esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Top-\(k\) Spatial Dataset Search Processing
###### Abstract  
In this paper, we introduce two novel top-\(k\) spatial dataset search schemes, KSDS and KSDS+. The core innovation of these schemes lies in partitioning the spatial datasets into grids and assessing similarity based on the distribution of points within these grids. This approach provides a robust foundation for spatial dataset search. To optimize search performance, we have developed an optimized scheme that incorporates two key strategies: a GMBR-based optimization strategy and a pooling-based optimization strategy. These strategies are designed to filter datasets to significantly improve search efficiency. Our experimental results demonstrate that KSDS and KSDS+ can perform top-\(k\) spatial dataset searches with both high effectiveness and efficiency, outpacing existing methods in terms of search speed. In the future, our research will explore other similarity-calculation models to further accelerate processing times. Additionally, we aim to integrate privacy-preserving techniques to ensure secure dataset searches. These advancements are intended to enhance the practicality and efficiency of spatial dataset searches in real-world applications.  
spatial dataset; top-\(k\) dataset search; index; two-layer filtering Article
# Kinetic Modeling and Analysis of Fuel Element Pneumatic Lifting System in Pebble Bed Reactors
## 1 Introduction
German scholars studied the motion of fuel elements along the axial pipeline direction during the transport process [21; 22; 23] but did not take account of the complex motion behavior of highly coupled fuel elements (axial lateral shift of the vertical pipeline, collision, and friction with the pipeline wall, etc. ), nor did they carry out experimental studies of the fuel-element operation process in the pipeline. [PERSON] et al. [24] obtained the transverse coefficients \(\mathrm{C_{d}}\) and \(\mathrm{C_{y}}\) and the torque coefficient \(\mathrm{C_{m}}\) of the fuel element based on CFD and established the equations of the motion of the fuel element accordingly. [PERSON] et al. [25] performed a dynamic analysis of the motion process of the fuel element  
Figure 1: Movements of the fuel element in the pipe. and derived the equations of the motion. [PERSON] et al. [26] simulated the flow field using a structural mesh of fluid dynamics and performed a force analysis of the fuel element. However, previous studies focused on typical bends (pipe axis in the vertical plane) and did not consider atypical bends (pipe axis not in the vertical plane, as in Figure 2), which leads to a large error in the model calculation. In this paper, the kinetic model of the fuel element in an atypical bend is established in Section 2. The kinetic characteristics of the fuel element and the influence of the pipe parameters on its motion are analyzed and discussed by numerical simulations in Section 3. The experimental results are verified by building an experimental platform in Section 4. Section 5 discusses the hazards and ideas for solutions regarding collisions between fuel elements and piping. Section 6 concludes the paper with suggestions for the design and optimization of the fuel-element lifting system of a pebble-bed reactor and provides a direction for future research.
## 3 Three-Dimensional Freezing Temperature-Field Calculation Model
### Initial Temperature and Boundary Conditions  
Field measurements showed that the initial ground temperatures of the fine and medium sand layers before freezing were 8.3 \({}^{\circ}\)C and 12.0 \({}^{\circ}\)C, respectively. The field-measured brine temperature served as the thermal boundary condition for the freezing pipe during the calculation process [25]. Figure 4 shows the measured temperature change curve of the brine. Based on the measured brine temperature curve, the entire freezing process can be divided into two stages: 1 Active freezing period: During this stage, the brine temperature drops rapidly between days 0 and 12, reaching approximately \(-27\)\({}^{\circ}\)C; 2 Maintenance freezing period: In this phase, the temperature remains relatively stable, with the brine temperature remaining below \(-20\)\({}^{\circ}\)C. The difference between the temperature of the input brine and output brine is 1-3 \({}^{\circ}\)C, which is adequate to meet the excavation requirements of the open-excavation section.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**Soil Properties**} & \multirow{3}{*}{\begin{tabular}{c} **Depth of** \\ **Embedment/m** \\ \end{tabular} } & \begin{tabular}{c} **Specific Heat** \\ **Capacity of** \\ **Unfrozen Soil/** \\ **(J\(\cdot\)g\({}^{-1}\cdot\)K\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **Specific Heat** \\ **Capacity of** \\ **Frozen Soil/** \\ **(J\(\cdot\)g\({}^{-1}\cdot\)K\({}^{-1}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Thermal** \\ **Conductivity of** \\ **Unfrozen Soil/** \\ **(W m\({}^{-1}\cdot\)K\({}^{-1}\))** \\ \end{tabular} &
\begin{tabular}{c} **Coefficient of** \\ **Thermal** \\ **Conductivity of** \\ **Frozen Soil/** \\ **(W m\({}^{-1}\cdot\)K\({}^{-1}\))** \\ \end{tabular} \\ \hline Fine sand & 0–4.75 & 1.304 & 1.241 & 1.335 & 1.624 \\ medium sand & 4.75–25.91 & 1.424 & 1.181 & 1.305 & 1.854 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Thermal physical parameters of soil.  
Figure 3: Three-dimensional finite element calculation model. (**a**) Finite element calculation model. (**b**) Model grid division. (**c**) Arrangement of the soil and freezing pipes to be excavated.
# A Survey of Vehicular VLC Methodologies
## 1 Introduction
The survey also discusses emerging research areas, including realistic channel models, different vehicular VLC links, the integration of RISs, and multi-hop relaying, all of which offer promising opportunities to enhance performance, reliability, and scalability. Looking to the future, two key directions are highlighted in this survey. The utilization of digital twin technology is explored, emphasizing its role in optimizing RIS elements and enabling smart, connected, and automated VLC-based vehicles. Additionally, the application of AI and ML algorithms in vehicular VLC systems is discussed, showcasing their potential to optimize resource allocation, enhance security, and enable intelligent decision making. Furthermore, the integration of vehicular VLC with mm Wave networks is proposed as an open research topic, highlighting its potential for achieving continuous connectivity, low latency, and advanced vehicular services. The remaining parts of this paper are organized as follows: In Section 2, we explain the basics of VLC technology, explaining the main system components; we also discuss the advantages and challenges of VLC technology and explain the main applications of VLC in indoor scenarios. Section 3 provides an overview of the vehicular VLC system, explaining the motivations for its use in the vehicular field and then reviewing the existing surveys related to technical research works in the literature. Section 4 illustrates several technical challenges in vehicular VLC systems and potential solutions. Section 5 discusses vehicular VLC connections/links and applications. Section 6 provides a detailed discussion of vehicular VLC channel modeling and propagation characteristics, explaining the main differences between indoor and outdoor channel models. Section 7 discusses new research trends, related technologies, and their roles in vehicular VLC systems and gives insights into our future research work. Finally, the paper is concluded in Section 8.
# Simulation and Validation of a Steering Control Strategy for Tracked Robots
## 5 Simulation Analysis of Obstacle Avoidance Steering Control
### Center Steering Simulation Analysis
Furthermore, the tracked robot had a large skid rate at the moment of deceleration, and the entire body moved forward under the action of inertia, as shown at point C. The slip rate of the track of the robot changed within a certain range during the steering process, as shown at point D. The relevant data obtained for the tracked robot through center steering are listed in Table 2.  
Figure 11: Simulation of center steering: (**a**) strategy 1: the influence of the position relationship between the robot and the obstacles has not been considered; (**b**) strategy 2: the influence of the position relationship between the robot and the obstacle has been considered. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Point** & **Strategy 1** & **Strategy 2** \\ \hline point A & 0.36 & 0.13 \\ \hline point B & 0.01–0.1 & 0.01–0.1 \\ \hline point C & \(-1\) & \(-1\) \\ \hline point D & 0.28–0.46 & 0.25–0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The skid and slip rates of robot through in situ steering. Figure 12: Simulation data of center steering: (**a**) The theoretical and actual velocity of left track in strategy 1; (**b**) the theoretical and actual velocity of right track in strategy 1; (**c**) the theoretical and actual velocity of left track in strategy 2; (**d**) the theoretical and actual velocity of right track in strategy 2. (**e**) Skid and slip rate of two tracks in strategy 1; (**f**) skid and slip rate of two tracks in strategy 2. From the simulation results, it is evident that the use of control strategy 1 results in the tracked robot easily colliding with surrounding obstacles but takes less time. On the other hand, control strategy 2 reduces collisions with obstacles but takes more time. Additionally, both steering control strategies exhibit higher skid and slip rates.
## 1 Introduction
In addition to the established main material classes of metals, ceramics and polymers, composites, especially polymer-matrix composites (PMC), allow for a physical property tailoring using different type of fillers [7,8]. Depending on the particle size, particle shape, specific surface area and chemical nature, the following polymer matrix properties can be modified:  
* electrical and thermal conductivity
* polymer phase behavior and thermal stability
* mechanical properties like stiffness, Young's modulus, wear, fatigue, and others
* flame retardancy [9]
* density
* physical properties such as magnetic, optic, or dielectric properties. In principle, the whole bandwidth of polymer processing technology can be used for shaping, molding or replication of the polymer-based composites enabling a low cost fabrication of components and devices. On the one hand new potential applications can be realized using nanoparticles with small sizes, but on the other hand they complicate the realization of homogeneous and highly filled composites. Comprehensive books and reviews covering polymer matrix composites containing different kinds of nanosized fillers like clay, carbon nanotubes, and others, can be found in [9-11]. Depending on the synthesis conditions and the surface chemistry, the nanoparticles tend to form soft or hard agglomerates. Hard agglomerates consist of smaller particles which are connected to each other by sinter necks. They can be destroyed only by high energy milling. Soft agglomerates are accumulations of isolated particles which are connected to each other by attractive physical interactions like van-der-Waals or hydrogen bridge forces. Soft agglomerates can be disrupted into smaller particles by shear forces generating mechanical stress gradients. The interparticle interactions  
Figure 1: Schematic representation of the increasing surface area while decreasing particle size, using a 50 kg quartz cube. The cubes are not true to scale. depend mainly on the particles surface chemistry, the shape, aspect ratio and dimensionality, the interparticle distance and the polydispersity [12].
# Analysis and Design of Tunable THz 1-D Leaky-Wave Antennas Based on Nematic Liquid Crystals
## 3 Optimization and Design
### Design of the AMC Walls  
Thus far, we have assumed ideal PMC lateral walls, which, in practice, have to be realized with AMC-like materials, such as _high-impedance surfaces_ (HIS) [65, 66]). Simple and accurate design rules for HIS can be found in [65]. Here, we consider a patch unit-cell on top of a grounded slab of Zeonor of thickness \(d_{1}=45\,\upmu\)m whose design parameters are the period \(p_{1}=30\,\upmu\)m and gap \(g_{1}=8\,\upmu\)m.  
The parameters have been optimized to obtain a zero-phase reflection coefficient at the desired frequency of \(1.049\)  THz, for a TE mode propagating impinging at grazing incidence over the \(xy\)-plane (i.e., almost parallel to the propagation \(x\)-axis). It is worth noting here that the propagating TM leaky mode will have no \(E_{y}\) component; thus, it will be TE with respect to the \(y\)-axis, which corresponds to the normal axis for the AMC wall.  
The full-wave results confirm that the designed unit-cell behaves as an AMC over a narrow frequency bandwidth centered around the working frequency (see Figure 4a); since we are interested in a LWA operating at a fixed frequency, this aspect should not be an issue. The lateral walls will, thus, be modeled as _ideal_ PMC walls.
# Augmented EMTCNN: A Fast and Accurate Facial Landmark Detection Network +
## 5 Conclusions  
In this paper, we proposed a new method for extracting the 68 most popular feature points to represent facial landmarks in real time. More specifically, we first extended the original MTCNN model to increase the number of facial landmark points from 5 to 68. Then, to improve the accuracy of facial landmark extraction, we augmented the EMTCNN model by using two state-of-the-art convolution techniques--dilated convolution and CoordConv. In our experiments, we compared the number of extracted facial landmark points, processing speed, and extraction accuracy of four methods--Dlib, MTCNN, EMTCNN, and Augmented EMTCNN. The Augmented EMTCNN model extracted the 68 most popular feature points at a sufficient speed for real-time processing, and its accuracy was almost the same as the best accuracy achieved by Dlib. Our scheme can be applied to  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  &
\begin{tabular}{c} **Augmented** \\ **EMTCNN** \\ \end{tabular} & **EMTCNN** & **MTCNN** & **TCDCN** & **Dlib** \\ \hline No. of landmark points & 68 & 68 & 5 & 68 & 68 \\ \hline Speed (fps) & 68 & 70 & 99 & 23 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of extraction speeds.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**(x-axis Learning Weight/\(y\)-axis Learning Weight)**} \\ \cline{3-6}  & & **5:5** & **6:4** & **7:3** & **8:2** & **9:1** \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **Mean** \\ **normalized** \\ **distance** \\ \end{tabular} } & EMTCNN & 6.94 & 6.72 & 6.36 & **5.66** & 6.83 \\ \cline{2-6}  & Augmented EMTCNN & 5.42 & **4.65** & 5.22 & 5.43 & 6.07 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of extraction accuracy depending on weights.  
various applications that require real-time object recognition, such as face recognition in payment services and pedestrian detection in autonomous driving. In the near future, we will investigate the face segmentation model and apply its segmentation results to input images for training to further improve the extraction accuracy.  
Conceptualization, H.-W.K.; methodology, H.-W.K. and H.-J.K.; validation, H.-W.K., H.-J.K. and S.R.; formal analysis, S.R. and E.H.; data curation, H.-W.K.; writing--original draft preparation, H.-W.K. and H.-J.K.; writing--review and editing, S.R. and E.H.; visualization, H.-J.K.; supervision, E.H.; project administration, S.R. and E.H.; funding acquisition, S.R. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1F1A1060668).  
We greatly appreciate the anonymous reviewers for their comments and suggestions.  
The authors declare no conflict of interest.
# Dynamic Ensemble Selection for EEG Signal Classification in Distributed Data Environments
## 3 Datasets and Experimental Results
Among the evaluated classifiers, Gradient Boosting demonstrated the most robust and consistently high performance across varying levels of data dispersion. Notably, the unified groups strategy, particularly when applied at the measurement level, achieved the highest classification scores. In the configuration with nine local tables (9 LT), Gradient Boosting combined with measurement-level unified coalition selection achieved an F1-score of 0.987, balanced accuracy (BAacc) of 0.987, and overall classification accuracy of 0.987--the best results across all tested configurations. AdaBoost also yielded high performance, particularly with three to seven local tables, achieving F1-scores above 0.93 in multiple scenarios. In contrast, Random Forest achieved moderately strong results but consistently lagged behind the boosting methods. Logistic Regression and k-Nearest Neighbors (kNN) exhibited significantly lower performance, especially as the number of local tables increased, indicating sensitivity to data sparsity and limitations in capturing complex patterns in a decentralized setting.
## 2 Principles
### Steps of the SSST-TD TF Correlation Analysis  
To obtain the local correlation between the signals, a synchrosqueezed S transform-time series decomposition time-frequency correlation method is proposed. The main steps are as follows:  
1. For the time domain signal \(x\) and \(y\), obtain the time-frequency spectrum by the SSST.
2. Perform time series decomposition on the time-frequency spectrum. That is, the inverse Fourier transform is applied to the frequency-axis of the SSST to obtain the two-dimensional complex valued time series matrix.
3. Carry out correlation analysis on each local time \(b\) between the two-dimensional time series matrix of \(x\) and \(y\), thus getting the local correlation between the signals.
4. Observe the time-frequency distributions of the signals in the ST/SSST time-frequency spectrum and then analyze and judge the local correlation between the signals.
# SNR Enhancement of an Electrically Small Antenna Using a Non-Foster Matching Circuit
## 3 Fabrication and Measurement of Non-Foster Circuit
### Measurement of Signal-to-Noise Ratio  
As described in the introduction, the broadband S\({}_{11}\) does not guarantee the effectiveness of the NFC since the NFC itself can generate additional noise that may adversely influence a systematical figure-of-merit, SNR. With this issue in mind, we proceeded to measure SNR with and without the NFC, following the measurement setups depicted in Figure 9. The transmitted signals, originated by a signal generator, were captured by a spectrum analyzer in the receiving end. The received power and noise power read by the spectrum analyzer are denoted as (S\({}_{\text{ESA}}\), N\({}_{\text{ESA}}\)) without the NFC and (S\({}_{\text{ESA+NFC}}\), N\({}_{\text{ESA+NFC}}\)) with the NFC, respectively. With them, the SNR enhancement due to the NFC can be calculated by  
\[\text{SNR enhancement}=(S_{ESA+\text{NFC}}-S_{ESA})-(N_{ESA+\text{NFC}}+N_{ESA}) \tag{2}\]  
\(\left(S_{ESA+\text{NFC}}-S_{ESA}\right)\) means Received power enhancement, and \((N_{ESA+\text{NFC}}+N_{ESA})\) means Added noise.  
Figure 10a shows the received power recorded by the spectrum analyzer with and without the NFC. Their difference is labelled as \"Improvement\". There are some fluctuations, but the power  
Figure 8: The measurement of the reflection coefficient of the monopole and non-Foster circuit assembly: (**a**) A picture of the fabricated non-Foster circuit connected to the monopole. (**b**) The measured reflection coefficient with and without the non-Foster circuit.  
Figure 9: The measurement setup for signal-to-noise ratio measurement without and with the non-Foster circuit connected.  
received with the NFC equipped shows higher values than the antenna-only case (i.e. without the NFC) throughout the frequency range of 150-300 MHz. The average power improvement achieved by using the NFC is 17.3 dB. Subsequently, Figure 10b shows the noise power added by the NFC. It can be observed that the NFC adds fluctuating noise, which peaks at more than 7 dB for some frequency points. These fluctuations come from the thermal noise increase and decrease as the currents travel in the feedback loop of the NFC. The added noise base is around 7 dB, and for the noise-peaking frequencies, it is around 14 dB. Finally, the SNR enhancement by the NFC was calculated by subtracting the received power enhancement from the added noise power. As depicted in Figure 11, the SNR enhancement values also significantly fluctuate due to the noise fluctuation, implying that the NFC is not effective in improving the reception of the communication system for certain frequencies. The fluctuation does not exceed the stability limit but can adversely affect the SNR of the system. Figure 11 also shows a smoothed curve of SNR enhancement. It indicates that an average of 7.2 dB of SNR enhancement can be achieved if the noise fluctuation can be controlled by implementing a cooling or damping device on the NFC.
# Orthogonal Subblock Division Multiple Access for OFDM-IM-Based Multi-User VLC Systems
## 2 Osdma with DFT-Spreading and Interleaving for Bandlimited MU-VLC
### Interleaved Subcarrier Multiplexing  
In order to address the adverse effect of the low-pass system frequency response on the performance of the \(K\)-user OSDMA-MU-VLC system, interleaved subcarrier multiplexing is performed at the transmitter side. Figure 2a,b illustrate the transmitted spectra without and with interleaving for \(K=2\), respectively. For the case without interleaving, the subblocks for user 1 are distributed in the low frequency region while the subblocks for user 2 are distributed in the high frequency region. In contrast, for the case with interleaving, the subcarriers in each subblock are not adjacent each other and the subblocks of each user are distributed over the overall frequency band. Figure 2c,d show the received spectra without and with interleaving for \(K=2\). Due to the low-pass frequency response of the system, as can be seen from Figure 2c, the subblocks for user 2 suffer from much more significant power attenuation than that of user 1. As a result, user 1 might achieve a much better BER than that of user 2, leading to a remarkable BER imbalance and hence a degraded overall BER performance of the system. However, when interleaving is performed, as shown in Figure 2d, user 1 and user 2 suffer from a comparable low-pass frequency response. Consequently, user 1 and user 2 might achieve comparable BER performance, which results in a much improved overall BER performance of the system.  
Figure 2: Illustration of transmitted spectrum (**a**) without interleaving and (**b**) with interleaving, and received spectrum (**c**) without interleaving and (**d**) with interleaving.