esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Particle Filter Weight for Bayesian Direct Position Estimation in a GNSS Receiver
## 8 Simulations and Real-World Results  
The previous chapters presented the optimal weight update function in Equation (32) for DPE when using a Bayes filter and dicussed in Figure 4 the problem with the resulting narrow probability function. In Equation (50), a possible solution is proposed to overcome the presented issues. In order to understand the behaviour of the proposed weight update function, an analysis is performed for Equation (50) within this chapter for a single signal \(N=1\) and assuming a uniform distribution from the previous epoch, i.e., \(\bar{w}_{k-1}^{i}=0\). All simulations have been performed completely with MATLAB, except the real-world scenarios, which take as input the correlation values from the GNSS software receiver. The MATLAB simulation performs a correlation with a simulated GNSS signal. In order to see clearly the impact of the different parameters, the simulated signal was generated without noise. All following plots show the correlation values \(|P|\) from Equation (15) and the resulting weight \(w_{k}^{i}\) from Equation (36) when using Equation (50) as weight update function. For visualisation, Equation (36) is plotted instead of Equation (37). This is because of the normalized amplitude to 1, which makes a comparison more easy to visualize. It is noted that the analysis is based on the herein presentedequations. The derived equations do not consider any present multipath signal, thus there is no estimation of multipath parameters nor a handling or mitigation of multipath.  
The next subsections analyse the impact on the resulting probability function for  
* different signal strengths,
* different code delay bias variances,
* constructive and destructive multipath,
* short, medium and far multipath,
* different multipath amplitudes,
* two significant real-world scenarios under open-sky and urban conditions.
## Passive Radars  
### General Description and Operating Principle  
The basic architecture of passive bistatic radars is depicted in Figure 2:  
* The reception stage includes the antenna system, the Radio-Frequency (RF) front-ends and the Analog-to-Digital Conversion (ADC) stages for generating the digital reference and surveillance channels.
* The processing stage includes the necessary preprocessing to reject interferences and the matched filtering process. The interference can be composed of clutter (interfering radar echoes caused by reflection from objects other than the target), the selected IoO signal present in the surveillance channel (Direct Path Interference (DPI)) and IoO multipath.
* Finally, the output of the matched filter is applied to the detection stage in order to decide between target absence or target presence.  
To perform the matching filtering process, delay and Doppler-shifted copies of the reference signal are correlated with the surveillance channel to generate the Cross-Ambiguity Function (CAF).  
Figure 2: Basic architecture of a PRS with one surveillance channel and one reference channel.  
For each Coherent Processing Interval (CPI) of duration \(T_{int}\) (s), and a sampling frequency \(f_{s}\) (Hz), the discrete time expression of the CAF is presented in Equation (1):  
\[S_{CAF}\left[m,p\right]=\sum_{n=0}^{N_{CAF}-1}s_{ref}^{*}\left[n-m\right]\cdot s \left[n\right]\cdot\exp^{-\beta\pi\frac{p}{N_{CAF}}n} \tag{1}\]  
where:  
* \(N_{CAF}\) is the number of samples in the CPI (\(N_{CAF}=T_{int}f_{s}\)).
* \(m\) represents the time bin associated with a delay \(\tau_{m}=m/f_{s}\).
* \(p\) is the frequency bin corresponding to a Doppler-shift \(f_{Dop}(p)=f_{s}\left(p/N_{CAF}\right)\).
* \(s_{ref}[n]\) is the reference signal at the output of the pre-processing stage.
* \(s[n]\) is the surveillance signal at the output of the pre-processing stage.  
In PRS, one of the key issues is the suppression of the DPI, which correlates perfectly with the reference signal. Although it is confined to the zero-Doppler and zero-range bin of the RDS, it produces range and Doppler sidelobes that are several orders of magnitude greater than the echoes that are sought. Actually, the target echo power is usually much lower (e.g., 80-100 dB) than the DPI power. Due to the variability of the signal to be suppressed, adaptive algorithms are used [17; 18]. The Extensive Cancellation Algorithm (ECA) is widely used [17; 19].
## 3 Simulation Results
### Root-Mean-Square Delay Spread and Cumulative Density Function  
In most used communication protocols, as for example the OFDM, a guard interval is placed between successive symbols, so that the intersymbol interference is eliminated and full equalization is achieved. The drawback of this technique is that the presence of the guard interval reduces the transmission efficiency.  
A different option (to reduce the loss in transmission efficiency) is to set the guard interval to approximately three-times the root-mean-square delay spread (RMSDS) of the channel [20] (the square root of the second central moment of the power-delay profile). This design choice does not totally eliminate intersymbol interference: it reduces the transmission efficiency loss but causes some performance degradation.  
Reducing carrier spacing, that is, increasing the number of carriers for the same bandwidth, would have the effect of increasing transmission: in this approach, the transmission efficiency loss is compensated by increased complexity of the transmission protocol.  
In the following Table 1, the RMSDS has been calculated according to the definition reported in [20].  
Figure 13: Theoretical channel capacities as a function of the signal to noise power ratio.  
The RMSDS values reported in Table 2 represent important information for the choice of the guard interval duration. As previously observed for frequency responses and crosstalk attenuations, the RMSDS values do not differ significantly for distances less than 75 m, and the need of a repeater every 75 m has been underlined. As a result, a guard interval of 150 ns would be sufficient for the transmission, and considering that the typical OFDM symbol duration for broadband PLC is 40 us, the efficiency reduction due to the use of the guard interval is of the order of 0.4%.  
To evaluate the channel performances, the cumulative density function (_CDF_) relating to each channel for configurations has been calculated. The _CDF_ is defined as follows, and it gives information on how much bandwidth, in the chosen frequency range, is below a certain amount of attenuation:  
\[CDF(G)=\ \frac{|\{f:|S_{21}(F)|<G\}|}{|\{f\}|}. \tag{9}\]  
The results are shown in Figure 14 for the case of termination on the characteristic impedance. For the shortest path channel of 25 m, the magnitude of the frequency response is larger than \(-20\) db for 100% of the frequency range, while for the channel of 75 m, 65% of the frequency range is below \(-30\) db.
## 1 Introduction
Therefore, in the modified TCK model, not only the tensile damage characteristic, but also the strain rate effect of materials is able to be considered. In general, brittle materials undergo plasticity during compression. Moreover, the yield strength increases evidently with the increase of strain rate. The JH2 model [4] (the Johnson-Holmquist Ceramic model) is able to describe these characteristics very well. For this reason, JH2 is widely used in the dynamic simulation of glass. For example, the response of laminated glass under blast loading is calculated with the JH2 constitutive model by [PERSON] [13]. A study of the laminated glass response to blast and windborne debris with the JH2 constitutive model was conducted by [PERSON] [14; 15]. Since the material constants of glass of JH2 in [14; 15] are originally used for military ballistic penetration analysis, an error occurs when these constants are adopted in the analysis of window glass. As a result, [PERSON] [6; 16] updated the constants of the JH2 model for window glass through a series of experiments. Based on JH2, a series of improved models, which are applied to the analysis of the transparent armor, were proposed by [PERSON] and [PERSON] [17; 18; 19]. These models describe the compressive characteristics of glass very well, and yet the description of the tensile characteristics is not accurate enough. In this paper, in order to describe the tensile damage characteristic of annealed glass, a rate-dependent TCK model is employed. The JH2 model is adopted when the glass material is under compression. After the implementation of this combined TCK-JH2 model is described, some experiment tests were conducted with various PVB laminated glass panes under impact loading. The experimental results are compared with the numerical results in order to verify the \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multicolumn{2}{c}{**Static Strength (MPa)**} & \multicolumn{2}{c}{**Dynamic Strength (MPa)**} \\ \cline{2-5}  & **Tensile** & **Compressive** & **Tensile** & **Compressive** \\ \hline [PERSON] [4] & 150 & 1023 & — & 1150 (250 s\({}^{-1}\)) \\ [PERSON] [5] & 54.5 & 1087 & 89.5 & 1179 (1109 s\({}^{-1}\)) \\ [PERSON] [6] & 19.27 & 256.13 & 33 (696 s\({}^{-1}\)) & 600 (241 s\({}^{-1}\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Static and dynamic strength of glass material. combined TCK-JH2 model. Furthermore, the effects of the thickness of glass layer and PVB interlayer are investigated.
# On the Modeling of Automotive Security: A Survey of Methods and Perspectives
## 3 Automotive Security Models
### Security Modeling Methods for Automotive Industry
#### 3.1.5 Tvra  
The European Telecommunications Standards Institute (ETSI) proposed a threat, vulnerability and risk analysis (TVRA) methodology for their standards developers to deal with security issues originally in the telecommunications industry. Then ETSI adapts this method for ITS based on European V2V communication platform [82]. The target of the TVRA method evaluation is used to identify the threats and risks of the communications and services of vehicle-to-vehicle and vehicle-to-roadside network infrastructure in the ITS. The security objectives are evaluated by specifying the CIAAA attributes, namely confidentiality, integrity, availability, authenticity and accountability. The TVRA is a systematic method to identify the assets of the system and the threats the system may be subject to. The classifications of threats are defined as interception, manipulation, denial of service, and repudiation of sending and receiving [100]. The potential of the threats is evaluated based on the parameters like knowledge, time, expertise, opportunity, and equipment. The intensity level of an attack is scaled with three levels from 1 to 3. Then the likelihood and the impact of a risk can be quantified and the security requirements are derived.
# Multi-Device Security Application for Unmanned Surface and Aerial Systems
#### 1.1.2 Phase 2  
In the second phase, we expanded the implementation from Phase 1 by integrating with the Robot Operating System (ROS). ROS was chosen because it is the primary operating system used in the UxS platforms chosen for this research. The test environment consisted of two Ubuntu Virtual Machines (VMs) installed with ROS to simulate the platforms. The VMs were tested with both 802.11n wireless network and military-grade mesh network radio configurations. We then ran our MLS ROS application on both VMs, one VM created the MLS group and the other joined the group.  
For the initial metric of interest in this phase we considered MLS update frequency. We varied MLS update frequency using a range of 2, 10, 50, and 300 updates per 1000 messages, where \"messages\" refer to data transfer. After completing our initial test in this phase, we ran two benchmark tests to observe MLS protocol performance: initial group setup and message handling. We first tested and measured the time it took to set up and initialize an MLS group for different ciphersuite choices; initializing a group requires the creation of user credentials and the group itself. For MLS, this only happens once for any given group of devices. For message handling, we tested and measured how long it took to encrypt and send 1000 messages of 15 bytes each while updating the encryption key twice during the sending period. We cycled through all ciphersuite options with the same hash function and frequency parameters for baseline comparison.
## 2 Theory and Simulation
### Seismic Wave Selection and Working Condition Setting
#### 2.3.2 Working Condition Setting  
According to the \"Seismic Technical Code for Oil and Gas Transmission Pipeline Engineering\" (GB/T50407-2017) [31], the consideration of vertical and horizontal seismic waves is omitted when the seismic wave amplitude is less than 0.2 g. Considering the significant magnitude of the seismic wave amplitude employed in this study, it was necessary to separately calculate the three-dimensional seismic waves. Moreover, in cases where local seismic waves are bidirectional or tri-directional, the ratio of ground motion along the longitudinal bridge, transverse bridge, and vertical bridge should be 1:0.85:0.65, respectively. Consequently, a total of six working conditions were established by considering both the independent and joint actions of each vibration component, as presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parts** & **Materials** & **Density (kg/m\({}^{3}\))** & **Poisson’s Ratio** & \
\begin{tabular}{c} **Modulus of** \\ **Elasticity** \\ **(MPa)** \\ \end{tabular} &
\begin{tabular}{c} **Yield Strength** \\ **(MPa)** \\ \end{tabular} \\ \hline Bridge tower & Q345 steel & 7850 & 0.3 & 2.06 \(\times\) 10\({}^{5}\) & 378 \\ Truss beams & Q345 steel & 7850 & 0.3 & 2.06 \(\times\) 10\({}^{5}\) & 378 \\ Bearings & Cast iron & 7850 & 0.3 & 1.5 \(\times\) 10\({}^{5}\) & 400 \\ Pipes & X60 & 7850 & 0.3 & 2.06 \(\times\) 10\({}^{5}\) & 425 \\ Dragline & G\multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the cable-stayed tube bridge.  
Figure 1: Overall finite element model of the cable-stayed tube bridge span structure.
###### Abstract  
In order to meet the massively increasing requirements of big-data applications, data centers (DCs) are key infrastructures to cope with the associated demands, such as high performance, easy scalability, low cabling complexity and low power consumption. Many research efforts have been dedicated to traditional wired data center networks (DCNs). However, DCNs' static and rigid topology based on optical cables significantly limits their flexibility, scalability, and even reconfigurability. The limitations of this wired connection can be addressed with optical wireless technology, which avoids cable complexity problems while allowing dynamic adaption and fast reconfiguration. Here, we propose and investigate a novel optical wireless data-center network (OW-DCN) architecture based on nanoseconds semiconductor optical amplifier (SOA)-based wavelength selectors and arrayed waveguide grating router (AWGR) controlled by fast field-programmable gate array (FPGA)-based switch schedulers. The full architecture, including the design, packet-switching strategy, contention solving methodology, and reconfiguration capability, is presented and demonstrated. Dynamic switch scheduling with a FPGA-based switch scheduler processing optical label and software-defined network (SDN)-based reconfiguration were experimentally confirmed. The proposed OW-DCN was also achieved with a power penalty of less than 2 dB power penalty at BER \(<\) 1 \(\times\) 10\({}^{-9}\) for a 50 Gb/s OOK transmission and packet-switching transmission.  
Keywords:data center architecture; free-space optical communication; fast optical switch +
Footnote †: journal: _Photonics_
## 3 Testing the Manikin Prototype and Results
### Calibration of the Advanced Thermal Manikin
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **[PERSON] Name/Body** \\ **Name/Body** \\ **Part** \\ \end{tabular} } & **[PERSON] [46]** & **[PERSON] [48]** & **[PERSON] [49]** & **[PERSON] [50]** & **[PERSON] [51]** & **
\begin{tabular}{c} **[PERSON] \\ **(Current Study)** \\ \end{tabular} \\ \cline{2-7}  & & & & \(h_{\text{c}}\) **(W/m2 \({}^{\circ}\)C)** & & \\ \hline Left foot & 9.48\% & 4.41\% & 0.86\% & 5.17\% & 29.31\% & 12.17\% \\ \hline Right foot & 0.00\% & 7.96\% & 11.90\% & 4.76\% & 50.00\% & 12.30\% \\ \hline Left leg & 15.61\% & 25.26\% & 30.38\% & 28.27\% & 5.06\% & 17.02\% \\ \hline Right leg & 21.88\% & 25.51\% & 31.64\% & 23.83\% & 12.11\% & 18.90\% \\ \hline Left thigh & 19.91\% & 20.07\% & 19.91\% & 15.58\% & 4.76\% & 13.04\% \\ \hline Right thigh & 11.06\% & 15.11\% & 6.25\% & 6.25\% & 3.37\% & 6.74\% \\ \hline Pelvic region & 52.30\% & 52.73\% & 55.71\% & 52.30\% & 25.04\% & 39.83\% \\ \hline Head & 20.94\% & \(-\)0.23\% & 3.85\% & 8.12\% & 31.62\% & 10.99\% \\ \hline Left hand & 3.02\% & \(-\)6.00\% & 11.64\% & 14.22\% & - & 14.73\% \\ \hline Right hand & 13.46\% & 16.70\% & 19.23\% & 21.15\% & - & 14.23\% \\ \hline Left arm & 23.39\% & 17.18\% & 27.42\% & 25.40\% & 17.34\% & 19.00\% \\ \hline Right arm & 23.39\% & 107.08\% & 23.39\% & 27.42\% & 19.35\% & 16.66\% \\ \hline Left upper arm & 50.22\% & 55.36\% & 54.61\% & 53.15\% & 38.51\% & 41.91\% \\ \hline Right upper arm & 27.51\% & 41.91\% & 29.64\% & 27.51\% & 16.84\% & 24.11\% \\ \hline Chest & 1.96\% & 23.57\% & 28.10\% & 24.84\% & \(-\)1.31\% & 13.97\% \\ \hline Back & 45.38\% & 53.86\% & 45.38\% & 41.18\% & 28.57\% & 36.11\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Convective heat transfer standard deviations—manikins from literature and the current prototype.
## 2 Proposed Architecture
### Beam-Steering Mechanism  
As shown in Figure 4, two cameras used in this experiment detect the taillights as objects, using the pre-registered features. Unlike the traditional visible light positioning systems based on trilateration or triangulation from separable light sources, the tail-lamp tracking method used in this work utilizes the disparity between images from the two cameras, constructing a type of stereo vision. The blue LD beam is steered to the FC-coupled license plate located in the middle of the two taillights. The relative location of the license plate is calculated in real time by applying the following equations:  
\[\begin{bmatrix}x_{L}=\frac{x_{1/2}+x_{1/2}}{2},&y_{L}=\frac{y_{L1}+y_{L2}}{2} \\ \parbox[t]{0.0 pt}{$\left[x_{R}=\frac{x_{R1}+x_{R2}}{2},&y_{R}=\frac{y_{R1}+y_ {R2}}{2}\right]$}\end{bmatrix} \tag{1}\]  
There are two pairs of location information representing the relative position of the taillights seen from each camera. The microcontroller connected to camera sends the information to the PC via a serial port. \(x_{L1}\), \(x_{L2}\), \(y_{L1}\), and \(y_{L2}\) in Equation (1) mean the horizontal and vertical locations of the two taillights seen from the left camera, respectively. Likewise, \(x_{R1}\), \(x_{R2}\), \(y_{R1}\), and \(y_{R2}\) indicate such information seen from the right camera, respectively. Here, \(x_{L}\), \(y_{L}\), \(x_{R}\), and \(y_{R}\) denote the calculated horizontal and vertical location of the license plate using the information from the left and right camera. Using this information and Equation (2), the relative distances and angles from each camera (\(L_{1}\), \(L_{2}\), \(\theta_{L}\) and \(\theta_{R}\)), as well as the perpendicular distance (\(D\)) described in Figure 4, can be calculated [13].  
\[D=L_{1}\cdot tan\left(\frac{\pi}{2}-\theta_{L}\right)=\ L_{2}\cdot tan\left( \frac{\pi}{2}-\theta_{R}\right) \tag{2}\]  
\[\varphi_{hor}=tan^{-1}\left(\frac{L_{hor}}{D}\right) \tag{3}\]  
Figure 3: Schematic block diagram and experimental setup of the proposed V2V-VLC system.  
\[\varphi_{ver}=tan^{-1}\left(\frac{L_{ver}}{D}\right) \tag{4}\]  
Therefore, the horizontal angle (\(\varphi_{hor}\)) and vertical angle (\(\varphi_{ver}\)) between the receiver and blue LD module can be calculated by Equations (3) and (4), where \(\varphi_{hor}\) and \(\varphi_{ver}\) are the values that are ultimately used to adjust the angle of the steering mirror. The mirror also has a horizontal and a vertical axis that can be adjusted up to 50 degrees in each direction. The beam-steering module can control the beam to the target with tracking information updated in real time. This method can be applied to the vehicle in front and other neighboring vehicles.
# Detection of Yunnan Pine Shoot Beetle Stress Using UAV-Based Thermal Imagery and LiDAR
## 1 Introduction
In agricultural research, the underlying surface of crops is homogeneous, and the temperature of the crop canopy receives little disturbance, making it easy to obtain. The complex structure and irregular shape of forestry canopies result in the heterogeneity of the canopy [30], which increases the uncertainty of canopy temperatures extracted from UAV-based thermal images. Light detection and ranging (LiDAR) data provide a method for obtaining detailed three-dimensional (3D) structural parameters of tree crowns with dense cloud points, which are used to obtain the spatial distribution of foliage [31; 32; 33]. The intensity of laser signal can produce a reflection characteristic in the near-infrared region of foliage within a canopy. Therefore, LiDAR has significant potential for monitoring forest health by detecting the severity of damage from forest pest infestations [34]. However, obtaining fine-grained measurements that reflect the health of an individual canopy is limited when using TIR or LiDAR only. The mixed radiation from trees and the background presents difficulties for accuracy accessing the canopy structures using thermal remote sensing techniques. The data obtained by LiDAR serves as an additional source of information for capturing the fine structures of tree crowns, such as the leaf area index (LAI), fractional cover, and canopy density [35; 36]. Few studies have used TIR for monitoring forest infestations at the canopy scale to assess the damage caused by pine-shoot beetles in Yunnan pine forests. Accordingly, this article investigated the capacity of thermal imagery for monitoring the shoot damage ratio (SDR) of the crowns of Yunnan pine trees. The degree of damage to Yunnan pine crowns from attack by _Tomicus_ spp. was assessed using the SDR [23; 37]. The study also presents and discusses the impact of the LAI on the CST-SDR relationship using TIR and LiDAR datasets at canopy and plot scales in addition to corresponding field measurements.