esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this work, the coating mask width at different segments can be obtained using optical simulation combined with flux mapping method. The dimensions of the coating mask can be precisely obtained, which can increase the thickness uniformity of the different segments. From the analysis results of different oxide films, it can be seen that the uniformity is less than 0.4%. A good uniform film thickness distribution can be obtained by electron beam evaporation with IAD technique. The thickness uniformity is 0.38% for SiO\({}_{2}\) film, 0.36% for Ta\({}_{2}\)O\({}_{5}\) film, and 0.15% for Nb\({}_{2}\)O\({}_{5}\) film, respectively. The results show that Nb\({}_{2}\)O\({}_{5}\) film has the best thickness uniformity. We also solved the problem of the thinner film thickness in the area farthest from the evaporation source. For the optical performance, the average transmittance of the SiO\({}_{2}\) film in the visible light band is higher than 90%. The average transmittance of the Ta\({}_{2}\)O\({}_{5}\) film is higher than 85%. The average transmittance of the Nb\({}_{2}\)O\({}_{5}\) film is higher than 80%. In addition, the extinction coefficients of the three oxide films are small, and their values are in the order of 10\({}^{-3}\). These results show that three kinds of amorphous thin films have good optical properties. These results show that the three kinds of amorphous films have good optical properties, which is helpful to develop new thin-film components.  
Conceptualization and methodology, C.-L.T.; writing--review and editing, C.-L.T.; validation, C.-L.T. and K.-S.C.; software, K.-S.C.; data curation, K.-S.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the National Science and Technology of Council, under project number 111-2622-E-035-003.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Simulation Examples
### Examples with Consideration of Multiple Uncertainties
#### 5.2.3 Example 6--Response to Real Weather Forecast Data  
In this example, all the assumptions outlined in the previous Example 3 (Section 5.1.3) still apply, with the key difference being the inclusion of multiple uncertainties in the algorithm. The total demand remains at \(P_{D}=1200\) kW.  
The algorithm initialization follows the settings in Table 8. The fuel prices for all sources, \(\mu_{c_{i}}\), \(\mu_{s,w_{i}}\), and \(\mu_{b_{i}}\), remain constant throughout the simulation. The drop-off packet is also modeled to occur once every \(n=5000\) steps, following the approach used in the previous example.  
Figure 14: Example 5 results for \(\lambda_{i}\), \(P_{i}\) and time delay on the first generator with more uncertainties.  
Figure 13: Example 5 results for BESS capacity \(E_{b_{i}}(t)\) and fuel price \(\mu(t)\) with more uncertainties.  
In this example, a unique rule was utilized for resetting the learning gain index due to the fluctuating performance of renewable resources at each step. Resetting the index at every change would be impractical. Instead, the decision was made to reset the index once every 900 steps, equivalent to once every fifteen minutes. This interval aligns with the standard trading interval observed in the electricity market [39; 40].  
Based on the results in Figures 15 and 16, maximizing the integration of renewable sources in the network reduces the overall incremental price. The lowest price observed, 8.1801 USD/kWh, occurred from \(k=30\),953 s to 41,176 s and then from \(k=48\),527 s to 54,455 s. In contrast, at the end of the simulation, when renewable resources were minimal and BESS was depleted, the price was 8.5476 USD/kWh. Notably, these values represent steady-state conditions, and transient events during input changes were not considered as minimum achieved values.  
Additionally, the BESS was in discharge mode from the beginning, specifically until step \(k=17\),351 s. Subsequent discharge sequences occurred from \(k=41\),593 s to 48,004 s and then from \(k=54\),126 s to 62,360 s. Charging sequences for BESS ran from \(k=31\),215 s to 41,594 s and from \(k=48\),005 s to 54,125 s. Compared to the example without uncertainties, it is apparent that uncertainties caused a shift and modification of the intervals.  
Uncertainties and resetting the learning gain index also influenced convergence to the optimal solution. The algorithm approaches the desired value over hundreds of steps but falls slightly under before the following change, resulting in a value approximately 3-4 kW lower, similar to Example 3 (Section 5.1.3). This example confirms the applicability of the described algorithm when using real weather forecast data, even for more uncertainties.
# Web3 DGIS-Based System for Reservoir Landslide Monitoring and Early Warning
## 6 Conclusions  
This study focused on the development of a landslide monitoring and early warning system in the Danjiangkou Reservoir area, China. The proposed real-time disaster forecasting system is based onWeb3 DGIS and uses analysis results from landslide monitoring data and linked forecasting models. After reviewing local conditions and historical landslide data, we set up 31 landslide monitoring sites around the Danjiangkou Reservoir in order to collect data on surface displacement, rainfall, and groundwater. After receiving the data, the system consults the early warning model and information from experts before deciding whether to issue timely disaster information to decision-makers and emergency personnel. The system incorporates many technologies, including those of the sensors, WebGIS, three-dimensional visualization, and landslide forecasting. The final systems offers: (1) a comprehensive combination of multi data and landslide forecasting models that support forecasting accuracy; (2) real-time data collection by different sensors and timely wireless data transmission; and (3) Web3 DGIS technology for three-dimensional analysis and expression of spatial information, allowing it to accurately identify the environmental conditions at the monitoring site, and to support the analysis of relevant three-dimensional space.  
This work was supported by the National Natural Science Foundation of China (Grant Nos. 41501558 and 41571377).  
[PERSON], [PERSON] and [PERSON] conducted the primary experiments, cartography and analyzed the results. [PERSON] and [PERSON] offered data support for this work. [PERSON] and [PERSON] provided the original idea for this paper.  
The authors declare no conflict of interest.
###### Abstract  
The investigations examined a potential reduction in discrepancies between the values of the unperturbed radiofrequency (RF) electromagnetic field (EMF) and values of the EMF measured by wearable equipment (personal exposure meters) impacted by the proximity of the human body. This was done by modelling distributed wearable (multi-location, with up to seven simultaneously locations) measurements. The performed numerical simulations mimicked distributed measurements in 24 environmental exposure scenarios (recognized as virtual measurements) covered: the horizontal or vertical propagation of the EMF and electric field vector polarization corresponding to typical conditions of far-field exposure from wireless communication systems (at a frequency of 100-3600 MHz). Physical tests using three EMF probes for simultaneous measurements have been also performed. Studies showed that the discrepancy in assessing EMF exposure by an on-body equipment and the parameters of the unperturbed EMF in the location under inspection (mimicking the contribution to measurement uncertainty from the human body proximity) may be significantly reduced by the appropriate use of a distributed measurement system. The use of averaged values, from at least three simultaneous measurements at relevant locations on the body, may reduce the uncertainty approximately threefold.  
Keywords:radiofrequency electromagnetic fields; personal exposure meters; measurements; workers' exposure +
Footnote †: journal: Journal of Low Temperature Physics
## 3 Methodology
### Data Preprocessing
#### 3.2.2 Training and Test Data  
An ML prediction model is based on the observation of data. Once the learning is complete, it can perform complex tasks and make predictions with greater precision [3]. For this reason, we divide the input data into training and testing data. This segmentation aims to acquire knowledge to simulate forecasts and evaluate their performance. In this study, the Pareto proportion was used, where 80% (48,793 cases) were used for training and 20% (12,199 cases) were used for tests.
## 4 Orbits with Critical Inclination
We can see that when \(e\) is small, for example \(e<0.2\), the critical inclination monotonically decreases with respect to the altitude of the orbit. On the other hand, when \(e>0.4\), the critical inclination monotonically increases. It can also be concluded that there exist some critical values for e, such that the critical inclination is independent of the altitude of the orbits. Figure 6b shows that in the case of Jupiter, critical inclinations depend on the semi-major axis and eccentricity, while for the Earth considering only the \(I_{2}\), they are fixed (63.435 deg and 116.565 deg). The evolution of \(\omega\) and \(e\) for two different values of the semi-major axis and eccentricity are shown in Figures 7 and 8. For the first case in Figure 7, the amplitude of \(\omega\) is about 5 deg over a time of \(25T\). The amplitude of \(e\) is about 0.013. For the second case, the amplitude of \(\omega\) is about 1.8 deg over the same time. One can also see from Figure 8 that the amplitude of \(\omega\) in the first case is  
Figure 6: (a) Variation of \(h(1)\) as a function of the semi-major axis \(a\) and the eccentricity e. (b) Variation of the critical inclination i as a function of the semi-major axis for different values of the eccentricity. much larger. This is reasonable, since the semi-major axis of the second case is larger than that of the first case.
## 2 Theoretical Background
### Presence Factors for Immersive VR Experiences  
In this paper, we define the presence factor as any element of a VR system or its usage context that has a positive effect on presence. Presence factors can help users to experience presence, thereby contributing to memorable VR experiences. Examples of potential presence factors are high-quality displays in the HMD, realistic virtual characters, realistic and contextually appropriate virtual environments, accurate and natural hand gestures, high-quality positional sound effects, comfort wearing the HMD, well-tested code free of obvious bugs, and a quiet obstacle-free physical environment. Many studies have investigated the effects of presence factors on the presence experienced by users. For example, [PERSON] et al. [9] explored how the technical affordances of VR equipment and network infrastructure, such as resolution, latency, frame rate, and jitter, affect presence. They proposed a method of measuring presence using these technical factors to complement subjective instruments [28; 29] that are typically used in presence research. [PERSON] et al. [11] conducted a qualitative study to explore the factors that affect presence in a social VR environment. They concluded that presence increased when the VR contents elicited cognitive, emotional, and behavioral responses in the users, as well as when the users were able to create their narrative about the events. Similarly, [PERSON] and [PERSON] [12] found significant correlations between presence and cognitive factors including absorption, creative imagination, empathy, and willingness to experience presence. Based on their findings, [PERSON] and [PERSON] proposed a presence equation that can be used to predict presence by measuring cognitive factors. Finally, [PERSON] et al. [10] reviewed the early literature on presence, including the studies behind some of the presence questionnaires. From other studies, they gathered several factors that contribute to presence in VR. Table 2 lists previously identified presence factors that are related to immersive VR experiences in general.
# Control Strategy of Multiple Battery Energy Storage Stations for Power Grid Peak Shaving
###### Abstract  
In order to achieve the goals of carbon neutrality, large-scale storage of renewable energy sources has been integrated into the power grid. Under these circumstances, the power grid faces the challenge of peak shaving. Therefore, this paper proposes a coordinated variable-power control strategy for multiple battery energy storage stations (BESSs), improving the performance of peak shaving. Firstly, the strategy involves constructing an optimization model incorporating load forecasting, capacity constraints, and security indices to design a coordination mechanism tracking the target load band with the equivalent power. Secondly, it establishes a quantitative evaluation system using metrics such as peak-valley difference and load standard deviation. Comparison based on typical daily cases shows that, compared with the constant power strategy, the coordinated variable-power control strategy has a more obvious and comprehensive improvement in overall peak-shaving effects. Furthermore, it employs a \"dynamic dispatch of multiple BESSs\" mode, effectively mitigating the risks and flexibility issues associated with single BESSs. This strategy provides a reliable new approach for large-scale energy storage to participate in high-precision peaking.  
b
# Evaluation of Standing-Up Motion from a Forward-Sloping Toilet Seat for Older People
## 4 Discussion
### Effect of Age on Toilet Seat Angle during the Standing-Up Motion  
In previous studies, the standing-up motion in older individuals showed a consistent pattern of trunk and lower extremity motion, with two distinct upper extremity movement strategies [16]. Older individuals used a technique consisting of forward trunk inclination motion. To stand up easily, it was thought that the center of mass for the forward trunk inclination motion at the time of getting off the seat was important. However, it is unclear whether the forward trunk inclination motion is affected by the toilet seat angle, which in turn affects the ease of the standing-up motion. In these studies, the effect of head posture on the standing-up motion with different seat angles was not measured. In this study, forward-backward and up-down head motions during the standing-up motion decreased as the seat angle increased. More head motion among older participants was seen as compared to younger participants. Regarding characteristics of the standing-up motion, older participants had less head motion than younger participants. However, muscle activity and COP displacement were the same in both groups. Forward trunk inclination angle and head motion in two directions affected the standing-up motion. Specifically, older participants had a larger forward trunk inclination angle than younger participants. We think that older individuals prefer to use a forward trunk inclination motion (which moves the center of gravity) for motions like standing up because physical strength decreases with age. In this study, static parameters, such as peak-to-peak displacement of head movement and COP, were analyzed instead of temporal changes during the standing-up motion. Future work will focus on determining the ease of the standing-up motion for older individuals while taking into account time elements and dynamic data.
# Chaotic Features of Decomposed Time Series from Tidal River Water Level
## 4 Chaotic Features Analysis of Decomposed Time Series
### Close Returns Plot for the Decomposed Time Series  
The attractor and correlation dimension show the nonlinearity of the water level time series and the three components. In addition, a close returns plot was used to determine whether the nonlinear time series has chaotic or stochastic behavior. Regular horizontal line segments in CRP indicates that the data has the characteristics of a periodic function. On the other hand, horizontal line segments are drawn at a certain period with irregularity to indicate chaotic characteristics, and random dot segments indicate stochastic characteristics (Figure 10). Figure 10a shows the pattern of CRP for the periodic model with the repeated appearance of regular line. Figure 10b shows the CRP of the Henon map indicating a typical example of chaotic characteristics which have irregular horizontal line segments. Meanwhile, Figure 10c shows a time series with stochastic characteristics. In the Henon map, the chaotic characteristics are seen in the irregular horizontal lines in the uniformed pattern. However, it is found for the stochastic time series that the random dots appear without a regular trajectory, forming a random distribution.  
For the water level time series, tide, wave, and rainfall-runoff components, the CRPs were drawn, as shown in Figure 11. Figure 11a shows the CRP using the time series of Figure 12, and the enlarged part in Figure 11a is the square area of Figure 12. Here, the \(x\)-axis means the \(i^{\text{th}}\) time series, and the \(y\)-axis means the distance (T) from the i-th time series. For the period when there was no rainfall, the Ulsan water level data showed the periodic characteristics, with the irregular horizontal segments appearing similar to those of the tide component. However, when rainfall occurred, the data had stochastic characteristics (top right of Figure 11a). As can be seen from time series, the water level periodically fluctuated and maintained the trajectory when the rainfall did not occur. However, they show a diagonal shape deviating from the orbit when the water level rises due to the rainfall. As for the tide component, there was aperiodic recurrence of irregular horizontal line segments, indicating chaotic characteristics (Figure 11b). For wave height, noise, and rainfall-runoff components, there is no horizontal line and it has random points, which means stochastic characteristics (Figure 11c-e).  
Figure 11: Close returns plot for (**a**) water level time series, (**b**) tide component, (**c**) wave component, (**d**) noise component, (**e**) rainfall-runoff component when r = 0.005.  
Figure 12: Water level time series in tidal river used for CRP.
# Goal Oriented Time Adaptivity Using Local Error Estimates
## 3 Time Adaptivity Based on Local Error Estimates
### Convergence in the QoI
Summing up Equations (29) and (24) gives  
\[\Delta t_{n}^{\hat{p}+1}\left(\left|\frac{\partial j(t_{n},\mathbf{u}(t_{n}))}{ \partial\mathbf{u}}\mathbf{\phi}_{-}(t_{n},\mathbf{u}(t_{n}))\right|+|\varphi_{-}(t_{n}, \mathbf{u}(t_{n}))|+\mathcal{O}(\Delta T_{\text{ref}})\right),\]  
since we have \(\mathcal{O}(\Delta t_{n})=\mathcal{O}(\Delta T_{\text{ref}})\) by the induction hypothesis. Inserting the above term into the controller in Equation (26), separating the resulting \(\mathcal{O}\) term from the denominator gives  
\[\Delta t_{n+1}=\Delta t_{n+1}^{\text{ref}}+\tau^{1/(\hat{p}+1)}\mathcal{O}( \Delta T_{\text{ref}})=\Delta t_{n+1}^{\text{ref}}+\mathcal{O}(\Delta T_{ \text{ref}}^{2}).\]  
This concludes the induction step. Consequently, we can use Lemma 1 for the steps in Equation (26), since they fulfill Equation (11), which gives convergence in the solution. The statement now follows from Theorem 2 provided that Condition (16) holds. A single step of size \(\Delta t_{n}^{*}\) from \(t_{n}\) to \(t_{n}^{*}\in(t_{n},t_{n+1})\), with the scheme \(\hat{\mathcal{N}}^{t_{n},\Delta t_{n}^{*}}\), gives the error  
\[\mathbf{e}_{n}^{*}=\mathcal{M}^{t_{n},\Delta t_{n}^{*}}\mathbf{e}_{n}+\left(\hat{ \mathcal{N}}^{t_{n},\Delta t_{n}^{*}}-\mathcal{M}^{t_{n},\Delta t_{n}^{*}} \right)\mathbf{u}_{n}=\mathcal{O}(\tau^{p/(\hat{p}+1)})+\mathcal{O}\left((\Delta t _{n}^{*})^{p}\right).\]  
Here we have \(\Delta t_{n}^{*}\leq\Delta t_{n}=\Delta t_{n}^{\text{ref}}+\mathcal{O}(\Delta T _{\text{ref}}^{2})=\mathcal{O}(\tau^{p/(\hat{p}+1)})\Rightarrow\mathbf{e}_{n}^{*} =\mathcal{O}(\tau^{p/(\hat{p}+1)})\). Thus, Equation (16) holds for \(q=\hat{p}+1\), which gives us convergence in the QoI with \(e^{\mathcal{J}}=\mathcal{O}(\tau^{\min(r,p)/(\hat{p}+1)})\) by Theorem 2. **Remark 4**. : _The assumption of Equation (27) is a requirement of controllability in the asymptotic regime, ensuring non-zero error estimates. Using both the quadrature and time-integration error estimate makes a scheme more robust, as \(\psi_{\min}=0\) would require a common zero in both principle error functions. Remark 1 likewise applies here._  
**Example 1**. : _In [16] [PERSON] describes using the lift-coefficient of the flow around a cylinder as QoI. This \(j(t,u(t))\) is oscillating in time, changing signs. Attaining very small absolute values would thus lead to very large timesteps and \"catastrophical results\" [16]. This is a prime example of Criterion (27) not being fulfilled and thus not guaranteeing convergence in the QoI. In such scenarios, [PERSON] proposes using multiple quantities for error estimation, for example, drag and lift, combined with global norms of pressure and velocity._  
_The authors of [18] present a numerical experiment with an oscillating beam and \(j(t,\mathbf{u})\) being the displacement in the \(x\)-direction. This likewise implies vanishing error estimates, but the author obtains good results. This is likely due to their strict limits on step-size changes, cf. Equation (32), and enforcing a maximal timestep._
## 3 Design and Analysis  
### Dimensional Optimization of FGIL Enclosure Apropos of Stranded Conductor  
The selection of a stranded conductor for an FGIL requires reconsideration regarding enclosure diameter because the FUF for GILs is normally kept in the range of 0.5 to 0.6 and is directly related with their dimensional specifications [24; 41; 42]. In a standard GIL, enclosure and conductor dimensions are normally selected to have approximately 1 as the solution of the logarithmic expression in Equation (2). That is, the enclosure diameter is approximately three times the conductor diameter [24; 41; 42]. However, in order to have an optimized enclosure size regarding the required FUF, Equation (2) was rearranged for enclosure dimension and expressed as Equation (3). Considering that Equation (3) appears as an implicit equation, its solution was performed through the Newton-Raphson iterative (NR) method in MATLAB(r) with the required accuracy up to four decimals and an initial estimate of 50 for the unknown parameter (i.e., enclosure radius). The estimated values and their errors showed a converging trend, and the enclosure radius finally converged in eleven iterations up to the required accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sr. No.** & **Conductor Type** & **Strand Geometry** \\ \hline
1. & Concentric strand & Circular \\
2. & Compact strand & Circular \\
3. & Compact strand & Trapezoidal \\ \hline \hline \end{tabular}
\end{table}
Table 1: Stranded conductors used in conventional power transmission systems.  
Figure 1: (**a**) Circular strand and (**b**) trapezoidal strand conductors used in conventional power transmission schemes.  
Dimensional appraisal of conductor and enclosure revealed that the enclosure was approximately three times the conductor size and resulted in the achievement of the desired FUF.  
\[R\ =\ \textit{rexp}\Big{(}F\big{(}\Big{(}\frac{R}{r}\Big{)}-1\big{)}\Big{)} \tag{3}\]