esa-sceva/satcom-chunk-collection huggingface dataset:
# Classic Discrete Control Technique and 3D-SVPWM Applied to a Dual Unified Power Quality Conditioner
## 2 UPQC in Dual Topology
### Series Active Power Filter (Series APF)
From the state-space model, the current loop transfer function \(Gi(s)\), which relates the output current of the inverter \(I_{\text{cs\_p}}(s)\) with the duty cycle of the circuit breakers \(D(s)\), a new expression is obtained as shown in Equation (7):  
\[Gi(s)=\frac{I_{\text{cs\_p}}(s)}{D(s)}=V_{B}*\frac{s^{2}k_{1}+sk_{2}+k_{3}}{s^{3 }k_{4}+s^{2}k_{5}+sk_{6}+k_{7}}, \tag{7}\]  
where \(k_{1}-k_{7}\) are terms that represent the equivalent resistances and inductances of the circuit and they are defined follows:  
\[k_{1}=L_{\text{eq}_{1}}L_{m}\] \[k_{2}=R_{\text{eq}_{3}}R_{m}\] \[k_{3}=R_{\text{eq}_{4}}L_{m}+R_{m}L_{m}+R_{m}L_{\text{eq}_{5}}\] \[k_{4}=L_{\text{eq}_{1}}L_{\text{eq}_{3}}L_{m}\] \[k_{5}=R_{p}L_{\text{eq}_{1}}L_{m}+L_{\text{eq}_{1}}L_{\text{eq} _{2}}R_{m}+L_{\text{eq}_{1}}R_{m}L_{\text{eq}_{3}}L_{m}+R_{m}L_{m}L_{\text{eq }_{5}}\] \[k_{6}=R_{p}L_{\text{eq}_{1}}R_{m}+L_{m}R_{\text{eq}_{1}}+R_{p} R_{m}L_{m}+L_{\text{eq}_{1}}R_{\text{eq}_{3}}R_{m}+R_{m}L_{m}R_{\text{eq}_{5}}\] \[k_{7}=R_{p}R_{\text{eq}_{3},s}R_{m}.\]  
Herein, \(R_{m}\) is the magnetizing resistance, \(L_{m}\) is the magnetizing inductance, \(R_{\text{eq}_{1},s}\) is the equivalent resistance in the secondary of the transformer, \(L_{\text{eq}_{1},s}\) is the equivalent inductance in the secondary of the transformer, \(L_{\text{eq}_{1},p}\) is the equivalent inductance in the primary of the transformer, and \(R_{p}\) is the primary scattering resistors of the transformer. To represent the equivalent voltage in the DC bus (\(\overline{V}_{B}\)), the bidirectional three-phase inverter will be considered as a current source (\(\overline{l}_{\text{eq}}\)) that feeds the circuit in the DC side of the same inverter, and with a current value equal to the average value of the load current \(\overline{l}_{b}\), as illustrated in Figure 8.  
In the circuit of Figure 8, a fictitious load \(R_{b}\) has been included because the capacitor (\(C_{b}\)) does not consume active power, so it would not make sense to represent an average current \(\overline{l}_{\text{eq}}\) charging the capacitor in the steady-state. However, the power is considered null from the point of view of the power grid frequency, but not so for the switching frequency of the inverter switches, in which case if  
\[Gi(s)=\frac{I_{\text{cs\_p}}(s)}{D(s)}=V_{B}*\frac{s^{2}k_{1}+sk_{2}+k_{3}}{s^{3 }k_{4}+s^{2}k_{5}+sk_{6}+k_{7}}, \tag{7}\]0 Equivalent circuit of the DC bus. \[Gi(s)=\frac{I_{\text{cs\_p}}(s)}{D(s)}=V_{B}*\frac{s^{2}k_{1}+sk_{2}+k_{3}}{s^{3 }k_{4}+s^{2}k_{5}+sk_{6}+k_{7}}, \tag{7}\]1 Representation of the switching period. an instantaneous power consumption is required to maintain the desired average voltage between DC bus terminals and for which this fictitious load \(R_{b}\) is included. By means of power balance analysis, the power supplied to the DC bus (\(P_{b}\)), is given by the sum of the power per phase in the inverter (\(P_{cs}\)), according to Equation (8).
## 2 Materials and Methods
### Image Data Collection and Processing
#### 2.2.3 Sentinel-2 Data  
Data from the multispectral instrument sensor on-board the two ESA Sentinel-2 satellite platforms (i.e., Sentinel-2A and -2B) was downloaded free of charge from the Copernicus Open Access Hub [48]. After cloud and cloud shadow screening, we identified six Sentinel-2 images for further analysis that were acquired within \(\pm\)5 days of the UAV flight dates (Table 1). These images had already been atmospherically corrected by ESA, from Level-1C top-of-atmosphere to Level-2A bottom-of-atmosphere reflectance products, using the Sen2 Cor algorithm [49]. These images were also available in cartographic geometry (UTM/WGS84 projection).  
Although the spatial resolution of Sentinel-2 near-infrared and visible bands is 10 m, observations in the red-edge band centred on 705 nm, which have been demonstrated to improve the retrieval of wheat variables [26], are available at 20-m resolution. In our analysis we, thus, use only the Sentinel-2 Level-2A output image products that were provided by ESA at a ground sampling distance of 20 m.
## 4 Analysis of Test Results
### Analysis of Uniaxial Compression Test Results
#### 4.2.1 Deformation Characteristics
It can be seen from Figure 9b that the deformation modulus \(E_{50}\) of the group A samples increases slightly with the increase of temperature within 300 \({}^{\circ}\)C. When the temperature increases from 25 \({}^{\circ}\)C to 300 \({}^{\circ}\)C, the average deformation modulus increases from 16.17 to 17.3 GPa, the increment is 7%; when the temperature exceeds 300 \({}^{\circ}\)C, the deformation parameters of A14 sample is obviously lower, the deformation parameters of A16 sample is obviously higher, and the deformation modulus of other samples decreases sharply with the increase of temperature; The average modulus of deformation of the sample from 500 to 600 \({}^{\circ}\)C is reduced from 14.95 to 7.19 GPa with a 50% decrease. The influence of the deformation modulus of the sample in the high temperature range of 600-900 \({}^{\circ}\)C is slowed down. Compared with the normal temperature of 25 \({}^{\circ}\)C, the average deformation modulus decreased by 52.3%, 55.4%, 58.1% and 64.5 after 600 \({}^{\circ}\)C, 700 \({}^{\circ}\)C, 800 \({}^{\circ}\)C and 900 \({}^{\circ}\)C, respectively. Comparing Figure 9c with Figure 9b, it is not difficult to find that the relationship between deformation modulus, elastic modulus and temperature of group A samples is basically similar after  
Figure 9: Relationship between uniaxial compressive deformation parameters and temperature. When the temperature exceeded 500 \({}^{\circ}\)C, the compressive strength of the sample decreased with the increase of temperature. Compared with the average compressive strength of 80.3 MPa at room temperature of 25 \({}^{\circ}\)C, the average compressive strength of group A samples after 100 \({}^{\circ}\)C, 200 \({}^{\circ}\)C, 300 \({}^{\circ}\)C, 400 \({}^{\circ}\)C, 500 \({}^{\circ}\)C, 600 \({}^{\circ}\)C, 700 \({}^{\circ}\)C, 800 \({}^{\circ}\)C and 900 \({}^{\circ}\)C increased by 13.7%, 9.3%, 27.4%, 15.9%, and 34.4%. \(-\)6.0%, 7.4%, \(-\)3.8% and \(-\)5.6%, respectively. The compressive strengths of the three samples at 500 \({}^{\circ}\)C are 115.4 MPa, 105.6 MPa and 102.8 MPa, respectively, and the average compressive strength is 107.9 MPa. The high temperature effect in the range of 100-500 \({}^{\circ}\)C was observed, the ability has a strengthening effect. The high temperature action of more than 500 \({}^{\circ}\)C slightly reduces the bearing capacity of the sample, which is roughly equivalent to the peak intensity of 25 \({}^{\circ}\)C at room temperature, which indicates that the high temperature effect at 900 \({}^{\circ}\)C has no obvious influence on the uniaxial compressive strength of the sample.
## 3 Results and Discussions
### Oil Spill Transport Behavior under Various Influencing Factors
#### 3.2.2 Wind
Taking the scenarios of Q = 5000 m\({}^{3}\)/s and wind speed of 5 m/s with northeast, southwest, and northwest winds as an example, as shown in Figure 10, under northeast and southwest winds, the oil spill will migrate along the south and north banks of the river channel respectively, and the migration time to the 2\(\#\) interception point will be 4.2 h and 3.8 h; under the northwest winds, the oil spill will migrate to the 3\(\#\) interception point will be 3.2 h. Emergency personnel can select the interception and recovery point according to the migration speed of the oil spill under the current wind conditions and reasonably deploy personnel and equipment on both sides of the river according to the gathering orientation of the oil spill to improve the efficiency of interception and recovery of the oil spill.
## 2 Related Work
This paper takes a significant step towards that goal by proposing a new antenna for digital TV reception. Our design makes use of a microstrip patch antenna as a radiating element with applications at relatively low frequencies (in the DTV-UHF band). The advantage of the proposed design is that it can easily be integrated into printed circuit boards (PCBs). It is also versatile in adjusting the antenna resonant frequency by simply changing the patch length. However, antennas of this type have a relatively narrow bandwidth, which can result in performance/efficiency degradation. Nevertheless, such antennas are expected to have enough bandwidth to cover at least a single 8 MHz channel with a suitable resonant frequency (754 MHz, single-frequency network in Portugal).
## 1 Introduction  
Ultrasound elastography is a useful non-invasive tool for the diagnosis of liver fibrosis in adults [1; 2; 3; 4; 5; 6; 7]. It plays a similar role in children, with some differences in the confounding factors and in the etiological spectrum of the liver disease; however, guidelines and recommendations have not been published yet. Preliminary data using transient elastography (TE), point shear wave elastography (pSWE) and two-dimensional shear wave elastography (2D-SWE) techniques, have shown that they are all feasible and accurate for the evaluation of liver fibrosis due to several etiologies in children [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29]. Nonetheless, data on the use of ultrasound elastography in children younger than 6 years is still scarce. Assessment of liver stiffness is the most studied application in children. However, there are other applications of ultrasound elastography such as for the evaluation of the thyroid, renal parenchyma, bowel and testis.  
Specific considerations relating to pediatric investigations include: (a) feasibility, related also to the differences in anatomy, anthropometrics, metabolic profile and psychology of each age group, the lack of cooperation to stop breathing, and so on; (b) the type of probe that should be used; (c) some differences in etiology and pathology in children; (d) cut-off values (are they the same of the adults?); (e) definition of preventable fibrosis in liver diseases.
# Fast 10-Point DFT Algorithm for Power System Harmonic Analysis
## 3 Computing \(L\times 10\)-Point DFT  
The \(N=2^{M}\)-point DFT can be quickly computed with radix-2 or radix-4. There are many applications where the sequence length varies from \(2^{m}\) or \(4^{m}\). Abnormal radix-based DFTs are often proposed in this case [45; 46; 47]. When \(N=L\times 10^{M}\)-point DFT, in which \(L\) and \(M\) are integers, several algorithms can be used, such as the prime-factor algorithm (PFA) [48], double factors algorithm (DFA) [49], or Winograd Fourier transform algorithm  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline  & **DFT** & **MFFT** & **SWIFT** & **WFTA** & **PFA** & **Baseline [38]** & **Proposed** \\ \hline Number of real multiplications & 400 & 48 & 32 & 20 & 20 & 20 & **16** \\ Number of real additions & 380 & 92 & 84 & 88 & 88 & 84 & **92** \\ \hline \end{tabular}
\end{table}
Table 1: The number of real multiplications and additions for the proposed algorithm, the baseline algorithm [38],DFT [43], MFFT [43], PFA [44], WFTA [44], and SWIFT algorithms [43].  
(WFTA) [50]. The length \(N\) of DFT can be subdivided into smaller transforms of length \(N=N_{1}N_{2}\).  
Using a mixed-radix in an electric power system is not a new solution [51]. For the harmonic analysis of a 50 Hz or 60 Hz signal, it is convenient to assume \(N=10^{M}N_{2}\) and \(N_{1}=10^{M}\), which solves the problem of sampling a non-integral number of periods and thus spectral leakage [37; 39]. If we let \(M_{R1}\), \(A_{R1}\), \(S_{R1}\), and \(M_{R2}\), \(A_{R2}\), \(S_{R2}\) denote the number of real multiplications, additions, and assume right-shiftings by 1 bit of the \(N_{1}\) and \(N_{2}\)-point DFT, then the total number of real multiplications and additions of the \((N=N_{1}N_{2})\)-point DFT in the DFA [49] are  
\[M_{R}=N_{2}M_{R1}+N_{1}M_{R2}+4(N_{1}-1)(N_{2}-1),\]  
\[A_{R}=N_{2}A_{R1}+N_{1}A_{R2}+2(N_{1}-1)(N_{2}-1),\]  
\[S_{R}=N_{2}S_{R1}+N_{1}S_{R2}.\]  
The total number of real multiplications and additions of the \((N=N_{1}N_{2})\)-point DFT in the PFA [43] are  
\[M_{R}=N_{2}M_{R1}+N_{1}M_{R2},\]  
\[A_{R}=N_{2}A_{R1}+N_{1}A_{R2},\]  
In Table 2, a comparison of the computational requirements for the \(N=10^{M}\)-point DFT for DFA and PFA algorithms and the related \(N=2^{M}\)-point and \(N=4^{M}\)-point DFTs is shown. The proposed algorithm can be used in any mixed-radix or split-radix algorithms. The best algorithm was a PFA with radix-10. The spectral leakage can be reduced, and the computed harmonic parameters are more approximate to the actual value, as shown in [37; 39]. Therefore, the proposed radix-10 DFT can be efficient for use in harmonics analyses in power systems.
## 4 Conclusions  
In summary, ZIF-8 and CNTs were fabricated in situ in the 3D interconnected network structure of CBs via a feasible strategy. Subsequently, carbonization treatment could effectively change the hydrophobic property of the sample. The characterization results suggested that CBs could be used as a porous structure for embedding ZIF-8 and CNTs, effectively improving the oil-water separation performance after carbonization. As an oleophilic and hydrophobic adsorbent, CZIF-8/CNTs/CCBs shows high sorption capacities of 48 to 84 times its own weight. Moreover, CZIF-8/CNTs/CCBs exhibits a stable oil  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sorbent Material** & **Absorbed Substances** & **Sorption Capacity (g.g\({}^{-1}\))** & **Ref.** \\ \hline CNT/wood aerogel & oils and organic solvents & 16–39 & [39] \\ Silylated wood sponges & oils and organic solvents & 17–41 & [40] \\ Carbon nanotube sponges & oils & 49–56 & [41] \\ Polyvinyl-alcohol-reinforced wood sponge & oils and organic solvents & 4–27 & [42] \\ Microwinkled reduced graphene oxide & oils & 36–84 & [43] \\ Cellulose-based aerogels & oils and organic solvents & 45–99 & [48] \\ Winter melon carbon aerogel & oils and organic solvents & 16–50 & [45] \\ Carbon nanofiber aerogel & oils and organic solvents & 37–87 & [46] \\ MOF-coated cotton fiber composite & oils and organic solvents & 25–48 & [31] \\ MOF-reduced graphene oxide aerogel & oils and organic solvents & 45–147 & [47] \\ Polyimide/MXene aerogels & oils and organic solvents & 18–58 & [44] \\ CZIF-8/CNTs/CCBs & oils and organic solvents & 48–84 & This work \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of various sorbent materials.  
Figure 9: (a) Adsorption capacities Q\({}_{1}\) of CZIF-8/CNTs/CCBs and CCBs for oil or organic solvents. (b) Adsorption capacities Q\({}_{2}\) of CZIF-8/CNTs/CCBs and CCBs for oil or organic solvents. (c) Adsorption capacities of CZIF-8/CNTs/CCBs for n-heptane after 9 cycles.  
absorption capacity with reuse. This work provides a novel strategy for designing a superior adsorbent candidate for oil-water separation.  
Conceptualization, Y.S. and R.S.; methodology, Y.S.; validation, Z.K., C.S., and Q.W.; writing--original draft preparation, Y.S. and C.S.; writing--review and editing, Z.K., W.Y., and Z.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Anhui Nature Science Research Program (KJHS2019B07), Anhui Provincial Nature Science Research Project (2008085 MH269), Anhui Provincial Teaching Team Project (2020)xtd250), and Huangshan University Talent Introduction Funding (2018 skjq005).  
Data Availability Statement: Data sharing is not applicable.  
The authors declare no conflict of interest.
# Analyzing and Predicting Students' Performance by Means of Machine Learning: A Review
## 4 Objectives
### Student Dropout  
Several studies focused on the dropout rate in nursing courses have tried to find the causes rather than predicting the likelihood of students dropping out. A useful method for trying to make this type of prediction is the induction of rules, using IBM SPSS Answer Tree (AT) software [4] for this purpose. The authors [5] found that the following factors are highly informative in predicting school dropout: family history, socioeconomic status of families, high school grade and exam results.  
It was noticed that unbalanced class data was a common problem for prediction [6]. In addition, classification techniques with unbalanced datasets can provide deceptively high prediction accuracy. To solve this problem, the authors compared different data balancing techniques (including SMOTE) to improve accuracy. All these techniques improved the accuracy of predictions, although Support Vector Machine (SVM) combined with SMOTE data balancing technique achieved the best performance.  
Nowadays, higher education institutions are attempting to use data collected in university systems to identify students at risk of dropping out [64]. This study uses the data to validate the Moodle Engagement Analytics Plugin learning analysis tool. High dropout rates are a very important problem for e-learning. The authors propose a technique that considers a combination of multiple classifiers to analyze a set of attributes of students' activities over time [7]. Other authors [8] selected students' personal characteristics and academic performance as input attributes. They developed prediction models using ANN, Decision Trees (DT) and Bayesian Networks (BN). Along these lines, another study [65] identified the most important factors for predicting school dropout risk: those that showed student commitment and consistency in the use of online resources. For this purpose, Exploratory Data Analysis was applied.  
In particular, higher education institutions in the United States faced a problem of university student attrition, especially in the areas of Science, Technology, Engineering and Mathematics. More than 60% of the dropouts occurred in the first two years. One study develops and evaluates a Survival Analysis (SA) framework for early identification of students at risk of dropping out of school and early intervention to improve student retention [10].
# A Novel Hierarchical Adaptive Feature Fusion Method for Meta-Learning
## 4 Experimental Results
### Ablation Study
#### 4.4.2 Comparison of Different Classifiers  
The effects of different classifiers on the experiment were tested, and multiple classifiers were used for validation on three public datasets. The experimental classifiers include nearest neighbor classification, support vector machine, cosine similarity, and logistic regression. The experimental results are shown in Table 6. The performance of each classifier varies depending on the dataset, but the logistic regression classifier is better than other classifiers in the experiment; hence it is selected for subsequent research.
## 4 Results and Discussion  
The infrared image analyzed is shown in Figure 3. The image was collected with the excitation source directly above the surface and two defects under it. The defect on the right side of the image is not clear.  
By verifying the value of the step-size, we determined that the method was most efficient when the step-size was 8. Figure 8 shows the result when the step-size was 8. Compared with the initial infrared image, it is evident that the swell caused by the excitation source was eliminated.  
Figure 9 shows the contrast between the processed infrared image and the initial image. The infrared image after processing was significantly clearer than the initial image. The bright area influenced by the excitation source was eliminated in the processed infrared image. Furthermore, the bright area in the right box was caused by the defect inside the object became clearer.  
The swell in the infrared image caused by the excitation source was eliminated by using the algorithm provided in this paper. Furthermore, the outlines of the defects were made more distinct. The method presented in this paper improved the defect detection accuracy by enhancing the two crack images, and most importantly, it reduced the background noise which could increase the credibility of crack estimation compared with the original detected infrared image.  
It should be noted that this algorithm is designed to reduce the background noise imported through the temperature of the environment and the excitation. Furthermore, in the pulsed eddy current infrared imaging test, multiple cracks will influence the distribution of the temperature so that the low temperature area of a crack will be covered in the background. This is because the thermal imager should adapt to the temperature in the whole vision area to make sure the image is clear. Thus, the proposed method is motivated by this problem. From the results, it can be inferred that when multiple cracks exist in the visible area, this method could reduce the influences produced by the thermal transforming. At stable temperatures, because the temperature between every two crack is similar and smooth but the temperature in the crack area is higher than that of the other areas, this method could be used.
# MSLCFinder: An Algorithm in Limited Resources Environment for Finding Top-\(k\) Elephant Flows
## 3 Top-\(k\) Measurement Scheme Based on MSLCFinder
### Measurement Scheme Overview  
The overall design of the measurement scheme is shown in Figure 4. The scheme parses each message arriving by the traffic to be measured packet by packet, calculates the hash value corresponding to the data flow ID through the hash function, and is used  
\begin{table}
\begin{tabular}{c c c c} \hline
**Research Category** & **Strategy** & **References** & **Method** \\ \hline \multirow{6}{*}{Based on Data Flow} & \multirow{2}{*}{_count-all_} & CM sketch [13] & sketch \& min-heap \\  & Count sketch [14] & sketch \& min-heap \\ \cline{2-4}  & \multirow{4}{*}{_admit-all-count-some_} & Frequent [8] & constraint rules \\  & & Efficient counting [9] & constraint rules \\  & & Lossy counting [10] & error variable \\  & & SpaceSaving [11] & stream-summary \\  & & CSS [12] & compact space-saving \& sliding window \\ \hline \multirow{6}{*}{_separate elephant/mouse flows_} & HeavyKeeper [7] & count-with-exponential-decay \\  & Cold Filter [39] & two-layer sketch structure \\ \cline{1-1}  & HeavyGuardian [40] & heavy and light part \\ \cline{1-1}  & ActiveKeeper [41] & two-mode active counter \\ \cline{1-1}  & FastKeeper [42] & sliding window \& bitmap voting \\ \cline{1-1}  & ActiveCM + [43] & ActiveCM + \& LUS \\ \hline \multirow{2}{*}{Based on Sampling} & Sampled NetFlow [5] & sampling\(\Rightarrow\)recording \\ \cline{1-1}  & Sample and hold [6] & query\(\Rightarrow\)sampling\(\Rightarrow\)recording \\ \hline \end{tabular}
\end{table}
Table 1: Research on top-\(k\) elephant flow identification methods.  
to query, judge, and update the information of the MSLCFinder and flow sampling. The sampling probability is calculated by the given threshold and updated by the size of the counter in MSLCFinder.  
At the beginning of the measurement, the hash value is calculated for the parsed FlowID, and the sampling process will use the initial sampling probability. If the FlowID is sampled, the counter at the corresponding location of the FlowID in MSLCFinder will be updated. Otherwise, the counter is not updated to reduce the probability of counting small flows in the MSLCFinder structure and reduce the pressure on on-chip resource storage.  
Here, the size of the counter in the MSLCFinder is a fixed \(n\) bit. It uses a \(n\)th-level multi-sampling module to sample the flow. When the value of the MSLCFinder counter exceeds \(u\) thresholds, the sampling probability is recalculated, respectively, and a new sampling probability resamples the arriving flow. If the sampling is successful, the counter is incremented by 1; otherwise, it will be forwarded directly. When the count exceeds the last-level threshold, the flow corresponding to the location flow ID in the MSLCFinder is the top-\(k\) flow. The flow information is stored in the information storage table through the flow label recording algorithm. At the end of the measurement cycle, the entire information storage table is reported to the control end to obtain the specific information of the top-\(k\) flow.