esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussions
### Successful of Finding a Solution
#### 5.2.3 Simulations for 20 Nodes  
In this simulation, we added five nodes from the CONFIG 15-1, as follows. Nodes 16, 17, 18, 19, and 20 are located at (-100 m, -180 m), (0 m, 100 m), (200 m, -180 m), (200 m, 180 m), and (170 m, 130 m), respectively. Similar to the simulation for 15 nodes, we define four edge configurations in the new graph, namely CONFIG 20-1, CONFIG 20-2, CONFIG 20-3, and CONFIG 20-4. The CONFIG 20-1 connectivity configuration is described in Figure 5. CONFIG 20-2 is constructed by modifying CONFIG 20-1, i.e., adding edges connecting Nodes 1 to 16, 4 to 16, 2 to 20, 6 to 9, 8 to 19, and 12 to 17. CONFIG 20-3 is performed by adding Edges 2 to 17, 3 to 5 to 9, 7 to 15, and 9 to 15 and eliminating Edges 4 to 16, 4 to 10, and 14 to 17 on CONFIG 20-2. CONFIG 20-4 is performed by the addition of Edges 3 to 17, 4 to 16, 4 to 10, and 5 to 14 to CONFIG 20-3.  
Table 5 reveals the simulation results of such connectivity configurations. It can be shown that the solution can be found for \(\phi\leq 0.02\). It means that, from the 10 trials, it can be concluded that there always exists at least one successful trial at the range of the \(\phi\).  
\begin{table}
\begin{tabular}{c} **Success** \\ **Rate (\%)** \\ \end{tabular} \\ \hline
3 & 0.06 & 100.00 & 0.05 & 100.00 & 0.05 & 90.00 & 0.05 & 90.00 \\
4 & 0.07 & 90.00 & 0.07 & 90.00 & 0.06 & 90.00 & 0.06 & 100.00 \\
5 & 0.09 & 10.00 & 0.09 & 20.00 & 0.08 & 10.00 & 0.08 & 50.00 \\
6 & 0.11 & 0.00 & 0.11 & 0.00 & 0.10 & 0.00 & 0.09 & 0.00 \\
7 & 0.13 & 0.00 & 0.13 & 0.00 & 0.11 & 0.00 & 0.11 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Number of Degree of Nodes**} \\ \hline
**Node** & **CONFIG 20-1** & **CONFIG 20-2** & **CONFIG 20-3** & **CONFIG 20-4** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 5: Simulation results for 20 nodes.
## 1 Introduction
The exact same algorithms can be used for OP-MATSP. We firstly build a two-objective integer programming (ILP) model of OP-MATSP. Then, we solve the small-scale problems using the Gurobi solver [26]. The Gurobi solver is the fastest and most powerful mathematical solver available for ILP problems. To solve its non-inferior solution, we transform the two-objective integer linear programming (ILP) model into a single-objective problem by the linear weighting method [27]. We assign weight \(\omega_{1}\) and \(\omega_{2}\) to each objective function, and then add them together to form a new objective function and obtain a solution to the two-objective integer linear programming (ILP) model by solving this new objective function. However, it becomes complicated to solve the large-scale problem. In this paper, the multi-chromosome coding-based genetic algorithms [28, 29] are used to solve the large-scale problems. The multi-chromosome coding genetic algorithm has been proven to have a smaller search space and faster search speed in solving the multiple traveling salesman problems (mTSP) [30]. We treat each robot's task sequence as a chromosome and each task as a gene in the chromosome. GEATPY is a high-performance genetic algorithm library. In this paper, the multi-chromosome coding-based genetic algorithms are selected in this library to solve the large-scale multi-robot task assignment problem. We first solve small-scale problems using the multi-chromosome coding-based genetic algorithm in GEATPY and compare the results with the Gurobi solver. We then use the algorithms with higher solution performance to solve large-scale problems. The main contributions of this paper can be summarized as follows:  
* This paper regards the multi-robot task assignment in an intelligent warehouse as an open-path multi-depot asymmetric traveling salesman problem (OP-MATSP). Moreover, the OP-MATSP is proposed for the first time in this paper. * A two-objective integer linear programming (ILP) model of OP-MATSP is established. Through this ILP model, we solve small-scale multi-robot task assignment problems using the Gurobi solver. * Multi-chromosome coding-based genetic algorithms are implemented to solve large-scale multi-robot task assignment problems. The remainder of this paper is structured as follows. Section 2 gives the problem description and the two-objective ILP model. Section 3 presents the small-scale and large-scale experiments' results and discussions. Section 4 provides the conclusions. Figure 3: The diagram of MTSP and OP-MATSP. (**a**) Multi-depot traveling salesman problem (MTSP). (**b**) Open path multi-depot asymmetric traveling salesman problem (OP-MATSP). \(d_{1}\) and \(d_{2}\) represent depot. \(a\) to \(c\), \(e\) to \(i\) represent the city nodes that the traveling salesman will visit.
## 2 Methods and Materials
### Error Measures  
In order to estimate the quality of the implementation of the homogenization algorithm (see Section 3.1) or the quality of the approximation from ANN (see Section 3.3), its results were compared with the references, i.e., published in [PERSON] and [PERSON]'s paper [10] or computed by the homogenization algorithm implemented for this study research, respectively. Let us introduce the error, \(e\), which is accounted by the following  
\[e=\left|\frac{y-\overline{y}}{y}\cdot 100\%\right|, \tag{17}\]  
in which \(y\) is the reference, while \(\overline{y}\) is the computed value.  
Also, in order to compute the quality of ANN approximation by the scalar measure, the Mean Squared Error (MSE) was introduced. The MSE is accounted by the following  
\[MSE=\frac{1}{10}\sum_{i=1}^{10}\left(\mathbf{ABDR_{i}}-\overline{ABDR_{i}}\right) ^{2}, \tag{18}\]in which, the vectors \(\mathbf{ABDR_{i}}\) and \(\overline{\mathbf{ABDR_{i}}}\) are laminate theory stiffnesses vectors: \(\{A_{11},A_{12},A_{22},A_{33},D_{11},D_{12},D_{22},D_{33},\ R_{44},\ R_{85}\}\), the reference obtained by numerical homogenization and the values derived by ANN approximation, respectively.  
```
1:Require : \(\alpha\) (stepsize)
2:Require : \(\beta_{1},\beta_{2}\in[0,1)\) (exponential decay rates for the moment estimates)
3:Require : \(f(\theta)\) (stochastic objective function with parameters \(\theta\))
4:Require : \(\theta_{0}\) (initial parameter vector)
5:
6:\(m_{0}\gets\ 0\) (initialize 1 st moment vector)
7:\(v_{0}\gets\ 0\) (initialize 2 nd moment vector)
8:\(t\quad\leftarrow\ 0\) (initialize timestep)
9:
10:while\(\theta_{t}\) not converged do:
11:\(t\gets t+1\)
12:\(g_{t}\leftarrow\ \
abla_{\theta}f_{t}(\theta_{t-1})\)
13:\(m_{t}\leftarrow\ \beta_{1}\cdot m_{t-1}+(1-\beta_{1})\cdot g_{t}\)
14:\(t_{1}\leftarrow\ \beta_{2}\cdot v_{t-1}+(1-\beta_{2})\cdot g_{t}^{2}\)
15:\(\hat{m}_{t}\leftarrow\ m_{t}/\left(1-\beta_{1}{}^{t}\right)\)
16:\(\hat{\sigma}_{t}\gets\ v_{t}/\left(1-\beta_{2}{}^{t}\right)\)
17:\(\theta_{t}\leftarrow\theta_{t-1}-\alpha\cdot\hat{m}_{t}\ /\left(\sqrt{\hat{v}_{t}\ +\epsilon}\right)\)
18:end while
19:return\(\theta_{t}\ v\)
```  
**Algorithm 1** The pseudo code of the Adam algorithm.
# Participatory Exhibition-Viewing Using Augmented Reality and Analysis of Visitor Behavior
## 3 Methodology
### Establishment of AR Exhibition-Viewing System
#### 3.1.2 User Modes  
To understand the differences between passive and participatory exhibition-viewing behaviors, we created two modes for the AR app. Visitors could download our app onto their mobile devices, and after installing it, freely switch between the exhibition-viewing mode and message mode. Both modes shared the same app, physical space, and AR positioning method. Additionally, neither mode restricted the way or order in which AR content was viewed. In the participatory mode, the messages left by participants were not overlaid onto the exhibition-viewing mode. This was primarily done to enable users to concentrate on the AR content presented in both modes, preventing potential biases in the analysis results due to the participatory mode offering more experiential content to the visitors. The reason for adopting this research approach primarily lies in the ability to compare the differences in exhibition-viewing behaviors within the same space under different AR modes. Comparing different modes in different spaces might not be feasible due to variations in scene size and spatial arrangements.  
(1) Exhibition-viewing mode  
In this mode, visitors scanned the frames hung on the walls using the AR lens to view the exhibition content (in this case, photos and videos of the achievements of each center). The videos were all uploaded to YouTube and streamed to save on storage space. Visitors could also select from among a series of videos, which achieved a simple interactive effect (Figure 4).  
(2) Message mode  
In message mode, visitors could leave text messages at any location within the exhibition space and see them on the screen of their mobile devices (see Figure 5). These messages were also uploaded to Google Sheets. When visitors used this mode, it would take about 2-3 s for the messages left by previous visitors to load onto their screens. All of the messages were kept until the end of the exhibition, becoming part of the exhibit. Although some messages might have appeared peculiar, such as someone leaving comments like \"Hehehe\", \"Haha\", \"three\", \"YA\", \"Good morning\", and \"Go National Taiwan University of Arts\", which had no particular significance to the exhibit itself, they reflected the diverse and genuine reactions of visitors interacting with the exhibition content through our AR system.  
Figure 4: AR video content positioned within physical frames (The Chinese text displayed in the image are introductions to the two centers.).
# A Data-Driven Situational Awareness System for Enhanced Air Cargo Operations Emergency Control
## 1 Introduction
While important, none of the available solutions provide a complete solution for the remote monitoring and control of the container regarding its structure, status, location, handling fire and smoke warnings, and monitoring data. Working towards this direction, IATA, through its project called \"Interactive Cargo\" [22], aims to provide a regulatory regime to enable and ease the use of connected devices that interact with air cargo. The project involves a large number of stakeholders, but mainly focuses on air cargo tracking. To address these issues, the \"INTELLICONT\" project integrates the best available technologies with novel solutions to Enhance Situational Awareness and support relevant crew and technical staff in the execution of their duties and involvement. In our previous work (under evaluation) we focused on the description of a novel intelligent Remote Monitoring System that is capable of receiving a feed from sensors in real-time  
Figure 1: Percentage of safety accidents related to human error, for different air carrier operations. The majority of the accidents are related to air cargo operations. and performing a preliminary analysis of sensor data and data from other sources (e.g., battery status). The objective of the current work is to describe the intelligent Human Machine Interface, which enables an adaptive smart working environment for air cargo operations crews, considering restrictions and needs of the harsh environment. Pillars of our architecture are (a) the novel visualization tool, (b) data fusion architecture, and (c) the standardized interconnection methods that will secure accurate predictions. To the best of the authors' knowledge, no similar system that involves different aspects of ULD safety has been presented. The main innovation of our work is the development of a system capable: (i) of creating strategies and methods regarding the safety of passengers and FRs, easily integrated into the existing ULD systems and technologies; (ii) of creating a complex collaborative system for surveillance, detection, and monitoring of safety-related incidents and events; (iii) of creating situational awareness, future escalation projection, and early warning methods to prevent safety issues; (iv) of allowing easy engagement and smooth cooperation of different FR levels (crew, headquarters, authorities, emergency response teams) in an air cargo related crisis; (v) of modelling, classifying and preserving pieces of evidence and easily reporting a safety event. The rest of the article is organized as follows. Section 2 briefly presents the general concept of the project. The functionality of the intelligent ULDs is introduced in Section 3. Section 4 describes how the ULD is connected with the HMI developed for the data visualization to the users. The structure of the HMI is presented in Section 5. In Section 6, the proof-of-concept testing procedure followed is described. Finally, the conclusions are summarized in Section 7.
## 4 Conclusions  
The stability of blue TADF OLEDs was successfully analyzed considering the HTL and ETL structures, and the analysis results were in accordance with the chemical properties of the device material. The C-V characteristics were measured to analyze the degradation features of each fabricated device using a non-destructive and quantitative impedance spectroscopy method. After the device was electrically aged, the peak capacitance value decreased, and the voltage of the peak capacitance increased compared to that of the fresh devices. By comparing the amount of peak capacitance shift, the stability properties were determined at different EML interfaces in terms of the HTL and ETL. For the HTL, the TCTA/EML interface was the most stable of the three types of HTL materials, with a peak capacitance shift ratio of approximately 1.3%. This shift ratio was the smallest in comparison with those of the mCP/EML (3.7%) and NPB/EML (7.4%) interfaces. This could be explained by the higher T\({}_{\text{g}}\) and BDE of TCTA, which are key indicators of the thermochemical stability of OLED materials. For the ETL, a 10 nm thick ExBL clearly enhanced the EL's characteristic properties. However, after removing the ExBL, the peak capacitance shift ratio decreased from 3.7% to 2.8%. In addition, for the device containing a 40 nm thick ExBL, the shift ratio increased to 5.8%. Therefore, although the poor stability and low carrier mobility of DPEPO contribute to the high degradation of the device, a 10 nm thick ExBL is necessary to achieve high EL performance. We also successfully demonstrated that investigating the C-V characteristics can analyze the operational stability of the device by measuring the operation time.  
Conceptualization, S-J.P.; methodology, S-J.P.; validation, S-J.P., J-Y.P., and B.-K.J.; formal analysis, S-J.P. and J-Y.P.; investigation, S-J.P. and J-Y.P.; resources, S-J.P. and J-Y.P.; data curation, S-J.P. and J-Y.P.; writing--original draft preparation J.-Y.P.; writing--review and editing, S-J.P.; visualization, S-J.P. and J-Y.P.; supervision, B.-K.J.; project administration, B.-K.J.; funding acquisition, B.-K.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Samsung Display Co., Ltd. and the Brain Korea 21 Plus Project in 2022.  
Not applicable.  
Figure 6: (**a**) Normalized luminescence of blue TADF OLEDs at J = 10 mA/cm\({}^{2}\) as a function of operating time of devices 1, 2, 4, and 6, and (**b**) J-V-L characteristics of device 6.
## 3 Results
### Characterization of Microcapsules
#### 3.2.4 Thermogravimetric Analysis (TGA)
TGA was used as an indicator of thermal stability to determine decomposition kinetics according to the rate of sample weight loss and then to estimate sample stability. The results demonstrated the thermal stability of SD and FD powders during low temperature storage. In addition, encapsulation can be considered to protect TC from thermal degradation, and the TGA diagram could be useful to inform about temperatures at which powders can be used for food and beverage applications.
# Study on the Performance of Optical Lenses under High Fluxes of Solar Radiation
## 2 Materials and Methods
### Irradiation Tests
#### 2.2.2 Irradiation with Concentrated Natural Sunlight  
Irradiation tests with concentrated natural sunlight were conducted at the Very High Concentration Solar Tower (VHCST) [18,19], an installation available at the IMDEA Energy Institute (Mostoles, Madrid, Spain). The VHCST has a customized heliostat field (see Figure 8a) and a tower with optical height of 15 m (see Figure 8b). As in the previous experiments conducted with the xenon arc lamp radiation, the lenses were tested separately i.e., one by one, and duration of exposure of each lens to the radiation was also 60 min. The lenses were positioned on a target, made of alumina foam, at the top of the tower and measurements of radiation flux were performed with the same type of radiometer (Vatell Circular-Foil Heat Flux Transducer TG100-1, Vatell Corporation, Christiansburg, VA, USA). Figure 8c depicts the image provided by infrared camera software, showing the temperature distribution on the target (made of alumina foam) at the top of the tower. The radiometer was placed close to the lens in a zone where the radiation flux is homogenously distributed (see Figure 8c). The two small spots at the center of the yellow zone at Figure 8c correspond to the locations of the lens (above) and the radiometer (below). Photos in Figure 8d,e provide more details on the setup used to test the lenses on the inclined target at the top of the tower.
## 2 Channel width Quantization Method Based on Deep Learning  
### Deep Convolutional Neural Networks  
A machine learning algorithm has the advantages of automation and high accuracy. Since entering the 21 st century, machine learning has developed explosively, and deep learning is one of the most important algorithms, and the main driving force for the speedy advances in machine learning in recent years. It has been applied to various fields, such as image recognition, speech processing, automobile manufacturing, and engineering construction [20; 21; 22; 23; 24]. Nowadays, it is difficult to find fields unrelated to deep learning. Combining deep learning with traditional theoretical research in reservoir modeling and oil and gas prediction can often produce better results; examples include CNN-based seismic log interpretation [25; 26], and a GAN-based approach to reservoir geological modelling [27; 28]. The biggest advantage of deep learning is that by providing it with a large amount of sample data, it can automatically learn to extract the key features, and the identification effect is more accurate than that of traditional indicators.  
Deep convolutional neural networks (CNNs) are feed-forward neural networks that contain convolutional computation and have a deep structure. Common deep convolutional neural network models are AlexNet, VGG-Net, ResNet, and GooLNet. The Inception-Resnet-v2 model based on GooLNet introduces the residual network jump connection on the basis of Inception V3 [29], which integrates the \"residual\" structure of Resnet into the Inception structural module to optimize the convergence efficiency of the network and avoid the gradient caused by network deepening. Inception-Resnet-v2 extracts the features of the input layer image through the process of convolution, pooling, and tensor connection, and finally uses the SoftMax classifier to identify the features and predict the probability that the image belongs to a category [30].  
Assuming the number of input images is \(N\). Define the input image as \(x_{i}\), its category label as \(y_{i}\), and the total number of categories output by the model as \(m\) (\(m\geq 2\)). Assume that the probability of the function \(f_{\theta}(x_{i})\) corresponding to the category is \(P(y_{  
The random gradient descent method is used to minimize the error function and obtain the final weight values. Practical applications of deep convolutional neural networks are usually implemented using transfer learning. Transfer learning involves migrating the parameters of a trained model (pre-trained model) to a new model to help train the new model. The basic idea is to use pre-training, take the weight values trained on an existing large dataset as the initialization value, and transfer it to the dataset of the actual problem for fine-tuning the training. Transfer learning avoids the drawbacks of learning from zero training and speeds up the learning efficiency of the optimized model by sharing the already learned model parameters with the new model.
## 3 Numerical Experiments and Discussions
### Fault Detection
#### 3.4.2 Vision Fault  
In addition, the VA-RAIM may also consider any potential faults within the visual measurements. In this paper, the vision fault is defined to be a fault bias on one of the feature detection results. The fault detection results with different DE on a 300 dpi image are shown in Figure 10. The experimental results show that the vision/GPS integration system can detect the fault of the vision system effectively. With more accurate DE, the method can obtain higher fault detection rate of the vision system, and future research will cover the protection levels associated with visual measurement faults.  
Figure 9: Fault detection rate of GPS RAIM and VA-RAIM. (**a**) GPS RAIM and VA-RAIM with different resolutions with 1 pixel DE; (**b**) GPS RAIM and VA-RAIM with different DE on a 300 dpi image.
# Evaluating the Impact of Mobility on Differentially Private Federated Learning +
## 6 Performance Evaluation
### Scenario 2. Random Waypoint  
Figures 7 and 8 illustrate performance in terms of model accuracy and training loss in vanilla FL and mobile FL, respectively, in the RWP mobility scenario. The figures also show  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Loss** \\ \hline Vanilla FL, \(z=0\) & 80.07 & 0.6301 \\ Vanilla FL, \(z=0.5\) & 77.35 & 0.6970 \\ (privacy-enhanced) & & \\ \hline Mobile FL, \(z=0\) & 82.86 & 0.5542 \\ Mobile FL, \(z=0.5\) & 80.28 & 0.6327 \\ (privacy-enhanced) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Enumerated model accuracy and loss in the RPGM model.  
Figure 5: Vanilla FL with RPGM: performance of model accuracy (**left**) and training loss (**right**).  
Figure 6: Mobile FL with RPGM: performance of model accuracy (**left**) and training loss (**right**).  
the impacts of different noise levels applied to them for privacy. In the graphs, a noticeable observation is that performance in vanilla FL drops more significantly than that in mobile FL as the noise multiplier value increases. In the latter, a coordinator makes use of its knowledge base maintaining vehicles' semantic information and selects vehicles that have more data needed for learning as participants before training. This method is particularly important in this scenario because a small number of vehicles may store data associated with accidents, and most of them are highly likely to be selected as participants, which thus prevents dramatic performance degradation.  
Table 2 summarizes the experimental results. Just like in scenario 1, mobile FL performs best, and the privacy-enhanced mobile FL model shows a 2.96% lower accuracy performance than the former. The latter also performs better than vanilla FL by 9.16%, which is a much larger gap than that in scenario 1. The worst performance is from the privacy-enhanced vanilla FL model. Vehicles having no knowledge about accidents are equally likely to be selected as participants, and their update values are noised, which results in a sharp drop in performance, from 73.17% to 41.73%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Loss** \\ \hline Vanilla FL, \(z=0\) & 73.17 & 0.8369 \\ Vanilla FL, \(z=0.5\) & 41.73 & 1.7769 \\ (privacy-enhanced) & & \\ \hline Mobile FL, \(z=0\) & 82.26 & 0.5691 \\ Mobile FL, \(z=0.5\) & 79.87 & 0.6388 \\ (privacy-enhanced) & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Enumerated model accuracy and loss in the RWP model.  
Figure 8: Mobile FL with RWP: performance of model accuracy (**left**) and training loss (**right**).  
Figure 7: Vanilla FL with RWP: performance of model accuracy (**left**) and training loss (**right**).