esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, we introduced a hard security solution, i.e., the improved Hyperledger Fabric, to implement a blockchain-enabled IoV for safe vehicle information sharing. This paper comprehensively highlighted the issues related to IoV. The literature review concluded that most of the security services can be achieved by the implementation of blockchain. Hyperledger Fabric is one of the major implementations of blockchain for achieving security services. This paper provides a brief introduction to IoV, Hyperledger Fabric, consensus algorithms, privacy, and anonymization techniques, in conjunction with the additional terminology necessary to understand the problem statement and proposed scheme. Then a critical analysis is undertaken of existing consensus algorithms and conditional privacy schemes in the context of the IoV environment. The paper also discusses the non-applicability of existing schemes to the IoV environment. Thus, there is a requirement for an efficient decentralized scheme for IoV that can address security issues and fulfill the latest security requirements of vehicular communication. Hyperledger Fabric appears to be the most suitable emerging solution for a resource-constrained environment, and addresses some of the functionality issues of IoV. The security analysis indicates the proposed scheme will address some of the limitations of existing schemes and fulfill the security criteria of CPPA schemes for IoV. The two main contributions of this research relate to the manner in which it addresses the issue of the leader selection process. The first is to select leaders based on their reputation. A reputation-based scheme is utilized to calculate the accurate reputation of RSUs. Second, this paper presents an anonymous and traceable CPPA approach that can be utilized in a vehicular network. We also evaluated the performance of the proposed solution. In future work, we will choose a more effective and scalable consensus algorithm, and a more efficient scheme to increase the accuracy of the leader's reputation. We will also create a version of the suggested approach for real-world experimentation in a permissioned system, enabling us to analyze and modify the scheme to make it more realistic.  
Conceptualization, K.N.Q. and L.S.; methodology, A.A. and T.A.E.E.; software, T.A.E.E.; validation, A.A.-D.; writing--original draft preparation L.S.; and K.N.Q.; visualization, I.T.J.; writing--review and editing, I.T.J. and B.A.; supervision, N.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 11: Reputation of a malicious miner.
# A Framework for Market State Prediction with Ontological Asset Selection: A Multimodal Approach
## 4 Materials and Methods  
As illustrated in Figure 1 (presented in Section 2), the methodology of this study is structured into two main stages: (i) Ontology-Based Stock Selection and (ii) Market State Prediction.  
This framework integrates multiple components to enhance predictive accuracy, starting with the ontological classification of stocks based on financial indicators such as liquidity, profitability, debt, and growth. These selected stocks are then analyzed using multimodal techniques, including sentiment analysis with BERT, volatility modeling with GARCH and the Fear Index (VIX), and dynamic market regime transitions modeled by the Non-Stationary Markov Chain (NMC). The predictive power is further enhanced by incorporating Long Short-Term Memory (LSTM) networks, fine-tuned through hyperparameter optimization. The final output determines the market state (bull, neutral, or bear), providing a robust foundation for future work on trading strategy development.
## 3 KGs Learning
### Dynamic and Task-Specific Learning
#### 3.3.2 Extensions: Recommendations, Reinforcement Learning, and Beyond  
When user-item, item-attribute, or item-item interactions are represented as a graph and GNNs are used to learn node embeddings, the recommendation accuracy improves significantly [120]. EMKR employs multi-task learning (recommendation + KGE) to enhance the performance of both tasks. Reinforcement Negative Sampling (KGRec-RNS) uses reinforcement learning (RL) to identify \"meaningful\" negative samples [121], which provide far more informative negatives than random sampling, improving training efficiency and interpretability.  
For fully inductive learning in evolving KGs, one method has been proposed to classify or predict links using GNNs on sampled subgraphs rather than the entire graph [96]. Techniques such as personalized PageRank (PPR) are employed to extract neighboring nodes, which helps mitigate over-smoothing and enables flexible inductive reasoning. Ref. [122] proposed a sequential recommendation system based on knowledge graphs and transformers, and ref. [123] introduced an enhanced multi-task learning framework that integrates recommendation and KG embedding tasks. Additionally, some approaches combine first-order neuro-symbolic and logic rule embeddings to improve the explainability of recommendation tasks [124]. For example, logical rules such as \"If X is related to Y, and Y is similar to Z, then X is related to Z\" can be used in conjunction with neural embeddings. Moreover, BiG-Fed [125] further enhances the recommendation performance by combining hierarchical optimization with federated learning.  
Ref. [126] introduced a method for extracting local subgraphs from large-scale graphs to reduce the training time and improve prediction accuracy. This approach performs link prediction based on subgraphs, achieving high efficiency and performance without requiring the entire graph to be trained, making it particularly useful for large-scale datasets like recommendation systems. Ref. [127] proposed a model for knowledge graph-based question generation using subgraphs. By focusing on specific nodes and relationships, the model generates questions that reflect the structural information from the graph, enabling more precise responses in question-answering systems.  
Global-Local Anchor Representation (GLAR) [128] is an inductive link prediction model centered on subgraphs, capable of effectively predicting links in dynamic environments where new nodes and relationships are continuously added. This model leverages the properties of local subgraphs to enable scalability and achieves high performance with an efficient computation.  
In summary, dynamic and task-specific learning models offer significant advantages over static embedding methods by adapting to new data using meta-learning and dynamic relation learning. This adaptability not only improves the overall accuracy but also enhances the robustness of the knowledge graphs by reducing errors, such as hallucinated relationships.
## 4 Results
### Straight-Through Labyrinth Comparison Results
The dependence of the predicted flow coefficient on Reynolds number, which is admitted by some correlations, is not expected to play a role in these results, since the same trend is achieved as the results at constant Reynolds (i.e., same Reynolds number for all clearance values) are considered (even if not shown in the paper). According to Figure 10b, the percentage values of all the correlations except [PERSON] range from \(-25\%\) to \(+25\%\). The shift between negative to positive values is related to the crossing of the experimental and analytical trends of Figure 10a. This variation is far more limited for the [PERSON] approach (from about \(-15\) to \(-5\%\)), thus showing, albeit with a certain offset, a more adequate modeling of the actual clearance behavior. Overall, results point out that Vermes correlation, even if it was among the worst performing as far as the analysis was limited to the nominal clearance size (Figure 7), was revealed to be the only one able to accurately predict the seal behavior with changing clearance gap; significant mispredictions can be expected with the adoption of the other approaches, as the actual clearance size changes during the engine operability and lifetime. As anticipated, it is also worth considering that all the employed equations were developed especially for straight-through seals with perfectly straight teeth, having a constant width moving from their hub to the tip. Thus, some geometrical features which defines the current seal, such as the \(\alpha\) and \(\delta\) angles, are not considered in those models, but they are expected to play a major role in the flow field and hence, in determining the effective value of the discharge coefficient. Figure 9: Pressure distribution across the straight-through labyrinth for two values (\(PR=1.2\) and \(PR=2.5\)) of the overall pressure ratio (nominal \(c\) value).
## 3 Experimental Results
### Effect of Layering in \(Z\)-Direction of the Cavity  
The purpose of layering the cavity in the Z-direction of is to prevent the particles/powder from accumulating under the action of gravity, to enhance the movement ability of the underlying particles/powder, and on the other hand, to adjust the collision gap between the particles and the cavity in the Z direction.  
The number of Z-direction layers corresponding to the cavities A1, B1, and C1 are 1, 2, and 5, respectively, and the Z-direction collision gaps of B1 and C1 are 1/2 and 1/5 of A1, respectively.  
This part of the test was carried out with 2 mm stainless steel particles and 32 mesh tungsten carbide powder. The test results are shown in Table 6. The amplitude-frequency response curves corresponding to Table 6 are shown in Figures 8 and 9.  
The structural frequency variation caused by different Z-direction layering schemes is within 3%; the best solution for damping effect of stainless steel particles is C1_304_0.9, which corresponds to a decrease of 41.8% for the measuring point response amplification factor \(A_{\text{sin}}\), and \(A_{\text{spec}}\) decreases 46.5%; The best solution for tungsten carbide powder is A1_WC_0.9, which corresponds to a decrease of 86.30% for \(A_{\text{sin}}\), and a decrease of 52.14% for \(A_{\text{spec}}\).  
The test results show that for 2 mm stainless steel particles, Z-direction stratification is beneficial to improve the damping effect of stainless steel particles. The more Z-layers, the lower the response peak. However, from the perspective of the variation of the RMS value, the promotion effect of layering is not obvious. The Z-direction layering has some negative effects on the tungsten carbide powder. The Z-direction layering of the cavity under sinusoidal excitation reduces the particle damping, while the layering has no obvious influence on the particle damping under random excitation. In view of the above results, it is not recommended to layer the cavity in the Z-direction.
## 4 Evacuation Model Design
### Pedestrian Sheltering (Destination)  
By researching the evacuation of residents in San Diego, California, due to the wildfire of 2007, [PERSON] et al. found out that the first preferred option for evacuation destination was relatives' or friends' house. The other chosen destinations were hotels, holiday houses or campsites, and public shelters at the bottom of this list [59]. In other research carried out by [PERSON] et al. [53], it was realized that in a no-notice evacuation, the first chosen destination was public shelters, and after that, relatives' or friends' houses. This research also studied subjects that could affect the preference for one option over the other. Returning home after the incident, which before this work was not considered an option for a WUI wildfire, was also considered. Overall, the anticipation model for the choice of one destination option over the other by evacuees was modeled based on environmental and societal clues.  
Studies on other incidents, such as hurricanes, reveal that evacuees prefer friends' or families' homes and hotels for their evacuation destination rather than public shelters. Churches, workplaces, and other locations were also investigated as possible evacuation destinations in hurricane research [60]. Discrete choice modeling was utilized in this research to anticipate the tendency of individuals to choose a destination following clues they perceive from society or the environment during an incident.
## 4 Simulation Results and Performance Analysis
### Performance Metrics  
The performance of the proposed scheme CDS_SC is evaluated in terms of cluster stability, cluster density, cluster head density, and percentage vehicles acting as a cluster head, cluster member, and gateways. The cluster stability was measured by how many times the color (state) of a vehicle changed during the simulation represented in terms of percentage of vehicles. The cluster density was measured as a number of members belonging to one cluster with the increase in the number of vehicles per kilometer. Cluster head density was measured as the average number of cluster heads per kilometer, cluster state distribution was measured as the percentage of vehicles in each state per kilometer and packet delivery ratio was calculated as the percentage of vehicles that receives the message. End-to-end delay was computed as the average time required for disseminating the alert message from the source vehicle to all the vehicles in the 5 km range approaching towards the region of interest. The packet collision ratio was calculated as the percentage of packets that collided during transmission and the packet loss ratio was calculated as the percentage of packets that were lost during the transmission.
## 2 Experimental Part
### Methodology
That is why by measuring the electric current one must be aware that parallel reactions with a different number of exchanged electrons are possible. Moreover, redox reactions in the bulk competing with the target electrochemical reactions are also possible. That is why Equation (1) will be used very cautiously to help estimate the obtained numerical results for the electric current. According to the theory, the reversible electromotive force (EMF) of a net electrochemical reaction in fuel cells is given by the well-known Nernst equation [29]:  
\[E_{Nernst}=E_{0}+\frac{RT}{nF}ln\left(\frac{\Pi_{i}a_{reactant,i}^{c_{i}}}{ \Pi_{j}a_{product,i}^{c_{j}}}\right) \tag{2}\]  
where:  
\(E_{0}\) is the standard EMF at unit activity and 1 bar pressure; \(i\) and \(j\) are the numbers of reactants and product species; \(a_{ij}\) are the activities of the species and \(c_{ij}\) are the stoichiometric coefficients in the net reaction; \(R\) is the universal gas constant, and \(T\) is the temperature. In the case of various possible reactions (as shown in Table 3) the calculation of the reversible EMF becomes quite uncertain. The operation of an electrochemical system, such as the fuel cells, is associated with electric currents, i.e., with irreversible processes and certain losses in cell efficiency [29], like:  
* Activation losses, expressed by the measured overpotential at the electrochemical reactions;
* Concentration (or mass transfer) losses;
* Losses due to incomplete conversion of reactants to products;
* Ohmic losses and;
* Losses due to fuel crossover and internal current. The latter are strongly influenced by the ionic conductivity of the separation membrane. Nevertheless, the open circuit electromotive force of the cell is considered as its reversible thermodynamic potential. The output (load) voltage \(V\) of the fuel cell is related to the Nernst EMF and the cell Ohmic resistance \(r\) by the equation [29]:  
\[V=E_{Nernst}-i_{FC}r, \tag{3}\]  
Figure 2: Distribution of different forms of sulfide ions depending on the pH value [28]. where \(i_{FC}\) is the electric current. The voltage fuel cell efficiency is given by the ratio:  
\[\varepsilon=\frac{V}{E_{Nernst}} \tag{4}\]  
The real fuel cell efficiency comprises the reversible thermodynamic efficiency, the voltage efficiency and the fuel utilization one [30].
# Study on the Impact of the Coastline Changes on Hydrodynamics in Xiangshan Bay
## 2 Materials and Methods
### Hydrodynamic Analysis  
The hydrodynamics in the bay are driven mostly by tidal currents. Hence, the tidal currents have significant effects on the transport of substances, the dispersion and distribution of pollutants, as well as the ecological environment within the bay. We analyzed residual currents and tidal currents before and after the coastline changes in Xiangshan Bay.  
As shown in Figure 7, the main characteristics of the residual currents in the Xiangshan Bay in the control experiment were as follows: there was a significant correlation between the distribution of residual currents and the coastline orientation of Xiangshan Bay. The residual current was relatively weak inside the three secondary bays, whereas it was strong and chaotic at the entrance of the three secondary bays. The currents in the middle of Xiangshan Bay were relatively fast and chaotic. In contrast, the tidal currents in the eastern part had lower velocities and a more consistent direction, and they gradually transitioned from a southwest-northeast direction initially to a west-east direction. The maximum flow velocity of the residual current throughout the bay was located in the area from the inner bay towards the outer region. The residual current in this area was divided into two branches: one strong branch surged towards the Fodu channel in the southwest-northeast direction with a uniform flow direction, and the average velocity was \(\sim\)10 cm/s. After passing through the narrow area below Meishan Island, the velocity weakened to \(\sim\)5 cm/s. Another weaker current flowed along the northwest-southeast direction along the coastline towards the Niubishan channel, with a velocity of \(\sim\)5 cm/s.
## 5 Conclusions  
In conclusion, our research provides the first detailed description of the structure of the gut microbiome of juvenile pike (_E. lucius_). The dominant phyla were _Proteobacteria_ and _Actinobacteria_, followed by lower percentages of _Bacteroidetes_ and _Firmicutes_. These phyla are the main components of the gut microbiome in many other fish species [56; 65]. Despite the fact that there were no significant differences in the percent abundance of gut bacteria species, some taxa were more abundant at certain levels of salinity. At 7%o salinity, _Planctomycetes_, _Rhodobacterales_, and _Alphaproteobacteria_ were more abundant than at other levels of salinity; at 3%o, in contrast, _Betaproteobacteria_ and _Burbolderiales_ were more abundant; and in the control tanks, _Aeromonas_ were more abundant. Furthermore, the alpha diversity based on the OTU index showed that salinity affected the number of species. Taken together, these findings indicate that salinity influences the bacterial biodiversity. Our results also suggest that salinity influences the composition of the pike gut microbiome in terms of the percent abundance of bacteria. At 3%o salinity, mortality was low (similar to that in the control tanks), suggesting that this concentration is tolerated by the pike, beneficial for their development, and protects them from pathogens at an early life stage. Our findings suggest that salinity adjustment can improve fish welfare and aquaculture practices. To test this hypothesis, future interspecific studies should test a wider range of salinity levels and include long-term exposition.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/7/2506/s1](http://www.mdpi.com/2076-3417/10/7/2506/s1)]([http://www.mdpi.com/2076-3417/10/7/2506/s1](http://www.mdpi.com/2076-3417/10/7/2506/s1)): Figure S1: Alpha diversity metrics. Rarefraction curves of the gut microbiome structure from pike kept at different levels of salinity. (A) Observed OTU's, (B) species richness (Chao1), and (C) [PERSON]'s diversity index; Table S1: Statistical analysis of the mean relative abundance (%) \(\pm\) SD of phyla, classes, orders, and families between groups.  
Conceptualization, S.C. and T.D.; methodology, T.D. and S.C.; software, T.D.; validation, T.D.; formal analysis, T.D. and M.G.; investigation, T.D.; resources, R.K.; data curation, T.D.; writing--original draft preparation, T.D.; writing--review and editing, T.D., S.C., R.K., and M.G.; visualization, T.D.; supervision, S.C.; project administration, S.C. and T.D. All authors have read and agreed to the published version of the manuscript.  
The project was financed as part of a grant statutory project (No. 29.610.024-110) of the University of Warnia and Mazury in Olsztyn and financially co-supported by the Minister of Science and Higher Education in the scope of the program entitled \"Regional Initiative of Excellence\" for the years 2019-2022, Project No. 010/RID/2018/19, amount of funding 12.000.000 PLN.
## 2 Data and Methodology
### Data Analysis from Sampling and Response Designs
#### 2.4.2 Accuracy of the Detected Time of Disturbance  
For evaluating the accuracy of time detection of disturbance by CCDC, we used the set of 294 disturbed samples by clearing and fire. We tracked them over time in the Landsat images and VI profiles using the Collect Earth Online. This tool was customized here to record the type and date of each disturbance in the attributes of the validation points (shapefile). Thus, the date of change was obtained visually for the reference data and was compared with the CCDC output to obtain the difference between them. The evaluation of the date of disturbance occurrence was affected by the number of clear observations in the images. In other words, the reference date of disturbance occurrence can be different from the exact time of the event due to the limitations imposed by cloud cover, cloud shadows, and temporal resolution of the instruments.  
The date of change for clearing events considered the time when the vegetation was completely removed (clear-cutting). The date of change for fire events considered the time when active fire or burned areas were first recorded in the reference samples. The accuracy of time detection of disturbance by the CCDC algorithm was then calculated by comparing this reference dataset with the CCDC outputs of the time of occurrence of each disturbance type in 2019.
## 4 Case Study  
To validate the feasibility and performance of the proposed system, a case study was conducted, implementing the CVROSS in Kwun Tong District, Hong Kong. Due to the seriousness and urgency of the problem of traffic congestion in Hong Kong, particularly in Kwun Tong District, an IoT-based system for surveillance of roadside loading and unloading bays is much needed. The entire implementation was divided into three phases: (i) site selection, (ii) deployment of the CVROSS and (iii) establishment of web-based user interfaces. The project commenced with data collection mainly from selected site visits focusing on Kwun Tong District. Having obtained a better understanding of traffic situations and occupation, a simulation model was built based on traffic features and real cases in Kwun Tong District. Subsequently, a solution with the CVROSS system architecture was deployed to tackle the issue with the help of a web application. Thus, implementing the CVROSS involved the application of computer vision, cloud computing, big data analysis and reusable energy (solar power), to detect, recognize and match vehicles and objects, hence providing road users with comprehensive and real-time information, after image processing. The information was also visualized using a front-end web interface to enhance understandability.