esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Non-Bracket Construction Method
### Nonlinear Analysis Method  
Structural nonlinearity includes state change, material nonlinearity, and geometric nonlinearity. For tension structures, nonlinear analysis must be adopted because the structure has strong geometric nonlinearity. For complex nonlinear problems, a common effective method in nonlinear analysis is to use linear equations to gradually approximate the nonlinear solution. Therefore, the essence of solving a nonlinear problem is to linearize the nonlinear equations and choose linear approximation strategies and algorithms.  
The common methods for solving nonlinear algebraic equations include direct iteration, full and modified Newton-Raphson, and load increment methods. The full Newton-Raphson method is used in this paper. This method linearizes the nonlinear equations, and the tangent stiffness matrix is modified every iteration until the convergence condition is reached [35].  
For nonlinear problems, the following nonlinear equations are obtained by discretizing the structural elements:  
\[[\mathrm{K}(\mathrm{u})]\mathrm{u}=\mathrm{F} \tag{12}\]  
The above formula can be expressed as:  
\[\{\Psi(\mathrm{u})\}=[\mathrm{K}(\mathrm{u})]\mathrm{u}-\mathrm{F}=0 \tag{13}\]  
where \(\mathrm{u}\) is the displacement vector, the stiffness matrix \(\mathrm{K}(\mathrm{u})\) is the n-order square matrix about \(\mathrm{u}\), and \(\mathrm{F}\) is the load vector.  
After the approximate solution \(\mathrm{u}^{\mathrm{n}}\) is obtained, in order to further obtain the approximate solution \(\mathrm{u}^{\mathrm{n}+1}\), \(\{\Psi(\mathrm{u})\}\) can be expanded by the Taylor series at \(\mathrm{u}^{\mathrm{n}+1}\) and only retain the linear term:  
\[\left\{\Psi(\mathrm{u})^{\mathrm{n}+1}\right\}=\left\{\Psi(\mathrm{u})^{ \mathrm{n}}\right\}+\left[\frac{\mathrm{d}(\Psi)}{\mathrm{d}(\mathrm{u})} \right]_{\mathrm{n}}\cdot\Delta\mathrm{u}^{\mathrm{n}} \tag{14}\]  
where \(\mathrm{k}=\mathrm{d}(\Psi)/\mathrm{d}(\mathrm{u})\) is the tangent matrix.  
Repeat the above iterative solution process until the iterative convergence condition is satisfied:  
\[\left\|\Delta\mathrm{u}\right\|_{\infty}=\left\|\mathrm{u}^{\mathrm{n}+1}- \mathrm{u}^{\mathrm{n}}\right\|_{\infty}<\varepsilon \tag{15}\]  
where \(\varepsilon\) is an infinitesimal quantity.  
The positive definiteness of \(\mathrm{d}(\Psi)/\mathrm{d}(\mathrm{u})\) is a necessary condition for the system of equations to have a solution. The analysis process of using the Newton-Raphson method to solve the single degree of freedom system is shown in Figure 7.
## 1 Introduction
For instance, building upon the cam-roller spring mechanism, [PERSON] et al. [33] proposed a nonlinear isolation system based on cam-roller-spring-rod stiffness., [PERSON] et al. [35] successfully implemented an effective nonlinear damping approach to enhance the vibration isolation performance of electromagnetic structures. [PERSON] et al. [36] improved the stability of the three-spring QZS isolator in low-frequency by introducing the inerter element. [PERSON] et al. [14; 27; 24; 31] developed X-shaped and limb-like structures that exhibit beneficial nonlinear stiffness, damping, and inertial characteristics. Their studies revealed that careful parameter tuning could readily achieve a QZS region featuring substantial load capacity, large stroke, broad vibration isolation bandwidth, and superior stability. Similarly, [PERSON] et al. [28] devised an innovative toe-like anti-vibration configuration comprising two rods and a spring, with both theoretical and experimental results confirming effective vibration reduction for excitation frequencies above 3 Hz. Complementing these stiffness innovations, researchers have developed advanced damping strategies to optimize isolation performance across different frequency regimes. These include velocity-dependent nonlinear damping for resonance control [37], displacement-dependent geometric damping for broadband attenuation [9; 38], and hybrid approaches combining multiple damping mechanisms. In this study, to solve the ultra-low frequency vibrations of the floating platform, a novel quadrilateral-shaped vibration isolation platform (QVIP) is proposed with tunable and enhanced QZS properties. The mechanism of the QVIP for the QZS property based on three-spring element and its dynamic equation are shown in Section 2. Section 3 shows parametric influences on static stiffness force and summarizes the key factors for achieving the QZS property, while Section 4 builds up the simulation model of the QVIP and verifies the vibration control effect on the offshore platform under harmonic excitations and ocean wave conditions. Finally, a conclusion is given in Section 5.
# Asymmetric Propagation Delay-Aware TDMA MAC Protocol for Mobile Underwater Acoustic Sensor Networks
## The APD-TDMA
### Initialization Phase
Each UN determines whether the UN should respond to the RIRQ packet by checking the list of the received RIRQ packet. UN keeps silent if the ID of a UN is equal to one of the UNs' IDs in the RIRQ packet. Otherwise, UN responds to the RIRQ packet by transmitting RIRP packet with a suitable random-backoff to resolve RIRP packet collisions at the SN. The SN finishes the initialization phase if the positions of all UNs are successfully obtained, or if the number of the initialization retry, denoted by \(Init_{\text{NUM}}\), reaches pre-defined maximum value, \(Init_{\text{max}}\). \(Init_{\text{max}}\) can be different according to network environments, such as number of UNs, data rate, and network range. We do not specify a certain value of \(Init_{\text{max}}\) in this paper. At the end of the initialization phase, the SN determines the transmission schedules of UNs in ascending order of UNs' propagation delays based on the position. If we denote the position vector of UN\({}_{i}\) and the SN by \(X_{i}\) and \(X_{SN}\), respectively, the propagation delay between UN\({}_{i}\) and the SN can be expressed by  
\[\frac{|X_{SN}-X_{i}|}{c}, \tag{7}\]  
in which \(|X_{SN}-X_{i}|\) is the Euclidean distance between the SN and UN\({}_{i}\). The scheduling algorithm for UNs' transmission and the scheme for time slot allocation are detailed in following subsections. We assume that all UNs maintain their positions during the initialization phase, which is usual in operating AUV.
# Experimental Study on Grouting Visualization of Cover Layer Based on Transparent Soil
## 2 Visualization Grouting Test System
### Test Materials
When preparing transparent soil, we first weigh each component according to the accurate mass ratio, and then fully mix it by mechanical mixing until there is no visible stratification in the mixture. In the filling process, the method of combining layered laying and light vibration-assisted compaction is adopted to ensure that the thickness of each layer is consistent, so as to effectively reduce the non-uniformity of density distribution. (2) Grouting slurry  
In the transparent soil grouting test, the slurry displaces the pore-filling mineral oil under pressure. However, water released from the slurry can alter the refractive index of the transparent soil, thereby affecting the accuracy of visual monitoring of the slurry front movement. Therefore, in this model test, a stable slurry with a low water separation rate was selected as the grouting material ([PERSON] et al., 1991) [16]. The mass ratio of the stable slurry components was water/cement/superplasticizer/bentonite = 0.7:1:0.005:0.015, with a measured water separation rate of 3% after 2 h.
###### Abstract  
Fractional delay-Doppler (DD) channel estimation in orthogonal time-frequency space (OTFS) systems poses a significant challenge considering the severe effects of inter-path interference (IPI). To this end, several algorithms have been extensively explored in the literature for accurate low-complexity channel estimation in both integer and fractional DD scenarios. In this work, we develop a variant of the state-of-the-art delay-Doppler inter-path interference cancellation (DDIPIC) algorithm that progressively cancels the IPI as estimates are obtained. The key advantage of the proposed approach is that it requires only a final refinement procedure reducing the complexity of the algorithm. Specifically, the time difference in latency between the proposed approach and the DDIPIC algorithm is almost proportional to the square of the number of estimated paths. Numerical results show that the proposed algorithm outperforms the other channel estimation schemes achieving lower normalized mean square error (NMSE) and bit error rate (BER).  
OTFS; channel estimation; (DD) delay-Doppler domain; fractional parameter estimation; inter-path interference 202424414. [[https://doi.org/10.3390/s24134414](https://doi.org/10.3390/s24134414)]([https://doi.org/10.3390/s24134414](https://doi.org/10.3390/s24134414))
## 4 Evaluation Methodology
### Experiment Results of Feature Selection with Binary Decoding
#### 4.5.2 Result of Detection Rate and F1-Measure, ETS/CSI/PAG/ACC  
The detection rate and F1-measure for this experiment are given in Table 6, and are expressed as a graph in Figure 13. This experiment showed, as in the previous experiments, that the proposed algorithm resulted in a better detection rate performance and F1-measure than wrapper-DR. The performance difference in F1-measure was somewhat larger than that of the detection rate, with values of 0.000876 and 0.000402, respectively. The proposed algorithm showed better performance overall in binary decoding.  
Figure 11: ETS/CSI/PAG/ACC result in binary: Proposed vs. PCA.  
Figure 12: Fitness curve of proposed algorithm in binary decoding.  
To objectively compare the model performance, published research datasets should be used. In the experimental dataset for research, there is a part where the data for each normal and attack class is biased; therefore, in general, the performance index of the measure of the classification model is already high. However, the proposed method attempted to obtain a more optimized feature set by reflecting the error rate, and the experimental results are considered appropriate for verification. Better results are expected for multi-object classification or less biased datasets.
# Current Sensorless MPPT Control for PV Systems Based on Robust Observer
###### Abstract  
Photovoltaic (PV) systems are among the most used alternatives for electrical power generation from renewable sources. To ensure that PV systems make the most of the available solar energy, maximum power point tracking (MPPT) schemes must be implemented, which usually require voltage and current sensors to track the PV power. This paper presents the design of a robust observer using the Attractive Ellipsoid Method to achieve a precise estimation of PV current under parametric uncertainty and output perturbations. The application of such an observer enables the PV generation system to operate in a current sensorless mode, which reduces the overall cost of the system and enhances its reliability. The convergence of the observer is guaranteed by solving an optimization problem which generates the optimal gains using Linear Matrix Inequalities (LMI). To prove the effectiveness of the proposed sensorless scheme, simulations are performed in Matlab under test profiles based on the EN50530 standard and parameter uncertainty conditions, obtaining an accurate estimation which is used for MPPT operation.  
PV system; current sensorless operation; boost converter; observer; maximum power point tracking
## 2 Materials and Methods
### Experimental Analysis Methods  
#### 2.2.1 Compressive Strength Analysis  
The values of the compressive strength of the experimental samples were measured using a mechanical tester (ZCYA-W300C, Jinan Xinghuo testing machine Co., Ltd., Jinan, China). For each group, six cubical samples (50.8 mm \(\times\) 50.8 mm \(\times\) 50.8 mm) were tested and then the average of the compressive strength of each group was used as the final test result to ensure the accuracy of the test results.  
The simulated compressive strength was calculated through the HYMOSTRUC3D model. [PERSON] et al. [47; 48] concluded that the effective contact area (A\({}_{\text{EC}}\)) between particles is the main parameter to determine the bearing capacity of a cementitious material. In the HYMOSTRUC3D model, A\({}_{\text{EC}}\) is defined as the contact area in the direction normal to the direction of the applied load. Taking the load applied in the y direction as an example, the particle contact relationship can be divided into four cases, as shown in Figure 3. A\({}_{\text{EC-y}}\) is the sum of the effective contact area of all particles in the representative element volume (REV) in the y direction, A\({}_{\text{EC-y}}\) = \(\sum_{i=1}^{n}A_{\text{EC-y,i}}\). The total effective contact area A\({}_{\text{EC}}\) is defined as the average value of A\({}_{\text{EC}}\) in x, y, and z directions, that is, A\({}_{\text{EC}}\) = (A\({}_{\text{EC-x}}\) + A\({}_{\text{EC-y}}\) + AE\({}_{\text{C-z}}\))/3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Composition** & **Action** & **1** & **2** & **3** \\ \hline _w/c_ ratio & & 0.40 & 0.44 & 0.50 \\ defoaming agent & eliminate aeration & 0.5 & 0.5 & 0.5 \\ antifiltrating agent & reduce cement slurry & 6 & 6 & 6 \\  & filtration & 100 & 100 & 100 \\ GOC & sets the cement slurry & 100 & 100 & 100 \\ \hline \hline \end{tabular} All components in % by mass of cement.  
\end{table}
Table 2: Selected compositions of cement slurries.  
Figure 3: Effective contact area between different cement particles, modified from references [47; 48]: (**a**) two particles interconnected in a plane perpendicular to the y-axis; (**b**) two particles not interconnected in a plane perpendicular to the y-axis; (**c**) three or more particles interconnected; (**d**) the larger particle O\({}_{1}\) contained the smaller particle O\({}_{2}\) completely.
## 5 Research on Slope Treatment Plan
### Evaluation of Stability of the Slope After Treatment  
The slope stability under two water level scenarios, \(-50\) m and \(+50\) m, after implementation of the backfilling scheme, is discussed herein. The simulation results of the slope stability after treatment are shown in the corresponding figures. Although the backfilled body was included in the numerical simulations, to better evaluate the effect of the internal benching measures on slope stability, the backfill is rendered semi-transparent in the visualization. This allows for clearer observation of changes in the stability of the original slope beneath the backfilled area.  
The simulation results after backfilling are shown in Figure 9. The comparison results before and after backfilling show that the backfilling scheme significantly improves the stability of the water side slopes. Before backfilling, the minimum safety factors (MSFs) of the north slopes were 0.85 and 0.78 at water depths of \(-50\) m and \(50\) m, respectively, which showed high landslide risks. After backfilling, the minimum safety coefficients increased to 1.05 and 1.02, respectively, indicating that backfilling effectively improved the slope stability. In addition, the application of backfill material significantly reduced the erosion of the original rock mass by water and the accumulation of pore water pressure, only the slope rock mass near the water level was affected by water erosion, and the local instability of the shallow surface layer appeared, but the range of its influence was small. Compared with the area before backfilling, the unstable area in the eastern part of the north slope was reduced by about 66%.  
Figure 8: Water storage schemes with backfilling treatment: (**a**) backfilling with water level at \(-50\) m; (**b**) backfilling with water level at \(+50\) m.  
Further analysis of water storage to \(-50\) m and \(50\) m shows that the unstable range of the slope expands with the increase in water level. When the water storage reaches \(-50\) m, the eastern part of the north slope becomes unstable; while when the water reaches \(50\) m, not only is the eastern part unstable, but also the western part starts to appear unstable area. Combined with the significant increase in the safety factor after backfilling and the simulation results, it is recommended that the maximum storage level be limited to less than \(50\) m. This water level limit can effectively prevent the sidewalls from becoming unstable. This water level limit can effectively avoid the further expansion of the unstable area of the slope and ensure the long-term stability and safety of the slope.
# Design Procedure of a Mono-Anchor Head for 2360 MPa High-Strength Steel Strand
## 4 Finite Element Modeling of Mono-Anchor Head
### Material Test of Steel for Mono-Anchor Head  
Conventionally, the material used for anchor heads is C45 [18], which has a yield strength of 340 MPa; stress in the local region of the anchor head will inevitably exceed the yield strength. Therefore, in this study, a carburized 42 CrMo4 [19] anchor head was adopted to increase the yield strength and reduce inelastic deformation during tensioning  
Figure 5: Load-displacement curve of anchorage system.  
Figure 6: Hoop strain of mono-anchor head.  
of strands. Tensile tests were conducted on non-carburized (H0) and 1 mm-deep carburized specimens (H1) in an effort to understand the effect of carburization. During the carburizing process, the surface is hardened by the carbon penetrating into a surface layer, and the carbon content decreases as the depth increases from the surface, resulting in lower yield strength and hardness. In order to consider the hardened layer with higher strength in FEM modeling, the mechanical properties for each layer should be defined. In this study, hardness tests for H0 and H1 were conducted, and correlation between the strength and hardness was used to indirectly identify the material properties in each layer of heat-treated anchor heads.
# Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique
## 2 Materials and Methods  
### Study Site  
The study used a commercial site of 56.4 ha (Figure 1), composed of four fields, located in the municipality of Botucatu, Sao Paulo, Brazil (22\({}^{\circ}\)41\({}^{\prime}\)42.8\({}^{\prime\prime}\) S; 48\({}^{\circ}\)16\({}^{\prime}\)54.0\({}^{\prime\prime}\) W; 480 m) during the 2017/2018 and 2019/2020 sugarcane growing seasons. The climate in the study region is mesothermal, Cwa--humid subtropical, which includes drought in the winter (from June to September) and rain from November to April. The average annual rainfall in the municipality is 1433 mm. The air relative humidity is 71% with an annual average temperature of 23 \({}^{\circ}\)C. All fields of the study site had the same variety of sugarcane, SP83-2847 (4 th ratoon), planted with a row spacing of 1.5 m in Argisol [33], and it was mechanically harvested in the late season (wet time of the year-October and November). The previous harvesting dates (10/14/2017 and 11/11/2018) were considered as a reference to determine the days after cutting (DAC) associated with the date of the orbital images and related to the phenological crop stage.
# HSVNet: Reconstructing HDR Image from a Single Exposure LDR Image with CNN
## 7 Conclusions  
It is challenging to reconstruct an HDR image from a single random exposure LDR image. To solve this problem, we propose an end-to-end model, HSVNet, which is a U-net-based network. The main difference from previous HDR reconstruction models is using HSV information of images to allow the network to learn the relationships between saturated regions and colors. We develop a customized loss function that is suitable for HSV color space based on characteristics of Hue space to produce satisfying optimal results for human eyes. By applying masks created from \(S\) and \(V\) information, we restrain the impact of \(H\) on saturated regions. The performance quality is proven through comparisons to other existing inverse tone mapping models after experiments.  
Conceptualization, M.J.L., C.-h.R. and C.H.L.; methodology, M.J.L. and C.-h.R.; software, M.J.L.; validation, M.J.L., C.-h.R. and C.H.L.; formal analysis, M.J.L.; investigation, M.J.L.; resources, M.J.L.; writing--original draft preparation, M.J.L.; writing--review and editing, C.H.L.; visualization, M.J.L.; project administration, C.H.L. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the National Research Foundation of Korea (NRF) funded by the Ministry of Education grant number NRF-2013R1A1A2060582.  
Not applicable.  
The authors declare no conflict of interest.
# Effects of Central Cut Width on the Dynamical Characteristics of Box Assembly Structure
## 3 Effect of the Central Cut on the Box Assembly Characteristics: Computational Modeling
#### 4.3.2 Harmonic Vibration Testing Investigating Results from the X-Direction
The Y-direction quality factors' plots show that as the input excitation level increases, the quality factor decreases, as shown in Figure 13a,b. Since the quality factor is decreasing, the nonlinear trend has a higher effect as the excitation level increases. This means that the nonlinear damping has a higher effect as the excitation level increases. The outlier is the 0.25\" cut system in the side accelerometer Y plot. The trend is not the same as the others, instead decreasing and then increasing back at the higher excitations. One of the possible reasons is the presence of several harmonics in the time histories of the experimental data and hence, the possible uncertainties in the estimation of the amplitudes of oscillation at the quality factor's corresponding frequencies. It should be mentioned that efforts are made during experiments to ensure that all the joints are torqued the same before each test, but some uncertainties from preload loss could be credited to the damping in each system. It is possible that the torque of the joints changed throughout each test, which cannot be controlled without disturbing each experiment. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \multicolumn{2}{c}{**0.5” Cut (Hz)**} & \multicolumn{2}{c}{**0.25” Cut (Hz)**} & \multicolumn{2}{c}{**0.1” Cut (Hz)**} & \multicolumn{2}{c}{**No Cut (Hz)**} \\ \cline{2-7}  & **L. Peak** & **R Peak** & **L Peak** & **R Peak** & **L Peak** & **R Peak** & **Peak** \\ \hline
**50 mV** & 109.2 & 114.8 & 104.9 & 113.4 & 106.4 & 110.5 & 166.3 \\
**100 mV** & 109.1 & 114.3 & 104.8 & 113.3 & 106.4 & 109.9 & 165.8 \\ \hline
**125 mV** & 109.1 & 114.3 & 104.8 & 113.5 & 106.4 & 109.8 & 165.4 \\
**150 mV** & 109.1 & 114 & 104.7 & 113.1 & 106.2 & 109.3 & 164.6 \\
**\% Change** & 0.09\% & 0.70\% & 0.19\% & 0.26\% & 0.19\% & 1.09\% & 1.02\% \\ \hline \end{tabular}
\end{table} 
Table 11: Tabulated dominant frequencies from left and right peaks of mode 1 found through harmonic vibration testing from top accelerometer. Second, in order to directly explore the effects of the central cut width on the dynamical response of the box assembly, an investigation into individual cut widths comparing the input excitations was also conducted, as depicted in Figure 14. Inspecting this figure's plots, it is clear that a little change in the central cut width results in a significant variation in the box assembly's responses. In fact, a decrease in the central cut width results in a linear softening in the first modes of vibration, as shown in Table 9. In addition, the broadband range between the two resonant frequencies varies depending on the cut width. As for the amplitudes of oscillations and the coupling/interaction between the two modes, they are strongly dependent on the cut width.