esa-sceva/satcom-chunk-collection huggingface dataset:
# A Decentralized Blockchain-Based Trust Management Framework for Vehicular Ad Hoc Networks
## 1 Introduction  
### Problem Statement  
VANETs are a fundamental component of the Internet of Vehicles (IoV) [1], which is one of the newest technology designed for Smart transportation in Smart Cities. In VANETs, a myriad of smart vehicles, smart devices, and roadside units (RSUs) communicate to provide plenty of applications to the passengers [2]. These applications range from safety to entertainment aiming to facilitate and improve the passengers' journey. Particularly, safety applications in VANETs are based on a frequent exchange of alert messages aiming to share information reporting the road state, the traffic crowding, and sudden events such as vehicle crashes. The ultimate aim of the safety applications is to enhance road safety via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. Nevertheless, safety applications require a reliable, authentic, and appropriately-timed exchange of data to avoid severe damage [3]. The vehicles should not only authenticate their peers, originators of messages but also assess the reliability of the received data to react accordingly. According to the IEEE 1609.2 standard [4], a public key infrastructure (PKI) would be used to authenticate vehicles using certificates. However, PKIs cannot be used to ensure the reliability of the data. Besides, a PKI allows mitigating only outsider attackers, an authenticated vehicle holding a valid certificate may forge messages without being detected. The concept of trust is used to overcome this shortcoming of PKI. In VANETs, trust management schemes have the potential to mitigate messages alteration and spoofing attacks, also to detect forged messages and intruder attackers [5, 6]. Furthermore, trust schemes allow vehicles to communicate with unknown peers. Plenty of approaches hasbeen put forward to establish and maintain trust communications in VANETs [6; 7; 8; 9]. In our previous work [11], recent approaches have been reviewed. The existing approaches have been classified into two categories: centralized-based and distributed-based. In centralized-based trust management approaches, the trust values are processed and stored in a central server or authority. However, given the large scale of VANETs in terms of long distances and the high number of vehicles, besides the time-sensitivity of the safety applications, the centralized-based approaches are ill-suited for VANETs. In decentralized-based trust management approaches [12], trust computing and storing are performed locally by the vehicles and/or the roadside Unit (RSU). However, due to the different conditions and capabilities to perceive and assess the target vehicles, trust values computed by only one vehicle cannot be always reliable.
# Routing Schemes in FANETs: A Survey
###### Abstract  
Flying ad hoc network (FANET) is a self-organizing wireless network that enables inexpensive, flexible, and easy-to-deploy flying nodes, such as unmanned aerial vehicles (UAVs), to communicate among themselves in the absence of fixed network infrastructure. FANET is one of the emerging networks that has an extensive range of next-generation applications. Hence, FANET plays a significant role in achieving application-based goals. Routing enables the flying nodes to collaborate and coordinate among themselves and to establish routes to radio access infrastructure, particularly FANET base station (BS). With a longer route lifetime, the effects of link disconnections and network partitions reduce. Routing must cater to two main characteristics of FANETs that reduce the route lifetime. Firstly, the collaboration nature requires the flying nodes to exchange messages and to coordinate among themselves, causing high energy consumption. Secondly, the mobility pattern of the flying nodes is highly dynamic in a three-dimensional space and they may be spaced far apart, causing link disconnection. In this paper, we present a comprehensive survey of the limited research work of routing schemes in FANETs. Different aspects, including objectives, challenges, routing metrics, characteristics, and performance measures, are covered. Furthermore, we present open issues.  
ad hoc networks; FANETs; routing; network topology +
Footnote †: journal: Sensors
## 6 Metal Complexes Based on Terpene Ligands and Their Biological Activities
### Oligodynamic Effect  
The oligodynamic effect enables certain metals to self-clean by destroying microorganisms with their metal ions, which would otherwise be toxic to many bacteria [215; 216]. This effect can be seen in brass door handles, water tanks on certain aircraft, and silver-ware. The simple composition of metal surfaces provides protection against bacteria, even in the absence of a disinfectant. Owing to the changing nature of bacteria, the study of antimicrobials based on metals and metal ions has been slow and difficult, but it has been demonstrated after several experiments that mineral compounds disrupt biofilm production and synergistically exert antimicrobial effects by inhibiting biofilm production and enzymatic activity, altering membrane stability and function, damaging DNA, and generally inhibiting plankton growth [217; 218]. Metals have been used as antimicrobial agents for thousands of years, dating back to the Egyptians' use of copper salts as an astringent. Copper and silver were also used by Indians, Egyptians, Persian kings, Phoenicians, Greeks, and Romans to preserve food and disinfect water [219]. A variety of metal-coated surfaces have antibacterial capability against _Staphylococcus aureus_, _Escherichia coli_, and _Listeria monocytogenes_, including silver, titanium, copper, iron, molybdenum, zinc, etc. [220]. In fact, some metal compounds, especially those that do not show substantial metal complexation might just serve as vehicles for metal ions. Given that metals are used to kill bacteria, it is not surprising that research has looked at the direct antibacterial applications of these metals, particularly in the form of nanoparticles. It has been shown that the metal ions released by these nanoparticles are involved in their antibacterial activity [221; 222; 223]. Copper has a high affinity for carboxyl (COOH) and amine groups present on the cell surface. Released Cu ions can bind to DNA and disrupt the helical structure by cross linking nucleic acid strands. It also disrupts the biochemical processes of bacterial cells [224]. Silver ions disrupt the function of membrane-bound enzymes and respiratory enzymes, leading to the complete destruction of the bacterial cell [225].
# Method for Handling Massive IoT Traffic in 5G Networks
## 8 Multiplexing Gain  
Multiplexing gain is achieved by increasing the resource block utilization in every multiplexing process. For determining the multiplexing gain, the average resource block usage \(k\) without multiplexing (represented by \(k_{no\_max}\)) as well as with multiplexing (represented by \(k_{max}\)) are determined in accordance with Equation (9). Multiplexing gain in percentage is given as:  
\[G=100-\left(\frac{k_{no\_max}}{k_{max}}\times 100\right) \tag{10}\]
###### Abstract  
Free space optical communication is developing towards laser communication networking. A novel method of quadrant detector (QD) multi-spot position detection based on orthogonal frequency division multiplexing (OFDM) is proposed for multi-spot laser communication systems. The mathematical model is constructed, and the Monte Carlo method is used to validate it. The position calculation of three beams incident on QD is simulated. The influence of key parameters on the accuracy of position detection is analyzed when the spots are at the same position and a different position. The results show that when the SNR of the system is 31.74 dB, the radius of the Gauss spot is 2 mm, the number of FFT (fast Fourier transform) points is 1024, and the center of the multi-spot is at the same position of the detector target; the accuracy of signal position detection calculated by the equation is 1.433 \(\upmu\)m, and the simulation results are 1.351 \(\upmu\)m, 1.354 \(\upmu\)m, and 1.389 \(\upmu\)m, respectively. When the center position of the multi-spot is at different positions of the detector target, the detection accuracy calculated by the formula is 1.438 \(\upmu\)m, 1.433 \(\upmu\)m, and 1.434 \(\upmu\)m, respectively, and the simulation results are 1.419 \(\upmu\)m, 1.387 \(\upmu\)m, and 1.346 \(\upmu\)m, respectively. The experimental results verify the effectiveness of the proposed multi-target simultaneous detection method. This article proposes a new multi-spot position detection method which can not only achieve one-to-multiple node laser communication but also improve the accuracy of point position detection.  
orthogonal frequency division multiplexing; fast Fourier transform; Monte Carlo simulation method; quadrant detector; position detection accuracy
## 2 Materials and Methods
### Resources for Readily Available EO and Other Data (Research for Step 2)
#### 2.2.4 Digital Elevation Model (DEM)  
The DEM generally refers to a representation of a bare terrain surface or a subset of it, excluding features such as vegetation, buildings, or bridges. DEM is often useful for flood disaster evaluations or water-flow estimation models, land-use studies, and geological applications, aspect, slope, etc. DEM can be obtained from different data types (e.g., satellites and Lidar), but in this study, we used Google Earth Engine (GEE) [92], which provides free open access to the NASA Shuttle Radar Topography Mission (SRTM) Digital Elevation 30 m. SRTM digital elevation data are based on an international research effort that obtained digital elevation models on a near-global scale [93].
## 4 Experimental Evaluation
### Baseline Models  
Six previously proposed few-shot models are tested as baselines: Prototype Network (ProtoNet) [9], Relation Network (RelationNet) [8], Hybrid Attention-Based Prototypical Network (HABPN) [15], Induction Network (InductionNet) [14], SNAIL [38], and Meta-SGD [39]. All of these few-shot models mentioned above use the same embedding architecture described in [8] to project samples. In particular, we replace the ReLU activation function in feature attention module of HABPN with Leaky ReLU to address gradient vanishing problem encountered in experiments.  
To make more comparisons, we consider three classic machine learning methods as baselines: kNN using Gist descriptors [16; 54], kNN using byte n-gram frequency [17], and kNN based on image pixels. We mainly use kNN as classifier for its non-parametric nature to avoid severe overfitting. Gist and n-gram work on original PE files whereas pixel kNN uses malware images.
## 4 Work Undertaken
In some cases, the abstract contained enough information relative to data quality dimensions and, therefore, the authors had to look for the complete paper. However, as CDC is a global center with high recognition and authority, the dimensions mentioned in the different reports were taken into account even if a lesser weight was given to those reports, compared to journals or conference papers. The final number of papers included for further reviewing amounted to 41. The 42 papers retained for the integrative review were analyzed to understand and assess the importance of the DQDs discussed by the authors. An initial organization of the ideas is summarized in Table 4 below. A weight (L:Low, M:Medium, H:High) was assigned to each of the 41 papers. The weight was attributed taking into consideration the degree of alignment between the ideas presented within a paper and the context of the evaluation of DQDs for big datasets in the healthcare industry. This method of data evaluation was inspired by integrative review discussions in nursing, where reports were coded on a 2-point scale [34]. Low weights were assigned whenever the research lacked both the Big Data and health industry context, but some association could be made between the importance of DQDs cited and Big Data within health industry according to the authors' judgment. Medium weights were assigned when either a Big Data or health industry context was present. High weights were assigned when the two concepts (Big Data and health industry) were present. The judgment of the authors was again used to resolve semantic differences in jargon used by different authors to express specific data quality dimension. Each weight was assigned a numerical value as follows: L: 1, M: 2, and H: 3. The total of weighted counts per DQD was computed to ascertain which DQDs were the most important following the application of the inner hermeneutic cycle. A staggering unique count of 43 distinct DQDs was noted following the IHC analysis. This confirmed the impression of a lack of a universal data quality framework and the possible fact that different authors might be using different jargon to express the same idea. DQDs cited only once with a low weight were discarded from further analysis, and the remaining number of DQDs became 38. The total count results of the dimensions listed tended to confirm previous general research on DQDs, with 'accuracy' being one of the most cited.
# Structural Optimization of MicroMED Dust Analyzer
## 3 Results
### Current OB  
The MicroMED current OB was manufactured with the AI/075-T6 aluminium alloy, leading to a mass budget of about 68.5 g. The material allocated to the ribs grid--which is the one eligible to be reduced--was estimated to be about 7 g (i.e., approximately 10% of the OB mass).  
The results of the performed FEAs set are summarized in Table 1 and the most critical loading conditions (i.e., 1000 m/s\({}^{2}\) acceleration along the Y-axis for the quasi-static analysis and the cold case for thermoelastic stresses) are reported in Figure 6. The numerical investigation pointed out that the current OB's first natural frequency was about 407 Hz.  
Figure 5: FEM for the thermal deformations quantification (**a**) and detail on the positions of the probes (**b**). The included dissipated thermal powers are of the Proximity Electronics (PE), the Laser Group (LCG), and the Motor Pump Group (MPG), respectively.  
Moreover, the maximum Von Mises stress arising for the quasi-static and thermoelastic analyses was approximately 84 MPa and 12 MPa, respectively. A largely positive _MoS_ of about 3 and 28 was obtained for the former and the latter case studies.  
Finally, the thermal deformations estimated within the sampling volume in the OH are reported. The results are shown for both the cold case and the hot case in Figure 7, completing the preliminary activities for the baseline identification. As can be noticed, no significant relative displacement was highlighted, suggesting the thermal stability of the current OB assembly. Indeed, a maximum relative displacement of about 2.1 \(\upmu\)m and 710 nm was computed, values which are three orders of magnitude smaller than the sampling volume ellipse major axis (i.e., about 1 mm) obtained crossing the laser beam with the multiphase flow in the OH. The displacements measured at the probes in the three directions with respect to the reference frame XYZ are reported in Appendix B.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Resonances [Hz]**} & \multicolumn{3}{c}{**Von Mises Quasi-Static Stresses [MPa]**} \\ \hline f\({}_{1}\) & f\({}_{2}\) & f\({}_{3}\) & f\({}_{4}\) & f\({}_{5}\) & \(\sigma_{X}\) & _MoS_ & \(\sigma_{Y}\) & _MoS_ & \(\sigma_{Z}\) & _MoS_ \\
407 & 530 & 798 & 926 & 1193 & 29 & 10.7 & 84 & 2.99 & 45 & 6.50 \\ \hline \multicolumn{10}{c}{**Von Mises Thermoelastic Stresses [MPa]**} \\ \hline  & Cold case & & _MoS_ & & Hot case & & _MoS_ & \\  & 12 & & 28 & & 4 & & 88 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: MicroMED current OB FEAs outcomes.  
Figure 6: MicroMED current OB first resonance mode shape (**a**); Von Mises stress distribution [MPa] with 1000 m/s\({}^{2}\) load in Y direction (**b**) and thermoelastic cold case (**c**).  
Figure 7: Overview of MicroMED current OB assembly relative thermal deformations for the cold case (**a**) and displacements at the defined probes (**b**).
## 5 Conclusions and Future Work  
Over the course of this study, a multitude of ecosystem structure parameters were systematically collected at the studied vineyard. The precise localization of individual vines was achieved through the analysis of a 3D point cloud generated at an early phenological stage, focusing exclusively on the woody structures of the plants. Subsequently, vine heights spanning the entire vineyard were extracted from the resultant nDSM, which was computed from a DTM using a robust cloth filtering method. The segmentation of vine vegetation and its differentiation from other objects in the scene were executed through an OBIA framework, leveraging the geometrical attributes of the vines. Additionally, the computation of LAI and LSA was predicated using 3D point cloud analyses, capitalizing on the high-resolution data obtained through cross-grid flights and oblique data-capturing methods. Although timing constraints precluded the validation of the latter parameters, these methodologies demonstrate technical feasibility in acquiring geometric and morphological datasets from RGB-equipped UAS platforms. Looking ahead, the research will extend into the incorporation of multispectral data, soil-specific data, and satellite data, promising to further enrich our understanding of vineyard dynamics and providing a more comprehensive assessment of physiological parameters. The correlation matrix shows the interaction of the derived parameters at a single time point in the development cycle of the vine plants. Future projects will analyze the monthly changes in the vine plants and thus create a basic concept for a harvest prediction model.  
Conceptualization, S.R., S.L. and G.P.; methodology, S.R. and G.P.; software, S.R.; validation, S.R., S.L. and G.P.; formal analysis, S.R.; investigation, S.R., S.L. and G.P.; resources, G.P.; data curation, S.R.; writing--original draft preparation, S.R.; writing--review and editing, S.R.; visualization, S.R.; supervision, S.L. and G.P.; project administration S.L. and G.P.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 14: The matrix displays the correlation coefficients between the variables. Strong correlations are shown in dark red shades.  
Data Availability Statement: Datasets available on request from the authors.  
The authors want to thank [PERSON] for making it possible to access the vineyard at any time for data capturing and reference measurements, and for providing valuable reference datasets at the harvest stage of the vines. Thanks to [PERSON] from the SIENA research Team, for performing the data collection missions in the field. The printing costs were kindly supported by the Paris Lodron University Salzburg.  
The authors declare no conflict of interest.
# A Survey of Secure Time Synchronization
## 2 Time Synchronization  
### Problem Formulation  
In recent years, the time synchronization problem in wireless networks has been widely studied in the literature, aimed at achieving a higher order of accuracy with greater scalability of topology and application. However, so far, there is no specific reliable time synchronization scheme available due to the complexity associated with the collaborative nature of nodes [6]. Time synchronization is a method to synchronize the nodes [19]. Computer clocks contain two parts, i.e., an oscillator and a counter. _C(t)_ represents the clock if a network node counter increases its value based on the oscillator angular frequency. In ideal situations, angular frequency is constant but it can be changed because of physical variations such as temperature, vibration, and pressure. The local clock of node \(i\) and real time \(t\) can be related as [20]:  
\[C_{i}(t)=a_{i}t+b_{i} \tag{1}\]  
where \(a_{i}\) is the clock drift of node \(i\)'s clock and \(b_{i}\) is the offset. Drift is the frequency rate of the clock, and offset is the difference in value from the real time \(t\). Local clocks of Nodes 1 and 2 are compared as [20]:  
\[C_{1}(t)=a_{12}\times C_{2}+b_{12} \tag{2}\]  
where \(a_{12}\) is the relative drift and \(b_{12}\) is the relative offset of the clocks of Nodes 1 and 2. If relative drift is 1 and relative offset is 0, then both clocks are perfectly synchronized. Clock rate of the network node and offset can be used to adjust its local time according to above equations [21]. Currently, there are many protocols to achieve time synchronization; however, those protocols are vulnerable to many attacks.
## 4 Simulation Setup, Result Analysis, and Discussion  
For the simulation setup of the proposed SDAA protocol models, we simulate an underwater vehicular wireless sensor network (UVWSN) scenario. We simulated this scenario using low-power radios. These radios include a high asymmetrical coverage ratio communication range and stochastic link features. Simulating UVWSN application occurs in realistic results scenarios.  
Therefore, UVWSN propagation utilizing USN (underwater sensor node) channel model is simulated through the proposed SDAA protocol. The simulation was developed and implemented in the network using the Omnet++ network simulation tool. We used IEEE 802.15.4 low power UVWSN compatible medium access control protocol and considered using an area size 1400 \(\times\) 1400 square meters. The simulation time is 600 s, and 250 sensor nodes are used in the entire simulation area.  
We chose 90% of sensor nodes to be static USNs (underwater sensor nodes), and the remaining 10% are considered mobile USNs that could move due to water waves and other physical interference. We generated a malicious node attacker (MNA) scenario for 1-18 attackers to assess and determine the effectiveness of the proposed SDAA protocol methods. The proposed SDAA protocol compares with other state-of-the-art similar types of approaches, including self-sustaining, efficient, and forward-secure cryptographic (SEFSC) secure data aggregation (SDA) and energy-efficient and secure transmission (EEST), which were deployed in the UVWSN in similar simulation condition.  
The simulator parameters are shown in Table 3. Based upon this, we observed that similar scenarios of the simulation parameters are used throughout the investigation, which have been generated for the proposed SDAA protocol and the other competing standard secure MAC protocols, including SFSC, SDA, and the EEST. This is used to evaluate the effectiveness of the protocols. Based on the simulation results, we determine the following performance metrics.  
End-to-end delay  
Reliability ratio  
Drop package  
Energy consumption