dataesr/scientific-paragraphs-categorization huggingface dataset:
Most of these works put the Digital Twin at the center of DevOps to serve as a knowledge base for organizing and automating DevOps processes. While all these and similar works that we have found postulate that the DT can facilitate DevOps principles for CPS, we could not find any work that addresses CPS and DT service design for DevOps. To close this gap, we have recently proposed self-adaptive CPS design models  [31]  that use the DT as a shared knowledge base to orchestrate DevOps activities in a modern industrial environment, with a particular focus on distributed and networked embedded control units. However, evaluating these models' effectiveness and practical implementation is still necessary. In addition, a more precise and explicit connection between the generic software models and the DT concept's realization in practice is required  [31].
Design of a DT-Enhanced Self-Adaptive Software Framework for Mission-Critical Industrial Process Control In this section, we discuss our solution for a DT-enhanced control service designed with the aim to improve the autonomy and adaptability of CPS in operating and managing their mission-critical physical space and cyberspace. We introduce the service software architecture to establish an explicit map between architecture elements and their corresponding MAPE-K elements. Then, we present our SA DT model and explain relevant implementation aspects of the model and the control service. Finally, we conclude this section by discussing the applied design patterns and approaches.
e. , analyze and plan) for context-aware decision-making. Notably, the DT-enhanced control service is also self-aware since the software probes sense the states of the microservices, the host operating systems, and the host system hardware. The DT-enhanced control service adaptation and self-adaptation capabilities are designed according to the hierarchical control pattern  [39]  with one knowledge repository per MAPE loop to support fully autonomous operation alongside hierarchical co-operation and orchestration. In the hierarchical control pattern context, the DT-enhanced control service responsibilities are threefold: 1. The PA and SA control services are responsible for the autonomous monitoring and control of its associated PAs and SAs under normal operating conditions (R1, R2, R3, R4); 2.
Overall, the sensor plugin principle can support the self-organized dynamic composition of services based on contracts  [91]. The CLT can read commands from the command line or scripts. Scripts contribute to the infrastructure-as-code paradigm  [92, 93]  by the following means: They can be version-controlled and enable the flexible definition of complex orchestration sequenced. They can also be tested and support the reproducible creation of environments through reliable deployment, reconfiguration, and adaptation of the CPS. A noteworthy property of our setup is its support for the dynamic reconfiguration of the MAPE loop. The CLT implements the MAPE loop in our setup to control and manage the secondary assets. To that aim, the MAPE loop uses the command interface of the script engine. Reusing the script engine allows us to describe the behavior of the MAPE steps via a list of commands.
The RT PF service processes and then forwards all received messages to the Real-Time Protocol Stack Output service (RT PS output service). These three services thereby implement the behavior of a real-time closedloop software stack that controls and interacts with its physical environment. All services operate at a cycle time of 1 ms under the real-time first-in-first-out (FIFO) scheduling policy at priority 99. Each service offers a management interface or instrumentation and the PA and SA interfaces for twinning and adaptation. Considering the overall structure, the experiment setup implements the hierarchical control pattern with knowledge repositories as per MAPE loop as of  [39] , Figure  9.
During the execution of the experiment, we observed model and SWF invariants via remote monitoring dashboards. In the subsequent analysis, we followed a similar but more accurate approach. We created multiple interactive high-resolution time series plots with the Plotly library for Python. We used these interactive plots to check and compare the observed system behavior against the data flow model. In addition, we checked all build-in error states to identify potential failures. We can confirm that no violations of invariants occurred during any of the experiments.
The RT PS input and output services are configured to not limit the maximum end-to-end throughput characteristics of the RT PF service. The RT PF service load is primarily determined using the following timing and data flow factors:  One timing factor and one data flow factor are modified to reduce the number of experiments. All others are kept constant during the experiment, as defined in Table  3. The cycle time of all services is set to 1 ms, and the cycle portions are kept constant during all experiments. Only the sub-portions of the primary function (as shown in Figure  9 ) are modified. These sub-portions represent the timing factor, i. e. , factor 1. To obtain a configuration with maximum end-to-end throughput, the PF input portion PF input. portion must equal the PF output portion PF output.
In particular, it can be assumed that commercial industrial-grade software and hardware platforms offer similar or higher performance at the control layer. Furthermore, the timing requirements and resource constraints are less stringent in any layer above the embedded control layer, so the SWF and the implemented self-adaptive system models remain applicable. 9. 2. Design Space: Self-Adaptive Software Models to Realize DevOps for Smart CPS Section 1 states the two main research questions addressed by the case study presented in this work. In this section, we discuss how the demonstrated experiments can answer these questions. To structure our discussion, we refer to the design space and the AdEpS model shown in Figure  1  and to the outline of this work's contributions provided in Table  1.
In this work, we argue that modern software systems-such as CPS-, cloud-, and service-oriented systems-must be designed for technical sustainability to satisfy short-and long-term stakeholder concerns in today's dynamic industrial environments. We further argue that technical sustainability can be achieved by adhering to DevOps principles. While DevOps is a well-established concept in the IT domain, it is de facto not implemented in the CPS, OT, or embedded system domains. Its transfer to these domains is considered a challenging problem. We propose implementing the AdEpS model (Adaptation and Evolution processes for Sustainability) to address this challenge. To achieve that aim, we follow an architectural approach and propose self-adaptive system design models for the integrated management of CPS adaptation and evolution processes.
CCD-based X-ray detector systems often suffer from spatial distortions. Reproducible distortions can be quantitatively measured with a calibration grid and described as a displacement matrix or as spline functions. The measured distortion can be used afterwards to undistort raw images or to refine the actual position of each pixel, e. g. for azimuthal integration. This article describes a method using a regular grid, not necessarily orthogonal, to measure the distortions. The graphical user interface (GUI) Python software that is used to implement this method is available under a GPLv3 license on ESRF GitLab, and produces a spline file that is usable with data-reduction software such as FIT2D or pyFAI.
, 1972; Muehllehner et al. , 1980; Chakraborty, 1987). In the X-ray field, FIT2D  (Hammersley et al. , 1994; Hammersley, 2016)  has been used for a long time for this kind of calibration and correction of 2D patterns, using regular orthogonal hole grids and splines. Several software packages rely on the distortion splines created by FIT2D to perform distortion correction, like SPD  (Boesecke, 2007)  or pyFAI  (Kieffer, 2012). However, it appears that there is no software for creating this file independently, or using non-rectangular grids. So, a new specific Python software, with a graphical user interface, has been developed and made available for the community.
Practical software implementation The ideas presented above have been implemented in a Python software named SpatDistCalib  (Chevremont, 2022) , with the help of NumPy  (Oliphant, 2021) , SciPy  (Virtanen et al. , 2020)  and Matplotlib  (Hunter & Droettboom, 2021)  libraries. The FabIO  (Knudsen et al. , 2013)  library is also used to read the raw image from a file. This library can read various file formats from a large range of detectors. Once read, the image is transformed into a NumPy array, allowing one to use the functions of this software with images from almost any source. The user is requested to provide the origin and the base vectors by clicking on the origin (O), O + e 1 and O + e 2. The user input is refined by the method described in Section 3. 2. The software then displays the regular grid, as well as the actual grid detected on the image.
The user is then requested to provide the physical length of the base vectors, d 1 and d 2 , as well as the angle between them, in order to deduce the pixel size in both directions. If pixel-size correction is enabled, the software will introduce the enlargement or contraction to correct the pixel size to the ideal pixel size. After that, the software displays the displacement maps, the splines computed and the errors, as well as the calibration image corrected by the splines, with the regular grid superimposed. The splines can be saved in text format, which is usable by online data-reduction software, such as SPD  (Boesecke, 2007) , FIT2D  (Hammersley, 2016)  or PyFAI  (Kieffer & Karkoulis, 2013; Kieffer & Wright, 2013).
Conclusions In this article, a method to measure image deformation and calibrate 2D detectors using a calibration grid has been described in detail. The proposed method is able to correct low-frequency deformations of an image, measure effective pixel size and correct the size to the theoretical one, so that the corrected image is like that taken by a virtual perfect sensor at the calibration-grid position. The only hypothesis on the calibration grid is that it is a regular grid, described by two base vectors. This method has been implemented and provided to the user community as a Python software package. The method has been tested on a 2D detector that exhibits image deformation, FReLoN 4M at ID02 beamline, ESRF, demonstrating how the calibration grid can be reshaped as a regular grid using spatial-distortion correction splines.
The correction has also been used on monodisperse polystyrene beads, which show a large number of oscillations. Applying this correction clearly improves the resolution of these oscillations and reduces the standard deviation of the averaged 1D curve. To conclude, this software provides a standalone alternative to generate displacement files for the spatial calibration of 2D detectors. This software also allows one to use any kind of regular grid for the calibration, not just orthogonal ones. The Python source code is available for the community and can be easily extended or improved for specific needs. The spline files generated by this software can be used directly with software like FIT2D, SPD or pyFAI for distortion correction. The calibration has been demonstrated to work well using triagonal calibration grids, and the spatial-distortion correction, when applied on samples exhibiting oscillations over a wide dynamic range, has been shown to recover them accurately on the 1D azimuthal reduced curve.
1 is given in Appendix 7. Let us introduce the dimensionless initial Robin data for the errors Ḡ0 i := ∆t ν Ξ 0 i -(B i U i )(0) , i = 1, 2. (4. 8) Let us also extend (4. 7) to the case ℓ = 0, for ᾱj ̸ = 1, j = 1, 2, β 0 i := ᾱj I N - √ A -1 Ḡ0 j , j = 3 -i, i = 1, 2, (4. 9) E 0 i (x) := e -|x| √ ν∆t √ A β 0 i , ∀x ∈ Ω i , i = 1, 2. (4. 10) For ℓ ≥ 1, the dimensionless Robin data for the errors are denoted by Ḡℓ i := ∆t ν (B i (E ℓ j ))(0) = ᾱi I N - √ A β ℓ j , j = 3 -i, i = 1, 2. (4. 11) Let H H H 1 (Ω i ) := (H 1 (Ω i )) N and L L L ∞ (Ω i ) := (L ∞ (Ω i )) N , equipped respectively with ∥U ∥ H H H 1 (Ωi) := j∈[[1,N ]] (∥U j ∥ 2 H 1 (Ωi) ), ∥U ∥ L L L ∞ (Ωi) := max j∈[[1,N ]] (∥U j ∥ L ∞ (Ωi) ). With these notations and Theorem 4. 1, we can now prove the convergence of Algorithm 4. 1. Theorem 4.
2 (OSWR convergence). Let α i > 0, i = 1, 2. Then, Algorithm 4. 1 converges in H H H 1 (Ω 1 ) ∩ L L L ∞ (Ω 1 ) × H H H 1 (Ω 2 ) ∩ L L L ∞ (Ω 2 ) to the solution of (2. 4). Moreover, setting * M ( ᾱ) := ᾱi I N + √ A -1 ᾱi I N - √ A ᾱj I N + √ A -1 ᾱj I N - √ A , (4. 12) we have the following relations on the Robin data for the errors, for i=1,2, Ḡ2ℓ i = (M ( ᾱ)) ℓ Ḡ0 i , ∀ℓ ≥ 0, (4. 13a) Ḡ2ℓ+1 i = (M ( ᾱ)) ℓ Ḡ1 i , ∀ℓ ≥ 0, (4. 13b) as well as the discrete-time relations for the errors on the interface * * β 2ℓ i = (M ( ᾱ)) ℓ β 0 i ∀ℓ ≥ 0, (4. 14a) β 2ℓ+1 i = (M ( ᾱ)) ℓ β 1 i ∀ℓ ≥ 0, (4. 14b) from which we deduce the following convergence estimates, for even and odd iterations ∥E 2ℓ i (0)∥ ∞ ∥E 0 i (0)∥ ∞ ≤ ∥(M ( ᾱ)) ℓ ∥ ∞ , ∀ℓ ≥ 0, (4. 15a) ∥E 2ℓ+1 i (0)∥ ∞ ∥E 1 i (0)∥ ∞ ≤ ∥(M ( ᾱ)) ℓ ∥ ∞ , ∀ℓ ≥ 0.
Nevertheless, such universal correlation that would cover the two regimes has not been found up to now. Furthermore, few articles propose numerical simulation of AM surfaces. In the aim of building new rough-wall models dedicated to AM surfaces, the creation of a roughness-resolved Large-Eddy Simulation (RRLES) database of representative channel flows emerges as a worthwhile process. The present paper describes a methodology to perform parametric RRLES from a limited set of surface parameters to control the surface height distribution and the streamwise and spanwise spatial auto-correlation functions. The first section of the article describes the numerical setup, configurations and the used methodology for performing RRLES. The post-processing and obtained results are presented and discussed in the second section.
Configurations and Meshes First of all, rough surface (RSG) and body-fitted unstructured mesh (RRMG) generators have been developed  [18]. The former creates triangulated surfaces stored in the STL format that are used by the latter to compute level set functions. The level set functions are then employed to refine an existing 3D unstructured mesh and to create a conforming body-fitted mesh with controlled cell size, quality and size gradation. The resulting meshes are suitable for performing RRLES with higher-order finite-volume schemes. The RRMG has been developed within the YALES2 code. YALES2 is an unstructured low-Mach number code developed at CORIA for the Direct Numerical Simulations and Large-Eddy Simulations in complex geometries  [19]. It heavily relies on the parallel volume and surface mesh adaptation.
10) is taken as a reference in order to compare friction factor values obtained in the present RRLES database to the Moody diagram. The definition used in this correlation is Darcy's one and the relationship between Darcy and Fanning friction factors is f Darcy = 4f. Therefore, in Fig. 12 , friction factors from the Colebrook equation are divided by a factor of 4 for getting comparative results. Two relative roughness S a /D h values (S a /D h = 10 -2 and S a /D h = 5. 10 -3 ) for Colebrook formula are plotted as the relative roughness of the present cases is intermediate: S a /D h = 7. 8 10 -3. 1 f Darcy = -2 log S a 3. 7D h + 2. 51 Re f Darcy (10) As expected, for each Reynolds number, friction factor is higher with increasing ES. For the front and isotropic cases, values are above the Colebrook correlation ones and tend to slightly increase with the Reynolds number.
CONCLUSIONS This paper presents roughness-resolved Large-Eddy Simulations that are representative of the flow obtained in additive-manufactured heat exchangers. The aim of these simulations is to provide a rich database that will ease the development and assessment of rough-wall models. The most challenging task in the building of this database is the generation of representative rough surfaces and conformal unstructured meshes. Three different configurations of parallel rough plane channels with the same roughness height distribution but different effective slopes have been chosen and modeled. The impact of the effective slope parameter, which is directly linked to the alignment of the wall roughness with the flow, has a strong impact on the flow topology, velocity and temperature profiles, as expected.
acknowledgment The STREAM project has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreement No 865378. The JU receives support from the European Union's Horizon 2020 research and innovation program and the Clean Sky 2 JU members other than the Union. This article reflects only the authors' view, the Joint Undertaking is not responsible for any use that may be made of the information it contains. This work was granted access to the HPC resources of CINES/TGCC/IDRIS under the allocation 2020-A0092B06880 and 2021-A0112B06880 made by GENCI and of CRIANN under the allocation 2012006.
For a pair of configurations (a, b) in C 2 , we now call q = (q i ) i=0. M an array of M + 1 elements of the configuration space C joining a = q 0 and b = q M as well as w = (w i ) i=1. M an array of M elements of the configuration space lie algebra c which represent the M tangential vectors that maps q i to q i+1 through the group exponential map  [11]. We solve the multiple shooting problem: min w∈c M ,q∈C M+1 M-1 ∑ k=0 F(q k , w k ) 2 q k+1 = Exp q k (w k ), k = 0. M -1; q 0 = a, q M = b, (12) where Exp q is the exponential map of the configuration space Lie group in configuration q implemented through the Pinocchio library  [9]. We solve this minimization program using an off the shelf optimizer based on the interior point method  [25]  because we can construct an initial guess that respects the constraints.
no. CZ. 02. 1. 01/0. 0/0. 0/15 003/0000468)", the Inria-NYU collaboration agreement, the Louis Vuitton Louis Vuitton ENS Chair on Artificial Intelligence, and the French government under management of Agence Nationale de la Recherche as part of the "Investissements d'avenir" program, reference ANR-19-P3IA0001 (PRAIRIE 3IA Institute).
From this data, the meso/macro porosity and tortuosity of this material were evaluated at 49% and 1. 95, respectively, using the SegmentPy  [37]  and Taufactor  [38, 39]  softwares (see Figure  S2 , Supporting Information for the segmentation justification and the representativeness calculation). These porosity and tortuosity values are consistent with those from Li-O 2 binder carbon electrode obtained by FIB/SEM 3D imaging. [35]  The porosity of the electrode has a critical impact on the reaction intermediates at the surface while the tortuosity affects the electrochemical kinetics enhancing Li-ion diffusion inside the wetted porous electrode. The pore network distribution of over 400k pores detected in this volume was obtained using Porespy algorithm. [40]  It is depicted in Figure  2c  with a ball-and-stick model, showing pores and channels respectively.
End-of-Life Electrode Treatment After the cell death, two electrode recovery treatment paths were investigated: 1) reconditioning without further nanotube dispersion and 2) recycling of the CNT material via dispersion and filtration to form new electrodes. For the reconditioning process, an acid and deionized water treatments were investigated, whereas for the CNT recycling process, they had first washed them in abundant deionized water, and then re-dispersed them using the same procedure shown in Figure  1. SEM images show efficient removal of solid deposits by either soaking the aged electrode in a pH = 3 acid solution, or water respectively (Figure  5d ,e), and or recycling the CNTs (Figure  5f ), (detailed in Experimental Section). EDX data in small magnification over a large area for different electrodes (Figure  S5 , Supporting Information) confirms the disappearance of oxygen-containing species after washing.
5 camera (2560 × 2160 pixels 2 ) along a 360°rotation with an exposure time of 45 ms per projection using an incident X-ray beam having an energy of 17. 5 keV and a high flux of 1. 4 10 11 ph/s -1. The total acquisition time was ≈20 min per full holo-tomography scan. 3D reconstructions were achieved in two steps: (i) phase retrieval calculation using an in-house developed octave script based on a Paganin-like approach using a delta/beta ratio of 303, and (ii) filtered backprojection reconstruction using ESRF software PyHST2,  [52]  for a final volume of 64 × 64 × 54 μm 2 and a voxel size of 25 nm. For tomography measurements, samples have been carved as small tip using carving a Zeiss Palm laser beam and subsequently retrieved thanks to an epoxy-wetted pencil lead. The cycled electrodes were soaked in dimethylacetamide to leach the salt and then dried in a vacuum without heating.
Acknowledgements This research was supported by the  French Ministry Higher Education, Research and Innovation. The authors acknowledge the  European Synchrotron Radiation Facility  for provision of beam time (in-house research time) using the ID16B beamline. They also acknowledge use of the Cambridge XPS System, part of  Sir Henry Royce Institute -Cambridge Equipment ,  EPSRC  grant  EP/P024947/1  and  Dr. Carmen Fernandez-Posada  for XPS data acquisition and processing. I. T. and C. P. G. acknowledge funding from the  European Research Council (ERC)   BATNMR  project. A. A. F. acknowledges the  European Union 's  Horizon 2020 research and innovation program  for the funding support through the  European Research Council  (grant agreement  772873 , " ARTISTIC " project). A. A. F. acknowledges  Institut Universitaire de France  for the support.
The heat-treated matchstick samples were then polished down to 0. 1 µm using diamond suspension. Backscattered electrons (BSE) in the scanning electron microscope (SEM) were used to obtain the microstructure images. The SEM characterisation was performed on a JEOL  JSM-7900F microscope with an accelerating voltage of 20 kV. Vickers microhardness tests were performed on each heat-treated sample to understand the effect of heat treatment on the mechanical properties. A total of 10 indents were performed on each sample and an average of the data is presented in section 3. The hardness measurements were performed on a PRESI HZ50-4 indenter; using a 5 N load with a dwell time of 10 s.
Their number and siting thus need to be optimized in order to compose an op-Published by Copernicus Publications on behalf of the European Academy of Wind Energy e. V. timal network of sensors in an offshore wind development area. Such networks are expected to capture most of the dominant wind dynamics from a minimum number of sensors. Numerous efforts have been undertaken in different scientific fields to optimize sparse sensor siting, a combinatorial problem not solvable by standard approaches such as convex optimization. Sparse sampling is about selecting salient points in a highly dimensional system. It then requires a dimension reduction of the data, such as the use of empirical orthogonal functions  (EOFs). EOF analysis projects the original data into an orthogonal basis derived by computing the eigenvectors of a spatially weighted anomaly covariance matrix.