dataesr/scientific-paragraphs-categorization huggingface dataset:
Bilateral, Anisotropic Diffusion and Non-Local Means vs. Ilastik The main difference observed with these filters was an enhanced description of specific surface area, 9% higher than with median and mean filters. However, edge and wedge mismatches had a negative impact on the results. Some tight pores and pore-throat conformance are poorly represented. Some of the permeability calculations on the simulation differ by 3 to 4% compared to the mean and median filters, despite a coordination number 5% higher. Locally, Figure  5  shows the effects of these filters on a cropped 3D image. This study sought to quantify the impact of image processing on effective properties using a standard filtering and segmentation process versus the use of A. I. software, making it possible to perform multi-label segmentation and not just gray-level segmentation.
We clearly showed the impact of the segmentation method and the advantages of using Artificial Intelligence (A. I. ) software integrating multiple classifiers. Of course, image processing is a decisive step, but all the other steps are equally important: experimental capability, reconstruction, and image processing. Several other techniques could be used to remove current limitations, and the solution undoubtedly resides in combining techniques, whether this involves enhancing phase identification or addressing the multiscale challenge. We drew four main conclusions from this study: -A synchrotron or even a 3D X-ray microscope produces high-clarity, contrasted raw tomographic images, in which noise is very much reduced compared to those obtained by conventional X-ray microtomography. The filtering step in this case is no longer required.
- The results obtained with Ilastik, as an A. I. multi-label segmentation software package, appear quite robust and relatively conform to those that an experienced scientist would obtain by performing a pixel-by-pixel classification. -Some effective properties of the porous media are strongly affected by very small differences in porosity. We concluded that it was essential to define these properties for each image processing workflow in order to qualitatively and quantitatively determine error sensitivity in Digital Rock Physics. Given the results obtained, we can have every confidence in such a promising technique.
Acknowledgments:  Alastair MacDowell  and  Dula Parkinson  at the  Advanced Light Source ,  Beamline 8. 3. 2 , supported by the  U. S. DOE Office of Science, Office of Basic Energy Sciences  ( DE-AC02-05CH11231 ) helped to operate the microXRT. The Advanced Light Source is supported by the Director,  Office of Science, Office of Basic Energy Sciences , of the  US Department of Energy  (award No. DE-AC02-05CH11231 ). Jonathan Ajo-Franklin  and  Tae-Hyuk Kwon  performed the original experiment. We especially acknowledge  Alexandre Lapene  and  Eric Chaput  for fructuous scientific exchanges. This work was granted access to the HPC resources of CALMIP supercomputing center under the allocation p21016.
With respect to the existing anti-Trojan Design-for-Trust techniques, our methodology is purely software-based; therefore, it can be applied both when the system is still to be designed as well as on already designed and deployed systems. Moreover, our methodology does not require any redundancy or modification to target microprocessor. The only similar idea has been proposed in  [17]  where software diversity is achieved by substituting program instructions with equivalent ones during the fetching procedure. Nevertheless, the proposal in  [17]  only considers sequentially-triggered changefunctionality HTHs and equivalent instructions substitution is solely employed. The main contributions of this paper are: • the definition of software obfuscation procedures to protect the execution of a program from information stealing HTHs infesting the underlying microprocessor, • the definition of a Genetic Algorithm-based engine to optimize the software obfuscation procedure, and • an experimental campaign aimed at assessing the feasibility of software obfuscation for security purposes and its optimization on a set if MiBench benchmarks  [18]  and other criptographic programs executed on the 64bit Ariane RISC-V microprocessor  [19].
Moreover, we assume that the attacker has an idea about which operating system and programs will be executed on the attacked microprocessor but, on the other hand, he/she cannot have all the details about software versions and implementations. In order to make our threat model dangerous and realistic at the same time, we assume that the HTH is able to monitor and exfiltrate data from a reduced number of registers of the infected microprocessor. We believe that this assumption is totally reasonable if we keep in mind that: i) HTHs need to be small enough not to be detected via optical inspection, ii) HTHs need to have a very small impact on power consumption, electromagnetic emission and timing of the infected system, and iii) HTHs cannot occupy the transmission channels for a long time in order not to be discovered.
B. Fitness Function Since the best solution identified by the GA has to maximize the average register heat (H r ) while minimizing the introduced overhead (∆E), we defined two distinct values that are calculated based on the execution the obfuscated programs. The first value, dubbed heat, measures how hot all the registers in the microprocessor are kept during the program execution. The second value, dubbed enlargement, measures how longer is the obtained obfuscated program w. r. t. the original one. The value range of both heat and enlargement is [-10000, 10000]; the fitness of each solution is calculated as the sum of these two values, thus ranging between -20000 up to 20000. In order to calculate the value of heat, the execution time of the program is divided into windows of c instruction cycles each (being c the number of registers in the microprocessor).
E. Mutation Operator The mutation operator is meant to provide exploration of the solution space. We implemented a mutation operator consisting in a single bitflip in the value of a gene. In order to provide solution space exploration without compromising the exploitation provided by the cross-over operator, the probability of applying mutation to a chromosome grows with the drop of positions in the chromosome ranking. Finally, it is worth mentioning that, since mutation is applied to every gene individually, it is possible to have multiple mutations in a single chromosome. V. EXPERIMENTAL ANALYSIS We implemented the proposed software obfuscation methodology and the companion Genetic Algorithm-based optimization engine as a set of automatic Python scripts and C programs. We targeted the 64bit Ariane RISC-V  [19]  microprocessor that counts 32 user registers, its ISA and toolchain and we considered the set of benchmark programs reported in Table  II , where the program names and corresponding number of assembly lines of the plain, unprotected version of the program are reported in the first two colums.
As a first validation note, we checked that the obfuscated programs were always functionally equivalent to the corresponding plain ones through a set of random simulations. When considering the effectiveness of proposed optimized software obfuscation procedure, if we look at Figure  2  we can see that the average aggregated registers' heat (calculated over the entire program execution and considering all the registers) is always much higher in the obfuscated program than in the plain one, with an average increase about 281%. This actually demonstrates that all the registers are more (and more frequently) used in the obfuscated versions of the programs. More in details, the difference between the average H r for the plain and obfuscated programs ranges between 99% for SHA and 375% for Idea.
VII. CONCLUSIONS We presented an automatic methodology for software obfuscation aimed at protecting program execution over possibly untrusted microprocessor-based systems against information stealing HTHs. Moreover, we presented a Genetic Algorithm-based engine for the identification of the best parameters for the software obfuscation procedure, thus obtaining an optimal obfuscation both in terms of effectiveness and introduced overhead. We proved the correctness, effectiveness and efficiency of our proposal by applying it to the Ariane 64bit RISC-V microprocessor running a set of MiBench and cryptographic programs.
[21] o further help that electrochemical analysis, herein we describe the Electrochemistry Visualization Application (EVA), a program developed for the visualization of the potential vs. capacity, ICA and DVA curves; the reconstruction of a full-cell curve through fitting of the half-cells curves; and the data export. The possibility of simultaneous visualization of the potential vs. capacity and both the differential techniques by selection in a friendly interface for the fitting turns the analysis more intuitive. Additionally, those functions are possible for multiple cycles and without the need of data pretreatment for BioLogic EC-Lab ® Software entry data, turning EVA a powerful and practical tool for the user.
Description of the Program The design of the application is based on PyQt5, a well-known comprehensive set of Qt5 application packages and Python bindings, offering modules for the development of multi-platform desktop Graphical User Interface (GUI) applications, including Windows, Unix and Mac OS. [26, 27]  The main modules of PyQt5 embed functionalities like the QtCore, a module for the assessment of data types, files, or directories. QtGui has dedicated functionalities to structure the window-based application with common graphics, fonts and 2D plots. QtWidgets offers tools to create the different styles and objects for the GUI. The front-end and the back-end of the GUI are both developed using Python 3. 7, embedding the detailed modules above to deal with the treatment of the electrochemical data.
In our case study, we developed the application ready to be used under Windows 10 and Unix platforms. [27] n this GUI, the user loads the electrochemical results coming from EC-Lab ® software (treated as. mpt files) in order to analyze automatically without manual settings, the capacity and voltage values included into such type of files that are not easy to handle from a common desktop program. In this regard, the GUI facilitates the pre-treatment and post-treatment of the electrochemical curves using dedicated Python classes from the back-end, storing and retrieving the measurements from the files, but also the fitting processes that are proposed by the GUI. When the user is satisfied with the extraction of data and the calculations of fittings, the GUI offers different 2D representations to render plots implemented using PyQt5 toolkits which embed matplotlib modules.
Program Functionalities Once EVA is initialized, the first step to be taken is to load the entries. The present alpha version of EVA accepts up to three files simultaneously as entries. They are labeled "Positive", "Negative" and "Full", meaning a positive electrode half-cell, a negative electrode half-cell and the full-cell, respectively. All of them can be generated through saving as. mpt file any EC-Lab ® galvanostatic cycling with potential limitation (GCPL) raw data containing solely constant current charge and discharge cycling profiles. These files contain from the recorded points of the cycling information such as current, potential, time and capacity. For positive electrode half-cell and full-cell data, the cycle is taken as a charge phase followed by a discharge phase while for negative electrode half-cell data it's the opposite since in half-cell the negative electrode of the full-cell is the positive electrode (once commonly Li is used as a counter electrode whose potential is the lowest available).
Other factors such as the choice of separators, of the electrolytes and how data is recorded might impact the fitting. Even though the conditions to apply these techniques might seem restrictive, they can track degradation under multiple ageing regimes if inserted as control cycles. However, impedance rise, selfdischarge and important capacity losses during the control cycle tend to make difficult the fitting once they move away from the validity of Eq. (  1 ) and Eq. (  2 ) or would importantly locally change the fitting parameters within a charge phase. In Figures  12  and 13 , the curves presented are obtained after 3 formation cycles and thus do not contain the initial irreversible capacity associated to each electrode during first charge. Those initial irreversible capacities though impact the initial slippage of the curves because their causes (SEI formation, limited relithiation of positive electrode, reaction of impurities and others) are directly related to cyclable lithium ion inventory.
[34] EVA is available for download from a Github repository upon publication of this paper through the following URL: https://big-map. github. io/big-map-registry/apps/EVA. html. The application has a friendly display and a simple operation in order to support electrochemical analysis and to spread the use of the differential techniques of ICA and DVA, as well as the fitting to track degradation mechanisms. The present version of EVA to the publication also turns those analyses practical without the need of data treatment and the fitting intuitive with the addition of the capacity vs. potential fitting to the ICA or DVA fitting windows. Moreover, forthcoming upgraded versions are intended to add functionalities, rendering EVA more flexible to data entry.
Acknowledgements The authors acknowledge the European Union's  Horizon 2020 research and innovation programme  for funding via the  BIG-MAP  project (grant agreement  957189 ). AAF acknowledges the support of the  European Union 's  Horizon 2020 research and innovation program  for the funding support through the  European Research Council  (grant agreement  772873 , " ARTISTIC " project). AAF and MD acknowledge the  ALISTORE European Research Institute  for the funding support. AAF acknowledge Institut Universitaire de France  for the support.
Simulation of voltage sag The model was produced with the SimScape Toolbox from MATLAB/SIMULINK. To be able to simulate a fault at any point of any line, it is essential to model each line with two blocks, the first will have the distance equal to the distance between the start of the line and the fault and the second will have a distance equal to the total length of the line minus the distance from the fault. The loads were coupled to the nodes to reproduce a real environment. Generally power lines are modelled with sequential impedance and parallel admittance  [15]. As the lines are short, we have neglected the parallels. All the parameters necessary for the construction of the model of the line are indicated in Table  1. These are the lengths of the sections, unit impedances of the sections, nominal powers of the transformers.
CONCLUSION With a view to optimizing and reducing the consumption of electrical energy, researchers have embarked in recent decades on the development of non-intrusive supervision solutions for electrical loads in the residential sector. These solutions are based on the known characteristic quantities of these loads such as the rms value of voltage, current, active and reactive power, power factor, current harmonics etc. However, these characteristics can be influenced by non-deterministic factors such as the appearance of a fault on the network. The study carried out in this article aims to evaluate the impact of these faults which disturb the quality of the network so that their impact is considered in the development of more realistic NILM solutions. The study carried out through simulations of faults in the Matlab/Simulink environment shows that faults such as voltage sags which are very frequent in electrical networks, as well as the flicker voltage, have a considerable impact on these signatures on which the development of the NILM solutions is based considering the simulation's (Vrms, Irms, …) result.
ACKNOWLEDGEMENTS This research is supported by the  'Schlumberger Foundation 's  Faculty for the Future Program ', which encourages and supports women to pursue research in STEMs. The authors therefore thank FFTF for its financial support and its commitment to women for a better diversity in STEM research. The authors also wish to thank the  University of Orléans (France)  and especially the  PRISME Laboratory of Orléans' Polytechnics  in which the FFTF laureate is carrying out her research.
(2021) ;  Chau, Urco, et al. (2021) ;  Conte et al. (2021). Notice that the vertical estimates from SIMONe Argentina and SIMONe 2018 show a diurnal pattern, while those from SIMONe Peru show a dominant semi-diurnal (12 hr) oscillation. To check whether these tidal features are artifacts of the observational geometry, we selected another set of 7 days from Multistatic-Multi-frequency Agile Radar for Investigations of the Atmosphere (MMARIA)/ SIMONe Germany and SIMONe Peru. Figure  2  shows the 7-day composite daily mean plots of two data sets from Germany and Peru at different heights. Figure  2a  shows data from SIMONe 2018 during the campaign   a-c ) show the estimated vertical velocities using SIMONe Argentina, SIMONe 2018 (Germany) and, SIMONe Peru for seven days. These winds were estimated using specular meteor events observed within 4 hr and 4 km time-altitude bins.
SIMONe Systems The inspiration for the SIMONe systems conceived by  Chau and Clahsen (2019)  originated from the following factors: The MMARIA concept  (Stober & Chau, 2015) , radar signal processing techniques for coded continuous wave meteor radar transmissions  (Vierinen et al. , 2016) , multiple transmitters and multiple receivers (MIMO) for radar interferometry  (Urco et al. , 2018) , and compressed sensing  (Urco et al. , 2019). A special campaign was conducted in northern Germany to test this concept for 7 days in November 2018. Results from this campaign were later used in Charuvil  Asokan et al. (2021); Vargas et al. (2021) ;  Vierinen et al. (2019). Henceforth, we call this data set SIMONe 2018. The campaign gave us a substantial amount of specular meteor observations that is 10 times larger than those of a typical monostatic SMR.
Then, based on the SIMONe geometries and meteor samplings from each system, we probe the UA-ICON atmosphere to measure the parameters in a way in which multistatic SMRs conduct actual measurements. It is to be noted that, in the current study, we do not compare the results from the UA-ICON and SIMONe systems. Instead, we are creating a tool VMMR to validate the methods used by the multistatic SMRs to estimate winds and gradients. This tool can be extended to validate all the analyses done by meteor radar observations in the future. A block diagram of our VMMR is shown in Figure  5. UA-ICON provides the zonal (u), meridional (v), and vertical (w) wind component values at each grid position in space and time. However, the SIMONe meteor data contains one-dimensional projections (Doppler shift) of the velocity field and other parameters also depending on the location of meteors, such as Bragg wave vector and angle of arrival.
Since the meteor measurements are sparse and randomly measured in nature, they have an irregular sampling structure in space and time. First, we place the four SIMONe geometries (SIMONe 2018, SIMONe Peru, SIMONe Argentina, and SIMONe-X) onto the UA-ICON model base. A visualization example of one of such placements can be seen in Figures  6a-6c. Figures  6a-6c  displays a time frame (5 min) at 90 km altitude in the latitude-longitude cut data of u, v, and w from the UA-ICON model. The small gray stars in these plots show meteors' locations within 5 min from SIMONe 2018. Similarly, Figures  6d-6f  show the average of such time frames for 30 min, and the gray stars correspond to the meteors observed in 30 min. After placing the SIMONe geometries on the UA-ICON data, the next step is to find the spatially and temporally closest grid position in the UA-ICON for each meteor observation.
Validation Statistics We estimated the mean winds and gradients using two different averaging kernels: 1 hr -2 km and 4 hr-4 km time-altitude bins from both the simulated data from VMMR and UA-ICON model data to validate the estimation techniques. Their performance metrics are shown in Figure  7. Figures  7a  and 7d  correspond to the CC between the VMMR simulated and UA-ICON data for the two datasets, respectively. In all graphics of Figure  7 , the x-axis labels correspond to mean zonal wind (u), 𝐴𝐴 𝐴𝐴𝑥𝑥𝑢𝑢 = 𝐴𝐴𝑢𝑢 𝐴𝐴𝑥𝑥 , 𝐴𝐴 𝐴𝐴𝑦𝑦𝑢𝑢 = 𝐴𝐴𝑢𝑢 𝐴𝐴𝑦𝑦 , 𝐴𝐴 𝐴𝐴𝑧𝑧𝑢𝑢 = 𝐴𝐴𝑢𝑢 𝐴𝐴𝑧𝑧 , 𝐴𝐴 𝐴𝐴𝑡𝑡𝑢𝑢 = 𝐴𝐴𝑢𝑢 𝐴𝐴𝑡𝑡 , mean meridional wind (v), 𝐴𝐴 𝐴𝐴𝑥𝑥𝑣𝑣 = 𝐴𝐴𝑣𝑣 𝐴𝐴𝑥𝑥 , 𝐴𝐴 𝐴𝐴𝑦𝑦𝑣𝑣 = 𝐴𝐴𝑣𝑣 𝐴𝐴𝑦𝑦 , 𝐴𝐴 𝐴𝐴𝑧𝑧𝑣𝑣 = 𝐴𝐴𝑣𝑣 𝐴𝐴𝑧𝑧 , 𝐴𝐴 𝐴𝐴𝑡𝑡𝑣𝑣 = 𝐴𝐴𝑣𝑣  and, mean vertical wind (w). The four different lines in each plot correspond to each SIMONe geometry used for the VMMR network simulation.
Vertical Velocity Estimation: Tidal-Like Features In our VMMR analysis using the model data, we find that the small-scale structures of the order of ±1-2 m/s are due to horizontal wind contamination. However, this result does not explain the tidal-like features found in the actual SIMONe data from Germany, Peru, and Argentina (Figure  1 ). These tidal-like features are more significant at altitudes around and above 95 km (Figure  2 ) and sometimes reach more than 5 m/s in amplitude. Since we used various SIMONe geometries in our VMMR, we can claim that these tidal patterns are not the result of sampling or geometry errors. Due to the lack of independent observations of vertical winds, it is challenging to assert geophysical implications to the unexpected mean values and tidal features observed in our SIMONe systems.
Conclusions In this work, we present a validation study of mean winds and gradient estimation analysis of multistatic SMRs. Our study is motivated by the tide-like large-scale structures and unexpected mean values in the vertical velocity estimates in the MLT from our SIMONe systems in Argentina, Germany, and Peru. It is not a comparative study between the winds obtained from models and multistatic SMRs. Instead, we created a tool called VMMR based on data from the UA-ICON high-resolution model to validate the quantities estimated from SIMONe systems in particular and multistatic SMRs in general. This tool, the VMMR, can validate all the analyses connected to multistatic SMRs. However, in our study, we focus only on the estimation techniques related to the mean winds and gradients in the MLT.
The other existing systems, such as SIMONe Peru and Argentina, give a good correlation, but not as excellent as SIMONe 2018. The validation tests on the vertical velocity estimation show a poor correlation. However, our VMMR results could only explain the variability of ±1-2 m/s in the estimates. These variabilities are due to the small-scale structures present in the horizontal winds and their leakage to the vertical velocity estimates. These tests did not explain the large-scale tide-like structures observed in the actual SIMONe observations in Peru, Argentina, and Germany. Further tests using virtual radar systems by inducing diurnal tides to the UA-ICON data suggest that the SIMONe systems can estimate the large-scale variations in the MLT vertical winds if they have an amplitude larger than 2 m/s.