esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Development of Analytical Model
### Fracture Toughness Determination  
In general, researchers divide fractures into three basic modes, i.e., the opening mode (I), the sliding mode (II), and the tearing mode (III) [35]. The fracture toughness, \(K_{IC}\), refers to the critical stress intensity factor of mode I, which can be described as the resistance of a material against crack propagation [36; 37; 38]. The measurement methods of the fracture toughness of brittle materials at the microscale to the nanoscale mainly include the indentation method (IM) [38; 39], micropillar splitting [40; 41], and microcantilever testing [42; 43]. Numerous studies regarding the indentation method were carried out over the past half-century, starting with [PERSON] initially putting forward the potential values of indentation-induced cracking to characterize the toughness of brittle materials [44] because of its advantages such as simple operation, prepared sample facilities, and no sample shape or size requirements. However, so far, no single model exists that accurately describes the fracture toughness of most materials due to mechanical analysis complexity.  
In an indentation test for Berkovich indenter of brittle material, the crack length of the indentation corner can be measured using a microscope to calculate the fracture toughness. There are two main crack systems illustrated in Figure 3. A method to compare the ratio of the length from the indentation center to the radial crack tip, \(c\), to the distance from indentation center to the indentation corner, \(a\), is used to identify the crack system. The Palmqvist crack system shown in Figure 3b tends to appear during tests when the ratio, \(c\)/\(a\), is less than 2.5, in comparison with the other study results, which appear between 2 and 3 [45]. If the ratio, \(c\)/\(a\), is more than 2.5, the crack can supposed to be resulting from the half-penny crack system, as shown in Figure 3c. The half-penny crack is considered to be the result of a Palmqvist crack propagating downward at a higher load in some brittle materials. A well-known equation developed by [PERSON] et al. which can be used to determine the fracture toughness of a half-penny crack is given in [46]  
\[K_{IC}=\alpha\!\left(\frac{E}{H}\right)^{1/2}\!\frac{P}{c^{3/2}} \tag{5}\]  
where \(\alpha\) is a constant related to indenter shape (for a Berkovich indenter, \(\alpha=0.036\)[47]), \(E\) is the elasticity modulus, \(H\) is the hardness, and \(P\) is the maximum indentation load.  
Figure 3: Illustration of typical radial crack propagation for a Berkovich indenter: (**a**) the top view of indentation-induced crack geometries, (**b**) the Palmqvist crack system, and (**c**) the half-penny crack system.  
The fracture toughness calculation formula of a Palmqvist crack for a Berkovich indenter is given by [PERSON] [48; 49]:  
\[K_{\text{IC}}=1.073x_{\text{c}}\Big{(}\frac{a}{l}\Big{)}^{1/2}\Big{(}\frac{E}{H} \Big{)}^{2/3}\frac{P}{c^{3/2}} \tag{6}\]  
where \(x_{\text{c}}\) is a constant (for Berkovich \(x_{\text{c}}=0.015\)) and \(l\) is the length of the crack.
## 2 String System
### Dynamics Model  
The model of the uncertain flexible string under time-varying distributed disturbance and boundary disturbance is defined by a non-homogenous PDE and ODEs [43] in the following expressions:  
\[\rho_{s}u_{tt}(n,t)-\hat{T}_{s}u_{nm}(n,t)=f(n,t) \tag{1}\]  
with the boundary conditions being  
\[u(0,t)=0 \tag{2}\]  
\[\hat{T}_{s}u_{n}(L,t)=u_{c}(t)-\hat{M}_{s}u_{tt}(L,t)+d(t),\forall(0<t<T,0<n<L) \tag{3}\]  
where \(\hat{T}_{s}\) and \(\hat{M}_{s}\) are the unknown string tension and unknown tip mass, respectively.  
**Assumption 1.** There exist the constants \(\overline{F}\in R^{+}\) and \(\overline{D}\in R^{+}\) such that unknown distributed disturbance and boundary disturbance satisfy \(\left|f(n,t)\right|\leq\overline{F},\left|d(t)\right|\leq\overline{D},\forall (0<t<T,0<n<L)\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Description** \\ \hline \(t\) & independent time variable \\ \(n\) & independent position variable \\ \(u(n,t)\) & deflection of the string \\ \(u_{n}(n,t)=\partial u(n,t)/\partial n\) & deflection angle of the string \\ \(u_{m}(n,t)=\partial^{2}u(n,t)/\partial n^{2}\) & deflection angular velocity \\ \(u_{n}(n,t)=\partial u(n,t)/\partial t\) & velocity of the string \\ \(u_{tt}(n,t)=\partial^{2}u(n,t)/\partial t^{2}\) & acceleration of the string \\ \(f(n,t)\) & time-varying distributed disturbance \\ \(d(t)\) & boundary disturbance \\ \(\rho_{s}\) & string system density \\ \(T_{s}\) & string tension \\ \(M_{s}\) & string tip payload mass \\ \(L\) & actual string length \\ \(u_{c}(t)\) & boundary control input \\ \(N\) & position subdivision points \\ \(T\) & time interval \\ \hline \hline \end{tabular}
\end{table}
Table 1: String system variables.  
Figure 1: String system structure.  
\[B_{S1(N\times N)}\left[\begin{array}{cccccccc}0&0&0&0&0&0&\cdots&0\\ 0&0&0&0&0&0&\cdots&0\\ 0&0&0&0&0&0&\cdots&0\\ 0&0&0&\frac{1}{\rho_{s}}&0&0&\cdots&0\\ 0&0&0&0&0&0&\cdots&0\\ 0&0&0&0&0&\frac{1}{\rho_{s}}&\cdots&0\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\ddots&\vdots\\ 0&0&0&0&0&\cdots&\frac{1}{M_{s}}\end{array}\right],\ B_{S2(N\times N)}=\left[ \begin{array}{cccccccc}0&0&\cdots&0\\ 0&0&\cdots&0\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&\cdots&\frac{1}{M_{s}}\end{array}\right]\mathcal{C}_{s}=ones_{1\times N}\]
# Cooperative Caching and Resource Allocation in Integrated Satellite-Terrestrial Networks
## 6 Performance Evaluation
### Performance Comparison with Three Different Algorithms
Specifically, the average total deployment costs of each user service for CCRA can reduce by 18.71% for Greedy and 14.53% for BFS, respectively. To further evaluate the performance of the proposed CCRA algorithm, the experiments are conducted for \(M=\{200,220,\cdots,300\}\), \(Q=\{5,10\}\), and \(N_{a}=\{4,8\}\), as well as the simulation parameters in Table 2. The simulation results for CCRA, Greedy, and BFS in different simulation environments are provided in Table 3, which includes the average results of storage costs, bandwidth costs, and total deployment costs for each user service. From Table 3, it can be found that the average storage costs of each user service for CCRA are also less than that of Greedy and BFS in all the cases. For example, compared with Greedy and BFS, CCRA can reduce the average storage costs of each user service by 13.53% and 10.28% for the case of \(M=260,Q=5,N_{a}=8\), as well as 4.91% and 3.34% for the case of \(M=260,Q=10,N_{a}=8\). Note that more contents can be deployed on satellites to provide different content services for users as the number of contents increases. Therefore, the average storage costs of each user service can be correspondingly increased. Furthermore, it can be found that the average bandwidth costs of each user service for CCRA may be more than that of Greedy and BFS in several cases, such as \(M=260,Q=5,N_{a}=8\) and \(M=300,Q=5,N_{a}=8\). The reason is that the contents  
Figure 6: Performance comparison for content service in three different algorithms. (**a**) Storage costs. (**b**) Bandwidth costs. (**c**) Total costs. can be deployed on more satellites to provide content services for users as the number \(N_{a}\) of access satellites increases. Considering that the content sharing approach can be given priority for all the satellites in the neighboring sub-network, the network bandwidth consumption will be increased when content services for users are provided by the satellites that are far from the access satellite. However, for all the cases, the average total deployment costs of each user service for CCRA are less than that of Greedy and BFS. Specifically, for the case of \(M=260\), \(Q=5\), \(N_{a}=8\), the average total deployment costs of each user service for CCRA can reduce by 11.05% for Greedy and 8.1% for BFS. In addition, for the case of \(M=260\), \(Q=10\), \(N_{a}=8\), CCRA can also reduce the average total deployment costs of each user service by 5.87% for Greedy and 4.03% for BFS.
# Obstacle Avoidance Drone by Deep Reinforcement Learning and Its Racing with Human Pilot
## 2 Related Work
### Robot Navigation using Segmentation Map  
Recently, there are several studies where the segmentation model is adopted to extract simple and handy features for robot navigation. For example, a wheeled robot navigating the outdoor street has been trained using the CNN-based segmentation model [35]. Here, the navigation direction is controlled by a fuzzy logic that receives a segmented image from the RGB input. Another work has shown that a semantic segmentation model trained in a virtual environment can minimize the gap between the real and virtual environment [36]. In this study, the segmentation model plays an essential role in visually guiding a robot where to go, and an RL agent trained in the simulation can be transferred to the real environment to control a car. It has also been shown that the segmentation model can learn from the simulated image jointly with the real image by combining a recurrent neural network (RNN) so that the robot in the real environment can use the model directly without having suffered from the gap between real and simulation [37]. Besides, a study shows that a depth map generated from an RGB image using a CNN-based model can be used for training a network controlling a drone [38] by utilizing supervised learning. With this model, it was shown that a network trained in a simulation environment could navigate in the real world during testing.  
However, none of the studies have applied the model on the drone using reinforcement learning where it does not require manual labeling of the data. In this study, for the continuous control, we have adopted segmentation model to simplify the information exhibited in the raw image from the first person view (FPV) of the drone, so that an RL model can learn to navigate in the continuous action space, which has been considered very challenging due to the curse of dimension. Moreover, designing the reward function is replaced by taking advantage of mutual interaction between the segmentation network and actor-critic networks.
# Design and SAR Analysis of a Dual Band Wearable Antenna for WLAN Applications
## 2 Design Methodology  
### Conventional Approach  
All the designs and simulations in this project were carried out in CST MWS software with a frequency range of 2 to 5.5 GHz. The geometrical model of the proposed dual band wearable patch antenna is shown in Figure 1. The 3 mm thicker substrate having a compact size of 68 mm \(\times\) 73 mm was made of polyethylene foam with a relative permittivity of 1.05 [34]. The overall size of the radiating patch was 44.75 mm \(\times\) 47 mm. A 50 \(\Omega\) microstrip feed line was used in the design of the proposed dual band textile antenna. The dimensions of the proposed dual band textile antenna were obtained by using the theory of transmission lines [35].  
Table 1 presents the optimized values of the parameters mentioned in Figure 1.  
Figure 1: Geometrical model of the proposed dual band microstrip patch antenna.
## 4 Experimental Setup  
The following experiments aim to demonstrate and investigate the feasibility of using a sensor chip with 8 channels in the PAR region to obtain the PPFD and the RLQI. For the PPFD measurement, the PAR data of our sensor system is compared with a reference (LI-190). The RLQI was determined when the AS7341 was illuminated by a light source with a known emission spectrum. The channel counts of the corresponding spectral interval were compared to the SPD of different light sources. All experiments were performed with the AS7341 embedded in its developed housing. The ADC was configured with an integration time of 100 ms with a gain of four for all eight channels in the visible spectrum and a gain of one for the remaining IR channels. The settings are chosen in a way that the channel outputs are about half of the maximum possible count number on a cloud-less bright summer day. The settings are not changed throughout all experiments to circumvent the necessity of a re-calibration.  
For the calibration of our sensor to determine the PAR, we have used the method of referencing the measured data to a calibrated reference sensor, similar to the open-sky tests from [PERSON] et al. (1977), since an optical radiation calibrator was not available. The AS7341 was exposed to different light scenarios together with a reference PAR sensor (LI-190 with LI-250A light meter). Both the eleven output channels of the AS7341 and the reference PPFD were recorded under solar radiation outside and in an insulator box with artificial light. Outside, 31 data-sets at different weather and canopy situations were measured and taken into account for the PAR calibration procedure. The measured values are in the range of 1.7 umol m\({}^{-2}\) s\({}^{-1}\) to 594 umol m\({}^{-2}\) s\({}^{-1}\). In the insulator box, a set of LEDs with narrow-band emission and two standard halogen 40 W and 75 W light bulbs were used as light source. The set of LEDs consisted of 12 LEDs of the manufacturer LUMILEDS, each emitting at a non-overlapping spectral region with a half-width of about 30 nm. The specification of the used \"L1C1\"-LED series can be found in their datasheet ([PERSON], 2018). Two white LEDs with 2700 K and 5700 K were also used beside the light bulbs as two broadband light sources. All LEDs were facing both sensors with a distance of 100 mm. The selected LEDs were used to perform a coarse spectral sweep over the PAR region in the steps according to the LED center wavelengths by using them individually. The obtained 19 data sets in the insulator box cover a PAR range from \(0\,\mathrm{\SIUnitSymbolMicro mol\,m^{-2}\,s^{-1}}\) to \(45\,\mathrm{\SIUnitSymbolMicro mol\,m^{-2}\,s^{-1}}\) with a mean value of \(14\,\mathrm{\SIUnitSymbolMicro mol\,m^{-2}\,s^{-1}}\).  
For the determination of the RLQI, the data set was extended by two additional measurements of a warm and cold white LED. With the known SPD of each LED, it was verified qualitatively that the ratio of the AS7341 channel output corresponds to the expected distribution.
# An EMD-SARIMA-Based Modeling Approach for Air Traffic Forecasting
## 6 Conclusions and Future Work  
A novel hybrid forecasting framework of Empirical Mode Decomposition and Seasonal Autoregressive Integrated Moving Average (EMD-SARIMA) has been proposed in this paper. The forecasting framework is tested using the data from the year 2006 to 2014 from the official website of the Civil Aviation Administration of China (CAAC) and is compared with other widely used forecasting models (SARIMA, Holt-Winters, and a naive model) in four different cases: domestic cargo, domestic passenger, international cargo, and international passenger, and the results show that the EMD-SARIMA framework can improve the forecasting accuracy to a great level and that the forecasting result is consistent in the four different cases as well as for different forecasting steps.  
In the future, the robustness of the EMD-SARIMA framework will be discussed by testing it with data where there is more seasonality (or less seasonality); also, the data sensitivity of the framework will also be discussed by testing it with more data (or less data); last but not least, the comparison of this framework to AI-based models will also be done so as to examine the performance of forecasting.  
Figure 8: Forecasting results of the international passenger series.  
**Acknowledgments:** This work is supported by National Key Research and Development Program of the 13 th Five-year Plan in China under Grant No. 2016 YFB1200401, Fund of Shanghai Cooperative Center for Maglev and Rail Transit, and Fund of Shanghai Fei Tu You Science and Technology Company Limited under Grant No. 0217013.  
**Author Contributions:** [PERSON] and [PERSON] proposed the idea of employing hybrid EMD-SARIMA algorithm into the air traffic flow forecasting; [PERSON]. [PERSON] conceived and designed the experiments; [PERSON] collected the data, and set up as well as performed the experiments, [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Application of Digital Twin in Handling and Transportation of Hazardous Chemicals
## 4 Application of DT in LNG Road Transportation
### Liquid Unloading Stage  
(1) Consumers information management.  
The distribution of gas-using terminals is more extensive than that of filling sites, and statistics on the distribution, business type, gas consumption scale and price affordability of these sites are presented on a map. Additionally, the previous site qualification and information filing can also be transferred online.  
(2) Operations Visualization.  
The three-dimensional structure of buildings and internal facilities and equipment around the gas-using terminal site is constructed. Then, a visual view of the site is established by integrating on-site video surveillance, sensors, and personnel positioningdata. Then, the station management scheme and operation procedures are integrated to monitor abnormal values during operation and provide early warning. It provides a comprehensive display of 2D maps, equipment, and corridor 3D models, and provides functions such as roaming tour, partition display, spatial measurement, station integrated intelligent monitoring and linkage control, inspection personnel positioning and inspection track query.  
(3) Unloading monitoring.  
There are several types of LNG terminals in general: industrial point supply, power plants, city domestic gas, and chemical companies, and each type of terminal has its own accident-prone locations. These key locations are monitored, and the interface incorporates document management, procedure management, and rescue measures to \"treat\" their safety management points.  
(4) Emergency scenario simulation.  
Based on the simulation environment and the characteristic parameters of each object, the emergency plan to be activated is determined according to the type and extent of the accident, and the characteristic parameters of each object involved in the accident simulation, such as the escape of personnel, the spread of fire, the change in the residual liquid in the tank, and the structural deformation of the auxiliary facilities in the station, are dynamically loaded into the environment model for simulation. The simulation is carried out by repeated simulations and calculations to analyze the accident state comprehensively [37].
## 2 Related Works  
### PPG Physiological Signal Measurement  
The flow rate of human blood typically fluctuates with the cardiac cycle, which can lead to variations in the light intensity reflected by pulse oximeters. Consequently, the periodicity of PPG signals corresponds to the heart rate [13]. Current research on heart rate estimation using PPG signals has seen some success. For instance, [PERSON] progressively decomposed PPG signals and employed noise removal techniques before reconstructing the power spectrum from PPG sparse signals for heart rate estimation [14]. [PERSON] proposed an enhanced spectral subtraction method based on composite motion artifacts for heart rate estimation, which leverages dual-channel PPG signals and three-axis accelerometer data to calculate heart rate by analyzing the attenuation of noise and motion artifacts in PPG signals [15]. In the domain of emotion recognition, [PERSON] utilized PPG-derived autonomous neural signals to extract two-dimensional emotion feature vectors, achieving a 63.67% accuracy in classifying happiness and sadness [16]. [PERSON] extracted heart rate variability (HRV) features from PPG signals for detecting and recognizing emotions based on physiological signals [17], while [PERSON] achieved a 66.67% classification accuracy using HRV features from PPG signals for emotional stimulus detection [8]. Obviously, the above models need to be improved, and it is necessary for us to introduce new ideas. Firstly,the application of wavelet transform offers a novel approach for localized analysis of PPG signal time-frequency characteristics. Employing spectral information, emotion recognition can be achieved through two-dimensional image processing methods [18]. Additionally, the TCN utilizes dilated convolution and residual modules to address the memory limitations of traditional recurrent and convolutional networks, making it suitable for modeling and processing PPG time-series data [19].  
Based on the above considerations, we carry out PPG-based emotion recognition processing according to the framework shown in Figure 1. We focus on the frequency-domain features of the PPG signal through the attention mechanism, and then mine spatial-temporal information through the TCN to improve the accuracy of classification.
## 2 Materials and Methods
### Data Acquisition  
The first stage of data processing has the role of reading the data from the LIDAR interface and transforming it to a suitable format for further processing. After the collection of the raw sensor data from the LIDAR with the ROS driver, the points are transformed to Cartesian coordinates and represented in sensor_msgs/PointCloud2 format.  
A rotating 3D LIDAR outputs a scan as an array of distances measured from a single point sweeping the angular field of view. The returned values may be interpreted as points in spherical coordinates  
\[P^{S}_{i,j}=(d,\theta_{i},\phi_{j})^{T}, \tag{1}\]  
Figure 2: Activity diagram of point cloud processing.  
where \(d\) is the measured distance at polar angle \(\theta\) and azimuth angle \(\phi\). The angles depend on the mechanical construction of the sensor and are the same in each consecutive scan. To obtain the point cloud representation, the points from the 3D scan are transformed to the Cartesian coordinates. To conform to the ROS standards, the points are represented in the local coordinate system with the X axis directed to the front of the sensor, the Y axis to the left, and the Z axis up. The transformation to the Cartesian coordinates is given by  
\[P_{i,j}^{C}=(x,y,z)^{T}=(d\cos\theta_{i}\cos\phi_{j},d\cos\theta_{i}\sin\phi_{ j},d\sin\phi_{j})^{T}. \tag{2}\]  
In Section 3.1, we provide the details on the data transformation for the specific 3D LIDAR used in the experiments.
## 3 Results
### Optimization of the Proposed Mathematical Models  
The application of _classical optimization_ method for finding the maximum of the response function/proposed optimization criterion, i.e., \(Y^{*}\) = f(\(X_{1}\)*,\(X_{2}\)*,\(X_{3}\)*), consists of the solving of specific equations' systems with first- and second-order derivates equalized with zero.  
The result for \(Y_{1}\) response/decision function optimization leads us to the conclusion that the \(Y_{1}\)* response function (maximum dye removal by biosorption onto biosorbent 1) has _no distinct maximum_ and only a _local maximum_ experimentally found as corresponding to \(X_{1}\)* = +1.00, \(X_{2}\)* = +1.00 and \(X_{3}\)* = +1.00, meaning a dye removal of 52.878%, working at least 8 h with 70 mg/L BRed dye and 18 g/L of biosorbent 1.  
In the case of \(Y_{2}\) function optimization, the result leads to the conclusion that the \(Y_{2}\)* function (maximum dye removal by biosorption onto biosorbent 2) has _a distinct maximum_ (\(Y\) = 75.3375%), corresponding to the following values of three independent values: \(X_{1}\)* = +1.6849, \(X_{2}\)* = -0.0755 and \(X_{3}\)* = +1.3999. Transposed to real values, these independent variables for maximum \(Y_{2}\)* function correspond to a biosorbent 2 concentration of 22.1094 g/L (0.553 g per 25 mL), a BRed dye concentration of 48.49 mg/L, and a S/L contact time for biosorption of at least 8.80 h.