esa-sceva/satcom-chunk-collection huggingface dataset:
# SLR, GRACE and Swarm Gravity Field Determination and Combination
## 2 Materials and Methods
The observations used for orbit determination are either the 1D-ranges (normal points [54]) observed by SLR, the kinematic orbits of Earth observing LEOs determined by precise point positioning (PPP) [55] from high-low GPS data that are used as pseudo-observations [56], or low-low range-rates derived from the K-band inter-satellite link in case of the two satellites of the GRACE mission [57]. The sampling of these observation types is very diverse. In case of SLR, it depends on the inhomogeneous global distribution of the SLR stations [19]. Certain regions of the Earth, such as the polar regions, are not covered by observations at all due to the lack of suitably positioned stations. All information about gravity variations is derived from the orbit dynamics. Therefore, the orbit modeling of SLR satellites has to be mainly dynamical (based on physical models) and the pseudo-stochastic parameterization of the orbits has to be very limited to allow for gravity field determination. On the other hand, GPS and K-band observations are normally given at very high sampling rates of 1 s, 5 s or 10 s and in case of polar orbits the observation distribution is global and densest near the poles. Several pseudo-stochastic parameters may be set up per orbital revolution of the Swarm or GRACE satellites. The spatial resolution of the resulting gravity fields depends on the orbital altitude and ground track pattern of the satellites. In case of sparse SLR tracking, gravity variations beyond degree 2 can only be determined by the combined evaluation of several satellites (see [10; 11; 12]). Satellites at lower inclinations are helpful to decorrelate the individual gravity field coefficients [19]. In case of the Earth observing LEOs, the achievable temporal resolution of subsequent gravity field solutions directly depends on sub-cycles of the ground track pattern, which vary depending on the orbit altitude (decreasing with time in case of the LEOs). The GRACE mission was designed to deliver monthly gravity fields [1] and since then monthly temporal resolution has become the standard, even if 10-day [58], and even daily \"snapshot\" solutions [59] of the gravity field are also available.
## 6 Numerical Experiments
### Comprehensive Evaluation of the Algorithms
#### 6.4.3 Evolution of the Self-Adaptive Algorithmic Parameter Values  
Unlike the crossover and mutation probabilities for the AEA, DPCEA, and EA algorithms, the crossover and mutation probabilities for the SAEA algorithm are encoded in the chromosome (see Figure 4) and evolve during the search process. Throughout the numerical experiments, the crossover and mutation probability values were recorded after the offspring selection for the whole population at each generation for each one of the generated large size problem instances. Figures 11 and 12 illustrate evolution of the average crossover and mutation probabilities (over all the chromosomes of the population) within the SAEA algorithm for the first replication of problem instances 49-60 (12 large size problem instances with the largest number of vessels, calling for service at the MCT). Note that similar tendencies in evolution of the average crossover and mutation probabilities within the SAEA algorithm were observed for the rest of replications and problem instances.  
It can be observed that the pattern of changes in the crossover and mutation probability values within the SAEA algorithm is complex as compared to other EA-based algorithms (e.g., DPCEA, where changes in the crossover and mutation probability values can be described using the piecewise function--see Section 6.2 of the manuscript for more details). Typically, relatively high crossover and mutation probability values are assigned by SAEA at the beginning of the search process (i.e., the crossover probability is set to \(\approx\)0.60 \(\div\) 0.70, while the mutation probability is set to \(\approx\)0.35 \(\div\) 0.45). On the other hand, SAEA substantially reduces the crossover and mutation probability values towards convergence (i.e., the crossover probability is reduced to \(\approx\)0.40 \(\div\) 0.50, while the mutation probability  
Figure 11: Evolution of the average crossover probability values for problem instances 49–60.  
is set to \(\approx\)0.15 \(\div\) 0.25). High crossover and mutation probability values at the beginning of the search process allow the SAEA algorithm efficiently exploring promising domains of the search space, while lower crossover and mutation probability values allow the SAEA algorithm focusing on exploitation of the identified promising domains towards convergence.
## 3 A Hybrid AC/DC Topology  
In this section, we first introduce a general overview of the MVDC interface circuit topology that is used to turn an AC distribution grid into a hybrid AC/DC system. The MVDC interface has been shown in Figure 3 and is used to provide a seamless connection between end feeders. In practice, the MVDC interface starts with a link between two substations as in the \"Angle-DC\" demonstration project [6] and then expands to the nearby substations to form a DC platform. The development progress is not necessarily continuous instead it can be done in stages as needed. This work is carried out to showcase the benefits to be gained after it forms the MVDC interface.  
As mentioned in Section 1, the role of the MVDC interface is two-fold (i) to provide a platform for the seamless integration of DG resources with integrated DC links (e.g., MV wind parks or solar farms), as well as various types of typically DC flexible demand (e.g., electric vehicles, and battery storage systems) and (ii) to provide maximum operational flexibility through the extra levels of control afforded by the VSCs for improved voltage regulation as well as load balancing between feeders. This configuration guarantees the continuity of supply constraint. In the next sub-sections, the mathematical model of the MVDC system (to be included in the ANM framework) is described.  
Figure 3: The MVDC (multi-terminal medium voltage DC) interface applied in a hybrid network
# Cryptography in Wireless Multimedia Sensor Networks: A Survey and Research Directions
## 4 Cryptography Approaches for WMSNs
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Approach** & **Scalar** & **Image** & **Video** & **Audio** & **Cryptography** & **Encryption** & **Selective** & **Compression** & **Year** \\ \hline [PERSON] et al. [69] & - & X & - & - & - & Image correlation & & Based on & 2009 \\  & - & - & X & - & - & from multiple sensors & - & correlated images & 2009 \\ \hline [PERSON] et al. [70] & - & - & X & - & Symmetric & AES & X & H.264 & 2010 \\ \hline [PERSON] et al. [62] & - & - & - & X & Symmetric & AES & X & MDCT & 2010 \\ \hline [PERSON] et al. [71] & - & X & - & - & Symmetric & skipjack & - & Quadtree & 2011 \\ \hline [PERSON] et al. [72] & - & X & - & - & Symmetric & Transposition & - & \begin{tabular}{c} Burrows-Wheeler \\ Transform (BWT) \\ \end{tabular} & 2012 \\ \hline [PERSON] et al. [73] & - & - & X & - & Symmetric & \begin{tabular}{c} AES, Message \\ Authentication Code \\ \end{tabular} & X & H.264 ; H.263 & 2012 \\ \hline [PERSON] et al. [74] & - & - & X & - & Symmetric & AES; Message & - & H.264 & 2013 \\ \hline [PERSON] et al. [48] & - & X & - & - & Symmetric & RC4 & X & JPEG2000 (DWT) & 2013 \\ \hline [PERSON] et al. [56] & - & - & X & - & Symmetric & RC5 & X & H.264 & 2014 \\ \hline [PERSON] et al. [75] & - & X & - & - & Symmetric & RC4 & - & Voronoi tessellation & 2014 \\ \hline [PERSON] et al. [76] & X & X & X & \begin{tabular}{c} X \\ Authentication Code \\ \end{tabular} & X & X & \begin{tabular}{c} Synthetic \\ Authentication Code \\ \end{tabular} & \
\begin{tabular}{c} Kestel: Message \\ Authentication Code \\ \end{tabular} & - & Compressive sensing & 2015 \\ \hline [PERSON] et al. [77] & - & X & - & - & Symmetric & RC4 & - & - & 2015 \\ \hline [PERSON] et al. [78] & X & - & - & - & Symmetric/Asymmetric &
\begin{tabular}{c} AES, Elliptic Curve \\ Integrated Encryption \\ Scheme (ECIES) \\ \end{tabular} & - & - & 2016 \\ \hline [PERSON] et al. [79] & - & X & - & - & Symmetric & CS-based & - & Compressive sensing & 2016 \\ \hline [PERSON] and [PERSON] [80] & - & X & - & - & Symmetric & AES & X & DWT & 2016 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Cryptography in Wireless Multimedia Sensor Networks (WMSNs).
## 2 Materials and Methods
### Optical Gauge Amplifier  
A Fibos Optical Gauge Amplifier (OGA) was used to conduct wavelength measurements and spectral analysis of the returned wavelength from the FBGs. The OGA scans the wavelength of the connected FBG using a narrow linewidth laser and records the reflected power and wavelength of each FBG specimen. The spectral plots (power vs. wavelength) were used to determine whether the FBGs experienced optical distortion during the experiments. The OGA uses a proprietary technique to measure the FBG's characteristic wavelength (patent US20180372566A1) which provides wavelength measurements with an uncertainty of \(\pm\)0.005 nm. The Fibos OGA wavelength measurement is insensitive to changes in the reflected power of the FBG. Research conducted by [PERSON] et al. suggested that exposure to radiation may induce an attenuation of \(\sim\)0.1 dB/m along the fiber optic cable, therefore reducing the reflected power of a FBG. As the Fibos OGA's method of wavelength measurement is not affected by the intensity of the FBG's reflection power, the presented fiber optic measurement system is ideal for applications in a radiation environment where darkening of optically translucent materials can occur.
# Fault Location Method of Multi-Terminal Transmission Line Based on Fault Branch Judgment Matrix
## 4 Research on Multi-Branch Line Fault Ranging Technology Based on Fault Branch Judgment Matrix
### Multi-Terminal Transmission Line Fault Location Method
#### 4.2.3 T-T Inter-Node Failure  
When the fault occurs at nodes T1-T2, as shown by 2 in Figure 5, the same principle of determination can be applied as follows: when the value of the line element corresponding to only one end measurement point in the fault branch judgment matrix is less than 0, and either only the value of the first element in its previous line is equal to 0 or only the last element in the next line is equal to 0, the fault is determined to occur between the T node of the branch where the endpoint is located and the T node corresponding to the endpoint in the previous or next line.
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 3 Methodology
### Step 1: Collect System Information
#### 3.1.1 Step 1.1: Develop Nanogrid System Information  
Basic information about potential nanogrids must first be developed following [PERSON] et al.'s six criteria for understanding electrical systems that power military loads including: (1) System Purpose (2) Stakeholders (3) System Boundaries (4) Functional Requirements (5) System Architecture (6) Operating Modes [2]. This information is useful to ensure accurate and explicit communications between all stakeholders (e.g., base energy managers, tenant commands, critical load owners, etc.) and helps to elucidate the architectural process to find weaknesses and validate checkpoints within the grid development [40]. We advocate that this information is captured in a Model Based Systems Engineering (MBSE) tool such as Papyrus, Magic Draw, Innoslate, etc. which helps with later analysis.  
As part of developing the information for [PERSON] et al.'s six criteria, stakeholder needs are elicited. The stakeholder needs are then developed into requirements which will be used later in a decision matrix to help judge different nanogrid configurations for suitability. Potential requirements include but are not limited to efficiency, reliability, ease of integration, and communication cohesion.
# Computationally Efficient Channel Estimation in 5G Massive Multiple-Input Multiple-output Systems
## 4 Proposed Algorithm
### Complexity Analysis
#### 4.3.3 LLR calculation  
The computational complexity of this part mainly came from the calculation of the effective channel gain and the NPI variance after equalization. It can be known from Equations (20) and (21) that all the elements of the matrix \(\widetilde{\mathbf{U}}\) and the pair of matrices \(\widetilde{\mathbf{E}}\) need to be calculated. Obviously, the former requires \(2\mathbf{K^{2}}\) multiplications, while the latter only requires \(2\mathbf{K}\) multiplications. Therefore, a total of \(\left(\mathbf{2K^{2}}+\mathbf{2K}\right)\) multiplications were required for this step.  
In summary, the total complexity required for the joint iterations to be applied to the soft decision was \(2\mathbf{K^{2}}(i+\mathbf{2})+\mathbf{12 Ki}\), which reduced the computational complexity by an order of magnitude, compared to the traditional MMSE algorithm. The complexity of the number of iterations was kept at \(\mathbf{O}\left(\mathbf{K^{2}}\right)\). In addition, considering the application scenarios of hard decision detection, Table 1 also gave a comparison of the computational complexity in the four detection algorithms.
# Design Analysis of a Passive Buffer System for Space-Assembled Segmented Mirrors
## 2 Establishment of Double-Elastic Contact Model
According to the equilibrium condition, the product of the volume of the hemisphere mentioned above and \(\frac{f_{0}}{u}\) should be equal to the driving force, which can be shown as:  
\[\frac{f_{0}}{u}\frac{2}{3}\pi u^{3}=F. \tag{8}\]  
From Equations (7) and (8), the radius \(u\) of contact circle and the maximum deformation \(\delta\) can be obtained by:  
\[\left\{ \begin{array}{l}u=\left[\frac{3\pi FRR_{1}R_{2}}{4(R_{1}+R_{2})} \right]^{\frac{1}{3}}\\ \delta=\left[\frac{9\pi^{2}F^{2}k^{2}(R_{1}+R_{2})}{16R_{1}R_{2}}\right]^{ \frac{1}{3}}\end{array} \right.. \tag{9}\]  
The maximum compressive stress\(f_{0}\) occurs at the center of the contact surface, and its value is:  
\[f_{0}=\frac{3F}{2\pi}\bigg{[}\frac{4(R_{1}+R_{2})}{3\pi FKR_{1}R_{2}}\bigg{]} ^{\frac{2}{3}}. \tag{10}\]  
Since \(k\) is related to the Young's modulus \(E\), and the Poisson's ratio \(v\) of the elastic pin head and the elastic wall surface, the value of the maximum compressive stress \(f_{0}\) can be determined as long as we know the wall radius, \(R_{1}\) at the collision point \(O\), and the magnitude of the driving force.
## 3 Results and Discussion
### Antidiabetic and Antiobesity Activity  
Pancreatic \(\alpha\)-amylase and \(\alpha\)-glucosidase are enzymes that hydrolyze bonds in complex carbohydrate molecules, breaking them down into simple sugars, compounds suitable for absorption. In turn, pancreatic lipase participates in the breakdown of lipid bonds into free fatty acids and monoacylglycerol hypercoels. Inhibiting the activity of these enzymes may play an important role in the strategy of preventing and/or treating type II diabetes and obesity. Hence, the search for new sources of inhibitors among materials of plant origin is important in contemporary phytotherapy. The ability of _C. sativa_ leaf extract to inhibit the activity of \(\alpha\)-amylase, \(\alpha\)-glucosidase and lipase is shown in Table 4.  
The methanolic extract showed higher activity against the obesity-promoting enzyme than against antidiabetic enzymes. The obtained values were 0.72 mg/mL (for pancreatic lipase) and 1.62 and 2.20 mg/mL (for pancreatic \(\alpha\)-amylase and intestinal \(\alpha\)-glucosidase, respectively). So far, only the antiobesity and antidiabetic activity of _C. sativa_ shell has been assessed. [PERSON] et al. (2021) found comparable activity against lipase (IC\({}_{50}\) 0.457 mg/mL) and overall weak activity against \(\alpha\)-amylase (15.22% at 1 mg/mL) [42]. This means that _C. sativa_ extracts have higher anti-obesity than anti-diabetic activity. Nevertheless, the reports of [PERSON] et al. (2011) showed a beneficial effect of _C. sativa_ leaf extracts on rat pancreatic \(\beta\) cells, reducing the risk of cell death as a result of oxidative stress, thus preventing the development of diabetes. Importantly, this effect was attributed to the total polyphenol content, not to individual compounds isolated from the extract matrix [28]. Our own analyzes in the assessment of the Pearson coefficient showed a positive correlation between the sum of phenolic compounds and \(\alpha\)-amylase (r = 0.918, \(p\) < 0.05), \(\alpha\)-glucosidase (r = 0.958, \(p\) < 0.05) and pancreatic lipase (r = 0.983, \(p\) < 0.05). Also, [PERSON] et al. (2021) showed a strong correlation between TPC and \(\alpha\)-amylase activity and a negative one between TPC and pancreatic lipase [42], which may indicate that _C. sativa_ polyphenols have a significant impact on inhibiting the activity of digestive enzymes. Nevertheless, these are the first reports on the inhibitory effect of _C. sativa_ leaves on digestive enzymes. Further research should be conducted to elucidate these relationships in detail.
## 3 Results
### BER Performance under Interference and Multipath Conditions  
The performance of communication systems in general is limited by interference. In case of MPSK modulations, it is of particular importance. Therefore, the BER performance of RP-MPSK is investigated under multipath and interference conditions occurring at the same time. The case of co-channel interference from single user is considered [27] assuming matched central frequency. Equation (15) can be rewritten for this case as:  
\[r_{m,\ell}=q_{m,\ell}H+\mu q_{(m,\ell)i}H_{i}+w \tag{21}\]  
Here, \(q_{(m,\ell)i}\) is the replica of the symbol transmitted by the interfering user, \(H_{i}\) is the quaternion fading coefficient of the interfering user, and \(\mu\) is the relative level of interference. Two interference cases are considered: a low interference (\(\mu=0.3\)) and a high interference (\(\mu=0.9\)). In Figure 9, BER curves of RP-MPSK are obtained for \(M=2,\ 4,\ 16\) and \(N_{p}=7\). Since interference always degrades the BER performance, a higher value of \(N_{p}\) compared with Figure 6 is selected. The result shows that for low to moderate BER, RP-BPSK and RP-QPSK must be preferred. According to Figure 8, RP-16-PSK can also be used provided a high value of \(N_{p}\) is selected. The same argument is also valid for RP-8-PSK though its results are not shown. Comparing Figure 9 with Figure 6 reveals that the performance of RP-BPSK (\(N_{p}=7\)) with interference and multipath is consistent with the performance of RP-8-PSK (\(N_{p}=3\)) with multipath only, for low values of \(\alpha\).  
Figure 8: Recommended sampling rates for RP-8-PSK and RP-16-PSK for compatibility with RP-BPSK/RP-QPSK.
## 2 Materials and Methods
### Improvements in YOLOv5s Network Model  
A challenge in pavement crack detection is the significant variation in crack shape and size from image to image. The crack images in our constructed dataset have scale and shape diversity. The original YOLOv5s model, despite its obvious advantage in detection speed, does not perform well on targets with multiple scales and shapes. To solve this problem, we train model weights on the channel dimension of the feature layer extracted by the backbone network so that the network strengthens the part with pavement defects and weakens the useless information in the image. This strategy reduces the network's focus on irrelevant image information, which improves the network's feature extraction efficiency in complex contexts. We employ an enhanced YOLOv5s framework that integrates the ECA-Net (efficient channel attention network) [29], an improvement upon the Squeeze-and-Excite Network (SE-Net) [30]. This integration serves to reduce model complexity and dependence on dimensionality reduction.  
We introduce a decoupled head structure for the independent optimization of dimension prediction and category confidence, enhancing model accuracy without extra computational load. The standard CIoU (Complete Intersection over Union) loss function is replaced with the GIoU (Generalized Intersection over Union) loss function to offer a more general metric for assessing target box overlap. The GIoU is particularly beneficial for targets with significant size variations. The above adjustments more effectively correct discrepancies between predicted and actual boxes.  
Integrating the ECA attention mechanism, implementing a decoupled head structure, and utilizing the GIoU loss function result in a more efficient and robust YOLOv5s-based model. The proposed model significantly improves the accuracy of multi-scale pavement crack detection in complex real-world environments.
## 1 Introduction  
Industry 4.0, formally defined in 2011, transformed manufacturing and society by integrating emergent technologies to enhance efficiency and high-quality production [1]. This paradigm prioritized efficiency and high-quality production through the adoption of Artificial Intelligence (AI), Big Data Analytics, the Internet of Things (IoT), edge computing, cloud computing, and cyber-physical systems (CPSs) [2, 3, 4]. However, Industry 4.0's strong emphasis on automation and optimization has raised ethical and sustainability concerns, potentially threatening the future of human labor and the environment [5]. In response to these challenges, the concept of Industry 5.0 has gained significant attention in recent years [6]. Industry 5.0 builds upon Industry 4.0 but shifts the focus from pure automation to human well-being, resilience, and sustainability, emphasizing human roles, skills, and rights in the workplace [5, 6, 7]. Driven by emerging applications such as assistive technologies, human-robot collaboration (HRC), and human digital twins [4, 8, 9], Industry 5.0 aims to augment rather than replace human capabilities with intelligent robotic systems [7].  
A key challenge in developing Industry 4.0/5.0 applications is the safe and seamless integration of intelligent robotic systems into real-world environments [5; 9]. As robots become increasingly embedded in social, service, and industrial settings, their capabilities must extend beyond basic automation to enable advanced perception, decision-making, and adaptability. In this context, the Robot Operating System (ROS) has emerged as a fundamental framework for robotics development, providing a modular, open-source architecture that facilitates communication, visualization, and algorithm integration. However, deploying intelligent robotic systems requires rigorous development, testing, and validation of complex algorithms for vision, navigation, localization, mapping, and control. Moreover, early-stage research and real-world deployment of intelligent robotic systems can be risky and resource-intensive. To mitigate these challenges, robotic simulators play a crucial role by offering a safe and cost-effective environment for refining and validating algorithms before physical implementation [3]. Additionally, simulators enhance collaboration among research groups, accelerating development timelines and fostering innovation [6; 10; 11].  
Given the complexity and multifaceted nature of Industry 4.0/5.0 applications, selecting the appropriate simulators requires a comprehensive approach. Therefore, this article aims to systematically examine the role of robotic simulators in the context of Industry 4.0/5.0. This review specifically focuses on ROS-enabled simulation platforms due to ROS's critical role in modern robotics research and deployment.