esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Diagnosis System of Power Grid Based on Multi-Data Sources
## 4 Fault Tracking Quasi-Real-Time Processing System and Fault Diagnosis Batch Processing System
### Implementation of Fault Diagnosis Batch Processing System  
At present, the networking work aimed at realizing the remote transmission of the entire power grid's fault recording data has been completed in many provinces of China. The batch processing system at the dispatching center can input the fault recording data documents in batches of multiple sets of documents through the fault recording information net and schedule them in the form of workflow. Under the premise that a real-time processing system provides diagnosis results, the batch processing system diagnoses elements in _V_e5_, verifies the set of faulty elements (_V_e4_), outputs the information of fault phases, fault types, fault locations and fault times.  
Fault recording data processing flow is shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Project** & **Data** & **Normal Value** \\ \hline Current RMS of tripping/closing coil & 3.2 A & 2.2–2.4 A \\ Current time of tripping/closing coil & 44 ms & 56–60 ms \\ voltage at two ends of tripping/closing coil & unknown & 210–230 V \\ \hline \hline \end{tabular}
\end{table}
Table 6: Abnormal data and unknown data of CB4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Prior Probability\%** & **f1** & **f2** & **f3** & **f4** & **f5** \\ \hline r1 & 3.21 & 0.172 & 0.086 & & & \\ r2 & 8.84 & & 0.237 & 0.182 & & \\ r3 & 4.57 & & & 0.235 & 0.352 & \\ r4 & 12.63 & 0.828 & 0.677 & & 0.648 & \\ r5 & 21.23 & & & 0.583 & & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Conditional probability table of CB4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fault Reason** & **r1** & **r2** & **r3** & **r4** & **r5** \\ \hline Bayesian suspected degree & 0 & 0.091 & 0.2935 & 0.324 & 0.2915 \\ Revised suspected degree & 0.0141 & 0.0836 & 0.2695 & 0.3651 & 0.2677 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Suspected degree of fault reasons.  
The COMTRADE recording document format, Fourier algorithm, mutation monitoring algorithm, symmetrical component method, fault direction algorithm, single-end fault location method and fault phase and type judgment rule have been introduced in detail in references [10,13]. Due to limited space, this article does not repeat.
## 3 Results and Discussion
### Characterization of MGONCs
#### 3.1.3 FTIR  
The results of the FTIR analysis for the surfaces of Fe\({}_{3}\)O\({}_{4}\), GO, and MGONCs are presented in Figure 6. The broad band observed in the 3400-3000 cm\({}^{-1}\) region corresponds to the stretching vibration of -OH groups. The absorption peak at a wave number of 1717.74 cm\({}^{-1}\) was attributed to the C=O stretching vibration from carbonyl or carboxyl groups, while the peak at 1610.00 cm\({}^{-1}\) corresponded to the C=C aromatic stretching vibration. The peak at 1221.38 cm\({}^{-1}\) was associated with C-O bonds. Additionally, the strong Fe-O bond in Fe\({}_{3}\)O\({}_{4}\) was indicated by the low stretching vibrations observed at  
Figure 4: Fe\({}_{3}\)O\({}_{4}\) nanoparticles: (**a**) TEM image; (**b**) particle size distribution histogram; (**c**) high-resolution image of a single Fe\({}_{3}\)O\({}_{4}\) nanoparticle.  
Figure 5: Characterization results of MGONCs: (**a**) TEM image; (**b**) selected area electron diffraction pattern.  
598.37 cm\({}^{-1}\)[33; 39]. The surface of the GO samples, C=O bonds, C-O peaks, and multiple peaks of O-H vibrations were observed. This indicated that the functional groups on the surface of MGONCs are predominantly derived from GO. In contrast, the Fe\({}_{3}\)O\({}_{4}\) nanoparticles mainly contributed to the magnetic properties. The combination of GO with magnetic nanoparticles resulted in a mixture that possessed both magnetic properties and a large number of active functional groups for subsequent experiments.
## 2 Materials and Methods
### Potentialor Polarization (PDP) Measurements  
The corrosion rate was evaluated from the results of electrochemical tests by the potentiodynamic polarization (PDP) method using an SP-300 potentiostat (Bio-Logic SAS; Seyssinet-Pariset, France). Corrosion studies were carried out in physiological solution with pH = 7.4 at room temperature. A flat PAR cell (Ametek Instruments, Oak Ridge, TN, USA) with a \"three-electrode configuration\" was used. The cell is a double-jacketed 250 mL cell for flat samples with a maximum thickness of 1 cm. Integrated into the cell Pt gauze is the counter electrode. The test samples with a minimum diameter of 3 mm and a maximum thickness of 0.6 mm were used as the working electrode. The area exposed to the solution is about 1 cm\({}^{2}\). The electrode surface was polished to a mirror finish. The Ag/AgCl electrode was inserted into the bridge tube as a reference electrode. The distance between the tip of the bridge tube and the working electrode was kept constant to minimize the \(IR\) drop in the solution. Scanning was performed at a rate of 1 mV/s in the scanning range: \(-\)1000 V, \(E_{\text{OC}}\) +300 mV, where \(E_{\text{OC}}\) is the open circuit potential. The time for determining the open circuit potential was 20 min. Preliminary studies have been carried out to determine the stabilization time for open circuit potential. \(E_{corr}\) vs. time curves were measured with different exposure times in the range of 15-60 min. The PDP curves obtained after 20- and 25-min exposure time for open circuit potential were almost the same, indicating that the surface had reached a more or less stable potential. It should be noted that the Fe-Mn-Si alloy is degradable, so there is a risk of changing the surface area due to corrosion, which in turn affects the results of the experiment. Six scans were carried out for each state of the alloy. Corrosion potential, corrosion current density and corrosion rate (\(C_{R}\)) were obtained using EC-Lab software V11.31 (BioLogic, Seyssinet-Pariset, France) according to [40] using the following equation:  
\[C_{R}=\frac{3.27\cdot 10^{-3}.i_{corr}.\ EW}{\rho\cdot S},\ \text{mm}/\text{year} \tag{1}\]  
where \(C_{R}\) is the corrosion rate (mm/y), \(i_{corr}\)--the corrosion current density extracted from the polarization curve, \(\mu\)A cm\({}^{-2}\); \(EW\) is the equivalent weight, g/eq; and \(\rho\) is the alloy density, g/cm\({}^{3}\).
# An FPGA-Based Four-Channel 128k-Point FFT Processor Suitable for Spaceborne SAR
## 4 Experimental Evaluation and Results  
In this section, we evaluated the performance of the proposed design through several experiments. First, we implemented the proposed processor on FPGA. Four sets of experimental data were used to verify the function of the FFT processor, with the accuracy of the calculation results and the real-time performance of the calculation evaluated. Then, the occupation of hardware resources was also evaluated which was compared with other FFT processors. The experimental settings and detailed experimental results are shown below.
## 4 Analysis of Test Results
### Comparison between Simulated Data and Experimental Data  
Based on the above finite element analysis and actual test data, a summary is shown in Table 4.  
In Table 4, it can be seen that the simulation results of ABAQUS are consistent with the test data, and the errors vary between 0.4 and 2.81. Therefore, the simulation method of ABAQUS is reasonable and applicable. It was also found that the propagation speed of the transmitted wave increases as the degree of defects increases, and the frequency shifts towards high frequencies. It is known that the propagation velocity of elastic waves in pre-stressed steel is higher than that in concrete. Normally, the propagation velocity  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Pipeline \#** & \multicolumn{2}{c}{**Wave Velocity (m/s)**} & \multicolumn{2}{c}{**Frequency (Hz)**} & \multicolumn{2}{c}{**Error \%**} \\
**Simulated** & **Tested** & **Simulated** & **Tested** & **Wave Velocity** & **Frequency** \\ \hline
1 & 4315.25 & 4340.00 & 3680 & 3662 & 0.57 & 0.4 \\
2 & 4276.57 & 4325.00 & 3390 & 3448 & 1.11 & 1.68 \\
3 & 4243.96 & 4310.00 & 3200 & 3143 & 1.53 & 1.78 \\
4 & 5395.80 & 5274.00 & 6333 & 6516 & 2.26 & 2.81 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Signal comparison analysis.  
Figure 24: Tested spectrum diagram of pipeline 4 signal.  
of elastic waves in pre-stressed steel is more than about 5000 m/s, and it is more than about 4000 m/s in concrete. The propagation velocity of elastic waves will be higher in pre-stressed pipelines, as there are more defects (slurry detachment). This is because elastic waves propagate in pre-stressed steel to a greater degree. The higher the propagation velocity of elastic waves, the lower the quality of the grouting in the pre-stressed pipeline (the more defects). The lower the propagation velocity of elastic waves, the higher the quality of the grouting in the pre-stressed pipeline. The propagation velocity of elastic waves in pre-stressed pipelines is an indicator of the quality of the grouting.
## 2 Materials and Methods
### Background and Related Work
#### 2.1.2 Threat Model
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **(3)** & **(2)** & **(1)** \\ \hline D & full trust, admin level & leak sensitive information & leak trivial information \\ R & always reproducible, any time & reproducible in specific time and condition & difficult to reproduce \\ E & noviee, in a short time & skilled, script & extreme skilled, no script \\ A & all users, confs, and key customers & some users, no default confs & few users, obscure feature, and anonymous \\ D & published info explains attack, easy to spot & rare users, requires some knowledge & bug is obscure, unlikely potential for damage \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Risk quantification in DREAD [68]—table used as a basis for risk quantification in each category. The artifact is organized as follows: in the leftmost column is the list of categories, then the conditions classified as risk 3 (major), risk 2 (medium), and risk 1 (minor). If risk does not exist, it is classified as risk 0 (zero). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \# & **Surface** & **Vulnerabilities** & **STRIDE** & **CVSS** & **DREAD** & **Total Score** \\ \hline v01 & & information disclosure & I & 0 & 1.2 & \\ \cline{3-6} v02 & s01 & leakage & I & 0 & 1.4 & 1 \\ v03 & & DoS & D & 1 & 1.8 & \\ \hline v04 & & device impersonation & S & 2 & 1.4 & 3 \\ v05 & & brute force attack & E & 1 & 2 & \\ \hline v06 & & device impersonation & S & 2 & 1.4 & 4 \\ v07 & & tampering data & T & 2 & 2.2 & \\ \hline v08 & s04 & information leakage & D & 0 & 0.8 & 0 \\ \hline v09 & & message tampering & T & 3 & 1.6 & \\ v10 & s05 & message fabrication & T & 2 & 1.6 & 8 \\ v11 & & unauthorized access & E & 3 & 2.2 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Vulnerability classification and prioritization. The classification according to STRIDE. Contrastingly, prioritization according to CVSS is presented, where priorities are assigned from 0 to 3. Finally, the quantification of threat impact risk through DREAD is presented. With this evaluation of three criteria, it is possible to have a complete understanding of the current threats in the MFA_R. In short, methodical threat analysis was carried out to support formal verification. As such, it was possible to approach from the definition of the attack surface, going through the threat analysis process in three stages, ending with the countermeasures expressed through the aspects table (security requirements). Moreover, although being a long process, the steps were presented in an intelligible, step-by-step, well-documented way so they could be reused in future works.
# An Improvement to the 2-Opt Heuristic Algorithm for Approximation of Optimal TSP Tour
## 3 2-Opt++ Algorithm  
### Basis for Solution  
Initially, the algorithm needs to be provided with a Hamiltonian cycle as a base input. A random Hamiltonian cycle is quickly computed by using a greedy technique (least cost edge) and then an edge swap technique is used to iteratively improve upon the original tour. It is pertinent to mention that although we have computed the Hamiltonian cycle in a greedy manner for this work, any method can be utilised to compute the Hamiltonian cycle, as the effectiveness of the 2-Opt++ algorithm is beneficial in cases where the initial Hamiltonian cycle is of higher quality in terms of the total edge weight. However, it is not entirely dependent on such optimistic scenarios only, as the proposed edge swap technique and the graph compression/candidate list are also key to the 2-Opt++ algorithm's success.
# Mapping Radar Glacier Zones and Dry Snow Line in the Antarctic Peninsula Using Sentinel-1 Images
## 5 Discussion
We hope that the addition of SAR textural features in future studies can help eliminate these misjudgments. As the highest limit of snowmelt, winter DSL in the AP was mapped based on the SAR mosaic. DSL mainly distributed in the areas melted for one to three days in summer, indicating that a slightly larger accumulative summer melt area was recognized by the SSM/I. The largest dry snow patch was located on the Palmer Land, where the DSL elevations increased from south to north. Higher temperatures at the lower latitudes enable snowmelt to reach the higher altitudes.
# Combined Fusion Rules in Cognitive Radio Networks Using Different Threshold Strategies
## 4 The Proposed Combination Algorithms
### Adaptive Combined Algorithm  
Unlike Algorithms 1 and 2, a variable eligibility threshold, \(\varphi\), will be used in this case. An iterative algorithm is devised to update the value of \(\varphi\). The eligibility threshold, \(\varphi\), varies to minimize the total detection error rate denoted by \(Q_{ER}\) and is computed as follows  
\[Q_{ER}=P\left(H_{0}\right)Q_{f}+P\left(H_{1}\right)\left(1-Q_{d}\right) \tag{13}\]  
where \(P\left(H_{0}\right)\) and \(P\left(H_{1}\right)\) are the probabilities of the PU being absent and present, respectively.  
```
1:SET\(\tilde{Q}_{fc}\) and \(\varphi\)
2:Let\(Q\leftarrow\varnothing\)
3:Compute\(Q_{dc}(i)\) for all clusters in the system, where \(i=1,2,\ldots,C\).
4:Let\(\mathbf{U}=[Q_{dc}(1),\ldots,Q_{dc}(C)]\)
5:Sort the rows of \(\mathbf{U}\) in a descending order.
6:for\(i=1:C\)
7:\(I=U(i,:)\)
8:for\(j=1:length(I)\)
9:if\(I(j)>\varphi\)
10:\(Q=I(j)\)
11:else
12:\(\varphi=I(j)\)
13:break
14:end
15:end
16:Compute the global probability of false alarm of the CRN using Equation (5)
17:if\(Q_{f}(i)<\tilde{Q}_{fc}\)
18:Compute the global probability of detection of the CRN using Equation (6)
19:else
20:\(Q=[Q\ \varphi]\)
21:Compute the global probability of detection of the CRN using Equation (6)
22:Compute the global probability of false alarm of the CRN using Equation (5)
23:end
24:end
```  
**Algorithm 3** Adaptive Combined SchemeThe first step of the adaptive combined algorithm is to set the initial value of the maximum allowable global probability of false alarm, \(\tilde{Q}_{fc}\), and the adaptive threshold \(\varphi\). The \(\tilde{Q}_{fc}\) is set as either 0.05 or 0.005 which the maximum expected probability of false alarm over a cluster, while the adaptive threshold, \(\varphi\), is set as the average of probabilities of all clusters in the CRN, when all clusters employ one-bit fusion rule. In other words, each cluster computes its probability of detection using Equation (8).  
The second step is to update \(\varphi\) through an iterative algorithm to ensure improving \(Q_{d}\) and minimizing \(Q_{ER}\), simultaneously. This is achieved by sorting the probabilities of detection of the clusters and then comparing them to \(\varphi\). The \(\varphi\) is updated to exclude clusters with a low probability of detection. Furthermore, to guarantee attaining minimum error detection for involved clusters, each cluster computes its probability of false alarm using Equation (7) then compares it by the \(\tilde{Q}_{fc}\). The \(i\)-th cluster will be excluded if \(Q_{f}(i)\geq\tilde{Q}_{fc}\).  
As a conclusion, the threshold, \(\varphi\), in the adaptive combined algorithm varies adaptively to increase total probability of detection, i.e., detection performance, which results in decreasing the total detection error rate, \(Q_{ER}\). Algorithm 3 illustrates the mechanism of the adaptive combined fusion algorithm.
## 4 Analysis Example and Methods
### Bidirectional FSI Simulation and Experimental Methods for Bionic Pipelines  
#### 4.2.1 Simulation Analysis of Bidirectional FSI for the BHP  
To study the influence of different types of BHP on the vibration of the pipe wall when the flow pulsation is the same, the bidirectional FSI analysis for the BHP is carried out in the ANSYS Workbench software. The grid division of the solid and fluid regions is shown in Figure 8. The structural parameters and fluid parameters are shown in Table 12; where, the parameters for the RTV are \(C_{01}=1.213\) MPa, \(C_{10}=-0.01679\) MPa. The standard k-\(\xi\), turbulence model is adopted for the fluid, the wall shear force in the BHP with RTV as the elastic layer is the wall shear force in the Brunone model, and the inner wall of the BHP with RTV-SWCNTs composite materials as the elastic layer is set as the smooth wall. The boundary condition for the pipeline inlet is set as the flow pulsation at the outlet of the axial piston pump, as shown in Figure 9. The pipeline outlet is set as the pressure outlet and the pressure is set as 2 MPa.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Simulation (Hz)** & **Experimental (Hz)** & **Error (\%)** \\ \hline First-order mode & 78 & 81.26 & 4.01 \\ \hline Second-order mode & 331 & 333.68 & 0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the modal analysis simulation and experimental data.  
Figure 7: The frequency response curve for the BHP.  
1. Influence of the thickness of the elastic layers on the vibration of the BHP  
To study the effect of different thicknesses of the elastic layer and materials on the vibration of the pipeline wall in the BHP, the thickness of the stainless steel on the outer layer of the BHP is set at 2 mm, the length of the pipe is set at 100 mm, and the thickness of the elastic layer is, respectively, set at 5 mm, 10 mm, 20 mm, and 25 mm.
2. Influence of the length of the BHP on the vibration of the pipe wall  
To study the effect of the different lengths and materials in the elastic layer on the vibration of the pipeline wall in the BHP, the thickness of the stainless steel on the outer layer of the BHP is set at 2 mm, the thickness of the elastic layer is set at 5 mm, and the length of the pipeline is, respectively, set at 100 mm, 150 mm, 250 mm, and 300 mm.
## 3 Materials and Methods
### Accuracy Assessment  
The accuracy of the predicted ESTARFM time series was assessed in different ways. Similar to a cross-validation, the first approach was to exclude one Landsat scene from the input time series and predict this scene with ESTARFM on the basis of the remaining image acquisitions. The predicted scene was then correlated with the actual, formerly excluded Landsat scene and differences between the scenes were calculated. By doing so for every Landsat image (except for the first and last scenes since these cannot be predicted without shoulder scenes) an accuracy assessment of each tile of the ESTARFM time series was conducted. For this purpose, the interval of the output time series was adapted to the 16-day acquisition scheme of the Landsat path 195 (Figure 2) so that the Landsat scenes of the tiles 195/52 and 195/53 could be directly used for validation. For the Landsat path 194 with a different acquisition interval, additional images at the dates of existing Landsat scenes were generated for validation and, in this way, a total number of 195 images have been produced.  
In addition to this cross-validation, since the used Landsat scenes do not cover a major part of the rainy season, eight additional Landsat scenes with cloud cover of up to 80% were used for validation of this period. In this way, the quality of predictions for the main rainy season can be estimated, although the results might represent only a very limited part of the study region due to the dominating cloud cover on these scenes.  
Furthermore, in order to display the small-scale spatial patterns of the prediction quality, a subset area was monitored over several time steps. Based on these images, the prediction of different land cover types related to different phenological stages can be assessed.  
In addition, the consistency of ESTARFM predictions between the eastern and western Landsat path was analyzed for the overlapping areas. Generally, low differences between the paths indicate a  
Figure 5: Map of the study area showing how often a pixel in the input Landsat time series of 2014 was filled.  
high prediction quality and also reduce the amount of subsequent processing of the time series for further geoscientific applications.
## 3 Proposed Research Methodology
### Word Embedding Techniques
#### 3.3.1 fastText  
Vector representation by word embedding has been used in various natural language processing (NLP) tasks. Generally, pre-trained word embedding that is trained on texts such as Wikipedia and Google News predicts the word context in an unsupervised way. They consider the words to appear close to each other and have similar contexts. The fastText [23] technique is created by Facebook's FAIR Research Lab. It contains 2 million word vectors (600 billion tokens) having 300 dimensions. fastText embedding is a good choice for vector representation because of the word difficulty detection feature, which uses morphological information. This capability makes it possible to improve the results of text classification by generalizing them well. Vectors of fastText word embedding are obtained by the sum of n-grams that make it possible to generate a vector for unfamiliar words.
## 7 Evaluation of the Intruder Tracking Algorithm
### Impact of the Sink Mobility  
In this set of simulation experiments, we analyze the behavior of the WSN when varying the sink speed. Notice that, in our experiments, the sink used the intruder location estimations in order to continuously move towards the intruder. However, higher sink mobility levels required a faster adaptation of the network topology. Also, the intruder mobility pattern was intimately related to the sink mobility pattern. Thus, our purpose was to determine how these parameters affected the tracking error.  
The parameters used in this series of simulation experiments were similar to those of the previous section, but now fixing the intruder speed at 4 m/s and varying the sink speed instead. Figure 4 presents the obtained results. It shows that the routing algorithm was quite robust in the presence of sink mobility, being that the estimated error for the straight and curve mobility patterns is kept stable, with error values of about 5 and 6 m (see Figure 4(a)). For the random path, the estimated error increased slightly (from 7 m to almost 9 m) when the sink speed increased. To understand this difference, notice that the greater error associated with the intruder location estimation makes the trajectory followed by the sink slightly irregular, thus causing more topology changes and more routing-related losses.  
In terms of routing overhead, we found that it depends more heavily on the intruder mobility pattern than on the sink speed. In particular, more irregular intruder mobility patterns (in this case, the random pattern) are associated with more routing updates. Notice that the sink mobility pattern is intimately related to the intruder's mobility pattern, which explains the phenomenon observed. In addition, we  
Figure 3: Mean position estimation error for different mobility patterns when varying the intruder speed.  
found that the MRLG routing protocol is highly efficient at managing high sink mobility levels, being that the routing overhead barely varies for high sink speeds.