esa-sceva/satcom-chunk-collection huggingface dataset:
# Side-Scan Sonar Image Augmentation Method Based on CC-WGAN
## 4 Discussion
### Impact of Different Generation Algorithms  
In order to test the performance of the generative models in this paper, CC-WGAN is compared and analyzed with the basic GAN and WGAN models. From Section 4.2, it can be seen that the YOLOv5l detection model is the best for target detection in sonar images, so in this section, we use the YOLOv5l detection model for experiments to evaluate the performance of the three generative models.  
The FID, MMD, PSNR, and SSIM metrics are calculated to evaluate the quality of images generated by the three algorithms, as shown in Table 6.  
As can be seen from Table 6, the CC-WGAN algorithm generates images with the lowest FID value, which indicates that this paper's algorithm generates higher-quality images, and its MMD is the smallest, which indicates that there is a significant statistical similarity between the generated images and the real images. For the PSNR metric, this paper's algorithm generates images with the highest PSNR, which indicates that the proposed model obtains a better image quality. For the SSIM metric, the three groups are similar.  
Next, YOLOv5l was used to evaluate the performance of target detection on the images generated by the three algorithms, as shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Algorithm** & **FID** & **MMD** & **PSNR** & **SSIM** \\ \hline basic GAN & 165.16 & 0.2899 & 14.0637 & 0.5940 \\ WGAN & 155.69 & 0.2585 & 14.6373 & 0.5896 \\ CC-WGAN & 123.50 & 0.2358 & 16.0296 & 0.5956 \\ \hline \end{tabular}
\end{table}
Table 6: Calculations of FID, MMD, PSNR, and SSIM metrics for three algorithms to generate augmented images.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Detection Model** & **1** & **2** & **3** \\ \hline YOLOv5n & 88.1\% & 96.8\% & 85.1\% \\ YOLOv5s & 88.2\% & 97.2\% & 95.1\% \\ YOLOv5m & 89.7\% & 97.1\% & 94.8\% \\ YOLOv5l & 89.8\% & 95.3\% & 97.4\% \\ \hline \end{tabular}
\end{table}
Table 5: Precision comparison of different models.  
As can be seen from Table 7, the images generated by the CC-WGAN algorithm are optimal in terms of precision, recall, and mean average precision for target detection, further reflecting the advantages of the proposed algorithm.
## 3 Results
### COM-COP Inclination Angle  
We also examined the COM-COP inclination angle at five stance Events, i.e., FDS1, MST1, FDS2, MST2, and SDS2, and Event interaction with Condition. Significant findings were revealed for the COM-COP inclination angle for both Event and Event interaction with Condition (Table 7). For the post hoc interaction analysis for the CON and PD groups by Event, the only finding for a significant difference was a larger inclination angle for the CON group at second double support for step 2 (Table 8, Figure 9).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Measurement** & **Condition** & **Event** & **Interaction** \\ \hline COM–COP Angle (\({}^{\circ}\)) & 0.940 & \textless{}0.001 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 7: ANOVA F \(p\)-values for main effects of Condition and Event and interaction.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metric** & **Event** & **CON** & **PD** & **(CON–PD)** & **\(p\)-Value** & **Diff CI** \\ \hline COM–COP & FDS1 & 12.73 (0.229) & 13.1 (0.244) & \(-\)0.389 (0.334) & 0.262 & (\(-\)1.097, 0.320) \\ \hline COM–COP & MST1 & 2.64 (0.229) & 3.11 (0.244) & \(-\)0.476 (0.334) & 0.174 & (\(-\)1.184, 0.233) \\ \hline COM–COP & FDS2 & 13.49 (0.229) & 13.25 (0.244) & 0.244 (0.334) & 0.475 & (\(-\)0.464, 0.953) \\ \hline COM–COP & MST2 & 2.73 (0.229) & 3.18 (0.244) & \(-\)0.447 (0.334) & 0.200 & (\(-\)1.155, 0.261) \\ \hline
**COM–COP** & **SDS2** & **16.66 (0.229)** & **15.51 (0.244)** & **1.159 (0.334)** & **0.003** & **(0.450, 1.867)** \\ \hline \hline \end{tabular}
\end{table}
Table 8: COM–COP inclination angle during the obstacle crossing.  
Figure 9: The COM–COP inclination angle for the control (CON) and Parkinson’s (PD) groups at all five Events. FDS1 = first double support step 1; MST1 = midstance step 1; FDS2 = first double support step 2; MST2 = midstance step 2; SDS2 = second double support step 2. Note: FDS2 = second double support step 1. The 95% confidence intervals for each mean are included to illustrate significant differences, i.e., confidence intervals not overlapping, both within and between conditions. Notably, this also suggests differences between Events within each group (CON and PD).
# Influence of Magnetic Field on Amplification without Inversion Induced by a Femtosecond Pulse Train
## 2 Theoretical Model
\begin{array}{l}\frac{\partial\rho_{11}}{\partial t}=\frac{1}{T_{2}}\rho_{22}- \frac{i}{2}\Omega(\rho_{12}-\rho_{21}),\\ \frac{\partial\rho_{12}}{\partial t}=-\frac{1}{T_{2}}\rho_{12}+i(\Delta+\Delta _{B})\rho_{12}-\frac{i}{2}\Omega(\rho_{11}-\rho_{22}+\rho_{13}),\\ \frac{\partial\rho_{13}}{\partial t}=2i\Delta_{B}\rho_{13}-\frac{i}{2}\Omega( \rho_{12}-\rho_{23}),\\ \frac{\partial\rho_{21}}{\partial t}=-\frac{1}{T_{2}}\rho_{21}-i(\Delta+\Delta _{B})\rho_{21}+\frac{i}{2}\Omega(\rho_{11}-\rho_{22}+\rho_{31}),\\ \frac{\partial\rho_{22}}{\partial t}=-\frac{1}{T_{1}}\rho_{22}+\frac{i}{2} \Omega(\rho_{12}+\rho_{32}-\rho_{21}-\rho_{23}),\\ \frac{\partial\rho_{23}}{\partial t}=-\frac{1}{T_{2}}\rho_{23}-i(\Delta-\Delta _{B})\rho_{23}+\frac{i}{2}\Omega(\rho_{13}-\rho_{22}+\rho_{33}),\\ \frac{\partial\rho_{31}}{\partial t}=-2i\Delta_{B}\rho_{31}-\frac{i}{2} \Omega(\rho_{32}-\rho_{21}),\\ \frac{\partial\rho_{32}}{\partial t}=-\frac{1}{T_{2}}\rho_{32}+i(\Delta- \Delta_{B})\rho_{32}-\frac{i}{2}\Omega(\rho_{31}-\rho_{22}+\rho_{33}),\\ \frac{\partial\rho_{32}}{\partial t}=\frac{1}{T_{2}}\rho_{22}-\frac{i}{2} \Omega(\rho_{32}-\rho_{23})\end{array}  \tag{5}\]  
where \(\rho_{11}+\rho_{22}+\rho_{33}=1\). The spontaneous relaxation time of \({}^{87}\)Rb is \(T_{1}=28\) ns and \(T_{2}=2T_{1}\)[19]. The real part of the off-diagonal elements (\(\rho_{21}\) and \(\rho_{23}\)) represents dispersion, and the imaginary part represents absorption. Therefore, the gain of the atomic system is related to the imaginary part of the off-diagonal element. Since the duration of each pulse in the pulse train is much shorter than the pulse repetition period \(T_{R}\) and the atomic spontaneous relaxation time \(T_{1}\) of 28 ns, a semi-analytical method developed in [20; 21; 22] was adopted to solve the above density matrix equations. In each period of the pulse train, this process is divided into two parts. During the interaction time \(0<\tau\leq\sigma\), the atomic relaxation behavior, which is much slower than the pulse duration \(\sigma\), is ignored and only the pulse excitation is considered which can be treated as an undamped Rabi process. During \(\sigma<\tau\leq T_{R}\), the atom only experiences spontaneous emission. For every input pulse, the obtained solution \(\rho\) from the previous pulse is taken as the initial state of the atomic system for the next pulse interaction, and then the iteration is carried out repeatedly to obtain atomic states of this system after the pulse train interacting with the atoms. For problems about interactions between atoms and short pulse trains, it was proven that the result of this kind of semi-analytical method is consistent with the numerical fourth-order Runge-Kutta method [23].
# Molecular Science of Lubricant Additives +
## 1 Introduction
### The Functions of Lubricants  
Lubricants are those substances that intervene between rubbing surfaces, thereby preventing any negative influences upon moving. The three major functions of lubricants are below.  
* **Controlling friction--**This is the primary role of lubricants. Reducing friction and preventing wear and seizure (or failure) are necessary in most applications. Well-controlled, high friction is required for clutches, breaks, etc. Since this is the central objective of the lubricant, we discuss the phenomena with a lubrication model (Stribeck curve, see Figure 1). Optimized performances with minimized side effects could be achieved by proper selection of lubricants for specific machine elements. As modern machines are required to work in a more energy efficient way, the roles of lubricants are increasing. The importance of additives is emphasized (see Section 2.1 tribo-improvers).
* **Cooling the contact--**Heat generated by rubbing motion can have many negative influences on surface materials, such as transformation of microstructure or thermal failure. Ageing of lubricants is accelerated at higher temperatures. Heat accumulation could be prevented by circulating a liquid lubricant. The heat capacity of a lubricant is the controlling factor for this function. This function is mainly supported by the properties of base fluids. Some additives contribute to the heat conductivity of primary (see Section 2.5.2) or secondary (see Section 2.3.5) base fluids.
* **Cleaning the contact--**Wear particles, external dusts, or deposits by aged lubricants could appear during machine operation. These contaminants negatively influence lubrication performances. Circulating a lubricant can wash out these nuisances physically. Advanced lubricants contain some substances to help the cleaning process (see Sections 2.3.2 and 2.3.3).  
Lubricants can appear as a liquid, as a semi-solid (greases), or as a solid (including particles and coatings) form. Since cleaning and cooling functions need fluidity, greases and solid lubricants could be applied to machine elements where heat and contamination are not serious factors. Because of the three functions and the benefit of handling, liquid lubricants are most ubiquitous in many practical applications. Therefore, various additives have been developed and are being applied in liquid lubricants. In this regard, this review mainly focuses on additives in liquid lubricants. Of course, the demands of greases and solid lubricants are also substantial mainly for task-specific purposes.
# Synthetic Microstructure Generation and Multiscale Analysis of Asphalt Concrete
## 2 Materials and Methods
### Microstructure Generation  
The algorithms for the AC microstructure generation can be roughly divided into two main groups. The distinction is based on the modeled domain. If the task is to generate the microstructure for a given real sample, then the X-ray computed tomography (XRCT) is typically used. This approach is very versatile and reliable, as it enables consideration of a real sample in the numerical model. A number of the XRCT scans is generated. Then, they are sharpened and binarized to reflect the material spatial distribution. Finally, the microstructure is generated on a set of such scans. In the context of the further numerical analysis, this approach is very often prohibitively expensive. For the AC modeling purposes, the resolution of the scans was reported in [7] as the main limitation. It is due to the fact, that 1 voxel is assosiated in the finite element method (FEM) with a single element. It leads to the overkill meshes that are unecessary in such a relatively simple geometry as the shapes of particles. In [7], it was presented that \(1000^{3}\) voxels were used to model a cube of the edge length equal to 24.5 mm. In order to make the analysis of such a domain feasible, a geometry simplification is performed (c.f. [4]).  
The alternative approach is to generate a fully synthetic microstructure without any mapping of the real domain. In the case of AC, this approach is justified, as its microstructure is random. There are two main parameters that are to be preserved in the numerical modeling of this material: total volumetric ratios of the matrix and the inclusions as well as the particle size distribution determined by the gradation curve. As reported in [4], the angularity of the aggregate particles plays also a vital role in the behavior of the structures (cylindrical samples, therein) made of AC. There is a large body of the literature devoted to the AC microstructure generation (see, e.g., in [3; 4; 5; 7; 13; 14; 16; 17]). The analized geometry was substantially simplified (c.f. [17]), where the research was focused on the ravelling process. The approach based on th XRCT scans was used for the real samples analyzed in a laboratory experiment to compare the results with the numerical analysis [4] or in the numerical simulations reproducing the laboratory tests [13]. However, the synthetic microstructure generation is frequently used to overcome the resources limitations [7; 16]. This approach is also used in the cases, where the focus is on the feasibility of the nonlinear analysis or new constitutive model introduction [3; 5; 13; 14].  
The algorithm we propose in this paper is a combination of the approaches presented in the aforementioned papers with some modifications. We have implemented it in 2D but it is not limited to this case.
## 1 Introduction
This article presents a novel approach that overcomes the limitations of vibration-based thermography as a stationary ground-based system by replacing the shaker and the IR-camera with piezoelectric wafer active sensors (PWAS; as typically applied for various SHM methods) and an array of surface temperature sensors that are permanently bonded to the structure of interest and discusses its applicability as an SHM method. A challenge of the novel vibration-based thermal health monitoring method is the weak excitation by PWAS in contrast to heavy shakers. The lower excitation levels are compensated by the application of the LDR principle. To evaluate the potential of the novel thermal health  
Figure 1: Composite spoiler of a civil aircraft. monitoring approach for face layer debonding detection and identification, laboratory experiments with different excitation sources are performed. First, the general applicability of vibration-based thermography for detecting and identifying this type of damage is investigated experimentally by means of a piezoelectric stack actuator as excitation source and an array of surface temperature sensors and an IR-camera for temperature measurements. Notwithstanding, that this setup is a typical NDT setup, it is required to proof the basic measurement concept, as there is a lack of research so far in NDT and SHM literature for sandwich debonding. Furthermore, it allows the comparison of the results of the surface temperature sensor and the IR-camera measurements. Second, the applicability and potential of the novel vibration-based thermal health monitoring is demonstrated experimentally using the same damaged structure by means of a small and lightweight PWAS as excitation source and the validated surface temperature sensor array. To find the resonance frequencies of the considered face layer debonding and to investigate local vibration amplitudes and its consequent heating by the different excitation sources, vibration measurements with a scanning laser Doppler vibrometer (SLDV) are performed.
## 3 Results
### Robot Platform Application
\begin{table}
\begin{tabular}{c} \hline
**Initialize**\(G\)\(\in\)[0.5 1]; \(S_{D}\)\(\in\)[\(D_{ij}=0,j=1,2,\ldots,6\); \(i=1,2,\ldots,2^{n}\)]; \(B\)\(\in\)[0, 1]; n = 0; \(T_{i}\)\(\in\)[1, 2, \(\ldots,6\)] \\
**Repeat:** \\ (1) Collect terrain images: color, depth, and infrared; \\ (2) Run the obstacle detection module and output \(B\) \\
**if**\(B=1\)**then** \\
**Run** image infilling processing I \\
**jump to (2)** \\
**else if**\(B=0\)**then** \\
**continue** \\
**end** \\ (3) Run the terrain classifier module and output \(S_{D}\) \\
**for**\(i=1\); \(i\leq 2^{n}\); \(i\)++ \\
**if**\(\max\) [\(D_{ij}\), (j = 1, 2, \(\ldots,6\))] \(<0.3\)**then** \\
**n++** \\
**Run** image segmentation processing \\
**Run** image infilling processing II \\
**jump to repeat (3);** \\
**Else if** \\ output the subscript \(j\) of max {\(D_{ij}\), (j = 1, 2, \(\ldots,6\))}; \\
**\(T_{i}=j\)** \\
**end** \\ (4) Output classification results and gait \(G\) \\
**for**\(i=1\); \(i\leq 2\)\(n\)–\(1\); \(i\)++ \\
**if**\(T_{i}=1\) or 2 **then**\(G=0.5\) \\
**else if**\(T_{i}=3\) or 4 **then**\(G=0.75\) \\
**else if**\(T_{i}=5\) or 6 **then**\(G=0.83\) \\
**end** \\ \(T=T_{1}\),\(T_{2}\),\(T_{3}\), \(\ldots,T_{2n\)-\(1}\) \\ (5) Run the robot \\
**Until:** The robot is switched off. Note: \(G\) is the walking gait; typically, 0.5 for tripod gait, 0.75 for quadruped gait, and 0.83 for wave gait. \(S_{D}\) is the confidence score; \(j\) refers to terrain type: 1 for asphalt, 2 for tile, 3 for soil, 4 for gravel, 5 for sand, 6 for grass; \(i\) is the serial number of images; \(B\) refers to the result of obstacle detection: \(B=1\) means there is an obstacle, \(B=0\) means no obstacle. \(T_{i}\) is the output label of the terrain classifier. Image infilling processing I represents the ILI module, and image infilling processing II represents the SPI module.
## 4 Conclusions  
The results in this study demonstrate the great potential of freely available Sentinel-2 satellite data and the respective derived ratios to map fire damage in the fire-prone landscape of the Southern Cape in South Africa. Using the differenced normalized burned ratio (dNBR) derived from Sentinel-2, we successfully tracked the extent of burned areas and the corresponding severity of the damage to the vegetation that succumbed to fire with accuracies exceeding 97% based on RF. The RF also identified texture, NBR, and NIR as the most important variables in the classification. The forested area was the most affected class. We also analyzed the pattern of carbon monoxide (CO) plumes recorded by Sentinel-5, with a marked peak during the fire incident. Most of the region was accounted for by forestry, whereas the remainder of the area represented the fynbos biome. Overall, CO was a reliable indicator for tracking fires and can be used in combination with remotely derived vegetation indices for a national fire assessment and monitoring framework. Moreover, our study reaffirmed the GEE-based tools as efficient resources in facilitating the rapid generation of burned area outputs.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON] and [PERSON]; validation, [PERSON] and [PERSON]; formal analysis, [PERSON], [PERSON] and [PERSON]; investigation, [PERSON] and [PERSON]; resources, [PERSON] and [PERSON]; data curation, [PERSON] and [PERSON]; writing--original draft preparation [PERSON], [PERSON] and [PERSON]; writing--review and editing, [PERSON], [PERSON] and [PERSON]; visualization, [PERSON] and [PERSON]; funding acquisition, [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Research Foundation (NRF) of South Africa (Grant number: 114898).  
The datasets used and/or analyzed in this study are available from the corresponding author on reasonable request.  
The authors are thankful to [PERSON] for manuscript editing and proofreading and to the anonymous reviewers and editors for their invaluable comments and suggestions, which have improved the quality of the manuscript. The authors are also thankful to [PERSON] for her support with the GEE scripting and the Google Earth Engine Team for the GEE cloud platform. The authors would also like to thank three anonymous reviewers for their valuable comments and suggestions.  
Figure 9: The importance of each variable in burned area mapping.
# Analyzing Meta-Heuristic Algorithms for Task Scheduling in a Fog-Based IoT Application
## 4 Evaluation
### Analysis Based on the Number of Devices
#### 4.5.3 Comparison with Meta-Heuristic Methods  
The HHS algorithm is compared with two meta-heuristic methods: NSGA-III [13] and MaOPSO [14]. The chromosome encoding is as described in Section 3.2.1. The parameters of these two algorithms are as follows.  
* NSGA-III: population size = 100, crossover probability = 0.9, mutation probability = 0.5, and max iterations = 50.
* MaOPSO: swarm size = 100, archive size = 100, mutation probability = 0.5, and max iterations = 50.  
The average energy consumption and total execution cost of MaOPSO and NSGA-III are better than that of HHS. Of course, MaOPSO and NSGA-III reduce the energy consumption and total execution cost with a great deal of time, which is not a good result for an IoT application. The delay of the application loop is decreased by HHS compared to  
Figure 5: Energy consumption of GA, PSO, ACO, SA, and HHS.  
Figure 6: Total network usage of GA, PSO, ACO, SA, and HHS.  
MaOPSO by 28.1% and compared to NSGA-III by 40.8%. Therefore, reducing the delay is an advantage relative to the MaOPSO and NSGA-III algorithms.  
The results in Figure 7a,b show that the energy consumption and the total execution cost of NSGA-III and MaOPSO are less than that of the HHS algorithm. This is due to the use of low-level algorithms in HHS. NSGA-III and MaOPSO are good at finding the optimal answer, but as Figure 7c shows, their application loop delay is greater than that of HHS. Since many IoT applications need to execute in real-time, thus HHS algorithm with its low delay is more suitable.  
Figure 7: Comparison of HHS algorithm to MaOPSO and NSGA-III: (**a**) Energy consumption (**b**) Total execution cost, and (**c**) Delay of application loop.
## 4 Evaluation
### Communication Complexity  
A logical read from the client is translated into an ORAM Read, which responds with a block to the client. The physical reads in ORAM Read include (1) a block read from the main ORAM, and (2) a path read from the posMap ORAM. Hence, the communication complexity for a logical read is \(D_{m}+S\cdot\log N\cdot D_{p}\).  
A logical write from the client is translated into an ORAM Read and an ORAM Write. The ORAM Write writes the blocks from the client to the server. First, a physical read is executed in Mwrite to read a random bucket from the main ORAM. Then, to check the states of the blocks in the random buckets, at most \(Z\) paths are physically read from the posMap ORAM in Mwrite. Afterwards, a physical write is executed to write the random bucket back. In the end, both a physical read and physical write of a deterministic path are executed in Pwrite to update the position map of the blocks in the main ORAM. Comparing a logical read, the extra communication complexity for a logical write is \(2Z\cdot D_{M}/A+Z\cdot S\cdot\log N\cdot D_{p}/A+2S\cdot\log N\cdot D_{p}/A^{\prime}\).  
As \(D_{M}\)=\(O(B)\) and \(D_{P}\)=\(O(logN)\), the total communication complexity is \(O(B)\) or \(O(log^{2}N)\). If \(B\)=\(\Omega(log^{2}N)\), the total communication complexity per logical write or logical read is \(O(B)\). When \(N\) and \(B\) are fixed, we can trade off \(Z\) and \(A\) to reduce the communication complexity in the main ORAM. Likewise, \(S\) and \(A\) can be traded off to reduce the communication complexity in the posMap ORAM.  
Furthermore, for a logical read, the physical reads in ORAM Read can only be executed sequentially, which results two communication rounds. For a logical write, ORAM Read and ORAM Write can be executed in parallel. In ORAM Write, the first communication round is the physical read to a random bucket and the second communication round is the physical read to a set of paths in posMap ORAM to check the states of blocks in the random bucket. For the remaining operations, all of them can piggyback on the two physical reads. Specifically, the physical write to the random bucket as well as the physical read and physical write to update the position map could be executed together with the following physical reads to a random bucket or a set of paths. As a result, two communication rounds are required for a logical write.