esa-sceva/satcom-chunk-collection huggingface dataset:
# Method to Measure Light Spot Position in Beacon-Less Inter-Satellite Communication System
## 3 Method of Separating the Two Light Signals
### Cross-Correlation Detection for Weaker Beacon Light Signal  
A reference signal \(z(t)=\cos(2\pi\cdot f_{1}t)\) with the same frequency of \(x(t)\) is generated. Obviously, \(z(t)\) has good time correlation with the signal \(x(t)\), but no time correlation with the signal \(y(t)\) and the noise \(n(t)\). The cross-correlation operation process can be written as:  
\[\begin{array}{c}R(\tau)=E[s_{i}(t)\cdot z(t+\tau)]\\ =E[x(t)\cdot z(t+\tau)]+E[y(t)\cdot z(t+\tau)]+E[n(t)\cdot z(t+\tau)]\\ =R_{xz}(\tau)+R_{yz}(\tau)+R_{xz}(\tau)\end{array} \tag{7}\]  
where \(E[\cdot J\) represents mathematical expectation, and the first part of the Equation (7) is written as:  
\[\begin{array}{c}R_{xz}(\tau)\\ =\lim\limits_{T\rightarrow\infty}\frac{1}{T}\int_{0}^{T}kA_{i1}\cos(2\pi\cdot f _{1}t+\varphi_{1})\cdot\cos[2\pi\cdot f_{1}(t+\tau)]d_{t}\\ =\lim\limits_{T\rightarrow\infty}\frac{kA_{i3}}{2T}\int_{0}^{T}\cos[2\pi\cdot f _{1}(2t+\tau)+\varphi_{1}]d_{t}+\frac{kA_{i3}}{2}\cos(2\pi\cdot f_{1}\tau+ \varphi_{1})\end{array} \tag{8}\]  
It can be proven that when the integration time \(T\) is an integer multiple of the cosine-signal period, the integral result in Equation (8) is equal to zero [18; 19]. This means that when \(T\) matches the follow Equation (9):  
\[T=M_{1}/f_{1}\quad M_{1}\in Z \tag{9}\]  
\(T\) consists of \(M_{1}\) signal periods, each of which is equal to \(1/f_{1}\). Then we can get:  
\[\int_{0}^{T}\cos[2\pi\cdot f_{1}(2t+\tau)+\varphi_{1}]d_{t}=0 \tag{10}\]  
Then, the result of the Equation (8) is:  
\[R  
Thus, the result of the Equation (12) is \(R_{yz}(\tau)=0\).  
The third part of the Equation (7) is written as:  
\[R_{nz}(\tau)=E[n(t)\cdot\cos[2\pi\cdot f_{2}(t+\tau)]] \tag{16}\]  
Since \(n(t)\) is white noise, \(z(t)\) and \(n(t)\) are independent of each other and the value of \(R_{nz}\) is very small [20]. Therefore, \(R_{nz}\) can be denoted as a new smaller noise \(n_{2}(t)\), and the final result of Equation (7) can be written as:  
\[R(\tau)=r(t)=\frac{kA_{i1}}{2}\cos(2\pi\cdot f_{1}t+\varphi_{1})+n_{2}(t) \tag{17}\]  
Obviously, when the integration time \(T\) is an integer multiple of the periods of both \(x(t)\) and \(y(t)\), \(T\) satisfies the requirement of Equation (9) and Equation (13) simultaneously. Therefore, the constraint on the integration time \(T\) can be rewritten as Equation (18):  
\[T=M_{4}/f_{1}=M_{5}/f_{2}\quad M_{4},M_{5}\in Z \tag{18}\]  
where \(M_{4}\) is the number of \(x(t)\) periods contained in \(T\); \(M_{5}\) is the number of \(y(t)\) periods contained in \(T\).  
According to the above analysis, when \(T\) matches Equation (18), we can obtain Equation (17). Then, the amplitude \(A_{i1}\) can be extracted from Equation (17) and can be substituted into Equation (1) to calculate the spot position.
# Vision, Enabling Technologies, and Scenarios for a 6G-Enabled Internet of Verticals (6G-IoV)
## 3 Future Scenarios for the Internet of Verticals
### The Internet of Smart Energy Grids
It can offer virtualized and scalable communication architecture for a customized communication service expected by future smart distribution grids [27]. Consequently, the above evolving landscape in smart grids necessitate the availability of the 6G network infrastructure supporting the often-stringent requirements (in terms of latency, reliability and security) for both wide area connectivity and mobility for management of an Internet of Smart Energy Grids (IoSG).
## 3 Simulation-Based System Architecture
### Requests Queue Scheduler  
This component manages the queue of pending requests in the Outer Dispatcher and decides when a request is dispatched, discarded, left for a future dispatch or requires an additional task.
## 4 Application to Steel Buildings in Downtown Area
### Effect of Rotational Constraints in Environmental Performance  
From an environmental point of view, while the contribution of the concrete panel to embodied CO2 emissions is very low, the rotation constraints from the concrete panel can greatly improve the overall environmental performance of the double-beam floor system by reducing the material quantity. To evaluate the environmental performance according to the presence or absence of concrete panels, the global warming potential (GWP) was calculated through Equation (16) from the quantity of steel and concrete derived by the proposed optimal design method. In the DBO system, the concrete quantity of the concrete panel was considered to calculate the GWP.  
\[\text{GWP}=\text{CO}_{2,\text{ }Embedid}\times\text{CF} \tag{16}\]  
where CO2, _Embedid_ represents the embodied CO2 emission of whole building materials (see detailed calculation process in [19]) and CF is the characterization factor for the GWP (1.00 kgCO2-eq/kg from IPCC 2013 [33]).  
Figure 11 shows the comparison of GWP normalized to the floor area at low live load (below 6.0 kN/m2) and high live load (exceed 6.0 kN/m2) for each structural system. While the DBX system had a slightly smaller GWP value than the GBG system due to the decrease in the material quantity of sub-beams at low live load, the GWP value of the DBX system increased more than the GBG due to the increase in the material quantity of double beams at high live load. The DBO system exhibited the smallest GWP value compared to the other two structural systems at all live loads. The reason that the GWP value of the DBO system was the smallest is that the rotational constraint induced by the concrete panel greatly influenced the reduction of GWP value in the double-beam. The structural characteristic such as the rotational constraint could improve environmental performance, and the GWP value of the DBO system was 13.1% smaller than the GWP value of the GBG system at high live loads. In other words, the steel double-beam floor system improves environmental performance by adding concrete panels to buildings that require a high live load. In addition, the proposed optimal design method can be effectively used in the optimization process of the material quantity requiring for evaluation in environmental performance.  
Figure 11: Comparison of global warming potentials (GWPs) occupied by the structural members in the different structural systems according to the level of live load.
# Current Progress in Femtosecond Laser Ablation/Ionisation Time-of-Flight Mass Spectrometry
## 5 Applications
### Material Science
#### 5.3.3 Ceramic Materials  
The LI-O-TOFMS instrument equipped in VIS ns- and IR fs-laser ablation was used to investigate ancient ceramics [177; 187]. Using ns-laser ablation ion source, a piece of Longquan celadon shard made in Song Dynasty and a piece of an imitation of ancient celadon shard were investigated using elemental composition analysis of the body and glaze form of both porcelain shards. The studies concluded that a difference exists in the element composition for different shards. Further, a piece of blue and white porcelain shard of Ming Dynasty was analysed by elemental imaging of Co, Mn, Fe, Ni, Ba, Ca, Mg, Na, Al, Si, P, K, Cu, Zn and Rb. The studies proved the capabilities of ns-LIMS for elemental imaging analysis and showed that this technique can be an important tool in porcelain provenance study and identification study of early precious porcelain. In the studies with IR fs-laser ablation source, both the spot-wise elemental and chemical imaging measurements were conducted on the porcelain body, as well as glaze of several samples originating from Yue kiln (in Southern China) and Yaozhou kiln (in Northern China) and different cultural eras. Basic sample preparation involved sherds slicing and cleaning with ethanol solution in an ultrasonic bath. The results indicate that Ti and Fe of the porcelain body and Ca, Fe, P, Mn, Mg of the glaze from Yue kiln can be considered as characteristic elements to classify porcelains among the different eras and discriminate them from the contemporary counterfeits. The high concentration of P also reveals that the glaze was fabricated out of plant materials. The differences in trace element concentrations of La, Ce, and Nd found in Yaozhou porcelain body from the Coin Ming Dynasty were interpreted as the historical record about the alteration of kiln site due to the warfare. The comparison of glazes between the two kilns revealed a difference in the aspects of the raw material, the structure of layers, and firing techniques, being responsible for the distinction of porcelain between southern and northern China. The elemental distribution in porcelain is usually inhomogeneous. The elemental mapping of a cross-sectioned Yaozhou porcelain helps to determine the elemental distribution in glaze transition body structure, which in turn helps in understanding the formation mechanism of the transition layer. Figure 15 shows the 2D-plots of elemental densities from the analysed cross-sections.
## 3 Results
### Seasonal Variation of Linear Correlation Between EVI2 and Crop Yields  
A multiple linear regression (Equation (2)) was conducted using crop yields from all the 14 years (2003 to 2016) and in all the counties combined, and using EVI2 extracted with a general cropland mask (GM), and crop-specific masks (SM) derived from classification. The intention was to explore the general explanatory power of EVI2 as a function of time across a typical growing season. The correlation coefficients are shown in Figure 4 for the three crops. This allows for discrimination of positive versus negative correlations. A clear seasonal pattern can be observed for all cases. EVI2 derived using a general cropland mask had a negative correlation with the yields of the three annual crops during a period around the end of May (day-of-year (DOY) = 150) and the end of October (DOY = 300), and a positive correlation during a period around the end of July (DOY = 210) corresponding to the peak growth stage of corn and soybean, the two dominant crops in the region. The transition from negative to positive correlations for the three crops occurred between the end of June and the start of July. Using crop-specific masks, the strongest positive correlation for winter wheat changed to mid-to-late May, whereas the time of the strongest positive correlation did not change for corn and soybean. When crop specific masks were used, the strongest positive correlation occurred approximately at peak growth stages of respective crops, that is, late May for winter wheat and late July and early August for corn and soybean. This was because vegetation indices at the peak stage captured the variability of maximum green biomass accumulation up to this stage, which was largely carried over to the variability of final yields. The negative correlation between the yields of corn (and soybean) and EVI2 at around the end of May and end of October was reduced when crop specific masks were used.
## 4 Testbed Environments--Physical and Virtualized  
The implementation of the experiments required preparation of two environments which were:  
* Physical environment (hereinafter referred to as Host);
* Virtual environment using Docker (hereinafter referred to as Docker)--virtualized at the operating system level (containerization).  
Due to several optimizations during source code compilation, it was necessary to install the GCC compiler and the OpenMPI library in each of these environments. Computing clusters were created from both environments. The SSH protocol was used for setting up communication between the nodes (used public and private keys). During the experiments, no other calculations were performed within the environments. The test execution times were measured by the time program. In order for the tests on the Host and Docker to run in the same hardware configuration, all test runs used the -hostfile parameter of the mpirun command, which allowed specific (the same) machines to be used to perform the task.
## 2 Fast Finite Element Simulation Method for PAUT
### Numerical Model of PAUT of Type-B Sleeve Fillet Weld  
In the process of welding repair and the application of the type-B sleeve, the fillet weld has welding defects, which are tested using the phased array ultrasonic testing method. During detection, the phased array ultrasonic probe is placed on the sleeve, and the internal defects in the triangular area of the weld can be detected through phased array ultrasonic sector scanning. According to the basic principle of phased array ultrasonic testing and the phased array ultrasonic testing method of the type-B sleeve fillet weld, a two-dimensional numerical calculation model of the phased array ultrasonic testing of the type-B sleeve fillet weld defects was established, as shown in Figure 2. The phased array ultrasonic excitation source can be approximated as multiple impulse \(P_{i}(t)\) loads arranged according to a certain delay sequence, and the expression of the \(i\)th excitation load can be written as follows:  
\[P_{i}(t)=\left\{\begin{array}{cc}A\cdot\sin[2\pi f\cdot(\mathrm{t}-\Delta t_{ i})]&0\leq\mathrm{t}-\Delta t_{i}\leq 1/f\\ 0&Others\end{array}\right. \tag{10}\]  
where \(A\) is the peak value of the excitation signal amplitude, \(f\) is the excitation signal frequency, and \(\Delta t_{i}\) is the delay time of the \(i\)th excitation signal. The size array element position \(x_{i}\), phased array ultrasonic focusing depth \(d_{f}\), deflection angle \(\theta\), and ultrasonic wave velocity \(v\) can be written as follows:  
\[\Delta t_{i}=\left(\sqrt{d_{f}{}^{2}+{x_{i}}^{2}-2*d_{f}*x_{i}*\cos(\theta+ \pi/2)}-d_{f}\right)/v \tag{11}\]  
Figure 1: Schematic diagram of the non-zero element compressed storage method for sparse matrix.  
The size of each array element of the phased array ultrasonic probe used in this paper was 0.6 mm, the spacing between each array element was 0.2 mm, the center frequency of the probe was 2 MHz, and the number of excited array elements was 16. The type-B sleeve and fillet weld were made of X70 pipeline steel, with an elastic modulus of 210 GPA, Poisson's ratio of 0.3, and density of 7.85 g/mm\({}^{3}\). The chemical compositions of the X70 pipeline steel are shown in the Table 1. The thickness of the pipe and sleeve was 12.8 mm and 20 mm, respectively, and the gap between them was 2 mm.
# A Deep Reinforcement Learning Approach for Efficient, Safe and Conformtable Driving +
## 3 Design and Implementation of the DRL Framework
### Integrating the Drl Model in the Comove Framework  
A realistic simulation environment is vital for a DRL agent to learn the desired behavior, which includes the movements of surrounding vehicles and environmental circumstances. We realize such a simulation environment through CoMoVe, our sophisticated simulation framework. The CoMoVe framework [12], shown in Figure 2, integrates widely known simulators from Mobility, Communication, and Vehicle Dynamics domains. It combines (i) the ns-3 simulator, with the LENA module, simulating LTE-based V2X communications; (ii) the SUMO simulator for vehicle mobility; (iii) the MathWorks module, which models the vehicle on-board sensors and vehicle dynamics; and (iv) the Python engine, which serves as an interface to facilitate the exchange of data between the modules, and it also features a DRL agent to control the vehicle's movements.  
The Python engine used a combination of Python libraries to envision efficient interactions with other simulators. It includes TraCI, MATLAB Engine, and ns3 Python bindings to interact with SUMO, extract vehicle-related information, and simulate vehicular communication, respectively. Concerning the DRL state components, the ns3 V2X communication model facilitates the reception of lead vehicle acceleration value (\(\alpha(t)\)), the headway (\(\vartheta(t)\)) and headway derivative (\(\Delta\vartheta(t)\)) values are computed using the velocity and distance measurements acquired from the vehicle sensor models, and the Simulink Vehicle Dynamic model provides the longitudinal slip (\(\xi(t)\)) and friction coefficient (\(\mu(t)\)) values. The Python Engine gathers these values and provides them to the DRL framework as observed state components. The Vehicle Dynamics Model utilizes the action (desired acceleration) of the DRL framework as a reference signal for the lower-level controller of the ego vehicle. The vehicle is modeled using Simulink with fourteen Degree-of-Freedom. The key aspects of the vehicle model are an eight-speed automatic transmission, rear-wheel driven, and a Spark Ignition engine.
# Shear Band Characterization of Clayey Soils with Particle Image Velocimetry
## 4 Experimental Results and Discussion
### Shear Band Characteristics: Angle and Thickness
In the case of OC clay, there is no existing research, but the final shear band slopes of 56.3\({}^{\circ}\) (for OC1) and 55.2\({}^{\circ}\) (for OC2) in this study (i.e., steady state) were similar to \(\theta_{A}\). During shear band development, [PERSON] et al. [17] showed that the inclination of the shear band of a granular soil decreased from the softening to the steady-state stage (which lies between \(\theta_{MC}\) and \(\theta_{A}\)). These results indicate that \(\theta_{MC}\), which considers only the internal friction angle and is widely adopted to represent the shearing behavior of clay, is not enough to calculate the final shear band slope of both NC and OC clays and that the dilatation angle should also be considered. In summary, the dilatation angle mainly influenced the softening stage when the shear band was continuously developing (while internal friction was influential in the softening stage of granular soils), and the internal friction angle was influential in the steady-state stage (i.e., after shearing was completed). There are several previous studies on the thickness of shear band. For sandy soil, [PERSON] [35], [PERSON] et al. [34], [PERSON] et al. [8], and [PERSON] et al. [17] investigated the thickness of shear band, and they excluded the effect of the particle size on the shear band thickness by normalizing the thickness with an even particle distribution to the average particle diameter (\(D_{50}\)). For clay, [PERSON] [22], [PERSON] et al. [37], and [PERSON] et al. [20] assessed the thickness of the shear band after shearing was completed. As the \(D_{50}\) of clay is very small, they did not normalize the shear band thickness. In this study, referring to previous relevant studies, the shear band was defined as the area where a shear strain increment of at least 1.0% occurred [17], and the thickness of this area was evaluated, as shown in Table 4, without normalization process [20; 22; 37]. In the softening stage, the thicknesses of the NC1, NC2, OC1, and OC2 were 4.42, 5.20, 3.84, and 4.66 mm, respectively. As the shearing progressed, the thicknesses gradually increased up to 7.56, 6.34, 6.56, and 7.01 mm, respectively (i.e., in the steady-state stage); these values showed good agreement with previous studies, indicating that the shear band thickness of clayey soil was 6-8 mm after shearing was completed [20; 22; 37]. It is interesting to note that the shear band thickness increases with the shearing process, unlike sandy soil, which gradually decreases with the development of shear band [17]. In this study, the thickness of shear band of clayey soil did not show any particular tendency between NC and OC clays. However, the effective confining stress of OC clay was less than that of NC clay, thus further research work with the experiments should be performed under the same effective confining stress.
## 2 Existing Approaches
### Evolving Studies on Ad Hoc Security  
An ad hoc network is an integral part of cloud computing and IoT, which will eventually mean that it is now exposed to a higher range of security threats. The conventional security protocols for ad hoc networks are restricted in their capability when integrated with IoT, which is a massive-scale deployment compared to conventional ad hoc networks. Fog computing, another revised version of cloud computing, is increasingly used, also has security concerns associated with it. [PERSON] et al. [51] developed a secure game model for resisting lethal threats in fog computing. A dynamic Stackelberg model was developed to improve the interaction among the actors in order to resist threats. A study on securing IoT was carried out by [PERSON] et al. [52] using a collaborative game model: the stochastic Petri-net model. There are also other studies that have depicted the ongoing issues despite various existing IoT security schemes [53; 54].  
Hence, there is a need for a study to address the problems associated with different forms of security in the present state of challenging wireless networks. The following section highlights the problems identified.
## A Remote-Sensing Method to Estimate Bulk
While the applicability of our developed method to other water areas is unknown, our findings in the current study demonstrate that the method presented here can serve as a proof-of-concept template to remotely estimate \(n_{\text{p}}\) in other coastal optically complex water bodies. bulk refractive index of suspended particles; particulate backscattering ratio; PSD slope; remote sensing reflectance; spatiotemporal distribution; GOCI; Bohai Sea and Yellow Sea
## 3 Face Recognition System
### Face Recognition Algorithms  
#### 3.2.1 Geometric Approach  
The approach on face recognition based on geometric features of the face extracts features such as face landmarks' positions and their distances from one another. The feature vector consists of these positions of face features and their distances. Thus, each face is represented by such a feature vector. The decision of whether two faces match or not is made based on whether the feature vectors representing the two faces are more similar than a given similarity threshold. The main advantage of the geometric approach for face recognition is the speed of the comparison, taking into account the compactness of the representation. A main disadvantage of this approach is the difficulty of accurate automated detection of face landmarks. Whoever implements face recognition based on geometric properties of the face must make arbitrary decisions regarding the selection of face landmarks and face dimensions to be taken into account. This is important because face models will be built based on this choice, thus the better the selection of geometric face features, the easier it is for the face recognition system to differentiate between faces of different individuals.
# Gyre Precoding and T-Transformation-Based GFDM System for UAV-Aided mMTC Network
## 2 System Model
### Signal Model
#### 2.2.2 UAV Segment
Considering \(H_{\bar{k}}W_{\bar{k}}=\bar{H}_{\bar{k}}\) and multiplying Equation (32) \((\bar{H}_{\bar{k}}^{H}\bar{H}_{\bar{k}})^{-1}\bar{H}_{\bar{k}}^{H}\), a new form of the processed signal for mMTC user \(\bar{k}\) is obtained as:  
\[\vec{x}_{\bar{k}} =(\bar{H}_{\bar{k}}^{H}\bar{H}_{\bar{k}})^{-1}\bar{H}_{\bar{k}}^ {H}\bar{H}_{\bar{k}}\,\vec{x}_{\bar{k}}+(\bar{H}_{\bar{k}}^{H}\bar{H}_{\bar{k}}) ^{-1}\bar{H}_{\bar{k}}^{H}H_{\bar{k}}\sum_{j=1,j\
eq\bar{k}}^{j=8}W_{\bar{j}} \bar{x}_{\bar{j}}+(\bar{H}_{\bar{k}}^{H}\bar{H}_{\bar{k}})^{-1}\bar{H}_{\bar{k}} ^{H}\widehat{\bar{n}}_{\bar{k}}\] \[\bar{S}_{d}^{\bar{k}} =(\widehat{\bar{H}}_{\bar{k}}^{H}\widehat{\bar{H}}_{\bar{k}}+\mathfrak{ H}I_{N_{T}})^{-1}\widehat{\bar{H}}_{\bar{k}}^{H}Y_{d}^{\bar{k}}\]0  
where the first, second, and third terms of Equation (35) are indicative of signal, interference, and noise components \(\widehat{\bar{n}}=(\bar{H}_{\bar{k}}^{H}\bar{H}_{\bar{k}})^{-1}\bar{H}_{\bar{k}} ^{H}\widehat{\bar{n}}_{\bar{k}}\) and is \(\sim\mathbb{C}\mathbb{N}(0_{N_{R}},\widehat{\bar{\sigma}}_{\bar{k}}I_{N_{R}})\), respectively. With further processing for necessary steps with the execution of the inverse T-transformation, achievable ergodic rate, SINR, and SNR values can be estimated.