esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Edge Detection and Enhancement
#### 3.2.1 Structure-Oriented Semblance  
We examined several structural attributes, and the results showed varying degrees of success. For example, the Structural Oriented Semblance attribute computes the lateral changes in the seismic response due to structure and stratigraphy variation. It is based on a cross-correlation algorithm and directly applies to the seismic data to create a fault edge preservation cube. It does not need a dedicated dip and azimuth steering volume for orientating itself to the seismic reflector. This attribute calculates the correlation between the neighboring points and identifies the faults and other near-vertical structural features due to low correlation along the neighboring seismic events. It is very sensitive to changes in phase but less susceptible to amplitude changes. Therefore, the areas with high amplitude such as fault edges are not detected with semblance. This is a major drawback of the semblance attribute and it fails to capture discontinuities depending upon seismic amplitude variations in semblance volume. Semblance is not influenced by high dipping seismic reflectors due to the structurally oriented nature of the filter. Semblance depends upon the filter size (lateral extent in-line and crossline direction) and filter depth (vertical extent). Semblance gives us the structure-oriented averaging. However, in order to map the faults properly, fault-oriented averaging should also be known, which is not derived in the traditional semblance attribute. Hence, the standard semblance works well only for vertical or near vertical faults. Therefore, automated fault tracking on such a volume may not produce optimum results until faults have a mostly vertical and consistent throw.
# AI Agents Meet Blockchain: A Survey on Secure and Scalable Collaboration for Multi-Agents
## 8 Research Challenges and Future Directions
### Ethical and Regulatory Concerns  
The rise of AI agents introduces a set of unique ethical and regulatory challenges. One major concern is the difficulty in assigning liability for unintended consequences, particularly when AI agents interact across complex and multi-agent ecosystems without direct human intervention [112]. Specifically, the absence of a central authority results in challenges such as biases in training data, unethical decision making, and harmful behaviors that can occur when agents collaborate autonomously [113]. As AI agents often access and share data autonomously, it becomes difficult to enforce data protection regulations and maintain user confidentiality [31]. Additionally, the lack of standardized global frameworks creates inconsistencies, particularly in cross-border contexts where laws and enforcement mechanisms vary significantly.  
To address these issues, future research should concentrate on developing ethical guidelines and regulatory frameworks that can be applied globally to decentralized systems. For instance, the ETHOS framework [114] suggests leveraging decentralized governance models to enhance accountability and transparency. It also introduces AI-specific legal entities and reputation-based systems to manage risk and incentivize compliance with ethical norms. Another promising direction is the integration of blockchain-based accountability mechanisms, such as immutable on-chain logs of AI agent activities [70]. Moreover, decentralized frameworks such as multi-weight reputation systems can enhance fairness and reduce ethical violations [113]. In terms of tackling global challenges, the regulatory aspects of AI and blockchain integration need flexible and technology-neutral regulations. For example, the European Union has implemented the GDPR and proposed the AI Act to ensure data protection and promote ethical AI practices [115]. Similarly, the United States adopts a sector-specific regulatory framework, while China integrates regulations at the national, provincial, and local levels to maintain state authority [87]. A critical aspect of  
Figure 8: Hybrid quantum computing framework integrating AI agents.  
this effort is the promotion of \"Responsible AI\" [116], which ensures that AI systems are designed and deployed in ways that prioritize fairness, transparency, and accountability. In essence, the collaboration among policymakers, industry stakeholders, and academia will be essential to create adaptive and proactive regulatory measures that can keep up with the rapid pace of AI agent development.
###### Abstract
Additionally, Transformers, known for their attention mechanisms, have demonstrated potential in handling multi-source datasets and reconstructing three-dimensional thermal structures [14]. These models excel in analyzing complex datasets, such as satellite observations and Argo float measurements, enabling accurate predictions of ocean-typhoon interactions, crucial for improving typhoon intensity forecasts and mitigating their impacts. Moreover, news reports play a crucial role in documenting typhoon events and disseminating disaster prevention information. In this study, we apply text mining and natural language processing (NLP) techniques to analyze a large corpus of news reports, extracting key information such as affected regions, disaster severity, and preventive measures. By quantifying the data from these reports, we reveal the spatiotemporal distribution of typhoon events, identifying patterns in media coverage across different regions and time periods, and offering valuable insights into public perception and disaster preparedness [15; 16]. Furthermore, we integrate the news reports with actual data from satellite observations and oceanographic models to assess discrepancies between media coverage and the real impacts of typhoons [17; 18]. This comprehensive analysis helps to identify biases in reporting and provides scientific evidence to improve disaster prevention and response strategies in coastal areas, offering a more complete understanding of typhoon impacts. The main contributions of this study are twofold. First, we apply deep learning techniques, particularly CNNs and transformer models, to reconstruct the three-dimensional thermal structure of the ocean in regions affected by typhoons. These models are capable of processing multi-source datasets, including satellite observations and Argo float measurements, to accurately predict the interactions between the ocean and typhoons. Second, we integrate the analysis of news reports on typhoon events to explore their impact and assess how well the media reflects the spatial and temporal distribution of these natural disasters. By combining physical oceanographic modeling with media analysis, this interdisciplinary approach provides valuable insights for improving typhoon forecasting accuracy and enhancing disaster preparedness. The structure of this paper is organized as follows: Section 2 introduces the data sources used in this study; Section 3 provides a detailed description of the research methodology; Section 4 evaluates the accuracy of the results and discusses the findings; finally, Section 5 concludes with the main conclusions drawn from the study.
###### Abstract  
The shale oil reservoirs of the Lower Permian Fengcheng Formation in the northern Mahu Sag are promising targets. However, complex geology and strong heterogeneity in the area pose great difficulties in the numerical simulation of in situ stress fields, which have for a long time been poorly understood. This study provides a systematic and accurate 3D in situ stress numerical simulation workflow based on comprehensive data. In this research, optimized ant tracking was applied to construct refined geological models. Acoustic impedance is taken as what we refer to as \"hard\" data to reflect variations in geomechanical parameters. Logging and mechanical tests were taken as \"soft\" data to restrict the numerical range of the geomechanical parameters. With the integration of \"hard\" data and \"soft\" data, accurate 3D geomechanical models can be attained. The finite element method was ultimately utilized to simulate the 3D in situ stress field of the Fengcheng Formation. Numerical simulation results reveal that the stress state of the Fengcheng Formation is quite complicated. The magnitude of the horizontal principal stress, horizontal stress difference and horizontal stress difference coefficient are correlated with burial depth, faults, and geomechanical parameters to some degree. The parameter \(A\varphi\) was introduced in this research to better analyze the stress regime, the result of which demonstrates that the main stress regime in the study region is the reverse faulting stress regime. By evaluating the fault stability, it was found that there is basically no possibility of slippage regarding the faults in northern Mahu Sag. The results of this research provide evidence for well deployment optimization, borehole stability, and so on, all of which are of great significance in hydrocarbon exploration and exploitation.  
in situ stress; 3D geomechanical modelling; Fengcheng Formation; shale oil reservoir; Junggar Basin +
Footnote †: journal: Applied Sciences
## 3 Advantages and Limitations  
Hyperspectral (HIS) and multispectral imaging (MSI) are becoming more attractive for the analysis of several raw materials and foods. The main advantages of these techniques are related with the possibility of acquiring either single or multiple images as needed, at selected wavelengths in the NIR, MIR, and Vis range, providing with the ability to detect individual traits or properties directly associated with quality, in an extensive range of raw materials and products used in the manufacturing of foods. However, the potential of these techniques to obtain spatial, spectral, and multi-constituent information about the sample being analysed is the most attractive advantages in food analysis. Apart from infrared (IR), other vibrational spectroscopy technologies have been reported as fingerprinting techniques and they have been used to analyse several agricultural products and foods. In recent years, such methods have been prominently re-introduced by several researchers in several applications (feasibility studies) or real world applications. The main advantages that these techniques that are present over the traditional chemical and chromatographic methods are the timeliness and the simplicity of use in routine operations. However, several limitations still exist in the use of these technologies. Issues that are related with the availability of commercial and robust instrumentation, the large amount of data generated during the analysis, the need to complex data analysis and algorithms, the small number of samples in most of the applications reported in the literature are still a drawback on the use of this technology in the food industry. However, the lack of academic training is still a barrier for the worldwide application of these technologies in research and by the industry. Table 1 summarises the advantages and limitation of HSI and MSI.
## 2 Materials and Methods
### Description of Datasets
#### 2.1.1 Simulated Data
To match the different SSIs of the simulated \(E_{g}\), the reflectance and SIF spectra were resampled by splines as they are continuous and smooth in the visible and near-infrared regions [37]. In order to simulate different SNR conditions, random Gaussian-distributed noise was added [38]. The SNR is defined as the ratio of the signal intensity (outside the absorption bands) to the standard deviation of the random Gaussian-distributed noise; SNR values of 300, 500, 1000, 5000, and 10,000 were used.
# BIM-Based Management System for Off-Site Construction Projects
## 5 System Application and Effects
### Production Management  
The project participants efficiently managed the production process for each component, from planning to production, using the BIM-based system. In general, in OSC projects, production and installation are performed in different places and considerable communication arises between agents using various data for the timely procurement of components. In the past, the production managers at the plant periodically recorded the production status in a spreadsheet and shared it with the site managers. After they ordered the components according to the given data, the producers confirmed the order and started production. Again, the production managers shared the production status of the component with the site, and the subsequent processes were repeated. Data in various forms and versions were exchanged via e-mail or cloud storage, and in certain cases, information was exchanged over the phone without documents. In many cases, the person in charge of sending and receiving documents and the person in charge of communication were different. Because of this inconsistency, important data were often not accurately shared with all participants. Moreover, the presence of several versions of the documents led to human errors during production and exchange, and the participants had to spend additional time and labor to reconfirm or change the contents of orders and production. These data were periodically updated, which was inconvenient.  
When the proposed management system was implemented, the production and onsite personnel could communicate promptly, conveniently, and accurately without additional data production and contact, and the production was managed accurately based on the data. The site managers could easily place an order by selecting the required component in the 3D view of the system, and the producers could immediately confirm these requests via notifications from the system to initiate production. In this manner, the system enabled faster and more convenient communication and minimized the possibility of the omission of exchanged information by providing a unified channel to the participants. Additionally, the production managers could save the time and human resources required to record production logs for each component by using the RFID technology implemented in this system. The proposed BIM-based system could manage a vast amount of multidimensional information, including the component status acquired by RFID in an integrated manner in the system DB. Using these data, the production managers could replace the production log by tagging the RFID once the component was produced. Consequently, the system application reduced the existing workload in the test projects, increased the reliability of the data, and shortened the time required for overall production management. In addition, the project participants could not only visually check the status of the components on the system in a 3D view but could also print the status in the form of a spreadsheet, if necessary, in parallel with the existing work.
# Learning Accurate Pseudo-Labels via Feature Similarity in the Presence of Label Noise
## 3 Materials and Methods
### Generate Feature Labels
Based on the above, this paper also embraces a straightforward and efficient approach to sample reweighting. Defining the training target as \(t_{i}\), we set the weights, \(w_{i}\), as \(w_{i}=\max_{j}t_{i}\), and the sample weights, \(w_{i},\in\left[\frac{1}{\varepsilon},1\right]\) signify the confidence associated with the respective samples. Additionally, during the pre-training phase, all samples receive equal treatment. As the target undergoes updates, the FPL algorithm diminishes its focus on potentially noisy labels, shifting emphasis toward learning more from potentially clean samples. This strategy enables noisy samples to regain attention only if they can be confidently rectified. Finally, the network's parameters undergo updating via the cross-entropy loss computation between the predicted outcomes and the pseudo-labels assigned to the clean samples. This updating process can be expressed as follows:  
\[\mathcal{L}=-\frac{1}{\sum_{i}w_{i}}\sum_{i}w_{i}\sum_{j}s_{i,j}\log p_{i,j} \tag{8}\]  
Following the completion of training, we proceed to output and preserve the trained network model parameters. Algorithm 1 summarizes the essence of our proposed methodology. Additionally, as the DNNs initially adapt to the features of the clean labels, we initiate a warm-up phase. This warm-up phase enables the model to assimilate features that facilitate fine-tuning across various tasks, thereby improving the performance and accelerating the convergence of the model. Subsequently, we employ the proposed pseudo-label method to train the model. ```
0: Training set \(D\), \(\alpha\), \(\beta\), \(\gamma\),Optimizer \(O\)
0: Model parameters \(\theta\)
1:for\(t=1\); \(t<T\)do
2:for\(b=1\); \(b\leq\frac{|D|}{B}\)do
3: Fetch mini-batch B from D;
4:if\(t<T_{warm}\)then
5:\(L_{CE}=-\frac{1}{n}\sum_{i=1}^{n}\sum_{j=1}^{\mathcal{L}}\tilde{y}_{ij}\log p _{ij}\)
6: Update \(\theta\)
7:else
8: Feature normalization using \(\widehat{f}_{i}=\frac{f_{i}}{\|f_{i}\|}\)
9: Calculate the feature center for each class using \(O_{c}=\frac{\sum_{i=1}^{N_{c}}\widehat{f}_{i}}{\|\sum_{i=1}^{N_{c}}\widehat{ f}_{i}\|}\)
10: Calculate the cosine similarity using \(COS_{ij}=\widehat{f}_{i}\cdot O_{j}\)
11: Select feature label using \(u_{i}=argmax(COS_{i1},COS_{i2},COS_{i3}\ldots COS_{ic})\)
12: Update pseudo-labels using \(s_{i}=\alpha s_{i-1}+\beta p_{i}+\gamma u_{i}\)
13: Update \(\theta\) using \(\mathcal{L}=-\frac{1}{\sum_{i}w_{i}}\sum_{i}w_{i}\sum_{j}s_{i,j}\log p_{i,j}\)
14:endif
15:endfor
16:endfor
```  
**Algorithm 1** FPL
# Can Walnut Serve as a Magic Bullet for the Management of Non-Alcoholic Fatty Liver Disease?
## 3 Results and Discussion
### Pathogenesis of NAFLD and Progression to NASH, Fibrosis, and Primary Liver Cancer
#### 3.1.3 Immune Reactions  
Immune imbalance in the liver plays a pivotal role in the maintenance and progression of chronic inflammation in NAFLD. Recruitment and activation of immune cells induce the release of inflammatory mediators. In the liver, macrophages consist of unique tissue-resident macrophages, the so-called Kupffer cells, and bone marrow monocyte-derived macrophages. Although the primary function of Kupffer cells is to remove bacteria and microbial products from the portal system as the first immune cells in the liver, they also paradoxically contribute to the progression of simple steatosis into NASH via the production of TNF-\(\alpha\) and C-C motif chemokine ligand 2 (CCL2) [14; 43]. Kupffer cells express high levels of inflammasome components leading to the progression of NASH. The recruitment of bone marrow-derived monocytes is also important in the progression of NASH as they promote inflammation and fibrosis [44] (Table 1).  
The role of liver dendritic cells in NAFLD remains unclear, but dendritic cells in conditions of increased intracellular lipid content exhibit a proinflammatory phenotype [45]. Neutrophphils promote the progression of NASH through the release of cytokines, chemokines, active molecules, and neutrophil extracellular traps [46]. Neutrophil extracellular traps are composed of nucleic acids and antimicrobials and have been suggested to be important in chronic inflammatory conditions and in cancer progression. Markers of neutrophil extracellular traps are elevated in the serum of patients with NASH. T cell subsets also play differential roles in the progression of NASH. T-helper cells promote NASH through increased production of cytokines such as interferon-\(\gamma\) (IFN-\(\gamma\)) and IL-17, and decreased production of anti-inflammatory cytokines IL-4, IL-5, and IL-13 [47]. Cytotoxic T cells increase the production of IFN-\(\gamma\) and TNF-\(\alpha\), leading to inflammation in the liver [48; 49]. The role of B cells in NAFLD is unclear, even though B cells have recently emerged as critical regulators of obesity-induced adipose tissue inflammation [50].
###### Abstract  
The objective of this study was to evaluate the applicability of a flow model with different numbers of spatial dimensions in a hydraulic features solution, with parameters such a free surface profile, water depth variations, and averaged velocity evolution in a dam-break under dry and wet bed conditions with different tailwater depths. Two similar three-dimensional (3D) hydrodynamic models (Flow-3D and MIKE 3 FM) were studied in a dam-break simulation by performing a comparison with published experimental data and the one-dimensional (1D) analytical solution. The results indicate that the Flow-3D model better captures the free surface profile of wavefronts for dry and wet beds than other methods. The MIKE 3 FM model also replicated the free surface profiles well, but it underestimated them during the initial stage under wet-bed conditions. However, it provided a better approach to the measurements over time. Measured and simulated water depth variations and velocity variations demonstrate that both of the 3D models predict the dam-break flow with a reasonable estimation and a root mean square error (RMSE) lower than 0.04, while the MIKE 3 FM had a small memory footprint and the computational time of this model was 24 times faster than that of the Flow-3D. Therefore, the MIKE 3 FM model is recommended for computations involving real-life dam-break problems in large domains, leaving the Flow-3D model for fine calculations in which knowledge of the 3D flow structure is required. The 1D analytical solution was only effective for the dam-break wave propagations along the initially dry bed, and its applicability was fairly limited.  
dam break; Flow-3D; MIKE 3 FM; 1D [PERSON]'s analytical solution +
Footnote †: journal: Applied Soft. 2018, 8, 2456; doi:10.3390/app8122456
## 5 Experimental Evaluation and Results
### Recovery  
Recovery is an essential feature of a system that aims to restore data to its normal state in the event of failures. System failures can occur for various reasons, such as power outages or unknown causes, leading to service unavailability or slowdown. In the case of a persistent hash table, similar to Dash, we employed a comparable testing method to ensure a fair comparison. This involved loading a set of records for a certain duration and then intentionally terminating the process responsible for the loading operation. Subsequently, we measured the time taken by each scheme to resume accepting incoming requests.  
The results presented in Table 2 demonstrate that both ESH and Dash exhibit rapid recovery times, even for large amounts of data, while CCEH requires more time compared to its competitors and exhibits scalability issues as the amount of data increases. The swift recovery times of Dash and ESH can be attributed to their utilization of specific markers: Dash employs a clean marker, whereas ESH employs a \"state\" marker. These markers serve to check whether the system was cleanly shutdown in a previous operation. During the recovery process, the system reads these markers and restores the persistent memory to a consistent state. It is worth noting that ESH, which operates on a per-bucket basis, requires slightly more time for recovery compared to Dash, which is negligible even for large record sets. Additionally, both schemes address other consistency concerns during multi-threaded operations through the inclusion of a \"lock\" signal in the metadata.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Hashing Schemes**} & \multicolumn{5}{c}{**Record Size in GB**} \\ \cline{2-6}  & **1** & **5** & **10** & **20** & **30** \\ \hline CCEH & 50 & 121 & 256 & 503 & 1082 \\ Dash & 62 & 63 & 65 & 65 & 65 \\ ESH & 62 & 65 & 66 & 66 & 67 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Recovery time (ms) comparison with respect to data size.  
Figure 10: Load factor with respect to the number of items inserted into the hash table.