esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Meshless Numerical Method for Heat Conduction Studies in Particle Aggregates
## 5 Results and Discussion
### Comparison with Experimental Data  
The performance of the MLPG method with DC PSE and local refinement was compared with experimentally studied reference problems. In [32], the effective thermal conductivity of granular porous media was determined experimentally. Samples of sand were used, the void space of which was filled either with air or with water. During the present simulations, the structures were reconstructed using the Monte Carlo method, Figure 3. The mean radius of the sand is 0.9 mm with 10% deviation. The mean radius of 0.9 mm was used as the uniform particle radius for simulations. Figure 10a,b show the experimental data for the effective thermal conductivity as functions of the porosity (\(1-f_{p}\)) for the two fluids in the same porous medium. The thermal conductivity ratio of sand to water is 13, while that to air is 317. Each point represents the average results of 20 simulations with the same characteristics (porosity and ratio of thermal conductivities) and their standard deviation. Additionally, in this figure the predictions of the effective medium theory are shown. It is evident that the simulations coincide with the experimental data. Also, the DEM approach of the effective medium theory seems to predict adequately the conductivity. The SC approach appears to overestimate the result, while [PERSON]'s approach underestimates it.  
Figure 10c presents the effective thermal conductivity as a function of the conductivity ratio (k\({}_{\text{r}}\)). The experimental data were obtained from the same reference work [32], with behavior similar to the results with varying porosity. That is, the MLPG with the DC PSE method with local refinement near the interfaces produces results in excellent agreement with the experiments. DEM is also closer to the experimental data and the simulations than the rest of the analytical approaches studied here.
# Internet of Food (IoF), Tailor-Made Metal Oxide Gas Sensors to Support Tea Supply Chain
## Appendix A
\begin{table}
\begin{tabular}{c c} \hline
**RT** & **Compound Name** & **Abundance Mean** \\ \hline
5.165 &.beta-Myrcene & 932,205.5 \\
6.028 & D-Limonene & 20,772,492 \\
6.083 & Cyclobutane, 1,2-bis(1-methylethenyl)-, trans- & 28,705,656 \\
6.680 & 2-Hexenal & 350,397 \\
6.970 & 4-Methylacetbech, bis(trimtrimelysilyl) ether & 99,509 \\
7.608 &.gamma\_Terpinene & 2,668,285.5 \\
8.218 & 3-Nonen-1-yne, (Z)- & 185,302 \\
8.577 & Benzene, 1-methyl-3-(1-methylehyl)- & 1,380,619 \\
9.032 & (+)-4-Carene & 369,547.5 \\
10.477 & Decane, 2,4-dimethyl- & 59,486 \\
10.489 & Undecane, 2,7-dimethyl- & 147,641 \\
11.408 & Cyclopropaneethanol & 87,457.5 \\
11.920 & 1-Hepten-6-one, 2-methyl- & 216,541 \\
13.323 & 1-Butanol, 3-methoxy- & 82,569 \\
14.474 & 3-Hexen-1-ol & 72,691 \\
14.653 & Nonanal & 227,838 \\
15.801 & 2-Penten-1-ol, 4-methyl- & 123,248 \\
17.298 & 2,6-Octadiene-1,8-diol, 2,6-dimethyl- & 143,284 \\
18.203 & Ammonium acetate & 594,590.5 \\
18.619 & Pyrazole, 1,4-dimethyl- & 146,710 \\
18.640 & Furfurral & 251,584 \\
18.926 & Cogene & 187,151 \\
19.482 & Acetic acid, hexyl ester & 223,391 \\
20.390 & Octane, 1-azido- & 48,281 \\
20.413 & Decanal & 82,253 \\
21.002 & Benzaldehydehyde & 147,588 \\
22.121 & 1,6-Heptadiene, 2,5,5-trimethyl- & 81,076 \\
22.161 & 1,5-Heptadiene, 2,3,6-trimethyl- & 245,420 \\
23.162 & 5-Nonononic acid, methyl ester & 231,994 \\
23.204 & 1,2-Dihydrolinaol & 226,441 \\
23.628 & Cyclohexanol, 5-methyl-2-(1-methylehyl)-, acetate, (1.alpha,2.beta,5.beta.)- & 54,549 \\
23.656 & Methyl acetate & 82,100 \\
24.079 & Linalyl acetate & 3,330,821 \\
24.326 & 1,1-Cyclopropanedicarbonitrile, 2-butyl-2-methyl- & 176,787 \\
24.694 & trans-alpha.-Bergamotene & 209,911 \\
26.318 & Benzenemethanol, alpha-(1-ethenylpentyl)-alpha.-methyl- & 92,881 \\
26.945 & Arabino-Hex-1-eantid, 1,5-amylydro-2-deoxy- & 102,198 \\
27.845 & Oxiranmethanol, 2-phenyl- & 190,581 \\
27.871 & Cyclopropane, 1-bromo-2,2,3,3-tetramethyl-1-prop-1-ynyl- & 49,869 \\
28.261 & d-Menthol & 142,424.5 \\
28.595 & 2-Bromopropionic acid, 2-pentyl ester & 79,505 \\
29.070 & Cycloisolongifolene & 120,477 \\
30.001 & 2,6-Octadienal, 3,7-dimethyl-, (Z)- & 535,579.5 \\
30.495 & 2-Methyll-methylmannopyranoside & 167,380 \\
30.806 & 3-Cyclohexene-1-methanol, alpha\_alpha,4-trimethyl-, acetate & 635,343 \\
30.824 & alpha.-Terpine & 702,887 \\
31.110 & 1-Cyclohexyl-2,2-dimethyl-1-propanol acetate & 79,379 \\
31.364 & L-alpha.-Terpineol & 953,500.5 \\
31.644 & Acetic acid, 1-(R)-phenylethyl ester & 152,399.5 \\
32.078 & (-)-Carvone & 238,609 \\
32.618 &.beta.-Bisabolene & 292,323 \\
32.907 & 2,6-Octadienal, 3,7-dimethyl-, (Z)- & 517,386.5 \\
33.259 & Linalyl acetate & 180,568.5 \\
33.570 & 7,8-Dibromo-4,4,7-trimethyl-hexah dro-benzo[1,3]dioxin-2-one & 31,710 \\
33.899 & Butanoic acid, 3-methyl- & 116,192 \\
34.202 & Methyl salicylate & 140,347 \\
34.653 & 2,6-Octadien-1-ol, 2,7-dimethyl- & 735,215 \\ \hline \end{tabular}
\end{table} 
Table 10: _Cont._
# Distributed Weighted Coverage for Multi-Robot Systems in Non-Convex Environment
## 4 BUVC under Limited Communication Distance in Non-Convex Environment  
### BUVC under Non-Convex Environment  
Considering the limited communication distance in reality and the limitations of the non-convex environment of the traditional CVT, the task environment can be partitioned into convex and non-convex environments according to the geometry. A non-convex environment primarily refers to an environment with holes and large obstacles. Herein, the connection between any two points in an area is not blocked. For a strictly convex environment, based on the Lloyd algorithm, the controller can ensure that the robot asymptotically converges to the centroids of their respective Voronoi regions. However, this process is no longer applicable to an internal non-convex environment, as shown in Figure 2(a), or an edge non-convex environment, as shown in Figure 2(b). Therefore, for a non-convex task environment, the traditional CVT method needs to be improved.  
First, BUVC division under a convex environment is performed for the non-convex environment \(Q\subset R^{N}\) containing a group of robots \(P=(p_{1},p_{2},\ldots,p_{n})\). A group of divisions is obtained as follows:  
\[\left\{\begin{aligned} & V=\{v_{i}^{b,u}\}_{i=1}^{n}\\ & v_{i}^{b,u}=\{q\in Q\;||\;p_{i}-q\;||^{2}\leq\parallel\;p_{j}-q \;||^{2}-\omega_{ij}\}.\end{aligned}\right. \tag{9}\]  
Each robot computes all the discrete points within its designated region that satisfy the BUVC conditions, resulting in the partitioning of Voronoi cells.  
Next, relevant information about the non-convex region \(O_{i}\) can be obtained using moving robots. By constraining the non-convex region \(O_{i}\) for division in the convex environment \(V\), the BUVC division \(V_{o}\) under the constraint of the non-convex environment can be obtained as follows:  
\[\left\{\begin{aligned} & V_{o}=\{v_{o,i}^{b,u}\}_{i=1}^{n}\\ & v_{i}^{b,u}=\{q\in Q\;||\;p_{i}-q\;||^{2}\leq\parallel\;p_{j}-q \;||^{2}-\omega_{ij}\}.\\ & v_{i}^{b,u}=v_{i}^{b,u}\cap\mathbb{Q}_{Q}O\end{aligned}\right. \tag{10}\]  
Figure 3: Non-convex environment.  
Figure 2: Multi-robot distribution before and after introducing uncertainty and robot size wright.  
Figure 4 lists the division of the BUVC in three non-convex environments. The areas indicated by \(O\) in Figure 4a-c are holes, obstacles, and edge depressions, respectively. The small circle represents the robot, the colored area indicates the location of the robot in the Voronoi cell, and the red cross indicates the centroid of that area.
# Impacts of GPS Spoofing on Path Planning of Unmanned Surface Ships
## 1 Introduction
The authors in [20] present a structure of repulsion potential for APF, adding a rotational avoidance force, which can effectively reduce oscillations and avoid conflict when the target is near obstacles. The authors in [21] propose a modified APF method, which introduces virtual target points and changes the repulsion field function to solve the problem of unreachable target points and local extreme points to avoid obstacles in real-time. To make the target reach the goal, avoid obstacles, and avoid the target getting stuck in the local minimum, the authors in [22] propose an improved APF method by looking for an appropriate cost function. There are many papers studying GPS spoofing. The authors in [23] describe a series of attack methods based on GPS spoofing in detail. The authors in [24] study attackers using civil or military GPS receivers to spoof victims and determine the GPS spoofing parameters, such as position and signal accuracy. There are also some examples of GPS attacks. For instance, in June 2021, GPS attacks made a warship of the U.K. near a Russian naval base by mistake [25]. This incident fully shows that GPS attacks have significant impacts on ship navigation safety. Attacks are almost in every cyber or physical related field [26]. Our research interests focus on attacks on ships and the functions of ships. The motivation of this paper is to systematically study the path planning of unmanned ships impacted by the security of GPS, implement the practical applications, and promote the research of the network security of unmanned ships. The research on the influence of GPS spoofing on path planning of artificial potential fields is beneficial for unmanned shipbuilders, researchers, and students. The contributions of this paper are listed as follows:  
* To the best of our knowledge, this is the first time the influence of GPS spoofing on the path planning of unmanned ships is researched using the artificial potential field method;
* We study the security of GPS and propose a GPS attack using a low-cost software-defined radio, which can cause deviations to the result of path planning based on the artificial potential field method, leading to path yaw. Our simulation results show that this method can influence the path planning results of the artificial potential field method;We study the feasibility of using low-cost and portable GPS spoofers to implement GPS spoofing and conduct a practical test. The rest of this paper is organized as follows. Section 2 introduces the traditional and modified artificial potential field methods. Section 3 first describes the principle of GPS positioning and GPS spoofing attacks based on the APF method, and then proposes a feasible GPS spoofing attack scheme and analyzes the influence of GPS spoofing on the path planning results of unmanned ships based on the APF method. Section 4 presents experiments/simulations of a GPS spoofing attacker for the APF model of unmanned ships. Finally, we conclude this paper in Section 5.
# Video Super-Resolution Network with Gated High-Low Resolution Frames
## 5 Conclusions  
The GHLRF network designs a gated motion compensation method for high-low resolution adjacent frames, which can better improve the reconstruction effect in scenarios where there is a large amount of inter-frame motion variation, distant targets, and blurred targets. The aim is to solve the problem that the existing methods do not make full use of the high-low resolution adjacent frame information and have large motion estimation errors. A pre-initial hidden state network is designed to address the impact of unbalanced output effects that can occur in a one-way cyclical framework. In addition, a scale local hierarchical salient feature fusion network is proposed, which is used to fuse features from aligned video frames, allowing for better extraction of contextual information, locally salient features, and extracting high-frequency detail information from the edges and textures of video frames to improve the reconstruction quality of the video. The methods adopted in this paper are all based on local ideas for extraction and are not able to extract global targets better, which may result in poor reconstruction when there is large movement  
Figure 12: Comparison of the reconstruction results of \(\times\)4 on PRVTG_012 for different methods of video super-resolution.  
Figure 13: Comparison of the reconstruction results of \(\times\)4 on Hiltachi_ise5_001 for different methods of video super-resolution.  
or occlusion. In the future, it is intended to adopt global-based ideas to deal with scenes such as large motion and occlusion.  
Conceptualization, N.O. and Z.O.; funding acquisition, N.O. and L.L.; methodology, Z.O.; writing--original draft, Z.O.; writing--review and editing, Z.O. and L.L. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Natural Science Foundation of China under Grant No. 62001133. This research is supported by Guangxi Science Foundation and Talent Special Fund under Grant No. AD19110060. This research is supported by Guangxi Thousands of Young and Middle-aged University Backbone Teachers Training Program, Guangxi Natural Science Foundation under Grant No. 2017 GXNSGBA198212. This research is supported by the Guangxi Key Laboratory of Wireless Broadband Communication and Signal Processing under Grant No. GXKL06200114.  
Not applicable.  
The authors declare no conflict of interest.
# A Method of Barkhausen Noise Feature Extraction Based on an Adaptive Threshold
## 1 Introduction  
The magnetic Barkhausen noise (MBN) as a non-destructive evaluation method is mainly used to detect stress of ferromagnetic materials [1; 2; 3]. Feature extraction of MBN signals is important for stress detection of ferromagnetic materials. In order to preserve more information of the MBN signals, data of the collected MBN signals are relatively large. If a stress model is established directly with the original MBN signals, a large amount of redundant and irrelevant information will lead to a prolonged time spent on training the model and using the model for stress detection, and will reduce measurement accuracy. Therefore, how to extract MBN features is critical to the results of stress detection.  
However, previous methods for extracting MBN features require the choice of a suitable threshold so that these features can have good linearity and low dispersion. Figure 1 shows two novel features of MBN signals, namely 'pulse count' and 'event'. It was found that new features had a good linear correlation with residual stress [1]. However, the linearity and dispersion of the pulse count feature are closely related to the choice of threshold.  
Similarly, MBN features extracted by the commercial non-destructive testing instrument 3 MA are full width at 25, 50 and 75% of the maximum amplitude [2,3], which is shown in Figure 2. These three thresholds are not necessarily the best choice for stress detection or other parameter measurements.  
In this paper, a new approach has been proposed for selecting the appropriate threshold for MBN features adaptively. The criterion for selecting the threshold is the lowest standard deviation and 'overlap' of features. We use a genetic algorithm (GA), a global optimization algorithm, which is adopted to find the optimal threshold. The adaptive pulse count feature and adaptive full width feature have better linearity and stability than features of previous fixed threshold methods [1,2]. Modeling the traditional feature root mean square (RMS), mean value, energy, entropy and new adaptive pulse count feature by multivariable linear regression (MLR) and multilayer perceptron (MLP), the results obtained have proven that adaptive threshold features can effectively improve the accuracy of stress detection.  
The rest of the paper is organized as follows. In Section 2, the details of the sample, MBN measurement systems and process of adaptive feature extraction are illustrated. The results of adaptive threshold features selected by genetic algorithm and 'overlap' criterion are given in Section 3.1. The multi-threshold adaptive extraction method is introduced in Section 3.2. Adaptive pulse count feature and traditional MBN features are compared in Section 3.3. In order to verify the effectiveness of adaptive pulse count feature for stress detection, different modelling techniques are compared in Section 3.4. In Section 4, the summary and conclusion are provided.
## 6 Mobile Networks
### Evolution Toward 6G
The more precise knowledge and advanced deep integration with AI can further contribute to network management and orchestration of automation and optimization [136]. The 6G network is expected not only to fulfil unmet 5G promises but also to pave the way for the next generation of use cases. To this end, several new service classes that target more specialized use cases are being proposed, such as Human-Centric Services (HCS), Multi-Purpose Services (MPS), reliable eMBB, Mobile Broadband Reliable Low Latency (MBRLLC), or Massive Ultra-Reliable Low Latency Communication (mURLLC) [137], which will offer 10-100\(\times\) improvements in the connection QoS parameters (in comparison to 5G). The categorization, as well as the precise requirements for each new service class,are still a matter of discussion and speculation. A detailed comparison of the network evolution in terms of performance is presented in Table 3.  
In terms of spectrum allocation, the 6G operating bands are expected to overlap the ones already occupied by 5 GS, i.e., the FR1 (410-7125 MHz, sometimes split into the FR1 low band 410-1000 MHz and FR1 mid-band 1000-7125 MHz), FR2-1 (24.25-52.6 GHz), and FR2-2 (52.6-71 GHz) bands, as well as open new bands in the 5G and sub-THz range. Most often, three 6G spectrum ranges are projected (cf. Figure 1): _low_ (410-1000 MHz), _mid_ (lower mid, 1-7.1 GHz, and upper mid, 7.1-24 GHz), and _high_ (mmWave range of 24.25-92.0 GHz and sub-THz above 92 GHz and up to 300 GHz). It has to be also noted that since Release 16, 5 GS supports operation in an unlicensed spectrum, i.e., the n96 (5.925-7.125 GHz) and n263 (57.00-71.00 GHz) bands, which are potentially exploitable by 6 GS. Some sources also expect 6 GS to exploit the THz bands; however, there exist overwhelming obstacles such as i.a. high penetration loss, very poor propagation characteristics, and very high losses due to channel attenuation and scattering [141], resulting in short coverage ranges (the approximate cell radius is up to 200 m) and limiting real-life deployments in urban spaces. Moreover, operation in the THz bands requires specifically crafted transceivers [142], further questioning the possibility of the effective exploitation of this frequency range in commercial deployments.
## 5 Mirror Facet Alignment Method  
The mirror installation error consists of the axis vector error and vertex position error. In this paper, only the alignment of the axis vector error of the mirror unit was investigated. There are three reasons for this: (1) the axis vector error of the mirror unit (similar to the concentrator tracking error) affects the optical performance of the dish concentrator and is much larger than the vertex position error; (2) the vertex position error can only be completely eliminated by the translational motion of the mirror unit however the mirror facet adjustment process is a series of rotational motions (Section 2) such that it is difficult to completely eliminate the vertex position error; and (3) the truss structure in the dish concentrator will only have a certain level of accuracy determined by the manufacturing process, which controls the vertex position error.
# Smooth 3D Dubins Curves Based Mobile Data Gathering in Sparse Underwater Sensor Networks
## 2 Preliminaries and Problem Statement
The investigated 3D-smooth-curves-based MDG in this paper collects sensory data from all underwater sensors one by one with predefined 3D smooth curves. We let \(\mathcal{L}_{ij}=\int_{0}^{1}\big{|}C_{ij}(s)\big{|}\,ds\) be the length of 3D smooth curve \(C_{ij}(s)(i\
eq j)\); \(P(\mathcal{L}_{ij})\) and \(E_{i}\) denote the energy consumption of cruising along smooth curve \(\mathcal{L}_{ij}\) and the data collecting approaching the sensor node \(S_{i}\), respectively. The indicator \(k_{ij}=1\) denotes that the AUV is assigned to pass sensor node \(S_{j}\) as the the next sensor node after \(S_{i}\), and otherwise \(k_{ij}=0\). Therefore, the optimization objective of the 3D smooth curves planning-based MDG is given as  
\[\text{minimize} \mathcal{P}(k)=\sum_{i=1}^{n}\sum_{j=1}^{n}k_{ij}\cdot P( \mathcal{L}_{ij})+\sum_{i=1}^{n}E_{i},\] (2) subject to \[\sum_{i=1}^{n}k_{ij}=1,\ \ \ \forall j=1,2,\cdots,n, \tag{3}\] \[\sum_{j=1}^{n}k_{ij}=1,\ \ \ \forall i=1,2,\cdots,n,\] (4) \[C_{ij}\in 3D-Smooth-Curves,\forall i,j=1,2,\cdots,n, \tag{5}\]  
where Equation (2) is our objective function representing the total energy consumption in collecting all sensory data. For the sake of simplicity, \(E_{i}\) is neglected in the following section, as it is much smaller than the cruising energy consumption. Equations (3) and (4) guarantee that all underwater sensors are visited by the AUV exactly once. The novel constraint in Equation (5) is our focusing point of this paper. The above problem can be modeled as a type of a new DTSP and can be solved by a certain heuristic method. As far as a 3D smooth trajectory is concerned, the AUV can perform the data collection task using shorter-range acoustic communication to reduce the energy consumption in a cruising manner.
## 4 Performance Evaluation and Discussion
### Computational Complexity  
The computational complexity of our proposed model was determined by the computational complexity of forming the instantaneous QoE vector \(Q_{t}=(q_{0},q_{1},...,q_{t})\) predicted by the LSTM-QoE model. It is important to note that, at the time instant \(t\), the previous instantaneous QoE values \(\{q_{0},q_{1},...,q_{t-1}\}\) have already been predicted and cached in the memory. Since the LSTM-QoE model takes up only a very small computational overhead to predict \(q_{t}\), to form the vector \(Q_{t}\), the cumulative QoE of each second \(CQ_{t}\) can be predicted in real-time. To demonstrate this, we calculated the required computing time for training LSTM-QoE model and predicting the instantaneous QoE at the end of a session \(q_{L}\). All the timing experiments were carried out on a 18.04 Ubuntu LTS Intel i7-8750H @ 2.20 GHz and 16 GB RAM system. The LSTM-QoE model took 620.740 s to train and 0.4917 ms to predict \(q_{L}\). Furthermore, the cumulative QoE \(CQ_{L}\) prediction took 0.5103 ms. Thus, our proposed model is suitable for real-time cumulative QoE prediction.
# Impact of Texture Information on Crop Classification with Machine Learning and UAV Images
## 3 Results and Discussion
### Classification Methods  
In this study, two machine learning algorithms including RF and SVM were applied to crop classification. Recently, deep learning algorithms including convolutional neural network (CNN) were widely applied to remote sensing data classification [54; 55; 56]. Despite the promising performance of CNN, [PERSON] et al. [57] reported that the training sample size has greater effects on the accuracy of CNN than that of SVM in crop classification, indicating a need for numerous training samples for improved CNN classification performance. Furthermore, [PERSON] et al. [58] also reported that SVM with adjacent region features showed better accuracy than CNN for moderate-resolution land-cover classification. Therefore, deep learning is not always superior for all cases, and conventional machine learning algorithms can achieve classification performance comparable to, or even better than deep learning algorithms if proper spatial contextual features are combined with spectral information. To further evaluate the usefulness of texture features for crop classification, comparison with a patch-based CNN classifier will be conducted.