esa-sceva/satcom-chunk-collection huggingface dataset:
# A Learning-Based Image Fusion for High-Resolution SAR and Panchromatic Imagery
## 3 Results and Discussion
### Comparison of Fusion Results
Although visual analysis is direct and intuitive, it is also highly subjective and, therefore, may not allow for fully accurate evaluation. Thus, the performance of the fusion results was further evaluated quantitatively based on FQI, AG, and SF, which are summarized in Table 2. Regarding the FQI, the proposed method performed better than the conventional image-fusion methods in all sites. For site 1, the proposed method showed improvements of 8.51%, 6.24%, 27.14%, and 19.90% over ATWD, DWT, NSCT, and NSCT-PCAA, respectively, in addition to respective improvements of 2.78%, 0.53%, 21.92%, and 24.63% for Site 2 and respective improvements of 2.77%, 0.45%, 20.25%, and 14.68% for Site 3. The higher FQI of the proposed method indicates that its fusion results contain more of the information of the SAR and panchromatic images. In contrast, AG yielded different results for each site, as follows. For Site 1, the NSCT-PCNN had the highest value, whereas at Sites 2 and 3 the proposed method had the highest value. AG represents the spatial information in the panchromatic image in addition to the object information and surface roughness in the SAR image. As mentioned above, Site 1 consisted mostly of vegetation, and the result of the NSCT-PCNN contained most of the surface roughness information of the SAR image with a lack of the spatial information of the panchromatic image of the vegetation area. Because of this, the texture features of the vegetation were best highlighted owing to the influence of surface roughness in the calculation of AG. However, for Sites 2 and 3, which consisted mainly of developed structures, the spatial information of the panchromatic image and the object information of the SAR image were the main information, and the result of the proposed method had the highest abundance with regard to both aforementioned sets of information. Regarding the SF, which is primarily a metric for assessing the spatial information derived from the panchromatic image, the proposed method exhibited the best performance in all sites. In other words, it is confirmed that the proposed method would be more useful than the conventional image-fusion methods in visual and quantitative evaluations. Figure 9: Enlargement of the area marked with a red rectangle: (**a**) Site 1, (**b**) Site 2, (**c**) Site 3. From left to right: proposed method, a-trous wavelet decomposition, discrete wavelet transforms, non-subsampled contourlet transform, and non-subsampled contourlet transform-pulse couple neural network.
###### Abstract  
Element and mineral associations are fundamental parameters for palaeoceanographical reconstructions but laboratory methodologies are expensive, time-consuming and need a lot of material. Here, we investigate the quality and reliability of XRF measurements of major elements (Fe, Ti and Ca) using BTX II Benchtop, by comparing them with previous ICP-OES elemental analysis for a set of Late Pleistocene marine sediments from Campos Basin. Although the numerical values of the logarithmic form of the elementary ratios were different, the lnTi/Ca and lnFe/Ca ratios measured by both techniques (XRF and ICP-OES) presented similar downcore results. To correct the XRF intensity data, a linear regression model was calculated and, based on the linear equation generated, the logarithmic values of the elementary XRF ratios were corrected. After the correction, One-Sample \(t\)-test and Bland-Altman plot show that both techniques obtained similar results. In addition, a brief paleoceanographic interpretation, during the MIS 5 and MIS 4 periods, was conducted by comparing mineralogical and elementary analysis aiming to reconstruct the variations of the terrigenous input to the studied area. As a conclusion, the results from XRF measurements (BTX II) presented to confirm the viability of such a technique, showing that analysis using BTX II is a reliable, cheap, rapid and non-destructive option for obtaining elementary ratios and mineralogical downcore results at high resolution, allowing stratigraphic and paleoceanographic interpretations.  
X-ray fluorescence; ICP-OES; elementary ratios; Fe/Ca; Ti/Ca; Campos Basin (Brazil); MIS 4; MIS 5
## 6 Discussion
### Potentialities of HCA
#### 6.1.1 Case Study for AMR Parsing
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **PLM** & **Span** & **Nuc.** & **Rel.** & **Full** \\ \hline \multirow{5}{*}{Top-Down} & XLNet & \(96.6\pm 0.07\) & \(93.5\pm 0.20\) & \(87.0\pm 0.57\) & \(86.9\pm 0.56\) \\  & XLNet \({}^{\dagger}\) & \(96.7\pm 0.27\) & \(93.6\pm 0.33\) & \(\mathbf{87.6\pm 0.36}\) & \(\mathbf{87.6\pm 0.37}\) \\  & DeBERTa & \(96.6\pm 0.02\) & \(93.3\pm 0.07\) & \(87.2\pm 0.04\) & \(87.2\pm 0.10\) \\  & DeBERTa \({}^{\dagger}\) & \(\mathbf{97.0\pm 0.14}\) & \(\mathbf{94.0\pm 0.29}\) & \(87.6\pm 0.69\) & \(87.6\pm 0.61\) \\ \hline \multirow{5}{*}{Bottom-Up} & XLNet & \(96.9\pm 0.10\) & \(93.6\pm 0.09\) & \(87.4\pm 0.33\) & \(87.3\pm 0.31\) \\  & XLNet \({}^{\dagger}\) & \(97.0\pm 0.34\) & \(93.7\pm 0.46\) & \(87.6\pm 0.67\) & \(87.6\pm 0.67\) \\ \cline{1-1}  & DeBERTa & \(97.0\pm 0.10\) & \(94.0\pm 0.17\) & \(87.8\pm 0.39\) & \(87.7\pm 0.38\) \\ \cline{1-1}  & DeBERTa \({}^{\dagger}\) & \(\mathbf{97.4\pm 0.08}\) & \(\mathbf{94.5\pm 0.13}\) & \(\mathbf{88.6\pm 0.27}\) & \(\mathbf{88.5\pm 0.28}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Clause-parsing results with large versions of pretrained language models (PLMs), XLNet and DeBERTa (RST-Parseval). \({}^{\dagger}\) indicates PLMs with a large version. Standard deviations for three runs are shown in parentheses. **Bold** numbers are the best scores for each model. dotted red edges were missed by the parser when compared with the gold AMR, while two solid red edges were mistakenly predicted. From the HCA tree given in Figure 1a, the clause \"_I get very anxious_\" mapping to the subgraph \(G_{2}\) and the clause \"_but often the anxiety is so much_\" mapping to subgraph \(G_{4}\) were coordinate and contrastive. Therefore, we provide a transformation rule:  
1. Transform the inter-clause relation but to an AMR node contrast-01 and two AMR edges directing to the root nodes of \(G_{2}\) and \(G_{4}\). Meanwhile, the clause \"_that I can not wait that long_\" mapping to subgraph \(G_{5}\) is a resultative adverbial clause subordinated to the clause \"_but often the anxiety is so much_\" mapping to the subgraph \(G_{4}\). Note that the verb of the matrix clause is the copular \"_is_\", and the subordinate clause modifies the complement \"_much_\" in the matrix clause. Therefore, a new transformation rule can be derived:  
2. Transform the inter-clause relation resultative to an AMR node cause-01 and two AMR edges directing to the root node of \(G_{5}\) and the node much in \(G_{4}\). With these two transformation rules, we can delete the solid red edges, which were mistakenly predicted, and add the dotted red edges, which were missed.
## 3 Effect of Surfactants on Mechanico-Thermal Properties
### Effect of Cationic Surfactant
Furthermore, the degradation temperature of the nanocomposites improved by up to 23% due to the high thermal stability of modified G NPs that acted as an efficient physical barrier, which hindered thermal decomposition by slowing down the vaporization of volatile molecules. Moreover, the glass transition temperature of the nanocomposites also increased by up to 554% because of the physical barrier effect of the crumpled modified G NPs, which induced mechanical interlocking with PUA chains, and subsequently confined segmental movements at the modified G NP-PUA interface [13]. BHHB could modify inorganic nanofillers such as MMT and modified MMTs have been utilized for the preparation of MMT/HDPE nanocomposites [1]. The modification of MMT improved the tensile strength and Young's modulus by up to 31% and 66%, respectively, compared to the neat HDPE; this is attributed to the homogeneous distribution and a large aspect ratio of BHHB-modified MMTs that induced the strong interfacial interaction between modified MMTs and the HDPE matrix for better interfacial stress transfer efficiency. Nonetheless, the elongation at break of the nanocomposites significantly decreased because of the cross-section of HDPE resisted deformation, which was efficiently reduced by the modified MMTs [1]. The storage modulus of the nanocomposites increased by up to 99% because an interconnected network of modified MMTs within the HDPE matrix formed, which restricted the long-range movement of HDPE chains. Moreover, the degradation temperature of the nanocomposite improved by up to 5.3% because the modified MMTs acted as a mass transport barrier to the volatile products generated during thermal decomposition [1]. On the other hand, DDAB could also modify inorganic nanofillers like MMT and modified MMTs have been applied for the preparation of MMT/PS nanocomposites [32]. The modification of MMT improved the mechanical properties of the nanocomposites, such as tensile strength, Young's modulus,and elongation at break, by up to 32%, 30%, and 18%, respectively, compared to the unmodified MMT/PS nanocomposite. The increases were related to the exceptional dispersion of DDAB-modified MMTs in the PS matrix and the remarkable adhesion between the modified MMTs and the PS [32]. Moreover, the degradation temperature of the nanocomposites also increased by up to 7.1%, possibly because DDAB, with two long alkyl chains, could provide outstanding thermal stability [32]. Table 8 clearly displays that the modification of inorganic and organic nanofillers by cationic surfactants has improved the mechanico-thermal properties of the prepared polymer nanocomposites, regardless of the type of the cationic surfactant.
# Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning
## 2 Background  
This section provides background information about commonly used SSL approaches. Most current SSL methods rely on three assumptions. First, the smoothness assumption which states that if two points in a high-density region are close, their output labels must also be close. Second, the cluster assumption states that the points belonging to one class tend to form a cluster, meaning if certain points are in the same cluster, they likely belong to the same class. Lastly, the manifold assumption states that samples that are close in high-dimensional space are also close in a low-dimensional manifold [21; 22]. The state-of-the-art (SOTA) semi-supervised methods for medical image classification can be divided into three broad categories of consistency-based, graph-based and adversarial [19; 23]. Each of these is described next.
# Probabilistic Seismic Hazard Assessment for United Arab Emirates, Qatar and Bahrain
###### Abstract  
A probabilistic seismic hazard assessment in terms of peak ground acceleration (PGA) and spectral acceleration (SA) values, for both 10% and 5% probability of exceedance in 50 years, has been performed for the United Arab Emirates, Qatar, and Bahrain. To do that, an updated, unified, and Poissonian earthquake catalog (since 685 to 2019) was prepared for this work. Three alternative seismic source models were considered in a designed logic-tree framework. The discrimination between the shallow and intermediate depth seismicity along the Zagros and the Makran regions was also considered in this assessment. Three alternative ground-motion attenuation models for crustal earthquakes and one additional for intermediate-depth ones have been selected and applied in this study, considering the predominant stress regime computed previously for each defined source. This assessment highlights that the maximum obtained hazard values are observed in the northeastern part of the studied region, specifically at Ras Al-Khairah, Umm Al-Quwain, and Fujaira, being characterized by mean PGA and SA (0.2 s) pair values equal to (0.13 g, 0.30 g), (0.12 g, 0.29 g), and (0.13 g, 0.28 g), respectively, for a 475-year return period and for B/C National Earthquake Hazards Reduction Program (NEHRP) boundary site conditions. Seismic hazard deaggregation in terms of magnitude and distance was also computed for a return period of 475 years, for ten emirates and cities, and for four different spectral periods.  
seismic hazard; peak ground acceleration; deaggregation; uniform hazard spectra; Zagros-bilitis fold thrust belt; makran subduction zone; United Arab Emirates; Qatar; Bahrain 2020, 10, 7901; doi:10.3390/app10217901
# Joint Radar and Communications Waveform Design Based on Complementary Sequence Sets
## 3 JRC-CWG and DR-JRC-CWG Design
### JRC-CWG Design with Low Sidelobe Level
#### 3.1.1 Problem Formulation  
When the relative radial velocity \(v\) is 0, (3) can be rewritten as  
\[\mathbf{y}_{g}^{r}=\delta_{g}\mathbf{f}_{g}+\sum\limits_{m=1}^{M}\mathbf{n}^{ \prime}_{g,m} \tag{8}\]  
where \(\mathbf{f}_{g}=\sum\limits_{m=1}^{M}\mathbf{r}_{g,m}\) is defined as the SACF of the \(g\)-th JRC-CWG. To suppress the sidelobe level of \(\mathbf{f}_{g}\) while designing the unimodular JRC-CWG, the optimization problem can be formulated as [39]  
\[\begin{array}{ll}\min\limits_{\mathbf{S}_{g}^{K}}&\frac{1}{M}\mathbf{f}_{g}^ {H}\mathbf{f}_{g}-1\\ s.t.&\left|s_{g,m}(n)\right|=\frac{1}{\sqrt{N}}\ \ m=1,2,\ldots,M,\ \ n=1,2,\ldots,N \end{array} \tag{9}\]  
where \(\mathbf{S}_{g}^{K}=\left[\mathbf{s}_{g,1},\ldots,\mathbf{s}_{g,M_{c}}\right] \in\mathbb{C}^{N\times M_{r}}\) is the matrix formed by all WTOs of the \(g\)-th JRC-CWG. The \(g\)-th optimal JRC-CWG can be obtained by solving the optimization problem  
Figure 4: Receive signal at the communications user.  
in (9). To obtain \(G\) optimal JRC-CWGs, \(G\) optimization problems similar to (9) should be solved in parallel. The proposed optimization problem is a fourth-order polynomial minimization problem with nonlinear constraints, which is hard to be solved. Note that the objective function can be transformed into a frequency domain using the Wiener-Khinchin theorem, which can be performed by applying the FFT algorithm to reduce the computational complexity. Moreover, by only optimizing the phase of WTOs, the constraints in (9) can be cancelled. Hence, (9) can be transformed to be  
\[\min\limits_{\mathbf{\Theta}_{g}}\left[\sum\limits_{m=1}^{M}\mathbf{x}_{g,m} \odot\mathbf{x}_{g,m}^{*}\right]^{H}\left[\sum\limits_{m=1}^{M}\mathbf{x}_{g, m}\odot\mathbf{x}_{g,m}^{*}\right] \tag{10}\]  
where \(\mathbf{\Theta}_{g}=\left[\mathbf{\Theta}_{g,1},\ldots,\mathbf{\Theta}_{g,M_{r}}\right] \in\mathbb{C}^{N\times M_{r}}\) is the phase of WTOs in \(\mathbf{S}_{g}^{R}\). \(\mathbf{x}_{g,m}=\tilde{\mathbf{F}}_{K}\tilde{\mathbf{g}}_{g,m}\), where \(\tilde{\mathbf{s}}_{g,m}=\left[\mathbf{s}_{g,m}^{T}\quad\mathbf{0}_{(N-1)\times 1 }^{T}\right]^{T}\), and \(\tilde{\mathbf{F}}_{K}\) is the \(K(K=2N-1)\) point discrete Fourier transform (DFT) matrix.
# Research on the Trajectory and Operational Performance of Wheel Loader Automatic Shoveling
## 3 Wheel Loader Shoveling Trajectory Planning
### Trajectory Parameters  
The test vehicle has a rated shovel weight of \(G\) = 5000 kg, a rated bucket volume of \(V_{2}\) = 3 m\({}^{3}\), and a bucket width of \(B\) = 2.9 m. It is also necessary to analyze and investigate the material characteristics to determine the bucket tip trajectory of the flat bucket and to calculate the relevant essential test data later.  
The material object of this shoveling test is gravel, so the characteristics of gravel are analyzed. The main objects of analysis are the diameter, rest angle, porosity, and density of gravel, as shown in Table 1.  
Since the bucket tooth trajectory curve is a function of \(Z\), the trajectory for this shoveling test was planned by changing the parallel shovel depth \(Z\) and keeping the cross-sectional area \(S\) formed by the bucket in the shoveled material constant. In other words, the automatic shoveling performance of the loader at different shoveling depths is investigated by maintaining a specific full bucket rate. The \(Z\) values of the trajectory planning are shown in Table 2.  
The \(Z\) values of Table 2 are brought into Equation (1) to calculate the shoveling trajectory for different \(Z\) values, and the shoveling trajectory diagram is shown in Figure 9. Since the bucket is lifted and turned while shoveling, the bucket is set to be evenly turned during the shoveling stage, with a total turning angle of 47.3\({}^{\circ}\) and a shovel cross-sectional area of 1.05 m\({}^{2}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Materials** & **Diameter (mm)** & **Rest Angle (\({}^{\circ}\))** & **Porosity (\%)** & **Density (kg/m\({}^{3}\))** \\ \hline gravel & 20–60 & 35.34 & 39.9 & 2684 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Analysis of gravel characteristics.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Parallel Shoveling Trajectory** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline \(Z\)/mm & 400 & 425 & 450 & 475 & 500 & 525 & 550 & 575 & 600 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Values of \(Z\).
## 3 Experiments  
Highly efficient InGaN NUV, blue, and green LEDs with emission wavelengths at 400, 455, and 525 nm were grown by metal-organic chemical vapor deposition (MOCVD) and fabricated using standard mesa structure. InGaN LED epitaxy wafers were grown on 2-inch \(c\)-plane (0001) patterned sapphire substrates (PSS). LED structures were composed of a 3.0 \(\upmu\)m undoped GaN layer, a 3.0 \(\upmu\)m Si-doped \(n\)-GaN layer, InGaN/GaN multiple quantum wells (MQWs), a 20 nm \(p\)-Al\({}_{0.15}\)Ga\({}_{0.85}\)N electron blocking layer, and a 250 nm \(p\)-GaN main layer. X-ray measurements indicate that indium composition is about 6%, 15%, and 30% for the InGaN NUV, blue, and green LEDs respectively [23]. High power LED chips with a size of 1 \(\times\) 1 mm\({}^{2}\) were fabricated. Indium Tin Oxide (ITO) (100 nm) was firstly deposited on top of \(p\)-type GaN as an ohmic contact and current-spreading layer. An \(n\)-type GaN layer was exposed by inductively coupled plasma etching around 1.3 \(\upmu\)m. An \(n\)-contact of Ti/Al/Ni/Au (10/100/100/100 nm) was deposited onto the nGaN and Cr/Pt/Au (25/20/500 nm) layers were deposited as \(p/n\) pads. A patterned design of metal electrodes with multiple-branches for the \(p/n\) contacts were employed to avoid the current crowding effect [24]. LEDs were packaged with silica gel and output power was measured in an integrating sphere. Pulse trains with 10 \(\upmu\)s duration and 1% duty cycle were used to avoid a self-heating effect.
## 2 Control of USV Swarm  
In this study, we employed the long short-term memory (LSTM) algorithm, a type of recurrent neural network (RNN), to learn patterns of disorders impacting movement control. We investigated methods for forecasting future disturbances using the trained model and enhancing the performance of the artificial neural network-proportional-integral-derivative (ANN-PID) control algorithm by utilizing data on predicted disturbances. Building upon this, our paper suggests a more practical approach compared to existing research, by leveraging established methods of behavior-based swarm control techniques and leader-follower techniques.  
* Behavior-based Swarm Control  
We crafted a swarm control algorithm rooted in leader-follower principles and devised a sophisticated swarm control system that amplifies efficiency through behavior-based techniques. We disseminated the current state information (position, speed, status, etc.) to establish rules for each USV. We devised a methodology and framework to understand the underlying behavior and constructed a swarm control simulation that incorporates the decision-making process, including adjustments to speed.  
* Learning of Ocean Currents  
Using measured ocean current data, we employed LSTM, a type of RNN, to forecast ocean currents. These forecasts are utilized to estimate the speed (magnitude) of the ocean currents and implement them as control inputs. If the disturbance magnitude can be accurately assessed, it will enhance the performance of the ANN-PID control algorithm utilized in this study.  
* USV Control Algorithm  
The ANN-PID controller utilizes an algorithm capable of adjusting the control gain based on the current error. This algorithm is rational for regulating the output by incorporating external information to manage the movement of the USV, and it can effectively govern the USV due to its swift computational processing. The three inputs consist of the error value, the integral of the error value, and the derivative of the error value, which serve as the foundation of the PID control algorithm. It calculates the gain until the final value falls within the predetermined target range. When the error is sufficiently minimizedand the control performance within a specific range satisfies system stability requirements, the algorithm concludes, and the calculated gain value is retained.
## 2 System Model
### Single Rotating Antenna Model  
The single rotating antenna model used in this article is shown in Figure 1. The antenna is fixed at an angle \(\beta\) to the rotating base, which rotates at an angular velocity \(\omega\)[16].  
Due to the rotational motion of the antenna, the position of the antenna changes regularly, and the carrier phase of the same navigation satellite signal received by the receiver also changes regularly. During the antenna rotation process, the incident direction of the navigation signal changes in transmission, which leads to a regular change in the gain of the received signal. Their variation patterns are the same for different spoofing signals from the same direction, while their variation patterns are different for real signals from different directions. Therefore, spoofing detection can be carried out based on the above characteristics. First, there is a requirement to establish a Cartesian coordinate system with the center of the rotating circle at the center of the antenna as the origin of the coordinate system. The axis points toward the direction from the origin to the antenna's phase center at time 0, as shown in Figure 2. The following will provide a detailed introduction to the changes in signal characteristics caused by rotating the antenna.  
Figure 1: Schematic diagram of a single rotating antenna model.