esa-sceva/satcom-chunk-collection huggingface dataset:
# Jointly Optimize the Residual Energy of Multiple Mobile Devices in the MEC-WPT System
## 4 Formalization of the Problem
### Problem Analysis  
Problem \(P1\) is a non-convex problem that is difficult to solve. However, through the analysis of problem \(P1\), we can first transform the main problem into a problem of smaller dimensions. Here we introduce Lemma 1, which is the basis of transformation.  
Lemma 1: _We always have \(\inf_{x,y}f(x,y)=\inf_{x}\tilde{f}(x)\), where \(\inf_{x}\tilde{f}(x)=\inf_{y}f(x,y)\)._  
Proof of Lemma 1.: See [31].  
Lemma 1 shows that the objective function can be minimized by first minimizing some of variables, and then minimizing the other ones. For this reason, we are able to find the optimal \(f\) firstly.  
According to Equation (3), we can find that the energy consumption of local computation increases monotonically with the increase of \(T1\), the smaller the \(T1\) is, the less energy consumed. However, the small \(f_{i,j}\) lead to the violation of constraint \(C2\). In other words, local task execution cannot be completed within time block \(T\). Therefore, when the time \(T\) is taken as the maximum available computation time, the best value \(f_{i,j}^{*}=\frac{(R_{ij}-R_{ij}^{off})C_{j}}{T}\).  
It can be seen that the objective function and \(f(\frac{R_{ij}^{off}}{t_{i,j}^{off}})=0\) satisfy the constraint 6 so that the constraint 6 can be eliminated. When all tasks are offloaded to the edge server, \(R_{i,j}-R_{i,j}^{off}=0\), which means that no local computation is required. At the same time, \(f_{i,j}^{*}=0\) satisfies constraint 7. By replacing \(f_{i,j}^{*}\) in \(P1\), we can simplify the original problem \(P1\) to \(P2\):  
\[\begin{split} P2&\max_{R^{off},t^{har},t^{off}} \sum_{i=1}^{N}\sum_{j=1}^{M}E_{i,j}^{res}(R_{i,j}^{off},t_{i}^{har},t_{i,j}^{ off})\\ s.t.& C3,C4,C5,C7,C8\end{split} \tag{11}\]  
where \(E_{i,j}^{res}(R_{i,j}^{off},t_{i}^{har},t_{i,j}^{off})=f(i,j)t_{i}^{har}-(\frac{ k_{j}((R_{i,j}-R_{ij}^{off})C_{j})^{3}}{T^{2}}+\frac{t_{i,j}^{off}}{h_{i,j}^{ {}^{\prime}}}f(\frac{R_{i,j}^{off}}{t_{i,j}^{off}})+p_{m,j}t_{i,j}^{off})\), C1, \(C2\) and \(C6\) can be eliminated.  
Through the above replacement steps and constraint elimination, the problem \(P1\) is converted to \(P2\), and a variable is eliminated simultaneously. Next, we will solve the problem \(P2\) to get the best combination of offload data size, energy harvesting time and task offload time.
# Stable Evaluation of 3D Zernike Moments for Surface Meshes
## 2 Moments from 3D Shapes
### Computing the Zernike Polynomials from the Geometric Moments  
Although the Zernike polynomials are usually defined with respect to spherical coordinates, they are actually polynomial functions on Cartesian coordinates \((x,y,z)\). To show this, let us first rewrite the Zernike polynomials  
\[\begin{split} Z_{nl}^{m}(\mathbf{x})&=\sqrt{2n+3}R_{nl} (r)Y_{l}^{m}(\theta,\phi)\\ &=\sqrt{2n+3}\sum_{v=0}^{k}Q_{nlv}(x^{2}+y^{2}+z^{2})^{v}r^{l}Y_{ l}^{m}(\theta,\phi)\\ &=\sum_{v=0}^{k}Q_{nlv}\sqrt{2n+3}(x^{2}+y^{2}+z^{2})^{v}e_{l}^{ m}(\mathbf{x}),\end{split} \tag{15}\]  
where \(k=(n-l)/2\) and \(e_{l}^{m}\) is the harmonic polynomial (see Equation (5)) and \(\mathbf{x}=(x,y,z)=(r,\theta,\phi)\). The harmonic polynomial can be expressed in Cartesian coordinates [22], but it is enough to know that it is a polynomial of total degree \(l\). We can thus write  
\[\sqrt{2n+3}(x^{2}+y^{2}+z^{2})^{v}e_{l}^{m}(\mathbf{x})=\sum_{r+s+l\leq l+2v}\zeta _{nlv}^{rst}x^{y}y^{z}z^{t}, \tag{16}\]  
with \(r\), \(s\) and \(t\) non negative integers and \(\zeta_{nlv}^{rst}\) some known complex numbers. We now have  
\[Z_{nl}^{m}(\mathbf{x})=\sum_{v=0}^{k}Q_{nlv}\sum_{r+s+t\leq l+2v}\zeta_{nlv}^{rst}x ^{y}y^{z}z^{t}=\sum_{r+s+t\leq n}\chi_{nln}^{rst}x^{y}y^{z}z^{t}, \tag{17}\]  
with  
\[\chi_{nln}^{rst}=\sum_{v=0}^{k}Q_{nlv}\zeta_{nlv}^{rst}. \tag{18}\]  
Finally, after replacing the Cartesian expression for \(Z_{nl}^{m}\) (Equation (17)) into Equation (12), we get an expression of the Zernike moments \(c_{nl}^{m}\) as a function of the geometric moments \(G\):  
\[c_{nl}^{m}=\sum_{r+s+t\leq n}\chi_{nln}^{rst}G_{rst}.\]  
This allow for a simple algorithm for computing the Zernike moments of a shape from its geometric moments. Similar algorithms have been proposed [22; 52] for the same task. Such an algorithm is theoretically very efficient, once the geometric moments have been computed, as it is independent of the size of the mesh representing the shape or the number of grid point in a voxel representation of the shape. There are, however, numerical issues with this formula that we discuss below.
# GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning
## 4 Security Analysis of the Proposed Algorithm
### Robustness against Various Attacks
#### 4.3.4 Sybil Attack  
Each client is registered with the admin in our model and has a valid GS. The admin first validates the client's identity and then adds it to the group. It also continuously monitors suspicious participants; if it finds any, it immediately revokes their group membership and updates the GS. It also maintains a table (the \"revocation table\") where it shows the reasons for all members' revocations. If a client is blacklisted, they cannot be added to the group.
## 1 Introduction
### Related Works and Motivation
Then, the decisions are coded, modulated and are transmitted to the FC via the PAC with channel errors. The FC demodulates, decodes the received signal and derives binary results. Then, all of these binary results are combined to give the final detection result about the presence/absence of a target. Based on this model, the received signal is usually decoded first, and a fusion rule is then applied at the FC. Hence, it is the decoded data that determine the final fusion performance. The accuracy of such demodulated data can be characterized by the average bit error rate (BER), which is the final output of the non-ideal PAC. The decode-then-fuse detection system was first given in [24] for MIMO channels. However, it mainly studied the detection performance of the two-stage CV fusion rule, which combines the maximum-likelihood (ML) or minimum mean square error estimate of the transmitted symbols. The optimal fusion rule design in a decode-then-fuse detection system, which combines corrupted binary local decisions that are transmitted from sensors via wireless channels, to the best of our knowledge, is an unaddressed problem. Our goal here is to design the fusion rule to combine corrupted binary local decisions that are transmitted from sensors and to evaluate the detection performance of the decode-then-fuse strategy. Figure 1: A block diagram of the parallel fusion model in the presence of fading and noisy channels between sensors and the fusion center (FC).
# Three-Dimensional Division of Visible Light Communication Irradiation Area
## 3 Theoretical Simulation and Analysis of VLC
### Simulation Results and Analysis
Our analysis of the above indicators suggests that the minimum BER of the VLC system between the transmitter half angle and the irradiance half angle at the source is related to the irradiance half angle at the detector. Similarly, the obtainable communication distance increases as the transmitter half angle decreases and the concentration of the light increases. However, the transmitter and the receiver need to be accurately aligned, which has many advantages for the communication system of the fixed transceiver, which is difficult to achieve in mobile transceiver systems. In particular, mobile transceiver systems require a large receiving space. Based on the above findings, in this study we increased the communication distance from 40 cm to 10 m using a lampshade. We built a 10 m VLC system indoors, and the receiver was arranged in such a way that it could be moved longitudinally and laterally on the track. The longitudinal distance was varied from 0 to 10 m. The lateral variation was varied from \(-\)20 cm to +20 cm. We ensured for a fixed transmitter that the receiver had enough receiving space.
# Particle Swarm Optimization of a Passivity-Based Controller for Dynamic Positioning of Ships
## 4 The PSO Method for DP of Ship
### An Overview of PSO Method  
Since its introduction in [41], particle swarm optimization (PSO) algorithm has received wide attention and has been widely used for various optimization applications. Inspired by the social and intelligent collective behavior of animals (such as insects, herds, birds and fishes) and the learning experiences they develop conforming cooperatively some ways in searching for food [42; 43]. The PSO design is a population-based stochastic optimization algorithm which has the advantages of simple implementation and simple integration with other optimization methods as well as its rapid convergence capability [43]. In this algorithm, a group of particles called population moves in the search-space looking for the optimal value of a performance index (fitness function). The position and the velocity of each particle is updated according to mathematical equations which are:  
\[x_{ij}^{n+1}=x_{ij}^{n}+v_{ij}^{n+1} \tag{19}\]  
\[v_{ij}^{n+1}=wv_{ij}^{n}+r_{1}c_{1}(p_{ij}^{n}-x_{ij}^{n})+r_{2}c_{2}(g_{j}^{n }-x_{ij}^{n}), \tag{20}\]  
where \(x_{ij}^{n}\) is the position of the particle \(i\) at the current iteration in the \(j\) dimension, \(x_{ij}^{n+1}\) is the position of the particle at the next iteration, \(v_{ij}^{n}\) is the velocity of the particle \(i\) at the current iteration, \(v_{ij}^{n+1}\) is the velocity of the next iteration, \(w\) is the inertia weight, \(c_{1}\) is the self-weight acceleration coefficient, \(c_{2}\) is the social-weight acceleration coefficient, \(r_{1}\) and \(r_{2}\) are uniformly distributed numbers, \(p_{ij}^{n}\) is the best previous position (with minimum fitness value) of the particle \(i\) in the \(j\) dimension until the current iteration \(n\) and \(g_{j}^{n}\) is the position of the best particle in the dimension \(j\) at the current iteration. The flow-chart of the PSO algorithm is illustrated in Figure 3.
# Time-Sensitive Networking Mechanism Aided by Multilevel Cyclic Queues in LEO Satellite Networks
## 3 LEO Satellite Network Management Architecture  
### Network Architecture  
For the management, operation, and maintenance of existing ultralarge-scale LEO satellite networks, we designed an LEO satellite network management architecture as shown in Figure 1. The overall network adopts a multilayered SDN architecture that divides the network into data and control planes. The data plane includes the terrestrial network and the LEO satellite network. Moreover, the LEO satellite network divides the satellites into multiple orbit planes (OPs) according to their orbits, and each plane has a satellite controller. ISL resources within each plane are stable due to the relatively stable link state within the LEO satellite orbit. A terrestrial controller mainly manages the terrestrial network, which manages the terminal users and gateway stations associated with satellite services, and interacts with the terrestrial control center. In addition, the terrestrial network includes the traditional IP bearer network whose network management is carried out independently. However, the IP bearer network and the satellite network can be accessed through and interconnected with satellite terminal users and gateway stations.  
To manage satellite resources, a robust control plane is required to interact with heterogeneous network nodes while satisfying the network state changes introduced by topological variation [23]. The control plane shown in Figure 1 contained two segments: the satellite controller and the terrestrial controller. The satellite controller needs to build an onboard cloud platform to manage heterogeneous virtual network elements to realize the network element management of large-scale dynamic topology. The fault manager, virtual network manager, and topology manager related to computing resources operate and maintain the satellite network characteristics through the onboard cloud platform. Inaddition, the satellite controller includes two main feature types, i.e., communication and computing features. Communication features include a protocol stack manager, routing manager, and time-sensitive manager, which can ensure traditional Ethernet service forwarding while effectively handling time-sensitive services. These features collaboratively control the communication resources of large-scale satellites. Computing features involve the computing-related management services of the onboard elements, which can manage and assign onboard tasks, and process the onboard computing services. At the same time, various resources of the satellite need to be managed to provide essential support for the onboard cloud platform. The terrestrial control is similar to the traditional SDN controller, with the difference that it needs to manage the access control of the satellite and the offload management of traffic flows. The control and data planes are interconnected through various southbound interfaces (SBIs). Different controllers collaborate with each other to manage a large-scale LEO satellite network.
## 3 Establishing the Proposed Approach
### Stiffness Matrix Establishment  
The stiffness matrix of a pin-pin-element based on the presented approach can be presented as follows.  
The geometry of the member before loading that shown in Figure 3a can be found as follows:  
\(h_{o}=x_{2}-x_{1}\) and \(v_{o}=y_{2}-y_{1}\) while \(L_{o}=\sqrt{{h_{o}}^{2}-{v_{o}}^{2}}\), where \(h_{o}\) and \(v_{o}\) are the horizontal and vertical distance between the two joints of the element, respectively, while \(L_{o}\) is the initial length of the member before loading. Moreover, the element stiffness matrix can be expressed as follows.  
The stiffness matrix for the member before loading can be written as  
\[k_{o}=\frac{EA}{L_{o}}\left[\begin{array}{cccc}c^{2}&cs&-c^{2}&-cs\\ cs&s^{2}&-cs&-s^{2}\\ -c^{2}&-cs&c^{2}&cs\\ -cs&-s^{2}&cs&s^{2}\end{array}\right]\]  
where, \(c\) and \(s\) are cosine and sine of the oriented angle of the element and they can be found as \(c=\frac{h_{o}}{L_{o}}\) and \(s=\frac{v_{o}}{L_{o}}\), respectively.  
After applying Equations (3)-(9), the geometry of the element will be changed as presented in Figure 3b and the procedure will be as follows:  
Figure 3: The geometry of a hinged member (**a**) before and (**b**) after loading in step (i).  
Figure 2: The flow chart of the algorithm of the current technique.  
\[h_{i}=(x_{2}+d_{x2})-(x_{1}+d_{x1})\text{ and }v_{i}=(y_{2}+d_{y2})-(y_{1}+d_{y1})\text{ while }L_{i}=\sqrt{h_{i} {}^{2}-v_{i}{}^{2}}\]  
The stiffness matrix for member 1 can be written as \(k_{i}=\frac{EA}{L_{i}}\begin{bmatrix}c^{2}&cs&-c^{2}&-cs\\ cs&s^{2}&-cs&-s^{2}\\ -c^{2}&-cs&c^{2}&cs\\ -cs&-s^{2}&cs&s^{2}\end{bmatrix}\) Here, \(c=\frac{h_{i}}{L_{i}}\) and \(s=\frac{v_{i}}{L_{i}}\).
## 2 System Model
### Channel Model  
Consider a downlink cellular system that consists of \(C\) cells. Each cell consists of one base station and \(M\) users that are randomly distributed. Each base station has transmitter antennas, \(N_{t}\), and each user has receiver antennas, \(N_{r}\). Figure 1 shows an example of a \(C=7\) cellular network for a downlink JP-CoMP system. This illustration also provides an example of how the base station clusters transmit data to the selected user in the cluster. Each base station will exchange data and CSI with the cooperating base stations in order to create a cell cluster. The cooperating base stations that act as a cluster member will transmit data to all users in the cluster.  
Figure 1: Downlink JP-CoMP configuration system with seven cells.  
The transmission medium conducted from the base station to the users can be represented by a channel. Suppose that a base station in the \(i\)th cell will transmit data to users in the \(g\)th cell with a single antenna. The relationship between them can be represented by channel \(\mathbf{H}_{ig}\). Channel \(\mathbf{H}_{ig}\) can be defined, as follows,  
\[\mathbf{H}_{ig}=[\mathbf{h}_{i1}{}^{T}\ \ \mathbf{h}_{i2}{}^{T}\ \...\ \ \mathbf{h}_{ig}{}^{T}], \tag{1}\]  
where \(\mathbf{h}_{ig}\in\mathbb{C}^{1\times N_{t}}\) is a channel vector between a base station in the \(i\)th cell and the selected user in the \(g\)th cell. In addition, \(\mathbf{H}_{ig}\in\mathbb{C}^{M\times N_{t}}\) represents the channel matrix between a base station in the \(i\)th cell and all users in the \(g\)th cell. The channel will become an important parameter in the JP-CoMP.
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 3 Threats and Challenges in Underwater Wireless Sensor Networks (UWSNs)
### Replay Attack  
In a replay attack, the attacker (A) intercepts a message sent by sender (S) to receiver (R), after a short delay, then resends it to R. Even though the packet is received, but the arrival time at R is changed due to the delay by A. As a consequence, the fake propagation time and signal strength result in R receiving imprecise locations and distance between them, which is usually estimated according to signal arrival time or difference in signal strength. Authentication and anti-replay techniques can protect against a replay attack.  
In [28], an underwater media access control protocol based on the RTS/CTS mechanism with a cipher block chaining-message authentication code is proposed to provide data confidentiality, authenticity, and replay attack protection. In [29], a secure MAC protocol for cluster-based UWSNs is proposed to ensure the security of data transmission. A random nonce is used to prevent a replay attack, which is updated each time after the message was transmitted. Therefore, the attacker cannot pass the authentication even if the attacker had made a copy of the previous message.
## 2 Materials and Methods
### Experimental Set-Up  
For each subject, upper and lower dental impressions were taken and dental casts were obtained. A resin model of the vestibular inferior arch was designed to fit the inferior dental arch. Two copies of a rigid splint were designed using Catia v. R2019x (IBM, New York, NY, USA) and 3D-printed in resin for each volunteer from dental cast to fit the volunteer's buccal dental anatomy in a stable position (3D-Printed dental adapter) (Figure 1).  
Four retro-reflective spherical markers (diameter 10 mm) were attached with a distance of 30 mm from each other to a standardized external support. The external support was then connected by a metal connector to the 3D-printed dental adapter to form the Mandibular Tracking Support (MTS). Upon mounting of the MTS, the metal connector was scanned with a professional 3D structured-light scanner (inEos X5, Sirona, Charlotte, NC, USA) in order to determine the distance between the markers and the dental arch [21; 22]. This geometrical relationship was used to derive the position of the teeth in the data analysis. Four additional retro-reflective markers were located on a standardized 3D-printed external set-up on the glabella (Cranial Tracking Support, CTS), well fixed to the head (Figure 2). The cranial support CTS defined a cranial plane of reference. The mandibular support MTS defined the movement plane and its movements were described relative to the cranial plane of reference.  
Movements were recorded using a portable optoelectronic three-dimensional motion tracking system operating at 60 Hz (Northern Digital Inc., Polaris Spectra, Waterloo, ON, Canada). Briefly, the tracking device consists of two high-resolution infrared-sensitive charge-coupled-device video cameras coupled with a video processor with a working volume of 1500 \(\times\) 1300 \(\times\) 1450 mm\({}^{3}\) (W \(\times\) H \(\times\) L). A built-in software able to recognize the three-dimensional coordinates of the geometric center of up to eight passive markers during movement acquisition converted all the landmarks of each captured frame into a set of x, y, and z spatial coordinates. The tracking device was placed on a stable tripod at a standard distance of 1.5 m from the location of the markers. Its orientation was set with a double spirit level and controlled between measurements. All metal items were removed from the field of view (FOV) of the device, including personal objects worn by the volunteers during the data acquisition (i.e., earrings or glasses).  
Figure 1: Dental resin model designed to fit the patient’s inferior dental arch anatomy without interfering with occlusion. (**a**) Top view; (**b**) Detail of the connector for the standardized external support carrying the markers; (**c**) side view of the object in patient’s mouth.
## 4 Results and Discussion
### Monitoring Results
#### 4.1.4 Double Layer Capacitance (C\({}_{\rm DL}\))  
Figure 8 indicates the double layer capacitance values obtained during the 4-month monitoring period. These data were acquired with the new protocol implemented in the INESSCOM monitoring system based on the analysis of the sensor's response to CSV-type disturbance.  
Double layer capacitance repr  
Figure 8: Double layer capacitance monitoring: (**A**) the column free of aggressive agents; (**B**) the column with the chloride-contaminated lower zone; (**C**) the column with the carbonated lower zone.  
LZ (chloride-contaminated concrete) obtained levels that were between two- and three-fold higher. Although values between 1500 and 2000 \(\upmu\)F/cm\({}^{2}\) were obtained on the first few days, they became stable as of day 40 and then remained constant until the end of the study within the 650-900 \(\upmu\)F/cm\({}^{2}\) range. Moreover, the sensors in the UZ (passive rebars) behaved similarly to what was found in column A, with values between 200 and 600 \(\upmu\)F/cm\({}^{2}\).  
Despite the LZ of column C being carbonated, no differences were found in the results obtained for the sensors in both the LZ and UZ (values ranged between 100 and 900 \(\upmu\)F/cm\({}^{2}\)). The fact that there were no differences in this parameter between the rebars with high corrosion levels and the passive rebars is aligned with corrosion type. The rebars affected by the carbonation front corroded in a uniform manner, which led to the formation of a layer of oxides on its surface. Likewise, when rebars maintained the passive layer that protected metal from corrosion, a layer of oxides also formed and covered the entire surface. This phenomenon has also been observed by other authors [55; 66; 67] and has been proven in former studies [33].
## 2 Materials and Methods
### Quantitative Evaluations of Image Quality  
Parameters that confirmed the spatial resolution and similarity were used as quantitative evaluation methods to evaluate the usefulness of the proposed algorithm. In the simulation study, the spatial resolution was analyzed by deriving the FWHM value through intensity profile acquisition, and the similarity was evaluated using the PSNR and SSIM evaluation parameters. We attempted to prove the usefulness of the proposed algorithm by analyzing the PSNR and SSIM of real clinical ultrasound images. The formulas for calculating PSNR and SSIM are as follows:  
\[PSNR=10\times\log\left(\frac{MAX_{I}^{2}}{MSE}\right) \tag{4}\]  
\[\text{SSIM}(x,y)=\frac{\left(2\mu_{x}\mu_{y}+C_{1}\right)\left(2\sigma_{xy}+C_{ 2}\right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+C_{1}\right)\left(\sigma_{x}^{2}+ \sigma_{y}^{2}+C_{2}\right)} \tag{5}\]  
where \(MAX_{I}\) is the maximum pixel value, \(MSE\) is the mean squared error, \(\mu_{x}\), \(\mu_{y}\) are the average luminance values of each image, respectively; \(\sigma_{x}\), \(\sigma_{y}\) are the standard deviations of each image, respectively; and \(\sigma_{xy}\) is the covariance between the two images.