esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Clustering Routing Method for Wireless Sensor Networks Considering the Locust Search Scheme
## 5 Experiments and Simulation  
### Metrics  
A simulation was carried out to measure the performance of the proposed method. The metrics used to validate the technique contemplate the network lifetime, the total residual energy, the network stability period, the number of received data packets by the sink node, and the residual energy deviation. In the following, the description of these metrics is presented in detail.  
The network lifetime considers the period in which the network starts the transmission and when the first node dies. This measure allows the analysis of the network stability in which all the sensors are alive and working. On the other hand, the network instability contemplates the period from the first dead node to the end of the network life, which occurs when all nodes are dead.  
The residual energy is determined by the sum of the residual energy of the alive nodes in the network divided by the sum of the initial energy of all nodes. This metric indicates the percentage of available energy in the network in every round and can be formulated as:  
\[p(t)=100\left(\frac{\sum_{i=1}^{N}E_{r}\left(n_{i},t\right)}{\sum_{i=1}^{N}E_{ o}\left(n_{i}\right)}\right) \tag{22}\]where \(t\) is the current round, while \(E_{r}(n_{i},t)\) is the residual energy of the node \(i\) in the round \(t\). The initial energy node \(i\) is defined by \(E_{o}(n_{i})\). Therefore, the percentage of the total residual energy is given by \(p(t)\).  
The residual energy deviation measures the difference between the node with the highest residual energy and the node with the lowest residual energy, divided by the total initial energy. This metric is expressed in percentage and is calculated as follows:  
\[D_{R}(t)=100\Bigg{(}\frac{E_{r_{max}}(\mathbf{n},t)-E_{r_{min}}(\mathbf{n},t)}{\sum_{i= 1}^{N}E_{o}(n_{i})}\Bigg{)} \tag{23}\]  
In the round \(t\), the node with the maximum residual energy in the network and the node with the minimum residual energy in the network is given by \(E_{r_{max}}(\mathbf{n},t)\) and \(E_{r_{min}}(\mathbf{n},t)\), respectively.  
The last metric considered in the experiments is the throughput, which measures the total number of packets sent to the BS by every cluster head.
# Preliminary Propulsion and Power System Design of a Tandem-Wing Long-Range eVTOL Aircraft
## 3 Results and Discussion of the Propulsion System Design
### Noise Analysis Results  
Under cruise conditions, the reference noise level \(L_{1}\) for the Wigeon was determined to be 107 dB. The tip Mach number for the Wigeon is slightly below 0.3, for which \(C_{Mach}\) is \(-19\) dB. Using Equation (12) with these values, the noise from one propeller during cruise at 100 m from the aircraft was determined to be 65.06 dB. The 100 m distance was chosen as a sample distance to evaluate the noise. Similarly, for 1000 m, the noise representing the cruise height is 45.03 dB. For the 12 propellers combined, the noise levels at 100 m and 1000 m are 75.85 dB and 55.83 dB, respectively. The semi-empirical method used was not developed for highly loaded propellers under hovering conditions, and as such, only cruise estimates were presented here. Since hovering is a critical part of the noise performance of eVTOLs, this remains an important point for future work, as mentioned in Section 3.3.
# In Situ Experimental Study of the Friction of Sea Ice and Steel on Sea Ice
## 3 Results
### Effect of Normal Stress  
The normal stress was determined by dividing the total weight of the slider, frame, and ballast by the apparent contact area. Figure 7 shows that \(\mu_{\mathrm{k}}\) varied with respect to normal stress in the cases of CS-Lateral\(\perp\), Lateral\(\perp\)-Lateral\(//\), Lateral\(\perp\)-Bottom, and Lateral\(//\)-Top, and a general trend was detected for \(\mu_{\mathrm{k}}\) to decrease with increasing normal stress. Similar trends were obtained by [PERSON] and [PERSON] [21], [PERSON] et al. [24], and [PERSON] et al. [36]; one reason is that as normal load increases, the natural roughness of both contact surfaces or that of the softer surface is smoothed, yielding lower friction coefficients [36; 37]. [PERSON] and [PERSON] [42] and [PERSON] and [PERSON] [43] found that the decrease of \(\mu_{\mathrm{k}}\) with normal stress \(P\) could be expressed as a power law \(\mu_{\mathrm{k}}\propto P^{n}\), with the power \(n\) fitted as \(-0.32\) and \(-0.25\) using laboratory tests and a theoretical model respectively. To compare the current results with these previous reports, the same power law was used to fit the current test data. The results gave values of the power \(n\) ranging from \(-0.17\) to \(-2.22\), with a mean value of \(-0.73\). Note that the objects in current study were sea ice while were fresh ice in studies of [42; 43]. The salt in sea ice makes it softer than fresh ice, which is the possible reason for the lower \(n\) obtained in the study.  
Figure 6: (**a**) U-shape friction regime on Top-Lateral\(//\) at a velocity of 0.04 m\(\cdot\)s\({}^{-1}\) and a normal stress of 4.56 kPa; (**b**) Fourier analysis showing that the frequency of load oscillations was 0.28 Hz in (**a**).  
Figure 7: Variations of \(\mu_{\mathrm{k}}\) with normal stress \(P\) for CS-Lateral\(\perp\), Lateral\(\perp\)-Lateral\(//\), Lateral\(\perp\)-Bottom, and Lateral\(//\)-Top. Also shown are the power-law regression lines (dashed lines).
# An Enhanced Precoder for Multi User Multiple-Input Multiple-Output Downlink Systems
## 3 Conventional Precoding Schemes
### Conventional BD Precoder  
The typical property for the conventional BD precoder is that IUI signal is completely eliminated by multiplying the precoding matrix by the data signal. Thus, the precoding matrix consists of null space for the channel matrix of other UEs. The conventional BD precoder obtains orthogonal vectors to IUI signal from SVD of \(\mathbf{\tilde{H}}_{i}\). The SVD of \(\mathbf{\tilde{H}}_{i}\) is given by,  
\[\mathbf{\tilde{H}}_{i}=\mathbf{\tilde{U}}_{i}\mathbf{\tilde{\Sigma}}_{i} \Big{[}\begin{array}{cc}\mathbf{\tilde{V}}_{i}^{(1)}&\mathbf{\tilde{V}}_{i}^ {(0)}\end{array}\Big{]}^{H}, \tag{8}\]  
where \(\mathbf{\tilde{U}}_{i}\in\mathbb{C}^{\big{(}N_{R}-N_{R_{i}}\big{)}\times\big{(} N_{R}-N_{R_{i}}\big{)}}\) and \(\mathbf{\tilde{\Sigma}}_{i}\in\mathbb{C}^{\big{(}N_{R}-N_{R_{i}}\big{)}\times N _{T}}\) are left singular vector matrix and singular value matrix. \(\mathbf{\tilde{V}}_{i}^{(1)}\in\mathbb{C}^{N_{T}\times L_{i}}\) is right singular vector matrix correlated with non zero singular values. \(\mathbf{\tilde{V}}_{i}^{(0)}\in N_{T}\times\big{(}N_{T}-L_{i}\big{)}\) is right singular vector matrix correlated with zero singular values. In other worlds, \(\mathbf{\tilde{V}}_{i}^{(0)}\) forms null space for \(\mathbf{\tilde{H}}_{i}\). Then, the conventional BD algorithm uses \(\mathbf{\tilde{V}}_{i}^{(0)}\) as the precoding matrix \(\mathbf{F}_{i}^{\text{BD}}\). Consequently, IUI signal is completely eliminated and received signal \(\mathbf{y}_{i}\) can be expressed as follows,  
\[\mathbf{y}_{i}=\mathbf{G}_{i}\left(\mathbf{H}_{i}\mathbf{\tilde{V}}_{i}^{(0) }\mathbf{x}_{i}+\mathbf{w}_{i}\right). \tag{9}\]  
Figure 1: The multi user multiple-input multiple-output (MIMO) downlink system model.  
Figure 2: The equivalent single user (SU) model of the \(i\)-th user equipment (UE).  
By using the BD precoder, the complex MU MIMO channel turns into parallel SU MIMO channels due to zero IUI. However, the BD algorithm wastes spatial resources by accomplishing perfect nulling and does not obtain additional BER performance by disregarding the noise element.
###### Abstract  
The thermodynamic gas hydrate suppression behavior of four Deep Eutectic Solvents (DESs) was evaluated in this paper. The mixtures of Hydrogen Bond Acceptors (HBA), Tetrahylammonium Acetate (TEAAC), and Tetraethylammonium Bromide (TEAB) with Hydrogen Bond Donors (HBD), Mono-Ethylene Glycol (MEG), and Glycerol were used to make the DES. The DESs were made at a 1:7 molar ratio for the combinations of TEAAC:MEG, TEAAC:Glycerol, TEAB:MEG, and TEAB:Glycerol. The Hydrate Liquid-Vapor Equilibrium (HLVE) data for CO\({}_{2}\) were evaluated through the T-cycle method at different temperature (273.15-283.15 K) and pressure (2-4 MPa) conditions in the presence and absence of 5 wt % aqueous DES solutions. The inhibition effects showed by the DESs, including average suppression temperature (\(\Delta\)T) and gas hydrate dissociation enthalpies (\(\Delta\)H\({}_{\text{diss}}\)), were also calculated. The average suppression temperature values of the DESs ranged between 0.4 and 2.4, with the highest inhibition to lowest inhibition order being TEAB:Glycerol \(>\) TEAB:MEG \(>\) TEAAC:Glycerol \(>\) TEAAC:MEG. A comparison of the DES with conventional Thermodynamic Hydrate Inhibitors (THIs) showed that studied Deep Eutectic Solvents had better gas hydrate inhibition. The results proved that DES has the potential to be one of the promising alternatives in gas hydrate inhibition.  
gas hydrates; deep eutectic solvents; HLVE data +
Footnote †: journal: Applied Sciences
## 6 Reviewed Papers on Machine-Learning-Based Path Loss Models
The proposed model is grounded in multidimensional Gaussian process regression (GPR), which predicts local shadow fading to give channels spatial consistency in propagation in indoor environments. To test and validate the suggested model, though, more varied environments must be used. In [93], the authors conducted path loss prediction at 7 GHz within an urban environment by employing a model-assisted deep learning approach. Their proposed model utilizes a distinct set of input features, encompassing both fundamental and engineered attributes. The numerical results demonstrate that the deep learning model outperforms the chosen empirical models in terms of prediction performance. The proposed approach must be enhanced in the context of an environment with additional obstacles because it is still a hybrid model. In similar work by [94], a viable alternative to improve path loss prediction with the use of the random forest model was proposed. The test carried out in their work showed that the use of the random forest technique with attributes such as geographical coordinates, distance, azimuth, and antenna gain presented better results than other considered models [95]. Although additional features need to be considered to train and test the model to improve its performance.
Additionally, network performance and user experience would be dramatically impacted due to the different channel surroundings caused by user mobility [45; 46; 47]. Further improvements are possible in 6G and beyond by employing opportunistic HetNet. Connections in an opportunistic HetNet are made between network equipment based on the efficiency of the possible connections [48]. When a mobile device's connection is lost, it stores the data until it finds other BSs to forward the stored data [49]. Recently, research has expanded the idea of opportunistic HetNet to include fully connected networks. Here, connections are made between nodes only after their efficacy has been ensured. Resources opportunistic HetNet discussed this scenario [50], which utilizes non-real-time traffic to search for the most beneficial connectivity opportunities. Thus, it is essential to improve throughput further to have accurate system modeling and critical evaluation of mode selection with mobility. The design of the current ultra-high frequency (UHF) wireless communication will not be able to meet the requirements of the 6G standard because the spectrums that are available in the range of 300 MHz to 3 GHz are almost fully used up and are close to reaching the Shannon limit [51; 52; 53]. Investigating the use of a high spectrum between 3 GHz and 300 GHz could be one solution to the problem of the insufficient spectrum being made available [22; 54]. Except for the ranges from 57 GHz to 64 GHz and 164 GHz to 200 GHz, there are broad frequency bands that have the potential for development. Since signals at these frequencies can be absorbed by water vapor and oxygen, these frequency bands are not suitable for HetNet [55; 56]. In addition, research is currently being conducted on THz frequency bands ranging from 300 GHz to 3 THz. These bands can offer even wider bandwidths, allowing them to accommodate future applications requiring high data rates [57; 58]. Radio networking for mmWave, which operates in higher frequency bands, will be e different from current wireless networks. Due to the high path losses, signal penetration, and blocking effects of mmWave signals [59; 60], Line of Sight (LoS) propagation, reflection, and scattering are especially important [61]. In order to improve mmWave connectivity, it is essential to limit the gap between users and BSs [62]. Because of the need for an ultra-dense network, small-cell networks can increase coverage in every unit area by being deployed in a highly concentrated manner [63; 64; 65]. Figure 2 shows the system structure of future HetNets [66].
# TransPGC: Point Cloud Geometry Compression Based on Transformers
## 3 Methods
### Encoder and Decoder Modules  
The encoder-decoder of TransPCGC primarily consists of Transformer blocks, which extract both global information and local neighborhood features from the point cloud. As the decoder shares the same module composition as the encoder, it is introduced in the following subsection together.  
The transformer encoder consists of two components. One is the attention module for mixing information among tokens and we term it a token mixer. The other component contains the remaining modules, such as channel MLPs and residual connections. The computational complexity of multi-head attention increases quadratically with the number of tokens [57]. In related work, Ref. [58], attention was replaced with Fourier Transform and still achieved an around 97% accuracy compared to vanilla Transformers. Inspired by [59], we introduced a pooling operation that does not require any learnable parameters as a token mixer to compute long-distance dependencies. This approach ensures global feature extraction while significantly reducing both computations and parameters. Additionally, we utilized convolutional layers for feature extraction. These convolutional layers preserve the spatial structure of the point cloud, and the parameter count is independent of the input point cloud's size, leading to a further reduction in parameters. Specifically, we used a convolution kernel size of \(1\times 1\times 1\). Consequently, our developed Transformer module excels at extracting local features with translation invariance using convolution operations and capturing long-distance information in sequences. We applied the proposed Transformer block to compress point cloud geometry data, resulting in promising experimental outcomes. Figure 2 illustrates the schematic diagram of the structure.  
Figure 2: Transformer block.  
First, the input \(\hat{T}_{n}^{loc}\) undergoes an input embedding process [60], where it is partitioned into patches and transformed into a token sequence, as demonstrated in Equation (3). Then, the tokens are processed by the Token Mixer within the Transformer, enabling the exchange of information between tokens, as depicted in Equation (4).  
\[Q=\text{InputEmb}\Big{(}\hat{T}_{n}^{loc}\Big{)}, \tag{3}\]  
\[G=\text{TokenMixer}(\text{Norm}(Q))+Q. \tag{4}\]  
Norm(\(\cdot\)) represents normalization techniques such as Group Normalization, Layer Normalization, and Batch Normalization. TokenMixer(\(\cdot\)) is a token mixer constructed using a pooling operation without any learnable parameters. ConvMLP consists of a convolution operation with a kernel size of \(1\times 1\times 1\), as shown in Equation (5). \(W_{1}\) and \(W_{2}\) represent the learnable parameters within ConvMLP(\(\cdot\)), and they are accompanied by a nonlinear activation function such as GELU, ReLU, or Swish.  
\[Z=\sigma(\text{Norm}(G)W_{1})W_{2}+G. \tag{5}\]
# Classification Performance of Thresholding Methods in the Mahalanobis-Taguchi System
## 2 The Concept of Mahalanobis Distance (MD)
### Type-I and Type-II Errors Method  
Several attempts have been reported in the literature to minimize Type-I and Type-II errors in finding the optimum threshold of the MTS [14; 15; 18; 31]. Generally, Type-I error is a misclassification error associated with the true normal samples when they were classified as abnormal while Type-II error occurred when the true abnormal samples were predicted as normal. For a two-classification problem, the normal samples can be regarded as positive, and the abnormal samples can be regarded as negative. Consequently, there will be four classification results such that:  
1. TP (True Positive) = an observation is positive and predicted as positive,
2. FP (False Positive) = an observation is negative but predicted as positive,
3. TN (True Negative) = an observation is negative and predicted as negative, and
4. FN (False Negative) = an observation is positive but predicted as negative.  
The four classification results could be further understood in a tabular representation as shown in Table 4 which is also known as Confusion Matrix.  
Thus, from Table 4, Type-I error is derived as \(\alpha=\frac{FN}{FN+IP}\) while Type-II error is expressed as \(\beta=\frac{FP}{TN+FP}\). To determine the optimal threshold (MD\({}_{\text{T}}\)) is to minimize the sum of \(\alpha_{Type-I}+\beta_{Type-II}\) such that:  
\[MD_{T(min)}=\alpha_{Type-I}+\beta_{Type-II} \tag{15}\]  
The optimal threshold (MD\({}_{\text{T}}\)) that minimizes the Type-I and Type-II errors could be illustrated in Figure 4:  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Predicted Class**} \\
**True Class** & **Positive** & **Negative** \\ \hline Positive & TP & FN \\ Negative & FN & TN \\ \hline \hline \end{tabular}
\end{table}
Table 4: A typical Confusion Matrix.  
Figure 4: Thresholding (MD\({}_{\text{T}}\)) point using Type-I and Type-II errors.  
Figure 3: Illustration on \(\omega\) determination when normal and abnormal Mahalanobis Distances (MDs) are overlapped.
# A Novel Dictionary-Based Image Reconstruction for Photoacoustic Computed Tomography
## 2 Methods  
### Analytical Reconstruction  
In a PACT system, after laser excitation, acoustic waves are generated. The waves are collected by ultrasonic transducers all around the imaging target, stored in a computer by a data acquisition (DAQ) card, and processed in a reconstruction algorithm. The acoustic wave generation is based on thermoelastic expansion effect. Based on the principle of acoustic theories, in a homogeneous medium, the pressure at position \(r\) and time \(t\), \(p(r,t)\), follows the thermoacoustic equation shown in Equation (1). In this equation, the initial pressure, \(p_{0}(r)\), is generated by a short pulse, which can be mathematically considered as a delta function (\(\delta\)(t)) [20].  
\[\bigtriangledown^{2}p(r,t)-\frac{1}{c^{2}}\frac{\partial^{2}}{\partial t^{2} }p(r,t)=-p_{0}(r)\frac{d\delta(t)}{dt} \tag{1}\]  
where \(c\) indicates the speed of sound. The pressure at \((r,t)\), generated from the initial pressure at \(r^{\prime}\), is given in Equation (2), known as the Forward Problem.  
\[p(r,t)=\frac{\partial}{\partial t}\bigg{[}\frac{1}{4\pi c^{3}t}\int\;dr^{ \prime}p_{0}(r^{\prime})\delta(t-\frac{|r-r^{\prime}|}{c})\bigg{]}. \tag{2}\]  
In PACT, we look for initial pressure (\(p_{0}(r^{\prime})\)) calculation using the pressure measured at different view angles/times (\(p(r,t)\)). To this end, Equation (3), known as the Backward Problem, would be used [27].  
\[p_{0}(r)=\int\bigg{[}2p(r_{0},\bar{t})-2\bar{t}\frac{\partial}{\partial\bar{t }}p(r_{0},\bar{t})\bigg{]}\frac{d\Omega_{0}}{\Omega_{0}} \tag{3}\]  
where \(\bar{t}=|r-r_{0}|\), and \(\frac{d\Omega_{0}}{\Omega_{0}}\) denotes the weight that must be allocated to the detected pressures by transducers [27]. A favorite reconstruction algorithm for the PACT system could be the one that generates a high-quality image with fewer number of view angles. Analytical algorithms such as BP and TV-based have an inherent limitation (necessity of a large number of view angles around the target object) for accurate estimation of the optical absorption. In other words, such algorithms cause artifacts in the reconstructed images when a limited number of view angles is available in the PACT system. One solution to address this problem is a model-based algorithm in which artifacts and noise are iteratively degraded using a model. It should be noted that reconstruction algorithms are mostly simplified, and the effects of transducer size, imaging noise, and directivity of sensors are not considered in the reconstruction procedure. All these assumptions lead to negative effects called artifacts in this paper.
# A Simple and Stable Atmospheric Pressure Electrodeless Water Vapor Microwave Plasma Torch
## 1 Introduction  
Non-thermal plasma is a weakly ionized plasma that can work at room temperature and atmospheric pressure while still producing highly active species and electrons. The electronic energy works well to excite molecular and atomic species and destroy chemical bonds, so the non-thermal plasma is widely used in various applications, such as surface modification [1, 2], air-cleaning [3] and assisted combustion [4], etc. When water vapor is used as the supporting gas of atmospheric pressure non-thermal plasma, it can produce reducing and oxidizing free radicals such as H, OH, and O so that the plasma can more effectively treat various hydrogen-rich compounds such as methane, natural gas, ethanol, and coal and use them as raw materials to produce hydrogen [5, 6, 7, 8, 9, 10, 11]. The commonly used atmospheric pressure non-thermal plasma sources include glow discharge, corona discharge, dielectric barrier discharge (DBD), radio frequency (RF) discharge, and microwave discharge [12, 13, 14]. Among these plasma sources, the microwave discharge is used to generate water vapor plasma, which has many advantages, because the strong interaction between 2.45 GHz microwave and water molecules can produce higher electron temperature and higher plasma density, realize high-level dissociation, and realize high-efficiency microwave power transmission from the microwave generator to the plasma [15, 16, 17, 18]. For example, Canadian researchers have studied a method based on a microwave plasma source, which uses microwaves to generate plasma to decompose water vapor into hydrogen. The energy efficiency can reach 53% [15].  
However, some issues still exist in producing continuous and lasting water vapor plasma with high microwave power. One of the problems is that the strong chemicalreaction between the active atoms produced by microwave plasma and the surface of the metal electrodes will lead to electrode corrosion, which will limit the service life of plasma devices [18,19]. Therefore, many studies have proposed electrodeless discharge devices to overcome the problem of electrode poisoning caused by electrode discharge [20], such as surface-wave-sustained discharges [21, 22, 23], microwave plasma jets [24], plasma guns [25], etc. However, when we apply liquid water to these devices, many additional devices are often added to generate water vapor plasma, such as heating and evaporation equipment or vacuum sealing device. Which undoubtedly greatly aggravates the use restriction of the device itself, and there is also the risk of water vapor condensation [15,20,26].  
Based on the above problems, a stable and simple water vapor electrodeless microwave plasma device is developed and designed in this paper. In this device, the ceramic tube can quickly absorb water after heating, and the water in the ceramic can soon evaporate and ionize on the ceramic tube top to form a water vapor plasma torch.