esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methodology
#### 2.3.3 Trend Analysis  
A trend analysis can describe the temporal variation in wind speed, and we use the least squares method and the Mann-Kendall trend test as a trend analysis method [40; 41; 42]. In the least squares method, we want to find a linear relationship, \(y=kx+b\), such that the line is as close as possible to all data points. Parameters \(k\) and \(b\) of the linear relationship can be estimated by minimizing the sum of the squares of the differences between the actual observations \(y_{i}\) and the predictions of the linear model. This can be expressed as solving the following system of equations to obtain least squares estimate:  
\[\left\{\begin{array}{l}k=\frac{\sum_{i=1}^{n}x_{i}y_{i}-\frac{\sum_{i=1}^{n}x _{i}y_{i}}{2}}{\sum_{i=1}^{n}x_{i}^{2}-\frac{\left(\sum_{i=1}^{n}x_{i}^{2} \right)^{2}}{n}}\\ b=\frac{\sum_{i=1}^{n}y_{i}}{n}-k\frac{\sum_{i=1}^{n}x_{i}}{n}\end{array}\right. \tag{3}\]  
where \(k\) is the slope, and \(b\) is the intercept. If \(k\) is greater than 0, the random variables tend to be positively correlated; if \(k\) is less than 0, the random variables tend to be negatively correlated.  
In the Mann-Kendall Trend Test, for given wind speed time series data, \(v_{1}\), \(v_{2}\), \(\ldots\), \(v_{n}\), the statistic \(S\) is defined as  
\[S=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}sgn\big{(}v_{j}-v_{i}\big{)} \tag{4}\]  
for each difference value, we compute the sign function according to Equation (5):  
\[sgn\big{(}v_{j}-v_{i}\big{)}=\left\{\begin{array}{l}1,if\ v_{j}>v_{i}\\ 0,\ if\ v_{j}=v_{i}\\ -1,\ if\ v_{j}<v_{i}\end{array}\right. \tag{5}\]  
The trend analysis Z statistic was calculated using Equation (6), where \(Var(S)\) is the variance in statistic s, calculated using Equation (7):  
\[Z=\left\{\begin{array}{l}\frac{S-1}{\sqrt{Var(S)}},if\ S>0\\ \frac{S+1}{\sqrt{Var(S)}},\ if\ S<0\\ 0,\ if\ S=0\end{array}\right. \tag{6}\]  
\[Var(S)=\frac{n(n-1)(2n+5)-\sum_{p=1}^{q}t_{p}\big{(}t_{p}-1\big{)}\big{(}2t_{ p}+5\big{)}}{18} \tag{7}\]  
where \(n\) is the length of the sequence, \(q\) is the number of equal data groups in the sequence, and \(t_{p}\) is the number of data points in group \(p\) of equal data. Subsequently, the significance of the trend is determined based on the \(Z\) value and the chosen significance level.
## 2 PFC Converters Power Components Design Analysis and Selection
### The Boost Inductor Design and Manufacturing  
#### 2.1.1 Conventional PFC Boost Inductor  
There are many considerations to consider when selecting the PFC boost inductors, such as the inductance value, the direct current resistance (DCR), and the saturation current value. The first step to designing the required boost inductor is to calculate the required inductance value based on the required circuit ripple current which is usually calculated as the percentage (10-40%) of the maximum input current to the boost converter [33]. For the conventional CCM PFC, the boost inductor value can be expressed as given [5]:  
\[\text{L}_{\text{b}}=\frac{1}{\text{\%Ripple}}\times\frac{\text{V}_{\text{in,min}}^{2}}{\boldsymbol{\eta}\ \text{P}_{\text{o}}}\times\left(1-\frac{\sqrt{2}\ \text{V}_{\text{in,min}}}{\text{V}_{\text{o}}}\right)\frac{1}{\text{F}_{ \text{sw}}} \tag{1}\]  
where \(\text{V}_{\text{in,min}}\) is the minimum supply input voltage, %Ripple is the inductor ripple current percentage, \(\boldsymbol{\eta}\) is the desired efficiency, \(\text{P}_{\text{o}}\) is the power rating, and \(\text{F}_{\text{sw}}\) is the converter switching frequency. The circuit operation of the conventional PFC boost converter working in the continuous current conduction mode (CCM) can be represented by two operation modes. Figure 3 depicts the waveforms of the inductor voltage and currents for the boost converter at different operation modes, where \(\text{V}_{\text{L}}\) represents the inductor voltage, \(\text{I}_{\text{L}}\) represents the inductor current, D is the converter duty cycle, \(\text{T}_{\text{on}}\) is the on-switching time, \(\text{T}_{\text{off}}\) is the off-switching time, \(\text{T}_{\text{s}}\) is the switching time, \(\text{V}_{\text{in}}\) is the boost converter input voltage, \(\text{V}_{\text{o}}\) is the output voltage, and \(\Delta\text{I}_{\text{LPPP}}\) is the inductor ripple current peak to peak value. Figure 3: Boost inductor voltage and currents waveforms of the conventional PFC topology. Mode-1, when the switch (Q\({}_{\text{b}}\)) is closed, the energy is stored in the inductor, generating a magnetic field. Mode-2, when Q\({}_{\text{b}}\) is opened, the current circuit impedance increases, thereby reducing the current, and the magnetic field previously created is reduced to let the current toward the load, thus, the inductor voltage polarity is reversed, which places the two sources in series, and charging the capacitor (C\({}_{\text{b}}\)) through the fast switching diode (D\({}_{\text{b}}\)).
## 3 Results
### Computational Model Setup
#### 3.2.3 Pollution Source Distribution Characteristics and Settings  
Table 2 presents the summary statistics of geographical data. Since it is unrealistic to obtain precise continuous data across the entire regional plane, discrete point samples are usually collected. Before being converted into attribute fields, the unit of Shape_Length is meter (m) and the unit of Shape_Area is square meter (m\({}^{2}\)). After the conversion, the data type of the feature points is double precision, with a numerical format. Subsequently, spatial interpolation techniques are used to integrate the data from monitoring stations. We employed the Inverse Distance Weighting (IDW) method to analyze and synthesize information such as wind speed, wind direction, and pollution indices, thus generating a comprehensive regional dataset.  
We systematically collected the AQI for NO\({}_{2}\) values from various regions of Urumqi during the winter season. Utilizing the inverse distance weighting (IDW) method, we simulated the monthly average NO\({}_{2}\) AQI for the period from November 2023 to June 2024 (winter), as depicted in the schematic diagram in Figure 9.  
Urumqi's geographical layout, characterized by a pronounced north-south elongation and east-west constriction, combined with the limitations imposed by the outer-ring road,  
\begin{table}
\begin{tabular}{c c c} \hline
**District** & **Shape\_Length** & **Shape\_Area** \\ \hline Midong & 4.760512 & 0.384539 \\ Shuimogou & 1.248445 & 0.031278 \\ Xinshi & 1.038806 & 0.029396 \\ Saybag & 1.192238 & 0.046956 \\ Tianshan & 0.824248 & 0.019073 \\ \hline \end{tabular}
\end{table}
Table 2: Geographic data information statistics.  
Figure 9: Characteristics of pollutant distribution.  
results in a significant north-south disparity in NO\({}_{2}\) levels. Notably, there is a distinct accumulation of NO\({}_{2}\) in the city-center districts of Shuimogou, Tianshan, and the New Urban District, where the average AQI values exceed 39. Conversely, NO\({}_{2}\) AQI values gradually decrease in the eastern and western directions, remaining consistently below 23 in Dabancheng District and Urumqi County. Local vehicle exhaust, industrial emissions, and suburban contributions are the primary sources of NO\({}_{2}\) in Urumqi. This insight enables us to better observe and manage NO\({}_{2}\) pollution at the northwestern air inlet. Given that suburbs are a key source of NO\({}_{2}\), we can more effectively monitor pollutant diffusion and implement countermeasures. Therefore, we set the NO\({}_{2}\) sources as both the northwestern air inlet and local sources.
# Verifiable (2, \(n\)) Image Secret Sharing Scheme Using Sudoku Matrix
## 5 Conclusions  
This paper proposes a verifiable image secret sharing method that can divide a secret image into several shares, and then embed the shares containing authentication codes into cover images as stego images. Later, the secret image can be completely reconstructed, and the authentication codes can be extracted via any two stego images. Experiments show that the hiding capacity of the proposed method exceeds that of other approaches for both smooth and complex types of images. Moreover, the visual quality of the stego images in our approach is superior to that found in the experiments, while the BER values are much higher than those achieved.  
Methodology, Y.-H.C. and M.-H.C.; formal analysis and validation, Y.-H.C., M.-H.C. and J.-Y.L.; writing--original draft preparation, Y.-H.C., J.-Y.L. and S.-H.C.; writing--review and editing, Y.-H.C. and S.-H.C. All authors have read and agreed to the published version of the manuscript.  
The work was supported in part by the Ministry of Science and Technology of the Republic of China, Taiwan, under Grant MOST 110-2221-E-182-026-MY3, and in part by the Kaohsiung Chang Gung Memorial Hospital, with grant number CMRPD3M0011.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 9: An authentication example. (**a**) Special image (fake logo). (**b**) Standard image (fake logo). (**c**) Uniform noise (fake logo). (**d**) Image with tampering (fake cover). (**e**) Image without tampering (real cover). (**f**) Reconstructed secret image. (**g**) Detected inauthentic region (painted in black).
## 3 Results
### Microbial Community Profile of the Samples from Production and Control Wells
To further explore microbial diversification after flooding, a comparative community analysis of the production and control well was conducted at the genus level (Figure 5). The bacterial community composition of the two samples was similar at the genus level but the proportion of the dominant bacteria was different. In the control well W202, the hydrocarbon-degrading bacterium _Arcobacter_ was the most abundant genus, representing 60.1% of the retrieved sequences; this was followed by _Desulfovibrio_, with a proportion of 14.7%. After treatment with nutrients, the abundance of _Arcobacter_ increased to 71.0% in the W226 well, whereas the abundance of _Desulfovibrio_ dropped sharply to 0.96%. The populations of other SRBs, including _Desulfoverus_, _Desulfovaro_, and _Desulfogaleba_, decreased in the W226 well, from 3.4% to 2.5%, 0.87% to 0.15%, and 0.64% to 0.02%, respectively. Nevertheless, the abundance of hydrocarbon-utilizing _Thalasslituus_ spp. increased to 3.7% at low temperatures. The community analysis at the genus level indicated that the bacteria had been enriched in fewer genera. However, the community composition of the archaea at the genus level was very different between the two samples. In the control well W202, _Methanolobus_ was the most abundant genus, accounting for more than 66%; it was followed by _Methanolicnia_ (9.7%), _Methanoliculus_ (7.3%), and _Methanosaeta_ (5.9%). In the W226 sample, _Methanolobus_ spp. decreased significantly to 0.3%, and the dominant _Methanocalculus_ (50.8%) and _Methanothermococcus_ (30.6%) genera accounted for more than 80% of the retrieved sequences. The injection of indigenous activators has a significant impact on archaea at the genus level.
## 2 Synthetic Aperture Radar Interferometric Processing
### Interferometric Processing of SAR Images  
There are five steps in the procedures for processing InSAR images. They can be summarized as shown in Figure 5 as follows:  
1. Acquisition of SAR images;
2. Co-registration of two SAR images;
3. Generation of interferogram;
4. Phase unwrapping of the wrapped interferometric phase;
5. Geocoding of DEM.
6. DEM is the final product of InSAR image processing.
# Multi-Color Channels Based Group Sparse Model for Image Restoration
## 3 Proposed Multi-Color Channels Based GSR Model
### Implementation of the Proposed Method
#### 3.2.2 Adaptive Parameters  
In general, \(\lambda\) is set to a fixed value empirically. However, a fixed value cannot guarantee the stability of the whole algorithm. In order to overcome this challenge, an adaptive parameter-setting scheme is used to make the proposed multi-color-channels-based GSR model more stable and practical. Using spatially adaptive Laplacian priors, the regularization parameters adaptively update in each iteration [36]:  
\[\lambda_{i}=\frac{2\sqrt{2}\sigma_{w}^{2}}{\sigma_{i}+\epsilon} \tag{17}\]  
Here \(\sigma_{i}\) is the \(i\)th local estimated variance, and \(\epsilon\) is a sufficiently small constant to guarantee a no-zero denominator. The likelihood estimation [36] of \(\sigma_{i}\) can be represented as  
\[\hat{\sigma}_{i}=\sqrt{\max\left(\hat{\gamma}_{i}^{2}/m-\sigma_{m}^{2},0 \right)} \tag{18}\]  
where \(\hat{\gamma}_{i}\) denotes the singular value of the estimation \(r_{i}^{C}\), and \(m\) is the number of similar patches. The proposed method adopts an iterative regularization strategy [8] to alternatively update the estimation of the noise variance \(\sigma_{w}\) and the signal variance \(\sigma_{i}\).  
\[\hat{\sigma}_{w}^{k}=\kappa\sqrt{\sigma_{w}^{2}-\left\|y-y^{k+1}\right\|_{2}^{2}} \tag{19}\]  
\[\hat{\sigma}_{i}^{k+1}=\sqrt{\max\left(\left(\hat{\gamma}_{i}^{k}\right)^{2}/m- \left(\sigma_{m}^{k}\right)^{2},0\right)} \tag{20}\]  
Here \(\kappa\) is the scale factor controlling the noise variance, and \(k\) is the number of iterations. The complete description of the proposed GSR model based on multi-color channels is shown in the following Algorithm 1.
## 2 Background and Related Work
### Graph Signal Processing
For instance, two popular graph filters are (a) personalized PageRank [20; 21; 22], which arises from Markovian-like equivalents to random walks with restart within graphs and have parameters \(f_{k}=(1-a)a^{k}\) controlled by one hyperparameter \(a\in[0,1]\); and (b) HeatKernel [23; 24] which emulates heat diffusion dynamics in graphs with parameters \(f_{k}=e^{-t}t^{k}/k!\), where \(k\in\{1,2,3,\dots\}\) is the number of hops away in which maximal importance is placed. These filters are low-pass in the sense that parameters \(f_{k}\) are generally larger for smaller \(k\), which in turn translates into a lesser impact on eigenvalues with absolute values closer to \(0\) than high-frequency eigenvalues with larger absolute values. In practical terms of node domain operations, low-pass filters place more emphasis onto diffusing node values of fewer hops away and thus introduce a type of graph signal smoothing that removes non-local implicit node relations, which can be thought of as high-frequency noise. The above spectral analysis is tailored to the symmetric normalization of graph adjacency matrices and undirected graphs, i.e., for which \(\hat{A}[u,v]=\hat{A}[v,u]\). However, some filters, such as personalized PageRank, are better known for non-symmetric normalizations arising from Markov chain modeling, such as \(\hat{A}=AD^{-1}\), where graphs are defined by directed edges. Spectral theories are also available for these filters, but lay outside the scope of our work. Instead, when analyzing bipartite project-library graphs, we work with undirected edges that allow the transfer of graph signal values from both projects to libraries and libraries to projects (otherwise, filtering with graph signals would be stuck at recommending only immediate neighbors). Therefore, we adopt the adjacency matrix normalization of (2).
# UAV-Based Privacy-Preserved Trustworthy Seamless Service Agility for NextG Cellular Networks
## 6 Security and Performance Analysis
### Performance Results
#### 6.3.2 Authentication Scalability  
Here, we evaluate our designed authentication solution's ability to expand for realistic large networks. Table 3 numerically reveals our delegated-based proposed authentication framework scalability based on the total count of the transmitted and received messages for each entity involved in the authentication (UEs, UAVs, and Guest eNBs). In particular,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**UE–Home eNB Connection Establishment**} \\ \hline \hline  & \multicolumn{2}{c}{0.04 ms} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Authenticated Inter-Operator Connection Performance.  
the authentication scalability test is a pressure qualitative test to our proposed scheme to check the scalability of our framework under an extensive number of UEs requesting data trafficking services at the same time. Hence, this scalability test measures our proposed scheme's capability of handling more extensive networks.  
In Table 3, we increase the number of UEs supported by 1 UAV to show the increasing load effect on the UAV battery Life. The table demonstrates a linear increase in the total transmitted and received messages for both the UAV and the Guest-eNB and an indifferent UE experience. It is worth noting that the proposed authentication does not add any message exchange payload toward the home network core; therefore, it is suitable when no communication is guaranteed toward the home network. Moreover, all the UAV transmitted messages are short-ranged, which is more energy-efficient than reaching the home network core for authentication, and it obsoletes the need for key management.
## 2 Data and Methods
### Analysis Methods
#### 2.3.2 Buffer Analysis  
Buffer analysis is a method of spatial data analysis that automatically establishes polygonal buffer zones of a certain width around point, line, and polygon features in a database, thereby extending the spatial data in the horizontal direction. A buffer zone represents the range of influence or service area of a geographical spatial object in terms of scale. It is a dependent variable that changes with the form of the element being studied. From a mathematical perspective, a buffer zone is the neighborhood obtained for a given spatial object or set, and the size of the neighborhood is determined by the radius of the  
Figure 2: Site distribution of Sichuan Province.  
neighborhood or the conditions for establishing the buffer zone. Therefore, for a given object A, its vector buffer can be defined as:  
\[P=\{x|d(x,A)\leq r\} \tag{2}\]  
In the formula, \(A\) is a given spatial object or collection, \(P\) represents the buffer range of object \(A\), and \(d\) generally refers to the Euclidean distance, although other types of distances can also be used. In this study, the Euclidean distance is employed. Additionally, \(r\) is the radius of the neighborhood or the condition for establishing the buffer zone. In this study, 1 km and 3 km buffer zones around rivers were created to investigate the relationship between the distribution of archaeological sites and their distance from water sources.
# Finger-Gesture Recognition for Visible Light Communication Systems Using Machine Learning
## 4 System Implementation
### Training and Evaluation  
As a proof of concept, data were collected for four different hands. The smallest span (from extended little finger to thumb) was measured as 16.3 cm and the largest hand had a span of 21.4 cm. Data were collected for the four hands on two separate measurement campaigns. During a first session, data were collected for training the neural network algorithm. A second validation session was conducted on the same day for evaluating the performance of the trained neural network. The data were divided equally into training and verification sets; that is, the training to verification ratio was 50% of all data. This figure is common in ML research and some systems use higher amounts of training to achieve high accuracies. Over-fitting can occur if the system is trained with too much data, and conversely, under-fitting if the training ratio is too low. The LSTM algorithm predicts the next sample in a sequence, and hence the most likely gesture classification, subject to the noise, variation, and irregularities present in human movement. The LSTM was trained using the stochastic gradient descent with momentum (SGDM) optimizer. This is a commonly applied solver with accelerated gradients to reduce the solving time [40]. After training, the LSTM was switched to validation mode in which a section from the nontraining set is evaluated. The output of the stochastic gradient solver can be sensitive to the initial random seed used and, therefore, a Monte Carlo type simulation was set-up averaging results over 50 cycles each with a different random seed. The accuracy and loss versus iteration performance for one of the random seed settings is shown in Figure 7.
## 4 Discussion  
### Performance on Different Data SMRs  
The experiments presented in Section 3.3 compared the ordinary DINEOF and the proposed CS-DINEOF methods, demonstrating that the overall reconstruction results of the CS-DINEOF method were superior to the DINEOF method. However, the impact of the data missing rate on reconstruction results should also be considered. In spatiotemporal datasets, the information required for reconstructing the missing value at a specific point is derived from data at the same location but in other images (considered as temporal modes) and from data in non-missing areas within the same images (spatial modes). As the missing rate increases, the decline in the volume of valid data diminishes the reliability of relevant information. Consequently, these spatial and temporal modes no longer dominate the variance contribution in reconstruction. Instead, missing values are approximated from the mean value or other secondary modes, thereby reducing the accuracy of the reconstruction.  
To further investigate the optimization effect of the CS-DINEOF method under different missing conditions, the SMR and the RMSE between the original and reconstructed values at cross-validation points were calculated for each image in the dataset. As shown in Figure 12, although the RMSE trends of both the CS-DINEOF and ordinary DINEOF methods were similar across different SMRs, the CS-DINEOF method consistently obtained lower reconstruction errors in almost all cases, with an average reduction of 0.0281 mg/m\({}^{3}\). Hence, it is evident that the CS-DINEOF method can achieve superior reconstruction performance over the DINEOF method under different missing conditions.  
Figure 11: Density scatterplots of original and reconstructed chl-a values by the CS-DINEOF method at cross-validation points on (**a**) 15 February, (**b**) 26 April, (**c**) 22 September, and (**d**) 28 November in 2019.