esa-sceva/satcom-chunk-collection huggingface dataset:
# Outlier Detection for Multivariate Time Series Using Dynamic Bayesian Networks
## 3 Methods  
METEOR is portioned in four phases including pre-processing, modeling, scoring, and score-analysis, which together form a complete and automatic anomaly detection system. Data is assumed to be complete, lacking missing values or hidden variables. A diagram comprising all phases is depicted in Figure 2. The _pre-processing_ phase studied is comprised by (an optional) discretization and dimensionality reduction technique discussed in Section 3.1, especially relevant when considering data descendant from sensor devices. Discrete MTS datasets are then employed to the tDBN _modeling_ algorithm, which generates a DBN according to the parameters chosen: the Markov lag \(m\), the maximum number of parents \(p\) from preceding slices and a flag \(s\) deciding the stationarity of the model. Afterward, the MTS dataset, together with the trained model, are delivered to a _scoring_ phase. The aforementioned capitalizes on the structure and parameters of the DBN to analyze each subject transition using a sliding window algorithm. Entire series are likewise scored. Subsequently, scores are delivered to a _score-analysis_ strategy which creates a threshold differentiating abnormal and normal scores. Two possible strategies are discussed in Section 3.5 and later compared; both output a threshold for the final binary classification. Observations associated with scores below the threshold are classified as outliers, being suspected of not have been generated by the learned model.  
Figure 1: Example of a stationary first-order Markov DBN. On the left, the prior network \(B^{0}\), for \(t=0\), and on the right, the transition network \(B^{t}_{t-1}\) over slices \(t-1\) and \(t\), for all \(t\geq 1\).
# A Key Node Mining Method Based on K-Shell and Neighborhood Information
## 2 Related Works
### Hybrid Methods  
Hybrid methods often employ a combination of local and global information to measure a node's influence, leveraging the strengths of both approaches. For instance, local-and-global centrality (LGC), proposed by [PERSON] et al., integrates local and global centrality metrics, considering the network's topology to identify key nodes [40]. Similarly, improved K-shell (IKS), proposed by [PERSON] et al., merges K-shell analysis with node information entropy to rank nodes based on both their local and informational significance [41]. K-shell based on gravity centrality (KSGC), an enhancement over the local version of GM (LGM) by [PERSON] et al., incorporates node positional data and degree disparities to differentiate node importance [42]. Additionally, the extended K-shell hybrid method (KS+), as proposed by [PERSON] et al., evaluates node importance by comprehensively considering the K-shell, degree, contact distance, and the extent of neighborhood influence [32]. Although these hybrid methods outperform single-method approaches by incorporating diverse criteria, they may still overlook influential nodes on the network's periphery. This highlights a potential area for further development or refinement in hybrid methodologies to capture the significance of such nodes effectively.  
[PERSON] [43] proposed a hybrid method named Mixedinf, which considers a node's degree, its neighbors, and structural holes (SH) to determine its importance. The method acknowledges that different nodes contribute differently to the importance assessment, and it uses hyperparameters to balance the contributions of degree and SH. [PERSON] emphasized that the contributions of degree and SH vary and thus need careful consideration. [PERSON] et al. proposed a centrality method called semi-local centrality and structural holes (LSH), which combines SH with the modified local centrality (MLC) method to better evaluate the importance of bridging nodes and core nodes with multiple neighbors [44]. The MLC method, proposed by [PERSON] et al. [45], is an improved local centrality approach that considers the order of a node's neighbors based on expansion frequency. They argue that nodes with higher expansion frequencies yield greater influence, necessitating comprehensive consideration of the neighbor order. Both the Mixedinf and LSH methods incorporate SH into their criteria for measuring node importance, thereby enabling the recognition of influential spreaders, even among peripheral nodes in the network. These hybrid approaches, leveraging both global and local information, address some limitations inherent in purely global or local centrality methods. Consequently, this study adopts a hybrid strategy to enhance the KS method by incorporating the network's local structural information.
## 3 Results and Discussion  
### Characterization Studies  
#### 3.1.1 UV-Vis Adsorption Spectra  
The UV-vis adsorption spectra of the Cd(II) solution, MA, AN and Cd(II)-MA/AN complex are displayed in Figure 1. The major adsorption peaks for the Cd(II) solution, MA, and AN occur at 225, 235, and 254 nm, respectively. The major adsorption peak for the Cd(II)-MA/AN complex appears at 264 nm. According to the superposition principle of ultraviolet adsorption spectrum, if AN and MA do not coordinate with the Cd(II), the adsorption peak of the complex will overlap with that of the Cd(II) solution, AN and MA, and there will be no new adsorption peak. The results show that a new adsorption peak appears in the long-wavelengths range, proving that the ligands coordinate with the central ion Cd(II). This can be attributed to the empty orbital of Cd(II), the lone pair electron on nitrogen atoms of AN and the hydroxyl oxygen atom of MA. The decreased density of the electron cloud around the functional monomer is caused by the coordination bond among Cd(II), AN and MA. When illuminated by ultraviolet light, the electron transition can occur at lower energies and generate an adsorption spectrum [28; 29]. Thus, the adsorption peak of the Cd(II)-MA/AN complex appears at long wavelengths.
## 2 Software-Defined GNSS Receiver
### BeiDou Signal Tracking
#### 2.2.1 Carrier Tracking  
The main objective of signal tracking is to wipe off the code and the carrier. The carrier tracking loop synchronizes the carrier frequency and phase with that of the incoming signal. One of the most commonly used frequency discriminator in a conventional GPS Frequency Locked Loop (FLL) is a four-quadrant arctangent discriminator. This discriminator is optimal at high and low C/N\({}_{0}\) with a wide frequency pull-in range and it offers a linear relationship between the discriminator output and the real frequency error. This discriminator can not only enhance the robustness of signal tracking but also tolerate large acquisition frequency errors coming from the acquisition. But, this four-quadrant arctangent discriminator is sensitive to data bit transition, meaning that two adjacent integration samples  
Figure 4: Acquisition metric for BeiDou GEO, IGSO and MEO satellites.  
should be within the same data bit interval. In case of legacy GPS L1 signal, the data bit transition may occur only after every 20 ms offering a low data bit rate. Due to the low data bit rate, the GPS receiver can meet this condition in most occasions. Therefore, the probability that the data bit transition affects the discriminator is relatively small (less than or equal to 5%), and the four-quadrant arctangent discriminator based FLL can work correctly [19]. However, in case of BeiDou D1 or any modern GNSS signals with an extra tier of modulation on top of data bits, the bit transition usually occurs rather quickly, and therefore, the four-quadrant arctangent discriminator based FLL is not an appropriate choice for BeiDou D1 signal tracking. The same observation is true for BeiDou D2, where the data bit lasts for only 2 ms, with a maximum probability of data bit transition of 50%. Therefore, the BeiDou receiver should choose an FLL discriminator that is insensitive to data bit transition. In view of this particular situation, a two-quadrant arctangent discriminator is implemented in the software receiver, which is first proposed in [8]. The implemented two-quadrant arctangent discriminator is insensitive to data bit transition, but it has reduced tolerance of frequency uncertainty coming from the acquisition stage. It was shown in [8] that the frequency uncertainty tolerance is reduced by half, as compared to the conventional four-quadrant arctangent discriminator. This restriction in frequency uncertainty can be overcome by proper selection of coherent integration time (_i.e._, frequency bin size) at the acquisition stage. The signal tracking is switched from FLL to a costas Phase Locked Loop (PLL), once the FLL is locked.
## 3 Results and Discussion
### SEM of Natural Fibers
The obtained SEM images were post-processed with ImageJ 1.50 software, in order to determine the diameters of the fibers, by randomly measuring at least fifty individual fibers of each kind. Although the studied fibers exhibit both irregular shapes and variable dimensions, an assumption is made for reasons of simplification, considering all fibers as cylinders with circular cross sections. The results of the respective measurements areexhibited in Table 3. It is obvious that the greater mean value of fiber diameter is exhibited by kenaf, following hemp, then ramie, and lastly, flax. The great differentiation between kenaf and flax, which are the fibers exhibiting the greater and the lower diameter values, is more than obvious in Figure 3, where both SEM micrographs have been obtained under the same magnification of 2700\(\times\). Figure 3: SEM images of (**a**) kenaf and (**b**) flax under the same magnification of 2700\(\times\). Figure 2: SEM microphotographs of fibers at different magnifications: (**a**) ramie \(\times\)350; (**c**) ramie \(\times\)900; (**i**) ramie \(\times\)2000; (**b**) hemp \(\times\)350; (**f**) hemp \(\times\)900; (**j**) hemp \(\times\)2000; (**c**) flax \(\times\)350; (**g**) flax \(\times\)900; (**k**) flax \(\times\)2000; (**d**) kenaf \(\times\)350; (**h**) kenaf \(\times\)900; and (**l**) kenaf \(\times\)2000. Red arrows highlight specific areas of interest in terms of the fibers’ sizes, shapes, surface roughness, and surface-lump materials. \begin{table}
\begin{tabular}{l c} \hline \hline
**Sample** & **Average Diameter (\(\mu\)m)** \\ \hline Ramie & 21.7 \(\pm\) 7.0 \\ Hemp & 24.2 \(\pm\) 11.1 \\ Flax & 14.2 \(\pm\) 6.5 \\ Kenaf & 44.9 \(\pm\) 14.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Diameters of natural fibers. It is of great importance that kenaf and hemp are characterized by the presence of both individual fibers and formed bundles, explaining the corresponding greater values of the standard deviation of the mean fiber diameters. The co-existence of attached and individual fibers is clearly observed in Figure 4.  
So, the overall conclusion concerning the studied natural fibers by means of SEM is summarized in the following sentences. * The roughness of the fibers follows the trend \"kenaf\" > \"flax\" > \"hemp\" > \"ramie\";
* The amount of surface impurities on the fibers follows the trend \"flax\" > \"kenaf\" > \"hemp\" > \"ramie\";
* Fiber diameter follows the trend \"kenaf\" > \"hemp\" > \"ramie\" > \"flax\".
## 3 Methodology
### Reliability Importance Measure Of Components
#### 3.2.3 Joint Reliability Importance  
A new joint reliability importance measure is presented herein that is expressed as JRI. For \(s\)-independent components, the JRI of two components \(i\) and \(j\) can be defined as  
\[\text{JRI}(i,j;t)=h(1_{i},1_{j},R_{s}(t))-h(1_{i},0_{j},R_{s}(t))-h(0_{i},1_{j },R_{s}(t))+h(0_{i},0_{j},R_{s}(t)) \tag{12}\]  
where \(R_{s}(t)\) is the system reliability at time \(t\), and \(h(1_{i},0_{j},R_{s}(t))\) represents the system reliability with component \(i\) working and component \(j\) failed. If component \(i\) and \(j\) are of same type \(k\), Equation (12) can be written as follows  
\[\text{JRI}(i,j;t)=\sum_{l_{1}=0}^{m_{1}}\cdots\sum_{l_{k}=0}^{m_{k}-2}\cdots \sum_{l_{K}=0}^{m_{K}}\bar{\Phi}_{JRI}^{w}(l_{1},\cdots,l_{K})\prod_{k=1}^{K} P\{C_{k}(t)=l_{k}\} \tag{13}\]  
where  
\[\bar{\[PERSON]}_{JRI}^{w}(l_{1},\cdots,l_{K})=\bar{\Phi}_{1,1}^{w}(l_{1},\cdots,l_ {K})-\bar{\[PERSON]}_{1,0}^{w}(l_{1},\cdots,l_{K})-\bar{\[PERSON]}_{0,1}^{w}(l_{1},\cdots,l_{K})+\bar{\Phi}_{0,0}^{w}(l_{1},\cdots,l_{K}) \tag{14}\]  
Generally, for a system with \(n\) independent components \((c_{1},c_{2},\cdots,c_{n})\), the JRI can be derived as  
\[\text{JRI}(c_{1},\cdots,c_{n})= h(1_{1},1_{2},\cdots,1_{n},R_{s}(t))-h(1_{1},1_{2},\cdots,0_{n},R_{s} (t))\pm\cdots\] \[\pm h(0_{1},0_{2},\cdots,1_{n},R_{s}(t))\pm h(0_{1},0_{2},\cdots, 0_{n},R_{s}(t)) \tag{15}\]  
where the sign before each function \(h\) follows the principles: (i) while number \(n\) is odd, the sign before the function \(h\) is positive if the number of corresponding 1's is odd, otherwise, it is negative; (ii) while number \(n\) is even, the sign before the function \(h\) is positive if the number of corresponding 1's is even, otherwise, it is negative; (iii) the sign of last term \(h(0_{1},0_{2},\cdots,0_{n},R_{s}(t))\) is negative if \(n\) is odd, otherwise, it is positive [14].  
The JRI is introduced to quantitatively measure the interaction of multiple components in a system in contributing to system reliability. For any \(n\) components, \(\text{JRI}(n;t)\in[-1,1]\), and in particular: (a) JRI \(>0\) indicates that one component becomes more important when the other is functioning (\"complements\"); (b) JRI \(<0\) indicates that one component becomes less important when the other is functioning (\"substitutes\"); (c) JRI \(=0\) indicates that one component's importance is unchanged by the functioning of the other [13].
# Effects of the Configuration of Trailing Edge on the Flutter of an Elongated Bluff Body
## 2 Experiment Setup
### Model Details  
As is shown in Figure 2, the overall aspect ratio of the model is 1:10, the height \(H\) is 20 mm, the width \(B\) is 200 mm, and the axial length is 430 mm. In order to study the effect of wake flow on the flutter stability of the bridge, the streamlined front edge (FE), whose section is an isosceles triangle with an apex angle of 30\({}^{\circ}\), is used to reduce the separation of flow at the FE of the model. For the FE, rounded corners are used in the transition section to minimize the separation here, which is illustrated in Figure 2c. For the trailing edge (TE), a different apex angle, \(\beta\), is configured to construct TEs with different degrees of streamline to obtain different wake flow. In this paper, five cases of apex angle of the TE \(\beta\), from 30\({}^{\circ}\) to 180\({}^{\circ}\) (\(\beta\) = 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\)), were studied to investigate the effects of wake flow on the flutter stability of bridge decks, using \(\beta\) to quantify the effects of wake flow. The details of the section are shown in Figure 2b. The test model consists of three parts: one FE, five TEs, and a foundation deck. Since the mass of the FE and TE is not negligible relative to the mass of the entire model, simply designing the TE with different shapes will cause the model of bridge deck to be seriously off-center, which may cause large errors in the test results. In order to reduce this error as much as possible, masses are added at appropriate locations inside the TE, so that the mass of each TE and moment of inertia of each TE relative to the center axis of the bridge deck are nearly equal to that of the FE (\(\pm\)3% theoretically). The perspectives of the FE and 5 TEs are shown in Figure 2d. Each part is made of resin material through 3D printing technology. Unfortunately, due to the limitations of 3D printing technology, the error mentioned above may be magnified.  
Figure 1: Wind tunnel.  
The overall rigidity of the foundation bridge deck is guaranteed by a model shell with a thickness of at least 3 mm and 3 steel bars fixed inside, and the overall rigidity of the TE and FE is guaranteed by a model shell with a thickness of at least 2 mm and the internal mass block.  
A test model was installed in the test section of the wind tunnel through a spring suspension system. As shown in Figure 3, the test model is elastically supported by eight springs of equal stiffness. The tests of free vibration were performed on the five cases to obtain their natural frequency. The vertical and torsional natural frequency between each case, \(\omega_{v}\) and \(\omega_{t}\), are basically equal, 2.6 Hz and 5.0 Hz, respectively. \(f_{v}\) and \(f_{t}\), representing the vertical and torsional damping ratio, respectively, are both less than 0.2%, and the details of the model's dynamic characteristics are shown in Table 1. The blockage ratio is 4.25%.
## 4 Discussion
### Security Frameworks and Scalability  
Integrating 5G, IoT, and AI expands not only educational possibilities but also the network's attack surface. The success of AI-driven intrusion detection and encryption solutions shows that strong security is technically feasible. Nevertheless, the inherent complexity and cost of these measures can strain school IT departments, especially where resources are limited. Furthermore, adding biometrics compounds the data-protection burden: biometric data, once compromised, cannot be reissued like passwords.  
Edge computing has emerged as a partial remedy, bringing analytics closer to campus networks to reduce latency and minimize data exposure. Offloading time-sensitive tasks to edge nodes allows for near-real-time responses without flooding the cloud with raw data. While initial results suggest performance benefits, widespread adoption remains contingent on schools' capacity to deploy and maintain specialized hardware [69]. International frameworks encouraging modular network architectures can help smaller institutions adopt partial edge solutions, but the path to comprehensive coverage is far from linear.  
Legal Harmonization Across Jurisdictions  
A further complication is the global variance in data protection laws and regulatory compliance demands. Some educational networks operate across multiple states or countries, each with its own guidelines for data privacy, student consent, and usage of biometric identifiers [18]. This landscape complicates the widespread adoption of AI-driven 5G solutions, necessitating legal harmonization or at least mutual recognition of compliance. Although consortia, such as the European Union, have established frameworks like the GDPR, many other regions rely on fragmented or nascent policies. As a result, technology suppliers must design flexible systems capable of conforming to multiple regulatory regimes, underscoring the need for modular architectures and multi-level access controls.
# A Single-Launch Deployment Strategy for Lunar Constellations
## 3 Design of the Deployment Strategy
### Deployment to the Final Orbit
To overcome this issue, if required, a final corrective maneuver can be performed at the periselenium, providing a \(\Delta V_{c}\) to reduce the semimajor axis.
# A Proof-of-Multiple-State Consensus Mechanism for Mobile Nodes in Internet of Vehicles
## 3 Proposed System Model
### Implementation Process  
The vehicles in the model travel on the road at different speeds depending on the predetermined density of the area. The driving process of the vehicles regularly sends their own driving data, including driving direction, driving speed, coordinate position, etc., to the RSU in the sub-region, where the RSU has larger storage space and stronger computing power of the collected vehicle data to calculate In the next action, the vehicles, which are still within the communication range of the RSU within a certain period of time, are included in the group. The schematic diagram of regional division is shown in Figure 2, where vehicles of the same color represent being within the jurisdiction of the same RSU.  
In order to improve message forwarding speed, this article chooses a special grouping method based on relative position information and travel speed prediction. This method means that vehicles with similar driving status in a specific area are classified as the same group. After completing the first round of message transmission, RSU nodes collect position information and speed information for all vehicles involved in communication,  
Figure 1: System Architecture.  
and each individual RSU selects a set of vehicle nodes that meet the conditions for sending the grouping identifier in close proximity and the vehicles with the same identifier are recognized by the network as the same group in the subsequent consensus process. The execution process of the consensus algorithm in each round is shown in Figure 3.  
After completing a certain number of rounds of consensus, the state of vehicle movement has changed significantly compared to that before consensus, and at this point the RSU needs to regroup the nodes again using the collected vehicle information. The regrouping process is similar to the initial grouping, in which the RSU removes the vehicles that have left or are about to leave a certain area from the previous group and the newly entered vehicle nodes that meet the entry criteria for the group into the Partitioned network involves participating in the subsequent consensus.