esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Numerical Results
### Numerical Test 1  
This section considers a standard test image, called the _Lena_ image, of size \(200\times 200\) pixels. The goal is mainly to investigate the accuracy of the reconstruction by the proposed accelerated LS approach for different amounts of missing pixels. The speedup of the reconstruction time will be considered later in the next two numerical tests. We consider the cases when \(30\%\), \(50\%\), and \(65\%\) pixels are missing uniformly on the original image, as shown in Figure 1. The reconstruction results for all these cases are shown in Figure 2 when using the standard POD-LS approach with \(k=30\) and the proposed accelerated POD-LS approach with \(k=30\) and \(m=50\). Figure 3 demonstrates the average relative errors of the reconstructed missing components using the accelerated POD-LS approach for \(k=30\) and different values of \(m=10,20,30,\ldots,80\) for the cases of \(30\%\), \(50\%\), and \(65\%\) missing pixels. It shows the convergence of the accelerated POD-LS approach to the standard POD-LS approach as dimension \(m\) increases. When there are more missing pixels, this convergence seems to be slower, i.e., the error of the accelerated POD-LS method approaches the error of the standard method at a larger dimension \(m\). Note that, in Figure 3, the accelerated POD-LS approach for both cases (i) and (ii) in Algorithm 4 are performed. The errors for case (ii) seem to be larger than the ones for case (i), and these errors for case (ii) seem to be oscillating with no trend when \(m<30\). This suggests that to select the DEIM components it is more appropriate to use the matrix constructed from the rows of POD basis matrix corresponding to the indices of the know components in case (i) than to use its orthogonalized basis in case (ii).  
Figure 1: (Numerical Test 1) The original Lena image and incomplete images with \(30\%\), \(50\%\), and \(65\%\) missing pixels distributed uniformly over the image.  
Figure 2: _Cont._
# Driving Fatigue Detection Based on the Combination of Multi-Branch 3D-CNN and Attention Mechanism
## 2 Related Works
### Detection Based on Driver's Physiological Characteristics  
[PERSON] et al. [3] proposed an efficient wavelet packet transform (FMIWPT) feature extraction method based on fuzzy mutual information (MI) to classify driver fatigue status and to extract fatigue-related information from EEG, EOG and ECG signals. Information and the quality of extracted features are evaluated based on simulation tests conducted on a data set collected from 31 drivers. The experimental results prove the key role of FMIWPT in feature extraction. The average accuracy of the experiment reached 97%. [PERSON] et al. [4] also used a multi-feature fusion method to monitor drowsiness. First, the EEG signal was decomposed into wavelet sub-bands and nonlinear features were extracted from the fusion, then the eyelid movement information was fused with it and finally a support vector machine (SVM)was used for state classification. [PERSON] et al. [5] studied the driver's fatigue state based on the EMG and ECG during driving and proposed a non-contact data acquisition system to collect the driver's physiological signals, using fast independent component analysis (FastICA) and digital filters to process the original Signal, according to the EMG crest factor and the maximum value of the cross curve of EMG and ECG to detect the driver's fatigue state. The results show that the proposed method can distinguish the normal state and the fatigue state well. It is worth mentioning that the proposed method is non-invasive, which helps to improve the stability of the detection results.  
The method based on the driver's physiological characteristics is an accurate, effective and objective fatigue detection method, which is very convincing. Biological signs provide good signs of the early onset of fatigue and can be used to prevent accidents in time. However, this type of method is invasive and requires physical contact with the driver, which will greatly compromise the driving experience and cause inconvenience to the driver.
# A Transformer-Based Channel Estimation Method for OTFS Systems
## 4 Performance Evaluation
### NMSE and BER Performance
As \(SNR\) increases, the performance of the Transformer estimation algorithm  
Figure 6: NMSE performance curves between different algorithms under different \(SNR_{p}\). Figure 7: NMSE performance curves between different algorithms under different velocities. acquires acceptable BER results. The threshold method exhibits the worst BER due to its inferior channel estimation performance.
## 3 Results and Discussion
### Characterization of the Salled Water-Based Fluid Formulations
The pH and density values, in lb/gal, of the palygorskite formulations are presented in Table 8.  
In general, regardless of the formulations, the fluids presented a basic pH, with a range of values between 12.69 and 13.87. Furthermore, it is possible to observe a slight influence of the palygorskite content on this property, since the formulations without palygorskite (F0) and with the lowest content of this clay (F1 and F3) presented the highest pH values, and the formulations with the highest content (F2 and F4) presented the lowest values. A similar trend was observed by [PERSON] et al. and [PERSON] et al. [17; 44], in which palygorskite suspensions in salt water had their pH values slightly reduced with increasing clay concentrations. Regarding the density of the fluids, the results demonstrated that there was no variation considered significant in this property, with values between 9.4 and 9.6 lb/gal. Considering the application of the palygorskite fluids as barrier elements for well abandonment, it is possible to predict that the density values observed are suitable for the operational window of most of the wells to be abandoned, since they are, in their majority, depleted. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **F0** & **F1** & **F2** & **F3** & **F4** & **F5** & **F6** & **F7** \\ \hline
**pH** & 13.87 & 13.43 & 13.04 & 13.37 & 12.69 & 13.18 & 13.27 & 13.32 \\
**Density (lb/gal)** & 9.4 & 9.4 & 9.6 & 9.5 & 9.6 & 9.5 & 9.5 & 9.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: pH and density values for the studied formulations of the salted water-based fluid with palygorskite. For wells whose operational window requires higher densities, it is possible to meet this requirement using more concentrated brines or the addition of weighting agents to the formulation. Finally, it is important to emphasize that this study focused on developing a fluid formulation considered promising for the temporary abandonment of petroleum wells, with properties suitable for maintaining hydrostatic pressure. However, further testing is required to assess its performance under field conditions, considering the time and temperature to which it will be exposed to throughout the operation.
## 5 Applications of 3D-SNOM
### 3D Probing of Near-Fields of Nanostructures  
Surfaces composed entirely of metallic nanoparticles or composite films embedded with nanoparticles may be incorporated in the architectures of optoelectronic devices to optimize their performance. Nanoparticles can impart increased efficiency or functionality to traditional device architectures if their implementation is well understood. 3D SNOM has been demonstrated to represent an outstanding tool for a deeper understanding of similar systems, whereas other scanning probe techniques are surface-sensitive only. Figure 6 summarizes the use of 3D SNOM to map plasmonic waves and electric field enhancement. Gold nanoparticles embedded in a polyimide thin film were investigated using SNOM equipped with an 80 nm aperture tip with illumination provided by 532 nm laser light (Figure 6e) [54]. Gold nanoparticles are promising for use in nanoplasmonic devices because they are strongly resistant to environmental oxidation, while displaying an appropriate plasmon resonance suitable for harvesting light in the visible range. Gold nanoparticles nucleated within the film layer with different annealing temperatures and atmospheres were visible in simultaneous AFM (left side of Figure 6a-d) and c-SNOM (right side of the same) images. Comparison of AFM and c-SNOM images for these transparent samples qualitatively indicates the different depth of various nanoparticles within the film as on the surface detectable by AFM or below the surface only detectable by SNOM. AFM indicates large (up to 2 \(\upmu\)m) metallic microclusters near the surface of the films, while smaller (<500 nm) nanoparticles are observed much deeper below the surface. 3D SNOM imaging scanning pattern in Figure 6e is used to image the plasmonic waves and electric field enhancement at a distance \(z\) above the surface (Figure 6f). The mapping shows enhanced light scattering intensity close to the surface of the films, with areas of constructive and destructive interference evident moving away from the sample surface up to a distance of several microns.
# Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product
## 4 Result Analysis
### Comparison with GEOV1 FCover Product
#### 4.1.1 Spatial Consistency
For deciduous needleleaf forests, the TRAGL and GEOV1 FCover products show good consistency in all months except February, October, November, and December. The largest relative difference of 90.75% is observed in December. For grasses/cereal crops, shrubs and deciduous broadleaf forest, there are large discrepancies between the TRAGL and GEOV1 FCover products during the growing seasons, which may result from the difficulty of acquiring cloud-free images in this time period due to the high humidity in the atmosphere. In addition, there are high relative differences between the TRAGL and GEOV1 FCover products in the spring or winter for some biome types, such as evergreen needleleaf forests and deciduous needleleaf forests, which may partly result from the limited number of pixels to calculate the mean values and standard deviations (Table 3). For instance, there are only 788 pixels for which TRAGL and GEOV1 FCover products provided FCover values in February for the deciduous needleleaf forests. In other words, less than 1% of the pixels in summer contributed to the February values. The main reason for this limited number of pixels is that deciduous needleleaf forests are mainly situated in the higher northern hemisphere and optical remote sensing in February is extremely difficult in high latitudes. Figure 5: Mean values of TRAGL and GEOV1 FCover products for different biome types in each month for 2001–2005 and their relative differences.
# A Phase Angle-Modulated Bat Algorithm with Application to Antenna Topology Optimization
## 3 Phase Angle Modulated Bat Algorithm (P-AMBA)  
### Amba  
Even though traditional BBA can be optimized in the binary search space, the optimization time is longer, and the optimization ability is limited [24]. Thus, the idea of angle modulation was introduced. The AMBA is an improvement of the BBA [22], and its core idea is to introduce a trigonometric function as a bit vector generator to map the real-valued solutions into binary ones. This method was first used as the angle-modulated (AM) technique for communication signal processing [37; 38; 39].  
This angle modulation idea uses a compound \(sin/cos\) generating function:  
\[g(x)=sin(2\pi(x-a)\times b\times cos(2\pi(x-a)\times c))+d \tag{10}\]  
where \(x\) is a sequence of equal intervals, the number of which needs to be determined by the number of optimized dimensions, the coefficient \(a\) represents the horizontal shift of the function; \(b\) represents the maximum frequency of the sine wave; \(c\) represents the frequency of the cosine wave (which changes the rate at which the frequency of the sine wave changes); and \(d\) represents the vertical shift of the function. The default value of the four parameters in the generating function is \(a=0\), \(b=1\), \(c=0.5\), and \(d=0\), and a function diagram is shown in Figure 2.  
After introducing the angle modulation strategy, a high-dimensional binary search space can be obtained by optimizing the four-dimensional continuous search space. The updated range of the four variables \(a\), \(b\), \(c\), and \(d\) is as follows:  
\[X=(a,b,c,d)\in[-1,1]. \tag{11}\]  
The change from a high-dimensional search space to four-dimensional search space greatly reduces the optimization time under the premise of ensuring optimization accuracy.
# Wave Orbital Velocity Effects on Radar Doppler
###### Abstract  
The orbital velocity of sea wave particles affects the value of sea surface parameters as measured by radar Doppler altimeters (also known as delay Doppler altimeter (DDA)). In DDA systems, the along-track resolution is attained by algorithms that take into account the Doppler shift induced by the component along the Earth/antenna direction of the satellite velocity, V\({}_{\rm S}\). Since the vertical component of the wave particle orbital velocity also induces an additional Doppler effect (in the following R-effect), an error arises on the positioning of the target on the sea surface. A numerical investigation shows that when the wavelength of sea waves is of the same order of magnitude of the altimeter resolution, the shape of the waveform might be significantly influenced by the R-effect. The phenomenon can be particularly important for the monitoring of long swells, such as those that often take place in the oceans.  
radar Doppler altimeter; significant wave height; orbital velocities; waveforms; swell 0000-0002-4000-0000]J. Mar. Sci. Eng. 2020, 8, 447
# Smart Agriculture Applications Using Deep Learning Technologies: A Survey
## 4 Applications of Deep Learning in Agriculture
### Identification of Water Stress  
Agricultural production is heavily dependent on water and is also increasingly exposed to water risks. Furthermore, it is the most water-consuming sector, and a significant water polluter as spraying pesticides and applying fertilizers can pollute both underground and surface water resources. Therefore, improving water management in agriculture is essential for ensuring the agricultural sectors are sustainable and productive. In this regard, article [6] proposed a convolutional neural networks model to identify the water-stressed and typical areas in the maize crop field. The performance of the proposed framework has been compared with ResNet50, VGG-19, and Inception-v3, and the results show that the proposed model gave better output with an accuracy of 93%. While paper [50] developed and tested a novel deep learning-based pipeline model for detecting phenotyping plant water stress areas using pictures of chickpea plant shoots.  
In [51], a deep learning technique was proposed to recognize water requirements from aerial pictures of irrigation systems. This automatic detection could aid in the management of the irrigation system, which reduces the maintenance time and expense of the system. Using the Mask R-CNN neural network, the initial results suggested that it is possible to recognize water from images taken by a UAV. The goal was to find and avoid malfunctioning irrigation that could cause under or over-watering by the proper implementation of irrigation plans.
## 5 Results and Discussion
### Characterization of Archaeological Stone Materials by XRD
\begin{table}
\begin{tabular}{c c c c c} \hline \hline _Tesserae_ & & & & \\ \hline \hline
**Group** & **Sample ID** & **Type** & **Classification** & **Mineralogical phases by XRD** \\ \hline \hline \multirow{6}{*}{I} & TTB1 & & & & Cal; Qz \\ \cline{2-5}  & TTB2 & & & & Cal; Qz \\ \cline{2-5}  & TTB3 & & & & Cal \\ \cline{2-5}  & TTB4 & & & & Cal \\ \cline{2-5}  & TTB5 & & & & Cal \\ \cline{2-5}  & TTB6 & & & & Cal; Qz; Ab \\ \hline \multirow{6}{*}{II} & TTB11 & & & Calcarenite (intrasparite/packstone) & Cal; Qz \\ \cline{2-5}  & TBI1 & Sedimentary & & Calcarenite (biomicrites/wackestones) & Cal \\ \cline{2-5}  & TBI2 & & & Calcarenite (biomicrites/wackestones) & Cal \\ \hline \multirow{6}{*}{III} & TTN1 & & & Cal; Sa; Hbl; Cpx; Fo \\ \cline{2-5}  & TTN2 & & & & Cal; Or; Sa; Cpx \\ \cline{2-5}  & TTN3 & & & & Cal; Ab; Cpx \\ \cline{2-5}  & TTN4 & & & & Cal; Cpx; Fo; Sa; Hbl \\ \hline IV & TTNi1 & Sedimentary & & Calcarenite (biomicrites/wackestones) & Cal; Qz \\ \hline V & TTGi1 & Sedimentary & & Calcarenite (biomicrites/wackestones) & Cal; Qz; Ab \\ \hline \multirow{6}{*}{VI} & TTR1 & & & Calcarenite (pelsparite/packstone) & Cal \\ \cline{2-5}  & TTR2 & & & Calcarenite (intrasparite/packstone) & Cal; Qz \\ \hline VII & TTRi1 & Sedimentary & & Calcarenite (biomicrite/mudstone) & Cal; Qz \\ \hline \multicolumn{5}{c}{_Vointed white marble_} & & & \\ \hline \hline
**Group** & **Sample ID** & **Type** & **Classification** & **Mineralogical phases by XRD** \\ \hline \multirow{6}{*}{VIII} & TLM1 & & & & Cal; Qz \\ \cline{2-5}  & TLM2 & & & & Cal \\ \cline{2-5}  & \multicolumn{1}{c}{} & & & & Cal \\ \hline \hline \multicolumn{5}{c}{_Brick fragment_} & & & & \\ \hline \hline
**Group** & **Sample ID** & **Type** & & **Mineralogical phases by XRD** \\ \hline \multirow{6}{*}{IX} & TL1 & & & Cal; Qz; Ab; Di \\ \cline{2-5}  & TL2 & & & Cal; Qz; Ab; Ilt \\ \cline{2-5}  & TL3 & & & Cal; Qz; Di; Gh \\ \cline{2-5}  & TL4 & & & Cal; Qz; Ab; Ilt \\ \cline{2-5}  & TL5 & & & Cal; Qz; Ab; Ilt \\ \cline{2-5}  & TL6 & & & Cal; Qz; Ab; Ilt; Di; Gh \\ \cline{2-5}  & TL8 & & & Cal; Qz; Ab; Ilt; Di; Gh \\ \hline \multicolumn{5}{c}{_Concrete base_} & & & \\ \hline \hline
**Group** & **Sample ID** & **Type** & & **Mineralogical phases by XRD** \\ \hline \hline X & TC1 & Mortar & & Cal; Qz; Ab \\ \hline \hline \end{tabular} Notes: Ab: Albeit; Cal: calcite; Cpx: Clinopyroxene; Di: Diopside; Fo: Forsterite; Gh: Gehlenite; Hbl: Hornblende; Ilt: Illite series; Qz: quartz; Or: Orthoclase; Sa: Sanidine. Mineral abbreviations were made according to [PERSON] [44].  
\end{table} 
Table 3: Main features of the examined samples (by POM) along with the main mineralogical phase and accessory minerals (by (XRD) occurring in the analyzed samples.
# At the Nexus of Blockchain Technology, the Circular Economy, and Product Deletion
## 3 Framework and Propositions
### Meso: Circular Economy at Networks and Supply Chain Level
#### Industrial Symbiosis and Eco-Industrial Parks  
An eco-industrial park contains several companies that cooperate to share their resources and manage their wastes in an environmentally sound way. Waste management is an important part of a circular economy. Although based on the sustainability principles, the primary focus is on a no-waste strategy, in most cases, complete waste elimination is not possible, and thus produced wastes need to be managed effectively. Waste exchange programs are important aspects of industrial symbiosis and require collaboration among companies to address environmental issues and support the circularity of resources.  
Blockchain technology can provide a platform to connect companies to exchange and trade their wastes and recreate value. Companies can interact directly to exchange their wastes without any middle-men and improve profit margins. Smart contracts can further facilitate waste exchanges by automatic execution of waste exchanges based on factors such as the condition of wastes, their volume and quality. In addition, electronic sensors and tracking devices can capture the location and value of wastes and make data available on blockchain ledgers. Traceability of wastes is critical, especially for hazardous wastes [34]. Stakeholders can use blockchain information to evaluate the efficiency of waste exchange programs.  
Information regarding the waste exchange can be recorded on blockchain ledgers. Blockchain can present information about the number of waste exchanges in a network and the value and quality of exchanges. This accurate information can be used for product deletion management. Those products that generate wastes with a low likelihood of waste exchanges can be candidates for removal from supply chains. The waste exchange information may also help supply chain participants assess how well products and materials are selling at their final stages and thus make sounder product deletion decisions. Byproduct synergies are another aspect of product management decisions. For example, if an important byproduct that is profitable is made with waste from a product targeted for deletion, the decision may be impacted the value of the by-product. Information on this by-product and other potential verified byproducts can be managed in the blockchain.
# Open-Set Specific Emitter Identification Based on Prototypical Networks and Extreme Value Theory
## 2 Prototypical Networks and Extreme Value Theory  
### Prototypical Networks  
Prototypical networks, combining neural networks and prototype learning, use the distance between the sample features and the prototypes to measure the attribution of samples, leading to a wide range of applications in fields such as few-shot learning [26]. Prototype learning is a classical algorithm in pattern recognition [34]. With the development of neural networks, prototype learning methods have been integrated into the CNN framework for better performance. Previous CNN models used SoftMax to normalize the output of the fully connected layer and classify the input sample \(x\) with the highest probability. The classification is based on:  
\[\left\{\begin{array}{l}x\in\textit{class}\;\arg\max\limits_{y=1}^{N}p(y|x) \\ p(y|x)=\frac{\exp(\xi_{y})}{\sum\limits_{i=1}^{N}\exp(\xi_{i})}\end{array}\right. \tag{1}\]  
where \(p(y|x)\) represents the probability that sample \(x\) belongs to the class \(y\); \(\xi_{i}\) represents the network output of class \(i\), where \(i\in\{1,2,\cdots,N\}\); and \(N\) represents the number of known classes. When using prototypical networks for classification, the networks can learn a prototype \(a_{i}\) for each class. Then, the samples can be classified into the class with the closest prototype. The process can be expressed as:  
\[x\in\textit{class}\;\arg\max\limits_{i=1}^{N}g_{i}(x) \tag{2}\]  
where \(g_{i}(x)\) is the classification function of class \(i\)[33]. It can also be expressed as:  
\[g_{i}(x)=-\|f(x;\theta)-a_{i}\|_{2}^{2} \tag{3}\]  
where \(f(x;\theta)\) represents the neural network feature extractor, and \(\theta\) represents the network parameters. \(f(x;\theta)\) and the prototype \(a_{i}\) can be learned jointly [33]. During the learning process, the prototype of a certain class is continuously pushed towards the sample features of that class, while the prototypes of other classes are kept away from the sample features of that class.  
Prototype learning transforms the classification into the nearest neighbor problem in the feature vector space. The addition of the feature extraction advantages of neural networks can effectively improve the generalization performance and alleviate overfitting. We can also see that the classification principle of prototypical networks and the metrics method based on distance are more conducive to recognizing unknown classes.