esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Enhanced Noise Model Performance Based on Analysis of Civilian GPS Data
## 5 Quantitative Analysis of Noise Models
Although both the ARMA(\(p\), \(q\)) and Ornstein-Uhlenbeck models could achieve comparable AIC values (with the exception of the North Stradbroke Island dataset), the quantitative comparison of AIC values always favoured the high-order AR model. We note here that slightly lower AIC values could be achieved for autoregressive models by increasing their order, but the gain is becoming negligible around \(p\approx 10\) (see Figure 7). Figure 7 also captures two typical phenomena: For low values of \(p\), the AR(\(p\)) models do improve significantly with increasing values of \(p\), but there is a 'kink' in the AIC curve around approximately \(p=10\), where some improvement can be still achieved by increasing \(p\), but these maynot be worthwhile considering the increasing computational workload. In many GPS applications, the sampling period is often shorter than a few minutes or an hour, so the aim of developing a noise model is to focus on the short-term behaviour of the time series. For this reason, the choice of \(p=9\) seems a good compromise between modelling performance and computational load (cf. Table 2). In summary, the Gaussian white noise model is the worst fit to the observation. The next worst model, according to the AIC, is a low-order MA process, followed by the Ornstein-Uhlenbeck model. A higher-order AR scores better, with the higher-order AR processes scoring best. The AR process has the further advantage of robust parameter estimation.
# On Hybrid Energy Utilization in Wireless Sensor Networks
## 3 Simulation Results
Additionally, the performance metrics with different energy quanta (discrete values) of the solar energy unit arrival are used. This creates various cases of solar energy unit arrival as follows:  
1. Arrival units could be zero or one. 2. Arrival units could be zero, one, or two. 3. Arrival units could be zero, one, two, or three. The aforementioned figures show that the performance metrics improved when the energy quanta probability of solar harvested energy arrival increased for all cases. In Figure 6, the MDP scenario outperformed the non-MDP scenario up to 45% in terms of the power consumption ratio difference. Additionally, the percentage of power consumption ratio is decreased in Cases 2 and 3 rather than in Case 1. In particular, the battery utilization based on solar harvested energy is improved significantly in  
Figure 4: Power consumption ratio for different values of \(\alpha\). MDP: Markov decision process. Figure 5: Packet dropping probability (PDP) for different values of \(\alpha\). the MDP scenario. In addition, \(\alpha\) reflected on the MDP scenario results, since each action is determined by the cost function of the minimum cost value. In contrast, the average power consumption ratio is fixed when the MDP scenario is not considered. In both scenarios, Figure 7 shows that the PDP decreases when the probability of solar harvested energy arrival increases--especially in Cases 2 and 3, where the battery could accumulate more solar energy to transmit packets without dropping a packet. Moreover, the percentage of PDP will be high and fixed for the MDP scenario when \(\alpha\) is larger than or equal to 0.5. Based on the value of \(\alpha\), the cost value of PDP will be higher than the cost of constant energy source for all cases. In the scenario without MDP, \(\alpha\) will be negligible, and packets are dropped for all cases. Simulation results show that the design framework can efficiently balance the constant energy source usage and the PDP by adjusting the weighting factor \(\alpha\), based on the proposed MDP model. When the value of the weighting factor \(\alpha\) increases, the power consumption ratio decreases, since the cost of the constant energy source usage is high. On the contrary, the PDP increases, since the cost of the PDP is less important. In addition, when the value of \(\alpha\) is larger than or equal to 0.5, the power consumption ratio is zero and the PDP remains constant. The reason for the tendency in figures lies in that the cost of the PDP is less important than the cost of the constant energy source usage when  
Figure 6: Power consumption ratio for different values of \(\alpha\). Figure 7: PDP for different values of \(\alpha\). \(\alpha\geq 0.5\) according to (4). Additionally, hybrid energy utilization will be increased when the model sizes or the energy quanta probability of solar harvested energy arrival increase, as emphasized in previous figures.
# Developing a Novel Design for a Tubular Solid Oxide Fuel Cell Current Collector
## 2 Numerical Model  
The tubular anode-supported SOFC type is investigated in this study. There are three main layers: a thin electrolyte, a thick anode, and a cathode. The material properties and cell dimensions for the cell components are summarized in Tables 1 and 2, respectively [31; 32; 33]. The boundary conditions applied to the model are summarized in Table 3. Hydrogen is used as a fuel, while air is used to supply the cell with oxygen. The fuel is fed in on the anode side, and the outer surface of the cathode is exposed to air. The model includes all the solid oxide fuel cell-governing equations, such as the fluid flow, mass transport, heat transfer, electrochemistry, and charge transport. The sections below describe the governing equations used in the model in detail.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Dimension, mm** \\ \hline
**Cell length** & 11.8 \\ \hline
**Anode thickness** & 0.15 \\ \hline
**Cathode thickness** & 0.015 \\ \hline
**Electrolyte thickness** & 0.012 \\ \hline
**Anode flow Diameter** & 1.35 \\ \hline
**Cathode flow Diameter** & 3.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cell component dimensions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Anode** & **Cathode** & **Electrolyte** \\ \hline
**Material** & Ni-YSZ & LSM & YSZ \\
**Density (kg/m3)** & 4200 & 6350 & 6010 \\
**Specific heat (j/kg-K)** & 377 & 377 & 2000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cell components’ material properties.
# Automated Generation of Hybrid Metaheuristics Using Learning-to-Rank
## 4 Methodology
### Feature Extraction and Model Training
Similarly, the _Label_ for the WOA and HHO are adjusted to \"2\" and \"1\", respectively. This strategy more properly reflects each algorithm's relative performance during the optimization process, as well as providing more discriminative annotations for predictive model training. After completing the optimization phase of each algorithm, the feature sets corresponding to the optimal solutions provided by the three algorithms are merged to form the training feature set for the prediction model \(LTRMHA\). The detailed implementation of the training set construction for the LTR-MHA is shown in Algorithm 1, with the specific feature extraction and construction process of the metaheuristic algorithm exemplified by the WOA in Algorithm 2. ```
0: Metaheuristics set \(Alg=\{\)WOA, GA, HHO\(\}\), Update behaviors set Actions, Dimension \(D\), Maximum iterations \(I_{\max}\), Population size \(m\), Bounds \(lb,ub\), fitness function func;
0: Training feature set, label set;
1: Initialize search population \(P=\{X_{i}\mid i=1,2,\dots,m\ \land\ |X_{i}|=D\}\);
2:for algorithm \(alg_{j}\in Alg\)do\(\triangleright\) See detailed WOA update steps in Algorithm 2
3: Compute fitness values for all search agents via \(fitness_{i}=func(X_{i}(0))\);
4: Select the best search agent \(X^{*}\);
5:while\(t<I_{\max}\)do
6:for search agent \(X_{i}\in P\)do
7: Execute strategy of \(alg_{j}\);\(\triangleright\) Update strategy follows Equation (16)
8: Record selected action \(act_{\text{selected}}\);
9: Construct feature vector \(F_{\text{selected}}\) according to Equation (19);
10:for candidate action \(act_{k}\in Actions\)do
11:if\(act_{k}=act_{\text{selected}}\)then
12:\(Label=1\);
13:else
14:\(Label=0\);
15: Construct feature vector \(F_{\text{unselected}}\) for unselected actions via Equation (19);
16:endif
17:endfor
18:endfor
19: Adjust search agents exceeding search space boundaries via \(rand(lb,ub)\);
20: Recalculate fitness values for all search agents via \(fitness_{i}=func(X_{i}(t+1))\);
21: Update \(X^{*}\) when a better solution is available;
22:\(t=t+1\);
23:endwhile
24: Record best fitness value \(\textit{fitness}^{*}_{j}\) for algorithm \(alg_{j}\);
25:endfor
26: Sort algorithms by \(\textit{fitness}^{*}_{j}\) values;
27: reassign \(Label_{j}=\text{rank}(\textit{fitness}^{*}_{j})\), where rank is the descending order;
28:return Training feature set, label set;
```  
**Algorithm 1** The procedure of training set construction of LTR-MHA
```
0: Initialize search population \(P\), max iterations \(I_{\text{max}}\), fitness function \(func\), WOA parameters. 0: For each agent: construct feature vectors and Label for both the selected and unselected actions.
## 2 Materials and Methods
### Methodology
#### 2.3.4 Change Detection Analysis and Land Cover Updating
The decomposition model is expressed as follows:  
\[Y_{t}=T_{t}+S_{t}+e_{t}(t=1,\ldots,n), \tag{9}\]  
where \(Y_{t}\) is the observed value at date \(t\), \(T_{t}\) is the trend component fitted by piecewise linear models, \(S_{t}\) is the seasonal component expressed by harmonic seasonal models, and \(e_{t}\) is the noise component which represents the remaining variation beyond seasonal and trend components. The BFAST algorithm is free of parameterization but requires a maximum number of breakpoints (also regarded as the minimum time between breakpoints according  
Figure 9: Example of change pixels for 3 \(\times\) 3 grids (C: Cropland, F: Forest, G: Grassland, W: Water body, B: Built-up land, and U: Unused land). to the length of given time series). Figure 10 demonstrates an example of detecting the breaks using BFAST algorithm for the time series NDVI. The final step is the land cover updating for the missing years in 1980-1990. Since complete annual AVHRR data for 1981 are absent as mentioned in Section 2.2, this paper assumed that the land cover type in the study area did not change during 1980-1981. Land cover change was considered once in the interval 1982-1990 because of a low probability of frequent changes such as two or three conversions during the 9-year interval (e.g., forest-cropland-water or grassland-cropland-grassland-unused land). Therefore, the maximum break was set to 1 without considering the multi-time changes in the algorithm. Consequently, the land cover products for 1982-1990 were updated as follows: unchanged pixels retain the land cover type consistent with 1980 and 1990, whereas changed pixels were adjusted based on the year of change as detected by the BFAST algorithm. This involved maintaining the original land cover type before the change year in alignment with 1980, and transitioning to the land cover type after the change year in alignment with 1990. For instance, if a pixel shifted from grassland to cropland in 1986, it was classified as grassland from 1980 to 1985, and then updated to cropland from 1986 to 1990.
# XI2S-IDS: An Explainable Intelligent 2-Stage Intrusion Detection System
## 2 Materials and Methods
### Detailed Framework Workflow
#### 2.4.2 Stage II: Attack Classifier  
The second stage of the framework focuses on multi-class classification. To enhance the models' ability to distinguish between various attack behaviors, an attacks-only dataset was created by removing all normal records from the dataset. This step allowed the models to concentrate solely on attack patterns. Figure 4 illustrates the general workflow of the multi-class classification phase.  
The CICIDS2017 and UNSW-NB15 datasets exhibit significant class imbalances, with rare attack types like Heartbleed and Worms severely underrepresented. To address this, the Synthetic Minority Oversampling Technique (SMOTE) was applied to oversample these low-frequency classes. This augmentation ensures that the multi-class classifier effectively learns the patterns of rare attacks without being dominated by more frequent classes.  
Two deep learning (DL) models were developed and tested exclusively on the attack data. The first model incorporated Bidirectional Long Short-Term Memory (BI-LSTM) layers. The second model combined BI-LSTM and Convolutional Neural Network (CNN) layers, drawing inspiration from a previous study [15].  
The BI-LSTM model was chosen due to its demonstrated superior performance in intrusion detection systems (IDSs), as evidenced in recent studies [51; 52]. It consists of two BI-LSTM layers, separated by a max-pooling layer. Critical feature extraction occurs in the multi-class classifier, where the max-pooling layer after BI-LSTM layers encodes the most important temporal features, enabling the model to focus on the characteristics that best distinguish between different attack types.  
Additionally, both models were tested with and without the use of SMOTE upsampling on the attacks-only dataset. The final results were compared to identify the most effective model for multi-class classification of IDS, across both the UNSW-NB15 and CICIDS2017 datasets.  
Figure 4: General workflow of the multi-class classifier phase.
## 3 Results and Discussion
### Identification Performance of the Improved MobileNetV3  
To evaluate the identification performance of the improved MobileNetV3 model in this paper, it is compared with the MobileNetV3, ResNet34, GoogleNet, and VGG16 models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Training Set** & **Validation Set** & **Testing Set** & **Total** \\ \hline Defective & 1936 & 242 & 242 & 2420 \\ Non-defective & 1932 & 240 & 244 & 2416 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The dataset division for binary classification.  
Figure 8: Bearing ring images of defective and non-defective patches from the dataset. (**a**) Defective patches; (**b**) Non-defective patches.  
[PERSON] is selected as the optimizer of the CNNs, the number of iteration steps is set to 100, and the learning rate is set to 0.0001. The CNNs are trained using the dataset we split, and the training process is shown in Figure 9.  
Figure 9a show the trend of the validation accuracy as the number of iterations increases. The validation accuracy of CNNs varies from low to high and gradually tends to be stable. After 20 epochs, the difference between the five models is obvious. Figure 9b show the curves of the cross entropy loss function for each epoch. With the increasing training iterations, the trend of validation loss is decreasing. The validation loss value gradually becomes smoother to prove that the CNNs are convergent. The testing results of CNN models are shown in Table 3.  
The testing results show that the accuracy of the improved MobileNetV3 is 94.9%, higher than that of the above classical CNN models. Compared to the original MobileNetV3, the accuracy of the improved MobileNetV3 is 3.1% higher than that of MobileNetv3. Therefore, the CA attention module is stronger than the SE attention module in the feature expression ability of crack defects. Additionally, it is helpful to improve the recognition accuracy of the model. Table 3 also show the size of the weight files generated by each model and the average run time for a single image. The parameter memory of the improved MobileNetV3 is 12.6 MB, and the average execution time of that is 0.675 s, which has a relatively excellent performance considering speed and memory.
# Sensitivity Analysis of a Numerical Model for Percutaneous Auxicular Vagus Nerve Stimulation
## 2 Materials and Methods  
### Simulation Platform  
We used the Sim4 Life platform [17] to conduct the numerical simulations. Electromagnetic simulations were performed using the low frequency solver. Neuronal simulations were performed using the neuronal tissue models from Sim4 Life. The time response of the axonal membrane potential to external injected currents was simulated using the spatially extended nonlinear node (SENN) [18; 19] model. The threshold amplitude of a pulse to depolarize the axons was calculated using the titration mechanism. Titration consists of stimulating a neuron with a varying series of pulses to find the threshold above which a single action potential is generated. The titration process provides a scaling factor of the actual modulated current needed to generate an action potential. Details of the simulation platform can be found in Reference [12]. The SENN model is based on the Frankenhaeuser and Huxley [19; 20] equations to describe the time course of the membrane potential at all nodes of a myelinated fiber given by  
\[\frac{dV_{n}}{dt}=\frac{1}{C_{m}}[G_{a}(V_{n-1}-2V_{n}+V_{n+1}+V_{e,n-1}-2V_{e, n}+V_{e,n+1})-G_{m}V_{n}] \tag{1}\]  
where \(V_{n}\) is the voltage difference across the membrane at node \(n\). It is equal to  
\[V_{n}{=}\ V_{i,n}-V_{e,n} \tag{2}\]\(C_{m},\ G_{a}\), and \(G_{m}\) are given by  
\[G_{m}=g_{m}\pi dl \tag{3}\]  
\[G_{a}=\frac{\pi d^{2}}{4\rho_{i}L} \tag{4}\]  
\[C_{m}=c_{m}\pi dl \tag{5}\]  
where  
* \(V_{i,n}\) and \(V_{e,n}\) are the internal and external nodal voltages, respectively;
* \(C_{m}\) is the nodal capacitance;
* \(G_{a}\) is the axial internodal conductance;
* \(G_{m}\) is the nodal membrane conductance;
* \(t\) is time;
* \(d\) is the axon diameter;
* \(L\) is the internodal distance;
* \(g_{m}\) is the membrane conductivity (30.4 mS/cm\({}^{2}\));
* \(l\) is the nodal gap (2.5 \(\upmu\)m);
* \(\rho_{i}\) is the axoplasm resistivity (110 \(\Omega\)-cm); and
* \(c_{m}\) is the membrane capacity (2 \(\upmu\)F/cm\({}^{2}\)) [18; 19].
## 3 Preliminaries
### Validity Indices  
To ensure the robustness of the clustering results obtained from the FCM algorithm, various cluster validity indices are employed. These indices help in determining the optimal number of clusters by evaluating the quality of the clustering solution.  
A cluster validity index is a numerical function of the number of clusters C; it measures the compactness of clusters and the separability between clusters. A cluster validity index is an effective method for determining the optimal number of clusters; its use does not require any a priori knowledge of the structure of the dataset. Generally, the optimal number of clusters C is determined as the value that maximizes or minimizes the index.  
The following describes the three validity indices used in the proposed method, which are among those most widely adopted in fuzzy clustering: the Xie-Beni [36], Davies-Bouldin [37], and Fukuyama-Sugeno [38] indices. A complete discussion of the various validity indices proposed in the literature is provided in [39].
# A Review of the Artificial Neural Network Models for Water Quality Prediction
## 3 Three Basic Model Structures in Water Quality Prediction
### Recurrent Architectures  
Compared with feedforward ANNs, RNNs differs in that neurons within a layer are interconnected and allow feedback [53]. Different types of RNNs are developed so that the neural networks have better memory ability (see Figure 1). LSTM, an improvement over RNN, adds a processor called \"memory cell state\" to its hidden layer to determine whether the information is useful or not [66], and this is also suitable for SRU (Simple Recurrent Unit) [67]. Furthermore, the forget gate also determines what information should be discarded from the cell state [66]. TLRN has a similar structure to MLPs, but has local recurrent connections in the hidden layer (see Figure 3), with the advantages of low noise sensitivity and adaptive storage depth [55]. NARX networks are also sub-classes of RNNs and can be utilized to establish a long-term temporal relationship. The recurrent connections of NARX networks come from the output (see Figure 3) [12]. In addition to the input, hidden, and output layers, the Elman neural network has a context layer to store the internal states [3]. The Elman neural network is sensitive to the historical information of inputs because of the self-connections of the context nodes (see Figure 3). The three layers of ESN are different from the above recurrent neural networks. The three layers are input, reservoir, and readout layer. The feature of the reservoir layer is randomly and sparsely connected. The echo state property whose internal states are particularly dependent on the inputs is the key to the ESN. To overcome the ill-posed problem existing in the ESN, an RESN method using the ridge regression algorithm instead of linear regression to calculate output weights is proposed [38].
# Data-Aware Path Planning for Autonomous Vehicles Using Reinforcement Learning +
## 3 Problem Formulation
### Reinforcement Learning Formulation
#### 3.2.2 State Space (S)  
The state space \(S\) of our environment is defined by several key parameters that encapsulate both the physical position and the communication objectives of the agent.  
\[S=\{(x,d,B,T)\mid x\in X,d\geq 0,B\in\mathcal{B},T\in\mathcal{T}\}\]  
where  
* \(x\) represents the current node position of the agent within the network.
* \(d\) is the remaining amount of data that needs to be transferred. This component reflects the agent's communication objectives, crucial to ensuring that data transfer requirements are met before the journey ends.
* \(B\) denotes the set of bandwidths available on the links connected to the current node \(x\). Each element in \(B\) represents the bandwidth on a specific link, which affects the rate at which data can be transferred as the agent considers its next move.
* \(T\) indicates the set of traffic densities on the links connected to the current node \(x\). Each element in \(T\) reflects the traffic density on a specific link, which affects the agent's travel time and decision-making for route optimization.  
The bandwidth and traffic density vectors \(B\) and \(T\) are restricted to the links directly connected to the current node \(x\); the agent therefore observes only its immediate neighboring nodes and not the entire network. The quantities \(B\) and \(T\) are observations that include zero-mean Gaussian noise to emulate sensing uncertainty and avoid the unrealistic assumption of perfect measurement.  
Together, these parameters provide a multidimensional snapshot of the environment from which the agent can make its decisions.