esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Future Work  
In this research work, a blockchain-based resource efficient and secure data sharing mechanism for VN is proposed. A distributed file storage system (known as IPFS) is considered to tackle the issues related to a centralized storage system. To maintain the trust value of vehicles and to ensure fair payment against given services, IVTP is introduced. For each service, pre-defined amount of charges are to be paid by the ordinary nodes. We introduced blockchain technology to optimize resource utilization of the proposed network and to ensure establishment of a secure data sharing environment. Reviews given by the ordinary nodes are considered as feedback against services delivered by the edge vehicle nodes. Depending on these reviews, the reputation of each edge vehicle node is determined. The edge vehicle nodes are awarded with incentives depending upon the reviews they get from the ordinary nodes. The simulation results show that the gas consumption of the proposed system model is decreased by almost 15%-20% when using PoA instead of PoW. Moreover, it is analyzed that our proposed system provides an efficient and secure data sharing mechanism for VN. However, the tradeoff lies between the system's cost and the data size. The cost of authenticating the vehicles and storing the data increases with the increase in the size of data.  
In future, a fake review detection system will be considered for ordinary vehicles, which will lead to verification of the reputation of ordinary vehicles. To find an equilibrium between a system's cost and data size, different algorithms will be designed.  
**Author Contributions:** [PERSON] and [PERSON] proposed and implemented the main idea; [PERSON] and [PERSON] performed simulations and wrote the simulations section; [PERSON], [PERSON] and [PERSON] organized and refined the manuscript; [PERSON], [PERSON] and [PERSON] worked together and responded to the distinguished reviewers' comments. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Deanship of Scientific Research, King Saud University through the Vice Deanship of Scientific Research.  
**Acknowledgments:** The authors are grateful to the Deanship of Scientific Research, King Saud University, KSA for funding through the Vice Deanship of Scientific Research Chairs.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Study Area  
Zabala river catchment is located in the central-eastern part of Romania (Figure 1), measuring an area of approximately 600 km\({}^{2}\). The altitude values range from 312 m in the sub-Carpathian region, at the confluence with Putna river, to 1700 m (Figure 1) in the Curvature Carpathians zone (Vrancei Mountains), where the largest area of the basin is located. The lithology, which is represented by the internal flysch of the Carpathians, alternating between hard and impermeable rocks (such as sandstones, tuffs, or shales), favors the surface runoff [53; 54].  
The mean relief slope angle in the study area, calculated through the geoprocessing of the digital elevation model, is equal to 12.7\({}^{\circ}\). The areas recording high slope angles (above 15\({}^{\circ}\)), where runoff is highly increased, cover 31% of the total catchment area, demonstrating the exposure of the study area at risk for phenomena associated with surface runoff. According to the results of the supervised classification of the Landsat 8 scene from 2019, the forest vegetation covers approximately 60% of the basin total area, forming extended and compact patches in the mountainous area. In the sub-Carpathian zone, the absence of forest vegetation and the dominance of pastures overlapping a loamy-clay soil texture increase the runoff potential.  
The high slope angles, combined with the hard lithological substrate and the presence of pastures covering a soil with a fine texture determine the augmentation of the flash-flood susceptibility within the perimeters of human communities from the sub-Carpathian zone. According to the General Inspectorate for Emergency Situations of Romania (GIES), the following localities were frequently affected by flash floods: Nistoresti, Nereju, Paltin, and Spulber (Figure 1). The destructive consequences of flash floods have occurred along the Zabala river during the last few years (20.10.2009, 11.05.2010, 22.04.2016, 28.05.2017, and 05.04.2019), leading to important damages to the road network and households, as well as the loss of human lives [55].
# A Framework for Market State Prediction with Ontological Asset Selection: A Multimodal Approach
## 4 Materials and Methods
### Ontology-Based Stock Selection
#### 4.3.4 Sentiment Analysis with BERT  
BERT processes financial news to extract sentiment, providing a forward-looking perspective on market dynamics by predicting sentiment trends at \(t+1\), as demonstrated by [PERSON] (2020) [7]. Unlike traditional sentiment analysis approaches, such as Bag-of-Words or lexicon-based methods, BERT excels in capturing contextual nuances and complex sentiment dynamics. Its bidirectional architecture, as noted by [PERSON] (2020) [36] and [PERSON] (2024) [37], enables the understanding of semantic relationships by analyzing both preceding and succeeding contexts in a sentence, making it particularly suitable for financial texts.  
The innovation in this study lies in employing BERT for predicting future market states (\(t+1\)) rather than merely classifying current sentiment. Prior works, such as [PERSON] (2023) [8], aggregated daily sentiment scores or focused on immediate market impacts, while [PERSON] (2024) [37] highlighted the integration of BERT with financial indicators for trend prediction. This work extends these approaches by applying BERT outputs directly as predictive features for \(t+1\) states, enabling preemptive decision-making in market strategies.  
Furthermore, this study fine-tunes BERT on a curated dataset of financial news, ensuring that the embeddings capture domain-specific vocabulary and contexts. As highlighted by [PERSON] (2020) [36], unlike general-purpose sentiment models, this adaptation significantly enhances the accuracy and relevance of the extracted sentiments. The integration of BERT with the Sentiment Index (SI) and Illiquidity Index (ILLIQ) ensures a multimodal analysis, leveraging textual and fundamentalist data in tandem.  
Empirical evaluations further validate this approach, demonstrating superior performance. While prior studies, such as [PERSON] (2024) [37], achieved accuracy levels around 70%, this framework, integrating BERT with \(t+1\) forecasting, delivers substantial improvements. This result underscores the role of BERT as a cornerstone for robust and forward-looking market predictions, surpassing traditional methods and highlighting its potential to uncover nuanced market dynamics.
## 2 Relevant Techniques
### Tensor Decomposition (TD)  
Due to application requirements of high-order data, tensor decomposition (TD) is used as a tool to analyse high-order data. TD is a high-order extension of matrix decomposition in multi-linear algebra, and is an efficient technique used in many fields [22; 23]. CANDECOMP/PARAFAC (CP) and Tucker decomposition are two particular ways to implement tensor decomposition; the well-known Tucker decomposition is always selected to implement TD.  
Tucker decomposition can be considered to be higher-order extensions of the matrix singular value decomposition (SVD). The Tucker decomposition was introduced by [PERSON] [24] and has been successfully applied to data dimensional reduction, feature extraction, tensor subspace learning, face image recognition [25], data compression, image quality evaluation [26], noise reduction [27], and data analysis [28]. In the present paper, Tucker decomposition is used to construct a watermark embedding domain.  
A third-order tensor \(T\in R^{M\times N\times O}\) is decomposed by Tucker decomposition, there will be obtained three orthogonal factor matrices \(U^{1}\in R^{M\times P}\), \(U^{2}\in R^{N\times Q}\), \(U^{3}\in R^{O\times R}\), and a core tensor \(K\in R^{P\times Q\times R}\)[24]. Figure 1 shows Tucker decomposition of a third-order tensor \(T\).  
Each element in the core tensor \(K\) represents the degree of interaction between different slices. The Tucker decomposition [22] is defined in Equation (1).  
\[T\approx K\times_{1}U^{1}\times_{2}U^{2}\times_{3}U^{3}\approx[[K;U^{1},U^{2}, U^{3}]]. \tag{1}\]  
Figure 1: Tucker decomposition: a third-order tensor \(T\) decomposed by Tucker decomposition can obtain three orthogonal factor matrices \(U^{1}\), \(U^{2}\), \(U^{3}\), and a core tensor \(K\).  
For each element of the original tensor \(T\), the Tucker decomposition [22] is expressed in Equation (2).  
\[T\approx\sum_{p=1}^{P}\sum_{q=1}^{Q}\sum_{r=1}^{R}k_{pqr}u_{p}^{1}\circ u_{q}^{2 }\circ u_{r}^{3}. \tag{2}\]  
where \(P\), \(Q\), and \(R\) correspond to the number of column vectors of the factor matrices \(U^{1}\), \(U^{2}\), and \(U^{3}\), respectively. \(P\), \(Q\), and \(R\) are generally less than or equal to \(M\), \(N\), and \(O\), respectively. The symbol '\(\circ\)' represents outer product between two vectors. where the symbol '\([\![\) I\(]\!]^{\prime}\) is a concise representation of Tucker decomposition given in [22]. The core tensor \(K\) has the same dimension as tensor \(T\), and it is expressed in Equation (3).  
\[K\approx T\times_{1}U^{1}\times_{2}U^{2}\times_{3}U^{3}. \tag{3}\]  
\(K\) has full orthogonality, that is, any two slices of the core tensor \(K\) are orthogonal to each other, and the inner product between the two slices are zero.
## 4 Measurement Results and IL Prediction  
### _Impedances of the Equivalent Modal Circuits of the Motor Drive System Setup_  
Based on the enhanced single-probe setup in Section 2 and the measurement procedure in Section 3.2, the modal impedances of the motor drive system equivalent modal circuits are determined (see Figure 10).  
Figure 8: Setups to determine CM equivalent circuit: (**a**,**b**) in the presence and (**c**,**d**) in the absence of the motor drive system.  
Figure 9: (**a**) Setup for S-parameter measurement of a three-phase filter and (**b**) filter arrangement inside a metallic enclosure equipped with SMA connectors.  
The impedances of the LISN and cable in the equivalent modal circuits (see black dash line in Figure 10: \(Z_{LISN\_Cable\_DM}\) and \(Z_{LISN\_Cable\_CM}\)) are almost equal to 50 \(\Omega\) and 50/3 \(\Omega\) in the intermediate frequency range as expected. However, at higher frequencies, these impedances increase owning to cable effects, thus confirming the necessity to evaluate not only the source but also the load CM and DM actual impedance.  
The impedance \(Z_{MDS\_DM}\) of the motor drive system shows a significantly different behavior depending on the power being turned OFF and ON (see the black curve and other curves in Figure 10a). Thus, the online DM impedance cannot be represented by the one measured offline in this motor drive system setup. This conclusion is in line with other research works [17]. Except for a small portion below 70 kHz, where the switching frequency and its harmonics affect measurement accuracy, the impedance \(Z_{MDS\_DM}\) does not exhibit significant variations if the switching frequency is changed (sf = 5 kHz, 10 kHz, and 15 kHz). When power is ON, and the motor drive system is OFF (grey curve), it displays almost the same impedance as the other three operating conditions but with less noise at low frequency. This result suggests measuring the online DM impedance by simply turning ON the power while leaving the motor drive system OFF.  
None of the cases exhibit significant variations of the CM equivalent impedance \(Z_{MDS\_CM}\), except for frequencies below 100 kHz, where the measured impedance is more affected by the switching frequency and its harmonics than in the DM case. However, no significant variations are observed if the CM impedance is measured online or offline. As a consequence, offline impedance measurement can be effectively used for the CM. These conclusions are consistent with those in [17; 18].
# Blockchain Application in Internet of Vehicles: Challenges, Contributions and Current Limitations
## 5 Findings and Future Directions
### Review Findings: Limitations of Using Blockchain in IoV-Assisted Smart Grid
**Limitation 5--IoV-Specific and Optimized Consensus:** As found in the analysis of the studies, several consensus algorithms have been used for the demonstration of IoV scenarios, which resulted in the following issues. Because it requires a significant amount of energy to confirm a transaction, PoW is a computationally costly consensus algorithm [113]. In addition, the PoS algorithm faces a problem with rich rules [114]. In addition, BFT-related algorithms are unsuitable for large public blockchain networks with large numbers of players [115; 116]. As a result, several consensus mechanisms have been developed to address the limitations and enhance the performance of currently popular mechanisms. One drawback shared by most of the consensus algorithms is their single-purpose use, such as their use in cryptocurrencies exclusively. However, due to the distinctive nature of IoV, there are several challenges associated with implementing these algorithms in the IoV, such as block validation, security, reward schemes and energy usage. Thus, IoV-specific and optimized consensus algorithms have the potential to expand the usefulness of incorporating blockchain into IoV [59]. Even though there are some preliminary studies and implementations of IoV-specific consensus algorithms [108; 117; 118], they still lack reliability, since they need to be further tested and evaluated. **Limitation 6--Incentive Mechanisms:** In a common public blockchain network, a miner or validator is generally rewarded for successfully generating a block. This incentive is distributed to the participants in the case of a group of miners or validators who work together. For instance, a miner who accomplishes the computationally demanding work first will be paid with a number of BTCs. Meanwhile, an Ethereum transaction will be charged a gas-free to compensate miners for contract execution. However, the lack of smart grid-centric incentive mechanisms continues to be a challenge, since there are limited incentivization schemes available in the literature [119; 120; 121]. Providing incentives, such as bitcoin, money, carbon credits and reputation value, through the blockchain eventually helps increase the consumption of clean and renewable energy, as well as promoting participation in smart grids. As a result, one important future research direction is to create effective and beneficial incentive mechanisms with equal distribution to incentivize all stakeholders, including producers, consumers and miners/validators, to participate in the blockchain network toward clean and renewable energy consumption. Furthermore, penalty systems are required to prohibit harmful acts by any party.
## 2 Numerical Models and Schemes
### Time Step and Numerical Scheme  
Unsteady numerical simulations were conducted by commercial software ANSYS FLUENT 14.0. During the numerical simulations, the results of steady RANS simulations were used as the initial flow field for the transient simulations. In the steady RANS simulations, the multiple reference frame approach was used for the runner zone; in the transient simulations, a sliding mesh approach was used. The time-step was set to 0.0002 s, corresponding to 1.2\({}^{\circ}\) of the runner rotating at initial steady condition. Even at the maximum speed during the runaway process, the rotation angle at every time-step is less than 1.6\({}^{\circ}\). It is small enough to simulate the influence of the rotor stator interaction. The maximum number of iterations per time-step was set to 40, and the residual convergence criterion at each time-step was 1.0 \(\times\) 10\({}^{-5}\). The SIMPLEC algorithm was chosen to achieve the coupling solution for the velocity and pressure equations. The second order discretization schemes in time and space were used.
# A Quality Control Method for High Frequency Radar Data Based on Machine Learning Neural Networks
## 1 Introduction
They examined the latitude, longitude, average radial direction, and speed of the radial data. Using this information, they assessed the data quality. Moreover, they compared the measured radial velocity with theoretical values, serving as a benchmark for quality control. Parameter monitoring: [PERSON]'s team [15] recognized that radars output parameters beyond just radial velocities. They utilized these parameters for quality control, believing that these parameters provide added evidence for the quality of radial velocity data. Real-time monitoring: [PERSON]'s group [16] introduced a practical approach. They installed real-time monitoring equipment on buoys within the radar area, enabling instant diagnostics of non-speed parameters. This offered real-time quality feedback for radar data, assisting researchers in adjusting their quality control strategies. Although these methods significantly improved the quality of radar data, anomalies might still emerge during the vector field synthesis. Thus, we present a new machine learning method specifically tailored for the quality control of these synthesized vector fields. In this study, we employed machine learning techniques for the quality control of the synthesized radar ocean-current vector velocity data. We used the Bi-LSTM model to analyze the time-domain data and utilized its predictive residuals for anomaly detection. Compared to traditional methods, machine learning offers a more automated, efficient, and real-time solution. Conventional approaches often involve multiple steps, such as manual filtering, threshold setting, and geographical validation, which are not only time-consuming but may also introduce human errors. In contrast, deep learning models can automatically extract data features, substantially reducing the need for manual intervention and providing more consistent and reliable results. By applying machine learning for data anomaly detection, we demonstrated its potential in streamlining processes and enhancing data quality. The results of this research provide valuable guidance for improving radar data quality. In this section, we have discussed the high-frequency radar technology and its applications and challenges in marine observations. In response to these challenges, we introduce a novel method utilizing machine learning for radar data quality control. In the sections that follow, we will describe in detail the data and methods used, then present our research outcomes, and wrap up with a summary of the entire study.
## 1 Introduction
Finally, existing crop phenotyping systems are primarily provided by companies such as Zealquest (Zealquest Science and Technology Co., Ltd., Shanghai, China) and Phenospex (Phenospex B.V. Heerlen, the Netherlands), which are not only expensive but also fail to address the issue of synchronized data collection timing. For example, the minimum configuration price for the PhenoWatch imaging unit developed by Zealquest is 220,000 USD. Each parameter is collected independently, without achieving synchronized collection, making it unsuitable for application in fast-moving crop phenotyping monitoring systems. This severely hinders its development and application in the field of smart agriculture. Therefore, considering the issues of asynchronous data collection and high costs in current fast-moving crop phenotyping systems (such as UAVs), this paper selects relatively low-cost hardware and designs a low-cost, high-synchronization crop phenotyping data collection system based on the PTP between the MAC layer and physical layer of the network, laying the foundation for equipping fast-moving crop phenotyping platforms with multiple sensors and achieving synchronous collection of crop phenotyping parameters.
# Fast Screening Algorithm for Satellites Based on Multi-Constellation System
## 2 Analysis of Satellite Selection Algorithms in Satellite Positioning System
### GDOP Values for Multi-System Satellite Position System  
In a multi-constellation satellite positioning system affected by the different time bases between systems, it is necessary to consider adding clock biases between the different systems as state variables when forming the navigation observation matrix \(G\)[10]. Taking the GPS/BDS/Galileo three-system satellite positioning system as an example, if a set of \(M\) BDS satellites and a set of \(N\) Galileo satellites are added, the navigation observation matrix \(G\) is modified as follows:  
\[\left[\begin{array}{cccccc}l_{1}&m_{1}&n_{1}&1&0&0\\ \dots&\dots&\dots&\dots&\dots&\dots\\ l_{N}&m_{N}&n_{N}&1&0&0\\ l_{N+1}&m_{N+1}&n_{N+1}&0&1&0\\ \dots&\dots&\dots&\dots&\dots\\ l_{N+M}&m_{N+M}&n_{N+M}&0&1&0\\ l_{N+M+1}&m_{N+M+1}&n_{N+M+1}&0&0&1\\ \dots&\dots&\dots&\dots&\dots\\ l_{N+M+O}&m_{N+M+O}&n_{N+M+O}&0&0&1\end{array}\right] \tag{6}\]  
At this point, the number of columns in the observation matrix has reached six. With the addition of the BDS and Galileo satellites, the number of observable satellites will significantly increase. Assuming that at a certain epoch, a total of 22 GPS/BDS/Galileo satellites are observed simultaneously, and due to the introduction of clock bias, the number of unknowns in the linearized pseudo-range observation equations reaches six. Therefore, at least six visible satellites need to be selected for positioning. The optimal GDOP value selection method needs to carry out the \(C_{22}^{m}(m\geq 6)\) times of the GDOP value operation in order to obtain better satellite spatial configurations. If we continue to improve the positioning accuracy by invoking the weighted least squares solver, a total of \(C_{22}^{m}\times 2\) matrix multiplications and \(C_{22}^{m}\) inversion operations are required in this calendar element to complete the star selection.  
It can be seen that with the increase in the number of satellite systems and satellites, both the number of columns in the observation matrix and the number of satellites increase exponentially. The traditional GDOP-based satellite selection method, which calculates the minimum GDOP value based on permutations and combinations, takes too much system computation time during the satellite selection process. Therefore, the traditional optimal GDOP-based satellite selection method is not suitable for multi-constellation single-point positioning.