dataesr/scientific-paragraphs-categorization huggingface dataset:
Numerical approach: The proposed numerical approach consists to simulate the thermo-mechanical phenomena acting in the cutting zone during turning of 7075-T651 aluminum alloy in orthogonal cutting configuration. In this approach a plane-strain coupled thermo-mechanical analysis was performed using the finite element code Abaqus-Explicit. For a better prediction of residual stress distribution induced by orthogonal cutting, a Lagrangian formulation is used to describe motions where material points are considered connected to the mesh nodes. The workpiece and tool geometries, thermo-physical properties, material behavior model, cutting conditions, and friction law are introduced in the finite element model to simulate machining operations. Then, cutting forces and residual stress retained as main machinability indicator were numerically determined and compared to those established experimentally on the 7075-T651 aluminum alloy to prove the numerical procedure efficiency.
Acknowledgements Velimir Meded,  Rodrigo Prieto-Gonzales  and  Jan van Ruitenbeek  are gratefully acknowledged for fruitful discussions and giving valuable advice. This work is partly supported by the  NanoSci-E+ ERA program  INTERNET and by the  Netherlands Organization for Scientific Research  via a  NWO-Vidi grant  (SJvdM). Technical support from the  STnano facilities and Labex   NIE 11-LABX-0058_NIE  are also gratefully acknowledged. T. E. K. , Ú. P. and T. L. gratefully acknowledge the  Irish Government 's  Programme for Research in Third-Level Institu-.
The device photoresponse and photocurrent characteristics measured at low temperatures show clearly oscillations in the current-voltage (I-V) curves. The number of the oscillations corresponds to the number of the QWs in the intrinsic region as reported by us elsewhere  [26, 27]. In this paper, we aim to understand the underlying mechanisms for the observed oscillations via comparing our results with an extensive simulation model. The semiconductor simulation software, Simwindows32  [28] , is used successfully to account for the experimental results.
Modelling results In order to support our explanation of the PC oscillations, we modelled the current-voltage curves of our devices using the semiconductor simulation package SimWin-dows32  [28]. This software is able to model carrier escape from the QWs mainly via thermionic emission by considering the lowest energy subband; nonetheless, it has been able to recreate the oscillations and helped improve our understanding of the mechanisms involved in our samples. SimWindows32 is fundamentally a 1D drift-diffusion simulator that solves Poisson's equation, the current continuity equations, the photon rate equation and the energy balance equation in steady state. The simulation presented here refers to the device AsN3134, using the values present for GaAs in the Sim-windows32 material parameter file and in the literature for GaInNAs  [35] [36] [37].
Acknowledgements We would like to thank the  Optoelectronics Research Centre at Tampere  and the  National Center for III-V technologies at Sheffield University  for providing the GaInNAs samples. This work was partly supported by  Scientific Research Projects Coordination Unit of Istanbul University. Project number:  IRP 9571. COST  action  MP0805  entitled ' Novel Gain Materials and Devices Based on III-V-N Compounds ' is also gratefully acknowledged.
Acknowledgements We wish to express our gratitude to  M. Peyrard  for numerous useful discussions. We thank  J. K. Trømborg  for a careful reading of the manuscript. This work was supported by the  EGIDE/Dnipro  grant No. 28225UH  and by the  Programme (Marie Curie Actions) of the European Unions 7th Framework Programme  (FP7/2007-2013) under  Research Executive Agency Grant Agreement 303871. O. B. acknowledges a partial support from the  NASU "RESURS" program.
Acknowledgments This work, partly realized on NANOLYON platform, was supported by the  French Agence Nationale de la Recherche (ANR)  through the project  PIEZO2POWER , reference:  ANR-11-NANO-0015. The authors are very grateful to  R. Mazurczyk  for his technical help. One of the authors (Q. Liu) would like to thank the  Chinese Scholarship Council (CSC)  for its financial support.
Luminescent signals from immunoblots, recorded using the LAS-3000 imaging system and quantified with the MultiGauge software, were within the linear range, allowing quantitative determination of oleosin content. For each oleosin, data were expressed as the percentage of the mature seed content of that oleosin, regardless of its relative amount among OB proteins. The inset in B shows OB proteins resolved by SDS-PAGE and stained with silver nitrate (column a) or Coomassie blue (column b) to illustrate the relative content of each oleosin in purified Arabidopsis OBs. of living cells could be stained. Unfortunately, as Nile Red was not compatible with cell wall stains, no fluorescent dye allowed discriminating OBs from cell walls in living embryos (Fig. 3, A1  and B1 ). Consequently, individual plant cells could not be detected, and we used estimators of the distance between each OB and its neighbors to answer the question of distribution in the three-dimensional (3D) space.
e. the ratio of volumes between a given OB and the Voronoï cell containing it), and therefore estimating densities of OBs and compaction processes, independently of the plant cell walls (Fig. 4B ). To evaluate the spatial distribution of OBs during seed development, we designed a pipeline based on morphological operators for the quantitative analysis and characterization of OB size and organization. The pipeline was implemented in a 3D imaging and analysis software, Avizo-Fire (Burlington). It processed laser scanning microscope image stacks of OBs, yielding a mathematical representation of segmented OB images built on watershed-based techniques. Three estimators were used: the V OB , the f loc , and the distance of each OB to its nearest neighbor (Fig. 4 ). These estimators are detailed in "Materials and Methods.
Quantitative PCR Frozen tissues were ground in liquid nitrogen, and total RNA was extracted using the Sigma-Aldrich GenElute Mammalian Total RNA Miniprep Kit (Sigma-Aldrich). RNA extraction of dry seeds was performed as described  (Suzuki et al. , 2004 ). An on-column DNase treatment was performed using the RNase-free DNase 1 (Qiagen). For RT-PCR studies, the DNA-free RNA was converted into first-strand complementary DNA (cDNA) using the Super-Script II RT preamplification system for first-strand cDNA synthesis (Invitrogen) with oligo(dT) 22. Quantitative PCR was performed with a LightCycler using SYBR Green mix (Roche). Specific primers for OLE1, OLE2, OLE3, OLE4, and OLE5 were designed using the LightCycler probe design software version 3. 5 (for oligonucleotide sequences, see Supplemental Table  S2 ) and tested for efficiency rates as well as sensibility to dilutions of cDNA samples.
Saturation and incubation with antibodies were carried out according to D'  Andréa et al. (2007). Luminescent signals from western blots were recorded using a LAS-3000 luminescent image analyzer (Fujifilm) and quantified with the MultiGauge software (Fujifilm). Mature dry seed extract was used as a standard to determine the linear relation between luminescent signal and oleosin content. Using this linear relation, the amount of oleosin in developing seeds was determined from the luminescent signal (provided that this signal was within the linear range) and expressed as the percentage of mature seed content.
Every sample was analyzed in 4 repetitions. Comments: CEC eff was determined according to Gillman -CSN ISO 11260, C ox spectrophotometrically a er oxidation by chromsulphuric mixture -ISO 14235, N t according to Kjeldahl  (Zbíral et al. , 1997). Statistical analysis of the data was performed through one-way ANOVA (analysis of variance) and Fisher's LSD test (least signifi cant diff erence). All statistical analyses were undertaken using the Statistica 9. 0 program.
Acknowledgments The authors would like to acknowledge the support of  French Research National Agency  which has financed this work through  SRAMM  project. Parts of this work were also supported by the  National Natural Science Foundation of China  (nos. 61401158  and  61302056 ),  National Engineering Technology Research Center for Mobile Ultrasonic Detection  (no. 2013FU125X02 ), the  Fundamental Research Funds for the Central Universities of SCUT  (nos. 2014ZM0040 ,  2013ZM0082 , and  2014ZB0030 ), and the Project-sponsored by SRF for ROCS, SEM.
III. SYSTEM ARCHITECTURE Similar to other work in literature (e. g. ,  [7] ,  [8] ), the reference system architecture proposed in this project is as depicted in Figure  1. It is a three-tiers architecture, where at the lowest tier a Bluetooth-enabled WBAN connects sensors to a local collector (i. e. , a hub), which can be a portable embedded PC or a PDA. The hub needs to communicate with WBAN devices through a Bluetooth radio module and then send measurements reports towards a residential gateway, through a ZigBee/IEEE 802. 15. 4-based multi-hop WSN. The gateway is able to perform local computation and forward data to the public IP-based network towards the professional caregivers for real-time analysis. The scope of the WSN4QoL project is focused on the efficient data transmission over the multi-hop WSN network, as well as to support real-time people localization in a fully distributed way.
(iii) Middleware Services. This layer represents an interface between the underlying protocol stack and the application layer, and is the core of the novelties introduced in the WSN4QoL project. (iv) Application Layer. This last layer mainly focuses on gathering measurements from the sensors and data compression. For instance, at this layer, compressed sensing techniques (e. g. ,  [9] ,  [10] ), aiming to exploit intrinsic properties of the biometric data in order to provide energy efficient tele-monitoring solutions, are implemented exploiting the APIs offered by the middleware. [11]. While this figure abstracts from the majority of interfaces and some configuration components, it illustrates one important aspect, namely how access to the platform specific radio driver (PHY) is structured.
A component on this level typically provides a certain MAC MLME/MCPS primitive to the next higher layer, it is responsible for assembling the particular data or command frame and it accepts and processes incoming frames. Among the options offered by the IEEE 802. 15. 4 standard, in WSN4QoL we have chosen to refer to the NBEM, i. e. , the fully asynchronous mode. This choice is motivated by the fact that mobile nodes usually have a variable duty cycle and that, to achieve efficiency, NC schemes require mobile nodes to transmit data in broadcast. Moreover, in the classical scenario, where patients' data need to be collected at a central station, the asynchronous mode allows for flexibility in accessing the medium only when sensor data are available at the node and a transmission is required.
ACKNOWLEDGMENT The authors would like to thank the  German Aerospace Center (DLR)  for providing the TerraSAR-X and TanDEM-X SAR images through the  MTH0828  and the NTIINSA2171 projects. Thanks to  Dr. Pau Prats  ( DLR ) for the useful discussions regarding the refocusing of high-resolution spotlight SAR images acquired in the TanDEM-X mission. The authors would also like to thank the  Électricité de France (EDF) company  for providing the in situ measurements for the Puylaurent dam.
2005) , and (what is important for the optimization problem model definition) price dispersion. One should also notice that consumers may choose from a large number of Interbookstores. Yahoo. com lists more than 100 online bookstores. Data for our sample were collected 32 stores and covered the largest US-based stores, including Amazon,  Bar-nesandNoble. com, Borders. com, Buy. com, and Booksamillion, and  top sellers among Internet bookstores in Poland such as empik. com and merlin. pl. We decided to review and upgrade the model presented by  Blazewicz et al. (2010). Our goal was to create a new, more sophisticated (focused not only on books), and even more realistic model than the previous one. The working model was prepared on the basis of data from the above-mentioned publications as well as our own observations of many Internet stores.
Capillary Electrophoresis. The CE experiments were carried out on a P/ACE MDQ TM CE system from Beckman Coulter (Brea, CA) equipped with a temperature controlled autosampler and a power supply able to deliver up to 30 kV. A 32 Karat TM (Beckman Coulter, Brea, CA) was used for instrument control, data acquisition and data handling. Polymicro bare fused-silica capillaries of 50 µm i. d. were obtained from Photonlines (St-Germain-en-Laye, France). Concerning modified capillaries, capillaries were coated in laboratory with hydroyxypropylcellulose (HPC) following the protocol described by Shen et al. [49]. For model protein separation, 83. 3mM ionic strength ammonium acetate (pH 4. 0) has been used as BGE. Samples were introduced into the capillary by electrokinetic injections (3 kV, 8 min), and the separation of proteins was performed by applying 20 kV across the capillary.
MALDI-TOF-MS and MALDI-TOF-TOF MS/MS. For intact protein and intact mAb separations, the matrix was prepared by dissolving 2,5-dihydroxybenzoïque (DHB) (2g. L -1 ) in 0. 1% trifluoroacetic acid/acetonitrile (TFA/ACN) (50/50). Fraction collection fraction was realized using Ground Steel MALDI target (Bruker Daltonics, Bremen, Germany). For peptide mapping approach, α-cyano-4-hydroxycinnamic acid (CHCA) (5g/L) matrix was prepared following the classical AnchorChip protocol provided by Bruker Daltonics. Fraction collection was realized using AnchorChip MALDI target (Bruker Daltonics, Bremen, Germany). When the samples were directly collected on a MALDI target with 0. 6µL sheath liquid, 0. 6 µL of the matrix solution were automatically deposited on the same spot. Mass spectra of the CE fractions were recorded using an Autoflex III MALDI-TOF (Bruker Daltonics, Bremen, Germany), operating in reflector mode and with FlexControl software.
Positively charged ions were detected and sums of 1500 single-shot spectra were acquired automatically from each sample by using the AutoXecute software. For peptide mapping study WarpLC 1. 2 software was used. Data processing was performed with FlexAnalysis 3. 0 and Biotools 3. 2 provided by the mass spectrometer manufacturer. All spectra were calibrated according an external calibration using Protein calibration standard I (Bruker Daltonics, Bremen, Germany) for intact protein separation and Peptide calibration standard II (Bruker Daltonics, Bremen, Germany) for peptide mapping study. Data obtained from CE-MALDI-MS/MS experiments were processed using Mascot search algorithm developed by Matrix Science (Boston, MA, USA).
Evaluation of the CE-UV/MALDI-MS/MS for peptide mapping approach Peptide mapping is essential for the characterization of a complex mixture of digested proteins. In comparison to peptide mass fingerprinting (PMF), MS/MS peptide identification is not only based on digested peptide m/z ratio measure, but also on the fragmentation of those peptides, increasing the confidence of the identification. WarpLC 1. 2 (Bruker Daltonics, Bremen, Germany) is a software initially dedicated to perform peptide mapping strategy using off-line coupling between HPLC and MALDI-MS/MS. This software is fully compatible and could be easily adapted to the CE-UV/MALDI-MS/MS approach. Due to the complexity of biological samples, a low carryover may involve the presence of the same m/z ratio in two or three adjacent spots.
In that case, the software selects the spot with the most intense m/z parent ion to perform MS/MS. After MS/MS spectra acquisition, WarpLC 1. 2 proceeds to the exclusion of the same m/z parent ion present in the adjacent spots. The software allows a drastic reduction in the possible redundant peptides and improves the number of selected m/z ratios which increase the number of possible unique peptides. To further characterize the fraction collection interface, 100 nL of a peptide mixture sample containing the tryptic digests of βCas, RNase A, CAII, BSA, Lys, Ins, Cyt c, αLac and Myo (each at 3 µM) has been analyzed. Amounts of protein tryptic digests with 300 fmol of each proteins were injected. In this study, a neutral coated capillary has been used and a transient isotachophoresis (t-ITP) preconcentration step has been implemented.
Fig. 5a  shows a separation obtained for the tryptic digest of mAb (500 fmol injected). The total duration of the separation was less than 50 minutes with a detection window of 20 minutes between 25 and 45 minutes. In this work, peptide identification was automated and based on MS/MS data meaning that identification was performed on parent ion mass measure and fragment identification. Results of Mascot search showed sequence coverage of 92% for the HC and 100% for the LC of mAb corresponding to a total of 92 unique identified peptides (Fig. 5b ). Some peptides were selected and fragmented several times during the analysis leading to the same identification. These phenomenon reinforce the confidence of the identification by enabling peptide overlapping. mAbs are heterogeneous by nature due to different structural modification which may experience the protein during the lifetime.
In spite of the lack of resolution of the MALDI-MS, this work represents the first analysis of intact mAb charge variants by CZE using a MS detection and opens new ways of research for the characterization of intact mAbs. Moreover, the CE-UV/MALDI-MS/MS fragmentation allowed 100% sequence coverage of the LC and 92 % on the HC and the separation of the 4 major glycosylated peptides and their structural characterization in the same analysis data set. These results analysis demonstrates the power of CE-UV/MALDI-MS/MS as a sensitive method in mAb characterization and more generally in bottom-up proteomic approach.
Acknowledgments Michael Biacchi  would like to thank the  ANFH  for funding his Ph. D work. LSMIS would like to thank  Dr. Bernard Capolaghi  ( Hopital Bel Air, Thionville ) for his support. LSMIS would like to thank  Paape Rainer ,  Pierre-Olivier Schmit  and  Anja Resemann from Bruker  for their help. The authors would like also to express their gratitude  Dr. E. Wagner-Rousset ,  Dr. D. Ayoub ,  MC. Janin-Bussat ,  O. Colas  ( Centre d'immunologie Pierre Fabre, St Julien en Genevois, France ) and  Rabah Gahoual  ( LSMIS, Strasbourg, France ) for helpful discussions around antibody structural characterization. Dr. Yannis François  would like to thank  Dr. Jean-Marc Busnel  for very fruitful discussions.
Acknowledgements The authors acknowledge the financial support of the  Agence Nationale de la Recherche  through the grant  ANR   ProCoMedia , project  ANR-10-INTB-0914. A. C. and J-F. M. acknowledge the funding from the  European Community 's  Seventh Framework Program  ( FP7/2007-2013)  under grant agreement no  285549 :  SIMPOSIUM  project.
Our approach Our interest in LASV  (Kong et al. , 2010)  is motivated by the possibility of using it as a pre-segmentation for our application. Our implementation aims to work on a vehicle platform equipped with a 30 FPS camera. This is why we needed to accelerate its computation to meet the real time requirements. We tested this algorithm on several datasets  (Geiger et al. , 2012; Kong et al. , 2010; Leskovec et al. , 2008) , in our case this algorithm will be used with continuous capture. This is why we have tried to improve this method on datasets using successive frames instead of random environment pictures datasets like the ones used by Kong. The original code was written in MATLAB by  Kong. During the initial tests, the original implementation required a computation time of 18 seconds per image.
c) These operations are done on every image. Method VI Temporal + Downsampling + Reset a) Image size is decreased by half before computing the vanishing point research. b) Image size is then increased and the best vanishing point candidate is only selected from a 10% by 10% image size zone around the coordinates of the vanishing point previously found on the same but smaller image in order to refine the vanishing point position. c) The following image size is decreased by a ratio We also wanted to see the differences in performance between gradient images and edge images. We have compared methods using pre-processed Prewitt images with same methods using in turn pre-processed Canny images. In order to evaluate the methods we have manually labelled a dataset with the ground-truth location of vanishing points.
We used raw data from the Kitti Road dataset  (Geiger et al. , 2012). We used two datasets for benchmarking, one also used by LASV author was already labelled with vanishing points, the second has been labelled by our own. All images were used with the resolution 180x240 following author's strategy. The LASV dataset is composed of 1003 independent images and taken in different environments. This dataset is difficult for algorithm but useful because most of these pictures are missing lane marking and asphalt which make path harder to detect. We used this dataset only on original method and method V. Indeed all temporal method aren't able to be used on independent images datasets. The second dataset is a 250 images dataset which are following each other. Ground truth has been labelled using different students estimation.
After explaining the vanishing point theory, each student has labelled vanishing points in the datasets. Following these labelling period, we have taken every labelled dataset to compute the average coordinates of vanishing points in each image. This average coordinates are considered as ground truth. The computation of the local error consists of computing the Euclidean distance between location of the computed vanishing point and its ground-truth location. Global error of each method is the average of all local errors. This comparison allowed us to created 3 classifications : precision classification, speed classification and speed + precision classification.
CONCLUSIONS AND FUTURE WORKS The obtained results show the entire potential that our improved version of the LASV could have for the pre-segmentation stage. We have improved its precision and speed it up by more than 32 times to come closer to the real time requirements (30 FPS), indeed our improvements are around 2FPS on Matlab compared to the 0. 06 FPS of the original LASV. Moreover, our method is almost as precise as the original algorithm. We now plan to create an embedded version of this code and expect to see an additional speed-up compared to the MATLAB implementation. The results would be highly increased and even closer to real time requirements. We plan to use this pre-segmentation as an aid for road segmentation using watersheds  (Vincent and Soille, 1991; Marcotegui and Beucher, 2005)  employing this method to limit the region of interest area and reduce computation time.
In all cases, we realize a random positionning of N gp glare points along the pre-defined form. We have thus developed a MATLAB library of predefined objects (Figure  2  shows some typical examples). For each object, we can choose in our simulator: -the form of irregular object and its dimension (see Figure  2 ), -the number of glare points to define the particle (N gp ), -the amplitude of the electric field emitted by each glare point (α j ), -the phase of each glare point (ϕ j ), The 3D position of each glare point (a j ,b j ,c j ) is defined randomly over the pre-defined form. In the simulations that will be presented in the next sections, the amplitude of all glare points will be identical. We are interested in the characterization of particles in the size domain from a tenth of micrometers to hundreds of micrometers.