esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions and Future Work  
This paper developed, discussed and evaluated a number of sensor networks architectures for monitoring underwater pipelines. The reliability issues of five sensor network architectures for monitoring long underwater pipelines were discussed. These architectures are underwater wired sensor  
\begin{table}
\begin{tabular}{c l c c c} \hline \# & **Architecture** & **BW** & **BWF** & **EC** & **PSC** \\ \hline
1 & Underwater wired sensor network & High & 0 & No & High \\
2 & Underwater acoustic wireless sensor network & Low & Low & Yes & Low \\
3 & RF Wireless sensor network & Med & Med & Yes & High \\
4 & Integrated wired/acoustic wireless sensor network & High & Low & No & Low \\
5 & Integrated wired/RF wireless sensor network & High & Med & No & Low \\ \hline \end{tabular}
\begin{tabular}{c} BW: Bandwidth; BWF: Bandwidth with fault condition; EC: Energy constrains; PSC: Physical security concern. \\ \end{tabular}
\end{table}
Table 2: A Summary of the reliability issues for different sensor network architectures for underwater pipelines.  
networks, underwater acoustic wireless sensor networks, RF wireless sensor networks, integrated wired/acoustic wireless sensor networks, and integrated wired/RF wireless sensor networks. The paper developed an analytical model to compare the reliability of different architectures for monitoring underwater pipelines. The newly introduced integrated wired/wireless networks can provide good reliability in terms of network connectivity, continuity of power supply, and physical network security. In this architecture, the node batteries will be used only if there is a fault in the wires providing power to the nodes. Unlike wireless sensor networks, if there is no cut for some period, the batteries will be fully charged during that period. As a result nodes will have better chance to have power supply in integrated wired/wireless sensor network than wireless sensor networks. In addition integrated wired/wireless sensor networks can have less physical security concerns than underwater wired sensor networks as physical attacks on the network will not stop the connectivity of the network in some degree. Although the integrated wired/wireless sensor network can provide good sensing and communication reliability, it is a new technology that may face some challenges in the design and implementation. As a result and as future work, we plan to design, analyze and evaluate in details the integrated wired/wireless sensor network architecture for monitoring long underwater pipelines. We plan to study the feasibility and effectiveness of this network. We intend to study the implementation issues of the mentioned networks as well as evaluating different technologies that can be used to implement these architectures. In addition, we plan to develop and evaluate cross-layered optimizations to enhance communication in the proposed architectures.
## 1 Introduction
[27] introduced a method for generating high-resolution digital outcrop models (DOMs) combining UAV photogrammetry and 3D structure reconstruction technology, extracting and analyzing rock discontinuities parameters such as orientation, group sets, and trace length. Airborne laser radar (Lidar) and UAV technology can be combined to collect the high-resolution and high-precision point cloud data of slope surfaces, providing technical support for the digital characterization of exposed rock layers that cannot be directly measured due to complex terrain or safety restrictions [28; 29]. [PERSON] et al. [10] extracted rock discontinuity information from the scanned point cloud using the random sample consensus algorithm based on the rock block identification method. As can be seen from previous studies, UAV photogrammetry technology is a reliable and efficient replacement for manual measurement, making up for the deficiencies of traditional manual measurement. However, the denoising process of the obtained rock slope point cloud data may lose some original details, and rock discontinuity recognition and orientation dominant grouping are mostly based on normal vector determination, which cannot achieve the accurate extraction of discontinuity information for smooth slope surfaces. Therefore, using UAV photogrammetry technology combined with the detailed analysis of high-resolution images obtained from the site can help us to comprehensively understand the rock mass discontinuity information of high and steep layered rock slopes, which has important significance for guiding slope stability analysis and landslide disaster prevention. In this study, we first introduce the geological background of the slope for the Fushun West Open-Pit mine, and the studied slope area is given in Section 2. The modeling process of the studied slope using UAV photogrammetry technology is given in Section 3. The rock mass discontinuities are analyzed using the established slop model in Section 4. The distributions of rock mass discontinuities in different rock layers are compared in Section 5. The accuracy validation of calculated discontinuities, the slope stability effected by the obtained discontinuities within different rock layers, and the limitations of UAV technology are discussed in Section 6.
# Application of a Hybrid Model in Landslide Susceptibility Evaluation of the Western Tibet Plateau
## 2 Materials and Methods
### Landslide Susceptibility Modeling
#### 2.5.3 Logistic Regression (LR)  
The LR model is mainly used to infer the relationship between a group of relatively independent geological environmental factors and the occurrence of geological disasters. In other words, the model seeks to determine whether geological disasters can occur under such conditions, and if yes, the probability of their occurrence [62; 63; 64]. In this model, the probability of geological disasters is defined between 0 and 1, and the probability of hazard distribution in the working area is then calculated [34]. Compared with the analytic hierarchy process, the geological environment factors selected by this model can be either discrete or continuous, which can more reasonably fit the probability of landslide occurrence [65]. The specific calculation process is as follows:  
\[\text{A}=\frac{\text{exp(M)}}{1+\text{exp(M)}}. \tag{7}\]  
A indicates the probability of a landslide occurrence from 0 to 1. M is a linear combination, and can be expressed as  
\[\text{A}=\frac{\text{exp(M)}}{1+\text{exp(M)}}. \tag{8}\]  
X1, X2, \(\ldots\), and Xn denote the variables. N1, N2, \(\ldots\), and Nn are the slope coefficients. N0 refers to the intercept.
## 3 Methodology and Process  
The design science research process model (DSRP) [50] was followed. The research model includes problem identification and motivation, objective identification for the new framework, design, and development of the cyber threat intelligence integration in the risk management process, demonstration, and evaluation of the framework. The phases' details are presented below.  
1. Problem identification and motivation: The existing literature and documentation of cybersecurity risk management and threat intelligence frameworks were reviewed to identify the gaps, including frameworks, methodologies, and industry best practices. Current risk management methodologies often focus on technical vulnerabilities, overlooking the motives and tactics of threat actors, which limits their effectiveness in addressing emerging threats [51]. Integrating cyber threat intelligence into risk management is still uncommon, and there is a need for systematic approaches to incorporate threat intelligence feeds effectively [10, 13]. Existing frameworks often struggle to adapt to the dynamic threat landscape, highlighting the importance of considering adversary techniques for proactive risk mitigation [14, 52].
2. The solution's objective: This research aims to propose novel threat intelligence integration into an existing risk management process to enhance the process and response to emerging cybersecurity threats.
3. Design and development: This phase includes selecting an existing cybersecurity risk management process and integrating threat intelligence into its different phases. The novel modifications in the EBIOS Risk Manager process aim to integrate the threat intelligence feeds into the different risk management phases. This leads to more accurate risk assessments that consider cyber threat actors' capabilities and objectives and better treatment prioritization.
4. Demonstration: This phase presents the use of the proposed framework to manage the risks in the context of a national telecommunications gateway. The main aim is to demonstrate the framework phases and aspects by assessing and managing risks of a defined scope based on threat intelligence information.
5. Evaluation: This phase aims to evaluate and present the enhancements in the novel framework in contrast with the existing problem. This is achieved by analyzing the proposed framework, the results of its conducted application, and the existing frameworks in addressing the existing problem. This includes limitations such as the necessity of threat intelligence resources and implementation validation, which will be part of future work.
## 3 Method  
The main contribution of this work is a method with which to detect oil slicks in SLAR images. Figure 3 represents a complete scheme of our method, based on image processing techniques and computer vision. It particularly shows all the processing stages in the detection of oil slicks in SLAR images acquired from an onboard sensor. First, a 'denoising caused by aircraft' phase detects the noise caused by aircraft maneuvers while the mission is being carried out, after which an 'oil slick region detection' phase detects and locates the oil slicks in the image. The method is discussed in the following sub-sections.
# Review of Flexible Piezoresistive Strain Sensors in Civil Structural Health Monitoring
## 3 Elaboration of Sensor Materials
### Conductive Materials
#### 3.1.1 Metallic Materials  
Metallic nanomaterials, including 0D nanoparticles [55; 56; 57] and 1D metal Cu/Au/Ag nanowires [58; 59; 60], have good electrical properties and mechanical flexibility. The distance between metallic nanoparticles is usually small; thus, most electrons are transmitted via the tunneling effect. Because the quantum tunneling effect is extremely sensitive to the distance change between particles, a small strain on the metallic nanoparticle array will cause destruction and regeneration of the conductive network, resulting in a large change in resistance. Therefore, metallic nanoparticles can be used to fabricate highly sensitive piezoresistive strain sensors with assisted microcracks.  
AgNW-based conductive inks have been developed and applied to print large-area stretchable strain sensors [61], but the weak connectivity between AgNPs will lead to defects, such as a small sensing range, low sensitivity and nonlinear mechanical deformation. In addition, silver is relatively expensive, which significantly increases the manufacturing cost [43]. As an alternative, CuNWs have similar conductivity to AgNPs but their cost is only one-third that of AgNPsNWs. Nevertheless, CuNWs easily oxidize and require further methods to reduce or prevent oxidation [62].
## 2 Numerical Modeling
### Validation of the Numerical Method  
In this study, the capability to simulate tip-leakage flow/vortex and shock wave structure is of significant importance. Since there is no available experimental data of this turbine to validate the simulation directly, two cases are respectively used to validate these two aspects.  
On the one hand, the 1.5 stage turbine LISA (Turbomachinery Laboratory of the Federal Institute of Technology (ETH), Zurich) [25] is used to assess the performance of the numerical methods to capture the tip-leakage flow/vortex. The same conditions as the experimental setup are considered in the simulations [26; 27]. Figure 4 shows the span-wise distribution of pressure coefficient at the outlet of the first-stage turbine rotor. The pressure coefficient is defined as follows:  
\[c_{\text{pt}}=\ (p_{\text{t}}-p_{\text{s,outlet}})/(p_{\text{t,inlet}}-p_{\text{s, outlet}}) \tag{1}\]  
Here, \(p_{\text{t}}\) is the local mass-averaged total pressure in the circumferential direction, \(p_{\text{t,inlet}}\) is the mass-averaged total pressure at the inlet and \(p_{\text{s,outlet}}\) is the mass-averaged static pressure at the outlet.  
It is observed that the predicted tip-leakage vortex, casing passage vortex and the peak value of the low pressure are consistent with the experiment. Figure 5 shows the circumfer  
Figure 4: Span-wise distribution of pressure coefficient at the turbine outlet.  
Figure 3: Grid independence.  
ential distribution of static pressure on the casing within the rotor tip gap. Accordingly, it is inferred that the selected numerical method can effectively capture the leakage flow.  
On the other hand, the turbine cascade BRITE [12; 28] was simulated to validate the capability of the numerical method in predicting the shock wave structure in the transonic flow of turbine. The outlet Mach number is 1.05 and the Reynolds number based on the blade chord length and outlet velocity is about 1.0 \(\times\) 10\({}^{6}\). Figure 6 shows the Mach number contour in the blade passage, where the shock waves formed by supersonic flows at the trailing edge and the separated flows originating from the interaction between the shock waves and the boundary layer on the suction surface can be observed. [PERSON] et al. [12] studied the shock wave structure near the trailing edge. Figure 7 indicates that the predicted static pressure on the blade has a good consistency with the experiment. Meanwhile, it is found that the shock wave/boundary interaction is consistent with the experiment.  
Accordingly, it is inferred that the applied numerical method can be used to accurately simulate the tip leakage vortex in transonic turbine flows and predict the losses.  
Figure 5: Circumferential distribution of the static pressure at the casing within the tip clearance.  
Figure 6: Contour of Mach number in the blade passage.
## Appendix C Qualitative Analysis
### Impact of Temperature on Prompt Techniques  
#### c.1.1 Zero-Shot Prompting  
In all three models, low-temperature zero-shot prompting produced clear, precise, and concise diagnostics with strong clinical justification. Diagnoses are well-organized and focused on probable conditions based on available clinical data. In models like Mistral and Gemini, the clarity of reasoning and linking of clinical markers to diagnoses stands out. However, rare or speculative conditions are often underrepresented, particularly in Gemini, which tends to avoid more creative diagnostics at low temperatures.  
High-temperature zero-shot prompting introduces more speculative reasoning and diagnostic variety, which can be beneficial for exploring broader possibilities. However, this also results in verbosity and less precise justifications. In Mistral, the trade-off between breadth and depth becomes more volatile, while Gemini shows a broader exploration of differential diagnoses. Overall, high temperatures in zero-shot prompting reduce diagnostic focus and precision.
# Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data
## 5 Conclusions
So, the use of multiple textural vectors has a better performance than that of a single textural vector to aid spectral information for lithological classification; (iii) it is necessary to select optimal feature vector combinations using appropriate methods, such as the J-M distance. Furthermore, more useful indicators, similar to the textural features derived from remote sensing imagery, need to be explored and integrated with spectral features using suitable classifiers to improve the accuracy of lithological classification. Nevertheless, we propose a referable technique to improve the classification of silicate rocks by fusing textural features with spectral features, which will aid in lithological mapping at macroscopic scales and guide further geological exploration in harsh natural environments. This work was supported by the project \"Survey and Evaluation of Geology and Mineral Resources of the Metallogenic belt at the peripheral and adjacent area of Qaidam Basin, Qinghai province\" (project ID: 1212011121188) and the project \"The national XX major project of remote sensing application system in XX area\" (project ID: 01-Y3 XXXX-90 XX-14/16). The authors are thankful to the Institute of Geological Survey, China University of Geoscience (Beijing) for providing geological data, and the Public Security Engineering Technology Research Center of Remote Sensing Applications, People's Public Security University of China for providing the technical support and basic geographic data, and USGS EROS data center for providing Landsat 8 data. The authors wish to thank the anonymous reviewers for their constructive comments that helped improve the scholarly quality of the paper. [PERSON] conceived and designed the experiments and wrote the paper. [PERSON] supervised the research and provided significant comments and suggestions. [PERSON] wrote the main programs of the fractal dimension method in MATLAB R2014b, MathWorks, Natick, Massachusetts, USA, 2014) and contributed to the editing and review of the manuscript. The authors declare no conflict of interest.
# Proteomic Landscape of the Mature Roots in a Rubber-Producing Grass _Taraxacum Kok-saghyz_
###### Abstract  
The rubber grass _Taraxacum kok-saghyz_ (TKS) contains large amounts of natural rubber (cis-1,4-polisoprene) in its enlarged roots and it is an alternative crop source of natural rubber. Natural rubber biosynthesis (NRB) and storage in the mature roots of TKS is a cascade process involving many genes, proteins and their cofactors. The TKS genome has just been annotated and many NRB-related genes have been determined. However, there is limited knowledge about the protein regulation mechanism for NRB in TKS roots. We identified 371 protein species from the mature roots of TKS by combining two-dimensional gel electrophoresis (2-DE) and mass spectrometry (MS). Meanwhile, a large-scale shotgun analysis of proteins in TKS roots at the enlargement stage was performed, and 3545 individual proteins were determined. Subsequently, all identified proteins from 2-DE gel and shotgun MS in TKS roots were subject to gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and most proteins were involved in carbon metabolic process with catalytic activity in membrane-bounded organelles, followed by proteins with binding ability, transportation and phenylpropanol biosynthesis activities. Fifty-eight NRB-related proteins, including eight small rubber particle protein (SRPP) and two rubber elongation factor(REF) members, were identified from the TKS roots, and these proteins were involved in both mevalonate acid (MVA) and methylerythnitol phosphate (MEP) pathways. To our best knowledge, it is the first high-resolution draft proteome map of the mature TKS roots. Our proteomics of TKS roots revealed both MVA and MEP pathways are important for NRB, and SRPP might be more important than REF for NRB in TKS roots. These findings would not only deepen our understanding of the TKS root proteome, but also provide new evidence on the roles of these NRB-related proteins in the mature TKS roots.  
Keywords:natural rubber biosynthesis; mass spectrometry; rubber grass; rubber latex; shotgun proteomics; _Taraxacum kok-saghyz_; two-dimensional gel electrophoresis; visual proteome map +
Footnote †: journal: Molecular Sciences
## 3 Experiments and Comparisons
### Comparing Model Settings  
Five GNNs, each exhibiting unique characteristics--namely, the ChebyNet [15], GAT [16], GCN [17], Graph Isomorphic Networks (GINs) [18], and Graph Sample and Aggregate (GraphSAGE) [19] neural networks--were selected as benchmark models. By comparing these models, the advantages of the proposed ChebyAT model in variable operating conditions fault diagnosis can be highlighted from various perspectives. Among these, the ChebyNet and GAT are directly related to the proposed method. Using them as comparison models not only highlights the technical relevance of the proposed method but also allows for an intuitive assessment of how the ChebyAT addresses and integrates the limitations of each.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Name** & **Parameter** \\ \hline \multirow{4}{*}{Input Layer} & Sample length & 1024 \\  & Batch size & 64 \\  & Epochs & 100 \\  & Learning rate & 0.001 \\ \hline \multirow{2}{*}{ChebyNet Layer1} & K & 2 \\  & Batchnorm1 & 1024 \\ \hline \multirow{4}{*}{GAT} & Heads number & 4 \\  & Batchnorm2 & 1024 \(\times\) 4 \\  & Fully connected & 512 \\  & Dropout & 0.2 \\ \hline \multirow{2}{*}{ChebyNet Layer2} & K & 2 \\  & Batchnorm3 & 10 \\ \hline \multicolumn{2}{c}{Node Reconstruction} & Fully connected & 1024 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model hyperparameter settings.  
A GCN is a GNN that realizes the local feature aggregation of graph structure data through spectral graph theory. It is used as a comparison to intuitively demonstrate the improvement of the ChebyAT in local feature extraction and aggregation; A GIN is a GNN that captures the overall characteristics of the graph structure through a multi-layer perceptron (MLP) as a node update function. It is used as a comparison to verify the ability of the ChebyAT to represent global information of complex graph structures. The GraphSAGE is an inductive learning GNN method that learns node representation through the sampling and aggregation of neighbor nodes. It can effectively learn node representation on unseen graphs. It is used as a comparison model to evaluate the generalization ability of ChebyAT and the effect of processing large-scale graph data.
## 3 Methodology
### Sentiment Analysis Models
#### 3.4.1 Baseline  
We started by proposing a CNN model, as a baseline, to detect the sentiment of Arabic tweets and to be used as a baseline for our experiments. CNN is a special type of feed-forward neural network originally utilized in the field of computer vision [58]. Afterward, CNN showed success in NLP tasks, specifically in the classification problems [59], due to its ability in extracting important features that assist with the classification tasks.  
The proposed model consists of 11 layers as shown in Figure 3. Each convolution layer applies a set of kernels to extract the features from the input word vectors. We used kernels of size \(5\times 5\) with the'same' padding and stride of \(1\times 1\) selected empirically. The first convolution layer accepts the words' vector embeddings resulting from the text representation phase. The input to this layer is \(n\times 300\), where \(n\) is the number of wordsin the tweet, and 300 is the vector embedding dimension of each word. Each convolution layer in this model is followed by a nonlinear ReLU activation function. This function takes an input of a real-valued number and threshold it at zero when it is less than 0. Compared with other activation functions, such as Tanh and Sigmoid, ReLU is fast to converge and easier to compute with better performance [60]. In addition, global maximum pooling layers of size 3 are used to down-sample the obtained features of each convolution layer, as shown in Figure 3.  
To overcome the overfitting problem, we regularized the network by dropping out 30% of the learned features at the dropout layer. This layer is followed by a fully-connected layer and a Sigmoid classifier. This classification layer outputs 11 output units that correspond to the number of the tweets' emotions that will be predicted by the model. We avoided using the Softmax function since it forces the total predicted probabilities to sum to one that violates our objective of predicting multiple classes or no class.