dataesr/scientific-paragraphs-categorization huggingface dataset:
After clustering of the entire sample, the application displays the charts and routes on the map, and then compiles a report in the TXT format. The implementation of the server end is represented by a special system developed for the web platform  [23] [24] [25]. Python was used for the development as the main programming language. The Flask framework  [26]  was used for displaying in the web environment. The cartographic service Google Maps v3  [27]  was used in this system. The JavaScript programming language [23] was used for manipulating the map: constructing points and routes. ORM SqlAlchemy  [28]  and the SQLite database management system  [25]  were used for working with the database. • speed -minimum average speed obtained as a result of the analysis; • table with intermediate data -table containing all the tracks for this route in the form of the total travel time, • straight-line distance -distance between points in a straight line; • road distance -distance between points along the road in compliance with traffic rules on this route; The FOREL algorithm was chosen for the clustering of this data set, since the number of clusters that should be obtained at the output was not known in advance.
As part of this work, a software system has been developed that includes modules for collection of navigational data, storage and verification of track data, management of street and road data, calculation of the Herman-Prigogine model indicators, and classification of streets according to these parameters on the basis of passive processing of navigational data on vehicle traffic on transport highways and street and roads networks of cities. The main advantages and novelty of the developed system are as follows: • cloud computing (using to store large amounts of cloud storage data); • availability of a mobile client application for the Android OS for collecting data in addition to the ability to work with already collected navigation data from the navigation and information center and other sources; • support for formats of modern GPS trackers; • use for calculating and processing the data of modern web technologies; • scalability and customizability of the system.
This paper presents a dataset obtained from fifty four laboratory experiments of the breaching of fluvial dikes due to flow overtopping. Data were collected on two complementary experimental setups, each consisting of a main channel representing the river, an erodible lateral dike and a floodplain. The dataset covers seven test series, involving varying hydraulic boundary conditions (e. g. inflow discharge, downstream boundary conditions), main channel dimensions, as well as bottom and dike material. The following experimental data were produced: time series of water levels in the main channel, time series of flow discharges in the main channel and through the breach, as well as high resolution 3D reconstructions of the breach geometry during its expansion. The latter measurements were performed using a novel non-intrusive laser profilometry technique developed for this research.
Dike overtopping occurs if the river discharge exceeds the design value of the dike during a flood event, or, broadly, if the water level exceeds the dike crest or the flow overtops a weak dike segment. The flow over the dike crest can trigger external erosion, eventually leading to breaching of the dike and catastrophic damage on the adjacent floodplain. Therefore, the breach growth dynamics and subsequent breach flow hydrograph have to be accurately estimated as they are key inputs for flood risk analysis and management. The present dataset is the outcome of a research project aiming at improving our understanding of the physical processes underpinning breach expansion and breach hydraulics in the case of fluvial dike failures  3, 12. A comprehensive test program was conducted (54 tests) on two experimental models, shedding light on dike breaching dynamics in various realistic configurations, including varying: (i) channel flow conditions, (ii) channel dimensions, (iii) floodplain confinement level, (iv) bottom erodibility, and (v) dike material composition.
Each test was densely monitored using a 3D continuous reconstruction (i. e. digital elevation model) of the breach geometry, which allows a full depiction of the breach expansion stages. Water levels in the main channel were measured, allowing the estimation of the flow discharge through the breach. The present dataset  13  complements and comprises findings, counting datasets, discussed in two previous research papers: (i) the experiments conducted by Rifai et al. 3  which focuses on the channel flow condition effects on dike breaching and (ii) the work conducted by Rifai et al. 12  investigating tailwater effects, i. e. water level increase on the floodplain side, on breach expansion dynamics. Details of the experimental method and scientific analyses of the present dataset were also presented in the PhD thesis of Rifai  14.
Data Records Data structure. The present dataset  13  is structured as presented in Online-only Table  1. Data corresponding to each test is stored in a directory named according to the test number. The file "tree. txt" presents a depth-indented listing of test directories and data stored within. Each test directory comprises: • "<test name>_data_structure. txt" file which presents the tree structure and content of the test data full file "<test name>. h5" (see 'Supplementary File 1' for an example of such file, and Fig. 5  for a sketch of the corresponding data structure for Test 1), • "<test name>. h5" file containing the test full data. The data file is under the HDF5 format (Hierarchical Data Format 5). HDF5 libraries exist for different programing languages (e. g. Python, Matlab, Fortran, C, and R).
More details can be found on the HDF5 website (https://support. hdfgroup. org/HDF5/), • "<test name>_Exp_config. txt" file which summarises the experimental configuration and test boundary conditions. This file allows an easy reading of the experimental configuration data stored in the HDF5 file, Data stored in the HDF5 files is structured flowing a directory, subdirectory, and file tree structure, corresponding to groups, subgroups and datasets, respectively (https://support. hdfgroup. org/HDF5/). The data is organized under four groups, namely: (i) experimental configuration, which summarizes the model dimensions and test boundary conditions, (ii) hydraulic variables, including, the measured water levels at different gauging locations as well as discharges, (iii) raw 3D point cloud reconstructions, and (iv) rasterized reconstructions.
In addition, a Python script, "read_datah5. py", is provided to serve as an example of data retrieving and display from the HDF5 files. The script can be used to start processing and analysis of data. The "Extract_example. mp4" video is a screen recording showing how the retrieving and display of the data, using the provided Python script, works. The videos "Test_6. mp4" and 'Test_49. mp4" are stop-motion videos recording during the experiments for tests conducted on the ULiège model and LNHE model, respectively. Notations and units. Datasets in the HDF5 are labelled following the physical variable name. Table  2  lists names of variables and associated units.
technical Validation Hydraulic variables. Water levels were measured using ultrasound probes. The probe measurement accuracy was ±1%. Record frequency was set to 100 measurements per second for the ULiège model and 10 measurements per second for the LNHE model. Measurements were then processed and outliers were deleted based on a moving average and standard deviation thresholding. For the sake of visibility and further processing, the measured signals were smoothed using a low-pass filter, namely the Savitzky & Golay  22  filtering function of Matlab 2014b (sgolayfilt). The polynomial order and the frame length parameters of the functions were adjusted depending on the measurement frequency. The use of the Savitzky & Golay  22  filter better preserves the distribution features (i. e. position and width of the extrema, area below the curve) in comparison to averaging techniques  22.
In Test 6, correction for refraction could not be performed. In Test 37, the camera did not operate between t = 1590 s and 3570 s. During Test 41, the ultrasound sensors did not operate between t ≈ 1000 s and 4600 s, liner interpolation of the water level time series was deemed sufficient in this case. Interpolated water level time series were then used for discharge calculation and water surfaces used for refraction bias correction. In Test 42, data from the ultrasound sensor G2 could not be processed. Finally, in Test 54, with the highest inflow discharge combined with an erodible bottom, the dike geometry reconstruction was affected by strong free surface variations due to flow turbulence.
Usage Notes This dataset is intended to bring insight into fluvial dike breaching processes and dynamic under various conditions. This dataset can be used to improve standardly used conceptual or numerical models to enable a comprehensive grasp of flow and erosion characteristics occurring throughout the different stages of the breaching process in a fluvial configuration (e. g. effects of saturation and apparent cohesion, secondary currents adjusted to abrupt flow deviation). Another use of the dataset can include derivation of a simplified proxy representation of the breach geometry and expansion processes. This is relevant to practitioners as it can represent a more efficient implementation of dike breaching process in large scale numerical models used for flood risk assessment studies.
acknowledgements The authors acknowledge the support of the  Fonds Spéciaux de la Recherche (FSR)  of the  University of Liège, Belgium. This work is partially funded by the  Association Nationale de Recherche et de la Technologie (ANRT) , the  European Fund FEDER  ( Programme Opérationnel Interrégional Rhône-Saône 2014-2020 ), EDF R&D, and  Saint-Venant Laboratory for Hydraulics.
Label in the dataset Variables Units Q_i Q i Test inflow discharge (m 3 /s) L_mc L mc Main channel length (m) Experimental configuration l_mc Z_fp l mc z fp Main channel width Fixed water level on the floodplain area (m) (m) z_bt z bt Model rigid bottom level (m) d_50 d 50 Median diameter of material (m) t t Time (s) z_Gi z Gi Water level at the gauging station G i (m) Hydraulic variables Q_b Q b Breach discharge (m 3 /s) Q_d Q d Drainage discharge (m 3 /s) Q_o Q o Main channel outflow discharge (m 3 /s) t t Time (s) Reconstructions x y x y Coordinate on the x axis Coordinate on the y axis (m) (m) z z Coordinate on the z axis (m).
The observation was made on low-lying remnants of the prominence after a partial eruption using the 1 ′′ × 120 ′′ slit. These data were used to derive the prominence properties by using the hydrogen Lyman lines profiles and differential emission measure analysis, combined to a 2D non-LTE prominence fine-structure modeling of the Lyman spectra  (Gunár et al. 2011 ). Among the results from the analysis of these datasets were the differential emission measure distributions between the transitionregion and coronal temperatures, which we define in Sect. 3. 1. These results were obtained using previous versions of the CHIANTI atomic database (v. 4. 2) and suggested photospheric composition for both prominences. In  Parenti & Vial (2007)  and  Gunár et al. (2011)  we pointed out two aspects which affect the DEM solution at lower and higher temperatures, and thus introduce uncertainties on the determination of the DEM at such temperatures.
Lines emitted at low transition region temperatures may have opacity effects  (Doyle & McWhirter 1980; Lanzafame 1994; Brooks et al. 2000; Giunta et al. 2015). The intensity of these lines will be compatible with an emission measure smaller than that predicted for optically thin lines emitted at a similar temperature, introducing an inconsistency in the DEM inversion. For higher temperature lines, there is an additional uncertainty on the amount of emission coming from the prominence-corona transition region (PCTR) with respect to the off-limb environment. For instance,  Parenti et al. (2012)  showed that the PCTR can be hotter than expected. For our datasets we could not separate the two components, introducing an additional uncertainty on the DEM inversion. Finally, it is well known that density-dependent effects can shift the formation temperature of an ion, and thus could in principle affect the FIP bias measurement based on a DEM analysis.
This is done by using an atomic database to calculate the synthetic spectral lines intensity (I th ) and minimizing the ratio I th /I ob. The theoretical line intensity is calculated assuming a tabulated abundance (in our case  Asplund et al. (2009)  photospheric), see Table  1. If the composition of one element in the observed plasma is different from that assumed, all of the lines from this element will show a similar (coherent) inconsistency within the DEM inversion of the amount equivalent to the correction to be made to its tabulated value. We also introduce the effective temperature, which is the DEM-weighted average temperature T eff = DEM(T ) × T dT DEM(T ) dT. (4) The DEMs as function of the temperature shown in this work have been obtained with a method based on a simple chi-square minimization.
We used essentially a modified version of the xrt_dem_iterative2. pro DEM inversion routine  (Weber et al. 2004 ) in order to have more flexibility in the choice of input parameters. This revised routine is being made available via CHIANTI version 9  (Dere et al. 2019 ). The DEM is modeled assuming a spline, with a selection of the nodes, and the program then uses the robust chi-square fitting routine (mpfit. pro).
Atomic processes and data In our previous analyses we used the CHIANTI atomic database for calculating the emissivity of the lines, and assumed the standard CHIANTI ion abundances, calculated assuming ionization equilibrium in the zero density approximation. This means that the ion charge state distributions are calculated assuming that all ions are in their ground states, and balancing the collisional ionization with dielectronic and radiative recombination rates. However, there are several processes that depend on the electron densities and that can affect the ion populations, as shown by  Burgess & Summers (1969). These effects are present even at coronal densities, and become significant at transition region densities and temperatures (see, e. g. , the review by Del Zanna & Mason 2018).
The inclusion of these effects requires a complex collisional-radiative modeling (CRM), as fully implemented in  Summers et al. (2006)  within the atomic data and analysis structure (ADAS) 1  project and database, and freely available through OPEN-ADAS 2. Such modeling has also recently been implemented by  Dufresne & Del Zanna (2019). For the present work we have used the results of the CRM available via OPEN-ADAS in the form of effective ionisation and recombination rates, calculated for a grid of densities and temperatures. We have calculated the ion balances of the main elements using these rates, assuming constant pressure. Figure  1  shows one example for the carbon ions: the net effect is to shift the temperature of formation of the ions toward lower values and change the peak ion abundances.
We modified the CHIANTI software to input the photo-exciting radiation for a specific line in the level population solver. As the process is a resonant one, the addition of photo-exciting radiation is taken into account by modifying the transition probability for spontanous decay. We note that the CHIANTI software allows the inclusion of incident disk profile, but this is equivalent to the method used here (and tested in Del Zanna & DeLuca 2018), as long as the coronal line profile is broader than the disk profile. We have neglected Doppler dimming effects as they would require velocities much higher than those typically observed in the quiescent prominences considered here. For the calculations of line emissivities we used CHIANTI v. 8  (Del Zanna et al. 2015) , which has significantly improved atomic data (over previous versions) for a range of ions.
In particular, CHIANTI v. 8 introduced new data for Fe iii, which we have added to the analysis, as lines from this ion are abundant in the SUMER spectrum. This is an important addition as it constrains the low transition -region temperature FIP bias. We encountered several discrepancies whilst testing different ionization equilibria and comparing predicted and observed line radiances for this ion. The cross-sections for collisional excitation should be reliable, but the ion abundance is very uncertain, due to large differences in the dielectronic recombination rates for the low-charge Fe ions as calculated by various authors. Nevertheless, as our main aim is to compare the prominence abundances relative to the quiet Sun, any systematic differences can be accounted for, as discussed below.
Another aspect to keep in mind is that in general atomic data for low charge states are difficult to calculate, and few are available. This is the reason why CHIANTI does not yet contain atomic calculations for all ions. Ni ii (another low FIP element) produces a few weak SUMER lines, but atomic data were not available in CHIANTI. Recently,  Cassidy et al. (2016)  published a calculation of radiative rates for this ion, while previous cross-sections for collisional excitation were made available  (Cassidy et al. 2011 ). However, several discrepancies in the atomic structures of the two independent calculations have been found, so we could only build a limited ion model to estimate the emissivity of two of the UV lines observed by SUMER. We excluded Fe ii in our analysis, as the atomic data are still not complete.
Aside from Ni and Fe , the other low-FIP element that we observed is Si. Finally, only in one dataset could we add a Mg v line to constrain the FIP bias at higher temperatures. Sulfur is a mid-FIP element producing several lines in prominences. We note that S often shows variations consistently with the high-FIP elements, as measured in remote-sensing observations of active regions (see, e. g. , the review by Del Zanna & Mason 2018). There are many high-FIP elements observed by SUMER. Nitrogen is particularly useful because four ionization stages are observed. Their temperature formation covers almost the whole of the range we are considering, allowing us to map any possible N FIP bias change within it.
The intensities are given in ergcm -2 s -1 sr -1. The lines indicated with "•" extend the lists published in  Parenti et al. (2005b)  and GUN04. The lines marked with "•" will be measured by Solar Orbiter/SPICE. As pointed out in PAR07, SUMER records about 40 Å at a time so there was a time lag of about 2h between the shortest and longest wavelengths observed. We list in Table  3  the additional lines that we have considered. The list includes, among others, the C ii multiplet (4 lines) at around 1323. 9 Å and the doublet at around 1335 Å. These are slightly density-sensitive and strongly temperature-sensitive lines. We discuss their behavior within our datasets in the following sections. As a general choice, we adopted lines above the hydrogen Lyman continuum (912 Å) to avoid absorption effects.
This behavior is expected, given their sensitivity to density (see Sections 4. 3 and 4. 4 below). The multiplet at around 1335 Å appears to be affected by opacity, so we have excluded it from the DEM inversion, even though in some cases the lines intensity can be reproduced by our DEM. We tested the PRM04 data for both pressures used for PRM1 and found that the best solution for the DEM was given by p = 2 × 10 14 cm -3 K, which is shown in Figure  4. Photospheric is also the appropriate composition for this prominence. The newly added Ni ii (log T = 4. 15) and Fe iii (log T = 4. 5) give consistent results. And the new Mg v (log T = 5. 45) line constrains the FIP bias at the highest temperature. Interestingly, we found this dataset to behave in the same way as that from PRM1: the S ii, Article number, page 6 of 14 Fig.
Opacity It is well known that some of the lower-temperature lines can be affected by opacity; line profiles can become self-reversed and total intensities decrease. However, these effects are more prominent near the solar limb, and less in quiet Sun areas on-disk and prominences. Our datasets include the C ii resonant doublet 1037. 018 -1036. 337 Å whose ratio should have a value of two if the plasma is optically thin. This is not the case both in QS (1. 2) and prominences (1. 2 for PRM1 and 1. 4 for PRM04), as already noticed in PAR07. Indeed the brightest 1037. 018 Å line is incompatible with our solution, suggesting that some opacity effect may be at work. A substantial opacity of these lines in the QS was also reported by other authors (e. g. ,  Chae et al. 1998; Brooks et al. 2000; Giunta et al.
This result is at odds with the lower intensities in the prominence than in the QS (Table  2 ). One can suspect that this has to do with the hypothesis of constant and identical source functions used in Dumont et al. 's technique. We discuss this issue in Section 4. 4. We have also looked at the multiplet at around 1335. 5 Å (1335. 708+11135. 663 and 1334. 577 Å); both lines are well reproduced by our DEM. However, the CHIANTI database predicts the (1335. 708+1135. 663)/1334. 577 Å ratio to be about two in optically thin conditions around the prominence density, while in our case this ratio is 1. 45 for the QS, 1. 28 for PRM1 and 1. 51 for PRM04, thus suggesting that some opacity is present. We note that in on-disk QS, chromospheric modeling of these lines gives optically thick conditions (e.
Bottom: Same as Top, but for two C ii lines, at 1037. 01 Å and 1335. 66 Å. Article number, page 11 of 14 filling process of the filament channel, have to be excluded or further improved to be compatible with our results. The analysis of the upcoming data from the ESA-NASA Solar Orbiter mission will benefit from our results, as we provide several guidelines for the data interpretation. The SPICE spectrometer will cover, co-temporally, the spectral ranges 704-790 Å and 972-1005 Å (first order). It will observe several lines that have been used in this work, and it will cover most of the ions that we listed (see Tables  2  and 3 ). Among these there are the C ii 1031-37 Å and C iii 977 Å lines, which we have shown to have a complex line formation. These lines will be observed frequently, as they are among the brightest lines in the UV prominences and QS spectra.
We then have provided a guide to the interpretation of their radiance, by clearly showing the differences in their properties between the QS and prominence plasmas, and the need for taking into account multiple processes in the line formation. We also recommend that future observations should be accompanied by the development of accurate atomic models. The existing CHIANTI and ADAS databases are already an excellent reference for this. However, improvements are foreseen, and we identified a few aspects that can be further investigated and for which the interpretation of the SPICE data, and all the future missions data, will benefit. It has already been shown that the analyses made using less accurate atomic models, or using diagnostic techniques that are correct only under simplified plasma atmospheres, resulted for instance in a too strong coronal FIP bias.