esa-sceva/satcom-chunk-collection huggingface dataset:
### Reliability Concerns  
Like traditional communication networks, the software-defined IIoT-Edge network encounters stochastic disruptions, resulting in intermittent network service interruptions [131; 132]. The reliability of the software-defined IIoT-Edge network addresses architectural robustness, failover mechanisms, and performance under stress conditions. Given that these networks are deployed in extreme environments with unpredictable weather, there is a need to deploy ruggedized equipment that can withstand the harsh conditions of the offshore environment.  
Failures can occur at various levels, including the data plane (like switches and routers), the control plane (controller software or the applications running on it), and the data links connecting different network elements [100]. The impact of such failures is magnified in offshore wind farms due to their remote locations, harsh operating conditions, and the critical nature of the operations they support, which include safety systems, power generation monitoring, and environmental controls. Fast failover mechanisms are crucial in SDN to ensure that the network services remain uninterrupted during and after failures. SDN controllers can pre-compute alternate paths in the network, which can be quickly activated in the event of a failure [133]. This proactive approach significantly reduces the recovery time compared to traditional methods. Several reliability metrics are used to evaluate how well a network performs over time [134; 135], such as Failure rate, which measures the frequency with which a network component or service fails; Availability, which measures the percentage of time that the network is operational and available for use; Mean time between failures (MTBF), which measures the average time between inherent system failures during operation; and Mean time to repair (MTTR), which measures the average time required to repair a failed network component and restore it to operational status.  
Industrial communication networks adhere to industrial SLAs. For example, the desired communication network availability is 99.999% (_5 nines_), which implies only 5 min of downtime in a year [136]. In practice, the network performance and availability are constantly affected, and, as a result, more advanced fast failover recovery methods are studied to tackle the availability issues by defining reliability or failure models and making inferences from the model results to determine the availability [137].
# Feasibility of a Quasi-Static Approach in Assessing Side-Wind Hazards for Running Vehicles
## 2 Quasi-Static Approach for the Evaluation of Tire Forces
### Equations for Trailers with One Axle and One Hitch Point  
Tractor-trailers are exceedingly vulnerable to side winds. The trailer portion, in particular, is vulnerable to side winds due to its large surface area, and it only has one rear axle to resist overturning. Therefore, the present study simulated the quasi-static approach only for the trailer portion, as shown in Figure 2, with the assumption that the hitch point that connects trailer to tractor cannot resist the  
Figure 1: Applied actions and corresponding tire forces of two-axle vehicles.  
rolling, yawing, and pitching moments. Six equilibrium equations were developed for the same procedure in the case of two-axle vehicles, as shown in Equations (9)-(14).  
\[T-f_{R}\sum_{i=\,2}^{3}F_{zi}\ =\ F_{D} \tag{9}\]  
\[\sum_{i=\,1}^{3}F_{zi}\ =\ (m_{s}+m_{r})g-F_{L} \tag{10}\]  
\[F_{yf}+F_{yr}\ =\ F_{S} \tag{11}\]  
\[\frac{c}{2}(F_{z3}-F_{z2})+h_{T}\cdot F_{yf}\ =\ M_{R}+h_{c}\cdot F_{S} \tag{12}\]  
\[aF_{yf}-bF_{yr}+\frac{c}{2}f_{R}(F_{z2}-F_{z3})\ =\ M_{Y} \tag{13}\]  
\[b(F_{z2}+F_{z3})-a(F_{z1})+h_{T}\cdot T\ =\ m_{r}\cdot b\cdot g+M_{P}+h_{c} \cdot F_{D} \tag{14}\]  
In Equations (9)-(14), \(T\) is the traction force by the tractor, and \(h_{c}\) and \(h_{T}\) are the height of the center of gravity and the hitch point, respectively. Since Equations (9)-(14) include only six unknowns (one traction force, two lateral friction forces, and three vertical reaction forces), all the tire forces could be determined from the equilibrium equations.
# Wireless Energy Harvesting for Internet-of-Things Devices Using Directional Antennas
## 5 Simulation Results
### Comparative Performance Evaluation
#### 5.3.3 Different Offset Values  
In the following experiment, we show the charging time and overflow energy for different schemes by using different offset angles. Figure 14 shows the charging time for three different offset angles, 10, 5 and 3, where the number of devices is 30 and the transmission angle is 90. The charging time of our greedy algorithm is 53.7, 51.6, and 51.4 s for 10, 5, and 3 degrees, while the charging time of DE is 54.1, 52.2, and 51.5 s. Because a smaller offset degree also indicates more transmission directions, the greedy algorithm can thus achieve a shorter charging time. Our DE algorithm also provides a performance comparable to that of the greedy algorithm. However, the charging efficiency of Fix, RAN and RR cannot be improved with a smaller offset. The RAN and RR methods have more sectors to choose from with a smaller offset and result in a longer charging time.  
Figure 11: The overflow energy for different numbers of devices.  
Figure 10: The charging time for different numbers of devices.  
The overflow energy of different algorithms for different offsets is shown in Figure 15. The wasted energy of DE is better than Greedy by 12.9%, 7.4% and 1.6% for 10, 5 and 3 offset degrees, where DE achieves better improvements for larger offset. This is because the greedy algorithm can also benefit from a small offset to reduce overflow energy. The greedy algorithm outperforms the Fix method by 6.1-15%. Both RAN and RR still have the most overflow energy.  
Figure 14: The charging time of different offset.  
Figure 12: The charging time for different transmission angles.  
Figure 13: The overflow energy for different transmission angles.
## 1 Introduction  
The major techniques for monitoring gas concentration include the Non Dispersive Infrared (NDIR) sensor, and the solid electrolyte type of gas sensor. There are different considerations to choose the NDIR or solid electrolyte type of sensor, which is more affordable than that of NDIR. Nevertheless, the NDIR gas sensor has more technical advantages than the solid electrolyte type, likes long term stability, high accuracy, and low-power consumption [1]. According to [PERSON]'s law, a non-dispersive infrared spectrometer is capable of measuring the light intensity absorbed by the particular gas. The basic architecture of the NDIR includes an infrared light source, an optical tube containing the target gas, and an infrared sensor with a specific wavelength filter.  
In general, NDIR instruments use a fixed periodic heating source and two infrared sensors with different filters for two channels. One is the reference channel used to detect the infrared radiation that not be absorbed by gas concentrations. The other channel gives a corresponding reading used to detect the infrared radiation in specific wavelength that will be absorbed by gas concentrations to achieve wavelength selectivity [2]. The Analog-to-Digital Converter integrated circuit (ADC IC) was used to convert the signal readings of these two channels from analog signals into digital signals. The advantage of the dual channel is that it can remove the factors that cause the signal to drift by comparing the differences in light intensity between different channels.  
Besides multi-filters type sensing, there is a new approach applied in NDIR gas sensor, which comprises of a sensor and a Fabry-Perot tunable filter by modulating two parallel mirrors to achieve selected band. One selects a band tuned to the absorption band of the gas being measured and the other is fixed at a band of reference light [3].  
The \(\Delta\)-\(\Sigma\) architecture we proposed earlier is an NDIR channel with a closed-loop feedback system that amplifies the signal readings of the sensor and compares it with the threshold voltage, then through microprocessor to count and adjust the heating duty of the light source that can quickly stabilize the entire system signal readings. However, temperature changes will cause the signal readings drifting especially before the system stabilized [4; 5]. Therefore, we recorded the relationship between the signal readings of sensor and its temperature changes to explore the dynamic performance of signal readings.
# Quantitative Testing and Analysis of Non-Standard AEB Scenarios Extracted from Corner Cases
###### Abstract  
Existing testing methods for Automatic Emergency Braking (AEB) systems mostly rely on standard-based qualitative analysis of specific scenarios, with a focus on whether collisions occur. To explore scenarios beyond the standard conduct, a comprehensive testing model construction and analysis, and provide a more quantitative evaluation of AEB performance, this study extracted three typical hazardous driving scenarios from the KITTI (The Automated Driving dataset was created by the Karlsruhe Institute of Technology in Germany and the Toyota Institute of Technology in the United States) naturalistic driving dataset using kinematic data. A DME (Data Missing Estimation) scene construction method was proposed, and these scenarios were simulated and reconstructed in PRESCAN (PRESCAN is an automotive simulation software owned by Siemens, Munich, Germany). A C-AEB (Curve-Automatic Emergency Braking) testing model was developed and tested based on simulations. Finally, a BCEM (Boundary collision evaluation model) was proposed to quantitatively evaluate AEB performance. The focus of the analysis was on the identified cornering scenario A (severely failed AEB scenario). A C-AEB testing model was constructed based on the DME scene construction method for this cornering AEB failure scenario, and it was evaluated using the BCEM. The study found that the average performance degradation rate (performance degradation rate refers to the ratio of AEB performance in the current scenario compared to the standard straightaway test) of the AEB system in this cornering scenario reached 75.44%, with a maximum performance degradation rate of 89.47%. It was also discovered that the severe failure of AEB in this cornering scenario was mainly caused by sensor system perception defects and limitations of traditional AEB algorithms. This fully demonstrates the effectiveness of our testing and evaluation methodology.  
intelligent networked vehicles; safety; dangerous scene extraction; rebuild; simulation test 4.0/. +
Footnote †: journal: Applied Sciences
# Critically Leveraging Theory for Optimal Control of Quadrotor Unmanned Aircraft Systems
## 2 The Structure and Mathematical Model of the Quadrotor
### Kalman Filter  
We used Matlab to simulate [PERSON]'s noise filtering ability with random noise input signal, as shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Value** & **Unit** \\ \hline Mass (m) & 1.74 & kg \\ Length from wing to center (l) & 0.21 & m \\ Second-level heading & 9.81 & m/s\({}^{2}\) \\ \(\mathrm{I_{xx}}\) & 0.004 & kgm\({}^{2}\) \\ \(\mathrm{I_{yy}}\) & 0.004 & kgm\({}^{2}\) \\ \(\mathrm{I_{zz}}\) & 0.0084 & kgm\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of the Quadrotor used in the simulation.  
The provided simulation imagery showcases a comparative analysis of the noise filtering capabilities of the Kalman filter applied to the dynamic states of a quadcopter: position along the x-axis (horizontal), y-axis (vertical), z-axis (altitude), and the angular orientations of pitch (tilt forward and backward), roll (tilt side to side), and yaw (rotation around the vertical axis).  
Measured Data: The red lines represent the noisy measurements. This noise may originate from various sources, such as sensor inaccuracies, environmental interference, or the unstable dynamics of the quadcopter. True Data: The green lines depict the actual values of the system, unaffected by measurement noise. This represents the 'clean' information that we aim to estimate accurately. Kalman-filtered data: The dashed blue lines illustrate the values post-Kalman filtering. These lines are smoother and closely aligned with the true data, demonstrating the efficacy of the Kalman filter in removing noise and providing accurate estimates.  
Across all graphs, the Kalman filter adjusts its estimates to closely follow the true values, even amidst significant measurement fluctuations, showcasing its resilience to noise and system dynamics. In cases of strong measurement volatility (as seen in the roll and yaw graphs), the Kalman filter maintains stability and is not overly influenced by noise, reflecting the filter's robustness. The Kalman filter also appears to 'ignore' random spikes in measurement data, updating its estimates based on the consistent trends of the actual data. This imagery validates the superior noise-filtering performance of the Kalman filter when tracking the state of a quadcopter, an essential aspect in the research and development of autonomous control systems for unmanned aerial vehicles.
###### Abstract
Table 1 summarizes the types of auxetic structures with the corresponding additive manufacturing technology and the materials used for their creation. In addition to polymer 3D printing, metal additive manufacturing technologies were incorporated to produce auxetic cellular structures [8; 39; 40; 41]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Technology** & **Auxetic Type** & **Material** \\ \hline Polyjet & 2D/3D chiral, 2D re-entrant, missing ribs & Veroblue, VeroWhite, TangoPlus, VeroGray \\ FDM & 2D/3D chiral, 2D re-entrant & PLA \\ SLA & 2D/3D chiral, 2D re-entrant & Tough resin, Photosensitive resin \\ SLS & 2D/3D chiral & Polyamide 12 \\ SLM & 3D chiral, 3D re-entrant & AlSi\({}_{10}\)Mg, SS 316L, Ti\({}_{56}\)Ni\({}_{44}\) \\ EBM & 3D re-entrant & Ti-6 Al-4V \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of different types of auxetic structures with corresponding additive manufacturing technology and materials used for their creation [42]. Figure 1: Literature review and classification of existing auxetic structures: e.g., (**a**) chiral [33], (**b**) re-entrant [34], (**c**) origami [35], (**d**) perforated [36], (**e**) rotating rigid [37], and (**f**) “Bucklicrystal” [38].
## 3 Results
### FC system  
#### 3.1.1 Fuel Consumption  
The behavior of the FC system using the strategies sFF, air-PFC, fuel-PFC, and air/fuel-PFC is presented in Figures 4-7 under the same load profile (see the 1 st plot).  
Figure 4: FC system behavior using static feed-forward (sFF) strategy.  
Figure 5: FC system behavior using air-PFC strategy [34].  
Figure 6: FC system behavior using fuel-PFC strategy [34].  
Figure 7: FC system behavior using air/fuel-PFC strategy.  
The plots of Figures 4-7 represent the same waveforms as follows: the 1 st plot shows the DC load profile; the 2 nd plot, OER; the 3 rd plot, ESS power; the 4 th and 5 th plots, \(AirFr\) and \(FuelFr\), respectively; the 6 th plot, \(Fuel_{T}\); the 7 th plot, \(Fuel_{eff}\); and the 8 th plot, \(\eta_{sys}=P_{FCnet}/P_{FC}\).  
OER varies under the recommended limits for all strategies (see the 2 nd plot), but minor differences appear due to the specific operation under each strategy (see the next sections). \(P_{ESS}\) is almost zero (see the 3 rd plot), except the load transitions, validating the charge-sustaining mode of the battery.  
The load profile is followed by the both \(AirFr\) and \(FuelFr\) in the sFF strategy, then by \(AirFr\) and \(FuelFr\) in the strategies air-PFC and fuel-PFC respectively, and by \(FuelFr\) and then by \(AirFr\) in the air/fuel-PFC switching strategy (see plots 4 and 5).  
The fuel consumption of the FC system using the strategies sFF, air-PFC, fuel-PFC, and air/fuel-PFC is mentioned in Table 1 (see the 6 th plot). The last two plots show the variations of the performance indicators \(Fuel_{eff}\) and \(\eta_{sys}\). It should be noted that the ranges of variation for \(Fuel_{eff}\) and \(\eta_{sys}\) using the air/fuel-PFC strategy are the same as those using the strategy air-PFC for \(P_{Load}>P_{ref}\) and the strategy fuel-PFC for \(P_{Load}\leq P_{ref}\), respectively.  
The fuel economy of the strategy air/fuel-PFC compared to the strategies sFF, air-PFC, and fuel-PFC is estimated in Table 2 using (15):  
\[\%Fuel_{T(sFF)} = 100\left(\frac{Fuel_{T(sFF)}-Fuel_{T(Air/Fuel-PFC)}}{Fuel_{T(sFF) }}\right) \tag{15a}\] \[\%Fuel_{T(Air-PFC)} = 100\left(\frac{Fuel_{T(Air-PFC)}-Fuel_{T(Air/Fuel-PFC)}}{Fuel_{T( Air-PFC)}}\right)\] (15b) \[\%Fuel_{T(Fuel-PFC)} = 100\left(\frac{Fuel_{T(Fuel-PFC)}-Fuel_{T(Air/Fuel-PFC)}}{Fuel_{ T(Fuel-PFC)}}\right) \tag{15c}\]
## 3 Generalized Algorithm
### Algorithms for Equivalent Circuit Transformation  
In the generalized algorithm, the transformation of equivalent circuits is carried out according to the flowchart shown in Figure 14. It provides four options for transforming the equivalent circuit, depending on the shape and internal filling of the enclosure. Thus, for empty enclosures, the first transformation step calculates the values of \(Z_{g\,mn}\) and \(k_{g\,mn}\). The subsequent calculations are carried out in full accordance with the analytical model from [20]. For internally-filled enclosures, transformation of equivalent circuits is performed in accordance with the flowcharts shown in Figures 15 and 16.  
Figure 15 shows the algorithm used to transform an equivalent circuit for a filled rectangular enclosure. It starts by processing vector \(D\) containing all the \(d_{x}\) points that define the positions of enclosure content. First, the \"Sort (\(D\))\" block sorts points \(d_{x}\) in ascending order (from 0 to \(d\)). The algorithm then determines the size (\(x_{max}\)) of vector \(D\), and the number of the element \(x_{d}\) corresponding to the observation point \(p\). Next, for each point \(d_{x}\) in the range \(x=1,2,\,\ldots,\,x_{d}-1\), the algorithm sequentially calculates filling parameters and intermediate values of voltages \(V_{x}\) and impedances \(Z_{x}\). When \(x=x_{d}\), the transformation for the left-hand side of the equivalent circuit ends. The last values of \(V_{x}\) and \(Z_{x}\) are stored in the \(V_{L}\) and \(Z_{L}\) variables, and the algorithm proceeds to calculate the load impedance. The right-hand side of the circuit is transformed in reverse order from \(x_{max}\) to \(x_{d}+1\). In each segment from \(d\) to \(p\), intermediate values of \(Z_{x}\) are calculated. The final load impedance \(Z_{R}\) is determined when \(x=x_{d}+1\). The values of \(V_{L}\), \(Z_{L}\), and \(Z_{R}\) from the algorithm are then used to calculate \(V_{p}\) and \(i_{p}\).  
Figure 16 presents a flowchart of the algorithm for transforming an equivalent circuit of filled cylindrical enclosure. It operates in a similar way to the algorithm in Figure 15, but the calculation does not take into account the conductive structures that fill the enclosure.  
Figure 13: Algorithm of \(Z_{qp}\) calculation for a group of apertures (Flowchart 4).  
Figure 14: Algorithm of equivalent circuit transformation (Flowchart 5).  
Figure 15: Algorithm of equivalent circuit transformation for a rectangular enclosure with internal filling (Flowchart 6).
## 3 Results and Discussion
### Results of Problem 2 and CNr Made of Capacitors or Inductors  
The Pareto front of Problem 2 is represented in Figure 8a. In this case, the OFs (13), to be maximized, and (14), to be minimized, are used and, among the whole 75 optimized individuals, 36 individuals satisfy the three conditions stated in Section 3.1. Their power vs. position and per unit power vs position profiles are reported in Figures 8b and 9. Two individuals whose per unit power profiles are similar to those of A and B are selected and are denoted as E and F. As a matter of fact, the power, \(\text{P}_{\text{L}}\)(0), transferred using individuals E and F is roughly twice the power relevant to A and B, respectively. The individual E gives nearly the same \(\text{P}_{\text{L,p-u-,max}}\) and \(\text{xp}_{\text{max}}\) as the individual A, while F originates a lower \(\text{P}_{\text{L,p-u-,max}}\) than B but with a shorter \(\text{xp}_{\text{max}}\).  
Figure 6: Per unit power vs. position (Problem 1).  
Figure 7: Efficiency vs. position (Problem 1).  
Considering the individuals with the highest power transferred, the one marked with \"G\" gives the most balanced performance because its relevant \(\mathrm{P_{L}}(0)\) and \(\mathrm{x}\mathrm{p}_{\mathrm{max}}\) are very near to the highest among the 36 selected individuals and \(\mathrm{P_{L,p\text{-u},max}}\) is sensibly lower than the maximum. Considering the power profiles, individual G outperforms D, but unfortunately it originates an efficiency about 3% lower, as can be seen in Figure 10, which plots the efficiency profiles of G, E, and F, and in Table 3. All the analyzed individuals in terms of power profile are marked with green dots in Figure 8a.  
This result regarding the efficiency is general, and indeed all the profiles of the 36 selected individuals lie below the profiles of the 11 elements selected in Section 3.2.  
Figure 8: (**a**) Pareto front of Problem 2 (CNs made of capacitors and inductors) and (**b**) Power vs. position. In panel (**a**) the green stars show the selected individuals for the analysis.  
Figure 10: Efficiency vs. position for (Problem 2 and both CNs made of capacitors and inductors).  
Figure 9: Per unit power vs. position (Problem 2 and both CNs made of capacitors and inductors).  
Figures 7-10 confirm that the individuals E, F, and G are characterized by lower efficiency than A, B, C, and D. Moreover, in this case there is not an inverse relation between \(\mathrm{P_{L}(0)}\) and \(\eta\) because G, which gives the maximum \(\mathrm{P_{L}(0)}\), has an efficiency higher than F and G. The latter ones have nearly equal efficiency profiles.