dataesr/scientific-paragraphs-categorization huggingface dataset:
Catoire and Naudet Model It has been observed that flash points of pure compounds and mixtures are highly correlated with the thermodynamic properties and vapor-liquid equilibria of the systems. Indeed, the flash point temperature depends on the composition of the vapor phase in equilibrium with the liquid. It also depends on the vaporization enthalpy of the liquid since the heat of combustion must compensate the vaporization enthalpy. Catoire and Naudet  [11]  proposed a phenomenological expression to predict the flash point of pure compounds from the normal boiling point, the number of carbon atoms and the enthalpy of vaporization at 298. 15K. This equation has been shown to be predictive for all chemical families and has been used for the search of erroneous flash points data reported in the literature and databases.
Biodiesels Biodiesels are renewable fuels that can be manufactured from vegetable oils, animal fats, or recycled restaurant grease. They are used in diesel vehicles. Six biodiesel fuels are considered here. Their compositions are reported in the supplementary materials (Table  A5 ). The properties of most components of biodiesels can be found in the DIPPR database  [24]  (simulis thermodynamics® software). For ethyl esters we readjusted the parameters of vapor pressure correlation to the available experimental data taken from the literature. These parameters are reported in the Supplementary materials (table  A7 ). All models predict about the same values for flash points of a given biodiesel, as shown in Table  4. This is also to due to the fact that the components of biodiesels are very similar in nature, so biodiesels behave as ideal solutions.
Even if only a small number of those are achievable, the search space is still humongous. Our challenge is to devise a neural architecture and a training method such that, using a training dataset illustrating only a few grid tolopogies, power flows can still be accurately predicted for topologies never seen before. In this paper, the approach that we present uses a neural network to emulate the power grid y = S( x ; τ ) in which the inputs x are so-called "injections" (productions and consumptions) and the outputs y are the power flows on all the connecting lines of the grid. The system is parameterized by structural parameter vector τ encoding various grid topologies, which includes our actionable parameters. We use τ to encode changes in our neural network architecture. The rest of the paper is organized as follows.
4  ResNet + Importance Sampling. Same ResNet architecture, but during training the data points are weighted to compensate for the imbalance with regards to τ  [3]. The idea is to give samples with τ = 0 a weight of 1 1 -p , and samples with τ = 1 a weight of 1 p , so that the same emphasis is put on the two cases. 5 For each of those four modeling strategies, and for each selected value of p , we trained 100 model instances using the Adam optimizer, with default parameters except for the learning rate that has been set to 1. 10 -3 and minibatches of 32 points. All instances are trained for 20 0 0 epochs, ensuring that all architectures have fully converged. The training of one model takes approximately 5 minutes on a single Nvidia Titan Xp GPU. In all cases, the training set counts N = 128 data points drawn according to: In Fig.
A typical way in which we train LEAP nets is to use a lot of training data in the reference topology τ ∅ , very few examples for each of the unary changes τ i , and we expect the network to generalize to target domains corresponding to double τ ij or higher level changes. 6   6 τ ij is defined as a vector that is 1 in both its i th and j th elements, and 0 every- where else. Our LEAP net architecture proceeds in the following way : we first embed x in a latent space by applying E. Then, based on τ and the location of E ( x ) within the latent space, we compute the corresponding leap L τ •E ( x ). 7 Then we decode the signal by applying D. Those latent leaps contain information about how much the system actually deviates from the reference state, and in which direction. Hence, in our architecture L τ only needs to learn to modulate the system response around its nominal value, disregarding of the system complexity, encoded in E ( • ) and D ( • ).
Illustrative experiments on a toy power grid The objective of these first experiments is to conduct a series of comparisons of LEAP net against other neural network architectures (Fully Connected network and ResNet). We use the toy power grid illustrated in Fig. 9. In terms of system identification objective, our goal is to train a neural network emulator of a load flow simulator, which is capable of making good predictions for all possible disconnections of a single line (so-called (N-1) situation) and for all possible disconnections of two lines (so-called (N-2) situation). In the jargon of power systems, a "N-1" test (with capital N) means evaluating ALL possible single line disconnections, while a "N-2" test would mean evaluating all possible disconnections of pairs of lines. In our experimental setting, for the distribution of x , we simulate data using semi-realistic injections, inspired by real production and consumption time series.
The encoder E has 4 hidden layers, with a latent dimension of 15 and ReLU activations. e , d are single layer linear neural networks with respective output sizes of 8 and 15. The decoder D has 2 hidden layers, with a latent dimension of 15 and leaky ReLU activations. The training of one model takes approximately 5 minutes on a single Nvidia Titan Xp GPU. Our evaluation method is to use the l 2 -loss on test data sampled in a way that topologies corresponding to line disconnections are re-balanced. Two distinct test sets are used, namely the "N-1" and the "N-2" test sets. In the "N-1" test single line, disconnections are uniformly distributed. Fig. 10  shows the results on the "N-1" test after training on the imbalanced training set parameterized by p. For each value  of p , we have repeated experiments for 11 instances of Fully Connected neural nets, ResNets, and LEAP nets.
Case 118 synthetic data experiment To further assess the performance of our method, we conducted controlled experiments on a standard medium-size benchmark from "Matpower"  [25]  , a library commonly used to test power system algorithms  [1]  : case118, a simplified version of the Californian power grid (dim x = 153 injections and dim y = 186 power lines). Power grid topology changes consist in reconfiguring line connection patterns in one or more substations (see Fig. 1  ). Such changes are more complex than simple line disconnections considered in the previous section and in  [7]. There are 11,558 possible unary actions (corresponding to single node splitting or merging, compared to the reference topology). From this medium size power grid, we derived 3 distinct datasets: • The training set is made of 150,0 0 0 independent data points: Then, for each of these 1500 τ ij , we sampled 100 inputs x (with the same distribution as the one used for the training and simple generalization test set).
Conclusion The LEAP net architecture has been evaluated on a number of real and artificial test cases, as well as on a synthetic example. Training was performed on data triplets ( x , τ , y ), for which τ is an actionable variable. The LEAP net generalizes not only by approximating well y for new values of x when τ follows the observational distribution on which it was trained, but also when τ is re-sampled uniformly on a domain included in the support of the original distribution. This property, which we called supergeneralization, allows us to use LEAP nets for system identification in application, in particular, to power systems. We analyzed theoretically the properties of LEAP nets and demonstrated in some restricted case that super-generalization properties could be explained and guaranteed.
We discussed the relationship between super-generalization and transfer learning and showed that LEAP nets could be used either for Domain Adaptation or for Multi-Task learning. In our experiments, we achieved a speed-up of ≈ 300 times using the LEAP net, compared to running the physical simulator, on the synthetic dataset (power grid of 118 nodes). With data stored in computer memory, our experiments on the Toulouse area attain a speed of 20 0 0 times compared to running the physical simulator. These computational evaluations were carried out using a single high-end Graphical Processing Unit (GPU) Nvidia Titan X. Further work includes scaling up our method computationally to the entire French extra high voltage power grid. We also need to improve prediction accuracy before our system could be deployed to production.
Mott-Schottky analysis A three-electrode system has been used for electrochemical detection by using; a platinum counter electrode (Pt), a silver/silver chloride (Ag/AgCl) reference electrode and the biosensor acted as a working electrode. This latter was sandwiched between two parts of a conventional 1 mL electrochemical Teflon cell. Electrical contact was taken from the backside of the biosensor  [41, 42]. The Mott Schottky experiments were associated with a VMP3 potentiostat monitored by EC-Lab software (Biologic Science Instruments, France). The Mott-Schottky analyses were performed by sweeping the potential from -0. 5 V to +4 V at a constant frequency of 10 KHz and a step rate of 25 mV. All the measurements were carried out in PBS (10 mM, pH 7. 4) and in AS with a constant pH = 7. 4, at room temperature (20 ± 2 °C) inside a Faraday cage.
A JEOL 6100 scanning electron microscope (SEM) was employed to detect initiated cracks and to observe fracture surfaces. The local crystallographic orientations nearby the cracks were characterized using the same microscope which is equipped with an EBSD detector provided by EDAX. A step below 1. 5 µm was chosen to accurately describe the local crystallographic orientations. EBSD data were processed using the OIM analysis software. Cross-section views were obtained via focused ion beam milling using a FEI Helios Nanolab G3 CX. The cumulated plastic strain versus the number of cycles for fatigue and dwell-fatigue tests is shown in figure  1a. The mean cumulated plastic strain at failure is 9% and the average number of cycles to failure is about 3700 for dwell-fatigue tests carried out up to complete fracture (i.
Our observations are also consistent with prior studies showing that the orientation of the crack matches the basal plane trace  [19, 23, 24, 27, 28]. However, the specific role of (0001) twist boundaries had not been reported yet. For instance, Bridier et al. , who investigated fatigue crack initiation in Ti-6Al-4V with a similar microstructure, have not reported the presence of such boundary at crack initiation sites  [19]. The data are presently revisited in order to clarify the potential role of (0001) twist boundaries. In that study, strain controlled fatigue tests were carried out with a frequency of 1 Hz, a strain ratio of 0 and a total strain amplitude of 0. 4 %. A micrograph of a secondary crack alongside with the associated crystallographic orientation map are presented in figure  4a  and 4b.
Acknowledgments This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 636835 -The authors would like to thank them. CAVAC, industrial partner of the  ISOBIO  project, is gratefully acknowledged by the authors for providing raw materials. Thanks are due to  To ny Hautecoeur  for his participation in the completion of this work. Thanks are due to  Yann Lecieux  (Mechanical tests). Thanks are also due to  Céline Leutellier  for having reviewed the English language. r e f e r e n c e s.
Funding: The research leading to this result has been supported by the project CALIPSOplus under the Grant Agreement 730872 from the EU Framework Programme for Research and Innovation HORIZON 2020, for the beamtimes 20170928 and 20190321 at Soleil Synchrotron, and by the project titled "Effect of water on the local structure and phase behavior of hydrophilic ionic liquids" funded by Sapienza University of Rome (Progetto Ateneo 2018, prot. RM11816427031C31).
Acknowledgements The authors want to acknowledge the support obtained by  PEGASUS (Plasma Enabled and Graphene Allowed Synthesis of Unique Nanostructures)  project, funded by the  European Union   Horizon research and innovation program  under grant agreement No. 766894. The authors would also like to acknowledge support obtained by  French National Research Agency via projects ANR PlasBioSens  and  ANR Plasma-Bond  and to thank  Helmholtz Zentrum Berlin  for the allocation of synchrotron radiation beam time at BESSY II. Experiments at BESSY have also been supported with  H2020 Calypso Plus  project, grant Nr. 19208955-ST  and  19108281-ST/R.
Power management of a one-converter parallel structure with battery and supercapacitor is addressed in this paper. The controller is implemented on a DSP from Microchip and uses a Controller Area Network (CAN) bus communication for data exchange. However, the low data transmission rate of the CAN bus data impacts the performances of regular power management strategies. This paper details an initial strategy with a charge sustaining mode for an application coupling a battery with supercapacitors, in which low performances have been witnessed due to the high sampling time of the CAN bus data. Therefore, a new strategy is proposed to tackle the sample time issue based on a depleting mode. Simulation and experimental results with a dsPIC33EP512MU810 DSP based on a 10 kW hybrid system proves the feasibility of the proposed approach.
Nowadays, embedded and networked automotive bus communication such as the Controller Area Network (CAN) is widely used for vehicle networks. It is used for the communication between the controllers, the sensors and the actuators  [25] [26] [27] [28] [29]. The controller can retrieve data from each component (i. e. voltage, current, temperature of the BT and SCs) and the DSP send periodic messages necessary to control the DC/DC converter  [30] [31] [32] [33]. It should be pointed out that the sampling frequency of the data on the CAN bus is relatively low compared to a regular implementation on a DSP/µC that use analog inputs and the PWM peripherals  [34]. In fact, in the case of a regular implementation, the CPU and the peripherals have a sampling time nearly equal to 100µs for the inner current loops and nearly equal to 1ms for the outter voltage loops.
Therefore, the performances of the controllers are not degraded by the sampling (see  [3]  for more information and  [35, 36]  for theoretical details). In practice, the CAN bus data sampling frequency is defined by the manufacturers of top-of-the-shelf equipment and modifying it in a wide range is not always possible, at least in a range defined by the manufacturer. is not always possible, at least in a defined range. It follows that designers need to face such issue by defining an appropriate controller with low data transmission rate  [28]. Therefore, this paper aims to detail experimental knowledge about the power management of a hybrid system controlled by a Controller Area Network (CAN) bus communication where one fundamental issue to be addressed concerns the closed-loop control stability under sampling with such network control  [29].
The main contribution of this paper is focused on the description of an initial strategy  [37]  with a charge-sustaining mode. Experimental results show that the proposed controlled  [37]  failed under high sampling time and quantization of the CAN bus data that deteriorate the closed-loop performances. It is the reason why a new rule-based strategy is proposed in this paper to tackle the sample time issue based on a depleting mode. Experimental results based on a 10 kW hybrid power pack coupling battery and supercapacitors prove the feasibility of the proposed approach. The paper is organized as follows. In section 2, a 10 kW experimental system is detailed. Section 3 details a regular controller for power management of a hybrid electrical system, where the closed-loop controller performance degradations are being emphasised with the CAN bus.
Finally, Figure  1  shows the experimental system and Table  1  gives the electric characteristics of the 10 kW hybrid system. All the control and the monitoring data are transmitted by the battery and PowerBOOST converter through one CAN bus network. It is worth to mention that the VISEDO inverter integrates an internal current control loop, where the desired set point current i * sc of the SCs is transmitted through the CAN bus. The different nodes involved are the battery pack, the supercapacitors, the DC-DC converter and the reversible load (parallel coupling of a load and a power supply). The three first variables of table 2 are measurements (DC bus voltage v b , SCs voltage v sc , battery current i bt ) available on the target test bench. These data are obtained from the CAN bus according to the indicated features (sampling time, precision, data type).
Discussions Experimental results have shown that the control of a one-converter structure with a CAN bus is not suitable and lead to such low performance results. Some of the solutions listed below are feasible: • Components software modifications: The most effective solution consists on a software update of the sampling time of the data send by all the components at around 1 to 5ms if this option is allowed. This option lead to good performances of the hybrid system. • Additional sensors: If the first solution is not feasible for top of the self-equipment, additional current and voltage sensors associated with local microcontrollers can be added. This option leads to a flexible solution for the designer but increase the cost and reduce the reliability due to additional materials. • Additional converter: A two-converters structure is probably an effective solution is such configuration because it allows a separate control of the two current sources and therefore doesn't lead to high battery peak current during load current transient.
Here, it is proposed to compare ETFA and FBA models by using a customized version of the previously proposed "Angiogenesis Analyzer"  18  , a simple and precise tool built in the ImageJ environment  19  and formerly used to quantify the ETFA experiment images. The "Angiogenesis Analyzer" program was conceived to extract characteristic points and elements of endothelial cells network. This image analysis software was successfully used to characterize meshed and/or branched structures in more than 150 different studies (http://image. bio. methods. free. fr/ImageJ/?Angiogenesis-Analyzer-for-ImageJ&artpage=6-6#outil_sommaire_6), which include endothelial in vitro ETFA cell differentiation in phase contrast  20, 21  , fluorescence microscopy  22  , and in vivo studies using the mouse retina angiogenesis model  23, 24.
The "Angiogenesis Analyzer" has also been used to analyze branching in alternative meshed or branched biological scaffolds unrelated to angiogenesis, as reported for the characterization of diatom silicate structures in phytoplankton research  25  , or for in vitro neuritic branching analysis in cultured neurons  26. The "Angiogenesis Analyzer" software works as an integrated program, avoiding the requirement for image pretreatment using additional plugins, or online computation services on distant servers, such as required by other available solutions  27. The "Angiogenesis Analyzer" is free and open source, easy to customize and compatible with several OS platforms (Linux, MacOS and Windows) thanks to a Java execution environment. Moreover, it can be used to obtain basic hierarchical data in the form of series of vectorial objects by modeling the analyzed structure at different levels, while most other solutions afford basic measurements as total tree lengths and meshes areas under the form of binary measurements  28.
Results fBA image analysis. Image analysis for FBA was performed using a program developed for the ImageJ software  19. This plugin is an extension of the "Angiogenesis Analyzer" for ImageJ  18  written in the macro language of ImageJ. The analysis can be divided into three main steps: (1) sphere detection (Fig. 1 ), (2) tree detection (Fig. 2 ) and (3) tree structure analysis of Junctions and Extremities (Fig. 3 ) as well as Segments, Branches and Anchorage Junctions (Fig. 4 ): Sphere detection (Fig. 1 ): Sphere analysis in ideal conditions does not present particular difficulties. However, sphere clustering, dirt, uneven lighting due to the meniscus of the culture medium and the walls of the culture wells introduce difficulties, especially defaults of object closure. A series of image manipulations were necessary to enable sphere detection using simple threshold methods as follows: §1 Light field correction (lfc) was performed using an artificial flat field obtained by applying a Gaussian convolution with a sigma value (σ) corresponding to the average sphere diameter.
Discussion In this comparative analysis between ETFA and FBA methods, we extended the "Angiogenesis Analyzer" to enable FBA analysis, including sphere detection and pseudo-vessel organization. The extended "Angiogenesis Analyzer" is written in the macro language of ImageJ, free image analysis software from the National Institutes of Health  19. While other software offers some solutions for analysis of images acquired by fluorescence complex confocal imaging  35  , here we focused on the analysis of images issued from phase contrast high-throughput microscopy. Although three-dimensionally distributed, the extensions of micro-capillaries preferentially developed in the horizontal plane. Thanks to a short, but sufficient depth of field, microscopy observation in phase contrast was found convenient enough to get a good sampling of field deepness containing pseudo-capillary developments (not shown).
Reactant structures Biotin has an imidazolidinone structure and thiolane ring (Figure  1a ). The isolobal analogs considered here are: oxybiotin, N-biotin and C-biotin. They have the same structure as biotin but S is substituted by O, NH, CH 2 (also shown in Figure  1 ). Although  The geometries of the different structures were optimized at Hartre-Fock (HF) and B3LYP PM6  28  and PM7. 29 The resulting optimized geometries of each tautomeric form are reported in Figures  2  and S1-S3. From these calculations, it was possible to obtain the proton affinities of all the structures considered at different levels of theory. HF and B3LYP calculations were performed using Gaussin 09 Rev D. 01 software,  30  while semi-empirical Hamiltonians calculations with MOPAC 2016. 31.
Fragmentation yields and products Reactivity yield is about 10% for biotin, but then it decreases when moving to oxybiotin (7. 8%), N-biotin (4. 2%) and C-biotin (2. 4%), which roughly reflects the increasing in C-X bond strength from S to C (where X = S, O, N and C). Details are reported in Table  S1  of the supporting material. Fragmentation products of biotin are compared with what reported from experiments  38  in the MassBank database. 39 The two fragmentation spectra are shown in  Globally, the match between experiments and simulations is quite good: the main experimental peaks are found also in simulations with some differences in relative intensity. In particular, experimentally, the most intense peak is m/z 227. 08 which is found less intense in simulations. Simulations report ion m/z 199.