dataesr/scientific-paragraphs-categorization huggingface dataset:
). During the years 1993 to 2002, evaporation measurements were taken. The significance and rate of evaporation are primarily determined by the evaporative power of the atmosphere, which, in our case, is measured by a Piche evaporometer. This evaporation represents the atmosphere's evaporative demand. A DEM (digital elevation model) and the TOPAZ (topographic parameterization) module for simulating the flow direction were integrated in the WMS software for determining the morphological parameters, the boundaries of the watershed, and the hydrographical network.
Remote Sensing Data The Copernicus Sentinel-2 mission comprises a constellation of two polar-orbiting satellites, Sentinel-2A, launched in 2013, and its twin Sentinel-2B, put in orbit later on, in 2015. They have a wide swath width of 290 km and a high revisit time (10 days at the equator with one satellite and 5 days with the 2 satellites under cloud-free conditions). The MSI optical sensor onboard provides 12 spectral bands (458-2294 µm) from RGB and NIR (near infrared), to SWIR (shortwave infrared), with a high resolution of 10 m, 20 m, and 60 m, respectively  [25]. Two Sentinel-2 images were acquired for the present study (Figure  4 ), one before and one after the flash-flood event (Table  1 ). These images are freely available, atmospherically, and geometrically corrected from the Theia-Land platform (https://www.
Hydraulic Model The delimitation of the watershed is the first step for hydraulic modeling, using the WMS and software developed by the Environmental Modeling Research Lab at Brigham Young University in collaboration with the US Army of Engineers and currently being developed by Aquaveo LLC based in Provo, Utah, USA. It consists of extracting from the DEM the boundaries of the SAKIA EL HAMRA catchment area and its hydrographical network (flow directions). Morphological parameters and physical characteristics of the watershed are also extracted. The methodology for hydraulic modeling is based on coupling WMS and HEC-RAS. The latter is described in the next section. The daily stream flows were computed using the HEC-HMS 3. 4 model, which incorporated the prepared data maps. Meteorological and watershed data were combined to simulate the hydrologic responses.
The hydraulic model implemented in the open-source HEC-RAS software that has been successfully applied and yielded in several international publications and thesis Water 2022, 14, 3582 6 of 18 works  [26, 27]  uses the 1D Saint-Venant shallow-water equations to link water heights and discharges: y 2 + z 2 + α 2 v 2 2 2g = y 1 + z 1 + α 1 v 2 1 2g + b e (1) In this equation, y 1 and y 2 are water depth in two cross-sections, z 1 and z 2 are the floor heights of the main channel, v 1 and v 2 are average velocities of discharge, α 1 and α 2 are coefficients of mass momentum speed, g is acceleration due to gravity, and be is the head loss of the energy level. This equation is deduced from the Navier-Stokes equations through simplifications related to the river model. The application of the HEC-RAS model is based on three fundamental steps: (1) Creating, using the ArcGIS tool, the Hec-GeoRAS extension, the Digital elevation model (DEM), and aerial images, the geometrical data of Saquia El Hamra River with the minor and major riverbeds and cross-sections; (2) Applying permanent flow modeling with the Hec-RAS 4.
1. 0 model, which generates an export file for ArcGIS Hec-Ras, software independent of ArcMap but complementary to the analysis processes; and (3) generating the results of water stain: flood surfaces and depth grids. Steady flow is a condition in which depth and velocity at a given channel location do not change with time. Therefore, gradually varied flow is characterized by minor changes in water depth and velocity from one cross-section to another  [28]. The crosssection sub-division for the water conveyance is calculated within each reach using the following equations: Q = KS 1 2 f ; While K = 1. 486 n AR 2 3 ( 2 ) where K is the conveyance for subdivision, n is the Manning roughness coefficient, A is the flow-area subdivision, R is the hydraulic radius for subdivision (wetted area/wetted perimeter), and Sf is the friction slope.
Satellite Images Classification Images collected by satellites provide reliable, extensive, and high temporal and spatial resolution data describing surface parameters. Currently, image classification algorithms are the most common mapping method used in satellite imagery. Using this remotely sensed approach, we focus on the classification and identification of the changes in land cover/land use (LCLU) before and after a flash-flood event based on a survey of affected areas, ground truth samples, and temporal images gathered from the Sentinel-2 before and after the event. RGB images were checked visually and showed that reliable discrimination would be based on four main classes: water, vegetation, urban areas, and barren soil. In addition, flooded water was apparent due to the existence of mud.
Calibration and Validation As part of this study, we conducted a field visit to a preliminary investigation survey in order to identify the situation in areas vulnerable to flooding at the level of Laayoune city and the rural commune of Foum El oued after the flash flood, with the help of the team of the Water Basin Agency of Saquia El Hamra Oued Eddahab. The hydraulic model was validated using cross-sections and water-line profiles realized at the tributary level. LCLU samples were collected from different parts of the area during field campaigns using a GPS system and labeling the nature of each observed LCLU type in the field using a map of the area. A total of 276 parcel-like polygons of ground-truth data representing all of the classes (resampled into 119 polygons as closer and similar ones were merged) were divided into calibration and validation samples and overlaid on the images (Figure  7 ).
The mean, min, and max slopes observed are 0. 03 m, 0 m, and 35 m, respectively. The concertation time Tc, defined as the time of the most distant water droplet to reach the outlet, is about 21. 3 h. The highest amount of evaporation occurs in August, the hottest month of the year, and the lowest amount occurs in December. The annual mean evaporation value is approximately 158. 99 mm (Figure  8 ). An estimate of the periods of return of the extreme values of rain can be obtained by adjusting the laws on pluviometric data. Using the Hyfran-Plus (Hyfran, 1998) and several statistical distributions, such as normal, log-normal, Gamma, Weibull, and exponential, to validate the results, the rainfall depth for various return periods was determined (5, 10, 25, 50, and 100 years). According to the analyses of the graphs of the adjustment of the statistical laws from the data of annual maximum precipitations from a series recorded at the Laayoune rainfall station over a period spanning 31 years (1985-2016) (Figure  9 ), the exponential distribution is the law best-adapted to the data for analyzing the periods of return, followed by the normal distribution; the other distributions have poor adjustment.
ater 2022, 14, x FOR PEER REVIEW  The following Table  2  displays the precipitation estimate along with its respective exponential law confidence intervals for various return times. The stream flows were computed using the HEC-HMS 3. 4 model, which incorporated the prepared data maps. Meteorological and watershed data were combined to simulate the hydrologic responses. The adjustment of laws to hydrological data yields an estimate of the return periods of extreme flow values, which is crucial for planning, forecasting, and protection activities. The results of the frequency analyses of samples of the maximum annual flows recorded at the Laayoune hydrometric station during the selected 31-year chronicle between 1985 and 2016. The statistical adjustments of each Qmax sample are performed in accordance with various distributions (log-normal, Gamma, Gumbel, and exponential) to identify the distribution that best fits the sample under consideration.
Acknowledgments: The authors would like to thank the  Laboratory of Molecular and Ecophysiological Modeling of Department of Physics, faculty of science at Semlalia Cadi Ayyad University Marrakech , the watershed agency of  Laayoune Saquia El Hamra Oued Eddahab (ABHSHOD) , and the regional directorate of meteorology (DRM) Laayoune, Morocco.
Secondly, SR-IOV requires additional logic in the hardware for each VF, therefore limiting the maximum number of VFs that can be implemented on each device  [2]. This limitation leads to a VF allocation problem, namely: in a situation with more VMs needing VFs than is available, which VMs should be allocated a VF? We conducted a study on the servers of two research data centers: Grid5000  [3]  and CloudLab  [4]. We found that the majority of servers are equipped with one SR-IOV-enabled dualport Ethernet card for network communication, and that most cards have support for up to 64 VFs per network port. On Grid5000 servers in particular, out of these two ports, one port is used exclusively for host administration and management purposes, leaving only one port (and therefore 63 VFs on their Ethernet card model) available to all VMs that could be executed on the host.
Assessment We carried out evaluations to estimate application performance using the previously-mentioned network device virtualization techniques. Section 6 presents more details on the experimental environment, including hardware and software configurations. The experiment consists of executing a network benchmark in a VM with 4 GB of memory, 4 vCPU and a network device provided using each of the mentioned techniques. We used the results of the Xapian benchmark from TailBench  [9] , a benchmark suite for latency-critical applications providing request latency numbers under various workloads. Figure  1  presents the 95th percentile of request latency as obtained from Xapian, an online search workload that performs queries on a English Wikipedia-based corpus. We first observe that SR-IOVenabled VMs equipped with a VF performs better than any other technique, whether PV (+22.
We used various TailBench workloads, including Xapian, Sphinx (a speech recognition workload), Masstree (C++-based key-value store), Silo (a in-memory database) and img-dnn (a handwriting recognition application). We set up TailBench in its networked configuration, where each application under test runs on a VM configured with VFs or PV, and with the application client (load generator) running on a separate physical machine. Figures  2  and 3  respectively present each benchmark's packet rate and the performance gain from switching to VFs. We note a large variability in results depending on the workload; for instance, Masstree shows a performance gain of 12% compared to only 1. 6% on the same configuration with Sphinx, which almost parallels the packet rates emitted by these two benchmarks.
FlexVF in VMs FlexVF's VM-side subsystem consists of three components: Network teaming. This component aggregates multiple network interfaces on each VM into a single logical teamed interface. On Linux, network teams can operate in multiple modes: active/backup, multiple load balancing modes or link aggregation. We configure VMs to use network teams in active/backup mode to prioritize VFs for network transfers whenever available. This component helps ensure VMs' network connectivity even in cases where the VF is disconnected from the VM, for example during VM migration as described in  [12]. Network activity hook. This component is a tracing hook installed in each VM's to monitor its network activity. The hook extracts network statistics such as packet rate, then transmits this information to the collector for processing.
Implementation This section presents the implementation details of a FlexVF. We implemented FlexVF on Ubuntu 18. 04 with KVM/QEMU version 5. 1. 0-git on Linux 4. 1 using Libvirt as the VM control interface. For our prototype, we opted for an implementation based mostly in user-space. Such an implementation has several advantages: Firstly, it takes advantage of existing libraries (e. g, shared memory tools that can be used in a VM), and also network statistics available in the Linux kernel, thus minimizing the development effort. Secondly, it allows easy reuse of our system across different virtualization technologies, for example the Xen hypervisor  [15]. Finally, it facilitates the installation of our system by system administrators, as we don't require an upgrade of the current hypervisor before using our system, and it can also be easily integrated with standard cloud computing platform such as OpenStack  [11].
FlexVF in the VM Our synthetic networking feature in FlexVF is based on the network teaming driver, and the network activity hook extracts its information from the /sys/class/net directory, both of which are existing features in the Linux kernel. For the data exchange between FlexVF collector and VF allocator, we rely on the KVM-based zero-copy memory sharing mechanism as described in  [16]  to let the host map a memory buffer allocated in the guest environment. Upon VF assignment from the VF allocator, the FlexVF collector is responsible for reacting to the hotplug/unplug event initiated by the VF allocator. Each time the VF allocator plugs/unplugs a VF to a VM, it triggers a corresponding virtual interrupt to the VM, which is then handled by the FlexVF collector running inside. The functionality of the FlexVF allocator can be integrated into existing guest services provided by most current hypervisors.
Experimental setup Hardware and software. Our hardware and software setup are described in Table  1. Benchmarks. We used well-known micro-and macro-benchmarks to evaluate the performance of FlexVF, using network latency as the main evaluation metric. The list of benchmarks used in our evaluations is as follows: • TailBench. [9]  A benchmark suite for latency-critical applications, it provides request latency figures for multiple types of workloads including search, databases and machine learning. • Sysbench and MySQL. We tested FlexVF on a system running the MySQL relational database, using Sysbench as the load generator software. • Sockperf. A socket-based network benchmarking utility, it provides network latency figures under various background loads at predefined packet rates.
Macro-benchmark evaluations MySQL. In this section, we investigate the impact of FlexVF on application latencies using the MySQL relational database. We deployed MySQL on the VM under test, used Sysbench to generate a OLTP mixed read/write workload, then measured the resulting request latency. After approximately 15 seconds of benchmarking, we triggered the network switchover using FlexVF and observed the change in database performance. Figure  6  shows the resulting request latency as recorded by Sysbench. We observe that with a PV interface, the request latency fluctuates between 7 to 10 msec throughout the benchmark; as soon as FlexVF assigns a VF to the server VM, this latency drops to a stable 2 msec with little to no fluctuation, representing a 75% latency reduction. Additionally, we observe no increase in   Multiple VM evaluation.
Other approaches and evaluations. These works investigate general opportunities for increasing I/O performance, for example by improving software-hardware interfaces as used by operating systems. Taguchi et al. [30]  introduces a packet aggregation solution that dynamically merges and splits packets in a VNF dataflow to reduce virtual network packet routing overhead, thus increasing throughput while reducing latency. Dong et al. [12]  evaluates the characteristics of SR-IOV-based virtual networking, designs an architecture for providing said virtual interfaces to VM guests, and details how SR-IOV can be utilized in a migration context by combining an SR-IOV virtual interface with a paravirtualized interface through use of a bonding driver. The same technique is used by Microsoft Azure to ensure VM connectivity in case the VF is disconnected  [8].
Acknowledgements This work is supported by the  French National Research Agency  ( ANR-20-CE25-0005 ) and the  CIMI research program. Some experiments presented in this paper were carried out using the Grid'5000 testbed, supported by a scientific interest group hosted by Inria and including  CNRS ,  RENATER  and several Universities as well as other organizations (see https://ww w. grid5000. fr/).
In this paper, we selected one event from the XMM database that occurred during solar minimum and estimated a neutral density at the 10 R E subsolar location, considering that a typical subsolar magnetopause is located at about 10 R E  (Kivelson & Bagenal, 2014). Finally, we compared our solar minimum exospheric density with the solar maximum density of CC2019 and with the neutral density in other studies. This paper is structured as follows. In Section 2, we introduce the data and models used in this study. In Section 3, we explain our methodology for deriving the exospheric density from the XMM observations. In Section 4, we discuss our event selection process using the 11 years of XMM soft X-ray data. In Section 5, we present our case study of the 12 November 2008 event and estimate a neutral density during solar minimum.
We need to identify the SWCX component for estimating the Earth's exospheric density. Section 5. 1 explains the details of the background removal process. We downloaded Original Data Files (ODFs) from the XMM Science Archive (XSA) and processed the data using the XMM Science Analysis System (SAS) software package version 18. 0. 0 (de la Calle, 2021), provided at the XMM data analysis web page: https://www. cosmos. esa. int/web/xmm-newton/sas. We used the MOS full-frame observation data, since some SAS commands needed in this study can be applied only to the full-frame mode data. In this paper, the XMM observations between revolutions 21 and 1990 are used, which corresponds to the period from January 2000 to October 2010.
To remove this additional contamination period, we calibrated event files from the original data files using the SAS subroutine emproc. Then, we created a good-time-interval (GTI). The GTI file contains a record of the the times when there was no proton flaring. We applied the GTI files to the event file to remove the contaminated time intervals. It is common for about 36% of the XMM time to be lost to soft protons  (Kuntz & Snowden, 2008). After applying the GTI file, we removed astrophysical point sources from the FOV. Generally, we followed the method of  Carter et al. (2011) , except that we used edetect_chain to detect the point sources. We applied a 35-arcsec extraction radius at each point source. After the automatic astrophysical point source removal process, we visually checked relevant images generated from the cleaned XMM data, after which we applied an additional source removal of a 500-arcsec radius about the telescope boresight to ensure that any bright on-axis astrophysical source was largely eliminated.
Since SAS v. 16, the task evqpb has been available to generate a tailored FWC event file that corresponds to an observation. As XMM science observation and FWC data are not recorded simultaneously, they may have different intensities of instrumental backgrounds. We renormalized the instrumental background of the FWC data to obtain the background of our observation by following the methods of De Luca and Molendi (  2004 ) and  Carter et al. (2011). We calculated the scaling factor by comparing our observation with the FWC data in a high energy band (7. 5-9 keV) where, for the period free of soft proton flaring, the contribution is believed to be entirely from the instrumental background. We obtained the instrumental background of our observations by multiplying the FWC background by the scale factor.
Our background signals are comparable to or higher than their values. We took higher background signals obtained from our techniques and subsequently underestimated the SWCX signals because we are interested in the lower-end exospheric density, an important quantity for the SMILE and LEXI teams to understand the minimum SWCX signals and thus prepare their mission accordingly. We also performed a spectral analysis of the entire event interval using the X-ray fitting software Xspec to double check if our XMM observation included SWCX signals. We found that our fitting results were significantly improved by the addition of the SWCX emission lines. Finally, we performed the same background removal processes for an XMM event that was expected to see no near-Earth SWCX signal. We found that the residual soft X-ray count is nearly zero or 0.
Neutral Density Estimation We derived an exospheric neutral density from the 12 November 2008 event. About 3. 3 hr of soft X-ray data (03:43-07:03 UT) were available from the EPIC MOS observations. Figure  3  summarizes the orbital and observational details of the spacecraft observations, as well as the IMF and solar wind conditions during the period of interest. Figure  3a  displays the XMM orbit (blue line), its LOS direction (black line), the THEMIS-B orbit (green line), the magnetopause location (red line), and the bow shock location (yellow line) projected on the Geocentric Solar Ecliptic (GSE) XY (left) and XZ (right) planes during the observation period. The orange/red dots present the starting locations of XMM/THEMIS B for this event, respectively. Figure  3b  shows, from top to bottom, the IMF, solar wind velocity, number density, plasma pressure (P = nkT), and solar wind proton flux (n sw V sw,x ).
The green and gray shaded areas indicate intervals of XMM near-Earth SWCX observation and the THEMIS-B magnetosheath observation, respectively. We used the WIND plasma and magnetic field observations, downloaded from the NASA CDAWeb. WIND was located at 239. 4 R E sunward, and its data were time-shifted by 1. 24 hr to consider the solar wind propagation from WIND to a subsolar bow shock using the minimum variance method explained in  Raeder et al. (2008)  and the references therein. The magnetic field observed from the Magnetic Field Investigation (MFI;  Lepping et al. , 1995)  instrument had a 1-min resolution, and the plasma data from the Solar Wind Experiment (SWE;  Ogilvie et al. , 1995)  instrument had a 92-s resolution. Both the magnetic field and the solar wind data were adjusted to have a 1-min resolution using a linear interpolation method before they were submitted to the OpenGGCM model.
85 cm -3 and a minimum of 3. 64 cm -3 ), and that of the IMF B Z was 3. 07 nT (from 0. 66 to 3. 73 nT) We simulated the 12 November 2008 event using the solar wind and IMF conditions in Figure  3b  as inputs for OpenGGCM. ) is shown because this flux is a key parameter that determines potential reaction rates (Q) and thus, contributes to the estimation of the characteristic neutral density (N 0 ), as seen in Equations 5 and 10. While THEMIS B crosses the magnetosheath (the gray shaded area), the model-to-data ratio is nearly 1, which suggests that the MHD model reasonably reproduced the magnetosheath plasma conditions. The modeled By and Bz components show good agreement with the THEMIS observations, while the Bx component shows a larger discrepancy with the data. However, this model-data gap in Bx is expected because MHD models (not only OpenGGCM but also other MHD models) often set the IMF Bx at a constant value or put constraints on Bx to save IMF By and Bz in the model input  (Raeder et al.