esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
1. A bond-slip model of bird's nest anchor cable is established on the basis of a statistical damage constitutive model. The shear stress and slip curve of the anchorage structure are divided into a nonlinear elastic deformation stage, a debonding slip stage, and a residual stage. With an increase in the pull-out load, the shear stress of the anchorage agent interface increases exponentially to the peak value from the load end, then decreases, and finally stabilizes to the residual strength. The process transmits every point with the anchorage depth. The model parameters are acquired by fitting data, and the theory curve and test curves are similar, thereby verifying the reasonability of the proposed mode.
2. The axial force of the anchor cable declines negatively along the load direction; the maximum value (148 kN) is near the load end, and the minimum value (28 kN) is at the other end. The shear stress of the rock mass decreases negatively along the load direction and is transferred from the load end to the other end, but the shear stress value near anchorage cable value is higher than that far away from the anchorage structure.
3. The failure of the anchorage structure is divided into three stages stage according to PFC simulation: an elastic deformation stage, a debonding slip stage, and a splitting rock failure. The anchorage structure of the load end first exhibits a small number of cracks (crack number: 4000), then extends along the interface of the anchorage agent (crack number: 62000), and then penetrates into the anchorage agent-rock mass interface (crack number: 87000). The failure mode of the anchoring structure is mixed failure, namely debonding slip of the interface and shear failure of rock mass. PFC can simulate the crack evolution process of the bird's nest anchor cable well.  
Methodology, X.L.; Writing--original draft, W.Z.; Writing--review & editing, C.Z. All authors have read and agreed to the published version of the manuscript.  
The research was funded by the National Natural Sciences Foundation of China (No. 51874119), the Education Department of Henan Province (No.2011A440003), and the Doctor Foundation of Henan Polytechnic University (No.B2009-96).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Forecasting Models and Evaluation Metrics
### Forecasting Models
#### Limitations of Residual Forecasting  
The residual forecasting method is a statistical technique that involves fitting a model to historical data and then using the residuals, which are the differences between the actual values and the predicted values, to forecast future values. The residual forecasting method can be a useful tool for forecasting time series data, particularly when the data contain complex patterns that are difficult to model using traditional methods. However, it also has some limitations. Outlined below are some advantages and limitations of using the residual forecasting method.  
* Simplicity: The method is easy to implement and requires minimal technical expertise.
- Flexibility: The method can be applied to a wide range of forecasting problems, including time series forecasting and regression analysis.
- Improved accuracy: By accounting for the residual errors in the forecasting model, the residual forecasting method can lead to more accurate predictions.
- Transparency: The method is transparent and provides insight into the forecasting process, which can help decision-makers understand the sources of uncertainty in the forecasts.
* Limited by time horizon: Residual forecasting is most effective in predicting outcomes over short to medium time horizons. Over longer time horizons, the accuracy of the predictions may decrease, particularly if there are significant changes in external factors that were not accounted for in the historical data.
- Dependence on underlying model: The accuracy of the forecasts is heavily dependent on the accuracy of the underlying model used to estimate the residuals.
- Assumption of stationarity: The method assumes that the residual errors are stationary over time, which may not always be the case.
- Ignoring other sources of uncertainty: The method only accounts for residual errors and ignores other sources of uncertainty, such as measurement errors or random fluctuations.
## 3 Methodological Framework  
### Scenario Definition  
#### 3.1.1 Building Configuration and Renovation Action  
As part of the project \"AUORAL: a European initiative aimed at enhancing smart communities through the integration of digital tools and interoperability\" ([[https://www.auroral.eu](https://www.auroral.eu)]([https://www.auroral.eu](https://www.auroral.eu)) (accessed on 1 August 2024)/, Grant Agreement No. 101016854), a case study was conducted on a public kindergarten located in Southern Burgenland, Austria. This single-story building, constructed primarily with bricks, spans a total floor area of 168.85 m\({}^{2}\) and operates year-round. The kindergarten's energy demands, particularly for heating during winter and cooling in summer, are covered using conventional energy systems, namely, a heat pump and basic air conditioning. The energy upgrade action implemented under the AUORAL project focused on the installation of a photovoltaic (PV) system as a key step toward sustainability. The roof-slanted PV system was designed to reduce reliance on grid electricity by generating renewable energy during daylight hours. The building operates under a net billing buyback scheme, enabling it to export surplus electricity to the grid while remaining connected for additional energy needs. This targeted sustainable energy measure represents a practical application that constitutes a basic use case for demonstrating how scenario-based analysis can be used to analyze and optimize the cost-efficiency of sustainability investments in real-world contexts. Table 1 outlines the basic structural and operational characteristics of the SBA1 Kindergarten, providing an overview of the building. Table 2 provides detailed information about the primary building components and systems, including the heating and cooling systems, insulation, glazing, and the domestic hot water (DHW) system. These details reflect the existing infrastructure and its associated costs and performance metrics. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Details** \\ \hline Project Name & SBA1 Kindergarten \\ Location & Southern Burgenland, Austria (Latitude: 47.0145, Longitude: 16.4579) \\ Building Type & Public Kindergarten \\ Building Structure & Single-story, primarily brick construction \\ Total Floor Area & 168.85 m\({}^{2}\) \\ Operation Schedule & Year-round \\ External Wall Material & Brick \\ Number of floors & 1 \\ Operational Parameters & Net billing buyback scheme \\ Primary Energy Source & Electricity \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Basic Key Attributes of SBA1 Kindergarten. Table 3 summarizes the energy upgrades implemented in the SBA1 Kindergarten, focusing on the photovoltaic (PV) system and its associated environmental metrics. The values in Table 4 for Primary Energy (PE) demand and Global Warming Potential (GWP) (Stage A quantities) were obtained using SimaPro v9.5.0.2 software with the EcolInvent database v3.9.1 [48], and the same impact assessment method was applied to all components. The Life Cycle Inventory (LCI) data are consistent across components, ensuring comparability.
# Analysis of Dynamic Characteristics of Attached High Rise Risers
## 3 Dynamic Analysis of VNA System under Combined Excitation
### Vibration Response of the VNA System under First Order Frequency Locking  
To obtain the reduced-order model equations of the Hualong-1 VNA system, the Galerkin discretization method can be used. Perform modal expansion on the wake oscillator lift coefficient \(q\), and if the vortex shedding frequency \(\omega_{s}\) is close to the \(r\)-th order natural frequency of the VNA system pipe, the contribution to \(q\) only exhibits in the \(r\)-th order mode, which is also a manifestation of the frequency locking effect. Therefore, the VNA system pipe displacement and lift coefficient \(q\) are written in the following form:  
\[\eta=\sum_{i=1}^{n}\Phi_{i}(\xi)\overline{\eta}_{i}(\tau) \tag{22}\]  
\[q=\Phi_{r}(\xi)\overline{\eta}_{r}(\tau) \tag{23}\]  
where, \(\overline{\eta}_{i}(\tau)\) is the time-dependent displacement, and \(\Phi_{i}(\xi)\) is a modal function. From Section 2, it can be seen that when the constraint spring stiffness is small, that is, less than \(10^{7}\) N/m, the vibration mode of the pipe is approximately the vibration mode of a cantilever beam containing axial force, and the vibration mode function can be expressed as:  
\[\Phi_{i}(\xi)=A_{i}\left(\sin(\beta_{i}\xi)+\chi_{i}\cos(\beta_{i}\xi)-\frac{ \beta_{i}}{\alpha_{i}}\text{sinh}(\alpha_{i}\xi)-\chi_{i}\cosh(\alpha_{i}\xi)\right) \tag{24}\]  
\[\text{where, }\chi_{i}=\frac{-EI\beta_{i}^{3}\cos(\beta_{i})-EI\alpha_{i}^{2} \beta_{i}\cosh(\alpha_{i})+m_{i}\omega_{i}^{2}\sin(\beta_{i})-\frac{m_{i}\beta \omega_{i}^{2}\text{sinh}(\alpha_{i})}{\alpha_{i}}}{-EI\beta_{i}^{3}\sin(\beta_ {i})+EI\alpha_{i}^{3}\text{sinh}(\alpha_{i})-m_{i}\omega_{i}^{2}\cos(\beta_{i })+m_{i}\omega_{i}^{2}\cosh(\alpha_{i})};\]  
\[\alpha_{i} = L\left(\left(\left(\frac{T}{EI}\right)^{2}+\frac{m_{S}\omega_{i} ^{2}}{EI}\right)^{\frac{1}{2}}+\frac{T}{2 EI}\right)^{\frac{1}{2}};\ \ \beta_{i} = L\left(\left(\left(\frac{T}{EI}\right)^{2}+\frac{m_{S}\omega_{i}^{2}}{ EI}\right)^{\frac{1}{2}}-\frac{T}{2 EI}\right)^{\frac{1}{2}}.\]  
\(T=-m_{t}g+m_{f}V_{in}{}^{2}\), \(A_{i}\) is the normalized amplitude satisfying \(\int_{0}^{1}\Phi_{i}(\xi)\overline{\Phi_{i}(\xi)}\text{d}\xi=1\). And the vibration mode function satisfies the following boundary conditions:  
\[ \begin{array}{l}\eta(0,\tau)=0;\ \eta^{\prime}(0,\tau)=0;\\ \eta^{{}^{\prime\prime}}(1,\tau)+j\tilde{\eta}^{{}^{\prime}}(1,\tau)=0;\ \eta^{{}^{ \prime\prime\prime}}(1,\tau)-\mu_{mt}\tilde{\eta}(1,\tau)+T\eta^{\prime}(1,\tau )=0\end{array}  \tag{25}\]  
where \(j_{t}=\frac{I_{t}}{m_{0}L}\), \(\mu_{mt}=\frac{m_{t}}{m_{0}}L\). \(\eta^{\prime}(\xi,\tau)\) represents the first order partial derivative for \(\xi\). \(\tilde{\eta}(\xi,\tau)\) represents the second order partial derivative for \(\tau\), and so on. By substituting Equations (22)-(24) into Equations (17) and (21), the ordinary differential equations of the parameterized model under the combined action of vortex-induced vibration and foundation excitation of the VNA pipeline can be obtained:  
\[
## 1 Introduction
For the data interaction scheme using dynamic link libraries, the time consumption for calculating the diffraction characteristics of microstructures, especially when SRG and PVG are used as coupling devices, is huge, and it takes several days to complete a simulation of diffraction waveguide systems. Meanwhile, the optimization algorithms, such as damped least squares and orthogonal descent method used for iteration, cannot solve the problem of multi-objective optimization. Therefore, the work in this paper is carried out to solve the above problems; we have established a diffraction grating ray tracing model and proposed a honeycomb-shaped coupler grating array solution to achieve 2D-EPE, while simultaneously improving pupil brightness uniformity and optical energy utilization. To shorten the development cycle of AR-HMD, we have implemented 2D ray tracing using a customized DLL diffraction function within the Zemax OpticStudio optical simulation software. Additionally, we have developed an electromagnetic calculation program called Holo-RCWA. By utilizing a multi-objective genetic algorithm, we have efficiently performed inverse design of process parameters, resulting in a shortened optimization time for the entire system.
# A Stackelberg Game Approach for Price Response Coordination of Thermostatically Controlled Loads
## 4 Simulation  
In this part, some case studies are analyzed to demonstrate the price response coordination of TCLs. The proposed Stackelberg game model and control scheme are validated by the simulations in MATLAB 2014a. Besides, we use the interior-point method to solve the optimization problems and the computational time of all cases are limited in 2.0 s.  
We adopt a typical 15-min based pricing by dividing 9-h into 36 equal time instants [19], as shown in Figure 3. An ambient temperature profile from 11:00 to 20:00 in a typical summer day is shown in Figure 4.
# Joint Hybrid Beamforming Design for Millimeter Wave Amplify-and-Forward Relay Communication Systems
## 3 Hybrid Beamforming Design for HD AF Relay Systems
### Algorithm Evaluation  
(1) Convergence AnalysisThe HBF-MO algorithm mainly includes two loops, which are the outer loop of Step 3 to Step 12 and the inner loop of Step 4, 6, 7, and 9 in Algorithm 1, respectively. With respect to the inner loop, by the definition of Theorem 4.3.1 in [29], the MO-based algorithm guarantees to converge to a point at which the first-order optimality condition is met [21; 27]. As a result, the objective function is ensured to decrease in each iteration of the alternating algorithm. For the outer loop, the DBF matrixes are obtained by taking the derivative, guaranteeing the convergence.  
_(2) Complexity Analysis_: The complexity consists of three parts: (a) calculating the gradient, with the complexity of \(\mathcal{O}\big{(}N_{\text{R}}^{2}N_{\text{MAX2}}\big{)}\), (b) orthogonal projection and retraction, whose complexity is \(\mathcal{O}(N_{\text{MAX1}}N_{\text{RF}})\), and (c) line search, whose complexity is \(\mathcal{O}\big{(}N_{\text{R}}^{2}N_{\text{MAX2}}\big{)}\), where \(N_{\text{MAX1}}=\max(N_{\text{T}},N_{\text{R}},N_{\text{D}})\), \(N_{\text{MAX2}}=\max(N_{\text{T}},N_{\text{D}})\) and \(\min(N_{\text{T}},N_{\text{R}},N_{\text{D}})>N_{\text{RF}}\). Therefore, the total complexity is \(N_{\text{out}}N_{\text{in}}\mathcal{O}\big{(}N_{\text{R}}^{2}N_{\text{MAX2}} \big{)}\), with \(N_{\text{in}}\) and \(N_{\text{out}}\) denoting the number of iterations of the inner and outer loops.
## 3 Proposed Scheme
### Iteration Method Review  
#### 3.1.1 Conventional SOR Method  
We consider a linear system [36] whose mathematical equation can be expressed as  
\[A\mathbf{x}=\mathbf{b}, \tag{5}\]  
where the matrix \(A\) is symmetric positive-definite, \(\mathbf{x}\) is the unknown vector and we can decompose \(A\) as follows:  
\[A=D-L-U, \tag{6}\]  
in which \(D\), \(-L\), and \(-U\) are the diagonal, strict lower part, and strict upper part of \(A\). As for the iterative equation, the Jacobi iteration [25] is given by  
\[\mathbf{x}^{(k+1)}=D^{-1}(L+U)\mathbf{x}^{(k)}+D^{\text{-1}}\mathbf{b}, \tag{7}\]  
Likewise, the Gauss-Seidel iteration [26] also can be expressed as  
\[\mathbf{x}^{(k+1)}=\left(D-L\right)^{-1}U\mathbf{x}^{(k)}+\left(D-L\right)^{ \text{-1}}\mathbf{b}. \tag{8}\]  
Here, we can define a matrix \(G\) as  
\[G=M^{-1}N. \tag{9}\]  
Therefore, for the Jacobi iteration, \(M=D\) and \(N=(L+U)\). Moreover, for the Gauss-Seidel iteration, \(M=(D-L)\) and \(N=U\). Then, Equations (7) and (8) can be rewritten as  
\[\mathbf{x}^{(k+1)}=G\mathbf{x}^{(k)}+\mathbf{d}, \tag{10}\]  
where \(G\) is called the iteration matrix and \(\mathbf{d}\) is \(M^{-1}\mathbf{b}\). Based on this result, we multiply the relaxation factor \(\omega\) in Equation (10), and we have  
\[\omega\mathbf{x}^{(k+1)}=\omega(G\mathbf{x}^{(k)}+\mathbf{d}), \tag{11}\]  
which gives the SOR iteration [27]. Take the Gauss-Seidel iteration as an example, which is defined as  
\[\mathbf{x}^{(k+1)}=(D-\omega L)^{-1}\Big{\{}[(1-\omega)D+\omega U]\mathbf{x}^ {(k)}+\omega\mathbf{b}\Big{\}}. \tag{12}\]  
According to [27], we can determine that the SOR iterative algorithm is convergent.
## 3 Results and Discussion
### Change Mechanism of Volatile Flavors and Spoilage Microorganisms in Yulu after B. subtilis Addition  
The correlation network map can clearly show the relational degree between different indicators [17; 36; 37]. In this study, the correlation network map was constructed based on the Pearson's correlation among different indexes in the BS45 and C45 groups (Figure 4). The Sobs, Chao, ACE, and Shannon indexes showed significantly negative relation with _Bacillus_ but obviously positive relation with _Pseudomonas_, _Achromobacter_, _Stentrophomonas_, _Cyanobium_, _Rhodococcus_, _Brucella_, _Tetragonococcus_, and _Chloroplast_. Meanwhile, the Simpson index exhibited the totally opposite correlation. These results indicated that the increase in _Bacillus_ played a key role in decreasing microbial contamination which was mainly due to the significant decrease in _Pseudomonas_, _Achromobacter_, _Stentrophomonas_, _Cyanobium_, _Rhodococcus_, _Brucella_, _Tetragonococcus_, and _Chloroplast_ after _B. subtilis_ addition. Meanwhile, except unpleasant trimethylamine, most of the characteristic pleasant volatile flavor compounds showed significantly positive correlation with _Bacillus_ but were significantly negatively related with _Pseudomonas_, _Achromobacter_, _Stentrorophomonas_, _Cyanobium_, _Rhodococcus_, _Brucella_, _Tetragenococcus_, and _Chloroplast_. It indicated that the improved volatile flavor in Yulu mainly resulted from the higher abundance of _Bacillus_. _B. subtilis_ possessed good protease production ability under high salt conditions, contributing to the increase in enzymatic hydrolysis of fish proteins and production of free amino acids. As important precursors of volatile compounds, the increasing free amino acids were beneficial for the increase in volatile compounds.
## 4 Simulations and Results  
### Simulation Setup  
To evaluate the performance of the proposed protocol, simulations were conducted with the original LEACH protocol [5], the KMMDA algorithm [24], the improved clustering algorithm proposed in Section 3.2, which has the same transmission scheduling algorithm as the original LEACH protocol, and the proposed protocol without using replication computing, respectively. The experiments were based on the NS3 simulation platform. The experimental parameters are given in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of nodes \(N\) & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of experimental simulation.
# Reshaping the Digital Twin Construct with Levels of Digital Twinning (LoDT)
## 2 Functional Architecture of Digital Twinning
### The Architecture
#### 2.2.4 Analysis  
Data can be analysed using various techniques, which should be chosen based on the initial objectives. Data analysis can involve descriptive statistics (measuring trends, central or relative location, dispersion, etc.), statistical modelling and predictive models, classification techniques, pattern identification, optimisation, simulation, and more. In general, data analytics serves three types of data services (or applications):  
* Monitoring, which can be descriptive (if perceptions focus on performance without analysing the underlying causes) or diagnostic (if there is a root cause failure analysis);
* Prediction and simulation, where the goal is to predict the future behaviour of a phenomenon based on historical data and the combination of multiple scenarios. This application includes predictive models, stochastic and deterministic simulations, etc.;
* Optimisation, which can be considered prescriptive (if recommendations for improving asset/system performance are proposed) or cognitive (if these improvement actions are performed autonomously by artificial intelligence mechanisms).  
Artificial intelligence methodologies, which aim to mimic human reasoning when supported by significant computing and data processing capabilities, unlock new types of applications and business opportunities. Some of these techniques include rule-based logic, conditions (\"if-then\"), decision trees, and machine learning (which includes deep learning techniques such as neural networks) [43]. These techniques are applicable to various purposes, such as diagnostics, pattern recognition, and task optimisation. Some effects include an increased capacity for perception generation, substitution of human labour in these functions, and improved task performance. Despite being a controversial topic, especially when discussing the role of humans, the use of artificial intelligence techniques should begin by focusing on tasks where there are mutual benefits, such as increased operational performance and allowing humans to focus on more tactical/strategic, supervisory, and lower-risk tasks.
# Optical Lensless-Camera Communications Aided by Neural Network
## 4 Results and Discussion
As seen in Figure 12b, the BER curves under the first two thresholds are significantly higher than the third threshold, which indicates that BER performance is very relevant to the threshold setting method in this decoding method. However, it is difficult to find a good threshold setting method that can adapt to environmental changes. Overall, this method cannot resist the interference of background light source to provide a reliable performance for our system. Figures 11 and 12 shows that our system can work well indoors in most cases. To analyze the performance of our system while working outdoors, we simulated the images taken in the bad weather (e.g., snowy days) and performed simulation analysis. The snowflake density factor in Figure 13 indicates the intensity of the snowflake distribution. The larger the factor is, the denser the snowflake distribution is. Figure 14a shows our image decoding algorithm can meet the communication requirements on snowy days as long as the appropriate length of the training sequence is provided. However, Figure 14b shows the poor BER performance of the threshold determination method in the outdoor snowy environment. Figure 11: Images of of LED lights indoor during day: (**a**) “off” image of LED light, (**b**) “on” image of the LED light, (**c**) “on” image of the blue LED light, (**d**) “on” image of LED light with blue noise source interference. Figure 14: BER performance using different decoding algorithms on snowy days (the transmitter is illumination LED light): (**a**) the BER using our proposed decoding algorithm and (**b**) the BER using threshold determination method at different thresholds. Figure 13: Images of the states of the illumination LED light on snowy days: (**a**) “off” image of the illumination LED light and (**b**) “on” image of the illumination LED light. Figure 15 describes the BER performance of the blue LED at the transmitting end, which is regarded as the signal light of communication device. We can see the \"on\" image of the blue LED captured by the lensless camera from Figure 11c. Figure 15a shows that our proposed image decoding algorithm can also work well when the transmitter is a blue LED. Figure 15b indicates that the decoding performance of the threshold decision method is not good when the blue LED light is used as the sender.