esa-sceva/satcom-chunk-collection huggingface dataset:
# Scattering of Plane Waves by Cylindrical Cavity in Unsaturated Poroelastic Medium +
###### Abstract  
The scattering of elastic waves by underground cavities is an active research topic for its broad applications in various fields, such as earthquake engineering, the blast resistance of underground structures, geophysical exploration, etc. In most previous studies, the sounding medium was treated as an ideal elastic medium or a saturated poroelastic medium. The understanding of the scattering of elastic waves by cavities in unsaturated porous media is limited. In this study, the scattering of plane P\({}_{1}\) waves and SV waves by a cavity with a permeable surface in an infinite unsaturated porous medium is solved by the wave function expansion method. The dynamic stress concentration at the cavity surface is investigated by taking P\({}_{1}\) wave incidence, for example. Numerical results illustrate that the scattering of plane waves around the cavity strongly depends on the frequency of the incident waves, and the saturation, Poisson's ratio, and porosity of the surrounding medium.  
unsaturated porous media; cylindrical cavity; plane waves; scattering and diffraction; analytic solutions +
Footnote †: Copyright © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Research on Improved GRU-Based Stock Price Prediction Method
## 3 Materials and Methods
### Gru  
This paper focuses on time series prediction in particular, for which the commonly used deep learning method is the RNN method. However, RNNs can encounter issues such as gradient explosion and vanishing, particularly when learning long-term dependencies in the data. To resolve these problems, studies have proposed LSTM, which improves the gradient flow within a network by employing a gating mechanism. The GRU is a simplified version of LSTM, reducing the three gates in LSTM to two. Consequently, the GRU exhibits enhanced proficiency in capturing and learning long-term dependencies in time-series data while also reducing model complexity and computational costs, thus providing superior training efficiency. The improved ability of the GRU to handle long-term dependencies in time-series data makes it the preferred choice. Additionally, the GRU has lower storage requirements, rendering it suitable for processing large-scale datasets. Therefore, the basic GRU model was selected as the primary model in this study. The architecture of the GRU model is illustrated in Figure 1.  
\[r_{t}=\sigma(W_{r}x_{t}+U_{r}h_{t-1}+b_{r}) \tag{1}\]  
\[z_{t}=\sigma(W_{z}x_{t}+U_{z}h_{t-1}+b_{z}) \tag{2}\]  
\[\widetilde{h_{t}}=\tanh(W_{h}x_{t}+U_{h}(r_{t}*h_{t-1})+b_{h}) \tag{3}\]  
\[h_{t}=z_{t}*h_{t-1}+(1-z_{t})*\widetilde{h_{t}} \tag{4}\]  
Here, * represents the element-wise product formula; \(W_{r}\) and \(W_{z}\) are the weight matrices of the \(r_{t}\) gate and the \(z_{t}\) gate, respectively; \(U_{h}\) represents the weight matrix for the output; \(x_{t}\) represents the input data at time \(t\); \(\widetilde{h_{t}}\) and \(h_{t}\) represent the candidate state and output state at time \(t\); \(b_{r}\), \(b_{z}\), and \(b_{h}\) are constants; and \(\sigma\) and \(\tanh\) are the sigmoid and \(\tanh\) activation functions, respectively, used to activate the control gates and candidate states.  
After the information enters the GRU unit, the process of flow transmission includes the following steps:  
1. The input data \(x_{t}\) at time \(t\) and the output of the hidden layer \(h_{t-1}\) at time \(t-1\) are concatenated. The output signal of the reset gate \(r_{t}\) is obtained with Formula (1);
2. The output signal of the update gate \(z_{t}\) is obtained with Formula (2);3. The current-state hidden-unit candidate set \(\widetilde{h_{t}}\) is obtained with Formula (3), which mainly integrates the input data \(x_{t}\) and the hidden layer state at time \(t-1\) after filtering by the reset gate;
4. The output of the hidden layer \(h_{t}\) at time \(t\) is obtained with Formula (4), which represents the forgetting of the hidden layer information \(h_{t-1}\) passed at time \(t-1\) and the selection of important information from the candidate hidden layer at time \(t\).
# A Knowledge Graph-Enhanced Attention Aggregation Network for Making Recommendations
## 4 Experiment and Results
### Results
#### 4.3.3 The Performance in Top-K Recommendation  
In the Top-K recommendation task, we recommend the K items with the highest matching degree for each user in the dataset after the model training is completed. Recall@K and Precision@K (K = 1, 2, 5, 10, 50, 100) are used as evaluation indicators to evaluate the performance of each model.  
As shown in Figure 4, KANR achieved the best performance in the Top-K recommendations of the three datasets. In Recall@10, the performance of KANR on the movie, music, and book datasets was 6.04%, 11.38%, and 14.03% higher than the best baseline. In Precision@10, it also achieved a performance gain of 9.09%, 7.6%, and 16.91%, which shows that KANR can effectively use the semantic information in the knowledge graph to enhance the recommendation model.  
In general, the performance of the model based on the knowledge graph is superior to that of the traditional model. In addition, the performance of KANR, which uses the multi-task learning framework and information interaction model, is superior to that of other types of model, especially in the book dataset. The reason for this is that, in the case of the information interaction model, the knowledge triples in the dataset. The vector features can still be shared, and the recommendation system obtains effective semantic information in the knowledge graph and is not affected by knowledge sparseness.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Reconfigurable Intelligent Surfaces
#### 3.2.2 Reconfigurable System Requirements  
RIS systems are required to be designed and implemented with specific requirements. Programmability is the most significant feature, as it will allow the control of electromagnetic properties using mechanical, thermal, or electrical modifications (ref. [60]). The most practical and efficient is electrical control, especially in combination with field-programmable gate arrays (FPGAs) (refs. [60; 63]). Graphene and semiconductors are used the most because of their outstanding performance and tunability (ref. [60]). To smoothly fit with current wireless infrastructure, the sub-6 GHz and mmWave bands are chosen (ref. [63]). In contrast to passive RIS, which uses very little power, optimal power management is necessary in active RIS, so the advantages of signal amplification outweigh the energy requirements (ref. [61]).
## 3 Results and Discussion
### Results of Biodegradation
#### Degradation Rates  
The observed degradation followed a first-order order kinetic model, so the rates were calculated based on the first-order decay rate law according to [55] (Table 5). The decay rates for the different fractions correspond to the slope of the trend lines (Figure 3B). As the concentration profiles of DOC indicate, the highest decay rate in the exponential phase was determined for the fraction 630 \(\upmu\)m to 1000 \(\upmu\)m. This was followed by the fractions 1000 \(\upmu\)m to 2000 \(\upmu\)m, 200 \(\upmu\)m to 630 \(\upmu\)m and 125 \(\upmu\)m to 200 \(\upmu\)m.  
The results indicate that soils with lower WFPS corresponding to bigger mean pore diameter and a wider range of pore sizes (Tables 2 and 3) are more suitable for the metabolization of organic substances than the fractions with higher WFPS and more uniform pore size distribution. This can be explained by better transport of oxygen and nutrients as well as sufficient moisture to support microbial activity (Figure 4).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Degradation** & & **Degradation** & & **Degradation** & & **Degradation** \\
**Runtime** & **Rate for** & **Runtime** & **Rate for** & **Runtime** & **Rate for** & **Runtime** & **Rate for** \\
**[d]** & **Fraction** & **[d]** & **Fraction** & **[d]** & **Fraction** & **[d]** & **Fraction** \\
**125–200 \(\upmu\)m** & & & **[d\({}^{-1}\)]** & & **[d\({}^{-1}\)]** & & **[d\({}^{-1}\)]** \\ \hline
0–12 & No degr. & 0–12 & No degr. & 0–4 & No degr. & 0–4 & No degr. \\
13–30 & 0.011 & 13–47 & 0.015 & 5–29 & 0.038 & 5–34 & 0.018 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Calculated degradation rates for each fraction.
# Sea Ice Detection Based on Differential Delay-Doppler Maps from UK TechDemoSat-1
## 1 Introduction
Several data processing strategies such as dataset selection, noise floor subtraction, and normalization of DDMs are employed to reduce the impact of noise floor and speckle noise. In addition, an adaptive incoherent averaging technique is employed to improve the computational efficiency. Finally, the performance of the proposed methods is evaluated against the existing methods using a large number of TDS-1 DDMs and ground-truth sea ice edge maps. The next section of this paper presents the details of the two proposed ice detection methods, including the definition of the new observation variable, the derivation of the formulas, the data processing techniques, and the detection procedures. Section 3 provides the experimental results obtained through processing the TDS-1 DDM dataset using the proposed methods and the existing methods. The final section summarizes the scientific results and indicates future directions for performance enhancement.
# 3D-STARNET: Spatial-Temporal Attention Residual Network for Robust Action Recognition
## 3 Proposed Methods  
### Model Overview  
In this section, we introduce a skeleton-based 3D CNN human action recognition model called the Spatial-Temporal Attention Residual Network for 3D human action recognition (3D-STARNET). Figure 1 shows the overall framework of the model. A spatiotemporal attention mechanism is embedded in the model and a staged residual structure is introduced. The spatiotemporal attention mechanism enables the network to enhance its feature learning ability in both temporal and spatial dimensions, while the staged residual structure improves the network's information transmission efficiency without changing the model's complexity. The overall network has been significantly improved in terms of robustness, scalability, and recognition performance. The details of 3D-STARNET will be introduced in the following chapters.  
First, the conversion process of 2D skeleton key points to 3D heat maps is introduced, and then the two improvements of the article are introduced: the spatiotemporal attention mechanism and the multi-staged residual structure.
# Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems +
## 4 Node Classification
### Estimating Neighbor Counts
#### 4.2.2 Real Deployment  
The grid model is a simple and computationally inexpensive method. However, it cannot be applied to this medical asset tracking system because the anchor nodes cannot be deployed in a grid pattern within the emergency room, which is not a rectangle, as well as it has several walls. In addition, anchor nodes were not allowed to be installed on a marble wall. Under these restricted circumstances, the number of neighbors of a mobile node is estimated in two phases, the filter phase and the refinement phase, to shorten the processing time. In the filter phase, the candidate neighbors of the node are found using a minimum bounding rectangle (MBR) in which the communication coverage of the node is contained. This phase is not computationally expensive because at most four comparisons are required to determine whether an anchor node is within the MBR. The candidate neighbors construct the set \(C\) defined as \(C=c_{1},c_{2},\ldots,c_{q}\), where \(C\subset A\), the set of anchor nodes. Each element in \(C\) has the properties, \(\forall c_{i}\in C\) and \(c_{i}\in\{(x_{i},y_{i})|(MinX\leq x_{i}\leq MaxX)\)_and_\((MinY\leq y_{i}\leq MaxY)\}\), where \((x_{i},y_{i})\) is the location of \(c_{i}\), \(1\leq i\leq q\), and \(MinX,MinY,MaxX\) and \(MaxY\) are the coordinates of the MBR of a mobile node.  
In the refinement phase, the candidate neighbors are checked for whether they are really the neighbors of the mobile node. The number of nodes processed in this phase is reduced due to the initial filter phase. To select the actual neighbors of the node from \(C\), the distance between the node and \(c_{i}\) in \(C\) is calculated. The actual neighbors of the node construct the set \(N\) defined as \(N=n_{1},n_{2},\ldots,n_{k}\), where \(N\subset C\). Each element in \(N\) has the property \(dist(m,n_{j})\leq r\), where \(dist\) is the distance function, \(m\) indicates the mobile node, \(n_{j}\) indicates that of each element in \(N\), \(1\leq j\leq k\) and \(r\) is the communication range of mobile node \(m\). Therefore, the number of neighbors of the mobile node equals \(|N|\).
### Introduction of Virtual Interface with Multihoming Based on PMIPv6  
A virtual interface (VI) is a physical port connected to a layer 3 virtual LAN (VLAN) configured on a switch to the layer 3. On the virtual interface, the routing parameters can be modified to allow the layer 3 transition to route network traffic from one layer 3 VLAN to another, devoid of utilizing an external router. This facilitates the continuous transfer of packets from correspondent node (CN) to mobile node (MN), and vice versa. The virtual interface (VI) is a common solution to implementing pseudo-interfaces and is usable on most operating systems such as Free-/Net-/OpenBSD, MAC OS X, Linux, and smooth MS Windows. The VI is utilized to execute a tunnel interface, a loop back interface, etc. Figure 2 summarizes the Proxy Mobile IPv6 (PMIPv6) support for the VI. The VI abstracts the associated physical interfaces (PIs) and supplies the host with a secure interface, avoiding any device-specific interruptions. From an application perspective, the VI is the only interface available, and all PIs are concealed. Applications often connect to the VI-assigned virtual interface and address [1]. The main motivation of this work is to provide flawless handover for Proxy Mobile IPv6 protocol in any environment.
###### Abstract  
Unmanned aerial vehicles (UAVs) can support low-cost, highly mobile communications while making it possible to establish dedicated terrestrial networks. To overcome the pointing error (PE) and beam misalignment of millimeter-wave large-scale multiple-input multiple-output/free-space optics (MIMO/FSO) caused by UAV jitter, a millimeter-wave massive MIMO/FSO hybrid beamforming method based on tensor train decomposition is proposed. This approach is used for reconfigurable intelligent surface (RIS) network-assisted UAV millimeter-wave massive MIMO/FSO to improve system spectral efficiency. Firstly, the high-dimensional channel of RIS-assisted millimeter-wave massive MIMO/FSO in UAV is represented as a low-dimensional channel by tensor training decomposition. Secondly, the two-way gated recursive unit attention neural network model can effectively solve the FSO PE caused by UAV jitter, and the fast fading channel and Doppler frequency shift are estimated by the Fast Cyclic Tensor Power Method (FCTPM) based on tensor training decomposition. Finally, the RIS phase shift matrix is optimized by singular value decomposition. The hybrid beamforming and RIS phase-shift matrices were estimated by using the low-complexity phase extraction alternating minimization method to solve the beam misalignment problem. Simulation results show that by using the proposed method, the spectrum utilization rate is improved by 23.6% compared with other methods.  
U
## 2 Study Area and Data Analysis
Therefore, the images have the same illumination conditions and shadow changes are not significant. With radiometric resolutions of 16 bit/pixel, the images provide a higher dynamic range than the traditional 8- or 12-bit/pixel images. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sensor Type \& Acquisition Date** & **View** & **Acquisition Time** & \multicolumn{3}{c}{**Incidence Angles (\({}^{*}\))**} & **Area** & **B/H** & **Convergence** \\ \cline{3-10}
**Aquisition Date** & & **(hh:h:h:h:s:s:s)** & **Across** & **Along** & **Overall** & **(lon\({}^{2}\))** & **Ratio** & **Angle (\({}^{*}\))** \\ \hline \multirow{3}{*}{Pleiades 13-06-2017} & Forward (F) & 10:09:51.5 & \(-\)2.23 & \(-\)6.75 & 6.75 & 158.73 & 0.10 (FN) & 5.71 (FN) \\  & Nadir (N) & 10:10:03.7 & \(-\)3.31 & \(-\)1.13 & 3.50 & 158.49 & 0.11 (NB) & 6.30 (NB) \\  & Backward (B) & 10:10:14.0 & \(-\)5.00 & 4.95 & 7.02 & 158.78 & 0.21 (FP) & 12.0 (FP) \\ \hline \multirow{3}{*}{WorldView-3} & Forward (F) & 10:22:07.0 & 7.71 & 11.00 & 13.57 & 100.00 & 0.20 (FN) & 11.52 (FN) \\  & Nadir (N) & 10:22:25.5 & 7.23 & \(-\)0.62 & 7.36 & 100.00 & 0.20 (NB) & 11.51 (NB) \\ \cline{1-1}  & Backward (B) & 10:22:44.1 & 6.72 & \(-\)12.20 & 13.97 & 100.00 & 0.40 (FB) & 23.04 (FB) \\ \hline \end{tabular} (FN): Forward-Nadir, (NB): Nadir-Backward and (FB): Forward-Backward image pairs.  
\end{table} 
Table 1: Acquisition parameters of Pleiades and WorldView-3 data. Figure 1: Study area: (**a**) overview map of Austria with location of the study area (coordinates in UTM zone 33N); (**b**) satellite imagery–blue: Pleiades tri-stereo pair and orange: WorldView-3 tri-stereo pair. From a visual inspection, some radiometric effects were observed in the WorldView-3 forward image (Figure 2). Here, the reflective roof surface, in combination with the imaging incidence angle and sun elevation, caused saturation and spilling effects. In contrast, no radiometric artefacts were observed in the Pleiades satellite images.
## 5 Quality of Experience (QoE) Assessment
### Subjective QoE Assessment
Freezing affects visual attention but switching between different levels of degradation does not affect visual attention to the experience. QoE assessment metrics are still in development and subjective methods are yet to be standardized. A comprehensive QoE assessment model is required to evaluate the performance of different approaches such as tiling, machine learning and viewport adaptive approach that strive to improve the QoE of 360-degree video.
# Trajectory Tracking Control Method Based on Adaptive Higher Order Sliding Mode
## 2 Design of Adaptive High-Order Sliding Mode Trajectory Tracking Controller
### Adaptive Higher-Order Sliding Mode Algorithms
The controller is transformed into the linear controller \(v=-k_{\rho}s_{\rho}\), thereby reducing the control chattering. 2. If the value of the time derivative of \(|s|\) and \(s\) is sufficiently small, a higher-order sliding mode surface is established; that is, \(s=\dot{s}=\cdots=s^{(\rho-1)}=0\), and the \begin{table}
\begin{tabular}{c c} \hline \hline \(\rho\) & Parameters \\ \hline
2 & \(\gamma_{1}=1.26\) \\
3 & \(\gamma_{2}=9.62,\gamma_{1}=1.5\) \\
4 & \(\gamma_{3}=739.5,\gamma_{2}=8.1,\gamma_{1}=2\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Value of Parameter \(\gamma_{i-1}\). exponential term \(\overline{\alpha}\) will approach 1. The controller is transformed into the linear controller \(v=-k_{\beta}\varepsilon_{\beta}\), thereby reducing the control chattering. 3. Parameters \(\varepsilon_{s_{i}}\) and \(\overline{\beta}\) are used to adjust the accuracy of the controller.
# Clove Oil Protects \(\beta\)-Carotene in Oil-in-Water Emulsion against Photodegradation
## 1 Introduction  
While \(\beta\)-Carotene (\(\beta\)-Car) is an important provitamin A, it is sensitive to light and degrades rapidly in plant oils or in plant oil emulsions during storage under ambient conditions [1, 2, 3, 4]. Vitamin A deficiency is a major challenge worldwide, especially for children's nutrition, and urgently calls for practical solutions [5, 6].  
Recently, it was found that plant phenols regenerate \(\beta\)-Car and other carotenoids (Car) from their initial photooxidation product, the carotenoid radical cations (Car\({}^{\bullet+}\)), through electron transfer from the reducing phenol group \(\varphi\)-OH [7]:  
\[\text{Car}^{\bullet+}+\varphi\text{-OH}\rightarrow\text{Car}+\varphi\text{-O}^{ \bullet}+\text{H}^{+} \tag{1}\]  
The regeneration of \(\beta\)-Car corresponding to the reaction of Equation (1) was surprisingly found to be the most efficient for moderately reducing plant phenols, such as eugenol, while strongly reducing plant phenols, like tea catechins, showed no regeneration of \(\beta\)-Car, but displayed enhanced photobleaching [7, 8, 9].  
Eugenol and isoeigenenol, the main constituents of clove oil [10], are moderately reducing plant phenols that have been found to regenerate \(\beta\)-Car efficiently from the radical cation formed by photolysis of \(\beta\)-Car. This reduction occurs in alkaline chloroform/methanol as an electron-withdrawing solvent [11]. The ordering of the anions of the plant phenols according to the rate of regeneration of carotenoids could further be accounted for by the [PERSON] theory of electron transfer [12]. According to this theory, the maximal rate of electron transfer corresponds to a driving force matching the reorganization energy in the transition state for electron transfer. Notably, for a larger driving force, the rate of electron transfer enters the so-called inverted region with a higher activation barrier, and accordingly, lower rates are seen for quercetin and tea catechins [7, 12].  
The more practical aspects of the [PERSON] theory for electron transfer have not yet been exploited in relation to food preservation. However, the protection of \(\beta\)-Car, as a provitamin A in an oil-in-water emulsion in a functional food, could provide a proof ofconcept, where the use of [PERSON] theory could be moved from model systems involving chlorinated solvents of high pH into a real food system. Accordingly, clove oil, with high content of moderately reducing plant phenols and its worldwide use in food and beverages, was combined with \(\beta\)-Car in an oil-in-water emulsion and stored under illumination in ambient conditions with the objective of protecting provitamin A against degradation during retail display. The present study aimed to explore whether the [PERSON] theory for electron transfer could be used to design optimal protection of a light-sensitive vitamin.
## 3 Results and Discussion
### Sorption/Desorption  
The reusability of materials is crucial for bioremediation technology; thus, sorption and desorption studies were performed. Three cycles were conducted and the results are presented in Table 7, as it is well known that the choice of eluent has a high impact on the ions desorption percentage. In this particular study, citric acid had the highest efficiency for the removal both of Cr(VI) and Cd(II) ions from AP and PAP material. Water was used as a control eluent and no desorption of ions was observed, which also proves the chemical nature of the adsorption process.