esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Studies
### All-Island Irish Transmission System  
In this case study, we consider a real-world model of the AIITS. The topology and the steady-state operation data of the system have been provided by the Irish transmission system operator, EirGrid Group, whereas the dynamic data have been defined based on our knowledge about the technology of the generators and the controllers. The dynamic model has been also validated while using frequency data from a severe event that occurred in the real system in 2018, see [71]. The system consists of 1479 buses, 796 lines, 1055 transformers, 245 loads, 22 synchronous machines, with Automatic Voltage Regulators (AVRs) and Turbine Governors (TGs), 6 Power System Stabilizers (PSSs), and 176 wind generators. In total, the dynamic model has \(n=1443\) state variables and \(m=7197\) algebraic variables.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Library**} & \multicolumn{4}{c}{**Data Formats**} & \multicolumn{3}{c}{**Computing**} & \multirow{2}{*}{**2-Sided**} & \multirow{2}{*}{**Real/**Complex**} & \multicolumn{2}{c}{**Releases**} \\ \cline{2-2} \cline{6-11}  & **Dense** & & & & & & & & **First** & **Latest** \\ \hline LAPACK & ✓ & ✗ & ✗ & ✗ & ✓ \({}^{a}\) & ✓ \({}^{b}\) & ✓ & ✓ & 1992 & 2016 \\ ARPACK & ✗ & ✗ & ✗ & ✓ & ✗ & ✓ & ✗ & ✓ & 1995 & 2019 \({}^{c}\) \\ SLEPc & ✓ & ✓ & ✗ & ✓ & ✓ & ✓ & ✓ \({}^{d}\) & ✓ & 2002 & 2020 \\ Anasazi & ✓ & ✓ & ✗ & ✗ & ✗ & ✗ & ✗ & ✓ & 2008 & 2014 \\ FEAST & ✓ & ✓ & ✓ & ✗ & ✓ & ✓ & ✓ & 2009 & 2020 \\ z-PARES & ✓ & ✓ & ✗ & ✓ & ✗ & ✓ & ✗ & ✓ & 2014 & 2014 \\ \hline \hline \end{tabular}  
* With MAGMA. \({}^{b}\) With ScaLAPACK. \({}^{c}\) Now as ARPACK-NG. \({}^{d}\) In SLEPc, only the power and the Krylov–Schur methods are two-sided.  
\end{table}
Table 4: Relevant features of open-source libraries for non-symmetric eigenvalue problems.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Library (Version)** & **Dependencies (Version)** \\ \hline LAPACK (3.8.0) & ATLAS (3.10.3) \\ MAGMA (2.2.0) & NVidia CUDA (10.1) \\ ARPACK-NG (3.5.0) & SuiteSparse KLU (1.3.9) \\ z-PARES (0.9.6a) & OpenMPI (3.0.0), MUMPS (5.1.2) \\ SLEPc (3.8.2) & PETSc (3.8.4), MUMPS (5.1.2) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Versions and dependencies of open-source libraries for non-symmetric eigenvalue problems.  
The results of the eigenvalue analysis of the AIITS are discussed for both LEP and GEP and for a variety of different numerical methods, namely, QR and QZ algorithms by LAPACK, GPU-based QR algorithm by MAGMA, subspace iteration, ERD-Arnoldi, and Krylov-Schur methods by SLEPc, IR-Arnoldi by ARPACK; and, CI-RR by z-PARES. In particular, we provide, for each method, the rightmost eigenvalues, as well as the time that is required to complete the solution of the eigenvalue problem. The dimensions of the LEP and GEP for the AIITS are shown in Table 6.
## 2 Method
### Preliminary Building Extraction by CNN
#### 2.2.1 Convolution Layer  
Each convolutional layer can obtain multiple feature maps, and generating one feature map includes two processes: convolution and nonlinear activation. The convolution process consists of two parts, the convolution kernel and a bias parameter, which are shared during the generation of a featured image [34]. For an N-channel image of size W\(\times\)H, using a two-dimensional convolution kernel of size \(w_{f}\times h_{f}\), the convolution calculation at \(x(ii,\ jj)\) is as follows:  
\[x_{k}(i,j)=\sum_{n=1}^{N}\left\{\sum_{p=1}^{w_{f}-1}\sum_{q=1}^{h_{f}-1}x_{n} \Big{(}i\cdot s_{f}+p,j\cdot s_{f}+q\Big{)}\cdot h_{k}(p,q)\right\}+b_{k} \tag{17}\]  
where \(x_{k}(ii,jj)\) is the result of the value obtained by the \(k\)th convolution kernel, \(x_{n}(i,j)\) is the pixel value of the (\(i\),\(j\)) position of the n channel, and \(h_{k}\) (\(p\),\(q\)) is the filter kernel (\(p\),\(q\)) the weight of the position, \(b_{k}\) is the weight value of the \(k\)th convolution kernel, and \(s_{f}\) is the stride of the convolution kernel movement. The size of the obtained feature map is \(\Big{(}\Big{(}W-w_{f}\Big{)}/s_{f}+1\Big{)}\times\Big{(}\Big{(}H-h_{f}\Big{)}/s _{f}+1\Big{)}\).  
The nonlinear activation process enhances the nonlinear characteristics [34]. It is necessary to use a nonlinear activation function. The process takes \(x_{k}(ii,jj)\) as an input. It is activated by a nonlinear function to obtain an activation value, which constitutes the final feature image. The specific formula is as follows:  
\[Z(x_{k}(ii,jj))=f(x_{k}(ii,jj)) \tag{18}\]  
where the nonlinear activation function f(\(\cdot\)) is in many forms, such as sigmoid, tanh, and ReLU. Because the ReLU function can better solve the gradient disappearance problem in the training process compared to other activation functions, it can make the network sparse, reduce the parameter interdependence, and alleviate the over-fitting problem [35]. Therefore, we use the ReLU function as the nonlinear activation in this paper. Its expression formula is as follows:  
\[\text{ReLU}(x)=\left\{\begin{array}{cc}x&x>0\\ 0&x\leq 0\end{array}\right. \tag{19}\]
## 2 Output Saturation Limits for Classical Stochastic Resonance Models
### Output Saturation Limit  
Temporarily disregarding the impact of the input signal and input noise on the system, the bistable system's approximate Langevin equation at this juncture is [16]  
\[x^{{}^{\prime}}=ax-bx^{3} \tag{8}\]  
Solving this differential equation gives an expression for the amplitude of the output \(x(t)\) of the system,  
\[\left|x(t)\right|=\sqrt{\frac{a}{b+e^{-2 at}}} \tag{9}\]  
From Equation (9), when the system stabilizes, i.e., at time \(t\rightarrow\infty\),  
\[\left|x(t)\right|_{t\rightarrow\infty}=\sqrt{\frac{a}{b}} \tag{10}\]  
In the adiabatic approximation, the output amplitude of the bistable system is influenced by the system parameters \(a\) and \(b\). When the system parameter \(b=1\), there is a saturation value. Figure 3 shows how the system output amplitude changes with the variation of parameter \(a\) while parameter \(b\) is fixed at 1. Similarly, Figure 4 illustrates the change in the system output amplitude with the variation of parameter \(b\) while parameter \(a\) is set to 1.  
It is apparent from Figures 3 and 4 that both system parameters \"\(a\)\" and \"\(b\)\" of the bistable system impact the saturation value of the output of the stochastic resonance. However, it is not possible to alter the nature of the saturation of the output, and the output saturation value does not change significantly with the system parameters. Therefore, the performance of the output of the stochastic resonance cannot be significantly improved by changing the system parameters.  
Figure 3: Stochastic resonance output amplitude for different parameter \(a\) values.  
The output of the stochastic resonance system is equivalent to the displacement trajectory of the equivalent proton. The motion of the equivalent proton is equivalent to the process of converting mechanical energy. To analyze the saturation of the system output, we need to start by examining the system's potential function curve. We analyze the slope of the potential function curve as  
\[U^{\prime}(x)=-ax+bx^{3} \tag{11}\]  
From Equation (11), it can be observed that the slope of the potential function curve contains cubic terms. This means that the slope of the curve will increase significantly as the displacement increases, as illustrated in Figure 5. After crossing the positive and negative stability points, the displacement increase will result in a substantial increase in potential energy. However, the constraints of the adiabatic approximation conditions cause the input signals and the noise to be of a smaller value, and the energy may not meet the demand of a significant difference in potential energy. As a result, the output of the system will tend to become saturated.  
Figure 4: Stochastic resonance output amplitude for different parameter \(b\) values.  
Figure 5: Potential function curve saturation analysis.  
From the above analysis, it can be seen that the shape of the system potential well has a significant effect on the output performance of the system, and the different performances of the system can be improved by adjusting the system's potential function.
## 3 Routing Protocols for UWSN  
Any routing protocol in UWSN depends on different metrics to route the sensed data from sensor node to the sink. The performance of the routing also varies depending on these metrics. For example, some routing protocols compare the RSSI values of the signals of its neighboring nodes and forward the data to the node that has better RSSI values. This type of routing protocol always maintains a good link quality while forwarding the data but it does not take energy balancing into consideration. Some other routing protocols may depend on depth information of the neighboring nodes while data forwarding. These protocols transfer the data to its neighboring node which has the lowest depth from the water surface. These type of routing protocols may have better energy efficiency and lower overheads as it always finds the shortest path to forward the data but their delivery ratio is usually poor as they always choose the distant nodes which may not always have good link quality.  
As already mentioned, knowing the location information of the underwater sensor nodes is one of the challenging issues in terms of accuracy, energy and cost-efficiency. Some researchers have used the location information for defining their routing protocols whereas some other routing protocols do not need the location information of the sensor nodes to route the data from the source to the sink. Depending on whether the routing protocol needs the location information for routing or not, all the routing protocols can be divided broadly into two categories--1. Location-based scheme 2. Location free scheme [49, 50].  
It is also a fact that every routing protocol cannot address all the challenges and issues altogether. Rather, different routing protocols address different challenging issues and improve them. For example, some routing protocols perform energy balancing among all the nodes of the network which improves the network lifetime and does not create void holes in the network. Besides, some other routing protocols may emphasize the reliability and the data transmission of the network with highest priority ignoring the fact of energy balancing. So, all the routing protocols can also be categorized into a few types depending on which of the routing metrics they are emphasizing or featuring [46]. In this classification, one routing protocol can be listed in two or more categories if they feature more than one challenging issue but authors have classified the schemes mutually exclusively as the schemes surely prioritize one factor over another. Based on the features, all the considered protocols are divided into five types--protocols featuring node mobility, energy balancing, channel conditioning, energy efficiency and network void hole avoidance. Table 2 depicts the classification of the routing protocols based on both localization and the features they are offering. The listed protocols are described categorically with their working procedure, key features, improvements and shortcomings as follows.
# A Novel Interpretable Deep Learning Model for Ozone Prediction
###### Abstract  
Due to the limited understanding of the physical and chemical processes involved in ozone formation, as well as the large uncertainties surrounding its precursors, commonly used methods often result in biased predictions. Deep learning, as a powerful tool for fitting data, offers an alternative approach. However, most deep learning-based ozone-prediction models only take into account temporality and have limited capacity. Existing spatiotemporal deep learning models generally suffer from model complexity and inadequate spatiality learning. Thus, we propose a novel spatiotemporal model, namely the Spatiotemporal Attentive Gated Recurrent Unit (STAGRU). STAGRU uses a double attention mechanism, which includes temporal and spatial attention layers. It takes historical sequences from a target monitoring station and its neighboring stations as input to capture temporal and spatial information, respectively. This approach enables the achievement of more accurate results. The novel model was evaluated by comparing it to ozone observations in five major cities, Nanjing, Chengdu, Beijing, Guangzhou and Wuhan. All of these cities experience severe ozone pollution. The comparison involved Seq2 Seq models, Seq2 Seq+Attention models and our models. The experimental results show that our algorithm performs 14% better than Seq2 Seq models and 4% better than Seq2 Seq+Attention models. We also discuss the interpretability of our method, which reveals that temporality involves short-term dependency and long-term periodicity, while spatiality is mainly reflected in the transportation of ozone with the wind. This study emphasizes the significant impact of transportation on the implementation of ozone-pollution-control measures by the Chinese government.  
recurrent neural network; O\({}_{3}\) forecasting; attention mechanism; spatiotemporal information +
Footnote †: journal: Applied Sciences
## 3 Experimental Content and Methodology
### Monitoring Points
#### 3.1.1 Temperature Measuring Points  
A JMT-36B semiconductor temperature sensor is used to measure and collect the temperature of the structure under the action of daily temperatures. The Jmt-36B temperature sensor uses the conductivity of semiconductor (PN junction) to change regularly with the change of temperature. The PN junction is packaged as a chip to achieve high precision, high reliability and high stability of the temperature measurement [25]. At the mid-span section of the tested beam, a total of seven rows of temperature sensors are buried in the upper, middle and lower parts of each layer of the ballastless track system, and the data were collected every 30 min. The specific layout of the measuring points is shown in Figure 3.
## 2 Materials and Methods  
### Microalgal Strain and Medium  
_E. gracilis_ LIMS-1351 was obtained from the Library of Marine Samples (Geoje-si, Korea) and subcultivated under photoautotrophic cultivation conditions in 75 cm\({}^{2}\) cell culture flasks (SPL Life Sciences, Pocheon-si, Korea). The subcultivations were carried out using an initial inoculum concentration of 5 \(\times\) 10\({}^{4}\) cells mL\({}^{-1}\) at a temperature of 27 \({}^{\circ}\)C with hand shaking on a daily basis. The light sources were fluorescent lamps with an intensity of 80 umol g m\({}^{-2}\) s\({}^{-1}\). Light was continuously provided without a dark period. Modified Hutter medium (HUT) or modified Cramer-Myers medium (CM) were used as the synthetic medium. The chemical composition of the HUT was based on a previous study [9]. The chemical composition of the CM in 1 L was as follows: 1 g KH\({}_{2}\)PO\({}_{4}\), 1 g (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\), 0.2 g MgSO\({}_{4}\).7H\({}_{2}\)O, 0.2 g CaCl\({}_{2}\).2H\({}_{2}\)O, 0.758 g EDTA-2 Na\(\cdot\)2H\({}_{2}\)O, 3 mg Fe(SO\({}_{4}\))\({}_{2}\)\(\cdot\)6H\({}_{2}\)O, 1.8 mg MnCl\({}_{2}\)\(\cdot\)4H\({}_{2}\)O, 1.5 mg CoSO\({}_{4}\).7H\({}_{2}\)O, 0.4 mg ZnSO\({}_{4}\).7H\({}_{2}\)O, 0.2 mg Na\({}_{2}\)MoO\({}_{4}\).2H\({}_{2}\)O, 0.02 mg CuSO\({}_{4}\).5H\({}_{2}\)O, 100 \(\mu\)g vitamin B\({}_{1}\), and 0.5 \(\mu\)g vitamin B\({}_{12}\). Stock solutions were autoclaved at 121 \({}^{\circ}\)C for 15 min and stored at 4 \({}^{\circ}\)C until use. The pH of the medium from the mixed stock solutions was adjusted to pH 3.5 using 2 M hydrochloric acid (HCI). The medium was filtered through a vacuum filtration unit of 0.22 \(\mu\)m pore size (Guangzhou Jet Bio-Filtration Co., Ltd., Guangzhou, China).
## 2 Background
### End-to-End DDPG  
In this study, we designed a model-free end-to-end DRL framework based on the DDPG algorithm, as illustrated in Figure 2. The DDPG algorithm is a DRL method based on the Actor-Critic framework, well-suited for decision making in continuous action spaces.  
Within this framework, we integrated steps such as state acquisition, feature extraction, reward adjustment, DDPG-network updating, decision making, and environmental interaction. Initially, in the state acquisition phase, to comprehensively capture three-dimensional spatial and geometric information, we employed the Kinect depth camera for data perception. The Kinect is capable of collecting multimodal signals, including color and depth images [31]. Initially designed for indoor human-computer interaction, it has been successfully applied in various automation scenarios. As described in [32], algorithms for contour and spatial positioning of planar shapes can be detected using Kinect. Figure 3 shows RGB and depth images captured at 640 \(\times\) 480 px resolution in our work, used for extracting features such as obstacles, current location, and target positioning. This step is crucial for processing high-dimensional input data and extracting key information.  
Subsequently, in the reward-adjustment phase, we introduced an FRS to dynamically adjust and optimize the reward values. This system generates fuzzified rewards based on the current state and actions of the robotic arm, as well as environmental feedback. The network-updating phase is the core of the DDPG algorithm. In this phase, the Actor network is responsible for generating decision actions, while the Critic network evaluates the expected return of these actions. The algorithm continuously learns and optimizes the control strategy for the robotic arm by minimizing the Critic network's loss function and updating the Actor network using policy gradients. Finally, in the decision making and  
Figure 2: Model-free end-to-end DRL framework.  
environmental interaction phase, the algorithm interacts with the environment based on actions generated by the Actor network and learns from the environmental feedback. This process continues in a loop until the algorithm converges and identifies the optimal control strategy. The following is the pseudocode for this Algorithm 1.  
Figure 3: RGB-D images captured using Kinect in the simulation environment; (**a**,**c**,**e**) are RGB images and (**b**,**d**,**f**) are grayscale maps representing depths.
# AI-Driven Pilot Overhead Reduction in 5G mmWaveMassive MIMO Systems
## 3 Results
### Random Forest Regression NNSVD with QPSK Moduation  
The next manipulated parameter in our simulation is the modulation technique. To enhance the realism of the simulation, we expanded the modulation schemes to include quadrature phase-shift keying (QPSK) instead of BPSK. This scheme reflects practical scenarios in mmWave communication systems, enabling us to analyze the impact of pilot overhead reduction across varying levels of modulation complexity.  
Figure 22 shows a decrease in the pilot overhead of the system after applying QPSK, where size decreased from around 62 to around 53.7. This is expected due to the fact that BPSK transmits fewer bits per symbol, so it requires more symbols to carry the same amount of information.  
Figure 23 depicts that with QPSK, the system gives a higher BER than BPSK, which is logical since BPSK uses only two symbols (0 and 1), making it more robust against noise and interference at lower SNR, while QPSK uses four symbols (00, 01, 10, and 11). While this increases the rate of data transmission, it also makes the system more susceptible to noise, as the symbols are closer in the constellation. It is clear that at a lower SNR of \(-10\) dB, the BER was \(1.05\times 10^{-4}\) using BPSK and became \(1.65\times 10^{-3}\) at the same SNR.  
Figure 22: RFR-NNSVD QPSK pilot overhead vs. SNR.  
As for the SE study using QPSK modulation instead of BPSK, the results are shown in Figure 24. It is clear that there is an increase in SE, reaching \(55\)\(\mathrm{bps}/\mathrm{Hz}\) at \(30\) dB compared to \(40\)\(\mathrm{bps}/\mathrm{Hz}\) using BPSK modulation.  
The results obtained from varying the number of Tx and Rx antennas and from changing the modulation technique from BPSK to QPSK clearly shows that the system proposed in our work reflects practical scenarios in mmWave communication systems, enabling us to reduce the pilot overhead across varying levels of antenna numbers and modulation complexity and still reach realistic and improved results.
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using Off-Policy
#### 3.2.1. Motion Planning  
In [69], the authors propose a framework for collision avoidance called \"_GPU/CPU Asynchronous Advantage Actor Critic for Collision Avoidance with Deep RL (GA3C-CADRL)_\". This approach helps robots avoid collisions in unpredictable multiagent environments, overcoming the challenge of conventional feedforward neural networks' fixed input size. To overcome that, the authors used Long Short-Term Memory (LSTM) networks (for more info about LSTM, please refer to [90]), which excels in processing large amount of information efficiently. In this work, an ROS was used to integrates sensors, communication, and hardware control. This ensured smooth execution of the GA3C-CADRL algorithm on a physical robot.  
Figure 7: ROS-based information flow architecture. For more information, please refer to Table 4.  
The authors in [69] implemented their approach in simulations and in a real-world experiment. This work is pivotal for collision avoidance, as it introduces a learning approach independent of specific behaviors and an architecture based on LSTM to enhance robot performance. The LSTM approach improves the collision avoidance algorithm by eliminating the need for predefined predictions about agents' behavior, enhancing their robustness and adaptability as interactions become more complex. The simulation results demonstrate the effectiveness and scalability of GA3C-CADRL, particularly in scenarios with a large number of agents where traditional methods struggle. This approach consistently outperformed the previous SA-CADRL approach in environments with more than four agents. For more information on the hardware and software used, please see Table 4.  
The above-mentioned work highlights the potential of addressing complex human behavior prediction tasks in multiagent systems. It shows that their approach can work in a real-world application with a less expensive sensing suite. Their method also demonstrated robustness in various scenarios, including crowded environments and with sudden changes in agent behavior. In general, this research establishes the foundation for autonomous systems to navigate complex social interactions effectively, enhancing their adaptability and responsiveness in dynamic environments.