esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Case Study 2: Scattering from a Cable Bundle in Air
(**a**) Map of the amplitude of the scattered field, normalized to that of the incident field. The axes \(x\) and \(y\) are normalized to the wavelength value. (**b**) Maximum value of the scattered field, normalized to amplitude of the incident field, versus the number of mesh nodes: The DtN and PML solutions are compared to the reference one.
## 5 Performance Evaluation
### Stability Assessment  
We now evaluate the stability of DDNC. Since DDNC's verification protocol is based on filtering out abnormalities among 'normal clones', the entire SN system can be subverted if the abnormal nodes occupy a 51% majority simultaneously. Therefore, the shorter the time it takes to execute the verification protocol, the more overhead it may have, but the more robust it is. In order to conduct experiments to evaluate the stability of DDNC, we measure how much time it took to restore after applying an intentional forging for arbitrary SNs in a single domain of the Service layer. Each experiment was conducted to obtain the time taken to blacklist all forged SNs with a DDNC set to 10-minute periods. Then we randomly and deliberately forged SNs among normal SNs.  
Table 4 shows the average time taken for the experiments which were performed 10 times respectively. Intuitively, the more nodes are forged, the longer it takes to cleanse the entire system. Not only it has more forgeries to find, but it takes more time for each node because larger ratio of forgery makes more difficult to determine whether it is normal or forged.  
As can be seen from Figure 8, the case of (a) _9 forgeries in 20 SNs_ is mostly blacklisted in the first period except the last or second node. On the other hand, in case of (b) _3 forgeries in 20 SNs_ which forged less number of SNs, DDNC blacklists all forged nodes within first period in every experiments. This result is consistent with when comparing (b) and (c). Also in (c) _3 forgeries in 10 SNs_, several experiments were observed that took more than one period to find the last forgery.
# Current Status and Future Developments of Micromegas Detectors for Physics and Applications
## 8 Micromegas for UV Detection  
Micromegas can be used as a UV photo-detector. Similar to the detection of neutrons, a photon/charged particle converter needs to be coupled to a Micromegas. The most common method of converting photons to electrons is the photoelectric effect. Photons with sufficient energy interact with electrons bounded to an atomic orbital or a lattice band. Photons can transfer enough energy to the electrons and remove them from the bound state. Those free electrons are further amplified in the Micromegas with the same mechanism as any other primary electron.
## 4 Results from Case Studies
### Validation of Synchrophasor Applications Using Dynamic Grid Simulations  
#### 4.3.1 Results from Synchrophasor Disturbance Detection  
The _disturbance detection module_ is evaluated for different contingencies using dynamic grid simulations from the _HVSim_ dataset as described in Section 4.1. A fixed hyperparameter set has been chosen according to Table 9.  
The _disturbance detection module_ is applied to assess features from 13 different PMU stations. For each PMU measurement channel, the last 50 observations (corresponds to 2 s) are used to generate the features in time and frequency domain. The resulting feature vector is compared with the features from the last N samples to compute the anomaly scores (see also Section 3.4). Some exemplary results are shown in Figures 17 and 18 for two different contingencies. It can be seen  
Figure 16: Outlier samples (**left**) and non-outlier samples (**right**).  
Figure 15: Total ranks for time-domain (**top**) and frequency-domain (**bottom**) features.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline S transform max. frequency & 10 Hz \\ \# of S transform frequency bins & 50 \\ \# of samples for feature assessment \(N\) & 20 \\ \# of time steps per sample & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Hyperparameters used for the _disturbance detection module_.  
occur mainly within the first PMU samples, which represents the beginning of a disturbance and usually causes high signal variations and fluctuations. During this time period, there is a transition from steady to transient system states. For subsequent PMU samples, the anomaly scores decrease depending on the measurement and the disturbance type. Especially in case of the line outage, high anomaly scores can be observed for voltage magnitudes over the whole timespan, while the frequency values almost remain unchanged. For a better understanding, some exemplary signal patterns of low and high anomaly scores are given in Figure 19. Each frequency, voltage magnitude or voltage angle signal represents a single PMU station.  
Figure 17: Anomaly scores for selected PMU samples from a DKW generator outage at station 1D1.  
Figure 18: Anomaly scores for selected PMU samples from a line outage at L32.  
Clear differences can be observed between patterns with low and high anomaly score. At low anomaly scores below or equal 1.0, only small signal variations occur over time for frequency, voltage magnitude and voltage angle signals. At high anomaly scores between 3.0 and 4.0, high frequency oscillations are present as well as significant changes of voltage magnitude and angle signals over time. The necessary calculations are performed in Python(r) with additional packages for wavelet decomposition [67] and statistical analysis [68].
## 5 Case Study
### Performance Analysis
#### 5.2.4 Analysis of Additional Time Ratios of Passengers  
Figure 9 illustrates the distributions of additional time ratios of passengers with different direct travel times in the OSP and the ISP. It shows a trend that the additional time ratio would decrease with an increase in direct travel time. This is because when the direct travel time of an O-D pair is short, passengers mainly adjust their departure and wait at intermediate stops in their journeys and the additional time would account for a large proportion of the travel time of this O-D pair, while with an increase in the travel distance, the proportion of direct travel time gradually increases, and the additional time ratio would decrease.  
In Figure 9, the additional time ratios in the OSP show a descent trend compared to those in the ISP, especially in the zones marked by the squares. Table 7 shows the average additional time ratios in different ranges of direct travel time in the two stop plans. It also presents an approximate descent trend like Figure 9. This indicates that the proposed approach is beneficial to improving the travel efficiency and service level for passengers, especially for medium-distance and long-distance passengers.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Line Plan** & \multicolumn{2}{c}{**OSP**} & \multicolumn{2}{c}{**ISP**} & \multicolumn{2}{c}{**PSP**} \\ \hline
**Indexes** & **PTN** & **SF** & **SR** & **SF** & **SR** & **SF** & **SR** \\ \hline Overall & 6701 & 3300 & 0.4925 & 3323 & 0.4959 & 3342 & 0.4987 \\ PS & 1180 & 1180 & 1 & 1180 & 1 & 1172 & 0.9932 \\ DS & 4153 & 1799 & 0.4332 & 1835 & 0.4418 & 1861 & 0.4481 \\ CS & 1368 & 321 & 0.2346 & 308 & 0.2251 & 309 & 0.2259 \\Figure 8: Illustration of the distributions of train stop ratios on each segment.
## Materials and Methods
### Prediction Models  
We applied three machine learning algorithms (i.e., RF, BRT, and SVM models) to predict STN. These models have a strong ability to model complex nonlinear relationships between soil properties and environmental variables. We optimized the parameters in these models through the grid search approach using the 'caret' package in the R software.  
First introduced by [PERSON] et al. [38], the SVM model originated from statistical learning theory and was applied to classification or regression. Based on the kernel function, the SVM model projects the input data onto the new hyperspace, optimally dividing all the data into different classes [39]. The four commonly used kernel functions are as follows: Linear, sigmoid, polynomial, and radial basis function (RBF). The selection of kernel functions can affect the prediction accuracy of the SVM model. The RBF kernel performs best at capturing nonparametric features [40] and has been successfully applied to soil mapping studies [5]. In this study, RBF was selected as the kernel model of the SVM model.  
As a nonparametric method, the RF model is an ensemble machine learning algorithm used to perform classification and regression. During model training, the RF model generates a large number of random trees that are combined into a single prediction [41]. A unique bootstrap sample from the original training data was used to build each tree in the forest [42]. The samples in the RF method are divided into \"in-bag\" samples and \"out-of-bag (OOB)\" samples, and the latter are used to estimate errors [43].  
Developed by [PERSON] et al. [44], the BRT model is a machine learning technique that combines the advantages of boosting and regression trees. The regression tree is a model based on a decision tree that analyzes the variation of the response variables of a set of predictor variables [45]. As a machine learning technique similar to model averaging [46], boosting is a forward and stage-wise procedure that uses an iterative approach to develop the final model, gradually adding the tree to the model [47]. The BRT model relies on a stochastic gradient boosting procedure to improve model prediction accuracy and prevent data overfitting [48].
# A Predictive Model of Chlorophyll a in Western Lake Erie Based on Artificial Neural Network
###### Abstract  
The reoccurrence of algal blooms in western Lake Erie (WLE) since the mid-1990s, under increased system stress from climate change and excessive nutrients, has shown the need for developing management tools to predict water quality. In this study, process-based model GLM-AED (General Lake Model-Aquatic Ecosystem Dynamics) and statistical model ANN (artificial neural network) were developed with meteorological forcing derived from surface buoys, airports, and land-based stations and historical monitoring nutrients, to predict water quality in WLE from 2002 to 2015. GLM-AED was calibrated with observed water temperature and chlorophyll a (Chl-a) from 2002 to 2015. For ANN, during the training period (2002-2010), the inputs included meteorological forcing and nutrient concentrations, and the target was Chl-a simulated by calibrated GLM-AED due to the lack of continuously daily measured Chl-a concentrations. During the testing period (2011-2015), the predicted Chl-a concentrations were compared with the observations. The results showed that the ANN model has higher accuracy with lower Chl-a RMSE and MAE values than GLM-AED during 2011 and 2015. Lastly, we applied the established ANN model to predict the future 10-year water quality of WLE, which showed that the probability of adverse health effects would be moderate, so more intense water resources management should be implemented.  
ch chlorophyll a; western lake erie; artificial neural network Article  
A Predictive Model of Chlorophyll a in Western Lake Erie Based on Artificial Based on Artificial Neural Network. April. 2021, 11, 6529. [[https://doi.org/10.3390/app11146529](https://doi.org/10.3390/app11146529)]([https://doi.org/10.3390/app11146529](https://doi.org/10.3390/app11146529))
# Estimation of Mental Distress from Photoplethysmography
## 2 Materials and Methods
### Statistical Analysis  
Shaphiro-Wilks and Levene tests have proved that distributions are normal and homoscedastic for all features studied. Consequently, the results are expressed in terms of mean \(\pm\) standard deviation for all samples belonging to a same group. The statistical differences between both groups, calm and distress, are assessed by a \(t\)-Student test. A value of statistical significance \(\rho<0.05\) has been considered as significant.  
Moreover, a ten-fold stratified cross-validation is used to assess the discriminant ability of each feature. This kind of cross-validation allows to obtain a highly reliable performance generalization of the metric under study [35]. Indeed, this approach makes use of all available data both for training and testing. This avoids the possibility of classification results to be highly dependent on the choice of a given training-test segmentation. Thus, the database is firstly partitioned into 10 equally sized folds, rearranging data to ensure that each fold is a good representative of the whole. Then, 10 training and validation iterations are performed, such that a fold of the data is held out for test, whereas the other ones are used for learning within each iteration. A receiver operating characteristic (ROC) curve is used to obtain the optimal discriminant threshold between calmness and distress for each learning set. The ROC curve is created by plotting true positive (TP) rate against false positive (FP) rate at various threshold settings. Here, TP rate (or sensitivity) is considered as the percentage of distress condition correctly classified. On the other hand, FP rate (or 1-specificity) corresponds to the rate of calm condition identified improperly. The optimal threshold is selected as the value which provides the highest accuracy, i.e., highest number of conditions correctly classified. Finally, global accuracy is obtained by averaging this procedure 5 times.  
A decision tree (DT) classifier is used in order to assess the possible relationships among the different temporal, morphological and frequency features. This methodology is chosen due to its easy implementation and low computation burden when addressing a binary classification problem. Indeed, DT is based in consecutive if-else decisions. Regarding the DT configuration, each split is performed after considering the best optimization criterion, based on the Gini diversity index. Moreover, some rules are programmed to prevent an uncontrolled three overgrowth. Thus, the growth of every tree is always stopped when any node only contains samples from the same group (pure node) or less than 20% of all samples.
## 4 Experiments
### Results  
We evaluated the three syllable-based multi-POSMORPH annotation methods using the proposed end-to-end model illustrated in Figure 3. The evaluation results are listed in Table 6. As the process became more complex in the higher stages of the annotation, the performance in terms of the eojeol F1-score reflected this complexity by increasing in the order of restoration (99.41%), segmentation (98.02%), and tagging (96.12%).  
The Sejong corpus is generally used to evaluate KMAP tasks because it is publicly available and large. However, there are some annotation errors, such as inconsistent annotations that could degrade the performance of the system. Some researchers used a subset of the corpus and/or modified it for the evaluation, which results in Sejong corpus evaluation sets with various sizes. In contrast, we used the full set of the corpus for the evaluation, with minor corrections or deletions, as described in Section 4.1.  
Table 7 presents the tagging performance of previous models on the various evaluation sets. Because the models were evaluated on different subsets of the corpus with some corrected data, the performance comparison is not rigorous but approximate. Most of the models [10; 11; 19] that used comparatively smaller test datasets with screened or corrected data achieved better results than the others. However, among the models evaluated on test datasets of more than 40K sentences, our proposed model performed best at the eojeol level, with the exception of [PERSON] et al.'s eojeol-level result, which was obtained on correctly annotated test data. If we were to use correctly annotated test data in the Sejong corpus, we would expect an increase in the performance. This is discussed in Section 5.4.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{1}{c}{**Syllable**} & \multicolumn{3}{c}{**Morpheme**} & \multicolumn{1}{c}{**Eojeol**} \\ \cline{2-6}  & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline Restoration & 99.83 \(\pm\) 0.01 & - & - & - & 99.41 \(\pm\) 0.02 \\ Segmentation & 99.28 \(\pm\) 0.02 & 98.94 & 98.82 & 98.88 \(\pm\) 0.01 & 98.02 \(\pm\) 0.02 \\ Tagging & 98.41 \(\pm\) 0.02 & 97.78 & 97.70 & 97.74 \(\pm\) 0.02 & 96.12 \(\pm\) 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation result of three syllable-based annotation methods.
# An Innovative Adaptive-Dimension Accordion Honeycomb for Circumferential Deformation
## 5 Results and Discussion
### Geometric Nonlinear Stiffness of \(\{\mathbf{5,2}\}\) Honeycomb
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(\varepsilon\)} & \multicolumn{2}{c}{\(k_{1}\)} & \multicolumn{2}{c}{\(k_{2}\)} & \multicolumn{2}{c}{\(k_{3}\)} & \multicolumn{2}{c}{\(k_{4}\)} & \multicolumn{2}{c}{\(k_{5}\)} \\ \cline{2-11}  & **ND** & **PD** & **ND** & **PD** & **ND** & **PD** & **ND** & **PD** & **ND** & **PD** \\ \hline
0.02 & 29,202.67 & 29,900.55 & 21,046.94 & 21,556.10 & 15,172.66 & 15,533.86 & 10,935.27 & 11,195.62 & 7881.10 & 8068.75 \\
0.04 & 28,881.40 & 30,285.28 & 20,818.27 & 21,821.82 & 15,004.90 & 15,728.35 & 10,814.36 & 11,335.82 & 7793.96 & 8169.80 \\
0.06 & 28,573.85 & 30,666.47 & 20,596.55 & 22,105.52 & 14,845.07 & 15,932.89 & 10,699.16 & 11,483.26 & 7710.92 & 8276.07 \\
0.08 & 28,280.71 & 31,080.36 & 20,385.22 & 22,404.05 & 14,692.75 & 16,148.14 & 10,589.37 & 11,638.43 & 7631.73 & 8387.98 \\
0.10 & 28,001.23 & 31,516.15 & 20,183.77 & 22,718.42 & 14,574.54 & 16,374.81 & 10,484.66 & 11,801.88 & 7556.01 & 8506.05 \\
0.12 & 27,734.72 & 31,975.38 & 19,917.67 & 23,049.71 & 14,409.06 & 16,613.73 & 10,384.69 & 11,974.27 & 7483.09 & 8631.17 \\
0.14 & 27,480.54 & 23,459.70 & 19,808.47 & 23,399.15 & 14,276.94 & 16,865.79 & 10,289.03 & 12,156.43 & 7415.91 & 8760.72 \\
0.16 & 27,238.10 & 32,970.96 & 19,633.72 & 23,768.09 & 14,150.81 & 17,132.04 & 10,197.06 & 12,349.42 & 7350.52 & 8898.96 \\
0.18 & 27,006.86 & 33,511.20 & 19,467.02 & 24,158.01 & 14,030.24 & 17,413.66 & 10,112.55 & 12,554.67 & 7288.17 & 9045.06 \\
0.20 & 26,786.29 & 34,082.72 & 19,307.95 & 24,570.62 & 13,914.76 & 17,712.07 & 10,030.04 & 12,764.59 & 7228.71 & 9199.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The nonlinear analysis stiffnesses of five unit cells vs. \(\varepsilon\).
# Robust Wireless Local Area Networks Based on Compressed Sensing
###### Abstract  
A wireless local area network (WLAN) is an important type of wireless network which connotes different wireless nodes in a local area network. Network traffic or data traffic in a WLAN is the amount of network packets moving across a wireless network from each wireless node to another wireless node, which provide the load of sampling in a wireless network. WLAN's network traffic is the main component for network traffic measurement, network traffic control, and simulation. In addition, traffic classification technique is an essential tool for improving the Quality of Service (QoS) in different wireless networks in the complex applications, such as local area networks, wireless local area networks, wireless personal area networks, wireless metropolitan area networks, and wide area networks. Network traffic classification is also an essential component in the products for QoS control in different wireless network systems and applications. Classifying network traffic in a WLAN allows one to see what kinds of traffic we have in each part of the network, organize the various kinds of network traffic in each path into different classes in each path, and generate network traffic matrix in order to identify and organize network traffic, which is an important key for improving the QoS feature. In this paper, a new architecture based on the following algorithms is presented for improving the QoS feature in a wireless local area network: (1) Real-Time Network Traffic Classification (RTNTC) algorithm for WLANs based on Compressed Sensing (CS); (2) Real-Time Network Traffic Monitoring (RTNTM) approach based on CS. This architecture enables continuous data acquisition and compression of WLAN's signals that are suitable for a variety of other wireless networking applications. At the transmitter side of each wireless node, an analog CS framework is applied at the sensing step before an analog to digital converter in order to generate the compressed version of the input signal. At the receiver side of the wireless node, a reconstruction algorithm is applied in order to reconstruct the original signals from the compressed signals with high probability and enough accuracy. The proposed architecture allows reducing Data Delay Probability (DDP) to 15%, Bit Error Rate (BER) to 14% at each wireless node, False Detection Rate (FDR) to 25%, and Packet Delay (PD) to 15%, which are good records for WLANs. The proposed architecture is increased Data Throughput (DT) to 22% and Signal to Noise (S/N) ratio to 17%, and 10% accuracy of wireless transmission. The proposed algorithm outperforms existing algorithms by achieving a good level of Quality of Service (QoS), which provides a good background for establishing high quality wireless local area networks.  
Keywords:real-time network traffic monitoring; compressed sensing; wireless local area networks; bit error rate; sampling rate; signal to noise ratio; real-time network traffic classification; false detection rate; packet delay; quality of service +
Footnote †: journal:
# The Evolution and Optimization Strategies of a PBFT Consensus Algorithm for Consortium Blockchains
## 3 An Introduction to Mainstream Consensus Algorithms
### The Mainstream Consensus Algorithms Used in Consortium Blockchains
#### 3.3.3 PBFT
The PBFT consensus algorithm lacks an effective reward and punishment mechanism, which hinders the ability to incentivize honest nodes to participate in the consensus process. Additionally, it lacks effective means to penalize malicious nodes, thus limiting the system's robustness when defending against attacks. 4. The PBFT algorithm requires a fixed number of consensus nodes during the consensus process, meaning that nodes cannot be dynamically added or removed within the consensus network. To address the shortcomings of the PBFT consensus algorithm, researchers both domestically and internationally have explored the limitations of traditional PBFT, primarily focusing on the following four aspects: hierarchical group consensus methods, the introduction of incentive and penalty mechanisms to reduce the number of consensus nodes, improvements to consensus through multiple leader nodes, and the optimization of the consensus algorithm based on optimistic mechanisms. Table 9 provides a summary of PBFT consensus optimization strategies.