esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results of Roadway Tunneling Simulation
### Determination of Burst-Prone Area around the Roadway
#### 4.1.2 Velocity Analysis of Coal and Rock Mass during Roadway Tunneling  
Based on a previous displacement analysis, a comprehensive analysis was conducted under the combination of velocity and displacement to examine the movement of the coal and rock mass throughout the roadway tunneling process. Figure 4 illustrates the curves of the coal and rock mass velocity recorded at the monitoring points. The velocity curves of the coal and rock mass at points A, B, and C exhibit distinct differences. The velocity curves at points A and C are relatively stable compared to point B. The velocities of the coal mass in the vicinity of the three points demonstrates slight fluctuations, predominantly around 0.02 m/s. During the middle stage of the roadway tunneling, the velocity at point B suddenly increased, rising to 1.12 m/s, and then a rapid decline ensued within a brief time interval.  
A comparison with Figure 3 in the previous section shows that a sudden drop in displacement at point B was accompanied by a significant change in its velocity. This observation suggests that the coal mass at the roadway side continuously accumulates elastic strain energy until a state of dynamic instability arises. The rapid release of the strain energy propels the coal mass from the roadway side, leading to the movement of the coal mass during rock burst events. In contrast, the velocity at point A demonstrates no significant variations during displacement fluctuations, indicating a static failure phenomenon, with no eruptions. Based on the results derived from Figures 3 and 4, it can be concluded  
Figure 3: Plot of displacements at monitoring points versus time steps.  
that during the tunneling process, although large coal rock blocks detach from both the roof (A) and the floor (C), no ejection of blocks occurs. Therefore, the coal mass in the roadway side region is most likely to undergo failure and burst due to stress disturbances.
## 3 Materials and Methods
### Proposed Method
#### 3.2.2 Proposed Residual Dual-Attention Mechanism
Softmax converts the output into a probability distribution, emphasizing the importance of various regions within the input feature map by assigning weights based on their significance. * Residual Connection and Attention Weight Reduction Simultaneously, a residual connection is established directly from the initial input feature map to preserve the original information. Softmax is applied again to this residual connection to prevent attention weight degradation, maintaining the relevance of the original information while incorporating new features. * Combination of Attention Weights The attention weights derived from the original feature map and the residual connection are combined. This integration enables the model to leverage both refined features and preserve original information. * Operation on Patch-Wise and Tile-Wise Channels The attention mechanism operates on both patch-wise and tile-wise channels. Patch-wise processing focuses on capturing local information within specific regions, while tile-wise processing considers broader contexts. This dual approach ensures a comprehensive understanding of the input feature map. * Fusion of Attention Weights from Patches and Tiles Finally, the attention weights obtained from both patch-wise and tile-wise operations are fused. This fusion synthesizes the strengths of both local and global attention mechanisms, resulting in more robust and effective attention weights. \[X^{\prime}=X+Xr,\quad Y^{\prime}=Y+Yr \tag{2}\]  
\[Z=X^{\prime}+Y^{\prime} \tag{3}\]  
The residual dual-attention mechanism employs a multi-scale approach, integrating original and refined features through residual connections while combining local and global attention. This method enhances the model's ability to capture intricate patterns and relationships in the input data.
# [PERSON] SOM Architecture for Intrusion Detection on In-Vehicle Communication Networks
## 5 Experimentation  
The research goal addressed in this paper is to implement an anomaly-based IDS for the identification of attack messages on an in-vehicle CAN bus. To this aim, we compared two methods on four different CAN bus attack datasets. The first method was a supervised XYF Kohonen network, and the second was a supervised XYF Kohonen network combined with a K-means clustering algorithm in order to improve the performance of the model and correctly identify the attack messages injected on the CAN bus.  
The research goal and research questions were defined according to the goal-question-metric (GQM) paradigm [52; 53] as follows:  
* **Research goal:** Analyze car hacking datasets in order to test the efficiency of a supervised Kohonen SOM network as an anomaly detector for a in-vehicle CAN bus.
* **Research questions:**
* Can the supervised Kohonen SOM network be used as an anomaly detector for in-vehicle communication networks?
* How does the proposed method perform in terms of the system's accuracy compared to other IDS approaches to detect attack messages on the CAN bus?
* **Metrics:**
* Given a car hacking dataset, the efficiency of the XYF and the XYF-K algorithms was tested computing the following classification metrics: accuracy, detection rate, false positive rate (FPR), precision, and F1-score.
* Given a car hacking dataset, the accuracy of the XYF-K algorithm is higher or, at least, equal compared to the accuracy of other IDS approaches, also including the analysis of periodic and aperiodic CAN messages.  
For this aim, the experimentation was performed in three main steps:  
1. Data preprocessing: preprocessing of the car hacking datasets concerning the traffic data messages on a CAN bus and containing the injection of attack messages. The datasets regard different kinds of attack including _DoS_ attack, _spoofing the drive gear_, _spoofing the RPM gauge_ and _fuzzy_ attack. The datasets are available at [20].
2. Experimental process: implementation of the XYF Kohonen network and of the XYF-K algorithm first on each dataset separately and then merging all of them in a single _mixed_ dataset including all kinds of attack. The network was performed on random samples of 10,000 split into the training set and test set, for each dataset and for the _mixed_ one. The training set was used to train the network and the test set for prediction.
3. Evaluation metrics [54]: evaluation of the performances of the two models.
## Results
### Effect of Storage Time  
During transportation and storage of air samples, concentrations of analytes can decrease due to their decomposition, losses via leaks and adsorption to internal walls of a vial and septum. The approach proposed by [PERSON] et al. (2015) was used to minimize the losses of analytes. The goal of this experiment was to estimate losses of analytes during their storage on the autosampler tray.  
Figure 2 shows the effect of storage time on responses of analytes. Two-sample two-tailed \(t\)-tests indicated that the changes in responses of \(n\)-undecane, acenaphthene and fluorene in relation to initial values were significant (\(p<0.05\) at RSD = 10%) after 24 h of storage (Table S4 in Supplementary Materials), while responses of other 22 analytes were stable. After 48 h of storage, changes in responses of \(n\)-undecane, \(n\)-hexadecane, acenaphthene and fluorene were significant. Greater losses of more hydrophobic analytes could be explained by their adsorption to a hydrophobic surface of a PTFE-lined septum being in direct contact with air. Despite these septa being considered highly inert, adsorption of very hydrophobic compounds by PTFE was earlier reported in the literature (S
# Proactive Service Caching in a MEC System by Using Spatio-Temporal Correlation among MEC Servers
## 5 Performance Evaluation
### Effect of User Mobility  
To inspect the effect of user mobility on the service cache performance, we conducted the same simulation but with a change in the user mobility model from the previous one to a random mobility model, while maintaining the same experimental environment. In a random mobility model, a user changes its moving direction from \((0,360^{\circ})\) and speed from \([0,\zeta]\) at each time slot according to the Uniform random distribution. Considering the size of the simulation topology, we conduct simulations for the cases where the \(\zeta\) is 0.2 km and 0.5 km.  
In Figure 10, we show the distribution of the relative cache hit rate \(\rho_{m}(t)\) with different \(\zeta\) when the cache size is \(\eta=8\). In this figure, we observe that the cache hit rate decreases as \(\zeta\) increases. This is attributed to the fact that the increased mobility speed leads to a larger change in the service popularity distribution across each MECS. As a result, the popularity prediction accuracy decreases. In this figure, we also observe that for all the moving speeds, the median \(\rho_{m}(t)\) at all MECS is higher when using the proposed method compared to using the LSTM method. When \(\zeta=0.2\) km, the average cache hit rate rate rate across all MECS (\(\rho(t)=\frac{1}{M}\sum_{i\in\mathcal{M}}\rho_{m}(t)\)) is 85.32% when using the proposed method, while it is 80.88% when using the LSTM method. When \(\zeta=0.5\) km, the average \(\rho(t)\) obtained through the proposed method is 80.92%, while the average \(\rho(t)\) is 74.79% when the LSTM method is used.  
Figure 9: Comparison of caching behavior.
## 2 Materials and Methods
### Data Acquisition  
OpenBCI Cyton Board was used for EEG acquisition. Eight passive Ag/AgCl electrodes were placed at the locations POz, PO3, PO4, PO7, PO8, O1, O2, and Oz according to the standard international 10-20 system. The reference and ground electrodes were placed on the mastoid bony surfaces behind the right and left ear, respectively. The signals were sampled at 250 Hz. The electrode-skin impedance was kept below 10 k\(\Omega\) during all experiments.
# Self-Similar Markovian Sources
###### Abstract
Its covariance function can be written as follows [17]:  
\[r(k)=\text{Cov}\,(X_{n},X_{n-k})\stackrel{{\Delta}}{{=}}\mathbb{ E}\left[(X_{n}-\mu)(X_{n-k}-\mu)\right] \tag{5}\]  
and the autocorrelation is equal to  
\[\rho(k)\stackrel{{\Delta}}{{=}}\frac{r(k)}{r(0)} \tag{6}\]  
LRD occurs when the autocorrelation function of a self-similar process follows a power law, which means that the value of the autocorrelation function is slowly decreasing as \(k\rightarrow\infty\):  
\[\rho(k)\sim k^{-\beta}\ \ \ \ as\ \ \ k\rightarrow\infty, \tag{7}\]  
where \(\beta\in(0,1)\). Returning to Equation (1); for \(H\in\langle 0,0.5\rangle\), LRD does not occur, and for \(H>0.5\), the process is correlated, which means that LRD occurs. For the correlated processes, the value of H determines the \"length of the memory\". For the value \(H=0\), the process is a random curve (it is memoryless) and for \(H=1\) it is deterministic. The number of time-scales for which the plot changeslinearly defines a number of time-scales for which the autocorrelation persists. A straight-line fit can be used to determine the averaged value of the Hurst parameter over the examined time-scales. We will refer to it while displaying the results of experiments in the form of figures in Section 3. The physical consequence of the self-similar intensity of flows in computer network transmissions is that very long periods of high traffic may happen during which queues and waiting times continuously increase, and we observe massive losses of packets. Many methods are used to simulate self-similar internet traffic, among which fractional Gaussian noise (FGN) (introduced in [19]) and Poisson Pareto burst process (introduced in [20]) are the most popular. Nevertheless, the Markovian models can still be useful in this field [18]. In Section 2 we briefly describe two Markovian models: the special semi-Markov process (SSMP), and Markov modulated Poisson process (MMPP) and one non-Markovian Poisson Pareto burst process (PPBP). SSMP may be a part of a DTMC queueing model, MMPP may be used in a CTMC model, and PPBP is often recommended for simulations of self-similar Internet traffic; however, neither of them is a perfect self-similar process. In Section 3 we present the results achieved in the experiments. We investigate and compare their ability to mimic self-similarity over several time scales. Section 4 concludes the article.
# Data Chunks Placement Optimization for Hybrid Storage Systems
## 4 Heuristics for Finding an Optimal Data Chunk Placement
### Moving Chunks from the Transmitter to Receiver Nodes  
Let us suppose that the chunk transfer combination specified as the vector \(\mathsf{Y}=(\Lambda^{\prime}{}_{1},\Lambda^{\prime}{}_{2},\ldots,\Lambda^{ \prime}{}_{n-1},\Lambda^{\prime}{}_{n})\) and the object itself is \(\Omega=(\Lambda_{11},\Lambda_{12},\ldots,\Lambda_{\delta n})\). If the single chunk was transferred from \(\mathsf{Y}\) to \(\Omega\), then both vectors change as \(\mathsf{Y}=(\Lambda_{1},\Lambda_{2},\ldots,\Lambda_{n-1})\)and \(\Omega=(\lambda^{\prime}{}_{11},\lambda^{\prime}{}_{12},\ldots,\lambda^{\prime}{}_{ \beta\text{n}},\Lambda_{\text{n}})\), where \(\lambda^{\prime}\) represents existing chunks in the storage. The reliability has been increased by this state change as an additional chunk is available for rebuilding object in the case where the object will be corrupted or totally lost. However, in this case, the cost object rebuilding is also increasing, but the transformation is defined in such a way that the required minimal cost of the chunk rebuilding is defined as an optimal for the current type operation. In the next section, we describe this process more briefly.
# Multi-Domain Modeling and Analysis of Marine Steam Power System Based on Digital Twin
## 2 Mathematical Model of Marine Steam Power System Equipment
### Mathematical Model of Pipeline  
The pipeline model is mainly used to simulate the flow process of the medium in the system, calculate the fluid flow state parameters in the pipeline equipment, and consider the effects of heat transfer, friction, etc., which can invoke the physical parameter model library for associated use with the medium.  
(1) Mass conservation equation:  
\[V_{p}\frac{d\rho_{p}}{dt}=q_{out}-q_{in} \tag{15}\]  
where:  
\(V_{p}\)--The volume of pipeline, m\({}^{3}\);  
\(\rho_{p}\)--The fluid density of pipeline, kg/m\({}^{3}\);\(q_{out}\)--The fluid outlet mass flow, kg/s;  
\(q_{in}\)--The fluid inlet mass flow, kg/s.  
(2) Energy conservation equation:  
\[Q_{p}=q_{out}h_{out}-q_{in}h_{in}=\alpha_{p}A_{p}\big{(}T_{p}-T_{e}\big{)} \tag{16}\]  
where:  
\(Q_{p}\)--The heat of pipeline fluid, kJ/s;  
\(h_{out}\)--The enthalpy of outlet fluid, kJ/kg;  
\(h_{in}\)--The enthalpy of inlet fluid, kJ/kg;  
\(\alpha_{p}\)--The convective heat transfer coefficient between fluid and pipe, kW/(m\({}^{2,\circ}\)C);  
\(A_{p}\)--The heat exchange area of pipeline, m\({}^{2}\);  
\(T_{p}\)--The temperature of pipeline fluid, K;  
\(T_{e}\)--The temperature of pipeline wall, K.  
(3) Frictional loss:  
\[\Delta p_{\zeta}=\zeta\frac{l_{p}}{d_{p}}\rho_{p}\frac{v_{p}^{2}}{2} \tag{17}\]  
where:  
\(\zeta\)--The frictional loss coefficient of pipeline fluid;  
\(l_{p}\)--The length of pipeline, m;  
\(d_{p}\)--The diameter of pipeline, m;  
\(v_{p}\)--The speed of pipeline, m/s;  
\(\mu_{p}\)--The viscosity coefficient of pipeline fluid;  
\(\varepsilon_{p}\)--The roughness of pipeline wall.
# SINR-Based MCS Level Adaptation in CSMA/CA Wireless Networks to Embrace IoT Devices
## 4 Demonstrations Using Simulations  
### Simulation Environments  
For demonstrating and evaluating the proposed MCS level adaptation algorithm, we consider IEEE 802.11a wireless networks composed of access points (APs) and wireless devices (WDs) in a given area. All the nodes in this paper use 100 mW for transmit power and the wireless devices are associated with (i.e., connected to) their serving access points chosen for the strongest received signal strength using Equation (1) before starting the simulations. For the radio transmission model in Equation (1), we modify the two-ray ground model [31] in ns-2 to use the fixed path loss exponent (i.e., \(\alpha=4\)).  
In this paper, we consider only downlink traffic from APs to their associated wireless devices (i.e., one-hop transmissions). Thus, there is no direct data traffic between wireless devices. For fixed and bidirectional routing paths between APs and their associated wireless devices, we use the NO Ad Hoc Routing Agent (NOAH) [32]. All participating nodes use the distributed coordination function (DCF) [8] to avoid collisions. When more than one wireless device are associated with the same access point, the wireless devices share the resource of their serving access point.  
A packet is recognized as received (or arrived) at a node when the received signal strength of the packet is higher than a power monitoring threshold, which is set to \(-129\) dBW in this paper. Thus, the power monitoring range (i.e., packet delivery range) is \(\sim\)1417 m from each AP of 100 mW transmit power. However, packets are recognized as _received successfully_ only if the SINRs of the packets remain higher than the minimum SINR requirements of their MCS levels during the time they are being received (Note that the SINR of a packet can change during the time it is being received owing to the change in cumulative power).  
Access points are assumed to be saturated during simulations (i.e., APs are continuously backlogged by a constant-bit-rate (CBR) traffic of 30 Mbps to each wireless device associated with them). The packet size of the CBR traffic is fixed at 1000 bytes and output queue sizes of APs and wireless devices are set to 50 packets. The RTS/CTS scheme is not used and the (physical) carrier sensing range is set to \(\sim\)532 m with the 100 mW transmit power. All other parameters for simulations remain at the default values suggested for IEEE 802.11a wireless networks in [29] unless stated otherwise.
# Identification of Critical Road Links Based on Static and Dynamic Features [PERSON]
## 5 Experimental Results  
### Parameter Sensitivity Analysis  
The TraLink2 vec model is composed of four key hyperparameters: \(p\), \(q\), \(\alpha\), and \(\beta\). To investigate the impact of these hyperparameters on the clustering results, we analyze different time periods (morning and evening rush hours) and time intervals (T = 5 min, T = 15 min, T = 30 min). For simplicity, we fixed the dimension of embedding vectors (\(d=3\)), random walk length (\(l=50\)), the number of walks (\(r=10\)), and window size (\(w=5\)). Research data were collected on 12 January 2022.  
Figure 4 shows the change of the CHI with the change of the return parameter \(p\) and in-out parameter \(q\) in static attributes. The figure shows that as the parameter \(q\) increases, the CHI decreases regardless of the time period, suggesting that smaller values of \(q\) result in better clustering results with higher CHI values. Moreover, the different colored curves in the figure correspond to different values of the parameter \(p\). As the parameter \(p\) increases, the overall CHI curve shifts downward, indicating that CHI becomes smaller, and the clustering results worsen. This indicates that smaller values of \(p\) lead to better clustering results with higher CHI values.  
Figure 5 displays the variation of CHI as the static and dynamic characteristic control parameters \(\alpha\) and \(\beta\) change. These parameter variations reflect the model's attention to the traffic network structure and traffic information during the random walk. For T = 5 min and T = 30 min, the CHI curve generally increases as the static control parameter \(\alpha\) increases, indicating that larger values of \(\alpha\) lead to better clustering effects and higher CHI values. However, for T = 15 min, the curve shows fluctuations, indicating that parameter adjustments are necessary to achieve optimal results. The curves of different colors in the figure represent different parameters \(\beta\). In general, the models obtain better clustering effects when \(\beta<1\), and no significant shift in the curve is observed when \(\beta>1\). The same dynamic control parameter \(\beta\) has different CHI values at different time intervals during different time periods, indicating that the effect of clustering changes with time periods and intervals. These findings demonstrate that the proposed model successfully captures the dynamic features in the urban traffic system during the learning of road link information. In practical applications, the optimal \(\beta\) value needs to be determined by adjusting the parameters.
## 3 Modelling of Wave Propagation in Wooden Poles
### Modelling of Soil  
In the field, the wood utility poles are generally embedded in the soil. Under this condition, boundary influences from the surrounding soil should also be considered so as to accurately characterise the wave propagation. In this work, to study the effect of soil modelling method, two models are built up based on a 5 m long pole using two different soil modelling methods: elastic linear model (linear) and Drucker-Prager model (nonlinear). Table 2 gives the all the properties of materials and Figure 2 compares the calculated acceleration responses of two models, when the sensor is located at 1.6 m from the bottom of the pole. In this comparison, the wave velocity is considered as main evaluation index, the change of which is generally caused by internal damage of the wood pole.  
In Figure 3, the dashed curve shows the results of the elastic linear soil model and the solid curve denotes the results using the Drucker-Prager model. The comparative results demonstrate that different soil modelling methods may lead to some difference in the structural responses. However, the difference is just about the wave amplitude rather than the wave pattern. It can also be found that the acceleration amplitude of the Drucker-Prager model is larger than that of the linear elastic model, even though the locations of wave peaks appear at almost the same time point. This result contributes to the conclusion that different soil modelling methods have less effect on the accuracy of wave propagation in this work and the linear elastic model is reasonable to portray the soil performance.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Material Type**} & \multicolumn{2}{c}{**Elastic Modulus (MPa)**} & \multicolumn{2}{c}{**Shear Modulus (MPa)**} & \multicolumn{2}{c}{**Poison’s Ratio**} \\ \cline{2-9}  & \(E_{R}\) & \(E_{T}\) & \(E_{L}\) & \(G_{LR}\) & \(G_{RT}\) & \(G_{LT}\) & \(V_{RL}\) & \(V_{RT}\) & \(V_{TL}\) \\ \hline Orthotropic & 1955 & 850 & 23,000 & 1513 & 357 & 1037 & 0.040 & 0.682 & 0.023 \\ Transversely isotropic & 1955 & 1955 & 23,000 & 1513 & 581 & 1513 & 0.023 & 0.682 & 0.023 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties for orthotropic and transversely isotropic materials.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Model**} & **Density (kg/m\({}^{3}\))** & **Elastic Modulus (MPa)** & **Poison’s Ratio** \\ \hline Linear-elastic & 1520 & 100 & 0.3 \\ Drucker-Prager & 1520 & 100 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties of the soil models.  
Figure 2: Comparison of numerical results according to different soil modelling methods.