esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of 3D Concrete Printing Extrusion Efficiency
## 2 Materials and Methods
### Additives  
The effect of fiber additives on extrusion stability was verified. The use of dispersed reinforcement in cementitious composites is a technique that enhances flexural strength. Additionally, the incorporation of fibers helps reduce cracking in printed components due to shrinkage [29; 30; 31; 32].  
Polypropylene (PP) fibers serve as reinforcement in cement-based materials due to their lightweight nature, reduced susceptibility to alkaline environments, and high resistance to corrosion and deformation, resulting in composites with improved long-term performance characteristics [33].  
The length of fibers used in concrete has a significant impact on its mechanical and rheological properties. Longer fibers more effectively bond larger areas of the material, increasing its tensile and flexural strength. However, they are more challenging to distribute uniformly within the mixture, which can lead to fiber clustering and weaken the structure. Additionally, longer fibers have a greater influence on the mixture's rheology, potentially reducing workability and thereby affecting the efficiency of pumping and extrusion. The optimal fiber length depends on the specific application of the concrete and the technical requirements of the project, necessitating a balance between strength and workability [34; 35].  
In the experiment, polypropylene (PP) fibers with a diameter of 0.04 mm were used in two lengths: 6 mm and 12 mm (Figure 1). These fiber sizes are standard for construction applications involving prefabricated elements, making them widely available. They also align with sizes studied by various research teams, with their effects on the properties of 3D-printed concrete components being well documented [34; 35; 36; 37; 38]. The fiber size selection was additionally constrained by the printing infrastructure capabilities, as longer fibers caused blockages in the extruder screw, impeding material flow. Testing these two fiber  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Content (\%)** \\ \hline Portland cement CEM 1 52,5R & 25.0 \\ \hline Quartz sand (0.1–1.0 mm) & 65.0 \\ \hline Activating additives & 2.0 \\ \hline Rheological additives & 1.0 \\ \hline Mix fillers & 7.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The mixture composition used for 3D-printing tests.  
lengths aims to assess their impact on extrusion stability, which is a critical factor due to the potential for fiber adhesion and clogging within the internal components of the extruder.  
PP fibers, in the form of single filaments (Figure 1b), were added to the dry material batch. The measured dry ingredients were initially intensively mixed using a glue and mortar mixer to ensure uniform fiber distribution throughout the mass. Following this, the material batch was mixed with water and subjected to consistency and extruder performance tests.
## 5 Design of Shutters Based on Side Wing Efficiency
### Optimization of Shutters' Geometry
#### 5.1.3 Optimization of Fin's Angle of Attack  
Five groups of angles of attack--\(2^{\circ}\), \(6^{\circ}\), \(8^{\circ}\), \(10^{\circ}\), and \(12^{\circ}\)--are selected for simulation to explore the influence of the angle of attack on the effect of shutters and to improve the high-pressure area on the upper surface of side wing. The other parameters corresponding to the flat airfoil data in Table 2 remain unchanged. The simulation results are shown in Figure 15.  
The meaning of each curve in Figure 15 is the same as that in Figure 14. The simulation results show that with the increase in wing attack angle, the wing lift gradually increases, and the resultant force of wing and shutters' negative lift first increases and then decreases.  
Figure 14: Influence of chord length L to side wing aerodynamic performance.  
Figure 15: Influence of fin’s angle of attack \(\alpha\) to side wing aerodynamic performance.  
The side wing drag decreases with the increase in angle of attack, but the overall drag is mostly unchanged. Under the free inflow of 15 m/s and assuming that the ambient temperature is 20 \({}^{\circ}\)C, the characteristic length is chosen as fin chord length \(L=90\) mm, and the Reynolds number \(Re=91,216\). The flow velocity at the actual shutters is lower, and the Reynolds number should be smaller. In accordance with the Profili airfoil library data, the 2D airfoil (BABIC) is about to reach a stall state at approximately \(\alpha=9^{\circ}\) when \(Re=90,000\), and the fins in front of the actual side wing may have a larger stall angle. The simulation results show that \(8^{\circ}\) is the best angle of attack of fin. When the angle of attack is greater than \(10^{\circ}\), the fin stalls, and the aerodynamic drag of shutters increases rapidly. The specific data can be found in Table 5.
## 3 Smart and Secure HIE Systems: SSHES
### Integrating AI and Blockchain into HIE Systems: Practical Challenges and Solutions
#### 3.6.6 Security Risks and Mitigation Strategies  
Typically, AI-blockchain-based frameworks are secure due to both the consensus mechanisms implemented in blockchain network and the cryptography used for sharing patient data. However, such systems are not totally immune to attacks and they have some security issues. Security risks in AI-blockchain frameworks may happen at the following three levels:  
* Consensus algorithms: despite their strong natural security, attackers have been increasingly trying to discover bugs and vulnerabilities in consensus algorithms (such as PoW, PoS, PoA, etc.) to have unauthorized access to the data stored in the blockchain. The authors of [57] categorized such attacks into several types (i.e., identity, manipulation, reputation, service, and cryptanalytic) according to the attacked layer of the blockchain (e.g., physical, data, network, consensus, incentive, contract, and application). Then, they mentioned different mitigation and protection strategies to detect these attacks such as a distributed incentive-based approach, an elliptical curve-based approach, distributed voting algorithm, homomorphic Paillier encryption, lower priorities for new agents, calculating reputation score from neighbors, and network encryption randomized port negation [57].
* Smart contracts: mostly, smart contracts should communicate with on-chain and off-chain data storage; thus, the reliability of such data is not guaranteed. Hence, constructing smart contracts is considered to be a critical operation in HIE systems. To combat against smart contract vulnerabilities, researchers have proposed recently a significant number of mitigation strategies and systems such as Oyente, Town Crier, ekiden, Engima, etc. [57]. The main objective of such strategies is to adopt the best practices in smart contract development, conduct regular audits, and employ bug bounty programs to identify and address vulnerabilities early.
* AI platform: centralization of AI models to detect risky transactions on HIE systems can become a single point of failure or target for attacks. A trivial mitigation strategy is to utilize decentralized AI models where computations are distributed across nodes in a blockchain network, enhancing resilience against attacks.
## 3 Comparative Analysis of Monitoring Data and Numerical Simulation
### Space-Time Deformation Analysis of Foundation Pit Structure
#### 3.3.1 Foundation Pit Column
To better reflect the rule between column deformation and construction, the statistical data of the maximum uplift value Vlz of each column in different construction stages along with the excavation depth He are given, as shown in Figure 7. The comparison with relevant research results shows that the maximum value of column uplift caused by excavation in the foundation pit is roughly distributed between 0.54% and 0.65%. He, which is greater than the value of column uplift range in the Shanghai hard clay area calculated elsewhere in the literature [7; 24; 25]. The reason for this phenomenon is that the foundation pit is located in the sandy soil layer, and the unloading modulus of sand is smaller than that of clay. At the same time, the mass of the foundation pit is large, and the unloading load of earth excavation is much larger than that of the foundation pit mentioned in the above references. At the same time, the project area has more rain during the construction stage, the sand moisture content increases, the pore water pressure increases, the effective stress decreases, and the friction resistance of pile end and pile side decreases. To sum up, the uplift deformation of the foundation pit column is large. Figure 7: Relationship between column deformation and excavation depth [7; 24; 25].
# Post-Quantum Biometric Authentication Based on Homomorphic Encryption and Classic McEliece
## 1 Introduction
### Post-Quantum Security  
With the availability of large-scale quantum computers in the future, many of the current proposed biometric protection schemes will be broken. In order to provide long-term security, post-quantum cryptography should be considered. Currently, several quantum-resistant biometric protection schemes have been proposed based on lattice cryptography [6; 7; 8; 9; 10; 14; 15]. However, further research is needed to improve the performance and employ standard solutions.  
In 2016, the National Institute of Standards and Technology (NIST) began a process with multiple evaluation rounds to develop standards for post-quantum cryptography. In July 2022, selected algorithms for standardization and round 4 candidates were announced [16]. Among public-key encryption and key-establishment algorithms, CRYSTALS- Kyber based on lattice-based cryptography using Module Learning with Errors (Module-LWE) was selected for standardization and Classic McEliece, which employs Goppa code-based cryptography, was selected as a candidate of the round 4. In the Third Round Status Report [17], NIST showed its interest to know specific use cases for which Classic McEliece would be a good solution. Although Classic McEliece requires higher key sizes and generation times than other proposals, this may not be a drawback for biometric recognition systems because keys only need to be generated and transmitted at the enrollment phase. On the other side, Classic McEliece provides the smallest protected data size of any of the NIST post-quantum cryptography candidates, which is interesting to reduce biometric data storage and transmission. These reasons motivated us to analyze Classic McEliece for biometric authentication in non-device-centric architectures.
# Synchronizing Many Filesystems in Near Linear Time
## 3 Filesystem Synchronization
### Mergers Have an Operational Characterization  
The synchronized state defined by the merger \(M\) as \(\Psi=M\Phi\) has a clear operational characterization. The local replica \(\Phi_{i}\) can be transformed into the merged state by first rolling back some of the local operations executed on that replica, then applying additional commands executed on other replicas. By Proposition 3, \(M\cap A_{i}\) is an initial segment of both \(A_{i}\) and \(M\), and thus,  
\[A_{i}\equiv(M\cap A_{i})\circ(A_{i}\smallsetminus M).\]Rolling back the commands in \(A_{i}\smallsetminus M\), that is, executing the canonical set \((A_{i}\smallsetminus M)^{-1}\) on \(\Phi_{i}\), gives \((M\cap A_{i})\Phi\). Then, applying the canonical set \(M\smallsetminus A_{i}\) yields the filesystem  
\[(M\cap A_{i})\circ(M\smallsetminus A_{i})\Phi=M\Phi=\Psi.\]  
In summary, the \(i\)-th replica should execute the command set  
\[(A_{i}\smallsetminus M)^{-1}\circ(M\smallsetminus A_{i})\]  
on its local copy \(\Phi_{i}\) to transform it into the synchronized state \(\Psi\). The rolled back commands in \(A_{i}\smallsetminus M\) give a clear indication of the local changes that are discarded. This command set could be presented to the user to decide whether some of them should be reintroduced.  
Choosing the merger \(M_{3}=\{\sigma_{1},\tau,\rho_{1}\}\) in the example above, the first replica should roll back \(\sigma_{2}\) and \(\sigma_{3}\) by executing \(\{\sigma_{2}^{-1},\sigma_{3}^{-1}\}\). These commands restore the directories at \(\sfrac{\mathsf{a}}{\mathsf{a}}\) and \(\sfrac{\mathsf{a}}{\mathsf{b}}\). They are followed by executing \(\{\tau,\rho_{1}\}\) which adds the two files. To reach the same synchronized state, the second replica need not roll back any of its commands. Executing \(\{\sigma_{1},\rho_{1}\}\) directly deletes the file at \(\sfrac{\mathsf{a}}{\mathsf{a}}\)/\(\sfrac{\mathsf{a}}{\mathsf{b}}\) and creates the new file at \(\sfrac{\mathsf{a}}{\mathsf{a}}\)/\(\sfrac{\mathsf{a}}{\mathsf{z}}\). Finally, the third replica should roll back \(\rho_{2}\) (after this, the precondition in \(\tau\) holds, so \(\tau\) will not break the filesystem when executed), and then execute the commands \(\tau\) and \(\sigma_{1}\) in any order.  
Observe that after synchronization, none of the rolled-back commands is applicable anymore. \(\sigma_{2}\) and \(\sigma_{3}\) would delete directories which are not empty, while \(\rho_{2}\) would create a file which already exists. This is true in general. The maximality of the merger set \(M\) implies that none of the rolled-back commands can be executed directly on the synchronized state \(\Psi\). Either its input condition would fail (modification by some other replica on that node took precedence), or the command would destroy the tree property (deleting a non-empty directory, or creating a file under a non-existent directory). Therefore, the changes represented by the rolled-back commands can only be reintroduced in a different form.
# A Petri Net Approach for Business Process Modeling and Simulation
## 7 Algorithm to Create PNs from Writing a BP  
A business process is a series of activities that are combined to achieve the goals of an organization. The measurement of the performance of the process is related to the critical success factor selected for such measurement. Factors may be tangible, such as execution time, profits, savings (money), and productivity,or intangible, such as customer satisfaction and better decision-making. The objective of this research is to create an algorithm to generate Petri nets based on the written description of the business processes of a WOA, with the ultimate aim of analyzing and improving them. The algorithm operates as follows:  
1. Read the activities related to BPs.
2. For each activity, create a new single structure with place \(p_{i}\in P\) and \(t\in T\) // Sequence
3. If \(p_{i}\) has an activity \(p_{j}\) before it, then create an arc connection from \(p_{j}\) to \(t\in{}^{\bullet}p_{i}\). // Selection
4. If \(p_{i}\) belongs to a selection structure, create an arc connection from place \(p_{j}\) that represents the decision phase to \(t\in{}^{\bullet}p_{i}\). // Distribution
5. If \(p_{i}\) denotes an activity that must be executed in parallel with activity \(\in\)\(p_{j}\), then \(t_{x}\in{}^{\bullet}p_{i}\) is removed and \(t_{y}\in{}^{\bullet}p_{j}\) is also connected to \(p_{i}\). // Disjunction
6. If \(p_{i}\) is already connected with a previous activity \(p_{j}\) and can also be executed after activity denoted by \(p_{k}\), a new transition \(t\) is created and the connections from \(p_{k}\) to \(t\) and from \(t\) to \(p_{i}\) are established. // Conjunction
7. If the execution of \(p_{i}\) depends on two activities denoted by \(p_{j}\) and \(p_{k}\), create connections from \(p_{j}\) and \(p_{k}\) to \(t\in{}^{\bullet}p_{i}\).  
With the PN model generated, the simulation phase begins, which involves emulating the execution time of each activity and selecting the probability distribution that best represents their behavior. The simulation results provide a mathematical representation of the process, including the incidence matrix, trigger vector, and time vector. The time vector is particularly useful for identifying the duration of each activity in different scenarios and determining the optimal option for the studied business process.
## 3 Results  
### Participants' Demographic Characteristics and Cattle-Herd Ownership  
A total of 250 small-holder cattle farmers participated in the study. The chi-square tests were conducted to examine the relationship between demographic characteristics and cattle-herd ownership in the sample (Table 1). The results in indicate that the majority of cattle-herd owners are male, accounting for 59.2% of the sample. A significant association was found between gender and cattle-herd ownership (chi-square = 6.21, \(p\) = 0.030), suggesting that gender may play a role in cattle ownership. Regarding age-group distribution, the highest proportion of cattle-herd owners falls within the 46-60-year-old age group, representing 28.8% of the sample. A significant association was observed between age group and cattle-herd ownership (chi-square = 14.61, \(p\) < 0.010), indicating that age group may influence cattle ownership. In terms of educational background, the highest proportion of cattle-herd owners has secondary education, accounting for 28.8% of the sample. A significant association was found between education level and cattle-herd ownership (chi-square = 24.39, \(p\) < 0.01), suggesting that education level may impact cattle ownership. The distribution of income sources among cattle-herd owners varied, with the largest proportion relying on a salary as their income source, representing 36.0% of the sample. A significant association was observed between income source and cattle-herd ownership (chi-square = 18.23, \(p\) < 0.001), indicating that the source of income is associated with cattle ownership.  
The duration of involvement in cattle farming was distributed across different time periods. The highest proportion of cattle owners had been engaged in cattle farming for more than 10 years, accounting for 39.2% of the sample. However, the chi-square test did not show a significant association between years in cattle farming and cattle-herd ownership (chi-square = 2.09, \(p\) = 0.351). Regarding herd sizes, the majority of cattle owners had a herd size between 10 and 20, representing 38.8% of the sample. A significant association was found between herd size and cattle-herd ownership (chi-square = 7.38, \(p\) = 0.025), suggesting that herd size may be related to cattle ownership. In summary, the results indicate that gender, age group, education level, income source, and herd size are associated with cattle-herd ownership among the surveyed population. However, years in cattle farming did not show a significant association with cattle ownership. These findings provide insights into the demographic characteristics of cattle-herd owners and their potential influence on cattle ownership patterns.
# Neural Radiance Fields with Hash-Low-Rank Decomposition
## 3 Preliminary  
### Tensor Decomposition  
Tensor decomposition has long been a common technique for model acceleration and information compression in machine learning [16; 17]. In the modeling of neural radiance fields, rendering a novel view requires the color components \(c\), and the volumetric density \(\sigma\). Predicting volumetric density is relatively straightforward. We first create a feature grid with a specified resolution \(r\), where \(r\) determines the number of feature vertices in the grid (a feature grid with a resolution of 300 has \(300^{3}\) vertices for example). When querying the density for a point at coordinate \((x,y,z)\), the feature grid identifies the eight nearest feature vertices surrounding this point, forming a cube. The Euclidean distance between the point and each of these vertices is used to calculate a tri-linear interpolation weight, which is then applied to the feature value at each vertex. These weighted values are combined to determine the feature at that location.  
Through matrix decomposition, the entire feature grid can be decomposed into a combination of vertical vectors (CP decomposition) or a combination of vertical vectors \(V\) and matrices \(M\) (VM decomposition). The process of VM decomposition [13] can be described with Equation (1)  
\[G=\sum_{m\in\{XYZ\}}\sum_{r=1}^{R}V_{m}^{r}\otimes M_{XYZ-m^{r}}^{r} \tag{1}\]  
where \(G\) is a three-dimensional feature grid, and \(R\) represents the number of decomposed components. This formula is similar to the feature grid discussed in later sections. In Sections 4.1 and 4.2, we will elaborate on how we leverage this feature grid with hash encoding and the modeling of volume density and color.
# Time Delay Optimization of Compressing Shipborne Vision Sensor Video Based on Deep Learning
## 4 Time Delay Optimization of Compressing SVSV Based on Deep Learning  
### HEVC Intraframe Prediction of CU Partitioning Mode  
The specific CU division process for HEVC intra-frame PC is shown in Figure 9. The CU division process is a hierarchical and recursive search, and a CTU can be regarded as a combination of one or more different CU sizes. According to the quadtree division rule in HEVC, the CU is divided into four possible depths or sizes according to the texture features of different image contents, which are 64 \(\times\) 64, 32 \(\times\) 32, 16 \(\times\) 16 and 8 \(\times\) 8. In the process of CU division, HEVC has to traverse a total of 85 CUs from 64 \(\times\) 64 to 8 \(\times\) 8 in size, and  
Figure 8: Coding quality comparison of shipborne visible light camera video under different QPs: (**a**) Original image; (**b**) QP = 22; (**c**) QP = 27; (**d**) QP = 32; (**e**) QP = 37.  
decide on a CU division scheme with the lowest rate distortion cost as the actual coding structure. The partitioning process is as follows.  
1. Firstly, calculate the CTU rate distortion cost of 64 \(\times\) 64 size and continue to loop down to divide into four sub-CUs of 32 \(\times\) 32 size.
2. The distortion cost of four sub-CU rates of size 32 \(\times\) 32 is calculated separately, and continues to be cyclically divided down to sub-CUs of size 16 \(\times\) 16.
3. The current four 16 \(\times\) 16 size sub-CU rate distortion cost is calculated sequentially in the process of cyclic downward division and continues cyclic downward division into 8 \(\times\) 8 size sub-CUs.
4. The current 4 sub-CU rate distortion costs of 8 \(\times\) 8 size is computed sequentially in the process of cyclic downward division.
5. The four 8 \(\times\) 8-sized sub-CU rate distortion cost are compared with the current 16 \(\times\) 16-sized CU rate distortion cost in turn, and the solution with the lower rate distortion cost is selected as the current CU division structure.
6. Similar to step 5, the comparison of sub-CU rate distortion cost of 16 \(\times\) 16 size is repeated, and the solution with lower rate distortion cost is selected as the division structure of the current CU.
7. Compare the rate distortion cost of 32 \(\times\) 32 size sub-CU with the current CTU to get the lowest rate distortion cost of the current CTU and the final division structure of the current CTU.  
The above description of the specific process of CU division shows that in order to decide the final CTU division structure, it is necessary to calculate and compare the rate distortion cost for all possible sizes of CUs. The exhaustive search approach of HEVC not only has high computational complexity, but also generates a large number of redundant calculations, which is very unfavorable to the intelligent ships with limited computational resources.