esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Discussion
### Analysis and Discussion  
As shown in Figure 2, the sensing threshold of the FC increases with the rise of the sensing point, which is the same as the classical scheme. Additionally, the sensing threshold of the proposed scheme is always higher than that of the classical scheme. The reason for this phenomenon is that when Poisson approximation is applied to the results of different independent Bernoulli variables, a high-order small quantity is always produced. The possible reason is that the rounding operation is needed for the evaluated decision threshold since the decision threshold is always an integer, which increases the decision threshold at the FC.  
Note that the sensing threshold of the proposed scheme is closer to that of the classical scheme compared with the AND rule and the OR rule. This is because that when the probability of the Poisson approximation is less than 0.5, the \(\lambda\) produced by Poisson approximation will be very small, and the corresponding high-order small quantity will be even smaller. Therefore, the decision threshold obtained by this method will be much better than the threshold error produced by other methods.  
As shown in Figure 3, the minimum error probability of the proposed scheme is basically identical to the classical scheme. This is because the threshold obtained by the proposed method is basically the same as that obtained by the classical scheme, and the error occurs only at a few jump points. As a result, if the proposed method is different from the classical scheme, the error probability at the bifurcation point will be greater than that calculated by the classical scheme. In addition, the trend of the three lines in Scenario I, II, III always increases gradually from left to right. This is because the performance of the CSS increases as the optimal \(N\) value decreases. However, the minimum total error probability under scenario IV decreases at first and then increases.  
As shown in Figure 4, when \(x\) is small or large, the maximum deviation increases, because when \(N_{1}\) is larger or smaller, the \(\frac{N_{1}}{N_{2}}\) is farther away from 1, thus increasing the deviation. In comparison, when \(N_{1}\) is too large, the deviation is larger. This is because \(SNR2>SNR1\), and the geometric mean is used in Poisson approximation, which leads to a greater error caused by a larger \(N_{1}\) when \(\frac{N_{1}}{N_{2}}\) also deviates from 1. Similarly, when \(SNR2\) increases, the maximum deviation will increase due to the rise of the average value.  
Figure 5: The error with the variation of \(N_{1}\) and \(SNR2\).  
Consequently, if it is possible to screen the population within the detectors, one should try to ensure that the \(\frac{N_{1}}{N_{2}}\) is close to 1, and at the same time, try to select an SNR with a small deviation to form the detector group.
# Numerical Insight into the Kelvin-Helmholtz Instability Appearance in Cavitating Flow
## 3 Numerical Procedure
### Boundary Conditions  
Modeling cavitating flow in the microchannel was performed at several mass flow rates. The boundary condition was set at the inlet of the Venturi channel computational domain as average velocity. The inlet turbulence intensity was set to zero. The vapor volume fraction at the inlet was set to zero also.  
At the outlet of the Venturi channel computational domain the second boundary condition was set prescribing the absolute pressure 1 bar and backflow turbulence intensity 5% for all mass flow rates. The vapor volume fraction at the outlet was set to zero.  
On all walls, the no-slip stationary wall boundary condition was used with a standard wall roughness model. The temperature was in all cases set to 20 \({}^{\circ}\)C.
## 3 Results
### Relation between Landslide Area Change and Rainfall-Derived Parameters during Typhoon Events  
No cloud-free satellite image was available between typhoons Megi and Nepartak (cf. Figure 5). Hence, the landslide area mapped for December 2016 could represent either of those events. To avoid any uncertainty, both typhoon events were removed from the correlation analysis.  
CHIRPS daily precipitation data for the exact date when the typhoon passed over Taiwan were correlated with the landslide area change for 17 typhoon events. The results show that there was not enough evidence of a significant correlation between the variables (\(p=0.186,p=0.47\)).  
For the CWB rain gauges, we tested for correlations between the landslide area change and the cumulative rainfall, duration and mean intensity of the rainfall events during 15 typhoon events for the stations Meishan and Fuxing, and for 14 typhoon events for the Xiaoguanshan station. Figure 7 shows the rainfall parameters per CWB station plotted against the landslide area change and the resulting Spearman's rank correlation coefficients. Moderate positive correlations were found at a 90% confidence level between the landslide area change and the duration of the heavy rainfall event for stations Fuxing and Xiaoguanshan. This means, there is less than 10% chance that the found relationship was due to chance. Cumulative rainfall and mean intensity did not present strong significant correlations with the landslide area change. For the Meishan station, no significant correlation was found, which may be explained by the rain gauge location within a tributary valley.  
Figure 7: Spearman correlation results between the Butangbunasi landslide area change and rainfall event parameters calculated for three CWB stations for selected typhoon events.
###### Abstract  
In a complex network, some nodes are relatively concentrated in topological structure, thus forming a relatively independent node group, which we call a community. Usually, there are multiple communities on a network, and these communities are interconnected and exchange information with each other. A node that plays an important role in the process of information exchange between communities is called an inter-community bridge node. Traditional methods of defining and detecting bridge nodes mostly quantify the bridging effect of nodes by collecting local structural information of nodes and defining index operations. However, on the one hand, it is often difficult to capture the deep topological information in complex networks based on a single indicator, resulting in inaccurate evaluation results; on the other hand, for networks without community structure, such methods may rely on community partitioning algorithms, which require significant computing power. In this paper, considering the multi-dimensional attributes and structural characteristics of nodes, a deep learning-based framework named BND is designed to quickly and accurately detect bridge nodes. Considering that the bridging function of nodes between communities is abstract and complex, and may be related to the multi-dimensional information of nodes, we construct an attribute graph on the basis of the original graph according to the features of the five dimensions of the node to meet our needs for extracting bridging-related attributes. In the deep learning model, we overlay graph neural network layers to process the input attribute graph and add fully connected layers to improve the final classification effect of the model. Graph neural network algorithms including GCN, GAT, and GraphSAGE are compatible with our proposed framework. To the best of our knowledge, our work is the first application of graph neural network techniques in the field of bridge node detection. Experiments show that our designed framework can effectively capture network topology information and accurately detect bridge nodes in the network. In the overall model effect evaluation results based on indicators such as Accuracy and F1 score, our proposed graph neural network model is generally better than baseline methods. In the best case, our model has an Accuracy of 0.9050 and an F1 score of 0.8728.  
social network analysis; bridge node detection; graph neural network; community
# An Algorithm for Rescheduling of Trains under Planned Track Closures
## 4 Joint Problem of Train Rescheduling and Track Closure Planning
### Representation of a Railway Network  
Tracks and stations are basic elements of a railway network for the considered problem. A track is a link connecting directly two different stations. Every pair of stations \(i\) and \(j\) can be linked by \(K\) tracks of the same length, i.e., \(d_{ij}=d_{j,i}\) (\(d_{i,j}\) is an element of a matrix \(\mathbf{D}=[d_{ij}]_{i,j\in\mathbf{V}}\)). Track numbers constitute a set \(\mathbf{K}=\{0,1,2,\ldots,K\}\) the same for every pair of stations. A railway network is described by a multigraph \(\mathbf{G}=<\mathbf{V},\mathbf{E}>\). The stations from a set \(\mathbf{V}=\{0,1,2,\ldots,V\}\) represent railway stations, while entries of the incidence matrix \(\mathbf{E}=[e_{i,j}]_{i,j\in\mathbf{V},k\in\mathbf{K}}\) express tracks between particular railway stations. A virtual railway station denoted as '0' is distinguished to serve as the beginning and the end of each train run in the model. It is located in zero-distance to other stations (\(d_{0,j}=d_{j,0}=0\)). The existence of the track number '0' enables taking into account a substitute transport between stations of the same distance as real-world links. Consequently, a current entry \(e_{i,j,k}\) of \(\mathbf{E}\) is equal 1(0) if there exists track \(k\) from \(i\) to \(j\) (otherwise), as in Figure 1. \begin{table}
\begin{tabular}{l l l l} \hline
**Symbol** & **Description** & **Symbol** & **Description** \\ \hline \(\mathbf{D}\) & matrix of distances between stations (tracks lengths) & \(\mathbf{V}\) & set of stations in a railway network \\ \(d_{i,j}\) & element of \(\mathbf{D}\) representing distance between stations \(i\) and \(j\) (tracks length) & \(V\) & number of stations in a railway network \\ \(\mathbf{K}\) & set of numbers indicating tracks between two stations (by default \(\mathbf{K}=\{0,1,2\}\), ”0” means a substitute connection which is not a track) & \(\mathbf{E}\) & incidence matrix describing existence of tracks between stations \\ \(K\) & maximal number of tracks allowed between two stations (by default \(K=2\)) & \(e_{i,j,k}\) & element of \(\mathbf{E}\) indicating the existence of track \\ \(\mathbf{G}\) & multigraph representing railway network & \(CAP_{i}\) & capacity of station \(i\) (the number of trains that can possess the station in the same time moment) & \\ \hline \end{tabular}
\end{table} 
Table 1: Notation regarding railway network model. Each station \(i\), as a place where trains can stop, start, or finish their runs, has limited capacity \(CAP_{i}\) restricting the number of trains which are allowed to dwell on there at the same time. Additionally, it is assumed that each train fits every slot at a station, and a single train may occupy only one station slot. A virtual station (\(i=0\)) can hold up to \(CAP_{0}=TR\) trains.
# Hierarchical LSTM-Based Network Intrusion Detection System Using Hybrid Classification
## 4 Performance Analysis
### Network Intrusion Detection Rate  
The detection performances of the proposed algorithm, ELM, DNN, CNN, and TSE are shown in Figures 12 and 13[27]. The comparison results using the ISCXIDS2012 dataset from Figure 12 confirm the superiority of the proposed method over ELM, DNN, and CNN in terms of all the performance metrics. In particular, the proposed method has a stable performance for all metrics with a very small variation, whereas the performances of the other methods vary for each metric. Thus, false positives and false negatives are few, which confirms that the performance is far superior to those of the existing methods. Among the algorithms used for comparison, DNN and ELM had the best and worst performances, respectively. However, the performance of DNN was 1.67% lower than the proposed method based on the F1-score.  
Figure 11: Hyperparameter tuning using the CIC-IDS2017 dataset.  
The experimental results for the CIC-IDS2017 dataset, shown in Figure 13, are also very similar to those for the ISCXIDS2012 dataset. Although the performance of DNN was the best among comparison methods, it was 1.39% lower than the proposed method in terms of the F1-score. The proposed method exhibited consistently high performance for all metrics. Therefore, regardless of the type of dataset, it exhibited a higher detection rate than other methods while keeping false positives and false negatives low.  
Such high detection performance is achieved because the proposed NIDS uses the entire packet data rather than partial data packets for intrusion detection and uses most packets of a session as session features. The creation of fixed-size features that accurately reflect packet information using a double LSTM structure for a large amount of packet data is also considered to have played an important role in achieving stable and high detection performance for all metrics.
# An Integrated Capacity Allocation and Dynamic Pricing Model Designed for Air Cargo Transportation
## 3 Results, Discussion, and Conclusions
### Methodology and Generalization of Findings
By setting a variable price rather than a fixed price, many companies are able to make more profit and offer prices that reflect ever-changing market conditions. In order to provide accurate pricing recommendations, it is essential that the data used in the model is of high quality. For air cargo carriers, dynamic pricing is about filling available capacity with profitable cargo, thus optimizing fleet and resources [5]. In order to build a dynamic pricing model, which is the second objective of the study, the first sub-step is to estimate future demand, which is an important input to the pricing model. Multiple linear regression model, time series model (Winters), and ANN were used to forecast demand. As a result of applying the models using historical data, actual data, and model results were evaluated using many statistical methods. As a result of the evaluation, we decided to use multiple linear regression analysis for demand forecasting as it provides the most appropriate result. For the dynamic pricing model, we used the output of the capacity allocation model and the demand forecasting model as inputs. In addition to these inputs, we added inputs such as flight time, remaining flight time, cargo weight, aircraft type, and flight route to the model based on literature review and expert opinion. We used the SARSA algorithm for the dynamic pricing model and solved the algorithm using Python. We used 25,203 rowsof data to test the accuracy of the model. We used 20% of these data for testing and the remaining data to train the model. Finally, we created an integrated model by using the revenue output of the dynamic pricing model as an input for the capacity allocation model in future periods. Our model differs from other studies in the literature due to its integrated structure and scope, and the use of a reinforcement learning algorithm for dynamic pricing. The inclusion of expert opinions in the study has helped the model to better reflect real-life situations. The fact that we applied the model using real data from an important airline in the air cargo industry helped us to correctly interpret the model outputs. As a result of our model and the expert's thought, the correct allocation of capacity will increase the overall load factor of the aircraft and the revenue to be earned by the company. As a result of the dynamic pricing model we have developed, the system's automatic pricing will provide the company with labor savings, greater control over pricing, and will help the company to generate more revenue.
# A Survey on an Energy-Efficient and Energy-Balanced Routing Protocol for Wireless Sensor Networks
## 5 Taxonomy for Energy-Efficient and Energy-Balanced Routing Protocols
### Multi-Hop Communication Method  
A typical example of the concept of the multi-hop communication method is as shown in Figure 5. In multi-hop communication, the sensed information is conveyed to the BS from the source node using one or more intermediate nodes depending on the distance between the BS and the source node. Multi-hop communication has many benefits when used in WSNs. Besides improving the connectivity of a WSN and extending the network's coverage, multi-hop communication helps in prolonging the network lifetime and functionality. In such a way, multi-hop communication allows higher transmission data rates and the effective use of the wireless communication channel. Different routing protocols or algorithms have been developed using the multi-hop communication method in order to optimize several single and multi-objective problems associated with WSNs. With heed to prolonging the network lifetime and functionality, researchers and practitioners have extensively used the concept of multi-hop communication to develop different energy-efficient and energy-balanced routing protocols for WSNs. The key reason for using the multi-hop communication concept is to reduce the distance in transmitting the sensed information from the source node to the destination. As mentioned earlier, the transmission distance plays a vital role in either reducing or increasing the energy dissipated by the SNs in the network. Nonetheless, solving the distance issue using the concept of multi-hop communication does not necessarily guarantee an optimal use of the limited battery energy in the network as explained in Section 4.2.  
In an attempt to use the concept of multi-hop communication to solve the distance problem associated with WSNs, the multi-hop communication technique gave rise to other design issues such as selecting the route with optical hop count to BS and/or the route to the BS with optimal energy consumption (both in terms of the energy consumed by the participating nodes and the total energy dissipated using that route). Therefore, researchers came up with different multi-hop techniques to solve these problems, where the ultimate goal is to conserve the energy in the network. We classify these multi-hop techniques into two main categories: multi-hop clustering technique and load-balanced tree technique. In fact, all the state-of-the-art energy efficient and energy-balanced routing protocols studied fall under any of these multi-hop communication classes, if they are not based on single-hop communication. The concept and principle of these classes of the multi-hop communication method are discussed one after the other. Subsequently, we grouped the surveyed energy efficient and energy-balanced routing protocols accordingly, pinpointing their strengths and weaknesses. Finally, we grouped the surveyed routing protocols based on the solution types or algorithms, and the decision variables used in designing each of these algorithms.
## 3 Condition Monitoring of the Impedance Bonds
### Condition Monitoring Device and Sensors Used for Monitoring the Impedance Bonds
#### 3.2.1 Current Transformers  
For measuring the currents through the windings of the impedance bond, a current transformer (inductive type) is used, with the transformation ratio of 250/5 A. Each transformer is specially designed to be mounted in measuring circuits without the need to undo the connections of the connecting conductors between the terminals of the impedance bond and railway tracks. For current measurements, the precision class is 1, and the apparent power is 1.5 VA.
# Research of Enzyme-Induced Carbonate Precipitation on Strength Behavior of Reinforced Sand
## 3 Results and Discussion
### Unconfined Compressive Strength
#### 3.1.3 Influence of Cementation Solution Concentration on Strength  
Figure 6 displays the variations in UCS of EICP-reinforced sand as influenced by cementation solution concentration (C) after a curing time of 7 days. It can be seen that the UCS of EICP-reinforced sand increases with cementation solution concentration (C) at different \(D_{\rm r}\) values. At 0.5 mol/L, strength remains low and positively correlates with \(D_{\rm r}\). Significant strength improvement occurs when the concentration increases from 0.5 mol/L to 1 mol/L, with strength enhancements exceeding 200% for \(D_{\rm r}\) values of 0.6, 0.7, and 0.8. However, no significant increase is observed at concentrations above 1 mol/L, indicating  
Figure 5: Variation in UCS of EICP-reinforced sand with relative density.  
that the reinforcement effect is limited beyond this threshold, likely due to urease catalysis inhibition. Consequently, it can be concluded that the optimal \(C\) value for this study is 1 mol/L. Under this concentration, the soil sample with \(D_{\rm r}=0.7\) exhibits the maximum UCS value of 413 kPa.
## 6 Results and Discussions
### Scenario 2: Nakagami-m-Weibull Fading  
A plot of the outage probability vs. \(\tilde{\gamma}\) for scenario 2, Nakagami-m-Weibull fading, for \((\gamma_{th})\) = 1 dB and 10 dB, is shown in Figure 3. System performance was evaluated at each threshold value for Weibull parameters \(c\) = 0.1, 0.5 and 1.5, which represent fading scenarios worse than Rayleigh fading, and Nakagami parameter \(m\) = 1, which represents Rayleigh fading. It was observed that the general system's behavior as the value of \(c\) and the threshold parameter \((\gamma_{th})\) varied was similar to that observed in Rayleigh-generalized gamma fading; thus, the performance improved with increased \(c\) values but worsened with increased \((\gamma_{th})\) values. For example, at \(c\) = 1.5 and an average SNR of 20 dB, an improvement of 85.4515% was observed in the outage performance when \((\gamma_{th})\) varied from 1 dB to 10 dB.  
Figure 2: Outage Probability under Rayleigh-Generalized gamma fading for (**a**) fading parameter \(m\) = 1, 2; \(\gamma_{th}\) = 5 dB, 10 dB, 20 dB and (**b**) Fading parameter \(v\) = 1, 2; \(\gamma\) = 5 dB, 10 dB, 20 dB.  
By setting \(m=1\), a Rayleigh faded link was evaluated, and by setting \(c<2\) on the other link, a worse-than-Rayleigh fading scenario was evaluated. It could therefore be observed that the performance of the system in this scenario, at \((\gamma_{th})=10\) dB was noticeably worse than the performance of the Rayleigh-generalized gamma fading for \(m=v=1\). However, at \(c=1.5\), the performance of the system was very close to that of the Rayleigh-Rayleighfading scenario.