esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Modeling
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Values** \\ \hline
**EOS: Orthotropic** & & \\ Density (g/cm\({}^{3}\)) & \(\rho\) & 0.98 \\ Young’s modulus 11 (GPa) & \(E_{11}\) & 3.62 \\ Young’s modulus 22 (GPa) & \(E_{22}\) & 51.1 \\ Young’s modulus 33 (GPa) & \(E_{33}\) & 51.1 \\ Poisson’s ratio 12 & \(\upsilon_{12}\) & 0.013 \\ Poisson’s ratio 23 & \(\upsilon_{23}\) & 0 \\ Poisson’s ratio 31 & \(\upsilon_{31}\) & 0.5 \\ Shear modulus 12 (GPa) & \(G_{12}\) & 2.0 \\ Shear modulus 23 (GPa) & \(G_{23}\) & 0.192 \\ Shear modulus 31 (GPa) & \(G_{31}\) & 2.0 \\
**Volumetric response: Shock** & & \\ Grüneisen coefficient & \(\Gamma\) & 1.64 \\ Parameter C1 (m/s) & \(c_{0}\) & 3570 \\ Parameter S1 & \(S\) & 1.3 \\ Reference temperature (K) & \(T_{0}\) & 293 \\ Specific heat (J/kg-K) & \(c_{v}\) & 1850 \\
**Failure: Orthotropic softening** & & \\ Tensile failure stress 11 (GPa) & \(S_{11}\) & \(1.01\times 10^{20}\) \\ Tensile failure stress 22 (GPa) & \(S_{22}\) & 1.15 \\ Tensile failure stress 33 (GPa) & \(S_{33}\) & 1.15 \\ Maximum shear stress 12 (GPa) & \(S_{12}\) & 0.575 \\ Maximum shear stress 23 (GPa) & \(S_{23}\) & 0.12 \\ Maximum shear stress 31 (GPa) & \(S_{31}\) & 0.575 \\ Fracture energy 11 (J/m\({}^{2}\)) & \(G_{11C}\) & 790 \\ Fracture energy 22 (J/m\({}^{2}\)) & \(G_{22C}\) & 30 \\ Fracture energy 33 (J/m\({}^{2}\)) & \(G_{33C}\) & 30 \\ Fracture energy 12 (J/m\({}^{2}\)) & \(G_{12C}\) & 1460 \\ Fracture energy 23 (J/m\({}^{2}\)) & \(G_{23C}\) & 1460 \\ Fracture energy 31 (J/m\({}^{2}\)) & \(G_{31C}\) & 1460 \\ Damage coupling coefficient & C & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The parameters for UHMW-PE used in non-linear orthotropic material model (20). In the simulation analysis, the quarter model was applied to reduce calculation time. The projectile and the Al 7075-T651 bumpers were modeled using SPH coding with a smooth length of 0.5 mm. The rear wall was meshed by a Lagrange grid with all the configurations and a hierarchical partition of thirty 0.5 mm fixed-size cells were assigned to fifty cells in each in-plane direction. A gap of 0.01 mm was set between the layers of the rear wall, according to the experimental situation.
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 5 Technological Infrastructure
### High-Performance Computing (HPC)  
High-Performance computing (HPC) leverages the combined power of supercomputers, cluster centers, and parallel processing techniques to tackle complex computational problems beyond standard desktop computers' capabilities [125]. HPC systems are essential in various domains, such as scientific research, design engineering, and data analysis, enabling large-scale simulations, systems modeling, and processing of massive datasets. Supercomputers and HPC clusters of interconnected nodes perform calculations at incredible speeds by dividing tasks into smaller sub-problems solved simultaneously. Advanced data centers equipped with high-performance servers and GPUs are essential for processing the vast amounts of data required for real-time metaverse interactions [105]. Specialized software and tools, like MPI (message passing interface), OpenMP (open multi-processing), and CUDA (compute unified device architecture), support the development and execution of HPC applications, ensuring efficient use of the vast computing resources available [126]. Applications range from climate modeling and molecular dynamics in scientific research to computational fluid dynamics and structural analysis in engineering, as well as big data analytics and machine learning in data-intensive fields.  
Despite the immense capabilities of HPC for a real-time metaverse, it faces several challenges, including scalability, energy consumption, cost, and complexity [127]. Scaling applications across thousands of processors efficiently requires optimizing code to minimize communication overhead. The significant power consumption of supercomputers and large clusters necessitates the development of energy-efficient designs. The high costs of HPC infrastructure and the specialized knowledge required for developing and maintaining applications further complicate its adoption. However, the future of HPC is promising, with advancements in exascale computing, quantum computing, and AI integration. Exascale systems will enable even more complex simulations and data analyses, while quantum computing could revolutionize fields like cryptography and material science [128]. Combining HPC with AI and machine learning will drive innovations across various domains, and research into energy-efficient technologies aims to reduce the environmental impact of HPC.
## 5 Key Technologies Supporting Deliberate Attack Defense for IECPS
### System Defense Assessment Technology
#### 5.5.2 Resilience Assessment Indicators  
The resilience index system is a standard for evaluating the resilience of integrated energy systems. The rationality of the selection of resilience indicators is related to the accuracy of resilience assessment. At present, system resilience indicators can be divided into two categories: resilience indicators based on system structure and resilience indicators based on system performance.  
Resilience indicators based on system structure include system topology, component redundancy, resource abundance, etc. [220; 221; 222; 223]. Resilience indicators based on system performance are mostly based on the system performance curve shown in Figure 18. This curve describes the trend of system performance changes over time before and after extreme events occur.  
In the IECPS resilience assessment, it is critical to determine which indicators are used to quantify system performance. Table 5 summarizes the existing resilience indicators based on system structure and system performance.  
As can be seen from the above table, although the current resilience indicators are constructed from multiple perspectives based on system characteristics, there is still a lack of discussion on information system losses. Although the damage to the information system will be reflected in the loss of the physical system, measuring the physical system alone cannot reflect the true loss of the entire IECPS. Therefore, multi-dimensional evaluation indicators are necessary.
## 2 Materials and Methods
### Instrumental Colour Measurement  
Colour measurements were conducted on storage days 1, 2, and 3 on raw samples and on cooked samples that were prepared on storage day 2. Colour measurements were carried out using a Minolta CR-410 colorimeter (Konica Minolta Company, Osaka, Japan) with a 50 mm measuring area (aperture size) using illuminant source C and 2\({}^{\circ}\) standard observer angle. The light projection tube (CR-A33a, Konica Minolta, Japan) was placed over the aperture port while measurements were conducted. Before measurement, calibration was performed using a white calibration plate (Y = 93.9, x = 0.3161, y = 0.3329). Sample colour was expressed using the L* a* b* system (L* a* b* represent luminosity, redness, and yellowness respectively). Chroma (colour saturation) and hue angle values were determined according to the following equations, as reported in [PERSON] et al. [28].  
\[\text{Chroma}=\sqrt{\left(\text{a*}^{2}+\text{b*}^{2}\right)}\]  
\[\text{Hue angle}=\tan^{-1}\left(\frac{\text{b*}}{\text{a*}}\right)\times \left(\frac{180}{\pi}\right)\]  
All L*, a*, and b* values are the instrumental average of three independent measurements collected from random sites across the sample, avoiding small areas of severe discolouration.  
Total colour difference (\(\Delta E_{\text{Lab}}\)) between control (HF0) and samples containing hep flour (HF4 and HF6) within the same storage day was also calculated according to the following equation:  
\[\Delta E_{\text{Lab}}=\sqrt{\left(\text{L*}_{\text{C}}-\text{L*}_{\text{T}} \right)^{2}+\left(\text{a*}_{\text{C}}-\text{a*}_{\text{T}}\right)^{2}+\left( \text{b*}_{\text{C}}-\text{b*}_{\text{T}}\right)^{2}}\]  
where C = Control (HF0) and T = treated samples containing hep flour (either HF4 or HF6 treatments).
## 3 Materials and Methods
### Ensemble of DIR Systems
For example, if all weak retrievals consistently rank a particular document as the most relevant to query Q, but the strong DIR system does not assign it the highest relevance based on its specific criteria, VBDIR, guided by the assigned weights, may prioritize the consensus of the weak retrievals over the judgment of the strong DIR system. This allows for a balanced consideration of multiple retrieval sources, helping to improve the overall accuracy of the document ranking.
## 4 Experiment and Application: Lane-Level Trajectory Matching
### Lane-Level Trajectory Matching
#### 4.2.2 Trajectory Matching in the Intersection Domain  
Intersection domain map matching is performed with a topological map matching algorithm (TMM). Our map data include lane-level topological relationships and construct a topological relationship graph with intersection nodes and directional edges to capture trajectories to the most appropriate edges using an if-then decision rule approach [38]. The algorithm defines a search area with a fixed radius around the estimated vehicle position. For the experiments, the search radius is set to 4 m. If the distance from a trajectory point to the centerline of a lane is larger than this threshold, this point will not be matched to this lane.  
The basic idea of the algorithm is that the trajectory determines the lane node into the intersection, and based on this node, determines the possible path of the vehicle's lane into the next lane. According to the lateral driving behavior of the intelligent vehicle at the intersection, the lane matched with the trajectory on next road section can be determined based on the topological relationship between each lane. As shown in Figure 12, if a vehicle arrives at an intersection through lane A, then the next lane it can access is predicted based on the lane nodes connected to lane A at that intersection (determined by the topological relationship between each lane at the intersection), which is shown as lane B and lane C. That is, if the vehicle goes straight at lane A, its trajectory at next road section will be matched to lane B. While if it turns left at lane A, then its trajectory at next road section will be matched to lane C. Similarly, if the vehicle reaches lane D, then next lane is probably Lane E or Lane F according to the topological relationship. At the intersection area, the next lane matched with the vehicle trajectory is analyzed based on the smart vehicle's driving behavior of going straight or turning. The trajectory points are matched to the corresponding directional edges in the topology diagram through orthogonal projection.
# Finite-Time Tracking Control of a Flexible Link Manipulator Based on an Extended State Observer
## 2 Problem Formulation  
### Platform Introduction and Operating Principle  
In this paper, the Quanser Flexible Link System is taken as the research object, as shown in Figure 1, and the nominal parameters of the system refer to [24]. The FLM is composed of a servomotor, a set of gears, and a flexible link. The shaft of the servo motor is connected to the gear, and the motor drives the rotation of the flexible link through the gear. Specifically, two sensors are integrated into the system, an encoder with a resolution of 4096 counts/rev, which is used to measure the rotation angle of the system, and a strain gauge with a constant calibration of 1/16.5 rad/V, which is used to measure the deformation of the flexible link.  
The whole experimental platform consists of an FLM unit, a voltage amplification unit, a data acquisition unit, and a computer. The computer is the core of the experimental platform and is used to implement real-time algorithms. The estimation algorithm and controller run on the computer. The data acquisition unit is used to realize the mutual conversion of analog signals and digital signals in the system. On the one hand, the unit is used to receive feedback information from the FLM, including the encoder angle and the deformation angle of the strain gauge. On the other hand, the unit receives the control signal output from the computer and outputs it to the voltage amplifier unit. The voltage amplifier unit amplifies the control signal to drive the servo motor to realize the rotation and vibration suppression of the FLM.
## 3 Results and Discussion
### Radio Frequency Range
The results are presented in Figure 5 as a dependence of the absorption on the real and imaginary parts of permittivity. The addition of the SiC whiskers strongly improves the absorption and its value reaches 56%. In contrast to highly reflective composites filled only with MWCNTs [40], hybrid SiC + MWCNT composites provide shielding mostly by means of absorption.
## 2 Materials and Methods
### Effect of Substrate Concentration  
Ammonium maylybdate tetrahydrate (HMA) (Merck SA, Darmstadt, Germany) and ammonium sulfide (AS) (Sigma Aldrich, Saint Louis, MO, USA) were used as reaction substrates and citric acid (Sigma Aldrich, USA) as a reducing agent for the reactions (Equations (5) and (6)). The molar concentrations of the substrates were in a Mo:S:CA ratio of 1:2:2, and the molybdenum concentrations in the HMA-CA aqueous solution varied from 0.2 M to 0.8 M. To prepare the HMA-CA solution, 1.766 g of solid HMA was dissolved in a small amount of distilled water in a 50 mL volumetric flask by stirring it on a magnetic stirrer at 90 \({}^{\circ}\)C. Subsequently, 3.842 g of solid CA was added to the stirring solution. Then, distilled water was added up to 50 mL and mixed for the next 30 min until a clear solution was obtained. Next, 50 mL of AS solution was obtained by diluting 3.8 mL of a concentrated ammonium sulfide solution (20 wt.%) with distilled water. The impinging jet reactor (1) (S-type) and volumetric flasks with the reagents (5 and 6) were kept in a thermostat (2) at a temperature of 20 \({}^{\circ}\)C. During the synthesis, the syringe pump (3) was operated with a constant flow of 20 mL/min. To avoid heterogeneous nucleation on the impurities, the reactants initially flowed through filters (4) with pore sizes in the following order: 1.00; 0.45; 0.22 \(\upmu\)m. One of the inlets was fed with a mixture of HMA-CA and the second with AS solution. The brown slurry product was collected into a separate container (8). The scheme of the used system is shown in Figure 3.  
\[(NH_{4})_{6}Mo_{7}O_{24}+21(NH_{4})_{2}S\overset{H_{3}O^{+}}{\to}7 MoS_{2}\downarrow+7S \downarrow+24H_{2}O+48 NH_{3}\uparrow \tag{5}\]  
\[(NH_{4})_{2}S+2C_{6}H_{8}O_{7}\to H_{2}S\uparrow+2C_{6}H_{7}O_{7}NH_{4} \tag{6}\]
# Predictive Business Process Monitoring Approach Based on Hierarchical Transformer
## 4 Results and Discussion
### Experiment Analysis  
#### 4.4.1 Ablation Experiment  
In contrast to existing predictive business monitoring methods based on deep learning, the advantage of the HiP-Transformer model proposed in this paper is that it introduces the idea of concept drift detection to split business process cases. Traditional sequence segmentation methods usually use Piecewise Aggregate Approximation (PAA) for segmentation, which cannot reflect the distribution changes within the sequence. To verify the positive effect of concept drift detection on business process instance prediction, this experiment replaces the concept drift point detection (DPD) in the HiP-Transformer model with equally spaced sequence segmentation. It keeps the structure of the three-layer Transformer model unchanged and then compares the prediction effects of the model before and after the replacement.  
Figure 2 shows the prediction accuracy of the two segmentation strategies on the next activity prediction task. Since segmentation of business process cases only involves longer event sequences, only the prediction results of business process cases with event sequence lengths greater than 20 are considered in this experiment. It can be found that the concept drift detection method for event sequence segmentation can improve the prediction results on most datasets, especially as the number of training iterations increases. It is because the encoding of events and their subsequences can be continuously optimized during the training process, which in turn continuously improves the accuracy of concept drift detection and makes the segmented subsequences more consistent with the overall distribution characteristics of the business process cases.
# Determination of the Optimum Amount of Superplasticizer Additive for Self-Compacting Concrete
## 1 Introduction
In addition, the concrete will require higher energy consumption for its correct vibration, if this is necessary/possible. On the other hand, an excessive amount of SPA will result in an increase in cost without improving the properties of the concrete, since the SPA will cease to be efficient above the saturation point determined by the Marsh cone test, the quantity of SPA above which the concrete's fluidity gain is minimal despite a notable increase in the amount of SPA [18; 19; 20]. As [PERSON] states, \"When self-compacting concrete becomes so widely used that it is seen as the \"standard concrete\" rather than a \"special concrete,\" we will have succeeded in creating durable and reliable concrete structures that require very little maintenance work.\" [21]. To achieve this objective, it is necessary to have a design method for SCC that is as fast, economical and robust as possible. According to the method proposed by [PERSON], the design of a self-compacting concrete begins with the determination of the SPA in mortars, after which, it is adjusted in concrete mix trials. Other methods are based on the definition of the saturation point determined in pastes. The advantage of this method is that the volume and cost of mortars and pastes is smaller than of concrete. [PERSON] et al. [22] proposed a method to optimize the SPA in self-compacting concretes by mean of tests performed in pastes and mortars. [PERSON] et al. [23] concluded that the effect of SPA varies according to the w/c ratio and the fine/total aggregate ratio, making it even more difficult to design these SCCs. [PERSON] et al. [24] defined a design method for conventional strength SCC based on minimizing the volume of mortar. All these different methods that have been developed, according [PERSON] et al. [25], could be divided in six categories, namely, empirical design methods, statistical factorial design methods, strength-based design methods, rheology of paste methods, particle packing methods and Eco-SCC mixture design methods. At present, the process of determining the amount of SPA in SCC involves carrying out a wide range of tests on cement pastes, mortars and concretes [26]. In the present work, an alternative method has been proposed for determining the optimum amount of SPA. In order to validate this hypothesis, in this study, the relevant tests were carried out on cement pastes and concretes to arrive at the appropriate mix proportions of an SCC and, subsequently, a concrete mix was made while recording the evolution of the mixer's power consumption. This concrete mix was based on an initial situation with a 0% SPA/cement (SPA/C) ratio. Subsequently, small quantities of SPA were added so that the effect of increasing this quantity of SPA on the consumption of the concrete mixer could be seen, with the consumption of the concrete mixer being correlated to the fluidity of the mixture.
# Force Analysis of Masonry Cave-Dwelling Structure Based on Elastic Center Method
## 2 Research Object and Methodology
### Derivation of Internal Force Calculation Formula Based on Unhinged Arch Model
#### 2.3.8 Internal Force Calculation Formula of Any Section of the Arch Ring  
The force diagram of any section of the right half-arch ring is shown in Figure 6. According to the equilibrium conditions, the internal force calculation formula of any section of the right half-arch ring are as follows:  
Bending function equation:  
\[\begin{array}{ll}M(\varphi)&=\overline{M}_{1}X_{1}+\overline{M}_{2}X_{2}+M_ {P}\\ &=X_{1}+X_{2}R\Big{(}\frac{\sin\varphi_{B}}{\varphi_{B}}-\cos\varphi\Big{)}- \frac{\eta}{2}R^{2}\sin^{2}\varphi\\ &-\gamma_{2}R^{3}\Big{(}\frac{\sin^{2}\varphi}{2}-\frac{\varphi\sin\varphi}{2 }-\frac{\sin\varphi\sin 2\varphi}{4}-\frac{\cos^{3}\varphi}{3}+\frac{1}{3}\Big{)} \end{array} \tag{30}\]  
Figure 6: Force diagram of any section of the right half-arch ring.  
Shear force function equation:  
\[\begin{array}{ll}Q(\varphi)&=N_{C}\sin\varphi-\left(\int_{0}^{x}Pdx\right)\cos \varphi\\ &=X_{2}\sin\varphi-P_{t}R\sin\varphi\cos\varphi-\left[\gamma_{2}\int_{0}^{ \varphi}\left(R-R\cos\varphi\right)d(R\sin\varphi)\right]\cos\varphi\\ &=X_{2}\sin\varphi-P_{t}R\sin\varphi\cos\varphi-\gamma_{2}R^{2}\cos\varphi \left(\sin\varphi-\frac{\varphi}{2}-\frac{\sin 2\varphi}{4}\right)\end{array} \tag{31}\]  
Axial force function equation:  
\[\begin{array}{ll}N(\varphi)&=-N_{C}\cos\varphi-\left(\int_{0}^{x}Pdx\right) \sin\varphi\\ &=-X_{2}\cos\varphi-P_{t}R\sin^{2}\varphi-\left[\gamma_{2}\int_{0}^{\varphi} \left(R-R\cos\varphi\right)d(R\sin\varphi)\right]\sin\varphi\\ &=-X_{2}\cos\varphi-P_{t}R\sin^{2}\varphi-\gamma_{2}R^{2}\sin\varphi\left( \sin\varphi-\frac{\varphi}{2}-\frac{\sin 2\varphi}{4}\right)\end{array} \tag{32}\]  
The bending moment and axial force of any section of the left half-arch ring are symmetrically equal to those of the right half-arch ring, while the shear force is equal in magnitude and opposite in direction.
# Nondestructive Tomographic Imaging of Rust with Rapid THz Time-Domain Spectroscopy
## 2 Experimental Setup  
A schematic of the TOF imaging equipment is shown in Figure 1 Our rapid THz-TDS system is based on the commercialized OSCAT (TERA-OSCAT, MenloSystems GmbH, Germany), which allows for a sampling rate of 100-200 Hz/pixel; for instance, a measurement time of 16 s was allowed for each image with a size of 40 \(\times\) 40 pixels at 100 Hz/pixel [28]. The details of the experimental setup can be obtained from published literature, except that the THz imaging system was equipped with spring-loaded flexible arms, as shown in Figure 1. This made our THz-TOF system portable, enabling us to inspect metallic objects at various locations. Importantly, we incorporated a galvano-scanner to obtain the high-speed images with a scan rate that is limited by the OSCAT system. The THz signal was focused onto the rusted metal sample using a convex lens with a diameter of 3.8 cm and a focal length of 5 cm. Conversely, the combined galvano-scanning (\(x\)-axis) and slow stage scanning (\(y\)-aixs) method can be used for larger samples (extending over 20 mm in one direction) and for inspecting the series of samples located in a row. The reflected THz signal was collected by the receiving antenna (denoted by Rx) using a beam splitter. Using home-built software, we recorded and analyzed 3D binary data as a function of the time delay, \(x\)-axis, and \(y\)-axis. We converted the time delay (\(T\)) into depth information (i.e., in the \(z\)-axis), where 1 ps corresponds to 0.15/\(n\) mm and \(n\) is the refractive index of the material.