esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of Basalt Fibers and Graphene Enriched Polymers on Bond Behavior of FRP Bars in Concrete
## 3 Numerical Model
### FE Model  
The specimens were modeled as 2D with axisymmetric geometry. This was possible because the size of the cubic specimens is much larger than the minimum cover to avoid splitting, so bond results are supposed to be insensitive to the specimen shape. Rebar was positioned along the axis of symmetry. Standard 4-node bi-linear quadrilateral FEs were used in the volume. Cohesive FEs are 4-node linear elements. The boundary conditions consisted of a displacement applied to the nodes at the loaded end of the bar, while vertical displacements for concrete nodes in contact with a support was blocked (see Figure 5). In addition, the nodes of the bar positioned at the symmetry axis were constrained in the horizontal direction. The meshes employed in the numerical analysis are shown in Figure 5. It is worth to mention that meshes with a double number of elements were tested in relation to the meshes shown in Figure 5 and no major differences were found in the numerical results, therefore results obtained can be considered mesh independent. Bars and concrete were considered elastic-linear according to properties in Tables 1, 3 and 4. Therefore, all non-linearity and rupture process is confined at the interface.  
Figure 5: Meshes used in numerical modeling: (**a**,**b**) full meshes for combinations with 8.0 and 12.5 mm bar diameter, respectively; (**c**,**d**) zoom of the interface region.  
Integration of stresses \(\tau(s)\) at the cohesive interface of the FEs gives nodal forces \(F\) related to bond. These forces are added to the finite element forces. Equilibrium is obtained using a Newton-Raphson scheme until a relative error of 0.1% is achieved. Tangent matrices were calculated by numerical derivation. Finally, \(F\) in equilibrium was used in Equation (1) to obtain the numerical average bond stress.
# Design of Multi-Modal Ship Mobile Ad Hoc Network under the Guidance of an Autonomous Ship
## 3 Ship Ad Hoc Network Construction
### Design of Network Mode  
The ship will be in various navigation scenarios dynamically during navigation, and the communication mode should be different in different navigation scenarios. We only consider the dynamic impact of other ships on ships. The main difference in the navigation scene lies in the number of target ships (TSs) within a certain range from own ship (OS) and the threat to own ship. Accordingly, we designed two kinds of network architecture  
Figure 1: The composition of the ship MANET system.  
as the main form of ship MANET, which is named intercommunication mode and cluster mode, and their topologies are called network topology and star-ring hybrid topology respectively.
# Impact of Natural and Human Factors on Dryland Vegetation in Eurasia from 2003 to 2022
## 2 Materials and Methods
### Method
#### 2.3.2 Partial Correlation Analysis  
Several natural variables tested as predictors of vegetation indices (NDVI, GPP and SIF) exhibiting covariation. When detecting the relationship between the detrended natural factors and the detrended vegetation variables, it is crucial to address this covariance [51]. Partial correlation analysis is a widely utilized statistical tool for isolating the relationship  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**LC Types Used**} & **Original LC Class Codes in** \\  & **the ESA-CCI-LC Dataset** \\  & **Combined to Form the LC** \\  & **Types Used in This Study** \\ \hline \multirow{6}{*}{Cropland} & 10, 11, 12 & Rainfed cropland \\  & 20 & Irrigated cropland \\  & 30 & Mosaic cropland (\textgreater{}50\%)/natural vegetation \\  & & (tree, shrub, herbaceous cover) (\textless{}50\%) \\  & 40 & Mosaic natural vegetation (tree, shrub, herbaceous cover) (\textgreater{}50\%)/cropland (\textless{}50\%) \\ \hline \multirow{2}{*}{Grassland} & 130 & Grassland \\  & 150 & Sparse vegetation \\ \hline \hline \end{tabular}
\end{table}
Table 1: The LC types analyzed in this research and the original ESA-CCI-LC classes.  
between two variables from the confounding effects of many correlated variables [8; 51]. In assessing the relationship between NDVI, GPP, or SIF and natural factors, we employed four-order partial correlation analyses from the \"pingouin\" Python package to eliminate the confounding effects of the other variables. For instance, the partial correlation coefficient between NDVI and MAT was calculated by restricting MAP, SM, VPD and TWS.
# Passive Radar-Based Parameter Estimation of Low Earth Orbit Debris Targets
## 3 The Baseline Crossing Event
### BCE Doppler  
Feature extraction often starts with Doppler analysis as it is inherently tied to the velocity of a target and/or signal source. Bistatic radar Doppler is described in Equation (6), given by  
\[f_{D}=\frac{2v}{\lambda}\text{cos}(\delta)\text{cos}\left(\frac{\beta}{2}\right) \tag{6}\]  
where \(v\) is the target relative velocity, \(\beta/2\) is the bistatic bisector angle (see Figure 1), and \(\delta\) represents the angular difference between the target velocity vector and the bistatic bisector. As \(\beta\) approaches 180\({}^{\circ}\), \(\text{cos}(\beta/2)\) approaches 0. This means that, at the crossing point, there is no path difference between the direct and reflected signals, i.e., the target lies along the baseline between \(T_{x}\) and \(R_{x}\) in the bistatic plane. This effect can be highlighted by taking the Short-Time Fourier Transform (STFT) of the BCE signal model, shown in Figure 3.  
Figure 3 displays a similar magnitude modulation as Figure 2. It is shown that maximum power is reflected while the target is approaching and leaving the baseline with minimum power at the crossing point. Equation (5) is also confirmed, as there is no Doppler information at the crossing point. However, some unique frequency-spreading effects are noticed. The frequency harmonics appear to increase in power along with the signal magnitude. This effect follows a similar trend as the Doppler behavior due to a space debris target's translational motion [38].  
Figure 2: Simulated BCE received signals with varied target height displacement.
## 3 System Model and Proposed Scheme  
### System Model  
We considered a 7-cell hybrid network consisting of cellular user equipment (CUE) and D2D user equipment (DUE) with a spectrum-sharing D2D-enabled cellular network and focused on uplink transmission with regard to cellular users. The entire cell was divided into two main regions: a center cell region (inner region) and cell-edge region (outer region), as can be seen in Figure 3. All UEs were uniformly distributed within the cell where resources and power for cellular links and D2D links were controlled by the eNB. The network structure used soft fractional frequency reuse (FFR). The entire frequency spectrum was used in a single cell and the same frequency spectrum was reused in the neighboring cells. For soft FFR, each cell was divided into a cell-center (inner area) region and cell-edge (outer area) region. The entire frequency band was divided into four portions, namely, \(S_{0}\), \(S_{1}\), \(S_{2}\), and \(S_{3}\), with the corresponding bandwidth represented as \(F_{c}\), \(F_{1}\), \(F_{2}\), and \(F_{3}\), as can be seen in Figure 3. To make sure that each CUE in the same cell or in neighboring cells did not interfere with each other, resources were allocated in an orthogonal manner. Through the use of a soft FFR scheme, interference among CUEs was mitigated efficiently within the network.
## 2 Methodology
### RUL Prediction Model  
For a specific unit, let \(v(t)\) denote the degradation rate of the extracted RMS. We constructed \(v(t)\) as  
\[v(t)=\lambda\cdot\varphi(t;\theta)+\sqrt{m\lambda}\cdot dB(t), \tag{1}\]  
where \(\lambda\) represents the basic degradation rate, \(\varphi(t;\theta)\) is a function of time with parameter \(\theta\), \(m\) is a constant and \(B(t)\) is the standard Brownian motion. Below are some interpretations of Equation (1).  
Figure 1: A descriptive illustration of the degradation process of wheel tread.  
1. The basic degradation rate \(\lambda\) is determined by material properties. Due to the heterogeneity among different units, \(\lambda\) is a random variable instead of deterministic. Here Gaussian distribution is chosen to describe such uncertainty, i.e., \(\lambda\sim N\big{(}\mu_{\lambda},\sigma_{\lambda}^{2}\big{)}\)[31];
2. The function of time \(\varphi(t;\theta)\) is determined by specific failure mode;
3. Standard Brownian motion \(B(t)\) represents temporal uncertainty of the degradation process.  
Accordingly, the constructed degradation rate \(v(t)\) follows a normal distribution, i.e., \(v(t)\sim N(\lambda\cdot\varphi(t;\theta),m\lambda)\). Note that \(\lambda\cdot\varphi(t;\theta)\) denotes the deterministic term related to material performance and failure mode, and \(\sqrt{m\lambda}\cdot dB(t)\sim N(0,m\lambda)\) denotes the stochastic term which describes random errors caused by inhomogeneity of material. The fluctuation caused by inhomogeneity of material is proportional to the material itself.  
Taking integration on both sides of (1), we could obtain the degradation path as  
\[x(t)=\int_{0}^{t}v(\tau)d\tau=x_{0}+\lambda\int_{0}^{t}\varphi(\tau;\theta)d \tau+\sqrt{m\lambda}B(t), \tag{2}\]  
where \(x_{0}\) describes initial value. Without loss of generality, we assumed \(x_{0}=0\). Since measurement errors are inevitable in condition monitoring, the observation function is formulated as  
\[y(t)=x(t)+\varepsilon, \tag{3}\]  
where \(\varepsilon\) describes measurement uncertainty, which follows a normal distribution with zero mean and variance \(\sigma_{m}^{2}\).  
A notable feature of the above-mentioned degradation model is that it integrates multiple uncertainties extensively existed in wheel tread, which can better reflects the actual health condition. In addition, the model relates degradation variation (described by the diffusion parameter in Equation (2), i.e., \(\sqrt{m\lambda}\)) to degradation rate (described by the drift parameter in Equation (2), i.e., \(\lambda\)) quantitatively. This is consistent with actual observation that a severer degradation provides an acute vibration variation [28].
## 6 Therapeutic Approaches of Dentin Hypersensitivity
### In-Office Therapeutic Treatments of Dentin Hypersensitivity
#### 6.2.4 Silver Diamine Fluoride  
Silver diamine fluoride (SDF) is a topical medicament used in dentistry to treat various dental issues, including DH. When SDF comes into contact with the tooth surface, it reacts with the hydroxyapatite in the dentin and enamel, forming silver fluoride (AgF) and silver chloride (AgCl) compounds. These compounds can occlude the tubules present in the dentin [116], preventing the external stimuli from reaching the nerve endings, thus reducing sensitivity [117]. It is important to note that while SDF can be highly effective in managing DH [118], it may cause temporary discoloration of the treated area. The darkening of the tooth surface occurs due to the formation of silver compounds and for this reason, it has been suggested to be accompanied by potassium iodide (KI) [119]. This potential aesthetic concern should be discussed with patients before using SDF (38%) as a treatment option. Consequently, SDF is usually considered for use in cases where conventional treatments for DH, such as desensitizing toothpaste or fluoride varnishes, have not provided adequate relief.
## 2 Related Work
This highlights the significance of the present study, which examines the performance of MIMO-VLC systems using various modulation techniques and different ambient noise sources. Our analysis specifically focuses on the systems' ability to transmit over varying distances and their resilience to interference from ambient light, filling a critical gap in the existing research. The main contributions of this research are outlined as follows. 1. Performance Evaluation of MIMO-VLC: The paper evaluates the performance of a MIMO-VLC system under various modulation techniques, specifically NRZ, RZ, and QPSK. 2. Comparison of modulation schemes for ambient light interference mitigation: The paper compares the effectiveness of different modulation schemes (NRZ, RZ, and QPSK) in mitigating ambient light interference and optimizing performance. 3. Resilience to Ambient Light Interference: The study investigates how MIMO--LC systems perform in the presence of ambient light interference, focusing on the system's robustness. 4. Distance and Transmission Capability: It analyzes the system's ability to transmit over different distances and assesses the performance in terms of distance coverage. The remainder of this paper is structured as follows: Section 3 introduces the MIMO-VLC system model, encompassing both the MIMO-VLC channel model and the ambient light noise model. Section 4 discusses the implementation of MIMO-VLC using OptiSystem. Simulation results and discussions are presented in Section 5. Lastly, Section 6 concludes the paper and outlines potential future research directions.
# An Advanced AFWMF Model for Identifying High Random-Valued Impulse Noise for Image Processing
## 4 Experiment Results and Discussions
### Restoration Performance Measurements  
This evaluation standard of the performance test uses Equation (19), and the Lena image is first used to compare the proposed AFWMF model with other filters proposed by other studies. The characteristic Lena image has an approximately normal distribution; that is to say that the detail, flat regions, shading, and texture are all included. Thus, the Lena image is an excellent comprehensive testing image for assessing restoration results and comparing restoration performance. Table 3 shows the comparison of the PSNR measurement values in the Lena image; the noise is set from 10% (i.e., 0.1) to 90% (i.e., 0.9). (Note: to lower the overabundance of figures and tables, this study only uses the empirical results of the Lena image as a representative of the demonstration here, but it uses a summary report of a table for completely concluding the results of the five images  
Figure 11: Pictures used for testing in experiments.  
in this subsection.) From Table 3, it can be seen that the AFWMF model has a slightly worse PSNR value than that of the DWM and ROR filters in the low 10% noise density instance and high 80-90% noise density instance. However, the AFWMF model's PSNR value apparently outperforms the other filter techniques at higher ratios of 20% to 70% noise densities. Importantly, when the ratio of noise density is within 20-70%, the proposed AFWMF model significantly outperforms all the existing filters selected and used for the Lena image in this study. For processing the Lena image, it is ranked in the order AFWMF \(>\) DWM \(>\) ROR \(>\) ASMF \(>\) SDOOD \(>\) SBF \(>\) FRDFM \(>\) EAIF \(>\) BDND \(>\) EBDND in PSNR performance. The proposed AFWMF model is the best filtering model used for the Lena image.  
For further evaluating the performance of all the five images used in this study, Table 4 and Figure 12 show the additional comparisons of the PSNR average measurements for noise of 10% to 90%. From the above table and figure, this is ranked in the same order as the Lena image of AFWMF \(\rightarrow\) DWM \(\rightarrow\) ROR \(\rightarrow\) ASMF \(\rightarrow\) SDOOD \(\rightarrow\) SBF \(\rightarrow\) FRDFM \(\rightarrow\) EAIF \(\rightarrow\) BDND \(\rightarrow\) EBDND in terms of the performance of the PSNR average value for the five images. Thus, this is effective evidence that the proposed AFWMF model has the best filter technique in this study. Furthermore, Table 5 presents the best filter model at different noise densities in the PSNR average value for the five experimental images. It is found that it is best to use the DWM model for a low 10% noise density, the ROR model for high 80% to 90% noise densities, and the AFWMF model for 20% to
## 5 Result and Discussion
### Results from the Customer Survey
#### 5.2.1 Part 1: Current DSO Status and Practices  
From Figure 6, it is clear that LV grid operation is a resource-intensive business, constituting a significant share of DSOs' yearly expenditure. A total of 81% of respondents either strongly agreed or agreed that loss detection (both technical and non-technical) was still critical for DSO business. This is in line with the Council of European Energy Regulators' (CEER's) latest report on losses, where many DSOs in Europe have recorded losses above  
Figure 5: Pareto analysis for the RESOLVD value propositions using the critical factor analysis (CFI).  
5% [36]. Most DSOs were in favor of the opinion that increased renewable penetration was likely to cause problems with traditional operations of the grid. There has been clear evidence from various studies on the impact of high renewable energy penetration, particularly on distribution grids, including a report by the European commission [28]. The report also proposed a broader role of DSOs in the future to tackle the challenges associated with renewable energy source integration. Still, 14% of the responding DSOs thought that growing renewable energy was not going to cause any problems for their businesses. The reason could have been that they had already invested in a grid upgrade and/or were confident with their current grid capacity. However, the reason was not clear and needs further investigation.  
Regarding observability, 48% of DSOs were still limited to secondary substation monitoring. Only 9% of DSOs were using real-time data from smart meters at the customer level to know the grid status, while 24% were making use of smart meter data at the aggregated level for observing the grid (see Figure 7a). About 19% of DSOs were using smart meter data only for billing purposes. Figure 7b shows that more than 50% of DSOs still relied on customers to detect faults in the grid. Studies have shown that smart meter data can be used for detecting faults in the distribution network faster than traditional approaches [1]. The results showed that DSOs were missing out on getting this added benefit, even though most of the respondents were from countries with a high smart meter penetration. There was a clear gap between having access to data and the utilization of data for benefits. To take advantage of smart meter data, DSOs require specific tools, which RESOLVD has proposed to deliver through its LVD-DST, thereby filling the gap in market.  
Figure 6: Customers’ opinions on four topics related to LV grid operation.  
Most of the DSOs considered cybersecurity issues important, which were mostly addressed internally (by 86% of DSOs). A small proportion of DSOs outsourced these issues to external experts, while 9% did not address cybersecurity issues at all. This has a profound implication on the business strategy for cybersecurity methodology being developed in the project. About 76% of respondents either strongly agreed or agreed that DSOs could benefit from flexibility markets, thus reflecting a positive attitude towards them.
## 3 Survey of Techniques for Updating
### Dissemination
#### 3.1.14 Cord  
CORD [28] is a reliable, bulk-data dissemination protocol, with a design focus on minimizing energy consumption. A two-phase approach is used: in the first phase, a connected dominating set (CDS) of _core_ nodes is selected and the update reliably propagated to them; in the second phase, the core nodes disseminate the update to their non-core neighbours. This significantly reduces the number of control message that need to be sent, which reduces both energy use and collisions. An aggressive, coordinated sleep schedule, enabled by the use of a CDS, is also used to further reduce energy consumption. Link quality and the sleep schedule are built into the CDS generation. One additional feature for CORD is its suitability for heterogeneous networks, where a subset of more highly powered nodes can be selected for the CDS, extending the lifetime of the entire network.  
CORD has support for reducing energy consumption, but is not an energy-aware protocol. It has no planning features, and provides no model for the anticipated energy use. CORD is a data dissemination protocol, so has no built-in support for activation; it also has no feedback mechanisms.
# Design and Evaluation of Novel Submerged Floating Tunnel Models Based on Dynamic Similarity
## 3 Model Design  
In hydrodynamic testing, both the scale model and the fluid domain must adhere to congruent scaling relationships. Thus, the scale ratio of the SFT scale model necessitates a balance between the model's dimensions and the environmental conditions. On the one hand, a larger scale ratio is beneficial to minimize scale effects, leading to more accurate and reliable results. On the other hand, a smaller scale ratio would allow the wave tank to simulate a higher water depth. According to the Buckingham \(\pi\) theorem, if a physical process involves \(n\) physical quantities, out of which \(k\) fundamental physical quantities can be selected, then this physical process can be described by \(n\)-\(k\) dimensionless numbers derived from the fundamental physical quantities. Fundamental physical quantities refer to those quantities that are dimensionally independent and cannot be derived from each other [30]. For the SFT scale model in this paper, length, acceleration, and elastic modulus are designated as fundamental physical quantities.
###### Abstract  
Modeling seismic isolators, one of the most effective installations in the design of earthquake-resistant buildings, is a very important challenge. In this study, we propose a new energy-based approach for the optimization of seismic isolation parameters. The hysteretic energy represents the dissipation of isolated structures in the isolation system. The minimization of input energy ensures that structural components are exposed to reduced seismic energy. For these reasons, this study aims to minimize the input energy and maximize the hysteretic energy. Additionally, an objective function is also generated with the energy ratio obtained from the input and hysteretic energy. The gray wolf optimizer (GWO) was applied to the optimization process. A four-story, 3D, and reinforced concrete superstructure was prepared and lead rubber bearings were placed under the base story. The isolation system is modeled nonlinearly, which requires two parameters: isolation period and characteristic strength. The inter-story drift ratio was selected as the structure constraint, while the isolator displacement and effective damping ratio were selected as the isolator constraints in the optimization process. The prepared base-isolated structure was optimized using 11 scaled ground motions. Nonlinear time history analyses were run in ETABS finite element software. Firstly, the optimum isolation parameters were obtained using peak roof story acceleration (PRA), in accordance with the methodology in previous studies. The outcomes generated by the PRA and energy components are compared considering the isolation parameters and structural responses. The energy ratio produced better results in terms of inter-story drift ratio than the other energy components. Secondly, the energy ratio was re-optimized with different constraints and its effectiveness was examined.  
energy-based optimization; seismic isolation; rubber bearings; gray wolf optimizer +
Footnote †: journal: Applied sciences