esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System and Signal Model
### Mode of Relay Selection
#### 2.4.1 Case 1 (Max-Select)  
In this mode, the best relay is selected according to the CSI from both the \(SR_{i}\) and \(R_{i}D\) links. The end-to-end SNR of the \(i\) th link is \(\gamma_{SD_{i}}=\min\bigl{\{}\gamma_{SR_{i}},\gamma_{R_{i}D}\bigr{\}}\). The relay \(I_{1}\) is selected according to  
\[I_{1}=\arg\max_{i=1,2\cdots,N}\min\bigl{\{}\gamma_{SR_{i}},\gamma_{R_{i}D} \bigr{\}}, \tag{7}\]  
where \(I_{1}\in\{1,2\cdots,N\}\) denotes the best relay selected in case 1. It is worth noting that both hops require feedback channels. In this case, the source node requires the CSI of all the \(SR_{i}\) and \(R_{i}D\) links. Since the source node and the destination node may not have a direct LoS connection, it even be necessary for the relay node to feedback the CSI of the \(R_{i}D\) link back to the source, resulting in a highly complex system.
## 2 LS-SVM Time-Delay Prediction Model
\[y_{i}=W^{T}\mathbf{Q}(x_{i})+b+e_{i} \tag{4}\]  
wherein \(x_{i}\) is a time-delay input set, \(y_{i}\) is a time-delay output prediction set, \(x_{i}\) and \(y_{i}\) can be expressed as \(x_{i}=[ \begin{array}{cc}d_{i}&d_{i+1}&\cdots&d_{i+n-1}\end{array} ],y_{i}=[ \begin{array}{cc}d_{i+n}\end{array} ]\) and \(m\) is the embedding dimension; and the current time and past \(m-1\) time-delay value can be used to predict the next time-delay value of the system. \(\lambda\) is the regularization parameter. \(b\) is the constant value deviation. The Lagrange function can be established to solve the above optimization problem:  
\[L(w,b,e,a)=J(w,e)-\sum_{i=l}^{N}a_{i}(w^{T}\Phi(x_{i})+b+e_{i}-y_{i}) \tag{5}\]  
\(a_{i}\) is the Lagrange multiplier. Calculate the partial differential of \(w\), \(b\), \(e\), \(a\), and simplify it; then, the next equation can be obtained:  
\[ \begin{bmatrix}0&l^{T}\\ l&\Omega+\gamma^{-1}l\end{bmatrix}\begin{bmatrix}b\\ a\end{bmatrix} = \begin{bmatrix}0\\ y\end{bmatrix}  \tag{6}\]  
For the given training set \(\{x_{i}y_{i}\}\), Equation (6) is used to calculate parameters \(a\) and \(b\), at the same time. When the given set \(x\) combines with the actual training set \(x_{i}\), the following equation can be used to calculate \(y\), the prediction output of the system. \[y=\sum_{i=l}^{N}a_{i}K(x,x_{i})+b \tag{7}\]  
In this paper, the radial basis function (RBF) is chosen as the kernel function of LS-SVM, it is:  
\[K(x_{i},x_{j})=\exp\left(-\frac{\left\|x_{i}-x_{j}\right\|^{2}}{2\sigma^{2}}\right) \tag{8}\]  
From the above modeling process, based on the kernel function of RBF, LS-SVM performance depends mainly on \(\gamma\) and \(\sigma^{2}\), but the selection of two parameters lacks uniform standards and theoretical guidance; therefore, the selection of LS-SVM parameters is an unsolved problem [19]. At the same time, for the time-delay prediction problem, the embedding dimension \(m\) is an important parameter also; \(m\) that is too small will reduce the prediction accuracy; \(m\) that is too large will increase the computational complexity. The following Tables 1-3 give the mean square error (MSE) comparison of three different values of the parameters. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(m\) & \(\gamma\) & \(\sigma^{2}\) & **MSE** \\ \hline
2 & 10 & 0.001 & 9.0126 \\
4 & 10 & 0.001 & 4.6340 \\
6 & 10 & 0.001 & 1.8533 \\
8 & 10 & 0.001 & 1.9399 \\
10 & 10 & 0.001 & 1.9049 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The influence of \(m\) on prediction precision. From the above data in the Tables, three parameters can greatly affect the prediction accuracy, and only optimizing a parameter separately cannot guarantee that the other parameters are optimal, leading to the prediction precision being not ideal. In order to obtain the best prediction results, it is necessary to make \(m,\ \gamma\,\ \sigma^{2}\) achieve the optimal values at the same time.
# Privacy-Preserving Electricity Trading for Connected Microgrids
## 5 System Description
### System Operation
#### 5.4.4 Billing  
At the end of a billing period:  
1. The meters deposit all the unspent e-coins stored in their wallets (Section 5.3, Procedure 3). * All these deposits are carried out through identified connection-oriented secure communications. * The deposited e-coins can either be unspent coins acquired at the beginning or during the billing period, or coins received from paired energy offers.
2. For each meter, the substation computes the difference between the quantity of withdrawn e-coins and the number of deposited ones. * A positive difference determines the quantity to be charged on the next bill. * Meters with a negative difference are paid.
# An Information Theoretic Approach to Privacy-Preserving Interpretable and Transferable Learning
## 2 Mathematical Background
### Review of Membership-Mappings-Based Privacy-Preserving Transferable Learning
#### 2.5.2 Semi-Supervised Transfer Learning Scenario  
The aim is to transfer the knowledge extracted by a classifier trained using a source dataset to the classifier of the target domain such that the privacy of the source dataset is preserved. Let \(\{\mathbf{Y}_{c}^{sr}\}_{c=1}^{C}\) be the labeled source dataset where \(\mathbf{Y}_{c}^{sr}=\{y_{sr}^{i,c}\in\mathbb{R}^{p_{nr}}\mid i\in\{1,\cdots, N\}\}\).  
\(\{1,\cdots,N_{c}^{sr}\}\)) represents the \(c\)-th labelled samples. The target dataset consist of a few labeled samples \(\{\mathbf{Y}_{c}^{lg}\}_{c=1}^{C}\) (with \(\mathbf{Y}_{c}^{lg}=\{y_{lg}^{i,c}\in\mathbb{R}^{p_{lg}}\mid i\in\{1,\cdots,N_{ c}^{lg}\}\}\)) and another set of unlabeled samples \(\mathbf{Y}_{s}^{lg}=\{y_{lg}^{i,s}\in\mathbb{R}^{p_{lg}}\mid i\in\{1,\cdots,N_{ s}^{lg}\}\}\).
# Analysis of the Efficiency of Slope Evacuation for Dense Crowds in Urban Street-Type Public Spaces
## 2 Related Works
### Pedestrian Movement in Groups  
The small groups formed by family, friendship, and other social relationships often account for more than 70% of the number of tourists [22], so it is necessary to focus on the effect of group movement of pedestrians on the overall evacuation of the crowd. [PERSON] developed a method for automated analysis of pedestrian group behavior in urban settings using movement trajectory data from videos [23]. [PERSON] highlighted the importance of understanding influences on pedestrian movement, particularly in large group relations and counterflow scenarios. They discussed the psychological factors that can influence interactions between large groups in pedestrian dynamics [24]. [PERSON] conducted experiments on the movement characteristics of social groups in unidirectional flow, emphasizing the influence of social groups on pedestrian movement in various situations [25].  
From the aforementioned literature, it can be observed that the research on group movement of crowds mainly focuses on large open public places. They have largely ignored the unique characteristics and challenges presented by narrow street-type spaces and slopes, and they have failed to consider the influence of the age composition of the group on the overall evacuation of the crowd. There is a gap in research on group movement in these specific spatial environments that limits our ability to develop effective safety evacuation strategies in street spaces with complex topography, and our study aims to fill this gap by considering group movement during slope evacuation of dense crowds in street spaces.
# Nanotomographic Analysis of Orthodontic Molar Tubes for Direct Bonding
## 4 Discussion
No studies were found in the literature that could be used for comparison with the results of the present study regarding the metallurgical imperfections of orthodontic brackets. Another important aspect of the quality of orthodontic materials is a smooth surface of the bracket slot and of the inner walls of orthodontic molar tubes. The present analysis of the molar tubes in nanotomography allowed to visualize their structural imperfections. The defects found in the molar tubes by a single manufacturer may potentially accumulate biofilm or food remnants and thus enhance corrosion or increase friction. No other studies were found in the literature that analyze orthodontic molar tubes or brackets in computed nanotomography that could be compared or discussed with the present results. In a perfect clinical setting, an ideal positioning of precise, perfectly manufactured smooth brackets and tubes on all the teeth, followed by the insertion of properly sized smooth archives, should allow for unimpeded performance of straight wire orthodontic treatment without the necessity of introducing compensatory wire bends and without hindering sliding mechanics by excessive friction. A proper selection of high-quality orthodontic materials might possibly significantly influence the treatment time, the clinical excellence, and patients' satisfaction. Manufacturers should constantly make efforts to provide brackets and tubes with comfortable shapes, smooth surfaces, perfect slot orientation, and precision in order to enhance ideal treatment results. Referring to future studies, it could be interesting to measure the roughness of the inner walls of the molar tubes. However, due to limited access to the inner walls of the molar tubes for instrumental measurement, an investigation of their roughness would require carefully planning the study design and methods. Moreover, it could be interesting to compare molar tubes and check for uniformity between the sides or dental arches within the same brands of molar tubes. In fact, it was noticed that tubes of the same brand had slightly different geometric features. The present study is of clinical importance, as a practitioner is not able to clinically assess the quality of orthodontic molar tubes in terms of the nanofeatures of the tube canal. The authors are of the opinion that a limitation of the study refers to its laboratory nature. The clinical significance of the findings can only be supposed. Moreover, the study is limited to one tube size and does not comprise tubes soldered to orthodontic bands. The authors are convinced that studies on orthodontic brackets, wires, and tubes published in scientific journals independent from manufacturers provide a constant motivation to improve the quality of orthodontic materials.
###### Abstract  
With the explosion of traffic demand in recent years, the integration of satellite optical networks and terrestrial networks (ISTN) creates a promising networking solution for future low-latency, high-rate, and high-capacity communications. Owing to the high cost of deploying and maintaining a satellite optical network, it is critical to carefully design and plan the network to ensure the performance of the network. Thus, a semi-physical simulation platform based on software-defined networks (SDNs) is developed to simulate a satellite optical network and evaluate the performance of the proposed label-based advanced orbiting system (AOS) frame switching method that adheres to the Consultative Committee for Space Data Systems' recommended standard (CCSDS). The semi-physical simulation platform has two major innovations: (1) adapting and integrating network protocols between the CCSDS and open system interconnect (OSI) reference models, particularly at the data link layer, and (2) the foundation for an SDN-based satellite optical network. In the control plane, real-time VxWorks Simulators serve as controllers to establish and manage various network protocols and the link manager protocol (LMP). Here, network protocols include open shortest path first (OSPF) for routing managing and controlling messages, constraint shortest path first-traffic engineering (CSPF-TE), and constraint-label distribution protocol (CR-LDP) for routing data services. LMP is used to assign and reserve satellite optical link resources. The performance of the architecture and protocols is evaluated via a semi-physical simulation platform.  
software-defined networks; integrated satellite-terrestrial network; semi-physical simulation platform; label-based CCSDS AOS frame switching +
Footnote †: journal: Applied  
Article
## 4 Performance Evaluation  
We selected four indicators to evaluate the performance of the model: sensitivity (Sens), specificity (Spec), F1-Score (F1) and Matthews' correlation coefficient (MCC). Sens, Spec, and MCC are often used to evaluate prediction results in bioinformatics [33; 34]. On this basis, we have added F1-Score to balance the accuracy and recall of the classification model. The following are the mathematical definitions of these four indicators:  
Sensitivity:  
\[Sens=\frac{TP}{TP+FN} \tag{21}\]  
Specificity:  
\[Spec=\frac{TN}{TN+TP} \tag{22}\]  
F1-Score:  
\[\frac{2\times Precision\times Recall}{Precision+Recall} \tag{23}\]  
where \(Precision=TP/(TP+FP)\), \(Recall=TP/(TP+FN)\).  
Matthews correlation coefficient:  
\[MCC=\frac{TP\cdot TN-FP\cdot FN}{\sqrt{(TP+FP)\cdot(TP+FN)\cdot(TN+FP)\cdot(TN +FN)}} \tag{24}\]  
In all the above formulas, \(TP\) represents the number of samples where the actual disordered residues are predicted to be disordered residues, \(FP\) represents the number of samples where the actual ordered residues are predicted to be disordered residues, \(TN\) represents the number of samples where the actual ordered residues are predicted to be ordered residues, \(FN\) represents the number of samples where the actual disordered residues are predicted to be ordered residues.
# Optimal Design of Switchable Wearable Antenna Array for Wireless Sensor Networks
## 3 Results
The results of the comparison of optimization algorithm variants in terms of performance are presented in Table 1.  
Recognizing the fact that we achieved the best solution using the single-objective min-max approach, we tested some other representative optimization strategies, namely Nelder-Mead [27, 28], \"interior-point\" [29, 30, 31], and Powell [32]. Comparison of convergence and objective function value for these algorithms using the single-objective min-max formulation is presented in Table 2. Solutions in the space of objective function components are shown in Figure 14. The single objective EStra  
Figure 14: Solutions obtained for the single-objective min-max formulation with the Nelder–Mead, “interior-point”, Powell, and EStra algorithms, respectively. Figure 13: The history of optimization in the space of objective function components for optimization with P-EStra algorithm. algorithm needed 109 iterations, and the value of the objective function was increased from 0.84 to 1.96. The \"interior-point\" algorithm implemented in Matlab function _fmincon_ identified the solution with the objective function value of 1.7 with 628 iterations. The Nelder-Mead algorithm implemented in Matlab _fminsearch_ function found the value of the objective function equal to 1.47 in 445 iterations. The value of the objective function equal to 1.66 was found by the Powell algorithm with 415 iterations.
# A Broadband Active Microwave Monolithically Integrated Circuit Balun in Graphene Technology
## 3 Results
### GFET Description  
The transistor channel is composed of a bilayer graphene. The gate length is about 240 nm. A top gate is used with an insulator thickness of about 15 nm made of Al\({}_{2}\)O\({}_{3}\). The channel width of each device is 24 \(\upmu\)m. The DC ID-VGS and its associated transconductance characteristics of a GFET, as well as the AC-characteristics were measured on a transistor situated at close proximity of the balun for minimal dispersion. The influence of the pads has been removed through appropriated test-structures and de-embedding. Maximum transconductance is about 5 mS. The gate-source capacitance CGS is about 26 fF while the gate-drain capacitance CGD is about 8 fF. The device is characterized by a cut-off frequency of 19 GHz and a maximum oscillation frequency of 6 GHz.
## 2 Materials and Methods
### Measurement Unit and Measurment Data Aquisition  
The Sonal PQM-711 (orange measurement unit shown in Figure 2) versatile power quality analyzer for remote analysis in the A class is a device suitable for use on all types of networks, with a rated voltage range from 110 V to 1000 V either directly or indirectly through transformers. Based on advanced technology, the Sonal PQM-711 power quality analyzer allows comprehensive measurement, analysis, and recording of 50/60 Hz power network parameters and power quality according to the European standard EN 50160 [22].  
All data gathered during the experiments were recorded in the form of logs with a time stamp, the required values, and measurement units. Data were observed and recorded using dedicated Sonal Analysis software. This application is delivered as a standard accessory, indispensable for working with PQM-series analyzers. Then, data logs were processed and analyzed in MS Excel sheets, along with the necessary calculations and chart generation. Figure 8 shows exemplary views of the window for the observation and recording of measurement data from the Sonal PQM-711 analyzer.  
Figure 8: Sonal Analysis 4 software (screenshot with the Polish interface and numbers in European nomenclature): (**a**) measurement data and (**b**) phasor chart.  
Figure 7: Smart home socket modules: (**a**) tplink and (**b**) Gosund.
# A Near-Far-Field Model for Bubbles Influenced by External Electrical Fields
## 2 Mathematical Model
### Electrical-Field Approach for the Near-Field Model  
In the first step, we model the influence of the electrical-field in the near-field model.  
Here, we are able to use the additional influence of the electrical field to the dielectric liquid, which can be modeled with the Maxwell stress tensor, see [27].  
The electrical-field influences the normal stress or pressure \(p_{E}\) of the underlying liquid, which is given as:  
\[p_{E}=(T\;\mathbf{n})\cdot\mathbf{n}, \tag{1}\]  
and \(\mathbf{n}\) is the unit normal vector to the surface. The Maxwell-stress tensor \(T\) is given as:  
\[T=\epsilon\mathbf{E}\otimes\mathbf{E}-\frac{1}{2}\epsilon(\mathbf{E}\cdot \mathbf{E})\mathbf{I}, \tag{2}\]  
where \(\mathbf{E}\) is the electrical field and \(\epsilon\) is the permittivity.  
In the following, we assume a uniform electric field \(\mathbf{E}_{0}\) and the relation of the normal stress or pressure of the bubble suspended in a dielectric liquid can be calculated with Equation (1) and can be given by, see [27]:  
\[p_{e}\approx\frac{9}{8}\epsilon|\mathbf{E}_{0}|^{2}\sin^{2}(\Theta), \tag{3}\]  
where \(\Theta\) is the angle of the elevation to the plane and we have a maximum with \(\Theta=\pi/2\), which means in the equatorial plane.  
The additional pressure term based on the electrical field is used in the near-field model in the following Section 2.2.
## 4 Experimental Evaluation
### Experimental Setup
In a second scenario, we evaluated the sensitivity of the regularization parameter \(\lambda\) on the performance of our method for the UMist, NORB, Stanford Cars, and Caltech256 datasets. This tuning term was chosen using cross-validation for each algorithm and database. Table 3 shows the optimal value of \(\lambda\) selected of \(\{10^{-5},10^{-4},\ldots,10^{4},10^{5}\}\), obtained by comparing the RMSE between the generated models, chosen as the \(\lambda\) that gives a lower RMSE. The experiment only considered the case \(L=2000\) for the selected databases. The other parameters of the FISTA, G-FISTA, and LC-FISTA algorithms follow the same selection criteria of the first scenario. For example, the steplength for FISTA and LC-FISTA has been \(\rho=\frac{1}{L_{f}}\), but for G-FISTA, \(\rho=\frac{13}{L_{f}}\), \(\xi=0.96\), and \(S=1.1\). The Lipschitz constant \(L_{f}\) and the strong convexity parameter \(\mu\) of \(f\) are taken as the maximum and minimum eigenvalue of the matrix \(\mathbf{H}^{T}\mathbf{H}\). The mathematical analysis and numerical experiments presented in [42; 43; 44; 45; 49] motivated the choice of our parameters. To avoid the problem of overfitting when the number of neurons is increased, we applied a 10-fold cross-validation technique. This control tool allowed us to restrict the training to a certain number of neurons. We then used the test set specified in Table 1 and the metrics defined in Table 2 to evaluate the performance of each algorithm. Figure 1 shows the values of the RMSE for the training, validation, and test sets for the G-FISTA algorithm. A graphical view is not shown for FISTA and LC-FISTA since all three algorithms had a comparable level of accuracy. According to the above analysis, \(L=900\) and \(L=1300\) were the maximum neuron thresholds that could be assigned in ELM-AE for the MNIST and CIFAR10 databases. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{4}{c}{**Regularization Parameter \(\lambda\)**} \\ \cline{2-5}  & **UMist** & **NORB** & **SCars** & **Caltech256** \\ \hline FISTA & 0.001 & 10 & 0.001 & 1 \\ G-FISTA & 0.001 & 100 & 0.01 & 0.1 \\ LC-FISTA & 0.01 & 100 & 0.1 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Choice of \(\lambda\) for each algorithm and selected database.
## 5 Results and Discussion
### Antenna Performance with Watch Case  
Since the proposed antenna is for SW applications, its performance was studied by placing it inside a round dial watch case of 23 mm radius and a height of 9 mm, as displayed in Figure 14. The watch body case was considered as stainless steel with a relative permittivity of 1. The watch top was covered with reinforced glass of PTFE material with a relative permittivity of 2.5. The antenna was placed at a height of 2.5 mm from the bottom of the watch case. At 25.5 GHz, a slight decrease in impedance occurs due to the formation of a negative image current on the metallic surface of the watch case [21; 22]. However, at 38 GHz, the impedance is significantly increased. The reflection coefficient \(|\,S_{11}\,|\) is shown in Figure 15. Nonetheless, other than an increase in the front-to-back ratio of radiation due to the metallic casing, the antenna performance is not strongly affected. The electric field distribution in and around the watch case is presented in Figures 16 and 17. On the XY-plane at 25.5 GHz, the E-field is concentrated inside the watch case mostly along the \(x\)-axis due to \(\mathrm{TM}_{10}\), as illustrated in Figure 16a. Outside the case, a minor E-field distribution can be seen at the front and back of the watch lug. Figure 16b,c show the E-field along the XZ- and YZ-planes. It is shown that the E-field intensity is in the broadside direction. The radiation pattern in Figure 16d shows a directional beam with an HPBW of 30\({}^{\circ}\) on the XZ- and YZ-planes with a maximum gain of 9.9 dBi. In the case of 38 GHz, the E-field distribution is on the \(y\)-axis of the XY-plane due to \(\mathrm{TM}_{02}\), as depicted in Figure 17a. The E-field is mostly concentrated inside the case, and some intensity can be seen along and opposite the watch crown side. Figure 17b,c show the E-field on the XZ- and YZ-planes. The intensity of the field is maximum on the YZ-plane in two directions, approximately \(\pm\)36\({}^{\circ}\). The radiation pattern with two beams on the YZ-plane is shown in Figure 17d with an HPBW of 46\({}^{\circ}\) and a maximum gain of 10.9 dBi.  
Figure 14: Model of the smartwatch with the antenna inside.  
Figure 13: Simulated and measured radiation patterns. (**a**) The XZ-plane at 25.5 GHz, **(b**) the YZ-plane at 25.5 GHz, and (**c**) the YZ-plane at 38 GHz.