esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Dynamic Response of the Slope Based on the Frequency-Domain Analysis  
### Modal Analysis  
Modal analysis has been a common method for the dynamic analysis of structures in the frequency domain [33; 34; 35; 36]. The modal analysis of slopes can clarify the natural frequencies and corresponding vibration modes. The dynamic control equation of the modal analysis is as follows [40]: \([\dot{M}]\left\{\dot{U}\right\}+[K]\left\{U\right\}=0\), where, \([[\dot{M}]\) and \([K]\) are the mass matrix and stiffness matrix of the model, respectively. \(\{\dot{U}\}\) and \(\{\dot{U}\}\) are the acceleration vector and displacement vector of the model, respectively. Generally, only a few vibration modes are selected in the modal analysis. Abaqus/frequency is adopted as the solver for the modal analysis. The modal analysis results are shown in Figures 11 and 12. Figure 11 shows that the first four natural frequencies are 2.53 Hz, 7.21 Hz, 10.64 Hz, and 17.47 Hz, respectively, and the natural frequencies of the bedded slope increase gradually with the order number of the modal mode. The first-order modal shows that the \(U\) at the slope crest is significantly greater than other areas (Figure 12a). The \(U\) increases with the elevation from the bottom to the slope crest, suggesting that the slope has a typical elevation amplification effect. The \(U\) near the slope surface is significantly greater, showing that the slope has a surface amplification effect. The \(U_{max}\) appears in the top area, which indicates that the slope crest is more prone to be unstable under the action of the earthquake, which is consistent with the analysis results, using PGA (Figures 8-10). Moreover, the second-order mode shows that \(U_{max}\) is mainly concentrated at the top of the slope (Figure 12b), which indicates that the surface area is prone to large deformation. Figure 12c,d show that the \(U_{max}\) of the third- and fourth-order modes are mainly concentrated in the local area of the slope crest. The low-order mode suggests that the low-order natural frequency (2.53 Hz) mainly induces the overall sliding deformation of the slope crest area, while the high-order natural frequency mainly induces the local deformation of the slope crest.  
Figure 11: Natural frequency of the slope according to modal analysis by using FEM.  
Figure 12: Cont.  
According to the modal analysis, the dynamic failure mechanism of the slope can be summarized as follows: under the action of high-order natural frequency, local failure deformation appears at the surface slope that is above the topmost structural plane (potential slip surface); under the action of low-order natural frequency, the surface slope appears as overall shear sliding failure along the potential slip surface; with the increase in seismic load, under the action of higher and lower natural frequencies, the slipping mass gradually expands downward, and further shear sliding failure continues to occur along the lower structural plane.
## 5 Experimental Results
### Geometric Attacks
#### 5.3.4 Translation Attack  
In an image, a translation attack shifts the image to a specific direction on the X and Y axes. The proposed system shifts the image to two pixels in the X direction and two in the Y direction.  
Different types of attacks are applied to test the robustness of the method. In addition to this, the method is also compared with related watermarking schemes [4,8,27,28], as shown in Table 5. The comparison is tested with various attacks where the proposed scheme has better robustness results than other methods.  
Figure 9: Geometric attacks on sample images and extracted watermarks.
# Distributed Attitude Synchronization for Spacecraft Formation Flying via Event-Triggered Control
###### Abstract  
The distributed attitude synchronization control problem for spacecraft formation flying subject to limited energy and computational resources is addressed based on event-triggered mechanism. Firstly, a distributed event-driven controller is designed to achieve attitude coordination with the limitation of energy and computing resources. Under the proposed control strategy, the controller is only updated at the event triggering instants, which effectively reduces the update frequency. Subsequently, an event-triggered strategy is developed to further decrease energy consumption and the amount of computation. The proposed event-triggered function only requires the latest state information about its neighbors, implying that the trigger threshold does not need to be calculated continuously. It is shown that the triggering interval between two successive events is strictly positive, showing that the control system has no Zeno phenomenon. Moreover, the update frequency of the proposed controller can be reduced by more than 90% compared to the update frequency of the corresponding time-driven controller with an update frequency of 10 Hz by choosing appropriate control parameters and the control system can still achieve high-precision convergence. Finally, the effectiveness of the constructed control scheme is verified by numerical simulations.  
spacecraft formation flying; event-triggered strategy; attitude control; distributed control
## 5 Simulation and Evaluation Results
### Privacy Protection Effectiveness Evaluation
#### 5.2.1 Experimental Settings  
To fairly demonstrate the effectiveness of our privacy-preserving scheme for task offloading in satellite-terrestrial networks, we assume that the real tasks \(L_{r}\) of 5 terminals arrive randomly in a Poisson distribution-\(\pi(\lambda=100)\), and ask each terminal to generate dummy tasks \(L_{d}\) with a random size following Gaussian distribution-\((\mu,\sigma)\) when a new offload task is transmitted in each time slot. Both real tasks and dummy tasks are sent to a visible satellite, and the satellite then decides on the offloading position. We define the redundancy rate \(\omega\) as the ratio between the real task size \(L_{r}\) and the dummy task size \(L_{d}\), i.e., \(\omega=L_{d}/L_{r}\). Experiments with average redundancy rates \(\omega=0.05,0.10,0.15,0.20\), and 0.25 over 30,000 time slots are conducted to present the privacy protection performance and cost. Detailed settings of the five group experiments in each time slot are listed in Table 2.
## 6 Results and Discussion
### End-to-End Delay Analysis  
End-to-end delay of AMCTD protocol is demonstrated in Figure 12 for variations of attacker nodes. The results depicted in Figure 12 demonstrate the significant influence that attacker nodes have on the end-to-end delay of the AMCTD protocol. In cases with four attacker nodes, eight attacker nodes, and twelve attacker nodes, there is a significantly increased end-to-end delay.  
Figure 13 displays the AMCTD protocol's end-to-end delay under various conditions, including AMCTD without an attack, 4, 8 and 12 attacker nodes. According to the findings in Figure 13 for AMCTD, the end-to-end delay without attack is 20% lower than it is in the 12 attacker nodes scenario. The findings in Figure 13 demonstrate that an active attack has a significant impact on the AMCTD protocol's end-to-end delay.  
Figure 12: AMCTD: End-to-End Delay without attack and 4, 8 and 12 Attacker Nodes.  
Figure 13: End-to-End Delay of AMCTD without Attack, AMCTD with 4, 8 and 12 Attacker Nodes.
# Energy-Efficient RIS-Enabled SISO-OFDMA Communication via Lower Bound Optimization
## 1 Introduction  
Energy efficiency (EE) has become a crucial topic in the research and development of next-generation wireless networks, particularly in sixth generation (6G). However, the deployment of 6G in the industry is quite challenging. Due to non-line of sight (NLOS), the energy efficiency and throughput of the network decrease. NLOS also gives rise to multipath fading, attenuation, and interference due to reflection and refraction. To overcome this problem, reconfigurable intelligent surface (RIS) is the primary solution. RIS reduces attenuation and improves network performance [1].  
A RIS is a meta-surface made up of numerous inexpensive passive antennas that may effectively reflect the electromagnetic waves impinging on it to improve performance [2]. In wireless communication networks with RIS assistance, a base station (BS) communicates control signals to a RIS controller to enhance incident wave characteristics and user communication quality. As a reflector, the RIS does not engage in any digitization processes. Therefore, if implemented properly, a RIS promises to use substantially less energy than conventional amplify-and-forward (AF) relays [2, 3].  
When using a RIS in wireless communications, radio resource allocation to optimize the network performance is a prime concern [4]. Effective resource allocation can aid to further enhance a RIS-assisted wireless network's energy efficiency.
## 3 Experimental Results and Discussion
### Basic Physical Parameters
#### 3.2.4 The Static Constitutive Models of Basalt Fiber-Reinforced Concrete  
There is significant meaning to the investigation of the constitutive models of concrete, as they could be used to study the concrete structure and nonlinear analysis [27]. The static compressive strength was tested in the experiment by raising the temperature of basalt fiber-reinforced concrete and cooling it in air or water. A reasonable constitutive model of basalt fiber-reinforced concrete was chosen to provide feasible suggestions for the practical application in engineering [28]. The parameters of this constitutive model were determined by the experimental results.  
According to the experimental results, the static compressive strength of concrete could be impacted by high temperature. Therefore, the function expression of the strength-temperature constitutive model established through static compressive strength is as follows:  
\[\text{F}=\text{t}_{1}+\text{t}_{2}\text{T}+\text{t}_{3}\text{T}^{2}+\text{t}_{ 3}\text{T}^{3} \tag{1}\]  
F--Static compressive strength (Mpa).  
T--Temperature (\({}^{\circ}\)C).  
Figure 14: Static compressive stress–strain curves of two kinds of concretes.  
On account of the function Equation (1), the fitting curve for the compressive strengthening-temperature relationship of basalt fiber-reinforced in air cooling was showed in Figure 15, and the fitting parameters were recorded in Table 10.  
The expression of the static compressive strength of basalt fiber-reinforced concrete in air cooling is as follows:  
\[\text{f}=34.75-0.11\text{T}-0.00039\text{T}^{2}+0.00000027\text{T}^{3} \tag{2}\]  
R\({}^{2}\) = 0.99.  
On account of the function Equation (1), the fitting graph of basalt fiber-reinforced in water cooling was showed in Figure 16, and the fitting parameters were recorded in Table 11.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **t\({}_{1}\)** & **t\({}_{2}\)** & **t\({}_{3}\)** & **t\({}_{4}\)** \\ \hline Basalt fiber-reinforced concrete & 34.75 & 0.11 & \(-3.9\times 10^{-4}\) & \(2.7\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: The fitting parameters of the static compressive strength of basalt fiber-reinforced concrete in air cooling.  
Figure 16: Static compression of basalt fiber-reinforced concrete in water cooling.  
Figure 15: Static compression of basalt fiber-reinforced concrete in air cooling.  
The expression of the static compressive strength of basalt fiber-reinforced concrete in air cooling is as follows:  
\[\text{f}=38.4-0.02\text{T}-0.00005\text{T}^{2}=0.000000029\text{T}^{3} \tag{3}\]  
\(\text{R}^{2}=0.97\).
# A Sub-6 GHz MIMO Antenna Array for 5G Wireless Terminals
## 2 Antenna Geometry
### Multiple-Element Antenna Array  
The design of single element is extended to another type of the array technique, which is a most famous gain enhancement technique. In Figure 5, the corporate feed array is presented, where each element holding a symmetry is placed at a distance of 0.477\(\uplambda\) to achieve a particular gain enhancement. The gap between the neighbor patches is optimized step by step to have minimal performance degradation effect on each other, and finally, 0.477\(\uplambda\)0 is chosen as an optimized gap where reflection coefficient, gain, and efficiency could be quite satisfactory. The back of the substrate is fully coated with copper material. Some of the design parameters of the corporate feed array have been shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value (mm)** & **Parameter** & **Value (mm)** \\ \hline a & 9.0 & d & 62.0 \\ b & 10.00 & e & 7.0 \\ c & 30.0 & f & 2.4 \\ g & 160 & h & 70.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Design parameters of array antenna.  
Figure 4: Antenna element: (**a**) gain pattern (polar); (**b**) gain pattern (3D).  
The width of the transmission line is calculated by using the following relationship [26].  
\[\mathrm{w}_{\mathrm{z}_{0}}=\left(\frac{377}{Z_{\mathrm{c}}\sqrt{\varepsilon_{ \mathrm{r}}}}-2\right)\times\mathrm{h} \tag{3}\]  
where \(\varepsilon_{\mathrm{r}}\) is the relative permittivity and \(Z_{\mathrm{c}}\) is the characteristic impedance of the transmission line. In the proposed antenna design a 50 \(\Omega\) transmission line is used.
# Evaluation of Methods Based on CPTu Testing for Prediction of the Bearing Capacity of CFA Piles
## 2 Materials and Methods
### Cone Penetration Testing and Pile Bearing Capacity Prediction
#### 2.3.2 LCPC Method  
The Laboratoire Central des Ponts et Chaussees (LCPC) was developed by [PERSON] and [PERSON]. It is based on the analysis of 197 static pile load tests on 96 pile foundations at 48 sites in various soil types such as clay, sand, gravel, weathered rock, mud, peat, or weathered chalkstone. The method also considers various pile manufacturing or installation technologies [59].  
To determine the pile tip bearing capacity \(q_{b}\), an averaging approach of characteristic value of cone resistance \(q_{c}\) below pile tip in interval \(1.5d\) above and \(1.5d\) below pile tip is adopted, where \(d\) is a pile diameter. The unit cone resistance at pile tip \(q_{b}\) is calculated as follows (Equation (7)):  
\[q_{b}=k_{b}\;\times\;q_{c,eq}, \tag{7}\]  
where \(k_{b}\) is the reduction coefficient for bearing capacity at the pile tip and \(q_{c,eq}\) is the equivalent average penetration resistance. Coefficient \(k_{b}\) = 0.40 for non-displacement piles with the tip in clay and silt and \(k_{b}\) = 0.15 for piles with the tip in sand and gravel [60].  
Shaft friction is calculated from penetration resistance and the limit value of unit shaft friction \(f_{p}\) is determined according to the soil type and pile manufacturing technology (Equation (8)):  
\[f_{p}=\frac{q_{eq}}{k_{s}}\;\leq\;f_{p,max}, \tag{8}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No. of Zone** & **Soil Type** & **Average \(C_{\kappa}\) (\%)** \\ \hline
1a & very soft sensitive clay & 8.32 \\
1b & soft clay and silt & 6.57 \\
2a & silty marine and carved clays & 4.69 \\
2b & stiff weathered clay, clay till & 3.63 \\
3a & firm to medium soft silty clay & 3.10 \\
3b & clayey silt, mudstone & 2.47 \\
4a & sandy silt, medium dense silt & 1.58 \\
4b & silty sand, very dense silt & 1.09 \\
5a & uniform loose sand & 0.82 \\
5b & medium dense sand & 0.61 \\
5c & dense sand, gravel–sand mix & 0.34 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Correlation coefficient \(C_{\kappa}\) according to the zone in classification chart [22].  
Figure 5: Classification chart for UniCone method. Numbers and letters are noted in Table 1 [22].  
where \(k_{s}\) is reduction coefficient of pile shaft bearing capacity; \(q_{eq}\) is equivalent average of penetration resistance in a soil layer; and \(f_{p,max}\) is maximum value of unit shaft friction in a soil layer.
## 2 Materials and Methods
### Measurement of Force Accuracy and Steadiness  
The equipment used to measure force accuracy is shown in Figure 1. A detailed technical description of the device can be found in our previous work [11]. Measurements were taken in a sitting position with the back resting on a backrest. The tested leg was placed on a pad attached to the board, while the untested leg rested on the ground. The seat was moved linearly to obtain a knee flexion of 60 degrees. To measure maximum isometric voluntary contraction (MIVC), the subject pressed the pad with maximum force and maintained pressure for 3 s. Three such trials were performed for the right and left legs, and the mean force value was calculated for each trial. The highest mean value (for each leg) served as a reference for determining the target force (50% MVC) that the subject was expected to reproduce.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **EXP** & **CON** & **p-Value** \\  & **(_n = 20_)** & **(_n = 20_)** & **p-Value** \\ \hline Age (years) & 22.0 \(\pm\) 1.7 & 21.1 \(\pm\) 1.8 & 0.304 \\ Body mass (kg) & 76.9 \(\pm\) 7.2 & 73.0 \(\pm\) 8.7 & 0.292 \\ Height (cm) & 179.9 \(\pm\) 6.0 & 176.9 \(\pm\) 7.8 & 0.367 \\ BMI & 23.7 \(\pm\) 1.6 & 23.3 \(\pm\) 2.0 & 0.777 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Participants’ characteristics; mean \(\pm\) SD.  
The force-matching task was to press the pad at 50% MIVC for 3 s. The learning phase (5 reps for each lower limb) was conducted with visual control to facilitate force matching (50% MIVC). The actual measurement was made without visual feedback. Subjects pressed the pad five times for 3 s with each leg (in randomized order), trying to match the target force. We used a 10 s intra-limb rest and a 30 s inter-limb rest. The force signal was sampled at 100 Hz. The software calculated the mean force value for each 3 s trial.  
The force matching task was conducted one week before (Pre) and one week after the intervention (Post), in the morning and without a warm-up. Using the following formulas, we calculated the values of three force errors: (1) absolute error (AE), (2) constant error (CE), and (3) variable error (VE).  
\[\text{AE}=\left|\frac{\sum(x_{i}-T)}{n}\right| \tag{1}\]  
\[\text{CE}=\frac{\sum(x_{i}-T)}{n} \tag{2}\]  
\[\text{VE}=\sqrt{\frac{\sum(x_{i}-\overline{x})^{2}}{n}} \tag{3}\]  
where \(x_{i}\) is the force reproduced in trial \(i\), \(\overline{x}\) is the mean value of force from five trials, \(T\) is the target force (50% MIVC [N]), and \(n\) is the number of trials.  
The AE determines the accuracy in the force matching task, the VE determines the steadiness (variability), and CE indicates a tendency to apply the force above (positive values) or below the target (negative values) [19].
# Towards Flexible Integration of 5G and IIoT Technologies in Industry 4.0: A Practical Use Case
## 5 Use Case
### IIoT Network Slice  
Similar to the NS deployed for the broaching process, the sensor network uses its own VLAN-segmented slice of the infrastructure. The NS deployed in the IIoT network slice is meant to provide the WSN network in the CFAA with access control mechanisms. The NS is composed of one VNF with one VM. It makes use of a total of 100 GB of disk, 8 GB of RAM, and 2 virtual CPUs from the resources available in the EIB data center. Therefore, this slice spans both the EIB and CFAA datacenters and is connected through the WAN infrastructure of SN4I. The reason for physically locating the access control services in the EIB data center is that it is also used to support other WSN deployments, which shows the flexibility provided by NSs and the isolation between them.  
Figure 8: Broaching process slice.  
The VM used is based on an Ubuntu 18.04 operating system. It contains the Hidra server, responsible for implementing all the Access Control Server functions. This machine receives every request from subjects wanting to access any CDS deployed in the WSN of the CFAA. In this context, the subject requesting access to the sensors is the client of the broaching process slice aforementioned, but there could also be clients from other processes. Therefore, the Hidra server implements all the functions involved in the authentication and preliminary authorization phases, including the conveyance of the corresponding security policy to the final CDSs. Moreover, it also implements a LOG server to receive from the sensors the details of all the access attempts in order to perform a real-time monitorization of different parameters. This monitorization allows an efficient management of users' activity, detailing which user did what, when, and in which CDS.  
The topology of this slice is shown in Figure 9.
# Understanding the Fracture Behaviors of Metallic Glasses--An Overview
## 2 Fracture Behaviors of MGs  
The fracture criterion is an important parameter to understand the deformation and fracture mechanism of a material. For MGs, due to the lack of work-hardening effect, the fracture strength in uniaxial compression tests generally will be equal to or close to the yielding strength [13]. The intensity and direction of stress at the moment of yielding or fracture reflect the critical stress condition to trigger the shear band avalanche [14; 15]. From the microcosmic aspect, the yielding criterion reflects the critical stress condition to trigger the localization of shear transformation of atomic groups [16; 17]. Previous studies on this issue were mostly based on the uniaxial tensile and compression experiments or simulations [18; 19; 20]. It has been discovered that MGs generally show a shear mode failure along a shear/fracture angle (the angle between the shear band/fracture surface and the load axis) near 45\({}^{\circ}\)[18; 19; 20]. The angle will be slightly larger than 45\({}^{\circ}\) under tension, and will be slightly smaller than 45\({}^{\circ}\) under compression. Some brittle MG systems can show cleavage or split mode failure, which will be discussed later. To understand the asymmetric compression and tension behaviors, [PERSON] et al. indicates that the microstructure of MGs is analogous to that of randomly packed particles in a granular solid [18]. Therefore, the yielding criterion of MGs could be described by the Mohr-Coulomb criterion:  
\[\tau_{y}=\tau_{0}-\alpha\sigma_{n} \tag{1}\]  
where \(\tau_{y}\) stands for the shear yield stress, \(\tau_{y}\) is a constant, \(\sigma_{n}\) is the normal stress on the shear plane, \(\alpha\) is a coefficient that reflects the degree of internal friction in the system. This criterion explains the asymmetric compression and tension strength of MGs, but show deviations in the estimation of shear angle. On this basis, [PERSON] et al. further proposed the elliptical criterion [19; 20]:  
\[\frac{\sigma^{2}}{\sigma_{0}^{2}}+\frac{\tau^{2}}{\tau_{0}^{2}}\geq 1 \tag{2}\]  
where \(\tau\) and \(\sigma\) stand for the shear stress and normal stress on a shear plane, \(\tau_{0}\) and \(\sigma_{0}\) are material dependent constants. The elliptical criterion provides a applicable model to comprehensively explain the strength and shear angle of MGs under the simple loading condition of uniaxial compression/tension. However, the fracture behaviors of MGs under bending, fast loading, fatigue loading and other loading conditions are still complicated to be understood.