esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of an Integrated System for Spaceborne SAR Imaging and Data Transmission
###### Abstract
Existing research on FBMC signals in SAR primarily focuses on investigating the ability of ground-based SAR systems to detect airborne targets [18; 19]. In terrestrial applications, the communication system operates independently of the SAR system for message and data transmission. Therefore, integrated SAR and communication are less critical compared to spaceborne systems. Spaceborne SAR systems require communication capabilities to transmit imaging data back to Earth or to other satellites with less spectrum, power, equipment weight, and envelope than ground-based SAR systems. By integrating SAR and data transmission functions with DRFC technology, the satellite eliminates the need for additional resources like transmission spectrum and equipment space. In order to achieve the FBMC-based integration of radar and data transmission, we propose a power allocation scheme for integrated spaceborne SAR and data transmission using FBMC-OQAM signals. We adjusted the transmission power of each subcarrier to maximize the SNR of SAR echoes while also ensuring the echo data transmission rate and adhering to PAPR constraints. The primary research focuses and innovations are as follows:  
The integration of SAR satellite with data transmission systems has been achieved, allowing ground mobile terminals to obtain real-time SAR imagery data of their surrounding environment. An algorithm has been designed for integrated spaceborne spotlight SAR and data transmission to achieve a balanced match between data transmission capacity and SAR imaging data volume. Given the limited power capacity of spaceborne SAR payloads, we aim to find an optimal power allocation strategy to meet power and PAPR constraints, while simultaneously optimizing performance and validating the simulation of SAR and data transmission systems. In order to address the interference problem with SAR signals' transmission and reception, we developed a Simultaneous Transmission and Reception (STAR) signal sequence based on spaceborne SAR echo and data transmission and reception. On the basis of the above functions, we designed an integrated system for SAR and data transmission, as well as a system workflow.
## 5 Discussion
### Consequences for City Planning and Smart Cities  
Remote sensing can always just play a minor, yet indispensable role for designing and managing smart cities. In smart city research there seem to be an emphasis on energy efficiency, digital services for citizens and mobility. These aspects are without any doubt highly valuable. In fact, smart cities need energy-efficient systems that reduce energy use and reliance on fossil fuels. They need intelligent transport systems that enable citizens to travel more efficiently in private vehicles or using public transport, and they need sophisticated communication technologies that improve public safety and emergency response services.  
In addition, smart cities put the citizen in the center of consideration while empowering citizens to improve their lives through better access to data and public services. Nevertheless, smart cities need precise and updated planning documents including maps. This is where remote sensing comes into play. While the degree of changes per year in land use and urban pattern may be relatively small e.g., for European cities, research demonstrates that the rate of changes in countries like China can be impressive. The authors have recently studied urbanization velocity in regard to mapping and monitoring exercises for fast growing cities in the Pearl River Delta in China [49; 50]. This study on ISA may only be a very small piece in the puzzle of how remote sensing may contribute to smart city research. However, in combination with these recent findings, we may conclude that for fast growing areas in many parts of the world, remote sensing imagery, both high resolution and medium (e.g., Landsat-like 30 m resolution) build an essential base for smart city planning measures.
## 3 Methodology
### Extreme Gradient Boosting (XGBoost)  
Nowadays, there are a number of AI models for implementing inventory forecasting. These models include the last-value forecasting model, simple averaging forecasting model, moving average forecasting model, weighted moving average model, multiple linear regression forecasting model, ridge/lasso linear regression forecasting model, and random forest regression forecasting model. Our study is based on the Extreme Gradient Boosting (XGBoost) model developed by [PERSON] and [PERSON] (2018). It is used in the investigation not only due to the advantage that the algorithm can make use of the Graphical Processing Unity (GPU) to speed up the training, but its performance is also suitable for handling the data-intensive big data that predict the expected inventory and forecasting. Suppose a data set \(D=\left\{(x_{i},y_{i})\right\}\) labelled \(y\) has \(n\) rows and \(m\) indices. Including \(|D|=n,x_{i}\in R^{m},y_{i}\in R\), the regression tree model \(\phi\) predicted values might be defined as:  
\[\widehat{y}_{i}=\phi(x_{i})=\sum_{k=1}^{K}f_{k}(x_{i}),f_{k}\in F \tag{1}\]  
where \(K\) represents the number of trees and \(f_{k}\) shows the number \(K\) tree, and \(F=\left\{f(x)=w_{q(x)}\right\}\) represents the set of regression trees. The scores of each leaf node were calculated, and the set constructed by the scores of all nodes \(i\) was shown in \(w_{i}\) table. \(q:R^{m}\to T\) represents the function that maps R to \(T\), and \(T\) represents the leaf tag set.  
The XGBoost target function \(L\) is defined as follows:  
\[L^{(t)}(\phi)=\sum_{i=1}^{n}l(\widehat{y}_{i},y_{i})+\sum_{k=1}^{K}\Omega(f_{k}) \tag{2}\]  
Among them, \(i\) represents residual error function between the \(y_{i}\) and \(\widehat{y}_{i}\), and \(\Omega\) means to prevent excessive fitting the regularization of the problem.  
\[\Omega(f)=\gamma|T|+\frac{1}{2}\lambda\parallel w\parallel_{2}^{2} \tag{3}\]  
where \(\gamma\) is the regularization parameter, \(w\) is the leaf node weight. \(\lambda\) is generally a constant term used to describe the penalty coefficient of the weight \(w\), and \(T\) is the number of leaf nodes.  
XGBoost brings the predicted value closer to the actual value by increasing the number of tree nodes. The goal is to find an \(f_{k}\) that minimizes the target function L. Data through theoretical derivation, expressed in \(I_{L}\), are divided into the left subtree of the sample label set; the parent node label set with \(I=I_{L}\cup I_{R}\) is described, including \(I_{R}\) said right subtree label sets, so there are:  
\[L_{split}=\frac{1}{2}\Bigg{(}\frac{\left(\sum_{i\in I_{L}}g_{i}\right)^{2}}{
# Pyramid Spatial-Temporal Graph Transformer for Skeleton-Based Action Recognition
## 5 Conclusions  
This paper proposed a pyramid spatial-temporal graph transformer backbone (PGT) for skeleton-based action recognition. We introduced the self-attention mechanism into graph convolution blocks to extract spatial-temporal correlations. According to the pyramid-alternated architecture, the local information extracted by the graph embedding module was fused by the spatial-temporal and joint transformer blocks layer by layer. Both the local information and global information were iterated alternately through the model. Meanwhile, the graph embedding module adopted a separate convolution design to make the model more efficient and deeper. The spatial-temporal separate self-attention proposed for the former layers in the PGT was able to handle a wealth of tokens of skeleton joints. At  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Top-1 (\%)** & **Year** \\ \hline Lie Group [1] & 74.2 & 2014 \\ \hline Ensemble TS-LSTM [50] & 89.2 & 2017 \\ \hline Separable STA [48] & 92.4 & 2019 \\ \hline SGN [43] & 92.5 & 2020 \\ \hline VPN [49] & 93.5 & 2020 \\ \hline
4 s Shift-GCN [12] & 94.6 & 2020 \\ \hline DC-GCN+ADG [30] & 95.3 & 2020 \\ \hline PGT (Ours) & 95.4 & 2022 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparisons of the experiment results on the Northwestern-UCLA dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Acc. (\%)** & **FLOPs (G)** & **Param. (M)** \\ \hline PGT & 90.9 & 4.01 & 12.36 \\ \hline PGT (Only Joint) & 87.3 & 1.00 & 4.09 \\ \hline ST-GCN [10] & 81.5 & 16.32 & 3.10 \\ \hline AS-GCN [25] & 86.8 & 26.76 & 9.50 \\ \hline
3 s RA-GCN [13] & 87.3 & 32.80 & 2.61 \\ \hline
2 s AGCN [11] & 88.5 & 37.32 & 6.94 \\ \hline
4 s Shift-GCN [12] & 90.7 & 6.12 & 0.79 \\ \hline Efficient-GCN [15] & 91.7 & 15.24 & 2.03 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The model complexity on the NTU-RGBD 60 CS benchmark denoting the efficiency of the PGT.  
the same time, the joint transformer self-attention focused on the long-range correlation fusion in both the spatial and temporal domains. Extensive experiments were conducted on the NTU-RGBD 60, NTU-RGBD 120 and Northwestern-UCLA datasets, demonstrating the effectiveness of our work.  
Conceptualization, S.C., K.X., X.J. and T.S.; methodology, S.C. and K.X.; software, S.C.; validation, K.X., X.J. and T.S.; formal analysis, S.C., K.X., X.J. and T.S.; investigation, S.C. and X.J.; resources, X.J. and T.S.; writing, S.C., K.X., X.J. and T.S. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Nature Natural Science Foundation of China (62002220).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Description of the Used Models  
### Multi-Layer Perceptron (MLP)  
The idea of an artificial neural network (ANN) was first suggested in 1943 by [PERSON] and [PERSON] [34]. Originally, ANNs are inspired by the human neural network and has been satisfactorily used for several fields of research [35; 36]. The main advantage of this model lies in the ability of mapping the non-linear relationship between sets of input-output pairs. Among various kinds of ANNs, multi-layer perceptron (MLP) is a reliable tool which is designed for the case of this study to predict the HL and CL of the residential buildings. As is shown in Figure 1, an MLP is composed of three layers that each layer contains a number of computational units (i.e., neurons). During the MLP performance, it has been aimed to find the optimal values of weights (W) and biases (b) to obtain the best accommodation between the targets and outputs of the system. More specifically, let T be the input vectors; then, the output of the k-th neuron is calculated as follows:  
\[Y_{k}=F\!\left(\sum_{m=1}^{z}T_{m}\ W_{mk}+b_{k}\right) \tag{1}\]  
where \(T\) denotes the input value, \(W\) and \(b\) are the assigned weight and bias, respectively. Also, the term \(F\) symbolizes the activation function.
## 3 Underwater Routing Protocols
### Tc-Vbf  
The topology control VBF (TC-VBF) addresses the issue of nodes' communication in sparse conditions and selects forwarders in the manner of VBF in addition to considering the density of the network [18]. However, it suffers from the same problems and issues as VBF, described above.  
LBDR  
The authors in [19] proposed a localization-based dynamic routing (LBDR) protocol and argued that localization is beneficial in dynamic underwater networks where nodes frequently change their positions. The network is divided into layers. A routing pipe is constructed within these layers. The movement of the nodes with water currents causes the nodes to move in and out of the routing pipe. The protocol achieves high throughput as packets are forwarded directionally in the pipe towards the water surface. However, the data load is high on the nodes in the pipe if the water currents do not frequently bring new nodes into the pipe.
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 5 Issues and Challenges in 5G and Edge-Based V2X
### Data Privacy Management  
Privacy is of critical importance for the protection of private and sensitive information about the vehicles in 5G V2X based on MEC. The vehicle users sometimes put their trust in MEC data centers trying to manage applications without knowing how information flows and who is actually using their information [44]. If the 5G-based MEC servers are under attack, a huge volume of heterogeneous information may be leaked and vehicle users' privacy may be breached. Some of the privacy problems have been resolved in existing vehicular networks, like the use of a group signature for secure vehicle communications. However, these schemes have to be well exploited before implementing them in 5G-based MEC networks.
## 4 Use of the Controller-Driven OppNet Architecture to Manage Congestion
### Applying a Directive  
Applying a directive \(\delta_{l}=(H_{l},l^{\prime})\) entails two actions: (1) using the encapsulated replication limit \(l^{\prime}\) when creating new data messages and (2) updating all the buffered messages according to the new \(l^{\prime}\). The first action consists of using the new replication limit \(l^{\prime}\) when an application creates a new message (\(g=(a,l^{\prime},\varrho,\varphi)\)). The second action consists of updating the message's field \(l\) of the buffered messages, considering that some are already copies.  
Indeed, it would not be accurate to update the buffered messages with the new replication limit \(l^{\prime}\) received through \(\delta_{l}\), as some of these messages are already copies. Therefore, the number of times this message has been relayed (message's \(\varrho\) field) is considered. This information is used to calculate the current replication limit of the buffered message, considering that: (1) the message was created with the replication limit set by the last received \(\delta_{l}\) and (2) \(\lceil 1/2\rceil\) copies of the message are forwarded at each encounter:  
\[l^{\prime\prime}=\frac{l^{\prime}}{2^{\varrho}} \tag{8}\]  
where \(l^{\prime\prime}\) is the remaining replication limit after relaying the message \(\varrho\) times.  
Algorithm 4 shows the procedure for modifying the replication limit of the buffered messages. For each of the buffered messages (_line_ 2), provided the replication limit it was assigned when created was \(l^{\prime}\) (from the received directive \(\delta_{l}=(H_{l},l^{\prime})\)) and taking into account the number of times that the message has been relayed (\(\varrho\)), the remaining replication limit (\(l^{\prime\prime}\)) is calculated using (8) (_line_ 3). In case the calculated \(l^{\prime\prime}\) is less than one, it would mean that by starting with the replication limit specified in the received directive, the message would not have any copies left to disseminate at this point and, therefore, the message would not reach the current node. If this is the case, the message's field \(\varphi\) is set to _false_ (_line_ 5), indicating that this message is marked to be deleted in case buffer resources are required.
## 4 Experimental Setup and Results Analysis
### Baselines  
We assessed our model against state-of-the-art methods by (1) utilizing molecular charts; (2) infusing drug fragment insights; and (3) evaluating their role in DDI predictions.  
DeepDDI [6] incorporates structural similarity profiles in the representation learning process and uses the Jaccard coefficient to predict DDI.  
DeepSynergy [51] uses chemical and genetic information as input and is applied to predict drug synergy.  
MHCADDI [25] uses a collaborative attention mechanism to integrate drug combination information.  
MR-GNN [19] captures different-sized structures of each drug using each graph convolutional layer of the nodes. These representations are subsequently channeled into a recurrent neural mechanism to collectively learn drug duo depictions for forecasting.  
CASTER [51] uses an end-to-end dictionary learning framework to encode drug chemical structures.  
SSI-DDI [38] perceives every node's concealed attribute as a substructure, calculating the interplay among them to conclusively pinpoint the DDI.  
EPGCN-DS [52] achieves drug structure permutation invariance using a graph convolutional network and DeepSets.  
DeepDrug [53] learns graph representations of drugs and proteins using graph convolutional networks, e.g., residual structures, to optimize training.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Task** & \(\mathbf{D}\) & \(\mathbf{C}\) & \(\left|\mathbf{Y}\right|\) \\ \hline TwoSides & Polypharmacy & 644 & 10 & 499,582 \\ DrugBankDDI & Interaction & 1706 & 86 & 383,496 \\ DrugComb & Synergy & 4146 & 288 & 659,333 \\ DrugCombDB & Synergy & 2596 & 112 & 191,391 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of drugs in the drug dataset (\(\left|D\right|\)), management context \(\left|C\right|\), and number of labels (\(\left|Y\right|\)).  
GCN-BMP [54] layers numerous graph convolution tiers (amounting to L strata) to assimilate depictions of each node within the diagram and fabricates a focus-driven graph pooling stratum.  
DeepDDS [55] discerns oncological drug pairings employing a profound learning framework hinging on graph convolutional structures or attention-centric processes.  
MatchMaker [56] uses drug chemical structure information to predict in a deep learning framework.  
We reproduced these methods using PyTorch and made minor modifications to some of them to achieve better performance under fair comparison.
# Application Scenarios of Digital Twins for Smart Crop Farming through Cloud-Fog-Edge Infrastructure
## 5 Possible Application Scenarios with Digital Twin
### Scenario 4: Nutrient Deficiency Risk Identification  
Nutrient deficiency risk identification is a traditionally onerous task in which farmers may heavily rely on experience and generational knowledge [4]. Identifying a nutrient deficiency is often done through visual inspection of crops, as nutrient deficiencies result in symptoms appearing on the plant [4]. If visual symptoms are missed, a farmer may only realise the potential for nutrient deficiencies when observing a reduced yield and/or poor plant quality. Problematic crops may prompt a soil test, which serves as the most accurate representation of soil pH and key nutrient levels. However, when a deficiency has manifested, it is often too late to treat it effectively. Therefore, due to the variable nutrient requirements of different crops, farmers often utilise strategies such as crop rotation and intercropping to maintain a balanced soil nutrient profile.  
Conventional smart agriculture techniques have been developed to identify nutrient deficiencies in soils and crops. This is primarily achieved through satellite imagery [50], machine learning [51], and IoT sensors [52]. Some techniques relying on imagery may prove insufficient in some scenarios, as treating nutrient deficiencies can be ineffective by the time it is visually present. IoT sensor solutions facilitate rapid data collection, thereby enhancing decision-making efficiency [52]. The adoption of IoT sensors has proven to improve the quantity and quality of yield [52].  
An implemented DT architecture offers a basis to integrate heterogeneous, dynamic, and disparate data sources into a homogenous system. This uniform system can underpin mechanisms that proactively monitor for coinciding and compounding risk factors for key nutrients, such as a field of a particular soil being waterlogged during the winter months. The result of this could be a notification to a farmer that a field is exposed to a greater risk of certain nutrient deficiencies. The use of edge services, as in the proposed DT, ensures the integration of real-time data into risk identification processes. The DT transforms the approach to nutrient deficiency, as the seamless integration of interoperable data enables holistic techniques for risk identification in real-time. Furthermore, this strategy is enhanced by being employed by a multi-agent system. The distributed nature of agents allows for simultaneous monitoring of multiple fields in parallel. The autonomous aspect of agents ensures a proactive risk identification process. This transitions a farm's nutrient deficiency management strategy from being reactive to preventative, as actions can be taken to mitigate risk as the dynamic data is updated in the DT.
## 4 Results and Discussion
### De-Blurring Image
The results show the impact of resizing the input training image from \(256\times 256\) pixels to \(112\times 112\) pixels on the correlation coefficient at different depths. These findings demonstrate the importance of optimizing the input image size to achieve optimal performance in terms of the correlation coefficient at different depths, as shown in Figure 13. The observed trends can be ascribed to the interplay of scattering phenomena and the depth of the absorbing structure. With increasing depth, the scattering effects intensified, leading to diminished correlation coefficients. Moreover, the selection of the input image size exerted a notable influence on performance, primarily by affecting the model's ability to capture intricate features amidst scattering influences. In particular, the optimal input size exhibited variability depending on depth, thus facilitating improved adaptability to varying degrees of scattering. These discernments underscore the imperative of factoring in depth and input size while addressing scattering-induced de-blurring tasks, thereby providing valuable insights for optimizing model efficacy across diverse scenarios. Further studies could delve into the intricate dynamics connecting depth, scattering effects, and input size, thereby advancing the potential for refining the applicability and precision of de-blurring models. The validity of the diffusion approximation is based on the condition that the thickness of the scattering medium is significantly greater than the average free-path length of \(1/\mu_{s}^{\prime}\). Consequently, caution must be exercised when applying Equation (1) in cases where \(\sqrt{\rho^{2}+d^{2}}\) is not greater than \(1/\mu_{s}^{\prime}\). As shown in Figure 1, the observing plan is considered to be significantly larger than the light distribution on the surface. Therefore, it is better to generate an appropriate wide image for training to ensure a result with a deep-absorbing structure. The light distribution on the surface has a Gaussian distribution shape. The image size in a dimension should be more significant than three times the standard deviation of the light distribution on the surface of the medium when calculating the deepest light point source distribution by Equation (1) in the turbid medium, as shown in Figure 13.  
Figure 13: Optimizing input image size across various depths.