esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Shortest Paths Methods for the Time-Dependent TSP
## 3 A Branch-and-Price Algorithm
### Additional Techniques
#### 3.5.3 Primal Heuristics  
We use an incremental construction heuristic to obtain a path \(P\) starting at \(s_{0}\). At each turn, we append an arc leading to vertices whose counterparts in \(D\) are still unexplored by \(P\), finishing when the path forms a tour. The selection of the arcs is based on the current fractional solution \((x^{*},y^{*})\). Arcs that are fixed to zero are always disregarded. If there are multiple possible arcs to be added, we score each arc \((u,v,\theta)\), and then choose one with probability proportional to the score. We use three different scoring functions:  
* The inverse of the travel time \(c_{uv}(\theta)\) (breaking ties arbitrarily);
* The variable value of \(x^{*}_{uv,\theta}\) using travel times to break ties or
* The compound value \(y^{*}_{uv}\) using the same tie-breaking rule.  
Since this construction is computationally inexpensive, we can increase the chance of finding an improved tour by using all scoring functions and performing several iterations with different random seeds.
# A Comparative Analysis of Arabic Text Steganography
## 2 Steganography
### Steganography Types
#### 2.2.2 Video Steganography  
Combining image and sound, a video file such as MPEG, AVI, or MP4 carries the capability of hiding a massive amount of sensitive information. A computed tomography (CT) scan, which is applied for image steganography, can likewise be implemented for video steganography by embedding sensitive information in each image of the video [11; 30; 31]. Other commonly applied techniques for video steganography include the Least Significant  
Figure 1: Scenario of steganography.  
Bit (LSB); Tri-way Pixel-Value Differencing (TPVD), which embeds the secret bits in the Inline frame (Iframe); and Bit Plane Complexity Segmentation (BPCS), which is also utilized for embedding secret bits within the MPEG video.
# Analysis of Earth Pressure Variation for Partial Displacement of Retaining Wall
## 3 Results and Discussion
### Distribution of Earth Pressure along the Height  
To reveal the distribution of earth pressure along the height of thee moveable plates, earth pressure exertion on the moveable plate under the test condition of a 10 cm trap-door width was plotted. For comparison, the data from load cells on the fixed wall located at \(x=0.35\) m was also selected for analysis.  
Figures 17-19 show the distribution of active earth pressure along the height of the trap-door and fixed wall in different displacement modes. It appeared that the distribution of horizontal earth pressure along the height of the trap-door was obviously nonlinear. Its nonlinearity performed that the lateral soil pressure near the middle of the trap-door had a significant increase, which was similar to the research of [PERSON] and [PERSON] [4]. The horizontal earth pressure on the fixed wall at the location of \(x=0.35\) m was slightly larger than the static earth pressure when the trap-door produced displacement. The distribution of earth pressure on the fixed wall basically presented a linear-distribution along the height.  
Compared with the condition of movement of the entire retaining wall, only the movable plates had a displacement in the horizontal trap-door test, while the surrounding box wall was fixed. Thus, it was evident that the horizontal soil arching phenomenon dominated the backfill performance behind the trap-door, making the earth pressure distribution different with traditional soil pressure.  
Figure 17: Distribution of earth pressure on the trap-door and fixed wall in T mode: (a) earth pressure on the trap-door, (b) earth pressure on the fixed wall.  
The active earth pressure coefficient is defined as:  
\[K_{a}=P/\left(\frac{1}{2}\gamma H^{2}\right) \tag{2}\]  
where \(P\) is the resultant force of the horizontal earth pressure acting on the trap-door, and \(\gamma\) is the bulk density of the filler.  
The variation of the active earth pressure coefficient corresponding to the displacement of the trap-door is shown in Figure 20. It showed a distinct nonlinear relationship of \(K_{a}\) and \(S_{max}/H\). The active earth pressure coefficient in the three modes all decreased rapidly at first, and then increased gradually with the increasing displacement, and reached a stable value eventually. This tendency was similar to the variation in active earth pressure. The ultimate active earth pressure coefficient was less than Coulomb and Rankine theory in T and RB mode, which equaled about 0.15. However, the active earth pressure coefficient in RT mode at any displacement was a little greater than Coulomb active earth pressure coefficient and its stable value could reach about 0.26.  
Figure 19: Distribution of earth pressure on the trap-door and fixed wall in RT mode: (**a**) earth pressure on the trap-door, (**b**) earth pressure on the fixed wall.  
Figure 18: Distribution of earth pressure on the trap-door and fixed wall in RB mode: (**a**) earth pressure on the trap-door, (**b**) earth pressure on the fixed wall.
# Advancements in EEG Emotion Recognition: Leveraging Multi-Modal Database Integration
## 3 Multi-Modal System EEG with Facial Expressions--Proposed Model
### EEG Electrode and Frequency Band Selection
Each average topography represents a level of the valence axis. The next step is to add up all of these absolute differences for each pixel among the \(64\times 64\) pixels to obtain a single number that expresses the difference between these two diametrically opposed feelings. Although optimization approaches normally yield just the ultimate optimal answer, this work documented and preserved all of the alternatives examined by the SHGO methodology for future examination. These different input possibilities tested during the optimization process helped in a way showing how we can choose different electrodes' placements with frequency band to reach the optimum solution according to the criteria we have. For example, and as we are going to show in this study, we have reduced the number of sensors to increase the system's simplicity while maintaining almost the same system efficiency. A chart showing the maximum objective function values for the valence emotional axis plotted against the number of channels is shown in Figure 7. The findings show that there is no big change in the valence emotional axis between using ten and nine sensors system. Nine channels, AF3, F7, F3, FC5, T7, O2, T8, FC6, and F4, are the best choice to have since more channels result in more noticeable differences between valence levels. Using four electrodes initially causes a reduction in the objective function value, but it subsequently increases again with five electrodes, although it remains lower than the value achieved with three electrodes. The objective function continues to rise until the use of ten electrodes, after which it gradually declines until reaching its lowest point when all 14 sensors are employed. Remarkably, this approach--and by choosing nine electrodes, which results in almost the same value of the objective function--yields a reduction in sensor complexity of over 35% compared to utilizing the full 14-sensor configuration, as illustrated in Figure 8.  
Figure 7: Maximum objective function value against the number of channels—valence axis.
# Wind Turbine Power Curve Modelling with Logistic Functions Based on Quantile Regression
## 2 The Proposed Logistic Functions Based on Quantile Regression  
### Logistic Functions  
Logistic functions (LF) have been successfully applied in WTPC modelling due to their good nonlinear mapping ability. Among them, 4-parameter logistic function (4 PL) has been commonly used, expressed as [9]:  
\[P(v,\ \Theta)=a\cdot\frac{1+m\cdot\exp(v/\tau)}{1+n\cdot\exp(v/\tau)} \tag{1}\]  
where \(P(v,\ \Theta)\) is the predicted power output; \(v\) is the wind speed; and \(\Theta=[a,\ m,\ n,\ \tau]\). We can obtain the estimated parameters \(\hat{\Theta}\) of 4 PL by minimizing the following cost function:  
\[\hat{\Theta}=\text{argmin}\sum_{i=1}^{N}{[y_{i}-P(v_{i},\ \Theta)]^{2}} \tag{2}\]  
where \(N\) is the number of samples in training set and \(y_{i}\) is the observed power output.  
Fitting curves obtained by 4 PL, however, are point symmetric on the semi-log axis about its midpoint, which cannot accurately fit the power curves with asymmetric features [34]. Accordingly, researchers proposed a 5-parameter logistic function (5 PL) to enhance the mapping ability for asymmetric data, expressed as:  
\[P(v,\ \Theta)=d+\frac{a-d}{\left(1+(v/c)^{b}\right)^{8}} \tag{3}\]  
where, in 5 PL, \(\Theta=[a,\ b,\ c,d,g],\ c,g>0\); parameters \(a\) and \(d\) determine the position of the horizontal asymptote of the fitting curve; and \(g\) is the asymmetry factor. The curvature of the fitting curve is jointly controlled via \(b,\ c\) and \(g\). Although 5 PL has good nonlinear mapping ability in power curve modelling, it can only provide deterministic fitting results.
# The Development of the Pipe Jacking Guidance Technology
## 3 Laser Target Method
### Measuring Principle
#### 3.2.2 Angular Measurement  
Angle measurements include the pitch angle \(\lambda\), roll angle \(\beta\) and azimuth angle \(\alpha\) of the pipe jacking machine. The dual-axis inclinometer built into the laser target provides the pitch angle \(\lambda\) and roll angle \(\beta\) of the pipe jacking machine. The azimuth angle \(\alpha\) needs to be further calculated using the laser spot position inside the laser target.  
The azimuth calculation is shown in Figure 5. A temporary coordinate system, O\({}^{\text{t}}\)-X\({}^{\text{t}}\)Y\({}^{\text{L}}\)Z\({}^{\text{t}}\), is established, with point O\({}^{\text{t}}\) representing the laser spot and the three axes being parallel to the three axes of the laser target coordinate system, respectively. In the arrangement of the laser target, it is imperative that the optical axis within the laser target is aligned parallel to the central axis of the pipe jacking machine. Therefore, the angle between the laser beam and the central axis of the laser target is equivalent to the angle between the laser beam and the central axis of the pipe jacking machine. The straight-line O\({}^{\text{t}}\)A (translation axis O\({}^{\text{t}}\)A) is parallel to both the central axis of the pipe jacking machine and the optical central axis of the laser target. The blue dashed line forms a horizontal plane \(\psi\) with the X\({}^{\text{t}}\)-axis and \(Y\)-axis, and O\({}^{\text{t}}\)A\({}_{1}\) and O\({}^{\text{t}}\)B\({}_{1}\) are the projections of the straight-line O\({}^{\text{t}}\)A and the laser beam O\({}^{\text{t}}\)B on the horizontal plane \(\psi\). The horizontal azimuth (\(\varepsilon\)) of the laser beam and the angle (\(\lambda_{1}\)) between the laser beam and the horizontal plane are measured by the total station. The angle between O\({}^{\text{t}}\)A and its projection, O\({}^{\text{t}}\)A\({}_{1}\), in the horizontal plane is the pitch angle (\(\lambda\)) of the pipe jacking machine.  
Figure 5: Schematic diagram of the azimuth calculation principle.  
The angle \(\delta\) between the laser beam O\({}^{\text{t}}\)B and the line O\({}^{\text{t}}\)A is equal to \(\delta_{\text{L}}\) in Equation (1), which is shown in Equation (2):  
\[\delta=\delta_{L} \tag{2}\]  
\(\delta_{1}\) can be calculated according to the cosine theorem as shown in Equation (3).  
\[\delta_{1}=\arccos\left(\frac{\cos\delta-\sin\lambda\sin\lambda_{1}}{\cos \lambda\cos\lambda_{1}}\right) \tag{3}\]  
The angle \(\alpha\) between the horizontal plane projection O\({}^{\text{t}}\)A\({}_{1}\) of the straight-line O\({}^{\text{t}}\)A and the X\({}^{\text{t}}\)-axis of the temporary coordinate system is the azimuth angle. According to the geometric relationship, the azimuth angle \(\alpha\) is calculated in Equation (4).  
\[\alpha=\varepsilon+\delta_{1} \tag{4}\]
# A Distributed Approach to the Evasion Problem
## 2 Materials and Methods
### Distributing the Algorithm
#### 2.2.9 Examples  
Figure 2 shows a running example of the distributed algorithm. In the local view figures, the red dot denotes the sensor we observed from, purple dots denote its Delaunay neighbors and blue dots denote the two-hop Delaunay neighbors. During the bootstrap phase, the sensor collects information from the entire network and computes a Voronoi diagram from its local view. After the bootstrap process, it will only communicate with its Delaunay neighbors and keep information on its two-hop Delaunay neighbors. As we can see from the figure that local Voronoi diagram does differ from the global view, due to the two-hop limit and the delay on the location update. However, the calculation of Delaunay neighbors is not affected, which is the key to our algorithm. Then it will calculate and update the Delaunay neighbors and regions accordingly.  
Now let us consider the example in Figure 1, and label the nodes clockwise from the top as \(a,b,c,d,e\). In step (c), all five nodes are tracking the same region \([a,b,c,d,e]\) as the (enclosed) region they are adjacent to. When moving from (c) to (d), two new edges grow shorter, causing \(Del-\alpha\)-updates introducing the edges \(ac\) and \(ad\). As \(ac\) is introduced, node \(a\) takes responsibility to notify all five nodes of the change in regions: \(a\), \(b\) and \(c\) are informed of the new region being \([a,b,c]\) while \(a,c,d,e\) are informed of the new region being \([a,c,d,e]\) (\(a\) and \(c\)--as they are part of the same new edge--receive two new regions to track). When \(ad\) is introduced, \(a\) informs \(a,c,d\) of the new region \([a,c,d]\) and \(a,d,e\) of the new region \([a,d,e]\).  
By the time that step (d) is reached, all three of the resulting triangular regions have also come close enough to their respective Voronoi vertices to trigger the event \(\Delta+\), labeling the three regions--independently of each other--with the label _False_ which denotes the impossibility of an evasion path within them.  
As we go from (d) to (e) in Figure 1, the two edges \(ac\) and \(ad\) grow too long for the \(\alpha\)-complex, and trigger one \(\alpha-Del\) event each. As \(ac\) triggers its event, the node \(a\) takes responsibility to inform \(a,b,c,d\) that the regions \([a,b,c]\) and \([a,c,d]\) have merged into a region \([a,b,c,d]\) and that since both regions were labelled _False_, so is the new region. Then when \(ad\) triggers its event, the node \(a\) again informs all the nodes that the regions \([a,b,c,d]\) and \([a,d,e]\) have merged into a single region \([a,b,c,d,e]\) which again inherits the labeling _False_ from both regions having been labelled _False_.
## 2 Problem Setting
### Dynamic Response
#### 2.4.1 Main Hypothesis  
The quasi-static problem, as formulated in the former section, is given by:  
\[\int_{\Omega}\mathbf{P_{1}}:\boldsymbol{\
abla}\mathbf{v}\ \mathrm{d}\Omega-\int_{ \Omega}\mathbf{f}\boldsymbol{\cdot}\mathbf{v}\ \mathrm{d}\Omega-\int_{\Gamma_{f}}\mathbf{F}_{0}\boldsymbol{\cdot}\mathbf{v}\ \mathrm{d}\Gamma=\mathbf{R}_{c}(\mathbf{u_{1}})\ \ \ \ \forall \mathbf{v}\in\mathcal{V} \tag{26}\]  
where \(\mathbf{R}_{c}\) is the virtual work of the contact forces. The virtual work due to the inertial forces is added to the weak formulation. The dynamic problem is given by:  
\[\int_{\Omega}\rho\dot{\mathbf{u}}_{2}\boldsymbol{\cdot}\mathbf{v}\ \mathrm{d}\Omega+\int_{ \Omega}\mathbf{P_{2}}:\boldsymbol{\
abla}\mathbf{v}\ \mathrm{d}\Omega-\int_{\Omega}\mathbf{f} \boldsymbol{\cdot}\mathbf{v}\ \mathrm{d}\Omega-\int_{\Gamma_{f}}\mathbf{F}_{0} \boldsymbol{\cdot}\mathbf{v}\ \mathrm{d}\Gamma=\mathbf{R}_{c}(\mathbf{u_{2}},\dot{\mathbf{u}}_{2})\ \ \ \ \ \forall \mathbf{v}\in\mathcal{V} \tag{27}\]  
where \(\rho\) is the density. The dynamic response \(\mathbf{u_{2}}\) is supposedly the sum of the quasi-static response \(\mathbf{u_{1}}\) and the vibrations field \(\mathbf{w}\). In other words, the inertia effects are only considered for the vibrations problem.  
\[\mathbf{u_{2}}(\mathbf{X},t)=\mathbf{u_{1}}(\mathbf{X},t)+\mathbf{w}(\mathbf{ X},t)+o(\mathbf{w}) \tag{28}\]  
An homogeneous Dirichlet boundary condition is applied on \(\Gamma_{u}\) since the prescribed displacement is the same for both problems.  
\[\mathbf{w}=\mathbf{0}\ \ \ \ \text{on}\ \ \Gamma_{u} \tag{29}\]  
The active contact zone \(\Gamma_{c}^{t}\) and the virtual work of the contact forces are supposedly the same for the dynamic and quasi-static problem.  
\[\mathbf{R}_{c}(\mathbf{u_{2}},\dot{\mathbf{u}}_{2})\approx\mathbf{R}_{c}( \mathbf{u_{1}}) \tag{30}\]  
The active contact zone is not exactly the same since the inertia terms will change the contact stiffness and thus alter the contact patch [22]. Another difference between the virtual works is the friction law formulation. The sliding velocity is approximated in the quasi-steady regime whereas it is equal to the tangent velocity in the dynamic regime.  
Given the hypothesis that the inertia effect does not change the contact patch, the normal displacement in the quasi-steady regime does not change in the dynamic regime since it is determined by the rigid plane position. Therefore, the normal vibrations field is null on the contact active zone.  
\[\mathbf{w\cdot n}=0\quad\text{ on }\ \Gamma_{c}^{t} \tag{31}\]  
This boundary condition is an interpretation of the contact constraint as a Dirichlet-like boundary condition. The active contact zone \(\Gamma_{c}^{t}\) is not related to a material domain, which is problematic from a mathematical point view; however, it is easily implemented after a finite element discretization.
## 5 Data Analysis and Results
### Reliability and Validity Tests
#### 5.2.2 Validity Tests  
The validity of the sample data was examined using KMO and [PERSON]'s spherical test. Table 3 demonstrates that the data may be factor analyzed, the statistical test results are significant, the significance level is less than 0.001, the KMO value is more important than 0.7, and [PERSON]'s spherical test chi-square value is 6079.531.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Measured Variables** & **Scale Items** & **Cronbach’s Alpha** & **CR** \\ \hline PE & 3 & 0.835 & 0.842 \\ EE & 4 & 0.812 & 0.824 \\ SI & 3 & 0.803 & 0.825 \\ FC & 2 & 0.732 & 0.739 \\ TEC & 1 & 0.823 & 0.806 \\ TAS & 1 & 0.831 & 0.832 \\ TTF & 1 & 0.845 & 0.835 \\ PR & 3 & 0.867 & 0.871 \\ IN & 3 & 0.873 & 0.862 \\ NA & 3 & 0.907 & 0.925 \\ FL & 3 & 0.926 & 0.955 \\ IU & 4 & 0.864 & 0.866 \\ \hline \end{tabular}
\end{table}
Table 2: Reliability test analysis.  
Figure 4: Descriptive statistical information of the official questionnaire.
# Sentinel: Automated Detection of Android Vishing Apps Using Optical Character Recognition
## 3 Background
### Malicious Behavior in Vishing Apps
#### 3.1.2 Display Overlay  
Android supports APIs that facilitate the output of specific pop-up screens or full-screen displays, known as Display Overlay [23, 24]. Vishing applications exploit these APIs to perform malicious activities by using Display Overlay to cover the victim's device screen with a fake interface, thereby deceiving the victim about the outgoing caller ID. Specifically, when vishing applications change the outgoing call, they overlay a fake screen to disguise the altered outgoing number display. When victims receive a call from attackers, vishing applications overlay a screen that appears to be from a legitimate institution (e.g., police, bank, public institution) to deceive the victim.
# Towards Efficient Sink Mobility in Underwater Wireless Sensor Networks
## 5 Simulation Results
### Mobile Sink Utility Ratio  
It can be seen in Figure 6 that the mobile sinks in the proposed scheme is used more for data packets collection compared to the SEEC. Please note that SEEC is the only protocol that uses mobile sinks amongst the compared protocols to our proposed scheme. Therefore, MUR and packet dropped ratio are compared only with SEEC to properly analyze the efficiency of mobile sink usage. The motion pattern of the mobile sinks in the proposed scheme results in the high MUR. In SEEC, the mobile sinks are used in the sparse regions only. The sparse region array is not updated periodically and, hence, this is the reason that, after 500 rounds, MUR becomes zero. The high MUR in the proposed scheme is also the reason for high throughput and less packet drop ratio. As discussed in Section 3, for SEEC, the case may appear when both mobile sinks come in adjacent sparse regions of either the left or right side. In this scenario, the sensor nodes of the sparse region on the other side starts dropping the received packets. Thus, the total packets collected by the mobile sinks in SEEC remain lower, which is the reason for less MUR in SEEC.  
Figure 4: Residual energy  
Figure 5: Packets received per round.
# DP-YOLO: Effective Improvement Based on YOLO Detector
## 2 Related Work
### Deformable Convolution  
The conventional convolution operation involves dividing the feature map into rectangles of the same size as the convolution kernel and performing the convolution. In contrast, group convolution groups the number of channels and uses different convolution kernels for each group, reducing the number of parameters and making the network more lightweight. Depth-separable convolution splits the convolution process into depth-wise convolution and point-wise convolution, further optimizing the number of convolution parameters. Dilated convolution introduces dilation rates to increase the receptive field of the convolution operation. However, these operations still perform convolution on an N \(\times\) N grid, resulting in a theoretical receptive field that is close to a rigid square, not adequately capturing the actual shape of objects. To address this limitation, [PERSON] et al. [24] proposed DCN (Deformable Convolution), which introduces a learnable offset to the sampling position of the convolution. This deformation makes the receptive field adaptive to geometric changes, allowing for more accurate feature extraction and enhancing the network's adaptability to various deformations.  
[PERSON] et al. [25] raised concerns about the introduction of irrelevant contextual information in DCN due to the offset module. To address this, they proposed DCNv2, which incorporates new learnable weights into the deformable convolution. This modification helps mitigate the interference of irrelevant factors during model training. They demonstrated that DCNv2 layers, when applied more extensively in the backbone network, enhance the network's ability to model geometric transformations effectively. On the other hand, [PERSON] et al. [26] initiated their work with the concept of constructing a large-scale vision base model. Drawing insights from DCNv2 and taking inspiration from MHSA [8] along with depth-separable convolution, they devised a more lightweight deformable convolution known as DCNv3. Implementing DCNv3 in their approach called InternImage, achieves a state-of-the-art level in the detection task at the time.  
Due to the diversity of visual task objectives, it is important for models to have deformational representations, and adding deformable convolution to the network to improve performance is a viable means of doing so, but it is more worthwhile to explore how to achieve more efficient utilization.
# Performance Evaluation of Multi-UAV Network Applied to Scanning Rocket Impact Area +
## 6 Results
### Experimental Results  
Throughput and RSSI tests were performed in scenarios 1 and 2 in order to verify which configuration is more efficient for structuring the network. In these conditions, transmission speeds with different baud rates were measured. The throughput was calculated based on the simultaneous bidirectional packet transmission between nodes. To increase the reliability of results, the test was repeated 72 times in scenario 1. In scenario 2, three different experiments were performed, each one of them repeated 36 times while sending 500 bidirectional packets per test.