esa-sceva/satcom-chunk-collection huggingface dataset:
# Positioning Methods and the Use of Location and Activity Data in Forests
## 4 Location-Based Services, the Internet of Things, Wearable Technology, and Big Data
### Big Data  
At its simplest, big data refers to the unprecedented volume of data currently being generated as the total quantity of devices and their ability to measure and store data continues to increase over time. Since size is relative, the term also embodies the technological limitations for storing and processing digital information efficiently [220]. Based on existing, available storage capacities, big data is usually measured in terabytes, petabytes (1,024 terabytes), or even exabytes (one million terabytes) [221; 222]. One terabyte is equivalent to 1500 compact discs (CDs) or 16 million Facebook photographs [221]. Definitions of big data typically also account for its high variety and high velocity [220; 221; 222]. Variety refers to the heterogenous nature of the data, which can be tabular (e.g., spreadsheets), structured, or unstructured (e.g., text, image, audio, video). Velocity denotes both the rate at which data is generated and the speed at which it needs to be analyzed to inform actions or provide services.  
Diverse fields generate big data, including health (genomics, medicine, pharmaceuticals), science (astrophysics, quantum mechanics, modeling, simulation), government (statistics, campaigning), higher education (admissions, student evaluation), and business (inventory, advertising, decision-making) [223; 224]. Daily interactions with objects like smartphones and other wearable technology or interactions with other people through social media sites also produce big data [221; 222]. In natural resources, big data from global satellite imagery or terrestrial monitoring systems (e.g., LiDAR) can help assess environmental changes and inform management decisions [224]. Emerging individual,tree-based forest inventory, particularly using airborne, terrestrial and mobile laser scanning, to support precision forestry is expected to generate considerably larger volumes of data than conventional forest sampling [225; 226].  
Multiple challenges stem from the creation of big data. Technical solutions are required to meet the need for advanced data management (acquisition, storage, and transfer) and analytics (extracting useful information from the data) [220; 221; 222; 223]. Some data sets include personal, sensitive information, such as electronic health records, which necessitate increased privacy and security measures [220; 223; 224]. Additionally, issues of ownership and legal rights arise over big data, such as individual information collected through social media sites or intellectual property related to pharmaceutical research [220; 223; 224]. Ownership also entails determining who is accountable for inaccuracies, particularly when those inaccuracies have negatives outcomes [223].
# An Enhanced Multi-Constraint Optimization Algorithm for Efficient Network Topology Generation
## 4 Algorithm Design
### Link Weight Calculation Using Multi-Attribute Decision Making
#### 4.2.3 Calculating Comprehensive Weight Value of Links  
In Section 4.2.1 of the paper, the normalized decision attribute matrix was acquired. Subsequently, the weights of each attribute were calculated, resulting in a row vector \(Q\) representing the attribute weights. These two matrices are then multiplied to obtain a comprehensive weight value matrix \(W\) for n potential links of a satellite node as follows:  
\[W=D\times Q=\left[\begin{array}{ccc}d_{11}&\cdots&d_{13}\\ \vdots&\ddots&\vdots\\ d_{n1}&\ldots&d_{n3}\end{array}\right]\times\left[\begin{array}{c}q_{11}\\ q_{21}\\ q_{31}\end{array}\right]=\left[\begin{array}{c}w_{11}\\ w_{21}\\ \ldots\\ w_{n1}\end{array}\right]. \tag{21}\]  
By following these steps, the multi-attribute decision-making weight values for all potential links of each satellite node can be calculated. These weight values are subsequently assigned to all potential links in the optical communication constellation network for the purpose of constructing link decisions. The multi-attribute weighting algorithm process is depicted in Algorithm 2.  
```
Input: Satellite constellation link network topology graph \(D\)
1. Output: The weight matrix \(W\) of the topology
2. begin
3 Initialize constellation network topology;
4 Calculate average link length \(al\);
5 Calculate average link duration \(dl\);
6 for\(V_{i}\in V\)do
7 Construct a multi-attribute decision matrix \(D\) for node \(v_{i}\in V\);
8\(r_{ij}=\frac{\frac{1}{\gamma_{ij}}}{\frac{\max\limits_{i\in N}\left(\frac{1}{ \gamma_{ij}}\right)}{x_{ij}}}=\frac{\frac{x_{ij}^{-}}{\gamma_{ij}}}{\gamma_{ij} };//\text{Compute normalized standard matrix }R\);
9\(p_{ij}=\frac{x_{ij}^{-}}{\sum_{i=1}^{n}x_{ij}}\);
10\(S_{j}=-k\sum_{i=1}^{n}p_{ij}\ln p_{ij}\);
11\(h_{j}=1-S_{j}\);
12\(q_{j}=\frac{h_{j}^{-}}{\sum_{i=1}^{n}\beta_{i}h_{j}}\);
13
14 end for
15 Determine Attribute weight vector \(Q\);
16\(W=D\times Q\);
17 return\(W\);
18
19 end for
```  
**Algorithm 2**Multi-Attribute Decision-making Weight Algorithm.
# Spatio-Temporal Distribution of Deep Convection Observed along the Trans-Mexican Volcanic Belt
###### Abstract  
Complex terrain features--in particular, environmental conditions, high population density and potential socio-economic damage--make the Trans-Mexican Volcanic Belt (TMVB) of particular interest regarding the study of deep convection and related severe weather. In this research, 10 years of Moderate-Resolution Imaging Spectroradiometer (MODIS) cloud observations are combined with Climate Hazards Group Infrared Precipitation with Station (CHIRPS) rainfall data to characterize the spatio-temporal distribution of deep convective clouds (DCCs) and their relationship to extreme precipitation. From monthly distributions, wet and dry phases are identified for cloud fraction, deep convective cloud frequency and convective precipitation. For both DCC and extreme precipitation events, the highest frequencies align just over the higher elevations of the TMVB. A clear relationship between DCCs and terrain features, indicating the important role of orography in the development of convective systems, is noticed. For three sub-regions, the observed distributions of deep convective cloud and extreme precipitation events are assessed in more detail. Each sub-region exhibits different local conditions, including terrain features, and are known to be influenced differently by emerging moisture fluxes from the Gulf of Mexico and the Pacific Ocean. The observed distinct spatio-temporal variabilities provide the first insights into the physical processes that control the convective development in the study area. A signal of the midsummer drought in Mexico (i.e., \"cancula\") is recognized using MODIS monthly mean cloud observations.  
deep convection; associated severe weather; precipitation; complex terrain; MODIS; CHIRPS +
Footnote †: journal: 23 March 2021
## 3 Results and Discussion  
### Stress Distribution on Rock Mass under Different Confining Pressure  
To study the stress distribution in rock mass under different working conditions, a cross-section of the rock mass is selected which is named Section B as shown in Figure 7. The distance between the section and the free surface, Plane A, is 400 mm. Take several equally spaced points along the \(x\)- and \(y\)-directions on the cross-section, denoted as \(A_{i}\) and \(B_{i}\), as shown in Figure 7b, to analyze the stress and strain at various positions under different boundary conditions and cutting conditions. The spacing between points in the \(x\)-direction is 470 mm, and the spacing between points in the \(y\)-direction is 300 mm. The distance from each point \(A_{i}\) in the \(x\)-direction to plane A is OA\({}_{i}\), and the distance from each point \(B_{i}\) in the \(y\)-direction to plane D is PB\({}_{i}\). The distances OA\({}_{i}\) and PB\({}_{i}\) can be found in Table 2.  
Stress distribution on rock mass under confining pressure can be found in Figures 8 and 9. Figure 8 shows the equivalent stress distribution along the y-direction on the rock mass. According to the figure, it can be seen that the equivalent stress close to the region where confining pressure is applied is the highest. As the distance between the sampling point  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**OA\({}_{1}\)/mm** & **OA\({}_{2}\)/mm** & **OA\({}_{3}\)/mm** & **OA\({}_{4}\)/mm** & **OA\({}_{5}\)/mm** & **OA\({}_{6}\)/mm** \\ \hline
470 & 940 & 1410 & 1880 & 2350 & 2820 \\ \hline PB\({}_{1}\)/mm & PB\({}_{2}\)/mm & PB\({}_{3}\)/mm & PB\({}_{4}\)/mm & PB\({}_{5}\)/mm & \\ \hline
300 & 600 & 900 & 1200 & 1500 & \\ \hline \end{tabular}
\end{table}
Table 2: The distance from the sampling point to planes A and D.  
and the region for applying the confining pressure increases, the equivalent stress gradually decreases.  
Figure 9 shows the equivalent stress distributions on the rock mass along the \(x\)-direction. Compared to the distribution along the \(y\)-direction, the differences of equivalent stress in the \(x\)-direction are relatively small. When the confining pressure is 5.0 MPa, the differences of equivalent stress in the \(x\)-direction were less than 5.0 MPa; When the confining pressure is 10.0 MPa, the differences of equivalent stress in the \(x\)-direction were also within 5.0 MPa; When the confining pressure is 15.0 MPa, the differences of equivalent stress increased, but they are still smaller than those differences along the \(y\)-direction.  
Figure 8: Stress distributions on the rock mass along the \(y\)-direction.  
Figure 7: Positions of Section B for studying stress distribution in the rock mass. (**a**) positions of Section B; (**b**) sampling points on Section B.  
Figure 9: Stress distributions on the rock mass along the \(x\)-direction.  
According to the comparisons of stress distribution in the \(x\)- and \(y\)-direction, it could be found that more obvious differences of stress occur in the \(y\)-direction.
# Research on Communication Stability of Inter-Cannonball Network Based on OPNET
## 1 Introduction
The communication network architecture can meet the fundamental requirements under the harsh environment. Figure 2 depicts the flowchart of the research procedure. The structure of this paper is as follows: Section 1 gives background information on inter-cannonball ad hoc networks. Section 2 gives the theoretical model of the inter-cannonball communication network and explores mathematically the node coordinates, communication distance, and communication parameters. To lay the groundwork for future study, Section 3 examines the features of routing protocols and picks the proper routing protocol for the inter-cannonball self-organizing network based on the characteristics of the network itself and simulation findings. In Section 4, the OPNET simulation program is introduced, the impact of node failure on routing is described, the final node topology model is shown, and the required simulation parameters are explained. In Section 5, the node movement speed and failure rate are used as variables to examine their effects on communication stability. Simulation analysis is conducted for harsh environments to determine the key technical indicators for the viability of inter-cannonball self-organizing network communication. Section 6 of this paper outlines the study outcomes and future work.
###### Abstract  
Current research widely acknowledges that the subcellular localization of mRNA is crucial for understanding its biological functions. However, current methods for mRNA subcellular localization based on k-mer frequency features may overlook the sequential information of the sequence, and a single encoding method may not adequately extract the sequence's features. This paper proposes a novel deep learning prediction method, CSpredR, specifically designed for predicting the subcellular localization of multi-site mRNAs. Unlike previous methods, CSpredR first employs k-mer to tokenize the mRNA sequences, then converts the tokenized sequences into de Bruijn graphs, thereby enabling a more precise capture of the structural information within the sequences. To mitigate the impact of lost sequential information and better capture sequence features, we combine word2 vec and fasttext models to extract the features of each node in the graph and retain the sequence order. They can encode the k-mer units in the sequence into word vectors, thus serving as the node feature vectors of the graph. In this way, each node in the graph is assigned a feature vector containing rich semantic information. Subsequently, we utilize multi-scale convolutional neural networks and bidirectional long short-term memory networks to capture sequence features, respectively, and fuse the results as input for a multi-head attention mechanism model. The information from these heads is integrated into the node representations, and finally, the attention-processed data are fed into an MLP (Multi-Layer Perceptron) for prediction tasks. Extensive experiments reveal that CSpredR achieves a 2% improvement over the best existing predictors, offering a more effective tool for mRNA subcellular localization prediction.  
mRNAs; subcellular localization; multi-label prediction; word2 vec method; fasttext method; graph construction; deep learning; sequence-based prediction +
Footnote †: journal: _Algorithms_
# An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
## 2 Domain Adaptation Techniques
### Evaluation of Domain Adaptation Techniques
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Paper** & **Year** & **Scope** & **Methods** & **Contributions** \\ \hline
[278] & 2019 & A comprehensive survey of transfer learning domain adaptation, and multi-source learning methods & Transfer learning, domain adaptation, and multi-source learning. & Provides a comprehensive overview of the three related fields of transfer learning, domain adaptation, and multi-source learning. & Discusses the challenges and future directions of each field. \\
[279] & 2020 & A comprehensive survey of domain adaptation methods for computer vision & Instance-based, feature-based, decision-based, generative, and meta-learning methods & - Provides a comprehensive overview of the field of domain adaptation in computer vision. \\
[280] & 2021 & A survey of recent advances in domain adaptation for visual recognition & Instance-based, feature-based, decision-based, generative, and meta-learning methods & - Provides a comprehensive overview of the challenges and future directions of domain adaptation for visual recognition. \\
[299] & 2022 & A survey of recent advances in domain adaptation for computer vision & Instance-based, feature-based, decision-based, generative, and meta-learning methods & - Provides a comprehensive overview of the field of domain adaptation in computer vision. \\
[295] & 2022 & A survey of recent advances in domain adaptation for computer vision & Instance-based, feature-based, decision-based, generative, and meta-learning methods & - Provides a comprehensive overview of the challenges and future directions of domain adaptation for computer vision. \\ \hline
[281] & 2022 & A systematic literature review of domain adaptation methods for computer vision & Instance-based, feature-based, decision-based, generative, and meta-learning methods & - Provides a systematic literature review of domain adaptation methods for computer vision. \\  & & & - identifies the most important research topics in domain adaptation for computer vision. \\
[281] & 2022 & A systematic literature review of domain adaptation methods for computer vision & Instance-based, feature-based, decision-based, generative, and meta-learning methods & - Provides a review of the challenges and future directions of domain adaptation for computer vision. \\ \hline
[284] & 2019 & A study of domain adaptation for object detection in video & Instance-based, feature-based, decision-based, generative, and meta-learning methods & - Can improve the performance of object detection in videos with limited labeled data. \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Publication surveys and trends.
# An Information Theoretic Approach to Privacy-Preserving Interpretable and Transferable Learning
### Goal and Aims  
Our goal is to develop a novel approach to trustworthy AI based on the hypothesis that information theory enables taking into account the privacy, interpretability, and transferability aspects of trustworthy AI principles during the development of machine learning and deep learning models by providing a way to study and optimize the inherent trade-offs. The aims focused on the development of our approach are the following:  
**Aim 1:**: To develop an information theoretic approach to privacy that enables the quantification of privacy leakage in terms of the mutual information between sensitive private data and the data released to the public without the availability of prior knowledge about data statistics (such as joint distributions of public and private variables).
**Aim 2:**: To develop an information theoretic criterion for evaluating the interpretability of a machine learning model in terms of the mutual information between non-interpretable model outputs/activations and corresponding interpretable parameters.
**Aim 3:**: To develop an information theoretic criterion for evaluating the transferability (of a machine learning model from source to target domain) in terms of the mutual information between source domain model outputs/activations and target domain model outputs/activations.
**Aim 4:**: To develop analytical approaches to machine and deep learning allowing for the quantification of model uncertainties.
**Aim 5:**: To develop a unified approach to \"privacy-preserving interpretable and transferable learning\" for an analytical optimization of privacy-interpretability-transferability trade-offs.  
Figure 1: An information theoretic unified approach to “privacy-preserving interpretable and transferable learning” for studying the privacy–interpretability–transferability trade-offs while addressing benefcience, non-maleficence, autonomy, justice, and explicability principles of TAI.
## 2 Seawater Channel Modelling Elements
### Scintillation and Turbulence  
Scintillation and turbulence can affect light propagation through the water column in underwater optical communication channels. While they are not typically classified as IOP or AOP in the same sense as parameters like absorption and scattering coefficients, they influence the apparent behaviour of light in the water. They can be considered in the context of AOP to some extent.  
While scintillation and turbulence are not typically listed as separate AOP, their effects on the apparent behaviour of the light in underwater environments are certainly relevant considerations when analysing and modelling the optical properties of the water column for underwater optical communication systems. They are essential factors to account for when assessing the performance and reliability of such systems. Here is how scintillation and turbulence relate to AOPs.
## 3 Investigation of Scalability and Impact of Virtualization on Performance of the System
### Testbed Description
#### 3.1.2 Device Simulator Software and Hardware  
In the proposed model devices send notifications to the middleware server. These devices were represented as actual software clients running in a cluster composed of 29 physical machines. Each has an Intel Xeon CPU E5345 @2.33 GHz and 8 GB RAM, running CentOS 6. Stations were connected with Gigabit Ethernet. Client software was written in Java as a multithreaded application. In total we simulated as many devices as 29 (number of machines in cluster) multiplied by the requested number of threads per machine. We began with 870 devices with a step of 290 devices to simulate load. Detailed overview of the client application model can be found in [33]. The main application loop performs:  
* access check--authorization,
* send notification periodically--randomly generated 36 bytes of data,
* random decision to disconnect (connection problem simulator).
## 3 Security Threats
### Reviewed Security Literatures  
In traditional centralized systems, components are grouped and organized so that every transaction or information exchange follows a defined route dictated by established policies or security controls. This arrangement helps minimize the potential scope of exploitation. In contrast, blockchain-based decentralized architectures lack well-defined boundaries among components, making it difficult to design and implement robust measures against adversarial manipulation. Furthermore, as noted in [18], authentication is the primary security control in blockchain systems, requiring open access to network nodes (or users) for autonomous operation. This structure limits the implementation of additional security measures beyond strong, cryptography-based authentication.  
Security vulnerability analysis is a critical area of blockchain research, with various studies offering different perspectives on potential attacks. For example, the work in [30] identifies six different types of attacks. In [27], blockchain security threats are broadly classified into four types, discussing eleven distinct attacks. Similarly, [PERSON] et al. [31] groups eighteen different attacks into eight categories, while [32] divides sixteen attack types into seven classes. In [33], the authors identify nine classes covering twenty-four different attacks, and [PERSON] et al. [28] categorizes blockchain attacks into three broad types encompassing twenty-two attacks. Moreover, [PERSON] at al. [18] documents twenty-three different types of attack, and [PERSON] et al. [29] discusses five different security threat types, under which thirty attack incidents are identified.  
Together, these studies underscore the complexity and diversity of security vulnerabilities in blockchain systems and highlight the pressing need for systematic analysis and effective countermeasures. Table 1 provides a summary of attack classifications by different works.
## 3 Experiments
### Image Evaluation  
Image evaluation consisted of qualitative and quantitative evaluation. Qualitative evaluation was achieved by observing the reconstructed images and the regions of interest (ROI). The feature similarity (FSIM) and the information weighted SSIM (IW-SSIM) were used for quantitative evaluation, which outperforms other evaluation methods on accuracy [46].  
In addition, the relative improvement ratios (\(relI\)) for the above two evaluation indexes are defined in Equation (14), where \(M_{FBP}\) and \(M\) represent the image evaluation indexes of the results from FBP and other methods.  
\[relI=\frac{M-M_{FBP}}{M_{FBP}} \tag{14}\]