esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Result  
With the tunnel inspection vehicle, tunnel lining image acquisition was carried out in 66 highway tunnels in Zhejiang and Yunnan provinces, as shown in Figure 12.  
According to the tunnel inspection requirements, the hardware parameters of the camera system are determined by the experiment and theoretical analysis of the tunnel lining image acquisition system to realize the selection of shooting system hardware. Furthermore, the calculation method of shooting-related parameters zoom and focus value is formed, ensuring that the shooting parameters can be quickly calculated before entering the tunnel, by simply adjusting the aperture and starting tunnel image acquisition when in the tunnel. The typical tunnel lining crack images taken inside the tunnel are shown in Figure 13.  
Figure 12: Tunnel inspection vehicle to carry out image collection (**a**) in the Shiziling tunnel, and (**b**) in the Zhaitouling tunnel.  
Figure 13: Images of tunnel lining cracks, (**a**) cracks picture with breakage, (**b**) cracks picture with seepage, (**c**) cracks picture with stains, (**d**) cracks picture with expansion joint.  
Under various interference factors, the machine vision system can clearly present the cracks in the tunnel lining, which verifies the rationality of the hardware selection of the shooting system and the accuracy of the parameter calculation method.  
Cracks at different crack width stages were found in the tunnel by using a crack observer with a resolution of 0.05 mm. As shown in Figure 14, the width of the crack in the No.1 linear interval is 0.50 mm, the width of the crack in No.2 linear interval 2 is 2.05 mm, and the width of the crack in the flat interval is 3.25 mm by onsite measurement. Since the crack width in the smooth area is too large to find the crack of the corresponding width in tunnels, the expansion joint is used to replace the crack for the width extraction test. The crack width calculated by the crack width model is shown in Table 4.  
In three different crack width stages, using the corresponding fracture width calculation method of the proposed fracture width model, the extraction of the crack width is accurately achieved. The maximum error rate is only 9.2%, and the overall error rate is lower than 10%, which verifies the rationality of the crack width extraction model.
# The Performance Study of a Novel Vertical Variable Stiffness Disc Spring Seismic Isolation Bearing
## 4 Test Results and Discussion
### Results and Analysis of the Ultimate Load Test  
The load-displacement curve of the bearing is shown in Figure 8. As observed, the protection phase begins at a displacement of 31.2 mm and ends at 42 mm. By calculating the slope of the curves in the isolation and protection phases, it is determined that the vertical stiffness of the bearing in the isolation phase is 11.66 kN/mm. When the loading displacement reaches 31.2 mm, the bearing transitions into the protection phase, where the stiffness increases to 37.06 kN/mm. Furthermore, after each loading cycle, the bearing fully recovers to its initial height without any signs of damage, demonstrating stable vertical load-bearing capacity.  
Table 3 presents the experimental and theoretical peak loads and vertical stiffness of the bearing during the loading stage. As shown in the table, the maximum error for the peak load is 6.78%, while the maximum error for the equivalent stiffness reaches 8.47%. These results validate the accuracy of the proposed theoretical hysteresis model for the VVSDSI bearing.  
Figure 8: The load-displacement curves of the VVSDSI bearing under the ultimate load.
## 5 Results and Analysis
### Thermal Prediction Under Variable Speed Conditions
#### 5.2.1 Comparison of Predicted and Real Temperature
This thermal gradient aligns with empirical observations, confirming the physical plausibility of all the temperature nodes results. Uncertainty analysis plays a critical role in verifying prediction model accuracy and systematically evaluating uncertainty and aleatory variability inherent. To further validate the thermal prediction accuracy of the digital twin-driven hybrid virtual-real framework, the identified parameters shown in Equation (26) are imported into the LPTN model of the bearing, and the predicted temperature of the reference nodes is compared with the real temperature measured under the condition of rapid acceleration test. The temperature comparison and uncertainty analysis results are shown in Figure 12.  
Figure 11: Twin temperature data of all temperature nodes. Figure 10: Temperature and errors comparison of reference nodes under variable speed conditions (Real Tor/Real Tir: real measured temperature acquired from the PEL. Predicted Tor/Predicted Tir: Predicted temperature generated by the bearing LPTN model from the VML). From Figure (a)a, it can be seen that the predicted temperature of the reference node is basically consistent with the real measured temperature. Figure (b)b,c present the uncertainty analysis results for the real and predicted temperatures at the reference node, respectively. As shown, the R\({}^{2}\) values for the temperature curves at the reference node are 0.9821 and 0.9939, indicating high accuracy of the thermal prediction model under varying rotational speed conditions. Uncertainty is quantified using prediction intervals (PI). Both subfigures exhibit relatively wide PI, with nearly all real temperature data points falling within the 95% PI of the predicted temperatures, confirming the robust predictive capability of the implemented framework.
## 2 Materials and Methods
### Methodology
#### 2.3.2 Model-Based Processing of TLS Data and Estimation of Oscillation Amplitude and Frequency
.3.3 Interferometric Processing of GB-RAR Data and 2D Visualization of Oscillation Frequency Spectra  
The stack of complex-valued GB-RAR acquisitions is processed by means of the interferometric radar technique by computing the phase differences of each pixel with respect to the first GB-RAR acquisition. The phase is first unwrapped and then mapped to propagation delay. Hence, the contribution of delay due to propagation in the atmosphere is modelled and removed, resulting in the measurement of the range R\({}_{\text{P,GB-RAR}}\) between the GB-RAR location and a target on the wind tower. A spectral analysis of the stack of range vectors {R\({}_{\text{P,GB-RAR}}\)(t)\({}_{\text{P}\text{-TOWER}}\), results in the 2D spectrum of the vibration frequencies. This procedure has been described in [18] and applied to the monitoring of bell-towers and monuments in [30]. An important step of the adaption of the estimation of the GB-RAR estimation of vibration frequencies to the study of the characterization of the dynamical properties of a wind tower is the co-registration of TLS and GB-RAR spectra as sketched in Figure 6. After co-registering TLS and GB-RAR measurements, it is possible to compare vibration spectra obtained by TLS and GB-RAR by a spectral analysis of the time series of distances. It is worth noting that due to the different spatial resolutions of TLS and GB-RAR data, more TLS spectra correspond to the a given frequency spectrum provided by GB-RAR interferometry. In fact, a GB-RAR range resolution of 0.75 m corresponds to a coarser spatial resolution along the wind tower, depending on the radar looking angle and the antenna irradiation pattern (Figure 4). Figure 5: Polynomial fitting of points along the wind tower of a single line scan.
# Underwater Image Super-Resolution via Dual-aware Integrated Network
## 3 Methods
### Swin Transformer Layer (STL)  
The swin transformer originates from the design of the initial transformer layer, which relies on regular multi-head self-attention to model long-range dependence and thus enhance network representation.  
As illustrated in Figure 1, with an initial input of \(F_{MCCB}\in\mathbb{R}^{H\times W\times C}\), the swin transformer reshapes the input \(\frac{HW}{M^{2}}\times M^{2}\times C\) feature first, where \(\frac{HW}{M^{2}}\) represents the total number of windows. Then it performs self-attention calculations simultaneously \(h\) times, where \(h\) represents the quantity of self-attention heads. For a local window feature \(F_{in}^{sut}\in R^{M^{2}\times C}\), the query, key, and value matrices \(Q\), \(K\), and \(V\in R^{M^{2}\times C}\) are expressed as  
\[Q=F_{in}^{sut}W_{Q},K=F_{in}^{sut}W_{K},V=F_{in}^{sut}W_{V} \tag{11}\]where \(d=\frac{C}{H}\),\(W_{Q}\), \(W_{K}\), and \(W_{V}\) are shared learnable projections over different windows. The attention matrix \(Attn(Q,K,V)\) is then computed via the self-attention in the local window.  
\[Attn(Q,K,V)=SoftMax(\frac{QK^{T}}{\sqrt{d}}+b)V \tag{12}\]  
Here, \(b\) is the trainable relative positional coding. Multi-head self-attention (MSA) is combined to maintain consistent embedding dimensions. After the attention operation, there are two Multilayer Perceptrons (MLPs) with GELU activation in them. The Layer-Norms (LNs) are then added ahead of MSA and the MLP and use residual connections. The transformer's overall functionality can be summarized as follows:  
\[\begin{array}{l}\begin{array}{l}\begin{array}{l}F^{surt}_{int}=H_{MSA}(H_ {LN}(F^{surt}_{in}))+F^{surt}_{int}\\ F^{surt}_{out}=H_{MLP}(H_{LN}(F_{inter}))+F^{surt}_{inter}\end{array}\end{array}\end{array} \tag{13}\]  
where \(H_{LN}(\cdot)\) denotes the LN operation, \(H_{MSA}(\cdot)\) represents MSA operation, and \(H_{MLP}(\cdot)\) denotes the operation function. On the one hand, the STL exploits regular and shift window partition alternately, realizing efficient information transmission and interaction between different windows. On the other hand, it uses local attention and a shifted window mechanism to greatly reduce computational effort while maintaining strong modeling power. Therefore, we introduce the STL to better catch local features and details, thus enhancing underwater image processing.
## 4 Modelling & Simulation Results
### Consistency Check of the Multi-Group Monte Carlo Results  
To assure the quality and reliability of the results delivered by the multi-group version of KENO-VI using the v7-252 cross-section set of the SCALE package [25] as a master library for the CSAS-MG cross-section generation procedure, the result for the critical reference case will be compared to a reference continuous energy Monte Carlo solution. The results for the comparison are presented in Table 3. The application of the continuous Monte Carlo approach delivers a slightly different critical reflector position at \(-22\) instead of \(-29\) cm. The results demonstrate that the use of the weighted master library has some influence on the results, but the influence seems to be limited and of sufficient quality for the delivered scoping study. This justifies the use of the significantly faster multi-group Monte Carlo approach as sufficiently accurate for the current level of this feasibility study. Nevertheless, for future application, a two-fold strategy seems to be indicated using the multi-group approach, with deterministic as well as Monte Carlo approaches, for serial fast track studies to improve the systematic understanding of the system which should be backed up with several continuous energy calculations to justify the absolute values as a reference result for the design. In general, the envisaged zero-power experiments will exactly deliver the validation basis for the future code application to assure a good validation level of the code systems. This is an important step in the case of a new, completely different reactor system. It is essential to demonstrate that code systems developed for other applications, e.g., mainly LWR, can be validated and used for the design of future molten salt reactor systems. However, a first level of modeling and simulation results will be required for the design of a zero-power experiment; thus, the careful use of different tools and databases as well as critical analysis of the differences will be required in the same way as the improvement in the thermo-physical data as input for the modeling and simulation tools. Thus, Table 3 provides a first insight into what quality of the results and which deviations have to be expected, and thus it points out the importance of validation experiments for quality assurance in the modeling and simulation of innovative reactor systems.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reflector Position** & **Multiplication Factor** & **Std Dev** & \\ \hline \(-22\) & 1.00052 & 0.00011 & CE Monte Carlo \\ \hline \(-29\) & 1.0003 & 0.0001 & MG Monte Carlo \\ \hline \hline \end{tabular}
\end{table}
Table 3: Consistency check between the multi-group (MG) Monte Carlo result, as used for this study, and a continuous energy (CE) Monte Carlo result using the same cross-section basis of SCALE.
# Phishing Attacks Survey: Types, Vectors, and Technical Approaches
## 3 Phishing Methods and Techniques
### Phishing Technical Approaches
#### 3.3.6 QRishing  
A QR (quick response) code is a matrix containing a layout of black and white pixels that are used to store and communicate compressed information. Two-dimensional QR codes are quickly replacing outdated one-dimensional barcodes because they are more readable and contain more information. To access the information stored within a QR code, an optical scan is used to read them, which in many cases means photographing the codes. Then, a QR code reader decodes the information contained within the QR code and processes it, for example, by opening an app store if the QR code is advertising a new application for mobile devices.  
Due to the growing number of smart mobile devices, QR codes are being more commonly used by businesses both internally (e.g., for tracking, payment, and discounts) and externally to direct people to their websites, apps, and products [48]. QR codes can now often be seen on the packaging of products, newspapers, and billboards.  
Unfortunately, the ease with which QR codes can be made and distributed has made them an ideal method for phishing attacks. This is further enhanced by the fact that humans are unable to understand the content of a QR code before it is deciphered with a QR code reader. Furthermore, many QR code readers perform the action necessary to see the QR code's content without first seeking approval from the user; for example, opening a URL in a browser [49]. Continuing with this example, a phisher could post QR codes around an area that pretended to be advertisements for a legitimate product or company. Then, the QR codes direct scanners of these code to a malicious URL, where a drive-by download takes place, thus infecting the victim's device, before redirecting them to the legitimate website. The victim would be unaware of the attack but would now have a compromised device that transmits their personal data to the phisher. Alternatively, the link could direct them to a spoof of the legitimate site, asking them to login, and simply stealing their credentials. Even if the QR code reader does first present the URL for inspection by the victim, the use of URL shortening techniques means that it is harder for users to determine whether a URL is legitimate.  
Thus, QRishing is a dangerous variant of phishing that can easily be combined with other techniques explained in this paper in potentially devastating attacks.
## 4 Literature Review on DAS+PRS
### Pattern Classification  
Pattern classification approaches for DAS+PRS are divided according to the type of model/approach these employ, as described in Section 3.3: rule-based pattern classification systems, generative model-based pattern classification systems, and discriminative model-based pattern classification systems. A summary of the systems that belong to each group is presented in Table 8. Next, we describe the main proposals in the literature.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference Pattern Classification Category** & **Pattern Classification Method** \\ \hline
[3] & & \\
[28] & Rule-based & Threshold \\
[29] & & \\
[31] & & \\ \hline
[24] & Generative model-based & GMM + system combination \\ \hline
[17] & - & None: just visual analysis of FE \\ \hline
[26] & Rule-based & Threshold \\ \hline
[19] & & BP ANN \\ \hline
[21] & & RVM \\ \hline
[46] & & SVM (RBF) \\ \hline
[27] & & BP ANN \\ \hline
[23] & Generative model-based & GMM \\ \hline
[12] & & BP + momentum ANN \\
[13] & & \\ \hline
[14] & Discriminative model-based & \\
[15] & & FF ANN \\
[16] & & \\ \hline
[22] & & GMM \\ \hline
[25] & & GMM + postprocessing \\ \hline
[45] & & SVM (RBF) \\ \hline
[20] & & SVM \\ \hline \hline \end{tabular}
\end{table}
Table 8: Summary of the pattern classification approaches reported in the literature for DOFS+PRS. ’GMM’ stands for gaussian mixture model, ’BP’ for backpropagation, ’ANN’ for artificial neural network, ’RVM’ for relevance vector machine, ’SVM’ for support vector machine, ’RBF’ for radial basis function, ’FF’ for feedforward, and ’FE’ for feature extractor.
## 8 Averaging at Different Radial Directions Considering the Incident Angles, \(\theta\)  
The effects of variation of angle \(\theta\) on the mean response of the bridge columns using the averaging procedure in different radial directions are also investigated.  
A similar method as used for the case of the averaging method using the maximum absolute displacements, as previously described, can be used to predict the mean response of the bridges at different radial directions (from 0 to 360\({}^{\circ}\)). For this case the maximum responses are determined in m different radial directions (i.e., m = 37 radial directions with intervals of 10 \({}^{\circ}\) from 0 to 360\({}^{\circ}\)). Assuming that the data are Normally distributed, for each record there are m values of mean and standard deviation due to the variability of angle \(\theta\) along m sets of directions. The angle \(\theta\) in this study ranged from 0 to 180\({}^{\circ}\) with increments of 5\({}^{\circ}\) (i.e., 37 different angles). Therefore, the mean and standard deviation of the data, due to variation of angle \(\theta\), from record \(i\) in direction \(j\) can be determined as \(M_{ij}\) and \(S_{ij}\), respectively. Therefore, with reference to Equations (1) and (2), the mean and standard deviation of the average response in each direction \(j\), \(M_{j}^{\prime}\) and \(S_{j}^{\prime}\), then can be estimated using Equations (3) and (4), by averaging the results from m ground motion records in each direction. The maximum value of the m Normal random variables with a mean and standard deviation of \(M_{j}^{\prime}\) and \(S_{j}^{\prime}\) can then be determined to predict the critical displacement. The probability distribution of the maximum displacement in the different directions can be predicted numerically by computing the maximum displacement within m random variables at different probabilities of non-exceedance. The predicted probability distributions of the displacements of the central column are shown by dashed line in Figure 14 for the regular and irregular bridges.  
\[M_{j}^{\prime}=\frac{1}{n}\sum_{i=1}^{n}M_{ij} \tag{3}\]  
\[S_{j}^{\prime}=\frac{1}{n}\sqrt{\sum_{i=1}^{n}S_{ij}^{2}} \tag{4}\]  
For example, the predicted displacements of 84 and 114 mm for the central columns of the regular and irregular bridges, when the angle \(\theta\) was not considered (see Table 2), corresponds to 95% and 70% probabilities of non-exceedance, respectively (see Figure 14).  
It should be noted that the Performance-Based Design (PBD) concepts have been introduced in the 2014 edition of the Canadian Highway Bridge Design Code (CHBDC) [60].  
The subjects studied in this research can be also investigated for bridges designed based on the new design concepts of 2014 CHBDC. For example, the effects of directivity on the maximum strains in the steel and concrete considering different displacement directions can be studied and be compared to the strain limitations given in 2014 CHBDC. Some researchers such as [PERSON] et al. [63] have concluded that the strain limitations in 2014 CHBDC are conservative. More research is needed on this subject.
# Multiscale Superpixelwise Locality Preserving Projection for Hyperspectral Image Classification
## 3 Proposed Method  
A remotely sensed HSI scene usually comprises irregular ground objects in various shapes and sizes, which leads to the irregular spatial dependency characteristic of an HSI, thus leading to the local homogeneities on the connected HSI manifold. In the following, we develop a new method called MSuperLPP to deal with the manifold local homogeneities. Different from a conventional LPP which applies an unified projection on the entire HSI data, our method adaptively determines homogenous subregions and thus the HSI manifold local homogeneity and then employs a divide-and-conquer strategy to perform LPP processing based on those subregions. Therefore, our method is able to fully explore local homogeneities on the HSI manifold and thus collects more useful discriminative information, which poses the potential to enhance the final classification performance.
## 3 Simulation Results
### Out-Of-Band Leakage  
The power spectral density (PSD) of CP-OFDM and GFB-OFDM are shown in Figure 5, with eight subbands and one subband chosen, respectively, as in Table 1. Due to the implementation of the polyphase filtering, the GFB-OFDM exhibits lower out-of-band leakage compared to CP-OFDM, as evident from the results in Figure 5a. This reduction in the out-of-band leakage allows for a decrease in the guard band  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **CP-OFDM** & **GFB-OFDM** \\ \hline Total number of subcarriers & 8192 & 8192 \\ Number of subbands & 8 & 8 \\ Number of subcarriers in each subband & 1024 & 1024 \\ Size of total IFFT & 16,384 & 16,384 \\ Size of first-level IFFT & / & 2048 \\ Size of second-level IFFT & / & 16 \\ Subcarrier spacing & 480 kHz, 960 kHz & 480 kHz, 960 kHz \\ Filter bandwidth & / & 1 times the subband width \\ The length of the time domain filter & / & 4 times the subsymbol length \\ Roll down factor \(\alpha\) of frequency domain DRRC filter & / & 0.15 \\ Roll down factor \(\beta\) of the DRRC window filter & / & 0.5 \\ MCS & 3/4 LDPC, 64 QAM & 3/4 LDPC, 64 QAM \\ Length of CP & 1/16 of the symbol length & 1/16 of the symbol length \\ Channel & AWGN & AWGN \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters.  
in the spectral efficiency. In Figure 4(b), the out-of-band leakage of one subband is depicted, which reflects the inter-subband interference.  
It can be observed that the out-of-band leakage of one subband is significantly higher than that of GFB-OFDM. Specifically, the subband with a 960 kHz subcarrier spacing demonstrates heavier out-of-band leakage than that with a 480 kHz subcarrier spacing. Conversely, in GFB-OFDM, the out-of-band leakage across different subcarrier spacings remains consistently low.