esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Analysis
### Simulation Assumptions  
The simulations were conducted using MATLAB, focusing on an interference-limited environment. In this study, we conducted 500 simulations per scenario to balance computational efficiency and result reliability. All the assumptions are justified and compared with the existing work with similar assumptions that have been referenced. All the simulation parameters are listed in Table 4. The following assumptions have been made for the assessment of the proposed dynamic DOSS EE strategy. Rayleigh fading channel is considered in this research which provides significant tractability and simplicity in the analysis of stochastic geometry-based HetNets.  
The effect of long-term shadowing has been ignored for the sake of reducing computational complexity. The long-term shadowing effect is usually ignored in much existing research [37; 53]. However, authors in [54], showed that the accuracy of the long-term shadowing effect can be overcome by using Monte Carlo simulations. A universal frequency reuse pattern is adopted. It means that all BS in the entire network can access all the bandwidth available. Single Input Single Output antenna transmission is used. It should be noted that a lot of similar existing work of HetNets ignores the impact of MIMO [37].  
Figure 6: Traditional network topology (**a**) Vs. Real HetNet topology (**b**).  
Figure 7: Wireless Network channel model.
## 3 A Machine Learning Approach for Predicting Dementia  
The research proposes a novel approach referred to as the Neighbourhood Component Analysis and Correlation-Based Filtration (NCA-F) method for the selection of the important primary features for the prediction of dementia. The research highlighted the impact of selected features in the early-stage detection of dementia. The ML-based model relies on a technique that enhances the feature reduction and selection processes of the relevant features. The process of combining cognitive and neuroimaging features resulted in the formulation of dementia biomarkers. Figure 1 provides an overview of the three-stage methodology used in this research.  
As this research focuses on predicting dementia, neuroimaging measurements, along with cognitive scores, have been employed. Neuroimaging measurements and cognitive scores are considered sensitive features [14]. For the prediction of dementia, a diverse set of ML algorithms was selected based on their compatibility with our data. Specifically:  
1. SVM: This was chosen for its suitability for high-dimensional data.
2. ANN: An advanced algorithm optimized and parameterized for high feature numbers.
3. NB: A probabilistic method ideal for smaller datasets due to its assumption of feature independence.
4. AdBE: An ensemble method that improves upon decision trees by emphasizing corrections from previous iterations.  
These algorithms, AdaBoost Ensemble (AdBE), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Naive Bayes (NB), were trained for multiple combinations of features.  
In addition to neuroimaging measurements, we leveraged cognitive test scores as an integral part of our feature set. While neuroimaging provides detailed structural and functional insights into the brain, cognitive tests offer a more accessible and immediate means of assessing an individual's cognitive functions. These tests are not only easier to administer but also critical in real-world clinical settings where quick and non-invasive evaluations are often necessary. By combining both of these types of data, we aimed to provide a comprehensive and clinically relevant model for dementia prediction. Furthermore, the inclusion of non-imaging cognitive tests from the ADNI dataset ensures that our research remains pertinent to a broader range of clinical scenarios beyond just those with imaging facilities.  
Figure 1: Overview of the proposed research methodology.
## 4 Performance Analysis
### Ergodic Capacity Analysis  
The EC is defined as \(\bar{C}=\mathbb{E}[\log_{2}(1+c\gamma)]\), where \(e=1\) for \(r=1\) and \(e=e/2\pi\) for \(r=2\). More specifically, the EC can be expressed in terms of \(\gamma_{e2e}\) by using integration by parts as follows:  
\[\bar{C}=\frac{1}{2}\int_{0}^{\infty}\ln(1+e\gamma)f_{\gamma_{e2e} }(\gamma)d\gamma. \tag{16}\]  
Now, substituting (6) into (16) and using Equation (4.293/10) in [16], Equation (1.1) in [12], a closed-form expression of the EC for the proposed system can be obtained as  
\[\bar{C}=\frac{\Delta_{1}}{2}H_{4,4}^{4,2}\Bigg{[}\frac{\theta_{1 }}{e\bar{\gamma}_{1}}\Bigg{|}\begin{matrix}(0,1)(1-b_{1},r_{1})\big{(}1+z_{1} ^{2},r_{1})(1,1)\\ (a_{1},r_{1})\big{(}z_{1}^{2},r_{1})\big{(}0,1)(0,1)\end{matrix}\Bigg{]}-C_{1}. \tag{17}\]  
By inserting the second part of (11) into (16) and utilizing Equation (4.293/10) in [16], Equation (A.1) in [12], after some manipulations, \(C_{1}\) can be derived as  
\[C_{1}=\frac{r_{2}\Delta_{1}\Delta_{2}}{2}H_{1,0,3,4,3,2}^{0,1;1, 3,3,2}\Big{[}\frac{e\bar{\gamma}_{1}}{\theta_{1}},\frac{C\theta_{2}}{\bar{ \gamma}_{2}}\Big{|}\begin{matrix}(1;1,1)\\ -\end{matrix}\Bigg{|}\begin{matrix}\psi_{8}\\ \psi_{9}\end{matrix}\Bigg{|}\begin{matrix}\psi_{3}\\ \psi_{4}\end{matrix}\Bigg{]}, \tag{18}\]  
where \(\psi_{8}=\{\psi_{1},(1,1)\}\) and \(\psi_{9}=\{\psi_{2},(0,1)\}\).
###### Abstract  
This research proposed a portable wideband horizontally-polarized directional antenna scheme with a radome for digital terrestrial television reception. The operating frequency band of the proposed antenna scheme is 470-890 MHz. The portable antenna scheme was an adaptation of the Yagi-Uda antenna, consisting of a folded bowtie radiator, a semicircular corrugated reflector, and a V-shaped director. Simulations were carried out, and an antenna prototype was fabricated. To validate, experiments were undertaken to assess the antenna performance, including the impedance bandwidth (\(1\,S_{11}\leq-10\) dB), gain, and unidirectionality. The measured impedance bandwidth was 75.93%, covering 424-943 MHz, with a measured antenna gain of 2.69-4.84 dBi. The radiation pattern was of unidirectionality for the entire operating frequency band. The measured xz- and yz-plane half-power beamwidths were \(150^{\circ}\), \(159^{\circ}\), \(160^{\circ}\) and \(102^{\circ}\), \(78^{\circ}\), \(102^{\circ}\) at 47, 600, and 890 MHz, with the corresponding cross-polarization below \(-20\) dB and \(-40\) dB. The radome had a negligible impact on the impedance bandwidth, gain, and radiation pattern. The power obtained for the outdoor test, at 514 MHz, was 38.4 dBiV (\(-70.4\) dBm) with a carrier-to-noise ratio (C/N) of 11.6 dB. In addition, the power obtained for the indoor test was 26.6 dBiV (\(-82.2\) dBm) with a C/N of 10.9 dB. The novelty of this research lies in the concurrent use of the Yagi-Uda and bowtie antenna technologies to improve the impedance bandwidth and directionality of the antenna for digital terrestrial television reception.  
corrugated reflector; digital television; portable antenna; radome; wideband antenna 2022225338. [[https://doi.org/10.3390/s22145338](https://doi.org/10.3390/s22145338)]([https://doi.org/10.3390/s22145338](https://doi.org/10.3390/s22145338))
# Energy-Efficient Power Allocation in Non-Linear Energy Harvesting Multiple Relay Systems
###### Abstract  
In this paper, to maximize the energy efficiency (EE) in the two-hop multi-relay cooperative decoding and forwarding (DF) system for simultaneous wireless information and power transmission (SWIPT), an optimal power allocation algorithm is proposed, in which the relay energy harvesting (EH) adopts a nonlinear model. Under the constraints, including energy causality, the minimum transmission quality of information and the total transmission power at the relays, an optimization problem is constructed to jointly optimize the transmit power and power-splitting (PS) ratios of multiple relays. Although this problem is a nonlinear fractional programming problem, an iterative algorithm is developed to obtain the optimal power allocation. In particular, the joint power allocation at multiple relays is first decoupled into a single relay power allocation, and then single-relay power allocation is performed by the Dinkelbach iteration algorithm, which can be proven that it is a convex programming problem. Its closed form solutions for different polylines of EH models are obtained by using mathematical methods, such as monotonicity, Lagrange multipliers, the KKT condition and the Cardan formula. The simulation results show the superiority of the power allocation algorithm proposed in this paper in terms of EE.  
SWIPT; multi-relay; nonlinear EH; energy efficiency; power allocation Article  
[PERSON], [PERSON], [PERSON] Energy-Efficient Power Allocation in Non-Linear Energy Harvesting Multiple Relay Systems. _Algorithms_**2021**, 14, 155. [[https://doi.org/10.3390/414050155](https://doi.org/10.3390/414050155)]([https://doi.org/10.3390/414050155](https://doi.org/10.3390/414050155))
## 2 Preliminaries
### Semijoin in Distributed Databases  
In distributed databases, a join \(R\bowtie_{A=B}S\) could be a distributed one: \(R\) and \(S\) reside at different sites, and the join result is to be consumed at a third site (_i.e._, result site). There could be several implementations of this join, each of which would incur different amounts of data transmission. In a naive one, \(R\) and \(S\) are respectively sent to the result site, where the final join is conducted. The external join in WSNs corresponds to this naive implementation. In another one, _semijoins_[13] are executed first to filter out non-joinable tuples of \(R\) and \(S\). Then, only those tuples of \(R\) and \(S\) that have survived (_i.e._, joinable) are sent to the result site for the final join.  
Given a distributed join \(R\bowtie_{A=A}S\), two semijoins are possible: \(R\rtimes_{A=A}S\) and \(R\bowtie_{A=A}S\). The semijoin \(R\rtimes_{A=A}S\) is executed in three steps: (1) At the site of \(R\), the set of values of the joining attribute \(A\) of \(R\) (_i.e._, \(R[A]\)) is retrieved; (2) \(R[A]\) is sent to the site of \(S\); (3) At the site of \(S\), \(R[A]\) is joined with \(S\) to produce \(R\rtimes_{A=A}S\).  
\(R\rtimes_{A=A}S\) is a subset of \(S\). It contains only those tuples of \(S\) that are joinable with \(R\) on \(A\). A semijoin could be employed in processing a distributed join if it is _cost-effective_ in communication [13; 14]. For \(R\rtimes_{A=A}S\), its cost is defined to be the cost of sending \(R[A]\) while its benefit is defined to be the difference between the cost of sending S and that of sending \(R\rtimes_{A=A}S\). A semijoin is said to be _cost-effective_ if the benefit exceeds the cost. To figure out if a semijoin is cost-effective, the _semijoin selectivity_ (or _semijoin selectivity factor_) of a relation on the joining attribute needs to be estimated. Let \(f_{R_{A}}\) denote the semijoin selectivity of relation \(R\) on joining attribute \(A\). \(f_{R_{A}}\) is the probability that a value in \(A\) appears in \(R\) assuming that the values of \(A\) are uniformly and independently distributed onthe join operand relations [14]. It is estimated as \(|R[A]/|D_{A}|\), where \(D_{A}\) is the domain of \(A\)[15]. Given a join \(R\bowtie_{A=A}S,f_{R_{A}}\) indicates what fraction of \(S\) would join with \(R\) on \(A\) (_i.e._, \(|R\;\rtimes_{A=A}\;S|=f_{R_{A}}\cdot|S|\)).  
Given a distributed join query \(q=R\bowtie_{A=A}S\bowtie_{B=B}T\), a semijoin, \(R\rtimes_{A=A}S\), produces \(S\) It is obtained after eliminating all the tuples of \(S\) not joinable with \(R\) on \(A\). This semijoin is said to _reduce_\(S\) to \(S\). Since \(S\) might contain those tuples not joinable with \(T\) on \(B\), \(S\)'is a _partial reduction_ of \(S\). A subsequent semijoin, \(T\rtimes_{B=B}S\)' produces \(S\)'. Since it contains only those tuples necessary for the result of \(q\) without any superfluous non-joinable tuples, \(S\)' is the _full reduction_ of \(S\).
## 2 Problem Formulation
### Description of the Flocking Task  
As shown in Figure 1, the flocking task is divided into five parts: avoiding collisions, maintaining connectivity, achieving consistency of velocities, one-hop distance and graph importance:  
1. Avoiding collisions: we assume that there are no obstacles in the environment, and the collisions may occur only between robots. The safe distance between robots is denoted as \(d_{min}\), then \(\forall v_{i},v_{j}\in\mathcal{V},\|\mathbf{r}_{i}-\mathbf{r}_{j}\|>d_{min}\) should be satisfied;
2. Maintaining connectivity: the swarm connectivity is determined by whether there are isolated vertices in the graph, i.e., whether the graph is a connected graph. Meanwhile, as for the each robot, \(\forall v_{i}\in\mathcal{V},\exists v_{j}\in\mathcal{V},\|\mathbf{r}_{i}- \mathbf{r}_{j}\|<d_{max}\) should be satisfied;
3. Achieving velocity consistency: the average velocity of the robot swarm is expressed as \(\mu\), and the target is to minimize \(\sum_{v_{i}\in\mathcal{V}}(\|\mathbf{v}_{i}\|-\mu)^{2}\);
4. Achieving one-hop distance consistency: the one-hop distance is defined as the distance between the robot and its one-hop neighbor. The average of the one-hop distances is expressed as \(\delta\), and the target is to minimize \(\sum_{e_{ji}^{d}\in\mathcal{E}}(\|e_{ji}^{d}\|-\delta)^{2}\);
5. Achieving graph importance consistency: the graph importance consistency is achieved when the graph importance is balanced distributed, which is evaluated by the graph importance entropy proposed in Section 3.1.  
Figure 1: The flocking task. (**a**) Avoiding collisions and maintaining connectivity. (**b**) Achieving velocity consistency. (**c**) Achieving one-hop distance consistency. (**d**) Achieving graph importance consistency.
## 2 Proposed Method
### CNN Encoder for Multi-Scale Features
#### 2.1.2 Bottleneck Transformation  
In our preliminary experiments, it was found that utilizing a direct feature transformation between CNNs and TDNNs results in inferior performance. We consider that is because the CNN encoder and TDNNs have different latent spaces of features. To obtain frame-level feature maps matching the input of the following TDNN blocks, we propose the bottleneck transformation structure located at the junction of CNNs and TDNN blocks. The feature maps of each branch from the CNN encoder are first flattened, then expanded through the transformation. As is shown in Figure 2, for each branch with a shape of \(R^{C_{in}\times F\times T}\), the transformation is expressed as:  
\[\begin{split}& M=Concat[M_{1},M_{2},\dots,M_{C_{in}}]\\ & X_{neck}=BN\{ReLU[Conv1d]_{k=1}(M)]\}\\ & X\ =BN\{ReLU[Conv1d]_{k=3}(X_{neck})]\}\end{split} \tag{1}\]  
where \(M_{i}\in R^{F\times T}\) indicates the feature map of channel \(i\) and \(M\in R^{[C_{in}\times F]\times T}\) represents the reshaped 2D matrix. 'BN' means batchnorm. The channels are firstly squeezed to \(C_{out}/4\) and obtain \(X_{neck}\in R^{C_{out}/4\times T}\), then we extend the feature map to \(X\in R^{C_{out}\times T}\). The transformation layer compresses the feature maps, which is beneficial to more major information with less computation. We suppose that \(X\in R^{C\times T}\) is the output of main branch, and \(X^{i}\in R^{C/2\times T/2^{i}}\) refers to output of the \(i\)th minor branch with a downsample factor of \(2^{i}\) in time dimension, \(i=\{1,2,3\}\). The shape of \(X\) is maintained through all the TDNN blocks to ensure a high-resolution expression, while minor branches are applied to fuse with the main branch repeatedly in every TDNN block.  
Figure 2: The structure of bottleneck transformation. \(C_{in}\) means number of channels of input branch and \(C\) is the number of output channels.
## 3 Results and Discussion
### TEM for AuNP Samples with pH Adjusted after Production  
Unwashed samples were unsuitable for TEM analysis, because the high amount of organic material present covered the surface of the grids and prevented the acquisition of ideal TEM images. In addition, the samples at pH 2 and 4 were agglomerated after the washing process, so we only analyzed the washed samples at pH 6 and 10. TEM Results are shown in Figure 10.  
As can be seen in Figure 9, samples at pH 2 and 4 did not have MAPs, which indicates that they were polydispersed nanoparticles.  
Figure 8: pH adjustment after AuNP preparation. Two samples with acidic and alkaline pH were used (pH 4 and 10): (**A**) initial color of the sample at pH 10 in contrast to control; (**B**) color-changed AuNPs after second pH adjustment; (**C**) initial color of the sample at pH 4 in contrast to control; (**D**) color-changed AuNPs after the second pH adjustment.  
The statistical analysis of the measured AuNP diameters (Figure 11) for the sample with the pH adjusted to 6 after AuNP preparation is shown in Table 2. The mean diameter of AuNPs for this sample was 20 \(\pm\) 7 nm. The value of the mean diameter of the AuNPs that had an initial pH before production that was six was different from that of the above sample. These data demonstrate the differences between nanoparticles that had a pH that was adjusted before or after their preparation. A comparison of these two samples based on their mean diameter values (boxplots and \(t\)-test) is shown in Figure 12. The ratio shows that the particles were not perfect spheres but had a similar shape to each other.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Vars** & \(N\) & **Mean** & **SD** & **Median** & **Trimmed** & **Mad** & **Min** & **Max** & **Range** & **Skew** & **Kurtosis** & **SE** \\ \hline Diameter,Meat & 189 & 20.8 & 7.3 & 20.6 & 20.7 & 7.8 & 3.6 & 43.9 & 40.3 & 0.2 & \(-\)0.2 & 0.5 \\ Diameter,Min 1 & 189 & 18.5 & 6.8 & 18.4 & 18.3 & 7.3 & 2.2 & 41.3 & 39.1 & 0.2 & \(-\)0.1 & 0.5 \\ Diameter,Max 1 & 189 & 22.4 & 7.7 & 22.1 & 22.3 & 8.3 & 4.0 & 47.7 & 43.7 & 0.2 & \(-\)0.2 & 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of descriptive statistics of the size of a washed sample at pH 6 (the pH was adjusted after the preparation of the AuNPs) shown in Figure 10A,B. Abbreviations are shown in Table 1.  
Figure 9: Visible spectra of five samples with different pH values (here the pH values were adjusted after the preparation of AuNPs with an initial pH of 10). The samples were diluted 1:2 with ddH\({}_{2}\)O.  
As shown in Figure 12, although the shapes of the prepared nanoparticles were similar in the two different methods of nanoparticle preparation (i.e., pH was adjusted before and after the preparation of AuNPs), the AuNPs were smaller in the sample that had the pH value adjusted before the preparation of the nanoparticles than the other one.
## 3 Methodology: Proposed SybilSocNet Algorithm
### ML Terminologies and Methodologies Applied in This Study
#### 3.1.2 Supervised Learning
\[dist(A,B)=\sqrt{\frac{\sum_{i=1}^{m}{(x_{i}-y_{i})^{2}}}{m}} \tag{1}\] \[sim(A,B)=\frac{\overrightarrow{A}\cdot\overrightarrow{B}}{ \left|\overrightarrow{A}\right|\left|\overrightarrow{B}\right|} \tag{2}\]\[dist\_Minkowsky(A,B)=\left(\sum_{i=1}^{m}\left|x_{i}-y_{i}\right|^{r}\right)^{1/r} \tag{3}\]  
\[dist\_Chi\_square(A,B)=\sum_{i=1}^{m}\frac{1}{sum_{i}}\bigg{(}\frac{x_{i}}{size_ {Q}}-\frac{y_{i}}{size_{I}}\bigg{)}^{2} \tag{4}\]  
\[dist\_correlation(A,B)=\frac{\sum_{i=1}^{m}\left(x_{i}-\mu_{i}\right)(y_{i}-\mu_ {i})}{\sqrt{\sum_{i=1}^{m}\left(x_{i}-\mu_{i}\right)^{2}\sum_{l=1}^{m}\left(y_ {i}-\mu_{i}\right)^{2}}} \tag{5}\]  
Each equation is used to calculate the distances between the instance under study and other instances in the dataset, leading to varying outputs and accuracy measurements. These aspects were explored in ongoing research projects. [PERSON] et al. [57] investigated breast cancer diagnoses using KNN algorithms and their dependency on diverse distance metrics. [PERSON] et al. [31] conducted comparable studies on the impact of these distance equations in stroke disease detection. After calculating the distances between the studied instance and all other dataset instances, the algorithm picks K-nearest neighbors, i.e., K determining the count of neighbors. For instance, with K set to 100, it chooses the closest 100 neighbors, with such neighbors bearing the smallest distances to the instance. However, this step can be computationally intensive, especially for large datasets, requiring distance calculations and comparison operations. Once the K-nearest neighbors are identified, the algorithm predicts the instance's class based on their classes, a step called \"Voting\", offering Weighted and Majority options. Weighted Voting assigns varying weights to classes, while Majority Voting treats all classes equally, so adaptability accommodates developers aiming to emphasize specific class attributes.
## 3 A Methodology to Differentiate Dissolution from Gas Losses in Gas-Metal Systems
### Measurement of Diffusivity and Sieverts' Constant from Experimental Gas Pressure Evolution  
The application of this model allows one to simulate the evolution of pressure inside a closed metal container, at a given temperature, if the dissolution parameters \(D_{\infty}\), \(S_{\infty}\), \(E_{d}\) and \(E_{s}\) are known. However, dissolution parameters are not always known (e.g., literature scattering for Pb-Li, lack of steel manufacturer's data); in these cases, comparison of the simulated pressure decrease curve with an experimental one allows for the determination of the dissolution parameters through a \"curve matching\" strategy.  
To apply this curve-matching strategy, the simulation must be run several times. Each time a pressure decrease curve is simulated with a particular combination of dissolution parameters (\(D_{\infty}\), \(S_{\infty}\), \(E_{d}\) and \(E_{s}\)). These combinations are made by varying each parameter inside its respective range between extreme values (maximum and minimum), derived from literature using logarithmic regression (_vide_ Section 2). The resulting simulated curves can be compared to the experimental pressure curve; the one that displays smallest least-squares deviation is assumed to have been simulated using dissolution parameters closest to the real values.  
The determination of dissolution parameters through the curve-matching strategy applied to an experiment of gas loss from a metal chamber effectively provides one with the diffusivity and Sieverts' constant for that gas-metal pair as a function of temperature. Notice how modelling double metallic barriers allows one to apply the model to experiments containing two metals (e.g., steel and Pb-Li).  
Subsequent comparison of the diffusivity and Sieverts' constant to literature measurements allows for the validation of the proposed model and methodology. Section 4 presents how an experiment with deuterium in a steel container was modelled using this approach, and the resulting dissolution parameters for the 316L steel that made up that container.  
Methodology Validation by Comparing Simulations and Experiments of a Deuterium-Steel System (without Pb-Li)
## 3 Results and Discussion
### Plasma-Treatment Stabilization
On the other hand, concentrations of H\({}_{2}\)O\({}_{2}\) and NO\({}_{3}\)\({}^{-}\) in the present work were lower than those reported for other plasma sources employing water flow, such as an electrospray system with corona (about 450 \(\mu\)M of H\({}_{2}\)O\({}_{2}\), 100 \(\mu\)M of NO\({}_{2}\)\({}^{-}\), and 4500 \(\mu\)M for NO\({}_{3}\)\({}^{-}\)) and spark discharge (about 700 \(\mu\)M of H\({}_{2}\)O\({}_{2}\), 750 \(\mu\)M of NO\({}_{2}\)\({}^{-}\), and 10,500 \(\mu\)M of NO\({}_{3}\)\({}^{-}\)), and plasma over a flowing-water film (about 2000 \(\mu\)M of H\({}_{2}\)O\({}_{2}\)), or air DBD plasma (about 300 \(\mu\)M of H\({}_{2}\)O\({}_{2}\), 150 \(\mu\)M of NO\({}_{2}\)\({}^{-}\), and 2000 \(\mu\)M of NO\({}_{3}\)\({}^{-}\), no water flow) reported elsewhere [16; 48; 54]. However, considering the higher flow rate in the present work (10 mL/min) compared to other systems (1 mL/min for electrospray system and 4 mL/min for flowing water film), it could be concluded that AAGD treatment is comparable to analogs employing liquid flow for RONS production [16; 54].