esa-sceva/satcom-chunk-collection huggingface dataset:
# Reallocating Charging Loads of Electric Vehicles in Distribution Networks
## 2 The Control Algorithm
### Control Algorithm  
Aggregated EV charging loads are considered to be the decision variables. These aggregated EV charging loads are optimally reallocated across the three phases to maintain the network constraints within their limits. The flowchart of the proposed control algorithm (see Figure 1) can be illustrated step by step as follows. Decision variables are evaluated using the generalized reduced gradient \"GRG Nonlinear\" solver (Frontline Systems, Incline Village, NV, USA) with Microsoft Excel (Microsoft Office 365, Microsoft Corporation, Reading, UK, 2013). However, this solver is limited in terms of assigning the number of decision variables and constraints. Therefore, these decision variables are evaluated with hourly time resolution (_i.e._, \(3\ \times\ 24\) values) across the three phases during a day. Accordingly, the evaluated decision variables are interpolated into a lookup table of (\(3\ \times\ 1440\) elements) using MATLAB (R2015a, MathWorks, Cambridge, UK, 2015), as shown in the next step. This lookup table represents the hosting capacity of the network components (_i.e._, \(\left[F\left(t\right)_{\text{a,b,c}}\right]_{3\times\ 1440}\)) during a day with one-minute time resolution across the three phases. The fluctuations of unbalanced residential loads and unbalanced EV charging loads are considered based on real daily load profiles from CLNR trials. The hosting capacity for each minute (_i.e._, _Optimized_\(\left[F\left(t\right)_{\text{a,b,c}}\right]_{3\times 1}\)) is then compared to the _Requested_\(\left[F\left(t\right)_{\text{a,b,c}}\right]_{3\times 1}\), as shown in Figure 1. The _Requested_\(\left[F\left(t\right)_{\text{a,b,c}}\right]_{3\times 1}\) is directly calculated in this step using Equation (2) based on the charging power of each EV per minute per phase (\(P\left(t\right)_{\text{aEV}},P\left(t\right)_{\text{DEV}},P\left(t\right)_{ \text{cEV}}\)).  
Figure 1: The flow chart of the centralized control algorithm.  
If \(Optimized\left[F\left(t\right)_{a,b,c}\right]_{3\times 1}\geq Requested\left[F\left(t \right)_{a,b,c}\right]_{3\times 1}\) is true, all requested EV charging loads can occur at this minute considering randomized delays (_i.e._, less than 60 s) among them. Otherwise, a number of EVs with the amount of power of \(\left(Requested\left[F\left(t\right)_{a,b,c}\right]_{3\times 1}-Optimized\left[F\left(t \right)_{a,b,c}\right]_{3\times 1}\right)\) will be reallocated to charge at other time steps, whenever the predefined constraints can be maintained. Then, EV users can accordingly reschedule their smart charging. If the aggregated EV charging power is not achieved during that day, the algorithms allow the remaining EVs to complete their charging demand on the next day depending on Boolean signals from \"AND\" gate (see Figure 1).
# Effects of Water-Soaking Height on the Deformation and Failure of Coal in Uniaxial Compression
## 2 Materials and Test Procedure
### Water Soaking Experiment  
Five groups of coal specimens with different water-soaking heights (0 mm, 25 mm, 50 mm, 75 mm, 100 mm) were prepared for the tests, which are listed in Table 2 and shown in Figure 4.  
Figure 3: Acoustic emission sensor and strain rosette layout.  
Figure 2: Testing system schematic.  
Before the start of the water-soaking test, all the coal specimens were placed in the oven, maintained at 105 \({}^{\circ}\)C temperature [26; 27], and dried for 24 h. Then the weights of the dried specimens were measured. Subsequently, the water soaking test was carried out. The first group of coal specimens were placed in a glass bucket without water.  
The other four plexiglass barrels were filled with the same volume of distilled water, then the coal specimens were placed in the plexiglass barrels according to their respective groups. The upper end of the coal specimens were clamped on the plexiglass lid by the holster, and the height of the holster was adjusted to achieve the soaking heights of the coal specimens, which were 25 mm, 50 mm, 75 mm, 100 mm, respectively. The coal specimens were taken out every 2 h during the first eight hours as the amount of water soaked during that period was relatively large. Then the surfaces of the coal specimens were wiped with wet towels so that the surface did not drip, and then weighed with an electronic scale. After 8 h, the weights of the coal specimens were measured every 12 h according to the above operation. The water-soaking test was continued until the mass change of the coal specimen was less than 0.01 g.
# Fast Frequency Response of a Doubly-Fed Induction Generator for System Inertia Support
## 5 System Layout  
Dynamic simulations are conducted to investigate the performances of the proposed STFS strategy from a DFIG, as shown in Figure 5. The test power system comprises six synchronous generators, an aggregated DFIG-based wind power plant with 100-MW installed capacity, and motor and static loads.  
The detailed parameters of synchronous generators are shown in Table 1. All synchronous generators are assumed to be the IEEEG1 steam governor model with tandem-compound, single-reheat  
Figure 4: Power-rotor speed trajectory of the proposed method: (**a**) power-rotor speed trajectory of the proposed method; (**b**) \(\Delta p(\omega_{C})\).  
Figure 5: Model system with a DFIG.  
(type B) [19]. IEEEX1 is selected for voltage control. In addition, Figure 6 displays the IEEEG1 steam governor model and Table 2 presents its coefficients. Note that the secondary frequency control is not considered, therefore the frequency will not return to the nominal value.  
The stable \(\omega_{r}\) operating range of the DFIG is from 0.7 p.u. to 1.25 p.u. The inertia constant for the DFIG is 5.0 s. The settings of power limit and torque limit are 1.1 p.u. and 0.88 p.u., respectively. Furthermore, the setting of the rate limit of power is 0.45 p.u./s [20]. The detailed parameters of the DFIG are presented in Table 3.
# Intelligent Fault Diagnosis of Rotating Machinery Using Hierarchical Lempel-Ziv Complexity
## 3 Proposed Fault Diagnosis Framework  
### Support Vector Machine  
In this paper, support vector machine (SVM) [30] was taken as the classifier to accomplish the pattern identification. SVM is a typical supervised learning method for recognition and regression analysis. The kernel function plays a significant role in the SVM classifier, which is not only important to reduce the computation cost but also useful in transforming the features into high dimension so as to construct the hyper-plane [28; 31].  
Three different used kernel functions of SVM consist of linear kernel function, polynomial kernel functions, and radial basis function (RBF) kernel function, which can be expressed as follows:  
(1) Linear kernel  
\[K(x,x_{i})=<x\cdot x_{i}> \tag{12}\]  
(2) Polynomial kernel  
\[K(x,x_{i})=(<x\cdot x_{i}>+c)^{d} \tag{13}\]  
(3) Radial basis function (RBF) kernel  
\[K(x,x_{i})=\exp\left\{-\gamma\|x-x_{i}\|^{2}\right\} \tag{14}\]  
where \(\gamma>0\), and \(\gamma\) is the kernel parameter.  
Figure 8: The simulated bearing signal with outer race fault: (**a**) the waveform, (**b**) its corresponding fast Fourier transform (FFT) spectrum, (**c**) the envelope spectrum.  
Among these functions, the RBF function is most widely used due to its good performance, in which there are two parameters--penalty parameter \(C\) and kernel parameter \(\gamma\)--which require optimization. Here, the grid search method was utilized to optimize the two parameters [28]. It is worth mentioning that the dataset was randomly split into training and testing subsets through a k-fold cross-validation (CV). Every k subset takes turns to perform as an independent test set for the rest of the \((k-1)\) training subsets. The test sets being independent provides the necessary compensation for CV so as to enhance the consistency in the output. In this paper, the fivefold CV was adopted to adjust the model parameters. The coarse grid points were firstly selected through the exponential growing sequence \(2^{-l}\) to \(2^{+l}\), where \(l\) is an integer. The optimal parameters for \(C\) and \(\gamma\) are assumed as \((j,k)\), and the values are further optimized by using finer grid, in which the respective search area is \(2^{j\
eq f}\) and \(2^{k\
eq f}\). In this paper, we set the range of f as \(-0.75\leq\)\(f\)\(\leq\) 0.75, with an interval of 0.25.  
It is worth noticing that the one-against-one approach is applied to solve the multi-class classification problem in this paper [32]. To deal with the \(k\) number classes, the \(k(k-1)\)/2 SVM models are required for classification. For the present work, LIBSVM software package was used to deal with the multiple-fault diagnosis of rotating machinery.
## 2 Differential Pulse Spectrum Analysis and 3D Forward Modeling
### Validation of Magnetic Flux Density for Differential Pulse  
In this study, a differential pulse with a pulse width of 20 ms is used to validate the 3D forward modeling method. The resistivity of the air layer is set to 10\({}^{6}\)\(\Omega\cdot\)m, and the resistivity of the homogeneous half-space is 200 \(\Omega\cdot\)m. The grounded wire has a length of 1 km and is placed along the \(x\)-axis, with the coordinates of the two endpoints at (\(-\)500 m, 0 m, 0 m) and (500 m, 0 m, 0 m), respectively. The current intensity is 10 A. The coordinates of the four measurement points are (100 m, 100 m, 0 m), (200 m, 200 m, 0 m), (500 m, 500 m, 0 m), and (1 km, 1 km, 0 m). Since the current analysis primarily focuses on the electromagnetic data during the OFF-Time stage, the sampling time ranges from 1 \(\upmu\)s to 100 ms after the current is turned off, with a total of 51 time channels logarithmically spaced.  
Figure 4a shows the \(z\)-component of the magnetic flux density, where the dashed line represents the 1D results, and the circles represent the 3D results. The two are in good agreement. Figure 4b presents the relative error curve, with the overall numerical error being less than 5%, which validates the accuracy of the 3D numerical method used in this study.
###### Abstract  
In this work, we aimed to establish a stable and accurate procedure with which to perform feature selection in datasets with a much higher number of predictors than individuals, as in genome-wide association studies. Due to the instability of feature selection where many potential predictors are measured, a variable selection procedure is proposed that combines several replications of shrinkage regression models. A weighted formulation is used to define the final predictors. The procedure is applied for the investigation of single nucleotide polymorphism (SNP) predictors associated with Alzheimer's disease in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Furthermore, the two following data scenarios are investigated: one that solely considers the set of SNPs, and another with the covariates of age, sex, educational level, and \(\varepsilon\)4 allele of the Apolipoprotein E (_APOE4_) genotype. The SNP _rs2075650_ and the _APOE4_ genotype are provided as risk factors for Alzheimer's disease, which is in line with the literature, and another four new SNPs are indicated, thus cultivating new hypotheses for in vivo analyses. These experiments demonstrate the potential of the new method for stable feature selection.  
penalized regression; [PERSON]'s information criterion; high-dimensional data; stability; overall weighted coefficients; Alzheimer's disease; SNP
# Neighborhood Aggregation Collaborative Filtering Based on Knowledge Graph
## 2 Related Work  
### Recommendation Based on Knowledge Graph  
The existing recommendation schemes based on KGs can be divided into three categories.  
1. Embedding-based method [6; 7; 19]. It uses knowledge graph embedding (KGE) algorithm [9] to pre-process the recommended entity in the KG and input the embedding vector into a  
Figure 1: High-order link of entities in the movie knowledge graph.  
recommendation framework. For example, DKN [19] combines entity embedding and title embedding as different initial features for news recommendation. CKE [7] combines knowledge graph embedding, text and item images into collaborative filter for unified recommendation. SHINE [6] designed a deep auto-encoder for embedding emotional network, social network and personal data network. However, knowledge graph embedding is usually used to extract the language association of each entity in the knowledge graph, and is more suitable for applications such as link prediction rather than the recommendation system.
2. Path-based method [20; 21; 22]. In the knowledge graph of recommendation, various entities form complex connections, and multiple paths between them can be explored. These paths provide additional information for recommendation, especially on the interpretability. For example, PER [20] uses potential features of meta-path in the KG to represent the connectivity between users and items along different relationships. RKGE [21] and KPRN [22] introduce RNN to model multiple links of the user to items in the KG. The path-based approach makes it easy to take advantage of the connective features of KG. But these methods rely on manually set meta-paths and do not solve the cold-start problem very well. This is not conducive to practical application.
3. Graph-based method [18; 23; 24]. Both the graph-based and path-based method are modeled by knowledge graph structure. The difference is that the graph-based method is not limited to the specific connection between entities, but regards KG as a heterogeneous network centered on a specific user or item. This method propagates from the center entity to extract the characteristics of the corresponding entity. RippleNet [23] proposed the idea of user interest diffusion, which spreads the users' interest characteristics by various entities of recorded interactions. KGCN [18] combines GCN with KG to extract embedded features of recommended item. Our method is also an example based on knowledge graph structure. But unlike the above algorithm, we embed the user into the KG and personalize the aggregation of user's characteristics.
## 4 Performance Evaluation
### Detection Performance
#### 4.2.2 Detection Performance
Meanwhile, the results show that it takes less detection time with a smaller \(\theta_{AS}\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \((\theta_{AS},\theta_{S})B_{\textit{ccl}}\) & **20 Mbps** & **10 Mbps** & **5 Mbps** & **2 Mbps** \\ \hline \((2,3)\) & 1.00 & 1.00 & 1.00 & 0.99 \\ \((2,4)\) & 1.00 & 1.00 & 1.00 & 0.47 \\ \((2,5)\) & 1.00 & 1.00 & 1.00 & 0.03 \\ \hline \((3,4)\) & 1.00 & 1.00 & 0.99 & 0.80 \\ \((3,5)\) & 1.00 & 1.00 & 0.98 & 0.28 \\ \((3,6)\) & 1.00 & 1.00 & 0.93 & 0.03 \\ \hline \((4,5)\) & 0.87 & 0.86 & 0.11 & 0.01 \\ \((4,6)\) & 0.84 & 0.84 & 0.07 & 0.00 \\ \((4,7)\) & 0.63 & 0.72 & 0.02 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Detection performance for the TCP protocol. Figure 10: Impact of selfish intensity on the performance of well-behaving nodes. Similarly, the impact of the _second alarm threshold_\(\theta_{S}\) can be seen in Figure 13. The figure compares the detection time for different values of \(\theta_{S}\) with the given \(\theta_{AS}=3\). We can see that the detection time increases proportional to \(\theta_{S}\). However, there is a tradeoff between the false alarm ratio and the detection time according to the choice of \(\theta_{S}\). For example, in case of the temporal behavior of the detection filter in Figure 9c, the use of a small value of \(\theta_{S}\) can cause false alarms (if \(\theta_{S}\) is set to be less than \(\theta_{AS}+1\), it would issue several false alarms.) although it can reduce the detection time unless the hidden node exists. Thus, it is recommended to use a value of \(\theta_{S}\) larger than \(\theta_{AS}+1\), in order to avoid false alarms, although it might take more time to detect.
## 3. Results and Discussion
### CO\({}_{2}\) Emissions Derived from Japanese Music and Videos Streaming in 2019  
Based on the above-mentioned results, another estimation revealed that CO\({}_{2}\) emissions from online music and video streaming in 2019 was approximately 922 thousand t-CO\({}_{2}\). The ratio of the CO\({}_{2}\) emissions of videos and music accounted for 87.7% and 12.3%, respectively.  
The validity of this result was confirmed in this study. CO\({}_{2}\) emissions from domestic commercial power generation in 2019 were 432 million t-CO\({}_{2}\), with the above result corresponding to 0.23% of this estimate [46]. According to the Center for Low Carbon Social Strategy of the Japanese Science and Technology Agency [6], the domestic power consumption for the data centers is equivalent to 1% of the annual domestic power consumption. Furthermore, business and consumer usages accounted for 18% and 82%, respectively, of global traffic in 2017 [47]. Online music and video streaming accounted for 32.1% of the domestic digital content market in 2018 [48]. Based on this information, 82% of communication volume used in the domestic data centers was assumed to be consumer usage. The data traffic for consumer usage was also assumed to be mainly from digital content. Based on these assumptions, the domestic power consumption derived from online music and video streaming in the data centers was estimated to be 0.26%. Therefore, 0.23%, as the ratio of CO\({}_{2}\) emissions, might be reasonable if approximately 93% of domestic CO\({}_{2}\) emissions are derived from energy generation [46].
# Parameter Identification Method of a Double-Layer Supercapacitor by Using a Real Voltage Source
###### Abstract  
This article presents a new method for obtaining the electrical parameters of a supercapacitor (SC) modeled as a constant resistor in series with a capacitance that linearly varies with its internal voltage. This model provides sufficiently accurate results when the SC is subjected to rapid, short-term charging and discharging. In other methods described in the literature, the parameters are obtained by charging or discharging the SC with a constant current source of high value. In this study, the electrical parameters are calculated by charging or discharging the cell with a real constant voltage source (RVS) or by discharging the SC through a known and constant resistance. The calculation procedure requires the measurement of the cell voltage as a function of time. Two alternative estimation methods have been employed: the three-point method (3 PM) and the least squares method (LSM). A series of experimental tests were conducted on cells from various manufacturers, with capacitances ranging from 150 F to 600 F. The laboratory measurements were then compared with the results obtained from theoretical models incorporating the parameters obtained for the variable capacity model. The results demonstrated that this straightforward procedure is capable of accurately characterizing the main branch of any SC.  
electrical parameters estimation; model validation; supercapacitors; voltage source +
Footnote †: journal: Applied Sciences
# A Real-Time Network Traffic Classifier for Online Applications Using Machine Learning
## 3 System Design
### Architecture  
As illustrated in Figure 1, the run-time system for NetScrapper is organized with parts executing on the network and application layers. Layer 1 shows that live traffic flows are extracted from the Network Interface Card (NIC), stored, and preprocessed in real time. The traffic stream is then fed into CICFlowMeter [12], an open-source tool that extracts the time-related features from the network flow stream to train the ML models. CICFlowMeter can generate these features from bidirectional flows, purifying attributes from an existing feature set, and control the duration of flow timeout for both TCP and UDP protocols.  
Layer 2 describes the machine learning models used in NetScrapper, including the KNN, RF, and ANN models. It also shows the MI pipeline, which runs underneath the three ML models. The pipeline automates the ML workflow by enabling live traffic flows to be transformed and correlated into each ML model to achieve the desired classification outputs. It also works as a coordination interface between the Flask GUI and the ML models. The ML pipeline transformed our ML workflows into independent, reusable, modular components that can then be pipelined together to build a more efficient and simplified real-time traffic classifier. Layer 3 illustrates the web-based interface of NetScrapper running on the cloud server. We used Python Flask Framework to develop a user-friendly web application that enables users (shown in layer 4) to interact with the system.  
Figure 1: System architecture.
## 3 Results and Discussion  
### General Summary of the Results  
Using a quasi-random Sobol' sampling, 5000 training samples are generated on the 242 uncertain parameters for coal-based operation and 243 uncertain parameters for biomass-based operation, while the other parameters are consider fixed. These samples are simulated with the Thermoflex(r) models. In the course of the study, a relevant observation emerged: not all output parameters depend (equally) on the same input parameters. This insight highlights the need to conduct specific analyses for each individual output, given the varying sensitivities to underlying input parameters. Therefore, SPCE surrogate models for each output parameter have been constructed using the deterministic response of the Thermoflex(r) simulations. The summary of the comparison between coal 100% load and biomass CHP 80% load of the resulting means and standard deviations can be found in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Output** & & **Coal** & **Biomass** & **Unit** \\ \hline Plant gross power & mean & 703 & 382 & MW \\  & std & 17.2 & 15.9 & MW \\ \hline Plant gross electric efficiency & mean & 45.4 & 30.8 & \% \\  & std & 1.1 & 1.3 & \% \\ \hline Plant CHP efficiency & mean & 41.5 & 65.3 & \% \\  & std & 1.4 & 1.5 & \% \\ \hline Live steam pressure & mean & 235 & 185 & bar \\  & std & 14.2 & 7.9 & bar \\ \hline Live steam flow rate & mean & 543 & 411 & kg/s \\  & std & 53.1 & 21.7 & kg/s \\ \hline Adiabatic flame temperature & mean & 2026 & 1839 & \({}^{\circ}\)C \\  & std & 327 & 279 & \({}^{\circ}\)C \\ \hline FEGT & mean & 1276 & 1132 & \({}^{\circ}\)C \\  & std & 73.6 & 52.8 & \({}^{\circ}\)C \\ \hline Effective radiation temperature & mean & 1655 & 1489 & \({}^{\circ}\)C \\  & std & 201 & 166 & \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the resulting means and standard deviations of the simulations of the power plant before and after the conversion.  
Figure 4: The evolution of the quantified mean and standard deviation of the coal-fired plant gross power with respect to the size of the design of experiment.  
There are 18 input parameters with Sobol' index (impact) above 1% for at least one of the output parameters for both scenarios, and 17 of the 18 impactful input parameters are the same for both fuels (see Table 4). The order of importance of the input parameters per output parameter are shown in Table 5 for coal and in Table 6 for biomass and will be discussed in Section 3.2.  
The fuel parameter 'weight percent of S' has a significant impact only in the case of coal, and the parameter 'high-pressure preheater 3: condensing zone heat transfer coefficient correction factor' has a significant impact only in the case of biomass.