esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Curing Time/Shear Strength of Non-Conductive Adhesives Using a Neural Network Model
## 2 Materials and Methods
### Specimens and Analysis  
#### 2.2.1 Curing Time Measurement  
In this study, the snap curing time of NCA quantitatively is measured by DEA using a change in the rate of dielectric loss of adhesives as a function of time at a fixed temperature. The curing time measuring equipment consisted of an impedance analyzer, a heating state, and a DEA sensor as shown in Figure 1.  
The DEA sensor was made by FR-4 PCB, and the dimension was 2 mm \(\times\) 3 mm \(\times\) 1 mm. After adhesive was drooped in the DEA sensor heated at 280 \({}^{\circ}\)C, the dielectric loss factor was measured using an impedance analyzer (E4991A from Agilent) at 1 MHz for 1 min. The variation of dielectric loss measured from the impedance analyzer is shown in Figure 1. The curing time of NCA by DEA was measured twice per condition.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ingredient** & **Chemical Name** & **Abbreviation** & **Chemical Structure** & **Molar Mass** & **Melting Point** \\  & & & & **(g/mol)** & **(\({}^{\circ}\)C)** \\ \hline Novacure & HX3941 & Microencapsulated & & & \\  & & & 2 MZ & & \\ \hline Imidazole & Z & & & 68 & 88–92 \\ \hline
2-methylimidazole & 2 MZ & & & & \\  & & & & & \\ \hline
2-ethyl-4-methylimidazole & 2E4 MZ & & & & \\  & & & & & \\ Catalyst & 1-cyanoethyl-2-ethyl-4-methylimidazole & 2E4 MZ-CN & & & \\  & & & & & \\  & & & & & \\ \hline
2,4-diamino-6-[2\({}^{\prime}\)-methylimidazol]-ethyl-s-triazine & 2 MZ-A & & & & \\  & & & & & \\ \hline Dicyandiamide & DICY & & & & \\  & & & & & \\ \hline Triphenylphosphine & TPP & & & & \\ \hline Coupling agent & gamma-Methacryloxymethyltrimethoxysilane & A-174 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.  
Dielectric data measured by the impedance analyzer can be expressed as dielectric constant, \(\varepsilon^{*}\), and the equation can be expressed as  
\[\varepsilon^{*}=\varepsilon^{\prime}-\mathrm{i}\ \varepsilon^{\prime\prime}, \tag{1}\]  
where \(\varepsilon^{\prime}\) is the bulk permittivity, \(\varepsilon^{\prime\prime}\) is the bulk loss factor, and \(\mathrm{i}\) is \(\sqrt{-1}\).  
\(\varepsilon^{\prime}\) can measure the polarization of the adhesive, and \(\varepsilon^{\prime\prime}\) is related to the energy loss and the conductive nature of the adhesive. \(\varepsilon^{\prime\prime}\) can be expressed as a parameter showing the dielectric loss factor because \(\varepsilon^{\prime\prime}\) is directly proportional to the dielectric loss factor when conditions, such as frequency, time, temperature, etc., are fixed [17, 24].  
In this study, curing time was measured when the degree of cure reached 80 % [17]. Deviation of a cumulative line of dielectric loss curve was calculated, and two inflection points were considered as curing start and completion points. In the previous study [17], DEA-based curing time measurements were verified to be compatible with the DSC method.
# Composite Foundation Settlement Prediction Based on LSTM-Transformer Model for CFG
## 4 Engineering Example
### Engineering Overview
Therefore, the original model is normalized based on the formula, and after normalization, a sliding window approach with a window size of k = 15 is applied to generate the model's input sequence. Grey relational analysis [37] is a method that involves normalizing the original data and measuring the correlation and similarity between factors by calculating the correlation coefficient and determining the degree of association. The grey relational grade (GRG) is employed to assess the degree of correlation between each input value and the settlement amount of the CFG pile composite foundation. A GRG value closer to 1 signifies a higher degree of correlation between the two; when the GRG value exceeds 0.6, the correlation between them is considered extremely high. If the GRG value falls within the range of 0.5 to 0.6, it indicates a correlation between the two factors. When the GRG value is less than 0.5, it suggests a lack of correlation between them [16]. Using \(S_{t-3}\), \(S_{t-9}\), \(S_{t-15}\), \(V_{t}\), and \(H_{\text{t}}\) as the model's input, Table 2 illustrates the grey relational grades between each influencing factor and the settlement of the CFC piles. From Table 2, it is evident that factor \(S_{t-3}\), \(S_{t-9}\), \(S_{t-15}\) exhibits notably high grey relational grades concerning the settlement at all four monitoring points, with GRGs exceeding 0.96. Similarly, factor b demonstrates a close correlation with the settlement of the CFG pile composite foundation, showing GRGs above 0.90 across all four monitoring points. The above indicates that the selection of the five variable factors is reasonable. The learning capability and prediction performance of the model sequence are influenced by the length of the input sequence. If the sequence is too short, the neural network model may struggle to effectively extract the intrinsic features of the data. On the other hand, if the sequence is too long, it increases the complexity of information and makes \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Monitoring** & \(S_{t-3}\) & \(S_{t-9}\) & \(S_{t-15}\) & \(V_{t}\) & \(H_{\text{t}}\) \\ \hline DK23 + 200 & 0.99 & 0.97 & 0.98 & 0.94 & 0.73 \\ DK23 + 250 & 0.98 & 0.99 & 0.98 & 0.92 & 0.68 \\ DK23 + 300 & 1.00 & 0.99 & 0.96 & 0.95 & 0.80 \\ DK23 + 350 & 0.99 & 0.98 & 0.97 & 0.91 & 0.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Analysis of grey relational coefficient for each section. Figure 9: Relationship between fill height, time, and ground settlement. feature extraction more challenging. Therefore, selecting an appropriate length for the model sequence is crucial [16]. In this study, the first 150 data points were chosen, following the principle of using 70% of the data as the training set and 30% as the validation set. Consequently, the training set consists of the initial 105 data points, which are used for training, and the subsequent data points (106 th to 150 th) are used for prediction.
# Implementing Deep Convolutional Neural Networks for QR Code-Based Printed Source Identification
## 4 Experimental Results
### Performance Evaluation of BW QR Codes
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model Name** & **Optimizer** & **Accuracy** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline \multirow{3}{*}{AlexNet} & SCDM & 0.9956 & 0.979 & 1 & 1 & 1 & 0.999 & 1 & 1 & 0.987 \\ \cline{2-9}  & Adam & 0.125 & – & – & – & – & – & – & – \\ \cline{2-9}  & RMSprop & 0.125 & – & – & – & – & – & – & – \\ \hline \multirow{3}{*}{GoogleNet} & SCDM & 0.7366 & 0.997 & 1 & 0.865 & 0.999 & 0.605 & 0.353 & 1 & 0.669 \\ \cline{2-9}  & Adam & 0.7188 & 0.897 & 0.991 & 0.685 & 1 & 0.464 & 0.561 & 1 & 0.145 \\ \cline{2-9}  & RMSprop & 0.5095 & 0.743 & 1 & 0.017 & 0.967 & 0.255 & 0.030 & 0.212 & 0.855 \\ \hline \multirow{3}{*}{DenseNet201} & SCDM & 0.9666 & 1 & 1 & 0.996 & 0.988 & 0.913 & 0.910 & 1 & 0.925 \\ \cline{2-9}  & Adam & 0.9234 & 1 & 1 & 1 & 1 & 0.943 & 0.991 & 1 & 0.447 \\ \cline{2-9}  & RMSprop & 0.905 & 1 & 1 & 1 & 1 & 0.939 & 0.965 & 1 & 0.328 \\ \hline \multirow{3}{*}{MobileNetv2} & SCDM & 0.9685 & 1 & 1 & 1 & 0.969 & 0.936 & 0.996 & 0.996 & 0.995 \\ \cline{2-9}  & Adam & 0.9857 & 0.999 & 1 & 1 & 0.887 & 1 & 1 & 1 \\ \cline{2-9}  & RMSprop & 0.9998 & 1 & 1 & 1 & 1 & 0.999 & 1 & 1 & 1 \\ \hline \multirow{3}{*}{ResNet18} & SCDM & 0.9773 & 0.999 & 1 & 0.947 & 1 & 0.879 & 0.996 & 0.997 & 0.999 \\ \cline{2-9}  & Adam & 0.9744 & 0.999 & 1 & 0.875 & 1 & 0.960 & 0.962 & 1 & 1 \\ \cline{1-1} \cline{2-9}  & RMSprop & 0.9268 & 1 & 1 & 0.895 & 0.999 & 0.978 & 0.987 & 1 & 0.551 \\ \hline \end{tabular}
\end{table} 
Table 7: Performance Metrics for COLOR QR Code.
# Correcting Ski Resort Trajectories Extracted from Video
## 4 Conclusions  
In this study, we have explored various methodologies to correct the trajectories extracted from skiing videos, focusing on improving the accuracy of the data through different calibration techniques. Our goal was to identify the most effective method for trajectory correction by comparing the outcomes of these different approaches.  
Initially, we used two reference lines as the baseline to calculate the scaling factor and correction coefficient. This approach is based on the assumption that the slope of the hill is constant. To incorporate the effect of changing slopes, we expanded the reference system to include five lines and conducted a regression analysis based on this new setup. During the regression process, both linear and nonlinear methods were employed, allowing for a comparison of their outcomes. Ultimately, we concluded that incorporating additional factors into the model could result in improved outcomes, offering potential for further refinement beyond the current results.  
This study highlights the importance of using corrected trajectory data for accurate skier behavior analysis. By addressing perspective distortions and slope variations, our methods provide a reliable framework for identifying high-risk zones and supporting the design of safety measures. The results obtained from our comparisons provide valuable insights into the effectiveness of various approaches, which can be applied to future studies or practical implementations. Although we encountered challenges along the way, the final outcomes demonstrate that with the right methodology, accurate and reliable trajectory corrections can be achieved. We are optimistic that these findings can be further refined and utilized in real-world scenarios, contributing to the advancement of sports video analysis and data accuracy in motion tracking.
## 2 Materials and Methods
### Finite Element Model Preparation and Validation  
A commercial motorcycle helmet available for pre-school children in Thailand was selected as a reference in this study. It was a typical half helmet type with a 52-inch circumferential, as shown in Figure 1. The shell and foam liner of the helmet were scanned in 3 dimensions in order to obtain the correct profile of the helmet and liner. The liner was cut in half to measure the thickness, as shown in Figure 1b. The liner thickness was not uniform. The total weight of the outer shell and inner liner was 0.355 kg. The CAD model was created based on the 3D scan surface. The outer shell as well as the liner thickness were measured.  
The outer shell was modelled using shell elements of 3.3 mm thickness, while solid elements were used for the inner liner. The inner liner had non-uniform thickness that varied from 12 mm at the front to 20 mm in the vertex, as shown in Figure 1b. The outer shell was made of acrylonitrile butadiene styrene (ABS). Its behavior was described using the plastic kinematics material model [34] in LS-DYNA with a mass density of 1200 kg/m\({}^{3}\), a [PERSON]'s modulus of 4000 MPa, and a Poisson's ratio of 0.37 [35]. The inner liner was made of expanded polystyrene (EPS). Its compression behavior was described using the low-density foam material model in LS-DYNA [34] with a mass density of 90 kg/m\({}^{3}\), a Young's modulus of 8.64 MPa, and a Poisson's ratio of 0 [35; 36]. The head of a 6-year-old child human body model (6 YO THUMS) was employed as a tool to measure the injury during the impact. The head was developed by Toyota Motor Cooperation and validated in terms of compression and drop tests as reported in [37]. The peak linear acceleration  
Figure 1: **(a)** A child motorcycle helmet sold in Thailand **(b)** the half section of the inner liner.  
was measured at the centre of gravity (C.G.) of the head, while the rotational acceleration was obtained by calculating the interpolation of the values from all nodes of the brain parts, as shown in Figure 2.  
The 6 YO THUMS head was assembled to the helmet. The head-helmet model was set to simulate the impact tests. Two protocols of impact tests, the department of transportation (DOT) FMVSS 218 helmet safety standard [38] and the modified helmet drop test to reflect an oblique impact proposed by [PERSON] et al. [16], were considered. The standard helmet drop test based on the DOT FMVSS 218 required an anvil to place horizontally, while the modified drop test required a flat anvil to make an angle of 45\({}^{\circ}\) with the horizontal plane. This head-helmet finite element model was validated against the DOT drop tests conducted by [PERSON] et al. [39].
# Probabilistic Seismic Safety Assessment of Railway Embarnments
## 5 Seismic Safety Probability Assessment  
This study performed an analytical probabilistic seismic safety assessment (PSSA) for railway embankments. The PSSA procedure of this study is based on a nonlinear response history analysis. In the literature, various methods are available for developing fragility curves from numerical simulations [(33; 37; 38)]. It is common to use incremental dynamic analysis [(39)] to find levels of intensity by scaling ground motions. Ten appropriate records of natural earthquakes were selected and utilized for IDA. For each embankment-earthquake set, an incremental dynamic analysis was conducted. Hence, 2400 analysis cases were performed, and eventually, fragility functions according to the simulated damage data were obtained by numerical analysis. Fragility curves indicate the probability that the demand on an embankment will exceed its capacity given a level of ground motion [(40; 41)].  
The PSSA of a particular structure is generally associated with a variety of uncertainties. According to previous research, embankment seismic performances depend on embankment heights (h) and mechanical properties [(42; 43)]. In other words, they can be considered important sources of uncertainty in the seismic safety evaluation of embankments. Hence, the selected railway embankments were categorized into three groups based on their heights (H = 2, 3 and 4 m). Moreover, the soil cohesion (c) and the friction angle (\(\phi\)) are two of the most important geotechnical parameters. Many of the soil's mechanical properties are directly  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Earthquake** & **Station** & **PGA (g)** & **Year** & **Magnitude** & **R (km)** \\ \hline \multirow{4}{*}{Far-field} & Griva, Greece & Edessa (bsmt) & 0.103 & 1990 & 6.1 & 32.84 \\  & Kern county & Taft Lincoln School & 0.18 & 1954 & 7.36 & 38.42 \\  & Sanfermando & Pasadena—CIT Athenaeum & 0.109 & 1971 & 6.61 & 25.47 \\  & Taiwan SMART & SMART1 E02 & 0.142 & 1986 & 7.3 & 51.35 \\  & Trinidad & Rio Dell Overpass—FF & 0.147 & 1980 & 7.2 & 76.06 \\  & Cape Mendocino & Petrolia & 0.661 & 1992 & 7.01 & 0 \\  & Chuetsu-oki, Japan & Oguni Nagaoka & 0.455 & 2007 & 6.8 & 10.31 \\ \multirow{2}{*}{Near-field} & Montenegro, Yugoslavia & Bar-Skuprtsina Opstine & 0.372 & 1979 & 7.1 & 0 \\  & Northridge-01 & Pacoima Kagel Canyon & 0.43 & 1994 & 6.69 & 5.26 \\ \multirow{2}{*}{Near-field} & Tabas, Iran & Tabas & 0.861 & 1978 & 7.35 & 1.79 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ground motion parameters of selected earthquakes.  
Figure 2: Ground motion response spectra, (**a**) near-field, (**b**) far-field.  
or indirectly dependent on c and \(\phi\). For this reason, several different friction angles (\(\phi\) = 30\({}^{\circ}\), 32\({}^{\circ}\), 34\({}^{\circ}\)) and cohesions (c = 16, 32, 40 kPa) were considered for each group.
# Generalizability of Deep Learning System for the Pathologic Diagnosis of Various Cancers
## 3 Results
### External Validation of the Tissue Classifiers  
H&E-stained slides undergo multiple processing from formalin fixation to staining, resulting differences in color, brightness, and contrast [9]. Furthermore, differences in scanning devices and image compression methods could contribute to the differences in the WSIs [2]. Therefore, different cohorts of WSIs may have different appearances even for the same cancer types depending on the preparation processes and the scanning devices. Furthermore, ethnic differences may contribute to the differences in the tissue appearance in the different cohorts. Thus, we validated the classifiers trained on the TCGA-STAD and TCGA-COAD/READ FFPE WSIs datasets with the WSIs of the stomach and colorectal cancer FFPE tissue slides from the Seoul St. Mary's hospital (SMH-STAD and SMH-COAD/READ). Figure 5a demonstrates the classification results for the SMH-COAD/READ dataset by the classifier trained with the TCGA-COAD/READ FFPE datasets. The AUC was 0.9926. For SMH-STAD dataset, the AUC was 0.9912 (Figure 5b). These results demonstrated that the classifiers trained for the FFPE WSIs of the TCGA datasets can discriminate normal and tumor tissues in the FFPE WSIs obtained from other institutes quite well.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **By BLCA** & \begin{tabular}{c} **By LUAD-** \\ **LUSC** \\ \end{tabular} & \begin{tabular}{c} **By COAD-** \\ **READ** \\ \end{tabular} & \
\begin{tabular}{c} **By STAD** \\ **CHOL** \\ \end{tabular} &
\begin{tabular}{c} **By LIHC** \\ \end{tabular} \\ \hline BLCA & 0.9823 & 0.9778 & 0.9816 & 0.9782 & 0.9390 & 0.9449 \\ LUAD- & 0.9212 & 0.9892 & 0.9417 & 0.9492 & 0.8548 & 0.9152 \\ LOSC & 0.9833 & 0.9878 & 0.9986 & 0.9907 & 0.9256 & 0.8754 \\ STAD & 0.9373 & 0.9126 & 0.9453 & 0.9935 & 0.8890 & 0.8278 \\ CHOL & 0.4389 & 0.8991 & 0.9398 & 0.9368 & 0.9964 & 0.9852 \\ LIHC & 0.6054 & 0.8219 & 0.7967 & 0.8196 & 0.8958 & 0.9910 \\ \hline \hline \end{tabular} BLCA, bladder cancer; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; COAD, colon adenocarcinoma; READ, rectal adenocarcinoma; STAD, stomach adenocarcinoma; CHOL, cholangiocarcinoma; LIHC, liver hepatocellular carcinoma  
\end{table}
Table 1: Cross-validation results for the TCGA-FFPE slides of the six different cancer types.
# Research and Design of a Decentralized Edge-Computing-Assisted LoRa Gateway
## 3 Design of the EC-Assisted LoRa Gateway  
### LoRa Gateway Hardware System Design Based on Zynq SoC  
The LoRa gateway hardware system is based on a XINLIX Zynq7000 processor. Zynq SoC architecture combines the software programmability of a processor with the hardware programmability to form a dual-core ARM Cortex-A9 processor and a conventional FPGA logic component, providing unparalleled system performance, flexibility, and scalability.  
The communication module of the LoRa gateway uses the LoRa SX1278 baseband chip, which features a small size, low power consumption, long transmission distance, and high interference immunity. The LoRa gateway can receive, process, store, and forward the data of end devices. By assigning LoRa gateway edge computing capability, it can effectively enhance data processing capability and reduce data transmission delays. The proposed EC-assisted LoRa gateway hardware system architecture is shown in Figure 2.  
As shown in Figure 2, the main modules connected to the PS resource include the sensor interface, the system debugging interface, the storage module, the communication module, the Ethernet interface, etc. The storage module mainly includes QSPI FLASH, DDR3, and eMMC, which are used to store the running memory, system files, application files, and data files. The wireless communication method for uplink in the system is via 3G, 4G, or LTE modules, which are connected to the network server, while the wireless communication for downlink relies on four LoRa modules, which are connected to numerous end devices. The communication interface between the LTE module and the Zynq is USB, and the communication interface between the LoRa module and the Zynq is UART pass-through. Furthermore, the system also includes interfaces for sensor information acquisition and debugging. In order to connect to multiple serial devices, we utilized multiple AXI_UARLITE IP core ports in our system. We developed the software for multi-threaded design, communication interface development, and distributed computing using the Xilinx Software Development Kit (SDK) environment. The program was compiled using appropriate tools to generate an executable file that was subsequently executed on the Zynq platform.  
Algorithmic IP cores for data processing have been integrated into the PL side based on edge requirements to further improve the efficiency of edge computing. However, the end device access and management calculations in this article were run on the dual-core ARM Cortex-A9 on the PS side.
# Integration of Artificial Intelligence in Art Preservation and Exhibition Spaces
## 5 Discussion
### Advantages and Disadvantages of the Automatic Exhibition Guide System  
#### 5.3.1 Advantages  
* User-friendly design, easy to operate: The system is designed with the user in mind, ensuring that it is intuitive and easy to use. This accessibility allows visitors of all ages and technical abilities to navigate the system without difficulty, enhancing their overall experience.
* Reduces labor costs, supports multiple languages, suitable for international visitors: By automating the guide process, the system significantly reduces the need for human guides, thereby cutting labor costs. Additionally, its support for multiple languages makes it ideal for international visitors, ensuring that language barriers do not hinder their experience.  
* Automatic guide system provides personalized experiences, enhancing visitor satisfaction: The system uses AI to tailor the tour experience to individual visitors based on their interests and behavior patterns. This personalization not only makes the visit more engaging but also increases visitor satisfaction by providing relevant and interesting information.
* Reduces guide vehicles, simplifies processes: By eliminating the need for physical guide vehicles, the system simplifies the logistics of managing exhibitions. This reduction in complexity allows for smoother operations and a more streamlined visitor experience.
# Hybrid Impedance-Admittance Control for Upper Limb Exoskeleton using Electromyography
## 4 Materials and Methods
### Gyroscope Error  
We performed tests with the POC in which the gyroscope was placed on the axis of rotation. The experiments showed that the gyroscope varied from \(-1^{\circ}\) to \(+1^{\circ}\) when at rest and from \(-0.5^{\circ}\) to \(+0.5^{\circ}\) when in motion. We then applied a five-position moving average filter to reduce the average error to 0.1\({}^{\circ}\).