esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Responses Measured by Optical Fiber Sensor for Structural Health Monitoring
## 4 Shock and Vibration Tests
### Base Excitation  
The beam is the same as in the previous section. The clamped end is connected to a shaker, as shown in Figure 7, which makes the beam vibrate. The shaker is able to excite the test specimen with four different frequencies simultaneously. The natural frequency of a cantilever beam can be determined by using the following equations.  
\[1+\cos\beta L\cos\hbar\beta L=0 \tag{6a}\] \[\omega_{i}=\beta_{i}^{2}\sqrt{\frac{EI}{\rho A}} \tag{6b}\]  
where \(\beta_{i}\) is the solution of Equation (6a); \(\rho\), \(E\), \(A\), \(L\), and \(I\) represent the density, Young's modulus, cross-section area, length and moment of inertia of the cantilever beam, respectively.  
Figure 6: Relation between the strain and normalized light intensity.  
The first five natural frequencies calculated from Equation (6) for the cantilever beam are 7.37 Hz, 46.21 Hz, 129.39 Hz, 253.63 Hz and 419.23 Hz, respectively. In the following tests, the cantilever beam is subjected to base excitation with various frequencies.
## 1 Introduction
As we are becoming closer to the standardization of 6G, this paper aims to provide a comprehensive overview of the incorporation of AI and advanced ML techniques in a RIS-enabled wireless communication environment and constitutes a contribution for enabling the deployment of AI/ML towards the realization of a 6G native AI system. We \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **5G** & **6G** \\ \hline User experienced data rate & Up to 200 Mbps (20 Gbps peak) & Up to 10 Gbps (1 Tbps + peak) \\ Coverage & Primarily terrestrial with limitations & Ubiquitous 3D coverage through space-air-ground integration (99\%) \\ Connection density & Up to 1 million & 10\({}^{6}\)–10\({}^{8}\) devices/km\({}^{2}\) \\ Latency over the air interface & 1–10 ms & 0.1–1 ms \\ Frequency bands & Up to 100 GHz & 0.1–10 THz \\ Security capacity & Robust (security-as-an-addition) & Enhanced (security-by-design) \\  & Enhanced mobile broadband & Integrated AI and Communication \\ New usage scenarios & IoT & Ubiquitous Connectivity \\  & Mission-critical applications & Integrated Sensing \\  & & and Communication \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Capabilities of 6G compared to 5G. present a framework that leverages the strengths of both centralized and decentralized ML methods, tailored specifically for the unique requirements of RIS-enabled 6G networks. By exploring the transition from supervised to FL in the context of RIS-aided networks, we seek to uncover new methodologies for enhancing network performance, security, and efficiency. Our study investigates the potential of these technologies to leverage opportunities, as well as to address key challenges, in 6G implementation, including ultra-high data rates, massive connectivity, and ultra-low latency. Our research lays the groundwork for future investigations into these novel possibilities that pave the way for the next generation of wireless innovation.
## 4 Results and Discussion
### Suppression of the Twin-Image Effect
#### 4.1.2 Boundary Condition for the Gerchberg-Saxton Method  
In this work, the Gerchberg-Saxton method was used in the reconstruction of holographic images of experimentally recorded digital holograms of a circular opaque model particle with a diameter of d = 124.2 \(\upmu\)m (Figure 8a) located at a distance z from the hologram recording plane. The size of digital holograms: 2048 \(\times\) 2048 pixels; pixel size: 3.45 \(\times\) 3.45 \(\upmu\)m; wavelength: \(\lambda\) = 0.66 \(\upmu\)m.  
The results of this iteration are shown in Figure 8 for a circular model particle. Note that, despite the large number of iterations, it is not possible to completely eliminate the twin-image effect.  
Let us define z\({}_{\text{R}}\) as the minimum distance at which the quality indicator of the particle image boundary after applying the method increases compared to the quality indicator of the particle image boundary before applying this method.  
Figure 6: (**a**) Image of model particles reconstructed from digital holograms. (**b**) Image of model particles after digital filtering.  
Figure 7: Dependence of the determination error of the best image plane \(\Delta=|\text{z}-\text{z}_{\text{P}}|\) on the distance between particles for digital holograms of two particles recorded at a distance z = 50 mm from the recording plane.  
Calculations of the dependence of the quality indicators of the reconstructed images of this model particle with a diameter \(d\) = 124.2 \(\upmu\)m on the position of the particle \(z\) (Figure 9) showed that under the given experimental conditions this position is \(z=z_{R}=\)50 \(\pm\) 4 mm, while the boundary intensity jump \(\Delta\)I almost doubles (increases by 1.9 \(\pm\) 0.2 times).  
Further, \(z_{R}\) values for model particles of different diameters (Figure 10, points) under the same conditions were experimentally determined. Since we know the parameters of a model particle, PSNR was used as the quality indicator in these experiments. It was found that \(z_{R}\) depends on the size of the studied particles and the wavelength of the recording radiation, while the empirical dependence is described well by the formula \(z_{R}=\frac{2d^{2}}{\lambda}\) (solid line in Figure 10).  
Figure 8: Examples of model particle images: (**a**) model particle on a glass substrate; (**b**) particle image reconstructed from a digital hologram at a distance of 200 mm; (**c**) same after 50 iterations of the complex field amplitude method based on the Gerchberg–Saxton algorithm.  
Figure 10: Dependence \(z_{R}\) for the complex field amplitude reconstruction based on the Gerchberg–Saxton algorithm according to PSNR vs. the size of the studied particle. Points—experimental data, line—theoretical calculation.  
Figure 9: Dependencies \(\Delta\)I of reconstructed images of a model particle with diameter \(d\) = 124.2 \(\upmu\)m before and after applying the complex field amplitude reconstruction vs. the distance between the particle and the plane of the digital hologram for different numbers of iterations.
## 3 Numerical Examples and Results  
A computer program developed by MATLAB programming language is used for coupled fluid-structure-thermal analysis of the HCS plates. A validation example is presented firstly. Then thermal buckling and pre-stressed modal analysis in thermal environments are conducted. Finally, coupled fluid-structure-thermal analysis is performed with parametric studies.
## 2 Materials and Methods
### Testing and Quality Evaluation  
All welded joints were verified through a combination of non-destructive and destructive tests to ensure compliance with relevant standards and industrial quality requirements.  
Visual inspection was performed first to detect surface discontinuities such as undercuts, lack of fusion, excessive spatter, and misalignment, serving as the initial quality control step. Liquid penetrant testing (PT) followed ISO 3452-1:2021 [16] to reveal any additional surface-breaking defects that may not be visible to the naked eye.  
Destructive testing included bend tests, conducted at 30\({}^{\circ}\) or 60\({}^{\circ}\) depending on the material and expected loading conditions, to assess ductility and root integrity of the welded joints in line with EN ISO 14555:2017 [15]. Radiographic testing and macrostructural examinations were performed to detect internal flaws such as porosity, slag inclusions, and incomplete fusion, and to evaluate weld penetration and heat-affected zone morphology.  
All tests and acceptance criteria for weld imperfections were applied according to EN ISO 14555:2017 [15] and ISO 5817:2014 [17]. This integrated testing protocol provides a robust quality assessment framework, supporting the validity and reproducibility of the experimental results under realistic industrial conditions. The mechanical performance of the welded joints was strongly influenced by heat input and welding current [25].
## 2 Materials and Methods
### Image Dataset  
The image dataset, collected as described in Section 2.1, consisted of images captured by visible, NoIR, and thermal cameras. There was a total of 672 images containing:  
* 133 images taken by the visible camera during the day (06:00-17:00 h);
* 275 images taken by the NoIR camera during the day and at night (06:00-05:00 h (next day)); and
* 264 images taken by the thermal camera during the day and at night (06:00-05:00 h (next day)).  
It is noted here that all three cameras captured the same scene at the same time interval, where each scene consists of four or five _Veiteria zizanioides_ plants. The data loss during transmission from the camera modules on the site to the cloud storage causes differences in the number of images captured by the NoIR and thermal cameras. This loss is affected by several problems, such as the camera module's image acquisition error, the internet connection problem, and the error while accessing the Google Drive cloud storage. Since the visible camera only captured images during the day, the number of captured images is half those of the NoIR and thermal cameras.  
Since the image data are time-stamped in intervals of 5 min, the image intensity during a whole day can be plotted, as shown in Figure 6. The blue, red, and green lines represent the intensity of visible, NoIR, and thermal images, respectively. It is clearly shown in the figure that at night, the intensity of the visible image is zero (no objects captured), and the image intensity varies during the day due to variations in sunlight (for visible and NoIR images) and temperature (for thermal images).  
Figure 6: Intensity of visible, NoIR, and thermal images during a whole day.  
Since infrared LEDs are the light source for the NoIR camera, the camera can capture objects at night. However, due to the low power of the LEDs, only objects near the camera are captured. Therefore, the image intensity is low, as shown in Figure 6.  
Figure 6 also shows that there is a significant difference in image intensity between daytime and nighttime images with the NoIR camera. This can be observed in Figure 7, which shows NoIR images at 13:00 h and 19:00 h. In the thermal image, the difference is insignificant, indicating that the temperature did not drop abruptly at night. This situation can be explained by observing Figure 8, which shows thermal images at 13:00 h and 19:00 h. In the images, brighter colors (orange and white) represent higher temperatures than dark colors (blue and purple). As shown in Figure 8, in the night image, some background parts are orange, indicating high temperature. This condition produces images with medium intensity as shown in Figure 6.
## 3 Results and Discussion
### Fluidity
#### 3.1.1 Effect of One-Component Retarder on Fluidity of HBCSA Paste  
According to the above test results of single-doped PCE, the content of PCE is selected as 1.2%, and different retarders are, respectively, compounded with PCE, as shown in Figure 4. Figure 4a shows the fluidity of the cement slurry after compounding \(\beta\)-CD and PCE. It can be seen that the addition of \(\beta\)-CD and the increase in the content have little effect on the initial fluidity of HBCSA cement. When the content of \(\beta\)-CD is 0.1%, the fluidity decreases greatly over time, and the fluidity drops to 158 mm at 30 min and disappears after 60 min. When the dosage reaches 0.3%, the fluidity loss decreases, and the fluidity disappears after 90 min, and when the dosage is between 0.5% and 0.7%, the fluidity will gradually decrease with the increase in time, and the fluidity can still reach 90 min at about 120 mm. Second, the introduction of CA (Figure 4b) reduced the fluidity of HBCSA cement paste, and the initial fluidity decreased sharply with the increase in its content. When the content of CA is 0.1%, the initial fluidity is 279 mm, and the fluidity disappears after 30 min, and when the content of CA reaches 0.5%, the initial fluidity drops below 150 mm, but the fluidity changes little in each period, and the loss of fluidity decreased significantly with time. This is inconsistent with the conclusion in the literature [22] that the combination of CA and PCE has almost no negative effect on the fluidity of sulphoaluminate cement paste. However, with the introduction of another retarder, B (Figure 4c), compared with retarder CA, the fluidity decreased more slowly with the increase in B dosage. This is consistent with the research results on the influence of borax on the dispersion of PCE in CSA [25]. A large amount of borax dominates the competitive adsorption between B and PCE, thus reducing the adsorption of PCE and the dispersion. However, the fluidity will disappear after 30 min.  
Figure 3: Effect of PCE on fluidity of HBCSA.  
Therefore, under the condition of the same dosage, the initial fluidity of the cement paste mixed with \(\beta\)-CD is relatively large, which is similar to the initial fluidity of the single-mixed polycarboxylate superplasticizer (blank group), and with the increase in time, the liquidity decreased regularly, indicating that the liquidity retention ability was excellent. However, the initial fluidity of CA and B was reduced to some extent, and the fluidity over time would exceed the initial fluidity when the dosage was high. Since PCE improves the initial fluidity of cement paste, it is determined by the adsorption amount of PCE on the surface of cement particles [27]. However, the introduction of CA and B will affect the adsorption behavior of PCE, but with the continuous hydration of cement paste, the effect of the retarder is weakened, and the fluidity will increase.
# Bounded-Error-Pruned Sensor Data Compression for Energy-Efficient IoT of Environmental Intelligence
## 3 Bounded-Error Sensor Data Compression (BESDC)  
In an IEI application, the application-specific bounded error is given from a fog-computing machine called the bounded error regulator according to the QoS2 or QoD required by IEI decision-makers. Then, a two-tier BEA scheme is applied based on the current topology of the deployed IoT to assign the error bounds of pdEB to the fog-tier machines like sink and the sensor-tier devices of SN and ED. With the given error bounds, EDs will use temporal and data correlations to compress the receiving sensor data and send the compressed sensor data to the corresponding SN. With the incoming compressed data between different neighboring EDs, SN will use spatial and data correlation compression to compress incoming sensor data further. Then, SN will send out the compressed data to the corresponding sink and the fog-tier sink will forward these compressed data to the fog-tier servers. Finally, the corresponding servers will store and decompress the compressed sensor data according to the pdEBs. Then, they will save the decompressed bounded-error-pruned data to the database for later query from users.
## 2 Theory
### Image Reconstruction  
The accuracy of reconstruction was enhanced using a Gaussian kernel to filter the noisy simulation data and experimental data, the kernel size of the filter was \(7\times 7\times 3\) voxels, and the standard deviation was \(1.2\times 1.2\times 1.2\) pixels. The proposed method was compared with the CM and the double regularization method (DRM). All calculations were conducted in MATLAB (The MathWorks, Natick, MA, USA).  
For the DRM and proposed method, several parameters need to be set. The RMSE was used to estimate the performance of reconstruction for different parameters, and then the proper parameters were selected. Table 1 presents the actual regularization parameters.
# Time-Delay Vibration Reduction Control of 3-DOF Vehicle Model with Vehicle Seat
## 5 Dynamic Performance Simulation and Analysis
### External Incentive Input  
To further evaluate the feasibility and effectiveness of the method of optimizing the time-delay control parameters proposed in this paper, random excitation is selected as the vertical disturbance to the wheel axle, and the 3-DOF suspension established model is subjected to random excitation as an example for time-domain analysis. In this paper, a time-domain model of random excitation is established by the superposition of random sine waves. The power spectrum density of road displacement is expressed by \(G_{q}(f)\). In time frequency \(f_{1}<f<f_{2}\), it is divided into n small intervals. The power spectrum density value \(G_{q}(f)\) corresponding to the central frequency of each cell is taken to replace the value of the whole cell. Then, a sine wave function with an intermediate frequency \(f_{\text{mind}-i}(\text{i}=1,2,\cdots,\text{n})\) and standard deviation \(\sqrt{G_{q}(f_{\text{mind}-i})\Delta f_{i}}\) is found. Such a sine wave function can be expressed as [48]:  
\[\sqrt{G_{q}(f_{\text{mind}-i})\Delta f_{i}}\sin(2\pi f_{\text{mind}-i}t+\theta _{i}) \tag{24}\]  
Equation (24) is superimposed on the sine wave function corresponding to each cell, and the time domain expression of the random displacement input is obtained as follows:  
\[q(t)=\sum_{i=1}^{n}\sqrt{G_{q}(f_{\text{mind}-i})\Delta f_{i}}\sin(2 pif_{ \text{mind}-i}t+\theta_{i}) \tag{25}\]  
where random numbers are uniformly distributed on \(\theta-[0,2\pi].q(t)\) is the time domain expression of random excitation displacement.
## 2 Literature Review
### LHTES Systems in Greenhouses
#### 2.1.4 Categorization of Previous Works on Greenhouse LHTES System Based on Efficient Factors  
Table 1 categorizes the results from earlier publications linked to using PCMs in greenhouses as a TES system. Different aspects of these works are focused on, including the year of publication, type of work (experimental, numerical, analytical, etc. ), type of PCM, product or crop of the greenhouse, evaluated factors, and outcomes (qualitative and quantitative). Indeed, as evidenced by the items listed in the table, research related to this topic was published between 2005 and 2023. This extensive timeframe, spanning nearly two decades, underscores the thorough examination and analysis researchers undertake. The results have been promising, inspiring researchers to enhance PCMs' coupling efficiency within greenhouse environments across various operational scenarios. Table 1 also demonstrates that most of the studies in this sector have been conducted experimentally, with less statistical or analytical investigation of the usage of PCMs in greenhouses. This issue might be viewed as a research gap that should be addressed in future studies. A precise and more detailed understanding of this material's phase change process is required to understand better the effect of using a PCM inside greenhouses for storing energy in the charge state and releasing it in the discharge state. Numerical simulation, due to its tremendous capabilities, can be an outstanding idea for achieving this. Concerning the PCM employed in greenhouses equipped with a PCM as TES, the details in Table 1 demonstrated that, in general, six different types of PCMs were used in these systems, including pure paraffin, calcium chloride hexahydrate (CaCl2.6H2O), GH-20, AC27, RT types (RT12, RT15, and RT18 HC), and shape-stabilized PCMs. It can be concluded that paraffin is the most commonly used PCM among the materials employed. In terms of the kind of greenhouse usage and the product or crop utilized in the research, Table 1 demonstrates that the type of product is indicated in some studies but not in others. Knowing the product type in the greenhouse aids future analysis; if the researcher plans to model and evaluate the intended system mathematically, the quantity of heat and humidity required for the desired product is essential. Modelling will be intricate without it. Figure 21: The suggested experimental setup’s schematics by [PERSON] et al. [39].
## 3 Experimental Validation
### Circuit under Test
#### 3.1.2 CSTV Filter Circuit  
Figure 7 shows the CSTV filter circuit with a cut-off frequency of 1 kHz. A square wave signal with a frequency of 200 Hz, duty cycle of 50% and amplitude of 5 V is used as the stimulus of the CSTV filter circuit. The nominal values of the components in the CSTV filter circuit are shown in Figure 7. The sensitive components of the CSTV filter circuit are \(R1\), \(R2\), \(R5\), \(C1\), and C2 [29]. Sensitive components, together with R3 and R4, are used for fault setting and they mainly have 15 fault states, which are represented as \(R1\)\(\downarrow\), \(R1\
# A Neural-Network-Based Watermarking Method Approximating JPEG Quantization
## 4 Proposed Method
### Quantized Activation Function  
We propose a quantization table-based QAF for neural networks to implement the quantization process of the JPEG compression according to the standard. The QAF consists of \(n\) hyperbolic tangent (tanh) functions and is defined as  
\[\text{QAF}\big{(}x|t_{Q}\big{)}=\sum_{i=0}^{n}\frac{t_{Q}}{2}\tanh \bigg{\{}\beta\bigg{(}x\pm t_{Q}\bigg{(}\frac{1}{2}+i\bigg{)}\bigg{)}\bigg{\}}, \tag{13}\]  
where \(t_{Q}\) is the value of the quantization table \(T_{Q}(u,v)\) when the quantization level is \(Q\). The parameters \(n\) and \(\beta\) denote the number and slope of the tanh functions, respectively. The red dashed line in Figure 6 represents \(\text{QAF}(x|16)\) when the slope \(\beta=1000\) and the value of the quantization table \(T_{Q}(u,v)=16\). In addition, the values of the DCT coefficients after JPEG quantization are plotted with a black line. We can see that they are almost the same.  
Quantization is essentially a rounding operation to integer values. It should therefore be represented by a discontinuous, step-like function. In other words, a sign function should be used for the representation of JPEG quantization (13). However, the tanh function was used in the proposed method. When training a neural network, the differentiable function works better for training. Therefore, we chose the tanh function, which is a continuous function. Figure 7a shows the tanh functions for different slopes \(\beta=1,10,100,1000\). We can see that the slope becomes steeper and asymptotically closer to the sign function as \(\beta\) increases. When the slope of tanh is set to \(\beta\rightarrow\infty\), it asymptotically approaches the sign function. For practical use, a large value of \(\beta\) can approximate the quantization with sufficient accuracy.  
Let us see how the quantization with QAF differs from the quantization with JPEGdiff. Figure 7b shows a comparison of JPEGdiff (11) with QAF functions. The green curve represents the JPEGdiff, and the blue and orange curves represent the QAF at slopes \(\beta=10,1000\). We can see that the JPEGdiff has discontinuities, while the QAF is smooth. Since the QAF has no discontinuities, the network may be better trained.  
Finally, we consider the number \(n\) of tanh functions. The number \(n\) depends on the value of \(T_{Q}(u,v)\). If the minimum value of \(T_{Q}(u,v)=1\), because the maximum value of the DCT coefficients is 2040, then at most, 2039 tanh functions are required. A sufficiently large constant \(n\) is chosen because it does not matter if the possible values of the QAF exceed the maximum value of the DCT coefficients.