esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Things and Blockchain Integration: Security, Privacy, Technical, and Design Challenges
## 3 RQ 1 and RQ 2--Current Challenges and Recommendations to Enhance BIoT Integration
### BC-Based Security Issues  
The security model incorporates confidentiality (the capacity to keep data hidden from unauthorized users), integrity (the consistency and correctness of data throughout its life cycle), and availability (i.e., ensures that data are available to the users at a required range of performance in any situation); it is regarded as one of the most critical concepts for ensuring security in any form [141; 152; 21]. Furthermore, security challenges associated with the nature of BC including BC security vulnerabilities, smart contract vulnerabilities, trust management, and authentication (i.e., using personal identification to validate requests)  
Figure 2: BIoT integration current challenges.  
are other concepts that are required for implementing policy, and limiting access should be discussed [141; 153; 21].  
Although all BCs utilize cryptographic protocols to protect their data and activities, this does not mean that they are without flaws [68; 153]. Accordingly, for heterogeneous devices, a multilayer security architecture must be developed. Before any amenities are delivered to users, the framework should first adapt to current resources and make judgments about the security methods to apply at the IoT levels. Intelligence is required for such an adjustable security framework that is susceptible to resource standardization for deployment in IoT systems [85]. In this section, we look at how employing BC in IoT applications might jeopardize the security concepts and requirements mentioned above. In addition, the recommended solutions are discussed. Table 6 highlights the solutions recommended for addressing security challenges.
## 1 Introduction
### Motivation and Challenges  
Within the scope of this study, our primary focus is on the safety of the UAM attitude control system to guard against both crashes and theft, which are cause by cyberattacks such as jamming and spoofing. Since the majority of research [1; 2; 3; 4; 5; 6; 7; 8] on unmanned aerial vehicles (UAVs) has focused on how to exploit vulnerabilities in received signal indicators (RSSIs) or communication streams. Unlike that, our method is new and distinct, since we are not only detecting the cyber threat but also providing analytics on the risk of a cyberattack. In addition, a significant portion of the research literature has focused on the detection of intrusions in aerial vehicles without providing or expanding upon the attack mechanism excluding the attitude control system. One of our main goals is to conduct an extensive experimental study of the exploratory cyber-physical safety framework that incorporates artificial intelligence and statistical models. As a result, our study becomes unique in identifying and validating cyberattacks, following attitude control system protocols while exploring the vulnerabilities of communication streams.  
In order to achieve such a goal, we have faced several technical challenges. A few of the technical challenges are summarized as follows:  
* First, the prediction of UAM cyberattacks must be taken as a fraction of the time (e.g., 1-10 ms). Then, the question is how we can meet such a requirement while, in general, AI mechanisms are computationally expensive.
* Second, designing a lightweight AI mechanism can be one of the suitable methods, where offline training and online execution can solve such challenges. However, it is hard to detect the root cause of cyberattacks dynamically when a pretrained model is used. Now, the challenge is how to ensure analyticity so that the UAM service providers can autonomously find the risk of theft and crashes.
* Finally, an AI-enabled exploratory cyber-physical safety analyzer framework for civilian UAM operations can overcome the above challenges. However, it is imperative to meet the distinct characteristics of several UAM examples, since the manufacturing and working principles vary among them.
# Fingerprint Based Codebook for RIS Passive Beamforming Training
## 6 Conclusions  
In this work, we propose a new CB-based PBF scheme. Firstly, a fingerprint CB is designed by dividing the area of interest into a number of points, and then, in the offline phase, the CE-PBF scheme is adapted to determine the best RP corresponding to each point. Thereafter, using the estimated RX location and the constructed CB, a candidate group of RPs whose points' positions are near the RX position is determined. Then, the system searches only these RPs to select the suboptimal one to be used in the data transmission period. The proposed scheme proves that constructing PBF codebooks and determining the PBF vector based on positioning information is a promising direction to achieve high network performance and reduce system overhead, especially with rapidly changing channels. For example, using a group of 10 candidate RPs for searching obtains a 9.98 bps/Hz effective rate, which is 0.32 bps/Hz higher than that obtained by CE-PBF. Moreover, even if the localization service has a 10 m error in the estimated RX location, using 10 candidate RPs, the FP-PBF method will obtain a 10.16 bps/Hz achievable rate, which is not a big decrease compared to the 11.07 bps/Hz rate that can be obtained if the error is 2.5 m. As a future extension, dynamic CB based on PI can be designed to mitigate the impact of environmental changes on the fingerprint CB. Furthermore, RIS with a higher number of elements can be considered.  
Conceptualization, A.M.N.; methodology, A.M.N.; software, A.M.N.; validation, A.M.N.; formal analysis, A.M.N.; investigation, A.M.N.; resources, A.M.N.; writing--original draft preparation, A.M.N.; writing--review and editing, O.F. and S.H.; visualization, A.M.N.; supervision, O.F.; project administration, S.H. All authors have read and agreed to the published version of the manuscript.  
Figure 6: The effect of channel coherence time on the proposed FP-CB scheme and CE and AO schemes.  
**Funding:** This study has been conducted under the project 'MObility and Training fOR beyond 5G eco-systems (MOTOR5G)'. This project has funding from the European Union's Horizon 2020 program under the Marie Sklodowska Curie Actions (MSCA) Innovative Training Network (ITN) under grant agreement No. 861219.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# An Overview of Machine Learning and 5G for People with Disabilities
## 2 Machine Learning for People with Disabilities
### Visually Impaired
#### 2.1.3 Face Recognition Systems  
A real-time _face recognition system_ for people with visual impairments is developed [25]. A camera sends a video to be processed; Haar-Like Features (HLF) Cascade is used as a classifier with AdaBoost to detect a face within an image; 42 characteristic points of the face are extracted using the Active Appearance Model (AAM), and a new hybrid model named Amx\(\beta\)-CMKNN (Alpha-Beta Associative memories (Amx\(\beta\)) with Correlation Matrix (CM) and K-Nearest Neighbors (KNN)) is used as a classifier to determine to whom a particular face belongs.  
Another _face recognition system_, based on computer vision algorithms (region proposal networks, ATLAS tracking and global, and low-level image descriptors) and deep convolutional neural networks [26], is able to detect, track, and recognize people from video streams in real-time. It includes a novel video-based face recognition framework that is able to construct an effective global, fixed-size face representation method with independence of the length of the image sequence.  
_Month-based emotion recognition using deep learning_[27] helps people with disabilities who have difficulties seeing or recognizing facial emotions. The position and curve of the lips has been analyzed through CNNs. Four convolutional neural networks have been tested: VGG16, InceptionResNetV2, InceptionV3, and Xception. InceptionResNetV2 delivers the best result with an accuracy of 79.5%.
## 3 Modeling of SMEHS
### Position Servo System of SMEHS  
In Figure 1, servo valve 1 is used to control the displacement of the piston rod to track the given displacement signal \(x_{d}\). Choosing \(u_{i}\) as the system input, and choosing \(x_{L}\) as the system output, the system state variable is presented, as follows.  
\[x_{i}^{T}=\left[\begin{array}{cccccc}x_{i1}&x_{i2}&x_{i3}&x_{i4}&x_{i5}\\ \end{array}\right]^{T}=\left[\begin{array}{cccccc}x_{L}&\dot{x}_{L}&p_{1}&x _{v1}&\dot{x}_{v1}\\ \end{array}\right]^{T} \tag{22}\]  
Assuming that \(\delta_{i2}=\frac{-p_{2}A_{2}}{m}\), \(c_{1}=\frac{A_{1}}{m}\), \(c_{2}=-b/m\), \(c_{3}=\frac{\beta_{e}}{V_{L1}}K_{v1}\sqrt{\Delta P_{1}}\), \(c_{4}=-\frac{\beta_{e}}{V_{L1}}A_{1}\), \(c_{5}=K_{s1}\omega_{v1}^{2}\), \(c_{6}=-\omega_{v1}^{2}\), \(c_{7}=-2\omega_{v1}\xi_{v1}\), \(\mathrm{V}_{L1}=V_{L01}+A_{1}x_{L}\), the state-space function of the metering-in side can be illustrated, as follows.  
\[\left\{\begin{array}{ll}\dot{x}_{i1}=x_{i2}\\ \dot{x}_{i2}=c_{1}x_{i3}+c_{2}x_{i2}+\delta_{i2}+\theta_{i2}\\ \dot{x}_{i3}=c_{3}x_{i4}+c_{4}x_{i2}+\theta_{i3}\\ \dot{x}_{i4}=x_{i5}\\ \dot{x}_{i5}=c_{5}u_{i}+c_{6}x_{i4}+c_{7}x_{i5}\\ \end{array}\right. \tag{23}\]  
where \(\theta_{i2}\) and \(\theta_{i3}\) are the disturbance combining of uncertainties, unmodeled items, and external disturbances in the model. In Section 4.1, a disturbance observer is designed to compensate for the disturbance in order to achieve perfect dynamic performance.
# A Game Theoretic Approach for Energy Sharing in the Italian Renewable Energy Communities
## 2 Proposed REC Model  
The energy community is supposed to be composed by a set of users and a set of generators. Each passive user is connected to the grid by mean of a specific POD, while generators can be connected to a dedicated POD as well as to one of the POD of the passive users. Given this configuration, the energy consumed by each user can be categorised in three different ways:  
* Self-consumed--It is produced and consumed under the same POD.
* Shared--It is produced by a generator of the community and it is consumed by users of the community connected to different PODs.
* Bought--It is bought from the market if the production of the generators is not enough to satisfy the request of passive users.  
The methodology considers a base case in which there are no generators owned by the community and the overall energy is bought from the market. Starting from this, it is possible to consider costs, revenues and savings obtained with the installation of new generators. The self-consumed energy can generate a saving by buying less energy and at a reduced network tariff thanks to a minor usage of the grid. In addition, the shared energy can generate savings, but at a less convenient tariff reduction than self-consumption, as the distribution network is still affected by power fluxes. Nevertheless, given the high importance of energy sharing in the REC configuration, the model considers the possibility to introduce an economical incentive based on the quantity of energy shared. Finally, the energy produced but not consumed can be sold on the market, generating additional revenues. It is clear that the value of the savings, incentive and revenues depend on the regulation in place. Even if the application has been tested only in the Italian context, it may be possible, adapting these values, to apply it for different countries.  
The energy balances are evaluated hourly to quantify the energy produced, self-consumed, shared, bought and sold from the single users' and the community's perspectives. The installation of new generators for the community is evaluated, and the relative economical revenues will provide different incomes for the community. Since the benefits are mainly given by the energy shared and self-consumed, there will be an optimal choice for the type and size of the power plants, given the local availability. Operational cost of the generators are considered, and also some administration cost are taken into account. The complete model of the renewable energy community and the investment in new power plants is detailed in the following paragraphs.  
Figure 1: Flowchart of the proposed methodology.
## Abbreviations  
\begin{tabular}{l l} \(a\) & Distance from center of gravity to the middle axle \\ \(\beta\) & Vehicle sideslip angle \\ \(a_{x}\) & Longitudinal acceleration \\ \(a_{xdes}\) & Desired longitudinal acceleration \\ \(a_{y}\) & Lateral acceleration \\ \(B\) & Vehicle width \\ \(H\) & Height of center of gravity \\ \(F_{xi}\) & Longitudinal force of the \(i\)th tire (\(i=1,2,\cdots,6\)) \\ \(F_{xid}\) & Desired longitudinal force of the \(i\)th tire (\(i=1,2,\cdots,6\)) \\ \(F_{yi}\) & Lateral force of the \(i\)th tire (\(i=1,2,\cdots,6\)) \\ \(F_{zi}\) & Vertical force of the \(i\)th tire (\(i=1,2,\cdots,6\)) \\ \(F_{x}\) & Total longitudinal force at the center of gravity \\ \(F_{y}\) & Total lateral force at the center of gravity \\ g & Acceleration of gravity \\ \(I_{z}\) & Yaw moment of inertia of the vehicle \\ \(J_{w}\) & Rotation moment of the wheel \\ \(l_{1}\) & Distance from front axle to middle axle \\ \(l_{2}\) & Distance from middle axle to rear axle \\ \(m\) & Vehicle total mass \\ \(M_{Tx}\) & Resultant moment produced by longitudinal force of tire \\ \(M_{Ty}\) & Resultant moment produced by lateral force of tire \\ \(R\) & Wheel effective radius \\ \(T_{i}\) & Torque of the \(i\)th wheel (\(i=1,2,\cdots,6\)) \\ \(T_{di}\) & Desired torque of the \(i\)th wheel (\(i=1,2,\cdots,6\)) \\ \(v_{x}\) & Vehicle longitudinal velocity \\ \(v_{xdes}\) & Desired vehicle longitudinal velocity \\ \(v_{y}\) & Vehicle lateral velocity \\ \(\omega\) & Vehicle yaw rate \\ \(\omega_{des}\) & Desired vehicle yaw rate \\ \(v_{xi}\) & Longitudinal velocity of the \(i\)th wheel (\(i=1,2,\cdots,6\)) \\ \(v_{yi}\) & Lateral velocity of the \(i\)th wheel (\(i=1,2,\cdots,6\)) \\ \(\omega_{wi}\) & Rotation velocity of the \(i\)th wheel (\(i=1,2,\cdots,6\)) \\ \(\alpha_{i}\) & Sideslip angle of the \(i\)th tire (\(i=1,2,\cdots,6\)) \\ \(\lambda_{i}\) & Slip rate of the \(i\)th tire (\(i=1,2,\cdots,6\)) \\ \(\eta_{i}\) & Longitudinal force workload rate of the \(i\)th tire (\(i=1,2,\cdots,6\)) \\ \(\mu_{i}\) & Coefficient of friction between road and the \(i\)th tire (\(i=1,2,\cdots,6\)) \\ \end{tabular}
## 2 Immersive Simulation Platform Design
### Kinematic Modeling  
In the simulation of the takeoff, landing, and transfer processes of carrier aircraft, dynamic and kinematic models are employed to simulate the aircraft's trajectory. Interpolation techniques are applied to smooth the takeoff and landing trajectories, ensuring a natural and fluid simulation of these processes.  
Due to the slow movement speed of the unmanned carrier aircraft in the process of transportation and the flat surface of the aircraft carrier deck, it can be assumed that the tires do not slide during the movement. Due to the slow movement, the inertial force and lateral force can be ignored, and the relationship of the rodless traction system can be simplified, as in Figure 2.  
In Figure 2, \(x_{0}(t)\) represents the azimuth angle of the tractor; \(x_{1}(t)\) represents the angle difference between the tractor and the unmanned carrier aircraft; \(x_{2}(t)\) represents the traction azimuth angle of the unmanned carrier aircraft under the traction system; \(x(t)\) represents the horizontal position of the two rear wheels of the unmanned carrier aircraft; \(u(t)\) represents the steering angle of the tractor; \(y(t)\) represents the vertical position of the center of the two rear wheels of the unmanned carrier aircraft; \(L_{1}\) represents the front and rear wheelbase of the unmanned carrier aircraft; \(L_{2}\) denotes the front and rear wheel pitch of the tractor. According to Figure 2, its kinematic equation is obtained as follows.  
\[\left\{\begin{aligned} x_{0}(t+1)=& x_{0}(t)+ \frac{v\cdot\Delta t}{L_{1}}\cdot\tan(u(t))\\ x_{1}(t)=& x_{0}(t)-x_{2}(t)\\ x_{2}(t+1)=& x_{2}(t)+\frac{v\cdot\Delta t}{L_{2}} \cdot\sin(x_{1}(t))\\ x(t+1)=& x(t)+v\cdot\Delta t\cdot\cos(x_{1}(t)) \cdot\cos(\frac{x_{2}(t+1)+x_{2}(t)}{2})\\ y(t+1)=& y(t)+v\cdot\Delta t\cdot\cos(x_{1}(t)) \cdot\sin(\frac{x_{2}(t+1)+x_{2}(t)}{2})\\ \end{aligned}\right. \tag{1}\]  
Considering that the traction system is limited by the wheel structure, the movement path is constrained by the minimum turning radius:  
\[r\geq r_{\min} \tag{2}\]  
Figure 2: Schematic of the mathematical model.  
The turning radius is constrained by limiting the maximum angular velocity. Since the speed of the unmanned carrier aircraft is small when it is transferred on the deck, the linear velocity and angular velocity are constrained:  
\[\left\{\begin{array}{l}v\leq v_{\max}\\ \omega\leq\omega_{\max}\end{array}\right. \tag{3}\]
## 2 Data and Methods
### Method for Extracting GW Activities from RO Temperature Profiles  
In the research on the characteristics and distribution patterns of GW activities based on satellite observation data, GW activities are often measured by GW energy (e.g., [2; 9; 16]), which includes kinetic energy (Ek) and potential energy (Ep). According to the linear wave theory, the ratio of the kinetic energy to the potential energy, Ek/Ep, is approximate to a constant in the atmosphere. Therefore, the characteristics of GW activities can be directly characterized by Ep [16; 43], and Ep is calculated by the formulas below:  
\[E_{p}=\frac{1}{2}\frac{g^{2}}{N^{2}}\left(\frac{T^{\prime}}{\overline{T}} \right)^{2} \tag{1}\]  
\[N^{2}=\frac{g}{\overline{T}}\left(\frac{\partial\overline{T}}{\partial z}+ \frac{g}{c_{p}}\right) \tag{2}\]  
\[T^{\prime}=T-\overline{T} \tag{3}\]  
where, \(g\), \(N\), \(c_{p}\), \(z\) are respectively the gravitational acceleration, the Brunt-Vaisala frequency, the isobaric heating capacity, and the altitude. \(\overline{T}\) represents the background temperature, and \(T^{\prime}\) represents the temperature perturbation caused by the GWs. In the present study, for each original atmPrf profile, the corresponding GW Ep profile is derived using the horizontal detrending method brought forward by [PERSON] and [PERSON] [21; 37], and its flow chart is shown in Figure 1.  
The original RO dry temperature profile is of the vertical resolution varying from tens of meters to around a hundred meters, and in the data processing, each temperature profile is interpolated with a height interval of 0.1 km between 20 and 35 km, at first. Then, the daily dry temperature profiles are binned into 15\({}^{\circ}\)\(\times\) 10\({}^{\circ}\) longitude-latitude grids, and the mean temperature profile of each grid is derived and then used to construct a three-dimensional single day global temperature grid data. Next, at each height level, S-transform is applied on each zonal component in the global temperature grid data of the single day to derive the zonal wavenumbers, 0-6 of the corresponding latitude band, which are used to construct the three-dimensional background temperature's grid data. By interpolating back to the positions of the raw temperature profiles in the background temperature's grid data, the background temperature profiles \(\overline{T}\) corresponding to the raw temperature profiles are obtained. The profiles of temperature perturbations, \(T^{\prime}\), are further extracted by subtracting the background temperatures from the raw temperatures using Equation (3). In the next step, S-transform is applied the second time to filter out the 0-3 wavenumber in \(T^{\prime}\), which aims to remove the residual background information in the temperature perturbations. Finally, the GW Ep is calculated using Equations (1) and (2). What needs to be mentioned is that, theoretically, the GWs with vertical wavelengths longer than twice the vertical resolution of the GNSS RO data (-1 km), can be resolved in the present analyses [37].
## 2 Theoretical Model
### Pressure Control Valve  
The three-way control valve is the key component that regulates the pressure of the outlet chamber near the preset pressure. For small perturbations at the initial working point, the flow of the control valve can be linearized by means of an incremental method as below:  
\[\Delta q_{v}=\left.\frac{\partial q_{v}}{\partial x_{v}}\right|_{0}\Delta x_{ v}+\left.\frac{\partial q_{v}}{\partial p_{l}}\right|_{0}\Delta p_{l}=K_{q} \Delta x_{v}+K_{c}\Delta p_{l} \tag{14}\]  
where \(K_{q}\) is the flow gain of the valve and \(K_{c}\) is the flow pressure coefficient.  
Hence, the transfer function of the valve flow can be given by the following:  
\[Q_{v}(s)=K_{q}X_{v}(s)-K_{c}P_{2}(s) \tag{15}\]  
For the moving spool of the control valve, the transfer function can be derived as follows:  
\[P_{1}(s)A_{v}=m_{v}s^{2}X_{v}(s)+b_{v}sX_{v}(s)+K_{v}X_{v}(s) \tag{16}\]  
The continuity equation of flow in the chamber between the spool and the pipe:  
\[Q_{1}(s)-Q_{v}(s)=A_{v}sX_{v}(s)+\frac{V_{1}}{K_{oil}}sP_{1}(s) \tag{17}\]  
To summarize the dynamics of the control valve, Equations (15)-(17) are solved simultaneously to give the following result as a four-pole form:  
\[\left[\begin{array}{c}P_{1}(s)\\ Q_{1}(s)\end{array}\right]=G_{1}(s)\left[\begin{array}{cc}K_{c}&1\\ G_{2}(s)&G_{3}(s)\end{array}\right]\left[\begin{array}{c}P_{2}(s)\\ Q_{v}(s)\end{array}\right]=\mathbf{A}_{1v}\left[\begin{array}{c}P_{2}(s)\\ Q_{v}(s)\end{array}\right] \tag{18}\]  
where \(G_{1}(s)\), \(G_{2}(s)\), and \(G_{3}(s)\) can be given as follows:  
\[G_{1}(s)=\frac{m_{v}s^{2}+b_{v}s+K_{v}}{K_{q}A_{v}} \tag{19}\]  
\[G_{2}(s)=K_{c}s(\frac{A_{v}^{2}}{m_{v}s^{2}+b_{v}s+K_{v}}+\frac{V_{1}}{K_{oil}}) \tag{20}\]  
\[G_{3}(s)=\frac{K_{q}A_{v}+A_{v}^{2}s}{m_{v}s^{2}+b_{v}s+K_{v}}+\frac{V_{1}s}{K_ {oil}} \tag{21}\]The flow of the chamber behind the valve spool \(V_{2}\) consists of the flow from the valve and the flow to the pipe and actuator piston chamber. The continuity equation of flow in this chamber is given as follows:  
\[\left[\begin{array}{c}P_{2}(s)\\ Q_{v}(s)\end{array}\right]=\left[\begin{array}{cc}1&0\\ \frac{V_{2}}{K_{oil}}s&1\end{array}\right]\left[\begin{array}{c}P_{2}(s)\\ Q_{2}(s)\end{array}\right]=\mathbf{A}_{\eta 2}\left[\begin{array}{c}P_{2}(s)\\ Q_{2}(s)\end{array}\right] \tag{22}\]
# Navigational Risk Assessment in Offshore Wind Farms Using Spatial Ship Domain Models
## 2 Materials and Methods  
### Defining the Method for Assessing the Navigational Risk Indicators \(R_{\rm N}\)  
The R\({}_{\rm N}\) evaluation is calculated using formulas based on [PERSON]'s research [23,24], focusing on maintaining safe distances around a ship (Appendix A). According to [PERSON]'s ship domain concept, a ship is considered safe if it is the only potential hazard within its domain. For the vertical component of navigational risk (R\({}_{\rm ND}\)), safety is ensured if the ship's domain depth (SD\({}_{\rm D}\)) is less than the actual sea depth (h), which indicates the risk related to under-level clearance (Formula (1)). When sea depth is less than or equal to the ship's maximum draft (T\({}_{\rm max}\)), the navigational risk is high, possibly reaching 100% (indicating grounding). For sea depths between T\({}_{\rm max}\) and SD\({}_{\rm D}\), the risk is between 0 and 1, showing varying degrees of potential danger. Similar principles apply for risks involving objects above the water, as described in Formula (2).  
In the horizontal plane, two components of R\({}_{\rm N}\): R\({}_{\rm NLF}\) and R\({}_{\rm NLA}\), represent the risk related to maintaining safe distances ahead and astern of the ship. If the distance to the nearest danger ahead (d\({}_{\rm NF}\)) is less than or equal to the forward ship domain (SD\({}_{\rm LF}\)), the risk will vary between 0 and 1, potentially reaching 100% if the danger is very close (Formula (3)). A similar method is used for analyzing the risk on the port and starboard sides (R\({}_{\rm NWP}\) and R\({}_{\rm NWS}\)) using formulas that account for safe distances to the nearest objects (Formulas (5) and (6)).