esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application of the \(EDNN\) in Denoising
### Denoising of Second-Order Damped Free Oscillation Signal via EDNN
#### 4.2.1 Acquisition of Second-Order Damped Free Oscillation Signal
The maximum error of the first-order derivative of the _NRSNN_ signal is 0.49 percent of the maximum amplitude of the first-order derivative of the _SODFO_ signal. Figure 6b is the second-order derivatives and the errors. The second-order derivatives of the _NRSNN_ signal and the _NREDNN_ signal coincide with the second-order derivative of the _SODFO_ signal. The maximum error of the second-order derivative of the _NREDNN_ signal is 0.42 percent of the maximum amplitude of the second-order derivative of the _SODFO_ signal. The maximum error of the second-order derivative of the _NRSNN_ signal is 2.22 percent of the maximum amplitude of the second-order derivative of the _SODFO_ signal. Figure 6c is the third-order derivatives and the errors. The third-order derivative of the _NREDNN_ signal coincides with the third-order derivative of the _SODFO_ signal. Additionally, no trend discrepancy occurs in the third-order derivative of the _NREDNN_ signal. However, there is a trend discrepancy occurs on the third-order derivative of the _NRSNN_ signal (Figure 6d). The maximum error of the third-order derivative of the _NREDNN_ signal is 4.02 percent of the maximum amplitude of the third-order derivative of the _SODFO_ signal. The maximum error of the third-order derivative of the _NRSNN_ signal is 12.05 percent of the maximum amplitude of the third-order derivative of the _SODFO_ signal. Figure 6e is the fourth-order derivatives and the errors. The fourth-order derivatives of the _NRSNN_ signal and the _NREDNN_ signal coincide with the fourth-order derivative of the _SODFO_ signal. The maximum error of the fourth-order derivative of the _NREDNN_ signal is 39.92 percent of the maximum amplitude of the fourth-order derivative of the _SODFO_ signal. The maximum error of the fourth-order derivative of the _NRSNN_ signal is 114.24 percent of the maximum amplitude of the fourth-order derivative of the _SODFO_ signal. The error of _NREDNN_ signal is less than that of the _NRSNN_ signal (Figure 6f). The standard deviation of the first- through fourth-order derivatives are shown in Table 1. The standard deviation error between the derivatives of the _SODFO_ signal and that of the _NREDNN_ signal is less than 62.5 percent of the standard deviation errors between the derivatives of the _SODFO_ signal and the _NRSNN_ signal. In summary, the noise reduction performance of the _NREDNN_ is better than that of the shallow neural networks. Additionally, no trend discrepancy occurred in the first-through fourth-order derivatives of the _NREDNN_ signal. Those are the advantages of the _EDNN_. However, the _EDNN_ requires more memory to store the derivatives and more computational time to calculate the derivative and the extreme feature error.
## 4 Numerical Results
### One-Way FSI Analysis  
Next, a one-way FSI analysis method was developed using the ANSYS Workbench (ver. 2020R2). Using the System Coupling module, the transient structural and CFX analyses were coupled. This section reviews the effect of the anti-wave plate on the free surface motion in the water tank and its structural consequences. The results of the sloshing and the transient structural analysis are also discussed.  
Figure 13: Structural response comparison of the steel beam frame between the quasi-static (CASE I-3) and transient (CASE II) structural analysis. (**a**) Total deformation; (**b**) von Mises stress.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Item** & **PDF Panel** & **Steel Beam Frame** \\ \hline \multirow{4}{*}{CASE II (Transient, No Plate)} & Total Deformation (mm) & 8.090 & 6.146 \\  & von Mises Stress (MPa) & 4.779 & 119.080 \\ \cline{1-1}  & \multirow{2}{*}{Shear Stress (MPa)} & XY-Plane & - & 24.089 \\ \cline{1-1}  & & YZ-Plane & - & 40.425 \\ \cline{1-1}  & & ZX-Plane & - & 38.739 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of structural response in the transient analysis.  
In this section, FVM modelling methods were developed to supplement the FEM modelling methods used in the transient analysis. Interaction interfaces were set and used to transfer the calculated pressure on the tank wall from the flow analysis. The convergence of the sloshing analysis in FVM was defined as the root mean square of the residual coefficients falling to less than \(1\times 10^{-4}\), and the size of time step was 0.01 s.  
The developed analysis process of the one-way FSI analysis is as follows. Initially, sloshing analysis in FVM was performed until the solution converged under seismic loads. Second, pressure results for the tank wall were transferred from the FVM to the FEM model to be mapped. Finally, the transient structural analysis was performed at the mapped pressure on the tank wall to obtain a structural response.
# State-Bust Feedback Control for Fault Recovery of Input/State Asynchronous Sequential Machines
## 5 Illustrative Examples
### Asynchronous Error Counter
To determine the existence of \(C\), we now investigate stable reachability between \(x_{i}\) and \(\Gamma(x_{i})\). An examination of Figure 4 shows that for all \(i=1,\ldots,6\), \(x_{i}\) is stably reachable from every state of \(\Gamma(x_{i})\). For instance, \(x_{1}\) is stably reachable from \(x_{2}\) via an input string \(b_{2}b_{4}a_{6}\) (\(s(x_{2},b_{2}b_{4}a_{6})=x_{1}\)), and from \(x_{6}\) via an input string \(a_{6}\) (\(s(x_{6},a_{6})=x_{1}\)). By Theorem 1, therefore, \(C\) exists which achieves fault recovery against transient faults by \(A_{d}\). \(C\) is constructed in line with (6)-(8). Let us keep focusing on the fault recovery to \(x_{1}\). As addressed above, we employ \(b_{2}b_{4}a_{6}\) and \(a_{6}\) in activating the correction procedure from \(x_{2}\) and \(x_{6}\), respectively. Upon receiving \(b_{2}b_{4}a_{6}\), \(\Sigma_{2}\) traverses two intermediate stable states \(s(x_{1},b_{2})=x_{4}\) and \(s(x_{4},b_{4})=x_{6}\). With \(m\) fixed to \(1\), \(C\) is designed with respect to \(x_{2}\) and \(b_{2}b_{4}a_{6}\) as follows:  
\[ \begin{split}& C(x_{2},u,1)=b_{2},\ \forall u\in\{a_{6},b_{5}\}\\ & C(x_{4},u,1)=b_{4},\ \forall u\in\{a_{6},b_{5}\}\\ & C(x_{6},u,1)=a_{6},\ \forall u\in\{a_{6},b_{5}\}.\end{split}  \tag{11}\]  
On the other hand, \(\Sigma_{2}\) transfers from \(x_{6}\) directly to \(x_{1}\) in response to \(a_{6}\), so \(C\) is designed as \(C(x_{6},u,1)=a_{6}\), \(\forall u\in\{a_{6},b_{5}\}\). As (11) already contains this operation, it serves as the correction procedure realizing fault recovery to \(x_{1}\). When \(\Sigma_{2}\) reaches \(x_{1}\), \(m\) is reset to \(0\). Upon receiving \((x_{1},0)\) from \(G\), \(C\) terminates the recovery procedure. To this end, set \(C\) as follows:  
\[C(x_{1},u,0)=u,\ \forall u\in\{a_{6},b_{5}\}. \tag{12}\]  
It is clear from (11) and (12) that the selected control input sequences preserve the implementability. The operation of \(C\) for the other states is attained by adopting (11) and (12). Since all the interactions between \(C\), \(G\), and \(\Sigma_{2}\) are conducted in an asynchronous mechanism, the correction procedure can be accomplished instantaneously before further change of the external input.
# 3D Point Cloud Completion Method Based on Building Contour Constraint Diffusion Probability Model
## 3 Methods
### Contour Constraint
#### 3.1.2 Definition of Building Surface Polygon Attributes  
Based on the grouping results of building surface polygons in Section 3.1.1, and in accordance with the standard _Code for design of building foundation_[42], which specifies  
Figure 4: Building grouping results schematic diagram: The figure divides the surface polygons fitted from the incomplete building point cloud into four groups, with each color representing a polygon association group. The white irregular region represents the missing point cloud area.  
Figure 3: Schematic diagram of common polygonal convex–concave connections on building surfaces: (**a,b,e**) have angle \(\theta_{i}>\theta_{j}\), and there is a convex connection between the polygons; (**c,d**) have angle \(\theta_{i}<\theta_{j}\), and there is a concave connection between the polygons.  
that the wall inclination of buildings below 24 \(m\) should be less than 0.23\({}^{\circ}\), we define the polygon attribute \(F_{V}\) to determine whether the polygon attribute is a wall or a roof.  
\[F_{V}\ =\ \left\{\begin{array}{lll}Wall&,&\theta=\arccos\frac{\overset{ \rightarrow}{n}_{i}\cdot\overset{\rightarrow}{n}_{horizon}}{\left\|n_{i} \right\|n_{horizon}}\in\left(90^{\circ}\pm 0.23^{\circ}\right)\\ Roof&,&\theta=\arccos\frac{\overset{\rightarrow}{n}_{i}\cdot\overset{ \rightarrow}{n}_{horizon}}{\left\|n_{i}\right\|\overset{\rightarrow}{n}_{ horizon}}\in\left[0^{\circ},90^{\circ}-0.23^{\circ}\right]\cup\left[90^{\circ}+0.23^{ \circ},180^{\circ}\right]\end{array}\right., \tag{4}\]  
where \(\overset{\rightarrow}{n}_{i}\) represents the normal vector of the building surface polygon, \(i\in\left\{1,2,\cdots,n\right\}\); \(\overset{\rightarrow}{n}_{horizon}\) represents the normal vectors of the ground plane; and \(\theta\) represents the angle between the building surface polygon and the ground plane.  
Based on the building surface polygon attribute \(F_{V}\), attributes are defined for each polygon contained within the individual building, and they are divided into wall polygons \(\left\{W^{\left(Z_{1}\right)},\cdots,W^{\left(Z_{a}\right)}\right\}\) and roof polygons \(\left\{R^{\left(Z_{1}\right)},\cdots,R^{\left(Z_{a}\right)}\right\}\) based on these attributes.
# Parameter Design and Optimization of Grass Aerial Seeding Tower Based Computational Fluid Dynamics
## 3 Parameter Design and Optimization of Basic Nozzle Unit
### Criteria for Velocity Direction Assessment  
If an initial velocity is given at the inlet, the final velocity direction after acceleration through the nozzle may not strictly align with the initial velocity direction. The ratio of the velocity magnitude in the same direction as the initial velocity to the overall velocity can be used as a criterion to assess the directionality of the outlet velocity.  
If the inlet velocity is in the \(x\) direction, then using \(S_{x}=V_{x}/|V|\) represents the directionality of the velocity in the \(x\) direction. A larger \(S_{x}\) indicates better directionality.
# Using Graph Neural Networks for Social Recommendations
## 3 Methodology
### RelationalNet Algorithm Architecture
#### 3.2.8 Time Complexity  
Following a similar analysis as in DiffNet++ [13], the additional time cost lies in the influence and interest diffusion layers for users and items, when compared to the classical matrix factorization-based algorithms. Given \(m\) users, and \(n\) items and diffusion depth of \(K\), let us denote the maximum degree of \(G_{S}\) by \(maxDeg_{S}\), the maximum degree of \(G_{T}\) by \(maxDeg_{T}\), the maximum items per user in \(G_{R}\) by \(maxI\), and the maximum users per item in \(G_{R}^{\prime}\) by \(maxI\). At each layer, we need to update the embeddings using Equations (12) and (13). As noted, in practice, the MLP layers are two, so the time cost for the graph attention modeling is \(O(D\{m(maxDeg_{S}+maxI)+n(maxDeg_{T}+maxU)\})\), where \(D\) is the dimension of the embeddings. Since there are K layers, the total additional time complexity of our algorithm is \(O(KD\{m(maxDeg_{S}+maxI)+n(maxDeg_{T}+maxU)\})\). Additionally, we have practically that \(maxDeg_{S},maxU<<m\) and \(maxDeg_{T},maxI<<n\), thus being almost linear to the number of users and items.  
Figure 6: Example of the graph attention at the \((k+1)^{th}\) layer for the user embedding \(u_{a}^{k+1}\) only.
# A Preliminary Study on the Potential of the New Red-Fleshed 'JB' Clone Apple for Cider Production
## 2 Materials and Methods
Potassium pyrosulfite (0.07 g/L) and diammonium hydrogen phosphate (0.3 g/L) were added to the fermentation slurry, which was then inoculated with _Saccharomyces cerevisiae_ UCLM S325 yeast (Fermentis, Warsaw, Poland). The inoculum was prepared according to the manufacturer's recommendation by weighing the yeast preparation _S. cerevisiae_ UCLM S325 (dose 1 g/L) into 10 mL of tap water at room temperature. After stirring  
Figure 1: Red-fleshed apple clone ’JB’ and juice obtained from it. gently to break up lumps, the yeast was rehydrated for 20 min at 25 \({}^{\circ}\)C, and then totally transferred into a tank filled with 80% must. The active acidity of the must was 2.53. The must was fermented for 21 days at 15 \({}^{\circ}\)C, and then the resulting \"young\" cider was racked off the lees and aged for 8 weeks at 6 \({}^{\circ}\)C. The total sugars, active acidity, total acidity, polyphenol content, and color were determined in the cider using the same methods as those used in the analysis of the apple must. In addition, total extract content, volatile acidity, and alcohol content tests and an organoleptic evaluation were carried out. The determinations of total extract content, ethyl alcohol content, total acidity, and volatile acidity were conducted using the methods recommended by the International Organisation of Vine and Wine [24]. Total extract and ethanol content in cider were determined by distillation methods using pycnometric density determination, and volatile acidity was determined using titration. All experiments were performed in triplicate. The preliminary quantitative descriptive analysis (QDA) of the cider was conducted following the Polish Standard PN-A-79529-2:2005 [25]. The assessment was conducted at the Department of Food Biotechnology and Microbiology, WULS, by 10 appropriately trained individuals (5 men and 5 women, mean age 42 \(\pm\) 7 years) during a single session in a ventilated room at room temperature, lasting 30-45 min. The selected panelists had prior experience with similar assessments (including cider) and were familiar with cider consumption. Each assessor received one 50 mL individually coded cider sample in a transparent glass vessel. They rinsed their mouths with clean water and then assessed the cider's color, followed by its aroma and taste. A five-point scale was used, with points assigned based on the following quality features: color (1--pale red; 5--dark red), clarity (1--cloudy; 5--clear), characteristic cider aroma (1--imperceptible; 5--very intense), sweetness (1--imperceptible; 5--very intense), acidity (1--very intense; 5--imperceptible), characteristic cider taste (1--imperceptible; 5--very intense), effervescence (gas bubbles) (1--imperceptible; 5--very intense), and alcohol intensity (1--imperceptible; 5--very intense). Additionally, each assessor provided an overall assessment, scored from 1 (undesirable) to 5 (very desirable). When evaluating the intensity of the individual features, the assessors were permitted to mark intermediate values along the scale.
## 3 Results
### LA-1 Alleviates Brain Ischemia-Reverfusion Injury
(**A**) The experimental timeline for in vivo evaluation of LA-1 and loganin. (**B**) Non-invasive, trans-cranial laser  
Doppler perfusion imaging to verify occlusion at 2h,22h after MCAO. **(C)** Representative images of TTC-stained brain slices after treatment with LA-1 and loganin for 22 h in MCAO rat model. **(D)** Corresponding infarct size statistics. **(E)** Neurological scores of MCAO rats after treatment with LA-1 and loganin for 22 h. **(F)** MRI imaging of MCAO rats before and after the i.v. injection of the LA-1 and loganin. **(G)** H&E staining, scale bar = 50 \(\upmu\)m. **(H)** Nissl staining, scale bar = 100 \(\upmu\)m and **(I)** tunel staining, scale bar = 100 \(\upmu\)m, scale bar = 20 \(\upmu\)m of brain tissues from different treatment groups. **(J)** Recorded the number of tunel cells by image J. Data were presented as the mean \(\pm\) SD, *_p_ \(<\) 0.05, **_p_ \(<\) 0.01, and ***_p_ \(<\) 0.001. ***_p_ \(<\) 0.0001, ns represents not significan. LA-1 Alleviates Oxidative Stress Damage and Inflammation Induced by Cerebral Ischemia-Reperfusion Injury  
Both oxidative stress and inflammation contribute to the neurological deficit induced by cerebral ischemia-reperfusion. To further investigate the neuroprotective mechanisms of LA-1, we evaluated its antioxidant activity by measuring the levels of superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GSH-Px) in brain tissue. The results exhibit that ischemia-reperfusion injury (MCAO group) led to decreased levels of SOD, CAT, and GSH. Compared to the MCAO group, the LA-1 treatment restored the levels of SOD, CAT, and GSH (Figure 6A-C), confirming the antioxidant activity LA-1 in vivo. We next assayed the anti-inflammation activity of LA-1 by measuring the levels of TNF-\(\alpha\), IL-1\(\beta\), and IL-10 in brain tissue. Compared to the sham group, the MCAO group exhibited increased levels of the pro-inflammatory factors TNF-\(\alpha\) and IL-1\(\beta\), and decreased levels of IL-10. After treatment with LA-1, the levels of TNF-\(\alpha\) and IL-1\(\beta\) were significantly reduced, while the levels of IL-10 increased (Figure 6D-F). These results indicate that LA-1 can effectively reduce inflammation and oxidative stress damage induced by cerebral ischemia-reperfusion injury in vivo. Figure 6: LA-1 modulates antioxidant enzyme levels and inflammatory cytokine expression in rat model of cerebral ischemia/reperfusion injury. Expression levels were shown as **(A)** superoxide dismutase (SOD), **(B)** catalase (CAT), **(C)** glutathione peroxidase (GSH-Px), **(D)** TNF-\(\alpha\), **(E)** IL-1\(\beta\), and **(F)** IL-10 in the brain tissue of different treatment groups. Data were presented as the mean \(\pm\) SD, *_p_ \(<\) 0.05, **_p_ \(<\) 0.01, and ***_p_ \(<\) 0.001. ***_p_ \(<\) 0.0001, ns represents not significant, n = 3.
## 4 DCSK and QCSK Communication Systems for UWA  
Figure 2 shows the transmitter and receiver block of a DCSK system. Firstly, the \(l\)th input datum is mapped to symbol \(b_{l}\in 1\); then, the DCSK transmitter signal \(S_{k}\) for the first input datum is given by [21]:  
\[S_{k}=\left\{\begin{matrix}x_{k}&\qquad\qquad k=1.\cdots,\beta\\ b_{1}x_{k-\beta}&\qquad\quad k=\beta+1,\cdots,2\beta\end{matrix}\right. \tag{15}\]  
where \(2\beta\) represents the spreading factor. At the receiver, the output of the correlator represents the decision variable of the \(l\)th bit, \(c_{l}\), which is expressed as:  
\[c_{l}=\sum_{k=1}^{\beta}r_{k}\cdot r_{k+\beta} \tag{16}\]  
where \(r_{k}\) is the received sequence. Here, we do not need to pass the decision variable through thresholding since the output of the correlator is directly connected to the polar decoder.  
Figure 3 shows the transmitter and receiver block of the QCSK system. Firstly, the \(l\)th parallel two symbols \(a_{l},b_{l}\in 1\) are multiplied by the reference chaotic sequence and Hilbert transform of the reference chaotic sequence, respectively. After that, the two multiplied sequences are summed and modulated using the same scenario for the DCSK system. The QCSK transmitter signal \(S_{k}\) for the first two input data is given by [22]:  
\[S_{k}=\left\{\begin{matrix}x_{k},&\qquad\qquad k=1,\cdots,\beta\\ \frac{1}{\sqrt{2}}(a_{1}x_{k-\beta}+b_{1}\tilde{x}_{k-\beta}),&\qquad k=\beta+ 1,\cdots,2\beta\end{matrix}\right. \tag{17}\]  
where \(\tilde{x}_{k}\) is the Hilbert transform of the \(x_{k}\) signal. At the receiver, the two outputs of the correlators represent the decision variables \(c_{1,l}\) and \(c_{2,l}\), which are expressed as:  
\[\begin{split} c_{1,l}&=\sum_{k=1}^{\beta}r_{k}\cdot r _{k+\beta}\\ c_{2,l}&=\sum_{k=1}^{\beta}\tilde{r}_{k}\cdot r_{k+\beta} \end{split} \tag{18}\]  
where \(\tilde{r}_{k}\) is the Hilbert transform of the received sequence \(r_{k}\). Similar to the DCSK system, it is not required to pass the decision variable through thresholding since the output of the correlator is directly connected to the polar decoder.  
Figure 2: Proposed DCSK transmitter and receiver system.  
Figure 4 shows the scheme diagram of the underwater acoustic system (UWAS) employing a differential chaos modulation system. The input data are encoded using a polar coder and then DCSK or QCSK modulation is employed to generate the UWA transmitted signal. At the receiver, the transmitted signal is summed with the noise signal which belongs to a t-distribution, as described in (2) (here, only the noise signal is considered). The received signal under the UWAN channel can be expressed as:  
\[r_{k}=S_{k}+n_{k} \tag{19}\]  
where \(n_{k}\) is the noise signal that follows a t-distribution with zero mean and variance \(N_{0}/2\). After that, differential chaos demodulation is employed with a soft output and applied to the polar decoder to recover the original data bits.
## 4 Fragility Analysis Based on IDA Method
### Damage Measure Selection  
The IDA curve reflects the damage state of the structure under different ground motion intensity measures, so the horizontal and vertical axes of the IDA curve represent the structural ground motion intensity measure (IM) and the structural damage measure (DM),  
Figure 10: Acceleration response spectra of the selected ground motion records.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Earthquake Name** & **Year** & **Station Name** & **Magnitude** \\ \hline
1 & Imperial Valley & 1979 & Delta & 6.5 \\
2 & Imperial Valley & 1979 & El Centro Array \#11 & 6.5 \\
3 & Landers & 1992 & Yermo Fire Station & 7.3 \\
4 & Northridge & 1994 & Beverly Hills-Mulhol & 6.7 \\  & & & Canyon Country-W & 6.7 \\
5 & Northridge & 1994 & Lost Cany & 6.7 \\
6 & Kobe-Japan & 1995 & Nishi-Akashi & 6.9 \\
7 & Kocaeli, Turkey & 1999 & Duzce & 7.5 \\
8 & Chi-Chi-Taiwan & 1999 & CHY101 & 7.6 \\
9 & Duzce, Turkey & 1999 & Bolu & 7.1 \\
10 & Manjil, Iran & 1990 & Abbar & 7.4 \\ \hline \end{tabular}
\end{table}
Table 3: Ten ground motion records.  
respectively. The structural damage index must intuitively and effectively reflect the damage and performance state of the structure, which is a crucial parameter in IDA. Common damage measures used by scholars in IDA include maximum inter-story displacement angle, structural vertex displacement, maximum floor acceleration, base shear force, and isolation bearing displacement [37; 48; 49].  
For the overturning problem identified in shaking table tests of isolated structures, the IDA of its anti-overturning effect is conducted by using two finite element models of isolation proposed in this paper. Research shows that several factors contribute to the overturning of isolation structures: (1) excessive compressive stress in the isolation bearing; (2) excessive horizontal displacement of the isolation bearing; (3) tensile stress exceeding the limit value; (4) excessive overturning torque.  
According to the results of the vibration table test of the isolated structure in this study, the compressive stress does not exceed the code limit, but the tensile stress and horizontal displacement of the bearing may exceed the limit behavior under high seismic intensity. When the bearing is under compression, tensile stress is negative. To facilitate statistical calculations, the parameters TP and \(\gamma\) related to the tensile stress of the bearing are selected as the structural DMs. Tensile stress is the tension divided by the effective cross-sectional area of the bearing, and shear strain is defined as the horizontal displacement divided by the thickness of the rubber layer. These two indicators are defined in Formulas (3) and (4).  
\[TP=T+5\;\text{MPa} \tag{3}\]  
where \(T\) is the tensile stress result calculated by dynamic elastic-plastic analysis.  
\[\gamma=S/h \tag{4}\]  
where \(S\) is the maximum horizontal displacement of the bearing and \(h\) is the total thickness of the rubber layer of the bearing.