dataesr/scientific-paragraphs-categorization huggingface dataset:
To determine the smallest spatial scale of roughness to which the infrared data are sensitive, we compared our surface roughness maps with the global 20-cm-facet shape model of Bennu derived from data acquired by the OSIRIS-REx Laser Altimeter (OLA)  (39) (40) (41). For a direct comparison, we first calculated the RMS slopes of the global 20-cm shape model with respect to the equivalent 6-m shape model. The results were then degraded to the spatial resolution of the OTES thermal roughness map for direct comparison. The global mean roughness value from the OLA map is 39° ± 7°, which is in excellent agreement with the OTES-derived global roughness (40° ± 2°). The two maps correlate spatially (figs. S5 and S6), and we therefore conclude that ~20 cm is indeed at or near the smallest spatial scale to which the infrared data are sensitive.
Other large, low-reflectance boulders that were well resolved in the global thermal inertia map (Fig. 1,  A  and B ) yield similar values. The high-reflectance boulders tend to be smaller (≲10 m in diameter)  (21) , which prevents the direct determination of their thermal inertia in our global-scale OTES and OVIRS datasets collected during Equatorial Stations. However, higherresolution data were acquired during Recon A flybys of the OSIRIS-REx mission's primary and backup sample sites, known as Nightingale and Osprey, respectively. These data provide an opportunity to test at the local scale whether the global spatial trends are linked to boul-der type and to better estimate the thermal inertia of the highreflectance boulders. The Recon A flybys were conducted at an altitude of ~1 km, leading to an OTES spot diameter of ~10 m.
The data were acquired during a narrow range of local times of day (~9:00 a. m. to ~2:00 p. m. ). As a result, it is not possible to reliably separate thermal inertia and surface roughness in the ATPM model fits of the Recon A data owing to the strong degeneracy between these two parameters. To circumvent this degeneracy, we measured local variations in surface roughness from the OLA-derived digital terrain models of the candidate sample sites and converted them to OTES-equivalent roughness variations using the relationship derived from the Equatorial Stations global analysis. Therefore, in the ATPM fits to the Recon A data, surface roughness was kept fixed at the OLA-based values, and only thermal inertia was varied to find the best model fit. Figure  4  shows the resulting local thermal inertia maps for the primary (Nightingale) and back-up (Osprey) sample sites.
The Equatorial Stations on October 19, 2020 http://advances. sciencemag. org/ Downloaded from were executed in the order given above, approximately a week apart from each other: 25 April 2019; 2, 9, 16, 23, and 30 May 2019; and 6 June 2019. The spectral observation sequences started between 17:19 and 18:02 UT on these days. Integration times were ~2 s for OTES and ~1 s for OVIRS, leading to ~3000 OTES spot spectra and ~5000 to ~7000 OVIRS spot spectra per station. The observing sequence for the BBD 1 station was slightly different. That station was optimized for imaging and included N-S slews pointed 45° to the east and west along with the nadir-relative slew. These slews did not extend fully to the poles and include large gaps in the OTES and OVIRS data between slews. BBD 1 was executed on 7 March 2019 and spectral observations started at ~17:00 UT.
Fitting a single OTES or OVIRS spot The OSIRIS-REx infrared observations were fitted in terms of brightness temperature because the OTES measurements were primarily reported in these units. As in previous work that fitted similar datasets for other planetary bodies, e. g. ,  (7, 93) , we calculated the RMS error, RMSE(,f R ), between the measured and modeled brightness temperatures, T OBS,n () and T MOD,n (sf n ,,f R ,), respectively. For fitting a single OTES or OVIRS spot, RMSE(,f R ) was calculated using RMSE(, f R ) = √ _____________________________ 1 ─ N   N  ( T OBS,n () -T MOD,n ( sf n , , f R , )) 2 (10) where N  is the number of wavelengths that were observed by OTES or OVIRS. In addition, the T MOD,n (sf n ,,f R ,) were calculated from the modeled radiance spectra using the inverse of the Planck function given by T MOD,n ( sf n , , f R ,  ) = hc /  k B ln ( 2 hc 2 ────────────  5 sf n R MOD,n (, f R , , t) + 1 ) ( 11 ) where h is the Planck constant, c is the speed of light, and k B is the Boltzmann constant.
The fitting of multiple spots also allowed the uncertainties of the derived thermal inertia and roughness values to be independently quantified through a Monte Carlo method. It was not possible to quantify the uncertainties through a χ 2 analysis because measurement errors were not reported by OTES for all the wavelengths that it observed. However, this was not usual because previous work using similar datasets for other planetary bodies also used Monte Carlo methods to quantify uncertainties for similar reasons, e. g. ,  (7). For our Monte Carlo uncertainty analysis, we use the bootstrap method described in  (94). In particular, if given a suitable number of independent observations, then the bootstrap method estimates the uncertainties by performing multiple fits of different randomly chosen combinations of those observations.
Second, the OTES RMS slopes were then mapped back to the 6-m shape model by averaging the RMS slopes of all OTES spots that projected onto each 6-m facet. As a result, this method produced the degraded-resolution OLA surface roughness map shown in fig. S5 , which was in good spatial agreement with the OTES thermal roughness map shown in Fig. 1. Figure  S6  shows a comparison between the surface roughness RMS slopes derived from OTES and OLA. As shown, there was a good correlation between the two datasets, but the relationship was not one to one. In particular, a polynomial fit gave a best-fit cubic function of  OTES = 3. 062 × 10 -4  OLA 3 -4. 526 × 10 -2  OLA 2 + 2. 332  OLA  (15)  which can be used to predict the OTES RMS slope from an OLA-derived RMS slope with a typical RMS error of 1.
Local analysis of OTES Recon A data To produce local maps of thermal inertia for the Nightingale and Osprey candidate sample sites, we analyzed the OTES data collected by OSIRIS-REx during the low-altitude Recon A passes using the single-spot fitting method described above. For generating suitable temperature lookup tables, we carved out subsections from the 1. 6-m-resolution shape model that were 80 m in radii about the central points of the Recon A scan patterns. Temperature lookup tables were then built by the ATPM using these shape model subsections with the positional geometry of Bennu given in table S1. To avoid boundary issues with the edge of these shape model subsections, we only analyzed and produced model radiance spectra for OTES spots that fell within 75-m radii of the subsection central points.
Most facets were only observed a few times at a single local solar time of day despite there being a ~7-hour range for the collected OTES spots. Therefore, it was not possible to independently constrain thermal inertia and surface roughness from the Recon A OTES data alone. To circumvent this, we measured the surface roughness of the OLAderived v16 20-cm shape model for these regions by comparing its facets against those of the 1. 6-m shape model subsections using Eq. 14. This allowed us to calculate  OLA for each OTES spot, which could then be converted to  OTES using the previously derived cubic function given in Eq. 15. Last, these calculated roughness values were given as an input to the single-spot fitting procedure to determine a best-fit thermal inertia for each OTES spot analyzed.
Lateral dust mixing analysis To determine whether a fractional mixture of dust with very low thermal inertia on boulders with high thermal inertia could explain the low thermal inertia of Bennu's boulders, we fitted a two-component model to OVIRS data of the regions with the lowest and highest thermal inertia identified earlier. OVIRS data were used for this investigation, rather than OTES data, because the short wavelengths observed by OVIRS were more sensitive to the potential hot dayside temperatures induced by the presence of any very low thermal inertia dust. In the two-component model fits, the total radiance, R TOT,n ( ROCK ,  DUST , f DUST , f R , , t), was calculated from the single-component model radiances by R TOT,n (  ROCK ,  DUST , f DUST , f R , , t) = (1 -f DUST ) R MOD,n (  ROCK , f R , , t ) + f DUST R MOD,n (  DUST , f R , , t) ( 16 ) where f DUST was the fractional surface coverage of dust and  ROCK and  DUST were the rock and dust thermal inertia values, respectively.
Albedo trend analysis We began with a photometrically corrected MapCam v-band equirectangular mosaic that has been calibrated to units of normal reflectance  (97). A Gaussian convolution kernel is applied to the map to smear it to approximate OTES spectrometer resolution during the Equatorial Stations. The size of the kernel was set to approximate the OTES spot sizes. The resulting smeared maps were then facetized to the SPC/OLA v34 6-m shape model of Bennu by mapping each pixel centroid to the nearest facet and averaging the binned values. These facetized albedo maps were plotted against the corresponding OTES thermal inertia map from Fig. 1  to generate the histogram data shown in Fig. 3.
Acknowledgments: We are grateful to the entire OSIRIS- REx Team  for making the encounter with Bennu possible,  C. Wolner  for editorial help, and  H. Roper  and  D. Worden  for help with figure formatting. We thank the  J-Asteroid/JMARS  development team for assistance with the production of map figures. Funding: This material is based on work supported by  NASA  under contract  NNM10AA11C  issued through the  New Frontiers Program. Some of the research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with  NASA. B. Ro. acknowledges funding support from the  Royal.
Introduction Gu Dezhen has established the concept of rock mass structure in the early 1960s  [1]. It is suggested that the characteristics of rock mass mainly depend on the internal structure of rock mass  [2, 3]. Fine description, rapid measurement, and visual representation of the structure of rock mass and its spatial distribution of quality grade are the foundation and key to solve many underground engineering problems. Geological survey of traditional joints and fractures is complicated and cumbersome. e observation points are determined by the surveyor's line first, and then the joint measurement is carried out one by one on the site of the bad working environment by means of a scale and geological compass, and the data of the nature, occurrence, length, width and spacing of each joint fracture, and the position of the intersection point between the structure surface and the measuring line are recorded.
Compared with other methods, the digital image of field rock is obtained by digital camera equipment, and then image processing is carried out by computer, and the relevant fracture data can be obtained. is method can transfer a large number of field works to the indoor to complete, with the advantages of precision, high efficiency, practicality, comprehension, and few environmental disturbances, which effectively solve the disadvantages and shortcomings of the traditional method, provide a new idea for obtaining geometric information of rock mass structure surface, and have been favored by the majority of scholars  [14] [15] [16] [17] [18] [19]. Wu  [20]  introduced the information processing scheme of digital image of beach rock mass developed according to modern computer vision theory; Fan  [21]  studied the intelligent recognition of digital images of rock mass fractures based on image processing and recognition technology to realize automatic interpretation of fractures; Li et al.
At the same time, a program automatically processes the collected basic information and directly gives the rock mass quality evaluation results of each set of information according to the relevant classification methods. e correlation between RMR and BQ is analyzed and discussed on the basis of engineering example  [34] [35] [36]. Finally, a threedimensional model of rock mass classification is established based on the distance power-inverse ratio method. e research idea is shown in Figure  1. On the basis of MATLAB platform, the intelligent evaluation system of image fracture AI identification and rock mass quality is developed, the quality grade of rock mass and the dynamic distribution of regional rock mass under mining disturbance are finely described, rapidly measured, and directly visualized, and the digital, intelligent transition and 3D visual management of quantification of rock mass and rock mass quality identification are realized.
Information collection of engineering factors affecting rock mass strength is shown in Table  1. e structure surface conditions A, B, C, D, and E, respectively, mean "discontinuous, closed, very rough, and the unweathered rock cliff," "<1 mm, slightly rough wall, width slightly weathered rock," "<1 mm, slightly rough wall, width serious weathering rock," "surface smooth or soft interlayer thickness <5 mm, the width of 1∼5 mm, continuous," "surface smooth or soft interlayer thickness >5 mm or opening >5 mm, continuous. " e groundwater conditions A, B, C, D, and E, respectively, mean "completely dry," "wet," "wet cave wall," "dripping water," and "running water. " After the on-site statistical data are recorded in this form, the rock mass quality information database on the computer side is input uniformly for storage and retention (the information database on the computer side has been made into a software form that can be dynamically changed at any time), paving the way for the automatic rock mass quality classification calculation by the self-programmed program later.
Basic Information-Related Processing and Rock Mass Quality Classification    Fracture Information Statistics Table. It is urgent to improve and innovate the traditional collection method, which is characterized by high labor intensity, low efficiency, high risk, and high accuracy under the influence of human and environmental factors. is paper processes the close-up photographs of the joints and cracks on the computer through self-programming and identifies and obtains the basic information of each fissure. J v = K × n/A ∑ n i=1 S(i) × p(i) S = R c = 22. In this paper, the self-compiled MATLAB program is used to obtain the pixel-length scale (cm/pixel) of the photos by selecting the flaky reference objects in each close-up photography picture. Click the two end points of the crack to read the coordinate values of the two end points from the current coordinate system.
is standard adopts the comprehensive index method composed of multiple parameters. Taking the quantitative indexes R c and K v of the two grading factors as parameters, the basic quality index of rock mass (BQ) is calculated according to the formula, and BQ is used as the quantitative index of the classification level, as shown in the following equation. When R c > 90K v + 30, take R c � 90K v + 30. When K v > 0. 04R c + 0. 4, take K v � 0. 04R c + 0. 4: BQ � 100 + 3R c + 250K v. ( 15 ) In this paper, the self-programming process is used to process and calculate the above-mentioned indicators. According to the specification, the scores of 97 groups of data are given. After the BQ is modified according to the specifications, the underground engineering rock mass quality index [BQ] is obtained, and the BQ is calculated according to the maximum score of 100.
e value is approximately the same as the RMR score of the percentile system, and the average error rate is 8. 26%. e results are shown in Table  6. Among the 97 graded test sections, 20 of them had the same grade, with an agreement rate of 20. 6%, and 63 of them had a grade difference, accounting for 64. 9%. According to the information in Table  5 , the distribution statistics of the RMR values of 97 groups of data can be obtained. As shown in Figure  9 , the level IV rock mass in the measurement block is the largest, accounting for 54. 6%, while the rest are level II and III rock mass, without any level I or V. Software has been developed to make the grading evaluation process automatic. Only the database files prepared in the early stage need to be updated, and then the rock mass quality rating results and distribution statistical charts at the sampling measuring points in the mine can be obtained through rapid computer calculation.
n), the distance between the point to be estimated and the sample point is recorded as d i , and the attribute value P of the distance to be estimated by the power-inverse ratio method is as shown in the following equation  [50]. In the formula, m is the power value of the power-inverse ratio method. e larger the power, the greater the weight of the nearest point to the estimated point. In this paper, the power is 2: P � 􏽐 n i�1 P i / d i (x, y, z) 􏼂 􏼃 m 􏼁 􏽐 n i�1 1/ d i (x, y, z) 􏼂 􏼃 m 􏼁. ( 16 ) In this paper, RMR value is selected as attribute value to get rock mass grade distribution at -540 m level. According to the horizontal geological plane map of each level, the boundary information of rock mass can be obtained, and the solid model of rock mass can be established preliminarily by importing the boundary information into the 3DMine software as a constraint condition, as shown in Figure  11.
Further evolution of this scheme introducing volume preservation is the Empirical Cubature Method(ECM)  [27]. ECSW is an 'a posteriori' method as it relies on data from full-scale simulations to train a hyper-reduced integration scheme, as opposed to 'a priori' methods which use the knowledge of the physical problem rather than training data  [25]. The optimization problem that arises in the ECSW is NP-hard and is in practice suboptimally solved by mean of the Sparse Non-Negative Least Square (SNNLS) greedy algorithm. Similar numerical integration schemes, in which it is necessary to preserve the consistency and compatibility during the shape functions integration, arise in Galerkin meshless methods such as Diffuse Elements  [28] , and Element Free Galerkin  [29] [30] [31]. Present work is motivated by the observation that, proceeding greedily by subsequent enrichments, the SNNLS algorithm becomes computationally expensive in building large hyper-reduced integration schemes , and that a reference method is needed to compare performances of heuristic approaches.
This is in part due to the incapacity to express the constraints on training data integration in L2 norm in the proposed linear methods, thus, they don't share the same admissible space with the SNNLS. Moreover, the MIP formulations do not minimize the offline training data approximation error but only keeps it under a prescribed threshold. On the other hand, quadratures obtained with the MIP approach may also be unattainable with the SNNLS algorithm due to its greedy nature. Unassembled training data approximation quality is overall quite similar between the different approaches and adding consistency constraints does not deteriorate the training data approximation at a given quadrature size. The maximal online work of internal forces approximation error is presented in Fig. 5. While the SNNLS shows very good online performances for an heuristic approach, the proposed MIP approach offers better online work of internal forces reconstruction on the given example.
Conclusion The contribution of this paper is the formulation of the hyper-reduction problem in terms of a linear mixed-integer optimization problem, imposing a threshold on the infinity norm of the training data reconstruction error. This formulation enables optimal solution by IBM CPLEX algorithm, which is then compared to the standard SNNLS heuristic from the literature. The impact of additional consistency constraints on volume preservation and polynomial integration is studied. Due to high computational cost, the problem is reformulated using reduced constraints set. All results are compared in both offline and online phases of projected Galerking ROM on an explicit nonlinear transient dynamics case. The different methods are implemented in a research development branch of the industrial FE solver Altair Radioss  [39].
UML interactions, aka sequence diagrams, are frequently used by engineers to describe expected scenarios of good or bad behaviors of systems under design, as they provide allegedly a simple enough syntax to express a quite large variety of behaviors. This paper uses them to express formal safety requirements for safety critical systems in an incremental way, where the scenarios are progressively refined after checking the consistency of the requirements. As before, the semantics of these scenarios are expressed by transforming them into an intermediate semantic model amenable to formal verification. We rely on the Clock Constraint Specification Language (CCSL) as the intermediate semantic language. An SMT-based analysis tool called MyCCSL is used to check consistency of the sequence diagrams.
e. its consistency with a class diagram and a state diagram. Shen et al. [17]  present a template semantics, for describing the operational semantics of behavioral notations, especially the combined fragments and other constructs of sequence diagrams. Muram et al. [18]  translate sequence diagrams into temporal logic based constraints (LTL) and formal behavior specifications (i. e. , symbolic model language (SMV) ), respectively. The model checker NuSMV is used to verify the containment relationship. Various petri nets are used. For example,  [6]  defines and explains the relationship between sequence diagrams and normal Petri nets. Cunha et al. [19]  also translate UML sequence diagrams to Petri nets and verify deadlock freeness, reachability, safety and liveness properties by using a model checker.
Methodology In this work we study four liquid electrolytes (three in aqueous solvent and one in a common organic solvent mixture) mirroring the extremely diverse range of experimental systems in which these behaviors were observed in Refs. 1] [42]  The structures of all the constituent molecules and ions are shown in Fig. 1. This work is centered on state-of-the-art molecular dynamics (MD) simulations using the Gromacs simulation software. 43 The simple salts are modeled using the Dang force field,  44, 45  the water is modeled using the SPC/E force field,  46  while for TFSI we use the Siqueira Due to the low dielectric permittivity of the solvents DOL and DME (" ' 7 for both  40  ), the electronic polarizability e↵ects, which are at the origin of more than 20% of the total static permittivity,  50  have to be considered.
Acknowledgement S. W. C. and B. R. acknowledge financial support from the  Ville de Paris (Emergences , project  Blue Energy ). The research of S. W. C. leading to these results has received funding from the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  under  REA  grant agreement n. PCOFUND-GA-2013-609102, through the  PRESTIGE programme  coordinated by  Campus France. B. R. acknowledges fi-.
Introduction Recent years have witnessed a massive push towards reproducible experiments in neuroscience. Some of the leading projects include OpenfMRI (now OpenNeuro,  Poldrack & Gorgolewski, 2017) , allowing researchers worldwide to test hypothesis on a massive cohort, Neuro-Synth  (Yarkoni et al. , 2011)  a brain mapping framework to automatically conduct large-scale, high-quality neuroimaging meta-analyses or the development of the Nipype framework  (Gorgolewski et al. , 2011) , a very useful tool developed initially for the MRI field and which facilitates data analysis by wrapping commonly-used neuroimaging software into a common Python framework. Nipype was originally designed to provide rapid comparative development of algorithms and to reduce the learning curve necessary to use different packages.