esa-sceva/satcom-chunk-collection huggingface dataset:
# A Critical Review of SCWG in the Context of Available Gasification Technologies for Plastic Waste
## 6 Gasification of Plastic Waste: An Overview
### Plastics Mix
A summary view of the operating conditions and of the obtained results in the various works is shown in Table 5. As can be seen, the gasification of the plastic mix is carried out using mainly air or steam and temperature of 800-850 \({}^{\circ}\)C. Moreover, in this process olivine or dolomite are used as a catalyst to reduce tar content. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Reactor** & **Feedstock** & **Bed Material** & \begin{tabular}{c} **Gasification** \\ **Temperature** \\ \end{tabular} & \begin{tabular}{c} **Tar Content** \\ **Agent** \\ \end{tabular} & \begin{tabular}{c} **Gasifying** \\ **Agent** \\ \end{tabular} & \begin{tabular}{c} **Heating Value** \\ \end{tabular} & **Reference** & **Year** \\ \hline \hline Two-stage gasifier & PP, PE, FS, PVC, PMMA, PET & \begin{tabular}{c} Olivine and \\ activated carbon \\ \end{tabular} & 800 \({}^{\circ}\)C & 2-2170 mg/Nm\({}^{3}\) & air & \begin{tabular}{c} LHV: From 3.9 \\ to 8.2 MJ/Nm\({}^{3}\). \\ \end{tabular} & [PERSON] et al. [89] & 2013 \\ \hline Two-stage gasifier & PP, PE, FS, PVC, PMMA, PET & \begin{tabular}{c} Calined \\ dolomite and \\ activated carbon \\ \end{tabular} & 800 \({}^{\circ}\)C & 20-3490 mg/Nm\({}^{3}\) & air & \begin{tabular}{c} LHV: 13.4 \\ MJ/Nm\({}^{3}\) \\ \end{tabular} & [PERSON] et al. [138] & 2013 \\ \hline Dual fluidized bed & \begin{tabular}{c} PE, PP, \\ and mixtures of PE \\ + PS, PE + PET and \\ \end{tabular} & olivine & 850 \({}^{\circ}\)C & 100 g/Nm\({}^{3}\) & steam & \begin{tabular}{c} LHV: From 27.2 \\ to 41.2 MJ/Nm\({}^{3}\) \\ \end{tabular} & Wilk and Hofbauer [115] & 2013 \\ \hline drop-tube fixed bed & \begin{tabular}{c} LDPE, HDPE, PP, \\ and PS \\ \end{tabular} & \
\begin{tabular}{c} Dolomite \\ supported Ni \\ catalyst, \\ and PS \\ \end{tabular} & 850 \({}^{\circ}\)C & - & steam and \\ \end{tabular} & - &
\begin{tabular}{c} [PERSON] et al. [139] & 2020 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Plastics mix gasification summary table.
# A Staring Tracking Measurement Method of Resident Space Objects Based on the Star Tacker
## 3 Results
### Simulation Settings of the Proposed STM Method  
As demonstrated in Section 2.1, there are three orbits in the staring-tracking process: the observation satellite orbit, the estimated initial RSO orbit from original TSA measurements, and the real RSO orbit. In Section 2.2, the observation measurement method of the star tracker based on the ADI principle was analyzed. We analyze the orbit elements and the star tracker parameters to design appropriate simulation parameters for the STM method.
# Intelligent Transportation System Technologies, Challenges and Security
## 2 Intelligent Transportation Systems
### Intelligent Transportation Communication Technologies
#### 2.2.2 Detection Technologies  
Detection technologies are sensor-based systems for measuring road, environmental, air, and vehicle information that ITS needs. In this field, vehicle counting is used to provide data on traffic density, road, air, and emission status. In particular, thanks to the processing and use of data obtained from mobile devices on which they have travel planning software installed, as well as data from sensors located on the roadside, drivers can make alternative route suggestions on the route [23].
# Lightweight Semantic Segmentation Network for Real-Time Weed Mapping Using Unmanned Aerial Vehicles
## 4 Results
### Optimization of the Inference Process  
To reduce the model size, the data precision requirement of the network was changed from FP32 to FP16. The Jetson TX2 module uses half-precision mode to reduce the size of the model to half of its original size. Table 2 lists the results of precision calibration for the modified AlexNet-FCN. One can see that precision calibration improves inference efficiency by approximately three to four times on both the GTX 1060 (Nvidia Corporation, Santa Clara, CA, USA) and Jetson TX2 module. Although the prediction accuracy decreases slightly, the overall accuracy is acceptable for agricultural applications because it can provide rapid judgments regarding weed density.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Device** & \begin{tabular}{c} **Overall** \\ **Accuracy (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Mean IoU** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Frames Per** \\ **Second** \\ \end{tabular} \\ \hline The proposed FCN-Alexnet model & GTX 1060 & 91.2 & 70.5 & 12.5 \\ VGGNet-FCN by [PERSON] et al. [19] & GTX 1060 & 92.3 & 72.8 & 3.1 \\ GoogLeNet-FCN by [PERSON] et al. [21] & GTX 1060 & 91.9 & 71.3 & 5.9 \\ ResNet-FCN by [PERSON] et al. [18] & GTX 1080 TI & 94.2 & 77.2 & 9.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons of the proposed FCN-Alexnet with other mainstream network architectures.  
Figure 8: The loss and accuracy curves during training process.  
Figure 9 presents the output images corresponding to several testing samples before and after precision calibration. In Figure 9c, one can see that our modified AlexNet-FCN can correctly distinguish the rice and weed areas. Following precision calibration, the prediction accuracy decreases by a small amount. The weed area in the third sample (blue dotted lines) was classified as the \"other\" category by the model following precision calibration. However, following precision calibration, our model still correctly classifies most areas in the input images, demonstrating its capability to provide decision support information for spraying machines.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Device** & **Precision Clibration** & **Overall Accuracy (\%)** & **Mean IoU (\%)** & **Frames Per Second** \\ \hline GTX 1060 & FP32 & 91.2 & 70.5 & 12.5 \\ Jetson TX2 & FP32 & 91.2 & 70.5 & 1.2 \\ GTX 1060 & FP16 & 80.9 & 62.8 & 35.6 \\ Jetson TX2 & FP16 & 80.9 & 62.8 & 4.5 \\ \hline \end{tabular}
\end{table}
Table 2: Inference optimization in the desktop and the embedded devices.  
Figure 9: (**a**) UAV imagery, (**b**) corresponding labels, (**c**) results obtained by FCN-AlexNet, and (**d**) results obtained by FCN-AlexNet optimized with precision calibration.
# Precise Point Positioning Using Triple GNSS Constellations in Various Modes
## 6 Conclusions  
This paper developed a triple-constellation GNSS PPP mode in various positioning modes. Post-processed PPP solutions are obtained in both of the un-differenced and BSSD modes, while a RT-PPP solution is obtained the un-differenced mode only. All post-processed PPP and RT-PPP solutions are compared with the traditional un-differenced GPS-only counterparts. Three scenarios are considered when forming BSSD, each uses a reference satellite from a different GNSS constellation. All of the RTS-IGS satellite corrections currently produced by the IGS are used to produce the RT-PPP solutions.  
It has been shown that combining the GPS, Galileo, and BeiDou observations in an un-differenced post-processing PPP model improves the convergence time by about 25% in comparison with the GPS-only counterpart. However, no noticeable improvement is obtained in the PPP solution precision. The use of BSSD linear combination improves the PPP solution convergence time by about 50% and the precision of the estimated PPP parameters by about 25%, in comparison with the GPS-only post-processed PPP solution. RTS IGS03 satellite corrections provided the shortest RT-PPP convergence time, which was about 25 min. In addition, the precision of the RT-PPP solution was better when IGS03 was used, in comparison with the cases when IGS01, IGC01, and IGS02 satellite corrections were used.  
**Acknowledgments: This research was partially supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada. The authors would like to thank the International GNSS service--Multi-GNSS Experiment (IGS-MEGX) network.**  
**Author Contributions: [PERSON] and [PERSON] conceived the idea and wrote the paper. [PERSON] and [PERSON] elaborated the problem formulation. [PERSON] implemented the experimental testbed and performed the evaluation under the supervision of [PERSON].**  
**Conflicts of Interest: The authors declare no conflict of interest.**  
Figure 9: Summary of the RT-PPP positioning standard deviations in East, North, and Up directions. (**a**) East standard deviation; (**b**) North standard deviation; (**c**) Up standard deviation.
# Secure Aviation Control through a Streamlined ADS-B Perception System
## 2 Background Review
### ADS-B Security Threats and Vulnerabilities  
ADS-B suffers from various attacks and vulnerabilities. Below are the most known attacks that threaten an ADS-B system and their description [4].  
* **Message injection**: if the attacker has the right equipment, an actual and legitimate counterfeit ADS-B message could be generated and injected into an ADS-B System.
* **Message deletion**: physically deleting ADS-B messages using constructive or destructive interference differs from jamming because it drops the whole message rather than blocking it.
* **Message modification**: altering the contents of an ADS-B message using different techniques such as message injection and bit flipping.
* **Signal jamming**: interrupting the signals of ADS-B communications channels using radio frequency devices that send multiple requests leads to jamming and disrupting the communication between the aircraft and ground stations.
* **False alarm attack**: modifying a message to indicate a false alarm. Such an attack could indicate that an aircraft has been hijacked while operating normally.
* **Virtual trajectory modification**: changing the received trajectory of an aircraft by using message manipulation techniques.
* **Ghost aircraft injecting**: this attack creates an imaginary aircraft by faking ADS-B messages within a communication channel.
* **Ghost aircraft flooding**: like ghost aircraft injecting, this attack injects multiple imaginary aircraft simultaneously to cause disruption and confusion and could cause a denial of service in different surveillance systems.
* **Aircraft disappearance**: the objective here is to cause failure within the collision avoidance systems and cause confusion with the ground stations where suddenly all of the messages received from an aircraft are deleted and no longer transmitted; this could force an aircraft to perform an emergency landing to avoid any risks.
* **Aircraft spoofing**: the objective of this attack is to spoof an aircraft's ICAO number, a special identifier for the aircraft transmitter; the main risk here is that any aircraft, even if unauthorized, could pass into the premises of a country as a normal aircraft without triggering any alarms.
# Comprehensive Survey on VLC in E-Healthcare: Channel Coding Schemes and Modulation Techniques
## 6 Future Directions and Challenges
### Mitigating the Effects of Electromagnetic Interference (EMI)  
One of the significant advantages of VLC over traditional RF systems is its immunity to EMI. Research has shown that exposure to EMF from RF systems can cause health issues, including damage to the nervous system, and other physiological effects [64]. VLC, which operates outside the electromagnetic spectrum used by RF systems, offers a safer alternative, especially in sensitive healthcare environments. However, future work should focus on further exploring the impact of VLC on human health, particularly in prolonged exposure scenarios. Furthermore, hybrid systems that combine the VLC and RF technologies could be developed to capitalize on the strengths of both, while mitigating their weaknesses.
## 8 Recent Advances in the Design of IPB Braced Frame
### Connection Influence on the Ductility of the Braced System  
Past research on the OOPB braced system has established that allowing the secondary hinges to form at the connection enhances the structure's ductility. The hinges in the connection are introduce by providing clearance at the gusset plate, as discussed earlier. The effectiveness of the IPB braced system as compared to its counterpart, the OOPB braced system, is shown in Figure 10. The backbone curves of the respective connections were generated from past experimental investigations [24]. From Figure 10, it can be seen that the IPB connection performs better than the OOPB connection. It is observed that the rotational fixity of the connection has influenced the ductility of the braced system. Less rotational restrain delays the fracture of the brace member, provided other limit states are satisfied.  
Various studies in the past have been conducted to evaluate the fracture ductility of the components of the brace frame. These studies are based on the database generated from experimental investigations conducted in the last four decades. The parameters observed mostly govern the fracture ductility found as slenderness ratio and compactness ratio [42; 43; 44; 45]. Parameters, such as grade of steel, also profoundly impact predictions of fracture ductility [46; 47]. It has been observed that the analytical formula used to calculate the fracture ductility shows a lower coefficient of determination value (\(R^{2}\)). This may be due to other factors, such as hinge plate thickness, out-of-plane stiffness of the hinge plate, and effective brace length, which may affect the fracture ductility of the brace component. However, past studies neglected these effects due to sparse experimental results that explicitly include these effects. Table 3 highlights the various damage stages of the IPB braced system observed in the test conducted by [PERSON] and [PERSON] [24].  
Figure 10: Comparison of the backbone curve of the IPB and OOPB connection braced system.  
The knife gate clearance that facilitates the in-plane buckling in the IPB braced system has evolved in the last decade. The first set of experiments on the IPB braced system conducted by [PERSON] [31] used the 2 tp clearance on the detailing of the knife plate. [PERSON] [32] further changed the clearance to 3 tp to enhance the performance and remove the problems of the earlier study. [PERSON] and [PERSON] [24] observed that the knife plate's flexibility is retrained due to the insertion of the gusset plate in the middle. This led to the IPB braced system's lower ductility compared to the OOPB braced system. This also led to the initiation of the crack at the knife plate, as observed in the past experimental study. Hence, a clearance of 4 tp-6 tp (based on the rotational flexibility of the knife; \(t_{p}\) = thickness of the knife plate) is proposed for the IPB braced system to enhance the seismic performance of the braced system.
## 3 Verification of the Strength of Age-Hardened Maraging Steel Samples Produced with SLM Technology
### Pressure Fatigue Testing  
The pressure fatigue tests were carried out on a test rig (Figure 4). The internal diameters of the samples (Figure 5) were 22, 27, and 33 mm and the wall thicknesses were 0.4, 0.5, and 0.6 mm. The build orientation of the sleeves was 0 degrees with respect to the sample's rotational axis. The samples were sandblasted and machined to achieve good sealing surfaces, leaving the internal and external cross sections in the middle with an as-built surface finish and a 50 um layer setup. The samples were placed in a steel block in the test chamber of the rig. On the basis of the measurement of the hydraulic oil pressure, the cylindrical stresses were determined using the following relationship:  
\[\sigma_{\theta}=\frac{Pd}{2t} \tag{1}\]  
Figure 3: The S–N curve for additively manufactured managing steel, R = 0.05.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Sample No.** & \(\sigma_{\text{max}}\) **(MPa)** & \(\sigma_{\text{min}}\) **(MPa)** & \(N_{f}\) **(Cycles)** \\ \hline R1/128/01 & 1200 & 60 & 216 \\ R1/128/02 & 1000 & 50 & 1237 \\ R1/128/03 & 800 & 40 & 3504 \\ R1/128/04 & 600 & 30 & 22,077 \\ R1/128/05 & 450 & 22  
where \(\sigma_{\theta}\) is cylindrical stress, \(P\) is pressure, \(d\) is internal diameter, and \(t\) is wall thickness.  
The stresses arising in the sample were controlled by the maximum values of the pressure pulses (Figure 6) at a frequency f = 5 Hz. The control system counted the pressure pulses until the sample was broken. The results of the pressure fatigue tests are presented in Table 4. Based on Table 4, the S-N curve was created (Figure 7). The pressure fatigue endurance limit was more than 50% higher in comparison with tensile tests of the AM samples.  
Figure 4: The pressure fatigue test rig.  
Figure 5: Pressure fatigue samples.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample No.** & **t (mm)** & **P (psi)** & **\(\sigma_{\text{max}}\) (MPa)** & **N\({}_{f}\) (Cycles)** \\ \hline X0937 & 0.4 & 2900 & 592 & 15,052 \\ X0938 & 0.4 & 3600 & 735 & 20,452 \\ X0939 & 0.4 & 2200 & 449 & 52,125 \\ X0940  
The Goodman diagram shows the influence of build orientation on the fatigue properties. AM parts that were loaded in the same direction as the build orientation indicated lower fatigue strength. This effect is widely known in AM technology, and has been extensively analyzed in the literature [36; 37; 38; 39]; however, the diagram presented in Figure 8 is the quantitative result obtained by the authors of the work in relation to the selected material and technology in which the presented actuators are made.
## 2 Methods  
### Car-Following Model  
The car-following model, which is also called the time-continuous model, can simulate the behavior of a driver following a leading vehicle. Another form of car-following model is safety distance or collision avoidance. This means that the driver tries to maintain a safe distance from the leading vehicle to avoid collisions. The car-following model was determined by an ordinary differential equation that showed the position dynamics as follows:  
\[\ddot{x}_{a}(t)=\ \dot{v}_{a}(t){=}\ F(v_{a}(t),s_{a}(t),v_{a-1}(t)), \tag{1}\]  
where \(s_{a}\) refers to the bumper-to-bumper distance to the leading vehicle, \(v_{a}\) is a host vehicle's actual speed, and \(v_{a-1}\) refers to the leading vehicle's speed.  
To deal with realistic driving interactions, we applied the IDM paradigm to this traffic simulation, considering the longitudinal dynamic for the behavior of human-driven vehicles (HVs) [27]. In this work, the velocity, headway, and identification of the leading vehicle were obtained by the \"get\" function in the SUMO simulator. The command of acceleration (\(a_{IDM}\)) was determined as follows:  
\[a_{IDM}=a\left[1-\left(\frac{v}{v_{0}}\right)^{\delta}-\left(\frac{s^{*}(v,\Delta v )}{s}\right)^{2}\right], \tag{2}\]  
where \(v\) refers to the actual velocity, \(v_{0}\) is the desired velocity, \(\delta\) is the acceleration exponent, \(s^{*}\) is the desired headway, and \(s\) is the actual headway. In addition, the formula for desired headway is expressed as follows:  
\[s^{*}(v,\Delta v)=s_{0}+max\left(0,vT+\frac{v\Delta v}{2\sqrt{ab}}\right), \tag{3}\]  
where \(s_{0}\) refers to the minimum headway, \(T\) is the time headway, \(\Delta v\) is the different velocity, \(a\) is the acceleration term, and \(b\) is the comfortable deceleration.  
In this study, the typical parameters for IDM were taken from [PERSON] and Kesting [10] and are shown in Table 1.
# Antenna Arrays for Line-of-Sight Massive MIMO: Half Wavelength Is Not Enough
## 2 Antennas and Propagation Model  
In this paper, we will use a simplified model for antennas and propagation; in particular, we will consider isotropic radiating elements; we will neglect the effect of mutual coupling and polarization, and we will consider a simple 2D propagation model, i.e., BS antennas and user terminals will lie on the same plane. Furthermore, we will consider a fixed frequency \(f\) and the corresponding wavelength \(\lambda\).  
These assumptions have a two-fold reason; first, the model we will discuss, and the achieved results, could be easily reproduced by any interested reader; second, the simplified model will allow us to remove the aspects that play a minor role in the definition of the channel matrix (polarization and mutual coupling), focusing only on the aspects, like the antenna positioning, that play the main role in the overall system performance.  
In particular, we will consider a linear antenna array of \(N\) identical elements, with the antennas aligned along the \(x\) axis, whose abscissas are stored in the vectors \(\mathbf{x}_{s}=[x_{s,1};\cdots;x_{s,N}]\). The position of the \(K\) terminal antennas will be instead described by the vectors \(\mathbf{x}_{t}=[x_{t,1};\cdots;x_{t,K}]\) and \(\mathbf{y}_{t}=[y_{t,1};\cdots;y_{t,K}]\) (see Figure 1). The transfer function between the \(n\)-th antenna of the BS and the \(k\)-th terminal will be achieved as:  
\[h_{n,k}=\gamma\frac{e^{-j\beta R_{n,k}}}{R_{n,k}} \tag{1}\]  
where \(\gamma\) is an inessential multiplicative constant, \(\beta=2\pi/\lambda\) is the free space wavenumber and:  
\[R_{n,k}=\sqrt{(x_{s,n}-x_{t,k})^{2}+y_{t,k}^{2}} \tag{2}\]  
The MU-MIMO channel matrix can be thus built as:  
\[\mathbf{H}=\ [\mathbf{h}_{1}\cdots\mathbf{h}_{K}] \tag{3}\]  
where the column vector \(\mathbf{h}_{k}\) represents the channel response of the \(k\)-th terminal.  
This channel matrix can be properly normalized, according to the guidelines in [20] before performing its singular value decomposition or calculating the spectral efficiency (thus making the constant \(\gamma\) inessential). It has also to be underlined that the use of Equation (1) allows us to take into account the different power received by the antennas at the base station because of their different distance from the terminals, as well as the spherical shape of the wavefronts.