esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 An Architecture for Self-Organization
### Self-Configuration  
This module is responsible for setting the parameters of the routing protocols OLSR and AODV based on the information available to the NS-Agent. The parameters are defined to improve the computation of routes in the network.  
The chief factors influencing the parameters are: the size of the network, the mobility rate, the transmission distance, the strength of the transmission signal, and the network overhead, jitter, and delay.  
The system administrator has to assist the configuration of the parameters to avoid parameter set-ups that compromise the network performance, specially so in complex networks. Typically, the system administrator does not tune the parameters for lack of knowledge or time, selecting default values that in many situations influence network performance drastically and potentially create security breaches. Therefore, it is desirable to have a self-configuration module for obvious reasons: reduced intervention from the part of the system administrator and consequently a reduction in installation and maintenance costs. Self-configuration relieves the system administrator from these tedious tasks who can concentrate his efforts on more critical decisions. To that end, Algorithm.1 has been designed to configure the network parameters based on three different network sizes: small, medium, and large. That is, the self-configuration module selects a suit of routing protocol parameters that have been optimized by the self-optimization module to improve network performance depending on the size of the network. The identification of the network size is based on the information received from the NS-Agent, this way allowing the network to gracefully adapt to the prevailing conditions as new clients arrive.  
The idea behind the algorithm is to set the routing protocol parameters for OLSR and AODV to the predefined values depending on the network size. Therefore, the algorithm maintains a list of the client nodes currently connected to the network, which can be obtained from the NS-Agent at any given instant. Once the self-configuration module is up and running, the system administrator not longer needs to configure routing protocol parameters manually to optimize network performance in response to dynamic changes in network size (_i.e._, number of client nodes).  
The boundaries between the three network sizes were defined according to works in the literature [5, 28]. Namely, a network is said to be small if it has from 1 to 30 nodes, medium if it has more than 30 but not more than 70 nodes, and large if it has more than 70 and does not exceed 100 nodes. Evidently, the algorithm described above can be augmented with other policies for self-configuration that improve network performance at particular locations of the network.
## 3 Results and Discussion
### Optimal Parameters of Member Damping  
The damping value of each member of the original model is treated as undamped. The damping values of driving rod 1, connecting rod 1 and end g in the original model are set according to the 20 groups of data in Table 3. Under the condition that other steps remain unchanged, the amplitude frequency curve of the displacement value of the controllable mechanism welding robot end can be obtained by the simulation calculation. The simulation results demonstrate that the vibration amplitude in the Y direction is the largest. The maximum vibration displacement amplitude of the end harmonic response of the controllable mechanism welding robot actuator occurs at two frequencies: 16 Hz and 25  
As seen in Figures 19 and 20, the damping value of driving rod 1 has the greatest impact on the end displacement value of the controllable mechanism welding robot, whereas the damping value of connecting rod 1 and end G has some impact on the end amplitude displacement value of the actuator.  
Taking the end amplitude displacement value of the controllable mechanism welding robot 0 mm as the optimization target, the optimization results are shown in Figure 21.  
The results shown in Figure 21 are detailed in Table 7.  
The results presented in Figure 21 and Table 7 indicate that if the target end displacement of the actuator of the studied welding robot prototype is 0 mm at 16 Hz and 25 Hz,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Response** & **Fitting Value** & **Fitting Value Error** & **95\% Confidence Interval** & **95\% Prediction Interval** \\ \hline Deformation (16 Hz) & 0.0147 & 0.0433 & (\(-\)0.0833, 0.1127) & (\(-\)0.1025, 0.1319) \\ Deformation (25 Hz) & 0.0169 & 0.0164 & (\(-\)0.0203, 0.0541) & (\(-\)0.0276, 0.0614) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Response optimizer optimization results of member damping design.  
Figure 21: Optimization diagram of member damping design.  
Figure 20: Interaction diagrams of the amplitude displacement at the end of the welding robot: (a) 16 Hz; (b) 18 Hz. (The sign * in the figure means the interaction of two factors).  
the optimal result is obtained when the damping values of driving rod 1, connecting rod 1 and end g are 0.0161, 0.050 and 0.0020, respectively, and the vibration displacement amplitudes of the actuator end are 0.0147 mm and 0.0169 mm, respectively. Compared with the initial amplitude response displacement, the displacement is 0.1615 mm and 0.0309 mm, respectively. The amplitude response displacement is reduced by 91% and 45%, respectively, after optimization.  
The optimized parameters are substituted into the simulation model for calculation, and the maximum displacement amplitude of the actuator end at 16 Hz frequency is 0.018 mm, which is consistent with the optimization result of the response optimization solver.
# Numerical Simulation of Liquid Film Characteristics during Atomization of Aluminum Alloy Powder
## 2 Atomization Model and Basic Theory
### Boundary Conditions and Meshing  
Based on the actual working conditions, the outlet of the tundish is set as the mass inlet boundary, and the mass flow rate is 1.5 kg/min, which is converted into a flow rate of approximately 1.3 m/s. The molten metal temperature of the aluminum alloy is 720 \({}^{\circ}\)C. The rotating wall boundary is adopted for rotating atomization and the temperature wall boundary condition is adopted for the cooling wall with a temperature of 25 \({}^{\circ}\)C. The bottom outlet of the atomization chamber is the pressure outlet, and the pressure is 0 Pa.  
The materials involved in the simulation are nitrogen and liquid aluminum alloy, and the material parameters, including the thermodynamic parameters, are shown in Table 2, where the data were obtained from the software's material database.  
For mesh division, due to the large amount of calculation in the 3D model, and in order to ensure the calculation accuracy and the smoothness of the large amount of computation, the overall grid of the three-dimensional model is a cutting body grid, the mesh size is 4 mm, and the atomization disk, inlet area, and edge area of the atomization disk are mesh-encrypted. The mesh sizes are 0.4 mm, 0.4 mm, and 0.8 mm, respectively. The number of boundary layers on the disk is 6, so as to accurately capture the gas-liquid interface and particles. The number of meshes in the flat disk and the spherical disk is 1.61 million and 1.79 million, respectively. The specific mesh division is shown in Figure 4.
## 1 Introduction
Therefore, although this research makes significant progress in change detection methods, we still believe that future research needs to pay more attention to how to handle the bottlenecks of high-frequency image acquisition and change detection in order to better meet the high demands of land managers for real-time performance and accuracy. We further emphasize this issue in the Introduction Section, clearly point out the crucial role of high-frequency image acquisition in farmland change monitoring and land management, and propose possible directions for future research. This study is structured as follows: Section 1 reviews the theoretical foundations and current research on farmland change detection, highlighting this study's innovations. Section 2 details the proposed network's architecture, key modules, and technical specifics. Section 3 covers the datasets, experimental environment, and parameter settings. Section 4 presents the model's performance and visual analysis. Section 5 discusses the effectiveness of multi-scale feature extraction and attention mechanisms, identifying areas for improvement. Section 6 concludes with a summary of the findings and discusses the model's potential applications and future directions.
# A Cost-Aware Framework for QoS-Based and Energy-Efficient Scheduling in Cloud-Fog Computing
## 6 Service Management Framework
### Problem Formulation: Minimum Cost of QoS and Energy Penalty  
The problem is modeled by analyzing the performance penalty cost of QoS and energy for allocating and serving a fog job \(J_{i}\) across the cloud-fog computing environment, represented by \(\mathcal{C}\) and \(\epsilon\), respectively.
## 2 Related Work
### Imputation Methods  
A major issue in feature selection using an incomplete data set is that the traditional methods become invalid. A number of works have addressed this issue and presented a two-stage method to select features from incomplete data sets, primarily in which the missing values are imputed or deleted.  
A straightforward and simple method of complete case analysis (CCA) was proposed in [14], in which the samples or features containing missing values are deleted. However, this in turn becomes invalid when the proportion of missing instances is high and the total amount is small. Imputation methods are another more intuitive option, and the classic form of this method is mean imputation. The mean imputation method takes the mean value of the observed feature values as the estimated value of the missing values [15]. However, this method reduces the uncertainty of features and the variance. Another imputation method based on statistics is the expectation maximization (EM) imputation method [16], which uses edge distribution of existing data to conduct maximum likelihood estimation method for missing data, which is used to obtain the corresponding imputation value. In recent years, [PERSON] et al. proposed KNN imputation [12], in which the mean value of the nearest neighbor samples was taken as the estimated value of the missing value. An improved k-nearest neighbor imputation method was also proposed to improve the classification accuracy further and achieve a filling effect [17]. [PERSON] et al. proposed and evaluated an iterative imputation method (missForest) based on random forest, with the results showing that missForest could successfully handle missing values, particularly in datasets including different types of variables [18].  
Recently, the use of deep learning models has been explored for missing-value imputation [19; 20; 21; 22]. [PERSON] et al. proposed an imputation model based on deep denoising autoencoders for multiple imputation [19]. A probabilistic framework based on deep generative models for missing value imputation was proposed in [20]. [PERSON] et al. proposed a simple framework for performing approximate maximum likelihood training with an incomplete data set [21].  
However, the results of missing value imputation of unobserved information may be non-useful or even noisy [23]. The reason for this is that there is no ground truth for unobserved information, so the correctness of the imputed information cannot be evaluated.
## 3 Microseismic Evolution Characteristics of Rockburst Formation Process
### Special Types of Rockbursts
#### 3.3.2 Delayed Rockburst
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Strainburst** & **Structure-Type Rockburst** \\ \hline Microseismic energy and rockburst level & For rockbursts with similar microseismic radiation energy, the rockburst level is relatively lower. & The damage range is wide, and the rockburst level is relatively higher. \\ \hline Spatial distribution of microseisms & From dispersion to local concentration. & Concentrated distribution along the structural plane. \\ \hline Microseismic evolution with time & The activity of microseisms increases, and the energy and magnitude increase gradually. & Microseisms are frequent, and there are high-energy microseismic events in the early stage of excavation. \\ \hline Rupture mechanism of microseismic events & The proportion of tension rupture events is dominant. & The proportion of shear failure events is dominant. \\ \hline Rockburst failure characteristics & Rockburst pits are usually arc shaped or V shaped. The exfoliated rock has plate-like or lamellar characteristics. & The pit boundary is affected by the structural planes and is usually larger in depth and range. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of typical characteristics between strainbursts and structure-type rockbursts. creep tests. Their results showed time-dependent failure and significant volume expansion in the last loading stage. With the increase in the confining pressure, the time-dependent behavior decreased significantly. Combined with on-site microseismic monitoring and laboratory tests, [PERSON] et al. [41] proposed that the time effect of rock failure is the time effect of crack propagation. When the failure accumulates to a certain extent, the rock mass becomes unstable and then triggers a delayed rockburst. Overall, research on delayed rockbursts has made some progress, the law of delayed rockbursts and the characteristics of the microseismicity in the formation process have been summarized in detail, and the formation mechanism has been made clear. However, it remains difficult to predict the occurrence time, which should be focused on in future research for an accurate early warning.
## 6 FWA Transport Network Solutions
### Wired Solution
#### 6.2.3 WDM-Based Solution  
The WDM solution helps in reducing the need for a dedicated fiber and is also a good approach in scenarios with limited fiber resources. Besides, the demand for high-capacity and low-latency transmission makes the WDM-based network more relevant for the backhaul/fronthaul links. Moreover, a large amount of DU can be cascaded and supported by the WDM solution. This is because a large number of optical wavelengths can be supported by a single fiber in this approach. At large, WDM-based fronthaul can be categorized into active and passive solutions. In the active approach, other protocols are normally employed in the CPRI traffic encapsulation. Furthermore, it presents network topologies with significant robustness and flexibility. In addition, when optical amplifiers are employed, there can be a considerable extension in the network reach. Besides, the requirement for a power supply at the cell site demarcation point is a distinguishing characteristic of an active approach. In contrast, a passive solution is primarily based on the CPRI links passive multiplexing (MUX)/demultiplexing (DEMUX). Moreover, power supply and battery backup are not mandatory at the demarcation point for effective operation. Nevertheless, active equipment is usually utilized for system monitoring at the CO demarcation point in this solution [384, 386, 387, 370, 388, 47, 371, 389, 47, 372, 380, 47, 47, 373, 381, 4  
the most out of the access infrastructure [386; 388]. Consequently, multiple channels per fiber can be supported and the required fiber resources for an application can be reduced significantly. Moreover, the latency being introduced by the associated optical components is negligible. This can help in fulfilling the CPRI transport specified jitter and latency requirements. As previously mentioned, owing to its passive nature, the power supply is not essential for its operation, resulting in high power efficiency and lower cost solution. Moreover, this scheme is not only an economical solution, but it is also easy to maintain. However, the wireless equipment expenditure implication demands considerable attention. This can be attributed to the required pluggable colored optical interfaces at the CU and DU. Similarly, other issues that demand consideration are the inadequate optical power budget and limited transmission range of a comparatively complex topology like chain network or ring network. This is a result of the accumulated insertion loss produced by multiple passive WDM components. Moreover, apart from the fact that the solution does not offer potentials for robust operations, administration, and maintenance (OAM), also, it does not present the line protection feature. Besides, the requirement for well-defined network demarcation points can also be a limiting factor for its implementation [47; 370; 386; 371; 385].
## 4 Simulations and Results  
In this section, we describe the most relevant aspects of the configuration in our simulations. We include a short description of the operation workflow of the channel and physical layers in a network simulator. Additionally, we describe the traffic application scenario in which we test the different building attenuation models and the simulation settings of the scenarios. This section ends up with an analysis of the results that we obtained from simulations using two different urban areas.  
Figure 5: Pre-computed attenuation file format with its corresponding localization value algorithm used in this work. (**a**) Output format; (**b**) Position computation algorithm.
## 3 Fibre Bragg Grating
### Results of the Characterization of the FBG  
In Figure 6, the results of measurements of three fibre Bragg grating are shown. It shows the comparison between the Bragg wavelength \(\lambda\) which is set by software and the actually measured Bragg wavelength \(\lambda\) which is measured at different points. A step size of 0.1 nm and 0.2 nm was chosen. For the sake of confirmation, reproducibility and accuracy, the latter was measured twice [56; 57]. The curves marked in the legend with V and R represent the ascending (increase) and descending (decrease) in wavelength, respectively. The curves show here a linear dependence between the Bragg-wavelength-difference and the actually set wavelength. When the Bragg wavelength is set to 1538 nm, the deviation is between 0.1 and 0.25 nm. The minimum wavelength difference is reached when the FBG is working at about 1541 nm. If the Bragg wavelength is set to around 1546 nm, the actually measured wavelength is greater than the set one and a negative wavelength difference of up to \(-0.25\) nm can be observed.  
This behaviour could be caused by the control concept of the manufacturer. It is assumed that the fibre Bragg grating is optimized for the production of a working software[58], and a linear approximation around this regime is therefore, provided. Table 1, therefore, provides the parameters of the linearised curves that are shown in Figure 6. The slopes of all the curves are between 0.037 and 0.047. In view of these reasons, for the control of the fibre Bragg gratings in the overall system, this behaviour must be taken into account, which has been discussed in detail with the technical team of the vendor.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Step Size** & **Direction** & **a** & **b** & **Mean Squared Error (MSE)** \\ \hline
1 & 0.1 nm & V & \(-\)0.048 & 73.62 & _0.0194_ \\ \hline
2 & 0.1 nm & R & \(-\)0.047 & 72.70 & _0.0088_ \\ \hline
3 & 0.2 nm (a) & V & \(-\)0.037 & 56068 & _0.0184_ \\ \hline
4 & 0.2 nm (a) & R & \(-\)0.037 & 57.63 & _0.0107_ \\ \hline
5 & 0.2 nm (b) & V & \(-\)0.045 & 69.73 & _0.0078_ \\ \hline
6 & 0.2 nm (b) & R & \(-\)0.039 & 60.54 & _0.0090_ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the linearly adapted FBG difference characteristic curve. a is the slope and b is the intersection point at the y-axis. MSE is the mean squared error of the linearization.  
Figure 6: Measurement of wavelength difference between the set and measured Bragg wavelengths.
## 2 System Description and Problem Statement  
As shown in Figure 1, a robotic machining system for milling is being developed. The system is composed of a Kuka KR 300-2 PA robot, a cylindrical helical end milling cutter mounted the robot end-effector, a work table located within the robot workspace, a modular fixture placed on the work table, and a workpiece to be machined. Both the work table and the fixture have pin holes for positioning reference and bolt holes for fastening, and the workpiece is fixed on the fixture. In addition, the inclination angle \(\theta_{p}\) between the fixture and the work table can be adjusted. As a result, the pose of the workpiece relative to the robot can be represented by the position of the fixture on the work table denoted by \(x_{p}\) and \(y_{p}\) and the inclination angle \(\theta_{p}\) as displayed in Figure 1.  
During the milling operation, the robot is subjected to periodic milling forces due to the intermittence of the cutter teeth. Since the machining operations can excite vibrations inside the robot structure, it is necessary to guarantee that the offset of the cutter tool maintains certain tolerances during the milling operation. In other words, the robot must be able to the hold the cutter tool firmly under the periodic milling forces. The machining forces can be changed by adjusting the machining parameters like the spindle speed and the feed rate; however, the adjustment of these parameters is constrained by many other factors such as the process efficiency requirement and the capability of the spindle and the robot. On the other hand, as mentioned above, the vibration characteristics of the system are also influenced by the workpiece pose relative to the robot, and the workpiece pose can be adjusted relatively easily. Then, the problem under study can be defined given the required milling tasks and milling parameters to find the optimal workpiece pose relative to the robot that minimizes the cutter tool offset under the periodic milling forces.