esa-sceva/satcom-chunk-collection huggingface dataset:
# Cluster-Based Control Plane Messages Management in Software-Defined Flying Ad-Hoc Network
## 5 Conclusions and Future Work  
This article presented CAPONE, a cluster-based control plane management protocol that reduces the network overhead while guaranteeing the UAV management and control message delivering in an SDN-FANET. The controller node considers the UAV contextual information to predicts the UAV movements without the need for constant control message transmissions, reducing bandwidth consumption and signaling overhead. The prediction and acknowledgment mechanisms not only reduce the number of control packets in the network but also improves the control message transmission as less packet occupies the network bandwidth.  
CAPONE also divides the network into groups of UAVs while guaranteeing the control message delivering in FANET scenarios. It shows considerable advantages in terms of PDR, energy consumption, and overhead, even with a slight reduction of position accuracy. CAPONE determines the number of clusters using the Gap statistics method. This technique defines the best number of clusters according to the current locations of the UAVs. Fuzzy C-means method uses the number of clusters as input, defines the cluster heads, and groups the nodes of each cluster. It allows the controller to consider an association or membership degree for each node, ensuring communication between a UAV and the controller node.  
For future work, we are planning to consider more dynamic and heterogeneous scenarios, with different kind of data source nodes, such as vehicles, ground users, and others. Moreover, the drawback of a single point of failure, usually present in a centralized approach, shall be attenuated with more controllers in the network. Thus, we are planning to define the best number of controllers for different scenarios and analyze the impacts on the network in terms of control management and service provisioning.  
Figure 6: Overhead for different control plane message management mechanisms.  
Figure 7: Remaining energy during the simulation for different control plane message management mechanisms.  
**Author Contributions:** Conceptualization, P.C.; Formal analysis, P.C. and T.T.; Software, K.M.; Supervision, D.R. and E.C.; Validation, K.M.; Writing--original draft, P.C. and T.T.; Writing--review & editing, H.O., D.R. and E.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was financed by FAPESPA (Fundacao Amazonia de Amparo a Estudos e Pesquisa) ICAAF N 003/2017. The author [PERSON] would like to thank the grant 431474/2016-8, CNPq (National Council for Scientific and Technological Development) for the financial support.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
This paper focuses on the challenges of the free-piston internal combustion linear power generation system, which is characterized by a wide frequency and amplitude variation range of output electric power and difficulties in rectification. The study delves into the power generation characteristics during the system's stable operation and explores strategies for rectifying three-phase electric power. A mathematical model of the power generation system has been established. The model of the linear motor has been verified, and an in-depth analysis of the electric power output characteristics has been carried out. On this basis, this paper proposes several rectification schemes, including uncontrolled rectification, PWM rectification based on a PI control strategy, and compound control based on model predictive sliding mode control. In terms of control strategy, a model predictive sliding mode compound control algorithm is introduced to optimize traditional PWM rectification. By comparing and analyzing the electric power output effects under different rectification schemes, this paper aims to explore the rectification strategy most suitable for the system. Simulation results indicate that while uncontrolled rectification can achieve power conversion, it leads to further phase current distortion on the linear motor side. This paper examines the last two types of rectification strategies in terms of robustness, accuracy, and responsiveness. It is found that model predictive sliding mode compound control performs better than PI control. It can stably regulate the output voltage, and the phase current on the linear motor side will not be distorted. It is more suitable for power processing in this system.  
free-piston linear generator; uncontrolled rectification; PWM rectification; model prediction; sliding mode control Article
# Transformer-Based Cascading Reconstruction Network for Video Snapshot Compressive Imaging
## 2 Related Works
### Reconstruction Methods  
Video snapshot compressive imaging is a computational imaging method that requires decoding to reconstruct the original video scenes. The reconstruction method is the key to affect the quality of SCI imaging. The classical variational methods mainly use the structural prior as the regular term to construct models. GMM-TP [11] used the Gaussian mixture model to represent video scenes and estimated the parameters of the Gaussian mixture model through the expectation maximization algorithm. MMLE-GMM [9] used the maximum boundary likelihood estimator to learn the distribution parameters from measurements. GAP-TV [13] used the total variation regularization as a prior constraint and solved the problem by the generalized alternating projection algorithm. DeSCI [8] used the weighted kernel norm minimization to capture the low-rank structure of video patches. However, the models mentioned above need to be solved iteratively with high complexity.  
Recently, researchers have applied deep networks to SCI tasks by training the network to learn the mapping from training datasets to original signals. Then, the fast reconstruction can be achieved by inputting the measurements of video into the trained network. The key problem of such methods is the design of a reconstruction network. A codec structure network was proposed in [18], which represented the inverse mapping from the measurement to the reconstruction result. [PERSON] et al. expanded the iterative optimization model into a network module based on the generalized alternating projection and alternating direction multiplier method and used different deep denoisers as a prior constraint to form a plug-and-play reconstruction network model (PnP-FFDNet) [17]. [PERSON] et al. proposed the residual integration network RE\({}^{2}\)-Net [19], which consisted of four subnets of different structures to capture the temporal and spatial correlation between frames to reconstruct video scenes. [PERSON] et al. proposed a multi-hypothesis motion compensation structure [20], which can exploit the similarity between neighboring frames to improve the reconstruction quality. Although the existing network models based on convolutional operations can effectively extract local features, there are still shortcomings in capturing global context information and modeling complex video scenes, and the reconstruction quality needs to be further improved.
## 3 Results
### Optimisation Problem Formulation
#### 3.1.3 Heuristic Solution via Adaptive Scheduling  
The optimisation problem above is solved heuristically by Algorithm 1. Thus, for each event \(e\in E\), the algorithm performs the following:  
1. Computes the set of feasible slots \(\mathcal{T}_{e}\);
2. Evaluates the cost of assigning \(e\) to each slot \(t\in\mathcal{T}_{e}\) using \(\text{CostImpact}(S^{\prime},e,t)\);
3. Selects the slot \(t^{*}\) that minimizes the cost;
4. Updates the schedule \(S^{\prime}\) accordingly.  
This greedy, local optimal assignment approach enables dynamic adaptation and real-time responsiveness while approximating the global optimum. The adaptive scheduling algorithm ensures that, even with unexpected changes (like sick leave or sudden room unavailability), the timetable is quickly re-optimised. More specifically, an updated schedule \(S^{\prime}\) is initialised by copying the current schedule \(S\), ensuring minimal disturbance to existing allocations.  
```
0:\(S\)\(\triangleright\) Current schedule
0:\(E\)\(\triangleright\) Set of events to be scheduled
0:\(R\)\(\triangleright\) Resource matrix
0: Updated schedule \(S^{\prime}\) with minimised conflicts
1:\(S^{\prime}\gets S\)
2:for all\(e\in E\)do
3:\(\mathcal{T}_{e}\leftarrow\{\,t\mid\text{isFeasible}(e,t,R)=\text{True}\,\}\)\(\triangleright\) Valid time-room-instructor slots
4:\(t^{*}\leftarrow\arg\min_{t\in\mathcal{T}_{e}}\text{CostImpact}(S^{\prime},e,t)\)\(\triangleright\) Slot with minimal disruption
5:\(\text{schedule}(S^{\prime},e,t^{*})\)
6:endfor
7: broadcastNotifications\((S^{\prime})\)
8:return\(S^{\prime}\)
```  
**Algorithm 1** Adaptive Scheduling and Resource Allocation.  
Then, an iteration takes place over each event \(e\) in the event set \(E\). The feasibility of assigning \(e\) to each time-slot \(t\) is verified by consulting the resource matrix \(R\), which checks whether the required room capacity, instructor availability, and timing constraints are satisfied. The specific time-slot \(t^{*}\) is identified that minimises conflict or disruption cost, ensuring minimal effect on previously scheduled events. Next, the new assignment (event \(e\)\(\rightarrow\) slot \(t^{*}\)) is assigned to the updated schedule \(S^{\prime}\). After processing all events, notifications of any modifications are broadcast to relevant stakeholders (such as instructors and students), and finally, the algorithm returns the revised schedule \(S^{\prime}\).
# Decision Tree-Based Adaptive Reconfigurable Cache Scheme
## 5 Results and Analysis
### Software Full-System Simulation Results and Analysis  
Figure 12 shows the number of occurrences of each associativity under different reconfiguration periods, which are respectively marked as 5 M, 10 M, 30 M, and 100 M. The 16-way set-associative cache appears the most frequently, followed by the 8-way set-associative cache. Generally, the larger associativity appears more frequently than the smaller associativity, because in most situations, large associativity reduces the miss rate and improves the performance of the last-level cache. When running with a smaller reconfiguration period, some applications use small associativity in some reconfiguration periods. This is because the shorter the reconfiguration period, the easier it is to recognize and capture the time-varying behavior of the application, and the more frequent associativity tuning. However, frequent tuning of the associativity wastes time in the dirty blocks writing back and cache flush options. Compulsory miss after flush will increase the miss rate, making the average memory access time longer, which may offset the performance gains benefit from reconfiguration and even lead to performance degradation.  
Figure 11: Multicore system with the proposed decision tree-based adaptive reconfigurable cache.  
Figure 12: Associativity distribution.  
To determine the optimal reconfiguration periods, it is necessary to compare the AMAT under different reconfiguration periods. The calculation method of L2 cache AMAT is revised to Equation (3) after considering the influence of reconfiguration overhead:  
\[AMAT=Hit\ Time+Miss\ Rate\times Miss\ Penalty+Average\ Config\ Time \tag{3}\]  
where the _Average Config Time_ is the average reconfiguration overhead, calculated as in Equation (4):  
\[Average\ Config\ Time=\frac{Config\ Penalty\times Config\ Number}{Total\ Memory\ Access} \tag{4}\]  
where the _Config Penalty_ represents the reconfiguration overhead, including dirty blocks writing back and cache flush, which is 500 clock cycles in this work; the _Config Number_ is the number of L2 cache associativity reconfiguration; and the _Total Access Number_ is the total number of L2 cache accesses during the reconfiguration period. Figures 13 and 14 show the L2 cache miss rate and L2 cache AMAT under different reconfiguration periods, respectively.  
ity becomes more frequent. After the associativity tuning, cache flush introduces a large number of compulsory misses and increases the miss rate. The reconfiguration overhead offsets the reduction in miss rate, benefiting from reconfiguration at 50 million clock cycles. It can be seen from Figure 14 that the AMAT of the L2 cache decreases significantly as the reconfiguration period becomes longer. Similarly, it is increased again at 50 million clock cycles except for the case of the application of Barnes and Blackscholes. Combining the above analysis, the optimal reconfiguration period of the system is determined to be 30 million clock cycles.
## 2 Optimization Method
### Rigid-Body Motion and Surface Shape Error of a Mirror  
In this paper, the surface equation processing method and the best fit by the least-squares method [5; 25; 26; 27] were adopted to calculate the rigid-body motion for an optical surface with multiple nodes based on area-weighted average motion. As is well known, the rigid-body motion of an optical surface includes six components (three translations, \(T_{x},\ T_{y},\ T_{z}\), and three rotations, \(R_{x},\ R_{y},\ R_{z}\)), and the nodal rigid-body displacements, \(d_{\vec{x}_{i}},\ d_{\vec{y}_{i}},\ d_{\vec{z}_{i}}\), generated at a given node, \(x_{i},y_{i},\ z_{i}\), can be expressed as follows:  
\[\begin{cases}d_{\vec{x}_{i}}=T_{x}-z_{i}R_{y}-y_{i}R_{z}\\ d_{\vec{y}_{i}}=T_{y}-z_{i}R_{x}-x_{i}R_{z}\\ d_{\vec{z}_{i}}=T_{z}-y_{i}R_{x}-x_{i}R_{y}\end{cases} \tag{1}\]  
The squared difference, \(E\), between the actual nodal displacements, \(d_{x_{i}},\ d_{y_{i}},\ d_{z_{i}}\), and the nodal rigid-body displacements, \(d_{\vec{x}_{i}},\ d_{\vec{y}_{i}},\ d_{\vec{z}_{i}}\), of an optical surface can be obtained as follows:  
\[E=\sum_{i}\left[\left(d_{x_{i}}-d_{\vec{x}_{i}}\right)^{2}+\left(d_{y_{i}}-d_{ \vec{y}_{i}}\right)^{2}+\left(d_{z_{i}}-d_{\vec{z}_{i}}\right)^{2}\right] \tag{2}\]  
For Equation (2), by taking the partial derivatives of each of the six rigid-body motion components, the squared difference, \(E\), on the left side is zero. Thus, six simultaneous equations for solving the average rigid-body motion are obtained. After getting the average rigid-body motion of the surface, the rigid-body motion components can be removed from the actual nodal displacements by using Equation (3). Thus, the deformation of each node (\(\Delta_{x_{i}},\Delta_{y_{i}},\Delta_{z_{i}}\)) on the surface of the mirror can be obtained.  
\[\begin{array}{l}\Delta_{x_{i}}=d_{x_{i}}-d_{\bar{x}_{i}}\\ \Delta_{y_{i}}=d_{y_{i}}-d_{\bar{y}_{i}}\\ \Delta_{z_{i}}=d_{z_{i}}-d_{\bar{z}_{i}}\end{array} \tag{3}\]  
Furthermore, the error of the mirror deformation relative to the ideal mirror surface can be obtained, and the root mean square value (RMS) of the error can be calculated. Using the above method, the root mean square values of the surface shape error in three directions can be conveniently obtained.
# The Cascade Control of Natural Gas Pipeline Systems
## 3 Case Studies of the Cascade Control of Natural Gas Pipelines
Currently, the goal of the scheduling given by the dispatchers is to stabilize the outlet pressure connected with end users. The scheduling of the pipeline working conditions are to satisfy the need of the users in the mid-long term. However, the dispatchers will not able to adjust the pipeline following the wave of the consumption of end users. A better operation is to make the actual flow rate of the terminal station vary according to the users' requirement. Here, it was assumed that the flow rate changes during 24 h, as shown in Figure 5. The pipeline was in transient state because of the continuous change of flow rate. The dynamic control of natural gas pipeline was to let the flow rate of the natural gas in the terminal end following the need the of the users. The criterion is that the transient process is stable and fast. With only the PID controllers and the cascade control algorithm, the output of the compressor is shown in Figure 6. The transient process of the entire line was 38.13 h for only local PID controllers with the help of an dispatcher. The adjustment of the compressor's speed was not smooth and steady. The dispatcher might need to improve the scheduling process. However, it would require more practice and experience. The time under cascade control was 29.55 h. The adjustment process of the compressor was perfect. Therefore, the cascade control is an effective control algorithm for the pipeline in transient situations. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Process Variable** & **Disturbance** & **Final Result/MPa** & **Absolute Error/Mpa** & **Relative Error** \\ \hline
1 & Outlet Pressure & 5.2 MPa & 4.9991 & \(-\)0.0009 & 0.45\% \\
2 & Outlet Pressure & 4.8 MPa & 5.0006 & 0.0006 & 0.30\% \\
3 & Outlet Pressure & 6.0 MPa & 4.9940 & \(-\)0.0060 & 0.60\% \\
4 & Outlet Pressure & 4.0 MPa & 5.0467 & 0.0467 & 4.67\% \\
5 & Terminal flow & 75 m\({}^{3}\)/s & 5.0010 & 0.0010 & 3.23\% \\
6 & Terminal flow & 65 m\({}^{3}\)/s & 4.9987 & \(-\)0.0013 & 3.51\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The validation results of controller. Figure 5: Flow rate changes of natural gas pipeline terminal station.
# Characterization of Ancient Marquetry Using Different Non-Destructive Testing Techniques
## 5 Results
### Air-UT Results  
As mentioned in Section 2, the scanning area had to be divided in three sections to adjust the UT-transducers perpendicular to the panel surface as the samples has a curvature along its surface. Figure 5a shows an image, where the three sections (I, II, and III) were fused almost into its original shape. Note that Figure 5 resulted by rotating the original image (as scanned in Figure 2) by 90\({}^{\circ}\) for better comparison with IRT results. Additionally, Figure 5b depicts the amplitude signals over time of two arbitrary points in section I, representing the bright (high amplitude) and the darker areas. The latter indicates low amplitude and poor signal-to-noise ratio, which is most likely caused by poor US-wave transmission through the thick and, more importantly, anisotropic sample. This assumption is supported by the fact that three dark vertical lines (i) are present in the image. Their location agree perfectly with the wooden texture that is visible in Figure 1, indicating that the strong anisotropic properties strongly affect the US-wave attenuation. There are plenty more lines visible: convex in the left middle section (ii) and concave in the right middle section (iii), both agreeing perfectly with the wooden texture of the panel. Thus, concluding that the wooden texture plays a dominant role for the amplitude contrast.  
However, the multitude of visible bright areas and features allow to identify certain areas on the marquetry sample and to further conclude on existent defects. To start again with areas of poor transmission, the attention is drawn to the dark ring-shaped structure surrounding the middle (iv). This zone can clearly be assigned to the circular pattern of the white pearl material.  
Adjacent to the circular dark pattern are several small but bright zones (v), whose location match with the bigger prism-shaped tiles. Therefore, one may notice the repeating pattern of said bright zones, which are most noticeable on the upper and left side of the image. As these ornaments are also composed of the pearl material as the circular dark zone, this is no material contrast and thus indicating possible delaminations.  
Continuing further towards the middle part of the image, several darker areas follow in a circular pattern, corresponding to the red, gray, and black horn evident in Figure 1. This also applies for the corners of the sample, where red, gray and black horn is also present. Therefore, a slight material contrast may be the cause. Even though, the interpretation should be handled carefully, as the overall image contrast is extremely low and different effects may be superimposed.  
However, Figure 5 reveals some bright areas of high amplitude in the middle and upper middle section (vi). Small ornaments of the different aforementioned materials are located in this area (Figure 1), meaning absence of a material contrast. Nevertheless, different effects may contribute to the high amplitude, i.e., better transmission due to the wooden texture, delaminations, or flaws between neighboring ornaments.
# Preventive Maintenance Decision-Making Optimization Method for Airport Runway Composite Pavements
## 2 Methods
### Long-Term Maintenance Decision-Making Optimization Method
#### 2.3.5 Objective Function and Basic Equation  
The objective function was used to estimate the maintenance decision in each stage, which is defined as \(V_{k}(s_{k},x_{k})\) and consists of state variable \(s_{k}\) and decision variable \(x_{k}\). \(V_{k}(s_{k},x_{k})\) represents the total cumulative benefit or cost from stage \(k\) to the final stage. The basic equation \(f_{k}(s_{k})\) is the combination of all the optimum decisions from stage \(k\) to the final stage, which means it is the extremum of \(V_{k}(s_{k},x_{k})\). The state transfer equation \(T_{k}(s_{k},x_{k})\) represents the benefit and cost of each stage; therefore, \(f_{k}(s_{k})\) can be calculated based on a backward algorithm, as shown in Equation (14):  
\[f_{k}(s_{k})=opt\big{\{}V_{k}(s_{k},x_{k})\big{\}}= opt\big{\{}T_{k}(s_{k},x_{k})+f_{k+1}(s_{k+1})\big{\}},\ k=n,\ n-1,\ \ldots,\ 1 \tag{14}\]  
Both the maximum total improvement in the _PCI_ and minimum total maintenance cost from stage \(k\) to the final stage are required. The state transfer equations are used to calculate the average variation of \(PCI_{kni}\) and \(c_{bin}\) in stage \(k\), as shown in Equations (15) and (16), respectively.  
\[T_{k}^{1}(s_{k}^{1},x_{k})=\frac{1}{N}\sum_{n=1}^{N}\sum_{i=0}^{l}(s_{kni}^{1}-s_{( k-1)ni}^{1})x_{kni}=\frac{1}{N}\sum_{n=1}^{N}\sum_{i=0}^{l}\Delta PCI_{kni}x_{kni} \tag{15}\]  
\[T_{k}^{2}(s_{k}^{2},x_{k})=\frac{1}{N}\sum_{n=1}^{N}\sum_{i=0}^{l}(s_{kni}^{2}-s_ {(k-1)ni}^{2})x_{kni}=\frac{1}{N}\sum_{n=1}^{N}\sum_{i=0}^{l}\Delta c_{kni}x_{kni} \tag{16}\]  
where \(\Delta PCI_{kni}\) and \(\Delta c_{kni}\) are calculated based on Equations (10)-(13).  
Thus, the basic equations are as follows:  
\[f_{k}^{1}(s_{k})=\max\Bigl{\{}T_{k}^{1}(s_{k},x_{k})+f_{k+1}^{1}(s_{k+1}) \Bigr{\}} \tag{17}\]  
\[f_{k}^{2}(s_{k})=\min\Bigl{\{}T_{k}^{2}(s_{k},x_{k})+f_{k+1}^{2}(s_{k+1}) \Bigr{\}} \tag{18}\]