dataesr/scientific-paragraphs-categorization huggingface dataset:
Finite-difference time-domain simulations FDTD simulations were performed using the Lumerical FDTD Solutions software. The geometry of the nanoparticle arrays was described using Cartesian coordinates with the x-and y-axis in the plane and the z-axis perpendicular to the plane of the arrays. Infinite arrays were considered in the simulations by choosing periodic boundary conditions (symmetric or antisymmetric) along the x-and y-axis and using perfectly matched layers (PML) above and below the structure. For the field profile simulations, the simulation mesh was set to 2 nm over the volume of the unit cell. A transmission monitor was placed 0. 4 μm below the nanoparticle arrays and a 2D monitor in the xz-plane was employed for simulating the near field distribution of the electric field intensity.
Acknowledgements DK acknowledges funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 642787 ,  Marie Sklodowska - Curie Innovative Training Network BIOGEL. SF, JS, JD, JH and UJ were supported by  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 633937 , project  ULTRAPLACAD. PV, ML, and MDLC are grateful for the financing from the project jointly funded by  Agence Nationale de la Recherche (ANR)  and  Austrian Science Fund (FWF)  under the grant agreements  ANR-15-CE29-0026  and I 2647, respectively. SF and JD received support by the  Austrian Research Promotion Agency (FFG)  with grant agreement no. 861578  ( ERANET  project  PLABAN ). JS and JH acknowledge financial support from the  Czech Science Foundation , grant agreement # 19-02739S.
Acknowledgements This work was performed within the  LABEX Plas@par project  and received financial state aid managed by the  Agence Nationale de la Recherche  under the reference  ANR-11-IDEX-0004-02. ES is also funded by the ' Chaire Energie Durable ' at  École Polytechnique  under the reference EXXI with the financial aid from EDF foundation. AS would like to thank the  ANR  for the "invited international expert" Grant, hosted by  École Polytechnique.
Specimens for OM were first polished with 1 µm diamond paste, then finished with Struers OPS solution before etching with a Klemm reagent. EBSD maps were acquired from the same OM samples after repolishing. The EBSD data was acquired using an EDAX TSL system on a Nova NanoSEM 650 field emission gun scanning electron microscope (FEG-SEM). The spot spacings used were 200 nm and 80 nm. Vickers microhardness was measured using a Clemex MMT-X7B tester under 300 gf force and 10 s dwell time according to the ASTM E384 standard. The in-situ HEXRD experiments were carried out at the Petra (Hamburg) P07 beam line operated in transmission mode under powder diffraction conditions. A high-energy monochromatic beam with 100 keV energy and 0. 12 Å wavelength permitted data acquisition in the transmission mode at a frequency of 10 Hz.
A 2-D Perkin-Elmer charge coupled device (CCD) detector was positioned 1 m behind the sample, giving access to full Debye-Scherrer rings with a maximum 2θ angle of 12 •. The X-ray beam cross section was 400 µm × 400 µm so that the analyzed volume contained at least 10,000 austenitic grains at the start of each experiment. The applied thermal cycles were controlled using another Bahr dilatometer available at the P07 beam line. Specimens were heated by induction and cooled by argon gas. The temperature was regulated and recorded by a thermocouple spot-welded at the center of the sample. The setup was the same as that used in  [14]. The 2-D diffraction patterns produced during the synchrotron experiments were circularly integrated using the Fit2D software [15] after calibration using Si powder and the resulting 1-D diffractograms were analyzed with a full Rietveld refinement procedure.
The diffraction peaks were fitted by pseudo-Voigt functions using the FullProf code  [16]  with 16 degrees of freedom (background, lattice parameters, peak shape and temperature effects) to determine the phase fractions and the lattice parameters. In all the following experiments only two phases were detected in the diffraction patterns: a face-centered cubic phase, corresponding to austenite; and a body-centered phase. The latter could correspond to either body-centered tetragonal (bct-martensite or supersaturated bct-bainitic ferrite) or body-centered cubic (bcc) ferrite. It was not possible to isolate the contribution of each based on tetragonal distortion. Therefore, during the Rietveld refinement procedures the lattices of the two phases were considered to be cubic (Fm3m for austenite and Im3m for martensite/bainite).
The carbon content in martensite, bainite and retained austenite phases was measured by EELS using a standards based second difference acquisition technique developed by the authors  [18]. All equilibrium and kinetic thermodynamic calculations were carried out using the MatCalc 6. 02 (rel 1. 003) software [19] using the mc_fe_v2. 011 diffusion data base, the mc_fe_1. 003 physical database and the mc_fe_2. 059 thermodynamic database.
• Any cementite formed during reheating should be dissolved to maximize the interstitial carbon content in austenite before the bainite transformation. The equilibrium transformation temperatures Ae1 and Ae3 for Ref and Ref+V were calculated using the MatCalc 6 code and compared with experimental dilatometry data  [20]  in Table  2. The bainitic start temperature Bs and the martensite start temperature M s calculated from the empirical equations of Kircaldy and Venugopalan  [21]  and Andrews  [22]  respectively are also shown. Ref 707 • C 700 • C 831 • C 933 • C - 423 • C 375 • C Ref+V 713 • C 748 • C 840 • C 975 • C 1067 • C 423 • C 375 • C As expected, the addition of vanadium tends to increase the equilibrium transformation temperatures and stabilize the ferrite phase. The most important effect is on the kinetics-during continuous heating vanadium greatly reduces the austenite nucleation and growth rate in the intercritical region, resulting in a >40 • C increase in A c1 and A c3 at a constant heating rate of 5 • C/s.
Microstructure and Mechanical Properties The microstructure of the Ref and Ref+V alloys transformed at 375 • C can be seen after Klemm etching in Figure  1. The matrices (colored dark) are fully bainitic with second phase retained austenite and/or martensite (MA) colored light. The second phase consists of two distinct morphologies-alignments of thin films or small islands along interlath boundaries and prior austenite grain (PAG) boundaries and coarser blocky islands with a more random distribution. The area fraction and the mean size of second phase islands were determined using the ImageJ analysis software for all three transformation temperatures. The results are listed together with the corresponding synchrotron HEXRD, EBSD and TEM data in Tables  3  and 4. Metals 2020, 10, x FOR PEER REVIEW 5 of 19 is no significant temperature undershoot at the transition point.
Microstructure and Mechanical Properties The microstructure of the Ref and Ref+V alloys transformed at 375 °C can be seen after Klemm etching in Figure  1. The matrices (colored dark) are fully bainitic with second phase retained austenite and/or martensite (MA) colored light. The second phase consists of two distinct morphologiesalignments of thin films or small islands along interlath boundaries and prior austenite grain (PAG) boundaries and coarser blocky islands with a more random distribution. The area fraction and the mean size of second phase islands were determined using the ImageJ analysis software for all three transformation temperatures. The results are listed together with the corresponding synchrotron HEXRD, EBSD and TEM data in Tables  3  and 4. The EBSD data from specimens transformed at 375 °C are presented in Figure  2a-f.
Consequently, some undissolved vanadium-containing particles are present before the bainitic transformation occurs (Figures  8  and 9 ). In those images a majority of rather coarse (20-40 nm) vanadium particles can be seen, along with some finer (5-10 nm) precipitates. It must be emphasized that many areas showed little or no evidence of V precipitation, so that the average particle density observed in the TEM was actually quite low. Further, a close examination of the carbon and nitrogen maps suggests that many of these vanadium-rich regions are not carbides or nitrides and in fact could be due to local segregation. An anisothermal kinetic precipitation calculation was carried out with the MatCalc 6 code in order to determine the amount of V(C,N) remaining undissolved before the bainitic transformation.
Strengthening from V Precipitation in Bainite Here the question of whether or not new vanadium precipitates can nucleate and grow during the fast cooling and isothermal bainitic transformation stage is addressed. It can be assumed that any further growth of the pre-existing vanadium particles is limited, as this would require low temperature long-range diffusion of vanadium across the (presumably) depleted zones surrounding those precipitates. Figure  12a  is a calculated precipitation-time-temperature (PTT) diagram for V(C,N) formation in Ref+V calculated using the MatCalc 6 code [32] assuming a constant dislocation density of 1 × 10 14 /m 2 in ferrite. The blue dots show the cooling path of the Ref+V alloy. The high defect density decreased the nucleation time for V(C,N) by several orders of magnitude, but it is still not possible to nucleate vanadium particles in any ferrite that might form during cooling.
Acknowledgments: The authors would like to express their gratitude to the  Canadian Office of Energy Research and Development (OERD)  and the  LABEX DAMAS  ( ANR-11-LABX-008-01 ) from  Lorraine, France  for their support. Finally, we wish to acknowledge the help and professionalism of all the staff at  CanmetMATERIALS  who participated in the study and in particular  Jian Li  for aid with the EBSD analysis.
Performance Evaluation We have used micro-architecture simulator, zsim  [16] , for performance evaluation of IE-Cache. Table  2  shows the configuration used in our experimental setup. A 4-way IE-Cache is introduced at the L3 in the cache hierarchy and it is designed for a different level-of-search, i. e. 2 and 3 levels. We have evaluated the IE-Cache for each level-of-search against baseline architecture by executing 11 workloads of PARSEC using medium input set. In each run, we have taken one program at random for each domain (secure and non-secure). We have calculated the weighted speedup metric, which is a sum of ratios of the program's IPC executing in a group to its IPC when it is executing in isolation, and normalized it to baseline architecture. Figure  5  shows the normalized weighted speed up metric of IE-Cache.
PA R A M E T R I C S T U DY Based on the reference model given in Table  1 , we conducted a qualitative analysis of the inversion input data V ph , V g and the PVD with respect to frequency, dV ph /df, to highlight the effects of the shear wave velocity and first layer depth variations. The Geopsy software  (Wathelet et al. 2004 ) is used to calculate the theoretical phase and group dispersion curves. The computation of the theoretical dispersion curve is based on the eigenvalue problem originally described by  Thomson (1950)  and  Haskell (1953)  and then modified by  Dunkin (1965). The propagator matrix method  (Gilbert & Backus 1966 ) is used to solve the eigenvalue problem in a 1-D stratified medium. The root search is based on the Lagrange polynomial, which efficiently speeds up the calculations.
I N V E R S I O N T E S T S In the previous two sections, we discussed the sensitivity of the PVD dV ph /df both qualitatively and quantitatively. In this part, we apply dV ph /df in a numerical inversion problem to verify its feasibility. Three types of dispersion data are taken as inversion input data: V ph , V g and dV ph /df. For that, theoretical dispersion curves of V ph and V g are calculated using Geopsy software  (Wathelet et al. 2004 ). The PVD is then calculated using eq. (  8 ). Note that, since only the fundamental mode is tackled, the dispersion curve calculated is that of the fundamental mode only. The parameters of the reference model are presented in Table  1  and we assume that the medium has undergone minor changes such that its shear wave velocities change with variation ratios α(V s i ) ≤ 5 per cent.
Misfit function and a priori information The dispersion data are tested below for an inversion process by a global optimization through the NA, as indicated in Section 1: the NA computes the misfit function in the parameter space, where the pseudo-random samples are generated at each iteration by making use of Voronoi cells, and then guided and improved by the samples generated at the previous iteration. Each sample corresponds to one ground model (i. e. a set of the research parameters: V s of each layer in the model) and corresponds to one calculated dispersion curve using Geopsy software. The objective (also called misfit) function is defined as the L2 norm relative difference between measured and calculated inversion data values, x mes and x cal respectively, in the frequency domain misfit = 1 N f N f i ( x mes -x cal x mes ) 2 , ( 23 ) where N f is the number of inversion input data in the frequency domain, x is the inversion input data, V ph , V g or dV ph /df.
Fig. A1 (b) in Appendix A shows the position of the piezo-electric source and the measurement points of the laser receivers (maximum offset equal to 90 mm with a space of 1 mm). Like all the recordings published from the MUSC bench, the measurements are available to the scientific community and can be obtained as free data on request by email. Seismograms measured on the two models were then analysed and processed. Fig. 11 (a) shows the measured baseline and repeatline seismograms. The phase velocity dispersion diagrams are extracted from the measured seismograms, using the phase-shift method (Fig. 11b ). The dispersion diagrams are calculated in the frequency range  [10, 150]  Hz and at each frequency, the phase velocity is searched in the velocity range [500, 1500] m s -1 with the velocity resolution equal to 2 m s -1.
The ANSYS 7  software was used to generate the volumetric meshes of the four test objects and the Co-rotational Linear method from the Vega FEM library  (Sin et al. , 2013)  was used to implement the deformation model. As for the force sensor model, we used the same network architecture described in Mohy el Dine et al. (  2018 ) but trained the network with motions of the robot arm that were adequate for our experiments.
FUNDING This work has been sponsored by the  French government research program Investissements d' Avenir through the RobotEx Equipment of Excellence  ( ANR-10-EQPX-44 ) and the  IMobS3 Laboratory of Excellence  ( ANR-10-LABX-16-01 ), by the  European Union  through the program Regional competitiveness and employment 2007-2013 ( ERDF -Auvergne region ), by the Auvergne region and by the project  CoMManDIA  ( SOE2/P1/F0638 ), which is cofinanced by the  Interreg Sudoe Programme (European Regional Development Fund).
Protocols and cohort effect In order to illustrate the applicability of M2M to metagenomic samples and cohorts of individuals, we reused the work presented in  (Diener et al. , 2020)  and analysed the gut metagenomes of 170 individuals from a Danish (MHD) cohort and a Swedish (SWE) cohort  (Forslund et al. , 2015)  in the context of Type-1 (T1D) and Type-2 (T2D) diabetes. Based on species-level dereplicated MAGs, metagenomic species (MGS), we built GSMNs and bacterial communities for each individual. We relied only on the available metagenomic data to perform analyses, and used qualitative information (presence/absence of MGS or species in the sample) to build the communities as M2M works with qualitative information. Two experiments were performed: M2M was run on each sample firstly using communities of newly reconstructed GSMN from MGS, and secondly using communities consisting of curated GSMNs of the AGORA resources  (Magnúsdóttir et al.
A classification experiment on the composition of the community scope can, to some extent, (AUC = 0. 75 ± 0. 15) decipher between healthy or diabetes statuses (Figure  4  d) but classification between T1D and T2D was not achievable (Appendix 2 Figure  4 ). Although metagenomic data would more precisely perform such a separation, it is informative to observe that despite metabolic redundancy in the gut microbiota, there are differences at the metabolic modelling level. Qualitative differences are noticeable between healthy and diabetic individuals: it is possible to distinguish them to some extent using the set of metabolites predicted to be producible by the microorganisms found in their faeces. We then computed for each sample the key species (essential and alternative symbionts) associated to the cooperation potential.
This allowed us to characterise metabolic complementarity in a large collection of draft reference genomes. We further assessed the robustness of M2M to data incompleteness by performing analyses on collections of MAGs. Finally, we applied M2M to a common use-case in metagenomics: the study of communities associated to individuals, in a disease context. Our method is robust against the uncertainty inherent to metagenomics data. It scales to typical microbial communities found in the gut and predicts key species for functions of interest at the metabolic level. Future developments will broaden the range of interactions to be modelled and facilitate the incorporation of abundance data. This software is an answer to the need for scalable predictive methods in the context of metagenomics where the number of available genomes continues to rise.
Results and Discussion Once the 3D RIC is obtained, the 3D spatial mappings of refractive indices are realized by simply adding the refractive index of CaF2 at 632. 8 nm, n = 1. 4348, over the entire ensemblerange of 3D RIC values. Thus, using Import object for the (n,k) data tool in the Lumerical software, the possible TE and TM eigen modes propagated are simulated inside each waveguide. The numerical simulation results indicated that the waveguides are multimodal. The experimentally observed guided modes are highly consistent with the simulation results, see Table  1. The corresponding simulation results are given in parentheses. Table  1. The experimental (simulation) near-field mode field radii (MFR) of the five double-track waveguides inside CaF2 along the horizontal (x) and vertical (z) axes and for the injected HeNe beam.
Funding: This work was supported by the  Association Nationale de la Recherche et de la Technologie (ANRT)  under  Cifre program  and  Amplitude Laser Group  and by the  French State , managed by the  French National Research Agency (ANR)  in the frame of "the Investments for the future"  Programme IdEx Bordeaux -LAPHIA  ( ANR-10-IDEX-03-02 ).
Analysis NOTE: Data analysis is in general performed by first correcting the whole stack for drift, finding the beads in each frame, tracking their movements with respect to a reference frame (taken in absence of cells), interpolating the displacement field and inverting the problem to obtain the stress using Fourier transform  29. To this end, we suggest using a combination of ImageJ Macro and MATLAB programs downloadable from an online repository  30. 1. Open the movie in ImageJ as stack of images 2. Run the macro "Crop_and_save. ijm" 1. Select the regions of interest (ROI) with the "Rectangle" tool and add them to the ROI list using the 't' key. 2. When cropping the cell, be sure to include a region of at least 5-10 pixels of immobile beads. Exclude cells that are too close to the boundaries or to other cells from the analysis.
When finished click on 'OK'. 3. The macro proposes a mask of the cell: if this is satisfactory click on "OK". If not satisfactory, click on "Not ok" and then manually select a closed region with any selection tool (e. g. "Freehand" or "Oval") and click on "Continue". 3. Open MATLAB and run "TFM_v1. m". 1. Input the required parameters: in particular check the image properties (pixel size, time interval of acquisition) and the gel properties (Young modulus E, Poisson ratio). 2. The reference image is set to be the first by default. Set it to another frame if needed or set it to "0" to load an external file.
Representative Results Given the size of the cells, algorithms that extract the displacement map of the beads via correlative techniques (such as particle image velocimetry) are in general not very precise. However depending on the degree of resolution required, one can easily obtain qualitative results using a free Fiji/ImageJ plugin  31 , 32. While this approach is sufficient to compare stimulating versus non-stimulating conditions, for a thorough analysis we recommend using our software downloadable from an online repository  30  , that tracks the beads individually and provides the displacement field map at a given time point as the interpolation of the individual bead displacements  33. Several quantifications are possible at this point. For example (by assuming the displacement is caused only by stress tangential to the gel surface) the software also provides the stress at each point causing that specific displacement map.
Acknowledgments The authors thank  M. Bolger-Munro  for critical reading and acknowledge the  Nikon Imaging Center@CNRS-InstitutCurie and PICT-IBiSA, Institut Curie, Paris , member of the  France-BioImaging national research infrastructure , for support in image acquisition and the  Curie Animal Facility. PP was supported by  CNRS. AK and JP were supported by  Paris Descartes PhD fellowship and Ecole Doctorale FIRE-Programme Bettencourt. This project was funded by grants to  PP  ( ANR-10-JCJC-1504-Immuphy ) and  AMLD  ( ANR-PoLyBex-12-BSV3-0014-001 ,  ERC-Strapacemi-GA 243103 ).
At 2000 m depth in the oceans, one can hear biological, seismological, meteorological, and anthropogenic activity. Acoustic monitoring of the oceans at a global scale and over long periods of time could bring important information for various sciences. The Argo project monitors the physical properties of the oceans with autonomous floats, some of which are also equipped with a hydrophone. These have a limited transmission bandwidth requiring acoustic data to be processed on board. However, developing signal processing algorithms for these instruments requires one to be an expert in embedded software. To reduce the need of such expertise, we have developed a programming language, called MeLa. The language hides several aspects of embedded software with specialized programming concepts. It uses models to compute energy consumption, processor usage, and data transmission costs early during the development of applications; this helps to choose a strategy of data processing that has a minimum impact on performances.
Measurements are done during any step of the dive by sensors integrated into the float to measure conductivity, temperature, depth, chlorophyll, nitrate, as well as acoustic signals and others. In more recent works, Underwater Passive Acoustic (UPA) measurements have been integrated into profiling floats for different monitoring applications, such as whale tracking in marine ecology  [11]  and above-surface wind speed or rainfall estimations in marine meteorology  [12, 13]. In this field, a breakthrough has recently been obtained by seismologists with the Mobile Earthquake Recording Device in Marine Areas by Independent Divers (Mermaid), an autonomous float equipped with a hydrophone and a smart acquisition board able to recognize seismic sounds  [14]. The recognition of seismic sounds allows it 1) to trigger the ascent of the float in order to obtain a precise estimation of the recording position and 2) to transmit only relevant seismogram data through the low bandwidth satellite link.