esa-sceva/satcom-chunk-collection huggingface dataset:
# Side-Scan Sonar Image Augmentation Method Based on CC-WGAN
## 3 Experiments
### Evaluation Metrics  
Image generation quality is primarily evaluated based on image clarity, feature diversity, and structural similarity. In this study, we chose the Frechet Inception Distance (FID), Maximum Mean Discrepancy(MMD), Peak Signal-to-Noise Ratio (PSNR), and Structural  
Figure 5: Integration of the CBAM into the discriminator.  
Figure 6: Part of the dataset.  
Similarity Index Measure (SSIM) to evaluate the quality of the generated images. The FID quantifies the dissimilarity between the feature vectors of authentic and generated image sets, reflecting the similarity between the two sets of images. It involves extracting features from both images using a pre-trained network on a publicly available dataset, modeling the feature space using a Gaussian model, and determining the distance by considering the mean and covariance of the Gaussian model. This approach captures the differences between the two distributions more effectively by considering both the mean and the covariance matrix. The MMD is a statistical test utilized to quantify the similarity between two sets of features. It involves transforming real and generated datasets into a kernel space using a predetermined kernel function, followed by computing the average disparity between the two distributions to determine if the samples originate from different distributions. The PSNR is a metric used to quantify the disparity between two images. It begins by determining the average squared error between the images and subsequently derives the PSNR, which is utilized to assess the training set against the generated images. The SSIM takes the structural details that are present in the image into account. It defines structural information as a characteristic that represents the arrangement of objects in the scene, regardless of their brightness and contrast. The SSIM characterizes distortion by considering three distinct elements: brightness, contrast, and structure. The average value is utilized to estimate brightness, while the standard deviation serves as an indicator of contrast, and covariance measures structural similarity. For FID and MMD, smaller values indicate better performance, suggesting that the distribution of the generated images is more similar to that of real images, which implies a higher quality. For the PSNR, larger values indicate smaller differences between the images, signifying a better quality of the generated images. Similarly, higher SSIM values denote a greater similarity between the images.
# An Efficient Imaging Method for Medium-Earth-Orbit Multichannel SAR-GMTI Systems
## 5 Experimental Results  
In this section, numerical experiments are conducted to validate the proposed imaging method and the analysis presented in Section 4. The MEO SAR parameters used for simulation are given in Table 1. Five targets are simulated, including four moving targets and one stationary target. The parameters of these targets are given in Table 2.  
The results of RCMC are presented in Figure 7. Comparing Figure 7a with Figure 7b, it is evident that the five targets' RCMs are corrected at the same time. In addition, the residual RCMs of five targets are also calculated by (34), which are 0.42 m, 0.85 m, 0.53 m, 0 m, and 0.16 m. They are so much smaller than a range c  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Target 1** & **Target 2** & **Target 3** & **Target 4** & **Target 5** \\ \hline \(v_{tr}\) (_m/s_) & 4 & \(-3\) & 4 & 0 & 5 \\ \hline \(v_{tu}\) (_m/s_) & 6 & 8 & 12 & 0 & 15 \\ \hline \(a_{tr}\) (_m/s_\({}^{2}\)_) & 0.1 & 0.15 & \(-0.1\) & 0 & 0 \\ \hline \(a_{tu}\) (_m/s_\({}^{2}\)_) & 0.2 & 0.3 & 0.2 & 0 & 0.4 \\ \hline \(\delta_{lat}\) (\({}^{\circ}\)) & 10.016 & 10.008 & 10.02 & 10.012 & 10.002 \\ \hline \(\delta_{lmg}\) (\({}^{\circ}\)) & 30 & 30 & 30 & 30 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the targets.  
The azimuth compression results are given in Figure 8, from which it can be found out that the targets are focused simultaneously. Furthermore, it can be seen that the static target (target 4) is finely focused. In addition, the four moving targets are also well focused in range direction with defocusing only in the azimuth direction.  
In order to verify the target's signal characteristic analysis presented in Section 4, the range and azimuth displacements and the ATI phases of the four moving targets are measured, with the results shown in Table 3. Note that the measured displacements are calculated based on target's position in the SAR image, and thus they can only be the integer multiples of the size of a pixel. From Table 3 it can be seen that the measured values are very close to the theoretical ones. The differences between the theoretical range displacements and the measured ones are all less than half of the range length of a pixel (i.e., 3.75 m), which indicates the measured values being exactly consistent with the theoretical ones. The differences between the theoretical azimuth displacements and measured ones are slightly larger. This could be caused by the residual cubic azimuth modulation (see Equation (37)) and azimuth defocusing.  
Figure 8: SAR image of the five targets.  
Figure 7: RCMC results. (**a**) Five targets before RCMC; (**b**) Five targets after RCMC.
# Contrasting Dual Transformer Architectures for Multi-Modal Remote Sensing Image Retrieval
## 2 Materials and Methods
### Encoders Architecture  
Given the limitations imposed by the small-scale nature of the available RS cross-modal datasets, we expect that learning the model from scratch may not be a good solution. Instead, we propose to transfer knowledge from backbones pre-trained on large-scaledatasets such as ImageNet (1.2 M images) and English Wikipedia (2500 M words) for vision and language tasks, respectively. In addition, as our model involves a joint learning from both images and text pairs, different types of architectures pre-trained in different manners can be investigated (Table 1). Our aim is to identify the most suitable pre-trained configuration for transferring knowledge to our task.  
The first configuration is based on dual transformers pre-trained on matching text to computer vision images known as Contrastive Language Image Pre-training (CLIP) [16]. The related image-text encoders based on ViT32 and BERT were learned for matching 400M image-text pairs using a contrastive loss. In the second configuration, we consider transformers pre-trained independently in a self-supervised mode. For images, we use a ViT16 encoder pre-trained on ImageNet using the Distillation with no-labels (DINO) self-supervised learning approach [17]. For text, we use RoBERTa as encoder [18] pre-trained by the Simple Contrastive Learning of Sentence Embedding (SimCSE) approach [19]. In the third configuration, we use the Data efficient Transformer (DeiT16) pre-trained in a supervised mode on ImagNet [20]. While for text, we adopt the original BERT model [14].
## 3 Results
### Results on Elemental Quantification Using ICP-MS  
Mineral nutrients are very important for human health, and their presence or absence has a direct impact on beneficial microorganisms. In plants, their presence increases plants' disease resistance and enhances plant growth and development [53].  
Figure 4: TFC—total flavonoid content in _Salicornia europaea_ L. grown on different substrates. Values followed by the same letter(s) do not differ significantly at \(p=0.05\).  
In our study, the mineral composition of _Salicornia europaea_ L., depending on the cultivation substrates, is presented in Table 1. The analysis of the chemical composition of _Salicornia europaea_ L. showed that Na content varies with the growing substrate, the differences being distinctly significant. High values of Na accumulation were obtained in the case of the aerial parts of _Salicornia europaea_ L. grown on the substrates S2 and S1. Also, regarding Mg content, the concentration depended on the cultivation substrate. High values in Mg content were found in aerial parts harvested from the R and S2 substrates compared to the indigenous plant (I).  
The value of K content is significantly different, with high values for the plant grown on acidic soil (R) (40.867 g\(\cdot\)kg\({}^{-1}\)). What is interesting is that _Salicornia europaea_ L., in its indigenous environment, has a lower K content. One explanation is related to the high Na\({}^{+}\) concentrations in indigenous salimized soil where _Salicornia_ I is grown. According to [49], high levels of Na\({}^{+}\) inhibit the uptake of K\({}^{+}\) ions, which is an essential element for the growth and development of plants, resulting in lower productivity.  
Regarding Ca content, in the case of indigenous _Salicornia europaea_ L., a concentration of 3.943 g/kg\({}^{-1}\) was found. According to [PERSON]. [54], in an _Arabidopsis_ experiment, the roots exhibit Ca\({}^{2+}\) signals in response to K\({}^{+}\) deficiency using the decoding complex that regulates the transport of K\({}^{+}\). In this work, the highest values were observed in the case of the S3 cultivation substrate (non-saline, pH 7.45). Similar studies on _S. bigelovii_ found significant amounts of minerals in stems: Na (1218.1 mg/100 g), Ca (158.8 mg/100 g), K (740.1 mg/100 g), and Mg (52.2 mg/100 g) [32]. It is important to mention that, generally, the adopted extraction and analysis procedure has an important role in the elemental analysis, so small differences obtained in quantification may be due to the method of extraction [55] and also to the sample preparation.
## 2 SIMO System Model in UWACs  
Suppose a binary bit sequence \(\mathbf{b}=[b_{0},b_{1},\ldots,b_{N_{b}-1}]\) is emitted in the transmitter, where the \(N_{b}\) is the length of the bit sequence. This sequence is denoted as \"0\" or \"1\" by channel encoding with binary code bit \(\mathbf{c}\). The coded bits are interleaved and then mapped to the sequence of symbols \(\mathbf{x}=[x_{1},x_{2},\ldots,x_{K}],x_{k}\in\mathcal{A}\), where \(x_{k}\in\mathcal{A}\), \(\mathcal{A}\) is a set of mapping symbols, and \(\mathbf{x}_{k}\) is mapped by Q bit coded bits \(\mathbf{c}_{\mathbf{k}}=[c_{k,1},c_{k,2},\ldots,c_{k,Q}]^{T}\), where \(c_{k,q}\in\{0,1\}\). Subsequently, the training sequence is then inserted into the front of the information blocks. The blocks are modulated to carriers, transmitted through UWAC channels, and received by a hydrophone array, as shown in Figure 1.  
The UWAC channels between the transmitter and hydrophones are time-varying. Each symbol block remains unchanged, and the channel varies randomly between different blocks. After synchronization and sampling, the received symbols are expressed as  
\[\mathbf{y}=\mathbf{H}_{t}\mathbf{x}+\mathbf{n}, \tag{1}\]  
where \(\mathbf{y}\) is the received vector of \(M\) channel hydrophones, where \(\mathbf{y}=[\mathbf{y}_{1}^{T},\mathbf{y}_{2}^{T},\ldots,\mathbf{y}_{K}^{T}]^ {T}\) with \(\mathbf{y}_{k}=[y_{1,k},y_{2,k},\ldots,y_{M,k}]\), \(\mathbf{n}\) is the additive noise vector where \(\mathbf{n}=[\mathbf{n}_{1}^{T},\mathbf{n}_{2}^{T},\ldots\mathbf{n}_{K}^{T}]^ {T}\) with \(\mathbf{n}_{k}=[n_{1,k},n_{2,k},\ldots n_{M,K}]^{T}\), and \(n_{m,k}\sim\mathcal{CN}\big{(}0,\sigma_{n,m}^{2}\big{)}\) for ease of modeling, where \(\mathcal{CN}(\cdot)\) denotes the complex Gaussian distribution, and \(\sigma_{n,m}^{2}\) is the variance of the  
Figure 1: UWACs system model.  
noise. \(H_{t}\) is the UWAC channel state and block circulant matrix with the first column \(\left[\mathbf{h}_{0}^{T},\mathbf{h}_{1}^{T},\ldots,\mathbf{h}_{L-1}^{T},\mathbf{ \mathbf{0}}_{M,K-L}\right]^{T}\), where \(\mathbf{h}_{l}=[h_{l,1},h_{l,2},\ldots,h_{l,M}]\), and \(L\) is the channel length.
# Evaluation of Emotions from Brain Signals on 3D VAD Space via Artificial Intelligence Techniques
## 2 Materials and Methods
### Attributes
The largest Lyapunov exponent (LLE) is used chiefly for the nonlinear analysis of physiological signals, and it is sufficient for chaos evaluation. Many algorithms can be used to calculate LE and LLE [30]. The Higuchi fractal dimension (HFD) method was first developed in 1988 by [PERSON] et al. For a finite set of time series, if a new time series is obtained and the Lm(k) average value is calculated, this indicates the curve length (m is the initial time and k is the interval time). The HFD is then calculated in the slope between ln(L(k)) and ln(1/k) values [35]. First developed by [PERSON], the Hurst exponent (HE) statistically calculates the time series' variability [24]. Considering a time series that increased previously, it is possible to identify if it is likely to increase more, less, or equally like the previous steps with the Hurst exponent [35]. The HE value can range from 0 to 1. If it is ranged as 0 < HE > 0.5, then the time series has long-ranged anti-correlations; if it is equal to 0.5, there are no correlations; if it is ranged as 0.5 < HE > 1, then the time series has long-ranged correlations; and, if the value equals to 1, then there is self-similarity in the time series [24]. Detrended fluctuation analysis (DFA) also uses a remarkably similar parameter to HE called the Hurst parameter H [35]. Lempel-Ziv (LZ) complexity is used frequently in the irregularity analysis of biomedical signals with its interpretability and suitability. LZ complexity measures the nonparametric complexity of the irregularity of time series [36]. LZ complexity evaluates the algorithmic complexity and determines the emergence rate of new patterns [37]. A greater LZ complexity means the new changes occur at a faster rate, indicating data change irregularly. In contrast, a lower LZ complexity means a slower rate of changes, and the data change regularly and periodically [36].
## 4 Results
### Performance of the Bidirectional LSTM-Sentiment Model  
The Bidirectional LSTM-Sentiment model, implemented with a bidirectional LSTM architecture as discussed in Section 3.3.3, achieved competitive performance on the test dataset. The Bidirectional LSTM produced an MAE of 0.00253 and an RMSE of 0.00350. Although slightly higher than the tuned Multi-LSTM-Sentiment, these values indicate robust predictive performance.  
A visual comparison of the actual and predicted prices is presented in Figure 9. The Bidirectional LSTM model captured the general trend of Bitcoin price fluctuations  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MSE** & **RMSE** & **MAE** \\ \hline Multi-LSTM-Sentiment & 9.80247 & 0.00990 & 0.00934 \\ Tuned Multi-LSTM-Sentiment & 9.29112 & 0.00304 & 0.00196 \\ Univariate RNN LSTM [14] & 340,685,452.13732 & 18,446.6821 & 13,094.2243 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance metrics comparison for Multi-LSTM models.  
Figure 8: Tuned Multi-LSTM-Sentiment: actual vs. predicted prices.  
effectively. However, it occasionally struggled to accurately track sudden price spikes or drops, mirroring challenges faced by the other models.  
A performance comparison between the tuned Multi-LSTM-Sentiment model and the Bidirectional LSTM-Sentiment model is shown in Table 3. While the Bidirectional LSTM-Sentiment demonstrated excellent results, the tuned Multi-LSTM-Sentiment outperformed it slightly, reinforcing its suitability for this dataset.
## 6 Case Studies
### Case II: Modified IEEE 69-Node Distribution System  
As shown in Figure 3, the modified IEEE 69-node test case is used to examine the scalability of the proposed method for larger networks. The case of the IEEE 69-node distribution system is modified by replacing five load nodes with generation nodes, and substituting five distribution lines with boundary lines. Thus, when the boundary lines are disconnected, the system comprises six independent microgrid subsystems. The capacities of the five added generation nodes are listed in the last two columns of Table 2. While the capacity of node 1 is obtained from the original system data, the capacities of the other five nodes are related to the power demands of the load nodes within their corresponding microgrid subsystems. The parameters associated with MES devices are similar to those in Case I.  
It is assumed that there are four MES devices and ten arbitrarily selected damaged lines, labeled in Figure 5. The pre-positioning and re-allocation scheme of MES devices and the utilization of their capacities are detailed in Tables 7 and 8, respectively. Figure 5 illustrates that in the first stage, the MES devices are pre-positioned at nodes 17,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Boundary Line** & **First Stage** & **Second Stage** \\ \hline Line (2, 19) & open & \(\rightarrow\) & open \\ Line (3, 23) & open & \(\rightarrow\) & closed \\ Line (6, 26) & open & \(\rightarrow\) & open \\ Line (12, 13) & open & \(\rightarrow\) & closed \\ \hline \hline \end{tabular}
\end{table}
Table 6: The switch operation scheme of boundary lines in Case I.  
Figure 4: The optimization solution for the modified IEEE 33-node distribution system. (**a**) The first stage. (**b**) The second stage.
# A Proposal of a Troposphere Model in a GNSS Simulator for VANET Applications
## 6 Performance Evaluation
### Experimental Results
The graphics show that the errors distribution are very similar to the normal/Gaussian distribution. These figures prove that the model is correctly representing a GPS signal and, in particular, they show how the error committed by simulator considering the proposed module implementing the Collins model is lower than considering experiments without the troposphere correction module. In particular, Figure 9(a) represents the estimated probability density function for the latitude error committed without troposphere correction. It is possible to note a error range between about 0.25 to 0.55 m, with a mean of 0.4023 m and a variance of 0.0470 m. Figure 9(b) shows the latitude error range for experiments with the troposphere error compensation. In this case, it is possible to observe a lower error range with value between 0.12 and 0.3 m, with a mean of 0.2136 m and a variance of 0.0234 m. Instead, Figure 9(c),d represent the estimated probability density function for the longitude error committed without troposphere correction and with troposphere correction, respectively. It is possible to note that the proposed module implementing the Collins model is lower than considering experiments without the troposphere correction module. In particular, Figure 9(a) represents the estimated probability density function for the latitude error committed without troposphere correction. It is possible to note that the proposed module implementing the Collins model is lower than considering experiments without the troposphere correction module. an error range between about \(-1.5\) to \(3\) m, with mean of \(0.7953\) m and variance of \(0.5813\) m in the first case and an error range with value between \(-0.3\) and \(0.7\) m, with mean of \(0.2136\) m and variance of \(0.1213\) m for the second one.
# Definition and Determination of Fin Substitution Factors Accelerating Thermal Simulations
## 3 Results
### Case Study: Natural Convection on Vertical Rectangular Finted Arrays
#### 3.3.2 Objectives  
Taking a closer look at Formula (31), the \(F\!SF\) consists of three components: geometry ratios (indicated by index \(A\)), heat conduction within the fin (indicated by index \(\eta\)), and the ratio of the heat transfer (indicated by index \(h\)). Especially the computational effort for the heat transfer coefficients is high. Therefore, the question is answered whether it is necessary to consider all components or whether a simplified calculation is sufficient. The following definitions of the \(F\!SF\) are used, whereby the indices indicate the considered components.  
\(F\!SF\) depending on geometric quantities:  
\[F\!SF_{A}=\frac{\left(A_{fb}+A_{f}\right)}{A_{0}} \tag{32}\]  
\(F\!SF\) depending on geometric quantities and heat conduction in the fins:  
\[F\!SF_{A,\eta}=\frac{\left(A_{fb}+\eta_{f}\cdot A_{f}\right)}{A_{0}} \tag{33}\]  
\(F\!SF\) depending on geometric quantities and heat transfer:  
\[F\!SF_{A,h}=\frac{h_{f}}{h_{0}}.\frac{\left(A_{fb}+A_{f}\right)}{A_{0}} \tag{34}\]  
\(F\!SF\) depending on geometric quantities, heat conduction, and heat transfer:  
\[F\!SF_{A,\eta,h}=\frac{h_{f}}{h_{0}}.\frac{\left(A_{fb}+\eta_{f}\cdot A_{f} \right)}{A_{0}} \tag{35}\]  
In addition to the components, it is examined which Nusselt number correlation presented under Section 2.2.3 provides the best results for the considered parameter combinations, listed in Table 3.
## 4 Conclusions  
Two BS-based adsorbents were successfully synthesized by grafting GMA on BS using the RIGP technique, followed by a ring-opening reaction and quaternization. The adsorption performance of BS-GMA-EDA-Q and BS-GMA-PEHA-Q adsorbents for phosphate removal was investigated.  
1. After quaternization, BS-GMA-EDA-Q and BS-GMA-PEHA-Q showed a high adsorption capacity for phosphate. The adsorption capacity was independent to pH.
2. The adsorption kinetics of BS-GMA-EDA-Q and BS-GMA-PEHA-Q well obeyed the pseudo-second-order model. The phosphate uptake reached equilibrium within 180 min.
3. BS-GMA-EDA-Q and BS-GMA-PEHA-Q adsorbents for phosphate removal well obeyed the Langmuir model. The maximum phosphate adsorption capacities of BS-GMA-EDA-Q and BS-GMA-PEHA-Q were 85.25 and 152.21 mg/g, respectively.
4. The repeated use showed that the adsorption performance of the BS-GMA-EDA-Q and BS-GMA-PEHA-Q adsorbents displayed a negligible decrease after five cycles. Therefore, the BS-GMA-EDA-Q and BS-GMA-PEHA-Q adsorbents exhibit good recyclability and can be used repeatedly in practical applications.  
Conceptualization: [PERSON]; formal analysis: [PERSON], [PERSON]; investigation: [PERSON], [PERSON]; data curation: [PERSON], [PERSON]; writing--original draft preparation: [PERSON], [PERSON]; writing--review and editing: [PERSON], [PERSON]; visualization: [PERSON]; supervision: [PERSON]; funding acquisition: [PERSON], [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"Hubei Provincial Natural Science Foundation, China, grant number 2020 CFB852\", Research Project of Department of Education of Hubei Provincial, China, grant number BXLBX0781, and Foundation Project of Hubei University of Science and Technology, grant number BK201820.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Results
### Numerical Results: Cysts Phantom  
The results obtained with the cyst phantom are shown in Figure 9. The images are laterally limited due to the narrow transmission beam profile (see Figure 2a). The proposed scheme (Figure 9a) is more laterally limited because of the smaller effective aperture size than the CBC GT (Figure 9b). The red arrows in Figure 9a show the effects of grating lobes. To statistically compare these images, pixel intensities within two regions of interest (ROI1 and ROI2, the red and yellow circles in Figure 9b) are used. For the proposed imaging sequence, the mean of ROI1 is 6 dB higher than GT (see Figure 9c) due to the effects of the grating lobes and sidelobes (see Figure 8c). The 3 dB higher mean in ROI2 provided by the proposed imaging sequence (Figure 9c) is also due to the larger FWHM and more constructive interferences of the point scatterers. See the Supplementary Materials (Figure S2) for the results obtained using the multi-cyst phantom.  
Figure 7: **(a–c)** The PSFs obtained for different Nr = 5, Ns = 3. The green rectangular regions show a reduction of sidelobes with respect to the red rectangles. **(d,e)** The lateral shift variance for full-aperture transmission, Nr = 5 and Ns = 3, for a scatter at a depth of **(d)** 10 mm and **(e)** 20 mm. Green arrows: location of grating lobes.