esa-sceva/satcom-chunk-collection huggingface dataset:
## 3D Target Tracking  
### Overall Framework  
In Section 4, the FDA-SSD model construction with normalized depth information was completed. After the model network was used to obtain the target's 2D anchor frame, the edge information of the sample and the required characteristic corner points can be obtained following specific needs. Given the target space positioning of geometric constraints, a  
Figure 6: Schematic diagram of the normalized depth value.  
target-space-tracking framework based on monocular vision was proposed, as shown in Figure 7.  
The framework consists of three parts:  
1. Two-dimensional-detection based on a neural network (red part): The FDA-SSD model was adapted to obtain the anchor frame area of the sample in real-time. Considering the real-time requirements, the key corner points can be extracted according to the typical geometric characteristics of the target, such as triangles, parallelograms, and circles, for use in subsequent positioning algorithms. Finally, the motion information of the target related to the camera in the current frame was employed to extract the depth information of the target relative to the camera, obtain the restored depth value based on the target scale and the sample scale, and feed it back to the input of the FDA-SSD as the reference depth value of the next frame of the image.
2. Three-dimensional-location method based on geometric constraints (blue part): The targets in this article were abstracted as a circle or a parallelogram, and the improved high-order Newton iterative algorithm was used to realize the efficient real-time numerical solution of the depth information of the target feature points. Then, the spatial motion of the target related to the camera was restored using depth information. Finally, the real-space motion trajectory of the target was calculated with the pose of the camera.
3. Camera-pose acquisition (gray part): The monocular camera pose can be obtained by open-source visual-slam methods such as VINS [24], ORB-SLAM3 [25], or refer to the author's previous work [26; 27]. This article focuses on two parts, part1 and part2. In the experimental verification of this study, the camera pose was acquired by the motion-capture system. In practical applications, the absolute scale information learning of the target can be obtained by multiple methods such as multi-frame initialization in the monocular vision slam, the baseline of the visual motion platform, and the prior information of the cooperative target.  
Figure 7: The overall framework diagram of the visual target-detection-and-tracking algorithm of the monocular moving platform.
# Reliable Design for a Network of Networks with Inspiration from Brain Functional Networks
## 2 Basic Principles of NoN Models
### Influence Identification in a Network of Networks  
The identification of highly influential nodes, which play important roles in robustness and diffusion, has been studied in many research domains, such as social networks [14; 23], biology [24; 25], marketing [26; 27], and computer networks [16]. Because searching for the optimal influencers over a given network is an NP-hard problem [14], a number of heuristic methods have been proposed to date [28; 29; 30].  
Herein, we also deal with influencer design to enhance NoN reliability, and therefore we focus on the recently proposed Collective Influence (CI) algorithm for influencer identification [31]. Not only does the CI algorithm outperform other existing methods in detecting influencers, it is also optimized for an NoN [13]. The influence on the network centered around node \(i\) is represented by CI\({}_{i}\), which is defined as  
\[\text{CI}_{i}=(k_{i}-1)\sum_{j\in\partial\text{Ball}(i,l)}(k_{j}-1), \tag{6}\]  
where \(k_{i}\) is the degree of node \(i\) and \(\partial\text{Ball}(i,l)\) is the set of nodes located exactly \(l\) hops away from node \(i\) (Figure 2). CI\({}_{i}\) is calculated as the product of the degree of node \(i\) and the sum of the degree of node \(j\) in \(\partial\text{Ball}(i,l)\). Furthermore, the definition of CI\({}_{i}\) is expanded for an NoN as follows:\[\text{CI}_{i} = (k_{i}^{intra}+k_{i}^{inter}-1)\sum_{j\in\partial\text{Ball}(i,l)}(k_{ j}^{intra}+k_{j}^{inter}-1) \tag{7}\] \[+\sum_{j\in\mathcal{F}(i):k_{j}^{inter}=1}\biggl{[}(k_{j}^{intra}+k _{j}^{inter}-1)\sum_{m\in\partial\text{Ball}(j,l)}(k_{m}^{intra}+k_{m}^{inter}- 1)\biggr{]},\]  
where \(k_{i}^{intra}\) and \(k_{i}^{inter}\) are the degree of node \(i\) for intranetwork links and internetwork links, respectively. \(\mathcal{F}(i)\) is the set of nodes connected to node \(i\) via internetwork links. The first term of Equation (7) corresponds to the CI of node \(i\) for Network 1 in Figure 2. The second term represents the sum of the CI of node \(j\) connected to node \(i\) via internetwork links, corresponding to Network 2 in Figure 2. The condition \(k_{j}^{inter}=1\) indicates that node \(j\) is taken into account only if it has a single intermodular link. This is attributed to the NoN characteristic that the state of node \(j\) is not directly affected by node \(i\) if node \(j\) has more than one intermodular link according to Equation (1).
# The Analysis of Experimental Deployment of IGLUNA 2019 Trans-Ice Longwave System
## 1 Introduction
Despite this severe limitation in performance, the redesigned system was successfully deployed, providing many data useful for the special-purpose AM/LW (longwave) broadcasting systems and remote sensing of the antenna surroundings (ice and rocks) [8], as well as for the development of the future omnipresent lunar communication system, especially as an adaptation/pilot experiment for the use as a subsystem of a larger network, employing among LW also other frequencies/communication links [9].
# NB-IoT for D2D-Enhanced Content Uploading with Social Trustworthiness in 5G Systems
## 7 Conclusions  
In this paper we proposed a trust-based solutions for effective D2D-enhanced cooperative content uploading in narrowband-IoT cellular environments. To limit the impact of the malicious nodes either dropping or corrupting the data packets in a cooperative multihop coalition, social awareness has been modeled to evaluate the reliability for the nodes and to suitably weigh the recommendations exchange for the reputation definition. A simulative analysis validated the proposed solution in a wide range of settings for small-scale IoT scenarios. The results showed how the social-based trusted solution guarantees higher gains in the content uploading time, in the energy consumption, and has the ability to increase the amount of successful cooperative interactions by filtering out the malicious nodes.  
**Acknowledgments: The publication was financially supported by the Ministry of Education and Science of the Russian Federation (the Agreement number 02.a03.21.0008).**  
Figure 4: Impact of feasibility threshold (50% of malicious nodes, half of type A and half of type B).  
(a) Uploading time gain; (b) Average energy gain; (c) Data loss. UE: user equipment.  
**Author Contributions:** In this paper, the first three authors [PERSON], [PERSON] and [PERSON] conceived the idea, organized the work and designed the analytical model and the proposed algorithms; [PERSON] and [PERSON] conceived and designed the experiments, analyzed the results and wrote the paper; [PERSON] performed the experiments; [PERSON] supervised the work; all the authors reviewed the writing of the paper, its structure and its intellectual content.
# Efficient Distributed Method for NLOS Cooperative Localization in WSNs
## 1 Introduction
### Contributions  
Up to now, most NLOS mitigation techniques applied to WSN cooperative localization still utilize centralized optimum algorithms. In this paper, we propose a parallel distributed algorithm based on a tight relaxation technique to both decrease computational complexity and improve accuracy in NLOS environments.  
First, we propose a modified convex model based on projection relaxation, which relaxes the original nonconvex problem into its convex envelope. This model can be applied to tough situations where NLOS connections are unidentifiable in all range measurements. According to the bounds of range measurements, we formulated the problem in the form of projection distances. Then, we relaxed this formulation into the projection on convex sets by using Cauchy-Schwartz inequality. Compared with SDP, this approach improves the decomposition property of the convex model and the accuracy of the estimation results.  
Second, we developed a parallel distributed algorithm to solve the convex model. We designed a consensus form to decompose the large-scale problem into numerous local subproblems and provide the relevant theoretical proof. The proposed consensus form is more suitable for an ADMM framework. It enables each node to solve each subproblem in parallel. We derived the concrete procedure of how to handle the problem in a parallel way. The distributed algorithm had much lower computational complexity than that in existing papers about NLOS cooperative localization.  
Third, we further improved the convergence rate of local updates. The local subproblems are convex and nonquadratic differentiable, which makes them less appropriate for the Newton method and interior-point algorithm. Hence, with the guarantee of Lipschitz continuity, we propose an iterative algorithm to solve the untypical convex subproblems based on the proximal method.  
The paper is organized as follows. Section 2 formulates the cooperative localization problem and the corresponding convex relaxation. Section 3 presents the consensus form and solves it in a distributed way. Section 4 derives the iterative method for local subproblems. In Section 5, the simulation results are reported. Section 6 concludes the paper.
# Stochastic Generator of Earthquakes for Mainland France
## 3 Application to Mainland France
### Temporal Description of the Instrumental Catalogue
#### 3.2.2 Consideration of Magnitudes' Uncertainties through a Monte Carlo Scheme  
Taking into account magnitudes' uncertainties in the calculation of FMD through a Monte Carlo scheme allows us to obtain a stochastic set of PMD and FMD that corresponds to the global representation of main shocks' proportion and occurrence (Figure 7). This Monte Carlo process consists of producing 1000 initial catalogues of 15,567 earthquakes with \(M\geq 2\). Only the magnitude differs from one catalogue to another, since the magnitude of each earthquake is drawn in a normal law \(N(\mu,\sigma)\), where \(\mu\) is its magnitude and \(\sigma\) its magnitude's uncertainty.  
In Figure 7, each magnitude step is characterized by a set of annual frequencies of main shocks. Thus, for each magnitude step, the annual frequency of main shocks can be described by a probability density function (pdf). These pdfs are visible in Figure 8 for six magnitude steps. For a given magnitude \(M\), the annual frequency of main shocks of magnitudes greater than or equal to \(M\) is thus defined as a pdf. The pdf values are normalized by their sum. In this way, these normalized values are dimensionless and their sum converges to 1, which makes them probabilities.  
These pdfs of annual frequencies as a function of magnitude are inputs of the generator of earthquakes for mainland France (\"_Stochastic FMD of main shocks_\"), whereas the mean PMD of main shocks constitutes another input (\"_PMD of main shocks_\", Figure 1).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\mathbf{M}\mathbf{\geq}\) & \(\mathbf{2}\) & \(\mathbf{3}\) & \(\mathbf{4}\) & \(\mathbf{5}\) & \(\mathbf{6}\) & \(\mathbf{7}\) \\ \hline Return periods & \(2.4\pm 0.1\) & \(31.9\pm 0.3\) d & \(1.14\pm 0.05\) y & \(15\pm 1\) y & \(200\pm 24\) y & \(4700\pm 700\) y \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mean return periods of main shocks in function of magnitude \(M\) computed in the instrumental catalogue. These return periods are calculated from G85-GR application (Figure 7). Standard deviations are also reported. d: days; y: years.
## 2 Theoretical Analysis and Improvement Research on Finite Element Models
### Helical Coil Excitation Calculation  
As shown in Figure 2, according to [PERSON]'s circuital law, the magnetic field \(H_{inp}\) inside the helical coil, which is induced by the current source \(I_{in}\) satisfies the following condition:  
\[2\pi\rho H_{in\varphi}=\left\{\begin{array}{ll}NI_{in}&|\rho-R_{0}|<r_{s}\\ 0&|\rho-R_{0}|>r_{s}\end{array}\right. \tag{11}\]  
According to [PERSON]'s law of electromagnetic induction, it is known that the relationship between the magnetic field and the electric field at any point within the helical coil is as follows:  
\[\
abla\times E_{in}=-\hat{\varphi}j\omega\mu H_{in\varphi} \tag{12}\]  
Inside the helical coil, because the magnetic field in the \(\varphi\) direction is equal, the magnetic flux caused by the magnetic field at any point on the \(\rho\)z plane is equivalent to a point source of magnetic current in the \(\varphi\) direction, which means:  
\[I_{m}=\
abla\times E_{in}=-\hat{\varphi}\frac{j\omega\mu NI_{in}}{2\pi\rho} \delta(z-z^{\prime})\delta(\rho-\rho^{\prime}) \tag{13}\]  
Therefore, in simulation modeling, a magnetic current loop can be used to equivalently replace the helical coiled loop antenna, which provides a reliable method for efficiently simulating and analyzing electromagnetic wave transmission.  
Where:  
\[\left|z-z^{\prime}\right|<\frac{h}{2},\left|\rho^{\prime}-R_{0}\right|<r_{s}\]  
The total equivalent magnetic current \(I_{M}\) of the helical coil can then be obtained by integrating \(I_{m}\):  
\[I_{M}=\iint I_{m}d\rho dz \tag{14}\]  
Bringing (13) into (14) gives  
\[I_{M}=\frac{j\omega\mu hNI_{in}}{2\pi}\ln\frac{R_{0}+r_{s}}{R_{0}-r_{s}} \tag{15}\]  
Referring to Equation (15), we understand that a linear relationship exists between the total magnetic current of the helical loop antenna and the applied current, given a fixed number of turns, dimensions, and core permeability. In practical applications, this implies that we can precisely calculate the required magnetic current for simulation environments by pre-setting the number of turns, cross-sectional area, and core permeability of the helical loop and adjusting the magnitude of the input current. Consequently, the magnetic current  
Figure 2: Helical loop physical diagram.  
values used in simulations can also provide direct guidance for the customized design of the helical loop antennas.
## 5 Methodology for Reliability and Availability Evaluation
### Device Redundancy  
Regarding to redundancy issues, we consider that there are two types of field devices: with redundancy and without redundancy. The latter are simple field devices, while the former are composed by multiple devices arranged according to a fault-tolerant architecture based on a _hot standby sparing_ approach. Hot standby sparing provides redundancy through the use of spare devices. A device is kept operational whereas the others devices (spares) are in standby. When the operational device fails, a spare module assumes the operation. We assume that this is an internal arrangement of the field device. That is, from the perspective of an external observer the behavior of a redundant device is indistinguishable from a device without redundancy. The number of spare devices available for each field device is an input of the model.  
In the proposed model, devices are represented based on the failure event. The failure event for a redundant device is represented by the boolean \(AND\) of all spare devices whereas a device without redundancy is represented by a basic failure event.
# A Game Theoretic Approach for Balancing Energy Consumption in Clustered Wireless Sensor Networks
## 4 Cluster Head Election Game
### Achieving the Maximum Payoff
Combining Equations (6) and (10), the payoff function \(H_{i}\) corresponding to node \(i\) can be re-expressed as follows:  
\[H_{i}=\bigg{(}e_{i}\frac{E_{i}}{E_{\max}}-v_{i}\bigg{)}\Theta^{2}-e_{i}\Theta +v_{i}-e_{i}p_{i} \tag{11}\]  
where \(v_{i}\) is the payoff when node \(i\) chooses the strategy \(ND\) while there exists at least one CH; \(e_{i}\) is the extra cost when node \(i\) chooses \(D\); \(\Theta\) is the multiple formative of probabilities not to be the CH for all sensor nodes and \(p_{i}\) is the probability of being a CH for node \(i\). We calculate the derivative of \(H_{i}\) with regards to \(p_{i}\) as follows:  
\[\frac{\partial H_{i}}{\partial p_{i}}=-2\bigg{(}e_{i}\frac{E_{i}}{E_{\max}}-v _{i}\bigg{)}\frac{\Theta^{2}}{1-p_{i}}+e_{i}\frac{\Theta}{1-p_{i}}-e_{i} \tag{12}\]  
Letting this derivation to be zero, we can get the maximum point \(p_{i}{}^{\prime}\) of \(H_{i}\) as follows:  
\[p_{i}{}^{\prime}=1-\Theta^{\prime}-2\bigg{(}\frac{v_{i}}{e_{i}}-\frac{E_{i}}{ E_{\max}}\bigg{)}\Theta^{\prime 2}<1,\,s.t.\,p_{i}{}^{\prime}\in R,0\leq p_{i}{}^{ \prime}\leq 1 \tag{13}\]  
where \(v_{i}\) is the payoff when node \(i\) chooses the strategy \(ND\) while there exists at least one CH; \(e_{i}\) is the extra cost when node \(i\) chooses \(D\); \(E_{i}\) is residual energy of node \(i\); \(E_{\max}\) is the maximum residual energy of sensor nodes participated in the clustering game and \(\Theta^{\prime}\) is the multiple formative \((1-p_{i}{}^{\prime})\prod\limits_{j\in N_{G},j\
eq i}\big{(}1-p_{j}\big{)}\). Equation (13) shows that the maximum point of \(H_{i}\) not only depends on the strategy of node \(i\), but also relies on the combination of all other sensor nodes that participated in the \(CEG\). A sensor node which has more energy has a bigger probability to be CH at the maximum point. In addition, a sensor node with too little energy may have a negative value at the maximum point that is inconsistent with the term probability. As \(H_{i}\) is a convex function, its reasonable maximum point within the range \((0,1)\) can be expressed as follows:  
\[p_{i}{}^{m}=\max\{0,p_{i}{}^{\prime}\},\,s.t.\,i\in N_{G},p_{i}{}^{m}\in R,0 \leq p_{i}{}^{m}\leq 1 \tag{14}\]  
where \(p_{i}{}^{\prime}\) is the previous optimal probability of being a CH for node \(i\) that can maximize its own payoff \(H_{i}\) and \(p_{i}{}^{m}\) is the revised optimal probability of being a CH for node \(i\). Equation (14) indicates that the payoff of node \(i\) can be maximized when its probability to be CH is either \(0\) or \(p_{i}{}^{\prime}\).
# Passive Radar-Based Parameter Estimation of Low Earth Orbit Debris Targets
## 5 Simulations
### Moving Target over a Forward Scatter Receiver Array
#### 5.2.3 FFT Results  
The simulation parameters are displayed in Table 1. The FFT results are displayed in Figure 8.  
Figure 8a highlights a linear trend of Doppler increasing as the target is further away from the baseline. At the crossing point, there is no Doppler, thus indicating a successful target detection. The graphs show absolute Doppler values. According to Equation (5), \(\delta\) is positive as the target approaches the baseline and negative as the target departs the baseline. Therefore, asymmetry as the Doppler shifts from positive to negative is expected.  
Figure 8b displays a similar trend as Figure 8a but over all four elements in the receiver array. The plot is zoomed in for clarity. In this simulation, the target is traveling leftward. Therefore, it arrives at \(R_{x1}\) first and departs \(R_{x4}\) last. This is obvious from the Doppler behavior as \(R_{x1}\) sees a zero Doppler instance first. \(\lambda/2\) m later, \(R_{x2}\) sees the zero Doppler. This information can provide an initial estimate direction value for the target translational motion with respect to the receiver array.
## 1 Introduction  
Dual-energy X-ray absorptiometry (DEXA) is the most widely used method to measure bone mineral density (BMD), chiefly because it uses X-rays, which are noninvasive and inexpensive [1; 2; 3]. This measurement tool calculates the density of bone mass by measuring the rate differences of radiation transmittance in the analyzed bone mass when the radiation from the tool penetrates the body [4; 5]. By using a tube voltage of 80 Kv Cerium filter, DEXA utilizes photon energies of 40 keV and 80 keV [6]. Thus, it can be used to assess bone health conditions in any part of the body. It is widely used to measure and assess the regions of the body and is typically used to assess full-body BMD, such as the lumbar spine and proximal femur [7].  
Bones undergo mineralization and remodeling during their life cycle [8; 9]. An imbalance in the absorption and generation of osseous tissue generally occurs in both men and women as they age. This process causes mineral synthetic imbalances of different body parts, which leads to reduced average BMD. The rate of mineral synthetic imbalance has been reported to be 26.59% for women and 14.56% for men [10].  
A reduction in BMD, which causes diseases such as osteoporosis, is responsible for pain in various body parts and contributes to disabilities associated with old age [11]. More specifically, a decrease in BMD due to aging exponentially amplifies the risks of bone fracture [12], which can lead to disabilities and death and, ultimately, to increased social costs [13]. Moreover, a decline in BMD increases the risk of additional secondary fracture [14]. Mortality due to a reduction in BMD has been reported to be higher in men than in women [15].  
Although studies on the process of mineralization and bone reformation have been conducted, there is a paucity of data on therapies, such as hormone therapy, diet therapy, and therapeutic exercise, which have been deemed important for adults with decreased BMD. These interventions not only delay bone loss but also help in the maintenance and development of bones [16]. The importance of maintaining a high BMD is highly emphasized in the medical community.  
DEXA is necessary for accurately assessing and identifying bone conditions. According to the International Society for Clinical Densitometry (ISCD), DEXA is recommended as the most appropriate BMD measurement method that can then be applied to the diagnostic criteria of the World Health Organization [17; 18]. According to research on osteoporosis and fractures, DEXA measurement can predict and assess the risk of bone fracture. A BMD-estimated T-score of \(<\)-2.5 (defined as osteoporosis) has a close correlation with the development of bone fractures [19].  
In light of these findings, this paper seeks to provide an important reference for the pathophysiology and treatment of bone diseases, including osteoporosis, by analyzing the BMDs of Koreans based on sex and age.
# Optimal Resource Allocation Policies for Multi-User Backscatter Communication Systems
## 2 System Model
### Tag Configuration
#### 2.2.1 Passive Tag  
For a passive tag, if the receive power exceeds a threshold, this tag is activated so that it can enter the active state directly without staying in the sleep state due to the absence of batteries. In the active state, the passive tag backscatters its signals using a portion of instantaneous excitation energy and harvests the remainder to support circuit power over the active time \(t_{i}^{a}\), where \(t_{i}^{a}=T\). The working model is shown in Figure 2. Without loss of generality, assume that the circuit power and energy harvesting efficiency of all passive tags are same, denoted by \(P^{c}\) and \(\eta\), respectively. Since BPSK is employed, define \(n_{i}\) as the reflection ratio for signal backscattering of the \(i\)-th passive tag, following the definition in [24]. Hence, the backscattered power of the \(i\)-th passive tag (denoted by \(P_{i}^{0}\)) is given as \(P_{i}^{0}=n_{i}P_{i}^{\text{rec}}\). The harvested energy to cover the circuit energy consumption is given as \(E_{i}^{h}=\eta(1-n_{i})P_{i}^{\text{rec}}T\). If the receive power is lower than the threshold, the passive tag cannot be activated, so no signals will be backscattered.  
Figure 1: Multi-user backscatter communication system.