esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
The major drawbacks of high-speed VANETs such as security and communication overhead are taken into consideration. To overcome these drawbacks, privacy-aware secure routing is introduced with the combination of Genetic algorithm (GA)-based optimal RSU distribution and ECC-based Authentication. Optimal RSU distribution is mainly employed to control the communication overhead, and ECC is used to improve the security of the VANETs. The simulation is performed using NS2, and three types of analysis are conducted for performance evaluation that are in terms of number of vehicles, varying speed, and increase in malicious vehicles. The performance of the proposed ECC-GA-PASR is calculated and then compared with the earlier works, such as S-AODV, ES-AODV, and ECC-ACO-AODV. According to number of vehicles, the efficiency of the proposed method reaches up to 95%, which is around 15% higher than the earlier works. The Overhead of the proposed ECC-GA-PASR is very low in terms of vehicles. According to varying speeds, the efficiency is reduced for all the methods but is only 3% for ECC-GA-PASR, which is 10% better than others. The Overhead of the proposed ECC-GA-PASR is very low in terms of increase in speed. According to the increase in the malicious vehicles, the efficiency of the proposed ECC-GA-PASR is reduced by around 3%, which is significant when compared with the earlier works such as S-AODV, ES-AODV, and ECC-ACO-AODV. In the future, the research will be continued by providing hybrid security so that an increase in speed should not affect the performance of the network.  
**Author Contributions:** Conceptualization, Funding acquisition, Resources, Investigation, Writing--original draft, G.G.S.; Data curation, Funding acquisition, D.A.M.; Investigation, Funding acquisition, Funding acquisition, Methodology, Validation, Writing--review and editing, Visualization, Supervision, A.H.A.; Investigation, Funding acquisition, N.F.A.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** Partial support by Imam Ja'afar Al-Sadiq University.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
The present study aims to analyze the physiological and agronomical response to drought among seven local and traditional field-grown Monastrell clones (4, 94, 188, 360, 276, 372, and 373) over four seasons (2018-2021) under optimum irrigation conditions (control) and water stress (stress). We have focussed on measuring Monastrell interclonal variability in plant water relations and leaf gas exchange, vegetative growth, leaf mineral nutrition, yield, water use efficiency (WUE), and grape and wine quality. A classification of the different clones according to drought-tolerance degree and agronomical/oenological performance was established. The classification revealed that (a) The most drought-tolerant clone (i.e., clone 4) was neither the most productive (8600 kg ha\({}^{-1}\)), the most efficient in terms of water use (average of 25 kg m\({}^{-3}\)), nor the one that presented a better grape quality; (b) The most productive and efficient clone (i.e., clone 94) (11,566 kg ha\({}^{-1}\), average of 30 kg m\({}^{-3}\)) was also a drought-tolerant clone, but it provided the worst berry and wine qualities with the lowest aromatic/nutraceutical potential, and it is not recommended for premium red wine production; (c) Conversely, clone 360 provided the highest berry quality, but at the expense of a greatly reduced vigor and yield (4000 kg ha\({}^{-1}\)) and a lower WUE\({}_{\text{yield}}\) (average of 10 kg m\({}^{-3}\)); (d) Low-vigor clones 372 and 276 were the most sensitive to drought conditions and put more water conservation mechanisms into play, i.e., a tighter control of vine water use and reduced leaf transpiratory surface, under soil water deficit and high vapor pressure deficit (VPD). In addition, these clones reached a balance between drought tolerance, productive water use efficiency (WUE\({}_{\text{yield}}\)), and berry and wine quality, because they provided moderate yields (7400-7700 kg ha\({}^{-1}\)), a high WUE\({}_{\text{yield}}\) (average between 17-19 kg m\({}^{-3}\) applied water), and an enhanced berry and wine quality with greater oenological, nutraceuticalital, and aromatic potential; (e) High-vigor clone 188 also displayed several mechanisms of drought tolerance (tighter stomatal control of water use), maintained a higher yield (10,500 kg ha\({}^{-1}\)) and a very high WUE\({}_{\text{yield}}\) (29 kg m\({}^{-3}\)), enhanced berry quality (similarly to 276 or 372), and improved oenological/aromatic potential, and can also be recommended for the application of low water volume deficit irrigation (DI) strategies under semi-arid conditions.  
berry and wine quality; climate change; clone; intracultivar genetic diversity; Monastrell; water stress tolerance; water use efficiency
# Evaluation of a Reputation Management Technique for Autonomous Vehicles
## 2 Background
### Defensive Measures  
In this subsection, the methods and approaches used by existing vehicles for cybersecurity are reviewed.  
[PERSON] and [PERSON] [36] discussed how cyber-attacks may be prevented through the use of secure communications, firewalls, and device authentication. Through encryption methods, secure communications can ensure the confidentiality of data and prevent malicious actors from interacting with it. Some encryption schemes may also rely on key pairs to verify the identity of the sender [36]. A firewall is a networking system component that controls incoming and outgoing connections using a set of rules. In AV security, firewalls may block malicious communications from being sent within the vehicle-to-everything (V2X)--communications between AVs and infrastructure, vehicles, and others [37]--domain. [PERSON] et al. [38] noted that device authentication protocols are another way of authenticating nodes using a controller certificate, which includes a controller identifier, public key, and list of authorized actions.  
Several studies have also been conducted on controller area network (CAN) and electronic control unit (ECU) security architectures. [PERSON] et al. [39] proposed an architecture for securing internal communications. The system utilized a proof-based architecture to address time-constraints presented by a vehicle operating in the real world. Another approach to securing the CAN was designed by [PERSON] et al. [40]. This system, CANAuth, was designed to ensure the data being transmitted across the CAN is secured. It can be applied to existing nodes to effectively defend against injection attacks. [PERSON] and [PERSON] [38] also proposed a framework for detecting and mitigating injection attacks by calculating the latency of messages between a sender and receiver.  
Over the past several years, there has been growth in vehicular ad hoc network (VANET) security research. [PERSON] et al. presented a password-based conditional privacy-preserving authentication and group-key generation protocol. This protocol aims to maintain privacy, while leveraging a hashing function to securely exchange data, allowing the system to defend against impersonation attacks [41]. [PERSON] and [PERSON] [42] explored the application of regional blockchain implementations in VANETs, which they found secured their system from 51% of attacks. Along with this, they evaluated factors such as puzzle calculation and message-delivery time and their impact on reducing the success rate of attacks [42].  
This assessment of the benefits and drawbacks of current defensive measures for vehicle cybersecurity provides a useful framework upon which to base current research.
aureus_) and _Escherichia coli_ (_E. coli_) very efficiently for \(1\,\mathrm{h}\)[17]. Later, we encapsulated carbon polymerized dots (CPDs) into polyurethane films and established their strong antibacterial potential, especially _E. coli_, for \(1\,\mathrm{h}\)[18]. Other authors encapsulated metal nanoparticles (Cu, Au) into polyurethane films and described their antibacterial activity against _S. aureus_ and _E. coli_[19; 20; 21; 22]. Many bactericidal applications of carbon dot composites rely on combining the photodynamic reactive oxygen species production of carbon dots with the antibiotic activity of a transition-metal-based nanoparticle, often containing Ag, Cu, Zn or Ti oxides cores, among others [23]. Fullerenes can inhibit bacterial growth and metabolism. It was established that Gram-positive bacteria are more sensitive to fullerenes due to their higher membrane permeation [24]. The antibacterial activity of fullerenes is based on the electrostatic interaction between fullerenes and bacterial membranes or due to the reactive oxygen generation of fullerenes [25]. In this paper, we investigated the structural, chemical, antibacterial, antibiofouling and cytotoxic activities of CPD/polyurethane (CPD/PU) composite films and C\({}_{60}\)/PU composite films. We conducted electrostatic force microscopy (EFM) of composites, which is an electrical mode in atomic force microscopy (AFM) to monitor the variation in samples'electric fields to obtain information on the charge content and distribution of nanocarbon fillers. We tested both composite film samples on seven different bacterial strains, including _Klebsiella pneumoniae, Proteus mirabilis, Salmonella enterica, Enterococcus faecalis, Enterococcus epidermidis, Shigella flexneri, Pseudomonas aeruginosa_ and fungi _Aspergillus niger_. Apart from antibacterial activity, we conducted antibiofouling tests against the following bacteria: _Pseudomonas aeruginosa_, _Staphylococcus aureus_ and _Escherichia coli_.
## 3 Results and Discussion
### Ozone Deposition Velocities
#### 3.1.3 Comparison with Data in the Literature
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Material** & **Description** & **Age** & **Chamber** & **ACH** & **Temp.** & **RH** & **Ozone** & **Deposition** & **Source** \\  & & & & **(h\({}^{-3}\))** & **(\({}^{\circ}\)C)** & **(\({}^{\circ}\)s)** & **(ppm)** & **(ppm)** & **Velocity (cm s\({}^{-1}\))** & **Velocity (cm s\({}^{-1}\))** & **Source** \\ \hline \multirow{2}{*}{OSB} & Green-core & \(>\)1 year & 50 L & 1.52 & 23 & 20 & 100,300 & 0.040,051 & This study \\ \cline{2-9}  & No data & No data & 35 mL & \(-\)174 & 23 & \(<\)2 & 100 & 0.011,0.033 & [46] \\ \hline \multirow{2}{*}{Plywood} & Luan & \(>\)1-2 years & 50 L & 1.52 & 23 & 20 & 100,300 & 0.035,0.026 & This study \\ \cline{2-9}  & One-side varnshed & No data & 2.38 m\({}^{3}\) & Decay method & 22 & \(50\pm 5\) & 1000,500 & 0.03 & [47] \\ \hline \multirow{4}{*}{Fine} & Pain-free & \(>\)1-2 years & 50 L & 1.52 & 23 & 20 & 100,300 & 0.089,0.005 & This study \\ \cline{2-9}  & & & & & & \(47\pm 1\) & 109 \(\pm\) 1 & 0.108 & \\ \cline{2-9}  & & & & & & \(48\pm 1\) & 129 \(\pm\) 2 & 0.069 & \\ \cline{2-9}  & \multirow{2}{*}{Raw, untraoted, without knots} & New and unused & \multirow{2}{*}{17 L} & \multirow{2}{*}{12} & \multirow{2}{*}{23 \(\pm\) 2} & \multirow{2}{*}{50 \(\pm\) 1} & \multirow{2}{*}{167 \(\pm\) 1} & \multirow{2}{*}{165 \(\pm\) 0.041} & \multirow{2}{*}{[48]} \\ \cline{2-9}  & & & & & & \(41\pm 1\) & \(33\pm 1\) & 0.053 \(\pm\) 0.157 & \\ \cline{2-9}  & & & & & & \(41\pm 1\) & \(92\pm 1\) & 0.203 \(\pm\) 0.184 & \\ \cline{2-9}  & & & & & & \(38\pm 1\) & 165 \(\pm\) 1 & 0.142 \(\pm\) 0.033 & \\ \hline \multirow{4}{*}{Carpet} & Synthetic & 1-2 years & 50 L & 1.52 & 23 & 20 & 100,300 & 0.024,0.034 & This study \\ \cline{2-9}  & & & & & & 0 & 0.051 & \\ \cline{2-9}  & \multirow{2}{*}{Wool} & & & & & 30 & 0.062 & \\ \cline{2-9}  & & & & & & 50 & 0.073 & \\ \cline{2-9}  & & & & & & 70 & 0.100 & \\ \cline{2-9}  & & & & & & 90 & 0.130 & \\ \cline{2-9}  & & & & & & 0 & 0.057 & \\ \cline{2-9}  & \multirow{2}{*}{Synthetic} & & & & & 30 & 0.069 & \\ \cline{2-9}  & & & & & & 50 & 0.081 & \\ \cline{2-9}  & & & & & & 70 & 0.113 & \\ \cline{2-9}  & & & & & & 90 & 0.145 & \\ \hline \multirow{2}{*}{Nylon, GlassLuc backing} & \multirow{2}{*}{Recycled} & \multirow{2}{*}{10 L} & \multirow{2}{*}{12} & \multirow{2}{*}{25} & \multirow{2}{*}{50} & \multirow{2}{*}{150-200} & \multirow{2}{*}{0.068} & \multirow{2}{*}{[50]} \\ \cline{2-9}  & & & & & & 0.054 & \\ \hline \multirow{2}{*}{Nylon, SoftDisc platinum backing} & \multirow{2}{*}{\(>\)1 year} & \multirow{2}{*}{35 mL} & \multirow{2}{*}{\(-\)1543} & \multirow{2}{*}{\(21\pm 2\)} & \multirow{2}{*}{50 \(\pm\) 5} & \multirow{2}{*}{50 \(\pm\) 1} & \(\begin{array}{c}07\pm 0.4\text{ (sample 1)}\\ 0.023\pm 0.004\text{ (sample 2)}\end{array}\) & \multirow{2}{*}{[51]} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of ozone depositions measured in this study and the data from the literature.
## 5 Analysis of the Transmission Delay
### One-Hop Delay/Distance for Intra-/Inter-Lane Scenario
#### 5.2.3 One-Hop Timer Delay for the Inter-Lane Scenario  
Different from the intra-lane scenario, in the inter-lane scenario, the timer for the receiver vehicles, considering the influence of both the inter-relay distance and the link lifetime, can be represented as:  
\[T^{(1,2)}=t_{\min}+t_{\Delta}\left(1-\epsilon\cdot\frac{R-D^{(1,2)}}{v_{\Delta} \cdot L_{\max}}-(1-\epsilon)\cdot\frac{D^{(1,2)}}{R}\right), \tag{21}\]  
where \(v_{\Delta}\) is the difference between the speed levels of the two lanes.  
Following a similar reasoning as the one used to derive the CDF of \(T^{(1,1)}\), we obtain:  
\[\Pr\left\{T^{(1,2)}>t|N=n\right\}=\left[1-(\alpha_{2}t+\beta_{2})\right]^{n}, \tag{22}\]  
where \(n>0\), and \(t\in[t_{\min},t_{\max}]\). Moreover, we have:  
\[\alpha_{2}=\frac{v_{\Delta}L_{\max}}{\epsilon Rt_{\Delta}-(1-\epsilon)v_{ \Delta}L_{\max}t_{\Delta}},\qquad\beta_{2}=\frac{(\epsilon R-v_{\Delta}L_{ \max})t_{\Delta}-t_{\min}v_{\Delta}L_{\max}}{\epsilon Rt_{\Delta}-(1-\epsilon )v_{\Delta}L_{\max}t_{\Delta}}. \tag{23}\]  
Based on the total probability formula, we have the CDF of the minimum intra-lane timer delay as:  
\[\begin{split}\Pr\{T^{(1,2)}\leq t|N>0\}=& 1-\sum_{n=1}^{\infty}\Pr\left\{T^{(1,2)}_{\min}>t|N=n \right\}\Pr\{N=n|N>0\}\\ =&\frac{1-\exp\left(-\lambda_{2}\left(\alpha_{2}t+ \beta_{2}\right)\right)}{1-\exp(-\lambda_{2})}.\end{split} \tag{24}\]  
Based on the expression of the conditional CDF, the conditional pdf can be obtained as:  
\[f_{T^{(1,2)}|N>0}(t)=\frac{\lambda_{2}\alpha_{2}\exp\left(-\lambda_{2}\left( \alpha_{2}t+\beta_{2}\right)\right)}{1-\exp(-\lambda_{2})}. \tag{25}\]  
for \(t\in I_{2}\triangleq[t_{\min},t_{\min}+t_{\Delta}]=[t_{\min},t_{\max}]\) and zero otherwise.  
Let \(t^{(1,2)}\) denote the expectation of \(T^{(1,2)}\), and we have:  
\[t^{(1,2)}=\frac{1}{\lambda_{2}\alpha_{2}}+\frac{t^{-}_{1,2}-\exp(-\lambda_{2} )t^{+}_{1,2}}{1-\exp(-\lambda_{2})}. \tag{26}\]
# Study of Urban Unmanned Aerial Vehicle Separation in Free Flight Based on Track Prediction
## 2 Separation Formulation Method
### Separation Formulation Based on Position Error
#### 2.2.3 Target Level of Safety  
For UAVs, JARUS [18] proposes defining the UAV collision TLS as the number of mid-air collisions per flight hour, with a TLS of \(1\times 10^{-6}\). The SORA definition of the TLS differs from and is lower than that for manned aircraft. By comprehensively considering domestic and international research findings [27], a TLS value of \(10^{-6}\) is chosen.
# Seismic Response Analysis of Multi-Story Steel Frames Using BRB and SCB Hybrid Bracing System
## 3 Multi-Story CBF  
[PERSON] et al. [6] designed a six-story BRBF that complies with the requirement of National Earthquake Hazards Reduction Program (NEHRP) [42], assuming the location is Los Angeles downtown. This frame is used in the following analysis. The same parameters of BRBs and frame members are used to be consistent with the original design. Figure 2 shows that the CBF, which was noted as 6 vb2 in original study, has a chevron-bracing placement. In this study, it is renamed as S1. It has a bay width of 9.0 m for each single bay, and its 1 st-story height is 5.5 m, and the upper stories are 4 m. More building information and design procedure can be found in a related work by [PERSON] et al. [6]. With the aim to isolate the influence of framing mechanism, the beam-to-column rigid connections are amended to hinge type. Such a treatment also helps to release bending moment suffered by the connection area and to adapt to large rotations while maintaining the connections damage free [43]. The connection amendment weakens the lateral stiffness of the system and thus leads to a longer structural vibration period to a certain degree. Incidentally, the story number is one parameter that needs in-depth investigations in the future. The corresponding effect can be quantified by increasing and decreasing the number of stories. However, the parametric analysis on story number is not included in this work due to space limitation and current research focus.  
In terms of the other frames under consideration, they are produced by substituting the BRBs in frame S1 with SCBs at different locations. Figure 2 shows the notation of the frames. The BRBF is denoted as S1, and the identical SCCBF which uses SCBs throughout building height is named as S2. The effect of varying the combinations of BRBs and SCBs is intensively examined. The structures of S3 and S4 are achieved by replacing the BRBs with SCBs at the lower and upper three stories, respectively. For the structures of S5 and S6, the BRBs are replaced alternately throughout building height. Specifically, the BRBs are replaced with SCBs at the odd and even stories in S5 and S6,  
Figure 2: Concentrically braced frames (CBFs) using various bracing systems.  
respectively. The yield strength \(F_{y}\) and the elastic stiffness \(k\) are defined identical for the BRBs and SCBs in every single story. Consequently, the framing structures, although they are installed with various bracing system, have exactly the same dynamic properties in the initial elastic state. According to the eigen value computation, these CBFs have a fundamental period of approximately 0.82 s. It is also worth noting that the performance objectives under earthquakes are not explicitly specified, since the current focus is paid on seeking a solution to reduce the residual deformation of BRBFs through comparing the frames installed with different bracing systems.
## 3 UAV Deployment and Trajectory  
As explained in previous sections, UAV will be a key component in 5G and beyond networks due to its cost-effective and fast deployment along with its unique channel characteristics. However, to extend the coverage or improve the throughput of 5G and the beyond networks, it is necessary to analyze the performance of UAV wireless networks considering the constraints on UAV such as limited resources. The channel conditions change as the position of UAV changes, hence, it could enhance or degrade the performance of UAV wireless networks. Therefore, the deployment of UAV is the first step to construct an optimal UAV network configuration. In addition, UAV could be static at a given position, or move around a target area. The optimal trajectory of UAV is a main issue on mobile UAV use cases to reduce the energy consumption and prolong the lifetime of operation. In this section, we survey the recent research on UAV deployment and trajectory conducted for the best utilization of static and mobile UAVs in wireless communication networks.
## 4 Experimental Analysis
### Collision Detection Efficacy
#### 4.4.1 Effectiveness of the Improved HS Algorithm  
Take two frames of a traffic road video as an example. The comparison results of the optical flow vectors obtained by the improved HS algorithm and the classic algorithm are shown in Figure 8. We used red vector arrows to represent optical flow vectors.  
In Figure 8a, using the original Horn and Schunck algorithm, the optical flow vectors displayed discontinuities in multiple areas, particularly around the car on the left side of the image. In contrast, the results from the improved algorithm Figure 8b showed smoother and more coherent optical flow vectors, particularly along the vehicle's edges, thereby reducing errors.  
Figure 8: Detailed visualization comparison of the optical flow estimations highlighting enhancements: (**a**) original Horn and Schunck algorithm results with a focus on specific flow details; (**b**) improved Horn and Schunck algorithm results showcasing enhanced flow accuracy.  
Figure 7: Vehicle tracking and PSR analysis. (**a**) Tracking visualization shows a vehicle tracked over several frames with blue bounding boxes; (**b**) PSR variation over 70 frames: graph displaying PSR metric changes over time.  
The comparison between the improved and classic algorithms was analyzed through the average endpoint error (AEE), angular error, and FPS, with the performance results detailed in Table 5.  
Although some computational efficiency was sacrificed, the improved algorithm significantly enhanced the AEE and slightly reduced the angular error, thereby increasing the accuracy of optical flow estimation.
## 5 Hybrid Localization Algorithms
### Distributed Localization Algorithms
#### 5.2.3 Underwater Localization Based on Directional Beacons (UDB)  
Instead of traditional omnidirectional localization, the authors in [71] proposed a novel Underwater localization approach based on Directional Beacons (UDB) for hybrid UWSNs, where mobile AUV and stationary unknown nodes coexist. The directional beacons are transmitted by an AUV, which moves according to a predefined route navigated by compass, as shown in Figure 12(a). The sensing range of AUV is \(R_{a}\). A directional antenna is used on the mobile AUV and the angle of signal is controllable. For simplicity, two lines is used to imply the scope of the directional beacons in a 2D plane. The speed of the AUV is \(V_{sound}\) and it is a stable value. \(R_{u}\) is the sensing range of the unknown node. The distances in Figure 12(b) are expressed by equations listed as follow:  
\[\Delta x_{1}=\frac{R_{u}}{\cos(\frac{\alpha}{2})},\Delta x_{2}=\frac{x_{2}-x_{ 1}}{2},\Delta y_{1}=(\Delta x_{2}-\Delta x_{1})\times\cot(\frac{\alpha}{2}) \tag{1}\]  
\[x=x_{2}-\Delta x_{2},y=y_{1}+\Delta y_{1} \tag{2}\]  
Figure 11: Range-Free Scheme Based on Mobile Beacons (RSMB).  
However, the example in Figure 12(b) is the ideal case. The two edges of the beacon triangle are the tangent lines. Since beacons are not emitted continuously, some parts of the sensing area may not be covered. As illustrated in Figure 12(c), the biggest estimation error can be obtained as follows:  
\[\Delta x=2R_{u}\cos\frac{\alpha}{2},\Delta y=2R_{u}\sin\frac{\alpha}{2} \tag{3}\]  
It is shown that if the sensing range \(R_{u}\) is small enough, the ideal case of UDB can work in general case with an acceptable estimation error. When the AUV emits beacons within a fixed time interval, an optimization problem about the number of beacons is considered. As shown in Figure 12(d), the shadow part \(G_{p}\) is the area that beacons can cover twice. Correspondingly, \(G_{m}\) implies the area that beacons can cover once. The minimum number of beacons sent by the AUV can be optimized by minimizing \(G_{m}\) and maximizing \(G_{p}\):  
\[G_{p}=\left\lceil\frac{L}{l}\right\rceil D^{2}\tan(\frac{\alpha}{2}),G_{m}=LD- \left\lceil\frac{L}{l}\right\rceil D^{2}\tan(\frac{\alpha}{2}) \tag{4}\]  
where,  
\[l=2(R_{a}-D)\tan(\frac{\alpha}{2}) \tag{5}\]  
The total numbers can be calculated by the following equation:  
\[N=\left\lceil\frac{L}{l}\right\rceil+1 \tag{6}\]  
To the best of our knowledge, this is the first time to introduce the idea of using directional beacons to localize unknown nodes in UWSNs. UDB is an range-free localization scheme that uses angle information to localize unknown nodes. However, localization coverage is limited. Hence, UDB is  
Figure 12: The Underwater Localization Approach Based on Directional Beacons (UDB).  
not suitable for large scale UWSNs. Moreover, if the AUV sends beacons with too long intervals, many unknown nodes may not be localized.