esa-sceva/satcom-chunk-collection huggingface dataset:
# Multidisciplinary Reliability Design Optimization Modeling Based on SysML
## 5 Evaluation and Discussion  
### SysML Optimization Metamodel Extension Based on CSM  
Stereotype defines how to extend existing metaclasses. This section extends the optimization metamodel defined above using the Cameo System Modeler 19.0 (CSM) software. By adding extension elements to the Profile Diagram and based on the theory described in Section 3, the Multidisciplinary Design Optimization metamodel was constructed, as shown in Figure 11. It includes the optimization variable metamodel, the optimization constraint metamodel, and the optimization objective metamodel.  
Figure 11: SysML element extension metamodel (partial).  
Figure 10: Extraction rule metamodel.  
The extended optimization element metamodel can be utilized in future optimization modeling by publishing it. For example, the metamodel for an aleatory design variable includes attributes such as name, mean, variance, and distribution type. In practice, the metamodel for an aleatory design variable can be directly dragged in, and its attribute information can be set through the specification. The specific extension relationships of the optimization metamodel are shown in Figure 12.  
In Figure 12, the MDO metamodel encompasses design variables, discipline classification, optimization constraint, and optimization objective. The design variables are further divided into continuous variables, aleatory variables, and epistemic variables. The constraints are categorized into equalities and inequalities, with both optimization constraints and objectives classified under the constraint block stereotype.
# Energy Efficient and Reliable Routing Algorithm for Wireless Sensors Networks
## 6 Conclusions  
In order to improve the energy efficiency and the reliability of the whole network, this paper innovatively applies the data fusion rules of DS evidence theory to WSN routing decision-making and proposes an energy-efficient and reliable-routing algorithm based on weighted DS evidence theory. For each node in the network, three attribute indexes are established: transmission energy efficiency ratio, idleness degree and energy density factor, and the entropy weight method is adopted to dynamically determine the weight of each index, and the combination of the above two can get the BPA function. Then the DS evidence fusion rule is used to fuse the reliability value of the index in turn, which can objectively make the optimal routing decision. The simulation results show that, compared with MCRP and FLEOR algorithm, DS-EERA can effectively reduce network energy consumption, prolong the network lifetime and also improve packets loss rate, showing a good performance in transmission reliability.  
In future work, we will focus on combining mobile charging technology and routing optimization strategies to enhance the performance of wireless sensor networks.  
Conceptualization, L.T. and Z.L.; methodology, L.T. and Z.L.; software, Z.L.; validation, Z.L. and B.F.; formal analysis, L.T. and Z.L.; resources, B.F.; writing--original draft preparation, Z.L.; writing--review and editing, L.T., Z.L. and B.F.; supervision, L.T. and B.F.; project administration, L.T. and B.F.; funding acquisition, L.T. and B.F.; All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant number 51677065. The authors declare no conflict of interest.
## 3 System Model  
### Network Model  
Consider a static rechargeable wireless sensor network modeled as an undirected graph \(G=(V,A)\), where \(V\) is the set of \(n\) rechargeable sensor nodes and sink nodes within the wireless sensor network. \(A\) is the set of links, \(A=\{A|(i,j)\in A,i,j\in V\}\). \(G\) consists of a finite nonempty vertex set \(V\) and edge set \(A\) of ordered pairs of distinct vertices of \(V\). Two nodes \(i\) and \(j\) are connected by a link if they can transmit a packet to each other with a transmission power less than the maximum transmission power at each node, where a sensor node generally obtains a maximum transmission range depending on its maximum transmission power and neighbors only within the transmission range can receive the packets from the node. The maximum transmission power is affected by the hardware of wireless radio transmitting device. Considering the condition of energy supplement, at present, the maximum transmission range is set to be less than 200 m in the real scenario.  
In the networks, all links are assumed to be bi-directional. This assumption is not necessary for the convergence of the distributed algorithms; however, it can make the presentation clearer. A graph is simple if it has no loops and no two of its links join the same pair of vertices. An acyclic graph is one that contains no cycles. The set of nodes are connected to node \(i\) by links denoted as \(S_{i}\), which indicates that all sensor nodes in the set \(S_{i}\) can communicate with sensor \(i\) directly without forwarding other sensor nodes. We assume that the network graph is connected, i.e., there always exists a path between any pair of nodes \(i\) and \(j\) in \(V\). Isolated nodes are not considered in the work, even though they exist. The data generation rate and the remain energy of node \(i\) are expressed as \(g_{i}\), \(e_{i}\), respectively.
###### Abstract  
To address the issue of declining prediction accuracy caused by the lack of data in newly constructed wind and solar power stations, this paper introduces a transfer learning-based forecasting approach for wind and photovoltaic power. The method incorporates sensitive meteorological feature selection and utilizes a Temporal Convolutional Network-Attention-Long Short-Term Memory (TCN-ATT-LSTM) model. [PERSON]'s rank correlation, mutual information entropy, and Pearson correlation are employed to investigate the relationship between meteorological features and power output. Through evidence theory, meteorological features with a cumulative contribution exceeding 85% are selected as inputs for the wind and solar power forecasting model. The TCN-ATT-LSTM network is pre-trained to extract common knowledge, and transfer learning is applied to fine-tune (FT) the model through network parameter adjustments. This enables the adaptive model to be quickly constructed for target wind and solar power stations with limited data, improving the prediction accuracy. Finally, the effectiveness of the proposed method is validated through its application to data from a projected wind and solar power station planned for a region in northwestern China. The proposed method not only enhances forecasting accuracy for emerging wind and solar power stations with limited data but also has significant implications for the renewable energy industry.  
transfer learning; wind and solar; sensitive meteorological features; power forecasting
## 4 EWM-TOPSIS-Gray Relational Degree Model
### Determining Indicator Weights Using the Entropy Method  
Entropy weight method (EWM) is an objective assignment method, and its core idea is to determine the weight of each indicator based on the information entropy of the data. The information entropy reflects the degree of disorder of the data, and the smaller the entropy value, the greater the amount of information contained in the indicator, which should be given a higher weight in the comprehensive evaluation. The specific calculation steps are as follows:  
The original data are first standardized, followed by the calculation of each indicator's entropy and entropy weight. The detailed steps are as follows:  
(1) Construct a Normalized Decision Matrix Utilize the standardized sample data to create a normalized decision matrix comprising 24 evaluation objects and 19 evaluation indicators \(Y=(y_{ij})_{24\times 19}\).  
(2) Calculate the Entropy of Each Evaluation Indicator Initially, compute the entropy values for each evaluation indicator. \(e_{j}\), Then, calculate the entropy weight for each indicator based on the entropy values \(w_{j}\), where \(1-e_{j}\), represents the coefficient of variation for the \(j\)-th indicator:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Secondary Indicator** & **Indicator Code** & **Tertiary Indicators** \\ \hline \multirow{8}{*}{Perceptual Ability} & A1 & Average Fixation Duration \\  & A2 & Average Pupil Diameter \\  & A3 & NN50 \\  & A4 & HF (High Frequency) \\ \cline{1-1}  & A5 & Fixation Duration \\ \cline{1-1}  & A6 & Average Blink Frequency (Inverse) \\ \cline{1-1}  & A7 & Saccade Duration (Inverse) \\ \cline{1-1}  & A8 & Number of Fixation Points (Inverse) \\ \cline{1-1}  & A9 & Average Saccade Frequency \\ \hline \multirow{4}{*}{Information Understanding Ability} & B1 & Information Reception and Filtering \\ \cline{1-1}  & B2 & Information Integration And Association \\ \cline{1-1}  & B3 & Information Storage and Retrieval \\ \cline{1-1}  & B4 & Information Interpretation and Judgment \\ \cline{1-1}  & B5 & Information Updating and Adaptation \\ \hline \multirow{4}{*}{Information Processing and Decision-Making Ability} & C1 & Information Acquisition Efficiency \\ \cline{1-1}  & C2 & Information Analysis Quality \\ \cline{1-1}  & C3 & Timeliness of Decision Making \\ \cline{1-1}  & C4 & Communication and Coordination \\ \cline{1-1}  & C5 & Average Fixation Duration \\ \hline \hline \end{tabular}
\end{table}
Table 5: Indicator system for assessing the level of situational awareness of remote tower controllers.  
\[e_{j}=-\frac{1}{\ln(24)}\sum\limits_{i=1}^{24}\frac{y_{ij}}{\sum\limits_{i=1}^{24 }y_{ij}}\ln\frac{y_{ij}}{\sum\limits_{i=1}^{24}y_{ij}} \tag{1}\]  
\[w_{j}=\frac{1-e_{j}}{\sum\limits_{j=1}^{19}\left(1-e_{j}\right)} \tag{2}\]
## 3 Our Construction
### The Proposed Scheme  
#### 3.1.1 Setup  
Key generation center (KGC) performs the following operations [2]:  
1. Choose a security parameter \(1^{\lambda}\)\((\lambda\in\mathbb{N})\).
2. Choose integers \(n\) and \(q\)\((\)a prime\()\), \(q=poly(n)\).
3. Choose dimension \(m\geq 6n\log q\) and a bound \(L=O\big{(}\sqrt{m}\big{)}\).
4. Choose a Gaussian parameter \(s\geq L\cdot\omega\big{(}\sqrt{\log(m+m^{\prime})}\big{)}\), where \(m^{\prime}=\ poly(\lambda)\in\mathbb{N}\).
5. Choose a set \(M=\Big{\{}\mathbf{B_{1}},\dots,\mathbf{B_{Q}}\in\mathbb{Z}^{n\times m^{\prime}}\Big{\}}\), where \(Q=\ poly(\lambda)\) and \(\mathbf{B_{i}}\) is independently chosen with uniform distribution. Note that \(\mathbf{B_{i}}\) is the public parameter for user \(i\).
6. Let \(H(\ )\) denote the function which converts a vector into a key of symmetric encryption.
7. Let \(E_{k}\) denote the symmetric encryption.
8. Publish system parameters chosen as above.
# Exploiting Concurrent Wake-Up Transmissions Using Beat Frequencies
## 4 Concurrent Wake-Up Protocol Design
### Concurrent Wake-Up Flooding  
Wake-up messages are usually energy-expensive and require a certain amount of time to be sent. In the cases of large wireless sensor node deployments, waking up of all sensor nodes can, as such, pose a considerable amount of energy and time with respect to the wireless sensor network. However, waking up of all nodes is required for example during the initialization phase of a wireless sensor node deployment or more generally required for network configuration tasks and in cases when data need to be pulled out of a complete network. In such cases, it is possible to use flooding algorithms, as they provide a tool to quickly broadcast messages throughout a complete wireless sensor network.  
This section introduces a concurrent wake-up flooding protocol similar to Zippy as introduced by [PERSON] et al. in [8] and as reviewed in Section 2.3. In comparison to Zippy, our newly-developed protocol uses both radios, the main and the wake-up radio. The main radio is used to transmit the on-off keying-modulated wake-up calls (WUC), the concurrently-transmitted wake-up calls (CWUC) and the synchronization (SYNC) messages.  
Figure 16 schematically illustrates the concurrent wake-up flooding protocol. An initiator Node a starts the flooding by sending a WUC. Each receiver node of this seed turns its radio on and listens to the wireless channel for a SYNC packet. Based on the timing provided by the SYNC packet, the nodes transmit a CWUC using the beat frequencies. The initiator retransmits the SYNC packet after each CWUC, in order to provide timing information to the newly-awoken nodes. The algorithm finishes at a predefined maximum number of CWUCs.  
To the authors' best knowledge, the herein proposed algorithm is the first concurrent wake-up flooding algorithm that supports address encoding in wake-up packets, which is a clear advantage over Zippy, as it removes virtually all occurrences of false wake-ups [6]. Furthermore, the strength of the beat signal increases due to the concurrency, as shown in the previous Section 3, which leads to a more robust and reliable wake-up reception. Synchronizing on the main radio signals provides improved timing, as well as low latencies, especially in larger network deployments where we expect our proposed algorithm to outperform Zippy.
# Formation Control of Nonlinear Multi-Agent Systems with Nested Input Saturation
## 1 Introduction  
### Motivation  
In light of the remarkable technological progress in recent decades, a substantial amount of research effort has been dedicated to the control of multi-agent systems (MAS), owing to their practical potential across diverse applications and the theoretical complexity in coordinating and controlling them [1; 2; 3]. The primary theoretical challenge involves controlling a multi-agent system using only the local information available to each agent and employing decentralized control laws for swarm coordination [4].  
As part of cooperative control, formation control addresses the development of efficient controllers aiming to guide a fleet of agents to maintain a desired formation configuration while tracking a reference trajectory or following a designated path. Employing distributed formation control schemes for MAS becomes crucial for executing intricate cooperative missions. The aforementioned task becomes even more challenging when practical issues including actuator nonlinearities, unknown system dynamics and environmental disturbances are taken into account. Moreover, in real-time applications, there is a need for control protocols that prioritize time efficiency while avoiding excessive computational complexity. Such complexity could adversely impact system performance and even lead to instability. Despite the urge for low computational complexity algorithms, standard performance specifications have to be provided by the control laws so that the desired system response is guaranteed. Hence, the fundamental motivation for this work arises both from the practical as well as theoretical challenges outlined in this paragraph and concerns the development of a robust decentralized control strategy, capable of addressing internal and external nonlinearities and imposing performance specifications while avoiding the explosion of computational complexity.
# A Method for Precise Tracking Control of Pneumatic Artificial-Muscle-Driven Exoskeletal Robot
## 4 Results and Discussion
### Analysis of Trajectory Tracking Results  
The fitting results of the actual elbow angle, angular velocity, and angular acceleration with the target value and the corresponding time-varying error are shown in Figure 15.  
Figure 15a shows the fitting curve of the actual angle of the elbow joint of the Upper Limb Exoskeletal Robot and the target angle. From the trajectory curve, it can be seen that the elbow joint movement is relatively stable and smooth, and the corresponding real-time  
Figure 15: Left and right elbow joint tracking results. (**a**) Comparison of elbow angle tracking. (**b**) Real-time image of elbow angle tracking error. (**c**) Comparison of elbow angular velocity tracking. (**d**) Real-time image of angular velocity tracking error of elbow joint. (**e**) Comparison of elbow angular acceleration tracking. (**f**) Real-time map of elbow angular acceleration tracking error.  
error diagram is shown in Figure 15b. Figure 15c shows the fitting curve between the actual angular velocity and the target angular velocity of the elbow joint of the Upper Limb Exoskeletal Robot. The initial velocity and end velocity of the elbow joint are both 0. The angular velocity function is continuous. The robot is stable in the process of movement without rigid impact. The corresponding real-time error diagram is shown in Figure 15d. Figure 15e shows the fitting curve between the actual angular acceleration and the target angular acceleration of the elbow joint of the Upper Limb Exoskeletal Robot. The angular acceleration at the initial position and the end position of the joint is 0, and there is no flexible impact. The corresponding real-time error diagram is shown in Figure 15f. During the trajectory tracking of the arm lifting action of the Upper Limb Exoskeleton, the process runs smoothly, with continuous speed and acceleration, and no rigid and flexible impact. At the same time, it can be seen from Table 2 that the maximum angular error in the tracking process is 0.0175 rad, MAE value is 0.0038 rad, RMSE value is 0.0048 rad, and ITAE value is 4.6426 rad. At the same time, combined with the error value of acceleration and angular acceleration, it can be seen that the control method in this paper has the ability to complete Upper Limb Exoskeleton handling assistance, and the tracking control process has high accuracy.
## 5 Conclusions  
The suspended-sediment dynamics in the River Drava were studied using different measurement methods and under different conditions (mean discharge and flood events). The problem of sedimentation in the River Drava is of particular importance for hydropower utilisation, as it leads to the clogging of reservoirs and reduces the efficiency of hydropower plants.  
The results at selected gauging stations show that the dependence of discharge and the concentration of suspended sediments can be described with the aid of rating curves, which can be used to determine the cumulative amounts of suspended-sediment transport. The concentration of suspended sediments in the River Drava is subject to temporal and spatial fluctuations, which can only be recorded with a suitable combination of indirect and direct measurement methods. An example of a combination of the acoustic method and point sampling in a selected cross-section was shown, giving a representative picture of suspended-sediment transport through the cross-section.  
The findings from the analysis of the high-water event in October 2018 suggest that the correlation between the concentration of suspended sediments and runoff follows a clockwise hysteresis loop. This phenomenon can be attributed to several factors, including the sediment sources being located far away from the area where surface runoff is generated, or the sediment being deposited on the slope without being washed away. Nevertheless, additional research is necessary to identify the source of the suspended sediment in the stream during the high-water event.  
In order to establish an efficient model of suspended-sediment dynamics in the River Drava, continuous monitoring should be established. In addition to the existing turbidity sensors at four hydropower stations, physical samples and other methods should be used to obtain a representative picture of the selected cross-section that could be used for further predictions of sediment transport. Based on an accurate model of sediment transport, sediment management can be determined in terms of flooding sediments into the core of the stream or constructing sediment fences, sediment traps, or sediment curtains. However, all measures should be sustainable and not adversely affect other users and life in the vicinity of the river.  
Conceptualization, J.K.S., R.J. and M.N.P.; Methodology, J.K.S. and M.N.P.; Investigation, J.K.S., R.J. and M.N.P.; Writing--original draft, J.K.S. and M.N.P.; Writing--review & editing, J.K.S., R.J. and M.N.P.; Visualization, J.K.S. and M.N.P.; Supervision, R.J. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Synchronous Rectification Method for CLLC Resonant Converters Based on State-Trajectory Models
## 3 SR Method for CLLC Resonant Converters Based on State-Trajectory Models
### Above-Resonant Synchronous Rectification Method
According to the principle of volt-second equilibrium, one can be derived as follows:  
\[i_{L\text{mN}}=(t-t_{1}-t_{\text{s}}/4)\frac{nZ_{1}U_{\text{outN}}}{L_{\text{ m}}} \tag{12}\]  
By taking (12) into (11), it can be derived as follows:  
\[I_{\text{outN}}=\frac{f_{\text{N}}}{\pi}\int^{t_{\text{3}}}_{t_{\text{1}}} \lvert i_{L\text{rN}}-i_{L\text{mN}}\rvert dt=\frac{f_{\text{N}}}{\pi}[(u_{C \text{r1N}_{\text{E}}}-u_{C\text{r1N}_{\text{G}}})+(c_{\text{r1N}_{\text{F}}} -u_{C\text{r1N}_{\text{E}}})]=\frac{2f_{\text{N}}}{\pi}u_{C\text{r1N}_{\text{F}}} \tag{13}\]  
As shown in Figure 5, the resonant capacitors \(C_{r1}\) and \(C_{r2}\) have equal voltages at the time indicated in the figure, corresponding to the points in the trajectory diagram. Therefore, the following relationship can be derived:  
\[u_{C\text{r1N}_{F}}+u_{C\text{r2N}_{F}}=I_{outN}\pi/f_{N} \tag{14}\]  
By taking (14) into (11), it can be obtained as follows:  
\[u_{C\text{r1N}_{E}}+u_{C\text{r2N}_{E}}=nU_{outN}I_{outN}\pi/f_{N} \tag{15}\]  
From Figure 5, \(i_{L\text{r1N}}\) and \(i_{L\text{mN}}\) are equal at times \(t_{0}\) and \(t_{2}\), so the voltage difference \(i_{L\text{r2N}}\) is zero at these two time points. Therefore, from Equation (12), the following relationship can be derived:  
\[i_{L\text{r1N}_{F}}=\frac{nZ_{1}U_{\text{outN}}}{4f_{\text{s}}L_{\text{m}}} \tag{16}\]  
The delayed duty cycle of the synchronous rectifier gate signal in the over-resonant state can be obtained by substituting Equations (14)-(16) into Equation (8):  
\[ \begin{split}& D_{SR\_Delay}=(\arccos\frac{nU_{outN}I_{outN}\pi/f_{N }+(1+N)}{\sqrt{\left(\left(I_{outN}\pi/f_{N}+1+N\right)^{2}+\left(nZ_{1}U_{ outN}/\left(4f_{\text{s}}L_{m}\right)\right)^{2}}\right.}\\ &-\arccos\frac{I_{outN}\pi/f_{N}+(1+N)}{\sqrt{\left(I_{outN}\pi/f_ {N}+1+N\right)^{2}+\left(nZ_{1}U_{outN}/\left(4f_{\text{s}}L_{m}\right)\right)^ {2}}})/(\frac{1}{\sqrt{L_{r1}\text{C}_{r1}}})\end{split}  \tag{17}\]