esa-sceva/satcom-chunk-collection huggingface dataset:
# MMNET: A Multi-Modal Network Architecture for Underwater Networking
### Contribution  
MMNET, with the idea of multimodality, uses NFV and SDR functions to support heterogeneous communication, multi-mode routing and forwarding and multi-application support within a unified architecture, while meeting the networking requirements of diversified and personalized underwater applications [7; 9].  
Heterogeneous communication. On the basis of general hardware, MMNET connects communication equipment with standard interface, describes different communication modes in the form of software, and provides physical and medium access control (MAC) layer functions for upper layer network protocol in the form of a unified interface, so as to reduce the difficulty of heterogeneous communication caused by different communication modes due to hardware differences, and provide better heterogeneous communication support for UWSN [14; 15; 15; 9; 11; 10].  
Multi-mode routing and forwarding. MMNET realizes the network routing and forwarding function in the form of software. It virtualizes different network foundations in the network layer and runs different routing and forwarding modes at the same time, which can meet the personalized requirements of different routing methods for UWSN at this stage and provide more networking options for UWSNs [24; 25; 26; 27].  
Multi-application organization. MMNET uses virtualization technology to create multiple virtual networks. Each virtual network hosts different applications and runs under its own application. It does not conflict with other applications to meet the personalized needs of different applications.  
Intelligent network management is backed on application requirements. In the face of the complexity of a multi-mode heterogeneous network and the personalized demand of network resources for network applications, resource allocation is expressed as a personalized recommendation problem. The distributed personalized recommendation algorithm is used to configure personalized network resources for applications. The advantages of a multimodal heterogeneous network are fully utilized to provide personalized network support for underwater applications [27].  
The rest of this paper is arranged as follows. In the Section 2, a new network architecture MMNET is proposed, and the basic principles and key technologies of the network architecture are introduced in detail. The Section 3 will introduce the intelligent network management algorithm based on application requirements. In the Section 4, a specific case is studied to further illustrate the working process of network architecture. Finally, the Section 5 summarizes the thesis.
# Hydrogeochemistry of Fault-Related Hot Springs in the Qaidam Basin, China
## 4 Discussion
### Hydrochemical Characteristics of the Hot Springs
#### 4.1.2 Water-Rock Interaction  
The relative contents of Na, K, and Mg in the thermal water in full equilibrium with a thermodynamically stable mineral system derived through the isochemical recrystallization of an average crustal rock remain, at a given temperature and salinity, uniquely fixed. These content values, along with the water compositions resulting from isochemical rock dissolution, are useful references for determining the degree of attainment of fluid-rock equilibrium [45]. According to the current study's findings, water samples from the study region were distributed below the partial balance line (Figure 4). The degree of water-rock interaction balance was relatively low, which could be attributed to ascent mixing with cold water [46]. The results for the AK and YTBLK spring water samples were closer to the partial equilibrium water line than those for the other spring water samples, indicating that they were closer to the water-rock equilibrium state. This also indicated that the cold-water mixing effect was relatively weak for these two springs. This could be attributed to the YTBLK and AK springs' spatial location, which is closer to the intersection of the Altun Tagh fault and the East Kunlun fault belt, where fracture activity is relatively high [47]. The  
Figure 3: Enrichment coefficients of trace element in the hot springs.  
results for the other water samples were close to the immature water at the Mg terminal element, indicating that the water-rock interaction was low and a certain degree of shallow cold-water mixing occurred. In addition, the water samples in the study region reached equilibrium in the water-rock interaction at a lower temperature (Figure 4).
## 4 Experiment and Analysis
### Experimental Results  
#### 4.3.1 Enhanced Processing Qualitative Analysis  
In this paper, some scenes were selected to carry out experiments on the adaptive autostep enhancement algorithm. The relevant experimental results are shown in Figure 5 below. The adaptive autostep enhancement algorithm proposed in this paper had an obvious effect and could significantly improve the image clarity in image defogging, laying the groundwork for subsequent target detection. In the dark night scene, the algorithm proposed in this paper could achieve a better self-enhancement, and the target in the image was obvious, which showed that the algorithm in this paper had a good feasibility to a certain extent.
## 3 Possibilities for the Efficient Design of FEA for the Calculation of the Load-Notch-Strain Curve
### Reduction in the Number of Load Steps to Be Simulated
#### 3.4.1 Linear Interpolation by Polygonal Chain  
A simple possibility for interpolation between the calculated results is to form a polygonal chain of 1 st-degree polynomials. For the number of load steps \(\text{n}_{\text{H}}\) (with index H for the hysteresis branch) calculated to determine the load-notch-strain curve, \(\text{n}_{\text{H+1}}\) support points result for the linear interpolation, since in addition to the support points \(\Delta\varepsilon_{\text{FEA,2}}\)\(\dots\)\(\Delta\varepsilon_{\text{FEA,AH+1}}\) at \(\Delta\sigma_{\text{el,FEA,2}}\)\(\dots\)\(\Delta\sigma_{\text{el,FEA,nH+1}}\) from the FEA, point \(\Delta\varepsilon_{\text{FEA,1}}\)\(=0\) at \(\Delta\sigma_{\text{el,FEA,1}}\)\(=0\) is available as a support point. The polygonal chain consists of \(\text{n}_{\text{H}}\) sections to calculate the strain range \(\Delta\varepsilon_{\text{L}}\) (index L for linear interpolation), which is defined in a sectionwise manner; see Equation (13). Slopes \(\text{a}_{\text{i}}\) and offsets \(\text{b}_{\text{i}}\) can be calculated directly from the FEA results using Equations (14) and (15).  
\[\Delta\varepsilon_{\text{L,i}}(\Delta\sigma_{\text{el}})=\text{a}_{\text{i}} \cdot(\Delta\sigma_{\text{el}}-\Delta\sigma_{\text{el,FEA,i}})+\text{b}_{ \text{i}}\qquad\text{if }\Delta\sigma_{\text{el,FEA,i}}\leq\ \Delta\sigma_{\text{el}}\leq\ \Delta\sigma_{\text{el,FEA,i}+1} \tag{13}\] \[\text{with i}=1,\dots,\text{n}_{\text{H}}\]  
\[\text{a}_{\text{i}}=\frac{\Delta\varepsilon_{\text{FEA,i+1}}-\Delta\varepsilon _{\text{FEA,i}}}{\Delta\sigma_{\text{el,FEA,i+1}}-\Delta\sigma_{\text{el, FEA,i}}} \tag{14}\] \[\text{b}_{\text{i}}=\Delta\varepsilon_{\text{FEA,i}} \tag{15}\]Figure 7 shows the procedure for two simulated load steps \(\mathrm{n_{H}}=2\). As a result of the FEA, the strains \(\Delta\epsilon_{\mathrm{FEA},2}\) and \(\Delta\epsilon_{\mathrm{FEA},3}\) are obtained for the applied loads \(\Delta\sigma_{\mathrm{el,FEA},2}\) and \(\Delta\sigma_{\mathrm{el,FEA},3}\). The values for the load-notch-strain curve at the class limits are taken from the interpolated red curve by inserting the elasticity-theoretical stresses \(\Delta\sigma_{\mathrm{el}}\) at the class limits into Equation (13). As the number of simulated loads increases, the interpolated load-notch-strain curve approaches the true curve.
## 2 Results and Discussion
### Tocopherols, Chlorophylls and Carotenoids  
Tocopherols, chlorophylls and carotenoids content of the studied oils are shown in Table 3. All the examined compounds were most abundant in _Leccino_ oil, while in _Masnjaca_ and _Krvavica_ VOOs their content was lower. Variations in the content of these compounds is most probably influenced by variety, considering that olive fruits are from the same growing area, picked at the same ripening index and processed at the same conditions. These variations are in accordance with previous reports of other authors [24]. Research on the occurrence and levels of tocopherols in VOOs has shown that \(\alpha\)-tocopherol comprises about 90% of total tocopherols content and the amount of \(\alpha\)-tocopherol in the oil depends on the cultivar potential and technological factors [25]. According to some authors, there is a wide range of tocopherols content in VOOs. For example the quantity of tocopherols in Italian and Spanish oils has been variously reported to be 147-187 mg/kg [26]; 160-253 mg/kg [27]; 55-234 mg/kg [28]. Compared to them, Greek oils reached levels of \(\alpha\)-tocopherol that are among the highest reported (127-370 mg/kg in 1994-1995; 98-333 mg/kg in 1995-1996 and 100-365 mg/kg in the 1996-1997 crop seasons) [29]. The levels of \(\alpha\)-tocopherol may be related to the high levels of chlorophyll pigments and the concomitant requirement for singlet oxygen deactivation. Low amounts of \(\alpha\)-tocopherol homologues \(\beta\)-tocopherol (\(\sim\)10 mg/kg), \(\delta\)-tocopherol (\(\sim\)10 mg/kg) and \(\gamma\)-tocopherol (\(\sim\)20 mg/kg) are usually reported [24].  
The amounts of carotenoids in the investigated olive oils were relatively low, especially for those deriving from Croatian cultivars _Kravvica_ and _Masnjaca_ that contained about two times less than that obtained from _Leccino_ oil. The fraction of \(\beta\)-carotene and other carotenoids in VOOs ranges from 0.5 to 15.0 mg/kg and depends on the variety of olive, degree of ripeness of the fruit, storage conditions, as well as the oil extraction method [30].  
Similarly, chlorophylls content in all the samples was low and was higher in _Leccino_ oil than in _Kravvica_ and _Masnjaca_ oils. The quantity of total chlorophylls in VOOs ranged from 5 to 35 mg/kg [30]. Like for other mentioned components, the quantity and composition of chlorophylls depend on the variety, degree of ripeness and storage conditions of fruits as well as on the method of the oil extraction. Three-phase centrifugation system gives oils with higher quantities of chlorophyll pigments in comparison with two-phase system, while the lowest quantities of chlorophyll are found in the oils obtained by pressing [30] which is in accordance with our results.
## 2 Materials and Methods
### Deep Learning Architecture Description
#### 2.3.2 U-Net Architecture Description  
The U-Net architecture is a deep learning model designed explicitly for semantic segmentation in image processing, characterized by an encoder-decoder structure. Input images of size 224 \(\times\) 224 pixels with three channels are normalized to [0, 1] before processing. The encoder consists of traditional convolutional layers followed by ReLU activations to extract hierarchical features, with max pooling used to reduce spatial dimensions and capture increasingly abstract features. A bridge section connects the encoder to the decoder  
Figure 2: Multiple MRI brain images depicting tumors with corresponding masks from a 2D brain tumor segmentation dataset [44]. **(a)** Original MRI image showing a tumor. **(b)** Binary mask highlighting the tumor region. **(c)** Additional MRI scan. **(d)** Corresponding binary mask indicating the tumor.  
using convolutional layers, incorporating dropout to prevent overfitting. The decoder employs transposed convolutions for upsampling and ReLU activations to introduce non-linearity and capture complex feature relationships. The network concludes with a softmax-activated convolutional layer for pixel-wise class probabilities and a specialized dice pixel classification layer for precise semantic segmentation.  
Different variants of the U-Net architecture were employed to segment brain tumor images, each utilizing distinct parameter configurations. Table 1 below summarizes the variations in architecture, including kernel size, number of channels, and dropout rate:  
These variations allowed for exploring different architectural configurations within the U-Net framework to assess their impact on brain tumor segmentation performance. Figure 3 depicts the modified version of U-Net, which significantly enhances brain tumor segmentation performance. It shows the various modifications made to the encoder, decoder, and bridge to reconstruct the segmented image to match the original image size. Additionally, the figure visually represents the image sizes at each stage, from encoding through the bridge to decoding. The figure illustrates the changes made to the structure by replacing ReLU with Leaky ReLU, switching max pooling to average pooling, adjusting the kernel size to \(5\times 5\) with 32 channels, and adding a dropout layer of 0.3. These modifications enhance and optimize the existing U-Net architecture for brain tumor segmentation.
# Critically Leveraging Theory for Optimal Control of Quadrotor Unmanned Aircraft Systems
## 2 The Structure and Mathematical Model of the Quadrotor  
### Structure and Working Principle  
The Quadrotor-type UAV (referred to as Quadrotor) is a flying device belonging to the helicopter type, with four propellers located in the same plane and equipped with four engines placed symmetrically through the center of the cross (Figure 1). The four propellers are identical in size, with two identical pairs, a pair of reverse blades, and a pair of forward blades interlaced in a clockwise rotation with the Quadrotor center as the center of rotation. The engines are also designed in the same way and operate so that two opposite blades rotate in the same direction, and two adjacent blades rotate in opposite directions to balance the torque generated on the frame.  
Assuming that the front and rear propellers rotate counterclockwise while the right and left propellers rotate clockwise, the control of the Quadrotor involves managing the speed ratio between these four propellers. During takeoff (throttle up) and landing (throttle down), all four propellers must generate an equal thrust to achieve a combined force greater or less than gravity. To adjust the roll angle \(\Phi\), which involves rotating the Quadrotor around the axis formed by the front-back motor pair, the speed difference between the right and left propellers is varied while maintaining the same thrust produced by this pair. Similarly, to control the pitch rotation \(\theta\), which involves rotating the Quadrotor around the axis formed by the right-left motor pair, the speed of the front and rear propellers is  
Figure 1: Quadrotor’s structure and operation.  
adjusted while keeping the total thrust constant. The yaw angle \(\Psi\), which entails rotating the Quadrotor around an axis perpendicular to the plane containing all four rotors, is achieved by changing the speed difference between the front and rear propellers, as well as between the right and left propellers while maintaining the total thrust. This allows the Quadrotor to maintain its original altitude (Figure 2).
## 4 Signal Models
### Downlink Signal Model for Different Satellite Systems  
The received signal in the downlink scenario is constructed similarly to the uplink scenario, consisting of the signal vector \(\mathbf{s}\), noise vector \(\mathbf{v}\), steer vector \(\mathbf{A}\), and channel vector \(\mathbf{H}\), as presented in Equation (8). However, a multipath environment with Rician fading is considered in the downlink scenario. Hence, unlike in the uplink scenario, the channel vector [23] is not set to unity. The ULA geometry in Figure 4 only illustrates the direct paths for better visualization and does not include indirect paths, such as diffracted or scattered paths. Moreover, the indirect paths are assumed to be only reflected paths to simplify the model. Thus, the expression for \(\mathbf{H}\) is given as follows:  
\[\mathbf{H}=[h_{0},h_{1},\cdots h_{n},\cdots h_{(N-1)}], \tag{15}\]  
The component \(h_{n}\) includes the Rician fading, which is expressed as:  
\[h_{n}=\sum_{m=1}^{M}g_{(n,m)}, \tag{16}\]  
where \(M\) indicates the number of paths. Thus, as per the model, when \(m=1\), the direct path is obtained. Consequently, the transmitter's direction \(\theta\) from Equation (13) for the direct path in the downlink scenario is considered as \(\theta_{1}\), i.e., \(\theta=\theta_{1}\). Furthermore, this paper accounted for a normalized Rician channel with two paths (\(M=2\)). Therefore, \(\theta_{2}\) is the angle of the reflected path, generated randomly. Thus, the channel component reduces to the below:  
\[h_{n}=g_{(n,1)}+g_{(n,2)}, \tag{17}\]  
The channel coefficients for the line-of-sight (LOS) and non-line-of-sight (NLOS) paths are denoted by \(g_{(n,1)}\) and \(g_{(n,2)}\), respectively. The LOS path is assumed to have a fixed coefficient of 1, while the NLOS path coefficient \(g_{(n,2)}\) is expressed as follows:  
\[g_{(n,2)}=\sqrt{(\omega_{1}^{2}+\omega_{2}^{2})}, \tag{18}\]  
The parameters \(\omega_{1}\) and \(\omega_{2}\) are two independent Gaussian random variables with a variance of \(\sigma_{R}^{2}/(2K)\) each, where \(\sigma_{R}^{2}\) equals 1, and \(K\) represents the Rice factor value [13], which is expressed as below:  
\[K=\frac{\left|g_{(n,1)}\right|^{2}}{\left|g_{(n,2)}\right|^{2}}. \tag{19}\]
## 3 Results
### Simulation of Dust Storm on 14 February 2018
#### 3.3.1 Spatial Pattern of Vertical Dust Flux Using AFWA Wind Erosion Scheme  
Figure 6 shows the three-hour vertical dust flux for 14 February 2018 storm simulated by the AFWA scheme. One may see that the model is able to reproduce the distribution of dust storm in MODIS image at 09:00 UTC; in particular, the peak of concentration in central Iran and in its south-eastern part are well detected.  
Figure 5: Backward trajectories of dust from the National Oceanic and Atmospheric Administration Hybrid Single Particle Lagrangian Integrated Trajectory (NOAA HYSPLIT) Model.  
Figure 6: Spatial pattern of dust flux (\(\mu\)g/m\({}^{2}\)s) with 3 h interval by Air Force Weather Agency (AFWA).  
The analysis of the vertical dust flux maps on 14 February 2018 using AFWA Wind Erosion Model in the Central Plateau of Iran shows a dust source in the south-western part of the Arabian Desert mainly from 6:00 to 9:00 UTC on 14 February 2018, and another source in Central Iran. This is shown more clearly in Figure 6, where the average spatial pattern of dust flux during the event is shown. As one may see, the dust flux reaches as high as 2838 \(\upmu\)g/(m\({}^{2}\)s). The most dust rise is in parts of the Gavkhouni wetland and Salt Lake, as shown in Figure 7. During winter, which is generally a wet season, these areas should have a wet surface; however, the results show that if the soil moisture is reduced, as was the case in 2018, the region may serve as a source of dust as well, potentially becoming a dust hotspot.
# 3D Avatar Approach for Continuous Sign Movement Using Speech/Text
## 3 Materials and Methods
### Translation of ISL Sentence from English Sentence
#### 3.2.1 Preprocessing of Input Text Using Regular Expression  
If a user mistakenly enters an invalid/misspelled word, the \"edit distance\" function is used to obtain an equivalent valid word. A few examples of the misspelled words, along with the corresponding valid words, are presented in Table 2.  
The edit distance function takes two strings (source and target) and modifies the source string such that both source and target strings become equal. NLTK divides the English sentence into separate word-PoS pairs using the text tokenizer. The regular expression identifies the meaningful English sentence using the lexicon rule. During the preprocessing of input text, we define the regular expression (1) using the PoS tokens of the NLTK module. The regular expression starts with at least one verb phrase (VP) and is terminated with one noun phrase (NP). In the middle part, the regular expression can take zero or more number of any words that match PoS tokens (preposition (PP) or a pronoun (PRP) or adjective (JJ)). In a regular expression, \(+\) refers to one or more symbols, whereas \(*\) refers to zero or more symbols. Therefore \((VP)^{+}\) represents one or more verb phrases. For example, our first sentence, \"Come to my home\", starts with the verb phrase (\"come\"), followed by a preposition (\"to\"), pronoun (\"my\"), and ends with a noun phrase (\"home\").  
\[(VP)^{+}(PP|PRP|JJ)^{*}(NP) \tag{1}\]  
where VP \(\in\) (VB,VBN), NP \(\in\) (NN), VB \(\in\) (\"hello\",\"Thank\",\"Please\"), VBN \(\in\) (\"come\"), PP \(\in\) (\"to\",\"with\"), PRP \(\in\) (\"my\",\"you\",\"me\"), JJ \(\in\) (\"Good\"), NN \(\in\) (\"home\", \"morning\").  
\begin{table}
\begin{tabular}{c c} \hline
**Misspelled/Invalid Word** & **Equivalent Valid Word** \\ \hline \"Helllo\" & “Hello\" \\ \"Halo\" & “Hello\" \\ \"Happyyy\" & “Happy\" \\ \"Happyyy\" & “Happy\" \\ \"Nooooooo\" & “No\" \\ \hline \end{tabular}
\end{table}
Table 2: Mapping of misspelled/invalid word into equivalent valid word.  
Figure 3: Model of ISL sentence generation from English sentence. PoS: Part-of-Speech; CFG: Context-Free Grammar.
## 2 Design Methodology
### Proposed Compact FSS Topology  
The unit cell design, composed of a new Meander Square Nested Resonator (MSNR), is proposed in Figure 2, where _Ls_ and _hs_ are the length of the square unit cell and the thickness of the substrate, respectively; _Lq_ is the length of the FSS square structure; _Lt_, _Ld_ and _Lg_ are the meander-line dimensions (length, depth and gap); \(s\) is the space between the needed square loops and \(w\) is the line width.  
\begin{table}
\begin{tabular}{l c} \hline
**Service** & **Freq. Band (MHz)** \\ \hline
3 GPP & 1875–2200 \\ WiFi I & 2375–2500 \\ WiFi II & 5150–5850 \\ \hline \end{tabular}
\end{table}
Table 1: Services and Frequency Bands.  
Figure 2: Meander-line nested square structure dimensions.  
The proposed MSNR draws inspiration from the Hilbert curve [31; 32]. This fractal structure has a particular compact size and therefore exhibits a small electrical size of the overall surface when compared to the wavelength of the operating resonant frequency. However, two main downsides are now addressed for this particular application. Firstly, due to the asymmetry of the Hilbert curve, dual polarization cannot be achieved at the same frequency. To overcome this issue, the MSNR is a closed structure without any opening, which makes it dual-polarized in its filtering capabilities. Secondly, while traditional Hilbert curve designs occupy the internal space of the structure, MSNR concentric feature enables nested resonator accommodation and, therefore, multiband performance. In this way, the MSNR exhibits similar compactness to the Hilbert curve but with dual-polarization and multiband features.  
The FSS design simulation is generally carried out using the unit cell boundary condition, which virtually repeats the modeled structure periodically in two directions up to infinity. When Floquet modes are used to solve FSS scattering, the induced currents are uniform across the array excited with a plane wave. In CST software, this can be implemented as shown in Figure 3, where \(\Gamma\) and \(T\) are the reflection and transmission coefficients, respectively.  
The unit cell can be defined as the basic building block of the array and repeats itself in simulation infinitely with periodicity \(D_{x}\), \(D_{y}\). The simulation, based on Floquet modes, imposes that all the elements of the infinite array are identical. As the space among the elements of the array should be reduced as much as possible to achieve a compact low-profile FSS, the mutual coupling between the elements must be considered.