esa-sceva/satcom-chunk-collection huggingface dataset:
# Progress of Process Monitoring for the Multi-Mode Process: A Review
## 4 Steady-State Mode Process Monitoring Methods
### Supervised Process Monitoring Method
#### 4.1.2 Overall Modeling Method  
The overall modeling method considers the multi-mode process as a whole and involves establishing a unified monitoring model for the multi-mode process and building a global monitoring statistical index. [PERSON] et al. [17] proposed a process monitoring method based on global and local models. This method involves learning a global model from historical data, treats multi-mode data as a whole and constructs a global monitoring statistical index to monitor the current process. The authors of [18] proposed an extended principal component analysis (PCA) algorithm by assuming that the covariance matrix of each independent mode had the same eigenvector quantum space, and solved the monitoring problem of the multi-mode process using a modeling method based on a common subspace. [PERSON] et al. [19; 20] combined a single-mode support vector data description (SVDD) algorithm with a nearest neighbor difference (NND) algorithm and an improved local neighbor standardization (ILNS) algorithm. After preprocessing the original data, eliminating the multi-model characteristics and converting them into single-mode data using NND, they constructed an SVDD monitoring model. The control limit of data statistics for monitoring and solving the problem was calculated, as the traditional SVDD algorithm was unable to deal with multi-mode data.  
The overall modeling method is simple and easy to implement because it does not need to model each mode separately. However, it can only be used when the data of each mode do not differ greatly and the non-linear relationship between variables is not strong as, otherwise, the overall modeling will be difficult to describe uniformly. Due to these limitations, the method is rarely used in practice.
## 2 Materials and Methods
### Accuracy Assessment  
To assess the performance of estimations employing various data and phenological identification portfolios, several common statistical measures of agreement were adopted in this study. These measures include the coefficient of determination (\(R^{2}\)), root-mean-square-error (RMSE), correlation, and standard deviation. \(R^{2}\), RMSE and correlation assess the agreement between the estimated growth stages and the observed or referenced growth stages, while standard deviation provides information about the precision of the estimations.  
For the comparison between different data sources, data-measured time periods, phenological modeling methods and transition characterization methods, the estimations from specific categories were sequentially evaluated using the aforementioned indices. As outlined in Section 2.2, estimations with in situ data were assessed based on site observations within CN-SQ station, while those with satellite data were evaluated using area observations, encompassing the adjacent agricultural landscape. The accuracy of estimation for each crop growth stage, especially for key growth stages (KGS), was evaluated based on the reconciled time-series phenological characteristics with the appropriate method.  
The accuracy assessment results were used to identify the better dataset, data-measured time period, phenological modeling method or transition characterization method with superior accuracy. Ideally, these components could contribute to forming a phenological estimation portfolio suitable for various scenarios. Finally, the estimations made by the portfolio were evaluated through the \(R^{2}\) and RMSE of linear regression with the corresponding observations, depending on the data utilized in the portfolio. It is important to note that the \(R^{2}\) and RMSE for the portfolio are based on the total accuracy of several growth stages, while the accuracy assessment for specific components is based on individual growth stages.
# Game theory-based Routing for Wireless Sensor Networks: A Comparative Survey
## 4 Comparison of the Routing Protocols
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Protocol** & **Technical strength** & \multicolumn{2}{c}{**Technical weakness**} \\ \hline \multirow{4}{*}{GTEB \({}^{1}\)[40]} & \multirow{2}{*}{\(\bullet\)} & Creates an energy balance and & \multirow{2}{*}{\(\bullet\)} & If the destination node is far away, this may lead to \\  & & increases network & & energy wastage; \\  & & lifetime significantly; & & The protocol is more energy efficient when there are \\  & & Intelligent method to evenly & & more nodes in a subregion. If there is only one node \\  & & distribute network traffic in & in a subregion, the data packet is to be transmitted \\  & & a WSN. & & directly to the destination. This is a serious flaw in \\ \hline \multirow{3}{*}{EEREG \({}^{2}\)[41]} & \multirow{2}{*}{\(\bullet\)} & Cluster size is & \multirow{2}{*}{\(\bullet\)} & Network lifetime is considered as the time taken for \\  & & maintained optimally; & & the first node to dies in the network. This is \\  & & Avoidance of the low energy & & impractical because WSNs may still remain fully \\  & & nodes becoming a CH. & & functional even after the death of the first node. \\ \hline \multirow{4}{*}{GEEC \({}^{3}\)[42]} & \multirow{2}{*}{\(\bullet\)} & Introduces rationality to the low & \multirow{2}{*}{\(\bullet\)} & \multirow{2}{*}{Energy threshold determination procedure can be \\  & & energy-offs were considered & & elaborated for the betterment of the protocol. \\  & & differently for nodes with higher & & \\  & and lower energy levels. & & & \\ \hline \multirow{3}{*}{ERG \({}^{4}\)[45]} & \multirow{3}{*}{\(\bullet\)} & Players can not only learn & \multirow{2}{*}{\(\bullet\)} & One hop transmissions among the nodes are \\  & & strategies, but can also & & discouraged by imposing lower pay-off. Multi-hop \\  & & modify them. & & transmissions can be energy consuming. \\ \hline \multirow{4}{*}{GBRA \({}^{5}\)[43]} & \multirow{3}{*}{\(\bullet\)} & Good mechanism to stop & \multirow{2}{*}{\(\bullet\)} & No mathematical formulation or definition regarding \\  & & packet drops; & & who can be the neighbors of a sending node \\ \cline{1-1}  & & Mechanism to avoid malicious & & was presented. \\ \cline{1-1}  & & nodes from routing path. & & \\ \hline \multicolumn{4}{l}{\({}^{1}\) Game Theoretic Energy Balanced Routing; \({}^{2}\) Energy-Efficient Routing Protocol based on Evolutionary Game; \({}^{3}\)} \\ \multicolumn{4}{l}{Game-Theory-Based Energy-Efficient Clustering; \({}^{4}\) Evolutionary Routing Game; \({}^{5}\) Game-Theory-Based Routing Algorithm.} \\ \end{tabular}
\end{table} 
Table 6: Comparison of routing protocols based on evolutionary and reputation-based games in terms of technical strengths and weaknesses.
## 3 Methodology
### Overview  
DAPONet achieves efficient and accurate feature extraction and analysis through the combination of multi-layer and multi-module. The whole network consists of two main parts, Backbone and Neck, which undertake the tasks of initial feature extraction and further fusion and output, respectively, and finally the Head part generates the specific detection results.  
As shown in Figure 1, in the Backbone part of DAPONet, the network extracts and processes key features from the input image layer-by-layer by combining multiple CPDA modules and MCD modules. The CPDA module effectively captures multi-scale features through partial overparameterization techniques to enhance the model representation. The MCD module further compresses the size of the feature map through feature downsampling to save computational resources for subsequent processing. In the Neck section, DAPONet uses multiple CPDA and MCD modules to perform multiple up- and down-sampling and feature fusion on the features extracted from the Backbone to ensure that the model can accurately analyze the image content at different levels, and the feature concatenation and up-sampling operations in the Neck further integrate the features from different scales to generate a more detailed high-level feature map. This process ensures that the network is able to process the features at the same time. This process ensures that the network is able to handle different levels of features simultaneously, providing strong support for object detection in complex scenarios. Finally, the Head part of DAPONet transforms the high-level feature maps from the Neck into concrete detection results. Through the design of the multi-scale Head, the network is able to simultaneously process feature maps from different scales to generate detection results such as the location and category of the target.
# Decentralized Multi-Agent Control of a Manipulator in Continuous Task Learning
## 3 Methodology  
### Preliminaries  
In this paper, the RL problem is modeled as a discrete time-continuous Markov decision process (MDP). Markov decision processes are the mathematical foundation for RL [3; 34]. A Markov decision process is a tuple \((S,P,R,A)\), where \(S\) is a state set, \(P\) is the transition probability matrix, \(R\) is a reward function of the agent based on the current state and the chosen action, and \(A\) is a finite set of actions available to the agent. The objective of the agent is to learn a policy \(\pi\) that maximizes the expected return, whilst achieving the maximum cumulative reward. A policy \(\pi\) represents the description of behaviors of an agent. A stationary policy \(\pi:S\to A\) is a probability distribution over actions to be taken in each state. A deterministic policy has a probability of one for some action in a state.  
Reinforcement learning methods can be divided into three main categories:  
* Value-based methods, which learn a value function that is then used to derive a policy;
* Policy-based methods, which have a parameterized policy and directly search for the optimal policy;
* Actor-critic methods, which learn both the value function and the policy. The role of the _critic_ is to judge the actions taken by the _actor_ on the basis of the learned value function. The actor then updates its policy parameters based on the critic's feedback [35].  
In this paper, the actor-critic method is used to learn both the policy and the value function. Based on the way the algorithms generate and use experience data, a further classification can be made into _On-policy_ and _Off-policy_ algorithms. On-policy methods evaluate the same policy that is used to select actions; whereas, off-policy methods learn the target policy that is different from the behavioral policy used to generate experience. In this paper, two main algorithms are used, the on-policy PPO algorithm and the off-policy SAC algorithm. Further information on these algorithms can be found in [36; 37].
# Semantic Segmentation of Aerial Imagery Using U-Net with Self-Attention and Separable Convolutions
## 3 Proposed Method  
### Self-Attention Mechanism  
In computer vision, self-attention acts as a guide for models to focus on relevant visual information. By allowing pixels or features to communicate and adapt their importance dynamically, self-attention enhances the model's ability to understand images, recognize objects, and extract meaningful patterns as can be seen in Figure 1. This adaptive and context-aware mechanism contributes to the overall effectiveness of computer vision models in various tasks. We can think of an image as a sequence of pixels. Self-attention helps each pixel consider the relevance of other pixels in the same image. This is akin to pixels \"talking\" to each other, focusing on what is important nearby. Self-attention allows a computer vision model to be flexible. It can pay attention to both the big picture (entire image) and small details (local features) based on what is important for the task at hand. In scenarios where images are part of a time series, self-attention enables the fusion of temporal and spatial information. This is beneficial for tasks like tracking changes in landscapes over time [23].  
\[\text{Attention}\left(\mathbf{Q},\mathbf{K},\mathbf{V}\right)=\mathbf{ softmax}\left(\frac{\mathbf{Q}\cdot\mathbf{K}^{T}}{\sqrt{d}}\right)\cdot\mathbf{V} \tag{1}\]  
Figure 1: Self-attention mechanism representation in terms of computer vision.
# Study of Surface Defect Detection Techniques in Grinding of SiCp/Al Composites
## 5 Conclusions  
In this paper, a surface defect detection technique for grinding a SiCp/Al composite has been developed, and a surface quality evaluation criterion based on defect characteristic parameters has been proposed. The conclusions of this paper are as follows:  
1. A deep learning-based surface defect detection model is developed for grinding SiCp/Al composites. After training the defect detection model, the average accuracy of the defect detection is 97.4%, and the average detection time for a single image is 47 ms, which meets the requirements for accurate and fast defect detection.
2. OpenCV is used to perform a series of image processing steps such as cropping, edge detection, and morphological operations on the detected defect regions, which achieves the accurate extraction of the characteristic parameters of surface defects. A surface quality evaluation method using the number of defects and the total area of defects in the same machining area as indicators was established.
3. The number and total area of surface defects in SiCp/Al composite grinding increase with the increasing feed rate, tool diameter, and abrasive size, and they decrease with the increasing spindle speed and ultrasonic vibration amplitude. The grinding depth has little effect on the number of surface defects and the total area, as it is less than 20 um. When it is greater than 20 um, an increase in the grinding depth leads to a rapid deterioration in the machined surface quality.  
Figure 12: Influences of tool parameters on \(A_{\mathrm{t}}\) and \(N_{\mathrm{d}}\): (**a**) tool diameter; (**b**) abrasive size.  
**Author Contributions:** Conceptualization, H.W.; methodology, H.W. and H.Z.; software, H.W.; validation, H.Z.; formal analysis, C.G.; investigation, S.B.; resources, H.L.; experiments, H.W.; writing--original draft preparation, H.W.; writing--review and editing, H.W. and M.Z.; supervision, M.Z.; funding acquisition, M.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 51975153.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data used in this research can be provided upon request.  
**Acknowledgments:** Thanks are due to [PERSON] for assistance with the experiments.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 2 Methods
### Limiting Procedures
#### 2.2.2 The EigenWeno Limiter  
For a conservation law system (Equation (1)), the value of the conservative variable \(\underline{U}\) is a column matrix, for which the following limiter is recommended. The first step is to obtain the \(\
u\)-split form of Equation (1) on the interface shared by \(E_{i}\) and its neighbor \(E_{k}\) (for each \(k\in K_{i}\)):  
\[\partial_{t}\underline{U}+\partial_{\
u}\underline{F}^{\
u}=\underline{Q}, \tag{11}\]  
where \(\partial_{\
u}:=\vec{v}\cdot\
abla\) is the directional derivative operator and \(\underline{F}^{\
u}:=\vec{v}\cdot\vec{\underline{F}}\) is the normal flux (as in Equation (3)). It can be treated as a one-dimensional conservation law system whose flux Jacobian can be approximated by the average value of \(\underline{U}\):  
\[\underline{A}^{\
u}=\left.\frac{\partial\underline{F}^{\
u}}{\partial \underline{U}}\right|_{\langle\underline{U}\rangle_{i}}. \tag{12}\]  
For a hyperbolic system, it is guaranteed that the \(K\times K\) matrix \(\underline{A}^{\
u}\) has \(K\) real eigenvalues and has the eigenvalue decomposition  
\[\underline{A}^{\
u}=\underline{R}\begin{bmatrix}\lambda_{1}&&\\ &\ddots&\\ &&\lambda_{K}\end{bmatrix}\underline{R}^{-1},\qquad\underline{R}:=\begin{bmatrix} r_{1}&\cdots&r_{K}\end{bmatrix}, \tag{13}\]  
where \(r_{\underline{R}}\) (the \(k\)-th column of \(\underline{R}\)) is an eigenvector corresponding to the \(k\)-th eigenvalue \(\lambda_{k}\) (for \(k=1,\ldots,K\)). Once obtaining the \(\underline{R}\), the original conservative variable \(\underline{U}\) can be projected into the space spanned by the \(\underline{r}\)'s, which gives the characteristic variable  
\[\underline{V}:=\underline{R}^{-1}\,\underline{U}, \tag{14}\]The next step is then to apply the ScalarWeno limiter (Equations (6)-(10)) on each scalar component of \(\underline{V}\), which gives \(\underline{V}^{\text{new}}\). After obtaining the reconstructed characteristic variable \(\underline{V}^{\text{new}}\), it can be turned back into the original conservative variable  
\[\underline{U}_{k\to i}^{\text{new}}\coloneqq\underline{R}\,\underline{V}^{ \text{new}}, \tag{15}\]  
in which the subscript \(k\to i\) means that it is a function defined on \(E_{i}\), which is constructed with the help of \(E_{k}\). The final step is to weight these reconstructed conservative variables by the measure of the corresponding adjacent element:  
\[\underline{U}_{i}^{\text{new}}\coloneqq\frac{\sum_{k\in K_{i}}\underline{U}_ {k\to i}^{\text{new}}|E_{k}|}{\sum_{k\in K_{i}}|E_{k}|}. \tag{16}\]  
Since the eigenvalue decomposition (Equation (13)) plays a central role in this limiting procedure, we would like to name it as the EigenWeno limiter.
# Real-Time Precise Point Positioning during Outages of the PPP-B2b Service
###### Abstract  
The precise point positioning service on B2b signal (PPP-B2b) is a real-time decimeter-level positioning service provided by the BeiDou-3 Global Navigation Satellite System (BDS-3). The service provides users with high-precision orbit and clock corrections through geostationary orbit (GEO) satellites, which means that the PPP-B2b service would be unusable if GEO satellites were blocked. In this study, the performance of PPP-B2b corrections and real-time positioning results during outages of the PPP-B2b service are comprehensively investigated. The results showed that PPP can achieve satisfactory accuracy during outages of the PPP-B2b service by extending the nominal validity of the received PPP-B2b corrections. After extending the PPP-B2b corrections for 10 min, for BDS-3 medium earth orbit (MEO) satellites, the mean root-mean-square error (RMSE) values of the extended orbit were 0.16 m, 0.26 m, and 0.23 m in the radial, along-, and cross-track directions, respectively. The accuracy of the BDS-3 inclined geostationary orbit (IGSO) satellites was slightly worse than that of the BDS-3 MEO satellites; for Global Positioning System (GPS) satellites, the mean RMSE values of the extended orbit were 0.11 m, 0.45 m, and 0.33 m in the radial, along-, and cross-track directions, respectively. In terms of the extended clock, the mean standard deviation (STD) reached 0.17 ns, 0.20 ns, and 0.22 ns after 10 min for the BDS-3 MEO, BDS-3 IGSO, and GPS satellites, respectively. The positioning performance maintained with the extended corrections during the PPP-B2b service outage was evaluated based on five stations in and around China. Our experiments showed that, as long as the interruption time does not exceed 10 min, the real-time positioning with extended PPP-B2b corrections can achieve a comparable accuracy with that obtained following PPP-B2b correction.  
BDS-3 PPP-B2b service; corrections extension; outage; positioning Article Real-Time Precise Point Positioning PPP-B2b Service 2023 15 784. [[https://doi.org/10.3390/rs15030784](https://doi.org/10.3390/rs15030784)]([https://doi.org/10.3390/rs15030784](https://doi.org/10.3390/rs15030784))
## 3 Information Centric Networking (ICN)
### Content Naming  
Content-naming has a direct influence on the other aspects of ICN-based networks, such as routing and forwarding [49]. As ICN changes the focus of traditional networks from host-based to content-based, the data being requested is considered to be an object and given a name that helps users to identify it. Named data objects (NDOs) are the core of addressing and forwarding in the ICN network. NDOs are addressable data units in ICN networks that can represent a collection of bytes or a piece of information. Every NDO, including documents, videos, music, and photos must have a globally unique name.  
Four types of naming techniques are used in the ICN literature: hierarchical, flat, attribute-based and hybrid.  
Figure 5: Typical interest and data communication in NDN communication.  
* Hierarchical names are given in plain, human-readable components, separated by \"/\". CCN and NDN follow this naming convention.
* Flat names are self-certifying and used in ICN architectures such as MobilityFirst [50] and DONA [45]. The content name has two parts of the form \(P:L\) and is associated with metadata.
* The attribute-based technique identifies content by attribute-value pairs (AVP). Contents are requested by applying constraints (also called predicates) to the attributes.
* Hybrid-naming combines the previous three techniques.
## 4 Analysis of Experimental Results
### Quantitative Analysis of ISS Feature Points
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Radius of Spherical Neighborhood of Covariance Matrix/m**} & \multicolumn{3}{c}{**Number of ISS Eigenpoints**} & \multirow{2}{*}{**Run Time/s Cloud**} & \multirow{2}{*}{**RMSE/mm**} \\ \cline{2-2} \cline{4-4}  & **Source Point Cloud** & & & \\ \hline
4.605 & 188 & 996 & 0.124 & – \\
4.223 & 332 & 1502 & 0.265 & 10.728 \\
3.93 & 493 & 2000 & 0.507 & 8.242 \\
3.72 & 714 & 2500 & 1.07 & 5.865 \\
3.535 & 943 & 3003 & 2.021 & 4.934 \\
3.335 & 1152 & 3497 & 2.946 & 3.972 \\
3.16 & 1460 & 3998 & 4.59 & 2.438 \\
2.97 & 1847 & 4496 & 6.57 & 1.38 \\
2.8 & 22,71 & 4998 & 9.461 & 1.272 \\ \hline \end{tabular}
\end{table} 
Table 2: Impact of the number of feature points on coarse alignment.
# Business Intelligence through Machine Learning from Satellite Remote Sensing Data
## 2 Satellite Systems in Remote Sensing  
An artificial satellite is an item that is placed in orbit, usually with the use of a rocket, to be used in a variety of fields. It is typically equipped with an antenna that enables communication with a space station and a source of power (i.e., battery, solar panel, etc.), and it can operate individually or within a larger system. Their positioning varies as they are placed at different heights and follow different paths/orbits depending on their use case. Geostationary orbits (GEO), Low Earth Orbit (LEO) and Medium Earth Orbit (MEO), Polar Orbit and Sun-Synchronous Orbit (SSO), Transfer Orbit, Geostationary Transfer Orbit (GTO), and Lagrange points (L-points) are some of the common satellite orbits. LEO is relatively close to Earth, with an altitude of fewer than 1000 km, and Polar Orbit reaches down to 160 km, while GEO is where the satellite circles above the equator.  
Since the launch of the first satellite in 1957, they have been used for various purposes, which can be categorized into communications, navigation, and weather satellites but also as military or civilian Earth observation satellites.  
Satellites have a variety of features, the comprehension of which is important to select the best one for our use case. Therefore, next, we briefly present the main characteristics of the compounds that can impact our study and the accuracy of our analysis.