esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Experimental Results
### Experimental Parameter Settings and Evaluation Indices  
The code implementation in this article was partially performed on a CUDA 11.2, PyTorch version 1.11 framework, and the hardware module was a server with four Nvidia RTX2080 Ti processors, manufactured by Lenovo, Beijing, China. The loss function utilized was cross-entropy, with L2 regularization applied to enhance generalization. The Adam optimizer was used, with a learning rate of 0.001 and a dropout rate of 0.5. A 10-fold cross-validation method was used. To determine the optimal number of iterations for subsequent experiments, a range of values was assessed, and epoch = 50 was chosen based on the achieved accuracy of the model. The outcomes of the comparative analysis are presented in Table 3.  
Various performance metrics can be employed to assess the performance of a system. Although accuracy is widely used as the evaluation criterion to indicate the percentage of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Epoch** & \
\begin{tabular}{c} **Test Accuracy (\%)** \\ **(2-Class)** \\ \end{tabular} & **F1-Score** &
\begin{tabular}{c} **Test Accuracy (\%)** \\ **(4-Class)** \\ \end{tabular} \\ \hline
50 & 97.84 & 96.61 & 88.46 \\
100 & 97.53 & 96.49 & 86.93 \\
150 & 96.81 & 95.12 & 86.10 \\
200 & 97.04 & 96.83 & 85.79 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental comparison for different epoch cases.  
Figure 9: DEAP dataset acquisition process.  
correctly predicted samples, it is not always the most appropriate metric, particularly in the case of imbalanced data. In this regard, the F1-Score is used as an additional performance metric in this study. The F1-Score, which is the harmonic mean of precision and recall, is a statistical measure that assesses the accuracy of a binary classifier. Specifically, the equation for F1-Score is expressed as follows:  
\[\text{Acc}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{FN}} \tag{25}\]  
\[\text{Pre}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{26}\]  
\[\text{Rec}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{27}\]  
\[\text{F}-\text{score}=\frac{2\times\text{Pre}\times\text{Rec}}{\text{Rec}+\text {Pre}} \tag{28}\]  
where TP and TN indicate that the data labels are positive and negative classes, respectively, and are consistent with the recognition results, and FP and FN indicate that the data labels are positive and negative classes, respectively, but are inconsistent with the recognition results.
## 1 Introduction
The proposed framework not only enhances decision making by integrating reinforcement learning (RL) for optimal intervention strategies but also ensures that predictive models remain reliable and accurate through self-supervised retraining workflows. Therefore, in connection with this aim, several research questions were formulated. **RQ1:**: Is it reasonable to use different models to increase the reliability of predictions of process variables? In this case, can the different models play different roles depending on the uncertainty for different time horizons? **RQ2:**: Is there a way to deploy created ML models in a less resource-demanding context with appropriate update capabilities (with respect to the DevOps perspective)? **RQ3:**: Can the global accuracy of the prediction system be tracked continuously? Is it useful in this regard? This paper is organized as follows: Section 2 provides a summary of previous articles related to our research. Section 3 shows an industrial case adopted as a data source to develop this study and the methodology followed. Section 4 describes the proposed framework and the tools used to achieve the research objectives. Section 5 presents the results obtained in the selected use case and the performance of the different models under consideration. These results are highlighted, discussed, and compared by answering the research questions presented. Section 6 presents the conclusions, limitations, and possible future research directions drawn from this study.
## 6 Evaluation
### Maximum Storage Usage of Carrier Terminals  
The simulation result of storage usages of carrier terminals is shown in Figure 11. Since the size of bundle data is 100 Bytes, the maximum storage usage is below 60 KB in any method. The storage usage of _Spray and Wait_ (number of copies: 5) is considerably smaller than _Epidemic Routing_ or _Spray and Wait_ (number of copies: 10). In the case of the proposed protocol, the storage usage is about 5 KB, which is far smaller than that of _Epidemic Routing_ or _Spray and Wait_.
## 4 Case Simulation and Result Analysis
### Comparison of Method Computational Efficiency  
Taking the voltage transient signal detection experiment as an example, the computing time consumption of the three stochastic resonance detection systems is recorded, and the detection real-time comparison is made with the plus-window interpolation FFT, VMD, EEMD, and empirical wavelet transform methods, and the results are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \(\mathbf{D=1}\) & \(\mathbf{D=5}\) \\ \hline SR & 0.6132 & 0.1743 \\ UBSR & 0.6439 & 0.3972 \\ ASR & 0.9879 & 0.9794 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of correlations between the input and output signals of various stochastic resonance detection systems.  
Figure 26: ASR detection system output signal waveform and spectrum at \(D=5\). (**a**) Time domain waveform. (**b**) Spectral amplitude.  
As can be seen from Table 3, the classical stochastic resonance detection algorithm has a lower computational amount compared to the traditional methods such as time-frequency domain detection and has good real-time detection. Additionally non-saturated stochastic resonance detection methods and accelerated stochastic resonance detection methods, relative to the classical stochastic resonance detection methods, just slightly increase the amount of computation, and they still have a higher arithmetic efficiency.
# Optimization of Buoy Shape for Wave Energy Converter Based on Particle Swarm Algorithm
## 2 Theory and Methodology
### Optimization Methods
Therefore, how to improve the energy capture capacity of the buoy is the key. Therefore, the objective function could be defined as the average power captured by the buoy in a wave, represented by \(P_{int}\). By calculating and comparing the value of the objective function, the average power captured by the buoy was maximized for a given wave condition. When the PSO was applied to the buoy line optimization problem, the particles of the PSO were selected as the coordinates of the control vertex \(X_{1}\), \(X_{2}\), \(X_{3}\), \(X_{4}\), \(X_{5}\) of the B-spline curve that controlled the shape of the buoy, which formed the shape vector \((\alpha,\beta,\delta,\theta,\lambda)\). As shown in Figure 6, \(\beta\) controlled the buoy draft height, \(\delta,\theta,\beta\) jointly controlled the buoy draft volume, \(\lambda\) controlled the buoy waterline cross-sectional area, and \(\alpha\) controlled the buoy bottom shape, with a maximum of 20 iterations. The shaded area enclosed by the B-spline curve and the x and z axes was rotated 360 deg about the axis of rotation z, which was the shape of the buoy below the surface line. The upper and lower bounds were set to 15-20% of the difference between the upper and lower bounds so that the buoy shape was not unreasonably sharp or concave, and the algorithm was restricted to search for the optimal solution within a certain range. The upper and lower bounds of the constraint can be expressed as  
\[\left\{ \begin{array}{l}0\leq\alpha\leq 1.0\;\mathrm{m}\\ -1.5\;\mathrm{m}\leq\beta\leq-1.0\;\mathrm{m}\\ 1.0\;\mathrm{m}<\delta\leq 2.0\;\mathrm{m}\\ -1.0\;\mathrm{m}<\theta\leq-0.5\;\mathrm{m}\\ 2.0\;\mathrm{m}<\lambda\leq 3.0\;\mathrm{m}\\ \end{array} \right. \tag{9}\]  
The equation of the B-spline curve can be expressed as  
\[C(u)=\sum_{i=0}^{n}P_{i}N_{i,k}(u) \tag{10}\]  
where \(P_{i}(i=0,1,2\cdot\cdot n)\) are the control points--typically, there are \(n+1\) control points-- and \(N_{i,k}(i=0,1,2\cdot\cdot\cdot n)\) is the \(k\)-_th_ B-spline basis function--the highest number of times  
Figure 5: Buoy shape optimization flow chart for the PSO algorithm. is \(k\). The basis function is the \(k\)-\(th\) segmented polynomial determined by a node vector \([u_{0},u_{1}\cdots u_{n+k+1}]\). The nodes in the node vector are uniformly or equidistantly distributed along the parameter axes, and the B-spline curve is called a uniform B-spline curve. In this paper, a uniform 3 rd-order B-spline curve was used to describe the buoy shape.
## 6 Algorithm 3: Secure Data Aggregation  
Let us define the following variables:  
* \(D_{i}\): Raw data from the \(i\)-th participant institution.
* \(E(D_{i})\): Encrypted version of the data \(D_{i}\) using a secure encryption algorithm.
* \(A\): Aggregated encrypted data.
* \(f()\): Secure aggregation function.  
The mathematical model for secure data aggregation can be represented as follows:  
\[A=f(E(D_{1}),E(D_{2}),\ldots,E(D_{n}))\]  
Here, \(f()\) is a secure aggregation function that takes as input the encrypted data from each participating institution (\(E(D_{i})\)) and computes the aggregated encrypted data \(A\). The function \(f()\) ensures that the aggregation process preserves the security and privacy of the individual data contributions while producing meaningful results for further analysis [43]. The Algorithm 3 explains secure data aggregation as shown below stepy by step:  
```
1: Secure Aggregation \(D_{1},D_{2},\dots,D_{n}\)
2:\(i=1\) to \(n\)
3: Encrypt \(D_{i}\) using a secure encryption algorithm
4: Aggregate the encrypted data using a secure aggregation protocol
5: Transmit the aggregated encrypted data to the blockchain network
```  
**Algorithm 3** Secure Data Aggregation  
The specific implementation details of the secure aggregation function depend on the cryptographic protocols and techniques employed in the proposed approach. It may involve techniques like homomorphic encryption, secure multi-party computation (MPC), or privacy-preserving aggregation protocols.
# A Maximum Power Point Tracking Control Method Based on Rotor Speed PDF Shape for Wind Turbines
## 4 Simulation and Results
### Controller Determination
#### 4.1.1 Goal PDF Is Gaussian Distribution  
The control design is carried out considering that the desired rotor speed PDF obeys the unimodal Gaussian distribution. The goal PDF expression is:  
\[p_{trf}(x)=\frac{1}{\sqrt{2\pi}\sigma}exp\left(-\frac{\left(x-\mu\right)^{2}}{ \sigma^{2}}\right) \tag{30}\]  
where \(\mu=0.9495\) and \(\sigma=0.056\).  
According to the controller design principle and method described in Section 3.1, the polynomial in Equation (24) is fitted with different orders. When the order of the controller is taken as two, the tracking effect of rotor speed PDF shape is shown in Figure 5a.  
It can be found that when \(l=2\), the output PDF curve completely coincides with the desired PDF curve. At this time, the tracking error between the approximate solution of FPK equation and the goal PDF is \(e(l)=3.4078\times 10^{-26}\), which indicates that the tracking accuracy is very high and the tracking effect of the output PDF is very good.  
The two-order controller of this system is:  
\[T_{g}{}^{*}=K_{opt}\omega_{g}{}^{2}-J\left(c_{1}+2c_{2}\omega_{g}+3c_{3}\omega _{g}{}^{2}\right) \tag{31}\]  
where \(c_{0}\), \(c_{1}\), \(c_{2}\) and \(c_{3}\) are the coefficients in the corresponding parametric set (\(C_{2}\)) and are listed in Table A1 in Appendix A.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Symbol** & **Value** & **Unit** \\ \hline \(R\) & \(R\) & 33.05 & m \\ Stiffness of rotor & \(K_{s}\) & 48,376 & N\(\cdot\)m/rad \\ The moment inertia of rotor & \(J\) & 35,000 & kg\(\cdot\)m\({}^{2}\) \\ nominal voltage & \(U_{nom}\) & 575 & V \\ nominal frequency & \(f_{nom}\) & 60 & Hz \\ Number of poles & \(p_{n}\) & 48 & \\ Stator resistance & \(R_{s}\) & 0.027 & pu \\ Stator inductance & \(L_{d}\), \(L_{q}\) & 0.5131 & pu \\ Flux induced by magnets & \(\psi_{f}\) & 1.18842 & pu \\ Air density & \(\rho\) & 1.12 & kg/m\({}^{3}\) \\ Rated power & \(P_{rate}\) & 1.5 & MW \\ Rated wind speed & \(v_{rate}\) & 12.1 & rad/s \\ Rated rotor speed & \(\omega_{r\ rate}\) & 1.1 & rad/s \\ Pitch angle & \(\beta\) & 0 & \(\circ\) \\ Optimum TSR & \(\lambda_{opt}\) & 10.5 & \\ Maximum power coefficient & \(C_{p\ max}\) & 0.44 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the 1.5 MW wind turbine system.
## 3 Energy and Information Beamforming
### Strongest-Path Beamforming Scheme
#### 3.2.2 MUI Mitigation
* In the third step, for the low received SNR scenario, we pick one sensor from each group to serve a particular time-frequency resource. Thus, the maximum number of simultaneously served sensors is equal to \(B_{x}B_{y}\). In the high received SINR scenario, the scheduled sensors are selected in the same way, but further divided into two clusters, as shown in Figure 2, where the sensors from red regions are assigned to Cluster 1 and those from white regions are assigned to Cluster 2. Each cluster is served on different time-frequency resources. Thus, the maximum number of simultaneously served sensors is equal to \(\frac{1}{2}B_{x}B_{y}\). To ensure the fairness between all sensors, the remaining sensors in each group can be served on the other time-frequency resources using the same scheduling procedure. Since in the first step, we have made the length of each block much larger than the spread of virtual AoD of each sensor, i.e., \(\rho_{x,l}^{\max}-\rho_{x,l}^{\min}\) and \(\rho_{y,l}^{\max}-\rho_{y,l}^{\min}\), the proposed scheme can make Condition (a) and/or (b) satisfied approximately. In this case, the power of MUI is greatly mitigated. From the first and third steps, \(B_{x}\) and \(B_{y}\) can be viewed as the parameters that give a tradeoff between the allowed residual MUI and the number of simultaneously served sensors. Thus, considering the constraint in the first step, \(B_{x}\) and \(B_{y}\) should be designed to maximize some performance metric (e.g., the system SE). In practical application, since the feasible sets \(B_{x}\) and \(B_{y}\) are discrete and finite, this task can be completed by simple off-line searching algorithms based on the analytical results in Theorem 1 and (31). **Remark 1**. : _Note that both the strongest-path beamforming and sensor scheduling require the knowledge of virtual AoDs of sensors. By using the class of estimation methods based on discrete Fourier transform with zero padding [38], the virtual AoDs can be estimated at AP from the pilots transmitted by sensors with a certain quantification error._  
**Remark 2**. : _If the remaining battery power of a sensor is not enough to transmit the pilot signal, AP cannot obtain its AoD estimation. In this case, this sensor becomes a \"dead sensor\", which can no longer be severed._  
Figure 2: Illustration of the MUI-aware sensor scheduling scheme. To deal with this problem, the common signal of AP can be exploited. Usually, AP will broadcast periodically some common signals within its coverage region for the purpose of synchronization, access control, etc. The sensor with low battery power can harvest energy from these common signals until it has enough battery power to access the network actively. Some methods of designing an omnidirectionally common signal in a massive MIMO system can be found in [39].
# Does Intensive Land Use Contribute to Energy Efficiency?--Evidence Based on a Spatial Durbin Model
## 2 Literature Review and Theoretical Hypotheses  
### Energy Efficiency  
Energy efficiency, or full-factor energy efficiency, has been the focus of research in the field of population, resource and environmental economics in recent years. As early as 1995, the World Energy Commission gave an explanation of the concept of energy efficiency. Energy efficiency can be defined as: \"reducing energy input to provide equivalent energy services.\" From an economic point of view, [PERSON] (1996) defines energy efficiency based on the concept of energy consumption per unit of output: energy efficiency refers to the use of less energy to provide the same service or produce the same effective output. [PERSON] believes that the improvement of energy efficiency should be based on ensuring economic growth, rather than reducing investment [8]. [PERSON] (2003) added the concept of environment to the goal of energy efficiency on the basis of predecessors, further enriching the concept of energy efficiency [9]. From early energy efficiency research, the measurement of energy efficiency comes from the ratio of energy consumption to GDP, which belongs to single-factor energy efficiency. As a method to measure factor input and output, single factor energy efficiency has the characteristics of good data openness, simple calculation, and clear economic significance. However, it ignores the substitution between factors, and in the actual production process, the inputs of production factors can replace each other to a certain extent, and the substitution rate can be expressed by the marginal substitution rate; single-factor energy efficiency does not take into account the externality of energy consumption, and in the process of energy consumption, waste water, waste gas and solid waste will inevitably be generated. Therefore, energy efficiency will be overestimated if single-factor energy efficiency is used [10].  
Based on the shortcomings of single-factor energy efficiency, scholars began to look for better indicators to describe the relationship between energy consumption and output. [PERSON] and [PERSON] (2006) made a breakthrough study on the concept of energy efficiency, and they also combined data envelopment analysis to measure total factor productivity [11]. The addition of labor and capital to the input factors changed the analytical framework of previous studies that only considered energy factors, and at the same time introduced the concept of marginal substitution of factors. This method of calculation has also been widely used by later scholars. On the basis that the output remains unchanged, the ratio of the optimal energy input to the actual input can better present the connotation of efficiency because the coordination of different input elements is considered.
## 5 Performance Evaluation
### Configuration  
There are two types of benchmarks: user and kernel level. The benchmark performs operations on linked list and hash table data structures. The linked list is initially set to 1000 nodes. The chained hash table is set to 1000 buckets and 5000 initial nodes. The key of the initial node is generated by a uniformly distributed random integer. Thus, an average of five nodes are created for each bucket. To compare the performance of each scheme according to the amount of write, the update ratio is set to 2%, 20%, and 50%, respectively. The update comprises of inserting and deleting nodes in equal parts. As a result, the total number of nodes remains nearly constant at all times. The operation is random and uniformly distributed to all nodes, and the execution time per experiment is 10 s. The write log size for each thread of MV-RLU is 128 KB, and all other settings use the default. The benchmarks pin running threads to the cores to minimize the performance interference caused by migration between the cores. For this experiment, we use the hyperthread feature of the CPUs. The benchmark's main thread is always executed on core 0 of CPU 0, and threads created in the benchmarks are pinned in the order of core 1 to 17 of CPU 0 on NUMA node 0 and core 0 to 17 of CPU 1 on NUMA node 1. As a result, the hyperthread is not activated until the 36 threads have been created. The additional threads are again pinned to core 0 to 17 of CPU 0 and core 0 to 17 of CPU 1 in order, and they perform with the hyperthread on the cores.
## 3 Mathematical Model
### Model Development  
The mathematical model strives to reach the aim of the minimum TC under the condition of meeting the material demand with the optimal configuration of the production scheduling system. Before a scheduling plan is carried out, the data of orders, time windows, material inventory of each supplier, vehicles, roads and other information are collected. In the model (as shown in Figure 1), \(D=(V,F,E)\) is defined as a directed graph. where \(V=N\cup P\) represents a set of network nodes. N is the set of SSs, and \(P\) is a node of \(FS\). The arc set \(B\) is defined as \(B=\{(i,i),\,i\in N,j\in P\}\). Each arc corresponds to a transportation route between \(FS\) and \(SS\). \(F\) represents a set of fuel vehicles, and \(E\) represents a set of electric vehicles. For a production scheduling plan, there are \(h\) SSs that will be selected as material suppliers to serve \(FS\). The transportation facility for materials includes two types of electric vehicles and fuel vehicles. In addition, electric vehicles and fuel vehicles have different fixed costs and carrying capacities.
## 1 Introduction
### Abnormal Propagation Paths in the Power Grid During Tropical Cyclones  
Based on Equations (20)-(25) in Section 4.1, the transition of grid abnormal states from one period to the next is a complex process governed by both power flow redistribution and extreme meteorological factors. During the \(T\) periods of a tropical cyclone, each period corresponds to a unique system state distribution, forming the abnormal propagation path illustrated in Figure 3. The fault events caused by extreme weather factors at longer time scales (minutes or hours) are denoted as \(F^{D}\) in Figure 3, with \(T_{D}\) representing the development time of extreme weather. At shorter time scales (seconds or milliseconds), cascading overload faults due to power flow redistribution are denoted as \(F^{C}\) in Figure 3, with \(T_{C}\) representing the development time of cascading faults. The radial derivatives in Figure 3 represent the power flow transfer process that occurs when an initial branch failure leads to a redistribution of system power flow. If this redistribution results in line overloads, the radial arrows illustrate the flow shift, while the blue dots indicate lines that disconnect due to these overloads.  
Section 3 has already detailed the methods for calculating branch fault probability and wind power anomalies under extreme meteorological conditions. To derive the abnormal  
Figure 3: Schematic diagram of the power grid anomaly propagation process, accounting for the coupling effects of meteorological factors and power flow.  
Figure 2: Schematic diagram of the power grid anomaly coupling process considering extreme weather factors and power flow redistribution.  
propagation path, the key step is to analyze the power flow redistribution process. This section focuses on two main aspects, (1) generator rescheduling and (2) islanding management, to explain the power flow redistribution in detail. Finally, the procedure for deducing the abnormal propagation path is outlined.