esa-sceva/satcom-chunk-collection huggingface dataset:
# 5G-Enabled E-Textiles Based on a Low-Profile Millimeter-Wave Textile Antenna +
## 1 Introduction  
Wireless Body Area Networks (WBANs) are widely regarded as the most scalable and convenient method of connecting wearable devices. On-body WBANs, where wearables communicate between each other on the same body, as well as off-body WBANs, where the wearables communicate with a gateway away from the body, have attracted significant research interest [1].  
Recently adopted for 5G communications, millimeter-wave (mmWave) bands are expected to enable high data-rate communications as well as energy-efficient networks based on wireless power transmission (WPT) [2]. Nevertheless, at mmWave frequencies, antennas are typically fabricated on low-loss, dielectric-stable substrates to achieve high gain and radiation efficiency. Recently, a range of textile-based antennas have been proposed for mmWave wearable applications [3, 4]. A range of e-textile fabrication methods, including the laser ablation of copper films [3], photolithography on polyimide flexible circuit filaments [4], as well as screen printing combined with heat transfer [5], have been used to realize mmWave components such as antennas, transmission lines, and capacitors on textile substrates. While the radiation efficiency of textile-based mmWave antennas remains below their non-textile counterparts, the antennas' gains, characterized through anechoic radiation pattern measurements, show the potential for high-speed communications and WPT. In most mmWave network architectures, directional antenna arrays with high gain are used [1]. Therefore, it is essential to experimentally characterize the link budget between e-textile, wearable mmWave antennas, where the highly dynamic and mobile body-centric environment may hinder effective communication between mmWave wearables.  
In this paper, a state-of-the-art, textile-based mmWave microstrip antenna implemented on a woven polyester substrate is investigated for wearable applications in line of sight and non-line of sight cases. Through empirical channel gain measurements, it is shown that e-textile mmWave antennas are suitable for operation in this environment.
# Fabrication of Alumina-Doped Optical Fiber Preforms by an MCVD-Metal Chelate Doping Method
###### Abstract  
This paper reports on the fabrication of alumina-doped preforms using a modified chemical vapor deposition (MCVD)-vapor phase chelate delivery system with Al(acac)\({}_{3}\) as the precursor. The objectives of this work are to study the deposition process, the efficiency of the fabrication process, and the quality of the fabricated fiber preforms. Two parameters are studied, the Al(acac)\({}_{3}\) sublimator temperature (T\({}_{\rm Al}\)\({}^{\circ}\)C) and the deposition direction (i.e., downstream and upstream). Other parameters such as the oxygen flow and deposition temperature are fixed. The results show that high uniformity of the refractive index difference (%RSD < 2%) and core size (%RSD < 2.4%) was obtained along the preform length using downstream deposition, while for the combined upstream and downstream deposition, the uniformity deteriorated. The process efficiency was found to be about 21% for T\({}_{\rm Al}\)\({}^{\circ}\)C of 185 \({}^{\circ}\)C and downstream deposition. From the EDX elemental analysis, the refractive index was found to increase by 0.0025 per mole percent of alumina.  
MCVD (Modified Chemical Vapor Deposition); chelate delivery system; Al(acac)\({}_{3}\); alumina; vapor phase doping; EDX (X-ray spectroscopy) Article
# An Efficient Optimization of the Monte Carlo Tree Search Algorithm for Amazons
## 1 Introduction
However, the addition of extra layer nodes means that more nodes need to be traversed and evaluated in the worst-case scenario, where pruning has not occurred, potentially leading to increased time-consuming overhead. This overhead is incurred as the algorithm strives to find the local optimal solution. * We propose using a Parallel Evaluation mechanism to address the potential shortcomings of the Move Groups strategy. By parallelizing the evaluation function for the Amazons game, we aim to accelerate the expansion process of the Monte Carlo tree search and minimize the likelihood of the Move Groups strategy falling into a local optimum. Additionally, we conducted rigorous performance analysis and game experiments. The performance analysis indicates that--compared to the traditional MCTS algorithm--the MCTS algorithm with the Move Groups strategy can enhance the playing ability of Amazons by 20-30 times, while the Parallel Evaluation optimization component can improve the playing ability of Amazons by 2-3 times. Experimental results demonstrate that--compared to the traditional MCTS algorithm--the MCTS algorithm with the MG-PEO strategy achieves an average 23% increase in the game-winning rate and exhibits stronger playing ability. The remainder of this paper is structured as follows: In Section 2, we present preliminary information about the Amazons game and discuss its relevance to the MCTS algorithm. In Section 3, we provide a detailed description of the MCTS algorithm with the MG-PEO strategy. In Section 4, we offer performance analyses of the Move Groups strategy and Parallel Evaluation strategy, respectively. Section 5 outlines a series of experimentsconducted to verify the efficiency of the MCTS algorithm with the MG-PEO strategy. Finally, conclusions and avenues for future research are summarized in Section 6.
## 3 Results
### Influence on First Order Critical Speed by the Integral Squeeze Film Bearing Damper  
The novel integral squeeze film bearing damper designed in Section 2.3 has a much lower stiffness than the rigid support and rolling bearing. This results in decreased overall stiffness of the unbalanced single-side cantilevered rotor system, so the critical speed of the system will also change. The effect on the critical speed of the rotor system is explored by changing the installation position and quantity of integral squeeze film bearing dampers. The speed measurement range is from 2200 r/min to 3000 r/min. The displacement response of the shaft at the cantilevered disk is extracted, and the critical speed can be changed under different working conditions, as shown in Figure 13 and Table 2.  
As can be seen from Figure 13 and Table 2, integral squeeze film bearing dampers can reduce the critical speed of the unbalanced single-side cantilevered rotor system. When both supports are rigidly supported, the critical speed is 2673 r/min. When the support away from the cantilevered end  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Working Condition** & **Critical Speed (r/min)** \\ \hline Two rigid supports (working condition 1) & 2673 \\ ISFBD installed away from cantilevered end (working condition 2) & 2667 \\ ISFBD installed near cantilevered end (working condition 3) & 2641 \\ Two ISFBDs (working condition 4) & 2620 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Critical speed under different working conditions.  
Figure 13: Influence of ISFBD on critical speed.  
is integral squeeze film bearing damper, the critical speed is 2667 r/min. When the support near the cantilevered end is integral squeeze film bearing damper, the critical speed is 2641 r/min. When both supports are integral squeeze film bearing dampers, the critical speed is 2620 r/min.  
It can be found that when the support away from the cantilevered end is an integral squeeze film bearing damper, the critical speed is substantially similar to when it is a rigid support. However, when the support near the cantilevered end is an integral squeeze film bearing damper and both supports are integral squeeze film bearing dampers, there are significant critical speed changes. This change in critical speed can effectively avoid resonance and reduce the vibration of the rotor system. This structure can protect the rotor for safe operation.
###### Abstract  
We present a novel approach to providing greater insight into the characteristics of an unlabelled dataset, increasing the efficiency with which labelled datasets can be created. We leverage dimension-reduction techniques in combination with autoencoders to create an efficient feature representation for image tiles derived from remote sensing satellite imagery. The proposed methodology consists of two main stages. Firstly, an autoencoder network is utilised to reduce the high-dimensional image tile data into a compact and expressive latentfeature representation. Subsequently, features are further reduced to a two-dimensional embedding space using the manifold learning algorithm Uniform Manifold Approximation and Projection (UMAP) and t-distributed Stochastic Neighbour Embedding (t-SNE). This step enables the visualization of the image tile clusters in a 2D plot, providing an intuitive and interactive representation that can be used to aid rapid and geographically distributed image labelling. To facilitate the labelling process, our approach allows users to interact with the 2D visualization and label clusters based on their domain knowledge. In cases where certain classes are not effectively separated, users can re-apply dimension reduction to interactively refine subsets of clusters and achieve better class separation, enabling a comprehensively labelled dataset. We evaluate the proposed approach on real-world remote sensing satellite image datasets and demonstrate its effectiveness in achieving accurate and efficient image tile clustering and labelling. Users actively participate in the labelling process through our interactive approach, leading to enhanced relevance of the labelled data, by allowing domain experts to contribute their expertise and enrich the dataset for improved downstream analysis and applications.  
Keywords:manifold exploration; dimension reduction; labelling samples; remote sensing data +
Footnote †: journal:
# Coverage, Rate, and Last Hop Selection in Multi-Hop Communications in Highway Scenarios
## 4 Numerical Results  
In this section, we elaborate on our numerical results. Specifically, we start characterizing the trade-offs between the data rate and ISD. Then, we proceed with the impact of the system, road traffic parameters, and the last hop selection scheme. Finally, we demonstrate the use of different propagation paths. The default system parameters are shown in Table 3.  
Figure 3: Simulation algorithm of establishing relaying communication.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Notation** & **Value** & **Description** \\ \hline \(l_{p}\) & 4.5 m & Vehicle length \\ \(t_{r}\) & 0.5 s & Minimal inter-vehicle distance \\ \(\lambda\) & 0.02 un./m & Mean vehicle density \\ \(H_{T}\) & 0.4, 0.7, 1.2 m & Antenna heights \\ \(H_{V}\) & 0.2 m & Vehicle clearance \\ \(v\) & 25 m/s & Default vehicle speed \\ \(f_{c}\) & 304.2 GHz & Carrier frequency [23] \\ \(\mathrm{P_{T_{W}}}\) & \(4.2\cdot 10^{-6}\) W & BS/vehicle antenna emitted power [29] \\ \(P_{T}\) & \(-23.7\) dBm & Emitted power in dBm [29] \\ \(N_{0}\) & \(-84\) dBm & Noise power [29] \\ \(\sigma_{min}\) & \(-56\) dBm & Minimal SINR [29] \\ \hline \hline \end{tabular}
\end{table}
Table 3: The default system parameters.
# Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
## 3 Networking Technologies toward Collaborative Intelligence
### Radio Access Technology Selection in IoV
In tedious scenarios such as the future IoV, the network slicing paradigm has been studied as technology used to solve the dynamic, isolation, and programability of mobile networks [46]. This is the layering of an infrastructure into many logical network slices (NSs) so that intelligent systems can flexibly allocate system resources to these network slices based on requirements and network conditions. [PERSON] et al. [45] proposed Dynamic Radio Access Selection and a Slice Assignment algorithm suitable for a heterogeneous network at 5G or above. Multi-attribute decision making and analytical hierarchy process are used in the selection process, which takes into account multiple entities in a network. Their approach handles overload using collaborative game theory, allowing slices to be reallocated on demand and accepting more users with sufficient service awareness. The distinction between premium and ordinary customers ensures that high-priority users receive excellent service quality. The algorithm combines SDN and network function virtualization methods to provide a comprehensive solution for ratio access selection and slice allocation. Since the above methods are proposed in IoT scenarios and slow mobile users are the experimental objects, whether they can also achieve good results in high-speed mobile IoV scenarios needs further experimentation and discussion.
## 1 Introduction
For the Spanish case, and after a long series of changes in the regulatory and legal framework of renewable energy installations in Spain (RD1699/2011, RDL 1/2012, L15/2012, OM1491/2013, RD413/2014), the regulation of self-consumption and net-metering facilities through RD900/2015 [31] implies a series of taxes that must be paid by the facilities connected to the grid when they inject power into the grid. Further information focused on self-supply and net-balance Spanish policies can be found in [32]. Figure 1: Integration of the agricultural sector into a smart grid: supply and demand-side active roles. By considering previous contributions and the lack of analysis from a multidimensional perspective regarding PV power plants connected to the grid for groundwater pumping solutions, this paper aims to:  
* Analyze and identify, from a socioeconomic, environmental, and energy perspective, the problems of current agriculture groundwater pumping systems based on fossil fuel technologies. * Propose and analyze PV solar pumping alternatives connected to the grid by including surplus energy and its injection into the grid, evaluating the possible economic profits from the sector. * Evaluate a case study based on including this alternative in a real environment and crops located in the southeast of Spain (Castilla La Mancha Region). The rest of the paper is structured as follows: Section 2 discusses the methodology, focused on a global analysis of the problem in agriculture, describing the problems and their most important impacts, as well as the process of determining the surplus energy and the possible economic return from the sale of such additional energy. Section 3 describes the case study. Results are given in Section 4, including estimations of the surplus energy and the potential economic benefits of the sale of energy. In addition, benefits provided to the agriculture sector with the integration of this solar resource are also included. Finally, conclusions are given in Section 5.
## 4 Implementation and Results
### Training and Validation  
#### 4.4.1 Model Training  
The model is trained using the random forest model, which has been very successful as a general-purpose classification and regression method. We chose the random forest algorithm as it can handle vast amounts of data, is less prone to overfitting, and can be analyzed with less time complexity [59].  
For the model to be effectively trained, we need to define thresholds for image classification and feature detection [60; 61]. In our case study, the land cover threshold has been defined based on heuristic experiences. In this study case, the threshold is defined for both the oil palm area and the distance between patches. For the area, if the alert contains 30% oil palm and contains at least 1 ha oil palm, then it could be considered deforestation related to oil palm, or else it will be considered deforestation related to other drivers. With regard to the distance between patches, only patches within distance mean superior or equal to 300 m will be clustered together. Figure 8 shows an example of an alert represented on the raster map.
## 3 Experiments
### Experimental Results
#### 3.4.3 Effects of the Dimension of Sentiment Resource Words  
Since the sentiment attention mechanism was added in the word-embedding layer, in order to verify the influence of different dimensions of sentiment resource words on the classification accuracy, we used SDNN to analyze the accuracy under different dimensions on the MR dataset. The experimental results are shown in Figure 4.  
From Figure 4, we can see that when the dimension of the word vector is less than 200, the accuracy increases rapidly with the increase of the dimension. One main reason is that the model can adjust more component parameters of the sentiment resource word vector to learn the sentiment information of the sentence along with the increase of the dimension of the word vector. However, when the dimension of the word vector exceeds 200, the classification accuracy will fluctuate instead. This phenomenon is related to the meaning of the dimension of the word vector. In a word vector, each dimension represents a deep semantic feature, which is obtained by pre-training a specific corpus. Since the parameters of a word vector remain unchanged during the training process, only the parameters of the attention mechanism associated with it are optimized. In other words, when the dimension of the word vector exceeds a certain threshold, the word vector is burdened with many irrelevant parameters of vector dimension. For the optimization of the sentiment attention mechanism, these parameters are redundant, which will affect the parameter adjustment of the sentiment attention mechanism so as to affect the learning quality of sentiment linguistic knowledge. Thus, we used 200 as the dimension of the sentiment resource word vector in the experiment.  
Figure 4: Classification of different dimensions of sentiment resource words on the MR dataset. When the dimension of the word vector is equal to 0, it means that the sentiment attention mechanism was not used in this model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **MR** & **SST** \\ \hline Bi-GRU+CNN & 83.7 & 51.2 \\ CNN+Bi-GRU & 77.0 & 46.1 \\ CNN-Bi-GRU & 81.9 & 48.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test accuracies (%) of different combinations of Bi-GRU and CNN.
# A Survey of Vehicle to Everything (V2X) Testing
## 5 V2X Testing Methods
### Field Testing  
The V2X network and its applications must be subjected to field testing and large-scale demonstration running before being officially promoted and used, such as the US's Safety Pilot Model Deployment [99], Security Credential Management System (SCMS) for vehicle-to-everything (V2X) communications [100], HarborNet [101], M-City, Safe intelligent mobility (sim\({}^{\text{TD}}\)) [102], Korea Intelligent Transportation Demonstration in Jeju Island, China's 5+2 Internet of Vehicle Pilot Zones, etc. Waymo, Google's subsidiary for automatic driving, has conducted more than four million km of road testing [103]. Tesla has used simulators to test over several million km in conjunction with actual roads. Chinese Internet companies such as Tencent and Baidu have also used simulators and road-testing methods to conduct extensive testing in demonstration zones. Unlike the traditional mobile applications, the V2X applications are deployed in the vehicles. So the tests of them need to be done in the field which is a big cost for the developer. In general, field testing requires a large number of basic network facilities and transportation facilities, test vehicles, testers, etc. So how to effectively reduce the cost of testing is particularly important [104].  
Parallel testing can effectively reduce the cost of large-scale testing [105]. It belongs to the closed-loop hardware in the loop (HIL) test, which is mainly aimed to test the communication process and applications in the vehicle network and can provide a test environment for automatic driving. As shown in Figure 8, parallel testing can build a virtual test field based on an actual test field or create a complete virtual test field. The virtual test field includes a large number of virtual vehicles and virtual host vehicles. The host vehicles establish mappings with actual test vehicles or simulation cockpits. Other virtual vehicles use augmented reality (AR), sensor deception, mapping simulator communication devices, etc., to communicate with the test vehicles or simulation cockpits. Through the mapping of this virtual test environment to the real test environment, a large-scale test for V2X is realized.
## 3 Mapping
### Image Preprocessing  
Principal component analysis (PCA) [32] was applied to 2D images to remove noise and decrease dimensionality as well as the number of features.  
Given a 2D input image (\(640\times 480\) pixels), we assumed that there were \(n=s\times s\) patches from which to obtain \(m\) pieces of sample:  
\[\{x_{1},x_{2},\ldots,x_{m}\}\in R^{n} \tag{1}\]  
For each patch, we first calculated the mean \(\mu\):  
\[\mu= \frac{1}{m}\sum_{j=1}^{m}x_{j} \tag{2}\]  
where \(x_{j}\) denoted the \(j\)th piece of sample. To obtain the eigenvectors, we calculated the covariance matrix \(\Sigma\):  
\[\Sigma= \frac{1}{m}\sum_{i=1}^{m}(x_{j}-\mu)(x_{j}-\mu)^{T} \tag{3}\]  
After obtaining the eigenvalues and eigenvectors, we selected the top \(k\) eigenvectors as the basis for the construction of a low-dimensional space. The original data was mapped into the low-dimensional space, thereby completing dimension reduction.  
For 3D images, we used the intrinsic parameters of the camera to obtain 3D point cloud coordinates of the depth values, as follows:  
\[\left[\begin{array}{c}u\\ v\\ 1\end{array}\right]=\left[\begin{array}{cccc}f&0&u_{0}&0\\ 0&f&v_{0}&0\\ 0&0&1&0\end{array}\right]\left[\begin{array}{c}X_{c}\\ Y_{c}\\ Z_{c}\\ 1\end{array}\right]=K[\mathbb{I}0]P \tag{4}\]where \(\left[u,v\right]^{T}\) were the coordinates of the image plane and also the perspective projection of points \(\mathbf{P}=\left[X_{c},Y_{c},Z_{c}\right]^{T}\) in a 3D space on the image plane, \(\mathbf{K}\) was the intrinsic parameter matrix of the camera, and \(f\) represented the focal length of the camera.  
The relationship between the world coordinate system and camera coordinate system can be expressed using the extrinsic parameters of the camera, as shown in Equation (5), using rotation matrix \(\mathbf{R}\) and translation matrix \(\mathbf{t}\). We generally assumed that the camera coordinate system applied to the first frame is the world coordinate system.  
\[\left[\begin{array}{c}X_{c}\\ Y_{c}\\ Z_{c}\\ 1\end{array}\right]=\left[\begin{array}{cccc}r_{11}&r_{12}&r_{13}&0\\ r_{21}&r_{22}&r_{23}&0\\ r_{31}&r_{32}&r_{33}&0\\ 0&0&0&1\end{array}\right]^{-1}\left[\begin{array}{cccc}1&0&0&T_{x}\\ 0&1&0&T_{y}\\ 0&0&1&T_{z}\\ 0&0&0&1\end{array}\right]\left[\begin{array}{c}X_{w}\\ Y_{w}\\ Z_{w}\\ 1\end{array}\right]=\left[R|t\right]\left[\begin{array}{c}X_{w}\\ Y_{w}\\ Z_{w}\\ 1\end{array}\right] \tag{5}\]