esa-sceva/satcom-chunk-collection huggingface dataset:
# Effective Bus Travel Time Prediction System of Multiple Routes: Introducing PMLNet Based on MDARNN
## 3 Materials and Methods
### Method
#### 3.2.1 Data Pre-Processing
: _The value \(D_{fused}\) is divided into four sub-travel chain datasets, denoted as \(D_{805-248}\), \(D_{202-121}\), \(D_{805-202}\), and \(D_{121-202}\), respectively. The corresponding information for each travel chain is presented in Table 4. The sub-travel chain dataset is denoted as Equation (8), where m is the data record amount of route X and Y. \[D_{X-Y}=\left[ \begin{array}{l}IF^{X_{1}}_{local_{1}},\ldots,IF^{X_{88}}_{ local_{1}},IF^{X_{1}}_{macro_{1}},\ldots,IF^{X_{9}}_{macro_{1}}\\ IF^{X_{1}}_{local_{1}},\ldots,IF^{X_{88}}_{local_{1}},IF^{X_{1}}_{macro_{1}}, \ldots,IF^{X_{9}}_{macro_{1}}\\ \vdots\\ IF^{Y_{1}}_{local_{m}},\ldots,IF^{Y_{88}}_{local_{m}},IF^{Y_{1}}_{macro_{m}}, \ldots,IF^{Y_{9}}_{macro_{m}}\end{array} \right],\quad i,j=1,2,\ldots,m;i<j \tag{8}\]  
According to Equation (1), travel time T is the sum of \(t_{CDSTT}\), \(t_{SSTT}\), \(t_{BSDT}\), and \(t_{TPWT}\), which should be predicted separately. Therefore, for each travel chain, the dataset \(D_{X-Y}\) should be divided into 4 sub-datasets based on geographic regions of data generation. The corresponding division rule is as follows, where \(t_{CDSTT}\), \(t_{SSTT}\), \(t_{BSDT}\), and \(t_{TPWT}\) are used to train sub-models and predict time parts. * Bus stop region: Regions within 0.0001 latitude and longitude from a stop are defined as stop regions. It should be noted that transfer points are not considered as bus stops. Data pieces whose coordinates belong to bus stop regions are then classified into \(D_{BSDT}\). \(D_{BSDT}\) is labeled by the dwelling time of all preceding stops. * Inter-stop region: The region between two adjacent bus stop regions is defined as the inter-stop region. It should be noted that the start/end points of inter-stop region are located on the edge of bus stop regions. We classify data pieces whose coordinates belong to inter-stop regions as \(D_{SSTT}\). \(D_{SSTT}\) is labeled as being between all preceding inter-stop regions. * CDS region: The region between the current location of the bus and next bus stop is the CDS region. Data pieces that belong to the CDS region are sorted into \(D_{CDSTT}\). \(D_{CDSTT}\) is labeled according to the cruising time between the current location and next bus stop. * Transfer region: Regions within 0.0001 latitude and longitude from the transfer point are defined as transfer point regions, and corresponding data pieces are used to generate \(D_{TPWT}\). \(D_{TPWT}\) is labeled according to the transfer waiting time.
## 3 Material & Methods
### Risk Analysis
#### 3.2.5 Attack Feasibility Rating  
The fifth step of the framework conducts a rating of an attack path's feasibility. This rating is based on the following criteria, listed below, in which each criterion is split into different possible ranges. Those ranges are then converted into a quantitative value and summed up to obtain the Aggregated Attack Feasibility Level (AAFL), as shown in Table 4. This rating represents the overall feasibility of the attack based on each of the criteria that composes it:  
* Elapsed time: how much time the attack execution requires (1 week/1 month/6 months/3 years/more than 3 years);
* Expertise: skill and experience required to execute the attack, as well as how many people are needed (Layman/Proficient/Expert/Multiple experts);  
Figure 4: Attack tree showing three attack paths, each from lowest child to root.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Damage** & **Attack Path** & \\
**Scenario** & **Scenario** & **Attack Path Description** \\
**ID** & **ID** & \\ \hline \hline \multirow{4}{*}{D3} & AP.3 & An attacker can impersonate the server identity to send a rogue update, thereby compromising the integrity of the legitimate data. \\  & AP.4 & An attacker can execute a Man-in-the-Middle attack to modify transmitted data, compromising, as a result, the integrity of the legitimate data. \\ \cline{1-1}  & AP.5 & An attacker can impersonate the identity of a 3G/4G antenna and send falsified data, compromising, as a result, the integrity of the legitimate data. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sample attack path scenarios for damage scenario where attacker modifies transmitted data.  
* Equipment: availability of the tools needed to perform the attack (Standard/Specialised/Bespoke/Multiple Bespoke);
* Knowledge of the item or component: how much information is needed to perform the attack (Public information/Restricted information/Confidential information/Strictly confidential information);
* Window of opportunity: ease of access and time limitation (Unlimited/Easy/Moderate/Difficult).  
An illustration of the previously outlined attack paths and their feasibility ratings is provided in Table 5.
## 1 Introduction
In fact, the examined channel is the skin which is totally different and more complex for the optical signal in comparison with atmosphere. Thus, apart from non-zero boresight pointing errors which arise from random movements between transmitter and receiver terminals, skin-induced attenuation that experiences the information-bearing optical signal is now considered, while atmospheric turbulence is obviously neglected. In short, although SIMO and \(L\)-PPM techniques have been also considered in outdoor FSO communications, this is the first time that both techniques are applied this way in the biomedical and transdermal communication systems where the requirements for very low energy consumption and negligible biological effects must be fully met. Thus, motivated and inspired by our previous works in outdoor FSO [2; 4; 46], in the current work we introduce, for the first time ever, SIMO with \(L\)-PPM in an entirely different field of optical wireless communications--i.e., transdermal optical communications with medical implants--as an attempt to enhance the outage probability of such systems. Thus, the scope of this work is to outbalance the negative side-effects of this joint impact by introducing \(L\)-PPM SIMO TOW configurations. Therefore, an ABER analysis is presented and novel closed-form mathematical expressions are derived for its estimation. In this context, a performance comparison in terms of ABER between SISO and SIMO TOW configurations with either OOK or \(L\)-PPM formats is performed, as an attempt to reveal possible significant ABER performance enhancements and/ or IMD's lower power consumption achievements that stem from the selection of proper SIMO \(L\)-PPM TOW system configurations.
## 1 Introduction
### Paper Contribution  
The aim of this work is firstly to analyze the latest 3-D channel model for 5G (described in [21]) by 3 GPP, in order to provide a tutorial of its implementation in MATLAB. Then we proceed to study the aforementioned channel model in a particularly interesting scenario, namely a trisectorial cell site with 5G-compliant communication parameters. We assume an Orthogonal Frequency-Division Multiplexing (OFDM) modulation and planar antenna arrays at the Base Station (BS) and User Terminals (UTs).  
We resort to spatial multiplexing capabilities only to assess the system performance: UTs in the sector coverage area are served on the same time and frequency resources, by means of Space Division Multiple Access.  
We finally simulate our system with different parameters (such as the antenna array size, the traffic loading, the total transmit power, etc.) in order to evaluate the link quality and the capacity with this new 3-D channel model. One of the target is to investigate the impact of different MIMO configurations on the per-user rate. Our main contributions are:  
1. Provide a step by step tutorial on the 3 GPP Technical Report (TR) 38.901 channel model implementation along with MATLAB scripts to generate channel coefficients [39].
2. Design an OFDM MIMO system with uniform planar arrays and dual polarized antenna elements with realistic radiation patterns.
3. Implement _SLNR_ precoding and MMSE combining to exploit Multi-User Multi-Layer MIMO and provide simulation results on the system behavior.
# Theoretical Study on Prestress Loss in Cross-Tensioned Concrete Pavement with BFRP Tendons
## 4 Comparison of the Prestress Loss with Steel Strands
### Results for Steel Strands  
The same input parameters for concrete slabs and distribution angles of BFRP tendons were used in the calculation of prestress loss for steel strands. The diameter of the steel strands was assumed to be 15.2 mm with the nominal cross-sectional area being 184 mm\({}^{2}\), and the ultimate tensile strength and elastic modulus of steel strands were 1860 MPa and 195 GPa, respectively. The tension control stress of steel strands was 75% of the ultimate tensile strength, namely, 1395 MPa in this case. Given the distribution angle of 30\({}^{\circ}\) and spacing of 1000 mm, the mean longitudinal compressive stress for steel strands was calculated to be 3.85 MPa with Equation (7), and therefore the corresponding spacing of steel strands at each distribution angle (20\({}^{\circ}\), 25\({}^{\circ}\), 30\({}^{\circ}\), 35\({}^{\circ}\), 40\({}^{\circ}\), and 45\({}^{\circ}\)) were 1721 mm, 1296 mm, 1000 mm, 780 mm, 609 mm, and 471 mm, respectively.
## 3 Results and Discussion  
### Adsorption Characteristics' Evolution during Coal Structure Deformation  
#### 3.1.1 Excess Adsorption Curve  
The experimental measurement of the adsorption capacity is known as Gibbs excess adsorption capacity. Figure 4 shows that isotherms of four coal specimens are not monotonous at the pressure of 0-7 MPa. The excess adsorption capacity of CO\({}_{2}\) presents an increase at first, and then a decrease with increasing pressure, and shows the single peak curve, which coincides with type I of Gibbs adsorption isotherms proposed by [PERSON] and [PERSON] [15]. An increase of coal deformation degree causes the overall increase in the excess adsorption capacity. According to granulitic coal and crumple coal with strongly structural deformation, the adsorption isotherms present crossing points; in the low pressure zone of 0-3.5 MPa, the adsorption capacity of granulitic coal is greater than that of crumple coal; as the pressure exceeds 3.5 MPa, the adsorption capacity of crumple coal is greater than that of granulitic coal. The maximum values of excess adsorption capacity of undeformed coal, cataclastic coal, granulitic coal, and crumple coal were 9.48 m\({}^{3}\cdot\)g\({}^{-1}\), 12.04 m\({}^{3}\cdot\)g\({}^{-1}\), 15.83 m\({}^{3}\cdot\)g\({}^{-1}\), and 16.74 m\({}^{3}\cdot\)g\({}^{-1}\), respectively. The maximum excess adsorption capacity increased with increasing degrees of coal deformation.  
The maximum excess adsorption capacity is present at pressure ranging from 4 MPa to 5 MPa, and with increasing coal deformation degree the pressure corresponding to maximum excess adsorption amount shifts to the high pressure. As shown in Figure 4, the maximum adsorption amount is located at the pressure of 4 MPa in cases of undeformed coal and cataclastic coal, and at the pressure larger than 5 MPa in cases of strongly deformed granulitic coal and crumple coal. More researches will be carried out in the future to validate the results attributed to the limited number of specimens in the present study.
## 3 Uncertainty Representation of Uncertain Distribution Parameters  
### Available Information from Two Sources  
\(m\) point entries \(a_{i}(i=1,2,\cdots,m)\) and \(n\) interval entries \(\left[\underline{b_{j}},\overline{b_{j}}\right](j=1,2,\cdots,n)\) from Source 1 constitute insufficient data for every distribution parameter \(\xi\). There are seven candidate distribution types, which are Normal distribution, F distribution, Gamma distribution, Weibull distribution, Extreme value distribution, Uniform distribution, and Lognormal distribution. The probability density function (PDF) under candidate distribution type \(\theta_{k}(k=1,2,\cdots,7)\) and distribution parameters \(\xi\) are represented with \(f_{\xi}(\xi|\xi,\theta_{k})\). For the \(k\)-th distribution type \(\theta_{k}\), the likelihood estimation function \(L(\xi,\theta_{k})\) considering both probability density function of sampling points and cumulative probability density function of sampling interval data is constructed in Equation (1).  
\[L(\xi,\theta_{k})\sim\left[\prod_{i=1}^{m}f_{\xi}(\xi=A_{i}|\xi,\theta_{k}) \right]\left[\prod_{j=1}^{n}\int_{\underline{b_{j}}}^{\overline{b_{j}}}f_{ \xi}(\xi|\xi,\theta_{k})\mathrm{d}\xi\right]. \tag{1}\]The maximum likelihood estimation values of \(\xi\) under determinate distribution type \(\theta_{k}\) are calculated by maximizing \(L(\xi,\theta_{k})\). Further, the probability density function \(f_{\xi}(\xi|\theta_{k})\) of \(\xi\) is calculated using [PERSON] theorem ([PERSON], 1972) in Equation (2).  
\[f_{\xi}(\xi|\theta_{k})=\frac{L(\xi,\theta_{k})}{\int L(\xi,\theta_{k})\text{ d}\xi}. \tag{2}\]  
The evidence theory information from Source 2 for each distribution parameter \(\xi\) is transformed into a continuous probability density function using the uniformity method. For the \(i\)-th sub-interval \(A_{i}\), its BPA, lower and upper bounds are represented as \(\Theta(A_{i})\), \(LB_{i}\), and \(UB_{i}\), respectively. If \(\xi\) is located in the independent sub-interval \(A_{i}\), the PDF \(f_{\xi}(\xi)\) of the corresponding probabilistic variable is calculated using Equation (3). There are overlap intervals, and \(f_{\xi}(\xi)\) is the sum of the overlapped sub-intervals. For example, when \(\xi\) is located in the overlapped region of \(A_{1}=[LB_{1},UB_{1}]\) and \(A_{2}=[LB_{2},UB_{2}]\), \(f_{\xi}(\xi)\) is calculated using Equation (4).  
\[f_{\xi}(\xi)=\frac{\Theta(A_{i})}{UB_{i}-LB_{i}}(\xi\in A_{i}). \tag{3}\]  
\[f_{\xi}(\xi)=\frac{\Theta(A_{1})}{UB_{1}-LB_{1}}+\frac{\Theta(A_{2})}{UB_{2}- LB_{2}}(\xi\in A_{1}\text{ and }\xi\in A_{2}). \tag{4}\]
## 4 Simulation Results
The RMSE, average RMSE (ARMSE) and peak RMSE (PRMSE) can be defined as  
\[RMSE(k)=\sqrt{\frac{1}{n_{MC}}\sum_{i=1}^{n_{MC}}\left(\hat{X}_{k/k}^{(i)}-X_{ k}\right)^{2}} \tag{19}\]  
\[ARMSE=\frac{1}{m}\sum_{k=n}^{n+m}RMSE(k) \tag{20}\]  
\[PRMSE=\max_{n\leq k\leq m+n}\left(RMSE(k)\right) \tag{21}\]  
where \(n_{MC}=100\) is the Monte-Carlo simulation number; \(\hat{X}_{k/k}^{(i)}\) is the predicted state at discrete time \(k\) at the \(i\)-th Monte-Carlo simulation; \(X_{k}\) is the real state at discrete time \(k\). The RMSE of \(r\) and \(V\) using different algorithms in scenarios 1 and 2 are shown as Figures 3 and 4, respectively. The ARMSE and PRMSE of \(r\) and \(V\) using different algorithms in scenarios 1 and 2 are shown in Tables 1 and 2, respectively. Figure 4: The performance for tracking scenario 2: (**a**) \(V\) RMSE using multiple model algorithm, single model algorithm and hybrid model algorithm; (**b**) \(r\) RMSE using multiple model algorithm, single model algorithm and hybrid model algorithm  
Figure 3: The performance for tracking scenario 1: (**a**) \(V\) RMSE using multiple model algorithm, single model algorithm and hybrid model algorithm; (**b**) \(r\) RMSE using multiple model algorithm, single model algorithm and hybrid model algorithm, ARMSE: average root mean square error; PRMSE: peak root mean square error.
# Propagation Characteristics of High-Power Vortex Laguerre-Gaussian Laser Beams in Plasma
## 3 Numerical Methodology
In fact, we can first introduce the auxiliary quantity:  
\[\mathbf{S}(\omega)=-\frac{\omega_{\text{p0}}^{2}\exp\bigl{[}-\alpha E_{\text{m}} ^{2}(r,z)\bigr{]}}{\omega^{2}-\mathrm{j}\omega v_{\text{c0}}\exp\bigl{[}- \alpha E_{\text{m}}^{2}(r,z)\bigr{]}}\mathbf{E}(\omega)=\frac{\bigl{(}\omega_{\text {p0}}\Delta t\bigr{)}^{2}\exp\bigl{[}-\alpha E_{\text{m}}^{2}(r,z)\bigr{]}}{ \bigl{(}\mathrm{j}\omega\Delta t\bigr{)}^{2}+\bigl{(}\mathrm{j}\omega\Delta t \bigr{)}\bigl{\{}v_{\text{c0}}\Delta t\exp\bigl{[}-\alpha E_{\text{m}}^{2}(r, z)\bigr{]}\bigr{\}}}\mathbf{E}(\omega), \tag{14}\]  
so that:  
\[\mathbf{D}(\omega)=\varepsilon_{0}[\mathbf{E}(\omega)+\mathbf{S}(\omega)]. \tag{15}\]  
Subsequently, by applying the bilinear transformation \(\mathrm{j}\omega\Delta t\Leftrightarrow 2(1-Z^{-1})/(1+Z^{-1})\) to transform Equation (15) from the frequency domain to the \(Z\) domain and noting that \(\mathbf{E}^{n}Z^{-1}=\mathbf{E}^{n-1}\) and \(\mathbf{S}^{n}Z^{-1}=\mathbf{S}^{n-1}\), we have the updating equation for the discrete time-domain relationship between \(\mathbf{S}\) and \(\mathbf{E}\):  
\[\mathbf{S}^{n}=C_{1}\left(\mathbf{E}^{n}+2\mathbf{E}^{n-1}+\mathbf{E}^{n-2}\right)-C_{2}\mathbf{S}^ {n-1}-C_{3}\mathbf{S}^{n-2}, \tag{16}\]  
where \(C_{1}=\bigl{(}\omega_{\text{p0}}\Delta t\bigr{)}^{2}\beta/(2v_{\text{c0}} \Delta t\beta+4)\), \(C_{2}=-8/(2v_{\text{c0}}\Delta t\beta+4)\), and \(C_{3}=(-2v_{\text{c0}}\Delta t\beta+4)/(2v_{\text{c0}}\Delta t\beta+4)\) with \(\beta=\exp\bigl{[}-\alpha E_{\text{m}}^{2}(r,z)\bigr{]}\). By substituting Equation (16) into Equation (15), we have the final updating equation for \(\mathbf{E}\):  
\[\mathbf{E}^{n}=\frac{1}{1+C_{1}}[\frac{\mathbf{D}^{n}}{\varepsilon_{0}}-C_{1}(2\mathbf{E}^ {n-1}+\mathbf{E}^{n-2})+C_{2}\mathbf{S}^{n-1}+C_{3}\mathbf{S}^{n-2}] \tag{17}\]  
after some mathematical manipulation. However, the coefficients \(C_{1}\), \(C_{2}\), and \(C_{3}\) are dependent on \(\beta\) that is related to \(E_{\text{m}}^{2}(r,z)\). Thus, we also need to extract the parameter \(E_{\text{m}}^{2}(r,z)\). According to the time-harmonic property of a coherent laser beam, \(E_{\rm m}^{2}(r,z)\) can be numerically extracted by applying the composite trapezoidal rule within the discrete framework of FDTD method [20],  
\[E_{\rm m}^{2}(r,z)\approx\frac{2}{N_{\rm T}}\Bigg{\{}E^{2}[r,z,(n-N_{\rm T}/2) \Delta t]+E^{2}(r,z,n\Delta t)+2\sum_{m=n-N_{\rm T}/2+1}^{n-1}E^{2}(r,z,m\Delta t )\Bigg{\}}, \tag{18}\]  
where \(N_{\rm T}=T_{\rm L}/\Delta t\) is an even number of time steps per period of laser oscillation.
## 1 Introduction
Note that the torque balance between both rotors is taken into consideration. Furthermore, [PERSON] et al. [12] used the same method to evaluate the power and thrust coefficients using the pitch angle, diameter ratio, and rotor speed of the CCRR as variables. Note that some of these studies are related to the CCRR, which was developed in South Korea around 2010, which has an FR diameter that is 1/2 that of the RR, and the inboard sections of the RR blades are not expected to generate torque. In addition, both the rotors are located at the front and rear of the nacelle, and the distance between rotors is relatively large. In these modified BEM methods, the interaction of the FR with RR is treated as a fully developed wake flow, and the interaction of the RR with FR is ignored. Therefore, it is necessary to verify the reliability of the method for CCRRs in which the FR and RR are relatively close to each other and have almost the same diameter. 1. CFD with actuator disk model (ADM)  
[PERSON] [13] investigated the characteristics of a tandem rotor using a one-dimensional ADM that ignores swirling flow, with the aim of applying it to vertical axis wind turbines. The results show that the power coefficient increased by 18% compared to that for a single rotor at the Betz limit. Of course, this method is not applicable for blade design. In the design of floating tidal current/ocean current turbines, a high accuracy and low analysis load are required for the fluid dynamics analysis. Among the above methods, the conventional (1) full-model CFD, (2) ALM, and (3) MP model can be used to comprehend many phenomena, but it is difficult to use them for an actual motion/response analysis due to the heavy load required in calculation. On the other hand, the conventional (5) modified BEM is not acceptable when it is applied to the CCRR which the diameters of FR and RR are same, and for which the rotors are placed close together. Considering the above, individual rotor mutual induction (IRMI) was developed for use on the CCRR based on the fluid force characteristics of the individual rotors. This eliminates the need for calculations seeking to find the balance point between the torques of the FR and RR, which is required in conventional analysis methods (1) and (2). It can handle mutual induction, and the analysis time can be significantly reduced while maintaining analysis accuracy. Section 2 provides an overview of the analysis method, Section 3 provides the case study model, and Section 4 provides the steady-state CFD results for the MP model CCRR for verification. Sections 5 and 6 present the CFD results for individual rotors and the mutual induction factors. Section 7 shows the analysis results using the IRMI method developed in this research. Then, Section 8 summarizes the research overall.
## 1 Introduction
The scaled distance is defined as \(Z=\text{R}/\text{W}^{1/3}\), where R is the range (distance between the explosive and the target) and W is the charge weight equivalent to a TNT explosive [21]. Different from far-field explosions, blast wave released from close-in explosions could produce both localized and global failure for the structural elements. The localized failure takes place in the form of localized punching and spalling, while global failure usually occurs as flexural failure. Field blast test is a reliable experimental approach to investigate the dynamic behavior of blast-resistant structures. Blast tests conducted in the field face many challenges, such as test setup, high cost, people injury, environmental risks, and measurement accuracy. Alternatively, finite element analysis (FEA) using hydro-code programs allows researchers to observe the response and failure modes of structural components subjected to blast loading [22]. Numerical results obtained by the FEA are usually verified by the data obtained from the field blast test. So reliable numerical models validated against measured field data is an effective tool to analyze the structural performance under blast impact. In this study, the numerical model is validated using the experimental work done by [PERSON] et al. [23; 24]. Once the numerical model has been validated, detailed numerical models were conducted to investigate the behavior of the proposed core topology. This paper proposes a new core topology for sandwich structures named ribbon core configuration. The purpose of this configuration is to provide a sandwich structure with many advantages, such as being very light weight, the least expensive, simplicity in manufacturing, great flexibility, and with curvature that can be built in two directions. The ribbon configuration consists of folded strips placed in two perpendicular directions forming the ribbon pattern. This technique exploits the material for a better stress distribution, which enhance the capability of the ribbon cores against blast loads. Various blast mitigation parameters such as front and back plate deflection, and energy absorption were assessed to highlight the outstanding behavior of the ribbon configuration compared with ordinary corrugated cores. A parametric study has been conducted considering design parameters such as the front plate's thickness, back plate's thickness, the core layer's thickness, height of the core layer, and the angle of corrugation to investigate their influence on its blast performance.