esa-sceva/satcom-chunk-collection huggingface dataset:
# Faudistic Platooning: Subdivide Platoons, Unite Networks, and Conquer Efficiency and Reliability
## 3 Vehicular Communication  
Two types of communication technologies are now in use to exchange data between two or more nodes in vehicular environments: these are ad hoc or cellular networks. Before we discuss each network in detail, we briefly introduce the following concepts of unicast, broadcast, and multicast:  
* Unicast: data are sent from the transmitter to one receiver using a dedicated link. In the event of multiple nodes receiving the same message, the same number of transmissions and links as receivers are necessary.
* Broadcast: data are sent from the transmitter over a single link that is shared by _all_ nodes. Even though only one transmission is necessary, all nodes are able to receive the data.
* Multicast: similar to broadcast, but the link is only shared by _some_ nodes. Geocast is a special form of multicast, where nodes are identified by their geographical locations.
## 6 Conclusions
\begin{tabular}{l l} _Latin Letters_: & \\ \(\underline{I}\) & identity tensor \\ \(\underline{K}\) & permeability tensor [m\({}^{2}\)] \\ \(\underline{S}\) & strain rate tensor [s\({}^{-1}\)] \\ \(\underline{k}\) & thermal conductivity tensor [W m\({}^{-1}\) K\({}^{-1}\)] \\ \(g\) & gravity field [m s\({}^{-2}\)] \\ \(x\) & position vector [m] \\ \(q_{R}\) & thermal radiation flux [J] m\({}^{-1}\) s\({}^{-1}\)] \\ \(v\) & average velocity [m s\({}^{-1}\)] \\ \(a_{1},..,a_{7}\) & Janaf coefficients \\ \(A\) & chemical species \\ \(\mathcal{A}\) & Arrhenius law pre-exponential factor \\ \(C_{1},C_{2}\) & Sutherland coefficients \\ \(C_{B1},C_{B2}\) & Boussinesq approximation coefficients \\ \(C_{p}\) & specific heat at constant pressure [J] kg\({}^{-1}\) K\({}^{-1}\)] \\ \(D\) & mass diffusivity [m\({}^{2}\) s\({}^{-1}\)] \\ \(\mathcal{E}\) & Arrhenius law activation energy [J mol\({}^{-1}\)] \\ \(F_{i,j}\) & mass fraction of sub-phase j in phase i \\ \(f\) & puffing frequency [Hz] \\ \(h\) & specific absolute enthalpy [J] kg\({}^{-1}\)] \\ \(k\) & turbulent kinetic energy [J] \\ \(L,l\) & macroscopic and microscopic characteristic lengths [m] \\ \(\mathcal{M}\) & mean molar mass [kg mol\({}^{-1}\)] \\ \(m,n\) & Arrhenius law parameters \\ \(N_{g},N_{p}\) & number of gaseous and solid species \\ \(p\) & average pressure [Pa] \\ \(P_{i}\) & number of sub-phases in the solid phase i \\ \(Q\) & heat flux [J] m\({}^{-2}\) s\({}^{-1}\)] \\ \(R\) & perfect gas constant [J mol\({}^{-1}\) K\({}^{-1}\)] \\ \(s\) & generic solid phase \\ \(T\) & average temperature [K] \\ \(\mathcal{T}\) & time period [s] \\ \(y\) & species mass fraction \\ _Greek Letters_: & \\ \(\frac{\tau}{\alpha}\) & stress tensor [N m\({}^{-2}\)] \\ \(\alpha\) & thermal diffusivity [m\({}^{2}\) s\({}^{-1}\)] \\ \(\epsilon\) & volume fraction \\ \(\mu\) & dynamic viscosity [Pa s] \\ \(\xi\) & mass stoichiometric coefficient \\ \(\pi_{k}\) & pyrolysis production rate of species k [kg m\({}^{-3}\) s\({}^{-1}\)] \\ \(\Pi\) & total pyrolysis gas production rate [kg m\({}^{-3}\) s\({}^{-1}\)] \\ \(\rho\) & average density [kg m\({}^{-3}\)] \\ \(\tau\) & overall pyrolysis advancement \\ \(\chi_{i,j}\) & advancement of pyrolysis reaction j within phase i \\ \(\omega_{k}\) & combustion rate of reaction of the specie k [kg m\({}^{-3}\) s\({}^{-1}\)] \\ _Subscripts and Superscripts_: & \\ _comb_ & combustion \\ _eff_ & effective \\ \(f\) & fluid \\ \(g\) & gas \\ \(i\) & index for the solid phases \\ \(j\) & index for the sub-phases produced from pyrolysis \\ \(k\) & index for the gaseous species \\ \end{tabular}
## 2 Experimental Setup  
### Microscopic and Macroscopic LIF/Mie Setup  
The optical setup for the simultaneous detection of microscopic and macroscopic LIF and Mie signals, generated from individual monodisperse droplets, is displayed in Figure 1.  
For droplet illumination, a laser beam (Multi-Nd:YAG-Laser, Thales, Paris, France, 532 nm with top-hat beam profile) was formed to a light sheet (height: 15 mm, thickness: approximately 0.7 mm). The LIF- and Mie-signal intensities were recorded simultaneously with a micro and a macro imaging system, respectively. Each imaging system was equipped with two identical sCMOS cameras (Imager, LaVision, Goettingen, Germany, resolution 2560 pixel \(\times\) 2160 pixel). The design of the systems and  
Figure 1: Optical setup for the simultaneous microscopic and macroscopic LIF/Mie detection (**a**). Detail for the microscopic detection and the droplet generator (**b**).  
the associated adjustment possibilities simplify the alignment for obtaining the same field-of-view for both cameras. The individual filters were mounted directly in front of the camera chips. The LIF signal was detected using a 532 nm (17 FWHM) notch filter to exclude the excitation wavelength. The Mie scattering signal was detected using a 532 nm (1 nm FWHM) laser line filter. To optimize the signal detection at constant laser power, motorized filter wheels equipped with a set of ND-filters (ND 0, ND 0.3, ND 0.6, ND 1, ND 1.3) were mounted in front of the individual cameras. All optical elements of the objective systems were characterized with a photo spectrometer (UV/VIS Spectrometer Lambda 40, Perkin Elmer, Waltham, USA, wavelength range 300-900 nm, 3001 pixels, spectral bandwidth 0.2 nm) to calibrate the spectral properties of the detection system. The microscopic objective system was equipped with a high-resolution, long-range microscope (K2 DistaMax, Infinity, Centennial, USA) with a nominal pixel resolution of 0.28 \(\upmu\)m/pixel. The macroscopic detection system was equipped with a conventional objective (135 mm, 2.8/135, Pentacon, Dresden, Germany) with a nominal pixel resolution of 6.67 \(\upmu\)m/pixel.
# Resolution Aspects for Near-Field Projections
## 2 Materials and Methods
### Testing Procedure  
The two collectives consisted of different participants. The second study was conducted three months after the first, but the testing procedure was identical. All participants participated within two weeks in a single 30-min-session.  
The testing procedure for both studies was identical: first, the participants read an instruction sheet while the experimenter adapted the ambient luminance of the respective scenario. Before the test started, examples of the high and low resolution of every projection content were shown to the participants, which were not included in the testing set. The examples ensured that every participant had a similar prior perceptual threshold due to different individual experiences of near-field-projections. Second, the participants got used to the test procedure with a test run (Figure 3A). To prevent learning effects, ten different pre-set image qualities that occurred three times in the total set were presented in a random order (Figure 3B), resulting in a total of 30 images per sequence, and participants had 2.5 s to log in their decision whether the displayed projection had a good or bad resolution in reference to the displayed examples in prior testing. The presentation time of every projection was 2.5 s (Figure 3A). The experiment followed a forced choice procedure of constant stimuli [33; 38; 39; 40]: while focusing on the projection area, the participants had to either press 'Y', if the displayed quality of the projection had a high quality, or 'N' if not. There was a random delay from 0.5 to 1 s between sequences to prevent learning effects. After the delay, visual feedback was displayed if the answer was logged in successfully. The participants rated the projection's quality subsequently from one of the three testing distances, e.g., 1, 3, or 5 m. The order of distances was randomized between participants. The same procedure was repeated for both projection contents (text- or symbol-based), resulting in a total of 180 potential ratings of perceived projection quality per participant (10 qualities \(\times\) 3 repetitions \(\times\) 3 viewing distances \(\times\) 2 projection contents) (Figure 3B). Finally, a questionnaire was answered with general personal information and questions about near-field projections. However, the questionnaire analyses are not part of this paper [10].  
Figure 3: Schematic illustration of the testing procedure and used data set in both studies. (**A**) Visualization of the testing procedure including the welcoming screen, test run phase and the test procedure phase. The stimulus illustrates a projection appearing on the projection area. In addition, the log-in time is displayed with shading in the test procedure. (**B**) Display of all ten different quality levels of the displayed pictures in descending order of quality. Additionally, an example of the possible test procedure output is displayed after applying the randomization algorithm to the data set of 30 images (10 qualities \(\times\) 3 repetitions) in the lower part of (**B**).
## 2 Model Test
### Test Results  
#### 2.5.1 Support Pressure Ratio  
Figure 6 shows the relationship between the support pressure ratio (\(\sigma_{\mathrm{s}}\)/\(\sigma_{0}\), where \(\sigma_{0}\) is the initial support pressure at the excavation face and \(\sigma_{\mathrm{s}}\) is the support pressure at the excavation face at different moments of backward displacement) and the relative displacement (\(S\)/\(D\)%) of the excavation face from the model tests for four cases: one case with no existing tunnel, as well as \(L\)/\(D\) = 1, 0, and \(-\)1. The trend of support pressure change can be divided into two phases. After the excavation surface is destabilized, the support pressure shows a sharp decrease in the first phase, with the decrease concentrated in the range of 49-58%. The second phase is characterized by the gradual stabilization of the support pressure ratio with the increase in the excavation surface backward displacement. At this stage, the support pressure is the ultimate support pressure. Compared to the ultimate support pressure in the case without the existing tunnel, the ultimate support pressure is shown to be lower by 17.6% and 8.7%, respectively, when the \(L\)/\(D\) = 1 and the \(L\)/\(D\) = 0. This is because the existing tunnels play a supporting role for the upper soil above, preventing the direct transfer of soil stress to the lower strata and effectively limiting the development of the damage zone. When \(L\)/\(D\) = 1, there is a large overlap between the area where the existing tunnel is located and the destabilization zone of the excavation face. In this scenario, the ultimate support pressure of the excavation surface is at its lowest because the existing tunnel uses its deformation to counteract the stress release of the soil in the original destabilized area. When \(L\)/\(D\) = \(-\)1, the ultimate support pressure is the closest to that without the existing tunnel, which indicates that the influence of the existing tunnel is the least at this time.  
Figure 6: Support pressure ratio (for different horizontal spacings).
# Performance Estimate of a Spin-Stabilized Drag Sail for Spacecraft Deorbiting
## 2 Mathematical Model
### Solar Radiation Pressure  
Solar radiation pressure is usually negligible within LEO orbital range, but because of the significant area-to-mass ratio \(A/m\) of a spacecraft equipped with drag sail, its effect is considered in this work. The acceleration generated by the solar radiation pressure \(\mathbf{a}_{\rm SRP}\) is estimated with a modified ideal force model, which assumes a flat drag sail and takes into account a nonperfect reflection [63], yielding  
\[\mathbf{a}_{P_{\oplus}}=\eta(c_{r}+1)P_{\oplus}A(\mathbf{\delta}\cdot\mathbf{\hat{n}}_{ \rm SRP})^{2}\mathbf{\hat{n}}_{\rm SRP} \tag{14}\]  
where \(P_{\oplus}\simeq 4.5632\times 10^{-6}\,\mathrm{Pa}\) is the solar radiation pressure at the Sun-spacecraft distance, which is assumed constant, and \(\mathbf{\hat{s}}\) is the Sun-spacecraft unit vector. In addition, \(c_{r}\) is the reflection coefficient of the drag sail, which is assumed to be small because drag sails, unlike solar sails, are not designed to take advantage of solar radiation pressure as a source of thrust. For this reason, drag sails are usually low reflective to reduce light pollution [64]. Accordingly, and in analogy with the drag sail models discussed in Ref. [65], a value of \(c_{r}=0.1\) is assumed in this work. The parameter \(\eta\in\{0,\,1\}\) in Equation (14) is used to determine whether eclipses occur with a cylindrical shadow model, i.e., \(\eta=1\) if the Sun-spacecraft line does not intersect the Earth (modeled as a sphere of radius \(R_{\oplus}\)), and \(\eta=0\) otherwise. With reference to Figure 2, the value of \(\eta\) can be determined from the instantaneous Sun-Earth vector \(\ell\mathbf{\hat{s}}\) (with \(\ell^{\rm\,\triangleq\,}1\,\mathrm{au}\)) and the Earth-spacecraft vector \(\mathbf{r}\).  
Finally, with reference to Equation (14), since the unit vector normal to the sail surface must point in the direction opposite to the Sun, \(\hat{\mathbf{n}}_{\text{SRP}}\) in Equation (14) must be chosen such that \(\hat{\mathbf{n}}_{\text{SRP}}\cdot\mathbf{\delta}\geq 0\). This consideration removes the ambiguity about the definition of \(\hat{\mathbf{n}}\) that arose in Section 2.1, that is,  
\[\hat{\mathbf{n}}_{\text{SRP}}=\begin{cases}\hat{\mathbf{n}}&\text{if }\hat{\mathbf{n}}\cdot\mathbf{ \hat{s}}\geq 0\\ -\hat{\mathbf{n}}&\text{if }\hat{\mathbf{n}}\cdot\mathbf{\hat{s}}<0\end{cases} \tag{15}\]  
where \(\hat{\mathbf{n}}\) is a freely selected normal unit vector \(\hat{\mathbf{n}}\).
## 2 Experimental Descriptions
### Cryostat Design
#### 2.1.2 Residual Magnetic Field Simulation  
The high ambient magnetic field at the location of the superconducting cavity can significantly decrease the RF performance of the cavity significantly. Equation (1) [33] provides the additional surface resistance \(R_{mag}\) due to the cavity-trapped magnetic field, where \(B_{ext}\) represents the trapped field, \(B_{c2}\) denotes the upper critical field, and \(R_{n}\) is the normal state surface resistance. For conventionally processed 1.3 GHz Nb cavities, \(B_{c2}=240\) mT, \(R_{n}=1.7\) m\(\Omega\), and \(R_{mag}=0.35\times B_{ext}(\text{mG})\) n\(\Omega\)[33], as follows:  
\[R_{mag}=\frac{B_{ext}}{2B_{c2}}R_{n} \tag{1}\]  
The trapped magnetic field is positively correlated with the ambient magnetic field. To address this issue, two magnetic shields were designed for the cryostat, and the material was permalloy 1J79. Figure 3 presents the simulation results of the environmental magnetic field at the axis of the superconducting cavity using CST Studio Suite(r) (Dassault Systemes, Pairs, France). The maximum magnetic field is 2.4 mG, and the environmental magnetic field on the surface of the superconducting cavity is consistently lower than this value. If the ambient magnetic field of 2.4 mG is fully trapped by the Nb cavity, the resulting additional \(R_{s}\) is 0.84 m\(\Omega\), which does not significantly affect the performance of the superconducting cavities. In the experiments, the magnetic fields monitored by the three magnetometers were sometimes higher than this value, due to the additional dynamic ambient magnetic field generated when cooling down or turning on the heaters.  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Pathway** & **Position** & **50 K** & **10 K** & **4 K** \\ \hline \multirow{3}{*}{Radiation} & Vacuum vessel—outer thermal shield & 42.4 & - & \\  & Outer thermal shield—inner thermal shield & & 0.393 & \\  & Inner thermal shield—SRF cavity & & & 0.001 \\ \hline \multirow{3}{*}{Conduction} & G10 or titanium rods & 0.3 & 0.24 & 0.003 \\  & RF cables & 0.6 & 0.17 & 0.013 \\  & Thermometer leads & 0.1 & 0.006 & 0.001 \\ \hline Total & & 43.4 & 0.809 & 0.018 \\ \hline \end{tabular}
\end{table}
Table 1: Heat leakage (W) in each temperature zone of the cryostat obtained by simulation.  
Figure 3: Magnetic field distribution on the center axis of the cryostat obtained by simulation; the horizontal axis is the distance from the top of the inner magnetic shield, and the superconducting cavity is located at a distance of about 100 to 520 mm from the top.
# Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter
###### Abstract
To improve the accuracy of vehicle stand \(-\)alone localization in highly dynamic driving conditions during GNSS outages, [PERSON] [25] proposed a vehicle localization system based on vehicle chassis sensors considering vehicle lateral velocity. The Kalman filter combines vehicle states obtained from vehicle kinematics and dynamics to improve the reliability and accuracy of autonomous driving. A consensus \(-\)based and vehicle kinematics/dynamics integrated autonomous vehicle sideslip angle estimation algorithm based on GNSS/INS was proposed [26; 27]. [PERSON] [28] proposed a Kalman filtering method based on attitude error states. The error state Kalman filter (ESKF) is designed for covariance estimation, which serves as a weight for the inertial odometry optimization process [29; 30; 31]. These results validate the ESKF's effectiveness for error \(-\)state estimation, although a full \(-\)state estimation based on ESKF is not available yet. Further exploration of state estimation based on ESKF is thus favorable. The major challenge in designing a sensor fusion algorithm for state estimation is to address GNSS signal errors and low \(-\)cost hardware limitations with a smoother approach. The errors of inertial navigation systems accumulate over time, while GNSS signals are heavily influenced by satellite signal quality and urban obstructions [32; 33; 34]. These would lead to significant localization errors. Using INS alone cannot provide accurate position estimation for a long term in signal\(-\)obscured environments [35; 36; 37]. In such cases, real\(-\)time high\(-\)accuracy localization is necessary, and data fusion from multiple sensors is often required [38; 39]. In order to provide reliable localization information, simultaneous localization and map construction (SLAM) systems can be used, as well as multi\(-\)sensor integrated systems that combine LiDAR [40] and navigation systems to solve problems caused by GNSS antenna failures. To reduce GNSS and Inertial Measurement Unit (IMU) fusion costs and achieve desired accuracy, different navigation satellite [41; 42; 43] constellations need to be combined, especially in urban working environments. In this paper, the low\(-\)cost sensor combination of only GNSS and IMU is considered, and effects of GNSS signal error and different fusion algorithm design are investigated. The extended Kalman filter and error state Kalman filter are designed in Section 2. The application of the error\(-\)extended Kalman filter and the RTS optimal smoothing algorithm is investigated in Section 3 for combined inertial guidance and GNSS localization in urban working environments. The insights and conclusions on the performance of the designed sensor fusion algorithms are further analyzed.
# Efficient On-Off Keying Underwater Acoustic Communication for Seafloor Observation Networks
## 3 Receiver Design Adapted to Channel
### IrCC Design under Rayleigh Fading Channel
Compared with the RSC, a gain of 1 dB is obtained by using IrCC at the BER level of \(10^{-5}\). In Figure 7, the simulated performances of the proposed decoding scheme with ITE and a conventional decoding scheme without ITE under the flat Rayleigh fading channel are compared. Furthermore, we investigate the performance of the above three encoding and decoding schemes under different iterations. Three conclusions can be concluded. First, a performance gain of about 0.8 dB is achieved by adopting the proposed decoding scheme with ITE. Second, the decoding process of the RSC is almost unaffected by the number of iterations. In contrast, with the increase in the number of iterations, the decoding performance of IrCC is improved obviously, and the improvement is more significant with the aid of the ITE. Finally, at a maximum number of
# Functional Safety Networks and Protocols in the Industrial Internet of Things Era
## 4 The Fail Safe over EtherCAT Protocol Implemented on WiFi
### Implementation  
The FSoE stack has been developed and implemented on two different device types, namely a Raspberry Pi board and a PC running the Linux operating system. As can be seen in Figure 3, the FSoE protocol exploited the User Datagram Protocol (UDP) to carry safety data. With such an approach, as evidenced in Figure 3, the black channel comprises the communication medium, the WiFi modules of the adopted devices, and their TCP/IP suites (indicated as protocol stacks) up to the interface with the respective FSoE stacks.  
The transmission of safety data is started by the safety application running on the master that issues a request to the FSoE protocol stack. The safety data are hence encapsulated within the FSoE frame which provides all the countermeasures to detect communication errors. Beyond this point, any further data manipulation/encapsulation is carried out within the black channel. On the slave side, if the integrity of the received safety PDU is positively assessed by the FSoE Protocol Stack, the safety data are forwarded to the slave safety application, which sends the response primitive carrying the response data. The service is then concluded with the arrival of the confirm primitive at the master.  
Clearly, a WiFi network has a completely different topology with respect to EtherCAT; thus, a different addressing technique needed to be devised. In practice, the FSoE Master hence assigns a \"connection ID\" to each slave and associates it with the IP address of the slave itself. This allows the delivery of safety data to the right slave. Remarkably, this custom addressing technique has neither impact on FSoE, i.e., it has not required any modification to the FSoE protocol stack, nor on the safety performance. Moreover, this type of addressing techniques adds a further useful safety feature, because it allows to obtain a double check of the correct addressing of the slave, thanks to the univocal correspondence between IP address and connection ID. Indeed, both slave and master know both their connection ID and IP address. Therefore, upon receiving a frame, they can check whether the addressing is correct or not.  
To test the performance of FSoE over WiFi, an experimental setup has been prepared where the FSoE master protocol was implemented on the PC, whereas the FSoE slave was  
Figure 3: Implementation of the FSoE Protocol Stack.  
the Raspberry Pi board. The network was configured to use the IEEE 802.11n at a rate of 72 Mbits/s in the 2.4 GHz band with the Request To Send/Clear To Send (RTS/CTS) mechanism enabled. Notably, RTS/CTS is usually disabled, when WiFi is used for industrial real-time applications, since it might introduce additional latency. In this case, however, it has been used since it contributes to increase communication reliability [28].  
From the IEEE 802.11 network point of view, the node acting as FSoE master is configured as an Access Point (AP), whereas the FSoE slave is a Station (STA), as shown in Figure 4.