esa-sceva/satcom-chunk-collection huggingface dataset:
_Applied_  
_sciences_  
Application of a Hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) Model in Behavior Prediction of Channel Shear Connectors Embedded in Normal and High-Strength Concrete  
[PERSON] 1,2, [PERSON] 3, [PERSON] 4, [PERSON] 3, [PERSON] 5, [PERSON] 6, [PERSON] 7,*, 2, [PERSON] 8,*, 3, [PERSON] 9 and [PERSON] 10  
1 Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh 758307, Vietnam; [EMAIL_ADDRESS]
2 Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam
3 School of Civil Engineering, College of Engineering, University of Tehran, Tehran 1417466191, Iran; [EMAIL_ADDRESS] (M.S.M.); [EMAIL_ADDRESS] (A.B.)
4 MSc graduate, Department of Civil Engineering, K.N. Toosi University of Technology, Tehran 15875-4416, Iran; [EMAIL_ADDRESS]
5 Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz 5157944533, Iran; [EMAIL_ADDRESS]
6 School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia; [EMAIL_ADDRESS]
7 Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
8 Civil and Environmental Engineering, Nagaoka University of Technology, 1603-1, Kami-Tomioka, Nagaoka, Niigata 940-2188, Japan
9 Center for Spatial Information Science, the University of Tokyo, 5-1-5, Kashiwa 277-8568, Japan; [EMAIL_ADDRESS]
10 Construction Research Center (CRC), Institute for Smart Infrastructure & Innovative Construction (ISIIC), School of Civil Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia; [EMAIL_ADDRESS]
* Correspondence: [EMAIL_ADDRESS] (H.N.); [EMAIL_ADDRESS] (J.D.)  
Received: 4 November 2019; Accepted: 9 December 2019; Published: 16 December 2019  
conducting costly experiments to some extent. In addition, in this case, the ANN-PSO model showed better performance than the ANN-BP model by resulting in superior performance indices.  
**Keywords:** artificial neural network (ANN); particle swarm optimization (PSO); hybrid ANN-PSO; channel shear connector; high-strength concrete (HSC); behavior prediction
###### Abstract  
The application of active magnetic bearings (AMBs) in high-speed rotating machinery faces the challenge of micro-vibration. This research addresses the vibration control of a high-speed magnetically suspended turbo molecular pump (MSTMP) with rotor mass imbalance vibration and multi-stage-blade modal vibration. A novel integrated AMB controller consisting of parallel co-frequency adaptive notch filter (ANF) and cascaded multi-frequency improved double-T notch filters (DTNFs) is proposed. To suppress rotor mass imbalance vibration, a bandwidth factor rectification method of the ANF based on displacement stiffness perturbation is designed. To suppress multi-stage-blade modal vibration, a multi-objective constrained optimization method of cascaded improved DTNFs based on linear normalization is designed. Simulation and experimental results validate that the proposed structure improvement of the addition of an AMB controller and multi-parameter optimization of the algorithm can effectively improve not only the phase stability margin and the notch vibration performance of the magnetically suspended rotor (MSR) system but also the efficiency and practicability of the algorithm. At rotational speeds of 12,000 rpm, 15,000 rpm, 18,000 rpm, and 21,000 rpm, the suppression of co-frequency synchronous vibration is approximately maintained between \(-\)30.94 dB and \(-\)30.56 dB. At the rated speed of 24,000 rpm, compared with other algorithms, the value of the rotor displacement converges from 0.08 mm to 0.03 mm, a reduction of 62.50%. The convergence time decreases from 3.67 s to 2.85 s, a reduction of 22.34%.  
active magnetic bearing; magnetically suspended turbo molecular pump; rotor mass imbalance vibration; multi-stage-blade modes; notch filter; multi-objective constrained optimization
# Machine Learning-Based Satellite Routing for SAGIN IoT Networks
## 2 Architecture Design
### Satellite-Based IoT (SIoT)
#### 2.1.3 Related Works on SIoT  
Author [PERSON] mainly introduces the architecture of a LEO satellite constellation for IoT, which contains some access and routing protocols, the structure of a LEO satellite constellation, and compatibility of heterogeneous networks [10]. Author [PERSON] introduces a satellite IoT based on micro-satellite technology and SDN and the impact of orbital configurations [11].  
A satellite-based IoT network is shown in Figure 2. Satellites and IoT are connected by wireless links. The ellipse below represents the satellite coverage area, and there are IoT networks in it. The red dots represent the sensor nodes and the triangular towers represent the IoT trans-receivers. The SIoT network is formed between the satellites and IoT network on the Earth's surface through the above methods.
# Recording a Hologram Transmitted over a Communication Channel on One Sideband
## 1 Introduction
These problems are first of all connected with the necessity to make fast Fresnel transforms describing the propagation of the complex amplitude of the electromagnetic field in the space from the object to the hologram, and from the hologram to the observation plane within the scalar approximation of diffraction theory [20], as well as with the practical difficulties in the realization of point-by-point recording of large data arrays. In addition, this type of hologram is not reconstructed with white light--firstly, because the spectral blooming \(\Delta y\) of the points in the image, which is proportional to spectrum width \(\Delta\lambda\) and the distance \(Z\) from the hologram plane to the point of holographed image, is characterized by substantial extension at a large distance from the hologram, as shown by Equation (1), where \(\theta_{R}\) is the angle of reference beam inclination:  
\[\Delta y_{\lambda}\approx(\Delta\lambda/\lambda)\cdot Z\cdot\theta_{R}, \tag{1}\]  
Secondly, the angular size \(\theta\) of white light sources is much larger than that of a laser source, which also leads to blurring. See Equation (2):  
\[\Delta y_{\theta}\approx\theta\cdot Z. \tag{2}\]  
For a white light source (\(\Delta\lambda\) = \(\lambda\)), with the angular size of the luminous body \(\theta\) = 1/200, placed at a distance of best viewing distance (\(Z\) = 250 mm), these values will be \(\Delta y_{\lambda}\) = 250 mm and \(\Delta y_{\theta}\) = 1.2 mm, respectively. One can see in Equations (1) and (2) that the values of both types of blurring are proportional to the distance from the image to the hologram, so it is convenient to consider so-called focused image holograms [21], because blurring is minimal in this case. A dynamic monitor operating in the real time mode can recover the image by laser radiation, and then three lasers will be necessary for a full-color image (RGB color coding); otherwise, white color may be used ([PERSON]'s thick hologram and [PERSON]'s thin rainbow holograms). We chose the holograms recovered with white light, and among them, [PERSON]'s holograms [22], because it is very difficult to synthesize [PERSON]'s material holograms from their digital models. In addition, in rainbow holograms, the entropy decreases due to the elimination of the vertical parallax, which causes a decrease in the amount of information in the hologram; information overload not only hinders the transmission along communication channels [23], but also brings complications into computer synthesis. Moreover, the horizontal parallax is in much higher demand than the vertical one, so at the first stage we chose [PERSON]'s rainbow hologram for experimental recording.
# A Long Short-Term Memory Network-Based Radio Resource Management for 5G Network
## 3 LSTM-RRM Method
### GSO-Based Hyperparameter Tuning for LSTM  
In this phase, the GSO [33] is used for optimizing the hyperparameters of the LSTM. The set of hyperparameters processed under hyperparameter tuning are the amount of neurons, learning rate, regression rate (reg_rate), size of batch, and epochs, as given in Table 1. This GSO is used to obtain optimal results when the hyperparameters are not significant. The values given in Table 1 are the range of values in the search space, and the hyperparameter tuning is done according to the power and bandwidth. Here, GSO with 10-fold cross-validation is performed to select the optimal value within the given range of hyperparameters.  
Therefore, this LSTM network is trained using the bandwidth, power, and data rate of the UE in the 5G network. The number of data used to train the LSTM is 3628800, which are taken from the simulation when the 5G network is operated without LSTM.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Range of Values** \\ \hline epochs & 1–200 \\ Neurons & 10–200 \\ reg\_rate & 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5 \\ learning rate & 0.1–0.9 \\ batch\_size & 73, 146, 219, 500, 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of LSTM.  
Accordingly, the UEs with higher QoS level are maintained by allocating an adequate power and bandwidth using the LSTM. Consequently, the bit rate of the 5G network is increased during the communication process.
## 4 Results and Discussion
### Comparison of the Results of Different Prediction Models
The SSA-SVR, SSA-GRNN, and SSA-ELM models exhibited significantly enhanced predictive performance in contrast to the standard SVR, GRNN, and ELM models, respectively. Among them, the SSA-ELM model attained the highest degree of predictive accuracy, with MAE, MAPE, MSE, RMSE, and TIC values of 2.18, 0.10, 7.57, 2.75, and 0.07, respectively. When compared to the RMSE values of the SSA-SVR and SSA-GRNN models, the SSA-ELM model displayed a reduction of 24.90% and 30.84%, respectively. In comparison with the SVR model, the MSE and RMSE values of the SSA-SVR model decreased by 82.21% and 57.82%, respectively. In a previous study, the SSA was used to finely adjust the penalty factors and kernel function parameters of an SVR model for drilling risk assessment, and it was discovered that the model's accuracy surpassed that of the BPNN [41]. When contrasted to the GRNN model, the MSE and RMSE of the SSA-GRNN model were reduced by 14.27% and 7.41%, respectively. In opposition to the ELM model, the MSE and RMSE of the SSA-ELM model were lessened by 81.00% and 56.41%, respectively. Researchers have previously demonstrated that the SSA-ELM model performed better than the ABC-ELM, PSO-ELM, and GA-ELM models in terms of accuracy in evapotranspiration prediction [42]. These results emphasize the importance of parameter optimization for the SVR, GRNN, and ELM models, mainly because the SSA is outstanding in search accuracy, convergence speed, stability, and the avoidance of local optima, thereby effectively optimizing model parameters and enhancing the models' predictive capabilities [24; 42]. (3) Analysis of the results of combined prediction models  
Table 3 shows that there were differences in the fit quality among the 10 predictive models, and the optimal neural network residual correction prediction method using the IOWA operator achieved the best fit. The proposed model presented the MAE, MAPE, MSE, RMSE, and TIC values of 1.52, 0.07, 5.20, 2.28, and 0.06, respectively. Compared with the SVR, GRNN, and ELM models, the RMSE of the proposed model showed reductions of 73.76%, 46.94%, and 63.88%, respectively. When contrasted to the PSO-SVR, PSO-GRNN, PSO-ELM, SSA-SVR, SSA-GRNN, and SSA-ELM models, the RMSE of the proposed model exhibited decreases of 69.20%, 43.83%, 30.70%, 37.77%, 42.69%, and 17.14%, respectively. This indicates that the proposed model has achieved varying levels of enhancement in predictive outcomes. Compared with the PSO-SVR, PSO-GRNN, PSO-ELM, SSA-SVR, SSA-GRNN, and SSA-ELM models, the proposed model was found to have lower MAE, RMSE, MAPE, and TIC values. Therefore, the predictive performance of the proposed model is good, and the model can better capture the complex characteristics of agricultural product prices. Thus, it can obtain more accurate forecasting results.
# Mind, Machine, and Meaning: Cognitive Ergonomics and Adaptive Interfaces in the Age of Industry 5.0
## 3 Ergonomic Techniques in Human-Machine Interactions
### Modern Solutions for Improving Human-Machine Interactions
#### 3.3.3 Augmented and Virtual Reality in Operator Training: Applications, Methods, and Examples
The University of Zilina study showed measurable performance gains in AR/VR groups versus traditional instruction. In corporate HR trials, VR onboarding modules improved user retention and reduced time-to-productivity by over 30% [144]. The more recent challenges are as follows [145; 147; 148]:  
* Cost--high upfront investments in hardware, software licenses, and content creation;
* Scalability--difficulties in deploying across geographically distributed teams;
* Resistance to change--especially among older employees unfamiliar with digital tools;
* Technical limitations--VR sickness, display resolution, latency issues, and limited field of view;
* Privacy and security--the use of cameras and sensors raises concerns in sensitive environments;
* Content maintenance--updating 3D models and procedures as processes evolve requires dedicated resources. In conclusion, combining augmented and virtual reality in operator training represents a leap forward in industrial education. From immersive learning to real-time assistance, AR/VR offers scalable, efficient, and engaging methods for upskilling workers. Future research should focus on integrating these technologies with AI, digital twins, and IoT systems to unlock their full potential. Tailored, adaptive, and data-driven training platforms \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Hardware Used** & **Benefits** & **Measured Outcomes** & **Method** \\ \hline VR Simulator & HTC Vive Pro & \begin{tabular}{c} Safe, immersive \\ learning \\ \end{tabular} & \begin{tabular}{c} Reduced errors, \\ faster completion \\ \end{tabular} & VR Simulator [142] \\ \hline AR Step Guidance & Microsoft HoloLens & \begin{tabular}{c} Hands-free, real-time \\ support \\ \end{tabular} & \begin{tabular}{c} Improved accuracy \\ and efficiency \\ \end{tabular} & \begin{tabular}{c} AR Step \\ Guidance [142] \\ \end{tabular} \\ \hline MR Collaborative & CAVE System & \begin{tabular}{c} Multi-user training \\ scenarios \\ \end{tabular} & \begin{tabular}{c} Enhanced \\ coordination \\ \end{tabular} & \begin{tabular}{c} MR \\ Collaborative [144] \\ \end{tabular} \\ \hline CNN Object & \begin{tabular}{c} Epson Moverio AR \\ \end{tabular} & \begin{tabular}{c} Automated guidance, \\ fast training \\ \end{tabular} & \
\begin{tabular}{c} Reliable \\ identification in AR \\ task \\ \end{tabular} &
\begin{tabular}{c} CNN Object \\ Detection [144] \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparative table of methods [142; 144]. powered by immersive tech may become the new standard in industry, ensuring continuous learning and workforce resilience.
## Materials and Methods  
### Materials  
Multi walls carbon nanotubes (MWCNTs) with lengths of \(\sim\)5 \(\upmu\)m and purity of \(>\)90%, were supplied from Sigma-Aldrich (USA) and utilized without extra refinement. Methanol (Analar, 99.8%, Sigma-Aldrich, Germany), resorcinol (99%, Sigma-Aldrich, Germany), formaldehyde (37-41% in water, Sigma-Aldrich, Germany), Na\({}_{2}\)CO\({}_{3}\) (anhydrous, ACS, 100%, Fisher, Germany), HNO\({}_{3}\) (70.4%, Sigma-Aldrich, Germany), ammonium hydroxide (24%, Fluke, Germany), acetone (99%, BDH, England) and acetic acid (Analar, BDH, USA) were utilized as obtained. Ultra-purified H\({}_{2}\)O generated from the Milli-Q integral purification system (Elix(r)70, France) was used in all experiments. Carbon aerogels were synthesized in-house as described in Sections 2.2 and 2.3. CO\({}_{2}\), CH\({}_{4}\) and N\({}_{2}\) gases were supplied by the National Industrial Gas Plants (NIGP, Qatar) with high purities (\(>\) 99.999%).
# Faster MDNet for Visual Object Tracking
## 4 Experiments
### Results on TrackingNet  
The TrackingNet [26] dataset contains more than 30,000 videos in 27 categories, with a larger number of videos and annotations than any previous dataset. Simultaneously, the dataset makes a distinction between the training set and the testing sets, with no overlap  
Figure 4: Comparisons of different trackers in terms of parameters, FLOPs, and EAO values on VOT2018. The horizontal coordinate represents the floating-point operations (FLOPs) of the model, and the vertical coordinates denote the ratio of the EAO value to the number of parameters measured. Our tracker achieves a better result than other current methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Trackers** & **Accuracy** & **Robustness** & **EAO** \\ \hline ATOM [14] & 0.59 & 0.20 & 0.401 \\ C-COT [5] & 0.50 & 0.32 & 0.267 \\ DaSiamRPN [10] & 0.59 & 0.28 & 0.383 \\ DiMP [15] & 0.60 & **0.15** & 0.440 \\ ECO [6] & 0.48 & 0.28 & 0.276 \\ MDNet [20] & 0.55 & 0.38 & 0.383 \\ PrDiMP [16] & **0.62** & 0.17 & **0.442** \\ SiamFC [7] & 0.50 & 0.59 & 0.188 \\ SiamRPN [8] & 0.59 & 0.28 & 0.383 \\ SiamRPN++ [11] & 0.60 & 0.23 & 0.414 \\ Ours-conf & 0.60 & 0.21 & 0.409 \\ Ours & **0.62** & 0.16 & 0.432 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of our model with other high-performing trackers on VOT2018 with three metrics; accuracy, robustness, and EAO value.  
between the two sets. The large-scale video provided by this dataset can effectively alleviate the current problem of insufficient training data in the field of object tracking.  
As shown in Table 4, TransT [18] achieves the highest AUC score, PrDiMP [16] gains an AUC score of 0.750, and our model follows closely. The proposed method a the precision of 0.696 and reaches results comparable to PrDiMP [16] for normalized precision.
## 2 Materials and Methods
### Training and Testing the Models  
To train the model, Python 3.12.0 and XGBoost 2.0.3 [49] were used. XGBoost is an optimized gradient boosting library that builds decision tree ensembles to solve classification and regression tasks efficiently. An important aspect of model training is mitigating overfitting and underfitting. To achieve this, XGBoost implements various techniques. To address underfitting, it allows for controlling model complexity through parameters such as the maximum tree depth and the number of boosting rounds. To prevent overfitting, XGBoost incorporates mechanisms such as regularization, the minimum child weight for leaf nodes, and random sampling of data and features, effectively reducing model complexity and improving generalization.  
The algorithm was calibrated, and 10-fold cross-validation was employed, generating a model with 90% of the data and testing it with the remaining 10%. This means that alternately in 10 subgroups, all the data were used to test the model, obtaining more robust validation data and preventing overfitting and underfitting of the model. This procedure was carried out analogously to [43].  
The input variables for training the models include physical and geographic characteristics. The target variables to be predicted are the energy information, the NRPEC in kWh/(m2y) and the GHG emissions in kgCO2 eq/m2y, using as predictor variables the geographic and physical information.  
The data used were:  
* Energy information
* NRPEC (kWh/m2y)
* GHG emissions (kgCO2 eq/m2y)
* Physical characteristics
* Use (residential, offices, etc.)
* Number of floors
* Number of building units by use
* Housing size (m2)
* Year or construction
* Building typology
* Building type according to the Spanish Long-Term Renovation Strategy (LTRS)
* Building quality
* Roof area (m2)
* Year of renovation
* Renovation cost
* Type of municipality
* Geographical characteristics
* Georeferenced building
* Building perimeter
* Climate zone
* Percentage of exposed surface of the envelope
* Volume
* Compactness
## 2 Materials and Method
### Finite Element Analysis (FEA)  
In this study, the DEFORM 3D (Version 11.0) software was employed to conduct a FEA to simulate the Vo-CAP process. The simulation setup, as illustrated in Figure 2, consisted of four main components: the workpiece, punch, die, and die holder. The workpiece was modeled as a deformable body using AA6082 material properties, with a diameter of 20 mm and a length of 40 mm. Importantly, the workpiece temperature was maintained at a constant 210 \({}^{\circ}\)C throughout the analysis, simulating isothermal processing conditions.  
Based on the mesh convergence study, the workpiece was meshed with 60,000 tetrahedral elements, which provided optimal accuracy for strain and stress calculations while maintaining computational efficiency. Furthermore, to account for substantial deformations during the analysis, an automatic re-meshing technique was implemented.  
The punch and die were modeled as rigid bodies to simplify the computational requirements while accurately capturing the deformation behavior of the workpiece. This approach allows the software to focus on the material flow and strain distribution within  
Figure 2: FEM setup of Vo-CAP.  
the deformable workpiece. A critical aspect of the simulation was the interface between the workpiece and die. The shear coefficient of friction was determined to be 0.1, a finding that was arrived at through a comprehensive review of the extant literature, a rigorous testing program of lubricants conducted within an experimental environment, and a detailed analysis of the catalog values of lubricants, and the punch velocity was set at 1 mms\({}^{-1}\). The objective was to press at the slowest possible speed, despite the fact that, in theory, this parameter does not appear to be effective in the process, given its impact on the recovery process during material flow. In addition, the punch movement was constrained in all directions except for its motion axis, providing stability during the pressing process.  
The die stroke was set as a control parameter, with appropriate stopping controls to define the process limits. To stop the simulation, the value of the stroke was set to be equal to the length of the workpiece, i.e., 40 mm. Thanks to this comprehensive setup, an accurate modeling and analysis of the Vo-CAP process under hot forming conditions has been made possible.  
The FEM model used in the present study has been validated by comparison with the study carried out by [PERSON] et al. [24]. In this context. the same material (AA1050) and same process parameters were applied and resimulated. The obtained effective strain and highest pressing load results were compared for validation.
# Wearable Flexible Antenna for UWB On-Body and Implant Communications
## 3 Methodology  
The novel antenna is evaluated with (1) electromagnetic simulations and (2) measurements using the prototype of the antenna. These methodologies are described in the following subsections.