dataesr/scientific-paragraphs-categorization huggingface dataset:
2. Forecast module: measured values of the controller's stochastic inputs (GHI, grid load, and water demand inferred from incoming flow rate) are added to the sliding databases of respective GPR models, which are then used to update the models' parameters. Afterwards, the module produces updated forecasts of all three stochastic quantities over the forecast horizon, along with their respective confidence intervals. Lastly, GHI values and their corresponding confidence intervals are converted into PV power generation ones.
5 mm is proposed. The researchers in  [11]  outline several approaches with data rates equal to or greater than 100 Mb/s. Generally, there are two categories of optical receiver: photodiode and solar cell  [12]. In this work a Bpw34 photodiode is used. The receiver collects hybrid light composed by direct and reflected light. As it is known that the rate of direct light is about 95%, the first reflected light is 3. 57%, and the second reflected light is 1. 27%. In this paper, a VLC-based Indoor Positioning System (VLC-IPS) using a trilateration technique and RSS information is proposed. The localization accuracy in different ambient light scenarios is compared. Different indoor illumination and SNR distributions are evaluated in our simulation by considering the first-order light reflection at each wall, as well as direct and indirect sunlight exposure.
Those scenarios are illustrated by a mixed optical wireless channel model, which was studied in research about wireless infrared communication  [13, 14]. The researchers analyze the performance and present the benefits of partial pre-equalization for indoor optical wireless transmissions based on asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) with intensity modulation and direct detection (IM/DD). In those papers some parameters are not studied, Bit-Error-Rate (BER) performance is presented in  [15]. Reference points of the model are the light sources and the target is the optical receiver. The simulation of the model was carried out using MATLAB software. The remainder of this paper is organized as follows: In Section 2 the optical channel and the positioning algorithm are described.
Type of data Table, Image, Figure How data were acquired The porous and composites membranes were tested in direct contact configuration cell with an active surface of 40 cm². The centrifuge pumps, temperature, and flux sensors are provided from RS Components, the bench conductivity meter is a Jenway 4520 and the balance is from Sartorius (0. 01 g). The data related to mass transfer were recorded using LabVIEW software, the images were recorded in 12 megapixels size with the camera of a smartphone (Samsung 7). The contact angles were measured with the apparatus Dataphysics Instruments GmbH (DI) Germany equipped with a goniometer OCA-PSA Drop 8 using the software SCA20.
Acknowledgments David Brunello  (david. brunello@univ-lorraine. fr) and  Charly Koenig  (charly. koenig@univlorraine. fr) and his team are gratefully acknowledged for their skillful contributions respectively to the online acquisition system and to the conception and fabrication of the experimental direct contact cell. The authors are very grateful to  Région GrandEst  and to Europe,  programme ERASMUS  (project  EU-METALIC ), for their financial support and grant for  Dr. T. Eljaddi.
Experimental set-up The comprehensive experimental study on the stress recovery behaviour of Ecoflex rubber is conducted by a testing system called Instron 5567 universal test machine. The machine frame can move up to 800 mm with a maximum crosshead speed of 8 mm/s. The loading conditions are controlled by the Instron built-in software, Bluehill. The testing machine equips with two force sensors with the capacity of ±500 N and ± 50 N, respectively, which are used for this study. In addition, specific grippers for different deformation modes are equipped. Note that the Digital Image Correlation (DIC) technique is adopted to capture the deformation of specimens more accurately, see Appendix A.
Shore hardness dependence In addition to the strain-dependence, the Shore hardness of the materials could also affect the stress recovery. illustrates the second cycles applied 0 second after the first loading. At both cycles, the stress level is higher for a higher Shore hardness. With the data of first cycles and second cycles after 0 second, the initial softening dissipation energy density w s0 and the viscoelastic dissipation energy density w v can also be decoupled and obtained, as Figure  19  shows. Again, w s0 is significantly larger than w v. Except that both dissipations of Shore hardness 00-10 are slightly higher than those of 00-20, overall a higher Shore hardness yields a larger w s0 and w v. Note that according to the product information of Ecoflex series, the viscosity for 00-10 is higher than 00-20.
Therefore, it takes a longer recovery time for the material to recovery to the same level of softening dissipation ratio. This work gives a comprehensive investigation of the factors that influence the stress recovery process and reveals that the microscopic reasons for the dependence of these factors coincide. Since Ecoflex silicone rubbers and other congeneric polymers often experience multiple complex deformations, their stress softening and recovery are of great importance to predict the long-term mechanical responses of a product made of such soft polymeric materials. Our experimental data shed light on modelling the complete constitutive mechanical response as depicted in Figure  3  including hyperelasticity, viscoelasticity, stress softening, and stress recovery.
Acknowledgements This work is partially supported by the  National Science Fund for Distinguished Young Scholar  (No. 11925203 ), the  National Natural Science Foundation of China  (No. 1672110 ), and the  Open Project Program of State Key Laboratory of Traction Power  under Grant (No. TPL2003 ). MH also acknowledges the support by  EPSRC  through the  Supergen ORE Hub  ( EP/S000747/1 ), who have awarded funding for the Flexible Fund project Submerged bi-axial fatigue analysis for flexible membrane Wave Energy Converters ( FF2021-1036 ).
To compare the relative proliferation of NRCs in different culture conditions, a ratio of cell densities was used to assess an increase or a decrease in cell proliferation. b) Macroscopic imaging. Mosaic fluorescence images were stitched using the ZEN Tiles module (Zeiss) and segmented to extract the total area of lumens for each culture condition (mean ± SEM, N=3 experiments per condition, significant difference at p=0. 0273). Fluorescein accumulates in lumens, thus their gray level intensity is higher than in other structures (cells in 2D and cells in 3D but without lumen), which was previously confirmed by confocal analyses with orthogonal views of 3D cell structures after a fluorescein secretion test. Based on this principle, using Fiji (National Institutes of Health), a threshold of gray levels was applied on images after background removal (500 pixels rolling ball radius).
Results are expressed as mean ± SEM, p values <0. 05 were considered statistically significant. A Kruskal-Wallis test was used to compare sample means with Matlab analysis software The obtained p-values from multiple comparisons were corrected with an implemented function by David Groppe (2020) named "fdr_bh" (https://www. mathworks. com/matlabcentral/fileexchange/27418-fdr_bh), MATLAB Central File Exchange. Retrieved May 2, 2020. *** Conflict of interest statement: the authors declare no conflicts of interest that pertain to this work. Financial support statement: This work received the financial support of both the iLite RHU program (grant ANR-16-RHUS-0005) and the DHU Hepatinov Authors contributions: E. G. , L. L. , C. E. K. , L. B. , D. A. , E. M-A. , A. F. and P. D-W. conceived and designed the experiments; E.
Acknowledgments We thank  Isabelle Garcin  from the  laboratory Physiopathogénèse et traitement des maladies du Foie (Université Paris-Saclay , INSERM,  U1193 ) for her support in confocal imaging,  Elodie Grellier  from the  Paris-Saclay Institute of Neuroscience (CERTO-Retina France ,  CNRS ,  Université Paris-Saclay ) for her technical support in using the stereomicroscopy imager AXIOZoom. V16 (Zeiss),  Marjorie Juchaux  from the  Irène Joliot-Curie laboratory (IJCLab)  for her technical assistance in using the Nikon eclipse TS2R and a Nikon Eclipse  TE300  inverted microscopes for imaging of co-cultures. We thank  Jean-Charles Duclos-Vallee  for his constant support and interest and his invaluable role in creating a stimulating environment in liver bioengineering. ***.
Statistical analysis. analysis Results are expressed as mean ± SEM, p values <0. 05 were considered statistically significant. A Kruskal-Wallis test was used to compare sample means with Matlab analysis software The obtained p-values from multiple comparisons were corrected with an implemented function by David Groppe (2020) named "fdr_bh" (https://www. mathworks. com/matlabcentral/fileexchange/27418-fdr_bh), MATLAB Central File Exchange. Retrieved May 2, 2020. *** Conflict of interest statement: the authors declare no conflicts of interest that pertain to this work. Financial support statement: This work received the financial support of both the iLite RHU program (grant ANR-16-RHUS-0005) and the DHU Hepatinov Authors contributions: E. G. , L. L. , C. E. K. , L. B. , D. A. , E. M-A. , A. F.
Multi-Objective and Multi-Physics Optimization Methodology The global optimization framework is shown in Figure  1. The optimization methodology has been developed on MATLAB ® and based on 2D finite element-modeling tool XFEMM ® to calculate the electromagnetic performances of the machine  [13]. For the choice of optimization algorithms in optimization process, we used the platform PlatEmo  [14]. PlatEmo is an open source MATLAB platform for evolutionary multi-objective optimization, which includes more than 50 multi-objective evolutionary algorithms with several used performance indicators.
For geometrical variables, we had to add the control parameter variables. Two variables for each operating point, the current (Imax) and the torque angle control ψ. The optimization variables combine geometric variables and supply variables. Then, based on the optimization variables defined, we used GA to generate randomly the first generation of the population (by MATLAB ® "rand" function) or directly imposed by the user. Step 3: we computed the multi-physical FEA (electromagnetic, thermal, and mechanical) to evaluate the objective functions and constraints which will be used by GA to optimize the electrical machine. Step 4: From objective functions and constraints evaluation, the GA performs some operations such as selection, crossovers and mutation to produce the following generations. The GA parameters used by default in PlatEmo are given in  [18] : probability of crossover (proC): 1; distribution index of crossover (disC): 20; probability of mutation (proM): 1; distribution index of mutation (disM): 20 The optimization process is terminated when the number of generations reaches a given value.
Electromagnetic Models Stepped Rotor Position (abc Model) vs. Fixed Rotor Position (dq Model) The electromagnetic performances are evaluated by FE calculations to have the most generic design tool for modeling complex geometries. Indeed, the FE modeling allows an easy adaptation of the models developed for the evaltion of the performances of the machine in the case we have to change the geometry of the machine. The main disadvantage of FE modeling is the high computation time. To overcome the computation time issue, we developed two electromagnetic models (stepped rotor position and fixed rotor position) and computed magnetostatics FEA on XFEMM ® , which is an "open source code" directly integrated into MATLAB ® and allows a fast evaluation of FE calculations  [13]. Indeed, XFEMM ® does not have a graphical interface unlike commercial software finite element calculations, which significantly reduces the computation time during the optimization process.
PV production and building load The PV production throughout the year was simulated with the help of EnergyPlus software for the estimation of incident solar radiation on each outdoor surface. For the load curve, real consumption profile were used. Referred to as l(t), they correspond to a non-dimensional aggregation of the consumption profiles in French Geneva. Therefore, the consumption of the building is defined here as L(t) = K × A T × l(t) (14) where K (kWh/(m 2. y)) is the annual energy consumption density of the building and A T = S r × N f is the total living area. The time step for both the PV production and the building energy consumption is ∆t = 10 min. Except when explicitly mentioned in section(3. 2), in the rest of the manuscript the focus will be on the tertiary building load profile.
4. 3, the economic parameters S 0 , I 0 , p w and OM k also depends on the total size of the PV installation and therefore on S PV,i. Therefore it appears that for each objective q = 1, 2, 3 the optimization problem can be simply written as min S PV,i f q (S PV,i ), S PV,i ∈ [0, S u,i ] , i ∈ {r, e, w, s}. The objective functions f 1 and f 2 are respectively E grid and σ(RR) whereas f 3 = -NPV. Indeed, the minimization of f 3 corresponds to the maximization of NPV. In order to minimize the objective functions, the 'fmincon' optimization solver from the Matlab environment is used  [36]. 'fmincon' algorithm uses gradient based methods and can be used with continuous multivariable objective function. It can be noted that I R , I F , p w and S 0 , are non-continuous piecewise defined functions, which make the direct use of the 'fmincon' optimization solver impossible.
Acknowledgements Authors would like to thanks the program  INTERREG V Suisse-France  for providing financial support to conduct this study in the framework of the project  G2   Solar  which aims at extending the solar cadaster to the Greater Geneva and intensify energy solar production at this level. This work has been supported by the  French National Research Agency, through Investments for Future Program  (ref. ANR-18-EURE-0016 -Solar Academy). The research units LOCIE and FRESBE are members of the  INES Solar Academy Research Center.
The feature encoder 336 based techniques utilize the trained models over different 337 datasets and retrain the last layer with the new input. The 338 AlexNet [60], VGG [61], ResNet [62], and Xception [63] 339 pre-trained models are the most prevalent methods for feature 340 extraction. The founder of the deep CNN network (AlexNet) 341 that won the ImageNet large scale visual recognition chal-342 lenge (ILSVRC) with as the top-5 test accuracy of 84. 6%, 343 Krizhevsky et al. [60] introduced the architecture, consist-344 ing of five convolution layers, max-pooling layers, rectified 345 linear units (ReLUs) as non-linear, three fully-connected 346 layers, and dropout. Later in 2014, the University of Oxford 347 Visual Geometry group launched the VGG network [61] and 348 becomes the runner up for ImageNet ILSVRC.
Huang et al. [139] implemented U-Net for segmentation of prostate cancer tissue in mp-MRI; Dynamic contrastenhanced (DCE) and DWI MRI images. Öcal et al. [140] fused the Nested 3D dimensional volumetric convolutional neural network (Nested-Vnet3d) and 2D volumetric convolutional neural network (V-net2d) for segmentation of prostate trained and tested on PROMISE-12 challenge dataset. In [141], Kang et al. proposed a 3D segmentation technique using volumetric convolutional network and temporal stream modeled using recurrent neural networks with long short-term memory (LSTM) units. Ma et al. [142] modified the U-840 Net with multi-scale dilated convolution (MDC) and pooling 841 block (MPB) to extract and encode multi-level features. The 842 attention gate [143], along with adversarial loss, resulted in 843 enhanced PZ segmentation.
The network was trained and tested on 859 multi-dataset to generalize both intra-and cross-dataset. Their 860 results revealed that the SE blocks allow recalibration of 861 adaptive features, thus providing an excellent generalization 862 of cross-datasets. 863 Astono et al. [147] implemented a simple 2D U-Net model 864 for prostate segmentation based on a private dataset of T2 865 weighted MRI images with fixed 2D resolution and voxel 866 size across the entire dataset. 867 Da Silva et al. [148] developed coarse segmentation that 868.
The short and long residual 888 convolution enhanced the prostate MRI segmentation accu-889 racy, and the rank in the open challenge of PROMISE-12 890 dataset in 3D segmentation. 891 Mooij et al [152] introduced aniso-3DU-Net for segmen-892 tation of anisotropy MRI volumes of prostate. Milletari et al. 893 [153] performed the 3D segmentation of prostate gland MRI 894 using volumetric CNN with a novel objective function. 895 Yu et al. [154] implemented residual connections within 896 the 3DCNN to perform segmentation of prostate MRI vol-897 umes and evaluated the network using DSC, hausdorff dis-898 tance (HD), average boundary distance (ABD) and relative 899 absolute volumes difference (RVD). 900 Yuan et al. [133] developed a novel encoder-decoder 901 densely connected convolutional network (ED-DenseNet).
Multi-level hybrid global convolution 1144 blocks and boundary refinement blocks are used in the de-1145 coder part of the network. Hu et al. [194] developed a novel 1146 GAN called prostateGAN. The author combines the idea of 1147 DCGAN and cGAN to generate prostate DW MRI images 1148 with a specified glean score. The network is trained with 1149 a dataset of 104 patients having a 1490 prostate DW MRI 1150 slice. The 3D convolutional layer, along with the adversarial 1151 loss function, achieved prostate gland better localization and 1152 classification. 1153 Cem Birbiri et al. [195] evaluated the cGAN, Cycle GAN 1154 and U-Net for the segmentation of prostate T2W MRI. 1155 Among the evaluated networks, the cGAN performed very 1156 well in terms of DSC for the mp-MRI dataset. Jun He et al.
COMMON PROBLEMS IN PROSTATE MRI SEGMENTATION Data scarcity is the main limitation in the segmentation of medical images via DCNN, usually leading to over-fitting, which refers to a model that performs well on a training dataset but does not perform well on new data. In the majority, medical image analysis tasks, sufficient dataset with concerned masks are not available for training of DCNN because the dataset delineation is time-consuming and sometimes prone to error due to less number of experts in this field. Special care must be taken during DCNN training with limited images to prevent over-fitting. Consequently, by reducing the layers or parameters, the complexity of DCNN can be reduced or, without changing the network architecture, focus on methods that artificially augment [207], [208] the training data.
, and background. The dataset was used in the evaluation of patch-wise FCN, SegNet, U-Net and DeepLabV3+ [173]. A. DISCUSSION Large dataset with precise annotation by the radiologist and pathologist are still lacking. The process of annotation of prostate MRI needs sufficient effort to find the pathology in the MRI images. In this study, we explored the techniques of machine learning and new, deep-learning segmentation methods of different modalities prostate MRI. The latest state-of-the-art segmentation approaches are mainly based on CNN for the segmentation of prostate MRI and can be categorized into four groups: feature encoder based techniques, upsampling based techniques, increased resolution based techniques and region-based proposal. The detail of the literature work of machine learning and deep learning methods is given in Table1, 2, 3, 4, 5, 6 and 7.
In addition, the use of the generative adversarial network with CNN for segmentation of prostate MRI is given in Table7. In the medical image dataset confronting an issue of class differences, to resolve this issue, the different loss is explained in this survey, as shown in Figure6. The challenging task with prostate MRI is the delineation of the prostate that varies from patient to patient and having an impact in different modalities. Making a fair distinction between quantitative analysis of the various studies is not easy. This fact is interpreted by different factors which play its part. Different datasets are utilized for the assessment of the developed algorithm and faces a lack of standardization regarding experimental evaluation. The similar view concluded by [219]  supporting the above argument, that experimental results are not validated using a common technique which results in the inability to compare the various studies.
The literature work shows that deep convolutional neural networks have performed better than machine learning techniques. Several deep learning methods have been trained and tested on personal data and publicly available datasets, which provide a better platform for transparent evaluation and comparison of DCNN and also to exploit the capabilities of DCNN in segmentation of prostate MRI. Enhanced feature-based techniques are computationally less expensive, and have fewer parameters than the feature encoder based techniques, upsampling based techniques and increased resolution based techniques.
The main problem in research on prostate cancer diagnosis is the availability of a large dataset with precise annotation 1521 by the radiologist and pathologist. Especially, the process of 1522 annotation of prostate MRI needs sufficient effort to find the 1523 pathology in the MRI images. Based on this survey, it is apparent that the ability of DCNN 1527 to learn the appropriate features of input images has given 1528 them an edge over traditional methods of prostate MRI.
To further refine segmentation results, Savitzky-Golay (SG) filtering is employed as the postprocessing step. Huang et al. [139] implemented U-Net for segmentation of prostate cancer tissue in mp-MRI; Dynamic contrastenhanced (DCE) and DWI MRI images. Öcal et al. [140] fused the Nested 3D dimensional volumetric convolutional neural network (Nested-Vnet3d) and 2D volumetric convolutional neural network (V-net2d) for segmentation of prostate trained and tested on PROMISE-12 challenge dataset. In [141], Kang et al. proposed a 3D segmentation technique using volumetric convolutional network and temporal stream modeled using recurrent neural networks with long short-term memory (LSTM) units. Ma et al. [142] modified the U-840 Net with multi-scale dilated convolution (MDC) and pooling 841 block (MPB) to extract and encode multi-level features.
A. COMMON PROBLEMS IN PROSTATE MRI SEGMENTATION Data scarcity is the main limitation in the segmentation of medical images via DCNN, usually leading to over-fitting, which refers to a model that performs well on a training dataset but does not perform well on new data. In the majority, medical image analysis tasks, sufficient dataset with concerned masks are not available for training of DCNN because the dataset delineation is time-consuming and sometimes prone to error due to less number of experts in this field. Special care must be taken during DCNN training with limited images to prevent over-fitting. Consequently, by reducing the layers or parameters, the complexity of DCNN can be reduced or, without changing the network architecture, focus on methods that artificially augment [207], [208] the training data.