esa-sceva/satcom-chunk-collection huggingface dataset:
# DEM Simulation of the Load Transfer Mechanism of a GRPS Embankment with a Fixed Geogrid Technique
## 4 Conclusions  
To investigate the load transfer mechanism of a GRPS embankment using the fixed geogrid technique, three discrete element method (DEM) models of pile-supported embankments were established, including an unreinforced embankment, a geogrid reinforced embankment, and a fixed geogrid reinforced embankment. The embankment settlement, relative displacement between the pile and the subsoil, soil stress, deformation of the reinforcement, and evolution of the contact force chain were analyzed. In addition, the  
Figure 14: Comparison of the geogrid displacements and the axial forces.  
embankment load transfer mechanism and efficacy of the pile were compared. The main conclusions are summarized as follows:  
(1) The differential settlement between the pile and subsoil could be effectively controlled using geogrid reinforcement. Compared with the traditional technique of a GRPS embankment, the fixed geogrid technique had a better effect to reduce the total and differential settlement.  
(2) In the three models, the stress of the embankment fill was concentrated at the pile top. The GRPS embankment with the fixed geogrid technique had less soil stress concentration than the other two cases, and the stress distribution along the embankment-soil interface was more uniform. The reinforcement directly transferred part of the load to the pile top, which was more conducive to the load transfer performance.  
(3) The fabric parameters could reflect the evolution of the soil arching structure to a certain extent. The principal direction of the normal contact force chain in the arching area in the fixed geogrid case showed that the fixed geogrid technique could limit the displacement of the soil particles.  
(4) The fixed geogrid technique could reduce the proportion of the load transferred to the pile from the soil arching effect, where the vertical load transferred to the pile top by the tensile membrane effect accounted for 22-28% in this study. Under the combined effect of the fixed reinforcement and the soil arching, the efficacy of the pile could increase by 10%.  
(5) Compared with the other two cases, in the fixed geogrid model, the axial force of the reinforcement was greater, indicating that the higher tensile strength of the reinforcement was required to ensure the stable connection and the full utilization of the tension performance.  
Writing--original draft preparation, Y.J.; writing--review and editing, J.Z. and Y.Z.; Software, Y.J. and C.M.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (grant number: 2018 YFB1600100) and the National Natural Science Foundation of China (no. 52178341, no. 52078392, and no. 51809191).  
Not applicable.  
The authors gratefully acknowledge [PERSON] for his technical help in the numerical modeling.  
The authors declare no conflict of interest.
# Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement
## 3 Proposed Speech Enhancement Algorithm
### Parameter Estimation  
In is known that \((\{a_{i}\},\sigma_{w}^{2})\) are very sensitive to real-life noises. Since clean speech, \(s(n,k)\), is unavailable in practice, it is difficult to accurately estimate these parameters. Therefore, we first focused on noise estimation, \(\hat{v}(n,k)\), for each noisy speech frame using speech presence probability (SPP) method (described in Section 3.2) [26] to compute \(\hat{\sigma}_{v}^{2}\). Given \(\hat{v}(n,k)\), \(\hat{\sigma}_{v}^{2}\) is computed as:  
\[\hat{\sigma}_{v}^{2}=\frac{1}{M}\sum_{n=0}^{M-1}\hat{\sigma}^{2}(n,k). \tag{34}\]  
To reduce bias in the estimated \((\{\hat{a}_{i}\},\hat{\sigma}_{w}^{2})\) for each noisy speech frame, we computed them from the corresponding pre-whitened speech, \(y_{w}(n,k)\) using the autocorrelation method [23]. The framewise \(y_{w}(n,k)\) was obtained by applying a whitening filter, \(H_{w}(z)\) to \(y(n,k)\). \(H_{w}(z)\) is given by [23]:  
\[H_{w}(z)=1+\sum_{j=1}^{q}\hat{b}_{j}z^{-j}, \tag{35}\]  
where the coefficients, \(\{\hat{b}_{j}\}\) (\(q=20\)) are computed from \(\hat{v}(n,k)\) using the autocorrelation method [23].
To the best of our knowledge, no researcher has investigated low power, cost effective IoT-based tissue paper monitoring systems that can be mounted on existing tissue dispensers. Our contribution is to propose a cost-effective, low power IoT-based tissue paper monitoring system that can be deployed beyond the laboratory scale. The system is designed using free and open-source software platforms. The application targets existing Wi-Fi network infrastructure. The current amount of tissue remaining and battery voltage levels are displayed on a Grafana dashboard. In addition, notification emails are sent to the user when the paper supply or battery voltage is low. We discuss simple methods for current measurement and practically verify them as applicable to many other low power applications. The application power profile and power budget are also modeled and practically verified. All the parameters presented in this work are application-oriented, and the limitations are meaningful compared to the standard protocols and technologies. The rest of the paper is organized into Section 3. Section 4 provides our methodology, consisting of a system overview, hardware architecture, distance measurement methods, the software architecture, our adaptive power estimation, the network architecture, the system's security features, and data storage and visualization. In addition, power consumption is also analyzed in detail. Section 2 after that presents experimental results and provides discussion. Finally, Section 5 draws conclusions and recapitulates the early stages of development for future work.
# Conceptual Design and Preliminary Verification of Distributed Wireless System of Weigh-in-Motion
## 3 Design of Distributed WIM System
### Wireless Transmission Module  
Wireless data transmission technology is the key to realizing the routing without grooving under the pavement surface. The following requirements should be considered when selecting a wireless transmission module: (1) stability of data transmission should be included because it is very important for the signal generated by the sensor to be sent and received accurately, considering data loss will cause the measurement error of the whole system; (2) the data transmission distance should be relatively long to meet the requirements of sending and receiving data of short and medium distance WIM sensor; (3) power consumption should be considered because the whole system can provide self-power, but the electricity provided will be very limited, so the wireless module needs to have the characteristics of low power consumption. By comparing the characteristics of existing wireless modules and combining them with the requirements of vehicle WIM system, this paper chooses the NRF24L01 wireless module, which mainly has the following characteristics: (1) high speed because the air transmission rate of NRF24L01 module can reach 2 Mbps, reducing the data transmission time and effectively avoiding the consumption of the signal itself in the transmission process; (2) low power consumption which means when the module is in response mode for communication, it can be transmitted and started quickly, greatly reducing the current consumption; (3) low operating voltage, which is to guarantee that the module can work normally under the voltage provided by STM32 MCU; (4) automatic retransmission function, which is to effectively prevent the omission of signals in the process of wireless data transmission.  
According to the design requirements and referring to the usage document of NRF24L01, the wireless transmission module is designed here. In order to facilitate the connection with the STM32 module, the SPI interface is reserved, as shown in Figure 8, to achieve the purpose of wireless data transmission. Figure 9 shows a prototype of the wireless transmission module.  
Figure 7: A prototype of charge-amplifying and conversion circuit.
## 2 Global Appearance Descriptors
### Homomorphic Filter  
In order to solve the localization task, typical situations may happen such as lighting variations and changes in the position of some objects (chairs, tables, open doors, etc.). Hence, descriptors must be robust against these circumstances.  
[PERSON] et al. [41] showed that some pre-treatments could improve the localization accuracy in indoor environments with different lighting levels. Among the studied techniques, the use of the homomorphic filter [42] can be highlighted. The homomorphic filter permits filtering the luminance and reflectance components from an image separately.  
The use of this filter has proven to provide especially good results when it is used in combination with the HOG descriptor [31], whereas in the FS and _gist_ cases, the results were similar to or worse than without this pre-treatment filter. Hence, in the present paper, the following configurations will be used throughout the experiments: FS without filter, HOG with filter, and _gist_ without filter.
# Synchronization and Application of a Novel Hyperchaotic System Based on Adaptive Observers
## 5 Application for Secure Communication  
In secure communication applications, hyperchaotic synchronization is highly relevant. The simplicity of its implementation in secure communication systems serves as a crucial example of this. The suggested communication system is shown in Figure 13.  
At the receiving end of the connection, there is a transmitter (driving) and a receiver (response).  
Figure 11: The synchronization errors.  
Figure 12: Estimation of unknown parameter \(b\).  
The information signal and the chaotic transmitter's output combine to generate the sent signal. The information is deliberately included into the driving mechanism to create the transmitter. Therefore, the transmitter is the synchronized hyperchaotic system with the additional information signal. The system (36) expresses the used system:  
\[\begin{array}{l}\dot{x}=Ax+Bf(x)+Bg(x)\theta+B\eta(x,t)\\ y_{1}^{\prime}=y_{1}+s(t)\end{array} \tag{36}\]  
Here, \(s\in\mathfrak{R}\) represents the information signal and \(y_{1}^{\prime}(t)\) is one of the transmitted signals guiding the receiver in the hyperchaotic system. Another transmitted signal is formulated as \(y_{2}^{\prime}=y_{2}=C_{2}x\). Following synchronization, the recovery of the information signal \(\delta(t)\) can be achieved as detailed in (37):  
\[\delta(t)=y_{1}^{\prime}(t)-C_{1}\delta(t) \tag{37}\]  
We acknowledge that the error dynamics are governed by (14) during the synchronization design. Therefore, akin to its proof, it is concluded that the transmitter can be synchronized. Upon the completion of synchronization, the following function is attained (38):  
\[\lim_{t\rightarrow\infty}\hat{s}(t)=\lim_{t\rightarrow\infty}\left(y_{1}^{ \prime}(t)-C_{1}\hat{x}(t)\right)=\lim_{t\rightarrow\infty}(C_{1}x(t)+s(t)-C _{1}\hat{x}(t))=\lim_{t\rightarrow\infty}s(t) \tag{38}\]  
This signifies the successful recovery of the information signal. For the simulation, the values employed during synchronization are utilized and \(s(t)=1.2\sin(2t)\) is considered as the information signal. The information signal is depicted and the two transmission signals are formulated as \(y_{1}^{\prime}(t)=C_{1}x(t)+s(t)\) and \(y_{2}^{\prime}=C_{2}x\). The information signal \(s(t)\), the signal \(y_{1}^{\prime}\) masked (i.e., encrypted, with the transmitted information signal) and the \(s(t)\) signal obtained after synchronization and the recovery error after secure communication are illustrated in Figure 14.  
As can be seen in Figure 14c, the encrypted signal shown in Figure 14b is decrypted by the receiver in a very short time and the information signal is obtained. The simulation results prove that the secure communication design is successful.  
Figure 13: Secure communication system with chaotic masking.
# Experimental Studies on Low-Latency RIS Beam Tracking: Edge-Integrated and Visually Steered
## 4 Discussion  
Table 1 presents a comparison of tracking latency among the three approaches: the traditional scheme has a feedback delay of up to 3-4 s, needs to interact with the remote server via UE for the RIS feedback mechanism, and relies on cross-device time synchronization. The EIR-CM architecture proposed in this paper reduces the delay to 1-2 s by relocating the RIS feedback computation logic to the local computation unit and eliminating the need for cross-device synchronization, while the VSR-CM architecture selects the optimal codebook based on the visual perception without feedback and with a delay of less than 0.5 s. Obviously, the two methods proposed in this paper can provide more stable real-time communication links in the application scenarios with high dynamics and the need for accurate and fast beam tracing.
## 5 Results and Discussion
### Analysis of the Bending Strain Energy Density  
Asphalt mixture can be regarded as an elastic material at low temperatures, for which the destructive process is the process of energy dissipation. Specifically, first, the work of external force on the material will be converted into elastic strain energy, then, elastic strain energy turns into surface energy when a new surface appears with the occurrence and development of cracks. Normally, the more elastic strain energy that is stored in the asphalt mixture, the better the low temperature crack resistance. Hence, to some extent, the bending strain energy density function is a preferable method for evaluating the low temperature performance of asphalt mixture [42]; the function is shown as (5):  
\[W=\int_{0}^{\varepsilon_{0}}\sigma_{ij}d\varepsilon_{ij} \tag{5}\]  
where \(W\) is the strain energy density function. \(\sigma_{ij}\) is the stress value. \(\varepsilon_{ij}\) is the strain value. \(\varepsilon_{0}\) is the strain value corresponding to the maximum stress.  
According to the test results shown in Figure 13, the stress-strain curve was plotted. In order to achieve a good correlation between the fitted curve and the test data, the fourth power parabolic equation was adopted. Then, the critical strain energy density could be obtained by integral calculation according to the regression equation. The calculation results are shown in Table 20:  
Table 20 indicates that the bending strain energy density of rubberized SMA-II mixtures is significantly greater than that of SBS-modified SMA-I mixture. The strain energy density of SMA-II-1, SMA-II-3 and SMA-II-2 increases by 20.1%, 38.2% and 64.6% more than that of the SMA-I mixture. The SMA-II-2 mixture, with high asphalt content and polyester fiber, has the highest bending strain energy density. Therefore, from the perspective of crack resistance, SMA-II mixtures are better than SMA-I mixtures.
# Wind Turbine Power Curve Modeling with a Hybrid Machine Learning Technique
###### Abstract  
A power curve of a wind turbine describes the nonlinear relationship between wind speed and the corresponding power output. It shows the generation performance of a wind turbine. It plays vital roles in wind power forecasting, wind energy potential estimation, wind turbine selection, and wind turbine condition monitoring. In this paper, a hybrid power curve modeling technique is proposed. First, fuzzy c-means clustering is employed to detect and remove outliers from the original wind data. Then, different extreme learning machines are trained with the processed data. The corresponding wind power forecasts can also be obtained with the trained models. Finally, support vector regression is used to take advantage of different forecasts from different models. The results show that (1) five-parameter logistic function is superior to the others among the parametric models; (2) generally, nonparametric power curve models perform better than parametric models; (3) the proposed hybrid model can generate more accurate power output estimations than the other compared models, thus resulting in better wind turbine power curves. Overall, the proposed hybrid strategy can also be applied in power curve modeling, and is an effective tool to get better wind turbine power curves, even when the collected wind data is corrupted by outliers.  
extreme learning machine; support vector regression; wind turbine power curve modeling; fuzzy c-means clustering; outliers +
Footnote †: journal: Applied Soft. 2019, 9, 4930; doi:10.3390/appr9224930
# JSSTR: A Joint Server Selection and Traffic Routing Algorithm for the Software-Defined Data Center
## 7 Conclusions  
In order to avoid the inefficient use of network resources or some network issues caused by choosing the destination server from the server cluster without considering its impact on the network when handling user requests, the joint server selection and traffic routing problem has been proposed in this work. As that problem belongs to NP-hard problems, a heuristic algorithm called JSSTR has been proposed to find the approximate optimal solution of that joint server selection and traffic routing problem in polynomial time. The proposed JSSTR algorithm has been evaluated using Mininet. Several metrics including the routing path hop degree, network load, network load balancing degree, server load and server load balancing degree have been proposed and used to evaluate the proposed algorithm. The evaluation results showed that the proposed algorithm can achieve high network utilization, network load balancing and server load balancing simultaneously.  
[PERSON], [PERSON] and [PERSON] initiated and discussed the research problem. [PERSON] and [PERSON] designed and discussed the proposed idea. [PERSON] and [PERSON] implemented the algorithms. [PERSON] performed the simulation and analyzed the results. [PERSON] prepared and wrote the paper. [PERSON], [PERSON] and [PERSON] revised the original paper.  
This research was funded by the Research on the Key Technology of Information Security in Railway Signal System (grant number 2017Y0030), the Key Project of China Railway (grant number 2016X008-D), the Fundamental Research on Security Monitoring using Optical Sensing in Intelligent Railway System (grant number 61735015) and the National Natural Science Foundation of China (Grant No. 61401374)  
The authors declare no conflict of interest.  
Figure 10: Calculation time comparison for JSSTR, Travs and WLC-GA.
# Experimental Investigation on Effective Distances of Acoustic Emission in Concrete Structures
## 2 Experimental Program
### Test Parameters  
Several parameters indicate the characteristics of AE waves; those considered in this study are count, duration, amplitude, average frequency, signal strength, and absolute energy. Duration is the time interval between the triggering time of one AE signal and the time of its disappearance. Count is the number of times one signal exceeds the threshold within the duration. Amplitude is the peak voltage of the signal waveform, which is closely related to the magnitude of the source event [30]. These parameters identify the waveform characteristics of the AE signal. In addition, average frequency was calculated from the count divided by the duration obtained over each AE signal; the signal strength was calculated by integrating the absolute value of the voltage signal with respect to time; and the absolute energy is the square of the wave signal with respect to time. These parameters were elected as indicators of the AE signal intensity. All test parameters were determined using AEwin for Express-8 software in data acquisition equipment [27]. This means that the parameter-based AE evaluation was employed, and not the streaming mode in this study. Figure 4 illustrates the AE parameters considered in this study.
# Numerical Analysis of the Installation Process of Screw Piles Based on the FEM-SPH Coupling Method
## 4 Numerical Results
### Analysis of the Installation Process of Screw Piles
#### 4.1.2 Analysis of the Installation Process in the Middle Soil  
Figure 15 shows the movement displacement curves of the middle soil particles (300 mm) during the installation of NT3. It can be found that the middle (300) soil particles begin to move at the same time as the shallow soil particles during the installation process, indicating that the effect of the screw pile installation on the shallow soil also influences the motion of the middle soil particles. This shows the transferability of the pile-soil interaction during the installation process. As shown in Figure 15, it can be found that when the installation depth of the screw pile is 300 mm (Time = 0.636), the soil particles in the middle layer begin to move violently. At this moment, the conical pile tip starts to contact the soil in the middle layer (300), combined with the reasons for the movement of soil particles occurring at the surface, indicating that the violent movement of soil particles here is also caused by the installation of the conical pile tip. Unlike the shallow soil particles' displacement curves, the motion displacement curves of the middle soil particles exhibited a single linear increase in the radial direction, indicating that the soil is influenced differently by the installation of the screw piles at different depths.  
As shown in Figure 15, there is a consistency in the beginning time and duration of the violent motion of soil particles in the axial and radial directions, indicating that the motion of the soil particles in the radial and axial directions occurs simultaneously during the installation process. Compared with the motion direction of the shallow soil particles, the middle soil particles exhibit motion displacement in both directions (Figure 15). Therefore, it can be concluded that the motion of the middle soil particles does not show a continuous overall downward or upward movement during the installation process, rather, the movement reaches a stable state after a certain displacement. With the increase in installation depth, especially after the installation of the tapered pile tip is completed at this depth, the middle soil particles will gradually reach a stable state. Compared with the shallow soil particles' displacements, the motion displacements of the middle soil particles are generally smaller in both radial and axial directions. This indicates that the disturbance displacement of the soil caused by the installation of the screw pile will gradually decrease with the increase in the installation depth. Due to the existence of soil overburden pressure and the action of the spiral blades, unlike the heave failure mechanism in the shallow soil, the installation of the screw pile exhibits a cylindrical shear failure mechanism (CSFM) to the middle soil.
# A Performance Analysis of VANETs Propagation Models and Routing Protocols
## 4 Performance Metric and Simulation Setup
### Results  
The simulation is conducted for propagation models and routing protocols over IEEE 802.11p stack with varying simulation time and number of nodes(vehicles). Based on the varying number of nodes and simulation time, the specified routing protocols and propagation models are examined and the performance metrics such as packet delivery ratio, throughput, delay, goodput, and overhead are discussed. Various scenarios taken for the analysis of network performance are given below.