esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Task-Space Nonholonomic Motion Planning Algorithm Based on Lie-Algebraic Method
## 3 Simulations
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & \multicolumn{8}{c}{**The Newton Algorithm**} \\ \cline{3-10}  & & & \multicolumn{8}{c}{**Basic**} & \multicolumn{8}{c}{**Null-Space Optimization**} \\ \hline
**Output** & **Control** & \multirow{2}{*}{\(\delta\)} & **b.a.** & **f.** & **Ctime** & **b.a.** & **a.** & **f.** & **Ctime** \\
**Function** & **Param.** & & & **[\%]** & **[\%]** & **[\%]** & **[s]** & **b.a.** & **[\%]** & **[\%]** & **[s]** \\ \hline  & & 0.05 & 0.000 & 100 & 0 & 1.7 & 0.001 & 100 & 0 & 1.7 \\  & & 0.1 & 0.001 & 100 & 0 & 1.6 & 0.001 & 100 & 0 & 1.6 \\  & & 0.2 & 0.002 & 98 & 0 & 1.5 & 0.003 & 100 & 0 & 1.5 \\  & & 0.5 & 0.009 & 49 & 0 & 1.5 & 0.011 & 62 & 0 & 1.5 \\  & & 0.7 & 0.017 & 43 & 0 & 1.4 & 0.020 & 42 & 0 & 1.4 \\  & & 1 & 0.037 & 29 & 0 & 1.2 & 0.040 & 31 & 0 & 1.3 \\ \hline  & & 0.05 & 0.000 & 100 & 0 & 0.6 & 0.000 & 100 & 0 & 0.6 \\  & & 0.1 & 0.000 & 100 & 0 & 0.5 & 0.001 & 100 & 0 & 0.5 \\ ident. & (23) & 0.2 & 0.000 & 100 & 0 & 0.4 & 0.002 & 100 & 0 & 0.5 \\  & & 0.5 & 0.006 & 100 & 0 & 0.4 & 0.010 & 100 & 0 & 0.5 \\  & & 0.7 & 0.012 & 100 & 0 & 0.4 & 0.020 & 100 & 0 & 0.4 \\  & & 1 & 0.028 & 100 & 0 & 0.4 & 0.040 & 100 & 0 & 0.4 \\ \hline  & & 0.05 & 0.002 & 100 & 0 & 0.5 & 0.005 & 100 & 0 & 0.6 \\  & & 0.1 & 0.010 & 99 & 0 & 0.5 & 0.017 & 100 & 0 & 0.5 \\  & & 0.2 & 0.032 & 82 & 0 & 0.4 & 0.049 & 100 & 0 & 0.6 \\  & & 0.5 & 0.157 & 0 & 0 & 0.4 & 0.195 & 0 & 0 & 0.5 \\  & & 0.7 & 0.262 & 0 & 0 & 0.4 & 0.322 & 0 & 0 & 0.5 \\  & & 1 & 0.454 & 0 & 0 & 0.4 & 0.546 & 0 & 0 & 0.4 \\ \hline  & & 0.05 & 0.001 & 6 & 5 & 42.3 & 0.006 & 1 & 0 & 42.4 \\  & & 0.1 & 0.004 & 7 & 3 & 43.1 & 0.018 & 1 & 0 & 43.2 \\  & & 0.2 & 0.007 & 4 & 0 & 38.5 & 0.051 & 1 & 1 & 38.5 \\  & & 0.5 & 0.136 & 1 & 0 & 42.7 & 0.172 & 0 & 0 & 42.8 \\  & & 0.7 & 0.155 & 2 & 2 & 40.3 & 0.257 & 0 & 0 & 40.3 \\  & & 1 & 0.328 & 0 & 3 & 39.2 & 0.381 & 0 & 0 & 39.2 \\ \hline  & & 0.05 & 0.005 & 10 & 15 & 13.7 & 0.042 & 0 & 0 & 13.8 \\  & & 0.1 & 0.016 & 2 & 20 & 13.0 & 0.083 & 0 & 1 & 13.2 \\  & & 0.2 & 0.095 & 0 & 21 & 13.0 & 0.164 & 0 & 0 & 13.4 \\  & & 0.5 & 0.388 & 0 & 5 & 12.6 & 0.406 & 0 & 1 & 12.8 \\  & & 0.7 & 0.546 & 0 & 3 & 12.4 & 0.567 & 0 & 2 & 12.5 \\  & & 1 & 0.783 & 0 & 2 & 12.6 & 0.809 & 0 & 1 & 12.7 \\ \hline  & & 0.05 & 0.009 & 7 & 11 & 15.6 & 0.005 & 68 & 2 & 16.6 \\  & & 0.1 & 0.033 & 0 & 13 & 15.0 & 0.017 & 62 & 4 & 15.2 \\  & & 0.2 & 0.069 & 0 & 3 & 15.0 & 0.037 & 43 & 7 & 15.1 \\  & & 0.5 & 0.103 & 1 & 5 & 15.3 & 0.207 & 0 & 8 & 15.4 \\  & & 0.7 & 0.296 & 0 & 2 & 15.0 & 0.329 & 0 & 17 & 15.1 \\  & & 1 & 0.553 & 0 & 4 & 14.7 & 0.539 & 0 & 11 & 14.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy, numeric failures, and computing time for the unicycle tasks.
## 3 Results  
### Transcriptional Activity of the Cellular Antioxidant System-Related Genes  
RT-qPCR analysis showed statistically significant changes in the expression of the cellular antioxidant system-related genes such as _SOD1_, _SOD2_, _GSH_ and _NFE2L2_ among the tested cell groups (Table 1). The mRNA level of _SOD1_ was significantly higher in the cells that had been co-exposed to fluoride and the SMF with a 0.65 T flux density compared to the cells that had been co-exposed to fluoride and the SMF with a 0.45 T ([PERSON]'s post hoc test, \(p=0.026\)) or with a 0.55 T ([PERSON]'s post hoc test, \(p=0.005\)) flux density. In the case of the _SOD2_ gene, there was statistically significant increase in its expression in the NaF-treated fibroblasts and simultaneously exposed to the SMF with a 0.65 T flux density compared to the control cells ([PERSON]'s post hoc test, \(p=0.008\)) and those that had only been treated with fluoride ([PERSON]'s post hoc test, \(p=0.022\)). Moreover, the transcriptional activity of glutathione reductase increased significantly in the NaF-treated cells and simultaneously exposed to the SMF with a 0.65 T flux density compared to the control cells ([PERSON]'s post hoc test, \(p=0.002\)),the cells that had only been treated with fluoride ([PERSON]'s post hoc test, \(p=0.005\)) and the cells that had simultaneously been exposed to fluoride and the SMF with a 0.45 T flux density ([PERSON]'s post hoc test, \(p=0.001\)). In turn, the level of the _NFE2L2_ expression decreased statistically significant due to the action of fluoride. We did not detect statistically significant changes in the gene expression of _CAT_, _GPx1_ or _MGST1_ under the influence of fluoride and a SMF.  
The increased activity of the antioxidant enzymes suggests a restoration of the cell redox homeostasis, which had been disturbed by fluoride, and the highest tested magnetic induction had the strongest effect on cells.
# A Review on Methods and Systems for Remote Collaboration
## 5 Communication Methods and Cues
### Pointers and Annotations
In [107], a user was able to navigate in a virtual space in order to collaborate with other users, making use of 2D gestures in 3D reconstructed scenes. This method can be applied in virtual and augmented reality. [PERSON] et al., in their study [108], proposed a remote assistance platform that gave the remote user the ability to use video and virtual annotations to give explanations to the other user. The platform was compared with an instant messaging application that could be used to send text instructions for each captured image. The results showed that visual annotations are valuable when objects have a similar appearance. AlphaRead [109] is a tool developed to reference objects by annotating them in a remote collaboration context. The system introduces the feature of object tracking, a solution for cases when the objects or the camera are moving. Each object has a readable label that the users can read in order to give instructions. This study showed that users found the object annotations useful and readable.
## 2 Materials and Methods
### Statistical Analysis  
The effects of FTT on the oil yield, quality, and chemical composition of Croatian VOOs were evaluated using a full factorial experimental design with two independent variables: (i) treatment temperature (15, 20, 25, 30, 35, and 40 \({}^{\circ}\)C) and (ii) olive variety (Istarska Bjelica, Levantinka, and Oblica). Three consecutive production batches of VOOs were carried out for each variety under each FTT condition. All resulting oil samples were analyzed in triplicate.  
To assess the impact of variety on the measured parameters, a one-way analysis of variance (ANOVA) was conducted. Additionally, the effect of FTT temperature on each variety included in the study was also evaluated using one-way ANOVA. When a significant effect of the temperature was observed, [PERSON]'s post hoc test was applied to perform multiple comparisons and identify differences among the produced samples. To further explore the influence of specific components on oxidative stability and antioxidantcapacity, partial least squares (PLS) regression analysis was performed. The PLS model for oxidative stability included fatty acid composition (sum of saturated fatty acids--SFAs, monounsaturated fatty acids--MUFAs, and polyunsaturated fatty acids--PUFAs) and bioactive compounds (\(\alpha\)-tocopherol and phenolic compounds) as predictors. For the antioxidant capacity model, only bioactive compounds were used as a predictor. Variable Importance in Projection (VIP) scores were calculated to determine the most influential variables, and model performance was evaluated using the coefficient of determination (R\({}^{2}\)).  
To identify optimal FTT temperatures for each olive variety, response surface methodology (RSM) was employed. Optimization aimed to maximize the oxidative stability index (OSI), antioxidant capacity (AC), and volatile compounds resulting from the lipoxygenase pathway (\(\Sigma\) LOX), while minimizing the levels of volatiles resulting from oxidation (\(\Sigma\)OX) and microbial activity (\(\Sigma\)MBA). For each response variable, a polynomial regression model was fitted and evaluated using ANOVA, lack-of-fit tests, and R\({}^{2}\) values. Numerical optimization was performed using a desirability function approach, with individual goals and relative importance set for each response variable to reflect their contribution to overall oil quality. Optimal pretreatment temperatures were identified for each variety, and predicted response values at these optimal conditions were generated from the fitted models to illustrate the expected improvements in oil quality.  
All statistical analyses were conducted at a significance level of \(p=0.05\). ANOVA, [PERSON]'s test, and PLS regression were carried out using XLSTAT 2023 (Lumivero, Denver, CO, USA), while RSM analyses were performed using Design-Expert 10 (Stat-Ease, Inc., Minneapolis, MN, USA).
# A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network
## 1 Introduction
### Proposal of the Intelligent Satellite
In 2017, the Institute of the Chinese Academy of Sciences and the Association of Software-Defined Satellite Technologies, initiated by more than 20 units, launched the \"Tianzhi No.1\" satellite, based on space-based cloud computing, with an open system architecture and powerful real-time information processing and massive data storage capabilities [9]. These new conceptual satellites have not solved the problems associated with a long design and manufacturing cycle, high cost, weak universality, single application mode, and function, and they cannot meet the development requirements of a space-based network system. [PERSON] proposed the concept of an intelligent satellite, \"iSAT\", based on software-defined satellite research [10; 11]. The goal of iSAT study is to change the traditional satellite service model, from functional to service-oriented customization, standardizing the design and manufacture of satellites through, for instance, designing and manufacturing computers and decoupling user and developer processes. The iSAT satellite can run different application software, according to different task requirements, provide corresponding in-orbit services, share satellite resources in real time, and realize one-satellite multi-energy, multi-satellite coordination, and intelligent services. The core idea of the iSAT concept is a general satellite architecture. Based on this, research and mass production will be carried out, and finally, there will be multiple intelligent satellites with complete functions in orbit. Each iSAT satellite is a node of the space-based information network. In the process of intelligent satellite application, the underlying physical resources of the satellite are abstracted, and the resource pool is built in the satellite network to separate the infrastructure from the service, so that the resources can be flexibly and efficiently matched, according to the user's business requirements. It is foreseeable that the emergence of iSAT will change the satellite's resource application model, from the traditional task-specific design satellite function load to the multi-satellite in-orbit resource service, for specific tasks.
# Fault Detection and VSC-HVDC Network Dynamics Analysis for the Faults in Its Host AC Networks
## 3 Simulation Model and Results
### Simulation Model  
The HVDC network connecting four host AC systems is presented in Figure 2. For the higher voltage level of the converter, the modular multilevel converter (MMC) topology was preferred. For the high-voltage network, the VSC-based HVDC network proved its strength over the LCC-based HVDC network. The modular multilevel converter (MMC) topology was accepted over two- and three-level VSC topologies for the bulk amount of power transfer through HVDC network. The modeling was performed following the guidelines of the CIGRE B4 DC grid guide [16; 17; 18]. The control and protection of an HVDC network is still a challenging task. In the VSC-HVDC network, fast and indigenous control of the active power and reactive power was implemented using pulse width modulation. Different faults were analyzed at four different locations--F\({}_{1}\), F\({}_{2}\), F\({}_{3}\), and F\({}_{4}\)--with different fault scenarios and conditions tested for the simulation model [19; 20]. The HVDC network parameters are presented in Appendix A.
## 1 Introduction
In addition, the behavior of granting permissions requested by applications is observed as a user behavior that contributes to cyber attacks. Based on the challenges mentioned in detecting APTs, a comprehensive systematic survey was conducted in this study. A total of 1351 papers analyzed from the past ten years (2012 to 2023) were analyzed. The primary aim of the conducted systematic literature review (SLR) is to understand and analyze existing studies and present concrete findings by detecting APT on mobile sensors, applications, and services. Subsequently, a conceptual framework was also proposed for the situational awareness model in line with adopting game theory as an AI technique. The cyber cognitive situational awareness (CCSA) model was explored, and its suitability to detect APT attacks on smartphones was tested. The study aims to fulfill the following research objectives: (i) to conduct a comprehensive SLR concerning APTs on mobile sensors with game theory as an AI technique. (ii) To provide a concrete discussion and analysis of findings for the SLR and generate recommendations on detection. (iii) To analyze the open challenges of APTs on mobile sensors, applications, and user behavior. (iv) To propose a conceptual model and an open discussion to identify solutions for APTs on smartphone sensors. This study provides an SLR of APT detection for mobile devices and an exploration of AI techniques, such as game theory, as a potential solution. It can be used to design guidelines and policies to secure against APT attacks on mobiles and raise awareness of the challenges, effects, and consequences of APT attacks. It has identified, explored, and evaluated various detection mechanisms used to detect APTs on smartphones and proposed a conceptual framework for the situational awareness model. Finally, the review and proposed conceptual framework (FORMAP) have addressed the gaps concerning the detection of APTs in smartphone sensors, applications, and behavior. Figure 1 illustrates the outline structure of this research study. The remainder of this paper is structured as follows. After this introductory section, the background of APTs on smartphone sensors is introduced in Section 2. The SLR protocol and the relevant steps are presented in Section 3. Subsequently, Section 4 focuses on the SLR analysis  
Figure 1: The Outline Structure. of the selected studies to respond to the formulated research questions. Finally, the discussion, recommendations, limitations, and conclusion are outlined in Sections 5 and 6, respectively.
# GRACE/GFO and Swarm Observation Analysis of the 2023-2024 Extreme Drought in the Amazon River Basin
## 4 Results
### Drought Process
In contrast, the spatial distribution of drought durations for the 2010-2011 and 2017-2018 droughts were just the opposite of the current drought, that is, long in the southeast and the short in the northwest. The spatial distributions of drought severity were essentially the same as those of drought duration (Figure 11e-h). Figure 8 shows that the peak of this drought has a discrete distribution. The larger peaks are mainly concentrated in the east, while the smallest peaks are distributed in the east and parts of the south. However, the peak distributions of the other three droughts are more concentrated, and the maximum peaks are higher than the current drought (Figure 11i-k). Comparing Figure 11m,n,p, the regions with the largest water storage deficit for these three droughts are in the northeast. In summary, this drought is most similar to the characteristics of the 2015-2016 drought, but the drought severity is much lower. Moreover, the most severely affected regions in this drought are mainly concentrated in the northeast, while the eastern alpine region and the southern plains region are the least affected. Figure 12 plots the trajectory of the center of gravity of this extreme drought. Initially, the center of gravity of the drought was in the central north. Subsequently, the center of gravity of the drought shifted to the southwest month by month over time. After December  
Figure 11: Spatial distribution of drought characteristics of four extreme droughts in the ARB during January 2003 and February 2024. 2023, the location of the center of gravity of the drought was basically fixed with only minor changes. Essentially, this southwestward movement is a process of redistributing drought intensity driven by anomalies in tropical atmospheric circulation. During the Southern Hemisphere winter of 2023, the subtropical high over the South Atlantic strengthened abnormally, causing the path of water vapor transport from the Atlantic carried by the southeast trade winds to shift southward [63]. This resulted in the dominance of descending air currents in the north-central part of the ARB. The orographic lifting effect on the eastern slope of the Andes in the southwest prompted the remaining water vapor to form local PRE there. However, this PRE was still insufficient to offset the long-term ET loss, leading to the migration of the drought core area to ecologically fragile regions with low PRE response.
## 5 Pairwise Clock Synchronization under Unknown Random Delays
### Least Squares Estimators  
The least squares approach in [39] provides a general framework for joint estimation of clock offset and skew, irrespective of the network delay distribution type. The model adopted by the least squares approach is obtained by adding two equations in Equation (2) together and reduces to:  
\[\frac{T_{i}^{b}+T_{i}^{c}}{2}=\theta+f\frac{T_{i}^{a}+T_{i}^{d}}{2}+f\frac{X_ {i}-Y_{i}}{2} \tag{41}\]  
Thus, we can regard Equation (41) as a linear regression model in terms of \(\{(T_{i}^{b}+T_{i}^{c})/2\), \((T_{i}^{a}+T_{i}^{d})/2\}_{i=1}^{N}\), where \(\theta\) represents the intercept, \(f\) stands for the slope and \(f(X_{i}-Y_{i})/2\) denotesthe error term. Based on the model Equation (41), \(\theta\) and \(f\) can be naively estimated via a standard least squares approach as follows:  
\[\hat{f}_{\text{LS}}=\frac{\sum\limits_{i=1}^{N}(T_{i}^{b}+T_{i}^{c})(T_{i}^{a}+T _{i}^{d}-\overline{T}^{a}-\overline{T}^{d})}{\sum\limits_{i=1}^{N}(T_{i}^{a}+T _{i}^{d}-\overline{T}^{a}-\overline{T}^{d})^{2}} \tag{42}\]  
and:  
\[\hat{\theta}_{\text{LS}}=(\overline{T}^{b}+\overline{T}^{c})/2-\hat{f}_{\text {LS}}(\overline{T}^{a}+\overline{T}^{d})/2 \tag{43}\]  
where \(\overline{T}^{j}\) for \(j=a,b,c,d\) represent sample means.  
However, in general, neither the mean nor the covariance of the error term \(f(X_{i}-Y_{i})/2\) is zero, which may result in poor estimation performance. Therefore, several alternative least squares estimators were proposed in [39] to further improve the estimation accuracy of clock offset and skew. An example of an enhanced least squares estimator is referred to as the constrained least squares (CLS) estimator, and it is found by minimizing the sum of squared residuals subject to a set of constraints on \(\theta\) and \(f\) formed by considering the time stamp model in Equation (2):  
\[T_{i}^{b}\geq\theta+fT_{i}^{a},\;\;\;T_{i}^{c}\leq\theta+fT_{i}^{d} \tag{44}\]  
The disadvantage of the CLS lies in the challenging computations to solve the minimization problem. Therefore, two additional least squares estimators, referred to as the feasibility checked least squares (FLS) estimator and the Paxson-based estimator, respectively, were proposed in [39] to reduce the computational complexity. Experimental results showed that the FLS estimator and the Paxson-based estimator perform comparably and sometimes better than the CLS estimator, while exhibiting the advantage of much less computational complexity.
# Spectra Prediction for WLEDs with High TLCI
## 2 Materials and Methods
### Experimental Methods
\(Q_{G-R}\) indicates the conversion efficiency  
Figure 4: Spectral shift of green phosphor spectrum in this study. of the emission spectrum of the green phosphor to excite the red phosphor. A higher \(Q_{G-R}\) means a higher conversion efficiency of the green light to the red light. \(Q_{G-R}=0\) means that all the green light absorbed by the red phosphor is absorbed, and no red light is excited. According to Equation (5), by fitting the spectrum of the WLED with dual phosphors, we can obtain the proportion of red phosphors reabsorbing green light. For simplicity, the reabsorption proportion is assumed to be constant. Then, we have obtained the prediction model for calculating the spectrum of WLEDs with any proportion of phosphor powder. Finally, colorimetric analysis was carried out on the spectrum, and the TLCI was calculated. In this study, we followed the TLCI-2012 standard. The calculation steps have been explained in detail in this reference [13], with a brief explanation as follows: Firstly, determine the signals, \(R_{c}\), \(G_{c}\), and \(B_{c}\), generated by the sensors for each color from the spectrum. This is followed by balancing. Then, optimize the color performance using a linear matrix and deliver RGB signals. This is followed by gamma correction. Derive CIE tristimulus values XYZ for each color, as seen on the display via colorimetric analysis, and then calculate the color difference, \(\Delta E_{00}\) (CIEDE2000). The TLCI is derived from the color difference, \(\Delta E_{00}\):  
\[\text{TLCI}=\frac{100}{1+(\frac{\Delta E_{00}}{3.16})^{2.4}}. \tag{6}\]  
The highest level of TLCI is 100, and the higher the value is, the higher the degree of color restoration will be and the better the color rendering effect will be. Film-style production and live multi-camera production typically use a TLCI \(>90\) as a minimum starting point to provide cinematographers and gaffers with a quick and accurate way to quantify the light quality, thereby avoiding extensive testing and minimizing during post-processing. Based on the predicted results of the model, high-TLCI WLED samples can be fabricated. The simulated and experimental measurement results were compared to verify the model.
# Construction of a Numerical Model for Flow Flash Evaporation with Non-Condensable Gas
## 2 Construction of the Numerical Model
### Model Construction and Governing Equations
#### 2.3.4 Turbulence Equations  
In addition, the RNG k-\(\epsilon\) model was selected to simulate the turbulent flow in the flash tank. This model has a better result of turbulence simulation compared to the Standard k-\(\epsilon\) and RSM models in the VOF model for stratified flow simulation [33; 34]. This model calculates the turbulent kinetic energy in the flow by solving the k and \(\epsilon\) equations. The control equations are shown in Equations (14) and (15).  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial(\rho ku_{\rm i})}{\partial x _{\rm i}}=\frac{\partial}{\partial x_{\rm i}}(1.393\mu_{\rm eff}\frac{ \partial k}{\partial x_{\rm i}})+G_{\rm k}+\rho\epsilon \tag{14}\]  
\[\frac{\partial(\rho\epsilon)}{\partial t}+\frac{\partial(\rho\epsilon u_{\rm i })}{\partial x_{\rm i}}=\frac{\partial}{\partial x_{\rm i}}(1.393\mu_{\rm eff }\frac{\partial\epsilon}{\partial x_{\rm i}})+\frac{C_{\rm k}^{*}\epsilon}{k }G_{\rm k}-1.68\rho\frac{\epsilon^{2}}{k} \tag{15}\]  
where \(k\) represents the turbulent kinetic energy, \(\epsilon\) represents the turbulent dissipation rate, \(G_{\rm k}\) represents the generation of turbulence kinetic energy due to the mean velocity gradients, and \(\mu_{\rm eff}\) is the corrected turbulence viscosity.