esa-sceva/satcom-chunk-collection huggingface dataset:
# Electromagnetic Analysis of Vertical Resistive Memory with a Sub-nm Thick Electrode
## Materials and Methods
### Device Characterization and Modeling  
For high-resolution transmission electron microscopy (HR-TEM) imaging, a Tecnai TF-20 Field Emission Gun/TEM (FEI Company, UK) functioning at 200 kV was used. For the _I-V_ voltage sweep measurement, a Keithley 4200-SCS parameter analyzer with an integrated remote pre-amplifier (Keithley, OH, USA), a Keithley 707B Switch Matrix, and a Keysight 81150A arbitrary signal function generator (Keysight, CA, USA) were used. The compliance currents (I\({}_{\text{CC}}\)) for the conventional metal (Pt) and graphene-based VRRAMs were 30 \(\upmu\)A during SET with a maximum sweep of 2.5 V, and 5 \(\upmu\)A with a maximum voltage sweep of 1 V, respectively. The modeling of the device response was performedin Verilog-A language, and the simulation was compiled in Simulation Program with Integrated Circuit Emphasis (HSPICE, I-2013.12-SPI 64-BIT, Synopsys Inc., Mountain View, CA, USA). In addition, computational analysis was conducted in MATLAB (R2017a, MathWorks, MA, USA).
## 2 Failure Pattern and Basic Hypotheses  
When the axial load on the pile top increases to a certain extent, settlement occurs at the pile tip. As the axial load continues to increase, portions of the soil around the pile tip deform into a plastic state and the plastic zone develops. With the expansion of the plastic zone, the pile tip resistance increases to the ultimate bearing capacity. The possible failure mechanisms around the pile tip at the ultimate state generally include general shear failure, cavity expansion failure, and local shear failure. They are shown in Figure 1. Figure 1a shows the general shear failure with the failure surface extending to the ground. Figure 1b shows the spherical cavity expansion failure pattern in which it is assumed that outside the rigid cone, the soil expands as a spherical cavity. Figure 1c shows the local shear failure with the failure zone limited below the pile end plane. Figure 1d shows the Prandtl failure patterns considering equivalent overload. Figure 1e shows the Meyerhof failure mode with the failure surface reverting to the pile shaft. Experimental research has found that general shear failure only occurs in shallow foundations, while in deep foundations, the failure zone around the pile tip does not revert to the pile shaft but is only limited below the pile end plane [23]. Therefore, considering a failure zone larger than that in engineering practice is difficult to prove as reasonable.  
In this study, a limited region failure with a failure zone limited below the pile end plane is considered and the soil under the pile end is considered to be divided into four parts at the ultimate state, as shown in Figure 2a. Outside the rigid cone beneath the pile end, zone I is in a state of high shear failure, zone II, surrounding zone I, is not damaged, and zone III is mainly under vertical compression deformation. Under the considered partitioning conditions, the end-bearing capacity of piles is intended to be computed based on the following assumptions:  
1. The end-bearing capacity of piles is independent of the pile shaft resistance; that is, the pile shaft resistance does not affect the stress state at the boundary of the failure zone;
2. When the pile tip resistance reaches the ultimate state, the failure zone around the pile tip is limited below the pile end plane;
3. When the pile tip resistance reaches the ultimate state, the boundary between zone I and zone II is in the passive state;
4. Soil obeys a non-associated flow rule that takes into account the difference between the dilation angle and the soil friction angle.  
Figure 1: Different failure patterns around the pile tip: (**a**) general shear failure; (**b**) cavity expansion failure; (**c**) local shear failure (**d**) Prandtl failure mode; (**e**) Meyerhof failure mode.  
The stress boundary condition at the boundary of the failure zone limited below the pile end plane is shown in Figure 2b. \(P_{Y}\) represents the vertical load acting on the boundary of the failure zone, and \(P_{X}\) represents the horizontal load acting on the boundary of the failure zone.
## 4 Simulation and Results  
In this section, the optimal placement for the microgrid is carried out on the targeted test system. The targeted system is a radial 69-bus IEEE test system, which is depicted in Figure 3 in the form of a single-line diagram. In addition, in order to have a better appraisal of the effectiveness of the proposed method, the proposed method is also tested on a radial 33-bus IEEE test system, which is depicted in Figure 4. However, the input data is the same for both grids. In this paper, all the methods are implemented in MATLAB 2018a in a Core i5 PC with 3 GHz processing frequency of CPU and 8 GB of RAM. The convergence behavior is another aspect with which the methods are compared to each other.  
The characteristics corresponded with the lines and the buses of this system refer to [43; 44], respectively. The normalized demand of the microgrid is shown in Figure 5. The demand for this microgrid at the peak is considered to be 250 kW. The peak of the load curve of the grid is supposed to be the mid-peak demand obtained from [43; 44]. Moreover, the hourly solar insulation profile is normalized and illustrated in Figure 6.  
The maximum generation capability of the PV panel per month is shown in Table 1. The placement mechanism is executed in the worst condition. Thus, the calculations are conducted for the month of August. This month indicates the lowest level of mean solar radiation.  
Figure 4: The single-line diagram of the 33-bus test system.  
Figure 5: The normalized demand of distribution network for both systems.  
Figure 3: The single-line diagram of the 69-bus test system.  
Figure 6: The normalized daily solar irradiance for both distribution systems.  
The economic data for different sources were extracted from reference [44]. The investment cost of the PV panel is considered to be 7.44 \(\upupupup\)/kW, and the O&M cost is supposed to be 40 \(\upupup\)/year. The cost of each battery is determined as 0.283 \(\upupup\)/Ah, and each battery incurs 50 \(\upupup\)/year for O&M costs. The operation cost and maintenance cost of the diesel generator is 0.55 \(\upupup\)/W and 0.2 \(\upupup\)/h, respectively.  
The demand in the targeted microgrid is supposed to be composed of residential and industrial loads. Table 2 delineates the price of electricity corresponded with the types of loads and the demand level.
# Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter
## 4 Discussion and Conclusions  
In this paper, we propose a fusion positioning method based on the genetic-particle filter. In order to optimize the particle degradation problem, we design an optimization mechanism that utilizes the gene mutation method and the method of reconstructing particle set to ensure the reliability and diversity of particles. The experiment results show that the average position estimation error is 1.72 m and the RMSE is 1.89 m, the positioning accuracy and stability are improved. Contrary to the high computational burden caused by increasing the number of particles in papers [26; 28; 29], the size of the particle set in this paper is 100 and the computational load is lower compared with them. Furthermore, the single-point-based magnetic positioning (SPMP) method is more flexible and easier to be implemented and its computational load is also not very high. Therefore, it is possible for us to implement this fusion positioning algorithm on smartphones. Moreover, five geomagnetic  
Figure 15: Positioning trajectories of different methods.  
features are extracted in our work and they can improve the fingerprint specificity of a single point and geomagnetic positioning accuracy. Besides, better heading angle estimation performance of the Mahony complementary filter (MCF) algorithm also provides another effective approach for us to calculate the heading angle by using smartphones. All of these results can support that our proposed fusion-positioning method has application value in indoor positioning and navigation.  
However, there are also some limitations in our fusion-positioning method. The linear interpolation method can reduce data collection work. Whether there is another effective approach that can reduce more geomagnetic database construction work is still needed to explore in depth. We did not consider the influence of different walking speed on fusion positioning. The effect of different motion states on heading angle estimation is not studied. All of these limitations are our future research work.  
Conceptualization, M.S. ([PERSON]); Formal analysis, M.S. ([PERSON]) and S.X.; Funding acquisition, Y.W.; Methodology, H.C.; Project administration, Y.W.; Data curation, H.C., M.S. ([PERSON]); Writing-original draft preparation, M.S. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China [grant number 2016 YFB0502102], in part by the State Key Laboratory of Satellite Navigation System and Equipment Technology [grant number CEPNT-2018 KF-03].  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the writing of the manuscript; and in the decision to publish the results.
# UAV Attitude Angle Measurement Method Based on Magnetometer-Satellite Positioning System
## 5 Measurement Error Compensation Based on Kalman Filter Algorithm
### Continuous-Discrete System Kalman Filter Algorithm  
According to Equation (33), the system equation and measurement equation are described in the following form:  
\[\dot{X}(t)=F(t)X(t)+G(t)W(t) \tag{42}\]  
\[Z_{k}=H_{k}X_{k}+V_{K} \tag{43}\]  
The continuous-discrete system Kalman filter equation is expressed as follows. The time correction equation is:  
\[\dot{\hat{X}}(t)=F(t)\hat{X}(t) \tag{44}\]  
\[\dot{P}(t)=F(t)P(t)+P(t)F^{T}(t)+G(t)Q(t)Q^{T}(t) \tag{45}\]  
Here, \(t_{k-1}\leq t\leq t_{k}\).  
The measurement correction equation is:  
\[\dot{\hat{X}}_{k}=\hat{X}_{k,k-1}+K_{k}[Z_{k}-H_{k}\hat{X}_{k,k-1}] \tag{46}\]  
\[K_{k}=P_{k,k-1}H_{k}^{T}(H_{k}P_{k,k-1}H_{k}^{T}+R_{k})^{-1} \tag{47}\]  
+++ ==ERROR: No output for this page==
\[P_{k,k-1}=\phi_{k,k-1}P_{k-1}\phi_{k,k-1}^{T}+\Gamma_{k-1}Q_{k-1}\Gamma_{k-1}^{T}\] (48)  
\[P_{k}=P_{k,k-1}-K_{k}H_{k}P_{k,k-1}\] (49)
# Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping
## 3 Materials and Methods
### Proposed Method  
This study proposes a new customer-churn prediction system consisting of feature construction, stacking model and soft votingas shown in Figure 1.  
The original features with customers' consumption and behavior are equidistantly grouped to construct new features. The stacking model consists of two levels with four algorithms: Xgboost (XGB), Logistic regression (LR), Decision tree (DT) and Naive Bayes classifier (NBC) to achieve better prediction accuracy. The third step consists of a soft voting. The results of the stacking model are input to the soft voting.
## 5 Conclusions  
In this paper, we have studied the preamble channel estimation based on compressive sensing for FMBC/OQAM systems under an IEEE 802.22 sparse multipath channel. A new sparse adaptive regularized compressive sampling matching pursuit algorithm for channel estimation is proposed, which is associated with adaptive, regularized and CoSaMP. The proposed algorithm can accurately estimate the multipath components. Simulation results demonstrate that a CS based preamble approach can achieve significantly better BER and MSE performance than conventional LS methods. The proposed scheme outperforms SAMP for channel estimation with lesser time complexity and can provide approximate results than the state-of-the-art CoSaMP algorithm without a prior sparse knowledge of the channel. It has been verified that the ARCoSaMP scheme is an efficient method for sparse adaptive channel estimation in FBMC/OQAM transmission networks.  
This work is supported by the Hainan University Excellent Paper Cultivation Plan, and the National Natural Science Foundation of China, Grant No.61561017, No.61561018. The authors would like to thank the editor and the anonymous reviewers for their valuable comments.  
[PERSON] proposed the sparse adaptive regularized compressive sampling matching pursuit algorithm; [PERSON] conceived and designed the simulations and provided some comments on the paper organization; [PERSON] contributed towards the performance results and analytic evaluations.  
The authors declare no conflict of interest.
## Network Setting and Computational Model
### Communication Model  
The computational task of an MD can either be performed on the local MD or offloaded to the MEC server. Therefore, we show the communication model when the computational task is offloaded to sMEC through a wireless link or continues offloading to the mMEC via fiber-optic links. In this study, we used orthogonal frequency division multiple access for communication between MDs and SBSs. To reuse the spectrum, we assumed that the spectrum is used in an overlaid manner. The transmission rate of the computational task \(I_{m}\) from the MD \(m\) to the SBS \(n\) through the wireless channel is expressed as follows:  
\[R_{mn}=W\log_{2}\left(1+\frac{p_{m}h_{mn}}{n_{0}+Q_{mn}}\right), \tag{1}\]  
where \(W\) is the channel bandwidth, \(p_{m}\) is the fixed transmit power of the MD \(m\), \(h_{nm}\) is the channel gain between the MD \(m\) and the SBS \(n\), \(n_{0}\) is the noise power, and \(Q_{mn}\) is the inter-cell interference.
# Knowledge-Based Fuzzy Expert System to the Condition Assessment of Historic Road Bridges
## 2 Materials and Methods
### Proposed Model to Historic Road Bridge Condition Rating
After the assigned fuzzy ratings and structural importance, the fuzzy ratings of the components (superstructure, substructure, and equipment) are calculated, and furthermore, they are defuzzified by the centroid method. The defuzzified grades of the components are then compared with each other using the principles of the AHP method and the Saaty comparison scale. The results of the comparison are of relative importance. Multiplying the relative importance and the rating of component, and then adding up the products gives an estimate of the condition of the bridge, i.e., the value of the HRBCAI. Structural importance determines the negative importance element has on the condition of a component concerning the element rating relative to the rating of other elements. Since the elements are given crisp values of grades, it is necessary to find fuzzy sets that the given grade belongs to. For this reason, all conditions in which the element can be according to a given grade are determined. This way, combinations of all elements in all conditions are created and each of the combination gains average structural importance. The average structural importance is obtained by summing the importance of construction experts and dividing by the total number of experts. Once the ratings and importance of the elements have been defined, their aggregation is approached, which further uses the fuzzy weighted geometric mean (FWGM) approach to obtain a bridge component fuzzy grade for all combinations. Given its fuzzy value, \(\alpha\)-cuts are defined which determine the upper and lower values of fuzzy sets of component evaluation. The average value of the upper and lower boundary is then calculated and a crisp evaluation of the bridge component is determined by the defuzzification process using the centroid method. The process is repeated until all components receive their crisp grade. After that, using the multiciteria AHP method, the importance (weights) are defined for each component. Using the Saaty scale, the components are compared with each other, followed by normalization of the assigned weights and checking the consistency of the comparison matrix. If the matrix is consistent, the defined weights are adopted and the calculation of the condition of the bridge is calculated. The bridge condition assessment is defined as the HRBCAI and is calculated by multiplying the obtained component ratings by the assigned weights and summing them together.
# Escalator: An Autonomous Scheduling Scheme for Convergecast in TSCH
## 5 Analysis of Escalator
### Proof of the Conflict-Freeness of Convergecast Schedule
There is no primary conflict between parent and child nodes by Theorem 2 and Theorem 3. Therefore, the schedule of the convergecast slotframe does not cause the primary conflict. The secondary conflict may occur when two or more links are communicating simultaneously on the same channel, as defined in Definition 2. Escalator uses the channel offset selection mechanism to isolate channels between links using the same timeslot to prevent the secondary conflict. Theorem 4 proves that the convergecast slotframe does not generate the secondary conflict. Theorem 4: _There is no secondary conflicts between the links generated by the convergecast slotframe._  
Proof. : By the channel offset selection mechanism in Section 4.3, the communication channels used by the two links having a rank difference of two or more are different. Furthermore, by Theorem 2 and Theorem 3, the communication of two links whose rank difference is one or less does not execute in the same timeslot. Therefore, since the links using the same timeslot generated by the convergecast slotframe communicate using different channels, the secondary conflict does not occur. We prove that the schedule of the convergecast slotframe is conflict-free through Corollary 1 and Theorem 4.
# Full-Scale Measurements of Wind Characteristics on a High- Rise Building during Typhoon Sarika
## 4 Conclusions  
Wind-field characteristics are monitored synchronously during the passage of Typhoon Sarika in Haikou. A large number of samples covering different wind directions are obtained. The mean wind and fluctuating wind characteristics atop the building under the action of typhoon Sarika are counted. The main conclusions are as follows:  
1. Turbulence intensity decreases with the increasing wind speed and remains slight changes under high wind speed, and the ratios between longitudinal and lateral turbulence intensities are basically within the range of 1.0-2.0. The measured longitudinal and lateral turbulence integral scales increase with the increasing mean wind speed  
Figure 14: Probability density of total wind speed samples in the whole landing process of Typhoon Sarika.  
and S.D of fluctuating wind. The ratio of the turbulence scale between the turbulence components \(L_{\rm u}^{x}\):\(L_{\rm v}^{x}\) increases with the increasing mean wind speed and the increasing rate of longitudinal turbulence integral scales is more significant than that of lateral turbulence integral scales.
2. The gust factor is related to turbulence intensity, sample duration, and gust duration; the gust factor increases as turbulence intensity increases; and the findings show that the relationship between turbulence intensity and gust factor is approximate to linear. The gust factor increases with the rise of sample duration, while it decreases with the increasing gust averaging time.
3. The Von Karman-type spectrum has a slightly higher prediction of the longitudinal power spectrum of wind speeds when the dimensionless frequency is in the range of \(10^{-2}\)\(\sim\)\(10^{-1}\). Moreover, the measured longitudinal power spectrum of wind speed decays faster than the lateral power spectrum, and the Von Karman-type spectrum has a slightly higher prediction of the longitudinal power spectrum of wind speeds when the dimensionless frequency \(X\) is in the range of \(10^{-1}\)\(\sim\)\(10^{1}\).
4. The time-frequency analysis of non-stationary samples of fluctuating wind speed are conducted by wavelet transform, which shows that the amplitude of wavelet coefficients fluctuates greatly in time-domain coordinates, indicating that the higher the wind speed, the larger the amplitude of wavelet coefficients in time-domain.  
[PERSON] contributed to the overall study design, analysis, and writing of the manuscript. [PERSON] provided technical support and supervision. [PERSON] provided technical support and modification-polis. All authors have read and agreed to the published version of the manuscript. This research was financially supported by the Natural Science Foundation of Hunan Province, China (Grant No. 2020 JJ5205), Scientific Research Project of Hunan Provincial Department of Education, China (Grant No. 21B0730) and the National Natural Science Foundation of China (Grant No. 91215302). Not applicable. The authors declare no conflict of interest.
## 5 Results and Discussions
### Analysis of Accuracy  
From the simulations established in Section 5.3, we can analyze the accuracy of the result, i.e., minimum traveling time of the slowest vehicle. The standard deviation \(\sigma_{\rm T}\) can identify the accuracy of the simulation. As shown in Tables 1 and 2, the averages of \(\sigma_{\rm T}\) for \(\rm t_{\rm occ}>10\) s and \(\rm t_{\rm occ}>150\) s are 373.48 s and 631.2 s. It indicates that the results in lower \(t_{\rm occ}\) are more accurate than the higher ones.  
In addition, we analyzed the relationship between the average \(T_{\rm avg}\) and \(\sigma_{\rm T}\) by using the same technique as the one used in Section 5.2, i.e., statistical correlation. The results are described as follows. The correlations between \(T_{\rm avg}\) and \(\sigma_{\rm T}\) for \(\rm t_{\rm occ}>10\) s and \(\rm t_{\rm occ}>150\) s are 0.92 and 0.84, respectively. It indicates that there is a relatively strong positive relation between \(T_{\rm avg}\) and \(\sigma_{\rm T}\): larger \(T_{\rm avg}\) tends to lead to a larger \(\sigma_{\rm T}\), or, in other words, a larger \(T_{\rm avg}\) tends to lead to lower accuracy.  
Figures 6 and 7 show the searching progression for \(\rm t_{\rm occ}>10\) s and \(\rm t_{\rm occ}>150\) s, respectively, each with \(\rho=0.1\) and \(\rho=0.5\). It can be concluded that the algorithm's ability to converge is better for \(\rm t_{\rm occ}>10\) s than for \(\rm t_{\rm occ}>150\) s. It can be concluded from the final values until the 3000 th iteration. Moreover, Figures 6 and 7 confirm the accuracy conclusions conducted from the statistical correlation analysis. Accuracy analysis can infer other behavior, that is, the ability to reach \(T_{\rm avg}\). Figure 7 shows that, for \(\rm t_{\rm occ}>10\) s, the algorithm's ability to reach \(T_{\rm avg}\) is better than that for \(t_{\rm occ}>150\) s. Furthermore, it can be seen that, for \(t_{\rm occ}>150\) s, even until the 1500 th iteration, the algorithm still can update the minimum of maximum times. However, it cannot go further to \(T_{\rm avg}\). The interesting situation occurs for \(\rho=0.5\) in the same \(t_{\rm occ}\). In this situation, mostly, once a solution is found, the algorithm cannot find another better solution.  
Figure 6: The progression of searching the optimal solution for \(\rm t_{\rm occ}>10\) s: **(a)** for \(\rho=0.1\); **(b)**\(\rho=0.5\).