esa-sceva/satcom-chunk-collection huggingface dataset:
# Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery
## 2 Proposed Methodology
### ResNet-50 Model  
A deep neural network architecture called ResNet-50 [33], or residual network, was created to overcome the difficulties involved in training extremely deep networks; the architecture is shown in Figure 6. Since its introduction in a 2015 study, it has developed into a key model in the domains of computer vision and deep learning. ResNet-50 uses residual blocks with skip connections to achieve both performance and depth. The vanishing gradient issue, which frequently impedes training in deep networks, is lessened by these connections, which allow the gradient to move through the network more efficiently.  
Figure 4: Architecture of Inception-V3 model with proposed fully connected layers.  
Figure 5: The architecture of DenseNet121 model with proposed fully connected layers.  
ResNet-50 models are, therefore, capable of becoming extremely deep, with hundreds of layers, which enhances their accuracy and efficiency for many uses for computer vision, such as image classification, object identification, and segmentation.  
ResNet-50 topologies are available at different depths, with the total number of layers indicated by numbers such as ResNet-18, ResNet-50, and so forth. Because of their novel approach to residual connections, which has been widely adopted and expanded to other domains outside image recognition, these models have had a significant influence on the evolution of deep neural networks. The success of deep learning has been greatly aided by the ResNet-50 concept, which has made it possible to create deep and highly accurate models that remain at the front of ML and AI research.
## 3 Results  
### Measurement Points Used on Metro Tunnel Model  
As demonstrated in Figure 7, the entire tunnel structure was divided into 300 elements. At the base of the metro tunnel model, the measurement points were collected with a spacing range of ten elements (10 m) along the simulated moving load path. The moving load was used to obtain the raw acceleration signal data from the vibration, and the time domain force is shown in Figure 8. At a 1000 Hz sampling frequency with a sampling time duration of 10 s and a time step of 0.02 s for each sample, the dynamic responses were measured from the model, and the tunnel finite element simulation diagram is given in Figure 8. The dataset is acquired at three different locations using different percentages of moving load, and the setting of nine different damaged and undamaged conditions was carried out on the finite element model of an underground tunnel structure under a moving load. Figure 9 depicts the excitation curve force of a simulated moving load.  
Figure 6: Complete mechanical model system.  
Figure 7: Segment of the finite element model.
# MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication
## 3 System Model
### Device Selection Based on the Stability of Network with Back-Pressure Strategy  
Considering that the mobility of devices helps prevent a high-speed device from participating in the route selection procedure, this leads to the selection of a highly stable route and reduces the routing overhead. The Random Waypoint (RWP) mobility model has been utilized to calculate the devices' speed [47]. Here, we consider a factor called the mobility factor \(M_{(i,j)}\) of a device \(j\) with respect to device \(i\) in the D2D communication-basedIoT 5G network. The mobility factor is employed to categorize the devices based on their mobility, which is measured based on pause time \(p\), speed \(v\) and direction of the mobile devices \(\theta\). The mobility of devices is based on the mobility-aware route selection introduced in [48], where if the value of the mobility factor is high, it indicates a high pause time, suitable direction and less speed. The minimum mobility factor value for selecting devices in an optimum route is known as the threshold value, which has a range from zero to one. In cases where the mobility factor value of the devices is greater or equal to the threshold value, those devices will be selected as an intermediate device between the source and destination; otherwise, the device is avoided. This device selection based on the mobility of devices, i.e., the \(DS_{mob(i,j)}(t)\) metric, improves the _network stability_ for the unpredictable motion of devices in the D2D communication-based IoT 5G network. The \(DS_{mob(i,j)}(t)\) value is estimated based on the mobility of devices with maximum weight back-pressure as follows:  
\[DS_{mob(i,j)}(t)=\max_{f_{C}\
eq i,j}\{Q_{i}^{fc}(t)-[\frac{1}{2}\times\frac{1} {m}\sum_{n=1}^{m}v_{j}^{n}(t)+\theta_{j}^{n}(t)+\frac{1}{p}\sum_{l=1}^{p}t_{j} ^{l}]\times Q_{j}^{fc}(t)\} \tag{6}\]  
Here, \(t^{l}(1\leq l\leq p)\) refers to the \(l^{th}\) time interval of devices' pause state and \(t^{m}\) is the \(n\)th \((1\leq n\leq m)\) time interval motion of devices.
## 3 Results
### Sea Level Determination Based on the Tidal Fluctuations and Borometric Pressure Changes  
Figure 10 shows the effect of barometric pressure on the simulated signal with seven frequencies. The simulated sea level signal has a similar trend to the observed sea level signal with a residual remaining between the simulated and observed signal. When the sea level signal is simulated using the tidal components and the inverted barometric effect, the RMSE decreases to 0.1057 [m] and the Pearson correlation coefficient increases to 0.777 [-], further highlighting the effect of barometric pressure and its contribution to the observed sea level definition at the location of interest (Table 3). The decrease in the residual values when both the barometric pressure and tidal effects are involved in the procedure for the sea level determination implies the relevance of those two mechanisms in the vertical movement of sea surface elevation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline Data Sets (h) & 0- & 1000– & 2000– & 3000– & 4000– & 5000– & 6000– & 7000– & 8000– & 10,000– & 11,000– & 0– \\  & 1000 & 2000 & 3000 & 4000 & 5000 & 6000 & 7000 & 8000 & 9000 & 11,000 & 12,444 & 12,444 \\ \hline RMSE & 0.144 & 0.103 & 0.061 & 0.062 & 0.083 & 0.061 & 0.113 & 0.189 & 0.091 & 0.126 & 0.076 & 0.106 \\ Pear. Corr. Coef. & 0.735 & 0.776 & 0.886 & 0.886 & 0.936 & 0.865 & 0.886 & 0.794 & 0.812 & 0.891 & 0.872 & 0.777 \\ \hline \end{tabular}
\end{table}
Table 3: Pearson coefficient and root mean square values obtained between the measured and simulated sea water elevation by Equation (4).  
Figure 10: **(a)** Observed and simulated sea level by Equation (4) for the first 1000 h of observed sea level signals, **(b)** Observed barometric pressure, **(c)** Residual between simulated and observed sea level signals.
# Imogolite Nanotubes: A Flexible Nanoplatform with Multipurpose Applications
## 2 Imogolite Synthesis  
### Synthesis Routes for Aluminosilicate INTs  
The synthesis of single-walled imogolite nanotubes (SWINT) was first reported by [PERSON] et al. in 1977 [22]. It consists of the co-precipitation under vigorous stirring of Al and Si precursors. This solution is adjusted to pH 5 with the addition of NaOH, and then immediately re-acidified to pH 4.5 with a mixture of HCl and acetic acid (Figure 2). Finally, the solution is aged upon heating under reflux or in autoclave. Since then, several improvements have been proposed in the literature by changing either the nature of the precursors, their initial concentration, their ratio (e.g., the hydrolysis ratio R = [OH]/[Al]) or the reaction pathway [35; 36; 37; 38; 39; 40; 41]. Among them, [PERSON] et al. obtained similar INTs as those reported previously, but without performing the acidification stage [38]. In that case, the hydrolysis step was directly controlled initially by adjusting the amount of NaOH added (typically R = 2, Figure 2). Most of the current studies dealing with INTs use one of the two protocols presented  
Figure 1: Structure of a single-walled imogolite nanotube and detail of the arrangement of tetrahedron units on the octahedral vacancies. Color code: aluminum (blue); silicon (yellow); oxygen (red); hydrogen (white).  
in Figure 2, with minor variations more or less. For instance, the [Al]/[Si] ratio is set between 1.5 and 2, a slight excess of Si precursor preventing the formation of aluminum hydroxides such as gibbsite and boehmite [36]. A \"seeding\" approach has also been explored by adding a certain amount of a processed synthetic imogolite sol in a solution of Al and Si precursors [42; 43]. Although it avoids the pH adjustment step and allows the imogolite formation after heating the mixture for 2 days, this synthesis route remained quite confidential and has not yet been explored in detail.
## 6 Conclusions
### Nomenclature  
\begin{tabular}{l l} BPNN & Back-Propagation Neural Networks \\ CART & Classification and Regression Trees \\ CCI & Climate Change Initiative \\ CHIRPS & Climate Hazards Group InfraRed Precipitation \\ CC & Correlation Coefficient \\ CHPclim & Climate Hazard group Precipitation climatology \\ DL & Deep Learning \\ DEM & Digital Elevation Model \\ EVI & Enhanced Vegetation Index \\ ESA & European Space Agency \\ ET & Evapotranspiration \\ ERT & Extremely Randomized Trees \\ FPAR & Fraction of Photosynthetically Active Radiation \\ GPR & Gaussian Process Regression \\ GFSAD & Global Food-Support Analysis Data \\ GPP & Gross Primary Production \\ LAI & Leaf Area Index \\ LOOCV & Leave One Out Cross Validation \\ MAE & Mean Absolute Error \\ MAPE & Mean Absolute Percentage Error \\ MBE & Mean Bias Error \\ MODIS & Moderate Resolution Imaging Spectroradiometer \\ MLR & Multiple Linear Regression \\ NASA & National Aeronautics and Space Administration \\ KNN & K-Nearest Neighbors \\ RF & Random Forest \\ RMSE & Root Mean Square Error \\ SM & Soil Moisture \\ SMAP & Soil Moisture \\ SM & Soil Moisture Active Passive \\ SOM & Soil Organic Matter \\ SVM & Support Vector Machine \\ SVR & Support Vector Regression \\ NDVI & Normalized difference vegetation index \\ TIR & Thermal infrared \\ USDA & United States Department of Agriculture \\ USGS & United States Geological Survey \\ \end{tabular}
## 6 Applications of UAV Computing
### Natural Disaster  
UAVs have the features of high mobility and ease of deployment, allowing for the provision of required communication services [76] quickly. UAV computing may deliver required computing services for mobile users with on-demand mobility in a more responsive way than fixed communication infrastructures. The advantages of UAV computing are underlined in situations where fixed communication infrastructures cannot meet the computing requirements of ground users, such as sites far from communication facilities and places where natural catastrophes have damaged communication infrastructure. The UAV may fly to specific locations to assist everything with computational needs, such as monitoring equipment to complete tasks. UAV computing can considerably reduce the strain of computing in specific locations due to its on-demand mobility. A timely and efficient reaction is essential during natural disasters to aid people, avoid increasing the number of victims, and reduce the economic damage [173; 174]. For example, UAVs can promptly respond to earthquake assistance requests, locate missing individuals, and assist in monitoring and rescuing flood victims [175]. Furthermore, using UAV computing with AI to collect vast data from various sensors installed in the environment might assist in the forecasting and acting in natural disasters such as tornadoes, volcanic eruptions, tsunamis, and storms [176].
# Lightning-Ignited Wildfires beyond the Polar Circle
## 3 Results
### Relationship of Fire to Lightning Occurrence and Climate Variables: Multiple Regression Analysis  
The effect of lightning strikes and climate variables on wildfire occurrence was tested by hierarchical regression analysis. The equations obtained are presented in Table 2.  
Estimated lightning input in the wildfire occurrence for the circumpolar Arctic, Eurasia, entire Russia and East Siberia varied within the 40-43% range. Alongside lightning, moisture parameter (terrestrial water content) is also a primary variable that influences wildfire occurrence in the Arctic (Table 2). Partial correlations indicated that soil moisture (0-7 cm upper layer) similarly influenced fire occurrence (Figures 6c, 7c and 8c). Significant correlations were also observed between air temperature and fires (Figures 6a, 7a and 8a). Meanwhile, air temperature indirectly influences fire occurrence, i.e., by drying fuel formed  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sector** & **Equation** & **Adjusted \(R^{2}\)** & **Explaned** & **Fraction of** \\  & & & **Variance** & **Variance** \\ \hline \multirow{2}{*}{Entire Arctic} & \(F=-0.68\times TW(M|JA)\) & \multirow{2}{*}{0.78} & \multirow{2}{*}{83\%} & \(TW(M|JA)=42\%\), \\  & \(+\) 0.67 \(\times\)\(L+\) 0.12 & & & \(L=41\%\) \\ \multirow{2}{*}{Eurasia} & \(F=-0.85\times TW(Jun)\) & \multirow{2}{*}{0.89} & \multirow{2}{*}{92\%} & \(TW(Jun)=50\%\), \\  & \(+\) 0.72 \(\times\)\(L+\) 0.01 & & & \(L=42\%\) \\ \multirow{2}{*}{Russia} & \(F=-0.85\times TW(Jun)\) & \multirow{2}{*}{0.89} & \multirow{2}{*}{92\%} & \(TW(Jun)=49\%\), \\  & \(+\) 0.73 \(\times\)\(L+\) 0.01 & & & \(L=43\%\) \\ \multirow{2}{*}{Eastern Siberia} & \(F=-0.87\times TW(Jun)\) & \multirow{2}{*}{0.87} & \multirow{2}{*}{90\%} & \(TW(Jun)=48\%\), \\  & \(+\) 0.73 \(\times\)\(L\) & & & \(L=42\%\). \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dependence of wildfire occurrence on the environmental variables. Abbreviations: \(F\)— number of fires; \(TM(M|JA)\)—terrestrial water content storage in May—August; \(L\)—lightning index. The equation’s coefficients are significant at \(p<0.05\). All equations passed (\(p>0.05\)) Shapiro-Wilk’s, Kolmogorov–Smirnov’s, and Gauss–Markov’s tests, except for the Entire Arctic (Link Function test only, i.e., the dependent variable is continuous or categorical). The analyzed period is 2010–2020.  
Figure 11: Norward fire migration in (**a**) West and (**b**) East Siberia, (**c**) Eurasia, and (**d**) the entire Arctic (\(p<0.05\)). Fire extreme shifts synchronized with May–June air temperature extremes (or “heat waves”) indicated by arrows for 2004–2006, 2010, 2012–2014, and 2020.  
by moss, lichens, woody debris, etc. Precipitation also indirectly influenced fires by decreasing fuel flammability (Figures 6b, 7b and 8b).
# Optimal Attitude Determination for the CR200 Underwater Walking Robot
## 2 Problem Definition and Method of Simulation  
The subject of this study, the submersible walking robot CR200, is designed to overcome strong currents by featuring a blunt, vertically inverted airfoil-shaped cross-section in its body and adjusting the angle of attack and seafloor clearance. The downforces created in the flow of the fluid enhance vertical resistance, ensuring ground contact and generating appropriate moments to prevent overturning. Therefore, the independent variables chosen are the angle of attack and distance from the seabed, with downforce selected as the dependent variable.
# Objectively Scoring the Human-Likeness of Artificial Driver Models
## 5 Results
### Human-Likeness of Investigated Datasets  
As described in Section 3.5, the metric is applied to retained real driving (inD12) and artificial driving data, assuming that the human-likeness score of real vehicles should be significantly higher compared to synthetic behavioral. The used database is described in Section 4.1 and results of the objective scores obtained by the metric are shown in Figure 7. The scores were calculated once with the initial thresholds and without weighting according to Table 3 (right), and once with the tuned quality function, incorporating weights from the survey and adjusted thresholds from Table 3 (left). First of all, the difference in those two figures demonstrates the sensitivity of results to weights and thresholds within the quality function. Regarding the initial setting, only the comparison of real data and artificial behavior of interSection 2 showed significant differences in the human-likeness scores when using the Mann-Whitney U test (\(U=82\),\(727.0\), \(p_{value}=4.06\times 10^{-13}\)), whereas the  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameter** & **Spearman Correlation** & \(p\)**-Value** & **Conversion to Weight** \\ \hline KS Longitudinal Vel. & 0.073555 & 0.820285 & 0.016269 \\ KS Lateral Vel. & 0.178634 & 0.578567 & 0.03951 \\ KS Longitudinal Acc. & \(-\)0.30823 & 0.329698 & 0.068174 \\ KS Lateral Acc. & 0.021016 & 0.948312 & 0.004648 \\ KS Jerk & \(-\)0.51839 & 0.084229 & 0.114656 \\ Max. Longitudinal Acc. ratio & \(-\)0.42732 & 0.165877 & 0.094514 \\ Min. Longitudinal Acc. ratio & \(-\)0.50794 & 0.0918 & 0.112346 \\ Max. Longitudinal Vel. ratio & \(-\)0.12151 & 0.706773 & 0.026876 \\ Max. Lateral Vel. ratio & 0.309234 & 0.328046 & 0.068396 \\ Min. Distance to partners ratio & \(-\)0.3632 & 0.245869 & 0.080333 \\ Max. Critical Time Gap (s) & \(-\)0.94286 & 0.004805 & 0.208539 \\ TET (s) & \(-\)0.52179 & 0.288343 & 0.115409 \\ PET (s) & \(-\)0.22755 & 0.587845 & 0.050329 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Spearman correlation between the parameters and average human-like driving behavior score from the survey, with correlations converted into weights.  
Figure 6: Relationship between the fine-tuned objective human-like driving behavior scores provided by the proposed methodology and subjective ratings of participants during the survey.  
comparison of behavior on interSection 1 compared to real humans showed no significant difference (\(U=58\),\(561.0\), \(p_{value}=0.78\)). This can be explained by the quite far-developed driver model, which was used to create the artificial data. Therefore, without weighting or tightening the thresholds of the quality function, only _harsh_ outliers of behavior can be detected. When using the tuned quality function, (Figure 7 left) a clear difference between real and artificial behavior could be measured (human-like grades of inD12 vs. interSection 1: \(U=103\),\(568.5\), \(p_{value}=4.34\times 10^{-68}\) ; human-like grades of inD12 vs. interSection 2: \(U=113\),\(910.0\), \(p_{value}=1.54\times 10^{-60}\) ).
# Failure-Robot Path Complementation for Robot Swarm Mission Planning
## 1 Introduction  
### Background and Objectives  
Currently, unmanned vehicles are applied in various fields. For example, unmanned aerial vehicles (UAV) monitor earth, provide emergency aid, perform disaster control and prevention, and execute aerial photography [1, 2]. Along with the mission scale, mission areas are growing much wider and may soon be beyond a single vehicle's reach. Compared to a traditional single robot system, multi-robots cooperating to achieve a global mission can be used to solve problems for a wide variety of application domains. For example, a multi-robot team is usually beneficial for shortening the time required for a search and rescue mission as well as for reducing energy consumption in large area cargo delivery, etc. All team members cooperate to fulfill a mission by dividing the labor of execution. Controlling unmanned vehicle swarms via human supervision is of great interest in military sectors as well as private logistic companies. While the scope and area of robot vehicle missions is expanding, to complete tasks efficiently (in time or in energy), a well-planned path program for an unmanned vehicle swarm is crucial.  
The aim of this research was to develop a group of robot cars as a multi-agent system (MAS) with multi-path programming under the constraint of a maximum distance limit, which is the maximum capable range (usually limited by the onboard battery/fuel energy capacity) of a robot car. With a series announcement of unmanned vehicles' prohibited zones by government sectors, in addition tooptimal multiple-path programming, the system should feature a no-travel zone avoidance function. The no-travel zones serve as a e-fences, which enclose the areas unmanned vehicles are not permitted to enter. The system will process all the target points and then dispatch the optimal paths, which causes the robot cars to avoid the no-travel zone and to reach targets efficiently without wasting precious energy on redundant paths. In addition, for a robot swarm mission, it is recognized that failures (among robot agents) may have a higher chance to occur due to some external/internal factors, such as external attacks or internal mechanical failures, which might affect the success of this endeavor. Thus, there is also an increasing need to develop an algorithm to automatically perform a failure robot's mission complementation.
# Recognition of Manual Welding Positions from Depth Hole Image Remotely Sensed by RGB-D Camera
###### Abstract  
The proportion of welding work in total man-hours required for shipbuilding processes has been perceived to be significant, and welding man-hours are greatly affected by working posture. Continuous research has been conducted to identify the posture in welding by utilizing the relationship between man-hours and working posture. However, the results that reflect the effect of the welding posture on man-hours are not available. Although studies on posture recognition based on depth image analysis are being positively reviewed, welding operation has difficulties in image interpretation because an external obstacle caused by arcs exists. Therefore, any obstacle element must be removed in advance. This study proposes a method to acquire work postures using a low-cost RGB-D camera and recognize the welding position through image analysis. It removes obstacles that appear as depth holes in the depth image and restores the removed part to the desired state. The welder's body joints are extracted, and a convolution neural network is used to determine the corresponding welding position. The restored image showed significantly improved recognition accuracy. The proposed method acquires, analyzes, and automates the recognition of welding positions in real-time. It can be applied to all areas where image interpretation is difficult due to obstacles.  
working posture; welding position; motion capture; depth hole; CNN; RGB-D; shipbuilding assembly +
Footnote †: 2021: 11, 10463. [[https://doi.org/10.3390/app112110463](https://doi.org/10.3390/app112110463)]([https://doi.org/10.3390/app112110463](https://doi.org/10.3390/app112110463))