esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Applications
### Determination of Peak Velocities during Blast Operation at Schindgraphen Bridge
accelerations, it is expected that results of higher accuracy will be found with future phones, making smartphones an even more attractive choice. Since the smartphone is highly mobile and the smartphone application can trigger and control measurements, a second smartphone was attached at the bottom of the pier without much effort to collect additional information about the structural behavior. The presented measurement system also allows simultaneous measurement at all bridge piers while the RPi-based wireless sensor network is employed. RPi nodes can also be used as a WiFi-extender in the case of long distances, and even smartphones can easily be integrated into the network. The determination of natural frequencies and integral values such as velocities and displacements can be obtained from a single sensor attached to the structure at a suitable position. The benefit of having a mesh-based wireless sensor network providing synchronized sensor nodes is that dynamic properties such as mode shapes and the corresponding structural damping can also be identified. This advantage will be demonstrated in the following sample application.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Research Group (N = 10)** & **Control Group (N = 20)** & **Z** & \(p\) \\ \hline
**MABC parameters** & & & & \\ Total score & 0.8 (0.5–5.0) & (37.0–63.0) 37.0 & \(-\)4.443 & \(<\)0.001 \\ Manual dexterity score [\%] & (0.5–1.3) 0.5 & (25.0–50.0) 37.0 & \(-\)4.447 & \(<\)0.001 \\ Visual-motor coordination & (8.0–40.3) 31.0 & (50.0–75.0) 75.0 & \(-\)3.764 & \(<\)0.001 \\ score [\%] & (1.0–7.0) 5.0 & (25.0–50.0) 37.0 & \(-\)3.593 & \(<\)0.001 \\ \hline
**COP parameters** & & & & \\ AP range [cm] & 4.3 (2.9–17.1) & 5.8 (3.9–8.3) & \(-\)0.453 & 0.651 \\ ML range [cm] & 10.1 (4.9–18.0) & 6.5 (3.8–8.5) & \(-\)0.906 & 0.365 \\ Path length [cm] & 118.1 (78.0–212.2) & 101.3 (84.0–139.3) & \(-\)0.728 & 0.467 \\ \hline
**Game parameters** & & & & \\ Score in game & **275.0 (157.5–297.5)** & **335.0 (226.2–522.5)** & \(-\)**2.036** & **0.042** \\ Right hand path length [m] & 20.8 (7.8–37.9) * & 39.0 (19.4–74.2) & \(-\)1.760 * & 0.078 * \\ Left hand path length [m] & 38.9 (17.5–59.5) & 47.0 (26.5–75.3) & \(-\)0.880 & 0.379 \\ \hline MABC = Movement Assessment Battery for Children; COP = Center of Pressure; AP = Anterior-Posterior; ML = Medio-Lateral * When removing an outlier from the research group, found using [PERSON]’s test, the median and interquartile percentage of the right hand path length is 19.0 (6.1–30.1), and statistically significant between-group difference is found (Z = \(-\)2.168, \(p\) = 0.030). & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Movement Assessment Battery for Children (MABC) scores, Center of Pressure (COP)-related parameters, hand path lengths and game score of the two groups, presented as median and interquartile percentages. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Manual Dexterity [\%]** & **Visual-Motor Coordination [\%]** & **Balance [\%]** & **Total [\%]** \\ \hline AP range of COP [cm] & 0.294, 0.443 & 0.102, 0.795 & \(-\)0.128, 0.743 & \(-\)0.196, 0.614 \\ ML range of COP [cm] & 0.606, 0.084 & 0.085, 0.828 & \(-\)0.085, 0.828 & \(-\)0.088, 0.823 \\ COP path length [cm] & 0.330, 0.385 * & 0.017, 0.965 & \(-\)0.238, 0.537 & \(-\)0.101, 0.795 \\ Right hand path length [cm] & 0.274, 0.443 * & **0.902, \(<\)0.001 * & **0.769, 0.009 * & **0.667, 0.035 * \\ Left hand path length [cm] & \(-\)0.254, 0.480 & 0.202, 0.575 & 0.185, 0.610 & 0.071, 0.845 \\ Score in game & \(-\)0.329, 0.353 & 0.622, 0.055 & 0.551, 0.098 & 0.276, 0.441 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: [PERSON]’s correlations (r,p) between scores of the MABC, its subcategories and COP-related parameters, hand paths, and game score in the research group (N = 10).
## 4 Results and Discussions
### Simulation-Optimization Model Results  
Table 4 shows the simulation-optimization model results. Our SEGA model evolved for 45 generations and evaluated a total of 1350 chromosomes to obtain the optimal objective function value of 0.35. The pollution source is located in the 19 th column and 28 th rows of the model. The pollution source leaked at a concentration of 6631 mg/L\({}^{-1}\) Mn\({}^{2+}\) on the 2000 th day after the chemical plant was put into operation, leaking for 354 days. Figure 8 shows the convergence of the optimal objective function while solving for the pollution source. The model found a good solution after 19 iterations and then entered a smoother convergence process until it converged (Figure 8). The model runs in 411 s, which is an acceptable amount of time spent.  
Figure 9 shows the fit of the pollutant concentrations at the two observation points. The blue and green curves in the figure are the calculated pollutant concentrations at Obs1 and Obs2, respectively, and the red points are the actual observed concentration values. Figure 9 shows that the simulated pollutant concentrations match the observed pollutant concentrations well.  
Figure 10 shows the location and extent of the contamination source as obtained by the simulation-optimization model. The pollution source is located inside the square. The initial site investigations did not indicate any trace of pollution leakage and no industrial equipment was present where leakage may have occurred inside the square. The site investigation results, therefore, contradict the simulation results. A subsequent site visit to the people around the square brought to light that the square was used to dry sludge in the biochemical pond about two years ago. This sludge contains a large amount of sewage, which seeped into the ground during drying, causing groundwater contamination. This history of the site contamination investigated agrees with the traceability results. Therefore,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline \(x_{1}\) & 19 \\ \(x_{2}\) & 28 \\ \(x_{3}\) (mg-L\({}^{-1}\)) & 6631 \\ \(x_{4}\) (d) & 2000 \\ \(x_{5}/\)(d) & 354 \\ Evaluation number & 1350 \\ Execution time (s) & 411 \\ Best objective value & 0.35 \\ Total number of generations & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Inversion results of the pollution sources.  
Figure 8: Best objective value trace plot.  
the source locations obtained from the simulation-optimization model are verified to be reliable. However, the leak times and concentrations of the sources cannot be accurately verified. The purpose of exploring the pollution source location and release history at this site was to stop the pollution in time and to develop subsequent treatment plans. The simulated pollutant transport history is consistent with the actual observation, making the simulation results of the simulation-optimization model reliable. This study, therefore, achieved its research purpose.  
Figure 10: Pollution traceability map.  
Figure 9: Observation point concentration changes over time.
# Studying the Impact of Different TCP DoS Attacks on the Parameters of VoIP Streams
## 2 Related Work
The authors' proposal is to switch (change) the transport protocol for voice traffic from User Datagram Protocol (UDP) to TCP (Transmission Control Protocol) when the system is subjected to DoS attacks. Validation of the applicability of the proposed framework was accomplished through an established simulation model. As can be seen from the reviewed works of other authors, as well as from studies not presented in this work, authors mainly propose methods, techniques, or algorithms to detect DoS/DDoS attacks or propose a mechanism to protect against such attacks or improve the protection of the SIP protocol. However, no study has been done on the impact of the DoS attack itself on the quality of the voice and video streams during an attack on a VoIP server through which VoIP streams are processed/exchanged.
# Data Gathering Techniques in WSN: A Cross-Layer View
## 2 WSN Architecture--Arising Platforms and Novel Infrastructure Concepts
### Application-Oriented
[29] propose a sensor node topology that uses low-cost and low-power components for energy-efficient waste management in the context of smart cities. The architecture described in [29] suggests a node architecture for measuring the filling level of trash bins and utilizes LoRa LPWAN (low-power wide-area network) technology for real-time data transmission to collect the measured data in a remote data collection center. The design of a sensor node that can detect the presence of water on home floors and provide early warning of water leaks is suggested by [PERSON] et al. [30]. The paper presents and deploys both hardware and software of the network components (flood sensing nodes, actuator nodes, and a control central); communication within the sensor network relies on the IEEE 802.15.4 standard. [PERSON] and [PERSON] [31] present a low-cost agriculture-oriented system. The suggested system is based on LoRa technology and can collect various measurements, such as humidity, ambient temperature, soil moisture, and temperature, and enables a farmer to access all of the information necessary to achieve efficient irrigation management of crops in real time. The developed wireless sensor node has been optimized both in hardware and software and exhibits very low power consumption.
## 4 SDOF Systems with Nonlinear Eddy Current Dampers
### Response to Seismic Excitations
#### 4.3.3. Energy Dissipation Analysis Under Seismic Excitations  
For elastic SDOF systems, the input energy of seismic excitations will be finally dissipated by the structural inherent damping and supplemental damping devices. In this paper, the energy dissipated by the inherent damping is expressed as  
\[E_{in}=\int_{0}^{T_{\mathrm{t}}}ci\dot{u}dt=\int_{0}^{T_{\mathrm{t}}}2\zeta ma \omega_{n}\dot{u}dt \tag{10}\]  
where \(T_{\mathrm{t}}\) is the duration of the ground motions. The energy dissipated by the supplemental damping devices (ECDs or FVDs) is  
\[E_{ECD(FVD)}=\int_{0}^{T_{\mathrm{t}}}F_{ECD(FVD)}(\dot{u})\dot{u}dt \tag{11}\]  
Figure 10: Acceleration reduction ratios under different earthquakes. Figure 9: Displacement reduction ratios under different earthquakes. The total dissipated energy is  
\[E_{total}=E_{in}+E_{ECD(FVD)} \tag{12}\]  
The time history results of the normalized dissipated energy of FVD and ECD subjected to the Elcentro ground motion are shown in Figure 11. As shown in Figure 11a, the normalized energy dissipated by the FVD (\(\alpha\) = 1) is 0.9648, which is 27.38 times of that dissipated by the corresponding inherent damping (0.0352), indicating that the FVD improves the energy dissipation capability of uncontrolled systems by 27.38 times. As a contrast, it is clear that in Figure 11b, the energy dissipated by the ECD is larger than the energy dissipated by the FVD. Specifically, the normalized energy dissipated by the ECD in Figure 11b is 0.9808, which is about 50.98 times of the energy dissipated by the corresponding inherent damping (0.0192). These results indicate that the ECD could improve the energy dissipation capability of uncontrolled systems by about 50.98 times. As indicated by Equation (10), if the inherent damping is prescribed, the energy dissipated by the inherent damping depends on the response amplitude. To protect the structure, the supplemental damping devices are required to dissipate the majority of the input seismic energy. Here, we define a new criterion named as the energy dissipation ratio (ER) to represent the energy dissipation capacity of supplemental damping devices. The larger the energy dissipative ratio, the larger the energy dissipation capability of supplemental devices. \[\text{ER}_{ECD(FVD)}=\frac{\text{E}_{ECD(FVD)}}{\text{E}_{in}} \tag{13}\]  
The energy dissipation ratios of the SDOF systems (\(\zeta\) = 2%) with FVDs and ECDs subjected to different ground motions are compared in Table 3 and Figure 12. From Figure 12, the points above the zero line indicate that the energy dissipation capacity of ECD is better than that of FVD, and vice versa. It is found that the energy dissipation capacity of ECDs are larger than those of FVDs in most cases. Figure 11: Dissipated energy of SDOF systems (\(\zeta\) = 2%) with FVD and ECD subjected to the Elcentro earthquake.
# Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation
## 3 Methods
### Overall Network Architecture  
Our network is improved with DeepLabv3+ [13] as the baseline. Although DeepLabv3+ has achieved outstanding results in image segmentation, there are still some problems in the spacecraft image segmentation task, such as blurred object boundary segmentation, incomplete contour, inaccurate segmentation pixels, and image noise affecting the learning effect of the model. In order to solve these problems of DeepLabv3+, we have carried out a series of optimizations. First, in the backbone, we did not choose Xception-65 or ResNet-101, used by DeepLabv3+ for feature extraction. We use the DRN-D-54 [16] as a feature extraction network and achieve better results than Xception-65 or ResNet-101, with only about the same number of layers as ResNet-50. After the deep feature extraction in the backbone, the extracted features are divided into two different branches: high-level features and low-level features. Then, both branches should enter the SEM module of the channel attention mechanism to calibrate the channel feature response adaptively to improve the representation quality generated by the network. Finally, we improve the decoder structure. We add the second ASPP module. The two ASPP modules form a parallel ASPP structure. The high-level features enter the first ASPP module, and the low-level features enter the second ASPP module. In addition, the network still retains the low-level features branch without passing through ASPP module, which is fused together with the high-level and the low-level features passed through the ASPP module in the decoder structure. After a \(3\times 3\) convolution, the fused features are up-sampled by four times bilinear interpolation,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Segmentation** & **Deep Learning** &
\begin{tabular}{c} **Spacecraft** \\ **Object** \\ \end{tabular} & **Dataset** \\ \hline [PERSON] et al. [25] & no & no & yes & no \\ [PERSON] et al. [36] & no & yes & yes & Pose \\ [PERSON] et al. [4] & no & yes & no & Pose \\ [PERSON] et al. [7] & yes & yes & no & Segmentation \\ DeepLabv3+ [13] & yes & yes & no & no \\ \hline Ours & yes & yes & yes & Segmentation \\ \hline \hline \end{tabular}
\end{table}
Table 1: The comparison table of related work.  
and, finally, the predicted spacecraft mask is obtained. We will describe the details of the improvements in the following sections. The overall network structure is shown in Figure 2.
# Silica Mesoporous Structures: Effective Nanocarriers in Drug Delivery and Nanocatalysts
## 3 Factors in the Synthesis of a Mesoporous Material
### Silica Source  
Oligomeric precursors and silicone monomers are commonly used for the fabrication of mesoporous materials, thus preventing irreversible polymerization in acidic conditions. Precursors such as colloidal solutions, sodium silicates, and organosilanes like tetraethyl ortho-silicates can be used for the syntheses of ordered mesoporous materials [74]. Sodium metasilicate (Na\({}_{2}\)SiO\({}_{3}\)), when used as a precursor, produces small oligomers upon acidification. The tetramethyl ortho-silicate (TMOS), Tetraethyl orthosilicate (TEOS) tetrapropyl ortho-silicate (TPOS), trimethoxysilane (TMS), tetrabutoxysilane (TBOS), and other typical silica-based precursors are used as mesoporous precursors [75]. Among these precursors, TMS is the fastest in the formation of silicate mesoporous structures. It should be noted that since the porous meso-structures produce slowly, TBOS should also be added slowly [76]. Although other factors, such as pH and the molar ratio of surfactant to silica, affect the syntheses of meso structures, the key roles of silicate sources cannot be ignored. [PERSON] et al. [77] reported the preparation of spherical silica mesoporous via the use of sodium silicate and TEOS under acidic conditions, producing pore sizes in the ranges of 1 to 3.5 nm and 2 to 10 nm, respectively. They demonstrated that the mesoscopic material should be synthesized with fumed silica at a temperature of 850 \({}^{\circ}\)C, whereas if the TEOS precursor is used at 750 \({}^{\circ}\)C, the mesoporous structure will collapse. The wall thicknesses of the porous materials obtained via these two precursors are the same under identical conditions [77]. However, despite being formed under the same conditions and having walls of equal thickness, they have different thermal stabilities coming from other silicate sources [77].
## 3 Results and Discussion
### Anti-Lipid Peroxidation Capacity  
Results on the inhibition of lipid peroxidation ranged between 0% (no inhibition) and 100% (total inhibition). Figure 4 only shows the strains with the activity (50% of the total strains tested).  
In general, 25% of the strains from pork fermented sausages presented this capacity, while around 80% was shown in game meat sausages. Only one strain (_K. unispora_ GD100) showed 69.9%, which was higher than the positive control (62.3%). As occurred in the antioxidant activity assay, the anti-lipid peroxidation capacity has turned to be strain dependent.  
Figure 4: Lipid peroxidation (% inhibition) of the yeast strains studied and positive control (Vitamin D). Strains with code PA means yeasts strains from factory A (Pork), while GC, GD, GE codes indicated strains from game meat samples from factory C (GC), D (GD) and E (GD).  
Furthermore, it is known that the cell wall polysaccharide fraction can inhibit the lipid peroxidation. Some studies showed that the anti-lipid peroxidation increased rapidly when the cell wall polysaccharides concentration was up to 3.2 mg/mL (\(\beta\)-glucans) and 1.6 mg/mL (mannans) [8; 13]. Additionally, these authors found that the inhibition percentage of the lipid peroxidation did not reach 40%, which is lower than those values found in this research. Therefore, wild yeasts may be a new useful tool in food protection.
## 2 Models and Problem Formulation
### Conflict Graph  
The link assignment problem can be converted to a _conflict graph_ problem, which is first addressed in [11]. A conflict graph is used to model the effects of interference.  
**Definition 7**: Conflict Graph. _We define a graph in which every link from a link set \(LS\) can be represented by a vertex. Two vertices in the graph are connected by an edge if and only if the two links conflict. Such a graph is called a conflict graph. The conflict graph \(G\) constructed from link set \(LS\) is denoted as \(G(LS)\)._
## 3 Map Construction Method
### Road Centerline Extraction  
The core of extracting the road centerline is to detect the proximity relationship between boundaries. Delaunay triangulation as a method to construct the topological relationship of the data set that describes the interrelationship between data objects distinctly [31]. The road centerline extraction process based on Delaunay triangulation consists of three steps. First, the constrained Delaunay triangulation is constructed using densified road edge line points and road edge lines. Then, the road segments and intersection domains are extracted based on the constructed triangle network. Finally, the centerlines of the road segments and the intersection domains are then extracted.
The sensors cooperate with each other to form a MIMO structure and in fact lead to better energy efficiency and smaller end-to-end delay. The basic idea of C-MIMO was first proposed by [PERSON] in [4]. Later this idea has been improved in [5] by [PERSON] considering channel estimation (training overhead) in the DGN side and is further modified in [9] by [PERSON] and in [8] by [PERSON]. The issue of applying error control codes in WSNs is the topic of recent interest. The performance of block codes and Viterbi decoded convolutional codes is investigated in [10, 11]. The iterative decoding algorithm using turbo code is used to prolong the network lifetime [12]. Low-density parity-check (LDPC) codes are more reliable than the block and convolutional codes and are serious competitors of turbo codes. In particular, LDPC codes exhibit an asymptotically better performance than turbo codes and admit a wide range of trade-offs between performance and decoding complexity [13]. [PERSON] and [PERSON] [14] compare the performance of the LDPC codes and the Reed Solomon (RS) codes [15]. From the recent works, it is known that LDPC codes are attractive in WSNs because of their applications in compression, joint sourcechannel coding and distributed source coding [14, 16]. However, to the best knowledge of the authors, there has been no document on the implementation of LDPC encoder/decoder in a wireless sensor node using cooperative communication. More precisely, none of the recent works have addressed the problem of reducing the energy consumption using error control coding. In this paper, LDPC code is incorporated in cooperative communication as an error control code. Later the idea is compared with SISO communication. In Section 2, the system model is shown and the error correction using LDPC code is analyzed in Section 3. Section 4 shows the energy model for both cooperative MIMOand SISO considering error correction codes. In Section 5, simulation results are shown and discussed. Finally, Section 6 concludes the paper.
# Path Planning Optimization of the Load Transport Process Using Heuristic Algorithms
## 5 Discussion  
From the obtained results, it can be seen that the determined solutions of inverse kinematics agree with the specified trajectories. Changes in length \(L_{2}\) and angle \(\alpha_{2}\) are the smallest for the optimal trajectory (\(l_{pop}=50\)). The \(\alpha_{2}\) angle reached the maximum value for the \(l_{pop}=30\) variant, while the \(l_{pop}=40\) variant included the operation of the crane with the most extended actuator. The position in which the actuator is in the most retracted position was obtained for the population number of 50. Based on the obtained results, it can be concluded that due to the operation safety criterion, more favorable results were obtained for the PSO algorithm. Both the boom length (\(L_{2}\)) and tilt angle change (\(\alpha_{2}\)) have been optimized to minimize the load on the crane actuators, indicating that PSO not only improves the performance but also increases the overall operational safety and service life of the crane components. Both the length of the boom (\(L_{2}\)) and the change in inclination angle (\(\alpha_{2}\)) are smaller than those obtained using the genetic algorithm. As a result, the crane's actuators will be less loaded, and ultimately, the maximum lifting capacity of the transporting machine will be increased.  
Comparing the obtained results of optimization studies, a similar trend can be observed in other works presented in the literature. The effectiveness of PSO in minimizing the trajectory and computation time has been confirmed in studies such as [41], where the PSO algorithm found the optimal path with a shorter computation time. This is consistent with the results in this paper, reinforcing PSO as a more efficient choice for dynamic optimization tasks. In [41], as in the case of our work, it was found that the PSO algorithm found the optimal path in less computation time. A similar tendency can be found in [42]. As the number of iterations increases, the difference in simulation time between these algorithms increases in favor of PSO. In the case of [43], better results of the optimized function for GA than PSO were noted, but the differences in this article are larger than those obtained in this article. In [44], however, it was stated that in the case of a small scale of research, these algorithms can be used interchangeably, which confirms the results presented in the previous sections.