esa-sceva/satcom-chunk-collection huggingface dataset:
# Virtual Reality or Videoconferencing for Online Learning? Evidence from Comparative Meta-Analyses
###### Abstract  
This study compares the effectiveness of virtual reality (VR) and videoconferencing (VC) platforms for online learning. Comparative meta-analyses of 29 articles from 2003 to 2023 (15 for VR, 14 for VC) revealed that both technologies positively affect learning outcomes, with VR demonstrating a larger effect size (ES = 0.913) compared with VC (ES = 0.284). VR proved more beneficial in regular-sized classes, especially for natural science subjects, and excelled in experiential or collaborative learning environments. VC showed a greater impact in smaller classes, with significant variations depending on the program brand and camera options; it also favored natural science. These findings provide valuable guidance for educators and learners in choosing the most suitable technology for different online learning scenarios and shed light on future research in this field.  
virtual reality; videoconferencing platforms; online learning; learning outcomes; meta-analysis +
Footnote †: journal: Applied sciences
# Energy Harvesting over Rician Fading Channel:
## Appendix C Proof of Theorem 3  
The PDF of the random variable \(X_{i}\) is given by \(f_{X_{i}}(x)=\zeta_{i}e^{-K}\sum\limits_{l=0}^{\infty}\frac{(\zeta_{i}K)^{l}} {(l!)^{2}}x^{l}e^{-\zeta_{i}x}\). Hence, the asymptotic outage probability at node \(S_{1}\) can be written as  
\[P_{out\_1}^{\infty} =\Pr(\gamma_{1}^{\infty}<\gamma_{th})=\Pr\left(\frac{X_{2}}{X_{1 }+X_{2}}<a\gamma_{th}\right)=\Pr\left(X_{2}<\frac{a\gamma_{th}X_{1}}{1-a\gamma _{th}}\right)\] \[=\int\limits_{0}^{\infty}F_{X_{2}}\left(\frac{a\gamma_{th}x_{1}}{ 1-a\gamma_{th}}\right)f_{X_{1}}(x_{1})dx_{1}\] \[=1-\int\limits_{0}^{\infty}e^{-K}\sum\limits_{l=0}^{\infty}\sum \limits_{m=0}^{l}\frac{K^{l}\zeta_{2}^{m}}{l!m!}\left(a\tilde{\gamma}_{th}x_{1 }\right)^{m}e^{-\zeta_{2}a\tilde{\gamma}_{th}x_{1}}\zeta_{1}e^{-K}\sum\limits_ {k=0}^{\infty}\frac{(\zeta_{1}K)^{k}}{\left(k!\right)^{2}}x_{1}^{k}e^{-\zeta_{ 1}x_{1}}dx_{1}\] \[=1-\zeta_{1}e^{-2K}\sum\limits_{0}^{\infty}\sum\limits_{l=0}^{ \infty}\sum\limits_{k=0}^{\infty}\sum\limits_{m=0}^{l}\frac{K^{l+k}\zeta_{1} ^{k}\zeta_{2}^{m}}{l!m!\left(k!\right)^{2}}\left(a\tilde{\gamma}_{th}x_{1} \right)^{m}\int\limits_{0}^{\infty}x_{1}^{m+k}.e^{-(\zeta_{2}a\tilde{\gamma}_{ th}+\zeta_{1})x_{1}}dx_{1}.\]  
Using the Formula (3.381.4) in [32], we obtain the asymptotic formula:  
\[P_{out\_1}^{\infty}=1-\zeta_{1}e^{-2K}\sum\limits_{l=0}^{\infty}\sum\limits_ {k=0}^{\infty}\sum\limits_{m=0}^{l}\frac{K^{l+k}\zeta_{1}^{k}\zeta_{2}^{m}}{l! m!\left(k!\right)^{2}}\left(a\tilde{\gamma}_{th}\right)^{m}\frac{\Gamma(m+k+1)}{( \zeta_{2}a\tilde{\gamma}_{th}+\zeta_{1})^{m+k+1}}, \tag{19}\]  
where \(\Gamma(\cdot)\) is the complete gamma function.  
The asymptotic throughput can be obtained by substituting Equation (19) into the definitive formula of the throughput.
## 2 Materials and Methods
### Automatic Classification of CNN
#### 2.3.2 CNN-Based Text Classification Model  
CNN, as an efficient recognition algorithm, can accurately extract and analyze semantic features from text and image data [32; 33], as well as automatically extract and combine these features from the data without human intervention [34]. In the field of text classification, the core idea of CNN is to search for relationships between neighboring words in the text using convolutional kernels, extract useful features, and use these features for text classification. The CNN model consists of five parts: inputting layer, convolutional layer, pooling layer, fully connected layer, and outputting layer, as shown in Figure 4.  
In the CNN model, the preprocessed text is transformed into a text matrix through word embedding, and then convolutional kernels extract local features from the text matrix, generating a series of two-dimensional feature maps. Next, the pooling layer reduces the dimensionality of each feature map, resulting in a high-dimensionality text vector representation. The fully connected layer takes the text vector as input and maps it to scores for all possible categories, and finally, the softmax function is used to calculate the probability for each category. Since CNN is a supervised learning machine learning method, category labels must be added to each incident description. During the classification process, activation functions and dropout techniques need to be added. Activation functions increase the model's nonlinearity characteristics, enabling it to better adapt to complex input data. In the CNN model constructed in this study, relu and softmax are used as activation functions, and dropout is introduced to reduce the occurrence of overfitting. The pseudocode of the CNN-based text classification model is shown in Table 3.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{2}{l}{**Pseudocode for CNN-Based Text Classification Models**} \\ \hline
1: & Load CSV file \\  & df = pd.read\_csv (‘accidents.csv’) \\
2: & Split dataset into train and test sets \\  & train\_data, test\_data = splitData (df) \\
3: & Preprocess data \\  & trainLabel, testLabel = preprocessLabels (train\_data, test\_data) \\  & trainCut, testCut = preprocessText (train\_data, test\_data) \\  & saveWordData (trainCut + testCut) \\
4: & Train Word2 Vec model \\  & w2v\_model = trainWord2 Vec (loadWordData (0)) \\
5: & Tokenize and pad sequences \\  & tokenizer = Tokenizer (0) \\  & vocab = tokenizeAndPreprocess(loadWordData (0), tokenizer) \\  & trainSeq, testSeq = convertAndPad (tokenizer, trainCut, testCut) \\
6: & Encode labels \\  & trainCate, testCate = encodeOneHot (trainLabel, testLabel) \\
7: & Build and train CNN model \\  & model = buildAndTrainCNNModel (vocab, w2v\_model, trainSeq, trainCate) \\
8: & Evaluate and visualize model \\  & evaluateAndVisualize (model, testSeq, testCate) \\
9: & Generate reports \\  & generateReports (model, testSeq, testCate) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Pseudocode for CNN models.  
Figure 4: The CNN-based classification framework.
# Use of Gradient-Based Shadow Detection for Estimating Environmental Illumination Distribution
## 3 Results
The local feature-based matching was performed when the shadows overlapped. Otherwise, this final module did not need to be performed. The proposed method generated the final rendering image in about 77.1-88.7 ms (11.3-13.0 frames/s). In general, since the change of the environmental illumination distribution does not happen in every frame, the proposed method can be applied to real-time applications. To further improve the performance, we will consider a parallel implementation in Compute Unified Device Architecture (CUDA), thus offering greater computation efficiency, in the near future. The proposed method assumes that the distance to the light source was constant. In this experiment, the radius of the hemisphere with the candidate light source was set to 2 m. To estimate the illumination information in a dynamic environment (one in which the light sources are moving), we will consider the illumination environment of a 3-D space instead of a hemisphere in the near future. Our proposed method can reduce the unwanted effects caused by the threshold values during edge-based shadow detection, in addition to those caused by the sampling position during point-based illumination estimation. Our proposed method was tested on an untextured shadow surface, but it can also be applied for the detection of shadows on textured shadow surfaces by using the illumination invariant conversion [2,5]. The proposed method is applicable for model-based line segment detection in the image, with non-salient and cluttered gradient distribution. In further studies, we will consider the case where there are area light sources of a certain size, such as incandescent lamps, in the scene. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **AR Marker** \\ **Recognition** \\ \end{tabular} } & \begin{tabular}{c} **Candidate** \\ **Shadow** \\ **Generation** \\ \end{tabular} & \begin{tabular}{c} **Image** \\ **Gradient** \\ **Computation** \\ \end{tabular} & \begin{tabular}{c} **Gradient-Based** \\ **Matching** \\ \end{tabular} & \begin{tabular}{c} **Non-Maximum** \\ **Suppression** \\ \end{tabular} & \
\begin{tabular}{c} **Local** \\ **Feature-Based** \\ **Matching** \\ \end{tabular} &
\begin{tabular}{c} **Total** \\ **Factuating** \\ \end{tabular} \\ \hline
8.5 & 15.0 & 10.6 & 42.0 & 1.0 & 11.6 & 88.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Computation time (ms) of modules. Figure 8: **(a–d)** Input images for Cases 2 to 5 (from left to right); **(e,f)** the teapot rendering images by the previous method and the proposed method in Case 4. We will include the shading technique for more realistic images in augmented reality according to changing environment illumination.
# RTK with the Assistance of an IMU-Based Pedestrian Navigation Algorithm for Smartphones
## 2 Materials and Methods
### Implementation of RTK
#### 2.1.1 EKF Formulation  
RTKLIB is based on the extended Kalman filter (EKF). The EKF is a nonlinear version of the Kalman filter (KF), which can help solve nonlinear state estimation. A discrete nonlinear system at epoch \(t_{k}\) can be described with two formulas:  
\[\hat{x}_{k}=f\left(x_{k-1},u_{k},w_{k}\right) \tag{1}\]  
\[y_{k}=h\left(x_{k},v_{k}\right) \tag{2}\]  
Here, \(\hat{x}_{k}\) and \(y_{k}\) are the estimated state vector and measurement vector at epoch time \(t_{k}\), respectively. \(w_{k}\) and \(v_{k}\) are the process and observation noises, which are both assumed to be multivariate Gaussian noises with zero mean and covariance \(Q_{k}\) and \(R_{k}\), respectively. \(u_{k}\) is defined as the control vector. We can utilize the process function \(f\) to compute the predicted state at epoch \(t_{k-1}\) from the state at epoch \(t_{k}\) and utilize measurement function \(h\) to compute the predicted measurement at epoch \(t_{k}\) from the predicted state at epoch \(t_{k}\). We define two Jacobian matrices \(F_{k-1}^{k}\) and \(H_{k}\) as the matrix of partial derivatives of function \(f\) and function \(h\), respectively:  
\[F_{k-1}^{k}=\frac{\partial f}{\partial x}|_{\hat{x}_{k-1}} \tag{3}\]  
\[H_{k}=\frac{\partial h}{\partial x}|_{\hat{x}_{k}^{-}} \tag{4}\]  
The procedure of EKF can be divided into two parts at each epoch: predicting and updating.  
Predicting aims to provide predicted state estimate \(\hat{x}_{k}^{-}\) and covariance estimate \(P_{k}^{-}\) as follows:  
\[\hat{x}_{k}^{-}=f\left(\hat{x}_{k-1},u_{k},0\right) \tag{5}\]  
\[\hat{P}_{k}^{-}=F_{k-1}^{k}\hat{P}_{k-1}{F_{k-1}^{k}}^{T}+Q_{k} \tag{6}\]  
Updating aims to provide updated state estimate \(\hat{x}_{k}\) and updated covariance estimate \(P_{k}\) as follows:  
\[K_{k}=\hat{P}_{k}^{-}{H_{k}}^{T}(H_{k}\hat{P}_{k}^{-}{H_{k}}^{T}+R_{k})^{-1} \tag{7}\]  
\[\hat{x}_{k}=\hat{x}_{k}^{-}+K_{k}(y_{k}-h(\hat{x}_{k}^{-},0)) \tag{8}\]  
\[\hat{P}_{k}=\left(I-K_{k}H_{k}\right)\hat{P}_{k}^{-} \tag{9}\]
## 5 Simulation Results and Discussion
### Test Systems and Results
#### 5.1.2 Test System 2  
The medium-size ELD problem with 40 thermal units with the valve-point loading effect and transmission loss is considered. The total power demand is 10,500 MW, with input data taken from [24]. The total power output and fuel cost were determined for the 40-unit systems using several metaheuristic techniques, including OIWO [24], BBO [13], DE/BBO [22], SDE [39], and GAAPI [40], alogn with the proposed RLGWO, and the results are given in Table 3. A comparison of the convergence characteristics of the 40-unit system between the original GWO and RLGWO algorithms with respect to fitness value is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Generation Cost**} & \multirow{2}{*}{**Time (s)**} & \multicolumn{2}{c}{**No. of Hits of the**} \\ \cline{2-2} \cline{4-6}  & **Max** & & **Min** & **Average** & **Minimum Solution** \\ \hline RLGWO & 24,514.78 & 24,514.78 & 24,514.78 & 3.7 & 50 \\ ORCCRO & 24,513.91 & 24,513.91 & 24,513.91 & 8 & 50 \\ OIWO & 24,514.83 & 24,514.83 & 24,514.83 & 5.3 & 47 \\ SDE & NA & 24,514.88 & 24,516.31 & NA & NA \\ DE/BBO & 24,515.98 & 24,514.97 & 24,515.05 & 11 & 46 \\ BBO & 24,516.09 & 24,515.21 & 24,515.32 & 15 & 44 \\ ICA-PSO & 24,589.45 & 24,540.06 & 24,561.46 & 10.4 & NA \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between different methods taken after 50 trials (13-unit system with loss).  
Figure 4: Comparative convergence characteristic of original GWO and proposed RLGWO algorithm for the 13-unit system.  
Figure 5: Results for different methods for the 13-unit system (50 trials): (**a**) boxplot of the final generation cost; (**b**) time and number of hits of the minimum solution.  
in Figure 6. The magnified results in Figure 6 show that RLGWO obtained a lower cost for this system.  
Table 4 presents the maximum, minimum, and average fuel cost obtained using the RLGWO, DE/BBO, SDE, OIWO, and GAAPI methods over 50 trials. These results are visualized in Figure 7. The OIWO produced the lowest and densest results of the five algorithms, as seen in Figure 7a. The RLGWO was second in terms of identifying the lowest cost, but was still always able to identify the lowest cost. As seen in Figure 7b, RLGWO and ORCCRO found the minimum result 50 times among 50 trials, but the solution found by ORCCRO was higher than that determined using RLGWO. Furthermore, RLGWO found the solution the fastest among the five algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Unit Number**} & \multicolumn{8}{c}{**Methods**} \\ \cline{2-9}  & **RLGWO** & **OIWO** & **DE/BBO** & **ORCCRO** & **BBO** & **SDE** & **GAAPI** \\ \hline
1 & 113.9954 & 113.9908 & 111.04 & 111.68 & 112.54 & 110.06 & 114 \\
2 & 114 & 114 & 113.71 & 112.16 & 113.22 & 112
# Adaptive Navigation in Collaborative Robots: A Reinforcement Learning and Sensor Fusion Approach
## 3 Methodology
### Obstacle Avoidance Algorithm  
For obstacle avoidance, the suggested approach makes use of a model-free reinforcement learning (RL) algorithm. This approach is recommended over other machine-learning (ML) algorithms because it may learn optimal behavior directly from interactions with the environment, making it ideal for dynamic, real-time decision-making tasks. Unlike other algorithms, RL does not require large, labeled datasets and can adapt to changing conditions over time [34]. This flexibility makes RL particularly suited for applications like robotics, gaming, and autonomous vehicles, where traditional machine-learning approaches may struggle. Model-free RL continuously adjusts its approach based on real-time feedback. Also, for microcontrollers with less programming memory, this model-free RL algorithm can be used due to low memory requirements as compared to other supervised ML algorithms [35; 36].  
In this reinforcement learning-based algorithm, the test vehicle navigates its environment by integrating sensor inputs and learning from interactions. The process begins with the front ultrasonic sensor measuring the distance to the nearest obstacle. If an obstacle is detected within a specified range (40 cm or 60 cm), the test vehicle must avoid it. The test vehicle then chooses a direction to turn--left or right--based on a 50/50 probability. Post-turn, the test vehicle continues forward. The algorithm tracks the success rate of each direction: If turning in a given direction successfully avoids the obstacle, the success rate for that direction increases; if it results in another obstacle, the success rate decreases. To balance exploration and exploitation, a random element is introduced. A random number between 1 and 100 determines the test vehicle's behavior: If the number is between 1 and 10, the test vehicle performs a random turn to explore new paths; if the number is between 11 and 100, it exploits the direction with the higher success rate. Meanwhile, the left and right ultrasonic sensors help maintain the vehicle on its path by avoiding side crashes through vehicle tilting rather than detecting hard obstacles. One potential source of the problem that can arise from this algorithm (also known as reward hacking issue in reinforcement learning) is when difference between the success rate values becomes too large, causing the turn decision with the higher value to dominate. To avoid this, a maximum value can be predefined for success rate [37]. Once a success rate reaches this maximum value, it can be reset to the initial value.  
This approach exemplifies how reinforcement learning can be effectively applied to small robot vehicles such as the test vehicle used in this study, balancing the need for exploration of new strategies and exploitation of successful ones to optimize performance in real time. The flow diagram is shown in Figure 4.
# Imaging the Permittivity of Thin Film Materials by Using Scanning Capacitance Microscopy
## 5 Conclusions  
By using the double scanning technology, the topography and SCM images of the sample could be obtained almost at the same time. From the topography image of the film, the rise and fall of every point corresponded to the thickness change \(\Delta h\). The thickness \(h\) of the film at every point could be set as \(h=\overline{h}+\Delta h\). Every signal of SCM could be changed to the value of \(\frac{\mathrm{d}C^{d}_{\mathrm{ML}}}{\mathrm{d}\Delta z}\) at the corresponding point. With the tip radius \(R\), and cone half angle \(\theta\), etc., the SCM image could be reconstructed to the 2D \(h/\varepsilon_{\mathrm{r}}\) image. Then, the two-dimensional (2D) image of the relative permittivity could be acquired. The average value of \(\varepsilon_{\mathrm{r}}\) of MoO\({}_{3}\) film was about 8.0, and that of MoO\({}_{3}\) film-doped NaCl was about 9.5. The experimental results were quantitatively consistent with other experimental results of the same material. The technique could provide a novel method for imaging the relative permittivity with nanometer resolution and be helpful for the study of photoelectric materials.  
Conceptualization, X.D. and G.L.; data curation, Y.L. and T.C.; formal analysis, Y.L.; funding acquisition, X.D.; investigation, Y.L.; methodology, X.D.; writing--original draft, G.L. and Y.L.; writing--review and editing, T.S. and D.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 61871410).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors acknowledge [PERSON] for preparing the sample, and [PERSON] for helpful discussion about the experiment.  
The authors declare no conflict of interest.
# Anthocyanin Delivery Systems: A Critical Review of Recent Research Findings
## 3 ACN Delivery Systems  
Different types of colloidal particles can be used as viable delivery systems for ACNs (Figures 3 and 4), including, nanostructured lipid carriers (NLCs), solid-lipid nanoparticles (SLNs), liposomes, emulsions, and others [65]. In encapsulation, highly sensitive constituents are enclosed within a wall material before delivering inside a system and could be formed by nanoencapsulation (<1 \(\upmu\)m) or microencapsulation (1-1000 \(\upmu\)m) depending upon the particle size [66]. This significantly improves the stability, water solubility, and bioavailability of bioactive compounds [67] and prevents the enzymatic and chemical degradation of ACNs [68]. However, conventional microencapsulation has not proven effective in the context of stability studies for ACNs, primarily because of their bigger particle size and low encapsulation efficiency [69]. Figure 3: Pictorial representation showing the interaction of polysaccharide-based wall material (pectin) with the ACNs color pigments as encapsulating agents to increase the stability of ACNs. Different formulation and storage techniques related to encapsulation are summarized in Table 1. Protein-based delivery systems are frequently used in microencapsulation owing to various favorable attributes, such as cost-effectiveness, porous structure, surface-active nature, and ability to self-assemble, bind water molecules, and form stable, biodegradable hydrogels [70]. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Source** & **Encapsulation Material** & **Study Outcome** & **Reference** \\ \hline Spray drying & Grape juice & Maltodextrin, whey proteins, soybean & 120 days of ACNs stability at 35 \({}^{\circ}\)C & \\ \hline  & Pomegranate powder & Gum arabic and modified starch & Higher ACNs retention (60\%) & [72] \\ \hline  & Jussara & Starch, inulin, and Maltodextrin & ACNs color maintained together with light stability at 50 \({}^{\circ}\)C for 38 days & [73] \\ \hline  & Barberry & Gum arabic, Maltodextrin, and gelatin & Microencapsulation efficiency of 92.8\% was achieved during the study & [74] \\ \hline  & Grape pomace & Maltodext & 11 days’ half-life stability of encapsulated ACNs into apple pure matrix at 35 \({}^{\circ}\)C & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Methods for encapsulation of anthocyanin using different material and their outcomes. Figure 4: Formulation of different conventional and advanced delivery systems used for encapsulation of ACN pigments in different biopolymer complexes (carbohydrates, protein, lipids), and wall materials.
# Multi-Scale Recurrence Quantification Measurements for Voice Disorder Detection
## 2 Extraction of Multi-Scale Recurrence Quantification Measurements (MRQMs)
### Gammatene Filter Bank  
The research on auditory perception found that analyzing and processing acoustic signals by the basement membrane in the human cochlea is equivalent to filtering and frequency decomposition. The gammatone filter bank is very close to the acoustic characteristics of the auditory, and only a few parameters can simulate the basement membrane's filtering and frequency decomposition functions. The gammatone filter bank is a band-pass filter whose impulse response is composed of the Gamma function [32].  
\[g_{i}(t)=\partial t^{(N-1)}e^{-2\pi Bt}\cos(2\pi f_{i}+\varnothing)u(t) \tag{1}\]  
where \(\partial\) is a constant, \(N\) is the filter order. \(f_{i}\) is the center frequency of the \(i\)th channel of the gammatone filter. \(\varnothing\) is the phase, usually set to 0 because the human auditory perception system is not sensitive to the phase. \(u(t)\) is the unit pulse function. B is determined by th ERB of the gamma pass filter [33].  
\[\text{B}=1.019\text{-}ERB(f_{i}) \tag{2}\]  
The relationship between the ERB and the center frequency of the \(i\)th channel of the gammatone filter is as follows [34]:  
\[ERB(f_{i})=24.7+0.108f_{i} \tag{3}\]  
In the frequency range of 0-12.5 kHz, this work uses a 24-channel gammatone filter bank to decompose the glottal signal after iterative adaptive inverse filtering. The corresponding magnitude response is shown in Figure 2.  
It can be seen from Figure 2 that, in the low-frequency part, the bandwidth of the gammatone filter bank is relatively narrow, and the frequency intervals of the filter banks are very close. In the high-frequency part, the bandwidth of the gammatone filter bank is relatively large, and the frequency intervals of the filter banks are relatively far apart. Most of the effective information of the pathological voice exists in low-frequency bands, so multi-scale features extracted by gammatone processing signals can effectively detect pathological voice. The specific fixed values of the gammatone filter band used in this work are listed in Table 1. In particular, in the first eight frequency bands, the bandwidth is less than 100 Hz, and the processing of low-frequency signals is very me  
Figure 2: A 24-channel gammatone magnitude response.  
frequency of the eighth frequency band is 549 Hz, which is similar to the main frequency upper limit of the glottal signal, so the combination of glottal signals and gammatone can produce good results.