esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### BDS-2 Broadcast Navigation Message and Precise Product Availability  
Figure 3 displays the time series (a) and availability histogram (b) of BDS-2 broadcast navigation messages and the precise product. Blue indicates available broadcast navigation messages, while red indicates the precise product. The availability statistics in Figure 3b are calculated as  
\[Availability=\frac{\#\ of\ Available\ Messages}{\#\ of\ Expected\ Messages\ During\ The\ Period\ of\ Service}\times 100\% \tag{6}\]  
Figure 3a shows that all satellites have been providing consecutive service except for C13. Neither broadcast nor precise product was available for C13 from DOY 294, 2014 to DOY 139, 2016. The data absence was caused by satellite replacement. In the first phase, C13 acted as an MEO satellite. After the C13 retirement, a new IGSO satellite C15 was launched. A pseudo-random noise (PRN) number switch from C15 to C13 was made by the ground control segment after 146 days.  
Meanwhile, we can also see from Figure 3 that four MEO satellites (C11-C14) suffered from frequent broadcast navigation message missing in 2013. However, the situation for the precise product was not the same. The BDS precise product availability was more stable than broadcast navigation messages during the same period.  
Figure 3b shows the availability comparison between the broadcast and precise product. Discarding the C13 satellite replacement issue, we can see that the broadcast navigation message availability (\(>\)98.51%) is greater than that of the precise product (\(>\)91.79%) for all GEO and IGSO satellites. As for MEO satellites, the data missing issue in 2013 led to the relatively worse availability of broadcast navigation message (\(<\)90.03%) than the precise product (\(>\)96.67%).  
Figure 3: _Cont._In the preprocessing stage, 32293-invalid BDS-2 broadcast navigation messages were excluded based on three conditions defined in Section 3.1. A total of 651038 broadcast navigation messages were retained for further analysis.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 3 Wireless Technologies
### Visible Light Communication (VLC)
For simulation evaluations, the OOK signal can be generated by an arbitrary waveform generator (AWG), while the transmission optical channels can be modelled by using the impulse response [115]. Another important parameter is the signal-to-noise ratio (SNR), which estimates the effects of the system. Commonly, the noise is considered to be Gaussian and is estimated as the sum of the contributions from shot and thermal noise. The shot noise is induced by the ambient light, which is proportional to the total light power reaching the PD, whereas the thermal noise is a white noise raised from the system's parts [116]. The SNR can be 10 dB lower in sunlight-exposed room environments, as compared to indirect sunlight exposure [113]. It is also affected by the LEDs' arrangements, i.e., for symmetrical arrangement at the ceiling corners, the SNR is low and the system's performance is downgraded when the user moves far away from the LEDs. From the different configuration arrangements, i.e, circular, square, and hexagonal, the circular arrangement of PDs outperformed the others, in terms of accuracy [112]. For the light sensors, the Skellam distribution [44] can be adapted for determining the overall noise for two consecutive images captured at the same scene.
# The Integration of AI and Metaverse in Education: A Systematic Literature Review
## 2 Background
### Research Gap  
As summarized in Table 1, the study found no systematic review focusing on the use of AI within the metaverse for education. The keywords of related works revealed a lack of any research into \"education\", \"metaverse\" and \"AI\" subjects except [13]. The keywords in [33] explicitly exclude AI by using a NOT operation to avoid articles reporting on AI without including the human learning context. In addition, most papers failed to address the issue of AI, either superficially or in depth [14; 27; 30; 31; 32; 33; 34; 35]. These reviews mainly focus on the use of the metaverse to make learning more effective and engaging, stressing aspects such as (1) the adoption of the metaverse [30; 31]; (2) the main disciplines covered by the metaverse [31]; (3) the design of immersive learning content [32; 33]; (4) the contributors to the metaverse [34]; (5) metaverse technologies [14]; (6) future research [30; 35] and (7) challenges [27; 30; 35].  
Nevertheless, we also found only a limited number of systematic reviews examining AI from a specific perspective [13; 28; 29]. Ref. [13] focused primarily on IoE and XR technologies, examining how AI could be integrated with these technologies to support education. Furthermore, the review in [28] discussed the role of intelligent non-player characters (NPCs) within the metaverse, as well as the application of a number of AI techniques (i.e., neuro-symbolic AI, convolutional neural networks, machine learning) in the metaverse for education. In addition, ref. [29] considered AI as part of an analysis of the perceived usefulness of the metaverse, with the authors arguing that this can be enhanced by the use of chatbots.  
While it is conceivable that, in the future, the metaverse and AI will enhance the advancement of education, their use currently remains at an early stage of development. We found this area currently largely unexplored in systematic literature reviews and therefore aim to fill this gap in the existing literature, which could also encourage future studies on this subject.
## 2 Model Description and Methods
### Turbulence Modeling  
The \(k\)-\(\varepsilon\) model was utilized to depict turbulence flow as previous investigations [21; 22; 23] have confirmed that it is appropriate for describing gas-liquid hydrodynamic characteristics and is not computationally intensive. As the volume fraction of the secondary phase (gas phase) is dilute in the stirred tank, the dispersed \(k\)-\(\varepsilon\) turbulence model was used here. In this dispersed \(k\)-\(\varepsilon\) model, the turbulence quantities for the dispersed phase (gas phase) are not obtained from the transport equations. Even though the turbulence quantities for dispersed phase were significant only around the impellers, their effect on the predicted results is quantitatively negligible [4]. Therefore, the governing equations to solve \(k\) and \(\varepsilon\) transport equations were used only for the continuous liquid phase.  
Among three different \(k\)-\(\varepsilon\) models (i.e., the standard, RNG, and realizable model), the realizable \(k\)-\(\varepsilon\) model was utilized as follows [24]:  
\[\frac{\partial}{\partial t}(\rho_{l}k)+\
abla\cdot\left(\rho_{l}\overset{ \rightarrow}{u}_{l}k\right)=\
abla\left(\mu+\frac{\mu_{t,l}}{\sigma_{k}} \
abla k\right)+\mu_{t,l}S^{2}-\rho_{l}\varepsilon \tag{3}\]  
\[\frac{\partial}{\partial t}(\rho_{l}\varepsilon)+\
abla\cdot\left(\rho_{l} \overset{\rightarrow}{u}_{l}\varepsilon\right)=\
abla\left(\mu+\frac{\mu_{t,l}}{\sigma_{k}}\
abla\varepsilon\right)+\rho_{l}C_{1}S\varepsilon-C_{2} \frac{\rho_{l}\varepsilon^{2}}{k+\sqrt{\
u\varepsilon}} \tag{4}\]  
where  
\[\mu_{t,l}=\rho_{l}C_{\mu}\frac{k^{2}}{\varepsilon} \tag{5}\]  
\[S=\sqrt{2S_{mn}S_{mn}} \tag{6}\]  
\[S_{mn}=\frac{1}{2}\left(\frac{\partial u_{m}}{\partial x_{n}}+\frac{\partial u _{n}}{\partial x_{m}}\right) \tag{7}\]  
\[C_{1}=\max\left[0.43,\frac{\eta}{\eta+5}\right] \tag{8}\]  
\[\eta=S\frac{k}{\varepsilon} \tag{9}\]  
The difference between the realizable \(k\)-\(\varepsilon\) model and the other, standard and RNG \(k\)-\(\varepsilon\), models, is that \(C_{\mu}\), which is used for obtaining the turbulence viscosity, is a function dependent on the mean strain, rotation, and turbulence flows rather than a constant [25].
# Joint Radar-Communication Optimization of Distributed Airborne Radar for AOA Localization
## 2 System Model  
This section first provides a visual description of the scenario, and the specific mathematical modeling is explained in the subsections.  
As shown in Figure 1, the passive localization model of an active target by UAV swarms was considered. Each UAV in the swarm performed AOA measurements on the target in each time slot. Each UAV carried radar and communication equipment, where the radar was applied to measure the AOA of the target. The communication equipment was applied to broadcast the information obtained to its neighboring UAVs (specific information will be explained in Section 3.3). In each time slot, each UAV needed to plan the power of the radar system, the power of the communication system, and motion based on the information it sensed (i.e., the information broadcasted by the communication system). The ultimate goal of JRCO was to maximize the accuracy of target localization. In the initial state, all the UAVs departed from the departure area. When the iteration of the JRCO algorithm terminated, all UAVs returned to the departure area and fused all obtained AOA information in the base station.  
In this context, this article designed a fully distributed JRCO method to maximize the accuracy of target location estimation (Namely the CRLB, which will be derived in Section 2.6). \"Distributed\" refers to each UAV making independent decisions based on its sensed information at each time slot without being commanded by any other UAV. The reason for considering distributed algorithms instead of centralized algorithms is that when there are a large number of UAVs, centralized algorithms rely on multiple relays for command distribution, which can cause significant communication delays and load.
## Appendix A Self-Consistent Approximation (SCA) of [PERSON] [25]  
This method is applicable to isotropic media. All heterogeneities are represented by ellipsoids of revolution. The shape of ellipsoids is described by the aspect ratio. This means that no embedding matrix is assumed in this method. If a model of rock is a polycrystalline matrix with voids, the particles of isotropic polycrystal are usually considered as spheres. The aspect ratio can vary in a wide range describing shapes from very thin disks (an aspect ratio smaller than 1) to elongated thin needles (an aspect ratio greater than 1). Commonly, to model pores and cracks, an aspect ratio smaller than 1 is considered. All the inclusions are randomly oriented.  
The formulas for calculating the effective elastic properties according to this method are as follows:  
\[\sum_{i=1}^{N}v_{i}(K_{i}-K_{SCA}^{*})P^{*i}=0,\ \sum_{i=1}^{N}v_{i}(\mu_{i}- \mu_{SCA}^{*})Q^{*i}=0, \tag{1}\]  
where \(K_{SCA}^{*}\) and \(\mu_{SCA}^{*}\) are the effective bulk and shear moduli, \(K_{i}\) and \(\mu_{i}\) are the bulk and shear moduli of ellipsoidal inclusions, \(N\) is the number of inclusion types, and \(v_{i}\) is the volume concentration of inclusion of type \(i\). The coefficients \(P^{*i}\) and \(Q^{*i}\) are controlledby the aspect ratio of inclusions of type \(i\) and the elastic moduli of the effective medium (\(K^{*}\) and \(\mu^{*}\)) and inclusions of type \(i\) (\(K_{i}\) and \(\mu_{i}\)). The formulas for these coefficients are rather cumbersome and are not presented here. Specifically, these formulas are given in a convenient form in [28]. In the case of a model of a polycrystalline matrix with pores of equal shape which are filled with the same material, \(N=2\). If the model contains both pores and cracks, \(N=3\). In this case, the shape and substance filling the cracks should be the same for all cracks.  
Equation (A1) can be rewritten in a more convenient form:  
\[K^{*}_{SCA}=\frac{\sum\limits_{i=1}^{N}v_{i}K_{i}P^{*i}}{\sum\limits_{i=1}^{N} v_{i}P^{*i}},\ \mu^{*}_{SCA}=\frac{\sum\limits_{i=1}^{N}v_{i}\mu_{i}Q^{*i}}{\sum\limits_{i=1}^{ N}v_{i}Q^{*i}},\] (A2)  
These equations contain unknown values of the effective elastic moduli in both the left- and right-hand parts. Such equations are solved by iterations. Commonly, no more than 15-20 iterations are needed to obtain a solution for very thin cracks.
# Improving the Energy Efficiency of the Modular Buildings
## 4 Results and Discussion
### Drying Room Setup in Wincham
#### 4.3.4 Drying Time  
Figure 22 illustrates the drying time of each of the trousers, waterproof jacket, and softshell jacket in the Wincham drying room experiment that took place in the morning from 7:45 a.m. to 15:15 p.m. The drying time was estimated based on the duration of garment drying from initial moisture content to final moisture content (0.4-2%). Data from Figure 22 has been translated into Table 8 for a clear view of the drying time. A general observation from Figure 22 is that the moisture reduction rate was higher in H (heater), DH (dehumidifier), and AM (air mover) cases, whereas in the baseline case, it was reducing slowly. Regarding DH and AM and H and AM, the moisture reduction rate was in the middle between the best and worst scenarios. This indicates that the combination of a  
Figure 21: Moisture content in garments tested and relative humidity variation in four tests: (**a**) heater ON, (**b**) dehumidifier ON and air mover ON, (**c**) heater and air mover ON, (**d**) dehumidifier, air mover, and heater ON.  
heater, air mover, and dehumidifier accelerates the drying process. Data from Table 7 suggest that the longest drying time, exceeding 7 h for all types of garments, is in the baseline scenario when only the heater was running. Adding an air circulation fan reduced this time from 4 to 6 h depending on the type of garment; interestingly, the introduction of a dehumidifier to this setup has helped to reach the shortest time to dry all clothes to 3 to 4 h. In the meantime, it should be noted as well that combining a dehumidifier with an air mover has also been able to dry garments from 5 to 7 h. However, drying time is not a good indicator of the energy efficiency of the different drying setups used; hence, the forthcoming section will outline the criteria for determining the optimal drying system, with a focus on energy consumption.
## 3 Simulation Results and Analysis
### Results and Discussion: Safety Factors (Maximum Stress and Temperature Rise)

Table 8: The setup of material parameters. The winding temperature of the equivalent model is close to the test data when the power is equal to 30 kW and 50 kW. Hence, the cooling model design method can be used to assess the optimized cooling model and evaluate the temperature rise in the rated condition (30 kW and 115 A). But in the limited condition (60 kW and 250 A), the simulation and evaluation results can only be used as a trend reference, but they are not very precise. The temperature mismatch between the original and test data is recorded in Table 9. The percentage of the mismatch at the 30 kW and 50 kW output power are in the acceptable range, especially at 30 kW, and the simulation original data are even higher than the test, which can further improve the safety of the motor at the rated power before manufacturing. The test data are higher than the simulation original data at 50 kW, which is mainly caused by two reasons. First, the actual iron loss in the experiment is bigger than the simulation one. Further, the effect of the distribution of the iron loss to the stator temperature  
Figure 17: The original equivalent cooling model: (**a**) 3-D view; (**b**) radial direction view; and (**c**) axial direction view. Figure 18: The winding temperature data comparison of original model and real motor: (**a**) 30 kW; and (**b**) 50 kW. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**The Output Power** & **Original Data** & **Test Data** & **Maximum Mismatch/Time** \\ \hline
30 kW & 102.0 \({}^{\circ}\)C & 100.0 \({}^{\circ}\)C & +2.0 \({}^{\circ}\)C/+2.0\%/20 min \\
50 kW & 133.8 \({}^{\circ}\)C & 140.3 \({}^{\circ}\)C & \(-6.5 \({}^{\circ}\)C/\(-4.6\%/4\) min \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The maximum winding temperature mismatch at different output powers. field is more obvious, which causes the temperature of the windings to be higher. Secondly, in the simulation, the cooling system adopts the typical and ideal axial water jackets. However, the test cooling structure is not completely the same as the axial water jackets, which is more complex. Then, the real water jackets worsen the heat dissipation of the cooling system. With these differences, the test data are higher than the simulation result. Based on the analysis above, using the same cooling model design method to build the optimized cooling model is acceptable. The steady-state highest temperature rise data are compared with the original data to evaluate the temperature at the rated output power. In the rated power condition, the temperature of the major motor component is listed in Table 10 and the temperature rise process is illustrated in Figure 19 as an example of the simulation. Under the same external cooling conditions, the windings' temperature is 5% higher than the original one.
## 5 Results and Discussion
### RQ1: What Cyber-Attacks in the MSCN Have Been Reported in the Academic Literature?  
Our SLR identified eight primary categories of cyber-attacks that present substantial risks to the MSCN. These types of assaults include DDoS, malware/ransomware, identity fraud, navigation system attacks such as hijacking, spoofing, and jamming, phishing, spear phishing, social engineering, watering holes, and brute force attacks. The following analysis offers a comprehensive examination of each type of assault, their documented occurrences, and a recently established classification model that effectively addresses these risks.
# Innovative Integration of Severe Weather Forecasts into an Extended Arrival Manager
## 4 Meteorological Modeling  
The three new meteorological forecasting techniques, i.e., Weather Research and Forecasting (WRF), the PHAse-diffusion model for STochastic nowcasting (PhaSt), and the Radar Nowcasting Density of the Vertical Integrated Liquid (RaNDeVII) model, were developed and extended to better nowcast severe weather events affecting tactical approach operations. For this purpose, short-range severe weather forecasts with very high spatial resolution were elaborated, starting from radar images, through an application of nowcasting techniques combined with the Numerical Weather Prediction (NWP) model and data assimilation.
## 3 Non-Thermal Technologies in Food Processing
### High-Pressure Processing
#### 3.4.2 Application of HPP in Meat and Fish Products  
Fish and seafood are among the most vulnerable foods, and they pose a major threat to public health, especially when eaten raw or undercooked. Foodborne bacterial pathogens, such as L. monocytogenes and S. enterica, significantly affect the quality, and the HPP technology can contribute to effective microbiological safety [215]. In a very recent study, [PERSON] et al. applied the HHP technique in frozen fish fillets and caused a reduction in L. monocytogenes and S. enterica without significant effect on the quality [215].  
A total of 500,000 tons of HPP product are traded annually on the planet with ready-to-eat meat products occupying the largest share in this market [200]. The HPP technique has caused the inactivation of pathogens such as Campylobacter jejuni [216], E. coli [217], L. monocytogenes [218; 219], and S. enterica [220]. Although HPP is effective against spoilage and pathogenic microorganisms in meat, it can cause some unpleasant effects on its quality, related to color, appearance, and texture completely dependent on the intensity of the pressure to be exerted [221; 222; 223].  
The application of the HPP technology begins with the sealing of meat samples in flexible plastic containers in vacuum conditions. These containers are placed in vessels/chambers, which are filled with the pressure transmitter medium. The pressure transmission is independent of direct or indirect contact with the sample and takes place in a uniform manner [222].  
The HPP technique may have a synergistic effect with essential oils in the treatment of foodborne pathogens such a Salmonella and Listeria. [PERSON] et al. (2020), reported 5.25 log reduction against L. monocytogenes and 6.01 log reduction against S. enterica in fresh ground chicken meat after HPP treatment at 350 MPa for 10 min and 4\({}^{\circ}\)C with 0.60% carvacrol treatment [221].
## 3 Methodology
### Efficient Gradient-Based Federated Unlearning (EG-FedUnlearn)
This storage requirement can be resource-intensive for large-scale systems. Meanwhile, EG-FedUnlearn addresses this using gradient tracking at each node, eliminating the need to store extensive historical updates centrally. It also uses an adaptive learning rate to ensure that model quality is preserved. By comparing it with retraining from scratch we will find that retraining is the most straightforward but computationally expensive approach to unlearning. While EG-FedUnlearn offers significant speed-up by directly reversing the influence of the target data instead of retraining the entire model from scratch, EG-FedUnlearn, in contrast, uses gradient-based negative updates that go directly after data influence, making unlearning more precise and effective. Here, the pseudocode of EG-FedUnlearn represents where each node has a local model and dataset, and stores the gradient updates for each data point during training, the \"_train_local_model_\" function trains the local model and stores gradients; the 'unlearn_data_point' function reverses the influence of a specific data point by applying the negative of the stored gradient; and the \"_send_model_update_\" function sends the current model to the central server. The CentralServer Class aggregates model updates from the nodes, and the method \"_aggregate_model_updates_\" averages the models received from the nodes with respect to an adaptive learning rate as a mitigation against unlearning on the quality of the resultant model, while the \"_distribute_global_model_\" function distributes the aggregated model back to the nodes. Thus, the Algorithm 1 thus aims to reduce extremely costly inverse computations by avoiding full model retraining. Influence reversal is selectively performed through the gradient tracking procedure. Summarized gradient tracking at a central server makes EG-FedUnlearn suitable for large-scale federated networks. The summarization process allows the server to efficiently manage the influence of multiple data points over time. Communication between nodes and the server is minimized by focusing only on the relevant gradient updates during unlearning, rather than retraining entire models. This reduces the size of transmitted data. The adaptive update strategy helps ensure that the model's performance is not heavily impacted during the unlearning process, which is a common concern in federated learning environments. Figure 5: Flowchart of the EG-FedUnlearn methodology.