esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Path-Planning Design and Dynamic-Obstacle Avoidance Strategy
### Trajectory Optimization Based on the Resultant Path of 3D JPS Algorithm
#### 3.2.1 An Any-Angle Path-Planning Strategy Based on the Parent Node Transfer Law
\begin{array}{cccccccc}p_{m,0}&p_{m,1}&\cdots&p_{m,k }\end{array} \Big{)}^{\top} \tag{12}\]  
In this paper, the minimum snap is used as the optimization objective of the polynomial as shown in Equation (13). \[J_{n}=\min\!\int_{0}^{T}{(f^{(4)}(t))}^{2}dt \tag{13}\]  
The quadratic solution equation for the PQ problem can be obtained by expanding it as shown in the following expression. \[J_{n}=\sum_{i=1}^{n}P^{T}\!\int_{T_{i-1}}^{T_{i}}\Bigg{(} \begin{array}{cccccccc} 0&\cdots&0\\ \underbrace{\cdots&0}_{4}&24&\cdots&\frac{kt}{(k-4)!}t^{k-4}\end{array}  \Bigg{)}^{T}\Bigg{(} \begin{array}{cccccccc}0&\cdots&0&24&\cdots&\frac{kt}{(k- 4)!}t^{k-4}\end{array} \Bigg{)}dt\cdot P \tag{14}\]  
\[J_{n}=\min\!\sum_{i=1}^{n}P^{T}\!\left( \begin{array}{cccccccc}Q_{1}&&\\ &\ddots&\\ &&Q_{n}\end{array} \right)\!P \tag{15}\]  
The position (\(P=f(t)\)), velocity (\(v=f^{\prime}(t)\)), acceleration (\(a=f^{\prime\prime}(t)\)), jerk function (\(\mathit{jerk}=f^{(3)}(t)\)), and snap function (\(snap=f^{(4)}(t)\)) of the nodes in the path process are used as equation constraints in the quadratic programming. The set of smooth-trajectory nodes is obtained by solving the expansion interval (expansion radius of \(r\)) of each node as an inequality constraint, as shown in Equation (16). \[s.t.\left\{ \begin{array}{l}f_{1}(T_{0})=P\\ f_{n}(T_{n})=P_{n+1}\\ f_{i}^{(k)}(T_{i})-f_{i+1}^{(k)}(T_{i})=0\quad(i=1,2,\ldots,n-1;k=1,2,3,4)\\ P_{i}-r\leq f_{i}(T_{i-1})\leq P_{i}+r\quad(i=2,3,\ldots,n)\end{array} \right. \tag{16}\]
## 2 Background
### WebAssembly
#### 2.2.1 Security  
**Environment.** WebAssembly modules run in a sandboxed environment which uses fault isolation techniques to separate it from the host environment. As a result of this, modules have to go through APIs to access external resources. For instance, modules  
Figure 1: WebAssembly serves as the intermediate bytecode bridging the gap between multiple source languages and host environments. The host environments compile the WebAssembly binaries into native code for the specific hardware architecture.  
that run in the web browser must use JavaScript APIs to interact with the Document Object Model (DOM). Similarly, stand-alone runtimes must use APIs, like WASI, to access system resources like files. In addition to this, modules must adhere to the security policies implemented by its host environment, such as the Same Origin Policy (SOP) [32] enforced by web browsers, which restricts the flow of information between web pages from different origins.  
**Memory**. Unlike native binaries, which have access to the entire memory space allocated to the process, WebAssembly modules only have access to a contiguous region of memory known as linear memory. This memory is untyped and byte-addressable, and its size is determined by the data present in the binary. The size of linear memory is a multiple of a WebAssembly page, each being 64 KiB in size. When a WebAssembly module is instantiated, it uses the appropriate API call to allocate the memory that is needed for its execution. The host environment then creates a managed buffer, typically an ArrayBuffer, to store the linear memory. This means that the WebAssembly module accesses the physical memory indirectly through the managed buffer, which ensures that it can only read and write data within a limited area of the memory.  
**Control Flow Integrity**. WebAssembly enforces structured control flow, organizing instructions into well-nested blocks within functions. It restricts branches to the end of surrounding blocks or within the current function, with multi-way branches targeting only pre-defined blocks. This prevents unrestricted go-to or executing data as bytecode, eliminating attacks like shellcode injection or the misuse of indirect jumps. Additionally, execution semantics ensure safety for direct function calls through explicit indexing and protected returns with a call stack. Indirect function calls undergo runtime checks for type signatures, establishing coarse-grained, type-based control-flow integrity. Additionally, the LLVM compiler infrastructure has been adapted to include a fine-grained control flow integrity feature, specifically designed to support WebAssembly [33].
# EMI Threat Assessment of UAV Data Link Based on Multi-Task CNN
## 3 Data Preprocessing
### Normalized Density Constellation  
The constellation diagram is a widely used two-dimensional image, which maps the signal sample into scattering points to express the amplitude and phase information of the sampling points. In order to reflect the correlation characteristics of the I/Q signal and extract the phase feature, the I/Q signal is plotted in the coordinate system with the in-phase component as the horizontal axis and the quadrature component as the vertical'axis.  
The amplitude of the received signal of the data link is different under different EMI signals, so it is necessary to select an appropriate region to observe the points on the constellation diagram [21]. If the selected area is too large, the signal samples will be compressed in a small area and the distribution of sampling points cannot be effectively observed. If the selection area is too small, some signal samples may be excluded from the image.  
Therefore, in order to obtain the normalized constellation diagram (NDC) with a uniform size, the amplitude of the sampling point is normalized to [\(-\)1,1], as shown in Table 2. Due to the superposition of the operating signal of the data link, interference signal and noise, the sampling points overlapped on the constellation diagram, so it is difficult to distinguish their distribution characteristics. However, the distribution density of sampling points in different regions is also different, so the points density can be used to strengthen the characteristics of constellation images [22].  
The normalized density \(\rho(i,j)\) is obtained by calculating the ratio of the number of sampling points \(d(i,j)\) to the circular window area with its radius of \(r\) centered at \((i,j)\)  
\[\rho(i,j)=\frac{1}{\pi r^{2}}\sum_{\sqrt{i^{2}+\beta^{2}}\leq r}d(i,j) \tag{6}\]  
The constellation diagram is colored according to the density, as shown in Table 2. The color code represents the density \(\rho\) (\(i,j\)) of the constellation sampling points normalized to [0, 1]. Therefore, each point in the preprocessed constellation is equally informative and no longer independent. This preprocessing method accumulates the time dimension of the points in the constellation diagram, which makes the data feature dimension higher and condenses more prior knowledge of the modulation signal. In consequence, the NDC can realize the feature enhancement of the constellation diagram.  
It should be noted that the communication of the data link is not ideal. Due to the influence of interference, noise, RF circuit design error, and physical limitations of electron  
\begin{table}
\begin{tabular}{c c c c c}
**EMI Signal** & & & & \\
**Types** & & & & \\ Constellation & & & & \\ \end{tabular}
\end{table}
Table 2: The constellation diagram and NDC under different EMI signals when the data link loses lock.  
devices on the data link, there is a deviation between the obtained constellation diagram and the standard constellation diagram of the modulated signal.
## 3 Results and Discussion
### Preparation of the Alpha-Particle Solid Source onto BDD Substrates
#### 3.1.2 Alpha-Particle Spectroscopy  
To check the quality of prepared solid sources, alpha-particle spectrometry was recorded and the FWHM value was estimated. All FWHM values of \({}^{241}\)Am sources are given in Table 1. It shows that \({}^{241}\)Am spectra displayed a high resolution with FWHM values varying from 13.2-14.5 keV at the energy of 5.485 MeV for \({}^{241}\)Am source onto BDD substrate. These values are very close to those of stainless steel e.g., 13.8 KeV and of \({}^{241}\)Am standard source of 12 keV. This result confirms that (i) BDD could be used as an alternative material for alpha-particle solid-source preparation, and (ii) NaNO\({}_{3}\) electrolyte solution could also be used as an alternative electrolyte with respect to Na\({}_{2}\)SO\({}_{4}\) electrolyte to enable high-resolution alpha-particle spectrometry. Typical alpha-particle spectra displays are of very high resolution and thus enable identification of very distinct peaks attributed to four alpha energies of the \({}^{241}\)Am sources at 5388, 5443, 5485.6, and 5544 MeV (Figure 6A). Furthermore, with such spectroscopic resolution, a clear identification of actinides mixture (\({}^{239}\)Pu, \({}^{241}\)Am, and \({}^{244}\)Cm) could be practically achieved for solid sources prepared onto BDD substrates using Na\({}_{2}\)SO\({}_{4}\) as well as NaNO\({}_{3}\) electrolyte solutions (see in Figure 6B).  
Figure 4: Autoradiographs of the mixture of \({}^{239}\)Pu, \({}^{241}\)Am, and \({}^{244}\)Cm alpha sources electro-precipitated in 0.3 M Na\({}_{2}\)SO\({}_{4}\) at pH 3.50 electrolyte solutions with a current density of 5 mA.cm\({}^{-2}\) onto BDD substrates at different precipitation times: (**A**) 15 min; (**B**) 30 min; (**C**) 60 min; (**D**) 90 min; and (**E**) 120 min.  
Figure 5: Autoradiographs of the mixture of \({}^{239}\)Pu, \({}^{241}\)Am, and \({}^{244}\)Cm alpha sources electro-precipitated in 0.3 M NaNO\({}_{3}\) at pH 3 electrolyte solutions with a current density of 2.5 mA.cm\({}^{-2}\) onto BDD substrate at different precipitation times: (**A**) 15 min; (**B**) 30 min; (**C**) 60 min; (**D**) 90 min; and (**E**) 120 min.
# Resource Allocation of Multiple Base Stations for Throughput Enhancement in UAV Relay Networks
## 2 System Model  
We consider two-hop downlink relay networks, where an \(U_{o}\) cannot have a direct transmission link from the nearby mBS and sBS due to severe blockages or under an emergency state, as shown in Figure 1. A relay transmission in the URN consists of two time slots, as shown in Figure 2. In the first time slot, the transmission from mBS or sBS is considered, where mBS uses a non-orthogonal transmission to transmit a superposition-coded signal to the UE in the macrocell (\(U_{m}\)) and UAV simultaneously, or sBS utilizes orthogonal transmission to transmit the data to UAV, respectively. It is well known that a non-orthogonal transmission at mBS in the URN achieves superior performance in terms of the overall throughput of the UE in the macrocell over an orthogonal transmission [30]. sBS supports mBS when the wireless backhaul link cannot provide a reliable relay link to \(U_{m}\) and sBS has extra time resources. Therefore, we assume that sBS utilizes orthogonal transmission to maximize the throughput enhancement. In the second time slot, UAV forwards the received data in the first time slot to \(U_{o}\). It is assumed that the channel condition between mBS and \(U_{m}\) is better than that between mBS and UAV in general, because UAV remains closer to \(U_{o}\) to set a strong LOS air-to-ground link.  
Figure 1: System model: multiple BSs in the URN.
## 2 Materials and Methods
### Analytical Model  
The equation of motion with initial displacement \(X_{0}\) is as follows:  
\[\left\{\begin{array}{l}m_{0}\ddot{x}+c_{i}\dot{x}+k_{i}x=0\\ x(0)=X_{0},\,\dot{x}(0)=0\end{array}\right. \tag{1}\]  
where \(m_{0}\) represents the accelerometer's mass, \(c_{i}\) the damping coefficients and \(k_{i}\) the stiffness coefficients for a cantilever beam with lengths \(L_{i},\ i=\overline{1,4};x,\dot{x},\ddot{x}\) are the displacement, velocity, and the recorded acceleration of the mass \(m_{0}\), respectively.  
\[k_{i}=\frac{3 EI}{L_{i}^{3}},\,i=\overline{1,4} \tag{2}\]  
where \(E\) is the Young's modulus, and  
\[I=\frac{bh^{3}}{12} \tag{3}\]  
is the second moment of cross-section area, \(b\) the width, and \(h\) the thickness of the beam.  
The damped frequencies \(f_{i},\,i=\overline{1,4}\) can be measured from FFT spectra, as well as from the pseudo-periods of free vibration time records for cantilever lengths \(L_{i},\ i=\overline{1,4}\).  
The relative damping coefficients (damping ratios) of oscillating system (1) can be evaluated by  
\[\zeta_{i}=\frac{c_{i}}{2\sqrt{m_{0}k_{i}}}=\frac{\delta_{i}}{\sqrt{4\pi^{2}+ \delta_{i}^{2}}}, \tag{4}\]  
where \(\delta_{i}\) represent the measured logarithmic decrements from free vibration records.  
The undamped natural frequencies of systems (1) are given by \(f_{0i}=\frac{1}{2\pi i}\sqrt{\frac{k_{i}}{m_{0}}}\).  
The relation between undamped and damped natural frequencies of the systems (1) is \(f_{0i}=\frac{f_{i}}{\sqrt{1-\zeta_{i}^{2}}}\).  
Using the relations (2) and (3) yields the following:  
\[f_{0i}=\frac{1}{4\pi}\sqrt{\frac{bh^{3}E}{m_{0}L_{i}^{3}}}=\frac{C}{L_{i}^{3 /2}}. \tag{5}\]  
The constant \(C=\frac{1}{4\pi}\sqrt{\frac{bh^{3}E}{m_{0}}}\) is obtained by fitting the experimental data \((L_{i},f_{0i})\) with analytical model \(f(L)=\frac{C}{L^{3/2}}\). Within the presented NDT method, the value of longitudinal modulus of elasticity \(E\) is approximated as follows:  
\[E=\frac{16\pi^{2}C^{2}m_{0}}{bh^{3}}. \tag{6}\]
## 3 Results  
### Performance Comparison Between Baseline Methods and MTLNFM  
We evaluated the performance of our proposed model, MTLNFM, against several baseline methods across three clinical prediction tasks (Table 1). Overall, MTLNFM consistently outperformed the baselines, with particularly notable gains in AUROC, where it achieved the highest performance across all tasks. On average, MTLNFM demonstrated a 2.55% improvement in AUROC over the second-best performing model. For the hospital length of stay prediction task, MTLNFM achieved the most significant performance gains, surpassing all competing models across all four metrics: AUROC (+5.29%), accuracy (+0.54%), precision (+0.64%), and F1 score (+0.17%). Interestingly, the performance of certain traditional models, such as XGBoost, indicated that not all algorithms are equally effective when applied to our labeled-missing-data preprocessing strategy. While MTLNFM may not always achieve the top score on every individual metric or task, it benefits from the shared learning across tasks, which helps reduce overfitting and improves generalizability. This performance trade-off reflects the model's ability to balance task-specific and shared representations, prioritizing global learning over isolated task optimization. As a result, MTLNFM is better aligned with real-world clinical applications, where models must contend with heterogeneous, noisy data and deliver robust, unbiased predictions across multiple correlated outcomes. These findings underscore the model's effectiveness in managing complex feature interactions and its adaptability to multi-outcome prediction settings.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Predictive Task** & **Models** & **Accuracy** & **Precision** & **F1-Score** & **AUROC** \\ \hline \multirow{4}{*}{Frailty} & LR & 0.8447 & 0.5140 & 0.4902 & 0.7322 \\  & XGBoost (raw) & 0.8398 & 0.4936 & 0.3959 & 0.7400 \\ \cline{1-1}  & XGBoost (embed) & 0.8268 & 0.4518 & 0.4309 & 0.7463 \\ \cline{1-1}  & FM & 0.8308 & 0.4674 & 0.4517 & 0.7359 \\ \cline{1-1}  & MTLNFM & 0.8607 & 0.6278 & 0.4390 & 0.7514 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison between our proposed MTLNFM and other baselines for three tasks of patient clinical outcome prediction on the test set.
# A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks
## 5 Simulation Performance and Analysis
### Satellite Network Traffic Forecasting
#### 5.3.4 Variable Step Forecasting  
The above comparison experiments validate the forecasting performance of the proposed hybrid model for satellite network traffic. In order to verify whether the forecasting accuracy fluctuates significantly with the change in step size, the forecasting accuracy of the hybrid model is reanalyzed when varying step size [36]. The raw step size selected in this paper is measured in minutes. Therefore, \(N\)-step forecasting means that the satellite network traffic can be forecasted after \(N\) minutes. In particular, \(N=1\) indicates the forecasting target for the comparison experiments in the previous section. The variation in forecasting accuracy with different step sizes is shown in Figure 15.  
The _MAPE_ of EMD-ARIMA-BPNN tends to increase slowly as the forecasting step size increases. Therefore, EMD-ARIMA-BPNN can better describe the stage-by-stage trend of satellite network traffic, which provides an advantage for strategies such as the load-balanced routing of LEO mega constellations.  
Figure 14: Forecasting error in satellite network traffic prediction.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Forecasting Schemes**} & \multicolumn{5}{c}{**Indexes**} \\ \cline{2-6}  & _RMSE_ & _MAE_ & \(\mathbf{R^{2}}\) & _MAPE_ & **Time (s)** \\ \hline LSTM & 0.0054126 & 0.0294802 & 0.97899 & 8.22\% & 4.159 \\ ARIMA & 0.0044703 & 0.0397500 & 0.99475 & 11.06\% & 3.187 \\ ARIMA-BPNN & 0.0025979 & 0.0237420 & 0.98643 & 6.61\% & 4.420 \\ EMD-ARIMA-BPNN & 0.0010770 & 0.0063580 & 0.98643 & 1.77\% & 2.366 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Analysis of the forecasting performance for satellite network traffic.
## 3 Results and Discussion  
### FTIR Analysis  
Figure 2 shows the FTIR spectra of the PS, PMMA, poly(St-_co_-MMA), and poly(St-_co_-MMA)/30 wt.% FA composites. The signal at 1449 cm\({}^{-1}\) belongs to the bending vibration of -CH\({}_{2}\)- deriving from St [23]. The peak at 1728 cm\({}^{-1}\) was ascribed to C=O stretching vibration, which revealed the existence of C=O groups in MMA [19]. The peak at 1068 cm\({}^{-1}\) was attributed to Si-O stretching vibration. For the poly(St-_co_-MMA) copolymer, peaks at 3025 cm\({}^{-1}\) and 2919 cm\({}^{-1}\) indicate the C-H stretching vibration in St and MMA, re  
Figure 1: Schemes of the synthetic process of poly(St-co-MMA)/FA composite microspheres.  
spectively. A strong peak at 1728 cm\({}^{-1}\) represents the C=O stretching vibration; the C-O stretching vibration in the range of 1000-1200 cm\({}^{-1}\) can be assigned to the MMA. Peaks at 700-760 cm\({}^{-1}\), corresponding to the C-H bending vibration, belong to the mono-substituted benzene ring, and the peak at 1500 cm\({}^{-1}\) belongs to the C=C stretching of the benzene ring in St. For the poly(St-_co_-MMA)/30 wt.% FA composite, except for the vibration signals of MMA and St, more signal peaks can be observed. The peaks at 3538 cm\({}^{-1}\), 1035 cm\({}^{-1}\), and 400-600 cm\({}^{-1}\) were attributed to the -OH bending of the silicate layers, Si-O stretching, and Si-O-Si bending vibration, respectively [24; 27]. These indicated the presence of FA in the final poly(St-_co_-MMA)/30 wt.% FA composite. Overall, the FTIR investigations have clearly shown that the FA was successfully incorporated in the polymer matrix [28].
## 3 Results and Discussion
### Effect of Soil Type
#### 3.1.5. Horizontal Stresses on the Slope Face  
Figure 9 shows the influence of the soil density on the horizontal stresses at the slope face at the middle of the slope during construction stages and loading stages. The figure depicts that, in general, when the soil density increases, the horizontal stresses increase. The percentage increase in horizontal stresses at the slope face increases with the increase of surcharge loading. The percentage increase in horizontal stresses at the slope face subjected to higher surcharge loading is about 20% when the soil density increases from 35% to 69%.  
Figure 8: Influence of soil density on the vertical stresses under the nails.  
Figure 9: Influence of soil density on the slope face horizontal stresses at mid-height.  
3.1.6. Horizontal and Vertical Stresses behind Reinforced Soil Mass  
The horizontal and vertical stress behind the soil mass at different soil densities during construction and loading stages are given in Figures 10 and 11. It is clear from the figures that at construction stages, the vertical stresses behind the soil mass increase as the soil density increases. The behavior of horizontal stresses behind the soil mass with the soil density shows an inverse behavior, i.e., the horizontal stresses behind the soil mass decrease when the soil density increases. A similar trend of variation of horizontal and vertical stresses is also obtained at loading stages. The increase or decrease in horizontal stresses at various stages is more as compared to vertical stresses behind the soil mass at different soil densities.  
Figure 11. Influence of soil density on vertical stresses behind the nailed soil mass.  
Figure 10. Influence of soil density on horizontal stresses behind the nailed soil mass at mid-height of slope.
# Human Factors Analysis of Air Traffic Safety Based on HFACS-BN Model
## 2 Methodology and Materials
### Bayesian Network Model  
Bayesian network, also known as belief network or causal network, is a probabilistic directed acyclic graphical (DAG) model [23]. Nodes in DAG denote random variables, directed line segments represent conditional dependencies among random variables, and conditional probability tables store joint conditional probabilities between corresponding nodes [24; 25; 26]. It is assumed that node B directly affects node A and connects the two nodes with A directed line segment, i.e., B \(\rightarrow\) A, where B stands for \"Parents\" and A stands for \"Children\". The connection strength between the two nodes is expressed by conditional probability P (A|B), as shown in Figure 2a. A Bayesian network is formed when variables involved in a system are plotted in a DAG according to a certain causal relationship, as shown in Figure 2b, which is a simple example.  
In a BN, any trajectory consists of the connection of the three structures in Figure 3.  
BN is especially well suited to safety issues and risk assessment, including the aviation field [27]. [PERSON] et al., focusing on personal safety behavior, established the BN model to identify the most critical organizational factors that improve safety behavior [28]. [PERSON] et al. focused on the human error of flight crew behind aviation accidents [9], while [PERSON] et al. focused on the safety awareness of airport staff [6]. Although these applications are in different settings, it can be concluded that BN can perform human factor and safety analysis, including prediction, diagnosis, decision making, and provide insight into the relationship between variables. This study thus aims to introduce BN as an appropriate method to predict ATC performance and to diagnose human factors in ATC failures.  
Figure 1: Overview of the human factors analysis and classification system (HFACS).