esa-sceva/satcom-chunk-collection huggingface dataset:
# Area Division Using Affinity Propagation for Multi-Robot Coverage Path Planning
## 2 Literature Review
The strengths of this approach encompass handling the weight allocation problem, thereby enhancing coverage performance, and the ability to manage large-scale scenarios. Despite its improvements over the original DARP algorithm, it retains the limitation of being unable to find a solution when the initial positions of the robots are proximate. Another DARP-based algorithm was proposed in [25]. The authors presented an A*-modified DARP algorithm. This modified version of the DARP algorithm assigns tasks to the appropriate robot and based on an Up-First approach the Spanning Trees are constructed in order to ensued full coverage of the initial area. The authors claim that, compared to the original DARP algorithm, their modifications yield higher efficiency and a higher coverage rate. A different approach for multi-robot CPP was proposed in [26] that uses Ant Colony Optimization. The authors introduced an improved Ant Colony Optimization (ACO) algorithm for single-robot CPP, which optimizes the energy and time consumption by building the best possible Spanning Tree (ST) and, consequently, an optimal path. For multi-robot scenarios, the study employed the DARP algorithm [18], dividing the total area into smaller, equally sized sections, thus simplifying the complex computation. Each subarea then constructs a spanning tree using the improved ACO. In the final stage, the end nodes are shared among subareas to develop ideally-shaped spanning trees that minimize the number of turns in the coverage path. The algorithms are proven to be near polynomial, and simulation results highlight benefits including complete coverage, no backtracks, minimum path length, zero preparation time, and the least number of turns. However, the implementation of this approach still suffers from the drawbacks of the DARP algorithm, meaning the arbitrary choice of robot's initial positions. Given the literature review, it is evident that multi-robot CPP has garnered considerable attention from researchers. However, a direct evaluation and comparison of these research works is challenging as each paper addresses a subtly different problem. A commonality that most offline multi-robot CPP algorithms share, including those mentioned above, is their reliance on arbitrarily defining the number of robots and sensitivity to the initial environmental conditions. Further research is imperative to devise new multi-robot CPP algorithms that are devoid of these limitations, are more efficient, and tailored for real-world applications. The Affinity Propagation algorithm proposed in this paper demonstrates the potential to address these issues, and thus presents a significant advancement in this field.
## 3 Results and Discussion
### Theoretical TS using the DWBA Model
The lowest TS value was on the anterior (\(0^{\circ}\)) and posterior (\(180^{\circ}\)) from both species. The zooplankton theoretical TS value from different acoustic incidence angle measurements proved that acoustic measurements using upward-looking and downward-looking methods were not different in acoustic backscatter response for zooplankton studies, with the highest possible TS values obtained from individual zooplankton. In future studies, direct measurement of TS, density contrast, and sound speed contrast should be conducted to characterize the source of the MVBS recorded by the ADCP instruments associated with DVM. By using two frequency in ADCP instruments, the theoretical TS from each species was used to differentiate between general classes of scatterers. The relationship between the TS and MVBS was \(\text{MVBS}_{\text{freq}}=~{}10\log\rho\text{-TS}_{\text{freq}}\), while when we compared the MVBS for each frequency, the equation \(\text{dB}_{\text{diff}}=\text{MVBS}_{\text{750 kHz}}-\text{MVBS}_{\text{307.2 kHz}}=\text{TS}_{\text{750 kHz}}-\text{TS}_{\text{307.2 kHz}}\). This assumption of the MVBS and TS was used to estimate the major scatterers in the water column of the observed area. So, based on the equation, \(\text{TS}_{\text{750 kHz}}-\text{TS}_{\text{307.2 kHz}}\) for each dominant zooplankton was around \(15\) dB, while \(\text{MVBS}_{\text{750 kHz}}-\text{MVBS}_{\text{307.2 kHz}}\) was \(5.64\) dB. If a single zooplankton species was assumed to dominate the MVBS in the SSL, then \(\Delta\text{MVBS}750\)-\(307.2\) in the SSL was equal to \(\Delta\text{TS}750\)-\(307.2\) of the scatterers [57]. The dominant zooplankton species was not the main scatterers based on the \(\Delta\text{MVBS}\) and \(\Delta\text{TS}\). Instead, it was assumed that the strong signal was caused by all zooplankton species found in the waters which aggregated into schools. This study did not directly measure zooplankton TS at measured conditions in the laboratory against acoustic angle variations due to equipment limitations. Further studies involving more sample collections every day are needed, especially during the night and daylight.
## 2 Materials and Methods
### Mathematical Model
#### 2.3.2 Statistical Parameters  
Several metrics, each with its own strengths and limitations, can be used to compare the performance of various AI models. The following are some common metrics:  
* Mean absolute error (MAE) is a measure that captures the average absolute disparity between predicted and true values. By focusing solely on the magnitude of the error, irrespective of its direction, it provides an evaluation of the model's effectiveness in accurately forecasting the actual values.
* Root mean square error (RMSE) is a performance metric like MAE, but it considers the square of the errors, thus placing more penalty on larger discrepancies. RMSE is typically employed when substantial errors pose a greater problem than minor ones.
* Root mean square log error (RMSLE) is a useful metric when dealing with a target variable that spans a broad range of values. It employs the logarithms of both predicted and actual values, which lessens the effect of substantial discrepancies between these values. When the distribution of the target variable is skewed, employing this metric can be particularly beneficial.  
R-squared (R\({}^{2}\)) is a statistical measure that measures the degree to which the model matches the data, relative to a simple, baseline model. R\({}^{2}\) values can range from 0 to 1, with higher values signifying a better fit. However, it is important to note that R\({}^{2}\) can provide a skewed perspective if the underlying baseline model is unfitting, or the data are contaminated with outliers. Equations (2)-(5) show these metrics:  
\[MAE=\frac{\sum_{N}\left(X_{m}-X_{p}\right)}{N} \tag{2}\]  
\[RMSE=\sqrt{\frac{\sum_{N}\left(X_{m}-X_{p}\right)^{2}}{N}} \tag{3}\]  
\[RMSE=\sqrt{\frac{\sum_{N}\left(log\left(X_{m}+1\right)-log\left(X_{p}+1\right) \right)^{2}}{N}} \tag{4}\]  
\[R^{2}=\left[\frac{\sum_{i=1}^{N}\left(X_{m}-\overline{X_{m}}\right)\left(X_{p} -\overline{X_{p}}\right)}{\sum_{i=1}^{N}\left(X_{m}-\overline{X_{m}}\right)^{ 2}\sum_{i=1}^{n}\left(X_{p}-\overline{X_{p}}\right)^{2}}\right]^{2} \tag{5}\]  
where \(N\) is the number of datasets, \(X_{m}\) and \(X_{p}\) are actual and predicted values, and \(\overline{X_{m}}\), \(\overline{X_{p}}\) are the average of actual and predicted values, respectively. The model should ideally have an \(R^{2}\) value of 1 and _MAE_, _RMSE_, and _RMSLE_ values of 0.
# Large-Scale Experimental Static Testing on 50-Year-Old Prestressed Concrete Bridge Girders
## 3 Loading Test Setup
### Instrumentation  
The sensors' layout was designed to measure key parameters representative of the structural response of the girder during the load test. Table 3 summarizes the details of the designed monitoring system, which consists of 69 devices for measurement of strains, displacements, accelerations, acoustic emissions and forces. In this paper, the results in terms of displacements, strains and forces are presented.  
The applied load was measured by load cells on the actuators to read the actual applied load and check the symmetric loading condition. The layout of the sensors for beam B3-P47/46 is reported in Figure 9. The linear variable displacement transducers (LVDTs) were mounted onto two different configurations of the aluminium frame with the aim of measuring the average strain referred to the measurement base: the LVDTs close to the supports are oriented at 45\({}^{\circ}\) with a base of 700 mm, whereas the LVDTs in the bending zone were installed with a horizontal direction around the midspan and have a base of 400 mm. The test setup for the first girder B3-P47/6 is shown in Figure 9.  
Figure 8: Three-point bending test configuration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Sensor** & **Location** & **No.** \\ \hline Deflection & Wire displ. transducer & D04-06 & 3 \\ \hline Deflection & Displ. potentiometer transducer & D01-03 D07-09 & 6 \\ \hline Displacement & Linear displ. transducer & V01-02 H01-02 & 4 \\ \hline Strand sliding & Linear displ. transducer & STR-01/02 & 2 \\ \hline Shear strain & Linear variable displ. Transducer 1 & 1-10 & 20 \\ \hline Bending strain & Linear variable displ. Transducer 1 & IT-7T & 11 \\ \hline Bending strain & Strain gauge & 2C-1T & 2 \\ \hline Acceleration & Accelerometer & 1-102 10Y & 10 \\ \hline Acoustic emission & PZT transducer & Midspan & 8 \\ \hline Load & Load cell & Hydraulic jacks & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristics of the sensors’ layout.  
From the second test on the girder B8-P47/46, three out of five pairs of LVDTs were moved close to the transverse beam to intercept a more pronounced cracking pattern evidenced during the first test. For test B9-47/46 (the beam without the cast in situ slab), six out of seven LVDTs were used to measure strains to the bottom flange in bending zone (1T-6T) and two LVDTs in top flange of precast beam (1C and 2C) were moved to the same vertical alignment of 1T and 6T (see Figure 9). Indeed, two strain gauges were used to compare LVDTs measurements in 2C and 1T position. For all the girders, the vertical deflection was measured by nine displacement potentiometer transducers connected to the bottom of the girders (see from D01 to D09 in Figure 9). Furthermore, displacement transducers were installed at the ends of the girders to measure vertical and horizontal displacements, settlement at the supports and the slipping of one unbonded strand at each end. During the tests, the data were collected at a 10 Hz sampling frequency and synchronized.
# A New Hybrid Positioning Method by Fusion of BDS and 5G Signal Using the Particle Swarm Method
## 2 System Model
For TDOA receivers, we can measure the difference between the transmission time of the ranging signal between the receiver and the two base stations. Assuming that the transmission time of the ranging signal between the receiver and base station is \(t\), we have  
\[d_{k}-d_{1}=c(t_{k}-t_{1})=c\Delta t_{k} \tag{3}\]  
where \(c\) is the signal propagation speed 299,792,458 m/s, and \(\Delta t_{k}\) is the time difference between the time from the 5G base station signal to the receiver measured for the kth measurement and the time between the 5G base station signal and the receiver measured for the first time. From Formula (2), we obtain:  
\[\left\{ \begin{aligned} 0=\sqrt{\left(x_{b1}-x\right)^{2}+ \left(y_{b1}-y\right)^{2}+\left(z_{b1}-z\right)^{2}}-d_{1}\\ c\Delta t_{2}&=\sqrt{\left(x_{b2}-x\right)^{2}+\left(y_{ b2}-y\right)^{2}+\left(z_{b2}-z\right)^{2}}-d_{1}\\ c\Delta t_{3}&=\sqrt{\left(x_{b3}-x\right)^{2}+\left(y_{ b3}-y\right)^{2}+\left(z_{b3}-z\right)^{2}}-d_{1}\\ &\vdots\\ c\Delta t_{n}&=\sqrt{\left(x_{bn}-x\right)^{2}+\left(y_{ bn}-y\right)^{2}+\left(z_{bn}-z\right)^{2}}-d_{1}\end{aligned} \right. \tag{4}\]Simultaneous Equations (1) and (4) can be obtained:  
\[\left\{ \begin{array}{l}\rho_{1}=\sqrt{\left(x_{s1}-x\right)^{2}+\left(y_{s1}-y \right)^{2}+\left(z_{s1}-z\right)^{2}}+\delta\\ \rho_{2}=\sqrt{\left(x_{s2}-x\right)^{2}+\left(y_{s2}-y\right)^{2}+\left(z_{s2 }-z\right)^{2}}+\delta\\ \rho_{3}=\sqrt{\left(x_{s3}-x\right)^{2}+\left(y_{s3}-y\right)^{2}+\left(z_{s3 }-z\right)^{2}}+\delta\\ \vdots\\ \rho_{n}=\sqrt{\left(x_{sn}-x\right)^{2}+\left(y_{sn}-y\right)^{2}+\left(z_{ sn}-z\right)^{2}}+\delta\\ 0=\sqrt{\left(x_{b1}-x\right)^{2}+\left(y_{b1}-y\right)^{2}+\left(z_{b1}-z \right)^{2}}-d_{1}\\ c\Omega_{2}=\sqrt{\left(x_{b2}-x\right)^{2}+\left(y_{b2}-y\right)^{2}+\left(z_ {b2}-z\right)^{2}}-d_{1}\\ c\Omega_{3}=\sqrt{\left(x_{b3}-x\right)^{2}+\left(y_{b3}-y\right)^{2}+\left(z _{b3}-z\right)^{2}}-d_{1}\\ \vdots\\ c\Omega_{n}=\sqrt{\left(x_{bn}-x\right)^{2}+\left(y_{bn}-y\right)^{2}+\left(z _{bn}-z\right)^{2}}-d_{1}\end{array} \right. \tag{5}\]  
multiple measurements can solve the above equation to obtain accurate positioning coordinates, and due to the advantages of the particle filter algorithm in solving nonlinear equations, the particle filter (PF) is used to calculate it, which is an approximate Bayesian filtering algorithm based on Monte Carlo simulation. Its core idea is to use some discrete random sample points (particles) to approximate the probability density function of the system random variables and replace the integral operation with the sample mean to obtain the minimum variance estimation of the state.
# An Efficient and Secure Certificateless Aggregate Signature Scheme for Vehicular Ad hoc Networks
###### Abstract  
Vehicular ad hoc networks (VANETs) have become an essential part of the intelligent transportation system because they provide secure communication among vehicles, enhance vehicle safety, and improve the driving experience. However, due to the openness and vulnerability of wireless networks, the participating vehicles in a VANET system are prone to a variety of cyberattacks. To secure the privacy of vehicles and assure the authenticity, integrity, and nonrepudiation of messages, numerous signature schemes have been employed in the literature on VANETs. The majority of these solutions, however, are either not fully secured or entail high computational costs. To address the above issues and to enable secure communication between the vehicle and the roadside unit (RSU), we propose a certificateless aggregate signature (CLAS) scheme based on hyperelliptic curve cryptography (HECC). This scheme enables participating vehicles to share their identities with trusted authorities via an open wireless channel without revealing their identities to unauthorized participants. Another advantage of this approach is its capacity to release the partial private key to participating devices via an open wireless channel while keeping its identity secret from any other third parties. A provable security analysis through the random oracle model (ROM), which relies on the hyperelliptic curve discrete logarithm problem, is performed, and we have proven that the proposed scheme is unforgeable against Type 1 (\(FGR_{1}\)) and Type 2 (\(FGR_{2}\)) forgers. The proposed scheme is compared with relevant schemes in terms of computational cost and communication overhead, and the results demonstrate that the proposed scheme is more efficient than the existing schemes in maintaining high-security levels.  
vehicular ad hoc network; security; certificateless aggregate signature; hyperelliptic curve cryptography +
Footnote †: journal: Future Internet  
0203 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# AR-Assisted Guidance for Assembly and Maintenance of Avionics Equipment
## 2 Related works  
### Object 3D Tracking Algorithm  
One of the core techniques of AR-assisted guidance method is stable, accurate, and efficient virtual-real fusion [10]. The key to virtual-real fusion is to track the correct poses of assembly parts, which allows the display of virtual assembly guidance information, such as the assembly docking site and visual blind areas of the assembly parts.  
Region-based methods use image statistics to model the probability that a pixel belongs to an object or background areas in the environment [11]. And the pose and the corresponding contour of the object that best fit the image segmentation can be found, and a change in the pose of the object can be tracked.  
The region-based method allows robust tracking in many challenging scenarios. Such methods typically extract the contour as an initial step, followed by optimization to find the optimal pose [12]. [PERSON] [13] developed PWP3D, a real-time algorithm that uses level set pose embedding, which laid the foundation for a region-based method. On the basis of PWP3D, many algorithms have been proposed to add additional information, extend the segmentation model, or improve the efficiency of segmentation [14]. To combine depth-based and region-based information, [PERSON] [15] extended the energy function of PWP3D using the iterative closest point (ICP) algorithm. [PERSON] [16] tightly coupled region and depth information in a probability formula using a 3D symbolic distance function. Then, [PERSON] [17] developed adaptive weighted region binding and local constraints on edge information to improve the robustness of 3D target tracking methods in complex situations. [PERSON] proposed SRT3D [18], ICG [19], and other methods to further accelerate the speed and robustness.  
In order to improve the robustness of partial occlusion, [PERSON] [20] designed a local area definition strategy and an occlusion detection strategy based on a pixel weighting function. [PERSON] [21] matched the affinity scores calculated for local feature embeddings and motion descriptors of tracking objects, improving the robustness of tracking in cases of occlusion and omission.  
While deep learning has proven to be very successful in 6 DoF pose estimation, pure tracking methods are also evolving rapidly. Many tracking methods are inspired by pose refinement and predict the relative pose between an object's rendering and subsequent images [22]. For example, se(3)-TrackNet [23] proposed a neural network framework to identify the best relative pose of an object in a case driven by synthetic image data. PoseRBPF [24] employed a rao-blackwelzed particle filter for a potential code with pose representation. And 6-Pack [25] tracked key points based on anchor points. [PERSON] [26] employed a learning-based active contour model to improve the accuracy of tracking. However, it is difficult for the learning-based method to meet the computing speed and hardware configuration requirements of mobile devices. To be used in AR devices, more region-based and image processing methods are being considered at present.
# QoS Enabled Layered Based Clustering for Reactive Flooding in the Internet of Things
## 1 Introduction
The proposed technique is an interoperable solution both for physical layer and network layer devices. CBF divides the network into different clusters, local queries are proactively maintained by the intralayer clustering (IALC), while interlayer clustering (IELC) is responsible for reactively obtaining the routing queries to the destinations outside the cluster. CBF is a hybrid approach, having the potential to be more efficient against traditional schemes in terms of query traffic generation. However, in the absence of appropriate redundant query detection and termination techniques, the CBF may generate more control traffic compared to standard flooding techniques. Interlayer clustering (IELC), composed of an advance query detection and termination technique (QCM), uses link signal strength and a query limit value for detecting flooding and it is capable of minimizing the energy consumption, network flooding, identifying, and eliminating unwanted and redundant flooded queries in IoT networks. A QoS-enabled QCM model is developed and the results of the simulation show the superior performance against state of the art approaches in term of traffic delay, QoS throughput, and energy consumption, under various performance metrics compared with traditional flooding and is state of the art. In order to determine real understanding of flooding in IoT networks we provide modeling of the redundant queries which leads to flooding in Figure 2.  
Figure 2: Flooding as a result of redundant queries. Following is the organized remaining of the paper: Some state of the art related works are outlined in Section 2 on quality of service, energy consumption, searching, flooding, and redundant queries in IoT networks. The network model and problem solution are mainly defined in Sections 3 and 4. Moreover, Section 4 also explains a cluster based flooding strategy their principles in detail. In Section 5, we design the algorithm for our CBF strategy. Section 6 provides the experimental results and compares our strategy with the state of the art strategy. Finally, in Section 7 we conclude the paper.
## 4 Our DRL-JCBRTP Framework
### System Model  
In this section, we evolve the system model of the MAB\({}^{2}\) scheme in [17] for integration with our DRL framework. Basically, our system model can interact with multiple access-layer technologies, for example, the DSRC/WAVE protocol suite [22; 23; 24] and/or cellular V2X technologies [25; 26; 27]. This is because the beaconing agent determines the high-level parameters of the access layer, such as the rate of the beacon broadcast and the TP to cover a given transmission range \(D_{T}\), that can be adapted to the underlying access technologies. In our model, time is segmented into a series of frames \(\{n\}\) with duration \(T\). It is assumed that vehicle kinematics, including position and speed at frame \(n\), are sampled at the beginning of each frame, marked as \(t=nT\). This beacon information is subsequently broadcast to neighbor vehicles during the frame period, \(t\in[nT,(n+1)T)\).  
Figure 4 shows the architecture of a beaconing agent in an MDP framework. Due to a time-varying and lossy wireless channel, a neighbor vehicle may not receive the latest beacons of the sending vehicle. If none of the past beacons from the sending vehicle is successfully received by a neighbor vehicle within \(D_{T}\), the sending vehicle becomes a ghost vehicle (detection failure) in the V2X communication framework, as shown in Figure 3b,c. On the other hand, if at least one beacon is successfully received by a neighbor vehicle, it belongs to one of the \((K+1)\) vehicle groups \(\mathcal{V}=\{\mathcal{V}_{1},~{}\mathcal{V}_{2},~{}\cdots,~{}\mathcal{V}_{K },~{}\mathcal{V}_{K+}\}\). Vehicle group \(\mathcal{V}_{k}\) represents a subset of neighbor vehicles whose latest beacon is the \(k\)-th previous beacon  
Figure 3: Awareness of ego vehicle (green) at three timepoints (\(t_{1}<t_{2}<t_{3}\)).  
of the sending vehicle. In our DRL-JCBRTP framework, we consider the awareness of a neighbor vehicle in the first \(K\) vehicle groups \(\mathcal{V}_{G}=\mathcal{V}-\mathcal{V}_{K+}\) only, excluding vehicle group \(\mathcal{V}_{K+}\) due to its severe wireless channel conditions. \(K\) constant-speed position estimators are assumed to be used as the remote kinematics estimators that predict the current position of each sending vehicle in \(\mathcal{V}_{G}\). Then, the _tracking error (TE)_ of a neighbor vehicle in \(\mathcal{V}_{k}\) is formally defined as the Euclidean distance from the actual position of the sending vehicle to the position of estimator \(k\). If the TE of a neighbor vehicle exceeds a tracking threshold \(E_{T}\), then its kinematics estimation is considered a _tracking failure_ (See Figure 3c).