dataesr/scientific-paragraphs-categorization huggingface dataset:
Toolboxes and Hyperparameter Setting The current applications are based on different Python toolboxes. The GP model is constructed with the GPFlow toolbox  [43] , and its parameters are optimised using the LBFGS algorithm  [38]. Next, the DGP model is generated with the doubly stochastic toolbox [25], and its deterministic parameters, namely the location of the pseudo-points and the lengthscales of the kernel of each layer, are optimised using Adam  [44]. With regard to the variational parameters, namely the mean and variance matrix of each layer, they are optimised using the natural gradient descent method, introduced by Salimbeni et al. [45]. For the DNN, the Scikit-Learn toolbox  [46]  is used, and the optimisation is carried out with the LBFGS algorithm. Some of the hyperparameters, shown in Tables 3, 5 and 6, are put aside in this sequel and are not studied based on the following reasons.
At this step of HHO, each Hawk is placed in a random position and waits to locate the prey according to two scenarios. Assuming that these two perching states are (q); in the event of q < 0. 5, the Hawk perches according to the prey (rabbit) the location, and in the case of q > 0. 5, according to other Hawks' position situated in the hunting region. The case of q > 0. 5, in computation of (t + 1), HHODE structure is obtained by employing five mutation operators of DE, only arbitrarily applying one of these mutation operators will be sufficient to apply. "The HHODE controls an overall population space which is shared by HHO and DE. The differential evolution (DE) optimization has the benefit that, in the course of local search, the diversity in the population". The pseudo code of the HHODE is given in Figure  7.
| Simulation setup In this subsection, all the simulations are performed using MATLAB environment on a laptop of 8 GB memory and i5-2. 4 GHz CPU to evaluate the performance of HHODE. In our simulation experiments, the sensor nodes are randomly deployed over a 50 m x 50 m meters square area. The number of sensors is 200 where 40 anchor nodes are taken. The transmission range of sensor node is 30 m. The initial deployment of sensor node is random, in each iteration; the number of localizable nodes is not expected to be the same. The unknown nodes' coordinates are calculated using HHODE.
2003; Boore and Asten 2008; Mital et al. 2021). Traditionally, invasive borehole (downhole, DH) methods were the standard practice for characterizing seismic site conditions and proved to be quite costly; some practitioners have ill-advisably considered DH methods to be the most accurate approach, and  Socco and Strobbia (2004)  and  Thompson et al. (2009)  (amongst others) have cautioned against the perceived superiority of DH methods (e. g. , data contamination from reflected or downward wave propagation). In recent years, an impressive number of noninvasive geophysical site characterization methods were developed to meet the demand to accurately and costeffectively provide these critical seismic parameters. Consequently, a number of studies have administered blind trials to evaluate different array and processing methodologies for extraction of V S profile information, or the equivalent inverse parameter shear-wave slowness S S.
They, however, did not considered the issue of which basic arrays with a limited number of sensors might be practical and under what circumstances. In this study, we address the important issue of how useful basic or sparse arrays can be in practical surveys for evaluation of site parameters. The study was conducted through the Consortium of Organizations for Strong Motion Observation Systems (COSMOS) using a set of blind trials which were conducted from June-November 2018. This COSMOS trial strategically employed a number of key ingredients  (Asten et al. 2018a (Asten et al. , b, 2019a, b), b). One such component was that the study used microtremor array data recorded from four sites in order to consider limitations imposed by differing geologies, sparse array geometries, and interpretation methodologies.
Another important element was that these sites differ in the azimuthal coverage of microtremor ambient noise at each location. The COSMOS trial also deliberately used a four-phase approach in order to evaluate changes in blind interpretations as each phase introduced additional array data. Furthermore, this trial also incorporated results from a range of different geophysical methods, including processing techniques (algorithms) coded in software packages (or customized in-house computer programs) which were independently selected for analyses by the Vol:. (1234567890)  invited participants. The range of their experiences with microtremor array methods was broad as they were comprised of graduate-level students, commercial practitioners, and topical experts-some were also developers of the open-source and commercial software packages used in this trial.
Site 3: La Salle, Italy Site 3 is located in a glacial valley in the foothills of the Alps, containing more than 200 m of firm sands and gravels  (Socco et al. , 2008). Location of the Geophones were connected by cabling in two circular evenly-spaced arrays, each consisting of 24 geophones, at radii of 20 m and 37. 5 m. The maximum inter-station spacing provided for this blind test is 64 m. Data were collected using a multichannel acquisition system and 48 vertical geophones with a natural frequency of 4. 5 Hz. A sampling interval of 16 ms was adopted while recording files with lengths of 32,625 samples. Multiple files provided a total recording time of approximately 1 h. SITE 1 SITE 4 SITE 3 SITE 2 R 0 R 1 R 2 R e R 0 R 1 R 2 R e R 0 R 1 R 2 R e R 0 R 1 R 2 R e Fig. 3  Phase velocity dispersion curves computed for the reference models provided for sites 1-4.
Site 4: Dolphin Park, Carson, California Site 4 (near Los Angeles) is situated on geology with a thickness of more than 400 m Holocene sediments. Drill hole Carson-1 (Well 4S/13 W-9H9; longitude -118. 2409°, latitude 33. 8369°) and is also known as ROSRINE Site 26  (Reichard et al. 2003). The borehole was previously surveyed with a borehole P-S suspension logger to a depth of 340 m (Fig. 2d ). Depth to basement is not known. Microtremor data were acquired using a nested triangular array, with maximum station spacing of 60 m. Sensors were 4. 5 Hz vertical geophones that operated at a 4-ms sample rate with approximately 11 min of recording time. Note that although the drill hole and P-S log provided data to 340 m depth, the maximum depth of investigation provided by this microtremor survey is estimated from the lowest usable frequency (1.
Choice of processing and techniques by analysts Each of the 34 analysts who contributed to this study used their preferred MAM processing and analysis technique, as well as software (Table  1 ). The righthand column of Table  1  indicates whether analysis was made assuming only the fundamental Rayleigh mode (R 0 ) or the effective mode (R e ). The majority heavily favored use of SPAC (equivalently, spatially averaged coherency methods) and their various extensions (ESAC-1 and ESAC-2 in Table  1 ). The terms ESAC-1 and ESAC-2 denote similar methods but implemented in independent software packages. See section Data and Resources for software availability. Only two analysts reported use of frequency wavenumber (FK) methods, an observation not surprising because FK methods are most applicable for arrays with larger numbers of sensors than were provided in phases 1 and 2 of this study.
A wide range of public-domain, commercial, and in-house software packages were used by the 17 analysts of group 1, but if we apply a test of consistency over at least three of the four sites, two unrelated techniques proved most effective in terms of bandwidth achieved: the ESAC and the multimode SPAC (MMSPAC) techniques for direct fitting of coherency spectra (orange bands on Fig. 12 ). CC (SI) methods also proved very effective for obtaining high-frequency data, but less so for low-frequency data. This limitation at low frequencies is a consequence of the fact that the time-domain approach imposes a restriction whereby velocities of propagating waves cannot be determined for wavelengths greater than half (or Phase1 Site2 Phase1 Site2 in practice a third) of the maximum inter-station distance  (Bensen et al.
6  and 9  and in Supplementary Information Figs. S5-S6, is not necessarily reflected in the successful estimation of the boundary depth. We use these boundaries and uncertainties to tabulate the rate of success by analysts in picking one or both V S contrasts. Table  2  shows the results compiled from interpretations of phase 1, 2, and 3 data submitted by up to 16 analysts. The three analysts in phase 3 that show major layer boundaries within the acceptable ranges shown on Fig. 15 , for both interfaces, used three different software packages. These were analyst #4 using ESAC-2 for phase 2 and analysts #5, #10, and #3 (in-house SPAC; SPAC Genetic Algorithm, GA; and ESAC-1) for phase 3. Thus, there is no obvious indication of a preferred software package. It is Fig. 13  Quality of blind results by site, expressed as summed M index values for phases 1, 2, and 3, averaged over all group 1 analysts providing an interpretation.
Site 1 is Guadalupe, CA, USA. Site 2 is Trois Rivieres, Quebec, Canada. Site 3 is La Salle, Italy. Site 4 is Dolphin Park, CA, USA notable that the success rate in correctly interpreting these two known boundaries from passive microtremor data at site 1 is on the of order 20% with phase 3 data and a poor 10% for the phase 1 and 2 sparse array data. It is reasonable to draw the conclusion that where depths to V S boundaries are important, as distinct from average velocities, then the use of dense arrays such as multiple triangles is a necessary part of the survey design. The role of microtremor horizontal-to-vertical spectral ratios (mHVSR) was not discussed by most analysts in these trials, presumably because only one site of the four provided 3C data  (Molnar et al. 2018). Two analysts noted using mHVSR on site 1 to determine the fundamental site frequency which was then used to guide the inversion process.
Use of the blind trial for graduate student training This COSMOS blind trial exercise was also used as a graduate student training exercise at the University of Western Ontario (UWO) by Sheri Molnar. A team of four was assembled with each member designated a particular site. The team composition included Molnar as the expert-trainer (site 4), one PhD student (site 2), and two MSc students (Sites 1 and 3). All three students had been trained by their supervisor (S. Molnar) in field data acquisition and processing of ambient vibration and surface wave array data using the Geopsy software during a 2-week training course. Sites were assigned blindly to each team member by the supervisor based on their experience, using the assumption that site complexity may increase sequentially from site 1 to site 4.
The team began the blind trial at phase 2 which included the three-sensor array data and comprised the datasets of two team members. Each team member analyzed the blind trial phase 2 array data independently for their assigned site, including picking of fundamental-mode Rayleigh wave phase velocity estimates and inversion. The Geopsy software was used by all team members to calculate modified SPAC (MSPAC) dispersion estimates (SPAC curves are reviewed in selecting dispersion estimates but not used directly in the inversion) which were validated by comparisons with high-resolution FK dispersion estimates. Student team members then discussed their phase 2 dispersion picks, trials in model parameterization (not submitted to the trial), and inverted models with their supervisor prior to submission of an optimal V S profile.
The four-member team discussed results in comparison to phase 4 information and shared lessons learned in regard to accuracy and frequency bandwidth of their dispersion estimates. Team members consistently obtain correct dispersion estimates in the mid-frequency dispersion curve range but tend to under-or overestimate dispersion estimates in the bottom (low frequencies) or top (high frequencies) of the curve. Overall, the blind trial provided empirical datasets with external expertreviewed answers. This formed an important empirical benchmark exercise for the UWO team to test their skills in dispersion curve interpretation.
14  used five different techniques/software packages. The two-station method does not directly address the critical factor relating to whether the energy source for microtremor wave propagation is directional. Thus, the two-station method should only be used when azimuthally distributed sources are known to exist. This cautionary point will also apply if linear or quasi-linear arrays are used. For the majority of sites in this study, azimuthal distribution of sources was in fact sufficient such that a two-station sparse array proved adequate for reliable estimation of averaged shear-wave velocities V S10 , V S30 , and V S100. With respect to processing techniques, the widest usable bandwidths of Rayleigh wave dispersion curves of microtremor data, as determined in these blind trials, was obtained with the ESAC method and with the MMSPAC method based on direct fitting of coherency spectra.
identified the need for these blind trials, for these guidelines for best-practices and provided funding and encouragement to facilitate the project. This material is also based upon work supported by the US Geological Survey under Cooperative Agreement No. G17AC00058. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the US Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the US Geological Survey. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the US Government, the Canadian Government, or COSMOS. This paper is Natural Resources of Canada contribution number 20200665. Open Access publication fees for this paper were directly funded by COSMOS.
Array data for site 1 was supplied by the US Geological Survey. Data for site 2 was acquired and supplied by the Port and Airport Research Institute, Japan. Data for site 3 was acquired for the Interreg III B-Alpinespace-Sismovalp project: "Seismic hazard and alpine valley response analysis" funded by the European Union. The borehole survey at site 4 was conducted as part of the ROSRINE project funded by the US National Science Foundation, Pacific Earthquake Engineering Center, California Department of Transportation, and other organizations. The microtremor array data was acquired in 2003 as an internally funded research project by GEOVision, Inc.
Acknowledgements The  Consortium of Organizations for Strong Motion Observation Systems  (COSMOS) consisting of the  US Geological Survey , the  Geological Survey of Canada , and a group of North American power companies, consisting of  Southern California Edison and Pacific Gas and Electric ,  J Seismol  The authors thank two anonymous reviewers for valuable comments and also thank the following  US Geological Survey (USGS)  colleagues for many remarks and recommendations:  Annemarie Baltay  and  USGS reviewers Jack Odum  and  Sean Ahdi. Final USGS reviews by  Shane Detweiler  and  Michael Diggles  improved this article.
However, the temporal-spectral and spatial domains are not totally separated from each other in ultra-short-pulse-based PW lasers. The induced space-time/spectral coupling effects are very specific features that could dramatically impact the targeted laser intensity even for the most carefully designed laser systems. In this work, with the help of the Miró software, a powerful 3D modeling code developed by the CEA DAM, France  [10]  , we present a synthesis of simulation studies, showing the need for careful consideration of a large number of parameters and the necessity of global optimization of PW-class systems, throughout the different sub-systems of the laser down to the end-chain target area. For our analysis a typical Ti:sapphire-based, multi-PW-class laser configuration is considered.
( 6 ) Focusing such a beam at a short focal distance F, compared to the F NL , will have as an impact the displacement of the focal point longitudinally to about (1/F + 1/F NL ) -1 , resulting in a chromatic focus spread of F (λ) = F 2 /F NL (λ). Expressing this spread in terms of Rayleigh length units Z r would result in F (λ) = Z r (π/λ 0 ) R 2 /F NL (λ) or equivalently in the following: F (λ) = 4Z r ϕ NL (λ). ( 7 ) To have a better insight into the effect and investigate its impact in real world PW-class systems, we used the Miró software in a series of simulation cases. We first consider a Gaussian beam with 140 mm diameter (1/e²) and a chirped pulse of approximately 1 ns stretched from 15 fs. The beam is going through an NL medium just before being focused by an ideal focuser of f = 3 m.
We have modeled a target building and calculated its dynamic response using the recordings of the 46 strongest earthquakes of the current crisis as the input. The dynamic calculations were performed using open source finite element software, Opensees © Berkeley. We analyzed the dynamic response at the top of the building in terms of the period of vibration, energy dissipated, and drift. We used the aforementioned 46 recordings from two different seismological stations in terms of site effects: YTMZ station, considered as rock, and MILA station, which shows strong site effects and therefore the strongest recordings in terms of acceleration. We try to find a correlation between the wavelet energy dissipation and the observational damage degree or driftbased damage degrees. We found out that about 25% of the 46 earthquakes selected on soils with site effects (MILA station) have building response parameters corresponding to degrees of damage D1 and D2 of the EMS-98 scale: negligible to light damage (no structural damage and slight non-structural damage) and moderate damage (light structural damage and moderate non-structural damage).
STRUCTURAL MODEL For the numerical modelling of the building, we used the opensource software OpenSees (Open System For Earthquake Engineering Simulation (c) Copyright 1999-2016 The Regents of the University of California) developed by the Pacific Earthquake Engineering Research  (PEER, 2006)  OpenSees to calculate the dynamic response of a building. We chose a simple RC frame building with a light reinforcement. The building has two levels, two bays in the longitudinal direction, and two bays in the transverse direction (Figure  4 ). The geometry of this building was chosen in order to represent, as well as a possible, a typology of residential buildings common in Mayotte. In addition, in order to take into account certain constructional problems present in Mayotte and likely to increase the vulnerability of the building, we have introduced several structural details: (i) weakly reinforced columns and beams (corresponding to a calculation under own weight) in order to represent a building designed without a seismic code, (ii) the first floor is slightly less rigid than the second to represent transparency and the presence of piles, and (iii) addition of a small torsion effect to represent a non-symmetrical wall distribution.
To model the structural elements, we used fiber sections of the beams and columns available in the OpenSees software (Figure  4 ). All elements that compose the building resistance (beams and columns) are inelastic elements available in the OpenSees software. They are based on force formulation. The nonlinearity of the concrete material is represented by a uni-axial material model  (Kent and Park, 1971 ) made of concrete with degraded linear unloading/reloading stiffness according to the work of Karsan-Jirsa and no tensile strength. Since it is assumed that there is no adequate shear reinforcement in the columns and beams of the buildings, the confining effect of the core concrete is minimized. The first three periods of the vibration of the building are 0. 28, 0. 28, and 0. 26 s, respectively.
Data acquisition During the experiment, the samples were scanned ex-situ on the x-ray microtomography instrument at the School of Geosciences, University of Edinburgh in regular intervals (for acquisition parameters see Table  A1 , appendix). To enable this, the oedometers were unloaded and temporarily removed from the loading frames. Nineteen scans (SBS sample) and ten scans (SB sample) were acquired over a total duration of 1619 hours and 1932 hours, respectively. At the beginning scans were collected in shorter time intervals to image the rapidly progressing deformation within the first 200 hours of the experiment. As the compaction slowed down, the intervals between each scan were gradually extended to monitor chemical compaction processes. The time-resolved 3D data series obtained in this way were then combined into three 4D data sets capturing the dynamic evolution of the porosity in the different samples.
Data processing After each scan, Octopus ® software v. 8. 9  (Dierick et al. , 2004)  was used to reconstruct the µCT data from the radiographic projections. The resulting stack of 2D images, which contains a virtual representation of half of the sample, covers a volume of 5 × 10 3 µm 3. Each time step comprises two scans, vertically translated to cover the entire sample including the sample base and the top piston. Reconstructed image stacks were merged using the image processing software AVIZO ® v. 9. 2. The relative shortening of the sample was calculated using ε t = L 0 -L t L 0 (1) with the relative vertical shortening ε t at the time t, the initial sample height L 0 and the sample height L t at the time t. The height of each sample was determined from the merged microCT scans. The µCT data were prepared for analysis by pre-processing in Fiji-ImageJ  (Schindelin et al.
NaCl redistribution measurements In order to quantify the amount of NaCl migrating within the sample we used two different methods to isolate a pure NaCl signal from the rest of the sample. The first method is similar to the measurement of the 2D porosity. For each slice along the loading axis of a microtomography scan we segmented the NaCl in the grey scale image using Trainable Weka Segmentation for the pure NaCl sample and the Deep Learning Segmentation of Dragonfly software for both biotite-bearing samples. From the segmented data we measured the relative area of NaCl in the 2-dimensional image and calculated its proportion relative to the cross section of the sample. The results were plotted as relative NaCl content on the abscissa and the number of slices along the loading axis on the ordinate (Fig.
Digital Volume Correlation Digital Volume Correlation (DVC) was used to quantify deformation on the grain scale between pairs of consecutive µCT data sets using the python packages SPAM  (Stamati et al. , 2020)  and TomoWarp2  (Tudisco et al. , 2017). A grid of regularly spaced measurement points was defined in the reference image. Each of these points is the centre point of a cubic correlation window. A corresponding cube in the deformed image was transformed with a homogeneous deformation function in order to minimise a sum-of-squared-differences error function. The correlation parameters were individually tuned for each data series and are documented in Table  1. The DVC analysis was completed by strain determination. A finite strain tensor U and its first two invariants -the volumetric strain (Eq.