dataesr/scientific-paragraphs-categorization huggingface dataset:
0, Anton Paar, Austria) with an EIGER2 R 1M area detector (Dectris, Switzerland) and a time resolution of 5 min. All recorded SAXS patterns were azimuthally averaged and normalized by transmission values. The SAXS background intensity was recorded separately for each cell after removing the cathode. The averaged and normalized background intensity was then subtracted from all recorded operando SAXS curves. The azimuthally averaged 2D operando WAXS data were corrected by subtracting the WAXS intensity prior to discharge (at OCV). Operando SANS measurements during potentiostatic discharge/charge were performed on the D-22 small angle neutron scattering beamline at the ILL neutron source (Grenoble, France) using a wavelength of 0. 5 nm, a beam diameter of 10 mm, and two areal detectors with a sample-to-detector distance of 17.
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie grant NanoEvolution , grant agreement No  894042. The authors acknowledge  TU Graz  for support through the Lead Project  LP-03. Likewise, the use of  SOMAPP Lab , a core facility supported by the  Austrian Federal Ministry of Education, Science and Research , the  Graz University of Technology , the  University of Graz , and  Anton Paar GmbH  is acknowledged. Electron microscopy measurements were performed at the  Scientific Scenter for Optical and Electron Microscopy (ScopeM)  of the  Swiss Federal Institute of Technology. C. P. and J. M. M. thank  A. Senol  for her support with the SANS beamtime preparation. S. D. T, A. V. and R.
Here, η is the magnetic diffusivity, and the term k 2 η is the electrical resistive contribution to the RT instability dispersion relation; ν is the ion kinematic viscosity, and the term k 2 ν is the viscosity contribution  (Green & Niblett 1960). We note that the relative importance of viscosity and resistivity as damping processes can be estimated from the ratio of these two terms, which is also equal to the ratio of the magnetic to the hydrodynamic Reynolds number (i. e. P = R m /R e = ν/η). Using the Spitzer conductivity  (Spitzer 1963 ) and the ion dynamic viscosity expression in the magnetised case given in  Ryutov et al. 1999 , we obtain P = 1. 2 × 10 -13 √ AT 4 /(ΛZ 5 ρ), where A = 12 is the atomic number for carbon and Z(T ) is interpolated from the FLYCHK database, considering a plasma density of 10 18 cm -3  (Chung et al.
Acknowledgements. The authors would like to thank the team of the PEARL facility for their expert support. This work was supported by funding from the  European Research Council (ERC)  under the European Unions  Horizon 2020 research and innovation program  (Grant Agreement No. 787539 ). The research leading to these results is supported by  Extreme Light Infrastructure Nuclear Physics  ( ELI-NP) Phase II , a project co-financed by the  Romanian Government and European Union  through the  European Regional Development Fund , and by the project  ELI-RO-2020-23  funded by  IFA (Romania). The experiments at the PEARL facility (IAP RAS) were supported by the  Russian Science Foundation (RSF)  in the frame of project No. 20-12-00395. This work was granted access to the HPC resources of MesoPSL financed by the  Region Ile de France  and the project  EquipMeso  (reference  ANR-10-EQPX29-01 ) of the programme Investissements d'Avenir supervised by the  Agence Nationale pour la Recherche.
This work was supported in part by the  National Natural Science Foundation of China  under Grant  62061030 , in part by the  Science and Technology Research Project of Education Department of Jiangxi Province  under Grant  GJJ181544 , in part by  Jiangxi Open University  under Grant  JKND2002 , in part by the  Open Research Fund of National Mobile Communications Research Laboratory Southeast University  under Grant  2021D02 , and in part by  EU   Horizon 2020 Program  through  6G   BRAINS  Project  H2020-ICT  under Grant  101017226.
Besides, the Kramers-Kronig approach remains inaccurate for low bandwidths and improves as bandwidth increases. Figs. 9a and 9b present the extracted permittivity ε r and loss tangent tanδ of the FR4-like and AD1000-like materials, respectively, based on the methods in Figs. 4  and 5. The extracted properties are compared with the input data considered in the simulation. Figs. 9a  and 9b  show that the extracted ε r and tanδ correlate well with the input data for the two materials. In addition, the permittivity error is smaller than 0. 08% and 0. 6% for the FR4-like and AD1000-like samples, respectively. However, the permittivity error rises at low frequency for three reasons: i) the line length becomes electrically short, ii) the line's internal inductance becomes non-negligible as it increases with a 1/ √ f -factor as frequency decreases, and iii) errors from the analytical formulas for the static and dispersive permittivities, ε res  [14]  and ε re  [9] , increase at low frequencies, as seen in Figs.
Compaction experiments on the compaction simulator The compaction kinematic was set up using the Profil'one software (Medelpharm, Beynost, France). Two main kinds of experiment were performed: monotonic compression and jump test experiments. In all cases, the die was first filled by gravity and the filling height was set in order to obtain a final tablet thickness around 3 mm. In all the experiments, a symmetrical compression was used (double action compression). Punch movement was set to obtain pressures above 200 MPa during the compression. A complete description of the compaction cycle in the case of the tableting machines used for pharmaceutical applications can be found elsewhere  [22].
Characterization The phase composition of the PbSe QDs and the resultant thin films were analyzed using X-ray diffraction (XRD). The data were collected using an X'Pert PRO diffractometer (PANalytical, Malvern, UK) with Ni-filtered Cu K α radiation (λ = 1. 541874 Å) and a PIXcel detector. The XRD patterns were matched to International Centre for Diffraction Data (ICDD) PDF-4 database using the HighScore software package (PANalytical, Malvern, UK). To investigate the morphology and the chemical composition of the synthesized PbSe QDs, high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) and energy-dispersive X-ray spectroscopy in STEM mode (STEM-EDX) were carried out using a JEM-ARM200F electron microscope (Jeol, Tokyo, Japan), operated at 200 kV. QDs' size distribution was estimated using ImageJ software package.
Acknowledgments: V. S. acknowledges support by the  Portuguese Foundation for Science and Technology (FCT)  PhD Fellowship under Grant No. SFRH/BD/143750/2019. This research was supported in part by the  European Union 's  Horizon 2020 research and innovation program  through the  EnABLES  project under Grant Agreement no. 730957. P. A. also acknowledges support from  National Funds  through the  FCT  in the framework of the Strategic Funding  UIDB/04650/2020. E. M. F. Vieira thanks to  CMEMS-UMinho Strategic  Project  UIDB/04436/2020  and  UIDP/04436/2020. InfrastructuresMicro&NanoFabs@PT , operation code  NORTE 01-0145-FEDER-022090 , PORNorte, Portugal 2020.
Total data centers (DCs) storage capacity has grown from 382 EB in 2015 to 1. 8 ZB by 2020  (Cisco, 2016). During this same period, BD represented 27% of total stored data and reached 247 EB by 2020  (Cisco, 2016). Since then, cloud companies have sought in-depth BD administration and distribution software, ensuring increased parallelism paradigm for unprecedented processing speed  (Tsai et al. , 2016). The availability of such Advanced cloud infrastructure also enabled a promising AI as a Services deployed for multidirectional FE and BE cloud-fog applications, through powerful VMs GPUs. In 2014, Google's cloud has opened a new AI division, called DeepMind  (DeepMind, 2021). Applying DeepMind's machine learning and deep learning models, Google DCs were able to build their own super-efficient servers and reduce their cooling energy by up to 40%  (Projects, 2016).
In 2018, a DeepMind's system  (Projects, 2018)  was also configured to predict US wind power availability, to make optimal hourly delivery commitments to the power grid a full day in advance. Since then, collaborative efforts between Google and DeepMind team have resulted in blossoming projects. However, when everyone argues over potential benefits of distributed BD technologies and AI models in cloud-fog systems, what about these both stakeholders' hidden harms? The current paper sets out to discuss through a developed inquiry, how far ongoing interests are conscious about the greedy power consumed while implementing hardware and software AI and BD processing models. Motivating by this target, the contributions of this article are expressed in the following hierarchy: -As a starting point, the analysis of responses to the first questions of our topical inquiry, examined existing interoperability between three major actors: cloud-fog systems, AI services and distributed BD technologies.
Sampling and validation sources For the sake of reaching decisive results, this survey was submitted to IT communities specialized in the field (Fig. 2a ). The first community initially included Google DeepMind scientists having already conducted cutting-edge research on Google products. Then, embedded systems engineers, Software & system engineers and cloud data engineers specialized in cloud hardware and process operations management. The last category enclosed BD and AI developpes, senior certified data scientists and Kaggle experts with broad experience in AI models development and distributed database processing. For further justification, the obtained two hundred survey results have been justified with regard to current literature advances, collected from sources presented in (Fig. 2b ).
Generic insights into intra and inter cloud-fog architectures Cloud computing extended its traditional three oriented services Infrastructure, Platform and Software as a Services (IaaS, PaaS, SaaS) to Everything as a Service (SaaS) concept, offering a wide panoply of on-demand services  (Deshpande, 2020) , including: AI as a Service, Blockchain as a Service, Database as a Services, Sensing as a Service, etc. Some particular services are not only profiled to customers but also implemented as a management BE for all cloud layers (Infrastructure, platform, and software). Following a PaaS Marketplace catalogue to implement such large-scale data services, the PaaS model stands up to handle: the necessary deployments using many IDE and ARE programming languages, versions updates and synchronization, applications scaling, then data recovery, segregation, and replication (Fig.
3 ). One example of this last model is the Red Hat OpenShift cloud  (Revuri et al. , 2021) , performing entire platform tasks using PHP programming and Apache databases management technologies. Platforms and services being intrinsically linked are necessarily executed on the top of a large computing and storage infrastructure, having virtualization and containerization as core technologies. Google  (Verma et al. , 2015)  is one of the providers of pure containerized resources, while Amazon cloud provides hybrid VM and containers instances. Cloud industry  (Revuri et al. , 2021)  has moved ahead with NVIDIA GPU computing, which will ramp up to 1000x by 2025. Incredibly powerful VMs are now available in many cloud industries. As an instance, AWS cloud delivers more than 223 computing instances (AWS, n.
Online Transaction Processing (OLTP) comes at almost equitable percentages in both computing systems, since it is a workload type that is responsible for maintaining database ACID features. A transaction refers to an indivisible task such as Read and Write database operations. 99% of OLTP transaction's execution time is spent for the server threads. Shore-MT  (Ailamaki et al. , 2013 ) is a traditional disk based OLTP system, while VoltDB (VoltDB, 2021) and HyPer  (Kemper et al. , 2011)  are both in-memory OLTP systems. Comparing the number of stall cycle on these three OLTP databases using Cache Miss-Based Methodology (CMBM) and Top-down Microarchitecture Analysis Method (TMAM)  (Analysis and Workloads, 2021) , the recently deployed Intel TMAM methodology represents sufficiently all stalls, where a wide FE and BE stall cycles were consumed by instruction and data caches.
BD technologies in support to cloud-fog systems Through the analysis of obtained survey results, we discuss how BD programming models and frameworks support parallel computing in cloud-fog systems. (Fig. 7 ) highlights major results related to obtained percentages taxonomy on the most solicited distributed BD computation field (Fig. 7a ), most deployed BD programming languages (Fig. 7b ), most used In-memory frameworks (Fig. 7c ), most solicited distributed graph processing frameworks (Fig. 7d ) and eventually the most solicited stream processing frameworks (Fig. 7e ). To provide parallel computation, many BD programming languages can be deployed in cloud-fog platforms. According to the survey results mentioned in (Fig. 7b ), the most deployed BD programming languages are respectively: Spark, Cloud based Dataflow, Hadoop/Mapreduce framework and Dyrad.
Depending on required computation speed, the input data to these programming languages are typically categorized into batch mode, in which data are generally aggregated and bounded, or stream mode, in which continuous unbounded data flows. Apache spark has become one of the dominant chosen frameworks when it comes to distributed BD processing, owing to its fast speed and its great potential to mobilize massive computing power. Spark's core is implemented in Scala and built on the abstraction of RDDs (Resilient Distributed Datasets), which is a partitioned collection of read-only records providing parallel and fault tolerant data structures  (Zaharia et al. , 2012). Executing a Spark application involves the following five key entities: a driver program, a cluster manager, executors, tasks and workers  (Salloum et al.
, 2016). While Spark cluster mandates some DevOps management efforts, Google cloud-based Dataflow is a unified self-managed service with a standalone load balancer and cost controller. Data in cloud Dataflow  (Akidau et al. , 2015)  operates as a tuple of key, value, event time and required processing time window, satisfying hence sophisticated users' requirements. The core primitives of Dataflow are therefore  (Akidau et al. , 2015) : the generic ParDo parallel processing, then the GroupByKey and the GroupByKeyandWindow aggregation operations. The third rank dedicated to MapReduce (Fig. 7b ) was surpassed by the real time data processing of cloud Dataflow, as MapReduce is primarily dedicated to batch processing. Dryad was the least solicited according to the survey results, as it is the latest released Microsoft programming language dedicated to academic purposes and implemented with Directed Acyclic Graph (DAG).
Applying DryadLINQ, Hadoop and CGL-MapReduce versions to many data/compute intensive applications having similar requirements  (Ekanayake et al. , 2009) , it has been shown that DryadLINQ and CGL-MapReduce technologies have comparable performances and perform well for applications with simple communication topologies. However, CGL-MapReduce performed slightly better in most application use cases. The above discussed BD programming languages are used in many BD parallel computation fields, among which the most solicited according to obtained results are respectively (Fig. 7a ): distributed In-Memory processing (PIM), distributed stream processing, distributed graph and eventually the distributed storage processing. Given the relatively slow processes of existing disk systems, iterative, distributed, and interactive PIM systems are widely privileged.
To overcome slow disk I/O operations and ensure rapid data access, PIM systems make use of fast Dynamic Random Access Memory (DRAM) and cache units  (Liu et al. , 2021). Since memory units are volatile, though, PIM systems must embrace advanced frameworks to ensure durable and fault tolerance data. Hence, the most solicited frameworks according to the survey results (Fig. 7c ) are respectively: Redis  (Choi et al. , 2016) , SAP HANA  (Lemke et al. , 2017) , Apach Ignite  (Tapekhin et al. , 2019)  and lastly RAMCloud  (Tinnefeld et al. , 2013). Redis  (Zhang et al. , 2015)  is an example of lightweight system for caching key-value objects with strict Least Recently Used (LRU) prevention mechanism. Furthermore, HANA is an In-Memory database combining OLAP and OLTP in the same data management system.
The most solicited graph processing frameworks according to the survey results (Fig. 7d ) are respectively: Pregel  (Han et al. , 2014) , GraphLab  (Han et al. , 2014) , PowerGraph  (Gonzalez et al. , 2012) , GraphX  (Salloum et al. , 2016) , Giraph  (Han et al. , 2014)  and Trinity  (Shao et al. , 2013). Comparing four algorithms over five datasets on Pregel like system such as GPS, Mizan, Giraph, and GraphLab  (Han et al. , 2014) , it was found that GraphLab and Giraph's synchronous mode have good overall performance in terms of computation, setup, memory, network and total time. Using further three algorithms over three real-world social network graph datasets, on the popular graph-parallel computing platforms (PowerGraph, Giragh and GPS)  (Yoshigoe et al. , 2014) , it was found that PowerGraph exhibits the best processing rate and resources utilization over other graph-parallel computing platforms.
However, Spark GraphX  (Yoshigoe et al. , 2014; Salloum et al. , 2016)  resulted in less ingress time when compared to graphparallel systems with large graph sizes. Coming up to scalability performance  (Yoshigoe et al. , 2014) , Giraph outperformed other platforms owing to its relatively lower communication overhead in multiple machines, but at an expense of lower computing rate. In this subsection, we have highlighted some achieved survey results with regard to the most solicited: distributed BD fields, BD programming languages and distributed BD parallel computing frameworks. We further analyzed their interaction with cloud-fog environments. To provide additional confirmation, we have proved how the inquiry results largely converged with many literature evaluations studies. Since most used metrics in above analyzed evaluations involved: response times, computation rates, then memory-network bandwidths, we noticed a significant disregard concerning consumed power and energy metrics.
, 2017) ) or Agilent LXI Digitizers  (García-Martín et al. , 2019). This low proportion is primarily due to the fact that hardware metrics are not practical when parallel HPC nodes are implemented in virtualized environments, often migrating among multiple hosts and clusters in a cloud-fog environment. In this case, it becomes necessary to switch to software measurement tools. Intel's was a pioneering developer of software measurement tools, among which: Intel Performance Counters (PMC) counting specific event types related to modern processors registers hardware, Intel Running Average Power Limit (RAPL) providing power reading of uncore, core and DRAM, then the Intel Power Gadget  (Timothy and Konsor, 2014)  and Intel SoC Watch with VTune profiler (Intel, n. d. ). Though, the most deployed energy and power software measurement tools in HPC contexts are Xilinx Vivado tools (Xilinx, 2018) and Synospsis SystemVerilog tools  (Zhou et al.
al. , Nomenclature AI Artificial Intelligence ARE Application Runtime Environment ACID Atomicity, Consistency, Isolation, Durability BASE Basically Available Soft-state Eventual consistency BD Big Data MAC Multiply-and-Accumulate BoTs Bag-of-Tasks SPM ScratchPad Memory CAP Consistency, Availability, and Partition GPUs Graphic Processing Units ConvNet Convolutional network PMC Performance Counters DAG Directed Acyclic Graph NFCAs Nanoscale Flash Computing Arrays DBN/s Deep Belief Network/s ASICs Application Specific Integrated Circuits DC/s Data Center/s EDP Energy Delay Product DNN/s Deep Neural Network/s NPEs Neural Processing Elements DSS Decision Support System SMC Smart Memory Cubes F/BE FrontEnd/ BackEnd SIMD Single Instruction Multiple Data FNN/s Feedforward Neural Network/s CNN Convolutional Neural Network GANs Generative Adversarial Networks RAPL Running Average Power Limit IDE Integrated Development Environment / ADCs Analog-to-Digital Converters LNU Natural Logarithmic Unit EW Element Wise LRU Least Recently Used VMM Vector Matrix Multiplication LSBs Least Signification Bits CSR Compressed Sparse Row LSTM Long Short-Term Memory FPGAs Field-Programmable Gate Arrays NFV Network Functions Virtualization NoC Network-on-Chip NLP Natural Language Processing LoB Logic-Base OLAP Online Analytical Processing LUT LookUpTable OLTP Online Transaction Processing ReRAM Resistive Random-Access Memory PEs Processing Elements SRAM Static Random-Access Memory PIM Processing In-Memory DRAM Dynamic Random-Access Memory RBN/s Restricted Boltzmann Network/s SNNs Spiking Neural Networks RDBMS Relational Database Management Systems MRAM Magnetic Random-Access Memory RL Reinforcement Learning EXPU Exponential units SDN Software Defined Network PCM Phase Change Memory SMT Simultaneous Multi-Threading DMA Dynamic Memory Access SOMs Self-Organizing Maps MAC Multiply-Accumulate ToR Top of Rack DACs Digital-to-Analog Converters VM/s Virtual Machine/s EPR VNs / VNE Virtual Networks / Virtual Network Embedding.
Physicochemical characterization of substrate and inoculum used Cattle manure (CM) was chosen as substrate to compare hydrodynamic parameters obtained with existing data  (André et al. , 2015). CM was sampled from the farm of the UniLaSalle Polytechnic Institute (Beauvais, France). Used CM did not undergo any preliminary treatment. The liquid part of the manure (i. e. liquid bovine manure (LM)) was used as inoculum to bring the microbial consortium. All the analyses were carried out in triplicate. The total solid content (TS), the volatile solid (VS) of the inoculum, CM and DG were determined by a 105 • C drying for 24 h and a combustion at 550 • C for 2 h  (APHA, 1998). The pH of the inoculum was determined using a pH meter (Mettler Toledo, Switzerland). The total volatile fatty acid content (VFA) and the buffer capacity (TAC) were determined by two titrations using sulfuric acid with an automatic titrator (Mettler Toledo, Switzerland).
Computational aspects 2. 5. 1. Model calibration and validation The model resolution, calibration and validation, and the sensitivity analysis were performed with Matlab R2019b. The optimization procedure to determine parameters values was done with the fmincon function. This function computes the minimum of a given function to perform the optimization of a non-linear optimization problem with constraints. Model calibration and validation were conducted on 2 different sets of data. The parameters determined by calibration part had to be validated with different data to ensure the model robustness. For parameters estimation, three experimental data were used: accumulated CH 4 yield, CH 4 flow and VFA concentration. The simulated CH 4 flow expression is described in Eq. (  36 ). The parameters were estimated using a least-squares criterion minimizing the Eq.
Model calibration The calibration of the kinetic parameters aimed to obtain the best fitting with the experimental results. Two different datasets were used for parameter estimation and validation: the average methane flow, the average accumulated methane yield and the average volatile fatty acid concentration before and after 15 days. The model calibration was carried out by trial and error in order to obtain a possible dataset. The first step was to determine the hydrolysis constant μ 0 with equation (  132 ) using a linear regression on experimental solid phase COD measurements. A mean value of 0. 04 d -1 was measured with R 2 = 0. 97 and was the selected value for calibration step. LBR simulation results were close to the experimental data as represented in Fig. 3  until day 15. Experimental data are represented as red dots and simulated data are the blue lines.