dataesr/scientific-paragraphs-categorization huggingface dataset:
The use of DVC in changing samples In our analysis, we used continuum DVC to analyse samples that are changing, which violates a core assumption of image correlation, that of the preservation of mass. The fact that material gets dissolved and re-precipitated elsewhere should, in principle, cause problems with convergence of the algorithm. However, across all analysed datasets and increments, the analysis did correlate very well (videos S2 in supplementary material), with only negligible proportions of the volumes not correlating. This observation was persistent across all analysed data. DVC is very sensitive to the choice of input parameters half-window size and node spacing which must be adapted to the underlying microstructure -here both of these parameters were carefully tuned to the grain size.
Conclusions To our knowledge, the use of silk materials as adsorbents has rarely been studied to date. Herein, the maximum adsorption capacity of SF hydrogels has been evaluated. The better fit of the experimental data with the Langmuir adsorption model agrees with most of the studies on organic dye adsorption found in the literature. Moreover, the results found in this section strongly suggest an electrostatic-mediated interaction between MB and SF hydrogels. This interaction is in fact impaired in the presence of salt, or within an acid media due to the positive charge of SF materials at this stage. In addition, the use of an anionic dye resulted in an opposite behavior confirming the electrostatic interaction hypothesis. This work also shows the possibility of using pH 3 solutions to restore and reuse SF materials.
A comparison in the literature was not found also in this case. Fig. 3b  shows the elastic modulus of the intermetallic phases. SiC particles were the microstructural feature with the highest elastic modulus, of ~415 GPa on average. The range measured is in line, although slightly lower, than the 450-480 GPa available in the database  [43]. The α'-Al 13 (Cr,Mn,Ni) 4 Si 4 phase had ~240 GPa while the other Feand Ni-based phases had in the range of 175-190 GPa. The ε-Al 3 Ni phase had ~130 GPa of elastic modulus, a value close to the one measured for Al-Si eutectic (Fig. 2b ). Several models have been developed for modelling the elastic modulus of composite materials  [45] [46] [47] , and they are summarised in Table  4. The rule of mixture (ROM) in Equation (  1 ), also known as the Voigt model, represents the upper-bound modulus and correspond to loading parallel to reinforcement.
Name Model Equation Voigt -Rule of mixture (ROM) Ec = VpEp + ( 1 -Vp ) Em (1) Reuss -Inversed rule of mixture (IROM) Ec = ( Vp Ep + 1 -Vp Em ) -1 (2) Halpin-Tsai Ec =Em •1 + 2s• ( Ep/Em -1 Ep/Em + 2s ) •Vp 1- ( Ep/Em -1 Ep/Em + 2s ) •Vp (3) Hashin-Shtrikman (H-S) Ec = 9K * G * 3K * + G * (4) G * = Gi + Vj [ 1 Gj -Gi + 6Vi(Ki + 2Gi) 5Gi(3Ki + 4Gi) ] -1 (4. 1) K * = Ki + Vj ( 1 Kj -Ki + 3Vi 3Ki + 4Gi ) -1 (4. 2) Eshelby Ec = [ 1 Em + Vp ( Km -Kp 24KpGm + Gm -Gp 3GpGm ) ] -1 (5) bounds. The ROM and IROM application to the matrix alloys, which can be seen as composite materials reinforced by the secondary phases, resulted in the ranges of elastic moduli listed in Table  5. The fraction of the main phases came from ThermoCalc simulations with the TCAL6 database. The phases α'-Al 13 (Cr,Mn,Ni) 4 Si 4 , (Al,Si) 3 (Zr, Ti), and T-Al 9 FeNi were grouped as "others" in Table  5 , and the average modulus of 202 GPa was assigned.
The improvement due to the combination of alloying elements and SiC particles is considerable compared to the 100-102 GPa of the reference composite material investigated by Jarfors et al. [49]. The elastic modulus is critical for brake discs, where stiffness is one of the main parameters with thermal conductivity and density to optimise the material selection. The selection indexes considered in the present study are I w = ρ/E 1/3 and I q = 1/(λ•E 1 3 ), designed for weight minimisation and heat transfer maximisation of a stiff panel, respectively. In both indexes, ρ [kg/m 3 ] is the density, E [GPa] is the elastic modulus, and λ [W/m*K] is the thermal conductivity. The GCI data from the Cambridge Material Selector database were used for calculations. Considering the H-S bounds, the I w of the investigated MMCs is 60 % lower than GCI  [5] , from 1.
Acknowledgements The Stiftelsen för kunskaps-och kompetensutveckling ( KK-Stiftelsen ), Sweden, supported this work through the  ProForAl  Project [diarienr. 20201702]. The  Erasmus+ Program for student from ENSICAEN, France is also acknowledged for the internship program  from  ENSI-CAEN, France. The authors gratefully acknowledge Automotive Components Floby AB,  Sweden , for providing the experimental material and the support of the technical staff at the  Department of Materials and Manufacturing of the School of Engineering, Jönköping University.
ACKNOWLEDGMENTS We acknowledge financial support in Chile from  FONDECYT  Grants No. 1200867  and  1190727 , and  Financiamiento Basal para Centros Científicos y Tecnológicos de Excelencia   FB 0807 (AFB 180001 ). D. M. -A. acknowledges Postdoctorado  FONDECYT 3180416 2018  and  Proyecto Postdoc DICYT ,  Código 042131AP POSTDOC ,  Vicerrectoría de Investigación, Desarrollo e Innovación. M. A. Castro acknowledges Conicyt-PCHA/Doctorado Nacional/2017-21171016. This work was supported in part by the  ERC  Grant MAGICAL (N o  669204 ).
Results We observe that olfactory ratings are indicative of COVID-19 status in a country, significantly correlating with national infection rates over time. More importantly, we observe indicative power at the individual level (79% sensitivity and 87% specificity). Critically, this olfactory screen remains effective in participants with COVID-19 but without symptoms, and in participants with symptoms but without COVID-19. Conclusions The current odorant-based olfactory screen adds a component to online symptom-checkers, to potentially provide an added first line of defense that can help fight disease progression at the population level. The data derived from this tool may allow better understanding of the link between COVID-19 and olfaction.
In a reported follow-up we then analyzed an added 1464 participants, culminating at 13,484 participants. This study size reflected a balance between two considerations: Web-based questionnaires on odor intensity and pleasantness gain sufficient power at 198 participants per odorant  16. We wanted this power for at least 40 odorants, and indeed here report on 42 odorants with more than 198 respondents. In turn, we did not wait for this number of respondents on all possible odorants, as we wanted to report on this in a timely manner, given the progression of the pandemic. Web-tool. We built an online odorant rating tool (www. smelltracker. org). The tool was written in open-source Drupal (drupal. org), and translated into 15 languages by the coauthors who are all respective native speakers. Using the tool, participants first created a unique login to facilitate repeated testing.
In addition to the graph, participants were presented with a text informing them whether their perception was within range of most participants, or aberrant. Based on the results now obtained and reported in this manuscript, we have only recently modified the feedback component such that the system now also informs participants to what extent they resemble a person who is COVID-19 positive or COVID-19 negative. Given regulatory restrictions, this is as close as we could get to giving a diagnosis. This extended feedback, however, was not provided to participates reported on in this manuscript. Statistics and reproducibility. All analyses were conducted using Matlab software, and the complete data file allowing full recreation of these results is in Supplementary Data 1. For initial analysis of intensity and pleasantness, we restricted our analysis to 23 odorants that had more than 25 C19+ raters.
Average intensity ratings of the five odorants were calculated for each entry. 2. Mean intensity ratings were inverted by subtracting them from 100. This was done so that higher values imply greater smell loss. 3. A 5-day moving average was calculated by averaging all ratings in the span of 7 days. We used this moving average to match the cases span. After obtaining these two values, a cross-correlation between the daily ratings and inverse intensity was then calculated (using the xcorr function in Matlab). The cross-correlation analysis resulted in a correlation between the two signals for different lags (between 14-days earlier to 14-days later response) in the inverse intensity signal. The lag that produced the maximal correlation between the two signals was chosen for the analysis. The Pearson correlation between daily cases and lagged inverse intensity was calculated.
4%) used it twice, and the remaining 6. 6% of participants used it various number of times (Fig. 1f ). In total, the 12,020 participants provided 171,500 ratings applied to 60 different odorants (i. e. , 11 odorants were never rated) (the entire raw data file is available in Supplementary Data 1). To probe for any gross differences in olfactory perception between C19+, C19-, and C19-UD, we plotted their overall odorant intensity and pleasantness estimations (Fig. 2a-c ). Consistent with previous reports 2,9,32-34 , these gross plots revealed pronounced differences between groups in intensity (two-sided Kolmogorov-Smirnov, D = 0. 45, p = 3. 96e-233, corrected) and pleasantness (D = 0. 31, p = 2. 36e-114, corrected) (Fig. 2a-c ). To provide a finer-grain view of this, we examined individual odorants, restricting our analysis to odorants that were rated by at least 25 C19+, thus retaining 23 odorants.
Because a Kolmogorov-Smirnov normality of distribution test revealed that data for some of the odorants was not normally distributed, we proceeded with a non-parametric approach. A Chi squared test comparing C19+ and C19-ratings was significant for each of the 23 odorants (all Chi Square > 7. 6, all p < 0. 0058, all Eta squared effect size > 0. 08) (Fig. 2d ), and the same test on pleasantness ratings was significant for 17 of the 23 odorants (all Chi Square > 8. 69, all p < 0. 0188, all Eta squared effect size > 0. 02) (Fig. 2e ) (we note that replicating this analysis using a parametric analysis of variance yielded nearly identical results). Having observed that, consistent with previous reports, these gross measures of perception implied altered olfaction in COVID-19, we asked whether they were related to the COVID-19 status over time in the different countries where we collected data.
Second, whereas we could retain many participants for the test of symptomatic C19+ vs. symptomatic C19-participants, we had much fewer participants for the asymptomatic C19+ vs. asymptomatic C19-participants. This is because we had very few completely asymptomatic C19+ participants (our strict criteria were to exclude for even just one somatic symptom reported). To address both of these limitations, we set out to download an additional ensuing data-set from our web-tool. In addition to testing the exact same algorithm in an independent dataset, the advantage of this is that this later (calendar-wise) data-set corresponds to an increase in testing frequency world-wide, thus making for a much higher proportion of asymptomatic C19+ participants. We downloaded data from asymptomatic users only, who participated between 03/25/2020 and 04/10/2021.
Case studies A large number of case histories exist where MAM investigations are supplemented with three-component horizontal-to-vertical spectral ratio data (HVSR). The HVSR method is reviewed in detail by  Molnar et al. (2018; 2021). We include HVSR data for the Mexico City case history discussed in this section. The M W 8. 0 earthquake that struck Mexico City on 19 September 1985 caused severe damage although the city was 400 km from the epicenter. The main reason for this is that the city is located on a very soft sedimentary basin  (Abe 1986 ). The distribution of these soft sediments has been delineated by drillhole records and microtremor measurements  (Lermo and Chávez-García 1994). Vs profiles were measured by downhole seismic logging  (Seo 1986 ) at only seven sites, and the regional Vs structure of the whole basin was not delineated.
Lessons from the COSMOS blind trials 2018-2020 The COSMOS blind trials of MAM methods involved 34 groups of analysts using about 13 different software packages to perform blind interpretations on MAM array data from four different sites in the USA, Canada, and Europe  (Asten et al. 2019a; 2021a). For each site, the array data were released to analysts in four phases, allowing objective analysis of the usefulness of simple two-station arrays, sparse arrays (simple triangles), and dense arrays (circles or nested triangles). The final phase was reinterpretation and reconciliation of the geophysical data with all available geological information. The phase 1 results illustrated the dangers of erroneous interpretations of Vs when using two-station arrays in the presence of directional seismic noise.
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, 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 COS-MOS. This paper is Natural Resources of Canada contribution number 20200665. Open Access publication fees for this paper were directly funded by COSMOS. Data from Hollister Municipal Airport were acquired by Geometrics, Inc. as part of an internal project and are used here by permission of Geometrics, Inc. Funding Funding to assist with the preparation of this manuscript was provided by the U. S. Geological Survey.
Encouraging the open sourcing of appropriate code on public git repositories is thus a critical step to ensure the reproducibility and durability of the developments, to maximize their impact, and to ultimately help establish DRL as a mature and stable technique for the analysis and design of complex flow systems. In this respect, it is disappointing to note that only 9 out of the 32 studies compiled in this review have come with such open-source releases  [14, 41, 43, 44, 45, 40, 16, 59, 15]. Creating and providing exhaustive benchmark datasets and metrics is another alternative that would certainly add value to the community, and lay the ground for solid further developments in the field.
1  below and Fig. 50  of  [1] ), along with an image of the target. The information gathered from these techniques will help us understand the history of Mars's water, climate, and habitability. The instrument is split between the mast unit (MU), mounted at the top of the rover's remote sensing mast, and the body unit (BU), located inside the rover's body. The MU and BU are connected by a 5. 8 m optical fiber, as well as data and power cables. The MU consists of the telescope, infrared spectrometer (IRS), RMI camera, laser, and associated electronics. The IRS is used for passive reflectance observations at wavelengths from 1. 3 to 2. 6 µm. Detailed descriptions of its design, testing, and calibration are provided elsewhere  [4, 5]. This paper details the calibration of only the BU spectrometers, which constitute three of four SuperCam spectrometers, excluding the IRS which resides in the MU.
The BU contains an optical demultiplexer to distribute the light from the telescope to the appropriate spectrometer, three spectrometers covering the wavelength ranges from approximately 243-853 nm, a high voltage power supply, and a control and data handling unit. The BU spectrometers are used for LIBS, Raman, TRL, and passive reflectance spectroscopies. The BU spectrometers are made up of two crossed Czerny-Turner spectrometers (of ChemCam heritage  [6] ) for the UV (243. 5-341. 7 nm) and blue-violet (VIO; 382. 1-467. 5 nm) spectral ranges and an intensified transmission spectrometer for the green, orange, and red (GOR; 535-853 nm) spectral ranges. The transmission spectrometer uses an image intensifier (made by Excelis, now Elbit Systems of America) with selectable amplification. A detailed description of the BU is available in  [2].
A. Data Collection Instrument optical response data were collected during two shifts at NASA JPL's Spacecraft Assembly Facility in August and December 2019. In August, 30 observations were collected at a range of 10 m using the EQ-99, interspersed with 30 "darks" where the light source was covered with a matte black-anodized aluminum plate. Room lights were required to be on during the calibration for safety reasons, but were turned to the minimum allowed brightness. In December, 10 light/dark pairs were collected using the Labsphere at a range of 5 m, along with an additional 35 light/dark pairs with the EQ-99 at 5 m. The instrument was at room temperature (approximately 25-30 • C) in both cases. Satisfactory alignment and focus were verified through the use of the RMI camera which is co-boresighted with the BU spectrometers.
B. Wavelength Calibration Method Wavelength calibration was calculated using a LIBS spectrum of a titanium plate and two calibration targets (an ilmenite/hematite mixture, and a clinozoisite, quartz, and orthoclase mixture) collected during the system thermal test in October 2019. The known positions of 535 emission lines for Na, Al, Si,  K, Ca, Ti and Fe were mapped to pixel positions on the CCD (Figure  S1  in Supplement 1), and a continuous wavelength distribution was fit to this map using a cubic spline. The procedure is described in section 2. 3. 3 of  [7]  and has been in use with ChemCam since 2012. Figures  3 4 5 show the wavelength calibrations for the UV, VIO, and GOR regions, respectively. The wavelength calibration results are included in Data File 1, Data File 2, Data File 3, Data File 4, and Data File 5.
C. "Difference of Differences" Technique Each observation consists of 30 individual spectra taken in rapid succession with the same settings. Since the instrument response function (IRF) data were collected with the spectrometers at room temperature, we discarded the first of these 30 spectra to avoid a spurious signal due to excessive dark current that is cleared by the first readout of the CCD. We then take the mean   of the remaining 29 spectra and perform all later calculations using the mean. Then, in order to remove the portion of the recorded signal due to dark current and readout, we performed a simple two-step correction, as used with ChemCam  [7]. First, each mean dark observation was subtracted from the corresponding light one. This removed the portion of the recorded signal due to dark current on the CCD.
With that value and the distance to the target (d ), we can calculate the area on the target using A = π(FOV • (d /2)) 2. Multiplying by the area of the FOV on the target leaves units of photons/sr. Finally, the solid angle subtended by the telescope ( ) depends on the limiting aperture (a ) and distance to the target = π • sin tan -1 a 2d 2. (1) The limiting aperture for the telescope depends on distance. Since the front aperture of the telescope is slightly smaller than the primary mirror (104 versus 110 mm  [1] ), targets that are within 2. 55 m of the telescope "see" the full primary mirror and have a limiting aperture of 110 mm. Targets that are farther away "see" the primary mirror slightly occluded by the front aperture and have a limiting aperture of 104 mm. Since all optical calibration data were collected at distances of 5 or 10 m, the limiting aperture is 104 mm for this dataset.
The types of data collected in the two campaigns were not completely redundant. In August, we collected data with 16 and 200 rows in the UV. In December, we collected data with 16, 40, and 200 rows, but all of the 200-row data were saturated due to the closer distance of the lamp in the latter campaign. We determined that although the shapes were different between the different sessions, the total signal difference due to the number of rows collected was consistent. To calculate a consistent set of IRFs, we used the mean of the August and December 16-row IRFs, and then scaled it by the December (40-row IRF/16row IRF) ratio and the August (200-row IRF/16-row IRF) ratios. This estimate has proven to be sufficiently accurate for quantitative LIBS  [3]. The final UV IRFs are shown in Fig. 13.
C. Transmission Spectrometer IRFs The transmission spectrometer calibration data were collected at gains of 2500 and 3200. Since we intended to use more than just those two settings, we derived a gain scaling curve from previous testing of the BU prior to integration onto the rover. This data were collected with the engineering qualification model of the MU attached to the flight BU. Since we are only comparing the amount of amplification from the intensifier, the difference can be ignored. This is acceptable and transferable to the integrated flight model because the quantity we wanted to calculate relies on the intensity of light incident on the intensifier, the number of rows used in the red region, and the intensifier gain setting. We calculated the ratio of the sums of the signals in the usable ranges of each of the spectrometer regions between the different gains and then regressed a second-order exponential fit.
The result of that fit is the curve shown in Fig. 15. This scaling function was verified for each spectrometer region individually, as well as all three regions simultaneously. The worst error for the individual regions was for the green region at a gain of 2900 with an error of approximately 6. 7%, and the worst error for all regions simultaneously was 2. 8%, also at a gain of 2900. The IRF for the red region using 200 rows was not measured directly with the Labsphere due to the very limited amount of  time allowed for calibration on the rover, and sharp features in the EQ-99 spectrum in that range prevent the direct calculation of an IRF from that source. (The calibration data do not adequately describe the sharp features. ) However, the ratio of the signal for 200 rows divided by 70 rows was a smooth function and was applied as a scaling factor to the 70-row IRF when needed for observations using 200 rows.
The pixel size was 15×15 μm 2 , and the chip was cooled to -39 • C. The sample was set on a piezoelectric stage, and the BT antenna to be measured was located in the centre of the detection zone. To center the structure in the focal point, the position and intensity of the particles were evaluated by scanning in the x, y and z directions using a homemade software program. The neighbouring nanoantennas were 5 μm apart; therefore scattered light from other antennas and grating effects were considered as negligible. The raw data were corrected according to the following equation: Scattering = I bt -I background I lamp -I dark ( 1 ) where I bt denotes the intensity of the light scattered by the BT structure, I background denotes scattered intensity from the substrate, I lamp denotes the incident light intensity, and I dark denotes the intensity caused by dark current.