esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigating the Effect of Motion Capture Suits on the Test-Retest Reliability of Gait Parameters
## 3 Results
### Knee Angle Variables  
PiG definition: Knee angle ICC values were similar between the suited and non-suited trials, and both classed as moderate to good (Table 1). ICCs were higher in HS, Max and Min events in the non-suited trials than in the suited trials. The ICC was higher at TO in the suited trials compared to the non-suited trials, with similar variation in ICC values across all events (range: suited 0.631-0.801, non-suited 0.731-0.899). Average ICC values across events were higher in the non-suited trials (0.832) compared to the suited trials (0.707) but did not differ significantly between conditions (\(p\) = 0.13) (Table 1). CGM2 definition. Compared to the PiG model, the CGM2 did not perform significantly better, with average ICCs in the non-suited trials being comparable (PiG ICC = 0.832; CGM2 ICC = 0.878; PDifference = 0.15); however, in the suited, there was a significant difference (PiG ICC = 0.707; CGM2 ICC = 0.855; PDifference = 0.01). Using the CGM2 definition,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Joint** & \multicolumn{3}{c}{**ICC**} & \multicolumn{3}{c}{**MDC**} \\ \hline  & **Non-Suited** & **Suited** & _p-Value_ & **Non-Suited** & **Suited** & _p-Value_ \\ \hline Ankle & 0.916 & 0.900 & 0.77 & 0.63 & 0.95 & 0.08 \\ Knee & 0.878 & 0.855 & 0.90 & 1.20 & 2.32 & 0.16 \\ Hip & 0.897 & 0.948 & 0.541 & 1.35 & 1.42 & 0.46 \\ \hline \end{tabular}
\end{table}
Table 2: Average ICC and MDC in Non-Suited and Suited trials at each joint across all events (CGM2).  
Figure 3: Mean and 95% CI of MDC values for each joint (PiG ankle, alternative ankle, knee, hip and CGM2 ankle, knee and hip) and for both suited and non-suited conditions.  
knee angle showed good to excellent reliability across all timepoints (0.829-0.910) in the non-suited trials. In the suited trials, reliability was good across all timepoints (0.769-0.895). There were no significant differences in the ICC values between the suited and non-suited trials (PDifference = 0.90) (Table 2).  
PiG definition: The average MDC in the non-suited trials was 3.98\({}^{\circ}\), compared to the non-suited trials, where average MDC was 2.05\({}^{\circ}\), though differences were not significant (\(p\) = 0.59) (Table 1). CGM2 Definition: There was no difference in the MDC when comparing the PiG and CGM2 definitions in the non-suited conditions (PDifference = 0.21). There was, however, a difference in the suited condition, with the MDC in the PiG significantly higher than the CGM2 (PDifference = 0.02) (Figure 3). When comparing the suited and non-suited condition in the CGM2 definition, there was no difference (PDifference = 0.16). However, it should be noted that the average MDC was higher in the suited condition (MDC = 2.32\({}^{\circ}\)) and almost double the non-suited condition (MDC = 1.20\({}^{\circ}\)).
## 3 Results and Discussion
### Various Pumping Scenarios from the Well Field/Boreholes Assessment
#### 3.4.1 Borehole's Assessment from Baseline Information  
The result of simulations in GIS over the period 2015 to 2020 revealed that the maximum and minimum simulated groundwater levels were 376.227 m and 253.925 m, respectively, as shown in Figure 18 below. This was in relation to the baseline information, which for this study was referred to as Scenario 1.
## 1 Introduction
This problem is clearly explained in [10], where it is demonstrated that such a DC-link voltage oscillation can be translated into the PV voltage, making the MPPT algorithm unstable and avoiding the extraction of the maximum power. In addition, the average value of the DC-link must be regulated to avoid non-feasible operation conditions for the first-stage, e.g., high DC-link voltage that could destroy the DC/DC converter. Therefore, the inverter of the second-stage  
Figure 1: Structure of a PV microinverter with DC-link regulation and grid connection. must regulate the average value of the DC-link voltage, as reported in [11], where the size of the capacitor defines two main aspects of the DC-link:  
* The amplitude of the low-frequency voltage oscillations at double the grid frequency, which introduce a perturbation on the first stage, as discussed in [10]; thus, the controller of the first stage must be designed to avoid the transmission of such a DC-link perturbation into the PV voltage. * The magnitude of the high-frequency voltage ripple generated by the DC/DC converter of the first stage, which introduces high-frequency current harmonics into the second-stage inverter. The current harmonics deteriorate the power quality provided to the inverter, introducing high-frequency noise that could interfere with the inverter control system and sensing circuits. With the aim of reducing the previous problems, high capacitances are usually adopted to design the DC-link, requiring electrolytic capacitors. For example, the microinverters reported in [12; 13] consider DC-link capacitors of 2200 uF, which is clearly in the electrolytic range. Since electrolytic capacitors have a much higher failure rate in comparison with other technologies such as film capacitors [10], using large DC-link capacitors introduces a reliability problem to the PV microinverter.
## 1 Introduction
To estimate the nominal hydrodynamic coefficients of the AUV, [PERSON] and [PERSON] [12] employed both the robust extended Kalman filter (REKF) and the extended H-infinity (EHoo) filter. [PERSON] and [PERSON] [13] employed a comprehensive approach combining the nonlinear hybrid extended Kalman filter (HEKF) observer, analytical and semi-empirical (ASE) formulas, as well as computational fluid dynamics (CFD) simulations. This combined method allowed for the estimation of all the hydrodynamic coefficients associated with a REMUS AUV. Scholars such as [PERSON] et al. have also employed the Kalman filter and its improved algorithms to identify the hydrodynamic coefficients of underwater vehicles [14,15]. Additionally, [PERSON] [16] and [PERSON] [17] employed the maximum likelihood estimation algorithm to identify a simplified model of underwater vehicles and their hydrodynamic coefficients in different directions. [PERSON] et al. [18] and [PERSON] et al. [19] utilized the least squares method to identify partial hydrodynamic coefficients of surface ships and underwater vehicles, respectively. [PERSON] et al. [20] used the Bayesian method to identify the four-degree-of-freedom hydrodynamic coefficients of surface ships through simulated motion data. Additionally, some scholars have explored support vector machines [21,22], adaptive particle swarm optimization algorithms [23], and other algorithms to identify the hydrodynamic coefficients of surface and underwater vehicles. This study introduces a new method for identifying the hydrodynamic coefficients of a six-degree-of-freedom underwater vehicle using the Bayesian ridge regression (BRR) model. The BRR algorithm was chosen due to its advantages of high data utilization, requiring fewer training samples, and avoiding overfitting [24]. Here, the BRR model is employed to solve the problem of extracting hydrodynamic coefficients for the complex motion of underwater vehicles with six degrees of freedom. Specifically, the CFD method is combined with the six-degree-of-freedom motion model, virtual propeller model, and overlapping grid technology to simulate the free navigation motion of a submarine model under specific rudder deflection. The motion and angular velocity data are collected, and then the model's hydrodynamic coefficients are identified using the BRR model combined with the standard equations of submarine motion. Meanwhile, the CFD method simulates the restrained model test of the submarine model and obtains the partial hydrodynamic coefficients. Finally, the results of the two methods are compared, and the feasibility of the BRR model for identifying hydrodynamic coefficients is analyzed.
## 4 Methodology of APSO-LS
### Overview of the Proposed Approach  
The proposed methodology addresses the dynamic nature of the problem by dividing the planning horizon into discrete stages. At each stage \(h\), the current set of tasks is optimized using the APSO-LS algorithm, which determines the optimal task allocation and routes for the UAVs. As new tasks emerge or existing task workloads change, the system replans at the subsequent stage, incorporating the updated information. This rolling horizon approach ensures that the UAVs can adapt to changes in real time while maintaining efficient operation.  
The APSO-LS algorithm is specifically designed to handle the complexities of the problem, including the need for rapid adaptation and the integration of energy constraints. It combines the global search capabilities of PSO with adaptive perturbation strategies to maintain diversity and a simulated annealing-based local search to refine solutions and escape local optima.
# Circular Orbit Flip Trajectories Generated by E-Sail
## 2 Problem Description and Mathematical Approach
Let \(\mathbf{\hat{t}}_{r}\) and \(\mathbf{\hat{t}}_{\theta}\) be the unit vectors of \(\mathcal{T}\), where \(\mathbf{\hat{t}}_{r}\equiv\mathbf{\hat{r}}\) and \(\mathbf{\hat{t}}_{\theta}\) is the transverse unit vector illustrated in Figure 1. The same figure also shows the sail pitch angle \(\alpha_{n}\in[-\pi/2,\,\pi/2]\) rad, that is, the angle between \(\mathbf{\hat{t}}_{r}\) and \(\mathbf{\hat{n}}\). Note that \(\alpha_{n}\) defines the orientation of the E-sail nominal plane with respect to the radial direction, that is, the E-sail attitude in the polar reference frame. The normal unit vector can be written as  
\[\mathbf{\hat{n}}=\cos\alpha_{n}\,\mathbf{\hat{t}}_{r}+\sin\alpha_{n}\,\mathbf{\hat{t}}_{ \theta} \tag{6}\]  
Figure 1: Polar reference frame and spacecraft states in a two-dimensional mission scenario. so that Equation (5) becomes  
\[\widetilde{\mathbf{a}}=\tau\,\frac{\beta}{2\widetilde{\tau}}\left(1+\cos^{2}\alpha_{n }\right)\,\hat{\mathbf{t}}_{r}+\tau\,\frac{\beta}{2\widetilde{\tau}}\,\cos\alpha_{n }\,\sin\alpha_{n}\,\hat{\mathbf{t}}_{\theta}. \tag{7}\]  
Note that \(\alpha_{n}\) and \(\tau\) are the two control variables of our two-dimensional problem.
## 3 Results and Discussion
### Trend Yield and Meteorological Yield of Winter Wheat in Typical Counties  
To reduce the amount of data to process, typical counties (Yanchang, Luochuan, Pucheng, and Linyou) with a large dry land area and suitable location were selected for study in subregions I, II, III, and IV, respectively. Using the yield data of winter wheat from 1997 to 2017 in typical counties of each subregion, the logistic equation mentioned in Section 2.1 was used to fit the trend yield of winter wheat in different typical counties. Trend yield and actual yield are shown in Figure 3.  
Figure 3: Comparison of actual yield and a trend yield of winter wheat.  
In the past 20 years, the yield per unit area of winter wheat in each subregion showed an upward trend (Figure 3). The average growth rate per annum of yield per unit area of winter wheat in subregion I was the largest (7.04%), followed by the growth rates of subregions IV, II, and III (4.49%, 2.86%, and 1.68%, respectively). The change in the trend yield of winter wheat in each subregion was basically the same as that of the corresponding unit area yield. By comparing the average fluctuation range of the relative meteorological yield (\(y_{w}/y_{t}\)) in each subregion, it was found that the average fluctuation range of subregion I was the largest, with a value 16.38%, and that of subregions II, III, and IV were 4.26%, 5.94%, and 6.08%, respectively.  
Notably, trend yields reflect changes in winter wheat yields, due to increased productivity over the long term. If the trend yield is overfitted with the actual yield, it is easy to exaggerate the impact of productivity levels on yield growth and ignore the impact of climate change on yields.
## 6 Case Study
### Process Robustness Analysis
#### 6.3.2 Process Robustness Comparison of Three Substitution Strategies under IB Failure Mode  
As shown in Figure 12, (1) in the IB failure mode, the RoS values under the no-substitution strategy (\"common\") and the three resource substitution strategies (\"internal\", \"external\", and \"inter&exter\") all show a decreasing trend as the number of node failures increases (i.e., as the cloud order completion rate decreases). This indicates that from a multi-agent simulation perspective, the robustness of the CMP gradually decreases. (2) The curves of the three resource substitution strategies are all located above the curve of the no-substitution strategy (i.e., their RoS values are larger than the no-substitution strategy's value, which means their order completion rates are higher). This indicates that all three resource substitution strategies can improve the robustness of the CMP in the face of initial node betweenness failure. (3) The \"internal\" curve is only slightly higher than the \"common\" curve, and when the number of node attacks is high, the RoS values of these two strategies decrease to 0, at which point all cloud orders fail to be processed. In contrast, the \"external\" and \"inter&exter\" curves are significantly higher than the \"common\" curve, and though the RoS values fluctuate when the number of node attacks is high, they still maintain a high level (above 0.6). This indicates that the external replacement strategy and the internal-external integration replacement strategy both offer more significant  
Figure 11: Variation of RoS under ID failure mode.  
robustness enhancement than the initial replacement strategy under the IB failure mode. In particular, when the number of node attacks is high, the slight fluctuations in the RoS value indicate that the cloud order completion rate tends to be stable at this time, but different resource substitution strategies will lead to changes in the service time, cost, reliability, and other factors.
# A Data-Driven Model of Cable Insulation Defect Based on Convolutional Neural Networks
## 5 Conclusions  
(1) This paper proposes a data-driven model based on convolutional neural network to calculate the electric field distribution of cables. After obtaining the basic data through finite element calculation, the convolutional neural network constructs a data-driven model that can replace the finite element calculation to construct the electric field distribution results of the inner insulation of cables containing impurities. By comparing the CNN with the BPNN, the data-driven model constructed by the CNN algorithm has a higher accuracy, with an overall accuracy of 94.3%. The calculation time of the data-driven model is 0.025 s, which is approximately one 360 th of the finite element calculation and much less than the finite element computational time required for the calculation.  
(2) To address the problem that the model needs to be changed at the same time when the operating parameters of the cable are changed in the finite element calculation. The data-driven model constructed in this paper contains 11 input characteristic quantities related to the actual operating parameters of the cable, so that the electric field distribution of the cable can be calculated directly by the data-driven model when the operating conditions of the cable are changed. The results show that when the cable parameters are in the training set, the error in the electric field distribution calculated by the data-driven model is within 3% when varying the core voltage and the material parameters of the impurities. In the actual working condition, the parameters of the cable in normal operation are included in the training data set of the model.  
(3) The data-driven model constructed in this paper based on convolutional neural networks can visualize the distribution of electric fields during cable operation in two dimensions. The CNN-based data-driven approach can also be used to quickly calculate the electric field of other cable arrangements and cables above 10 kV. It is only necessary to change the finite element model of the cable and reconstruct the characteristic inputs such as the electromagnetic parameters of the cable to build a new data-driven model of the cable. In the future, the cable model can be coupled with multiple fields to take advantage of the fast computation speed of the data-driven model to monitor the state of the cable during operation at various voltage levels in real time and realize the digital twin of the cable.  
Conceptualization, W.H.; methodology, G.Y.; validation, C.H.; writing--original draft preparation, G.Y. and Z.W.; writing--review and editing, Z.W. and D.K.; project administration, Y.D. and C.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Project of State Grid Hebei Electric Power Company (kj2021-060).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Methodology
#### 2.3.4 Accuracy Evaluation  
To evaluate the performance and sensitivity of the proposed ITDS strategy in this study, ITDS results were compared with reference tree locations calculated by the four RTK points at each tree. The numbers of true positives (TP), false negatives (FN), and false positives (FP) were counted. TP is the number of trees detected from UAV-LiDAR data that matches reference trees, FN is the unmatched reference trees (omission error), and FP is the unmatched trees extracted from UAV-LiDAR data (commission error). Based on these three indices (TP, FN and FP), the accuracy of the ITDS methods is evaluated using three indices (\(r\), \(p\) and \(F\); Equations (2)-(4)).  
\[r=TP/(TP+FN) \tag{2}\]  
\[p=TP/(TP+FP) \tag{3}\]  
\[F=2(r\times p)/(r+p) \tag{4}\]  
where \(r\), recall of the results, is the proportion of ground reference trees detected; \(p\), the precision of the results, is the proportion of detected trees that match ground reference trees; and \(F\) is an index of the overall accuracy that is calculated with \(r\) and \(p\).
## 4 Results and Discussion
### Empirical Correlation between Independent and Dependent Variables in Biomass Gasification
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \multirow{2}{*}{**Model**} & **CO** & **CO\({}_{2}\)** & **CH\({}_{4}\)** & **H\({}_{2}\)** & **N\({}_{2}\)** & **t-Stat** & **t-Stat** & **t-Stat** & **t-Stat** & **t-Stat** \\  & **(\%vol)** & **(\%vol)** & **(\%vol)** & **(\%vol)** & **(\%vol)** & **CO** & **CO\({}_{2}\)** & **CH\({}_{4}\)** & **H\({}_{2}\)** & **N\({}_{2}\)** \\ \hline M5A & 16.741 & 13.951 & 0.344 & 15.496 & 52.374 & \(-\)2.267 & 2.905 & \(-\)8.589 & 3.832 & \(-\)0.889 \\ M5B & 18.366 & 12.829 & 0.416 & 15.021 & 52.371 & 0.803 & 1.091 & \(-\)8.229 & 2.962 & \(-\)0.898 \\ M5C & 17.966 & 13.089 & 0.393 & 14.971 & 52.537 & 0.045 & 1.512 & \(-\)8.342 & 2.835 & \(-\)0.694 \\ M5D & 18.645 & 12.697 & 0.659 & 14.898 & 52.193 & 1.336 & 0.871 & \(-\)6.761 & 2.836 & \(-\)1.110 \\ M6O & 23.943 & 9.367 & 0.160 & 21.743 & 44.763 & & & & & \\ M6A & 16.744 & 13.944 & 0.320 & 15.517 & 52.375 & \(-\)1.473 & 2.658 & \(-\)8.919 & 3.770 & \(-\)1.147 \\ M6B & 18.370 & 12.807 & 0.322 & 15.101 & 52.382 & 1.630 & 0.837 & \(-\)8.910 & 2.975 & \(-\)1.155 \\ M6C & 18.130 & 13.287 & 0.436 & 14.952 & 52.167 & 0.295 & 1.979 & \(-\)8.405 & 3.055 & \(-\)1.197 \\ M6D & 18.653 & 12.711 & 0.754 & 14.823 & 52.174 & 2.216 & 0.678 & \(-\)6.041 & 2.615 & \(-\)1.402 \\ \hline \end{tabular}
\end{table} 
Table 12: Cont. \[\text{CH}_{4} = 2.0071\times 10^{40}\bigg{(}\frac{H}{C}\bigg{)}^{1.4861}\bigg{(} \frac{O}{C}\bigg{)}^{0.7418}\bigg{(}\frac{N}{C}\bigg{)}^{-0.0315}ER^{-0.2936}T^{- 13.5810};R^{2}\text{adj}\;=\;0.9960 \tag{50}\] \[\text{H}_{2} = 7719.5233\bigg{(}\frac{H}{C}\bigg{)}^{0.7330}\bigg{(}\frac{O}{C} \bigg{)}^{0.4827}\bigg{(}\frac{N}{C}\bigg{)}^{-0.0139}ER^{-0.2531}T^{-0.9627};R^ {2}\text{adj}\;=\;0.9354\] (51) \[\text{C}_{6}\text{H}_{6} = 6.1558\times 10^{12}\bigg{(}\frac{H}{C}\bigg{)}^{0.9082}\bigg{(} \frac{O}{C}\bigg{)}^{-3.2235}\bigg{(}\frac{N}{C}\bigg{)}^{-0.1833}ER^{-1.4669 }T^{-4.2424};R^{2}\text{adj}\;=\;0.8634\] (52) \[\text{LHV}_{\text{gas}} = 0.4931\bigg{(}\frac{H}{C}\bigg{)}^{0.0328}\bigg{(}\frac{O}{C} \bigg{)}^{0.0370}\bigg{(}\frac{N}{C}\bigg{)}^{-0.0162}ER^{-0.6884}T^{0.2085};R^ {2}\text{adj}\;=\;0.9950\] (53) \[CGE = 29.8215\bigg{(}\frac{H}{C}\bigg{)}^{0.1308}\bigg{(}\frac{O}{C} \bigg{)}^{0.3723}\bigg{(}\frac{N}{C}\bigg{)}^{-0.0128}ER^{0.0115}T^{0.1178};R^ {2}\text{adj}\;=\;0.8423 \tag{54}\]
# Compact Model for L-Shaped Tunnel Field-Effect Transistor Including the 2D Region
## 3 Results
The smoothing function only approximates surface potential saturation due to electron inversion, and is thus not very accurate at high bias. Considering the electron concentration term in the Poisson equation and doing a self-consistent solution for potential and electron concentration will also improve the model accuracy significantly. However, both numerical integration and self-consistent potential electron concentration solution will significantly add to the computational complexity of the model. This will make the model unsuitable for SPICE applications. It should also be mentioned that the model was bench-marked only against TCAD data and not experimental data. This is because the experimental LTFET demonstrated significant trap-assisted tunneling (TAT), and Shockley-Read-Hall (SRH) generation-recombination current- and ambipolar current-induced degradation of the \(I_{\text{ds}}\)-\(V_{\text{gs}}\) characteristics [4]. In particular, TAT in line TFETs is a significant topic and requires its own modeling framework [9]. This work with the equations for surface and junction potentials lays the foundation for the TAT model. However, due to space constraints, TAT, SRH, ambipolar, quantum confinement [15], and breakdown models [16] could not be included in this manuscript. Figure 8: \(I_{\text{ds}}\)–\(V_{\text{gs}}\) characteristics of LTFET with \(T_{\text{j}}\) = 4 nm, \(H_{\text{overlap}}\) varied, and \(V_{\text{ds}}\) = 0.5 V. Blue, red, and black represent \(H_{\text{overlap}}\) = \(H_{\text{s}}\) = 40, 50, and 60 nm, respectively. Symbols and lines denote TCAD simulation and model results, respectively. Error bar: 10%.
# A Survey on the Application of WirelessHART for Industrial Process Monitoring and Control
## 4 Challenges and Design Requirements
### Reliability and Interference  
In the industrial environment, compared with traditional wired networks, WHNCS has low reliable communication because of interferences such as noise, electromagnetic radiation, multipath distortion, temperature, and humidity from nearby industrial equipment and surrounding walls [47; 102]. These situations result in packet loss and delay, making the adoption of WHNCS in the industrial environment as a challenging one. Signal parameters such as the link quality indicator (LQI) and radio signal strength indicator (RSSI) can be utilized to identify the link quality and reliable data transmission [103; 104]. Other methods, such as path re-routing [39], efficient re-transmission techniques [105], link failure analysis, and redundancy devices [106], will be helpful to improve the reliability and data transmission. This might result in additional transmission overhead that wastes more energy and makes the network congested, which in turn affects the reliable transmission of data [107]. In the future, the necessity of efficient routing algorithms in multi-hop communication with optimized memory utilization is expected to improve the processing power and reliability of motes.