esa-sceva/satcom-chunk-collection huggingface dataset:
# Robustness Analysis on Graph Neural Networks Model for Event Detection
## 5 Experiments
### Model Performance on the Adversarial Data
#### 5.3.1 Model Robustness to Character-Level Transformations  
Keyboard, Ocr, SpellingError, Typos, and EntTypos all transform one or several characters in a word, which are character-level transformations. The experimental results show that the robustness of models to EntTypos is significantly (\(p=0.05\)) stronger than that of the other four. It is obvious that EntTypos is only for words with entity labels so that it causes less perturbation to the original sentence than other character-level transformations. Further, we analyze the robustness of the models to four other text transformations one by one:  
* The perturbation caused by Typos is irregular, and the transformed words will almost certainly be OOV words, so the robustness of models to Typos is very weak.
* Although Ocr simulates possible errors in reality, the robustness of the model to it is also weak. We believe that because the corpus for training word vectors is manually typed rather than recognized from pictures, errors caused by Ocr rarely appear in the corpus.
* SpellingError and Keyboard simulate errors that may be caused by humans and appear in the corpus, so models are more robust to these two text transformations than the other two.  
It can be seen from the above analysis that the robustness of the GNN-based ED models to character-level transformations is related to the training corpus. Although these models use the same pre-trained word vectors, A-MDL-EEGCN and EE-GCN are more robust to character-level transformations than MOGANED. We infer that since MOGANED only considers adjacency instead of dependency labels, it is more sensitive to noise from transformed words.
## 6 Current Challenges and Future Prospects
### Industry 5.0 as a Way to Overcome the Challenges of Industry 4.0
\begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline
**Concept** & **Definition** \\ \hline Cloud computing & Cloud computing is a new technology that can save industries from managing their own computer hardware. By sharing resources virtually, clouds can serve many users with different needs using a single set of physical resources. This can save costs and offer an alternative to owning hardware for both industries and scientists [190; 191]. \\ \hline Cyber–physical systems & Cyber–physical systems (CPSs) are a new kind of system that combine computing with physical actions, creating new ways for people to interact with them. By leveraging computation and communication, CPSs extend the capabilities of the physical world, emphasizing the significance of communication and control in driving future technological progress [192; 193]. \\ \hline Augmented reality & Augmented reality (AR) merges virtual and real environments by enriching real-world objects with computer-generated information or objects through various technologies. By harnessing human capabilities, AR offers effective and complementary tools to aid in manufacturing tasks [172; 194]. \\ \hline Simulation & Simulation serves as a potent tool for decision-making. As digitalization advances, simulation methods become more relevant, offering comprehensive, efficient, embedded, and cost-effective solutions [195]. \\ \hline Autonomous robots & Autonomous robots have the capability to identify issues and adapt their tasks independently to maintain smooth operation of processes. However, the level of a robot’s independence, from being controlled by humans to operating entirely on its own, influences how humans and robots interact [196]. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Cont. It prioritizes adaptability to changing market dynamics and places increased emphasis on sustainability [185]. An illustrative example of additive manufacturing within Industry 5.0 is found in the automotive sector, particularly in the production of complex, customized components for high-end or performance vehicles. Companies like BMW utilize 3D printing to create lightweight, tailor-made parts that seamlessly integrate into their vehicle designs. Additionally, Porsche has adopted 3D printing to manufacture engine pistons, demonstrating how metal 3D printing can optimize performance while reducing lead time and costs. In the context of Industry 5.0, which emphasizes the collaboration between humans and machines, additive manufacturing enables more flexible and personalized production processes, addressing specific customer needs through advanced technology and creative human input. This approach aligns with the principles of Industry 5.0 by combining automation with human creativity to deliver consumer-centered, highly customized solutions. The impact of this technology extends beyond automotive applications; for example, Swiss aerospace company RUAG has optimized satellite bracket designs using 3D printing, highlighting the technology's potential even in space [198].
###### Abstract
Since there are almost no existing algorithms for satellite-to-Moon visibility, this paper draws on the idea of satellite-to-site visibility calculations and identifies a problem when applying the adaptive interpolation algorithm to the satellite-to-Moon visibility calculation (explained in detail in the next section). To avoid this problem, an adaptive interpolation algorithm based on vertex protection is proposed, which is applicable not only to satellite-to-site visibility, but also to satellite-to-Moon visibility. At present, this paper might be the first to provide a fast solution for determining satellite-to-Moon visibility data. In Section 2, the satellite-to-Moon visibility model, which includes the elevation angle model and the Earth occultation model, highlights the drawbacks that arise from directly using the traditional adaptive interpolation algorithm to solve the satellite-to-Moon visibility problem. In Section 3, an adaptive interpolation algorithm based on vertex protection is introduced, and a specific scheme for calculating the satellite-to-Moon visibility using an adaptive interpolation algorithm based on vertex protection is described. In Section 4, the algorithm given in Section 3 is used to experimentally analyze the BDS satellite data. Section 5 concludes this paper.
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## Appendix A Additional Details on Sensor Variations  
The information here continues from Section 1.1 on the different aspects sensors (even from the same build) can differ in:  
* Spectral band and spectral response: A spectral band is defined by (i) its wavelength range and (ii) the spectral response over this range. Spectral response is photocurrent level per incident light level, measured in ampere per watt, as a function of wavelength [88]. Wavelength ranges are typically selected to maximise object-to-background contrast; for example, Sentinel-2 uses a narrow band at 865 nm because iron-oxide soil exhibits an absorption band in the 850-900 nm region [11; 89]. However, imagers configured to observe the same wavelength ranges can exhibit different spectral responses.
* Spatial resolution: This is a measure of the smallest angular or linear separation between two objects that can be resolved by the imager [89]. Equivalently, the spatial resolution is the area on the ground each pixel occupies. Spatial resolution can vary from one spectral band to another. Furthermore, imagers with the same spectral bands can provide different spatial resolutions.
* SNR: For a spectral band, the SNR is the mean of the measured radiances divided by their standard deviation [90]. Among the factors that determine the SNR are the sources of shot noise and thermal noise in the photodetector-preamplifier circuit [88]. SNR can vary from one spectral band to another. Imagers with the same spectral bands can have different SNRs.
* Calibration error and instrument drift: Imagers with the same spectral bands can be subject to different calibration errors and instrument drift characteristics. For example, brightness temperature data obtained from different geostationary meteorological satellites have been observed to vary for the same/similar scenes not only due to differences in the spectral characteristics but also due to calibration errors and sensor drift among these satellites [91].
## 2 System Model and Channel Model
### System Model
#### 2.1.2 Local Model Transmission  
After finishing local model computation, the local models are sent to BS via multiple channel access, such as the frequency division multiple access (FDMA) or the time division multiple access (TDMA) [19]. In this paper, we adopt FDMA for communication between BS and UDs. The total band width is equally divided into \(K\) sub-channels, each of which has a bandwidth \(b\). Let \(p_{k}\) denotes the \(k\)-th UD's transmit power, and the transmission rate of the \(k\)-th UD can be written as  
\[R_{k}=b\log(1+\frac{|h_{k}|^{2}p_{k}}{\sigma^{2}}), \tag{3}\]  
where \(h_{k}=h_{d,k}+g_{k}\Theta h_{r,k}\) is the composite channels spanning from the \(k\)-th UD to BS. The channel from the \(k\)-th UD to IRS and from IRS to BS are, respectively, modeled by \(h_{r,k}\in C^{N\times 1}\) and \(g_{k}\in C^{1\times N}\), and the direct channel from the \(k\)-th UD to BS is modeled by \(h_{d,k}\in C\). \(\sigma^{2}\) denotes the noise power spectral density. The phase shift effect of IRS is defined as a diagonal matrix, that is \(\Theta=diag\{e^{\beta_{1}},e^{\beta_{2}},\ldots,e^{\beta_{n}}\}\), where \(\theta_{n}\in[0,2\pi]\) for \(n\in\mathcal{N}\). Accordingly, the delay and energy consumed in this step can be written as  
\[\tau_{k}^{u}=\frac{d}{R_{k}}, \tag{4}\]  
\[e_{k}^{u}=\tau_{k}^{u}p_{k}, \tag{5}\]  
where \(d\) denotes the size of local model.  
Figure 1: An FL system model with IRS.
# Adaptive Robust Fuzzy Impedance Control of an Electro-Hydraulic Actuator
## 4 Adaptive Robust Velocity Control  
### Model Analysis  
Parameter uncertainties are unavoidable in the electro-hydraulic system, such as the variations of \(m\), \(b\), \(F_{fc}\), \(\beta_{e}\), \(k_{q}\), \(k_{x}\) and so on. For simplicity, only the prime parameters, \(m\), \(\beta_{e}\) and \(d_{n}\) (the nominal value of system uncertain nonlinearity \(d\), \(d=d_{n}+\widetilde{d}\)) are taken into parameters adaption.  
Define the unknown parameters set as \(\theta=\begin{bmatrix}\theta_{1}&\theta_{2}&\theta_{3}\end{bmatrix}^{T}= \begin{bmatrix}1/m&d_{n}&\beta_{e}\end{bmatrix}^{T}\). Thus, the system dynamics (7) is transformed to  
\[\left\{\begin{array}{l}\dot{x}_{1}=x_{2}\\ \dot{x}_{2}=\theta_{1}\Big{[}A_{1}\bar{x}_{3}-bx_{2}-F_{L}+G-F_{fc}\Big{]}+ \theta_{2}+\widetilde{d}\\ \ddot{\bar{x}}_{3}=\theta_{3}\big{[}-f_{1}x_{2}-f_{2}C_{t}(x_{3}-x_{4})+k_{qx} f_{3}u\big{]}\end{array}\right. \tag{11}\]  
In practical applications, the extents of parameters and uncertain nonlinearity are known. So, we made the following assumption.  
**Assumption 3**.: _The parameter uncertainties and uncertain nonlinearity are bounded as_  
\[\begin{array}{l}\theta\in\Omega_{\theta}\triangleq\{\theta:\theta_{\min} \leq\theta\leq\theta_{\max}\}\\ \Big{|}\widetilde{d}\Big{|}\leq\delta\end{array} \tag{12}\]  
_In (12), the operation '\(\leq\)' is performed in terms of the corresponding elements of the vectors. In practice,\(\theta_{\min}=\begin{bmatrix}\theta_{\min}&\theta_{\min}&\theta_{\min} \end{bmatrix}^{T}\), \(\theta_{\max}=\begin{bmatrix}\theta_{1\max}&\theta_{2\max}&\theta_{3\max} \end{bmatrix}^{T}\), \(\delta\) are all known, and \(\theta_{1\min}>0\), \(\theta_{3\min}>0\)._  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\(M\)} & \multicolumn{8}{c}{\(V_{e}\)} \\ \cline{2-7}  & LS & LS & LS & MS & B & LB \\ \hline \multirow{4}{*}{\(F_{c}\)} & LS & LB & LB & MB & B & M \\  & S & LB & MB & B & M & S \\ \cline{1-1}  & M & MB & B & M & S & MS \\ \cline{1-1}  & B & B & M & S & MS & LS \\ \cline{1-1}  & LB & M & S & MS & LS & LS \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fuzzy rules of desired mass \(M\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\(B\)} & \multicolumn{8}{c}{\(V_{e}\)} \\ \cline{2-7}  & LS & LS & S & M & B & LB \\ \hline \multirow{4}{*}{\(F_{c}\)} & LS & LS & LS & MS & S & M \\  & S & LS & MS & S & M & B \\ \cline{1-1}  & M & MS & S & M & B & MB \\ \cline{1-1}  & B & S & M & B & MB & LB \\ \cline{1-1}  & LB & M & B & MB & LB & LB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fuzzy rules of desired damping coefficient \(B\).
# The Real-Time Optimal Attitude Control of Tunnel Boring Machine Based on Reinforcement Learning
## 1 Introduction
For the tunneling axis problems caused by manual attitude control, namely the snake-like motion around the design tunnel axis and even exceeding the deviation limit, numerous technologies proposed in the above research, including the attitude and position prediction, specify attitude accurate control and deviation correction path planning can help to solve these problems to a certain extent. To achieve a better effect, this paper proposed an end-to-end optimal attitude control framework based on the actual engineering data, which can integrate the tunneling axis planning, the overall optimization of the tunneling axis, and attitude control. This control framework can predict the current geological information in real-time and provide the corresponding real-time optimal attitude control during tunneling. The main innovations of this study are as follows: (1) the GEC predictive model with the real-time excavation parameters as input is proposed to obtain the real-time GEC information as the input of the optimal attitude control policy during tunneling; (2) the TBMAP predictive models for four GECs are established based on the actual engineering data, which can be used as the interactive environment to train the attitude control policy under the reinforcement learning framework; and (3) for solving the hysteresis of TBM position response and the overall optimization of the tunneling axis, the optimization framework of attitude control policy using the PPO algorithm is proposed based on the TBMAP predictive model, which can easily provide real-time decision support for attitude control in actual engineering combined with GEC predictive model. The structure of this paper is organized as follows. Section 2 introduces the origin data from the actual engineering, training dataset construction, and data analysis. Section 3 introduces the overall modeling and training framework of the real-time optimal attitude control policy. Section 4 applies the proposed framework to the Xinjiang Yiner Water Supply Phase II Project to verify its effectiveness. Section 5 summarizes the paper.
## 2 Experimental Setup and Theoretical Analysis
### Interferometer Setup
For each point \(p\) in \((x,y)\) on the screen where the interference pattern is projected (in this case, each pixel of the CMOS sensor), the difference in the trajectories traveled is different, but directly related, according to  
\[h(x,y)\times\Delta n=m(x,y)\lambda=\frac{\phi(x,y)\lambda}{2\pi}, \tag{3}\]  
where \(h\) is the thickness of the object under study (through which the object beam was transmitted), \(\Delta n\) is the difference of the refractive index with respect to the reference beam, and m refers to the fringe order, which is proportional to the phase difference. In this work, the four-step +1 algorithm was used, since it is robust to detuning errors. The wrapped phase (\(\phi_{\text{W}}\)) is obtained by [30]  
\[\phi_{\text{W}}=\tan^{-1}\left(-\left[\frac{l_{2}-l_{4}}{\frac{1}{2}l_{1}-l_{3 }+\frac{1}{2}l_{5}}\right]\right), \tag{4}\]  
being the interference pattern subscripts (1-5) the corresponding time step numbers, as related to Equation (2). Expression (4) gives a phase value wrapped in the range from \(-\pi\) to \(\pi\) (hence the origin of its name), so that an unwrapping process is necessary to eliminate discontinuities not present in the actual measurement, and thus estimate a continuous phase.
# MPC for Optimized Energy Exchange between Two Renewable-Energy Prosumers
## 4 Conclusions  
This work describes the utility of an MPC approach for efficiently coupling two prosumers, each with their own renewable energy supply and battery storage. This builds upon earlier research which explored MPC for managing one prosumer's energy flows to another prosumer. Here, a single MPC dispatch algorithm manages all flows internal to the paired prosumers, as well as shared energy flows between them. By exploiting differences between the two prosumers' supply and load profiles, higher renewable energy penetration is achieved for the coupled system relative to the penetration achieved if the prosumers operate independently. An example of a residential prosumer paired with a commercial prosumer is presented to illustrate this point. The MPC approach utilizes forecasts of supply and demand, ensuring the batteries are drained prior to times of anticipated excess supply, and the batteries are full prior to times of anticipated excess demand.  
Renewable penetration could be further improved by networking more than two prosumers in this manner, although the current MPC formulation may not scale easily due to the number of variables involved. One approach for scaling may be to only apply MPC to manage shared energy flows, but not flows that are internal to each prosumer.  
Although this work does not explicitly consider energy pricing, the coefficients for the MPC objective function could be functions (changing in time, if necessary) of prices. The coefficients could similarly be adjusted to more precisely reflect battery efficiency and life, losses due to DC to AC conversion, physical distances between prosumers, and other electrical or economic considerations. Such approaches can further refine the potential of an MPC approach to optimized energy exchange. Future research will consider the algorithm's performance with a smaller time-step, and modifications necessary to handle rapid fluctuations in load and supply.  
[PERSON] designed the model, computational the framework, analysed the data, and wrote the manuscript. [PERSON] contributed to the design, implementation of the research, and supervised the project. [PERSON] provided critical feedback and helped shape the research. All authors discussed the results and contributed to the final manuscript.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Unpaired** & **Paired** \\ \hline Percentage of both loads satisfied by RER production & 71\% & 84\% \\ Percentage of total RER output transferred to other prosumers & 39\% & 32\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance for both operating modes.  
Figure 7: Average daily energy from external prosumers to both prosumers.  
**Funding:** This research was funded by University of Dayton, Dayton, Ohio, USA.  
**Acknowledgments:** This work has been supported in part by the University of Dayton Office for Graduate Academic Affairs through the Graduate Student Summer Fellowship Program.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Framework for the Joint Placement of Edge Service Infrastructure and User Plane Functions for 5G
## 2 Related Work and Motivation  
Two main research areas are involved in the solution of the problem presented in the previous section: Edge Node Placement and UPF Placement under 5G networking. The following sections present a comprehensive study of current literature on both topics.
# Explainable Machine-Learning Predictions for Peak Ground Acceleration
## 3 Establishment of the Machine Learning Model
### Analysis of XGBoost Model Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Bin Number**} & \multirow{2}{*}{**PGA(g) Range**} & \multicolumn{3}{c}{**Number of Motions**} \\ \cline{3-4}  & & **Training Set** & **Testing Set** \\ \hline
1 & 0.00–0.01 & 0 & 0 \\
2 & 0.01–0.02 & 18 & 2 \\
3 & 0.02–0.03 & 155 & 18 \\
4 & 0.03–0.04 & 449 & 70 \\
5 & 0.04–0.05 & 542 & 122 \\
6 & 0.05–0.06 & 370 & 102 \\
7 & 0.06–0.07 & 245 & 69 \\
8 & 0.07–0.08 & 164 & 46 \\
9 & 0.08–0.09 & 129 & 29 \\
10 & 0.09–0.10 & 76 & 25 \\
11 & 0.10–0.20 & 283 & 77 \\
12 & 0.20–0.40 & 78 & 10 \\
13 & 0.40–0.60 & 13 & 1 \\
14 & 0.60–1.00 & 7 & 2 \\
15 & \textgreater{}1.00 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Defined PGA bins and associated number of seismic records. Figure 9: The prediction residuals on the training and testing dataset for XGBoost. The comparison results of different site classifications and different PGA intervals are shown in Figure 11. It can be seen from Figure 11 that, in general, the prediction results of the machine learning models are better than that of the numerical simulation methods. Even if the non-integrated algorithm (DT) is used, the prediction results are still better than the numerical simulation methods, except for individual conditions (site class IV and 0.1 g < PGA < 0.2 g). For site class II, the prediction results of the machine learning models are quite different from that of the numerical simulation methods, and the prediction results of the machine learning models are obviously better than those of the numerical simulation methods. For site class III and site class IV, the MAE and RMSE values of several methods are similar, and the machine learning models are slightly better than the numerical simulation. For different PGA intervals, the prediction results of the machine learning models are better than those of the numerical simulation methods (except for 0.1 g < PGA < 0.2 g; the MAE and MAPE values of DT are slightly larger than those of the SR and SHAKE methods). The calculation error of numerical simulation methods increases significantly, while the prediction results of machine learning models are more stable, and the prediction error increases slightly with the increase in PGA. Figure 11: Comparison of prediction effects of numerical simulation methods and machine learning models on different site classifications and different PGA intervals. Figure 10: Histogram of the ratio of XGBoost for predicted and observed PGA.