esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Input Fusion Model for Privacy and Semantic Preservation in Facial Image Datasets
## 3 Preliminary
### Image Enhancement  
As the pictures generated are not always visually good, it is necessary to enhance the image texture. To recover high-quality faces from the generated images, GFP-GAN [6] is adopted in our proposed model. The architecture of the whole model can be divided into two parts. One of the parts is a module for removing image degradation, which is known as U-net, and the second part is a prior in the form of a facial generative adversary network that has been trained in advance. Two networks are connected by a mapping of latent embeddings and a series of several layers called Channel-Split Spatial Feature Transform (CS-SFT) layers. For the original input image, U-net retrieves useful features, forming two features: \(F_{latent}\) and \(F_{spatial}\). The formulation is as follows:  
\[F_{latent},F_{spatial}=U-Net(x) \tag{6}\]  
After passing through multiple layers of the network, \(F_{latent}\) is mapped to intermediate latent codes \(W\). The characteristics are recorded in the pre-trained StyleGAN2 model [63]and those stored features are utilized to generate convolutional features, denoted by \(F_{GAN}\). Equation (7) shows the above process.  
\[W=MLP(F_{latent}),F_{GAN}=StyleGAN(W) \tag{7}\]  
To enhance the preservation of accuracy, it employs a Spatial Feature Transform (SFT) [64] to modify the GAN features \(F_{GAN}\) by transforming the input spatial features \(F_{spatial}\). More specifically, the model utilizes a set of convolutional layers to generate the parameters \((m,n)\) for an affine transformation from the input features \(F_{spatial}\), as shown in Equation (8)  
\[m,n=Conv(F_{spatial}) \tag{8}\]  
After that, the \(F_{GAN}\) feature is manipulated by changing its scale and position based on the parameters obtained in the previous step, as represented by Equation (9)  
\[F_{output}=SFT(F_{GAN}|m,n) \tag{9}\]  
It takes advantage of input features \(F_{spatial}\) to modify certain GAN features in terms of space while keeping the remaining features unchanged. Finally, the restored face \(y\) is generated with the channel-split SFT layers implemented at every resolution level. The process is as shown in Equation (10).  
\[y=CS-SFT(F_{GAN}|m,n) \tag{10}\]
_Applied_  
_sciences_  
_Article_  
**The Generation Mechanism of the Flow-Induced**  
**Noise from a Sail Hull on the Scaled**  
**Submarine Model**  
**[PERSON] 1,2,3, [PERSON] 1,2,3,* and [PERSON] 1,2,3**  
1 Acoustic science and technology laboratory, Harbin Engineering University, Harbin 150001, China; [EMAIL_ADDRESS] (Y.L.); [PERSON]SS] (D.S.)  
Key laboratory of marine information acquisition and security (Harbin Engineering University), Ministry of Industry and Information, Harbin 150001, China  
College of underwater acoustic engineering, Harbin Engineering University, Harbin 150001, China  
*Correspondence: [EMAIL_ADDRESS]; Tel.: +86-156-4508-1512 check for updates  
Received: 22 November 2018; Accepted: 24 December 2018; Published: 29 December 2018  
**Featured Application: The results in this research can provide the design orientation for the flow control techniques to suppress the hydrodynamic noise from the sail hull. The results can also lay the foundation for the novel design of the sail hull with a low level of the flow-induced noise.**  
**Abstract:** Flow-induced noise from the sail hull, which is induced by the horseshoe vortex, the boundary layer separation and the tail vortex shedding, is a significant problem for the underwater vehicles, while has not been adequately studied. We have performed simulations and experiments to reveal the noise generation mechanism from these flows using the scaled sail hull with part of a submarine body. The large eddy simulation and the wavenumber-frequency spectrum are adopted for simulations. The frequency ranges from 10 Hz to 2000 Hz. The simulation results show that the flow-induced noise with the frequency less than 500 Hz is mainly generated by the horseshoe vortex; the flow-induced noise because of the tail vortex shedding is mainly within the frequency of shedding vortex, which is 595 Hz in the study; the flow-induced noise caused by the boundary layer separation lies in the whole frequency range. Moreover, we have conducted the experiments in a gravity water tunnel, and the experimental results are in good accordance with the simulation results. The results can lay the foundation for the design of flow control devices to suppress and reduce the flow-induced noise from the sail hull.  
**Keywords:** flow-induced noise; sail hull; horseshoe vortex; tail vortex shedding; boundary layer separation
## 4 Validation of New AI-FEM Multiple Cracks Coalescence Criteria
### Geometry and Material  
In Section 4, the newly proposed plastic zone size-based coalescence criterion was validated through comparison with experimental results, and the conservatism of the distance and shape criteria for multiple crack coalescence in plate geometries, as presented in the current codes, was investigated. In this section, further conservatism investigation was performed on multiple cracks present in the pipe geometry of major NPP structures using AI-FEM. This investigation was conducted through AI-FEM-based FCG simulations.  
Figure 9(a) shows a schematic diagram of a pipe with multiple cracks. \(a_{1}\) and \(c_{1}\) represent the crack depth and half of the crack length for multiple crack 1, respectively, while \(a_{2}\) and \(c_{2}\) represent the crack depth and half of the crack length for multiple crack 2, respectively. \(S\) indicates the distance between the two adjacent surface cracks.  
Figure 9(b) illustrates a schematic diagram of a pipe with a single crack formed by coalescence according to each methodology. \(a_{1}\) and \(a_{2}\) represent the maximum crack depths of crack 1 and crack 2, respectively, while \(a_{3}\) indicates the crack depth in the re-entrant region formed by the coalescence of the two cracks. The crack depth formed by the code methodology is equal to the depth of the deeper crack between multiple cracks 1 and 2. \(R_{o}\), \(R_{m}\), \(R_{i}\), and \(t\) represent the outer radius, mean radius, inner radius, and thickness of the pipe, respectively. The distances between multiple cracks, pipe geometry, and initial crack shapes used in the FCG simulation are summarized in Table 7. Crack 1 has a greater depth than crack 2, and two crack aspect ratios, a semi-elliptical crack with \(c/a=3\) and a semi-circular crack with \(c/a=1\), were considered.  
The material applied in this analysis is AISI 304L stainless steel, which is commonly used in NPP piping systems. The material properties and FCG rate curves for this material were referenced from the FCG experiment conducted by [PERSON] et al. [6]. The Young's  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(S/t\) & \(R_{m}/t\) & \(a_{1}/t\) & \(a_{2}/t\) & \(c_{1}/a_{1}c_{2}/a_{2}\) \\ \hline \hline \multirow{2}{*}{0.4} & \multirow{2}{*}{3.5} & \multirow{2}{*}{0.3} & \multirow{2}{*}{0.2} & 3 \\ \cline{3-3} \cline{5-5}  & & & & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Normalized pipe, crack shape, and distance between cracks for FCG simulation.  
Figure 10: Schematic diagram of a cracked pipe: (**a**) multiple cracks; (**b**) coalesced single crack.  
modulus is 200 GPa, the Poisson's ratio is 0.3, and the yield strength is 274 MPa. The Paris' law form of Equation (7) for the presented FCG curve is as follows:  
\[da/dN=1.586\times 10^{-12}(\Delta K)^{2734} \tag{7}\]  
The FCG curve was obtained under loading conditions with a \(R=0.1\) in an air environment.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### High-Resolution AGB Maps from Field Measurements and LiDAR Data
#### 3.3.4 LiDAR-Derived Biomass, Canopy Height and Cover, Sonoma County, California, 2013  
Using the same methods as [PERSON] et al. [81], [PERSON] et al. [82] generated a biomass map at 30 m spatial resolution for Sonoma County in California for the nominal year 2013. Validation results with FIA data at the plot level showed estimated accuracy with the R\({}^{2}\) of 0.67 and RMSE of 97.9 Mg/ha.
# Speech Inpainting Based on Multi-Layer Long Short-Term Memory Networks
## 3 Proposed Speech Inpainting Methods and Model Architecture  
### Long Short-Term Memory Networks  
LSTM is a type of Recurrent Neural Network (RNN) architecture [37], which is designed to address the limitations of traditional RNNs in capturing long-distance dependencies and avoiding the vanishing gradient problem [14]. The vanishing gradient problem of the RNN model often occurs when the gradient of the loss function relative to the network weight becomes extremely small, which makes it difficult for RNNs to learn long-term correlations.  
The structure of an LSTM cell is shown in Figure 1. By incorporating an input gate, forget gate, and output gate, the LSTM architecture has the ability to retain long-term memory of past inputs and their contextual information. Mathematically, let us denote the input time series as \(X_{t}\), the hidden state of memory cells as \(H_{t}\), the input gate as \(I_{t}\), the forget gate as \(F_{t}\), and the output gate as \(O_{t}\), where \(t\in[1,N]\) represents the time parameters. Then, we have the following notations:  
\[\begin{split} I_{t}&=\sigma(X_{t}W_{xi}+H_{t-1}W_{ hi}+b_{i}),\\ F_{t}&=\sigma\Big{(}X_{t}W_{xf}+H_{t-1}W_{hf}+b_{f} \Big{)},\\ O_{t}&=\sigma(X_{t}W_{xo}+H_{t-1}W_{ho}+b_{o}),\\ \hat{C}_{t}&=\tanh(X_{t}W_{xc}+H_{t-1}W_{hc}+b_{c}),\\ C_{t}&=F_{t}\otimes C_{t-1}+I_{t}\otimes\tilde{C} _{t},\\ H_{t}&=O_{t}\otimes\tanh(C_{t}),\end
## 3 Results and Discussion
### Corrosion Behavior  
The semi-logarithmic plots of the current density-potential measurements (see Figure 13) show that the reference coatings tested in both solutions (Ref-M1 and Ref-M2) exhibit similar values for the determined current density (see Table 4), indicating similar kinetics of the corrosion reaction. However, reducing the pH value of the testing solution shifts the corrosion potential in a negative direction, allowing corrosion attack to initiate earlier (see Ref-M2).  
Figure 12: Exemplary CLSM 3D scan of the wear track on the GC coating at R = 3 mm.  
Figure 13: Semi-logarithmic current density–potential curves of GC and WC-Co-Cr (Ref.) in M1: 3.5% NaCl solution and M2: 3.5% NaCl solution at pH 3.5 (with HCI).  
A similar shift in the corrosion potential was also registered for the GC coatings when the pH value of the testing solution decreased. However, faster corrosion kinetics for the sample GC-M2 were indicated by an increase in the current density (icorr). The U\({}_{\text{corr}}\) values of the tested GC samples were lower than those of the WC-Co-Cr reference coatings. Furthermore, the corrosion current density values determined were higher than those of the reference samples. This indicates that the corrosion attack generally occurs earlier and proceeds with higher kinetics in the GC coatings.  
Figure 14 presents a cross-section of one of the examined GC coatings after exposure to a 3.5% NaCl solution at pH 3.5 in order to assess the degree of corrosion attack more precisely.  
Examining Figure 14 reveals a localized attack caused by pitting corrosion induced by Cl\({}^{-}\) anions (marked in red). The main objective of this microscopic investigation was to determine whether the corrosion medium had penetrated the coating and reached the substrate. It was identified that the metallic areas (containing Fe) in the coating were more susceptible to corrosion attack. Based on the information provided by this SEM micrograph, it can be concluded that no corrosion attack was detected at the coating/substrate interface. Only aluminum oxide particles were observed in this region, as reported in the previous section (see Figure 7a). This suggests that corrosion resistance can still be assumed at this stage and that the GC coating is effective in providing a barrier.
# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 4 Experiments and Results
### Improved ML Models
#### 4.3.2 SVR Results  
This section introduces the results of the support vector regression model. Table 7 shows the performance metrics of experiments. Compared to the early testing results, utilizing an SVR model resulted in a considerable increase in model prediction accuracy. The hyperparameters are included for each experiment in Table 7.  
Figure 17 corresponds to SVR experiment #2 (Table 7). Although most extreme values are underestimated, the error rate of 33.6% is a significant improvement over the LSTM model in a similar experiment.  
Figure 18 corresponds to SVR experiment #10 (Table 7). This model averaged EPA NO\({}_{2}\) across all power plants, introducing bias as most of the averaged EPA NO\({}_{2}\) was around 0.08 to 0.10. Still, the model was not overtly overfitting and captured the trend mainly, but the low error rate of 15.52% was influenced by the bias towards the concentrated range of EPA NO\({}_{2}\) averaged values.  
Figure 19 shows a correlation plot of the predicted vs. actual EPA NO\({}_{2}\) for SVR experiment #2 (Table 7). The graph depicts a moderate positive relationship. The regression line is fitted around the cluster of points, indicating a good model fit.  
Figure 17: SVR model fit over actual EPA NO\({}_{2}\) actual values throughout the year using a single power plant.  
Figure 18: SVR model fit over actual EPA NO\({}_{2}\) values for training/testing sets throughout the year using TROPOMI/Date features for all coal-fired power plants second scenario.  
Figure 20 is similar and was generated from SVR experiment #10 (Table 7). It shows a strong positive correlation between actual and predicted EPA NO\({}_{2}\) values, with the regression line fitted around the cluster of points.  
SVR experiment #3 (Table 7) utilized TROPOMI, MERRA-2 daily, and date features. It resulted in the lowest error rate for the study's first scenario. This experiment resulted in a 13.36% improvement in the error rate in comparison to SVR experiment #1 (Table 7) using TROPOMI NO\({}_{2}\) input only. The MAE listed for the third experiment confirm its predictive ability by producing an MAE of 0.0483, indicating a slight difference observed between actual and predicted EPA NO\({}_{2}\) values.  
The SVR model had the best results across both study scenarios for the lowest error rates of 32.4% (SVR experiment #3) for the first scenario and 15.52% (SVR experiment #10) for the second scenario.
## 3 Method
### Iteration Filter  
The operating frequency and sampling frequency of the system are 1 kHz, so based on Nyquist sampling theorem, the effective spectrum bandwidth is 500 Hz. But in actual situation, excessive frequency bandwidth is meaningless, so the maximum frequency in this paper is set as 200 Hz. At the same time, due to the limitation of the driving stroke of the system, and the sensor's low frequency response is very poor, the minimum frequency in this paper is set as 0.2 Hz.  
In this paper, the filter is a simple first-order Butterworth high-pass filter with cutoff frequency at 0.2 Hz, and a first-order Butterworth low-pass filter with cutoff frequency at 200 Hz. The filter is applied on the drive correction and in the calculation process of the iteration coefficient.  
Figure 4: Control method sketch.
# Positioning Performance Limits of GNSS Meta-Signals and HO-BOC Signals
## 8 Conclusions and Outlook
As a final remark, the following recommendations could be extracted from this article: (i) if the receiver is constrained by the signal bandwidth and the user has access to restricted codes, it is recommended to use the E1-PRS or B1A signal under nominal conditions, and L1-M or E6-PRS under non-nominal conditions; (ii) if the receiver is able to operate at 60 MHz, it is recommended to exploit the full-bandwidth Galileo E5 signal; and (iii) in terms of robustness and performance, if the receiver can operate at 135 MHz, the best choice is to use the GNSS meta-signals E5 + E6 or B2 + B3. These meta-signals provided the best overall performances regardless of the positioning method used, the satellite constellation geometry, or the propagation conditions. It is worth noting that the present article provided the ultimate achievable performance using HO-BOC and GNSS meta-signals, but further analysis could be conducted: (i) performance loss w.r.t. the optimal under multipath conditions; (ii) the impact of high-dynamic conditions; (iii) coherent vs. non-coherent architectures in very weak signal conditions; (iv) analysis w.r.t. external errors such as ionospheric/tropospheric delays, orbital or satellite clock errors; or (v) a specific receiver design to avoid false locks to secondary ACF peaks or meta-signal tracking strategies. Conceptualization and methodology, J.V.-V., F.V. and E.C. ; software and validation, L.O. and D.M. ; writing--original draft preparation, L.O., D.M. and J.V.-V.; writing--review and editing, F.V. and E.C. ; supervision, E.C. ; project administration, E.C. and J.V.-V.; funding acquisition, J.V.-V. All authors have read and agreed to the published version of the manuscript. This research was partially supported by the DGA/AID projects (2019.65.0068.00.470.75.01, 2018.60.0072.00.470.75.01) and the TeSA Lab Postdoctoral Research Fellowship. The authors declare no conflict of interest.
# Advance on the Capitalization of Grape Peels By-Product in Common Wheat Pasta
## 3 Results
### Uncooked and Cooked Pasta Texture  
Uncooked pasta breaking force could give valuable information about its resistance to handling and transport. The supplementation of common wheat pasta with grape peel flour led to the increase in breaking force proportional to the amount added, the values being higher compared to the control, except for GP1% which did not show significant difference (Table 4). \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Uncooked Pasta**} & \multicolumn{2}{c}{**Cooked Pasta**} \\ \cline{2-5}  & **Breaking Force (N)** & **Elasticity \(\times\) 10\({}^{-2}\) (adim.)** & **Chewiness (N)** & **Firmness (N)** \\ \hline Control & 41.26 \(\pm\) 1.24 \({}^{\text{cd}}\) & 99.83 \(\pm\) 0.07 \({}^{\text{a}}\) & 48.79 \(\pm\) 2.19 \({}^{\text{a}}\) & 74.05 \(\pm\) 0.91 \({}^{\text{a}}\) \\ GP1\% & 41.00 \(\pm\) 1.01 \({}^{\text{d}}\) & 99.87 \(\pm\) 0.01 \({}^{\text{a}}\) & 39.57 \(\pm\) 0.09 \({}^{\text{b}}\) & 62.67 \(\pm\) 1.85 \({}^{\text{b}}\) \\ GP2\% & 42.73 \(\pm\) 1.95 \({}^{\text{cd}}\) & 99.87 \(\pm\) 0.00 \({}^{\text{a}}\) & 38.98 \(\pm\) 1.65 \({}^{\text{bc}}\) & 58.64 \(\pm\) 0.26 \({}^{\text{bc}}\) \\ GP3\% & 44.32 \(\pm\) 1.37 \({}^{\text{c}}\) & 99.91 \(\pm\) 0.08 \({}^{\text{a}}\) & 36.29 \(\pm\) 1.37 \({}^{\text{bd}}\) & 57.40 \(\pm\) 2.89 \({}^{\text{bc}}\) \\ GP4\% & 49.02 \(\pm\) 0.15 \({}^{\text{b}}\) & 99.92 \(\pm\) 0.07 \({}^{\text{a}}\) & 35.51 \(\pm\) 0.99 \({}^{\text{cd}}\) & 56.43 \(\pm\) 2.80 \({}^{\text{cd}}\) \\ GP5\% & 55.76 \(\pm\) 0.54 \({}^{\text{a}}\) & 99.94 \(\pm\) 0.06 \({}^{\text{a}}\) & 35.20 \(\pm\) 1.17 \({}^{\text{d}}\) & 54.04 \(\pm\) 1.83 \({}^{\text{cd}}\) \\ GP6\% & 58.46 \(\pm\) 0.17 \({}^{\text{a}}\) & 99.96 \(\pm\) 0.07 \({}^{\text{a}}\) & 34.11 \(\pm\) 1.01 \({}^{\text{d}}\) & 51.67 \(\pm\) 2.09 \({}^{\text{d}}\) \\ \hline \hline \end{tabular} Each experiment was carried out in triplicate and data was reported as means \(\pm\) standard deviation. Means in the same column with different letters are significantly different (\(p<0.05\)).  
\end{table} 
Table 4: Effects of grape peel flour on uncooked and cooked pasta texture.
###### Abstract  
**Objective:** This study aimed to investigate the correlation between condylar volume and airway dimensions in skeletal Class I malocclusion patients with different vertical growth patterns. Cone-beam computed tomography (CBCT) files were analyzed using AI-performed segmentation to ensure accurate measurements. **Materials and Methods:** A total of 93 individuals with skeletal Class I malocclusion (55 females and 38 males; average age 21.3 \(\pm\) 3.0 years) were classified into three groups (normodivergent, hyperdivergent, and hypodivergent) according to their vertical growth patterns. Upper airway and condylar volumes were calculated following AI-assisted segmentation, and their correlation was evaluated. **Results:** In the hyperdivergent group, both airway volume (11.2 \(\pm\) 5.0 cm\({}^{3}\)) and condylar volume (1.2 \(\pm\) 0.2 cm\({}^{3}\)) were significantly lower compared to the normodivergent (airway: 14.4 \(\pm\) 4.9 cm\({}^{3}\); condyle: 1.5 \(\pm\) 0.3 cm\({}^{3}\)) and hypodivergent groups (airway: 14.1 \(\pm\) 6.3 cm\({}^{3}\); condyle: 1.5 \(\pm\) 0.3 cm\({}^{3}\)) (\(p<0.05\)). Although no statistically significant correlation was detected between airway volume and right condylar volume across the three groups (normodivergent: r = \(-\)0.204, \(p\) = 0.280; hypodivergent: r = 0.015, \(p\) = 0.936; hyperdivergent: r = \(-\)0.007, \(p\) = 0.971), a strong positive correlation was identified between the right and left condylar volumes in all groups (r > 0.8, \(p<0.01\)). **Conclusions:** No significant statistical correlation was detected between condylar volume and airway volume across the evaluated groups. However, hyperdivergent individuals were found to have smaller condylar volumes and narrower airway volumes, which may contribute to increased airway resistance and a higher risk of respiratory dysfunctions. These findings highlight the importance of considering vertical growth patterns in orthodontic and orthopedic treatment planning, especially when evaluating airway dimensions. Additionally, a strong and statistically notable positive correlation was detected between the right and left condylar volumes across all groups.  
**Keywords:** pharyngeal airway; condylar volume; vertical skeletal pattern; artificial intelligence
## 3 Objective Function
### Elephant Herding Optimization  
Elephant herding optimization (Algorithm 2) [17] was proposed by [PERSON], [PERSON] and [PERSON] in 2015. The algorithm is inspired by the herding behavior of the elephant group. Elephants of different clans live together under the leadership of a matriarch, and male elephants leave the group when they become adults. These two behaviors are modeled into this algorithm, known as clan updating and separating operators. The position of an elephant updates the updating operator.  
Figure 6: Flowchart of the optimal value finding.  
Next, the population diversity is created by separating the operator. The required equations for this optimization given below.  
Clan updating operator:  
\[x_{new,ci,j}=x_{ci,j}+\alpha\Big{(}x_{best,ci}-x_{ci,j}\Big{)}r, \tag{13}\]  
where \(x_{new,ci,j}\) and \(x_{ci,j}\) are updated and previous positions of elephant \(j\) in clan \(ci\). \(\alpha\in[0,1]\) is a scale factor. \(x_{best,ci}\) is the matriarch in the clan. \(r\in[0,1]\) is a uniformly distributed random number.  
The fittest elephant in each clan should be updated using Equation (14):  
\[x_{new,ci,j}=\beta x_{center,ci}, \tag{14}\]  
where \(x_{center,ci,d}=\frac{1}{n_{ci}}\sum_{j=1}^{n_{ci}}x_{ci,j,d}\) and \(\beta\in[0,1]\).  
Separating Operator:  
\[x_{worst,ci}=x_{min}+(x_{max}-x_{min}+1)rand, \tag{15}\]  
where \(rand\in[0,1]\).  
```
1:Initialize X=Particle/Solution
2:while gen \(<\) max_gen
3: Sort all the elephants according to the fitness
4: Implement Clan updating operator by Equations (13) and (14)
5: Implement Separating operator using Equation (15)
6: Evaluate the population by updated positions
7: gen = gen + 1;
8:end while
```  
**Algorithm 2** EHO