dataesr/scientific-paragraphs-categorization huggingface dataset:
Energy management and control simulation Our algorithm is considered intuitively predictive, because we have assumed that the power demand variation depends on time slots, and the battery power is the average consumption/hour of the charge. Also solar irradiations can be assumed to vary significantly per hour. The power required by the load and the system behavior in response to this demand are explained in Fig. 9. The priority is given to solar production, battery, and in case of excess or lack of energy the grid intervenes, respectively. This is ensured by switches transition. We notice that when one of the sources intervenes, its switch takes the unit state to collaborate on the DC bus. The differences between the required power and the actual power consumed are due to the efficiencies of the conversion systems that were taken into account during MATLAB modeling and simulation.
Compared to  [6]  where the authors consider the uplink, in the present article, we consider the downlink communication. The BER of downlink NOMA for QPSK constellation has been expressed in a closed-form in  [7]  and  [8]. However, the authors limited their study to the two-users and three-users cases, whereas we propose a closed form expression which is valid regardless of the number of users. The associate editor coordinating the review of this manuscript and approving it for publication was Wei  Wang. Besides, error probability-based analysis has the advantage of being experimentally verifiable, e. g. , using software defined platforms such as the Universal Software Radio Platform (USRP) and GNU Radio  [9]. Some NOMA demonstrations have already been carried on with USRPs. In  [10] ,  [11] , USRPs are used for assessing the over-the-air performance of NOMA for 5G network.
We also compare the result of a Monte-Carlo simulation with the numerical evaluation of this formula. This comparison is given in figure  3  and we can easily check the accuracy of our derivations since the simulated points coincide with the bit error probability curve. The Monte-Carlo simulation here is independent from the formula (we multiply the channel experiences and we only count error events), and uses the same hypothesis as the ones used for the proof of the formula. The Python code used to plot the formula and the Monte-Carlo result of the simulation is provided. A pseudo-code implementation of an algorithm evaluating this formula is also given in Appendix VI.
V. EXPERIMENTAL VALIDATION A. EXPERIMENTAL SET-UP We have employed the USRP  [9]  Software Defined Radio (SDR) platform and GNURadio companion, an open-source software for USRP programming. In figure  9 , we give the schematic representation of the transmission and reception chains so as to improve the following description of our GNU radio implementation. In our set-up, a first USRP sends the symbols, while another USRP serves as a receiver. The antennas of both USRPs were distant by between 30cm and 1m. In some experiments, we also use a third USRP to generate noise on our channel (see our experimental results V-B for more details). We built custom blocks to generate a random NOMA-encoded signal for an arbitrary number of users. We then developed blocks to automatically generate the phaseshift induced by the transmission.
During the transmission,  each symbol is repeated multiple times (oversamping process), and FIR filters are used to smooth out the signal. A single sample is then selected for each symbol at the receiving end (downsampling process). The blocks used in GNURadio are described in detail in Appendix VI. The code is also available at https://gitlab-research. centralesupelec. fr/ garnier_jea/NOMA. In our formula, we assume the noise to be AWGN (Additive White Gaussian Noise). Actually, the received noise is far from being ideal, and we believe that this ambient noise was the reason why the experimental results were worse than the predictions, using the theoretical formula or simulations. In order to verify that theory, we added a third USRP with the purpose of generating an AWGN that was significantly more powerful than the real noise, i.
Xcompact3D is a Fortran 90-95 open-source framework designed for fast and accurate simulations of turbulent flows, targeting CPU-based supercomputers. It is an evolution of the flow solver In-compact3D which was initially designed in France in the mid-90's for serial processors to solve the incompressible Navier-Stokes equations. Incompact3D was then ported to parallel High Performance Computing (HPC) systems in the early 2010's. Very recently the capabilities of Incompact3D have been extended so that it can now tackle more flow regimes (from incompressible flows to compressible flows at low Mach numbers), resulting in the design of a new user-friendly framework called Xcompact3D. The present manuscript presents an overview of Xcompact3D with a particular focus on its functionalities, its ready-to-run simulations and a few case studies to demonstrate its impact.
The numerical parameters of a simulation (domain size, number of mesh nodes, number of cores, etc. ) can easily be changed without having to recompile the code. The manuscript focuses on exactly these aspects. Section 2 is dedicated to the software architecture of Xcompact3D including its parallel computing strategy, functionalities, main features and scalability. Subsequently, illustrative examples and impact case studies are shown in Section 3 and Section 4. Finally conclusions and future directions are briefly discussed in Section 5.
Software architecture An overview of the modular structure of the framework is presented in Fig. 1. Xcompact3D is build around the 2D Decomp & FFT library  [2, 6]  which is designed to implement a 2D pencil decomposition (as shown in Fig. 2 (a)) for data distribution on distributed-memory platforms. This library also provides a highly scalable and efficient interface to perform three-dimensional distributed Fast Fourier Transforms, which are used to solve the Poisson equation (needed in the Navier-Stokes equations subjected to LMN or incompressibility approximations). Equations 1 -5 are solved with high-order compact finite-difference schemes on a Cartesian mesh, with a conventional fractional step strategy based on the general principle of projection methods  [7, 8]. Since DNS of high Reynolds number flows is very often not possible due to its computational cost, LES can be performed with an implicit approach for which the sub-grid scale (SGS) stress tensor is modelled using numerical dissipation  [9, 10].
Different explicit SGS models are also available for completeness, namely the standard Smagorinsky model and its so-called dynamic version (see  [11]  and  [12]  for more details). For flows around relatively complex geometries, it is possible to use a customised Immersed Boundary Method (IBM)  [13]. IBMs, originally proposed by  [14]  are very attractive for Cartesian meshes, avoiding sophisticated body-fitted approaches and their associated loss of simplicity and increase of computational cost. Finally, please note that there exists a new Python library for the post-processing of the data, Py4Incompact3d, based on the same schemes as in Xcompact3D. 1.
) in Fig. 2(a). Each decomposition (in the x, y and z axes respectively) allows independent computation of derivatives/ interpolations/filtering. Global transpositions to switch from one pencil to another are performed with the MPI command MPI_ALLTOALL(V). The 2D Decomp & FFT library also provides a parallel I/O module to help Xcompact3D handle large data, using MPI-IO. More details about the parallel computing strategy implemented in Xcompact3D can be found in  [2]. The scalability of Xcompact3D has been tested recently on various Tier-1/0 supercomputers ARCHER, MareNostrum and HazelHen with simulation sizes of more than 68 billion mesh nodes. The scalability plots have been obtained for the Taylor-Green case (see details in the next section) with a very high Reynolds number of 10,000 in a DNS set-up.
Illustrative examples Example applications are defined in the Xcompact3D source under the examples/ directory. These demonstrate the use of different aspects of the framework and allow new users to begin using Xcompact3D immediately, even if they are not familiar with the framework and its numerical methods. In this section, we present four of these examples to illustrate the use of Xcom-pact3D to solve well-known CFD benchmarks. Each case consists of run-time settings specified in examples/<CASE>/input. i3d which are easily modifiable by the user and Fortran code implementing case-specific boundary/initial conditions, source terms etc. in src/BC-<CASE>. f90 which must be compiled. Note that a special case USER is supplied for users to create their own setups. The input files for each case described can be found in the source repository or can be downloaded here with the supporting data used to generate the figures in this section.
The numerical parameters for these two simulations can be found in the relevant input file. The channel case was performed on ARCHER with 512 computational cores for 6 h (300,000 time steps) while the cylinder case was performed on ARCHER with 512 computational cores for 60 min (160,000 time steps). Some results are shown in Fig. 4. It can be seen that a very good agreement is obtained with the reference data for the first and second-order moments for the channel flow, and for the mean and fluctuating streamwise components of the velocity downstream of the circular cylinder.
Acknowledgements The authors would like to thank  EPCC  for funding part of this work [grant numbers  eCSE10-02/eCSE13-03 ]. Computing time was provided on ARCHER by the  UK turbulence consortium  [grant number  EP/L000261/1  and  EP/R029326/1 ], on MareNostrum and HAZEL HEN by  PRACE  [grant numbers 2010PA5024 and 2018184381] and on Curie by TGCC under the allocation 2014/2015-2a0912 made by  GENCI. Additional computing time was provided by the  Imperial College Research Computing Service  and by the high-performance facility LAD at the  Pontifical Catholic University of Rio Grande do Sul. FS was supported by  Petrobras S. A. and financed in part by the  Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior -Brasil (CAPES) -Finance Code   88887. 154060/2017-00. GD would like to acknowledge funding from  EPSRC, UK  [grant number  EP/R007470/1 ].
The purpose of this study was to investigate the genetic variation, gene expression differences, and clinical significance of SUMOylation regulators in pan-cancers. Based on previous studies, we gained a better understanding of the biological process of SUMOylation and the status of current research. In the present study, we employed a wide range of bioinformatics methods. We used genetic variation and mRNA expression data in the Cancer Genome Atlas (TCGA) to construct a panoramic view of the single nucleotide variants, copy number variants, and gene expression changes in SUMOylation regulators in various tumors. Subsequently, we used the String website and the Cytoscape tool to construct the PPI network between these regulators. We used the GSCALite website to determine the relationship between these regulators and cancer pathways and drug sensitivity.
We constructed images of co-expression between these regulators using the R programming language. Using clinical data from TCGA, we performed hazard ratio analysis for these regulators in pan-cancer. Most importantly, we used these regulators to successfully establish risk signatures related to patient prognosis in multiple tumors. Finally, in KIRC, we conducted gene-set enrichment analysis (GSEA) of the five molecules in its risk signatures. We found that these five molecules are involved in multiple cancer pathways. In short, we have comprehensively interpreted the detailed biological process of SUMOylation at the genetic level for the first time, successfully constructed multiple risk signatures, and conducted GSEA in KIRC. We believe that these findings provide credible and valuable information that is relevant for future clinical diagnoses and scientific research.
Therefore, comprehensive investigation regarding the heterogeneity of SUMOylation regulators in pan-cancers is required. Extensive sequencing of cancer genomes has already begun, and one of the largest collections of these data can be found in The Cancer Genome Atlas (TCGA). Sequencing efforts have revealed variations in the cancer genome, providing a more comprehensive understanding of cancer biology  [14]. However, the discovery of a large number of mutant cancer genes has been accompanied by another arduous task: identifying the underlying genes that drive the cancer  [15]. In the present study, we systematically explored the gene mutations, mRNA expression, and clinical significance of SUMOylation regulators in various cancer types for the first time. We found that these SUMOylation regulators have a wide range of mutation frequencies and copy number variations in a variety of cancers, and identified that CBX4 may play an oncogenic role in many cancers.
Identification of SUMOylation regulators From recently published articles, we identified 20 SUMOylation regulators  [4, 17]. The SUMO family contains four regulators. Maturation and de-modification contain seven regulators. Activation contains two regulators. Conjugation contains one regulator. Ligation contains six regulators. A schematic diagram of the SUMOylation process is shown. In constructing the schematic diagram of Figure  1A , we use two softwares, Photoshop and PowerPoint. In drawing the image in Figure  1B , we used Photoshop and bioinformatics online tools (http://www. bioinformatics. com. cn/) to complete the image. Bioinformatics. com is a website focused on online data analysis, mapping and visualization.
Data collection To better identify, diagnose, and treat tumors, there is an urgent need to conduct in-depth research regarding their genetic changes and establish appropriate databases. In 2006, the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI) collaborated on the Cancer Genome Atlas (TCGA) database program. The program aims to identify gene changes caused by tumorigenesis and development through large-scale gene sequencing and comprehensive, multidimensional analyses, by building a comprehensive atlas related to tumor genes. The results of our study are based on 'omics' datasets generated by TCGA Research Network (http://cancergenome. nih. gov/). We analyzed 33 different TCGA projects, and each project represented a specific cancer type. We obtained RNA-seq data and clinicopathological data of these 33 types of cancer via this database.
Generation of PPI networks STRING is a high-coverage, high-quality protein-protein interaction network platform with a wide range of applications for interpreting biomedical 'big data' and visualization in the context of systems biology  [19]. We used the STRING online database to map SUMOylation regulators in the PPI network. We then used the visualization software Cytoscape to embellish the PPI network  [20]. The data in PPI were used to construct a quantization table.
Bioinformatics analysis and data processing In our research, we used bioinformatics to achieve our research goals. We made extensive use of the Perl and R programming languages to analyze and visualize data. We explored the single nucleotide variants (SNVs), copy number variants (CNVs), and gene expression variations of SUMOylation regulators in various tumors via the TCGA database. We used the Limma software package for differential analysis. TBtools was used for heat map postprocessing. To explore the expression relationship between these regulators, we plotted co-expression maps of SUMOylation regulators in ACC, BLCA, PRAD, and KIRP tumors. We used the Corrplot software package to complete the co-expression analysis. To explore whether these regulators are risk or protective factors in pan-cancers, we performed a hazard ratio analysis on the SUMOylation regulators in pan-cancers.
Our primary research area is renal clear cell carcinoma, and we displayed the hazard ratio results of these SUMOylation regulators in KIRC in the form of a forest chart. In addition, we observed that PIAS3 might play a more critical role in a variety of tumors, so we displayed the hazard ratio results of PIAS3 in several tumors in the form of a forest chart. We then used these SUMOylation regulators to establish risk signatures related to patient prognosis in five types of tumors, including ACC, KIRC, KIRP, LUAD, and SKCM. We plotted survival curves, ROC curves, and multivariate independent prognostic analyses. The Pheatmap software package was used to construct heat maps. The survival software package was used to analyze and construct survival curves. The SurvivalROC software package was used to explain and illustrate the ROC curve.
To investigate whether these genetic variations affect the expression of SUMOylation regulators, we explored the expression perturbations of SUMOylation regulators in 19 cancer types. We used the R language to construct a heat map of the expression alterations of these regulators across multiple tumors and graphically enhanced them using TBtools (Fig. 2A ). Compared with healthy tissues, CBX4, a regulator of SUMOylation with extensive CNV amplification, showed significantly higher expression in cancer tissues (Fig. 2B ). Past research has demonstrated that CBX4 is involved in DNA damage and repair processes  [22]. In contrast, PIAS4, a regulator with extensive CNV deletions, showed significantly lower expression in cancer tissues (Additional file 1: Table  S4 ). Therefore, we speculate that there is a correlation between CNV and gene expression alterations.
The interaction between SUMOylation regulators, their interaction with cancer pathways, and their sensitivity to anticancer drugs To further understand the potential mechanism of action of SUMOylation regulators in various cancers, we first analyzed the regulatory relationships between each SUMOylation regulator using the STRING website online tool, visualized these using Cytoscape software (Fig. 3A ), and finally quantified their relationship (Fig. 3B ). It is apparent that SUMO1, SUMO2, and UBE2I play more prominent roles. Subsequently, using the GSEALite website, the relationship between regulators and the activities of various cancer pathways was explored. We found that UBA2, SENP3, and SAE1 are related to activation of the cell cycle pathway, and UBA2, SUMO1, and SENP5 are related to inhibition of the RAS/MAPK pathway (Fig.
Acknowledgments This work was supported by the  National Natural Science Foundation of China  (Grant Nos. 81672553  and  81902572 ) and the  Natural Science Foundation of Shandong Province  (No. ZR2016HB61 ). We thank  TCGA  project for the detailed, available data. In addition,  Yingkun Xu  would also like to thank  Jianyi Li  for useful discussions.
D. The TFS pixel readout system verification overhead In order to have an idea of the processing time overhead required to run the necessary verifications when reading the pixel matrix, we have developed a Matlab model of the readout system and the TFS image sensor. This model input is a stimuli from a 10-second video: a video of a car on the road, which is a normal case scenario for image sensors. From the model simulation output, we measured the event rate and verifications required to pinpoint the correct events. Figure  4 , shows how severe is the amount of verifications required for uncertain events, and most importantly how verifications scale with the pixel matrix. We have found out that in the case of no spatial redundancy suppression, verifications represent 98% of the total data exchange between the pixel matrix and the readout system for a pixel matrix resolution of 600 × 1200 pixels, and 97.
In figure  5 , we present different sets of DVS and TFS combinations. (a) represents one DVS pixel (in Green) triggering one TFS pixel (in Blue), (b) features one DVS pixel triggering three surrounding TFS pixels and similarly in this order, in (c),(d) and (e) kernels the DVS pixel is surrounded by 5, 8 and 24 TFS pixels. Kernel (f) is particular, where the DVS pixel circuit uses the photodiodes of the surrounding 4 TFS pixels and takes the average photo-current of the four as input. Constructing a pixel matrix from one of these 5 kernels would be the result of repeating the kernels several times, like the pixel matrix (g) and (h) obtained from (a) and (b) kernel. In order to evaluate the kernels performances in terms of generated events, we have developed Matlab models of the two pixels and then run behavioral simulation of each kernel.
Based on numerous studies of the possible mechanisms involved in the growth of water trees, electric fields have been found to be the most significant influence on the growth of water trees in insulators. The forces resulting from the high electric field at the tips of the voids cause plastic deformation and subsequently lead to the formation of interconnecting channels between the voids. After initiation, water trees begin to propagate towards the conductor, leading to its corrosion and ultimately damaging the cable. In this article, a 3D finite element model of a water void in an insulator is developed using COMSOL Multiphysics software. Although it would be of significant interest, experimental study of the ageing of insulators due to water tree propagation is impractical due to the extremely long ageing experiment time that would be required.
Thus, the current study instead aims to widen the comprehension of this kind of ageing using numerical modeling. In this study, the water void was modelled as an ellipsoid in a cubic cell in reference to the model of  [19]. The main objectives of this study are to: (1) calculate the displacement of a single water void in a cubic cell with respect to applied voltage; (2) determine the influence of relative distance between two water voids and applied voltage on the void's displacement; and (3) to study the plastic deformation of water voids in accordance to the von Mises yielding criterion. The first section of this article describes the electromechanics module of COMSOL Multiphysics, which was the main computer tool used in this study. It should be noted that few previous studies have been conducted pertaining to the current topic, thus the presented simulation results cannot be compared to the literature.
FE Software COMSOL Multiphysics is a platform offering a simulation tool for electrical, mechanical, fluid flow, and chemical applications. A coupled Multiphysics electrical and mechanical phenomenon was considered for modelling water voids using COMSOL. A 3D model was created to study the deformation of water voids present in cable insulation. COMSOL provides a number of options relating to the type of solver configurations required to obtain the best numerical results. Problems are solved based on advanced numerical methods.
Electromechanics Theory The electromechanics module can be found under the structural mechanics physics tree. This module is used to study deformation of objects under the effect of an applied electric field. A model was created using COMSOL Multiphysics to study the influence of an electric field on water voids present in an insulator. Before discussing the mathematical formulations, we describe the coordinate system that was used. Continuum mechanics uses two coordinate systems, a material coordinate system and a spatial coordinate system. A material coordinate system was used to describe all material particles. The particles were identified by their position in the reference configuration. In the presence of external forces, the material coordinates are held constant and the spatial coordinates change with respect to time and applied force.