esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Some Statistical Properties
### Order Statistics  
Let \(X_{(1)},X_{(2)},\ldots,X_{(n)}\) be the corresponding order statistics of the sample \(X_{1},X_{2},\ldots,X_{n}\) selected from the CDF \(F(x)\) with pdf \(f(x)\). The \(i\)th order statistic, \(X_{(i)}\), has the pdf given by  
\[f_{(i)}(x)=\frac{n!}{(i-1)!(n-i)!}[F(x)]^{i-1}[1-F(x)]^{n-i}f(x),\ \text{for}i=1,2, \ldots,n. \tag{13}\]  
The pdf and cdf of the PDD is  
\[f_{(i)}(x;\alpha,\theta,\beta)=\frac{\beta\theta m!2^{i-m-1}x^{\beta-1}e^{- \frac{\theta x^{\beta}}{\alpha}}\left(4\alpha^{4}+\theta^{4}x^{2\beta}\right) \left(e^{-\frac{\theta x^{\beta}}{\alpha}}\left(e^{\frac{\theta x^{\beta}}{ \alpha}}-\frac{\theta^{3}x^{\beta}\left(2\alpha+\theta x^{\beta}\right)}{2 \alpha^{2}(2\alpha^{2}+\theta^{2})}-1\right)\right)^{i-1}\Psi^{m-i}}{\alpha^{ 3}(2\alpha^{2}+\theta^{2})\Gamma(i)\Gamma(1-i+m)},\]  
where \(\Psi=\frac{e^{-\frac{\theta x^{\beta}}{\alpha}}\left(4\alpha^{4}+2\alpha^{2} \theta^{2}+\theta^{4}x^{2\beta}+2\alpha\theta^{3}x^{\beta}\right)}{\alpha^{2 }(2\alpha^{2}+\theta^{2})}\). The pdfs' of lowest and largest order statistics \(X_{(1)}\) and \(X_{(n)}\), respectively, are  
\[f_{(1)}(x;\alpha,\theta,\beta)=\frac{\beta\theta mx^{\beta-1}e^{-\frac{\theta x ^{\beta}}{\alpha}}\left(4\alpha^{4}+\theta^{4}x^{2\beta}\right)\left(1- \Upsilon\right)^{m-1}}{2\alpha^{3}(2\alpha^{2}+\theta^{2})}, \tag{14}\]  
Assuming that the right censoring is not informative, the log-likelihood function can be expressed as:  
\[L_{n}(\gamma)=\sum_{i=1}^{n}\delta_{i}\text{lnh}(x_{i},\gamma)+\sum_{i=1}^{n}lnS( x_{i},\gamma)\] \[=\sum_{i=1}^{n}\delta_{i}[ln(\beta\theta)+(\beta-1)ln(x_{i})+\ln( \omega_{i})+\ln(\alpha)-\ln(\theta_{i})]+\sum\limits_{i=1}^{n}ln\Big{(}1-e^{- \upsilon_{i}}\Big{(}e^{\upsilon_{i}}-\tfrac{1}{2}u_{i}-1\Big{)}^{a}\Big{)}. \tag{16}\]  
We pose, \(\varphi_{i}=4\alpha^{4}+2\alpha^{2}\theta^{2}+\theta^{4}x_{i}^{2\beta}+2 \alpha\theta^{3}x_{i}^{\beta}\), \(u_{i}=\frac{\theta^{4}x_{i}^{2\beta}+2\alpha\theta^{3}x_{i}^{\beta}}{\alpha^{ 2}(2\alpha^{2}+\theta^{2})}\), \(v_{i}=\frac{\alpha x_{i}^{\beta}}{\alpha}\), \(\omega_{i}=4\alpha^{4}+\theta^{4}x_{i}^{2\beta}\).
## 4 Integration of Multi-Sensory Information for Target Identification
### Results of Classification Using Vibration, Acoustic and Radar Data
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|} \hline \multirow{2}{*}{**Classes**} & **Number** & \multirow{2}{*}{**Classifier**} & **Correctly** & **ROC** & **Kappa** \\  & **of Runs** & & **Classified (\%)** & **Area** & **Statistics** \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Trains \\ A and B \\ \end{tabular} } & \multirow{3}{*}{240} & Random Forest & 94.1 & 0.99 & 0.88 \\  & & MLP & 96.7 & 0.99 & 0.93 \\ \cline{1-1}  & & SVM & 97.1 & 0.97 & 0.94 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Three \\ Velocities \\ \end{tabular} } & \multirow{3}{*}{920} & Random Forest & 80.0 & 0.91 & 0.70 \\  & & MLP & 80.6 & 0.92 & 0.71 \\ \cline{1-1}  & & SVM & 85.7 & 0.89 & 0.79 \\ \hline \end{tabular}
\end{table} 
Table 7: Classification results using acoustic data. use radar data to complement other sensor modalities. We have obtained radar data using three different configurations, in which the target train moved with reflectors of different shape and material. For each of the three configurations, 640 runs were used for classification purposes. The results with radar data are presented in Table 8. In the first configuration, we used metal and paper reflectors of the tetrahedron shape to test if the radar can differentiate between different types of material. For this case, MLP produced the best classification result among the three classifiers. By applying MLP on the radar data, we were able to classify metal and paper reflectors correctly in 87.66% of the runs. For the second configuration, we changed the reflectors to a rectangular shape made of metal or plastic. Again MLP produced the highest classification accuracy (81.06%). For the last configuration, we tried to classify between different shapes of the reflectors, so we selected tetrahedron and rectangular shape reflectors, both made of metal. MLP still produced the best classification result (82.2%). In summary, we are successful in classifying reflectors made of different material as well as two shapes of the metal reflectors. A more in-depth study of the radar's capability in classifying different types of material can be found in [6].
## 3 High-\(k\) Dielectric Nanoparticles
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Nanoparticles** & **Dielectric Constant** & **References** \\ \hline Cerium oxide (CeO\({}_{2}\)) & 4.1 & [120] \\ ZnMn\({}_{2}\)O\({}_{4}\) & 16.5 & [121] \\ \({}^{\mathrm{a}}\) Strontium hafnium oxide (SrHfO\({}_{3}\)) & 17.0 & [110] \\ \({}^{\mathrm{b}}\) Iron oxide (Fe\({}_{3}\)O\({}_{4}\)) & 130.0 & [122] \\ Cadmium sulfide (CdS) & 163.0 & [123] \\ CoFe\({}_{1.6}\)Al\({}_{0.4}\)O\({}_{4}\) & 200.0 & [124] \\ Ba\({}_{0.9}\)Sr\({}_{0.1}\)ZrO\({}_{3}\) & 290.0 & [125] \\ Carbon coated silver (Ag@C) & 320.0 & [126] \\ Cerium oxide (CeO\({}_{2}\)) & 370.0  & [129] \\ CaCu\({}_{3}\)Ti\({}_{4}\)O\({}_{12}\) & 9000.0 [FOOTNOTE:]Footnote : \({}^{\mathrm{a}}\) nanoparticle size = 2.5 nm, \({}^{\mathrm{b}}\) stabilized by glucose, \({}^{\mathrm{c}}\) measured at 200 kHz, \({}^{\mathrm{d}}\) measured at 1 kHz, \({}^{\mathrm{e}}\) measured at 100 Hz.[ENDFOOTNOTE] & [130] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dielectric constant of some of the selected nanoparticles. approach in a solution followed by heat treatment, [PERSON], et al. synthesized a high dielectric constant titanium oxide-coated barium titanate (TiO\({}_{2}\)@BaTiO\({}_{3}\)) core-shell nanoparticle structures and embedded in the PVDF matrix [134]. The authors reported that the dielectric constant value obtained for neat PVDF was 9.2 and it was enhanced to 19.6 for 10-vol% of TiO\({}_{2}\)@BaTiO\({}_{3}\)/PVDF nanocomposites [134]. The amine functionalized carbon-coated Fe\({}_{3}\)O\({}_{4}\)/polyimide composite films showed a permittivity of 58.6 at 1 kHz, which for the Fe\({}_{3}\)O\({}_{4}\)@C-NH\({}_{2}\) composition was 1.13 vol% [135]. As reported by [PERSON], et al. we can notice exceptionally high permittivity values for the PVDF nanocomposites by loading novel titanium carbide@boehmite (TiC@AlOOH), and it was as high as \(1.8\times 10^{7}\) at 100 Hz when the content of the TiC@AlOOH nanoparticles was 41 wt% [136].
# Assessment of Quad-Frequency Long-Baseline Positioning with BeiDou-3 and Galileo Observations
###### Abstract  
Quad-frequency signals have thus far been available for all satellites of BeiDou-3 and Galileo systems. The major benefit of quad-frequency signals is that more extra-wide-lane (EWL) combinations can be formed with quad-frequency than with triple- or dual-frequency, of which the ambiguities can be fixed instantaneously in medium and long baselines. In this paper, the long-baseline positioning algorithm based on optimal triple-frequency EWL/wide-lane (WL) combinations of BeiDou-3 and Galileo is proposed. First, the theoretical precision of multi-frequency combinations of BeiDou-3 and Galileo is studied, and EWL/WL combinations with a small noise amplitude factor and a small ionospheric scalar factor are selected. Then, geometry-free methods are used to estimate the a priori precision of EWL/second EWL/WL signals for different combination schemes. Second, the double-differenced (DD) geometry-based function models of quad-frequency configurations and three different triple-frequency configurations are given, and the DD ionospheric delays are estimated as unknown parameters. In the end, the real BeiDou-3 and Galileo data are used to evaluate the positioning preference. The results show that, when using fixed EWL observations to constrain WL ambiguities, the proposed triple-frequency EWL/WL signals composed of (B1I,B3I,B2a) of BeiDou-3 and (E1,E5a,E6) of Galileo can achieve the same precision as the quad-frequency signals. Therefore, the method proposed in this article can realize long-baseline instantaneous decimeter-level positioning while reducing the dimension of matrix and improving calculation efficiency.  
BeiDou-3; Galileo; quad-frequency observations; long baseline; real-time kinematic (RTK); geometry-based (GB) +
Footnote †: journal: Remote Sensing
## 3 Methodology
### Performance Evaluation in Fuzzy Collaborative Forecasting  
Some performance measures of fuzzy collaborative forecasting are defined as follows.  
**Definition 1.**\(F(p)\) is a fuzzy forecasting method with parameter \(p\). The fuzzy forecast at period \(t\) using \(F(p)\) is indicated with \(F_{t}(p)=(F_{t_{1}}(p),F_{t_{2}}(p),F_{t_{3}}(p))\). The precision and accuracy of \(F(p)\) are indicated with \(Prec(F(p))\) and \(Accur(F(p))\), respectively. Some of the common functions for \(Prec(F(p))\) and \(Accur(F(p))\) are described below:  
(1) The average range (AR):  
\[Prec_{\rm{AR}}(F(p))= \sum_{t=1}^{T}(F_{t_{3}}(p)-F_{t_{1}}(p))/T \tag{62}\] \[\text{(1)}\quad\text{Mean absolute error (MAE):}\] \[Accu_{\rm{MAE}}(F(p))= \sum_{t=1}^{T}|D(F_{t}(p))-a_{t}\ |/T \tag{63}\]  
where \(D()\) is the defuzzification function; \(a_{t}\) is the actual value at period \(t\).  
Mean absolute percentage error (MAPE):\[Accu_{\text{MAPE}}(F(p))=\sum_{r=1}^{T}\frac{\left|\left.D(F_{r}(p))-a_{r}\right. \right|/\left.a_{r}\right.}{T} \tag{64}\]  
(2) Root mean squared error (RMSE):  
\[Accu_{\text{RMSE}}(F(p))=\sqrt{\sum_{r=1}^{T}\frac{\left(D(F_{r}(p))-a_{r} \right.\right)^{2}}{T}} \tag{65}\]  
All of these performance indicators are as small as possible.  
**Definition 2**.: _FCF_(_F_, _G_) is a fuzzy collaborative forecasting method on the basis of two forecasting methods \(F\) and \(G\). The quality of collaboration in the precision and accuracy are indicated with _QoCp_(_FCF_) and _QoCa_(_FCF_), respectively. Some of the common functions for _QoCp_(_FCF_) and _QoCa_(_FCF_) are described below:  
(1) Maximum percentage improvement (MPI):  
\[\text{QoC}_{\text{MPI}}(\text{FCF})=\ Max(\frac{Prec(F)-Prec(FCF)}{Prec(F)}, \frac{Prec(G)-Prec(FCF)}{Prec(G)})\cdot 100\% \tag{66}\]  
\[\text{QoC}_{\text{MPI}}(\text{FCF})=\ Max(\frac{Accu(F)-Accu(FCF)}{Accu(F)}, \frac{Accu(G)-Accu(FCF)}{Accu(G)})\cdot 100\% \tag{67}\]  
(2) Average percentage improvement (API):  
\[\text{QoC}_{\text{API}}(\text{FCF})=\ (\frac{Prec(F)-Prec(FCF)}{Prec(F)}+\frac{ Prec(G)-Prec(FCF)}{Prec(G)})\ /\ 2\cdot 100\% \tag{68}\]  
\[\text{QoC}_{\text{API}}(\text{FCF})=\ (\frac{Accu(F)-Accu(FCF)}{Accu(F)}+\frac{ Accu(G)-Accu(FCF)}{Accu(G)})\ /\ 2\cdot 100\% \tag{69}\]  
These functions can easily be extended to involve more than two objects.
## 4 Problem Formulation
### Scheme Definitions  
Consider \(U=\{u_{1},u_{2},\ldots,u_{n}\}\) as the set of users in the framework. Let \(L=\{1,2,\ldots,p\}\) represent the set of descriptive attributes. For each attribute \(i\), define \(G_{i}\subseteq U\) as the group of users possessing attribute \(i\), which is called the attribute group. \(G_{i}\) will serve as a list for granting or revoking access to attribute \(i\). The collection of all such attribute groups is denoted by \(G=\{G_{1},G_{2},\ldots,G_{p}\}\). Finally, let \(K_{i}\) be the key associated with the attribute group, shared by the users in \(G_{i}\) who have not been revoked.  
Figure 2: System model.  
**Definition 1**.: \(\bullet\)__Setup\((1^{\kappa},S_{A})\rightarrow(GP,MSK)\)_. It takes the security parameter_ \(\kappa\) _and the attribute universe_ \(S_{A}\) _as input, and it outputs a public parameter GP and a master key MSK._
* \(\mathsf{ldKgen}(GP,uid)\rightarrow(IS_{uid},IP_{uil})\)_. It takes as input the user identity_ _uid with GP, and it outputs the id key pair_ \((IS_{uil},IP_{uil})\)_._
* \(\mathsf{AttKgen}(GP,MSK,S,uid)\rightarrow\)__\(SKS_{uil}\)_. It takes an attributes set_ \(S\subset S_{A}\)_, the_ _uid, the GP, and the MSK as input, and it outputs the user-attributes secret key_ \(SK_{S,uid}\)_._
* \(\mathsf{EncML}(GP,m,\mathbb{M},L)\rightarrow\)__\(CT_{\mathbb{M},L}\)_. It takes as input a message_ \(m\)_, an access structure_ \(\mathbb{M}\)_, and a revocation list_ \(L\) _with GP, and it outputs the ciphertext_ \(CT_{\mathbb{M},L}\)__
* \(\mathsf{ldkEnc}(GP,Sw)\rightarrow\)__\(I_{w}\)_. It takes as input the keyword set_ \(Sw\) _with GP, and it outputs the keyword index set_ \(I_{w}\)_._
* \(\mathsf{AKgen}(GP,uid,IS_{uil})\rightarrow\)__\(AK_{uil}\)_. It takes as input the user identity_ _uid and secret key_ \(IS_{uil}\)_, together with GP, and it outputs an authorization key_ \(AK_{uil}\)_._
* \(\mathsf{Verify}(AK_{uil},IP_{uil})\rightarrow(0,1)\)_. It takes the authorization key_ \(AK_{uil}\) _and the public id key_ \(IP_{uil}\) _as input; it returns_ \(1\) _if the user's authentication passes and_ \(0\) _if it does not._
* \(\mathsf{Trapdgen}(GP,w^{\prime})\to T_{w}\)_. It takes a search keyword_ \(w^{\prime}\) _with GP as input, and it outputs the trapdoor_ \(T_{w}\)_._
* \(\mathsf{Search}(T_{w},I_{w})\rightarrow(0,1)\)_. It takes the trapdoor_ \(T_{w}\) _and the index_ \(I_{w}\) _as input; it returns_ \(1\) _if there is a match and_ \(0\) _if there is not._
* \(\mathsf{Dec}(GP,CT_{\mathbb{M},L},SK_{S,uid})\to M\)_. It takes as input the ciphertext_ \(CT_{\mathbb{M},L}\)_, and the secret key_ \(SK_{S,uid}\) _with GP, and it outputs the message_ \(M\) _correctly if_ \(S\in\mathbb{M}\) _and_ \(uid\
otin L\)_._  
**Correctness.** The proposed ABE-RS scheme is correct if Verify, Search, and Dec are successfully performed to recover the desired message \(M\).
## 1 Introduction
There are several numerical approaches which can solve conjugate heat transfer problems. The Lattice Boltzmann Method (LBM) is efficient in dealing with numerical models with complex geometry as well as multiphase flow problems. This approach is widely applied in nanoscale fluid flow or porous media fluid flow problems [29; 30]. The Lagrangian meshless methods, such as the Smoothed Particle Hydrodynamics (SPH) method, can handle large-scale deformation of the fluid domain as well as large displacement in solid domains [31]. For the problems regarding conjugate heat transfer in subatmospheric conditions, when the Knudsen number is less than 0.01, the fluid domain can be treated as a continuum medium and the Finite Volume Method (FVM) is a relatively robust approach that has been widely used in numerical studies [32; 33]. When the Knudsen number is greater than 0.01, the fluid domain can no longer be treated as a continuum, such that the LBM method can be considered. The maximum Knudsen number appearing in this study has a value less than 0.01; therefore, the FVM method is chosen as the numerical approach in this study. This study investigates the temperature rise process of a levitation electromagnet module in a maglev under different vacuum levels through an experimentally validated 3-dimensional numerical model. The least-favorable operating condition, which is the static levitation situation of the levitation electromagnet, is comprehensively investigated. Based on existing research conclusions and engineering applicability, the range of the vacuum level is set from 1.0 atm to 0.1 atm. Under the specified vacuum level range, the velocity and temperature distribution of the flow field, as well as the conjugate heat transfer characteristic in the fluid-solid interface, are analyzed. The influence of the flow field on the temperature profile of heating units is studied. By analyzing the convection heat flux and radiation heat flux under different vacuum levels, the low-vacuum heat transfer profile of the levitation electromagnet is characterized. The effective convection heat transfer coefficients under different vacuum levels are then calculated for evaluation of the influence of the vacuum level on the heat transfer characteristics.
# Adaptive Path Planning for UAV-Based Pollution Sampling
## 4 Experiments and Results
### Results of Analysis of the Selected Optimization Methods  
The initial tests examine the efficiency of several single-objective optimization algorithm variants utilized for global path planning. This task involves tackling the optimization problem (5) within a highly challenging environment. Whenever feasible, the algorithms were applied with their default configurations as outlined in the relevant literature: [32; 33] for I-GWO, [47] for PSO, [16; 36] for \(\text{ACO}_{\mathbb{R}}\), and [48] for GA. Certain settings required adjustments. The parameters were determined empirically through trial and error on a test map, refining them until the algorithm reliably converged to an optimal solution. The study utilized the MATLAB implementation of I-GWO by [PERSON] [33]. A summary of the final algorithm settings is provided in Table 2. Values that deviate from their default settings are highlighted in bold, while unspecified values remain at their default configurations. Following the calibration of each algorithm, a comparative study was carried out to assess their performance. The evaluation involved 10 test cases, with each case being repeated 5 times using various random seeds. The analysis consisted of 200 test cases in total. All test cases utilized the same airspace, terrain, and wind maps to ensure consistency. The initial and target waypoints, along with the positioning of the pollution source, were manually set and differed across the cases. The cost associated with pollution increased in a linear fashion, starting from 0 at the location of maximum pollution. All criteria outlined in Table 1 were active, each assigned an equal weight of 0.2. The intensity of each criterion was empirically adjusted through their scaling factors \(s_{f}\) to yield a cost that aligned with intuitive expectations. The initial run and the first test case results are illustrated in Figure 3 and summarized in Table 3. Figure 3 displays four global paths as distinct colored lines, with the starting point marked by a red tetrahedron and the goal by a green one. Wind conditions are depicted through a colored vector field, while NFZs are shown as translucent red prisms. A blue sphere highlights the area of maximum pollution concentration. The zigzag measurement path is slightly shifted to the south of the pollution peak, which is a proper behavior resulting from the safety margin. The algorithm tries to place the measurement path as close to the pollution center as possible while respecting the margin applied near obstacles and the map edges.
## 3 Performance Analysis of iPyroxy Block
### Antenna Specification  
A rectangular microstrip antenna operating from 1 to 15 GHz was designed using an FR4 substrate with a thickness of 1.6 mm, targeting dual-band operation at 4 GHz and 13.7 GHz. The antenna is impedance-matched to 50 ohms to ensure optimal power transfer and minimize reflection losses. The substrate's dielectric constant (\(\epsilon_{r}\) = 4.4) and thickness influence the antenna's bandwidth and radiation characteristics. The dual-band operation allows applications in the C-band (4 GHz), typically used in satellite communication and radars, and the Ku-band (13.7 GHz), suitable for high-speed data transmission and satellite broadcasting. Critical design challenges include maintaining efficiency and minimizing losses at higher frequencies due to the properties of FR4 as well as precise fabrication to achieve accurate resonances at both operating frequencies. The proposed antenna offers broadside radiation patterns and efficient performance across the designed frequency bands.
# Evaluation of Online Teaching Quality Based on Facial Expression Recognition
## 1 Introduction  
In recent years, information technologies, such as 5G and high-speed networks, have made significant progress, and information processing technologies represented by artificial intelligence and big data have been rapidly integrated into all aspects of human life. At the same time, people began to introduce artificial intelligence and computer vision into the field of education. Computer vision can be used to identify students' expressions and behaviors, thereby inferring students' participation in education. The emotions of learners in the learning process are precisely the key to improving the quality of teaching. Based on facial expression recognition technology, teachers can grasp the emotional state of students in time. According to the students' learning status, timely adjusted teaching strategies alleviate students' bad learning emotions and improve learning efficiency [1]. Affected by the epidemic, students in our country use the online classroom mode part of the time. In this mode, teachers cannot pay attention to each student's listening status in real-time. Therefore, this requires a technology that can collect students' listening status in real-time and give feedback to teachers in time so that teachers can customize personalized teaching by analyzing students' learning status data, stimulate students' enthusiasm for learningand thinking, and return to the educational essence of \"teaching students according to their aptitude\".  
Most of the teaching evaluation we have been using is the way of after-school investigation, which is not only cumbersome but also the information collected is not comprehensive and accurate. Moreover, we lack a complete and unified evaluation scheme to measure the teaching level of schools and teachers. In recent years, with the development of artificial intelligence, people have begun to apply face detection and expression analysis to classroom teaching evaluation. However, due to reasons, such as precision and accuracy, they have not been widely used in classrooms. Therefore, how to improve the accuracy of expression analysis is an urgent problem to be solved.
## 4 Result and Discussion
Comparing the model fitting speed allows for a comparison of the efficiency of autoencoder models employing traditional positional layer self-attention against those using cycling layer self-attention. This comparison is vital as it reveals the practicality and performance of the models in real-world applications, particularly in handling large and complex datasets, like the Jena Climate dataset. Understanding which configuration yields faster convergence to the best fit can significantly impact the choice of model architecture in future implementations and research. The key metric for this comparison is the overall number of iterations taken by each model to reach the best fit, which is determined by the lowest training loss achieved. This metric is chosen as it directly reflects the time efficiency of the model in learning from the data and reaching a point of optimal performance. Regression model performance comparison aims to evaluate and compare the performance of different regression models that utilize the representations learned by autoencoder models. This evaluation is crucial for understanding how effectively the learned representations can predict or reconstruct relevant outputs, thereby determining the practical utility of the autoencoder models in real-world applications. This comparison uses MSE, MAE, RMSE, and median MAE as the metrics for different regression methodologies. To assess the specific contribution of the cycling layer, we conducted an ablation study comparing different model configurations: (1) with only the positional layer, (2) with only the cycling layer.