esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Sets
### Water Surface Area Estimation  
Unsupervised classifications were performed on masked scenes to identify significant patterns in the images. While unsupervised k-means classification was performed by using the SNAP software of Sentinels Scientific Data Hub on creating our data subset that were clipped from the footprint images to extract the study area of SLSTR for period June 2016 to September 2019. The NIR band has been used to mask the land area from the image. The resulting unsupervised classified images showed different classes according to the class number assigned, as the water appears dark and has sharp edges discriminating water from land, which looks relatively bright (Figure 7).  
Figure 6: Flowchart showing the processing steps used to estimate water depth, area, and volume variations in AHDL by using Sentinel-3.  
Figure 7: Processes of unsupervised k-means classification method for extract water surface of AHDL. (**a**) showed the optical image of AHDL area after applied K-means unsupervised classified with K = 4 by SNAP; (**b**) showed the land and water classes by using QGIS; (**c**) (showed the water surface of AHDL after separate land area.  
The resulting classification images were opened in QGIS to extract water surface from the image using a raster calculator and mask land area from the images. Figure 8 shows the estimated water surface area of whole AHDL for all-time series. There is no atmospheric correction applied because AHDL is considered to have zero cloud cover, and the atmospheric path delays and extinction are considered constant.
## 2 Design and Analysis
### Four-Element MIMO Antenna  
This design is specifically required for a four-port MIMO antenna array that operates at two frequency bands with centers around 28, 35, and 38 GHz. The spacing between antenna elements is emphasized with the aim of improving MIMO isolation. The MIMO antenna assemblage consists of 4 different elements that occupy a common area of 50 \(\times\) 50 mm\({}^{2}\), alongside the substrate which is the same as for the single-element antenna. The MIMO antenna structure is displayed in Figure 4a for both top and bottom views, with each port connected to SubMiniature version A (SMA). As compared to those results obtained for a single-element antenna, the simulated reflection coefficient showed a minor degradation to \(-\)18 dB at 28 GHz and 35 GHz. However, there was a significant improvement to below \(-\)37 dB at 38 GHz. The slight deviation observed in the MIMO S-parameters, particularly S\({}_{11}\), compared to those of a single-element antenna, can be attributed to several factors intrinsic to MIMO systems. In MIMO configurations, where multiple antennas are closely positioned, interactions such as mutual coupling between antennas can impact individual antenna performance. This coupling can alter impedance matching and radiation patterns, leading to differences in S\({}_{11}\) parameters. Additionally, the placement of antennas in a MIMO array, the characteristics of the feed network, environmental conditions, and cross-coupling effects all contribute to the variations in S\({}_{11}\) parameters between MIMO setups and single-element antennas. Understanding and accounting for these complexities are vital in optimizing MIMO antenna designs to achieve the desired performance levels in real-world scenarios.  
In a MIMO configuration, significant coupling arising from surface wave interactions can profoundly impact the system's performance. Hence, it is imperative to minimize such interference or crosstalk behavior to the greatest extent possible. As a consequence of  
Figure 4: Four-port MIMO antenna; (**a**) front, (**b**) back view with SMA connectors, and (**c**) simulated S-parameters versus frequency.  
these efforts, reductions in coupling of \(-34.5\), \(-22\), and \(-30\) dB were achieved at 28 GHz, 35 GHz, and 38 GHz, respectively. These outcomes reveal successful mitigation of adverse coupling effects and the realization of the improved isolation and performance over the tri-band.
# The Identification and Assessment of Rail Corrugation Based on Computer Vision
## 3 Rail Corrugation Identification Based on Improved SPM Model
### Image Pre-Processing
#### 3.1.1 Image De-Noising  
The first stage of pre-processing is noise reduction, which is implemented by using image filters. The bilateral filter [27] is a non-linear filter that can achieve noise reduction and edge-preserving simultaneously. In addition, in comparison with other conventional filter-based methods, it can commendably preserve the rail surface texture characteristics. Hence, it is suitable for image de-noising. The bilateral filter is developed from the traditional Gaussian filter, which can be described as follows  
\[I_{out}(x)=\frac{1}{W_{p}}\sum_{x_{i}\in\Omega}I(x_{i})f_{r}(\|I(x_{i})-I(x)\| )g_{s}(\|x_{i}-x\|) \tag{1}\]  
where \(I\) and \(I_{out}\) represent the original and filtered image, respectively. \(x\) is the coordinates of the pixels to be processed while \(\Omega\) is the neighborhood window. \(f_{r}\) and \(g_{s}\) denote the distance and intensity filter, respectively. \(W_{p}\) stands for the normalization function. More details about BF can be found in Reference [27].
# On the Robustness of No-Feedback Interdependent Networks
## Simulation and Analysis
### Coupling Strength
The cascading failure process of interdependent networks under unilateral failure is equivalent to the failure of a single network, which is shown in Figure 5c. More generally, for the cases of \(0<CS_{A}<1\) and \(0<CS_{B}<1\), it indicates that only part of the nodes have dependency links in interdependent networks, and this is the coupling pattern of most real-world interdependent systems. The corresponding cascading failure of such interdependent networks is shown in Figure 5e. Compared with Figure 4, a clearer conclusion can be drawn from Figures 5 and 6: Coupling strength is an important factor that affects the robustness of no-feedback interdependent networks. The higher the coupling strength, the smaller the collapse threshold, and the robustness also becomes worse. Figuratively speaking, the curved surfaces in Figures 5 and 6 can be considered as snow-capped mountains, then avalanches easily happen in the green and light blue areas, and the red area represents a relatively secure zone. The avalanche region corresponds to a rapid decline of the collapse threshold, indicating that the robustness of interdependent networks deteriorates with the increase of \(CS_{A}\) and \(CS_{B}\). Comparing Figure 5 with Figure 6, it can be seen that the area of avalanche was larger when the no-feedback interdependent networks were faced with bilateral failure. It is further verified that the robustness of interdependent networks under bilateral failure was more sensitive to the change of coupling strength than unilateral failure. As a further supplement, we did additional simulations based on the other two types of subnetworks, the scale-free network (BA model) and the small world network (WS model). The results were shown in Figures S1-S3 of the supplemental material, the same conclusions were obtained from these results as that in ER-ER interdependent networks.
# The Stability of New Single-Layer Combined Lattice Shell Based on Aluminum Alloy Honeycomb Panels
## 4 The Stability of NSCLS for Different Defect Sizes  
The current Chinese \"Technical specification for spatial lattice structures\" [27] stipulates that when analyzing the stability of a lattice structure, the maximum initial geometric imperfection size (i.e., the initial deviation of the surface shape) should be set to 1/300 of the lattice shell's span. However, in this paper, the authors study a new type of single-layer aluminum alloy honeycomb combined lattice shell structure, and we find that the critical load obtained using the uniform defect mode is often not the smallest critical load. This is because the lowest-order buckling mode is frequently unable to characterize the most unfavorable distribution of defects in the structure. Therefore, the current specification of defect sizes may no longer be applicable. Therefore, it is necessary to investigate a reasonable maximum defect size for this type of structure. In this paper, we used the stochastic defect mode method to study the stable bearing capacity of a single-layer aluminum alloy lattice shell structure. To study the influence of different geometric defect sizes and plate thicknesses, the maximum defect sizes ranged from L/2000 to 16L/2000 (a total of 16 parameters), where L was the span of the combined lattice shell structure. The plate thickness was set to 0.5 mm, 1.0 mm, 1.5 mm, 2.0 mm, 2.5 mm, and 3.0 mm. The geometric dimensions and material properties that were used in the computational model were exactly the same as they were in the test model. A nonlinear finite element analysis was performed for lattice shell models with six plate thickness and 16 geometric defect sizes. Calculations were performed for 200 random samples (in total, 6 \(\times\) 16 \(\times\) 200 = 19,200 samples).  
By processing and analyzing the nearly 20,000 samples discussed above, the authors determined the critical load P\({}_{\sigma}\) by following the \"3\(\sigma\)\" principle. Figure 11 shows the results. The thickness of the plate and the standard bearing capacity of the structure for different defect sizes are comprehensively accounted for.  
Figure 11 shows that the single-layer aluminum alloy combined lattice shell structure is sensitive to defects. As the defect size increases, the stable bearing capacity of the structure obviously decreases. When the defect size is small, i.e., below 6L/2000, the stable bearing capacity of the structure changes approximately linearly with the defect size. The bearing capacity does not change significantly. However, when the defect size exceeds 6L/2000, the stable bearing capacity of the structure decreases nonlinearly with the defect size. The decreasing slope becomes steeper as the defect size continues increasing. Therefore, the authors recommend that when designing NSCLS in the future, the maximum size of the initial geometric imperfections should be held below 3/1000 of the span of the lattice shell.
# Pipeline Leakage localization Based on FBG Hoop Strain Sensors Combined with BP Neural Network
## 4 Leakage Localization Based on BP Neural Network
### BPNN for Leakage Localization  
The basic idea of this method is to use the BP neural network to recognize the leakage point of the pipelines through the hoop strain distribution acquired by some FBG hoop strain sensors. Different leakage accidents bring distinct hoop strain distributions, which can be seen as a pattern for leakage of a specific point.  
Thus, the BP neural network for leakage localization can be established as follows. Several hoop strain sensing points are chosen as the neurons of the input layer. The variable parameter in the output layer is the different leakage point. It is noted that the neurons both in the input and the output layer are from the nodes in MOC as mentioned in Section 3.1. The number of neurons in the hidden layer can be determined by the input and the output layer and optimized through trial calculation.  
In the training or learning mode, the hoop strain variations are fed to the input layer. A neuron of the network calculates the weighted sum of its inputs and passes it through a transfer function to generate an output. The most popular transfer function is the sigmoid logistic function, which is defined as:  
\[f(x)=\frac{1}{1+e^{-x}} \tag{6}\]  
A neuron of the output (leak point) goes to the inputs of all hoop strain neurons of the succeeding layer. The leakage point is collected at the output layer. The calculated outputs \(O_{k}\) and the error compared with desired outputs \(e_{k}\) can be expressed respectively as:  
\[O_{k}=\sum_{j=1}^{l}H_{j}w_{jk}-b_{k} \tag{7}\]  
\[e_{k}=Y_{k}-O_{k} \tag{8}\]  
where \(w_{jk}\) is the connection weight and \(H\) is the output of the hidden layer. The network learns by adjusting its weights; thus, the error is smaller in the next iteration. The weight can be shown in the following formula by considering the learning rate constant \(\eta\):  
\[w_{ij}=w_{ij}+\eta H_{j}(1-H_{j})x(i)\sum_{k=1}^{m}w_{jk}e_{k} \tag{9}\]  
\[w_{jk}=w_{jk}+\eta H_{j}e_{k} \tag{10}\]  
The parameters used in the leakage prediction network need to be set for a specific pipeline. There is no integrated theory to determine the number of neurons in the hidden layers. Thus, the number is often set  
Figure 6: Topological graph of the BP neural network.  
with a given performance by trial in simulation. Appropriate parameters of the network should be obtained by repeating tests.
## 4 System Communication Implementation Plan
### Communication between ROS and WEB Platform  
The connection between the ROS and the WEB platform is established as shown in Figure 8.  
In the ROS parameter server, the URDF model is converted into a 3D model usable by Three.js, and relevant configuration data such as robot arm parameters, rotation axis parameters, and collision information are transmitted. The system uses ROSBridge to build a communication bridge between the ROS and the WEB application, converting ROS  
Figure 8: Communication diagram between ROS and WEB platform.  
messages into a format suitable for the WEB [39]. Then, utilizing Three.js, a JavaScript library specifically used for rendering 3D models and environments on web pages, detailed and interactive robots and their operating environments are created.  
With the help of rosis, the system can achieve basic interactions with the ROS, while ros3 djs is used for 3D visualization of robots and environments [40], providing a real-time, dynamic, and interactive robot control and simulation environment on the WEB platform.  
The process is represented in pseudocode 2 as follows:  
```
1:Initialize ROS node and set up communication  Initialize ROS node 'web_robot_control_node' 2:Main function for the Web platform  Function web_platform_main():  Initialize Web Page  rosjs and ros3 djs  keyboard control event listeners  Set up WebSocket connection  Connect to WebSocket server  receiving user input:  handle_user_input(input_data)  receiving ROS messages:  update_web_page(ros_data) 3:Function to handle user input  Function handle_user_input(input_data):  user input  corresponding ROS control message  control message to '/robot/control' topic 4:Function to update the Web Page  Function update_web_page(ros_data):  robot state on the Web Page  three.js to update the rendering of the 3D model 5:Main function for the ROS  Function ros_system_main():  Initialize ROSBridge and Tf2_web_republisher  Set up ROS topic subscriptions and publications  Loop until ROS node is shut down:  Get robot state and sensor data  Publish sensor data and joint state to ROS topics  Get control commands and send them to the physical robot  Shutdown ROS node 6:Set up ROS topic subscriptions and publications  Function set_up_ros_topic_subscriptions_and_publications():  Subscribe to '/robot/state' topic  Create publisher for '/robot/control' topic
```  
**Pseudocode 2** Figure 8 process  
After completing these two communication steps, information exchange between the WEB page and the physical robot is realized. Through this integration, we can create an interactive, multi-user industrial robot simulation platform in the metaverse, which is not only suitable for education and training but also provides strong support for the research and application of industrial robots.  
This integration method demonstrates the great potential of combining advanced WEB technology with industrial robot simulation, offering new possibilities for virtual laboratories and the planning and testing of complex tasks.
## 4 Methodology
### Logical Rules-Guided in-Context Triples Retrieval and Extractor Pre-Training
#### 4.1.2 Supervised Extractor Pre-Training  
The in-context triple extractor is pre-trained based on the former training data \(D_{\text{rule}}=\{(h_{i},r_{i},?),\mathcal{I}_{i}\}\). It is an encoder-decoder generation model \(\theta\), and takes a query triple \((h_{i},r_{i},?)\) as input, then outputs the set of in-context triples \(\mathcal{I}_{i}\) guided by the logical rules. The encoder, decoder, and the process of pre-training are introduced as follows:  
**Encoder.** Given a query triple \((h_{i},r_{i},?)\), it is first converted into a sequence and then encoded to obtain the corresponding hidden states:  
\[\mathbf{M}_{i}=\text{Encoder}(h_{i}[SEP]r_{i}),\mathbf{M}_{i}\in\mathbb{R}^{d \times|S_{i}|}, \tag{2}\]  
where the input is the sequence \(S_{i}=\{h_{i}[SEP]r_{i}\}\), and \([SEP]\) is a separation token between \(h_{i}\) and \(r_{i}\). \(\mathbf{M}_{i}\) are the hidden states of the given sequence \(S_{i}\). \(|S_{i}|\) represents the length of sequence \(S_{i}\), and \(d\) is the dimension of the hidden states.  
**Decoder.** The decoder aims to decode the obtained hidden states \(\mathbf{M}_{i}\), and generate the set of the in-context triples \(\mathcal{I}_{i}\) for reasoning. The procedure of generating in-context triples \(\mathcal{I}_{i}\) at the \(k\)-th decoder step can be represented by:  
\[\begin{split}\mathbf{F}_{i}&=\text{Decoder}( \mathbf{M}_{i}),\mathbf{F}_{i}\in\mathbb{R}^{d\times k},\\ P(y_{k}|\{h_{i}[SEP]r_{i}\},\mathcal{I}_{i}^{<k};\theta)& =\text{softmax}(\mathbf{W}\mathbf{F}_{i}+\mathbf{b}),\\ P(\mathcal{I}_{i}|\{h_{i}[SEP]r_{i}\};\theta)&= \prod_{k=1}^{K}P(y^{k}|\mathcal{I}_{i}^{<k},\{h_{i}[SEP]r_{i}\};\theta),\end{split} \tag{3}\]  
where \(\mathbf{F}_{i}\) are the output hidden states of the extractor decoder. \(P(y_{k}|\{h_{i}[SEP]r_{i}\},\mathcal{I}_{i}^{<k};\theta)\) represents the probability distribution at the \(k\)-th decode step. \(\mathcal{I}_{i}^{<k}\) denotes the decoded sequence in the previous \(k-1\) decoding steps. \(\mathbf{W}\) and \(\mathbf{b}\) are trainable parameters.  
**Training.** During the training phase, the optimization of parameters is achieved by reducing the negative log-likelihood of the parallel training data \(D_{rule}=\{(h_{i},r_{i},?),\mathcal{I}_{i}\}\) to its minimum value:  
\[L(\theta)=-\frac{1}{|\mathcal{I}_{i}|}\sum_{k=1}^{|\mathcal{I}_{i}|}logP(y_{k} |\{h_{i}[SEP]r_{i}\},\mathcal{I}_{i}^{<k};\theta), \tag{4}\]  
where \(\mathcal{I}_{i}^{<k}\) is the generated sequence in the previous \(k\)-\(1\) decoding steps. \(|\mathcal{I}_{i}|\) is the length of the sequence of the in-context triples \(\mathcal{I}_{i}\), where \(i\) denotes the index of the training data.  
After pre-training, given a query triple \((h,r,?)\), the in-context triple extractor can output the corresponding in-context triples which can be used for LLM reasoning.
## 3 Results and Discussion
### Overview of Metabolites Discovered in Filed Soil (Contaminated Site)
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Metabolism** & **Total** & **Expected** & **Hits** & **Raw p** & **LOG(p)** \\ \hline \multirow{2}{*}{1} & Alanine, aspartate and & & & & & \\  & glutamate metabolism & & & & & \\ \hline
2 & Pyrimidine metabolism & 42 & 0.511 & 2 & 0.0895 & 1.048 \\ \hline
3 & Nitrogen metabolism & 8 & 0.097 & 1 & 0.0936 & 1.029 \\ \hline \hline \end{tabular} Three metabolisms presented in Table 3 are visualized in Figure 5. Predominant metabolism of soil bacteria in field soil. Metabolism less than the \(p\)-value of 0.05 is considered significant statistically. Accordingly, metabolism (LOG(p) more than 1) is meaningful metabolism. “Total” means the total number of metabolites in metabolism, and “Hits” means the number of discovered metabolites in field soil using LC–MS.  
\end{table} 
Table 3: Metabolic pathway analysis of field Soil (contaminated soil). \begin{table}
\begin{tabular}{l c c c c c} \hline \multicolumn{5}{c}{**Upregulated Metabolites in Field Soil (Contaminated Soil)**} \\ \hline
**tg (min), m/z** & **t.stat** & \(p\)-Value** & **Mass Error (ppm)** & **Metabolite** \\ \hline
15.43\_146.9797 & \(-\)4.3983 & 0.00060457 & \(-\)23.25573074 & 1,4-Dichlorobenzene & Derivative of benzene ring \\ \hline
15.47\_355.0095 & \(-\)2.2841 & 0.038491 & \(-\)9.933051337 & 2-Caffeoylisocitrate \\ \hline
15.47\_265.9916 & \(-\)2.3449 & 0.034295 & \(-\)12.07498001 & 2-Hydroxy-beta-keto-t-d-tryoyl-[kpc] & Novobicon biosynthesis \\ \hline
14.37\_139.9672 & \(-\)2.7065 & 0.017037 & 58.77534925 & 3-Nitroacrylate & Beta-nitroacrylate reductase \\ \hline
0.95\_182.9844 & \(-\)2.533 & 0.023891 & \(-\)13.43082261 & 5-Chloror-1,2,4-trihydroxybenzene & Chloroxycheane and chlorobenzene degradation \\ \hline
15.92\_686.1990 & \(-\)2.2594 & 0.04031 & 14.99536091 & Acartoose & Acartoose and validamycin biosynthesis \\ \hline
13.87\_149.0227 & \(-\)2.6871 & 0.017691 & \(-\)11.97984821 & -Chloroxycheane and chlorobenzene degradation \\ \hline
15.46\_371.1010 & \(-\)2.2147 & 0.04387 & \(-\)2.081103867 & Campothechen & -Bisosynthesis of alkaloids derived from kilamate pathway \\ \hline
14.60\_141.9025 & \(-\)2.9159 & 0.01128 & 52.71728653 & Carbamoyl phosphate & Purine metabolism \\ \hline
15.90\_371.1008 & \(-\)2.5188 & 0.024559 & \(-\)11.12892128 & Ciprofloxacinacin & ABC transporters \\ \hline
14.98\_166.0264 & \(-\)2.2939 & 0.037783 & 0.52975036 & Detali-Pireidine-6-L-carboxosyllate & Biosynthesis of plant secondary metabolites \\ \hline
15.00\_207.0162 & \(-\)2.4157 & 0.020955 & 21.6127894 & Dbenzofuran & Derivative of benzene ring \\ \hline
13.83\_279.1582 & \(-\)2.8061 & 0.014008 & 3.172566133 & Dibutyl phthalate & \\ \hline
15.47\_429.0878 & \(-\)2.1947 & 0.045551 & \(-\)3.571806454 & Dibutylstit