esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Results of Experiment 1  
Seventeen participants were initially enrolled in Experiment 1, and fifteen participants completed the study (age: 21.2 \(\pm\) 0.9 years, BMI: 20.2 \(\pm\) 2.2 kg/m\({}^{2}\), HbA1c: 5.2 \(\pm\) 0.2%, fasting plasma glucose [FPG]: 85.4 \(\pm\) 5.0 mg/dL; mean \(\pm\) SD); two participants withdrew due to COVID-19 infection. postprandial blood glucose and insulin levels at 40 min (88.6 \(\pm\) 2.4 vs. 100.8 \(\pm\) 5.6 mg/dL, \(p<0.05\) for glucose, and 21.4 \(\pm\) 2.3 vs. 52.1 \(\pm\) 5.8 \(\mu\)U/mL, \(p<0.001\) for insulin; Figure 3A,B), as well as the IAUC at 40 min for glucose (143.6 \(\pm\) 42.7 vs. 601.5 \(\pm\) 175.6 mg/dL, \(p<0.05\); Figure 3C) and the IAUC at both 40 min (316.2 \(\pm\) 35.2 vs. 943.1 \(\pm\) 110.3 \(\mu\)U/mL, \(p<0.001\)) and 80 min for insulin (1452.8 \(\pm\) 171.5 vs. 2320.5 \(\pm\) 265.1 \(\mu\)U/mL, \(p<0.01\)), were significantly lower for the full-course meal manner compared to the fast eating manner, due to delayed consumption of the carbohydrate dish in the former condition at these time points (Figure 3D). However, at later time points, postprandial blood glucose and insulin concentrations were significantly higher for the full-course meal manner than the fast eating manner. At 80 min, glucose (97.8 \(\pm\) 3.3 vs. 79.7 \(\pm\) 4.2 mg/dL, \(p<0.01\)) and insulin (46.6 \(\pm\) 6.9 vs. 26.7 \(\pm\) 3.3 \(\mu\)U/mL, \(p<0.01\)) levels were elevated (Figure 3A,B). Similarly, at 180 min, glucose (102.1 \(\pm\) 4.8 vs. 88.4 \(\pm\) 4.6 mg/dL, \(p<0.05\)) and insulin concentrations (44.6 \(\pm\) 4.6 vs. 28.1 \(\pm\) 2.0 \(\mu\)U/mL, \(p<0.01\)) remained significantly higher for the full-course meal manner (Figure 3A,B). Although the VAS for satiety showed no significant difference between the two eating manners, the satisfaction level was significantly higher for the full-course meal manner compared to the fast eating manner (67.2 \(\pm\) 10.9 vs. 55.0 \(\pm\) 10.0 mm, mean \(\pm\) SD, \(p<0.01\)).
# Particle Swarm Optimisation in Practice: Multiple Applications in a Digital Microscope System
## 4 Image Stitching  
Image stitching is a method of blending two or more overlapped images into a higher resolution image. The requirement for image stitching can arise when an object is larger than the field of view of the microscope or when high-resolution images of an object are needed for quality assurance. Figure 2 shows an example of two images of the same object taken with overlapping regions. In order to blend these images together the exact region of overlap needs to be found. In microscopy applications where the sample is moved manually, translation and rotation are required. When using a manual \(XY\) table only translation is required.  
The established methods for solving this problem largely fall into two categories, feature point matching and template matching:
## 3 HJ-1B images simulation  
### Airborne data  
The data used to simulate HJ-1B data in this paper was obtained from thermal infrared (TIR) images acquired by an airborne hyperspectral scanner (AHS). The AHS instrument has 80 spectral bands covering the visible and near infrared (VNIR), short wave infrared (SWIR), mid-infrared (MIR) and thermal infrared (TIR) spectral range. The instrument is operated by the Spanish Instituto Nacional de Tecnica Aerospacial (INTA) and it has been involved in several field campaigns since 2004 [27]. The simulated HJ-1B image in this paper was obtained from the fusion of the land surface temperature images from the 10 AHS thermal infrared bands, from 71 to 80, located in the region between 8 and 13 \(\mu\)m with a spatial resolution of 2.5 m at nadir. The study area was located at Cabauw, Loobos and Speulderbos in The Netherlands [40]. The dates of the airborne-AHS data mainly included the 15\({}^{\mathrm{th}}\) and 18\({}^{\mathrm{th}}\) of July 2004 and 13\({}^{\mathrm{th}}\) of June 2006. Grasslands, forests, croplands, bare soil, buildings, roads, rivers, etc. were covered in these areas. The grassland is mainly distributed in Cabauw and the forest mainly distributed in Loobos and Speulderbos [40].
## 7 Conclusions and Future Directions
### Future Directions  
Despite all of the progress and applications, MEE system and dual-tank system related thermal management are still far from mature. Although a large number of applications show the potential of these methods, more discussions are still needed to realize the advantages and shortcomings. Some topics that required further research in this area are as follows:  
1. To improve the accuracy of metering flowrate controlled by motor: In MEE system, the motor controlled gear pump replaced the metering valve and CPDCV, and the accuracy of fuel flowrate control is affected by internal leakage, fuel temperature, and operating time range. The fuel flow feedback can compensate the performance degradation of fuel pump by increasing the pump speed. Moreover, the improvement of low-speed control characteristics of the motor is also important. In order to solve this problem, a digital servo control system with variable sampling time will be introduced. With the decrease or increase of motor speed, the sampling time will be shortened or extended.
2. To strengthen the modeling algorithm of the output fuel flowrate of electric motor-based fuel pump by using machine learning and big data and to develop the flowrate meter with high response and high accuracy, thus, the FCS of more electric aeroengine will be put into practical application as soon as possible.
3. To enhance the studies on heat-sink scheduling of multi-heat-sink, including fuel oil, ram air, and variable cycle engine 3 rd bypass air, the synergetic combination of a multi-heat exchange loop with different working fluids, high efficient heat load collection, and heat transfer technology, advanced control theory and control method, sensor network and control network, data fusion, and health management for thermal topology, thus the performance of fuel thermal system can be improved.  
Design of the research supervision: Y.L. and S.D.; Performing of the articles overview and writing of the manuscript: D.L. and J.H. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Science and Technology Major Project (2017-V-0015-0067) and the National Key Basic Research Program of China (No. 2014 CB046403).  
Not applicable.  
No statement.  
The authors declare no conflict of interest with other researchers.
## 3 Experimental Results
### Comparative Experiments
#### 3.4.2 Feature Map Analysis  
To further validate the principles behind feature extraction in the proposed algorithm, feature maps generated at each stage were visualized and compared with the baseline model, as shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Backbone** & **P/\%** & **R/\%** & **mAP0.5/\%** & **Params/M** & **GFLOPs** \\ \hline Baseline & CSPDarkNet & 98.66 & 91.87 & 96.23 & 43.61 & 164.8 \\ Faster R-CNN & ResNet50 & 63.79 & 92.43 & 90.64 & 137.10 & 370.2 \\ SSD & VGG & 97.07 & 92.21 & 94.02 & 26.29 & 62.7 \\ FCOS & ResNet50 & 96.74 & 94.88 & 96.19 & 32.16 & 161.9 \\ DETR & ResNet50 & 74.57 & 80.91 & 71.11 & 36.76 & 114.2 \\ YOLOv5-L & CSPDarkNet & 96.73 & 93.61 & 95.90 & 46.56 & 109.6 \\ LDKA-NET (Ours) & WFVC Net & 99.42 & 95.13 & 99.01 & 45.84 & 117.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of experiment results.  
In the figure, the feature maps of the baseline model and the proposed algorithm are converted into visual images for comparative analysis of their differences. The feature map in Figure 8b displays more pronounced structures and patterns, with the representation of the target insulator being more prominent and concentrated. This helps to more accurately locate and highlight key information in the image, showing superior performance compared to Figure 8a. In contrast, Figure 8a performs poorly in focusing on the area of the ice-covered insulator.  
Furthermore, Figure 8b exhibits higher activation intensity in the mid-to-late stages of the backbone network, indicating that its feature extractor is more effective at capturing the local details of the image. Higher activation intensity is typically associated with better discrimination capability of the network, allowing it to learn more complex feature representations through deeper convolutional layers.  
Additionally, compared to the baseline model, the proposed algorithm, which utilizes a more optimized wide field of view convolutional network, enables the feature map to not only capture more critical information but also achieve better focus and analysis of complex scenes in terms of spatial distribution of features. Moreover, observing stages 12-19, the proposed algorithm enhances the model's capability in processing local details effectively through the use of full-dimensional dynamic convolution and Expectation Maximization Dynamic Convolutional Attention. In contrast, the baseline model, lacking targeted optimization for the scene, shows poorer feature clarity and cannot effectively discern the areas of focus for the target.  
In summary, the advantages of the feature map in Figure 8b in terms of structural clarity, activation effect, and stability are directly related to the model's performance in practical tasks, especially evident in the challenging detection task of ice-covered insulators.
# Distributed Software Build Assurance for Software Supply Chain Integrity
## 4 Our Scheme
### Building Blocks: Hash Function, Merkle Tree, and Blockchain  
The cryptographic hash function is a mathematical algorithm that turns input into a fixed-size string of characters, known as a hash value. It ensures data integrity by generating unique hash values for different inputs and exhibiting properties like collision and pre-image resistance, making it challenging for adversaries to tamper with or to reverse-engineer the data.  
Merkle trees, building on the cryptographic hash functions themselves, utilize these properties to create a hierarchical structure. Building the tree starts with a set of leaf node values \(x_{1},x_{2},\ldots,x_{n}\), where each node \(x_{i}\) would contain hash value \(H(x_{i})\). In the next level of the tree moving up would be the parent node that would contain the hash value of the concatenation of two of the children nodes, for example, \(H_{12}=H(H(x_{1})||H(x_{2}))\). The process will continue to build on the next level until a root hash is formed. This root hash represents the entire dataset concisely. A Merkle tree [12] based on a hash function is known to be efficient for verification and is popularly used in many computing applications, as described in Section 7.3.  
Blockchain provides a cryptographic system with robust security properties using the likes of the hash function, the Merkle tree, and the public-key digital signature. It provides a digital ledger with high integrity where one can securely track who wrote the transaction on the ledger. Specifically, blockchain utilizes the public-key digital signature to provide authenticity, non-repudiation, and data integrity [13]. Such robust security properties enable the blockchain to be applied to high-risk security applications. One notable example is the cryptocurrency blockchains processing digital financial transactions. However, while we use similar cryptographic construction for the ledger security as the cryptocurrency, our blockchain application environment having the software authorities (e.g., known software sources which are more reputable than the others) enables the use of a permissioned consensus protocol (e.g., as opposed to a permissionless protocol, such as that based on proof of work), providing significantly more efficient processing.
# Productivity Assessment of the Yolo V5 Model in Detecting Road Surface Damages
## 5 Performance Evaluation of Object Detection Models
### Performance Evaluation Using a Single Data Source
#### 5.3.2 Model Processing Speed  
In the field of computer science, the computational complexity of an algorithm signifies the resources it demands for its execution, notably encompassing time and memory requirements. As demonstrated in Table 5, the most intricate model in terms of computational complexity is Faster R-CNN ResNet 101, which involves the deep extraction of distinct features for identifying road damage using 101 convolutional layers. This model boasts around 62.4 million parameters. Subsequently, Faster R-CNN Inception ResNet V2, which combines Inception and ResNet for road damage feature extraction, comprises 59.4 million parameters.  
In contrast, models that utilize MobileNet as the foundational architecture exhibit considerably fewer parameters. Among these, the SSD Lite Mobile V2 model stands out as the least complex, with a mere 3.2 million parameters. The Yolo V5 SSD is an architecture with low parameters at 7.3 M, and the execution time excels in all tests including 0.12 s, 1.2 s, and 3.2 s. Fundamentally, the operational speed of a detection model hinges on an incorporated backbone architecture that employs numerous convolutional layers to identify distinctive attributes of target objects. It is important to highlight that dashcams are commonly configured to record videos and images in high resolution. Consequently, the computation duration for road damage detection should primarily be assessed with respect to 1920 \(\times\) 1080 (full HD) resolution.  
Considering that both precision in road damage detection and processing speed are pivotal for effective road maintenance, the Yolo V5 model emerges as a recommended choice for this purpose.
###### Abstract  
Forecasting the development of large-scale landslides is a contentious and complicated issue. In this study, we put forward the use of multi-factor support vector regression machines (SVRMs) for predicting the displacement rate of a large-scale landslide. The relative relationships between the main monitoring factors were analyzed based on the long-term monitoring data of the landslide and the grey correlation analysis theory. We found that the average correlation between landslide displacement and rainfall is 0.894, and the correlation between landslide displacement and reservoir water level is 0.338. Finally, based on an in-depth analysis of the basic characteristics, influencing factors, and development of landslides, three main factors (i.e., the displacement rate, reservoir water level, and rainfall) were selected to build single-factor, two-factor, and three-factor SVRM models. The key parameters of the models were determined using a grid-search method, and the models showed high accuracies. Moreover, the accuracy of the two-factor SVRM model (displacement rate and rainfall) is the highest with the smallest standard error (RMSE) of 0.00614; it is followed by the three-factor and single-factor SVRM models, the latter of which has the lowest prediction accuracy, with the largest RMSE of 0.01644.  
Keywords:large-scale landslide displacement rate prediction; multi-factor; support vector regression machine +
Footnote †: journal: Applied Sciences
# Thermal and Energy-Efficiency Assessment of Hybrid CLT-glass Facade Elements
## 5 Full-Size Numerical Analysis of the Live-Lab Facility  
### Solving Approach and Input Data  
Numerical simulations are presently one of the most powerful tools for design optimization and assessment. They are based on the concept that developing a virtual model (based on well-known physical processes and constitutive laws) consumes less money/time than developing a real experimental prototype. This applies especially to full-size structures and complex systems, such as the Live-Lab 3D facility investigated in this paper.  
In the first stage of this study, a series of numerical simulations were carried out with the EnCert-HR computer software (www.encert.hr) to calculate--as accurately as possible--the expected overall energy consumption for the 3D Live-Lab facility in Figure 4. In doing so, the reference modelling procedure in Figure 9 was applied in the EnCert-HR software and adapted to the goals of this study.  
For the Live-Lab facility, the numerical building prototype was assumed to be located at the Faculty of Civil Engineering of the University of Zagreb. Accordingly, the whole calculation process was developed with respect to the Croatian Technical Regulations on energy economy and heat retention in buildings (Official Gazette No. 128/15 [45]). The building characterization was also based on [46; 47; 48] and treated as an independent building unit. Special care was taken for the Northern side of  
Figure 8: Thermal bridges detected by thermal camera acquisitions: (**a**) top corner layout (indoor) with the (**b**) corresponding temperature field and (**c,d**) bottom corner detail (indoor and outdoor measurements).  
the Live-Lab, whose orientation toward the Faculty building was considered in the calculation by solar gains and losses. The calculation process also included the coefficient of heat loss due to ventilation.  
As the basic step in the calculation process, the building components of the Live-Lab facility were first separately described and characterized (see Figure 10). These characterizations included two full-size CLT-glass facade elements (East and West sides), two solid walls according to Figures 4 and 5, and the floor and roof panels. A door opening was also placed on the North side.  
Through the calculation steps, the nominal geometrical and thermo-physical properties were determined for the materials and building components used (see Section 3).  
The final advantage was then derived from the available Live-Lab meteorological data. These data included ambient and consumption records (for the energy efficiency assessment) and outdoor  
Figure 10: Arrangement of the building envelope components for the Live-Lab facility (EnCert-HR).  
Figure 9: Schematic process for the thermal and energy analysis of the full-size facility (EnCert-HR).  
average records (for the definition of realistic configurations in the definition of thermal performance indicators) (see Table 3).
# Game Theory-Based Load-Balancing Algorithms for Small Cells Wireless Backhaul Connections +
## Appendix A Data Traffic Modeling  
### Best Effort Traffic: FTP  
An FTP session is composed of a succession of file transfers separated by some time intervals, the so-called reading time. The main FTP session parameters are (see Table 11):  
* The size S of a file to be transferred;
* The reading time D, i.e., the time interval between the end of the download of the previous file and the beginning of the next file transfer.  
Based on experimental evaluation of the FTP sessions was observed that for each file transfer a separate TCP connection is used and that around 76% of the files were transferred using a Maximum Transmission Unit (MTU) of 1500 bytes and around 24% of the files were transferred using an MTU of 576 bytes.
###### Abstract  
This paper provides a comprehensive review of the future digitalization of microgrids to meet the increasing energy demand. It begins with an overview of the background of microgrids, including their components and configurations, control and management strategies, and optimization techniques. It then discusses the key digital technologies that can be used to improve the performance of microgrids, including distributed energy resources management systems, the Internet of Things, big data analytics, blockchain technology, artificial intelligence, digital twin technology, cloud computing, and augmented reality. The paper also highlights the importance of cybersecurity in microgrids, identifying the potential security vulnerabilities and threats to microgrid cybersecurity, as well as strategies for addressing these challenges. Finally, the paper discusses the barriers and challenges regarding the digitalization of microgrids, including technical complexity, high implementation costs, regulatory barriers, data privacy and security concerns, lack of standardization, interoperability issues, limited technical expertise, and integration with the main grid. Overall, this paper demonstrates the significant potential for digital technologies to transform the future of microgrids. By leveraging advanced technologies and implementing effective cybersecurity measures, microgrids can become more efficient, reliable, and resilient, enabling them to meet the growing demand for energy and contribute to a sustainable energy future.  
microgrids; digitalization; optimization; cybersecurity; renewable energy sources; IoT +
Footnote †: journal: _Energies_
# TrustShare: Secure and Trusted Blockchain Framework for Threat Intelligence Sharing
## 3 Materials and Methods
### Methodology
#### 3.2.6 Scalability and Adaptability  
The framework is designed with scalability in mind, accommodating a growing number of participants and increasing volumes of threat data. The modular nature of **Hyperledger Fabric** allows for easy integration with new partners and adaptation to different cybersecurity needs. Furthermore, the system's interoperability ensures compatibility with existing threat intelligence frameworks, such as **STIX** and **TAXII**, enhancing its applicability in real-world scenarios.  
This methodology combines advanced cryptographic techniques, decentralized storage, and structured threat intelligence frameworks to create a robust and secure system for threat data sharing. By leveraging **Hyperledger Fabric**, the **IPFS**, and **MITRE ATT&CK**, our design ensures the efficient, scalable, and trustworthy exchange of threat intelligence data among organizations. This approach not only addresses current cybersecurity challenges but also sets the foundation for future innovations in the field of collaborative threat intelligence.