esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview of Fog Data Analytics for IoT Applications
### Background  
Before we deeply understand the IoT paradigm, let us first understand the industry's evolution through the various revolutions taking place at stipulated time intervals, each time having a novel, promising, and disruptive concept upon which that revolution has been based. Figure 1 shows the timeline of manufacturing, starting from the first industrial revolution and ending at the current state of Industry 4.0. We start with Industry 1.0, the first industrial revolution, which took place around the late 18 th/early 19 th century. Initially, human resources were involved in the production processes. Due to this, there was a high chance of errors, and the industries needed to be more scalable. During this revolution, the mechanization of production took place, with the introduction of steam and water-propelled engines into the industrial realm, which resulted in a tremendous increase in efficiency and scaling. Small-scale industries could now serve large companies and increase their customer base. The next revolution, Industry 2.0, took place in the 19 th/early 20 th century. The main factor behind the second revolution was introducing and developing machines running on electrical energy and assembly line production techniques for increasing efficiency and bringing modularization into effect so that divisions of work or labor based on skill sets could be made possible. The mass production of products began during this era. The production efficiency increased significantly due to a further decrease in human intervention as humans were now required more for monitoring and maintenance purposes and less for production. The Industry 3.0 revolution started in the late 20 th century when electronic hardware such as transistors and integrated circuits were introduced to machines. These electronic devices needed software to operate them. The software also automated other tasks, including inventory management, optimum resource utilization, etc. The involvement of fully automated machines reduced the risk of errors and increased the production rate significantly. This era marked the era of computers and, lately, the Internet too.  
The next and most recent industrial revolution, which is still ongoing, is the Industry 4.0 revolution, which thrives on making machines or devices \"smart\" [9]. It adds more value to the Industry 3.0 generation by adding communication between the connected devices/machines to minimize human involvement in production. Industry 4.0 is a byproduct of the IoT paradigm and focuses on real-time decision making by the devices based on various external and internal stimuli.
# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
## 3 System Model and Problem Description
### System Model
#### 3.1.1 Fixed Device Model
In this context, \(sdin_{i}\) represents the input data volume for the task, \(sy_{i}\) quantifies the task load, and \(sdout_{i}\) specifies the output data volume. Referencing Figure 1 and the preceding description, it becomes clear that various potential candidate locations within the mining production line have been selected based on expert insights. These edge nodes are strategically deployed according to a scenario-specific node deployment plan. The selected locations must satisfy the dual objectives of minimizing the task response time for the equipment and optimizing the deployment costs associated with the nodes.
## 2 Materials and Methods
### Method Validation  
The analytical method was evaluated in terms of selectivity, linearity, sensitivity, accuracy, precision and recovery. Internal standard and deconjugation standards were included during the validation process. Analytical controls were included to identify any interference in the samples. Every analysis included a glassware blank (water processed without internal standard), reagent blank (reagents and solvents without matrix processed with internal standard), matrix blank (matrix low/without phytoestrogens, see Materials Section 2.1 for serum, Section 2.3 for food and Section 2.5 for urine, processed with internal standard), spiked matrix blank, and a solution containing the 16 phytoestrogens. A random real sample (women's biofluids and regional food) was processed in duplicate. Statistical analysis was carried on with NCSS 2001 statistical program.
###### Abstract  
The system reliability evaluation of a bridge structure is a complicated problem. Previous studies have commonly used approximate estimation methods, such as the wide bounds method and the narrow bounds method, but neither could obtain an accurate result. In recent years, the copula theory has been introduced into the system reliability evaluation, which can obtain more accurate results than the approximate methods. However, most studies simply construct binary copula functions to consider the joint failure of two failure modes. For a complex bridge structure composed of multiple components and failure modes, the joint failure of multiple failure modes needs to be considered. Before evaluating the system reliability, it is necessary to determine the failure criteria of the system. Different failure criteria for simply supported beam bridges have been proposed. However, there is no standard available to determine which failure criterion to choose, and the selection of failure criteria is ambiguous. In this paper, a novel method is proposed to evaluate the system reliability of a simply supported beam bridge by considering multiple failure criteria based on multivariate copulas and the analytic hierarchy process entropy weight (AHP-EW) method. The method first considers multiple failure criteria comprehensively and constructs multivariate copulas for the joint failure of multiple components in a bridge system reliability evaluation. The AHP-EW method is a comprehensive weighting method combining the analytic hierarchy process and entropy weight methods, which is used to establish the hierarchical analysis model between system reliability and multiple failure criteria. By considering the joint failure of multiple failure modes in the system reliability evaluation under a single failure criterion, multivariate copula functions were constructed. In order to verify the applicability of the proposed bridge system reliability method, a simply supported reinforced concrete (RC) hollow slab bridge composed of nine slab segments was selected as the numerical example. The results indicate that the method proposed in this paper could evaluate the bridge system reliability more comprehensively and reasonably.  
system reliability; copulas; failure criterion; analytic hierarchy process; entropy weight method; bridge structure
# Coordinate Descent for Variance-Component Models
## 3 Parameter-Expanded CD
### Linear Mixed Model Implementation
#### 3.3.1 QR Method  
The following QR factorization can be viewed as a data pre-processing step that allows all PX-CD and PXI-CD computations to be \(c\)-dimensional instead of \(n\)-dimensional. The QR factorization only needs to be computed once initially with a cost of \(O(cn^{2})\) operations. Let the QR factorization of \(\mathbf{Z}\in\mathbb{R}^{n\times c}\) be  
\[\mathbf{Z}=\overbrace{\begin{bmatrix}\mathbf{Q}_{[c]}&\mathbf{Q}_{[n-c]}\end{bmatrix}} ^{\mathbf{Q}}\begin{bmatrix}\mathbf{R}\\ \mathbf{0}\end{bmatrix},\quad\mathbf{R}=\begin{bmatrix}|&&|&&|\\ \mathbf{R}_{1}&\mathbf{R}_{2}&\cdots&\mathbf{R}_{m}\\ |&&|&&|\end{bmatrix},\]  
where \(\mathbf{R}\) is an \(c\times c\) upper triangular matrix, \(\mathbf{0}\) is an \((n-c)\times c\) zero matrix, \(\mathbf{Q}_{[c]}\) is an \(n\times c\) matrix, \(\mathbf{Q}_{[n-c]}\) is \(n\times(n-c)\), and \(\mathbf{Q}_{[c]}\) and \(\mathbf{Q}_{[n-c]}\) both have orthogonal columns. The matrix \(\mathbf{R}\) is partitioned such that the number of columns in \(\mathbf{R}_{i}\) is equal to the number of columns in \(\mathbf{Z}_{i}\). Let \(\widetilde{\mathbf{y}}=[\widetilde{\mathbf{y}}_{[c]},\widetilde{\mathbf{y}}_{[n-c]}]^{ \top}=\mathbf{Q}^{\top}\mathbf{y}\), where \(\widetilde{\mathbf{y}}_{[c]}\) are the first \(c\) elements of \(\widetilde{\mathbf{y}}\) and \(\widetilde{\mathbf{y}}_{[n-c]}\) are the last \(n-c\) elements of \(\widetilde{\mathbf{y}}\). Then,  
\[H(\mathbf{\Omega},\mathbf{C})=\widetilde{\mathbf{y}}_{[c]}^{\top}\widetilde{\mathbf{\Omega}}^ {-1}\widetilde{\mathbf{y}}_{[c]}+\sum_{i=0}^{m}\gamma_{i}\text{tr}(\widetilde{\mathbf{ C}}^{-1}\widetilde{\mathbf{V}}_{i})+\ln\det(\mathbf{C})-n+\alpha,\]  
where we define: \(\widetilde{\mathbf{V}}_{i}:=\mathbf{R}_{i}\mathbf{R}_{i}^{\top}\), \(\widetilde{\mathbf{V}}_{0}:=\mathbf{I}_{c}\), \(\widetilde{\mathbf{\Omega}}:=\sum_{i=0}^{m}\gamma_{i}\widetilde{\mathbf{V}}_{i}\), \(\widetilde{\mathbf{C}}:=\sum_{i=0}^{m}\widetilde{\gamma}_{i}\widetilde{\mathbf{V}}_{i}\) and \(\alpha:=\gamma_{0}\widetilde{\gamma_{0}}^{-1}(n-c)+\gamma_{0}^{-1}\widetilde{ \mathbf{y}}_{[n-c]}^{\top}\widetilde{\mathbf{y}}_{[n-c]}\). The details of this derivation are provided in the Appendix A. To implement PX-CD or PXI-CD after this transformation we run Algorithm 2 with inputs \(\widetilde{\mathbf{V}}_{0},\dots,\widetilde{\mathbf{V}}_{m}\in\mathbb{R}^{c\times c}\) and \(\widetilde{\mathbf{y}}_{[c]}\in\mathbb{R}^{c\times 1}\). In this simplification of \(H\), we have the additional term \(\alpha\), which is dependent on \(\gamma_{0}\). Therefore, when we update the parameter \(\gamma_{0}\) and implement [PERSON]'s method we must also add  
\[\frac{\partial\alpha}{\partial\gamma_{0}}=\widetilde{\gamma_{0}}^{-1}(n-c)- \gamma_{0}^{-2}\widetilde{\mathbf{y}}_{[n-c]}^{\top}\widetilde{\mathbf{y}}_{[n-c]} \quad\text{and}\quad\frac{\partial^{2}\alpha}{\partial\gamma_{0}^{2}}=2 \gamma_{0}^{-3}\widetilde{\mathbf{y}}_{[n-c]}^{\top}\widetilde{\mathbf{y}}_{[n-c]}\]  
to the corresponding derivatives derived for [PERSON]'s method in Section 3.1.
# Geometric Processing and Accuracy Verification of Zhuhai-1 Hyperspectral Satellites
## 2 Methods
### Geometric Calibration
\begin{array}{c}X_{s}\\ Y_{s}\\ Z_{s}\end{array} \right]_{\text{WGS84}}+m\mathbf{R}_{\text{J2000}}^{\text{WCS84}} \mathbf{R}_{body}^{\text{J2000}}\mathbf{R}_{u}\mathbf{R}_{cam}^{body}\left[ \begin{array}{c}a_{0}+a_{1}s+a_{2}s^{2}+\cdots+a_{i}s^{i}\\ b_{0}+b_{1}s+b_{2}s^{2}+\cdots+b_{j}s^{j}\\ 1\end{array} \right],i,j\leq 5 \tag{4}\]  
Considering the strong correlation between \(\mathbf{R}_{u}\) and \(a_{i}b_{j}\), the geometric calibration can be completed by using the iterative solution of \(\mathbf{R}_{u}\) and \(a_{i}b_{j}\) parameters so that the geometric calibration can be completed when the number of geometric control points of a single spectral segment is no less than \((3i+3j+6)/2\). As shown in Figure 2, the OHS camera has 32 spectral segments, which need to be geometrically calibrated. Since the spectral responses of the 32 spectral segments are quite different, if the control points are obtained by matching each spectral segment with the Digital Orthophoto Map (DOM) of the calibration field before geometric calibration, the calibration accuracy of each spectral segment will be inconsistent due to the difference in control accuracy. Considering that the imaging time interval of the adjacent spectral segments is short and the difference in the spectral response is relatively small, the registration accuracy between adjacent spectral segments will be higher than the accuracy between spectral segments and the DOM. Therefore, this paper proposes a recursive geometric calibration method for adjacent spectral segments to complete the geometric calibration of all spectral segments. The process involves the following steps:  
1. Using the DOM of the calibration field as a reference, select the spectral segment that is closest to the radiating characteristics of the DOM image as the starting spectral segment (assuming this is spectral segment N), use a high-precision matching algorithm to match N and DOM, obtain control points, and determine \(\mathbf{R}_{u}\) and \(a_{i}b_{j}\) of N using Equation (4);
2. According to the calibration parameters solved in step (1), construct the geometric positioning model of N using Equation (4);
3. Register spectral segment N-1 and spectral segment N to obtain the corresponding points \((x_{i}^{N-1},y_{i}^{N-1},x_{i}^{N},y_{i}^{N})\), and calculate the ground coordinates corresponding to the image points according to the geometric positioning model of N constructed in step (2). Taking \((x_{i}^{N-1},y_{i}^{N-1},X_{i}^{N},Y_{i}^{N},Z_{i}^{N})\) as the geometric control point of N-1, the offset matrix of N-1 is the same as that of N in step 1). Solve for \(a_{i}b_{j}\) of N-1 using Equation (4) and update the geometric positioning model of N-1;
4. Repeat step (3) until the geometric calibration of spectral segment 1 is completed;
5. For spectral segment N+1 to 32, follow steps (3) to (4) to recursively complete the geometric calibration.
# Characterization of Carrier Phase-Based Positioning in Real-World Jamming Conditions
## 1 Introduction
An L1/E1 PPD was used in a test setup off the north coast of Germany, and it was found that a tightly coupled GNSS/INS system could be used to mitigate the impact of jamming--mainly when jamming resulted in the receiver tracking too few satellites (<4) to determine a position. Further real-world tests have been conducted by [19; 20; 21; 22], but mostly with a focus on detection or localization of RFI, rather than the actual impact of jamming. A common theme for these investigations of jamming is the focus on code-based GNSS positioning. Carrier phase-based positioning has been investigated by [23] in a real-world experiment where several geodetic receivers were subjected to both static and kinematic L1/E1 band jamming. Positioning accuracy of the receivers in a real-time kinematic (RTK) positioning scenario was examined, and it was shown that, with sufficient jamming power, all receivers would eventually lose the ability to determine a position. In this paper, jamming incidents registered in the reference stations of the Danish TAPAS real-time kinematic (RTK) network are investigated. The purpose is to characterize the impact on current RTK/GNSS infrastructure, and thus carrier phase-based positioning accuracy of post-processed kinematic (PPK) solutions are examined. As in [8], this setup will use the jammed reference station as the base. In [8], a single GPS L1 jamming incident was evaluated on a short baseline. Now, this paper will further explore how jamming affects positioning based on carrier phase observations, and look into both single-baseline and network RTK positioning. PPK solutions will be evaluated for different baselines, using different constellations and frequencies to determine how positioning is affected in the various scenarios during jamming. Finally, code-based positioning will be compared to carrier phase-based positioning to evaluate which type of approach is most robust to jamming. The paper is structured in the following way. In Section 2, the background for the analyses is presented. The TAPAS network is introduced, and it is explained how data from the network may be used in order to assess carrier phase-based positioning during real world jamming events. In Section 3, the sum-of-squares (SoS) detector is briefly described. This algorithm is used to detect jamming incidents in archived RINEX observation files. Three selected jamming incidents found in the archived data are presented. Section 4 introduces six analyses performed for this paper. These analyses are based on PPK solutions made from data collected during the three previously introduced jamming incidents. The six following subsections contain comprehensive results and summaries for each of the analyses. In Section 5, the results of the analyses are discussed, and future work within this field is proposed. Finally, conclusions are presented in Section 6.
## 3 Results
### XRD Room Temperature Results
This was taken into account when performing the Rietveld fits but is not discussed further in this paper. It is remarkable that for materials obtained from PtIr crucible melts, despite using the same melting times and cooling rates as for ceramic crucible melts, a monoclinic phase (\(\alpha\)) (calculation based on ICDD no. 00-041-1449) was identified (which is consistent with the current state-of-the-art of Bi\({}_{2}\)O\({}_{3}\) phase stabilization in bulk materials [4]), instead of the preferred \(\delta\)-type phase. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Structure** & **Space** & **Lattice** & **Rwp** **Factor [\%]** \\ \hline Twin rollers, 1000 RPM & Fluorite & Fm3m & a = 5.537 & 7.37 \\ Ceramic crucible & Fluorite & Fm3m & a = 5.536 & 7.56 \\ \hline Free cooling & Fluorite & Fm3m & a = 5.536 & 6.56 \\ Ceramic crucible & Fluorite & & a = 5.849 & \\ Twin rollers, 1000 RPM & Monoclinic & P21/c & b = 8.166 & 8.83 \\ PtIr crucible & & & c = 7.510 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of calculations by the Rietveld method for samples melted at 900 \({}^{\circ}\)C.  
Figure 2: Representative phase matching performed by the Rietveld method for the sample melted at 1000 \({}^{\circ}\)C, quenched by the twin rollers technique at 1000 RPM. Blue points (+) are the experimental data; red—the background function; green—the fitted fluorite phase model. Turquoise bottom line is a difference plot and black plot shows the weighted difference between the observed and calculated diffraction runs. Based on the data presented, it can be concluded that the twin rollers technique was successfully used to produce glass-ceramic composites with nano-grains in a \(\delta\)-type phase that was stable at room temperature. As expected, the free cooling method produced polycrystalline materials that were devoid of a significant glass matrix volume inherent in the other samples with larger grains; however, still not exceeding 100 nm. Interestingly, also in these samples, a fluorite-like crystalline phase that was stable at room temperature was found, but with visible impurities that are not observed in composite glass-ceramic materials. Obtaining polycrystalline materials in alpha phase from PtIr crucible melts that are cooled by the twin rollers technique may indicate firstly that impurities from the ceramic crucible are introduced into the samples, as the crucible type is only significant difference in that case, and ceramic crucibles are more susceptible to degradation factors present during this process than PtIr ones; secondly, that these impurities may be necessary to stabilize the fluorite-like phase in Bi\({}_{2}\)O\({}_{3}\) based materials at room temperature.
# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 5 Methodology and Setups
### Simulation Scenarios  
Each scenario, devoid of attack, comprises three node types: the sink node, legitimate nodes, and malicious nodes. The proportion of malicious nodes ranges from 10% to 40% of the total node population, while legitimate nodes vary from 10 to 60. This range facilitates a thorough examination of the network's behavior across different entity proportions. Each scenario deploys four-group mobility models and strategically embeds an attacker node in the network. This standardized scenario design, consistent across environments with or without attacks, enables comparative analysis. Such an approach elucidates the influence of mobility models and the Hello Flood attack on attack efficacy and network resilience under varying conditions.
### Motivations and Contributions  
The increasing need to track, comprehend, and respond to technical and environmental acoustic signals in real time inspired this article. Sound is now a rich data source for intelligent machines and a carrier of information for human hearing, thanks to the rapid development of the IoT infrastructure and AI techniques. The increased availability of MEMS sensors, edge computing devices, and wireless networks allows for the widespread deployment of acoustic monitoring systems in urban planning, predictive maintenance, health monitoring, and public safety. However, a comprehensive overview of state-of-the-art solutions is necessary to identify trends, challenges, and research gaps, given the diversity of application domains, fragmentation of research efforts, and rapid technological advancements.  
This manuscript advances the field in the following ways. It begins by providing a cross-domain, organised overview of contemporary acoustic data collection and analysis techniques. Second, it identifies key technological enablers, including acoustic sensor  
Figure 1: Overview of acoustic sensing and analysis in IoT ecosystems.  
networks, edge and cloud computing paradigms, and artificial intelligence algorithms. Third, it showcases new applications and use cases with practical implications. The paper concludes by outlining potential avenues for future research and the possibility of further combining acoustic monitoring with cyber-physical system architectures and innovative city systems.  
This manuscript examines specific domain applications, such as medical auscultation, mechanical diagnostics, and environmental noise monitoring. This review provides a unified perspective on acoustic information in intelligent systems. Our work extends beyond summarising current methods, as it establishes conceptual connections between different use cases through the integration of AI and the IoT.  
The main contributions of this manuscript are as follows:  
* We provide a structured and comparative overview of acoustic sensing solutions across healthcare, industry, smart cities, and environmental monitoring.
* We identify common technological enablers and patterns, such as the role of MEMS sensors, edge computing, and neural network architectures.
* We highlight methodological gaps, particularly in how distributed systems handle uncertainty, interference, and real-time inference.
* We propose a conceptual synthesis that frames acoustic data as a unifying layer in cyber-physical systems, suggesting new directions for research into context-aware, adaptive, and resilient monitoring platforms. This broader view enables researchers and practitioners to recognise overlaps between currently fragmented efforts, paving the way for more holistic and interoperable solutions.
## 2 Theoretical Background
### CS Based on ISTA  
The ISTA algorithm is a gradient-based approach in which the differentiable gradient is projected at each iteration and then reduced to a specific value by a threshold. Given the compression measurement \(y\), ISTA achieves the reconstruction of the vibration signal by iterative solution (1) of gradient and signal. The signal to be reconstructed is updated by the \(x\)-shrinkage threshold operation, and the specific iterative formula is defined as follows:  
\[Z^{(k)}=y-\mathbf{A}x^{(k)}, \tag{2}\]  
\[x^{(k+1)}=\eta_{\lambda}\Big{(}x^{(k)}+\rho\mathbf{A}^{T}Z^{(k)}\Big{)}. \tag{3}\]  
where \(k\) is the number of iterations, \(Z^{(k)}\in~{}R~{}^{M\times 1}\) is the immediate reconstruction signal of the \(k\)th iteration, \(\rho\) is the step size, \(\eta_{\lambda}\) (-) is the soft threshold function. The specific expression formula is defined as follows:  
\[\left[\eta_{\lambda}(x)\right]_{i}\triangleq\text{sign}(x_{i})(|x_{i}|- \lambda_{i}), \tag{4}\]  
where \(\lambda_{i}\) is the threshold parameter, and \(\text{sign}(\cdot)\) is the sign function.  
The flow of the ISTA-based CS algorithm is shown in Figure 1. Where \(x_{0}\) is the initialization value, \(r_{k}\) is the residual error obtained during the calculation process, and \(\hat{x}\) is the final reconstruction signal value.  
Ref. [48] combines the above method with deep neural networks to propose the classic ISTA-NET method. In the following text, we will compare ISTA-NET with the proposed method in terms of overall performance.