dataesr/scientific-paragraphs-categorization huggingface dataset:
It consists of a collection library conceived in a component-oriented architecture and considers the proxemic variables between people, objects, and devices. The toolkit involves four main components: (1)Proximity Toolkit server, (2) Tracking plugin modules, (3)Visual monitoring tool and (4) Application programming interface. A proxemic designers' tool for prototyping ubicomp space with proxemic interactions is presented in  (Kim et al. , 2016). It is built using software and modeling materials, such as: photoshop, paper and Lego. Interactions can be defined in photoshop based on proxemic variables. The tool uses an augmented reality projection for miniatures to enable tangible interactions and dynamic representations. A hidden marker stickers and a camera-projector system enable the unobtrusive integration of digital images on the physical miniature.
Data recording and processing Joint kinematics was captured using eleven IMU sensors (5. 6 cm × 3. 8 cm × 1. 8 cm) positioned on the subject according to the « Lower body (with heels) plus spine » model of the iSen software. They were connected to the computer via a wireless Wi-Fi connection. IMU were fixed on the subject with adhesive scratch bands on the feet (rearfoot and forefoot), shanks, thighs, pelvis, chest and back (Fig. 1 ). Subjects were asked to remove their shoes before beginning the test, so that the IMU could be placed directly on the feet. iSen data were sampled at 100 Hz. Acquisitions were recorded simultaneously at 100 Hz by nine infrared cameras using OptiTrack Motive 2. 2. 0 (NaturalPoint, Inc. , Oregon, USA). Ground reaction forces were collected at 1000 Hz with two force plates under the treadmill.
Heel-strike recorded with MOCAP force plates was used for cycle segmentation on knee, hip and ankle joint at each gait speed. Thirty-two passive reflective markers were placed with double-sided hypoallergenic adhesive tape on the anatomical landmarks of the subject, according to Rizzoli Lower Body markerset protocol in Motive (NaturalPoint Product Documentation,  [15] ). Three more markers were placed on shoulders, elbows, and wrists (Fig. 1 ). Data were recorded simultaneously with the inertial and the MOCAP system and to synchronize the two signals, a cross-correlation between the norms of the angular velocities of the knee from the two systems was applied using Matlab (Version R2018b. Natick, Massachusetts: The MathWorks Inc). Data obtained with the optoelectronic system were exported as C3D files and kinematics calculations were conducted using MATLAB.
Data analysis and statistical calculation To assess the validity of IMU system, Root Mean Square Deviation (RMSD) was computed on kinematic walking cycle. Lin's Concordance Correlation (LCC) with 95% CIs was calculated to determine the strength of the agreement. Normality of the data was checked using Shapiro-Wilk test. RMSD and LCC calculations were conducted via MATLAB. Bland-Altman analysis was performed with XLSTAT (XLSTAT version 2021. 2. 2) to analyze bias and limits of agreement. For test-retest reliability, a comparison of gait parameters was made between first and second trials using interclass correlation coefficient (ICC) of type (2,1). SPSS 23. 0 statistical software was used for the calculation of ICC. Two-Way Random Model was considered. A two-way repeated-measure analysis of variance (ANOVA) was performed to compare joint angles.
Discussion The objective of the study was to validate iSen IMU kinematics with reference to the optical motion capture system (OptiTrack). The accuracy of iSen IMU was evaluated by comparing data obtained with both systems during treadmill walking. Results indicate that iSen has an acceptable and tolerable accuracy in the evaluation of lower-limb kinematics during gait in the sagittal plane. IMU measurements showed excellent repeatability during test-retest experiments. No significant difference was found between discrete variables (max, min and range of motion) of knee and ankle joint, regardless of the speed. IMU system captured well knee kinematic changes induced by walking speed. However, hip range of motion and ankle dorsiflexion were significantly different at high speed (1. 2 and 1.
Average gait cycle was identical between first and last 30 s (RMSD < 2 • ). Relative error on RMSD values highlighted a non-significant increase of kinematics discrete parameters within the three-minute acquisitions. IMU system seems to deliver stable output data over time. Results align with existing gyroscopes presenting drift biases in the order of 0. 05 • /h  [32]. Drift effect can be neglected during a few minutes of continuous use. Regular calibration, before launching each three-minute acquisition, to correct drift offset was well integrated by the iSen software. Drift errors were effectively compensated in IMU algorithms to provide an accurate motion follow-up. One of the limitations of this study is the focus of inertial sensors measurements in the sagittal plane. Future work should include validation in the three axes.
Accounting for 3-D Earth structure at shorter periods can increase the double-couple (DC) component of an event, compared to the GCMT solution, by tens of percent. This suggests that non-DC events in the GCMT catalog may result from unmodeled Earth structure and the related limitation to longer-period data. We also observe that significant changes in source parameters, and the DC component in particular, may be related to only small waveform changes, thereby accentuating the importance of a reliable Earth model. Posterior probability density distributions become increasingly multimodal for shorter-period data that provide tighter constraints on source parameters. This implies, in our specific case, that stochastic approaches to the source inversion problem are required for periods below ∼20 s to avoid trapping in local minima.
To ensure a rapid forward problem for probabilistic inference, we pre-compute and store the Green's strains in a database, taking advantage of spatial reciprocity. The merits of reciprocity for the computations of Green's functions have also been exploited in previous studies (e. g. ,  Eisner & Clayton, 2001; Hejrani et al. , 2017; Lee et al. , 2011; Okamoto et al. , 2018; Takemura et al. , 2020; Zhao et al. , 2006). The reciprocal formulation of Equation 3 can be found in  Fichtner and Simutė (2018).
Database of Green's Strains We compute Green's strains numerically with the spectral-element solver SES3D  (Fichtner et al. , 2009b; Gokhberg & Fichtner, 2016). Enabled by reciprocity, we treat seismic stations as virtual sources and save the wavefield across the actual source area of interest, that is, the Izu-Bonin trench. To ensure a continuous representation of the wavefield within the domain, we store the wavefield on Gauss-Lobatto-Legendre (GLL) points of the fourth-order spectral-element method (SEM) grid and use the built-in polynomial interpolation of SEM to extract the wavefield for any spatial coordinate. This contrasts with the common practice of storing the Green's functions on a pre-defined grid (e. g. ,  Hejrani et al. , 2017; Lee et al. , 2011; Takemura et al. , 2020; Vackár et al.
, 2017) , where one has to implement an interpolation routine or deal with a finite number of discrete locations and possibly limit the spatial resolution of the earthquake location. Storing the wavefield itself allows us to extract the strains for any potential source location and be exempt from any additional parametrization effects. We compute the database for over 50 selected F-net broadband stations (Figure  5 ) uniformly distributed across the network (National Research Institute for Earth Science and Disaster Resilience, 2021). The source area of interest extends between 140°E and 143°E, 30°N-35°N, and down to 110 km depth. The downsampled wavefield with a time increment of 2 s takes 27 Gb of space for a single virtual source, and the total storage requirements are 4. 2 Tb. The database is stored on Piz Daint supercomputer in the Swiss National Supercomputing Center, which we use to rapidly perform the inversions (Swiss National Supercomputing Center, 2021).
Study Events We study events of moderate magnitudes, which fall within the area of our strain database (Figure  5 ) and have a significant CLVD component. Preference is given to more recent earthquakes away from the database boundaries. We selected 13 events from the Global Centroid-Moment-Tensor (GCMT) catalog  (Dziewoński et al. , 1981; Ekström et al. , 2012; The Global CMT Project, 2021). The earthquakes have moment magnitudes between M w 4. 8-5. 3, are distributed within 13 and 64 km depth with CLVD component ranging between 7% and 62% (in absolute sense), with a median value of 36% (Figures  5  and 6 , Table  S1  in Supporting Information S1).
Our measurements include body waves and fundamental-and higher-mode surface waves. No arbitrary waveform shifting is required, as all relevant time shifts are taken care of by the previous full-waveform inversion. Assuming uncorrelated Gaussian data noise, our data covariance matrix C D is a diagonal matrix, entries of which we conservatively estimate from the pre-signal noise. We express prior probability density on model parameters as Gaussian distributions with standard deviations around 10 times larger than the parameter mean for the moment tensor elements (1 × 10 17 N m), and 2°, 20 km, and 2 s for horizontal location, depth, and centroid time, respectively. As a prior mean we commonly use the solution provided by the GCMT  (Dziewoński et al. , 1981; Ekström et al. , 2012). However, for some events  (IDs 20050816, 20040407)  we update the location first by running a preliminary inversion with a reduced number of samples, and then use the posterior maximum-likelihood model from this inversion as a prior mean for the main inversion.
Source solutions inferred with 50-80 s period data (Figure  15 ) have negligible deviations from the GCMT solutions, both in terms of moment tensor and location. This means that inversions at long periods either have weaker constraints from the data and are dominated by the prior or that the likelihood term at longer periods favors the same solution as that given by the GCMT. An overview of depth relocation across different inversions is presented in Figure  16. Similar to the source mechanisms, the largest deviations in depth come from the shortest-period band inversions. Events inferred with 50-80 s period data maintain almost the same depth as given by the GCMT catalog.
Increase of Double-Couple Component Accounting for heterogeneous Earth structure enables us to use shorter-period data (15-80, 20-80 s) in regional source inversion. This leads to a higher double-couple component compared to GCMT solutions, and it suggests  that at least part of the CLVD component, given by the GCMT catalog, is likely to be an artifact caused by forward modeling errors. This is supported by the comparison of posterior distributions for the 3-D full-waveform inversion model and its 1-D lateral average, shown in Figure  8. A similar trend of increasing DC component when 3-D Earth structure is taken into account has been shown by  Hejrani et al. (2017)  and X. Wang and Zhan (2019)  for earthquakes in the Papua New Guinea and Los Angeles regions, respectively.
Information Content Long-period data may not contain enough information to properly constrain the source parameters of relatively small-magnitude earthquakes. First, data are more correlated at longer-periods and hence, carry less independent information. This results in inherent trade-offs between moment tensor components, such as M zz and (M xx + M yy ), which trade off in the case of long-period surface wave inversion  (Dufumier & Rivera, 1997; Fitch et al. , 1981). Second, the amplitude spectrum of small-magnitude events tends to decrease with increasing period  (Aki, 1967) , and hence, the signal-to-noise ratio at long periods may therefore, be diminished. Long-period data used in this study, that is, body and surface waves in the 50-80 s period band, are comparable to the data used to construct the GCMT catalog.
This suggests that at least some part of the non-DC component in the GCMT catalog might be apparent-resulting from unmodeled Earth structure. Events inverted at longer periods (e. g. , 50-80 s) (Figure  15 ), do not show a significant change in mechanism or centroid location and stay close to the GCMT predictions. We have presented several possible mechanisms to explain the effective solutions, the most likely being near-source propagation effects, which cannot be resolved by long-period data, or weakening data constraints with increasing periods. Constraining isotropic component to zero is a common practice in order to stabilize the inversion and to prevent unexplained waveform differences from mapping into the additional parameter. Here, we observe that owing to the 3-D Earth model, isotropic component remains weak even when unconstrained.
The diagonal components of the moment tensor, which are responsible for the volumetric change, are significantly less constrained, compared to the off-diagonal ones (Figure  10  and Figure  S8  in Supporting Information S1). From the events which could be inverted in all period bands, we also see that shorter-period data constrain the source parameters better than the long-period data (Figure  10  and Figure  S8  in Supporting Information S1). Posterior probability density distributions of shorter-period data inversions appear multimodal and are more complicated than those of long-period. This illustrates non-uniqueness of short-period source inversions and highlights the need for stochastic approaches. We note that finding a solution, which has a significantly better waveform fit than that provided by the GCMT catalog, is a challenging task.
Data Availability Statement All seismic waveform data used in this study are freely available from the Full Range Seismograph Network of Japan (F-Net, http://www. fnet. bosai. go. jp), the Broadband Array in Taiwan for Seismology (BATS, http:// bats. earth. sinica. edu. tw), the Korea National Seismograph Network (http://www. kma. go. kr/weather/earthquake/ internationallist. jsp), and the China National Seismic Network, the New China Digital Seismograph Network, the Northeast China Extended Seismic Array, the Global Seismograph Network, and the Korean Seismic Network, made available by the IRIS Data Management Center (http://ds. iris. edu/ds/nodes/dmc/). The centroid moment tensors were obtained from the Global Centroid-Moment-Tensor Catalog (www. globalcmt. org, http://ds. iris. edu/ spud/momenttensor) and National Research Institute for Earth Science and Disaster Prevention Seismic Moment Tensor Catalog (https://www.
fnet. bosai. go. jp/). Seismic wave propagation software, SES3D, used to model the waveforms is available on https://cos. ethz. ch/software/production/ses3d. html. Seismic tomography model of the Japanese islands, developed and used in this study, can be downloaded from https://cos. ethz. ch/research/CSEM. html. Processed observed as well as synthetic waveform data for all the study events are available at https://doi. org/10. 5281/zenodo. 7384213 (Simutė, 2022).
Typically, the optimization of the purification relies on multiple experiments and analysis of the products. However, to the best of our knowledge, a strategy to determine a priori the most suitable temperature has not been reported yet. We demonstrate here that a thermogravimetric method, namely the constant decomposition rate thermal analysis (CRTA), is particularly well adapted to answer this question. An isothermal treatment based on the results obtained from a CRTA program allowed arc-MWCNTs to be successfully purified from graphenic shells while optimizing the yield of the MWCNTs. This strategy is believed to be valuable not only for purifying MWCNTs but also for the purification of other carbonaceous forms, including new carbon nanoforms.
CRTA Measurement First, 6. 7 mg of the raw powder was placed in a platinum crucible previously cleaned with hydrochloric acid (37 %-sigma Aldrich), in order to perform the CRTA analysis. The analysis was conducted in a thermogravimetric analysis machine (TGA-TA instrument TAQ50) by two gas cylinders (Air Liquide): the synthetic air needed for the combustion and nitrogen to protect the balance. The balance flow (N 2 , coming from above the crucible) was set at 40 mL/min, and the mass flow (synthetic air coming from one side) was set to 60 mL/min. The CRTA program used was the following: 1. Ramp 50 • C/min to 250 • C; 2. Abort next segment if %/min > 0. 20 ("%/min" corresponds to the weight-loss rate); 3. Ramp 2. 5 • C/min to 700 • C; 4. Abort next seg if %/min < 0. 05; 5. Isothermal for 1000 min; 6.
1 to ~0. 4, suggesting that at least one kind of material, the most defective one, was burned away by the treatment (purification). The Raman resonance effects being a priori different for the different materials, no quantitative analysis can be made from the measurement. In Figure  4b , one can see that the treatment has narrowed the G band from a mean FWHM of 23. 7 to 21. 0 cm -1. This is fully consistent with a purification of the material  [21]. Comparing the mean Raman spectra before and after treatment (see Figure  S1 ), almost no difference is actually visible, and it is quite remarkable that statistical analysis highlights the difference between those two datasets  [27].
2). Then, the controller starts to heat again to find the next thermal event, in a loop. This may allow the controller to self-find the temperature for the combustion of different species inside the powder. As a result, with a good choice of parameters for the CRTA program (that may need adjustment), the temperature at which the impurities start to burn can be precisely determined. Several types of impurities with distinct structural or chemical features would be even more easily discriminated than in the challenging example used here. If such impurities had been present here, more steps would be visible on the temperature profile, at lower temperatures, without necessarily implying a difference in the weight-loss rate.
In fact, stirring the material during the combustion reduces the diffusion limitations versus an immobile powder. While it has been shown over and over that the carbon combustion can easily suffer diffusional limitation  [14] , the use of "low" temperature, i. e. , temperature close to the ignition temperature, favors the kinetic regime  [13, 31]. In the CRTA program, the temperature at which the apparatus will stabilize first (first isothermal step), depends on the choice of the criterion for the weight-loss rate. This criterion should consequently be selected to allow the reaction to be slow enough compared to the oxidant feeding, in order for the reaction to occur in the kinetic regime. Finally, the use of the CRTA program in equipment allowing the homogenization of the powder by stirring the powder during the measurement could probably allow reaching a higher selectivity for the carbon combustion, e.
Conclusions The CRTA, an uncommon TGA program in which the material weight loss controls the temperature of the machine, was successfully employed to determine the good parameters for arc-discharged MWCNTs purification. Thanks to a good selection of the temperature which also avoided diffusion limitations, combustion was selectively performed on the curved part of the carbon materials, as far as possible. This allowed collection of about 100% of the starting MWCNTs (initially present at ~30%), mostly opened at their end and in a highly pure fraction. Beyond the purification of arc-discharge MWCNTs, the use of the CRTA program may prove very valuable for the purification of any kind of carbon material including those yet to be discovered. While here the method was demonstrated in the absence of a catalyst, the same strategy could also be successful in principle in the presence of a catalyst, which is the case of raw single-wall carbon nanotubes, although this would merit further investigations.
Tropical cyclones are responsible for a large share of global damage resulting from natural disasters and estimating cyclone-related damage at a national level is a challenge attracting growing interest in the context of climate change. The global climate models, whose outputs are available from the Coupled Model Intercomparison Project (CMIP), do not resolve tropical cyclones. The Cyclone generation Algorithm including a THERmodynamic module for Integrated National damage Assessment (CATHERINA) presented in this paper, couples statistical and thermodynamic relationships to generate synthetic tracks sensitive to local climate conditions and estimates the damage induced by tropical cyclones at a national level. The framework is designed to be compatible with CMIP models data offering a simple solution to resolve tropical cyclones in climate projections.
The objective of this paper is to fill this gap: we build synthetic cyclones based on the climate data produced by the global climate models, and evaluate the economic damage of these synthetic cyclones under various assumptions. The physics of tropical cyclones has been intensively studied in the literature. The thermodynamic cyclone theory builds upon the seminal contributions by  Emanuel (1988)  followed by  Holland (1997)  and  Emanuel (1999). Concerning the impact of Framework from climate models allowing to define locally the maximum potential intensity based on the simplified expression in Holland  (1997). Some controls such as the maximum pressure drop observed for the corresponding temperature and the decay relationship for cyclones evolving over land are also fitted for each basin and applied in the synthetic tracks generation algorithm.
Extracting the climate variables from different models allow us to correct the biases and evaluate model uncertainty. Then, we use the physical asset values  (Eberenz et al. , 2020b)  and regional damage functions from the CLIMADA module to evaluate the cyclone-related damage at a national level. This step requires to extract the local physical asset values and aggregate them on tiles of length defined proportionally to the average radius to maximum wind (∼ 50 km) along the cyclone path. Summing the losses along tracks for each year and for each country allows us to establish a national assessment of the damage generated 65 by tropical cyclones. The paper is organized as follows. Section 2 describes the datasets used to fit the model on ERA-5 and to generate synthetic cyclones based on both ERA-5 and CMIP5 models datasets.
Section 3 describes the statistical calibration process and the details of the thermodynamic instrumental variables. Section 4 recalls the calibration methods implemented in the CLIMADA environment to fit the regional damage functions, defines the parameters of these functions using  Eberenz et al. (2020a)  and applies them along the synthetic tracks produced to study the distribution of national annual damages. Section 5 explores the properties of the produced synthetic tracks, with ERA-5 and 7 CMIP models, assesses climate uncertainty and introduces the bias correction module in the context of changing climate conditions. To close this section, we present the global and regional projections of cyclone damage between 2070 and 2100 obtained with CATHERINA.
Climate model data (CMIP) CATHERINA aims at generating cyclones tracks with properties drawn from climate models to enable national damage assessments, bridging the gap between AOCGM outputs and damage assessments. To reduce the bias in the variables produced by climate models 1 and evaluate the performance of CATHERINA on past data by comparing the simulated cyclone damages to the realized ones we use historical simulations (as opposed to future climate projections) from the Coupled Model Intercomparison project (Phase 5) models  (Taylor et al. , 2012). We use the historical climate simulations at the monthly frequency for the relative humidity (RH) at two meters, sea surface temperature (SST), sea level pressure (MSLP) and tropauspose temperature (T tropo ) (at pressure level of 50 hPa) from Copernicus climate data store 2.