esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 V2X System Architecture When Integrating Blockchain Technology and MEC  
The proposed system architecture consists of roadside participants, several RSUs, distributed MEC units and the application server. Figure 1 presents the end-to-end system structure of the developed system. The V2X system participants, e.g., vehicles, pedestrians and cyclists, are the end-devices that periodically transmit and request traffic-related information.  
RSUs are used to link services to V2X objects moving on/around the road and provide route information and updates. Each RSU provides the coverage to a group of vehicles, using the appropriate communication interface. Recently, V2X communications implemented using two main technologies, namely, 3 GPP-based technology and dedicated short range communications (DSRC). IEEE 802.11p is the common DSRC that is widely used for V2X applications, while cellular vehicle-to-everything (C-V2X) is the alternative technology introduced by the 3 GPP [21].  
Distributed MEC units are introduced at the edge of the access network, by connecting each RSU with an MEC unit. These edge units provide computing resources to roadside participants. Moreover, they implement the developed offloading scheme to manage the network traffic in an energy-efficient way.
## 2 Literature Review
### Fuzzy Inference System (FIS)  
A fuzzy inference system (FIS) is a decision-making system that uses fuzzy logic rules to generate crisp output results. It takes input data and processes them according to specific rules to generate outputs. The outputs and inputs are real-world values, while the processing system is based on fuzzy arithmetic. Fuzzy inference is the process of formulating input and output mappings using a fuzzy logic system and its operation is shown in Figure 1 below.  
* Fuzzification This function matches up the pieces of input data sets to determine which series of membership functions best describes them. This allows the 3D VLE to map input values from 3D VLE to a range from 0 to 1. This process is useful for translating crisp input data into more meaningful linguistic variables [16].
* Fuzzy rule base In a fuzzy logic system, there are no specific model parameters or mathematical equations required--all constraints, model complexity, and non-linear relationships are incorporated in the fuzzy inference setup as IF-THEN statements. This makes the system more flexible and easier to use, since it can consider a wider range of possible fuzzy relationships between input and output data sets [16].
* Fuzzy inference engine The fuzzy inference engine helps to simulate the reasoning process of a human being by evaluating the inputs and using the IF-THEN fuzzy rules. It considers all of the rules in the fuzzy rule base and then learns to transform sets of input variables into output results [16].
* Defuzzification The fuzzifier helps to make the fuzzy outputs of the decision-making unit or inference engine more precise. This is done by using membership functions that approximate the ones used by the decision-making unit or inference engine itself [17; 18].  
Figure 1: Components of the fuzzy inference system.
# On the Trade-Off between the Main Parameters of Planar Antenna Arrays
## 3 Sparse Array Synthesis
### The Sparsification Effect  
The weighted \(\ell_{1}\) minimizations make the relative excitation \(a_{n}^{k+1}\) of some of the \(N_{k}\) points to be very small; by choosing a threshold \(\epsilon\), we can start the first step of the successive iteration with only the \(N_{k+1}\) excitations with an amplitude larger than \(\epsilon\).  
Now we are going to focus on a single iteration for a test case of Figure 4. In the left subplot, marked (a), we see four excitations in the \(xy\) plane; their amplitude is calculated solving Equations (12)-(14). In the middle subplot, marked (b), we can see the inflation of the sources with \(P=3\), and the amplitude of these is calculated using Equations (17)-(19). Finally in the right subplot, marked (c), each set of sources is substituted by a new source obtained by the minimization of Equation (21).  
Figure 4: Graphical analysis of a test case; a dashed white circle in (**b,c**) gives the position of starting sources of (**a**). Source 1 is not changed by the inflation/deflation. Source 2 and 3 are moved. Source 4 has a very small excitation, so it is removed.  
Summarizing, at each iteration, the \(N_{k}\) sources can be modified in two possible ways: they can be moved, or they can be removed. This means that the overall number of sources can only be reduced during the process of optimization, and the sparsification of the antenna can only increase. Then the algorithm can be stopped when the solution found does not change anymore, or when a chosen number of iterations has been performed.
# A Study on a Radio Source Location Estimation System Using High Altitude Platform Stations (HAPS)
## 3 Numerical Analysis and Results
### Comparison of LoS Rate between the HAPS and UAVs  
In Section 2, we discussed various location estimation methods. However, the accuracy of such methods varies greatly depending on whether the environment between the receiving antenna and the radio source is a LoS environment or an NLoS environment. Inthis section, we perform a ray tracing simulation using the SBR (Shooting and Bouncing Rays method) in MATLAB to compare the LoS rate between the case using the HAPS and the case using a UAV. In this analysis, the case in which a direct wave (a radio wave that is not reflected by any obstacle) reaches the receiving antenna was considered as the LoS environment and the case in which direct waves do not reach the receiving antenna considered an NLoS environment.
## 2 DPS Modeling of LEO-SIN
### Rmdes-Sp  
Considering the main path not at the center point of DPS, asymmetrical DPS, and multi-path with random AOA in LEO-SIN, the DPS of LEO-SIN must solve the problem of simulations of random AOA and its variable range. Hence, we combine advantages of RMEDS and MEDS-SP to design our new method RMEDS-SP. In the proposed RMEDS-SP, SOS would be parted into different sets, then each AOA would be random and be operated traversal in the sets. RMEDS-SP combines the advantages of high accuracy by RMEDS and the low complexity by RMEDS.  
In the proposed RMEDS-SP, the AOA would be added into a random number based on MEDS-SP, the model of MEDS-SP is shown in Figure 6.  
Figure 6: The models and performance of RMEDS-SP.  
The Gaussian process of RMEDS-SP can be expressed as,  
\[U^{(k)}\;(t)=Z_{1}^{(k)}(t)+jZ_{2}^{(k)}\;(t) \tag{13}\]  
\[Z_{i}^{(k)}(t)=\sqrt{\frac{2}{N_{i}}}\sum_{n=1}^{N_{i}}\cos\Bigl{(}2\pi f_{max} \sin\Bigl{(}\alpha_{i,n}^{(k)}\Bigr{)}t+\varphi_{i,n}^{(k)}\Bigr{)} \tag{14}\]  
\[\alpha_{i,n}^{(k)}=\frac{\pi}{2N_{i}}\left(n-\frac{1}{2}\right)+\frac{\pi}{KN_ {i}}(1-k)+\frac{\theta}{4 KN_{i}},i=1,2 \tag{15}\]  
where, \(Z_{1}^{(k)}(t)\) and \(Z_{2}^{(k)}\;(t)\) denotes two orthogonal components of the I component and Q component, \(\alpha_{i,n}^{(k)}\) denotes AOA of the nth path determined by the moving direction of UE, \(\varphi_{i,n}^{(k)}\) denotes the phase change of the nth path, and \(k\) denotes the number of partition sets. Referring to MEDS-SP, the large sets of sinusoids with random phase and AOA would be further parted into small sets of sinusoids. In the small sets, the sinusoids with random AOA \(\alpha_{i,n}^{(k)}\) would be superposed and the average would be calculated to obtain the optimal value.  
Since the range of AOA is random and not permanent, we set the AOA in the proposed RMEDS-SP independent and obey a uniform distribution range in \(\left(-\frac{\pi}{4 KN},\frac{\pi}{4 KN_{i}}\right)\).  
As Figure 6 shows, the random AOA range in \([-\pi,\pi]\), has each AOA corresponding to one point on the circle. For example, the AOA of sinusoids are parted into two sets; we have the number of sets \(K=2\). These two sets are \(\{\alpha_{i,n}^{(1)}\}\) and \(\{\alpha_{i,n}^{(2)}\}\). We can configure different AOA and the number of sinusoids in each simulation in the set, and get the suitable AOA with the least error and calculate the time-domain response of that to take as the time-domain response of the whole set. Then, the time-domain response of each set would be superposed to get an overall optimal time-domain response of the signal. Here, the overall number of sinusoids is \(N_{i}^{\prime}=KN_{i}\). Its ACF can be expressed as,  
\[R_{z_{i}z_{i}}^{(k)}=\frac{1}{N_{i}}\sum_{n=1}^{N_{i}}\cos(2\pi f_{max}\sin \Bigl{(}\alpha_{i,n}^{(k)}\Bigr{)}\tau) \tag{16}\]  
\[R_{z_{1}z_{2}}^{(k)}=R_{z_{2}z_{1}}^{(k)}=0,\;\alpha_{1,n}^{(k)}+\alpha_{2,m}^ {(k)}\;\
eq 1\pi \tag{17}\]  
\[R_{z_{i}z_{i}}=\frac{1}{K}\sum_{k=1}^{K}R_{z_{i}z_{i}}^{(k)} \tag{18}\]  
where, \(n=1,2,3,\ldots\), \(N_{1}\), \(m=1,2,3,\ldots\), \(N_{2}\), and the overall average ACF is the average of ACF of each set.
## 6 Conclusions  
While our understanding of UAV cellular communications has improved in recent years, there are still numerous fundamental hurdles to overcome. We conducted a trip from authentication in 6G UAV application cases, requirements and enabling technologies in this essay, blending academic and industrial perspectives. We demonstrated how NR improvements will considerably aid in meeting the rigorous control and payload data demands of network-connected UAVs throughout this decade through real results. In terms of sidelobe-based association, beamformed control signals make UAV cell selection and handovers easier. In both UMa and denser UMi scenarios, mMIMO combined with UAV-aware null steering or UAV-based beam shaping is required to ensure stable cellular connectivity. Surprisingly, this is because to a favorable mix of antenna sidelobes and powerful reflections, NR mm wave networks can also provide enough sky coverage. Such coverage could be further extended by rooftop-mounted uptilted mm wave cells, albeit this is subject to regulations. Underlaying these direct links with the ground uplink incurs little mutual interference for UAV-to-UAV applications, but this becomes more noticeable as UAVs fly higher. UAV-specific power control can efficiently balance UAV-to-UAV performance with ground-link performance. In this article, we have presented the detailed information on the blockchain-envisioned UAV communication using 6G networks. A discussion on the architecture, requirements and use cases of 6G technology was carried out. A solution taxonomy of several applications of UAV communication was discussed. Various unsolved issues were extracted from the study among UAVs and provide a viable solution by providing a view on the blockchain-envisioned UAV communication using 6G networks. Then, we discussed some future research directions based on our study. Finally, we presented a case study of package delivery in industry 4.0 applications that explores the role of the blockchain-envisioned UAV communication using 6G networks.  
Conceptualization: A.S.K. and M.A.S.; Methodology: M.A.S. and K.N.; Visualization: A.A.A.I. and N.B.A., Writing--original draft preparation: M.A.S., A.S.K. and K.N.; Writing review and editing: A.S.K., J.B.A. and S.K.M.; supervision, A.S.K., K.N. and J.B.A.; project administration, A.S.K., K.N. and S.K.M.; Funding acquisition: A.A.A.I., K.N. and A.S.K. All authors have read and agreed to the published version of the manuscript. This research is fully funded by Universiti Malaysia Sarawak under Grant number F08/GRADUATES/2188/2021.  
Not applicable.  
**Acknowledgments:** This research was conducted in collaboration with Universiti Malaysia Sarawak, Malaysia, Victorian Institute of Technology, Adelaide, Australia, University Malaysia Sabah, Malaysia and University of York, UK. Article Processing Charges (APC) is paid by University Malaysia Sabah.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Principal Component Analysis  
PCA analysis was applied to explore the association between the physiochemical and textural parameters of control breads and the fortified hemp protein breads. The PCA biplot of correlation coefficients among the examined parameters as located by the position of the breads on each principal analysis is presented in Figure 9, while the correlation coefficients of the parameters with the two major components are shown in Table 5. PC1 (Axis 1) accounted for 33.4% and PC2 (Axis 2) accounted for 35.9% of the total variation (69.3%). On Axis 1, cohesiveness, resilience, and springiness showed the maximum correlation coefficients. On Axis 2, the highest correlation coefficients were found for chewiness, moisture, and a* (color parameter). PC1 was negatively correlated to hardness and a* parameters, while PC2 was negatively correlated to L* and b* color parameters. The parameters cohesiveness, resilience, and springiness were highly associated with each other but negatively related to hardness. On the other hand, moisture was positively associated with chewiness, cohesiveness, resilience, and springiness. This was expected since moisture is the main parameter which affects textural properties. The control bread (0%) was highly correlated with L* and b* values while the breads with 10 and 15% hemp protein were related to a* and hardness values. On the other hand, the breads on the 1 st day of storage were positively positioned to resilience, cohesiveness, springiness, and moisture, while the breads stored on the 4 th day of storage were closely positioned to hardness, indicating that this parameter was affected during aging. Those results further confirm the findings obtained from physicochemical and textural analysis.
# Spatial Multiplexing Holography for Multi-User Visible Light Communication
## 3 Simulation Results
### Simulation Results for OAM Secure Communication  
In terms of secure communication, the users' information undergoes dual encryption following the principles outlined in Section 2.3. Firstly, as shown in Figure 6a, we demonstrate the dispersion resistance and arbitrary location OAM-forming ability of SMH. We created 5 th- and 10 th-order OAM multiplexing holograms using SMH, the conventional SDM method [17], and the MDM method [16]. The SDM method splits the forward space and forms two OAM modes in different directions, but the depths of the two OAM modes are similar, and their locations change as the distance increases. In the MDM method, two OAM modes are coaxially added, and users can only decode the signal at the same place. In contrast, SMH forms the two OAM beams at 0.6 m and 0.8 m, respectively, with smaller rings, indicating minimal dispersion. This highlights the superiority of SMH. Then, Figure 6b illustrates the relationship between power variation and OAM keys to highlight the critical role of accurate OAM decoding. Specifically, for user encoded with OAM modes \(l=20\), only the application of the inverse OAM phase \(l=-20\) yields maximal power reception. Moreover, the power reception quality incrementally improves as the decodingOAM modes more closely align with the correct values. This is because light spots decoded with OAM modes closer to the correct value tend to assume a Gaussian distribution.  
Figure 6: (**a**) Simulation of optical field at receiver plane under SMH and conventional method; (**b**) Power trend of user encoded with OAM modes \(l\) = 20 under different OAM keys; (**c**) Simulation results for OAM secret communication.  
Figure 6c displays the power distributions for five distinct users across five separate plates within the forward transmission space. Each user is encoded with unique OAM modes ranging from 1 to 5. The figure demonstrates that when the correct OAM key is applied to decoding at the appropriate plate, the respective user experiences high-power reception. Conversely, other plates, which encounter incorrect OAM keys, exhibit significantly lower power levels. Furthermore, the disparity between the intended and applied OAM keys directly influences the power received: the greater the discrepancy in the OAM keys, the lower the power that is captured. This pattern underscores the precision required in matching OAM keys for optimal power distribution and effective secure communication within VLC systems.
## 2 Materials and Methods
### Atmospheric Stability
#### 2.4.3 Richardson Number  
The properties and relative magnitudes of turbulence produced by mechanical or thermal convection can be determined by the Richardson number. This is a stability index that can take into account the density difference and wind speed difference in the atmosphere by simultaneously measuring the temperature and wind speed of the upper and lower layers. When defining the Richardson number, it is preferred to first define the stability parameter S (Equation (8)). The variable S is proportional to the rate at which the steady state of the atmosphere suppresses turbulence. The concept of the gravitational constant (g, 9.81 m/s\({}^{2}\)) and temperature (\(\theta\)) is introduced. Temperature is defined as the temperature at which a dry mass of a certain altitude moves adiabatically to a height of 1000 hpa. An increase in temperature with altitude means that the atmosphere is stable. Turbulence is also produced by mechanical convection at a rate proportional to \((\partial\overline{n}/\partial Z)^{2}\). The Richardson number can be said to be the ratio of these two processes, and is an indicator of thermal turbulence and atmospheric stability. The strong negative Richardson number characterizes strong vertical convection in an unstable atmosphere with dominant convection, and weak winds. As the mechanical turbulence increases, the Richardson number approaches zero as a neutral atmosphere (\(\partial\overline{n}/\partial Z\) = 0). Finally, when the Richardson number becomes positive, vertical convective mixing ceases, and the atmosphere is stably stratified, resulting in mechanical turbulence.  
\[S=\frac{g}{T}\bigg{(}\frac{\Delta\theta}{\Delta Z}\bigg{)} \tag{8}\]  
\[\theta=T\bigg{(}\frac{p_{0}}{p}\bigg{)}^{0.286} \tag{9}\]  
\[\text{Ri}=\frac{S}{\left(\partial\overline{n}/\partial Z\right)^{2}}=\frac{g }{T}\frac{(\partial\theta/\partial Z)}{\left(\partial\overline{n}/\partial Z \right)^{2}}=\frac{g}{T_{0}}\frac{(\partial/\partial Z)[\theta_{0}+\theta_{1 }]}{\left[(\partial/\partial Z)(u_{0}+u_{1})\right]^{2}} \tag{10}\]
# Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks
## 2 Related Work
### Traditional Mobile Routing  
Classic mobile routing protocols in vehicular networks include the ad hoc on-demand distance vector (AODV) [23; 24], dynamic source routing (DSR) [25], the destination-sequenced distance vector (DSDV) [26], and greedy perimeter stateless routing (GPSR) [13]. The DSDV routing protocol is a proactive routing protocol where every node in the network needs to maintain up-to-date routing information. This increases network overhead and requires frequent updates of the routing information due to the constantly changing topology. The AODV and DSR are on-demand routing protocols, and their simulation results in depicting various aspects that have not been satisfactory in rapidly changing network environments. Additionally, due to the internal maintenance mechanisms of these routing protocols, they can cause network congestion, consume significant bandwidth, and even degrade network performance [27; 28].  
Compared to previous routing protocols, location-based geographic routing is considered to be one of the best solutions for dynamic network topologies. In protocols employing a greedy strategy, vehicles forward data packets to the next hop that is closest to the destination node based on their neighbor table. GPSR is a classic location-based routing protocol that combines greedy forwarding with perimeter forwarding. Vehicles determine their own positions and broadcast their state information among neighboring nodes. GPSR offers low latency and control overhead characteristics. However, GPSR does not consider parameters such as beacon intervals, node velocities, or movement directions, which can lead to link instability and limited routing performance.
## 3 Experiments
### Evaluation Metrics  
For a fair evaluation, this paper uses two metrics as quantitative analysis indicators: tracking success rate and tracking precision [31; 32].  
We use the center location error (CLE) to evaluate tracking precision, i.e., the Euclidean distance between the object's predicted and real center position. CLE is calculated by:  
\[CLE=\sqrt{\left(x_{p}-x_{gt}\right)^{2}+\left(y_{p}-y_{gt}\right)^{2}} \tag{26}\]  
where \(\left(x_{p},y_{p}\right)\) is the object center position predicted by our proposed algorithm, \(\left(x_{gt},y_{gt}\right)\) is the object's ground truth location. The tracking precision is the ratio of the frames whose center position error obtained by the tracking algorithm is less than a certain threshold to the total number of the video frames. Therefore, the threshold has a significant influence on the object tracking precision. Since an object in the satellite videos is very small, we set the threshold to five, i.e., if the CLE value is within five pixels, we consider that the object is successfully tracked.  
The tracking algorithm's success rate is the ratio of the frames whose overlap rate exceeds a certain threshold to the total number of video frames. Accordingly, the overlap rate is the ratio of the overlap area between the predicted and the real object tracking frame to the total area of both areas, expressed as:  
\[S=\frac{Area\left|r_{p}\cap r_{gt}\right|}{Area\left|r_{p}\cup r_{gt}\right|} \tag{27}\]  
where \(r_{p}\) and \(r_{gt}\) are the predicted bounding and the real object tracking area, \(\cap\) and \(\cup\) are the intersection and union, respectively, and \(||\) is the number of pixels in the region.  
The threshold can be set to 0.5. If the overlap ratio of the current frame is greater than 0.5, we consider that the object tracking of the current frame is successful. Therefore, we evaluate the overall performance of our proposed algorithm on all satellite videos utilizing the precision plot, success plot, and area under curve (AUC). Moreover, the success score, precision score, and AUC can further rank the trackers.  
Moreover, the FPS denotes the number of frames that the tracker can process, so it is suitable to evaluate the tracking speed per tracker.