dataesr/scientific-paragraphs-categorization huggingface dataset:
Despite the prevalence of machining, tools and cutting conditions are often chosen based on empirical databases, which are hard to be made, and they are only valid in the range of conditions tested to develop it. Predictive numerical models have thus emerged as a promising approach. To function correctly, they require accurate data related to appropriate material properties (e. g. , constitutive models, ductile failure law). Nevertheless, material characterization is usually carried out through thermomechanical tests, under conditions far different from those encountered in machining. In addition, segmented chips observed when cutting titanium alloys make it a challenge to develop an accurate model. At low cutting speeds, chip segmentation is assumed to be due to lack of ductility of the material.
Introduction Machining is a widely employed industrial process because it provides good dimensional tolerances and good surface finishing in quite complex features. According to  [1] , 100 billion US dollars are spent on metal finishing processes. In addition, it is estimated that around 15% of all mechanical components are obtained from machining operations  [2]. Furthermore, it is estimated that savings up to 20% could be made by the proper selection of tool and cutting conditions, which is made many times based on empirical databases  [1]. Therefore, reliable predictive models are needed to reduce costs and time. Predictive models can be listed as analytical, numerical or hybrid, and their main advantages and drawbacks, compared to empirical databases, are shown in Table  1. These predictive models need to be supplied with reliable material properties (constitutive and ductile failure law), which are commonly obtained through thermomechanical tests under conditions which are usually far different from those of the machining process  [4].
During the 1950s, the evolution laws arose, which were ductile failure models with two stages: (i) damage initiation and (ii) damage evolution  [47, 48]. At the same time, the flow stress models with coupled ductile failure models started to be developed. For instance, Chen et al. [49]  used a two-stage ductile failure model, which was stress triaxiality-dependent, coupled with the Johnson-Cook flow stress model, to predict chip segmentation when carrying out orthogonal cutting on Ti6Al4V using the finite element software Abaqus. Additionally, Liu et al. [50]  analyzed the feasibility of using different ductile failure models in machining when the orthogonal cutting 2024-T3 aluminum alloy also used Abaqus. However, none of them focus on the optimization of the ductile failure parameters to improve the quality of the predictions.
Finite Element Model The FEM model employed is briefly explained in this section and more details can be found in  [35]. The commercial software AdvantEdge-2D V7. 502 (Third Wave Systems, Minneapolis, MN, USA) was chosen to carry out the simulations. AdvantEdge is a commercial finite element software which provides an easier approach than other generic software such as Abaqus to model the machining process. The software employs elastoplastic Lagrangian Metals 2021, 11, 1154 5 of 22 code with continuous remeshing and adaptive meshing. The remeshing technique ensures a minimum element size in the shear zone, where severe conditions (high strain, temperature and strain rate) are expected. The minimum element size was set to 2 µm to obtain accurate results, assuming that the material behaviour is homogenous.
Acknowledgements This work was supported by  ADEME  [ N° 1401C0066 ] and by the  European Commission  [ ERA-IB-14-055 ] in the framework of the  LIGBIO  project. The IJPB benefits from the support of the  LabEx Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ). Authors acknowledge  Dr. Paul-Henry Ducrot  and  Dr. Abdus Samad Jaufurally  for providing the DHP and for useful discussions.
Data treatment The measurement system for bubble hydrodynamics is composed of a high-speed camera, a LED light source with a diffuser, and associated to the CAM-WARE software The position of the high-speed video camera is fixed at 40 cm from the cell, and it records the bubble formation process with a frame grabbing of 500 frames/s for an exposure time of 0. 1 ms. Each acquisition of images (1280 x 1024 pixels) lasts for 10 seconds, corresponding to 5000 images. The contrast of the raw images are adjusted and blurred to reduce background noise. This is followed by removing noise from images through filtering the grey-scale images using a pixel-wise adapting low-pass wiener filter. Thereafter, the contour of air bubbles (hereafter referred as structures) is detected using the edge-detection algorithm, which is followed by returning a binarized image.
Holes that could appear inside the bubbles due to light refraction or the alignment of the light source are filled in order to have homogeneous binarized structure. After image treatment and detection of binarized structures (see figure  2 ), a series of horizontal lines is imposed in the MATLAB code, which could be considered as sensors, and are situated at different altitudes above the orifice. Each of these lines, when crossing the bubbles, will record 1 and 0 respectively corresponding to the dispersed phase (air) and the continuous phase (water) ( figure  3 ). Applying this technique to the acquisition of large number of images will reproduce the so-called XT diagram, shown in the figure  4. XT diagram represents the evolution of the bubbles at a certain altitude for the whole time of the acquisition.
Experimental Problem Definition The vibration signals investigated in this study were sampled at 40 kHz for 2 seconds at every 10 minutes and a dataset containing 73 measurements was obtained. The average rotational speed of the high-speed shaft was 279 Hz. In general angular resampling is necessary for a proper vibration based condition monitoring of a rotating machine. The angular resampling requires the knowledge of the instantaneous speed of the shaft so that the data can be transformed to the angular domain in order to compensate for the speed variations  (Peeters et al. , 2019). Nevertheless, the present dataset was sampled under negligible speed variations, hence the angular resampling is not needed. Accordingly, the signal spectrum is dominated by pronounced shaft harmonics. Another potential pre-processing method to improve the effectiveness of blind filtering is the deterministic content removal.
CONCLUSION This paper investigated the performance of sparsity-based blind filtering approaches on experimental data. It is proven that, with a known the average or the instantaneous shaft speed, a blind filtering approach is capable of detecting the incipient rolling element bearing fault of a complex industrial rotating machine. Furthermore, results of the broad parameter study stress that filter length is a significant parameter that influences the convergence characteristic of the iterative solution that maximizes the generalized Rayleigh quotient. For the given dataset, the conclusion can be drawn that filter initialization induces no effect on either the numerical stability or the computation time of the iteration process. In summary, the blind filtering method can be used as an autonomous health surveillance tool for complex industrial applications in which exact characteristic frequencies of mechanical components are lacking.
ACKNOWLEDGMENT This research was supported by  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Education, Science and Technology  ( NRF-2016R1D1A1B03931639 ,  NRF-2019R1F1A1060594  and  NRF-2020R1A2C1007227 ) and the " Human Resources Program in Energy Technology " of the  Korea Institute of Energy Technology Evaluation and Planning (KETEP) , granted financial resource from the  Ministry of Trade, Industry & Energy, Republic of Korea. (Project No:  2017 4010 201410 ).
Compared to the approach of many linearization of products of radial polynomials by Clebsch-Gordan coefficients, the approach by means of Eq. (  54 ) and with using the recursion allows to substentially decrease the computation time, typically 116ms for Eq. (  52 ) and 141ms for Eq. (  58 ), and to increase the maximal value of s. With s = 2. 5, the approach by many linearizations is not optimal in term of computation time because the time is arround 198s for (51) and 90s for (57). The Figs. (7)  demonstrate that a good agreement is obtained between the functions J nmk (β, ω, s), I nm (β, ω, s) and their integral forms evaluated under Mathematica software. Thus, this concordance will allow us to simulate a few holograms of opaque particles that we consider to be common in holography like opaque disks.
This would allow researchers to obtain the Earth's incident flux on each UVSQ-SAT face, given that the CubeSat has Earth radiative sensors and photodiodes on all its faces  [1]. To determine the attitude of the UVSQ-SAT CubeSat, two methods are used: the Tri-Axial Attitude Determination (TRIAD) method and the Multiplicative Extended Kalman Filter (MEKF) method. TRIAD is a basic method, which is implemented by considering that the instruments' measurements can be easily related to the information from models in an inertial frame of reference at the satellite's location. Then, it is necessary to find the perfect rotation matrix to move from one reference frame to another. The MEKF method improves the results obtained with the TRIAD method. It aims to calibrate and correct the data from noise and inaccuracies.
This can be done studying the gradient of the Sun LOS or nadir components. Studying the standard deviation of the gradient is representative of the short variations that could be due to the attitude determination error. The average of the standard deviations of the Sun LOS gradients is equal to 5. 17 × 10 -3 s -1 for the TRIAD method compared to 4. 1 × 10 -3 s -1 for the MEKF method (UVSQ-SAT data on the 2 and 3 February 2021). Therefore, the MEKF method seems to represent the satellite attitude variations better based on the previous indicator as it reduces discontinuities thanks to its smoothing asset. A more accurate analysis can be established based on the measurements of a sensor not used in the attitude determination process. UV sensors (UVSs) with a narrow field of view (FOV) are used to compare the results based on the two methods.
In our case, the sampling period of 30 s does not allow us to reach such accuracy. This is nevertheless important to note as feedback. It is possible to estimate the uncertainties on the incident flux from the uncertainties on the angles to the source. Indeed, fluxes are corrected using the cosine of the angle for the Sun and the view factor in the case of terrestrial flux. Those uncertainties are based only on the attitude determination uncertainties. Therefore, for the solar flux, the uncertainty is estimated at 4% (1σ) for both methods. For the eclipse phase and terrestrial flux, the TRIAD method allows us to compute the flux with around 19% uncertainty, although MEKF allows for around 13% accuracy (1σ). The MEKF method brings improvements. but it also presents limitations. The algorithm loads the data on-ground from in-orbit data to compute the CubeSat attitude.
Conclusions UVSQ-SAT CubeSat aims to validate innovative technologies in-orbit for a future constellation to study the Earth's energy imbalance. UVSQ-SAT has been in orbit since 24 January 2021. To improve the UVSQ-SAT's reflected solar radiation and outgoing longwave radiation measurements at the top of the atmosphere, the UVSQ-SAT's attitude must be accurately known. Two different methods were implemented to determine the UVSQ-SAT CubeSat's attitude, and they are based on real data from space observations. The first method developed is based on the TRIAD algorithm. The accuracy of the UVSQ-SAT attitude knowledge obtained with the TRIAD method is close to ±3 • (at one σ) in sunlight. During eclipse periods, the accuracy of the UVSQ-SAT attitude knowledge is ±14 • (at one σ). In this observation phase, accurate knowledge of the CubeSat attitude is more difficult to obtain.
Funding: This research was mainly funded by  Centre National de la Recherche Scientifique (CNRS, France) ,  UVSQ  ( Université de Versailles Saint-Quentin-en-Yvelines, France ), and  Agence Nationale de La Recherche (ANR, France). This work was supported by the  Programme National Soleil Terre (PNST)  of  CNRS/INSU (France)  co-funded by  CNES and Commissariat à l'énergie atomique (CEA, France).
In this setting, an object y ∈ B is represented by a vector (d(y, x1 ),. d(y, xn )), where { x1 ,. , x1 } is a collection of objects from B, for example a subset of the training dataset. These objects, called landmarks, may be selected at random or following heuristics and systematic methodologies  (Pekalska et al. , 2006). In particular,  (Cazzanti, 2009)  proposes to use class medoids (µ 1 ,. , µ C ) as landmarks. A class medoids is an element of a class which minimize the average dissimilarity with all other members of the same class: µ i = arg min y in class i 1 n i ∑ x in class i d(y, x) , ∀i ∈ 1. C , (1) with d the dissimilarity function, n i the number of points in class i and C the number of classes.
LEARNING PROBLEM The objective of the MLP models studied in this paper is to predict the basket of products which would be obtained by processing a log at a given sawmill, using dissimilarities between 3D scans of the logs surface as inputs. These scans are unordered point clouds containing an arbitrary number of points and, therefore, cannot be used directly by traditional MLPs, but would require specialized architectures or data processing, as was, for example, done in  (Martineau et al. , 2021). This motivates the use of a pairwise dissimilarity-based learning method. The outputs of these predictors, the baskets of products, were similarly modeled as vectors. More precisely, considering a set of p individual products present in the dataset, numbered from 1 to p, the basket of products is represented as a p dimensional vector.
The i th element of this vector represents the number of lumbers of type i obtained from digitally sawing the log with a simulator. This output is therefore multidimensional. Furthermore, it can be referred to as structured, as the different dimensions of the vector cannot be considered independent from one another. The problem of predicting the basket of products of a log given a set of features can be considered either as a classification problem, or as a regression problem. In the classification problem, each type of basket presents in the training dataset is considered as a distinct class, numbered from 1 to C, and the objective of the classifier is to assign one of these class labels to new logs. In the regression case, the model directly output a p-dimensional vector, as a prediction of the basket of products.
DESCRIPTION OF THE DATASET The dataset used in this study is a proprietary dataset originating from the Canadian forest industry. It is composed of 1207 scans of real logs. An example of such a log scan is presented figure  2. Each scan can be seen as a table of dimension N p × 3, with N p the number of points, which changes from one scan to another, with an average of 12 000 points per scan. Each of the 3 points coordinates corresponds to one column in the scan table. Additionally, points, are roughly organized as ellipsoids which, together, span the log surface. Originally, the scans contained sections with missing ellipsoids, which led to poor performances of the ICP algorithm when computing logs dissimilarities. This was corrected by filling these empty sections with repetitions of the ellipsoid which immediately precedes them.
The basket of products associated with each log was generated by the sawing simulator Optitek. The dataset contains 19 different types of individual products, characterized by their length, width and thickness. Therefore, the structured output vector one has to predict is of dimension 19. It might be noticed that no basket contains more than 5 different types of products and that, therefore, these vectors are sparse. For training, selection and evaluation purpose, the dataset was randomly divided into three parts, that is, a training set containing 600 logs, a validation set containing 400 logs, and an evaluation set containing the remaining 207 logs. The training set contains only 62 different classes, which should lead to 62 medoids used to build features. However, most of these classes appear only once in the dataset.
However, when comparing two such methods on their industrial dataset, they found no significant advantage to the use of a diversity based method, compared to the use of a performance based method. In the following of this paper, we will therefore focus on a method to select MLPs for the ensembles based on the MSE score only. This method proceed as follow: 1. Evaluate the MSE of all the MLPs on a validation set and order them from best to worst. 2. from the best to the worst model, add them to the ensemble, and evaluate the ensemble MSE on the validation set. 3. Select the ensemble showing the lowest MSE. The prediction of an ensemble are here the averaged predictions of its individual elements. Additionally, 150 individual NN were trained with random initialization for each output of each of the MISO models, and 150 other for each of the MIMO models.
These scores are evaluated on the evaluation set. These models were selected as being the ones with the lowest squared error over the validation set, among the 150 models with same architecture. In particular, in the MISO case, a complete prediction of the output vector is done by concatenation of the MISO which perform the best on their respective individual output. This table also displays, for each architecture, the total time needed to train all the 150 models. The models were implemented in Python and trained on an intel Core i7 vPRO 10 th generation CPU at 2. 70GHz. Matrix operations are performed with the library Numpy. It appears than on the dataset used in this study, the MISO model with sigmoid output performs best. In particular, it has the highest prediction-production score and F 1 and lowest MSE.
It might be noticed that, similarly, the model with higher prediction-production score and F 1 among the MIMO models only is the one with sigmoid output. While the selected models with Tanh architecture perform relatively well on the evaluation set, however, the results from figure  4  do not encourage the use of this particular activation function. Additionally, one should be careful that these differences in score, despite appearing important, are not always statistically significant. This is partly due to the high variability of the scores, and extensive tests on a larger dataset would be required to conclude on a difference. Here, in particular, when comparing the F 1 scores of the MISO sigmoid models with the scores of all the other selected models using Wilcoxon signed rank test, it cannot be said to be statistically different, at a 5% level, from the MISO model with hyperbolic tangent output (p-value at 18.
Ensemble Table  3  presents the averaged scores over the evaluation database of ensembles based on MLPs with different architectures. While the ensembles seem to improve upon the scores of several models, these differences are nearly never statistically significant, at a 5% level, under Wilcoxon signed ranks test. In particular, the scores of the MISO sigmoid ensemble are never statistically different from the scores of the previously selected MISO sigmoid model, despite appearing even slightly lower than the scores of the individual models. Concerning the size of the ensembles, the fact that the MISOs appears to need more models is due to the fact that 19 ensembles are needed to make a prediction of the complete output vector, and that all of that all of them are counted in table 3. As was observed in  (Thomas et al.
CONCLUSION The set of features based on dissimilarities to medoids used to train MLPs in this study gives satisfactory results. This study furthermore compares several MLP architectures to the task of predicting baskets of products from 3D scans of wood logs. It appears that using an output activation function constraining the individual outputs in a certain value range leads to better evaluations scores, as the MLPs cannot predict unrealistic values. Therefore, both MIMO and MISO with sigmoid outputs work well and can be trained in reasonable time, with MISO performing seemingly better on our dataset. This is of particular interest when considering the fact that the sawmill configuration used in this paper processes a relatively small number of products. For example,  (Morin et al. , 2020)  considers sawmills with as much as 83 products.
The fact that ensembles barely increase the performances of the models is however surprising and may raise doubts on the possibility to consistently increase further the evaluation scores, at least when using only this set of medoid-based features. Additionally, it appears that one should be extremely careful when working on this problem to avoid overfitting of the training database. While in this paper, the features were selected as dissimilarities to class medoids, other logs could have been selected has references. To use metaheuristics like genetic algorithms or other feature selection methods during training time to select these logs might appears as a promising research direction for future works. Similarly, other classic ML algorithms, such as Random forest, will be investigated. 2 : Mean evaluation scores for the different individual models with lower MSE on the validation set, in percent, and their asymptotic confidence interval at a 95% level.
The radio frequency identification (RFID) system is one of the most important technologies of the Internet of Things (IoT) that tracks single or multiple objects. This technology is extensively used and attracts the attention of many researchers in various fields, including healthcare, supply chains, logistics, asset tracking, and so on. To reach the required security and confidentiality requirements for data transfer, elliptic curve cryptography (ECC) is a powerful solution, which ensures a tag/reader mutual authentication and guarantees data integrity. In this paper, we first review the most relevant ECC-based RFID authentication protocols, focusing on their security analysis and operational performances. We compare the various lightweight ECC primitive implementations designed for RFID applications in terms of occupied area and power consumption.
• The RFID tag, associated with the identified element, includes an electronic chip with a memory containing a unique EPC (electronic product code) identifier. Besides, to communicate to the reader in a given frequency band  [35] , the chip connects to an antenna. of silicon. This is why originally few works focused on side channel attacks on RFID tags. In 2006, Oren was the first researcher who demonstrated in  [30]  the possibility of applying power analysis attacks on UHF tags. During this attack, Oren et al. considered that the attacker does not require any physical contact with the attacked device. This way, the attacker becomes completely passive during the data transmission, making the attack hardly detectable. Then in 2007, Hutter et al. published in  [31]  the first paper that examined the effectiveness of SCA attacks on RFID devices powered by a 13.
• Semi-actives tags: similar to active tags, semi-active tags also contain an energy source, but they do not use their battery to emit signals. They act as passive tags at the communication level. However, their battery allows them to record data during the transport of merchandise (temperature change, etc. ). The cost of this type of tag is, therefore, lower than active tags. Generally, this type of tag has a simple design; however, they have several disadvantages: Reliability: it is impossible to know if their batteries are still operational. Cost: the connection of their batteries with their circuits increases the cost compared to a passive tag. Environmental impact: their battery contains highly polluting substances  [36].