esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design and Analysis Methods
### Main Dimensions
\[T_{d}=\frac{S_{elm}\cos\varphi}{2\pi n_{s}}=\frac{\pi}{4}k_{\omega 1}D_{g}^{2}L _{e}B_{mg}A_{m}\eta\cos\varphi \tag{5}\]  
\[P_{o}=P_{in}\eta=m_{1}V_{1}I_{a}\eta\cos\theta=\frac{1}{\varepsilon}S_{elm} \eta\cos\theta \tag{6}\]  
where the back emf and phase voltage ratio for the excited motor \(\varepsilon=E_{pm}/V_{1}=0.6\ldots 0.95\)[28], \(E_{pm}\approx 0.9\ldots 1.1\) V [30], or \(\varepsilon\sim 0.98\)-0.005 p [31]. The output coefficient \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Slot fill factor (\%) & 45 \\ \hline Maximum flux density in stator tooth (T) & 1.8 \\ \hline Maximum flux density in stator yoke (T) & 1.5 \\ \hline Maximum flux density in rotor yoke (T) & 1 \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._or motor constant \(\sigma_{p}\) given in Equation (7) is the ratio of output power to rotor volume and speed [28]. \[ \begin{array}{l}\sigma_{p}=\frac{P_{e}\varepsilon}{D_{g}^{2}L_{e}n_{s}}=\left( \frac{\pi^{2}}{120}\right)k_{\omega 1}B_{mg}A_{m}\eta\cos\theta\\ \to D_{g}^{2}L_{e}=\frac{P_{e}\varepsilon}{\sigma_{p}n_{s}}\end{array}  \tag{7}\]  
Equations (4) and (7) show that the power developed in a PMSM is mainly proportional to the speed and volume of the rotor; that is, the output coefficient depends on both the rotor geometry and the speed [32]. In the sizing procedure, NdFeB is initially evaluated as \(B_{mg}\approx(0.6\ldots 0.8)\)B\({}_{\rm r}\) for magnet motors, while the stator current density \(A_{m}\)'s amplitude varies depending on the size of the motor, from 10 kA/m for small motors to 55 kA/m for medium-power motors [28]. Another critical step in sizing is the separation of \(D_{g}\) and \(L_{e}\). These values cannot be chosen arbitrarily, as they are essential for the losses and cooling of the machine. These values can be determined with the help of the ratio between the effective core length and the pole pitch, called the aspect ratio. This ratio can be selected in the value range given in Equation (8) [23; 31; 32]. Thus, the airgap diameter of the machine is shown in Equation (9). \[\lambda=\frac{L_{e}}{\tau_{p}}=\frac{L_{e}2p}{\pi D_{g}};\quad 0.15<\lambda<3 \tag{8}\]  
\[D_{g}=\sqrt[3]{\frac{2p}{\pi\lambda}\frac{P_{e}\varepsilon}{\sigma_{p}n_{s}}} \tag{9}\]  
In the next step, the slot and pole number must be selected. \(Q_{s}\) = 36 slot and 2\(p\) = 32 pole configurations are preferred for low-speed and high winding factors. Airgap \(g\) is in the range of 0.2-2.5 mm for small- and medium-power motors, and larger values for MW are suggested to limit losses due to field harmonics caused by windings and slots [32]. In [28], it is meant to be 0.3-1.0 mm for small-power PMSMs. In another reference [30], it is given as \(g\) = \((0.18+0.006\times{\rm P_{o}}^{0.4})/1000\) (m). Also, the following equations can be used to estimate the airgap as a function of pole pitch and number of poles [23]:  
\[g=3\cdot 10^{-3}\left(\sqrt{\frac{2p}{2}}\right)\cdot\tau_{p} \tag{10}\]
## 4 Simulation Results
### Comparison between MMMR and Other Routing Protocols
Besides, the high number of hops is not a drawback from the power consumption point of view, since in VANETs (contrary to WSN or MANETs), the on-board unit (OBU) of the vehicle is powered by the car battery. Hence, the communication equipment do not have battery constraint problems. Finally, Figure 7 shows the throughput achieved using each protocol. Clearly, protocols with high packet losses (I-GPSR in Figure 4) obtain a low throughput (I-GPSR in Figure 7). In contrast, low packet losses (MMMR in Figure 4) represent a high throughput (MMMR in Figure 7). The results confirm that the use of a four-metric combination used in our proposal to score candidates improves the selection of nodes in different environments compared to other routing protocols proposed for VANETs.
## 2 Background
### Apache Spark and Apache Kaffaa  
Apache Spark is an open-source computing framework, originally developed at the University of California Berkeley in 2009 [18]. Apache Spark consists of 5 main components: Spark Core, Spark Streaming, Spark SQL, MLlib, and GraphX [19]. Spark Core is the main component, which is the basic general execution engine to build other functions on it. Spark Core supports multiple application programming interfaces with languages such as Java, Scala, and Python.  
Spark's processing speed is achieved with the capability of in-memory computing and parallel and distributed computing on a cluster of computers. Given a task, Spark allows the division of this task into more manageable chunks. Spark will then run these small tasks in memory on clusters of many different computing nodes. Spark consists of a master node and multiple worker nodes. Spark Driver will contact the master node to coordinate the workers, where there are executors for executing the tasks (Figure 3).  
Figure 2: Knowledge development process.  
Spark allows real-time data processing, performing processing right on the received data with Spark Streaming. It enables powerful analytics on both real-time and historical data. Spark Streaming easily integrates with many popular data sources, including HDFS, Flume, Kafka, and Twitter. One thing that should be noted is that Spark does not have its own distributed file system; thus, users can use other file systems such as HDFS, HBase, and Cassandra.  
Apache Kafka is a high-performance streaming message platform that was first introduced in 2011 for collecting and delivering high volumes of data [20]. Kafka is distributed and scalable providing an API similar to a message passing system to process a huge amount of data in real time. There are some basic concepts used in the overall architecture of Kafka (Figure 4). Topic is a category name to which messages are stored and published. A topic is divided into multiple partitions in which data are stored in an immutable order and assigned an ID called offset. A producer can publish messages to a topic. The published messages from the producers are stored on a set of servers called brokers. A broker allows consumers to fetch messages by topic, partition, and offset. A consumer consumes messages of the subscribed topics by pulling data from the brokers. Exactly one consumer in a consumer group consumes each partition of a topic.
# Prediction of Total Organic Carbon Content in Shale Based on PCA-PSO-XGBoost
## 2 Materials and Methods
### Particle Swarm Optimization  
When using machine learning algorithms for modeling, the selection of model hyperparameters affects prediction results. The principle of hyperparameter selection is mainly judged by evaluation indexes. Only by setting the appropriate values can we achieve better evaluation results. When using different machine learning algorithms to predict TOC content, each algorithm must set hyperparameters. The problem of selecting algorithmic parameters is transformed into an optimization problem due to the difficulty of finding the optimal solution via conventional manual parameter tuning. The intelligent preferential selection of the best hyperparameters of the model is realized by particle swarm optimization.  
The Particle Swarm Optimization (PSO) algorithm is a population-based metaheuristic optimization technique inspired by the collective intelligence of bird flocking or fish schooling. Each particle in the algorithm represents a potential solution in the solution space. The particles adjust their positions and speeds according to their own optimal positions (individual optimal) and the optimal positions of the flock (global optimal), gradually approaching the global optimal solution [19; 20]. In constructing the TOC content prediction model, the evaluation index of the model was set as the objective function. The optimal hyperparameters of the model were determined by continuously updating the particle velocity and position. The process is shown in Equations (7) and (8).  
\[v_{i}^{t+1}=wv_{i}^{t}+c_{1}r_{1}\left(p_{i,pbest}^{t}-x_{i}^{t}\right)+c_{2}r_{2 }\left(p_{gbest}^{t}-x_{i}^{t}\right) \tag{7}\]  
\[x_{i}^{t+1}=x_{i}^{t}+v_{i}^{t+1} \tag{8}\]  
where, \(v_{i}^{t+1}\) is the velocity of the particle in \(t+1\) iterations; \(v_{i}^{t}\) is the velocity of the particle in \(t\) iterations; \(x_{i}^{t}\) is the position of the particle in \(t\) iterations; \(w\) is the weight; \(c_{1}\) is the learning factor of the individual; \(c_{2}\) is the learning factor of the group; \(r_{1}\) and \(r_{2}\) are random numbers with values ranging from 0 to 1; \(p_{i,pbest}^{t}\) is the individual optimal position of the particle in \(t\) iterations; \(p_{gbest}^{t}\) is the global optimal position in \(t\) iterations.
## 4 Discussion  
The Correlation between Indoor Air Exchange Rate and Wind Pressure Differential across Exterior Building Windows  
Figures 17 and 18 display the correlation between the wind pressure differential across window surfaces and the indoor air exchange rate for 54 classrooms in different scenarios. Row-by-row flow planning in Scenario 1 shows a notable positive link between the wind pressure difference at the windows and the interior air exchange rate. As the wind pressure differential increases, the interior air exchange rate likewise increases dramatically, showing a constant correlation between the two variables. The lack of wind blockage in Scenario 1's floor planning enables the outdoor wind field to directly impact the indoor air exchange rate.  
However, the positive correlation between the wind pressure difference and the air exchange rate is not as strong in Scenarios 2 and 4 as it is in Scenario 1. In some local variations, there is a certain degree of non-correlation. For example, in Scenario 2 (as indicated by the circled area in Figure 17), the air exchange rate of classrooms 13-19 actually decreases when the wind pressure difference at the exterior windows increases. In Scenarios 3 and 4, the changes in wind pressure difference and air exchange rate are generally consistent. However, there are instances where the air exchange rate increases significantly even when the wind pressure difference increases marginally, indicating non-correlation (as indicated by the circled area in Figure 18).  
Scenario 3 exhibits a similar trend to Scenario 4, with basic consistency between the wind pressure difference and the air exchange rate. However, in some classrooms, the indoor air exchange rate increases even when the wind pressure difference between the interior and exterior surfaces of the windows is relatively small.  
Therefore, only Scenario 1 maintains a strong positive correlation between the wind pressure difference of windows and the classroom air exchange rate. In contrast, the other  
Figure 17: The correlation between the indoor air change rate and the wind pressure differential across the building’s exterior windows in Scenarios 1 and 2.  
Figure 18: The correlation between the indoor air change rate and the wind pressure differential across the building’s exterior windows in Scenarios 3 and 4.  
three scenarios exhibit non-positively correlated phenomena. This is primarily attributed to the complexity of the building forms. The forms of Scenarios 2, 3, and 4 create enclosed and semi-enclosed spaces, where outdoor winds entering these spaces can create vortices or stagnant regions. This results in a smaller wind pressure difference between different facades of the building, leading to a decrease in air exchange rate even when the wind pressure difference on the external windows of classrooms exceeds 0.5 Pa.
## 2 Materials and Methods
### Feature Extractors
#### 2.5.2 Convolutional Neural Network  
The CNN [22] employs a different architecture optimized for processing structured data, such as images or time series, where local patterns and correlations are important. Instead of fully connecting each layer, CNNs apply convolutional filters to local regions of the input, allowing the network to extract hierarchical features. The operation of a convolutional layer is defined as  
\[z^{(l)}_{i,j}=\sum_{m,n}W^{(l)}_{m,n}a^{(l-1)}_{i+m,j+n}+b^{(l)} \tag{10}\]  
where  
* \(z^{(l)}_{i,j}\) is the activation at position \((i,j)\) in layer \(l\);
* \(W^{(l)}_{m,n}\) represents the convolutional kernel applied at position \((m,n)\);
* \(a^{(l-1)}_{i+m,j+n}\) is the input from the previous layer at the corresponding position;
* \(b^{(l)}\) is the bias term.  
CNNs are particularly advantageous for tasks where spatial or temporal relationships are crucial, such as identifying trends in financial time-series data. By sharing weights across different regions, CNNs are more parameter-efficient than MLPs, leading to faster training and fewer parameters to tune.
## 1 Introduction
Antimicrobial activity, mainly antibacterial and antifungal activity, has been reported in various studies using different plant extracts, and this effect has been shown to largely depend on the tested species, sample processing, and dosage. Components with proven antifungal activities predominantly include phenolic compounds such as gallic acid and thymol, flavonoids (especially catechins), and polyphenols such as tannins, terpenoids, and saponins [27; 28; 29]. We believe it is critical to thoroughly investigate the profile of biologically active organic compounds and their effects at a cellular level, as fruits of the _Cornus mas_ L. contain several significant active components [13]. The nonpathogenic yeast _Schizosaccharomyces pombe_ is a widely used model organism for investigating principal biological processes in eukaryotic cells. It has been reported that \begin{table}
\begin{tabular}{c c c c} \hline _Cornus mas_ L. & **Content in Fruits [mg kg\({}^{-1}\) fw]** & **References** & **Content in Extract [ug mL\({}^{-1}\)]** & **References** \\ \hline Vitamin C & 610.36–1344.1 & [15] & nd & \\ Neochlorogenic acid & 2.67–8.66 & [15] & nd & \\ Chlorogenic acid & 1.13–7.32 & [15; 16] & 23.189 & [13] \\ Caffe acid & 0.07–6.02 & [15; 16] & 57.15 & [13] \\ Rutin & 6.67–20.67 & [15] & 29.63 & [13] \\ Ellagic acid & 2.36–1.88 & [16] & nd & \\ Epicatechin & 2.17–6.7 & [16] & nd & \\ Catechin & 1.44–3.71 & [16] & nd & \\ Kaempfer-3-O-galactoside & 36.69–41.3 & [16] & 11.023 & [13] \\ Total Quercetin & 47.1–167.9 & [16] & nd & \\ \hline \end{tabular} nd is Not determined.  
\end{table} 
Table 1: Composition of bioactive compounds of _Cornus mas_ L. fruits and ethanolic extract. the _S. pombe_ genome shares almost 70% similarity with the protein-coding genes of higher organisms, including humans [30]. _S. pombe_ is, therefore, a useful system for evaluating the possible function of genes and proteins from complex eukaryotes, like humans, in the setting of an intact cell not only under physiological but also under challenging and non-physiological conditions. Thus, our aim in this study was to provide a comprehensive view of the cellular response of _Cornus mas_ L. fruit ethanol extract on the model organism, yeast _Schizosacharomyces pombe_, focusing on its pro- or antioxidant and antimicrobial properties. We selected _S. pombe_ as our model system in order to extend the understanding of the effects of the _Cornus mas_ L. ethanolic extract on eukaryotic cells on a molecular basis. Moreover, only a limited number of studies investigate the quantitative effect of the highly abundant biologically active substances inherent in the fruits of _Cornus mas_ L. on the biology of a model organism. Thus, there is generally no consensus on the concentration-dependent prooxidant effects of the _Cornus mas_ L. ethanolic extract. In this study, we investigated the regulation of gene expression and enzymatic activity in response to _Cornus mas_ L.-mediated oxidative stress in order to assess its potential health benefits.
## 2 Results
### Overall Knowledge, Attitude, and Perception Scores
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Complete Dataset** & **HICs** & **LMICs** & **Fisher Exact** \\  & **\% (N)** & **\% (N)** & **\% (N)** & **Fisher Exact** \\ \hline Total & 351 & 80.1\% (281) & 19.9\% (70) & \\ \hline \multirow{3}{*}{**Top 3 nationalities A**} & NLD 48.7\% (171) & NLD 60.9\% (171) & MMR 48.6\% (34) & \\  & ESP 27.6\% (97) & ESP 34.5\% (97) & IND 15.7\% (11) & \\  & MMR 9.7\% (34) & AUS 1.4\% (4) & NGA 12.9\% (9) & \\ \hline
**Gender** & & & & \\ Female & 51.9\% (182) & 40.8\% (15) & 45.7\% (32) & 0.252 \\ Male & 46.4\% (163) & 44.5\% (125) & 54.3\% (38) & \\ Undisclosed & 1.7\% (6) & 2.1\% (6) & 0.0\% (0) & \\ \hline
**Age** & & & & \\ Mean \(\pm\) standard deviation & 49.3 \(\pm\) 13.3 & 52.2 \(\pm\) 12.0 & 37.8 \(\pm\) 12.3 & \\ Median [IQR] & 52.0 [21.5–62.5] & 55 [46.5–63.5] & 33 [23.3–42.8] & \\ \hline
**Age group** & & & & \\ <40 & 26.8\% (94) & 16.4\% (46) & 68.6\% (49) & \textless{}0.001 *** \\
40–60 & 51.0\% (179) & 57.3\% (161) & 25.7\% (18) & \\ >60 & 22.2\% (78) & 26.3\% (74) & 5.7\% (4) & \\ \hline
**Position duration** & & & & \\ <1 year & 7.7\% (27) & 8.2\% (23) & 5.7\% (4) & \textless{}0.001 *** \\
1–3 years & 30.2\% (106) & 33.5\% (94) & 17.1\% (12) & \\
3–5 years & 17.1\% (60) & 12.5\% (35) & 35.7\% (25) & \\
5–10 years & 19.7\% (69) & 19.2\% (54) & 21.4\% (15) & \\ >10 years & 25.4\% (89) & 26.7\% (75) & 20.0\% (14) & \\ \hline Educational background & & & & \\ Master/PhD & 44.7\% (157) & 39.5\% (111) & 65.7\% (46) & \textless{}0.001 *** \\ Bachelor & 40.7\% (143) & 42.7\% (120) & 32.9\% (23) & \\ Lower levels & 14.0\% (49) & 17.1\% (48) & 1.4\% (1) & \\ Unknown & 0.6\% (2) & 0.7\% (2) & 0.0\% (0) & \\ \hline
**Expertise** & & & & \\ Scientific & 46.4\% (163) & 42.7\% (120) & 61.4\% (43) & 0.008 ** \\ Other background & 53.6\% (188) & 57.3\% (161) & 38.6\% (27) & \\ \hline
**Living condition** & & & & \\ (Sub)urban & 62.7\% (220) & 54.1\% (152) & 97.1\% (68) & \textless{}0.001 *** \\ Rural & 37.0\% (130) & 45.6\% (128) & 2.9\% (2) & \\ Unknown & 0.3\% (1) & 0.4\% (1) & 0.0\% (0) & \\ \hline
**Role** & & & & \\ Government & 86.3\% (303) & 97.2\% (273) & 42.9\% (30) & \textless{}0.001 *** \\ Non-government & 12.8\% (45) & 2.1\% (6) & 55.7\% (39) & \\ Unknown & 0.9\% (3) & 0.7\% (2) & 1.4\% (1) & \\ \hline
**Role (detailed)** & & & & \\ Municipal and regional & 52.1\% (183) & 64.1\% (180) & 4.3\% (3) & \textless{}0.001 *** \\ Province & 18.2\% (64) & 22.4\% (63) & 1.4\% (1) & \\ National & 7.1\% (25) & 4.6\% (13) & 17.1\% (12) & \\ Non-government and unknown & 22.5\% (79) & 8.9\% (25) & 77.1\% (54) & \\ \hline \hline \end{tabular}  
* Country abbreviations, as follows: AUS (Australia), ESP (Spain), IND (India), MMR (Myanmar), NGA (Nigeria), and NLD (The Netherlands).  
\end{table} 
Table 1: Sociodemographic description of all participants, stratified for low- and middle-income (LMICs) and high-income countries (HICs). Differences between subsets were analyzed by [PERSON]’s exact test. Significance: *** \(p\) < 0.001, and *** \(p\) < 0.01.
# Temporal Continuity Expression for Network Topology of Space Information Systems
## 3 Calculation of Time-Related Adjacency Matrix
### Calculation of the Geometric Linkable Factor  
In one system period, the visibility relationship of \(S_{11}\) with the three nodes are simulated, and the results are shown in Figure 2.  
Table 2 shows the expression of the geometric linkable factor.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Semi-Major Axis & 8500 km \\ Eccentricity & 0.0000 \\ Inclination & 60\({}^{\circ}\) \\ Longitude of the ascending node & 0\({}^{\circ}\) \\ Argument of periapsis & 0\({}^{\circ}\) \\ Mean anomaly at epoch & 0\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Walker Delta 32/4/132/4/1 constellation (orbital elements of the seed satellite) [20].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Satellite Pairs** & **Expression** \\ \hline \(S_{28}\) and \(S_{11}\) & \(\sigma_{1}(t)=1,t\in(0\sim 7798)\) \\ \(S_{34}\) and \(S_{11}\) & \(\sigma_{1}(t)=\left\{\begin{array}{l}0,t\in(1308\sim 3081,5208\sim 6980)\\ 1,t\in(0\sim 1308,3081\sim 5208,6980\sim 7798)\end{array}\right.\) \\ \(S_{47}\) and \(S_{11}\) & \(\sigma_{1}(t)=\left\{\begin{array}{l}0,t\in(0\sim 2257,2863\sim 6157,6762\sim 7798) \\ 1,t\in(2257\sim 2863,6157\sim 6762)\end{array}\right.\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The expression of the geometric linkable factor.  
Figure 2: The visibility relationship of \(S_{11}\) with the three nodes in one system period. (**a**) \(S_{28}\) and \(S_{11}\); (**b**) \(S_{34}\) and \(S_{11}\); (**c**) \(S_{47}\) and \(S_{11}\).
## 7 Numerical Results of NSDI
### 4 Story Steel Structure Model  
The third example is a 3D modular structure based on Phase II of the IASC-ASCE SHM benchmark four-story building [49; 50]. As illustrated in Figure 8, the model is built without side braces along the four floors. The structure has 12 degrees of freedom (DOFs) and 84 members. This structure has a base plan of 2.5\(\times\)2.5 m and height of 3.6 m. The members are hot rolled grade 300 W steel with a nominal yield stress 300 MPa (42.6 kpsi).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Element Number** & **PoDE** & **PDE** \\ \hline
57 & 8\% & 5\% \\
58 & 88\% & 68\% \\
87 & 0\% & 3\% \\
88 & 5\% & 14\% \\
89 & 85\% & 74\% \\
90 & 16\% & 25\% \\
103 & 0\% & 1\% \\
104 & 7\% & 9\% \\
105 & 95\% & 88\% \\
106 & 11\% & 35\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: _Cont._  
Figure 8: The IASC–ASCE SHM benchmark 4-story building model: (**a**) the original model, and (**b**) the developed model.  
This complex model can effectively illustrate the efficiency of structural damage tracking using the proposed non-probabilistic method. As indicated in Figure 9, one damage situation is considered by replicating the damage in element 7 by reducing its Young's modulus by 25%.  
Table 11 show PODEs, DMIs, and PDEs for the damage scenario. Furthermore, a comparison between the proposed ESMA and the original SMA is shown. Herein, the simulated mode shapes and frequencies consist of 5% and 2% measurement noise, respectively.  
The maximum numbers of function evaluations (NFEs) shown in Table 11 indicate that determining the global optimum with ESMA is substantially less computationally costly than with SMA.  
Based on the results displayed in Table 11, it can be determined that SMA has the ability to detect damaged elements with high certainty, whereas the resulting PDE, PoDE, and DMI for the undamaged elements is higher in comparison with ESMA. Furthermore, as can be seen in Table 11, the proposed methodology reduces the computation time by at least 30%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Element** & **ESMA** & **ESMA** & **ESMA** & **SMA** & **SMA** & **SMA** \\
**Number** & **(PoDE) \%** & **(PDE) \%** & **(DMI) \%** & **(PoDE) \%** & **(PDE) \%** & **(DMI) \%** \\ \hline
1 & 2.00 & 4.00 & 0.24 & 0.00 & 5.00 & 0.00 \\
7 & 100.00 & 99.00 & 25.00 & 100.00 & 98.00 & 25.00 \\
12 & 0.00 & 0.00 & 0.00 & 4.00 & 0.00 & 0.32 \\
13 & 8.00 & 14.00 & 0.96 & 10.00 & 14.00 & 1.2 \\
18 & 10.00 & 10.00 & 1.00 & 16.00 & 17.00 & 2.50 \\
28 & 0.00 & 0.00 & 0.00 & 3.00 & 9.00 & 0.15 \\
39 & 0.00 & 9.00 & 0.00 & 13.00 & 5.00 & 2.60 \\ Total time (s) & 202 & 612 & - & 370 & 815 & - \\ NFEs & 1250 & 1800 & - & 1730 & 2000 & - \\ RMSE & 6.00 \(\times 10^{-3}\) & 7.00 \(\times 10^{-2}\) & - & 8.11 \(\times 10^{-2}\) & 9.21 \(\times 10^{-2}\) & - \\ \hline \hline \end{tabular}
\end{table}
Table 11: PoDEs, PDEs, and DMIs for the four-story steel structure.  
Figure 9: The damage scenario.