esa-sceva/satcom-chunk-collection huggingface dataset:
# Modular Robotics Configurator: A MATLAB Model-Based Development Approach
## 2 Materials and Methods
### General Information
For the simulation of the robot, homogeneous transfer matrices will be generated, which are calculated with the following formula:  
\[A_{0,n}=\prod_{i=0}^{n-1}A_{i,i+1} \tag{7}\]  
The position of the characteristic point attached to the final effector in relation to the system \([T_{0}]\), expressed with the help of the transfer matrices, will be calculated using the relation  
\[P_{0,n}=\prod_{i=0}^{n-1}A_{i,i+1}\cdot P_{n} \tag{8}\]The Denavit-Hartenberg homogeneous operator method assumes that the elements of homogeneous transformation matrices of size 4 \(\times\) 4 can be expressed using four independent parameters, called Denavit-Hartenberg parameters. These are defined as follows:  
- angle \(\theta_{i}\)--the angle between the axes \(x_{i-1}\) and \(x_{i}\);  
- distance \(a_{i}\)--the distance between the origins \(O_{i-1}\) and \(O_{i}\) measured on the \(x_{i}\) axis;  
- distance \(d_{i}\)--the distance between the origins \(O_{i-1}\) and \(O_{i}\) measured on the \(z_{i-1}\) axis;  
- angle \(\alpha_{i}\)--the angle between the \(z_{i-1}\) and \(z_{i}\) axes. The transformation of the coordinates between the elements \(i\) and \(i+1\) results in a sequence of four elementary movements, namely  
- a rotation with the angle \(\theta_{i}\) performed around the \(z_{i}\) axis;  
- a translation with the distance \(d_{i}\) along the \(z_{i}\) axis;  
- a translation with the distance \(a_{i}\) along the \(x_{i+1}\) axis;  
- a rotation with the angle \(\alpha_{i}\) around the \(x_{i+1}\) axis. The homogeneous transformation matrix of size 4 \(\times\) 4 characterizes the relative movement between the elements \(i\) and \(i+1\). It is obtained after obtaining the product of the homogeneous matrices that characterize the four movements:  
\[A_{i,i+1}= \begin{bmatrix}\cos(\theta_{i})&-\sin(\theta_{i})&0&0\\ \sin(\theta_{i})&\cos(\theta_{i})&0&0\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix}\begin{bmatrix}1&0&0&0\\ 0&1&0&0\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix}\begin{bmatrix}1&0&0&a_{i}\\ 0&1&0&0\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix}\begin{bmatrix}1&0&0&0\\ 0&\cos(\alpha_{i})&-\sin(\alpha_{i})&0\\ 0&\sin(\alpha_{i})&\cos(\alpha_{i})&0\\ 0&0&0&1\end{bmatrix}  \tag{9}\]  
\[A_{i,i+1}= \begin{bmatrix}\cos(\theta_{i})&-\sin(\theta_{i})\cdot\cos(\alpha_{i })&\sin(\theta_{i})\cdot\sin(\alpha_{i})&a_{i}\cdot\cos(\theta_{i})\\ \sin(\theta_{i})&\cos(\theta_{i})\cdot\cos(\alpha_{i})&-\cos(\theta_{i}) \cdot\sin(\alpha_{i})&a_{i}\cdot\sin(\theta_{i})\\ 0&\sin(\alpha_{i})&\cos(\alpha_{i})&d_{i}\\ 0&0&0&1\end{bmatrix}  \tag{10}\]
## 7 The Algorithm Performance  
### The T-bracket Structure  
To examine the proposed Cellular Automata method combined with the refined mesh adaptation, more complicated geometries were investigated. The first of them is a T-bracket structure (see Figure 22).  
Half of the element was considered due to symmetry. The Young modulus and the Poisson ratio were E = \(2\times 10^{11}\) Pa and \(\
u\) = 0.3, respectively. A volume fraction equal to 0.5 was implemented, a concentrated force P = 1000 N was applied. As in the previous example, the dynamic mesh refinement was implemented for an initially regular mesh of 775 elements. The initial mesh, a selected iteration, and the final topology for this example are shown in Figure 23. The compliance value of the optimized structure equals \(1.741\times 10^{-4}\) Nm, the iteration history is presented in Figure 24.  
Figure 21: The final topology obtained using [37] with a small (**a**) and a large (**b**) filtering radius.  
Figure 23: Selected middle topologies and the final one for the T-bracket structure—for the last iteration the grid has not been shown to improve the clarity of the picture.  
Figure 22: The T-bracket example, an initial structure with applied loads and supports.  
At the end of the optimization process, the number of elements equaled 16,472, while the regular mesh needed 130,896 elements to cover the whole structure with elements of the same size as the smallest cell of irregular mesh.  
To illustrate the results obtained for a regular mesh, the final topologies for 1830, 7180, 15,359, and 130,896 elements are presented in Figure 25.  
For comparison, this example was calculated using the code based on [35]. The resulting topology is given in Figure 26.  
Figure 24: The iteration history for the T-bracket example.  
Figure 25: The final topologies for 1830 (**a**), 7180 (**b**), 15,359 (**c**), and 130,896 (**d**) elements. In the last case a grid has not been shown to improve the clarity of the figure.
## 4 Applications of Chaos in Robotic Motion  
In applications of robotic motion, there is the need to combine an efficient area coverage with unpredictable movements of the robot. Chaos enables both goals to be achieved, as shown in some recent manuscripts [14; 16]. For example, robotic systems can be used in the military for patrol missions or to find explosives [14]. In accomplishing these tasks, the robots should recognize their initial positions and those of the targets, in order to instantly update the map of the workspace [14]. Thus, efficient path planning methods for creating a trajectory in the workspace are required. Since in patrolling missions it is crucial for the robot to move randomly, the use of chaotic systems is proposed in [14] to generate unpredictable trajectories for the motion of the robots. In particular, the considered chaotic path generator is a modified logistic May map, which is exploited for implementing the robot's movement in four and eight directions [14]. Simulations results are carried out to show the capabilities of the method. In particular, the results in [14] highlight that the use of eight directions improves the behaviour of the robot, both in coverage percentage and in the reduction of the number of visits of the same cells in the square workspace.  
In [15], an efficient chaotic path planning algorithm for autonomous mobile robots is illustrated. The objective is to cover a given terrain using chaotic motion. The method in [15] exploits a logistic map with a chaotic tactic, which uses a modulo function to generate a sequence of eight directions on a grid. Using extensive simulations, it is shown that the approach is effective to guarantee path coverage, as the number of iterations increases [15]. In addition, the efficiency of the algorithm is further improved with a pheromone inspired memory technique [15].  
Referring to chaotic path planning, in [16], chaotic motion patterns are generated using chaotic pseudo-random bit generators based on modified logistic maps. Namely, the bitstream produced by the generator is converted into motion commands in different directions. Successively, the chaotic logistic map is combined with an inverse pheromone approach in order to get a reduction in the number of revisits in each cell of the workspace [16]. The considered robot is able to move in four or eight directions, even though the conducted analysis highlights that the motion in eight directions provides better results [16]. Simulation results show that the use of the pheromone can significantly increase the coverage percentage, leading to better performances in different scenarios, which include the presence of obstacles in the workspace [16].
# A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges
## 3 5G Coverage Enhancement Techniques
### Multiple Input and Multiple Output
Different antenna arrays, multiplexing techniques for M-MIMO have been proposed in recent research to enhance the data rates and capacity in 5G networks, as has been summarised in Table 15.  
MIMO--Future Challenges and Research Issues  
5G key parameters such as improved spectral efficiency, energy efficiency, high data rates, and enhanced capacity are achieved using the MIMO technology. There are still some challenges and issues in the implementation of M-MIMO that need to be addressed. The key issues that need to be explored in the future are:  
* Signal Detection: The UL signal detection becomes more complex in M-MIMO systems due to the large number of radiating elements, which decreases the system throughput. Along with this, the superimposition of user-transmitted signals at the BS causes interference, which also reduces the spectral efficiency and throughput. Therefore, to enhance the spectral efficiency and throughput of a M-MIMO UL, intelligent algorithms are required. Designing less complicated and accurate algorithms for UL signal detection is one of the future research challenges;
* Channel Estimation: For a perfect CSI, the M-MIMO system performance increases linearly with the number of transmitting and receiving antennas. The BS should know how to use CSI in order to identify and detect the user-transmitted signal at the UL and to precode the signals at the DL. Channel estimation at the DL and UL depends on the FDD and TDD duplexing modes. FDD Mode Channel Estimation: In this mode, the CSI needs to be estimated for both DL and UL. During the DL, the BS forwards the pilot signals to the user, and the user replies with the estimated CSI to the BS. Similarly, during the UL, the BS estimates the CSI using the orthogonal pilot signals forwarded by the user. The DL channel
## 2 Basic Problem Description  
As participants of an intelligent bus system, passengers receive intelligent bus information services during travel, and use the same limited bus resources, including bus stations, electronic bus stop boards, and buses. Under the system's information service, each passenger can choose the most suitable bus travel plan--such as the route with the shortest travel time, least number of transfers, lowest travel cost, or least congestion--to minimize their costs or maximize their own interests during travel. However, as part of the bus system, the bus choices of individual passengers will impact one another, and they could even cause passengers to lose interest. A representative of this is the impact of the perception of congestion. Take the bus selection among passengers in Figure 1 as an example. Assuming that the existing buses, \(k_{1}\) and \(k_{2}\), can meet the travel needs of passengers at bus station \(i\) and that, for passengers, the above two vehicles have no significant impact on their travel except for the crowding factor. Passengers obtain congestion information for each bus through the information provided by the intelligent bus system, and the congestion degree of \(k_{1}\) is higher than that of \(k_{2}\). The possible combinations of bus choices that passengers can make based on this information include (\(k_{1}\), \(k_{1}\)), (\(k_{1}\), \(k_{2}\)), (\(k_{2}\), \(k_{1}\)), and (\(k_{2}\), \(k_{2}\)). If all passengers choose to travel by bus \(k_{1}\), the congestion degree of bus \(k_{1}\) is likely to be higher than that of \(k_{2}\), which is called the \"congestion drift\" phenomenon in road congestion research. Another extreme situation is that platform passengers all take into account the \"congestion drift\" situation, so they choose bus \(k_{2}\) to ensure their own travel comfort, which results in bus \(k_{2}\) becoming more crowded. In fact, the most probable situation is that most passengers will choose to travel on bus \(k_{1}\) according to the bus guidance information, and a small number will choose bus \(k_{2}\) because they believe that \(k_{1}\) might become more crowded as it is the choice of most people. There is a game relationship between passengers, and passengers consider each other's bus choices to determine their own bus choices. Therefore, game theory can be used to analyze the bus choice behavior of waiting passengers.
## 1 Introduction
Different damping phenomena are associated with various aspects of the system: material damping in the concrete and prestressing steel, radiation damping in the soil, and aerodynamic damping from the train passage [25]. The appropriate consideration of these damping mechanisms is essential for accurate response prediction, particularly in cases where resonance effects may occur [26]. The development of sophisticated numerical models capable of capturing all relevant phenomena has proved challenging [27]. Current state-of-the-art approaches typically employ multi-scale modeling techniques, combining detailed local models of critical components with global models of the entire bridge-soil system [28]. The integration of various numerical methods, including finite elements, boundary elements, and spectral approaches, has enabled a more comprehensive analysis of complex dynamic interactions [29; 30]. This study aims to bridge the existing knowledge gaps by developing a comprehensive analytical framework that simultaneously considers seismic excitation, train-induced vibrations, SSI effects, and traveling wave phenomena. The research utilizes advanced numerical modeling techniques to capture these complex interactions. The findings are expected to contribute to the development of more reliable design guidelines for prestressed concrete bridges subjected to multiple dynamic loading conditions, ultimately leading to more resilient and economical infrastructure systems.
## 1 Introduction  
A dam blocking a river reduces the natural flow of the solid material causing a continuous decrease in the water capacity of the reservoir [1]. When its actual capacity is less than the initial reservoir storage capacity, the reservoir risks not guaranteeing. the users' demands, thus a planning of _extraordinary management_ operations will be necessary [2]. The choice of the site to dispose the dredged material is a crucial point for planning a drainage project [3; 4; 5]. Confined disposal was the approach originally employed to treat contaminated material which was neither disposable nor reusable [3]. Therefore, the opportunity to use valid disposal methods for particular classes of dredged material [6; 7] must be carefully evaluated through risk analysis [8]. Large diameter geotextile tubes, have been widely used to contain and dehydrate dredged material from river channels and ports [9]. In this approach, coarse-grained sediments, pumped into the geotextile tube, settle quickly and the water is discharged to the outside [10]. In this perspective geotextile tubes assume the role of alternative disposal sites. For instance, sand-filled geotubes are used as artificial banks and peninsulas to stabilize ports or shore protection.  
The idea of extending geotextile tubes as dehydration sites for even fine-grained materials has only been analyzed in recent years [11]. If the material to be dehydrated is clayey, the use of geotubes is complicated by the fact that the suspension is made of very fine materials with very long deposition times. To overcome this drawback, polymeric flocculants have been used to accelerate the precipitation of the material in the geotube. It is, therefore, clear that in the dehydration process of clays by means of geotubes, sedimentation plays a key role in planning a dredging project.  
The objective of this study is to assess the sedimentation role in the dewatering process, by geotexile tubes, of clayey material dredged from the Occhito reservoir (Italy). Results of sedimentation measurements are used to derive a semi-empirical method to optimize the filling process of geotexile tubes. The sedimentation behavior of mixed and non-mixed with polymers samples allows us to monitor the efficiency of the flocculant in the dewatering process.
## 3 The Proposed Protocol
### Three Proposed Authentication Methods
#### 3.3.3 Receiver Centric Authentication (RCA)
\begin{array}{l}e\big{(}g_{2 aggr},\sigma_{aggr}\big{)}=e\big{(}g_{2 aggr},sk _{aggr}\cdot H(m)\ \big{)}\\ =e\big{(}sk_{aggr}\cdot g_{2 aggr},\ H(m)\big{)}\\ =e\big{(}pk_{aggr},\ H(m)\big{)}\end{array}  \tag{13}\]  
```
Signature Generation:
1. Picks \(g_{22 ID}\) values for the neighboring communicated zones
2. Generate \(g_{2 aggr}=\ g_{21}+\ g_{22}+\ldots+\ g_{2N}\in G_{2}\ sk_{aggr}=sk_{1}+sk_{2}+ \ldots+sk_{N}\in\ F_{p}\)
3. \(pk_{aggr}=sk_{aggr}\cdot g_{2 aggr}\), where \(pk_{aggr}\in G_{2}\)
4. Generate \(\sigma_{aggr}=sk_{aggr}\cdot H(m)\), where \(\sigma_{aggr}\in G_{1}\)
5. \(v_{i}\) broadcasts \(\big{\{}L_{2 ID},\ phid_{i},m_{i},T_{s},pk_{aggr},\sigma_{aggr}\big{\}}\)
6. Signature Verification:
1. All vehicles in the neighboring zones receive \(\big{\{}L_{2 ID},\ phid_{i},m_{i},T_{s},pk_{aggr},\sigma_{aggr}\big{\}}\)
2. Receivers generate \(g_{2 aggr}\) for the neighboring zones, using \(g_{2 aggr},\sigma_{aggr}\), and \(pk_{aggr}\) to verify:
3. \(lf\ e\big{(}g_{2 aggr},\sigma_{aggr}\big{)}=e\ \big{(}pk_{aggr},\ H(m)\big{)}\), accept the message, else reject the message. ```  
**Algorithm 3**Receiver-Centric Authentication (RCA)  
Figure 9: Multi-zone authentication using Receiver-Centric Authentication (RCA) method.
## 4 Data Analysis of Gas-Liquid Separation Efficiency in Two-Phase Flow  
Fluent can accurately simulate an experimental target to obtain the parameters of the DGLS. Setting the gas holdup to 10%, the water holdup to 90%, and the total flow of the liquid inlet to be 20 m\({}^{3}\)/d (\"m\({}^{3}\)/d' is the unit of mass flow, i.e., cubic meters per day), 30 m\({}^{3}\)/d, 40 m\({}^{3}\)/d, 50 m\({}^{3}\)/d, 60 m\({}^{3}\)/d. The actual gas-water two-phase flow and oil-gas-water multiphase flow are complex three-dimensional motion processes. The detailed conditions are set as:  
1. When fluid flows in a circular pipe, whether it is laminar or turbulent, and its flow has axisymmetric characteristics. This paper focuses on the fluid's flow characteristics; thus the physical model is simplified into a two-dimensional model in the numerical simulation. 2. The inner surface of the vertical riser, the outer surface, and the inner surface of the gas-liquid separation device are ideal smooth surfaces without considering the friction resistance. 3. The results show that the fluid flow in the tube is isothermal, the density and viscosity of the fluid are measured at 20 \({}^{\circ}\)C, and the gas flow is fully developed laminar or turbulent, unsteady. 4. The pressure of velocity inlet pressure is standard atmospheric pressure. 5. The measurement error range is \(\pm\)10% or less. 6. In Fluent simulation settings, we set the oil phase to diesel, the gas phase to air, and the water phase to natural water. Water is used as the continuous phase, that is, the first phase. 7. The Reynolds number and turbulence intensity are shown in Table 1. According to the Reynolds number, 20 m\({}^{3}\)/d is laminar flow, 30 m\({}^{3}\)/d is transitional flow, and 40 m\({}^{3}\)/d\(\sim\)60 m\({}^{3}\)/d is turbulent flow. There is no calculation model for transition flow in Fluent, and therefore we set the theoretical model of fluid in the transition state as the laminar flow model for solution and calculation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Gas Holdup**} & **Total Flow** & **Gas Flow** & **Water Flow** & **Reynolds** & **Turbulence** \\  & **(m\({}^{3}\)/d)** & **(m\({}^{3}\)/d)** & **(m\({}^{3}\)/d)** & **Number** & **Intensity (\%)** \\ \hline \multirow{4}{*}{5\%} & 20 & 1 & 19 & 2514 & 6.01 \\  & 30 & 1.5 & 28.5 & 3771 & 5.72 \\ \cline{1-1}  & 40 & 2 & 38 & 5028 & 5.51 \\ \cline{1-1}  & 50 & 2.5 & 47.5 & 5602 & 5.44 \\ \cline{1-1}  & 60 & 3 & 57 & 7542 & 5.24 \\ \hline \end{tabular}
\end{table}
Table 1: Related parameters of gas–water two-phase flow with 5% and 10% gas holdup.
## 2 Characteristic Analysis of Industrial Power Consumption  
The industrial power consumption data used in this paper are from the official website of the Suzhou Bureau of Statistics [30]. A total of 36 data sets of \"Industrial Power Consumption\" in \"Transportation and Power consumption\", between the years 2003 and 2005, are selected as the research object. The monthly time series of each year is shown in Figure 1. It can be seen that there are two typical characteristics: (1) Seasonal fluctuation. The data curves representing the three years in Figure 1 basically maintain the same distribution characteristics: in each year, the trough appears in February and October, respectively, and the peak appears in July and December. In particular, the data in January and February 2004 changed significantly. According to the survey, the Spring Festival of China is usually in February, while the Spring Festival of 2004 took place in January, and the climate change in January and February also had an impact on power consumption. This shows that the industrial power consumption in Suzhou has the periodic characteristics of seasonal fluctuations. (2) Trend growth. The data of power consumption in the same month over three years are compared, which shows that they have an obvious growth trend.  
According to the typical characteristics of Suzhou's industrial power consumption, this paper proposes a forecasting method of a grey model with a seasonal index adjustment. In the following sections, the seasonal index is introduced; then, the grey model is established; finally, the forecast results are inversely adjusted by the seasonal index.
## 4 Results
### Assimilation of Remotely-Sensed LAI at the Regional Scale  
Remotely-sensed LAI are an important information source for describing the growth of regional winter wheat. In addition, these data are the main source of information for correcting the crop model simulation process. To map the regional winter wheat yield, 16 remotely-sensed LAI data from the crop growing season were averaged to allow scaling up to a 1-km spatial resolution and then assimilated into the CERES-Sheat model using the POD4 DVar-based strategy. The yield map showed that the yield per unit area generally decreased from south to north of Hengshui (Figure 8). The average yield over the study region was 6996 kg\(\cdot\)ha\({}^{-1}\); the yield range was from 4480-9558 kg\(\cdot\)ha\({}^{-1}\). The maximum productive capacity was observed in Gucheng County (7611 kg\(\cdot\)ha\({}^{-1}\)), followed by  
Figure 7: Comparisons of winter wheat yield estimated by the CERES-Sheat model with (**a**) no data assimilation, (**b**) the PF-based data assimilation and (**c**) the POD4 DVar-based data assimilation.  
Wuyi (7479 kg\(\cdot\)ha\({}^{-1}\)) and Wuqiang (7253 kg\(\cdot\)ha\({}^{-1}\)) counties. The minimum yield was 6493 kg\(\cdot\)ha\({}^{-1}\) in Raoyang County.  
The accuracy of the crop yield estimation was evaluated at the pixel and county scales. The estimated yields of the 55 pixels closely agreed with the actual yields of the field plots with an R\({}^{2}\) of 0.53, an RMSE of 543 kg\(\cdot\)ha\({}^{-1}\) and an RE of 5.91% (Figure 9a). Then, the estimated yields with a 1-km spatial resolution were aggregated at the county level so that the results could be validated using regional official yield statistics, which are compiled at the county level. The statistical mean yields of 11 counties showed that the R\({}^{2}\) was 0.67, the RMSE was 172 kg\(\cdot\)ha\({}^{-1}\) and the RE was 7.77% (Figure 9b). The results also showed that the estimated yields are generally slightly higher than the official statistical yields. The reasons are mainly because most of the field plots' observational yields (five of seven plots) in the agricultural experiment station were slightly higher than the official statistical yields, which led to the higher simulated yields and assimilated yields after the calibration of the crop model. However, on the whole, the estimated statistical mean yields own small deviations with the official statistical yields, and the accuracy is relatively high. These results suggest that the POD4 DVar-based assimilation of remotely-sensed LAI significantly improved the simulation of yields with CERES-Wheat at the pixel and county scales.  
Figure 8: Distribution of winter wheat yield estimates with a 1-km scale using POD4 DVar.  
Figure 9: Comparisons between the measured and estimated yields at the pixel (**a**) and county scales (**b**).