esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Types of Augmented Reality Applications
### Superimposition-Based AR  
Superimposition-based augmented reality is a type of AR where the original view of an object in the real world is either partially or fully replaced by a newly augmented view of that same object. This replacement is typically driven by object recognition, so the system needs to understand what the object is in order to replace it with an augmented version [41]. For example, in a furniture shopping app, when the phone camera is pointing to an existing piece of furniture in a home, the app could recognize the object and replace it with a 3D model of a new piece of furniture that could be considered to buy. In this way, it is possible to see how that new piece would look in place of the old one [42]. Another example is in the medical field, where doctors could point a device at a patient and have the system recognize certain anatomical features. The system could then overlay a view of the patient's internal organs or systems, allowing doctors to see inside the patient without invasive procedures [43]. There are two main types of superimposition-based AR:  
* **Partial Superimposition:** Only parts of the original view are replaced by augmented content. For instance, in an AR application that helps users visualize how new furniture might look in their room, the original room view remains visible, but the furniture is replaced with new models [44].
* **Full Superimposition:** The entire original view is replaced by augmented content. A well-known example of this is the IKEA AR furniture catalog, where users can see how furniture pieces would look in their homes [44].  
Currently, as with other AR technologies, superimposition-based AR also is involved in other fields such as retail, maintenance and repair, education, gaming, and entertainment [27]. For example, different AR learning experiences include the use of superimposition AR to create more engaging and interactive educational content. In [15], the authors examine AR learning prototypes in various education fields, which include the use of superimposition-based AR. They delve into how these AR systems can provide immersive, interactive, and enlightening educational content that can significantly enhance traditional learning methods. Furthermore, the authors analyze the technical and pedagogical challenges faced in implementing AR in education. They discuss issues like the need for accurate tracking, the creation of meaningful and engaging content, and the cognitive load placed on the learner when interacting with AR [15].  
As with all AR technology, superimposition-based AR faces challenges such as accurate object recognition and tracking, realistic rendering of augmented objects, and seamless integration of augmented content with the real-world environment [42].
## 4 Discussion
### Design Challenges and Trade-Offs
#### 4.1.4 Scalability Considerations in AI-Driven Matchmaking  
The matchmaking system must efficiently process and evaluate large data sets to match innovators with suitable experts. AI algorithms used for matchmaking could require significant computational resources, which may hinder the scalability of the platform as the number of users grows. The need for real-time performance and personalization also adds complexity to scaling the system.  
* _AI accuracy vs. computational resources_: AI-driven matchmaking systems often rely on complex models that require considerable computational power. As the platform scales, maintaining the same level of personalization and accuracy in recommendations becomes challenging due to the increased resource demands. In SPARK-IT, we consider a federated learning approach where multiple systems make predictions and can be rewarded based on their input. Some of these systems can be hosted by third parties, thus ensuring the overall system scalability.  
* _Real-time performance vs. scalability:_ The need for real-time matchmaking (matching experts to innovators on-demand) adds another layer of complexity. Real-time AI processing can lead to bottlenecks in systems with growing data inputs, especially when scaling to thousands of innovators and experts. In SPARK-IT, we devised an asynchronous workflow, where the innovator initiates the matchmaking process from its front-end application and, at a later time, receives a notification that the results are ready.
* _Data privacy vs. matching precision:_ For AI models to provide effective matchmaking, they need access to a large amount of data about both innovators and experts (e.g., skills, preferences, historical records, and reputation scores). However, the need for privacy and confidentiality may limit the amount of data accessible to AI systems, affecting the accuracy of the matches. In SPARK-IT, we defined a clear set of attributes that are available to the AI systems. These attributes are needed to achieve the objective of the matchmaking process without disclosing more information than needed. Moreover, an additional data anonymization layer can and will be deployed in the next versions of the platform.
# Sustainable Approach of Generating Water and Energy:
## 3 Mathematical Model
### Estimating Total Energy Consumption for a Residential Building  
A model for the residential building is constructed in this section that can predict building heat gains/losses and related heating/cooling requirements with the external environment, as well as estimates of energy consumption from plug-in loads, such as lighting, and other equipment. This aims to estimate the hourly energy consumption, and to predict building heat losses/gains with the exterior environment to estimate the cooling/heating energy, which is required to keep the indoor temperature at 24 \({}^{\circ}\)C. The residential building's overall energy usage may be calculated as follows:  
\[\dot{E}_{total}=\dot{E}_{HP}+\dot{E}_{baseline} \tag{11}\]  
In Equation (11), \(\dot{E}_{HP}\) refer to the energy requirement of heat pump express in kWh to keep the indoor temperature at 24 \({}^{\circ}\)C, which depend on the outdoor weather. \(\dot{E}_{baseline}\) is the baseline load of the house that is independent of the outdoor conditions. This load accounts for plug-in load such as lighting, computers, TV, etc.  
\[\begin{split}\rho_{\text{a}}C_{\text{a}}V\frac{\text{d}T_{\text{ th}\,b}}{\text{d}t}=-\dot{Q}_{\text{lost}}+\dot{Q}_{\text{HP}}\qquad\text{in heating mode}\\ \rho_{\text{a}}C_{\text{a}}V\frac{\text{d}T_{\text{cb}\,b}}{\text{d }t}=\dot{Q}_{\text{gain}_{c}}-\dot{Q}_{\text{HP}}\qquad\text{in cooling mode}\end{split} \tag{12}\]  
In Equation (12), \(\dot{Q}_{\text{-lost}}\) and \(\dot{Q}_{\text{gain}_{c}}\) are the heating lost/gain due to conduction, convection, and ventilation express in kWh. \(\dot{T}_{\text{h},b}\) and \(\dot{T}_{\text{C},b}\) are house indoor temperature during cold and summer months, respectively. Other parameters presented in Equation (12) indicates the air density \(\rho_{a}\), the volume of the house (V), and the specific heat capacity of air C\({}_{\text{a}}\).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(\mathbf{B_{0}}\) & \(\mathbf{B_{1}}\) & \(\mathbf{B_{2}}\) & \(\mathbf{B_{3}}\) & \(\mathbf{B_{4}}\) & \(\mathbf{B_{5}}\) & \(\mathbf{B_{6}}\) & \(\mathbf{B_{7}}\) & \(\mathbf{B_{8}}\) & \(\mathbf{B_{9}}\) \\ \hline
6.818 & 0.3703 & \(-1.4329\) & 1.113 & 0.0122 & 0.0014 & \(-0.0076\) & \(-0.0025\) & \(-0.0081\) & \(-0.0217\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The coefficients (\(B_{n}\)) used in Equation (10) [31].
# Machine Failure Prediction Using Survival Analysis
## 5 Results
### Model Evaluation Criteria  
#### 5.1.1 C-Index  
Assessing the accuracy of machine failure prediction models can be challenging due to the presence of right-censored failure events in the testing dataset. This means that some machines may have been operational for the entire duration of the testing period, making it impossible to observe when they would have failed. To address this challenge, a common metric used to evaluate the performance of machine failure prediction models is the concordance index (C-index).  
The C-index considers both the observed failure times and the predicted failure times, including those that are censored. This is achieved by creating pairs of machines, where the machine with an observed failure time is ranked higher than the machine with a censored failure time. The C-index ranges from 0.5 to 1, with a value of 1 indicating perfect prediction performance, where the observed failure times follow the same order as the predicted failure times, and a value of 0.5, indicating that the prediction model performs no better than random chance. Therefore, the C-index provides a reliable wayto assess the accuracy of machine failure prediction models, even in the presence of censored data [22].  
Calculate the total C-index by summing all values and dividing by the total number of possible pairs:  
\[C=\frac{1}{num}\sum_{i:d_{i}=1}\sum_{j:y_{i}<y_{j}}I[\widehat{y_{i}}>\widehat{y_{ j}}] \tag{10}\]  
where \(C\) represents the C-index; \(num=\) number of all comparable pairs; \(y_{i}\) and \(y_{j}\) represent the observed and predicted time to fail, respectively; and \(I\) is the indicator function. Therefore, this metric includes censored data, creating ranked pairs where the observed, uncensored events occur before the observed censored event.
# Study on the Influence of Consumers' Purchase Intention of Selenium-Rich Agricultural Products
## 1 Introduction
Using the rich natural soil, selenium-rich resources and ecological resources, each selenium-rich soil region in China has successfully developed pure natural selenium-rich tea, selenium-rich rice, selenium-rich wheat, selenium-rich corn, selenium-rich soybeans, selenium-rich rape, selenium-rich potatoes, selenium-rich vegetables, selenium-rich edible mushrooms, selenium-rich tea oil, selenium-rich fruits, selenium-rich pasture, selenium-rich seeds and other planting products, pure natural selenium-rich pork, selenium-rich beef, selenium-rich poultry, selenium-rich eggs, selenium-rich milk, selenium-rich aquatic products and other farming products, as well as selenium-rich gibberellic acid, Golden Partner, new Rare Treasure zinc and selenium tablets, selenium-rich Kang, selenium-rich spirulina, Anqi yeast selenium, Bao Kang selenium tablets, Kang Bi selenium and other selenium-rich health food drugs. In addition, through the use of microbial metabolism transformation biotechnology to develop ecological selenium-rich products, selenium-rich functional foods such as selenoprotein, selenium polysaccharide and other products have also made significant progress in research and development. At present, China's selenium-rich agriculture is better developed in areas such as Jiangxi, Hubei Enshi and Shaanxi Ankang. Various places play the \"selenium-rich card\", in selenium-rich agriculture, selenium-rich green food, drinks, drugs and health products), biomedical industry, inorganic selenium industry and rehabilitation health tourism industry, increasing the development and utilization of selenium resources, and cultivating a number of leading enterprises, representing the initial formation of selenium-rich industrial clusters. Through the survey of local consumers' willingness to purchase selenium-rich agricultural products, we gain an in-depth understanding of the actual situation of consumers' knowledge, purchase intention and purchase behavior of selenium-rich agricultural products, and analyze the factors affecting consumers' willingness to purchase selenium-rich agricultural products. In this paper, we put forward relevant policy suggestions to guide the government and relevant departments to formulate and promote the development of selenium-rich agricultural products and market policies and regulate the production and operation behavior of selenium-rich agricultural product producers and distributors. At the same time, during the survey, the knowledge of selenium-rich agricultural products will be publicized to the consumers to improve their awareness of selenium-rich agricultural products, increase their attention to selenium-rich agricultural products and food safety, and promote the further development of selenium-rich agricultural products in China.
## 4 Discussion
### Dependence of CO\({}_{2}\) Flux on Soil Moisture
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & **GEP** & **Re** & **Rs** & **NEE** \\ \hline \multirow{3}{*}{SS} & Moisture & 10 & 67 & 5 & 25 \\  & Temperature & 856 & 982 & 246 & 4 \\  & Interaction & 863 & 126 & 46 & 330 \\ \hline \multirow{3}{*}{F} & Moisture & 1 & 34 & 6 & 3 \\  & Temperature & 68 & 504 & 309 & 1 \\  & Interaction & 69 & 65 & 57 & 41 \\ \hline \multirow{3}{*}{P} & Moisture & 0.3647 & \textless{}0.0001 & 0.0124 & 0.0808 \\  & Temperature & \textless{}0.0001 & \textless{}0.0001 & \textless{}0.0001 & 0.4658 \\ \cline{1-1}  & Interaction & \textless{}0.0001 & \textless{}0.0001 & \textless{}0.0001 & \textless{}0.0001 \\ \hline \end{tabular} Degree of freedoms of the model and error were 3 and 646, respectively.  
\end{table} 
Table 4: ANOVA results of soil temperature and moisture on gross ecosystem productivity (GEP), ecosystem respiration (Re), soil respiration (Rs), and net ecosystem CO\({}_{2}\) exchange (NEE) in a semiarid grassland of the northwest China. dependence of GEP and NEE on soil moisture (Table 3), supported these hypotheses. These results indicated shifts in the response functions of CO\({}_{2}\) flux to soil temperature or moisture resulting from precipitation treatments, which is consistent with the acclimation of foliar gas exchange to different precipitation regimes in a pinon-juniper woodland [67]. Such shifts in the response functions could be a mechanism underlying the relatively stable ecosystem CO\({}_{2}\) flux in this semiarid grassland and should be incorporated in current models that do not consider such shifts in the response functions to better predict ecosystem processes at various precipitation change scenarios.
## 4 Failure Envelopes in the \(V\)-\(H\)-\(M\) Domain  
### Parametric Study  
Parametric studies are carried out to quantify the influences of the soil properties (\(\tan(\varphi)\gamma^{\prime}sD_{\rm p}/s_{\rm sum}\)), sand layer thickness (\(T_{\rm s}/L\)) and foundation geometry (\(D_{\rm w}D_{\rm p}/L^{2}\)) on the bearing capacity under combined \(V\)-\(H\)-\(M\) loadings. Figure 9 presents the influence of soil properties on the bearing capacities in the \(V\)-\(H\)-\(M\) domain by plotting failure envelopes with varying \(\tan(\varphi)\gamma^{\prime}sD_{\rm p}/s_{\rm sum}\). (\(D_{\rm w}D_{\rm p}/L^{2}\) = 0.062 and 0.08, \(T_{\rm s}/L\) = 0.1 and 0.7, \(e/D_{\rm p}\) = 15). Figure 9a shows that, the shape and value of failure envelopes are both related to the soil properties. From Figure 9b, it can be observed that the peak lateral capacity increases at an accelerating rate with the increase of \(\tan(\varphi)\gamma^{\prime}sD_{\rm p}/s_{\rm sum}\). Figure 10 shows the effect of the foundation geometry on the failure envelops in the \(V\)-\(H\)-\(M\) domain. The failure envelopes expand with the increase of \(D_{\rm w}D_{\rm p}/L^{2}\) under a constant upper sand thickness, which means the foundation geometry has little influence on the failure mechanism but affects the magnitude of the bearing capacity of the composite foundation. As can be seen in Figure 11, when \(T_{\rm s}/L\) = 0.1, under a constant \(D_{\rm w}D_{\rm p}/L^{2}\), the lateral bearing capacity decreases continuously when the vertical load increases. However, as \(T_{\rm s}/L\) exceeds 0.1, the peak bearing capacity can be observed in the failure envelopes. The normalized vertical load corresponding to the peak value grows as the thickness of the upper sand layer increases. For instance, it increases from 50 to 180 as \(T_{\rm s}/L\) increases from 0.3 to 0.7 when  
Figure 8: Effect of wheel thickness and height of wheel collar.
# An Overview of Design Techniques for Two-Dimensional Leaky-Wave Antennas
## 3 Fabry-Perot Cavity Antennas
### Theoretical Background
This relation between the directivity \(D\) and the attenuation constant \(\alpha\) is expressed in a closed formula for an FPCA radiating at broadside as [16]  
\[D\simeq\frac{k_{0}^{2}\pi^{2}}{8\alpha^{2}} \tag{6}\]  
It is worthwhile to point out that the attenuation constant is also strictly related to the bandwidth. In particular, it has been shown in [74] that, when broadside radiation is considered, the fractional bandwidth (FBW)--defined as the frequency range, normalized with respect to the central frequency, over which the power density at broadside decreases by less than 3 dB [70]--can be computed as [16]  
\[\text{FBW}=\frac{2\alpha^{2}}{k_{0}^{2}\epsilon_{r}} \tag{7}\]  
By observing Equations (6) and (7), an evident _trade-off_ between directivity and fractional bandwidth appears. The figure of merit (FoM) given by the product \(D\cdot\text{FBW}\) for an FPCA based on a single thin PRS is constant and equal to  
\[\text{FoM}=D\cdot\text{FBW}\simeq\frac{2.47}{\epsilon_{r}} \tag{8}\]  
It is thus clear that low-permittivity and low-loss materials are the best choice for maximizing this figure of merit. With these formulas in mind, one can easily find three different working conditions for FPCAs depending on the mutual comparison of \(\beta\) and \(\alpha\). When \(\beta<\alpha\), the FPCA radiates at broadside [74] in the so-called _reactive_ region. A deep analysis of FPCAs radiating in this frequency range has been presented in [75]. The condition \(\beta\simeq\alpha\) provides, instead,the best working point to maximize the radiated power at broadside [74]. In this context, a broadside pencil beam is produced by the FPCA with a beamwidth which depends on the \(X_{s}\) or, similarly, on the \(\alpha\) value. Finally, by increasing the frequency, the condition \(\beta>\alpha\) is reached. In this working region, FPCAs generate a scanning conical beam whose pointing angle mainly depends on the \(\beta\) value through (4) and the beamwidth on the \(\alpha\) value through (5) (and, in turn, on the \(X_{s}\) value). These aspects, along with additional design formulas, are rigorously addressed by exploiting the _transverse resonance technique_ discussed in the following subsection.
## 5 Discussion
### Motor Operation with Fault Detection  
The proposed fault-tolerant operation of the induction motor under the single-phasing fault is to monitor the vital signs (temperature and current) of induction motors and to control the operation of the motor based on the online reading of these signals.  
The simulation results in Figures 17 and 18 illustrate the main idea of the proposed idea of this research. Figure 17 shows that even if a single-phasing fault occurs at Section 4, the motor will continue operating because the current and the temperature do not exceed their limit values. However, Figure 18 shows that under the half load condition, the motor exceeds the threshold value and therefore should be shut down.  
By continuously monitoring the temperature and current of the motor, any abnormalities can be detected quickly, and the operation can be adjusted accordingly to prevent further damage. This proactive approach allows the motor to continue running smoothly even in the event of a single-phasing fault, ensuring that production processes are not disrupted. Additionally, the real-time monitoring of these vital signs can provide valuable data for predictive maintenance, helping to extend the lifespan of the motor and minimize downtime.  
Figure 16: Infrared thermal image of the stator windings under light load (healthy and faulty conditions).
## 3 Numerical Simulations
### Grid-Independent Solution  
The simulations are made in 3D. The discretization error is a factor affecting the accuracy of CFD modelling. To produce grid-independent results, the grid reliability must be checked on the basis of grid density. The grid convergence index (GCI) method is used to estimate the discretization error [18], achieved in the steps that follow:  
1. Let \(n\) denote cell number of the domain. Generate, for a 3D domain, three grids with \(n\) cells and calculate their reprehensive grid size (\(S\)) \[S=\left[\frac{1}{n}\sum_{i=1}^{n}\Delta V_{i}\right]^{1/3}\] (6) where \(\Delta V_{i}\) = cell volume.
2. Calculate apparent order \(p\), \[p=\frac{1}{\ln(r_{21})}\left|\ln\left|\varepsilon_{32}/\varepsilon_{21}\right| +\ln\left(\frac{r_{21}^{p}-\text{sgn}(\varepsilon_{32}/\varepsilon_{21})}{r_{ 32}^{p}-\text{sgn}(\varepsilon_{32}/\varepsilon_{21})}\right)\right|\] (7) where \(\phi_{i=1,2,3}\) = solutions by different grids, \(\varepsilon_{21}=\phi_{2}-\phi_{1}\), \(\varepsilon_{32}=\phi_{3}-\phi_{2}\), \(r_{21}=S_{2}/S_{1}\), and \(r_{32}=S_{3}/S_{2}\). The refinement is made around the steps, with refinement factor \(S_{\text{coarse}}\)/\(S_{\text{fine}}\) = 1.31.
3. Determine the GCI, \[\text{GCI}=\frac{1.25\big{|}(\phi_{1}-\phi_{2})/\phi_{1}\big{|}}{\big{(}S_{2}/ S_{1}\big{)}^{p}-1}\] (8)  
In the study, \(n\) = 1.00, 0.68 and 0.30 million. Define \(V\) = flow velocity, \(V_{\text{max}}\) = max. velocity, \(g\) = gravitational acceleration, and \(d_{c}=(q^{2}/g)^{1/3}\) = critical flow depth, two dimensionless parameters, \(V/V_{\text{max}}\) and \(P/(pgd_{c})\), are selected to check the grid independence.  
Tables 1 and 2 show the discretization errors in terms of \(P/(pgd_{c})\) and \(V/V_{\max}\), respectively. Let \(S_{1}<S_{2}<S_{3}\); define \(r_{21}=S_{2}/S_{1}\) and \(r_{32}=S_{3}/S_{2}\). Let \(X\) be the distance along an inclined step face, with its origin at the step corner, \(X/L=1.0\) corresponding to the step vertex, and \(y_{\max}=\) the maximum value of \(y\). Four \(X\) locations are selected to check GCI. The results show that the GCI values are below 4.5% for \(V/V_{\max}\) at all locations and 4% for \(P/(pgd_{c})\) except for at \(X/L=0.10\). The differences between the three grids are insignificant. For the sake of the computational efficiency, the medium-sized grid with \(n=0.68\) million is chosen.
## 4 Results
### Optimal Configurations for Different Models Across Different Performance Metrics
#### 4.2.4 Optimal Configurations for F1-Score  
Table 6 presents the configurations optimizing the F1-score, and the harmonic mean of precision and recall, offering a balanced performance measure across the seven models. ResNet50 achieved the best F1-score of 0.961672, using AdamW (learning rate 0.0001), CosineAnnealingLR, and FocalLoss. This setup effectively balances its precision (0.958333) and recall (0.965035), with FocalLoss managing the precision-recall trade-off by prioritizing difficult samples. ResNet101 follows with 0.956822, employing SGD (learning rate 0.01), StepLR, and CrossEntropy, leveraging momentum and periodic learning rate adjustments for robust balance in deeper models.  
EfficientNet-B2 achieves 0.953688 with an identical SGD setup to ResNet101, demonstrating consistency across scaled architectures. DenseNet121 (0.949033) and MobileNetV3 (0.947917) use SGD (learning rate 0.01) and AdamW (learning rate 0.0001), respectively, with ReduceLROnPlateau and CrossEntropy, where adaptive scheduling fine-tunes their balance. ConvNeXt records 0.946274 with Adam (learning rate 0.0001), CosineAnnealingWarmRestarts, and CrossEntropy, while ViT-B-16, at 0.935428, uses SGD (learning rate 0.001), ReduceLROnPlateau, and CrossEntropy, its lower score reflecting high recall (1.0000) offset by weaker precision (0.936842).