esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Challenges and Future Directions  
We showed that GraphNDT can be a key enabler for innovating communication networks in many aspects including optimization, management, and security. However, the deployment of GraphNDTs may entail overcoming various obstacles. In this regard, we outline some of these challenges along with potential research directions, as summarized in Table 7.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Challenges** & **Description** & **Future Directions** \\ \hline \multirow{4}{*}{Dynamicity} & The network topology is dynamic in a way that nodes or edges may appear or disappear or the input data change over time. & 1. Incremental Learning can allow GNN-based models learning from new data without having to retrain from scratch, which could be particularly beneficial in dynamic environments where data continually evolve [123]. & 1. Incremental Learning can allow GNN-based models learning from new data without having to retrain from scratch, which could be particularly beneficial in dynamic environments where data continually evolve [123]. & 2. FL plays a crucial role within the dynamic GNN model, particularly in handling complex, large scales, and adapting to shifts in the network structure over time [124; 125]. \\ \hline \multirow{4}{*}{Heterogeneity} & The different types of nodes and edges have different attributes, which are usually located in different feature spaces. & 1. Meta-path Aggregated Graph Neural Network (MAGNN) which can capture both the semantic and structural information in heterogeneous graphs. & 2. Contrastive learning-based method, where the model is pre-trained in a self-supervised manner to learn both the semantic and structural properties of heterogeneous graphs. \\ \hline \multirow{4}{*}{Robustness} & The ability of the model to maintain high performance even when faced with perturbations in the graph structure or feature information. & 2. Adversarial training methods involve integrating adversarial examples into the training process [128]. \\ \hline \multirow{4}{*}{Generalization} & The ability to generalize GNNs so that the GraphNDT can still provide accurate predictions in case of abnormal scenarios or configurations. This is mandatory for low-cost trial use cases. & 1. Train the models on additional data generated by simulations and testbeds. Transfer learning can be adopted to finetune the pre-trained models on the current NDTs. & 2. FL enables collaborative learning and model aggregation, allowing the GraphNDTs to benefit from a broader understanding of various configurations while preserving the privacy of individual network systems [129; 130; 131]. \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of challenges and future directions for GraphNDTs.
## 3 First Generation: Virtual Inertia Topologies
### Virtual Synchronous Generators: A Frequency-Power Response Based Topology
PLL systems are known to show steady-state errors and instability especially in weak grids with frequency variations, harmonic distortions, and voltage sags/swells [35; 36; 37]. In [60], it was shown that the problems with instability are even more pronounced when a proportional-integral (PI) controller is used to implement the inner-current control loop of the inverter. Accordingly, a VSG requires a robust and sophisticated PLL for a successful implementation [61]. Another disadvantage of the VSG approach is that the derivative term used to compute the ROCOF makes the VSG sensitive to noise which can lead to unstable operation.
## 4 System Model
### Simulation Environment  
The simulations were performed in OMNET++ [22] by considering two different scenarios: linear and mobile. It is assumed, that the popularity content follows the Zipf distribution. As per Poisson process, the users generate 1 requests per 300 s for the  
Figure 5: A mobile network consists of one server and \(\mathcal{N}\) ICN mobile nodes and deployed in a random position of probability-p as shown in Figure 5. The server is equipped with content catalogue \(\mathcal{C}\) is placed at the centre and connected with nodes in a multi-hop manner.  
interested content. The wireless communication in ICN node is enabled by using wifi-direct (IEEE 802.11 standard). The Random Walk mobility model is used for mobile nodes, as it randomly changes its position in a defined area, with random speed and direction at a time interval \(t\). Each node can transmit data with constant bit rate (CBR), with a maximum transmission capacity of 30 m. For each communication link between nodes, the bandwidth is 2 Mbps with 10 dB noise in channel and 0.33 s propagation delay. The nodes can have the size of two contention windows and it can transmit up to 250 kbps. Assume that each ICN node consumes 1 joule energy for bi-directional communication.  
For linear scenario, a fix storage size of node \(\mathcal{B}=25\) is assumed and for mobile scenario, a varying cache size from 10 MB to 500 MB is considered for each node. Initially, the cache nodes are empty and are of same size. The simulation are measured when the network is in steady state and the caches are full with the content. The simulations are run 10 times for each caching scheme and here the results are reported as average. The selection of requester node and content from the catalog \((e_{t},e_{2},\ldots,C_{\mathcal{N}})\) follows uniform distribution and Zipf distribution, respectively. The edge nodes may cover a large number of users, hence the average request rate \(\lambda\) at edge nodes follows a uniform distribution. The parameters used are given in Table 3.
# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## 5 Discussion
### Inter-Comparison and Quality Assessment
In their study, they mapped irrigated areas in a region (20 km \(\times\) 20 km) located in Urgell Catalonia, Spain (contained within our study site) using the SIGPAC data and the S1 multi-temporal SAR data. To map irrigated/non-irrigated plots, [PERSON] et al. (2018) used the statistical metrics including the mean, variance and correlation length of S1 SAR time series obtained at each plot in VV and VH polarization. They obtained an overall accuracy of 81.1  
Figure 11: Comparison of accuracy indices between RF and CNN classifications in three different scenarios: (**a**) Using the S1 SAR data, (**b**) using S2 optical data and (**c**) using S1 SAR and S2 optical data. % using the SVM (support vector machine) and similar accuracy using the RF classifier. For the study site examined in this study, the approach proposed by [PERSON] et al. (2018) was tested using the RF classifier. For each plot, the same metrics were calculated: The mean of the SAR time series, the variance and the correlation length of the SAR signal in both VV and VH polarizations. Results showed low accuracy compared to our proposed RF classifiers. The overall accuracy obtained was 80.1% with accuracy of irrigated class reaching 60.8% only while that of the non-irrigated class reaching 90.3%. Moreover, the addition of these metrics (mean, variance and correlation length) to our RF classifier of PC variables or WT coefficients did not enhance the accuracy of the classification obtained.
## 5 Experimental Verification
From Figure 20, when the grid voltage amplitude floats up or down by 10%, the grid current amplitude maintains constant. Meanwhile, its transient adjustment process is very short. From Figure 21, when DC side voltage floats up or down by 20%, the grid current amplitude also maintains constant. Meanwhile, its transient adjustment process is also very short. Figures 1 and 20 verify that the compound controller we put forward efficiently eliminate the effect of step disturbance of grid voltage and DC side voltage.
# Control of a Mobile Line-Following Robot Using Neural Networks
## 3 Control Strategies
### Lstm  
LSTM (Long-Short Term Memory) is an advanced type of RNN (recurrent neural network) developed by [PERSON] and [PERSON] [30]. Traditional RNNs are characterized by using the previous output as one of the inputs of the network, similarly to PID controllers. However, RNNs have difficulty learning long-term dependencies due to the vanishing gradient problem. LSTM solves this problem by introducing a memory cell that can retain information over extended periods of time. Figure 6 shows the structure of an LSTM cell [31].  
Figure 5: PID-controlled system.  
The equations for the forward pass of an LSTM cell are as follows:  
\[\begin{split} f_{t}&=\sigma(W_{f}\cdot[h_{t-1},x_{t}]+b_ {f})\\ i_{t}&=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i})\\ \tilde{C}_{t}&=\tanh(W_{C}\cdot[h_{t-1},x_{t}]+b_{C} )\\ C_{t}&=f_{t}*C_{t-1}+i*\tilde{C}_{t}\\ o_{t}&=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o})\\ h_{t}&=o_{t}*\tanh(C_{t})\end{split} \tag{4}\]  
where \(W\) terms denote the weight matrices, \([h_{t-1},x_{t}]\) the concatenation of the current input and the previous hidden state, the \(b\) terms denote bias vectors, \(\sigma\) is the sigmoid activation function, and \(i,f,o\), and \(C\) are, respectively, the input gate, forget gate, output gate, and cell activation vectors; \(*\) is the elementwise multiplication, and \(\tanh\) function is the activation function.  
The LSTM, by having the ability to capture long-term dependencies and process sequential data efficiently, has revolutionized many fields, including natural language processing, speech recognition, and time-series forecasting. Their advanced architecture, which incorporates memory cells and gates, enables them to learn and retain complex patterns over time, making them an essential tool in modern machine learning and artificial intelligence applications, including image classification [32].
## 5 Sensitivity Analysis and Design Optimization of Parameters
### Result Analysis for the Mechanical Properties of an Irregularly-Shaped Bridge  
#### 5.2.1 Parameter Optimization Based on the Overall Balance Method  
According to the orthogonal analysis results for indices, the overall balance method comprehensively considers the influence order of factors and the advantages of levels to determine the final optimal parameter combination. Ramp radius mainly affects the torsion vibration fundamental frequency, and the influence on maximum stress and the stress variation coefficient are negligible. Therefore, optimal ramp radius is selected based on the torsion vibration fundamental frequency. With increasing ramp radius, the torsion vibration fundamental frequency continues to increase. A ramp radius of 50 m is selected as the optimal level. Bifurcation diaphragm stiffness is the first influencing factor for the stress variation coefficient and the second influencing factor for maximum stress. The stress variation coefficient and maximum stress are all decreasing with an increase of the bifurcation diaphragm thickness. The optimal value of the bifurcation diaphragm thickness is 2.0 m. Box girder height affects maximum stress and the stress variation coefficient significantly. With the box girder height increasing, the maximum stress reduces and the stress variation coefficient becomes larger. Taking into account of optimization principles for both factors, the optimal box girder height is determined to be 1.8 m. Supporting condition mainly affects the torsion vibration fundamental frequency. In order to obtain the maximum torsion vibration fundamental frequency, 1 is the optimal supporting condition selected. Comprehensively considering all of the above influences for indices,  
Figure 9: Influence of varying parameters on the torsion vibration fundamental frequency.  
the optimal parameter combination for the mechanical properties of an irregularly-shaped bridge is: ramp radius \(R\) = 50 m, bifurcation diaphragm thickness \(D\) = 2.0 m, box girder height \(H\) = 1.8 m, and supporting condition 1.
## 3 Experiments
### Experimental Implementations  
To demonstrate the superiority of the proposed method, we compare it with several typical DW image denoising methods, including LPCA, AONLM, and BM3D, as well as some learning-based methods, such as DIP, Neighbor2 Neighbor (NB2 NB), patch2 Self (P2S), and SSECNN. All the typical comparison methods kept their default settings, and the learning-based methods were implemented with a Pytorch framework and trained on an Nvidia RTX A6000 GPU, with a batch size = 64 and epoch = 200. In our network, Node2 Node, in the image pair matching block, \(\sigma_{q}\) in Equation (1) and \(\beta\) and \(\sigma_{GFT}\) in Equation (9) were all set to 0.5. The GFT decomposition level \(Lev=3\) and scaling factor \(\gamma=2\). The optimizer was Adam with an initial learning rate of 0.001. A poly learning rate decaying policy (with a power of 0.8) was used to adjust the learning rate during the training.  
Note that, in the synthetic dataset, 1155 images were used to train the deep learning-based methods, and the rest 495 images were used as the test data; in the real dataset, only the partial ex vivo porcine dataset was used for training (90,000 DW images). The rest of the ex vivo porcine dataset (36,000 DW images) and all the in vivo human datasets were used for testing.
# Joint Acquisition Time Design and Sensor Association for Wireless Sensor Networks in Microgrids
## 5 Experiment Simulation  
In this part, we will demonstrate the effectiveness of the algorithm proposed in this paper through simulation. We consider a WSN, where \(N\) sensor nodes are deployed on a square area with an area of S, and the base station is located in the center of the network. The path loss between CMs and CH and between CH and BS is modeled as \(L(d)=34+40\log d\), where \(d>0\) indicates the Euclidean distance from the CH to the BS and from CMs to the CH; it is also used in [26]. According to the different methods used to obtain the user association in the second subsection, we will mark the three algorithms proposed in this paper as GREEDY, ATSAO and Relax-CVX and compare them with the classic topology control algorithm LEACH proposed in [27]. Since the classic LEACH algorithm only considers the topology control, and does not consider the acquisition energy consumption of sensor nodes. Therefore, we set the acquisition time of LEACH to \(T\) in simulations of this paper. It is obvious that the value of \(T\) has a great impact on the algorithm simulation results. In the simulation of this paper, we find that the optimal value of acquisition time \(t^{*}\) is between 0.01 s and 1 s. Therefore, the value of \(T\) is set to 0.1 s. It is worth noting that the acquisition time \(T\) is designed only to explore the performance of the algorithm not to be close to a certain standard. The simulation parameters used in this paper are shown in Table 1. The performance evaluation is operated in Matlab 2020b. All the simulations are performed on a desktop with an Intel Core i7-7700 3.6 GHz CPU and 16 GB memory.
## 4 Experiments and Analysis
### Experiment Settings
#### 4.1.2 Evaluation Metrics  
To assess the performance of continual learning of CLIP, we record the average classification accuracy of all seen classes at the end of each task training and denote the average accuracy on the \(i\)-th task after learning the \(j\)-th task as \(A_{ij}\). Following [15; 18; 21], we evaluate all methods with the metrics below.  
* Final average accuracy (FAA) refers to the last average accuracy after learning all the tasks: \[FAA=\frac{1}{T}\sum_{i=1}^{T}A_{iT}\] (10) where \(A_{iT}\) is the average accuracy of task \(i\) after learning task \(T\), and \(T\) is the number of tasks. A larger FAA indicates greater learning capacity and less forgetting.
* Cumulative Average Accuracy (CAA) is the average of historical FAA values after learning each task: \[CAA=\frac{1}{T}\sum_{j=1}^{T}\frac{1}{j}\sum_{i=1}^{j}A_{ij}\] (11) CAA reflects the overall performance after learning each incremental task, which can also be denoted as \"Inc-Acc\". Larger CAA indicates greater learning capacity and less forgetting.
* Final Forgetting Maximum (FFM) quantifies catastrophic forgetting by averaging the maximum performance drop of each task after learning the final task. A lower FFM indicates greater learning capacity and reduced forgetting. The FFM can be formulated as \[FFM=\frac{1}{T-1}\sum_{i=1}^{T-1}\max_{t\in\{i,\dots,T-1\}}(A_{iT}-A_{iT})\] (12) where \(A_{iT}\) is the average accuracy of task \(i\) after learning task \(T\), and \(T\) is the number of tasks.
* F1-score. This metric provides deeper insights into model performance, where a higher F1-score signifies better overall effectiveness. It is defined as: \[F_{1}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}}\] (13) where \[\text{Precision}=\frac{TP}{TP+FP}\] (14) \[\text{Recall}=\frac{TP}{TP+FN}\] (15) Here, TP (True Positive) is the correctly predicted positive cases, FP (False Positive) is cases incorrectly predicted as positive, and FN (False Negative) is cases not identified as positive by the model.
## 2 Materials and Methods
### Description of Test Sites
#### 2.1.2 Site B: Pohang, North Gyeongsang Province  
For the earthquake-induced ground deformation analysis, the city of Pohang was selected. This city stands on the alluvium deposit located in North Gyeongsang Province and serves as a main seaport in the southeast coast of South Korea. An earthquake with the moment magnitude M\({}_{w}\) = 5.4 struck the city on 15 November 2017, one of the strongest and most damaging earthquakes in the Korean Peninsula. It was a moderate-sized earthquake with an estimated focal depth of 5 km [35]. It has an oblique strike-slip mechanism with a reverse faulting. Over 1000 aftershocks followed the mainshock, and among them, the largest event was with the magnitude 4.6 on 11 February 2018. Table A1 shows the epicenters of the major seismic events of the Pohang earthquake [41]. Moreover, a local magnitude 3.1 earthquake was relocated during the hydraulic stimulation operations at an Enhanced Geothermal System (EGS) site in Pohang [42]. The mainshock caused widespread liquefaction, and heavily damaged the town of Heunghae and many neighboring villages which in turn resulted in over 80 fatalities and left thousands of people homeless.
## 2 Theoretical Background and Problem Statement
### Radial Basis Function Neural Network  
Neural networks are highly adept at modeling diverse nonlinear functions, as evidenced in references [32; 33; 34; 9]. Their streamlined learning protocols enhance computational efficiency, making them ideal for addressing the uncertainties discussed in this study. In neural network models, the function approximation process involves mapping input data \(H\) to the output \(f_{i}(H)\), where \(H\) is a feature vector from real-world applications and \(H=\left[h_{1},h_{2},\ldots,h_{q}\right]\in O_{H}\mathbbm{R}_{q}\). This mapping is achieved through the use of specific activation functions by each neuron in the network, typically Gaussian functions. The mathematical representation of this choice is as follows:  
\[f_{i}(H)=\Phi_{i}^{T}J_{i}(H),\;i=1,2,\ldots,n \tag{9}\]  
where \(\Phi_{i}\) denotes the weight vector corresponding to the \(i\)-th output, and \(J_{i}(H)\) represents feature mapping based on the input \(H\). This can be expressed as a collection of Gaussian functions:  
\[J_{i}(H)=[j_{1}(H),j_{2}(H),\ldots,j_{l}(H)] \tag{10}\]  
Each \(j_{n}(H)\) is a Gaussian basis function, and can be defined as follows:  
\[j_{n}(H)=exp\left[\frac{-(H-\mu_{n})^{T}(H-\mu_{n})}{{\eta_{n}}^{2}}\right],n =1,2,\ldots,l \tag{11}\]  
To handle complex data effectively, the number of nodes \(l\) in the neural network must be sufficiently large. The centers of the Gaussian functions are denoted by \(\mu_{n}\), and their widths \(\eta_{n}\) determine the range of these functions. By adjusting the weights \(\Phi_{i}\) and the parameters of the basis functions, this configuration can effectively model the uncertainty of the system.  
Working by approximation, the uncertain aspect of the system can be represented as follows [37]:  
\[f_{i}(H)=\Phi_{i}^{T}J_{i}(H)+\Delta_{i}(H),i=1,2,\ldots,n \tag{12}\]  
where \(\Delta_{i}(H)\) denotes the bounded error, enabling the model to approximate nonlinear behaviors within an error constraint. \(\Phi_{i}\) is the ideal weight.  
\[\Phi_{i}=\underset{\Phi_{i}\in R^{l}}{\text{arg min}}\Bigg{\{}\underset{H\in \mathcal{O}_{H}}{\text{sup}}\Big{|}f_{i}(H)-\Phi_{i}^{T}J_{i}(H)\Big{|}\Bigg{\}} \tag{13}\]  
This targets the best weight vector \(\Phi_{i}\), which minimizes the maximum deviation between the actual function and the neural network approximation across all inputs, enhancing the model's precision in uncertain environments.
## 3 Results and Discussion
### Impact of Liquid Nitrogen Boiling on the Stability of the DCM  
#### 3.3.1 Temperature Field Analysis  
Simulations were conducted for the DCM crystal temperature and the liquid nitrogen flow state for the pump's rated power operation. The temperature field distribution of a crystal is shown in Figure 14a, with the lowest temperature around 80.98 K near the entrance of the cooling circuit and the highest temperature at the geometric center illuminated by the beam, approximately 249.97 K. Figure 14b illustrates the temperature field distribution on the wall of the U-shaped circuit. In this contour plot, heat is mainly concentrated in the wall area closest to the beam, gradually decreasing along the axis. In the radial direction of the pipe, the temperature distribution is nearly symmetrical.  
Figure 14c displays the temperature field distribution inside the nitrogen circulation loop. The liquid nitrogen temperature gradually increases from the entrance to the loop exit. In the region close to the wall of the fluid, influenced by the near-wall effect and the proximity to the heat source in space, the temperature of liquid nitrogen rises more significantly. When the liquid nitrogen flows through the curved pipe, under the influence  
Figure 12: The vibration response curve of the crystal under the influence of amplitude of pulsation.  
Figure 13: The root mean square of amplitude.  
of turbulence and secondary flow, the fluid is mixed, resulting in a uniform temperature field near the exit of the curved pipe. Due to the secondary heating effect on the wall of the outlet segment, the temperature near the boundary layer reaches 84 K. At the outlet of the loop, the liquid nitrogen temperature is around 78.25 K, exceeding the saturation boiling point of liquid nitrogen at atmospheric pressure, leading to boiling.