esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In the present study, a new thermal design strategy was proposed for an 80 kg class lightweight active X-band small SAR satellite, S-STEP, for high-resolution earth observation. Considering the bus-payload integrated flat panel-type unique satellite configuration, the proposed thermal design of S-STEP mainly relied on the optimization of environmental heat flux on the satellite by passive thermal control approaches. A lightweight flexible graphite sheet with high thermal conductivity was used for heat dissipation of some high-power components via radiators. A thermal design of the key payload component of TRM assembly with dedicated radiators was proposed. To validate the effectiveness of the proposed thermal design, an on-orbit thermal analysis of S-STEP was performed in various cases of SAR-looking modes. The analysis results showed that the proposed thermal design ensured the required SAR imaging duration of 60 s regardless of left- and right-looking modes of SAR antenna while keeping all onboard components within the allowable temperature range. The spatial temperature differences in the TRM assembly and satellite primary structure of honeycomb panels were also evaluated through the analysis. The results verified that the proposed thermal design contributed to ensuring the thermal stability of the SAR payload. In the future, the effectiveness of thermal design using graphite sheets for high-power components will be experimentally verified through the TV test. In addition, the entire thermal design of S-STEP will be verified through structural-thermal model tests. The new thermal design strategy proposed in this study  
Figure 18: Temperature profiles obtained from thermocouples (T/C) of dummy TRM assembly.  
would be useful and applicable to the flat panel-type small satellite for SAR observation, communication, and internet service missions.  
Conceptualization, H.-U.O.; methodology, H.-U.O. and B.-G.C.; validation, B.-G.C., K.-R.K.; formal analysis, B.-G.C.; investigation, B.-G.C., K.-R.K., H.-U.O.; resources, S.-C.S., H.-U.O.; data curation, B.-G.C., T.-Y.P.; writing--original draft preparation, T.-Y.P.; writing--review and editing, T.-Y.P.; visualization, T.-Y.P., B.-G.C.; supervision, H.K., S.-C.S., H.-U.O.; project administration, H.-U.O.; funding acquisition, H.K., H.-U.O. All authors have read and agreed to the published version of the manuscript. This research was funded by the Agency for Defense Development (ADD).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request. This research was supported by the Agency for Defense Development (ADD). The authors declare that they have no conflict of interest.
# Methodology Proposal, Virtual Simulations and 3D Printed Prototype of a Car Steering Wheel
## 2 Methodology of Vehicle Steering Wheel Simulations
### Preparatory Stage
#### 2.1.2 Defining Material Characteristics
These are based on the principle of the Finite Element Method (FEM), where the investigated area of complex geometry with unknown structural behavior is divided into a finite number of simple geometric elements with known structural behavior, known as a mesh. At the boundaries of the elements, the displacements are aligned with the adjacent elements, and a matrix equation is formed. The equation is numerically solved, and the results are visible graphically and show the calculated stresses, deformations and displacements that occur in the model when forces are applied. 2.2.1. Choosing a Software Application  
Due to the fact that the 3D virtual model was created in the 3D CAD application Creo Parametric, we carried out the simulations in Creo Simulate. Both Creo Parametric and Creo Simulate work on a single platform, and Creo Simulate is embedded as a module in the Creo Parametric environment. A great advantage is that if the analyses point to the fact that it will be necessary to change the geometry to achieve the desired mechanical properties, it is possible to switch to modeling mode, modify the geometry, and switch back to the simulated environment again in a short time, where repeated analyses will be performed. Other best-known programs for creating simulations include Abaqus, Adina, ANSYS, Marc, Nastran, HyperWorks, Creo-Simulate, etc.
## 3 Model Validation for Sand-Fines Mixtures  
It can be inferred from the model presented above [Equations (1) through (6)] that estimating the binary mixture porosity as function of \(f_{f}\) requires the determination of \(n_{l}\), \(n_{f}\), \(R\), \(d_{l}\), \(d_{cri}\) and \(p\). The first four parameters (\(n_{l}\), \(n_{f}\), \(R\) and \(d_{l}\)) can be found using measurements of porosity and particle size analysis for the pure fine and large particles samples, while \(d_{cri}\) and \(p\) need to be determined for a given material. For binary mixtures of glass beads characterized by very small \(R\) (\(<\)0.1), \(p\) has an average value of 0.2, while \(d_{cri}\) was found to be 0.150 mm [41]. In the following, we aim to find the appropriate \(d_{cri}\) and \(p\) for binary sand-fines mixtures. Since the original model of [PERSON] et al. [41] was formulated for dry binary mixtures of glass beads, we focus here on dry sand-fines mixtures for model calibration and validation. It is however important to note that the presence of water can complicate the behavior of fines such as clay and impact the void ratio of sand-clay mixtures [46, 47, 48]. Clay swelling (i.e., expansive behavior of clays) is an example of the complication caused by the presence of water [46, 49, 50].
## 3 Results
### Accuracy Evaluation of Original Model
Furthermore, the ECM-LUE model with the multiplicative method outperformed the EC-LUE model (Figure 6f,h,l, and Figure A1), which was the same pattern as that in the results on sunny days. Among the seven models, the ECM-LUE model had the best simulation effect on the measured GPP, and the R\({}^{2}\), IA, and RMSE were 0.4906, 0.8292, 1.1689 g C m\({}^{-2}\) day\({}^{-1}\), respectively.
## 2 Mathematical Model
### Grid Dependence Study  
Grid density exerts a great impact on the numerical simulation results. When the grid number is relatively small, the dispersion error will be very large; when the grid number is large, not only will the rounding error increase, but the calculation quantity will increase as well. Figure 3 shows that the velocity profile in the symmetry plane is 30 m away from the outlet under different grid orders of \(0.08\times 10^{6}\), \(0.47\times 10^{6}\), \(1.05\times 10^{6}\), and \(5.78\times 10^{6}\). It can be seen that the number of grids has a great effect on the velocity profile. When the number of grids reaches \(1.05\times 10^{6}\), the velocity profile will no longer change with the increase of grid number.
###### Abstract
[15] improved the performance of TRM in damage detection through alleviating the influence of the time reversal operator. [PERSON] et al. [21] and [PERSON] et al. [22] proposed some different damage indices to improve the sensitivity of the TRM to damage. These studies are all aimed at improving the performance of TRP. However, it is still unavoidable that a large number of experiments are required to optimize excitation parameters, and to determine the time intervals in which the waveform of the reconstructed signal is compared with that of the original input signal. This study comprehensively discussed the effects of the two time intervals on the performance of Lamb wave TRP in composite laminates. Then, an efficient time reversal method (ETRM) is established, which could obtain multiple reconstructed signals related to different excitations through only one actuating-sensing step without determining time intervals. Firstly, a broadband excitation signal is used to acquire Lamb wave responses for a wide range of frequencies. Secondly, the short-time Fourier transform (STFT) combined with a time-varying filter is employed to extract the desired mode components in the time-frequency domain. Then the time-domain waveform of the extracted components is obtained through inverse STFT transform. After that, frequency domain deconvolution is applied to calculate multiple narrow-band reconstructed signals. The rest of this paper is organized as follows. In Section 2, the theory of the TRM for damage detection is introduced. Then the detailed steps of the proposed efficient time reversal method are given in Section 3. In Section 4, the effectiveness of the ETRM is demonstrated by experiments. The application of the ETRM for damage location in composite laminates is illustrated in Section 5. Finally, conclusions are drawn in Section 6.
# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 3 Methodology
### Five-Step Interactive Quality Control Based on Knowledge
#### 3.7.5 Step Five: Interactive Quality Control with Statistics Reports  
Some recognized statistical information, such as forest coverage, grassland area and cropland area, was recorded in government statistics reports and relevant documents, which were helpful for verification of the quality of the CLC-SW2010 product. Unfortunately, the differences of the definition for the same land cover classes have existed between the statistics report and the CLC-SW2010 product. Only when the differences of the area of a certain land cover class exceeded a predefined threshold value (10% of its area), should further revision be considered.
# Study on Spray Characteristics and Breakup Mechanism of an SCR Injector
## 2 Numerical Models and Methods
### Model Description and Methods of Primary Breakup
#### 3.1.4 Droplet Diameter and Velocity of the Primary Breakup  
The area of the first breakup droplet was measured by Image-J software after gray-scale processing of the primary breakup result in Figure 4f. The droplets are approximated to circles with the same area, and the range of droplet diameter is 6-187 \(\upmu\)m. In addition, the initial velocities of the primary droplets are also counted, and the initial velocities range from 6.42 to 30.68 m/s, with an average velocity of 23.52 m/s.
## 4 Countermeasures against Thermal Covert Channel Attacks
### Selective Noise Countermeasure  
The methodology is illustrated in Figure 6 and has the following main steps:  
* Step 1 (Record temperature): This step consists of monitoring and recording the temperature. The extra noise is added when the bit is \"1\". The current temperature is then recorded by the temperature sensor and used for the next step;
* Step 2 (Temperature monitoring of processors and adding selective noise): In the last step, the next bit's temperature is compared with the recorded temperature (TR) from the previous step. If the temperature of the current bit is less than TR or starting to decrease, the selective noise is added to keep the temperature at the same level, otherwise it is moved to the next bit. This progress continues until the end of the transmission process.  
Here, we adopt different countermeasure strategies that take into account both security requirements and power consumption. Specifically, adding selective noise effectively can change the pattern of the temperature signal. To compare with our previous method, the BER of the receiver does not show significant changes (94%) but the power consumption will not be extremely high. As Figure 7 shows, by adding selective noise, the thermal  
Figure 6: The process of adding selective noise.  
signal shows different behaviour. The temperature fluctuation is decreased, and we could keep it at the same level, which results in avoiding system overheating and low power consumption. When the transmission process is complete, at the receiver side, the receiver reads the temperature sensor and records the thermal signal, and then decodes the signal to recover the original data. Next, if the received packet is specified to be uncompromised and includes a special preamble field during the transmission, the receiver extracts the data fields from the packet, otherwise, the receiver drops the binary bitstreams and waits for retransmission [5].
## 3 MMPP/D/1 Simulation for Rare Event
### The Optimal IS Distribution  
In IS simulation, the optimal distribution determination is most important. If we choose the good optimal distribution, the variance of the estimator will be smaller and the estimate result will be more exact. There are some approaches to determine the optimal simulation distribution in [1; 3]. However, these methods are heuristic, exhaustive search in parameter space to determine the optimal distribution; therefore, they take much computation time. In this paper, based on the research in [4], we determine the optimal simulation distribution for IS simulation. This approach is theoretical; hence, it takes less computation time.  
The state of the MMPP and the queue length are denoted by \(S_{t}\) and \(Q_{t}\), where \(t\) is the time slot, \(t=0,1,2,\dots\). Denote \(X_{t}=(Q_{t},S_{t})\); then \(X_{t}\) satisfies the formula  
\[\begin{array}{l}X_{0}=X^{0}\ (\text{initial state})\\ X_{t+1}=X_{t}+\Delta(X_{t},W)\end{array}\]  
where \(W\) is an independent and identically distributed random vector; therefore, a random process \(X_{t}\) forms a Markov chain [5]. In this paper, for simplicity, \(X_{t}\) is represented by \(X\) at an arbitrary \(t\) time slot and \(M_{X}(\theta)\) is the moment-generating function of \(\Delta(X,W)\). \(M_{X}(\theta)\) is defined by  
\[M_{X}(\theta)=E[e^{\theta\Delta(X,W)}] \tag{5}\]  
where \(E\) is the expectation with respect to \(\Delta(X,W)\) and the parameter \(\theta\).  
\(P(X_{t},X_{t+1})\) is defined as the probability of moving from state \(X_{t}\) to state \(X_{t+1}\), according to the original distribution. The Markov chain is governed by the state transition probability, determined by the parameter \(\theta\), denoting the probability of transitioning between states.  
\[P_{\theta}(X_{t},X_{t+1})=\frac{e^{\theta\Delta(X,W)}}{M_{X}(\theta)}P(X_{t}, X_{t+1}) \tag{6}\]  
As stated in reference [9], the value \(\theta=\theta^{*}\) that solves the equation \(M_{X}(\theta)=1\) is considered the optimal distribution. The simulation distribution that is most advantageous, abbreviated as \(P^{*}\equiv P_{\theta^{*}}\), is given by  
\[P^{*}(X_{t},X_{t+1})=e^{\theta^{*}\Delta(X,W)}P(X_{t},X_{t+1}) \tag{7}\]  
In [4], the authors used the large deviation theory to find the optimal IS distribution. In the next section, we will determine the optimal distribution for our IS simulation based on [4].
# A Review on Deep-Learning-Based Cyberbullying Detection
## 4 Data Representation Techniques
### Text Data Representation
#### 4.1.4 GloVe  
GloVe [34] is an unsupervised ML technique that stands for Global Vector for Word Representation. Stanford created GloVe to construct word embedding by aggregating a corpus's global word to word co-occurrence matrix. The outcome of embedding in vector space reveals intriguing linear substructures of the word.
###### Abstract  
The aim of the study was the evaluation and mathematical modeling of the effect of active modified atmosphere packaging (MAP), by the incorporation of CO\({}_{2}\) emitters in the package, on the microbial stability and shelf life of gutted sea bass during refrigerated storage. Gutted sea bass samples were packaged in modified atmosphere (50% CO\({}_{2}\)-40% N\({}_{2}\)-10% O\({}_{2}\)) with and without CO\({}_{2}\) emitters (ACT-MAP, MAP) (gas/product volume ratio 3:1) and stored at isothermal conditions: 0 \({}^{\circ}\)C, 5 \({}^{\circ}\)C, and 10 \({}^{\circ}\)C. The gas concentration in the package headspace (%CO\({}_{2}\), %O\({}_{2}\)) and microbial growth (total viable count, TVC, _Pseudomonas_ spp., _Enterobacteriaceae_ spp., lactic acid bacteria) were monitored during storage. The microbial growth was modeled using the Baranyi growth model, and the kinetic parameters (microbial growth rate, lag phase) were estimated at the tested temperature and packaging conditions. The results showed that the ACT-MAP samples presented significantly lower microbial growth compared to the MAP samples. The growth rate of the total viable count at 0 \({}^{\circ}\)C was 0.175 and 0.138 d\({}^{-1}\) for the MAP and ACT-MAP sea bass, respectively (\(p<0.05\)). The shelf life of the MAP sea bass at 0-10 \({}^{\circ}\)C (based on a final TVC value: 7 log CFU g\({}^{-1}\)) was extended 4-7 days with the addition of a CO\({}_{2}\) emitter in the package. The CO\({}_{2}\) concentration in the ACT-MAP samples was stabilized at approximately 60%, while the CO\({}_{2}\) in the MAP samples was approximately 40% at the end of the shelf life.  
Keywords:modified atmosphere packaging; active packaging; CO\({}_{2}\) emitters; sea bass; shelf life modeling +
Footnote †: journal: Applied Soft. **2019**, 9, 5019; doi:10.3390/appr9235019
## 3 MSA-LMS Adaptive Filter
### MSA-LMS Adaptive Filter Implementation and Analysis
#### 3.2.3 Stage 2  
The error signal \(e(k)_{s1}\) is an unknown signal and cannot be measured. The frequency \(f_{2}\) of the error signal \(e(k)_{s1}\) is estimated from the number of times \(C\) the signal passes through the \(x\)-axis as sensed by the zero-crossing detector and indicated by the counter. Using the output of the sine wave generator at the estimated \(f_{2}\), the function \(u_{f2}\) between 2 and 2.9999 s is expressed as follows:  
\[u_{f2}=0.5\cdot\sin(2\cdot\pi\cdot f_{2}\cdot t_{2}) \tag{11}\]  
To avoid the amplitude of the input signal from increasing with the update of the filter coefficients, the signal is multiplied by 0.5. Thus, in Stage 2, the following signal appears additionally:  
\[u_{f1}=0.5\cdot\sin(2\cdot\pi\cdot f_{1}\cdot t_{2}) \tag{12}\]  
Therefore, the filter output of Stage 2 appears as follows,  
\[u_{f1}=0.5\cdot\sin(2\cdot\pi\cdot f_{1}\cdot t_{2}) \tag{13}\]  
The output signal \(y_{2}\) is expressed as  
\[y_{2}=u_{f2}\cdot w_{2}^{T}={u_{f2}}^{T}\cdot w_{2} \tag{14}\]  
Figure 3: Principle of frequency detector.  
Since there are two input signals in Stage 2, the total output signal of the filter \(y(k)_{2}\) appears as follows:  
\[\begin{split} y(k)_{2}&=y_{1}+y_{2}=u_{f1}\\ &=0.5\sin(2\cdot\pi\cdot f_{1}\cdot t_{2})\cdot w_{1}+0.5\sin(2 \cdot\pi\cdot f_{2}\cdot t_{2})\cdot w_{2}\end{split} \tag{15}\]  
In Stage 1, the error signal \(e(k)_{s1}\) is the \(d(k)\) of Stage 2. The filter output \(y(k)_{2}\) and error signal are expressed as follows:  
\[\begin{split} e(k)_{s2}&=e(k)_{s1}-y(k)_{2}\\ &=e(k)_{s1}-u_{f1}\cdot w_{1}^{T}-u_{f2}\cdot w_{2}^{T}\\ &=e(k)_{s1}-u_{f1}{}^{T}\cdot w_{1}-u_{f2}{}^{T}\cdot w_{2}\end{split} \tag{16}\]  
The \(e(k-1)_{s2}\) signal is applied one step before the error signal \(e(k)_{s2}\) to the MSA-LMS algorithm and is updated repeatedly.
# Fault Parameter Estimation Using Adaptive Fuzzy Fading Kalman Filter
## 4 Numerical Simulations
### Fault Parameter Estimation for Nonlinear System  
To verify the feasibility of AFFKF for a nonlinear system, the well-known van der pol model [20] is considered. The van der pol model is classically defined by a dynamic system of the following form in unforced case:  
\[\ddot{x}(t)-\mu\bigg{[}1-\frac{x^{2}(t)}{x_{0}^{2}}\bigg{]}\ddot{x}(t)+\omega^{2 }x(t)=0 \tag{23}\]  
where \(\mu\) is the friction coefficient, \(x_{0}\) is the reference amplitude for \(x\), and \(w\) is the natural frequency. Equation (23) will be restricted to the case when \(x_{0}=1\), as followings:  
\[\ddot{x}(t)-\mu\Big{[}1-x^{2}(t)\Big{]}\dot{x}(t)+\omega^{2}x(t)=0,\qquad\mu, \omega>0. \tag{24}\]  
The numerical simulations for parameter estimations are performed using Equation (24) with the following values:  
\[\mu_{0}=2,\omega_{0}=3,\quad\mu_{f}=0.2,\omega_{f}=2. \tag{25}\]  
where, \(\mu_{0}\) and \(w_{0}\) denote the values of normal condition, and \(\mu_{f}\) and \(\omega_{f}\) denote the values of fault condition. An abrupt fault is injected at t = 50 s. For the noisy case, the process and measurement noise \(w_{k}\) and \(v_{k}\) have the following statistics:  
\[\begin{array}{l}E[w_{k}]=E[v_{k}]=0\\ E[w_{k}w_{j}]=0.05\delta_{k-j}\\ E[v_{k}v_{j}]=0.05\delta_{k-j}\\ \end{array} \tag{26}\]  
The simulation results are shown in Figures 7 and 8 with initial values \(\hat{\theta}_{0}=\left[\begin{array}{cc}\hat{\mu}_{0}&\hat{\omega}_{no}^{2} \end{array}\right]^{T}=\left[\begin{array}{cc}1&7\end{array}\right]^{T}\). The oscillatory behavior of the system is shown in Figure 7, and the results of parameter estimations are shown in Figure 8. The conventional Kalman filter fails to estimate the true value of the fault parameter until the simulation is complete. On the other hand, both AFKF and AFFKF successfully estimate the true value of the fault parameter. However, the proposed AFFK converges much faster than AFKF does.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Performance Index**} & \multicolumn{3}{c}{**Approaches**} \\ \cline{2-4}  & **Standard KF** & **AFKF (M = 2)** & **AFFKF** \\ \hline \(J\)(\(\zeta\)) & 0.0633 & 0.2005 & 0.1875 \\ \(J\)(\(\omega_{n}\)) & 4.5716 & 1.9325 & 1.9125 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of performance index for estimation of incipient fault parameter.  
Figure 7: Estimated x and measurement y in van der pol model.