esa-sceva/satcom-chunk-collection huggingface dataset:
# Predictive Energy-Aware Routing Solution for Industrial IoT Evaluated on a WSN Hardware Platform
## 5 PEAR Solution: Hardware Integration and Results Evaluation
### The One Profile Test Series
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Energy Profile** & **Success Rate** & **Number of Overconsuming Devices** \\ (DPDUs/min) & **(\%)** & **Number of Overconsuming Devices** \\ \hline
100 & 48.57 & 36 \\
135 & 77.14 & 16 \\
170 & 84.28 & 11 \\
200 & 90 & 7 \\
250 & 92.85 & 5 \\
300 & 95.71 & 3 \\
400 & 97.14 & 2 \\
450 & 100 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The PEAR success rate with respect to the energy profile. Figure 8: Average field device energy consumption per day exceeding the energy profile. The PEAR impact on energy balance and overconsumption takes place in jumps. More specifically, three jumps can be observed namely, at 120, 160 and 210 topology changes. Each jump is preceded by an increase in energy overconsumption. This is the expected behavior since PEAR accepts intermediary energy inefficiencies for the benefit of more energy balanced network resources allocation. When trying to lower the energy consumption of a field device, the PEAR choices are limited by the set neighbours of each device. This leads to cases where an overconsuming field device cannot be freed from its child nodes due to limited choices. Therefore, PEAR triggers intermediary network topologies that create new opportunities to free the overconsuming field devices. With these opportunities PEAR can efficiently balance the energy consumption as it is illustrated by the overconsumption jumps. In terms of performance, PEAR reduces the overconsumption 10.4 times while having the average field device energy consumption only 1.8% larger compared with the Non-PEAR variant. This observation emphasizes that PEAR does not aim to reduce the overall energy consumption but rather to enable predictable energy consumption by balancing the energy consumption across the network. This observation emphasizes that PEAR prioritizes the predictability of energy consumption and the energy balancing across the network. The Table 2 comprises the results of six tests showing the number of topology changes and the time to stability for a 70 devices profile cluster having different energy profiles. It shows that for negative and small energy margins (e.g., under 50 DPDUs/min) PEAR reaches instability states whereas for larger energy margins PEAR reaches a stable state in a shorter time and with a smaller number of topology changes. Moreover, for an energy margin larger than 130 DPDUs/min the impact on the number of topology changes and time to stability decreases. This nonlinear behavior emphasizes on the PEAR ability to efficiently trigger the topology changes and have the most impact within a 130 DPDUs/min energy margin. Figure 9: Average field device overconsumption for a 24-month profile (i.e., 200 DPDU/min) with respect to the number of topology changes triggered by PEAR.
## 5 Cloud and Bandwidth Resource Allocation Problem  
The cloud and bandwidth resource allocation problem in NFV architectures is Nondeterministic Polynomial Hard (NP-Hard) [1]. For this reason, we will compare the computational complexity of two heuristics for the resource allocation in NFV architectures when a Traditional Orchestration (TO) solution and the proposed Scalable Orchestration (SO) solution are considered, respectively. The objective of both the heuristics is to minimize the total cost of the allocated cloud and bandwidth resources.  
For the TO case, the NFV orchestrator has the visibility of the internal structure of every NFVI-PoP (its network topology, the number of servers,...). For this reason, a modified version of the algorithm proposed in [7] can be applied for solving the SFC routing and cloud and bandwidth resource allocation problem in the TO case; the algorithm has been modified so that the total cost of allocated cloud and bandwidth resources is only minimized. It is shown in [7] that the computational complexity of the algorithm is \(C_{TO}=O(NM_{max}N_{s}^{2}log(M_{max}N_{s}))\), where \(N\) is the number of offered SCs, \(M_{max}\) is the number of SFs in the offered longest SFC and \(N_{s}\) denotes the number of servers in each NFVI-PoP.  
Next, we illustrate an algorithm, inherited by [7; 37; 38], for the SFC routing and resource allocation in NFV architectures in the Scalable Orchestration case proposed in Section 3. We will introduce the main notations in Section 5.1 while the main steps of the algorithm are illustrated in Section 5.2.
## 2 System and Channel Model
### One-Hop Hybrid FSO/RF Link Based on Selective Combination Scheme  
In one-hop hybrid FSO/RF link, the selective combination scheme is adopted, which is realized by comparing the SNR of the two link signals and outputting the signal with the maximum SNR. Therefore, the output SNR \(\gamma_{x,y}^{SC}\) of the selective combiner on the one-hop hybrid link can be expressed as [28]:  
\[\gamma_{x,y}^{SC}=\max(\gamma_{x,y}^{FSO},\gamma_{x,y}^{RF}) \tag{7}\]  
Therefore, the CDF of the SNR \(\gamma_{x,y}^{SC}\) can be expressed as [28]:  
\[F_{\gamma_{x,y}^{SC}}(\gamma)=\Pr(\max(\gamma_{x,y}^{FSO},\gamma_{x,y}^{RF}) \leq\gamma)=\Pr(\gamma_{x,y}^{FSO}\leq\gamma,\gamma_{x,y}^{RF}\leq\gamma)= F_{\gamma_{x,y}^{FSO}}(\gamma)F_{\gamma_{x,y}^{RF}}(\gamma) \tag{8}\]  
It is assumed that the fading parameters on all RF paths are the same--that is, \(m_{i,j}=m_{s,d}=m^{RF}\). By substituting Equations (4) and (6) into Equation (8), the CDF of the output SNR \(\gamma_{x,y}^{SC}\) of one-hop hybrid sub-link can be obtained:  
\[F_{\gamma_{x,y}^{SC}}(\gamma)=\frac{B_{x,y}}{\Gamma(m^{RF})}G_{12}^{1,1}\left( \left.\frac{m^{RF}\gamma}{\overline{\gamma}_{x,y}^{RF}}\left|\begin{array}{ c}1\\ m^{RF},0\end{array}\right.\right)G_{3,7}^{6,1}\left(\left.\frac{A_{x,y}\gamma}{ \overline{\gamma}_{x,y}^{FSO}}\right|\begin{array}{c}C_{x,y}\\ D_{x,y}\end{array}\right.\right) \tag{9}\]  
According to formula (07.34.16.0003.01) in Ref. [33], the product of two Meijer-G functions in Equation (9) can be replaced by the extended generalized bivariate Meijer's G-Function (EGBMGF),  
\[F_{\gamma_{x,y}^{SC}}(\gamma)=\frac{B_{xy}}{\Gamma(m^{RF})}G_{0:0:12:37}^{0:0:1 :61}\left(\left|\begin{array}{c}1\\ m^{RF},0\end{array}\right.\left|\begin{array}{c}C_{xy}\\ D_{xy}\end{array}\right.\left|\frac{m^{RF}\gamma}{\overline{\gamma}_{xy}^{RF}},\frac{A_{xy}\gamma}{\overline{\gamma}_{xy}^{FSO}}\right) \tag{10}\]
# Design and Research of Intelligent Assembly and Welding Equipment for Three-Dimensional Circuit
## 3 Overall Equipment Scheme Design
### System Coordinate Unity
\begin{cases}\overline{sp}=\frac{1}{n}\sum\limits_{i=1}^{n}{{}^{S}p_{i}}\\ \overline{r}=\frac{1}{n}\sum\limits_{i=1}^{n}{{}^{T}p_{i}}\end{cases}  \tag{3}\]  
(2) Decentralize the two-point set:  
\[ \begin{cases}{}^{S}p_{i}={}^{S}p_{i}-\overline{sp}\\ {}^{T}p_{i}={}^{T}p_{i}-\overline{r}p\end{cases}  \tag{4}\]  
(3) Calculate the covariance matrix H between point sets:  
\[H=\sum\limits_{i=1}^{n}{{}^{S}p_{i}({}^{T}p_{i})}^{T} \tag{5}\]  
(4) SVD decomposition of matrix H:  
\[H=UDV^{T} \tag{6}\]  
(5) The rotation matrix \({}^{Temp}_{Sensor}R\) and the translation matrix \({}^{Temp}_{Sensor}t\) can be obtained via the following formula:  
\[\left\{ \begin{array}{l}{{}^{Temp}_{Sensor}R=VU^{T}}\\ {{}^{Temp}_{Sensor}t=\overline{Tp}-{}^{Temp}_{Sensor}R\overline{sp}_{i}} \end{array} \right. \tag{7}\]  
Through the data processing of Formula (3) to Formula (7), the final pose transformation matrix can be solved, the coordinate system calibration of the point cloud camera can be completed, and then the pose information of the processing point can be converted to the Base coordinate system [Base] through Formula (1), which is convenient for subsequent welding paste spraying, resistance mounting, laser welding, and other processes. After each coordinate system of the intelligent assembly equipment system of the array antenna unit is defined, taking the laser welding process as an example, when welding a certain processing point, there is the following coordinate transformation relationship:  
\[ \begin{bmatrix}{}^{Base}_{M}P\\ 1\end{bmatrix} ={}^{Base}_{Sensor}T \begin{bmatrix}{}^{Sensor}_{M}P\\ 1\end{bmatrix} ={}^{Base}_{TCP1}T \begin{bmatrix}{}^{TCP1}_{N}P\\ 1\end{bmatrix} ={}^{[Base}_{N}P\\ 1\end{bmatrix} \tag{8}\]  
Where \({}^{Base}_{Sensor}T\) and \({}^{Base}_{TCP1}T\) represent the homogeneous transformation matrix between point cloud camera coordinate system [Sensor], solder paste spray end coordinate system [TCP1], and Base coordinate system [Base], respectively. \({}^{Base}_{M}P\) and \({}^{Sensor}_{M}P\) represent the coordinate of processing point M in the base coordinate system [Base] and point cloud camera coordinate system [Sensor], and \({}^{Base}_{N}P\) and \({}^{Sensor}_{N}P\) represent the coordinate of solder paste spray end point N in the Base coordinate system [Base] and point cloud camera coordinate system [TCP1]. The processing point and the laser welding end position are unified in the base coordinate system. The unified relationship of each coordinate system is shown in Figure 15.
## 5 Discussion
This proposed analytical approximation model can reveal the QPE distribution, compared with the extreme value method mentioned in Section 1. For the numerical simulation method, with \(M\) error samples for each true anomaly calculation point and a total of \(N\) true anomaly calculation points, the calculation complexity reaches \(O\left(MN\right)\). It is important to point out that \(M\) is usually a large number for better statistical results. No error sample set is generated in the analytical approximation model; thus, the calculation complexity is reduced. The QPE distribution result for \(N\) true anomaly calculation points is \(O\left(N\right)\), and for the distribution parameters, the validated true anomaly \(\
u\in\left[0,2\pi\right]\) is \(O\left(1\right)\), which can significantly reduce the total calculation and processing time. Spaceborne real-time SAR imaging mission designers can quickly determine the maximum QPE with the help of only a calculator and the appropriate parameters. Therefore, this model will accelerate the technical parameter iteration procedure during the early-stage development of an onboard real-time SAR imaging mission and provide strong proof for the feasibility of such spaceborne real-time SAR missions. There will always be some processing blocks in the design of a spaceborne SAR system. For each processing block, the errors introduced by processing must be limited for optimal performance of the whole system. From the design practice of the existing system, an azimuth impulse response width of 1.02 can be an acceptable limitation for a single processing block, which equals a QPE of no more than 0.27\(\pi\)[23]. The results in Section 4 are used here as an example. With the analytical approximation model, the result, and the three-sigma rule, it is clear that, with the simulation parameters given, if \(\mathbb{E}\left[\Delta f_{r,a}\right]\) is removed during the Doppler rate calculation, then the QPE introduced by the onboard orbit determination system will be less than 40.02\({}^{\circ}\), with a probability of 99.73%. This result equals an azimuth impulse response width of less than 2%, with a probability of 99.73%. In its current form, the proposed analytical approximation model is given when the attitude error is ignored; i.e., the orbit determination error and the attitude error of the platform are decoupled. The next generation of the analytical approximation model will take the attitude error into consideration in order to examine the coupled relationship. In addition, for spaceborne bistatic real-time SAR imaging missions, a bistatic form of the model will be needed. These two directions will be our future research topics.
# Interference Aware Resource Control for 6G-Enabled Expanded IoT Networks
## 3 Interference-Aware Scheduling of APs and Nodes  
In the communication scenario proposed in the paper, a large number of IoT nodes, mobile users and network terminals communicate through a set of APs. Since there are a large number of user nodes in the considered setup, each node is served by multiple APs. Also, each AP serves a multitude of user nodes. The interference is caused by the pilot reuse in the communication scenario. The user nodes sharing the same pilot are known as co-pilot user nodes and often led to interference with the intended recipient, known as pilot contamination, which degrades the channel estimation quality.  
Let us consider a user node \(k\) that is assigned a pilot \(\tau\) where \(\tau\in\{1,2\ldots\tau_{p}\}\). Since a set of fixed length orthogonal pilots are used, the pilots are reused among the communicating nodes. A set of nodes that are allocated the pilot \(\tau\) except node \(k\) are termed as interfering nodes to node \(k\) and are denoted as \(U_{\tau,k}\). Also, a node \(k\) with pilot \(\tau\) has interfering APs apart from the intended AP \(l\). The set of interfering APs of node \(k\) is denoted as \(L_{\tau,k}\).  
\[U_{\tau,k}=U_{\tau}\setminus\{k\} \tag{16}\]  
\[\mathcal{L}_{\tau,k}=\bigcup_{i\in U_{\tau,k}}A_{i}\setminus A_{k} \tag{17}\]  
To determine the strength of the intended AP-node link considering the interference caused by the interfering nodes and the interfering APs, a metric is defined as [54]  
\[I_{\tau,k,l}=\zeta_{kl}-\max_{i\in U_{\tau,k}}\zeta_{kl}.\max_{j\in\mathcal{L }_{\tau,k}}\zeta_{kj} \tag{18}\]  
for a particular node \(k\), AP \(l\) and pilot \(\tau\).
# Manufacturing of Micro-Lens Array Using Contactless Micro-Embossing with an EDM-Mold
## 4 Results and Discussion
### Micro-Optics of Micro-Lens Array  
The optical quality of the micro-lens array was the most important parameter. To verify the imaging performance of the micro-lens array, it can be detected by optical measurement system. Figure 10a shows the schematic diagrams of the system. The experiment system consisted of a charge-coupled device (CCD) camera, workbench, directional light source, and computer system. The system had the advantage of convenience and rapid response. Through the projection information, we can estimate the imaging quality.  
Figure 8: Profile of micro-lens measured using ContourGT-X 3D Optical Profiler. (**a**) Forming under conditions of 105 \({}^{\circ}\)C/200 N/120 s; (**b**) forming under conditions of 135 \({}^{\circ}\)C/800 N/120 s.  
Figure 9: The stress distribution of micro-lens array. (**a**) Von [PERSON] stress; (**b**) friction shear stress.  
Figure 10b-j shows the focused light spots of the embossed micro-lens array corresponding to micro-lens array of Figure 7. The light spots of the micro-lens array had a great difference in the same projection position. From the imaging performance of the micro-lens array, the processing parameters had a great influence on the profile of the lens array. The light spots are non-uniform due to the non-uniformity of the lens shape. In the embossing process, the deformation of the PMMA material had a recovery stage, which was related to the temperature and pressure. When the embossing temperature is 105 \({}^{\circ}\)C with embossing force of 200 N and holding time of 120 s, the micro-lens endured larger recovery deformation than that of other processing parameters. Large recovery led to irregular deformations if the process had no intervention. An apparent optical imaging difference can be observed among these micro-lenses under different processing parameters. The temperature and the embossing force were the two crucial parameters affecting the fabrication of micro-lens array.
# Energy Harvesting for TDS-OFDM in NOMA-Based Underwater Communication Systems
## 3 TDS-OFDM-NOMA Scheme
### Acoustic Propagation Loss  
The acoustic transmitted signal loss over a communication distanced \(b\) with carrier frequency \(f\) in dB over UWA communication in the ocean environments can be calculated as [26]:  
\[10\log PL(b,f)=\boldsymbol{\hat{\kappa}}.10\log b+b\cdot 10\log\alpha \tag{16}\]  
where geometric spreading factor \(\boldsymbol{\hat{\kappa}}\) is \(\boldsymbol{\hat{\kappa}}=1\) and \(\boldsymbol{\hat{\kappa}}=2\) for shallow and deep water, respectively. \(\alpha\) in dB/km is the absorption coefficient factor, and it can be written as [26]:  
\[\alpha=\frac{A_{1}P_{1}f_{1}f^{2}}{f^{2}+f_{1}^{2}}+\frac{A_{2}P_{2}f_{2}f^{2} }{f^{2}+f_{2}^{2}}+A_{3}P_{3}f^{2} \tag{17}\]  
The restrictions given in (17) can be categorized as boric acid parameters, i.e., \(f_{1}\), \(A_{1}\) and \(P_{1}\), magnesium sulfate parameters, i.e., \(f_{2}\), \(A_{2}\) and \(P_{2}\), and pure water parameters, i.e., \(f\), \(A_{3}\) and \(P_{3}\). For boric acid parameters, i.e., \(f_{1}\), \(A_{1}\) and \(P_{1}\), \(f_{1}\) is the boric acid relaxation frequency in kHz, and it can be written as [26]:  
\[f_{1}=2.8\left(\frac{\mathcal{S}}{35}\right)^{0.5}\times 10^{[4-1245/(273+ \mathcal{T})]} \tag{18}\]  
where \(\mathcal{S}\) is the water salinity in parts/1000, and \(\mathcal{T}\) is the water temperature at \({}^{\circ}\mathcal{C}\). \(A_{1}\) is the boric acid rate, and it is dependent on pH and the acoustic propagation speed of the ocean water obtained as [26]:  
\[A_{1}=\frac{8.68}{\mathcal{C}}10^{(0.78\text{ pH}-5)} \tag{19}\]  
\(P_{1}=1\) is the pressure at a given depth in ocean water.  
For the magnesium sulfate parameters, i.e., \(f_{2}\), \(A_{2}\) and \(P_{2}\), \(f_{2}\) is the magnesium sulfate relaxation frequency in kHz and is obtained as [26]:  
\[f_{2}=\frac{8.17\times 10^{[8-1990/(273+\mathcal{T})]}}{1+0.0018(\mathcal{S}-35)} \tag{20}\]  
\(A_{2}\) is the value of the magnesium sulfate and obtained as [26]:  
\[A_{2}=21.44\frac{\mathcal{S}}{\mathcal{C}}(1+0.025\mathcal{T}) \tag{21}\]  
The depth pressure of the magnesium sulfate, \(P_{2}\), is obtained by [26]:  
\[P_{2}=1-1.37\times 10^{-4}z+6.2\times 10^{-9}z^{2} \tag{22}\]  
where \(z\) is the water depth in meters.  
\(A_{3}\) is the viscosities of the pure water and can be obtained [26]:  
\[A_{3}=\left\{\begin{aligned} & 4.937\times 10^{-4}-2.59\times 10^{-5} \mathcal{T}+9.11\times 10^{-7}\mathcal{T}^{2}-1.5\times 10^{-8}\mathcal{T}^{3} \\ &\text{for }\mathcal{T}\leq 20\ ^{\circ}\mathcal{C}\\ & 3.964\times 10^{-4}-1.146\times 10^{-5}\mathcal{T}+1.45\times 10^{- 7}\mathcal{T}^{2}-6.5\times 10^{-10}\mathcal{T}^{3}\\ &\text{for }\mathcal{T}>20\ ^{\circ}\mathcal{C}\end{aligned}\right. \tag{23}\]  
\(P_{3}\) is the depth pressure of pure water and is written as [26]:  
\[P_{3}=1-3.83\times 10^{-5}z+4.9\times 10^{-10}z^{2} \tag{24}\]
# Research on Trajectory Planning and Tracking Methods for Coal Mine Mobile Robots
## 2 Trajectory Planning Based on MINCO Trajectory Classes  
### Differential Flatness-Based Control Optimization Trajectories  
In traditional trajectory planning, multidimensional trajectory problems can usually only be solved by solving multiple dimensions separately. [PERSON] et al. first proposed the concept of differential flatness for nonlinear systems [24], and it was valued in the field of robotic trajectory planning in the following years [25; 26; 27]. The differential flatness property is conducive to lowering the dimensionality in the optimization problem, decreasing the number of operations, and increasing the efficiency of the computation. Therefore, it has high application value in trajectory planning. The kinematic model of the downhole mobile robot used in the experiment will be given in the later section, and its differential flatness will be verified, so the differential flatness and its application in trajectory planning will be directly discussed first.
# A Dynamic Parameter Tuning Strategy for Decomposition-Based Multi-Objective Evolutionary Algorithms
## 5 Conclusions  
In order to solve the shortcomings caused by the fixed penalty parameter in the PBI decomposition strategy in the MOEA/D algorithm, this paper proposes the MOEA/D-DPA dynamic tuning algorithm. This strategy will dynamically adjust the penalty parameter according to the current iteration period, and then adaptively adjust the value of the penalty parameter according to the distribution of solutions around the current weight vector, so as to make the population better balance between convergence and diversity while improving the operational efficiency. The experimental results show that the improved dynamic tuning parameter substantially improves the solution quality of the PBI decomposition strategy with higher stability in solving bi-objective optimization problems. In solving the three-objective optimization problem, dynamic parameter tuning improves the quality of the solution of the PBI decomposition strategy to a certain extent, and there is a certain improvement in the population diversity in. However, it also brings the problem of unstable solution. Therefore, it is considered that the tuning strategy has a certain degree of performance optimization effect on the PBI decomposition strategy in multi-objective optimization problems. Tuning strategies offer significant advantages in addressing optimization problems, providing strong support for their application to real-world issues. The MOEA/D-DPA algorithm maintains similar time and space complexity to the basic MOEA/D algorithm, while also demonstrating effective scalability in parallelized and distributed environments. This increased scalability enhances the algorithm's applicability to real-world optimization problems.  
Conceptualization, J.Z. and J.N.; methodology, J.Z. and J.N.; software, H.M.; validation, J.N., H.M. and Z.L.; formal analysis, J.N.; investigation, J.Z.; resources, J.Z.; data curation, J.N.; writing--original draft preparation, H.M.; writing--review and editing, Z.L.; visualization, Z.L.; supervision, J.N.; project administration, Z.L.; funding acquisition, H.M. All authors have read and agreed to the published version of the manuscript.  
Figure 8: Box plots of the HV of algorithms on DTLZ test suites.  
**Funding:** This work was funded by the Scientific Research Top-Level Projects of the Liaoning Education Department, grant number LJKMZ2020613, and the National Student Innovation and Entrepreneurship Program of the Liaoning Education Department, grant number 202310144005.  
**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The authors declare no conflicts of interest.