esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Sensitivity Analysis  
The purpose of sensitivity analysis is to evaluate the impact of adjustments to different input and output variables on station efficiency, identify key variables, verify the robustness of the model, and provide scientific evidence for optimizing resource allocation and improving station efficiency. In this study, the DEA model was applied to calculate the spatial efficiency of 17 rail transit stations. To validate the robustness of the SBM model, Textile City Station was used as an example for sensitivity analysis using the variable disturbance method. The analysis involved setting single-variable increases of 10% and 20%, as well as multi-variable simultaneous increases of 10% and 20%. The resulting efficiency changes are shown in Table 5.  
Figure 10: Classification and analysis of Vrste and Scale.  
Single-variable adjustment analysis demonstrates that Y2 (metro passenger flow) and Y1 (land diversity) are key output variables for improving efficiency. When these two variables increase by 10% and 20%, Crste and Scale improve significantly, indicating that optimizing output variables has a direct and significant positive impact on station efficiency. In contrast, an increase in X1 (total land area) exacerbates resource redundancy, particularly when increased by 20%, leading to a decline in scale efficiency to 0.88, reflecting diminishing returns to scale. Therefore, limiting the growth of X1 is a crucial strategy for controlling efficiency decline. Meanwhile, the increase in Y7 (rental levels) has a relatively moderate effect on efficiency improvement but can still slightly enhance output benefits. The robustness of the model is validated, as the efficiency variation trend after single-variable adjustments aligns with expectations, further confirming the model's strong stability and reliability against variable fluctuations.  
Multi-variable adjustment analysis reveals that simultaneously optimizing multiple output variables produces the most significant efficiency improvements. For instance, when these variables increase by 20% simultaneously, Crste rises to 1.45, and Scale improves to 0.99, demonstrating the immense potential of comprehensive output optimization. In the combined input and output adjustments, increasing X1 and Y2 can partially offset the resource redundancy caused by the increase in X1, showcasing the effectiveness of synergistic optimization between input and output variables. Overall, prioritizing the optimization of output variables, reasonably controlling the growth of input variables, and combining multi-variable optimization are key strategies for improving station efficiency.
## 3 Results and Discussion
Such an effect has been reported earlier for flexible devices using PEDOT:PSS-based electrodes on a PET substrate studied at similar test conditions [35]. To confirm this assumption, Figure 7a shows the relative change of the bottom electrode's sheet resistance at different numbers of bends, together with the curve of the current flowing through the whole device. After 1500 bends, the electrode's resistance change against its initial value is less than 25%. Afterward, the sheet resistance remains relatively stable up to 10,000 cycles, with a variation of 7%. The moment of the current dropping down coincides with the sudden resistance change to almost 80%. At this point, the measured sheet resistance of the bottom electrode is almost 4 k\(\Omega\), which is an indication of the physical degradation of this film. Although scanning electron microscopic images of the ZnO nanobranched surface after bending are required for clarification of the process, we believe that the ZnO film remains still undamaged. In the second case (Figure 7b), the structure was constantly loaded with increasing mass weight and the voltage change was detected. As can be seen, the generated piezoelectric voltage increased exponentially with the mass load for the ZnO nanobranched-based harvesting. After around 3 kg of loading, the voltage reached approximately 1.4 V; afterward, at 3.6 kg, it dropped abruptly to zero. Separate microscopy testing of the bottom electrode film (not shown) demonstrated that degradation is not observed at this weight. Therefore, the sharp voltage drop to zero can be ascribed to ZnO nanobranched film rupture, and 3.6 kg can be considered a critical working mass load. Figure 7: (**a**) Change of the current flow through the multilayer structure and the electrode sheet resistance as a function of the number of bends at a constant frequency of 10 Hz, and (**b**) relation between the voltage generated and the weight at static load.
## 3 The Results and Discussion
### Concentration Distribution Characteristics of Leaked R290 in the Far-Field Flow Area  
The variations in the R290 concentration with time at different locations in the far-field flow area is shown in Figure 9. The flow field distribution of the leaked R290 gas in the external limited space is shown in Figure 10. It can be seen from Figure 9 that with the increase in the leakage time, the gas concentration of each measuring point increased rapidly and then tended to be stable. In the direction of the jet, as the distance from the measuring point to the leakage hole increased, the concentration response time (the time from the beginning of the leakage to the concentration increase at the measurement point) was prolonged, and the concentration of R290 decreased. The concentration response times of points 7 and 11 were 0.06 and 0.2 s, respectively. After the concentration field in the local flow area was stabilized, the R290 concentrations of the points 3, 4, 7 and 11 were 86.5, 1.67, 0.85 and 0.56 vol%, respectively. Because the distance from point 3 to the leak hole was small, the concentration was higher, which was in the potentially flammable area, whereas at points 4, 7 and 11, the gas concentration had rapidly decayed to a lower level, which was in the nonflammable area. In the horizontal direction, the concentration response times of each measuring point was longer, and the R290 concentration was maintained at a low level. The concentration response times of points 8 and 9 were approximately 0.16 s, and the concentration values were relatively close, approximately 0.2 vol%. The concentration response times of points 10 and 12 were approximately 0.28 s, and the concentrations were maintained at 0.3 vol%. As the leaked R290 in the vertical direction was greatly affected by gas expansion process, the diffusion velocity of the gas was larger, the diffusion in the horizontal direction was mainly driven by the concentration gradient, and the diffusion velocity of the gas in the horizontal direction was much smaller than that in the vertical direction; thus, the concentration response time of each measuring point in the horizontal direction was longer. In addition, it can be seen from Figure 10 that the concentration distribution of the leaked R290 in the horizontal direction was centered on the jet axis and symmetrically distributed in the circumferential direction.
# Multimodal Power Management Based on Decision Tree for Internet of Wearable Things Systems
## 2 Circuit Design of the PPG-Based Wearable System
### Healthcare Sensors
#### 2.3.1 Temperature Sensor  
The MAX30205 is a clinical-grade temperature sensor with 16-bit resolution, \(\pm\)0.1 \({}^{\circ}\)C accuracy, 0 to \(+\)50 \({}^{\circ}\)C operation range, and 2.7 V to 3.3 V supply voltage range. The sensor consumes 600 uA in typical operating supply current and 1.65 uA when the sensor is in sleep mode (with the I\({}^{2}\)C bus inactive). This sensor accomplishes the ASTM E1112 standard specification for an electronic digital thermometer.
# Flexible DEM Model Development and Parameter Calibration for Rape Stem
## 3 Results and Discussion
### Contact Parameter
#### 3.1.2 Significant Factor for Repose Angle
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multicolumn{2}{c}{**Coefficient Estimate**} & \multirow{2}{*}{**Sum of Squares**} & \multirow{2}{*}{**df**} & \multirow{2}{*}{**Mean Square**} & \multirow{2}{*}{**F-Value**} & \multirow{2}{*}{**\(p\)-Value**} \\  & & **(Coded Factors)** & & & & & \\ \hline Model & \(\backslash\) & 349.62 & 14 & 24.97 & 11.66 & \textless{}0.0001 ** \\ \(A\) & 1.03 & 12.8 & 1 & 12.8 & 5.97 & 0.0309 * \\ \(D\) & 0.5515 & 3.65 & 1 & 3.65 & 1.70 & 0.2163 \\ \(E\) & 3.92 & 183.94 & 1 & 183.94 & 85.85 & \textless{}0.0001 ** \\ \(F\) & 2.11 & 53.2 & 1 & 53.20 & 24.83 & 0.0003 ** \\ \(AD\) & 0.5345 & 1.14 & 1 & 1.14 & 0.53 & 0.4792 \\ \(AE\) & 1.80 & 12.89 & 1 & 12.89 & 6.02 & 0.0304 * \\ \(AF\) & \(-0.9551\) & 3.65 & 1 & 3.65 & 1.70 & 0.2164 \\ \(DE\) & 2.05 & 16.84 & 1 & 16.84 & 7.86 & 0.0159 * \\ \(DF\) & 2.13 & 18.19 & 1 & 18.19 & 8.49 & 0.013 * \\ \(EF\) & 0.1959 & 0.1535 & 1 & 0.154 & 0.072 & 0.7935 \\ \(A^{2}\) & \(-1.10\) & 6.47 & 1 & 6.47 & 3.02 & 0.1077 \\ \(D^{2}\) & \(-1.10\) & 6.47 & 1 & 6.47 & 3.02 & 0.1078 \\ \(E^{2}\) & \(-2.22\) & 26.23 & 1 & 26.23 & 12.24 & 0.0044 ** \\ \(F^{2}\) & 0.6495 & 2.25 & 1 & 2.25 & 1.05 & 0.3257 \\ \(\text{Intercept}\) & 26.30 & \(\backslash\) & \(\backslash\) & \(\backslash\) & \(\backslash\) \\ Residual & \(\backslash\) & 25.71 & 12 & 2.14 & \(\backslash\) & \(\backslash\) \\ Lack of Fit & \(\backslash\) & 21.94 & 10 & 2.19 & 1.16 & 0.5479 \\ Pure Error & \(\backslash\) & 3.77 & 2 & 1.89 & \(\backslash\) & \(\backslash\) \\ Cor Total & \(\backslash\) & 375.33 & 26 & & \(\backslash\) & \(\backslash\) \\ \hline \hline \end{tabular} Note: ** means highly significant (\(p<0.01\)), * means significant (\(0.01\leq p<0.05\)).  
\end{table} 
Table 6: ANOVA for contact-parameter-response surface test’s results. Using the physical test measurement of the stacking angle of 27.15\({}^{\circ}\) as the optimization target, the combination of the contact parameters' optimization model is:  
\[\left\{ \begin{array}{l}\text{s.t.}\left\{\begin{array}{l}y_{1}=27.15^{\circ} \\ 0.322\leq A\leq 0.611\\ 0.298\leq D\leq 0.623\\ 0.284\leq E\leq 0.730\\ 0.021\leq F\leq 0.042\end{array}\right.\end{array} \right. \tag{12}\]  
Based on Equations (11) and (12), the solution for the optimal combination of parameters was solved as \(A=0.514\), \(D=0.463\), \(E=0.568\), and \(F=0.029\). The rape stem repose angle simulation result was 26.55\({}^{\circ}\) through this combination of parameters. The relative error to the measured values from physical tests (27.15\({}^{\circ}\)) was 2.2%.
## 2 Problem Statement
For example, \(\{(a_{1},b_{1})^{\mathrm{e,}},(a_{2},b_{2})^{\mathrm{e}}\}\) is the primitive state of \(\{(a_{1},b_{1})^{\mathrm{e,st}},(a_{2},b_{2})^{\mathrm{e,st}}\}\) or \(\{(a_{1},b_{1})^{\mathrm{e,st}},(a_{2},b_{2})^{\mathrm{e,dn}}\}\). Furthermore, when CFs are the same from the viewpoint of the primitive contact states, we describe them as _isogenous_. For example, \(\{(a,b)^{\mathrm{e,st}}\}\) and \(\{(a,b)^{\mathrm{e,dn}}\}\) are isogenous, and \(\{(a_{1},b_{1})^{\mathrm{e,st}},(a_{2},b_{2})^{\mathrm{e,st}}\}\) and \(\{(a_{1},b_{1})^{\mathrm{e,st}},(a_{2},b_{2})^{\mathrm{e,dn}}\}\) are also isogenous. Figure 2: Frictional constraint on object’s motion in fixed and sliding cases: (**a**) A robot tilts an object with fixed contact on the floor; (**b**) a robot tilts an object with sliding contact; (**c**) two robots tilts an object with sliding contact. In this paper, we distinguish the CFs consisting of the contact states between an object and the environment, which we call environmental contact formation (ECF). We also distinguish the CFs consisting of the contact states between the robots and the environment, which we call robot contact formation (RCF); see Figure 3.  
Furthermore, we describe them as \(C^{\mathrm{E}}\) and \(C^{\mathrm{R}}\), respectively. As the result, we denote the mode \(s\) in the manipulation as the set of ECF and RCF:  
\[s=\{C^{\mathrm{E}},C^{\mathrm{R}}\}.\]  
A multi-contact set on an object was supposed to form the full constraints, so that the robots manipulated the object quasi-statically. Gravity closure [19; 20], which is force closure that includes the gravitational force, is introduced later in this paper as the requirement for a mode. Our first goal is to identify all possible modes from the viewpoint of gravity closure. If one mode can directly transform into another mode without any intermediate ones, it is a possible mode transition. Mode transitions can be described by a graph that comprises nodes and arcs, where the nodes represent individual modes, and the arcs between them represent the transitions; the value of an individual arc represented the cost of the state transition [21]. Our second goal is to generate this mode graph by taking into account some restrictions on robots and the geometrical relationships between an object and the environment. Using the resultant graph, the manipulation action sequences are determined by searching for paths from a given initial mode to a targeted mode before applying a sample-based method to probe the configuration spaces determined by modes. In the following sections, we have made the following assumptions. The shape and the gravity center of the object and the shape of the environment are given. Furthermore, we have assumed that the type of contact that a robot generates with the object is given, and it does not change in the manipulation. The number of robots is also given. We have considered only the possible RCF on the targeted object, and the contacts between the robots and the environment were ignored; the robots generally make contact with a face (a floor) in the environment.
## 4 Experimental Results
### Wavelet Packet-Based Damage Index  
In order to quantify the degree of structural damage during the applied load process, the damage index is computed by using the wavelet packet analysis. The results are shown in Figures 18-21. It can be seen from Figures 18-21 that the value of the damage indices increase as the loading progressing. When the applied load reached 10 kN, the damage index of the OS1 and OS2 area sensors mounted on the beam 1 increased by 14.5% and 28.1%, respectively. The damage index of the OS1 and OS2 area sensors mounted on the beam 2 increased by 2.4% and 6.1%, respectively. This indicates that crack appears in the tensioned end region of the beam 1 when the applied load is only 10 kN. When the applied load reached 20 kN, the damage index of the OS1 and OS2 area sensors mounted on the beam 1 increased by 13.4% and 38.6%, respectively. The damage index of the OS1 and OS2 area sensors mounted on the beam 2 increased by 8.1% and 63.4%, respectively. This indicates that when the applied load is only 20 kN, cracks appear in the tensioned end region of the beam 2. A new crack continues to appear in the tensioned end region of the beam 1. As the applied load increases, the crack of the beam further expands. When the applied load reaches 40 kN, the damage index of the OS1 sensor installed in beam 1 and beam 2 increases by 36.4% and 46.5%. When the applied load is only 40 kN, cracks occur in the bonded end regions of the beam 1 and beam 2. By comparing the damage indices of the OS1 and OS2 area sensors, it can be seen that the damage index of OS2 grows faster with the increase of applied load. It is indicated that the tensioned end region cracks earlier than the bonded end region due to the influence of the stress change. The OS2 region damage index of beam 1 increases fastest under 10 kN and 20 kN loads. The OS2 region damage index of beam 2 increases fastest under 20 kN load. It is shown that increasing the length of the non-pre-stressed bond end has an effect on the cracking of the tensioned end region. The OS1 region damage index of beam 1 and beam 2 increases fastest at the applied load of 40 kN. The complete debonding failure of beam 1 was found at the applied load of 105 kN. The complete debonding failure of beam 2 was found at the applied load of 130 kN. It shows that increasing the non-pre-stressed bond length can improve the bearing capacity and the damage degree.  
Figure 17: Frequency domain signal of OS2 in the beam 2.  
Figure 18: Damage index of OS1 in the beam 1.
## 6 Conclusions
#### Limitations and Future Scopes  
Despite the promising results achieved in this study, several limitations should be acknowledged. First, the permeability coefficient database was derived entirely from CFD-DEM simulations, which, although capable of capturing pore-scale flow behavior, may not fully replicate the heterogeneity and boundary conditions of real-world CGSs. Second, the model inputs were limited to gradation parameters and porosity; other potentially influential factors such as particle shape, angularity, and fabric structure were not considered. Moreover, the machine learning models were trained and validated on a relatively constrained parameter space, which may limit their generalizability when extrapolated to soils with markedly different gradation characteristics. Future research should focus on in corporating experimental datasets to validate and enhance the robustness of the predictive models, exploring additional microstructural descriptors as input features, and extending the framework to accommodate dynamic seepage conditions or coupled hydro-mechanical processes for broader geotechnical applications.  
Conceptualization, J.W. and H.D.; methodology, J.W.; software, L.G.; validation, [PERSON], H.D. and L.G.; formal analysis, [PERSON]; investigation, [PERSON]; resources, [PERSON]; data curation, Y.W.; writing--original draft preparation, Y.W.; writing--review and editing, Y.W.; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This investigation was funded by the National Key R&D Program of China (No. 2023 YFC3009400); the National Science Fund of Jianggi Province (No. 20223 BBG71018); Fujian Natural Science Foundation (No. 2021J011134); Science and Technology Innovation Development Fund of Wuyi University (No. N2017Y05); Engineering Research Center of Prevention and Control of Geological Disasters in Northern Fujian, Fujian Province University (No. WYERC2024-4).  
Data will be made available on request.  
The authors declare that they have no known competing financial interests.
# A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model
## 5 The Detection of a QS Magnetic Field
KF update: Obtain the measurement vector of heading by Equation (32); Performance the KF measurement update of Equations (13)-(16); Update the state noise covariance by Equation (23). Step 4. Correct the heading calculated by gyroscope: If Equation (48) is true, the heading calculated by Equation (31) is corrected by the updated heading of Step 3. Step 5. Terminate the process, otherwise return to Step 1. ```  
**Algorithm 1** The heading estimation with KF based on AR model
## 4 Preprocessing of the Measured Data
### STFT Analysis of the Measured Signal  
The short-time Fourier transform is a powerful tool for time-frequency analysis. Unlike the well-known Fourier transform (FT), it presents frequency content together with time information. The STFT can be thought of as a 2D image. Each column of this image contains the FT of the signal bounded by a window function. The window function is then shifted slightly on the signal, and the other columns of the STFT are formed by calculating the FT operation again for the received signal portion. The STFT can be calculated by Equation (1):  
\[STFT_{x}(m,k)=\sum_{n=-\infty}^{\infty}x[n]w[n-m]e^{-\frac{2\pi\mathrm{d}n}{N}}, \tag{1}\]  
where \(x[n]\) and \(w[n]\) are the discrete signal vector and the window function, respectively. \(N\) represents the FFT length. The frequency resolution improves as \(N\) increases. Using FFT, the signal's frequency content between \(-f_{s}/2\) and \(f_{s}/2\) is obtained (\(\pm 40\) Hz), where \(f_{s}\) denotes the sampling frequency. Only the positive and low-frequency parts of the STFT are considered in the classification for two reasons: 1. The absolute value of the STFT images is the same for positive and negative frequencies. Thus, the data size can be reduced by taking only the positive part. 2. Since the aim was to detect whether the debris was empty or not, the detection of respiration and motion was required. Since the respiration frequency ranges between 0.2 and 0.5 Hz, it was sufficient to focus on the low-frequency range (0-5 Hz).  
The STFT analysis was performed using a moving window with a constant length (\(L\)). The main signal was divided into a set of segments, and each segment was refined using the conventional FFT algorithm. The resolution of the STFT depends on the selection of the window function, overlapping value, and FFT length. In this study, \(N\), \(L\), and overlapping value were set to 1024, \(4f_{s}\), and \(5f_{s}/8\), respectively. A Hamming window was employed to select the signal portion, while various window functions were used to address the issue of spectral leakage in the STFT process.  
Figure 5 represents the STFT images (with dB values) of the measured respiration and movement phase data from a subject with different orientations in the debris. Similar to Figure 4, the STFT values were quite small for empty debris. Additionally, the signal energy changed significantly over the 0-5 Hz frequency range, depending on the orientation of the subject. The figure also shows that the energy was more localized at lower frequencies compared to high-frequency STFT values.
## 3 Materials and Methods
### Data Collection and Preprocessing
#### 3.1.2 Data Pre-Processing
For instance, \"randomized controlled trial,\" \"randomised controlled trial,\" \"randomized controlled-trial,\" and \"randomised controlled-trial\" were grouped with the united expression \"randomized controlled trial.\" Finally, the data were divided into three consecutive periods, 1999-2008 (91 articles), 2009-2013 (149 articles), and 2014-2018 (211 articles), for thematic evolution analysis. Before conducting the STM analysis, we conducted a pre-processing procedure to facilitate the analysis. First, abstracts of the 451 selected articles were collected, for which terms were extracted by a self-developed NLP program. Second, terms with similar meanings were unified (e.g.,'modeling' and'modelling' 'personalize' and 'personalise'). Third, stop terms were excluded (e.g., \"of,\" \"you,\" \"a,\" and \"the\"). Besides, terms containing little or no meaningful information were excluded (e.g., \"also,\" \"although,\" \"among,\" \"another,\" \"demonstrate,\" \"furthermore,\" \"however,\" \"include,\" \"possible,\" and \"towards\"). In addition, term frequency-inverse document frequencies (TF-IDF), as one of the common techniques of feature extractions [75], was further adopted to filter unimportant terms. We followed the suggestion by [PERSON] et al. [76] to include terms with TF-IDF values more than or equal to 0.05.