esa-sceva/satcom-chunk-collection huggingface dataset:
# Radar-Based Target Tracking Using Deep Learning Approaches with Unscented Kalman Filter
## 1 Introduction
### Significance of Machine Learning  
Advancements in computing power and access to large amounts of data have made the development of new machine learning algorithms a rapidly growing field. For example, Transformer-based models, such as GPT-3 [24; 25; 26], use attention mechanisms to process input sequences and various natural language processing tasks. Graph Neural Networks (GNNs) [27] are designed to handle graph-structured data and have shown promising outcomes in fields such as drug discovery and recommendation systems. Few-shot learning [28; 29], a relatively new area of study that has emerged in recent years, focuses on developing models that can learn from limited examples, making it well-suited for tasks where data are scarce. Gated Recurrent Units [30], first introduced in 2014, are a Recurrent Neural Network that handles long data sequences and has been applied to time series analysis, effectively capturing both short-term and long-term dependencies in the data. [PERSON] et al. [31] utilized the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm to identify the deformation state and controller coefficient of a quadrotor and subsequently employed a deep neural network to estimate the change in moment of inertia under deformation. Similarly, in Ref. [32], a deep neural network was employed to estimate the moment of inertia and proportional integral derivative (PID) coefficient to obtain several deformation states of a hex-rotor [33; 34].  
A Multilayer Perceptron (MLP) is a type of Artificial Neural Network (ANN) that is commonly used in deep learning consisting of one or more layers of Artificial Neurons (ANs), where each neuron is a simple mathematical function that takes in one or more inputs, performs a weighted sum of those inputs, adds a bias term, and then applies an activation function to the result [35; 36; 37]. On the other hand, CNN is a type of ANN specifically designed to process data with a grid-like topology, such as images or time-series data. The key feature of CNN is the use of convolutional layers, which apply a set of learnable filters to the input data [37; 38; 39; 40; 41]. Each filter is convolved across the input data to produce a set of feature maps, which capture different aspects of the input data. The feature maps are then passed through activation functions and pooling layers to reduce the spatial dimensionality of the data and increase their invariance to small spatial translations [42; 43; 44; 45]. The application of CNN in time series prediction is demonstrated by [PERSON] et al. [46].
# Multi-Module Combination for Underwater Image Enhancement
## 3 Proposed Method
### Fusion Module  
In this section, a multi-scale feature fusion strategy is employed to co-fuse the visibility-restored processed image with the contrast-enhanced image. A multi-exposure image fusion method, which utilizes adaptive weights that reflect relative pixel intensities and global gradients, aims to fully leverage multiple sources of information and construct more effective fusion weights. This method innovatively introduces a dual weighting mechanism that incorporates local weights based on pixel intensities and global gradients. This approach optimally fuses visibility restoration images with contrast enhancement images [25].
###### Abstract
We conducted Interferometry Synthetic Aperture Radar (InSAR) observations together with GPS/GIS data analysis and geophysical modelling to assess the vulnerability of Ulsan to surface deformation, and focused on the relationship between recent (2015-2018) and future potential seismic activities and the plausible risks of industrial infrastructure. InSAR techniques have been employed to investigate the effects of seismic activity and surface deformation in a large number of studies; it is a well-demonstrated method for determining potential hazards, particularly earthquakes [22; 23; 24], and for evaluating the associated damage [25; 26; 27]. However, our aim was to investigate the effects of relatively minor earthquakes and to monitor the potential damage from accumulated subtle strain over highly sensitive infrastructure facilities. Thus, InSAR time series analysis was employed as error components of InSAR processing need to be suppressed and the outcomes was carefully interpreted together with GIS and geophysical modelling. Technically, this study focused the feasibility of InSAR remote sensing approaches for the safety surveillance of industrial infrastructure, which requires high precision measurements, as well as the investigation of subtle pre/postseismic surface deformation over the target area. Thus, it differed from past studies that have used geophysical data to investigate obvious coseismic ruptures using multipass InSAR observations. The consequent technical challenges involved in mining the potential seismic risk elements from weak/continuous deformations were overcome via spatial/temporal interpolation of InSAR time series analyses and 3D decompositions. The extracted information was feed forwarded to geophysical models and again synthesized with probabilistic seismic hazard analysis (PSHA) [28] to extract corresponding risk maps. The associated target area context and data sets are described in Section 2. The processing methodologies for InSAR and auxiliary data sets with their outcomes are given in Sections 3 and 4, respectively. A discussion on the detected deformation and the geophysical inversion modelling work validating the DInSAR observations is presented in Section 5.
## 4 Autonomous Landing Method
### SLA Determination
#### 4.2.2 Procedure of SLA Determination  
The flowchart of the numerical simulation for the power line model using COMSOL software is shown in Figure 8. By setting the ground material and radius, the initial power line model is generated using the prior structure data of the OPTL. The gravity of the FPLIR is loaded as a concentrated force to the specified slope (or location) to obtain the power line model after the landing of the FPLIR.  
The FPLIR does not slip, and the maximum starting slope of the walking motor is \(15^{\circ}\). As an example, the shape of the power line was simulated using the parameters in Table 2, while eight points were analyzed with slopes of \(-15^{\circ}\), \(-14^{\circ}\), \(-13^{\circ}\), \(-12^{\circ}\), \(12^{\circ}\), \(13^{\circ}\), \(14^{\circ}\) and \(15^{\circ}\). In this study, a polynomial function is applied to fit the shape of the power line to easily calculate the slope of the power line. The shapes of the power lines before and after the FPLIR landing are fitted using 3 rd- and 7 th-degree polynomials, respectively. From Table 3, it can be seen that the slope decreases at the landing location. Combined with the variation of the slope in Figure 9, the following conclusions can be drawn. When the slope of the power line is less than zero, the slope decreases at the landing position and its right side; when the slope of the power line is greater than zero, the slope decreases at the landing position and its left side. Therefore, the safe slope for FPLIR landing in the section of the OPTL is \(-15^{\circ}\) to \(15^{\circ}\).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Voltage** & **Ground** & **Tower** & **Tower** & **High** & **Span (m)** & **Slope** & **Voltage** & **Ground** \\
**Level (KV)** & **Wire Type** & **Type** & **Height (m)** & **Difference (m)** & **Span (m)** & **Level (KV)** & **Wire Type** \\ \hline
500 & LGJ-95/55 & ZB3-31.5 & 127.008 & 18.283 & 349 & 1.7/10.9 & 16.69 & 4.7245 \(\times\) 10\({}^{-3}\) \\ \cline{2-9}  & ZB6-26 & 145.291 & 18.283 & 349 & 1.7/10.9 & 16.69 & 4.7245 \(\times\) 10\({}^{-3}\) \\ \hline \end{tabular}
\end{table}
Table 2: The prior structural data, for a 500 kv OPTL.  
Figure 8: Flowchart of power line shape simulation.  
For power lines in images, in this study, a polynomial function is used to fit segmented power lines, and its derivative is computed to obtain the line slope. The 100 segmented power line images were fitted using multiple polynomial functions. The sum of squares due to error (SSE), R-square, root mean square error (RMSE) and mean absolute percentage error (MAPE) are listed in Table 4. It can be seen that a good fit was achieved using the quadratic polynomial. Therefore, in this study, a quadratic polynomial is used to fit power lines in the images.
# Rethinking Figure-of-Merits of Liquid Crystals Shielded Coplanar Waveguide Phase Shifters at 60 GHz
## 2 Materials and Methods  
Sixteen well-established thermotropic types of microwave-engineered tunable LCs in nematic phase are evaluated in this work, with the material codes (and companies), namely, 5 CB, E7, BL006, BL037, GT3-23001, GT3-24002, GT5-26001, GT5-28004, GT7-29001, TUD-566, MDA-03-2838, MDA-03-2844 (Merck KGa, Darmstadt, Germany), CLHS-1 [6], QYPD036, QYPD142, and QYPD193 (Qingdao QY Liquid Crystal Co., Ltd., Qingdao, China). To facilitate comparison and analysis, LC material's FoM is denoted as FoM\({}^{\prime}\) and  
Figure 1: Modelled and fabricated prototype of an LC-based SCPW phase shifter device for 60 GHz.  
represented in Equation (1) and LC tunability is defined in Equation (2), where \(\varepsilon_{\parallel,LC}\) and \(\varepsilon_{\perp,LC}\) denote the two extreme values of the dielectric constant at parallel and perpendicular states, respectively. Here the max. dissipation factor represents the loss tangent state with the LC molecular director biased normal to the mmWave polarisation.  
\[\text{FoM}^{\prime}=\text{LC tunability}/\text{max. LC dissipation factor} \tag{1}\]  
\[\text{LC tunability}=\sqrt{\varepsilon_{\parallel,LC}}-\sqrt{\varepsilon_{ \perp,LC}} \tag{2}\]  
Comparatively, the LC-filled phase shifter's FoM is represented by FoM\" and defined in a like manner as Equation (3) below in the degrees/decibels scale.  
\[\text{FoM\"}=\text{max. phase shift (}^{\circ}\text{)}/\text{max. insertion loss (dB)} \tag{3}\]  
The geometry size of the SCPW is unified as per our experimentally optimised prototype [7], i.e., a core line width of 0.293 mm, coplanar channel (gap) width of 0.203 mm, LC layer thickness of 0.14 mm, and effective line length of 14.32 mm to produce a maximum differential phase shift of \(\pi\) at 60 GHz. To ensure the elimination of return loss and radiation loss due to impedance mismatching, port impedance in the simulation is intended to not necessarily be normalised to any specific impedance. In this way, the insertion loss is unambiguously related to dissipations by the materials' absorptions only (i.e., from both dielectrics and conductors).  
The cost of computational efforts for simulation analysis is economically viable by leveraging our existing experimentation knowledge with the GT3 series of LC, instead of fully probing via experiments with all 16 types of LC (the size and expenses would be non-manageable, and some of these LCs may be subject to export control protocols, even though no defence application is involved). The computer-based simulation analysis in this work is built upon our previous experimentations, which suffices to draw new insights from data, and it exhibits unique advantages, in particular as controlled experiments are infeasible to implement, with tremendous lead times and costs for the procurement of the 16 types of LCs (most of the materials are research grade and not commercially available) and the fabrication of 16 SCPW devices for measurements. Instead, parametric simulation is expandable with ease for a vast type of LCs with no additional expenses.
## 4 Application of the Method  
### Network Skeleton Identification Forvesilient Recovery  
As shown in Figure 6, the network's resilient recovery ability will stay almost unchanged if the link between 1 and 4 (1-4) is removed (network a \(\rightarrow\) network b). The analysis indicates that every link has unequal contribution to the resilient recovery ability. In other words, the resilient recovery of SN is hardly affected if certain links are removed. It has been discovered that many links in the network are actually redundant for resilient recovery, known as redundant links. The backbone of resilient recovery is the minimal network maintaining resilient recovery ability, which is the network when the redundant links have been removed. Disruption may occur anywhere in a SN, so the question of how to identify the backbone of resilient recovery is of great importance.  
In order to remove the redundant links, a greedy algorithm is designed based on \(R(t)\) in Equation (8), in which the resilient recovery ratio of the original SN is expressed as \(R^{(0)}\), and the resilient recovery ratio of the reduced network after removing \(l\) links is denoted as \(R^{(l)}\)[43].  
\[\Delta R=|R^{(l)}-R^{(l-1)}| \tag{20}\]  
Then, one can calculate \(\Delta R\) of each remaining link and select the link with lowest \(\Delta R\) to be actually removed from the network. Then, continue to remove links until \(R^{(l)}\) falls below 98% of \(R^{(0)}\) and the obtained network is denoted as the network backbone of resilient recovery. As shown in Figure 5 (network \(c\)), the network backbone of resilient recovery is obtained when the redundant links (1-4, 1-2, 3-5) are removed.
###### Abstract  
This paper proposes a novel control method for enhancing transient stability by using renewable energy sources (RES). The kinetic energy accumulated in a rotor of variable speed wind generator (VSWG) is proactively used as the active power source, which is controlled according to the frequency measured at the wind farm. In addition, coordinated reactive power control according to the grid voltage is also carried out to more effectively use the kinetic energy of the VSWG. The effects of the proposed control system were evaluated by simulation analyses performed using a modified IEEE nine-bus power system network made up of synchronous generators (SGs), a photovoltaic (PV) system and a VSWG-based wind farm. Furthermore, the coordinated reactive power control between the VSWG and PV system was also demonstrated.  
variable speed wind generator; photovoltaic; transient stability; kinetic energy; active and reactive power control
# Handover Decisions for Ultra-Dense Networks in Smart Cities: A Survey
## 4 Research Challenges
### Big Data Analysis  
Artificial intelligence and machine learning techniques are being increasingly considered to enhance HO decision-making in 6G networks. However, introducing AI into mobility management is met with several challenges, including being able to handle real-time processing of data, large dataset training, and achieving high computation efficiency. AI-facilitated HO prediction algorithms must be able to handle dynamic, volatile network behavior with minimal latency in processing. Additionally, skewed training datasets have the potential to lead to inaccuracy in HO prediction, negatively affecting network performance. Security attacks, such as data poisoning and adversarial attacks, can compromise AI-based HO mechanisms and lead to incorrect decisions. Secondly, explainability and interpretability of AI-based HO decisions are crucial to gain trust in automated mobility management. For resolving these concerns, lightweight and energy-efficient AI models must be designed to make proactive HO decisions with zero computational overhead [39].
# Towards Security Joint Trust and Game Theory for Maximizing Utility: Challenges and Countermeasures
## 3 Game Theory in Networks
By analyzing this literature, we can obtain the following conclusions. The introduction of game theory provides a new solution for improving network security and maximizing utility. The dynamic game model is used most widely in the use of game theory. This is because, in some networks/systems, players usually have some knowledge of the information and decisions of other players. Therefore, modeling it as a dynamic game is more appropriate. Cooperative game, non-cooperative game, and security game also occupy a certain proportion. When two or more metrics in the network are optimized for conflict, the introduction of game theory solves the problem. Researchers use equilibrium to solve multi-metric optimization conflicts, thereby maximizing network utility. In some networks, information or resources need to be shared. The introduction of game theory provides a good strategy for resource sharing. When there is a malicious attack in the network, whether it is an external attack or an internal attack, the combination of game theory and trust relationship provides a solution to solve the security problem. In general, the introduction of game theory has maximized utility from the following aspects. (1) It improves the security and secrecy rate of the network, against malicious attacks, and reduce malicious services; (2) it maximizes the profits of players in the network, thereby maximizing network utility; and (3) it promotes information/resource sharing to provide network players with better strategy selection.
# Mesoscale Modelling of the Mechanical Behavior of Metaconcretes
## 4 Results
### Effect of Material Properties  
This section examines the effect of varying the elastomer material coating and the core of the resonators. For this purpose, a case study was selected from the specimen modeled in Section 3 for mesh size analysis. In this specimen, 10.6% of the resonators are embedded in concrete. Each resonator has an overall diameter of 20 mm, a steel core diameter of 15.4 mm, and a rubber coating thickness of 2.3 mm.  
Considering that Young's modulus of an elastomer can range from \(10^{-4}\) GPa to \(10^{-1}\) GPa [19; 57], the following values were examined: 100, 50, 25, 1, and 0.1 MPa. Two core materials were considered for each coating material: 210 GPa for steel and 16 GPa for lead. All other parameters were kept constant.  
As illustrated in Figure 8, the values of Young's modulus and compressive strength for the diverse materials under consideration for the resonators are presented. The data show that modifying the core and coating materials does not substantially influence the composite's macroscopic mechanical properties. Our findings suggest that Young's modulus is less sensitive to these alterations than the compressive strength. However, it is noteworthy that  
Figure 7: Effect of rubber coating thickness (\(t\)) and core size (\(D_{c}\)) on the normalized Young’s modulus (\(E/E_{0}\)) and compressive strength (\(f_{c}/f_{c0}\)) for metaconcrete specimens with a constant resonator volume fraction of 10.6%. Source: own elaboration.  
altering the Young's modulus of the coating can result in an approximately 6% variation in compressive strength. Furthermore, the core material selection appears to exert minimal influence on the mechanical response, at least within the scope of the resonator dimensions considered in this study. Crack patterns were also not presented, as they remained unchanged when [PERSON]'s modulus of the resonator materials was modified. Furthermore, as previously shown in Figure 3, they are identical to those calculated with a mesh size of 1 mm. These trends indicate that, under quasi-static compression, each resonator functions mechanically as an almost stress-free cavity rather than as a load-bearing inclusion. The significant disparity in stiffness between the concrete matrix and the compliant coating effectively decouples the solid core from the external field, thereby negating the core's inherent stiffness (steel versus lead) from significantly impacting the global response. Consequently, variations in the core or coating materials within the examined ranges do not alter the crack trajectory or the overall load-displacement curve. The resonators replace an equivalent volume of concrete with void-like regions, which govern the observed 6% fluctuation in compressive strength while leaving Young's modulus virtually unchanged.
# Routing Schemes in FANETs: A Survey
## 2 Background and Motivating the Need for FANETs
### How Do FANETs Differ from Other Kinds of Ad Hoc Networks?  
Ad hoc network is composed of geographically distributed connected devices that can communicate with each other over a wireless medium. In general, it is deployed to support short-term applications, such as military usage, video conferencing, infotainment, as well as disaster relief and rescue operation, for a short time period. The ad hoc network is different from a cellular network because the former, which lacks a fixed infrastructure and powered by battery, must perform computation in a distributed manner; hence, energy efficiency is a major concern [42; 43]. Table 2 compares and contrasts the three main kinds of ad hoc networks, namely MANETs, VANETs, and FANETs, shown in Figure 2. Due to the differences among the ad hoc networks, novel routing schemes must be designed to cater to the main characteristics of FANETs.  
Figure 2: Two categories of ad hoc networks, namely mobile ad hoc networks (MANETs) and vehicular ad hoc networks (VANETs): The solid line represents the connectivity between two nodes.
## 2 Materials and Methods
### Disease Control Effects of the B. velezensis BP-1 Strain on Peanut Plants in Pot Experiments  
In the lab, pot experiments were carried out to evaluate the efficacy of the _B. velezensis_ BP-1 strain to control web blotch disease on peanut plants. The plants had total access to air, water, and light at room temperature (20-24 \({}^{\circ}\)C). The seeds were planted on the same day. After the peanut plants had grown for 20 days, the treatments were given. The peanut plant received the treatments, including the control group (fungicide tebuconazole), at a concentration of 400 \(\upmu\)L/plant. For peanut plant inoculation, a _P. arachidiola_ spore suspension of 400 \(\upmu\)L/plant was directly applied to the peanut leaf using an Eppendorf pipette. The above-mentioned optimized fermentation broth was used for bioassay activity. The following is the treatment approach:  
1. Inoculation with _P. arachidiola + B. velezensis_ BP-1 broth treatment
2. _B. velezensis_ BP-1 broth treatment + Inoculation with _P. arachidiola_  
The gap between the inoculations and treatments of _B. velezensis_ BP-1 was 3 days. The same method was used for the control group (fungicide tebuconazole). An evaluation of the disease control efficiency was carried out after the fourteen days of treatments. The number of disease lesions was measured to assess the disease control efficacy of _B. velezensis_ BP-1. A total of three replications were conducted in this experiment. The following formula was established:  
\[\text{Disease control efficacy}=\frac{Infected leaflets}{Total leaflets}\times 100\]