esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background  
### Annual Model of Loads and Distributed Generators  
The annual load model related to a load point (LP) \(i\) is usually obtained by means of the historical power demand of the customers connected to the LP. It is represented by considering several power demand levels with their occurrence probability, as reported in Table 1. Similarly, the annual generation model of a renewable DG (RDG) \(r\) is obtained by the historical generation data. The model can also be obtained by computing the potential power output by properly combining historical meteorological data and the hardware unavailability [16; 17; 18] (that is the forced outage rate, usually known as FOR [19]). Table 2 shows an example of annual generation model for a renewable generator. Finally, Table 3 reports the annual model of a conventional DG (CDG, e.g. diesel), whose output does not depend on a fluctuating primary energy source.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Level (I)** & **Power Demand (\% of Annual Peak)** & **Probability \(\left(\pi_{i,l}^{(D)}\right)\)** \\ \hline
1 & 20 & 0.17 \\
2 & 40 & 0.22 \\
3 & 60 & 0.26 \\
4 & 80 & 0.24 \\
5 & 100 & 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of annual load model for a load point \(i\) with five levels (\(N_{i}^{D}=~{}5\)).
# Social Internet of Vehicles for Smart Cities
## 5 SIoV Security Issues
### SIoV Trust Issues
#### 5.2.2 Trust and Reputation  
Reputation management is part of trust management. Although the concepts of trust and reputation are often used interchangeably, they are subtly different. Trust is active and indicates whether an entity believes in the trust quality of a peer. Reputation is passive and indicates an opinion about an entity. A reputation system is further classified as positive reputation and negative reputation. Positive entity behaviour is recorded in positive reputation systems, and negative entity behaviour is recorded in negative reputation systems [105]. The authors in [112] present a cooperative neighbour position verification mechanism based on V2V communications among vehicles; it can mitigate the impact of adversary users by excluding malicious nodes from the packet routing mechanism. The aim of the reputation system in the SIoV is to establish trust values for each entity in the SIoV, providing information for other entities to make trust-based decisions. It provides information on whether a peer is trustworthy or not, encourages peers to participate in a trustworthy way and isolates untrustworthy peers from acting in the process. Experience can be borrowed from reputation and trust management in social networks to support trust-based decision-making in the SIoV [113].  
Another aspect of the use of trust and reputation within a SIoV is related to the length of interactions between peers in the network. Most work on trust assumes that peers build and establish trust through a number of interactions and that trust can be verified eventually to influence future interactions or recommendations for that peer. While these assumptions hold in the context of electronic transactions and service provisions, they are more difficult to assess in the highly dynamic, reactive environment of the SIoV. In addition, many reputation-based systems rely on the predictability, rationality and repetition of behaviours. In a targeted cyber-attack scenario, these assumptions do not necessarily hold, especially if a malware's spread affects a large number of vehicles in a platoon.  
In sum, trust is a multidimensional concept. Trust-based management and decision making in SIoV faces big challenges. Trust establishment needs to address the trust characteristics of the SIoV. Future work should also focus on how to adapt the trust decision frameworks, models and systems in social networks to address similar issues in the SIoV.
# Demand Forecasting Model for Airline Flights Based on Historical Passenger Flow Data
## 2 Materials and Methods
### Description of a Typical Problem of Medium-Term Forecasting of Demand for Flights  
The demand for air transportation exhibits a complex nonlinear nature and is characterized by non-stationarity. In recent years, modern machine learning methods, particularly time series analysis and deep learning approaches, have been actively utilized for forecasting passenger demand. A common practice in the development of models for assessing and forecasting air transportation demand is the use of classical methods such as Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), and Deep Learning Neural Networks (DLNN) [18]. Unlike specific machine learning methods aimed at identifying patterns in data, predictive modeling encompasses the entire process of developing mathematical models to quantitatively assess the accuracy of predictions for future data based on historical data or training samples [19]. According to the development process of time series forecasting methods, existing approaches are classified into three categories: classical statistical methods, neural networks, and deep learning [20]. Hybrid models can be utilized in several ways. One approach involves combining forecasts, where predictions are first generated using each individual forecasting model, and the results are then integrated through weighted averages or a meta-model. Another approach involves sequential processing, where one method (e.g., ARIMA) is used to model the fundamental components (trend and seasonality), while another method (e.g., neural networks) analyzes the residuals. An additional option for using hybrid models is joint learning with parallel data processing, where different models are trained simultaneously, and their results are integrated [21]. The current focus in the field of time series forecasting is on the development of hybrid models that combine the advantages of statistical methods, neural networks, and deep learning. This approach enables the consideration of both internal dependencies and external influencing factors, which is particularly important in dynamic fields such as air transportation [22, 23]. This study analyzes international experience in the development of air passenger demand forecasting models. It examines trends in development, the forecast horizon and modeling objectives chosen, the tools used, and the resulting forecast accuracy. Table 1 presents the findings of an analysis of the experience of the international scientific community in the development of flight demand forecasting models.
## 1 Introduction
[PERSON] [12] employed ANSYS Fluent to develop a CFD model for analyzing the SMR process in a real reformer. Their investigations were focused on determining the temperature and the species distributions in the reformer at different operating conditions. [PERSON] et al. [13] numerically studied the flow development in a SMR reactor. The reformer was divided into segments by the reactor. The authors claimed that their design made a significant improvement in the reformer temperature distribution. [PERSON] et al. [14] applied CFD to investigate SMR for producing hydrogen in microchannel reactors. Some important design factors, including dimension, flow arrangement, heat/mass transfer, and catalyst, were examined. The authors quantitatively evaluated different transport phenomena and proposed several methods of improving the SMR process. [PERSON] et al. [15] proposed a scheme that can generate the distribution of the outer surface temperature of a catalyst tube. The proposed scheme has two major steps: prediction and correction. [PERSON] et al. [16] also introduced a procedure that can increase the reformer throughput by optimizing the reformer fuel input. In an earlier paper, [PERSON] et al. [17] developed a scheme that can improve the reformer thermal efficiency. [PERSON] et al. [18] also employed CFD approaches to analyze a practical steam methane reformer. They concluded that the CFD model could be applied to find out the risk of real reformer operation at unknown and possibly more favorable operating conditions. [PERSON] et al. [19] numerically investigated the chemical reaction and transport phenomena in an industrial-scale catalyst tube. The authors chose the outer surface temperature of the tube as the manipulated input and the hydrogen yield as the controlled output to analyze the hydrogen production. [PERSON] et al. [20] numerically analyzed the effect of various operating parameters on the SMR process. They found that higher methane conversion could be obtained when the steam/methane molar ratio was increased. Further, the way how the ratio was changed also had a significant effect. [PERSON] [21] developed a two-dimensional numerical model to investigate the reaction in a reformer. He pointed out that the SMR and WGS (Water Gas Shift) reaction rates reached their highest values at the inlet but decreased downstream. In a previous study [22], a simplified model of a steam methane reformer was investigated numerically. The effect of the burners on/off combinations on the catalyst tube temperature and hydrogen yield was discussed. In this paper, we use CFD to analyze the chemical reaction and transport in a practical steam reformer. The temperatures and pressures of the catalyst tubes are inspected. This paper aims to investigate the influence of the burner operating mode on the catalyst tube lifetime and to seek a feasible way of improving the reformer performance.
# Fast Prediction and Optimization of Building Wind Environment Using CFD and Deep Learning Method
## 3 Results and Discussions
### Prediction Efficiency Analysis  
Traditional methods for optimizing building wind environments often rely on atmospheric boundary layer wind tunnel experiments or CFD simulation. However, these methods typically require high costs in terms of both experimental and computational resources. Given that deep learning models can benefit from GPU acceleration, this study proposes a deep learning-based surrogate model for optimizing the building wind environment. As shown in Figure 13, a comparison is made of the time required for predicting building wind fields using two methods employed in this study: the CFD model and the CNN model on a computer equipped with an Intel i5-13600 KF CPU, Intel, Santa Clara, CA, USA (3.5 GHz, 20 cores, 32 GB RAM) and NVIDIA GeForce RTX 4080 GPU, Nvidia, Santa Clara, CA, USA (16 GB VRAM). For the optimization task presented in this study (196 cases), the CFD method requires approximately 15,680 s and the CNN method requires about 10,000 s, including the dataset generation (9600 s) and training time of the model (400 s) and the time for predicting the wind fields (10 s). This represents a reduction in time compared to using the CFD method alone. It is worth noting that the CFD method takes approximately 80 s to compute a single case, while the CNN model predicts a building flow field in just 9 milliseconds; the prediction time for a single building flow field using the CNN model is only 1/900 th of the time required by the CFD method. Therefore, as the amount of data required for the optimization task increases, the time saved using the CNN model becomes even more significant.  
Figure 11: Contours of dimensionless velocity magnitude (\(U/U_{0}\)) of the optimal building layouts with inlet velocity = 4 m/s (**A**), 5 m/s (**B**), 6 m/s (**C**), 7 m/s (**D**).  
Figure 12: Contours of dimensionless velocity magnitude(U/U\({}_{0}\)) of the worst building layouts with inlet velocity = 4 m/s (**A**), 5 m/s (**B**), 6 m/s (**C**), 7 m/s (**D**).
## 3 Methods  
### Accuracy Assessment  
Although the validation based on in situ leaves issues of scale unresolved, which might introduce uncertainties to the verification, it is still an important method in regions lacking long-term validation data [37; 38; 39]. Here, the direct comparison method was utilized because it is simple and easy to implement. To avoid the influence of data noise, geometric mismatch, and spatial heterogeneity on the validation results, the average of the 3 \(\times\) 3 pixels of MOD17A2H centered around tower coordinates in situ was used to match with in situ as suggested by [PERSON] et al. [40].  
Statistical indices, including the coefficient of correlation (\(R\)), root mean square error (RMSE), and Bias were used to indicate the accuracy of MOD17A2H [41]. R measures the consistency between MOD17A2H and in situ data, while RMSE measures the average absolute error of the MOD17A2H over a single in situ. Bias describes the average deviation between MOD17A2H and in situ measurements:  
\[R=\sum_{d=1}^{h}\big{(}P_{l}-\overline{P}\big{)}\big{(}O_{l}-\overline{O} \big{)}\big{/}\sqrt{\sum_{l=1}^{h}\big{(}P_{l}-\overline{P}\big{)}^{2}\sum_{l=1 }^{h}\big{(}O_{l}-\overline{O}\big{)}^{2}}, \tag{2}\]  
\[\text{RMSE}=\sqrt{\sum_{l=1}^{h}(P_{l}-O_{l})^{2}/h}, \tag{3}\]  
\[\text{Bias}=\sum_{l=1}^{h}(P_{l}-O_{l})/h \tag{4}\]  
where \(P_{l}\) and \(O_{l}\) are the MOD17A2H and in situ-based GPP on the \(l\) th time period, respectively. \(\overline{P}\) and \(\overline{O}\) are the averaged value of the MOD17A2H and in situ-based GPP time series, respectively. \(h\) is the total number of time periods.  
The assessment was twofold. First, the performances of the MOD17A2H product were assessed separately over each site. Second, by combining the data points of all sites within each specific land cover type, the accuracies of MOD17A2H over different land cover types were assessed and compared.
# Research on Data Transmission of Laser Sensors for Reading Ruler
## 3 Research on Coding Ruler Data
### Absolute Coding
\begin{table}
\begin{tabular}{l l l} \hline
**Decimal Value** & **Binary Value** & **Gray Code** \\ \hline
0 & 0000 & 0000 \\
1 & 0001 & 0001 \\
2 & 0010 & 0011 \\
3 & 0011 & 0010 \\
4 & 0100 & 0110 \\
5 & 0101 & 0111 \\
6 & 0110 & 0101 \\
7 & 0111 & 0100 \\
8 & 1000 & 1100 \\
9 & 1001 & 1101 \\
10 & 1010 & 1111 \\
11 & 1011 & 1110 \\
12 & 1100 & 1010 \\
13 & 1101 & 1011 \\
14 & 1110 & 1001 \\
15 & 1111 & 1000 \\ \hline \end{tabular}
\end{table} 
Table 3: Correspondence Between Four-Bit Binary code and Gray Code. regarded as the result of adding \"1\" or \"0\" at the rightmost end after the preceding encoding is shifted one bit to the left. For example, if the current code is 0100110100, the code after removing the leftmost bit is 100110100. Two possible subsequent codes, namely, 1001101000 or 1001101001, can then be obtained by moving the code one bit to the left and filling the bit at the rightmost end. Although there are two options for translation compensation, the subsequent encoding of each encoding is uniquely determined in practical application. It is worth noting that although there are two options in the process of translation compensation, in practical applications, the subsequent encoding of each encoding is uniquely determined. This is because continuous code shift encoding usually combines other encoding rules or constraints to ensure the uniqueness of the encoding. This uniqueness ensures the accuracy and reliability of encoding, enabling correct identification and parsing of encoded information in digital processing and communication processes. In shift continuous encoding with n bits, there are n\(-\)1 consecutive and identical digits between two adjacent encoding. An overlapping sequence of displaced continuous encoding can be formed if the adjacent codes in a displaced continuous encoding sequence are overlapped according to their identical features. As shown in Table 4, the sequence is the overlapping sequence of a displaced continuous code with a bit length of 4. In this sequence, each 4-bit adjacent 0/1 digital combination constitutes a unique code. A coding sequence closely related to the digital position can be obtained if the number at any position is taken as the starting point and forms a code together with the three numbers on the right [11].
## 2 State of the Art
Especially in the context of decision-making processes with respect to planning renewable energy systems, MCDA methods [35] are especially beneficial--for example, as qualitative data and knowledge from local stakeholders can be integrated [36] and can, for instance, be applied in the context of robustly selecting appropriate locations for solar plant sites [37], or for the general assessment of a country's ability to generate renewable energy [38]. Furthermore, the assessment of societal acceptance with regard to SET implementation [39] and its integration into MCDA approaches [40] is essential for sustainable energy system transformation. In this context, social indicators--manifested for example in the impact on the personal environment, the sociocultural sensitivity, the attitude towards a particular technology, or the general acceptance by the public--are of great importance for the evaluation process. Ambiguities in the assessment of acceptance or acceptability that is associated with uncertainties referring to equivocal human opinions may be handled, for example, with the help of fuzzy set theory [41; 42]. However, to the best of the authors' knowledge, the ambiguities that arise in the context of evaluating different decision alternatives--which result from changing perspectives on the decision subject, as well as the temporal influences that arise from possibly changing framework conditions and assumptions under which decisions are made--are not sufficiently taken into account in the literature. To integrate temporal aspects into MCDA, different approaches exist that combine MCDA methods with scenario planning, life-cycle analysis, or via stand-alone MCDA models that--for example--consider past and future data with the help of several decision matrices [43]. In conclusion, handling ambiguities resulting from taking a global and local view on the decision problem represents a fundamental issue in decision making, and constitutes a research gap that this work begins to fill.
## 2 Materials and Methods
Grid searches for tree-based models determined parameters such as maximum depth, minimum samples per leaf, and the number of estimators. For SVMs, kernel types and regularization strengths were tested [20]. Performance evaluation involved stratified 5-fold cross-validation, ensuring consistent representation of risk categories across folds. Accuracy, macro-averaged precision, recall, F1-score, and AUC-ROC, were measured [41]. Additional analyses included confusion matrices, ROC curves, and feature importance plots for clearer interpretation of results. All analyses were conducted using Python (version 3.10) and widely accepted libraries such as pandas for data handling, NumPy for numerical computations, Scikit-Learn for traditional machine learning methods, ARIMA from Statsmodels, and TensorFlow/Keras for neural networks [42]. Data visualizations were created using matplotlib and seaborn. Code was thoroughly documented and version-controlled, ensuring complete reproducibility in line with computational social science standards [43; 44]. Special attention was dedicated to avoiding future information in model training or selection, thereby preventing data leakage. Hyperparameter optimization occurred strictly within training subsets of each fold. Final evaluation was conducted on a separate independent test set--the last 20% of data--ensuring unbiased and reliable predictions [45; 46]. Figure 1: ARIMA-ANN model architecture. This detailed methodological framework guarantees robust and actionable findings, laying a solid foundation for evidence-based policy decisions in crime prevention and public safety. By clearly outlining each analytical step and its justification, this approach facilitates both replication and practical application of the study's results.
## 5 System Development and Specifications
### Detection and Classification Procedures  
The detection procedure generates the label feature, which consists of two classifications, and the output is either normal or an anomaly using the machine learning models mentioned earlier. The classification procedure generates the category feature, which consists of five classifications. The output is either normal, Mirai attack, DoS attack, Scan attack, or MITM attack using the earlier machine learning models.
## 2 Use of Reliability for Optimum Inspection Planning
### General Concept of Probability of Failure  
Reliability (\(p_{s}\)) is generally defined as the probability that a structure will survive for a specific time interval. It is the complement of probability of failure (\(p_{f}\)) (i.e., \(p_{s}=1-p_{f}\)) [26; 32]. The reliability (\(p_{s}\)) and probability of failure (\(p_{f}\)) can be expressed as:  
\[p_{s}=\int\limits_{g(\textbf{X})>0}f_{\textbf{X}}(\textbf{x})d\textbf{x} \tag{1a}\] \[p_{f}=1-p_{s}=\int\limits_{g(\textbf{X})<0}f_{\textbf{X}}(\textbf{x})d\textbf{x} \tag{1b}\]  
where \(f_{\textbf{X}}(\textbf{x})\) is the joint probability density function (PDF) of random variables (**X**); and \(g(\textbf{X})\) is the state function; and \(g(\textbf{X})>0\) and \(g(\textbf{X})<0\) indicate the safe and failure states, respectively. Figure 1 shows the relationship between the state function value, its PDF, and cumulative distribution function (CDF). The probability of failure (\(p_{f}\)) is equal to the area under the PDF of \(g(\textbf{X})\), which is upper-bounded by the vertical axis located at \(g(\textbf{X})=0\) (see Figure 1a). In addition, \(p_{f}\) is equal to the CDF value when \(g(\textbf{X})=0\), as shown in Figure 1b. Generally, the state function (\(g(\textbf{X})\)) is formulated as the difference between the resistance and the load effects of a structural component. The time-dependent state function (\(g(\textbf{X},t)\)) can be expressed by considering the changes in external and internal forces, associated stresses and strains, and the degree of damage (e.g., crack size and corrosion depth) over time [2]. Thus, time-dependent \(p_{f}\) and \(p_{s}\) can be computed for a structural component.  
Figure 1: State function value and the associated probability of failure: (**a**) probability density function (PDF) and (**b**) cumulative distribution function.  
Furthermore, the assessment and prediction of the \(p_{f}\) and \(p_{s}\) of a deteriorating structural system require (a) formulation of the state functions of the individual components involved in the system, (b) identification of critical deterioration mechanisms affecting the state function of the components, (c) appropriate system modeling (e.g., series, parallel, and series-parallel systems), and (d) estimation of the correlation among the safety margins of individual components. El Haj Chehade and Younes [33] and Shittu et al. [34] reviewed the software programs used to compute the \(p_{f}\) and \(p_{s}\) of structural systems.
# Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks
## 7 Performance Evaluation
### Data Transmission Performance Comparison
The average transmission delay of DSAT decreases with the increase of data arrival rate, and the reason is as follows. When the data arrival rate is very low, packets stay in the buffer until the channel state gets better, which makes the average transmission delay relatively long. With the increase of data arrival rate, some packets have to be transmitted, although the channel state is not good, in order to send a predetermined amount of data. Thus, the average transmission delay will be reduced. Figure 27: Average transmission delay for comparative strategies (Data 1). Figure 26: Average buffer length for comparative strategies (Data 2). Figure 31: Simulation results of the impact of buffer threshold and data arrival rate on the average buffer length. Figure 30: Theoretical results of the impact of buffer threshold and data arrival rate on the average energy cost. Figure 21.0 Simulation results of the impact of buffer threshold and data arrival rate on the average energy cost. Figure 21.1 Theoretical results of the impact of buffer threshold and data arrival rate on the average transmission delay. Figure 21.2 Theoretical results of the impact of buffer threshold and data arrival rate on the average buffer length. Figure 21.3 Simulation results of the impact of buffer threshold and data arrival rate on the average transmission delay.