esa-sceva/satcom-chunk-collection huggingface dataset:
# Failure Mode and Effects Analysis Considering Consensus and Preferences Interdependence
## 5 Case Study
### Comparisons and Discussion  
To further demonstrate the effectiveness of the proposed method, we use the result to compare some similar approaches, including the RPN method, the fuzzy TOPSIS [14], the fuzzy VIKOR [36], the fuzzy MULTIMOOR [20], and the IFHWED-based FMEA [54] in this section. Table 6 shows the ranking results of all nine failure modes obtained by implementing the six approaches. The ranking order of failure modes obtained by the proposed method is partly different from the ones according to the RPN value, but \(FM_{7}\) possesses the highest risk in the two methods. The \(FM_{2}\) and \(FM_{7}\) failure modes have the same risk in the RPN method, which indicates that different values of S, O, and D may produce the same value of RPN. In this situation, it is difficult to distinguish the risk between \(FM_{2}\) and \(FM_{7}\) for a decision-maker. However, the risk of \(FM_{2}\) is lower than that of \(FM_{7}\) in the proposed method. Therefore, this drawback of the RPN approach can be solved easily by using the proposed method. The priority of the nine failure modes produced by the fuzzy TOPSIS method is significantly different from the ones determined by the proposed approach. The ranking order obtained by the fuzzy TOPSIS method may be irrational because it does not take into account the interdependent relationships between the experts' preferences. However, the process of risk assessment based on the FMEA team is considered as a group decision process, and there are many different types of correlations between experts. In addition, each FMEA team expert was considered to have equal importance in the fuzzy TOPSIS method. In reality, FMEA team experts usually come from various \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Failure Modes**} & \multicolumn{5}{c}{**RPN Method**} & \multicolumn{5}{c}{**Proposed Method**} & \multirow{2}{*}{[14]} & \multirow{2}{*}{[36]} & \multirow{2}{*}{[20]} & \multirow{2}{*}{[54]} \\ \cline{2-2} \cline{6-11}  & **S** & **O** & **D** & **RPN** & & & & & & & \\ \hline \(FM_{1}\) & 6 & 6 & 5 & 180 & 7 & 6 & 7 & 6 & 7 & 7 \\ \(FM_{2}\) & 8 & 7 & 6 & 336 & 1 & 2 & 2 & 3 & 2 & 3 & 2 \\ \(FM_{3}\) & 7 & 5 & 8 & 280 & 3 & 3 & 3 & 4 & 5 & 4 & 5 \\ \(FM_{4}\) & 6 & 5 & 4 & 120 & 9 & 9 & 9 & 9 & 8 & 9 & 9 \\ \(FM_{5}\) & 7 & 6 & 4 & 168 & 8 & 8 & 8 & 7 & 6 & 6 & 6 \\ \(FM_{6}\) & 8 & 7 & 4 & 224 & 4 & 5 & 5 & 5 & 3 & 5 & 4 \\ \(FM_{7}\) & 7 & 8 & 6 & 336 & 1 & 1 & 1 & 2 & 1 & 2 & 1 \\ \(FM_{8}\) & 10 & 5 & 4 & 200 & 6 & 4 & 4 & 1 & 4 & 1 & 3 \\ \(FM_{9}\) & 6 & 6 & 6 & 216 & 5 & 7 & 6 & 8 & 9 & 8 & 8 \\ \hline \hline \end{tabular}  
* The crisp values of S, O, and D are obtained by transforming the elements of matrix C (Step2.3) by the equation \(c(c_{ij})=ceil(10\times(s(c_{ij})+1)/2)\), where \(ceil(x)\) is a function that returns the smallest integer \(\geq x\). RPN: risk priority number.  
\end{table} 
Table 6: Ranking of failure modes of different approaches.
# Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review
## 6 Applications
### Physiological Feedback and Training in ADHD  
There is wide scope for use of biological-based feedback to help train individuals to boost engagement and become more self-aware of when their attention is about to lapse, not only during measurements but as a training regime that aspires to enable transferable effects beyond monitoring settings.  
One specific clinical condition that has had wide testing for biofeedback training is ADHD. A systematic review [127] shows that EEG-based brain-computer interface (BCI) along with a video game can be feasible and effective in improving attention measures in children with ADHD. More broadly, quite a bit of research has examined the possibility of EEG-based neurofeedback as interventions for ADHD, especially targeted at the aforementioned theta-beta ratio (TBR), which has shown promise in some contexts [155], though limitations in others [156; 157]. This has potential to wider applications for any work role requiring minimal attention lapses. However, EEG is not always feasible in the home setting.  
Potentially more feasible in the non-lab setting is feedback based on eye-tracking. A systematic review of this question [158], which included ADHD but also other neurodevelopmental and neurological conditions, concluded that feedback based on eye-gaze tracking was effective in improving both cognition and emotions.  
As discussed in Section 3.2, pupil diameter also links to arousal and engagement state; interesting new findings show that dilation-based biofeedback can enable individuals to control pupil dynamics and linked psychophysiological measures [159]. However, this research is still limited and not yet tested in the context of ADHD or real-world environments. As mentioned above, also relevant for non-lab setting would be a wristwatch-style cardiac measurement that can dissociate engagement with task demands in ADHD [113] or risk of non-engagement as the time on task lengthens [114] and thus could provide biofeedback in these contexts.
# Analysis of Wafer Warpage in Diamond Wire Saw Slicing Sapphire Crystal
## 3 Finite Element Modeling
### Calculation of Wafer Warpage Based on Finite Element Method  
The warpage of a wafer is defined as the difference between the maximum and minimum distances between the wafer center plane and the reference plane, as shown in Figure 4, i.e., warpage = Smax - Smin. Among them, the reference plane is an imaginary reference plane.  
Figure 4: Schematic diagram of the definition of wafer warpage [5].  
Figure 3: Finite element simulation flowchart.  
[PERSON] et al. [5] proposed a method for calculating wafer warpage based on finite element simulation. The total thickness deviation of the wafer is assumed to be zero, i.e., TTV = 0. In finite element analysis, the sapphire wafer before deformation is considered as the reference plane, and the wafer surface after thermal deformation during sawing is considered as the center plane. Therefore, the deformation displacement of the wafer surface nodes during sawing is used to calculate the wafer warpage, as shown in Figure 5. In this paper, 25 nodes are uniformly taken along the direction perpendicular to the wire moving direction along the wafer cut surface. The wafer warpage is calculated by extracting the thermal deformation displacement of the nodes during the cutting process, similar to the point-by-point scanning method in experimental measurements, as shown in Figure 6.
# Residual Dense Swin Transformer for Continuous-Scale Super-Resolution Algorithm
## 3 Methodology
### Network Architecture
#### 3.1.2 Upsampling Module Using LIIF  
Inspired by [18], we use the local image implicit function \(f_{\theta}(\cdot)\) in the upsampling module to express the discrete image continuously, namely, \(I=f_{\theta}(z,x)\). The input of the function is any coordinate \(x\) to be predicted, the corresponding feature vector is \(z\), and the output is the RGB value \(I\) at this coordinate. The corresponding eigenvectors of the actual predicted coordinates cannot be obtained directly, so they are estimated by using the eigenvectors of the four nearest coordinates around the predicted coordinates.  
Figure 1: Flowchart of residual dense Swin Transformer.  
The specific super-resolution process is as follows: We first perform feature unfolding on the fusion feature \(F_{MF}\) in the upsampling module, and we use our own information to enrich each feature vector in the feature map. The specific method is expressed in the following formula:  
\[F_{MF}^{(n,i,j)}=Concat\left(\left\{F_{MF}^{(n,i+k,j+k)}\right\}\right)\ k\in-1,0,1 \tag{4}\]  
where \(F_{MF}^{(n,i,j)}\) represents the nth feature vector in fusion feature \(F_{MF}\) and its coordinates are \((i,j)\). Then, we use the nearby feature vector to predict the RGB value of the corresponding coordinate \(x_{q}\); the specific process is as follows:  
\[I^{(n)}\big{(}x_{q}\big{)}=\sum_{t\in 0,01,10,11}\frac{S_{t}}{S}\cdot f_{ \theta}(F_{MF}^{(n,i,j)},x_{q}-v_{t}^{(n)}) \tag{5}\]  
where \(v_{t}^{(n)}\) represents the coordinates of the corresponding feature vector \(F_{MF}^{(n,i,j)}\), \(S_{t}\) is the rectangular area of the diagonal coordinates of \(x_{q}\), \(v_{t}^{(n)}\), \(S\) is the total area corresponding to the four eigenvector coordinates, and \(f_{\theta}(\cdot)\) represents the function of the RGB value of the predicted coordinates. Considering that the relationship between the position of the pixel to be predicted and its surrounding pixels is different when the actual magnification is different, the cell parameter is also introduced into the function \(f_{\theta}(\cdot)\), which refers to the size of the pixel under different magnifications. In the actual prediction process, a five-layer MLP can be used to achieve a super-resolution, continuous-scale of image.
## 3 Results
### Assessment of Intensity Variability and Segmentation Performance
#### 3.2.2 Comparison Amongst Brain Extraction Methods
On the other hand, applying the N4 ITK correction showed statistically significant differences between the five BE methods, regarding the following RSVs (Table 5): CGM--differences between Manual, BET2, and SWS methods with SynthStrip; WM--differences between Manual, SWS, HD-BET, and SynthStrip methods with BET2; CSF--differences between Manual, BET2, SWS, and HD-BET methods with SynthStrip; DNGM--differences between BET2 and SynthStrip methods with SWS; CB--differences between Manual, BET2, HD-BET, and SynthStrip methods with SWS, and BET2 method compared with HD-BET; BS--differences between HD-BET and SynthStrip methods with SWS, and Manual method compared with HD-BET. Most statistically significant results showed a large effect size (\(r>0.5\)) [72], indicating a substantial difference between groups. Figure 5 provides a visual perspective of the regional segmentation differences when using the five studied BE methods. It is perceptible how they differ between them in the edges, exceeding the brain area (e.g., BET2) or the opposite (e.g., SynthStrip), compromising the regional segmentation of the remaining tissues differently. Considering the results from DC (Figure 4), the most suitable automated BE methods were SWS and HD-BET, with the following differences (Table 5) between them: (1) manual BE and SWS were significantly different regarding the CB (\(p<0.001\)), independently of the BFC method used; (2) manual BE and HD-BET showed differences regarding the CGM (\(p=0.017\)) and BS (\(p=0.003\)), with SPM-BFC, and differences regarding the BS volume (\(p=0.002\)), with N4 ITK correction. SWS and HD-BET differ from each other on the volumes obtained from CGM (\(p=0.014\)), CSF (\(p=0.010\)), DNGM (\(p=0.003\)), CB, and BS (\(p<0.001\)), after applying SPM-BFC (Table 5). However, when correcting with N4 ITK, they did not differ from each other on the cerebrum tissue volumes, only on CB and BS (\(p<0.001\)) (Table 5).
## 2 Background and Related Work  
### Cache and Memory Prefetching  
Hardware prefetching is a well-studied field, and various methodologies have been suggested by many computer architects. Among them, some basic ideas have had a particular influence on numerous prefetching algorithms, which still fit well to some workloads [14]. [PERSON] et al. [4] proposed a stride-based prefetching method. Two types of approaches are used to seek stride patterns using instruction program counters and physical memory addresses. To find stride patterns from various memory access streams, this method records recent deltas or addresses [6] (a difference of an address value between two consecutive demand memory requests). If the same stride pattern consecutively occurs, the prefetching operation is triggered by adding the stride to the recent memory request address.  
[PERSON] et al. [15] proposed a Markov predictor-based prefetching method. It analyzes correlations between physical memory addresses using an address history table. When a memory miss occurs, the address is recorded sequentially to observe the correlation. If the prefetcher finds the same memory access pattern in the history table, the next memory address in the list will be prefetched. This method is effective for covering temporal irregular patterns; however, this method has a disadvantage of requiring a large storage size for storing chains of memory requests. In addition, [PERSON] et al. [5] proposed a locality-based prefetcher. Most memory access patterns in applications do not show fixed strides. Some applications concurrently generate multiple memory streams of adjacent memory address requests, which look like multiple random memory access sequences. As analyzed by previous study, conducted by [PERSON] et al. [6], memory access sequences would sometimes appear random patterns, but in the view of different granularity and delta-based classification, in fact, those streams have multiple and separated regular patterns. To handle these kinds of irregular memory streams, the feedback directed prefetching (FDP) method [5] evaluates factors such as a prefetch accuracy, prefetch timeliness, and a degree of cache pollution caused by prefetching operation to control the aggressiveness or the conservativeness of the prefetching. This locality-based prefetcher shows effectiveness in a single-core environment. However, there are some disadvantages in the locality-based prefetcher, such as fixed stride detecting and a fixed feedback mechanism.
# The Static Frictional Behaviors of Rubber for Pipe-Laying Operation
## 4 Discussion
### Tangential Load Condition  
#### 4.2.1 Description of Fracture Mechanics  
It is known to us that partial slip takes place adjacent to the boundary of the central adhesive zone when tangential load is imposed to the contact system [9; 10; 11; 17]. In the description of the fracture mechanics, the central adhesive zone never slips as a whole until the critical value of the tangential load is reached. In addition, the boundary of the central adhesive zone is assumed to be a crack tip which propagates in the interface. Specifically, crack tips are moving towards to the core of central adhesive zone from both edges of contact with increasing tangential load. For the rigid sphere-rubber plate contact, the propagating velocity of crack tip at the front edge is quite lower than that at tail edge of contact. That is because that compression stress that is resulting from bulking at the front edge is much less than shear stress which is caused by peeling at the tail edge.  
The Griffith equilibrium is introduced and defined by \(G=w\) to reveal the energy balance at the edge of the crack tip [18], where \(G\) represents the strain energy release rate, and \(w\) is the thermodynamic work of adhesion. The equilibrium can be disrupted by the change of tangential load. In this study, it is deduced that the increasing tangential load leads \(G\) to increase while \(w\) is unchanging. When \(G\) is higher than \(w\), the interfaces start to separate and the central adhesive zone decreases. The difference of \(G\)-\(w\) can be seen as \"crack extension force\" and the crack should be accelerated to a limiting value due to the viscoelastic losses of crosslink rubber materials.  
The partial slip zone replaces the central adhesive zone continuously as crack tips are propagating in the interface and releasing shear stress. As soon as crack tips propagate across the whole apparent contact area, the central adhesive zone is replaced by slip zone entirely. The static friction reaches the peak value.  
The fracture mechanics can help us to identify the differences of evolution of the contact area which are under different increasing rates of the tangential load. Distinction of the deformations that are shown in Figures 6 and 7 should be attributed to the fluctuation of the maximum static friction. More details need to be discussed by taking adhesive dragging and viscoelastic property of rubber into consideration.
## 6 Results and Discussion
### Intrusion Detection Accuracy  
Compared with the CNN and MLP neural network models, the FT-CID significantly improves the detection accuracy when tested in a heterogeneous IIoT environment. Furthermore, the experimental results show that the neural network models have stronger intrusion detection performance than the neural network and ML, MLP, and LR algorithms. The FT-CID obtains the best detection performance, even when there is a minimum number of training data sizes. Figure 7 and Table 4 show the implementation results of the performance of the centralised and federated intrusion detection models in terms of accuracy under scenarios of different percentages of training data size and for 20 communication rounds. Figure 7b shows that the proposed FT-CID model integrated IGWO-based feature selection (FS) outperforms the FT-CID model without FS, with 5.49% higher intrusion detection accuracy when there is 80% training data. As shown in Figure 7, the performance  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Training Data Size (\%)**} & \multicolumn{3}{c}{**Recall (\%)**} \\ \cline{2-4}  & **Proposed FT-CID** & **MLP** & **LR** \\ \hline
40 & 82.12 & 52.95 & 50.62 \\ \hline
60 & 83.08 & 59.38 & 55.99 \\ \hline
80 & 84.94 & 65.36 & 57.91 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative experimental recall results.  
Figure 5: Recall performance for FT-CID, MLP, and LR.  
accomplished by the FT-CID is better than that of the neural network and ML models of the CNN, MLP, and LR in centralised settings. By applying the IGWO algorithm, the FT-CID model selects the optimal set of features from the raw IIoT dataset and thus ensures improved detection accuracy compared to the detection of intrusions by the MLP and LR models without FS of 22.7% and 24.69% 60% of training data, respectively.  
Figure 6: F-measure performance.  
Figure 7: Intrusion detection accuracy performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Number of** \\ **Communication** \\ **Rounds** \\ \end{tabular} } & \multicolumn{2}{c}{**Fem-Measure (\%)**} \\ \cline{2-4}  & \multicolumn{2}{c}{**Centralised**} & \multicolumn{2}{c}{**Federated**} \\ \cline{2-4}  & \multicolumn{2}{c}{**AMI**} \\ \cline{2-4}  & \multicolumn{2}{c}{**Proposed FT-CID**} & **CNN** & **Proposed FT-CID** \\ \hline
5 & 79.23 & 67.23 & 78.97 \\ \hline
10 & 80.16 & 68.98 & 80.12 \\ \hline
15 & 83.296 & 72.15 & 82.72 \\ \hline
20 & 85.91 & 69.15 & 85.75 \\ \hline
25 & 79.56 & 67.16 & 79.98 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative experimental results of F-measure.
## 3 Performance Optimization
### Performance Difference Formula  
We denote \(\hat{W}_{0}=\hat{W}\) and \(\tilde{W}_{0}=\tilde{W}\). Then we have the performance function as \(f(x)=\hat{W}_{0}x^{2}\mathbf{1}_{x\geq 0}+\tilde{W}_{0}x^{2}\mathbf{1}_{x<0}\). With the initial condition \(x_{0}=x\), by (5), (6), (11), and (13), the performance operator is  
\[(Pf)(x)=\hat{W}_{1}x^{2}\mathbf{1}_{x\geq 0}+\tilde{W}_{1}x^{2}\mathbf{1}_{x<0}, \tag{14}\]  
where  
\[\hat{W}_{1}=(a_{1}\hat{W}_{0}+a_{2}\tilde{W}_{0})\hat{C}^{2},\ \tilde{W}_{1}=(a_{1}\hat{W}_{0}+a_{2}\tilde{W}_{0})\hat{C}^{2},\]  
and  
\[a_{1} =\sigma\phi(\frac{1}{\sigma})+(1+\sigma^{2})\Phi(\frac{1}{\sigma}), \tag{15}\] \[a_{2} =-\sigma\phi(-\frac{1}{\sigma})+(1+\sigma^{2})\Phi(-\frac{1}{ \sigma}), \tag{16}\]  
with \(\phi(\cdot)\) as the probability density function of a standard normal distribution. We can verify that \(a_{1}\) and \(a_{2}\) are both nonnegative constants, with \(a_{1}+a_{2}=1+\sigma^{2}\). As \(P^{2}f=P(Pf)\), continuing this process, we obtain  
\[(P^{k}f)(x)=\hat{W}_{k}x^{2}\mathbf{1}_{x\geq 0}+\tilde{W}_{k}x^{2}\mathbf{1}_ {x<0}, \tag{17}\]  
where  
\[\tilde{W}_{k} =(a_{1}\hat{W}_{k-1}+a_{2}\tilde{W}_{k-1})\hat{C}^{2},\] \[\tilde{W}_{k} =(a_{1}\hat{W}_{k-1}+a_{2}\tilde{W}_{k-1})\hat{C}^{2}.\]  
We set \(W_{0}^{*}=\max(\hat{W}_{0},\tilde{W}_{0})\). In order to ensure the stability of the system, Reference [14] gives some assumptions. Here we assume \(\max(\hat{C}^{2},\hat{C}^{2})<1/(1+\sigma^{2})\leq 1\). Then we have  
\[\hat{W}_{k}\leq(1+\sigma^{2})^{k}(\hat{C}^{2})^{k}W_{0}^{*},\ \tilde{W}_{k}\leq(1+\sigma^{2})^{k}(\hat{C}^{2})^{k}W_{0}^{*}.\]  
Therefore, we have  
\[\lim_{k\rightarrow+\infty}\hat{W}_{k}=\lim_{k\rightarrow+\infty}\tilde{W}_{k} =0. \tag{18}\]  
We denote \(\hat{\mathbf{G}}_{k}:=\sum_{i=0}^{k}\hat{W}_{i}\) and \(\bar{\mathbf{G}}_{k}:=\sum_{i=0}^{k}\tilde{W}_{i}\). Based on the above claims, we obtain that \(\hat{\mathbf{G}}_{k}\) and \(\bar{\mathbf{G}}_{k}\) would converge when \(k\rightarrow+\infty\). Thus we denote  
\[\hat{\mathbf{G}}:=\lim_{K\rightarrow+\infty}\hat{\mathbf{G}}_{K}=\sum_{k=0}^{+ \infty}\hat{W}_{k},\ \bar{\mathbf{G}}:=\lim_{K\rightarrow+\infty}\bar{\mathbf{G}}_{K}=\sum_{k=0}^{+ \infty}\tilde{W}_{k}.\]  
Based on the definition of total rewards (3), we have  
\[\hat{W}_{1}=(a_{1}\hat{W}_{0}+a_{2}\tilde{W}_{0})\hat{C}^{2},\ \tilde{W}_{1}=(a_{1}\hat{W}_{0}+a_{2}\tilde{W}_{0})\hat{C}^{2},\]0  
By (17) and (18), we have  
\[\hat{W}_{1}=(a_{1}\hat{W}_{0}+a_{2}\tilde{W}_{0})\hat{C}^{2},\ \tilde{W}_{1}=(a_{1}\hat{W}_{0}+a_{2}\tilde{W}_{0})\hat{C}^{2},\]1Then we have proved that the closed-loop system (11) is \(L^{2}\)-asymptotically stable, i.e., \(\lim_{l\to\infty}E[(x_{l})^{2}]=0\). Therefore, the total rewards \(\eta(x)\) exists, that is, a piecewise quadratic function with positive semi-definite matrices \(\hat{\mathbf{G}}\) and \(\bar{\mathbf{G}}\).
## 1 Introduction  
Considering its transferability to industry, the impact of feature selection on the effectiveness of fault diagnosis techniques for induction machines (IMs) is frequently overlooked. Although some recent studies have incorporated optimal feature selection into their methodologies, they often fail to offer solutions that are intelligently tailored to industrial applications [1, 2]. Many recently developed methodologies focus heavily on utilizing highly sophisticated optimization algorithms to identify optimal feature sets; however, the results of these algorithms are often inherently biased by the formulation of the objective functions on which they heavily depend [3, 4, 5, 6]. On the other hand, the availability of multiple data sources and the relatively modest computational power of most personal computers, compared to industrial settings, often motivate researchers to capture as many features as possible to test their Artificial Intelligence (AI)-based developed algorithms. While this approach aims to enhance model performance, it frequently leads to redundancy and noise, underscoring the crucial role of feature selection. Typically, IM fault detection and diagnosis techniques can be categorized based on the features employed in vibration analysis, stator current measurements, temperature monitoring, thermal imaging, acoustic emissions, spectral analysis, or a combination of these methods [7]. Among these features, vibration analysis and stator current measurements are the most appealing for industrial applications. Therefore, the following is a summary of the latest fault diagnosis methodologies based on these fundamental measurements.