esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Stability  
The integration of the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) loss into the Hyper-CycleGAN architecture resulted in fundamental improvements, significantly enhancing both the stability of training and the quality of generated hyperspectral data. Unlike the original CycleGAN, which relies solely on adversarial loss for training, incorporating WGAN-GP loss presented several pivotal advantages.  
Primarily, including a gradient penalty term within WGAN-GP loss effectively mitigated mode collapse, a prevalent issue in traditional generative adversarial network (GAN) training. This phenomenon occurs when the generator fails to capture the full diversity of the target distribution, leading to limited and repetitive sample generation. Our training experiments, as illustrated in Figure 13, revealed instances of mode collapse where models struggled to produce diverse and realistic hyperspectral data samples. However, with WGAN-GP loss integration, we successfully mitigated mode collapse and achieved more stable training dynamics. The gradient penalty term regularized the discriminator by imposing constraints on gradients of generated samples, thus preventing excessive domination by the discriminator during training. Consequently, this enabled generators to produce a broader range of hyperspectral data samples, leading to improved quality and fidelity. Furthermore, employing the Wasserstein distance metric in WGAN-GP loss offered a more meaningful measure of discrepancy between real and generated data distributions compared to traditional GANs that rely on binary classification. This provided smoother and more informative gradient signals during training, facilitating stable convergence and better quality synthesis  
In summary, incorporating WGAN-GP loss into the Hyper-CycleGAN framework represents a significant advancement over the original CycleGAN architecture by effectively addressing issues like mode collapse and providing a more meaningful loss metric, thus enhancing the stability, diversity, and quality of generated hyperspectral data and improving the overall model performance in hyperspectral data synthesis tasks.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**DPaviaC**} & \multicolumn{2}{c}{**RPaviaHR**} \\ \cline{2-5}
**Methods** & **MPSNR (\(\uparrow\))** & **MSSIM (\(\uparrow\))** & **MPSNR (\(\uparrow\))** & **MSSIM (\(\uparrow\))** \\ \hline Ground Truth & N/A & \(1\) & N/A & \(1\) \\ \hline CycleGAN & 28.317 & 0.735 & 28.342 & 0.729 \\ \hline Hyper-CycleGAN & 29.23 & 0.787 & 29.461 & 0.802 \\ \hline \hline \end{tabular}
\end{table}
Table 5: MPSNR and MSSIM on the DPAviaC and RPAviaHR datasets. “\(\uparrow\)” indicates that higher values correspond to better performance.
## 4 Wind Loading and Vibration Mitigation
### Considered Load Cases  
Wind loading is numerically captured using a LES approach, typical in CFD for transient flow fields [39; 40]. The respective numerical domain follows the setup as presented in [41]. There are certain particularities to this setup, depending on whether the aim is to realistically model the natural turbulence in the Atmospheric Boundary Layer (ABL) or whether the scope is to trigger strong vortex shedding. The former case implies nonzero turbulence, whereas the latter can become critical for very low (to practically zero) turbulence intensity. In Figure 6, we show the main aerodynamic considerations and definitions.
# CNN-Based Defect Inspection for Injection Molding Using Edge Computing and Industrial IoT Systems
## 2 Background and Related Work
### Edge Computing  
Because data are increasingly generated at the edge of the network, it is more efficient to process data there. Previous work has been introduced to the community, such as micro data centers [50; 51], cloudlet [52], and fog computing [53]. This is why cloud computing is not always efficient in processing data when it is generated at the network edge. This section lists some of the reasons why edge computing is more efficient than cloud computing in some computing services and then provides definitions and an understanding of edge computing. Edge computing can do computations at the edges of a network on downstream data that replace cloud services and upstream data that replace IoT services. Here we define \"edge\" as all computing and network resources along the path between the data source and the cloud data center. For example, a smartphone is the edge between a body object and a cloud, a gateway to a smart home is the edge between a home object and a cloud, and a micro data center and a cloudlet are the edge between a mobile device and the cloud. The rationale for edge computing is that computing should occur near a data source. From our perspective, edge computing is interchangeable with fog computing, but edge computing is more focused on the object side, whereas fog computing is more focused on the infrastructure side. Edge computing can have as much an effect on our society as does cloud computing.
## 4 Results
### Participant Satisfaction in Ecuador, Participation, and Post-Event Interviews
Nevertheless, increased interest and advanced use of Sentinel data for various applications, including land cover mapping, crop and water quality monitoring, fire impact analysis, and disaster management, were noted. Some remarkable applications included extending historical land cover mapping from 2016 to 2024, as well as monitoring potatoes and cassava crops using Sentinel-2. Some achievements included increased motivation to use Earth observation data for security applications, shifting satellite data storage to Copernicus platforms, and improving workflows by integrating open-access data and cloud processing tools. The key collaborator emphasized the importance of sustainability through continuous training programs, effective knowledge transfer mechanisms, and the integration of Copernicus data products and services with national platforms to simplify access and avoid the overload of navigating multiple platforms. Suggestions focused on offering more practical content and virtual courses and maintaining participant engagement through ongoing capacity-building initiatives.
# Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model
## 5 Results and Discussions
### Comparative Methods  
In order to showcase the accomplishments of the UDAF model, a comparative analysis is performed. The techniques employed for the comparison consist of RF regressor [41; 42; 43; 44; 49; 50]. The models are evaluated based on Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), root mean square standardized error, and R-squared.
# A Distributed RF Threat Sensing Architecture
## 4 Test Environment and Experiment Setup
### Experiment Setup and Measurements  
Several scenarios were investigated during the system trials in the test environment. One scenario refers to a relative high-power sweep jamming attack detection, another to a relative low-power constant jamming attack detection, while the last was focused on drone RF profiling and detection thresholds. These scenarios are further analyzed below.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **[EMAIL_ADDRESS] GHz** & **[EMAIL_ADDRESS] GHz** \\ \hline Min (dBm) & \(-79.5\) & \(-71.8\) \\ \hline Max (dBm) & \(-71.5\) & \(-65.3\) \\ \hline Average (dBm) & \(-78.8\) & \(-70.9\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Laboratory RF spectrum profile.  
Figure 7: Small city airfield.
# Lifetime Prediction of Single Crystal Nickel-Based Superalloys
## 2 Experimental Procedure and Methodology
### Lifetime Assessment
#### 2.3.2 Modified Fatigue Damage Law  
Damage law is generally expressed by assuming that the fatigue damage accumulated at each cycle is a function of the amplitude and mean values of stress:  
\[\delta D=f(\sigma_{amp},\,\sigma_{mean})\,\delta N \tag{12}\]  
where D is the scalar variable representing fatigue damage evolution from the virgin state (\(D=0\)) up to fatigue fracture (\(D=1\)). [PERSON] and [PERSON] [16] proposed fatigue life models based on the Woehler rule for both uniaxial and multiaxial cases. Multiaxial fatigue  
Figure 3: Demonstration of opening modes of a crack.  
Figure 2: Octahedral (**a**) and cubic (**b**) slip planes of a FCC crystal.  
damage is estimated by using equivalent fatigue limit and stress values relevant to the loading conditions. Due to the anisotropic nature of single crystals, the crack initiation location and propagation path occur specific to the slip systems of the material. Since the consideration of both crack initiation and propagation is essential for the fatigue lifetime assessment of single crystals, the cleavage stress acting on the crack is considered instead of the macroscopic equivalent amplitude stress. The differential equation of the fatigue damage on the dominant cleavage plane is expressed as:  
\[\delta D^{clv}=\left[1-(1-D)^{\beta+1}\right]^{\alpha(\sigma_{amp}^{clv}, \sigma_{mean}^{clv})}\left[\frac{\sigma_{amp}^{clv}}{M(\sigma_{mean}^{clv})( 1-D)}\right]^{\beta}\delta N \tag{13}\]  
where \(\sigma_{amp}^{clv}\) and \(\sigma_{mean}^{clv}\) are the dominant cleavage stress amplitude and mean stress, respectively, and \(\beta\) is a temperature-dependent model parameter. The function \(\alpha\) represents the effects of nonlinear accumulation:  
\[\alpha(\sigma_{amp}^{clv},\sigma_{mean}^{clv})=1-a\left\langle\frac{\sigma_{ amp}^{clv}-\sigma_{l}(\sigma_{mean}^{clv})}{\sigma_{uts}^{clv}-\sigma_{max}^{clv}}\right\rangle \tag{14}\]  
where a is a model parameter, \(\sigma_{uts}\) is the ultimate tensile stress, and \(\sigma_{l}\) is the fatigue limit in terms of amplitude stress. The relation between fatigue limit and mean stress is represented by [PERSON]'s linear relation:  
\[\sigma_{l}^{clv}=\sigma_{l_{0}}^{clv}(1-b_{1}\sigma_{mean}^{clv}) \tag{15}\]  
where \(\sigma_{l_{0}}\) is the fatigue limit at zero mean stress i.e., endurance limit, and \(b_{1}\) is the model parameter establishing the relation between fatigue limit and mean stress. M is expressed with the same linear relation dependent on mean stress and model parameters \(b_{2}\) and \(M_{0}\) as  
\[M=M_{0}(1-b_{2}\sigma_{mean}^{clv}) \tag{16}\]  
Fatigue life, \(N_{f}\), of a material is determined by integrating the damage variable, D, in Equation (13) from 0 to 1 since \(D=1\) represents the fatigue failure of a virgin material.  
\[N_{f}=\frac{1}{(\beta+1)[1-\alpha(\sigma_{amp}^{clv},\sigma_{mean}^{clv})]} \left[\frac{\sigma_{amp}^{clv}}{M(\sigma_{mean}^{clv})}\right]^{-\beta} \tag{17}\]
## 2 Methods
### Path-Planning Improvement
#### 2.2.2 Environment-Adaptive Step Size Expansion Strategy  
Most of the underground roadway environment is a narrow channel, and the step size of the conventional RRT algorithm is fixed, which limits the exploration efficiency of the random tree. Therefore, this paper proposes an environment-adaptive step size expansion strategy to improve the algorithm and adjust the step size according to the current environment. Exploring the narrow channel leads to an enlargement of the random tree expansion's step size in this area, enabling rapid exploration of the region by the random tree.  
In this paper, the bridge detection algorithm [18] is used to identify whether it is a narrow channel. The bridge detection algorithm is a heuristic algorithm for detecting whether the sampling point is in a narrow space. The main principle of the bridge detection algorithm is to identify whether it is a narrow channel by detecting the state of three sampling points: First, a line segment is randomly generated to detect the two endpoints of the line segment and the intermediate nodes. If the two endpoints are in the impassable area, and the intermediate nodes are in the free area, the intermediate nodes are detected as the sampling points in the narrow channel. As shown in Figure 3, the number 1 indicates that the bridge detection algorithm successfully identifies the narrow channel, and number 2 is the useless sampling in the obstacle.  
The pseudocode of the bridge detection algorithm is as follows (Algorithm 1):  
Figure 3: Bridge detection algorithm diagram.  
```
1:Repeat
2:\(a\leftarrow\)Rand();
3:if CollisionFree(\(a\)) = False then
4:\(a^{\prime}\leftarrow\)Nearpoint(\(a\));
5:if CollisionFree(\(a^{\prime}\)) = False then
6:\(b^{\prime}\leftarrow\)Midpoint(\(a^{\prime}\));
7:if CollisionFree(\(b\)) = True then
9:\(G\leftarrow\)Insertpoint(\(G\), \(b\));
10:endif
11:endif
12:End
```  
**Algorithm 1**Bridge detection algorithm  
This document utilizes the bridge detection algorithm to identify sampling locations within the narrow channel, and they are added to the narrow channel sample set \(G\) in such a cycle. In each sampling, a point, \(a\), is randomly sampled in the space, and then the adjacent node \(a^{\prime}\) is randomly sampled. Then, taking \(a\) and \(a^{\prime}\) as the two endpoints of the bridge, the midpoint coordinate \(b\) of the bridge is calculated. Through obstacle collision detection, it is ensured that the two endpoints are collision points and the midpoint is a free point. If the conditions of the above three points are satisfied, the area where the midpoint b is located can be determined as a narrow channel, and a narrow channel detection can be completed. When the random tree searches for a narrow channel, the extended step size of the random tree is increased, so that the random tree can quickly search the region and get closer to the target point, which improves the computational efficiency.
## Abbreviations  
The following abbreviations are used in this manuscript:  
MIMIC-II Multi-parameter Intelligent Monitoring in Intensive Care II  
TCN-CBAM Temporal Convolutional Network-Convolutional Block Attention Module  
MTFF-ANN Multi-type Feature Fusion Artificial Neural Network  
BiLSTM Bidirectional Long Short-Term Memory  
AAMI Association for the Advancement of Medical Instrumentation  
Resnet Residual Neural Network  
mmHg Millimeter of Mercury  
LOSO Leave One Subject Out  
ECG Electrocardiogram  
BCG Ballistocardiogram  
PPG Photoplethysmogram  
MTL Multi-Task Learning  
PTT Pulse Transit Time  
PWV Pulse Wave Velocity  
ABP Arterial Blood Pressure  
SBP Systolic Blood Pressure  
DBP Diastolic Blood Pressure  
CNN Convolutional Neural Network  
RNN Recurrent Neural Network  
MAE Mean Absolute Error  
AI Artificial Intelligence  
BP Blood Pressure  
r Pearson's Correlation Coefficient
# Analysis and Design of Infrared Search and Track System with Afocal Zoom Telescope
## 2 Homogeneous Coordinate Transformation in IRST
### Imaging Model under Dynamic Condition  
The rotation of the platform and the FSM will lead to a change in the transform matrix between the object and image coordinate systems. When operating at a specified FOV, all components except the FSM in the IRST are fixedly located inside the platform, and their transform matrix is invariant.  
If the platform rotates around the y-axis of its own coordinate system C\({}_{p}\) at the angular velocity of \(\omega_{scan}\) after a certain integration time t, then the perturbation matrix of the platform coordinates is  
\[\mathbf{G_{p}}=\begin{bmatrix}\cos(\omega_{scan}\cdot t)&0&-\sin(\omega_{scan} \cdot t)&0\\ 0&1&0&0\\ \sin(\omega_{scan}\cdot t)&0&\cos(\omega_{scan}\cdot t)&0\\ 0&0&0&1\end{bmatrix} \tag{14}\]  
The transform matrix from the target coordinates to the platform coordinates becomes  
\[\mathbf{T^{\prime}_{p}}=\mathbf{G_{p}}\mathbf{T_{p}} \tag{15}\]  
Similarly, the FSM rotates around the \(y\)-axis of the FSM coordinates C\({}_{FSM}\) at the angular velocity of \(\omega_{FSM}\). With an integration time of \(t\), the perturbation matrix for the FSM coordinates is calculated as follows:  
\[\mathbf{G_{FSM}}=\begin{bmatrix}\cos(\omega_{FSM}\cdot t)&0&-\sin(\omega_{FSM} \cdot t)&0\\ 0&1&0&0\\ \sin(\omega_{FSM}\cdot t)&0&\cos(\omega_{FSM}\cdot t)&0\\ 0&0&0&1\end{bmatrix} \tag{16}\]  
Hence, the transform matrix from the telescope coordinates to the FSM coordinates becomes  
\[\mathbf{T^{\prime}_{FSM}}=\mathbf{G_{FSM}}\mathbf{T_{FSM}}\mathbf{G^{T}_{FSM}} \tag{17}\]  
Therefore, the imaging model considering the dynamic rotation of IRST is  
\[\mathbf{X^{\prime}_{i}}=\mathbf{T_{img}}\mathbf{T_{obj}}\mathbf{T_{TM4}}\mathbf{T_{FM}}\mathbf{T_{TM3 }}\mathbf{G^{T}_{FSM}}\mathbf{T_{FSM}}\mathbf{T_{TM2}}\mathbf{T_{tele}}\mathbf{G_{p}}\mathbf{T_{TM1}} \mathbf{T_{p}}\mathbf{X_{o}} \tag{18}\]Generally, \(R\) is much larger than \(l_{1}\), \(l_{2}\), \(l_{3}\), and \(l_{4}\), and the approximations of \(\mathbf{T_{TMI}}=\mathbf{T_{TMI}}=\mathbf{T_{TMI}}=\mathbf{E}\) can be made, where \(\mathbf{E}\) is the unit matrix. The imaging model can be rewritten as  
\[\mathbf{X}_{i}^{\prime}=\mathbf{T_{img}}\mathbf{T_{obj}}\mathbf{T_{FM}}\mathbf{G_{FSM}}\mathbf{T_{FSM}} \mathbf{G_{FSM}}\mathbf{T_{tele}}\mathbf{G_{p}}\mathbf{T_{p}}\mathbf{X_{o}} \tag{19}\]  
According to Equation (19), we can substitute the IRST requirements of object distance, rotation speed of platform, focal length, angular magnification, and integral time into the above model to help us obtain the image position and orientation on the focal plane. And the results will demonstrate the dynamic variation in the image wandering as well. Researchers and designers can use the variations to determine the telescope magnification and objective focal length promptly during the evaluation stage.
# Search-Space Reduction for S-Boxes Resilient to Power Attacks
## 3 Our Contributions: Reduction of the S-Boxes Search-Space into a Hamming Weight Class SEARCH-Space
### Examples Using Proposition 6  
In this subsection, we present some examples derived from Proposition 6.  
**Example 6**.: _Let \(n=3\). The number of classes is equal to \(2^{n}\prod_{r=1}^{n}C\big{(}\left[2^{n}-\sum_{i=0}^{r-1}C(n,i)\right],C(n,r) \big{)}=2^{3}\prod_{r=1}^{3}C\big{(}\left[2^{3}-\sum_{i=0}^{r-1}C(3,i)\right],C(3,r)\big{)}\) \(=8[C(8-1,3)][C(7-3,3)][C(4-3,1)]\) \(=8[C(7,3)][C(4,3)][C(1,1)]\) \(=8(35)(4)(1)=1120\)\(HWclasses\ of\ 3\times 3\)._  
This example illustrates the calculation of the number of HW classes by Proposition 6, according to which there are 1120 classes. This statement was tested experimentally and the 1120 classes obtained are shown in the Appendix C (see Table A5).  
**Example 7**.: _For \(n=8\), \((2^{n})!=(256)!\sim 10^{506}\) by [PERSON] (see Table 4). This example illustrates the difficulty in calculating the number of S-boxes even for small values of \(n(n=8)\) if it is necessary and convenient to use the Stirling formula._Finally, we establish a comparison of the number of classes estimated by Propositions 5 and 6. For \(n=3\), it is observed that both values coincide \((1120\)\(HWclasses\) of \(3\times 3)\), as expected since they are exact calculations. For \(n=8\), the Stirling formula was used in both cases to approximate different factorials, so there may be differences between the two estimates (see Table 5). The difference between both estimates is of the order \(10^{2}\). It can be considered acceptable, given the dimensions of the spaces being estimated. This comparison can be improved using the refinement of the Stirling formula.
# Mixed-Integer-Based Path and Morphing Planning for a Tensegrity Drone
## 6 Numerical Studies, Results
### Planning Drone Path and Deformation through a Series of Rooms  
In this section, the results of numerical studies of the algorithm are discussed. As stated previously, this requires generation of a configuration dataset, which in turn requires choosing an actuation pattern: which of the elastic elements are changing their lengths and which do not. We consider the case when only two vertical struts are changing their lengths. This is illustrated in the Figure 2.  
Consider the following experiment. Given three rooms: room A, room C, and room E, with rooms A and C connected via a narrow tilted shaft B, and rooms C and E connected via a narrow tilted shaft D; the robot needs to plan a path from the center of room A to the center of room E. Each room is 4.5 by 3 by 3.5 m, shaft B is 0.5 m wide, and shaft D is 0.7 m wide, with a slope of 45 degrees. For this task, the rooms A, C, and E are represented as H-polytopes \(\mathcal{A}_{1}\), \(\mathcal{A}_{3}\), and \(\mathcal{A}_{5}\), and the corridors B and D are represented as H-polytopes \(\mathcal{A}_{2}\) and \(\mathcal{A}_{4}\). The drone is based on the six-bar tensegrity structure and has 12 nodes. The rooms are modeled as rectangles, giving six linear inequalities describing each room. The configuration dataset used has eight elements.  
Two experiments are run: (1) 27 steps are allocated for the path from the starting point to the goal and (2) only 5 steps are allocated for the path. Both problems are solved using (13); the problems are written in CVX, using Gurobi 9.0 as a solver. In the first case, before simplifications, 351 binary variables and 77,760 scalar linear inequalities are expected, while in the second case, only 65 binary variables and 14,400 linear inequalities are expected. The problem was solved on a i7-9700K CPU, 3.60 GHz with 32 Gb RAM. Figure 3 gives a pictorial representation of a plan with 27 steps. The details of these two experiments are given in the Table 1.  
Let us observe that while both methods found solutions, the 27-step case took 733 s, while the 5-step case required only 2.8 s. These results depend on the solver settings; here, the Gurobi gap value of 0.05 is used. Notice that while 27 steps required a prohibitively long time, necessitating that the computations be done offline, the 5-step path was computed essentially in real time. In practice, one does not expect to compute long paths spanning more than two rooms, making this experiment more demanding than an average use case. Furthermore, the increase in time between cases is not linear, while the increase in the number of decision variables and constraints is. This is a natural consequence of the combinatorial nature of the branch and bound algorithm and mixed-integer programs. The next subsection gives a study of parameter and time-cost growth as the planned paths become longer, taking into account the solver settings.  
Figure 2: Actuation pattern on the tensegrity drone; static rest length struts are drawn in blue, the actuated struts in green, the nodes in magenta, and the cables in black.