esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
To compensate for the effect of non-linear dynamics on the actuator and suppress the influence of un-modeled dynamics and wind disturbance on missile during the ascent phase, and for the non-linear actuator with dead-zone, a control model suitable for the ADRC control method was constructed. Meanwhile, a model suitable for the ADRC control framework was constructed for the pitching plane of the missile considering wind disturbance. Later, the cascade ADRC control structure was constructed by combining the internal loop of the actuator and the external loop of pitch attitude control. The ADRC controllers, contrasted with PID controllers were designed separately.  
Simulation results show that accurate modeling is not necessary under ADRC control. For actuator close loop with voltage dead-zone, the presence of non-linearity under certain conditions causes the actuator to generate limit cycle oscillations. Meanwhile, the dynamic response varies greatly when the system is operating at different loads and different command angles, which can generate a serious impact on missile attitude control. Dead-zone non-linearity, model uncertainty and external disturbances can all be estimated and compensated for under ADRC control. The controller has excellent control performance and robustness under different dead-zones, different command angles, and rated load ranges.  
The vertical wind has a great influence on the attitude control of the pitch channel during the ascent phase. Under the traditional PID gain scheduling control, the pitching moment changes drastically, which will cause structural damage, and it is difficult to apply to suppress strong gusts. ADRC can estimate and suppress the external gust disturbance properly, and it has a very good capability in tracking attitude command; as a consequence, ADRC has stronger robustness and stability than PID.  
Conceptualization, J.X.; methodology, B.F., H.Q., and Y.Y.; software, B.F.; validation, H.Q., J.X.; formal analysis, B.F. and Q.G. investigation, Q.G.; resources, H.Q. and J.X.; data curation, Y.Y. and S.W.; writing--original draft preparation, B.F.; writing--review and editing, S.W.; visualization, B.F.; supervision, H.Q.; project administration, H.Q.; funding acquisition, J.X.  
This work was funded by the National Natural Science Foundation of China (No. 11372080), and China Scholarship Council (No. 201606685036).  
The authors declare no conflict of interest.  
Figure 23: Actual pitch movement under PID.
## 4 Application
### Model Parameters
Alternatively, we can initiate the starting point at a higher level such as two or three. This allows algorithms to run more efficiently in large-scale data sets. For a simple example, in a \(6\times 6\) matrix (see Section 3.3 for the first artificial example), the first row of variables is \(\{X_{1},X_{2},\dots,X_{6}\}\) and the second row of variables is \(\{X_{7},X_{8},\dots X_{12}\}\). At a starting point of two, we start with \(X_{8}\) to pass over with the rolling window, because this variable sits at the position in the second row and the second column. This is illustrated in the following matrix;  
\[\text{Starting point}=2: \begin{bmatrix}X_{1}&X_{2}&\dots\\ X_{7}&X_{8}&\dots\\ X_{13}&\vdots&\ddots\end{bmatrix} _{6\times 6}\]  
where the starting point is colored in red. This tuning parameter is particularly useful when the edge of the images is noisy and noninformative. For example, in Section 4.1, we notice from training images of the COVID-19 chest X-ray data set that the margins of the X-ray images are dark and do not contain the human body for a few pixels in length. This is an example of when this tuning parameter can be helpful as it speeds up the training process. **Computation of Dimensions. ** The above discussion introduced the tuning parameters: window size, stride level, and starting point. These parameters update our input matrix and generate a new matrix with different sizes. Let us denote window size as \(w\), stride level as \(l\), and starting point as \(p\). Given a \(s_{\text{in}}\) by \(s_{\text{in}}\) input matrix, the output matrix has new dimensions computed as:  
\[\lfloor\frac{s_{\text{in}}-p-w+1}{l}+1\rfloor\times\lfloor\frac{s_{\text{in}} -p-w+1}{l}+1\rfloor \tag{18}\]  
For simplicity in this investigation, we assume the input matrices are a square. In other words, in the application in this paper, we process the input images to have the same width and height, i.e., 128 by 128 pixels. In future work, this can be extended to different shapes.
## 3 Methodology  
The study is carried out in stages; this involves the selection of MLPs, developing the framework from the ISO standards, and determining the value of the three risk factors for each risk item with the aid of an Excel template for FMEA data collection. In addition, calculating the weight factors and assigning the corresponding weights leads to calculating the grey RPN. Therefore, the evaluation and analysis of the results from FMEA-FAHP.  
The procedures involved are further described in steps as shown below;  
1. The selected criteria were based on ISO/IEC 9126 and were arranged on the first column of the FMEA sheet. And the metrics (Occurrence, Severity, and Detection) were arranged on the subsequent columns of the same sheet.
2. The data on the columns were manually collected, as each MLP was observed based on individual criteria. Therefore, metrics were measured based on their corresponding rankings.
3. The classical Risk Priority number is then calculated, which is the multiplication of the three metrics: Occurrence, Severity, and Detection.
4. The grey priority number is calculated based on weights obtained through the FAHP technique.
5. The grey RPN is ranked through the application of the method of ranking [10].  
Figure 1 shows the study's procedural flowchart. In this flowchart, the MLPs are accessed based on the criteria from the adopted framework. The risk factors for each criterion are prioritized using the generated FAHP weights of the weighted criteria for occurrence, severity, and detection, respectively.
# A Secure and Scalable Authentication and Communication Protocol for Smart Grids
## 4 Experimental Setup
### Experimental Scenarios  
The following scenarios were assessed to validate the functionality and performance of the protocol:  
* **Secure Authentication and Communication:** Legitimate clients authenticated with the server performed ECDH-based key exchange and securely exchanged encrypted messages using AES. The decrypted message was verified on the server side to ensure successful decryption.
* **Authentication Failure:** Malicious clients attempting to access the server were denied after failed authentication attempts. Such events were logged for further investigation.
* **Network Delays:** Manual delays were introduced to assess the protocol's robustness under varying communication delays.
## 3 Results  
### Performances of the Relative Radiometric Correction  
Figure 7 shows the changes in DN value curves of a BNU-1 image in B1 band when the relative sensor normalization has been applied to correct its radiometric errors. The BNU-1 image was acquired on 5 July 2020 (orbit number: 2000021032). The black line in Figure 7 is the DN value curve of the BNU-1 image in P band, which is considered as the curve without deviation. The blue line in Figure 7 is the DN value curve of the original BNU-1 image in B1 band. An obvious negative deviation can be found in the curve from 0 to 4000 columns. Then, histogram matching is used to correct the deviation. A red curve in Figure 7 is obtained when the histogram of the B1 band is matched to the histogram of the P band. The red curve is very consistent with the black curve. But it retains many detail fluctuations, such as a DN value peak near 3200 columns. Since the radiances in different bands are different, we must apply the mean adjustment to the red curve. In this case, the radiance difference between the B1 and P band is defined as the mean difference of DN values in B1 and P band from 4324 to 5056. The cyan curve is the final DN value curve of the corrected BNU-1 image in B1 band. The negative deviation has been improved in the cyan curve.  
Figure 6: Schematic diagram of image stitching.  
Figure 7: The DN value curves of the original and corrected image. The DN value of every column is averaged in rows. The image was acquired on 5 July 2020 (orbit number: 2000021032). The blue curve shows the DN values from the uncorrected B1 band. The red curve shows the DN values from the B1 band after histogram matching. The cyan curve shows the DN values from the B1 band after histogram matching and mean adjustment, which is the corrected image. The black curve shows the DN values from the P band.
# Evaluation of Ionospheric Delay Effects on Multi-GNSS Positioning Performance
## 3 Data and Processing Strategy
\begin{array}{l}\sigma_{ion,0}^{2},\qquad\quad t<8 \;or\;t>20\;or\;B>\pi/3\\ \sigma_{ion,0}^{2}+\sigma_{ion,1}^{2}\cos(B)\cos(\frac{t-14}{12}\pi),\quad other \end{array} \right. \tag{22}\]  
where \(t\) is the local time at IPP in hours, \(B\) denotes the satellite elevation angle. Since the GIM products have an accuracy of 2-8 TECU, the variance of the zenith ionospheric delay \(\sigma_{ion,0}^{2}\) and the zenith ionospheric delay variation \(\sigma_{ion,1}^{2}\) are set to 0.09 m\({}^{2}\) and 0.09 m\({}^{2}\), respectively. Figure 1: Geographical distribution of the selective MGEX stations. \begin{table}
\begin{tabular}{l l} \hline \hline
**Items** & **Strategies** \\ \hline Data span & 1–30 September 2018 \\ Signal Selection & GPS: L1/L2/L5; BDS: B1/B2/B3; GLONASS: G1/G2; Galileo: E1/E5a/E5b \\ Estimator & SPP: LS PPP: Kalman filter [23] \\ Observation sampling rate & 30 s \\ Elevation cutoff & 7\({}^{\circ}\) \\ Satellite orbit and clock & SPP: broadcast ephemeris provided by MGEX \\  & PPP: Fixed by MGEX orbit and clock products \\ Satellite TGD & Correct using broadcast ephemeris for SPP \\ Satellite DCB & Correct using MGEX DCB products for PPP \\  & SPP: Modified Hopfield PPP: Modified Hopfield for dry part and estimated \\  & for wet part as random-walk process (10\({}^{-9}\) m\({}^{2}\)/s) [24] \\ Ionospheric delay & Eliminated or estimated as white noise Strategies in Table 2 \\ Relativistic effect & General relativistic models [25] \\ [PERSON] effect & Corrected model [26] \\ Phase windup effect & Corrected model [27] \\ Satellite and receiver antenna & Corrected with the values from MGEX and IGS [26] \\ Tide displacement & Corrected, including solid Earth, pole and ocean tide [26] \\ Station reference coordinates & IGS SINEX solutions \\ Station coordinate & Estimated as constants \\ Receiver clock & Estimated as white noise process (1\(\times\)10\({}^{5}\) m\({}^{2}\)) \\ Receiver IFB & Estimated as constants or absorbed by receiver clock biases \\ Phase ambiguities & Estimated as constants for float values \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data processing strategies for multi-GNSS combined positioning. Figure 2: F10.7 values and geomagnetic Kp index on 1–30 September 2018. To obtain the single-frequency SPP solution, three different schemes, ionosphere delay correction with BIM, NTCM and GIM were applied. The rapid GIM products for real-time ionospheric correction were provided by the Center for Orbit Determination in Europe (CODE). For single-frequency PPP, five different schemes including GRAPHIC approach, UC approach, BIM-, NTCM- and GIM-constrained approaches were utilized. For dual-frequency PPP, five schemes including the IF approach, UC approach, BIM-, NTCM- and GIM-constrained approaches, were also applied. The ionosphere processing strategy is summarized in Table 2.
# Wavelet-Based, Blur-Aware Decoupled Network for Video Deblurring
## 5 Ablation Study
### Effectiveness of the MSPF and BADE Modules  
We first validate the necessity of our wavelet-based task decomposition design through ablation studies. As shown in Table 5, removing the MSPF module leads to a 3.4 dB drop in PSNR. Similarly, removing the BADE module results in a 4.99 dB decrease. These results demonstrate that both modules are crucial for effective deblurring.  
To better understand the role of each module, we present visual comparison results in Figure 7. Without the MSPF module, the billboard's edge structure shows obvious distortion and blur. This is because the network lacks a dedicated module for the processing of low-frequency structural information. Without the BADE module, the text details on the billboard become completely indiscernible. This verifies the importance of high-frequency detail enhancement. Only with both modules working together can the network achieve optimal restoration results. This synergy enables accurate reconstruction of both structural integrity and fine details.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **STFAN [49]** &
\begin{tabular}{c} **CDVD-** \\ **TSP [7]** \\ \end{tabular} & **ESTRNN [39]** & **STDAN [9]** & **FGST [12]** & **Ours** \\ \hline Para. (M) & 5.37 & 16.2 & 2.47 & 13.8 & 9.7 & 8.9 \\ Time (s) & 0.16 & 2.03 & 0.21 & 3.24 & 0.78 & 0.41 \\ GMACs & 144.26 & 1275.71 & 203.74 & 1524.22 & 579.87 & 746.53 \\ PSNR & 28.59 & 31.67 & 31.07 & 32.62 & 32.90 & 34.87 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model parameters, inference time, and computational cost comparisons on the GoPro dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Exp.** & **(a)** & **(b)** & **(c)** \\ \hline MSPF & & ✓ & ✓ \\ BADE & ✓ & & ✓ \\ \hline PSNR & 31.47 & 29.88 & 34.87 \\ SSIM & 0.9215 & 0.8965 & 0.9729 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study of the proposed module on the GoPro dataset.
# Intelligent Resource Orchestration for 5G Edge Infrastructures
## 7 Results
### Resource Optimization Results
#### 7.3.2 Minimization of Number of Servers with Latency Violation Constraints  
In this subsection, we analyze the objective function that minimized the number of servers, with different limits on the number of latency violations allowed. Table 4 and Figure 9 summarize the results of these experiments.  
As observed in Table 4, when the limit on latency violations approached the global minimum (29 latency violations), it became necessary to utilize all 12 available servers. However, by relaxing the latency violation constraint, the number of servers in use could be reduced. For a limit of 60 latency violations, it was possible to achieve a solution with the minimum number of servers (9 servers). Increasing the latency violations limit beyond 60 did not lead to any significant improvement in solution quality. Thus, we can conclude that a solution with nine servers and around 60 latency violations represents a favorable trade-off between both variables. Regarding the solver's execution times, we observed that the introduction of constraints increased the time required to achieve an optimal solution. However, these times remained below 20 s in all cases.  
Figure 9 displays the load profiles of these solutions, showcasing only the maximum and minimum values of the actual average daily load, as well as the maximum peak load for simplicity. As observed, a significant disparity existed between the maximum and minimum values of the average daily load. The maximum value was close to 100%, while the minimum value fell below 40%. This imbalance in the load distribution among the different servers is significant and can result in the overloading of certain servers during specific time slots. This is evident in the graph displaying the maximum peak load, which exceeded 100% in all cases.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Limit on Latency** & **Total Servers Used** &
\begin{tabular}{l} **Total Latency** \\ **Violations** \\ \end{tabular} & **Solver Exec. Time** \\ \hline
30 & 12 & 30 & 3.2 s \\
40 & 11 & 40 & 10.7 s \\
50 & 10 & 50 & 18.6 s \\
60 & 9 & 60 & 10.6 s \\ None & 9 & 389 & 0.9 s \\ \hline \end{tabular}
\end{table}
Table 4: Allocation results for the ’number of servers’ objective function under different latency violation constraints.
# Evaluating MPTCP Congestion Control Algorithms: Implications for Streaming in Open Internet
## 2 Materials and Methods  
### Quality Measures  
In this analysis, we aim to identify the most suitable MPTCP CC algorithms for multipath streaming traffic. For quantitative comparison, the following metrics have been chosen: HoL Blocking Degree, protocol delay, Path Delay, mean drop rate, and Throughput.
# Tracked Wall-Climbing Robot for Calibration of Large Vertical Metal Tanks
## 2 Design of Tracked Wall-climbing Robot (TWCR)
### Design of Driving, Transmission, and Steering Mechanism  
Capacity calibrations are conducted before the first use or after maintenance, when the tanks are empty and free from the risk of explosion. Thus, the robot is powered by a wire delivering 220 V AC, which is readily available close to the tanks. An AC servo motor is used as the main power unit of the robot, because of its small size and lightweight. The total torque necessary for both sides that the motor outputs through the reducer are given by:  
\[T\geq\textit{G}\textit{h}+2(M_{1}-M_{2}), \tag{10}\]  
where \(T\) is the necessary torque that the motor outputs through the reducer, \(M_{1}\) and \(M_{2}\) is the torque of the first upper magnetic unit which is engaging with the surface and the lowest magnetic unit which is disengaging with the surface, respectively. \(M_{1}\) and \(M_{2}\) are changing during the motion of the TWCR, and as shown in Figure 5, we have,  
\[M_{1}-M_{2}<M_{1}=F_{m}l_{1}<F_{m}l_{0}, \tag{11}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Length of Air Gap/mm** & **0** & **0.5** & **1.0** & **2.0** & **3.0** & **4.0** & **5.0** \\ \hline Adhesion force/N & 244.1 & 150.2 & 80.6 & 43.5 & 29.6 & 20.2 & 18 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The relation between the length of the gap and the adhesion force of a single magnetic unit.  
where \(F_{m}\) is the adhesion force of each permanent magnetic unit, \(l_{1}\) is the horizontal distance of the center of the permanent magnetic unit and the center of the driven wheel and \(l_{0}\) is the length of the permanent magnetic unit, \(l_{0}\)\(=15\) mm. Hence, we have,  
\[T\geq 127.4\times 0.0312+2\times(80.6\times 0.015)=\ 6.4\ \mathrm{Nm} \tag{12}\]  
As can be seen in Figure 6, a 30 W AC gear motor with a reduction ratio of 1:36 is installed on the baseboard and delivers power to the gear train comprising two electromagnetic clutches. The rated torque of the motor is 0.35 Nm, and the transmission efficiency of the clutches are 95%, thus, we have,  
\[T_{1}=0.35\times 36\times 0.95=10.26\ \mathrm{Nm}\ >\ 6.4\ \mathrm{Nm} \tag{13}\]  
which means that the motor meets the requirement of the Equation (12).  
Compared to the use of two motors for traction, a single motor with two clutches, though the presence of clutches introduces extra power consumption and transmission loss, make sufficient room for the calibration system on the compact body. The torque transmitted by the electromagnetic particle clutch is linear with the control current, which means the torque can be easily controlled. When the torque transmitted by the two clutches is the same, the TWCR moves straight, and when there is a torque difference, the wall-climbing robot will steer.
# A Machine Learning Method with Hybrid Feature Selection for Improved Credit Card Fraud Detection
## 5 Results and Discussion  
The proposed method's classification performance and other baseline classifiers are presented and discussed in the section. The machine learning models were implemented using scikit-learn [63], a widely used library for machine learning in Python. Meanwhile, the proposed method is compared with the following baseline classifiers: AdaBoost [64], logistic regression (LR) [65], random forest (RF) [66], SVM [67], and decision tree [68]. Furthermore, the stratified 10-fold cross-validation method is employed to measure the performance of the prediction models. The stratified k-fold technique ensures that the proportion of fraudulent and legitimate instances in the dataset is preserved in each fold, and it is usually more suitable for imbalance classification problems than the k-fold cross-validation method [69].  
The following metrics are used to evaluate the performance of the models: sensitivity, specificity, the receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC). Sensitivity refers to the model's ability to predict a fraud transaction as fraud. Usually, a highly sensitive model is preferred in fraud detection, as it implies there are no or few false negative predictions [70; 71]. Meanwhile, the specificity of a model is its ability to predict non-fraudulent transactions as legitimate. A model with high specificity means there are hardly any false positive predictions [72]. Sensitivity and specificity can be computed as follows:  
\[\text{Sensitivity}=\frac{TP}{TP+FN} \tag{9}\]  
\[\text{Specificity}=\frac{TN}{TN+FP} \tag{10}\]  
The ROC curve is used to visualize the performance of binary classifiers. It plots the true positive rate against the false positive rate at various classification thresholds [73]. In contrast, the AUC summarizes the ROC curve, and its value ranges from 0 to 1. An AUC of 0 implies the model's predictions are all wrong, and a value of 1 implies all the model predictions are correct. The AUC is a crucial metric in imbalance classification problems, such as fraud detection, as it indicates the classifier's ability to differentiate between the fraud and non-fraud classes.  
Furthermore, in line with existing literature, this study estimates the best GA parameters by conducting several trials using different combinations [74; 75; 76]. The final GA parameters used in this study are outlined in Table 1.