esa-sceva/satcom-chunk-collection huggingface dataset:
# Beam Scanning and Capture of Micro Laser Communication Terminal Based on MEMS Microm mirrors
## 2 MEMS Micromirror Optical Scanning Model
From Equation (13), it is clear that for various horizontal mechanical deflection angles \(\alpha_{x}\) and vertical mechanical deflection angles \(\alpha_{y}\) of the MEMS micromirrors as well as the different initial light incident angles, the relationship between the horizontal and the vertical deflection angles of the initial output light vector could be expressed as Equation (14):  
\[\left\{ \begin{aligned} \varphi_{x}&=\tan^{-1}\frac{X}{L}= \frac{\cos^{2}\alpha_{y}\sin 2\alpha_{x}-\sin^{2}\alpha_{y}\sin 2\beta}{\cos^{2} \alpha_{y}\cos 2\alpha_{x}-\sin^{2}\alpha_{y}\cos 2\beta}\\ \varphi_{y}&=\tan^{-1}\frac{Y}{L}=\frac{\cos(\beta -\alpha_{x})\sin 2\alpha_{y}}{\cos^{2}\alpha_{y}\cos 2\alpha_{x}-\sin^{2}\alpha_{y}\cos 2 \beta}\end{aligned} \right.. \tag{14}\]
# RF-Access: Barrier-Free Access Control Systems with UHF RFID
## 4 Evaluation
### Detection of Motion Direction
#### 4.2.2 Impact of Antenna Angle  
In Figure 15(a), we study the impact of antenna angle on sensing performance. We adjust the antenna angle \(\gamma\) from \(0^{\circ}\) to \(40^{\circ}\) with the step of \(10^{\circ}\) and repeat the experiments 50 times at each angle, with half of the constant speed and half of the running speed. As we can see, the accuracy is close to 100% when the angle is no less than \(10^{\circ}\). The case of \(\gamma=0^{\circ}\) sees a slight decline. The reason is that the inclined angle helps two antennas better read tags along their facing directions, which benefits direction detection. When the angle is 0, the difference between the two antennas comes from only that of antenna positions, which is too small to properly identify all cases. However, large inclined angles make it hard to deploy the antennas in a gate. We recommend \(20^{\circ}\) in this case.
# Analysis of the Working Performance of a Back-to-Back Geosynthetic-Reinforced Soil Wall
## 2 Experimental Study and Numerical Modeling
### Numerical Modeling  
The numerical modeling was performed using the 2D finite element computer program PLAXIS [22]. Figure 4 shows the geometry of the numerical model adapted from the BBGRSW on-site. A fixed boundary condition in the horizontal direction was applied to the left and right lateral borders. At the bottom of the model, a fixed boundary condition was adopted along the horizontal and vertical directions.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Items** & \multicolumn{3}{c}{**Indexes**} \\ \hline Particle diameter (mm) & \(d_{10}\) & \(d_{30}\) & \(d_{60}\) \\ \cline{2-4}  & 0.32 & 0.84 & 1.95 \\ \hline Coefficient of uniformity & & 6.09 & \\ Curvature coefficient & & 1.13 & \\ Saturated unit weight (kN:m\({}^{-3}\)) & & 19.2 & \\ Cohesion (kPa) & & 3.1 & \\ Friction angle (\({}^{\circ}\)) & & 37.3 & \\ Moisture content (\%) & & 5.7 & \\ Optimum moisture content (\%) & & 7.8 & \\ Maximum dry density (g:cm\({}^{-3}\)) & & 2.234 & \\ Elasticity modulus (MPa) & & 12.00 & \\ \hline \end{tabular}
\end{table}
Table 2: Physical and mechanical properties of the filling soil.  
Figure 3: Curve of the particle size distribution of the filling soil.  
The PLAXIS virtual three-axis simulator was used to test the input parameters of the soil. The result is shown in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Indexes** \\ \hline Rib length/mm & 245 \\ Rib spacing/mm & 16 \\ Rib width/mm & 5.1 \\ Rib thickness/mm & 1.3 \\ Bar width/mm & 18.2 \\ Bar thickness/mm & 3.5 \\ Mass per unit area/(g-m\({}^{-2}\)) & 850 \\ Tensile strength/(kN\(\cdot\)m\({}^{-1}\)) & 141.6 \\ Tensile strength at 2\% strain/(kN\(\cdot\)m\({}^{-1}\)) & 41.6 \\ Tensile strength at 5\% strain/(kN\(\cdot\)m\({}^{-1}\)) & 85.9 \\ Peak strain/\% & 8.92 \\ Axial stiffness at 2\% strain/(kN\(\cdot\)m\({}^{-1}\)) & 2080 \\ Long-term stiffness/(kN\(\cdot\)m\({}^{-1}\)) & 2200 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Technical specifications of the high-density polyethylene (HDPE) geogrid.  
Figure 4: Layout of the BBGRSW on-site (baseline case).  
Figure 5: Triaxial test results of the filling soil.  
The input parameters of the soil and panel properties from the laboratory tests are listed in Tables 4 and 5, respectively. The reinforcement was performed using high-density polyethylene (HDPE) uniaxial geogrids modeled as a linear elastic material. The total railway load and load of the track board that was applied on the BBGRSW were 54.1 and 17.4 kN\(\cdot\)m\({}^{-2}\), respectively, as per the Chinese design code for railways.
## 5 Experimental Validation
### Experimental Result for Shading Pattern 2  
Figure 19a-c shows the results of the MPPT schemes applied in PSC 2. The conventional INC and P&O techniques failed to track the true GMPP location for this partially shaded pattern, as they canceled the searching process after reaching an LMPP on the PV curve (Figure 19a,b).  
The power of the LMPP tracked by both conventional MPPT schemes continuously oscillates between 48 and 50 W, indicating that the operating point moves around the local MPP (49.12 W). Thus, the methods provide lower tracking efficiency (62.1%) and require more time (0.23 s) to track the LMPP instead of the GMPP locus. Moreover, they never stop duty cycle perturbation (0.152-0.187), although one of the LMPPs is tracked under SSC. Therefore, both conventional MPPTs degrade severely and prevent the operating point to move closer to the GMPP area in this shading pattern. Figure 19c shows that  
Figure 19: Experimental comparison of discussed MPPT techniques at middle GMPP of PSC condition: (**a**) INC-MPPT; (**b**) P&O-MPPT; and (**c**) Proposed algorithm.  
the experimental results of the proposed coordinated algorithm in shading pattern 2 are satisfied with the simulation outputs on a similar PV curve. The operating point of the converter is first located nearer to an LMPP point (50.25 V, 49.12 W) and then slides back to track the GMPP point (30.71 V, 77.72 W). After tracking the GMPP within 0.18 s, the proposed algorithm stops searching for the MPP and is stuck at a duty cycle of 0.489 under SSC. Hence, the proposed algorithm ensures a tracking efficiency of 37.22% more than both conventional techniques and can track the GMPP rapidly with no power loss under the SSC on the PV curve with 99.87% power efficiency. Therefore, only the proposed algorithm can manage this worsening weather condition and enables the operating point to always be in the GMPP region.
## 4 Economic Analysis for the Proposed Method
### Performance Group of Structural and Non-Structural Members
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Performance Group**} & **Fragility ID** & **Costing Based** & **Number in** & **Total** \\  & **[**49**]** & **EDP** & **Upon in PACT [49]** & **Each Story** & **Number** \\ \hline Structural & Beam joints & B1041.101b & IDR & 1 EA & 24 & 144 \\ \hline \multirow{8}{*}{Non-structural member} & Curtain walls & B2022.001 & IDR & 30 SF & 34.82 & 208.92 \\  & Partitions & C1011.001a & IDR & 100 LF & 5.32 & 31.92 \\  & Wall finishes & C3011.001a & IDR & 100 LF & 1.34 & 8.04 \\  & Cold water piping & D2021.011a & PFA & 1000 LF & 0.07 & 0.42 \\  & Hot water piping & D2022.011a & PFA & 1000 LF & 0.40 & 2.40 \\  & Smitary waste piping & D2022.011a & PFA & 1000 LF & 0.16 & 0.96 \\  & WAC equipment & D301.011b & PFA & 1000 LF & 0.21 & 1.26 \\  & HVAC equipment & D3041.011a & PFA & 1000 LF & 0.24 & 1.44 \\  & Variable Air Volume & D3041.031a & PFA & 10 EA & 2.37 & 14.22 \\  & Variable Air Volume & D3041.041a & PFA & 10 EA & 1.90 & 11.4 \\  & (VAV) box & B3011.011 & PFA & 100 SF & 32.28 & 32.28 \\  & Concrete tile roof & Buccess ceiling lighting & C3033.001 & PFA & 1 EA & 71.21 & 427.26 \\  & Independent pendant lighting & C3034.001 & PFA & 1 EA & 71.21 & 427.26 \\  & Sprinkler water supply & D4011.021a & PFA & 1000 LF & 0.85 & 5.1 \\  & & D4011.031a & PFA & 100 EA & 0.38 & 2.28 \\  & Stairs & C2011.001a & IDR & 1 EA & 2.00 & 12 \\  & Low voltage switchgear & D5012.021a & PFA & 225 AMP & 1.00 & 6 \\  & Elevator & D1014.011 & PFA & 1 EA & 1.00 & 1.00 \\  & Motor control center & D5012.013a & PFA & 1 EA & 2.00 & 2.00 \\ \hline \hline \end{tabular} Note: HVAC, Heating Ventilation Air Conditioning.  
\end{table} 
Table 10: Statistical data on the performance groups of the 6-story frame. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Construction Items**} & **Construction Period (Unit: Workday)** \\ \hline Foundation project & 30 \\ Upper structure project & 305 \\ Decorate project & 60 \\ Elevator system installation & 50 \\ Heat-supply system installation & 50 \\ Air conditioning system installation & 65 \\ Electric power substation project & 40 \\ Total required workdays & 600 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Construction periods of key items of the 6-story RC frame. \(\times\) 4 + 15010 \(\times\) 3 \(\times\) 2)/10\({}^{5}\) = 16.2 MUD. The installation of a viscous damper needs 2 workers with 1 workday, and the cost is estimated as 45 \(\times\) 2 = 90 US dollars, in which 45 US dollars are estimated as the salary for 1 worker per day at the building's location. The estimated total number of required viscous dampers is 56, hence the total installation cost is (90 \(\times\) 56)/10\({}^{4}\) = 0.5 MUD. Therefore, the total retrofitting cost of the 3D structure is 16.2 + 0.5 = 16.7 MUD.
## 3 Preliminaries
### Basic Policy-Mining Problem in ABAC  
Besides the basic components of ABAC, the other components involved in traditional ABAC policy mining [20] can be presented as follows:  
1. \(A\) represents a set of all possible authorizations that occur in an ABAC system. Each element of the set is represented as \(a=\)\(<\)\(u\),\(o\),\(op\)\(>\), which allows user \(u\) to perform operation \(op\) on object \(o\), where \(u\in U\), \(o\in O\), \(op\in OP\).
2. \(P\) represents an ABAC policy, which is also referred to as a set of authorization rules \(AR\). Each element \(ar\) in \(AR\) is denoted in a 3-tuple form \(<\)\(U\)\(U\)\(A\)\(V\),\(O\)\(A\)\(V\),\(OP\)\(>\), where \(U\)\(A\)\(V\)\(\subseteq\)\(U\)\(A\)\(V\), \(O\)\(A\)\(V\)\(\subseteq\)\(O\)\(A\)\(V\), \(OP\)\(\subseteq\)\(OP\).  
For the sake of simplicity, assume that any rule \(ar\) comprises only one operation, and \(ar\) is simply represented as \(<\)\(U\)\(A\)\(V\), \(O\)\(A\)\(V\), \(op\)\(>\). Figure 1 presents the factors influencing the access decisions in ABAC, where single arrow heads represent which factors influence the access decisions and double arrow heads represent the one-to-many or many-to-many mappings between these influencing factors and other components of ABAC.  
The basic ABAC policy-mining problem [21; 22; 23], in terms of the above elements, states that, given a set of authorizations \(A=\{a_{1}\),\(a_{2}\),... }, a mapping list \(U\)\(U\)\(A\)\(V\) and a mapping list \(O\)\(A\)\(V\) must find a set \(AR\) of authorization rules that can cover all the authorizations in \(A\). Specifically, for any authorization \(a=\)\(<\)\(u\),\(o\),\(op\)\(>\), user \(u\) can perform operation \(op\) on object \(o\) if and only if some combination of several attribute-value pairs of \(u\) in the \(U\)\(U\)\(A\)\(V\), as well as those of \(o\) in the \(O\)\(O\)\(A\)\(V\), can match a rule with \(op\) in \(AR\). Furthermore, the number of m  
Figure 1: Factors influencing access decisions.
# A Comparative Study of Differential Quadrature Methods for METE Nanobeam Vibrations
## 3 Method of Solution
### Polynomial-Based Differential Quadrature Method (PDQM)  
To approximate the unknown function \(v\) and its derivatives at specific discrete nodes, the Lagrange interpolation polynomial acts as the shape function. This method expresses the approximation as a weighted combination of the nodal values \(v_{i}(i=1:N)\)[42].  
\[v(x_{i})=\sum_{j=1}^{N}\frac{\prod_{k=1}^{N}(x_{i}-x_{k})}{(x_{i}-x_{j})\prod_ {j=1,j\
eq k}^{N}(x_{j}-x_{k})}v\big{(}x_{j}\big{)},\quad(i=1:N), \tag{31}\]  
\[\frac{\partial v}{\partial x}\bigg{|}_{x=x_{i}}=\sum_{j=1}^{N}C_{ij}^{(1)}v \big{(}x_{j}\big{)},\quad\frac{\partial^{2}v}{\partial x^{2}}\bigg{|}_{x=x_{i}}= \sum_{j=1}^{N}C_{ij}^{(2)}v\big{(}x_{j}\big{)},\quad(i=1:N) \tag{32}\]Likewise, we can approximate \(\frac{\partial^{3}y}{\partial x^{3}},\frac{\partial^{4}y}{\partial x^{4}}\) and determine \(C_{ij}^{(3)},C_{ij}^{(4)}\). The number of grid points is denoted by N. For each variable, \(v\in\{W,\Theta,\Psi,\Phi\}\), the weighting coefficients \(C_{ij}^{(1)}\) are obtained by differentiating the Lagrange interpolation polynomial (Equation (31)):  
\[C_{ij}^{(1)}=\begin{cases}\frac{1}{(x_{i}-x_{j})}\prod_{k=1,k\
eq i,j}^{N} \frac{(x_{i}-x_{k})}{(x_{j}-x_{k})}&i\
eq j\\ -\sum_{j=1,j\
eq i}^{N}C_{ij}^{(1)}&i=j\end{cases} \tag{33}\]  
Matrix multiplication is employed to compute the matrices \(C_{ij}^{(2)},C_{ij}^{(3)}\), and \(C_{ij}^{(4)}\) as follows:  
\[\left[C_{ij}^{(n)}\right]=\left[C_{ij}^{(1)}\right]\left[C_{ij}^{(n-1)}\right],(n=2,3,4) \tag{34}\]
## 5 Results and Discussion
### Ablation Study  
For RQ2, we perform an ablation study by comparing our proposal with its variants to analyze the effectiveness of each component. Specifically, we produce four variants for comparison, including: (1) \"w/o demo\" that removes the whole demonstration learning module and utilizes a fine-tuning; (2) \"w/o demo (full)\" that removes the joint correlation scoring function and adopts a full demonstration following LM-BFF; (3) \"w/o demo (random)\" that removes our proposed scoring function and employs a random sample as a demonstration example; (4) \"w/o generation (man)\" that removes the template generation module and uses manual-crafted templates. The results are shown in Table 4.  
From Table 4, we can observe that the removal of any component in our proposal leads to a performance decrease, indicating that all components contribute to the model performance. Further, the removal of the demonstration module has the greatest impact on model performance, illustrating that providing random examples as demonstrations can help the language model to capture the answer patterns for prompts. Moreover, comparing \"w/o demo (full)\" and \"w/o demo (random)\", we can notice that \"w/o demo (full)\" outperforms \"w/o demo (random)\" in all cases. It can be explained by the fact that the random selection sometimes ignores the informative examples for the query. In addition, the comparison between \"w/o demo (full)\" and our proposal demonstrates that our proposed joint correlation scoring function can effectively select the informative demonstration example for each query and thus improve the model performance.  
In addition, \"w/o generation (man)\" loses the performance competition to our original proposal in terms of accuracy on all tasks. We attribute the reason that the manual-crafted templates are usually sub-optimal to the model training.
## Appendix C Gauss-Markov Processes  
The Gauss-Markov stochastic processes are stochastic processes that satisfy the requirements for both Gaussian processes and Markov processes. A common stochastic model is one whose auto-correlation function decays exponentially with time. A stationary Gauss-Markov process with variance \(\mathbf{E}\left[x^{2}(t)\right]=\sigma_{GM}^{2}\), where \(x\left(t\right)\) denotes the consecutive stochastic signal, has the following properties. On the one hand, the auto-correlation of \(x\left(t\right)\) is exponential, i.e.,  
\[R_{x}\left(\tau\right)=\sigma_{GM}^{2}e^{-\beta\left|\tau\right|} \tag{12}\]  
where \(\sigma_{GM}\) represents the temporal standard deviation of the process; \(\beta\) is the reciprocal of the time constant, \(\tau\). The random process of (12) is called the 1 st-order Gauss-Markov process. Then, the PSD of \(x\left(t\right)\) can be derived as  
Figure 14: Square root of Allan variance plot with the related asymptotes for a typical ovenized crystal oscillators [64; 66; 71; 72].  
\[S_{x}\left(j\omega\right)=\frac{2\sigma_{GM}^{2}\beta}{\omega^{2}+\beta^{2}} \tag{100}\]  
Next, the continuous system model with respect to the time variable is given by  
\[\dot{x}\left(t\right)=-\beta x\left(t\right)+\sqrt{2\sigma_{GM}^{2}\beta}m\left(t\right) \tag{101}\]  
where \(m\left(t\right)\) denotes the input white noise in time domain. Given  
\[w\left(t\right)=\sqrt{2\sigma_{GM}^{2}\beta}m\left(t\right) \tag{102}\]  
which satisfies  
\[\mathbb{E}\left[w\left(t\right)\right]=0,\quad\mathbb{E}\left[w\left(t\right) w\left(\tau\right)\right]=2\beta\sigma_{GM}^{2}\delta\left(t-\tau\right) \tag{103}\]  
where \(\delta\left(\cdot\right)\) denotes the Dirac delta function. Therefore, the variance intensity of \(w\left(t\right)\) is given by  
\[q_{w}=2\beta\sigma_{GM}^{2} \tag{104}\]  
The 1 st-order GM processes are commonly used since they tend to adequately approximate a wide range of physical processes. Furthermore, they are also simple to work with. A block diagram to describe this process is shown in Figure 14.
## 3 Results  
Three specific experiments were conducted to evaluate the SMF model and the CAIS. The first experiment fixed the irrigation interval and applied the SMF model to deduce the probable moisture value for the next CAIS irrigation. This experiment aimed to evaluate the accuracy of the SMF model. The second experiment applied the SMF model and dynamically adapted the irrigation interval to maintain the soil moisture at a constant level for a single irrigation point. The third experiment combined the SMF model and the CAIS to irrigate each VPA. This experiment evaluated whether the CAIS could maintain the soil moisture at a stable level at different locations across the whole experimental plot with multiple irrigation points.
## 4 Discussion
### Limitations  
A key limitation of this study lies in its cross-sectional design, which captures data at a single point in time and may not fully reflect the broader dynamics of the RAE. Because this study relied on publicly available competition data, it was not possible to examine factors such as athletes' biological maturity, training history, or selection processes. The findings primarily apply to elite-level athletes and may not be generalizable to younger or recreational gymnasts. Conducting longitudinal research that incorporates biological maturation measures and involves larger sample sizes would enable more robust and generalizable conclusions. Nonetheless, this study's sequential analysis across different age groups within an international cohort of elite junior RGs--and extending into the senior category--revealed notable birthdate disparities across quartiles. These findings, which have not been widely reported in the previous literature, underscore the importance of continued investigation into the presence and implications of the RAE in RG. Future qualitative research, such as interviews with coaches or athletes, could provide valuable insights and highlight areas that remain unexplored.
# Mobility Management Scheme with Mobility Prediction in Wireless Communication Networks
## 3 Modeling and Performance Analysis
### Urban Environment  
Now, let us consider state transition diagram for urban environment as shown in Figure 2. There are two kinds of transitions: one is entering a cell and another one is call occurrence.  
Let us consider the case of entering a cell before call occurrence. The probability that a UE moves to a neighboring cell before a call occurs, \(m\), is as follows:  
\[m=P[T_{c}>T_{m}]=\int\limits_{0}^{\infty}\int\limits_{t_{m}}^{\infty}\lambda_{c }e^{-\lambda_{c}t_{c}}f_{m}(t_{m})dt_{c}dt_{m}=f_{m}^{*}(\lambda_{c}) \tag{1}\]  
Therefore, the UE registering in cell 1 (state S\({}_{1}\)) can enter cell 3 (state S\({}_{2}\)) with a probability of \(qm\) or enter any one of the remaining 5 cells with a probability of (1 \(-\)\(qm\)/5. If it enters one of 3 neighboring cells that are not in the current TAL, it will register its location (state S\({}_{1}\)).  
Figure 3: State transition diagram for rural environment (big sector, 120\({}^{\circ}\)). (a) State of a big sector (120\({}^{\circ}\)). (b) State transition diagram of a big sector.  
If a call occurs to/from the UE before the UE registering in cell 1 (state S\({}_{1}\)) enters a neighboring cell (state S\({}_{2}\) or state S\({}_{3}\)), the state of the UE becomes state S\({}_{0}\). Such a state transition occurs with a probability of \(1-m=P[T_{c}\leq T_{m}]\). Note that, in this case, cell is not changed. However, a new TAL is set up (by implicit registration) and the cell becomes cell 1 in Figure 1.  
One more important probability in Figure 2 is that, given that a call has already occurred, UE enters a neighboring cell before another call occurs, \(m^{\prime}=P[T_{c}>R_{m}]\).  
To calculate \(m^{\prime}=P[T_{c}>R_{m}]\), let us consider \(R_{m}\). The density function of \(R_{m},f_{r}(t)\) is from a random observer property [20; 21]:  
\[f_{r}(t)=\lambda_{m}\int_{\tau=t}^{\infty}f_{m}(\tau)d\tau=\lambda_{m}(1-F_{m} (t)) \tag{2}\]  
The Laplace-Stieltjes transform for the distribution can be written as follows:  
\[\begin{split} f_{r}^{*}(s)&=\int_{t=0}^{\infty}e^{ -st}f_{r}(t)dt=\int_{t=0}^{\infty}e^{-st}\lambda_{m}(1-F_{m}(t))dt=\frac{ \lambda_{m}}{s}-\int_{t=0}^{\infty}e^{-st}\lambda_{m}F_{m}(t)dt\\ &=\frac{\lambda_{m}}{s}+\left[\frac{\lambda_{m}}{s}e^{-st}\lambda _{m}F_{m}(t)\right]_{t=0}^{\infty}-\frac{\lambda_{m}}{s}\int_{t=0}^{\infty}e^{ -st}f_{m}(t)dt=\frac{\lambda_{m}}{s}(1-f_{m}^{*}(s))\end{split} \tag{3}\]  
This gives:  
\[m^{\prime}=P[T_{c}>R_{m}]=\int_{0}^{\infty}\int_{t_{c}=r_{m}}^{\infty}\lambda_{ c}e^{-\lambda_{c}t_{c}}f_{r}(r_{m})dt_{c}dr_{m}=\int_{0}^{\infty}f_{r}(r_{m})e^{ -\lambda_{c}t_{c}}dr_{m}=\frac{\lambda_{m}}{\lambda_{c}}(1-f_{m}^{*}(\lambda_{ c})) \tag{4}\]  
From the state transition diagram, we can obtain transition probability matrix \(P_{urban}\) as follows:  
\[P_{urban}=\left[\begin{array}{cccc}1-m^{\prime}&\frac{1-q}{5}3m&qm^{\prime} &\frac{1-q}{5}3m\\ 1-m&\frac{1-q}{5}3m&qm&\frac{1-q}{5}3m\\ 1-m&0&0&m\\ 1-m&\frac{1}{2}m&\frac{1}{6}m&\frac{1}{3}m\end{array}\right] \tag{5}\]
## 5 Applications of DRA
### Vehicular Applications
Therefore, a conical beam radiation pattern is required to ensure that the radiation from the antenna fully covers a \(360^{\circ}\) latitude. Moreover, a conical-beam radiation-pattern antenna with a high gain and wide beam coverage is necessary to cope with the relevant movement between the antenna and the satellite [115]. To produce a wideband conical beam radiation pattern, several methods have been implemented. The most common method is by using a cylindrical DRA. This is because a cylindrical-shaped DRA is easy to design compared to the complex geometries such as an equilateral triangle. In [116], a DRA with conical radiation patterns is presented for vehicle-mounted surveillance equipment, as shown in Figure 28. The proposed antenna consists of a cylindrical DR and a loaded annular column. It is then placed on a metal disk and fed by a coaxial probe. With this configuration, four conical radiation pattern modes are excited and merged, providing an impedance bandwidth of 56% from 3.14 to 5.56 GHz. However, this antenna radiates in linear polarization. Figure 27: Proposed antenna mounted on vehicle’s rooftop [104]. Very few CP DRAs for vehicular applications or unmanned aerial vehicle (UAV) have been reported in the literature so far [52; 66; 117]. In [52], the shared-aperture merged-structure design of a DRA and slot-dipole antenna is applied to realize circular polarization features. The CP waves are controlled by a phase compensation method with tuning processes. It is easier to obtain impedance matching by using this feeding technique than a contacting feed. However, the manufacturing process of a stacked DRA is complicated and needs to be carefully optimized in order to avoid any errors. In [117], a wideband CP is realized from the orthogonally-placed conformal strips, off-centered circular slot, and curved path in the feedline. However, a slightly different outcome in the results occurred due to the presence of air gaps and errors during the fabrication process. Table 11 summarizes a comparison between various DRAs for vehicular applications.