esa-sceva/satcom-chunk-collection huggingface dataset:
# Imaging Top of Volcanic Mounds Using Seismic Time- and Depth-Domain Data Processing
## 3 Results of Seismic Data Processing
### Time-Domain Processing  
Time-domain data processing was performed on offshore seismic survey lines (Figure 1b) acquired from the study area (Figure 1a) using the Tamhae2 research vessel of KIGAM. The bold red line in Figure 1b is a representative seismic line, with approximately 37.5 line-km. Seismic data were acquired using a 1.2 km (96 channels) streamer and the air-gun source. Other detailed acquisition parameters are listed in Table 1.  
To confirm the effectiveness of the time-domain data processing techniques (Figure 3a) applied in this paper, the results before and after the signal processing were compared (Figures 4 and 5). Figures 4a and 5a show the shot gather and the stack section before the signal processing, and there is a multiple (refer to the arrows) at around 1.05 s. We can confirm from the results that the multiple was effectively attenuated (Figures 4b and 5b) after the signal processing was applied. In addition, the amplitude spectra before (black line in Figure 4c) and after (red line in Figure 4c) the signal processing were compared to confirm the effectiveness of time-domain data processing. From Figure 4c, we can confirm that the low-frequency signals were enhanced and the notches at higher frequencies were removed after the signal-processing techniques were applied. Therefore, through the application of time-domain data processing, the multiples and noise inherent in the field data can be effectively attenuated. In addition, from the stack section (Figure 5b), after applying the time-domain processing, the connectivity of the horizontal layers in the region of interest (0.9 to 1.2 s) greatly improved, and detailed subsurface structures could be imaged.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline \multirow{5}{*}{Streamer} & Streamer length (m) & 1200 \\  & No. of channels & 96 \\  & Group interval (m) & 12.5 \\  & Offset ranges (m) & 125–1325 \\  & Streamer depth (m) & \(7\pm 1\) \\ \hline \multirow{2}{*}{Recording} & Recording length (s) & 5 \\  & Sampling rate (ms) & 1 \\ \hline \multirow{5}{*}{Source} & Source type & Bolt long-life air guns \\  & Source volume (cu. in.) & 1254 \\ \cline{1-1}  & Source depth (m) & 5 \\ \cline{1-1}  & Source interval (m) & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Seismic data acquisition parameters.  
_Appl. Sci._**2021**, _11_, 4244  
**Figure 3. Seismic data processing workflows in the (a) time and (b) depth domains.**Figure 4: Shot gathers (**a**) before and (**b**) after the signal processing. (**c**) Amplitude spectra before (black line) and after (red line) the signal processing.
## 3 Experimental Work
### Material Characterization Based on the SPHB Data  
A total of 21 samples of ultra high-strength concrete were tested in the Split Hopkinson Bar (SPHB) test system and the primary measured values are given in Table 3. The tests also provided time variations of the stress, strain and strain rate values. Strain rates in the range of 353-1288 s\({}^{-1}\), which are typical values under impact and blast effects, were observed during the tests.  
The Johnson-Holmquist-Cook (JHC) model is one of the material models developed to numerically analyze the concrete behavior under impact and blast effects. It is a kind of elastic-viscoplastic model proposed by [PERSON] [35]. The model is known to handle the high strain, high strain rate and high pressure cases in concretes. The commercial hydrocode LS-DYNA included the JHC model as a material model in 1997 [36]. The JHC material model consists of the relations for strength, damage and equation of state.
## 1 Introduction
The dataset can be collected from various sources with the same characteristics, namely having a Southeast Asian race, frontal face position, and bright lighting. In this study, a GAN-based face sketch-to-photo generator is proposed, which combines U-Net and PatchGAN architectures on generators and discriminators inspired by pix2 pix [8], thereby avoiding bias toward Indonesian faces. We found a potential area of improvement for the pix2 pix framework, primarily since U-Net and PatchGAN have performed well in image translation tasks, such as creating images from image segmentation or sketches [8]. However, the translation of human faces is not easy because the resulting images look rough and retain some artifacts. To avoid this drawback, in this work, another term was also applied in addition to the loss from Conditional GAN and L\({}_{1}\), i.e., the Total Variation (TV) term. TV loss was imposed to reduce artifacts contained in the images generated by the generator. Several scenarios with different L\({}_{1}\) and TV weight values were carried out and evaluated using two evaluation parameters, namely, Structural Similarity Index Measure (SSIM) and Frechet Inception Distance (FID). The scenario that produced the best accuracy was analyzed and recommended as a system that could assist in Indonesian face photo generation using face sketches, namely cGAN-TV. Inspired by the sketch transformer [10], a color correction was added as postprocessing to improve the skin tone results. The rest of this paper is organized as follows. Section 2 reviews the basics of GAN and the proposed method, including the dataset and the experiment scenario. Section 3 describes the results of our proposed method. Section 4 discusses the proposed method's performance compared to the baseline methods and the potential future works. Finally, we conclude our work in Section 5.
## 3 Results
### Relationships under Different Sky Conditions
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Station** & **Period** & **SunRec1** & **SunRec2** & **Sky Conditions** & **MeanRec1** & **MeanRec2** & **MeanDif** & **RMSD** & **R\({}^{2}\)** & **n** \\ \hline Larnaca & 2009–2012 & **CS** & **Gl** & Cloudy & 1.05 & 1.12 & 0.07 & 0.564 & 0.771 & 60 \\  & & & Partly Cloudy\_D & 4.81 & 4.33 & \(-\)0.48 & 0.977 & 0.764 & 173 \\  & & & & Partly Cloudy\_B & 7.70 & 7.38 & \(-\)0.32 & 0.977 & 0.650 & 195 \\  & & & & Clear & 10.96 & 11.22 & 0.26 & 0.568 & 0.915 & 785 \\  & & & & All-sky & 9.06 & 9.23 & 0.16 & 0.717 & 0.960 & 1213 \\ \hline Larnaca & 2009–2012 & **CS** & **CSD3\_Men** & Cloudy & 1.05 & 1.08 & 0.03 & 0.690 & 0.658 & 57 \\  & & & Partly Cloudy\_D & 4.81 & 4.44 & \(-\)0.38 & 1.293 & 0.622 & 173 \\  & & & & Partly Cloudy\_B & 7.70 & 7.16 & \(-\)0.54 & 1.377 & 0.528 & 195 \\  & & & & Clear & 10.96 & 10.80 & \(-\)0.16 & 0.875 & 0.800 & 778 \\  & & & & All-sky & 9.06 & 8.94 & \(-\)0.13 & 1.032 & 0.918 & 1203 \\ \hline Larnaca & 2009–2012 & **CS** & **CSD3\_Pent** & Cloudy & 1.05 & 2.78 & 1.73 & 2.120 & 0.481 & 34 \\  & & & Partly Cloudy\_D & 4.81 & 5.61 & 0.80 & 1.935 & 0.665 & 95 \\  & & & & Partly Cloudy\_B & 7.70 & 8.37 & 0.67 & 1.635 & 0.430 & 104 \\  & & & & Clear & 10.96 & 11.01 & 0.05 & 0.922 & 0.753 & 497 \\  & & & & All-sky & 9.06 & 9.55 & 0.49 & 1.327 & 0.843 & 730 \\ \hline Pafos & 2009–2012 & **CS** & **Gl** & Cloudy & 1.41 & 1.22 & \(-\)0.19 & 0.781 & 0.583 & 78 \\  & & & & Partly Cloudy\_D & 4.73 & 3.90 & \(-\)0.83 & 1.364 & 0.632 & 147 \\  & & & & Partly Cloudy\_B & 8.01 & 7.18 & \(-\)0.83 & 1.346 & 0.611 & 204 \\  & & & & Clear & 11.07 & 10.77 & \(-\)0.31 & 0.792 & 0.807 & 783 \\  & & & & All-sky & 9.18 & 8.82 & \(-\)0.36 & 0.988 & 0.938 & 1212 \\ \hline Athalassa & 2012–2015 & **CS** & **Pr** & Cloudy & 1.32 & 0.98 & \(-\)0.34 & 0.589 & 0.861 & 87 \\  & & & & Partly Cloudy\_D & 5.15 & 4.48 & \(-\)0.67 & 1.107 & 0.819 & 237 \\  & & & & Partly Cloudy\_B & 8.68 & 8.01 & \(-\)0.67 & 1.028 & 0.803 & 305 \\  & & & & Clear & 11.06 & 10.82 & \(-\)0.25 & 0.686 & 0.877 & 767 \\  & & & All-sky & 8.94 & 8.51 & \(-\)0.44 & 0.847 & 0.955 & 1396 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of sunshine duration recorded by manual (CS) and automatic devices (Gl, Pr, CSD3) for the three synoptic stations and different time periods (n: number of cases; MeanDif: Automatic Sensor—CS recorder; RMSD: Root-mean-square deviation; R\({}^{2}\): Coefficient of determination). Means and RMSD are in h.
# Congestion Control Mechanism Based on Backpressure Feedback in Data Center Networks
## 6 Conclusions  
This article introduces a congestion control mechanism based on backpressure feedback, which divides congestion into different degrees, identifies real-congested flows by recognizing the occupancy of buffer queues, and generates the backpressure feedback accordingly. Meanwhile, the rate control on the switch is designed to achieve precise control of each flow group. In comparison to existing end-to-end congestion control schemes under two network topologies, switch-to-switch congestion control enhances throughput and significantly reduces transmission delay. The feasibility of the strategy is also demonstrated by implementing it on Mininet with BMV2. In future work, the approach will be validated in a real network environment.  
Conceptualization, W.L., M.R., Y.L. and Z.Z.; methodology, W.L., M.R. and Y.L.; software, W.L., M.R., Y.L., C.L. and H.Q.; validation, W.L., M.R. and Y.L.; investigation, M.R.; resources, W.L., Y.L. and Z.Z.; writing--original draft preparation, M.R.; writing--review and editing, W.L., M.R., Y.L., C.L. and H.Q.; visualization, M.R. and Y.L.; supervision, W.L., Y.L. and Z.Z; project administration, W.L., Y.L. and Z.Z. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Science and Technology Project of Hebei Education Department ZD2022102.  
No necessary datasets has been applied in this article.  
The authors gratefully appreciate the anonymous reviewers for their valuable comments.  
The authors declare no conflicts of interest.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 2 Image Encryption Algorithms
### Chaotic Systems
#### 2.2.7 Multiple Chaotic Maps  
The security and complexity of a chaos-based image encryption-based algorithm can be improved by using multiple chaotic maps instead of just one. Additionally, the limitations and vulnerabilities of one chaotic map can be mitigated by combining it with another chaotic map.  
[PERSON] et al. [28] used an enhanced logistic-tent map for key generation in an image encryption algorithm based on block scrambling and a modified zigzag transformation. The algorithm starts by dividing the plaintext image into four quadrants, and each quadrant is further divided into four sub-quadrants. Next, each sub-quadrant is rotated anti-clockwise by 90\({}^{\circ}\). Then, each color channel of the scrambled image is further permuted using a modified zigzag transformation, where the upper left pixel and its next neighboring horizontal pixels are exchanged with the base right pixel. Finally, an XOR operation is  
Figure 4: The Henon map bifurcation diagram.  
performed between the permuted image and the keystream generated by the enhanced logistic tent map.  
[PERSON] and [PERSON] [29] proposed an image encryption algorithm based on a 2-dimensional logistic-sine coupling map (2D-LSCM) and [PERSON]'s cat map, where [PERSON]'s cat map is used to shift the pixels of the plaintext image. Next, a chaotic matrix (matrix X) that has the same size as the plaintext image is generated by the 2D-LSCM chaotic map, and is used to perform another permutation on the pixel positions. The pixel values are then diffused using chaotic matrix X. This process of permutation and diffusion is repeated for two rounds to produce the cipher image.
## 3 Results and Discussion
### Identification of Key Components That Affect Sensory Qualities of Tea Samples
#### 3.3.2 Pca  
PCA was used to further analyze the correlation between phytochemicals and sensory profiles. Two PCs accounted for 75.13% of the original data on the variables. The loading values of variables in PC1 and PC2 are exhibited in Figure 6. For PC1, the overall acceptance was positively correlated with umami, sweetness, and astringency exhibited negative correlations with sourness and bitterness.  
For PC1 and PC2, \(a^{*}\) and \(b^{*}\) values were positively correlated with TF, TF-3-G, TF-3\({}^{\prime}\)-G, and TFDG. The higher the a and b values, the more yellow and red the tea infusion is. For PC2, astringency was positively correlated with these theaflavins. It is reported that TF, TF-3-G, TF-3\({}^{\prime}\)-G, and TFDG are important yellow-red compounds in black tea, which exhibit astringency taste in black tea [21]. This is consistent with our results that theaflavins strongly contributed to the yellow-red and astringency of the black tea infusion.  
In addition, umami exhibited positive correlations with glutamic acid and theanine, while astringency showed a positive correlation with tannic acid. This result was similar to previous studies in that glutamic acid and theanine were the main contributors to the umami of tea, and tannic acid strongly contributed to the astringency of the tea [27; 39]. Previous studies have reported that geraniol and \(\beta\)-ionnone can enhance the sweetness of tea infusion [21]. However, in our results, these two substances were negatively correlated with sweetness, indicating that the effects of geraniol and \(\beta\)-ionone on the sweetness of tea soups may are still limited. Safranal is a volatile substance with a sweet or herbal aroma; 1-octanol has a citrus aroma; 2-ethylhexanol exhibits a rose and green aroma, which is positively  
Figure 6: Principal component (PC) 1 vs. PC2 loading plots. Abbreviations: theaflavins (TF); theaflavin-3-gallate (TF-3G); theaflavin 3\({}^{\prime}\)-O-gallate (TF-3\({}^{\prime}\)-G); theaflavin 3\({}^{\prime}\)-digallate (TFDG); epigallocatechin gallate (EGCG); gallocatechin (GC); epigallocatechin (EGC); catechchin (C); epicatechin (EC); gallocatechin gallate (GCG); epicatechin gallate (ECG); gallic acid (GA).  
correlated with sweetness and overall acceptance, suggesting that it may contribute to the sweetness and overall acceptance of the tea infusion.  
Collectively, for the GT, it is necessary to keep the bitter and astringent substances such as EC, EGCG, GCG, and ECG in the tea leaves at an appropriate level, during the decaffeination. Their high removal rate results in a bland and tasteless DeCAF-BT. For BT, theaflavins are the main contributors to the color and taste of the tea infusion. Their high retention rate is the key to maintaining the quality of DeCAF-BT.
## 2 Scope and Structure  
The research begins with a review of currently available standards suitable for wireless networking in CPES. These are analyzed in the third section, which describes wireless sensor networks (WSN) and the class of low power wide area networks (LPWANs), both aimed at networking in smart microgrids. The Long Range (LoRa) standard, which is practically analyzed in one proposed research case, is presented in more detail. This critical review of the standards helped refine the initial research ideas and build the context for the standards currently available for wireless networking in CPES.  
The fourth section discusses standards that can be considered for the cyber representation of CPES. The OPC Unified Architecture (OPC UA) framework analyzed here is a particularly promising candidate and is more closely aligned with CPES requirements.  
The hands-on work of this research is presented in the fifth section, where we discuss the two representative case studies we are conducting in companies in Germany. We compare a retrofit using a non-standard product with a setup based on the LoRa and OPC UA standards. The first company is a retailer with low energy consumption that receives a retrofit of its utility energy meter through a glued-on optical character recognition (OCR) camera solution. In the second company, an automobile manufacturer, we measure energy consumption in three phases of a process plant using current transformers. In both case studies, the sensors for measuring energy consumption are connected to a universal EMS. The observation focuses on the complexity and effort of integrating CPES sensors into a universal EMS in a real-life context. A comparison shows the cost differences of implementation, configuration, and hardware costs.  
The discussion in the sixth section compares the two approaches and shortcomings of the standards applied for their use in CPES.
# Jointly Optimize the Residual Energy of Multiple Mobile Devices in the MEC-WPT System
## 5 Problem Solving  
Through the above simplification, we have obtained the simplification problem \(P2\). In order to get the best task-offloading time and energy harvesting time, we have the constraint \(t_{i}^{har}+\sum_{j=1}^{M}t_{i,j}^{off}\leq T\). After the time \(T\) is consumed, the maximum energy harvesting time and the minimum task-offloading time are the best solutions. Therefore, let \(t_{i}^{har}+\sum_{j=1}^{M}t_{i,j}^{off}=T\), then \(t_{i}^{har}=T-\sum_{j=1}^{M}t_{i,j}^{off}\). After we replace \(t_{i}^{har}\), we get the \(P3\) question:  
\[\begin{split} P3&\max_{R^{off},t^{off}}\sum_{i=1}^{N }\sum_{j=1}^{M}E_{i,j}^{res}(R_{i,j}^{off},t_{i,j}^{off})\\ s.t.& C4,C7,C8\\ & C9:\sum_{j=1}^{M}t_{i,j}^{off}<T\quad i\in\mathbf{N}\\ & C10:t_{i,j}^{off}>0\quad i=1,2,\ldots,N,j\in\mathbf{M}\end{split} \tag{12}\]  
where \(E_{i,j}^{res}(R_{i,j}^{off},t_{i,j}^{off})=f(i,j)T-(f(i,j)\sum_{j=1}^{M}t_{i,j }^{off}+\frac{\kappa_{j}((R_{i,j}-R_{i,j}^{off})C_{j})^{3}}{T^{2}}+\frac{t_{i,j }^{off}}{h_{i,j}^{{}^{\prime}}}f(\frac{R_{i,j}^{off}}{t_{i,j}^{off}})+p_{m,j}t _{i,j}^{off})\), and the C3 and C5 are replaced by C9 and C10.  
Through the above simplification, the problem is divided into two parts, one is a constant term and the other is a non-constant term with variables. It happens that the constant term subtracts the non-constant term, then we transform the \(P3\) problem into the \(P4\) problem:  
\[\begin{split} P4&\min_{R^{off},r^{off}}\sum_{i=1}^{N} \sum_{j=1}^{M}E_{i,j}^{con}(R_{i,j}^{off},t_{i,j}^{off})\\ s.t.& C1:\sum_{j=1}^{M}R_{i,j}^{off}C_{j}\leq F \quad i\in\mathbf{N}\\ & C2:0<R_{i,j}^{off}\leq R_{i,j}\quad i\in\mathbf{N},j\in\mathbf{M}\\ & C3:(E_{i,j}^{loc}+E_{i,j}^{off})-E_{i,j}\leq 0\quad i\in\mathbf{N},j \in\mathbf{M}\\ & C4:\sum_{j=1}^{M}t_{i,j}^{off}<T\quad i\in\mathbf{N}\\ & C5:t_{i,j}^{off}>0\quad i=1,2,\ldots,N,j\in\mathbf{M}\end{split} \tag{13}\]  
where \(E_{i,j}^{con}(R_{i,j}^{off},t_{i,j}^{off})=f(i,j)\sum_{j=1}^{M}t_{i,j}^{off}+ \frac{\kappa_{j}((R_{i,j}-R_{i,j}^{off})C_{j})^{3}}{T^{2}}+\frac{t_{i,j}^{off}} {t_{i,j}^{off}}f(\frac{R_{i,j}^{off}}{t_{i,j}^{off}})+P_{m,i}t_{im,j}^{off}\).  
Since \(f(x)\) is a convex function with \(x\geq 0\), its perspective function \(\frac{t_{i,j}^{off}}{t_{i,j}^{off}}f(\frac{R_{i,j}^{off}}{t_{i,j}^{off}})\) is convex relative to \(R_{i,j}^{off}\geq 0\) and \(t_{i,j}^{off}\geq 0\). We get that the objective function and constraint 3 of the \(P4\) problem are convex, and the other constraints are convex. Therefore, the \(P4\) problem is convex and can be solved by standard convex optimization techniques.  
The detailed solution for solving problem \(P4\) and main problem \(P1\) is described as follows.
## 4 Discussion
### Analysis of the Influence of Location (Seville/Barcelona)  
Although the \(DA\) and \(CSA\) values are slightly higher in Seville compared to Barcelona, the impact of location is less pronounced than that of WWR and interior reflectance. For bright coatings, \(DA_{300}\) values exceed 80% in Seville and 74% in Barcelona across various WWRs and orientations. Similarly, \(CSA2\) values for bright coatings remain elevated above 80% in both locations for south and north configurations, particularly with centered and upper windows with WWRs between 25% and 45%.  
Given these findings, it is recommended to prioritize larger WWRs and centered window configurations in both Seville and Barcelona, paying additional attention to maximizing daylight access in Seville, where possible. The observed differences in \(DA\) and \(CSA\) performance between the two locations do not necessitate distinct configurations but highlight the importance of optimizing other factors such as reflectance and window positioning.
# Thermal Strain-Based Simplified Prediction of Thermal Deformation Caused by Flame Bending
###### Abstract  
This paper describes a quick and accurate method for predicting thermal deformation due to flame bending of the curved plate located before and after the hull. Flame bending is a common method to deform the curved plate used in shipards. Three-dimensional thermo-elasto-plastic analysis is known as the most accurate method for predicting deformed shape in the automation of frame bending. However, the three-dimensional analysis takes a lot of computational time. The quick prediction method, strain as direct boundary (SDB), was introduced, which is a simplified prediction method based on thermal strain. This simplified method implements an equivalent load as a temperature difference that can simulate thermal deformation by flame. In the case of multiple heating lines by the flame bending, the residual strain generated by the first heating line affects the other lines. To consider the effect of residual strain, the plastic material properties are also considered. Then, the distance ratio from the center line is used to generate the same temperature field in grid mesh. The results of the prediction were evaluated for the heat affected zone (HAZ) of the specimen obtained through the flame bending experiment. Therefore, this paper introduced detail procedure of the proposed SDB method and the experimental results for the practical application.  
Keywords:plate deformation; heat-affected zone (HAZ); strain as direct boundary (SDB); line heating; flame bending +
Footnote †: journal: Applied