esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Progress on Solution-Processed CdTe Nanocrystals Solar Cells
###### Abstract  
Solution-processed CdTe nanocrystals (NCs) photovoltaic devices have many advantages, both in commercial manufacture and daily operation, due to the low-cost fabrication process, which becomes a competitive candidate for next-generation solar cells. All solution-processed CdTe NCs solar cells were first reported in 2005. In recent years, they have increased over four-fold in power conversion efficiency. The latest devices achieve AM 1.5 G power conversion efficiency up to 12.0%, values comparable to those of commercial thin film CdTe/CdS solar cells fabricated by the close-space sublimation (CSS) method. Here we review the progress and prospects in this field, focusing on new insights into CdTe NCs synthesized, device fabrication, NC solar cell operation, and how these findings give guidance on optimizing solar cell performance.  
solution processed; CdTe; nanocrystals; solar cells +
Footnote †: journal: Applied Sciences
## 3 Experimental Results
The optimum condition based on SNR plot is shown in Table 3.  
Based on Table 3, the ideal function plot of optimization step shown in Figure 10 described the dB gained between optimum and worst conditions. Figure 9: Result plot for SNR (**a**) and sensitivity, \(\beta\) (**b**).
# Landsat Surface Product Validation Instrumentation:
## 3 ASD and UAS Technology Assessment
### Accuracy
#### 3.2.4 Mystery Black Panel Accuracy  
Figure 13 graphically shows the accuracy results for the Mystery Black panel, which had a calibrated reflectance between 0.030 and 0.035, dependent on wavelength. SWIR UAS measurements are again not available, as described previously. For this panel, ASD-based accuracy error was small, less than 0.005 reflectance units in all cases. However, UAS error was substantially larger (0.01 to 0.05 reflectance units) and biased in the opposite direction from the ASD-based measurements (the plot is scaled vertically to show the detail of the ASD measurement error). In general, ASD measurements are capable of achieving accuracies in the range of 0.002 reflectance units. These results also suggest that measurement accuracy is somewhat higher in the SWIR wavelengths compared to the VNIR wavelengths.
# Coalition Formation Game for Cost-Efficient Multiparty Payment Channel in Payment Channel Networks
## 4 Multiparty Payment Channel
### MPC Creation  
As addressed in our previous study [34], where a PC is created by an L2 protocol and smart contract running in an Ethereum virtual machine, the MPC can be created similarly by multiple users. Users execute individually a registration function to open a channel with information parameters, e.g., an initial deposit, an account address, a host IP address, etc. To close the channel, a close function is called with a final channel state and the signatures of all users, which returns the amount to each user based on the final channel balance. The final MPC state is determined by the transaction with the highest round count, which increases for every transaction.
# Construction of Apple Leaf Diseases Identification Networks Based on Xception Fused by SE Module
## 2 Dataset  
### Building the Dataset  
The experimental apple leaf images were collected from the apple experimental and demonstration stations of Northwest A&F University in China, which were located in Bishui County of Weinan city, Qianyang County of Baoji city, Luochuan County of Yan'an city, Qian County of Xianyang, and Qingcheng County of Qingyang city. Images were either taken by an ABM-500 GE/BB-500 GE color digital camera or an Honor V10 mobile phone. The dataset contained color images of 5 apple leaf diseases and healthy leaves, including Alternaria leaf spot, gray spot, brown spot, Mosaic, and rust, which were common diseases that have great influence on apple trees. The image resolution was 2448 \(\times\) 3264.  
A total of 2975 images containing 5 kinds of diseased and healthy leaves were selected from the collected images as the experimental dataset, which was named ATLDD (Apple Tree Leaf Disease Dataset). The ALTDD contains 279 images with Alternaria leaf spot, 540 images with brown spot, 879 images with Mosaic, 430 images with gray spot,429 images with rust and 418 images of healthy leaves. Examples of the dataset are shown in Figure 1. We can see that the dataset includes the disease images in different disease stages and the images are photographed under different lighting conditions and weather conditions. There is a large discrepancy between different images within each category, and there is a certain similarity between different categories to increase the robustness of our model.
# Framework for Fast Experimental Testing of Autonomous Navigation Algorithms
## 1 Introduction
We propose a conceptual structure for navigation problems that permits to implement complete autonomous navigation systems in a fast and easy way (although demonstrated in a ground vehicle, these implementations can be tailored to different kind of robots, whether it be terrestrial, marine or aerial). This framework permits us to easily arrange the system complexity, enabling researchers to focus on their topics of interest while generating minimal but complete applications suitable for real-world experimental testing. This feature improves the research productivity and is a direct consequence of the proposed architecture. * A Kalman filter (KF)-based 2D SLAM and GNSS fusion module. To demonstrate how easy is to replace any module using the proposed framework, we developed a new localization module that became a contribution itself. This module is based on a Kalman filter that fuses the poses generated by two complementary localization sources as 2D SLAM and GNSS are. This module permits to recover the SLAM localization after exploring unmapped areas, so mixed navigation on-map/off-map can be performed. * A set of tools for basic system implementation. In addition to the conceptual framework, we provide a set of tools that brings the basic functionalities required to implement a fully operative terrestrial autonomous navigation system. It comprises planning, car-like control, and reactive safety modules. The rest of this paper is organized as follows: in Section 2, we describe some related works focusing on general frameworks for developing autonomous navigation systems. In Section 3, we explain the requirements and the design decisions to fulfill them, giving a conceptual architecture for our framework. Then, in Section 4, we explain the basic implementation and its features, from perception to control. Section 5 is devoted to explaining the second particularization of our framework, which implements a fusion of GNSS and Monte Carlo localization by means of a Kalman filter. In Section 6, the different experimental sessions are described and discussed, including real autonomous navigation in two different challenging outdoor environments. Finally, in Section 7, we give conclusions and future works.
# Overcoming Individual Discrepancies, a Learning Model for Non-Invasive Blood Glucose Measurement
## 6 Experiment and Results
### Comparision with other NGMs  
NGM is a hotspot in the field of glucose detecting nowadays. So far, a large amount of NGM methods have been proposed, among them some have achieved a pretty high accuracy, however most of these methods are lab-only with unsolved issues of transition and popularization. The limitations of these methods themselves lead to this awkward situation, since all the methods require support from large amount of sophisticated instruments at high expenses with complex operation procedures and strict experimental conditions. A comparison on input, accuracy, hardware cost and condition requirement of the methods proposed in this paper and three other recently published methods is listed in Table 5.  
Different measurement methods have different levels of accuracy. Among them, some have a lower accuracy but a higher practicability. The quality of a method is not only judged by accuracy, instead more indicators should be taken into consideration when it comes to a comprehensive evaluation. As demonstrated by these experiments, the method proposed in this paper not only has a preferable accuracy, but also has the advantages of low cost and good robustness. In a word, our method shows an excellent comprehensive performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Input** & **Accuracy** & **Hardware Cost** & **Condition Requirement** \\ \hline This paper & light absorbance & \(Corr=0.819\) & \(<550\) & open condition \\ \hline [PERSON], et al. [55] & spectroscopy & mean error = 1.02 mmol/L & \(>51000\) & open condition \\ \hline [PERSON], et al. [36] & impedance spectroscopy, optical properties, temperature and humidity. & \(Corr=0.831\) & \(>52000\) & laboratory environment \\ \hline [PERSON], et al. [37] & heart rate, blood oxygen, blood pressure & mean error = 2.43 mmol/L & unknown & laboratory environment \\ \hline [PERSON], et al. [38] & spectroscopy & \(Corr=0.570\) & \(>51000\) & laboratory environment \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of different Non-invasive Glucose Measurement (NGM) methods.
## 2 Numerical Simulation  
A carbon fiber reinforced polymer (CFRP) composite sheet with three plies, shown in Figure 1, was used to perform computer simulations. The dimensions and mechanical and thermal properties of this sheet are given in Table 1. The sheet was fixed at its left edge and free in the other edges. In the numerical simulation, the fact that during press forming, adjacent plies slip over each other was modeled. A uniform pressure load was applied on the surface of the sheet, and the middle ply was pulled out with different velocities.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Symbols** & **Quantities** & **Units** \\ \hline Longitudinal tensile modulus & \(E_{11}\) & 131,000 & MPa \\ Transverse tensile moduli & \(E_{22},E_{33}\) & 9100 & ” \\ Shear moduli & \(G_{13},G_{12}\) & 5100 & ” \\ Shear modulus & \(G_{23}\) & 1380 & ” \\ Density & \(\rho\) & \(1.52\times 10^{-9}\) & tons/mm\({}^{3}\) \\ Poisson ratios & \(\
u_{12},\
u_{13}\) & 0.34 & — \\ Poisson ratio & \(\
u_{23}\) & 0.4 & — \\ Ply thickness & \(h\) & 0.127 & mm \\ Laminate thickness & \(H\) & 0.381 & ” \\ Laminate length & \(l\) & 500 & ” \\ Laminate width & \(w\) & 250 & ” \\ Thermal conductivity & \(\rho_{T}\) & 0.074 & W/mm/K \\ Thermal expansion & \(\alpha\) & \(3.64\times 10^{-6}\) & 1/K \\ Specific heat & c & \(3.1\times 10^{7}\) & Joules \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of the fiber reinforced composite sheet.  
To conduct the computer simulations, ABAQUS commercial software was used. Finite element modeling of the composite sheet was based on coupled temperature-displacement (C3D8T: an eight node thermally coupled brick, tri-linear displacement, and temperature) element type. The initial temperature (as taken from the autoclave) of the composite sheet was set to be 393.15\({}^{\circ}\) K (120 \({}^{\circ}\)C). The fiber orientation angles of the two outer plies of the composite sheet were set to 0\({}^{\circ}\), relative to theply pullout direction, throughout all the simulations, whereas that of the middle ply varied between 0\({}^{\circ}\) and 90\({}^{\circ}\).  
In the ABAQUS modeling of the inter-ply contact, the penalty scheme was used to impose frictional constraints on the tangential interaction of adjacent plies. This scheme allows the relative motion of plies while remaining stuck together. Moreover, the ply pullout speed is deliberately limited to be no more than 2 mm/s, as shown in Table 2. Under this very slow forming speed, the shear stress is not affected by inter-ply friction, and hence, the coefficient of friction was set to a constant value (0.1) in the forming simulation. The initial temperature for each computer simulation was set to 393.15\({}^{\circ}\) K. During the forming process, for each sample data point, the temperature of the sheet (blank) drops down to its final value, which lies in the range given for the blank temperature parameter.
#### 1.1.1 Virtual Reality  
VR has emerged as a new treatment approach in stroke rehabilitation, involving exercise programs designed to simulate real-life objects and events through a computer interface. VR systems are classified into three immersion levels: fully immersive (which completely obstructs real-world perception), non-immersive (enabling simultaneous real and virtual environment experiences), and semi-immersive (utilizing screens or headsets for partial immersion) [17]. The potential benefits of VR are significant, as rehabilitation programs are often designed to be more engaging and enjoyable than traditional therapy tasks, encouraging more repetitions and concurrently addressing the patient's mental well-being. Physical exercise can improve health by reducing the risk of various health issues. Engaging in movement while enjoying virtual activities can help individuals stay motivated to adopt a healthier lifestyle, build confidence, and prevent hopelessness [18]. However, it is crucial to acknowledge the challenges, such as the necessary technological skills, expense, and cyber-sickness [19].
## 5 Experimental Results
### Cascade Approach
#### 5.4.2 Feature Selection
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Datasets** & **Sets** & **k-NN** & **ANN** & **Decision Tree** & **SVM** & **Naive Bayes** \\ \hline \multirow{3}{*}{Root dataset} & Set1 & 35.61 & **52.84** & 39.93 & 25.54 & 49.19 \\  & Set2 & **55.47** & 51.76 & 44.46 & 33.04 & 47.03 \\  & Set3 & **62.16** & 62.09 & 48.92 & 45.68 & 54.46 \\ \hline \multirow{3}{*}{Airport} & Set1 & **73.44** & 72.81 & 70.31 & 61.88 & 73.13 \\  & Set2 & 74.38 & 76.25 & 77.50 & 65.31 & **78.13** \\  & Set3 & **81.56** & 81.25 & 70.94 & 74.69 & 69.38 \\ \hline \multirow{3}{*}{Construction} & Set1 & 70.00 & 72.78 & 72.78 & 66.67 & **81.11** \\  & Set2 & 71.11 & **85.56** & 64.44 & 74.44 & 87.22 \\  & Set3 & 70.56 & 82.78 & 62.22 & 68.89 & **89.44** \\ \hline \multirow{3}{*}{Residence} & Set1 & 44.69 & **69.38** & 59.38 & 37.38 & 62.88 \\  & Set2 & 65.56 & 68.25 & 64.00 & 51.38 & **68.63** \\  & Set3 & **78.52** & 78.88 & 66.38 & 71.00 & 76.25 \\ \hline \multirow{3}{*}{Vehicles} & Set1 & 66.22 & 64.32 & **67.03** & 55.14 & 66.76 \\  & Set2 & 72.16 & **78.65** & 69.19 & 59.46 & 72.43 \\  & Set3 & 77.30 & **80.27** & 68.92 & 73.24 & 77.84 \\ \hline \multirow{3}{*}{Warming} & Set1 & 60.36 & **64.64** & 60.36 & 59.64 & 64.64 \\  & Set2 & 66.07 & **70.00** & 58.93 & 65.00 & 63.21 \\ \cline{1-1}  & Set3 & 65.71 & **72.86** & 65.00 & 65.36 & 65.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Average accuracy in percentage per data set. The best classifier per data set is highlighted in bold. Figure 10: Average timing of the classifiers running with their default configuration. One would expect that, for most of the classifiers, the increment of the number of features used by the classifiers would lead to more accurate results, but also to slower classifiers. The results summarized in Table 8 and Figure 10 confirm this assumption. Few exceptions exist. For instance, the classifier Naive Bayes reaches a higher performance using Set 1 instead of Set 2 for the _Root_ and the _Warning_ datasets. Moreover, the execution time of the ANN classifier slightly varies in most of the cases when enlarging the number of features.
# Investigation on Intelligent Early Warning of Rock Burst Disasters Using the PCA-PSO-ELM Model
## 3 Example Analysis
### Combined Early Warning Model of PCA-PSO-ELM for Rock Bursts  
The principal components Y\({}_{1}\), Y\({}_{2}\), Y\({}_{3}\), Y\({}_{4}\), Y\({}_{5}\), and Y\({}_{6}\) after the dimensionality reduction were taken as the input factors for the model, and the risk level of the rock burst was chosen as the output factor for the model. The training set samples were trained and the PSO algorithm was used to optimize the initial weight values of the ELM. The threshold values of the hidden layer were used to obtain the optimal parameter combination, which was input into the super parameters of the ELM model to predict the rock burst grade of the test set. Eventually, the rock burst prediction model with regard to the PCA-PSO-ELM combination algorithm was established.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Sample**} & \multicolumn{6}{c}{**Principal Component**} & \multirow{2}{*}{**Impact Level**} \\ \cline{2-2} \cline{5-7}  & \(\mathbf{Y_{1}}\) & \(\mathbf{Y_{2}}\) & \(\mathbf{Y_{3}}\) & \(\mathbf{Y_{4}}\) & \(\mathbf{Y_{5}}\) & \(\mathbf{Y_{6}}\) \\ \hline
1 & \(2.74\) & \(-1.24\) & \(-0.83\) & \(0.73\) & \(0.34\) & \(0.16\) & IV \\
2 & \(-3.57\) & \(-0.55\) & \(0.98\) & \(0.95\) & \(0.28\) & \(-0.25\) & I \\
3 & \(2.33\) & \(-0.94\) & \(-1.16\) & \(0.83\) & \(-0.22\) & \(-0.1\) & IV \\
4 & \(0.94\) & \(0.31\) & \(-1.36\) & \(-0.38\) & \(-1.24\) & \(-0.08\) & III \\
5 & \(0.42\) & \(0.81\) & \(-1.59\) & \(-1.1\) & \(-1.11\) & \(-0.9\) & III \\
6 & \(2.74\) & \(-1.24\) & \(-0.83\) & \(0.73\) & \(0.34\) & \(0.16\) & IV \\
7 & \(0.37\) & \(0.09\) & \(-0.03\) & \(-1.56\) & \(0.96\) & \(0.01\) & III \\
8 & \(0.21\) & \(-0.64\) & \(0.15\) & \(-0.61\) & \(0.37\) & \(-0.4\) & III \\ \hline \end{tabular}
\end{table}
Table 6: Test set samples after the PCA analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Sample**} & \multicolumn{6}{c}{**Principal Component**} & \multirow{2}{*}{**Impact Level**} \\ \cline{2-2} \cline{5-7}  & \(\mathbf{Y_{1}}\) & \(\mathbf{Y_{2}}\) & \(\mathbf{Y_{3}}\) & \(\mathbf{Y_{4}}\) & \(\mathbf{Y_{5}}\) & \(\mathbf{Y_{6}}\) \\ \hline
1 & \(-4.13\) & \(-0.52\) & \(0.22\) & \(0.38\) & \(0.23\) & \(0.13\) & I \\
2 & \(-4.15\) & \(-0.38\) &
Development of a Component-Level Hydrogen Transport Model with OpenFOAM and Application to Tritium Transport Inside a DEMO HCPB Breeder  
[PERSON]  
1 Karlsruhe Institute of Technology (KIT), P.O. Box 3640, D-76021 Karlsruhe, Germany;  
[PERSON]F.A.); [PERSON] (C.K.); [PERSON] (D.K.);  
[PERSON].S.); [PERSON]DRESS] (A.v.d.W.)1  
[PERSON]  
1 Karlsruhe Institute of Technology (KIT), P.O. Box 3640, D-76021 Karlsruhe, Germany;  
[PERSON]F.A.); [PERSON] (C.K.); [PERSON] (D.K.);
The design of a BZ has to take into account these parameters with regard to the breeding efficiency and the self-sufficiency of the reactor with fuel. The tritium inventory in the components plays a role with regard to safety and decommissioning requirements.  
The above conditions obviously ask for multiple physics capabilities when analyses are to be done. Multiphysics component-level simulations are done for several breeder concepts using different codes, e.g., for water-cooled lithium leads (WCLL) [3; 4], water-cooled ceramic breeders (WCCB) [5], and helium-cooled ceramic reflectors (HCCR) [6]. For HCPB, several system-level analyses exist [7; 8; 9].  
This work continues the development of a tritium transport model on the BZ level based on the open source CFD framework OpenFOAM [10]. The model is intended to become a direct support of the KIT HCPB design activities. As a first step in this work the model is applied to tritium transport inside a single pin of the KIT HCPB DEMO pin design.
## 4 Experiment and Analysis
### Comparison of Diverse Models on FLIR Dataset
Table 5 lists the experimental results of all the above models. The superiority of the presented strategy is clearly observable in Table 5, where mAP reaches the highest level of 84% of all comparison models. Specifically, the latest multispectral pedestrian detection CMPD method achieves more reliable multimodal feature fusion by attaching a confidence subnetwork; BU(AT, T) adopts a domain adaptation strategy to retain more visible domain feature information; and these methods achieve good results. However, the average -recision in the Person category is 15.5% and 9% lower than ours, respectively. Among the other methods, the highest mAP value was achieved by the most advanced multistage detector Cascade R-CNN and anchor-free detection algorithm YOLOX; and our method achieved values 3.5% and 2.8% higher than theirs. The contrasting results of the data mentioned above prove the validity and rationality of the module presented in this study. In addition, Figure 10 displays the F1 score curve and the PR curve for each category of our model. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Method** & **Size (MB)** & **Params (M)** & **mAP** & **F1** & **R** & **P** \\ \hline SSD & 181.3 & 91.7 & 67.9 & 68 & 66.2 & 69.8 \\ YOLOv3-tiny & 17.4 & 8.7 & 68.4 & 69 & 61.5 & 78.0 \\ YOLOv5n & 3.7 & 1.8 & 80.9 & 78 & 71.3 & 85.5 \\ YOLOv7-tiny & 11.7 & 6.0 & 83.1 & 79 & **77.3** & 81.6 \\ Ours & 4.4 & 2.1 & **84.0** & **81** & 75.7 & **86.8** \\ \hline \end{tabular}
\end{table} 
Table 4: Performance contrast of diverse one-stage models. The best data are described in bold. Figure 9: Various one-stage models’ performance indicators comparison chart.
## 3 Method
### Calculation Process  
#### 3.2.1 Thermal Model  
The simulation utilized a double ellipsoidal heat source [39]. As the welding heat source passed through the weld seam, the molten metal filling process in the weld seam was taken into account using the life and death unit technology to fill the gap. The welding unit was activated and filled the gap to the welding seam once the heating temperature of the component reached the melting point of the solder. The welding process involved first welding the two connecting welds between the horizontal flue side and the fin from top to bottom, followed by welding the connecting welds between the tail flue side and the fin.  
The ambient temperature and initial temperature of the workpiece were defined as 20 \({}^{\circ}\)C. The heat transfer coefficient was set as a constant of 20 W/(m\({}^{2.\circ}\)C). During the welding process, heat loss occurred primarily through radiation and convection from the workpiece's surface to the surrounding environment, with radiation being the primary form of loss. The radiation effect became stronger as the temperature increased. Typically, at temperatures above 1200 \({}^{\circ}\)C, radiation loss exceeds convective heat dissipation loss, while convection is the dominant form at lower temperatures. For calculation convenience, the radiation and convection coefficients were combined into a total heat transfer coefficient for simulation purposes. This allowed the heat energy lost due to boundary heat transfer to be expressed as follows:  
\[\mathrm{Q_{S}}=\beta(T-T_{0}) \tag{1}\]  
\[\beta=\beta_{\mathrm{e}}+\beta_{\mathrm{c}} \tag{2}\]  
where \(T\) represents the surface temperature of the welding piece (\({}^{\circ}\)C), \(T_{0}\) represents the temperature of the surrounding medium, \(\beta\) is the surface heat transfer coefficient (W/(m\({}^{2.\circ}\)C)), \(\beta_{\mathrm{e}}\) is the convective heat transfer coefficient, and \(\beta_{\mathrm{c}}\) is the radiation heat transfer coefficient.  
Figure 4: Schematic diagram showing the finite element model.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \(T\) & \(C\) & \(\lambda\) & \(\alpha\) & \(E\) & \(G\) & \(\
u\) & \(\sigma_{\mathrm{s}}\) \\ \hline
20 & 560 & 45.2 & 10.80 & 214 & 83.5 & 0.28 & 393 \\ \hline
100 & 569 & 45.2 & 13.00 & 211 & 81.8 & 0.28 & 374 \\ \hline
200 & 586 & 45.2 & 13.36 & 206 & 79.2 & 0.30 & 359 \\ \hline
300 & 611 & 42.7 & 13.55 & 195 & 74.0 & 0.31 & 308 \\ \hline
400 & 653 & 40.5 & 13.83 & 187 & 72.2 & 0.29 & 285 \\ \hline
500 & 682 & 37.7 & 14.15 & 179 & 68.6 & 0.30 & 266 \\ \hline
600 & 729 & 35.5 & 14.38 & 167 & 63.1 & 0.32 & 251 \\ \hline
700 & — & 33.4 & 14.62 & — & — & 0.32 & — \\ \hline \end{tabular}
\end{table}
Table 3: Material parameters of 12 Cr1 MoV [38].