esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Driven Damage Classification Using Guided Waves in Pipe Structures
## 5 Training and Validation of the Neural Network
### Activation Function Effect between Tanh and ReLU  
Two frequency-used functions tanh and ReLU were compared in this work. The ReLU function shown in Equation (7) was simple, but it is an important achievement in recent years.  
\[\text{ReLU}(x)=\max(0,x)=\begin{cases}\text{xif}x>0\\ \text{0 if}x\leq 0\end{cases} \tag{7}\]  
where \(\max(\cdot)\) refers to the larger value between two figures.  
The neural networks framework plotted in Figure 8 were established with Python. The loss function was usually set as the cross entropy as Equation (8). The regularization term is always brought in to avoid overfitting, so correspondingly, the final loss function is expressed in Equation (9), which can reflect the difference between the real label and the prediction label. When the final loss function \(L^{\prime}(y_{i},\hat{y}_{i})\) is equal to zero, this means that the prediction label is completely identical to the real label. In addition, the batch gradient descent back propagation method is adopted to update the MLP parameters, considering that the sample set is not very large.  
\[L(y_{i},\hat{y}_{i})=-\sum_{i=1}^{u}y_{i}\log(\hat{y}_{i}) \tag{8}\]  
\[L^{\prime}(y_{i},\hat{y}_{i})=L(y_{i},\hat{y}_{i})+\frac{\lambda}{2N}{\sum_{j=1 }^{N}}w_{j}^{2} \tag{9}\]  
where \(y_{i}\) represents the real labels, and in this paper, the labels \(y_{i}\) = [1, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 1, 0], and [0, 0, 0, 0, 1] correspond to the experimental conditions of no damage, corrosion, dent, groove, and pipe end; \(\lambda\) is the regularization coefficient; and \(N\) is the number of all the parameters \(w_{j}\).  
In order to accurately identify the signal type, an MLP with two hidden layers containing 32 neurons in each layer was employed. The final loss function \(L^{\prime}(y_{i},\hat{y}_{i})\) of the training set was set to be below \(5\times 10^{-3}\); the learning rate was \(3\times 10^{-3}\); and the regularization coefficient \(\lambda\) in Equation (9) was set as \(1\times 10^{-5}\). After training with 420 samples randomly selected in the gross 520 signal samples, the test result of what remained was obtained. The corresponding training processes for the training set are shown in Figure 9, from which it can be seen that the training process of the network containing two ReLU hidden layers converged faster. The L' of the neural network with tanh hidden layers dropped to 4.91 \(\times\)\(10^{-3}\) at 1244 epochs, and that with ReLU hidden layers dropped to 3.27 \(\times\)\(10^{-3}\) at 885 epochs. The training and test confusion matrices of these two kinds of multi-layer neural network are shown in Figures 10 and 11, respectively. It is easy to obtain the conclusion  
Figure 8: The multilayer feed-forward neural network framework.  
that the ReLU activation function is more effective for the pattern recognition of guided wave signals.
# Performance Analysis of Time Synchronization Protocols in Wireless Sensor Networks
## 3 Time Synchronization in WSNs  
### Problem and Challenge  
In a wireless network, each node has its own local clock, typically referred to as a hardware clock. This hardware clock's timing is calculated by counting the pulses of an oscillator operating at a particular frequency. However, the output frequency of each oscillator varies according to the hardware age or ambient environmental conditions [5]. Consequently, each hardware clock is subject to varying clock drift. Because of this clock drift, even when two nodes exhibit the same initial time, they will exhibit different clock values after some time. The oscillators used in sensor hardware typically exhibit a drift from 30-100 ppm (parts per million) [11]. The hardware clock value \(h_{i}(t)\) of node \(i\) can be modeled mathematically as follows:  
\[h_{i}(t)=\alpha_{i}t+\beta_{i} \tag{1}\]  
where \(\alpha_{i}\) is the hardware clock rate and \(\beta_{i}\) is the initial hardware offset of node \(i\). The difference between hardware clock rates is called the _clock skew_. Because the hardware clock operates continuously and should not be modified, a logical clock is defined to represent a global clock (synchronized clock). The value of the logical clock \(L_{i}(t)\) for node \(i\) is calculated as follows:  
\[L_{i}(t)=\tau_{i}(\alpha_{i}t+\beta_{i})+\delta_{i}=\tau_{i}\alpha_{i}t+\tau_{ i}\beta_{i}+\delta_{i} \tag{2}\]  
where \(\tau_{i}\alpha_{i}\) is the logical clock rate and \(\tau_{i}\beta_{i}+\delta_{i}\) is the logical clock offset of node \(i\). The goal of the time synchronization protocol is to estimate \(\tau_{i}\) and \(\delta_{i}\) such that the logical times \(L_{i}(t)\) of all nodes are equivalent.  
Designing a time synchronization protocol for WSNs is very challenging. Most time synchronization approaches rely on message exchange between nodes. However, whenever a node generates a timestamp and sends it to other nodes for synchronization, the packet is subject to variations in delay before it reaches and is processed by receivers. Authors of FTSP analyzed the causes of delays and the magnitude of each in message transmissions [1]. Assuming that deterministic delays can be calculated exactly, the role of time synchronization protocols is to eliminate or reduce the effect of nondeterministic delays. Additionally, implementing existing protocols in real-world networks present particular practical challenges, such as packet loss, fast clock drifting, and more limitations [37]. Therefore, the effects of these practical factors on the performance of time synchronization protocols should be analyzed and discussed carefully.
## 3 Methodology
### Gaussian Model  
The existing work on encounter prediction primarily employs the Gaussian model. The Gaussian model utilizes random variables that consist of a finite set, each possessing a joint Gaussian distribution. This model can also be characterized as a distribution over functions. The classification performed by the Gaussian process is non-parametric [28; 29]. This method is predominantly used for opportunistic mobile networks (OMNs), where connections are sparse. The Gaussian process is represented through a covariance or mean function (also known as the kernel), where \(m(X)\) is the mean function. The covariance function \(k(x,x^{\prime})\) for a real process, \(f(x)\), is illustrated in Equation (7). The Gaussian model for encounter prediction is also presented in Figure 4.  
The Gaussian model requires a matrix, \(X=[x_{1},x_{2},x_{3},\ldots,x_{n}]\), for training, with the dimensions \((n,d)\) to represent the data points. Additionally, the model requires a vector, \(Y\), denoted as \([y_{1},y_{2},y_{3},\ldots,y_{n}]^{T}\), for the training dataset, with the dimensions \((n,l)\). \(f\) represents the latent function, where \(f(x_{i})\) indicates the predictive class membership probability. The Gaussian model maps the latent space to the observation space, a process that is usually non-linear. The kernel function is employed to transform a non-linear input space into a space represented by various dimensions, allowing the problem's output to be expressed linearly. The significant challenge of this model arises when predicting the probability for a given test point, \(X\), where \(X\) is a member of one of the specified classes.  
\[p(y=C+|x)=sig(f(x)) \tag{8}\]  
Figure 4: Gaussian model for encounter prediction.  
The positive-class membership probability \(p(y=C+|x)\) is represented using Equation (8). The latent function \(f:R\to R\) is mapped in the interval \([0,1]\) through a sigmoid function, denoted as \(sig:R\rightarrow[0,1]\). Therefore, \(sig\) is a sigmoid transformation function and each data point, \(x\), belongs to the finite dataset. Supervised learning is employed for classification, whereby each data point is assigned a class label. Specifically, each data point, \(x\), is associated with a class label, \(Y_{i}\in C^{+},C^{-}\), where \(C^{+}\) represents the positive class and \(C^{-}\) represents the negative class [30, 31].
## 3 Results
### Lipid Peroxidation Estimation  
The highest lipid peroxidation contents after salts treatment were found in the Grenada cv. (Table 2). Salts did not significantly affect lipid peroxidation concentration in Ermioni or Wonderful cvs. (NaCl, KCl, and K\({}_{2}\)SO\({}_{4}\) treatments) (Table 1). At the highest concentrations (50 and 75 mM) of NaCl, KCl, and K\({}_{2}\)SO\({}_{4}\), cv. Grenada had the highest lipid peroxidation content, which was significantly greater than that of the Ermioni and Wonderful cvs (\(p\leq 0.05\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Treatment** & \multicolumn{3}{c}{**Proline Content (mg g\({}^{-1}\) FW)**} \\ \hline  & **Wonderful** & **Ermioni** & **Grenada** \\ \hline Control (0 mM NaCl) & 0.15 a & 0.13 a & 0.16 a \\
25 mM NaCl & 0.25 ab & 0.29 ab & 0.18 a \\
50 mM NaCl & 0.42 b & 0.49 b & 0.24 ab \\
75 mM NaCl & 0.65 c & 0.65 c & 0.31 ab \\
25 mM KCl & 0.26 ab & 0.32 ab & 0.16 a \\
50 mM KCl & 0.45 b & 0.50 b & 0.22 ab \\
75 mM KCl & 0.68 c & 0.67 c & 0.32 ab \\
25 mM K\({}_{2}\)SO\({}_{4}\) & 0.30 ab & 0.35 ab & 0.18 a \\
50 mM K\({}_{2}\)SO\({}_{4}\) & 0.48 b & 0.56 b & 0.23 ab \\
75 mM K\({}_{2}\)SO\({}_{4}\) & 0.70 c & 0.77 c & 0.34 ab \\ \hline \hline \end{tabular}  
* Proline content (mg g\({}^{-1}\) FW) in leaves of pomergnanate cvs. Wonderful, Ermioni, and under the effect salts, (a) NaCl (0, 25, 50, 75 mM), (b) KCl (0, 25, 50, 75 mM), (c) K\({}_{2}\)SO\({}_{4}\) (0, 25, 50, 75 mM). Each value is the mean of 8 replications \(\pm\) standard error for \(p\leq 0.05\).  
\end{table}
Table 1: Effect of NaCl, KCl, and K\({}_{2}\)SO\({}_{4}\) salts on proline content (mg g\({}^{-1}\) FW) in leaves of pomergnanate cvs. Wonderful, Ermioni, and Grenada. Each value is the mean of \(p\leq 0.05\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Treatment** & \multicolumn{3}{c}{**Lipid Peroxidation (MDA) Content (\(\mu\)mol g\({}^{-1}\) FW)**} \\ \hline  & **Wonderful** & **Ermioni** & **Grenada** \\ \hline Control (0 mM NaCl) & 1.8 a & 1.4 a & 1.5 a \\
25 mM NaCl & 2.4 a & 2.1 a & 2.7 ab \\
50 mM NaCl & 3.2 ab & 3.1 ab & 4.5 b \\
75 mM NaCl & 3.5 ab & 3.3 ab & 5.8 c \\
25 mM KCl & 2.6 a & 2.3 a & 2.6 ab \\
50 mM KCl & 3.6 ab & 3.2 ab & 4.8 b \\
75 mM KCl & 3.4 ab & 3.5 ab & 6 c \\
25 mM K\({}_{2}\)SO\({}_{4}\) & 2.7 a & 2.2 a & 2.8 ab \\
50 Mm K\({}_{2}\)SO\({}_{4}\) & 3.1 ab & 3.4 ab & 5.0 b \\
75 mM K\({}_{2}\)SO\({}_{4}\) & 3.7 ab & 3.6 ab & 6.1 c \\ \hline \hline \end{tabular}  
* Lipid peroxidation (MDA) content (\(\mu\)mol g\({}^{-1}\) FW) in leaves of pomergnanate cvs. Wonderful, Ermioni, and under the effect salts, (a) NaCl (0, 25, 50, 75 mM), (b) KCl (0, 25, 50, 75 mM), (c) K\({}_{2}\)SO\({}_{4}\) (0, 25, 50, 75 mM). Each value is the mean of 8 replications \(\pm\) standard error for \(p\leq 0.05\).  
\end{table}
Table 2: Effect of NaCl, KCl, and K\({}_{2}\)SO\({}_{4}\) salts on lipid peroxidation (MDA) content (\(\mu\)mol g\({}^{-1}\) FW) in leaves of pomergnanate cvs. Wonderful, Ermioni, and Grenada. Each value is the mean of 8 replications \(\pm\) standard error for \(p\leq 0.05\).
## 3 Traveling Waves (TW)
Fault Location in Multi-Terminal High-Voltage Direct Current (MT-HVdc) Grid Having High Grounding Resistance  
Typically, the traveling wave-based fault location identification technique is affected by the high grounding resistance of transmission lines because of abrupt and evolutionary changes in the voltage samples [50]. Therefore, a dc fault detection algorithm is required that can read the short window voltage samples and take appropriate actions according to the sampling information. For this, time frequency domain analysis of short window voltage samples obtained via \(S\) transform is carried out, which would help in determining the accurate location of wave front of traveling wave. Further, wave front position is used to detect dc fault location. As the characteristics of traveling waves are different for different faults, therefore, separate principles for faults are required for implementation. An \(S\) transform is generalization of short-term Fourier Transform, an extension of wavelet transform. It has the advantage of fixing modulation sinusoid with respect to time axis. Thus, localization of scalable Gaussian Window dilation and translation is done [69]. Consequently, a transformation matrix having complex elements is obtained as follows:  
\[S(t,f)=A(t,f)e^{i\beta(t,f)} \tag{1}\]  
where \(A(t,f)\) is amplitude and \(\varphi(t,f)\) is the phase function of the matrix. Every moment is acquired from the integration or by calculation of area under the curve of frequency spectrum at a specific instant. Therefore:  
\[I[t]=\int_{m}^{m}A(t,f)df \tag{2}\]  
Samples of voltage signals at instant of fault are analyzed into the instantaneous frequency spectrum matrix. Integration of every moment is calculated by instantaneous frequency spectrum matrix. Accuracy of fault location is found by maximum value of the wave front calculated by \(S\) transform. Location finding devices need to be deployed at both ends of the transmission line so that time and voltage signals could be analyzed and saved in a real time buffer memory for a time length \(t_{1}\). Another voltage signal is analyzed for time \(t_{2}\) with consideration that absolute value of voltage signal is less than the threshold value. Sum of these two samples of different time lengths forms a long sample of \(t=t_{1}+t_{2}\). An \(S\) transform is applied to analyze time frequency domain of long sample to achieve \(A(t,f)\).
###### Abstract  
To address the design and optimization of the ignition system for the microgravity single-droplet combustion experiment module within the Combustion Science Experimental System (CSES) aboard the Chinese Space Station (CSS), it is essential to first determine the ignition temperatures required for typical liquid fuel droplets. In this study, ignition experiments were conducted on droplets of three representative hydrocarbon fuels--ethanol, n-heptane, and n-dodecane--in static air at high temperatures ranging from 760 K to 1100 K. The experimental results show that the initial droplet diameter is inversely correlated with the ambient temperature at which ignition occurs. Subsequently, based on [PERSON]'s analytical method and combined with experimental data, a semi-empirical predictive model for droplet ignition temperatures in a normal-gravity environment was derived. Building upon this, and considering the characteristics of the microgravity environment, an appropriate empirical formula was applied to refine the model, resulting in a predictive model for droplet ignition temperatures in the microgravity environment. Furthermore, by comparing the experimental data and the observed phenomena from existing microgravity experiments, this semi-empirical predictive model used in the microgravity environment effectively reflects the trend of droplet ignition temperature variations.  
droplet ignition; predictive model; microgravity droplet combustion; ethanol; n-heptane; n-dodecane; Frank-Kamenetskii theory Article
## 5 Conclusions  
In this study, a U-Net deep learning model was developed for _M. polykrikoides_ red tide detection on the southern coast of Korea from PlanetScope imagery with a high spatial resolution of 3 m. The major results are as follows:  
(1) We identified a distinct _M. polykrikoides_ red tide pattern that distinguishes it from the surrounding waters using the PlanetScope image. Spectral analysis of the PlanetScope image revealed that the spectra in red tide waters showed a typical pattern of _M. polykrikoides_ red tide bloom. However, the spectral pattern in turbid waters is similar to that in red tide waters. Therefore, we confirmed that it is difficult to distinguish the spectra of red tide and turbid water using spectral shapes.  
(2) To find the optimal RTI thresholds for PlanetScope images, we performed a statistical analysis of red tide pixel and non-red tide pixel groups. From the overlapped histograms and their similar statistics for both groups, we confirmed that RTI may be insufficient to distinguish between red tide and non-red tide pixels via a threshold. We determined the median RTI of the red tide group for the PlanetScope image as a threshold.  
(3) We trained and tested four U-Net models to identify the effects of randomness on patch selection and the proportion of non-red tide pixels. As a result, the U-net #4 model, which was selected non-randomly even including non-red tide patches, showed the highest performance in qualitative and quantitative assessments compared to RTI and other U-net models. The predicted red tide map derived from the U-net #4 model showed the most reasonable red tide patterns in all water areas.  
Our U-Net model was trained and tested using the spectra of optically complex waters along the southern coast of Korea. Compared to RTI, which utilizes only the spectral characteristics of red tide waters, we confirmed that the U-Net model provides a reasonable _M. polykrikoides_ bloom distribution. We believe that these results will not only confirm the utility of U-Net for red tide detection but also provide a foundation for recognizing the potential of red tide utilization using terrestrial sensors with a high spatial resolution.  
Conceptualization, J.S. and S.M.K.; funding acquisition, B.-K.K. and S.M.K.; methodology, J.S. and S.M.K.; resources, J.-H.R.; supervision, S.M.K.; writing--original draft, J.S.; writing--review and editing, J.S., Y.-H.J., and S.M.K. All authors have read and agreed to the published version of the manuscript.  
This study was supported in part by the project titled \"Development of satellite-based system on monitoring and predicting ship distribution in the contiguous zone\" funded by the Korea Coast Guard and by research grants from Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2021R1A2C2006682).  
Not applicable.  
The authors thank the National Institute of Fisheries Science (NIFS) for providing red tide information.  
The authors declare no conflict of interest.
## 3 Numerical Experiments and Analysis
### Algorithm Parameter Setting  
All algorithm tests are carried out in MATLAB R2022a software. The test setup consists of Windows 11, an AMD Ryzen 7 5800H 3.20 GHz processor, and 16 GB of RAM (MAXSUN, Yichang, China). To guarantee the fairness of the comparison, it is necessary to uniformly set all algorithms to the same variables uniformly. For the specific parameters of various algorithms, we refer to the relevant literature and adopt the parameter values recommended in the standard version of the algorithm. The parameters are configured as presented in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **Parameters** & **Values** \\ \hline \multirow{2}{*}{For all algorithms} & Population size & 30 \\ \cline{2-3}  & Iterations & 500 \\ \hline \multirow{2}{*}{Walrus optimization algorithm (WaOA)} & \multirow{2}{*}{No specific parameters} \\ \cline{2-3}  & & \\ \hline Sand cat swarm optimization algorithm (SCSO) & & \\ \cline{2-3}  & Roulette selection & [0,360] \\ \hline \multirow{2}{*}{Seagull optimization algorithm (SOA)} & Control parameter \(A\) & [0,2] \\ \cline{2-3}  & \(f_{c}\) & 2 \\ \hline Ant lion optimization algorithm (ALO) & Random number \(F\) & [0.2/0.8] \\ \cline{2-3}  & Probability factor \(P_{CR}\) & 0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Different algorithm parameter settings.  
Figure 2: Search space diagram of the WaOA in benchmark functions.
## 3 Results and Discussion
### Effects on Atmospheric Variables
Vapor is mainly concentrated in the lower atmosphere, which is the source of precipitation. Weather systems in the lower atmosphere also play an important part in creating precipitation. The most suitable pressure for analyses is 850 hPa which can also avoid the impact of terrain. Figure 8 shows, for comparison, an 850 hPa wind field and the water-vapor flux that had a direct influence on the precipitation calculations of the two runs. The water-vapor flux (Q) was calculated as:  
\[\overset{\rightarrow}{\text{Q}}=\frac{1}{g}\int_{100}^{P_{a}}\overset{ \rightarrow}{V}\cdot qdp=\frac{1}{g}\int_{100}^{P_{s}}(\overset{ \rightarrow}{u},\overset{\rightarrow}{v})\cdot qdp \tag{9}\]  
Figure 6: Twenty-four-year (1982–2005) mean 2-m air temperature (TA, \({}^{\circ}\)C) from (**a**) the CTL run; (**b**) the NEW run; (**c**) NCEP data; (**d**) the difference between NEW and CTL. (Grid shaded regions are significant at the 95% confidence level according to Student’s \(t\)-test.). where \(u\) and \(v\) represent the zonal and meridional wind speeds (m s\({}^{-1}\)) respectively, \(q\) is the specific humidity (g kg\({}^{-1}\)), and \(P_{s}\) is the surface pressure (hPa). It was obvious that the NEW run changed the global low-level wind field (Figure 8a) by 1-2 m s \({}^{-1}\), mainly in tropical regions. In addition, more water vapor is present in the tropical atmosphere, and therefore water-vapor transport comes mainly from low-latitude areas. For example, warm air carrying water vapor from the sea results in more precipitation in Indonesia, central Africa, and the northern coast of South America; contrarily, in the southern region of India and southern China, there is less precipitation, mainly because of cold and dry air coming from northern Asia (Figure 7b,d). In the northeastern part of the Amazon, the west coast of Africa, and Indonesia, due to moisture convergence, large-scale continuous precipitation was increased by 100-200 mm year\({}^{-1}\). In South Asia, anticyclonic circulation led to a large water-vapor divergence, thereby reducing large-scale continuous rainfall by up to 100 mm year\({}^{-1}\) (Figure 7e). On the other hand, in the northeastern Amazon, the west coast of Africa, and Indonesia, due to increased evapotranspiration (Figure 4b), convective rain also increased (Figure 7f). Overall, local land-surface feedback and large-scale moisture advection resulted in an increase in total regional atmospheric precipitation, causing the humidifying effect. Figure 7: Twenty-four-year (1982–2005) mean precipitation (PR, mm year\({}^{-1}\)) for (**a**) total precipitation of the CTL run; (**b**) total precipitation of the NEW run; (**c**) total precipitation from GPCC (**d**) difference in total precipitation between NEW and CTL; (**e**) difference in large-scale precipitation between NEW and CTL; (**f**) difference in convective precipitation between NEW and CTL. (Grid shaded regions are significant at the 95% confidence level according to Student’s \(t\)-test. ).
# Mobility- and Energy-Aware Cooperative Edge Offloading for Dependent Computation Tasks +
## 2 Related Work
### Task Dependency Models  
The existing task offloading studies typically considered independent computation tasks. To the best of our knowledge, only the non-cooperative MEC task offloading study [51] considered task dependencies. However, the task dependencies in [51] are limited to a simple sequential (linear) task dependency, where each task depends only on the immediately preceding task in a linear task sequence. A general task dependency graph has been considered in the non-cooperative task offloading study [57]. However, both studies [51; 57] offloaded tasks in a non-cooperative fashion, i.e., only to installed stationary MEC and cloud servers (not to mobile helper end devices). In contrast, we consider arbitrary task dependencies that are represented by a general task dependency graph for cooperative task offloading to mobile helper end devices and stationary MEC servers.  
To the best of our knowledge, the present study is the first to develop and evaluate a task offloading algorithm that minimizes the battery energy consumption for arbitrary task dependencies in a cooperative edge computing setting with mobile helper nodes. The proposed EETO algorithm employs a state-of-the-art deep-learning-based trajectory prediction for general mobility settings.