esa-sceva/satcom-chunk-collection huggingface dataset:
# Information Fusion for Multi-Source Material Data: Progress and Challenges
## 2 Background
### Material Data Integration and Storage  
Material data from multiple data sources are likely inconsistent and have different data granularities and patterns. Therefore, material data integration is facing some difficulties. The following are some questions that need answering in material data integration:  
* How to establish a unified data schema and data model for material data?
* Does heterogeneous data require multiple model representations?
* How to solve the conflicts in multi-source data?
* How to accurately and effectively find the correct data?
* How to semantically identify ontology in material data?  
Material data storage is also a key issue in material data integration and fusion. Traditional, relational database systems cannot meet the requirements well when storing complex material data. They are especially inefficient for storing functional graphs and description documents. Relational database systems are used to store structural data in terms of the relational model, but material data include complex document data, recording reaction processes, performance test results, interaction between materials, etc. Material data also record how material properties vary with various conditions with functional graphs, as well as non-text data, such as material shape images, material production process images and molecular structure images. All of these data elements are difficult to be stored in relational databases.
# Leakage Detection Method of Coal Mine Power Supply System Based on 3 AC
); [EMAIL_ADDRESS] (J.C.)1 School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China; [EMAIL_ADDRESS] (Y.W. ); bql210040301 the DC component flowing through the detection circuit, influenced by factors such as modulation depth and carrier frequency, leading to increased measurement errors for insulation resistance [7; 8; 9; 10]. Therefore, there is an urgent need to develop new leakage protection methods to meet the unique requirements of long-distance power supply lines, including large, distributed capacitors, wide-voltage fluctuation ranges, high harmonic content, and significant differences in zero-sequence voltages at various fault locations [11]. In practical applications, power supply systems in underground coal mines must ensure both stable and safe power transmission while also coping with complex geological conditions and harsh working environments. To meet these needs, this paper proposes a novel leakage detection and protection method named 3 AC. This method involves adding a set of asymmetric three-phase capacitors at the head of the line to generate a continuous zero-sequence current that is monitored in real time. Even in the event of a three-phase leakage fault, the zero-sequence current can still be detected, allowing accurate measurement of the system's insulation resistance and identification of specific fault types [12]. This approach not only enhances the accuracy of leakage protection but also significantly improves the reliability and safety of the entire power supply system, ensuring safe and efficient coal mine operations [13; 14]. By introducing the 3 AC leakage detection and protection method, this paper aims to overcome the limitations of existing technologies and provide a more robust and efficient solution to address the complex challenges of long-distance power supply systems in underground coal mines. This innovation not only enhances the safety of mining operations but also offers valuable reference experience for similar application scenarios [15].
# Improved Bootstrap Method Based on RBF Neural Network for Reliability Assessment
## 2 Weibull Distribution  
The Weibull distribution is extensively used in the field of reliability engineering. It applies to several types of atypical electronic products [15] and can adequately describe the different cases of bathtub curves. Moreover, it can simplify the calculation steps using a transformed functional form.  
Several studies on the Weibull distribution have shown that, if a localized failure results in the malfunctioning of the entire system, the life of such a system generally obeys the Weibull distribution.  
The probability density function (PDF) of the two-parameter Weibull distribution is the following:  
\[f(t)=\frac{m}{\eta}\bigg{(}\frac{t}{\eta}\bigg{)}^{m-1}\exp\bigg{(}-\bigg{(} \frac{t}{\eta}\bigg{)}^{m}\bigg{)}, \tag{1}\]where \(m\) is the shape parameter, \(\eta\) is the scale parameter, and \(t\) is the time.  
The cumulative distribution function is as follows:  
\[F(t)=1-\exp\biggl{(}-\biggl{(}\frac{t}{\eta}\biggr{)}^{m}\biggr{)}. \tag{2}\]  
The reliability function is the following:  
\[R(t)=1-F(t)=\exp\biggl{(}-\biggl{(}\frac{t}{\eta}\biggr{)}^{m}\biggr{)}. \tag{3}\]  
The lapse rate function is as follows:  
\[\lambda(t)=\frac{f(t)}{R(t)}=\frac{m}{\eta}\biggl{(}\frac{t}{\eta}\biggr{)}^{m -1}. \tag{4}\]  
The shape parameter \(m\) has a strong influence on the Weibull distribution.  
Specifically, when \(m<1\), the density function \(f(t)\) and the failure rate function \(\lambda(t)\) are both decreasing functions, suggesting early failure.  
When \(m=1\), the Weibull distribution is exponential.  
Finally, when \(m>1\), the density function curve has a single peak, and, when \(m\geq 3\), the density function curve has a single symmetrical peak, resembling a normal distribution. The failure rate \(\lambda(t)\) is an increasing function, which suggests wear failure of the product. The density functional curves for different shape parameters \(m\)(\(n\) fixed) are shown in Figure 1.
# Extended Reality: Types and Applications
## 5 Software in XR
### Software Classification by Facilities and XR Toolkits
The distinctive feature of this engine is high-quality, photorealistic visualization, which is sometimes used in the creation of films, but the main application is games. The platform is quite flexible but requires significant knowledge of C++ to be used for non-gaming purposes. It has great hardware and community support [86]. Mixed Reality Toolkit (MRTK) is a set of tools developed by Microsoft that was created to accelerate the creation of AR applications for mixed reality headsets. It uses the game engine Unity as a base. MRTK is customized for the Microsoft HoloLens headset but also supports other headsets [88]. Magic Leap SDK (MLSDK) is used to speed up development of AR applications for Magic Leap headset only [95]. OpenXR is a free, open-source standard developed by the Khronos Group for creating high-performance applications for VR and AR. It aimed to simplify and speed up the development of apps by providing a unified interface and code for various hardware platforms. There is also a plugin available for Unity [90]. RViz is open-source visualization software that is a part of the ROS ecosystem. Using this tool, it is possible to simulate robot movements in virtual environments. It is very flexible and has a lot of plugins, including AR/VR plugins [93]. Having analyzed the existing development tools in the field of augmented reality, it is evident that not only the foundational game engine is important, but also the extensions that significantly speed up and simplify the development of such applications. Each solution has its own direction and is customized for certain hardware or operating systems. Therefore, many different extensions and the openness of some solutions allows them to be used not only for games and entertainment, but also in any areas of activity, including robotics, medicine, navigation, etc. After performing the analysis, there is one outcome. Specifically, the system that has good compatibility with robots has software packages, equipped with object tracking features. Other parameters, including the operation system and SDK integration support, did not reveal high compatibility issue.
## 4 Results
### Barriers and Enablers Identified for the Production Link
Although empirical studies present evidence of the effect of experience on the adoption of technologies [119], it is also evident that younger farmers have less risk aversion and are more willing to learn about digital technologies such as precision agriculture [128]. Other authors have associated experience and skills with attitudes and beliefs that can be considered when making decisions about the adoption of technologies [82]. Regarding ease of use, a factor associated with technology acceptance models that has been widely analyzed in the literature, it is evident that demonstration and networking processes facilitate adoption processes in the agricultural sector. This factor is associated with perceived usefulness, which has a direct impact on ease of use, thus positively influencing the adoption of technologies; however, it shows an inverse (negative) relationship with the complexity factor, which acts as a barrier [93]. Other research shows evidence of how the social context can play a role in the motivation towards technology adoption, especially the relationship between neighbors, which could imply the generation of knowledge spillovers through demonstrations and visibility of the technology [123], reducing the cost of learning [95; 129]. Likewise, some research has been aimed at demonstrating how agricultural cooperies play a role in innovation processes, acting as intermediaries for the promotion of technology adoption [123]. This social influence derived from the opinions generated by other users is a determinant of technology adoption [4], as well as advertising through digital media [123]. Finally, regarding formal and informal sources for access to information, some research has shown that formal sources increase perceived utility and, therefore, adoption. In contrast, informal sources do not reduce perceived utility [130]. In general terms, the relationship with actors such as suppliers, consultants, research institutions, and early adopters generates a key benefit primarily related to exchanging information and creating knowledge [127].
# Semantic Features Based N-Best Rescoring Methods for Automatic Speech Recognition
## 5 Experiments  
### Experimental Setup  
In this study the proposed rescoring method is carried out on the WSJ speech recognition task ([[https://catalog.ldc.upenn.edu/docs/LDC94S13A/wsj1.txt](https://catalog.ldc.upenn.edu/docs/LDC94S13A/wsj1.txt)]([https://catalog.ldc.upenn.edu/docs/LDC94S13A/wsj1.txt](https://catalog.ldc.upenn.edu/docs/LDC94S13A/wsj1.txt))). The dataset contains an 80-hour training set and a text corpus which has 37M tokens, and 162K words for language modeling. We use Kaldi [26] for acoustic model training and decoding. The acoustic model is a time-delay neural network (TDNN) with 6 layers, each layer with 250 hidden units. The acoustic model trained by the cross-entropy (CE) loss. The first-pass decode uses a 3-gram language model with Kneser-Ney (K-N) smoothing by srilm [27]. The large text corpus is used for n-gram and the small RNN language models and the training of the semantic models for LDA and GloVe. In this study, dev93 and eval92 are used equally to evaluate the proposed methods.  
The number of hypotheses used for re-ranking is 100 for both eval92 and dev93. We follow the kaldi's recipe to enumerate from 9 to 20 to find the best \(\lambda\) in Equation (19). The best choice for eval92 is 13. Both 12 and 13 yield the best results for dev93. We use 13 for both testsets in rescoring.
## 3 Cable Models and Methodology
### Consistent Cross-Sectional Area and Segmentation  
* Single-Segment (2D): The cable cross-section is one contiguous metallic cluster with about 421 hexagonal conductor strings. Each hex is sized so that the total copper area remains near 2711 mm\({}^{2}\). Figure 4a shows the layout for the single-segment 2D model;  
Figure 4: 2D segmented cable schematics for Single-segment (**a**), Three-segment (**b**) and Five-segment (**c**) arrangements; with all values in meter.  
Three-Segment (2D): The same total area is subdivided into three segments, each separated by a small air gap. Approximately 143 hexagons per segment (total 429). Figure 4b illustrates this three-segment cross-section;
* Five-Segment (2D): Further subdivision into five segments, each holding around 85 hexagons (total 425). All five segments are isolated by small gaps. Figure 4c depicts the five-segment cross-section.
# A Simplified Matrix Formulation for Sensitivity Analysis of Hidden Markov Models
## 4 Sensitivity Analysis of Smoothing Probability
### Observation Parameter Variation
#### 4.3.1 Algorithm Implementation  
In this algorithm, \(e\) is the sequence of observation and \(\theta=\theta_{o}\) and it solves the recursive backward probability sensitivity function Equation (10) in the backward matrices \(B^{k}\) for \(k=T,\cdots,t\) using SMF. Once the observation matrix is decomposed into the independent \(\tilde{O}\) and dependent \(\widehat{O}\) observation matrices on \(\theta_{o}\), the coefficients of the sensitivity function \(p(\mathbf{y}_{\mathbf{e}}^{t+1:T}|x_{v}^{t})(\theta_{o})\) are computed by filling the backward matrices \(B^{k}\) as follows: \(B^{T}\) is initialized as a vector of ones for \(n\) hidden states.  
\[B^{T}=ones(n,1)\]  
The remaining matrices \(B^{k}\) of size \(n\times k,k=T-1\leq k\leq t\), are computed by filling them as follows: Once the independent \(\tilde{O}^{k+1}\) and dependent \(\widehat{\tilde{O}}^{k+1}\) observation matrices on \(\theta_{o}\) is represented as a diagonal matrix, two temporary matrices \(Btmp_{1}\) and \(Btmp_{2}\) are used for computing the coefficients of the sensitivity function as previously explained. \(Btmp_{1}\) is computed as the matrix multiplication of \(A\), \(\tilde{O}^{k+1}\) and \(B^{k+1}\). A zero vector of size \(n\) is appended at its end. \(B^{k+1}\) represents the recursion in the backward probability. In the same way \(Btmp_{2}\) is computed as the matrix multiplication of \(A\), \(\widehat{O}^{k+1}\) and \(B^{k+1}\) and a zero vector of size \(n\) is appended to the front. Then \(B^{k}\) is set as the sum of \(Btmp_{1}\) and \(Btmp_{2}\),  
\[B^{k}=Btmp_{1}+Btmp_{2}\]
## 3 Methods
### VNIR-Based Prediction Methods
#### 3.3.2 Fitness Assessment of the VNIR-Based Prediction Model  
The prediction model can be evaluated by several assessment indices: the corrected Akaike information criterion (AICc) [26; 27], the coefficient of determination of simulation analysis (\(R_{c}^{2}\)), the coefficient of determination of prediction analysis (\(R_{p}^{2}\)), root of mean square simulation error (RMSEV), root of mean square prediction error (RMSEP), relative percent deviation (RPD), and Moran's I. AICc estimates the relative amount of information lost by a given model. When comparing a series of models for the same data, the less information a model loses with a lower AICc value, the higher the quality of that model. \(R_{c}^{2}\) and RMSEV are used to analyze the simulation precision and stabilization of the model. A low RMSEV value and high \(R_{c}^{2}\) value indicate the high stabilization and simulation precision of the model. The prediction precision of the model is estimated by \(R_{p}^{2}\), RMSEP, and RPD. If RMSEP is low and \(R_{p}^{2}\) is high, then the predictive capability of the model is considered good. In soil spectrographic analysis, if the RRP is less than 1, then the model is not recommended due to poor prediction capability; if the RPD is larger than 1 and less than 1.4, then the prediction capability is still deemed as poor; if the RPD is between 1.4 and 1.8, then the model can be used to perform prediction analysis; if the RPD is larger than 1.8, then the model should have very good prediction capability [28; 29]. Moran's I is utilized to test the randomness of residuals. A good model will yield a random series of residuals without autocorrelation [30].
## 4 Results and Discussions
### Results Statistics and Evaluation  
This section summarizes the results and evaluates the performance of the FWMB algorithm. The statistics of the leakage angle location results for two different holes are shown in Figure 11. The upper and lower ends of the error bar in the figure indicate the maximum and minimum values in the measurement data.  
As can be seen from the figure, the FWMB algorithm was able to get a close location result for different angles. Judging from the average error, reading errors of 1.33\({}^{\circ}\) and 1.93\({}^{\circ}\) were obtained in the holes of 1.5 and 2.0 mm, respectively, and the comprehensive error was 1.63\({}^{\circ}\). The above results prove that the FWMB method proposed in this paper is adaptive to different leaks. The above advantages are obtained by weighting the energy ratios of different frequency bands in the FWMB method. Due to the use of narrow frequency band, the dispersion problem of Lamb waves is solved, and the wave velocity parameter in the algorithm is more accurate.
# SatGuard: Satellite Networks Penetration Testing and Vulnerability Risk Assessment Methods
## 4 Implementation and Results
### Experimental Procedures and Results  
#### Step 1 Planning  
Rapid deployment pressures exacerbate risks by leaving services unpatched or misconfigured. To simulate a real-world satellite ground station environment, we select Kali Linux 2024.02 as the attacking virtual machine and Metasploitable as the target machine.  
The experimental setup utilized the Metasploitable virtual machine to emulate vulnerabilities commonly observed in satellite ground stations. While Metasploitable is a generic platform, its intentionally vulnerable configurations--such as outdated services, unsecured protocols, and misconfigured permissions--closely mirror weaknesses documented in real-world satellite infrastructure audits. For instance, the vsftpd backdoor vulnerability (CVE-2011-2523) replicates attack vectors observed in a 2020 Thales ground station security assessment, where unpatched FTP services exposed critical command-and-control interfaces to remote exploitation. Similarly, the Java RMI registry (port 1099) and SSH misconfigurations (port 22) in Metasploitable align with vulnerabilities reported in LEO ground station architectures, such as insecure remote management interfaces and weak authentication mechanisms. By virtualizing these components, the experiment prioritized ethical and operational safety while preserving the technical fidelity required to validate SatGuard's semi-automated penetration testing workflows.
## 1 Introduction
It is stated that the possibility of false alarms may affect system performance by reducing spectrum usage efficiency. In [25], the authors present performance comparisons by examining different methods, such as second-order statistics, for spectrum sensing in cognitive radio networks. This paper particularly emphasizes that being able to detect weak signals is one of the main challenges in these systems. In another study [26], the trade-off between energy consumption and throughput in sensor networks was investigated. An optimization problem was formulated to determine the optimal spectrum sensing and data transmission times, and it was determined that the sensing time was a factor that limited the performance of the system. In [27], the authors developed a deep learning-based method to detect spectrum gaps in underwater cognitive radio networks to improve spectrum sensing accuracy and shorten sensing time. Refs. [28; 29] examine the relationships between spectrum sensing and energy harvesting technologies. In [30], the authors used spectrum sensing and rate splitting multiple access (RSMA) techniques to block dynamic and suspicious communications and optimize communication with legitimate users. In [31], the authors showed that spectrum sensing performance can be increased with intelligent reflecting surfaces (IRS) technology. In [32], the energy efficiency of superior selective reporting (SSR) and opportunistic SSR (OSSR) methods used for spectrum sensing in cognitive radio networks was analyzed and their advantages over traditional methods were shown. In the system model in [33], two algorithms are developed to optimize the spectrum sensing time and transmission power to improve energy efficiency. The algorithms maximize energy efficiency by considering spectrum sensing errors. However, real-world heterogeneous network scenarios were superficially considered.
# A Spectral Gap-Based Topology Control Algorithm for Wireless Backhaul Networks
## 5 Experimental Results
### Performance Evaluation
On the other hand, even though the topologies generated by SGTC are more costly, we can observe that the rate at which this cost increases is similar to that of the Voronoi topologies. The reason is twofold. First, given that the cost of a network topology grows sublinearly with respect to the value of the LSG, the annealing process will not produce arbitrarily costly networks. Second, SGTC selects the Pareto efficient topology of minimum cost as its output.
# Determining Rotor Blade Multi-Mode Vibration Components +
## 6 Conclusions  
The new algorithm presented in this paper determines the multi-mode blade vibration components when the time of blade arrival is known from an experiment. The validation of the algorithm is presented in a numerical simulation, which assumes the blade vibration parameters. This shows the accuracy of the calculated vibration velocity amplitude and phase, as well as the good agreement between the calculated and assumed velocities. The accuracy of the calculations increased with the number of rotations up to N = 50. Therefore, N = 50 was used in further calculations  
The 1 st-stage compressor rotor blades of an ISKRA trainer aircraft SO-3 one-pass engine were analyzed using tip-timing and the Least Squares algorithm at nominal 15,000 rpm and non-nominal 12,130 rpm.  
The rotor blade vibrations were measured [14; 15] using a once-per-revolution inductive sensor and two inductive sensors in the casing.  
In the case of a nominal regime, only one blade component is dominant as a result of 2 EO.  
In the case of a non-nominal condition, the differences between blade amplitude components are smaller, showing multi-mode coupling as result of rotation stall.  
This algorithm can be used for the analysis of steam [11] and gas turbine blade vibrations, showing whether the blade vibration frequencies in the flow are similar to natural blade frequencies and the number of blade vibration components in the case of multi-mode coupling.  
Conceptualization J.M. and R.R.; methodology, R.R. and J.M.; software, J.M.; validation, L.K. and K.D.; formal analysis R.R.; writing--original draft preparation, R.R., L.K. and K.D.; writing--review and editing R.R. All authors have read and agreed to the published version of the manuscript.  
The authors wish to thank the NCBiR for their financial support of this work (POIR.04.01.04-00-0116/17) 1 MW steam turbine powered by steam using waste and process heat, co-financed in 2018-2021 from the European Regional Development Fund under the Smart Growth Operational Program 2014-2020, Priority IV: Increasing the scientific and research potential Measure 4.1 \"Research and development\", Sub-measure 4.1.4 \"Application projects\".  
The study did not require ethical approval.  
The research article does not contain any research involving human participants.  
The data cannot be made available because it comes from measurements of a military aircraft.  
The authors declare no conflicts of interest.
# Intrinsic Interference Use for FBMC-IOTA Systems
## 4 Evaluation of IIU  
In this section, we evaluate the proposed IIU to show its effectiveness. The Monte Carlo simulations are conducted over a multipath fading channel of type SUI-3 with 3 paths [22]. The FFT size is 64, the block length is 16, the modulation is 4-OQAM and the maximum number of iterations of IIU is 6.