esa-sceva/satcom-chunk-collection huggingface dataset:
# Elastic Computing in the Fog on Internet of Things to Improve the Performance of Low Cost Nodes
## 7 Conclusions  
Nowadays IoT is requiring a flexible and scalable network design but built on low cost nodes to access and connect almost everything at everywhere. In particular in this paper, we focused on nodes such as RPi. But with the new trends, the IoT is facing many scenarios that require high computation capabilities beyond the possibilities of these nodes alone. For this goal, we proposed an architecture leveraging fog computing, based on Linux containers and an orchestration platform, to run on the top of a cluster of these nodes in order to cooperate and schedule different tasks in an efficient way.  
As a proof of concept, we showed a scenario that requires high computing requirements, such as soundscape monitoring, and compared different alternatives in its implementation. With these results, we conclude that using and combining clustering techniques, Linux containers, and an orchestrator, we can improve the overall computation capabilities of these IoT nodes. We have used Linux Docker containers and compared two different COP as orchestrators, such as Docker Swarm and Kubernetes. The experimental results showed the improved performance in terms of execution time and throughput in a cluster of four RPis. We have seen that Docker Swarm always outperforms Kubernetes in this scenario.  
Thus, finally, an educated answer for \"how can I increase the overall computational power of a set of SBC devices in an IoT deployment?\" is: We can overcome the constraints imposed by single SBC devices using a cluster of interconnected nodes in a wireless mesh networks, since these devices have wireless capabilities. In addition, in order to improve the overall computational power, the use of Linux Docker containers add flexibility, adaptability, and responsiveness. However, it is necessary to orchestrate the resources among the nodes in the cluster by using a COP; in particular, Docker Swarm performs best.  
**Author Contributions:** conceptualization, methodology, and validation by [PERSON] and [PERSON]; software, [PERSON]-[PERSON]; resources, [PERSON]-[PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]-B.  
**Funding:** This work was supported by the Ministry of Economy under the project Urbauramon BIA2016-76957-C3-1-R and a grant by University of Valencia UV-INV-EPDI19-995284.  
**Conflicts of Interest:** The authors declare no conflict of interest
# Effect of Parameters on Vapor Generation in Ship-to-Ship Liquefied Natural Gas Bunkering
###### Abstract  
Liquefied natural gas (LNG) is attracting increasing attention as an alternative fuel in the maritime sector, as it can reduce harmful emissions for compliance with stricter environmental regulations. Owing to this environmental advantage, the number of ships using LNG as a fuel is increasing; thus, the demand for ship-to-ship LNG bunkering is increasing. One of the challenges of ship-to-ship LNG bunkering is boil-off gas (BOG) management, as it is more difficult than normal BOG management. This study analyzed the influences of the parameters on vapor generation, including the temperature difference between the bunker tank and receiving tank, bunkering flow rate, insulation performance, and compositions. A model based on a typical bunkering system was established, and a dynamic simulation was conducted using a commercial process simulator, Aspen HYSYS. The results indicated that as the initial temperature of the receiving tank increased, the amount of vapor return increased proportionally. In addition, increasing the bunkering flow rate decreased the amount of heat entering through the pipes and tanks; however, the heat dissipated by the pump shaft power increased. Different LNG compositions in the bunker tank led to changes in the initial pressure of the bunker tank, influencing the vapor return and vapor generation in the receiving tank. Through a parametric study, it was found that the pressure of the tank is the most important factor in terms of vapor return and vapor generation. As such, a pressure control method was proposed for the tank, so as to reduce vapor generation and vapor return. With pressure control, the total amount of vapor return to the bunker tanks is reduced from 7392 to 3317 kg. The net vapor generation in the receiving tank is reduced by up to 4047 kg and the net vapor generation in the overall system is reduced by 16.2%.  
liquefied natural gas; bunkering; boil-off gas; vapor generation; vapor return +
Footnote †: journal: Applied Soft. 2020, 10, 6861; doi:10.3390/app10196861
# FPGA-Based Methodology for Detecting Positional Accuracy Degradation in Industrial Robots
## 2 Materials and Methods
### Positional Accuracy Degradation FPGA Implementation
#### 2.6.1 DWT Soft-Core FPGA Module  
The DWT soft-core module calculates the decomposition of a signal in different approximation and detail frequency bands and performs the original signal reconstruction. This process is realized through a matrices multiplications. Figure 11 shows the module's architecture for the DWT process.  
Figure 10: General diagram of the FPGA system.  
Within this module, the data corresponding to the windowed signal of 256 samples and the coefficient module are read from RAM. The coefficients module contains the wavelet matrices with wavelet filter values for approximations and details of levels three and four of the mother wavelet Daubechies order six. The \"W. coef.\" module stores the wavelet coefficient vectors obtained by multiplying the wavelet matrix with the input signal. For reconstruction, the stored data in \"W. coef.\" is read and multiplied by the transposed wavelet matrix corresponding to the respective level, approximation, or detail. These matrices are stored in \"coefficients\" in RAM. The result of the decomposition and reconstruction of the frequency bands is saturated at 16 bits and is stored in the \"Data\" and \"W. coef.\" modules. Figure 11 depicts the bit size changes after each operation. A finite state machine (FSM) controls the signal decomposition and reconstruction process selection. It is done through the SEL signal and memory addresses of the coefficients, data, and wavelet coefficients modules. The address counters contain this information, and the FSM chooses the active module using the OPC signal. The STR signal indicates to FSM the beginning of the process, and once it is completed, the RDY signal is activated. To write and read memory, the modules use the WRC, WR, RD, and RDC signals. DWT module performs the process for the approximations and details of levels three and four of the signal stored in \"Data\". Once the process is finished, the reconstructed frequency bands are transferred to the statistical module for further processing.
## 5 Conclusions  
This paper proposes a transfer learning architecture based on the trained softmax-based model and SVM classifier to perform classification tasks on two histopathology image datasets. The proposed synthetic architecture involves a two-step procedure. The first step is to train a softmax classifier-based network using transfer learning on the target dataset. The second step is to use the already trained FC layer in the first step to connect to an SVM classifier. A pre-trained deep CNN architecture AlexNet is used for feature extraction for transfer learning. Knowing the tissue is cancerous or benign is vital for the doctor to diagnose the cancer of the patients. Thus, improving the classification accuracy of the histopathology image becomes a crucial task for machine learning applications. The softmax-based model, the SVM-based model, and the proposed synthetic model are tested and compared on a binary breast cancer dataset and a multiclass lung and colon cancer dataset. Four-fold cross-validation is used to ensure less bias of the evaluation of the three transfer learning models. Experimental results obtained show that the proposed method achieves the best histopathology cancer image classification accuracy, both in binary and multiclass histopathology image datasets.  
Conceptualization, Y.L. and J.L.; methodology, J.F., J.L. and Y.L; investigation, J.F.; writing--original draft preparation, J.F.; writing--review and editing, J.L.; supervision, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Korea institute of radiological and medical sciences. Grant number 2021-0222.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Softmax-Based** & **SVM-Based** & **Proposed** \\ \hline Fold 1 & 0.9922 & 0.9642 & 0.9942 \\ Fold 2 & 0.9962 & 0.9443 & 0.9952 \\ Fold 3 & 0.9920 & 0.9550 & 0.9930 \\ Fold 4 & 0.9912 & 0.9603 & 0.9952 \\ Average & 0.9929 & 0.9560 & 0.9944 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cross-validation accuracy of the three transfer learning models on colon and lung cancer dataset.  
Data Availability StatementRestrictions apply to the availability of these data. Data was obtained from [[https://www.kaggle.com/paultimothymooney/breast-histopathology-images](https://www.kaggle.com/paultimothymooney/breast-histopathology-images)]([https://www.kaggle.com/paultimothymooney/breast-histopathology-images](https://www.kaggle.com/paultimothymooney/breast-histopathology-images)) and [[https://www.kaggle.com/andrewmvd/lung-and-colon-cancer-histopathological-images](https://www.kaggle.com/andrewmvd/lung-and-colon-cancer-histopathological-images)]([https://www.kaggle.com/andrewmvd/lung-and-colon-cancer-histopathological-images](https://www.kaggle.com/andrewmvd/lung-and-colon-cancer-histopathological-images)).  
This work was supported by Seoul National University of Science and Technology, Seoul, South Korea. The authors declare no conflict of interest.
## 6 Security and Performance Analysis
### Security Analysis
Thus, \(\mathcal{C}\) runs the \(UKeyGen\) and returns the same update key \(UK_{m_{b}}\) that will hide the data. Finally, \(\mathcal{B}\) transmits \(UK_{m_{b}}\) to \(\mathcal{A}\). 5. **Phase 2:** This phase has the same process as mentioned in Phase 1. 6. **Guess:** The attacker finally outputs a guess \(b^{{}^{\prime}}\) of \(b\). If \(b^{{}^{\prime}}=b\), \(\mathcal{B}\) provides the output \(0\) so that \(\mathcal{T}=e(g,g)^{d^{\prime}+1}\). Contrarily, it provides output \(1\) to show that it believes \(\mathcal{T}\) is a random-group element in \(\mathcal{G}_{T}\). Here, \(\mathcal{T}\) is a tuple. Thus, \(\mathcal{B}\) implies \(\Pr\left[\mathcal{B}\left(\vec{y},\mathcal{T}=e(g,g)^{d^{\prime}+1}s\right)=0 \right]=\frac{1}{2}+Adv_{\mathcal{A}}\). Moreover, the \(\mathcal{T}\) is a random-group element, and the message \(m_{c}\) is completely hidden from the adversary at \(\Pr\left[\mathcal{B}\left(\vec{y},\mathcal{T}=e(g,g)^{d^{\prime}+1}s\right)=0 \right]=\frac{1}{2}\). Hence, \(\mathcal{B}\) plays the decisional q-parallel bilinear Diffie-Hellman exponent game with a non-negligible advantage. 7. **Anticollision:** Furthermore, in this research work, the attribute authority produces the private keys with respect to the end users. The private keys are related to \(t_{i}\), and each end user has a special connection. Therefore, combining components in different private keys is pointless. Assume that the access need is met by combining two or more end users with distinct attributes. End users cannot evaluate \(\mathcal{B}=e(g,g)^{s/2}\) in the decryption process. This technique in our work is considered as anticollision.
# Mechanism of HCB-Modified Asphalt and Dynamic Properties of Mixtures
###### Abstract  
Hydroymethyl carbon black (HCB) was prepared as an asphalt modifier with a high oxygen content and active surface chemical properties. The microstructure of HCB was analyzed by scanning electron microscopy, energy-dispersive X-ray spectroscopy, and Fourier-transform infrared spectroscopy. The improvement effect of HCB on asphalt's physical, dynamic shear, rheological, and aging properties was evaluated. To analyze the dynamic properties of the HCB-modified asphalt mixtures, a simple performance test (SPT) was conducted, and then the change laws of the dynamic modulus and phase angle for the HCB mixtures were clarified. The results showed that the surface of HCB is smooth and that the oxygen content increases with the generation of hydroxyl functional groups. Polar oxygen-containing functional groups and hydrogen bonds are helpful in improving the resistance to cracking and aging. The surface activity of HCB is susceptible to temperature and frequency, causing a slight influence of HCB on the viscoelasticity of asphalt mixtures at high and low frequencies. At low temperatures and high frequencies, the HCB enhanced the elasticity characteristics and weakened the viscosity characteristics of asphalt mixtures.  
Keywords:hydroxymethyl carbon black; modified asphalt; asphalt mixture; microscopic analysis; rheological properties; dynamic response +
Footnote †: journal: Applied Sciences
## 3 Case Study: XOR Problem
### Simulation Result  
Figure 6 plots the score and the number of chromosomes of the neural network structure with the highest score for each generation of the first simulation. The score of the highest performing species began from 0.65 and increased to 0.95. If a score of 0.9 or higher was obtained, the generated neural network could correctly obtain the exclusive or (XOR) value. This system found a structure for XOR in 48 generations. This solution structure had 32 chromosomes. As the generation progressed, the number of chromosomes gradually decreased. It took 218 generations to find the optimal form with 11 chromosomes for the first time. Since then, it appeared to oscillate and converge.  
Figure 6: Simulation result of first simulation.  
Figure 7 plots the results of the second simulation. In this simulation, species of the first generation were fully connected with 5 neurons. The score of the highest performing species began from 0.97 and decreased to 0.95, however, it increased again to 0.97. This system started from a completed ANN structure. However, it decreased the number of chromosomes after starting. Neurons and connections decreased together until the number of chromosomes reached \(11\).  
Table 1 shows a comparison between the results of structure learning with NEAT and the proposed method about the XOR problem. The NEAT methodology found a solution faster than the proposed methodology using VCGA. However, NEAT evolved networks with 2.35 hidden neurons and 7.48 disabled connections, while the proposed method's solutions used 2 hidden neurons and 0 disabled connections. Since NEAT only uses the add operation, it cannot evolve with the destructive approach. On the other hand, the proposed methodology with VCGA allows decreasing neurons or connections with the chromosome attachment. The result is important because it shows that a methodology using VCGA has the potential to be applied to large and complex problems while performing as well as NEAT.
###### Abstract
The spatial and temporal features play distinct roles in activity recognition. The spatial features analyze individual frames to extract static features such as shapes, textures, and poses, while the temporal features examine frame sequences to capture motion and repetitive gestures essential for detecting stereotyped movements. Both components are integral, with spatial analysis providing context and temporal analysis identifying dynamic patterns. Traditional computer vision approaches are good at extracting spatial and temporal features. However, prior knowledge of the topic of interest is necessary to extract significant features that may be further enhanced using machine learning (ML) approaches. In contrast, the deep learning (DL) technique outperforms ML approaches because of its ability to extract features from raw data right away [5]. In addition, the Convolutional Neural Network (CNN) is a robust deep structure designed to detect frames and learn related features efficiently without the need for intermediary engineering [8]. Furthermore, using a Vision Transformer (ViT) model as Temporal Swin with a ResNet-3D pre-trained model offers several advantages over traditional deep models, such as improved attention mechanism, better handling of long-range dependencies, flexibility in input size, reduced computational cost, and interpretability. Overall, combining a Temporal Vision Transformer model as Swin with a pre-trained ResNet-3D backbone can offer a powerful and flexible approach to various computer vision tasks, providing improved performance, interpretability, and efficiency compared to traditional deep models. In this endeavor, we aim to develop a model based on the combination of a pre-trained Temporal Vision Transformer (ViT) model (Temporal Swin) and a 3D-Residual Network architecture to detect Pre-Meltdown Crisis based on the SMMs of autistic children. The contributions of this research are summarized as follows:* We prepared the dataset using several methods, resulting in noise-free sequences that retain each child's traits and record full movement information (e.g., gestures and position). * We developed a high-performing predictive model using Temporal Swin Transformer with Renset-3D to extract and learn the spatial and temporal features of autistic children's SMMs during the Pre-Meltdown Crisis. * We conducted an empirical investigation to optimize the model structure and training parameters. The remainder of the paper is organized as follows: Section 2 summarizes the literature on normal human activity recognition and autism physical activity recognition. Section 3 discusses the MeltdownCrisis dataset. Section 4 presents our research methodology for the SMMs system of autistic youngsters during the Pre-Meltdown Crisis. Section 5 investigates and describes the effectiveness of our proposed technique. The Section 10 draws conclusions by summarizing the major results and stressing their importance for future study and practical applications.
# Investigation of Energy Cost of Data Compression Algorithms in WSN for IoT Applications
## 4 Hybrid Model for Run Length Encoding with Adaptive Huffman Encoding  
It is evident that the traditional Huffman algorithm necessitates probability distribution in order to generate the Huffman code which may not always be available. In addition, it may not be suitable in scenarios when the probabilities of the input symbols are dynamic. To address the limitations of Huffman coding, researchers have proposed Adaptive Huffman coding technique that uses a novel approach referred to as sibling property to form Huffman tree. As per adaptive Huffman code, the tree initially contains 0-node and maintains a counter for each symbol. As the tree is dynamically created, the generated codes are more effective than Huffman code. As the adaptive Huffman tree is dynamically created, it requires only 1 pass through the input data. Further, as discussed previously, RLE is one of the simplest compression methods that works most effectively for data that contains repeated symbols. However, the most concerning limitation of RLE is that the size of output can be twice to that of input in the worst case. Hence, to address this limitation, we propose a method that combines RLE with AHE as demonstrated in Figure 3.
# Raman Spectra of ZrS\({}_{2}\) and ZrSe\({}_{2}\) from Bulk to Atomically Thin Layers
## 3 Results  
Bulk crystals of ZrX\({}_{2}\) grown by CVT (see Appendix A) were mechanically exfoliated to obtain thin layers. Flakes with thicknesses ranging from hundreds to one nanometer were identified by optical contrast and AFM. Examples of thin layers can be seen in Figure 2 and Appendix B.  
Figure 2: Optical image (**top panel**), atomic force microscopy measurement (**middle panel**) with the corresponding height histogram (**bottom panel**) for a monolayer of (**a**) ZrS\({}_{2}\) and (**b**) ZrSe\({}_{2}\). For clarity, the contour of the flake has been marked with a white line in the optical image.  
The obtained flakes were also characterized by Raman spectroscopy. In Figures 3 and 4 we report Raman spectra of ZrS\({}_{2}\) and ZrSe\({}_{2}\), respectively, for different thicknesses, while a broader spectral window is shown in Figures C1 and C4 (Appendix C). Note that the estimated number of layers may vary by \(\pm 1\) layer due to an approximate estimation of the dead layer, as further discussed in Appendix B. Due to the overlap between the silicon peak and the A\({}_{\rm 1g}\) peak of ZrS\({}_{2}\), in Figure 3b we also report the Raman spectra of ZrS\({}_{2}\) flakes deposited on a silicon substrate covered with Ta (3 nm)/Au (30 nm). This coating masks the Si peak and allows a clearer view of the Raman mode for thin layers.  
Figure 3: Thickness dependence of the Raman spectra for ZrS\({}_{2}\) (\(\lambda\) = 532 nm) (**a**) on a silicon substrate with 285 nm of SiO\({}_{2}\) and (**b**) on a silicon substrate covered with Ta (3 nm)/Au (30 nm). Inset: zoom of Raman spectra in the characteristic peak for thin layers. The intensity has been normalized to the height of the silicon phonon peak. The Raman modes A\({}_{\rm 1g}\) and E\({}_{\rm g}\) are labeled. The shoulder marked with “**” is attributed to a broadening of the A\({}_{\rm 1g}\) peak due to non-harmonic effects.  
In order to better show the Raman peaks of the thin layers, all the spectra have been normalized to the height of the silicon phonon peak (which is inversely proportional to the flake thickness), except for the spectrum of bulk crystal, which has been normalized to the A\({}_{\rm 1g}\) peak. Bare spectra (counts/s) of Figures 3 and 4 are also shown in Figures 5, 5, and 5, and a detailed analysis of the fitted Raman peaks is presented in Tables 1-5. Finally, a comparison between the Raman signal measured at different wavelengths (532 nm, 638 nm, and 785 nm) is also provided in Appendix C (Figure 6), as well as the spectra obtained from the surface of non-cleaved and cleaved bulk crystals (Figure 7).
# Study on Load Transfer Mechanism of Pile-Supported Embankment Based on Response Surface Method
## 3 Analysis of the Numerical Simulation Results
### Single-Factor Test and Results Analysis
#### 3.1.1 Cohesion
In addition, the reduction in the pile-soil differential settlement limited the exertion of the membrane effect to a certain extent. Therefore, as shown in Figure 6b, as the cohesion increased gradually, the load-sharing ratio of the membrane effect decreased gradually. Figure 6: Influence of cohesion on load transfer; (**a**) variation of pile-soil stress ratio and pile-soil differential settlement; (**b**) variation law of load sharing ratio. The variation law of the pile-soil stress ratio reflects the load transfer under the combined action of the soil arching effect and the membrane effect. In a comprehensive analysis of the patterns shown in Figure 6a,b, it is seen that with the increase in the cohesion, the soil arching effect increased and the membrane effect decreased, while the pile-soil stress ratio gradually decreased. It can also be seen that the membrane effect was more efficient for the load transfer, i.e., within the same variation range more loads were transferred through the membrane effect.