esa-sceva/satcom-chunk-collection huggingface dataset:
# Bayesian Optimal Experimental Design for Race Tracking in Resin Transfer Moulding
## 2 Computational Model
### Numerical RTM Model
#### 2.2.2 Calculate Flow  
Given the finite element pressure distribution, the flow between CVs is calculated. The flow between filled CVs is not important, as the net flux will add to zero. \(CV_{i}\) has a saturation \(\Psi_{i}\) where  
* \(\Psi_{i}=0\) represents an empty CV.
* \(\Psi_{i}=1\) represents a filled CV.
* \(0<\Psi_{i}<1\) is a partially filled CV.  
CVs that increase in saturation during the current timestep must be considered. To this end, we define the set \(X\) that contains all the values for \(i\) such that \(CV_{i}\) has an adjacent CV that is filled and \(\Psi_{i}<1\).  
For a given finite element, we can express [PERSON]'s law for the flow rate \(\mathbf{q}\)--Equation (2)--in terms of the nodal pressures:  
\[\mathbf{q}=-\frac{1}{\mu}\mathbf{K}\begin{bmatrix}\frac{\partial p}{\partial x}\\ \frac{\partial p}{\partial y}\end{bmatrix},\qquad\begin{bmatrix}\frac{ \partial p}{\partial x}\\ \frac{\partial p}{\partial y}\end{bmatrix}=\mathbf{J}^{-1}\begin{bmatrix}\sum_{i= 1}^{3}p_{i}\frac{\partial\phi_{i}}{\partial\xi_{1}}\\ \sum_{i=1}^{3}p_{i}\frac{\partial\phi_{i}}{\partial\xi_{2}}\end{bmatrix}. \tag{12}\]  
Here, \(\mathbf{J}\) is the finite element Jacobian, defined as follows, based on the three coordinates of the element \((x_{1},y_{1})\), \((x_{2},y_{2})\), and \((x_{3},y_{3})\):  
\[\mathbf{J}=\begin{bmatrix}x_{1}-x_{3}&y_{1}-y_{3}\\ x_{2}-x_{3}&y_{2}-y_{3}\end{bmatrix}. \tag{13}\]  
Note the lack of dependence of \(\mathbf{q}\) on the element coordinates--the linear elements for pressure give a constant flow rate throughout an element. Through a boundary \(C\), the volume flow rate \(Q_{C}\) can be obtained by integrating \(\mathbf{q}\cdot\mathbf{n}\) across the boundary. Within one element, there is no spatial dependence, so we have the following for \(Q_{C}\):  
\[Q_{C}=(\mathbf{q}\cdot\mathbf{n})|C|, \tag{14}\]  
where \(|C|\) is the length of the boundary \(C\). By summing \(Q_{C}\) for all boundaries for the \(i\)th CV, the total flow \(Q_{i}\) is calculated.
## 4 Model Simulations with FEM-3D
### Simulation Results in Standstill Conditions
#### 4.3.5 Comparative Study of the Main Results Obtained in Standstill Conditions  
In this section, a comparative study between all models is presented. Figures 26 and 27 show the evolution of the thrust and levitation forces for each model. For model 4, an opening of the transversal slots equal to 55 mm was considered. Each model developed a thrust force greater than the previous model. For the dynamic conditions imposed (\(s=1\)), the levitation force presented a great change in each model. In conclusion, one can say that an improvement in the thrust force implied a reduction in the levitation force. In fact, in model 4, the levitation force changed from the levitation condition to attraction.  
Figure 27: Dynamic evolution of the levitation force in each model.  
Figure 26: Dynamic evolution of the thrust force in each model.  
Figure 25: Levitation forces versus increment of transversal slots.  
The dynamic evolution of the forces is important to design a control strategy over an MFLIM. All models presented a similar transitory state with a maximum value of the thrust force in the interval time (0.010-0.012 s). Figure 28 shows the average value of the principal forces in each model. The transversal force along the x-axis was not relevant, although this component cannot be neglected because an LIM can develop a motion through a curve path.  
Figure 29 represents the consumed electric currents in all models. The obtained results of each model showed that the electric currents always present the same behavior, see Equation (19):  
\[I_{Phase\_a}<I_{Phase\_c}<I_{Phase\_b} \tag{19}\]  
The current linked to phase B was higher than the currents in other phases with a maximum value in model 1 around 93.5 A. Instead, phase A always presented the most efficient current phase with only 67.496 A in model 3. The electric current associated with phase C had an intermediate value between the others in all experiences. In order to design the dimensions of the copper wire and the rest of the devices that protect the MFLIM, the current of phase B was a reference signal. Model 3  
Figure 28: Comparison of the principal forces in each model.  
Figure 29: Electric currents per phase in each model.  
implied a reduction of the currents consumed per phase with respect to the initial model around 20.5% (phase A), 15% (phase B), and 16.3% (phase C). The developed models illustrated the great relevance of the location of the coils of each phase inside the armature winding and showed also that the changes introduced into the geometry are very important for the consumed electric current.
## 3 Results
However, the difference between them did not reach statistical significance (T-A vs. M-A; MP-SN, \(-\)0.24\({}^{\circ}\) vs. \(-\)0.97\({}^{\circ}\); \(p\) > 0.05) The mandible opened more and the lower face was longer immediately after treatment (T2) in the T-A, than the M-A group (MP-SN: 1.74\({}^{\circ}\) vs. 0.53\({}^{\circ}\); N-Me, 3.14 vs. 1.25 mm; T1-T2 \(p\) < 0.05; Table 4). This resulted in a significantly greater increase in the length of the lower face in the T-A group even after follow-up (N-Me: 10.24 vs. 7.90 mm, \(p\) < 0.05; Table 3). The maxillary first molars were immediately and significantly more extruded due to the difference in odontogenic changes between the two groups after treatment in the T-A, than the M-A group (U6/NF: 1.88 vs. 0.49 mm, \(p\) < 0.001). A significant amount of extrusion was maintained in the T-A group even until follow-up (U6/NF: 4.56 vs. 2.60 mm, \(p\) < 0.001) (Table 3). The mandibular anterior teeth were linguuverted immediately after treatment (T2) in both groups because of the extraoral facemasks. Linguuversion was significantly greater in the T-A than the M-A group (IMPA: \(-\)5.04\({}^{\circ}\) vs. \(-\)2.61\({}^{\circ}\), \(p\) < 0.05). However, this trend recovered during follow-up, and changes from T1 to T3 did not significantly differ between the groups (IMPA: \(-\)0.31\({}^{\circ}\) vs. 0.53\({}^{\circ}\), \(p\) > 0.05; Table 3). Comparisons of changes between \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Measurement**} & \multicolumn{2}{c}{**T1**} & \multicolumn{2}{c}{**T2**} & \multicolumn{2}{c}{**T3**} & \multirow{2}{*}{**Adjusted \(p\) Value**} \\ \cline{2-2} \cline{4-9}  & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** & \\ \hline SNA & 79.76 \({}^{\text{a}}\) & 3.09 & 81.23 \({}^{\text{b}}\) & 3.14 & 82.25 \({}^{\text{c}}\) & 3.40 & \textless{}0.001 ** \\ SNB & 79.64 \({}^{\text{a}}\) & 3.17 & 78.93 \({}^{\text{b}}\) & 3.26 & 81.64 \({}^{\text{c}}\) & 3.95 & \textless{}0.001 ** \\ ANB & \(-\)0.13 \({}^{\text{a}}\) & 1.75 & 2.09 \({}^{\text{b}}\) & 2.01 & 0.60 \({}^{\text{a}}\) & 2.28 & 0.026 \({}^{\text{a}}\) \\ MP–SN & 28.29 \({}^{\text{a}}\) & 5.83 & 30.30 \({}^{\text{b}}\) & 5.38 & 26.05 \({}^{\text{c}}\) & 5.49 & 0.001 \({}^{\text{a}}\) \\ N–Me & 101.53 \({}^{\text{a}}\) & 4.45 & 104.68 \({}^{\text{b}}\) & 4.81 & 111.77 \({}^{\text{c}}\) & 6.04 & \textless{}0.001 ** \\ U6/NF & 14.27 \({}^{\text{a}}\) & 1.66 & 16.15 \({}^{\text{b}}\) & 1.56 & 18.83 \({}^{\text{c}}\) & 1.82 & \textless{}0.001 ** \\ \hline \hline \end{tabular} Data are results of repeated-measures analysis of variance tests. Significantly different at \({}^{\text{a}}\)\(p\) < 0.05 and \({}^{\text{a}}\)\(p\) < 0.001. Different letters indicate a statistically significant difference at adjusted \(p\) < 0.05 (post hoc Bonferroni correction).  
\end{table} 
Table 4: Comparison of treatment outcomes of tooth-anchored devices during T1, T2, and T3.
# Research on Feature Fusion Method Based on Graph Convolutional Networks
## 3 Methods
### Linear Fusion  
The linear layer is introduced here for two main purposes: (1) Feature transformation and dimensionality adjustment: The linear layer performs a linear transformation on the output from the BERT or GCN part to map the input features to the appropriate output space. This step ensures that features from different sources can be efficiently fused in the same space. (2) Nonlinear transformations: Although the linear layer itself is linear, in practice, it is usually used in combination with nonlinear activation functions (e.g., ReLU, sigmoid, etc.). This combination introduces a nonlinear transformation that enhances the expressive and fitting ability of the model, which, in turn, improves the performance of the model. The process of linear fusion is shown in Figure 4.  
The operation of introducing a linear layer in the process of fusing the BERT output with the conventional features can be expressed as follows:  
\[\text{h}_{\text{fusion}}=\text{ReLU}(\text{W}_{\text{BERT}}\cdot\text{h}_{ \text{BERT}}+\text{W}_{\text{traditional}}\cdot\text{h}_{\text{traditional}}+\text{b}) \tag{7}\]  
where \(\text{h}_{\text{fusion}}\) denotes the fused feature after linear layer fusion, ReLU denotes the activation function, \(\text{h}_{\text{traditional}}\) is the traditional feature, \(\text{h}_{\text{BERT}}\) is the BERT output, and WBERT, \(\text{W}_{\text{traditional}}\), and b denote the weight and bias terms of the linear layer, respectively. The above formulation describes the process of linearly fusing the BERT output and the traditional features, and, by learning the weight matrices \(\text{W}_{\text{BERT}}\) and \(\text{W}_{\text{traditional}}\), the model can automatically determine how to combine the two features in a weighted manner to generate the fused features.
# Unequal Interval Dynamic Traffic Flow Prediction with Singular Point Detection
## 2 Related Work
The spatiotemporal characteristics of traffic flow were input as a regression prediction layer to learn more key data features for improving performance. [PERSON] et al., proposed a method to obtain the spatiotemporal state and spatiotemporal dependency automatically based on a multigraph adversarial neural network, which extracted the spatiotemporal state of the data in real time and broke the constraints of traditional generative adversarial networks (GAN) [22]. Existing short-term traffic flow prediction models are constantly improved, and prediction accuracy is gradually increased. However, there are still some problems seldom discussed in the complex road network in an urban area: (1) There is no effective method or model to detect and analyze intrinsic properties based on the acquired real-time traffic flow information, which can reflect the change of status in the continuous process of traffic flow. (2) The same fixed interval is not suitable for the complex and changeable road network. The repetitive prediction interval should be dynamic and varying to balance between accuracy and computing time based on the analysis of the traffic flow signal. (3) The unequal matrix size problem should be considered during the prediction solution, which is causedby the unequal interval division. This paper mainly aims to solve these issues. Regarding the first issue, this paper selects singular points as the analyzed characteristic of traffic flow. Considering the application in a real traffic road network, the singular point detection and prediction are adaptively filtered based on multiscales. The frequent singular point selection benefits the prediction accuracy, while the sparse singular point selection can save on computing cost. A model with unequal intervals is extracted to balance two metrics, which is involved in a decomposition method during the solution.
# Elevation and Mass Changes of the Southern Patagonia Icefield Derived from TanDEM-X and SRTM Data
## 2 Materials and Methods
### Elevation and Volume Changes Rates and Mass Balance  
The area-wide DEMs for each timestep were differenced on the glaciated areas from the reference SRTM DEM and annual change rates were computed accounting for the exact timespan between both acquisitions. Glaciers were outlined by an updated Randolph Glacier Inventory Version 5 to the reference year 2000 (cf. Section 2.4). Outliers in the DEMs close to runataks, icefalls or processing artifacts were removed by a moving window function (Supplementary Materials Figure S3). For each pixel the median was calculated and differenced from the actual value for a 5 \(\times\) 5, 7 \(\times\) 7 and a 9 \(\times\) 9 Kernel. If the mean of those differences exceeded 33 m the center pixel was removed as an outlier.  
For the total SPI coverage (summer 2015/16) we calculated the pixel-based elevation change rates to volume by multiplying with the pixel area (30 \(\times\) 30 m\({}^{2}\)) and volume to mass changes using different density scenarios (cf. Table 2). For Scenario 1 we used a constant density value of 900 kg\(\cdot\)m\({}^{-3}\). Scenario 2 was calculated for 850 kg\(\cdot\)m\({}^{-3}\)[27], while for Scenario 3 the density was not constant. We applied a density of 900 kg\(\cdot\)m\({}^{-3}\) below the Equilibrium Line Altitude (ELA) and 600 kg\(\cdot\)m\({}^{-3}\) for areas above this elevation [21; 22; 28]. To derive the ELA we used the average ELA of the glaciers we studied from De Angelis [17] and divided the areas according to the hyposometric distribution we found to find an area weighted average ELA of 1050 m. The error for this latter scenario was estimated with a mean density of 750 kg\(\cdot\)m\({}^{-3}\).  
To provide a mean dh/dt on glacier catchment basis, we excluded glaciers \(<\)10 km\({}^{2}\) in the analysis due to a predominance of faulty or missing values. The area of valid TDX to SRTM elevation differences(surveyed area) amounted to 10,772 km\({}^{2}\) corresponding to 85.7% of the glaciated area (12,569 km\({}^{2}\)). We extrapolated the missing 14.3% using to the mean elevation change of the respective altitudinal bin of SRTM elevation. Our study does not account for mass changes of calving glaciers below lake or sea level. As a consequence, our estimates of mass changes are a lower bound.
# A Noise-Tolerant Carrier Phase Recovery Method for Inter-Satellite Coherent Optical Communications
## 4 Simulation Results and Analysis
### LRMSE Performance  
The performance of the noise-tolerant method, as measured by the LRMSE\({}_{\theta}\), is depicted in Figure 9. It is evident that with an increase in \(E_{b}/N_{0}\), the LRMSE\({}_{\theta}\) of the noise-tolerant method progressively converges to the MCRLB, demonstrating commendable continuity across the simulated SNR range. Conversely, the LRMSE\({}_{\theta}\) associated with the Diff-4 th methods remains substantially above the MCRLB when \(E_{b}/N_{0}\) is below 5.2 dB. This discrepancy can be attributed to the occurrence of phase cycle slips in the Diff-4 th methods at low SNR, leading to burst errors in their estimation outputs.  
To elucidate the observations presented in Figure 9, the phase estimation errors \(\theta_{\mathrm{err}}\  
Additionally, the natural frequency \(\omega_{n}\) in the initial stage influences \(D(\Delta\hat{\theta})\). As shown in Table 3, a smaller \(\omega_{n}\) corresponds to a reduced \(D(\Delta\hat{\theta})\). This observation, in conjunction with Figure 7, indicates that while the estimation errors decrease, the corresponding loop lock time increases, suggesting that the accuracy gains achieved by lowering \(\omega_{n}\) become progressively less significant. Therefore, a balance must be struck between \(\omega_{n}\) and \(D(\Delta\hat{\theta})\).
## 2 Mathematical Models
### Earth Occulation  
Since satellites are designed to serve the Earth, they rotate around the Earth and are oriented towards the Earth, so when applying satellites to the Moon, the Earth occultation problem needs to be considered. As shown in Figure 1, when the vertical distance between the satellite and the Moon is less than the radius \(R\) of the Earth, the satellite's line of sight to the Moon is blocked by the Earth, and the satellite is not visible to the Moon. The above problem can be described mathematically as follows:  
\[L(t)=\frac{||r_{s}\times\Delta r||}{||\Delta r||R}<1 \tag{2}\]  
where \(r_{s}\) is the coordinate vector of the satellite in an Earth-centered, Earth-fixed coordinate system, and \(\Delta r=r_{m}-r_{s}\). When \(L<R\), the line of sight from the satellite to the Moon is unobstructed at this time, and it is obscured by the Earth in all other cases.
# GPU-Accelerated Eclipse-Aware Routing for SpaceWire-Based OBC in Low-Earth-Orbit Satellite Networks
## 4 Experimental Results
### Experimental Setups  
In this paper, experiments on satellite network routing were conducted using CUDA-based KSP search and the EAR algorithm. The overall structure of the experimental setup is illustrated in Figure 7, while Figure 8 shows the actual equipment used in the experiment. The experimental environment consisted of an OBC device with both CPU and GPU, two SpaceWire Brick Mk4 devices, and a PC utilizing only a CPU.  
The OBC with CPU and GPU was responsible for executing the routing algorithm and measuring performance. Additionally, it was used to modify the routing tables of remote devices via the SpaceWire network. Detailed experimental results related to SpaceWire are provided in Section 4.3. The experiment was conducted by first generating candidate paths through a BFS-based KSP search and then selecting the optimal route using the EAR algorithm.  
The OBC with CPU and GPU employed in this experiment was the Jetson Xavier NX, which was used to simulate a satellite OBC with limited computational resources. As it is equipped with an NVIDIA GPU, it enabled parallel processing using CUDA. The PC  
Figure 6: Example graph for proposed parallelized KSP search.  
with only a CPU functioned as the receiving device, supplying power to the remote router and verifying the correctness of routing table modifications through the SpaceWire GUI. Additionally, the PC was used to validate the accuracy of the EAR algorithm by comparing its results with those obtained in the OBC environment. Since the accuracy validation involved ensuring that both environments produced the same results, the performance of the PC was not a critical factor in this experiment, and its specifications are therefore omitted.  
Table 1 summarizes the key specifications of the Jetson Xavier NX. To maximize performance, the device was configured in 20 W 6-core mode. It is equipped with an NVIDIA Volta GPU and a Carmel ARM CPU, enabling parallel computation using CUDA. Additionally, it supports 16 GB of LPDDR4x memory, providing a suitable environment for large-scale graph search computations.  
Figure 7: Structure for experimental environments.  
In this paper, the computational resources of the Jetson Xavier NX were utilized to evaluate the performance of CUDA-based KSP search and the EAR algorithm. The experimental results demonstrated that real-time routing is feasible even in constrained satellite environments.
# An Image-Encryption Algorithm Based on Stage-Merging Bit Scrambling
## 3 Security Analysis
### Key Space  
Exhaustive attack means that the attacker brute-forces the algorithm by trying every possible key combination, so the encryption algorithm needs to have a key space large enough to prevent brute force enumeration. If the key space is larger than \(2^{100}\), it means that the brute force attack can be resisted [22].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Image** & **NPCR/\%** & **UACI/\%** \\ \hline Ours & 99.6086 & 33.4635 \\ Ref. [4] & 51.0727 & 10.0694 \\ Ref. [7] & 99.6087 & 33.4652 \\ Ref. [8] & 99.6085 & 33.4617 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Differential attack test results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Key** & **Key after** & \multicolumn{2}{c}{**Encryption Key**} & \multicolumn{2}{c}{**Decryption Key**} \\ \cline{3-6}  & **Change** & **NPCR/\%** & **UACI/\%** & **NPCR/\%** & **UACI/\%** \\ \hline CD8C10890E0A & CD8C10890E0B & 99.5270 & 33.2301 & 99.6155 & 29.2486 \\ BC357C6061F9 & BC357C6061 FA & 99.5712 & 33.49  
The key used in this algorithm is a hexadecimal string with a length of 48, which is equivalent to 192 binary bytes. Therefore, the key space is \(2^{192}\), which is much larger than \(2^{100}\), indicating that this algorithm can resist exhaustive attacks.
# Fast Fourier Asymmetric Context Aggregation Network:
## 4 Experiments
### Comparison with Existing Approaches
#### 4.5.2 Qualitative Comparison
issues such as texture detail loss (e.g., texture propagation failure on the yellow knit vest in the first row of Figure 13) and inconsistency in dense texture content (e.g., loss of dense white dots on the green and white patterned top in the third row). Furthermore, the clothing images generated by the baseline methods still exhibit artifacts and color discrepancies (e.g., in the second and fourth rows). Figure 12: Qualitative comparison results under small texture patch sizes on the SKFashion dataset. Figure 13: Qualitative comparison results under medium texture patch sizes on the SKFashion dataset. Figure 14 presents the qualitative comparison results under the standard texture patch size condition on the SKFashion dataset. As the texture patch size increases, the quality of the generated images improves across all methods. However, the baseline methods still struggle when generating dense texture content, exhibiting issues such as texture loss or overflow. Leveraging the advancements in content reasoning and contextual modeling introduced by ACAM and FF-CDRB, our method seamlessly integrates the structural semantics of design sketches with the stylistic content of texture patches, thereby establishing coherent and consistent global contextual relationships. As a result, our method maintains stable performance across all three texture patch sizes. The generated images demonstrate significant advantages over the baseline methods in terms of color consistency, texture details, and structural-semantic alignment while also showing robust performance with respect to variations in texture patch size. Figures A1-A6 in the appendix present the experimental results on the VITON-HD and Fashion-Gen datasets. The results demonstrate that our method achieves excellent performance on both datasets. Even with a significantly reduced training dataset, our approach can still generate high-quality clothing images, guided by sketches and texture patches. Extensive comparative experiments conducted across the three datasets show that our method offers significant advantages in terms of objective performance metrics, visual quality of the generated images, and model size.