esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Overlay-Based Approach: Post Hoc Comparison  
The first approach establishes a baseline by treating damage assessment as a post-processing step following independent semantic segmentation. As described in the project's experimental design, a standard U-Net model, denoted as \(\mathcal{S}_{\theta}\) with trainable parameters \(\theta\), is trained to perform semantic segmentation on individual images.  
Let the pre- and post-disaster images be \(\mathbf{X}_{\text{pre}},\mathbf{X}_{\text{post}}\in\mathbb{R}^{3\times H\times W}\). The process is as follows:  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Advantage** & **Limitation** \\ \hline \multirow{3}{*}{Overlay} & \(\bullet\) & Simple architecture & \(\bullet\) & Susceptibility to misalignment \\  & \(\bullet\) & Fast inference & \(\bullet\) & Less sensitivity to fine-grained \\  & \(\bullet\) & Reusability of existing segmentation models & & damage variations \\ \hline \multirow{3}{*}{Difference} & \(\bullet\) & High sensitivity to change & \(\bullet\) & Susceptibility to noise \\  & \(\bullet\) & Capability of fine-grained & \(\bullet\) & Inconsistency in difference images \\  & & damage segmentation & \(\bullet\) & Possible loss of contextual information \\ \hline \multirow{3}{*}{Siamese} & \(\bullet\) & Effective capture of both temporal and & \(\bullet\) & Architectural complexity \\  & \(\bullet\) & spatial context & \(\bullet\) & Higher risk of overfitting \\ \cline{1-1}  & \(\bullet\) & Mitigation of image registration issues with & \(\bullet\) & Increased training difficulty \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristic comparison of three models.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 2 Background and Preliminaries
### Smart-Grid-Based Cybersecurity Standards
#### 2.5.1 NERC CIP  
The North American Electric Reliability Corporation's Critical Infrastructure Protection (NERC CIP) is very robust for centralized systems. However, it is less suitable for dynamic, distributed edge environments such as DERs or residential demand-response platforms [42]. Nonetheless, its standards enforce cybersecurity practices for Bulk Electric Systems (BESs) while including key directives such as CIP-003 [43] for cybersecurity policies, CIP-005 [44], for electronic security perimeters, CIP-007 [45] for system security management, and CIP-010 [46] for configuration and vulnerability management [47].
## 2 Grout-Injection Materials and Test Specim Products
### Properties of Test Specim Products
#### 2.2.3. Polyurethane Foam Resin Grout  
Polyurethane foam resin injection grout reacts with water to provide protection against leakage cracks in concrete structures. Their short reaction time with water transforms the resin into a foam structure inside the crack. Polyurethane foam grouts consist of a polyurethane resin that reacts with water to form a closed-cell foam layer or gel (hydrophilic types) inside the water leakage crack when injected. Polyurethane grouts are mainly used as a temporary measure to stop water-leakage, and a secondary injection installation of a reinforcing repair material is commonly applied afterwards [13]. The specifications of the three types of polyurethane foam injection grout materials tested in this study are outlined in Table 4 below.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Product Number** & **Components** & **Properties** \\ \hline \multirow{4}{*}{\(1\)} & & (1) Single-component rapid-curing elastic \\  & Reaction type polyurethane & polyurethane \\  & polymers & (2) Solubility: Insoluble \\  & Xylene: 4\% & (3) Specific gravity (20 \({}^{\circ}\)C): 1.26 \\  & & (4) Flashpoint: 65 \({}^{\circ}\)C \\ \hline \multirow{4}{*}{\(2\)} & & (1) Double-component high-strength \\  & Hydrophobic rigid Polyurethane & polyurethane \\  & foam & (2) Viscosity: 532(resin) cps/23.1 \({}^{\circ}\)C \\  & & (3) Initial/complete cure pot life: 60 min/24 h at room temperature \\ \hline \multirow{4}{*}{\(3\)} & & (1) Single-component polyurethane foam \\  & Polyurethane resin, Acetone & (2) Solubility: 1.3 g/100 mL \\  & (CH3 CoCH3) and other additives & (3) Specific gravity (20 \({}^{\circ}\)C): 1.37 \\  & & (4) Viscosity: 200–500 cps/25\({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 4: The polyurethane-foam injection-grout material properties.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Product Number** & **Components/Ratio** & **Properties** \\ \hline \multirow{4}{*}{\(1\)} & Epoxy resin + amine & (1) Non-shrinking low-viscosity epoxy resin \\  & Main Agent: Hardener = 2:1 & (2) Pot life: 90 \(\pm\) 5 min (20 \(\pm\) 1 \({}^{\circ}\)C) \\  & & (3) Cure time: About 8 h (30 \(\pm\) 5 \({}^{\circ}\)C) \\ \hline \multirow{4}{*}{\(2\)} & Epoxy resin + polyamide amine; & (1) Two-component low-viscosity wet-type grout \\  & Main Agent: Hardener = 2:1 & (2) Pot life: 60 \(\pm\) 2 min (25 \(\pm\) 1 \({}^{\circ}\)C) \\  & & (3) Cure time: 24–36 h(25 \(\pm\) 1 \({}^{\circ}\)C) \\ \hline \multirow{4}{*}{\(3\)} & Elastic epoxy sealant & (1) Two-component epoxy elastic sealing grout \\  & Main Agent: Hardener = 1:1 & (2) Pot life: 60 \(\pm\) 2 min (21 \(\pm\) 1 \({}^{\circ}\)C) \\ \cline{1-1}  & & (3) Cure time: 24–36 h (21 \(\pm\) 1 \({}^{\circ}\)C) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The epoxy-resin injection-grout material properties.
## 3 Methods
### Results Evaluation Strategy
#### 3.4.2 Assessment of Biomass and Yield Estimation  
Due to the small amount of observational data (\(N<100\)), it was quite inefficient to withhold part of the data to validate the crop biomass and yield estimation results; therefore, the leave-one-out cross-validation (LOOCV) approach was chosen to examine and validate the accuracy of the estimation models [48]. The LOOCV method involves using one observation as the validation sample and the remaining observations as the training samples. This procedure was repeated \(N\) times. The \(R^{2}\), RMSE and relative RMSE (RRMSE) of the algorithm were then estimated by averaging the values obtained from the \(N\) iterations.  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(O_{i}-E_{i}\right)^{2}} \tag{12}\]  
\[\text{RRMSE}=\frac{\text{RMSE}}{\overline{O}}\times 100\% \tag{13}\]  
where \(n\) is the number of observations, \(O_{i}\) is the observed value, \(E_{i}\) is the estimated value, and \(\overline{O}\) is the mean value of all the observed values.
# Progress and Summary of Photodarkening in Rare Earth Doped Fiber
## 7 Suppression Method of Photodarkening  
Because of the PD effect, the output power of the fiber laser and amplifier decreases continuously and the attenuation is large, which leads to the lack of stability and short operating lifetime of the system, which needs regular maintenance. This greatly restricts the application requirements of the system, limits the application scope and application field of the system. Therefore, the suppression of PD is particularly important.
# Coverage Area Decision Model by Using Unmanned Aerial Vehicles Base Stations for Ad Hoc Networks
## 4 Performance Analysis of the UAV-BS
### Experiment Results of the Coverage Area Decision Model for the UAVs-BS
#### 4.1.1 Delay Analysis with Path Loss Exponent  
In the first experiment, the network delay with extra users in the cellular network is evaluated with or without UAV deployment. The UAV's altitude is set at 200 to 500 feet  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**S\#** & **Parameters** & **Value** \\ \hline
1 & Network Size & 4 \(\times\) 4 Km \\ \hline
2 & Time & 900 s \\ \hline
3 & Mobility Model & SUMO \\ \hline
4 & No of UAVs & 80 \\ \hline
5 & Drone Speed & 0 to 60 Km/h \\ \hline
6 & Communication rage & 300 m for bases station and 1000 m for UAV/drone \\ \hline
7 & Data size & 1 to 1024 B \\ \hline
8 & UAV Altitude & 200–500 Feet \\ \hline
9 & UAV Transmission Power & 35 dBm \\ \hline
10 & System Bandwidth & 10 MHz \\ \hline
11 & Active Users & 400 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
because the high altitude provides less interference and a feasible line-of-sight (LoS). The delay threshold is set at 300 ms, whereas the distance of UAVs is set at 500 feet. Figure 4 shows the delay results in the presence of CADM-UAVs and without UAVs.  
It is observed that the proposed CADM-UAVs have less delay compared to those without UAV deployment in cellular networks. When the users are 300 in a single macrocell, the delay of the proposed model is around 150, whereas the delay with a BS without UAVs is noted as 235 ms. The same trend increases when the users are around 600 in the single macrocell, where the proposed model achieved the result of 166 ms. The proposed CADM-UAVs achieve less delay and provide better coverage to the cellular network.
# Onboard Centralized ISL-Building Planning for LEO Satellite Constellation Networks
## 1 Introduction
### Literature Review
LCTS is an algorithm with low computational complexity, but it is often used for inter-layer ISL-building rather than intra-layer inter-plane ISLs. Therefore, it is necessary to study the lightweight ISL-building planning algorithm to build an efficient dynamic satellite network topology.
# Deep Homography Estimation and Its Application to Wall Maps of Wall-Climbing Robots
## 3 Homography Estimation for a Center-Aligned Image Pair  
### HomographyFpnNet Structure  
Figure 3 shows the structure diagram of our regression-based HomographyFpnNet model for predicting the pixel offsets of the four corners of image_patch_b relative to those of image_patch_a when image_patch_b is warped to image_patch_a. HomographyFpnNet uses the VGG architecture (the deep convolutional network developed by Oxford's Visual Geometry Group (VGG) for object recognition, which is characterized by its simplicity, using only \(3\times 3\) convolutional layers stacked on top of each other to increase the depth of the network and using max pooling layers to reduce the dimensionality of the feature maps) [21], which was also used by HomographyNet [20]. The model input is a two-channel image of size \(128\times 128\times 2\) pixels. In other words, the two grayscale input images associated by the homography transformation are stacked in a channel manner and fed into the model network. After extracting features through stacked convolutional layers, HomographyNet [20] uses the features of the last convolutional layer to predict the four-point homography values through two fully connected layers, meaning that HomographyNet only uses single-scale convolutional features. In contrast, the ORB+RANSAC method commonly uses multi-scale ORB features to improve the accuracy of its homography estimation. Inspired by this, we propose the use of multi-scale deep convolution features for the homography estimation. Specifically, as shown in Figure 3, we used Feature Pyramid Network (FPN) [24] to fuse the convolution features of three different scales, and then globally averaged and concatenated these three scale convolution features (\(4\times 4\), \(8\times 8\), and \(16\times 16\)) to obtain the final one-dimensional features. That is, the \(4\times 4\) feature map of conv12's output is upsampled to \(8\times 8\) and then merged with the \(8\times 8\) feature map of conv10's output (which undergoes a \(1\times 1\) convolutional layer to adjust channel number to 256) by element-wise addition to generate new \(8\times 8\) feature map. Similarly, the new \(8\times 8\) feature map is upsampled and then merged with the \(16\times 16\) feature map of conv8's output to generate new \(16\times 16\) feature map. To reduce the aliasing effect of upsampling, each merged map undergoes a \(3\times 3\) convolutional layer to generate the final feature map. Finally, as in HomographyNet, two fully connected layers are used for the regression of the four-point homography values.
## 4 Experiments and Results
### Experiments and Analysis
#### 4.2.2 Comparison of Running Time and Speedup  
The speedup is used to measure the parallelization performance of parallel systems or programs. It is the ratio of the time that the same task runs in uniprocessor and parallel processor systems. The speedup is defined by the following formula [30]:  
\[S_{p}=\frac{T_{1}}{T_{p}} \tag{1}\]  
where \(p\) is the number of nodes, \(T_{1}\) is the execution time on a single node, and \(T_{p}\) is the execution time on \(p\) nodes.  
When \(S_{p}=p\), the speedup is a linear speedup. It is the ideal speedup that the parallel algorithm tries to achieve. However, the linear speedup is difficult to achieve because the communication cost increases with the increasing number of cores.  
Figure 5 shows the computational time of four algorithms for four data sets on a small number of nodes. Figure 6 shows the computational time of four algorithms for four data sets on large number of nodes. As the number of the nodes increases, the computational time of the four parallel methods becomes shorter. Due to the use of optimized compression, PCFMIS2 has less computational time than PCFMIS1. PCFMIS3 performs best, saving half the time compared to PCFMIS1. It indicates that the improvements on optimized compression and load balancing are effective. In particular, the EDS strategy proposed by PCFMIS3 divides the edges more rationally to provide a basis for grouping. As PaFuTM has to find all the candidate subtrees and duplicate redundant subtrees exist among nodes, PaFuTM has longer computational time than PCFMIS3.  
Figure 7 shows the speedups on four data sets with a small number of nodes. Figure 8 shows the speedups on another four data sets with a large number of nodes. As the results show, the speedup of PCFMIS1 performs worst on all data sets. PCFMIS2 performs better than PCFMIS1. The speedups of PCFMIS3 and PaFuTM are closest to the linear one, but PCFMIS3 performs better. As the number of nodes increases, the speedup keeps growing. However, in Figure 7e,f, when the nodes increase from 5 to 6, the speedup of PCFMIS1 and PCFMIS2 grow slowly. There is a clear salient point. This also appears in Figure 8a,b,f. PCFMIS3 maintains a steady growth.  
Figure 6: Computational time of four algorithms for four data sets on a large number of nodes.  
Figure 7: The speedups of four algorithms for four data sets on a small number of nodes.
## 4 Analytical Analysis of Two-Port Interconnections
### Sum of Integer and Fractional-Order Differentiator Responses  
Figure 5 shows interconnection resulting from the summing operation of integer and fractional-order differentiator responses. The transfer function can be expressed as:  
\[K_{D\_D}(s)\ =\ A_{1}\tau_{1}s+A_{2}\tau_{2}s^{\alpha} \tag{14}\]  
The rearrangement into complex form leads to:  
\[K_{D\_D}(\omega)\ =\ A_{2}\tau_{2}\omega^{\alpha}\cos\!\left(\frac{\alpha\pi}{2} \right)+j\!\left[A_{1}\tau_{1}\omega+A_{2}\tau_{2}\omega^{\alpha}\sin\!\left( \frac{\alpha\pi}{2}\right)\right] \tag{15}\]  
and after separation to magnitude and phase response we obtain:  
\[\left|K_{D\_D}(\omega)\right|\ =\ \sqrt{\left[A_{2}\tau_{2}\omega^{\alpha}\cos\! \left(\frac{\alpha\pi}{2}\right)\right]^{2}+\left[A_{1}\tau_{1}\omega+A_{2} \tau_{2}\omega^{\alpha}\sin\!\left(\frac{\alpha\pi}{2}\right)\right]^{2}} \tag{16}\]  
\[\varphi_{D\_D}(\omega)\ =\ \tan^{-1}\!\left[\frac{A_{1}\tau_{1}\omega+A_{2} \tau_{2}\omega^{\alpha}\sin\!\left(\frac{\alpha\pi}{2}\right)}{A_{2}\tau_{2} \omega^{\alpha}\cos\!\left(\frac{\alpha\pi}{2}\right)}\right] \tag{17}\]  
The Mathcad analysis of the transfer function (14) is shown in Figure 6. It shows that order variation (the same as in the previous case) causes a very similar point of break on the magnitude slope and occurrence of zero at low frequencies (especially for orders near to 1), and the cut of the plane projection of the phase response confirms that this starting phase value (low frequencies) clearly depends upon the value of the \(\alpha\) order.  
Figure 5: Practical solution of the summing response of fractional- and integer order differentiator.
## 5 Proposal for an Asset Concealment Ontology  
The proposal to develop an ontology for the asset concealment domain aims to validate the proposed process and tackle challenges related to integrating multiple databases, a prerequisite for using investigative tools to combat asset concealment. An ontology can be a powerful tool in this effort due to its ability to semantically organize and structure information, making it easier to integrate data and understand complex networks of relationships between entities, assets, and financial transactions.  
Additionally, an ontology aids in detecting hidden relationships, a significant hurdle in combating asset concealment. Concealed assets often involve complex networks of entities that appear unrelated on the surface. By modeling these connections and defining relationships, such as \"owns,\" \"transfers to\", or \"is part of\", an ontology can help uncover intricate webs of asset transfers. This is particularly useful in identifying typical concealment strategies like the creation of shell companies, the use of tax havens, or fraudulent asset transfers, all of which rely on obscuring ownership and relationships.  
Another essential benefit of ontologies is their ability to integrate data from multiple sources. Information about assets and their movements comes from various sources: public records, banks, court documents, social media, government databases, and several other sources. An ontology provides a structure that enables these diverse datasets to be aggregated and analyzed coherently, eliminating data silos and allowing for more comprehensive investigations.  
Moreover, ontologies support automation and artificial intelligence (AI) applications. By leveraging ontological frameworks, AI systems can more effectively detect fraudulent or concealed asset movements. AI algorithms can use ontology to identify patterns in transactions or asset ownership that might indicate attempts at concealment. For instance, these systems can automatically flag cases where an asset has been transferred multiple times between related entities, a typical red flag in money laundering or asset-hiding schemes.
## 2 Related Work
### E-Health Platforms  
E-Health applications can have different classifications, depending on their specific objective (fall detection, activity monitoring, localization, _etc._). Several authors [17,18] have identified a series of requirements for healthcare applications that are based on wireless technologies, including:  
1. Reliability: the transmission of precise data, which involves preventing the duplication of information, by implementing an efficient Quality of Service (QoS) which insures a high Packet Delivery Ratio (PDR) and reduces the Packet Loss Rate (PLR).
2. Energetic efficiency: the development or use of an energy-saving algorithm or technology to reduce the consumption of energy in devices, because this helps extend the life span of the network.
3. Data routing: the use of an efficient communication protocol that provides scalability, failure tolerance and best possible route selection, among others.
4. Node mobility: the ability of free moving wireless nodes to move within an indoor area, always maintaining optimum connectivity.
5. Security: the use of efficient security mechanisms to protect the data and privacy of highly sensible patient information, including its robbery or corruption.  
Taking into account the proposed requirements for the development of a robust e-Health, Table 1 shows a comparative among the developed systems and platforms. Considering the requirements an e-Health solution, and taking into account the characteristics already existing platforms, our workgroup selected WiSe [19] (primarily because of its low energy consumption) with the goal of developing a robust, reliable system that integrates the best characteristics of the platforms/systems depicted in Table 1.
## 3 Results
### Calculation of Actual Tool Temperatures  
The temperature distribution at the cutting edge can be differentiated between the spot drilling and the drilling process. Since the spot drilling process only represents a short period of the entire drilling process, the following evaluation is related to the constant temperatures during the drilling process. The CEL simulation was carried out with a cutting speed \(v_{c}\) = 80 m/min and a feed \(f\) = 0.05 mm/rev. After a simulation time of \(t_{sim}\) = 0.5 s, the temperature distribution of the cutting edge was exported. In addition, the geometrical information of the corresponding nodes was read out. Thus, a function can be determined over the cutting edge as a function of the coordinates. The cutting insert is divided into three sections. The first section \(T_{i}\) extends from the tool pivot point to the first cutting edge shoulder. The second section \(T_{a}\) extends further to the cutting edge corner, and the third section \(T_{f}\) represents the outer flank. From the simulation, the following exponential functions are obtained for the sections of the cutting insert:  
\[T_{i}(y)=1+0.0468e^{0.760y},[0<y<4.81]; \tag{9}\]  
\[T_{a}(y)=1+0.305^{0.281y},[4.81<y<8.83]; \tag{10}\]  
\[T_{f}(z)=1+31.690e^{0.887z},[2.39<z<10.63]; \tag{11}\]  
For test V001, the optimization via light results in a factor of \(k\) = 89.65. The temperature curves determined by measurement are close to the simulatively calculated temperature curves, as can be seen in Figure 17. Only the start time for the simulated temperature increase has to be offset, since a slight temperature increase in the real drilling process already occurs before the drill affects the workpiece. Therefore, the maximum temperature at the outer edge of the cutting edge is of particular importance since this can influence the properties of the bore hole wall in the form of introduced residual stresses, for example. The function of the second section of the cutting insert results in a temperature of \(T_{a}(Y=8.86)\) = 418 K and the function of the third section results in a temperature of \(T_{f}(Z~{}=~{}2.2)\) = 493.3 K.  
Figure 16: Effective area diagram of the temperature determined via thermocouple.  
The factor \(k\) can be represented by a linear influence of the applied temperature to the temperatures at the sensor positions. Thus, the temperatures at the sensors can be calculated in Kelvin by the linear equations as a function of factor \(k\):  
\[T_{\text{S1}}(k)=1.778k+160.57; \tag{12}\]  
\[T_{\text{S2}}(k)=2.041k+140.42; \tag{13}\]  
\[T_{\text{S3}}(k)=1.955k+147.28; \tag{14}\]  
From the heat transfer simulation, a temperature increase at sensor position S2 can be determined with a time offset of 0.5 s after the temperature is applied to the cutting edge. The sensors S1 and S3 experience a temperature increase after about 1.7 s.