esa-sceva/satcom-chunk-collection huggingface dataset:
# RF-Access: Barrier-Free Access Control Systems with UHF RFID
###### Abstract  
Traditional RFID-based access control systems use flap barriers to help manage pedestrian access and block unauthorized staff at any entrance, which requires visitors to swipe their cards individually and wait for the opening of the blocking body, resulting in low-frequency pedestrian access and even congestion in places with large passenger flow. This paper proposes a barrier-free access control system (RF-Access) with UHF RFID technology. The main advantage of RF-Access is that it provides non-intrusive access control by removing flap barriers and operations of swiping the card. The visitors just go across the system without any stay at the entrance. Meanwhile RF-Access performs the authentication, which greatly improves time efficiency and quality of service. RF-Access addresses two key issues of the non-intrusive access control: motion direction detection and illegal intrusion detection. In RF-Access, we first propose a dual-antenna system setup together with a time-slot-based model to monitor users' moving directions, which is robust to different environmental factors, such as multi-path effects. Afterwards, we use a tag array to detect illegal intrusion in case attackers do not carry any RFID tags. We implement a prototype of RF-Access with commercial RFID devices. Extensive experiments show that our system can detect the moving direction with 99.83% accuracy and detect illegal intrusion with an accuracy of 96.67%.  
RFID; access control; mobile sensing Article
# A Reliable Scheme for Synchronizing Multimedia Data Streams under Multicasting Environment
## 4 Proposed Solution
### Proposed Design Requirements
#### 4.2.8 Data Packets Playout Time Calculation and Synchronization  
As we know APP packet contains the playout time of the corresponding SR packet. Due to the presence of reference clock, it is very easy to extract the delay offset value from RTCP packet and then uses it to calculate the playout time of the data packets. Following are the three steps to calculate the playout time of the data packet.  
* Calculate the sent time of the data packet according to the reference clock.
* Extract the playout delay offset from the RTCP provided information.
* Add playout delay offset to the sent time of the data packet. \[d_{off}=t_{p-sapp}-NTP_{sr}\] (5)  
Playout time element in Equation (5) is same as calculated by Equation (4) earlier. As stated earlier, all the receivers receive the same RTCP packet from the sender, so they have the same value of playout offset. Thus, for a particular data packet, each receiver will have the same playout time as calculated by Equation (7) in the following subsection.
## 1 Introduction
Additionally, model tests to evaluate the reliability of a 5 cm-thick insulation layer were developed [13], and a mathematical optimization model of the insulation layer's parameters was constructed by taking the Daban mountain tunnel as an example [14]. This extends the distribution laws and factors of the tunnel temperature field of tunnels with insulation layers. Further, the temperature distribution of some cold-region tunnels was measured by advanced equipment and methods in 104 tunnels located in Gangwon Province of Korea [15] and the Hongfu tunnel [16], the Zuomutai tunnel [17], and the Wushaoling tunnel [18] in China. Their test results were in agreement that the air temperature in the tunnel changes periodically in the form of an approximate trigonometric function from the annual perspective. The temperature of the tunnel portal section is affected by the outside of the tunnel, and the influence of the middle section is small, which shows that the temperature fluctuation of the portal section is large and the temperature fluctuation of the middle section is small. The temperature changes in the sections at the two sides of the tunnel are different, and the temperature distributions are asymmetrical along the tunnel length. The test results of the temperature field distribution provide practical verification and theoretical support for the further understanding of the mechanism and reasonable design of tunnel frost damage. Lately, the distribution laws of daily freeze-thaw cycles in tunnels were studied [19], and it was revealed that unsaturated freeze-thaw cycles will also damage the strength and durability of concrete cube. This again draws researchers' attention to the deterioration of the lining structure. However, the influence of concrete deterioration on the stress and crack of lining structure has not been studied further in cold region tunnels. In this paper, based on the analysis of the service environment and distribution of freeze-thaw cycles in cold-region tunnels, the rule of the gradual deterioration of the lining structure was proposed. An experimental system was established to simulate the deterioration of the lining structure of cold-region tunnels; it was composed of a circular tunnel models, a cold air circulation system, and a temperature test system. The initial deterioration of the lining concrete was realized after 100 unsaturated freeze-thaw cycles took place, and the temperature field of the experimental model was tested during the freeze-thaw cycles. The bearing capacity and strain of the lining models were tested. Lastly, the influence of the deterioration of the lining structure on the service tunnels was discussed in the hope of providing support for the maintenance and design of tunnels in cold regions.
# A Synthetic Aperture Radar-Based Robust Satellite Technique (RST) for Timely Mapping of Floods
## 6 Results
### Sesia River--Italy 2020  
The CEMS system was activated for this event on 4 October 2020 [61]. The first estimate product generated by analyzing a Sentinel 2 image acquired on 3 October 2020 was considered the reference data for this case study. The flood map was generated using CEMS from an image by means of visual interpretation, with an estimated geometric accuracy (RMSE) of 7.5 m [61]. The SAR-RST-FLOOD map (Figure 5) was produced by considering the Sentinel 1 image collected on the same date.  
Figure 4: Flood Map of the Ouse River event (29 December 2015).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Event 1: England Flood 27 December 2015** & \multicolumn{2}{c}{**Reference Data**} & \multicolumn{4}{c}{**Accuracy Matrix**} \\ \hline  & Flood & Not Flooded & UA\({}_{f}\) & UA\({}_{nf}\) & PA\({}_{f}\) & PA\({}_{nf}\) & K \\
**RST-SAR-FLOOD** & Flood & 681,574 & 306,425 & & & & \\ Not Flooded & 39,209 & 10,312,432 & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix for the Ouse River event (29 December 2015).  
The user accuracy was relatively lower at 0.58, in contrast to other parameters, such as UAnf (0.92), PAf (0.98), and K (0.68) (Table 4). One factor contributing to the lower UAf is the inherent lack of direct comparability between the SAR and Sentinel 2 maps. This disparity arises because the SAR maps undergo topographic correction, allowing the detection of flood areas in vegetated zones that are undetectable by Sentinel 2. In addition, the method used for assessing flooded extension on the reference data may have added some uncertainty.
# Multi-View Multi-Attention Graph Neural Network for Traffic Flow Forecasting
###### Abstract  
The key to intelligent traffic control and guidance lies in accurate prediction of traffic flow. Since traffic flow data is nonlinear, complex, and dynamic, in order to overcome these issues, graph neural network techniques are employed to address these challenges. For this reason, we propose a deep-learning architecture called AMGC-AT and apply it to a real passenger flow dataset of the Hangzhou metro for evaluation. Based on a priori knowledge, we set up multi-view graphs to express the static feature similarity of each station in the metro network, such as geographic location and zone function, which are then input to the multi-graph neural network with the goal of extracting and aggregating features in order to realize the complex spatial dependence of each station's passenger flow. Furthermore, based on periodic features of historical traffic flows, we categorize the flow data into three time patterns. Specifically, we propose two different self-attention mechanisms to fuse high-order spatiotemporal features of traffic flow. The final step is to integrate the two modules and obtain the output results using a gated convolution and a fully connected neural network. The experimental results show that the proposed model has better performance than eight other baseline models at 10 min, 15 min and 30 min time intervals.  
graph neural network; passenger flow; self-attention mechanisms; gated convolution Article
# Shear Wave Velocity Estimation Based on Deep-Q Network
## 4 Geoacoustic Inversion Framework Based on DQN
### Environment Configuration  
As shown in Figure 1, the environment intakes the selected parameters and provides feedback for the agent to update the search results. During each iteration, the agent inputs \(k\) sets of selected parameters and the environment provides feedback to the agent.  
The configuration of the environment for the DQN framework is listed as follows:  
* The physical forward model: a theoretical program for calculating the replica.
* Observed data: the measured data or the data derived from the measured data.
* Misfit function: the root mean squared error (RMSE) measures the difference between the observed data and replica.
* Environment state: a special item for the DQN framework, which indicates the progress of the inversion. The environment state is formulated as: \[\mathbf{S}^{i}=[min(\mathbf{E}^{i}),mean(\mathbf{E}^{i}),std(\mathbf{E}^{i}),\Delta min(\mathbf{E }),\Delta mean(\mathbf{E}),\Delta std(\mathbf{E})]\] (6) where \(i\) refers to the \(i\)th iteration, \(min(.)\), \(mean(.)\), and \(std(.)\) are operators for calculating minimum, mean, and standard deviation, respectively. \(\mathbf{E}=[E_{1},\ldots,E_{k}]/E_{norm}\) refers to the normalized misfit values corresponding to \(k\) sets of parameters, where \(E_{norm}\) is the minimum misfit value in the initialization stage and acts as the normalization factor. \(\Delta\) is an operator for calculating the difference from the last iteration, e.g., \(\Delta min(\mathbf{E})=min(\mathbf{E}^{i-1})-min(\mathbf{E}^{i})\).
* Termination criteria: whenever one of the conditions expressed below is satisfied, the iteration stops. \[\begin{split}\text{Condition 1:}&\quad i=i_{max}\\ \text{Condition 2:}&\quad min(\mathbf{E}^{i})\times E_{ norm}<=e_{\text{threshold}}\\ \text{Condition 3:}&\quad 2 abs(\frac{max(\mathbf{E}^{i})- min(\mathbf{E}^{i})}{max(\mathbf{E}^{i})+min(\mathbf{E}^{i})})<=e_{\text{convergence}}\end{split}\] (7) where \(i_{max}\) is the maximum iterations, \(e_{\text{threshold}}\) is a preset threshold of misfit, and \(e_{\text{convergence}}\) is a preset threshold for convergence.
* Reward: a special item for the DQN framework, which is a signal for guiding the agent to learn a search strategy. For obtaining a fast and accurate search strategy, the reward rules are defined as: \[\begin{split}\text{reward}=\frac{e_{\text{threshold}}-min(\mathbf{E} ^{i})\times E_{norm}}{e_{\text{threshold}}}\times 100&,\text{iteration terminated}\\ \text{reward}=-1&,\text{otherwise}\end{split}\] (8)
* Feedback: the feedback includes misfit values \(\mathbf{E}^{i}\), the environment state \(\mathbf{S}^{i}\) and the reward.
# Optimal Fractional PID Controller for Buck Converter Using Cohort Intelligent Algorithm
## 1 Introduction
Such controllers have been used to control various DC-DC converters like the buck, boost, buck-boost converters for various applications [18; 19; 20; 21]. A review of the applications of fractional control in various power electronic systems is given in [22]. One of the main challenges in the use of the FOPID controllers is the tuning of its parameters. Numerical methods for the design of controllers use various optimization techniques, in which a particular algorithm is used to optimize one or more cost functions, which is decided by the design engineer based on the requirements of the application [23]. Some of the common parameters chosen as cost functions are the control performance indices like integral square error (ISE), integral absolute value error (IAE), integral of the absolute error weighted over time (ITAE), integral of the time squared error (ITSE), or time domain specifications like overshoot, rise time, etc. Optimization algorithms like the artificial BEE colony (ABC) algorithm [24], particle swarm optimization (PSO) [25], genetic algorithm (GA) [26], fuzzy/neural network based methods [27], etc., are commonly used for numerical techniques. Such multiple-objective optimization methods are very useful in tuning of fractional order controllers applied to power converters [28; 29; 30; 31]. The cohort intelligence (CI) algorithm is a socio-inspired meta-heuristic algorithm, which is based on the behavior of a group of people, referred to as cohort in society [32]. All the members in the cohort are competing with each other to achieve some common goal. Each candidate tries to improve his behavior by observing the others, so that the overall behavior of the cohort evolves, and improves. This method has been used for solving various unconstrained and combinatorial problems and in mechanical systems [33; 34; 35], for PID control in DC motors [36], etc. The objective of this paper is to study the effect of the CI algorithm on the optimal tuning of a fractional order PID controller for a buck converter. The paper is organized into five sections. Section 2 discusses the basics of the buck converter, fractional order systems, and the CI algorithm. Section 3 gives the basic methodology and system description. Results and comparisons with other methods have been detailed in Section 4. Finally Section 5 concludes the work.
## 3 Results and Discussion
### Performance of Cu-AAO Interposer
\(V_{\mathrm{Cu}}\) and \(V_{\mathrm{AAO}}\) are the volume fraction of copper and AAO. \(\lambda_{\mathrm{Cu}}\) and \(\lambda_{\mathrm{AAO}}\) are the thermal conductivities of copper and AAO. The thermal conductivity of copper and AAO are 383 W/(m\(\cdot\)K) and 1.249 W/(m\(\cdot\)K), respectively. For full filling the Cu-AAO film interposer, the volume fraction is equal to the area fraction in top-view, which was counted by pixels in Photoshop. The volume fraction of copper and the AAO film are 36.3% and 62.7%, respectively. Hence, the thermal conductivities in vertical and horizontal directions can be estimated to be 139.81 W/(m\(\cdot\)K) and 1.99 W/(m\(\cdot\)K). The thermal conductivity was also measured experimentally, which was achieved by the following equations:  
\[\lambda_{\mathrm{h}}{}^{\mathrm{e}}=\rho\cdot C\cdot\alpha_{\mathrm{h}} \tag{6}\]  
\[\lambda_{\mathrm{v}}{}^{\mathrm{e}}=\rho\cdot C\cdot\alpha_{\mathrm{v}} \tag{7}\]  
In which, \(\lambda_{\mathrm{h}}{}^{\mathrm{e}}\) and \(\lambda_{\mathrm{v}}{}^{\mathrm{e}}\) are the experimental value of thermal conductivities in vertical and horizontal directions. \(\rho\) is the density of the Cu-AAO interposer. \(C\) is the heat capacity of the Cu-AAO interposer. \(\alpha_{\mathrm{h}}\) and \(\alpha_{\mathrm{v}}\) are the thermal diffusion coefficient in horizontal and vertical directions. The test data results are shown in the Table 1.  
The experimental value of thermal conductivities in vertical and horizontal directions are 92.34 W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\) and 3.19 W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\). In the horizontal direction, the value of thermal conductivity is very small, so the measurement error of experimental value is relatively large. The experimental value is slightly less than the theoretical value in the vertical direction. This phenomenon could be attributed to two reasons. Firstly, a 100% filling percentage was considered in the calculation process, but the actual pore-filling-percentage is only 97.5%. Secondly, the thermal conductivity of the copper produced by the electroplating process is smaller than that of the bulk material [27]. In the vertical  
Figure 7: (a) X-ray diffraction (XRD) spectrum of CNPs formed with different electric current densities; (b) atoms arrangement on different crystal planes. direction, the thermal conductivity is comparable to silicon (149 W/(m\(\cdot\)K)) and far surpasses glass (0.77 W/(m\(\cdot\)K)). The thermal conductivity in the horizontal direction may be improved by an extra copper layer. It is also very important to test the mechanical properties of the copper nano-pillars. In future work, we will measure the fracture toughness of the copper pillars by dissolving the AAO film structure using a similar test method in Reference [28].
# GPS Multipath Analysis Using Fresnel Zones
## 2 Multipath Theory
### Fresnel Zones
 \tag{14}\]  
The orientation of the major axis \(a\) of the Fresnel zones is given by the orientation of the line, resulting from the intersection of the reflector plane and a plane defined by \(S\), \(A\) and \(A^{\prime}\). Using Equation (14), the Fresnel zones can be determined for every planar reflector, without any limitations regarding the satellite-reflector-antenna configuration. Generally, for arbitrarily oriented reflectors, valid statements regarding the size of the Fresnel zones are not possible, since, in this case, the size depends on both the orientation of the reflector and the satellite azimuth and elevation angles. In addition to determining Fresnel zones on the reflecting surface, the concept can also be considered for the line-of-sight (LOS) transmission in order to characterize the type of signal propagation [20]. In Figure 8, the Fresnel ellipsoid of the first order is shown for the LOS transmission between the satellite \(S\) and the antenna \(A\). The radius \(R_{F}\) of the Fresnel zone at any point \(D\) along the signal path can be determined by  
\[R_{F}=\sqrt{\frac{\lambda d_{S}d_{A}}{d}} \tag{15}\]  
where \(\lambda\) is the carrier wavelength, \(d_{S}\) denotes the distance between \(D\) and \(S\), \(d_{A}\) denotes the distance between \(D\) and \(A\), and \(d=d_{S}+d_{A}\) is the total length of the signal path [37]. An obstruction source in the signal path with larger dimensions than the Fresnel zone will lead to a complete blocking of  
Figure 8: Fresnel ellipsoid of the first order for line-of-sight transmission between the satellite \(S\) and the antenna \(A\) including a building obstructing the signal path and parts of the first Fresnel zone. the signal. If the obstruction source in the signal path is smaller than the Fresnel zone, the signal can be diffracted at the edges of the object. Without any obstruction source, the signal transmission will be undisturbed. In order to get an impression of the dimensions of the Fresnel zones along the signal path, \(R_{F}\) is shown in Figure 9 for \(d_{A}=1\ldots 1000\) m and \(\lambda=0.19\) m (GPS-L1). The total length of the signal path is set to \(d=20\),000 km. It becomes obvious that, especially for satellites with lower elevation angles, small objects in the environment can lead to diffraction effects. Thus, Figure 9 emphasizes that the choice of a proper elevation mask is an important step during data processing.
# Dielectric Slabs-Based Lens for Millimeter-Wave Beamforming
## 3 Discussion and Simulation Results
These antennas are arranged in \(xy\) plane to be symmetric with the lens center, as shown in Figure 7. The diameter of the aperture area of the designed lens is \(a=173\) mm, the optimized focal point is \(f=65.62\) mm, and the number of slabs is 13 with a thickness \(t_{\Delta}=2.67\) mm \(=\lambda/4\). Horn antennas are designed to work at 28 GHz and arranged to cover a circular area of a diameter of 150.7 mm. Figure 5: (**a**) The reflected scattering parameter and (**b**) cross-polarization gain. Figure 6: The normalized co-polarized radiation pattern of the horn antenna with a dielectric slabs-based lens using different full-wave simulators. The aperture area of the designed lens is \(a=64.52\) mm, and the horn antenna is placed at the optimized focal point (\(f=50.62\) mm). Figure 8 shows the co-polarized radiation patterns of 16 switchable horn antennas with the designed reconfigurable lens. The radiation direction is in both elevation and azimuth plane, which is missed in the metamaterials-based lens. The scanning \(\theta\) angle range cover from around \(\theta=-30^{\circ}\) to \(\theta=30^{\circ}\), and \(5.9^{\circ}\)-\(7.8^{\circ}\) HPBW. The \(\phi\) angle cover from \(-70^{\circ}\) to \(70^{\circ}\). The maximum gain range is between 26.6 and 27.9 dBi. The reflection coefficients of four selected antennas with the designed lens are shown in Figure 9a, which are below \(-10\) dB in all the investigated frequencies. Figure 9b shows the cross-polarization gain of these antennas with the designed lens, which are around 0 dBi at the desired directions. Figure 7: Sixteen switchable horn antennas with the designed reconfigurable dielectric lens. The diameter of the aperture area of the designed lens is \(a=173\) mm, the optimized focal point is \(f=65.62\) mm, and the number of slabs is thirteen slabs with a thickness \(t_{\Delta}=2.67\) mm. Figure 8: Co-polarized radiation patterns of sixteen switchable horn antennas with the proposed reconfigurable dielectric lens. (**a**) Antennas 1, 3, 5, 7, 9 and 11. (**b**) Antennas 2, 4, 6, 8, 10 and 12. (**c**) Antennas 13 and 15. (**d**) Antennas 14 and 16. Figure 9: (**a**) The reflected scattering parameter and (**b**) cross-polarization gain of four selected antennas. Table 1 summarizes the performance of the designed lens for millimeter-wave beamforming. The proposed dielectric slab-based lens results are compared to those of related published works, as shown in Table 2. The table shows that the designed lens gives compatible outcomes to the best-published one [7]. However, when concerning the design scalability, the proposed lens is more attractive.