esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### Weighted Bayesian Kernel Machine Regression
The prior is defined as follows:  
\[\pi(\gamma_{m}\mid\omega,a_{\gamma},b_{\gamma})=\omega\delta_{0}(\gamma_{m}) +(1-\omega)g(\gamma_{m}\mid a_{\gamma},b_{\gamma}),\ m=1,\dots,M, \tag{4}\]  
where \(\delta_{0}(\cdot)\) denotes a Dirac measure with point mass at \(0\) and \(\omega\) represents the probability that \(\gamma_{m}=0\). The mixing parameter \(\omega\) is assigned the Jeffreys' prior distribution (see, e.g., [PERSON] et al., 2014 [12]; [PERSON], 2022 [13]; [PERSON] et al., 2024 [18]) over the interval \((0,1)\), ensuring a non-informative prior that allows the model to adaptively determine which variables are important. The slab component \(g(\cdot\mid a_{\gamma},b_{\gamma})\) is modeled as an inverse uniform distribution on \(\mathbb{R}^{+}\), defined as  
\[g(\gamma_{m}\mid a_{\gamma},b_{\gamma})\propto\frac{1}{b_{\gamma}-a_{\gamma} }I(a_{\gamma}<\gamma_{m}^{-1}<b_{\gamma}), \tag{5}\]  
where \(I(\cdot)\) is an indicator function. The hyperparameters \(a_{\gamma}\) and \(b_{\gamma}\) are set such that the mean of \(\gamma_{m}\) is 5, and the variance is 5, implying that the model assumes the importance of most variables to be moderate on average, while allowing for a sufficiently wide range of variability. This configuration strikes a balance between emphasizing variables with significant contributions and excluding irrelevant ones, enabling the model to adapt flexibly to the data. In this prior, the mixing probability \(\omega\) plays a critical role by balancing the spike-and-slab components. When \(\omega\) is high, the variable is likely irrelevant, and its weight is set to zero. Conversely, a low \(\omega\) indicates that the variable contributes significantly to the prediction model, and its weight is sampled from the slab distribution. By incorporating the spike-and-slab prior, the model simultaneously achieves both variable selection and parameter estimation in a unified framework. This ensures that only the most influential environmental factors, such as dissolved oxygen, salinity, and nutrient supply, are included in the final predictive model, improving both interpretability and predictive accuracy.
## 3 Tracking Algorithm
### Hybrid Model Algorithm  
We are stimulated by the idea of a hybrid grid multiple model (HGMM) [16] algorithm and we propose an absolutely improved algorithm called the hybrid model algorithm. The model set in effect in the HGMM algorithm consists of two types of model subsets: a fixed coarse grid and an adaptive fine grid. The coarse subset is obtained by quantizing the mode space crudely and the fine subset is designed from the region surrounding the optimal estimate of the true mode. One cycle of HGMM consists of three steps: the first step is to obtain the mode estimate based on the fixed coarse grid; the second step is to design the fine grid using the mode estimate; third step is to run the variable structure-interacting multiple model algorithm. The hybrid model algorithm is not hybrid in every recursion like the HGMM algorithm but it is hybrid in the whole filtering stage (the initial filtering stage and the stable filtering stage). The differences between the hybrid model algorithm and the HGMM algorithm are shown in Figure 1.  
From Figure 1 we can know that the hybrid model algorithm is a hybrid of the multiple model algorithm and the single model algorithm in the whole tracking stage, but the HGMM algorithm includes many hybrids from the coarse grid and fine grid in the whole tracking stage.  
To solve the problem of big errors in the initial tracking stage using the single model algorithm because of the uncertain initial state and in the stable tracking stage using the multiple model algorithm  
Figure 1: Differences between hybrid model algorithm and hybrid grid multiple model (HGMM) algorithm.  
because of the excessive \"competition\" from the \"unnecessary\" models, we develop the hybrid model algorithm with multiple models in the initial tracking stage and a single model in the stable tracking stage. The turning point from the multiple model algorithm to the single model algorithm is an approximate time denoted as  
\[f=\frac{V}{\sqrt[2]{\left(R_{0}\sigma_{\text{A}}\right)^{2}+\left(R_{0}\sigma_{ E}\right)^{2}+\sigma_{R}^{2}}} \tag{10}\]  
\[t_{s}=fT \tag{11}\]  
where \(\sigma_{\text{A}},\sigma_{\text{E}},\sigma_{\text{R}}\) and \(R_{0}\) have been defined as parameters of the radar measurement in Section 2.2, respectively. \(T\) is the time interval for the Kalman filter recursion. The HGV tracking system enters into the stable tracking stage after the _f_-th Kalman filter recursion.  
More specifically, we use the multiple model algorithm before time \(t_{s}\) and use the single model algorithm after time \(t_{s}\). The tracking performance can be improved using the multiple model algorithm when the initial state is unknown in the initial tracking stage, and the tracking performance is better using the single model algorithm in the stable tracking stage.
# Methodology for Assessing Spatial Perception in Martial Arts
## 2 Materials and Methods
### Procedure  
The \"Spatial Perception\" application was installed on two 9 th-generation iPad mobile devices with a 10.2 inch screen. Athletes individually completed the test exercise. The research was conducted in several stages. At the preliminary stage, twenty taekwondo athletes (mean age: 11.4 years, SD = 2.48; qualification: 6 th-4 th Gup) participated in the initial testing of the developed application to assess its reliability and validity. Two measurements were conducted three weeks apart. At the main stage, the study aimed to further validate the application and analyze spatial perception differences among athletes of varying ages and skill levels. Participants were divided into two groups--group 1-33 athletes (mean age: 11.5 years, SD = 2.02; qualification: 8 th-3 rd Gup) and group 2-24 athletes (mean age: 18.2 years, SD = 2.40; qualification: 2 nd Gup-1 st Dan). Before testing at each stage, a warm-up session was conducted using tennis balls to activate visual and motor coordination mechanisms. The warm-up included throwing and catching the ball from the floor, in pairs, and against a wall. Each exercise lasted 2 min.  
A mobile application called \"Spatial Perception\" was developed for iOS devices using Swift 5 programming language and libraries such as UIKit, CoreData, AVFoundation, UniformTypeldentifiers, and SceneKit. The visual stimuli (2D and 3D geometric shapes) were created using the 3D modeling software Blender (version 4.0.2). The application includes four stages of tasks requiring participants to compare geometric shapes. Stage 1--comparison of 2D shapes (e.g., square, rhombus, hexagon, circle, triangle). Each shape is randomly rotated before display. Stage 2--comparison of 3D shapes (e.g., cube, cylinder, sphere, hexagonal prism, octahedron). Shapes are displayed in random positions within a three-dimensional space (Figure 1). During the 3 rd Stage, shapes begin rotating for 300 ms before stopping, after which the comparison task is performed. Stage 4--similar to Stage 3, but with additional distracting visual stimuli in the background (Figure 2). Participants responded by selecting the appropriate field: \"Same\" (green field) if the shapes were identical or \"Different\" (red field) if the shapes were different.
## 4 Experiments and Results
### Effects of LCCDNN and FCCDNN Parameters on Neuron Classification
#### 4.3.4 Comparison Experiment of Accuracy for Each Neuron Type  
According to the above experiments, the overall classification accuracies of different types of neurons were compared for two kinds of neuron datasets by using LCCDNNs and FCCDNNs. To further validate the advantages of the cumulative connected deep neural networks, the accuracy of each neuron type is analyzed in this section. The confusion matrix was used to compare the accuracy of each neuron type for the two deep learning models in the C. elegans dataset and zebrafish dataset. Figures 17 and 18 are the confusion matrices of the test sets for the LCCDNNs and FCCDNNs, respectively. The values on the diagonals represent the classification accuracies of interneuron, motoneuron, and somatic neuron for the LCCDNN model in Figure 17a and the FCCDNN model in Figure 18a. Similarly, the values on the diagonals represent the classification accuracies of large, mitral, output, and small neuron types in Figures 17b and 18b. The color is bluer in the matrix, so the deep learning model had a better classification performance for the corresponding neuron type. It can be seen that the classification accuracy of interneurons and somatic neurons in LCCDNNs was higher than that in the FCCDNNs for the C. elegans dataset. For the zebrafish dataset, the recognition rate of the output neuron was 100% for the two deep learning models, the classification accuracy of small neurons by LCCDNNs was higher than the result obtained by FCCDNNs, but did not improve the classification accuracies of large and mitral neuron types.  
Figure 16: Comparison of accuracy with different numbers of building blocks by FCCDNNs. (**a**) C. elegans dataset, (**b**) zebrafish dataset.  
Figure 15: Comparison of accuracy with different numbers of building blocks by LCCDNNs. (**a**) C. elegans dataset, (**b**) zebrafish dataset.
# Vision-Based System for Black Rubber Roller Surface Inspection
## 2 Visual Inspection System
### Defect Detection Based on Proposed Traditional Method
#### 2.3.5 Edge detection  
The first derivative of the image intensity is one of the most widely used methods for detecting edges because it has a constant value throughout the transition and is zero in all regions with low variance. Therefore, the change in intensity (potential edge) is revealed as an abrupt first derivative change. This feature is usually used for edge detection. The Canny algorithm is based on this technique.  
The Canny algorithm includes three main steps:  
* Find the intensity gradient of the image: in this step, the scale of the gradient vector is calculated for each pixel.
* Non-maximum suppression: the aim of this step is to 'thin' the edge to obtain a one-pixel width edge.
* Threshold hysteresis: finally, a two-step threshold hysteresis is applied in order to decrease the fake edges.  
After applying the Canny algorithm, the contours of defects are detected in order to proceed to coloring the error area in the following step. A contour search function is set up to merge small contours that are closely spaced (according to a predetermined weight) into one large contour; then, coloring the error area is easy, and the error is visible and displayed most clearly. After this step, the errors are identified completely correctly as shown in Figure 11.
# Low-Cost Goethite Nanorods for As (III) and Se (VI) Removal from Water
## 3 Results and Discussion
### Adsorption Experiments of As(III) and Se(VI)
#### 3.2.1 Adsorbent Dose Optimization Study
\[\log(q)=\log(K_{f})+n\log(C_{e}) \tag{6}\]  
Figure 4: Illustration graphs of (**a**,**d**) batch study, (**b**,**e**) Langmuir, and (**c**,**f**) Freundlich adsorption isotherms pertaining to As(III) and Se(VI) removal by \(\alpha\)-\(FeOOH\) NRs, respectively. WHO standard line for the concentration of these ions was plotted for reference. where \(n\) and \(K_{f}\) are constants that could be obtained from the slope and intercept of Freundlich isotherm plots of Figure 4c,f for As(III) and Se(VI), respectively. Specifically, \(K_{f}\) is the specific adsorption capacity and determined using Equation (4) for As(III) and Se(VI) to be 12.52 mg/g and 5.19 mg/g, respectively. Remarkably, the specific adsorption capacities calculated by Freundlich adsorption isotherm also show a lower adsorption affinity for Se(VI) as compared to As(III). Besides, the parameter, \(n\), a dimensionless parameter, indicates the nature and mechanism of adsorption. The values of \(n\) as calculated from Freundlich isotherm were 0.537 and 1.017 for As(III) and Se(VI), respectively. Of course, \(n<1\) indicates a strong adsorption affinity. These values, therefore, indicate that As(III) adsorption to \(\alpha\)-FeOOH surfaces also follows Freundlich isotherm, but Se(VI) does not follow Freundlich isotherm. All in all, by considering the values of \(R^{2}\), in Table 1 and Figure 4, there is evidence that the adsorption of As(III) and Se(VI) follows Langmuir isotherm, which suggests monolayer adsorption and uniformly distributed adsorption sites on the nanorods surfaces. Also, \(\alpha\)-FeOOH adsorbent shows more affinity towards As(III) than Se(VI), plausibly due to the electronic properties of both pollutants and the oxidation state [34; 35].
# SNR Enhancement of an Electrically Small Antenna Using a Non-Foster Matching Circuit
## 2 Non-Foster Circuit Design
### Characteristics of Electrically Small Antenna  
A short monopole was selected as the load. Figure 2 shows its measured resistance and reactance together with a picture of the monopole. The length of the monopole is 8.4 cm, corresponding to the quarter-wavelength of 893 MHz. Thus, it can be considered as an ESA at the frequency of interest (i.e., 150-300 MHz). The measured resistance and reactance in Figure 2a,b, respectively, clearly display impedance characteristics of an ESA: the resistance is low and reactance is high. One can easily notice  
Figure 1: Antenna reactance cancellation using (**a**) passive components which follow the [PERSON]’s reactance theorem. (**b**) Negative reactance generated by the non-Foster circuit matching technique.  
that the reactance behavior is similar to Figure 1a: the negative reactance reduces as the frequency increases in a rational manner. This means that as the frequency increases, the negative value of the capacitive reactance \(X_{C}\) decreases (i.e., \(X_{C}=-1/j\omega C\)).  
The purpose of the NFC is to generate a negative capacitance complimentary to the antenna's positive capacitance, and therefore, the input reactance can be 0 over a wide bandwidth. More specifically, the antenna capacitance of \(8\pm 1\) pF can be cancelled by the NFC's negative capacitance of \(-8\mp 1\) pF.
## 3 Materials and Methods
### Forest Aboveground Biomass Updating  
#### 3.2.1 Net Primary Productivity Simulation  
To drive the BEPS model, different layers of the raster images including LAI, baseline forest AGB, forest cover types, relative humidity, precipitation, temperature, daily solar radiation and soil data were resampled to the same spatial resolution (30 m) using the IDW method and converted to the same projection (i.e., UTM). With these spatially explicit input data on vegetation, soil, and meteorology, BEPS can be run pixel by pixel over a defined domain. By simulating the processes of photosynthesis, respiration, and carbon allocation for the forest ecosystem, which were determined by the input parameters, the BEPS model can be used for updating forest AGB [24; 50]. Prior to the actual simulation, the proper initialization of various carbon pools was required. It was then possible to alter the size of a given carbon pools based on the lost (decomposed) or added (from other carbon pools) carbon [40]. For the first day, carbon value for a given carbon pool was the value obtained from the baseline forest AGB. Then the numerical simulation was conducted at daily time step for various ecological processes, such as photosynthesis, respiration, carbon allocation, water cycle and energy balance [24]. In addition, sunlit and shaded leaves were separated in BEPS to better approximate real photosynthesis process. Therefore, the daily net assimilated carbon by plants (i.e., NPP) (gC/m\({}^{2}\)) can be obtained by subtracting energy consumed by the respiration process from gross primary productivity (GPP) (gC/m\({}^{2}\)) as Equation (5):  
\[NPP=GPP-R_{\rm g}-R_{\rm m} \tag{5}\]  
where \(R_{\rm g}\) is the respiration consumed by plant growth process (gC/m\({}^{2}\)); \(R_{\rm m}\) is the plant maintaining respiration (gC/m\({}^{2}\)). \(R_{\rm g}\) is assumed as 25% of GPP while \(R_{\rm m}\) is calculated as the summation of maintenance respiration of leaf, wood, and fine and coarse roots [51]. The maintenance respiration of individual vegetation pools is estimated according to pool sizes, maintenance respiration rates at the base temperature, and air temperature.
# Vision-Aided Localization and Mapping in Forested Environments Using Stereo Images
## 2 Direct Visual Odometry
### Optimization
#### 2.2.1 Pixel Selection  
Since minimizing photometric error is based on image gradients, only pixels with a non-zero gradient contribute to optimization. Therefore, including pixels with zero gradient in the Jacobian introduces unnecessary computations. In feature-rich environments, such as forests, selecting pixels with a non-zero gradient might not significantly reduce the number of pixels used in optimization. As shown in Figure 2a, selecting all non-zero gradient pixels results in using approximately 85% of the image. We can further reduce the number of pixels, thus decreasing computation, by thresholding pixels based on gradient magnitude. This approach, however, results in a Jacobian matrix that is subject to change in dimension between frames since the distribution of gradient magnitude is not guaranteed to be consistent; we must allocate enough memory to store the expected maximum number of non-zero gradient pixels across all frames, which cannot be determined in advance, or dynamically allocate memory prior to optimizing each frame. This issue can be resolved by selecting a percentage of the total number of pixels in the image either by performing binary search for a gradient magnitude threshold that results in the desired number of pixels or sorting the gradients in descending order and selecting the first \(N\frac{p}{100}\) pixels, where \(p\) is the desired percentage and \(N\) is the number of pixels in the image. In Figure 2b-d, we show the selected pixels resulting from desired percentages ranging from 25% to 75%.  
Prior to selecting pixels based on the gradient magnitude, we discard all pixels with a disparity value of zero since these pixels are projected to infinity during image warping. Zero disparity pixels are apparent in Figure 2 as areas in the images that obviously have non-zero gradients but are too distant to have a non-zero disparity value.
## 4 Variable Optical Coupler
### Operation of the Tunable Optical Coupler Used  
The general operation of an optical variable coupler is described here. By using a variable coupler it is possible to change the coupling length, and thus the coupling ratio. As a rule, this is achieved by a mechanical device. The coupler used by Evanescent Optics Inc. (Evanescent, Burlington, Canada) [49; 59] serves two degrees of freedom; a micrometer screw in front of the coupler and a piezoelectric actuator.  
The polished optical fibres are hereby inserted into a carrier (casing) plate. These support plates allow a transverse adjustment of the fibres, and, resulting in the displacement of the separating distance between the two fibre cores. An additional layer of oil ensures a smooth movement of these carrier plates. The fact that whether this oil also serves as a'matching oil' between the polished fibres [60], cannot be answered on the basis of the manufacturer's information.  
The micrometer rotation is transmitted to the carrier plates as a transverse movement via a toggle system. This means that upon rotation of the micrometer screw to 0.0254 mm, a transverse displacement of one of the two carrier plates to about 1 \(\upmu\)m takes place [52]. This changes the distance between the two fibre cores and thus the coupling ratio.  
This type of approach has a few limitations. The coupling ratio is defined by the use of a toggle lever system with a mechanical slot and associated with a hysteresis phenomenon. This is reinforced by additional hysteresis effects of the piezo-actuator. Thus, one can assign a specific micrometer position or input voltage, but not a fixed coupling ratio. Furthermore, the coupler is not suitable for fast switching. According to the manufacturer, generating a 1 Hz square-wave signal is still possible [52; 61], but a filter with a time constant of 20 ms is integrated in the piezo-controller. Although this filter is a limiting factor for later miniaturization, it can be neglected in further consideration at this level, since it is significantly faster in the laboratory than the spectrum analyzer [47] which is being used in the laboratory.
# Structure and Photoelectrical Properties of Natural Photoactive Dyes for Solar Cells
## 3 Results and Discussion
### Photoelectric Properties of DSSCs  
The photoelectrical conversion efficiency (\(\eta\)) of the DSSC is defined via the following equation [69]:  
\[\eta=\frac{J_{SC}\times V_{OC}\times FF}{P_{in}} \tag{1}\]  
where \(J_{SC}\) is the short-circuit current density, \(V_{OC}\) represents the open circuit voltage, \(FF\) is the fill factor, and \(P_{in}\) is the intensity of the incident light, in which the \(J_{SC}\), \(V_{OC}\) and \(FF\) are only obtained through the experiment.  
The fill factor (\(FF\)) is defined as the ratio of the maximum power obtained from the DSSC and the theoretical maximum power, which can be understood by the following equation:  
\[FF=\frac{I_{m}\times V_{m}}{J_{SC}\times V_{OC}} \tag{2}\]  
where \(I_{m}\) and \(V_{m}\) represent the current and voltage related to the maximum power, respectively.  
The measured current-voltage curves of the fabricated DSSCs sensitized by the six kinds of natural dyes are shown in Figure 7, and the photovoltaic parameters of the DSSCs, containing \(V_{OC}\), \(J_{SC}\), \(FF\) and \(\eta\) are listed in Table 5. It can be found from Table 5 that the PCE of the DSSCs sensitized by the six kinds of natural dyes are in the order of D1 \(>\) D4 \(>\) D2 \(>\) D5 \(>\) D6 \(>\) D3, in which the DSSC sensitized by the dye D1 exhibit the highest PCE of 1.08%, with \(V_{OC}\) of 0.58 V, \(J_{SC}\) of 2.64 mA cm\({}^{-2}\) and \(FF\) of 0.70. It is worth noting that the DSSC sensitized by the dye D1 possesses the highest \(J_{SC}\) among the investigated DSSCs, which can be promoted by the improved ability of dye regeneration can promote.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dye** & **E\({}_{\text{OX (V) }^{\text{a}}}\)** & **HOMO (eV)** & \
\begin{tabular}{c} **LUMO \({}^{\text{b}}\)** \\ **(eV)** \\ \end{tabular} & **Dye** & **E\({}_{\text{OX (V) }^{\text{a}}}\)** & **HOMO (eV)** &
\begin{tabular}{c} **LUMO \\ **(eV)** \\ \end{tabular} \\ \hline
**D1** & 0.34 & \(-\)4.74 & \(-\)2.873 & D4 & 0.07 & \(-\)4.47 & \(-\)2.607 \\
**D2** & 0.19 & \(-\)4.59 & \(-\)2.707 & D5 & 0.11 & \(-\)4.51 & \(-\)2.643 \\
**D3** & \(-\)0.07 & \(-\)4.33 & \(-\)2.461 & D6 & 0.10 & \(-\)4.50 & \(-\)2.636 \\ \hline \hline \end{tabular}  
* The onset oxidation potential of the dye; \({}^{\text{b}}\) estimation from HOMO and excitation energy [68].  
\end{table}
Table 4: Electrochemical properties of the six natural dyes.  
Figure 7: Current-Voltage curves of DSSCs sensitized with the six natural dyes.