esa-sceva/satcom-chunk-collection huggingface dataset:
# Radar-Assisted Multiple Base Station Cooperative mmWave Beam Tracking
## 4 Numerical Results
### Performance for Curvilinear Mobility Scenario  
In a curvilinear mobility scenario, the vehicle moves in a curve that is combined by uniformly accelerated mobility in two directions. We also first study the case of a single vehicle, i.e., 2 RSUs cooperate with each other to track a vehicle. For the kinematic state evolution noises, we set \(\sigma_{\theta}=0.4\), \(\sigma_{d}=2\) m, \(\sigma_{v_{y}}=1\) m/s, \(\sigma_{v_{x}}=1\) m/s and \(\sigma_{\beta}=0.5\). The AOD tracking performance and the spectral efficiency of different RSU switching methods are given in Figures 11-13, respectively. The results are similar to the case of the linear mobility scenario. It is worth noting that due to the higher nonlinearity of the vehicle curvilinear kinematic model, the AOD tracking effect will be slightly decreased compared with the linear mobility scenario, but the spectral efficiency is still improved greatly owing to multi-RSU cooperation. Specifically, when the transmit power is 20 dBm, the spectral efficiency is improved by at least 20% over the non-cooperative scheme due to multiple base station cooperation, regardless of the RSU switching method.  
Figure 11: AOD tracking performance for a single vehicle and 2 RSUs, with antenna number \(N_{t}=N_{r}=64\), \(M=16\), vehicle speed \(v_{x}=v_{y}=30\) m/s, vehicle acceleration \(a_{x}=8\) m/s\({}^{2}\), \(a_{y}=10\) m/s\({}^{2}\).  
Figure 12: AOD prediction RMSE variation for a single vehicle and 2 RSUs, with antenna number \(N_{t}=N_{r}=64\), \(M=16\), vehicle speed \(v_{x}=v_{y}=30\) m/s, vehicle acceleration \(a_{x}=8\) m/s\({}^{2}\), \(a_{y}=10\) m/s\({}^{2}\).  
Moreover, we compared the computational complexity of four schemes. For the beam traversal search scheme, i.e., the performance upper bound, the computational complexity is \(\mathrm{O}(N_{t}MLKN)\), where \(N_{t}\) denotes the number of transmitting antennas at the RSU, \(M\) denotes the number of antennas at the vehicle, \(L\) and \(K\) denote the number of RSUs and vehicles, respectively. \(N\) denotes the number of time slots. Similarly, the computational complexity of the proposed EKF-based and multi-RSU cooperation scheme is \(\mathrm{O}(N(LKn^{2}+3 LK+K))\), where \(n\) denotes the dimensionality of state variables. As a comparison, the computational complexity of the non-cooperation scheme and the DNN prediction-based scheme is \(\mathrm{O}(NLKn^{2})\) and \(\mathrm{O}((N_{train}+N)(n_{1}n_{2}+n_{2}n_{3}+n_{3}n_{4}+n_{4}))\), where \(N_{train}\) denotes the size of the training set, \(n_{1}\) and \(n_{4}\) denote the dimension of input features and output prediction. \(n_{2}\) and \(n_{3}\) denote the number of neurons in the two hidden layers. To make it more intuitive, the computational complexity is summarized in Table 2.
## 3 Results and Discussion
### Effect of Extraction Temperature  
The extraction carried out at ambient \(T\) (approximately 30 \({}^{\circ}\)C) was demonstrated to be the most efficacious (\(p<0.05\)), giving a Y\({}_{\text{TP}}\) of 16.86 \(\pm\) 1.75 mg CGAE g\({}^{-1}\) dm, whereas extractions accomplished between 40 and 80 \({}^{\circ}\)C were of lower performance (Figure 4). This fact strongly emphasized that \(T>30\)\({}^{\circ}\)C may not facilitate PP polyphenol extraction in the presence of HP-\(\beta\)-CD. Although a similar \(T\) of 35 \({}^{\circ}\)C has been proposed as the optimum for ultrasound-assisted PP polyphenol extraction [18], this finding contrasted with earlier investigations, which illustrated that the most favorable \(T\) for polyphenol extraction from PP may lie from 80 [16; 17; 19] and 90 [36] to as high as 190 \({}^{\circ}\)C [14]. However, intermediate levels of 50 \({}^{\circ}\)C have also been suggested [37].  
The aqueous solubility of sparingly soluble polyphenols, such as quercetin, has been explicitly shown to increase with increases in \(T\), in the presence of various cyclodext [38]. On this ground, the results illustrated in Figure 4 might appear paradoxical.  
However, in another study on polyphenol/cyclodextrin complexation, it was evidenced that the binding constants of polyphenol complexes with \(\alpha\)-, \(\beta\)- and \(\gamma\)-cyclodextrintrim, decreased with a rise in \(T\)[39]. In support of this finding were results from another investigation on interactions of the stilbene (E)-piceatannol with HP-\(\beta\)-CD, where a decrease in the complexation constant was observed as a response to raising \(T\)[40]. The authors ascribed this phenomenon to weakening of hydrogen bonds between the host and the guest molecule, due to heating. This hypothesis might justify the reason for reduced Y\({}_{\text{TP}}\) observed at \(T>30\)\({}^{\circ}\)C. Moreover, at \(T\) around 50-60 \({}^{\circ}\)C, there may be observed decomposition of polyphenol-CD inclusion complexes. Thus, selection of appropriate extraction \(T\) is of utmost importance [11].
## 3 IJA: Incremental Join Algorithm
### General Environment  
Suppose a sensor network consisting of \(N\) sensor nodes. We assume there are two virtual tables in the sensor network, \(R\) and \(S\), containing sensor readings distributed in sensors. Each sensor reading is a pair with two mandatory attributes, timestamp and sensorID, indicating the time and the sensor at which the pair is generated. A sensor reading may contain other attributes that are measurements generated by a sensor or multiple sensors, e.g., temperature, autoID. We are interested in the evaluation of static one-shot binary equi-join queries in sensor networks. We assume that \(R\) and \(S\) are stored in two sets of sensor nodes \(N_{R}\) and \(N_{S}\) located in to distinct regions known as R and S respectively. A BEJ query can be issued from any sensor node called query sink, which is responsible for collecting the join result. A set of nodes is required to process the join collaboratively, referred to as join nodes.  
When a join query is issued, a join node selection process is initiated to find a set of join nodes \(N_{F}\) to perform the join. \(R\) pairs are routed to a join region \(F\) where the join nodes \(N_{F}\) reside in. Each join node \(n_{f}\in\)\(N_{F}\) stores a horizontal partition of the table \(R\), denoted as \(R_{f}\). \(S\) pairs are transmitted to and broadcast in \(F\). Each join node \(n_{f}\) receives a copy of \(S\) and processes local join \(R_{f}\)\(\infty\)\(S\). The query sink obtains the join results by collecting the partial join results at each \(n_{f}\).  
The selection of \(N_{F}\) is critical to the join performance. Join node selection involves selecting the number of nodes in \(N_{F}\), denoted by \(|N_{F}|\), and the location of the join region \(F\). To avoid memory overflow, assuming \(R\) is evenly distributed in \(N_{F}\), \(|N_{F}|\) should be at least \(|R|/m\), where \(|R|\) denotes the number of pairs in \(R\) and \(m\) denotes the maximum number of \(R\) pairs a join node \(n_{f}\) can store. For the rest, it carries out the experiments in the same condition with previous researches.
# Simulation of Partial Discharge Induced EM Waves Using FDTD Method--A Parametric Study +
## 5 Cavity Discharge Simulation Results  
The cavity discharge was simulated by introducing a current filament flowing in the vertical direction in the middle of the XLPE sample. The PD path was varied between the minimum value of 1.5 mm, constrained by the FDTD cell size within the material, and the maximum value of 2.5 mm was constrained by the sample thickness. The non-uniform field, point-plate electrode configuration was used for this analysis. Figure 11 shows examples of typical time-domain signals generated by a 1-A pulse, with a width of 1 ns and a path length of 2 mm, demonstrating the need to place the UHF sensor closer to the discharge for increased sensitivity.  
The main parameters that were varied in this case are the pulse width, the PD pulse path and the UHF sensor location. The PD pulse amplitude was kept constant at 1 A. The results for two different UHF sensor positions are shown in Figure 12. First, it should be noted that the amplitudes of the radiated signals from the cavity discharge are much smaller than those obtained with surface discharge. This is because the EM waves propagate through the XLPE insulation and the surrounding air medium, which causes further attenuation. In practice, additional attenuation due to metallic enclosures in cable joints and cable metallic sheaths makes the detection of PD from internal cavities even more challenging because they shield the EM waves from the outside medium. The results show signal amplitudes increasing with PD path, with the highest increase being associated with fast PD pulses. A combination of measurements and FDTD simulations would enable a better understanding of the cavity discharge characteristics, such as deducing the PD pulse shape that would best fit the measurement results.  
Figure 11: Typical time-domain signals captured by the UHF sensor, cavity discharge, pulse width = 1 ns, pulse path = 2 mm, Pulse amplitude = 1 A.
## 4 Results and Discussion  
In this section, the influence of the azimuth axis tilt error on the tracking performance of an SDC system is studied in detail. The SPA [28] algorithm is used to calculate the solar position changes in different regions (Dunhuang, Naqu, Delingha, Kunming, Baotou, and Haerbin) during the summer solstice, as shown in Figure 4. Firstly, the influence of the radial and tangential angles of azimuth axis on the tracking performance of the SDC system is studied. Secondly, under the influence of azimuth axis error, the influence of the initial tracking position of SDC system on its tracking performance is studied. Then, the tracking performance variation law of an SDC system operating in different regions is studied. Finally, the tracking performance variation law of an SDC system running in a typical region (Dunhuang) for a long time (one whole year) is studied.  
Figure 4: Variation of solar position on the summer solstice in different regions: (**a**) azimuth angle; and (**b**) elevation angle.  
Figure 3: Geometric diagram of the effective tracking factor of an SDC system [29].
# Adaptive Clustering of Users in Power Domain NOMA
### Proposed Solution and Contributions  
Although several proposed approaches are in the literature, the NOMA approach was not included in the fifth generation of mobile systems (5G). In fact, the viability of NOMA systems depends on the efficiency of the techniques used for user clustering and power allocation. However, finding efficient clustering and power allocation approaches that suit different communication scenarios remains challenging. In this context, a fundamental attribute of the user-clustering method is that it can dynamically adapt the clusters to the dynamics of the communication systems, whether due to the entry of new users into the network or the variation of the communication channel over time. To the authors' knowledge, this has not been considered in the literature.  
Thus, in this work, we present an adaptive user clustering, which uses an evolutionary algorithm based on the DenStream algorithm in one of its stages. Evolutionary algorithms allow the creation of new clusters and their adaptation, i.e., it is adaptable to the number of users on the network and to the variation of the state of each user's channel, which is the feature used to perform the clustering.  
For the power allocation, the IFSPA algorithm is used. This algorithm is a dynamic method that is suitable for the mobile environment. The IFSPA has already proved to be the power allocation algorithm that best reconciles sum-rate capacity, fairness and bit error rate (BER) [7]. Next, the contributions of this work are detailed:  
* We propose using an adaptive clustering method, which considers the time-varying behavior of a mobile network, to increase the overall data rate of a NOMA downlink.
* We use an algorithm based on the DenStream to develop an adaptive and evolutionary clustering strategy, which adjusts to temporal variations in the channel state of each network user and the arrival of new users to the network, considering a downlink NOMA.
* We propose using a global fading to model the channel of each user, which considers a non-line-of-sight large-scale fading, modeled by COST 231 Hata, and a small-scale fading, modeled by the complex symmetric Gaussian.
* We evaluate the performance of the proposed solution for clustering and power allocation in downlink NOMA systems through the overall throughput.
# A Lightweight Entropy-Curvature-Based Attention Mechanism for Meningioma Segmentation in MRI Images
## 4 Experiments
### Visualization of the Segmentation Results
#### 4.3.2 Result on the BraTS2021 Dataset  
In this section, we show a comparison of the prediction results of the ECA model with other models on the BraTS2021 dataset, as shown in Figure 7. This figure shows the visual segmentation results of different models on the BraTS2021 dataset. Green, red, and yellow are used in the image to mark WT, TC, and ET, respectively. ECA showed high accuracy in the segmentations of all three regions. Compared with other models, the ECA model is able to capture the edges and shapes of the tumor more accurately, especially in the segmentation of the ET region. UNETR, SwinUNETR, and nnFormer may focus too much on capturing global information and the lack of local details, resulting in the accuracy in some regions, especially at the boundary between the TC and the ET, where the segmentation results of the model may not be sufficiently fine, with insufficient edge smoothing. dResU-Net and CU-Net are inadequate for edge extraction.
# Substrate-Driven Atomic Layer Deposition of High-\(\kappa\) Dielectrics on 2D Materials
## 4 Challenges for Applications
Figure 13 shows the comparison of the transfer characteristics (I\({}_{\text{D}}\)-V\({}_{\text{G}}\)) of three different kinds of back-gated field effect transistors on a SiO\({}_{2}\)/Si substrate, fabricated using (1) graphene monolayers without Al\({}_{2}\)O\({}_{3}\) on top (no Al\({}_{2}\)O\({}_{3}\)), (2) graphene monolayers with Al\({}_{2}\)O\({}_{3}\) deposited after being transferred onto SiO\({}_{2}\) (Al\({}_{2}\)O\({}_{3}\) ALD after transfer), and (3) graphene monolayers with Al\({}_{2}\)O\({}_{3}\) deposited before the transfer (Al\({}_{2}\)O\({}_{3}\) ALD before transfer). It is noteworthy that the third approach guarantees both very low doping of graphene (i.e., a charge neutrality point very close to V\({}_{\text{G}}\) = 0 V) and reduced hysteresis of the characteristics, which are attributable to the significantly reduced charges at the interface. These improved electrical performances have been correlated to the reduced contaminations at the Al\({}_{2}\)O\({}_{3}\)/graphene interface, since the direct contact between graphene and PMMA is avoided in this case. In this regard, a double application of the Al\({}_{2}\)O\({}_{3}\) layer deposited by ALD on the graphene on copper prior to the transfer process to SiO\({}_{2}\) substrates has been proposed. Aside from working as a protective layer for graphene during the transfer from copper and for passivation of the back-gated graphene transistors, the deposited high-\(\kappa\) films can be exploited as active layers (e.g., as dielectrics of top-gated field transistors or as a base collector barrier in graphene base hot electron transistors (GBHETs)). In this respect, the most crucial issue is the optimization of handling procedures to avoid damage or cracking of the high-\(\kappa\)/graphene stack during the transfer. Figure 12: Schematic representation of the transfer process from copper to the required substrate of the dielectric/graphene stack. Image adapted with permission from [75], copyright IOP, 2017. Similarly, the transfer of the dielectric/MoS2 stack from Au to an insulating substrate is mandatory for the field effect transistor applications of this system. To date, several authors demonstrated the transfer of large-area 1 L MoS2 exfoliated on Au to insulating substrates (using very similar methods to those used for CVD-grown graphene on copper), and excellent electronic and optical quality has been shown for the transferred 1 L MoS2 membranes [72; 76]. However, the transfer of Al2O3-coated 1 L MoS2 from Au to other substrates has not been reported so far. The transfer from the metal substrate is not required for other recently investigated devices, such as MoS2 memristors [77], exploiting the properties of Au-supported 1 L MoS2 membranes for non-volatile memory applications.
# Predicting Models for Local Sedimentary Basin Effect Using a Convolutional Neural Network
## 1 Introduction
[17] develop a ground motion amplification model based on three machine learning techniques (i.e., random forest, gradient boosting, and artificial neural network) using the record database of KiK-net site in Japan, and propose that machine learning is superior to the regression method. The data-driven method based on numerical simulation is widely used in machine learning to address the limitation of the lack of real observational data. [PERSON] et al. [14] adopt the fuzzy logic method and flexible interference rules to eliminate the uncertainty caused by the deviation of local data and established the peak ground acceleration prediction model of the Iranian plateau, which can also be improved and updated according to the newly added data. [PERSON] et al. [18] used a machine learning-based ensemble model to predict radon time series anomalies in earthquake likelihood. However, the traditional artificial neural network also has some shortcomings, such as slow convergence speed, local extreme value, low quantization accuracy, and data over-fitting. In addition, with the continuous expansion of the data set, the low computing speed of the traditional neural network similar to backpropagation neural network can no longer meet the computing needs. CNN has attracted wide attention due to its powerful feature learning and recognition ability. It is not a simple memory superposition but a comprehensive evaluation of training data, which has a certain degree of fault tolerance and has important application value. Based on this, this paper aims to establish an accurate and efficient model for predicting basin amplification characteristics using CNN. Considering that the one-dimensional (1D) basin model will underestimate the amplification value, and the computational cost of the 3D model is high and time-consuming,we used the simplified standard basin model and the commercial finite element software ABAQUS (version 6.14) to establish a series of 2D basin models, and relied on its PYTHON (version 2.7) interface for secondary development to realize batch modeling to quickly build the CNN sample database. In order to study the site effect, the key parameters affecting the basin effect were taken as the CNN input, and the basin amplification characteristics represented by the peak value of the amplification coefficient and the hazard location were taken as the CNN output to establish the prediction model. To improve the accuracy of prediction, this paper built a CNN model combined with a Bayesian optimization algorithm, analyzed the sensitivity of input parameters, optimized the input parameters to build the optimal CNN model, and used the CNN model to identify the amplification characteristics of basins. Finally, a reasonable surface amplification prediction model for local sedimentary basins was established, which achieved the purpose of rapid surface amplification prediction and provided a rapid and effective prediction method for the next step of seismic hazard assessment and seismic design.
# Design and Control of an Ultra-Low-Cost Logistic Delivery Fixed-Wing UAV
## 3 Overview of Image Recognition Technology
### Target Identification Applications  
#### 3.3.1 The Challenge and Demand of Target Identification  
As shown in Figure 24, the UAV delivery task uses a UAV to simulate a logistic delivery scenario and accurately deliver simulated packages to ground targets. The mission is characterized as follows:  
UAVs are required to complete reconnaissance and delivery tasks in an autonomous flight mode, without the use of any devices for real-time control or autonomous flights;  
The flight speed and altitude of each UAV are restricted, necessitating quick target identification and localization within a short timeframe;  
The target may be stationary or moving, and it may have various shapes, colors, and textures, potentially with obstructions and interference, requiring high recognition accuracy and robustness;  
The UAV's camera may be affected by lighting, noise, motion blur, etc., requiring image preprocessing and enhancement.  
Given these characteristics, the target recognition system in this study needs to meet the following requirements:  
Figure 23: Culture of convolutional neural networks.  
Figure 24: Schematic diagram of the throwing-task flow.  
Capable for detecting the target's position and size in real-time video streams and outputting the target's coordinates and framework;  
Able to recognize the target's category, outputting the target's label and confidence level;  
Adaptable to different targets and scenarios, with strong generalization capability and robustness;  
Able to operate with limited computational resources and storage space, ensuring high efficiency and portability.
## 3 Finite Element Analysis of a Railway Track Slab with a Gap
### Numerical Modeling of Track Slab
The element size is critically important for this kind of dynamic analysis problem using FEM. If element dimensions are too large, high frequencies are filtered. On the other hand, those that are too small cause numerical instability and require considerable computational resources [20; 24; 25]. The proper size of the finite element should be determined by taking into account the lowest velocity and the highest frequency of the wave propagation. Thus, the proper finite element size \(\Delta x\) must be decided by considering the minimum wavelength of a propagating wave (\(\lambda_{\text{min}}\)), the minimum wave velocity of the medium (\(V_{\text{min}}\)), and the highest frequency of a propagating wave (\(f_{\text{max}}\)). The maximum frequency of interest of a wave propagating into air generated by impact load was assumed to be 1700 H\({}_{\text{Z}}\). The maximum frequency (\(f_{\text{max}}\)) of a wave propagating into air was observed from field impact load test on a model concrete slab [26]. Therefore, the element size \(\Delta x\) can be computed based on the proposed criteria as the following given by [PERSON] et al. [27]. \[\Delta x\leq\text{C}\cdot\lambda_{min}\]  
where \(\lambda_{min}\) is the minimum wavelength of a propagating wave. C is a constant (C = 0.25 for consistent mass matrices whereas C = 0.2 for lumped mass matrices). Thus, considering the highest frequency range of a propagating wave generated by an impact load in air as described, the element size \(\Delta x\) = 0.05 m was selected for FEA where acoustic wave speed was 340 m/s at 15 \({}^{\circ}\)C.  
Infinite elements were applied to boundaries of the finite element mesh in order to reduce the influence of reflected waves induced by the impact load. For investigating the differences of transmitted leaky waves into the air from the concrete slab surface and elastic stress waves in the concrete body with a gap or without a gap, a ball-shaped gap assumed as the gap with a diameter of 1.0 m was simulated to be generated exactly underneath the track slab and at the top of the track bed foundation soil, as shown in Figure 1.  
Table 1 describes the input parameter values for the FEA modeling of railway track section. Leaky Rayleigh waves and stress waves were generated by the impact load applied at the center of the top surface of the concrete slab. Some of the stress waves generated in the concrete leaked and transmitted into the air. The transmitted leaked waves in the air and the stress waves generated in the concrete slab were obtained first in the time domain and were converted to the frequency domain by fast Fourier transform (FFT).