esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Steady-State Analysis
#### 4.1.3 Phase 3: Reinstating WG to Control Exhaust Manifold Pressure
For this study, the WG area is controlled indirectly by setting up a PID controller between the WG valve and the pre-turbine pressure. The targeted value is the pre-turbine pressure, which is set to increased values of 5%, 10% and 15% (multipliers of 1.05 to 1.15) compared to the baseline engine, but always within the set pre-turbine pressure limit. Figure 18 shows that for a 10% smaller turbine (multiplier of 0.9) than the baseline engine, the e-turbo can harvest energy (up to 4 kW) at most points of the map, except the 1000 rpm speed, where the results are not highly trusted due to non-convergence of the model. Although, the increased pre-turbine pressure leads to increased energy harvesting levels, it also, as expected, reduces the maximum power output of the engine. A 5% pre-turbine pressure increase leads to a 5% penalty on the engine's maximum torque, while for the case with the pre-turbine pressure set at 15% higher, the torque penalty is around 10%. Smaller Turbine: Variable Pre-Turbine Pressure Study  
The previous study showed that with the right component sizing, energy harvesting could be achieved across most of the speed/load map area of the engine. However, this leads to torque sacrifice  
Figure 18: Phase 3: DoE analysis of the motor-generator average energy for different turbine sizes and pre-turbine pressures; negative values indicate energy harvesting; positive values indicate the need for energy provision; the blue line represents the maximum torque line of the baseline engine’s model as provided by the manufacturer (\(\lambda<1\)); the \(\bigotimes\) symbol indicates limit violation; \(\square\) indicates energy provision area. at full load conditions. This penalty can be reduced or even eliminated by adjusting the targeted pre-turbine pressure of the engine when running at full load conditions. The present study shows the effects of various pre-turbine targeted values, larger and smaller than the baseline engine, to the maximum power output of the engine and the average power harvested or that needs to be provided by the motor-generator. As is shown in Figure 19, at full load conditions, the maximum power output of the baseline engine can be met by running the engine at the original pre-turbine pressures or slightly lower. However, this leads to energy provision demands by the e-turbo of up to 2 kW. Higher power outputs, which often work as selling points for automotive manufacturers, can be achieved by running at lower pressures and providing significant amounts of power to the compressor.
## 3 The Model
### Network Congestion and Plan-Leaving Characteristics  
Network congestion occurs when total full-speed data usage exceeds a threshold. Let \(G_{t}\) be a binary variable indicating whether network congestion occurs in period \(t\). \(G_{t}=1\) if network congestion occurs in periods \(t\), and \(G_{t}=0\) if not.  
For a subscribed customer of data plan \(i\), the expected volume of full-speed data usage in a single period is \(\mathbb{E}[U_{i}^{f}]\). By definition, we have:  
\[\mathbb{E}[U_{i}^{f}]=\int_{0}^{v_{i}}U_{i}\cdot f_{i}(U_{i})\ \mathrm{d}U_{i}+v_{i}\cdot[1-F_{i}(v_{i})]\cdot(1-w_{i})+\int_{v_{i}}^{ \infty}U_{i}\cdot f_{i}(U_{i})\cdot w_{i}\ \mathrm{d}U_{i}.\]  
When the subscribed customers feel unsatisfied with the service, they may quit the service. There are multiple reasons that lead to customer dissatisfaction, but we model only the two most important reasons, namely network congestion (NC) and individual speed restriction (ISR). Let \(q_{i,t}^{P}\) be the probability that a customer quits the service when NC happens but ISR does not and \(q_{i,t}^{E}\) be the probability that a customer quits the service when ISR happens, but NC does not.  
Let \(q_{i,t}\) be the probability that a subscribed customer quits the service. Because network congestion and individual speed restriction are two independent events, we have:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Total Data Usage** & **Willing to Buy** & **Full-Speed Data** & **Price to Pay in** & **Customer** \\  & \(\mathbf{U_{i}}\) & **SDP** & **Usage \(\mathbf{U_{i}^{f}}\)** & **One Period** & **Satisfaction** \\ \hline Category \(A\) & \(\leq v_{i}\) & Not necessary & \(=U_{i}\) & \(=b_{i}\) & Yes \\ Category \(B\) & \(>v_{i}\) & No & \(=v_{i}\) & \(=b_{i}\) & No \\ Category \(C\) & \(>v_{i}\) & Yes & \(=U_{i}\) & \(=b_{i}+k\cdot b_{i}^{s}\) & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Three categories of subscribed customers.  
\[q_{i,t}=\left\{\begin{aligned} &\bar{F}_{i}(v_{i})(1-w_{i})q_{i,t}^{ E}\\ & q_{i,t}^{P}+[\bar{F}_{i}(v_{i})(1-w_{i})q_{i,t}^{E}]-q_{i,t}^{P} \cdot[\bar{F}_{i}(v_{i})(1-w_{i})q_{i,t}^{E}]\end{aligned}\right.,\text { if NC does not happen in period t} \tag{1}\]
## 6 Results of the Simulations
### Observations for Cases with Spatial Side Information and Aggregation in the Fingerprint Domain
#### 6.8.1 Lower Bound in Performance for the Smallest 2D Sensor Array Considered
As a summary of the results obtained using the smallest 2D sensor array, we observe that the reduction in the error distance is significant when compared with fingerprinting-based positioning implementing pointwise samples and no side information (pattern-matching method 1-SingFingIn-PosOut). For a given use-case, it should be evaluated whether the use of a small 2D sensor array suffices in terms of the desired trade-offs between the cost, complexity, and intended target error distance at a given percentile in the CDF of the error distances (or, alternatively, target distribution of the error distances).
## 4 Numerical Simulation
### Fast Calculation of Structural Frequency Response  
Firstly, the FRF \(h_{aq}^{L}(\omega)\) of the undamaged structure is calculated according to the theoretical FE model. And the FRF \(h_{aq}^{L}(\omega_{1})\) of the first six frequencies of the damaged structure corresponding to the position of four measuring points is calculated respectively, where the amplitude of excitation used here is related to each mode shape of the undamaged structural model as explained in Section 3.3.2. Then, the virtual distortion of the associated substructure can be calculated by Equation (7). Finally, the FRF \(h_{aq}(\mathbf{\mu},\omega_{l})\) of the damaged structure can be obtained by Equation (6). The calculation process of \(h_{aq}(\mathbf{\mu},\omega_{1})\) of the damaged structure corresponding to the 1 st natural frequency is taken as an example to describe the above process introduced.  
Given the damage factor \(\mathbf{\mu}\), the 1 st mode shape \(\mathbf{\varphi}_{1}\) of the theoretical undamaged model is applied as a pulse excitation to the undamaged structure, and the frequency responses \(\gamma_{ij}^{L}(\omega_{1}),D_{ijkl}^{\gamma\gamma}(\omega_{1}),h_{aq}^{L}( \omega_{1}),D_{aij}(\omega_{1})\) in Equations (6) and (7) can be calculated. Then, the virtual distortion \(\gamma_{ij}^{0}(\omega_{1})\) of the substructure can be obtained by solving Equation (7), where the number of main virtual distortions is 48, i.e., the dimensions of the linear equations are 48 \(\times\) 48. Finally, the FRF \(h_{aq}(\mathbf{\mu},\omega_{1})\) of the damaged structure with damage factor \(\mathbf{\mu}\) is quickly calculated by Equation (6).  
When the real damage factor is given, the amplitudes obtained by SVDM at the four measuring points of the damaged structure are \([0.0743\ 0.3537\ 0.5538\ 0.6676]\ \times 10^{-3}\); the amplitudes obtained by the structural modes at the four measuring points of the damaged FE model are \([0.0769\ 0.3666\ 0.5743\ 0.6926]\ \times 10^{-3}\). The results obtained by the two methods are very close, which shows the accuracy of the structural frequency response constructed by SVDM.
## 5 Comparison of Calculation and Reality
### Summary of the Dynamic Characteristics  
The overall results in Table 4 show good agreement, especially with respect to the natural frequencies. Notably, as demonstrated in both 12,842 and 12,836, the first eigenmode achieves a high degree of agreement, while the second eigenmode shows a marginal discrepancy.  
Following this trend, the results for 13871 show excellent agreement in the second eigenmode but a slight overestimation in the first eigenmode. This observed discrepancy suggests an inadequate representation of the lateral stiffnesses, possibly due to the specific structure of the slab track.  
The predicted damping ratios for the bridges are very close to the measured values. Unexpectedly, bridge 12829, characterised by \(a_{0}=2.85>1\), also gives relatively accurate prediction results. However, it is important to note that reaching the application limit does not necessarily lead to poor predictions, as the shape of the impedance function for twin foundations has a strongly oscillatory character (Figure 3).  
Furthermore, the Calculation Model for 13871 shows a notable disparity in the damping ratios. This particular bridge has a directly adjacent retaining wall on one side, located in the transition zone between the railway and motorway bridges, at a distance of approximately 15 m. The omission of this additional interaction in the model explains the increased discrepancies observed in the predictions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Bridge** & **L [m]** & \(f_{1}\) **[Hz]** & \(f_{2}\) **[Hz]** & \(\xi_{1}\) **[\%]** & \(a_{0}\) \\  & & **Numeric/In Situ** & **Numeric/In Situ** & **Numeric/In Situ** & \\ \hline
12,842 & 17 & 9.9/10.1 & 13.8/14.7 & 6.9/6.4 & 0.90 \(<a_{0,C}\) \\
12,836 & 17 & 10/10.1 & 13.8/14.9 & 4.2/5.9 & 0.66 \(<a_{0,C}\) \\
12,829 & 9.5 & 22.4/21.8 & 24.8/25.7 & 8.5/10.9 & 2.85 \(>a_{0,C}\) \\
13,871 & 9.5 & 23.7/21.3 & 25.5/25.6 & 5.3/11 & 1.08 \(>a_{0,C}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the modal characteristics of the examined frame bridges.  
Figure 13: Illustration of the numerically determined first four eigenmodes using Abaqus.
# DeepST-Net: Leveraging Spatiotemporal Data Refinement for Enhanced Travel Time Prediction
## 1 Introduction
Using an extensive dataset from the New York City Taxi and Limousine Commission (TLC), our approach systematically examines the role of preprocessing in improving data quality and evaluates its impact on the performance of the model. This research addresses the following key questions:  
1. How do advanced preprocessing techniques, such as temporal clustering and spatial discretization, individually impact the accuracy and robustness of ANNs for travel time prediction? 2. How does the integration of these techniques influence the overall performance compared to established models on the same dataset? The contributions of our work when addressing these questions are as follows:  
1. The introduction of a novel deep learning framework that effectively integrates advanced spatiotemporal data refinement techniques with our deep learning model DeepST-Net. 2. A systematic evaluation of diverse preprocessing strategies, including temporal clustering, spatial distribution, and raw data refinement, to provide new insights into their relative importance for model performance. 3. A demonstration of the strong performance of DeepST-Net compared to established models, such as MURAT, ST-NN, and JSTC, using a real-world dataset from the New York City TLC. The remainder of this manuscript is organized as follows: Section 2 reviews and categorizes the existing research on travel time prediction. Section 3 provides the background, detailing how travel time prediction (TTP) is modeled and offering a general overview of the proposed framework. Section 4 presents the proposed framework, including spatiotemporal data refinement techniques and the DeepST-Net model. Section 5 describes the study area, dataset, use case, and model evaluation, focusing on an ablation study. Section 6 reports the findings, highlighting performance comparisons with established models. Finally, Section 7 summarizes the contributions, discusses limitations, and suggests directions for future research.
# Channel Estimation Algorithm Based on Parrot Optimizer in 5G Communication Systems
## 3 Numerical Simulations
### Simulation Parameter Settings
While the LS-based channel estimation algorithm is simple, its results are poor and cannot be used in real scenarios. Although the ideal MMSE offers superior performance, it often assumes known channel information and noise variance, which are typically not available in practical applications, thereby reducing its performance. In contrast to the ideal MMSE, the proposed method does not require pre-estimated noise power and correlation matrices, making it more feasible for practical applications. ```
Initialize parameters such as dimension, number of agents, and so on. Initialize the position \(H_{i}\ P_{i}(i=1,\ldots,\ N)\ (N\) represents the number of agents.) for each agent. For\(j=1\) to Max number of iterations, perform the following:  Calculate the fitness function. Find the best position and the worst position. For\(i=1\) to \(N\), do:  Update the position. Randomly select a strategy for each search agent and update the position according to Equations (35)-(40). End  Use the best position to estimate the channel \(H_{i}\). Perform signal equalization and clustering to obtain \(\hat{Y}_{i}\). Calculate MMSE_LOSS and find the optimal solution \(X*\). End Output the optimal channel estimation \(H*\). ```  
**Algorithm 1**Pseudocode of PO_CE  
Specifically, the computational complexity of the PO algorithm mainly depends on three aspects: solution initialization, fitness function calculation, and solution update. Let \(N\) be the number of solutions; the initialization complexity is \(O(N)\). The complexity of the update process is  
\(O(T\times N)+O(T\times N\times\text{dim})+O(T\times N\times\text{log}\,N)\), where \(T\) is the total number of iterations and \(\text{dim}\) is the problem dimension, including finding the optimal position and updating the positions of all solutions. The advantage of this algorithm is that the migration of group members is independent and can be performed in parallel on hardware, significantly reducing the algorithm complexity.
# Optimization Environment Definition for Beam Steering Reflectarray Antenna Design
## 2 Antenna and Optimization Environment Description
### Performance Parameters
For a given patch length \(L_{mn}\), it is possible to calculate the reflection proprieties (amplitude \(S_{MN}\) and phase \(\phi_{mn}\)). Thus, the reflected field from each patch is:  
\[\mathbf{a}_{mn}=\left( \begin{array}{c}E_{mn,x}^{R}\cdot S_{mn}e^{i\phi_{mn}} \\ E_{mm,y}^{R}\cdot S_{mn}e^{i\phi_{mn}}\end{array} \right) \tag{10}\]  
The combination of the radiated fields of all the patches is:  
\[\mathbf{E}^{R}(\theta,\phi)=\left( \begin{array}{c}E_{x}^{R}\\ E_{y}^{R}\end{array} \right)=\left( \begin{array}{c}\sum_{m=1}^{N_{x}}\sum_{m= 1}^{N_{y}}a_{mn,x}\cdot e^{i\phi_{0}(u\cdot x_{mn}+v\cdot y_{mn})}\\ \sum_{m=1}^{N_{x}}\sum_{n=1}^{N_{y}}a_{mn,y}\cdot e^{i\phi_{0}(u\cdot x_{mn}+v \cdot y_{mn})}\end{array} \right) \tag{11}\]  
Finally, this field is rotated in the \(\theta\), \(\phi\) reference system:  
\[\mathbf{E}(\theta,\phi)=\left( \begin{array}{c}-\frac{jk_{0}\tau_{ff}}{2\pi r _{ff}}(E_{x}^{R}\cos\phi+E_{y}^{R}\sin\phi)\\ \frac{-jk_{0}\tau_{ff}}{2\pi r_{ff}}(-E_{x}^{R}\cos\theta\sin\phi+E_{y}^{R} \cos\theta\cos\phi)\end{array} \right) \tag{12}\]  
The radiation pattern of an antenna is the module of the radiated field and generally it is expressed in decibels. Due to the scanning capabilities required for the antenna, several radiation patterns should be calculated to assess the radiating performance: in fact, the radiation pattern depends on the incident field. From each of the calculated radiation patterns, i.e., for each scan angle (indicated in the following equations by means of \(s\)), it is possible to define two important performance parameters that are affected by the design variables, denoted with the symbol **d**. The first performance parameter regards the general shape of the radiation pattern, and in particular it is useful to limit the side lobe levels. This objective can be achieved my means of the definition of a mask that defines the upper level that the radiation pattern can achieve for each scan angle. Thus, the radiation pattern error (RE) can be computed as follows:  
\[\mathrm{RE}_{s}(\mathbf{d})=\iint\Delta E(\theta,\phi)d\theta d\phi \tag{13}\]  
where \(\Delta E\) is the radiation pattern error, defined by means of the Heavyside function \(H\):  
\[\Delta E(\theta,\phi)=\left[\left|\mathbf{E}(\theta,\phi)\right|_{dB}-M( \theta,\phi)\right]\cdot H[\left|\mathbf{E}(\theta,\phi)\right|_{dB}-M(\theta,\phi)] \tag{14}\]  
The second performance parameter is the scanning direction error, i.e., the difference between the desired scanning direction (\(\theta_{scan}\)) and the direction of the maximum amplitude of the radiation pattern (\(\theta_{max}\)):  
\[\Delta\theta_{s}(\mathbf{d})=(\theta_{scan}-\theta_{max})^{2} \tag{15}\]  
These two objective functions can be used to properly direct the optimization process [26]. In fact, the integral error between the mask and the radiation pattern is a very common cost value, but it cannot detect accurately the scan angle error. Thus, the second cost should also be used in the optimization procedure.
# Review of GIS-Based Applications for Mining: Planning, Operation, and Environmental Management
## 5 Environmental Management Using GISs
### Mine-Induced Hazard Management
[63] investigated the feasibility of differential radar interferometry (DInSAR) using both ERS and Envisat images to monitor mine subsidence in Tang Shan, Hebei Province, China. They used GIS tools to analyze and validate the DInSAR results. They also discussed the disadvantages of using interferometric measurements for monitoring mine subsidence. Consequently, the results from Tang Shan showed that the subsidence was serious in the 1990s, and the resulting displacement was measured. [PERSON] et al. [64] conducted research to design a GIS database template for environmental management in mining operations in Indonesia. Using ArcCatalog ArcGIS 9.3 software, the GIS database was designed, followed by steps such as the inventory and evaluation of government regulations, inventory and evaluation of environmental quality standards, sorting and grouping parameters, classification of design features and attributes, and creation of a GIS database and dictionary. The newly designed GIS database template has many benefits for environmental management, including integration into a single database, prevention of duplicate data, volume data reduction, data uniformity, ease in finding and tracking data, and integration of spatial and attribute data. Furthermore, the template can be used as an input for GIS analysis for decision-making and development strategies. [PERSON] et al. [65] offered a different perspective on the rationale for research, preservation, and development of a mining heritage for educational purposes. To this end, the Lullumres iron mine in Spain was selected, and documents were analyzed and classified to investigate and reconstruct the 3D geometric structure of the mine. They also proposed ways to integrate this information into GIS to improve the management of the mine data. Consequently, the closed mine was found to be a live mine with factors, traditionally regarded as negative, that need to be included as patrimonial elements with associated implications. [PERSON] and [PERSON] [66] presented the main directions of implementation of GIS technology in mining through a management information system. They explained the role of GIS in Romania'smining sector and how it is used at each stage of mine development. The research shows that GIS can have a significant impact on the exploration and development stages of a mine and the mining process.
# Machine Learning for Predicting Neurodevelopmental Disorders in Children
## 2 Materials and Methods
### Data Measurements  
Most prediction models classify the data points they use into four categories: (i) true positive (TP): the individual being referred to does indeed have NDs, and our prediction correctly identified that the person has NDs; (ii) true negative (TN): the individual does not have NDs, and our prediction correctly identified that the individual does not have NDs; (iii) false positive (FP): despite the absence of actual NDs in the individual, our prediction erroneously indicated the presence of NDs (this type of problem is called a Type 1 error); and (iv) false negative (FN): despite the presence of NDs in the subject, our prediction erroneously indicated that the individual does not have NDs (this type of problem is called a Type 2 error).  
For the classification of the datasets, the reported accuracy is the average classification accuracy as measured in the test set. Accuracy is a metric that quantifies the likelihood of the classifier correctly predicting the number of outcomes. To put it simply, it represents the ratio of accurate predictions to the overall number of guesses. Accuracy is expressed in Equation (1):  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{1}\]  
Next, the precision metric measures how accurate our positive predictions were, i.e., what percentage of forecasted positive points actually happened. Precision is defined in Equation (2).  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
Next, the recall metric measures the ratio of correctly identified positive events by our model. Put simply, it evaluates the accuracy of our model in effectively classifying positive cases among all cases designated as positive. Recall and sensitivity are equivalent. Equation (3) defines recall.  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
Finally, the F1 score is a performance metric that assesses the efficacy of a model in terms of both precision and recall. It ranges from 0 to 1, with a higher value indicating better performance. It is particularly useful in situations where maintaining a balance between false positives and false negatives is critical, such as in medical diagnosis or fraud detection. Equation (4) defines the F1 score:  
\[\text{F1}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{4}\]
###### Abstract  
In order to study the aerodynamic performance of the quadrotor with different rotor spacings in hover, experiments were performed together with numerical simulations. For experimental study, an experimental platform was designed to measure the thrust and power consumption of the quadrotor with different rotor spacings (L/R = 2.2, 2.6, 3.0, 3.2, 3.6, and 4.0), and to attempt to find out the optimal rotor configuration which makes the quadrotor have the best aerodynamic performance. In addition, the pressure distribution, vorticity of the blade tip, and velocity vector of quadrotor in the flow field were obtained by Computational Fluid Dynamics (CFD) method to visually analyze the aerodynamic interference between adjacent rotors. By the comparison of experimental results and numerical simulations, the final results show that the aerodynamic performance of the quadrotor varies obviously with the change of rotor spacing, and it has a negative impact on hover efficiency if rotor spacing is too much small or large. The rotors pacing at L/R = 3.6 with larger thrust and smaller power is considered to be the best aerodynamic configuration for the quadrotor with better aerodynamic characteristics. Furthermore, compared with the isolated rotor, moderate aerodynamic interference is proved to help improve the aerodynamic performance of the quadrotor with a larger thrust, especially for a rotor spacing at L/R = 3.6.  
Quadrotor; rotor spacing; hover; experimental platform; numerical simulation +
Footnote †: journal: Applied Sciences