esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Strategy Hybrid Whale Optimization Algorithm Improvement
## 6 Comparison of Convolutional Neural Network Predictions
### Convolutional Neural Network  
Convolutional neural networks are divided into five parts: convolutional layer, pooling layer, activation function, fully connected layer, loss function, and optimizer. The convolutional layer uses the convolution operation to extract features, after which the dimension of the feature map is reduced by the pooling layer to reduce the computational cost while retaining important information. The activation function is applied to the outputs of the convolutional and pooling layers to introduce nonlinear properties. In the last few layers of CNN, there will usually be one or more fully connected layers. These layers \"squashed\" the output of the convolutional and pooling layers into a one-dimensional vector, which was fed into a classical feedforward neural network. The loss function is used to measure the difference between the model output and the true label, and the optimizer is used to adjust the model parameters to minimize the loss function. The specific process is shown in Figure 16. The operation of the CNN network first needs to receive input data (usually images) through the input layer, and then, it will normalize the pixel value to scale between \([0,1]\) or \([-1,1]\) to improve the training stability. Then, the features are extracted through the convolutional layer. After that, the data dimension is reduced through the pooling layer to reduce the calculation amount. Then the fully connected layer will convert the features extracted by the convolutional layer into the final classification result and, finally, output the final classification result or predicted value through the output layer.  
\(V_{t},\ l_{t},\ Temp_{t}\) represent the voltage data, current data, and temperature data in the time series, respectively. \(\omega_{V},\ \omega_{I},\ \omega_{T}\) are the time series weight matrices of voltage, current, and temperature, respectively. \(b_{V},\ b_{I},\ b_{T}\) denote the deviations of voltage, current, and temperature. Relu is used as the activation function for all three kinds of data. By cutting the feature into multiple regions and taking the maximum value, the new feature can be obtained in the pooling layer, and then the score of each category in the global feature is calculated through the fully connected layer. Finally, the feature is extracted.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Best** & **Mean** & **Worst** &
\begin{tabular}{c} **Comparison to** \\ **the Best Results** \\ \end{tabular} \\ \hline MHWOA & 2975 & 2981 & 2990 & 0\% \\ GWO & 3030 & 3034 & 3036 & 1.75\% \\ AVOA & 2992 & 2999 & 3008 & 0.60\% \\ MPO & 3002 & 3006 & 3011 & 0.83\% \\ AO & 4721 & 4943 & 5303 & 39.69\% \\ SO & 2988 & 3008 & 3024 & 0.90\% \\ BWO & 3166 & 3291 & 3402 & 9.42\% \\ WOA & 4850 & 4855 & 4901 & 38.60\% \\ GTO & 2997 & 3007 & 3010 & 0.86\% \\ \hline \hline \end{tabular}
\end{table}
Table 12: Weight minimization of a speed reducer design cost comparison.
# Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation
## 1 Introduction
We designed an end-to-end segmentation network for spacecraft objects and produced a spacecraft image segmentation dataset to validate the algorithm. 2. We optimized the backbone network and used dilated convolution to increase the receptive field while maintaining the image resolution. With only 53 layers, our backbone achieves better results than ResNet-101 with more layers and Xception-65 with a larger number of parameters. 3. We added the channel attention mechanism to the segmentation network of the encoder-decoder structure to form an encoder + attention + decoder structure. Our network focuses on both high-level and low-level feature branches to improve the learning effect of the segmentation network. Figure 1: The display of our work. 4. We designed a parallel-ASPP structure. Using the superposition of different dilated convolutions, our network achieves multi-scale feature fusion with different-depth feature maps, enabling the network to segment the contours of spacecraft objects at different scales more clearly and completely. The rest of the paper is organized as follows: in Section 2, we present the related work. In Section 3, we detail our proposed method. In Section 4, we detail the network training, comparison experiments, and ablation study of our approach. Quantitative and qualitative analyses are performed for the core benefits and performance of our network. In Section 5, we discuss the drawbacks of our method and the problems encountered during the research. Finally, in Section 6, we conclude the article.
# Secure Utilization of Beacons and UAVs in Emergency Response Systems for Building Fire Hazard
## 7 Conclusions  
In this paper, we have presented a proof of concept prototype of a monitoring and emergency response method that we call the UAV-EMOR system that is intended to address the weaknesses common in such smart systems, specifically limited monitoring range, inaccuracy of proximity data and data security. In order to enhance the real-time monitoring range, we present an approach that incorporates indoor/outdoor UAVs in coordination with smart sensors; to improve the accuracy of data received from beacons, we introduce the median method of proximity data calculation; and to address security issues, we use the AllJoyn security platform to provide strong encryption for all communications between all components used in the system.
## 1 Introduction
What parameters must be considered to decide the path to effect the fastest communication through the 2 nd base station? 4. How many base stations are required to guarantee a 100% fault tolerant system  
**Major Contributions of this research**  
1. A method to determine the shortest and fastest fault-free path for data transmission from cluster heads to microcontrollers en route to the redundant base station. 2. A method to facilitate communication between the base stations and the microcontrollers. 3. A method to convert a networking diagram to a Fault tree Analysis diagram considering different networking topologies used to connect both the device layer and the controller layer. **Outline of the paper**  
The rest of this paper is presented in Sections 3-10. The related work and the GAP are presented in Section 2. In Section 3, the overall method used for improving the fault tolerance of the IoT network through modifications effected in the controller layer is presented. Section 4 presents an updated and improved IoT network up to the device level, along with its fault tolerance diagram and computations. The revised IoT network and the changes effected in the controller layer of the IoT network are explained in Section 5. Section 6 explains the networking topology and the devices used in the network connecting the cluster heads and the base station. A novel fault-free, shortest and fastest path-finding algorithm is discussed in Section 7. Results of the experiments on the revised network and a discussion on the same are presented in Sections 8 and 9, respectively. Conclusions and future scope are presented in Section 10.
## 4 In-Situ Monitoring  
### Strain of Steel Tube  
#### 4.1.1 Monitoring Scheme  
JTM-V5000c strain meter made by Jiangsu gold civil construction group, China were used to monitor strain of steel tubes. The working range of the sensors is from 1200 \(\mu\varepsilon\) to 800 \(\mu\varepsilon\), with the accuracy of 0.1 \(\mu\varepsilon\). The dates were collected by a digital auto-reading instrument of DH3816N made by Donghua instrument group, China, and the stress values are calculated by the existing computing parameters. The installation of strain meter is shown in Figure 9.  
Considering circumferential strain meters merely measure the chord length corresponding to a circular arc of inner wall of steel tubes, in order to analyze the measurement error of circumferential strain, the feasibility and accuracy of monitoring schedule is investigated. Based on the installation in Figure 10, the chord length and in corresponding to ach length are D and I, respectively, and central angle and the inner radius are \(2\alpha\) and \(r\). In the jacking stage, the strain of chord length is \(\varepsilon_{\rm D}\), and the  
Figure 8: Connection between cylindrical steel columns and longitudinal beams: (**a**) Beam-column supporting system; (**b**) The connection nodes.  
Figure 9: Installation of strain meter.  
circumferential deformation and strain are \(\Delta_{l}\) and \(\varepsilon_{r}\). So, the relative error is calculated based on the Formula 1 [30].  
\[\begin{array}{l}\alpha\ =\ \arcsin(D/2r)\\ l\ =\ 2r\arcsin(D/2)\\ \varepsilon_{r}\ =\ \frac{\Delta l}{l}\ =\ \frac{2\arcsin[D(1+\varepsilon_{D}) /2l]}{2\arcsin(D/2r)}-1\\ \bar{\varsigma}\ =\ \frac{\varepsilon_{r}-\varepsilon_{D}}{\varepsilon_{D}}\times 100 \%\end{array} \tag{1}\]  
Based on the results of in-situ monitoring results, the monitoring values are within the limits of safety values. Applying the length of strain meter, inner diameter of steel tube and the monitoring value of strain meter to the Formula (1), the relative error between the linear strain monitored by strain meter and the circumferential strain of steel tube is 0.215%, and it is permitted. It is therefore reasonable for the installation to monitor the circumferential stain.  
In order to study the force behavior and the force differences between different jacking distance in the jacking construction, the two monitoring sections A and B are arranged in Steel tube 5#, and the distance from the initial end are 9 m and 15 m, respectively. Four monitoring points are prepared in every section, and each point is installed two strain meters, one longitudinal direction and one circumferential direction. The point above the steel tube is defined as 1#, and the others point are defined as 2#, 3# and 4# according to a clockwise direction, as shown in Figure 11.  
Figure 11: Diagram of strain measuring points: (**a**) Circumferential strain meter; (**b**) Longitudinal strain meter.  
Figure 10: Calculation of measurement error strain meter.
## 4 Design Scheme and VISSIM Simulation
### Safety Evaluation
#### 4.5.2 Analysis of Simulation Results  
In addition to evaluating the traffic efficiency and environment of the design scheme, safety is also an important factor that directly affects the safety of the driver's life and property. Traffic accidents in real situations can seriously affect traffic efficiency, so it is important to evaluate the schemes from a safety perspective. Trajectory files of each scheme were imported into SSAM for safety analysis, and the results are shown in Table 8.  
As can be seen from Table 8, Scheme 2, despite having the lowest traffic efficiency, exhibits better security with the fewest number of conflicts and the smallest CSI. The safety indexes of Scheme 4 are worse than the other schemes, and the security is the worst. Compared with Scheme 1, Scheme 3 has a certain degree of improvement in security. There are no crossing conflicts due to the small angle at which the traffic flows in the straight, diverging, and emerging directions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scheme** & **Rear end** & **Lane Change** & **Crossing** & **TTC** & **PET** & **CSI** \\ \hline
1 & 32 & 30 & 0 & 0.36 & 0.35 & 0.24 \\
2 & 22 & 16 & 0 & 0.23 & 0.31 & 0.17 \\
3 & 15 & 33 & 0 & 0.24 & 0.32 & 0.20 \\
4 & 173 & 10 & 0 & 0.15 & 0.03 & 0.27 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Safety analysis results.  
Collision frequency and severity were predicted by TTC, PET, and CSI indexes. When TTC and PET are large, it means that the driver has more time to adjust to avoid collisions. The results show that the collision frequency of the four schemes is Schemes 4, 1, 3, and 2 in descending order. In terms of collision severity, Scheme 2 has the lowest severity, followed by Scheme 3, while the collision between Schemes 1 and 4 is serious.  
In summary, it can be seen that the two new designs, Schemes 2 and 3, offer a good improvement in safety.
## 1 Introduction  
The longwall mining method in underground coal mines has been used worldwide for decades. After the coal in a longwall panel is extracted, the overlying strata tend to sag and fill the goat (voids created by the extracted coal). Such an operation process will induce a series of intensive strata responses and activities, including massive stress redistribution, strata movement and fracture extension. The overlying strata structure, as well as stress distribution, are disturbed by the mining operation in the order of severity from the immediate roof upward to the ground surface. As illustrated in Figure 1, the overlying strata can be generalized into three different zones of disturbance in response to the longwall mining [1].  
The mining-induced behaviors of the three zones are of paramount importance for studies related to stress redistribution and strata structure movement. For example, the abutment pressure ahead of a longwall face and periodic weighting of the main roof induced by the mining operation are essential parameters in the design of shield support in longwall faces [2] and studies on the rock burst and gas outburst control [3; 4].  
The strata in the fractured zone (FZ), which is located in the roof above the caved zone (CZ), is broken into blocks by vertical and horizontal fractures because of the bending and bed separations between layers. Because of the fracture propagation and its water-inflow capacity, the FZ is considered a major research target if there is underground water in the roof strata. Some research attempts aimed at roof water in brush prevention, for instance [PERSON] et al. [5] and [PERSON] et al. [6], have focused on the extent and development in height of the FZ induced by mining. Advances have been made in forecasting and monitoring the FZ development under various geological and engineering conditions [7; 8; 9] as well as analytical and numerical modeling [10; 11]. The rock mass in the FZ is heavily fractured, and it is well established that the properties of rock masses are directly related to the occurrence of fractures [12; 13; 14]. Therefore, the fracturing intensity of multiple strata in the FZ varies and directly affects the strata structural behavior (movement, failure, etc.) and stress distribution, so it is a factor that should not be ignored in addition to the extent and height of the FZs. However, the fracturing intensity of strata due to longwall mining, and most importantly, its induced weakening effect on stress readjustment and strata structural behavior are inadequately discussed.  
In the presented study, a numerical modeling approach was proposed by considering the mechanical behaviors of loose rock consolidation in the CZ and tension-induced weakening in the FZ. Based on a numerical model built according to a case study, a parametric study with respect to the fracturing intensity was performed to investigate the fracturing weakening effect on the mining-induced stress redistribution and strata behavior.
# Developments on Perovskite Solar Cells (PSCs): A Critical Review
## 4 Perovskite Solar Cell Device Architecture
The absorbed light creates electron-hole pairs (excitons) in the absorber layer, electrons are then collected at the anode electrode to the external circuit and through an electrical load. Electrons are thereafter reinjected into the absorber layer by a cathode counter electrode where recombination with holes occurs. The energy difference of the electron at the anode and cathode is used to determine the open-circuit voltage (V\({}_{\rm{oc}}\)) [58]. Figure 9a underlines the basic cell operation of a perovskite solar cell. [PERSON] (2015) studied the effect of transporting materials in a PSC and reported that the correct choice of cell transporting material is, at most, necessary for better PCE, FF, and V\({}_{\rm{oc}}\) [59]. A compatible transporting  
Figure 8: Characterized performance of triple cation perovskite solar cell. (**a**–**d**) SEM micrographs images; (**a**) Cs0/MA/FA, (**b**) Cs5/MA/FA; (**c**) Cs10/MA/FA; (**d**) cross sectional area at Cs5/MA/F; (**e**) absorption spectrum; (**f**) XRD pattern of Cs/MA/FA at 0, 5 to 10 wt% Cs addition; (**g**,**h**) I–V and IPCE curves [48]. material should have high charge extraction, high mobility, and proper energy alignment with the absorber layer at their interface, as indicated in Figure 9b. The traditional materials used as ETM and HTM in PSCs include titanium dioxide (TiO\({}_{2}\)) and Spiro-MeOTAD, respectively. Other common alternatives such as aluminium oxide (Al\({}_{2}\)O\({}_{3}\)), zinc oxide (ZnO), and PCBM are used as ETMs, while copper iodide (CuI), nickel oxide (NiO), PEDOT: PSS, and poly(triarylamine) (PTAA) materials are common alternative HTMs.
## 5 Simulation and Discussion
### Experimental Summary  
In this part, three sets of experiments are performed, which are, respectively, the simulation experiment based on the TSs keeping course and speed, the simulation experiment based on the TSs changing course, and the comparative experiment. It can be seen from the experimental results that the proposed method can identify the collision risk earlier and make corresponding avoidance measures. When the target ships alter course or change speed, the ship can also make adaptive maneuvering decisions.
## 1 Introduction
In this study, the ESTARFM was applied to fuse GOCI and Landsat 8 OLI data for coastal Chl-a in the North Yellow Sea, China, to obtain a clear sketch of local primary production. Progress was achieved by considering the spatial-temporal characteristics of the original datasets and adjusting the difference between inland areas and coastal waters. The impacts of time lags between GOCI and Landsat images were explored. Results from the ESTARFM and the improved one were compared with actual remote sensing observations. The overall contributions of this study are:  
(1) The development of an easy-to-use Chl-a processing method to support coastal eutrophication monitoring. The product has finer spatial and daily temporal resolution simultaneously and retains the accuracy of the GOCI Chl-a product. (2) The extension of the application of the ESTARFM from inland remote sensing to ocean color remote sensing. (3) The improvement procedure proposed in this study can be further used in other spatiotemporal fusion algorithms, especially for data with uniform spatial distribution and large numerical changes. This method can better handle the numerical conversion relationship and reduce the influence of the time interval between the basic data and the target data.
## 4 Damage Detection Using TRM  
### Wave Selection for TRM  
The propagation properties of guided waves along the rod are evaluated at frequencies from 500 to 1000 kHz, and the sweep frequency test is performed through exciting narrowband impulses in 10 kHz intervals. Continuous wavelet transform (CWT) is applied to analyze received signals, and the time and frequency information are presented along with the dispersion curve of a 7 mm diameter galvanized steel wire.  
As shown in Figure 4, only mode \(L(0,2)\) is generated when the central frequencies of excitation are below 740 kHz. With the central frequencies progressively promoted above 750 kHz, mode \(L(0,3)\) is gradually increasing and coexists with mode \(L(0,2)\). The 790 kHz is determined as the central frequency of excitation for damage detection, which will be elaborated in Section 5. Both the received modes \(L(0,2)\) and \(L(0,3)\) are approximately the same amplitude under the excitation of such frequency as illustrated in Figure 5.  
Figure 3: TRM process of multimode and multipath. (**a**) Original excitation signal; (**b**) Received signals of multimode and multipath due to original excitation signal; (**c**) Received signal after TRM; (**d**) Reconstructed input signal; (**e**) Comparison between normalized reconstructed input signal and original excitation signal.
###### Abstract  
The strain field can reflect the initiation time of solidification cracks during the welding process. The traditional strain measurement is to first obtain the displacement field through digital image correlation (DIC) or optical flow and then calculate the strain field. The main disadvantage is that the calculation takes a long time, limiting its suitability to real-time applications. Recently, convolutional neural networks (CNNs) have made impressive achievements in computer vision. To build a good prediction model, the network structure and dataset are two key factors. In this paper, we first create the training and test sets containing welding cracks using the controlled tensile weldability (CTW) test and obtain the real strain fields through the Lucas-Kanade algorithm. Then, two new networks using ResNet and DenseNet as encoders are developed for strain prediction, called StrainNetR and StrainNetD. The results show that the average endpoint error (AEE) of the two networks on our test set is about 0.04, close to the real strain value. The computation time could be reduced to the millisecond level, which would greatly improve efficiency.  
convolutional neural network; strain fields prediction; laser beam welding; solidification cracking +
Footnote †: journal: Applied sciences
# Foreground Objects Detection by U-Net with Multiple Difference Images
###### Abstract  
In video surveillance, robust detection of foreground objects is usually done by subtracting a background model from the current image. Most traditional approaches use a statistical method to model the background image. Recently, deep learning has also been widely used to detect foreground objects in video surveillance. It shows dramatic improvement compared to the traditional approaches. It is trained through supervised learning, which requires training samples with pixel-level assignment. It requires a huge amount of time and is high cost, while traditional algorithms operate unsupervised and do not require training samples. Additionally, deep learning-based algorithms lack generalization power. They operate well on scenes that are similar to the training conditions, but they do not operate well on scenes that deviate from the training conditions. In this paper, we present a new method to detect foreground objects in video surveillance using multiple difference images as the input of convolutional neural networks, which guarantees improved generalization power compared to current deep learning-based methods. First, we adjust U-Net to use multiple difference images as input. Second, we show that training using all scenes in the CDnet 2014 dataset can improve the generalization power. Hyper-parameters such as the number of difference images and the interval between images in difference image computation are chosen by analyzing experimental results. We demonstrate that the proposed algorithm achieves improved performance in scenes that are not used in training compared to state-of-the-art deep learning and traditional unsupervised algorithms. Diverse experiments using various open datasets and real images show the feasibility of the proposed method.  
visual surveillance; deep learning; object detection +
Footnote †: journal: Medical Image Computing  
1 Graduate School of Automotive Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea; [EMAIL_ADDRESS]  
2