esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Algorithm Performance Analysis  
In order to evaluate the performance of the filtering algorithm, the following two types of root-mean-square errors are introduced [18]:  
\[RMSE_{\text{time}}=\sqrt{\frac{1}{\text{M}}\sum_{j=1}^{\text{M}}\left(\hat{x} _{k}^{j}-x_{k}^{j}\right)^{2}},\;\text{k}=1,2,\cdots,\text{N} \tag{38}\]  
\[RMSE_{total}=\sqrt{\frac{1}{N}\sum_{k=1}^{N}\left(\hat{x}_{k}^{j}-x_{k}^{j} \right)^{2}},\;j=1,\;2,\cdots,M \tag{39}\]  
where \(\hat{x}_{k}^{j}\) and \(x_{k}^{j}\) are the estimated value and true value of the state variable in the \(j\)th Monte Carlo simulation at time k, \(N\) is the length of discrete time, \(M\) is the number of Monte Carlo simulations, the expression (38) describes the average convergence speed of the filtering algorithm in the simulation, and expression (39) describes the filtering performance of the filtering algorithm [18].  
The Figure 6 shows the corresponding \(RMSE_{\text{time}}\) value when the ASRCKF algorithm and the SRCKF algorithm are applied to the vector tracking loop when C/N\({}_{0}\) = 30 dB-Hz.  
Figure 6: \(RMSE_{\text{time}}\) value when the filter uses the ASRCKF algorithm and the SRCKF algorithm to filter respectively and C/N\({}_{0}\) = 30 dB-Hz.  
Similarly, the \(RMSE_{\text{time}}\) value when C/N\({}_{0}\) is 25, 23, 22, 21, and 19 dB-Hz, respectively, can be obtained. Table 2 describes the comparison of the filtering performance of the SRCKF and ASRCKF algorithms when the C/N\({}_{0}\) is 25, 23, 21, 20, and 19 dB-Hz, respectively.  
It can be seen from the Figure 6 and Table 2 that the numerical accuracy of the ASRCKF algorithm is higher than that of SRCKF, and the filtering performance of ASRCKF is better than that of SRCKF through the average value of \(RMSE_{total}\). However, the time consumed is similar, that is, the computational complexity is similar. This result is consistent with the simulation results of Figures 4 and 5.
## 5 Experimentation, Results, and Analysis
### Performance Metrics
#### 5.2.1 Computing Metrics  
The evaluation metrics are computed over the entire test set, comprising \(N\) test samples. Each metric value is an average score per test sample and is given by:  
\[\text{Avg. Metric Score}=\frac{1}{N}\sum_{i=1}^{N}\text{Metric}_{i} \tag{23}\]  
where \(\text{Metric}_{i}\) is the metric score for the \(i^{th}\) test sample.
# Prognostics and Health Management Based on Next-Generation Technologies: A Literature Review
## 3 Research History of PHM
### PHM Supported by New-Generation Technology
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**References**} & \multicolumn{6}{c}{**Field of Research**} \\ \cline{2-7}  & **Research on** & **Optimization of** & **Research on** & **PHM Combined** & **With New** & **Studies Based on** & **Related** \\  & **Algorithms** & **Sensors** & **Strategies** & **Technologies** & **Statistical Data** & **Design** \\ \hline [PERSON] et al. [3] & \(\backslash\) & \(\surd\) & \(\backslash\) & \(\surd\) & \(\surd\) & \(\surd\) \\ [PERSON] [4] & \(\surd\) & \(\backslash\) & \(\surd\) & \(\surd\) & \(\backslash\) & \(\surd\) \\ [PERSON] et al. [5] & \(\backslash\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ [PERSON] et al. [6] & \(\backslash\) & \(\backslash\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ [PERSON] et al. [7] & \(\surd\) & \(\backslash\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ [PERSON] et al. [8] & \(\surd\) & \(\backslash\) & \(\surd\) & \(\surd\) & \(\backslash\) & \(\surd\) \\ [PERSON] et al. [9] & \(\backslash\) & \(\backslash\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ [PERSON] et al. [11] & \(\surd\) & \(\backslash\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ [PERSON] et al. [12] & \(\surd\) & \(\backslash\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ [PERSON] et al. [13] & \(\backslash\) & \(\backslash\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ [PERSON] et al. [15] & \(\surd\) & \(\backslash\) & \(\backslash\) & \(\surd\) & \(\surd\) & \(\surd\) \\ [PERSON] et al. [16] & \(\backslash\) & \(\backslash\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ [PERSON] et al. [17] & \(\surd\) & \(\backslash\) & \(\backslash\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular} \(\backslash\) indicates not considered. \(\surd\) indicates discussed. After literature research, we found that most scholars and organizations have achieved more accurate predictions by continuously improving the sensing accuracy of sensors, and with the development of deep learning algorithms, many scholars have used them in intelligent O&M, which greatly improves the traditional failure prediction and maintenance processes in terms of the insufficiency of failure data, inaccuracy of prediction, complexity of the program, and other difficulties. In addition, some emerging technologies, including digital twins, AR, VR, UAVs (Unmanned Aerial Vehicles), etc., are slowly coming into everyone’s view, so some scholars consider combining them with traditional PHM to shift to a new era of intelligent factories and have achieved some results.  
\end{table} 
Table 1: Summary of classic papers.
## 2 Materials and Methods
### Water Quality Parameter  
Water quality parameters were directly analyzed on-site, including temperature, pH (WTW, pH315i/SET), conductivity (EC, WTW, LF330/SET), and dissolved oxygen (DO, WTW-Oxi, 3205 SET). Filtered water samples (1.6 \(\upmu\)m, Pall) measured ammonia (NH\({}_{3}\)-N, HANNA, HI 93715). Standard Methods analyzed alkalinity, total solids (TS), and suspended solids (SS). DO is regarded as the quality of water with high concentration, the better the water quality. TS is the total amount of solids dissolved in the water, which affect the taste of drinking water. SS is suspended solid matter in water, which is one of the indicators to measure the degree of water pollution. Chemical oxygen demand (COD) measures water quality by chemical oxygen consumption while the decomposing organic matter is in the  
Figure 1: Sampling locations of Wulo Creek.  
water. This study used COD analysis by the Hach method. A reagent (Hach 21259-25) was added to the water sample, then heated at 150 \({}^{\circ}\)C for two h. After cooling, the water sample was measured with a spectrophotometer (Hach DR/2010). Dissolved organic carbon (DOC) is defined as dissolved organic matter that can pass through a 0.45 \(\upmu\)m filter and be lost by evaporation during the analysis process.
# An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare
## 5 Signal Technologies
### Optical System  
Optical positioning systems can be divided into two main categories: systems in which a moving sensor must be located, whereby reference information is required, and systems in which fixed cameras detect images of moving objects without reference information. In the first case, it is possible to use LEDs that emit light as markers and a series of sensors photodiodes mounted in predefined positions that measure the angle of arrival of the light so that each segment of the space of interest falls within the field of vision of two units [65]. The data collected by the fixed sensors allow for calculating the position of the marker. In the second case, the images acquired by the cameras are combined with computer vision technologies to obtain the object's positioning [66]. Multiple static cameras can track objects with a high update rate. The obtained accuracies are of the order of tens of micrometers, and the cost of the system increases.
# Ultra-Small Fiber Bragg Grating Accelerometer
## 2 Theory
### Calculation of Pre-Stretch Based on the Mass and Stretch at Equilibrium  
To find the sensitivity and resonant frequency of an FBG accelerometer based on transverse forces, it is necessary to have the pre-strain (or the pre-stretch in terms of the resonant wavelength change) of the FBG [1; 15]. The pre-stretch is the stretch when the two ends of the FBG are fixed and the inertial object has not hung on the FBG. In the design of this accelerometer, as the ends of the FBG are fixed after the inertial object is hung, the pre-stretch needs to be calculated.  
The pre-stretch can be calculated based on the mass of the inertial object and the stretch of FBG at equilibrium by simulation. Firstly, estimate the pre-stretch based on the mass and stretch at equilibrium. For example, if the mass is 4.41 g and the stretch at equilibrium is 1.38 nm, the pre-stretch can be estimated around 1 nm. Secondly, based on the estimated pre-stretch, calculate the induced-wavelength-change by the mass, 0.29 nm for the example above. If the total of the pre-stretch and induced-wavelength-change is smaller than the stretch at equilibrium, increase the estimated pre-stretch. Vice versa. The pre-stretch is 1.13 nm for the example above, and the calculations are given in the Supplementary Material. The parameters of the optical fiber used here are from [16].
## 2 Materials and Methods
### Implementation
#### 2.2.6. Routine of Deliver Container  
This is the complete routine that integrates both navigation and manipulation processes. For this task, the user must specify through the interface the ID number of the container to be picked up, the name of the location where it is situated (e.g., table), the destination point for delivery, and, if the container is to be delivered to the user, whether it should be dropped or held for the user to access the contents only.  
The sequence is shown in the flowchart of Figure 16; the steps in this routine are as follows:  
1. Navigate to the container: The routine starts with the robot navigating to the point where the user locates the container.
2. Pick up the container: The point at which the container is located determines the routine that is executed. This is one of the previously presented routines, namely picking from a table or shelf, or picking from the user.
3. Navigate to the delivery point: With the container securely held, the robot is commanded to navigate to a position near the delivery point. If the point is the bed,  
Figure 16: Flowchart of the implemented assistive delivery routine: this routine consists of navigating to the container’s location, picking it up, and delivering it to the user or table.  
Figure 15: Sequence of frames demonstrating the routine for picking from the user.  
upon arrival, the robot notifies the user of its presence and the action it will perform (holding or dropping the container). Then, the robot navigates to the delivery point.
4. Deliver container: If the delivery point is a table, the robot performs the previously described routine of placing the container. If the delivery point is to the user, the process is illustrated in Figure 17 and is as follows: The routine begins by moving the end-effector forward to the user. The robot then notifies the user of the action it will perform (either holding or dropping the container). It executes the action by either maintaining its grip or opening the gripper to release the container. After a brief wait, the end-effector is retracted to a distance of 0.6 m. If the action involves dropping the container, the arm is then commanded to return to its home position.
5. Navigation to rest: Finally, the robot is commanded to navigate to a rest POI to finish and wait for another request.
# Reliability Modelling and Evaluation for LTD System Based on Load-Sharing Model
## 4 Numerical Simulation and Analysis
### Effect Analysis of Different Load-Sharing Rules on the Modules in a Level
Besides, the fault centrality can be broken with the local-equal assumption while the failure occurs in some case.
## 3 Results and Discussion
### Figures of Merit
indicating that the precision of the ICP-AES method is good, while the relative recoveries for all concentration levels ranged between 80.0% and 119.0%, indicating good accuracy. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multirow{2}{*}{\begin{tabular}{c} **Emission Line** \\ **(nm)** \\ \end{tabular} } & \multirow{2}{*}{**Regression Lines**} & \multirow{2}{*}{\begin{tabular}{c} **LOD** \\ **(mg kg\({}^{-1}\))** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **LOQ** \\ **(mg kg\({}^{-1}\))** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Clibration Curve** \\ **(mg kg\({}^{-1}\))** \\ \end{tabular} } \\ \hline Ag & 328.068 & y = 249x + 477 & 0.9999 & 0.04 & 0.12 & 500 \\ Ba & 233.527 & y = 19.5x + 25.5 & 0.9999 & 0.08 & 0.28 & 500 \\ Bi & 223.061 & y = 3.47x + 3.82 & 0.9994 & 1.32 & 4.39 & 125 \\ Ca & 396.847 & y = 29,057x + 58,815 & 0.9998 & 0.28 & 0.92 & 2000 \\ Cd & 226.502 & y = 15.6x \(-\) 8.77 & 0.9999 & 0.51 & 1.69 & 500 \\ Co & 238.892 & y = 23.8x \(-\) 43.2 & 0.9999 & 0.36 & 1.19 & 500 \\ Cr & 357.869 & y = 1004x \(-\) 2082 & 0.9999 & 0.05 & 0.15 & 500 \\ Cu & 324.752 & y = 1449x \(-\) 1311 & 0.9999 & 1.00 & 3.30 & 500 \\ Fe & 238.204 & y = 37.7x + 33.0 & 0.9999 & 0.43 & 1.44 & 500 \\ Mg & 280.271 & y = 895x + 4923 & 0.9994 & 0.13 & 0.43 & 2000 \\ Mn & 257.610 & y = 419x + 1463 & 0.9998 & 0.16 & 0.53 & 500 \\ Ni & 232.003 & y = 6.69x + 0.54 & 0.9997 & 0.46 & 1.54 & 125 \\ Pb & 217.000 & y = 1.01x + 7.09 & 0.9997 & 2.25 & 7.50 & 125 \\ Zn & 213.857 & y = 12.8x + 275 & 0.9999 & 0.16 & 0.55 & 125 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Regression analysis, LODs, LOQs and upper limits of the linear range. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multirow{2}{*}{ \begin{tabular}{c} **Added** \\ **(mg kg\({}^{-1}\))** \\ \end{tabular}  } & \multirow{2}{*}{**Found**} & \multirow{2}{*}{**R8\%**} & \multirow{2}{*}{**RSD\%**} & \multirow{2}{*}{**Found**} & \multirow{2}{*}{**RR\%**} & \multirow{2}{*}{**RSD\%**} \\  & & & & & **(mg kg\({}^{-1}\))** & \\ \hline \multirow{4}{*}{Ag} & 0 & ND & - & - & ND & - & - \\  & 25 & 20.2 \(\pm\) 0.2 & 80.8 & 1.0 &
## 3 Results and Discussion
### Influence of Rib Thickness on SIF of Crack Tip
The Figure 9 shows the comparison between the curve of the maximum SIF at the crack tip with rib thickness when the crack length is 5 mm and the influence of rib thickness on the SIF of the integral plate structure in the literature. The comparison of the two curves shows that the influence of rib thickness on the stress intensity factor in this study is consistent with the conclusion in the literature. As you can see from Figures 8 and 9, when the crack length a = 5 mm and the rib thickness t increases from 2 mm to 4 mm, \(K_{I}\) value decreases by 20%. When the rib thickness is large, the effect of increasing the rib thickness on the decrease of the crack tip \(K_{I}\) value is weak, when t increases from 4 mm to 6 mm, \(K_{I}\) value only decreases by 10%, and this effect does not change with the crack propagation. The change of rib thickness has a certain effect on the change of SIF at the crack tip of the integral panel. In order to ensure the fracture resistance of the integral panel, the thickness of the rib should be within a reasonable range, usually more than 4 mm. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Groups**} & **Model** & **Model** & **Skin** & \multirow{2}{*}{**Rib Spacing**} & **Rib Height** & **Rib** \\  & **Length** & **Width** & **Thickness** & & \(d\)**(mm)** & \(h\)**(mm)** & **Thickness** \\  & **L(mm)** & **\(W\)(mm)** & **\(t_{m}\)(mm)** & & & \(t\)**(mm)** \\ \hline
1 & 160 & 100 & 5 & 80 & 30 & 2 \\
2 & 160 & 100 & 5 & 80 & 30 & 3 \\
4 & 160 & 100 & 5 & 80 & 30 & 5 \\
5 & 160 & 100 & 5 & 80 & 30 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Structural parameters of integral wall panels with different rib thickness.
# Tunable RF Filters Based on Liquid Crystal for Space Applications
## 2 Materials and Methods
### Filter Design  
A 3-pole filter was designed, based on the resonator configuration above. A three-dimensional (3D) view is shown in Figure 6. Note the orientation of the x-, y-, and z-axis, which is consistent to previous figures.  
The resonator shape in the x-y-plane has evolved from a round shape (Figure 1 or Figure 2) to a rectangular shape with rounded corners, in order to allow for mounting of the biasing electrodes. The input and output ports are realized in waveguide technology. The individual resonators are coupled by irises either to the waveguide run or the adjacent resonator. Conventional tuning screws are foreseen for each resonator and for each iris.  
Figure 6: 3D view of 3-pole filter.
## 4 Active Load Alleviation Performance  
Besides aerodynamic benefits, the morphing winglet design can exploit the movable control surfaces to carry out load alleviation tasks. Active load alleviation capability is obtained in this work by moving the winglet surfaces during the manoeuvre condition to reduce the resulting aerodynamic loads [45]. The main reason supporting this choice is the slow response of the winglet actuators which are designed to adjust the winglet deflection in slowly varying steady flight conditions, thus requiring a limited bandwidth. Higher actuators' bandwidth would have led to an unnecessary weight and power consumption increase.
## 3 Results and Discussion
### Joining with Activating Materials
#### 3.2.1 Joining with SnAg Solders as Activating Material
The Ag nanopaste was applied with a thickness of 20 \(\upmu\)m, because this paste thickness was determined as an optimum for sintering [24]. The sintering process obviously causes shrinkage of the Ag material, leaving behind an even thinner Ag layer. The rolled solder foils had a thickness of approx. 20 \(\upmu\)m. A rolling out to thinner foils was not possible with the available rolling mill. Since the properties of the solder dominate the joining seam with this thickness ratio between solder and nanopaste, a thicker paste application was tested in further investigations (see also Section 3.2.2). With the solders SnAg4 and SnAg5, a significantly reduced formation of brittle phases can be observed due to the shorter holding time of 10 min, where the IMC were formed primarily at the edges of the joining seam (Figures 9 and 10). The experiments demonstrated that good bonding to the substrates can also be achieved with these solders. Despite the shortened holding time of 10 min, the diffusion depths (up to 3 \(\upmu\)m, Figures 14 and 15) are comparable with those of joining experiments with 30 min holding time (Figure 12). However, further tests showed that the flux contained in these two solders (2.2.3 in SnAg4 and 2.2.2 in SnAg5) is of critical importance for good bonding. If pure Sn foils are used without flux and the same holding time of 10 min, no comparable diffusion depths can be achieved (see Section 3.2.2). Figure 14: SEM image of the interface between the Q&P steel and the joining seam (activating material: SnAg4, with flux 2.2.3; joining temperature: 230 \({}^{\circ}\)C, holding time: 10 min). Figure 13: SEM image of the joining seam with SnAg3.5 as activating material (without flux) with residues of the Ag sintered seam (joining temperature: 230 \({}^{\circ}\)C, holding time: 30 min). After joining, the substrate exhibits the typical features of a predominantly martensitic, Q&P microstructure, Figure 16. This indicates that the joining process (solder: SnAg4, temperature: 230 \({}^{\circ}\)C, holding time: 10 min) only leads to negligible microstructural changes. Further work, particularly mechanical testing and additional microstructural analysis, is required to fully support this hypothesis.
# Improved DRUS 4H-SiC MESFET with High Power Added Efficiency
## 4 Conclusions  
An improved DRUS 4H-SiC MESFET with layered doping under lower gate is proposed and simulated in this paper to increase the PAE of the device. On the basis of the DRUS-MESFET, in order to maximize the power added efficiency, an upper layer region of 0.05 \(\upmu\)m thick is introduced under the lower gate, and the thickness of the partly undoped region of the DRUS-MESFET was optimized. The structure achieves a PAE of 64.1%, which is 85.8% larger than the DRUS-MESFET. In addition, the LDUS-MESFET has a certain improve in saturation current, threshold voltage, transconductance, and gate-source capacitance. Although the breakdown voltage is reduced, the drop value is within the acceptable range. Overall, the LDUS-MESFET has wider application in the radio frequency direction.  
Data curation, X.W.; formal analysis, T.Z.; project administration, H.J.; writing--original draft, Y.L.; writing--review and editing, T.L., Y.T., S.Z., and Y.Y. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (NSFC) under Grant No. 61671343. The authors declare no conflict of interest.
# A Novel Method to Mitigate the Multipath Error for BDS-2 Dam Deformation Monitoring
## 3 Results  
Position and Navigation Data Analyst (PANDA) software originally developed by Wuhan University was modified to obtain DD residuals between satellites and stations [30]. Firstly, the ZD residuals between station and satellites were attained directly by PANDA using the ZD processing mode [31]. Then, the ZD ambiguities were mapped to a maximum set of independent DD ambiguities, and the DD ambiguities could be solved and fixed sequentially.
## 2 Materials and Methods
### Uniaxial Tensile Test (UTT)  
ASTM-E8 [22] was the standard used to machine UTT samples. It requires a gauge diameter of 6.35 mm and a length of 7.62 cm. Following machining, samples were placed into a screw-driven mechanical actuator with an MTS 6.6120 \(\times\) 10\({}^{-1}\) load cell with a \(\pm\)22,000 pound force capacity and an Instron 8862 linear variable differential transformer with an operating range of \(\pm\)5.08 mm. UTT was performed to failure on all eight SLM-built tensile bars. A displacement rate of 5.08 mm/min was maintained until 2% strain was reached, after which the rate was increased to 12.7 mm/min. Strain data was obtained using a 2.54 cm extensometer calibrated to 50% strain. The MTS load cell obtained load data, which was subsequently converted to stress using the measured sample geometry.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample Number** & **HIP (Y/N)** & **Machine** \\ \hline
1 & N & M1 \\
2 & N & M1 \\
3 & N & M1 \\
4 & Y & M1 \\
5 & N & M2 \\
6 & N & M2 \\
7 & N & M2 \\
8 & Y & M2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the Inconel 718 SLM samples produced for this study.  
The stress-strain curves resulting from these tests were analyzed to determine modulus, yield strength, and UTS. ASTM-E8 [22] is a mechanical testing standard which is widely accepted throughout the aerospace industry and all of the UTT reported in this study were performed in accordance with ASTM E8.