esa-sceva/satcom-chunk-collection huggingface dataset:
# Truncated Newton Kernel Ridge Regression for Prediction of Porosity in Additive Manufactured SS316L
## 3 Applied Regression Models and Modelling
### Ridge Regression (RR)  
The least squares regression is the most fundamental regression model. Linear regression is the simplest form of the least squares method, where the relationship between the variables is described by a line, which in matrix form can be expressed as  
\[\mathbf{y}=\mathbf{X}\mathbf{\beta}+\mathbf{\epsilon} \tag{1}\]  
where \(\mathbf{y}\in\mathbb{R}^{N}\) is the dependent variable vector and \(\mathbf{X}\in\mathbb{R}^{N\times d}\) is the data matrix representing \(d-1\) independent variables and \(N\) rows, \(\mathbf{\beta}\) is the coefficient vector, and \(\mathbf{\epsilon}\ \in\mathbb{R}^{N}\) is the random error vector [37]. The solution to the minimization of the sum of squared errors (\(SSE\)) with regard to \(\mathbf{\beta}\) in Equation (2) is given by Equation (3):  
\[SSE=(\mathbf{y}-\mathbf{X}\mathbf{\beta})^{T}(\mathbf{y}-\mathbf{X}\mathbf{\beta}) \tag{2}\]  
\[\mathbf{\hat{\beta}}=\left(\mathbf{X}^{T}\mathbf{X}\right)^{-1}\mathbf{X}^{T}\mathbf{y} \tag{3}\]  
\(RR\) is an estimation method that can be used to improve prediction in regression situations involving strongly correlated parameters. This approach can prevent incorrect regression coefficients induced by multicollinearity in the least squares regression [38]. As a result, \(RR\) is frequently employed to avoid overfitting by adding a regularization parameter \(\lambda\geq 0\) to the SSE function, as presented in Equation (4).  
\[SSE=f(\mathbf{\beta})=\frac{1}{2}(\mathbf{y}-\mathbf{X}\mathbf{\beta})^{T}(\mathbf{y}-\mathbf{X}\mathbf{ \beta})+\frac{\lambda}{2}\left|\left|\mathbf{\beta}^{T}\mathbf{\beta}\right|\right|^{2} \tag{4}\]  
The solution to Equation (4) is given by  
\[\mathbf{\hat{\beta}}=\left(\mathbf{X}^{T}\mathbf{X}+\lambda I_{d}\right)^{-1}\mathbf{X}^{T} \mathbf{y} \tag{5}\]  
where \(\mathbf{I}\) is the \(d\times d\) identity matrix. Equation (5) shows that if \(\lambda=0\), the \(RR\) reduces to the linear regression. Compared to ordinary unbiased linear regression, \(RR\) decreases the complexity of the model and mitigates multicollinearity by accepting a small amount of bias to reduce the variance and the \(MSE\), thus helping to improve the prediction accuracy.
## 3 Results  
### Agreement with the Experts on a Patient-Level and PG-Level Basis in the 2010-2020 Group  
According to experts' diagnosis, the 2010-2020 dataset consisted of 198 subjects with no abnormal findings and 450 with at least one abnormal finding. The experts identified 504 abnormal PGs in the 450 abnormal scintigraphic scans. These findings were the ground truth for estimating the model's performance. Table 4 presents the results.  
On a patient-level basis, the model attains an accuracy of 0.9861, a sensitivity of 0. 9889, and a specificity of 0.9798. The PPV and NPV values are 0.9911 and 0.9749, respectively. The high F1 score (0.99) indicates that the model is not biased towards the majority class despite the class imbalance issue. The number of false positives was four, and the number of false negatives was five on a patient-level basis. The reader should note that the reported metrics are computed based on human-reader labelling. Therefore, the current experiment measures the agreement between the model and the experts' verdict.  
There is a decline in accuracy when inspecting the PG-level performance of the model. The latter inspection was performed with the aid of the Grad-CAM++ algorithm. Examples of the outputs are shown in Figures 5 and 6. Specifically, the model identified 462 of the 504 PGs. Eventually, 195 PGs were correctly identified as normal (true negatives). Subsequently, the model achieved an accuracy of 0.8831 (Table 4), 0.9167 sensitivity, and 0.8125 specificity. The positive predictive value was 0.9112, and the negative predictive value was 0.8228. The false-positive findings were 45, and the false-negative findings (abnormal PGs that the model ignored) were 42.
# A Novel Capsule Network Based on Wide Convolution and Multi-Scale Convolution for Fault Diagnosis
## 1 Introduction
The changing conditions of the actual industrial site (such as load and speed changes) require the algorithm to further improve its generalization. Finally, few algorithms can perform well in strong noise environments, as can be clearly seen in the experimental results in Section 4.3. This makes it difficult to apply the algorithm in actual factories. In view of the above shortcomings, a novel capsule network based on wide convolution and multi-scale convolution (WMSCCN) for fault diagnosis is proposed. The algorithm has the following innovations. 1. A novel capsule network for fault diagnosis is proposed which takes the original signal as input and does not require any time-consuming manual feature extraction processes. 2. The proposed WMSCCN algorithm has a high diagnostic accuracy in different working conditions and is superior to other advanced models, such as Deep Convolutional Neural Networks with Wide First-Layer Kernels (WDCNN) [22]. 3. By adding noise to the test set of data to simulate noise pollution in the industrial environment, the proposed model still achieves a higher accuracy when compared to other algorithms and has a better anti-noise ability. The rest of the paper is organized into four sections. The theory of CNN and capsule network is briefly introduced in Section 2, and Section 3 describes the technical details of the proposed WMSCCN algorithm. Two comparative experiments are carried out and some results are visualized in Section 4. In the last section, this paper presents the conclusions of the study.
# Automatic Wireless Signal Classification: A Neural-Induced Support Vector Machine-Based Approach
## 4 Simulation Results and Discussion
### Performance of NSVM with Different Channel Impairments  
Signal classifiers don't have any prior knowledge of when and where a transmission will occur. Hence, a wide signal is pre-processed to a based-band type, which is then considered for classification. There might be many channel variations still there due to the estimation error at the pre-processing. Therefore, to analyze the robustness of NSVM, the experiments are carried out on the test data with four different channel impairments, e.g., Carrier Frequency Offset, Timing Offset, Phase Offset and Velocity. The test set for each of these parts is generated separately, which consists of 2000 test realizations for each channel variation at each SNR value being generated for this section.  
Effect of Carrier Frequency Offset: The performance of NSVM due to different carrier frequency offset is shown in Figure 10. It can be observed that with the increase in carrier frequency, the performance of the proposed classifier degrades significantly. However, the performance is worse at a low SNR. This is due to the fact that the model is not using that data with offset. However, the performance of the proposed scheme at higher SNR manages to achieve reasonable performance. This is due to the fact that the higher order modulations are insensitive to offset in the carrier. To compensate the performance at a lower order, the proposed model can be retrained on the data, having random frequency offsets.  
Effect of Phase Offset: In this section, the effect of phase offset observed is investigated. Since the prior knowledge of the phase offset is symmetric; therefore, the influence of positive and negative deviations is the same. The performance of NSVM is observed for the test with the following \(\Delta P=(0^{\circ},7\cdot 5^{\circ},15^{\circ},22\cdot 5^{\circ},30^{\circ})\) phase offsets in the received signal. The performance of NSVM with \(\Delta P\) is given by Figure 11. The algorithm generalizes better despite different phase offsets, as the performance remains the same; however, there are slight changes in the performance at low SNR values.  
Effect of Timing Offset: Next, the performance of timing offset in the received symbol is discussed in Figure 12. Since, the proposed architecture considers 256 input symbols in a frame as an input, therefore, this effect can easily be compensated. However, this may not compensate for the bit-error rate due to the loss of information despite the correct signal classification.  
Effect of Velocity: In the real world, the signal is highly distorted due to small scale fading effects such as, multipath fading, scattering and Doppler shifts. Here, the performance of NSVM is observed under different Doppler shifts. The algorithm performance degrades with the increase in the Doppler shift caused by high velocity, which is given by Figure 13. Therefore, to overcome this, similarly to countering carrier frequency, offset can be used.  
Figure 10: Performance of NSVM with Different Carrier Frequency Offset in received signal.
## 3 Field Monitoring
### Analysis of Monitoring Results
#### 3.2.3 Ground Settlement  
It can be seen from Figure 8 that the maximum value of ground settlement also has an obvious spatial distribution effect. The maximum value of the surrounding ground settlement occurs in the middle of the long side. The variation of ground settlement with working conditions for different monitoring sections is shown in Figure 9. The excavation of the foundation pit will disturb the surrounding strata and cause the ground around the pit to settle. At the early stage of excavation, the ground settlement is small. As the work proceeds, the ground settlement increases significantly and the final settlement value tends to be stable. At the same time, the distribution rule of ground settlement around the pit can be seen: the maximum settlement value is not near the pit edge, but a certain distance  
Figure 5: Horizontal displacement curves of piles under different working conditions: (**a**) ZQT-7, (**b**) ZQT-1, (**c**) ZQT-34, (**d**) ZQT-33.  
Figure 6: Maximum horizontal displacement of piles.  
away from the pit edge. The maximum settlement position is about 6.5 m away from the pit edge.  
After excavation of the foundation pit, the piles lost the support of the original inner soil as the inner soil of the pit was excavated. The piles are subjected to active earth pressure exerted by the pit soil, which causes a change in the stress and displacement fields of the soil around the pit, resulting in deformation and displacement of the support structure, which is consistent with the results in Figure 5.  
The same row of piles on each side of the pit is restrained by the waist beam to form a single unit, and the forces of the structure are similar to those of a slab structure. The slab is restrained by the piles on one vertical side at the corner position of the pit and by the soil at the bottom of the pit, with the top of the piles in a free state. Both earth pressure and anchor cable forces act on the slab structure. As a result, the slab structure is subject to large deformations near the middle, with less deformation at the corners of the pit and near the bottom of the pit, in line with the results shown in Figures 6 and 7.  
Figure 8: Maximum ground settlement.  
Figure 7: Horizontal displacement of piles top.  
Figure 9: Distribution of ground settlement around pit under different case: (a) DBC-7, (b) DBC-33, (c) DBC-34.  
When the piles in the pit are deformed, the soil around the pit is displaced by the active earth pressure, which will fill the space formed by the deformation of the piles and cause the settlement of the ground outside the pit. As the pile deformation in the middle of the pit is greater than the deformation in the corner of the pit, the ground settlement appears similar in nature, with the ground settlement near the middle of the pit being greater than the ground settlement near the corner of the pit, the same as the results in Figure 8.
# GNSS Spoofing Detection and Mitigation Based on Maximum Likelihood Estimation
## 4 MLE-Based GNSS Anti-Spoofing Method
### Model of Spoofing Signal  
When there is a spoofing attack, the baseband signal can be expressed as:  
\[\mathbf{x}=\mathbf{G}(\mathbf{\rho}_{a})\mathbf{a}_{a}+\mathbf{G}(\mathbf{\rho}_{s})\mathbf{a}_{s}+\mathbf{w}, \tag{11}\]  
where \(\mathbf{\rho}_{a}\) and \(\mathbf{a}_{a}\) are the PVT and amplitude parameters of the authentic signals and \(\mathbf{\rho}_{s}\) and \(\mathbf{a}_{s}\) are the corresponding parameters of the spoofing ones. Since a spoofer aims to mislead the PVT results of the victim receiver, \(\mathbf{\rho}_{s}\) is different from \(\mathbf{\rho}_{a}\). Consequently, the authentic and spoofing signals will form different peaks in the cost function.  
In order to view the MLE cost function under a spoofing attack intuitively, it is assumed that only \(x_{u}\) and \(y_{u}\) are unknown, and the search space is limited to \([x_{u},y_{u}]\). The two-dimensional cost function \(J\) with eight authentic signals and eight spoofing ones is shown in Figure 1. The center of the search space corresponds to \(\mathbf{\rho}_{a}\) or \([\delta x_{u},\delta y_{u}]=[0,0]\). The position offset induced by the spoofing signals is \([\delta x_{u},\delta y_{u}]=[0,600]\) m, and the other parameters in \(\mathbf{\rho}_{a}\) and \(\mathbf{\rho}_{s}\) are the same. The \(C/N_{0}\) of each authentic signal is 45 dB-Hz, and the power of a spoofing signal is 0.8 dB higher than that of the corresponding authentic one. It can be seen that when \([\delta x_{u},\delta y_{u}]=[0,600]\) m, the cost function is maximized, and when \([\delta x_{u},\delta y_{u}]=[0,0]\), another peak, which is slightly lower than the maximum value of the cost function, can be found. If the MLE-based positioning is employed, since the power of the spoofing signals is higher than that of the authentic ones, the peak at \(\mathbf{\rho}_{s}\) will be the highest, and the ML estimate of \(\mathbf{\rho}\) will be equal to \(\mathbf{\rho}_{s}\). That is to say, when the conventional MLE-based positioning method is used, the spoofing attack can still mislead the PVT result.
# Lightweight Insulator and Defect Detection Method Based on Improved YOLOv8
## 4 Experimental Results and Analysis
### Experimental Result Analysis
Through iterations of the YOLO network version, the network structure has become more reasonable. The computational and parameter complexity of the YOLOv8s model is significantly lower than earlier versions like YOLOv3, mainly due to updates that reduced the number of residual blocks and convolutional kernels and reorganized module sequences to improve network performance. For the detection task in this study, YOLOv8s demonstrates high precision, with all metrics above 96% and an average accuracy of 96.3%. However, in terms of model parameters, YOLOv8s remains larger compared to lightweight networks such as MobileNetv3. Compared to conventional object detection networks, lightweight networks like MobileNetv3 and ShuffleNetv2 have lower computational and parameter complexity but also exhibit lower recall rates (R), indicating poorer detection capabilities. In practical detection scenarios, these lightweight models often miss detections, identifying many potential targets as background, especially smaller defect targets. Therefore, this study addresses the insufficient detection accuracy of lightweight networks by adopting the multi-scale fusion concept of the YOLO network, achieving practical effectiveness. Furthermore, the proposed algorithm reduces the model's parameter and computational complexity by incorporating MobileNetv3 modules to optimize the YOLOv8s Backbone network, achieving reductions of 46.6%. Meanwhile, our improved model has a slight increase in precision, recall, and detection accuracy compared to the original YOLOv8s network; for example, the [EMAIL_ADDRESS] increased by about 2% while the GFLOPs decreased. Our model has all metrics remaining above 97%, indicating that the performance of the proposed model far exceeds the task requirements. Thus, appropriate lightweight modifications do not compromise performance. The improved network outperforms the MobileNetv3 network itself and effectively accomplishes detection tasks, with computational complexity comparable to MobileNetv3 after lightweight optimization, significantly enhancing detection accuracy. Based on the comparative analysis above, the optimized method in this study introduces depthwise separable convolution to lightweighting, enhancing the YOLOv8s model. The incorporation of depthwise separable convolution into the YOLOv8s model improves its ability to sustain high accuracy and robustness in object detection tasks while significantly reducing resource consumption. This enhancement results in a substantial decrease in both parameter count and computational complexity, without compromising precision. This adaptation ensures that the YOLOv8s model meets the practical requirements for real-time applications on mobile platforms, balancing high performance with minimal computational overhead.
## 4 Simulation Results
### Simulation Tests Conducted
#### 4.3.3 Comparison with Original Controller  
The IQV-based control algorithm was also compared with the original algorithm in terms of performing the trajectory tracking task. The same trajectories, operating conditions and control coefficients (42) were assumed.  
The performance parameters of the original controller are shown in Table 4. Comparing the parameter values in Tables 2 and 4, it can be observed that the original algorithm gave better MIA results on the x direction than the IQV algorithm. However, this is an error difference of only 0.2 mm (sine) and 2.5 mm (complex). In the y direction, the error difference was 2.3 mm (sine). For the complex trajectory, the IQV controller delivered a lower value of 2.0 mm. The RMS index in both test cases had lower values after the IQV controller (2.7 mm--sine and 0.9 mm--complex). The smaller values of the MIAC and KE indexes are due to the fact that the original algorithm does not take into account the effect of vehicle asymmetry (additional mass components).  
It is also worth noting that the favorable results from the original algorithm cannot be directly compared with those from the IQV algorithm, because the latter works under more difficult conditions resulting from vehicle asymmetry.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**Sine Trajectory**} & **Sine Trajectory** & **Complex Trajectory** \\
**Index** & & **without Friction** & **with Friction** & **without Friction** \\ \hline MIA & \(\Delta p_{x}\) & 0.0713 & 0.1209 & 0.0260 \\  & \(\Delta p_{y}\) & 0.0955 & 0.1071 & 0.0487 \\ MIAC & \(\tau_{u}\) & 1.5790 & 2.4533 & 2.0831 \\  & \(\tau_{r}\) & 0.3539 & 0.4137 & 0.3547 \\ RMS & \(||e||\) & 0.2567 & 0.2657 & 0.1593 \\ KE & \(K_{m}\) & 0.6293 & 0.5866 & 0.9914 \\ \hline \end{tabular}
\end{table}
Table 3: Performance for TSMC controller.  
Figure 7: Simulation results for TSMC and complex trajectory (without friction): (**a**) desired (\(p_{d}\)) and realized (\(p\)) trajectory; (**b**) position errors \(\Delta p_{x},\Delta p_{y}\) (in inertial frame); (**c**) input signals components (applied force and torque) \(\tau_{u},\tau_{r}\); (**d**) actual velocities \(u,v,r\).
## 4 Results  
In this study, to measure participants' trust in AI, we utilized a questionnaire where respondents rated their trust level on a seven-point Likert scale, ranging from 1 (Strongly Disagree) to 7 (Strongly Agree). The responses were collected under three different task difficulty conditions, subsequently labeled \(T_{0}\), \(T_{1}\), and \(T_{2}\). To analyze the differences in trust levels across these conditions, we also calculated the differences between these values, such as \(\Delta T_{01}\) (\(T_{1}\) minus \(T_{0}\)) and \(\Delta T_{12}\) (\(T_{2}\) minus \(T_{1}\)).  
For data analysis, we employed the Kruskal-Wallis test, a non-parametric method used in K-independent sample analysis. This test was chosen for its ability to handle data from different groups without assuming a normal distribution, making it suitable for our study where data might not conform to normality due to the varied nature of human responses in trust levels. The Kruskal-Wallis test is particularly effective in comparing more than two groups, as in our case with three different levels of task difficulty. The analysis yielded results including the chi-square value, which represents the degree of correlation between the datasets and the degree of freedom of error, providing insights into the variability within the data. Additionally, the asymptotic significance obtained from the test offered an intuitive understanding of the trust trends across different task complexities. These results helped in assessing the impact of task difficulty on participants' trust in AI, forming a crucial part of our study's findings.
## 5 Technology and Case Studies
### Lightened AAC Grotus  
The AAC with M\({}_{\rm b}\) = 1.1 was chosen for lightened grouts for various work conditions (temperatures of 22-75 \({}^{\circ}\)C and pressures of 0.1-35.0 MPa) for gas-oil well cementing due to its high corrosion and temperature resistance [10; 78]. The use of admixtures is intended to regulate their viscosity and reduce shear stress [91; 92; 93].  
The compositions of the lightened grouts (Table 4) were designed based on the specific features of gas-oil well cementation of a casing pipe in two steps (two sections) (Figure 15). The first step required that Grout #1 would flow through the lowest point (well bottom) (t = 75 \({}^{\circ}\)C, P = 35 MPa) and rise to the upper point (head) of the first section (work conditions: temperature range of 60-50 \({}^{\circ}\)C and pressures of 20-25 MPa). The second step included the cementation of the second section, where Grout #2 flowed through its bottom (t = 50 \({}^{\circ}\)C, P = 20 MPa) with the following rise to the well heel (t = 35-22 \({}^{\circ}\)C and P = 0.1-10 MPa).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Grout**} & \multicolumn{6}{c}{**Components, \% by Mass**} \\ \cline{2-7}  & **GBFS** & **Clinker** & **Bentonite** & **Soda-Ash** & **Sodium Metasilicate** & **Sodium Nitrate** & **Sorbitol** \\ \hline \#1 & 80.94 & 4.65 & 7.44 & 2.79 & 1.86 & 1.86 & 0.46 \\ \hline \#2 & 73.05 & 8.91 & 7.13 & 6.24 & 2.67 & 1.78 & 0.22 \\ \hline \end{tabular}
\end{table}
Table 4: Compositions of the lightened AAC grouts.  
The rheological properties of the lightened grouts can be regulated by polyols. The values of plastic viscosity and dynamic shear stress comply with the recommended values for effective use in oil and gas well cementation. These characteristics do not change with the temperature increase, testifying to the long-term retention of the action effect of sorbitol. Sorbitol ensures stabilization of plastic viscosity at various rates of deformation, similar to the rheological properties of Bingham fluid, within the use temperature range of 22-75 \({}^{\circ}\)C.  
The formulated mixes of lightened AAC groups had properties that met the requirements for oil-gas well cementation of a well casing pipe in two steps (two sections).  
The properties of the designed lightened AAC groups:  
Grout #1: density--1.42 g/cm\({}^{3}\); consistency (flow)--250 mm; water separation--5.2 mL; plastic viscosity--8.5 mPa-s (22 \({}^{\circ}\)C) and 12.3 (75 \({}^{\circ}\)C); dynamic shear stress--2.0 Pa (22 \({}^{\circ}\)C) and 1.6 (75 \({}^{\circ}\)C); flexural strength at 2 days--0.7 MPa (22 \({}^{\circ}\)C) and 1.7 MPa (75 \({}^{\circ}\)C);  
Grout #2: density--1.43 g/cm\({}^{3}\); consistency (flow)--250 mm; water separation--4.6 mL; plastic viscosity--12.6 mPa-s (22 \({}^{\circ}\)C) and 11.8 mPa-s (50 \({}^{\circ}\)C); dynamic shear stress--2.7 Pa (22 \({}^{\circ}\)C) and 2.9 Pa (50 \({}^{\circ}\)C); flexural strength at 2 days--0.8 MPa (22 \({}^{\circ}\)C) and 2.1 MPa (75 \({}^{\circ}\)C).