esa-sceva/satcom-chunk-collection huggingface dataset:
# An Architecture to Manage Incoming Traffic of Inter-Domain Routing Using OpenFlow Networks
## 6 Further Discussion
### East/Westbound Discussion  
Regarding the East/Westbound SDN interfaces, SDN technologies (such as OF) can centralize the control of the network and require network functions such as monitoring capabilities or import and export data to exchange control information between the different controllers. As recent surveys have indicated, there is no standard to exchange network control information between different SDN controllers [40; 48], although some initiatives have tried to standardize an East/Westbound for SDN controllers, for example, ALTO [4; 49].  
To communicate among the SDN controllers in the inter-domain ecosystem, the authors in [40] proposed an interface for the SDN to exchange reachability and topology information. The goals were to present a peer-to-peer mechanism to exchange network information and be resilient and secure for ASes. As highlighted by [6], the SDN does not have a universal interface (East/Westbound API) for the integration of heterogeneous SDN controllers as it is easy to develop (it is just software, no hardware development involved). This interface is also dependent on niche markets where a given SDN controller has to satisfy the business requirements where it has to be deployed.  
The proposed architecture uses its East/Westbound interface to allow a more in-depth integration with the business systems of its customers. It is different from other approaches that only integrate legacy routers with SDN technology. For example, RouteFlow [50] uses Quagga as the main engine to control and configure flows of OpenFlow switches via a proxy for the OpenFlow controller, and all the network logic follows traditional network protocols, which in the case of inter-domain routing is the BGP protocol. Here, it is possible to refine the control task of managing inbound network traffic, when a customer is allowed to do so, by overriding the BGP behavior and exploring multiple available and valid paths in the inter-domain environment.  
This work is a step forward in the understanding of adopting an East/Westbound interface between different domains, especially in the inter-domain environment. Furthermore, regarding classification, the solution of using an East/Westbound interface for the proposed architecture makes it classified as a Peer-to-Peer application (P2P) based on the work of [PERSON] et al. [51].
## 3 Core Algorithm Analysis
### Representation of 4D Trajectory and Conflict Detection
#### 3.2.2 Causal Analysis  
The size (computational complexity) of the conflict resolution problem is typically very large, which represents a real challenge to deal with the exponential growth of the state space in a realistic environment containing dozens of UAVs. A highly efficient technique to reduce the problem size is clustering [22]. The scenario can be decomposed into several sets of independent clusters based on the interaction between planned trajectories for a period of time. The partition principle is mainly in view of the distances between the neighboring UAVs. These independent clusters can be processed in parallel to significantly improve efficiency. Figure 3 is the concept illustration.  
The original problem is decomposed into several clusters with treatable sizes with few trajectories. Thus, the overall solution is a combination of conflict-free solutions obtained by a causal analysis for these regional clusters. The core idea of the causal analysis is to explore the state space of the CA scenario to achieve the optimal resolution. It utilizes the alternative traject  
Figure 3: Concept illustration of clustering.  
generated by the DDOA, which includes the track adjustment, distance, and time costs. Therefore, for \(n\) UAVs involved in the same scenario, the total cost of the trajectories amendment can be defined as:  
\[Cost=\sum_{i=1}^{n}\sum_{r=0}^{N-1}C_{i}(m+r)+D_{i}(m+r)+T_{i}(m+r) \tag{13}\]  
The process state could be described by a specific four-tuple:  
\[\{conflict,cost,(active\quad UAV),(resolution\quad trajectories)\} \tag{14}\]  
An instance state a = {2,1,(1,2,3),(11,11,31,32)} is used to explain the tuple.
* Conflict: indicates the threat that will be resolved (conflict 2 is active in state a).
* Accumulated cost: Cost summation for the respective resolution trajectories (cost = 1 in state a).
* Active UAVs: is a processed tuple and represents all the UAVs in this scenario, among them the active one would be underlined (_ULAV_1 and _ULAV_3 are active to have conflict 2 in state a).
* Active resolution trajectories: used to store solution trajectories and the active resolution trajectories are represented with underlines to resolve the active collision (111, 31 or 32 can be used to resolve conflict 2 in state a). Here the number is composed with the aircraft identification \(i\) and amending strategy descend/climb (1/2).  
The causal exploration can be summarized with the following steps in Figure 4:  
As an explanatory case, Figure 5 illustrates the whole feasible solutions of a case scenario, and Figure 6 shows its reachability tree. Using the causal exploration algorithm, there are six final process states as the feasible solutions. Evidently, state d is preferred to be utilized than the others due to the minimum cost.  
Figure 4: Causal exploration flow chart.  
Figure 5: The feasible solutions.
# Construction and Evaluation of a Control Mechanism for Fuzzy Fractional-Order PID
## 4 Pneumatic Pressure Control System Modelling
### Fractional Order Model  
In the past years, there have been several greater amounts of accomplishments in the field of FO system identification. FO differential equations are commonly used to represent FO systems in the time domain.  
\[\mathrm{a_{n}D^{\alpha_{n}}y(t)+a_{n-1}D^{\alpha_{n-1}}y(t)+a_{n-2}D^{\alpha_{n-2 }}y(t)+\ldots+a_{0}y(t)=u(t)} \tag{2}\]  
The system input is u(t), whereas the system output is y(t). \(\alpha_{n}\), \(\alpha_{n-1}\), \(\ldots\)\(\alpha_{1}\) are fractional in Equation (2), the orders of differentiation. That is, \(\alpha_{n}\), \(\alpha_{n-1}\), \(\ldots\)\(\alpha_{1}\) could be real valued and not only integers. The Grunwald-Letnikov definition may be used to get the numerical solution for (2). The derivatives of the fractional model (1) are calculated using Grunwald-Letnikov approximation techniques [36]. The system that has been found is listed below (3).  
\[\mathrm{G_{P}(s)=\frac{7.01}{11.39s^{0.72}+1}e^{-1.4s}} \tag{3}\]
## 3 Performance-Based Optimization Model for the Life-Cycle Cost of SRHPC Frame Structures
### The Calculation Method of SRHPC Frame Structure's Optimization in Stages
#### 3.2.1 The First Optimization Stage under a Minor Earthquake  
1. Design Variables In the first stage, in order to minimize the sum of the initial cost of the structure and the expected loss at the \"no damage\" performance level, the beams, column section size, section area of section steel, and stirrup's reinforcement ratio of beam and column were optimized at this stage. Due to the influence of the height of beams and columns being greater than that of width on structural seismic behavior, this paper only takes the height of the section as one design variable, and the width of the section was determined by its aspect ratio according to standard design requirements. The design variable vector \(X\) in the first optimization stage is shown in Equation (7): \[X=\big{[}b_{ij,c}\;\;h_{ij,c}\;h_{ij,b}\;\;A_{ij,c}^{a}\;\;A_{ij,b}^{a}\;\;A_{ ij,c}^{w}\;\;A_{ij,b}^{sv}\;\;\rho_{ij,c}^{sv}\;\;\rho_{ij,b}^{sv}\big{]}.\] (7)
2. Optimization Objective The optimization objective in the first stage is shown in Equation (8): \[MinW(X)=\alpha_{1}C_{0}(X)+\alpha_{2}C_{fs}\widetilde{P}_{fs}.\] (8)
3. Constraints (1) Capacity Constraints The capacity constraint of the column is shown in Equation (9): \[V_{ij,c}\leq\frac{1}{\gamma_{RE}}\Bigg{(}\frac{0.16}{\lambda_{ij,c}+1.5}f_{c }b_{ij,c}h_{0 ij,c}+08f_{yv}\frac{A_{ij,c}^{sv}}{s_{ij,c}^{sv}}h_{0 ij,c}+\frac {0.58}{\lambda_{ij,c}}f_{a}\,A_{ij,c}^{w}+0.056N_{ij,c}\Bigg{)}.\] (9)The capacity constraint of the beam is shown in Equation (10):  
\[V_{ij,b}\leq\frac{1}{\gamma_{RE}}\Bigg{(}0.06f_{c}b_{ij,b}h_{0 ij,b}+0.8f_{yv}\frac {A_{ij,b}^{sy}}{s_{ij,b}^{sy}}h_{oij,b}+0.58f_{a}A_{ij,b}^{sy}\Bigg{)}. \tag{10}\]  
(2) Construction requirements;  
(3) The performance requirement under a minor earthquake is shown in Equation (11):  
\[\Delta si\leq[\Delta s]. \tag{11}\]  
(4) The requirements of conceptual design are shown in Equation (12):  
\[E^{c}I_{ij,c}^{c}+E^{a}I_{ij,c}^{a}\geq 1.4\Big{(}E^{b}I_{ij,b}^{b}+E^{a}I_{ ij,b}^{a}\Big{)}. \tag{12}\]
# Non-Classic Atmospheric Optical Turbulence: Review
## 6 Influence in Applications  
### Wireless Optical Communications  
Wireless optical communication (WOC) systems using laser light are attractive because of high data rates (due to extremely wide bandwidth) and enhanced security (supported by high laser directionality). However, WOC systems suffer from atmospheric turbulence-induced signal degradation. Non-classic turbulent regimes can also affect WOC in a number of ways. [PERSON] and [PERSON] used the extended non-Kolmogorov anisotropic power spectrum introduced in [71] to investigate the performance of a WOC system (with a Gaussian beam used as the information carrier) embedded into anisotropic turbulence [100]. Specifically, the scintillation index/flux with aperture averaging, the probability of fade, the mean signal-to-noise ratio (SNR) and the bit-error rate (BER) for the on-off key modulation were analyzed for a specific case of horizontal propagation path at high altitude.
## 2 Compatibility between Indoor Radon Exposure and Buildings' Energy Efficiency Improvement
### Energy Efficiency Management vs. Indoor Radon Exposure
According to the WHO, up to 30% of European buildings built between the 1970s and 1980s are likely to suffer from this type of syndrome, with possible sources of contamination being both pollutants that come from the outdoor air and those generated indoors [43]. To prevent issues related to \"sick buildings\" in 1987, WHO published the 1 st edition of the \"Air Quality Guidelines for Europe\" [44], containing the health risk assessment of 28 chemical contaminants in the air. In 2000, it published the 2 nd edition which includes a section on indoor air pollutants, being radon gas one of them [45]. In 2005, it updated the air quality guidelines and developed specific guidelines for IAQ [46]. In 2009, it published the guidelines for the protection of public health from health risks due to dampness, associated microbial growth, and contamination of indoor spaces [37]. In 2010 it published the standard values for 9 indoor air pollutants [47] following the recommendation that came out of the meeting with international experts held in 2006 within the framework of the European air quality program. Ideally, indoor environments must combine low radon concentration, IAQ, energy efficiency, and thermal comfort [48]. Energy efficiency projects aim to reduce energy consumption to acceptable levels, and consequently increasing thermal comfort, however, its compatibility with indoor radon concentration is not trivial, as it covers the interaction of numerous variables that increase the complexity of the study. On the other hand, thermal comfort depends not only on external variables such as color, shape, light, ambient temperature, relative humidity, air quality, air exchange rate, etc., but also on people's behavior [48; 49; 50; 51; 52; 53]. Radon levels and IAQ depend on variables that are common to thermal comfort, but contain others that are distinct, namely the presence of odors, the concentration of microorganisms or airborne dust, the noise level, and lighting, among others [54]. The \"sick buildings\" analysis has been an object of numerous scientific studies by several authors, who present a wide range of solutions [55; 56; 57; 43; 48; 54; 58].
# Joint Luminance Adjustment and Color Correction for Low-Light Image Enhancement Network
## 4 Experiments
### Quantitative Evaluation
#### 4.3.1 Peak Signal-to-Noise Ratio (PSNR)  
PSNR is currently the most widely used image quality evaluation method. This method evaluates image quality by measuring the average energy ratio between the maximum signal and the noise signal and requires a real reference image for calculations. The unit is dB. The larger the value, the better the image quality. The calculation formula is as follows:  
\[PSNR=10 log_{10}(\frac{MAX_{X}^{2}}{MSE}) \tag{14}\]  
\[MSE=\frac{1}{H\times W}\sum_{i=0}^{H-1}\sum_{j=0}^{W-1}[X(i,j)-Y(i,j)]^{2}, \tag{15}\]  
where \(X\) and \(Y\) represent the enhanced and real reference images, respectively. \(H\) and \(W\) are the height and width of the image, respectively. \(MAX(x)\) is the maximum pixel value of the image. Generally, the value of 8-bit integer data is 255, and the floating-point data value is 1. Since PSNR evaluates similarity based on the mean square error between corresponding pixels of two images, it does not consider the human eye's visual characteristics. The human eye is more sensitive to changes in regional differences with lower spatial frequencies and is less sensitive to brightness. Also, more so than chromaticity, sensitivity to changes in one area is affected by adjacent areas. Therefore, the evaluation results of PSNR sometimes need to be more consistent with the subjective perception of the human eye.
## 4 Results and Discussion
### Adhesion Stability Evaluation
#### 4.2.4 Considerations  
Using the peel-off load values for each section of the NHV-SPRG composite waterproofing sheet of the 4 types derived above, the coefficient of variation of the standard deviation, the linear regression line grade, and the percent relative ratio based on the maximum and minimum load were calculated and the adhesion stability was determined (as shown in Figure 13).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Specimen**}} & \multicolumn{1}{c}{**Max.**} & \multicolumn{1}{c}{**Min.**} & \multicolumn{1}{c}{**Average**} & \multicolumn{1}{c}{**Average Percent**} \\  & **Peel Load** & **Peel Load** & **Peel Load** & **Relative Range (\%)** \\ \hline \multirow{4}{*}{VG-1} & \multirow{2}{*}{185} & 225 & 205.45 & 19.5\% \\  & & 184 & 220.5 & 203.15 & 18.0\% \\  & & 182 & 223 & 203.28 & 18.5\% \\  & Avg. & 183.67 & 222.83 & 203.96 & 18.7\% \\ \hline \multirow{4}{*}{VG-2} & \multirow{2}{*}{12} & 235 & 315 & 285.55 & 28.0\% \\  & & 265 & 294.5 & 281.05 & 10.5\% \\  & & 79.5 & 285 & 224.15 & 91.7\% \\  & Avg. & 193.17 & 298.17 & 263.58 & 43.4\% \\ \hline \multirow{4}{*}{VG-3} & \multirow{2}{*}{11} & 110.01 & 284 & 246.591 & 70.6\% \\  & & 202 & 272.5 & 220.15 & 32.0\% \\  & & 216.5 & 251.5 & 235.7 & 14.8\% \\  & Avg. & 176.17 & 269.33 & 234.15 & 39.1\% \\ \hline \multirow{4}{*}{VG-4} & \multirow{2}{*}{1} & 88.5 & 101.5 & 93.1 & 14.0\% \\  & & 81.5 & 115.2 & 91.171 & 37.0\% \\  & & 71 & 134 & 101.8 & 61.9\% \\  & Avg. & 80.33 & 116.90 & 95.36 & 37.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 11: The percent relative range calculation results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{VG-1}} & \multirow{2}{*}{**Specimen**} & \multicolumn{1}{c}{**Slope**} & \multicolumn{1}{c}{**Regression Line Grade**} \\  & & & (\%) \\ \hline \multirow{4}{*}{VG-2} & \multirow{2}{*}{1} & 1.24 & 1.24 \\  & & 2 & 0.19 & 0.19 \\  & & 3 & 2.15 & 2.15 \\  & Avg. & 1.19 & 1.19 \\ \hline \multirow{4}{*}{VG-3} & \multirow{2}{*}{1} & 22.4 & 22.45 \\  & & \(\%\) & \(-\)0.92 & 0.92 \\  & & \(\%\) & \(-\)8.65 & 8.65 \\  & Avg. & 4.29 & 4.29 \\ \hline \multirow{4}{*}{VG-3} & \multirow{2}{*}{1} & \(\uparrow\) & \(-\)3.71 & 3.71 \\  & & \(\uparrow\) & \(-\)3.16 & 3.17 \\  & & \(\uparrow\) & \(-\)9.76 & 9.77 \\  & Avg. & \(-\)5.55 & 5.55 \\ \hline \multirow{4}{*}{VG-4} & \multirow{2}{*}{1} & 6.25 & 6.25 \\  & & 2.64 & 2.64 \\ \cline{1-1}  & & \(\uparrow\) & \(-\)0.36 & 0.36 \\ \cline{1-1}  & Avg. & 2.85 & 2.85 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The regression line grade calculation results.
# The Shear Behavior of the Curved Interface in Polyurethane-Concrete Composite Structures
## 3 Results and Discussion
### Comparison of Shear Behavior in Curved and Planar Composite Specimens
#### 3.1.2 Shear Stresses of Curved and Planar Specimens with Varying Polyurethane Densities  
The shear strength variation in composite specimens with varying polyurethane densities is illustrated in Figure 7. It is evident that both curved and planar specimens exhibit similar trends in shear strength variation. As polyurethane density increases, the shear strength of composite specimens gradually increases, albeit with a decreasing rate of growth as polyurethane density rises. Curved specimens demonstrate significantly higher shear strength than planar specimens, with the magnitude of shear strength variation being greater for curved specimens compared to planar ones. Specific shear strength values are detailed in Table 3, it is found that for composite specimens with the same polyurethane density, the shear strength of R250 curved specimens is approximately 1.37-1.39 times that of planar specimens.  
Figure 6: Shear stress-displacement curves of curved and planar composite specimens: (**a**) curved specimen R250, (**b**) planar specimens, (**c**) theoretical curve of curved specimen R250, and (**d**) theoretical curve of the planar specimen.
## 3 Processing Methods
### Evaluation Metrics  
To verify the prediction accuracy of the model, the root mean square error (\(RMSE\)) is used for evaluation, as shown in Equation (9). To fully assess the model's fitting performance across different concentration intervals, the coefficient of determination (\(R^{2}\)) is used to evaluate the prediction results for low (10-30%), medium (40-60%), and high (70-90%) gas concentration intervals, denoted as \({R_{L}}^{2}\), \({R_{M}}^{2}\), and \({R_{H}}^{2}\), respectively, as shown in Equation (10).  
\[RMSE=\sqrt{\frac{1}{N}{\sum_{i=1}^{N}}{[y(i)-y^{\prime}(i)]}^{2}} \tag{9}\]  
\[R_{L}^{2},R_{M}^{2},R_{H}^{2}=1-\frac{\sum_{i=1}^{N}{[y(i)-y^{\prime}(i)]}^{2 }}{\sum_{i=1}^{N}{\left[y(i)-\frac{1}{N}{\sum_{i=1}^{N}y(i)}\right]}^{2}} \tag{10}\]  
where \(N\) is the number of samples, \(i\) is the index of the sample, \(y\) is the true value of the sample, and \(y^{\prime}\) is the predicted value of the sample.
# Research on Offshore Vessel Trajectory Prediction Based on PSO-CNN-RGRU-Attention
## 2 Related Works
### Attention Layer  
The Attention Mechanism [23] is a commonly used technique in computer science and machine learning, designed to make models more accurate and efficient when processing sequence data. In traditional neural networks, the output of each neuron relies solely on the outputs of all neurons in the previous layer. In contrast, the attention mechanism allows the output of each neuron to be influenced not only by the outputs of all neurons in the previous layer, but also to be weighted according to different parts of the input data, assigning different weights to different parts.  
The specific details are shown in Figure 4. A method is chosen to calculate the similarity between the query and each key, resulting in a similarity score for each key that matches the dimension of the value. The similarity scores are normalized using the softmax function to obtain the attention weights for each key-value pair. By multiplying the attention weights by the corresponding value for each key and then summing them up, we obtain the final output. Here, a represents the inertia weight, s denotes the attention score, and * represents the multiplication operation between the weight value and the corresponding Value. The attention aggregation function f is expressed as a weighted sum of values in Equation (6), where the attention weight between the query \(q\) and \(k_{i}\) is calculated by mapping the two vectors into a scalar through Equation (6), and then running softmax to obtain Equation (7).  
\[f(q,(k_{1},v_{1}),\ldots,(k_{m},v_{m}))=\sum_{i=1}^{m}\ a(q,k_{i})v_{i}\in R^{V} \tag{6}\]  
\[a(q,k_{i})=softmax(a(q,k_{i}))=\frac{exp(a(q,k_{i}))}{\sum_{j=1}^{m}exp\big{(} a(q,k_{j})\big{)}}\in R \tag{7}\]  
Figure 3: Structure of the GRU Unit.
## 2 Literature Review
### Financial Data Prediction Using LSTM in Finance  
#### 2.1.1 Comparative Evaluation of LSTM and Conventional ML Models for Financial Time Series  
LSTM networks and random forests have been utilized to forecast the directional movements of S&P 500 stocks, showing that a multi-feature setting outperforms a single-feature setting [13]. Daily returns were 0.64% for LSTM and 0.54% for random forests, compared to 0.41% and 0.39% with the single-feature setting. The multi-feature approach also achieved higher Sharpe ratios, lower standard deviations, reduced maximum drawdowns, and lower daily Value at Risk (VaR). Additionally, LSTM outperformed random forests. Future research will focus on applying and comparing reinforcement learning methodologies, both model-free and model-based, for stock and cryptocurrency predictions.  
Various ML strategies for stock price prediction and portfolio management have been reviewed, including Autoregressive Integrated Moving Average Model (ARIMA), LSTM, CNN, and hybrid models [18]. The accuracy and limitations of these models were analyzed using standard measures like RMSE, Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). LSTM models are noted for their accuracy due to low MSE, RMSE, and MAE, while CNN models excel at capturing rapid changes and forecasting stock trends. Hybrid models leverage the strengths of both LSTM and CNN to offer efficient and accurate predictions of stock attributes.