esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### SHAP-Based Analysis and Its Practical Implications
#### Implications of SHAP Results for Landslide Dam Management  
The SHAP-based feature importance analysis provides valuable interpretability to the predictive model by quantifying the influence of individual features on landslide dam lifespan prediction. The results indicate that dam length and dammed lake volume are the most critical factors affecting lifespan prediction, which aligns with traditional engineering perspectives--larger dams with higher storage capacities tend to exhibit more complex stability dynamics. However, a notable new insight revealed by SHAP analysis is the significant influence of the upstream catchment area, which was previously considered secondary to geometric and material properties in many empirical models. The high SHAP value for upstream catchment area suggests that hydrological factors play a more dominant role than expected, particularly in regions where rainfall-induced erosion significantly impacts dam stability.  
This finding has important implications for landslide dam risk assessment and management strategies. Given the strong impact of upstream catchment area, future monitoring and risk evaluation should incorporate detailed hydrological assessments rather than relying predominantly on dam geometry and material composition alone. Additionally, the relatively lower SHAP values of triggering factors such as earthquakes and rainfall suggest that while these external forces are crucial for dam formation and sudden failures, their effect on long-term stability may be less dominant than internal structural factors. This insight can guide prioritization of mitigation strategies, emphasizing structural reinforcement and sediment control measures over purely event-driven risk mitigation.  
Furthermore, the transparent ranking of feature contributions through SHAP analysis enables adaptive dam management policies. For example, regions with a high upstream catchment impact may require enhanced flood regulation infrastructure, while areas primarily influenced by geomorphological factors may focus more on stabilization engineering measures. By integrating data-driven feature importance insights, SHAP analysis refines existing landslide dam assessment models, allowing for more targeted and proactive risk management strategies. These results highlight the potential of SHAP analysis not only as a tool for improving model interpretability but also as a foundation for practical engineering decision making.
## 4 Scenario and Parameterization
### Considered Scenario  
5G IAB NR network deployments are generally targeted at urban environments, where the signal propagation is heavily obstructed by large obstacles such as buildings. Specifically, the signal propagates mainly through the streets, blocked by buildings, preventing it from penetrating and reaching areas around their corners. As an example, for the numerical evaluation, we considered a Manhattan grid environment typical for city centers. Here, the natural deployment points of the BSs were street intersections, and their density was the main scenario parameter.  
The average size of a Manhattan block is 76 by 183 m [64], and the average avenue/street width is 30 m. We assumed that the nodes' antenna coverage radius is 210 m or less [5]. The nodes were installed according to the topologies depicted in Figure 5. An example of deploying Topology # 2 is shown in Figure 6. It was assumed that \(A_{1}=4\) and \(A_{n}=3\) for \(n=2,\ldots,N\).  
Let the UEs be evenly distributed across the streets' area. We modeled packet arrivals from all UEs associated with sector \((n,i)\) as a single Poisson process with parameter \(\lambda_{n,j}=L_{n,j}\lambda\), where \(L_{n,j}\) is the average number of UEs in sector \((n,i)\) and \(\lambda\) is the average packet arrival rate from one UE. Downlink packet arrivals destined to UEs in sector \((n,i)\) are represented as a Poisson process with parameter \(\alpha\lambda_{n,i}\), where \(\alpha\) is a coefficient that specifies the load ratio between downlink and uplink.  
The first hop in a multi-hop network is the \"bottleneck\" and must provide throughput for all IAB-nodes down the hop chain. This is one of the main limitations of IAB network scaling.
# The Long and Winding Road to Cardiac Regeneration
## 2 Stem Cell Therapy
### Adult Stem Cells
#### 2.1.5 Cardiac Progenitor Cells  
Cardiac progenitor cells (CPCs) are a specialized group of cells able to differentiate into various types of cardiac cells, making them a promising approach for cardiac regeneration [123; 124]. CPCs are derived from different sources, including the embryonic heart, fetal heart and adult heart. These cells already have a cardiomyocyte-like phenotype and can be used to construct cardiac tissue in vitro. Their multipotent nature and ability to generate new cardiac tissue provide several advantages for cardiac regeneration [125; 126].  
CPCs have the potential to differentiate into cardiomyocytes, endothelial cells, and smooth muscle cells, which are essential components to build a whole heart tissue [127]. This capacity allows them to integrate seamlessly into the existing cardiac structure, promoting functional recovery. Additionally, CPCs possess paracrine effects, secreting growth factors, and mRNAs that could stimulate endogenous repair mechanisms, angiogenesis, and tissue remodeling. This paracrine activity further enhances the regenerative potential of CPCs [128; 129].  
Despite their potential, CPCs also face certain limitations. The precise identification and isolation of CPCs remain challenging due to their heterogeneity and lack of specific markers. Ensuring a pure population of CPCs is crucial for successful regeneration. Additionally, the survival and engraftment of transplanted CPCs into the host myocardium is a major concern. Strategies to improve cell survival, integration, and long-term function are actively being investigated [130].
# Research on Frequency-Selective Output Constraint Algorithm for Active Vibration Control
## 3 Modified Rescaling Algorithm Development
### Stability Analysis  
Apparently, the proposed algorithm is the same as the NFXLMS algorithm before amplitude rescaling is triggered. When the amplitude rescaling of the \(i\)th frequency component is triggered, the \(i\)th output component \(u_{i}(n+1)\) can easily be drawn, as follows according to Equations (15)-(17):  
\[\begin{array}{c}u_{i}(n+1)=A_{ui}(n+1)\sin[\omega_{i}n+\theta_{ui}(n+1)][A_ {ui,\max}/A_{ui}(n+1)]\\ \qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\quad=A_{ui,\max}\sin[\omega_{ i}n+\theta_{ui}(n+1)]\end{array} \tag{21}\]  
Equation (21) shows that the amplitude \(A_{ui}(n+1)\) will be restricted at the constraint \(A_{ui,\max}\) while the phase \(\theta_{ui}(n+1)\) can still be tuned by the NFXLMS algorithm. According to Equation (10), the ratio part eliminates the rescaling factor \(A_{ui,\max}/A_{ui}(n+1)\). This means that the phase-tuning process will not be affected by the rescaling algorithm and is the same as the compensated NFXLMS algorithm. Thus, the conditions that guarantee the stability of the compensated NFXLMS algorithm are sufficient conditions for the stability of the modified rescaling algorithm. The stability and robustness analysis of the related NFXLMS algorithm is fully studied and is outside the scope of this paper.
# A Malware Detection Framework Based on Semantic Information of Behavioral Features
###### Abstract  
As the amount of malware has grown rapidly in recent years, it has become the most dominant attack method in network security. Learning execution behavior, especially Application Programming Interface (API) call sequences, has been shown to be effective for malware detection. However, it is troublesome in practice to adequate mining of API call features. Among the current research methods, most of them only analyze single features or inadequately analyze the features, ignoring the analysis of structural and semantic features, which results in information loss and thus affects the accuracy. In order to deal with the problems mentioned above, we propose a novel method of malware detection based on semantic information of behavioral features. First, we preprocess the sequence of API function calls to reduce redundant information. Then, we obtain a vectorized representation of the API call sequence by word embedding model, and encode the API call name by analyzing it to characterize the API name's semantic structure information and statistical information. Finally, a malware detector consisting of CNN and bidirectional GRU, which can better understand the local and global features between API calls, is used for detection. We evaluate the proposed model in a publicly available dataset provided by a third party. The experimental results show that the proposed method outperforms the baseline method. With this combined neural network architecture, our proposed model attains detection accuracy of 0.9828 and an F1-Score of 0.9827.  
network security; dynamic analysis; API sequences; deep learning; malware detection +
Footnote †: journal: Applied Sciences
# Energy-Efficient Internet of Drones Path-Planning Study Using Meta-Heuristic Algorithms
## 4 Result Discussion and Analysis
### Parameter Settings  
In this subsection, we explore the parameter settings of our model. These configurations are crucial in determining the model's behavior and performance.  
Formation of the IoD: Five drones navigate from their initial locations to designated destinations, as shown in Table 1.  
Environment: 12 obstacles are randomly distributed over 2000 m \(\times\) 2000 m.  
The population size is configured to 250 particles, with a maximum iteration limit of 500. The number of waypoints is set to 20, and a safety distance of 20 meters is established. The probabilities of mutation (Pm) and crossover (Pc) are set to 0.005 and 0.8, respectively. In the case of the artificial bee colony (ABC) algorithm, there are 300 employed bees, 500 onlooker bees, and 300 scout bees, with a limit of 20% of maximum iteration. The parameter settings are summarized in Table 2. It is worth noting that the selection of parameters involves experimentation and fine-tuning to attain optimal performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Drones** & **Start Point** & **Destination Point** \\  & **(xs, ys, zs)** & **(xd, yd, zd)** \\ \hline Drone 1 (x, y, z) & (00, 200, 50) & (2000, 400, 50) \\ \hline Drone 2 (x, y, z) & (00, 600, 50) & (2000, 800, 50) \\ \hline Drone 3 (x, y, z) & (00, 1000, 50) & (2000, 12800, 50) \\ \hline Drone 4 (x, y, z) & (00, 1400, 50) & (2000, 1600, 50) \\ \hline Drone 5 (x, y, z) & (00, 1600, 50) & (2000, 2000, 50) \\ \hline \hline \end{tabular}
\end{table}
Table 1: UAVs start and end points.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline No. waypoints & 20 waypoints \\ \hline Popsize & 250 population \\ \hline Max Iteration & 500 iterations \\ \hline No obstacles & 12 obstacles \\ \hline PSO parameters & c1 = 0.0596; c2 = 2.596, w = 0.5 \\ \hline CIPSO parameters & \(c_{max}\) = 3.5, \(c_{min}\) = 0.5, \(w_{max}\) = 0.9, \(w_{min}\) = 0.4, \(\mu\) = 2, \(\alpha\) = 2 \\ \hline GA parameters & Pm = 0.005, PC = 0.8 \\ \hline ABC parameters & employed bees = 300, onlooker bees = 500, scout bees = 300 \\ \hline SD & 20 m \\ \hline No runs & 30 runs \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters setting.
# A Dynamic Service Placement Based on Deep Reinforcement Learning in Mobile Edge Computing
## 3 Model and Problem Formulation
### QoS Model
#### 3.2.3 Updating Delay  
Due to the mobilities of users, it is inefficient to keep the locations of services unchanged all the time, which will increase the communication delay of users. Thus, we consider optimizing the user experience via dynamically migrating the services. We define a boolean variable \(\alpha(v_{i})\) to denote whether the service \(v_{i}\) that is serving user \(u_{i}\) is under the migration or toggling state. \(\mathrm{Y}(v_{i})\) is the updating delay of service \(v_{i}\), which includes service profiles transmission, rebooting software resources, and so on [13]. The updating delay \(D^{u}_{u_{i}}(t)\) of user \(u_{i}\) is defined as  
\[D^{u}_{u_{i}}(t)=\alpha(v_{i})(t)\cdot\mathrm{Y}(v_{i}) \tag{4}\]
## 2 Forecasting Models
### Forecast Error Measures  
To check the accuracy of a forecasting model, we usually split the data set \((y_{1},\ldots,y_{T})\) into a training set \((y_{1},\ldots,y_{N})\) and a test set \((y_{N+1},\ldots,y_{T})\). Then, we estimate the parameters of the model using the training set and use it to forecast the next \(T-N\) observations. The forecast errors are the difference between the actual values in the test set and the forecasts produced:  
\[y_{t}-\hat{y}_{t}\qquad\text{for}\;\;t=N+1,\ldots,T. \tag{15}\]  
The most commonly used scale-dependent error measures are the mean absolute error (MAE) and the root mean squared error (RMSE) defined as follows:  
\[\text{MAE}=\frac{1}{T-N}\sum_{t=N+1}^{T}\left|y_{t}-\hat{y}_{t}\right|, \tag{16}\]  
\[\text{RMSE}=\sqrt{\frac{1}{T-N}\sum_{t=N+1}^{T}\left(y_{t}-\hat{y}_{t}\right) ^{2}}. \tag{17}\]  
When comparing the performance of forecast models on a single data set, the MAE is interesting, as it is easy to understand, but the RMSE is more valuable as is more sensitive than other measures to the occasional large error (the squaring process gives disproportionate weight to very large errors). There is no absolute criterion for a 'good' value of RMSE or MAE: it depends on the units in which the variable is measured and on the degree of forecasting accuracy, as measured in those units, which is sought in a particular application. Percentage errors have the advantage of being scale-independent, and so are frequently used to compare forecast performance between different data sets. The most commonly used measure is the mean absolute percentage error (MAPE) defined as follows:  
\[\text{MAPE}=\frac{1}{T-N}\sum_{t=N+1}^{T}\left|\frac{y_{t}-\hat{y}_{t}}{y_{t} }\right|\times 100. \tag{18}\]  
Measures based on percentage errors have the disadvantage of being infinite or undefined if \(y_{t}=0\) and having extreme values when \(y_{t}\) is close to zero. Scaled errors can be used as an alternative to percentage errors when the purpose is to compare the forecast accuracy of time series on different scales [21]. A scaled error is given by \((y_{t}-\hat{y}_{t})/Q\), where \(Q\) is a scaling statistic computed on the training set. \(Q\) can be defined as the MAE of naive forecasts for nonseasonal time series:  
\[Q=\frac{1}{N-1}\sum_{t=2}^{N}\left|y_{t}-y_{t-1}\right|, \tag{19}\]  
and as the MAE of seasonal naive forecasts for seasonal time series:  
\[Q=\frac{1}{N-m}\sum_{t=m+1}^{N}\left|y_{t}-y_{t-m}\right|, \tag{20}\]  
\((y_{t}-\hat{y}_{t})/Q\) being independent of the scale of the data. Then, the mean absolute scaled error (MASE) is defined as follows:  
\[\text{MASE}=\frac{1}{T-N}\sum_{t=N+1}^{T}\left|\frac{y_{t}-\hat{y}_{t}}{Q} \right|=\frac{\text{MAE}}{Q}. \tag{21}\]  
When comparing several forecasting methods, the accuracy measures frequently lead to different results as to which forecast method is best.
# Video-Based Recognition of Human Activity Using Novel Feature Extraction Techniques
## 2 System Overview of Proposed Solution
### Proposed Features Using HEVC  
In this work, we propose the use of video coding standards to extract features from videos containing human activities. Namely, we modified the HEVC video coder to generate feature variables as detailed next. Rich feature sets can be generated from the HEVC coding process as the process of motion estimation is based on quadratic recursive splitting of the coding units. Such units vary in depth from 0, which is a block of 64 \(\times\) 64 pixels, to 3, which is a block of 16 \(\times\) 16 pixels. Coding units are further split into prediction units varying in size from 32 \(\times\) 32 to 4 \(\times\) 4 pixels.  
Consequently, the extracted feature variables are based on coding unit partitioning, count of coding bits spent on coding unit splits, percentage of intra-coded and skipped coding units, histogram of coding unit depths, sum of absolute differences in prediction errors (i.e., the difference between a current pixel block and its best-match location in a previous frame), coding distortion of coding units, histograms of motion vectors, variances  
Figure 1: System overview of proposed solution.  
in motion vectors, and count of coding bits spent on coding motion vector differences. This results in 67 feature variables per video frame.  
The authors previously used a similar set of features in their work on identifying key frames in video sequences [17]. The feature extraction solution is illustrated in Figure 2.  
The original videos of the dataset used in this work are in avi format; hence, as detailed in the flowchart in Figure 2, avi videos are converted into YUV sequences suitable for video coding. A HEVC video coder is used to encode the YUV sequences and generate feature variables and motion vectors which constitute the basis of our proposed solution. This process is repeated for every frame in human activity video.
## 3 Results and Discussion
### The Roles of HMDS Coating of the QD on the Improvement of QD-LED Characteristics
The data in Figure 2d,e indicate that the simple HMDS treatment of CdSe/ZnS QDs improves the property of luminance even though the current density is low as compared to the one of Ref. This result indicates that current loss via a Joule heat consumption was reduced by the simple HMDS treatment so that current effectively converted into light. Consequently, the efficiency of both HMDS1 and HMDS2 is higher than that of Ref in Figure 2f. As listed in Table 1, the simple HMDS treatment of QDs increased the maximum efficiency of test samples up to 1.7 and 1.8 times for HMDS-1 and HMDS-2, respectively. We believe that the deactivation of surface defects on QDs by the SiO(CH\({}_{3}\))\({}_{3}\) improves the amount of photon conversion. Additionally, based on the same reason, we observed the improvement of the maximum luminance up to 15-18%. The results of the reproducibility experiment are summarized in Table 2 of Appendix A, and the distribution of luminance values was not large depending on the conditions. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Turn-On Voltage (V)** & \begin{tabular}{c} **Maximum Luminance** \\ **(cd/m\({}^{2}\))** \\ \end{tabular} & \begin{tabular}{c} **Maximum Efficiency** \\ **(cd/A)** \\ \end{tabular} \\ \hline \begin{tabular}{c} Ref \\ (no HMDS treatment) \\ \end{tabular} & 4.3 & 9972 & 7.0 \\ \hline \
\begin{tabular}{c} HMDS-1 \\ (QD = 10 mL and HMDS \\ = 1 mL) \\ \end{tabular} & 5.5 & 11,538 & 12.1 \\ \hline
\begin{tabular}{c} HMDS-2 \\ (QD = 10 mL and HMDS \\ = 4 mL) \\ \end{tabular} & 5.5 & 11,783 & 12.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The effects of the hexamethyldisilazane (HMDS) treatment on the characteristics of quantum dot light emitting diodes (QD-LEDs). Note that the ratio of HMDS-1 to of quantum dot (QD) was 1/10 and the ratio of HMDS-2 to QD was 4/10.
# Load-Balancing Routing for LEO Satellite Network with Distributed Hops-Based Back-Pressure Strategy
## 4 Hops-Based Back-Pressure Routing
### Distributed Routing Algorithm
#### 4.3.2 Propagation Region Control  
In the LSN, every satellite generally contains four ISLs. Therefore, the number of available paths from source to destination increases exponentially with the network scale. If the permitted propagation region is not limited, the transmission range of the packet will be too large and the delay of the network will be affected. Therefore, in this paper, the permitted propagation region is limited to the rectangular area composed of source and destination satellites, so as to reduce redundancy while ensuring adequate available links. Figure 6 demonstrates the permitted area.  
The blue region denotes the permitted transmission area, while the rest is the prohibited area. DHBP allocates the link according to the defined maximum hop delay weight to transmit packets in the blue area. On the link \((s,d)\), when flow \(c\) reaches \(n\), packet \(p\) is listed at the head of transmission buffer first. Then, in the permitted propagation area, the next hop of \(p\) is determined according to the maximization criterion of \(\hat{\omega}_{nb}^{c}(t)\). Assume that \(b\) is the next hop obtained by the decision.  
\[b=\operatorname*{argmax}_{b\in N^{*}}\hat{\omega}_{nb}^{c}(t) \tag{14}\]  
\[\hat{\omega}_{nb}^{c}(t)=\hat{Q}_{n}^{c}(t)-\hat{Q}_{b}^{c}(t)=\sum\limits_{p \in\hat{Q}_{n}^{c}}H(p)-\sum\limits_{p\in\hat{Q}_{n}^{c}}H(p), \tag{15}\]  
Figure 6: Available rectangular region.  
where \(N^{*}\) is candidate satellites set for the next hop on link \((n,d)\). The distribution hops-based back-pressure routing (DHBP) is described in detail in Algorithm 1. Figure 7 demonstrates the flow chart of DHBP.  
```
1 Enqueue a new packet:
2 Assume packet \(p\) was injected by flow \(c\) from \(s\) to \(d\);
3\(n\) received \(p\) at time slot \(t\).
4 if\(n\) is the destination of \(p\)then
5 Transmit \(p\) to the upper layer;
6 else
7 Enqueue \(p\) into the buffer
8 Update the DHD to \(H(p)\)
9 Update the virtual DHD queue by \(\hat{Q}_{n}^{c}(t)\leftarrow\hat{Q}_{n}^{c}(t)+H(p)\)
10 end if
11 Schedule the transmission:
12 Dequeue \(p\) at the head of the sending buffer.
13 forall neighbors \(N*\) of \(n\)do
14 if\(N*\) belongs to the permitted regionthen
15 Calculate \(\hat{\omega}_{nb}^{c}(t)\) for all permitted \(N*\)
16 end if
17
18 end for
19 Get next hop \(b\) according to Eq.(14)
20 Transmit \(p\) on link \((n,b)\), then delete \(p\) in the buffer
21 Update virtual DHD queue by \(\hat{Q}_{n}^{c}(t)\leftarrow\hat{Q}_{n}^{c}(t)-H(p)\)
```  
**Algorithm 1**Distributed Hops-based Back-Pressure Routing  
Figure 7: Packet storage and forwarding process of DHBP algorithm.