dataesr/scientific-paragraphs-categorization huggingface dataset:
, others are statistically computed such as average, minimum, maximum. in this raw data, digital and nondigital features are included requiring a pre-processing step to be used by the behavioral-based IDS algorithm. More details are given in  [45]. (3) Pre-processing process: After extracting normal and malicious traffic from the network, the obtained raw data was pre-processed to be used. This processing is semi-manually made because raw data contains both digital and non-digital features. The non-numerical features are converted according to several rules. For IP addresses, only the last number of this latter is kept since the three others are the same for all the architectural elements. For each protocol, a number is assigned and a features list is split into a single value by row. (4) Labeling process: For classification purposes, the dataset is labeled with nine labels, one label for the normal traffic, seven labels for malicious traffic (one label for each attack) and one label for the equipment reaction against an attack.
For instance, among the extracted features, SYN flag count and MAC address features help us to label the SYN flooding and the MITM traffic respectively. (5) Extracted dataset: The used dataset is large which gives a good training of the neural network. It contains around nine millions lines in its csv file and has a size of 16 GB for its network traffic files. It was captured over five days, during which seven attacks were simulated. It contains 133 features. To train and test the neural network algorithm used in the behavioral-based IDS, the dataset is divided into training and testing datasets (70%/30%). More details are given in Table  1 :.
Neural network: Architecture and experimentation parameters To obtain the optimal neural network architecture, several tests were performed by changing either the layer's number, the layer's neuron number, the activation function, the epoch's number or the batch size. In the beginning of neural network development, either over-fitting or under-fitting phenomena were encountered. The first one occurs when the neural network is very good at learning its training set, but cannot generalize beyond the training set. The second one occurs when the network is not able to generate accurate predictions by using the training set. To overcome both of these problems, firstly the correct splitting percentage between training and test datasets had to be found. After using several splitting configurations, 70% of the whole data was kept for the training dataset and 30% for the testing one.
Performance results In the evaluation phase, one of the main concepts of the machine learning field which is a confusion matrix (Fig. 2 ), has been used. A confusion matrix is a tool for measuring the performance of a machine learning model. It checks in particular how often its predictions are accurate, compared to reality. On the x-axis, true labels are presented and the coordinate axis shows the predicted labels. This confusion matrix is computed after classification by using the testing dataset. According to these results, all traffic types are correctly predicted as shown in the diagonal line of this confusion matrix. All attacks have been classified with high detection accuracy. To evaluate the IDS performance in more depth, three metrics have been used: precision, recall and F1-score whose formulas are given below: (1) Precision: a metric that gives the number of the correct computed positive predictions.
The score must be near to 1. 0 for the best classification. F 1 -score = 2 * Precision * Recall Precision + Recall The neural network model has first been trained using the training dataset and tested later with the testing dataset. The results show a good rate of precision, recall and F1-score as shown in Table  3 : According to the results shown in the Table  3 , we notice that the normal traffic, malfunction, FTP, MITM and DoS are classified with high precision, recall and F1-Score values (more than 94%). However, the ''Reaction'' traffic is classified with only 88% precision. This value is due to the nature of this traffic. The ''reaction'' is not an attack but a reaction of the equipment against an attack. It is a specific traffic. SQL, Botnet and HTTP traffic also have lower precision values because a part of their traffic is falsely classified as ''Reaction'' as shown in the confusion matrix (see Fig.
Specification-based IDS: Results Specification-based IDS is developed to check the 13 identified anomalies. This checking is performed on the MES database which is compliant with the ISA-95 standard according to the set of rules detailed in Table  5. Depending on whether the rules are followed or not, the IDS detects if the system is working normally or if it is faulty. Specification-based IDS gives the possibility to check some production orders for a quick checking or an indepth analysis for the entire database. The anomalies are recorded in a logs report to be used by Decision Making System (DMS) for a final decision making. After testing all the anomalies and checking all the controls, specification-based IDS has detected all the played scenarios with a high accuracy in a short amount of time.
This paper presents a design methodology for a low-power, low-chip-area, and highresolution successive approximations register (SAR) analog-to-digital converter (ADC). The proposed method includes a segmented capacitive DAC (C-DAC) to reduce the power consumption and the total area. An embedded self-calibration algorithm based on a set of trimming capacitors was applied alongside a dynamic element matching (DEM) procedure to control the inherent linearity issues caused by the process mismatch. The SAR ADC and each additional algorithm were modeled in MATLAB to show their efficiency. Finally, a simple methodology was developed to allow for the fast estimation of signal-to-noise ratios (SNRs) without any FFT calculation.
After the calibration, the tuning capacitor Cc was determined through Equation  (7). Ideally, good matching means that the LSB segment presents the same contribution as the unit capacitor Cu in the MSB segment. However, if the bridge capacitor is oversized, the contribution of the LSB segment is greater than that of the unit capacitor. The progressive tuning of Cc can then be used to re-optimize matching in the circuit. This trimming process was automated in our design following the algorithm explained in reference  [22]. The simplicity of this trimming method, along with the low surface that is consumed, is a major advantage compared to some other digital calibration methods. 𝐶 = 2 -1 • 𝐶 -2 • 𝐶 (6) 𝐶 = 𝑉 < 𝑖 >• 𝐶 (7) The calibration process was developed with a MATLAB model. The segmented DAC was assumed to be almost ideal; then, an additional random mismatch coefficient was applied to the segmentation capacitor alone.
The simplicity of this trimming method, along with the low surface that is consumed, is a major advantage compared to some other digital calibration methods. The calibration process was developed with a MATLAB model. The segmented DAC was assumed to be almost ideal; then, an additional random mismatch coefficient was applied to the segmentation capacitor alone. According to Monte Carlo simulations, the random coefficient follows a Gaussian distribution with a process-dependent standard deviation. The efficiency of the trimming can be observed through the strong reduction in integral non-linearity (INL) after calibration (Figure  7 ). The INL after calibration is equal to 1 LSB (INL of ideal ADC), and the mismatch of the bridge capacitor is fully balanced.
Noise Estimation Usually, the SNR is calculated from the FFT measurement over several sinus periods. The SNR can be easily measured when the circuit is produced and tested. Unfortunately, when considering only simulation results, the data quantity needed for FFT calculation presents a critical issue. The following point can develop a faster SNR estimation methodology. A second method consists of summing the various noise power contributions independently and then estimating the overall converter performance. The main limitation that was examined derived from the comparator input's referred noise n comp , the capacitive array's kT/C thermal noise n th and noise from the reference buffers n re f P and n re f N (for the purposes of this study, the contribution of the power supply to the noise was not examined).
The conversion of a sine wave and FFT calculation regarding our MATLAB model provided an SNR of 82. 98 dB, which corresponds to a total noise of about 0. 822 LSB. The second method, using Equation (  9 ), allowed a mathematical estimation of about 0. 849 LSB. Finally, Figure  14  displays the simulated output code dispersion for a 600. 03 mV constant input. The standard deviation of the output code dispersion was about 0. 83 LSB, whereas, considering Equation (9), n tot was about 0. 85 LSB.
Conclusions In this paper, we propose a methodology for a high-resolution differential SAR ADC based on a segmented C-DAC. The method we present combines an automated mismatch calibration technique based on a trimming capacitor and a DEM algorithm that is applicable on thermometer-coded C-DAC to achieve a high resolution and compensate for mismatch errors while, at the same time, reducing the chip area. In addition, a MATLAB model was presented to illustrate the efficiency of the proposed algorithms. Thus, embedding these two algorithms simultaneously allows for a reduction in the worst-case INL from 28 LSB to 6 LSB when a mismatch of 2% is applied to each unit capacitor. Secondly, a simple method for estimating the noise is presented. It is based on two fast simulations, owing to a mathematical equation applied to each noise source or the consideration of the output codes' dispersion after a limited amount of simulation data.
These two methods were in good agreement with the usual FFT method when applied to a MATLAB model. These methods make it possible to quickly estimate the SNR for an ADC during the design simulation phase without FFT processing, which, for a high-resolution converter, would require a significant amount of data. Finally, Table  2  presents the performance of SAR ADCs published in ISSCC and VLSI between 2017 and 2022. This represents so far the state of the art as described in  [34]. In comparison to our ADC simulation results, the table firstly highlights the low power consumption results where we expect a total power dissipation of 30µW, including all contributing sources (DAC, comparators, switches, reference voltage, digital control, etc. ). Secondly, from the SNR point of view, we listed the SNR as expected by front-end simulation using the method explained in Section 4.
We investigate the conditions for alignment in Dirac Gaugino models with minimal matter content. This leads to several scenarios, including an aligned Dirac Gaugino NMSSM that allows a light singlet scalar. We then investigate the compatibility of minimal Dirac Gaugino models with an enhanced W boson mass, using a new precise computation of the quantum corrections included in the code SARAH 4. 15. 0. Contents 1 Introduction.
In this work we introduce a similarly precise computation in the package SARAH-4. 15. 0 and use it to examine the compatibility of our aligned DGNMSSM, along with four other scenarios -the "MSSM without μ term," the MDGSSM, the aligned MDGSSM and the general DGN-MSSM -with the new measurement of the W boson mass, or a naive world average value of M world average W = 80,411 ± 15 MeV. This work is organised as follows. In Sect. 2 we summarise the essential details of the class of Dirac Gaugino models, including the vacuum minimisation conditions and mass matrices. The conditions for alignment are reviewed and a comparison is made with the cases of the MSSM and NMSSM. We also introduce the different variants we shall consider: the MSSM without μ-term; the MDGSSM, the DGNMSSM and the aligned DGNMSSM.
Conclusions We have shown that an aligned Dirac Gaugino NMSSM is possible and compatible with current collider constraints; it can even lead to relatively light singlet scalars that may be of interest to future searches (although would be rather difficult to find directly as they are difficult to produce). Such a model favours a W boson mass compatible with or just above the SM prediction. We also showed how two different Dirac Gaugino scenarios can easily be compatible with an enhanced W boson mass, including a precise computation of the quantum corrections for the first time, which are now incorporated automatically in the package SARAH. We also used this computation to add more nails to the coffin of the "MSSM without μ term. " We have been conservative in our application of collider constraints and concluded that the MDGSSM models would typically contain underdense dark matter densities.
Acknowledgements The work of W. K. is supported by the  Contrat Doctoral Spécifique Normalien (CDSN) of Ecole Normale Supérieure-PSL. K. B. , P. S. and M. D. G. acknowledge support from the grant "HiggsAutomator" of the  Agence Nationale de la Recherche (ANR)  ( ANR-15-CE31-0002 ). M. D. G. also acknowledges support from the  ANR  via grant "DMwithLLPatLHC", ( ANR-21-CE31-0013 ). M. D. G. thanks  Werner Porod  and  Hannah Day  for interesting discussions. W. K. thanks  Pierre Fayet  for illuminating discussions during the early stages of this work.
Venant Kirchhoff, and adding additional more realistic models is straightforward. All of these fabrication material models differ only in their large-strain behaviors, which we argue does not significantly impact the behavior of flexible microstructures: deformations of flexible metamaterials feature primarily bending deformations that do not highly strain the base material, keeping strain magnitudes within the regime that a simple neo-Hookean model is reasonably accurate. Simulations and shape derivative calculations are implemented using our custom quadratic triangle finite element simulation code developed in C++. We use SciPy's nonlinear least squares solver  [Virtanen et al. 2020 ] for (6). Our implementation is fully parallelized using TBB apart from collision area calculation, yielding multifold speedups for individual material design tasks on a multi-core system.
Validation Methodology: Isotropic Hooke's Laws We systematically test our pipeline on the task of designing as-linearas-possible isotropic materials. This is a challenging test case that can be attacked comprehensively and one for which performance is easy to visualize and interpret. The resulting material catalog also has direct applications for multi-scale design problems, e. g. , for compliant mechanisms or soft robots. We first ran the pipeline of  [Panetta et al. 2017]  to generate linearly elastic designs and determine a feasible set of target Hooke's laws. That code optimizes designs on a regular grid in the (log(𝐸), 𝜈) plane. For each feasible grid point, the linearly elastic design tool produces multiple designs with different topologies and shape parameters, and we use these both as the baseline for the comparisons  of Section 7.
Preliminary analysis and scale of investigation The analyses performed in this work were based mainly on digital terrain models (DTM) and postprocessing in a GIS environment. The available datasets of the catchment were a 1 m resolution LiDAR-derived DTM from the 2008 flight, and a 1 m resolution UAV-derived DTM of the sub-catchment area performed in July 2018. The LiDAR DTM is characterized by a point cloud mean density of 1. 28 point/m 2 with an average point spacing of 0. 9 m (Ufficio Sistemi Informativi -Servizio autorizzazioni e valutazioni ambientali 2009). The second DTM was generated from an RPAS-derived point cloud. The point number was about 67 million points with a mean density of about 400 points/m 2. The first step focuses on choosing the most appropriate scale (pixel resolution) to produce the analyses  (Hengl 2006).
A detrended Index of Connectivity (DetIC) was introduced by a non-linear model fitting of IC values. In this way, it has been possible to compute a detrended IC without the influence of distance to the outlet. The upslope component has been filtered to discard the effect induced by the natural increase of drainage area from source to the outlet to highlight better the flow-spreading dynamic automatically computed within the IC framework. Thus, a detrended upslope component (DupDet) production allows changes induced by flow spreading in the selected flow path to be better discerned. The extraction of each variable value into a text file was performed using a data sampling algorithm available ArcGIS Pro 2. 5. 1 (or QGIS). Finally, we produced an R script to manage and process all the extracted values in the study area, including the computation of the detrended IC.
To validate the prediction of the 2008 analysis, a longitudinal analysis was also conducted on the 2018 DTM adding the information on the reach-averaged erosion or deposition depth ΔZ. The analysis aimed at checking (i) if connectivity/disconnectivity patterns discovered in the 2008 analysis were correlated to the erosion/deposition pattern which would mean that IC is indeed correlated to the actual sediment cascade dynamics, and also if (ii) these patterns were still present in the 2018 landforms and eventually enhanced or attenuated. The analysis of the 2018 dataset also allows an understanding of how the succession of debris flows impacted IC factors. The output is reported in Fig. 5 , highlighting changes in relevant areas (Fig. 5c-5d ). Most deposition occurred in Domain C (Fig. 5i ), i.
In addition, drops in both IC and DupDet are more evident in the 2018 dataset when compared to the 2008 analysis. Drops in these two factors indicate an increased disconnection of these areas from the sub-catchment outlet. Another observed phenomenon comparing the two periods is a thalweg avulsion that changed the flow with earlier conjunction with the left-hand watercourse. This is observable by a prior increase of drainage area in domain C (Fig. 5b ), as in the 2008 morphology (Fig. 4b ). The same is also observed in the classical IC maps (Fig. 3a-3b ), where the thalweg shifted to the left. Table  2  shows how the roughness index (RI) and derived weighting factor (W) did not change significantly in the analysed period (same behaviour since W is directly derived from RI). Fig. 6  reports the density plots for both variables (calculated over all domains) to confirm this observation, highlighting only small and not significant differences.
Summarising all the results, the integrated combination of DoD, IC and the longitudinal analysis resulted effective to explain the geomorphic evolution of the sub-catchment after a sequence of debris-flow events and (ii) to exploit hidden information with an alternative point of view. The open source version of the used R script can be found in  (Torresani and Piton 2022)  which has then been integrated in the R tool "R_IC" developed by  (Baggio et al. 2022; Martini et al. 2022 , with the source code available at-https://github. com/Tomm Bagg/R_IC/releases/tag/R_IC_v1. 0).
The drops in IC observed in 2008 and increased in 2018 help understand where debris flow starts depositing and that this trend seems to be aggravating. It was also possible to predict channel evolution from the 2008 longitudinal analysis by comparing the results of the 2018 dataset analysis. The analysis of the 2018 dataset and later field visits confirmed the lack of connectivity in fan areas observed in the 2008 dataset. As the disconnectivity tendencies were confirmed, it is possible to expect more deposition and autogenic avulsion in the fan area, potentially reducing the volume to the downstream grazing land during moderate magnitude events. Of course, this tendency could be overturned by extreme debris flow events or alterations in the magnitude-frequency relationship due to climate change.
Survey instrument A pilot survey was created by the coordinating investigating team (EKL, HS, CS, and JF) in order to address different decision-making steps in the work-up, management, and follow-up of individuals with suspected or confirmed phyllodes tumors of the breast. The survey was tested among twelve Swedish surgeons and oncologists for comprehension and clinical relevance, and was revised after completion. The collaborators from each participating country were then invited to further review the survey to ensure international applicability. The final version of the survey was created, collected and managed in REDCap (Research Electronic Data Capture tool)  [24, 25]  hosted at Karolinska Institutet. REDCap is a secure, web-based software platform designed to support data capture for research studies.
Statistical analysis Data were extracted from REDCap, and all data management and statistical analysis was performed in StataIC 13 (Stata-Corp, College Station, TX). Data was presented through descriptive analyses as frequencies, percentages and distributions. We performed pre-specified sub-analyses on all single-select multiple-choice items to see whether responses differed between more experienced and less experienced clinicians and/or by hospital type. For sub-analyses by clinician expertise, responses were divided into two categories based on number of cases of phyllodes that respondents had stated they had personally been involved in the management of in the last year (non-expert < 5 cases of benign, borderline, and/ or malignant phyllodes tumors, versus expert ≥ 5 cases). For sub-analyses by hospital type, responses from respondents who stated they work at a university hospital were compared against responses from respondents who work at nonuniversity hospitals (regional hospital or at an unspecified hospital type).
Our reconstruction, iPrub22, adheres to current convention standards and quality criteria, incorporating updated functional annotations, orthology searches with different GSMN templates, data from previous reconstructions, and manual curation steps targeting primary and specialised metabolites. With a MEMOTE score of 74% and a metabolic coverage of 45%, iPrub22 includes 5,192 unique metabolites interconnected by 5,919 reactions, of which 5,033 are supported by at least one genomic sequence. Of the metabolites present in iPrub22, 13% are categorised as belonging to specialised metabolism. While our high-quality GSMN provides a valuable resource for investigating known phenotypes expressed in P. rubens, our analysis identifies bottlenecks related, in particular, to the definition of what is a specialised metabolite, which requires consensus within the scientific community.
Additionally, particular attention was paid to specialised metabolism. Thus, the provided model could be compatible with future research on NPs pathways regulation, which could furnish insight into cryptic pathways. Thus, the iPrub22 model, composed of 5,919 reactions and 5,192 unique metabolites, combines data from previous reconstructions, an updated functional annotation, a homology search with various phylogenetically close and distant organisms, and manual curation based mainly on literature. Furthermore, our reconstruction provides the necessary framework for investigating the growth of P. rubens and exploring the production of its specialised metabolites under varying media conditions through constraint-based modelling.
rubens Wisconsin 54-1255. As the selected template networks were mostly reconstructed between 2010 and 2018 via the KEGG database  [29] , a mapping step on the MetaCyc database  [37]  was necessary for the interoperability between the OrthoFinder results and the subnetworks inference. The draft generation indicates that less than 50% of the detected and potentially informative reactions were lost during this mapping (S2. B Fig in the S1 File). Mapped reactions correspond to 46% of the total reactions for A. thaliana, 49% for A. nidulans, 49% for A. niger, 49% for P. rubens species complex, 49% for S. pombe and 46% for N. crassa. The best mapping rate (87%) is obtained with the S. japonica network, whose GSMN was reconstructed following a similar protocol to the one presented here and directly via the MetaCyc database  [37].