esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Present: 2010-2015
### Extensions of UPS Method
#### 3.2.2 Ultrasonic Backscatter Polar Scan (UBPS)  
During our UPS investigations, we persistently observed a small amount of ultrasonic energy being backscattered to the emitter, even for large incident angles \(\theta\). Recording of the backscattered signal according to the UPS principle then results in an ultrasonic backscatter polar scan (UBPS). Depending on the employed wave, we speak of P-UBPS for ultrasonic pulses [24] and H-UBPS for quasi-harmonic ultrasound [25] (similarly as done for H-UPS and P-UPS). A schematic of the UBPS method, together with a H-UBPS recording for a [0\({}^{\circ}\)]s C/E laminate, is presented in Figure 8.  
Figure 8: Schematic of ultrasonic backscatter polar scan (UBPS) method (**a**) and harmonic ultrasonic backscatter polar scan (H-UBPS) recording for a [0\({}^{\circ}\)]s C/E laminate (**b**).  
Figure 6: Experimental recordings of a [0\({}^{\circ}\)]s C/E laminate: P-UPS at \(f_{\rm c}\) = 5 MHz (**a**) and harmonic ultrasonic polar scan (H-UPS) at \(f\) = 5 MHz (**b**).  
Figure 7: H-UPS recordings for [0\({}^{\circ}\)]s carbon/epoxy laminate at \(f\) = 5 MHz: amplitude (**a**) and phase (**b**) analysis.  
Typically, a H-UBPS image is characterized by well-defined high amplitude backscatter spikes, which can be linked to diffraction phenomena under the Bragg condition [25; 26; 27; 28]. As such, these spikes directly expose geometrical characteristics of (sub)surface structures. For the [0\({}^{\circ}\)]s C/E laminate, the observed backscatter spikes originate at the imprint left by the peel-ply cloth during manufacturing of the laminate [24].
## 4 Results: Threat Analysis
### Threat 4: Dishonest Behavior by Auditor A  
In the proposed framework, the auditor \(A\) plays a crucial role in verifying transaction validity, detecting potential misconduct, and resolving disputes based on data from \(\mathbb{PC}_{M}\) and \(\widehat{\mathbb{PC}}_{M}\). However, this responsibility also introduces a risk: if \(A\) acts dishonestly, either by misreporting outcomes or by neglecting its verification duties, this could undermine the trust and credibility of the entire platform. The framework addresses this risk through the following safeguards:  
* **Issuing incorrect or misleading verification results:** All evaluations by \(A\) are based on data that are immutably recorded on-chain, including transaction hashes, smart contract logic, and execution results. Because these data are publicly accessible and verifiable, any incorrect or dishonest report by \(A\) can be independently verified and, if necessary, refuted.
* **Exercising exclusive control over validation:** The framework ensures that \(A\) does not rely on private information or privileged access. All necessary verification inputs are available on-chain, allowing others to replicate \(A\)'s role using independent or automated tools. This design supports the development of decentralized or community-driven auditing models and reduces the dependency on a single authority.
* **Legal accountability and institutional enforcement:** To complement technical safeguards, the framework assumes that \(A\) operates under a legally binding agreement or regulatory oversight. This institutional structure clearly defines \(A\)'s obligations, grants it authority to act independently, and holds it accountable for negligence or willful misconduct through enforceable consequences. Given the auditor's privileged role in verifying transaction validity and resolving disputes, assigning legal responsibility is not merely optional but practically indispensable to ensure trustworthy and reliable oversight.  
In addition, because \(A\) performs a transparent and non-privileged function, any misconduct is readily detectable and remediable. If dishonesty is observed, \(A\) can be replaced without impacting the platform's functionality.
# Research on the Impact of BDS-2/3 Receiver ISB on LEO Satellite POD
## 2 Gnss Isb
Therefore, the method to estimate the GPS receiver clock error as one parameter, and to estimate the BDS receiver clock error is another parameter together, equivalent to the way to estimate the GPS receiver clock error as one parameter and estimate the BDS-related ISB as one parameter together. This paper would adopt the first method to deal with the ISBs. With respect to the receiver, different components and modulations of the BDS-2/3 signal in the B1 band [27] would lead to different B1I hardware delay biases of BDS-2/3. Likewise, the B1I/B3I IF combination signals of BDS-2 and BDS-3 would have different hardware delay biases, and the receiver clock errors estimated of BDS-2 and BDS-3 are different, such as the next equation:  
\[dt_{HD}^{\text{C2}}\
eq dt_{HD}^{\text{C3}}\,\ dt_{R}^{\text{C2}}=dt_{r}+dt_{ HD}^{\text{C2}},\ dt_{R}^{\text{C3}}=dt_{r}+dt_{HD}^{\text{C3}}=>\ dt_{R}^{\text{C2}}\
eq dt_{R}^{ \text{C3}} \tag{8}\]  
For the onboard BDS ionosphere-free (IF) measurements from the LEO satellite, the observation equation would be expressed as follows:  
\[\left\{ \begin{array}{c}P_{IF}^{\text{C2}}=p^{\text{C2}}+c\cdot\left(dt_{R}^{ \text{C2}}-dt_{s}^{\text{C2}}\right)+\epsilon_{P_{IF}}^{\text{C2}}\\ P_{IF}^{\text{C3}}=\rho^{\text{C3}}+c\cdot\left(dt_{R}^{\text{C3}}-dt_{s}^{ \text{C3}}\right)+\epsilon_{P_{IF}}^{\text{C3}}\\ L_{IF}^{\text{C2}}=\rho^{\text{C2}}+c\cdot\left(dt_{R}^{\text{C2}}-dt_{s}^{ \text{C2}}\right)+\Lambda_{IF}^{\text{C2}}N_{IF}^{\text{C}}+\epsilon_{L_{IF}} ^{\text{C2}}\\ L_{IF}^{\text{C3}}=\rho^{\text{C3}}+c\cdot\left(dt_{R}^{\text{C3}}-dt_{s}^{ \text{C3}}\right)+\Lambda_{IF}^{\text{C3}}N_{IF}^{\text{C3}}+\epsilon_{L_{IF}} ^{\text{C3}}\end{array} \right. \tag{9}\]
# VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security
## 6 Conclusions  
This study successfully developed and validated a real-time anomaly detection framework for V2X communications, centered on a lightweight VAE-CNN architecture complemented by a sliding window mechanism for processing streaming BSM data. The core innovation lies in the synergistic integration of the VAE's probabilistic modeling for robust normality learning and the CNN's capacity for effective feature extraction from sequential vehicular data. Our comprehensive experimental evaluation rigorously demonstrated the framework's ability to achieve high-performance anomaly detection across a range of challenging attack simulations, consistently outperforming comparable autoencoder-based approaches. Remarkably, the system exhibited outstanding low inference times and efficient training, confirming its viability for real-time deployment on resource-constrained V2X hardware such as OBUs and RSUs. Ultimately, the demonstrated combination of robust detection accuracy, operational efficiency, and adaptability positions this VAE-CNN framework as a significant and practical contribution towards enhancing the security and trustworthiness of V2X communications. By offering a reliable means to identify and mitigate data falsification attacks, this work paves the way for safer and more dependable intelligent transportation systems.  
Conceptualization, Y.T.G.; methodology, Y.T.G., software, Y.T.G. and S.R.J.; validation, Y.T.G., S.R.J., S.G. and J.H.P.; formal analysis, S.G. and S.R.J.; investigation, Y.T.G. and S.G.; resources, J.H.P.; data curation, S.G.; writing--original draft preparation, Y.T.G. and S.R.J.; writing--review and editing, Y.T.G. and S.R.J.; visualization, S.G.; supervision, J.H.P.; project administration, J.H.P.; funding acquisition: J.H.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Strategic Networking & Development Program funded by the Ministry of Science and ICT through the National Research Foundation of Korea (RS-2023-00267476).  
Not applicable.  
Our code is available at [[https://github.com/yonishat/my-repository/blob/main/VAE_Based_Real_Time_Anomaly_Detection_Approach_for_En_hanced_V2X_Communication_Securitiyipynb](https://github.com/yonishat/my-repository/blob/main/VAE_Based_Real_Time_Anomaly_Detection_Approach_for_En_hanced_V2X_Communication_Securitiyipynb)]([https://github.com/yonishat/my-repository/blob/main/VAE_Based_Real_Time_Anomaly_Detection_Approach_for_En_hanced_V2X_Communication_Securitiyipynb](https://github.com/yonishat/my-repository/blob/main/VAE_Based_Real_Time_Anomaly_Detection_Approach_for_En_hanced_V2X_Communication_Securitiyipynb)) (accessed on 10 February 2025).  
The authors declare that they have no competing interests.
## 2 Variable Selection and Research Hypotheses
### Medical Level  
Research has shown that patients tend to choose Tier II and Tier III hospitals primarily due to their higher medical standards and advanced medical equipment [7]. In China, hospital rankings are commonly used to evaluate the medical level of hospitals, with core indicators such as medical technology and comprehensive assessments. Among these, the number of national key specialties (\(\theta_{1}\)) serves as a critical metric for evaluating medical technology, representing specialties in public hospitals with leading medical capabilities and high-quality services nationwide. On the other hand, this study considers hospital reputation as another measure of medical level. This is because the Chinese government does not publicly disclose quality information for medical institutions at all levels, leaving patients to rely heavily on a hospital's long-standing reputation and patient reviews when making decisions. Moreover, numerous studies indicate that, compared to complex medical quality indicators, patients are more responsive to straightforward hospital rankings [34]. Therefore, this study uses the number of positive comments after diagnosis (\(\theta_{2}\)), derived from hospital online data, as a proxy for online reputation. Accordingly, the following hypothesis is proposed: \(H_{2}\)--medical level significantly influences traffic attraction.  
\[H_{2}=v_{1}\theta_{1}+v_{2}\theta_{2}+\zeta \tag{2}\]  
In the formula, \(H_{2}\) represents the function of medical level; \(\theta_{i}\) denotes the observed variables of medical level; \(v_{i}\) refers to the factor loading of \(H_{2}\) on \(\theta_{i}\); \(\zeta\) represents the error term.
# Smart Contract-Based Right-of-Way Trade with Game Theory in Blockchain-Enabled Internet of Vehicles
## 2 An Intelligent Transportation Credit System Based on Blockchain
### Traffic Token-Based System at a Crossroad  
In this section, we study the convoy passing mechanism at a crossroad [23, 24, 25, 26, 27, 28, 29, 30], which is an important scenario for this intelligent transportation credit system. Specifically, we consider how convoys can pass a crossroad in a safe and orderly manner according to the vehicle-mounted smart contract.  
Figure 1 shows the proposed system architecture. A road side unit (RSU) at a crossroad, which is a full node, maintains an independent IoTA chain that records traffic information for its crossroad and neighboring roads. Vehicles are light nodes that only record traffic information related to the road they are currently traveling on. When a vehicle is about to enter a crossroad, it joins the chain, creates a crossroad passing trade record, and begins maintaining a local chain. When a vehicle leaves the current road for a while, in addition to the traffic events closely related to itself, it will automatically delete the previously outdated road information to simplify maintenance and ensure there is enough memory space.
# A Low Complexity Persistent Reconnaissance Algorithm for FANET
## 2 System Model and Problem Statement
### Problem Formulation  
Communication is the basis of cooperation and collaboration between UAVs, which is crucial and essential [10]. For simplicity, we assume that the communication between the UAVs follows the line-of-sight (LoS) model [28; 30]. In the following, we introduce the concept of network connectivity and the FANET threat metric.
## 4 Experiments and Results
### Preparation  
Ten synthetic datasets and ten UCI real datasets were prepared for this experiment, and the detailed information of these datasets is displayed in Tables 3 and 4, including the data size, dimensions, and the number of real clusters. All datasets were obtained from [[https://github.com/Chelsea547/Clustering-Datasets](https://github.com/Chelsea547/Clustering-Datasets)]([https://github.com/Chelsea547/Clustering-Datasets](https://github.com/Chelsea547/Clustering-Datasets)) (accessed on 21 January 2025).  
Secondly, this part compares some classical and advanced algorithms used to demonstrate the superiority of the algorithms, including DPC, DBSCAN, K-means, RNN-DBSCAN, LDP-MST, and HCDC. The content of these six algorithms has already been described in the introduction and related work, so they will not be repeated here. In particular, this paper uses three traditional external evaluation metrics, clustering accuracy [36] (ACC), adjusted Rand index [37] (ARI), and normalized mutual information [38] (NMI), to measure the algorithm's performance. Among them, ACC is an important metric for evaluating the performance of a classification model, which indicates the ratio between the number of samples correctly predicted by the model and the total number of samples, and it takes the value in the range of [0,1]. In this paper, ACC is used to evaluate the consistency between the clustering results and the true labels; ARI is used to measure the similarity between two clustering results, which takes into account the random assignment of element pairs in the clustering results and takes the value in the range of [-1,1]; and NMI is used to compare the consistency of different clustering results and takes the value in the range of [0,1]. Overall, ACC, ARI, and NMI are all used to describe the accuracy of the clustering results, with larger values indicating that the algorithm is more effective in clustering.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**NO.** & **Dataset** & **Instances** & **Dimensions** & **Clusters** \\ \hline
1 & Thyroid & 215 & 5 & 3 \\
2 & Wine & 178 & 14 & 3 \\
3 & Ionosphere & 351 & 33 & 15 \\
4 & Wireless & 2000 & 7 & 3 \\
5 & Ecoli & 366 & 7 & 2 \\
6 & Pima & 768 & 8 & 31 \\
7 & Yeast & 1484 & 8 & 3 \\
8 & Balance & 625 & 4 & 15 \\
9 & Satimage & 6435 & 36 & 3 \\
10 & Breast & 699 & 9 & 6 \\ \hline \end{tabular}
\end{table}
Table 4: Details of ten UCI real datasets  
\begin{table}
\begin{tabular}{c c c c} \hline
**NO.** & **Dataset** & **Instances** & **Dimensions** & **Clusters** \\ \hline
1 & Aggregation & 788 & 2 & 7 \\
2 & Spiral & 312 & 2 & 3 \\
3 & R15 & 600 & 2 & 15 \\
4 & Pathbased & 300 & 2 & 3 \\
5 & Jain & 373 & 2 & 2 \\
6 & D31 & 3100 & 2 & 31 \\
7 & Zelink1 & 299 & 2 & 3 \\
8 & S1 & 5000 & 2 & 15 \\
9 & DS577 & 577 & 2 & 3 \\
10 & T4.8K & 8000 & 2 & 6 \\ \hline \end{tabular}
\end{table}
Table 3: Details of ten synthetic datasets.
# Ring-Overlap: A Storage Scaling Mechanism for Hyperledger Fabric
## 3 Problem Description
### Ring-Overlap Storage Model  
The data stored in Hyperledger Fabric is composed of four parts: world state, block index, ledger data, and historical state (optional). The two most important components of the ledger, which together from the blockchain, are the world state and ledger data. The specific architecture is shown in Figure 1.  
The world state represents the current value of all ledger states and is usually implemented using a K-V database. The current value of a ledger state can be accessed directly through the world state, which is represented as a key-value pair. The ledger data is composed of blocks that are connected together by pointers. Each block contains a set of world state transaction logs with a Merkle Tree structure that cannot be tampered with, which records all changes that contribute to the current world state.  
In the Hyperledger Fabric, the peers on the same channel all store the same blockchain data. According to the properties of ledger data, blockchain data is the primary cause of data inflation, and the world state database is unsuitable for sharding. Therefore, the model in this paper takes blocks as the basic unit of shard data.  
Figure 1: Fabric data storage structure.
# A Scalable and Consistent Method for Multi-Component Gravity-Gradient Data Processing
## 4 Real Data Applications  
We applied our method to the Falcon AGG data from the Kauring gravity gradiometry test site, Australia [40] and FTG data from the Vinton salt dome, LA, USA [3,41]. In these real-world applications, whether using Falcon AGG data over Kauring gravity gradiometry or FTG data over the Vinton salt dome, we emphasize that the fitted data were computed at the original observation points, not at the reduced points. Aiming to process large gravity-gradient data, we also applied the method to more than 1,250,000 Falcon AGG data points from the Canobie Airborne Gravity Survey, Australia.
# Satellite Video Moving Vehicle Detection and Tracking Based on Spatiotemporal Characteristics
## 3 Methods
### Moving Vehicle Detection  
Moving vehicles in satellite videos are presented as faint and tiny targets, and direct detection using traditional background subtraction can result in serious under-detection. To compensate for vehicles being difficult to distinguish from the background owing to their low contrast, DTSTC optimized the KNN moving vehicle detection algorithm using the LAB color-space local contrast enhancement algorithm.  
Owing to the variety of colors in the background areas, the effect can be significantly reduced or even have side effects when contrast enhancement is applied to the image as a  
Figure 4: Schematic diagram of the discriminant condition. The left diagram shows frame \(t\), and the right diagram shows frame \(t+1\). The bottom layer of the diagram represents the original image, the middle layer represents the D-LinkNet extracted road mask, and the top layer is the motion heat map connectivity domain area. The area circled in green is the connectivity domain for inter-frame variations.  
whole. We begin with the contrast enhancement within the constrained region constructed in the previous section. Specifically, the mean \(\mathbf{I}_{\mathbf{H}}=\left[L_{\mu},a_{\mu},b_{\mu}\right]^{T}\) of the luminance (\(L\)) and color (\(a\), \(b\)) feature components of the LAB model are extracted within the constrained region, as shown in Equation (4):  
\[\mathbf{I}_{\mathbf{H}}=\begin{bmatrix}L_{\mu}\\ a_{\mu}\\ b_{\mu}\end{bmatrix}=\frac{1}{W\times H}\begin{bmatrix}\sum_{i=1}^{W}\sum_{j=1 }^{H}L(u,v)\\ \sum_{i=1}^{W}\sum_{j=1}^{H}a(u,v)\\ \sum_{i=1}^{W}\sum_{j=1}^{H}b(u,v)\end{bmatrix}. \tag{4}\]  
Subsequently, a Gaussian low-pass filter is applied to the image to obtain the filtered image \(\mathbf{I}_{\mathbf{g}}\), as shown in Equation (5):  
\[\mathbf{I}_{\mathbf{g}}=I(u,v)\otimes G(x,y,\sigma). \tag{5}\]  
The low-pass filtered image is converted to the LAB color space, and the \(L\), \(a\), and \(b\) channels are separated to obtain the three features, \(\mathbf{I}_{\textit{uhc}}\), as shown in Equation (6):  
\[\mathbf{I}_{\textit{uhc}}=\left[L_{g},a_{g},b_{g}\right]^{\top}. \tag{6}\]  
These features are fused to obtain a significant image value. The resulting highlighted moving vehicles can significantly improve the completeness of moving vehicle detection. Based on this, the KNN motion target detection algorithm was used to detect areas within the constraints, resulting in substantially improved detection results.
## 3 Results and Discussion
### Scenario 1
#### 3.1.1 BER Performance  
This part shows the BER performance of a high-gain SU, before and after pairing it with the interfering MU, using different modulation schemes. The results are provided in comparison of conventional NOMA with WHT-NOMA shown in Figures 6-9.  
In Figure 6, the results are derived using BPSK modulation. It can be observed from this investigation that using NOMA, the BER performance of SU deteriorates after pairing with the interfering MU, even after performing SIC, compared to its unpaired state. In terms of hybrid-access small-cells, this deterioration is unacceptable as SUs are the owners of the small-cell. Thus, to deal with the situation, WHT is applied to NOMA. It can be observed that using WHT-NOMA, for the paired SUs, there is a performance gain of almost 5-10 dB in SNR for different BER values in both perfect and imperfect SIC conditions compared to the conventional NOMA. Thus, it can be seen that using WHT reduces the BER significantly, resulting in an even better BER performance of the paired SU compared to its unpaired state. A similar trend can be seen in Figure 7, where QPSK modulation is used with the WHT matrix of order 4 (\(H_{4}\) from Equation (16)).  
The simulations in Figures 8 and 9 are derived using the higher-order modulation schemes, i.e., QAM and 8-QAM, respectively. It can be observed that the trend in performance gains for WHT-NOMA is similar to the results in Figures 6 and 7, i.e., approximately 5-10 dB in SNR for different BER values, compared to the conventional NOMA. Here also, the results signify the importance of using WHT-NOMA in small-cells by showing even better BER performance of the paired SU, compared to its unpaired state.
# Open-Pit Bench Blasting Fragmentation Prediction Based on Stacking Integrated Strategy
## 1 Introduction
The model performance of random forest, XGBoost, and SVM was compared, and the base learner was selected. An integrated model was developed using the stacking integration strategy to predict the size of rock fragments produced by blasting. The predictive performance of the model was evaluated using four mathematical indicators: \(R^{2}\), MSE, RMSE, and MAE. The importance of features using random forest and XGBoost was analyzed, and the impact of various input parameters on the size of blasting blocks was compared. Finally, the predictive performance of this model was validated using actual engineering blasting data.
## 3 Results and Discussion  
### OTPA Model Validation  
To ensure the reliability of the OTPA analysis results, the effectiveness of the improved OTPA model was validated. This was achieved by comparing the measured vibration signals at the target points on the chassis frame with the signals reconstructed by the model. The results of this comparison are presented in Figure 9.  
Figure 9 shows that the signals reconstructed by the improved OTPA model exhibit good overall agreement with the measured signals, particularly in the dominant vibration frequency ranges. This indicates that the application of SVD combined with the proportional truncation of singular values (with a truncation ratio of 0.4% used in this study) effectively addressed the issue of the ill-conditioned matrix and significantly improved the stability and accuracy of the transfer function estimation. The improved OTPA model is therefore capable of accurately describing the vibration transfer characteristics of the combine harvester chassis frame, which validates its effectiveness for practical applications.  
Despite the good fit, minor discrepancies still exist between the signals. These may be attributed, first, to the inherent coupling phenomenon among the multi-source excitation signals, which can lead to incomplete decoupling. Second, they may be due to the presence of uneliminated background noise or interference from other non-primary excitation sources in the actual operating environment. Nevertheless, the model is deemed sufficiently accurate for identifying the dominant vibration sources and transfer paths. This success  
Figure 8: The OTPA transfer path analysis model.  
Figure 9: Comparison of measured signals and OTPA-reconstructed signals.  
ful validation, showing good agreement between measured and reconstructed signals (Figure 9), is a critical first step, similar to the validation approach used by [PERSON] et al. [55], before proceeding with their source contribution analysis on an electric vehicle.