecasts performed are: 84. 13%, 99. 29% (for SVM), 95. 47% (for SVM), and 95. 27% (For KNN) respectively. Finally, an image-based gender classification dataset is employed to perform image classification on the Edge. Moreover, a novel image pre-processing Algorithm was developed that converts images into Time-series by relying on statistical contour-based detection techniques.
Introduction The Internet of Things paradigm is rapidly extending to many sectors of society because it allows to substantially improve the monitoring or control of complex and extensive processes, offering an innovative approach for multiple fields of application, such as quality of life, urban challenges, logistics, agriculture and livestock, climate change, mass production, health, energy and water production and distribution, and many more. The huge amount of data produced by the nodes of the networks of which the Internet of Things is made up must be processed in an efficient and effective way, and ML techniques are certainly among the most suitable for this purpose. Therefore, it is straightforward that Machine Learning tools can play a key role in further expanding the scope of applications, as well as their effectiveness.