esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Statistical Analysis of 3D Kinect Data: A Comparison of the Classification Methods
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Correct Classification**} \\ \hline
**HB by a Clinician** & **OLR** & **OLR** & **OLR** & **OLR** & **OLR** & **OLR** \\ \hline
1 & 48\% & 83\% & 66\% & 64\% & 74\% & 74\% \\
2 & 62\% & 10\% & 33\% & 52\% & 29\% & 43\% \\
3 & 65\% & 35\% & 39\% & 22\% & 52\% & 52\% \\
6 & 70\% & 25\% & 40\% & 15\% & 55\% & 70\% \\ \hline
**Overall accuracy** & 57\% & 52\% & 51\% & 46\% & 59\% & 64\% \\ \hline \hline \multicolumn{5}{c}{**Approximate classification**} \\ \hline
**HB by a clinician** & **OLR** & **OLR** & **OLR** & **OLR** & **OLR** & **OLR** \\ \hline
**1** & 78\% & 84\% & 78\% & 76\% & 76\% & 81\% \\
2 & 100\% & 100\% & 86\% & 90\% & 86\% & 100\% \\
3 & 78\% & 39\% & 48\% & 39\% & 70\% & 61\% \\
6 & 70\% & 25\% & 40\% & 15\% & 55\% & 70\% \\ \hline
**Overall accuracy** & 80\% & 69\% & 67\% & 61\% & 73\% & 79\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of classification of all models. Both the correct and approximate classifications are considered. is substantially higher when compared to the size of the training set, which makes them highly susceptible to overfitting. A comparison of the overall accuracy on the testing set with accuracy on the training datasets (for each step of cross-validation procedure) reveals that the OLR model provides stable results and does not suffer from overfitting, see Table 7. Meanwhile, the non-parametric model random forests displays overfitting: it achieves worse accuracy for testing set compared to accuracy on training sets. Non-parametric methods usually require (by their nature) rich training dataset to learn the systematic patterns and filter out non-systematic (random) noise. Otherwise, due to their flexibility, they tend to adapt to non-systematic random effects. As expected, neural network models exhibit strong overfitting to the training set for similar reasons as the non-parametric random forest model. However, due to early stopping and dropout regularisation, they can still achieve testing set accuracy that is on par with the parametric OLR model. The main advantage of the OLR parametric model over the neural network approach lies in the possibility to interpret intermediate steps of the classification process. This provides clinicians with more information on the recovery process, it also enables them to control and possibly adjust the automated evaluation procedure; see [13].
## 2 System Overview  
In this section, we introduce an AUV and a mobile recovery system, which are illustrated in Figure 1. The MRS includes an AUV (is called AUV-\(I\)) and a docking station. The docking station was rigidly fixed to the underbelly of AUV-\(I\). The entrance of the docking station was a funnel-like, 2 m in diameter. The AUV to be recovered is called AUV-\(II\). AUV-\(II\) was a torpedo-shaped vehicle that was 384 mm in diameter and 5486 mm in length. It was equipped with a GPS, an inertial measurement unit (IMU), a Doppler velocity log (DVL) and an acoustic sensor. The acoustic sensor, Evologics 32C R, was used for long-range positioning. The AUV-\(I\) carries a transponder, and AUV-\(II\) carries transceiver. The application scenario is: The MRS is moved with constant velocity and travels in a straight path, the AUV chases MRS to complete the homing process. The line-of-sight guidance algorithm is used to guide AUV to MRS.  
Figure 1: An AUV to be recovered (**a**) and the mobile recovery system (**b**).  
The AUV estimates the states of AUV and MRS based on TT-EKF with its own heading and forward speed. To estimate the PNCM, we model the heading and forward speed of AUV and MRS, and calculate the parameters of these distributions. The neural network is applied to compensate the prediction error. The framework of our proposed algorithm is shown in Figure 2.
# Urban Transportation Network Design for Fresh Fruit and Vegetables Using GIS-The Case of Bangkok
###### Abstract  
A cold chain for perishable fresh products aims to preserve the quality of the products under the control of a predefined temperature range. To satisfy the delivery conditions within appropriate time windows, the most critical operations in cold chain management is the transportation and distribution of fresh products. Due to rapid population growth and increasing demand for high-quality fresh foods, it becomes critical to develop advanced transportation and distribution networks for fresh products, particularly in urban areas. This research aims to design different scenarios based on mathematical models for fresh products transportation and distribution network in the Bangkok metropolitan area using Geographic Information system (GIS). The proposed methodology integrates location-allocation and vehicle routing problem analysis. The performance of all possible scenarios is evaluated and compared by considering the number of required distribution centers and trucks, total travel time, total travel distance, as well as fairness among drivers. The results of the scenario analysis highlight that the alternative scenarios show a better performance as compared to the current network. In addition, the administrator can make a different decision among several alternatives by considering different aspects, such as investment cost, operating cost, and balance of using available resources. Therefore, it may help a public officer to design the fresh products logistics network considering actual demand and traffic conditions in Bangkok.  
urban logistics; network design; location-allocation; vehicle routing problem; location-routing problem; GIS +
Footnote †: journal: Applied Sciences
# A Fingerprint-Based Verification Framework Using Harris and SURF Feature Detection Algorithms
## 3 Proposed Methodology
### System Model Block Diagram  
A functional block diagram of the automatic fingerprint recognition system (AFRS) model that shows the main components of the system in series is depicted in Figure 2. As illustrated in the figure below, firstly, the scanned fingerprint image is greatly improved in contrast and clarity using a contrast limited adaptive histogram equalization (CLAHE) technique, and then the enhanced image is normalized and segmented. The segmented fingerprint image is further improved by using an adaptive enhancement algorithm based on short time Fourier transform (STFT) analysis and contextual filtering. Thus, the orientation field can be estimated from a set of filtered images generated by applying a bank of Gabor filters; whereas the enhanced image is carefully binarized by applying a simple local-global binarization algorithm.  
For feature extraction, the binarized fingerprint image is thinned to form a skeletonized image where ridges are only one pixel wide, and all the redundant pixel information is removed [15]. Based on the robust technical characteristics of Harris and SURF algorithms, a Harris-SURF feature extraction algorithm is then applied to effectively detect and extract features from the skeletonized fingerprint image obtained by the above-mentioned pre-processing steps. In the next step, a matching algorithm is applied for matching these features. The fingerprint matching process also involves calculating a pairwise distance (i.e., Euclidean distance) between the corresponding feature vectors to make a final decision on fingerprint match, as shown in Figure 3.
# A Multi-Objective Path-Planning Approach for Multi-Scenario Urban Mobility Needs
## 2 Preliminaries
### Dynamic Weight-Adjustment Mechanism  
To adapt to the needs of different traffic scenarios, we have introduced a dynamic weight-adjustment mechanism based on real-world scenarios. This mechanism adjusts the weights in real time according to changes in the scenario and environment, enabling the path-planning process to meet diverse travel requirements. When assigning weights, we fully consider the potential correlations between different objectives. Generally, total time, congestion distance, and total distance are positively correlated. To avoid the amplification of such correlations affecting the optimization results, the dynamic adjustment mechanism reduces the weight proportions of strongly correlated objectives, ensuring the fairness of multi-objective optimization. The weights for multi-scenario urban mobility needs are shown in Table 1.
# Dynamic Load Redistribution of Power CPS Based on Comprehensive Index of Coupling Node Pairs
## 2 The Comprehensive Characteristics of the Coupling Node Pair
### Active-Business Degree Index  
A large amount of literature shows that the number of node degrees alone does not reflect the importance of nodes or node pairs in the system.  
As shown in Figure 1, on the power side, each power node is connected to multiple power lines, and there is power inflow or outflow on each node. The importance of each power node is evaluated by combining the magnitude of the power flow and its flow direction. On the cyber side, each cyber node is connected to multiple links, and each link carries one or more different services, and different services have different importance. So, the structural characteristics are combined with the business characteristics on the cyber side to quantify the cyber node importance.  
The importance of the power side node \(i\) can be characterized as \(I_{pi}\):  
\[I_{pi}=\frac{k_{ii}\sum p_{ii}+k_{io}\sum p_{io}}{<k_{p}>^{2}} \tag{1}\]  
where the average degree value of the power network node is represented as \(<k_{p}>;k_{ii}\) is the number of active lines injected into the \(i\) node, and \(\sum p_{ii}\) is the input active value of the \(i\) node; \(k_{io}\) is the number of active lines flowing out of the \(i\) node, and \(\sum p_{io}\) is the output active value of the \(i\) node.  
In order to simplify the calculation, the average importance of the service is taken according to the security partition [12], that is, the importance of the security zone I to IV is 0.835, 0.41, 0.315, 0.223, respectively. The cyber-side node service importance formula is as follows:  
\[I_{ci}=\sum_{a=1}^{n}\sum_{l=1}^{K}b_{al}h_{al} \tag{2}\]  
Figure 1: The active-business degree model.  
where \(b_{xl}\) is the importance of class \(x\) service on the service link \(l\) connected by node \(i\), \(h_{xl}\) is the number of class \(x\) services on the service link \(l\), and \(K\) is the collection of adjacent links of node \(i\). The service importance is evaluated according to the latency, bit error rate, real-time, reliability, etc. [13].  
Therefore, the active-business degree of the coupled node pair can be established according to the power node active degree and the service importance of the cyber node, which is used to characterize the importance of the node in the whole network:  
\[I_{vi}=(1-\beta_{cp})I^{\prime}_{pi}+\beta_{cp}I^{\prime}_{ci} \tag{3}\]  
where \(\beta_{cp}\) is the weight allocation coefficient for importance, \(I^{\prime}_{pi}\) and \(I^{\prime}_{ci}\) are normalized subnet node importance metrics, respectively.
# Seismic Isolation via I-Shaped and T-Shaped Large-Scale Phononic Metamaterials
## 3 Results
### T-Shaped LSPMs  
The T-shaped LSPM was also examined, and the results for this set of calculations are collected in Table 2. [PERSON] and [PERSON] [17] showed numerically that an abrupt reduction in the cross-section of a column, and therefore a T-shaped formation, could create wide band gaps. Following the same architecture model, the T-shaped trench was considered an LSPM. The results showed wide and complete PBGs, i.e., along all examined high-symmetry directions, \(\Gamma-\mathrm{X}-\mathrm{M}-\Gamma\), of the first Brillouin zone, although not as wide as for the I-shaped LSPM. The varying parameter for this set of calculations was the length L\({}_{\mathrm{T}}\), and the rest parameters were L = 9.0 m, w = 2.5 m, and w\({}_{\mathrm{T}}\) = 6.5 m. For L\({}_{\mathrm{T}}\) = 1 m, the two calculated PBGs were between 2.42 and 3.07 Hz and between 3.39 and 4.02 Hz, with \(\Delta f/f_{g}\) calculated at 0.24 and 0.17, respectively. For L\({}_{\mathrm{T}}\) = 2.5 m, there were also two calculated PBGs between 2.50 and 3.06 Hz and between 3.35 and 3.94 Hz, with \(\Delta f/f_{g}\) calculated at 0.20 and 0.16, respectively. The last examined case was for L\({}_{\mathrm{T}}\) = 4.0 m, where only one PBG was found between 2.63 and 2.97 Hz with \(\Delta f/f_{g}\) = 0.12. The band structure of the case where L\({}_{\mathrm{T}}\) = 1 m, with the relevant mode shapes of the limits of the first band gap, is shown in Figure 5. It is important to note at this point that apart from the complete PBGs that were calculated, there are also even wider PBGs along the \(\Gamma\)X direction. For the case presented in Figure 5, the PBGs along the \(\Gamma\)X direction are between 1.84 and 3.20 Hz with \(\Delta f/f_{g}\) = 0.54, and between 3.39 and 4.20 Hz with \(\Delta f/f_{g}\) = 0.21.  
The case where the T-shaped trench is filled with a coated steel core, where the rubber coating had thickness d = 1 m, although provided complete PBGs, did not significantly improve the performance of the structure. The most interesting case was for L\({}_{\mathrm{T}}\) = 4.0 m. For this case, a PBG was calculated between 1.73 and 2.16 Hz with a PBG width of 0.22. There were also two smaller gaps between 3.23 and 3.42 Hz and between 3.71 and 4.07 Hz with widths of 0.06 and 0.09, respectively. The band structure for this case is presented in Figure 6. In the same figure, the normalized displacement field for the band gap edges is also shown. For L\({}_{\mathrm{T}}\) = 2.5 m, a small PBG was calculated between 3.09 and 3.46 Hz with  
Figure 5: The band structure of the T-shaped LSPM for the case where L\({}_{\mathrm{T}}\) = 1.0 m (**top**) and the relevant mode shapes of the normalized displacement field for the lowest (f\({}_{\mathrm{I}}\)) and highest (f\({}_{\mathrm{h}}\)) frequencies of the first PBG (**bottom**), which are indicated with the blue asterisks in the band structure.  
\(\Delta f/f_{\rm g}=0.11\) and two other small gaps only along the \(\Gamma\)X direction between 1.64 and 2.11 Hz and between 3.57 and 4.24 Hz.
## 2 Finite Element Model of Cylindrical Roller Bearing
### Explicit Dynamic Theory  
The explicit dynamic algorithm based on nonlinear dynamic analysis theory must satisfy the following conditions: the displacement, velocity, and acceleration at time \(t=0\) are known, and the displacement, velocity, and acceleration at time \(t+\Delta t\) are solved based on these known quantities. The system dynamics at that time are presented in Equation (1) [16].  
\[M\ddot{x}_{t}+C\dot{x}_{t}+Kx_{t}=F_{t} \tag{1}\]  
where \(\ddot{x}_{t}\), \(\dot{x}_{t}\), and \(x_{t}\) are the acceleration vector, velocity vector, and displacement vector of the system nodes, respectively. \(M\) denotes the mass matrix of the system, \(C\) is the damping matrix of the system, \(K\) is the stiffness matrix of the system, and \(F_{t}\) denotes the nodal load vector of the system.  
Expand \(x_{t+\Delta t}\) into a Taylor series at time \(t\) to obtain an approximate analytical expression with finite terms:  
\[x_{t+\Delta t}=x_{t}+\Delta t\dot{x}_{t}+\frac{\Delta t^{2}}{2}\ddot{x}_{t}+ \cdots+\frac{\Delta t^{n}}{p!}x_{t}^{(p)} \tag{2}\]where \(x_{t}^{(p)}\) is the \(p\)th differential of \(x_{t}\).  
To combine Equation (2) with Equation (1), Equation (2) is transformed into a second-order differential equation as follows:  
\[x_{t+\Delta t}=x_{t}+\Delta t\dot{x}_{t}+\frac{\Delta t^{2}}{2}\dot{x}_{t} \tag{3}\]  
The derivation of Equation (3) can obtain Equation (4):  
\[\dot{x}_{t+\Delta t}=\dot{x}_{t}+\Delta t\ddot{x}_{t} \tag{4}\]  
In interval \([t-\Delta t,t]\), \(\dot{x}_{t}\) can be approximated by \(x_{t}\) as  
\[\dot{x}_{t}=\frac{1}{\Delta t}(x_{t}-x_{t-\Delta t}) \tag{5}\]  
Similarly, it can be deduced that  
\[\dot{x}_{t-\Delta t}=\frac{1}{\Delta t}(x_{t-\Delta t}-x_{t}) \tag{6}\]  
Combining Equations (4)-(6), the mathematical relationship between \(\ddot{x}_{t}\) and \(x_{t}\) can be obtained:  
\[\ddot{x}_{t}=\frac{1}{\Delta t^{2}}(x_{t+\Delta t}-2x_{t}+x_{t-\Delta t}) \tag{7}\]  
Then, the functional relationship between \(\dot{x}_{t}\) and \(x_{t}\) in the interval \([t-\Delta t,t+\Delta t]\) can be expressed as  
\[\dot{x}_{t}=\frac{1}{2\Delta t}(x_{t+\Delta t}-x_{t-\Delta t}) \tag{8}\]  
Combining Equations (7) and (8), the iterative equation for the displacement value at each time point can be obtained, as presented in Equation (9).  
\[\left(\frac{M}{\Delta t^{2}}+\frac{C}{2\Delta t}\right)x_{t+\Delta t}=F_{t}- \left(K-\frac{2}{\Delta t^{2}}M\right)x_{t}-\left(\frac{M}{\Delta t^{2}}-\frac {C}{2\Delta t}\right)x_{t-\Delta t} \tag{9}\]  
Finally, the stability conditions for the explicit dynamics algorithm are computed as  
\[\Delta t\leq\Delta t_{cr}=\frac{\tau_{n}}{\pi} \tag{10}\]  
where \(\tau_{n}\) is the minimum intrinsic vibration period of the system; \(\Delta t_{cr}\) denotes the critical value.
## 4 Vibration and Acoustic Sensors in Non-Destructive Testing of Port Cranes
### Acoustic Emission Sensors in Port Crane Maintenance
#### 4.2.4 Applications of Acoustic Emission Sensors in Port Cranes  
AE sensors play a crucial role in the SHM systems of port cranes. They enable the continuous, real-time assessment of critical components, such as crane booms, cables, and support structures. For example, in port cranes, AE sensors detect microcracks caused by cyclic loading during lifting operations [115; 116]. They can also monitor stress-induced failures during operations. Corrosion is a significant concern in port cranes because it can compromise the integrity of structural components.  
AE sensors detect stress-wave emissions from corrosion activity, aiding predictive maintenance and structural integrity testing. They can identify pitting corrosion, stress-corrosion cracking, and general corrosion in various components of the port cranes [117]. Furthermore, with the increasing use of advanced materials and complex components in port cranes, there is a heightened need for NDT methods such as AE testing. These materials often exhibit unique failure modes such as delamination and fiber breakage. AE sensors detect faults in crane components such as lifting cables, slewing bearings, and boom structures. For instance, they can identify delamination and fiber breakage in composite boom structures during operation [118; 119].  
The case study of the Red Sea Gateway Terminal highlights how IoT, machine learning, and artificial intelligence can be leveraged for real-time condition monitoring and fault prediction in super Panamax quayside container cranes. This integration enhances crane reliability, reduces inspection efforts, increases the mean time between failures, and boosts the uptime and availability. Autonomous crane monitoring, fault detection, and prediction contribute to saving maintenance team time and enhancing the overall reliability. The critical components monitored included motor DE-NDE bearing blocks, gearbox input, intermediate and output shafts, and drum supports. The AE sensors integrated with IoT achieved 83% accuracy in fault prediction and 19% improvement in overall reliability based on increased mean time between failures [120].
## 4 Influence of Each Component under Complex Load
### Influence of Stand Load  
To deeply analyze the influence of stand load on the strength and reliability, the simulation of group 2 (see Table 2) was conducted, and the variation of the structural strength and reliability with the increase in the stand load is discussed. As shown in Figure 7, with the number of stands increasing from 100 to 250, the stand load increases from 15.33 kN to 38.33 kN according to Eq.1. The maximum stress of the derrick increases from 52 MPa to 85 MPa, and the reliability index falls from 11.4 to 10.8. When the number  
Figure 5: The variation of the stress and reliability index with hook load increasing. (**a**) Stress contour plot. (**b**) Reliability index contour plot.  
Figure 6: Variation curve of column reliability index along with height under different hook load.  
of stands increases to 250, the maximum stress of the derrick appears at the bottom front column, and the reliability index is also the smallest. It can be seen that the increase in the stand load not only increases the stress at the bottom front column, but also reduces its reliability at the same position. Therefore, under the work conditions of the derrick, the stress monitoring of the bottom front column should be paid more attention. It is suggested that the operation of stacking too many stands on the cracking board for a long time should be avoided.  
Figure 8 shows the various trends of the column reliability index \(\beta\) along with the height under different numbers of stands. As the number of stands increases, \(\beta_{f}\) decreases at the height between 0 m and 34.42 m (racking board). When the height is larger than the height at 34.42 m, \(\beta_{f}\) maintains a stable value. When the number of stands is 250, \(\beta_{f}\) reduces from 12.13 to 10.87 at the height between 0 m and 4.30 m (bottom column), which is the smallest \(\beta_{f}\) of the whole column. Then, with the height rising to 8.15 m (the bottom of section I), \(\beta_{f}\) is 10.93. Above the height of 8.15 m, there is a fluctuating variation of increasing \(\beta_{f}\). Compared to the four curves under different numbers of stands, \(\beta_{f}\) at the height between 8.15 m and 28.64 m rose slightly with increasing numbers of stands. In other sections, \(\beta_{f}\) varied subtly due to the low influence of the stand load on the columns. Taking the stand number of 250, for example, it is found that \(\beta_{f}\) shows an overall reduction trend. The smallest value of \(\beta_{f}\) is found at the height of 32.65 m, which is close to the cracking board of the derrick. There is a similar varying trend for curves under other numbers of stands. It is concluded that when there are too many stands stacked at the cracking board, attention should be paid to the front bottom column and rear column close to the cracking board.