esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 RNN-Based Optimization of Mirror Placement for Maximizing SNR in ADRs
### RNN Methodology
#### 7.2.5 Algorithm for RNN-Based Optimization  
To provide a better understanding of the RNN algorithm used, we present the step-by-step algorithm to implement the RNN, including the initialization, training process, and iterative optimization of mirror positions under the MRC and SBC schemes, as we can see in Algorithm 2. Furthermore, the block diagram that briefly describes the system presented can be seen in Figure 3.  
The diagram illustrates a VLC system in which the LoS channel is completely obstructed, focusing the analysis solely on the NLoS channel. The system incorporates an IRS composed of flat or convex mirrors, which reflect the NLoS signals toward the ADR. These signals are then processed using SBC or MRC diversity mechanisms. Finally, the output of diversity processing is analyzed using an RNN to optimize IRS positioning.  
Figure 3: A block diagram of the developed VLC system.  
Figure 2: RNN structure to optimize IRS placement.  
```
Inputs: Initial mirror positions \((\mathbf{x}_{0},\mathbf{y}_{0},\mathbf{z}_{0})\), Received signal powers \(\mathbf{P}(t)\) for all photodiodes, Noise spectral density \(N_{0}\), bandwidth \(B\), Total time steps \(T\), learning rate \(\alpha\), Regularization weight \(\lambda\), and epochs \(N_{\text{epochs}}\). Outputs: Optimized mirror positions \((\mathbf{x}^{*},\mathbf{y}^{*},\mathbf{z}^{*})\). Initialization: Initialize RNN weights \(W_{h},U_{h},W_{o}\), and biases \(\mathbf{b}_{h},\mathbf{b}_{o}\). Set the initial hidden state \(\mathbf{h}(0)=\mathbf{0}\). Training the RNN: For each epoch \(e=1\) to \(N_{\text{epochs}}\) For each time step \(t=1\) to \(T\) Compute the hidden state: \[\mathbf{h}(t)=f_{h}(W_{h}\mathbf{P}(t)+U_{h}\mathbf{h}(t-1)+\mathbf{b}_{h})\]  Compute the output (optimized mirror positions): \[\mathbf{o}(t)=f_{o}(W_{o}\mathbf{h}(t)+\mathbf{b}_{o})\]  Update the mirror positions: \[(\mathbf{x}(t),\mathbf{y}(t),\mathbf{z}(t))\leftarrow\mathbf{o}(t)\]  Calculate the SNR: \[\text{SNR}(t)=\begin{cases}\sum_{k=1}^{N_{\text{pop}}}\frac{P_{\text{MRC}}}{N _{0}\mathbf{B}},&\text{for MRC}\\ \max_{k=1}^{N_{\text{pop}}}\frac{P_{\text{SRC}}}{N_{0}\mathbf{B}},&\text{for SBC} \end{cases}\]  Calculate the loss: \[\mathcal{L}(t)=\frac{1}{\text{SNR}(t)}+\lambda\|\mathbf{o}(t)-\mathbf{o}(t-1) \|^{2}\] endfor  Perform backpropagation and update the RNN weights and biases: \[\theta\leftarrow\theta-\alpha\frac{\partial\mathcal{L}}{\partial\theta}\]  where \(\theta\) includes all weights \(W_{h},U_{h},W_{o}\) and biases \(\mathbf{b}_{h},\mathbf{b}_{o}\) endfor Return: Optimized mirror positions \((\mathbf{x}^{*},\mathbf{y}^{*},\mathbf{z}^{*})\).
```  
**Algorithm 2** The Optimization Algorithm for Mirror Placement in an Indoor VLC Scenario Using RNN
## 4 Results and Analysis
### Analysis of the Prediction Result for a Real Tunnel  
A real tunnel outside the model training set was selected. Based on actual data obtained from this tunnel, the LGBM algorithm was employed to predict the service states of three types of elements. The predicted results were then analyzed in comparison with the actual inspection results.  
Data obtained from the Wallace Tunnel in 2021 were selected. This tunnel is located in downtown Mobile, United States, and was constructed in 1973. It has a length of approximately 3000 feet (914 m) and serves as a crucial link for traffic on Interstate 10. The prediction results obtained by applying LGBM models to the inspection data for three types of elements in the Wallace Tunnel are as follows: the structural section was given a rating of A (exceptional), the civil section was given a rating of B (good), and the non-structural section was given a rating of D (poor). The prediction results are consistent with the actual inspection results, thereby confirming the analysis conducted in Section 4.2 on the factors influencing the operational state of the tunnel. As of 2021, the service time of the Wallace Tunnel was 48 years, which is relatively shorter than that of other tunnels in the United States. Therefore, the structural section of the tunnel was in excellent condition at this time. The tunnel had a high traffic volume, with an AADT of 75,320. Compared to the structural section, factors such as traffic volume more directly affect the civil and non-structural sections, causing adverse effects.
## 4 Experimental Results
### Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case** & **RMS EVM (\%)** & **Avg EVM (dB)** \\ \hline PA output & 8.4 & \(-\)21.5 \\ With PD \(k=4\)\(m=1\) & 8.1 & \(-\)21.9 \\ With PD \(k=6\)\(m=2\) & 7.6 & \(-\)22.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Predistorter effectiveness for the QPSK 18–MHz signal through a virtual PA (Saleh model).
## Materials and Methods
### Data Preparation  
#### 2.3.1 Spatial Datasets and Model Initialization  
Most of the spatial datasets were generated based on the combination of GIS, remote sensing, field monitoring and modeling. All of these datasets were resampled to the same spatial resolution (100 m \(\times\) 100 m) and projected in World Geodetic System-1984 Coordinate System (WGS-84). The digital elevation model (Figure 3a) was obtained from a 30 m Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) dataset. It was crucial for the computing of lateral hydrological processes and spatial interpolation of various meteorological parameters. Watershed land cover (Figure 3b) was obtained from a 30 m global LC dataset, GlobaLand30, and merged as part of the classifications to fit DLM-Ecohydro. After model parameters localization and optimization, the biophysical parameters for the major land cover types in the Upper Heihe watershed are shown in Table 1. A soil texture map (Figure 3c) was obtained from a 1 km Harmonized World Soil Database (HWSD) dataset established by the FAO and IIASA. The key hydraulic properties of the four main soil types in the Upper Heihe watershed are shown in Table 2. The watershed annual maximum LAI (Figure 3d) was generated from Moderate Resolution Imaging Spectroradiometer (MODIS) LAI production (MOD15A2), July 2013. In addition to the spatial datasets above, an initial water table depth (WTD) and ecosystem C-pools (including four biomass C-pools and nine soil C-pools) were necessary for the eco-hydrological and biogeochemical module of DLM-Ecohydro. We used CLM4.0 [35] to spin up WTD based on 2013 meteorological and topographical data, and similar iteratively running processes for current sizes of ecosystem carbon pools were based on the CLM4.0 biogeochemical module. The spun up WTD and ecosystem carbon pools were input as 2013 initial datasets and subsequently used for two years of simulation.  
Figure 3: Main spatial dataset of the Upper Heihe watershed prepared for model input: (**a**) digital elevation model (DEM), (**b**) land cover, (**c**) soil texture, and (**d**) maximum leaf area index (LAI).
## 3 Methodology
### Secondary Structure Prediction Models  
In this study, we adopt a Bidirectional Long Short-Term Memory (Bi-LSTM) architecture due to its effectiveness in modeling sequential data and its ability to capture dependencies in both temporal directions. This bidirectional context modeling is particularly advantageous for protein secondary structure prediction (PSSP), where the structural identity of a residue is influenced by both its preceding and succeeding amino acids.  
The proposed Bi-LSTM network consists of five stacked layers with progressively decreasing hidden-layer sizes: 1024, 512, 256, 128, and 64 units. This hierarchical configuration facilitates the extraction of increasingly abstract representations from the input sequence. To mitigate overfitting, dropout layers are inserted between Bi-LSTM layers. Although a relatively low dropout rate of 0.0001 is used, it provides lightweight regularization suited for long sequences. A final dense layer with softmax activation maps the output to either Q3 (three-class) or Q8 (eight-class) predictions, depending on the classification scheme.  
This architecture is inspired by prior studies demonstrating the utility of deep recurrent structures in PSSP tasks [36; 37; 38]. The conservative dropout setting aligns with best practices for small and deep recurrent models in biological sequence modeling, where over-regularization can hinder the capture of subtle long-range dependencies [39; 40].  
The framework is flexible and scalable, supporting input tensors of shape \((L,D)\), where \(L\) is the fixed subsequence length and \(D\) is the embedding dimension obtained after autoencoder-based compression. This modularity allows the architecture to adapt to diverse sequence lengths and feature dimensionalities with minimal reconfiguration.
## 3 Results and Discussion
### Effect of Inorganic Nanofillers on CNC/Photopolymer Composites
#### 3.3.2 Morphological Properties of Photopolymer Composites with CNC and/or Inorganic Nanofilers  
Pure photopolymer composite had a smooth and homogeneous fracture surface, which resulted in no defects or other distinctive features (Figure 20). But, in the case of the composite with 0.25 wt% nano-PCC, the stress transfer direction was remarkable on the fracture surface, and for the composite with 0.25 wt% nanoclay, specific forms to infer orientation were observed (Figures 21 and 22). However, there was a difference in the fracture surface orientations of the composites filled with inorganic nanofillers. The fracture of the composite filled with 0.25 wt% nano-PCC proceeded rapidly at the place where the nano-PCC fracture started, and then passed through the nano-PCC particles, and consequently, thin and long shaped fracture traces with uniform frequency were found. On the other hand, the composite filled with 0.25 wt% nanoclay appeared to spread the polymer fracture as it passed through the plate-shaped nanoclay present in the polymer matrix.  
Figure 19: Impact strength of composites filled with CNC and/or inorganic nanofillers.  
Figure 18: Elongation at break of composites filled with CNC and/or inorganic nanofillers.  
Also, compared to the composite with nano-PCC, the fracture features of the composite with nanoclay were wider and shorter. The fracture failure patterns described above may explain the stress transfer mechanism mentioned in the mechanical properties. For the  
Figure 21: SEM images of a composite filled with 0.25 wt% nano-PCC: (**a**) \(\times\)500 and (**b**) \(\times\)10,000.  
Figure 22: SEM images of a composite filled with 0.25 wt% nanoclay: (**a**) \(\times\)500 and (**b**) \(\times\)10,000.  
Figure 20: SEM images of a neat photopolymer composite: (**a**) \(\times\)500 and (**b**) \(\times\)10,000.  
composite with nano-PCC, when the stress was transferred to individual cubic-like nano-PCC particles, appropriate loads were transferred to the nano-PCC particles, and the residual loads were turned sideways so that stress transfers developed in the original fracture propagation orientations. However, nanoclays have wider high-aspect-ratio layers to accommodate the transmitted stresses, which prevent the straight development of fractures. Thus, the nanoclays could accommodate large stresses and the stresses might be dissipated in composites. As shown in Figure 23, the different stress transfer mechanism models of these inorganic nanofillers provide a supporting basis for the mechanical strength values evaluated in this study.
## 6 Performance Analysis
### Results and Discussion
#### 6.3.2 Experiment II  
Figures shown in Figure 6 illustrate the average throughput and latency for GetBallot and CastVote functions for varying total numbers of transactions. The results indicate that there is no significant impact on total number of transactions, as long as the send rate remains below the scheme's peak throughput. As send rates increase beyond the peak throughput, the impact of the number of transactions increases. As seen in Figure 6,b, an increase to the transaction send rate before reaching the peak throughput of approximately 200 tps for GetBallot does not result in different throughput or latency between different volumes of total transactions. After the peak throughput is surpassed differences in performance emerge depending on the total transaction volume. There is not a clear relationship between transaction count and how throughput degrades after the send rate surpasses optimal throughput. For GetBallot, we observed that the throughput at high send rates was generally better for a total of 1000 tx than 20,000 tx, but this was not the case for CastVote. This could result from inconsistencies that arise in performance when max throughput is exceeded. For average latency, the impact of transaction count is more clear. As seen in Figure 6,d, there is an increase in latency as the transaction count increased for CastVote. This is also seen in Figure 6,b for GetBallot, with the exception of the observed latency at 20,000 total transactions, which is lower than the latency at 10,000 tx. These results are generalized as follows:  
* When the transaction send rate is lower than the peak throughput, increasing the number of transactions has no impact on throughput or latency.  
Figure 5: Impact of Transaction Send Rate on Throughput and Latency: 1000 Transactions. (**a**) GetBallot; (**b**) CastVote. (Author’s own processing).  
After the peak throughput is surpassed, increasing the number of transactions decreases the throughput and increases the average latency.
# Interpreted Petri Nets Applied to Autonomous Components within Electric Power Systems
## 3 Modeling Autonomous Components within Power and Energy Systems with Interpreted Petri Nets
```
1:An informal specification of an autonomous component within a power system Output: an interpreted Petri net IN = (P, T, F, M\({}_{0}\), X, Y)
2:Identify the steps of control algorithm realization and specify set \(Y\) of output signals to show the current state (progress);
3:Identify the conditions for control algorithm realization;
4:Create an empty set, \(E\), of elementary expressions;
5:Split the conditions into elementary expressions, \(e\), and add them to the set \(E\); If set \(E\) already contains expression \(e\), then check the next condition;
6:Specify set \(X\) of input signals by assigning each \(e\in E\) to a separate signal name;
7:Create the structure of the Petri net \(PN\) by adding places \(p\in P\), transitions \(t\in T\) and arcs
8:\(f\in F\), connecting them with each other to reflect the control algorithm;
9:Define the initial marking, \(M_{0}\), by specifying where the algorithm starts;
10:A Petri net \(PN\) = (P, T, F, M\({}_{0}\)) is obtained;
11:for each \(p\in P\)do;
12: Assign to \(p\) an output signal (or more output signals) \(y\in Y\), if necessary;
13:endfor;
14:for each \(t\in T\)do;
15: Check whether its firing should be guarded;
16: If not, consider the next transition, \(t\)
17: Otherwise, assign the appropriate expression using defined input signals \(x\in X\) and
18:elementary Boolean operators (and/or/negation);
19:endfor;
20:Return an interpreted Petri net IN = (P, T, F, M\({}_{0}\), X, Y). ```  
**Algorithm 1**Construction of an interpreted Petri net  
The appropriate assignment of conditions, also taking into account conflicting transitions results in the determinism. Algorithm 2 formally describes how to resolve the conflict among transitions. Following the algorithm, after identifying the transition with higher priority (line 1, Figure 4b), the appropriate logical expression is built (line 2) and assigned to the transition with the lower priority (Figure 4c). Finally, we can achieve strong determinism  
Figure 4: Input signals influencing the property of determinism: (**a**) non deterministic interpreted Petri net; (**b**) showing the priority; (**c**) deterministic (more precisely—strongly deterministic) interpreted Petri net. ```
0: two transitions in conflict, with distinct guards (logically)
0: two transitions without conflict
1: Identify the transition with a higher priority. Let it be \(t\_high\). Let the other one be \(t\_low\);
2: Form a conjuction of the guard from transition \(t\_low\) with the negation of the guard from transition \(t\_high\);
3: Change the guard of transition \(t\_low\) to be the logical expression from step 2;
4: Do not change the guard of transition \(t\_high\);
5: Return transitions \(t\_low\) and \(t\_high\). ```  
**Algorithm 2**Resolving conflicts among transitions
## 2 Related Works  
The concept of fog computing is preferred over cloud computing due to nearby processing with reduced delay. That is why there are multiple research studies on the prospects of fog computing. In vehicular networks, the placement of a server adjacent to a RSU creates a fog node. The utility of fog computing is being heavily studied due to its high utilities in vehicular networks such as content caching and task processing.  
An intelligent technique for task offloading to edge nodes by using a finite horizon Markov decision has been proposed for vehicular networks [36]. The main focus of this work determines the transition probabilities to offload the task to that edge node by considering driver behaviour, communication behaviour and road topology. The authors claim that their proposed task offloading scheme reduces delay significantly.  
In [37], a double bipartite matching task offloading algorithm is proposed for high-speed vehicles that are categorized in three different states. In this work, vehicles offload tasks to their nearby edge node, and the edge node can further forward the offloaded task to the next edge node that is in the direction of vehicle movement. However, the authors have not considered the task processing capacity of the next edge node.  
The authors in [38] proposed a task offloading scheme by optimizing the fog node selection. The authors execute the offloaded tasks through a load balancing technique by introducing fiber wireless technology in which all RSUs are backwardly connected through fiber with a software-defined network for information management and a centralized network. The software-defined networking node forwards the offloaded tasks to the edge node by applying the game theory. The authors in this work reduce the delay by applying offloading techniques that includes complex and costly fiber wireless technology. Moreover, the offloaded tasks are routed to a software-defined network through roadside units, causing unnecessary delay.  
[PERSON] et al., in [39] proposed a mobility aware task offloading scheme by considering the maximum allowed latency of an offloaded task along with the communication and computing capabilities of allocated fog node resources. The authors proposed a greedy scheme to assign tasks to other nearby vehicles in addition to a bipartite matching algorithm and claimed that their proposed scheme reduces the execution time of offloaded tasks. In [40], an adaptive learning task offloading algorithm was proposed for offloading task execution of vehicles. The algorithm is based on the multi armed bandit theory to minimize the offloaded task delay.  
All the abovementioned task offloading schemes for vehicular networks are either based on vehicle to vehicle bases or offer different load balancing schemes on different criteria basis. However, no one has proposed an efficient task offloading scheme for fog node, and no one has discussed the processing capacity of fog computing nodes. In this work, an optimal task offloading scheme is proposed by considering the task processing capacity of fog computing nodes.
## 4 Numerical Experiments  
For comparing the performance of LS-GA, HGA-PSO1, and HGA-PSO2, all algorithms solve nine test experiments 10 times, and the performance measures defined in [20], the average objectives value (\(\text{avg}(\cdot)\)), the runtime of the algorithm (Runtime (S)), the number of nondominated sets (\(M_{1}\)), set coverage measure (\(M_{2}\)), and mean ideal distance (MID), are used to analyze the performance of algorithms.  
The main parameters of the test experiments are listed in Table 6 (\(k=1\)), and other parameters are described in Tables 11-13 of Appendix A. Several parameters, such as the population sizes, the maximum number of generations, the probability of the crossover, and the mutation operator, may influence the performance of an algorithm. In this research, all the parameters of the GA and PSO algorithms are relatively efficient values obtained through many tests, which are shown in Table 7. Moreover, the backordering intensity coefficient \(k_{0}=0.25\), the waiting time resistance \(k_{1}=0.3\), the fixed cost part of product \(i\ f_{i}\)= \(0.05 PC_{i}\), the cost rate \(a_{i}=0.5f_{i}\), and the cost increasing rate \(b_{i}\  
Figure 13: Comparative analysis of performance measures.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Performance Measure**} & \multirow{2}{*}{**Hybrid Strategy**} & \multicolumn{8}{c}{**Experiment No.**} \\ \cline{2-10}  & & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline \multirow{3}{*}{MID} & LS-GA & 23.97 & 33.43 & 43.17 & 41.50 & 58.83 & 76.02 & 57.00 & 82.81 & 107.34 \\  & HGA-PSO1 & 23.99 & 33.36 & 42.82 & 41.49 & 58.67 & 75.64 & 57.03 & 82.99 & 107.38 \\  & HGA-PSO2 & 24.14 & 33.40 & 42.38 & 41.66 & 58.83 & 75.62 & 57.17 & 82.97 & 107.51 \\ \hline \hline \end{tabular}
\end{table}
Table 8: _Cont._From the comparison results of performance measures \(\text{avg}(Z_{1})\), \(\text{avg}(Z_{2})\), \(M_{1}\) and MID, there is little difference in the performance of LS-GA, HGA-PSO1, and HGA-PSO2 algorithms. From the algorithm running time, HGA-PSO1 and HGA-PSO2 algorithms have less average runtime than the LS-GA strategy, and the larger the problem scale, the greater the difference in running time. Moreover, the \(M_{2}\) in Table 8 shows that the overall performances of HGA-PSO1 and HGA-PSO2 are slightly better than that of LS-GA, and there is no significant difference between HGA-PSO1 and HGA-PSO2 strategies. The obtained solutions of three hybrid strategies are compared in Figure 14. The results show that there is no significant difference in the number of nondominated sets obtained by the three hybrid strategies.
## 4 Numerical Analysis and Discussion
### Parametric Study
#### 4.3.1 Effect of Mass Ratio  
To investigate the effect of the mass ratio, ten different mass ratios are considered in the analysis, and the mass ratios are selected as 0.5%-5%, in increments of 0.5%, to cover the range of the change of the mass ratio. Figure 10 shows the vibration reduction ratios of the maximum displacement with different ratios. The reduction ratio of the PTMD increases gradually with the increase of the mass ratio until 2%. However, the increase of the reduction ratio of the PTMD is slow when the mass ratio is larger than 2%. The reduction ratios in the longitudinal and transverse directions with the change of the mass ratio have the same trend. Therefore, considering the effect of the reduction ratio and economics, 2% is selected as the optimal result.