esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Propagation Characteristics of Tire Cavity Resonance Noise in the Automobile Suspension
## 2 An Introduction to the Power Flow Method  
To research the propagation of vibration energy in a structure, the power flow method is widely used. With it, the transmission path of the vibration energy in a structure can be identified [15]. Vibration power flow describes the energy consumption of structural vibrations or the ability of an external force to do work per unit time. The average vibration power flow in a period is generally used as a reference, and input power flow \(p\) in a structure is determined as follows:  
\[p(t)=F(t)V(t) \tag{1}\]  
where \(F(t)\) is the external force acting somewhere in the structure and \(V(t)\) is the velocity at that location produced by \(F(t)\).  
Average vibration power flow \(P\) corresponding to a certain period \(T\) is calculated as follows:  
\[P=\frac{1}{T}\int_{0}^{T}F(t)V(t)dt \tag{2}\]  
To calculate the power flow in a structure based on the finite element method, the nodal force and velocity vectors need to be calculated. The power flow per unit area of a certain section of the structure is its structural sound intensity, so stress can be introduced into the calculation. For an elastic body, the stress state at any point inside it can be expressed with six stress components: shear stress, i.e., \(\tau_{xy}\), \(\tau_{yz}\), and \(\tau_{zx}\), and normal stress, i.e., \(\sigma_{x}\), \(\sigma_{y}\), and \(\sigma_{z}\). As an example, the stress distribution of a micro-element is shown in Figure 1.  
The formula of structure sound intensity \(p_{n}\) is as follows:  
\[p_{n}=-\frac{1}{2}Re(\tau_{n1}v_{1}^{*}+\tau_{n2}v_{2}^{*}+\sigma_{n}v_{n}^{*}) \tag{3}\]  
where \(\tau_{n1}\) and \(\tau_{n2}\) are the shear stress in the 1-direction and 2-direction, respectively, \(\sigma_{n}\) is the normal stress in the normal n-direction, and \(v_{1}^{*}\), \(v_{2}^{*}\), and \(v_{n}^{*}\) are the velocity complex conjugates in the 1-direction, 2-direction, and n-direction, respectively.  
It can be seen from the literature [16] that stress and displacement can be evaluated by calculating the harmonic response results. A solid element has three directions, \(x\), \(y\), and \(z\), so the structural sound intensity in each direction is expressed as:  
\[p_{x}=-\frac{\omega}{2}I_{m}\Big{(}\tau_{xy}u_{y}^{*}+\tau_{xz}u_{z}^{*}+ \sigma_{x}u_{x}^{*}\Big{)} \tag{4}\]  
\[p_{y}=-\frac{\omega}{2}I_{m}\Big{(}\tau_{yx}u_{x}^{*}+\tau_{yz}u_{z}^{*}+ \sigma_{y}u_{y}^{*}\Big{)} \tag{5}\]  
\[p_{z}=-\frac{\omega}{2}I_{m}\Big{(}\tau_{zx}u_{x}^{*}+\tau_{zy}u_{y}^{*}+ \sigma_{z}u_{z}^{*}\Big{)} \tag{6}\]  
where \(\omega\) is the angular velocity, \(\tau_{xy}\), \(\tau_{zx}\), \(\tau_{yz}\), \(\tau_{yz}\), \(\tau_{zx}\), and \(\tau_{zy}\) are the shear stress in the \(x\), \(y\), and \(z\) directions, respectively, \(u_{x}^{*}\), \(u_{y}^{*}\), and \(u_{z}^{*}\) are the displacement conjugates of the node in the \(x\), \(y\), and \(z\) directions, respectively, and \(p_{x}\), \(p_{y}\), and \(p_{z}\) are the structural sound intensity in the \(x\), \(y\), and \(z\) directions, respectively.
# R-Curve Behavior of Polyhedral Oligomeric Silsesquioxane (POSS)-Epoxy Nanocomposites
## 1 Introduction  
Highly cross-linked thermosetting polymers, such as epoxy resins, can exhibit low values of fracture toughness and strain-to-failure. A conventional approach to overcome this limitation is to add fillers that lead to extrinsic toughening mechanisms, such as crack pinning, particle bridging, crack path deflection, shear yielding and microcracking. The use of nanofillers continues to be of interest because nanosized filler provides a necessary increase in the properties of the epoxy resin for lower weight fractions, as compared to micron-sized fillers because the surface area of the nanosized filler is much higher.  
Polyhedral oligomeric silsesquioxane (POSS) is a hybrid nanomaterial made up of the organic-inorganic monomer silsesquioxane (RSiO\({}_{1.5}\)). The POSS molecule has a size of 1-3 nm and possesses an inorganic rigid cage-type structure containing silicon and oxygen, and selectable organic groups (R) attached to the silicon atoms. Various investigations have been done on the characterization of POSS-reinforced nanocomposites. These have included the study of the formation of nanoscale structures, the synthesis of novel POSS molecules with different functionalities, and the improvement of physical properties such as thermal degradation and glass transition temperature [1; 2; 3; 4; 5; 6; 7; 8; 9]. It is generally believed that the quality of POSS dispersion in the epoxy resin is directly correlated to its efficiency in the improvement of mechanical, electrical, and thermal properties. Prior research on the fracture toughness behavior of POSS-epoxy composites have shown that based on theorganic functionality, POSS-POSS compliant domains can form at higher loadings [10,11]. These formations of soft domains lead to a change in the failure response, making the use of linear elastic fracture mechanics invalid. Therefore, the present study focuses on the use of the \(J\)-integral concept to quantify the initiation toughness and characterize the crack growth resistance curve.
# Control Strategies for Gait Tele-Rehabilitation System Based on Parallel Robotics
## 4 Discussion
Further assessment should be done with an RU located in similar geographical longitude as it might reveal additional issues regarding package loss and delay due to a higher concentration of network users. Moreover, we understand future works should be conducted to investigate the stability of such tele-rehabilitation system.
## 3 Parameters Studies and Discussions
### Three-Level Unreplicated Saturated Factorial Design Method  
[PERSON] et al. [31; 32] presented a three-level unreplicated saturated factorial design method, in which the statistical model can be expressed as:  
\[y=u+\alpha_{i}+\beta_{j}+\alpha\beta_{ij}+\epsilon_{ij},i,j=1,2,3 \tag{3}\]  
Figure 8: Multi-body model of vehicle.  
Figure 9: Elastic track foundation.  
where, \(y=(y_{1},y_{2},\ldots,y_{n})^{T}\) is the observation vector, \(\mu\) is the overall average, \(\alpha_{i}\) is the main influence of the first factor, \(\beta_{j}\) is the main influence of the second factor, \(\alpha_{i}\) and \(\beta_{j}\) shall satisfy the following constraint condition:  
\[\left\{\begin{array}{l}\sum_{i=1}^{3}\alpha_{i}=0\\ \sum_{j=1}^{3}\beta_{j}=0\end{array}\right. \tag{4}\]  
\(\alpha\beta_{ij}\) is the mutual influence when the first factor is \(i\) level and the second factor is \(j\) level, the satisfying the following constraint condition:  
\[\sum_{i=1}^{3}\alpha\beta_{ij}=0,j=1,2,3\text{ and }\sum_{j=1}^{3}\alpha\beta_{ ij}=0,i=1,2,3 \tag{5}\]  
\(\epsilon_{ij}\) is the experimental error, \(\epsilon_{ij}\) is the independent normal distribution variable, and its mathematical expectation of normal distribution is 0, the variance is \(\sigma^{2}\), which can be expressed by Equation (6).  
\[\epsilon_{ij}=N(0,\sigma^{2}) \tag{6}\]  
Then the most active influence factors can be selected according to all observation values and the assumed conditions as shown in Equation (7).  
\[\left\{\begin{array}{l}H_{0}:\alpha_{i},\beta_{j},\alpha\beta_{ij},i,j=1,2,3 \qquad\text{ all be zero}\\ H_{1}:\alpha_{i},\beta_{j},\alpha\beta_{ij},i,j=1,2,3\quad\text{not all be zero}\end{array}\right. \tag{7}\]  
If \(H_{0}\) is false, it is necessary to confirm which of \(\alpha_{i}\), \(\beta_{j}\) or \(\alpha\beta_{ij}\) is a non-zero value. When the deviation square and \(SS_{i}\) of the corresponding factors are considered in this method, the following can be adopted instead of the test assumptions of (7):  
\[\left\{\begin{array}{l}H_{0}:\frac{ EMS_{1}}{\sigma^{2}}=\frac{ EMS_{2}}{\sigma^{2}}=\ldots=\frac{ EMS_{m}}{\sigma^{2}}=1\\ H_{1}:\frac{ EMS_{2}}{\sigma^{2}},k=1,\ldots,m\text{existing results greater than 1}\end{array}\right. \tag{8}\]  
where, \(m\) is the total number of times the experiment has been conducted, \(EMS_{i}\) is the mathematical expectation of the mean square error. The equation for the mean square error \(MS_{i}\) is shown below [33; 34]:  
\[MS_{i}=SS_{i}/2 \tag{9}\]  
In the three-level unreplicated saturated factorial design method, three statistical methods, i.e., \(H/P/B\) statistical values, are adopted to select the significant influence factors.
## 3 Results and discussion
### Field Test  
#### 3.2.1 Meteorological and Flight Parameter Data  
The light airborne Beidou RTK differential system can accurately record the time of each flight and the flight parameter data of the helicopter, and the micro weather station recorded the meteorological data every 2 s. Corresponding to the data recorded by the Beidou system and the data of the micro weather station, the weather data and flight parameters of each test are shown in Table 5. During the test, the wind velocity, temperature, and humidity had been relatively stable. The wind velocity was maintained in the breeze range of 0.5-1.2 m/s, the average temperature was 17.2\({}^{\circ}\)C, and the average relative humidity was 72.3%. At the same time, the parameters of each flight operation were also normal, and the average flight height was 11.54 m, with the variation range of 1.24 m. The average flight velocity was maintained in the range of 16.36-17.58 m/s, and the real-time flight speed of the same flight over three droplet collecting belts was relatively close. The spray volume application rates were 12.83 L/ha (1\(\#\)), 18.49 L/ha (2\(\#\)), and 28.58 L/ha (3\(\#\)), respectively. In addition, the effective spraying distance of each sorting was more than 130 m, which met the test design requirements.
# Adaptive Clutter Intelligent Suppression Method Based on Deep Reinforcement Learning
## 4 Adaptive Clutter Intelligent Suppression Method Based on DRL  
In this paper, each range cell to be detected is regarded as an independent agent, and the echo signal data of the range cell are regarded as an environmental feature. After each agent obtains the observation data of the environment, its task is to adjust the parameters of the filter according to these data, so as to extract the target signal to the maximum extent and reduce the influence of clutter and noise. By continuously observing the environment, each agent can learn the optimal filter parameter configuration to achieve the optimization of filtering parameters
# Enhancing Botnet Detection in Network Security Using Profile Hidden Markov Models
## 5 Results
\begin{table}
\begin{tabular}{c l l l l l l} \hline \hline
**Scenario No.** & **Botnet Type** & **Training Accuracy** & **Testing Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline
1, 2, 9 & Neris & 0.9871 & 0.9844 & 0.7727 & 0.9883 & 0.8673 \\
3, 4, 10, 11 & Rbot & 0.9811 & 0.9767 & 0.6727 & 0.9610 & 0.7914 \\
5, 13 & Virut & 0.9402 & 0.9259 & 0.5823 & 0.9101 & 0.7102 \\
6 & Donbot & 0.9989 & 0.9970 & 0.7058 & 0.9999 & 0.8275 \\
7 & Sogou & 0.9853 & 0.9600 & 0.6646 & 0.9008 & 0.7649 \\
8 & Murlo & 0.9623 & 0.9498 & 0.6784 & 0.9885 & 0.8046 \\
12 & NSIS.ay & 0.9599 & 0.9480 & 0.7046 & 0.9801 & 0.8198 \\ Normal & None & 0.8122 & 0.7600 & 0.9538 & 0.5449 & 0.6935 \\  & (Normal & & & & & \\  & Traffic) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: HMM results for \(N=8\). \begin{table}
\begin{tabular}{c l l l l l} \hline \hline
**Traffic** & **Training Accuracy** & **Testing Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Botnet & 0.8319 & 0.8223 & 0.9177 & 0.7077 & 0.7991 \\ Normal & 0.8319 & 0.8223 & 0.7626 & 0.9366 & 0.8407 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: HMM results for \(N=2\). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Average Training Accuracy** & **Average Test Accuracy** \\ \hline HMM & 0.9533 & 0.9377 \\ PHMM & 0.9754 & 0.9710 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of average accuracies. Figure 10: Comparative confusion matrices. \begin{table}
\begin{tabular}{c l c c c c c} \hline \hline
**Scenario No.** & **Botnet Type** & **Training Accuracy** & **Testing Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline
1, 2, 9 & Neris & 0.9932 & 0.9911 & 0.8455 & 0.9776 & 0.9068 \\
3, 4, 10, 11 & Rbot & 0.9866 & 0.9715 & 0.6865 & 0.9355 & 0.7919 \\
5, 13 & Virut & 0.9612 & 0.9580 & 0.7406 & 0.9916 & 0.8480 \\
6 & Donbot & 0.9849 & 0.9739 & 0.8835 & 0.9125 & 0.8977 \\
7 & Sogou & 0.9946 & 0.9964 & 0.9846 & 0.9901 & 0.9873 \\
8 & Murlo & 0.9813 & 0.9933 & 0.8958 & 0.9247 & 0.9100 \\
12 & NSIS\_ay & 0.9970 & 0.9929 & 0.9074 & 0.9245 & 0.9158 \\ Normal & None (Normal & 0.9047 & 0.8915 & 0.9535 & 0.7906 & 0.8644 \\ Traffic) & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: PHMM results. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Average F1 score** \\ \hline HMM & 0.7849 \\ PHMM & 0.8902 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of Average F1 Scores. The PHMM model performed better than the HMM model, considering the different evaluation metrics. This might be because the PHMM could register subtle details in a sequence alignment, such as position-specific details and alignment characteristics. HMMs do not use positional details in observation sequences. However, the execution of HMMs is faster than that of PHMMs.
## 5 Applications and Discussions
### Waste Heat Utilizing System Modeling
#### 5.3.2 Simulation Tests  
Consider the whole system, the evaporating pressure and the superheating temperature are the main variables that should be regulated to improve the efficiency of the system and keep safety of the components. After running, we find that the evaporating pressure is mainly adjusted by the expander speed and the superheating by the fluid pump speed, and the channel of evaporating pressure is studied here. The zero mean write noise is adopted as the identification signal, and 1000 data samples are chosen in 1 s sampling interval when the pump speed runs from 500 rad/min to 550 rad/min. The comparison curve of the ABFT-SMM outputs with the actual system outputs are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Molecular Formula** & **CHCl2 CF3** \\ \hline Molecular weight & 152.931 g/mol \\ Critical pressure & 3.662 MPa \\ Critical temperature & 456.681 K \\ Critical density & 550.004 kg/m\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Critical properties of R123.  
Figure 4: Conceptual diagram of the waste heat utilizing system.
## 1 Introduction
Other studies also confirmed the changes of rock properties due to freeze-thaw weathering; therefore, further research is necessary to examine long-term rock durability in high latitude regions for effective estimation of maintenance and repair time against freeze-thaw cycles in nature [2; 16; 17]. The researchers emphasized that experience of freeze-thaw cycles of rocks is one of the most significant physical weathering factors. Therefore, changes of durability and mechanical properties of the rocks were examined with time. Rock weathering from freeze-thaw cycles also induced many engineering problems in roads, railways, energy pile lines, building constructions in cold regions [17; 18]. For these reasons, an accurate assessment of durability against rock freeze-thaw weathering is important [4]. Evaluation of reasonable freeze-thaw rock weathering properties are prerequisite for maintain important functions of cultural heritages; and it will bring us many benefits in terms of maintenance of repair of these structures [19]. Especially, protection of cultural heritage sites from freeze-thaw rock weathering is very important to prevent further deterioration of the heritage structures [16]. Most of the heritage structures are made of rocks, and understanding of material properties' changes with time is essential to protect these heritage structures [20]. [PERSON] [21] claimed that main factors influencing freeze-thaw rock weathering are freezing strength, velocity, maintained freeze time, and repetitive actions of freeze and thaw. In this study, we aimed to investigate the behaviour of rocks collected from Ny-Alesund, a high latitude region, with changes in rock and air temperatures simulating the Ny-Alesund region in a laboratory. We collected relatively fresh rock specimens exposed to the surface, and examined changes in their physical and mechanical rock properties of uniaxial compressive strength (UCS), shore hardness, absorption, and P-wave velocity with increasing freeze-thaw cycle.
# Adaptive Vector Quantization for Lossy Compression of Image Sequences +
## 4 Experimental Results
### Results Analysis (Dataset 2)  
Tables 5 and 6 summarize the achieved average C.R. when AVQ (second column) and AVQ\({}_{\text{IS}}\) (third column) are used, for each one of the used image sequences (first column). From these tables, it is possible to observe that the compression performances of the AVQ\({}_{\text{IS}}\) algorithm are better with respect to the AVQ algorithm in all of the tested sequences.  
Figures 13 and 14 show the trends of the PSNR metric for the AVQ algorithm (green line) and the AVQ\({}_{\text{IS}}\) algorithm (magenta line) when dictionaries of 4096 entries and of 8192 entries are used, respectively. As is observable from these figures and from Tables 7 and 8, which summarize the achieved average PSNRs, the trend obtained by the AVQ\({}_{\text{IS}}\) algorithm is generally slightly worse, except for a few cases (e.g., the 14-th frame of the sequence denoted as \"Dune\").
# A Multi-Service Adaptive Semi-Persistent LTE Uplink Scheduler for Low Power M2M Devices
## 7 Conclusions  
In this paper, we presented a new scheduling algorithm for supporting M2M traffic with different arrival patterns and delay tolerances in the LTE networks. The concept of semi-persistent scheduling was utilized to alleviate the heavy signaling load of dynamic scheduling that is one of the major concerns for small payload sizes of mass M2M device-initiated transmissions. Moreover, we introduced adaptation of the semi-persistent allocations based on buffer information to suit the needs of variable burst sizes and modulated the allocation periods with a control parameter and showed their impacts on the QoS. The observations can be utilized to customize the algorithm thus ensuring higher capacity  
Figure 21: Comparison of channel utilizations with DRX power saving.  
Figure 20: Average UE power consumption in mW per TTI.  
and efficiency for target-specific M2M traffic classes. We also designed a pre-emptive feature to prioritize highly delay sensitive M2M traffic by the SPS algorithm and divert more delay tolerant traffic to dynamic scheduling.  
With the adaptive SPS, the M2M UEs do not require to seek or process PDCCH grants before every uplink transmissions thus they can reduce power consumption which is an important requirement for cost-effective M2M communication. To realize this goal, we also coupled the adaptive SPS algorithm with DRX functionality for M2M traffic. The joint algorithm reduces UE power consumption by uninterrupted sleep and the sleep periods can be further increased by modifying a control parameter and using prediction-based larger allocations. These parameters can be further investigated to provide optimal solutions for diverse M2M scenarios. The results have shown significant reduction in the UE power consumption values as well as downlink control signaling with satisfactory QoS performance for M2M traffic as compared to the LTE dynamic scheduler.  
A further enhancement of our algorithm can be device-to-device allocation forwarding in cases where an M2M UE has already been assigned an adaptive SPS allocation but it does not require that any more. Such UEs can forward their allocations to a nearby UE that can utilize it without the need of new connection establishment and grant procedure. We also plan to support different ranges of desired QoS guarantees by allocating a range of the control parameter and adaptation functions for different M2M applications with diverse constraints and urgency levels. The adaptive SPS algorithm for low power devices has substantial importance for M2M communications and will be explored in depth in our future works.  
Conceptualization, N.A., J.B. and J.Y.K.; Data curation, N.A.; Formal analysis, N.A.; Supervision, J.Y.K.; Writing--original draft, N.A.; Writing--review & editing, J.B. and J.Y.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Study** & **Equipment\({}^{*}\)** & **Activity** & \begin{tabular}{c} **Add/Add (\(\tau\))** \\ **(X-axis)** \\ \end{tabular} & \begin{tabular}{c} **1/F Fact (\(\tau\))** \\ **(Y-axis)** \\ \end{tabular} & \begin{tabular}{c} **Flev/Fact (\(\tau\))** \\ **(Z-axis)** \\ \end{tabular} & \begin{tabular}{c} **AP Plan. (mm)** \\ **(X-axis)** \\ \end{tabular} & \
\begin{tabular}{c} **PD Plan. (mm)** \\ **(X-axis)** \\ \end{tabular} &
\begin{tabular}{c} **LM Plan. (mm)** \\ **(Y-axis)** \\ \end{tabular} \\ \[PERSON] et al. [53]\({}^{\circ}\) & Synchronous RXI & Dynamic & 0.31 (0.72) & \(-\)0.16 (0.61) & 0.37 (0.91) & 0.24 (0.16) & \(-\)0.11 (0.18) & \(-\)0.13 (0.18) \\ [PERSON] et al. [50] & Synchronous RXI & Dynamic & \(-\)0.11 (0.30) [0.54] & 1.01 (0.62) [1.41] & \(-\)0.30 (0.94) [1.75] & \(-\)0.68 (0.74) [1.54] & \(-\)0.16 (0.73) [0.69] & \(-\)0.49 (0.31) [0.69] \\ [PERSON] et al. [60] & Clinical RXI & Static & [0.34] & [0.53] & [0.62] & [0.51] & [0.49] & [0.53] \\ [PERSON] et al. [35] & Synchronous RXI & Dynamic & \(-\)0.03 (0.61) & \(-\)0.45 (0.69) & 0.03 (0.42) & \(-\)0.02 (0.49) & 0.08 (0.60) & 0.27 (0.71) \\ [PERSON] et al. [32]\({}^{\circ}\) & Synchronous RXI & Dynamic & \(-\)0.05 (0.71) & \(-\)0.17 (0.77) & \(-\)0.11 (0.45) & \(-\)0.05 (0.56) & \(-\)0.02 (0.38) & 0.16 (0.68) \\ Present-study & Clinical RXI & Dynamic & \(-\)0.04 (0.26) [0.26] & 0.38 (0.63) [0.73] & 0.02 (0.32) [0.32] & 0.14 (0.20) [0.28] & \(-\)0.17 (0.26) [0.31] & 0.22 (0.34) [0.40] \\ \hline \hline \multicolumn{10}{l}{\({}^{*}\) Data were extracted from _Xue_ 1. \({}^{*}\) Details regarding the definition of the joint axes were not provided in the study. \({}^{*}\) Data were extracted from an isolated extension trial. \({}^{*}\) Precision values were taken as the standard deviation of errors estimated from the Bland-Alman plots. \({}^{*}\) Abbreviation ”RXI” indicates the biplane-X-ray imaging system.} \\ \end{tabular}
\end{table} 
Table 2: Comparison of reported accuracies in the measurement of biofemoral kinematics obtained from biplane model-based tracking in the literature and the present study. The accuracy values are represented in the form of bias (precision) [Rms error].