esa-sceva/satcom-chunk-collection huggingface dataset:
# Rapid and Precise Zoom Lens Design Based on Voice Coil Motors with Tunnel Magnetoresistance Sensors
## 5 Discussion and Conclusions
The striking contrast of the zoom times between large-ratio zoom lenses with VCMs and those with traditional step motors demonstrates that our design using VCMs effectively decreases the zoom time to 0.2 s. Additionally, experiments to measure the positioning accuracy under ISO 230-2-1997 indicate that the maximum positioning deviation of our positioning control system is \(-\)0.0048 mm (\(\leq\)5 \(\upmu\)m), with high positioning repeatability. In conclusion, we propose a novel design for the integration of VCMs and TMR sensors into high-magnification zoom lenses, with the advantages of rapid zooming and high-precision positioning. This design directly converts the linear motion of the motor into the linear movement of the zoom lens group, significantly enhancing the zoom speed and positioning accuracy. Our design example ensures a sufficient margin for the zoom time and driving force to cope with various usage scenarios and environmental challenges, such as temperature variations, lens posture changes, and control errors. In these situations, the zoom time remains within 0.3 s. In the future, we will focus on precise multi-physics simulation analyses to further optimize and miniaturize the permanent magnet and magnetic yoke. Furthermore, exploring the integration of advanced control algorithms to enhance the precision and speed of the zoom groups is also crucial. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14166990/s1](https://www.mdpi.com/article/10.3390/app14166990/s1)]([https://www.mdpi.com/article/10.3390/app14166990/s1](https://www.mdpi.com/article/10.3390/app14166990/s1)). Conceptualization, [PERSON]; methodology, [PERSON]; software, J.G. ; validation, [PERSON]; formal analysis, J.G. ; investigation, J.G. ; writing--original draft preparation, J.G. ; writing--review and editing, J.L. ; supervision, J.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding.
## 3 LORAWAN Operation
### LoRa  
LoRa, a Chirp Spread Spectrum (CSS) technology [22], can operate in different ISM bands, including bands in the range 136-174 MHz, 433 MHz, 470 MHz, 780 MHz, 868 MHz, 915 MHz, and 920/925 MHz. The operating parameters; frequency, bandwidth, maximum power, and other factors are determined by the regulatory environment of the country of operation. In Europe, the typical operation is the 868 MHz ISM band and can include the frequency band from 863 to 869 MHz. The frequency band may be split into sub-bands and designated for other purposes to allow 'underlay' services limited to a Tx of 25 mW (+14 dBm) Effective Isotropic Radiated Power (EIRP) and a DC of 0.1%. The core channels for LoRa operation in EU are 868.1, 868.3, and 868.5 MHz, also limited to 25 mW Tx with a 1% DC limitation.  
Several LoRa modulation parameters are adjustable. One of the main parameter is the SF, which is used to trade-off transmission speed for receiver sensitivity. For LoRaWAN 868 MHz operation, there are seven SF used (SF6-SF12). LoRa signals can be modulated with SF7-SF12 [36]. The SFs are orthogonal and allows the concurrent decoding of messages sent on the same channel with a different SF without interference As the higher SF causes a slower transmission, the packet size is reduced to minimise the on-air time and to reduce the chance of packet corruption. To exploit the opportunity of multiple concurrent decodable transmissions, the SX1301 Semtech Base Band Processor emulates 49 LoRa Demodulators [37], and according to the Semtech LoRa FAQ [38], a LoRaWAN gateway utilizing the SX1301 component and operating on eight channels can handle approximately 1.5 million LoRaWAN packets per day.
## 4 Simulation Results
### Comparison of the Network Throughput  
The network throughput is an important indicator that fundamentally reflects the performance of a protocol. The network throughput refers to the number of packets in the network that are ultimately sent to the BS. The CMs transmit the sensed information to the CH in the form of packets, and the CH fuses this information with that sensed by itself and finally sends the information to the BS in the form of packets. As the nodes in the network die continuously, the number of CHs in the network decreases, and the success rate of transmitting packets to the BS also decreases. Therefore, the number of data packets transmitted in each round will gradually decrease. Until the last node dies, the network throughput reaches a maximum value, which is just the final network throughput.  
As seen from Figure 22, the final network throughput of LEACH is 11005, that of DEEC is 13901, and that of GSEN is 16239. Compared with these three, the EECRP-HQSND increases the final network throughput by 167.53%, 111.80%, and 81.30%, respectively. With an effective ICRM, the EECRP-HQSND-ICRM increases the final network throughput by 856 compared to EECRP-HQSND.  
Figure 21: The variance of the residual energy of the nodes in the network.
# Predicting Models for Local Sedimentary Basin Effect Using a Convolutional Neural Network
## 5 Basin Amplification Characteristics Prediction
### Optimize Input Parameters to Build Prediction Models
#### 5.3.1 Effect of Shape Ratio  
Eight conditions were set to observe the change in the predicted results of the amplification factor and the hazard location. Only the shape ratio parameter was modified and other parameters were kept constant. The shape ratio of working conditions 1-4 is 0.15, 0.2, 0.3, and 0.4, and the same working condition ratio is adopted for working conditions 5-8. The sensitivity analysis of the shape ratio is performed in both symmetric and asymmetric cases.  
From the predicted results of the amplification factor, Figure 17, it can be seen that the amplification factor increases with the increase in the shape ratio factor for both symmetric and asymmetric basins. The predicted amplification factor of asymmetric basins shows that the amplification factor was higher than that of symmetric basins under the same shape ratio. On the contrary, in the hazard location prediction, the hazard location is closer to the center of the basin with the increase in the shape ratio. The analysis of the above results may be due to the narrower basin with a larger shape ratio which results in a more pronounced basin focus effect. The hazard location of the asymmetric basin is closer to the center of the basin than the symmetric basin under the same shape ratio.  
Figure 16: Accuracy of CNN-AP-8 model in predicting amplification factor.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Direction** & **MAE** & **RMSE** & \(R^{2}\) \\ \hline CNN-AF & \(\backslash\) & 0.0385 & 0.0509 & 0.9995 \\ CNN-RL & 0.9470 & 1.3114 & 2.0410 & 0.9502 \\ CNN-AP-8 & 0.9249 & 0.0421/1.2109 & 0.0575/1.9128 & 0.9853/0.9538 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of CNN-AP-8 model with CNN-RL and CNN-AF models.
# Non-Vertical Well Trajectory Design Based on Multi-Objective Optimization
## 4 Information Entropy Guided Multi-Reference Vector NSGA-III Optimization Algorithm
### Multi-Granularity Adaptive Reference Vector Generation
For example, in the high-density region, \(\alpha\) is close to 0, which makes the reference vector \(w\) closer to the regional center \(C\), thereby enhancing the local search ability; in the low density region, \(\alpha\) is close to 1, which makes the reference vector \(w\) closer to the uniform distribution, thus covering the macro structure. The core idea of the strategy is to achieve the adaptive coverage of the target space by dynamically adjusting the density and distribution granularity of the reference vector. Specifically, the dynamic granularity proportion adjustment generates coarse-grained reference vectors in the early stage of the algorithm (\(t\ll T\)) through the formula \(\lambda t=max(0.3,1-0.6t/T)\), so as to quickly explore the global distribution; in the later stage of the algorithm, the proportion of fine-grained reference vectors is gradually increased to focus on high-density areas and improve convergence accuracy. The density-driven vector generation combines kernel density estimation and K-means clustering. The reference vector is dynamically generated by the formula \(w=\alpha u+(1-\alpha)C\), so that the distribution of the reference vector matches the density distribution of the population. For example, in high-density regions (such as the convex part of the Pareto front), fine-grained reference vectors are generated to enhance the local search; in the low-density region (such as the concave part of the Pareto front), a coarse-grained reference vector is generated to avoid excessive concentration. Through the above design, the algorithm can quickly cover the macro structure of the target space through coarse-grained reference vectors in the early stage and avoid falling into a local optimum. In the middle stage, the global exploration and local development are balanced by dynamically adjusting the coarse/fine-grained ratio. In the later stage, the high-density region is focused by the fine-grained reference vector to improve the convergence accuracy and distribution uniformity of the solution set.
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 3 Analysis of Resource Management in the MLSCS
### Primary Challenges in Resource Management  
In the design of MLSCS, the Walker constellation is commonly used. The Walker constellation offers several advantages:  
* Stable topology: The movement of all satellites in the constellation is similar, with minimal variation in perturbation and mutual position. This results in a stable constellation shape, which facilitates system control, management, and routing.
* Near-circular orbit: The Walker constellation adopts a near-circular orbit, where the angular velocity of satellite motion is nearly constant. This characteristic is beneficial for achieving global uniform coverage.  
In the MLSCS, resource scheduling is crucial for efficient operation. However, several adverse factors restrict effective resource scheduling. These factors include orbital perturbations, satellite failures, and communication latency, among others. Understanding and mitigating these factors is essential for optimizing the performance of the satellite constellation system.
## 5 Cluster Formation and Parameter Optimization  
The literature works studied in this section show that several clustering procedures concentrate primarily on MANETs and their power problems. However, there are a few specific solutions to VANETs. Most systems concentrate on the lane, road identification, and speed as the grounds for clustering, but none of the procedures, to our understanding, require the reputation of being a node to shape a cluster with speed, location, and lane. In [44], the authors obtained highway IDs and used them to form stable groups. Additionally, theauthors utilized the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for optimizing the Adaptive Weighted Clustering Protocol (AWCP). It has also been demonstrated that Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Differential Evolution (MODE) can play significant roles in ad hoc network modeling. According to [PERSON] et al. [45], referring to insects' behavior, which shapes organizations, we can come up with the most creative solution. The recommended approach is computer-based and is better suited for circumstances when a comprehensive survey is required to determine the best option. A multi-head clustering technique employed in [46] uses a master-slave strategy to create and maintain stable clusters. It is to be noted, however, that the methodology has not enabled all cars to follow the same course in many instances. In [47], the algorithm for the identification of cluster leaders was intended explicitly for roads. The approach has another parameter, differentiation metrics, which places cars traveling quickly into one group and cars traveling at a reduced speed in another group. The Weighted Clustering Algorithm (WCA) was suggested in [48], which selects a measured weight node including velocity, energy level, neighbor number, and median range. It is evident from all the clustering algorithms that the setup parameter has a significant impact on the created nodes. The enhancement of parameter settings such as cluster size, hello interval, and time out interval is therefore of utmost importance for distinct versatility circumstances. The authors of [49] used metaheuristic algorithms in genuine VANETs to enhance the AODV protocol's QoS and file transfer protocols (FTPs). Metaheuristic solutions have been discovered to fix VANET issues with channel allocation [50; 51]. An overview of clustering algorithms is given in Table 1. The study in [52] proposes a Reputation-Based Weighted Clustering Protocol (RWCP). To formulate the strategy, it is essential to take into account the number of times each node has become a cluster head (CH) for each cluster. Nodes that become CHs more frequently are considered more credible and are more likely to be asked to become CHs of specific groups. Furthermore, the study also examines how to optimize the algorithm by generating a multi-objective clustering problem in VANETs with the Firefly Algorithm to find the parameters necessary to tune the suggested clustering protocol (RWCP).
# Power Allocation and User Grouping for NOMA Downlink Systems
## 1 Introduction
### Contributions  
We propose an improved user grouping method and new power allocation methods to solve the problems in the above research. A mathematical model is established to optimize the sum rate of the system under the constraints of the total power and the minimum transmission rate. We use the idea of step-by-step optimization. We divide a complex non-convex optimization problem into two sub-problems to solve, respectively. Firstly, aiming at the disadvantage that users with a similar channel gain will be divided into one group in the user grouping method based on channel gain difference, when the remaining users are grouped, the grouping method is changed to expand the channel gain difference between users in the same group as much as possible. Secondly, we propose a DL algorithm for the power allocation among subcarriers for the first time. Thirdly, the closed-form solution of power allocation between multiplexed users is derived according to the constraint of the minimum transmission rate.  
In this work, we first propose the DL method for power allocation among subcarriers. In contrast to traditional DL methods, the proposed method does not require a large dataset to train the neural network and can optimize the sum rate of the system. The power allocation method we use can handle the constraints well, guarantee the minimum transmission rate of users, and avoid resource waste.  
The structure of this article is described as follows. We describe the system model and problem formulation in Section 2. The proposed algorithms are introduced in Section 3. The simulation results and detailed analysis are shown in Section 4. Finally, the work conclusions are drawn in Section 5.
## 4 The New Solution Procedure  
The stated optimization problem subject to the nonlinear differential-algebraic constraints takes a new form according to the multiple-shooting approach. Then, the optimization task is reformulated by the \(\alpha\)-model parametrization procedure. Finally, the ordered processing and computational steps of the designed procedure are presented as the \(\alpha\)DAE model optimization algorithm.  
**The \(\alpha\)DAE model optimization algorithm:**  
**Step 1.**: Define the optimized objective function according to Equation (1) with the system  
* of differential-algebraic constraints (2)
* Define \(N\in\mathcal{R}\) and apply the multiple-shooting approach to obtain:
* a parametrized form of the objective function (12),
* the matrix of the initial solution (13).
* the systems of the \(DAE^{i}\), for \(i=1,\ldots,N\), (15)
* the continuity constraints (16),
* the initial conditions' consistency constraints (17)
* Apply the \(\alpha\)-model parametrization algorithm to obtain:
* the systems \(\alpha DAE^{i}\), for \(i=1,\ldots,N\), (23)
* a matrix \(\mathbf{X}_{\alpha}\) (24)
* Define \(n\in\mathcal{N}_{+}\) and a sequence \(\{\alpha_{k}\}_{k=1}^{n}\) with \(\alpha_{1}=0\) and \(\alpha_{n}=1\)
* For \(k=1\) to \(n\), solve \(\min_{\mathbf{X}_{\alpha_{k}}}F(\mathbf{X}_{\alpha_{k}})\) subject to \(\alpha_{k}DAE^{i}(\mathbf{X}_{\alpha_{k}})\), for \(i=1,\ldots,N\), (23) the continuity constraints (16), the initial conditions' consistency constraints (17), to obtain \(\mathbf{X}^{\star}_{\alpha_{k}}\), and substitute \(\mathbf{X}_{\alpha_{k+1}0}=\mathbf{X}^{\star}_{\alpha_{k}}\)
* The matrix \(\mathbf{X}^{\star}_{\alpha_{n}}\) is a final solution.  
The finite number of \(n\) iterations needs to be assumed. Then, the unknown values in the decision variables' matrix \(\mathbf{X}^{\star}_{\alpha_{n}}\) can be obtained.  
The computational complexity of the proposed algorithm is related to two main aspects:  
* The complexity of the algorithm applied to solve finite-dimensional optimization tasks with pointwise-continuous constraints;
* The number \(n\) of \(\{\alpha_{k}\}\) with \(k=1,\ldots,n\).  
The computational effort related to the application of the \(\alpha\)DAE model optimization algorithm is dependent on the sequence of the \(\alpha_{k}\) parameters, the number of multiple-shooting subintervals, as well as the size of the DAE system:  
\[\underbrace{n}_{\text{sequence of the $\{\alpha_{k}\}$ parameters}}\times\underbrace{N}_{\text{shooting subintervals}}\times\underbrace{(n_{y}+n_{z}+n_{u})}_{\text{size of the DAE system}} \tag{34}\]  
The computational effort related to the problem solving with the proposed algorithm can improve the efficiency of the applied numerical optimization procedure. The appropriate values of the initial solution can prevent long-term calculations and the possibility of the premature termination of the algorithm as a result of finding a local minimum. The efficiency of the proposed procedure was tested in the task of a heat and mass transfer system's design.
# A Novel Rolling Bearing Fault Diagnosis Method Based on MFO-Optimized VMD and DE-OSELM
## 3 MFO Algorithm and Its Improvement
### Verification of the Algorithm  
To verify the effectiveness and superiority of the improved MFO algorithm, four commonly used functions were selected for the test, and the dimensions for the tested functions are all 30. The tested functions are given as follows.  
8. (1) [PERSON]'s Problem 1.2 function \[f_{1}(x)=\sum_{i=1}^{d}\left(\sum_{j=1}^{i}x_{j}\right)^{2}\] (15) where;\(x_{i}\in[-100,100]\), and the optimal value of this function is 0. (2) [PERSON]'s Problem 2.22 function \[f_{2}(x)=\sum_{i=1}^{d}\lvert x_{i}\rvert+\prod_{i=1}^{d}\lvert x_{i}\rvert\] (16) where; \(x_{i}\in[-10,10]\), and the optimal value of this function is 0. (3) Sum Squares function \[f_{3}(x)=\sum_{i=1}^{d}ix_{i}^{2}\] (17) where; \(x_{i}\in[-10,10]\), and the optimal value of this function is 0. (4) Ackley function \[f_{4}(x)=-20\exp\left(-0.2\sqrt{\frac{1}{d}\sum_{i=1}^{d}x_{i}^{2}}\right)- \exp\left(\frac{1}{d}\sum_{i=1}^{d}\cos(2\pi x_{i})\right)+20+e\] (18) where; \(x_{i}\in[-32,32]\), and the optimal value of this function is 0. In the verification, the moth population is set to consist of 30 individuals, with a maximum of 1000 iterations; the parameters m and \(p\) in the crossover operator are set as 15 and 5, respectively, which indicates that the first 15 flame positions after sorting are disordered five times. Each tested function was performed 20 times, and the results are compared with those obtained from the original MFO, GWO, and PSO optimization algorithms. A summary of the comparison results is shown in Table 1.  
The iterative optimization convergence curves of MFO, GWO, and PSO are shown in Figure 2. Additionally, Table 1 illustrates that the improved MFO algorithm exhibits superior optimization capability compared to three other algorithms. It produces the highest optimization accuracy across four test functions and is able to identify global optimal values. These findings provide convincing evidence that the algorithm improvement was effective.  
Figure 1: The flowchart of the improved MFO.  
Figure 2: The iterative optimization convergence curves of MFO, GWO, and PSO. **(a)** f1, **(b)** f2, **(c)** f3, and **(d)** f4.
## 3 Materials and Methods
### Real-Time Digitisation and Harmonics Extraction with Embedded Processing
For calibration-free WMS using first and second harmonics, background measurements are performed, whereby the non-absorbing transmitted signal, \(I_{0}(t)\), obtained without the absorbing species present, but using same hardware to minimise measurement variations, is similarly demodulated to obtain the background in-phase and quadrature components: \(S_{0,\ I,f_{m}},\ S_{0,Q,f_{m}},\ S_{0,1,2f_{m}}\), and \(S_{0,Q,2f_{m}}\). Finally, the background-subtracted, \(1f\)-normalised \(2f\) signal, \(S_{2f/1f}\), is calculated on the high-level processor using  
\[S_{2f/1f}=\sqrt{\left(\frac{S_{1,2f_{m}}}{A_{1,f_{m}}}-\frac{S_{0,1,2f_{m}}} {A_{0,1,f_{m}}}\right)^{2}+\left(\frac{S_{Q,2f_{m}}}{A_{1,f_{m}}}-\frac{S_{0, Q,2f_{m}}}{A_{0,1,f_{m}}}\right)^{2}}. \tag{10}\]  
As detailed in [20], the peak value of the \(S_{2f/1f}\) bears a strong correlation to the critical parameters of the gas sample. Hence, in this work, we extracted the peak value to determine the gas concentration as described in [14; 20]. The architecture shown in Figure 2 and described above was implemented on the embedded FPGA (xc7z01 oclg400-1) within the Redpitaya Soc [21]. The Redpitaya is an open source, commercial off-the-shelf platform consisting of a fast 125-MSPs ADC tightly coupled with the AMD's ZYNQ FPGA. An Ethernet port is also accessible for real-time data transfer from the SoC board to a PC. The firmware was coded with the Verilog Hardware Descriptive Language using the VIVADO (version 2022.2) development environment. Figure 3 shows the RTL of the implementation of the real-time demodulation process. The demodulation architecture was integrated into the Redpitaya native firmware. The resource overhead of the lock-in process is shown in Table 2. These are discussed in Section 4.