dataesr/scientific-paragraphs-categorization huggingface dataset:
Combining analysis of experimental curves, one can extract the operating bias and the dark current regimes at different biases of the devices. In addition to the electrical measurements and to better understand the device performance, dark current simulations were performed using the software SILVACO ATLAS which has already been used for midwave infrared photodetectors. 10, 11 ATLAS is a commercially available software used to simulate device performances by solving differential equations derived from Maxwell's equations, such as continuity and transport equations. By fitting the simulated dark current to the experimental data, we can estimate previously unknown material parameters such as the minority carrier lifetime, their effective mass and trap energy level within the bandgap. This fitting provides a complementary tool to identifying limiting factors of the device performance.
Despite its growing ubiquitous presence, the smart city continues to struggle for definitional clarity and practical import. In response, this study interrogates the smart city as global discourse network by examining a collection of key texts associated with cities worldwide. Using a list of 5,553 cities, a systematic webometric exercise was conducted to measure hit counts produced by searching for "smart city. " Consequently, 27 cities with the highest validated hit counts were selected. Next, 346 online texts were collected from among the top 20 hits across each of the selected cities, and analyzed both quantitatively and qualitatively using AntConc software. The findings confirm, first, the presence of a strong globalizing narrative which emphasizes world cities as "best practice" models.
;  NB Mora et al. 's [2017]  additional inclusion of some grey literature databases) and principally focus on analyzing the smart city as an emergent research agenda and evolving scientific knowledge domain. In contrast, the present webometric analysis (drawing on  Almind and Ingwersen, 1997 and Thelwall et al. , 2005 , among others) seeks to capture a broader online discourse which encompasses diverse policy and practice communications linked to actual cities worldwide. As such, it accesses global online sources that shed light on how the smart city is discursively constructed by a range of involved actors (municipal authorities, national agencies, international organizations, think tanks, consultants, etc. ). Relatedly, the study pursues a particular angle of inquiry informed by critical discourse theory and based on corpus linguistic analysis; namely, how the smart city is constituted as a discourse regime (of which, more below) and what critical insights this can offer for future directions in smart city research, policy, and practice.
Linking the online documentary search to a global list of cities provides useful insight into the structural dimensions of the discourse network; furthermore, the harvested collection of documents represents an important interface between the smart city discourse oriented internally within cities and externally across the global network. The global list of cities is based on the open-access GeoNames (n. d. ) database entailing 4,235 names of cities, to which 1,318 city names (with unusual spelling, or not recognized as having ≥100 K inhabitants by GeoNames) were added, resulting in a total of 5,553 cities (Step 1. 1, Box 1). The online documentary search-using the search query [city name] AND [smart city]-was conducted on Yahoo (Step 1. 2, Box 1). Following testing, this search engine was selected as it meets three concurrent criteria (unlike e.
g. , Google and Bing)-namely, the ability to use Booleans; apply semi-automated searches; and filter for PDF documents. Restricting online material to PDF has the dual benefit of returning not only more stable results (through reduced volatility of hit counts), but also more meaningful outputs: PDFs are more likely to entail relevant smart city texts (municipal documents, policy briefs, conference reports, etc. ) and less likely to yield arbitrary results owing to the ubiquitous linguistic use of "smart" (e. g. , "smart holiday;" "smart Christmas parties"). VBA Excel was used to run multiple online harvests over a 15-day period (See  Uyar, 2009)  and produce an aggregated dataset (August-September 2016). Two complementary validation techniques were applied to run checks on the raw data obtained.
Detection of symmetry axes is based on learning six-dimensional Veronese mappings of 3D vectors, and the median angular error of axis determination is less than one degree. We demonstrate the capabilities of our method on benchmarks with tandem repeated proteins and also with symmetrical assemblies. For example, we have discovered over 10,000 putative tandem repeat proteins that are not currently present in the RepeatsDB database. Availability: The method is available at https://team. inria. fr/nano-d/software/deepsymmetry. It consists of a C++ executable that transforms molecular structures into volumetric density maps, and a Python code based on the TensorFlow framework for applying the DeepSymmetry model to these maps.
With such a growing number of individual identification techniques, meta-methods for tandem repeat proteins' detection have also started to pop-up  [21]. These take scores of individual prediction methods as input and combine them into a unified output, which significantly increases the detection success rate compared to individual prediction methods. Recently, a database of repeat protein structures called RepeatsDB was made publicly available  [22, 23]. It is actively supported and contains annotated protein structures with tandem repeats, automatically selected  [9, 24]  and manually curated. In computational geometry, computer graphics and computer vision, pattern recognition  [25]  and symmetry detection  [26, 27, 28]  have been active research topics for some time. Historically, most of the developments in this domain were concentrated in 3D data represented as two-dimensional (2D) meshes or sets of 2D view-point projections and not so much has been done in volumetric data analysis.
Recently, many 3D datasets became available, which reinforced the interest in practical tools for the analysis of 3D data, and stimulated their active development. For example, deep learning methods after having revolutionized computer vision  [29]  being applied to 2D datasets, have started to be also widely used in 3D applications. Very recently, these were also applied to biological data. For example, deep learning has been used for protein structure quality assessment  [30] , protein contact predictions  [31, 32] , predicting the effects of genetic variations  [33]  or studying non-coding variants  [34]. Deep learning has also achieved computing molecular energies with the density functional theory (DFT) accuracy  [35, 36, 37]. Finally, deep learning methods, and more specifically, 3D convolutional networks, were applied to volumetric datasets for various 3D biological applications.
Data sets and data augmentation We trained the network on artificially generated data, as it is explained in section 1 of supplementary material. To test and validate our method, we applied it to several different datasets (please see section 2 of supplementary material). To compare the performance of our method with other published techniques, we used a benchmark for tandem repeated protein detection described by Do Viet et al. [21]. To assess the quality of the computed symmetry The input layer containing a 3D density map is followed by two convolutional layers. The output of these is then reshaped into a linear array and three layers of a fully connected network are added. The output vector contains information about the order and the axis of the putative symmetry. Please refer to supplementary material for a more detailed description of the layers.
axes, we used all the cyclic assemblies of order between 4 and 10 from the protein data bank (PDB)  [48]. Finally, we executed our method on all the protein chains that are present in the PDB (as for January 2018). This allowed us to identify new tandem repeat proteins that are not currently present in the RepeatsDB database. Absolute orientation of the input data is one of the major problems for the stability of NNs, as their output is generally not invariant with respect to the input orientation. A general workaround for this problem is a technique known as data augmentation. More precisely, the input data is randomly rotated multiple times during the training phase of NNs, and in such a way the training dataset is augmented. This technique makes the network less sensitive to the initial orientation of data.
This is typically the method of choice for 2D datasets. However, rotations in three dimensions result in much bigger augmentation of the initial dataset, as these need to be performed around three axes independently. Since we trained our network on already randomly rotated data (see supplementary material), we did not additionally augment it. However, to enhance the success rate in the prediction phase, we augmented input data as it is described below. For each structure in the test sets, we produce multiple volumetric maps of it. We then merge all the outputs, which allows to obtain more reliable results. To create these input maps, we apply 60 different rotations to the input structure. We then repeat this procedure also considering only the oxygen, nitrogen, and carbon atoms in the input maps.
Detection of cyclic assemblies To assess the quality of predicting the direction of symmetry axes, we ran our method on 2,183 cyclic assemblies extracted from the PDB, whose symmetry order is between 4 and 10. In our previous work we developed a technique called AnAnaS that analyzes the quality of symmetry of cyclic molecular assemblies, and also determines their symmetry axes  [7]. AnAnaS uses atomistic representation of the input data and is based on analytical minimization of a certain norm in the Euclidean space. Here we use the AnAnaS technique as the ground truth for the axis direction and the PDB annotations for the ground-truth order. On this dataset, 1,783 structures (82%) had their order of symmetry correctly predicted, which is a much better rate compared to the tandem repeat case discussed below.
This is because cyclic dataset is consistent with the training data, and also because the test structures are typically very symmetrical. Figure  2  shows the axis prediction results. We can see a very strong angular Figure  2 : DeepSymmetry errors in the axis prediction, as tested on 2,183 symmetric assemblies from the PDB. For all the cyclic structures in the PDB with an order between 4 and 10, we computed the axes with the DeepSymmetry method and compared them with the calculations of AnAnaS by Pageès et al. [7]. Please note that 90 • is the maximum possible error between two axes. separation between the correctly predicted axes and the others. Indeed, for 82% of the examples, the predicted axes are less than 2 • away from the ground truth directions. Let us mention that in 3D, the solid angle of this 2 • zone occupies only 0.
v) 2 natoms i=0 a i 2 2 -(a i. v) 2 , (9) which takes its values between 0, when all the atoms are located in a plane orthogonal to v, and +∞, when all the atoms are distributed along the v direction. We should mention that points evenly distributed on a sphere have an elongation of 1 along any direction. Figure  3  confirms that elongation along the ground-truth symmetry axis is the key factor that defines the accuracy of axis detection in our method. Indeed, for structures with elongation values bigger than 1. 7, the predicted result is always orthogonal to the ground truth. We believe that our training dataset did not contain sufficient number of such elongated examples to allow the model to properly learn this particularity.
Systematic analysis of PDB data For the final assessment of our method, we systematically analyzed all the protein chains in the PDB. To save computational time, we assumed that in a vast majority of the cases, if more than two chains are present in a PDB file, they are homologous to one of the first two, and thus we only tested the two first chains. As we have mentioned above, we used the benchmark of proteins with tandem repeats to set up a threshold for structures with putative Figure  5 : Eight structures that are not currently present in RepeatsDB and that our method considers as symmetrical. We chose to show four structures with very high symmetry scores (3k4x, 5kn1, 5c9p and 4tqm) and four structures with scores close to the chosen threshold (2g8z, 2hob, 4r8q and 5hqg). For the first four structures, three of them are almost perfectly symmetrical, and 5kn1 is less symmetrical but has an overall shape of a C 3 symmetry.
This number is consistent with the state of the art estimation of 19% by Do Viet et al. [21] , and we can roughly say that 17,000 of our predictions are true positives and 25,000 of our predictions are false positives. Among these chains, only 1,933 were already present in the RepeatsDB database  [22, 23]. Figure  5  shows eight structures detected by our method that are not present in the RepeatsDB. The four first structures in this figure are those, to which our method assigns very high scores. The four last structures are those, to which DeepSymmetry assigns scores close to the chosen threshold. All these structures are plotted along the predicted axes of symmetry. We should also note that we were not able to visually inspect all the 42,034 predictions. This experiment demonstrates that even though our method does not demonstrate results as good as some state of the art techniques in detecting proteins with tandem repeats, its algorithmic originality makes DeepSymmetry an interesting alternative to the existing methods.
Detection of symmetry in raw density maps As DeepSymmetry deals directly with density maps, it makes it possible to automatically detect symmetries in raw maps, e. g. those from electron microscopy (EM) experiments. These often have a resolution insufficient for the atomic reconstruction of the model. To present this capability, we chose 10 maps from the latest depositions to the Electron Microscopy Data Bank (EMDB) with a resolution coarser than 10 Å. For each of these maps, we computed the average ρ, the standard deviation σ ρ and the maximum ρ max of the density. We rescaled the input densities ρ as ρ = max(0, ρ -ρ -3σ ρ ρ max -ρ -3σ ρ ), (10) and cropped the rescaled maps to remove empty margins and keep a cubic bounding box. These cubic maps are then resized to 24×24×24 voxels and used as input of the deep learning algorithm.
Layer After, the result of the previous layers is reshaped to turn it into a mono-dimensional vector and three sets of fully-connected layers are applied to it. Each set consists of three layers, a fully-connected linear layer with a reduction of dimensionality, a dropout layer, and a ReLU nonlinear layer. The dropout layer is used to reduce NN overfitting by masking parts of the input (30% in our case) using binary samples from a Bernoulli distribution  [54]. Finally, our network uses a linear fully-connected layer that reduces the output to a 16-dimensional vector. Its first ten components give probabilities for a input map to have a certain symmetry order. The last six components predict the direction of the symmetry axis in the 6D Veronese space. Details about the training including training dataset, training algorithm, evolution of loss, etc.
To make sure that our model does not overfit the training data, we constructed a small validation dataset as described above, and regularly measured the difference of performance of our method on these two sets. Figure  S3  shows the evolution of the accuracy of the predictions on the training and the validation sets during the training phase. More precisely, we classify a prediction as correct when the ground truth symmetry order was considered as the most probable one by the model. We can see that the performance gap between the training and the validation data exists, but is not too big. In addition, the performance on the validation set tends to reach a plateau at about 92% of accuracy. We should mention that this accuracy is obtained on synthetic data, and such results cannot be expected on real examples.
Computational details and running time We implemented this method using the deep learning framework TensorFlow  [56]  for the deep learning part, and the C++ programming language for the data generation part. We ran all the computations on a Windows laptop equipped with an Intel Core i7 @ 3. 1 GHz CPU and a NVIDIA Quadro 4000M GPU. Generation of input data is the bottleneck for the computations, as it takes about 15 milliseconds to generate one density map from a PDB file with a few thousands of atoms, and it takes only 1 millisecond to score it. We should mention that the complexity of the method grows linearly with the number of input atoms, which makes this method suitable even for very large input structures.
Acknowledgements The authors thank  Georgy Derevyanko  from  Concordia University, Montréal , who designed a preliminary version of a 3D convolutional NN, which motivated us in constructing the Deep-Symmetry architecture. The authors also thank  Étienne Bamas  from  École Polytechnique , who was an trainee in our team, for his original ideas and developments in internal symmetry detection,  François Rousse  from  Inria, Grenoble , for his help to visualize 3D density maps, and  Marie Bernert  from  CEA, Grenoble , for her help on embeddings. Finally, the authors thank  Stephane Redon  from  Inria, Grenoble , for his permanent interest and motivated discussions in deep learning techniques, and also for his support on data visualization.
This work aims at analyzing an integrated system of a zinc-air flow battery with a zinc electrolyzer for energy storage application. For efficient utilization of inherently intermittent renewable energy sources, safe and cost-effective energy storage systems are required. A zinc-air flow battery integrated with a zinc electrolyzer shows great promise as an electricity storage system due to its high specific energy density at low cost. A mathematical model of the system was developed. The model was implemented in MATLAB and validated against experimental results. The validation of the model was verified by the agreement between the simulation and experimental polarization characteristic. The behavior and performance of the system were then examined as a function of different operating parameters: the flow rate of the electrolyte, the initial concentration of potassium hydroxide (KOH) and the initial concentration of zincate ion.
Nevertheless, HER has not been addressed previously in a mathematical model of a Zn-air flow battery. This work aims to develop a mathematical model of a Zn-air flow battery integrated with an electrolyzer. Hydrogen evolution reaction as a parasitic reaction is also considered in the model. The developed model was implemented in MATLAB and validated against the experimental data. Then, simulation was performed to examine the dynamic behavior of the battery system. The study consists of the following: (1) a brief overview of the Zn-air flow battery and Zn electrolyzer (2) experimental setup of the system for model validation (3) model development and validation of the model (4) simulation of the system and the effects of various parameters (5) final summary.
To validate the mathematical models of ZAFB and Zn electrolyzer, the polarization characteristic of ZAFB was examined. The cell voltage and current were measured by a BA500 battery analyzer using BA500WIN software. The current input can be adjusted manually and cell voltage is measured at the selected current continuously. The data of cell voltage was collected every second. For one current value, the voltage data had been collected for 10 s and 10 voltage values were used to calculate the average cell voltage. After that, the current value was changed to the next value. To measure the overpotential of the electrodes, a mercury/mercury oxide electrode was used as a reference electrode. The overpotential was calculated from the difference of potential between the reference electrode and the equilibrium potential of each electrode.
, 2006; Pouvreau, 2008 ) In order to meet the ambitious motivations and challenges, ILICO (started in 2016) inherited a great number of facilities and organizations, some of which were built over decades. In addition to these observation networks, ILICO runs a scientific transversal network in charge of general scientific potential and of optimizing the use of data in high-level research projects. per ecosystem) (Figure  4 ). Observation networks, mainly pre-existing ILICO for years, have combined their unique viewpoints and strengths in order to examine the global strategy. In this paper, we would like to highlight the contributions of three networks, given their particular characteristics: (i) SOMLIT, with a homogeneous sampling strategy, (ii) DYNALIT, challenged with its heterogenous observation sites in terms of physiographic conditions and monitoring strategies, and (iii) Mediterranean Ocean Observing System for the Environment (MOOSE), laying the groundwork for an integrated observation network.
SOMLIT Network Scientific Objectives SOMLIT (Coastal ocean observation service -Service Observation en Milieu LITtoral) studies the long-term evolution of coastal pelagic ecosystems and seeks to determine the natural and anthropic driving mechanisms of their functioning at local and regional scales. It focuses on the hydrological, biogeochemical, and ecological characteristics of the surface waters. It also aims at providing data sets to national and international communities for research, education, and stakeholder purposes, and serves as logistic support for research and education projects.
At the time of writing this paper, DYNALIT includes more than 120 researchers from 20 laboratories and 22 universities. Observations are collected at 35 coastal sites in metropolitan and oversea France (Figure  5 ), encompassing the three main coastal environments, namely sandy, rocky, and muddy coasts, including, e. g. , open coasts, embayments, sandpits, and estuaries, with a wide spectrum of behaviors and of erosion/accretion trends. Observed parameters are primarily Digital Elevation Models (DEMs) on sandy and rocky coasts, and turbidity in estuarine environments. DEM data are collected through various means, including diff erential global positioning system measurements, video monitoring, structure from motion using unmanned aerial vehicles, and Lidar, while turbidity is typically measured in situ using turbidity meters.
According to numerical weather and wave hindcasts, that winter was the most energetic one along the Atlantic coast of Europe since at least 1948  (Masselink et al. , 2016) , and most of western Europe's sandy and rocky coastline was severely impacted  (Castelle et al. , 2015; Masselink et al. , 2015; Autret et al. , 2016; Burvingt et al. , 2018). Masselink et al. (2016)  analyzed a unique dataset of decadal, at least bimonthly surveyed, beach morphological changes along the west coast of Europe comprising sites from DYNALIT, namely Vougot, Porsmilin, and Truc Vert. The authors showed that while extensive beach and dune erosion occurred on open coasts due to off shore sediment transport, more sheltered sites experienced less erosion and one of the sites even experienced accretion due to beach rotation induced by alongshore sediment transport.