esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Introduction to Digital Watermarking: Trajectory, Techniques, and Applications
### Robustness:  
Watermark robustness determines survival capability against typical signal processing operations, including compression, scaling, and filtering [10; 43]. High robustness protects against unauthorised removal and ensures that the watermark remains detectable and extractable even after the image undergoes processing or transmission.  
Data Payload:  
Data payload, also termed watermarking capacity, measures the quantity of information embeddable within an image without compromising quality [44]. This attribute indicates the efficiency of the watermarking system and determines the storable data volume within the medium for subsequent extraction.  
Security:  
Watermarking systems must resist unauthorised extraction or watermark removal [10]. Security typically relies on secret keys--public, private, or detection keys--used during watermark embedding and extraction.  
Computational Complexity:  
The computational demands of watermarking systems affect encoding and decoding speed [45]. Real-time applications require balanced systems that maintain security whilst ensuring efficient watermarking without robustness compromise.
###### Abstract  
Rolling element bearing is a vital component in rotating machinery, such as a wind turbine (WT) system. By accurately monitoring its health condition, the faults can be detected at an early stage, providing sufficient lead time to perform maintenance and hence reducing accidents and economic losses. Bearing usually suffers from various wears and tears, which result in a gradual increase in clearance through its lifetime. Insufficient understanding of vibration characteristics under different clearances brings difficulties for bearing condition monitoring. Thus, this paper presents a nonlinear bearing vibration model with six degrees of freedom (DOF) to investigate the vibration characteristics under different radial clearances and load conditions. Then, a dedicated bearing test is established to verify the reasonability and effectiveness of the vibration model. Furthermore, a comprehensive simulation analysis is conducted to study the vibration characteristics over an extended range of the internal radial clearance and external load. Results show that the dynamic force on each ball presents an impulse whose magnitudes increases whereas the pulse width reduces with clearance increases. Ball pass frequency of outer race (BPFO) is the dominant modulation component and the frequency is in accordance with the number of dynamic force impulses. Two indicators, i.e., root mean square (RMS) value and spectral centroid, are proposed to indicate clearance changes. In general, they show an uptrend with the increase in clearance, which is in line with the dynamic force increasing with clearance, especially the spectral centroid of the low frequency band. However, it should be noted that the RMS value and spectral centroid exhibit a fluctuating behavior due to nonlinear vibration responses. For the first time, this study shows the details of vibration characteristics with clearance variations and provides a foundation for monitoring the bearing conditions before any obvious local defects on raceways.  
rolling element bearing; radial clearance; nonlinear dynamic model; Hertz contact theory; condition monitoring +
Footnote †: journal: Applied Sciences
# Adsorption Separation of Various Polar Dyes in Water by Oil Sludge-Based Porous Carbon
## 3 Results
### Microstructure Analysis
#### 3.1.4 X-ray Photoelectron Spectroscopy Analysis  
A comparison of Figures 5a-d and 5e-i reveals that carbon (C) exists in two forms in the oil sludge (OS). The binding energies of carbon are 284.8 eV (C-C) and 286.0 eV (C-O-C) [46], as shown in Figure 5b. The C 1s spectrum (Figure 5f) indicates that carbon exhibits three types of binding states: C-C/C=C, C-O-C, and C-CO\({}_{3}\)[47], with the C-C bond being the predominant form. After thermal activation with ZnCl2, a substantial carbon network forms on the surface of OSC700, consistent with the results shown in Figure 3.  
In the OS, the binding energy of oxygen is 531.7 eV (C=O) [48], which shifts to 531.9 eV after ZnCl\({}_{2}\) thermal activation, corresponding to the formation of metal carbonates. This shift is primarily attributed to the synergistic binding of Zn with the carbon matrix. The silicon elements in the oily sludge primarily exist in the forms of Si-O (102.6 eV) and Si-O-Si (103.6 eV) [49] (Figure 5d), which shift to Si-O (102.4 eV) in the form of quartz [49] (Figure 5i). The activation process enhances the surface activity and porosity of the material, leading to improved adsorption performance. The binding energies of zinc are detected at 1022.0 eV (Zn 2p3/2) and 1044.0 eV (Zn 2p1/2), with no Zn LMM peak observed, indicating that Zn is fully covered by the carbon layer [50].  
In conclusion, the optimal preparation temperature for porous materials derived from oil sludge is 700 \({}^{\circ}\)C. The resulting OSC700 exhibits the largest specific surface area and total pore volume, with a relatively uniform pore size distribution. The use of ZnCl\({}_{2}\) as an activator at this pyrolysis temperature enhances pore formation.
# A Review of Flywheel Energy Storage System Technologies and Their Applications
## 4 FESS Applications
### Voltage Sag Control  
Voltage sag problems are created due to load unbalance or faults in the power grid, causing a decrease in voltage magnitude. Voltage sags due to unbalanced loads occur when large amounts of power for a short period of time is absorbed by the load, which will decrease the voltage and cause voltage drop problems [69; 76].  
Voltage sag has become one of the major power quality problems affecting sensitive loads such as modem industrial manufacturing like semiconductor production, food processing and paper making, sensitive microprocessors, and high frequency power electronic devices [76]. Further drawbacks of voltage sag in three-phase power networks are increased line losses, neutral conductor overloads, and extra rotating losses in drives [69]. About 92% of the power quality problems are as a result of voltage sag and 80% of these occasions last for only 20-50 ms [76].  
Traditionally, voltage sag has been compensated for by generation reserves adding power to the system, when demanded. The recent approach is to engage energy storage systems in mitigating the voltage problems in power networks. The energy storage system is used to store the energy in times when excessive power is required, in order to keep the grid voltage fixed. This reduces the cost and eliminates the need for oversized generating units [69].  
FESS, with their excellent characteristics, can be viable alternatives to other storage systems for this application. Particularly, a fast response, high power density, and frequent charge-discharge cycle capability, are the best attributes of flywheels for voltage compensation applications [69]. A 10 MJ flywheel energy storage system for high quality electric power and reliable power supply from the distribution network, was tested in the year 2000. It was able to keep the voltage in the distribution network within 98%-102% and had the capability of supplying 10 kW of power for 15 min [9]. In 2005, a flywheel-based grid stabilising generator (PowerStore) commenced operation in Flores Island, Portugal. It has been used to perform a frequency and voltage ride through to safeguard conventional grids and allows the integration of renewable generation from wind and solar sources. The PowerStore with a 500 kW rated power and 60 s duration capability is still operational [65].
## 2 MAP-DBSCAN Method  
### MAP as a Tool for the Detection of Seismicity Rate Changes  
In the first step, the temporal distribution of seismicity is approximated by a stochastic point model, the Markovian Arrival Process. The MAP is a two-dimensional Markov process \((N_{t},I_{t})_{t\in R^{+}}\), where \(N_{t}\) counts the number of earthquakes up to time \(t\) that occur with a rate \(\lambda_{t}\). Its value is associated with the unobservable states \(i=1,\ldots,K\), of the Markov process \(J_{t}\). In particular, when the process \(J_{t}\) is in state \(i\), earthquakes occur according to a Poisson process with rate \(\lambda_{i}\) and, therefore, the sojourn time in this state follows an exponential distribution with expected value \(1/\lambda_{i}\). When an earthquake occurs, the MAP can transit with probability \(p_{ij}\) to another state \(j\), so now, earthquakes occur according to a Poisson process with rate \(\lambda_{j}\), or remain in the current state \(i\) with probability \(p_{ii}\).  
To represent the MAP model, we need the \(K\times K\) rate matrices \(\mathbf{D}_{0}\) and \(\mathbf{D}_{1}\), where \(\mathbf{D}_{0}\) is a diagonal matrix whose non-negative elements we denote as \(\lambda_{1},\ldots,\lambda_{K}\), and which correspond to the \(K\) Poissonian rates, each one assigned to a hidden state of process \(J_{t}\), and \(\mathbf{D}_{1}\) consists of the transition rates among the states, along with the occurrence of an earthquake, which we denote as \(q_{ij}\). Additional details on the estimation procedure of the MAP model and its properties are given in Appendix A.  
Concerning our clustering algorithm, we are interested in the evaluation of the transitions among the hidden states, namely, the detection of changes in the seismicity rate. In particular, we define a rate threshold, \(\lambda_{thr}\), according to which a potential sequence starts when the rate of the counting process \(N_{t}\) achieves \(\lambda_{t}>\lambda_{thr}\) and ends as soon as the process \(J_{t}\) moves for the first time to a state with a Poisson rate below that threshold. Our main assumption is that each state corresponds to a distinct evolution phase of a seismic sequence, independently of its underlying mechanism. In this way, the model has the ability to approximate the temporal evolution of earthquake catalogs that incorporate both aftershock sequences and earthquake swarms [31], as well as datasets with non-stationary characteristics [34].
## 5 Conclusions  
Aiming at the problem of low computational efficiency caused by the three-layer nesting of the MRA, a decoupling strategy for reliability analysis of multidisciplinary system with aleatory and epistemic uncertainties is proposed. This paper integrates the multisource uncertainty quantification based on probability theory and convex set theory. Based on the decoupling principle of multidisciplinary reliability analysis and the idea of serialization, this paper proposes a serialization method of multidisciplinary reliability analysis under aleatory uncertainty and epistemic uncertainty. The three-layer nested loop process is decoupled, and a single loop recursive analysis process is composed of multidisciplinary probabilistic reliability analysis, nonprobabilistic reliability analysis based on convex model and multidisciplinary analysis. In the nonprobabilistic reliability analysis based on convex model, the global sensitivity analysis and KKT condition replacement in the CSSO strategy are integrated to ensure the computation efficiency of the method. The example shows that the method is effective in dealing with the multidisciplinary reliability analysis under aleatory uncertainty and epistemic uncertainty, and has good computational efficiency and accuracy. This method can provide multidisciplinary reliability analysis for RBMDO under aleatory and interval uncertainty, and expand and perfect the RBMDO theoretical system. It is worth noting that the method proposed in this paper also needs to be combined with specific multidisciplinary optimization framework, such as SORA framework, in order to play its specific role, which is our next research focus.  
The capability of dealing with uncertainties and the computational efficiency have become the main problems of MRA to be resolved since the current MDO engineering systems are becoming more and more complicated. Therefore, more efforts for future research will focus on: (1) the investigation of probability theory with other mathematical theories (evidence theory, possibility theory, etc.) in quantifying both random and epistemic uncertainties simultaneously, (2) the considerations of correlation of different convex models, and (3) the development of more efficient multidisciplinary reliability analysis methods such as saddlepoint approximation.  
Conceptualization, C.F. and J.L.; methodology, C.F.; software, C.F. and W.X.; validation, C.F. and W.X.; formal analysis, C.F.; investigation, C.F.; resources, J.L.; data curation, C.F. and W.X.; writing--original draft preparation, C.F.; writing--review and editing, C.F. and J.L.; visualization, C.F. and W.X.; supervision, C.F.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Program of China, grant number 2018 YFB1701703 and National Natural Science Foundation of China, grant number 51475027.  
Not applicable.  
Data Availability Statement: Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
###### Abstract  
The purpose of the no-reference image quality assessment (NR-IQA) is to measure perceived image quality based on subjective judgments; however, due to the lack of a clean reference image, this is a complicated and unresolved challenge. Massive new IQA datasets have facilitated the creation of deep learning-based image quality measurements. We present a unique model to handle the NR-IQA challenge in this research by employing a hybrid strategy that leverages from pre-trained CNN model and the unified learning mechanism that extracts both local and non-local characteristics from the input patch. The deep analysis of the proposed framework shows that the model uses features and a mechanism that improves the monotonicity relationship between objective and subjective ratings. The intermediary goal was mapped to a quality score using a regression architecture. To extract various feature maps, a deep architecture with an adaptive receptive field was used. Analyses of this biggest NR-IQA benchmark datasets demonstrate that the suggested technique outperforms current state-of-the-art NR-IQA measures.  
Inception-ResNet-v2; spinal network; image quality assessment (IQA); no-reference (NR); quality score +
Footnote †: journal: Applied Sciences
# Optimization of Slotted Steel Plate Shear Walls Based on Adaptive Genetic Algorithm
## 3 Optimization Analysis of SSPSWs
### Optimization Results
As detailed in Table 2, parametric trends mirror those of bilateral-joined configurations: increasing plate thickness inversely correlates with optimized ductility performance. This consistency across connection types validates the universal design principle of prioritizing thinner steel plates (t \(\leq\) 12 mm) for slit configurations, as thickened plates detrimentally affect energy dissipation capacity while providing marginal stiffness gains (\(\leq\)8%) in quadrilateral-joined systems.
# Quantitative Model for the Value of the 3D Product Model Use in Production Processes
## 2 Materials and Methods
### Smoother Transition to Field Use by Identifying Clashes Ahead of Install  
In another application of the 3D product model in the production process, a large automotive manufacturer uses the 3D product model to verify production and assembly interferences before launching a product model change. The automotive manufacturer has a virtual assembly lab that allows them to complete such studies and check how a 3D product model change will work in the context of how it will integrate with tooling and the existing products already assembled onto the vehicle on the production line. The manager of the digital manufacturing group at this organization stated that the application of the 3D product model through the virtual production lab provides the company with a key competitive advantage over other automakers. Such an improvement can lead to the reliability of the work being performed at the production and assembly stations as a result of a product and work station design that was developed with ease of manufacturing and assembly as a core intent which was enabled by the implementation of the 3D product model in the production planning and design processes. The model for the quantitative dollar value of this increase in reliability creates, as a result of the design for assembly enabled by using the 3D product model in the production planning and design process, is as follows:  
\(R_{m,n}=\) Reliability of station \(n\) on production line \(m\)  
\(R_{m}=\) Reliability of production line \(m\)  
\[R_{m}=R_{m,1}\times R_{m,2}\times R_{m,3}\times\ldots R_{m,N} \tag{25}\]  
Note that if the reliability of all stations on production line \(m\) are the same then  
\[R_{m}=(R_{m,N})\gamma N \tag{26}\]  
\(\eta=\) Number of assembled units reworked  
\[\eta=(1-R_{m})\times A_{m} \tag{27}\]  
\(\xi_{m,n}=\) Cost of rework for product \(a\) on production line \(m\)  
\(\overline{\xi}_{m}=\) Average cost of rework for a unit that requires rework on production line \(m\)  
\[\overline{\xi}_{m}=(\sum_{\delta=1}^{\eta}\xi_{m,n})/\eta \tag{28}\]\[Q_{m}=\text{Cost of rework on production line }m\] \[Q_{m}=\overline{\xi}_{m}\times\eta \tag{29}\] \[Q_{Without3D,m}=\text{Cost of rework for production line }m\ (Q_{m})\text{ prior to 3D product model implementation in production processes }\] \[Q_{With3D,m}=\text{Cost of rework for production line }m\ (Q_{m})\text{ after 3D product model implementation in production processes }\] \[P_{Q_{m}}=\text{Incremental profitability via reduction rework}\] \[P_{Q_{m}}=Q_{Without3D,m}-Q_{With3D,m} \tag{30}\]
# Satellite Image Super-Resolution by 2D RRDB and Edge-Enhanced Generative Adversarial Network +
###### Abstract
We use the feature map before the activation layer of VGG19 [7] to compute both the perceptual loss and edge perceptual loss, which can assist in generating more visually consistent results and sharper edges. Moreover, since satellite images usually cause more noise than ordinary images, the Canny algorithm [8] is required to be used for edge extraction to help the generator create distinguishable and clearer edge maps. In the end, we used several popular and publicly available datasets for training and testing, and compared the results with other state-of-the-art SR models. We not only consider PSNR but also look for several evaluation indices to evaluate the performance. As a result, we learned that our model can generate satellite images which look more natural and visually close to real images. The paper is an extension of our previous work in [9]. Compared to the original work, we add the following substantive new contents. First, we describe related works to this topic in Section 2, including a review of existing methods on image super-resolution, an introduction of Generative Adversarial Network (GAN), edge detection algorithms, and perceptual loss. Furthermore, in Sections 3.1 and 3.4, we perform a thorough ablation study on two sub-networks (UDSN, EESN) of the generator, edge detection algorithms, and loss functions to compare the performances. Moreover, in Section 4, we conduct more experiments on two additional data sets, including WHU-RS19 and SpaceWill data sets to prove the robustness of the proposed approach. The rest of paper is organized as follows. In Section 2, we give an introduction of image super-resolution and review some existing state-of-the-art SR methods and several possible techniques that could be used to improve the SR performance. Then we describe the proposed approach in Section 3, including the modifications in two sub-networks in the generator, edge extraction module, and perceptual loss on edges. Section 4 shows the experimental results and discusses the performance comparison on several publicly available satellite image data sets. Finally, we draw the conclusions in Section 5.
###### Abstract  
A newly available portable unit with GNSS raw data recording capability is assessed to determine static and kinematic position accuracy in various environments. This unit is the GPSMap 66, introduced by Garmin in early September. It is all-weather and robust for field use, and comes with a helix antenna. The high sensitivity chipset is capable of acquiring and tracking signals in highly attenuated environments. It can track single frequency GPS, GPS + GLONASS or GPS + Galileo and record code, Doppler and carrier phase data every second in the RINEX format. The evaluation presented herein focusses on GPS and Galileo. Static and kinematic test results obtained under a wide range of realistic field conditions are reported. Differential GNSS methods and Precise Point Positioning (PPP) are used to assess absolute position accuracy in ITRF coordinates, which is sufficiently close to the GPS and Galileo reference frame for the current purpose. Under low multipath conditions, measurements are found to be sufficiently accurate to provide single epoch, bias free position accuracy of a few metres. Accuracy is a function of signal attenuation and multipath conditions. The use of an external geodetic antenna significantly reduces measurement noise and multipath in high multipath environments. Carrier phase measurements, available more or less continuously under open sky conditions, significantly improve performance in differential mode. Accuracy in vehicular mode using code and carrier phase differential RTK solution is at the level of a few to several dm. Tests were conducted in parallel with a Huawei P10 Android 8.0 smartphone. The code measurement noise of this unit was found to be significantly higher than that of the GPSMap 66, a major reason being its lower performance PIFA antenna; carrier phase was only available for short time intervals, significantly degrading differential position accuracy performance.  
raw GNSS data; Android smartphone; hand held receivers +
Footnote †: journal: Sensors
## 4 Smart5 Grid NetApp Use Cases
### Uci--Automatic Power Distribution Grid Fault Detection  
#### 4.1.1 Description  
Real-time self-healing is a function in electricity distribution networks that consists of the automatic search and isolation of a portion of the distribution network affected by a grid fault, aiming to lower the impact on the involved energy users. This function requires very low latency and reliable communication in any environmental condition to allow self-reconfiguration of the network without an electricity supply interruption to end users. One of the critical aspects when investigating the cause of the malfunction of such an automation system is to be able to continuously monitor how the communication infrastructure works, for example, quickly identifying if the problem originated in the power circuit breaker, in the actuators, in the sensors, or due to communications errors.  
Currently, there are two types of communication technologies used for this advanced automation system--4G network technology and optical fiber. While the latter is recognized as a reliable communication technology for this automation system, its implementation is costly and less scalable for remote areas. On the other hand, implementations that use 4G technology are very scalable and cheaper than those with optical fiber, but they could not guarantee very low latency and highly reliable communication, especially in congested network situations. The goal of this UC is to reduce the complexity, time, and cost required in the troubleshooting process of the advanced self-healing power distribution automation system when an abnormal condition occurs. As such, this UC implements a telecommunications network monitoring mechanism, in the form of a NetApp, that continuously, and in real time, oversees the network status of all the remote field devices involved in the advanced self-healing automation system of a selected geographical area.