esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 6 Metaverse Engine
### Immersive Technologies
#### 6.1.1 Virtual Reality (VR)  
Virtual reality (VR) is a technology that immerses users in a computer-generated environment, providing a simulated experience that can be similar to or completely different from the real-world [156]. VR typically involves headsets equipped with displays, sensors, and controllers that track the user's movements and interactions within the virtual environment [157]. VR technology is widely used in gaming and entertainment, creating immersive experiences that engage users in new and exciting ways [158]. Beyond entertainment, VR has significant applications in education, where it enables interactive learning experiences,  
Figure 8: Immersive metaverse technologies.  
such as virtual field trips or simulations of complex scientific concepts, allowing students to explore and understand subjects more deeply.  
In addition to its impact on gaming and education, VR is transforming industries like healthcare, real estate, and training [159]. In healthcare, VR is used for surgical simulations, allowing surgeons to practice procedures in a risk-free environment and for therapeutic purposes, such as exposure therapy for patients with anxiety disorders. In real estate, VR provides virtual tours of properties, giving potential buyers a realistic sense of space without needing to visit in person [160]. In professional training, VR offers a safe and controlled environment for employees to practice skills and scenarios, such as emergency response or complex machinery operation [161]. As VR technology advances, with improvements in display resolution, motion tracking, and user interfaces, its applications are expected to expand further, offering increasingly sophisticated and practical uses across various fields.
## 4 Numerical Results and Discussion
### In-Plane Simulation
#### 4.2.2 Damage Kinetics  
The simulation failure process under 0.2 MPa loading is shown in Figure 46. The input bar was at the right side of the specimen. The corner of the specimen in contact with the input bar was squeezed vertically. This moment occurred at 25 \(\upmu\)s, which was similar to the experimental 23.69 \(\upmu\)s. After 55 \(\upmu\)s, gaps between sub-laminates began to generate. By comparing Figure 46c at 201.38 \(\upmu\)s with Figure 24d at 200 \(\upmu\)s, it can be seen that both simulation and experimental results showed that some sub-laminates were separate while some were still stuck to each other. Therefore, the simulation result had good agreement with the experimental result on failure morphology. It was discussed in Reference [17] that the slight general difference in fracture form at the four corners may be related to the experimental conditions; the geometry of the samples is not perfectly cubic and, thus, the parallelism of the faces in contact with the bars is not as perfect as in the numerical model.  
The contours of the simulation specimen under 0.2 MPa loading at a certain moment are shown in Figure 47. As shown in the simulation specimen in Figure 47a, the effective stress distribution at the center was relatively higher than the effective stress distribution near the vertical sub-layers. As shown in Figure 47b, the effective strain of the center at the end faces near the input and output bar had a higher value than the vertical corners and other regions of the specimen, which indicates that the elements in this region were squeezed the most. As shown in Figure 47c, a greater misalignment between two adjacent sub-layers resulted in a greater effective strain of cohesive elements in the corresponding region. At this moment, the misalignment of two adjacent sub-layers at the four vertical corners was greater than the misalignment at the center, and even greater than that in other regions.  
Figure 46: The simulated failing process of the specimen under in-plane loading: (**a**) 25 \(\upmu\)s; (**b**) 55 \(\upmu\)s; (**c**) 200 \(\upmu\)s.  
Figure 45: The experimental and simulated initial waves under in-plane loading.  
Thus, the effective strain of cohesive elements at the four corners was highest, followed by the center region. The cohesive element was deleted when the damage degree reached 1.0.
## 2 Materials and Methods
### Data Acquisition
The wrist sensors were selected for detecting hand and arm movements, such as when participants were grasping, lifting, and carrying the crate [33]. When standing, sensors on the chest, cervix, and lumbar region can record minimal acceleration and angular velocity, indicating stability with low variability [34]. During walking, the sensors can capture rhythmic patterns of acceleration and changes in angular velocity at the chest, reflecting the gait cycle [35]. The cervix and lumbar region sensors recorded dynamic posture shifts and alignment changes, while the wrist sensors recorded the movement of the arms. The end-to-end workflow for capturing human activity signatures using IMUs is illustrated in Figure 2. The left section of the graph shows a view of the IMU's internal components, while the central section demonstrates the placement of the sensors on the human body. The data capture process is then depicted, showing also how the IMUs are synchronized through the Capture.U 1.4.1 software [36], installed on an Apple iPad mini (64 GB). The required data (CSV files) are saved directly to the five IMUs, each of which contains information about the specific location of the body, and connected to a computer for supporting further analysis. Capture.U software, when paired with the iPad, enabled simultaneous video recording of the ongoing experiments. This capability was especially valuable for manually differentiating between activities and identifying critical moments of transition between them. Following the methodology of [23; 24], each activity begins with the subject standing still (designated as activity \"0\"), which serves a dual purpose: to establish a clear \"idle\" activity baseline and to enable accurate synchronization of the sensors before commencing the sequence. Subsequently, activity \"1\" begins as one foot leaves the ground, marking the start of the stance phase of gait. Activity \"2\" starts when the participant begins bending their trunk, kneeling, or doing both (known as stoop, squat, and semi-squat techniques [37]) to approach the crate. Activity \"3\" begins when the participant starts lifting the crate from the ground. Activity \"4\" starts when the participant enters the stance phase of gait while carrying the crate. Finally, activity \"5\" begins with bending, kneeling, or both actions and ends when the crate is fully placed onto the UGV. The continuous nature of these tasks means that the start of one activity marks the end of the previous one. Adhering to these criteria was vital for reliable results.
## 3 Results and Discussion
### Scanning Kelvin Probe Study of the CoCrMo Alloy Surface
CoCrMo alloys, particularly those used in partial dentures, are resistant to abrasion and temperature changes under artificial aging conditions, which proves that the alloy is resistant to repeated insertions and the removal of dentures [39]. The abrasion resistance of this material allows the hygiene of the prosthesis to be properly maintained [40]. Additionally, research shows that metal alloys such as CoCrMo are less exposed to the colonization of bacteria, in particular _Candida_ spp., which is responsible for the development of oral candidiasis and, consequently, the inability to use dentures [41]. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Electrolyte Type** & \begin{tabular}{l} **CPD\({}_{\text{av}}\)** \\ **[mV]** \\ \end{tabular} & \begin{tabular}{l} **CPD\({}_{\text{rms}}\)** \\ **[mV]** \\ \end{tabular} & \
\begin{tabular}{l} **CPD\({}_{\text{sk}}\)** \\ \end{tabular} &
\begin{tabular}{l} **CPD\({}_{\text{ku}}\)** \\ \end{tabular} \\ \hline (1) saliva with pH = 7.4 & \(-\)346.3 & 18.6 & 0.001 & 0.10 \\ (2) saliva with pH = 5.5 & \(-\)112.6 & 16.5 & 0.05 & \(-\)0.14 \\ (3) saliva with pH = 7.4 + 0.1 M NaF & \(-\)172.7 & 17.7 & \(-\)0.06 & 0.04 \\ (4) saliva with pH = 5.5 + 0.1 M NaF & \(-\)169.3 & 16.4 & \(-\)0.12 & 0.06 \\ (5) saliva with pH = 7.4 + 15 mL of Listerine(r) & \(-\)306.0 & 21.0 & 0.17 & 0.13 \\ (6) saliva with pH = 7.4 + 15 mL of Meridol® & \(-\)204.6 & 17.5 & \(-\)0.07 & 0.21 \\ (7) saline with pH = 7.4 & \(-\)235.6 & 16.6 & \(-\)0.08 & 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistical parameters calculated using CPD maps of the CoCrMo alloy. CPD\({}_{\text{av}}\) is the arithmetic average, CPD\({}_{\text{rms}}\) is the root mean square deviation, CPD\({}_{\text{sk}}\) is the skewness, and CPD\({}_{\text{ku}}\) is the excess kurtosis. The results of the conducted research in this study indicate that, in clinical practice, the use of toothpastes containing sodium fluoride as an ingredient, as well as alcoholic and water-based commercial mouthwashes, will also influence the durability of the Wirobond(r) C dental alloy.
## Result and Discussion  
### Fourier Transform Infrared Spectroscopy  
Figure 2 shows the infrared spectra of MMT (A), OMMT (B), PS (C) and PS-OMMT-3 (D). In Figure 2A, for the MMT sample, the absorption bands at 3446.4 cm\({}^{-1}\) and 1643.4 cm\({}^{-1}\) correspond to -OH stretching vibrations and H-O-H bending vibrations of the MMT interlayer water molecules, respectively. The characteristic adsorption bands at 3632.5 cm\({}^{-1}\) and 1036.1 cm\({}^{-1}\) were attributed to the Al (Mg)-OH bending vibrations and Si-O stretching vibrations, respectively. The characteristic band at 798.6 cm\({}^{-1}\) corresponds to the Si-O-Al stretching vibrations. In contrast, for the OMMT sample spectrum shown in Figure 2B, the characteristic adsorption bands at 2925.6 cm\({}^{-1}\) and 2860.2 cm\({}^{-1}\) respectively correspond to the asymmetric -CH\({}_{2}\) and symmetric -CH\({}_{2}\) stretching vibrations in the SLS molecular chain. In addition, the adsorption band at 1185.5 cm\({}^{-1}\) corresponds to an -SO\({}_{3}\)H asymmetric stretching vibration. Moreover, compared with those of MMT, the absorption bands at 3440.3 cm\({}^{-1}\) and 1632.7 cm\({}^{-1}\) of the OMMT weaken and shift to a lower wavenumber, which indicates that the water content in OMMT is reduced with the SLS modification. These observations indicate that the anionic surfactant molecules are present on the surface or intercalated into the interlayer spaces of the MMT. In Figure 2C, PS showed strong bands at 3027.9-3100.5 cm\({}^{-1}\) and 2850.3-2920.6 cm\({}^{-1}\), corresponding to CH aromatic stretching vibrations and CH\({}_{2}\) asymmetric stretching vibrations, respectively. There are two absorption bands at 1603.5 cm\({}^{-1}\) and 1447.1-1503.4 cm\({}^{-1}\) that correspondto absorptions from aromatic C=C stretching vibrations. The adsorption bands at 758.7 cm\({}^{-1}\) and 698.1 cm\({}^{-1}\) represent the C-H out-of-plane bending vibrations. In addition, the absorption band at 3449.2 cm\({}^{-1}\) corresponds to an absorption from O-H stretching vibrations, indicating the presence of hydroxyl groups. These observations suggest that the polymerization reaction of St has occurred. Compared with PS, PS-OMMT-3 clearly shows a characteristic adsorption band at 1043.9 cm\({}^{-1}\), which corresponds to the Si-O stretching vibrations in OMMT. The FTIR spectrum of PS-OMMT-3 contains the adsorption band corresponding to OMMT, confirming the inclusion of montmorillonite in the PS matrix.
# Cyber Threat Actors for the Factory of the Future
## 2 Analysis of Reports and Strategy Papers--Identifying Cyber Threat Actors
### Expert Organizations
#### 2.2.3 International Telecommunication Union (ITU)  
The International Telecommunication Union (ITU), a UN agency focusing on communications networks, identified nation-states, terrorist, disgruntled workers (insiders) and malicious intruders (malicious actors) as threat actors [17].
# Experience Replay Optimisation via ATSC and TSC for Performance Stability in Deep RL
## 2 Related Works  
With advancements in deep learning and deep reinforcement learning, researchers seek to retain the knowledge acquired by a deep neural network as it trains to find solutions to diverse problems. In recent times, the focus has been on continual learning, where a trained neural network continues to learn. However, continual learning faces the challenge of catastrophic forgetting [19; 20; 21; 22]. A neural network will adjust its weights to a specific problem, task A, but will forget these weights when the same network begins to train on a different task. This behaviour can be harmful to generalisation and continual learning. To ensure that the network is scalable and can train on multiple and complex tasks without forgetting, researchers suggest developing and adopting different strategies and algorithms. While some suggest training a neural network with regularisation approaches, such as learning without forgetting (LWF) [23] and elastic weight consideration (EWC) [21], others such as [PERSON] et al. [24] opt for dynamic network architecture, and some also suggest complementary learning systems (memory with replay strategies) [8; 9; 13; 15; 17; 25].  
ER is one of the oldest but relevant mechanisms employed to complement neural network training. [PERSON] [6] introduced the ER concept in his reinforcement learning research but the DQN algorithm [8] brought it to the limelight. In ER, previous data are stored in a buffer and repeatedly reused with new data to train the neural network. However, the successful operation of ER hinges on some selection and retention mechanisms that require careful design and implementation. This section briefly reviews experience retention and selection strategies but throws much on the latter.
# Inland Water Level Monitoring from Satellite Observations:
## 3 Review Results
### Satellite Missions for Inland Water Level Monitoring
#### 3.1.2 Laser Altimetry Satellite Missions
The evaluation of Lake Qinghai highlights the dense temporal resolution of the GEDI dataset, while the accuracy of these data is inferior to that of the ICESat-2 mission. Moreover, the factors affecting the accuracy extraction need to be further improved [102]. The factors that have an effect on the accuracy of GEDI elevations using two different data product releases were and analyzed in [103]. The results showed that the best criterion to filter less accurate GEDI data is based on the viewing angle, while the newest product release provides better precision of water levels. Also, a comparison between the three laser altimeters over the Great Lakes and Mississippi River showed that ICESat-2 provided water level measurements with an unprecedented accuracy (RMSE = 0.06 m, biases = \(-\)0.01 \(\pm\) 0.05 m), followed by ICESat-1 and then by GEDI missions [99]. In addition, the more accurate performance of ICESat-2 compared to GEDI data is shown in [102]. Furthermore, a comprehensive evaluation of the performances of radar and lidar altimetry missions over mountainous areas shows that very accurate results could be obtained using ICESat-2 data in contrast to GEDI data which need to be analyzed over longer time periods to clearly indicate their capability for inland water level monitoring [77]. As can be understood, the applications and the validation of ICESat-2 and GEDI data over inland water bodies are limited, since only 4 years of ICESat-2 and 3 years of GEDI data are provided. Table 3 showcases the publications analyzed in the current scoping review paper that employ laser altimeters for inland water level monitoring. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Publication** & **Satellite** & **Data** & **Validation** & **Accuracy Metrics** \\ \hline
[94] & ICESat-2 & ATL13 products–version V003 & In situ gauge and ATL08 products & RMSE = 0.08 m, r = 0.999 for ATL08 product \\ \hline
[104] & ICESat, ICESat-2 &
\begin{tabular}{c} GLAH14–V034 \\ (ICESat), ATL13 products \\ V005 (ICESat-2) \\ \end{tabular} & In situ gauge and G-REALM and HTDROWEB & For In situ: RMSE = 0.06 m/yr, MAPE = 15.58\%, r = 0.98. For databases: RMSE = 0.06 m/yr, MAPE = 22.79\%, r = 0.92 \\ \hline
[77] & ICESat-2, GEDI and L2A (GEDI) & ATL3 (ICESat-2), L1B and L2A (GEDI) & In situ gauge & ICESat-2: RMSE \(\leq\) 0.06 m, R \(>\) 0.95, bias (0.42 \(\pm\) 0.03) m, GEDI: RMSE \(\leq\) 0.16–0.51 m, R \(>\) 0.75, bias std: 0.08–0.3 m \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Laser altimeter applications that were found across the literature. RMSE stands for Root-mean-square-error, r: Pearson correlation, MAPE: mean absolute percent error, MAD: median of absolute deviation, ubRMSE: unbiased RMSE, std: standard deviation.
# Developing a Digital Twin and Digital Thread Framework for an 'Industry 4.0' Shipyard
## 2 The Digital Twin
### Current Digital Twin Application in the Industry  
DTWs attract interest from different industries' operations areas such as product design, logistics, manufacturing and maintenance. Also, DTWs can be used to increase the efficiency and automation levels of the manufacturing, maintenance and after-sales service (as shown in Figure 2) [7; 14; 15].  
In the product concept, design and production phases, DTW can be a very useful tool in the manufacturing system. Studies show that DTWs have been used successfully to understand the performance and behavior of individual machines, making it easier to integrate the production line [16; 17]. By leveraging the advantages of DTW, small manufacturing companies have achieved better performance in automation and adaptability to changes in customer orders or material properties such as hardness, strength and elasticity. These successes show that DTW can be used as a tool to increase efficiency in the production planning and optimization of the manufacturing implementation [16; 17]. DTW has also revealed potential in the predictive maintenance area where, based on the information collected from the physical component, multi-physics simulations and data analysis are performed to predict future performance and possible future failures. These can be used to generate early warnings and to feed into the maintenance plan continuously, thereby reducing the costs of unplanned disruption. However, these kinds of applications have not yet been widely adopted and further research is needed to generalize them for wider use [9; 15; 18].  
In the services area, such as after-sale service, DTW can be used as an information tool to provide added value to the customers by being able to produce better predictions of the future behavior and the remaining lifetime of an asset and its components. DTWs can also be used to collect useful data to drive design modification, improve product performance and improve the overall production planning cycle [15; 16; 19]. Despite some successful applications, the methods and tools to implement DTW in industry are still in their early stages of development and need more research. Also, many of the physical phenomena involved in the manufacturing of several products such as aircraft, vehicles and machining tools are complex and hard to simulate. Hence, these issues need more research to develop better models. Additionally, the large amount of data that can be collected by a DTW introduces new challenges in data handling, processing and storage [20; 21] and hence, a framework to build DTWs should address these challenges [9; 14; 15; 22; 23; 24; 25].
# Development of Analytical Model for Bonding of CFRP Rod in Concrete Subjected to Cyclic Loads
## 6 Analytical Approach
### Shape Function  
The direct stiffness method is employed to compute Degrees of Freedom (DOFs) at the element nodes. The shape function represents the linear displacement field within the element in terms of nodal displacements. The shape function is obtained using the Lagrange method for a 2D solid element, which can be considered as an isoperimetric element. In this case, the shape functions must be written in natural coordinates.  
In the finite element approach, the nodal values of the field variable are treated as unknown constants that need to be determined. The interpolation functions are typically polynomial forms of the independent variables, derived to satisfy certain required conditions at the nodes. These interpolation functions are predetermined, known functions of the independent variables, and they describe the variation of the field variable within the finite element.  
Figure 20: Interface Elements between CFRP rod and Concrete Membrane.  
To determine the stiffness matrix of the element [K], the [B] matrix and [D] matrix of the element need to be generated in advance. Six shape functions have been obtained, as per Table 12, which are crucial components in the [B] matrix.  
In general, the number of DOFs associated with a finite element is equal to the product of the number of nodes and the number of values of the field variable (and possibly its derivatives) that must be computed at each node. For this study, with 2 DOFs in both X- and Y-directions and a 2D model comprising six nodes, we have a total of 12 DOFs. Since the considered axes are planar, the 2D element in this research is considered as a plane-strain element.
# Solution of Bin Packing Instances in Falkenauer T Class: Not So Hard
## 1 Introduction
### Metaheuristics  
Many kinds of metaheuristic approaches have been tried for Bin Packing. Such algorithms are the genetic algorithm (GA), particle swarm optimization, tabu search, variable neighborhood search, etc. These algorithms usually generate suboptimal solutions, but in many cases, they are able to find an optimal solution. We briefly list some metaheuristics below.  
[PERSON] [18] proposed the so-called grouping genetic algorithm for solving BP instances. He also introduced two benchmark types, the Falkenauer U class and the Falkenauer T class. We deal with the latter one in this article.  
There are also hybrid algorithms, e.g., [19]. [PERSON] [20] introduces a very efficient hybrid evolutionary algorithm, solving 1615 benchmark problems, and in 99.7% of these instances, the solutions are optimal. On the hard28 test set, this algorithm outperforms the earlier heuristics, finding the optimal solution for all 28 instances.  
The P-SAWMBS algorithm by [PERSON] and [PERSON] [21] is also worth mentioning. P-SAWMBS is a variable neighborhood search (VNS) variant and is very efficient, as shown also in the paper of [PERSON] [20]. After creating an initial packing, P-SAWMBS performs a series of perturbations to improve the initial solution. The number of such perturbations is at most 2000. It finds all optimal solutions for the Falkenauer T instances (and is also efficient for other classes, but in this paper, we are interested in the Falkenauer T class only).  
The algorithm CNS_BP by [PERSON] and [PERSON] [22] uses a consistent neighborhood search to solve the one-dimensional Bin Packing problem, where tabu search is incorporated. It is notable for solving more benchmark instances to optimality than other heuristic approaches at the time of its publication.