esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Band Unmanned Aerial Vehicle Antenna for Integrated 5G and GNSS Connectivity
## 2 Dual-Port Inverted T-Shaped MIMO Antenna and Analysis
### Total Active Reflection Coefficient  
In multi-port elements, the total active reflection coefficient (TARC) is utilized to analyze the diversity characteristics of the antenna. A multi-port element must have a TARC value of less than \(-10\) dB. Figure 17 displays the proposed FSS-based MIMO antenna with the simulation value \(\leq-10\) dB in the dual-frequency bands of 4.4-5 GHz (n78) and 1.2-1.3 GHz (GNSS).  
Figure 14: Envelope correlation coefficient of the dual-port antenna and the dual-port antenna with an FSS obtained from the radiation patterns.  
Figure 15: Diversity gain of the MIMO and MIMO + FSS antennas.
# A Novel Complex-Valued Blind Source Separation and Its Applications in Integrated Reception
## 4 Objective Function
After obtaining the unmixing matrix \(W\), the separated signal is  
\[\mathbf{y}(t)=\mathbf{Wz}(t) \tag{36}\]  
The separated matrix is  
\[\mathbf{B}=\mathbf{WV} \tag{37}\]  
The estimated mixing matrix is  
\[\hat{A}=\left(\mathbf{B}^{H}\mathbf{B}\right)^{-1}\mathbf{B}^{H} \tag{38}\]
## 5 Numerical Results
### Investigation of the Proposed Method's Performance on the IEEE 118-Bus System
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Min.** & **Avg.** &
\begin{tabular}{c} **Average** \\ **Time (s)** \\ \end{tabular} & \(N_{sqe}\) & \(Ti\) (pu)** & **CV** & **DV** & **SR (\%)** \\ \hline ICOA & 0.06061 & 0.0608 & 100.05 & 8000 & 1 & X & 90 \\ ICOA & 0.06064 & 0.0610 & 102.8 & 8000 & - & X & 90 \\ WCA & 0.060731 & 0.061371 & 132.32 & 8000 & 1.32 & X & 87 \\ SSA & 0.0639 & 0.0655 & 104.07 & 8000 & 1.04 & X & 59 \\ SFOA & 0.0619 & 0.0644 & 108.53 & 8000 & 1.08 & X & 54 \\ COA & 0.06123 & 0.06303 & 139.75 & 8000 & 1.40 & X & 48 \\ COA1 & 0.06072 & 0.062112 & 117.45 & 8000 & 1.17 & X & 88 \\ COA2 & 0.06077 & 0.062221 & 123.40 & 8000 & 1.23 & X & 67 \\ CLPSO [16] & 0.0965 & - & - & 24,000 & - & X & - \\ PSO [16] & 0.1388 & - & - & 24,000 & - & X & - \\ PSO-AIV [19] & 0.0606 & 0.0607 & 119.66 & 8000 & 1.31 & X & - \\ PSO-AACE [19] & 0.0607 & 0.0609 & 119.22 & 8000 & 1.31 & X & - \\ SPSO-AAC [19] & 0.0607 & 0.0608 & 119.16 & 8000 & 1.31 & X & - \\ PSO-CC [19] & 0.0606 & 0.0607 & 119.86 & 8000 & 1.31 & X & - \\ PSO-PG [19] & 0.0654 & 0.0656 & 119.65 & 8000 & 1.31 & X & - \\ PSO-SWT [19] & 0.0587 & 0.0608 & 58.45 & 8000 & 0.64 & X & - \\ PSO-PGSWT [19] & 0.0574 & 0.0605 & 56.43 & 8000 & 0.62 & X & - \\ PSO-IPG [19] & 0.0568 & 0.0569 & 55.62 & 8000 & 0.61 & X & - \\ QOTLBO [33] & 0.0608 & 0.0631 & - & 10,000 & - & X & - \\ TLBO [33] & 0.0613 & 0.0626 & - & 10,000 & - & X & - \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Comparison of results obtained from the IEEE 118-bus system with voltage stabilization index optimization. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study Case** & **Min** & **Avg.** &
\begin{tabular}{c} **Average** \\ **Time (s)** \\ \end{tabular} & \(N_{sqe}\) & **CV** & **DV** \\ \hline TPL & 111.2344 & 115.433 & 92.4 & 8000 & X & \\  & 112.211 & 115.768 & 94.0 & 8000 & X & \\ TVD & 0.1561 & 0.217 & 92.8 & 8000 & X & \\ \(L\) index & 0.1606 & 0.228 & 94.2 & 8000 & X & \\ \(L\) index & 0.5678 & 0.6042 & 99.8 & 8000 & X & \\  & 0.5694 & 0.6103 & 101.8 & 8000 & & X \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Results obtained by the proposed method from the IEEE 118-bus system.
# A Framework for Coverage Path Planning Optimization Based on Point Cloud for Structural Inspection
## 1 Introduction
3D point cloud data are applied for two major applications, including 3D model reconstruction and geometry quality inspection, to represent structures in search of deformations or models [22]. One of the objectives for using point clouds is the temporal comparison looking for deformations in structures in reconstructions performed in different periods. In order to compare two structures, both reconstructions must have points of interest in common [23]. Other recent literature methods include techniques, such as [24; 25] that apply GA to obtain shortest path solutions in 3D space. In [26], a GA is used to find viable paths considering radio signal intensity. However, these methods do not include any optimization in terms of image and inspection quality. Other similar methods have also been developed in the last few years for deployed 3D environments, such as Rapidly-exploring Random Tree [27] and Visibility Graph [28]. Still, none of these focused on the specific application shown here. A comprehensive review of similar methods can be found in [29]. Therefore, this research's main contribution is developing an optimization-based point cloud methodology for application in inspection tasks of large and complex structures, such as slope and dams, which require periodic inspections to verify structural changes. The technique was applied in a simulated and real scenario to show its effectiveness. The other contributions of this research work can be summarized as follows:  
* Coverage Path Planning algorithm that optimizes photometric characteristics such as intersection, intersection and incidence angle and UAV flight time using metaheuristics;
* Path change algorithm to increase the quality of points of interest for coupling 3D reconstructions;  
The rest of this article is organized as follows. Section 2 shows the proposed optimization-based point cloud methodology and the necessary mathematical foundations for developing it in a real environment. Section 3 presents the results and discussions. The final concluding remarks and ideas for future works are in Section 4.
# Engine Optimization Model for Accurate Prediction of Friction Model in Marine Dual-Fuel Engine
## 3 Engine Optimization Model  
### Overview of the Numerical Engine Model  
The optimization model developed in this study integrates WAVE 2024.1 from Realis Simulation [53], as a 1D engine simulation software, with a nonlinear optimizer in Matlab, as shown in Figure 1. The results from the optimization model are subsequently imported into an Excel tool developed by Realis Simulation, which uses the Chen-Flynn model to fit the optimized data and determine the optimal coefficients for the friction equation.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Bore (mm) & 128 \\ Stroke (mm) & 157 \\ No. of cylinders & 12 \\ Displacement (liter) & 24.24 \\ Number of valves per cylinder & 4 \\ Compression ratio & 19:1 \\ BMEP (bar) & 17.66 \\ Piston speed (m/s) & 10.99 \\ Engine speed (rpm) & 2100 \\ Brake-specific fuel consumption (g/kW.h) & 208 \\ Power-to-weight ratio (kW/Kg) & 0.329 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main engine specifications [55].  
Figure 1: Overview of the developed optimization model for FMEP prediction.  
The model has been developed to ensure high accuracy in engine calibration and to find the optimal values of the FMEP along the propeller curve. The engine model optimization finds the combination of the engine parameters, such as the turbocharger, injection system, and FMEP, to achieve a steady state at different operating points.  
The model evaluates four specific operating points along the propeller curve to predict both the FMEP and maximum in-cylinder pressure: (1) rated power at 2100 rpm; (2) maximum and minimum speeds (1900 rpm and 1300 rpm) within the maximum torque range; and (3) the minimum engine speed at the propeller curve (600 rpm). The entire FMEP prediction process is conducted in liquid fuel mode, which is then used to compute the engine performance in both dual-fuel engine modes. Once the friction model's coefficients are optimized, they can be applied directly to simulate any operating points along the engine load diagram. This approach enables more accurate friction prediction across varying load conditions and engine modes, providing a robust tool for evaluating engine performance.
# ICRSSD: Identification and Classification for Railway Structured Sensitive Data
## 5 Conclusions  
In this paper, we propose a sensitive data identification and automatic classification method for structured data in the railway domain. We classify structured data into canonical and semi-canonical types and compute attribute sensitivity using different methods tailored to each type. In addition, we propose a hybrid classification approach that combines both dynamic and static methods. Specifically, a clustering-based dynamic classification strategy is employed to classify attribute-level sensitivity, while a threshold-based static classification is used to determine the sensitivity levels of data tables and systems. This integrated approach effectively mitigates the subjectivity associated with manual threshold setting and provides greater flexibility and adaptability to diverse data scenarios. To enhance the accuracy of sensitive data identification and classification, we also developed a rule base specifically tailored to the railway domain, incorporating domain knowledge to guide the identification and classification processes more effectively. We conducted experiments on a real-world dataset from the railway domain, and the results demonstrated strong performance. Our method achieved an average accuracy of 62.03%, highlighting its effectiveness in identifying sensitive data. Furthermore, we evaluated the performance of our designed prompts for generating regular expressions. Among them, Prompt 5 achieved an impressive accuracy of 98.98%, underscoring the effectiveness of our prompt design in supporting precise and reliable regular expression generation.  
Nevertheless, there are still some limitations in our approach. One of the main limitations of our approach lies in the discrepancy between information entropy-based sensitivity calculations and expert-based sensitivity judgments. While entropy measures sensitivity based on the amount of information contained in an attribute, experts often assess sensitivity based on the attribute's semantic meaning. Consequently, this presents a significant challenge for accurately identifying and classifying sensitive attributes. Additionally, the construction of the rule base depends heavily on the expertise of professionals in the railway domain, and the current rule base remains incomplete. These represent several potential directions for future work.
## 3 Research Background
### FLAME Clustering  
The fundamental goal of data clustering is to reduce the data dimensionality and size by combining similar data points through grouping or classification. To date, there have been many algorithms for data clustering, such as FLAME. This algorithm was used to determine the clusters in the data based on their density. The algorithm is based on neighborhood relations between objects employed to force the memberships of neighboring objects in a fuzzy membership space [47]. The FLAME algorithm performs clustering in three steps [48]:  
Step 1. Extracting the structure information:  
Create a neighborhood graph to link every object with its k-nearest neighbors (kNNs).  
Estimating the density for every object based on its distance from the kNNs, and  
Classifying the objects. In general, the objects are of three kinds:  
Cluster-supporting objects (CSOs) have densities greater than those of any of their neighbors.  
Cluster outliers are objects with densities are smaller than the density of any of their neighbors, and are even smaller than the predefined threshold.  
The remainder of the objects.  
Step 2. Assigning the fuzzy membership by local approximation through:  
Initialization of fuzzy membership:  
Every CSO is allotted full and fixed membership with itself so that it will represent one cluster.  
Outliers receive full and fixed membership of the outlier group.  
The remaining objects are allotted memberships that equal the memberships of all the clusters and the outlier group.  
The fuzzy members of the three object types were adjusted using the local/neighbourhood approximation of the fuzzy membership. This iterative process updates the fuzzy membership of each object using a linear combination of the fuzzy membership of its closest neighbor.  
Step 3. The clusters were constructed using fuzzy membership. In this regard, two clustering types are common.  
One-to-one object clustering involves allocating every object to the cluster with the highest membership.  
One-to-multiple object clustering entails assigning every object to a cluster with a higher threshold than the preset threshold.
# Deep Learning-Based Estimation of Muckpile Fragmentation Using Simulated 3D Point Cloud Data
## 3 Methodology
### Deep Learning  
Deep learning is a subset of machine learning that employs neural networks to learn complex patterns and representations from large datasets. One of the challenges in applying deep learning to 3D point cloud data is that point clouds are simply unordered sets of points, lacking the regular structure found in images or voxel grids [35]. This structure difference, when compared to the regular grid pattern seen in the example of the Multi-view Stereo Pipeline illustrated in Figure 4, makes point clouds particularly challenging. PointNet is a pioneering deep learning method designed to work directly with 3D point cloud data as input [36]. While voxel grids and images have traditionally been the preferred data formats for deep learning, PointNet can effectively perform classification and segmentation tasks on unordered 3D point cloud data. When dealing with point clouds, several issues need to be addressed, such as the unordered nature of the points and the invariance to geometric transformations. To tackle the issue of unordered points, PointNet employs a symmetric function, which is invariant to the order of the input points. As illustrated in Figure 5, each point is independently transformed and processed before a max-pooling operation is applied to extract global features of the point cloud without depending on the order of the points.  
In addition to order invariance, point clouds also exhibit transformation invariance. A point cloud representing the same shape can have different positions and orientations but should be recognized as the same object. To address this issue, PointNet introduces a subnetwork called T-Net, shown in Figure 6. The T-Net estimates an affine transformation matrix that aligns the input point cloud to a canonical space, thus providing approximate invariance to translations and rotations [36]. This paper leverages the classification capabilities of PointNet to analyze blast muckpile. The proposed approach involves preprocessing the point cloud data to ensure that it is suitable for PointNet, followed by training the neural network on a labeled dataset of muckpile. The trained network can then be used to classify and segment new muckpile data, enabling the estimation of particle size distributions and other relevant properties. Several studies have demonstrated the effectiveness of PointNet in various applications, such as object recognition, scene understanding, and semantic segmentation of point clouds [24; 35; 37].  
Figure 4: Example of Multi-view Stereo Pipeline.  
Figure 5: Using Logarithmic Functions.
## 3 Case Analysis
### Evaluation and Analysis of Model Prediction Results
#### 3.4.3 Comparative Analysis with Other Combined Models
In comparison, the iTransformer-BiLSTM model, by integrating global variable features and temporal context dependencies, adapts more effectively to dynamic changes, resulting in minimized error fluctuations and a smoother overall prediction curve. Figure 14: Comparison Results between the Main Model and Other Combined Models and the Partial Diagram (Taking the First Day Mentioned Above). Figure 13: Comparison Results between the Main Model and Other Combined Models (Three Days). As a multivariate regression-based predictive model, BPNN-Adaboost relies on traditional regression mechanisms, making it less effective in capturing the dynamic characteristics of time series, which results in weaker predictive performance. Meanwhile, CNN-BiLSTM integrates a Convolutional Neural Network (CNN) for local feature extraction, enabling it to capture short-term temporal patterns. However, its limited receptive field restricts its ability to model long-term dependencies. Additionally, Transformer-BiLSTM leverages self-attention mechanisms to enhance long-sequence modeling, but its approach tends to overlook fine-grained interactions between variables, resulting in the need for improvement in prediction accuracy. The proposed iTransformer-BiLSTM model enhances feature extraction capabilities by treating each variable as an independent token in iTransformer, thereby capturing inter-variable dependencies more effectively. By integrating BiLSTM, the model simultaneously leverages both local and global information, leading to superior performance in load forecasting tasks. This approach provides a more reliable technical foundation for load prediction and energy efficiency optimization in central air conditioning systems within manufacturing plants.
# Fast Implicit Surface Reconstruction for the Radial Basis Functions Interpolant
## 4 Fast Evaluation  
### Black-Box FMM  
To efficiently evaluate the function values of the RBF implicit function \(f(\mathbf{x})\), the fast multipole method is used to compute the sums of RBFs based on the low-rank approximations. Taking the 1-D black-box FMM [23] as an example, the background of the kernel independent FMM is reviewed briefly.  
Given a set of target points \(\{x_{i}\}\) and source points \(\left\{y_{j}\right\}\), the kernel \(K(x,y)\) is expanded in a low-rank approximation using Chebyshev polynomials  
\[K(x,y)\approx\sum_{n=1}^{p}\sum_{m=1}^{p}K\Big{(}\overline{x}_{n},\overline{y }_{m}\Big{)}S_{p}(\overline{x}_{n},x)S_{p}\Big{(}\overline{y}_{m},y\Big{)} \tag{4}\]  
where \(p\) is the expansion order of series, \(\{\overline{x}_{n}\}\) and \(\left\{\overline{y}_{m}\right\}\) are Chebyshev nodes, \(S_{p}(\overline{x}_{n},x)\) is the interpolation function at node \(\overline{x}_{n}\), and \(S_{p}\Big{(}\overline{y}_{m},y\Big{)}\) is the interpolation function at node \(\overline{y}_{m}\).  
To make the expansions with a rapid convergence, the evaluation points are well separated from the source points (far field). If the evaluation points and source points (near field) are not well separated, the kernels will be evaluated directly. Then the sum of kernels can be efficiently evaluated by changing the order of computation  
\[f_{K}(x) \approx\sum_{j=1}^{N}\omega_{j}\sum_{n=1}^{p}\sum_{m=1}^{p}K\Big{(} \overline{x}_{n},\overline{y}_{m}\Big{)}S_{p}(\overline{x}_{n},x)S_{p}( \overline{y}_{m},y)\] \[\approx\underbrace{\sum_{n=1}^{p}S_{p}(\overline{x}_{n},x)\sum_{ m=1}^{p}K\Big{(}\overline{x}_{n},\overline{y}_{m}\Big{)}}_{M2L}\underbrace{ \sum_{j=1}^{N^{\prime}\varpi}\omega_{j}S_{p}\Big{(}\overline{y}_{m},y_{j} \Big{)}}_{P2M}+\underbrace{\sum_{j=1}^{N^{\prime}\varpi}\omega_{j}K\Big{(}x,y _{j}\Big{)}}_{P2P}\]  
where the number of source points \(N\) satisfies \(N=N^{far}+N^{near}\). To divide the near field and far field adaptively, the multilevel FMM approach [32] is developed for hierarchical evaluation using the hierarchical data structure, as shown in Figure 1. The source points are divided into rectangle boxes in different levels using the Quadtree data structure. The well-known FMM operations of the near field and far field are divided into several steps, including P2M, M2M, M2L, L2L, L2P and P2P. The operations correspond to a tree structure.
# Ad-BBR: Enhancing Round-Trip Time Fairness and Transmission Stability in TCP-BBR
## 5 Experiment and Evaluation
### Retransmission  
To evaluate the impact of buffer size and the number of competing streams on the retransmission rate in multi-stream contention scenarios, this experiment configures 1, 2, 4, 8, and 16 streams with bottleneck buffers set to 0.1 BDPs and 1 BDP, respectively, and plots the relationship between retransmission rate and the number of streams for each buffer size.  
Figure 15 shows that the retransmission rates of all six algorithms increase as the number of streams rises. Packet loss-based algorithms, such as Cubic and Reno, exhibit lower retransmission rates than BBR-based algorithms. This is likely because with a buffer size of 0.1 BDPs, BBR-based algorithms aggressively multiply the sending rate during proactive bandwidth probing, rapidly filling the buffer and causing quick queue buildup, which leads to higher retransmission rates. When the buffer size increases from 0.1 BDPs to 1 BDP, the retransmission rates decrease slightly, as the larger buffer makes the link more tolerant to bursty traffic. Notably, under multi-stream contention, Ad-BBR consistently maintains a low and stable retransmission rate, achieving approximately a 13% improvement over the original BBRv1 algorithm and about a 7% improvement over BBRv3.
## 5 Parametric Analysis
### Parametric Analysis and Discussion
#### 5.2.1 Coupling Effect of Sediment Thickness and Pile Parameters
However, the decrease in the resonant frequency due to the increase in the sediment thickness is not affected by the pile radius. The coupling effect of the sediment thickness and pile radius on the velocity response is presented in Figure 7. For a fixed sediment thickness, the peak value of the signal reflected by the pile tip is higher and its time delay is longer when the pile radius is larger. In addition, it is observed that the reflected signal for \(r_{\text{p}}=0.5\) m weakens at a faster rate with an increasing sediment thickness compared to that for \(r_{\text{p}}=0.4\) m. This implies that the sediment thickness would exert greater influence on the pile dynamic response as \(r_{\text{p}}\) increases. Additionally, the delay in the reflected signal due to an increase in sediment thickness remains largely unaffected by variations in pile radius. Figure 8 portrays the coupling effect of the sediment thickness and the pile's longitudinal wave velocity on the pile complex impedance. As \(v_{\text{p}}\) increases, both the oscillation amplitude and resonant frequency of the complex impedance increase. When the longitudinal wave velocity of the pile is higher, an increase in the sediment thickness would result in a more notably decreased oscillation amplitude and an unchanged resonant frequency of the pile complex impedance. The coupling effect of the sediment thickness and the pile's longitudinal wave velocity on the velocity response is providedin Figure 9. When \(v_{\text{p}}\) is higher, the signal reflected by the pile tip becomes more notable and reaches the pile head more quickly. Thisapplies to the synthetic reflected signal before the reversed reflected signal is caused by the sediment. This would cause more serious interference in the judgment of the reflected signal and the support condition at the pile tip. As the sediment thickness increases, the peak value of the signal reflected by the pile tip decreases while that of the synthetic reflected signal before the reversed reflected signal increases. These phenomena can be more easily observed in a pile with a larger \(v_{\text{p}}\). It is worth noting that the delay in the peak time of the reflected signal with the sediment thickness is not affected by \(v_{\text{p}}\). When comparing curves on the same horizontal coordinate scale, a secondary reflected signal from the pile tip is observed for those corresponding to a \(v_{\text{p}}\) of 3800 m/s. Moreover, because of the sediment interference at the bottom of the pile, the secondary reflected signal is not an ideal co-directional reflected signal like that of a fully end-bearing pile. In fact, when using the low-strain method to judge the quality of pile foundations, the first reflected signal at the bottom of the pile is more meaningful, while the reference value of the secondary reflected signal is not significant.