esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment and Results
### Experimental Equipment  
The SHPB equipment conducted in the experiment is shown in Figure 3. The incident rod and the transmission rod were \(\Phi\)2000 mm \(\times\) 75 mm in size. The spindle bullet was used to adjust the waveform shape. The material parameters of the rod are given in Table 3.  
In the real-time high-temperature SHPB experiment, the SHPB equipment was modified, and the insulation device was installed in the same position as the loading specimen.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & **Density** & **Length** & **Equivalent** & **Melting Point** & **Elongation at Break** & **Elastic Modulus** \\  & **(g/cm\({}^{3}\))** & **(mm)** & **Diameter** & **(\({}^{\circ}\)C)** & **(\%)** & **(MPa)** \\ \hline Parameter & 1.18 & 3–9 & 9–30 um & 220 & \(\geq\)15–25 & \(\geq\)13,000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main performance parameters of polypropylene fiber characteristics.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Water**} & **Cement** & **Fly Ash** & **Silica Fume** & **Sand** & **Pebble** & **Polypropylene** & **Water Reducing** \\  & & & & & **Fiber** & **Admixture** \\ \hline
160 & 234 & 108 & 18 & 714 & 1166 & 1 & 7.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Concrete proportions (Kg/m\({}^{3}\)).  
Figure 3: The SHPB equipment used in the experiment.  
Figure 2: \(\varphi\)75 mm \(\times\) 40 mm polypropylene concrete specimen for SHPB test.  
The strain gauges were positioned 1.8 m and 0.6 m away from the contact of the specimen at the incident rod and transmission rod, respectively.  
In this experiment, the XH7L-12 muffle furnace produced by Zhengzhou Xinhan Instruments and Equipment Co., Ltd. was used to heat the specimens. The rated power was 5 kW, the voltage was 380/220 V, the maximum working temperature was 1200 \({}^{\circ}\)C, and the temperature control accuracy was 1-3 \({}^{\circ}\)C. The furnace used special ceramic fiber materials and composite fiber materials, which were characterized by their fast heating rate, and these fibers were 300 mm \(\times\) 200 mm \(\times\) 120 mm (length \(\times\) width \(\times\) height) in size.
## 2 Materials and Methods
### Design of ADRC and ST-SMC Hybrid Control
#### 2.3.1 Active Disturbance Rejection Control
\tag{15}\]  
The expansion of the above system is simplified to the following form:  
\[y=f(x_{1},x_{2},d(t))+bu \tag{16}\]  
where the system state variables are \(x_{1}\) and \(x_{2}\), the control input is \(u\), and the disturbance variable is \(d(t)\). Expanding the above equation leads to the corresponding ESO for the system, as shown below:\[\left\{ \begin{aligned} & e(t)=z_{1}(t)-x_{1}(t)\\ &\dot{z}_{1}(t)=z_{2}(t)-\beta_{01}\cdot e(t)\\ &\dot{z}_{2}(t)=z_{3}(t)-\beta_{02}\cdot\textit{fall}(e(t),\alpha_ {1},\delta)+bu(t)\\ &\dot{z}_{3}=-\beta_{03}\cdot\textit{fall}(e(t),\alpha_{2}, \delta)\end{aligned} \right. \tag{17}\]  
\[\textit{fall}(e,\alpha,\delta)=\left\{ \begin{aligned} &|e|^{\,\alpha}\textit{sign}(e),|e|> \delta\\ &\frac{e}{\beta^{\frac{e}{\alpha-\alpha}}}\cdot|e|\leq\delta \end{aligned} \right.\delta>0 \tag{18}\]  
where the observed values of \(x_{1}\), \(x_{2}\), \(x_{3}\) are \(z_{1}\), \(z_{2}\), and \(z_{3}\), respectively. The error between the estimated and actual values is represented by \(e\). The observation gains \(\beta_{01}\), \(\beta_{02}\), and \(\beta_{03}\) are all greater than 0. The filtering factor is denoted as \(\delta\), and the nonlinear function is \(\textit{fall}(\cdot)\), where \(\alpha_{1}\), \(\alpha_{2}\), and \(\alpha\) are the nonlinear factors. 3. Nonlinear State Error Feedback Control (NLSEF)  
Traditional PID control generates control inputs by applying proportional, integral, and derivative weighting to the error. However, in high-precision control applications, the performance may not be ideal. NLSEF combines nonlinear feedback structures with state errors, adjusting the control gains based on different error intervals. This approach allows for more precise and efficient control while also enhancing the system's robustness and adaptability. In a second-order system, the state errors \(z_{1}\) and \(z_{2}\) are typically obtained by subtracting the output signals \(v_{1}\) and \(v_{2}\) from the observed signals, respectively. Thus, the state errors are represented as:  
\[\left\{ \begin{aligned} & e_{1}=v_{1}-z_{1}\\ & e_{2}=v_{2}-z_{2}\end{aligned} \right. \tag{19}\]  
By performing a nonlinear combination of the state errors in the above equation, the NLSEF control law is obtained as follows:  
\[u_{0}=k_{1}\textit{fall}(e_{1},\alpha_{1},\delta)+k_{2}\textit{fall}(e_{2}, \alpha_{2},\delta) \tag{20}\]  
where the control feedback gains are \(k_{1}\) and \(k_{2}\).
## 3 Method
### Spatial-Channel Coordinated Attention Mechanism (SCCA)
#### 3.2.1 Mathematical Model
** The final fusion combines temporal (channel) and spatial attention through  
\[F_{SCCA}=\underbrace{InvariantNorm(A_{c}\odot X)}_{\text{Channel Stream}}+ \underbrace{D(A_{s})\otimes X}_{\text{Spatial Stream}} \tag{13}\]where \(InvariantNorm\) implements illumination-invariant normalization, which normalizes the features while preserving their relative magnitude relationships, regardless of the overall illumination level. The dual-domain projection \(D\) applies learnable transformations in the spatial and frequency domains:  
\[D(A_{s})=W_{spatial}*A_{s}+FFT^{-1}(W_{freq}\odot FFT(A_{s})) \tag{14}\]  
where \(FFT\) and \(FFT^{-1}\) are the Fast Fourier Transform and its inverse transform, \(W_{spatial}\) and \(W_{freq}\) are learnable weights, \(*\) represents convolution, and \(\odot\) represents the Hadamard product in the frequency domain.
## 5 Responses to Formulated Research Questions  
**Research question 1**: What are the most important WSN platforms and EH techniques, reported in the research articles from 2007 to 2023, that have been utilized for the SHM of bridges?  
**Answer**: A total of 46 articles (from 2007 to 2023) have been identified using a well-defined systematic process, mentioned in Section 2 of this article. The selected articles are arranged into three groups.  
Twenty-eight articles have been included in the WSN group (Section 4.1);  
Nine articles have been included in the energy harvesting group (Section 4.2);  
Nine articles have been included in the combined group.  
**Research question 2**: Which of the EH techniques is more effective for the SHM of bridges, based on the research articles from 2007 to 2023?  
**Answer**: The vibration-based (11 studies) and solar (6 studies) methods are the most used techniques. The vibration-based energy harvesting techniques are classified into electromagnetic and PZT methods. Further details are available in Tables 10-12.  
**Research question 3**: What are the most important sensor types that have been employed for the SHM of bridges, based on the research articles from 2007 to 2023?  
**Answer**: The most frequently used deployed sensors are accelerometers, strain gauges, and temperature sensors. The accelerometer is widely used to assess the dynamic response of the bridge structure, whereas the temperature sensors and strain gauges are deployed to assess the static response of the bridge structure. Further details are available in Table 5. There are other sensors that have been deployed in the SHM for bridges such as humidity, PZT, peak displacement, and ultrasonic sensors.  
**Research question 4**: Which of the system inspection scale techniques and response types are most frequently utilized in the process of SHM in bridges, based on the research articles from 2007 to 2023?  
**Answer**: The global scale investigations are the most used techniques in the SHM for bridges (31 studies out of 46), as shown in Table 6. Only five selected studies deploy the local scale investigation techniques. In addition, the dynamic response analysis is the most implemented approach applied for the SHM of bridges (18 studies out of 46) as shown in Table 7. These studies deploy mainly the accelerometer that is capable of assessing the response of the bridge structure due to an excitation force. The static response analysis approaches have been applied in four selected studies. Both dynamic and static response analyses have been implemented together in 15 studies.
# A Novel Approach to EEG Speech Activity Detection with Visual Stimuli and Mobile BCI
## 4 Methods  
The audio and EEG signals were recorded together. The audio recording was used to create speech and non-speech labels. EEG signals recorded using the graphical user interface OpenBCI, and stored in a text file format, where the individual channels are written in columns. The file format also contains an additional column which contains the information about the Unix time of recording each sample. The main challenge was in the synchronisation of the recorded EEG and audio signals in the post processing. To solve is was created a simple script which, after starting the audio recording, automatically stored the Unix time of record start. This was used to synchronize an audio with EEG in post-processing.  
For the experiment, 9 basic features was assembled based on which the models in the shallow Feed-Forward Artificial Neural Network was created. The important part of the signal processing was the conversion of the signal into minimum-phase signal. Tures were calculated for the raw EEG signal as well as for the minimum-phase signal. In summary, for the total of 16 recorded EEG channels in the experiment a calculation was completed for ehe assigned feaach channel identifying 9 features from the raw signal and 9 features for the minimum-phase signal. All input features composed an input vector of size 288.
## 3 Results and Discussion
### Acceleration Response  
Figure 7 displays the acceleration time history at the tunnel vault, invert, and two side walls along the fault rupture plane. It can be observed that the acceleration peaks at the four monitoring locations are amplified to a certain extent. Notably, the acceleration peak at the tunnel vault is the highest, reaching 0.94 g.  
In order to investigate the distribution of the peak acceleration of the tunnel structure at different locations, peak accelerations were extracted at distances of 60 m and 120 m on  
Figure 6: Flow chart.  
both sides of the fault plane. These measurements were taken from the tunnel vault, the tunnel invert, and the side walls on both sides of the tunnel, as detailed in Table 3 below.  
Table 3 reveals that the acceleration peaks at each monitoring point exceed the peak acceleration of the input ground motion (0.2 g) when the tunnel experiences fault dislocation superimposed with an earthquake. Figure 8 illustrates the distribution curve of the acceleration amplification coefficient (AAC) at each monitoring point, indicating an amplification phenomenon at various monitoring points. The maximum AAC value is 4.7, observed at the top of the tunnel vault at the fault plane.  
Figure 8: PGA amplification results at different longitudinal distances of the tunnel.  
Figure 7: Acceleration time history of the tunnel.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name**Location** & **120** & **60** & **0** & \(\mathbf{-60}\) & \(\mathbf{-120}\) \\ \hline Vault & 0.49 & 0.62 & 0.94 & 0.73 & 0.57 \\ Invert & 0.51 & 0.63 & 0.88 & 0.7 & 0.55 \\ Right side wall & 0.48 & 0.61 & 0.86 & 0.68 & 0.58 \\ Left side wall & 0.52 & 0.58 & 0.87 & 0.71 & 0.57 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Peak acceleration in various parts of the tunnel.
## 4 Experiment Results and Analysis
### Properties of Hardened Concrete
#### 4.2.5 Chloride Ion Penetration Resistance  
Figure 9 shows the experiment results for penetration resistance of chloride ion in the carbon-capturing concrete with respect to the cement replacement ratio. The S90 Ca20 Na20 variable with the lowest cement replacement ratio of 10% relative to the weight of blast-furnace slag showed the lowest total passed charge of 1264 (coulombs). Subsequently, the total passed charge tended to increase with a declining cement replacement ratio. In the case of the S60 Ca20 Na20 variable with the highest cement replacement ratio of 40%, the total passed charge was measured at 1544 (coulombs), which is higher by approximately 22% than that of the S90 Ca20 Na20 variable.  
The penetration resistance of chloride ion in the carbon-capturing concrete was evaluated as 'low' as the passed charge ranged from 1000 to 2000. It was analyzed that the increased water-tightness of the voids in the concrete due to the mixing of blast-furnace slag powder increased the chloride ion penetration resistance. Furthermore, the differences in chloride ion penetration due to the increase of cement replacement ratio resulting from the use of blast-furnace slag above a certain level were generally insignificant.
# An Energy-Efficient Fail Recovery Routing in TDMA MAC Protocol-Based Wireless Sensor Network
## 5 Results and Discussion
### Energy Efficiency of NE-MCR
Figure 12: Simulation results for energy efficiency considering: (**a**) ADT versus network density; (**b**) energy consumption versus network density; (**c**) energy consumption versus wireless range; (**d**) ADT versus wireless range. For example, for the wireless range of 90 m, NE-MCR consumes about 40% lower energy than DFCR. For the same wireless range, ESCFR consumes 48% more energy than the proposed method. Figure 12d illustrates ADT measured over various wireless ranges for the proposed NE-MCR and comparing DCFR and ESCFR algorithms, respectively, for an example network of 600 nodes. The nodes that transmit data to a longer distance consume higher energy and, therefore, drain their battery faster. NE-MCR shows better performance due to its key advantage that minimize the transmit distance for backup parents, while balancing the number of child nodes to meet the data size constraints. Consequently, Figure 12d demonstrates that NE-MCR performs around 30% more data collection rounds than the compared algorithms for the network with a 40 m wireless range.
## 2 Materials and Methods
### Model Formulation
#### 2.4.2 Justification for Introducing Kcp  
In this formulation, the Kcp parameter will replace the Kc that has been widely used for crop water balance studies around the world to simulate crop water-use patterns during a crop growing season. Researchers have shown that NDVI is linearly related to Kc ([18]; [19]; [20]; [21]). [PERSON] [22]demonstrated the use of the soil adjusted vegetation index (SAVI) to extrapolate Kc for corn throughout the growing season. In this study, a simple linear relationship is formulated to convert LSPs into water-use patterns for operational applications. The designation Kcp (the \"p\" denoting the phenology-based coefficient) is used to distinguish it from the Kc that is derived from experimental plots in the FAO method [6].  
Since Kc is a step-function, the following assumptions and simplifications have been introduced to simulate the Kc water-use patterns from LSP profiles. The Kc function is based on four growth stages of the vegetation: early, vegetative, maturity, and senescence. The early and mature stages are constant functions of time, while the vegetative (increasing) and senescence (decreasing) stages are linear functions of time.  
Similarly, the Kcp function is developed by assuming a constant water-use pattern in the early and late stages when the LSP value falls below a certain threshold. On the other hand, Kcp values between the initial and mature stages will vary according to the NDVI (LSP) profile, while the Kc values are derived as a linear function of time. The challenge has been the establishment of the threshold values that signal the start of a season that marks the end of the initial period. Although this model parameter can be adjusted to reflect regional or sensor conditions, a trial-and-error method was used to assign the initialization threshold to an NDVI value of 0.30 when using NDVI derived from AVHRR data sets. Although this threshold is appropriate for a large area of the Earth where there is reasonable vegetation cover for parts of the year, the threshold is dynamically adjusted to handle landscapes in mainly arid and some semiarid regions with little or no vegetation cover.  
The Kcp parameter has major advantages over the predecessor Kc. Since Kcp is derived from LSPs that are unique to each modeling cell in the world, there is no need to redefine Kcp by region and climate as is the case for Kc. Kcp includes the vegetation calendar, i.e., start of season, length of growing period, and end of season from the LSP. By contrast, crop-specific growth stage, timing, and length have to be assumed in the case of Kc. Because of the embedded vegetation calendar in LSPs,  
Figure 1: A simplified conceptual diagram of the VegET model. Major inputs are: precipitation (PPT), reference ET (ETo) and NDVI/Kc. Estimated parameters are phenology-based crop coefficient (Kcp) and soil-water stress factor (Ks). Model outputs are ETa and runoff.  
vegetation water balance studies can be initiated without the need to assume or model separately the start of the growing season for operational vegetation and crop monitoring.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & & \multicolumn{3}{c}{**TIME**} & \multicolumn{3}{c}{**TIME + CROP**} \\
**Sites** & **R** & **RMSE** & **NSE** & **MAE** & **R** & **RMSE** & **NSE** & **MAE** \\ \hline BE-Lon & 0.807 & 2.838 & 0.645 & 1.448 & 0.809 & 2.823 & 0.649 & 1.449 \\ CH-Oe2 & 0.580 & 15.507 & 0.337 & 7.014 & 0.570 & 15.661 & 0.323 & 7.066 \\ DE-Geb & 0.401 & 6.524 & 0.160 & 2.281 & 0.405 & 6.509 & 0.163 & 2.283 \\ DE-Kli & 0.674 & 7.107 & 0.454 & 3.270 & 0.679 & 7.069 & 0.460 & 3.273 \\ DE-RuS & 0.743 & 8.222 & 0.521 & 5.355 & 0.738 & 8.290 & 0.513 & 5.405 \\ DE-Seh & 0.849 & 7.057 & 0.705 & 4.451 & 0.849 & 7.060 & 0.704 & 4.451 \\ FR-Gri & 0.687 & 7.673 & 0.471 & 3.428 & 0.689 & 7.645 & 0.475 & 3.412 \\ US-CRT & 0.757 & 8.912 & 0.565 & 5.082 & 0.749 & 9.021 & 0.555 & 5.179 \\ \hline \end{tabular} Note: IT-BCi, US-Tw2, and US-Tw are not included because they are monoculture. CROP is the crop rotation information.  
\end{table} 
Table 3: The influence of crop rotation as a feature in XGBoost models for E prediction.
## 5 Case Study for Mitigating Security Attacks in Smart Healthcare System
### Application Layer  
This layer comprises all the recipients for whom the healthcare data are intended. It consists of a destination sensor, medical device, hospitals, pharmacy, laboratories, ambulances, and the government healthcare organization that collects the data from the secure pipeline (data layer to blockchain layer). Here, all the components are connected to a next-generation wireless network, i.e., a 6G network interface, which offers several advantageous features such as high data rates, low latency, high reliability, and availability. Furthermore, incorporating a 6G network interface improves the latency (response time); therefore, one can quickly send the data to the intended recipient who can use it for patient care.
# Synchronization, Control and Data Assimilation of the Lorenz System
## 2 Master-Slave Synchronization
### Pecora-Carrol Synchronization
Using a simple Euler scheme, we have \(t=n\Delta t\) and \(\mathbf{r}(t)=\mathbf{r}(n\Delta t)=\mathbf{r}_{n}\), and  
\[\mathbf{r}_{n+1}=\mathbf{r}_{n}+\mathbf{F}(\mathbf{r}_{n})\Delta t. \tag{12}\]  
In the tangent space  
\[\delta\mathbf{r}_{n+1}=(1+J(\mathbf{r}_{n})\Delta t)\delta\mathbf{r}_{n}, \tag{13}\]  
and the maximum Lyapunov exponent \(\lambda_{\text{MAX}}\) is  
\[\lambda_{\text{MAX}}=\lim_{n\to\infty}\frac{1}{n\Delta t}\sum_{n}\log(1+J(\bm {r}_{n})\Delta t)\simeq\lim_{n\to\infty}\frac{1}{n}\sum_{n}J(\mathbf{r}_{n}). \tag{14}\]  
The average growth of the distance \(\delta\mathbf{r}\) is  
\[\delta\mathbf{r}(t)=\delta_{0}\exp(\lambda_{\text{MAX}}n\Delta t)=\exp(\lambda_{ \text{MAX}}t), \tag{15}\]  
for time intervals such that the linearized approximation is valid. Notice that, in the continuous-time version, the Jacobian and the Lyapunov exponents are defined in units of the inverse of time. In this paper, we always use the Euler integration scheme. We checked that other, more sophisticated integration schemes do not qualitatively affect our results.
# Tripartite Quantum Key Distribution Implemented with Imperfect Sources
## 4 Security Analysis
 \tag{22}\]  
Afterward, [PERSON] measures her subsystems in the \(Z\), \(X\), and \(Y\) bases, respectively, to effectively emit the states \(|\phi_{j\beta}\rangle\) sent to [PERSON] and [PERSON]. Using the same method previously described for the yield of virtual states, the expression for the yield of actual states is obtained as  
\[ \begin{split} Y^{\omega}_{j_{\alpha}k_{\beta}m_{\beta}}=p(j_{\alpha}) \text{Tr}(\hat{D}_{k_{\beta}}\rho_{j_{\alpha}})\text{Tr}(\hat{D}_{m_{\beta}} \rho_{j_{\alpha}})&=p(j_{\alpha})\sum_{s=X,Y,Z}\mathbf{n}_{s}q_{k _{\beta}|s}\sum_{t=X,Y,Z}\mathbf{n}_{t}q_{m_{\beta}|t}\\ &=p(j_{\alpha})\chi^{\omega}_{j_{\alpha}k_{\beta}}Y^{\omega}_{j_{ \alpha}m_{\beta}}\end{split}  \tag{23}\]  
with \(p(j_{\alpha})\) denoting the probability that [PERSON] measures her subsystems as state \(j_{\alpha}\). The state \(\rho_{j\alpha}\) corresponds to one of the four states defined in Equation (6). The parameters \(Y^{\omega}_{j_{\alpha}k_{\beta}}\) and \(Y^{\omega}_{j_{\alpha}m_{\beta}}\) correspond to the yields of the states sent to [PERSON] and [PERSON], respectively. Now, based on the cases where [PERSON] ([PERSON]) measured the states sent by [PERSON] in the \(X\) basis, the transmission rate of Pauli operators is determined as follows  
\[\big{[}Y^{Z}_{0;X_{X}(m_{X})},Y^{Z}_{1_{Z};X_{X}(m_{X})},Y^{X}_{0 _{X}k_{X}(m_{X})}\big{]}^{T}=\frac{1}{64}\mathbf{A}[q_{k_{X}(m_{X})|t},q_{k_{X} (m_{X})|x},q_{k_{X}(m_{X})|z}]^{T} \tag{24}\]  
where  
\[\mathbf{A}= \begin{bmatrix}1&\sin\delta_{1}&\cos\delta_{1}\\ 1&\sin\delta_{2}&-\cos\delta_{2}\\ 1&\cos\delta_{3}&\sin\delta_{3}\end{bmatrix} . \tag{25}\]  
The same logic can be applied to determine the yield of the virtual states based on the transmission rate as  
\[[Y^{Z,\text{vir}}_{0_{X};k_{X}(m_{X})},Y^{Z,\text{vir}}_{1_{X};k_{X}(m_{X})}]^ {T}=\frac{1}{48}\mathbf{B}[q_{k_{X}(m_{X})|1},q_{k_{X}(m_{X})|x},q_{k_{X}(m_{ X})|z}]^{T}, \tag{26}\]  
where  
\[\mathbf{B}= \begin{bmatrix}\frac{1}{2}\Big{(}1+\sin\frac{\delta_{1}}{2}\cos \frac{\delta_{2}}{2}\Big{)}&0&\frac{1}{2}\Big{(}\cos\Big{(}\frac{\delta_{1}- \delta_{2}}{2}\Big{)}+\frac{1}{2}\Big{(}\sin\frac{\delta_{1}}{2}+\sin\frac{ \delta_{2}}{2}\Big{)}\Big{)}\\ \frac{1}{2}\Big{(}1-\sin\frac{\delta_{1}}{2}\cos\frac{\delta_{2}}{2}\Big{)}&0& \frac{1}{2}\Big{(}\cos\Big{(}\frac{\delta_{1}-\delta_{2}}{2}\Big{)}-\frac{1}{2 }\Big{(}\sin\frac{\delta_{1}}{2}+\sin\frac{\delta_{2}}{2}\Big{)}\Big{)}.\end{bmatrix} . \tag{27}\]  
Based on Equations (24) and (26), the yield of the virtual states sent to [PERSON] and [PERSON] can be deduced. The results can then be used to obtain the virtual yield in Equation (18) and, as a result, obtain the expression for the error rate, \(E_{X_{A}X_{B}X_{C}}\).