dataesr/scientific-paragraphs-categorization huggingface dataset:
5 ). The above observations clearly suggest that the downcore XRF-CS total-S signal is controlled primarily by the presence of sulfide phases within the sediment. Note that these concentrations are above DL of WD-XRF previously given. Using the concentrations of total-S and sulfate-bearing S determined by WD-XRF, we can calculate estimates for pyrite concentrations in the sediment, yielding values ranging from 0. 28 to 9. 58% (m/m). Second, we also calibrated the total-S profiles obtained by XRF-CS with the AvaaXelerate software package, using calcium as the denominator (see Section 1b). The obtained high resolution calibrated total-S profile is expressed in relative concentrations (Fig. 6 ). The above-mentioned hypothesis is also further supported by mineralogical analyses obtained by XRD on the same samples, which show that pyrite (FeS 2 ) is the only S-bearing crystalline mineral phase in our 200 discrete sediment samples (Fig.
Method and parameterization For all our simulations we used the OKMC code LAKIMOCA, thoroughly described in  [10]. The approach we adopted is explained in detail in  [8, 11, 12]. For convenience, we highlight here the fundamental ideas. The OKMC is a stochastic method used to describe the evolution of defects and their clusters in materials subjected to irradiation, disregarding the detail of processes directly involving atoms and focusing instead only on the properties of defects, treated as objects. Neutron irradiation produces point defects, namely vacancies (V) and self-interstitial atoms (SIA), which may form clusters. The objects we consider are therefore V and SIA clusters or any other nanostructural feature that needs to be included, like carbon (C) atoms or carbon-vacancy (C 2 V, see  [11]  ) complexes.
The cas-cade defect populations are chosen from a database  [17] [18] [19]  of displacement cascades that were simulated using molecular dynamics (MD) for different ener gies and the accumulated dpa is calculated using the NRT formula  [13]. In the model, grain boundaries act as a spherical surface sink for defect clusters of all sizes and the absorption of defects is taken into account by applying the algorithm described in  [20]  : each object has two sets of coordinates, one, subjected to periodic boundary conditions, that expresses its position in the simulation box, and a second one, not subjected to periodicity, that expresses its distance from the center of a supposedly spherical grain. If the object in its migration inside the simulation system happens to interact with grain boundaries it is immediately absorbed and removed from the system.
Acknowledgments This work has been carried out within the framework of the  EUROfusion Consortium  and has received partial funding from the  Euratom research  and training programme  2014-2018  under grant agreement No  633 , 053. The research leading to these results was also partly funded by the  Euratom 's  Seventh Framework Programme   FP7/2007-2013  under grant agreement No. 604 , 862  ( MatISSE  project) and contributes to the  EERA (European Energy Research Alliance)   Joint Programme on   Nuclear Materials  (JPNM). The views and opinions expressed herein do not necessarily reflect those of the  European Commission.
Acknowledgements The research leading to these results has received funding from the  European Union   Seventh Framework Program  ( FP7/2011 ) under grant agreement ESTABLIS no. 290022  and from the  Region Aquitaine  under agreement 2011 FULLINC. BAB acknowledges funding from  FAPESP  ( 2011/02205-3 ) and  CAPES  ( BEX 11216-12-3 ). MS acknowledges primary support from a fellowship by the  Portuguese Fundaça ˜o para a Ciencia e a Tecnologia  ( SFRH/BPD/ 71816/2010 ). CJB and AO gratefully acknowledge the  Cluster of Excellence 'Engineering of Advanced Materials at the University of Erlangen-Nu ¨rnberg' , which is funded by the  German Research Foundation (DFG). Dr M. Pe  ´deutour is warmly thanked for her administrative support.
15 M NaCl, 0. 01 M sodium phosphate buffer, pH 7. 4 with 0. 05% Tween 20). Aer that the beads were concentrated and the supernatant removed. Finally, the bead-bacteria complexes were resuspended into 100 ml of washing buffer for the SERS detection. For reference a concentration analysis was performed with 50 ml volumes of bead-bacteria complexes streaked onto differential selective agar Listeria acc. to Ottaviani and Agosti (ALOA) chromogenic agar (Labema) and incubated at 35 AE 0. 5 C for 24-48 h. SERS spectral acquisition of Listeria innocua and postprocessing of data Surface-enhanced Raman spectroscopy (SERS) spectra of L. innocua with AuNPs was detected from samples pipetted into PDMS wells integrated on top of SERS-active substrates. Sample amounts varied from 5 to 10 ml and well diameter varied from 1 to 2 mm.
9-1. 6 ns. The bacteria sample was pipetted on top of a glass slide prior to the detection. TimeGate measurements where analysed with TimeGate spectral processing tool and the acquired data was baseline corrected with a simple linear algorithm in Matlab (release 2015a, Mathworks Inc. , USA) aer opening the data with the PLS toolbox, version 2. 0 (Eigenvector Research Inc. , Manson, WA, USA). Further data handling and gure plotting was executed with Origin Pro (version 9. 4, Ori-ginLab corp. , USA).
The development of the detection process Commercial Dynabeads were used to capture the L. innocua cells for the SERS detection. The SERS signal recorded with IMS beads, possibly due to accumulation of more bacterial cells inside the excitation laser spot, was found to be 20 times stronger than the signal recorded without the beads. Fig. 6a  Fig. 4 (a-c ) TEM images of the different sized AuNPs: small, medium and large size AuNPs respectively. (d-f) The corresponding size distribution histograms calculated from TEM images of the AuNPs with Gaussian fit: small, medium and large size AuNPs respectively. Each histogram has been calculated from 100 particle sizes with ImageJ software. (g) UV-Vis spectrum for the small, medium and large size AuNPs. shows the results for the comparison of studies with and without IMS beads and Fig.
Acknowledgements This project was funded by  TEKES  (the  Finnish Funding Agency for Technology and Innovation)  through  FMA  project and  University of Oulu Graduate School through Infotech Oulu Doctoral Program  and by  Academy of Finland  through  FOUL-SENS  (Grant No. 292253 ),  M-SPEC  (Grant No. 284907 ) and multi  Diagnostics  (Grant No. 290596 ). The nancial support of the aforementioned institutes is gratefully acknowledged. We also thank  Tiina Väyrynen  ( National Resources Institute Finland (LUKE) ) for helping with the bacterial sample preparation. Yury Ryabchikov  acknowledges a support from  COST  project ( ECOST-STSM-BM1205-120416-072252 ) for performing experiments.
The computational cost is reduced by an optimal implementation of the adaptive strategy. The efficiency of the proposed implementation is tested using a C++ code which in turn uses appropriate open source libraries. The paper is structured as follows. The first section introduces the mechanical problem to solve in the framework of stochastic modeling. Section 2 presents the improvement of the error estimator presented in  [16]  by introducing a reduced basis approximation of the adjoint problem, along with some numerical examples that validate the presented technique. In section 3, a precise analysis of the Computational Processing Unit (CPU) gain obtained thanks to the proposed new optimal implementation is given. A parametric analysis of the method is performed on a 2D example, both regarding the computational cost and the quality of the provided approximations.
Improvement in the computational cost The proposed algorithms have been implemented in C ++ , using the Eigen library (FER numerical modelling software  [38] ). The Eigen library allows using a sparse storage for problems with a large number of degrees of freedom while using linear solvers involving a Cholesky decomposition of the form LDL T. The linear solver is the same for dense matrices (matrices involved in the reduced basis problems) and for sparse matrices (matrices involved in the resolution of the full finite element direct and adjoint problems). It is therefore well suited to use in the proposed algorithms since it is applicable for both dense and sparse linear systems, allowing to easily perform comparisons between the different CPU computational times. A study of the computational time gain, achieved using the proposed method, is given in the following.
Acknowledgements This work was financially supported by an  ERC  grant on 2DMA-TER and  EC under Graphene Flagship  (No. CNECT-ICT-604391 ). We also acknowledge the  Cfaed (Center for Advancing Electronics Dresden)  and the  Dresden Center for Nanoanalysis (DCN) at TU Dresden. P. L. and M. C. were supported by  JST-CREST ''Phase Interface Science for Highly Efficient Energy Utilization'', Japan Science and Technology Agency.
Analytic approach The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for the detection of meniscal and cruciate ligament tears were calculated separately for each of the MRI protocols, using surgical findings (arthroscopy) as the reference standard. To increase the statistical power for the comparison between the two imaging protocols  (4)  , the data from the independent reviews of both readers were assessed separately and then mathematically combined when calculating sensitivity, specificity, positive predictive value, negative predictive value and accuracy. Kappa statistics were used in order to measure the interobserver agreement, as well as to determine the intraobserver agreement between the two methods, for each reader  (15). To calculate the differences in diagnostic performance between the 3D TSE and the 2D TSE MRI techniques for dichotomized values (presence or absence of pathology), we used Fisher's exact test, with 95% confidence intervals, values of p < 0.
Semi-analytical models are often used for computationally efficient ultrasonic simulations. They typically apply plane-wave or quasi plane-wave approximations to the ultrasonic fields at the location of flaws in order to calculate diffraction coefficients. In favorable cases, the plane-wave approximations used for echo computations in the Civa software yield satisfying results. However, it can lead to inaccuracies in unfavorable cases, such as for wide probe apertures, outside of the focal region, or for beam-splitting or distortion due to irregular geometries. This communication presents an improved model, implemented in a development version of the software. The new approach describes the ultrasonic field as a sum of rays and applies the plane-wave approximation to each ray instead of the entire field.
INTRODUCTION The simulation of ultrasonic Non Destructive Evaluation has a wide range of industrial applications. It can, for example, help the design of inspection procedures or the interpretation of measurements. Simulation methods can rely either on semi-analytical methods, numerical methods (such as finite elements), or on a combination of both. Semianalytical methods apply approximations to perform faster computations than numerical ones, which can come at the cost of a loss of accuracy in their results. Plane-wave scattering coefficients are used by many semi-analytical methods, including those described by Thompson and Gray  [1]  and by Schmerr and Song  [2] , as well as those implemented in the Civa software  [3]. Planewave and quasi plane-wave approximations of the ultrasonic fields provide the input values for the calculations of these coefficients.
The interaction with the flaw is handled by CF, the amplitudes A of the fields are in the absence of defect. The directions and polarizations d and p correspond to approximations of the field as plane waves at the location x. In this formalism, the amplitudes A also contain phase information concerning the incident and received waves. In the strictest sense of the plane-wave approximation, the phase varies with angular frequency as ( ) (or its complex conjugate, depending on conventions), expressing only a time-of-flight. In the less quasi plane waveapproximation, the frequency dependence of A can account for some effects related to probe aperture. The semi-analytical echo computation of the Civa software relies on a strict plane-wave approximation, where the waves at a location are described by only their amplitude and time-of-flight.
RAY BASED APPROACH A decomposition of the field as a sum of rays is readily available in the Civa software, as the ultrasonic field computations of the Civa software rely on them. They can handle complex probe geometry and arbitrary numbers of interfaces with various shapes. To perform echo computations in the plane-wave approximation, the contributions of the rays are summed and the resulting field is approximated. A straightforward way to improve the accuracy of that approach would be to treat each ray as a different plane wave instead of approximating their sum as one. It could be written as follows: = , , ,. ( 3 ) The sums up to NEm x and NRe x are over the ensembles of rays found between the current point x of the defect and the surface of the emitting and receiving probes. This formulations implies that NEm•NRe coefficients CF need to be computed for each point x.
COMPUTATION RESULTS The ray-based model has been implemented in a development version of the Civa software. Computations were run using three approaches: the existing Civa model based on the plane-wave approximation, the new ray-based model, and the finite element Civa-Athena hybrid approach  [7]. The latter relies on the Athena finite element model for phenomena that occur in a small zone surrounding the defect, while the propagation between the probes and that zone is handled by the same paraxial ray model as the other two models. Finite elements are used here as a reference model for the computation of the interaction between waves and defect. As this approach is only available in 2D, computations with all three models have been performed under 2D assumptions to allow for meaningful comparisons.
CONCLUSION The ray-based method presented here allows overcoming limitations of the plane-wave approximation currently used in the semi-analytical ultrasonic echo computations of the Civa software. Examples of results illustrate some of the situations where the ray-based method is significantly better than the plane-wave approximation, due to the position of the defect relative to the focal zone or to the part geometry. The most noticeable improvement is the shape of the signals. The maximum amplitudes are also generally more accurate with the ray-based model. The improvement due to the new method comes at a cost in computation times, though that cost has been limited through optimization and parallelization. Ongoing work aims at further reducing computation times and at making the ray-based method available in a future release of Civa.
Acknowledgements This work was supported by the  Erasmus Mundus international doctoral school IDS-FunMat  under the project number  2013-07  and the  German-French Doctoral School. The authors thank  Dr. Nicolas Penin  and  Mrs. Laetitia Etienne  for ICP-OES analysis at ICMCB. The authors are also grateful to  Dr. Michel Lahaye  for EPMA and  Mr. Eric Lebraud  for XRD measurements at ICMCB.
Calculations 3D Finite Difference Time Domain (FDTD) calculations are carried out using the commercial software FDTD Solutions from Lumerical. A gold NP array is modeled as a regular 41 × 41 array of discs, 170 nm in diameter and 50 nm high, with a square 450 × 450 nm 2 unit cell or a rectangular 330 × 550 nm 2 unit cell, on an ITO or gold film on a glass substrate. An odd number of gold NPs (i. e. , 41 NPs rather than 40) along each line and row is considered for symmetry reasons; thus, the problem has a four-fold rotational symmetry with respect to the vertical axis when the excitation source is located on the central NP of the array. The dielectric function of gold that is used is from Ref. [36]. IET in the tip-sample junction is modeled as a point-like, vertical, monochromatic, electric dipole (along the z axis) located on top of (i.
Acknowledgments The gold nanoparticle arrays were fabricated at the technological platform for nano and microfabrication Planète in Marseille. Planète is part of the  Technological Center of the Région Provence-Alpes-Côtes-d'Azur. The authors acknowledge financial support by the  Région Ile-de-France  in the framework of DIM Nano-K and by  "Investissements d'Avenir" LabEx NanoSaclay.
MATHEMATICAL MODEL AND PROBLEM STATEMENT The system considered in this paper consists in a small-scale variable-speed wind turbine with a PMSG generator connected to a battery charging scheme. It is shown in Figure  2. The PMSG generator is connected to the battery system through a passive rectifier and a DC-DC converter. The captured power by the wind turbine is given by P m D 1 2 r 2 C p. /v 3 w (1) where is the density of the air, r is the rotor swept radius, C p. / is the power coefficient, and v w is the wind speed. is the tip-speed ratio (TSR) and is given by the following expression: , r! m v w ; where ! m is the rotor speed. The power coefficient data is obtained by experimental measurements or by using blade-element moment theory, and it depends on the size and geometry of the blades.
COMPUTER SIMULATIONS In order to show the performance of the controller and the identification algorithms proposed in this work, numerical simulations were made in the Simulink/Matlab environment. A wind speed profile with low-frequency and high-frequency components (Figure  5 ) was used for carrying out simulations on three adaptive schemes: 1. the GD-BBSC and the optimal mechanical torque point-based wind speed estimator proposed in this paper, 2. a PI controller and the I&I wind speed estimator, 3. the SPBC proposed in  [2] , and the I&I wind speed estimator. The system parameters are the same in this last work and are shown in Table  I. The nominal power of the considered wind turbine is 5 kVA. The scaling factors in GD-BBSC were s 1 D 1; s 2 D 10,ands 3 D 0:005. The initial conditions of the three controllers are shown in Table  II.
Textural characterization of raw and impregnated chars Surface areas were determined from nitrogen adsorption manometry at 77K and isotherms were obtained using an ASAP 2420 apparatus (Micromeritics). Each adsorption measurement was out-gassed overnight at 300 C prior to analysis. The BET model was applied to the N 2 adsorption data at relative pressures of 0. 01e0. 05. The sample pore volume was obtained from the amount of N 2 adsorbed at P/P 0 ¼ 0. 95.
Proteomics Three replicates were done of four different biological conditions: M9 with glucose (control); M9 with butanol 0. 5% as sole carbon source (butanol assimilation); M9 with glucose and butanol 0. 3% (long-term tolerance response to butanol); and a butanol shock was added to exponentially growing cells (short-term tolerance response to butanol). The proteins associated with the soluble and insoluble material were extracted and analysed by high-throughput tandem mass spectrometry as two separate fractions. The data set recorded from the 96 Liquid Chromatography-Mass Spectrometry/Mass Spectrometry (nanoLC-MS/ MS) runs comprised 707 041 MS/MS spectra. A total of 430 701 and 69 076 MS/MS spectra were assigned to peptide sequences for the soluble proteome and the insoluble-associated proteins respectively.
A total of 11 584 and 4243 different peptides were confidently listed respectively. Peptides validated the presence of 1086 and 591 proteins with at least two different peptides respectively. When considering the whole data set, a total of 1236 (without redundant) proteins were validated. Their relative quantities were estimated for each condition based on their respective spectral counts and normalized spectral abundance factors (NSAF). Proteins involved in central metabolism, and translation and transcription were found to comprise 38% and 37% of total proteins (soluble and insoluble, respectively) in terms of quantities of the whole cell proteome when merging data from all four conditions. Proteins involved in biogenesis of the outer membrane represent 5% of the detected soluble proteins in terms of total MS/MS assigned.
This global view of P. putida BIRD-1 protein content indicates no specific bias in our proteomic strategy and points to central metabolism, and transcription and translation as key butanol-related functional categories for systemic analysis. Regarding butanol assimilation candidate proteins, we compared the control condition (C fractions) with cells grown in butanol as sole carbon source (B fraction) in terms of protein enrichment using the Tfold method of the PATTERNLAB program designed for label-free shotgun proteomic data. The 1086 proteins from the whole-cell proteome and the 591 proteins identified in the membrane-associated proteomes were quantified and compared on the basis of their detection in at least two out of three replicates. The data are reported in supplementary Tables S5-S8.
Rockhopper analysis Considering all the samples and replicates, a total number of 34 267 239 reads were recorded to achieve an average sequence mapping for 91. 5% of the cases. The average length of sequences was 100 bp. The reads were mapped onto the P. putida BIRD-1 annotated reference genome (GenBank accession no. NC_017530) using ROCKHOPPER software  (McClure et al. , 2013)  that is based on BOWTIE 2. For visualization, we used IGV software  (Robinson et al. , 2011) , which allowed us to study expression of RNAs and mRNAs within their genomic context. Expression values reported by ROCKHOPPER for each transcript in each condition were normalized by the upper quartile of gene expression. A two-sample Student's t-test was performed on the average expression of the mRNAs to determine those with differential expression between the two conditions tested (P-value < 0.
MS/MS data processing Peak lists were generated with the MASCOT DAEMON software (version 2. 3. 2; Matrix Science) using the extract_msn. exe data import filter (Thermo Fisher Scientific) from the XCALIBUR FT package (version 2. 0. 7; Thermo Fisher Scientific). Data import filter options were set to 400 (minimum mass), 5000 (maximum mass), 0 (grouping tolerance), 0 (intermediate scans) and 1000 (threshold) as described previously  (Christie-Oleza et al. , 2012a). The mgf files from each sample were merged and MS/MS spectra were assigned using the MASCOT DAEMON 2. 3. 2 (Matrix Science) and the database containing the non-redundant RefSeq protein entries for P. putida BIRD-1 comprising 4960 protein sequences totalling 1 656 176 amino acids (National Center for Biotechnology Information download, 2014/01/07).
The search was performed using the following criteria: tryptic peptides with a maximum of two miscleavages, mass tolerances of 5 ppm on the parent ion and 0. 5 Da on the MS/MS, fixed modification for carbamidomethylated cysteine and variable modification for methionine oxidation. Mascot results were parsed using the IRMA 1. 28. 0 software  (Dupierris et al. , 2009). Peptides were identified with a P-value threshold below 0. 05. Proteins were considered validated when at least two distinct peptides were detected. The false discovery rate for protein identification was estimated with a reversed decoy database to be less than 1% using these parameters. Proteins were compared based on their spectral counts using the TFold Test using PATTERNLAB v2. 0  (Carvalho et al. , 2008; 2012)  with a false discovery rate (Benjamini-Hochberg q-value) fixed at 0.
Bioinformatics Predictions for subcellular localization, clusters of orthologous groups (COG) number and COG functional category were obtained from the Pseudomonas Genome Database (http://www. pseudomonas. com/viewAllGenomes. do). Functional connections between proteins were analysed with the multiple sequences module from the STRING-DB tools (http://string-db. org/) after extracting their respective COG numbers. The highest confidence level (0. 900) was applied for the network display  (Franceschini et al. , 2013).