esa-sceva/satcom-chunk-collection huggingface dataset:
# Virtual Reality or Videoconferencing for Online Learning? Evidence from Comparative Meta-Analyses
## 4 Results and Discussion
### Moderator Analysis for VC Platform-Supported Online Learning
#### 4.3.2 Technological Features in VC Studies  
Regarding the camera option, 17 studies turned on the cameras (48.6%), while more studies chose to turn them off (51.4%). A statistically significant moderating effect was found for the camera option (\(Q_{B}\) = 4.115, \(p\) < 0.05). In particular, the effect size of having cameras on (\(g\) = 0.433) was greater than when cameras were off (\(g\) = 0.148). This finding corroborates the ideas presented in previous research [65; 66] and further confirms the importance of camera use in online learning. When cameras are turned on, students can receive visual supervision from the teacher, which helps them focus on the course. They can also observe the status of other students, which can stimulate peer-driven learning motivation and encourage them to work harder to achieve better learning performance. When cameras are off, students often have to accept the learning content on the screen passively, and their connection with teachers and classmates is reduced. This learning mode not only causes fatigue but may also make students feel lonely, thus affecting their learning.  
Zoom was the most commonly used VC platform (45.7%), followed by Tencent Meeting (8.6%), Gather (8.6%), and video games (5.7%). The platform was also identified as a significant moderating variable (\(Q_{B}\) = 35.887, \(p\) < 0.001). Among the mainstream platforms, the intensity of the effect varied. Specifically, the use of a video game [85] as a VC platform had the strongest effect size (\(g\) = 1.270), followed by Gather (\(g\) = 0.927), Tencent Meeting (\(g\) = 0.663), and Zoom (\(g\) = 0.071). Surprisingly, although Zoom and Tencent Meeting have a considerable share of the VC market, their effect sizes were lower than those reported for Gather and video games. One explanation for this phenomenon could be that students may experience fatigue and burnout from prolonged use of traditional VC platforms [86], which may potentially weaken the effectiveness of these platforms in supporting online learning.
# Period Cycle Optimization of Integrated Energy Systems with Long-Term Scheduling Consideration
## 1 Introduction
[21] developed a multi-objective system optimization model to design strategies for electrical load (FEL) and thermal load (FTL), aiming to enhance system efficiency and reduce overall costs. [PERSON] et al. [22] employed a multi-objective bi-level optimization approach to improve the feasibility and overall benefits of planning schemes. [PERSON] et al. [7] utilized an Improved Particle Swarm Optimization method (ILPSO) to reduce the peak-to-average ratio (PAR) and energy usage costs in smart homes. [PERSON] et al. [23] employed a multi-objective operational optimization algorithm to decrease the operational costs of agricultural integrated energy systems while enhancing the photovoltaic penetration rate. [PERSON] et al. [24] proposed an electric-thermal coupling interconnect structure and an energy flow model for establishing a multi-energy complementary system in oilfield well sites. Additionally, a dynamic optimization method for source-load storage was employed to achieve overall performance scheduling of the system. The aforementioned achievements have laid a solid foundation for the research presented in this paper. However, considering the need for further in-depth exploration in the existing literature regarding energy equipment capacity, load variations, and long-term system scheduling optimization, as well as the limited discussion on the economic evaluation of systems, this paper aims to investigate performance optimization and economic feasibility issues in the long-term scheduling of integrated energy systems. A methodology based on genetic algorithms and deep learning is proposed for energy and component cycle optimization, which divides the system's long-term operating time into multiple operating periods and dynamically captures the component load conditions, energy dynamic interference, and response during each period. Through long-term, multi-period optimization in computational examples, real-time scheduling data, energy saving and emission reduction effects, and economic feasibility are obtained, thereby verifying the improved operational benefits of long-term scheduling optimization for the system.
## 3 Results and Discussion
A further decrease is expected when substituting elements with a smaller atomic radius (e.g., Si, as discussed in the present paper) on Fe sites. The situation was slightly different when comparing various stable intermetallic phases containing the same chemical elements. In this case, the crystalline structure and hence varying interatomic distances played a more significant role. Therefore, the discussion that excludes mentioned factors is rather incomplete, but some general trends are still valid. The complex Zr-Fe-Si system with a vast number of stable intermetallic phases can be treated as an example [47]. Intermetallics with a relatively low content of Si and Zr (in comparison to Fe) are characterized by enthalpy of formation values equal to approximately \(-\)52 kJ/mol and \(-\)56 kJ/mol for Fe\({}_{16}\)Si\({}_{7}\)Zr\({}_{6}\) and Fe\({}_{3}\)SiZr\({}_{2}\), respectively [48]. A decreasing content of Fe (Fe\({}_{4}\)Si\({}_{7}\)Zr\({}_{4}\)) leads to a more negative formation enthalpy equal to approx. \(-\)68 kJ/mol [48]. We observed a similar trend for the formation enthalpy of a solid solution in the Zr-Fe-Si system (Figure 2), with slightly less negative values of enthalpy compared to the intermetallics. The enthalpy changes from \(-\)28 kJ/mol for Fe\({}_{3}\)SiZr\({}_{2}\), through \(-\)31 kJ/mol for Fe\({}_{16}\)Si\({}_{7}\)Zr\({}_{6}\), to \(-\)48 kJ/mol for the Fe\({}_{4}\)Si\({}_{7}\)Zr\({}_{4}\) composition. In general, intermetallic phases are favored for stoichiometric (1:12 and 2:17) compositions, but it is believed that the further introduction of chemical disorder on rare earth and Fe sites can result in the formation of a solid solution (as in the case of high entropy alloys) or even amorphous phase. In this instance, the energy convex hull [12] can be used to determine the relative stability of the phases. The formation of a glassy structure can be also favored by the utilization of non-equilibrium synthesis methods.
## 5 Conclusions  
The preliminary design and the reliability analysis of a hybrid electric power system for the retrofitting of an ultralight airplane were addressed in this paper. The goals of the preliminary design were three: ensuring the safety of aircraft operation in case of engine failure, proposing a less complex power split device with respect to a previous investigation of some of the authors, and saving fuel despite the usage of a low-efficiency type of engine (Wankel). The results of the present investigation showed that all these goals can be achieved with series/parallel power systems, a configuration hitherto neglected in the aerospace industry. In particular, it was found that a series/parallel configuration with an adapted Honda hybrid system increases the reliability by 33% with respect to the conventional configuration. The simplicity of the device was guaranteed by replacing the planetary system with a helical gearbox used in a previous study and by considering a fixed speed propeller that also allows obtaining a relevant fuel saving (with respect to a full thermal configuration) with a very simple energy management strategy. The battery and the electric machines were sized by considering the power request of the power system in three different operating modes: all-electric (in case of engine failure), power assist (parallel), and series. In further investigations, a new aircraft prototype will be designed and the proposed power system will be optimized with the goal of reducing the weight and bulk in order to meet the requirements of maximum take-off weight for ultralight aviation according to Italian legislation.  
Conceptualization, T.D. and L.S.C.; methodology, T.D.; software L.S.C.; investigation, T.D. and L.S.C.; writing--original draft preparation, T.D. and L.S.C.; writing--review and editing, T.D.; supervision T.D.; project administration, T.D. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Configuration** & **Reliability** \\ \hline Conventional & 0.72519 \\ Series hybrid & 0.969327 \\ Power assist & 0.970835 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Reliability of different system configurations.  
**Funding:** The research was funded by the Regional program \"RIPARTI\", POC PUGLIA FESERTTSE 2014/2020, project code F87G22000270002.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The industrial partner's policy prevents public access to the data.  
**Acknowledgments:** The authors would like to thank Promecec Aerospace s.r.l. for supporting the research activities and in particular for providing valuable information about the reference aircraft.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Appendix  
Figure A1: Markov chain of the proposed series parallel HEPS.
## 3 Simulation Model
### First Stage--Routing Path Determination  
The aim of the routing strategies proposed is to produce edge-router-based routing solutions able to minimize burst contention using only topological network information. We assume that the network operates with source based routing, that is, the ingress edge node selects a path for a burst that enters the network from a set of \(K\) previously calculated paths. Therefore, this \(1^{st}\) stage comprises itself two parts: first, the calculation of \(K\) eligible paths for each pair of nodes, and second, the selection of one path from the set of \(K\) eligible paths for each pair of nodes so that the chosen paths minimize the global network contention. For the first part, the \(K\) most link disjoint shortest paths are determined. For the second part the following three optimization strategies for contention avoidance have been calculated: the minimize Maximum Congested Link (MCL), based on the idea that the more a certain link is included in the chosen paths for source-destination pairs, the highest the blocking probability can be [13]; the minimize Maximum End-to-end Congested (MEC) path, based on the idea that blocking may occur at any link traversed by a burst along the path [13]; and one Streamline Based Pre-planned Routing (SBPR) strategy [14] considering the streamline effect, a reported phenomenon unique to OBS networks wherein bursts traveling in a common link are streamlined and do not contend with each other until they diverge [15]. Mathematical formulations for these routing strategies will be presented in Section 4.
# Robust Estimators in Geodetic Networks Based on a New Metaheuristic: Independent Vortices Search
## 5 Results and Discussion  
For better comprehension, this section is divided into three subsections: Section 5.1 presents the optimization with IVS and comparison with the performances of other MHs. Section 5.2 shows the estimated impact of the trim limit and median position in some REs, when applied to the geodetic network adjustment. Section 5.3 presents the results for the network adjustments, applying IVS and testing all the REs (including the trim limit and median position variations) in 900 error scenarios. The adjustments were organized in three topics: no outlier case, small magnitude outliers, and large magnitude outliers.
## 5 Discussion and Conclusions  
A general approach for terrain and slippage classification has been proposed and validated through field experimental data. A SherpaTT prover testing campaign performed in Morocco and Utah deserts provided proprioceptive datasets on different soil types. A set of features have been defined and extracted from raw experimental data. Two labels for terrain type (Sand-Rock) and slippage level (High Slip-Low Slip) have been associated with each terrain patch, together with a set of statistical features (predictors), obtained from the computation of feature statistical moments on the terrain patch. Different selection methods based on validity index have been tested and proved to be effective for the extraction of relevant features. Different selection methods based on validity index have been tested. The tested validity indices are the \(WB\) index and a Pearson Coefficient (\(PC\))-based index. Both indices are demonstrated to be effective for the extraction of relevant features. Only the features selected by both \(WB\) and \(PC\) methods have been kept in the reduced feature set used for classifiers training and testing. Thanks to the selection, the initial statistical feature set of 60 features was reduced to 9 features  
Figure 8: Confusion matrix for training (**a**) and testing (**b**) of the terrain classifier.  
Figure 9: Confusion matrix for training (**a**) and testing (**b**) of the slippage classifier.  
and to 2 features for slippage classification. The reduced feature set observations, one per terrain patch, were randomly subdivided into a training set and a testing set, including, respectively, 75% and 25% of the observations. The two classifiers were trained on the training set and then tested on the new samples of the testing set, giving as result a global classifier accuracy of 96.7% for terrain classification and 98.8% for slippage classification.  
New experimental tests will be performed in the near future on different terrain types and during various rover maneuvers. Those new data will be used to further validate the proposed approach and to improve the classifier generalization. The combination of proprioceptive sensing with exteroceptive perception, as suggested in [18], will be also investigated.  
Conceptualization, M.D., G.R.; Methodology, M.D., G.R.; Data curation and experiments and validation, F.C.; writing--original draft preparation, M.D, G.R.; writing--review and editing, F.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the Horizon 2020 European Commission under grant agreement n. 821988 ADE. The authors declare no conflict of interest.
# Two Medoid-Based Algorithms for Clustering Sets
## 3 Medoid-Based Approaches  
In the approach proposed in this paper, we use _medoid_[9; 10] as the cluster representative instead of a histogram. Medoid is defined as the data object in the cluster with a minimal sum of the distances to the other data objects in the same cluster. The medoid-based approach has two advantages. First, medoid is a more natural cluster representative than a histogram and does not require any parameters such as the threshold for the histogram size. Second, we can use the same distance measures for sets as in [8] but with an adaption of the measures which considers the global frequency of using elementary items in the application dataset.  
Specifically, we present two novel medoid-based algorithms. The first algorithm is the classical K-medoids adopted to the sets. The second algorithm is the random swap variant, in which medoid is used instead of the mean of the objects as the clustering representatives.
# Optimization of Intelligent-Reflecting-Surface-Enabled Covert Communication in Multi-Sensor Systems
## 6 Problem Reformulation
```
1:Input: Learning rate \(\alpha\), maximum iterations \(T\), convergence threshold \(\epsilon\)
2:Initialization: Randomly initialize \(\theta_{1}^{0}\) and \(\theta_{2}^{0}\) in interval \([0,\pi]\)
3:Compute gradients:
4:\(\forall_{\theta_{1}}\leftarrow\frac{\frac{\partial\mathcal{R}(\theta_{1}, \theta_{2})}{\partial\theta_{1}}}{\partial\theta_{2}}\)
5:\(\forall_{\theta_{2}}\leftarrow\frac{\partial\mathcal{R}(\theta_{1},\theta_{2})} {\partial\theta_{2}}\)
6:Compute the covert transmission rate:
7:\(\mathbb{R}^{0}=\mathcal{R}(\theta_{1}^{0},\theta_{2}^{0})\)
8:for\(t=0\) to \(T\)do
9: Update parameters:
10:\(\theta_{1}^{t+1}\leftarrow\theta_{1}^{t}-\alpha\cdot\forall_{\theta_{1}}( \theta_{2}^{t})\)
11:\(\theta_{2}^{t+1}\leftarrow\theta_{2}^{t}-\alpha\cdot\forall_{\theta_{2}}( \theta_{1}^{t})\)
12: Update the covert transmission rate:
13:\(\mathbb{R}^{t+1}=\mathcal{R}(\theta_{1}^{t+1},\theta_{2}^{t+1})\)
14: Check for convergence:
15:if\(\|(\mathbb{R}^{t+1},\mathbb{R}^{t})\|<\epsilon\)then break
16:endfor
17:Output:\(\mathbb{R}^{t+1}\),\(\theta_{1}^{t+1}\) and \(\theta_{2}^{t+1}\)
```  
**Algorithm 1** Gradient Descent Optimization Algorithm with Random Initialization
# Hydrogen Production from Offshore Wind Parks: Current Situation and Future Perspectives
## 3 System Configurations
### Offshore Electrolyzer Scenario
#### 3.1.2 Individual Electrolyzers  
When sufficient wind is present, most of the electricity is fed into the rectifiers to power the electrolyzers and possibly refill the backup power source. The remaining power is used to power the seawater pumps, which need AC electricity. In the case of no offshore compression, the produced hydrogen exits the electrolyzer and is exported by a small dimension pipeline to a subsea collection manifold, which receives the hydrogen produced by each turbine-electrolytez system and exports it to shore using a bigger diameter pipeline. However, if offshore compression is needed, the hydrogen exits the electrolyzer and is exported by a small dimension pipeline to a collection manifold in a platform, compressed to a desired pressure, and exported to shore by a pipeline.  
This approach becomes more viable as the nominal power of a turbine keeps increasing, since more powerful electrolyzers can be installed individually, and economies of scale can play their part [23]. In [23], it is projected that the price per MW of a PEMEL will decrease from the current (2020) 0.75 M\(\&\)/MW to 0.2 M\(\&\)/MW by 2050. It is also projected that at around 10 MW, the cost per MW of hydrogen production power starts decreasing at a much slower rate, especially after the year 2030.  
Since bottom fixed and some floating options, such as a spur buoy, require significant modifications to be able to support the extra infrastructure, the semi-submersible platform like the one used in WindFloat Atlantic is the best choice for the individual electrolyzer approach [10]. To make the platform suitable for all the equipment, modifications need to take place, such as creating a floor on which to put the equipment that is shielded from waves and possible water splashing, as well as modifying the buoys and ballast to accommodate the additional weight.
## 3 Numerical Model
### Grid Independence and Model Validation  
To verify the independence of the grid, three grid members with cells of 655,132, 741,451, and 805,315 were adopted separately. The air flow rate into the tower was calculated using three individual grids. Then, the heat transfer rate of the three cases was compared, displayed in Figure 9. The heat transfer rate of 655,132 cells is 5% greater than that of 741,451 cells. The result varies negligibly when the number of grid nodes exceeds 800,000: the results of 805,315 cells is 1% less than that of 741,451 cells, indicating that further refinement of the grids has little influence on the numerical results. Thus, the grid number of 741,451 was chosen for the following computations.  
To validate the numerical model, thermal efficiency coefficients of the cooling tower from the numerical model are compared with those from the experiment, shown in Figure 10, which depicts  
Figure 8: Computational domain and air flow boundaries.  
Figure 9: Grid independence test.  
the effect of crosswind on the thermal efficiency coefficients. The maximum deviation of the thermal efficiency coefficient between the numerical simulation and experiment is 13.02% when the dimensionless velocity is 0.5, and the mean difference between them is 4.9%. The results of the numerical simulation agree well with those of the experiment.
# Fault Diagnosis Method for Rolling Bearings Based on Two-Channel CNN under Unbalanced Datasets
## 4 Experimental Analysis
### Model Parameters  
The paper is set with a sample of 1024 data points, \(p=0.5\), \(\lambda=0.5\) in GST: (1) The original test signal of \(1\times 1024\) dimension is performed FFT, and the \(1\times 513\) dimension frequency spectrum is obtained. (2) The original test signal of \(1\times 1024\) dimension is compressed to a \(64\times 64\times 3\) time-frequency image through GST. The purpose of compression is to highlight the primary feature information and not drown out other information, reduce the interference of background information, and improve the proportion of main features. The time-frequency image and frequency spectrum are the input of 2D-CNN and 1D-CNN, respectively. (3) The features extracted from the two CNN models are combined to obtain the final fault features. The network model parameters are obtained based on actual test results. The parameters set for the two networks are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Location** & **Fault Diameter (inch)** & **Fault Orientation** & **Label** \\ \hline \(f_{1}\) & Ball & 0.007 & - \\ \(f_{2}\) & Ball & 0.014 & - \\ \(f_{3}\) & Ball & 0.021 & - \\ \(f_{4}\) & Inner race & 0.007 & - \\ \(f_{5}\) & Inner race & 0.014 & - \\ \(f_{6}\) & Inner race & 0.021 & - \\ \(f_{7}\) & Outer race & 0.007 & Center @6:00 \\ \(f_{8}\) & Outer race & 0.014 & Center @6:00 \\ \(f_{9}\) & Outer race & 0.021 & Center @6:00 \\ \(f_{0}\) & Normal & - & - \\ \hline \end{tabular}
\end{table}
Table 1: Rolling bearing fault information.  
Figure 5: Rolling bearing experimental facility.  
The last fully connected layer \(FC_{2}^{*}\) is an SVM classifier, and the kernel function is Gaussian kernel. The kernel parameter \(\gamma=0.01\), and the penalty factor \(C=1\). The remaining parameters are as follows: the dropout ratio is 0.5, the learning rate is 0.005, the mini-batch size is 8, the total epochs is 500, and the loss function is cross-entropy.
## 3 Numerical Configuration
### Meshing Strategy
#### 3.1.2 Mesh Configuration 2D  
As is justified in Section 4, there was a stage where a 2D axisymmetric geometry was used for its optimization. Figure 6 shows the used domain, where the dashed line marks the correspondent axisymmetric part of the original geometry. Therefore, a 2D mesh configuration with its correspondent independence process was also needed. The chosen mesh followed a configuration analogous to the 3D case, using polygonal elements and a prism layer mesh looking for y+ = 1 and a maximum aspect ratio around 50.  
Figure 4: Global external surface reference mesh (75M elements).  
Figure 5: Mesh details at the leading edge of the rotor (reference mesh of 75M elements).