esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design Optimization
### Performance Metrics & Design Criteria  
This section presents a suite of metrics \(\{\mathcal{M}_{l}\}\) to be minimized in order to accomplish specific design goals towards fluidic LSI. Each of these metrics \(\{\mathcal{M}_{l}\}\) depends on several (experimental) parameters \(\{\gamma_{k}\}\) and their standard deviations \(\{\Delta\gamma_{k}\}\), i.e., \(\mathcal{M}_{l}=\mathcal{M}_{l}(\{\gamma_{k},\Delta\gamma_{k}\})\). By convention, these metrics \(\{\mathcal{M}_{l}\}\) are defined here to vanish towards the targeted design goal, and approach unity for its largest (practical) value, i.e., \(0\leq\mathcal{M}_{l}\leq 1\). Design optimization of a given structure \(\Gamma_{i}\) according to a metric \(\mathcal{M}_{l}\) thus comes down to their minimization in multidimensional space constituted by the variables \(\{\gamma_{k},\Delta\gamma_{k}\}\).  
Geometries \(\Gamma_{i}\) derived in isolation, i.e., from reducing a single metric \(\mathcal{M}_{l}\), tend to have very questionable benefit towards LSI. For example, a minimum spatial footprint may be associated with a band width \(\Omega_{i}\pm M\cdot\Delta\Omega_{i}\) of the same magnitude as the entire \(\omega\)-corridor, thus detrimentally undermining multiplexing. In the examples presented, we commonly optimize the metrics \(\mathcal{M}_{l}\) while concurrently keeping the standard deviation \(\Delta\Omega_{i}\) at bay.  
In theory, the entire (available) multiparameter space \(\{\gamma_{k}\}\) must be probed for a given \(\{\Delta\gamma_{k}\}\) to spot the absolute minimum of \(\mathcal{M}_{l}\), which would commonly require enormous computational power. Efficient numerical algorithms rather seek local minima by following the steepest local (downward) slope. By choosing, either statistically or via a clever, educated guess, a favorable starting point, a \(\mathcal{M}_{l}\) may still be identified. In practice, convergence of the numerical methods within reasonable boundaries in multidimensional parameter space \(\{\gamma_{k},\Delta\gamma_{k}\}\) involves iterative \"hunting\" of promising initial locations to the program. Therefore, the subsequently calculated structures \(\Gamma\) might not represent absolute minima of the target metric(s) \(\mathcal{M}_{l}\); they rather provide instructive design guidelines that can be further refined.  
Component- and device-level optimization of a valving structure \(\Gamma\) usually needs to consolidate an array of possible contradictory demands, e.g., on minimum field strength \(f_{\omega}\), (8), radially outbound staggering of the LUO-valve for serial processing, non-overlapping stacking bands \(\{\Omega_{i}\pm M\cdot\Delta\Omega_{i}\}\) (7), and the surface area required for holding volumes of compression chambers \(V_{\text{C}}\), liquid samples and reagents \(\{U_{0,i}\}\) imposed by the assay protocol. We discuss the concept of multi-parameter optimization in the final subsection.
###### Abstract  
The study compares the performance parameters of an Automated Guided Vehicle (AGV) equipped with optical navigation, focusing on two solutions: one utilizing reflective optocouplers and the other employing a camera. These components, commonly used in AGV optical navigation systems, differ in factors such as cost and the sophistication of control methods. The primary objective of the research was to evaluate the performance criteria of the analyzed optical navigation methods, with particular attention paid to electricity consumption, power profiles during specific transit tasks, and total transit time. The analysis also investigated two potential installation locations for the reflective optocouplers and the camera on the vehicle. The results indicate that the camera-based optical navigation method is more efficient. Specifically, the average energy consumption was approximately 26% lower when using the camera compared to the reflective optocouplers. Furthermore, the study revealed that the location of the camera had minimal influence on the vehicle's energy consumption, whereas the location of the reflective optocouplers significantly affected energy usage.  
optical navigation; AGV; reflective optocouplers; cameras; energy consumption Article
## 1 Introduction
Although various methods have been proposed to address the issue of low FVC accuracy under shadow conditions, there are still areas that need improvement. First, the computational efficiency of these methods still needs to be enhanced. Methods using deep learning and machine learning techniques are extremely time-consuming. Moreover, the accuracy of these methods is closely tied to the quality of data samples, which means that they are significantly influenced by human factors. Second, there is a lack of solutions for FVC calculation under complex shadow conditions. Previous algorithms such as SHAR-LABFVC, methods based on HDR images, and algorithms incorporating polarized images essentially aim to supplement pixel information in shadowed areas to improve FVC accuracy. These studies all indicate that reconstructing shadow pixel information is an effective and feasible solution. However, they have not addressed complex shadow situations more specifically. For instance, the SHAR-LABFVC algorithm enhances shadow pixels based on the overall light distribution of the entire image. To address the issue of shadows affecting the accurate recording of pixel information and subsequently reducing the FVC accuracy, a simple and effective solution is proposed in this study. The approach is based on a pixel-level classification strategy using a double-exposure algorithm with the ability to process images with different shadow levels. The double-exposure fusion data are derived from a normal-exposure image and a specific overexposed image. By increasing the exposure compensation during shooting, shadow regions are enhanced to show more vegetation details. Moreover, sunlit vegetation may be enhanced by overexposure. Therefore, a fusion operation is performed between the shadow pixels of the normal-exposure image and the corresponding pixels of the overexposed image. This process yields a fused double-exposure image containing a strengthened shadow region and a normal sunlit region. In the research process, multiple records of overexposed images at different exposure levels were captured. Various vegetation indices were assessed to identify the most suitable candidates for the double-exposure method and the optimal overexposed images under shadow conditions. This article is divided into four sections (materials and methods, experimental results and analysis, discussion, and conclusion) elaborating on the double-exposure method.
## 3 Results
### Total Underwater Sound Absorption  
Figure 4a shows the total absorption for August averaged over 21 years from 2000 to 2021 in the East Sea. Total absorption was higher in the deeper regions, but the variation in the coastal area was also influenced by temperature and salinity. Figure 4b reveals a seasonal variation of the standard deviation of the total absorption at 15 coastal points (green stars marked in Figure 4a). The standard deviation of the total absorption coefficient was not affected by the depth. These results suggest that, even when measurements are taken in the same region during the same season, sound absorption coefficients can vary. Higher absorption deviation values generally occurred during summer (August) and winter (December), primarily due to greater fluctuations in water temperature. Conversely, lower deviation values of the total absorption were observed in spring (April) and fall (October).  
In particular, the 15 points with the most pronounced seasonal variations (green stars) showed lower total absorption due to shallower depths. However, similar to the variations in temperature, salinity, and sound velocity shown in Figure 3, higher changes in sound absorption were observed at these points. The region with the greatest deviation exhibited a difference of approximately 14 dB. These seasonal and regional variations highlight the importance of considering such fluctuations when calibrating MBES data, as they can significantly affect sound absorption.  
Figure 4: (**a**) The total absorption in August averaged from 2000 to 2021 in the East Sea. (**b**) Graphs showing standard deviation of total absorption at the 15 selected points in the southern coastal region of the East Sea during one month (August).  
To enhance the comprehension of the trends discerned in Figure 4b, the acoustic absorption fluctuations for seasons apart from summer are incorporated in Supplementary Figures S6-S8.
## 2 Material and Methods
### Statistical Evaluation Procedures  
All measurements per condition were repeated three times, which were then averaged for evaluation. In order to evaluate the parameters statistically, the software program BIAS (Version 11.8; Epsilon-Verlag, Darmstadt, Germany) was used. The data were first tested for normal distribution using the Kolmogorov-Smirnov adjustment test. As the collected measurement data were not normally distributed, non-parametric tests, such as the Wilcoxon matched-pairs test to compare the two measurement conditions or the Wilcoxon-Man-Whitney U test to compare the men and women, were used. Subsequently, the data were subjected to Bonferroni-Holm correction. The significance level was 5%.
# Dynamic Routings in Satellite Networks: An Overview
## 4 Existing Works on Dynamic Satellite Routings
### Dynamic Routings in the Single-Layer Satellite Networks
#### 4.1.2 Virtual Nodes
The employed algorithm routes reply packets in the path discovery and a local repair strategy to quickly repair broken paths with low overhead, improving the real-time network performance. Compared to location-assisted on-demand routing (LAOR) [42], snapshot routing protocols, and source retransmission routing in case of link failure, DODR maintains low packet loss and end-to-end delay performance even in link failure [43].
## 3 Experimental Investigation and Results
### Modal Test and Model Validation
Figure 7: Motion snapshot from relative maximum deviation behavior of EMA: (**a**) at 13.2 Hz; (**b**) at 23.8 Hz. The natural frequencies of the CBF were obtained with a range between 0 and 25 Hz. The first six modes were examined. It can be seen from Figures 5-8 that there was a good agreement between the mode shapes in FEA and EMA. Figure 8 illustrates the motion snapshot of the CBF. Identified frequencies from the FEA and corresponding mode shapes obtained from EMA are summarized in Table 2. It obtained similar results, with differences ranging from 0.55 to 1.87 for the first six modes. There were some differences between the results obtained from the FEA and EMA. The data analysis yielded a correlation coefficient of R = 0.9875, a coefficient of determination (r-square) of 0.9752, and a standard error of 1.1148. These statistical measures assess the relationship between a simulated model using FEA as the independent variable and the  
It is thought that these differences resulted from the uncertainties in the structural steel weldment (boundary conditions) of types and material properties of steel. In the FEA, the properties are selected as a density 2.45 g/cm\({}^{3}\) and a Young's modulus of 88.7 GPa, respectively. In the comparison of natural frequencies in the ANSYS modal and experimental modal, the dominant mode was a lateral bending which occurred in the 4-5 Hz range with maximum translation experienced in alternate between front or rear of the CBF. The second to fifth modes were a torsion in the 8-15 Hz range and a 2-nodal-point lateral bending in the 21-24 Hz range in the sixth mode. Thus, the CBF may experience structural resonance at an idling condition. An increase in mass will reduce the natural frequency, while an increase in the stiffness will increase the natural frequency. Now, the CAD model and material properties can be used to evaluate other analytical models if the mechanical system meets its specifications.
## 4 Analyzing the Friction Impact on the Stiffness of the Laminated Membrane Coupling
### Analyzing the Influence of the Bolt Preload on the Stiffness of the Laminated Membrane Coupling  
In this section, the influence of the bolt preload on the stiffness of laminated membrane coupling with constant coupling dimensions is studied. In order to facilitate the comparison, only stiffness values obtained from the last sub-step of the second load step is taken for the analysis. The torque, axial displacement, radial displacement, and the rotational angle is set to 3000 N\(\cdot\)m, 1 mm, 0.5 mm and 0.5\({}^{\circ}\). It should be indicated that since the radial and angular stiffness along X- and Z-directions have negligible differences, they are analyzed along the X-direction.  
For the foregoing loads, the bolt preload is set to 5 kN, 10 kN, 15 kN and 20 kN, respectively, and the other boundary conditions are remained unchanged.  
Moreover, friction coefficient is set to 0.1, 0.2 and 0.3, respectively, to investigate the influence of the bolt preload on various stiffness settings.  
Figures 18-21 show that when the friction coefficient is set to 0.1, 0.2 and 0.3, increasing the bolt preload will increase torsional, axial, radial and angular stiffness of the laminated membrane coupling. Furthermore, when the preload increases from 5 kN to 20 kN, the variation of torsional, axial, radial, and angular stiffness are 1.6%, 66%, 1.1% and 5.2%, respectively. It is found that the bolt preload has the greatest impact on the axial stiffness, which is far greater than other stiffness.  
Figure 19: Influence of the bolt preload on the axial stiffness.  
Figure 18: Influence of the bolt preload on the torsional stiffness.
# Numerical and Experimental Investigation of Guided Wave Propagation in a Multi-Wire Cable
## 2 UGW Propagation in Multi-Wire Waveguides
### Energy Model of Multi-Wire Waveguides  
There are many limitations of modeling accurate UGW propagation of multi-wire structures taking into account the effect of mechanical contact, such as varying contact stress among the adjacent wires, wave mode conversion, and the CAN effect. In order to simplify the propagation model of an UGW in a multi-wire cable, a model that describes the transfer of the wave between an active and a passive wire from an energy perspective was used to analyze the propagation behavior of an UGW. A multi-wire system was composed of mechanically coupled wires. They were surrounded by a spring array along the wire axis [17; 18]. Thus, the wave energy of the \(i\)th wire of a cable with \(n\) wires can be mathematically described by a second-order nonlinear differential equation:  
\[\frac{\mathrm{d}^{2}E_{i}(z)}{\mathrm{d}z^{2}}=-d_{m}\frac{\mathrm{d}E_{i}(z) }{\mathrm{d}z}-c\sqrt{E_{i}(z)}\sum_{j=1}^{n}b_{ij}(\sqrt{E_{i}(z)}-\sqrt{E_{ j}(z)}) \tag{1}\]  
where \(E_{i}(z)\) is the average energy transfer from all \(n\) wires to the \(i\)th wire, \(d_{m}\) is the damping coefficient caused by the material, \(c\) is the mechanical coupling coefficient, and a binary constant \(b_{ij}\) indicates whether the wires \(i\) and \(j\) are in contact with each other, expressed as:  
\[b_{ij}=\left\{\begin{array}{ll}1&\text{in contact}\\ 0&\text{otherwise}\end{array}\right.. \tag{2}\]  
For a two-wire model, the equation can be solved using the ode-45 built-in algorithm in Matlab. The coupling coefficient, the material damping coefficient, and the initial conditions \(E_{i}(0)=E_{i,0}\) and \(\frac{dE_{i}(0)}{\mathrm{d}Z}=E_{i,0}^{\prime}\) were obtained using the method of least squares fitting to process the transient finite element (FE) simulation data shown in Section 3.2. The results are shown in Figure 4. With the propagation of the guided wave, the wave energy in the active wire was transferred to the passive wire. The wave energy in the two wires gradually decayed after reaching a balance, which was caused by material damping.  
Figure 3: A schematic drawing of contact acoustic nonlinearity (CAN) at the rough contact interface of a pair of coupled cylindrical waveguides.
## 1 Introduction  
Consolidation behavior is a complex phenomenon in clays and is of great importance to most construction activities. The accuracy of consolidation calculations is a key point in the geotechnical field. [PERSON] developed the classical consolidation theory for solving consolidation problems [1]. However, this theory is based on many assumptions, such as constant loading and a constant coefficient of consolidation. In particular, the constant consolidation coefficient will lead to incorrect results in the multilayered soil system [2]. To improve the calculation accuracy, many modifications of the assumptions have been proposed. Past studies mainly investigated the variations of material parameters, such as the permeability and the compressibility [3; 4; 5; 6; 7; 8; 9; 10], and many advances related to varying loading, which better reflect real constructions, were obtained [11; 12; 13; 14; 15]. As a simplification of inhomogeneity along depth, multilayered soil was studied [16; 17; 18; 19; 20]. Other important calculation factors, such as drainage boundary conditions [21; 22; 23; 24] and the non-Darcy fluid theory [25; 26; 27; 28], were also considered for more reasonable calculations. Although some of these early studies obtained analytical solutions, a majority of them have not been analytically solved. With the development of numerical methods, complex consolidation equations can be solved efficiently, thereby allowing many modifications to be incorporated into the consolidation calculations. The finite element method [2; 7; 29; 30] and the finite difference method [4; 27; 31; 32; 33] are the two most commonly used methods. Moreover, other effective numerical methods were also adopted [34; 35; 36; 37].  
However, the coefficient of consolidation used in calculation is usually considered as a constant value in aforementioned studies (i.e., a constant ratio of permeability to compressibility), and such a simplification may not be reasonable in some situations. In particular, there is little information available about the solution-dependent behavior of the compressibility, which significantly affects the coefficient of consolidation. Compression and recompression indexes, which are used in calculations of settlement, are used herein for evaluating the nonlinear compressibility. Subsequently, the nonlinear compressibility and a nonlinear permeability are combined to describe the nonlinear consolidation. Using the finite element method, the discrete equation in space and time domains is derived and the corresponding program with consideration of the time-dependent load is developed. A case history for verifying the proposed method and three comparative cases for proving the significance of the separation of overconsolidation and normal consolidation are conducted subsequently.
## 3 Results and Discussion
### QRLSTM PV Interval Prediction Simulation
It can be seen from Table 2 that at the 95% confidence level, the interval coverage rate of QRLSTM is only about 95%, and full coverage of the interval is not achieved; at other confidence levels, it is only slightly larger than the corresponding confidence level. For the QRNN method, the interval coverage is even lower than the corresponding confidence level. At the same time, the average width of the interval of the QRLSTM method is smaller than that of the QRNN method at any confidence level. In summary, Table 2 shows that the prediction effect of the QRLSTM method is better than that of the QRNN method on similar-day 2. The QRLSTM method achieves a relatively high interval coverage in a relatively narrow interval. It can be seen from Table 3 that the prediction effects of the QRLSTM and QRNN methods are not ideal on similar-day 3. The prediction effect of the QRNN method is significantly reduced, and the coverage of each prediction interval cannot reach the corresponding confidence level. However, the QRLSTM method can still achieve 95% coverage at the 95% confidence level, and the coverage under other confidence levels can also remain similar to the confidence level. Therefore, on similar-day 3, the prediction effect of the QRLSTM method is better than that of the QRNN method. To sum up, Tables 1-3 all reflect the excellent prediction effect of the QRLSTM method numerically.
## 3 Field Data Acquisition
### Field Sites  
A total of four field sites were selected and used for each agricultural operation. Sites 1, 2, 3, and 4 were located in Seosan (36\({}^{\circ}\)46\({}^{\prime}\)43.8\({}^{\prime\prime}\) N 126\({}^{\circ}\)33\({}^{\prime}\)38.3\({}^{\prime\prime}\) E), Dangjin (36\({}^{\circ}\)55\({}^{\prime}\)50.6\({}^{\prime\prime}\) N 126\({}^{\circ}\)38\({}^{\prime}\)00.3\({}^{\prime\prime}\) E), Buan (35\({}^{\circ}\)46\({}^{\prime}\)43.9\({}^{\prime\prime}\) N 126\({}^{\circ}\)45\({}^{\prime}\)00.0\({}^{\prime\prime}\) E), and Jeonju (35\({}^{\circ}\)53\({}^{\prime}\)04.5\({}^{\prime\prime}\) N 126\({}^{\circ}\)59\({}^{\prime}\)53.9\({}^{\prime\prime}\) E) in Korea, respectively.  
The size of sites 1, 2, and 4 were all 4000 m\({}^{2}\) (\(40\times 100\) m), while the size of site 3 was 6000 m\({}^{2}\) (\(60\) m \(\times\) 100 m). The physical properties of soil for each field site, such as soil texture, moisture content, and cone index, are provided in Table 4. The soil texture of sites 1, 2, and 3 was Loam, while the soil texture of site 4 was clay loam. These results are consistent with the results of previous studies, that most Korean paddy soils are loam [45]. The soil moisture content of sites 1, 2, 3, and 4 were 27%, 32%, 42%, and 37%, respectively. The cone index can be expressed as an indicator of soil strength, which is different for each depth.
## 2 Materials and Methods
### SW Support Architecture  
In order to understand how the individual sensor-, actuator-, and computing hardware nodes visualized in Figure 1 can mutually interact to build a single satellite-border trans-scending cooperating system, the SW architecture that has been used, albeit not required from theoretical point of view, is briefly introduced.  
As mentioned previously, the fractionated architecture consists of hardware nodes which are very heterogeneous in nature, both regarding their primary use/objective and their hardware composition. Sensor and actuator nodes for example usually have very limited processing capability due to their primary task of reading and forwarding sensor data or, in case of an actuator, receiving input commands and processing it on the connected actuator hardware. This is in contrast to the computing nodes where sensor-data filtering, control command computation and many more computationally intensive tasks require hardware with strong computing capabilities. In addition, the means by which two nodes are connected in the node network might differ. Nodes might have a wired or wireless connection to the node network. Two nodes might communicate directly or need to rely on intermediary nodes that relay the exchanged data.  
To account for the aforementioned peculiarities of such a multi-spacecraft node network, we rely on the RODOS middleware to provide an interface that abstracts the varying hardware and means of communication of the nodes. RODOS is specifically designed as software support infrastructure for heterogeneous distributed systems and serves a similar purpose as an operating system on PC hardware does. It runs on a multitude of hardware platforms ranging from small microcontrollers to PC grade hardware. RODOS takes care of multi-tasking, inter-process and inter-node communication via the publisher/subscriber principle, has a solid hardware-abstraction-layer and guarantees a robust information exchange between two nodes via flood-routing if they are reachable by any means in the network. Further information on RODOS and its architecture can be found in [5; 35] and details on the robust data exchange, hot-plug capability and cooperation advantages of nodes in a spacecraft context are elaborated in [26; 36].