esa-sceva/satcom-chunk-collection huggingface dataset:
# An Approach to Speed up Single-Frequency PPP Convergence with Quad-Constellation GNSS and GIM
## 2 Approach of Single-Frequency PPP with Quad-Constellation GNSS and GIM
In Equation (7), \(z\) is the zenith angle of the line of sight at the receiver, \(R\) is the average radius of the Earth, and \(H_{\rm{ion}}\) is the height of the assumed ionosphere single layer, which is 450 km for most of the GIM products. The variance of the code and carrier phase observations can be expressed as the function of the initial variance \(\sigma_{0}^{2}\) and the satellite elevation angle \(E\):  
\[\sigma^{2}=\sigma_{0}^{2}/(\sin E)^{2} \tag{9}\]  
For the pseudo-observables of the IHD, their variance can be expressed as:  
\[\sigma_{IB}^{2}=\sigma_{IB\_t}^{2}\cdot m^{2} \tag{10}\]  
where \(\sigma_{IB\_t}^{2}\) is the IHD pseudo-observable variance in the zenith direction. Since the GIM products only have an equivalent ranging accuracy of 0.32-1.28 m, the accuracy of the IHD pseudo-observables derived from the GIM and DCB products would be lower than 0.32 m. To account for this limitation, a time varying weight scheme is proposed for the pseudo-observables. The idea behind the scheme is that the pseudo-observables are assigned a larger weight at the beginning of data processing for the purpose of faster convergence but with gradually decreased weights during the convergence period for the purpose of better positioning accuracy. A Kalman filter algorithm is normally applied in the PPP processing. The \(\sigma^{2}_{IB\_t}\) in Equation (10) in this case can be expressed as a function of the filtered time in the Kalman filter:  
\[\Phi^{g}=\rho^{g}+cdT-cdt^{g}+d^{g}_{orb}+d^{g}_{trop}~{}-d^{g}_{ion}~{}+~{}B^{ g}+\epsilon^{g}_{\Phi} \tag{2}\]0  
where \(\sigma^{2}_{IB\_0}\) is the initial variance of the pseudo-observables, \(\alpha\) is the variance varying rate with time, \(\Delta t\) is the filtered time in the Kalman filter processing. Equations (1)-(5) are the observation equations of the single-frequency PPP model with quad-constellation GNSS and GIM. The precise satellite orbit and clock corrections from international GNSS service (IGS) are applied to mitigate the satellite orbit and clock errors. The mitigation of other error sources may refer to [1]. The carrier phase cycle slips are detected and repaired using a forward and backward moving window averaging algorithm and a second-order, time-difference phase ionospheric residual algorithm [21]. The unknown parameters of the proposed single-frequency PPP model include the three-dimensional position coordinates, the receiver clock offset, the system time differences, the zenith tropospheric delays, the ambiguities and the IHD. The static position coordinate and ambiguity parameters are considered as constants while the other parameters are modeled as random walk processes in the Kalman filter processing.
## 2 Related Work
### The Architecture of Digital Twin  
Digital Twins create a virtual counterpart of a physical system, offering a representation that mirrors its operational state. This virtual environment enables a 3D visual monitoring system for comprehensive oversight of the physical system. Digital Twin (DT) concepts vary by domain due to the specific nature of information in each field [33].  
The architecture of Digital Twins consists of the physical world, the virtual world, and their connectivity [34] as is designed in Figure 3. In the physical world, sensors gather data, edge computing enables initial analysis, and security measures protect information [35]. The virtual world includes a digital replica, advanced data processing using ML and AI, and databases. Connectivity ensures secure data transfer through interfaces like the internet and Bluetooth.
# AI-Driven Adaptive Communications for Energy-Efficient Underwater Acoustic Sensor Networks
## 2 State of the Art
Their system, evaluated on the DeepFish dataset for underwater fish recognition, achieved 97.78% accuracy with only 0.57 mJ energy consumption, significantly reducing memory, runtime, and power requirements. However, the absence of a communication mechanism raises open questions regarding how inference outcomes are transmitted or accessed in deployment scenarios. Similarly, in [11], authors introduced a battery-free underwater sensing architecture that performs on-board CNN-based inference using energy harvested from underwater acoustics, with results transmitted via binary backscatter. While this system proves the feasibility of end-to-end battery-free inference, achieving 63% accuracy in the classification of real-time marine mammals, the communication strategy remains static and lacks adaptability to dynamic conditions. In several studies, researchers have explored adaptive communication strategies in underwater acoustic networks, with a particular focus on modulation adaptation and power control, aiming to enhance energy efficiency and system performance under varyingchannel conditions. For example, in [12], the authors investigated adaptive power control in shallow-water communication channels using experimental data from the SPACE'08 experiment. They modeled the channel gain as a log-normal process and proposed a feedback-based linear prediction mechanism to adjust transmit power dynamically to maintain a target SNR, achieving an average reduction of 8.7 dB as compared to fixed-margin schemes. However, this approach is effective in improving energy efficiency, but it requires a periodic, error-free feedback of channel state information, which may be impractical in autonomous underwater deployments. On the other hand, the authors in [13], proposed an adaptive modulation approach for long-range underwater acoustic communications where the feedback from the receiver is not available due to long propagation delay. Their approach selects the best modulation scheme based on propagation modeling and a machine learning classifier trained on environmental parameters. They demonstrated approximately 80% accuracy in selecting the optimal modulation even under environmental mismatches. However, their method focuses on modulation adaptation and does not explore joint optimization with packet size, or power control, which could further improve performance. All the above works exploit environmental knowledge, adaptive learning techniques, and lightweight inference to enhance underwater sensing; however, they do not consider the integration of classification with SDR-based communication adaptation in the underwater environment. In contrast, our framework enables on-node inference while dynamically tuning key transmission parameters such as modulation scheme, packet length, and transmission power based on the classification output, offering a more responsive and energy-efficient approach suited to real-world underwater environments.
## 4 SkyDRL for Multi-Cloud Service Composition Problem
### Preliminary  
#### 4.1.1 Reinforcement Learning  
Reinforcement learning is a major branch of machine learning and serves as the core idea behind the proposed method presented in this paper. Five critical elements constitute reinforcement learning: agent; environment; state; action; and reward. The agent is responsible for making decisions and interacting with the environment by performing actions in a particular state. The environment represents the external context in which the agent operates, including states and transitions between them. The state describes the condition of the agent at a given moment within the environment. Action refers to the decision made by the agent in the current state, while the reward is feedback information that the environment provides based on the actions taken by the agent. Rewards can be positive or negative, with positive rewards serving as encouragements to the agent, while negative ones can be considered as punishments.  
The primary purpose of reinforcement learning is to enable the agent to learn by itself in the environment, continuously adjusting its strategy based on the feedback and eventually determining the optimal approach to maximize cumulative rewards [50]. The agent selects an action based on a specific policy and executes it, with the environment giving feedback based on the agent's action. The agent then moves to a new state, adjusts its strategy based on the newly acquired information, and performs a new action. This process continues until the optimal strategy that maximizes cumulative rewards is found. Figure 3 presents the basic framework for reinforcement learning. Assuming that the current agent is in a state \(s_{t}\), it executes a particular action \(a_{t}\), and the environment provides feedback \(r_{t}\), based on the action while simultaneously triggering a transition from state \(s_{t}\) to \(s_{t+1}\).
# Elevation Angle Characterization for LEO Satellites: First and Second Order Statistics
## Appendix C
### Interpolation for a Four-Point Mesh  
The bilinear method is proposed as the method to find the query point inside the mesh since it is a well-known method and widely applied for interpolation.  
We can obtain the values for a query point as in Figure 10 for a shape parameter value \(a\) using bilinear interpolation as follows  
\[a(\phi,i)=w_{1}a(\phi_{2},i_{1})+w_{2}a(\phi_{1},i_{1})+w_{3}a(\phi_{2},i_{2}) +w_{4}a(\phi_{1},i_{2}), \tag{11}\]  
similarly, we can obtain the scale parameter, \(b\), using bilinear interpolation as follows  
\[b(\phi,i)=w_{1}b(\phi_{2},i_{1})+w_{2}b(\phi_{1},i_{1})+w_{3}b(\phi_{2},i_{2}) +w_{4}b(\phi_{1},i_{2}), \tag{12}\]  
where the wights \(w_{1}\), \(w_{2}\), \(w_{3}\) and \(w_{4}\) are given by  
\[w_{1}=(i_{2}-i)(\phi_{1}-\phi)/\left[(i_{2}-i_{1})(\phi_{1}-\phi_{2})\right] \tag{13}\]  
\[w_{2}=(i_{2}-i)(\phi-\phi_{2})/\left[(i_{2}-i_{1})(\phi_{1}-\phi_{2})\right] \tag{14}\]  
\[w_{3}=(i-i_{1})(\phi_{1}-\phi)/\left[(i_{2}-i_{1})(\phi_{1}-\phi_{2})\right] \tag{15}\]  
\[w_{4}=(i-i_{1})(\phi_{1}-\phi_{2})/\left[(i_{2}-i_{1})(\phi_{1}-\phi_{2})\right] \tag{16}\]  
where the known values for \(a(i_{n},\phi_{m}),\ldots,a(i_{n+1},\phi_{m+1})\) and \(b(i_{n},\phi_{m}),\ldots,b(i_{n+1},\phi_{m+1})\), can be obtained from matrices \(\mathbf{A_{h}}\) and \(\mathbf{B_{h}}\).
# A Method to Construct an Indoor Air Pollution Monitoring System Based on a Wireless Sensor Network
## 4 Simulation and Results Discussion  
In this study, assuming that target area (RoI) A is two-dimensional plane (100 m\({}^{2}\)), it is digitized into \(100\times 100\) grids where each grid size is equal to 1. In the simulation, the WSN with 100 total numbers of sensors is deployed on the target area with two assuming specifications:  
1. All sensors have the same sensing range \(r=20\) m and communication range \(r_{c}=2\)\(r\)
2. Each sensor knows its location by a certain mechanism and the location information can be sent to the base station.  
This project is defined in order to find the best distribution of the deployed sensors to obtain full coverage of the monitoring area. To achieve our objective, the system implementation will be passing through three basic phases:  
* Coverage optimization of the monitoring area using PSO algorithm to find an optimal number of the deployed sensors to totally cover the RoI.
* Coverage hole detection based on the VD.
* Coverage hole healing using the algorithm of PiP to find sensors inside the hole then to use them to revalue the coverage of the hole using the coverage algorithm used in the first phase of work.  
The effectiveness of the network coverage optimization strategy will be determined through a simulation experiment. Using a computer with a 1.10 GHZ processor, the coverage optimization algorithm of the WSN is carried out in MATLAB (R2013a) environment.
# Deep Learning CNN-GRU Method for GNSS Deformation Monitoring Prediction
## 2 Method
### Modeling for GNSS Time Series  
The convolutional features extracted by CNN are inputted into GRU for modeling. Similar to the Long Short-Term Memory (LSTM) model, the GRU model is an evolution of the RNN model, aimed at addressing the issues of vanishing gradients and long-term dependencies inherent in the traditional RNN model.  
The GRU model includes an update gate and reset gate, while the LSTM model consists of a forget gate, input gate, and output gate. Due to that, the GRU model has fewer parameters with a simpler structure. This facilitates easier convergence during training for the GRU model and enhances its generalization performance on smaller datasets. As shown in Figure 1, the update gate is to update the dataset, while the reset gate is to reset the dataset. The formula of the update gate is as follows:  
\[z_{t}=sigm(W_{xz}x_{t}+W_{hz}h_{t-1}) \tag{3}\]  
where \(z_{t}\) is the update gate, \(sigm()\) is the sigmoid function, \(x_{t}\) is the input dataset and \(W_{xz}\) denotes its weight matrix, \(h_{t-1}\) is the hidden status of the prevent epoch, and \(W_{hz}\) denotes its weight matrix. The update gate determines the information from the previous epoch that needs to be passed on to the future. This mechanism is highly potent as it decides whether to retain all information from the past, thereby mitigating the risk of gradient vanishing.  
The reset gate serves a specific function, deciding the criteria for filtering previous state information, which effectively captures short-term dependencies in the dataset after the CNN method. The formula of the reset gate is as follows:  
\[r_{t}=sigm(W_{xr}x_{t}+W_{hr}h_{t-1}) \tag{4}\]  
where \(r_{t}\) is the reset gate, \(W_{xr}\) denotes its weight matrix of \(x_{t}\), and \(W_{hr}\) denotes its weight matrix of \(h_{t-1}\). The reset gate influences the model's retention of historical information. If the value of the reset gate is close to 0, the model will ignore the previous hidden state.  
The GRU model is a forward-propagating neural network, and a memory unit is designed to store the information from the past epochs. Based on Formulas (3) and (4), the memory unit is shown in Formula (5).  
\[c_{t}=\tanh[W_{xc}x_{t}+r_{t}\odot W_{hc}h_{t-1}] \tag{5}\]  
where \(c_{t}\) is the current memory unit, \(W_{xc}\) denotes the weight matrix of \(x_{t}\) in the hidden state \(h_{t}\), \(W_{hc}\) denotes the weight matrix of \(h_{t-1}\) in the hidden state \(h_{t}\), and \(\odot\) denotes the _Hadamard product_. Based on the above, the final memory unit of the current epoch is shown in Formula (6).  
\[h_{t}=(1-z_{t})\odot h_{t-1}+z_{t}\odot c_{t} \tag{6}\]  
where \(h_{t}\) is the final memory unit, which stores the current information and sends it to the next epoch.
# Learning Embedding for Signed Network in Social Media with Hierarchical Graph Pooling
## 2 Related Work
### Graph Neural Networks  
GNNs seek to implement deep neural networks to structured data. Due to the characteristics of the non-Euclidean data structure, traditional deep neural networks, such as CNNs, cannot be directly used in the graph domains [27]. To solve this problem, GNNs ignore the input order of nodes and propagate the information on each node separately. They can also do the propagation guided by graph structure instead of using it as part of node features. Graph convolution network (GCN) is a classic graph neural network. With the help of the Laplacian matrix, GCN uses feature decomposition and Fourier transform to obtain a convolution kernel to perform convolution on the graph network. GraphSage is an inductive framework that uses vertex feature information to construct embeddings for previously unseen vertices in an efficient manner. GAT assumes that neighbor nodes have different contributions to the target node, and attention is utilized to determine the relevance of each neighboring node to the target node.  
With continuous research on graph convolution, researchers have also proposed some signed network embedding methods based on deep graph convolution networks [28]. Signed Graph Convolutional Network (SGCN) [18] first expanded GCN to signed networks and designed both positive and negative aggregators to generate \"friend expression\" and \"enemy expression\" for each node in signed networks based on balance theory.  
However, the majority of existing approaches ignore the high-order information of the signed graphs. SHGP employs a graph pooling layer, which enables the collection and transmission of more meaningful high-order information to guide the representation learning process.
# On the Rate-Distortion Theory for Task-Specific Semantic Communication
## 5 Experimental Results
### Results for Generation Tasks
#### 5.2.2 Impact of Indirect Observation  
To investigate the impact of the indirect observation process, we demonstrate the simulation semantic rate-distortion function under different settings of the quantization levels \(L_{o}\) of the indirect observation in Figure 4. Here, \"direct observation\" corresponds to the case where the encoder directly observes the original samples from semantic source \(S\). From the simulation results on MNIST in Figure 4a and those on CIFAR10 in Figure 4b, we observe that the required rate to achieve the same distortion level increases when the quantization level decreases. This phenomenon can be attributed to the information loss incurred during the quantization process, which forces the system to compensate through higher channel capacity.  
Moreover, our experiments reveal a notable difference in how indirect observation affects performance across the MNIST and CIFAR10 datasets. While both datasets exhibit degraded performance under coarser quantization, the impact is significantly more pronounced for CIFAR10. Specifically, for MNIST at 12 bits, direct observation achieves MSE = 0.0061, while quantization degrades performance to 0.0072 (\(L_{o}=8,+18\%\)) and 0.011 (\(L_{o}=4,+80\%\)). For CIFAR10 at equivalent 384 bits (\(12\times 32\)), MSE increases from 0.013 (direct) to 0.018 (\(L_{o}=32,+38\%\)) and 0.026 (\(L_{o}=16,+100\%\)). This discrepancy stems from the inherent complexity difference between the two datasets. The CIFAR10 dataset contains more complex visual features and higher-dimensional structures, rendering it more susceptible to information degradation during indirect observation. In contrast, the MNIST dataset's simpler digit patterns and limited variability demonstrate greater resilience to quantization artifacts. This observation suggests that semantic communication systems handling complex, real-world data require more careful design of the observation process compared to those processing simpler, structured data.  
Figure 3: Experimental semantic rate-distortion functions for generation task. (**a**) Curve plots of semantic rate-distortion functions under different divergence metrics; (**b**) Surf plot of 3D semantic rate-distortion function.
## 4 Comparison Analysis  
In this paper, the proposed method is compared with the key component identification method based on the fault propagation intensity [38]. This method comprehensively considers the dual influence of the fault propagation structure characteristics of the CNC lathe and the reliability level of the system component itself. Based on the layered fault propagation directed graph of CNC lathe, the fault propagation cost was measured by selecting the fault impact degree of directed edge and edge mediums to find the key components that have great influence on the whole machine. According to the fault data of the above case, the hierarchical fault propagation directed graph is constructed as shown in Figure 6.  
The fault propagation intensity value of each directed edge in the graph is calculated as shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component** & \(\mathbf{f_{ASP\_i}}\) **(4000 h)** & **Rank** & \(\mathbf{f_{ASP\_i}}\) **(6000 h)** & **Rank** \\ \hline NC & 0.000284891 & 4 & 0.000278149 & 4 \\ F & 0.000078462 & 6 & 0.000071720 & 6 \\ M & 0 & 7 & 0 & 7 \\ E & 0.000538541 & 1 & 0.000509605 & 1 \\ S & 0.000337703 & 3 & 0.000320159 & 3 \\ T & 0 & 7 & 0 & 7 \\ H & 0.000434398 & 2 & 0.000404833 & 2 \\ A & 0.000199146 & 5 & 0.000184567 & 5 \\ B & 0 & 7 & 0 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Numerical control lathe fault propagation influence value and rank.  
Figure 6: Hierarchical model of fault propagation between components in CNC lathe.  
According to the ranking results in Table 8, the key components of the system at the current moment by the method based on the identification of key components of fault propagation intensity are the electrical system, followed by the hydraulic system, which is consistent with the identification results of key components based on the dynamic influence of fault propagation, verifying the correctness and reliability of the method proposed in this paper.  
Compared with the current methods and technologies, the method proposed in this paper mainly reflects the advantages of two aspects. On the one hand, the fault propagation structure model with each component of the system as the starting point can quickly and accurately analyze the fault propagation relationship of each component in the complex system. The operation program is relatively simple, and the fault propagation structure of each component is clearer. On the other hand, it provides a feasible and multi-faceted analysis method for the key component identification in view of the influence value of fault propagation, which is the core judgment criterion for identifying critical component and of great significance for ensuring system reliability.
## 5 Simulation Results
### Discussion of the Simulation Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**Scenario**} & \multirow{3}{*}{**No. Peak(s) of**} & **Method** & **Method** & **Method** & **Proposed in** \\  & & **Proposed in** & **Proposed in** & **Proposed in** & **this Study** \\ \cline{3-6}  & & **[3]** & **[18]** & **[24]** & **Average** \\ \cline{3-6}  & & **Average** & **Average** & **Tracking** & **Tracking** \\ \cline{3-6}  & & **Time** & **Time** & **Time** & **Time** \\ \hline
1 & Single-peak & 10.4 s & 8.6 s & 8.58 s & 3.4 s \\
2 & Double-peak & 19.5 s & 12.5 s & 20.01 s & 5.7 s \\
3 & Triple-peak & None * & 15.4 s & 31.3 s & 12.7 s \\
4 & Quadruple-peak & None * & 20.8 s & 24.21 s & 12.8 s \\ \hline \hline \end{tabular} Note: The symbol ”None *” indicates that this reference does not provide the test results of this scenario.  
\end{table} 
Table 8: Comparison of simulation test results for four scenarios with different MPPT methods. the MPPT tracking process is not significant because of its little variation in the shape of the P-V characteristic curve. In this paper, the maximum power tracking test has been conducted under five different shading conditions for the PVMA, and each of the shading conditions is equivalent to the change in the temperature and sunshine parameter so that it is identical to the robustness test of maximum power tracking (MPPT) under the consideration of parameter uncertainty in [43; 44]. Moreover, from the test results in Tables 7-9, the proposed improved FA MPPT method has better performance in tracking speed and efficiency than other methods for the location of the global maximum power point (GMPP) in different cases, so that it is evident that the proposed MPPT method is indeed robust.