esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Approach for a Inverse Kinematics Solution of a Redundant Manipulator
## 3 Inverse Kinematic Model and Computing Algorithm
### Joint Limit Avoidance Task  
The joint limit avoidance task deals with the range of motion of particular manipulator links. In the case of revolute joints, the construction of real manipulators usually does not allow full joint revolution (360\({}^{\circ}\)). During motion control of the manipulator, this limitation of link motion has to be considered in order to prevent the destruction of manipulator construction.  
For the joint limit avoidance task, we use an approach with changing of value of weight variable \(\mathrm{W_{li}}\) based on joint position. If the joint is in admissible range, the value of the weight variable is set to be zero. When the joint reaches the boundary of its range motion, the value of the weight variable increases. When the joint reaches a value out of its admissible range, the value of the weight variable increases to its maximum. This approach can be expressed by Equation (10) [33]:  
\[\mathrm{W_{li}}=\left\{\begin{array}{c}\mathrm{W_{W}}\leftarrow\mathrm{q_{i} }<\mathrm{q_{imin}}\\ \frac{\mathrm{W_{W}}}{2}\Big{\{}1+\cos\Big{[}\pi\Big{(}\frac{\mathrm{q_{i}}- \mathrm{q_{imin}}}{\mathrm{p_{i}}}\Big{)}\Big{]}\Big{\}}\leftarrow\mathrm{q_{imin }}\leq\mathrm{q_{i}}\leq\mathrm{q_{imin}}+\mathrm{p_{i}}\\ 0\leftarrow\mathrm{q_{imin}}+\mathrm{p_{i}}\leftarrow\mathrm{q_{i}}\gets \mathrm{q_{i}}\rightarrow\mathrm{q_{i}}\rightarrow\mathrm{q_{i}}\\ \frac{\mathrm{W_{W}}}{2}\Big{\{}1+\cos\Big{[}\pi\Big{(}\frac{\mathrm{q_{imax}} -\mathrm{q_{i}}}{\mathrm{p_{i}}}\Big{)}\Big{]}\Big{\}}\leftarrow\mathrm{q_{i} }\rightarrow\mathrm{q_{i}}\leq\mathrm{q_{i}}\mathrm{max}\\ \mathrm{W_{W}}\leftarrow\mathrm{q_{i}}>\mathrm{q_{i}}\mathrm{max}\end{array}\right. \tag{10}\]  
The value of the weight variable has to be set for every joint of the manipulator which needs to be limited in the range of motion. Particular weight variables \(\mathrm{W_{li}}\) are parts of the final weight matrix of the joint limit avoidance task \(\mathrm{W_{l}}\in\mathrm{R^{n\times n}}\). The final weight matrix is the diagonal matrix:  
\[\mathrm{W_{l}}=\left[\begin{array}{ccccc}\mathrm{W_{l1}}&&&&\\ &\mathrm{W_{l2}}&&\\ &&\mathrm{W_{l3}}&&\\ &&&\cdots&\\ &&&\mathrm{W_{ln}}\end{array}\right] \tag{11}\]  
The weight matrix \(\mathrm{W_{l}}\) is used with the corresponding Jacobian matrix \(\mathrm{J_{l}}\in\mathrm{R^{n\times n}}\). The Jacobian matrix for the joint limit avoidance task is \(\mathrm{J_{l}}=\frac{\mathrm{\partial e}}{\mathrm{\partial q}}\). If a particular joint does not consider the joint limit avoidance task, the value of \(\mathrm{J_{l}}\) is set to be zero; otherwise it is set to be one. The limit of all links of the manipulator investigated in this study is \(\pm\) 100\({}^{\circ}\).
# High-Reliability Wireless Pressure Measurement System Based on FEC Algorithm
## 3 Key Hardware Circuit Design
### Ethernet Communication Module  
In this system, the upper computer exchanges data with the outside world through a Gigabit Ethernet interface, taking advantage of its characteristics of large-capacity and high-speed transmission [26; 27]. In this system, the connection between the MAC of the FPGA and the PHY chip 88E1111 uses the GMI interface [28], with a transmission rate of up to 1000 Mbps. The chip interface design is shown in Figure 6. The model of the isolation transformer is HX5008 NL. During the data transmission process, the FPGA of the back-end test bench first receives the wireless data. After decoding and verification, if there are no errors, the MAC controller sends the data to the 88E1111 through the GMII bus. Then, the data are sent to the network port through the isolation transformer HX5008 NL to exchange data with the upper computer, thus completing the data transmission.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Pin Name** & **Pin Type** & **Pin Description** \\ \hline GND & reference ground & ground wire \\ VCC & power input & power supply input \\ MISO & output & data output \\ MOSI & input & data input \\ DIO0-DIO5 & input & configuration mode \\ SCK & — & clock \\ ANT & — & antenna \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pin definitions of the wireless module.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Module** & **LUT Occupancy** & **DSP Occupancy** & **BRAM (Block)** \\ \hline Galois field & & & \\ multiplier & & & \\ register check bit & & & \\ generation & & & \\ data packet & & & \\ formatting & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: FPGA resource allocation.  
Figure 6: Schematic diagram of Ethernet chip interface.
# A Layer-Wise Strategy for Indoor As-Built Modeling Using Point Clouds
## 3 Methodology
### Layer Contour Extraction
#### 3.4.2 Contour Extraction
Despite the redundant boundary between the adjacency of sub-contours, there may exist some isolated sub-contours (e.g., part 1 in Figure 17). However, the acquisition of point clouds is completed in the indoor scenes which means all the rooms are connecting to each other. Thus, we need to recover the connection information which has been ignored in the previous steps. To this end, we search the overlapping linear primitive along the boundary of isolated primitives. As shown in Figure 18b, the linear primitives underlying the boundary between the sub-contours 1 and 2 are overlapping to each other. Moreover, these two edges of boundaries share the same vacancy which denotes the location of the missing connection. The blue edges indicate the area of vacancies, and the green rectangles are the recovered connections. An illustration of the extracted contour is shown in Figure 18c, and the overall process of contour extraction is summarized in Algorithm 1. Figure 18: The process of sub-contour mergence: (**a**) Initial mergence of sub-contours (1–6 indicate the extracted sub-contours), (**b**) merging refinement (enlarged to show the connections between sub-contours), (**c**) the result of contour extraction (red lines: Virtual connection walls; blue lines: Wall vacancies; green rectangles: The extracted connections). ```
Input: Linear Primitives of the Sub-Contours \(C=\{C_{i}\ \mid\ i\in 1,...,n_{1}\}\) Output: Regularized Contour \(RC\)
1 begin
2 for\(i\gets 1\) to \(n_{1}\)do
31) classifying the linear primitives in \(C_{i}\): \((L_{a},L_{b})\gets classifyLinearPrimitives\{C_{i}\}\) ; // \(L_{a}\): primitive type I, II; \(L_{b}\): primitive type III
32) building the bounding box as the initial approximation: \(B\gets createApprox(L_{a},C_{i})\);
33) assigning \(L_{a}\) to the bounding box \(B\): \(B\gets assignPrimitive(L_{a})\);
34) shrinking process of part I (\(L_{a}\)): for\(j\gets 1\) to \(4\)do
4\(e_{curr}\gets B_{j0}\) ; // current processing edge \(B_{j}\gets B_{j}\setminus B_{j0}\); while\(B_{j}\
eq\varnothing\)do
5\(l_{curr}\gets findNearestPrimitive(B_{j})\); \(e_{curr}\gets extendPrimitive(e_{curr})\); checking shrinking rules: \(flag_{1}\gets checkInclusion(e_{curr},l_{curr},B_{j})\); \(flag_{2}\gets checkBlock(e_{curr},l_{curr},B_{j})\); \((D_{j},P_{j})\gets shrinkPrimitive(e_{curr},l_{curr},B_{j},flag_{1},flag_{2})\); \(B_{j}\gets B_{j}\setminus l_{curr}\) ; // delete processed primitive
6 end for
7
8 end for
9
10 5) shrinking process of part II (\(L_{b}\)): while\(L_{b}\
eq\varnothing\)do
11\((D_{j},P_{j})\gets adjustSubContour(D_{j},P_{j},L_{a},L_{b})\);
12
13 end for
146) creating sub-contour from determined and pending edges: \(SC_{i}\gets createSubContour(D,P)\);
15
16 end for
17) merging the sub-contours to generate the regularized contour: \(RC\gets mergeSubContour(SC,L_{a},L_{b})\)
18
19 end for
```  
**Algorithm 1**Contour Extraction.
## 4 Results
### Performance Evaluation Metrics
#### 4.1.1 Root Mean Squared Error (RMSE)  
As the wireless link lifetime and one-hop channel delay prediction using the DNNs are modeled as regression problems, RMSE is used as the performance evaluation metric, and it is computed as given in Equation (37) [66]:  
\[RMSE\ =\sqrt{\sum_{i=1}^{N}(\frac{(T_{i})^{2}-(\hat{T}_{i})^{2}}{N})} \tag{37}\]  
In Equation (37), \(T_{i}\) is the true wireless link lifetime/one-hop channel delay of the \(i\)th sample, \(\hat{T}_{i}\) is the wireless link lifetime/one-hop channel delay of the \(i\)th sample predicted by the machine learning model, and \(N\) stands for the total number of samples. A lower value for the RMSE is desirable.
## 5 Results
### Revisit Time and Data Transmission Analysis
Figure 15: Average revisit times of ground stations. present the average revisit times of satellite constellations for three orbital configurations--LEO, VLEO (300 km), and VLEO (200 km)--as a function of latitude. Figures 18 and 19 reveal a significant increase in the average revisit time for NB-IoT in high-latitude regions. This is attributed to the high inclination angles used in the LEO and VLEO (300 km) configurations for NB-IoT, which result in less frequent satellite passes at extreme latitudes. In general, as latitude approaches the equator, the average revisit time tends to increase. In Figure 18, Mioty exhibits a slightly higher average revisit time compared to the other technologies, while LoRa maintains the most stable performance across the entire latitude range. In contrast, Figure 19 shows that both NB-IoT and Mioty have higher average revisit times than LoRa, with a notable reduction in revisit time around latitudes between 50\({}^{\circ}\) and 60\({}^{\circ}\)--a region where the satellite inclination closely aligns with the observed latitude, leading to more frequent passes. Finally, Figure 20 displays a sharp increase in the average revisit time for NB-IoT near the equator, whereas Mioty maintains consistently high revisit times across all latitudes. Figure 17: Daily data transmission volumes of ground stations. Figure 18: Average revisit times of the LEO constellation by latitude. Focusing on the target region between 30\({}^{\circ}\) and 60\({}^{\circ}\) north latitude, all configurations satisfy the performance criterion of maintaining an average revisit time below 60 min. Moreover, most latitude ranges between 0\({}^{\circ}\) and 60\({}^{\circ}\) also meet this requirement, indicating robust and consistent performance across the analyzed scenarios.
## 3 Results and Discussion
### Evolution and Spatial Distribution of Temperature during the Composting Period  
Two-dimensional temperature contours of the compost pile, shown in Figure 5, were generated to better understand the temperature evolution and spatial distribution during the composting period. The temperatures are represented in different colors and vary along the width and height of the composting pile. From the figure, the average temperature was observed to steadily increase from day 1 to day 16, with the highest temperatures observed from the center of the pile. The regions of the pile near the exterior were cooler than the interior as heat dissipated into the surrounding environment. For instance, the regions with a close-to-ambient temperature (35-40 \({}^{\circ}\)C) were located around the exterior on day 1, while the highest average temperatures (\(\sim\)50 \({}^{\circ}\)C) were observed in the center of the pile, where heat dissipation is the lowest.  
In the succeeding composting days, the temperature from the center of the pile increased, while the cooler layers reduced in size. On day 5, for example, the regions \(<\) 40 \({}^{\circ}\)C were much thinner and the interior average temperature was higher, ranging from 55-65 \({}^{\circ}\)C compared with day 1. This day corresponds to the end of the acclimatization period and the beginning of the thermophilic phase. Day 7-16 covered this phase which is characterized by the highest temperature in the entire composting period. The cooler regions further decreased while the hottest ones grew in size. Most of the pile had a temperature range of 70-80 \({}^{\circ}\)C. In this study, the thermophilic phase took roughly 15 days. By day 23, the temperature dropped, signaling the cooling and maturation phase of the compost pile, with the latter phase reached on day 28. The temperatures dropped, with the outer layers falling within 30-40 \({}^{\circ}\)C, while the interior part of the pile was \(<\) 45 \({}^{\circ}\)C. These figures help to better understand the temperature evolution and spatial distribution during the composting process, which would be useful in heat recovery and utilization of this technology.  
Figure 4: pH variation during the composting period.
# Modified Cuckoo Search Algorithm with Variational Parameters and Logistic Map
## 2 Preliminary
### Nest Location of Each Host Are Initialized by Logistic Map of Each Dimension  
In this paper, the nest location of each host is initialized by logistic map of each dimension for four reasons. Firstly, the location \(x_{d}\) is found by the logistic map and will not repeat. This means that the speed of convergence can be accelerated. Secondly, logistic map is simple and easy to implement. Thirdly, logistic map is easy to embed into every part of the CS algorithm. Fourthly, since each dimension requires a chaotic map, using other chaotic maps will increase the complexity of the CS algorithm. Logistic map [31] is defined as:  
\[x_{n+1}=\mu x_{n}(1-x_{n}),n=0,1,2,3,\dots \tag{1}\]  
where \(x_{n}\in(0,1)\), the control parameter \(\mu\in[0,4]\) and \(n\) represents the \(n\)-th iteration. Chaos phenomenon occurs when \(\mu=4\)[32]. The pseudo code of initialization is shown in Algorithm 2.  
```
Input:d represents the dimension; \(\mathbf{Lb}\) is the lower bound of Objective function f(\(\mathbf{x}\)), \(\mathbf{Lb}=(Lb_{1},\dots,Lb_{d})^{T}\); \(\mathbf{Ub}\) is the upper bound of Objective function f(\(\mathbf{x}\)), \(\mathbf{Ub}=(Ub_{1},\dots,Ub_{d})^{T}\); Output:nest
1 Generate global random number r, r = \((r_{1},\dots,r_{d})^{T}\),\(r_{d}\in(0,1)\);
2\(n\) is equal to the number of nests;
3 for\(k=1:n\)do
4 r = 4*r.*(1-r); % Logistic map creates chaos when \(\mu=4\);
5 nest(\(k,:\)) = \(\mathbf{Lb}\) + \((\mathbf{Ub}-\mathbf{Lb})\).*r; % Initial population of n host nests;
6 final;
7 return nest;
```  
**Algorithm 2**Nest location of each host is initialized by logistic map of each dimension
## 3 Analysis of Test Results
### Single-Factor Analysis
#### 3.2.4 Consistency  
Based on Equation (5), the relationship curve among various factors and the slurry consistency is shown in Figure 9. It should be noted that the absence of the slurry particle size in Equation (5) indicates that its influence on the slurry consistency can be overlooked. The soil-binder ratio exerts a more significant impact on the slurry consistency than the water-binder ratio, water-reducing admixture content, fly flash-cement ratio, and binder-sand ratio. As the soil-binder ratio and muck content increase, the slurry fluidity decreases, directly affecting the consistency, which is tied to the free water content in the slurry.  
Figure 8: Relation curve between each factor and slurry fluidity.  
Figure 7: Relation curve between each factor and slurry bleeding rate.  
The silty clay in the muck adsorbs substantial water molecules, reducing free water and notably decreasing the slurry consistency. Other factors, such as the water-binder ratio and water-reducing admixture content positively correlate with the slurry consistency, while the binder-sand ratio negatively correlates with the fly flash-cement ratio. Additionally, the slurry consistency enhances as the water-reducing admixture content increases.
## 3 Results and Discussion
### Tensile Behavior  
Figure 6 presents the axial stress vs. axial strain curves obtained in the tensile tests of BVE (Figure 6a) and BVEL resins (Figure 6b). The resins presented a similar behavior up to failure, characterized by an initial linear response, a gradual stiffness reduction before attaining the maximum stress and failing in a brittle manner. Table 6 summarizes the tensile strength, tensile modulus (calculated for strains between 0.1% and 0.5%, for which the response was linear), and the ultimate tensile strain of both types of resins. As shown, BVE and BVEL resins presented similar tensile moduli (3.2-3.4 GPa), yet the incorporation of 4% by the weight of lignin resulted in a 27% increase in tensile strength, and 45% increase in average ultimate tensile strain, which offers a significant advantage for incorporation in FRP composites. This increase in mechanical properties is consistent with the results of GPC tests, namely with the increase in M\({}_{\mathrm{w}}\) with lignin incorporation. Performance increases have also been reported in previous studies using lignin as a source of hydroxyl groups in polyurethane synthesis, i.e., where lignin acts as a reactive component and is integrated into the polymer structure [49; 50]. On the other hand, performance reductions have also been reported in the literature, particularly when lignin is found to behave as a filler [51; 52].  
It is worth noting that, as shown in Figure 6b, the properties of the BVEL resin showed a slightly higher scatter than those obtained for the BVE resin, possibly owing to the heterogeneity introduced in the main polymeric chains of the former resin via the complex structure of lignin, during the first stage of the polycondensation reaction. This higher heterogeneity has been consistently suggested from the results of the different experiments presented here.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Atmosphere** & **Resin** & **Specimen** & **T\({}_{5\%}\) (\({}^{\circ}\)C)** & **T\({}_{10\%}\) (\({}^{\circ}\)C)** & **T\({}_{50\%}\) (\({}^{\circ}\)C)** & **Resolute (\%)** \\ \hline \multirow{4}{*}{Ox oxidative} & BVE & BVE-O-1 & 229 & 290 & 385 & 0.1 \\  & BVE-O-2 & 257 & 316 & 389 & 0.2 \\  & BVEL & BVEL-O-1 & 239 & 308 & 394 & 1.2 \\  & BVEL & BVEL-O-2 & 266 & 326 & 392 & 0.2 \\  & BVE & BVE-I & 247 & 313 & 393 & 5.7 \\  & BVEL & BVEL-I & 239 & 300 & 393 & 5.1 \\ \hline \end{tabular}
\end{table}
Table 5: Summary of TGA results.
## 2 Materials and Methods
### Data Reduction and Model Learners  
For the sake of completeness, this subsection revisits the main techniques that will be employed later: the Singular Values Decomposition -SVD-, the Dynamic Mode Decomposition -DMD-, and the Multi-Layer Perceptron -MLP-.
## Materials and Methods
### Explanatory Variable Selection and Data Acquisition
The daily PAR, VPD, and air temperature were obtained from the MERRA-2 [58], a NASA atmospheric reanalysis of the satellite era using the Goddard Earth Observing System Model, Version 5 (GEOS-5) with its Atmospheric Data Assimilation System (ADAS). The downwelling PAR was derived as the sum of the diffuse PAR and direct PAR. The VPD was calculated by the air temperature and the specific humidity.
## 2 The Description of the Dual-Rate Hammerstein System
* Moreover, the single-rate model in Equation (5) contains the products of the parameters of the nonlinear block and the linear block, _i.e._, the output of the model is a bilinear function about the parameter vectors \(\bar{\mathbf{b}}\) and \(\mathbf{M}\), and a standard least squares method cannot be directly applied to the model. The intent of this paper is to convert the Hammerstein system in Equations (1) and (2) into a dual-rate linear regression model about all parameters (linear-in-parameter model), by combining the polynomial transformation technique [20] with the key variable separation technique [12]. The obtained dual-rate linear regression model is suitable for the dual-rate sampled data, and a standard identification technique can be directly applied to the model to estimate the parameters of the model.
# An Overview of Key Technologies in Physical Layer Security
## 3 Secure Multi-Antenna Techniques
### Space-Time Coding (STC)
In Figure 16a we demonstrate the BER performance of the beamforming and AN schemes. The figure illustrates that the eavesdropper's channel has the worst bit error rate because it is corrupted by the AN signal; it is followed by a single-input single-output (SISO) system, which does not receive diversity from beamforming. Lastly, the \(1\times 2\) single-input multiple-output (SIMO) and \(2\times 1\) multiple-input single-output (MISO) systems receive the signal with better BER because of beamforming. The achievable secrecy rates of different precoding schemes are shown in Figure 16b. In particular, in a MIMO scheme, six transmit antennas, the intended receiver, and eavesdropper are each equipped with two receive antennas. In the figure we have the same MIMO configurations for no  
Figure 15: System block diagram. beamforming and use of beamforming. Lastly, we introduce an AN signal. The figure confirms that the use of AN indeed improved the secrecy rate of the communication network compared to beamforming only or no beamforming used.
# Substitute Seed Nodes Mining Algorithms for Influence Maximization in Multi-Social Networks
## 4 Substitutes Mining Algorithms for SMMNIM
### Greedy-Based Substitutes Mining Algorithm for SMMNIM (G_S)  
The literature [13] has proved that substitute nodes mining for influence maximization in a single social network is an NP-hard problem. SMMNIM can be regarded as finding \(t\) seed users in the set \(U/S\) that can maximize information influence, so it is also an NP-hard problem. The NP-hard problem can be approximated to the optimal based on the greedy algorithm. Therefore, we first design a Greedy-based substitutes mining algorithm for SMMNIM (G_S), which is shown in Algorithm 2.  
```
Input:\(G^{*}(V^{*},E^{*})\), seed node set \(S\), the subset of seed nodes that can be activated \(S_{1}\), number of seeds that cannot be activated \(t\); Output: New initial seed seeds set S'; (1) \(S^{\prime}=S_{1}\); (2) \(R(S)\): The number of active nodes obtained by \(S\); (3) for \(i=1\) to \(t\) (4) for \(v\) in \(V^{*}/(S\cup S^{\prime})\) (5) select \(v=argmax(R(S^{\prime}+v)-R(S^{\prime}))\) (6) end for (7) \(S^{\prime}=S^{\prime}\cup v\) (8) end for (9) Output S';
```  
**Algorithm 2** Greedy-based Substitutes Mining Algorithm for SMMNIM (G_S).