esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiuser Equalizer for Hybrid Massive MIMO mmWave CE-OFDM Systems
## 3 Precoder and Equalizer Design  
In this section, a low-complexity analog precoder, based only on average angles of departure (AoD), is presented. Then, the iterative hybrid analog-digital multi-user equalizer for CE-OFDM systems is derived. A decoupled transmitter-receiver optimization problem is assumed in this paper because the resulting joint optimization problem will be nonconvex, even without considering the analog part constraints, and is therefore, mathematically intractable [40].
## 3 Chemical Properties of Waste Cooking Oils and Purification Process
### Transesterification of Waste Cooking Oil
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Properties**} & **Homogeneous Base** & **Heterogeneous Base** & **Enzyme Catalysis (e.g.,** & **Supercritical Method (e.g.,** \\  & **Catalysis (e.g., NaOH,** & **Catalysis (e.g., CaO,** & **Lipase, Novozym 435)** & **Supercritical Methanol)** \\  & **KOH)** & **MgO, SrO, TiO2** & **Imobilized or soluble** & \\ \hline \multirow{2}{*}{Catalyst Solubility} & Soluble & **Insoluble** & **Imobilized or soluble** & \multirow{2}{*}{No catalyst required} \\  & & & & (enzyme) & \\ Reaction rate & Very fast & Moderate–fast & Slow–moderate & Very fast (under high \\ \multirow{2}{*}{Operating Conditions} & \begin{tabular}{c} Mild temperature \\ (60–70 \({}^{\circ}\)C), low \\ pressure \\ \end{tabular} & \begin{tabular}{c} Higher temperature \\ (>80 \({}^{\circ}\)C) \\ pressure \\ \end{tabular} & \
\begin{tabular}{c} Slow–moderate \\ (25–45 \({}^{\circ}\)C), atmospheric \\ pressure \\ \end{tabular} &
\begin{tabular}{c} High temp (200–350 \({}^{\circ}\)C), atmospheric \\ high pressure (100–200 bar) \\ \end{tabular} \\  & & Poor tolerance (<2\% & Poor to moderate (<2\% & Excellent tolerance \\ \multirow{2}{*}{FFA Tolerance} & FFA) & FFA ideal) & (high FFA tolerated) & Excellent (not affected by \\ \multirow{2}{*}{Soap Formation} & High if FFA >2\% & Possible at high FFA & None & None \\ \multirow{2}{*}{Product Purity} & Affected by soap, impurities, and moisture & Higher purity & Very high purity & Very high purity \\ \multirow{2}{*}{Catalyst Recovery} & Not reusable, requires extensive washing & Easily separable and reusable after immobilization & Not applicable \\ \multirow{2}{*}{Environmental Impact} & Generates soap, wastewater & Low (less effluent generation) & Eco-friendly and clean & Eco-friendly, no catalyst waste \\ \multirow{2}{*}{Corrosion Risk} & High (residual alkali, moisture) & Negligible & None & None \\ \multirow{2}{*}{Cost Implication} & Low catalyst cost, high purification cost & Higher catalyst cost, lower operational cost & High enzyme cost, requires immobilization & High energy and equipment cost \\  & & Reusable for several cycles & Requires & \\ \multirow{2}{*}{Reusability} & Not reusable & **Reusable** & **Requires** & \\  & Not reusable & cycles & immobilization for reusability & Not applicable \\ Separation of \multirow{2}{*}{Products} & Difficult (due to soap, water) & Easy & Very easy (no side products) & Easy due to one-step \\  & & & **products)** & **conversion** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary and comparison of reaction types used in FAME production [81,94,95,129].
## 2 TBD System Setup
### Measurement Model  
Generally, we use the results of the sensor signal processing, which are a series of images, and the signal is assumed to be two-dimensional images [8; 9; 10], each frame consisting of \(n\) cells in the \(x\) dimension and \(m\) cells in the \(y\) dimension. The measurement at time \(k\) is the intensity of each cell in the image, which is given by \(z_{k}=\{z_{k}^{(i,j)}:i=1,...,n;j=1,...,m\}\). As in Chapter 11 of [7], for each cell, the measurement model can be defined as follows:  
\[z_{k}^{(i,j)}=\begin{cases}h_{k}^{(i,j)}(\mathbf{x}_{k})+v_{k}^{(i,j)}&E_{k}= 1\\ v_{k}^{(i,j)}&E_{k}=0\end{cases} \tag{5}\]  
where \(h_{k}^{(i,j)}(\mathbf{x}_{k})\) is a contribution from the intensity of the target in the cell (\(i,\,j\)) and \(\
u_{k}^{(i,j)}\) is the measurement noise in the cell, which is a known Gaussian distribution with zero mean, variance \(\sigma^{2}\). In this work, we adopt the sensor point spread function, a point target of intensity \(I_{k}\) at position (\(x_{k}\),\(y_{k}\)); the contribution to cell (\(i,\,j\)) can be written as:  
\[h_{k}^{(i,j)}(\mathbf{x}_{k})=\frac{\Delta_{x}\Delta_{y}I_{k}}{2\pi\Sigma^{2} }\exp\left(-\frac{\left(x_{k}-i\Delta x\right)^{2}+\left(y_{k}-i\Delta_{y} \right)^{2}}{2\Sigma^{2}}\right) \tag{6}\]  
Here, the parameter \(\Sigma\) represents the extent of blurring and \(\Delta_{x}\) and \(\Delta_{y}\) denote the size of a cell in the \(x\) and \(y\) dimensions, respectively. Then, the set of complete measurements up to time \(k\) is denoted by \(\mathbf{Z}=\{z_{1},...,z_{k}\}\).  
Under the assumptions of Gaussian measurement noise, which is independent of cell-to-cell, the likelihood function in the presence and absence of a target can be written as:  
\[\mathrm{p}(z_{k}^{(i,j)}\mid x_{k},E_{k}=1)=\frac{1}{\sqrt{2\pi\sigma^{2}}}\exp \left(-\frac{\left[z_{k}^{(i,j)}-h^{(i,j)}(x_{k})\right]^{2}}{2\sigma^{2}}\right) \tag{7}\]  
\[\mathrm{p}(z_{k}^{(i,j)}\mid E_{k}=0)=\frac{1}{\sqrt{2\pi\sigma^{2}}}\exp \left(-\frac{\left[z_{k}^{(i,j)}\right]^{2}}{2\sigma^{2}}\right) \tag{8}\]  
Then:  
\[\mathrm{p}(z_{k}\mid x_{k},E_{k})=\prod_{i=1}^{n}\prod_{j=1}^{m}\mathrm{p}(z_{ k}^{(i,j)}\mid x_{k},E_{k}) \tag{9}\]  
The likelihood ratio for the cell is:  
\[l(z_{k}\mid x_{k},E_{k})=\left\{\begin{aligned} &\frac{p(z_{k}\mid x_{k},E_{k}=1)}{p(z_{ k}\mid E_{k}=0)}& E_{k}=1\\ & 1& E_{k}=0\end{aligned}\right. \tag{10}\]  
where:  
\[l(z_{k}\mid x_{k},E_{k}=1)=\prod_{i=1}^{n}\prod_{j=1}^{m}\exp\left(-\frac{h^{ (i,j)}(x_{k})[h^{(i,j)}(x_{k})-2z_{k}^{(i,j)}]}{2\sigma^{2}}\right) \tag{11}\]
# Progress in Electrodeposition of Zinc and Zinc Nickel Alloys Using Ionic Liquids
## Zinc Deposition in Ionic Liquids
In addition, liquid chlorozincates are easier to make and handle as they are less susceptible to hydrolysis, which makes the synthesis and process scale-up simpler. While ChCl-based ILs have been favoured, imidazolium-based cations are also gaining significant attention owing to their superior fluidity and conductivity and, of these, 1-butyl-3-methylimidazolium is the most preferred thanks to its high conductivity. There are few reports highlighting the feasibility and use of other ILs such as 1-butyl-3-methylimidazolium chloride ([BMIm]Cl, 1-butyl1-methylpyrrolidinium chloride ([BMP]Cl) and bis((trifluoromethyl)sulfonyl)imide anion ([TFSI]\(-\))-based owing to their lower viscosities and melting points. [PERSON] et al. [30] electrodeposited Zn particles in ZnCl\({}_{2}\)/[EMIm][Cl] IL with different acidity by adding different percentage of ZnCl\({}_{2}\). Scanning electron microscopy (SEM) images confirmed that the morphology of Zn deposits could be controlled by adjusting the deposition temperature and potential. However, the results from [PERSON] et al. [31] displayed a very inhomogeneous morphology of the coatings when ([BMIm]Cl) baths were used for the deposition of Zn on steel substrate. In addition, the results showed that the type of bath used ([ZnCl\({}_{2}\)]/BMIm)[Cl]), current density applied, and deposition temperature had an impact on the structure and corrosion behavior of the Zn deposit. [PERSON] et al. [32] demonstrated successful deposition of Zn on copper in bis((trifluoromethyl)sulfonyl)imide anion ([TFSI]\(-\))-based ILs employing 1-n-butyl-1-methylpyrrolidinium[BMP] as the cation. The Zn(II) species were introduced by either the anodic dissolution of Zn or the addition of Zn(TFSI)\({}_{2}\) orZnCl\({}_{2}\), respectively. ZnCl\({}_{2}\) exhibited good solubility in [BMP][TFSI] owing to the excellent interaction of the Cl\({}^{-}\) and TFSI\({}^{-}\) anions, which resulted in the formation of (i) soluble TFSI- co-ordinated Zn(II) species and (ii) partially Cl' co-ordinated Zn(II) species corresponding to the formation of two redox couples of Zn(II) species in ZnCl\({}_{2}\) solution. However, Zn deposited using Zn(TFSI)\({}_{2}\) and [BMP][TFSI] displayed higher cathodic current efficiencies than from ZnCl\({}_{2}\) and [BMP][TFSI]-based IL. The poorer efficiency in the latter was attributed to the fact that the liberated Cl\({}^{-}\) anions formed a complicated non-reducible complex by co-ordinating with the one of the redox couples of Zn(II) species in ZnCl\({}_{2}\), that is, partially Cl\({}^{-}\)coordinated Zn(II) species.
## 3 Named Data Networking Architecture  
The named data networking paradigm is proposed by the works of [1; 14; 15]. They stated that NDN is the next generation of the IP architecture. By design, NDN names the packets instead of naming the communication endpoints, therefore changing the network semantics from delivery of packets to the destination address, to the request and caching of packets with a given name.  
Figure 1 illustrates the layered hourglass designs of IP and NDN architectures. In NDN data, the name can be anything, for instance, a chunk of video, an endpoint, etc. NDN also derives/maintains the hourglass form but replaces the IP data delivery architecture with a receiver-based content retrieval architecture.  
The hourglass model is used as a means of describing Internet design. Today's Internet hourglass architecture represents a design in layered systems that aim to support a diversity of applications and implementations. The hourglass is based on a universal network layer such as Internet Protocol (IP). This thin waist is a key enabler of Internet growth, by letting the techniques of upper and lower layers innovate independently, as described by [1]. In summary, the IP was intended to establish a communication network between destination and source addresses. However, the growth of social networks and other applications has led to the use of the Internet as a content distribution network. Therefore, using the distribution networks via the communication network is error-prone and complex to solve. NDN keeps the same Internet hourglass-shaped architecture by replacing the thin waist with named data other than communication endpoints. This semantic advances the network from delivering the packet to the given destination address to a caching data/content packet that is identified by a given name.  
Figure 2 illustrates the NDN packet types. The consumer issues an interest and the producer issues a data--content packets. The interest packet express the request of consumer by giving named of the data--content (e.g., /ndn/uminho/stream-app/video-1). The data packet expresses a packet that is produced by the data/content producer. Natively, each of the data/content is signed by its producer to maintain the integrity of content and authenticate the producer, as described in the NDN security architecture [1; 14].
# Multi-Feature Guided Low-Light Image Enhancement
###### Abstract  
Due to the characteristics of low signal-to-noise ratio and low contrast, low-light images will have problems such as color distortion, low visibility, and accompanying noise, which will cause the accuracy of the target detection problem to drop or even miss the detection target. However, recalibrating the dataset for this type of image will face problems such as increased cost or reduced model robustness. To solve this kind of problem, we propose a low-light image enhancement model based on deep learning. In this paper, the feature extraction is guided by the illumination map and noise map, and then the neural network is trained to predict the local affine model coefficients in the bilateral space. Through these methods, our network can effectively denoise and enhance images. We have conducted extensive experiments on the LOL datasets, and the results show that, compared with traditional image enhancement algorithms, the model is superior to traditional methods in image quality and speed.  
low light enhancement; feature guided; denoise +
Footnote †: journal: Applied Sciences
## 4 Experiments
### Parameters Analysis of the Proposed MSGATN on the Guangzhou Dataset  
To further investigate the effectiveness of the proposed MSGATN, parameters analyses are performed on the Guangzhou dataset in this section. In our MSGATN, the number of superpixels is a hyperparameter. Furthermore, we selected six scales of superpixel segmentation, which, respectively, include 1000, 2000, 3000, 4000, 5000, and 6000 superpixels (as shown in Figure 6), to train our MSGATN in a Siamese manner. Generally, a larger number of superpixels indicates a smaller segmentation scale. Conversely, a smaller number of superpixels indicates a larger segmentation scale. Thanks to the characteristics of the inductive GATN, our MSGATN can easily exploit multi-scale superpixel features. By this way, features of different scales can be considered in our method. In this context, different BCMs can be generated by the proposed MSGATN at each scale, as presented in Figure 6. As the number of superpixels increases, the scale of superpixels also becomes finer. Similarly, the BCM of each scale is also finer as the number of superpixels enlarges for our MSGATN.  
Figure 7 more intuitively demonstrates the relationship between the number of superpixels and detection accuracy. Concretely, as the number of superpixels increases, all metrics show an upward trend. However, if the number of superpixels exceeds 3000, the accuracy gradually decreases. Hence, the performance of the proposed MSGATN may not continue to increase as the number of superpixels increases. Moreover, more superpixels can lead to larger graph structures, which can significantly increase the computational  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Precision** & **Recall** & **F1** & \(OA\) \\ \hline PCA\_K-means & 15.67 & 47.28 & 23.53 & 71.57 \\ ASEA & 16.26 & 50.69 & 24.62 & 71.28 \\ GaborPCANet & 3.71 & 12.89 & 5.76 & 60.99 \\ DBN & 19.17 & 34.53 & 24.66 & 80.47 \\ CWNN & 31.91 & 56.12 & 40.68 & 84.86 \\ Proposed MSGATN & **80.96** & **57.17** & **67.02** & **94.80** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy comparison (in %) of different methods on the Hongqi Canal dataset. The best evaluation value are presented in bold for different metrics.  
Figure 6: Segmentation results and the corresponding change detection results of different superpixel numbers in the proposed MSGATN on the Guangzhou dataset: (**a**) 1000 superpixels, (**b**) 2000 superpixels, (**c**) 3000 superpixels, (**d**) 4000 superpixels, (**e**) 5000 superpixels, (**f**) 6000 superpixels.  
cost. According to the above analysis, the number of superpixels cannot be continuously increased in our method.
## 3 Materials and Methods
### Methods
#### 3.2.3 Explainability Techniques  
To interpret the model's decision-making process, Grad-CAM (Gradient-weighted Class Activation Mapping) was applied to CNN-based models such as VGG-16, while attention map visualization was used for EfficientViT. These techniques help identify which regions of the input images the models focused on when making predictions.  
Grad-CAM calculates class-specific gradients with respect to the last convolutional feature maps and uses them as weights to highlight important areas. The method is formally expressed as shown in Equation (1):  
\[L^{c}_{\text{Grad-CAM}}=\text{ReLU}\left(\sum_{k}\alpha^{c}_{k}A^{k}\right), \tag{1}\]  
where \(A^{k}\) is the \(k\)-th activation map from the last convolutional layer and \(\alpha^{c}_{k}\) is the average gradient for class \(c\) over the feature map:  
\[\alpha^{c}_{k}=\frac{1}{Z}\sum_{i}\sum_{j}\frac{\partial y^{c}}{\partial A^{k }_{ij}}, \tag{2}\]  
Figure 4: Overall architecture of the proposed EfficientViT-based 1-stage beef carcass grading model. The model integrates convolutional layers for local feature extraction and multi-scale linear attention mechanisms to capture global contextual information, enabling efficient and accurate grading without a segmentation step.  
Here, \(y^{c}\) denotes the score for class \(c\) and \(Z\) is the normalization factor over spatial dimensions \(i,j\).  
EfficientViT adopts a lightweight linear attention structure instead of traditional multi-head attention. Attention maps are computed based on the inner product between queries (\(Q\)) and keys (\(K\)), as defined in Equation (3):  
\[\text{Attn}(i,j)=\frac{K_{i}^{\top}Q_{j}}{\sum_{j}K_{i}^{\top}Q_{j}+\epsilon}, \tag{3}\]  
where \(Q_{j},K_{i}\in\mathbb{R}^{d}\) are the query and key vectors at positions \(j\) and \(i\), respectively, and \(\epsilon\) is a small constant for numerical stability. During inference, the resulting attention scores were normalized to the range [0, 1] for visualization purposes.
# Improved Neural Differential Distinguisher Model for Lightweight Cipher Speck
## 3 Improved Neural Distinguishers Model for Speck32
### Design the Differential Neural Distinguisher  
In this section, a neural differential distinguisher is designed for the round-reduced Speck. The distinguisher uses the data structures generated in Sections 3.1 and 3.2 as input data and the neural network structure of Section 3.3 as the structure of the distinguisher. The training algorithm for the differential neural distinguisher is shown in Algorithm 1.  
Figure 6: The overall structure of the neural network.  
```
0: Speck cipher Oracle, Number of randomly selected plaintext pairs \(n\), Linear transformation \(Transform\).
0: Differential neural distinguisher \(N\)
1:\(TD\leftarrow\emptyset\)
2:\(Y\gets n\) random sample labels, assigned to 0 or 1
3:\((P_{l},P_{r},P_{l}^{\prime},P_{r}^{\prime})\gets s\) random plaintext pairs with a difference of \(\Delta=0x0040/0000\) ;
4:for\(i=0\) to \(n-1\)do
5:if\(Y_{i}=0\)then
6:\((P_{l}^{\prime},P_{r}^{\prime})\gets s\) random plaintexts
7:endif
8:\((C_{l},C_{r},C_{l}^{\prime},C_{r}^{\prime})\gets Oracle(P_{l},P_{r},P_{l} ^{\prime},P_{r}^{\prime})\)
9:\((C_{r},C_{r},C_{l},C_{r},C_{l}^{\prime},C_{r}^{\prime})\gets Transform(C_{l},C_{r},C_{l} ^{\prime},C_{r}^{\prime})\)
10:endfor
11:\(TD\leftarrow(X(C_{r},C_{r},C_{l}^{\prime},d_{l},C_{l},C_{r},C_{l}^{\prime},C_{r} ^{\prime}),Y)\)
12:\(N\gets trainNetwork(TD)\)
13:return\(N\)
```  
**Algorithm 1** The training algorithm for the differential neural distinguisher.
## 3 Results and Discussion
### Comparison of Model Outputs for Different Landscape Structures
#### 3.3.2 Analysis of Model Outputs in the Glape Basin  
Figure 10 shows the evolution of indicator \(Ic_{p}\) over the years according to the different types of rainfall. Tables 1 and 2 show the values of the indicators on a global scale (the indicators are related to the whole catchment area and not to the surface of a parcel).  
In general, it can be seen that connectivity increases significantly from 1947 to 2015. For a rainfall of 5 mm, the connectivity increased from 21% in 1947 to 67% in 2015. This is also the case for other rainfall events. For a 20 mm rainfall, the connectivity increases from 43% (in 1947) to 80% (in 2015). Similarly, for a 40 mm rainfall, it increases from 52% (in 1947) to 85% (in 2015). This is true for both types of indicators (\(Ic_{s}\) and \(Ic_{p}\)). However, the behaviour of this connectivity differs for the three dates. Indeed, in 1947, the connectivity  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(Ic_{s}\) Global & 5 mm & 20 mm & 40 mm \\ \hline
1947 & 21.80\% & 43.04\% & 51.72\% \\ \hline
1994 & 52.70\% & 70.53\% & 75.42\% \\ \hline
2015 & 67.06\% & 80.74\% & 84.51\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evolution of the connectivity indicator \(Ic_{s}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(Ic_{p}\) Global & 5 mm & 20 mm & 40 mm \\ \hline
1947 & 21.86\% & 40.99\% & 50.03\% \\ \hline
1994 & 52.17\% & 68.25\% & 73.76\% \\ \hline
2015 & 66.73\% & 78.33\% & 82.74\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evolution of the connectivity indicator \(Ic_{p}\).  
for a 5 mm rainfall is completely different from the connectivity for a 40 mm rainfall. For 2015, this difference is smaller. This means that for the current landscape configuration, connectivity tends to remain strong regardless of the rainfall intensity of the event.  
Figure 10: Effect of landscape structure and rainfall on the hydrological connectivity (\(Ic_{y}\)) in the Glape basin.  
Table 3 and Figure 11 show the evolution of the connected areas according to the years and the types of rainfall. The slope of the connectivity curve is steeper for 5 mm rainfall (slope = 0.77) than for 40 mm rainfall (slope = 0.56). This suggests that the evolution of the landscape structure from 1947 until now increasingly favours connectivity, especially for low intensity rainfall. This means that low rainfall, which previously would have favoured small-scale runoff trapped before reaching the outlet, can now cause much larger flows as the contributing connected areas increase.  
Furthermore, the difference between the connected areas for a 40 mm rainfall and for a 5 mm rainfall tends to decrease over the years (Figure 12). This is also the case for the difference between the connectivity for a 40 mm rainfall and for a 20 mm rainfall. This only supports the observation made earlier: the evolution of the landscape to the current configuration means that connectivity tends to remain high regardless of the rainfall intensity of the event (even for low intensity rainfall).
## 2 Basic Theory
### Echo Broadening Estimation  
The submarine echoes received by torpedoes usually have echo broadening characteristics due to the time delay generated by the submarine echoes along different parts of the radial direction [14; 15]; the echo broadening of the submarine echo signal can be obtained from the echo highlight time delay by:  
\[\tau=\tau_{\text{max}}-\tau_{\text{min}}-T \tag{1}\]  
Among them, \(\tau_{\text{max}}\) is the maximum value of highlight delay, \(\tau_{\text{min}}\) is the minimum value of highlight delay, and \(T\) is the pulse width of the torpedo launch signal.  
The target echo signal will appear when the extension of the width, which is due to the sound waves at a certain angle on the reflector with a certain scale, such as submarines, in different parts, will cause the sound range difference:  
\[\Delta\tau=\frac{2L\cos\varphi}{c} \tag{2}\]  
Among them, the target length is \(L\), and the angle between the incident wave and the target is \(\varphi\), and the underwater sound speed is c, usually 1500 m/s. Figure 4 shows the geometric significance of the target echo broadening.