esa-sceva/satcom-chunk-collection huggingface dataset:
# Negotiation-Free Encryption for Securing Vehicular Unicasting Communication
## 6 Performance Evaluation
### Performance Evaluation of NFEM
#### 6.2.1 Simulation Environment  
The road network represents the urban scenario of a \(5\times 5\) grid with 25 blocks, as illustrated in Figure 5. There are six vertical and six horizontal two-lane roads with 200 m spacing between them. To study the impact of vehicle density, the numbers of vehicles are varied from 25 to 125 with an increment of 25. Similarly, to study the impact of speed, the average speed of the vehicle is varied from 20 to 100 km/h with an increment of 20 km/h. The transmission range is 200 m and vehicles are deployed over an area of 1 km \(\times\) 1 km. The relative simulation parameter is shown in Table 6.  
With the continuous breakthroughs of VANETs, some excellent vehicular communication simulators are produced, such as TraNS, Veins and iTETRIS etc. These popular simulators work well in simulating vehicle behavior mode and emergency intervention. In our experimental scenario,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameter** & **Value** \\ \hline Number of vehicles & 25, 50, 75, 100, 125 \\ Vehicle average speed & 20, 40, 60, 80, 100 km/h \\ Beacon transmission frequency & 1, 4, 8, 10, 20 Hz \\ Simulation area & 1000 m \(\times\) 1000 m \\ Simulation duration & 50 s \\ packet size & 512 B \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simulation parameters.  
Figure 5: Road network, \(5\times 5\) Manhattan grid.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Transmission Method** & **Bandwidth** \\ \hline IPSec & 1.3 byte/s \\ Negotiation-free encryption method & 0.84 byte/s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average bandwidth.  
we divide the transmission process into two parts. We mainly measure the request and return delay of key packages in vehicle-to-cloud communication, and the delay of data transmission between vehicles. As a widely used open source simulation platform, NS-3 provides many integrated modules for different test requirements, including LTE-EPC Network Simulator (LENA) and Wireless Access in Vehicular Environment (WAVE). The architecture of LENA module includes LTE model and Evolved Packet Core(EPC) model, of which LTE model provides Ratio Access Network (RAN) protocol stack and EPC provides Core Network (CN) protocol stack. WAVE module supports IEEE1609 standard and can provide V2V and V2I communication. Considering the complexity of LTE system and the specificity of vehicle cloud communication simulation requirements, LENA can meet our LTE side test requirements. In the transmission process between vehicles, we use the WAVE module for ns-3. By default, each vehicle transmits 512 B beacons at varying transmission frequencies using an UDP-based application. The simulation parameters and values are shown below.
# An Improved Vicsek Model of Swarms Based on a New Neighbor Strategy Considering View and Distance
## 2 The Vicsek Model  
In the original VM, \(N\) particles move synchronously with the same velocity \(v_{0}\) in an \(L\times L\) square region with periodic boundary conditions. Initially, all particles are randomly distributed in the square region, and their initial directions are also randomly distributed within \([0,2\pi]\). At each discrete time step \(k\), particle \(i\) updates its direction as an average direction of its neighbors \(\langle\theta_{i}(k)\rangle\) with random noise \(\Delta\theta\) added. The range of the random noise added is \([-\eta,\eta],0\leq\eta\leq\pi\). The direction of particle \(i\) is updated according to Equation (1):  
\[\theta_{i}(k+1)=\langle\theta_{i}(k)\rangle+\Delta\theta,\quad i=1,2,\ldots,N. \tag{1}\]  
Therefore, the position of particle \(i\) is updated according to Equation (2):  
\[\overrightarrow{X}_{i}(k+1)=\overrightarrow{X}_{i}(k)+v_{0}e^{i\theta_{i}(k)} \tag{2}\]  
In Equation (2), \(\overrightarrow{X}_{i}(k)\) denotes the position vector of particle \(i\) at time step \(k\). The neighbors of particle \(i\) are defined as particles that fall into the circle with radius \(R\) centered at the position of particle \(i\), as is shown in Figure 1. Therefore, particle \(j\) is the neighbor of particle \(i\) if and only if \(\left\|\overrightarrow{X}_{i}(k)-\overrightarrow{X}_{j}(k)\right\|\leq R\).
## 4 Discussion
### MRONJ and Implant Failure  
MRONJ can be manifested through several signs and symptoms. Its development may present clinical manifestations such as the presence of pain, necrotic bone, bone exposure, the presence of purulent secretion, redness, abscess, swelling, paresthesia of the right inferior alveolar nerve, an ill-defined radiolucent area, bleeding upon probing, bone resorption around the implants, and the presence of mobility. These symptoms can persist for more than 8 weeks. It is a problem with a multifactorial origin; it is difficult to predict its occurrence.  
[PERSON] et al. [22] showed failure in four of the seven implants placed in the same patient that were related to the occurrence of MRONJ. In this case, the reported symptoms were essentially pain, the presence of purulent secretion, and paresthesia of the inferior alveolar nerve on the right side associated with an ill-defined radiolucent area that extended from the right posterior mandible to the opposite region of the premolar. These data were attributed to the patient's poor oral hygiene. As for the remaining implants that still showed acceptable osseointegration, it was not possible to conclude what would be the long-term prognosis, since the follow-up only occurred after 18 months.  
Similar results happened with [PERSON] et al. [24]. The patient had two implants presenting features compatible with MRONJ (necrotic bone, left lower lip paresthesia, and purulent secretion in only one of the implants). Also, [PERSON] et al. [25] reported a case with three implants placed; two of them presented pain, bone exposure, redness, and swelling.  
On the other hand, we had cases, in this study, where there was complete failure of the implants, and all patients developed MRONJ. [PERSON] et al. (2017) and [PERSON] et al. (2019) obtained the same results from evaluating a total of 23 implants, which all failed with reports of necrotic bone exposure, purulent secretion, pain, abscess, paresthesia, fistula, and swelling for more than 8 weeks.  
[PERSON] et al. (2020) reported a case of MRONJ in a 77-year-old female patient. After receiving oral implant rehabilitation and an immediate-load fixed prosthesis in the maxilla, she began to report pain and purulent secretions, which were neglected by the responsible professional. She returned to see the same professional after another episode of acute pain. The fixed prosthesis was removed and exposure of necrotic bone around the implants was observed. In this case, all implants failed. The patient was submitted to surgery to remove necrotic bone blocks. This was the most severe case analyzed in this systematic review.
## 5 Simulations Results
### The Nonlinear Mass-Spring-Damper Model  
A simple nonlinear Mass-Spring-Damper (MSD) mechanical system is shown in Figure 13. It can be described as follows [44]:  
\[M\ddot{x}+g\big{(}x,\dot{x}\big{)}+f(x)=\varphi\big{(}\dot{x}\big{)}u \tag{55}\]  
where \(M\) is the mass and \(u\) is the input force, \(f(x)\) is the nonlinear or uncertain term with respect to the spring, \(g\big{(}x,\dot{x}\big{)}\) is the nonlinear or uncertain term with respect to the damper, and \(\varphi\big{(}\dot{x}\big{)}\) is the nonlinear term with respect to the input term. Let, \(g\big{(}x,\dot{x}\big{)}=D\big{(}d_{1}x+d_{2}\dot{x}^{3}\big{)}\), \(f(x)=d_{3}x+d_{4}x^{3}\), and \(\varphi\big{(}\dot{x}\big{)}=1+d_{5}\dot{x}^{3}\), where \(x\in[-a\ a]\), and \(\dot{x}\in[-b\ b]\), \(a,b>0\). The parameters are listed in Table 2.  
Figure 12: Estimation error with measurement noise using N-ADRC, (a) \(e_{1}\) (b) \(e_{2}\).  
Figure 13: The nonlinear mass–spring–damper model.  
Figure 14: The results for the mass spring dumper model using C-ADRC, (**a**) output (\(y\)), (**b**) control signal (\(u\)), (**c**) the generalized disturbance.  
Figure 15: The result for the mass spring dumper model using N-ADRC, (**a**) output (\(y\)), (**b**) control signal, (**c**) the generalized disturbance.  
The numerical results are listed in Table 3. Adding an extra LESO to the C-ADRC significantly affected the output response (ITAE) of the N-ADRC controller. In Table 3, \(\text{ITAE}=\int_{0}^{20}t|y-r|dt\) is the integration of the time absolute error for the output signal, and \(\text{ISU}=\int_{0}^{20}u^{2}\ dt\) is the integration of the square of the control signal.
# Reliable Internet of Things: Challenges and Future Trends
## 2 Massive IoT and Application Requirements  
As the communication data rates are increasing and connectivity among devices is enhanced, many IoT applications will be implemented, thus leading to massive IoT networks. The scale of massive IoT will be billions of machines, cars, sensors all connected with the Internet. We list some of the requirements for massive IoT applications in Table 1[7; 8; 9]. Massive IoT will support many novel applications such as autonomous driving, augmented reality-based gaming, predictive maintenance of machines, automated surgery systems, and smart grids. As far as the communication requirements are concerned, massive IoT will require ultra-reliability of the order of 99.9999%. This is especially needed for critical applications where human safety is at stake, such as safe driving and health-related operations. Moreover, these massive IoT networks can afford a latency of less than 1 ms. This is needed to make sure that the data are delivered within time so that applications can make correct decisions.  
For the suitable wireless technology, there are many potential candidates. This selection depends on the nature of the application as well as constraints such as communication range, data rate etc., as shown in Table 2[9; 26; 27; 28; 29]. For example, if the communication is between two IoT sensors near each other (less than 100 m range), Zigbee or Bluetooth can be used. For energy-sensitive applications, Zigbee or Bluetooth Low Energy (BLE) can be a good choice. For applications that require connectivity of around 100 m, IEEE 802.11 technology can be used to meet these requirements [30]. Similarly, if the required connectivity range of sensors is high (greater than 300 m), 6G communication is the most suitable option. Similarly, for applications such as vehicular networks, Cellular V2X or IEEE 802.11p standards are proposed by different working groups, industrial manufacturers,  
\begin{table}
\begin{tabular}{c l} \hline \hline \multirow{3}{*}{**Applications**} & **Autonomous Driving** \\  & **Augmented and Virtual Reality Based Gaming** \\  & **Predictive Maintenance of Machines** \\  & **Automated Surgery Systems** \\ \hline Reliability (Packet success rate) & \(\geq\)99,99999\% \\ \hline Latency & \(\leq\)1 ms \\ \hline Data Rate & Upto 1 Gbps \\ \hline \multirow{4}{*}{Wireless technology} & Bluetooth \\  & Zigbee \\ \cline{1-1}  & IEEE 802.11 \\ \cline{1-1}  & 6G \\ \cline{1-1}  & Cellular V2X \\ \hline \hline \end{tabular}
\end{table}
Table 1: Massive IoT application requirements.  
and government organizations [31]. The 3 rd Generation Partnership Project (3 GPP) release 17 also contains the latest standards related to 5G communications [32].
## 2 Materials and Methods
### Computational mesh  
Given the nature of the geometry, a hybrid Tet/Hex mesh (see Figure 7) has been used to define the computational domain. Complex 3D features such as inlet and outlet DOC cones have been meshed by means of tetrahedral elements, and hexahedral meshes have been used for the monolith and the duct. In order to ensure the accuracy of the calculations, a grid independence study was conducted and mesh independency was achieved with a \(6.5\times 10^{5}\) elements grid with an average mesh size of \(3\times 10^{-3}\) m.  
Figure 6: Three-dimensional geometry model of the exhaust system.  
Figure 7: Mesh detail. Tetrahedral (DOC inlet and outlet cones) and hexahedral zones (monolith and pipe) can be distinguished.
## 3 Results and Discussion
### Analysis and Design of Optimal Compensate Criterion
In general, we can list the optimal compensate criterion: The optimal amplitude ratio is located within 0.6 to 0.7 for most of the frequency ratios. Besides, the frequency ratio should avoid 0.3 and 1, where it may cause resonance. Figure 4: Results for the frequency ratio-domain: (**a**) Displacement amplitude results; (**b**) Acceleration amplitude results. Figure 5: Results for the amplitude ratio-domain: (**a**) Displacement amplitude results; (**b**) Acceleration amplitude results.
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 6 Conclusions and Future Research Directions
### Future Research Directions  
The deployment of the 5G-NR technology in vehicular communication faces certain technical challenges related to the requirements on the backhaul network, millimetre wave communication and security issues [198]. Recently, the massive number of devices accessing the C-V2X network will cause congestion over the network; therefore, non-orthogonal multiple access (NOMA) is also considered for V2X communication [199]. However, NOMA techniques cannot be straightforwardly applied to C-V2X due to reliability and security constraints. Recently, [PERSON] et al. [200] (see also the references therein) looked to improve the reliability of NOMA which can be applied to C-V2X or V2V communication. Furthermore, these AVs will help form an Internet of Vehicles (IoV) which will generate big amount of data. Recently, artificial intelligence (AI), machine learning (ML) and deep learning (DL) techniques have been used to improve the state and position estimation and reference therein [201; 202; 203; 15]. Different AI, ML and DL frameworks have been proposed to learn and adapt these matrices online. Techniques such as artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), convolutional neural networks (CNN), deep belief networks (DBN), deep inference for covariance estimation (DICE), long short-term memory (LSTM), recurrent neural networks (RNN) and reinforcement learning have been adopted to estimate the noise co-variance and the transition matrix to improve the position accuracy of the AV [203; 204; 205; 206; 207; 208; 209; 100]. Further detail and discussion regarding these AI, ML and DL techniques for sensor fusion for AV can be found in [203] (and references therein). Hopefully, all these challenges will be rectified in the future paving the way for AVs and then finally IoVs.
## 3 Graphene Oxide
### Electrical Conductivity  
Graphene also has unique optical, electrical, and thermal characteristics that make it suitable for a range of different applications, such as biosensors, transparent conductors, and drug diagnostics. Both GO and rGO have residual functional groups, leading to faster heterogeneous electron transfer on the surface and better biocompatibility, dispersibility and charge transfer than is possible with pure graphene [32]. Several studies have demonstrated the effect of electrical conductivity of GO in combination with other polymers and its applications. [PERSON] et al. prepared chitosan/rGO nanocomposites with appropriate levels of conductivity, stability, charge density, and electrochemical properties for designing molecular detection systems [32]. In another study, chitosan and reduced graphene oxide sheets with a high conductivity of 1.28 S m\({}^{-1}\) were fabricated for various biological applications, such as biosensors and tissue-engineering scaffolds [33]. Highly reduced few-layer graphene oxide films with conductivities of up to 500 S cm\({}^{-1}\) were obtained by [PERSON] et.al [34]. Conductivity is strongly dependent on the properties of GOs (as these determine the effectiveness of the chemical reduction), structural and morphological defects of the flakes, and connectivity. Electrically conductive chitosan/GO based scaffolds have been prepared for cardiac tissue-engineering end-uses [35; 36]. Additionally, the electrical conductivity of GO has been found to be highly desirable for use as biosensors [37; 38], supercapacitors [39; 40], and cardiovascular tissue-engineering scaffolds [41; 42].
## 1 Introduction
### Why Rapid Damage Assessment Is Crucial after a Disaster
This section also includes an investigation of the factors influencing the coherence (de-correlation). Section 3 gives a literature review and comparison of interferometric coherence and intensity correlation: First, damage assessment procedures using the coherence are reviewed. Second, the ones using intensity correlation and third, techniques using both methodologies combined are presented. Section 3.4 reports studies using additional data (e.g., GIS and optical imagery) to support the damage assessment and increase its accuracy. Section 3.5 summarizes the achieved accuracies of the damage assessment methods reviewed in the Sections 3.1-3.4. Sections 3.6 and 3.7 show selected post-event SAR damage assessment techniques and examples of other applications of the interferometric coherence, respectively. Section 4 presents the preconditions for a successful worldwide application of multi-temporal SAR methods for damage assessment and the limitations of current SAR satellite missions. Next, Section 5 gives an outlook to the Sentinel-1 SAR mission showing possible solutions of these limitations to enable worldwide applicability of the reported damage assessment methods. Finally, concluding the article, Section 6 focuses especially on challenges for future research on multi-temporal SAR data based damage assessment methods and provides possible solutions hereof.
# Storing Set Families More Compactly with Top ZDDs
## 2 Preliminaries
### DenseZDD  
A DenseZDD [11] is a static representation of a ZDD with attributed edges [17] by using some succinct data structures. In comparison to the standard ZDD, a DenseZDD provides a much faster membership operation and less memory usage for most cases. When we construct a DenseZDD from a given ZDD, dummy nodes are inserted so that \(\ell(v_{0})=\ell(v)+1\) holds for each branching node \(v\) for fast traversal. A Spanning Tree consisting of all inverted 0-edges is represented by a simple BP. Notice that, each node always has one 0-edge, and ZDDs have no cycles and the terminal node is only \(\bot\) if we employ the attributed edge technique. Therefore, if you traverse only the 0-edges from any branching node, you will always end up at the \(\bot\). The inverted 0-edges thus form a spanning tree rooted at the \(\bot\). The DenseZDD is a combination of this BP and other succinct data structures that represent the remaining information of the given ZDD. Its size is much smaller than the standard ZDD size and it can execute fast membership operations. A DenseZDD represents 0-edges and 1-edges respectively in a different way. For 0-edges, the spanning tree of a ZDD formed by the 0-edges is stored. We give preorder id for each node by a depth-first traversal of the spanning tree. Some dummy nodes are added such that \(\ell(v_{0})=\ell(v)+1\) holds for each node \(v\). Spanning tree and dummy node information are represented by BP and Fully Indexable Dictionary (FID), respectively. For 1-edges, an array stores all the preorder ids of 1-children together.
## 4 Discussion  
Analysis of Variance of Resveratrol Content, TPC, and Antioxidant Activity in Leaves, Tendrils, and Shoots  
The cleaning of the vine generates considerable amounts of vegetable waste each year (tendrils, leaves, and shoots). This green waste could be an important source for high-value phytochemicals, as it (now) does not have any usage, and it could contribute to sustainability programs [25]. Trans-resveratrol is found in abundance in this vine waste, and its content can vary widely among the waste types [26].  
Results for samples analyzed after 42 days are represented in Table 1. As it can be seen, the largest amount of resveratrol is contained in vine shoots (1658.22 mg/kg D.W.), followed by tendrils (169.92 mg/kg dry weight (D.W.) and leaves (43.54 mg/kg D.W.). Vine shoots contain up to 10 times more trans-resveratrol than tendrils and up to 40 times more than leaves. Although, besides vine shoots, this vineyard waste also contains trans-resveratrol--extraction and purification are not economically recommended.  
These results agree with other studies, such as the study by [PERSON] et al., (2016), which determine the concentration of resveratrol on vine shoots, tendrils, and leaves. Vine shoots had the highest content of trans-resveratrol (12.5 mg/kg D.W.), followed by tendrils (0.51 mg/kg D.W.) and leaves (0.24 mg/kg D.W.) [26].  
A study conducted on vines from the Bohemian and Moravian Region, Czech Republic, determined the resveratrol content in different parts of grapevines (leaves, rachis, and cluster stems). The results varied from 6 to 490 mg/kg D.W. [27]. Cluster stems were found as the richest sources of resveratrol; this is in accordance with the results of this study.  
[PERSON] et al., (2017) reported that trans-resveratrol is undetectable in inflorescence and the highest content was found in cluster stems (344.5 mg/kg D.W.) after analyzing nine organs of _Vitis vinifera_ cv. Merlot: canes, shoot tips, roots, buds, clusters at the veraison, infloresces, mature berry skins, cluster stems, and seeds [20].  
TPC values for the three types of waste did not vary widely, although vine leaves had the highest content (30.42 (\(\pm\)0.32) mg GAE/g D.W.).  
Figure 3: DPPH % inhibition in vine shoots during the 90-day post-pruning time storage.  
The highest antioxidant activity recorded after the analysis of the extracts was found in the leaves (72.19%), although the highest amount of resveratrol was found in the vine shoots. The results are similar to those obtained by determining the total phenolic content. The lowest percentages were recorded in tendrils (14.69% DPPH inhibition) and vine shoots (52.72% DPPH inhibition).