dataesr/scientific-paragraphs-categorization huggingface dataset:
Including the CGMF prior via the Kalman filter smooths the evaluated ] p and further enhances second-and third-chance fission structures from 4. 5 to 6 MeV and 9-12 MeV, in agreement with the structures present in Marini data. Below 1 MeV, the evaluation (3) including Marini data and CGMF is systematically higher (on average 0. 5%) than ENDF/B-VIII. 0: Compared to evaluation (2), CGMF gives a smooth ] p close to the higher points of the second evaluation. This difference from ENDF/B-VIII. 0 is within the 1-σ evaluated uncertainties and the scatter of experimental data. While the evaluated uncertainties realistically assess the spread in evaluations, this difference will merit future continued studies given the importance of this observable. On the model side, these higher values could be caused by uncertainties in the energy dependence parametrizations, particularly in the mass distribution, Y(A| E inc ).
(n,f) cross section Three evaluations are discussed: 1) The 2018 standards evaluation (Std. 2018) that is in ENDF/B-VIII. 0 from 0. 04 to 20 MeV  [10] , (2) an evaluation building upon (1) from  [11]  differing in 239 Pu(n,f) cross section experimental covariances updated by templates of expected measurement uncertainties, and (3) evaluation (2) updated with NIFFTE TPC data. These evaluations differ by a maximum of 0. 5% until 14 MeV, as shown in the bottom of Figure  7. However, most of the previous data were measured with fission chambers, while NIFFTE TPC shape data were obtained with a time-projection chamber which allows accurate tracking of charged particles in three dimensions. Hence, this small difference up to 14 MeV in the shape of evaluations (  2 ) and (3) indicates that the TPC confirms the shape obtained from a bulk of data measured with fission chambers.
4 (Jezebel) by swapping them into ENDF/ B-VIII. 0 and comparing them to ENDF/B-VIII. 0 simulated results in Table  3. The simulated k eff values change distinctly: k eff decreases by 121 pcm if the new PFNS is used. This large change, largely driven by including CEA and Chi-Nu data, is counterbalanced by the new ] p guided by Marini experimental data and CGMF modeling that increases k eff by 139 pcm. The impact of the (n,f) cross section change is modest with 40 pcm as NIFFTE TPC shape data confirmed the general trend of ENDF/B-VIII. 0 data. The overall effect of updating all three observables is modest, -22 pcm, and well within the currently documented experimental uncertainties of 123 pcm; it decreases the bias slightly compared to ENDF/B-VIII. 0.
Pu ] p data set. The data confirmed evaluated results obtained from a bulk of past data using the ratio liquid scintillator technique from 1 to 4 MeV, further strengthening our trust in this technique. Marini data were able to resolve for the first time expected second and thirdchance fission structures that are now part of evaluated data. CGMF detailed fission modeling also predicts these structures. This fission event generator also links model parameters obtained by fitting to ] p to various fission quantities including yields as function of mass, the average total kinetic energy of fission fragment, and γ and neutron emission observables. It was shown that these predicted observables were mostly within the spread of experimental data, providing for the first time a validation of evaluated 239.
Data availability statement The evaluated data generated as part of this study can be found in https://www-nds. iaea. org/INDEN/ under Tab "Pu-239". The code GMAP is available on https://github. com/ IAEA-NDS/gmapy. The code CGMF is available on https:// github. com/lanl/CGMF. The code ARIADNE is currently under review for release at LANL. It cannot be made available before this review is successfully finalized per LANL rules. The code containing the exciton and Los Alamos model is a code by another LANL employee and cannot be made available by the authors per LANL rules. The MCNP code is distributed to its worldwide user community by the Radiation Safety Information Computational Center (RSICC), which is based at the Oak Ridge National Laboratory in Oak Ridge, Tennessee. Because the MCNP code is export controlled, the distribution through RSICC is governed by US laws and Department of Energy regulations.
In addition, the algorithm also provides a confidence index associated with the classification output, thus improving its reliability. This paper is organized as follows. Section 2 describes the problem statement and related works, and Section 3 presents the characteristics of the main voltage sag causes and the model of the industrial network used for data generation. Section 4 details the classification method. Section 5 is dedicated to the performance analysis of the proposal in terms of class separability, robustness to noise, and fundamental frequency variations. Finally, in Section 6, a minimum database size is obtained, the results on real data are presented, and the classification accuracy analysis is detailed. The main conclusions are given in Section 7.
presented a method for the detection and classification based on High Order Statistics (HOS) and Artificial Neural Networks (ANN). One of the main limitations of these approaches is their dependence on the choice of features, which should be done carefully to avoid the loss of relevant information in the process. In order to extract robust features, it is necessary to have a large and representative dataset for training, which can be a particularly limiting aspect, as voltage dips are disturbances of limited occurrence and therefore challenging to measure. Thus, building up an extensive training data set is a major obstacle to implementing these methods in real industrial applications. In addition, the transformations used in the pre-processing step and the retained features rarely take into account the physical properties of the events.
Thus, the generalisation capabilities of these algorithms are compromised, as it is difficult to provide guarantees on the behaviour of the selected features when applied to new data from the training data set. This is a well-known problem in machine learning, known as Domain Adaptation  [19] , defined as the ability to successfully apply an algorithm trained in a source domain to a different but related target domain. In our research area, this problem was highlighted by Bollen et al. in  [10]  when a model trained with a synthetic dataset was tested on measured data, and provided non-acceptable results. Classical machine learning algorithms only deal with scalar features in the examples mentioned above. This approach may be well suited to the analysis of steady-state disturbances. However, the time dependence of electrical waveforms is important for analysing short-duration disturbances such as voltage dips.
Indeed, these models have no feature extraction step as deep neural networks are designed to extract their own features during the training stage. Even if this can be an advantage when access to expert knowledge is not available, these approaches are highly data-driven, and their performance strongly depends on the characteristics of the training dataset (size, diversity etc. ). Although deep learning approaches are popular, there are other time series classification methods that are less dependent on the size of the training dataset. For instance, 1-Nearest Neighbor classifier with Dynamic Time Warping (1NN-DTW) is a recommended benchmark due to its simplicity and hardto-beat accuracy  [22]. However, one of the disadvantages of this method is the small number of neighbors involved in the classification, which may decrease its robustness.
Direct online motor startup The magnitude of the voltage sag caused by a motor startup depends on its power, torque, and total inertia. These parameters also determine the entire duration of the event and the sag as a consequence. It is generated by directly connecting the induction motor to the 400 V busbar of the industrial network (downstream). The generated voltage sags vary between 10% and 98% (residual voltage). Although a voltage sag is defined as the reduction of voltage RMS values lower than 90%, shallow voltage drops are also included in the dataset. Their identification can be more difficult than deep voltage sags since the variations on voltage and current can be very low.
CLASSIFICATION OF VOLTAGE SAG CAUSES The proposed classification method described in Figure  2  mainly relies on the pre-processing step. The voltage and The set of four-time series represents the voltage sag signature (four-dimensional signature). The signature of each of the seven defined classes can be visually identified and distinguished from the others. The pre-processing stage is based on the one briefly presented in  [28]. The main contributions on a new optimized and more robust distance-based classification strategy. For this purpose, a database containing the signatures of labelled events belonging to each class was constructed. The classification of a new event is based on the analysis of the distances between its signature and those in the database. A spatio-temporal alignment step is first performed between the new signature and those in the database.
(  14 ) The closer a new voltage sag signature q * is to the reference signatures r k of a given class C k of the database, the higher the confidence index NB-KDE will be. As expected, the number of reference signatures in the database influences the estimation of the likelihood function. An extensive and variate database will provide a reasonable estimate of the likelihood function by the KDE, thus, a more accurate NB-KDE confidence index. On the contrary, a reduced database will produce a poor likelihood function estimation, and the NB-KDE confidence index will not be considered as reliable.
p k (q * ) = ∏ j ≠k e d j (q * ) K ∑ i=1 ∏ j ≠i e d j (q * ) , k = 1, 2, … , K. ( 16 ) Figure  10  illustrates the distance to class and membership probability of a new voltage sag caused by an upstream symmetrical fault. Although an extensive and variate database would naturally improve the estimation of class distances d k (q * ), the relative distance-based index should be less sensitive to the size and diversity of the database than the NB-KDE index.
Synthetic dataset A synthetic dataset generated with the simulation model described in section 3 is built. The dataset consists of 700 voltage sags equally distributed in 7 classes (100 events per class). The defined classes are upstream balanced faults (A1), upstream unbalanced faults (A2), downstream balanced faults (B1), downstream unbalanced faults (B2), upstream transformer energizing (C1), downstream transformer energizing (C2), and downstream motor startup (D). The sampling frequency of the synthetic data is set at 12. 8 kHz to match the sampling frequency of the real data measurements. However, it can be noted that the sampling frequency could be reduced up to 400 Hz, while maintaining the main characteristics of the four-dimension signatures.
Class separability To verify the class separability capabilities of the proposed method, we analyze the characteristics of the entire synthetic dataset. We study the separability of two classes by analyzing their intra-class and inter-class distances. The intra-class distance distribution  k is defined as the ensemble of distances D(x k,i , x k, j ) of all the pairs of elements x k,i and x k, j belonging to the same class C k. The inter-class distance distribution  k-k ′ corresponds to the ensemble of distances D(x k,i , x k ′ ,i ) between all the pairs of elements x k,i and x k ′ ,i from two different classes C k and C ′ k , respectively. For this study, we use the Bhattacharyya coefficient (BC ), as defined in  (17). This coefficient which varies from 0 to 1, can be interpreted as the overlap between two distributions   (x) and   (x) defined in X.
Classification efficiency: Accuracy and robustness The standard metrics for classification algorithms that are used for evaluating the classification output performances of our algorithm are accuracy, recall, and F1-score  (18) , the latter being a good metric for summarizing the first two, mainly when applied to balanced datasets. A perfect classification is obtained with an F1-score equal to one. F 1 = 2 * precision * recall precision + recall. (18) However, the metrics mentioned above are not suitable for evaluating the confidence score associated with the classification output provided by the algorithm. A more appropriate metric for this task is Log-Loss. Globally, this metric penalizes outputs with a low confidence score. The Log-Loss definition is given in  (19) , where y = {0, 1} and p is the associated probability estimates with p = P (y = 1).
 log = -(ylog(p) + (1 -y)log(1 -p)). ( 19 ) Although raw Log-Loss values can be hard to interpret, lower values mean classification outputs with higher confidence. For instance, a perfect classifier would have a Log-Loss equal to 0. A random-guessing Log-Loss baseline score can be useful for interpreting this metric. The Log-Loss value for p = 0. 5 is  log = 0. 693. Any value higher than this baseline (represented as a red dashed line on the following figures) can be interpreted as worse than random guessing. We have also studied the robustness to noise of the proposed classifier. For this purpose, we performed two experiments with two databases. Database A is composed of the original 700 synthetic voltage sags from the synthetic dataset with no added noise. Database B comprises the original 700 synthetic voltage sags plus 700 events with additional white Gaussian noise at SNR=25 dB.
To evaluate the performance with increasing noise levels regarding databases A and B, we have created 16 test sets. Each one is composed of the original 700 synthetic voltage sags to which The NB-KDE index is less robust to variations between the database and the test set, as it rapidly degrades at noise levels higher than SNR=25 dB. We note that the extension of the database with noisy data (database B) slightly improves the performance of the NB-KDE index for SNR levels from 25 to 15 dB. However, beyond 15 dB, the NB-KDE index calculated with database B performs worse with database A, as its Log-Loss values increase. On the contrary, the addition of noisy data to the database does not seem to influence the RD index, as it is more stable regarding variations in the database. The global classification performance of our proposal is relatively robust in the standard range of noise levels from 40 to 20 dB.
Sensitivity to fundamental frequency variations To evaluate the sensitivity of the classification results regarding frequency variations around fundamental frequency (50 Hz), we proceed as before with two experiments based on two databases. Database A is composed of the original 700 synthetic voltage sags with a frequency of 50. 0 Hz, and database B is composed of the 700 synthetic voltage sags plus 210 events at 50. 25 Hz (910 events in total). In order to evaluate the performance with increasing levels of frequency variation, we build 6 test sets, each one composed of 210 synthetic voltage sags with a frequency of F = 50 Hz ± , where  = {0. 0, 0. 1, 0. 2, 0. 3, 0. 4, 0. 5} Hz. This range of frequency variations corresponds to the maximum frequency fluctuations (50 Hz ±1%) allowed by French and European regulation standards regarding the power supply at the distribution level for synchronous connection to an interconnected system  [46].
The results are plotted in Figure  13a. F1-score values are above 95% for the analyzed range of values for databases A and B. We note that the RD index slowly decreases as  increases but stays high near 90%, even for frequency variations of  = ±0. 5Hz. This is true for both databases. We note a significant improvement for the NB-KDE index when the extended database B is used, compared to database A. However, the RDindex is significantly more robust and stable than the NB-KDE index. Regarding Figure  13b , the Log-Loss values of the RD index remain stable despite frequency variations, and are significantly lower than the random-guess baseline for both datasets A and B. This is not the case for the Log-Loss values of the NBKDE index, which increase and perform worse than the random-guess baseline with only a ±0.
Comparison with benchmark method 1NN-DTW In this section, we compare the performance of the proposed method with the benchmark method 1NN-DTW presented in  [28]. One of the main limitations of 1NN-DTW is its sensitivity to outliers. Indeed, since a single closest neighbor is used for the classification, the presence of outliers or errors in the labeling of the training database can result in classification errors. To evaluate the sensitivity to outliers of both methods, we intro- duce labeling errors in the reference database using the synthetic dataset, ranging from 0 to 30%. The reference database comprises 60 events per class (420 in total) and the test set comprises 40 events per class (280 in total). The classification results in terms of F1-score are presented in Figure  14  for both methods.
We observe that the new method is more robust to outliers than the 1NN-DTW method. The F1-score for the benchmark method decreases rapidly to 0. 95 for 5% of outliers and 0. 67 for 30% of outliers. On the contrary, the proposal maintains an F1score of 1 even with 30% of outliers in the reference database, as the classification is performed taking into account the distance to an entire class instead of a single neighbor. Another significant advantage of the method presented in this work is the provision of confidence scores associated with the classification result, in contrast to 1NN-DTW, which does not provide this information.
VALIDATION USING INDUSTRIAL DATA In order to validate the efficiency of our proposal on real data, the performance on measurement data is presented in this section. The signature database is composed exclusively of The data consists of records from monitoring devices installed at three sites, each within a specific sector: metal equipment manufacturing, food processing, and chemical industries. The industrial sites under consideration are supplied from the 21 kV distribution network via a 21 kV/400 V Dyn11 transformer. The voltage drop detection algorithm is integrated into the monitoring device. The dataset consists of 385 measurements, representing 5 of the seven classes defined above, as described in Table  3. The analysis is performed under the scope of the confusion matrix, accuracy, and recall metrics.
Minimum database size setting In the literature, classification studies generally need a huge number of data to perform. Unfortunately in industrial prob- lems, the availability of data in different faulty conditions is not possible. For this reason, performing the classification with a reduced size is a key point in this area of research. In this section, we determine the minimum database size for synthetic data while maintaining good classification performances. We use a random permutation strategy of five balanced splits. The database is composed of a determined percentage of the synthetic dataset, and the test set is composed at each iteration of the entire experimental dataset. We note that the database contains data from 7 classes, and the test set contains events from the 5 available classes.
The results are illustrated in Figure  15. The F1-score reaches values higher than 95% even with very few samples per class in the database. However, the performance between the confidence indexes is significantly different. The RD-index is noticeably higher and more stable with the increasing number of samples. On the contrary, the NB-KDE index is close to 50% even with a database of maximal size. This poor performance is also reflected in the Log-Loss curve in Figure  15b  when compared to the red dashed baseline. These curves confirm the results obtained in the previous sections. The optimal size of the database is close to 20 samples per class, and the RD-index is the most reliable confidence index compared to the NB-KDE index.
Results The reference signature database is composed of 20 synthetic voltage sag events per class or 140 events in total for 7 classes. The synthetic dataset is randomly split into five balanced sets of 140 events; each set is used once as a database. The final results correspond to the average. The test set consists exclusively of experimental measurements. The obtained results are presented in Table  4. For three out of the five classes, the accuracy, recall, and F1-score are maximal. Only two events of class A2 are misclassified in class A1. The overall results of the classification are satisfactory. However, the mean values for the NB-KDE and RD confidence indexes are lower than those obtained with the synthetic data. This is especially true for the NB-KDE index, which is much lower for classes A1 and D.
Accuracy analysis In this section, we analyze the classification errors leading to the total accuracy of the classifier. Two voltage sags of the real dataset belonging to class A2 were misclassified in class A1, as illustrated in the confusion matrix in Figure  16. If we analyze the relative distance-based confidence index of all the voltage sags, we observe that only seven events out of 385 were classified with a confidence index lower than 60%, as shown in Figure  17. Among these events, two correspond to misclassification errors. The two events, classified in class A1 have an RD index of 46. 89% and 44. 11%, respectively. These values are significantly lower than the mean RD index for this class, close to 86%. In addition to this, the classification error appears between two classes that only differ on the balanced/unbalanced nature of the event.
CONCLUSION This paper has presented a method based on voltage and current waveforms analysis to classify the causes of voltage sags in LV industrial power networks. The proposal is based on four-dimension time series signatures, extracted after a pre-processing stage using STFT and Fortescue transform. A database composed of these signatures and a distance-based classification approach are used for the identification stage. The performance of the method has been analyzed in terms of class separability, classification efficiency (accuracy and robustness to noise), and sensitivity to fundamental frequency variations. The results have proved that the proposal is resilient regarding noise levels up to 15dB and fundamental frequency variations up to  = ±0. 5 Hz. Two confidence indexes, the NB-KDE and RD index, have been proposed and compared.
The RD index proved to be more robust and stable. The information provided by such an index increases the reliability on the classification process by alerting when classification results with low confidence scores are obtained while maintaining a high degree of automation in the analysis. Using a reference database composed entirely of synthetic data, we evaluated the algorithm with synthetic data at first, and finally with field data collected from three industrial sites representing five of the seven defined classes. In both cases, the classification metrics reach high values: 100% for synthetic data and higher than 99% for experimental ones. The main advantages of the proposed method are (1) the reduced amount of data necessary for building the database and (2) its generalization capabilities.