esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Comparison of Deep Learning Models for Damage Identification of Aging Bridges
###### Abstract
Comprehensive assessments of damage by combining visible-wavelength image data with sensor data for other wavelengths (e.g., radar and infrared) can yield more accurate and comprehensive results. In recent years, identifying and predicting facility damage using AI technologies, especially algorithms such as deep learning and reinforcement learning, has become a highly active field of research. However, the use of deep learning technology is not without problems. Performance varies depending on the training images, and classification can be challenging when the features of the target classes are limited or indistinct. For example, when classifying types of bridge damage such as efflorescence, corrosion, cracks, concrete scaling, and concrete spalling, deep learning may exhibit good performance in distinguishing efflorescence, which appears as a white powdery residue on bridges, from other damage types. However, it may have more difficulty differentiating among cracks, concrete scaling, and spalling, which are all similar in appearance. Another problem is that previous studies have predominantly focused on cracks, the most common type of damage in concrete structures. This has resulted in a scarcity of training data for other types of bridge damage, such as water leaks, efflorescence, concrete scaling, concrete spalling, and corrosion of reinforcing steel. The nature of deep learning technology dictates that classification accuracy improves with an increase in training data [12].
# Entropy Weighted TOPSIS Based Cluster Head Selection in Wireless Sensor Networks under Uncertainty
## 5 Numerical Experiment and Discussions
The findings indicate that in LEACH, the residual energy of all nodes depletes after 1300 rounds, whereas our proposed approach takes 2200 rounds to exhaust all residual energy shown in Figure 5. Compared with energy utilization, our proposed approach consumes 29% less energy than LEACH due to determining the right CHs.
# A Secure and Lightweight Group Mobility Authentication Scheme for 6 LoWPAN Networks
## 6 Performance Evaluation
### Computational Overhead  
A unified configuration of the parameters listed in Table 5 facilitates comparison. We consider only the execution time of cryptographic operations, as shown in Table 6, for all comparison schemes according to [11]. The simulations are conducted on a computer with Intel(R) Core(TM) i5-3317U CPU @ 1.70 GHz, 8 GB RAM, and Windows 7 (64-bit). \(T_{h}\), \(T_{L\text{i}C\!I}\), \(T_{AES}\), and \(T_{H\!MAC}\) represent the computational overhead associated with SHA-256, LiCi-128, AES-CBC-256, and (HMAC)-SHA-256, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scheme** & **Intra-Handover** \\ \hline
[11] & \(n+7\) \\ \hline
[16] & \(4n+10\) \\ \hline
[20] & \(2n+2\) \\ \hline SL\_GAS & \(n+6\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the signal cost.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Features** & [11] & [16] & [20] & **SL_GAS** \\ \hline
**S1** & Yes & Yes & Yes & Yes \\
**S2** & Yes & Yes & Yes & Yes \\
**S3** & No & No & No & Yes \\
**S4** & No & No & No & Yes \\
**S5** & No & No & No & Yes \\
**S6** & Yes & No & Yes & Yes \\
**S7** & Yes & Yes & Yes & Yes \\
**S8** & Yes & No & Yes & Yes \\
**S9** & Yes & No & No & Yes \\
**S10** & Yes & Yes & No & Yes \\
**S11** & Yes & No & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of security properties.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Size (Byte)** \\ \hline Timestamp, nonce, HNP, _TID_, _
SL_GAS demonstrates that it is more efficient than most related work GHSs that utilize other wireless technologies. According to [10], the computational overhead of related GHSs that involve \(n\) devices in their schemes, SGMS, SEIP, and CGM6, is summarized in Table 7.  
Various GHSs are evaluated by unified cryptographic operations when n is the number of devices in a group. Despite appearing less computationally intensive, the SEIP scheme has fewer nodes, reducing the computation overhead. The SEIP protocol consumes more time than the SL_GAS protocol when dozens of 6 LoWPAN devices roam in the same network. To demonstrate the high efficiency of the SL_GAS scheme, some GHSs rely on other wireless technologies compared to SL_GAS. A comparison of some GHS is shown in Table 7 and Figure 8 regarding computation overheads for SGMS, SEIP, and CGM6 based on n devices.
## 2 Modeling of Tethered UAV Dynamics
### Dynamics Modeling of UAV Platforms
#### 2.1.2 Modeling Assumptions for UAV Platforms  
The movement of the UAV is a very complex dynamic process, and its operating characteristics are affected by many factors. For example, the elasticity of the airframe, the change in weight acceleration with the altitude of flight, the Earth's curvature, the speed of rotation, and the movement of the surrounding atmosphere. If all of these factors are included, the whole equation is complicated. It is not even possible to carry out the processing [15]. For simplicity, appropriate assumptions can be made when modeling the tethered UAV.  
**Hypothesis 1**.: _The UAV platform is a rigid body with invariant mass and moment of inertia. In this way, the elastic vibration and deformation of the UAV can be ignored, and the displacement, velocity, and acceleration of the rigid body can be analyzed directly to study the law of motion of the object._  
**Hypothesis 2**.: _The geometric center and center of gravity of the UAV platform and the origin of the fuselage coordinate system coincide. The geometry of the UAV is symmetrical with the internal mass distribution, that is, the inertia product \(I_{xy}=I_{yx}=0\)._  
**Hypothesis 3**.: _Let the ground be an inertial frame of reference, ignore the effects of the earth's curvature, rotation, and revolution, and treat the earth's surface as a plane._  
**Hypothesis 4**.: _Acceleration due to gravity is constant. Since tethered UAVs usually fly at altitudes below 100 m, the change in gravitational acceleration due to the change in altitude can be ignored._  
**Hypothesis 5**.: _Wind resistance and gravity are not affected by flight altitude, and other external factors remain constant._
# A Software Defined Radio Based Anti-UAV Mobile System with Jamming and Spoofing Capabilities
## 6 Conclusions  
In this paper, we described an anti-UAV mobile system that was developed in order to protect areas from unauthorized drones. The system incorporated localization with jamming and spoofing techniques, which were used in a coordinated approach. The system was able to estimate the position of the invading vehicle and could then apply jamming to neutralize the GPS signal, followed by spoofing to gain control of the vehicle. In addition,  
Figure 14: Anti-UAV mobile system operator.  
Figure 15: Real test scenario using spoofing signals.  
it also supported jamming directed to the control signal, thus, making manual control unfeasible.  
Through carefully conducted real-life tests using the implemented prototype, we observed the effectiveness of the proposed solution, which was capable of blocking the autonomous flight of a UAV by jamming its GPS receiver, followed by the successful acquisition of the vehicle control through the transmission of spoofing signals, which allowed its forced landing on a predefined site.  
The proposed system can be used with the aim of helping to solve problems related to security and privacy. As it is a mobile system, it can be easily adapted to different geographical areas and different scenarios. As possible applications, it can be used for protection against possible terrorist attacks at political speeches, festivals, sporting events or others, where terrorists may resort to bombs attached to drones. It can be used to reduce the trafficking of illegal substances and objects into prisons, where criminals use drones to transport them by air. It can also help to solve situations of negligent drone pilots who fly these devices to capture images of forest fires, obstructing aerial firefighting operations. There are many other cases and situations where this system can act as a non-destructive approach capable of forcing an invading drone to land.
###### Abstract  
The purpose of this study was to analyze the contribution of anthropometric and strength determinants of 2000 m ergometer performance in traditional rowing. Nineteen rowers competing at national level participated in this study. Anthropometric characteristics, vertical jumps and bench pull tests were assessed to determine conditional factors, whereas the 2000 m test was used to set rowing performance. Pearson correlation coefficient, linear stepwise and allometric regression analyses were used to predict rowing performance (R\({}^{2}\) > 50%). Height, body mass and body muscle correlated with rowing performance in male and female rowers. Similarly, power output for squat jump and countermovement jump power correlated with performance. Finally, mean propulsive velocity, mean power and maximum power in bench pull also correlated with the test. Stepwise multiple regression analysis identified body mass (R\({}^{2}\) = 0.69, \(p\) < 0.001) and mean propulsive velocity in bench pull (R\({}^{2}\) = 0.76, \(p\) < 0.001) for male rowers and body muscle (R\({}^{2}\) = 0.89, \(p\) = 0.002) and maximum power in bench pull (R\({}^{2}\) = 0.62, \(p\) = 0.036) for female rowers as the best predictors of rowing performance. These results determine the relevance of anthropometric characteristics and, in contrast to Olympic rowing, support the greatest importance of upper body power in traditional rowing training.  
bench pull; vertical jump; power; talent detection; training +
Footnote †: journal: Applied Sciences
## 1 Introduction
The advantages of the MPC algorithm are: (1) It is easy to model; (2) It has a rolling optimization strategy with good dynamic control effect; (3) It can correct the output by feedback which improves the robustness of the control system; (4) As a computer optimization control algorithm, it is easy to realize on a computer. In recent years, the MPC algorithm has also been widely used to achieve optimized motion control of mobile robots. In [19], a novel visual servo-based model predictive control method was proposed to steer a wheeled mobile robot moving in a polar coordinate. In [20], a model-predictive trajectory-tracking controller, which used linearized tracking-error dynamics to predict future system behavior, was presented. In [21], a state feedback MPC method was proposed and applied for trajectory tracking of a three-wheeled OMR. The cost function in the proposed MPC method is constructed over finite horizon and is optimized in the linear matrix inequalities (LMI) framework. In [22], a virtual-vehicle concept and an MPC strategy were combined to handle robot motion constraints and the path-following problem. In this paper, the kinematic analysis of the OMR is conducted and then an MPC algorithm for point stabilization and trajectory tracking is proposed. In the proposed MPC controller, the rotation speeds of the three wheels and the state outputs of the robot are taken as manipulated variables and controlled variables, respectively. In stabilization control of the robot, a target point and an arbitrary initial direction, which lie within a given constraint range of the coordinate, are set and then the robot tracks the target point and stabilizes itself automatically. In the trajectory tracking of the robot, a pulsed route is planned and different target angles at each node point are set. The robot can move from the start point of the trajectory to the end point along the given route with target angles. This paper is organized as follows: A brief description and kinematics analysis of the considered OMR are given in Section 2. Section 3 presents the MPC strategy including MPC elements and the optimization method. Simulation results of the proposed MPC algorithm are presented in Section 4. Finally, Section 5 concludes the work in this paper.
# Impact of Partial Replacement of Cement with a Blend of Marble and Granite Waste Powder on Mortar
## 3 Results and Discussion
### Effects of MGWP on Microstructure Properties of Mortar
#### 3.4.4 Differential Thermal Analysis  
Endothermic reactions involve the absorption of heat and can result in phase changes such as melting, fusion, vaporization, sublimation, or transition between two structures. These reactions typically involve processes such as dehydration, decomposition, and oxidation. On the other hand, exothermic reactions release heat and may involve phase changes such as freezing or crystallization, transitions, and reactions such as decomposition, oxidation-reduction, and chemisorption. One application of DTA is to estimate the amount of Ca(OH)\({}_{2}\) in cementitious stages by determining the peak regions induced by the breakdown of Ca(OH)\({}_{2}\) to CaO+H\({}_{2}\)O. DTA can also be used as a monitoring tool in the preparation of a nearly Ca(OH)\({}_{2}\)-free product from hydrated Portland cement or hydrated C\({}_{3}\)S [41].  
Figure 16a,b show the DTA test outcomes for the mortar samples cured for 7 and 28 days, respectively. The DTA results presented in Figure 16a indicate that the mortar samples cured for 7 days containing 10% of MGWP displayed an early onset of the Ca(OH)\({}_{2}\) decomposition peak compared to samples with 0% and 30% replacement. On the other hand, for the mortar samples cured for 7 days with 30% replacement, a small peak at 166 \({}^{\circ}\)C was observed due to ettringite decomposition. The DTA curves for all mortars illustrate that the first peak is endothermic, followed by an exothermic peak, and then another endothermic peak. The endothermic peak around 150 \({}^{\circ}\)C observed in the DTA curves could be attributed to the breakdown of C-S-H, while the endothermic peak observed around 500 \({}^{\circ}\)C could be attributed to the breakdown of calcium hydroxide. The DTA results also show that the calcite present in the mortar samples underwent decomposition at a temperature of 800 \({}^{\circ}\)C.  
Figure 15: TGA curves of mortar samples at two curing ages: (**a**) 7 days, and (**b**) 28 days.
# Unlocking the Potential of RFA and Stabilizers in High Moisture Geotechnical Applications
## 2 Materials and Methods
### Test Equipment  
Unconfined Compressive Strength (UCS) Test Equipment: The UCS tests were conducted using a fully automatic UCS testing machine produced by Zhejiang Geo-Tech Co., Ltd. (Ningbo, China). The machine has a maximum load capacity of 10 kN, a range of 50 mm, an accuracy of 0.01 mm, and a compression rate of 1 mm/min. Following the Standard for Soil Test Methods (GB/T 50123-2019) [31], the test was terminated either at peak axial force or when axial strain increased by 3-5% beyond the strain at failure. For specimens with a peak value, the peak strength was recorded as the UCS. For specimens without a peak, the strength corresponding to 20% axial strain was taken as the UCS.  
The triaxial tests were conducted using a fully automated triaxial apparatus (model TKA-TTS-1) manufactured by Nanjing TKAO Instruments (Nanjing, China). The equipment can apply a maximum confining pressure of 2 MPa and withstand an axial load of up to 10 kN. The adjustable shear rate ranges from 0.0001 mm/min to 2.4 mm/min. During the tests, the failure axial strain was set at 15%, and the tests were terminated once this strain level was reached.  
Figure 3: XRD plots of soft soil stabilizers.  
Scanning Electron Microscope (SEM): A Hitachi SU8010 SEM model was used (Hitachi, Tokyo, Japan). After specimen preparation, gold sputtering was performed to ensure sufficient conductivity. Samples were then placed on the test platform, and the height was adjusted for observation under different magnifications.  
Laser Diffraction Particle Size Analyzer: The Malvern MS3000 (Malvern, UK) was used to analyze the particle size distribution (PSD) of soil samples. The device has a working range of 0.01 \(\upmu\)m to 3500 \(\upmu\)m and classifies particles into clay (<1.5 \(\upmu\)m), silt (1.7-75 \(\upmu\)m), and sand (>75 \(\upmu\)m) according to the USCS particle size standard [32].
## 3 Results and Discussion
### Heavy Metals
#### 3.2.2 Equilibrium Tests
Statistical analysis of the relationships between maximum adsorption capacities with regard to Cu determined at temperatures from 3 \({}^{\circ}\)C to 40 \({}^{\circ}\)C and the measured properties of reactive materials showed the lack of statistical significance for the specific surface area, average pore size and volume, pH of the materials and content of the fraction below 0.5 mm on the value of \(Q_{max}\). A positive correlation was observed only with the content of Na\({}_{2}\)O in the composition of the materials (Pearson's coefficient \(R\) larger than 0.936), which was 1.23% for activated carbon, 0.96% for zeolite and 0.37% for limestone sand, whereas Na\({}_{2}\)O was not observed among the oxides in diatomic and halloysite. Additionally, a distinct positive correlation was observed between the difference of \(Q_{max}\) determined for the most extreme temperatures (\(\Delta Q_{max}\)) and the content of K\({}_{2}\)O (\(R\) = 0.879), and a negative correlation with the content of Fe\({}_{2}\)O\({}_{3}\) (\(R\) = \(-\)0.926). The studied zeolite is characterized by the presence of K\({}_{2}\)O at the level of 3.51% and Fe\({}_{2}\)O\({}_{3}\) at the level of 1.58, diatomic at the level of 0% and 37.04% respectively, limestone sand at the level of 1.18% and 4.30% respectively, and halloysite at the level of 0.42% and 3.95% respectively. In equilibrium tests for zinc, there was a positive correlation between \(Q_{max}\) and the average pore size (\(R\) > 0.886), and also between \(\Delta Q_{max}\) and the CaO content (\(R\) = 0.965). The percentage contribution of CaO was 86.93% for limestone sand, 3.17% for zeolite, 1.37% for activated carbon, and 0.67% for halloysite, whereas CaO was not noted in the diatomic sample. It should be emphasized that in this case correlation with the reaction of equilibrium solutions and the pH of the reactive materials was also not observed. The possible explanation was that in batch studies, the ratio between the solution volume to the mass of the reactive material was large (0.500 g material per 50 mL solution), which influenced smaller changes of the reactions of equilibrium solutions (pH\({}_{\text{e}}\) in the range from 5.05 to 8.04 for Cu and from 5.87 to 8.74 for Zn) in relation to the initial solutions (pH\({}_{0}\) in the range from 5.83 for Cu and 6.51 for Zn to 7.00 for distilled water).
# Robust Speed Control of a Multi-Mass System: Analytical Tuning and Sensitivity Analysis
## 3 Development of the Control Algorithm  
The system in Figure 3 is accurately modeled as a nonlinear higher-order system of a multi-mass oscillator. However, because of the high complexity of this mathematical model, it will not be suitable for controller design. Hence, the nonlinearities may be ignored when designing the controller. Even so, the nonlinearities should be considered in the simulation [11; 23; 24].  
The three-mass system model has five poles \(0\), \(-2.65\pm\mathrm{j}\,108\), and \(-41.2\pm\mathrm{j}\,1310\), as shown in Figure 4. The pole at \(0\) makes the system marginally stable. The other poles characterize the plant system's resonance frequencies: \(108\) rad/s and \(1310\) rad/s, and the corresponding damping ratios: \(0.0244\) and \(0.0315\). The poles \(0\), \(-2.65\pm\mathrm{j}\,108\), which demote the original system's stability, are dominant. Obviously, the system requires a control algorithm that makes it more stable and enhances the damping properties. Suppose a shaft with relative flexibility connects the wheel-hub with the loading machine. In that case, a control algorithm is required to compensate for the mechanical losses and suppress the vibrations in the system [2]. These are achieved mainly by manipulating the angular speeds to minimize the twist angle differences between the shaft ends [10; 11; 12; 14; 16]. Other sources of torsional vibrations are the backlash between gears [6; 23; 33; 34] and the moment harmonics from the electrical machines [2; 23].
## 3 System Model of HAP Drones and LEO Satellite Collaborative Networks
### Overall Delay Analysis
#### 3.3.3 Processing on Forwarded HAP Drones  
After receiving the task from terrestrial IoT devices, the HAP drone can also forward it to other HAP drones connected to it. We assume that HAP drones can be connected through laser links [35], and the transmission bandwidth is very large. Therefore, we ignore the delay in data forwarding between HAP drones. The overall delay of task processing on directly connected HAP drones can be formulated as follows:  
\[t_{j}^{HAP,fd} =\sum_{n\in\mathcal{N}}\alpha_{j}^{n}(\frac{(y_{j}+z_{j}+w_{j})D_ {j}}{R_{j}^{n}}+\sum_{n^{\prime}\in\mathcal{N}/n}\beta_{n,n^{\prime}}t_{j}^{n,n^{\prime}}),\] \[=\sum_{n\in\mathcal{N}}\alpha_{j}^{n}(\frac{(y_{j}+z_{j}+w_{j})D_ {j}}{b_{j}^{n}\log_{2}(1+\frac{p_{j}s_{j}^{n}}{\text{N}_{0}b_{j}^{n}})}+\sum_ {n^{\prime}\in\mathcal{N}/n}\beta_{n,n^{\prime}}\frac{z_{j}D_{j}c_{j}}{f_{j}^ {n^{\prime},fd}}), \tag{9}\]  
where \(\beta_{n,n^{\prime}}\in\{0,1\}\) denotes whether HAP drone \(n\) is connected to HAP drone \(n^{\prime}\). If HAP drone \(n\) is connected to HAP drone \(n^{\prime}\), \(\beta_{n,n^{\prime}}=1\); otherwise, \(\beta_{n,n^{\prime}}=0\).
## 4 Optimization Problem Raising and Solving
### Description of the Capacity Optimization Problem  
Taking maximizing the capacity lower bound of the UWOC system shown in Figure 1 as an optimization target, and considering the limit of the tilting angle \(\theta\) of the receiver, the optimization problem can be expressed as  
\[\underset{\theta}{max}\ C_{Low},\ s.t.\ 0\leq\theta\leq\pi/2 \tag{38}\]
# Secure Video Communication Using Multi-Equation Multi-Key Hybrid Cryptography
## 4 Experimentation
### Time to Encrypt the Video Files  
This section gives information regarding the total amount of time that is necessary to finish the execution of the pipeline, which begins with the creation of video chunks and ends with encrypted video chunks. This section begins with creating video chunks and ends with encrypted video chunks. During the course of our investigation, we tested out a number of different sizes for video files, including 1, 2, 5, 10, 20, 30, 40, 50, 100, 200, 300, 400, and 500 megabytes. These file sizes have been selected with the applications that support real-time streaming in consideration. In addition to this, one of our goals was to investigate the behaviour of the increasing delay.  
The latency experienced by the entire procedure is illustrated in Figure 7. According to the data, the process's duration increases linearly as the size of the file grows. This is normal for all applications. In addition, it is important to note that the additional delay caused by every segment of the video clip is depicted in the figure. On the receiver's end, neither the video streaming nor the playback is being disrupted in any way. The results demonstrate that the increased complexity using the equation set in the proposed method does not increase the delay much.