esa-sceva/satcom-chunk-collection huggingface dataset:
# Lunar Satellite Constellations in Frozen Low Orbits
## 3 Design of Frozen Orbits
We also confirmed that for all the orbits we constructed, the eccentricity remains safely bounded even over a longer time interval (10 years) than was used during the optimization (1 year). This holds true within a more comprehensive dynamical model that now accounts for perturbations from Mercury, Venus, Mars, Jupiter, Saturn, Uranus, and Neptune, as well as lunar gravitational potential harmonics up to the 50 th degree and order. Figures 5 and 6 present polar plots illustrating the evolution of eccentricity and argument of periapsis for orbits at altitudes of 522 km and 261 km, both with a RAAN set to 0 degrees. Note that for  
Figure 3: Eccentricity vector evolution for the 73-cycle RGT periodic orbit (Adapted from [21], reprinted by permission of the American Institute of Aeronautics and Astronautics, Inc.). (**a**) Retrieved from [21], (**b**) reproduced. the 522 km orbit, a collision with the Moon would occur at an eccentricity value of \(e\approx 0.12\), and for the 261 km orbit, \(e\approx 0.07\). As shown in the figures, the maximum eccentricity attained over 10 years is approximately 0.01 for both orbits. Figure 4: Eccentricity vector evolution for the 328–cycle RGT periodic orbit (Adapted from [21], reprinted by permission of the American Institute of Aeronautics and Astronautics, Inc.). **(a)** Retrieved from [21], **(b)** reproduced. Once a set of frozen orbits uniformly distributed around the Moon is obtained, initial states for same-plane satellites in a constellation may be assigned as states along the reference frozen orbit equidistant in time over the nominal orbital period. Figure 5: Ten-year evolution of lunar orbit eccentricity and argument of periapsis (522 km altitude, 0\({}^{\circ}\) RAAN), considering Solar System planetary and lunar (up to degree/order 50) gravitational perturbations. The critical eccentricity (0.12) significantly exceeds the maximum observed value. Figure 6: Ten-year evolution of lunar orbit eccentricity and argument of periapsis (261 km altitude, 0\({}^{\circ}\) RAAN), considering Solar System planetary and lunar (up to degree/order 50) gravitational perturbations. The critical eccentricity (0.07) significantly exceeds the maximum observed value. A remark should be made that for a lunar orbiter, its elements are usually given with respect to the mean-Earth/mean-rotation (MER) reference frame which is employed to define the selenographic coordinates. At the same time, the spherical harmonic expansion of the lunar potential is given with respect to the so called Principal Axes (PA) reference frame. Its axes are very close to those of MER. The rotation matrix for the MER-PA transformation can be found in [10]. As for the PA-SCRF transformation, its matrix can be constructed using lunar libration angles included in the ephemeris information.
# Recognition of Hand Gesture Sequences by Accelerometers and Gyroscopes
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \(\mathbf{H_{1}}\) & \(\mathbf{H_{2}}\) & \(\mathbf{H_{3}}\) & \(\mathbf{H_{4}}\) & \(\mathbf{H_{5}}\) & \(\mathbf{H_{6}}\) & \(\mathbf{H_{7}}\) & \(\mathbf{H_{8}}\) & \(\mathbf{H_{9}}\) & \(\mathbf{H_{10}}\) & \(\mathbf{H_{11}}\) & **Ave.** \\ \hline Both & 70.35 & 90.19 & 72.14 & 86.43 & 99.12 & 97.93 & 98.23 & 98.16 & 94.05 & 100.00 & 99.54 & 92.36 \\ Gyroscope & 69.03 & 74.72 & 70.90 & 85.66 & 99.12 & 97.34 & 96.97 & 96.93 & 92.94 & 99.07 & 99.54 & 90.42 \\ Accelero. & 70.35 & 72.83 & 65.63 & 78.29 & 98.60 & 97.04 & 89.14 & 93.25 & 79.55 & 99.54 & 99.07 & 86.87 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The hit rates (in percentage) of the proposed PairNet algorithm with the employment of only accelerometer, gyroscope, or both.
## 2 The VFS Method
### RMD Function  
In VFS, attraction set and extended set are two important concepts to establish RMD function. Let \(X_{0}=[a,b]\) be the attracting set of \(x\) on the real axis (\(0<D_{A}(x)\leq 1\)) and \(X=[c,d]\) be the extended set containing \(X_{0}(X_{0}\in X)\). The M represents equilibrium point \(D_{A}(x)=1\) in the attracting set \([a,b]\). From the principle of VFS, it is clear that the intervals \([c,a]\) and \([b,d]\) are repelling sets of \(x\) (\(-1<D_{A}(x)\leq 0\)) (Figure 1).  
Now, suppose \(x\) is a random point among \([c,d]\) (Figure 1); when \(x\) is on the left side of M, the relative difference function is established as follows:  
\[\left\{\begin{array}{c}D_{A}(x)=\left(\frac{x-a}{M-a}\right)^{\beta}x\in[a, \ M]\\ D_{A}(x)=-\left(\frac{x-a}{c-a}\right)^{\beta}x\in[c,\ a]\end{array}\right. \tag{3}\]  
When \(x\) is on the right side of M, the relative difference function is established as follows:  
\[\left\{\begin{array}{c}D_{A}(x)=\left(\frac{x-b}{M-b}\right)^{\beta}x\in[M,\ b]\\ D_{A}(x)=-\left(\frac{x-b}{d-b}\right)^{\beta}x\in[b,\ d]\end{array}\right. \tag{4}\]  
According to Equations (2) and (3), the RMD function is expressed as follows:  
\[u_{A}(x)=0.5\bigg{[}1+\left(\frac{x-a}{M-a}\right)^{\beta}\bigg{]}x\in[a,M] \tag{5}\]  
\[u_{A}(x)=0.5\bigg{[}1-\left(\frac{x-a}{c-a}\right)^{\beta}\bigg{]}x\in[c,a] \tag{6}\]  
Figure 1: The position relation between random point \(x\) and intervals \([a,b]\) and \([c,d]\) and point M.  
According to Equations (2) and (4), the RMD function is expressed as follows:  
\[u_{A}(x)=0.5\bigg{[}1+\left(\frac{x-b}{M-b}\right)^{\beta}\bigg{]}\;x\in[M,b] \tag{7}\]  
\[u_{A}(x)=0.5\bigg{[}1-\left(\frac{x-b}{d-b}\right)^{\beta}\bigg{]}\;x\in[b,d] \tag{8}\]  
In Equations (5)-(8), \(\beta\) is a nonnegative index and, generally, \(\beta=1\). Equations (5)-(8) meet the following conditions:  
\[\left\{\begin{array}{c}u_{A}(x)=0.5\;x=a\;or\;b\\ u_{A}(x)=1\;x=M\\ u_{A}(x)=0\;x\leq c\;or\;x\geq d\end{array}\right. \tag{9}\]
# Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review
###### Abstract  
Landslides are one of the most devastating and recurring natural disasters and have affected several mountainous regions across the globe. The Indian Himalayan region is no exception to landslide incidences affecting key economic sectors such as transportation and agriculture and often leading to loss of lives. As reflected in the global landslide dataset, most of the landslides in this region are rainfall triggered. The region is prone to 15% of the global rainfall-induced landslides, and thereby a review of the studies in the region is inevitable. The high exposure to landslide risk has made the Indian Himalayas receive growing attention by the landslides community. A review of landslides studies conducted in this region is therefore important to provide a general picture of the state-of-the-art, a reference point for researchers and practitioners working in this region for the first time, and a summary of the improvements most urgently needed to better address landslide hazard research and management. This article focuses on various studies ranging from forecasting and monitoring to hazard and susceptibility analysis. The various factors used to analyze landslide are also studied for various landslide zones in the region. The analysis reveals that there are several avenues where significant research work is needed such as the inclusion of climate change factors or the acquisition of basic data of highest quality to be used as input data for computational models. In addition, the review reveals that, despite the entire region being highly landslide prone, most of the studies have focused on few regions and large areas have been neglected. The aim of the review is to provide a reference for stakeholders and researchers who are currently or looking to work in the Indian Himalayas, to highlight the shortcomings and the points of strength of the research being conducted, and to provide a contribution in addressing the future developments most urgently needed to obtain a consistent advance in landslide risk reduction of the area.  
Indian Himalayas; landslides; GIS; remote sensing +
Footnote †: journal: Applied Sciences  
1
# Three-Dimensional Velocity Field Interpolation Based on Attention Mechanism
## 2 Materials and Methods
### Joint Attention Mechanism
#### 2.2.2 CA Block  
In the TA Block, we calculate the attention weights between dimensions pairwise, involving three joint channels: inline-xline, inline-depth, and xline-depth. However, the patterns of changes in the horizontal and vertical directions in geoscience data are constrained by geological structures and features, and are not the same.This is because the \"inline\" and \"xline\" dimensions correspond to the planar distribution of geological structures, while the \"depth\" dimension corresponds to the temporal geological changes in geological ages. Therefore, the network needs to differentiate its focus on the three joint channels. To address this, we introduce a channel attention mechanism to enable the model to allocate and adjust attention weights more accurately.This helps in capturing the characteristics of horizontally distributed geological structures more effectively, allocating resources and attention more efficiently, and reducing interference from unrelated information, given that data exhibit rapid changes or discontinuities along the \"depth\" axis.  
The CA Block mainly includes two sub-modules [32]: the squeeze module and the excitation module. The squeeze module aims to pool the feature map globally to obtain global information for each channel. This extraction allows for capturing each channel's statistical characteristics, providing input for the subsequent excitation module. In this paper, a joint channel is pooled as a feature map. Based on the information provided by the squeeze module, the excitation module uses a small, fully connected network to learn the weights of each channel. Through a sigmoid activation function, the output range is limited between 0 and 1, representing the importance of each channel. These weights are then used to weight the original feature map, realizing the emphasis and de-emphasis of different channels.  
In the architecture of the CA Block, as shown in Figure 3, it can be seen that, after the TA Block outputs the feature data of the three joint channels, a squeeze operation must first be performed. This operation encodes the entire spatial feature on one channel as a global feature value, i.e., a scalar, achieved using global average pooling. Subsequently, through concatenation, an excitation operation captures the relationships between channels, learning the attention weights between channels. Finally, multiplying the channel attention weights with the output feature data of the joint channels completes the differentiation of attention to the joint channels.
## 3 Experiments and Analysis
### Improvement Experiments
#### 3.5.3 Comparison of Residual Edges  
In order to verify the superiority of introducing double residual edges into the grape disease identification model, the GFCD-YOLOXS model is compared with the GFCD-YOLOXS model without introducing residual edges, while the GFCD-YOLOXS model is compared with the introduction of single residual edges in separate experiments. The results are shown in Table 3. The introduction of single residual edges improved the mAP values for all disease identification methods by 0.18% compared to the method without double residual edges, indicating that introducing single residual edges can prevent degradation of the network. The introduction of double residual edges increased the mAP values for all disease identification by 0.21% compared to single residual edges, suggesting that introducing double residual edges can further enhance non-critical features and improve the accuracy of grape disease identification.
## 4 Training Datasets and Object Recognition Task Performance
### Training Datasets
#### 4.1.1 X-ray Dataset  
X-ray data were selected because contrast enhancement technology is actively being applied to increase diagnostic accuracy in the medical field, and in this paper, the performance of the classification task was used for measurement [56]. The X-ray dataset consisted of a total of 159,770 images, 512 \(\times\) 512 in size, and these were divided into 3 classes (COVID-19, pneumonia, normal). The original dataset and preprocessed images were applied.
## 2 Data and Methods
### Feature-Wise Fusion Strategy  
In this study, the deep residual U-Net, described as an end-to-end network with a ResNet backbone, was used. To implement feature-wise fusion for segmentation, the training was carried out in a sequential model where the satellite-based features and LiDAR-based features were both integrated. The model consists of two sequential parts connected to each other at the end of U-Net architecture. This was achieved by forwarding optical images through the encoder and decoder segments of the deep residual U-Net architecture, which makes up the first sequential part of the model, to extract high-level features from satellite images. In the second part, 13 geometric features derived from the LiDAR point cloud are fed to the sequential model without any computation to extract new features from them, as they are already linear features. Before the final convolution block of the model, these geometric features (512 \(\times\) 512 \(\times\) 13) were concatenated with the high-level features (512 \(\times\) 512 \(\times\) 16) along the channel dimension to form a multi-layer feature map with size 512 \(\times\) 512 \(\times\) 29. This combined feature map passed to the finalconvolution layer and an output layer with _Sigmoid_ as an activation function, to predict road pixels (see Figure 4).
## 4 Sparse Signal Transmission Framework
### Background
#### 4.1.2 Ubiquity of Sparse Signals  
It has been reported recently that many signals have sparsity in certain domains, such as temperature, light [15], image [10], acoustical signals [12], _etc._ Furthermore, [8] gives a relatively comprehensive summary of the signal's sparsity with \(\mathrm{CO}_{2}\) emission readings [30], temperature [31] and the seismic signal [32].  
To verify the ubiquity of sparse signal, we take the \(\mathrm{CO}_{2}\) emission in the Port of Oakland and the acoustical signal of a Porsche car as examples to show their sparsity. Figure 2 shows their sparsity in the wavelet and frequency domains, respectively. Since signal sparsity is the fundamental condition for using CS, the ubiquity of a sparse signal in sensor networks enables one to apply compressive sensing to sparse signal transmission.
## 3 Problem Description and Model Hypothesis
### Basic Assumptions and Parameters  
(1) Assuming that there is no difference in quality and performance between the remanufactured battery and the new battery, the new battery unit remanufacturing cost is \(c_{r}\) (recycling raw material cost), and \(f(v)=v^{r}\) indicates the battery remanufacturing rate, where \(v\) represents the efficiency of lithium-ion battery disassembly, \(r\) represents the coefficient of influence of lithium-ion battery disassembly efficiency on remanufacturing rate, \(r\) satisfies \(\partial f(v)/\partial v>0\), \(\partial f^{2}(v)/\partial e^{2}<0\), and \(r\in[0,1]\).  
(2) The amount of recovery can be expressed by the function \(Q=r_{i}e_{ij}+\theta+\xi\), where \(r_{i}\) indicates the real dismantling use level of the recycler, and \(e_{ij}\) indicates the recycler recovery effort. The label \(i\) represents the real information of the third party recycler, \(j\) represents the information reported by the third party recycler to the battery manufacturer, and \(i,j\in\{H,L\}\), H, L. The values \(r_{H}\) and \(r_{L}\) represent high and low capacity levels, respectively, with \(r_{H}>r_{L}\). Battery manufacturers can observe the proportion of high-capacity recyclers in the market at \(\delta\), and low-capacity recyclers at \(1-\delta\). The variable \(\theta\) indicates the amount of recycling when the recovery effort level is zero, \(\xi\) indicates a market random factor, and \(\xi\sim N(0,\sigma^{2})\).  
(3) The recovery effort cost is expressed as \(\mu=k\epsilon_{jj}^{2}/2\), and is a marginal increment function (\(\mu^{\prime}>0\)), where \(k(k>0)\) is defined as a recovery effort cost factor.  
(4) The level of third-party recycling efforts is private information, and battery manufacturing companies cannot observe and present asymmetric differences. The battery manufacturer gives its linear reward \(E(T_{j})\), \(E(T_{j})=T_{j}+f(v)\beta_{j}\mathcal{Q}\), \(j\in\{H,L\}\) based on the recycling yield of the third-party recycler. Here, \(T_{j}\) indicates that the third-party recycler reports a fixed remuneration for the disassembly use level of \(r_{j}\), and \(\beta_{j}\) indicates that the recycler reports the disassembly use level for \(r_{j}\). The number of products is divided into proportions (i.e., the battery manufacturer provides a revenue to the third-party recycler based on the number of remanufactured products), and \(f(v)Q\) indicates effective recycling.  
In the following, the variable subscripts \"M\", \"R\" and \"T\" respectively represent power battery manufacturers, new energy vehicle companies, and third-party recyclers. The superscript \"*\" indicates the optimal decision, and E indicates the expected utility.Decision variables and model parameters are shown in Table 1.
# Assessing Crisis Management Tools for Sustainability of Industrial Safety
## 3 Materials and Methods
\(M\)--Volume of the released substance (kg or m\({}^{3}\)). \(A\)--Area of the danger zone (m\({}^{2}\)). \(Z(x,y)\)--Color-coded hazard zone at point (\(x,y\)). Despite the versatility of ALOHA, certain limitations must be recognised, particularly in dynamic, rapidly changing environments. ALOHA depends on manual input for crucial environmental parameters, such as wind speed, temperature, and the quantity of the released substance. While this manual input ensures precise simulations under stable conditions, it introduces challenges when conditions shift unexpectedly. In situations where environmental factors change suddenly--such as rapid alterations in wind speed or direction--ALOHA cannot automatically adjust to the new data. Users are required to manually update the input parameters and rerun the model to account for these changes. This formula provides a direct link between the amount of hazardous material and the extent of the danger zone, which is crucial for defining evacuation perimeters and ensuring the safety of personnel. While simple, this relationship gives us a clear first estimate of the area potentially impacted by the release. While this approach is sufficient for predefined scenarios or environments with relatively stable conditions, it becomes less practical in fast-changing scenarios, where real-time adaptability is essential. Nevertheless, ALOHA remains a robust tool for pre-incident planning and response in predictable or stable conditions, allowing emergency responders to simulate potential hazard zones and assess risks in advance. This capability enables the preparation of evacuation plans and safety protocols. However, in scenarios where environmental conditions change in real time, ALOHA's reliance on manual updates can limit its effectiveness. In dynamic situations, ALOHA could be greatly enhanced by real-time data integration, continuously feeding updated meteorological data into the model. Although manual input would still be necessary, this integration would allow for quicker recalculations and improve the model's applicability in fast-evolving incidents. Automatic model retraining based on live data remains beyond ALOHA's current capabilities, but future research could explore opportunities to enhance ALOHA by incorporating advanced data streams or collaborating with platforms that support real-time recalibration of hazard models. Such advancements would enable ALOHA to better respond to rapidly changing conditions during an incident, providing emergency teams with more accurate and timely information.
## 3 Experimental Results
### Evaluation of the Influence of the Class Number in the Segmentation Step  
The performance of the system is dependent on the setting of parameters such as the class number (multilevel thresholding Otsu method) and the filter window sizes in the erosion and opening operations. The influence of the class number on the segmentation results was estimated. Eight setting possibilities of class numbers were analyzed. Additionally, the notation \(\alpha-\beta\) was adopted to represent the class numbers in B-mode and CEUS, respectively. Figure 6 shows the mean values of \(AUC\) and \(Acc\) calculated on the patient set using these eight configurations. It can be clearly observed that the highest \(Acc\) is achieved by selecting a large number of classes (e.g., 5-5). On the other hand, the highest \(AUC\) is obtained with a low number of classes (e.g., 3-2). When \(\alpha\) and \(\beta\) increase, the system becomes more selective or less sensitive. This means that the probability to detect highlighted structures, including tumor residuals and other hyperechogenic structures, is reduced. On the contrary, it becomes more sensitive when \(\alpha\) and \(\beta\) decrease (large values of \(AUC\)). Here, the probability to detect these highlighted structures is maximized. The first objective of the tool is rather the tumor remnants' localization and not accurate segmentation. Therefore, the optimal number of classes should be obtained when a balance between high values of both \(Acc\) and \(AUC\) is reached. A trade-off was obtained by setting \(\alpha\) and \(\beta\) to the values of 4 and 3.  
Figure 6: \(AUC\) and \(Acc\) performance rates computed for several numbers of class configurations in B-mode and CEUS.