dataesr/scientific-paragraphs-categorization huggingface dataset:
The complex phospholipid bilayer is typically described in the Monge gauge where to each point in the projected xy plane a height h(x,y) is assigned. Tension is pulling out the spontaneous fluctuations of the membrane, thus enhancing the apparent surface area, which is called excess area. b) The apparent surface area change upon controlled tension application can be measured by a micropipette assay. When irradiating included light activated bacterio-rhodopsin channels, an increased excess area can be measured. This was the first experimental proof for actively driven membrane fluctuations  (33) , that was later investigated in more detail (34) (Data replotted from  (34) , with permission). adjusts to conserve the absolute membrane area  (38). The Canham-Helfrich Hamiltonian is hence generally written : F = 1 2 κ A C(r) -C0 2 dA + σ A dA 1.
However, up to this point the passive origin was only supported by the success of fitting a a passive theory to the data. It is important to point out that these findings do not exclude active forces as the source of flickering, especially if these active forces are random and uncorrelated in nature. When comparing to the measurements of excess area, and mode analysis, it appears legitimate to describe these active membranes by passive models. However, using these passive approaches on active data leads to wrong estimates for mechanical properties of the membrane. Figure 4 (a) Schematic view of the red blood cell membrane with the organized spectrin network, ion pumps and channels. (b) Active rheology of RBCs. To determine the mechanical response function, an optical tweezer is used to apply an oscillating forces (blue) on the RBC membrane, while the displacement is measured (green).
ACKNOWLEDGMENTS The authors thank  P. Bassereau ,  J. Prost ,  C. Sykes ,  J. -F. Joanny ,  N. Gov  and  P. Sens  for helpful discussions over the past years,  N. Knubel  for help on the illustrations, and  P. Bassereau  for providing original data for replotting. H. T. acknowledges support from the  CNRS/Inserm program ATIP-Avenir , from the  Bettencourt-Schueller Foundation  and from the  Collège de France. T. B. was supported by the  Deutsche Forschungsgemeinschaft  via the  Cells-in-Motion Cluster of Excellence grant  ( EXC 1003 -CiM ) at the  University of Münster, Germany , as well as by an  ERC   Consolidator grant  (PolarizeMe).
For this purpose, the samples were painted with aerosol white and black spray paint on one face to create a speckle pattern with adequate contrast. A region of interest (ROI) was defined on the specimen surface. The correlation algorithm compared gray level of each pixel in subset defined in the region of interest (ROI). Once the matching was done, the displacement components of the center of the reference and deformed subset can be determined. In this study, strain computation was carried out by Vic3D ® software developed by Correlated Solutions ®.
The high finesse FP cavity ensures a high intracavity power enabling the simple observation of the Lamb-dip effect around 626 GHz, even for relatively weak rotational absorption lines of minor isotopologues, as illustrated in Fig. 4. The centre of the Lamb-dip was determined by fitting 10 spectra recorded at different pressures between 5 and 20 µbar using a Gaussian profile. A mean frequency value of 626 780 174 974 Hz was obtained with a standard deviation of 890 Hz for the J''=55 ¬ J'=54 rotational transition of 18 O 12 C 32 S. We note a discrepancy of 48 kHz with the tabulated value available in the Cologne Database for Molecular Spectroscopy  [51, 54]. Depending on the available signal to noise ratio, an absolute frequency accuracy between 1 and 5 kHz can be easily obtained by use of this FP-TAS.
Funding. The authors would like to acknowledge the financial support of this work by the  French Agence Nationale de la Recherche  project  Original Sub-millimeter Chirped pulse instrumentation for Astrochemical Reactivity (OSCAR)  under contract number  ANR-15-CE29-0017 , and the  European Fund for Regional Economic Development  through the  TERAFOOD  project (INTERREG V  FR-WA-VL 1. 2. 11 ).
Acknowledgements H. Meng was sponsored by  China Scholarship Council  as a joined Ph. D. student in  Ecole Centrale de Lyon. This work was supported by the  National Natural Science Foundation of China  ( 51528501  and  11532009 ) and the  Fundamental Research Funds for Central Universities  (2014qngz12). This work was performed within the framework of the  Labex CeLyA of Universite de Lyon , the program Investissements d' Avenir  ( ANR-10-LABX-0060/ ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR).
The architecture is based on collaboration between CPPS and CPES. The sustainable decision tools are based on a distributed control model based on an intelligent solving method (meta-heuristic/heuristic for scheduling and rescheduling) using a multi agent system coupled with IoT technology that enables devices (CPPS and CPES) to connect and collaborate with each other and act as an entire system. As real-time scheduling plays an important role in CPS  [25] , it is primordial to propose new reactive mechanisms to handle the energy consumption. Two rescheduling methods are also proposed to take into account fluctuations in renewable energy in real time. EasySched was validated in a completely distributed way, integrating hardware and software. Reactive mechanisms are proposed to adapt the consumption of production factories according to an energy threshold communicated in real time by CPES from energy providers.
• Monitoring data: Data acquisition via sensors is an important aspect of industry 4. 0 or smart systems in general. Sensors are the machine's gateway to sensing its surrounding physical environment. Using appropriate sensor installation in CPES, various signals such as vibration, pressure, or temperature can be extracted. This behavior is cyclic and repeated every fixed period P1. Raw data such as temperature, electric power, voltage, and energy consumption are collected and saved to be processed. The collected data is useful to find a good sustainable decision for maintenance, control, scheduling, etc.
Implementation of EasySched This section details the physical distributed systems used as an example to verify the feasibility of the proposed architecture and to asses the performance of EasySched. A physical distributed validation, on different systems, integrating the physical elements (hardware with connected sensors (IoT)) and the software is proposed in this article. These elements are connected via networks and cooperate to provide sustainable decisions. The real target system is the AIP-PRIMECA cell at the Polytechnic University Hauts-de-France. In the first experiment, the Acorn RISC Machine (ARM) embedded system was considered as a factory. The photovoltaic energy system was represented by the ARM embedded system with a luminosity sensor. The wind energy system was represented by the ARM embedded system with a temperature/humidity sensor.
Experimentation and Interpretation of Results The distributed implementation consists of a PC and five ARM A7 embedded systems (FSA1, FSA2, ESA1, ESA2, and MA). The PC has an Intel Core i7 processor with 8 GB of RAM. The integrated card operates at 900 Mhz with 1 GB of RAM. The target embedded system runs the Debian version of Linux. All the machines run Java SE Embedded. A LAN is used for communication between all IoT components. The multi agent model was developed using the Java Agent Development Framework (JADE) multi agent platform in Netbeans IDE. To test the efficiency and the performance of the proposed EasySched architecture through its distributed implementation, different tests were performed on various types of job-shop benchmarks using machines with different speeds. Instances were named "machines_Vmax_Pi" where (machines) is the number of machines, (Vmax) is the maximum number of tasks per job and (Pi) is the range of processing times.
The method first utilises the spectral correlation between wavelengths to distinguish between levels of noise present in different image planes of the data cube. Based on the level of noise present, the method adapts the use of the 2-D non-subsampled shearlet transform (NSST) coefficients obtained from each image plane to perform the spatial and spectral de-noising. Furthermore, the method was compared with two commonly used pixel-based spectral de-noising techniques, Savitzky-Golay (SAVGOL) smoothing and median filtering. The methods were compared using simulated data, with Gaussian and Gaussian and spike noise added, and real HSI data. As an application, the methods were tested to determine the efficacy of a visible-near infrared (VNIR) HSI camera to perform non-destructive automatic classification of six commercial tea products.
Since the noise is in the acquired signals, it can be observed in each individual spectrum. However, it can also be observed as pixel-to-pixel intensity variations in each spatial plane. For this reason, the noise is visible in both the spatial and the spectral domains. The need for methodologies to deal with the noise present in the data cubes generated by close-range HSI has already been highlighted in  [12]. A typical approach to deal with noisy signals in the chemometrics field is to remove the affected spectral range from the dataset. This approach can be seen very often when the extreme wavelengths of images are noisy, and the easiest option is to remove that part of the spectrum. However, the downside of this approach is that with the removal of noisy wavelengths, relevant information in the data might also be removed.
Finally, after de-noising, the inverse of the NSST is applied to reconstruct the image planes. The aim of the present work is to present a wavelength-specific shearlet-based image noise reduction method  [11]  for HS image denoising and to test its potential for de-noising close-range HS images. Furthermore, the method was compared with two pixel-based spectral smoothing techniques, i. e. , SAVGOL and median filtering. The potential of the method was tested using three different sets of HS images. The first two image sets comprised supervised images containing known amounts of Gaussian and mixed noise. The third image set was a real VNIR HSI dataset generated for the classification of six commercial tea products (oolong, black, green, yellow, Pu-erh and white). The performance of the de-noising techniques was evaluated through visual inspection, spectral correlation, peak signal-to-noise ratio (PSNR), and through classifications performed with a multi-class support vector machine (SVM).
A single image, comprising more than 2000 pixels per tea sample, was acquired of the six tea samples using a frame rate of 21 fps and an exposure time of 5 ms. The acquisition and management of data were performed using inhouse code developed in Matlab (R2016b, Mathworks Inc. , Natick, United States). Before data analysis, the radiometric calibration of images was performed using white and dark references. The correction was performed for every pixel in the HS image according to Eq. (  1 ), = - - I I I I I R i j k raw i j k dark i j k white i j k dark i j k ( , , ) ( , , ) ( , , ) ( , , ) ( , , ) where I R is the calibrated reflectance, I raw is the raw intensity measured from the test sample, I dark the intensity of the dark response, I white is the intensity of the uniform white reference, and i and j are spatial coordinates and k is the wavelength in the image.
De-noising performance Spectral correlation and peak signal-to-noise ratio (PSNR) were used to quantify the performance of the de-noising methods in the spectral and spatial domains, respectively. Spectral correlation provides a measure of the similarity of spectra after de-noising with the corresponding spectra in the absence of noise (i. e. , the clean spectra), and was estimated via calculation of the correlation coefficient between the de-noised and clean spectra utilising the corr function in Matlab. The PSNR was calculated as shown in Eq. (  9 ): = PSNR log peakvalue MSE 10 ( ) 10 2 (9) where the mean square error is given by = ∑ - = MSE Y i j Y i j N [ (, ) (, )] i j N , 1 1 2 2 2 and Y 1 and Y 2 are the two image planes to compare (i. e. , the image plane after de-noising and the corresponding image plane in the absence of noise, respectively) and peakvalue is either specified by the user or selected from a range that is dependent on the image datatype (e.
Multiclass support vector machine classification To perform the classification experiment on the HSI data sets, support vector machine (SVM) classifiers were developed. SVM utilises the hyperplanes to define the decision boundaries to perform classification. Furthermore, to deal with the data complexity, the SVM performs high dimensional mapping of the data using kernel functions. Mapping to higher dimensions is usually carried out to make the data linearly separable. Furthermore, in the high dimensional space, the choice has to be made to select the hyperplane that provides the largest separation of the classes. As in our case we have six different classes corresponding to six different tea products, the traditional SVM binary classifier was combined with the error correcting output code (ECOC) ensemble method.
The ECOC deals with the multiclass problem by creating several independent binary classification models. The SVM along with ECOC was implemented in Matlab via the Statistics and Machine Learning Toolbox (R2016b). The coding design used for the ECOC-SVM model was one-vs-one, where a model was developed with one class being assigned positive and another class being assigned negative and all other classes were neglected. The algorithm exhausts all combinations of class pair assignments leading to k (k-1)/2 models, where k is the number of classes to be considered. High dimensional mapping of the data was performed with a radial basis function (RBF) kernel function with a scale parameter of 2. 5. The spectra from the images for six different classes were selected in a supervised way using the "roipoly" function in Matlab.
The "roipoly" function provides the graphical interface to manually extract the information from the data cube. For each individual tea class, spectra were extracted from 200 pixels, which were selected at random from the image collected, leading to a total of 1200 spectra for classification model development. For the development of a robust model, the model was cross-validated with a 10-fold cross-validation method. In 10-fold cross-validation, the calibration data is divided into ten equal parts. For making the model, 9 out of 10 parts were used and to cross-validate, the 10 th part was used. This was then repeated ten times, and the average prediction accuracy was recorded. The whole process was performed with 100 iterations and the mean accuracy and standard deviation were recorded.
21 ± 0. 41%). The poor performance of SAVGOL smoothing is due to the spreading out of the effect of the noise over several consecutive wavelengths by the    Table  2  SVM classification model accuracies (%) obtained using different de-noising methods with simulated and real datasets (see Table  1 ). The value given is the mean ± one standard deviation resulting from 100 iterations of a 10-fold cross validated model. fitting of the polynomial inside the window. This reason can also explain the better performance of median filter compared to SAVGOL smoothing.
Conclusions The data generated from HSI often contain noise. For an efficient data processing strategy, it is important to deal with the noise present in the data by either reducing or removing it. However, simply removing noise (wavelengths) can lead to loss of information from the dataset. Therefore, exploiting different data pre-processing techniques to reduce the noise in the datasets is always the better option as the information in the dataset is largely retained. Commonly used methods such as SAVGOL smoothing and median filtering can deal with a small amount of noise and if the noise is not present in neighbouring wavelengths. However, when the noise level increases and the noise is present in consecutive wavelengths, SAVGOL smoothing and median filtering can result in distorted spectral profiles leading to spreading of the noise to adjacent wavelengths.
Acknowledgments PM and AN acknowledge the funding received from the  European Union 's  Horizon 2020 research and innovation program named MODLIFE (Advancing Modelling for Process-Product Innovation ,  Optimization, Monitoring and Control in Life Science Industries)  under the  Marie Sklodowska-Curie  grant agreement number  675251. The authors also acknowledge the  Centre for Hyperspectral Imaging at the University of Strathclyde, Glasgow ,  United Kingdom  and especially  Julius Tschannerl  and  Prof. Stephen Marshall  for their kind help in performing the HSI experiments.
The beam energy is adjusted to 19 keV and 1400 projections are needed to obtain the tomograms of deformed fibers. These radiographic images are obtained by a Frelon camera (2048 × 2048 pixels) developed at ESRF coupled to GGG scintillator (thickness 10 µm) and an optical × 20/NA 0. 45 objective from Olympus. Images are obtained at four different load levels up to the fiber rupture. The resolution of each image is 8. 6 × 10 9 voxels, with a voxel size of 280 nm. This corresponds to a field of view of 573 µm 3. Conversion of the 2D radiographic images into 3D tomograms is achieved using filter-back projection algorithm. Processing of the 3D images is conducted using the public domain ImageJ software. Four different configurations are studied including hemp bundles and elementary fibers with V-like and U-like notches.
ANSYS mechanical software (ANSYS, Inc. , Canonsburg, PA, United States) is used as a framework for all computations. A linear elasticity scheme is associated to the meshed structure. This scheme is able to represent truly the observed force -displacement response of the notched fibers. Elastic and isotropic material properties are associated to the meshed part, namely Young's modulus E(x, y, z) and Poisson's coefficient V(x, y, z). All these quantities are spatially dependent on the gray level g xyz of the fiber elements representing the sub-structure of the fiber. After segmentation, the two remaining gray levels (0 and 255) correspond to the air and the solid phases. The following relationships are considered to spatially assign the solid and air properties depending on the local gray level.
ACKNOWLEDGMENTS The authors would like to thank  Barbara Fayard  and  Olivier Guiraud  from  Novitom company  for their technical assistance during the experiments at  ESRF, Grenoble, France. The authors are also thankful to  Francois Gaudard  from  UMR614   FARE ,  Reims , and  Jean-Eudes Maigret  from  UR BIA, Nantes , for their technical assistance in the preparation of the experiments.
The two MNN structures are adopted to build a model allowing to improve the performance of nonlinear regression analysis. This model is established based on the collected training dataset, and it is used to map the three-dimensional gravity force ([F x , F y , F z ]) to the 3-D Euler angles of the end effector, namely [θ x , θ y , θ z ]. The performance of the two types of MNN models is compared in terms of modeling accuracy  [25] , prediction speed and training time. After the tool dynamics identification, the calibration procedure is achieved with the classic singular value decomposition (SVD) algorithm. The calibration results with different gravity compensation strategies are investigated. Finally, a bilateral teleoperation demonstration is performed to show the transparency of the bilateral teleoperation system when including the proposed model-free tool dynamic identification method.
System Description A brief description of the robot system developed in this project is shown in Figure  7. A redundant robot (LWR4+, KUKA, Augsburg, Germany) served as the slave manipulator torque-controlled through fast research interface (FRI), which could provide direct low-level real-time access to the robot controller (KRC) at rates of up to 1 kHz  [39]. The software system was developed with OROCOS (Open Robotic Control Software, http://www. orocos. org/) with a real-time Xenomai-patched Linux kernel and ROS (Robot Operating System, http://www. ros. org/) kinetic in Ubuntu  [40]. To guarantee control frequency, force sensor, ROS node and OROCOS torque controller were executed on separate computers with UDP communication  [41]  between each other: the control loop and the sensing ROS node was executed on separate computers, as shown in Figure  8.
The system consists of: • a seven DoFs LightWeight robotic arm (LWR4+, KUKA, Augsburg, Germany) as slave device. • a six-axis force sensor (M8128C6, SRI, Nanning, China)  [42]  that has the purpose of measuring interaction force between the surgical tool-tip and the environment. Overview of the developed software system. The "LWR4+ Impedance Controller" is adopted to allow hands-on control to move the surgical robot arm by hands. The "force sensor" is developed using Robot Operating System (ROS) and it communicate with the Open Robotic Control Software (OROCOS) by ROS topic with a frequency of 500 Hz.
To enhance the validity of the comparison results, four single hidden layer NN models (M5-M8) based on CF and FF networks, namely CF-single layer neural network (SNN) and FF-SNN, are chosen in the experiments. The four aspects for the evaluation the performance of the eight models comprise the training time, the online predictive time, the regression errors (i. e. , MSE and RMSE), and the correlation coefficient ρ. To avoid the phenomenon of over-fitting and under-fitting and to find the most stable NN, each of the models was tested 30 times. The first experiment was designed for proving the reconstruction ability of the adopted models. Figure  9  shows the results of comparing MSE, RMSE and correlation coefficient ρ among models M1 to M8 on the training dataset. The top three rows show the MSE, RMSE and ρ of each channel (x, y and z).
The fourth row displays the sum of errors (MSE and RMSE) and the average of coefficients ρ. By observing the eight pictures on the left two columns, M1 and M3 obtain the lowest errors for modeling the tool dynamic, while M1 had a lower error on the data collected from channel x than model M3. In addition, M1 was the most robust model due to its smaller standard deviation in respect to the other models. Similarly, by comparing the results of correlation coefficient ρ (in the third column), M1 proved to be the best reconstruction model for mapping the Euler angles to the force on the training dataset. The errors and coefficient only illustrate the ability of reconstruction accuracy and model stability. However, the time-consuming of training a NN model is an other significant issue which is needed to discuss.
Because both of them acquired lower errors and higher ρ values than the other models. The M1 and M3 approach obtain the worst errors with respect to the other models, but the difference between M1, M3 and other models is not too much. For example, the overall MSE of M1 model is about 0. 015, while the best results computed by M8 is 0. 018. The M1 and M3 had overfitting problems. The M2 and M4 models acquired better performance on the testing dataset because they are fit the outputs on that dataset. However, when they were adopted on the training dataset, they were underfitting. Finally, the M1 and M3 models were not suitable for predicting on the testing dataset. The average testing time was a significant index to evaluate detection speed of the built model. Table  4  displays the average and standard deviation of the testing time.