esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
A total of 1174 recreational skiers participated in this study, of whom 13.5% (\(n=158\)) reported at least one fall during the skiing day. Mean age of the total cohort was 37.8 \(\pm\) 13.3 years, mean height was 173.7 \(\pm\) 8.8 cm, mean weight was 75.1 \(\pm\) 14.2 kg and 46.7% (\(n=571\)) were females. In total, 81.7% of skiers (\(n=959\)) reported to be more skilled and 33.5% (\(n=993\)) participants reported to display more risky behavior during skiing. Table 1 presents the results of the univariate comparisons of risk factors between recreational skiers without and with reported falls. The two groups significantly differ within age, fitness level, skill level, preferred skiing speed, relativized ski length, sidecut radius, waist width of the ski, standing height of the front and rear components of the ski binding, snow and weather conditions and slope difficulty. \begin{table}
\begin{tabular}{c c c c c} \hline
**Risk Factors** & **Skier without Falls** & **Skier with Falls** & \(p\) **Value** & **Effect Size** \\ \hline \multicolumn{5}{c}{Individual factors} \\ \hline Sex (\%) & & & 0.844 & Phi. 0.006 \\ Male & 51.5 (\(n=523\)) & 50.6 (\(n=80\)) & & \\ Female & 48.5 (\(n=493\)) & 49.4 (\(n=78\)) & & \\ \hline Age, mean \(\pm\) sd & 38.2 \(\pm\) 13.4 (\(n=1016\)) & 35.3 \(\pm\) 12.3 (\(n=158\)) & 0.019 & [PERSON]’s d: 0.219 \\ \hline Body height (cm) mean \(\pm\) sd & 173.6 \(\pm\) 8.6 (\(n=1016\)) & 174.2 \(\pm\) 10.2 (\(n=158\)) & 0.763 & [PERSON]’s d: \(-\)0.068 \\ \hline Body weight (kg) mean \(\pm\) sd & 75.1 \(\pm\) 14.2 (\(n=1014\)) & 75.3 \(\pm\) 14.1 (\(n=157\)) & 0.765 & [PERSON]’s d: \(-\)0.014 \\ \hline Fitness level (\%) & & & \(<\)0.001 & Phi. 0.246 \\ Very good/good & 73.1 (\(n=743\)) & 44.3 (\(n=70\)) & & \\ Medium & 24.4 (\(n=248\)) & 43.0 (\(n=68\)) & & \\ Weak/very weak & 2.5 (\(n=25\)) & 12.7 (\(n=20\)) & & \\ \hline Skill level (\%) & & & \(<\)0.001 & Phi. 0.323 \\ More skilled & 86.6 (\(n=880\)) & 50.0 (\(n=79\)) & & \\ Less skilled & 13.4 (\(n=136\)) & 50.0 (\(n=79\)) & & \\ \hline Risk-taking behavior (\%) & & & 0.375 & Phi \(-\)0.026 \\ More cautious & 66.0 (\(n=671\)) & 69.6 (\(n=110\)) & & \\ More risky & 34.0 (\(n=463\)) & 30.4 (\(n=48\)) & & \\ \hline \end{tabular}
\end{table} 
Table 1: Univariate comparisons of individual, equipment-related and environmental factors between recreational skiers without and with falls. Table 2 presents the significant adjusted results of the multiple logistic regression analysis. Age, fitness level, skill level, moderate preferred speed, relativized ski length, sidecut radius, fresh and grippy snow conditions and an easy slope difficulty were found to be factors independently associated with the risk of falling in recreational adult skiers. Risk of falling decreased (OR < 1) with a lower age (OR 0.966), a very good/good fitness level (OR 0.251), a moderate preferred skiing speed (OR 0.435), a lower relativized ski length (OR 0.901), and fresh (OR 0.375) and grippy snow conditions (OR 0.451). In contrast,
# Global GNSS-RO Electron Density in the Lower Ionosphere
## 2 Data and Methods
### GNSS-RO Data  
Global Navigation Satellite System (GNSS) provides continuous broadcast of positioning and time data from space to the ground and other satellite receivers that can be used to determine their location. There are six major GNSS constellations either in operation or development: U.S. Global Positioning System (GPS), Russian Global Navigation Satellite System (GLONASS), European Navigation Satellite System Galileo (Galileo), Japanese Quasi Zenith Satellite System (QZSS), Chinese BeiDou System (BDS), and Indian Regional Navigation Satellite System (IRNSS). In addition, civil Satellite-based Augmentation System (SBAS) for aviation safety uses geostationary (GEO) satellites to improve the accuracy and reliability of GNSS information by broadcasting the augmentation data with signal measurement error corrections. Each of these GNSS constellations is assigned with a satellite system code: G: GPS, R: GLONASS, E: Galileo, J: QZSS, C: BDS, I: IRNSS, and S: SBAS, which is used in naming every GNSS-LEO link for the RO data.  
The GNSS-RO data used in this study are summarized in Appendix B (Table A1), which lists different RO missions with information such as operation period, LEO (low Earth orbit) satellite initial and final altitude, orbital precession, latitude coverage, RO top height, and number of daily RO profiles. The majority of GNSS-RO data is obtained from the COSMIC Data Analysis and Archive Center (CDAAC) distribution, which processes the data to Level-2. The Level-lb data are processed with different versions of software, namely, NRT, postprocessing (postProc), and reprocessing (e.g., with subfix \"2016\"). The satellites that contribute to the global GNSS-RO acquisition include CHAMP (Challenging Minisatellite Payload), SAC-C (Satellite de Aplicaciones Cientifico-C), COSMIC-1/2 (Constellation Observing System for Meteorology, Ionosphere, and Climate 1 or 2), METOP-A/B/C (Meteorological Operational Polar Satellite A/B/C), GRACE (Gravity Recovery and Climate Experiment), C/NOFS, TSX (TerraSAR-X), TDX (TanDEM-X), Korean KOMPSAT-5, Spanish PAZ, and Chinese Fengyun-3 (FY3).
## 3 Results and Discuss
### Flow Field Regulation Mechanism of Snow Prevention Fence  
Figure 3 depicts the panel-type snow fence used in the wind tunnel tests, featuring a configuration of three solid panels and three perforated panels. Other configurations of the snow fence include six solid panels (where all six panels are solid), six perforated panels (where all six panels are perforated), as well as combinations such as three solid and three perforated, three perforated and three solid, four solid and two perforated, two perforated and four solid, and alternating configurations of solid and perforated panels.  
Figure 13 shows the wind speed at different positions at a starting wind speed of 6.5 m/s for snow fences in different arrangements. Measurements were taken at horizontal intervals of 1 H (where H = 100 mm) within a range of 2 H upstream and 5 H downstream of the snow fence. Vertically, measurements were taken every 3 cm, corresponding to a prototype height of 0.9 m, with a total of seven measurement points up to 18 cm (equivalent to a prototype height of 6 m). Due to experimental limitations, the lowest measurement point was 0.3 cm above the ground or subgrade surface, corresponding to an actual height of 0.09 m.  
Figure 13: Distribution of flow field on both sides of the snow fence. (**a**) 2 H outside the snow fence; (**b**) 1 H from the snow fence; (**c**) In front of the fence; (**d**) Behind the fence; (**e**) 1 H after snow fence; (**f**) 2 H after snow fence; (**g**) 3 H after snow fence; (**h**) 4 H after snow fence; (**i**) 5 H after snow fence.  
As shown in Figure 13, the wind speed remains relatively consistent below a height of 10 cm for all tested snow fence types, and the wind speed patterns downstream of the different fences are also quite similar. The wind speed is lowest immediately upstream and downstream of the snow fences, generally below 1 m/s. Above 10 cm, the wind speed gradually recovers, and the effect of different configurations on the wind speed becomes less pronounced. A key difference is that when the bottom panel of the snow fence is perforated, the wind speed in the area below the height of the snow fence is higher than when the bottom panel is solid.
## 2 Internal and External Pose Parameters  
The 6-DoF pose estimation of a 3D object in an RGB camera image is to determine the external orientation and translation of the object from a reference coordinate system to the camera coordinate system. In addition, the projection of the transformed 3D object into the image plane is expressed by the internal camera parameters. If we know the 6-DoF pose of the object and the camera parameters, we can transform and project a shape-matching CAD model into the image space so that the projection exactly aligns with the object area.  
To project a 3D CAD model into an RGB image space, it is essential to understand the projection of a 3D point into the image plane. Let \(\mathbf{P}(X_{w},Y_{w},Z_{w})\) be a point in the 3D space (reference coordinate system), \(\mathbf{P}(X_{C},Y_{C},Z_{C})\) the corresponding point in the camera coordinate system, and \(\mathbf{p}(x,y)\) the projected point in the image plane (pixel coordinate system). Point \(\mathbf{p}(x,y)\) is determined through the following Equations (1)-(3). We assume \(f_{x}=f_{y}=f\) where \(f\) represents the focal length of the camera.  
\[\begin{bmatrix}X_{C}\\ Y_{C}\\ Z_{C}\end{bmatrix}=\begin{bmatrix}R_{1}&R_{2}&R_{3}&t_{x}\\ R_{4}&R_{5}&R_{6}&t_{y}\\ R_{7}&R_{8}&R_{9}&t_{z}\end{bmatrix}\begin{bmatrix}X_{W}\\ Y_{W}\\ Z_{W}\\ 1\end{bmatrix} \tag{1}\]  
\[\begin{bmatrix}u\\ v\\ w\end{bmatrix}=\begin{bmatrix}f_{x}&0&c_{x}\\ 0&f_{y}&c_{y}\\ 0&0&1\end{bmatrix}\begin{bmatrix}X_{C}\\ Y_{C}\\ Z_{C}\end{bmatrix} \tag{2}\]  
\[\mathbf{p}(x,y)=\begin{bmatrix}x\\ y\end{bmatrix}=\begin{bmatrix}u/v\\ v/w\end{bmatrix} \tag{3}\]  
In this paper, the 3-DoF rotation parameters are represented by the Euler angles, as shown in Figure 2. The other 3-DoF is related to the translation between the reference to the camera coordinate systems. Among the 3-DoF translation parameters, the translation along the \(z\)-axis affects the size of the object in the image plane, as the size is affected by both the translation along the \(z\)-axis and the camera's focal length (\(f\)). The camera translations along the x and y axes, \(t_{x}\) and \(t_{y}\), do not affect the object's size. However, estimation of \(z\)-axis translation \(t_{z}\) has inherent ambiguity because the object size is also affected by the focal length. Therefore, to estimate the 3-DoF parameters associated with the translation, both \(t_{x},\ t_{y}\) and the two scale factors (\(t_{z},\ f\)) should be estimated.
# Reducing the Total Product Cost at the Product Design Stage
## 4 An Example of Business Analytics for New Product Performance
### Prescriptive Analytics
#### 4.3.2 A Sensitivity Analysis for Evaluating the Net Present Value  
The sensitivity analysis investigates the impact of seven variables on the projected net present value (NPV) from a new product. These variables are related to the costs of a new product, its price, and the anticipated sales volume. The costs include the phases of NPD, production, and after-sales service. The unit production cost consists of costs related to materials, labor, and energy used in manufacturing.  
Figure 5 presents the sensitivity analysis for the above-mentioned variables changed at 10%. The strongest change was related to product price, for which the decrease at 10% reduced the NPV at 41% or the increase at 10% improved the NPV at 41%.  
Sales volume and product price depend on the market properties, mainly customers and competitors' strength. As a result, companies may control their costs in a higher grade compared to sales revenue. Among costs, the most significant impact on the NPV has the cost of materials that is the main factor of the unit production cost.  
Table 4 presents a 10% change in the unit production cost and its impact on the total NPV. The reduction of the unit production cost of 10% increase the NPV of 4.71%, if other parameters are unchanged.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Values of Variables** & **TC** \\ \hline \(V_{6}=7\), \(V_{7}=98\), \(V_{9}=290\) & 1099.54 \\... &... \\ \(V_{6}=7\), \(V_{7}=98\), \(V_{9}=284\) & 1098.83 \\ \(V_{6}=7\), \(V_{7}=99\), \(V_{9}=288\) & 1099.77 \\ \(V_{6}=7\), \(V_{7}=99\), \(V_{9}=281\) & 1099.05 \\ \(V_{6}=7\), \(V_{7}=100\), \(V_{9}=285\) & 1100.00 \\... &... \\ \(V_{6}=7\), \(V_{7}=100\), \(V_{9}=278\) & 1099.28 \\ \hline \hline \end{tabular}
\end{table}
Table 3: A set of possible variants of reducing the total product cost.  
The scenario analysis can be developed towards changes in more than one parameter (e.g., material cost, warranty cost, unit price, and sales volume), indicating then the cumulative impact of a few variables on the NPV.
# BDCOA: Wavefront Aberration Compensation Using Improved Swarm Intelligence for FSO Communication
## 3 Simulation Results
### Performance Analysis  
The performance of BDCOA is analyzed with three different algorithms, namely Mayfly Optimization Algorithm (MOA), Pelican Optimization Algorithm (POA), and COA. The reason for considering the MOA, POA, and COA algorithms is that they are all meta heuristic algorithms similar to BDCOA. The convergence analysis for BDCOA with other optimization algorithms is shown in Figure 5, where RMS value is considered as fitness. Figure 5 confirms that BDCOA has better convergence than MOA, POA, and COA. The BM incorporated in BDCOA escapes from the suboptimal solutions and ensures that BDCOA searches extensively to obtain the global optimum value. This adaptive and dynamic process helps enhance BDCOA's ability to effectively explore and exploit space. The DMS is used to enhance global searching efficiency during the searching process. The aforementioned advantages ensure a reliable convergence.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Names/Values** \\ \hline Communication channel & Free space \\ Optical power & 10 dBm \\ Modulation & DPSK \\ Number of actuators & 32 \\ Wavelength of laser & 1550 nm \\ Bit rate & 1.25 Gb/s \\ Turbulence Model & Kolmogorov turbulence model \\ Noise & Atmospheric turbulence noise \\ Refractive structure parameter & \(9\times 10^{-15}\) m\({}^{-2/3}\) \\ Length of FSO link & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.
## 7 Conclusions and Future Work  
The performance of satellite-to-ground coherent optical communication downlink with aperture averaging is investigated. The results demonstrate that aperture averaging can improve the reliability of communication efficiently, especially with stronger turbulence and larger zenith angles. With the aperture increasing continuously, the wave front aberration can degrade the communication performance inevitably, especially for the coherent optical link. An adaptive system installed in the large aperture receiver on the ground can compensate the wave front aberration effectively [24]. To improve the reliability of the communication system in further, a coherent multiple apertures diversity receiving system will be investigated. The amount and diameters of the apertures of a diversity receiving system will be analyzed.  
[PERSON] and [PERSON] proposed the research method of analyzing the performance of satellite to ground link with aperture averaging. The simulation experiments are designed and the manuscript is written by [PERSON] and [PERSON], [PERSON], [PERSON] and [PERSON] have proposed revision suggestions of this manuscript.  
This research was funded by National Natural Science Foundation of China, grant number: 61571096.  
The authors declare no conflict of interest.
## 2 Experimental Setup
### Instrumentation  
Performance evaluation requires the measurement of several quantities such as air temperature, air pressure, air flow rate, particle temperature, and particle flow rate. Several instruments are installed at different locations of the facility; the heat exchanger's instrumentation is presented in detail in this section. The particle inlet temperature of the heat exchanger is measured just before the particle inlet hopper using four thermocouples, while the particle outlet temperature is measured just after the discharge cone using three thermocouples. To ensure accurate measurement of bulk particle temperatures, the thermocouples are always kept immersed inside moving particle beds. Two thermocouples are installed at the inlet air pipe just before the heat exchanger's entrance to measure the air inlet temperature, and another two at the outlet air pipe to measure the air outlet temperature. Type-K thermocouples (KMQIN-062U-24 and KMQIN-125U-24, Omega Engineering Inc., Norwalk, CT, USA) are used for the temperature measurement. The air pressure drop across the heat exchanger can be determined by measuring the air pressures at the inlet and outlet of the heat exchanger. Three Omega pressure transmitters (PXM309-007 AI) are used to measure the air pressure at the inlet, the middle, and at the outlet of the heat exchanger. The air flow rate is measured using SDF flow meter made by SKL. It uses an averaging pitot tube installed at the inlet air pipe near the entrance of the heat exchanger; the flow meter has a bias error of 0.001 kg/s. All instruments are wired into a data acquisition unit (Keysight 34972A). The particle mass flow rate is measured upstream of the heat exchanger where a box with a diverting gate is installed at the bypass line. The measurement is  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Heat** & **Inlet** & **Discharge** & **Air** & **Olds** \\  & **Exchanger** & **Hopper** & **Cone** & **Pipes** & **Elevator** \\ \hline Ceramic wool (1”-thick) & 2 & 3 & 2 & 4 & 1 \\ Rock wool (3”-thick) & 3 & 1 & 3 & - & 2 \\ Glass wool (1”-thick) & 1 & - & - & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Thermal insulation layers.  
Figure 3: Schematic of the T100 unit.  
conducted using a simple weight and time measurements; the bias error of the weighing scale is 0.005 kg.
# Q-MeaMetaVC: An MVC Solver of a Large-Scale Graph Based on Membrane Evolutionary Algorithms
## 2 Related Research
### Introduction to Membrane Evolution Algorithms
#### 2.1.2 Membrane Evolution Algorithm Framework  
The membrane evolution algorithm framework (MEAF) is shown in Algorithm 1. The MEAF first uses the initialization algorithm to initialize the membrane structure and the initial population (line 1), and then it performs population parallel evolution (searching for optimization solutions) (lines 2-9). In each evolution, evolutionary operators are used sequentially for the population: fusion (line 3), division (line 4), cytolysis (line 5), and selection (line 6). Line 7 presents the corresponding algorithm for population repair to maintain the stability and diversity of the population, and line 8 presents the corresponding algorithm for individual repair to meet the solving goal requirements. When the evolutionary process meets the termination condition, MEAF outputs the optimal solution.  
```
Input: Problem description, requirements for the solution, end the condition; Output: Optimal solution; //(1) Initialization
1 Create a membrane structure and initial population;
2 Repeat
3//(2) Evolving each membrane in parallel
3 Fusion Operator: random or selecting the membrane for fusion according to the correlation between the membranes;
4 Division Operator: random or selecting the membrane for division according to the adaptability of the membrane;
5 Cytolysis Operator: random or selecting the membrane for cytolysis according to the adaptability of the membrane;
6 Selection Operator: expanding the membrane with high adaptation; //(3) Repairing population
7 Repair the population to maintain its stability and diversity;
8 Repair the individual (membrane) to meet the solving objectives;
9 Until meeting the end conditions;
10 Return optimal solution.
```  
**Algorithm 1****MEAF**  
Figure 3 shows an example of MEAF initialization with a population size of \(n\). The initial population is composed of membranes M\({}_{1}\) to M\({}_{n}\), denoted as P = [M\({}_{1}\), M\({}_{2}\),..., M\({}_{n}\)]. For any M\({}_{i}\) (\(1\leq i\leq n\)), its set of objects may be a solution to the problem. The 'best' is a special membrane whose object set is the optimal solution obtained by the population evolution to the current stage, which serves as the output membrane of the algorithm to output the solution results.  
MEAF only provides a framework for membrane evolution algorithms, where evolution operators, repair methods, and algorithm completion conditions are all related to the specific problem to be solved. This paper is based on the MEAF to study the MEA for solving the MVC of large-scale sparse graphs.
## 3 Non-Gaussian Reversible Jump Markov Chain Monte Carlo (NG-RJMCMC) Algorithm for Mixture Model  
### The Family of Location-Scale Distributions  
A random variable \(T\) is defined as belonging to the location-scale family when its cumulative distribution function (CDF)  
\[F_{T}(t|\mu,\sigma)=\Pr(T\leq t|\mu,\sigma)\]  
is a function only of \(\frac{t-\mu}{\sigma}\), as in Equation (6) [55]:  
\[F_{T}(t|\mu,\sigma)=F\left(\frac{t-\mu}{\sigma}\right);\mu\in\mathbb{R},\sigma >0, \tag{6}\]  
where \(F(\cdot)\) is a distribution without other parameters. The two-dimensional parameter \((\mu,\sigma)\) is called the location-scale parameter, with \(\mu\) being the location parameter and \(\sigma\) being the scale parameter. For fixed \(\sigma=1\), we have a subfamily that is a location family with a parameter \(\mu\), and for fixed \(\mu=0\), we have a scale family with a parameter \(\sigma\). If \(T\) is continuous with the probability density function (p.d.f.)  
\[f_{T}(t|\mu,\sigma)=\frac{dF_{T}(t|\mu,\sigma)}{dt}\]  
then \((\mu,\sigma)\) is a location-scale parameter for \(T\) if (and only if)  
\[f_{T}(t|\mu,\sigma)=\frac{1}{\sigma}\delta\left(\frac{t-\mu}{\sigma}\right), \tag{7}\]  
where the functional form \(g\) is completely specified, but the location and scale parameters, \(\mu\) and \(\sigma\), of \(f_{T}(t|\mu,\sigma)\) are unknown, and \(g(\cdot)\) is the standard form of the density \(f_{T}(t|\mu,\sigma)\)[56; 57].
## 3 Multiple Object Detection with YOLOv5
### Training YOLOv5  
In this study, we selected YOLO as the object detection model because of its multi-object detection and rapid inference capability. We used YOLOv5, which combines bottleneck [44] and cross-stage partial network (CSPNet) [45] techniques. The bottleneck technique controls the computational load and information loss through the channels of feature maps extracted from the images, thereby minimizing errors with minimal computation. The CSP technique performs convolution operations only on part of the basic layer before convolution with the next layer, and the remaining part is merged with the results after computation. This reduces the computational load of convolution operations, enhances the learning ability of the model, and reduces computing memory consumption. Therefore, YOLOv5, with its combined BottleneckCSP technology, is a model capable of accurate and fast object detection with low computing memory consumption. YOLOv5 provides five pretrained weight files based on the size of the weight network used.  
The development environment for training YOLOv5 is listed in Table 2. We used a single NVIDIA RTX 4090 Ti GPU and conducted training in an Anaconda environment. NVIDIA's CUDA was used as the GPU development tool, and training was based on Python and PyTorch.  
To implement a lightweight system, we conducted an ablation study by training and comparing the nano and small versions of the YOLOv5 backbone, which are smaller-sized weight files provided by YOLOv5. We used the default learning rate provided by YOLOv5 for parameter training [46]. The epoch and batch size were set to 100 and 16, respectively, and applied equally to both versions to compare the performance and size of the models. Table 3 presents the results of the comparative analysis of the two versions. Although approximately a fourfold difference in model size exists, the performance improvement is only approximately 1%. Therefore, to build an efficient lightweight system, the smaller nano model was used for system implementation.  
Figure 6 shows an example of the training results. The results show that multiple objects belonging to the four classes are detected. We can generate natural language sentences that describe the situation in advance using the information inferred from these images. For example, in an image where a crosswalk and bollard are inferred together, a natural language sentence can be generated to caution the user, such as \"Be careful, there is a crosswalk with bollards ahead\".