esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper presents an experimental study investigating the effects of water-in-gasoline emulsion (WiGE) on the performance and emissions of a turbocharged PFI spark-ignition engine. The emulsions were produced through a micro-channels emulsifier, potentially capable to work inline, without addition of surfactants. Measurements were performed at a 3000 rpm speed and net Indicated Mean Effective Pressure (IMEP) of 16 bar: the engine point representative of commercial ECU map was chosen as reference. In this condition, the engine, fueled with gasoline, runs overfueled (\(\lambda=0.9\)) to preserve the integrity of the turbocharged from excessive temperature, and the spark timing corresponds to the knock limit. Starting from the reference point, two different water contents in emulsion were tested, 10% and 20% by volume, respectively. For each selected emulsion, at \(\lambda=0.9\), the spark timing was advanced from the reference point value to the new knock limit, controlling the IMEP at a constant level. Further, the cooling effect of water evaporation in WiGE allowed it to work at stoichiometric condition, with evident benefits on the fuel economy. Main outcomes highlight fuel consumption improvements of about 7% under stoichiometric mixture and optimized spark timing, while avoiding an excessive increase in turbine thermal stress. Emulsions induce a slight worsening in the HC emissions, arising from the relative impact on combustion development. On the other hand, at stoichiometric condition, HC and CO emissions drop with a corresponding increase in NO.  
water-in-gasoline emulsion; micro-channel emulsifier; experiments; spark-ignition engine; performance; fuel consumption; emissions +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Extraction of High-Resolution Sea Ice Concentration  
As an important parameter reflecting the sea ice conditions in the region during the summer ice melt period, sea ice concentration can be used to infer the spatial distributional differences amongst sea ice categories to some degree. This feature is also key when considering the navigability of shipping services [19]. There are obvious differences in sea ice concentration between different sea ice categories; for example, the thin ice type tends to have a lower sea ice concentration, while that of multi-year ice is relatively higher, and one-year ice represents a kind of transition from thin ice to multi-year ice--it is rarely found in summer and has a sea ice concentration of effectively 100%.  
Sea ice concentration traditionally depends on active-passive remote sensing and is defined as the percentage of sea ice area within a single image element [20], a method which generally has a low spatial resolution. Given the high resolution of SAR images, we propose an optimized high-resolution sea ice concentration calculation method, defining the average sea ice concentration within the neighboring rectangular area of a specific image element as the sea ice concentration at that point.  
The whole extraction process of this method can be divided into two parts: ice water segmentation and sea ice concentration calculation. First, a regional ice water segmentation map with a 50 m spatial resolution is obtained using the K-means++ clustering algorithm [21] and land mask. Then, based on the above results, a convolution operation and array calculation are used to speed up the calculation and obtain the sea ice concentration at the same resolution as the SAR data.
## 4 Experimental Results
### Comparison to Existing Methods
#### 4.4.2 Optimization Algorithm Comparison
Furthermore, the GA's performance stabilizes after 600 min of runtime, based on the degree of similarity of the previous three consecutive accuracy measures.
# Anomaly Detection in Financial Time Series by Principal Component Analysis and Neural Networks +
## 4 Model Evaluation: Setting the Stage
### Calibration of the Latent Space Dimension  
When performing PCA, \(k\) is determined through a scree plot, which is the representation of the proportion of variance explained by each component. The optimal \(k\) corresponds to the number of principal components explaining a given level of the variance of the original data. However, this method has its limitation as stated in [13]. More importantly, it is not suitable for our approach. Indeed, in our case, the number of selected principal components must achieve a trade-off between information and noise in the latent space. If we consider a number of principal components that is too low, we may lose information regarding the normal observations, leading to false alarms. If too high a number of principal components is retained, we may include components representing noise, which prevents the model from detecting some anomalies.  
To select the optimal dimension of the latent space, we consider the distribution of anomaly scores obtained through the naive approach in Section 2.4, empirically calibrating a cutoff \(\hat{s}\) through the non parametric estimation of the distribution of the anomaly scores of uncontaminated and contaminated time series. We chose to calibrate the dimension of the latent space with the naive approach, because using the NN to this end would be very costly. Hence, for each \(k=5,10,\ldots,200\), we construct a PCA model from which we infer reconstruction errors, which are then converted into anomaly scores. Based on these anomaly scores, we tune the cutoff value \(\hat{s}\) thanks to the distributions and we finally convert the scores into labels. We evaluate the predictions of the naive approach for each value of \(k\). The results of the evaluation metrics on the training set, as legitimate to choose \(k\), are represented in Figure 4. The highest values are reached for \(k\in\{40,\ldots,145\}\). The performance seems to be stable in terms of \(F_{1}\)-score and accuracy. Therefore, for computational reasons, we choose the optimal \(k\) to be 40.  
Figure 4 also shows, as expected, a downward trend in the scores when presented against the highest values of \(k\). This demonstrates that when a high level of variance is explained, it become much harder to perform anomaly detection based on the reconstruction errors.  
Figure 4: Performance metrics for the training (**left**) and test (**right**) sets with respect to the number of principal components \(k\).
# Graph Neural Networks for Routing Optimization: Challenges and Opportunities
## 3 Supervised Learning for Network Modeling
### Literature Review
The proposed approach outperforms the full-mesh algorithm and approaches the performance of the globally optimal solution in terms of throughput. An intent-based networking (IBN) solution is proposed in [101] using RouteNet and LSTM models for optimized service path routing and computation resource prediction, respectively. Specifically, RouteNet is used for link utilization prediction. The proposed RouteNet-based IBN solution with end-to-end orchestration is successfully deployed for 8K and 4K video streaming services. A GNN-based multipath routing scheme is proposed in [102] for SDN to achieve a balance between flow transmission granularity, reordering, and end-to-end transmission efficiency improvement. The proposed scheme includes a route planning module, state information collection module, delay prediction module and adaptive flow splitting scheme, in which the delay prediction module is based on the MPNN. The proposed approach outperforms other baselines including the shortest path, per-flow routing, per-packet routing, and original flowlet routing schemes, in terms of time overhead, end-to-end delay, flow completion time, and throughput. An MPNN-based link delay model is proposed in [103], using the domain knowledge of network behaviors. The logic behind the proposed link delay model is that the end-to-end delay can be reflected by some typical network behaviors including jitter, packet loss, and throughput. Driven by this idea, an improved GNN model is proposed to aggregate messages in the modeling process. Both traditional Queuing model and MPNN-based RouteNet are used as baselines and the proposed approach outperforms Queuing model and RouteNet with an increased R\({}^{2}\) by 73% and 11%, respectively. The generalization ability of the proposed approach is further validated, which achieves a lower mean relative error under an unknown flow scheduling strategy. The relevant studies with supervised learning for network modeling are summarized in Table 3.
## 4 Result and Analysis
### Comparative Analysis of Models
This also indicates that the output of the PSO-BP neural network model has a good correlation with the actual values compared to the GA-BP neural network model, as a higher \(R^{2}\) value indicates a model with high precision. The prediction improvement of the PSO-BP neural network model over the BP neural network model was \(85.2\%\) compared to the GA-BP neural network model, which was \(65.7\%\), indicates that the PSO-BP neural network model has a better prediction improvement in comparison, as indicated in Table 4.  
Figure 8 shows a comparison curve between the predicted values and the actual values of the BP, GA-BP, and PSO-BP neural network models. From Figure 8c, it can be seen that the predicted values of the PSO-BP neural network almost match the actual values, making it the best prediction model. The analysis of the _MSE_, _MAPE_, \(R^{2}\), and global best fitness shows that the PSO-BP neural network model has the highest prediction accuracy. Therefore, in this paper, the PSO-BP neural network model is proposed as an improved model for the prediction and assessment of safety risk factors in underground mines. This study is in line with other studies that have also proposed the PSO-BP neural network model for the assessment and prediction of various issues. The PSO-BP neural network model has better robustness and accuracy in the assessment and prediction of risk factors compared to the other models [84]. Furthermore, in addition to having a simple algorithm, the PSO-BP neural network model is highly evaluative and intelligent [85]. For example, [PERSON] et al. proposed a model for the prediction of the number of coliform bacteria in Dai's Special Snacks, Sapie, based on the PSO-BP neural network model. [PERSON] et al. also proposed a thermal error model of the spindle system based on the PSO-BP neural network. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Model Prediction Improvement**} \\ \hline  & Train & Test \\ GA-BPNN & 58.9\% & 65.7\% \\ PSO-BPNN & 89.3\% & 85.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Models’ prediction improvement over the traditional BP neural network. Figure 8: _Cont_.
# Study on the Improvement of a Collision Avoidance System for Curves
###### Abstract  
Conventional autonomous emergency braking (AEB) systems derive the relative distance of a curve using a curvature calculated through an in-vehicle sensor. However, as the AEB system cannot reflect geometric factors of a curve with variable curvature, it does not accurately estimate relative distances, based on which the AEB performance is evaluated. Accordingly, an AEB system reflecting the geometric information of curves needs to be considered and developed to improve the AEB performance for curves. This study proposes a method to improve the performance of AEB systems for curves through curvilinear coordinate conversion, which is used to reflect the geometric information of roads for the navigation of an autonomous vehicle. Both the host and target vehicles are located by means of curvilinear coordinate conversion. The positions thus identified are used to calculate the relative distance and lanes. Finally, the hazard risk criterion--that is, time-to-collision (TTC)--is derived using the proposed AEB system. To demonstrate the effectiveness of the proposed AEB system, this study compares it with the conventional AEB system by analyzing the collision avoidance performance on curves through relative distances and TTC.  
advanced driver assistance system (ADAS); autonomous emergency braking (AEB); vehicle-to-vehicle (V2V) communication; curvilinear coordinates +
Footnote †: journal: Applied Sciences
# Spot Weld Inspections Using Active Thermography
###### Abstract  
Spot welds have a significant part in the creation of automotive vehicles. Since the integrity of, for example, a car, is dependent on the performance of multiple welds, it is important to ensure the quality of each spot weld. Several attempts have been made in order to determine the quality of spot welds, but most of them do not focus on the applicability in the manufacturing process. Spot weld inspections are often performed using back heating. However, during manufacturing, robotic inspections are desired, and since the bodywork of a car is a complex shape, the accessibility from the inside of the vehicle is minor. Therefore, inspections using front heating are more suitable. In this manuscript, multiple excitation methods are compared as well as different post-processing techniques. The used excitation techniques can be divided into light heating and inductive heating. Light heating is further divided in lock-in thermography and pulse thermography. The used post-processing techniques are principle component analysis and fast Fourier transform. Inductive heating turns out to be the most suitable measurement technique since it is fast and can be performed as front and back heating. Both investigated post-processing techniques deliver suitable information, such as relief images and information of the internal structure of the spot weld.  
active thermography; non-destructive inspection; spot welds +
Footnote †: journal: Applied Physics Letters
# An Adaptive Delay-Tolerant Routing Algorithm for Data Transmission in Opportunistic Social Networks
## 4 Simulation
### Simulation Setup  
#### 4.1.1 Introduction of Benchmark and the Compared Protocols  
In opportunistic networks, there are several paradigms of the mobile model that can be adopted to demonstrate the real mobile characteristics of peopel. To evaluate the resource consumption of DTCM algorithm, a suitable candidate, namely Epidemic [48], which heavily relies on resource supply, can be chosen as the benchmark protocol in this simulation. Moreover, to fully reflect the performance of DTCM algorithm, it will be compared against other four protocols: FCNS (a fuzzy routing algorithm using comprehensive node similarity) [13], P&F (Predict and Forward routing algorithm) [18], Spray and Wait [47], and Epidemic [48]. Performance comparison among the five algorithms is summarized at length, mainly focusing on three aspects: delivery ratio, end-to-end delay, and network overhead. To comprehensively evaluate the details of implementation and performance of each approaches, this paper develops Table 2 to demonstrate the significant differences among the five routing algorithms, from a perspective of theoretical analysis. This comparison provides an effective benchmark for the above five routing algorithms when data congestion has been controlled.
# Study on Flow Characteristics of Venturi Accelerated Vortex Drainage Tool in Horizontal Gas Well
## 4 Results and Discussion
### Performance Analysis of Venturi Accelerated Vortex Tool  
To further clarify the performance of the venturi accelerated vortex tool, the results when the liquid flow rate is 1-15 L/min and the gas flow rate is 100-600 m\({}^{3}\)/h were also investigated. The lengths of the spiral flow for the venturi accelerated vortex tool with different gas and liquid flow rates are shown in Figure 16. The average horizontal pressure drop and the average horizontal and vertical pressure drop are shown in Figure 17a,b, respectively.  
Figure 16: The length of spiral flow for the venturi accelerated vortex tool with liquid flow rate of 1-15 L/min and gas flow rates of 100–600 m\({}^{3}\)/h.  
Figure 15: The average total pressure drop of different tools in the horizontal and vertical tube with different liquid flow rates (1, 3, 5, 10, 15 L/min) and the gas flow rate of 300 m\({}^{3}\)/h.  
Figure 16 shows that with the decrease in the liquid flow rate and the increase in the gas flow rate, the length of the spiral flow gradually increases. This result can provide guidance for determining the liquid-carrying applicability of the tool under different production conditions. As shown in Figure 17, with the increase in the gas and liquid flow rates, the vacuum degree in the horizontal tube increases, and the total pressure drop in the horizontal and vertical tube increases. Similar results have also been reported by [PERSON] [24], suggesting that the vortex tool was able to reduce the wellbore pressure drop and enhance the production in gas wells. [PERSON] [18] argued that under the action of vortex downhole tools, the friction factor of the gas-liquid two-phase flow is reduced, as well as the total flow pressure drop. When the gas flow rate was 600 m\({}^{3}\)/h and the liquid flow rate increased from 1 L/min to 15 L/min, the vacuum degree in the horizontal pipe increased more than 2 times, while the total pressure drop in the horizontal and vertical pipes increased less than 2 times. In the case of a low liquid flow rate (3 L/min), when the gas flow rate increased from 100 m\({}^{3}\)/h to 600 m\({}^{3}\)/h, the overall flow resistance loss rapidly decreased by about 54%. These outcomes are in direct line with the analysis results of spiral flow length. Therefore, it can be argued that a longer spiral flow leads to a better spiral laminar flow, which reduces the flow resistance loss.
## 2 Materials and Methods
### Data Analysis
#### 2.3.2 Statistical Comparisons between Hotspots and Spectral Indices  
Linear and quadratic regressions were applied to compare sheep grazing behaviour (hotspots) with the spectral index values (Table 1) derived from the Sentinel-2 imagery. Index values were subjected to different transformations, i.e., logarithmic, reciprocal, square and square roots to find the best approximation to the normal distribution [88]. The normality of data distributions for all dependent and predictor variables was tested using normal quantile plots [89].
# Partial Discharge Source Classification in Power Transformers: A Systematic Literature Review
## 4 Recent and Related Research Works
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref. Year** & **Year** & **Data Source** & **ML Algorithm** & **PD Source** & **Summary** \\ \hline
[72] & 2019 & IET High Voltage & Artificial Neural Network & Experimental setup & Uses Artificial Neural networks to classify different discharges on outdoor insulators which were tested with the use of acoustic sensors. The results are compared to controlled samples tested in a laboratory and both achieve recognition rates higher than 85\%. \\ \hline
[73] & 2018 & IET Science, Measurement and Technology & Support Vector Regression, Artificial Neural Network & Experimental setup & Examines the use of a system of low-cost radio sensors for continuous partial discharge monitoring and develops models based on support vector regression and least squares support vector regression (LSSR), with IESVR being the recommended algorithm due to its low complexity. \\ \hline
[47] & 2018 & Analytics for Renewable Energy Integration & Convolutional Neural Network, Random Forest, Decision Tree, Support Vector Machine & Compares the classification performance of deep learning to traditional methods on three different partial discharge types which were collected using acoustic sensors on a transformer. \\ \hline
[74] & 2023 & IEEE Transactions on Power Delivery & Ridge Regression & Artificial PD Defect Model & Investigations partial discharges due to repetitive impulse excitation occurring in power electronic devices. The PD data is measured with the use of UHF sensors and processed with a ridge regression classifier improving accuracy to 98.6\% over classical deep learning models. \\ \hline
[75] & 2013 & IEEE 1 st International Conference on Condition Assessment Techniques in Electrical Systems & Support Vector Machine & Artificial PD Defect Model & Utilizes PCA to extract features from phase-resolved partial discharge patterns which are used as input to support vector machine algorithm for classifying PD data of six PD types. The highest classification results are achieved on corona discharge, discharge in oil and particle movement discharge, each at 100\% accuracy. \\ \hline
[46] & 2010 & Proceedings of the ninth international conference on machine learning and cybernetics & Artificial Neural Network & Power Transformer \\ \hline
[76] & 2023 & 8 th International Conference on Control and Robotics Engineering & Convolutional Neural Network, Support Vector Machine & PD images & Evaluates the use of a convolutional neural network for PD recognition versus traditional methods which include feature extraction steps. The results indicate that the proposed method can classify different types of PD. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Conf.
# Comparison of Nonlinear and Linear Controllers for Magnetic Levitation System
## 4 State Feedback Controller for Nonlinear Magnetic Levitation System
#### 4.1.1 Stabilizing Controller Design for Magnetic Levitation (Unit Circle)
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Angle (\(\varphi\))** & \(x_{e}\) & \(r\) & \(K_{p}\) & & \(K_{I}\) \\ \hline
70\({}^{\circ}\) & 0.7 & 0.99 & \([175.54\) & \(3.6675\) & \(-\) \(0.7527]\) & 1.0661 \\
70\({}^{\circ}\) & 0.8 & 0.99 & \([163.74\) & \(3.416\) & \(-\) \(0.4926]\) & 1.0013 \\
80\({}^{\circ}\) & 0.9 & 0.99 & \([157.76\) & \(3.2847\) & \(-\) \(0.4672]\) & 0.9927 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Parameters of LMI pole placement controllers designed for various damping angle (Angle-Ellipse region). \begin{table}
\begin{tabular}{c c c} \hline \hline \(K_{p}\) & \(K_{I}\) \\ \hline \([125.0566\) & \(2.9075\) & \(-\) \(0.7067]\) & 0.4094 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters of LMI stabilizing controller (Unit Circle). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Angle (\(\varphi\))** & \(K_{p}\) & \(K_{I}\) \\ \hline
86\({}^{\circ}\) & \([1994.1\) & \(15.552\) & \(-\) \(0.7836]\) & 79.109 \\
87\({}^{\circ}\) & \([1086.4\) & \(11.093\) & \(-\) \(0.7252]\) & 30.729 \\
88\({}^{\circ}\) & \([645.4\) & \(8.8646\) & \(-\) \(0.7411]\) & 11.552 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameters of LMI pole placement controllers designed for various damping angle (Elliptic region). only the output response but also control variable \(u\). Comparing Ellipse and AE results, control variable response for AE is significantly less agressive, the Ellipse control action has big overshoot for step change to lower value. This indicates vast control effort in practice. Figure 4: Step responses simulated on nonlinear model for the 1 st working point (small ball) with the proposed pole-placement controllers according to legend. Figure 5: Control variable responses simulated on nonlinear model for the 1 st working point (small ball) with the proposed pole-placement controllers according to legend. Figure 8: Step responses simulated on nonlinear model for the 3 rd working point (big ball) with the proposed pole-placement controllers according to legend. Figure 6: Step responses simulated on nonlinear model for the 2 nd working point (medium ball) with the proposed pole-placement controllers according to legend. Figure 7: Control variable responses simulated on nonlinear model for the 2 nd working point (medium ball) with the proposed pole-placement controllers according to legend.