esa-sceva/satcom-chunk-collection huggingface dataset:
# Extended Reality in Applied Sciences Education: A Systematic Review
## 3 Results
### Application of XR Technologies Across Educational Levels  
Based on the literature analysis results, Figure 4 illustrates that in higher education, universities and graduate schools are the primary settings for the application of XR technologies. At the university level, 26 studies (74%) utilized VR ([5;13;17;19;21;22;24;25;26;27;29;40;41;42;43;44])), 14 studies (50%) applied AR ([13;36;38;45;46;49;50;51;56;57;59;61;63])), and 3 studies (75%) employed MR ([9;64;65]). At the graduate level, five studies (14%) used VR ([18;20;21;32;35]), two studies (7%) applied AR ([48;56]), and only one study (25%) adopted MR ([64]). Additionally, five studies implemented XR technologies across both university and graduate levels ([21;32;35;56;64]). This concentration in higher education is largely due to the emphasis on integrating theoretical and practical learning in advanced curricula.  
Figure 4: Distribution of XR technology applications in the educational field.  
Figure 3: Distribution of XR technologies in the selected 56 studies.  
The results also indicate that MR, due to its high cost and operational complexity, was not employed at any educational stage below the university level. At the high school level, VR and AR were the dominant technologies, with three studies (9%) using VR ([11; 23; 28]) and four studies (14%) using AR ([47; 55; 60; 62]). AR was more widely applied in junior high schools and elementary schools, with seven studies (25%) in junior high schools ([7; 12; 16; 47; 53; 54; 58]) and one study (4%) in elementary schools ([52]). Additionally, one study (3%) applied VR in junior high school settings ([16]). AR's characteristics make it particularly suitable for the educational needs of middle and elementary schools, as it combines virtual information with real-world settings, offering highly interactive and engaging learning experiences.  
Although XR technologies are primarily used in higher education, the results reveal a growing trend of adopting emerging technologies at the middle and elementary school levels, enhancing student engagement in learning.
## 4 Parasitic Layer Design Example
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Param** & \(\mathbf{M_{1}}\) & \(\mathbf{M_{2}}\) & \(\mathbf{M_{3}}\) & \(\mathbf{M_{4}}\) & \(\mathbf{M_{5}}\) & \(\mathbf{M_{6}}\) & \(\mathbf{M_{7}}\) & \(\mathbf{M_{8}}\) & \(\mathbf{M_{9}}\) \\ \hline \(S_{11}\) & \(-11  
Even though, to the best of the authors' knowledge, this is the first effort to minimize the nonlinear distortion of a reconfigurable antenna, and the design technique has implications beyond the particular results presented as example, a comparison with similar works reported in the literature, in terms of basic antenna parameters, size, and nonlinearities when available, is presented in Table 3.  
Figure 16: EVM of the Minimum Nonlinearity Parasitic-Layer-based Reconfigurable Antenna. \[J(k) =\{J_{S}(k),J_{\Delta P}(k),J_{\Delta(\theta,\phi)}(k)\} \tag{19}\]0 Radiation diagrams at Port 1, for operating mode \(M_{t}\), \((i\in 1\dots 9)\), pointing towards \((\theta_{0},\phi_{0})\). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Ref.** & **\#_Sw_ & **\#Pt_** & **BW** & **Av G** & **\#Beams** & **Steer** & **Size** & **EVM** \\ \hline This work & 12 & 2 & 3.3–3.8 & 6.5 & 9 & 40 & \(0.76\times 0.87\times 0.10\) & 4 at 35 dBm \\
[30] & 17 & 1 & 3.1–3.9 & 9 & 3 & 30 & \(1.05\times 1.05\times 0.25\) & 6 at 30 dBm \\
[29] & 49 & 1 & 2.4–2.6 & 7.5 & & & \(1.38\times 0.71\times 0.007\) & 16 at 5 dBm \\
[28] & 12 & 1 & 2.4–2.5 & 6.5 & 9 & 30 & \(0.80\times 0.74\times 0.10\) & NA \\
[40] & 60 & 1 & 2.4–3.0 & 4 & 9 & 30 & \(2.16\times 1.08\times 0.05\) & NA \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison between the designed antenna and other antennas reported on literature. (_#Sw_: Number of switches, _#Pt_: Number of ports, BW: Bandwidth (GHz), Av. G: Average Gain (dB), # Beams: Number of beams, Steer: Beam steering angle (deg), Size: Occupied volume (wavelengths at central frequency), EVM: Mean EVM (%) at a given input power.
## 4 Experiments
### First-Stage: Automatic Pig Face Detection Module  
#### 4.1.1 Implementation Details  
To establish an effective pre-trained model for pig face detection, we carefully selected a small-scale dataset comprising 1586 high-quality images from a group of 20 pigs from our 50-pig dataset. These images were systematically divided into training, validation, and testing sets with a distribution ratio of 6:2:2. Each image was manually annotated with bounding boxes using the LabelMe tool to provide precise ground-truth data. This meticulous annotation process was crucial for training a robust model on a smaller dataset, which could then be applied to a larger unannotated dataset of 50 pigs, leveraging the predictive power of the pre-trained model to detect pig faces without the need for further manual labeling.  
We implemented four different state-of-the-art detection models on our built small-annotation pig face dataset in our first-stage module for pig face detection. We conducted end-to-end training of the model by fine-tuning a pre-trained model on the MS-COCO dataset. The training process involved using the SGD optimizer with a learning rate of 0.01, momentum of 0.937, and weight decay of 0.0005. The 2160 \(\times\) 3840 original images were resized to (224, 224) as input images. The final pig face detection weights were obtained after training the model for 300 epochs on a computer equipped with 4 Titan GPUs. We set 64 as our batch size and some advanced data augmentation to increase the variability in the training data, such as Mosaic, RandomAffine, Albumentation, MixUP, and RandomFlip.  
In assessing the models' performance, standard object detection metrics such as AP\({}_{50-95}\), AP\({}_{50}\), and AP\({}_{75}\) were utilized. The average precision across a range of IoU thresholds, AP\({}_{50-95}\), was computed by averaging the precision and recall at IoU thresholds from 0.5 to 0.95 in increments of 0.05. The metric AP\({}_{50}\) represents the model's average precision at the IoU threshold of 0.50, while AP\({}_{75}\) indicates the same at an IoU of 0.75. These specific metrics, AP\({}_{50}\) and AP\({}_{75}\), offer a nuanced view of the model's detection abilities at these particular IoU levels, supplementing the broader assessment provided by AP\({}_{50-95}\).
## 3 Flexible Substrate Antenna Design
### Measurements
#### 3.5.1 Introduction to the Frankonia Chamber  
To conduct the measurements, testing was performed in a Frankonia anechoic chamber with a frequency range of 30 MHz to 40 GHz at a measuring distance of 3.0 m. The external dimensions of the chamber were \(7355\times 3755\times 3300\) mm and the size of the uniform area was 1.5 \(\times\) 1.5 m. The chamber provides excellent RF-Shielding, of less than 90 dB for the frequency range of 1-40 GHz, which is typical for the pan-type module made of 2.0 mm galvanized steel; see Figure 8 VNA was used inside the Frankonia anechoic chamber.
## 2 MDPO Positioning Method
\tag{12}\]  
This is the typical over-determination set of linear equations, and the relative position vector \(\mathbf{b}\) can be estimated by the least-squares method. Equation (12) can be rewritten in general vector and matrix form as:  
\[\mathbf{y}=\mathbf{G}\mathbf{b}+\mathbf{e}, \tag{13}\]  
where \(\mathbf{y}\) and \(\mathbf{e}\) are the observation and the residual vectors, respectively, and \(\mathbf{G}\) is called the geometric matrix. Then the least-squares solution \(\hat{\mathbf{b}}\) that minimizes the sum of squared residuals \(\mathbf{e}^{\mathrm{T}}\mathbf{e}\) can be obtained from:  
\[\hat{\mathbf{b}}=\left(\mathbf{G}^{\mathrm{T}}\mathbf{G}\right)^{-1}\mathbf{G}^{\mathrm{T}}\bm {y}. \tag{14}\]  
In order to solve the relative position vector \(\mathbf{b}\) in Equation (12), the rover must be stationary for at least three observation periods. Further reduction in the stationary periods can be achieved if the topocentric height of the rover is known. Equation (12) can be easily transformed into the local ENU (East-North-Up) coordinate frame by writing the LOS vector \(\mathbf{f}_{1}^{S}(t_{i})\) as below:  
\[\mathbf{f}_{1}^{S}(t_{i})= \begin{bmatrix}\cos\delta_{1}^{S}(t_{i})\sin\gamma_{ 1}^{S}(t_{i})\\ \cos\delta_{1}^{S}(t_{i})\cos\gamma_{1}^{S}(t_{i})\\ \sin\delta_{1}^{S}(t_{i})\end{bmatrix} , \tag{15}\]  
where \(\gamma_{1}^{S}(t_{i})\) and \(\delta_{1}^{S}(t_{i})\) are the azimuth and elevation angles of satellite \(S\), respectively, as observed from the lander location. If the topocentric height of the rover can be pre-estimated using a lunar digital elevation model (DEM), the remaining two unknown parameters are the horizontal relative position vector of the rover. In this case, the minimum number of the stationary periods can be reduced to two. It is obvious that the satellite position difference must be large enough between the two consecutive observation periods to avoid the singularity of the least-squares solution of Equation (14). Many early studies utilize an elliptical lunar frozen orbit (ELFO) that has an orbital period of 24 h [10; 11]. It provides good south pole coverage, but the rover must remain stationary for a long time. On the other hand, the satellite motion is much faster in a low altitude orbit. The numerical simulation results in the early work showed that a set of double-differenced pseudorange measurements taken in a 30-s interval from the satellites in a 300 km altitude circular orbit could provide reasonable rover position estimation accuracies for the baseline length of up to a few kilometers [6; 7].
# A Rapid and Inexpensive Method for Finding the Basic Parameters of Involute Helical Gears
###### Abstract  
The paper proposes a rapid, straightforward, and inexpensive method for finding the basic parameters of helical gears with an involute profile. The basic parameters envisaged are the normal module, normal profile shift coefficient, and the helix angle. The proposed method uses balls introduced between the teeth and, thus, the contact with the measuring device surfaces is of the point type, and the centres of the balls are positioned symmetrically with respect to the measuring direction. The condition that the centre of the ball occupies an imposed position is mandatory. Additionally, there is the condition of the positions of the contact points between the balls and the flanks of the teeth. Two sets of balls of different sizes are necessary for a measurement. The conditions of the balls' positioning lead to a system of five unknowns. The methodology of solving the system is detailed and the method is exemplified for an actual helical gear. The new proposed method is based on the distance over pins but, using balls, presents the following advantages: It can be applied equally to all gears, regardless of the odd or even number of teeth. Furthermore, the dimension to be measured is singular compared to the dimension over pins when a maximum value must be found from several measurements.  
involute helical gear; inspection; module; profile shift coefficient; helix angle +
Footnote †: journal: Applied sciences
## 2 Related Work
### Federated Learning  
Federated learning (FL) has recently received much interest due to its ability to protect privacy in diverse data ecosystems. There are also many challenges that must be addressed, particularly the challenge of improving privacy protection in non-independently and identically distributed (non-IID) data. Non-IID data refer to instances in which data distributions between participating locations or organizations differ, which means that is heterogeneous and biased input data, creating significant problems for FL algorithms [11]. The work in [12] introduces a Paillier federated multi-layer perceptron (PFMLP) framework, which combines encryption and federated learning to provide privacy-preserving ML. Another study in [13] identifies numerous important issues, including privacy, communication, and distributed optimization in FL. The work in [14] demonstrates that adversarial inference attacks can be used to infer the properties of training data sets in collaborative learning situations. The work in [15] suggests a secure and efficient technique for collecting user data. These studies demonstrate that FL is a potential strategy for privacy-preserving ML (PPML), although several issues remain, such as robustness to adversarial attacks, especially in non-IID data, and scalability to large-scale data sets.
## 4 Evolution of the Smart Grid Infrastructure: SDN-Enabled Smart Grid
### Potential Benefits of the SDN-Enabled Smart Grid
All of the information received from each module can be monitored using SDN applications, and the service provider can access and control the system without the isolated stack regulations of the device. * Situational awareness and flexibility: In SDN, the service provider can monitor the real-time network situation by using feedback information obtained from each SDN device. For example, in a traditional smart grid system, a service provider may not understand the need to upgrade the reception stability of the metering data without the need for additional network analyzers, such as firewalls, packet inspection routers and systems, when new standard protocols are utilized. However, in SDN, the network analysis system can be provisioned by employing an Open Systems Interconnection Reference Model Layer 2-3 network-monitoring SDN application [29]. Meanwhile, the service provider can simply identify network-wide network situations, such as congestion, collisions, bottlenecks and the flow status of the data packet, without additional hardware installation. In addition, network functionalities, such as routing, load balancing and the QoS management function, can be dynamically optimized and provisioned using network-wide situation information. * Simplified service deployment: In the SDN-enabled smart grid, the application and network control plane can be removed from devices, such as routers, switches and AMI devices. Figure 4a-c shows an example of the SDN-enabled smart grid business model, SDN applications and system architecture. Generally, the SDN controller is directly managed by the service provider, and it is located in the HES. The service application is embedded within the application layer, which ensures hardware-independent system programming, and the new services and network policies can be dynamically deployed to the SDN devices without the need for new hardware installation and configuration. * Simplified business model: The SDN-enabled smart grid business model can be simplified to include policy development, service provisioning and service monitoring, as shown in Figure 4a. In this model, the system administrator develops an SDN application, such as topology construction, routing, QoS and traffic-filtering program. The implemented SDN application and policy need to be provisioned to the SDN controller, which is a part of the HES of the SDN-enabled smart grid system. In the application provisioning step, the service provider deploys an SDN application or policy to the SDN controller. The service monitoring is a key to developing a new application and profile. In this step, the service provider analyzes the network event history by using diagnostic SDN applications.
## 3 Experiments and Analysis
### Experiment 2: The Laboratory Measurement Bearing Dataset
#### 3.2.3 Influence of Scale Factor Variation on MPE  
The appropriate scale factor range was also analyzed in Experiment 2. The MPE curves of the seven bearing health conditions are obtained with the maximum scale factor selected as 20. As shown in Figure 26, when the scale factor is less than 10, the MPE curve of each state signal fluctuates greatly. Whereas when scale factor is greater than 10, the trend of the MPE curves of different bearing states tend to converge. This means that when scale factor is greater than 10, it has little significance for PE to describe the complexity of the signal. As such, the scale factor was also selected as 10 in Experiment 2. As in Experiment 1, with the selected parameters, MPE of the raw signal and the first five-order IMFs of each signal were calculated and fused, so dimension of the input feature vector is 60. And then MPE of all the experimental samples were extracted and used as input into the SSDAE network for training and testing.
# Lem-Detector: An Efficient Detector for Photovoltaic Panel Defect Detection
## 4 Experiments
### Dataset  
The dataset used in this study is the PVEL-AD dataset, collected and created by Hebei University of Technology. This dataset comprises 4500 images representing twelve common defect types in photovoltaic panels: Crack, Finger, Black core, Thick line, Star crack, Corner, Fragment, Scratch, Horizontal dislocation, Vertical dislocation, Printing error, and Short circuit. Each image is annotated with bounding boxes and class labels, specifying the class of the defect and the coordinates of the bounding box's top-left and bottom-right corners. All images have a resolution of 1024 \(\times\) 1024 pixels.  
Due to the insufficient number of defect instances for the classes Corner, Fragment, Scratch, and Printing error (each with fewer than 32 instances), these four classes were deemed insignificant for experimentation and were removed from the dataset. The remaining eight defect types were retained for further analysis. The dataset was then split into training and testing sets at a ratio of 7:3, resulting in 3155 images for training and 1345 images for testing. Table 1 provides a detailed breakdown of the number and distribution of defect instances for the eight retained defect types in both the training and testing sets.  
Following the application of the LFA, an additional 8761 training samples were generated. This augmentation strategy aimed to enhance the representation of linear texture features within the training data, further improving the model's ability to detect small-scale defects.
# Beyond 100G: All-Optical Processor for High-Capacity Access\(\sim\)Networks
## 2 Passive Optical Network Standardization Overview
### 50G Passive Optical Network  
The 50G PON standard, as officially standardized by the International Telecommunication Union (ITU-T G.9804.3), continues the point-to-multipoint (PtMP) architecture used in GPON and XGS-PON, rendering it suitable for diverse deployment scenarios, including fiber-to-the-home (FTTH) scenarios. Supporting symmetrical data rates of 50 Gbps for both downstream and upstream transmissions, 50G PON utilizes a downstream wavelength range of 1340-1344 nm, as agreed upon by both the IEEE [28] and ITU-T [29]. This specific wavelength range ensures compatibility with existing optical distribution networks (ODNs), allows for coexistence with legacy PONs, and minimizes dispersion to limit inter-symbol interference over the ODNs.  
One of the major challenges in PONs is achieving high link budgets without the use of optical amplification. This necessitates transmitters with an extremely high modulatedoutput power and high dynamic extinction ratio (ER). In certain scenarios, a link budget of up to 30 dB may be required. To meet these demanding power budgets, 50G PON systems rely on avalanche photodiode (APD)-based receivers due to their higher sensitivity compared to the more common and less expensive PIN receivers.  
Moreover, the use of APDs, especially low-rate and bandwidth-limited devices such as 25G APDs, significantly reduces the overall cost of the optical network units (ONUs). This cost reduction is crucial because, in real deployments, the number of ONUs is much larger than the number of optical line terminals (OLTs), making the combined ONU cost a major factor. The use of DSP enables mature 25G APDs to handle 50G signal transmissions, thereby enhancing overall cost effectiveness even more [30].
## 4 Proposed Methods
### System Architecture  
Figure 5 depicts the overall architecture of the proposed system. There are four major modules: (i) spatio-temporal image formation, (ii) knowledge transfer, (iii) fusion, and (iv) classification. First, we convert the skeleton joints into a spatial format called STIF by covering both spatial information and temporal changes for the three different views \(XY\), \(YZ\), and \(ZX\) planes (front, side, and top views). Then the images are passed through the pareto frontier pretrained model referred to as the backbone network (MobileNetV2, DenseNet121, and ResNet18) trained with the ImageNet dataset. A fully connected layer is attached to each branch of the backbone network for extracting discriminative features. The generated features obtained from three different views (\(f_{xy}\), \(f_{yz}\), and \(f_{zx}\)) are fused in three different manners. Again, we conduct a fully connected layer to reduce the dimensionality of the features map (\(f_{xyz}\)). Finally, a fully connected (\(fc\)) and a softmax (\(sf\)) layers are added to perform the classification.  
The input to the knowledge transfer module is the STIF representation of skeleton sequence which is indicated by n_features. Each branch including the front, side, and top of the backbone network produced 256-dimensional richer features that are fused in the fusion module. The input to the fusion module is the 256-dimensional features which are again passed through two fully connected layers and a softmax layer. The first fully connected layer reduces the 256-dimensional features to the 128-dimensional feature map. Finally, the second fully connected layer generates 27 output probabilities by following a softmax layer. The details of each module are described in the next sections.