esa-sceva/satcom-chunk-collection huggingface dataset:
# Coarse-to-Fine Structure-Aware Artistic Style Transfer
## 3 Proposed Method
### Coarse Network
#### 3.2.2 Architecture of Coarse Network  
The architecture of coarse network, which is shown in Figure 1, includes an encoder, a WCT module, and a decoder. (1) The encoder is a pretrained VGG-19 network, which is fixed during training. Given \(\overline{x}_{c}\) and \(\overline{x}_{s}\), the VGG encoder extracts the content feature \(\overline{f}_{c}\) and the style feature \(\overline{f}_{s}\) at ReLU_4_1. (2) Then, we apply a WCT module for whitening and coloring transformation. As shown in Figure 3(a), the whitening transform is adopted to linearly transform \(\overline{f}_{c}\) to obtain \(\overline{f}_{c}^{\prime}\). Next, the coloring transform is carried out to obtain \(\overline{f}_{cs}\) by using \(\overline{f}_{c}^{\prime}\) and \(\overline{f}_{s}\). (3) Finally, we adopt a reconstruction decoder to reconstruct the coarse stylized feature \(\overline{f}_{cs}\). The decoder is designed to be symmetrical to the VGG-19 network, where the nearest neighbor upsampling layer is used for enlarging the feature map. We take \(\overline{f}_{cs}\) as input for reconstruction and then generate these restructured stylized features \(\overline{f}_{r}^{(i)}\) as outputs. In this reconstruction decoder, these outputs are output before the second upsampling layer, before the third upsampling layer, and after the last convolution layer. These \(\overline{f}_{r}^{(i)}\) will become a part of the input of the fine network.
# Urban Flood Risk Assessment Based on a Combination of Subjective and Objective Multi-Weight Methods
## 3 Results and Analysis
### Hazard Assessment of Disaster-Causing Factors  
Using the ARCGIS 10.0 inverse distance weighting method, the related impact indicator information was interpolated and extracted, and the factors were spatially superimposed to obtain a vector map of each evaluation factor. Combined with the grading criteria in  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Target Layer**} & \multirow{2}{*}{**Citeria Layer**} & \multicolumn{3}{l}{**Indicator Layer**} & \multirow{2}{*}{**Nature**} \\ \cline{2-2} \cline{5-8}  & & & & **Weights** & & & **Indicator** \\ \cline{3-8}  & \multirow{4}{*}{**Indicator**} & \multirow{2}{*}{**Weights**} & \multirow{2}{*}{**Indicator**} & **Entropy Weight** & **CRITC** & **AHP** & **Combination** \\ \cline{5-8}  & & & & & **Maximum 24 h** & 0.1066 & 0.1246 & 0.2507 & 0.1919 & Positive \\  & & & & & **Maximum 3-day** & 0.0956 & 0.138  
Table 7, the risk distribution of flood disasters in different return periods in Liaocheng City was derived. The area share of each grade is shown in Table 10.  
The results of the hazard assessment of disaster-causing factors in Liaocheng City are shown in Figure 2, with no obvious boundaries in the spatial distribution. Overall, the risk is higher in the western and central parts and lower in the south and north. With the change in return periods, the medium-to-very-high-risk area gradually moves southward in the central part and gradually expands in the western part. Among them, the high-risk area is mainly concentrated in Dongchangfu District, and its scope increases with the increase in return period, expanding from 6.7% to 9.41%, indicating that the possibility of extreme floods in this area is increasing. Medium-to-very-high-risk areas are mainly located in Dongchangfu District, Chiping District, Dong's County, Guanzian County, and Yanggu County, where the severity of flooding is high due to the frequent and heavy rainfall. Very-low-severity areas are mainly distributed in Shenxian County and Gaotang County, which have low flooding severity due to their low precipitation variability and low rainfall.
## Appendix A Geodetic Methods Details
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Ascending Track 102**} & \multicolumn{4}{c}{**Descending Track 102**} \\ \hline
**Pick No.** & **Long.** (\({}^{\circ}\)E) & **Lat.** (\({}^{\circ}\)N) & \
\begin{tabular}{c} **Picked** \\ **Value (mm)** \\ \end{tabular} & **Model (mm)** & **Long.** (\({}^{\circ}\)E) & **Lat.** (\({}^{\circ}\)N) &
\begin{tabular}{c} **Picked** \\ **Value (mm)** \\ \end{tabular} & **Model (mm)** \\ \hline
20 & 23.42011 & 38.31878 & 0 & \(-2\) & 23.45319 & 38.30799 & 0 & 3.49 \\ \hline
21 & 23.43258 & 38.31604 & 0 & \(-0.37\) & 23.44426 & 38.31241 & 0 & 4.38 \\ \hline
22 & 23.447 & 38.31126 & 0 & 0.03 & 23.43577 & 38.31621 & 0 & 2.69 \\ \hline
23 & 23.45452 & 38.30693 & 0 & 0.4 & 23.42542 & 38.31913 & 0 & 0.76 \\ \hline
24 & 23.46142 & 38.30029 & 0 & 1.45 & 23.41595 & 38.32126 & 0 & 1.08 \\ \hline
25 & 23.46461 & 38.29206 & 0 & 1.43 & 23.41064 & 38.31843 & 0 & 1.82 \\ \hline
26 & 23.46054 & 38.28578 & 0 & 1.14 & 23.41055 & 38.31453 & 0 & 2.49 \\ \hline
27 & 23.45213 & 38.28277 & 0 & 1.32 & 23.41303 & 38.30701 & 0 & 2.3 \\ \hline
28 & 23.44019 & 38.28224 & 0 & 2.85 & 23.4186 & 38.29861 & 0 & \(-1.15\) \\ \hline
29 & 23.43311 & 38.30162 & \(-56\) & \(-52.85\) & 23.42506 & 38.2918 & 0 & \(-0.4\) \\ \hline
30 & 23.42948 & 38.30356 & \(-56\) & \(-55.72\) & & & & \\ \hline
31 & 23.42957 & 38.30639 & \(-56\) & \(-56.66\) & & & & \\ \hline
32 & 23.43355 & 38.30586 & \(-56\) & \(-51.44\) & & & & \\ \hline
33 & 23.43621 & 38.30374 & \(-56\) & \(-53.07\) & & & & \\ \hline
34 & 23.43532 & 38.30171 & \(-56\) & \(-54.08\) & & & & \\ \hline \hline \end{tabular}   
**Figure A2. ** Divergence diagrams of the set of control group of inversions for (**a**) planar position, (**b**) X in UTM, (**c**) Y in UTM and (**d**-**g**) for depth, length, azimuth and dip angle, accordingly (all versus standard deviation). Color-coding refers to the inversion number.
## 3 Results
### DEM Validation  
Table 4 shows the measures used for the accuracy assessment of the generated DEMs. The comparison of the statistical measures shows an underestimation of the minimum DEM value for the track 16 DEM, while the other two DEMs show similar minimum statistics as the ArcticDEM. In contrast, the maximum value for the track 16 DEM is the closest compared to the ArcticDEM while the other two DEMs (tracks 118 and 155) show lower values. The mean and standard deviation values for the track 16 DEM are closer to the values of the ArcticDEM.  
The RMSE for the track 16 DEM is the lowest of the three DEMs, which indicates less vertical variation between track 16 and the ArcticDEM, but at the same time it has the lowest Moran's I autocorrelation (0.92), which could be a result of the existence of more outliers compared to the track 118 and 155 DEMs, both of which have a Moran's I of 0.97. The latter DEMs have better autocorrelation due to more homogenous values.  
It is worth mentioning that both quality assessment indicators used (i.e., RMSE and Moran's I) are global indicators, and they don't take into account local variations.  
Figure 8: Elevation differences of the three Sentinel-1 DEMs compared to the ArcticDEM for the landslide deposition area used for calculation of the landslide volume. (**a**) elevation differences of DEM from track 16 (11 July 2018 and 17 July 2018); (**b**) elevation differences of DEM from track 118 (5 August 2018 and 11 August 2018); (**c**) elevation differences of DEM from track 155 (4 July 2019 and 10 July 2019). The respective hillshade derived from the Sentinel-1 DEMs was overlaid for each result.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sentinel-1 DEM** & **Orbit Track** & **Volume (Million m\({}^{3}\))** \\ \hline
11 July 2018 and 17 July 2018 & 16 & 109 \\
5 August 2018 and 11 August 2018 & 118 & 7 \\
4 July 2019 and 10 July 2019 & 155 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Volume estimation of the landslide based on the Sentinel-1 derived post-event DEMs and the pre-event ArcticDEM.  
From the statistics in Table 4, it is challenging to choose the most suitable DEM for volume estimation. Despite a clear indication of which DEM should give the best results, we see major differences in the measured volumes for the three DEMs (track 16, 118, and 155). According to the available literature [45], approximately 7 million m\({}^{3}\) of material was released from the landslide source area, and the volume of the debris in the depositions area is about 10-20 million m\({}^{3}\). According to these statistics, the track 16 DEM shows an unrealistic value of 109 million m\({}^{3}\), whereas the landslide volumes calculated from the other two DEMs are closer to the estimated reference value.
# Cross-Correlation and Fractal Analysis in the Images Diatoms Symmetry
## 1 Introduction
In signal processing, cross-correlation (or sometimes called \"cross-covariance\") is a measure of the similarity between two signals, often used to find relevant features in an unknown signal by comparing it with a known signal. It has applications in pattern recognition, single particle analysis, electron tomography, averaging, cryptanalysis, and neurophysiology. Deep learning and convolutional neural networks (CNN) are becoming extensively used techniques for image classification in a variety of problems. The use of this methodology in the classification of diatoms has been to demonstrate whether it is suitable for solving the classification problem [32]. In this manuscript, we propose three simple morphometric methods based on measurements of fractal dimension and 2D cross-correlation parameters to analyze diatoms images and thus achieve a correct identification. This novel approach demonstrates the differentiation feasibility between diatoms species with a wide variety of structures and shapes, which can also function as an image data pretreatment tool.
## 3 Results
### Effects of Different Treatments on the Content of Soil Active Organic Carbon  
#### 3.3.1 Effects of Different Treatments on Soil Soluble Organic Carbon  
The results presented in Figure 4 demonstrate that soil dissolved organic carbon (DOC) content initially increased rapidly, followed by a rapid decrease and eventual stabilization as the straw decomposition period extended. Specifically, soil DOC content peaked at 90 days for all treatments, with the following ranking: T2 \(>\) T3 \(>\) T4 \(>\) T1 across all stages. The trends observed in soil DOC content at 60 days, 90 days, and 120 days were similar, indicating no significant difference between T2 and T3 treatments, both of which were significantly higher than the other treatments. Additionally, T4 treatment exhibited significantly higher soil DOC content compared to T1 treatment. By 150 days, there was  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Treatments** & **WSS (g/kg)** & **HAC (g/kg)** & **FAC (g/kg)** & **HM (g/kg)** & **PQ (\%)** & **Homic Acid** & **Fulvic Acid** & **AlogK** \\ \hline T1 & 0.17 \(\pm\) 0.01 c & 3.11 \(\pm\) 0.01 c & 1.80 \(\pm\) 0.01 b & 10.14 \(\pm\) 0.18 b & 63.27 \(\pm\) 0.08 b & 0.66 \(\pm\) 0.01 b & 1.25 \(\pm\) 0.01 b \\ T2 & 0.23 \(\pm\) 0.01 a & 4.06 \(\pm\)  
no significant difference between T2 and T3 treatments, but both were significantly higher than the other treatments. At 150 days, T2, T3, and T4 treatments showed increases in soil DOC content by 11.82%, 8.84%, and 3.86%, respectively, compared to T1 treatments. Moreover, in comparison to T4, soil DOC content in T2 and T3 treatments increased by 7.66% and 4.80%, respectively.
# The Concept regarding Vehicular Communications Based on Visible Light Communication and the IoT
## 5 Conclusions  
When resource-intensive computing services employing big data are used and they contain location data or large-scale derivations, query requests also arise. They are encrypted and transmitted to vehicles to efficiently manage the processes carried out by the RSUs. The roadside control and management units can calculate the shortest routes to the desired destinations, a feature that transforms the network into a continuous flow of variants. In addition to these features, distance and location data are only captured at the user's physical level of inquiry and are shown in standard CPSs. The transfer of information and its sharing between vehicles is undertaken through a different environment that can control each physical level and a layer of the sharable service query. When the data are collected, they are adjusted with on-board units capable of branching the information into distinct stages and iterative processes through which the filtering of the usual information from that of control and management is distinctly achieved. Regardless of the area in which communications are used or their type, keeping personal information confidential and encrypting it are of paramount importance. The future is extremely promising for the application and development of such systems but, at the same time, the accumulation of factors and the dangers to which individuals and users are exposed can create more identity and authentication intrusions. Location and destination monitoring may make new systems veritable maps of possible targets for malicious individuals. Another approach that the scientific community can consider is that of generating fake data for the external environment and constantly updating IDs, producing a model capable of maintaining some degree of discretion in terms of safety and security.  
This research received no external funding.  
This work was supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS-UEFISCDI, project number PN-III-P1-1.1-TE-2021-1371, within PNCDI III.  
The author declares no conflict of interest.
## 3 Review of PSO, DE, and DEPSO  
### Particle Swarm Optimization  
The particle swarm optimization (PSO) algorithm [47] is one of the most popular swarm intelligence-based algorithms. It models the behavior of groups of individuals, such as flocks, to solve complex optimization problems in D-dimensional space. The algorithm performs a search for the global optimum using a swarm of \(NP\) particles. For the \(i\)-th particle of the swarm \(X_{i,t}\) (\(i=1,2,\ldots,NP\)), its position and velocity at iteration \(t\) are represented by \(X_{i,t}=\{x_{i,t}^{1},x_{i,t}^{2},\ldots,x_{i,t}^{D}\}\) and \(V_{i,t}=\{v_{i,t}^{1},v_{i,t}^{2},\ldots,v_{i,t}^{D}\}\), respectively. At each iteration, the position and velocity of each particle is updated, taking into account the information of the best solution found by the swarm \(X_{gbest,t}\) and the best solution found individually, \(X_{gbest,t}\), by each particle. In the standard version of the PSO algorithm, the way in which the updating of the position and velocity of each particle is performed is carried out according to the following expressions [48]:  
\[V_{i,t+1}=\omega\cdot V_{i,t}+c_{1}\cdot r_{1}\cdot(X_{gbest,t}- X_{i,t})+c_{2}\cdot r_{2}\cdot(X_{gbest,t}-X_{i,t}) \tag{18}\] \[X_{i,t+1}=X_{i,t}+V_{i,t+1} \tag{19}\]  
where \(c_{1}\) and \(c_{2}\) are the social and cognitive coefficients, respectively, which commonly take the values \(c_{1}=c_{2}=2\). \(r_{1}\) and \(r_{2}\) are two randomly selected values within the interval \([0,1]\). \(\omega\) is known as the inertia parameter and it aims to provide a better balance between global and local search. The inertia parameter \(\omega\) is updated at each iteration as follows:  
\[\omega=\omega_{max}-\frac{t\cdot(\omega_{max}-\omega_{min})}{t_{max}} \tag{20}\]  
where \(t\) refers to the current iteration, \(t_{max}\) is the maximum number of iterations, and \(t_{min}\) and \(t_{max}\) are the minimum and maximum values of the inertia factor, which are generally set to values \(t_{min}=0.4\) and \(t_{max}=0.9\).
## 2 Materials and Methods
### Sequence Data Analysis and Annotation  
Raw data (raw reads) of fastq format were firstly processed through in-house per scripts. In this step, clean data (clean reads) were obtained by removing reads containing adapter, reads containing poly-N and low-quality reads from raw data. At the same time, Q20, Q30, GC-content, and sequence duplication level of the clean data were calculated. All the downstream analyses were based on clean data of high quality. The left files (read1 files) from all libraries/samples were pooled into one big left.fq file, and right files (read2 files) into one big right.fq file. Transcriptome assembly was accomplished based on the left.fq and right.fq using Trinity [31] with min_kmer_cov set to 2 by default and all other parameters set to default. Gene function was annotated based on the following databases: NCBInon-redundant protein sequences (NR), Protein family (Pfam), Clusters of Orthologous Groups of proteins (KOG/COG/eggNOG), A manually annotated and reviewed protein sequence database (Swiss-Prot), Kyoto Encyclopedia of Genes and Genomes(KEGG), and Gene Ontology (GO).
###### Abstract
The dumbbell-shaped DGS pattern is realized between the output ports, to obtain comparable or preferably better isolation as compared to conventional WPD. Figure 1 shows the layout of the proposed WPD with its DGS section. The goal of the proposed circuit is to achieve better return loss in order of more than \(-20\) dB and improved isolation, i.e., \(S_{32}\leq-30\) dB in the operating bandwidth. Figure 1: Three-dimensional view of the proposed Wilkinson Power Divider (WPD) at the operating frequency of 3 GHz with the DGS section etched in the ground plane.
## 5 Issues Relating to the Fundamental Design Aspects
### Energy Management
#### 5.1.1 Importance of Energy Consumption in UAV-Assisted Data Gathering  
More energy is dissipated when UAVs accelerate or move at speed to collect new data in the least possible time, which helps to reduce AoI; however, higher energy consumption reduces the lifetime of the UAV. The nature of energy consumed is also architecture- and application-dependent. For instance, applications requiring continuous data collection by the UAV increase the amount of energy consumed by the UAV; thus, energy optimization in these scenarios is very important (see [1]). Another factor that significantly affects the amount of energy consumed by the UAV is the scheduling policy of the UAV, the choice of a node to visit, and the order of visits. In such situations, the UAV service time duration should be optimized. Scenarios where energy consumption is more sensitive are cases when the UAV embarks on special missions or an urgent need for update packets, such as military applications. Information freshness should be achieved in such scenarios while optimizing UAV energy consumption. The proper allocation of energy for each sensor node or CP visited is also important as it can help to optimize the overall energy consumed by the UAV.  
The energy consumed by the UAV can be a result of several factors, such as its propulsion, movement, hovering, as well as transmission. Usually, the energy for hovering and flying can be very significant and much higher than the energy required for communication with the sensor nodes or the ambient RF-EH delivered by the UAV. Sensor nodes have limited onboard energy, which makes it challenging to maintain highly reliable communication [92]. A solution for providing a reasonably stable energy supply is via energy harvesting. In this case, sensor nodes can harvest energy from the environment and also be charged via wireless energy transfer from UAVs. In other words, UAVs can transfer energy to sensor nodes, which can be used to upload data to the UAV.
## 3 Formulation and Numerical Modeling  
As mentioned above, plasma is a complex system involving nonlinear dynamics, particle-wave interactions, and chemical reactions on multiple spatial and time scales. In general, the motions of electrons and ions in a plasma, due to their large mass differences, have to be considered on different time scales, and electron transport should be described by the Boltzmann equation, which is a non-local continuity equation in a six-dimensional phase space. However, the Boltzmann equation is an extremely complicated integro-differential equation, and solving it in an efficient manner is difficult. To reduce the complexity, the Boltzmann equation can be approximated by fluid equations, multiplied by a weighting distribution function, and then integrated over velocity space, resulting in a three-dimensional, time-dependent fluid model. The fluid equations describe the electron number density, the mean electron momentum, and the mean electron energy as a function of space and time [42]. When the gas pressure is above about 1 Torr and the discharge is weakly ionized, the fluid or drift-diffusion (DD) approximation is suitable, and a Maxwellian distribution is assumed for the electron energy distribution function (EEDF). Based on the FEM simulations implemented using the commercial software COMSOL Multiphysics [43], the plasma, chemistry, and electromagnetic modules are coupled and tailor-made to model the low-temperature microwave plasma interaction in the MPJCVD system. These models help us to gain physical insight into plasma discharges and guide the performance of existing or potential designs, which will greatly benefit the applications of plasma technology in science and industry. Given their high degree of nonlinearity, plasma systems are highly complicated, and even a small change to the electrical input or plasma chemistry would significantly change the discharge characteristics. Modeling this behavior requires a high computational cost. The following subsections introduce the theory, governing equations, and numerical methods used in this work.
# Deep-Learning Integration of CNN-Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
## 3 Methodology
### Transformer Blocks  
The transformer branch includes several encoder layers, each incorporating layer normalization, multihead self-attention with adjustable head counts and projection sizes, and position-wise feedforward MLPs that utilize GELU activations and dropout for regularization (Figure 7). Residual skip connections between the layers improve gradient flow  
Figure 5: Architecture of the U-Net Decoder model.  
Each Transformer layer incorporates a multihead attention mechanism that operates on the tokenized patches to capture long-range dependencies between image regions. Num_heads specifies the number of attention heads, while the attention space's dimensionality is indicated by projection_dim. After each attention operation, a residual connection is included in the output to aid training. This is followed by a feedforward neural network that consists of two dense layers utilizing GELU activations and dropout for regularization. Layer normalization is applied after both the attention and feedforward layers to enhance training stability. The Transformer block's output is improved through multiple layers, enabling the model to capture local and global contextual information.  
The transformer block uses multihead attention and a multilayer perceptron (MLP) for feature extraction. The multihead attention operation is defined as:  
\[\text{Attention}(Q,K,V)=softmax((Q\,*\,K^{\ast}T)/sqrt(d\_k))\,*\,V \tag{5}\]  
where \(Q\) is the query matrix, \(K\) is the key matrix, \(V\) is the value matrix, \(d\_k\) is the dimension of the key.  
The output is fed into an MLP:  
\[\text{MLP}(X)=W1\,*\,\text{ReLU}(W2\,*\,X+b2)+b1 \tag{6}\]  
where \(W1\), \(W2\), \(b1\), and \(b2\) are learned weights and bias terms.