esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work  
### Methods for Low-Light Image Enhancement  
Low-light image enhancement (LLIE) aims to restore image details and reduce color shifts to improve the performance in computer vision tasks. Early methods were primarily based on traditional image processing techniques, such as Retinex theory [6] and histogram equalization [7]. While these methods can improve the brightness and contrast, they are limited in handling details, noise suppression, and color restoration. In recent years, deep learning has advanced LLIE. Convolutional neural network (CNN)-based methods [6, 7, 8, 9], such as KinD [10], restore details through adaptive network structures without requiring paired data. Zero-DCE [3] designs non-reference loss functions, eliminating the need for paired data and achieving excellent performance. Generative adversarial network (GAN) methods [11], like the unsupervised GAN by [PERSON] et al., have also gained widespread attention by restoring low-light images from unlabeled data through self-supervised learning. However, although low-light enhancement methods make images appear brighter and clearer to the naked eye, they still leave a lot of background noise, thus having a minimal impact in terms of improving the detection performance in low-light object detection.
## 2 Preliminaries  
### Fractal and Fractional Gaussian Noise  
Fractal analysis is especially useful when the data show self-similarity, power-law, scale-invariant and nonlinear properties [9; 10; 11]. From the fractal theory introduced by [PERSON] in [9], the traditional concept of three-dimensional space can be extended to the fractal (fractional) dimension (FD).  
Based on the definitions of fractional-order differential operators, many complex dynamic systems with complex memory behaviors can be more properly described by the fractional calculus. Some researchers found interesting the analytical results of the linear fractional-order differential equations are represented by the Mittag-Leffler function, which exhibits a power-law asymptotic behavior [12]. Therefore, the fractional calculus is being widely used to analyze the random signals with power-law size distributions or power-law decay of correlations [13; 14].  
The fractional order integral of the function \(f(x)\) with \(\alpha\in\mathbb{R}+\) is defined  
\[{}_{a}D_{t}^{-\alpha}=\frac{1}{\Gamma(\alpha)}\int_{a}^{t}\frac{f(\tau)}{(t- \tau)^{1-\alpha}}d\tau, \tag{1}\]  
where \(\Gamma(\cdot)\) is the Gamma function, \({}_{a}D_{t}^{-\alpha}\) is the fractional integral of order \(\alpha\) in \([a,t]\)[14].  
Based on fractional order integral, the relationship of white Gaussian noise wGn \(\omega(t)\), fractional Gaussian noise (fGn), \(\alpha\)-stable noise \(\omega_{\alpha}(t)\), fractional stable noise, multifractional Gaussian noise and multifractional stable noise can be described in Figure 1. Besides, the fGn can be expressed as the \(\alpha\)th order integration of wGn [2; 15]:  
\[X_{t}=_{0}D_{t}^{-d}\omega(t). \tag{2}\]  
Therefore, from the perspective of fractional signals and fractional-order systems the fGn can be simulated by the \(d\)th integrator with wGn as the input in the Figure 1.  
From the above figure, the major difference of monofractal and multifracal property is the time-depend variable \(H\). The linear multifractional stable motion is obtained by generalizing the constant Hurst parameter \(H\) to a time-dependent local H  
Figure 1: Fractional Gaussian noise and fractional Brown noise in monofractal analysis and multifractal analysis.
## 2 Overall Analysis Approach and Implemented Methods
### Thermodynamic Analyses
#### 2.5.2 Tank Heat Flow
For the liquid phase, the following equations were considered:  
\[Nu=0.0605\ Ra\ ^{1/3} \tag{39}\]  
\[Ra=\frac{g\ \beta\ \Delta t\ H^{3}\ Pr}{\
u} \tag{40}\]  
\[Pr=\frac{\mu\ Cp}{K} \tag{41}\]  
\[h_{l}=\frac{Nu\ K}{H_{l}} \tag{42}\]  
where all quantities are relative to the liquid phase, \(Nu\), \(Ra\), \(Pr\), are the Nusselt, Raleigh, and Prandtl numbers for the natural convection inside the liquid H\({}_{2}\), \(h_{l}\) is the heat transfer coefficient of the phenomenon, \(H_{l}\) is the level of the liquid part inside the tank, expressed as height from the bottom of the tank, \(K\), \(Cp\), \(\beta\), and \(\
u\) are, respectively, the thermal conductivity, the specific heat at constant pressure, the coefficient of volumetric thermal expansion, and the cinematic viscosity of the liquid H\({}_{2}\), whereas \(\Delta t\) is the temperature difference between the tank wall and the bulk of the liquid H\({}_{2}\). \(\beta\) was derived as reported in Appendix C. For the gaseous phase the following equations were considered ([25]):  
\[Pr=\frac{\mu_{air}\cdot Cp}{K_{air}} \tag{34}\]0  
\[Pr=\frac{\mu_{air}\cdot Cp}{K_{air}} \tag{34}\]1  
where all quantities are relative to the gaseous phase, \(Nu\) is the Nusselt number for the natural convection inside the gaseous H\({}_{2}\), \(h_{g}\) is the heat transfer coefficient of the phenomenon, \(H_{g}\) is the level of the gas inside the tank, expressed as distance from the top of the tank, and \(K\) is the thermal conductivity of the gaseous phase. As for assumptions (a), (b), and (c), the total internal thermal resistance is computed as in Equation (45), where \(S_{l}\) and \(S_{g}\) are the tank surfaces in contact with the liquid and gaseous phase, respectively. \[Pr=\frac{\mu_{air}\cdot Cp}{K_{air}} \tag{34}\]2The heat flow (\(Q\)) evaluation is computed using the method reported in [24]. The method assume that all the above-mentioned thermal resistances are in series, so the heat flow that crosses each resistance is the same. It is possible to write a linear system of equations in the form \(AX+B=0\), as in Equation (46), in order to determine \(Q\) and all the temperatures at each tank thermal stage interface:  
\[ \begin{pmatrix}1&0&0&TR_{ext}\\ -1&1&0&TR_{ins1}\\ 0&-1&1&TR_{ins2}\\ \ldots&\ldots&\ldots&\ldots\\ 0&0&-1&TR_{int}\end{pmatrix}\begin{pmatrix}T_{skin}\\ T_{ins1}\\ T_{ins2}\\ \ldots\\ Q\end{pmatrix} + \begin{pmatrix}-T_{ext}\\ 0\\ 0\\ \ldots\\ T_{h2}\end{pmatrix} =0 \tag{46}\]  
Since the thermal resistance \(TR\) of each thermal stage is dependent upon the temperatures at the thermal stage interfaces, the system is solved with an iterative procedure. It starts with temperature initialisation values identical to the environmental one, and lasts until the convergence on the temperatures at the outer skin and LH\({}_{2}\) interfaces, with a tolerance of 0.5 K.
# Influence of Solvent Selection in the Electrospraying Process of Polycaprolactone
## 3 Selection of Solvent Systems Used in PCL Electrospraying
### Solution Properties for Cone-Jet in PCL Electrospraying
#### 3.2.4 Boiling Point  
The formation of Taylor cone during electrospraying is also affected by the boiling point of working solution. The solvent evaporation in jetting occurs at the same time that the formation of Taylor cone at the tip of the nozzle. A too low boiling point or high vapor pressure value modify the shape of the cone-jet to change it in an unstable mode. Furthermore, it leads to the drying of the polymeric particles inside the tube or at the tip of the nozzle, until to stop the process. Therefore, the Taylor cone formation is promoted with high boiling point solvent, and is especially useful for high PCL concentrations.
## 6 Verification and Comparison  
The sensing signals captured by the sensor array were filtered to obtain clearer sensing signals. The sensing signals of case 13 at the same circumferential position and case 21 at different circumferential positions were decomposed into five levels of wavelet packets, and the specific damage index values were calculated. For case 13 and case 21, the damage index values obtained from the tests were compared with those obtained from the finite element simulation, and the errors between the test and the simulation were calculated. The specific numerical values are shown in Table 11.  
As shown in Table 11, the errors between the damage index values obtained from the experiments and those obtained from the finite element simulations were relatively small. This indicated that the finite element simulations could accurately represent the actual behavior of the pipeline structures, and confirmed the accuracy of the simulation model. Therefore, the finite element simulation could be considered a reliable tool for predicting the behavior of pipeline structures in real-world applications.  
To validate the accuracy of the conclusions drawn in this paper, we compared the damage index values for a single defect, same circumferential position, and different circumferential position, using the sensor signals obtained from experiments within the range of \(1.7\lambda\leq PD\leq 4\lambda\), after a five-level wavelet packet decomposition. The specific damage index values are shown in Table 12.  
Based on Table 12, the following relationships between single and double defects can be observed: when \(1.7\lambda\leq\) PD \(\leq 3.4\lambda\), \(I_{\text{double defect }90^{\circ}}<I_{\text{signal defect }}<I_{\text{double defect }0^{\circ}}\); when \(3.7\lambda\leq\) PD \(\leq 4\lambda\), \(I_{\text{double defect }90^{\circ}}<0.3<I_{\text{double defect }0^{\circ}}\); when \(1.7\lambda\leq\) PD \(\leq 4\lambda\), \(I_{\text{double defect }90^{\circ}}<I_{\text{double defect }0^{\circ}}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Source** & **Damage Index Value** & **Error\%** \\ \hline \multirow{2}{*}{case 13} & Experiment & 0.39323 & \multirow{2}{*}{0.4} \\  & Finite element simulation & 0.39166 & \\ \hline \multirow{2}{*}{case 21} & Experiment & 0.25878 & \multirow{2}{*}{0.82} \\  & Finite element simulation & 0.26093 & \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison between the experimental and simulation results.  
Figure 14: Experimental system for layered pipeline structures.  
The relationship between single and double defects obtained from the experimental system was consistent with the conclusions obtained from the simulation, which, to some extent, proves the accuracy of the conclusions in this paper.
# Numerical Study of Cone Penetration Tests in Lunar Regolith for Strength Index
## 2 Numerical Model
### Model Parameters  
Using a relative density of 0.83 as an example, we used a previously described particle refinement method for generating CPT simulations of the foundations [24; 25]. In the  
Figure 4: A comparison of stress-strain curves after triaxial simulation calibration and triaxial test results.  
Figure 3: Particle gradation curve.  
vicinity of the probe, the contact section was assigned smaller particle sizes, whereas the particle size in the boundary section, farther from the probe, was larger. An intermediate section was placed between these two, featuring particle sizes that ranged between those of the contact and boundary sections. This arrangement is depicted in Figure 5, and the particle size distribution curves for each section are shown in Figure 6.  
Relative density is a crucial variable affecting the soil parameters of granular soil. Samples with different particle size distributions prepared at the same relative density  
Figure 5: Simulated foundation diagram.  
Figure 6: Schematic diagram of particle size distribution of each section.  
exhibit similar soil strengths [26]. In this study, for each section, we employed a method for preparing simulated foundations at a specified relative density [27; 28]. The simulations involved 580,000 particles with frictionless boundary walls, ensuring stable settlement under 1/6 g. According to relevant studies on discrete element three-dimensional static penetration simulations [29; 30], a small \(D_{\mathrm{c}}/d_{50}\) ratio in the three-dimensional model satisfies the size effect. The probe size used in the simulation is depicted in Figure 7, which featured a smooth surface. The vertical force (N) acting on the conical surface was monitored during the simulation, and its variation with penetration depth was recorded. For data processing, the force acting on the conical surface was divided by the projected area of the probe in the vertical direction to obtain the cone tip resistance (\(q_{\mathrm{c}}\)).  
During the preparation of the triaxial simulation samples, the particle size distribution curve aligned with the contact part distribution shown in Figure 6. We generated 12,000 particles within a cylinder with a height of 0.050 m and a radius of 0.025 m. A flexible particle membrane was employed for boundary pressure. Lunar gravity, being only one-sixth of Earth's gravity, results in an estimated self-weight stress of approximately 9.3 kPa at a depth of 3 m below the lunar surface [31], which is accompanied by even lower horizontal stress. Consequently, the simulation accounted for low gravity by applying low confining pressure. The final three-dimensional simulation models are presented in Figures 8 and 9.  
Figure 8: A three-dimensional simulation model of the static penetration test.  
Figure 7: Probe size diagram.
# Radar-Based Target Tracking Using Deep Learning Approaches with Unscented Kalman Filter
## 2 Materials and Methods
### Deep Neural Network Design  
MLP and CNN are DNNs (Deep Neural Networks) typically used for supervised learning tasks such as classification and regression. DNNs can be explained using the following diagram. The following Figure 4 illustrates DNN.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{_scur_} & \multirow{2}{*}{\(\delta_{mg}\) **(m)**} & \(\delta_{lag}\) & \(\delta_{dev}\) & \multirow{2}{*}{_rng_ **(m)**} & \multirow{2}{*}{_brg_ **(deg)**} & \multirow{2}{*}{_V_ **(r/s)**} & \(V_{o}\) & _elev_ & _tcr_ & & & & & & _ocrs_ & _op_ \\  & & **(deg)** & **(deg)** & **(deg)** & **(deg)** & **(deg)** & **(deg)** & **(deg)** & **(deg)** & **(deg)** & **(deg)** \\ \hline
1 & 13.949 & 0.03174 & 0.482 & 58,364 & 271 & 497 & 34 & 76.694 & 270.0 & 65 & 300.0 & 54.5 \\ \hline
2 & 14.754 & 0.04715 & 0.477 & 48,773 & 154 & 275 & 26 & 57.621 & 89.99 & 53 & 119.9 & 116.4 \\ \hline \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ \hline
299 & 11.833 & 0.0375 & 0.617 & 56,903 & 66 & 400 & 35 & 68.734 & 90.00 & 53 & 60.00 & 64.5 \\ \hline
300 & 14.718 & 0.0399 & 0.0794 & 48,641 & 33 & 405 & 37 & 51.063 & 89.99 & 120 & 119.9 & 124.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Scenario parameters and initial conditions.  
Figure 4: DNN Architecture.  
Figure 3: Schematic diagram of proposed DL approaches, training, and testing.  
In a DNN, the network consists of multiple neuron layers, including input, hidden, and output. Each neuron in the network applies an activation function to its inputs and passes the result to the next layer of neurons. The equations for a feedforward DNN can be written as:  
\[z_{l}=W_{l}*a_{l-1}+b_{l} \tag{1}\]  
\[a_{l}=g(z_{l}) \tag{2}\]  
where \(W_{l}\) is the weight matrix between layer \(l\) and layer \(l-1\), \(b_{l}\) is the bias vector for layer \(l\), \(a_{l}\) is the output of layer l after applying the activation function g to its inputs \(z_{l}\). The forward pass through the network can be computed recursively as:  
\[a_{0}=x \tag{3}\]  
\[a_{l}=g(z_{l})=g(W_{l}*a_{l-1}+b_{l})\text{ for }l=1,2,\ldots,L \tag{4}\]  
where \(x\) is the input to the network and \(L\) is the number of layers in the network. During training, the network is trained to minimize a loss function that measures the difference between the predicted output of the network and the target output.  
\[f(x)=\frac{1}{1+e^{-x}} \tag{5}\]  
\[f(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{6}\]  
\[f(x)=max(0,x) \tag{7}\]  
where \(e\) is again Euler's number, and \(x\) is the input to the function; in this study, as it is a regression problem, ReLU was preferred because it addresses the vanishing problem, converges faster, and can help to produce sparse representations, where many of the neurons in the network have zero activations.
###### Abstract  
Soil salinization is a significant global ecological issue that leads to soil degradation and is recognized as one of the primary factors hindering the sustainable development of irrigated farmlands and deserts. The integration of remote sensing (RS) and machine learning algorithms is increasingly employed to deliver cost-effective, time-efficient, spatially resolved, accurately mapped, and uncertainty-quantified soil salinity information. We reviewed articles published between January 2016 and December 2023 on remote sensing-based soil salinity prediction and synthesized the latest research advancements in terms of innovation points, data, methodologies, variable importance, global soil salinity trends, current challenges, and potential future research directions. Our observations indicate that the innovations in this field focus on detection depth, iterations of data conversion methods, and the application of newly developed sensors. Statistical analysis reveals that Landsat is the most frequently utilized sensor in these studies. Furthermore, the application of deep learning algorithms remains underexplored. The ranking of soil salinity prediction accuracy across the various study areas is as follows: lake wetland (R\({}^{2}\) = 0.81) \(>\) aosis (R\({}^{2}\) = 0.76) \(>\) coastal zone (R\({}^{2}\) = 0.74) \(>\) farmland (R\({}^{2}\) = 0.71). We also examined the relationship between metadata and prediction accuracy: (1) Validation accuracy, sample size, number of variables, and mean sample salinity exhibited some correlation with modeling accuracy, while sampling depth, variable type, sampling time, and maximum salinity did not influence modeling accuracy. (2) Across a broad range of scales, large sample sizes may lead to error accumulation, which is associated with the geographic diversity of the study area. (3) The inclusion of additional environmental variables does not necessarily enhance modeling accuracy. (4) Modeling accuracy improves when the mean salinity of the study area exceeds 30 dS/m. Topography, vegetation, and temperature are relatively significant environmental covariates. Over the past 30 years, the global area affected by soil salinity has been increasing. To further enhance prediction accuracy, we provide several suggestions for the challenges and directions for future research. While remote sensing is not the sole solution, it provides unique advantages for soil salinity-related studies at both regional and global scales.  
soil salinity; remote sensing; machine learning; innovation; uncertainty; retrieval 2024 16 4812 1
## 3 Classification of Optimization Objectives
### Multi-Objective Optimization Focused on Placement Related Problems in WSNs
#### 3.4.3 Energy Conservation  
Energy conservation is one of the critical issues due to the peculiar characteristics of the wireless sensor networks. Therefore, it has been discussed at various levels namely, design, operation, deployment and placement. A multi-objective optimization algorithm has been proposed in [123] to find the optimal placement of sensor network while simultaneously optimizing energy consumption and detection capability. Relay node placement problem in wireless sensor network has been addressed in [124] by using a multi-objective optimization formulation to search a trade-off between the average energy consumption and average coverage.
# First Retrieval of Sea Surface Currents Using L-Band SAR in Satellite Formation
## 5 Results and Discussion  
Due to the absence of in situ measurement data in the experimental region, such as those from buoys and coastal radars, two approaches were adopted to validate the accuracy and effectiveness of the inversion results. First, a quantitative comparison was conducted between the inversion results and reference data from the CMEMS. Second, a qualitative analysis of sub-mesoscale oceanic phenomena, such as ship wakes, observed in the inversion results was performed based on oceanographic characteristics.
## 3 Methodology
### Exclusion Method of Non-Building Height Change  
The exclusion of non-building height change is a crucial step. The objectivity of exclusion is directly related to the accuracy of building height change statistics. In this study, two methods were employed and compared to determine which was more reasonable. Firstly, the construction lands in two phases were extracted separately from Landsat 7 ETM+ images through the decision tree classification in ENVI software (ENVI 5.5 from ESRI, USA), which was mainly based on the different reflectivity of different ground objects in different electromagnetic bands. Then, the scopes of the construction lands at two phases were merged, which was regarded as the limited scope of the building height change statistics. Therefore, elevation changes outside this scope were considered non-building land changes, and therefore should be excluded. Secondly, in the second method, through the previous detection, in the plain area with a slope less than 5 degrees, the greater the height change, the greater the possibility of real building height change. Therefore, based on previous experience [52; 53; 54; 55; 56; 57; 58; 59], elevation changes greater than a certain threshold were regarded as non-building changes, and therefore should be excluded.  
Based on the first exclusion method, Figure 7b results from the elevation change retained only in the construction area, regarded as the building height change. The built-up area was obtained by merging the two-phase construction regions in April 2000 and April 2009, as shown in Figure 7a.  
Based on the second exclusion method, non-building height changes were excluded by setting the exclusion threshold of elevation change, that is: (\(-\)4 m, 4 m), (\(-\)5 m, 5 m), (\(-\)6 m, 6 m), (\(-\)7 m, 7 m), (\(-\)8 m, 8 m), (\(-\)9 m, 9 m), (\(-\)10 m, 10 m), (\(-\)11 m, 11 m), (\(-\)12 m, 12 m), (\(-\)13 m, 13 m), (\(-\)14 m, 14 m), (\(-\)15 m, 15 m), (\(-\)16 m, 16 m), (\(-\)17 m, 17 m), (\(-\)18 m, 18 m), (\(-\)19 m, 19 m), and (\(-\)20 m, 20 m), respectively. For the purpose of saving space, only the results of (\(-\)7, 7) (Figure 8a), (\(-\)11, 11) (Figure 8b), (\(-\)14, 14) (Figure 8c), and (\(-\)20, 20) (Figure 8d) are shown in Figure 8. It can be seen that, with the increase of the exclusion threshold of the elevation change, the building height change pixels generally show a decreasing trend.  
Figure 6: Authenticity probability of whether the height difference is consistent with the building expansion.  
After comparing these different results derived from the two methods, it can be seen that the basic distribution characteristics are almost the same, but the second method will gradually reduce the construction volume with the increase of the threshold of heigh change exclusion.  
Figure 8: (**a**) Height changes of less than 7 m are excluded; (**b**) Height changes of less than 11 m are excluded; (**c**) Height changes of less than 14 m are excluded; (**d**) Height changes of less than 20 m are excluded.  
Figure 7: (**a**) The construction area extracted from the two original images. (**b**) The height change retaining only the construction area.
# A Real-Time Fabric Defect Detection Method Based on Improved YOLOv8
## 4 Experimental Results and Analysis
### Datasets
#### 4.1.2 Tianchi Dataset  
The Tianchi dataset is the fabric images collected on-site by the Tianchi platform deep into the textile workshop in Nanhai, Foshan, which contains a total of 5913 image samples, all with a resolution of 2446 \(\times\) 1000 pixels, covering all kinds of important defects of fabrics. In the textile industry, there are 34 categories. The defect categories are not balanced, as there is a small number of samples in some categories, and some of the defect types have similar characteristics that can be categorized into the same category. Therefore, we finally selected 20 common fabric defect categories after manual classification and screening. These categories include holes, three silk, stains, knots, repair marks, flower skips, hundred feet, etc., respectively, and some of the samples are shown in Figure 7.