esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Distance-Based Opportunistic Push/Subscribe Content Dissemination Model
### Network Layer  
The network layer deals with how to select the relay nodes so as to increase the delivery performance [17; 18; 19; 20; 21; 22; 23; 24; 25]. Selecting the next hop is the responsibility of routing protocols. We do not assign a specific opportunistic routing protocol for DOPS. Many opportunistic routing protocols could be employed here. Literatures [17; 18; 19; 20] are opportunistic routing developed for delay tolerant networks. Literatures [21; 22; 23] are opportunistic routing protocols specially designed for subscribe/publish.  
DPSP is publish/subscribe-based routing protocol for DTN networks [21]. When a router selects the relay nodes, it considers local resource constraints. [PERSON] _et al._ studied the way of improving the cost-effectiveness of multicast by social analysis [22]. When a node selects relay nodes, it selects thenodes based on social centrality and social community. This approach can reduce the number of relays. MuRIS scheme allows nodes to deliver content via chosen paths, which can reach more subscribers at intermediate hops [23].
# Enhancing Reliability in Wind Turbine Power Curve Estimation
###### Abstract  
Accurate power curve modeling is essential to continuously evaluate the performance of a wind turbine (WT). In this work, we characterize the wind power curves using SCADA data acquired at a frequency of 5 min in a wind farm (WF) consisting of five WTs. Regarding the non-parametric methods, we select artificial neural networks (ANNs) to make curve estimations. Given that, we have the curves provided by the manufacturer of the WTs given by some very precisely measured pair of wind speed and power points. We can evaluate the difference between the manufacturer characterization and the ones estimated with the data provided by the SCADA system. Before the estimation, we propose a method of filtering the anomalies based on the characteristics provided by the manufacturer. We use three-quarters of the available data for curve estimation and one-quarter for the test. One WT suffered a break in the test part, so we can check how the test estimates reflect this problem in its wind-power curve compared to the estimations obtained in the WTs that worked adequately.  
wind power curve modeling; artificial neural networks (ANN); wind turbine (WT); SCADA data; industrial AI 2024 14 2479 14062479
# Multi-Mobile Agent Trust Framework for Mitigating Internal Attacks and Augmenting RPL Security
## 3 Proposed Framework
### MMTM-RPL Layers
#### 3.1.1 Device Layer  
This layer consists of LLN-based IoT devices (e.g., sensors), which are well known for their constrained nature (Figure 1). There is a dire need to conserve their limited resources to enhance network lifetime and performance. Since these devices are highly resource-constrained and use RPL (which is not secure against internal attacks), ensuring their security is challenging. Therefore, MMTM-RPL aims to minimize the processing, storage, and energy drain for such devices while enhancing the security of RPL. To mitigate internal attacks, such as rank, Sybil, and sinkhole (in our scenario), these devices provide trust-related data to the upper layer (via an MA), which detects and isolates malicious nodes.  
Figure 1: Proposed device and control layers.
# A Method to Minimize the Effort for Damper-Blade Matching Demonstrated on Two Blade Sizes
## 2 Method  
### Model  
It has been shown in [24] that it is possible to define a modeling procedure and a related numerical treatment capturing the essential elements of a turbine blade for its damper-coupled optimization. In more detail:  
* the neck is represented through a series of beam elements;
* the platform, due to its size and shape behaves as a rigid body, in particular regarding the kinematic relationship between its central point P and its contact points with the dampers (see Figure 1);
* although it is sure that points R and N are connected rigid body kinematics and equilibrium, it is expedient here to represent the platform as a series of two beams;
* this modeling choice allows the beam area moments of inertia to be adjusted in such a way that local contributions to elastic deformabilities at the airfoil-platform junction (point R) and at the neck-platform junction (point N) are correctly taken into account [24].  
Contrary to the previous paper [24], where a simplified airfoil was represented by a series of beam elements, in this paper, a realistic airfoil with taper and torsion is introduced (see Figure 2). After a Component Mode Synthesis (CMS) reduction of the airfoil, its root cross-section DoFs are mutually constrained on a rigid translating and rotating plane, then coupled to the upper surface of the platform (point R), in a manner consistent with the model of the platform and of the neck beams. With reference to Figure 2, the reduced airfoil retains only those points that are strictly necessary for the application of the aerodynamic forces (a single point E is here considered), for monitoring of the main movements tracing the oscillation amplitudes (points A,B,C), for coupling the airfoil to the platform (points N and R).  
Figure 1: (**Left**) 3D view of the platform PCR representation through six DoFs (three displacements and three rotations) at point P. (**Right**) 3D view of the damper rigid body representation through six DoFs (three displacements and three rotations) at point D.  
Obviously, no one prevents that, in a case of special geometric complexity of the neck, it can be represented with a solid FEM model instead of beams (see Figure 2), reducing it with the CMS procedure after having constrained the points of the end surfaces on planes, as described above for the airfoil. However, the decisive advantage of representing the neck with a series of beam elements is to allow the exploration of different geometries without having to modify a CAD model and re-mesh it.
# An Efficient VQ Codebook Search Algorithm Applied to AMR-WB Speech Coding
## 2 ISF Coefficients Quantization in AMR-WB
### Linear Prediction Analysis  
In a linear prediction, a Levinson-Durbin algorithm is used to compute a 16 th order LPC, \(a_{i}\), of a linear prediction filter [1], defined as:  
\[\frac{1}{A(z)}=\frac{1}{1+\sum\limits_{i=1}^{16}a_{i}z^{-i}}. \tag{1}\]  
Subsequently, the LPC parameters are converted into the immittance spectral pair (ISP) coefficients for the purposes of parametric quantization and interpolation. The ISP coefficients are defined as the roots of the following two polynomials:  
\[F_{1}^{\prime}(z)=A(z)+z^{-16}A(z^{-1}) \tag{2}\]  
\[F_{2}^{\prime}(z)=A(z)-z^{-16}A(z^{-1}). \tag{3}\]\(F_{1}^{\prime}(z)\) and \(F_{2}^{\prime}(z)\) are symmetric and antisymmetric polynomials, respectively. It can be proven that all the roots of such two polynomials lie and alternate successively on a unit circle in the \(z\)-domain. Additionally, \(F_{2}^{\prime}(z)\) has two roots at \(z=1\) (\(\omega=0\)) and \(z=-1\) (\(\omega=\pi\)). Such two roots are eliminated by introducing the following polynomials, with eight and seven conjugate roots respectively on the unit circle, expressed as:  
\[F_{1}(z)=F_{1}^{\prime}(z)=(1+a[16])\prod_{i=0,2,\ldots,14}(1-2q_{i}z^{-1}+z^ {-2}) \tag{4}\]  
\[F_{2}(z)=\frac{F_{2}^{\prime}(z)}{(1-z^{-2})}=(1-a[16])\prod_{i=1,3,\ldots,13} (1-2q_{i}z^{-1}+z^{-2}) \tag{5}\]  
where the coefficients \(q_{i}\) are referred to as the ISPs in the cosine domain, and \(a[16]\) is the last predictor coefficient. A Chebyshev polynomial is used to solve Equations (4) and (5). Finally, 16 th order ISF coefficients \(\omega_{i}\) can be obtained by taking the transformation \(\omega_{i}=\arccos(q_{i})\).
## 3 Dataset Analysis
### Analysis of Literature
#### 3.4.1 Top 10 Most Cited Papers  
Overview  
Table 6 highlights the top 10 papers with the most global citations in the field of artificial intelligence applications in local and regional studies. Each paper is evaluated based on several criteria: number of authors, region of origin, total number of citations (TC), total citations per year (TCY), and normalized citations (NTC). The NTC indicator is used in order to be able to create a picture of how a paper scores in terms of the citations it has recorded compared to other papers that have a similar research topic and that were published in a given reference year. As a calculation formula, it is determined by the ratio between the number of citations recorded by a paper and the average number of citations in the same year that the rest of the papers we have in the dataset have, in similar fields of activity [97].  
With 519 total citations, [PERSON]'s study from Portugal has the most citations and an NTC score of 1.00 (obtained as the ratio between 519, representing the total citations, and the average number of citations recorded per document for 2009, which was also 519), indicating its significant influence in the field. With an average of 32.44 citations per year, this scientific paper is a benchmark in AI applications in engineering. The study by [PERSON] et al. from Bangladesh, published in _Geoscience Frontiers_, reached an impressive 226 citations in just one year, with a TCY of 56.50 and a very high NTC of 9.65 (226 citations divided by 23.43 citations), reflecting its contemporary relevance. Published in the _Environmental Impact Assessment Review_, [PERSON]'s research from China received 191 citations and a TCY of 47.75, with an NTC of 8.15, highlighting its significant contribution to environmental impact assessment. The study by the authors from Iran, with 167 citations and a TCY of 20.88, maintains an NTC of 1.00, indicating a consistent influence in the field. Published in _Heliyon_, the research by [PERSON] et al. from Jordan has 110 citations and a TCY of 22.00, with an NTC of 3.06, reflecting an important contribution to interdisciplinary studies. With 108 citations and a TCY of 21.60, the study by [PERSON] et al. from India published in _Stochastic Environmental Research and Risk Assessment_ has an NTC of 3.00, indicating its relevance in risk assessments. This analysis provides an overview of the most influential papers in the field of artificial intelligence applications in local and regional studies. By analyzing the citations and normalized scores, we can observe research trends and priorities in this field. Also, the geographical distribution of authors suggests a global collaboration and diversity of methodological approaches.
## 1 Introduction
[PERSON] and [PERSON] [36] analytically obtained the synchronization conditions by calculating the distance between trajectories and the synchronization manifold. Their synchronization conditions require knowledge of the synchronization trajectory, which is usually unknown before solving the system. As mentioned above, a shortcoming in the methods presented is the difficulty to analytically derive synchronization conditions for dynamical systems, especially for the delay differential systems. The influence of delay on synchronization conditions has seldom been analytically treated in previous research. In this paper, we analytically investigate the effects of time delay in signal transmission on synchronization between two coupled FHN neurons. It is easy to understand that synchronization between two neurons will be achieved when the energy of the synchronization error system derived from the original neural system disappears. To study the change of the energy of the error system, we first rewrite the original neural system in its corresponding energy coordinates according to the energy method proposed [37]. Then, we calculate the rate of change of the energy of the error system to judge whether the energy disappears. The disappearance of energy of the error system requires for the rate of the change of the energy to be less than zero. In this way, we derive the synchronization conditions for two coupled FHN neurons without and with delay, respectively. Therefore, we can analytically estimate the influence of time delay on the synchronization conditions and study how the time delay affects the synchronous dynamics of the FHN neural system. We show that whether or not synchronization occurs between two FHN neurons, it is entirely dependent on the value of the time delay in signal transmission between two neurons. If the time delay is ignored, synchronization does not appear for any given coupling strength. The rest of the paper is organized as follows. In Section 2, the synchronization between two coupled FHN neurons without time delay is investigated by using the energy method. In this case, a stability analysis is completed in order to prove that synchronous spikes do not occur, the correctness of which is demonstrated by numerical simulations. In Section 3, the influence of the time delay on synchronization conditions and synchronous dynamics are discussed analytically and numerically. Conclusions are drawn in Section 4.
# Study of Data Transfer in a Heterogeneous LoRa-Satellite Network for the Internet of Remote Things
## 8 Future Works  
The Internet of remote things (IoRT) is a recently introduced paradigm describing monitoring and control networks in hard-to-reach areas. These networks usually have very limited throughput and tend to be heterogeneous, which opens a way to new models, methods and algorithms for optimizing data transfer, energy efficiency and traffic balancing. We have identified several tasks that need to be addressed within the framework of IoRT and that can benefit from the findings of the present paper.  
We believe that the GDEP method can be further improved and generalized for the satellite-IoT scenario with any combination of initial data transfer technologies, encoding formats and container sizes. This will require additional simulation and experimental studies.  
The effect of data transfer optimization in a heterogeneous satellite-terrestrial network on the lifetime of battery-powered devices is another important issue. Normally, the question is raised in relation to end devices running LoRa, IEEE 802.15.4 and similar energy-efficient protocol stacks, while network gateways are considered to be supplied with a constant energy source. With the active development of micro-energy harvesters, more complicated and resource-intensive network devices can become fully autonomous as well.  
Finally, network clustering can be additionally applied to divide a complex data acquisition network into multiple components, each comprising a satellite gateway. Within each component, incoming data streams can be aligned in such a way that the GDEP container utilization rate is maximized.  
Conceptualization, IL. and L.V.; methodology, formal analysis, I.L., L.V. and S.E.; software, resources, data curation, IL.; investigation, writing--original draft preparation, I.L. and S.E.; validation, writing - review and editing, L.V., A.R. and S.E.; visualization, project administration, A.R.; supervision, L.V.; funding acquisition, L.V. and A.R.  
The publication was prepared within the framework of the Academic Fund Program at the National Research University Higher School of Economics (HSE) in 2019-2020 (grant No. 19-04-022) and by the Russian Academic Excellence Project \"5-100\".  
The authors declare no conflict of interest
# Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data
## 3 Methodology
### Near Real-Time FSR Calculation  
Himawari-8 provides an observation every 10 minutes, which makes it possible to retrieve FSR at near real-time by analyzing the spread progression of the fire center. Wildfire spread models that are based on Huygens' wavelet principle presume that there is an elliptical spread at each point of the fire-front, and that a fire spreads outwards elliptically and at a constant speed in windless and flat area conditions [58; 59; 60; 61; 62; 63]. Therefore, it can be assumed that the FSR can be represented by the rate of burned area centroid movement in a windy environment. Additionally, fire spread models define the fire spread direction (FSD) as the reverse of wind direction [23]. The wind direction is generally expressed in 16 directions on the land with an average of 22.5 degrees [64]. Following these principles, the azimuth of centroid movement (\(\alpha_{i}\), also called FSD) from time stamp \(i-1\) to \(i\) is first calculated using Equation (5), and then the difference (\(\triangle\)) between the azimuth of centroid movement and reverse wind direction (RWD) is calculated (Equation (6)).  
\[\alpha_{i}=\arctan\frac{Y_{c_{i_{1}}}-Y_{c_{i_{j-1}}}}{X_{c_{i_{1}}}-X_{c_{i_{ 1}-1}}} \tag{5}\]  
\[\Delta=|\alpha_{i}-(DR_{Wind}\pm 180^{\circ})| \tag{6}\]  
where \(X_{c_{i_{1}}}\), \(Y_{c_{i_{1}}}\), \(X_{c_{i_{1}+1}}\), and \(Y_{c_{i_{1}+1}}\)are the geographical coordinates of the burned area centroid in time stamp \(i\) and \(i+1\), \(DR_{Wind}\) is the reverse wind direction.  
Finally, the near real-time FSR, corresponding to \(\triangle\) lower than 22.5 degrees, is calculated at different timesteps using Equation (7).  
\[V_{(i-1,i)}=\frac{D_{(i-1,i)}}{T_{r}} \tag{7}\]  
where \(T_{r}\) is the time resolution of Himawari-8, \(V_{(i-1,i)}\) is the FSR during the timestep \(i-1\) to \(i\), \(D_{(i-1,i)}\)is the distance of the centroid movement during timestep \(i-1\) to \(i\). \(D_{(i-1,i)}\) can be obtained with Equation (8).  
\[D_{(i-1,i)}=\left(X_{c_{i_{i-1}}}-X_{c_{i_{1}}}\right)^{2}+\left(Y_{c_{i_{1}- 1}}-Y_{c_{i_{1}}}\right)^{2} \tag{8}\]
## 5 Proposed Approach and System
### System Architecture  
Figure 4 depicts our proposed framework, which consists of four primary components:  
1. Task Publisher; 2. Trusted Authority; 3. IoMT Edge Nodes; and 4. IoMT Devices, supported by 5. blockchain Infrastructure. The roles and interactions of these components are elaborated below.
2. Task Publisher: To address IoMT application needs, the task publisher (TP) designs the FL model training task, implements it through a blockchain smart contract (SC), and pays a fee as a bonus pool. IoMT devices that meet the requirements can apply to participate in the task. Then, the TP will initialize the model parameters and upload them to the blockchain by way of transactions.
3. Trusted Authority: The trusted authority (TA) initializes the system by generating and distributing public/private key pairs for IoMT edge nodes.
4. IoMT Edge Nodes: Edge nodes, functioning as blockchain clients, possess public/private key pairs \((pk_{m},sk_{m})\) issued by the Trusted Authority (TA). Their responsibilities encompass: (a) registering with the Smart Contract (SC), (b) downloading the global model from the blockchain, (c) collecting secret shares of all gradients and computing partial similarities between these secret shares, and (d) partially aggregating the secret shares of gradients from the selected models.
5. IoMT Devices: Devices are responsible for: (a) locally training the model, quantizing, and securely sharing gradients each round; (b) broadcasting gradient secret shares to all edge nodes; and (c) downloading the global model from an edge node to update local model parameters.
6. Blockchain: In the Internet of Medical Things (IoMT), blockchain technology substitutes the traditional federated learning (FL) parameter server. Its responsibilities are:  
\begin{table}
\begin{tabular}{c|c} \hline \hline
**Notation** & **Description** \\ \hline \(D_{i}\) & Training datasets of the device \(C_{i}\) \\ \hline \(\mathbf{w}_{i}^{(t)}\) & Local model of the device \(C_{i}\) in the \(t^{\text{th}}\) iteration \\ \hline \(\eta\) & Global learning rate \\ \hline \(\delta\) & Local learning rate \\ \hline \(\mathbf{g}_{i}^{(t)}\) & Gradient of the device \(C_{i}\) in the \(t^{\text{th}}\) iteration \\ \hline \(\mathbf{G}_{s}^{(t)}\) & Sum of the gradients in the \(t^{\text{th}}\) iteration \\ \hline \(cs_{i}^{(t)}\) & Cosine similarity between \(\mathbf{g}_{i}^{(t)}\) and \(\mathbf{G}_{s}^{(t)}\) \\ \hline \(S_{i}^{(t)}\) & Score of the device \(C_{i}\) in the \(t^{\text{th}}\) iteration \\ \hline \(b/e/l\) & Batch size/Global iterations/Local iterations \\ \hline \([\mathbf{g}_{i}^{(t)}]_{T}\) & \(j^{\text{th}}\) share of the secret \(\mathbf{g}_{i}^{(t)}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main notation used in this study.  
(a) verifying the public keys of registered nodes; (b) gathering secret shares of similarities from these nodes and reconstructing the similarities between local model updates; (c) collecting secret shares of aggregated gradients from all nodes and reconstructing the aggregated gradients.
## 2 Research on UAV Path-Planning Problem
### Fitness Function  
In UAV path planning, the fitness function is used to comprehensively measure the degree of merit of a flight path. In this paper, the UAV's flight distance, flight-altitude standard deviation, flight time, and stability (including yaw angle and pitch angle) are used as comprehensive evaluation indexes to construct the fitness function, and the lower the fitness indicates the higher quality of the flight path.
# SafeNet: SwArm for Earthquake Perturbations Identification Using Deep Learning Networks
## 1 Introduction
(2018) proposed a novel earthquake prediction framework based on inverse boosting pruning trees (IBPT), and achieved promising forecasting results in the validation of global earthquake cases retrospectively. In this study, we use deep learning techniques, combined with multi-year accumulated Swarm satellite data for pre-seismic ionospheric disturbance identification. The proposed method, known as SafeNet (SwArm for Earthquake study using deep learning networks), is a deep-learning method based on a sequence-based classification neural network for pre-earthquake perturbation identification. The suggested model was trained using 9017 recent independent 4.8+ magnitude earthquakes and 7-year plasma and magnetic field data from the Swarm A satellite. The results indicated that nighttime data provided the best performance in distinguishing pre-earthquake perturbations, with an F1 score of 0.846 and a Matthews correlation coefficient of 0.717. It also worked effectively in detecting pre-seismic ionospheric abnormalities as earthquake magnitude increased. Furthermore, the findings of this study enabled us to propose a hypothesis regarding the physical mechanism behind earthquake-induced ionospheric disturbances. In general, SafeNet could considerably enhance the effectiveness of pre-earthquake ionospheric perturbation identification. This paper is organized as follows. In Section 2, the used data from the Swarm satellite and its data pre-processing are discussed, together with the observation cases of the anomalies before two actual earthquakes. Section 3 describes the network structure design and performance evaluation metrics for the proposed deep learning model. The results are analyzed and discussed in Section 4, and a hypothesis of the earthquake mechanism is presented. In Section 5, we provide a conclusion and further possible orientation for future work.