esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Procedure  
Between January and March 2022, all testing was conducted indoors to eliminate the influence of weather conditions on the results. The participant's sprint skating profile was tested 60 min after the participants arrived. Body mass was measured using a calibrated scale and body length was measured using a tape measure stuck to the wall. Afterward, three players at a time performed their own preferred warm-up for 15 min, while wearing their regular training gear, to be ready for testing. After the warm-up, the participants were briefed on the testing procedure and a 16 g inertial measurement unit (IMU) integrated with a 3-axis gyroscope was attached on top of the skate on each foot. The IMU had a maximal measuring range of 2000\({}^{\circ}\)s\({}^{-1}\)\(\pm\) 3% (Ergotest Technology AS, Stattthele, Norway) and a sampling rate of 200 Hz, but was upsampled to 1000 Hz before step detection algorithm processing. After resting for 5 min, each participant performed two maximal 80 m sprint skating attempts with their club in their hand, as in a competition, starting with the club behind the starting line (See Figure 1: Testing procedure). A 5 min rest period was provided between each attempt.  
Nine timing gates (Ergotest AS, Stattthele, Norway) were placed at 10 m intervals from the start to the 80 m mark, at a height of 1.2 m. The goal was to measure the time taken to cover these distances, and then calculate the average velocities and accelerations per 10 m. The participants started 0.5 m behind the first pair of photocells and were asked to skate for a total of 90 m. The purpose of this was to ensure that they did not finish too early. The participants were encouraged to skate as fast as possible in a straight line.  
The IMUs were synced with the timing gates as part of the MUSCELLAB v10.57 system (Ergotest Technology AS, Stattthele, Norway). This allowed the software (Ergotest Technology AS, Stattthele, Norway) to automatically identify skating stride kinematics for each skating step. This methodology has been used in previous studies [7; 13; 14]. In skating, a stride consists of two phases, support and swing. The support phase can be divided into single and double support phases [31; 32]. During both these phases, propulsion takes place as the thigh rotates outward, coinciding with the initial extension of the hip and knee [32]. The time taken for both double and single support is called the glide time, while the total lateral distance covered during both these supports is called the glide length. Step length refers to the lateral distance covered during single support, while the step frequency is calculated by measuring the time elapsed between left and right foot touchdowns. All variables were analyzed separately, as follows: (i) the average over 80 m, (ii) over the first six steps (the acceleration phase), and (iii) over six steps at maximal velocity (i.e., the steady-state phase).
## Materials and Methods
### Construction of KO Mutant Strains and Complementation of the SLI_4383/_pptA Mutant Strain_  
PCR-based REDIRECT technology was used to disrupt _pptA_ and _SLI_4382_[44]. The genes were replaced in the cosmid StD84 [45; 46] by the _att3-aac_ cassette (originating from pOSV234), yielding pMES/_pptA:aac_ and pMES/_SLI_4382:aac_. These cosmids were introduced into _S. lividans_ TK24 by transformation of protoplasts. Double recombinants (Apr\({}^{\text{R}}\), Neo\({}^{\text{S}}\)) were detected. The replacement of the genes by the _aac_ cassette was confirmed by Southern blotting and hybridization in comparison with the original strain (data not shown). In order to obtain the in frame deletion mutant of _pptA_, the _att3-aac_ cassette of _pptA:att3-aac_ mutant was excised in vivo as described previously [47], yielding the \(\Delta\)_pptA_\({}^{\text{IED}}\) mutant.  
In order to complement the \(\Delta\)_pptA_\({}^{\text{IED}}\) mutant strain, a 1.5-kpb fragment containing the entire _pptA_ gene with its putative promoter was amplified by PCR using genomic DNA from _S. lividans_ as template and the primers listed in (Table S3) and cloned into pGEM-T Easy resulting in pMES17. The 1.5-kpb _Eco_RI fragment from pMES17 was ligated into pSET152 digested with _Eco_RI generating pMES18. Plasmids pSET152 and pMES18 were then introduced into the \(pptA^{\text{IFD}}\) mutant by conjugal transfer from _E. coli_ S17.1 (Table S1). The integration of each plasmid into the genome was checked by PCR.
# Improved Probability Prediction Method Research for Photovoltaic Power Output
###### Abstract  
Due to solar radiation and other meteorological factors, photovoltaic (PV) output is intermittent and random. Accurate and reliable photovoltaic power prediction can improve the stability and safety of grid operation. Compared to solar power point prediction, probabilistic prediction methods can provide more information about potential uncertainty. Therefore, this paper first proposes two kinds of photovoltaic output probability prediction models, which are improved sparse Gaussian process regression model (IMSPGP), and improved least squares support vector machine error prediction model (IMLSSVM). In order to make full use of the advantages of the different models, this paper proposes a combined forecasting method with divided-interval and variable weights, which divides one day into four intervals. The models are combined by the optimal combination method in each interval. The simulation results show that IMSPGP and IMLSSVM have better prediction accuracy than the original models, and the combination model obtained by the combination method proposed in this paper further improves the prediction performance.  
PV; probability prediction; sparse Gaussian process regression; least squares support vector machine; combination method Article
## 3 Results and Discussion
### \(\alpha\)-Glucosidase Inhibitory Activity  
\(\alpha\)-Glucosidase inhibitory activity of 80% ethanol extracts of Korean WS and NS germinated for 7, 9, and 11 days is presented in Table 1. \(\alpha\)-Glucosidase is present in the brush border of the small intestine and hydrolyzes carbohydrates, such as maltose, into glucose [32]. Glucose is then absorbed, resulting in an increase in blood glucose, therefore, the inhibition on \(\alpha\)-glucosidase exhibits a hypoglycemic effect, which is important for preventing and treating diabetes mellitus [33].  
The water extracts of WS and NS did not show \(\alpha\)-glucosidase inhibitory activity. The activity of 80% ethanol extract of WS germinated for 7 days was the lowest at 84.01% among all extracts, and there was no significant difference among the extracts except this one. All other extracts exhibited \(\alpha\)-glucosidase inhibitory activity higher than 90%. In the previous studies, germination for 5 days was shown to increase \(\alpha\)-glucosidase inhibitory activity of  
Figure 2: Antioxidant activities of Korean waxy and normal wheat sprouts germinated for 7, 9, and 11 days. (**A**) DPPH radical scavenging activity; (**B**) ABTS radical scavenging activity; (**C**) Reducing power. WSW, water extracts of the waxy wheat sprouts; WSE, 80% ethanol extracts of the waxy wheat sprouts; NSW, water extracts of the normal wheat sprouts; NSE, 80% ethanol extracts of the normal wheat sprouts. Values are shown as mean \(\pm\) SD (n \(\geq\) 3). Values with a same letter (a–l) are not significantly different by [PERSON]’s multiple range test (_p_ < 0.05).  
wheat compared to non-germinated wheat grain [23], and feeding wheat sprout extracts reduced blood glucose levels in BALB/c mice as also mentioned previously [14], which is consistent with the results of this study. Germination did not improve the activities in oat, buckwheat, and brown rice [23], implying that germination is an effective process for improving the hypoglycemic effects of wheat, although not for all grains.  
Phenolic compounds including quercetin, ferulic acid, and _p_-coumaric acid, are known to inhibit \(\alpha\)-glucosidase activity [34]. Phenolic compounds also increase the absorption and uptake of glucose into peripheral tissues and insulin secretion lowering the level of blood glucose [35]. Non-starch polysaccharides with \(\alpha\)-glucosidase inhibitory activity are also known to be present in wheat, and the isolated phosphatidic acids, including 1,2-dilinoleylglycerol-3-phosphate and 1-palmitoyl-2-linoleoyl glycerol-3-phosphate, in wheat germ exhibit high \(\alpha\)-glucosidase inhibitory activity [34]. These compounds might be responsible for \(\alpha\)-glucosidase inhibitory activity of wheat sprout examined in this study, although not for all.
# A No Reference Image Quality Assessment Metric Based on Visual Perception +
## 5 Feature Extraction of BRISQUE Algorithm
### Image Pixel Normalization  
Given an image which possibly distorted, we compute locally normalized luminances via local mean subtraction and divisive normalization. Formula (11) may be applied to a given intensity image \(I(i,j)\) to produce:  
\[\hat{I}(i,j)=\frac{I(i,j)-\mu(i,j)}{\sigma(i,j)+c} \tag{11}\]  
\[\mu(i,j)=\sum_{k=-K}^{K}\sum_{l=-L}^{L}\omega_{k,l}I(i+k,j+l) \tag{12}\]  
\[\sigma(i,j)=\sqrt{\sum_{k=-K}^{K}\sum_{l=-L}^{L}\omega_{k,l}[I(i+k,j+l)-\mu(i, j)]^{2}} \tag{13}\]  
where \(I(i,j)\) represents the gray value of the original image, \(I\in\{1,2,\ldots,M\}\), \(j\in\{1,2,\ldots,N\}\). \(M\) and \(N\) are the image height and width respectively; c is a small constant, in order to prevent the stability of calculated results when the denominator closes to 0. \(\mu(i,j)\) and \(\sigma(i,j)\) are weighted mean and variance. \(\omega=\{\omega_{\text{k,1}}|\,k=-K,\ldots,K,l=-L,\ldots,L\}\) is a two-dimensional circularly symmetric Gaussian weighting function. They called the normalized brightness value of \(\hat{I}(i,j)\) is MSCN (Mean Subtracted Contrast Normalized) coefficients.
## 2 Related Works
### The L(2,1,1) Algorithm  
**Definition 6**: Let U be a graph which is undirected and has a group of vertices T and a group of edges E. Let the function f:T \(\geq\) N, where f is a L(2,1,1) labeling of U, if for all a, b \(\in\) T, \(|\)f(a) \(-\) f(b)\(|\geq 2\) if d(a, b) = 1, \(|\)f(a) \(-\) f(b)\(|\geq 1\) if d(a, b) = 2 and the distance between colors assigned to a, b differs by at least two units in the color spectrum [7; 11].  
Figure 4 shows the colors used for the labeling, while Figure 5 shows the clique of the labeling. The reuse distance for this labeling is four (i.e., \(\phi\) = 4). According to [16], a graph's coloring problem, which is the problem of assigning colors to certain nodes of a graph based on some constraints, can be solved by using a clique; this clique is subsequently colored using an algorithm that is suitable for this purpose. The same method of clique coloring can be adopted in a matrix of any size in a cellular graph. Figure 6 shows the L(2,1,1) labeling of a \(5\times 5\) network graph, while Figure 7 shows a \(7\times 7\) network graph colored using the L(2,1,1) coloring. In formulation, the parities of i and j are considered, with the modulus (represented by %) being used in the formulation to achieve the optimal coloring of the clique.  
Figure 4: Coloring spectrum used for the graph.  
Figure 3: A \(7\times 7\) cellular graph network matrix colored with the L(2,1) coloring.  
Figure 2: A \(5\times 5\) cellular graph titled using the L(2,1) coloring.  
For the L(2,1,1) graph coloring, [16] proposed the following algorithm: for n rows and m columns, if n \(\geq\) 4 and m \(\geq\) 4, then the color f(a) is assigned to a node G = (\(i,j\)) thus:  
0 if (\(i+j\)) = 2 % 6, n is even, and m is even; 1 if (\(i+j\)) = 0 % 6, n is even, and m is even;  
2 if (\(i+j\)) = 4 % 6, n is even, and m is even; 3 if (\(i+j\)) = 1 % 6, n is odd, and m is even;  
4 if (\(i+j\)) = 3 % 6, n is odd, and m is even; 5 if (\(i+j\)) = 5 % 6, n is odd, and m is even;  
6 if (\(i+j\)) = 5 % 6, n is even, and m is odd; 7 if (\(i+j\)) = 2 % 6, n is odd, and m is odd;  
8 if (\(i+j\)) = 4 % 6, n is odd, and m is odd; 9 if (\(i+j\)) = 1 % 6, n is even, and m is odd;  
10 if (\(i+j\)) = 3 % 6, n is even, and m is odd; 11 if (\(i+j\)) = 0 % 6, n is odd, and m is odd.
# A Review of Commercially Available 3D Surface Imaging Systems for Body Composition Estimation
## 4 Discussion
### Study Limitations & Future Research  
This study focuses solely on commercially available whole body surface imaging systems, however, body composition estimations are available from app-based 3D surface imaging systems. With the increasing prevalence of mobile based systems, it is essential that future research also encompasses app-based systems. In addition, the study only explores the publicly available user and system details of systems, making note of the published research exploring the validity of the systems. Consequently, further research may seek to summarise the results of these studies. Furthermore, further research is required to explore the validity of body composition estimations from 3D surface imaging systems across sex, size, shape, age, ethnicity groups, atypical populations groups, and inter system agreement.
## 1 Introduction
### Importance of Study  
Production and use of hydrogen gas are increasing year by year. Hydrogen is the most potent means of achieving the zero-carbon goal. Republic of Korea will achieve carbon neutrality by 2050 by replacing coal-fired power generation with renewable sources and internal combustion engine vehicles with hydrogen-powered and battery-based electric vehicles. The Chinese government released the country's first-ever long-term plan for hydrogen, covering the period of 2021-2035 [20]. The plan laid out a phased approach to developing a domestic hydrogen industry and mastering technologies and manufacturing capabilities while pointing to the country's carbon peaking and neutrality commitments as overarching drivers. The Japanese government has set ambitious goals for a carbon-neutral future to enhance its energy security. It plans to establish a full-scale international hydrogen supply chain to cut the cost of hydrogen by 2030 [21]. In order to promote the development of a strong domestic market, Germany plans to build its first green hydrogen generation plants with a total capacity of up to five gigawatts (GW) by 2030, including the construction of the offshore and onshore infrastructure needed. Safe practices in the production, storage, distribution, and use of hydrogen are essential for the widespread acceptance of hydrogen technologies. Hydrogen gas is an aspect of critical infrastructure that requires attention regarding fire and explosion protection. If hydrogen gas were to leak at a hydrogen refueling station, the probability of ignition and possible subsequent pressure loads on the surrounding structures would be high. Therefore, it is necessary to design to protect against the potential for severe damage to the protection wall and serious harm to people in the region of an accident. China has the greatest number of hydrogen fuel stations of any country worldwide. As per April 2022, there were 250 operational hydrogen refueling stations in the country. This was followed by japan with 161 and Korea with 141 such stations [22]. Table 2 shows the number of hydrogen fueling stations of different countries and this data is not the worldwide data. Figure 2a shows the cumulative number of hydrogen fueling stations of different countries [23]. Figure represents the number of hydrogen fueling stations in different countries with forecasts up to 2030. It is expected that there will be around 520 hydrogen stations in Korea. Figure 2b shows the hydrogen demand worldwide from the 2019 to 2021, with a forecast for 2030 and it is expected to double by 2030 [24].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Country** & **Number of Hydrogen Fueling Stations (2022)** \\ \hline China & 250 \\ Japan & 161 \\ Republic of Korea & 141 \\ Germany & 93 \\ France & 21 \\ Netherlands & 11 \\ Canada & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of hydrogen fueling stations in different countries (2022).
## 5 The Cryptographic Suite
### Group Key Management
No additional information proving key authenticity is thus required. Notice that this is a great advantage in terms of communication overhead with respect to using digital signatures, for example. To fix ideas, let us suppose that group keys are 128-bit long and we use ECC-180 digital signature to authenticate them. ECC-180 is nowadays considered as secure as RSA-1024. In ECC-180 a digital signature is 360 bits and thus a rekeying message would be 488 bits, _i.e._, 3.8125 times longer than in the approach proposed here.
# Multi-Player Tracking for Multi-View Sports Videos with Improved K-Shortest Path Algorithm
## 3 Methodology
### Graph Construction
#### 3.4.1 Definition of the Nodes and Edges  
In our experiments, we define the node \(m_{i}^{t}\) as the location \(i\) of the court at time \(t\), with a total of \(G\) nodes for one frame. In order to reduce the complexity of the graph, we assume the node \(m_{i}^{t}\) can only move to its neighbor region in the following frame. The size of the neighbor region, also being named the depth of graph, can be determined in consideration to the speed of the players as well as the frame rate. The thick black lines in Figure 2 are possible links when the depth of the graph is assumed as 1,while the thin gray lines assume the depth of 2. Afterwards, given a pair of virtual nodes, \(v_{\text{source}}\) and \(v_{\text{sink}}\), any path between them is considered as a player's trajectory, such as the blue curve in Figure 2.  
On the whole, the nodes of the graph are defined as the discrete grids, each of which corresponds the location in the court. And the graph's edges consist of three types: (1) incoming edges: from the source node to the nodes in the first frame or the access nodes in other frames; (2) outgoing edges: from the nodes in last frame or the access nodes in other frames to the terminal node; (3) middle edges: from the node \(i\) to its neighboring nodes \(j\in\mathbb{N}(i)\).
## 4 Numerical Experimental Simulation and Analysis
### Comparison of Results  
In the matrix solving process, several factors influence the solution efficiency, stability, and convergence. These factors include the following:  
* A comparison and analysis of condition numbers;
* Solving time comparisons;
* Analysis of the distribution of matrices after preconditioning;
* Convergence performance during the matrix solving process;
* Accuracy of the matrix solving results.  
To ensure the stability and reliability of the solving tests, the same model was solved multiple times under each solving condition. Relevant data were recorded across these runs, and the results were compared and analyzed using their average values.
# Design of Wing Root Rotation Mechanism for Dragonfly-Inspired Micro Air Vehicle
## 3 Development of a Wing Root Rotation Mechanism
### Kinematics of Wing Root Rotation Mechanism  
Figure 6 shows a schematic of the proposed slide-crank mechanism with the three-bar link to define the relationship among variables, and the kinematics of this mechanism was analyzed by vector analysis.  
The wing root angle \(\theta_{r}\) is defined by \(\theta_{0}\) and \(\theta_{1}\), as in Equation (3).  
\[\theta_{r}=\theta_{1}-\theta_{0} \tag{3}\]  
where \(\theta_{1}\) is a constant value, and \(\theta_{0}\) is a dependent variable described by \(d_{2}\), which is an independent variable.  
\[\theta_{0}=\cos^{-1}{(\frac{d_{2}^{2}+s_{1}^{2}-s_{4}^{2}}{2s_{1}d_{2}})} \tag{4}\]  
As a result, \(\theta_{r}\) can be obtained as Equation (5) by substituting (4) into (3).  
\[\theta_{r}=\theta_{1}-\cos^{-1}{(\frac{d_{2}^{2}+s_{1}^{2}-s_{4}^{2}}{2s_{1}d_ {2}})} \tag{5}\]  
After the kinematic analysis, each variable was defined for the design of the wing root control mechanism in Table 2. \(s_{1}\), minimum value of \(d_{2}\), and \(\theta_{2}-(\theta_{0}+180^{\circ})\) are the values to avoid physical collision. These design parameters were obtained by trial and error in 3D modeling: \(s_{4}\) was obtained by applying the proposed values to (6).  
\[s_{4}=\sqrt{d_{2}^{2}+s_{1}^{2}\cos^{2}{(\theta_{2}-(\theta_{0}+180^{\circ})) -s_{1}^{2}}}+s_{1}\cos{(\theta_{2}-(\theta_{1}+180^{\circ}))} \tag{6}\]  
Lastly, \(\theta_{1}\), which makes \(\theta_{r}\) equal to zero when \(d_{2}\) is the minimum, is defined by (7).  
\[\theta_{1}=\cos^{-1}{(\frac{s_{1}^{2}+d_{2}^{2}-s_{2}^{2}}{2s_{2}d_{2}})} \tag{7}\]  
Figure 7 shows the results of the kinematics calculated by the defined design parameters. Figure 7a shows the simulation output motion (\(\theta_{r}\)) with respect to input motion (\(d_{2}\)) and describes that the output motion is normally driven by input motion. The blue line is the \(s_{4}\) link, the red line is the \(s_{4}\) link, the blue dot is the input motion of linear motion, and the red dot is output motion of the wing root.  
To implement the lift-based system and drag-based system of the dragonfly, each mean angle of attack should be \(0^{\circ}\) and \(30^{\circ}\). Therefore, it is necessary to define the input variable for each system. The wing root angle for the lift-based system was defined as \(0\) at the above link optimization, but \(d_{2}\) for the drag-based system, which is \(-30^{\circ}\), was not defined. Figure 7b shows relationship between \(d_{2}\) and \(\theta_{r}\), and the desired value of \(d_{2}\) when \(\theta_{r}\) is \(-30^{\circ}\). As a result, we  
Figure 6: Schematic kinematics of wing root rotation mechanism.  
implements a drag-based system when \(d_{2}\) is 16.25 mm. Table 2 lists the design parameters of the wing root rotation mechanism.
## 3 Payload Design
### Structure  
RSONAR II features a CubeSat form factor with a 4U-inspired structure. Triangular prisms were utilized at the base of each main structure as in the first iteration of the payload, which raised the subpayloads to a 45\({}^{\circ}\) viewing angle. This results in an improved RSO viewing geometry. The Computer-Aided Design (CAD) model of the payload is illustrated in Figure 2a, where subpayload 1 and subpayload 2 are located at the top-left and top-right, respectively. A 250 \(\times\) 320 \(\times\) 6 mm interface plate was utilized to mount the payload onto the CSA's gondola. Figure 2b displays the integrated payload and interface plate on the gondola of the CSA prior to launch. Both the primary structure and interface plate were  
Figure 1: Star tracker prototype launched on a stratospheric balloon in 2022.  
crafted from aluminum 6061-T6 (Ekos Precision Machining Inc, Concord, ON, Canada [13], with fasteners composed of AISI 30 (McMaster Carr, Princeton, NJ, USA) [14]. The total mass of the payload was approximately 5.3 kg, as indicated in the mass budget (Table 1).