esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Physics-Informed Neural Network in Dynamic System Simulation and Learning of Parameters
###### Abstract  
Artificial neural networks have changed many fields by giving scientists a strong way to model complex phenomena. They are also becoming increasingly useful for solving various difficult scientific problems. Still, people keep trying to find faster and more accurate ways to simulate dynamic systems. This research explores the transformative capabilities of physics-informed neural networks, a specialized subset of artificial neural networks, in modeling complex dynamical systems with enhanced speed and accuracy. These networks incorporate known physical laws into the learning process, ensuring predictions remain consistent with fundamental principles, which is crucial when dealing with scientific phenomena. This study focuses on optimizing the application of this specialized network for simultaneous system dynamics simulations and learning time-varying parameters, particularly when the number of unknowns in the system matches the number of undetermined parameters. Additionally, we explore scenarios with a mismatch between parameters and equations, optimizing network architecture to enhance convergence speed, computational efficiency, and accuracy in learning the time-varying parameter. Our approach enhances the algorithm's performance and accuracy, ensuring optimal use of computational resources and yielding more precise results. Extensive experiments are conducted on four different dynamical systems: first-order irreversible chain reactions, biomass transfer, the Brusselator model, and the Lotka-Volterra model, using synthetically generated data to validate our approach. Additionally, we apply our method to the susceptible-infected-recovered model, utilizing real-world COVID-19 data to learn the time-varying parameters of the pandemic's spread. A comprehensive comparison between the performance of our approach and fully connected deep neural networks is presented, evaluating both accuracy and computational efficiency in parameter identification and system dynamics capture. The results demonstrate that the physics-informed neural networks outperform fully connected deep neural networks in performance, especially with increased network depth, making them ideal for real-time complex system modeling. This underscores the physics-informed neural network's effectiveness in scientific modeling in scenarios with balanced unknowns and parameters. Furthermore, it provides a fast, accurate, and efficient alternative for analyzing dynamic systems.  
artificial neural networks; physics-informed neural networks; time-varying parameter; dynamical system modeling; COVID-19 Data; deep neural networks; error metrics +
Footnote †: journal:  
02033
# Data-Driven Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks
###### Abstract  
The successful rollout of fifth-generation (5G) networks requires a full understanding of the behavior of the propagation channel, taking into account the signal formats and the frequencies standardized by the Third Generation Partnership Project (3 GPP). In the past, channel characterization for 5G has been addressed mainly based on the measurements performed on dedicated links in experimental setups. This paper presents a state-of-the-art contribution to the characterization of the outdoor-to-indoor radio channel in the 3.5 GHz band, based on experimental data for commercial, deployed 5G networks, collected during a large scale measurement campaign carried out in the city of Rome, Italy. The analysis presented in this work focuses on downlink, outdoor-to-indoor propagation for two operators adopting two different beamforming strategies, single wide-beam and multiple synchronization signal blocks (SSB) based beamforming; it is indeed the first contribution studying the impact of beamforming strategy in real 5G networks. The time and power-related channel characteristics, i.e., mean excess delay and Root Mean Square (RMS) delay spread, path loss, and K-factor are studied for the two operators in multiple measurement locations. The analysis of time and power-related parameters is supported and extended by a correlation analysis between each pair of parameters. The results show that beamforming strategy has a marked impact on propagation. A single wide-beam transmission leads, in fact, to lower RMS delay spread and lower mean excess delay compared to a multiple SSB-based transmission strategy. In addition, the single wide-beam transmission system is characterized by a smaller path loss and a higher K-factor, suggesting that the adoption of a multiple SSB-based transmission strategy may have a negative impact on downlink performance.  
radio channel; outdoor-to-indoor environment; 5G; 3.5 GHz band +
Footnote †: journal:  
00184 Rome, Italy
# Data Naming Mechanism of LEO Satellite Mega-Constellations for the Internet of Things
## 6 Conclusions  
The LEO mega constellation facing the IoT is a new and important direction for the development of satellite internet. From the perspective of improving transmission performance, this paper proposes a hierarchical data naming mechanism based on the idea of ICN. The hierarchical nature of the name allows for the naming of content and services, and the naming mechanism also integrates names based on attribute-value pairs to help service identification and add more semantic ways to the name to meet the needs of multiple services in the IoT. In addition, prefix notation is used to overcome the unbounded nature of ICN names. System performance was verified by designing and developing an NS-3-oriented LEO mega constellation simulation platform for the IoT. Simulation results show that, compared with the IP address, the ICN-HMcH naming mechanism can increase throughput by as much as 54% and reduce the transmission delay of the LEO mega satellites for the IoT by 53.97%. The proposed data naming mechanism can provide high Quality of Service (QoS) transmission performance for the IoT-oriented LEO mega constellation and performs markedly better in terms of transmission performance compared to IP. In future research, in-network caching strategies, routing and forwarding strategies should be investigated.  
Conceptualization, S.H. and M.X.; methodology, M.X.; software, H.L.; validation, M.X., H.L. and Y.S.; formal analysis, T.Y.; investigation, M.X.; resources, Y.S.; data curation, H.L.; writing--original draft preparation, M.X.; writing--review and editing, S.H.; visualization, H.L.; supervision, S.H.; project administration, S.H.; funding acquisition, S.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the NSFC under award number 6156010183 and by Guizhou Province Education Department Projects of China (KY [2017]031 and KY [2020]007).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results
### Evaluation of Classification Accuracy Based on XGBoost for Different Altitude Tree Species
#### 3.4.1 Altitude 1600 m  
As shown in Table 10, in the area above 1600 m, the user accuracy (UA) of coniferous species ranged from 81.82% (_Larix gmelini_) to 100% (_Cunninghamian lancoolata_). Meanwhile, for broad-leaved species, the UA ranged from 53.13% (_Betula platyphylla_) to 94.29% (_Betula albosinensis_), indicating a higher classification accuracy of coniferous species compared to broad-leaved species. In addition, as indicated by the classification results in Figure 5, it was found that the producer accuracy (PA) suggested better accuracy for coniferous species compared to broad-leaved species. The PA accuracy for coniferous species ranged from 97.37% (_Cunninghamian lancoolata_) to 100% (_Pinus armandii_), while for broad-leaved species, it ranged from 75% (_Betula albsinensis_) to 89.47% (_Betula platyphylla_).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Species** & **P.arm** & **L.gme** & **B.pla** & **C.cor** & **C.** & **B.alb** & **Other** & **UA (\%)** \\ \hline Param & 32 & 0 & 0 & 1 & 0 & 1 & 0 & 94.12 \\ L.gme & 0 & 27 & 0 & 2 & 0 & 0 & 4 & 81.82 \\ B.pla & 0 & 0 & 17 & 3 & 0 & 8 & 4 & 53.13 \\ C.cor & 0 & 0 & 2 & 43 & 0 & 0 & 3 & 89.58 \\ C. & 0 & 0 & 0 & 0 & 37 & 0 & 0 & 100.00 \\ B.alb & 0 & 0 & 0 & 0 & 1 & 33 & 1 & 94.29 \\ Other & 0 & 0 & 0 & 3 & 0 & 2 & 59 & 92.19 \\ PA (\%) & 100.00 & 100.00 & 89.47 & 82.69 & 97.37 & 78.00 & 83.10 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Classification confusion matrix of tree species at altitude of 1600 m based on XGBoost.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & \multicolumn{2}{c}{**Altitude 1600**} & \multicolumn{2}{c}{**Altitude 1800**} & \multicolumn{2}{c}{**Altitude 2000**} \\  & **OA\%** & **Kappa** & **OA\%** & **Kappa** & **OA\%** & **Kappa** \\ \hline RF & 86.22 & 0.84 & 87.50 & 0.85 & 81.25 & 0.77 \\ SVM & 86.93 & 0.84 & 87.87 & 0.86 & 83.68 & 0.80 \\ KNN & 82.69 & 0.79 & 84.93 & 0.82 & 82.64 & 0.79 \\ XGBoost & 87.63 & 0.85 & 88.24 & 0.86 & 84.03 & 0.81 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Classification results of optimal characteristics of tree species at different altitudes.
## 3 Blasting Test and Vibration Safety Evaluation  
### Determination of the Blast Hole Type for Greatest Influence on Pipeline  
To evaluate the adverse effects of vibrations caused by upper bench blasting on pipeline safety, it is necessary to search the type and location of blast holes that have the greatest impact on pipelines. Based on a large number of in-situ interlaid rock blasting tests, [PERSON] et al. [33] studied the blasting vibration law of different blast holes in different areas. When the other blast holes are detonated, there are two or more free surfaces, and the energy decays faster than that at the cut holes. The energy of the cut holes remains higher than that of other holes after attenuation over a certain distance. Therefore, in long-range blasting, the PPV generated by cut holes is often the highest. But when the distance between the excavation face and the research object is close enough, the spacing of the blast holes will become a determining factor. The maximum PPV was induced by the blasting of the nearest holes (i.e., peripheral holes) in the near field of the tunnel.  
The near and far fields of the blasting division are generally divided by a scaled distance (\(SD\)), which is defined as follows:  
\[SD=\frac{R}{Q^{n}} \tag{1}\]  
where \(R\) is the distance from the blasting face to the measuring point (m). \(Q\) is the maximum simultaneous explosive charge (typically includes multiple holes) per delay (kg), and \(n\) is the attenuation index, which is related to the form of the explosive package. For columnar charges, \(n=1/3\) is generally used. When \(SD\leq 5.0\), the blasting is considered to be near-field, and when \(SD>5.0\), it is considered far-field. Specifically, when it is in the far field of the explosion source, the peak value of the vibration velocity is mainly affected by the blasting of the cutting holes with a large charge. In the near-field of the explosion source, the distance becomes the most important influencing factor, and the peak value of the vibration velocity is mainly affected by the blasting of the peripheral holes closest to the monitoring position.  
In this project, for cut holes, \(R=4.0\) m, \(Q=0.9\) kg, \(SD=4.143\); for peripheral holes, \(R=2.3\) m, \(Q=0.6\) kg, \(SD=2.727\). Irrespective of whether they are cut holes or peripheral holes, because \(SD<5\), the pipeline is located near the field of tunnel blasting. Therefore, the pipeline was primarily affected by the impact of the explosion on the closest peripheral holes [34].
# Timed-SAS: Modeling and Analyzing the Time Behaviors of Self-Adaptive Software under Uncertainty
## 5 Experiment Evaluation
### Experiment Results and Discussion
#### 5.2.2 Experiment Results  
Considering the experiment objective mentioned at the beginning of this experiment, i.e., evaluating the effectiveness and efficiency of the templates created in the Timed-SAS approach, we discuss the experiment results in this subsection.  
**Effectiveness.** The effectiveness is examined from the template reusability. According to Tables 4 and 5, the templates created in the Timed-SAS approach can be directly instantiated and reused and can satisfy the modeling and formal analysis requirements of the general SAS systems. In addition, from the application in Sections 4.2.3 and 4.3.2, it can be seen that the templates help to reduce modeling and formal verification difficulties of the time behaviors, and to optimize the self-adaptation logic. Thus, the templates created in the Timed-SAS approach are effective.  
**Efficiency.** Similarly, the efficiency depends on the formal modeling and analysis time with the templates. According to Table 4, the average working time when using the templates was shortened by 61.24%, compared with directly using the NPTA model. Thus, the Timed-SAS approach has a distinct advantage in improving the formal modeling and analysis efficiency of the SAS time behaviors.
## 5 Simulation Results
### Performance Evaluation of Proposed Method 1 with SSD-MobileNet Model  
We measured the predicted inference latency of the proposed method 1 in the SSD-MobileNet model and now compare it to the actual inference latency for the edge and cloud. Figures 14 and 15 show the actual and predicted inference latency with the SSD-MobileNet model for the cloud and edge, respectively. Overall, as shown in Figures 14 and 15, it can be observed that the predicted inference latency with the SSD-MobileNet model reflects the actual inference latency for the edge and cloud well. Therefore, it can be also confirmed that the proposed inference latency prediction approach is suitable for object detection.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**RMSE** & **Cloud** & **Edge** \\ \hline gRPC & 3.259 & 28.300 \\ REST & 5.372 & 15.554 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The RMSE performance of method 1 with the RFCN model for the edge and cloud.  
Figure 13: The actual and predicted inference latency of method 1 with the RFCN model for the edge: (**a**) gRPC; (**b**) REST.  
The SSD-MobileNet model has a tendency that is different from the results of the RFCN model because the difference in inference latency between the cloud and edge is small. In the SSD-MobileNet model, there is a large difference in terms of inference latency depending on the communication protocol rather than the difference in inference latency according to the device. When comparing the results of gRPC and REST, the similarity between the predicted and actual inference latency while using REST is greater than that while using gRPC. However, the RMSE of gRPC is much smaller than that of REST, as shown in Table 7.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**RMSE** & **Cloud** & **Edge** \\ \hline gRPC & 0.285 & 0.606 \\ REST & 0.689 & 1.705 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The RMSE performance of method 1 with the SSD-MobileNet model for the edge and cloud.  
Figure 14: The actual and predicted inference latency of method 1 with the SSD-MobileNet model for the cloud: (**a**) gRPC; (**b**) REST.  
Figure 15: The actual and predicted inference latency of method 1 with the SSD-MobileNet model for the edge: (**a**) gRPC; (**b**) REST.  
There is a trade-off relationship between gRPC and REST in the SSD-MobileNet model. The gRPC protocol has a lower inference latency and RMSE performance compared to the REST protocol, but it has a lower similarity between the prediction and the actual inference latency. Conversely, the REST protocol has a high inference latency and the RMSE performance compared to the gRPC protocol, but it has a high similarity between the prediction and the actual inference latency. When building a system environment, it is necessary to consider this trade-off relationship.
## 5 Results and Analysis
### Simulation Settings  
Table 1 displays the parameters for the simulation environment. In the scenario of an Industrial Internet intelligent factory, assume a flat square area of size 100 \(\times\) 100 m\({}^{2}\) with \(N\) small base stations of edge servers and \(M\) mobile smart industrial terminal devices randomly distributed within the area. The transmission bandwidth \(B\) of the subcarrier denotes 1 MHz. The channel gain at a reference distance of one meter is \(\alpha_{0}=-50\) dB, and the system noise power of the receiver is \(\delta^{2}=-100\) dBm. The minimum number of subcarriers is \(w^{\text{min}}=1\), and the maximum number of subcarriers is \(w^{\text{max}}=10\). The minimum transmission power \(p_{\text{min}}\) of the subcarrier is 1 w, and the maximum transmission power \(p_{\text{max}}\) of the subcarrier is 10 w. The total data size of the task chain \(I\) is 2 Mbits, and the value range of the task data volume \(I^{a}\) is randomly distributed in [60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70] Kbits. We set the minimum CPU clock frequency for the terminal device \(f^{\text{min}}\) is 0.1 GHz, and the maximum clock frequency \(f^{\text{max}}\) is 0.2 GHz. The quantity of CPU cycles needed to process one-bit  
data \(s\) is denoted by 1000 cycles/bit. The effective capacitance parameter is \(\kappa_{u}=10^{-27}\)[49]. Finally, we set the \(T_{t}^{\max}\) is 100 ms [50].  
In addition, in the IF-DDPG algorithm, the experimental setting discount factor \(\gamma\) is 0.001, the \(\tau\) of the target network is 0.01, and the reward parameter \(C\) is 100. The experiment uses TensorFlow 2 as the learning framework of IF-DDPG on a PC (CPU: AMD Ryzen 7 6800H, 16 GB RAM).  
The four algorithms compared in this paper are as follows:  
* DQN-based edge computing offloading and resource allocation algorithm: A comparison between the DQN based on discrete action space and IF-DDPG [51].
* AC-based edge computing offloading and resource allocation algorithm: A comparison between AC based on continuous action space and IF-DDPG [52].
* Dueling DQN-based edge computing offloading and resource allocation algorithm (DDQN): DDQN, in contrast to DQN, stores the experience sample data from the agent's interactions with the environment in an experience pool. A tiny batch of data from the experience pool is chosen at random for training [13].
* DDPG-based edge computing offloading and resource allocation algorithm: A comparison between the traditional DDPG without incorporating FL and IF-DDPG. The above comparison algorithms are similar to IF-DDPG, which are all DRL algorithms.
## 1 Introduction
Then, a series of image preprocessing algorithms were used to obtain the image edge contour. Finally, the image edge contour was measured via geometric algorithm, and thus the bottom radius of the cylinder contour and the height of the column were obtained. [PERSON] et al. [23] proposed a method for measuring the straightness, roundness and cylindricity of workpieces based on machine vision. A linear search algorithm and improved particle swarm optimization algorithm were proposed to evaluate the straightness and roundness deviation of workpieces. On this basis, a method to evaluate image cylindricity deviation using least squares fitting of circular center coordinates was studied. [PERSON] et al. [24] proposed a measurement method of cylindrical shaft diameter based on machine vision. [PERSON] et al. [25] proposed an axis diameter measurement method based on digital image processing technology. External parameters were calibrated by measuring the axis with known diameters, and new external parameters were used to improve measurement accuracy. However, with regard to coaxiality measurement, related applications and relevant research are scarce. Therefore, aiming at addressing the problem of the online detection of automobile brake piston components, this paper proposes a non-contact coaxiality measurement method based on machine vision and digital image processing technology. In order to obtain real-time images of automobile brake piston components, an image acquisition system is designed. Through a written algorithm program, the precise extraction of the geometric feature points of automobile brake piston components is realized, which reduces the manual operation, improves detection efficiency and accuracy, and lays a foundation for the on-line detection of the coaxiality of the stepped shaft parts. The rest of this article is organized as follows: Section 2 describes in detail the detection principle, image preprocessing process, and the method of extracting geometric feature points. Section 3 analyzes and discusses the experimental results, potential future research  
Figure 1: Automobile brake piston component. areas, and the reproducibility and scalability of other shaft parts. Section 4 summarizes the conclusions of this study.
# An Improved Energy-Efficient Routing Protocol for Wireless Sensor Networks
## 4 Simulation Results
### Residual Nodes Energy  
In this test, the effect of the residual nodes energy on the performance is shown. The residual energy of all SNs in the network for the protocols is illustrated in Figure 7. The proposed IEE-LEACH exhibits a slower decrease as the number of rounds increase than a variety of routing protocols. All nodes in the existing LEACH, LEACH-M, O-LEACH, LEACH-C, EE-LEACH, and SEEN protocols are dead after 2267, 2492, 2972, 2422, 3053, and 2889 rounds, respectively. However, all nodes in the proposed IEE-LEACH are dead after 3242 rounds. It can be seen that the new proposed threshold that considers the initial energy of nodes, residual energy of nodes, total energy of the network, and average energy of all nodes increases the network lifetime by about 6% to 43% compared with other routing protocols. Therefore, the strategy presented by the proposed IEE-LEACH protocol can effectively reduce the dissipated energy and extend the lifetime of the network.
## 4 Data Preprocessing
### Outliers  
Time-series data can be affected by isolated events, perturbations, or errors that create inconsistencies and produce unusual data that is not consistent with the general behavior of the time series; those unusual data points are called outliers. Outliers may result from external events, measuring, or recording errors (see [20]). There are two types of outliers: global and local. Figure 9 shows global (squares) and local outliers (circles). Different methods exist to detect outliers; in this work, we use the three-sigma method for detecting the global outliers and the local outlier factor (LOF) method for detecting the local ones.