esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation
### Evaluation Metrics
#### 5.2.1 Detection Accuracy  
In this experiment, the proposed approach computed the three variables (P\({}_{1}\), P\({}_{2}\) and P\({}_{3}\)) to detect the DDoS attack and the change of these three variables is shown in Figure 9. The baseline approach computed the two variables (P\({}_{2}\) and P\({}_{3}\)) to detect the DDoS attack and the change of these two variables is shown in Figure 10. As shown in Figure 9a, the variable P\({}_{1}\) for Flow Group 3 changed slightly. The ones for Flow Group 1 and Flow Group 2 changed greatly. Thus, it can be inferred that the DDoS attack does not exist in Flow Group 3 and the DDoS attack may exist in Flow Group 1 and Flow Group 2. As shown in Figure 9b,c, the variables P\({}_{2}\) and P\({}_{3}\) for Flow Group 1 changed greatly and for Flow Group 2 changed slightly over the time, especially after the twentieth second. Thus, it can be inferred that the DDoS attack exists in Flow Group 1. As shown in Figure 10a,b, the variables P\({}_{2}\) and P\({}_{3}\) for Flow Group 1 and Flow Group 3 all changed greatly over the time. Thus, it can be inferred that the DDoS attack exists in Flow Group 1 and Flow Group 3. However, the DDoS attack actually only exists in Flow Group 1. Therefore, the baseline approach made an error in the DDoS detection. The three variables P\({}_{1}\), P\({}_{2}\) and P\({}_{3}\) can completely characterize the DDoS attack and missing any factor will lead to the possibility of detecting errors.  
In addition, the proposed approach can narrow the scope of the detection through computing the variable P\({}_{1}\). By this way, it can detect the DDoS attack pointedly and in a timely manner.  
Figure 9: The change of three variables for the proposed approach when threshold =8 KB and T = 2 s.
## 5 Application to the Satellite Module Layout Problem
### Configurations of Both Stages
#### 5.1.2 Lower Stage Settings  
The lower stage solving the layout task for each container corresponds to the CSO-VF algorithm. For each container, \(P=10\) independently initialized instances solve the corresponding single-container optimal layout problem.  
The number of iterations should be adapted along with the occupation rate of the container given that rising occupation rates make it increasingly difficult to solve the constraints. Consequently, the maximum number of iterations allocated for each container is a function of its occupation rate determined by the assignation of the components to the container. The number of iterations varies linearly with the occupation rate and is calculated as follows:  
\[N_{it}(OR)=15,000\cdot OR \tag{46}\]  
where \(N_{it}\) is the number of iterations and \(OR\) is the occupation rate from 0 to 1.  
Moreover, the hyperparameters of the CSO-VF algorithm are optimized using Bayesian Optimization, following the same procedure as for the upper stage. The CSO-VF instances dedicated to each container are launched in parallel. Then, all the possible combinations of laid out containers are compared in terms of feasibility and objective function values, and the combination leading to the smaller global inertia corresponds to the output of the algorithm. In the specific case where no feasible combination is found, another assignment list is proposed and the containers' layouts are optimized once again. In this study, the maximum number of attempts is set to 1, i.e., the algorithm must find a solution in one assignment scheme.
# A ResNet1D-AttLSTM-Based Approach for Ship Trajectory Classification
## 3 Experiment
### Model Evaluation and Analysis
#### 3.3.2 Hyperparameter Tuning of ResNet1D-AttLSTM  
To evaluate the performance of the ResNet1D-AttLSTM model under different hyperparameter settings, we employed four-fold cross-validation and calculated the average classification accuracy across four independent validation sets. As shown in Table 3, the model achieved the highest classification accuracy under Scheme 5. This scheme includes two residual blocks, two LSTM layers (each with a memory cell dimension of 128), eight attention heads (with an embedding dimension of 256), and a dropout rate of 0.3 in the feature fusion layer.  
As indicated in Table 3, Scheme 1 and Scheme 2 achieved accuracies of 79% and 81.1%, respectively, suggesting that relying solely on either ResNet1D or LSTM for ship trajectory classification has limited performance. Scheme 3, which integrates features extracted by ResNet1D with the temporal information captured by LSTM, achieved an accuracy of 84.3%, higher than Schemes 1 (79%) and 2 (81.1%). This indicates that combining spatial and temporal features significantly improves model performance. Scheme 4 further improved the accuracy to 87.4%, demonstrating that introducing the attention mechanism allows the model to more effectively capture key information, thereby enhancing classification performance.  
However, Scheme 7 showed a slight decrease in accuracy to 88.5% compared with Scheme 6. This phenomenon could be ascribed to the heightened model complexity induced by an overabundance of attention heads and residual blocks, potentially resulting in overfitting.
## 3 Parameter Modeling and Error Analysis of the INS and GNSS Systems
### Inertial Navigation Error Analysis and Modeling
#### 3.1.1 Platform Error Angle Equation  
The attitude of the single-satellite integrated navigation algorithm based on broadband LEO constellation communication links is as follows:  
\[\delta\phi=\omega_{nb}^{n}=\omega_{ib}^{n}-\omega_{ie}^{n}-\omega_{cn}^{n}. \tag{16}\]  
After obtaining the total differential on both sides, the error angle equation is  
\[\dot{\phi}^{n}=\delta\omega_{ie}^{n}+\delta\omega_{cn}^{n}-(\omega_{ie}^{n}+ \omega_{cn}^{n})\times\phi^{n}+\epsilon^{p}, \tag{17}\]  
where \(\omega_{\alpha\beta}^{\gamma}\) is the rate of rotation of the \(\beta\)-frame axes with respect to the \(\alpha\)-frame axes resolved about the \(\gamma\)-frame axes; among them, \(\alpha=n,i,e\), \(\beta=b,e,n\), \(\gamma=n\), \(n\) represents the local navigation frame; \(i\) represents the Earth-centered inertial frame; \(e\) represents the Earth-centered Earth-fixed frame; \(b\) represents the body frame; \(\phi^{n}\) represents the error angle in the local navigation coordinate system; and \(\epsilon^{p}=\left[\varepsilon_{N},\varepsilon_{E},\varepsilon_{D}\right]^{T}\) represents the drift of each axis of the gyroscope. By expanding Equation (17) and ignoring the second-order infinitesimal expression, we can obtain  
\[\left\{\begin{array}{l}\dot{\phi}_{N}=\frac{\delta V_{N}}{\varepsilon_{E}+ \varepsilon_{D}}-\omega_{ie}\delta L\sin L-(\omega_{ib}\sin L+\frac{V_{E}}{ \varepsilon_{D}+\varepsilon_{D}}\tan L)\phi_{E}-\frac{V_{E}}{\varepsilon_{D} +\varepsilon_{D}}\phi_{D}+\varepsilon_{N}\\ \dot{\phi}_{E}=-\frac{\delta V_{N}}{\varepsilon_{D}+\varepsilon_{D}}+(\omega _{ie}\sin L+\frac{V_{E}}{\varepsilon_{D}+\varepsilon_{D}}\tan L)\phi_{N}-( \omega_{ie}\cos L+\frac{V_{E}}{\varepsilon_{D}+\varepsilon_{D}})\phi_{D}+ \varepsilon_{E}\\ \dot{\phi}_{D}=-\frac{\delta V_{N}}{\varepsilon_{D}+\varepsilon_{D}}\tan L-( \omega_{ie}\cos L+\frac{V_{E}}{\varepsilon_{D}+\varepsilon_{D}}\sec^{2}L) \delta L+(\omega_{ie}\cos L+\frac{V_{E}}{\varepsilon_{D}+\varepsilon_{D}}) \phi_{E}+\frac{V_{E}}{\varepsilon_{D}+\varepsilon_{D}}\end{array}\right. \tag{18}\]  
where \(V_{N}\), \(V_{E}\), and \(V_{D}\) are the velocities along the NED direction; \(L\), \(\lambda\), and \(H\) respectively represent the latitude, longitude, and altitude; \(\phi_{N}\), \(\phi_{E}\), and \(\phi_{D}\) respectively indicate the roll angle, pitch, and yaw angle of the carrier; \(\delta V_{N},\delta V_{E}\), and \(\delta V_{D}\) are the north, east, and down velocity errors of the carrier, respectively; \(\delta L\) is the latitude error of the carrier; \(\varepsilon_{N}\), \(\varepsilon_{E}\), and \(\varepsilon_{D}\) represent the first-order Markov drift of the gyroscope in the NED direction; and the angular velocity of the earth is \(\omega_{ie}\). The equatorial plane radius of the earth ellipsoid is \(R_{e}=6378137\); the curvature radius of the meridian circle is \(R_{m}=R_{e}(1-e^{2})/(1{-}e^{2}\sin^{2}L)^{3/2}\); the curvature radius of the prime unitary circle is \(R_{n}=R_{e}/\sqrt{1-e^{2}\sin^{2}L}\); and the ellipticity is \(e=1/298.257\).
## 4 Methodology for the Levelized Cost of Storage Estimation
### Battery Degradation Estimation for PRRC  
The RFC algorithm counts the number of cycles per DoD. This computation is performed using the Matlab \"rainflow\" function, which receives the time vector with the SOC values and returns a vector N\({}_{\text{cyc}}\) with the number of cycles per DoD. The battery degradation parameter over the simulated time is obtained by using:  
\[\text{D}(\%)=\sum_{\text{DoD=1}}^{100}\frac{\text{N}_{\text{cyc}}(\text{DoD} )}{\text{N}_{\text{max}}(\text{DoD})}, \tag{20}\]  
where N\({}_{\text{max}}\) is the maximum number of cycles per each DoD before achieving the end-of-life (EOL) and is specified by the manufacturer. Figure 6 shows the curve of N\({}_{\text{max}}\) against DoD for the Intensium Max High Power 20P cells LFP type from SAFT batteries, which are the battery banks used in Coto Laurel. This curve is approximated by:  
\[\text{N}_{\text{max}}(\text{DoD})=3\ \times\ 10^{7}\ \times\text{DoD}(\%)^{-1.825}. \tag{21}\]  
Once the overall battery degradation parameter D is obtained, it is required to transform it to a normalized number of cycles NC. Assuming a mean DoD of 80%, Figure 6 shows that the maximum number of cycles allowed before reaching the EOL is 10,000. Thus, the normalized number of cycles can be calculated using:  
\[\text{NC}(80\%)=\frac{\text{D}(\%)\times 10,000}{100\%}. \tag{22}\]  
Figure 6: Maximum number of cycles to achieve EOL for different DoD values for LFP batteries. The EOL capacity is defined by the manufacturer to be 70% of the initial capacity.  
Then, the NC value is evaluated in (19) to obtain the capacity fade in one year. To obtain the number of years to achieve EOL considering calendar capacity fade, it is necessary to solve the following equation:  
\[\text{RC}= \Big{\{}1-\Big{[}\text{C}_{\text{fade,cal}}\big{(}\text{y}_{\text{EOL}}, \text{T}\big{)}+\text{C}_{\text{fade,cyc}}(\text{NC},\text{T})\times\text{y}_{ \text{EOL}}\Big{]}\Big{\}}, \tag{23}\]  
where RC is the residual capacity (70% in this case), T is the temperature and yEOL is the number of years before achieving EOL. For this work, the operating temperature is assumed constant at 299.95 \({}^{\circ}\)K or 26.8 \({}^{\circ}\)C.
## 4 Conclusions  
A plasma-chemical method was used to process wood ash, fly ash, and slag (obtained during the incineration of household waste). A special cooled plasma-chemical channel was designed and manufactured to process these products and obtain the fiber. As shown by the SEM studies, the raw material (before plasma-chemical treatment) consisted of irregularly shaped granules or rod-shaped microforms. The fiber consists of elongated structures whose diameter ranges from 0.1 to 5 \(\upmu\)m. As shown by EDS research, although, in all cases, both the primary material and the products obtained after plasma-chemical processing are composed of various metal elements, but they make up a vanishingly small part, as the predominant elements are C, O, Cl, Si, and Ca. The temperature of the plasma-chemical process does not fall below 2000 \({}^{\circ}\)C throughout the length of the channel, which means that this condition is sufficient (the process temperature must be at least 1800 \({}^{\circ}\)C) for completely neutralizing various dangerous compounds in both the fly ash and the slag. A general tendency can be observed (by analyzing the results of the TG tests): the original material (unprocessed and untreated in the plasma-chemical channel) loses mass faster than the material after plasma-chemical treatment. After analyzing the XRD studies, it became clear that the obtained fiber has an amorphous structure. It is likely that the vitrification process takes place during the formation of fibers, during which a disordered structure (resembling a homogeneous melt) and amorphous formations are formed. The dominant fiber absorption bands were assigned to Ca-O, Si-O, and Ca-O groups for all raw material cases. The BET studies show that when fiber is obtained at 55.5; 55.6 and 74 kW of plasma torch power, respectively, the specific surface area of samples is 11.9; 8.5 and 3 m\({}^{2}\)/g. The summarized results of this study show that the fibers, fly ash, and slag obtained after the plasma processing were clean enough and harmless to the environment, so they can be widely used as secondary raw materials, e.g., for the production of thermal insulation; in the production of catalysts; as a component of concrete, cement, and asphalt; and in other industries. The high-temperature process (over 2000 \({}^{\circ}\)C) ensures that most harmful metals are vaporized or converted to stable oxide forms, reducing potential emissions and damage to the environment.  
Conceptualization, Z.K., R.K. ([PERSON]) and V.G.; methodology, M.M.; software, V.V.; validation, R.K. ([PERSON]) and Z.K.; formal analysis, M.M.; investigation, Z.K.; resources, V.V.; data curation, R.U.; writing--original draft preparation, Z.K.; writing--review and editing, Z.K.; visualization, A.B. and R.K. ([PERSON]); supervision, V.V.; project administration, V.V.; funding acquisition, V.V. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The data presented in this study are openly available in Zydrunas at www.lei.lt, reference number LK-109.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Evaluation Metrics  
The evaluation of the regression model is essential to determine how well the model fits the data. There are several metrics that are used to evaluate the regression model. In this study, we used the following evaluation metrics:  
Mean absolute error (MAE)  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\left|X_{i}-Y_{i}\right| \tag{4}\]  
Mean absolute percentage error (MAPE)  
\[\text{MAPE}=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{Y_{i}-X_{i}}{Y_{i}}\right| \tag{5}\]  
Correlation coefficient (\(R\)) and coefficient of determination (\(R^{2}\))  
\[R^{2}=1-\sum_{i=1}^{n}\left(X_{i}-Y_{i}\right)^{2}/\sum_{i=1}^{n}\left(\overline {Y}-Y_{i}\right)^{2} \tag{6}\]  
Mean squared error (MSE) and root mean squared error (RMSE)  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}\left(X_{i}-Y_{i}\right)^{2} \tag{7}\]  
\[\text{RMSE}=\sqrt{\text{MSE}} \tag{8}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of hidden layers & 3 \\ Number of neurons & 5, 4, 3 \\ Activation function & Tanh \\ Optimizer & Adam \\ Initial learning rate & 0.001 \\ Batch size & 200 \\ Epochs & 10,000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters of the deep neural network regression model.  
where \(X_{i}\) is the predicted \(i\)-th value and \(Y_{i}\) is the observed \(i\)-th value. \(n\) is the number of observations. MAE is a measure used to determine the average absolute difference between predicted and actual values. MAE is most effective when there are outliers in the data that can significantly impact the overall performance. On the other hand, MAPE is a metric that measures the percentage difference between predicted and actual values. This is particularly useful when it is important to consider the magnitude of the errors. Another useful metric is \(R\), which quantifies the strength and direction of the linear relationship between two variables. \(R^{2}\) is a metric that measures the proportion of the variance in the dependent variable that can be explained by the independent variables. RMSE is a metric that measures the average difference between predicted and actual values, and it places greater emphasis on larger errors than smaller ones when evaluating model performance. MAE and RMSE have the same unit of data. The unit of MAPE is percent. The best value for MAE, MAPE, MSE and RMSE is zero. For \(R^{2}\), 1 means a perfect linear relationship.
## 4 Literature Review and Data Extraction
### Dynamic Task Allocation
A task allocation optimization strategy for distributed edge networks to reduce energy usage while achieving a high quality-of-service was published by [PERSON] et al. [60]. ILP, PSO, and DRL are the three optimization techniques that were compared in this paper. PSO outperforms ILP in smaller problems, but DRL is better suited to larger problems and has the lowest upper bound for the optimality gap of the three techniques. According to the study's findings, the performance of the three techniques varies depending on the size of the job allocation problem in the edge networks. The PSO is more suited for smaller issue sizes, whereas DRL is better suited for bigger ones. According to the findings, the extended PSO algorithm is unreliable and useless for task allocations of greater than 20 and 60, respectively. The study highlighted the potential future research areas, such as investigating the trade-offs between speed and optimality in ILP problems that prioritize previously assigned tasks, and exploring the field of heterogeneous networks. Furthermore, [PERSON] [64] suggests a binary particle swarm optimization (BPSO)-based MEC application in the classroom assessment system for English education. It addresses the NP-hard problem of decoupling the channel resource allocation problem, proposes a multi-user and multi-MEC scenario based on mobile edge computing to maximize total revenue to finish the work, and formulates an objective function to reduce the task execution cost. This study uses big data allocation research from mobile edge computing resources for the application of the English teaching classroom assessment, accelerating the growth of classroom instruction. It offers recommendations on how to enhance and put into practice the English education assurance system.
## 3 Materials and Method
### Proposed Method
#### 3.3.1 Graph Modeling and Graph Attention Module  
This module is designed to capture structured relationships among signal nodes. As illustrated in Figure 2, each node receives an initial feature vector \(X_{i}\), which encodes raw observations and derived descriptors of signal \(i\). These inputs are linearly projected via a transformation matrix \(W\) to produce embedded vectors \(h_{i}=WX_{i}\).  
For a given node \(i\) and its neighbor \(j\), the attention coefficient \(e_{ij}\) is computed using a shared attention vector \(a\):  
\[e_{ij}=\text{LeakyReLU}(a^{T}[WX_{i}\|WX_{j}]),\]  
Here, \(\|\) denotes vector concatenation. The attention score is then normalized across all neighbors using the Softmax function to obtain the final attention weight \(\lambda_{ij}\):  
\[\lambda_{ij}=\frac{\exp(e_{ij})}{\sum_{k\in N(i)}\exp(e_{ik})},\]  
Figure 1: As illustrated in the figure, a time-evolving adjacency structure is constructed by computing correlation coefficients between historical signal sequences over a sliding window. Each node represents an independent signal, and the edge weights reflect the degree of inter-signal correlation. This dynamic graph is then input into a graph attention module to extract structure-aware embeddings, which enhance the capacity of the model to capture interactions across heterogeneous signal modalities.  
Here, \(\lambda_{ij}\) visually represents the edge weight from node \(j\) to node \(i\), quantifying the influence of node \(j\) in updating node \(i\)'s representation. The updated embedding \(h^{\prime}_{i}\) for node \(i\) is then obtained by a weighted aggregation of neighbor features:  
\[h^{\prime}_{i}=\sigma\left(\sum_{j\in\mathcal{N}(i)}\lambda_{ij}WX_{j}\right),\]  
where \(\sigma\) is a non-linear activation function (ReLU in this study). A multi-head attention mechanism performs this operation in parallel, with several independent attention heads. The results are aggregated via concatenation or averaging, forming the unified output representation. A three-layer stacked GAT is employed to progressively incorporate high-order neighborhood information. The structure graph itself is dynamically updated at each time step using a sliding correlation window, and edge connections are established based on inter-signal correlation measures. The final graph-based structural embeddings are concatenated with the outputs of the temporal and semantic modules for joint modeling in the signal forecasting task.  
Figure 2: The figure illustrates the construction of a graph based on inter-signal correlation and the computation process of attention weights within the Graph Attention Network. Each node receives an initial feature vector \(X_{i}\), which is transformed via a learnable weight matrix \(W\) to obtain embeddings. Attention scores \(e_{ij}\) are computed using a shared parameter vector \(a\) for each pair of neighboring nodes, then normalized to produce attention weights \(\lambda_{ij}\). The updated embedding \(h^{\prime}_{i}\) is obtained through a weighted sum of neighboring node features, modulated by \(\lambda_{ij}\).
# Context-Aware Trust and Reputation Routing Protocol for Opportunistic IoT Networks
## 3 Proposed CATR Routing Protocol for OppIoT Networks
### Complexity Analysis  
In the outer loop, the algorithm iterates over all encountered neighbor nodes of node \(i\). Let \(N\) represent the number of neighbors of node \(i\). The time complexity of the outer loop is O(\(N\)). Each method (\(calcBT()\), \(calcEF()\), \(calcPF()\), \(calcRC()\), \(calcCD()\)) is invoked once per neighbor. So, the time complexity for all trust metric calculations per neighbor is O(1).  
In the inner loop, for each neighbor \(n\), the algorithm iterates over all neighbors of \(K_{n}\), which represents the number of neighbors of \(n\). The combined time complexity for all neighbors is \(\sum_{n=1}^{N}K_{n}\).  
The threshold comparison and potential forwarding are O(1) per neighbor. The total time complexity is the sum of the complexities of all operations is O(\(N\)) + O(1) + O(\(\sum_{n=1}^{N}K_{n}\)).  
In the worst case, assuming each node \(i\) has \(N\) neighbors and each of those neighbors has an average of \(K\) neighbors, then the total time complexity consumed is O(\(N\) * K). If \(K\) scales linearly with \(N\), the complexity becomes O(\(N^{2}\)).
## 3 Results and Discussion
### Biospeckle Contrast and bOCT Results after 24 h of Exposure  
Biospeckle images were calculated at 0 h, 6 h, and 24 h after exposure to 200 nm and 2000 nm AlNPs. Figure 3a shows the bOCT images obtained after 24 h exposure along with that of the control at 0 h. The results clearly show that there was increased internal activity within the seed 24 h after its exposures to AlNPs, in comparison to that of the control. In order to attain a qualitative comparison, the average bOCT image contrast of six ROIs, as indicated by the rectangles, was calculated using bOCT images obtained at 24 h after exposure, and was further averaged over a total of three seeds, as shown in Figure 3b. A significant difference in the average bOCT contrast can be seen for the 200 nm and 2000 nm AlNPs compared to that of the control. The results of the study imply that the positive effect of AlNPs on seed germination can be visualized at a very early stage of germination. The addition of AlNPs is believed to enhance energy metabolism, which in turn would lead to increased dynamical variation or cellular movements within the seed, and thus result in the increased temporal variation or increased bOCT contrast. The effect was more significant as the particle size decreased.