esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Authentication in UAV Networks
### Light Weight Authentication Protocols  
The use of WiFi has significantly increased over the years both at individual and commercial levels. Due to no complexity involved in installation and operational use of WiFi technology, the popularity of this wireless communication system is ever-increasing [95]. Moreover, it is a cost-effective solution in comparison to typical cable network. wireless sensor nodes are exploding in popularity, with applications as diverse as in any possible fields for the future [96]. These sensors are expected to be a ground-breaking addition in the consumer and business world. For example, the information collected by these sensors in a market place, in a particular section of a store, can turned into meaningful data for targeted advertisement, thus engaging visitors through tapped data by these small sensors for attracting customers and providing better services in consumer field [97].  
In a wireless sensor network (WSN), each sensor collects a query from numerous wireless nodes and transports it to a database for subsequent analysis for converting data into meaningful information. The vital requirement in a secure network is authentication of network nodes. Similarly, in WSN, valid authentication of each element is vital. Light weight authentication is considered to be one of the time efficient schemes, mandatory in a heterogeneous network to reduce the period required for authentication process [98]. Reducing handoff latency is thought to be a difficult task. Once a mobile user requires to maintain utilizing the wireless service uninterrupted and remain connected while during a journey across the diverse communication network, this issue arises. For example, the access networks are switched by a user during traveling, staying connected on internet and accessing real-time mobile applications [99]. Interruptions, link quality and reliability issues, security concerns, loss of data packets is experienced whenever there is delay in vertical handoff. Security, reliability, negligible interruption, appropriate handoff scheme are demanded in such applications [100]. In this arena, a number of strategies for reducing authentication delays have been presented. These solutions, on the other hand, do not entirely solve all of the concerns in the problem area; for example, they have security, monetary cost, signaling cost and packet latency flaws.
## 3 Simulation and Discussion
### Effect of Nyquist Frequency Shift on the Coupled-MTF
The results show that when the deviation of position, the deviation of image element coupling, and the number of fibers (\(1000\times 1000\)), are fixed values, the spatial frequency of the input cosine target converges to a fixed value of 0.5. As the deviation of the spatial frequency of the input cosine target from the Nyquist frequency gradually increases, the convergence speed of coupled-MTF accelerates and finally converges to a fixed value of 0.5.
# A Connectivity-Based Clustering Scheme for Intelligent Vehicles
## 7 Conclusions and Future Work  
The highly dynamic and evolving structure of VANET is a big challenge for V2V and V2I communications. Clustering is an essential step towards the reliability, stability, and scalability of VANETs. The formation of clusters and CH selection are strongly related to the performance of a routing scheme. The proposed criteria for clustering in this work, i.e., link connectivity, considers the relative position and transmission range of a vehicle in a  
Figure 5: Intercomparison of route connectivity of our suggested connectivity-based clustering scheme with TCRP [1] and linear routing scheme.  
particular road segment. The suggested connectivity-based clustering uses link connectivity as a metric for cluster formation and CH selection. From the results, it is noticed that the proposed metric for clustering forms long-lasting groups of vehicles as well as significantly increases the probability of connectivity of a selected route. Moreover, it also reduces the computational cost by reducing the excessive number of RRMs. The spectral clustering optimally divides the evolving VANET into manageable groups based on the structure of network. This is the reason why the number of RRMs and packet loss never reach their worst performance threshold. Moreover, the CEG-Dijkstra and FAP play essential roles in discovering a strongly connected route in the intra-cluster and inter-cluster modes, respectively. The former discovers the destination node/vehicle within a cluster, while the latter is triggered in the case of outside discovery. Thus, the process of route discovery does not significantly increase the computational cost because of the specification of inter- and intra-cluster discovery. In the future, the proposed connectivity-based clustering scheme will be evaluated on real-road scenarios and an assessment will be performed through more comprehensive metrics.  
The conceptualization, methodology, and original draft writing were performed by [PERSON] The supervision, validation, and writing--review and editing--were conducted by [PERSON] Formal analysis and proofreading were performed by [PERSON] and [PERSON]. [PERSON] addressed all the comments of the reviewers in the third round. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1D1A1B07043302). It was also partially supported by Prince Sultan University, Kingdom of Saudi Arabia.  
Not applicable.  
This work was supported by the Robotics and Internet-of-Things Lab of Prince Sultan University, Riyadh, Kingdom of Saudi Arabia.  
The authors declare no conflict of interest.
## 1 Introduction  
In the near future, it can be expected that buildings will be equipped with a range of wireless sensors functioning as part of an overall building management system. Included in this set of sensors will be devices to monitor fire and smoke, allowing detection, localization and tracking of fires. It is expected such information could be used for a variety of purposes, including guiding building occupants to the nearest safe exit, and helping fire fighting personnel to decide on how to best tackle the disaster. Fire/smoke sensors are expected to be programmed to report periodically and also when they detect a sensor input that exceeds a threshold. In the latter case, there is a need for an emergency-adaptive,  
real-time and robust message delivery toward the sink. For example, a fire-tighter relies on timely temperature updates to remain aware of current fire conditions. In addition as the fire spreads throughout the building, it becomes likely that the sensing devices may become disconnected from the network or indeed be destroyed, so the network routes have to be changed or re-disovered to adapt to these emergency conditions in order for the network to continue operating. Most existing routing protocols consider the energy efficiency and lifetime of the networks as the foremost design factor. The routing mechanisms used in general wireless sensor networks and even routing for forest fire applications are not well suited for in-building disaster situations, where timeliness and reliability are much more critical. For forest fires the focus is on tracking of fires, rather than evacuation or guidance of fire personnel. This combination of real-time requirements coupled with dynamic network topology in a critical application scenario provides the motivation for our research. In this paper, we propose an emergency-adaptive routing mechanism(EAR) designed especially for building fire emergencies using wireless sensor networks (WSN), which provides timely and robust data reporting to a sink. We do not need to know the exact localization of each sensor and also no time synchronization is needed. To the best of our knowledge, this is the first time a real-time and robust routing mechanism adaptive to building fire emergency/sensing WSNs has been proposed. Also, this protocol could be easily used in other similar emergency applications.  
Section 2 presents the related work. In Section 3 we outline the routing problem. We present an emergency-adaptive routing mechanism in Section 4. In Section 5, we present a preliminary analysis. In Section 6, we give a simulation results. Finally, Section 7 concludes this paper.
## 2 Materials and Methods
### MODIS Time Series
#### 2.3.1 Phenology and Green Biomass Production Measures  
Yearly, pixel-based phenology and green biomass production measures were derived from the MODIS based complete 2001-2013 16-day NDVI time series using the Phenolo software (for a description see [47]). NDVI data were selected here due to robust seasonality of the signal directly associated with the plant 'greenness' [41]. The focus was given to two vegetation productivity metrics: Cyclic Fraction (CF, an approximation of annual seasonal growth), and approximation of Gross Primary Production (GPP, estimated as the integral value of the annual growing season); as well as two season information parameters: Season Length (SL) and Season Begin Day (SBD). All metrics together extensively characterize year-to-year vegetation variability [47; 64]. Since the Phenolo algorithm is based on the moving average approach [48], resulting time series comprised parameters only for the 2002-2012 period.
## 3 Proposed Methodology
### Proposed Reinforcement Learning with Snake Optimizer
Each state \(S_{t}\) encodes the availability of channels, current allocation, and observed interference levels, while each action \(A_{t}\) corresponds to a specific channel allocation decision for secondary users. The entire interaction process is modeled as a MDP, where SO functions as an agent that selects actions based on the observed state to maximize the expected cumulative reward. This formalism enables the efficient learning of optimal allocation policies under dynamic and uncertain network conditions.
# A Spatial 4-DOF Laser Collimation Measurement System
## 2 Optical Principle
### Crosstalk Analysis of Measurement Results  
#### 2.3.1 Using Two CMOS Detectors for Ranging and Angle Measurement Crosstalk Analysis  
The spot displacement x and y, received by the detector measuring displacement, are determined using the outgoing beam of the corner cone prism. According to the characteristics of the corner cone prism, the position and angle of the incident beam affect the position of the outgoing beam. Linear displacement Z and rolling angle do not affect the incident light spot of the angle cone prism, while the pitch angle and yaw angle will affect the incident light spot of the angle cone prism beam. The use of two CMOS detectors can eliminate the influence of the angle of deflection and pitch angle on the range of x/y direction straightness. A more detailed analysis is provided as follows. Analysis of Crosstalk of Angle Measurement and Range Using the Traditional Geometric Method  
The influence of pitch angle and deflection angle measurements on horizontal ranging: When the corner cone prism is rotated around an edge angle of O, take RR2 as an example for analysis, Hypothesis: Deflection angle \(\alpha\) will only cause measurement errors in the x direction range; deflection angle \(\beta\) will only cause error in the y direction range. Figure 7 is the coordinate system, the angle in the x direction is the pitch angle (yaw), and the angle in the y direction is the deflection angle (pitch). Figure 6: Principal diagram of angle measurement light path. As shown in Figure 8, assume the ideal case first. The errors of the pitch angle and deflection angle are \(\alpha\) and \(\beta\) respectively, when the cone prism RR rotates around the edge of angle O, which is the ideal case, and when the right angle radius of the cone prism is r, as shown in this figure. It can be seen that the influence of the two laser beams I1 and I2 in the x direction is twice as large as the deviation of the projection in the x direction of the center P and the P 'of the right angle radius of RR and RR\({}^{\prime}\) figures before and after the corner prism \(\delta_{x}\). \[\delta_{x}=2r(1-cos\alpha) \tag{9}\]  
Similarly, the influence in the y direction of by deflection angle \(\beta\) is \(\delta_{y}\)  
\[\delta_{y}=2r(1-cos\beta) \tag{10}\]  
The corner cone prism is rotated around the non-edge O point (Figure 9). Figure 8: The angular cone prism, rotated around edge angle O.  
Figure 7: System coordinates. Further considering the actual situation, assuming that RR1 and RR2 rotate around 0, as shown in the figure. In the actual model, the angular prism rotates around the non-edge O point.
## 2 Feature Analysis of Existing RRSA Concepts  
### Description of RRSA  
For ease of subsequent reference and quotation, we present a simple description of the rigid-reflector spaceborne antenna in this subsection. Figure 1 shows the process of deploying the antenna of the Spektr-R satellite after unfolding its solar array. Similar to the Spektr-R, the reflective surface of a typical RRSA usually consists of circumferential panels and a central hub. In the folded state, all of the panels are around the hub and above it in a circular arrangement. Therefore, the configuration profile of the antenna can be approximated as a cylinder.  
With respect to the cylinder profile, the ratio of aperture diameter to the maximum radial profile dimension of the antenna is referred to as the radial stowing efficiency (RSE).  
Figure 1: Deployment of Spektr-R. (**a**) Folded state. (**b**) Intermediate state. (**c**) Deployed state. The RSE is defined as \(D_{\text{a}}/D_{\text{f}}\) and VSE is defined as \(D_{\text{a}}/h_{\text{f}}\).  
For instance, for the particular antenna of Spektr-R, its RSE is the deploy/fold ratio \(D_{a}/D_{f}\). In addition, the ratio from aperture diameter to the vertical profile dimension is defined as the vertical stowing efficiency (VSE). For the Spektr-R, the VSE is \(D_{a}/h_{\mathrm{f}}\). If both RSE and VSE are large, the overall degree of system compactness is high. Unfortunately, these two values contradict each other, between which certain trade-offs must be made. This suggests that larger RSE implies lower VSE, and vice versa. In reality, the primary concern in practice is the RSE rather than the VSE, since manufacturing large-aperture fairing is far more difficult than manufacturing long fairing. For example, China's Long March-5B rocket possesses a fairing with a 5.2 m diameter and 20.5 m length [47]. Therefore, one advisable strategy is ensuring the RSE first and then improving the VSE as much as possible. In this research, RSE is the sole pursued index for convenience of discussion. To sum up, the following investigation primarily focuses on the RSE of the RRSA, and whenever the stowing efficiency is mentioned in this paper, it defaults to RSE.  
There are basically three concepts of the RRSA regarding the segmentation of the reflective surface of the antenna. The below subsection introduces these concepts and conducts a systematic analysis with respect to their characteristics, thereby forming a new RRSA concept. It has been acknowledged that the segmentation can be abstracted as follows. Firstly, the reflective surface is segmented into \(n\) (\(\geq\)6) identical units (axisymmetric elements) in the circumferential direction, and each unit is sub-segmented into \(m\) (\(\geq\)1) panels, with the rest performing as the central hub. Obviously, smaller \(m\) suggests higher axial symmetry.
## 4 Discussion
### Quantified MDD Superior to Line/Pair  
The quantified MDD can be transferred to the line pair/cm (lp/cm) in analyzing the gamma camera collected imaging quality, although these are defined on the basis of different scenarios in theory. The MDD is proposed to identify the minimum distance between two peaks and maintains a 95% confidence level, yet the lp/cm represents two lines of brightness and darkness. Furthermore, the MDD can be theoretically smaller than we derived if the confidence level is reduced from 95 to 68%; then, the constant of 1.96 can be disregarded in Equation (7). As seen in Figure 6, the diagram shows the difference between MDD and lp/cm. The definition of MDD can thus be interpreted as bearing a similar width to one line pair. Thus, the number of lp/cm approximately equals 10 mm/MDD, although MDD provides more precise information. Thus, the roughly estimated lp/cm is 1.2, 1.3, and 1.4 for the second group, conventional, and optimal, respectively. Nevertheless, the  
Figure 5: The three derived SPECT and original images from gamma camera scanned according to (**A**) group 2 of the original Taguchi’s eighteen groups and have the highest S/N, (**B**) conventional preset, and (**C**) the combination of the highest S/N in every specific factor.  
lp/cm offered only the digitized number, yet MDD is calculated based on two adjacent peaks from SPECT of the real gamma camera scanned images. In addition, Table 7 shows the various MDDs or lp/cm from other research and this work for comparison. As clearly shown, cardiac X-ray has the finest resolution for diagnosis, whereas a gamma camera is functioned on the collection of radioactive solution inside patient's body; thus, the imaging has comparatively inferior quality among all.
# Federated Learning for Secure In-Vehicle Communication
## 2 Backgrounds and Preliminaries
### Machine Learning for Intrusion Detection in Vehicular Networks  
Machine learning techniques are proposed to analyze CAN bus traffic and detect unauthorized access [27]. In this part, we focus on centralized machine learning that involves collecting, aggregating, and processing data in a central server or cloud, training a model, and performing computations for model training and evaluation [11].  
Machine learning techniques are proposed to analyze CAN bus traffic and detect unauthorized access [11]. Classical machine learning methods include Support Vector Machine (SVM), K-Nearest Neighbor (KNN), One-Class Support Vector Machine (OCSVM), and Isolation Forest are proposed, while deep learning techniques are increasingly employed [28]. The deep learning models developed are more in demand for IVN-IDS. The authors in [28] propose a Convolutional Neural Network (CNN)-based IVN-IDS to detect attacks within the CAN protocol. They created their dataset using three different automobile models, and they showed that their CNN-based classifier successfully detects CAN bus assaults with encouraging accuracy and efficiency of more than 99%. To address the lack of security features in the CAN bus, the researchers suggest a transformer-basedattention network (TAN) [16]. TAN uses a self-attention technique to categorize attacks. This model outperforms baseline techniques when given consecutive CAN IDs as input, and it uses transfer learning to adjust to smaller datasets from various car models. The outcomes demonstrate how well TAN can identify intrusions in automobile networks with a very high accuracy of 100%. The work in [10] outlines an LSTM-based IVN-IDS for identifying and mitigating attacks on the CAN bus. A custom dataset was created by collecting normal CAN traffic and injecting simulated attacks. The training and testing of this model achieved a detection accuracy of 99.995%. A lightweight BiLSTM-based anomaly detection model with an attention mechanism efficiently secures the CAN in vehicles lacking privacy safeguards. It detects replay attacks (92.79% accuracy), DoS attacks (92.85% accuracy), and fuzzing attacks (96.15% detection rate) by leveraging correlations in CAN ID sequences [11].  
Building upon these findings, we propose a BiLSTM model tailored to centralized deep learning scenarios, demonstrating enhanced effectiveness in detecting CAN-based attacks and reinforcing the robustness of IVN-IDS.
# Rotational-Shearing-Interferometer Response for a Star-Planet System without Star Cancellation
## 2 Theory
### Special Cases
#### 2.1.1 First Case, the Solitary Star  
When the star does not have a planet as a companion, the interference equation is reduced to the term \(M_{SS}\), described by the next equation:  
\[M=M_{SS}=M_{S}\bigg{(}2+2\cos\bigg{\{}\frac{2\pi}{\lambda}\big{[}2\rho\sin \theta_{S}\sin\Delta\varphi\sin(\varphi-\varphi_{S}-\Delta\varphi)+OPD\cos \theta_{S}\big{]}\bigg{\}}\bigg{)}. \tag{6}\]  
Equation (6) represents a fringe pattern. The spatial frequency of the fringes is given by \(2\theta_{S}\sin\Delta\varphi\). This equation denotes the dependency of the fringe density with the elevation angle of the star. The orientation of the fringe-pattern is given by \(\varphi_{S}-\Delta\varphi\). Note that the azimuth angle of the star determines the fringe direction. Because of the dependence between the fringe pattern and the star alignment, we may use this fringe pattern to align the optical axis with the star. When the star is perfectly aligned with the optical axis, the resultant pattern consists of a uniform incidence over the entire observation plane. If we change the interferometer OPD, the incidence level varies accordingly with:  
\[M=M_{S}\bigg{[}2+2\cos\bigg{(}\frac{2\pi}{\lambda}OPD\bigg{)}\bigg{]}. \tag{7}\]  
If we adjust the OPD to \(\lambda/2\), the star incidence is canceled at the observation plane. Under these conditions, it is possible to detect a planet if it is present around the planet.
# Millimetre-Wave Backhaul for 5G Networks: Challenges and Solutions
## 1 Introduction
The overall objective of this article is thus to offer an overview of a design framework of mm-wave backhaul based on current research and to provide solutions of utilizing mm-wave band techniques in building a 5G backhaul network. We structure the article as follows. After introducing the development of mm-wave techniques, we offer an overview of the mm-wave backhaul system and outline the associated challenges. Then, we introduce emerging progress in mm-wave physical layer techniques and propose an integrated design framework based on the new techniques to tackle the problems, with the demonstration of routing and scheduling based on the proposed optimization model. Finally, the capability of the proposed framework and design method is assessed via systematic simulations under a realistic scenario.