dataesr/scientific-paragraphs-categorization huggingface dataset:
Applied Techniques and Methods A detailed petrographic description was the first step of the study using optical microscopy (OpM) (Leica DM 6000, 20×, 10×, Wetzlar, Germany). This study was repeated after the SC CO 2 exposure. In order to obtain more detailed qualitative data about the rock petrography, Scanning Electron Microscopy was also applied (JEOL 6100 SEM, using W-Filament, acceleration voltage of 20 kV and Inca Energy-200 software (Inca Energy, Abingdon, UK)). By means of optical image analysis (OIA) the main parameters of optical porosity were quantified using ImageJ software on OpM images acquired by a Charge-Coupled Device (CCD) camera (ProgRes 5. 0, Jena, Germany). The chemical composition of the brine was determined by the combination of ion chromatography, inductively coupled plasma optical emission spectrometry (ICP-OES), pH and conductivity measurements, both before and after the experiment.
Similarly, the whole rock chemical composition was also measured twice (before and after SC CO 2 exposure) by XRF. These studies were developed in the facilities of the IGME and the University of Oviedo (UNIOVI). The mineral composition of the initial rock sample used in this study was determined from the XRF analyses performed at the IGME laboratory. To do so, we used Excel solver in order to determine the optimal mineral composition corresponding to the XRF analyses and the rock description. This mineral composition as well as the injected water solution were used as input for the mass balance calculation and the numerical modelling. The geochemical modelling was performed using the CrunchFlow code  [39]. The CrunchFlow code solves numerically the mass balance of solutes solving the reactive transport equation.
The best fit of the model to the experimental data (aqueous Ca, S, Mg, K, Si, Al concentrations) was performed by adjusting the values of the effective reactive surface areas of each mineral, which is a parameter that is highly difficult to evaluate and was treated here as a simple dummy fitting parameter to account for unmodelled mass transfer processes. Regarding the secondary minerals, the value of the initial surface area was set to 1000 m 2 /m 3 for initiating the precipitation. Preliminary simulations were done for determining the secondary minerals that are effectively precipitating among those given in the thermodynamic data base. The water inlet in the model is the same as the one used in the laboratory experiment. The calculated initial pH is 3. 32. All the equilibrium constants (log K at 25, 40 and 60 • C) and stoichiometric coefficients were taken from the EQ3/6 database  [48].
The described changes could be illustrated by the following simplified conceptual model (Figure  6a-d ). The CO 2 -rich brine reacts with the sample producing chemical and physical changes on the external surface of the carbonate blocks. These effects are a result of partial carbonate dissolution affecting calcite grains and carbonate cement. In order to validate the obtained results and the conceptual model, mass balance calculation and numerical modelling was applied. Using the initial mineral composition of the rock sample determined by the XRF measurement and the water chemistry, we evaluated the quantity of dissolved and precipitated minerals and identified the dissolution and precipitation reactions that occurred during the 970 h of CO2 rich water exposure. The numerical results are in agreement with the XRF data and water chemistry analysis.
It is important to take into account that the results obtained in this research must be In order to validate the obtained results and the conceptual model, mass balance calculation and numerical modelling was applied. Using the initial mineral composition of the rock sample determined by the XRF measurement and the water chemistry, we evaluated the quantity of dissolved and precipitated minerals and identified the dissolution and precipitation reactions that occurred during the 970 h of CO 2 rich water exposure. The numerical results are in agreement with the XRF data and water chemistry analysis. Both indicate calcite and dolomite dissolution coupled with secondary gypsum and illite precipitation. The described study is limited to two specific situations within the extremely complex system of CO 2 storage in geological structures.
e. , laboratory test). The study of a significantly larger number of samples together with the modelling data following the presented approach would make it possible to gain a clearer view of the precipitation and dissolution processes at a larger (i. e. , reservoir) scale. This would also provide important information about the potential of the study area for geological CO 2 storage. Flow-through experiments might also be an alternative option to characterize the chemical reaction during CO 2 injection taking into account the transport effect. Finally, numerical modelling at reservoir scale integrating these experimental results could predict the potential CO 2 sequestration in this reservoir. The real importance of the encountered mineralogical and porosity changes is in their influence on the functionality and integrity of a storage site.
Acknowledgments: The authors would like to thank the funding provided through the  ALGECO 2 -IRMC Project  ( Instituto Geológico y Minero de España :  2294-2013 ),  CO 2 -Pore Project  ( Plan Nacional de España :  2009-10934 ,  FEDER-UE). Thanks are due to  José Luis García Lobón ,  Roberto Martínez Orio  and  Pilar Mata  for providing help in sample collection and application of SEM techniques. We also would like to thank the editorial office for the editorial handling and two anonymous reviewers for their constructive comments and corrections.
The performance of the PWSL-EWMA approach is successfully tested on traffic data from the three lane highway portion of the Interstate 210 (I-210) highway of the west of California and the four lane highway portion of the State Route 60 (SR60) highway from the east of California, provided by the Caltrans Performance Measurement System (PeMS). Results show the ability of the PWSL-EWMA approach to monitor vehicle traffic, confirming the promising application of this statistical tool to the supervision of traffic flow congestion.
We apply the proposed PWSL-EWMA approach to real traffic data from a section of the Interstate 210 (I-210) freeway in California and a portion of the SR60 highway wich is a four-lane highway in east of California, provided by the Caltrans Performance Measurement System (PeMS). Results show the ability of the new PWSL-EWMA scheme to detect road traffic congestion. The following section briefly reviews macroscopic traffic modeling and the improved piecewise macroscopic traffic model. Section 3 introduces the EWMA chart and its use in anomaly detection. Section 5 reviews the PWSLbased approach and how it can be merged with the EWMA chart for traffic congestion detection. In Section 6, the performances of the proposed methods are illustrated in a practical application. Finally, Section 8 concludes the paper.
Shewhart monitoring chart Anomalies that occurs in a monitored process are detected by checking whether the current measurements are statistically different from the measurements that are known a priori to be faultless (i. e. , measurements without anomalies). In a Shewhart chart, a sequence of samples (denoted by x i ) are plotted against time  (Montgomery, 2007). Upper where σ 0 is the standard deviation of the anomaly-free data computed when the process is running under healthy conditions. Whenever the most recently measured point or a consecutive sequence of points is outside the control limits, an abnormal condition is encountered and the source of the problem can be diagnosed.
Cumulative sum (CUSUM) charts The history data obtained before current observation contains useful information for anomaly detection, but, the Shewhart charts do not make use of such information at all. For this reason, they are effective only for detecting large anomalies. Unlike Shewhart chart, CUSUM chart corporate all the information from past samples in addition to current samples in the decision procedure. The CUSUM charts have a good capacity to detect small shifts in the process mean due to an extensive memory of the process  (Page, 1961). CUSUM chart monitors cumulative sums of the deviations of the sample rather than plotting a sample itself. The CUSUM statistic (s i ) is defined as the following  (Montgomery, 2007) : s t = n ∑ j=1 (x j -µ 0 ), (19) where t denotes the current time point, s t is the cumulative sum of all samples, including the most recent, and µ 0 is the targeted process mean.
, x n } are individual observations collected from a monitored process. The expression for the EWMA is  (Montgomery, 2007) : z t = λ x t + 1 -λ z t-1 , t = 0,. , n. (22) The starting value z 0 is usually set to be the mean of the anomaly-free data, µ 0. The forgetting parameter λ ∈ (0, 1] determines how fast EWMA forgets the data history. We can see that if λ is small, more weight is assigned to past observations and the chart is tuned to efficiently detect small changes in the process mean. On the other hand, if λ is large, more weight is assigned to the current observations, and the chart is more suitable for detecting large shifts  (Montgomery, 2007; Kadri et al. , 2016). When λ = 1, the EWMA is equal to the most recent observation, x t , and provides the same results as the Shewhart chart.
Then, the anomaly detection procedure is executed by using the reference PWSL model with EWMA chart in the online monitoring phase. The proposed PWSL-EWMA approach is schematically summarized as shown in Table  4 , which is schematically represented in Figure  5. Step Action 1. Given: • A training congestion-free data set that represents the normal traffic flow and a testing data set (possibly congested data), • The parameters of the EWMA control scheme: smoothing parameter λ and the control limit width L,.
Data calibration results Data calibration or fundamental diagram identification is a necessary step for each macroscopic model validation  (Gomes and Horowitz, 2009). This step provides the density-flow relationship or FD parameters (Figure  8 ) which are the free flow speed, the critical density, congestion wave speed, the maximum flow and the jam density. For data calibration, we exploit the abundance of raw VDS data, obtained with a sampling period of five minutes, provided by the Caltrans Performance Measurement System (PeMS) database for the highways segments described above (Figure  6  and Figure  7 ). Using data collected over a period of 98 days (equivalent to 28225 data points) guarantees that there are sufficient data points corresponding to congested traffic status (Figure  8 ).
Model validation The constructed PWSL model is fitted to the free-flow training dataset from three different cells from I-210 dataset, and the goodness of fit is shown in Figure  9(a-c ). Here, the performance of PWSL model is compared to the CTM. Figure  10  shows the observed and predicted traffic density using a CTM. From Figure  9 , we can see that the two time series for cell 01, cell 02 and cell 03 are well-adjusted by the constructed PWSL models. From Figures 9and 10, it can be seen a high prediction accuracy of the proposed PWSL model compared to CTM. In addition, to evaluate the performance of the constructed PWSL model, three numerical criteria were used: r 2 , the mean absolute percent error (MAPE) and the root mean square error (RMSE). These were calculated as follows: RMSE = ∑ n t=1 (y t -ŷt ) 2 n , ( 30 ) MAPE = 100 n ∑ t=1 ( |y i -ŷi | |y i | ) /n, (31) r 2 = 1 - ∑ n t=1 (y t -ŷt ) 2 ∑ n t=1 (y t -mean(Y )) 2 , ( 32 ) where y t is the measured values, ŷt is the corresponding predicted values by the PWSL model and n is the number of samples.
Tables  5  and 6  summarize the goodness of fit statistics obtained for the two models studied (PWSL and CTM) applied to two experimental datasets I-210 and SR60, respectively. The results in Tables  5  and 6  show that the PWSL models describe well the traffic density. It was also seen that PWSL models used in this study exhibited high r 2 values (above 0. 95), indicating overall good predictive quality. According to r 2 , MAPE and RMSE values the PWSL model provided better results compared to the CTM. This demonstrates that the constructed PWSL models can be used for modeling traffic density effectively.
Detection results After the PWSL model is built from the free-flow traffic dataset (i. e. , uncongested data), it can be used for monitoring traffic flow. Three case studies are presented here to assess the performance of the developed PWSL-EWMA monitoring approach. The traffic density data in the first case contains abrupt congestion, the data in the second case study is contaminated by intermittent congestion and the data for the third case study has gradual congestion. To quantify the efficiency of the developed approach, we use two metrics: the false alarm rate (FAR) and the missed detection rate (MDR)  (Harrou et al. , 2016). The FAR is the number of normal observations that are wrongly judged as faulty (false alarms) over the total number of anomaly-free data. The MDR is the number of anomalies that are wrongly classified as normal (missed detections) over the total number of anomalies.
25 to the testing dataset. The CUSUM chart is shown in Figure  17(b ), from which it can be seen that the CUSUM statistic violate clearly the control limit and thus the ability of this chart to detect this congestion, but it resulted in several false alarms (i. e. , FAR=52. 015%) and some missed detections (MDR=10. 5%). Indeed, after the end of the abnormal conditions the CUSUM chart takes much time to return back to the nominal state and thus resulting in large number of false alarms. From Figure  17 (d), it can be seen that the CTM-EWMA chart with λ = 0. 25 can detect the abrupt congestion but with several false alarms (ie. , FAR=40. 02%). On other hand, Figure  17 (c) shows that PWSL-EWMA correctly detect this congestion with few false alarms (ie. , FAR=3. 39%). It is well-known that filters have been widely used for reducing false alarms caused by noisy data or model errors.
Results of the four charts are demonstrated in Figure  19 (a-d). Figure  19 (a) shows that the Shewhart chart cannot correctly detect this fault (i. e. , MDR=77%). The Shewhart chart is insensitive to this fault because it is designed to detect relatively moderate and large anomaly, while the magnitude of congestion in this case is small. This is mainly due to the fact that the Shewhart chart uses only the observed data at a particular instant to make a decision about the process performance, ignoring past data. CUSUM and EWMA charts, incorporates information from the entire process history, rather than just the most recent observations, so that they are more sensitive to small changes than Shewhart chart. The CUSUM chart is shown in it can be seen that this chart is able to detect this traffic congestion but with several false alarms (i.
Gradual congestion The aim of the third case study is to assess the potential of the proposed PWSL-EWMA monitoring approach to detect gradual congestion of vehicle traffic on a highway. To do so, a slow increase with a slope of 0. 01 was added to the simulated test data starting at sample number 3000. Shewhart and CUSUM methods and CTM-EWMA approach by detecting all anomalies with a smaller number of false alarms. These results also show that applying the proposed approach on pre-filtered residuals slightly enhanced its detection ability by reducing a number of false alarms. Page 32 of 36.
g. the possible impulsive nature of velocity records). Nevertheless, the exploitation of record databases is not always straight-forward and the interpretation of results can be difficult. In this paper, borehole recordings were processed to characterize the seismic site effects observed during the M W 6. 6-M JMA 6. 8 Niigata-Ken Chu ¯etsu-Oki (NCO) earthquake, occurred on July, 16th 2007 (10:13 UCT) off the coast of the Niigata prefecture (Japan). The NCO earthquake affected an area of approximately 100 km of radius along the coastal line of South-West Niigata prefecture, till a maximum depth of 17 km  (Pavlenko and Irikura 2012). The seismic sequence caused the shut-down of the Kashiwazaki-Kariwa Nuclear Power Plant (KK-NPP), located close to the epicenter location (see Fig. 1 ). The plant consists of seven generators (see map in Fig.
Records considered in this study Due to technical problems occurred during the NCO earthquake main shock, records from two free-field down-hole arrays and most structural arrays were lost, with the exception of the peak values (listed in Table  1 ). A single 4-device free-field array at the Service Hall (KSH) was recovered and provided a complete dataset, that was considered in this work to detect the variation with depth of earthquake ground motion during the main shock. In addition, the dataset recorded at Unit 5 (KK5) was considered, which, although having lost the main shock, provided a significant dataset during the aftershock sequence. Location of KSH and KK5 arrays is depicted in Fig. 2. The distance between the boreholes is relatively large (approximately 1 km) and the geology and topography quite complicated.
Acknowledgements This work, within the  SINAPS@ project , benefited from  French  state funding managed by the  National Research Agency  under program  RNSR Future Investments bearing Reference  No. ANR-11-RSNR-0022-04. The research reported in this paper has been supported in part by the  SEISM Paris Saclay Research Institute. TEPCO  ( Tokyo Electric Power Company ) is gratefully appreciated for disseminating the valuable strong motion records during the 2007  Niigataken Chuetsu-Oki  earthquake.
Grain sizes at the film surface were determined directly from SEM film surface images using SEM image analysis software ImageJ. [19] Because of the highly irregular shape of the grains, their sizes are presented according to three definitions: "bounding rectangle" gives size of the smallest rectangle the shape fits in, the "line intersection method" (LIM) consisting of counting the number of grains along straight lines of known length across the image and "Ferret's diameter", which gives the largest distance between two points on the shape boundary, giving thus, when compared to the values obtained by the two previously mentioned methods, an indication of grain shapes. The film thickness variation along the length of the sample was determined by SEM from the film cross-section. The thickness was measured at the extreme points of the substrate for all films assuming a linear variation in between.
SEM images of these film surfaces are shown in Figure  5a  and b , for the DC and HPPMS deposition methods, respectively. For T S ¼ 350 C, the clustering on the film surface was too fine to quantitatively estimate grain sizes, but it can be assumed that the sizes of the grains observed for T S ¼ 350 C are comparable to the crystallite sizes obtained from the Scherrer equation. Values for the crystallite sizes and granulation as a function of T S are shown in Table  2. Since the grains are of a very irregular shape, determining their size remains qualitative. To compensate for this, clusters were measured in three different ways, as described in the experimental section. The relatively large standard deviation indicates that variation in cluster sizes for the same sample is significant. From the data shown in Table  2 , for T S ¼ 350 C, the clustering at the film surface was too fine to be possible to objectively estimate grain sizes.
The modulating frequency f m is equal to 4 Hz. The modulation index β takes different values according to Table  2. To summarise the main changes from previous paragraph, the three-phase signals are perfectly balanced and they have the same noise levels on each phase. Thus, for each simulation cycle 5 three-phase signals have been generated with the only differences being the random noise (though the noise statistical characteristics have not changed) and the modulation index β according to Table  2. H S0 S1 S2 S3 β 0 0 0. 002 0. 004 0. 006 The first dataset (denoted H in Table  2 ) was used to estimate the reference PSD used for the fault indicator. The other 4 sets (denoted starting with the letter S) were used to compute the mechanical fault indicator. The parameters for the mechanical fault indicator are: only 1 measurement used for the estimation of the reference PSD and the considered bandwidth B = 4±0.
12 Hz, with 0. 12 Hz being the spectral resolution. Since the three-phase currents are perfectly balanced, only the current signal on phase 1 is used for comparison with i + (t). As previously mentioned, 1000 such simulations have been performed. For each simulation cycle the mechanical fault indicators' values have been saved and will be used for the statistical analysis. In order to build the Receiver Operating Characteristic (ROC) curves  [26]  for the indicators the detection thresholds have to be set. The considered thresholds belong to the interval [0 4], with an increment of 0. 01. The value of the indicator surpassing the threshold marks a fault detection. For each threshold for each value of the modulation index (denoted starting with S in Table  2 ) the true positive rate was computed as the proportion of detection out of the whole datasets for which the modulation was introduced.
Experimental set-up The proposed condition monitoring method has been tested on experimental data coming from a test-bench purposely designed and developed in the CETIM laboratory (Senlis, France)  [27]. The bench emulates the structure and behaviour of a wind turbine, with an electrical motor replacing the wind turbine rotor, followed by a low speed shaft with the main bearing, a gearbox, high speed shaft and three-phase electrical generator (three-phase induction machine SEW Eurodrive DRL160MC4 used as a generator with a maximum power of 10kW), as seen in Fig. 6a. The operating conditions are determined by the speed of the low speed shaft. It should be noted that the test bench generator is not connected to the main grid, thus its resulting stator frequency is not imposed by the grid but by the rotating speed of the high speed shaft.
6, 7 By identification, we mean here estimating the parameters of a presupposed constitutive model. In some cases, making explicitly a constitutive model and formalizing a constitutive law turn out to be extremely difficult. This is a case, for example, for ductile tearing, as the stress/strain and triaxiality levels occurring at crack tip cannot be reached in homogeneous stress/strain test configurations. At this point, the advantage of this framework (FE DIC combined with FE simulation) reveals also to be a weakness, as it requires the constitutive relation to be explicit. To circumvent the difficulty of formulating a constitutive equations, manifold learning and dimensionality reduction 8 are good candidates. The idea is to use databases of admissible material state (at least stress and strain) points to extract manifold and then to perform multidimensional interpolation/regression for calculating internal forces in FE simulations.
The data-driven computational mechanics framework recently proposed in Kirchdoerfer and Ortiz  9  goes one step further. Instead of minimizing the energy under the constraint that the constitutive relation is satisfied, the authors propose in this work to minimize the distance between the computed material states and those given by a database under the constraint of equilibrium. These non-parametric approaches of the mechanics of materials have a great potential to circumvent the limitations of parametric approaches concerning the formulation of constitutive laws. However, if the database uses results from conventional experiments using homogeneous stress/strain configurations, these promising methodologies would suffer from the same limitations concerning the lack of intense and/or complex loadings.