esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Mean Pressure Coefficient  
Based on the results of the wind tunnel test, Figure 5a shows the distribution of the mean pressure coefficient \(\overline{C}_{p}\) of M1 under various \(Re\) values. At \(Re\) = 2.2 \(\times\) 10\({}^{5}\), the value of \(\overline{C}_{p}\) was found to have an increasing section at 70-80\({}^{\circ}\), and tended to be stable thereafter. This phenomenon can be considered as a typical laminar separation in the subcritical regime, and the separation point is near the 70-80\({}^{\circ}\)[40]. For \(Re\) = 2.6 \(\times\) 10\({}^{5}\), the left side also had a laminar separation, while the right side of \(\overline{C}_{p}\) increased sharply and a platform appeared at 90-100\({}^{\circ}\), indicating the formation of a separation bubble. At the same time, the separation point moved to 140\({}^{\circ}\) with the transition to the critical regime [40]. When \(Re\) = 3.9 \(\times\) 10\({}^{5}\), both sides of M1 had platforms corresponding to the complete critical regime. With the further increase of \(Re\) = 5.5 \(\times\) 10\({}^{5}\), the platform on the right disappeared, indicating the transition to the supercritical regime [40]. As for the M2 model in Figure 5b, although the distribution of \(\overline{C}_{p}\) on both sides had a little difference at \(Re\) = 1.9 \(\times\) 10\({}^{5}\), there were no significant platforms similar to M1, and the value of \(\overline{C}_{p}\) was smaller than that of M1 in negative pressure range. Therefore, it can be concluded that the \(Re\) effect on the circular distributions of \(\overline{C}_{p}\) decreases with the decreasing \(AR\) for low-aspect-ratio cylinders.  
Figure 6a shows the distribution of the mean pressure coefficient \(\overline{C}_{p}\) of M1 under different wind fields. The value of \(\overline{C}_{p}\) in T1 was in a subcritical laminar separation state, and the separation point was between 70\({}^{\circ}\) and 80\({}^{\circ}\). At the same time, in the T2 and T3 wind fields, \(\overline{C}_{p}\) presented obvious platforms for 90-100\({}^{\circ}\) on the right side, corresponding to the critical regime. In the T4 wind field, a relatively symmetrical \(\overline{C}_{p}\) with no prominent platforms was observed. Compared with the T1 wind field, the separation points in T2, T3, and T4 were backward to 130\({}^{\circ}\), and the leeward pressure coefficient became larger with the turbulence increment. As for the M2 model, in Figure 6b, the value of \(\overline{C}_{p}\) is almost symmetric in T2, T3, and T4. Moreover, unapparent platforms 100-110\({}^{\circ}\) of the left side in T2 and T3 were detected, but no platforms were observed in T4. It can be deduced that, when the incoming turbulence increases, the wind pressure distribution of low-aspect-ratio cylinders tends to be in a higher \(Re\) state, that is, the supercritical regime is reached at a smaller \(Re\). Similar results were also obtained in [PERSON]'s research [41].  
Figure 5: The distribution of the wind pressure coefficient at 0.5 H of models under different \(Re\). (a) M1; (b) M2.
# Time-Dependent Vehicle Routing Problem with Drones Under Vehicle Restricted Zones and No-Fly Zones
## 5 Algorithm Design and Implementation
### Initial Population  
Traditional methods for generating initial populations typically rely on random generation, which fails to effectively utilize prior knowledge and heuristic information, often leading to insufficient population diversity. Therefore, chromosomes generated using the logistic chaotic map exhibit good randomness and uniform distribution characteristics. The specific form of the logistic map is given by Constraint (38), where the control parameter is set to 4.  
\[x_{i+1}=\alpha x_{i}(1-x_{i}) \tag{38}\]  
Based on the logistic map, a reverse learning strategy is introduced, where the chromosome is reversed from start to end, as shown in Constraint (39). Here, \(OP_{i}\) represents the inverse solution corresponding to each \(P_{i}\). Then, the individual with the higher fitness between the forward and reverse individuals is selected to participate in the composition of the initial population, as shown in Constraint (40), ensuring that the individuals selected for the initial population are of higher quality. Here, \(f\) represents the fitness of the individual.  
\[OP_{i}=\widetilde{P}_{i} \tag{39}\]  
\[P_{i}=\left\{\begin{array}{l}OP_{i}f(P_{i})<f(OP_{i})\\ P_{i},f(P_{i})>f(OP_{i})\end{array}\right. \tag{40}\]
# Cold Boot Attacks on the Supersingular Isogeny Key Encapsulation (SIKE) Mechanism
## 2 Background
### Previous Work  
Throughout this section, we present our literature review, describing works on cold boot attacks against multiples cryptographic schemes. These studies can be divided into several categories, namely the RSA setting, the discrete logarithm setting, the symmetric key setting and finally the post-quantum setting.
# Inversion of Forest Biomass Based on Multi-Source Remote Sensing Images
## 2 Materials and Methods
### Data Preprocessing  
#### 2.3.1 Landsat 8 OLI Data Processing Method  
Because Landsat 8 data have been geometrically and topographically corrected, preprocessing operations include radiometric calibration, atmospheric correction, image stitching and cropping. Then, the vegetation index, texture index (Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Second Moment, Correlation) and band information (band2-band7) were extracted. Vegetation index is an index to measure the status of ground vegetation. In this study, seven commonly used remote sensing indices (_ARVI_, _DVI_,  
Figure 1: A terrain and plot distribution map of Liaoning Province.  
_EVI_, _NDTI_, _NDVI_, _RVI_, _SVAI_) were selected, and the calculation formula of each index is as follows:  
\[NDVI=\frac{X_{NIR}-X_{RED}}{X_{NIR}+X_{RED}} \tag{1}\]  
Among them, \(X_{NIR}\) refers to the near-infrared band and \(X_{RED}\) refers to the red band.  
\[SAVI=(1+L)\frac{X_{NIR}-X_{RED}}{X_{NIR}+X_{RED}+L} \tag{2}\]  
Among them, \(L\) is the soil adjustment coefficient; generally, its value is 0.5.  
\[DVI=X_{NIR}-X_{RED} \tag{3}\]  
\[RVI=\frac{X_{NIR}}{X_{RED}} \tag{4}\]  
\[ARVI=\frac{X_{NIR}-(2X_{RED}-X_{BLUE})}{X_{NIR}+(2X_{RED}-X_{BLUE})} \tag{5}\]  
where \(X_{BLUE}\) represents the blue band.  
\[EVI=2.5\frac{(X_{NIR}-X_{RED})}{X_{NIR}+6X_{RED}-7X_{BLUE}+1} \tag{6}\]  
\[NDPI=\frac{X_{NIR}-(0.74X_{RED}+0.26X_{SWIR})}{X_{NIR}+(0.74X_{RED}+0.26X_{ SWIR})} \tag{7}\]  
where \(X_{SWIR}\) stands for short-wave infrared.
# Experimental Study on Vertical Shear Behaviors of an Immersion Joint with Steel Shear Keys
## 3 Loading Cases  
### Loading Protocol  
According to the research focus of this experiment, it is more appropriate to study the mechanical performance of the structure under clear load conditions. Hence, a load-controlled loading method is determined. Moreover, longitudinal compressive loading, transverse shear loading, and vertical shear loading are considered. A longitudinal compressive load is applied in front of element 2 to simulate water pressure on the joint. A transverse shear load is applied horizontally on the right side of element 2 to simulate possible transverse shear actions caused by earthquakes or wave impacts. A vertical shear load is applied vertically on the bottom of element 2 to simulate the possible vertical shear actions caused by differential settlements. To obtain time histories of the loading forces during the entire process, a semi-automatic servo-controlled actuator is adopted in the experiment. This servo-controlled actuator can control the jacks, thereby applying the loads in a stepwise manner until reaching the target values.  
Compared with the longitudinal compressive load and the transverse shear load, the vertical shear load is more difficult to implement due to the gravity of the element. The free body diagram during the vertical loading process is shown in Figure 7. Apparently, when the vertical shear load is applied, element 2 will have a tiny rotational displacement along the left endpoint of the support platform. The equations of equilibrium can be obtained as shown in Equation (2).  
\[\left\{\begin{array}{ll}\sum M{=\ 0,G\cdot\cos\theta\cdot L/2-(F_{v}-S)\cdot L =0}\\ \sum F{=\ 0,G\cdot\cos\theta+S-F_{n}\cdot\cos\theta-F_{v}=0}\end{array}\right., \tag{2}\]  
where \(G\), \(F_{n}\), \(F_{v}\), \(S\), \(L\), and \(\theta\) represent the gravity of the element, the supporting force provided by the support platform, the vertical shear load applied by the jacks, the total shear force carried by the joint, the length of the element, and the rotation of element 2 along the left endpoint of the support platform, respectively.  
The shear force (\(S\)) carried by the joint can be obtained by eliminating \(F_{n}\) from Equation (2).  
\[S=F_{v}-G\cdot\cos\theta/2 \tag{3}\]  
Because the rotational displacement of element 2 (\(\theta\)) is very small, \(\cos\theta\approx 1\). Therefore, Equation (3) can be simplified as shown in Equation (4).  
\[S=F_{v}-G/2 \tag{4}\]  
Figure 6: Arrangement of displacement gauges.  
Hence, theoretical values of the shear force carried by the joint can be obtained with Equation (4). Since the weight of a single element is 14 tons, i.e., the gravity (\(G\)) is 140 kN, there will be a positive shear force (\(S\)) after the vertical shear load (\(F_{v}\)) reaches 70 kN.
## 5 Results and Analysis
### Groundwater Potential Maps  
Groundwater potential maps were constructed using the training results of the models. The output values varying from 0 to 1 were classified into 5 classes (very low, low, moderate, high, very high) (Figure 6) using the natural breaks method, as it is the most suitable and popular technique for classification and construction of groundwater susceptibility maps [84]. Comparison of groundwater potential maps indicates that 51.56% of the study area is located in a very low groundwater potential zone and 11.62% in a very high zone using the BLR model. For the CGLR model, 55.53% of the study area is in a region with a very low groundwater potential and 11.95% is in a region with very high potential. In the case of the DLR model, 34.49% of the study area is located in a very low potential zone and 11.14% in a very high potential zone. For the RSSLR model, 26.02% of the study area lies in a very low potential zone and 11.11% in a very high zone. In the case of the LR model, 55.06% of the area is in a very low zone and 11.78% is in a very high zone. This suggests that 50% to 60% of the area of Dak Lak Province falls in the very low groundwater potential zone (Figure 7).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**Index**} & \multicolumn{8}{c}{**Phae**} \\ \cline{3-11}  & & \multicolumn{8}{c}{**Training**} & \multicolumn{8}{c}{**Validation**} \\ \cline{3-11}  & & **BLR** & **CGLR** & **DLR** & **RSSLR** & **LR** & **BLR** & **CGLR** & **DLR** & **RSSLR** & **LR** \\ \hline
1 & TP & 80 & 80 & 78 & 79 & 80 & 30 & 29 & 31 & 32 & 29 \\
2 & TN & 63 & 63 & 55 & 65 & 60 & 23 & 22 & 25 & 21 & 22 \\
3 & FP & 21 & 21 & 23 & 22 & 21 & 13 & 14 & 12 & 11 & 14 \\
4 & FN & 16 & 16 & 24 & 14 & 19 & 10 & 11 & 8 & 12 & 11 \\
5 & PPV (\%) & 79.21 & 79.21 & 77.23 & 78.22 & 79.21 & 69.77 & 67.44 & 72.09 & 74.42 & 67.44 \\
6 & NPV (\%) & 79.75 & 79.75 & 69.62 & 82.28 & 75.95 & 69.70 & 66.67 & 75.76 & 63.64 & 66.67 \\
7 & SST (\%) & 83.33 & 83.33 & 76.47 & 84.95 & 80.81 & 75.00 & 72.50 & 79.49 & 72.73 & 72.50 \\
8 & SPF (\%) & 75.00 & 75.00 & 70.51 & 74.71 & 74.07 & 63.89 & 61.11 & 67.57 & 65.63 & 61.11 \\
9 & ACC (\%) & 79.44 & 79.44 & 73.89 & 80.00 & 77.78 & 69.74 & 67.11 & 73.68 & 69.74 & 67.11 \\
10 & Kappa & 0.586 & 0.5855 & 0.469 & 0.5984 & 0.5501 & 0.391 & 0.338 & 0.472 & 0.3819 & 0.3375 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model performance in the training and validation phases.  
Figure 4: Magnitude of modeling error in the training and validation datasets.  
Figure 5: ROC curve of the models for the (**a**) training and (**b**) validation datasets.  
Figure 6: _Cont._  
Figure 6: Groundwater potential maps: (**a**) LR, (**b**) BLR, (**c**) CGLR, (**d**) DLR, and (**e**) RSSLR models.
# Semantic 3D Reconstruction for Robotic Manipulators with an Eye-In-Hand Vision System
## 2 Related Works
### Semantic 3D Reconstruction Based on a Hand-Held Camera  
After years of development, 3D scene reconstruction based on vision has been relatively mature and has produced a large number of excellent algorithms [21; 22; 23]. With the improvement of target detection and image segmentation algorithms, semantic 3D scene reconstruction has become a research hotspot in recent years [24; 25; 26; 27; 28; 29]. Its essence is the effective combination of semantic information with the SLAM system to generate 3D maps with semantic labels. Single Shot Detectors (SSDs) are introduced to handle geometric feather-based point cloud segmentation on the foundation of the orb-slam and processed map fusion through data association and the ICP [30]. Based on probabilistic methods, lots of previous works conduct 2D image segmentation through Random Decision Forests (RDF) and integrate 2D image labels into a 3D reconstruction map with a conditional random field and Bayesian updating model [31]. [PERSON] et al. [32] used the Convolutional Neural Network (CNN) to obtain the probability distribution of Classification for each 2D pixel, and then the Bayesian updating model would track the classification probability distribution of each curved surface, which would be updated based on the information regarding the data association provided by the SLAM system. In the subsequent work, they created a SLAM system with 6 degrees of freedom by merging 2D object detection with a 3D voxel foreground [33]. [PERSON] et al. [34] proposed a theoretical framework for the fusion of scale and semantic information, realizing the dynamic tracking of objects through ICPand RGB error and achieving the real-time object 3D reconstruction by asynchronous frame updating. Although the above works have established an environmental semantic map, the map scale is usually too large to reach a high accuracy, which limits its application in elaborate 3D modeling, such as desktop objects. The aforementioned 3D reconstruction method commonly use a hand-held camera and need a visual odometer, while in the eye-in-hand vision system in robotic manipulators, the 3D reconstruction method can be simplified through a forward kinematics analysis of robotic manipulators.
## 2 Formulation of the Problem  
### Physical Characteristic  
A view of the computational domain is disclosed in Figure 1. A two-dimensional obstructed duct with two side-by-side obstacles is modelled. Computational domain is subjected to a free stream with parabolic speed and uniform temperature (\(T_{h}=310\) K). It is assumed that the stream is incompressible, time dependent, and laminar. The obstacles and duct walls are kept at constant temperature (\(T_{c}=300\) K). Two side-by-side obstacles with sides \(D\) are mounted in the duct. \(S\) is the gap between the centers of the obstacles. The upstream and downstream lengths of the duct are fixed as 30\(D\) and 60\(D\), respectively. The duct height is considered to be 16\(D\). The gravity is in the stream-wise direction.  
The interactions between the liquid and aluminum oxide particles are taken into account for the simulations. Moreover, the collisions between particles are ignored. Note that for particles in the range of nanometer with low values of volume fraction, the chances that any two random particles would be close enough to interact with each other are extremely low [23]. Accordingly, the interactions between the particles in the range of nanometer can be ignored. However, for particles with higher diameter (e.g., 0.5 \(\upmu\)m), it is better to consider the effects of particle-particle interactions. It should be stated that in DPM, it is not possible to track all physical particles. Instead, representative particles or parcels should be tracked. Any parcel is representative of specified number of actual particles with the same physical property, which is characterized by the particle flow rate along each calculated particle trajectory. 5000 parcels [24] are assumed at \(\Phi=0.01\) for this research. Finally, it should be stated that all simulations are performed for the fixed values of Reynolds number (\(Re=100\)) and volume fractions of nanoparticles (\(\Phi=0.01\)) at variable particle diameter (30 nm-0.5 \(\upmu\)m) and space ratio (\(S/D=1.5\)-4.5).
# Research on Fault Diagnosis of Rotating Parts Based on Transformer Deep Learning Model
## 4 Conclusions  
This study seeks to address the limitations of existing methods in the extraction of both local and global fault feature information by proposing a fault diagnosis network model that integrates Transformer architecture with multi-head dilated convolution, referred to as MultiDilatedFormer. This model facilitates the comprehensive extraction of valuable fault information, thereby enhancing the accuracy of fault diagnosis. The main contributions of this paper are as follows: (1) A multi-head, multi-scale dilated convolution module has been designed, enabling the model to focus on local feature information from different subspaces within the input data, representing a pioneering effort in the application of multi-head convolution for fault diagnosis. (2) The innovative serial connection of multi-head self-attention and multi-head dilated convolution leverages the complementary advantages of both approaches, facilitating comprehensive extraction of global and local fault features. (3) A visual analysis of the entire decision-making process from model input to output has been conducted, enhancing the interpretability of the proposed model. (4) Experimental validation on three datasets demonstrates that the proposed method achieves higher diag  
Figure 16: The t-SNE cluster diagram of actual scene dataset.  
nostic accuracy and robustness in the fault diagnosis tasks of complex equipment. In the future, the aim will be to enhance the model's generalization and stability across different operating conditions and scenarios. This will involve further integrating multi-source data fusion techniques, leveraging information from various sensors and multimodal data to improve the model's ability to identify complex fault patterns. Additionally, the introduction of transfer learning will be explored to investigate cross-condition fault diagnosis methods, with the goal of achieving high accuracy and stability in fault diagnosis under varying operational conditions.  
Conceptualization, Y.D. and Z.Z.; methodology, Z.Z.; validation, Z.Z.; investigation, J.L.; resources, Y.D.; data collection and preprocess, X.L.; writing--original draft preparation, Z.Z.; writing--review and editing, Y.D. and Z.Z.; visualization, Z.Z. and J.L.; supervision, Y.D. and X.L.; project administration, Y.D.; funding acquisition, Y.D.; Z.Z. and Y.D. contributed equally to this paper. All authors have read and agreed to the published version of the manuscript.  
The project was financially supported by National Natural Science Foundation of China (Grant No. 52175457), Guangdong Science and Technology Plan Project (Grant No. 2023A0505050151), Guangdong Basic and Applied Basic Research Foundation (Grant No. 2022B1515120053), and the Key-Area Research and Development Program of Guangdong Province (Grant No. 2019B010154001).  
Not applicable.  
The datasets analyzed in this study are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.
# Parameterized Algorithms in Bioinformatics: An Overview
## 3 Genome Assembly and Sequence Analysis
### Identifying Common Patterns
#### 3.2.2 Longest Common Subsequence  
**Problem Description.** The Longest Common Subsesquence problem in its simplest version asks for a string of maximal length that is a subsequence of two input strings (see Figure 6(left)). It admits a classical dynamic programming algorithm, with complexity \(O(n^{2})\). However, the problem becomes NP-hard when we increase the number of strings, as follows.  
**Results.**: The foremost parameters for Longest Common Subsesquence are \(k\), \(\ell\), \(m\), and the alphabet size denoted \(|\Sigma|\). However, it is W[1]-hard (or worse) for all combinations of these parameters that do not yield a simple brute-force enumeration algorithm, i.e. \(k+m\) with any \(|\Sigma|\), and \(k\) with \(|\Sigma|=2\)[56; 57].
**Omitted letters.**: Another possible parameter is the number of _omitted_ letters, \(\ell-m\): Longest Common Subsesquence is FPT for parameters \(\ell-m\) and \(k\)[58], and the complexity is open for parameter \(\ell-m\) only.
**Sub-quadratic time? (\(k=2\))**: For two strings, there is no sub-quadratic algorithm unless the strong exponential time hypothesis fails [59]. However, parameterized algorithms can help subdue this lower bound to some extent: [PERSON] and [PERSON] [60] proposed an extensive study of the possible combinations of parameters yielding \"FPT in P\" algorithms (also sometimes referred to as \"fully polynomial FPT\", see [61; 62; 63]), with matching lower-bounds in each case.
**Constrained LCS. (\(k=2\))**: In this variant, the solution must contain each of a given set of \(f\)_restriction_ strings as subsequences. The problem is in P for \(f=1\)[64], NP-hard in general, and W[1]-hard for parameter \(f\)[65]. Finally, it is FPT for the total size of the restriction strings [66].  
Figure 6: Illustration of a longest common subsequence of length \(m=8\) (**left**) and of a shortest common supersequence of length \(m=12\) (**right**), each on a set of \(k=4\) strings.  
**Restricted LCS.**: Here, the restriction strings are _forbidden_ subsequences (rather than mandatory). The problem is FPT for the parameter \(\ell+k+f\)[67], as well as for the total size of restriction strings (with \(k=2\)) [66].  
**Open Problems.**: What is the complexity of Longest Common Subsesquence parameterized by \(\ell-m\), i.e., the maximal number of letters deleted in any string?
# A Method for Building a Mixed-Reality Digital Twin of a Roadheader Monitoring System
## 3 Construction of the Digital Twin Monitoring System
### Construction of the Exchange System Twin Data  
The roadheader twin data exchange processing program is the information hub in the architecture of the roadheader digital twin monitoring system and is an important program for data exchange. It adopts a C/S communication architecture and is responsible for data collection, forwarding, recording, and storage within the entire system architecture, corresponding to the data processing module. After the sensors on the physical entity of the roadheader obtain the position and movement of the roadheader, the data processing program on the roadheader body will process, encode, and package the sensor data, and then use the data transmission module to send the operating data of the experimental prototype to the twin data exchange processing program. Among them, the data storage module and the network connection module are the two main working modules.