esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
Table 2 summarizes the results of these analyses, considering the average of whole thermal zones and cities and excluding only the zones where the HVAC system is off for at least 95% of total hours since the thermal behavior of these zones is not considered representative of the behavior of the entire building. Values of MBE and CVRMSE were then compared with reference values from ASHRAE guideline 14 [64; 65], which gives the hourly calibration criteria for real building modeling. Indeed, most of the thermal zones in all the configurations meet the ASHRAE hourly criteria, with \(\mid\)MBE \(\mid\)\(\leq\) 10% and CVRMSE \(\leq\) 30% [64; 65]. All the results meet the criteria described before, except for the cooling CVRMSE of the small office, which was slightly over the threshold; in this case, the results were negatively influenced by the Fairbanks result, which can be considered nonrepresentative as it has a reduced functioning period (19% of total hours for small office, 17% for midrise apartment) and very low cooling energy consumption. Indeed, considering the Fairbanks yearly global HVAC consumption, the differences obtained by comparing reference and ideal-load models are very low: 1.1% and 1.5% for the office and apartment models, respectively. For these reasons, the model can be considered reliable. The performance assessment of the integration of PCMs in buildings was carried out considering a whole year. In order to properly account for the transient behavior of PCMs, each simulation hour was divided into 20 analysis timesteps. Moreover, 25 different European cities were considered in order to assess the benefits of the integration of PCMs into buildings. To define a broad and nonredundant city sample, different European cities--most of them characterized by common EU directives--were studied, selecting cities with very different climates, as described in Figure 2 and Table 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Heating**} & \multicolumn{2}{c}{**Cooling**} \\ \cline{2-5}
**Building Types** & **Average** & **Average** & **Average** & **Average** \\  & **1 MBE** & **CVRMSE** & **1 MBE** & **CVRMSE** \\  & **[\%]** & **[\%]** & **[\%]** & **[\%]** \\ \hline Midrise & 4.9\% & 9.3\% & 5.7\% & 12.9\% \\ apartment & 1.2\% & 20.7\% & 4.5\% & 44.9\({}^{\text{ 1}}\) \\ Small office & & & & \\ \hline \hline \end{tabular}  
* \({}^{\text{1}}\) Without considering Fairbanks’ cooling consumption, the cooling average CVRMSE is 19.6%.  
\end{table} 
Table 2: Average values of MBE and CVRMSE for the considered models, excluding the results of zones with a system functioning period of less than 5% of total hours. In order to adapt the models to the European climates, all thermal properties were adjusted to meet the different energy requirements [66; 67; 68; 69; 70]. The selected cities were then grouped in six different climatic zones--B, C, D, E, F, and G, from warmer to colder--in accordance with their values of heating degree days (HDDs). Hence, the thermal properties of roofs, walls, slabs, and windows were changed according to the climatic zones, as described in Table 4.
## 2 Robust Optimization Design for the Aerodynamic Shape of Hypersonic Vehicle  
### Parametric Geometry Modeling of the Aerodynamic Shape  
The parametric geometry modeling is the fundamental step for the optimization design of the aerodynamic shape. As shown in Figure 2a, an S-shape strike wing is adopted in the X-37B-like hypersonic vehicle. The wing profile can be fitted by a polynomial. Based on the original control points \(\{x_{i},y_{i}|i=1,2,\ldots,m\}\) on the leading edge of the wing, the polynomial can be expressed as:  
\[y=a_{1}x^{n}+a_{2}x^{n-1}+\ldots+a_{n}x+a_{n+1} \tag{1}\]  
where \(y(x)\) is the fitted profile, \(a_{i}(i=1,\ldots,n+1)\) is the polynomial coefficient.  
Figure 1: Framework of the manuscript.  
Here, 7 control points are chosen and the 5 th-order polynomial is utilized. As a result, the fitted profile is shown in Figure 2b. It can be seen that the wing profile is well fitted, as the maximum fitting error on the control points is less than 1.5%.  
Further, to describe the wing profile more clearly, the fitted S-shape strike wing is equivalently simplified into the traditional trapezoidal wing (see Figure 3). The aerodynamic shape can then be determined by several independent parameters, including wing reference area \(S\), span-chord ratio \(AR\), taper ratio \(TR\), leading edge radius \(LR\), dihedral \(D\) and coordinates of the leading edge of the wing root \((x_{0},y_{0},z_{0})\). The other parameters can be expressed as follows:  
1. Half-length of the wing \(b\): \[b=0.5\cdot\sqrt{AR\cdot S}\] (2)
2. Chord length of the wing root \(C_{root}\): \[C_{root}=2\cdot S/[b\cdot(1+TR)]\] (3)
3. Chord length of the wing tip \(C_{tip}\): \[C_{tip}=TR\cdot C_{root}\] (4)
4. Coordinates of leading edge of the wing tip \((x_{1},y_{1},z_{1})\): \[x_{1}=x_{0}+b\cdot\tan(D)\text{; }y_{1}=y_{0}+b\text{; }z_{1}=z_{0}+b\cdot\tan(D)\] (5)  
After that, the aerodynamic shape of the hypersonic wing can be determined with the above parameters by utilizing three-dimensional geometry modeling technology. Figure 4 gives the three-dimensional aerodynamic shape of the hypersonic vehicle with the parametric wing, where the initial values of the parameters are listed in Table 1.  
Figure 4: Three-dimensional aerodynamic shape of the hypersonic vehicle. **(a)** Top view; **(b)** Side view; **(c)** Front view.  
Figure 3: Description of the hypersonic wing.
# Seismic Response and Damage Analysis of an Isolated Bridge Subjected to Near-Fault Ground Motion
## 4 Conclusions  
In this paper, the seismic response and damage analysis of the isolated bridge and the non-isolated bridge under TCU068 ground motion in the Chi-Chi earthquake are conducted. The following research results are obtained:  
1. Under the TCU068 near-fault ground motion, the isolation bearing can decrease the girder acceleration, but it is not significant in the X-direction. Meanwhile, the isolation bearing increases the X-direction displacement and shear force of the pier. The displacement even increases by 1.2 times. This increment shows that the isolation bearing not only fails to work but amplifies the seismic response of the bridge.
2. The edges of piers P2 and P3 of the non-isolated bridges are all significantly damaged. The damage to piers P1 and P4 is mainly concentrated in the left upper and right lower. Piers P2 and P3 are more severely damaged than piers P1 and P4.
3. The damage degree of each pier of the isolated bridge is almost the same. Furthermore, the damage index in a large part of the area on the left side of the pier reached 1. The edge of the right upper part of the pier was significantly damaged, but the damage index did not reach 1. A large part of the concrete on the left side of each pier was completely crushed, which is the main reason for the residual displacement of the pier in the X-direction.
4. The non-isolated bridge may collapse due to severe damage to some piers. For the isolation bridge, the isolation bearings fail firstly, and then the bridge may collapse due to severe damage to the piers. In the case of the analyzed concrete bridge type and near-fault earthquake data, it can be concluded that the used isolating lead rubber bearings should not be recommended. In general, it can be stated that the selection of the types of bridge bearings in areas close to faults due to the specific characteristics of possibly generated seismic pulses should always be analyzed in detail. This should be conducted by designers in order to avoid unintentional amplifying of the bridge's seismic response.  
Conceptualization, Y.G.; methodology, Y.G., J.C. and L.Z.; software, Y.G., J.C. and L.Z.; formal analysis, Y.G., J.C. and L.Z.; writing--original draft preparation, Y.G. and J.C.; writing--review and editing, Y.G. and L.Z.; project administration, Y.G.; funding acquisition, Y.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 52108481), the Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi (Grant No. 2019L0547) and the Fundamental Research Program of Shanxi Province (Grant No. 201901D111134, Grant No. 201901D111169, and Grant No. 201801D221233).
# POP-ON: Prediction of Process Using One-Way Language Model Based on NLP Approach
## 4 Performance Analysis
### Hyperparameter Optimization  
In this experiment, experiments were conducted on six datasets (BPI 2012 A, BPI 2012 O, BPI 2012 W Complete, BPI 2013 Incidents, BPI 2013 Closed Problems, and Helpdesk). The experiment was conducted using the BPI 2012 O dataset. Training was performed on a NVIDIA V100 GPU with 25 GB RAM, with a batch size of 64, 200 epochs and, a learning rate of \(10^{-6}\). The time taken for each epoch was measured from about 3 s to 25 s depending on the setting.  
Table 5 shows a description of the hyperparameters we used, and the values for each setting for the experiments are as follows.  
* \(K_{1}=n_{head}=1,\ d_{model}=16,\ d_{attr}=128,\ d_{ff}=128\);
* \(K_{2}=n_{head}=2,\ d_{model}=32,d_{attr}=256,\ d_{ff}=256\);
* \(K_{3}=n_{head}=4,\ d_{model}=64,\ d_{attr}=512,\ d_{ff}=512\);  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Numerical Attributes** & **Categorical Attributes** \\ \hline BPI 2012 A & time:elapsed, & \\  & case:AMOUNT\_REQ & org:resource \\ \hline BPI 2012 W Complete & time:elapsed, & \\  & case:AMOUNT\_REQ & org:resource \\ \hline BPI 2012 O & time:elapsed, & \\  & case:AMOUNT\_REQ & org:resource \\ \hline  & & org:group \\  & & org:role \\  & & concept:name \\  & & product \\  & & impact \\  & & resource country \\  & & organization country \\  & & organization involved \\ \hline  & & org:group \\  & & org:role \\  & & concept:name \\  & & product \\  & & impact \\  & & resource country \\  & & organization country \\  & & organization involved \\ \hline Helpdesk & time:elapsed & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Numerical attributes and categorical attributes used in the POP-ON model for the different datasets.  
\(K_{4}=n_{head}=8\), \(d_{model}=128\), \(d_{attr}=1024\), \(d_{ff}=1024\).  
Table 6 shows the experimental results according to the hyperparameter setting. Accuracy was used as an evaluation index, and after defining the hyperparameter setting for the dimension of the layer in the hyper parameter of the Transformer Block (in this experiment, the hyperparameters of the Transformer Decoder Layer), we changed the number of blocks according to the setting (the number of decoder layers in this experiment) and proceeded.  
Table 6 shows the accuracy when the hyperparameter set and \(n_{layer}\) are different. As a result of the experiment, \(K_{4}=n_{head}=8\), \(d_{model}=128\), \(d_{attr}=1024\), \(d_{ff}=1024\), and \(n_{layer}=8\), where the performance was the best. Regarding the size of the model, the larger the model, the better the performance. In all hyperparameter sets, the higher the number of decoder layers \(n_{layer}\), the better the performance. However, when the size of the model is large, the performance improvement is smaller and when the size of the model is small, the difference of performance according to \(n_{layer}\) is greater.
# On the Secrecy Sum-Rate of Internet of Things Networks: Scheduling and Power Control +
## 3 Opportunistic User Scheduling with Information-Hiding Power Control
### Overall Scheduling Procedure
#### 3.2.4 Stage 3 (Uplink Communication and Post-Processing at Receiver)  
\(S\) selected transmitters simultaneously transmit their data to the base station. The base station receives the signals and decodes the desired data using the receive beamforming vectors.
## 4 Methodology
### LSTM-Based RUL Prediction with Self-Attention
#### 4.4.2 Loss Function and Metrics  
While training the LSTM-based RUL prediction model, the neural network tries to minimize the loss function. The RUL prediction network is trained as a regression model using the Mean Squared Error (MSE) loss between predicted and actual remaining life.  
The most popular and common performance metrics were applied to evaluate neural network performance. Many various metrics are used in NN applications, but there is no one standard set. That is why it was decided to use the Root Mean Squared Error RMSE, Coefficient of Determination R2, and Score function for comparison with other RUL prediction models from the literature.  
Figure 2: GMM latent feature distributions for three engine health states. \(x\)-axis: Latent dimension index; \(y\)-axis: Standardized feature value.
## 3 Results and Discussion
### Case-Study of Geomagnetic Storm on 25-26 August 2018
Our investigations of GPS RO opportunities from geostationary orbit for ionosphere soundings demonstrates that including an RO remote sensing payload as a scientific instrument will extend the GOES satellite monitoring capability of the near-Earth plasma environment and space weather. These new RO observations, when combined with the GOES Extreme Ultraviolet, X-ray, and Space Environment In Situ Suite detectors, will contribute to a deeper understanding of how the ionosphere responds to space weather.
## 5 Conclusions  
In this paper, an application for a recent optimization algorithm called the Archimedes optimization algorithm (AOA) has been proposed for reducing energy losses and to capture the size of incorporating battery energy storage system (BESS) and photovoltaic (PV) energy system in RDS. In this paper, all non-dispatchable PV energy systems have been transformed into a dispatchable energy resource with BESS integration with PV. AOA has been evolved for sizing several PV and BESS considering the changing demand over time and the probability generation. The proposed algorithm has been applied on the IEEE 69-bus distribution network with various daily demand configurations such as residential, industrial, and commercial loads demand. The obtained results demonstrate that the model can boost high penetration of the PV energy system accompanied with effective usage of BESS energy resources, which shows the strength of the presented algorithm for evaluating the best sizing of numerous PV and BESS with a significant reduction in energy losses. In addition, the AOA gives better results compared with other well-known optimization algorithms.  
Conceptualization, H.A.-M. and S.K.; data curation, M.T.-V. and E.E.E.; formal analysis, M.M.H.; methodology, M.T.-V. and E.E.E.; software, H.A.-M. and S.K.; supervision, M.M.H. and E.E.E.; validation, H.A.-M. and S.K.; visualization, M.T.-V.; writing--original draft, H.A.-M. and S.K.; writing--review and editing, M.T.-V., M.M.H. and E.E.E. All authors together organized and refined the manuscript in the present form. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Taif University Researchers Supporting Project number (TURSP-2020/86): Taif University, Taif, Saudi Arabia.  
Figure 34: BES output during the day by installing three PV with BES in commercial system load.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **AOA** & **Modified HGSO [36]** & **HGSO [36]** \\ \hline P\({}_{\text{loss }}\)(kW) Without PV and & & & \\ BES & 2173.851 & 2173.851 & 2173.851 \\ Location (PV size (kW)) & 61 (3517.3) & 61 (3517.488) & 61 (3187.526) \\ Location (PV size (kW)) & 18 (783.5) & 18 (784.1074) & 18 (860.6001) \\  & 11 (1065.6) & 11 (1064.323) & 11 (934.0223) \\ Location (BES size (kW)) & 61 (1878.5) & 61 (1878.5) & 61 (1911.138) \\ Location (BES size (kW)) & 18 (420.22) & 18 (420.843) & 18 (486.4727) \\  & 11 (564.685) & 11 (563.386) & 11 (595.803) \\ P\({}_{\text{loss }}\)(kW) & 688.1289 & 688.129 & 716.809 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison results between AOA, Modified HGSO, and HGSO algorithms in commercial system load.
# Privacy-Preserving Handover Optimization Using Federated Learning and LSTM Networks
## 4 Results
### Model Performance  
The figures illustrate the prediction results of heterogeneous federated learning Figure 7, homogeneous federated learning Figure 8, and the centralized LSTM model Figure 9.  
In Figure 7, the RSRP and near RSRP predictions using heterogeneous federated learning show a strong alignment with the actual values. This indicates that the model is highly effective in capturing the variations in signal strength and nearby cell signals under diverse conditions.  
Figure 8 presents the results for homogeneous federated learning. The RSRP predictions remain closely matched with the actual values, demonstrating the model's robustness in preserving data privacy while maintaining prediction accuracy. The near RSRP values also follow the actual data trends well, highlighting the model's capability in a more homogenous setup.  
Figure 9 showcases the centralized LSTM model's performance. The RSRP and near RSRP predictions again align well with the real values, indicating the centralized approach's effectiveness. However, the variations captured might be slightly less pronounced compared to federated learning methods, suggesting a potential trade-off between centralization and adaptability to diverse data sources.  
Overall, these figures demonstrate the effectiveness of our dynamic algorithm across different learning paradigms. The results underscore the model's robustness in predicting signal strength and nearby cell signals, adapting well to various network conditions simulated by the different datasets. This comprehensive analysis supports the potential of our approach to significantly enhance handover prediction and network performance.
# Exploring the Key Characteristics and Theoretical Framework for Research on the Metaverse
## 1 Introduction
### Metaverse Concept Overview  
The Metaverse is a virtual, parallel universe composed of countless digitized spaces. It comprises various online interactive experiences and digitized assets, providing new social and economic freedom of infinite possibilities. Within the Metaverse, users can interact with others through virtual avatars, participate in various activities, and even own and trade virtual goods and lands [2].  
The emergence of the Metaverse is primarily attributed to the rapid development and broad application of cutting-edge technologies such as virtual reality (VR), augmented reality (AR), artificial intelligence (AI), and blockchain. These technologies have provided technical support and possibilities for constructing the Metaverse, enabling its application in various scenarios and fields [5].  
Key features of the Metaverse mainly include asset digitization, application scenario-based, scope economics, industry universality, integrated innovation, and the platform ecosystem. Asset digitization allows everything in the Metaverse to be digitized and has unique ownership [6]. Application scenario-based enables the Metaverse to be applied to various scenarios, such as entertainment, education, shopping, etc. [7]. Scope economics implies that the Metaverse can provide users with infinite possibilities and opportunities [8]. Industry universality allows the Metaverse to be applied across various industries. Integrated innovation is reflected in the Metaverse's ability to merge multiple technologies and innovations, pushing the development of the Metaverse [9]. The platform ecosystem feature manifests as the Metaverse's ability to form a global, shared, decentralized digital ecosystem [10].  
To better understand and research the Metaverse, this study proposes a theoretical framework for Metaverse research, including rules of governance by law, rules of blending virtual with reality, and rules of technological associations. This theoretical framework will help us fully understand the characteristics of the Metaverse, guide the development of the Metaverse, and provide academic support for related decision making.
# A Jumping Robot Driven by a Dielectric Elastomer Actuator
## 2 Design Principle
### Jumping Mechanism  
The monopolodium jumping method was applied to the jumping mechanism of the robot. The jumping module included a spring-loaded leg, a foot, and a pull rope. The rope causes the spring to compress gradually when wound by the transmission mechanism and to exert energy stored for jumping when released.  
According to the locomotion status of the robot, we first referred to the SLIP (Spring-Loaded Inverted Pendulum) model. However, the mass of the spring-loaded leg and foot cannot be ignored; thus, the dual-mass SLIP model was applied to analyze the jumping locomotion of the robot. The jumping locomotion of the robot can be simplified into four states, as shown in Figure 5. The mass of the body is m\({}_{1}\), and we assume that the center of mass is at the body center; the mass of the leg and foot is m\({}_{2}\), and the center of mass is at the foot. In State 2 and State 4, there is no kinetic energy in the entire system, only potential energy. Therefore, the energy of State 2 and State 4 represents the maximum potential energy of the system.  
In State 2, the maximum potential energy Es\({}_{2}\) of the robot includes gravity potential energy (m\({}_{1}\)) and elastic potential energy:  
\[E_{s2}=m_{1}gh_{c}+\frac{1}{2}k(h_{d}-h_{c})^{2} \tag{11}\]  
where \(g\) is the acceleration of gravity, h\({}_{d}\) is the distance from the center of mass m\({}_{1}\) to the ground while h\({}_{c}\) is that distance after the spring-loaded leg is compressed, and k is the spring constant.  
In State 4, the maximum potential energy Es\({}_{4}\) of the robot includes the gravity potential energy of m\({}_{1}\) and m\({}_{2}\):  
\[E_{s4}=m_{1}g\big{(}h_{d}+h_{j}\big{)}+m_{2}gh_{j} \tag{12}\]  
where h\({}_{j}\) is the jumping height of the jumping robot. Note that the effects of m\({}_{1}\) and m\({}_{2}\) on the spring are ignored in the equation, as they are negligible when compared to energy stored.  
According to the law of conservation of energy,  
\[E_{s2}=E_{s4}, \tag{13}\]  
\[h_{j}=\frac{m_{1}g(h_{c}-h_{d})+\frac{1}{2}k(h_{d}-h_{c})^{2}}{(m_{1}+m_{2})g}. \tag{14}\]