esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Flow Mechanism for the Poor Self-Start Ability of DSBL Flow Mode
As for the flow of the body side, separation could spill broadwise through that triangle window. Figure 30 displays two spatial streamlines which came from the body-side separation and lip-side separation, respectively, both through the longitudinal section of Z-c = 0.062. Firstly, the spatial streamline from the body side shows that there also was a tendency to flow outward in body-side separation. More than that, the density of longitudinal vortexes was much sparser compared with the LSB flow mode, which implies that the broadwise spillage was stronger. The reason for this difference has also been analyzed: the increase in the lip-side separation with the approach to sideboards would have squeezed the body-side separation, which enhanced the body-side broadwise spillage. Then, we observed the spatial streamline from the lip-side separation. As shown, longitudinal vortexes were distributed in a wide transversal span of lip-side separation, and finally the streamline pointed downstream. No broadwise spillage could be observed. To sum up, the scale of separations on the body side could be diminished from broadwise spillage, both for the LSB flow mode and DSBL flow mode. As for lip-side separation, there was no broadwise spillage to promote separation vanishing, and the vanishment could only have been completed by being swallowed downstream. The concept of \"opened separation\" and \"closed separation\" could be analogized here. The opened separation with spillage which resembles the LSB flow mode can self-start easily, while the closed separation without spillage which has the characteristics of lip-side separation cannot easily self-start. Therefore, the self-start ability of DSBL is poor. Figure 28: Comparison of results between experiment and CFD calculation—DSBL unsstart mode. (**a**) Comparison of flow structures (Schlieren image and pressure contour of symmetry plane). (**b**) Comparison of pressure distributions along flow direction.
## 3 System Model  
In this section, the system model of the SDN-enabled integrated satellite-terrestrial networks is illustrated at first. Then, we model the joint placement problem as a Mixed Integer Linear Program (MILP) with network latency and network reliability formulated mathematically.
## 3 Experimental Setup
### LD and Transmitter
#### 3.1.2 Transmitted Waveform  
The data rate of the communication system is limited by the modulation bandwidth. Several studies have demonstrated that the M-PPM can achieve high-sensitivity communication by increasing the size of modulation order M, but the requirement for modulation bandwidth will be higher [38]. The higher modulation bandwidth means a shorter slot duration time, which leads to fewer received photons in a signal slot. To trade off the modulation bandwidth and M, in this current system, we chose 32-PPM for our modulation scheme. The transmitted data were organized into frames. The frame structure is shown in Figure 3. To achieve the slots and symbol synchronization, a special frame header was fixed at the beginning of the frame. The receiver can distinguish the slot sequences and the start of a frame from the received data sequence by identifying the frame header. The following 500 symbols are a payload sequence.  
Figure 3: The signal frame structure.  
Figure 2: Transmitter blue LD: (**a**) single mode fiber-pigtailed LD, (**b**) the optical spectral property, (**c**) 3 dB bandwidth of LD, (**d**) VI and P–I curves when the operating temperature was 25 \({}^{\circ}\)C.  
A frame acquisition sequence (FAS) with 24 PPM symbols (0, 3, 1, 2, 1, 3, 2, 0, 0, 3, 2, 1, 0, 2, 1, 3, 1, 0, 3, 2, 3, 2, 1, 0) for 4-PPM are provided by the standard for Optical Communications Coding and Synchronization recommended by the Consultative Committee for Space Data System [44]. Later on, the FAS is modified by reference [13] into [0, 3, 1, 2, 1, 3, 2, 0, 3, 2, 1, 0, 2, 1, 3, 1, 0, 2, 1, 3, 1, 0, 3, 2, 3, 2, 1, 0, 0, 1, 2, 3, 2, 3, 0, 1, 3, 1, 2, 0, 1, 2, 3, 0, 0, 2, 3, 1, 2, 1, 3, 0]. We converted 48 4-PPM symbols into 32-PPM symbols as shown in Figure 4 using the zero-fill operation as the FAS in this experimental setup. In this study, the modulation bandwidth in the system was set at 12.5 MHz, which means the signal slot duration time was 80 ns. After loading the waveform signal, the measured average transmitting optical power was 0.47 mW, and the single-pulse energy was 1.2 nJ.
# Two-Way Transmission for Low-Latency and High-Reliability 5G Cellular V2X Communications
## 4 Numerical Results
choosing the better channel and thus have higher SINR. Note that higher SINR leads to the higher probability of having perfect SIC operation and higher fading parameter \(m\). Figure 6 depicts the impact of imperfect SIC on the outage behavior, where the signal detection at D\({}_{1}\) is performed through SIC operation. The higher values of \(\lambda_{f_{1}}\) and \(\lambda_{f_{2}}\) result in the higher outage probability because they lead to smaller values of SINR. Figure 5: Outage probabilities with respect to the number of antennas at each vehicle with \(\rho=10\) [dB]. Figure 6: Impact of imperfect successive interference cancellation (SIC) levels to outage performance at the first vehicle with \(m=3\) and \(\rho=20\) [dB]. It is observed from Figure 7 that higher target rates result in higher outage probabilities of two vehicles. However, the throughput of vehicles does not grow as target rates get higher as shown in Figure 8. The maximum throughput is obtained at medium values of target rates. The values of rates maximizing the throughput depend on the number of antennas and SIC scheme. Figure 8: Throughput performance versus target data rates with \(a_{1}=0.95\), \(a_{2}=0.05\), \(m=2\), and \(\rho=5\) [dB]. Figure 7: Outage probabilities versus target data rates with \(a_{1}=0.9\), \(a_{2}=0.1\), \(m=2\), and \(\rho=5\) [dB].
## 5 Experiments and Results
### Recovery Results by Element  
The proposed methodology also allows the searching of natural scenes by their constituent elements, that is, from among all the scenes in the image base, retrieving all the scenes that contain a particular element of the scene (or common element). Figure 14 shows some examples of natural scenes i.e., fields, rivers, forests, beaches, and mountains. In the lower part of the Figure 14, some semantic concepts that constitute the scenes are shown i.e., clouds, sky, water, grass, mountains, etc.  
The protocol experimentation to quantitatively evaluate the scene recovery by element was as follows: one of the six elements to recover in the scenes was defined, each base element being cloud, sky, water, mountain, grass, and river. Subsequently, the recovery of 100 scenes containing the searched item was was carried out. It was quantified within the 100 recovered how many, indeed, contained the searched element.  
Table 4 gives the confusion matrix for the recovery of the four elements searched in the scenes. It can be seen that a good recovery is obtained since the proposal is ideal for searching one item at a time--for example water, thus having natural scenes that contain water.  
* Water Element: Figure 15 shows an example of recovering natural scenes containing the element _water_. For the proposed methodology, the type of scene is not important, and only items containing water (sea water or river water) are recovered. Among all the 20 images, only 14 images containing water were recovered.
* Cloud Element: Regarding the _cloud_ element, Figure 16 shows the recovery results, for the sake of clarity, first 20 images recovered are presented. It can be seen from the example that 15 out of 20 images contained _cloud_.  
Figure 14: Natural scenes constituent elements.  
Figure 13: Shullani database [16] performance.
## 4 Methods for Manipulation Protection and Detection
### ObjectFormer  
An intriguing method for detecting image manipulation within the realm of passive approaches is ObjectFormer [52], proposed by a team of researchers from Fudan University, the University of Maryland, Huya Inc., and Meta AI. ObjectFormer stands out for its multimodal approach to manipulation detection, which enables analysis in both the RGB and frequency domains. This dual-domain capability allows it to capture invisible traces of editing in the standard color spectrum. The architecture of this method is based on transformers [32; 33], which model spatial dependencies within the image (Figure 5). By leveraging the strengths of both RGB and frequency representations, ObjectFormer enhances its ability to detect subtle and sophisticated manipulation artifacts.  
Many image manipulation detection methods classify an image as either authentic or manipulated, often without considering the structure of objects within the image. However, the effective detection of visual modifications requires not only identifying pixel-level  
Figure 5: ObjectFormer high level architecture.  
anomalies but also evaluating whether entire objects in the image are consistent with each other. The input RGB image is transformed into the frequency domain using the Discrete Cosine Transform (DCT). A high-pass filter is then applied, retaining only the components that may potentially contain information about image tampering (low frequencies are removed). After this process, the image is converted back to the RGB domain. The architecture begins with two parallel processing paths: the spatial (color) and frequency domains. For data representation extraction, two feature extractors based on EfficientNetb4[53] (pre-trained on ImageNet[54]) are employed.  
Both representations--derived from the RGB and frequency domains--are then divided into spatial embeddings, which are vectors combined to form multimodal embeddings. In the next stage, the object encoder utilizes an attention mechanism and learnable object prototypes to analyze the consistency between elements in the image. These prototypes enable the model to identify various objects in the image and examine their interrelationships. Subsequently, the patch decoder leverages this information to refine the patch embeddings, enriching them with knowledge about the objects. The applied BCIM module (Boundary-sensitive Contextual Incoherence Modeling) is designed to detect contextual inconsistencies at the pixel level. This module improves the precision of boundary detection in manipulated regions, enhancing their sharpness by analyzing local pixel similarities and incorporating this information into the generated feature representations. The final stage includes a post-processing network consisting of interpolation and convolution operations aimed at upsampling the matrix to match the input image dimensions and producing a single-channel output to create a binary manipulation detection mask. The extracted image features are also passed through a simple fully connected layer [55], which classifies the image as either authentic or manipulated.
## 2 Materials and Methods
### Vulnerability Analysis of Coastal Flooding
#### 2.2.8 Wave Factor: Average Rate of Significant Waves (Fw)  
The wave factor indicates the maximum wave values that affect the coastline of Ria of Arosa, recorded at buoys located around the study area. These historical values have been obtained from the SIMAR dataset, retrieved from the Ports of State website (puertos.es) (Figure 5). The SIMAR dataset consists of time series of wind and wave parameters derived from numerical modeling, making them synthetic data and not directly measured from nature.  
The SIMAR series arises from the amalgamation of two extensive datasets of simulated wave data historically maintained by Ports of State, namely, SIMAR-44 and WANA. Its primary goal is to provide more extensive and up-to-date temporal series. Consequently, the SIMAR dataset offers information dating back to the year 1958 and extending up to the present year, 2023. Through these data, valuable information about wave direction (Figure 5), peak and mean wave periods, and significant wave heights can be obtained. Notably, information on the latter is used to calculate the vulnerability factor for significant wave conditions. From among the significant wave height values, the highest data points for each SIMAR location are selected. This selection process aligns with the aim of this study, which is to statistically evaluate the most likely flooding risk, even in less frequent scenarios.  
Once all significant wave heights from each SIMAR point were collected, the coordinates and data of the SIMAR points were entered into a Geographic Information System (GIS), and interpolation was performed. This allowed us to obtain a map of significant wave activity in the study area (Figure 6A). Considering that vulnerability is directly proportional to wave height, it can be assumed that the higher the wave, the greater the vulnerability in  
Figure 5: Distribution of buoys (green marks), SIMAR points (red marks), and tide gauges (yellow marks) on the Ports of State website, along with an example of information view.  
that area, and vice versa. Based on this assumption, the following vulnerability intervals have been established: 3.5-4 m (value 1), 4-5 m (value 2), 5-6 m (value 3), 6-7 m (value 4), and \(>\)7 m (value 5).
# A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning
## 5 Deep Transfer with Related Stock Information (DTRSI) Framework
### Base Model Construction
#### 5.1.2 Training Process  
For training the base model, a variety of network architectures which have a different number of layers, hidden units, and activation functions were examined. Then, from among the considered configurations, the most appropriate network architecture was selected based on the highest performance over the validation set. The topology of our LSTM model is as follows:  
* The input layer has units that equal the number of features and 20 timesteps.
* There are two LSTM layers, which are followed by two fully connected layers (marked FC in Figure 3b). Each layer has 16 units and dropout regularization is applied to the outputs of each hidden layer in order to mitigate the overfitting problem. Keep probability values are set to 0.5.
* The output layer has one sigmoid activation unit.  
Moreover, weight and bias values are initialized with the normal distribution. A learning rate in RMSprop optimization [50], the batch size, and the maximum training epochs are selected as 0.001, 512, and 5000, respectively.
## 4 Image Motion Modeling and Analysis
### Residual of Image Motion Compensation
#### 4.2.2 Distribution of the IMC Residual  
The magnitude and phase angle of \(v_{A_{F}}^{S}\) while \(A_{S}\) was located at a different position on the sensor plane were calculated using Equation (19), with the results shown in Figure 10. The parameters were the same as those in Figure 6. \(\omega_{F}\) was 3.29\({}^{\circ}\)/s, according to Equation (14). It can be learned that the linear velocity of the IMC varied on the sensor plane. The maximum magnitude of the IMC linear velocity was 6.252 mm/s, while \(x_{S}=\pm 30\) mm and \(y_{S}=0\) mm. The minimum magnitude of the IMC linear velocity was 5.73 mm/s, while \(x_{S}=0\) mm. The phase angle of the IMC linear velocity was 0\({}^{\circ}\), while \(y_{S}=0\) mm. The absolute value of the phase angle of the IMC linear velocity would increase with \(|x_{S}|\) or \(|y_{S}|\), and the maximum value of it was 4.79\({}^{\circ}\). The distribution of \(v_{A_{E}}^{S}\) is illustrated in Figure 11 according to Equation (20). \(v_{A_{E}}^{S}\) is represented by a purple line with an arrow on the sensor plane. The starting point of each purple line stands for the corresponding pixel. The phase angle of \(v_{A_{E}}^{S}\) varied with \(x_{S}\) and \(y_{S}\), so it was impossible to fully compensate the image motion for every pixel. \(E_{S}\) had the maximum magnitude of the IMC residual, which was 2.60 mm/s. If the image motion permitted was one half the size of one pixel, then the maximum exposure duration was 1.9 ms.  
Figure 11: Distribution of the residual of the IMC linear velocity on the sensor plane.  
Figure 10: Magnitude or phase angle of the linear velocity of the IMC with a position on the sensor plane.
## 4 Wear Evaluation Results
### Normal and Tangential Forces  
This section summarizes the analysis results of tangential and normal forces that cause wear. The force history is presented for each of the four sections defined above. Figures 19 and 20 show the tangential and normal force history of each section. As the penetration depth increases, the load received from the soil also increases. Since Sections 2, 3, and 4 have the same size and shape, the normal and tangential forces increase in proportion to the penetration depth. Since Section 1 has a different shape and size, the relative motion pattern of the particles is different, so the forces are not proportional to the penetration depth. Furthermore, the total amount of wear for each section was analyzed. The total amount of wear (\(\Delta\)V) is proportional to the shear work (\(\Delta\)W\({}_{shear}\)), as shown in Equation (13), and the shear work is calculated by multiplying the tangential force (\(F_{T}\)) by the sliding distance (\(S\)). If the sliding distance is in the same condition, the total wear amount is proportional to the tangential force. However, in the wear experiment of the reference study, since Section 4 is located on a free surface of soil, the sliding distance of the particle is larger than those of Sections 2 and 3, as shown in Figure 21. The total normal force and total tangential force of the particle for each section occur in proportion to the penetration depth shown in Figures 19 and 20. However, the history of shear work shows that the shear work in Section 4 is larger than those in Sections 2 and 3, as shown in Figure 22. The ability to visually represent the sliding distance of each particle is limited. Therefore, this study compared the results of shear work with the history of tangential force and normal force and confirmed that the sliding distance was increased in Section 4.  
Figure 19: History of total normal forces in each section.  
Figure 18: History of wear magnitude (total) in each section.  
Figure 21: History of average cumulative sliding distance.  
Figure 22: History of average cumulative shear work.
# Service Life Prediction and Life Cycle Costs of Light Weight Partitions
## 5 Life Cycle Cost Analysis
### Lifecycle Cost Analysis in Failure Conditions
#### 5.2.1 Lifecycle Costs Influenced by Inherent Construction Defects  
The findings of the analysis are presented in Table 13 and Figure 16. As mentioned, inherent construction defects cause a decrease in the predicted service life of partitions. The lifecycle cost analysis of these conditions shows that gypsum board partitions are the most economical in terms of lifecycle costs, even in conditions with inherent construction defects (i.e.,when the number of negative occupancy factors is less than four). Hollow concrete block partitions have an advantage in lifecycle costs over gypsum board partitions and aerated concrete block partitions in intensive occupancy conditions (\(\mu\geq 4\)).  
Comparing these findings to the lifecycle cost analysis of standard service conditions reveals that inherent construction defects significantly affect the lifecycle costs of gypsum board partitions and aerated concrete block partitions (an increase of about 6% in lifecycle costs), which clearly contrasts with their negligible impact on hollow concrete block partitions. It is evident that the lifecycle costs of hollow concrete block partitions subjected to the influence of inherent construction defects are identical to the lifecycle costs of counterparts without inherent construction defects (refer to Table 12 above). In contrast, the lifecycle  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Partition Type** & **Gypsum Board Partitions** & \
\begin{tabular}{c} **Autoclaved Aerated** \\ **Concrete Blocks** \\ \end{tabular} &
\begin{tabular}{c} **Hollow Concrete Blocks** \\ \end{tabular} \\ \hline
**Life Expectancy [years]** & & & \\ \hline Light Conditions (\(\mu=0\)) & 50\(<\) & 50\(<\) & 50\(<\) \\ Moderate Conditions (\(1\geq\mu>2\)) & 44 & 50\(<\) & 50\(<\) \\ Standard Conditions (\(2\geq\mu>4\)) & 24 & 30 & 50\(<\) \\ Intensive Conditions (\(\mu\geq 4\)) & 10 & 16 & 36 \\ \hline
**Lifecycle Cost [5/m]** & & & \\ \hline Light Conditions (\(\mu=0\)) & 23.4 & 32.6 & 38.4 \\ Moderate Conditions (\(1\geq\mu>2\)) & 23.4 & 32.6 & 38.4 \\ Standard Conditions (\(2\geq\mu>4\)) & 30.7 & 40.9 & 38.4 \\ Intensive Conditions (\(\mu\geq 4\)) & 55.5 & 56.5 & 38.4 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparative Analysis of Predicted Life Expectancy and Lifecycle Costs in Inherent Construction Failures.  
Figure 16: Lifecycle Cost of Partitions When Inherent Construction Failures Occur in Different Occupancy Conditions.  
costs of aerated concrete block partitions and gypsum board partitions are found to be 2-5.5% higher, when compared to equivalent service conditions where \(\mu>2\). This finding shows that hollow concrete block partitions reduce the economic risk to the user that results from inherent construction defects in a lifecycle planning perspective.
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 3 Methodology
### Edge Computing in VANETs
It can also improve the utilization of computing resources in VANETs by distributing computation and storage tasks to devices in the network. This can reduce the load on centralized servers and enable the more efficient use of available resources. Edge computing can also enable the use of low-power devices, such as smartphones and tablets, as computing nodes, which can further enhance resource utilization.