esa-sceva/satcom-chunk-collection huggingface dataset:
# Context-Driven Service Deployment Using Likelihood-Based Approach for Internet of Things Scenarios
### Combined Context  
Services under an application segment or those having functional similarities can be grouped together to form a logical cluster, and this is referred to here as a functional context. The provider and/or consumer nodes of similar services then form a logical network referred to as a functional-context network. Subsequently, as there are a variety of application segments, there will be more than one such functional-context network. We assume that there are \(N\) nodes in the physical network and the network provides \(S\) services; each node provides a subset of services from \(S\). Consequently, a context \(C\) may be described as a logical cluster of services with an equivalent functional structure, \(C=\{s_{1},s_{2},\ldots,s_{n}\}\). Whenever \(s_{i}\in C_{j}\) is true, then \(s_{i}\) is in context \(C_{j}\). There is no inherent restriction on a node being a member of more than one such context, depending on the service(s) it has consumed in the recent past or is currently providing. The nodes belonging to a functional context can form a functional-context network, and this is represented as \(N_{C_{i}}\). If \(n_{1}\in N_{C_{i}}\) and \(n_{2}\in N_{C_{i}}\), then \(n_{1}\) and \(n_{2}\) are members of the same functional-context network; even though they may be physically far (several hops) apart, \(n_{1}\) and \(n_{2}\) are now logically connected. Hence, a node in a functional-context network has a logical neighborhood with other nodes within the same functional-context network. These nodes also have physical neighbors (for any \(h\) hops) among the existing physical nodes. The complete physical network can thus be seen as an overlapping network of such usable context networks, as illustrated in Figure 1.  
Based on the above, corresponding to each individual node, we determine a physical context as well as a functional context. We can then consider combining the physical context and the functional contexts of a node to determine a more precise set of neighbors or contexts. This is referred to as a combined-context network, as discussed by [PERSON] and [PERSON] [10]. For a particular service, the search procedure will function only within the relevant combined-context network; this reduces the universal search space into a relatively smaller region or region, which will certainly reduce the search time and, thus, the chance of service drop.  
Let, \(C=\{c_{1},c_{2},\ldots,c_{|c|}\}\) be the set of overlapping combined-context networks. If we assume that the nodes are uniformly distributed over the combined-context networks and on average a node belongs to \(l\) contexts, then the average number of nodes per combined-context network will be given by  
\[|C_{i}|=\frac{|N|}{|C|}\times l \tag{1}\]
# Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings
## 4 Experimental Results and Analysis
### Simulation Results and Analysis of The Rolling Bearing Rolling Body Faults
#### 4.2.1 The Rolling Body Fault Simulation Results
Figure 11a-h describes the simulation results of \(T_{ij}^{2}\) and \(Q_{ij}\) statistics of the second-order subspace \(X_{21}\)-\(X_{24}\) based on the Deep DKPCA method, respectively. Figure 11b,g describes the detection results of incipient failures of rolling bearing outer rings of \(Q_{21}\) and \(T_{24}^{2}\), the rolling bearing outer rings fault could be successfully detected by \(Q_{21}\) and \(T_{24}^{2}\). From Figure 11e,  
Figure 10: Experimental results of the Deep KPCA fault detection method for the rolling bearing rolling body fault. (**a**) The result of \(T_{21}^{2}\), (**b**) The result of \(Q_{21}\), (**c**)The result of \(T_{22}^{2}\), (**d**) The result of \(Q_{22}\), (**e**) The result of \(T_{23}^{2}\), (**f**) The result of \(Q_{23}\), (**g**) The result of \(T_{24}^{2}\) and (**h**) The result of \(Q_{24}\). we could obtain that this fault had no obvious influence on \(T_{23}^{2}\). Compared with the Deep KPCA method, the incipient fault detection capability of Deep DKPCA method was improved. This is because the Deep DKPCA method considered the timing correlation characteristic, but it still had some disadvantages compared to the Deep ADKPCA method.
# Design of Cislunar Navigation Constellation via Orbits with a Resonant Period
## 4 Simulation Analysis
### Resonant Constellation Configuration Analysis for Distinct Regions
#### 4.1.2 Lunar Region  
The spherical simulation domain radius was configured at 10,000 km to comprehensively encapsulate the characteristic lunar orbital geometries. As in the near-Earth  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rank** & **Resonant Configuration** & \begin{tabular}{c} **Baseline** \\ **Period** \\ \end{tabular} & \begin{tabular}{c} **Resonance** \\ **Ratio** \\ \end{tabular} & \
\begin{tabular}{c} **Mean** \\ **DOP** \\ \end{tabular} &
\begin{tabular}{c} **DOP** \\ \end{tabular} \\ \hline
1 & L2 NH-L2 SH-L4P-L5P & 1.5715 & 1:1:4:4 & 16.90 & 3.14 \\
2 & L2 NH-L2 SH-L4V-L5V & 1.5715 & 1:1:4:4 & 17.00 & 3.19 \\
3 & L1 NH-L1 SH-L4V-L5V & 2.0947 & 1:1:3:3 & 22.01 & 3.98 \\
4 & L1 NH-L1 SH-L4P-L5P & 2.1812 & 1:1:3:3 & 24.12 & 5.01 \\
5 & L1 NH-L2 SH-L5V-L4V & 2.0947 & 1:1:3:3 & 22.30 & 21.70 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dominant configurations and spatiotemporal average DOP values in the near-Earth region.  
Figure 5: Schematic diagram of the optimal resonant constellation configurations and time-averaged DOP distribution in the near-Earth region.  
region, longitude and latitude intervals were set to 60\({}^{\circ}\) and 30\({}^{\circ}\), respectively. Using the aforementioned algorithm, five optimal and five suboptimal constellations for positioning performance in this region were identified. The optimal configurations and their corresponding spatiotemporal average DOP values and standard deviations are listed in Table 3. A schematic diagram of the best-performing resonant constellation and the temporal average DOP values across grid points in the target region are illustrated in Figure 6.  
Table 3 and Figure 6 demonstrate that the optimal constellation configurations for the lunar region significantly outperformed those for the near-Earth region. For instance, even the Rank 5 lunar configuration surpassed the Rank 1 near-Earth configuration, highlighting the advantage of leveraging periodic orbits for lunar navigation systems compared to geocentric ones. Furthermore, the mean DOP values for all lunar configurations were below 16, indicating high-precision navigation services for the lunar region. In terms of composition, the Rank 1 and Rank 2 configurations share similar orbital types, combining L1 vertical orbits, L2 southern/northern NRHO orbits, and L4/L5 vertical orbits.
## 3 System Model and Parameterization
### Analysis Metrics  
Consider that the synchrophasor data generated at each PMU are \(r\)\(bps\); then, we can define the Packet Delivery Ratio (\(PDR\)) as follows.  
Packet Delivery Ratio (\(PDR\)): For an SCN, if \(A\) and \(B\) represent the source and destination nodes, then \(PDR\) at node \(B\) corresponding to node \(A\) can be defined as:  
\[PDR_{B-A}=\frac{\sum\limits_{\begin{subarray}{c}t=t_{B_{0}}\\ t_{A_{0}}\end{subarray}}^{t_{B_{0}}}\alpha_{t}^{B}}{\sum\limits_{ \begin{subarray}{c}t=t_{A_{0}}\\ \end{subarray}}^{t_{A_{0}}}\beta_{t}^{A}} \tag{1}\]  
where \(\alpha_{t}^{B}\) is the number of packets received by \(B\) at time \(t\), \(\beta_{t}^{A}\) is the number of packets sent by \(A\) at time \(t\), \(t_{A_{0}}\) is the time at which the first packet is sent from \(A\), \(t_{A_{0}}\) is the time at which the last packet is seen from \(A\), \(t_{B_{0}}\) is the time at which the first packet is received by \(B\), and \(t_{B_{0}}\) is the time at which the last packet is received by \(B\).  
Figure 2: Simplified model of an SCN for the graph-theoretic approach.
## 3 Results
### Simulation of Test Conditions for the Self-Adjusting Transtibial Prosthesis  
The Master and Slave components developed have the capability to maintain fast wireless connections, enabling the implementation of a distributed control system, with each component handling part of the processing load, reducing complexity and increasing system efficiency. Bidirectional communication between the two components is achieved in real time, allowing the rapid transmission of data from the accelerometer in the Master component (which detects LLD) to the Slave component, which is responsible for the prosthetic adjustment via the EPM.  
The collaborative operation of the Master and Slave components in this prototype facilitates the detection and correction of limb length discrepancy (LLD) at the experimental setup level depicted in Figure 9.  
The Master component is strategically placed at the suprapatellar regions of the mannequin, ensuring monitoring of the lower-limb length inequality, while the Slave component is positioned on the base plate and connected to the EPM.  
The power supply for the self-adjustable prosthesis is provided by an external power source. This is to obviate the necessity for the patient to carry auxiliary electronic components with them at all times. This has a significant effect on the weight of the prosthesis.  
The experimental setup in this configuration enables the evaluation of the adaptable transtibial prosthesis, with specific reference to the accuracy with which the Master component detects the simulated limb length discrepancy (LLD) at the level of the mannequin's right lower limb. Concurrently, the Slave component is responsible for correcting the detected discrepancy in real time by sending an electrical command to the EPM. The precision and efficiency of the detection and correction process are validated using a DIC-type metrological system for high-accuracy measurements.  
Figure 8: Perspective view of the final experimental prototype of the Slave component.
## 3 Results
### Hand Function
#### 3.1.2 Muscle Activity
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \begin{tabular}{c} **Muscle Activity** \\ **(\%)** \\ \end{tabular} & \multicolumn{2}{c}{**Mean \(\pm\) SD**} & \
\begin{tabular}{c} **Shapiro [PERSON]** \\ **_p_ Value \\ \end{tabular} &
\begin{tabular}{c} **Wilcoxon** \\ **_p_ Value \\ \end{tabular} \\ \hline \multirow{5}{*}{WSS} & PS & 20.3 \(\pm\) 4.5 & 0.045 & \multirow{5}{*}{0.004} \\ \cline{2-4}  & NPS & NPS & 23.2 \(\pm\) 10.2 & 0.001 & \\ \cline{2-4}  & CT & PS & 40.7 \(\pm\) 6.2 & 0.048 & \multirow{5}{*}{0.004} \\ \cline{2-4}  & NPS & 29.2 \(\pm\) 8.6 & 0.001 & \\ \cline{2-4}  & PUSO & PS & 33.6 \(\pm\) 5.5 & 0.039 & \multirow{5}{*}{\textless{}0.001} \\ \cline{2-4}  & NPS & 21.8 \(\pm\) 4.0 & 0.045 & \\ \cline{2-4}  & PS & PS & 18.0 \(\pm\) 5.2 & 0.038 & \multirow{5}{*}{\textless{}0.001} \\ \cline{2-4}  & SF & NPS & 9.9 \(\pm\) 1.4 & 0.005 & \\ \cline{2-4}  & SC & PS & 25.8 \(\pm\) 5.1 & 0.012 & \multirow{5}{*}{\textless{}0.001} \\ \cline{2-4}  & NPS & NPS & 16.7 \(\pm\) 3.6 & 0.008 & \\ \cline{2-4}  & LLO & PS & 30.5 \(\pm\) 8.0 & 0.003 & \multirow{5}{*}{0.020} \\ \cline{2-4}  & NPS & 22.2 \(\pm\) 5.9 & 0.015 & \\ \cline{2-4}  & LHO & PS & 35.7 \(\pm\) 6.5 & 0.018 & \\ \cline{2-4}  & NPS & 31.6 \(\pm\) 10.7 & 0.001 & \\ \hline \multirow{5}{*}{ECRL} & WSS & PS & 33.2 \(\pm\) 7.6 & 0.037 & \multirow{5}{*}{0.074} \\ \cline{2-4}  & NPS & NPS & 31.9 \(\pm\) 4.7 & 0.002 & \\ \cline{2-4}  & CT & PS & 48.6 \(\pm\) 6.2 & 0.044 & \multirow{5}{*}{0.058} \\ \cline{2-4}  & NPS & 41.9 \(\pm\) 8.0 & 0.048 & \\ \cline{2-4}  & PUSO & PS & 39.4 \(\pm\) 7.9 & 0.016 & \\ \cline{2-4}  & NPS & 29.9 \(\pm\) 5.9 & 0.048 & \\ \cline{2-4}  & SF & PS & 35.2 \(\pm\) 9.4 & 0.045 & \\ \cline{2-4}  & NPS & 35.3 \(\pm\) 6.2 & 0.001 & \\ \cline{2-4}  & SC & PS & 34.6 \(\pm\) 7.9 & 0.032 & \multirow{5}{*}{0.096} \\ \cline{2-4}  & NPS & 40.2 \(\pm\) 9.6 & 0.034 & \\ \cline{2-4}  & LLO & PS & 38.2 \(\pm\) 4.9 & 0.033 & \multirow{5}{*}{0.036} \\ \cline{2-4}  & NPS & 32.5 \(\pm\) 5.7 & 0.047 & \\ \cline{2-4}  & LHO & PS & 49.7 \(\pm\) 6.6 & 0.015 & \\ \cline{2-4}  & NPS & 44.1 \(\pm\) 8.7 & 0.048 & \\ \hline \multirow{5}{*}{BB} & WSS & PS & 28.3 \(\pm\) 12.4 & 0.001 & \multirow{5}{*}{0.002} \\ \cline{2-4}  & NPS & NPS & 12.1 \(\pm\) 3.7 & 0.031 & \\ \cline{2-4}  & CT & PS & 36.4 \(\pm\) 9.5 & 0.008 & \\ \cline{2-4}  & NPS & 27.6 \(\pm\) 6.4 & 0.021 & \\ \cline{2-4}  & PUSO & PS & 26.4 \(\pm\) 5.4 & 0.031 & \multirow{5}{*}{0.823} \\ \cline{2-4}  & NPS & 27.5 \(\pm\) 4.6 & 0.006 & \\ \cline{2-4}  & SF & PS & 33.2 \(\pm\) 10.5 & 0.001 & \\ \cline{2-4}  & SF & NPS & 35.0 \(\pm\) 3.4 & 0.048 & \\ \cline{2-4}  & SC & PS & 25.2 \(\pm\) 8.2 & 0.001 & \multirow{5}{*}{0.750} \\ \cline{2-4}  & NPS & 20.6 \(\pm\) 4.1 & 0.002 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Differences in muscle activity of the upper extremities when performing writing a short sentence (WSS), card turning (CT), picking up small objects (PUSO), simulated feeding (SF), stacking checkers (SC), lifting lightweight objects (LLO), and lifting heavy objects (LHO) between the paralyzed side (PS) and nonparalyzed side (NPS).
## 3 Results and Discussions  
### Sample Conditions  
Figure 1a presents the photographs of Tb-doped LPS, YPS, GPS, and LaPS single crystal rods obtained from the floating-zone method, with the crystal rods length of 20-25 mm and diameter of 4 mm. The visible cracks throughout the entire crystal rods appear in every sample. The cracks are consistent with the previous attempt on the crystal growth of pyrosilicate crystals by the floating zone method [61; 62]. However, after cut and polished, relatively clear and transparent sample is obtained as presented in Figure 1b, with the bright yellow-green luminescence from the Tb-doped under the 254 nm UV lamp presented in Figure 1a. Figure 2 shows the XRD patterns of the Tb-doped LPS, YPS, GPS, and LaPS powders with an individual reference pattern. Similar to our previous reports, the Tb-doped LPS and YPS patterns belong to monoclinic Lu\({}_{2}\)Si\({}_{2}\)O\({}_{7}\) (COD 8400597) [63] and Y\({}_{2}\)Si\({}_{2}\)O\({}_{7}\) (JCPDS 82-0732) [64], respectively. The Tb-doped GPS pattern agrees with orthorhombic Gd\({}_{2}\)Si\({}_{2}\)O\({}_{7}\) (COD 901106) [65], and that of Tb-doped LaPS pattern matches with monoclinic Lu\({}_{2}\)Si\({}_{2}\)O\({}_{7}\) (JCPDS 82-0729) [66]. In all four samples, no impurity phases are detected. In addition, the substitution of 1 mol\(\%\) Tb to rare-earth site in the pyrosilicate structure is achieved and not affected the XRD pattern.  
Figure 3 shows the diffuse transmittance spectra of the Tb-doped LPS, YPS, GPS, and LaPS sample. From 400 to 800 all the samples have transmittance over 80% which indicated good transparency of the polished samples presented in Figure 1b. The absorption band of the Tb\({}^{3+}\) 4f-4f is start from 240-350 nm. However, the Tb-doped GPS sample have more complicated shape of the absorption band than the other Tb-d  
Figure 1: Tb-doped LPS, YPS, GPS, and LaPS samples photographs of (**a**) as-grown single crystal rods; (**b**) cut and polished specimens in room light and 254 nm UV.  
Figure 2: XRD pattern of Tb-doped LPS, YPS, GPS, and LaPS samples, with each individual reference pattern.  
study. The explanation of this behavior is discussed with the additional results from the later PL property.
# Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review
## 4 Technology Trends to Achieve the Requirements in the 5G Network
### Artificial Intelligence (AI) and Machine Learning (ML)  
Smart health-care applications in the wireless network are still at its early stages and slowly developing with the help of artificial intelligence and machine learning to make the network smarter. The network design, topology, and propagation models, along with the mobility of nodes in the 5G network, can be complicated. Therefore, AI and ML can play vital roles in assisting and managing different resources in the network to make the network smarter for smart health-care applications [78]. AI and ML can be deployed in the network in three different ways.  
* For rapid decision making and low computation capability, the AI and ML algorithms can be embedded within individual edge devices in the network.
* For low latency IoT services, AI and ML engines at the network edge can play an important role in performing real-time computation and quick decision making.
* For huge data storage and heavy computation for the analysis of medical data, AI and ML can be embedded in the centralized system to achieve these goals.
# False Information Detection via Multimodal Feature Fusion and Multi-Classifier Hybrid Prediction
## 4 Experimental Results and Analysis  
### Experiments  
Machine configuration and environment for this experiment: CPU: Intel Xeon E5-2630L v3, 62 G memory, 8 cores, GPU: NVIDIA GeForce RTX 3090, PyTorch(1.7.1), Python(3.8), Cuda(10.2). In our experiments, we use two publicly available datasets, namely Twitter and Weibo. The selected Twitter dataset is a public dataset: [[https://github.com/MKLab-ITI/image-verification-corpus](https://github.com/MKLab-ITI/image-verification-corpus)]([https://github.com/MKLab-ITI/image-verification-corpus](https://github.com/MKLab-ITI/image-verification-corpus)) (accessed on 18 October 2021). The selected Weibo dataset is a public dataset: [[https://drive.google.com/file/d/14](https://drive.google.com/file/d/14) VQ/EWPiFeGzxp3 XC2]([https://drive.google.com/file/d/14](https://drive.google.com/file/d/14) VQ/EWPiFeGzxp3 XC2) DeEHiBEsDINn/view?usp=sharing (accessed on 18 October 2021). The Twitter dataset contains text, related images and contextual information. The information in the training and test sets is collected from different events, so there is no temporal overlap between the training and test sets. This training set contains 14,483 tweets divided into three categories: false, true and humorous. A total of 6841 of them are fake, 5009 are true, and 2633 are humorous. The test set contains 3781 tweets. Since the test set does not contain humor class instances, we ignore these instances in our experiments. The Twitter dataset contains 360 training set images and 50 test set images. We use 20% of the training set as the validation set [18]. The Weibo dataset contains a total of 9563 news items. Of these, 4784 were false and 4779 were true. The test dataset includes 7567 messages. We use 20% as the validation dataset, and the test dataset includes 1996 messages, Table 2 shows the complete data distribution of the two data sets. All the hyper-parameters used for training the proposed model are listed in Table 3.
# A Survey of Generative AI for Detecting Pedophilia Crimes
## 4 Literature Review: AI Approaches for Detection
### Machine Learning Models
It seeks to balance the need for protecting children from cyber threats with the paramount importance of preserving user privacy. Federated learning is utilized to train a context-aware language model in a distributed manner. The focus of the study is on identifying linguistic markers within online dialogues that could indicate grooming behavior. By detecting these early signs, the system aims to facilitate timely intervention. The study demonstrates that federated learning offers a promising approach for the early detection of sexual predators, providing a balance between detection accuracy and user privacy. The proposed framework performs comparably to centralized models while ensuring that user data is protected. [PERSON] et al. [25] conducted an investigation into the effectiveness of machine learning algorithms for the early detection of cyberbullying in digital dialogues. Employing a sliding window methodology, the research examined segments of fixed-length message windows, incrementally traversing through conversational threads. This analytical approach facilitated the dissection of message clusters to identify suspicious patterns with temporal precision. The PAN2012 dataset served as the empirical basis for the study, providing a repository of chat dialogues that distinguish \"normal\" interactions from those characterized by potential predatory behavior, particularly involving sexual predators. Among the algorithms tested, the Multinomial Naive Bayes with Term Frequency (mult-nb-tf) model emerged as the most proficient, demonstrating superior accuracy over sliding window lengths of 5 and 10 messages. Despite the promising results, the study acknowledged several limitations. The most significant of these were the modest sample size of conversations and the rigidity of the fixed sliding window dimensions.
## 3 Methodology
### Data Pre-Processing  
A time-series-focused statistical features extraction process is implemented in three groupings including (1) central tendency, (2) variability and (3) measures of position, calculated through the Frequency, Mean, Median, Mode, Standard Deviation, Minimum, Maximum, 1 st Quartile and 3 rd Quartile, the 5 th Percentile and 95 th Percentile features (this is further outlined in [46]). A correlation of the features for the Device ID (a), Patient ID (b), User ID (c) and Routine ID (d) is displayed in Figure 4. The correlation is implemented using the _corplot_ ([[https://cran.r-project.org/web/packages/corplot/vignettes/corplot-intro.html](https://cran.r-project.org/web/packages/corplot/vignettes/corplot-intro.html)]([https://cran.r-project.org/web/packages/corplot/vignettes/corplot-intro.html](https://cran.r-project.org/web/packages/corplot/vignettes/corplot-intro.html))) R library package.  
The visualisations display the positive and negative correlation between the features, where blue refers to positive correlation and red negative. The plots are organised using the correlation coefficient, which displays hidden structures in the matrix by means of the angular order of the eigenvectors (AOE). The ordering of the visualisation is calculated in (1) from the order of the angles (\(a_{i}\)), with \(e_{i1}\) and \(e_{i2}\) referring to the two largest eigenvalues in the correlation matrix [47].  
\[a_{i}=\left\{\begin{array}{ll}\tan\bigl{(}\frac{e_{i2}}{e_{i1}}\bigr{)},&if \ e_{i1}>0;\\ \tan\bigl{(}\frac{e_{i2}}{e_{i1}}\bigr{)}+\pi,&otherwise.\end{array}\right. \tag{1}\]  
On visual inspection, there are clear positive correlation patterns in each of the plots. Based on the negative correlation patterns, an omission of the frequency for the User ID, Routine and Device Interaction would be beneficial to the detection process.  
Figure 3: System methodology.  
In terms of the Routine and Device Interaction, the frequency patterns would relate predominately to unique routine combinations. Logically, this feature is, therefore, less significant. However, it should be retained in the Patient ID classification process.