esa-sceva/satcom-chunk-collection huggingface dataset:
# Random Noise Suppression Method of Micro-Seismic Data Based on CEEMDAN-FE-TFPF
## 5 Conclusions  
This paper can draw the following conclusions:  
1. Through combining the advantages of CEEMDAN, FE, and TFPF, the CEEMDAN-FE-TFPF denoising method proposed in this paper can not only attenuate random noise well, but also preserve the amplitude to the maximum for micro-seismic data.
2. The method proposed in this paper introduces the concept of a discriminant factor. Through comparing the characteristics of different entropies, FE as the discriminant factor for the CEEMDAN-TFPF denoising method is the best choice.
3. The CEEMDAN-FE-TFPF denoising method can avoid the problem of TFPF not being able to achieve a balance between amplitude preservation and random noise suppression due to the fixed window length.  
Writing--original draft preparation, J.C.; writing--review and editing, J.F.; visualization, H.C.; data curation, Y.Y.; investigation, S.J. and D.Y. 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: 2017 YFC1503105).  
Not applicable.  
The authors are very grateful for all constructive comments that greatly help us to improve the original version of the manuscript.  
The authors declare no conflict of interest.
# Design of a Dynamic Secondary Mirror Truss Adjustment Mechanism for Large Aperture Telescopes
## 4 Finite Element Simulation Analysis  
### Statistical Analysis  
The lower base plate of the ring beam is restrained, and a preload force is applied to the blades to prevent flexural deformation due to pressure on the blades. A force of size 2000 N along the blade direction is added to the lower four blades, and a force of size 200 N along the blade direction is added to the upper four blades. These forces counteract the gravity of the secondary mirror assembly, as shown in Figure 6.  
When the telescope is at different zenith angles, a statistical analysis of the model yields the secondary mirror chamber deformation curve in different directions with the zenith angle, as shown in Figure 7.  
Figure 5: Correction unit mechanism.  
Figure 6: Finite element model of the SMAM.  
The deformation in the x-direction increases with the zenith angle, and the deformation in the z-direction decreases first and then increases with the increase of the zenith angle. Since the zenith angle increases, the deformation of the secondary mirror chamber along the z-direction increases due to the different sizes of the preloaded blades.  
Since the secondary mirror truss only introduces translation errors and does not include tilt errors, the effect of the x-directional shift on the imaging of the telescope system is calculated. For the Cassegrain system, the translation of the secondary mirror causes a tangential coma difference in the system, i.e.,  
\[(\delta u)^{\prime\prime}_{c}=\frac{-3m_{2}}{16N^{2}}\bigg{[}\bigg{(}m_{2}^{2} -1\bigg{)}+\frac{1}{1-R_{A}}\bigg{]}\frac{l}{f}\times 206265\]  
where \(m_{2}\) is the secondary mirror magnification, \(N\) is the system focal ratio, \(R_{A}\) is the axial light area obstruction ratio due to the secondary mirror, \(f\) is the system focal length, and \(l\) is the tangential offset of the secondary mirror. The obtained result is \(-0.0058\), which satisfies the requirements for use.
## 3 Results
### Test-Retest Reliability
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Outcome Measure** & **Side** & \multicolumn{2}{c}{**Mean \(\pm\) SD**} & **SEM** & **CV** & \(p\) & **Mean \(\pm\) SD** & **SEM** & **CV** & \(p\) & **ICC** \\ \hline Total & R & 280 \(\pm\) 124 & 34 & 44 & & 316 \(\pm\) 111 & 30 & 35 & & 0.86 \({}^{*}\) & 0.92 \\ length & L & 316 \(\pm\) 120 & 45 & 47 & 0.25 & 368 \(\pm\) 141 & 39 & 38 & 0.06 & 0.89 \({}^{*}\) & 0.89 \\ \hline Length x & R & 166 \(\pm\) 94 & 26 & 56 & & 190 \(\pm\) 71 & 19 & 71 & & 0.82 \({}^{*}\) & 0.88 \\ (ML) & L & 191 \(\pm\) 153 & 42 & 80 & 0.29 & 234 \(\pm\) 113 & 31 & 48 & 0.06 & 0.92 \({}^{*}\) & 0.94 \\ \hline Length y & R & 204 \(\pm\) 89 & 25 & 43 & & 229 \(\pm\) 82 & 23 & 35 & & 0.73 \({}^{*}\) & 0.84 \\ (AP) & L & 226 \(\pm\) 70 & 31 & 48 & 0.31 & 241 \(\pm\) 82 & 23 & 33 & 0.32 & 0.87 \({}^{*}\) & 0.91 \\ \hline Velocity x & R & 7.64 \(\pm\) 6.1 & 1.71 & 80 & & 7.61 \(\pm\) 2.8 & 0.79 & 37 & & 0.82 \({}^{*}\) & 0.88 \\ (ML) & L & 6.64 \(\pm\) 3.7 & 1.04 & 56 & 0.29 & 9.36 \(\pm\) 4.5 & 1.26 & 48 & 0.06 & 0.92 \({}^{*}\) & 0.94 \\ \hline Velocity y & R & 7.91 \(\pm\) 3.5 & 0.99 & 45 & & 8.86 \(\pm\) 3.3 & 0.92 & 37 & & 0.75 \({}^{*}\) & 0.84 \\ (AP) & L & 8.77 \(\pm\) 4.3 & 1.20 & 49 & 0.27 & 9.75 \(\pm\) 3.1 & 0.87 & 32 & 0.15 & 0.83 \({}^{*}\) & 0.86 \\ \hline Area 95\% & R & 2032 \(\pm\) 812 & 335 & 61 & & 2438 \(\pm\) 755 & 295 & 57 & & 0.72 \({}^{*}\) & 0.79 \\  & L & 2428 \(\pm\) 790 & 425 & 73 & 0.25 & 2847 \(\pm\) 843 & 380 & 60 & 0.26 & 0.65 \({}^{*}\) & 0.70 \\ \hline \hline \end{tabular} \({}^{*}\)—\(p\)-value \(<\)0.05; SD—standard deviation; CV—coefficients of variation; SEM—standard error of measurement; ICC—Intraclass Correlation Coefficients; r—Pearson correlation coefficient; R—right; L—left.  
\end{table} 
Table 2: Intra-rater reliability with eyes closed. For measurements with eyes closed, reliability for COP length and velocity in the M-L direction and for total COP length was also good to excellent (ICC = 0.78-0.95). Similar to the open-eyes measurements, COP length and velocity in the A-P direction, as well as COP area, showed only moderate reliability (ICC = 0.58-0.73) (Table 4).
## 3 Results
### Effect of the Irrigation System and Variety on the SPAD Value of Winter Wheat Flag Leaves  
Changes in the SPAD values of winter wheat flag leaves under different treatments are shown in Figure 3. The SPAD values of the W3 treatment of the three varieties gradually increased following the growth process. The SPAD values of the W1 and W2 treatments gradually decreased after reaching a peak at the flowering period. During the filling period, the SPAD values of the three varieties showed W3 \(>\) W2 \(>\) W1 and reached the highest at the filling stage of the W3 treatment, which were 62.23, 61.07, and 58.66, respectively. This result shows that irrigation can delay the aging of wheat flag leaves and enhance the stay-green of leaves, thus prolonging the late functional period of plants. The SPAD values of the three varieties were generally expressed as Hengmai28 \(>\) Hengguan35 \(>\) Hengmai4399 under the same irrigation system. The SPAD values of Hengmai4399, Hengguan35, and Hengmai28 at the filling stage under the W1, W2, and W3 treatments increased by 4.68-37.75%, 2.06-21.27%, and 5.17-15.81%, respectively, compared with the jointing stage. On comparing the mean SPAD values under different irrigation treatments, Hengmai4399 showed W1 \(>\) W2 \(>\) W3, while Hengguan35 and Hengmai28 showed W2 \(>\) W1 \(>\) W3.
# Communication-Traffic-Assisted Mining and Exploitation of Buffer Overflow Vulnerabilities in ADASs
## 2 Preliminaries  
### BOF Vulnerability  
BOF occurs when a computer fills a buffer with more data than its capacity, causing the overflowed data to overwrite legitimate data. The root cause of BOF attacks is an inherent flaw in modern computers that do not explicitly distinguish between data and code and can only rely on forward-compatible patches to mitigate the resulting damage [13].  
There are two types of BOFs: heap overflow and stack overflow [14]. Both result from programs writing data to the heap/stack without correctly controlling the data size, but they differ in data structure and their role in program execution. Due to the stack's particular structure and role, stack overflow can more directly disrupt or control the program flow. In contrast, the heap structure is often more closely related to the system and version, making it more difficult to exploit.  
Unmitigated BOFs commonly result in segmentation faults, causing the operating system to terminate the corresponding program, leading to program crashes and denial of service. By combining with return-oriented programming (ROP), constructing jump instructions, and gaining control of the operating system, arbitrary code/command execution can be achieved. In 1988, [PERSON]'s Morris worm virus exploited a BOF vulnerability, causing more than 6000 network servers worldwide to crash [15].  
As a specialized type of embedded system, it is hard for an ADAS to avoid BOF vulnerabilities. Once these vulnerabilities are detected, the reliability and security of ADAS devices will inevitably be compromised.
## 3 Results and Discussion
### Pigments and Astaxanthin Production  
The process of photosynthesis preserves energy input and provides supplies of carbon for life. Chlorophylls a and b as well as carotenoids are the main pigments used in photosynthetic processes. Carotenoids are often found in environments with high levels of stress [14]. Table 3 displays the pigment amounts (chlorophyll a and b, total carotenoids, and astaxanthin) produced by _C. zofingiensis_ grown in various concentrations of whey wastewater diluted in BBM medium. The amounts of chlorophyll a and b obtained in BBM medium (control) were higher than those obtained in whey wastewater. According to the data, chlorophyll a was the highest prevalent pigment discovered in _C. zofingiensis_, followed by chlorophyll b and total carotenoids. The reason for this outcome may be attributed to the fact that chlorophyll a serves as the primary pigment in microalgae, while chlorophyll b acts as an auxiliary pigment that absorbs energy and transfers it to chlorophyll a. In this study, _C. zofingiensis_ grown in BBM medium achieved high levels of chlorophyll a and b at about 36.59 mg g\({}^{-1}\)\(\pm\) 0.26 mg g\({}^{-1}\) and 31.85 mg g\({}^{-1}\)\(\pm\) 0.17 mg g\({}^{-1}\), respectively, compared to the results found by [PERSON] et al. [19] (chlorophyll a: 15.69 mg g\({}^{-1}\) and chlorophyll b: 7.31 mg g\({}^{-1}\)). This difference can be attributed to the light intensity, which was 15 times higher in the current research than that used in the study by [PERSON] et al. [19]. As the quantity of whey wastewater rose throughout the cell growth process, the levels of chlorophyll a and b decreased. Notably, 10%, 20%, and 50% whey wastewater displayed 11.49 mg g\({}^{-1}\)\(\pm\) 0.01 mg g\({}^{-1}\), 4.10 mg g\({}^{-1}\)\(\pm\) 0.09 mg g\({}^{-1}\), and 0.47 mg g\({}^{-1}\)\(\pm\) 0.01 mg g\({}^{-1}\) of chlorophyll a and 4.56 mg g\({}^{-1}\)\(\pm\) 0.05 mg g\({}^{-1}\), 1.77 mg g\({}^{-1}\)\(\pm\) 0.13 mg g\({}^{-1}\), and 0.33 mg g\({}^{-1}\)\(\pm\) 0.01 mg g\({}^{-1}\) of chlorophyll b, respectively. High pollutant concentrations and contact time may have been responsible for the decreases in chlorophyll concentrations [23]. Similar findings were obtained by [PERSON] et al. [14] using _C. zofingiensis_ grown in various concentrations of municipal wastewater. Table 3 depicts the increase in total carotenoid levels in _C. zofingiensis_ cultivated in 10% whey wastewater compared to BBM medium (control) and their decrease in 20% and 50% whey wastewater. Additionally, the content of astaxanthin was lower than in the control medium and decreased with increasing whey wastewater concentration. The maximum astaxanthin content using whey wastewater was obtained in the 10% concentration at about 0.71 mg g\({}^{-1}\)\(\pm\) 0.14 mg g\({}^{-1}\). It is important to note that wastewater bioremediation studies with _C. zofingiensis_ have not thoroughly examined astaxanthin accumulation simultaneously. [PERSON] et al. [21] found a high amount of astaxanthin, approximately 5.65 mg g\({}^{-1}\), in _C. zofingiensis_ growing
## 3 The Proposed Range-Division User Relay Selection Scheme
### Determination of ERSR Radius Set  
Corresponding to \(\theta\in\Theta\), the total number of URs is denoted by \(N^{\theta}\). The number of MG users who can successfully receive is \(N^{\theta}_{s}\). The coverage efficiency is  
\[C^{\theta}_{RDUR}=\frac{N^{\theta}_{s}}{N^{\theta}}. \tag{7}\]  
In RDUR, the \(\theta\), which maximize \(C^{\theta}_{RDUR}\), is selected to perform RDUR. The system coverage efficiency given by  
\[C_{RDUR}=\max\limits_{\theta\in\Theta}\{C^{\theta}_{RDUR}|\theta\}. \tag{8}\]  
Figure 3: The flow chart of RDUR.  
```
1: Every MG user achieves location information.
2: Every USU broadcasts location information and a training signaling.
3:\(SUl_{i,k_{i}}\) receives location information the training signaling. The distances from \(SUl_{i,k_{i}}\) to BS and USU \(u\) are measured. They are denoted by \(\gamma_{i,k_{i}}\) and \(\gamma_{k_{i},u}\). Simultaneously, the channel state information of \(\text{USU}\left|h_{k_{i},\mu}\right|^{2}\gamma_{k_{i},\mu}^{-\beta}\) is also obtained by \(SUl_{i,m_{i}}\).
4:\(SUl_{i,k_{i}}\) detects the AA he (she) locates in (e.g., \(A_{n}\)) and estimates the reception SINR of each USU. If USU \(u\) can satisfy \(\gamma_{k_{i},u}\leq r_{n}\) and the reception SINR of \(x_{2}\ SINR_{k_{i},\mu}^{x_{2}}\) is greater than \(\sigma_{0}\), this SU is selected as UR and the USU is a D2D MG member.
5: Every selected UR forwards reception signal in D2 MD.
```  
**Algorithm 1** User relay (UR) selection process.
## 2 Acetin (Glycerol Esters or Acetyl Glycerol)
### Influence of Reaction Parameters on the Acetylation of Glycerol
#### 2.2.2 Temperature
However, the use of lauric acid in glycerol esterification over layered double hydroxide (LDH, Mg-Al-CO\({}_{3}\)) catalyst accommodated the use of high temperatures. At 100 \({}^{\circ}\)C, only 25% of glycerol conversion was achieved in 2 h, but when the temperature was increased to 180 \({}^{\circ}\)C, glycerol conversion increased to 99%. Nevertheless, the selectivity towards monoacetin (>50%) was largely favoured [143]. [PERSON] et al. [89] also investigated glycerol esterification at lower temperatures (65-95 \({}^{\circ}\)C) over the CsPWA catalyst. Results indicate that in 2 h, molar ratio of 1:8, and temperature of 65 \({}^{\circ}\)C, the glycerol conversion was low (65%). However, as temperature increased to 75 \({}^{\circ}\)C, glycerol conversion increased to 92%. While at 85 \({}^{\circ}\)C, the conversion reached 98%, which appears to be the maximum because no further improvement was observed even when the temperature was further increased to 95 \({}^{\circ}\)C. The selectivity of di- and triacetin also improved with increased temperature while the monoacetin decreased substantially. The authors attributed the low performance of the reaction at low temperature to a low-level formation of acylium ion by the acetic acid, and as the temperature increased, the acylium ion formation also improved leading to high performance. A similar finding was reported using the same type of catalyst but with a temperature of 110 \({}^{\circ}\)C as the most desirable [121]. The influence of temperature in glycerol acetylation has been earlier reported in several literature articles [102; 133; 144; 145], and the results obtained were similar to the findings reported above. The findings from various researchers are in line with the Arrhenius equation (Equation (1)), which establishes a relationship between the temperature, activation energy, and rate of reaction. Increased temperature directly increased the number of collisions of the reactant, which is directly proportional to the rate of reaction [60; 146]. The increased temperature promotes rapid glycerol conversion, with a corresponding increase in diacetin and triacetin selectivity at the expense of monoacetin. This observation is exemplified in the pictorial representation in Figure 10:  
\[K=Ae^{-Ea/RT} \tag{1}\]  
where \(K\) is the reaction rate constant, \(A\) is the Arrhenius constant related to the frequency factor, \(Ea\) is the activation energy, \(R\) is the gas constant, and \(T\) is the temperature.
## 1 Introduction
In addition to the discrete data (ground reaction force (GRF), lower extremity joint angles, range of motion (RoM), moments, and power), waveform data (GRF and lower extremity joint angles, moments, and power) were also analyzed during different fatiguing stages. The hypotheses are (i) both discrete and waveform data of lunge biomechanical characteristics change significantly during the fatiguing process, especially post-fatigue; (ii) effects of fatigue vary with different periods of the stance phase; and (iii) the lower limb joints have different fatigue responses.
## 1 Introduction
### Polymer Liners--State-of-the-Art (SoA) in Manufacturing and Exploitation Related Issues  
The literature devoted to polymer liners used for hydrogen storage in type 4 containers is extensive and encompasses a large number of issues. Below, mainly material and technology-related problems influencing liner stability and hydrogen permeability were selected and are indicated:  
* Polymer aging and thermal degradation [5; 9];
* Chemical structure, including molecular weight and molecular weight distribution of polymer chains [5; 8; 9; 10];
* The degree of crystallinity of a polymer, which is affected by its cooling rate from the molten state [9; 10; 11];
* Processing techniques that can induce the orientation and extension of polymer chains affecting various properties [5; 9];
* The types and amounts of plasticizers, fillers, crosslinking agents, and other additives that are often incorporated to modify polymer properties [5; 9; 10; 11];
* Influence of long-term high-pressure hydrogen exposure on the physical properties and mechanical performance of polymers [10; 12; 13];
* Hydrogen permeability of polymers as a result of diffusivity and solubility [5; 8; 14; 15; 16; 17];
* Permeation rates, which are strongly dependent on the glass transition temperature [8; 10; 11; 16; 18];
* Parameters responsible for the liner collapse in hyperbaric type 4 hydrogen storage vessels [14; 19; 20; 21; 22];
* Blistering resulting from hydrogen sorption and the subsequent decompression of a hydrogen container [8; 9; 11; 14; 22; 23];
* Standardization of materials, container manufacturing, and testing technology, including liners [24];
* Limiting hydrogen permeability (e.g., by the use of barrier layers made using solgel technology, e.g., [1; 25; 26; 27]), or the use of constructions limiting the amount of hydrogen accumulated between a liner and composite (reduction of the so-called air gaps, or also the use of ventilation structures inside composites to allow natural hydrogen escape);
* Liner resistance to high temperature, e.g., when a fire occurs near a high-pressure hydrogen storage container [28; 29]. An additional goal is the so-called controlled defect, which allows for heat transfer through a composite shell and leads to liner melting [9; 28]. Then hydrogen accumulated inside the container leaks through pores in the liner, which, in such a case, grow in size, but it combusts immediately on the container surface, thus ensuring safety.
# Extended Smoothing Methods for Sparse Test Data Based on Zero-Padding
## 3 Calculation Process of Smoothing Theory
### Idft  
A new two-dimensional discrete signal is obtained after the newly generated domain angular spectrum in the wavenumber domain is translated to the spatial domain via two-dimensional IDFT. The regenerated discrete signal is the result of smoothing. The detailed steps are as follows.  
The angular spectrum \(P\) (\(k_{x}\), \(k_{y}\)) is performed by IDFT to obtain its spatial distribution:  
\[p(x,y)=\frac{1}{4\pi^{2}}\int\limits_{-k_{\max,x}}^{+k_{\max,x}}\int\limits_{- k_{\max,y}}^{+k_{\max,y}}P\big{(}k_{x},k_{y}\big{)}e^{i(k_{x}x+k_{y}y)}W\big{(}k_{x},k_{y}\big{)}\mathrm{d}k_{x}\mathrm{d}k_{y} \tag{7}\]  
in which \(W\) (\(k_{x}\), \(k_{y}\)) depict the \(k\)-space filter.  
According to Equation (7), the generated angular spectrum \(P^{\prime}\) (\(k_{x}\), \(k_{y}\)) is transformed from the wavenumber domain to the spatial domain to obtain new discrete data \(p^{\prime}\) (\(x\), \(y\)) by using the two-dimensional IDFT. This discrete signal \(p^{\prime}\) (\(x\), \(y\)) is the smoothing result.  
\[\begin{array}{l}p^{\prime}({m_{x}}^{\prime}\Delta x^{\prime},{m_{y}}^{ \prime}\Delta y^{\prime})=\frac{1}{2 NxNy^{\prime}\Delta x^{\prime}\Delta y^{ \prime}}\\ \sum\limits_{n_{x}^{\prime}=0}^{Nx^{\prime}-1}\sum\limits_{n_{y}^{\prime}=-1}^ {Ny^{\prime}-1}P^{\prime}({n_{x}}^{\prime}\Delta k_{x},{n_{y}}^{\prime}\Delta k _{y})W\big{(}{n_{x}}^{\prime}\Delta k_{x},{n_{y}}^{\prime}\Delta k_{y}\big{)} e^{\beta\pi\big{(}\frac{n_{x}^{\prime}m_{y}^{\prime}}{Nx^{\prime}}+\frac{n_{y}^{ \prime}m_{y}^{\prime}}{Ny^{\prime}}\big{)}}\end{array} \tag{8}\]  
in which \(W\) (\(k_{x}\), \(k_{y}\)) is the \(k\)-space filter to reduce the influence of high-frequency wavenumber error on the reconstruction result. The filter which was proposed by [PERSON] and [PERSON] [25] is expressible as follows:  
\[W\big{(}k_{x},k_{y}\big{)}=\left\{\begin{matrix}1-\frac{1}{2}e^{-(1-|k_{p}|/k_ {c})/\alpha}&|k_{p}|\leq k_{c}\\ \frac{1}{2}e^{(1-|k_{p}|/k_{c})/\alpha}&|k_{p}|>k_{c}\end{matrix}\right. \tag{9}\]  
in which \(k_{p}\equiv\sqrt{k_{x}^{2}+k_{y}^{2}}\), and \(\alpha\) is the steepness coefficient of the \(k\)-space filter. The empirical formula of the cut-off frequency is \(k_{c}=0.6\pi/a\), in which \(a\) is the sampling point interval on the measurement surface.