esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks
## 5 Result Analysis and Performance Evaluation
### Under Jellyfish Periodic Drop Attacks
#### 5.1.3 Throughput  
The throughput is the number of delivered data packets within a given time frame. It can be calculated as follows:  
\[\mathrm{Throughput}=\frac{\mathrm{Nbr}_{\mathrm{Packets}}}{\mathrm{T}_{ \mathrm{Period}}} \tag{10}\]  
where Nbr \({}_{\mathrm{Packets}}\) is the number of delivered packets and T Period is a particular time period.  
An ideal throughput is produced by a high PDR and a low end-to-end delay. Figure 12 shows the proposed SecE-V2X's generated throughput depending on the number of attacker nodes. We can observe, without a doubt, that our proposed protocol maintains better throughput than that of the competing schemes. In the absence of attacker nodes, the proposed protocol has an average throughput of 638.15 packets/s, while [37], 2017, had a throughput of 534.55 packets/s and [20], 2018, had a throughput of 596.33. The average throughput of the proposed protocol in the presence of three malicious nodes is 608.22 packet/s, whereas [37], 2017, has a throughput of 452.11 packet/s and the throughput of [20], 2018, is 556.65 packet/s. We can clearly see that the throughput performance drops drastically with an increase in the number of attacker nodes in the network. The throughput goes down to the lowest with nine malicious nodes, reaching 433.53 packets/s in the proposed protocol, 282.38 packets/s in [37], 2017, and 398.41 packets/s in [20], 2018. Through the Honesty metrics, our protocol can obtain better performance with regard to throughput.  
Figure 11: End-to-end delay under Jellyfish periodic drop attacks [20; 37].
## 4 Results
### Control Laws Performance Comparison
#### 4.3.2 Cooperative Formation Control Case  
In this case, a similar test as the one performed in Section 4.2 is selected for comparison, where the followers cooperate with the leader sharing their position so it can wait for them to keep a tighter formation. Villela controller is used instead in all three robots. Figure 21 shows the results with the previous DRL results superimposed (in dashed line).  
For a more quantitative comparison of controller performance, the absolute integral error (IAE) and the square integral error (ISE) are applied to the distance error \(d\) from its position to the target \(IAE=\sum d|dt\) and \(ISE=\sum d^{2}dt\), with \(dt\) being the sample interval. The results are shown in Table 1.  
Figure 19: Follower Villela control surfaces, showing a position history in blue and stating point in red.  
Figure 20: Position histories in the horizontal plane moving from right to left, DRL: blue and Villela: red. The black cross represents the target and the black arrow represents the initial orientation of the robot.  
From Figure 21 and Table 1, it is clear that DRL is outperforming Villela during the cooperative test.
## 2 Methods
### Data Acquisition  
A 16-channel g.USBamp acquisition device (g.tec medical engineering GmbH, Schiedlberg, Austria) was used to acquire EEG signals at a sampling rate of 256 Hz. EEG was acquired with 16 g.Ladybird electrodes (Fz, Cz, C3, C4, CPz, Pz, P3, P4, P07, P08, POz, Oz, FPz, FCz, FC1, and FC2) placed in a g.GAMMAcap2 (g.tec medical engineering GmbH, Austria) according to the extended international 10-20 System. The right earlobee was used as reference and the AFz channel for ground. All electrodes were connected to a g.GAMMAbox (g.tec medical engineering GmbH, Austria). Signals were filtered by a band-pass filter between 0.1 and 30 Hz and a notch filter at 50 Hz to eliminate the powerline interference. Data were acquired, processed, and classified in real-time in a Highspeed online processing Simulink framework.
## 3 Control System for Work Vehicle
### Control of the Work Vehicle's Walking System
#### 3.2.2 Operating Principle and Circuit Design  
The operational vehicle's movement is facilitated by rear-wheel drive, managed through an AC servo system. The electric control design allows for motor speed adjustments via a speed rotation button, a foot switch, and an emergency stop button.  
The working principle is as follows. First, ensure the emergency stop button and footswitch are both in the open position, then turn on the main power switch. Next, set the movement knob to the desired position: \"Forward\", \"Stop\", or \"Reverse\". After that, adjust the speed control knob and press the footswitch to send a signal to the servo motor, prompting the vehicle to move at the set speed. Control of the vehicle is achieved through the steering wheel, which has an electric power steering motor for easier maneuvering, as shown in Figure 14. In terms of comfort, the seat was ergonomically designed and adjustable in height and angle to reduce the fatigue of the driver during long hours of operation. The visibility is good, with a wide view from the driving position and a clear display on the instrument panel, which can provide real-time important information such as vehicle speed and battery power, facilitating the driver to grasp the vehicle status at any time and improving the safety and efficiency of the operation.  
Figure 14: Physical map of walking control. 1—Speed knob. 2—Emergency stop knob. 3—Forward and reverse knob. 4—Power switch. 5—Foot switch. 6—Power steering motor.  
Figure 13: Installation drawing of the drive motor.  
In the circuit design, the 72 V battery pack is transformed to 220 V via an inverter, connecting to three sets of wires. One set links to an air switch and subsequently to a 24 V power supply, converting the voltage to a stable 24 V to power the vehicle's low-voltage electrical appliances. The other two sets of wires connect to the control power supply and the input terminals (L1C, R, L2C, and S) for the left and right servo motor drivers, as illustrated in Figure 15.  
Figure 16 is the wiring diagram of the PLC host and the servo driver. The PLC controller is equipped with functions such as foot switches, speed adjustment control, emergency stop switches, and servo fault reporting. In terms of servo control, the PLC sends pulse and direction signals to the servo driver through ports Y0 and Y1, and sends direction signals to the left and right servo drivers through ports Y2 and Y3. The drivers are connected to the encoders and motor lines, forming a closed-loop control system.
## 5 Material and Methods
### Network Traffic Data Collection
#### 5.5.3 DDoS Detection Using Traffic Features  
The DDoS detection process begins by analyzing the traffic feature vectors \(X(t)\) derived from the flow statistics at time \(t\). The feature vector \(X(t)\) contains critical network statistics aggregated from all switches:  
\[X(t)=[\sum_{i=1}^{N}\text{packet\_count}_{i}(t),\sum_{i=1}^{N}\text{byte\_count }_{i}(t),\sum_{i=1}^{N}\text{flow\_duration}_{i}(t),\ldots] \tag{6}\]  
where  
\(\sum_{i=1}^{N}\text{packet\_count}_{i}\left(t\right)\): Total packet count across all switches at time \(t\).  
\(\sum_{i=1}^{N}\text{byte\_count}_{i}(t)\): Total byte count across all switches at time \(t\).  
Let \(X(t)\in R^{d}\) where \(d\) represents the number of traffic features. The DDoS detection model evaluates \(X(t)\) to classify the traffic as either normal or an attack. The binary output \(\hat{y}(t)\) of the detection model is defined as:  
\[\hat{y}(t)=\left\{\begin{array}{ll}1,&\text{if ddos attack is detected}\\ 0,&\text{if normal traffic is detected}\end{array}\right. \tag{7}\]  
The detection process can be formulated as a classification problem where a detection function \(f_{\text{detect}}\left(X(t)\right)\) maps the feature vector to a binary outcome:  
\[\hat{y}(t)=f_{\text{detect}}(X(t)) \tag{8}\]  
where  
\(f_{\text{detect}}\): DDoS detection model (e.g., a classification algorithm)  
\(\hat{y}(t)\): Detection result at time \(t\) (1: DDoS attack detected, 0: Normal traffic)
## 1 Introduction
As a consequence, most of the ACBC schemes proposed in the literature cannot be implemented in the cellular systems [9; 11; 14; 17]. [PERSON] et al. presented one of the few ACBC schemes that were specifically designed for 3 GPP cellular networks [18]. In particular, a reinforcement learning approach was utilized. However, results show that the performance achieved under the most typical mMTC scenario with the proposed ACBC scheme is lower than that of correctly selecting the fixed barring parameters beforehand. In our previous work, we proposed an ACBC scheme that incorporates a filtering block [19]. In this block, the well-known least-mean square (LMS) algorithm was implemented. In particular, two different applications of the LMS were investigated: the adaptive line enhancer (ALE) and a novel twist on the latter, referred to as \"pulling\" ALE (PALE) [19]; preliminary work on the benefits of the PALE can be found in [8]. During previous work, we found our ACBC scheme is one of the few that can be implemented in 3 GPP networks and that can provide a near-optimal performance, which greatly surpasses that of the ACB with fixed parameters, under different access request intensities [8; 19]. The main motivation of the present study is to investigate the potential benefits of our ACBC scheme with different filtering methods, namely, the recursive least-squares (RLS) algorithm or fixed finite-duration impulse response (FIR) filters instead of the LMS. Each of these presents different characteristics in terms of the rate of convergence, numerical stability, and simplicity. Therefore, the main contributions of this paper are described in the following. 1. Two different filtering methods are investigated, namely the RLS adaptive algorithm and a FIR filter. The benefits of these methods are compared to those of the LMS. The latter was the only algorithm considered in our previous work [8; 19]. An in-depth analysis of the tracking capabilities of the LMS and RLS adaptive algorithms in transient environments is presented. We observed several filtering methods lead to an adequate operation of our ACBC scheme under the most typical mMTC scenario. However, the main difference between implementing an adaptive algorithm and a simple FIR filter is that, for the latter method, an additional mechanism is needed to reduce the access delay of UEs in mMTC scenarios that can be handled by the RAP itself. That is, when the intensity of access attempts is low when compared to the capacity of the RAP. Clearly, increasing the access delay of UEs in these cases is not beneficial. On the other hand, such a mechanism is not needed if some adaptive algorithm configurations are implemented. The rest of the paper is organized as follows. The RAP and the ACB scheme are described in Section 2. Next, our ACBC scheme, the studied filtering methods, and the methodology used to assess the benefits of these methods are explained in Section 3. Our main results are presented in Section 4 and Section 5 concludes the paper.
## 1 Introduction  
Identification of the location of rail vehicles plays an essential role in transport systems control. Current information about the location of the monitored elements enables its users to effectively solve situations where transport element position has key importance, [1]. Long-term efforts have been made in railway transport towards the implementation of a harmonized ETCS (European Train Control System) within all the EU member states. The ETCS is a part of the ERTMS (European Rail Traffic Management System), which is designed for railway transport management. The system uses, among others, information about the current, real-time train position. However, there are already various studies on how to adapt the ERTMS using the GNSS [2]. However, the mentioned complex Europe-wide systems are primarily designed for main rail corridors with heavy traffic intensities. Application of the management systems to regional and single tracks is not counted on regarding their low traffic loads. Permanent crisis operational situations have unambiguously demonstrated that the lower frequency of regional and single-track traffic does not essentially reduce the risk of train collisions or nonstandard situations [3; 4; 5].  
With the aim to prevent crisis situations, we must pay attention to the development of systems capable to detect such situations, e.g., identify current positions of rail vehicles with the use of the worldwide GNSS within a multilayer railway network model reflecting the undirected data structure graph. The detection can be carried out either by selected analytical approaches or with the use of artificial neural networks or machine learning [6]. The use of the GNSS in rail transport has been the subject of a number of research studies for many years, focusing on a variety of issues. These have included testing in laboratory conditions [7], establishing methodologies for risk analysis and evaluation of GNSS-based localization systems [8; 9] and or detecting train integrity to quantify safety integrity level (SIL) criteria [10]. Additionally, the issues of signal reliability [11; 12] and the adaptation of localization systems in case of signal failure [13; 14; 15] must be addressed when using the GNSS.  
In the context of the localization of rolling stock within the combination of the ETCS and the GNSS, the use of so-called virtual balises [16; 17] can be mentioned as an alternative to physical balises, e.g., on regional lines.
## 5 Ultra-Wide Ionospheric Monitoring
### Experiment Facilities and Results  
On 16 March 2015, a large-area ionospheric monitoring experiment was carried out in Chongyang. During the experiment, WIOBSS-AA periodically transmitted the phase-coded pulse train waveform and received the echoes during the pulse interval. The applied waveform parameters of the radio system were 39.0625 kHz pulse repetition rate, 20% duty cycle, and 511-bit pseudo-random code used as the modulating signal [28; 29]. The range resolution was 3.84 km. The unambiguous Doppler measuring range was between [\(-\)3.81 Hz, \(+\)3.81 Hz] and the Doppler resolution was 0.0596 Hz. WIOBSS-AA was operated in the swept-frequency mode. The initial, end, and step frequencies were 6.6 MHz, 13.2 MHz, and 100 kHz, respectively.  
The location, as well as the five beam directions, of WIOBSS-AA is shown in Figure 10. The log-periodic antennas pointed northwest. Due to being a phased antenna array, the WIOBSS-AA is able to cover the large-area ionosphere with only 500 W peak power. The five beams pointed to 285\({}^{\circ}\), 300\({}^{\circ}\), 315\({}^{\circ}\), 330\({}^{\circ}\), and 345\({}^{\circ}\) in a clockwise manner. The beam elevation of the antenna array decreases with increasing operating frequency. The elevation is 42\({}^{\circ}\), 32\({}^{\circ}\), and 30\({}^{\circ}\) at 8, 10, and 12 MHz operating frequency, respectively. A receiver nearby with a broadband folded dipole antenna was used to record the echoes of the transmitting array.  
The ionograms of the five beams shown in Figure 11 all depict the distribution of scattered power as a function of group distance. Strong radio-frequency interference in the ionograms was removed [30]. The signals between 200 and 580 km are the vertical reflected echoes from F2-layer. The oblique backscatter echoes appear from 600 to 1400 km. The two-hop vertical-incidence echoes were overlapped with the oblique-incidence backscattered echoes. Usually, the range of the two-hop vertical-incidence echo is double that of the one-hop echo. So, by assessing the one-hop echo trace, the  
Figure 10: Map showing the location of the WIOBSS-AA in Chongyang, Hubei province, China and the antenna pointing in the experiment of 16 March 2015. The five main beams of the WIOBSS-AA are denoted by the different colors.  
two-hop echo trace can be removed and then the leading edge of the backscattered echoes can be well recognized. Due to focusing effects, the backscatter echoes have an obvious smooth leading edge at the minimum distance of each operating frequency. The leading edge of each beam is extracted and displayed in Figure 12. Initially, the leading edges are overlapped. With an increase of the operating frequency, the detecting area of each beam is further and further from each other, and the leading edges also gradually separate from each other. The further north the beam points, the higher the leading edge is in the group range.
## 3 Results and Discussion
However, the relative spatial distribution in water use (ETa) is expected to represent the situation in the field as it is demonstrated by flux tower data on two locations with different climate (Figures 5a and 5b). Figures 5a and 5b show a validation of the VegET ETa results using AmeriFlux tower eddy covariance latent heat flux measurements. Figure 5a shows these relationships using flux tower data from Brookings, South Dakota, in a crop/grassland environment, while Figure 5b shows the same relationship in the semiarid grassland of Audubon, Arizona. On both sites, the flux tower latent heat flux ETa and VegET ETa tracked well, capturing the seasonality of ETa with comparable magnitudes. The major difference between the two sites is that the ETa was much lower than the ETo during the dormant vegetation growing season in the Arizona site. This difference indicates the importance of rainfall/soil moisture or supplemental irrigation for achieving higher magnitudes of ETa in such environments. On the other hand, the relationship in Brookings, South Dakota, shows that, although both the VegET and flux tower latent heat ET track well, their magnitude is very close to ETo in much of the season. This indicates the limiting factor to increasing vegetation water use for more biomass production is the available energy in the form of net radiation. The overall performance of the VegET when compared to the latent heat flux data points is encouraging in the two different environments. The day-to-day correlation coefficient between VegET ETa and flux tower latent heat flux (LE) is 0.72 and 0.74 for South Dakota and Arizona. The respective correlation coefficients improved to 0.87 and 0.88 when the data were aggregated on 10-day time periods. These results are particularly encouraging considering the flux tower latent heat ETa data contain some suspiciously high magnitudes and extreme fluctuations from day to day in some cases. For example, in South Dakota, flux tower LE ETa was higher than ETo in 188 days out of 365. The ETo is expected to provide the upper boundary of ETa under unlimited moisture conditions. This is an indication that the flux tower LE is probably overestimating at the South Dakota site. Another example that shows the extreme fluctuation is the unseasonably high flux tower LE ETa estimate of 5.8 mm on 31 December 2005 (day number 365, Figure 5a). The problem with the Arizona flux tower data was that it was missing 11 days during the critical growing season (high ETa periods) and suspected too low values in the days next to the missing days. Overall, VegET ETa and flux tower latent heat flux data tracked very well even during the winter months at the Arizona site. Figure 5: Validation of VegET ETa using AmeriFlux data in a grass/cropland region of Brookings, South Dakota **(a)**, and a semiarid grassland region of Audubon, Arizona **(b)**, using daily data from 2005.
## 3 Results
### Hot Fracture Modelling  
Tensile tests at temperatures of 1173, 1273, 1373, and 1473 K and strain rates of 1 s\({}^{-1}\) and 10 s\({}^{-1}\) were carried out to determine the fracture strain (the relative cross-section reduction (\(\Psi\)) after failure). The engineering stress-strain curves, the fracture surface of the samples, and the values of \(\Psi\) are shown in Figure 4.  
Figure 4: Experimental tensile curves of the AISI 316 Ti steel at (a) 1 s\({}^{-1}\) and (b) 10 s\({}^{-1}\) (the values \(\Psi_{\mathrm{T}}\) is the relative cross-section reduction at the correspondent temperature).  
The modified Rice and Tracy fracture model in conformity with Equation (11) was used to simulate the fracture process. The dependences of the stress triaxiality (\(\sigma^{*}\)) on the strain in the center of the samples were calculated using the FE simulation of the tension process. As shown in Figure 5, the dependences have a similar tendency for all simulation conditions. The value of the stress triaxiality increases up to the beginning of the deformation localization. After the necking, \(\sigma^{*}\) decreases up to experimental fracture strain (circles at the plots). The obtained dependences were integrated to determine the critical values of the modified Rice and Tracy criteria at each test temperature and strain rate following Equation (11). As one can see in Figure 6, the critical values are dependent on the Zener-Hollomon parameter by a power law:  
\[mRT_{\sigma}=AZ^{n}=4.2\Big{[}\dot{\varepsilon}e^{\frac{48.5900}{RT}}\Big{]}^{ 0.03} \tag{33}\]  
The strain rate sensibility of the critical values is low, and the deformation temperature has a crucial influence on fracture behavior. The effective activation energy of the fracture 345.9 kJ-mol\({}^{-1}\) has a lower value in comparison with the \(Q_{HS}\) value (420 kJ-mol\({}^{-1}\) for a strain rate of 0.8). It may be explained by a lower energy barrier for the appearance and coalescence of the voids during hot deformation compared to the process of the dislocations' generation and movement.  
Figure 5: Calculated dependence of the stress triaxiality \(\sigma^{*}\) on the strain of the AISI 316 Ti steel at (**a**) 1173 K, (**b**) 1273 K, (**c**) 1373 K, and (**d**) 1473 K and the distribution of the strain in the samples at the value which correspondent the experimental fracture strain (circles).
## 3 Simulation Model of the Drive and Transmission Mechanism  
To analyze various parameters, such as the efficiency, acceleration, fuel consumption, driving forces, power, dynamic factors and related characteristics depending on the external and internal inputs, such as driving conditions, driver requirements and changing the parameters of the drive and transmission mechanism elements, it is necessary to create a complex simulation model (Figure 7), which includes the gearbox together with the main components of the vehicle's drive and transmission mechanism.  
The fundamental assumption for the creation of a functional model is in the transformation of the drive and transmission mechanism of the Iveco 4 \(\times\) 4 M65 vehicle into the simulation environment. For our purposes, Matlab software with its modules was chosen.  
Figure 6: Analytical dynamic characteristics of the Iveco LMV 4 \(\times\) 4 M65 vehicle.  
The model of the mechanical part of the drive and transmission mechanism of the vehicle involves all fundamental mechanical components organized into five subgroups:  
1. Engine: IVECO F1 CE0489C engine characterized by the parameters available in the technical documentation;
2. Torque converter: general model of the drive and transmission mechanism;
3. Gearbox: ZF 6 HP26 gearbox characterized by the parameters available in the technical documentation;
4. Additional gearbox: reduction gearbox and transfer case;
5. Vehicle body: subgroup consisting of rear (5) and front (6) differentials, wheel reductions (7) and driving wheels (8). The simulation model also includes driving shafts (9), axle shafts (10) and various different sensors for revolutions, torque, power, speed, etc., which create the output of the system. Subgroup 5, vehicle body, is also characterized by the dimensions, weights and external factors acting on the vehicle.  
In the created simulation model of the vehicle, the previously created numerical simulation model of the planetary gearbox was applied. The simulation model of the ZF 6 HP260 planetary gearbox used in the vehicle is described in detail in [52].  
The simulation model is designed to work reliable only if the input data are assigned correctly and in the predefined tolerance range and if they are complete. Otherwise, the created model will not perform the simulation. In the case of modification or replacement of components, it may be necessary to synchronize the processes to a 1 kHz sampling frequency.