esa-sceva/satcom-chunk-collection huggingface dataset:
# Extreme Learning Machines to Combat Phase Noise in RoF-OFDM Schemes
## 3 Complex Extreme Learning Machine  
Given a training set with \(L\) samples \((u_{i},v_{i})\), where \(u_{i}=[u_{i1},...,u_{in}]^{T}\in\mathbb{C}^{n}\) (\(n\) is the size of the inputs) and \(v_{i}=[v_{i1},...,v_{in}]^{T}\in\mathbb{C}^{m}\) (\(m\) is the size of the outputs), the actual outputs of the SLFN with \(\widetilde{L}\) hidden neurons are mathematically modeled as [12]:  
\[v_{i}=\sum_{k=1}^{\widetilde{L}}\beta_{k}g(w_{k}\cdot u_{i}+b_{k}),i=1,...,L, \tag{5}\]  
where \(\beta_{k}=[\beta_{k1},...,\beta_{km}]^{T}\in\mathbb{C}^{m}\) refers to the output weight vector between the \(k^{\text{th}}\) hidden neuron and the output neurons, \(g(\cdot)\) denotes the activation function that must be bounded and differentiable almost everywhere in the complex domain, \(w_{k}=[w_{k1},...,w_{km}]^{T}\in\mathbb{C}^{n}\) represents the input weight vector connecting the input nodes and the \(k^{\text{th}}\) hidden node, the term \(w_{k}\cdot v_{i}\) means the inner product of the column vectors \(w_{k}\) and \(v_{i}\), and \(b_{k}\in\mathbb{C}\) is the bias of the \(k^{\text{th}}\) hidden neuron. Both the input weights and hidden layer biases are randomly assigned based on any continuous distribution probability inside the radius of the converge of the mapping function. Equation (5) may compactly be written as:  
\[V_{L\times m}=H_{L\times\widetilde{L}}\beta_{\widetilde{L}\times m}\] \[\left[\begin{matrix}v_{1}^{T}\\ \vdots\\ v_{L}^{T}\end{matrix}\right]=\left[\begin{matrix}g(w_{1}\cdot u_{1}+b_{1})& \ldots&g(w_{\widetilde{L}}\cdot u_{1}+b_{1})\\ \vdots&\ddots&\vdots\\ g(w_{1}\cdot u_{L}+b_{1})&\ldots&g(w_{\widetilde{L}}\cdot u_{L}+b_{\widetilde{L }})\end{matrix}\right]\left[\begin{matrix}\beta_{1}^{T}\\ \vdots\\ \beta_{L}^{T}\end{matrix}\right], \tag{6}\]  
\(H\) is called the hidden layer output matrix of the ELM; its \(k^{\text{th}}\) column results from the \(k^{\text{th}}\) hidden node's output vector with respect to \(u_{i}\) inputs. Its smallest norm least-squares solution is given by [10]:  
\[\beta_{\widetilde{L}\times m}=H_{L\times L}^{\ddagger}V_{L\times m}, \tag{7}\]  
where \(H^{\ddagger}\) refers to the Moore-Penrose generalized inverse of the hidden layer output matrix, which can be solved by various methods, such as orthogonal projection, the Gaussian method, the iterative method, singular value decomposition, among others [11]. In the paper, this inverse matrix is found though the singular value decomposition approach in order to avoid time limitations and matrix requirements [10]. As can be seen, a standard SLFN subject to the ELM algorithm possesses the following benefits: high accuracy, least user intervention, and real-time learning. The overall ELMarchitecture is depicted in Figure 2, where all inputs, variables, and outputs are identified. In the next section, we explain how this artificial neural network can act as the equalizer in the OFDM receiver. Taking into account that the complex ELM comes from a direct extension from the real ELM to the complex plane [12; 14], the presentation of the fully-real ELM is discarded in the manuscript.
# Indoor Vehicles Geolocalization Using LoRaWAN
## 4 The Proposed Architecture
### The Devices  
Two basic devices constitute our architecture: the clients and the gateways. The client devices, also called \"tags\" are associated with whatever vehicle is required to be located. Their basic characteristics are (1) small size; (2) basic processing services; (3) low energy usage requirements. The latter requirement is especially critical in devices that are supposed to be used with not electrically autonomous vehicles, like a bicycle or for pedestrians. The client device does not have to perform any specific computation. It is required to periodically broadcast its associated id, (ttrack2 bis as in the example in Figure 3). Frequency of this action can be varied according to the degree of mobility of the device, that is, slowly moving ones can send this beacon every 5/10 min, rapidly moving ones can send it every 10/15 s. We have to be aware of the limitation imposed by the duty-cycle which is part of the LoRaWAN standard and that can be enforced by the Networks servers. The gateways can be simple single-channel devices that do not require any special software besides the basic standard forwarding software. All gateways must head to the same network server and must be configured to be part of the same \"application\", according to the LoRaWAN terminology.
## 5 Case Study: An Actual Deployment at the City Campus of the University of Auckland
### Application Prospects Based on Case Study Insights
#### 5.4.4 Adaptation to Other Urban Contexts  
The insights gained from the case study also suggest the system's adaptability to various urban settings. The system could help enforce noise regulations for quieter residential neighborhoods by monitoring disruptive sounds like construction activities or loud vehicles. The system's sensitivity to lower noise levels can be fine-tuned for such environments, improving residents' quality of life. Moreover, the system's successful operation in this controlled environment demonstrates its potential to scale for entire metropolitan areas, handling more complex topologies and diverse acoustic environments, making it suitable for large-scale urban deployments.
## 2 RadCom System Model and Processing  
### RadCom Approach  
In RadCom systems, overall radio resources must be first shared between multiusers (multivehicles; hence the MA technique), and second between two functionalities, radar and communication. In the following sections, we adopted a time-division multiplexing (TDM) approach to separate both aspects of the RadCom system. TDM is a straightforward approach that we applied to the RadCom system by simply separating radar signals and communication signals over the time domain, as depicted in Figure 1. Priority in terms of resource allocation is evidently given to the radar rather than communication due to the need for continuous detection. Nevertheless, the transmitter is always able to communicate; for instance, if the vehicle changes the cell, it needs to send a communicating message to the base station (BS). The transmitter sends repeated frames in order to detect the neighboring vehicles. After a certain delay \(\tau\), it receives its reflected signal. This delay \(\tau\) induced to the signal is inherently related to the application constrains, such as the radar's maximal detection range. However, other users may send communicating messages to the main user, thus interfering with the reflected signal, and detection is degraded. The TDM approach mainly indicates to the main user if he is either on a communication or a radar time slot in order to trigger the proper (radar/communication) receiver to decode the frame. This approach decreases the overall complexity of the RadCom system by overcoming the need for interference cancellation techniques. We chose the time-sharing method because it is the simplest approach for radar or communication integration. The goal of this study is to evaluate our proposition in vehicular channels. In the future, we aim to study other sharing approaches, such as frequency sharing and Sub-beamy.
## 3 Hierarchical Semi-Separable Compression
### Modified Algorithm for Diagonally Perturbed Matrices  
We will recall that for the backward Euler scheme, we solve a series of systems of linear algebraic Equation (3). The matrix of each system has the form  
\[\tilde{K}_{i}=\frac{M^{L}}{\tau_{i}}+K,\quad i=1,\ldots,m.\]  
It should be noted that the lumped mass matrix is diagonal and the only part of \(\tilde{K}_{i}\) that would differ between time steps is the main diagonal, and only when the time step changes \(\tau_{i}\
eq\tau_{i-1}\). On the other hand, we can notice that HSS compression does not change the diagonal values. Thus, we can compress the stiffness matrix \(K\) once. Then, only the diagonal is updated when needed. The developed modified Algorithm 1 is implemented using STRUMPACK functionalities.  
```
Input:\(K\), \(M^{L}\), \(t=[0,T]\), \(\mathbf{f}_{i}=f(t)\), \(\theta\), \(\mathbf{u}_{0}=u(0)\), \(\gamma\), \(\delta\) Output:\(\mathbf{u}_{i},\ \tau_{i}\), \(m\) \(i=0\), \(t=0\) \(H=\) compress(K) \(\triangleright\) Apply HSS compression on the stiffness matrix while\(t\leq T\)do\(\tau_{i+1}=\tau_{0}\)\(\triangleright\) Increase prognostic step \(\tilde{\mathbf{u}}_{i+1}:M^{L}\frac{\tilde{\mathbf{u}}^{i+1}-\mathbf{u}^{i}}{ \tilde{\tau}_{i}+1}+K\mathbf{u}^{i}=\mathbf{f}^{i}\)\(\triangleright\) Compute prognostic solution \(\tilde{\Psi}_{i+1}=\tilde{\mathbf{f}}^{i+1}-M^{L}\frac{\tilde{\mathbf{u}}^{i+ 1}-\mathbf{u}^{i}}{\tilde{\tau}_{i+1}}-K\mathbf{u}^{i}\)\(\triangleright\) Compute truncation error \(\overline{\tau}_{i+1}=\frac{\delta}{\|\tilde{\Psi}_{i+1}\|}\gamma\tau_{i}\)\(\triangleright\) Calculate time step if\(\overline{\tau}_{i+1}<\tau_{0}\)then\(\triangleright\) Apply size limitations to new time step \(\tau_{i+1}=\tau_{0}\) elseif\(\overline{\tau}_{i+1}>\overline{\tau}_{i+1}\)then\(\tau_{i+1}=\tilde{\tau}_{i+1}\) elseif\(\left|\frac{\tilde{\tau}_{i+1}-\tau_{1}}{\tilde{\tau}_{i}}\right|<\theta\)or\(\left|\frac{\overline{\tau}_{i+1}-\tau_{i}}{\tilde{\tau}_{i+1}}\right|<\theta\)then\(\tau_{i+1}=\tau_{i}\) endif if\(t+\tau_{i+1}>T\)then\(\tau_{i+1}=T-t\) endif if\(\tau_{i+1}\
eq\tau_{i}\)then\(H=\frac{M^{L}}{\tilde{\tau}_{i+1}}+H\)\(\triangleright\) Perturb the diagonal of \(H\) with \(M^{L}\) and \(\tau_{i+1}\)  ULV = factor(\(H\))\(\triangleright\) Calculate the ULV factorization of \(H\) endif\(\tilde{b}_{i}=M^{L}\Big{(}\frac{\tilde{\mathbf{f}}^{i+1}+\tilde{\mathbf{f}}^{i}}{ 2}+\frac{\mathbf{u}^{i}}{\tilde{\tau}_{i}}\Big{)}\)\(\triangleright\) Compute right hand side \(\mathbf{u}_{i+1}\) = solve(ULV, \(\tilde{b}_{i+1}\))\(\triangleright\) Compute the solution at time \(t+\tau_{i+1}\) \(i=i+1\), \(t=t+\tau_{i+1}\)\(i=i+1\), \(t=t+\tau_{i+1}\)\(\triangleright\) Prepare parameters for next time step if\(\tau_{i+1}\
eq\tau_{i}\)then\(H=H-\frac{M^{L}}{\tilde{\tau}_{i+1}}\)\(\triangleright\) Restore original compressed matrix endif endwhile\(m=i\)
```  
**Algorithm 1** Algorithm for diagonally perturbed matrices with HSS compression
## 2 Constraints and Challenges of Agricultural Sprayer UAV  
The main objective of a sprayer UAV is to spray the maximum farming area, to have an adequate spraying coverage and good droplet deposition. However, when spraying, there are some constraints within the agricultural field that have to be taken into consideration. These include (a) field shapes; (b) different weather conditions; and (c) obstacles in the field [59]. Other spraying performance constraints are inherent of the sprayer UAV itself, and these include (a) the liquid load, which will eventually decrease; (b) liquid capacity or vehicle weight; (c) battery/fuel capacity; (d) vehicle type; and (e) spray pressure [24; 60; 61]. For the purpose of brevity, the basic diagram of the agricultural sprayer UAV is shown in Figure 1.  
For the sprayer UAV to be able to perform within the abovementioned constraints, some significant farmland spraying issues have to be considered, and these \"spraying issues\" are highlighted in the subsequent sections.
# Multi-Granularity Mission Negotiation for a Decentralized Remote Sensing Satellite Cluster
## 2 Proposed Methods
### Mission Preprocess for All Levels of Granularity  
#### 2.1.1 Time Window Calculation  
Suppose that there are \(n\) targets, the set of all targets can be defined as \(T\):  
\[T=\{t_{1},\ldots,t_{i},\ldots,t_{n}\},i\in N^{+},1\leq i\leq n, \tag{1}\]  
where \(t_{i}\) expresses the information of the \(i\)th target, including latitude, longitude, height, and motion characteristics. Motion characteristics contain course, angular velocity, speed, and acceleration. All these motion characteristics can be unknown.  
The calculation of the time window is to find mission start time \(T_{i}^{S}\) and mission end time \(T_{i}^{E}\) to make sure that for \(\forall t\in[T_{i}^{S},T_{i}^{E}]\), two constraints are satisfied: the optical visibility constraint and the geometric visibility constraint. The optical visibility constraint for the \(i\)th target \(C_{o_{i}}(t)\) can be expressed as:  
\[C_{o_{i}}(t)=\left\{\begin{array}{ll}1&\quad\mathbf{R}_{i}\cdot\mathbf{R}_{ Sun}>0\\ 0&\quad\mathbf{R}_{i}\cdot\mathbf{R}_{Sun}\leq 0\end{array}\right., \tag{2}\]  
where \(\mathbf{R}_{i}\) is the position vector of \(t_{i}\) in the inertial coordinate, which can be calculated by integrating with time. \(\mathbf{R}_{Sun}\) is the position vector of the sun in inertial coordinate. The geometric visibility constraint for \(i\)th target \(C_{gi}(t)\) can be expressed as:  
\[C_{gi}(t)=\left\{\begin{array}{ll}1&\quad\alpha\leq\alpha_{max}\text{ {and} }\theta\leq\theta_{max}\\ 0&\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad else\end{array}\right., \tag{3}\]  
where \(\alpha\) is the angle between the target's position vector \(\mathbf{R}_{i}\) and its projection vector \(\hat{\mathbf{R}}_{i}\) in the orbital plane of the satellite, which can be expressed as:  
\[\alpha=arccos(\frac{\mathbf{R}_{i}\cdot\hat{\mathbf{R}}_{i}}{|\mathbf{R}| \cdot|\hat{\mathbf{R}}_{i}|}). \tag{4}\]  
\(\alpha_{max}\) is the maximum value of \(\alpha\), expressed as:\[\alpha_{max}=arccos(\frac{|\mathbf{R}_{i}|}{|\mathbf{R}|}), \tag{5}\]  
where \(\mathbf{R}\) is satellite's position vector in inertial coordinate. \(\theta_{max}\) in Equation (3) is the maximum attitude maneuver angle of the satellite. The desired attitude maneuver angle \(\theta\) is expressed as:  
\[\theta=arctan(\frac{|\mathbf{R}_{i}|\cdot sin(\alpha)}{|\mathbf{R}|-|\mathbf{ R}_{i}|\cdot cos(\alpha)}). \tag{6}\]  
Figure 1: The flowchart of the multi-granularity negotiation method.
# Application of Compressive Sensing in the Presence of Noise for Transient Photometric Events
## 4 Numerical Results
### Gravitational Microlensing Setup  
For all our simulations, we use a gravitational microlensing curve generated by a single lens. The amplification over time due to a single microlensing event is generated by the equation given in [4].  
\[A(t)=\frac{\mu_{0}^{2}+\left(\frac{t-t_{0}}{t_{E}}\right)^{2}+2}{\left[\mu_{0} ^{2}+\left(\frac{t-t_{0}}{t_{E}}^{2}\right)\right]^{1/2}}\bigg{[}\mu_{0}^{2}+ \left(\frac{t-t_{0}}{t_{E}}\right)^{2}+4\big{)}\bigg{]}^{1/2}} \tag{20}\]  
Here, \(t\) is the time sample, \(\mu_{0}\) is the impact parameter, \(t_{0}\) is the peak magnification time, and \(t_{E}\) is the Einstein ring radius crossing time. In our simulations, we use \(\mu_{0}=0.1\) and \(\mu_{0}=0.01\) to vary the amplitude of the photometric curve, in order to understand the effect of noise with the different magnifications. We use \(t_{0}=15\) and \(t_{E}=30\). Figure 5 shows sample magnification curves with \(\mu_{0}=0.1\) and \(\mu_{0}=0.01\). The lower \(\mu_{0}\) value provides a higher magnification of the light curve.  
In practical applications where the samples are not exactly zero, we can distinguish sparse signals as signals whose coeffecients decay at a high rate. In Figures 6 and 7, we show the rate of decay of the coefficients for \(\mu_{0}=0.1\) and \(\mu_{0}=0.01\).  
Figure 5: Magnification curve for \(\mu_{0}=0.1\) and \(\mu_{0}=0.01\).  
Hence, it is evident that photometric curves with \(\mu_{0}=0.01\) have a higher rate of decay compared to photometric curves with \(\mu_{0}=0.1\). Therefore, for CS, sparser signals should have better reconstruction accuracy.
## 7 Final Thoughts, Propositions, and Prospects for the Future
For preventing privacy exposure in sensitive data, strong encryption such as post-quantum cryptography with robust error detection and masking techniques is recommended, while to detect code vulnerabilities, the employment of AI and human experts is recommended;
* As blockchains cause additional resource expenditure in KDN systems, it is recommended to utilize intelligent networking with resource-efficient blockchain systems [13]. Firstly, this research's scope is limited to reviewing blockchain-based frameworks in Knowledge-Defined Networking domains. However, all types of networking applications of blockchain-based frameworks within Knowledge-Defined Networking domains with all forms of knowledge generation models are reviewed in this research. Finally, being a review article, this research does not explicitly propose and validate a blockchain-based framework for Knowledge-Defined Networking. However, this research proposes recommendations for a blockchain-based framework applied to Knowledge-Defined Networking, based on the challenges identified by surveying many existing, validated original research papers. Blockchains can revolutionize the knowledge creation approach in KDN systems. In conventional KDN, knowledge is generated by a centralized authority. By integrating blockchain, knowledge can be created and disseminated in a dispersed and trustworthy way, allowing collaboration. Self-executing contracts can be utilized to automate network processes such as network management, access control, etc. Future research may include efficient and cost-effective techniques for intellectual property management, licensing, authentication, etc. of intellectual property transactions within Knowledge-Defined Networks. Furthermore, future research may investigate how existing resources in KDN systems, such as machine learning frameworks that serve different network functions, may be leveraged to enhance the effectiveness of the underlying blockchain framework in order to make the two approaches more interoperable. Moreover, future research on blockchains deployed in KDNs may involve investigating efficient consensus approaches, cryptographic techniques, and optimization techniques. Additionally, quantum computing may be employed to improve the performance of both knowledge generation in KDN and computations related to blockchains for better performance of blockchain-based KDN frameworks. Conceptualization--P.A.D.S.N.W. ; software--P.A.D.S.N.W. ; validation--P.A.D.S.N.W. ; formal analysis--P.A.D.S.N.W. ; investigation--P.A.D.S.N.W. ; resources--P.A.D.S.N.W. ; writing original draft preparation--P.A.D.S.N.W. ; writing review and editing--S.G.; visualization--P.A.D.S.N.W. and S.G.; supervision--S.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. This research is part of a research project that principal and corresponding author Patikiri Arachchige Don Shehan Nilmantha Wijesekara is pursuing for a degree from the University of Ruhuna. The authors declare no conflict of interest.
## 2 Modeling and Problem Statement
### Problem Statement  
Recently, the DVR phenomenon has been a critical issue in terms of voltage stability because IM load penetration is growing significantly. When an AC load stalls, it behaves as a constant shunt conductance and reactance consuming active and reactive power, and motor stalling might also cause the tripping of transmission lines and transformers because of an increase in system impedance. In such conditions, sufficient reactive power cannot be transmitted to the load. Also, this problem would be aggravated by the growing use of low inertia IM loads in the transmission grid network. After the fault, for a system with a high proportion of IM loads, the problem can be spread throughout the system, resulting in wide-area blackouts [7].  
For the Korean power system, there is little literature on the DVR phenomenon and the impact assessment considering the characteristics of the IM load. That is, the current UVLS scheme does not reflect the DVR and the behaviors of IM loads. IM load models are not incorporated in dynamic simulations, and this makes it difficult to capture the DVR phenomenon, even though the proportion of IM load in the actual load configuration is increasing. For detailed simulations, representing large and small IM loads in various combinations is needed, because the IM load is sensitive to dynamic variation in voltage and frequency. When designing the UVLS scheme, the time, the location and the amount of load shedding needs to be determined. For the current UVLS scheme of the Korean system, the total amount of load shedding is decided only based on the results of steady-state analysis, and it is applied at once when the pick-up under voltage condition is satisfied. The control amount of load shedding is sometimes excessive when the post-disturbance condition is not much severe, and then in this case over voltage problem can happen. Also, the UVLS scheme is tested with transient stability simulation packages without considering IM load portion, so it could not capture the DVR problem when the amount of load shedding is not enough, because of a slightly large time delay. Thus, this paper proposes an approach to decide the multi-phase UVLS (MUVLS) scheme, considering an adequate portion of IM load, as well as the DVR problem. The MUVLS scheme can provide proper remedial actions in a reliable and economic way for various post-disturbance conditions.
## 4 Dual-Band Passive Metasurface Based Beam Steering Techniques
### Near-Field Meta-Steering Systems  
One disadvantage of a TA is its lateral height because the feed is placed at a specific distance equivalent to the focal length from the metasurfaces. Another technology known as near-field meta-steering (NFMS) is proposed in [23] to reduce the total lateral height of the antenna and to avoid feed displacement for steering the beam. The design presented in [23] is for single-frequency band operation. Several modifications of single band NFMS are also presented in [70; 71; 72; 73]. In a later work, an NFMS system that supports full-duplex communication is presented in [31]. In NFMS, the metasurfaces are placed within the near-field of a high-gain antenna system. The rotation of these metasurfaces about their axis in parallel to the antenna axis enables beam steering.