esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Window Decoding for Joint Source Channel Anytime Coding System
## 2 Preliminaries
### Decoding Algorithms of SC-RA-Based JSCAC System  
The red dashed rectangles in Figure 1 indicate the decoding windows expanding in size as decoding delay increases. The joint BP decoding for D-LDPC based JSCC [10] is implemented in each decoding window, which performs the source BP decoder and channel BP decoder in parallel, exchanging the mutual information through the public edges (red dashed lines in Figure 1). See [10] for more details of joint BP. This classical decoding method for JSCAC systems is the joint expanding window decoding (JEWD) [17], which directly implements the joint BP decoding in the expanding window with high complexities. Authors in [17] proposed a partial joint expanding window decoding (PJEWD) based on the observation of convergence inconsistency between the source and channel decoders, i.e., stop message updating for all channel nodes once the channel decoding convergence is detected. Compared with the JEWD algorithm, the PJEWD algorithm can improve the error performance. However, the reduction ratio of decoding complexity is not significant, as it is affected by the channel conditions.
## 2 Materials and Methods
An example build of one set of the 8 designs is given in Figure 5. After the PBF process, the samples were removed from the base plate via wire Electro Discharge Machining (EDM) using a V650G (Excetek Technologies Co. Ltd., Taichung City, Taiwan). The theoretical density of the structures was also calculated from the CAD data in SolidWorks CAD software (Dassault Systemes, Coventry, UK), using the CAD volume for each structure for comparison. Sample 3 and 4 were selected for metallurgical analysis as these samples contain a 45\({}^{\circ}\) re-entrant angle, which poses the largest risk for build failure and strut deformation, as the oriented struts in the unit cell are at the shallowest angle with respect to build orientation (compared to samples with a 70\({}^{\circ}\) re-entrant angle). The samples were cut along the cross-sectional and longitudinal axes using an AbrasiMet 250 manual abrasive cutter (Buehler UK, Coventry, UK). The samples were hot mounted in EpoMetTM G (Buehler UK, Coventry, UK). The samples were then ground and polished using standard metallurgical procedure using an Eco Met 250 Pro (Buehler UK, Coventry, UK). The samples were finished with colloidal silica polish (MasterPolish 0.06 um, (Buehler UK, Coventry, UK)). The strut thickness and porosity of the sample struts were analysed using optical microscopy, using an Eclipse MA200 inverted materials optical microscope (Nikon Metrology, Castle Donnington, UK) at 5-100x magnification. Samples were images as manufactured with no post-processing or surface finishing. The measure of porosity was taken at the centre of the cross-sectional area of each strut, taken in the XZ plane (see Figure 3), as carried out by [PERSON] (2018). As the edges are not uniform, it is difficult to specify the measurement area if the whole cross-section is taken for examination. The internal porosity was measured for five vertical, five re-entrant 1 and 5 re-entrant struts for each sample. The porosity and circularity of the pores were extracted from the images using ImageJ J2 software. Strut thickness was measured using image analysis of the optical microscope images using ImageJ J2 software (ImageJ, Open source software) with 20 measurements taken across each strut to obtain average values. Compression testing was performed using a 5800R tensile test system with 100 kN load cell (Instron, High Wycombe, UK). Two smooth steel plates were used on either side of the auxetic part in order to reduce friction and to prevent any damage to the fixtures. Testing was performed at 25 \({}^{\circ}\)C using a cross-head speed of 1 mm/min. An ARAMIS non-contact measuring system (GOM UK Ltd, Coventry, UK) was used to capture the deformation of samples during compression using the digital image correlation (DIC) technique. The DIC data were also used to measure strut lengths and re-entrant angles; 5 measurements were taken per feature measurement. The test directions, X and Y, are shown in Figure 6.  
Figure 6: Uniaxial compression test directions.
# On Modeling Eavesdropping Attacks in Underwater Acoustic Sensor Networks +
## 4 Analysis of Eavesdropping Attacks in UASNs  
In our eavesdropping model, we consider two scenarios of UASNs according to the two different types of hydrophones: Scenario (i), in which every node is equipped with a single isotropic hydrophone; Scenario (ii), in which every node is equipped with an unshaded uniform linear array hydrophone. Scenario (i) and Scenario (ii) correspond to IUSNs and AUSNs, respectively. In addition, we consider that all the nodes are randomly distributed in a 2-D plane according to a homogeneous Poisson point process with density \(\rho\) in both IUSNs and AUSNs. Then, we consider two types of eavesdroppers: isotropic eavesdroppers and array eavesdroppers in both IUSNs and AUSNs. Recall that an isotropic eavesdropper is equipped with a single isotropic hydrophone and an array eavesdropper is equipped with an unshaded uniform linear array hydrophone. Thus, we investigate the impacts of different eavesdroppers on the eavesdropping probability of IUSNs and AUSNs, respectively.  
Before we analyze eavesdropping activities, we have to analyze link criteria in both IUSNs and AUSNs in Section 4.1 and then derive eavesdropping success condition in Section 4.2. We next derive the eavesdropping probability in Section 4.3.
# A Semantically Aware Multi-View 3D Reconstruction Method for Urban Applications
###### Abstract  
The task of 3D reconstruction of urban targets holds pivotal importance for various applications, including autonomous driving, digital twin technology, and urban planning and development. The intricate nature of urban landscapes presents substantial challenges in attaining 3D reconstructions with high precision. In this paper, we propose a semantically aware multi-view 3D reconstruction method for urban applications which incorporates semantic information into the technical 3D reconstruction. Our research primarily focuses on two major components: sparse reconstruction and dense reconstruction. For the sparse reconstruction process, we present a semantic consistency-based error filtering approach for feature matching. To address the challenge of errors introduced by the presence of numerous dynamic objects in an urban scene, which affects the Structure-from-Motion (SfM) process, we propose a computation strategy based on dynamic-static separation to effectively eliminate mismatches. For the dense reconstruction process, we present a semantic-based Semi-Global Matching (sSGM) method. This method leverages semantic consistency to assess depth continuity, thereby enhancing the cost function during depth estimation. The improved sSGM method not only significantly enhances the accuracy of reconstructing the edges of the targets but also yields a dense point cloud containing semantic information. Through validation using architectural datasets, the proposed method was found to increase the reconstruction accuracy by 32.79% compared to the original SGM, and by 63.06% compared to the PatchMatch method. Therefore, the proposed reconstruction method holds significant potential in urban applications.  
three-dimensional reconstruction; semantic segmentation; SfM; SGM +
Footnote †: journal: Medical Imaging
# A Systematic Survey of Multi-Factor Authentication for Cloud Infrastructure
## 1 Introduction
### Outline and Contribution of Work  
Biological characteristics provide uniqueness to the user. This could be used as a potential means to tackle a known threat to any authentication system called an impersonation attack, where a false user tries to act as a true user. Many surveys have been conducted covering factors of authentication. However, to the best of our knowledge and available literature, for the first time, this research work makes an effort to bring out the fact that, _with no additional requirements other than the factory fitted System Accessories_, biometric uniqueness of users can be used as a factor of authentication.  
The broad guiding factors considered for the progress of this work are listed below, which are derived from the technological shortfalls associated with the adoption of authentication factors towards MFA and which signify the notable contributions of this research:  
1. It best utilizes only the Original Equipment Manufacturer (OEM)-fitted hardware and software without requiring additional or specialized Hardware and Software.
2. Systematic analysis is conducted of various factors used for authentication in the light of multiple threats and vulnerabilities concerning user authentication for cloud environment.
3. Systematic analysis is also conducted of available methods and means to be used to identify and make the best use of the least complex solution or combination of solutions towards implementing an efficient MFA for a Multi-Cloud scenario.  
The organization of the rest of the paper is as follows: After introducing this topic in Section 1, Section 2 describes the aspects of the authentication process along with Cloud Authentication mechanisms. Section 3 dwells upon the various factors of authentication. Section 4 presents an extensive analysis of security aspects associated with authentication in light of facial recognition. Section 5 concludes the paper along with a brief insight into the future scope of this research work.
# PrECast: An Efficient Crypto-Free Solution for Broadcast-Based Attacks in IPv4 Networks
## 6 Performance Analysis of PrECast  
In this section, we compare the performance of PrECast against the conventional ARP protocol. First, we present the asymptotic analysis dealing with the message complexity and the convergence time. These two parameters are inter-related and important for the overall network performance. In the second part of this section, we analyse the performance of PrECast using simulation.
## 1 Introduction
### Notations  
We use capital letters such as \(X\) to denote random variables, lowercase letters such as \(x\) to denote their corresponding instances, and calligraphic letters such as \(\mathcal{X}\) to denote sets of alphabets. Let \(\text{Prob}(\mathcal{X})\) denote the set of probability measures defined on \(\mathcal{X}\), and let \(P_{X}(x),P_{XY}(x,y)\), and \(P_{Y|X}(y|x)\) denote the probability distribution of \(X\), the joint distribution, and the conditional distribution of \(Y\) given \(X=x\), respectively. We omit the arguments of the distributions when doing so does not cause ambiguity. The \(n\)-sequence \((X_{1},X_{2},\dots,X_{n})\) is denoted by \(X^{n}\). Suppose that \(\{X_{i}\}_{i=1}^{\infty}\) and \(\{Y_{i}\}_{i=1}^{\infty}\) are memoryless sources; then, \(P_{X^{n}Y^{n}}=\prod_{i=1}^{n}P_{XY}=\otimes_{i=1}^{n}P_{X_{i}Y_{i}}\). We use \([m]\) to denote the set \(\{1,2,\dots,\lfloor m\rfloor\}\) for \(m>0\), and we use \(\mathcal{C}\) to denote the codebook. For sequences \((x^{n},y^{n})\in\mathcal{X}^{n}\times\mathcal{Y}^{n}\), the empirical distribution is defined as  
\[\hat{P}_{x^{n},y^{n}}(x,y)=\frac{1}{n}\sum_{i=1}^{n}\mathbf{1}\{(x_{i},y_{i}) =(x,y)\}. \tag{1}\]  
Extensions to multiple arguments can be defined similarly. Throughout the paper, we use \(\Delta(\cdot,\cdot):\mathcal{X}^{n}\times\mathcal{X}^{n}\mapsto\mathbb{R}\) to denote an additive distortion measure, and we use the calligraphic letter \(\mathcal{D}(\cdot,\cdot):\text{Prob}(\mathcal{X}^{n})\times\text{Prob}( \mathcal{X}^{n})\mapsto\mathbb{R}\) to denote the measure of perceptual fidelity. We define \(\overline{\mathbb{R}}_{\geq 0}:=\mathbb{R}_{\geq 0}\cup\{+\infty\}\).
## Appendix A  
In this appendix, the methodology to obtain the statistics for each KPI is detailed. The calculations have been performed using the statistical software for data science STATA ([[https://www.stata.com/](https://www.stata.com/)]([https://www.stata.com/](https://www.stata.com/))). To generate the KPIs, the minimum KPI values recommended by the manufacturer HUAWEI [35] for an acceptable network performance and a satisfactory quality of service perceived by the user are assumed. Considering these minimum values, we generate a Poisson distribution of lambda value 1 for all KPIs (except for Ec/N0 and RSCP), as it is the distribution that best resembles the characteristic distribution in these cases. The goal is that the probability of having better values is lower as we approach the theoretical maximums. For Ec/N0 and RSCP a Gaussian distribution is generated, with a mean given by the values provided in [35]. Table A1 collects the statistics used when generating the variables (KPIs) for each cluster according to the above methodology.  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Cluster** & **KPI** & **Cells** & **Mean** & **Minimum** & **Maximum** \\ \hline \multirow{8}{*}{**1: urban macro**} & CSSR PS & 5000 & 0.991 & 0.661 & 1.000 \\  & CSSR CS & 5000 & 0.998 & 0.808 & 1.000 \\  & DROP HSPA & 5000 & 0.011 & 0.000 & 1.000 \\  & DROP PS & 5000 & 0.010 & 0.000 & 0.375 \\  & DROP CS & 5000 & 0.008 & 0.000 & 0.500 \\  & TIRROUGHPUT & 500 & 1629.887 & 0.000 & 9642.500 \\  & INTRA-HO & 5000 & 0.999 & 0.750 & 1.000 \\  & INTRA-HO & 5000 & 0.968 & 0.067 & 1.000 \\  & Ec/N0 & 5000 & \(-\)317.28 & \(-\)26.403 & \(-\)6.227 \\  & RSCP & 5000 & \(-\)89.939 & \(-\)106.000 & \(-\)57.830 \\ \hline \multirow{8}{*}{**2: suburban**} & CSSR PS & 500 & 0.988 & 0.785 & 1.000 \\  & CSSR CS & 500 & 0.998 & 0.947 & 1.000 \\  & DROP HSPPA & 500 & 0.007 & 0.000 & 0.141 \\  & DROP R99 & 500 & 0.009 & 0.000 & 0.200 \\  & DROP CS & 500 & 0.008 & 0.000 & 0.308 \\  & THROUGHPUT & 500 & 1723.808 & 0.000 & 5709.760 \\  & INTRA-HO & 500 & 0.999 & 0.978 & 1.000 \\  & INTER-HO & 500 & 0.976 & 0.500 & 1.000 \\  & INTRA-RAT HO & 500 & 0.947 & 0.500 & 1.000 \\  & Ec/N0 & 500 & \(-\)9.658 & \(-\)18.860 & \(-\)5.840 \\  & RSCP & 500 & \(-\)86.942 & \(-\)101.65 & \(-\)66.661 \\ \hline \multirow{8}{*}{**3: urban micro**} & CSSR PS & 2000 & 0.995 & 0.800 & 1.000 \\  & CSSR CS & 2000 & 0.999 & 0.877 & 1.000 \\  & DROP HSPA & 2000 & 0.007 & 0.000 & 0.237 \\  & DROP R99 & 2000 & 0.005 & 0.000 & 0.089 \\  & DROP CS & 2000 & 0.008 & 0.000 & 0.250 \\  & THROUGHPUT & 2000 & 1807.736 & 198.490 & 11167.780 \\  & INTRA-HO & 2000 & 1.000 & 0.979 & 1.000 \\  & INTRA-HO & 2000 & 0.989 & 0.214 & 1.000 \\  & INTER-RAT HO & 2000 & 0.978 & 0.250 & 1.000 \\  & Ec/N0 & 2000 & \(-\)9.611 & \(-\)15.630 & \(-\)5.660 \\  & RSCP & 2000 & \(-\)89.267 & \(-\)101.260 & \(-\)75.280 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Statistics of variables (KPIs) according to clusters.
# Coordinated Multi-Platooning Planning for Resolving Sudden Congestion on Multi-Lane Freeways
## 2 Background and Related Work
The proposed strategy controls longitudinal motion based on feedback and feedforward architecture, which can ensure the safety and smooth merging of platoons. [PERSON] et al. [30] proposed a decentralized optimization control framework to provide the vehicle with the best driving trajectory. The proposed framework considered internal boundary conditions to eliminate stop-and-go driving. By coordinating vehicles, the framework can significantly reduce fuel consumption and travel time. [PERSON] et al. [31] proposed a decentralized platoon management architecture to realize platooning maneuvers, such as forming a platoon, lane changing, and disbanding a platoon. The management protocol also considered road condition changes and allowed each vehicle to dynamically adjust its distance from its preceding vehicle to maintain safety. [PERSON] et al. [32] restructured the vehicle queue based on the swarm algorithm, V2I, and V2V communication. The authors considered safety, passenger comfort, and fuel consumption to provide energy efficiency, a lower trip time, and passenger infotainment for a vehicle platoon. Meanwhile, the platoon vehicles can figure out the optimal cruise control by running a distributed Particle Swarm Optimization (PSO) algorithm. [PERSON] et al. [33] proposed an enhanced connectivity maintenance model for multiple platoons, where each vehicle shares its status information with neighboring vehicles. The vehicles must agree on the velocity and distance, which are collected by the radar sensors and then shared via V2V. On the other hand, [PERSON] et al. [34] proposed a model of a mixed traffic platoon, which considered the maximum platoon and platoon stability of road capacity. The results show that a larger platoon length can increase the road capacity, and, conversely, the smaller the platoon length, the more stable the traffic flow. [PERSON] et al. [35] proposed a cloud-based four-legged roundabout optimized coordination algorithm. The authors developed a control system with a bi-level framework, where the higher level carries out the vehicle cluster control and the lower level carries out the vehicle control individually. The objective is to minimize the total crossing time. [PERSON] et al. [36] propose two sub-systems: leader election and an incentive mechanism. Firstly, the elected leader constructs a reputation-based election scheme, where reputation is used for vehicle platoons. The trusted leaders are elected based on reputation. Secondly, the authors design an incentive mechanism to motivate platoon members to participate in the election.
# Influence of the Flexible Tower on Aeroelastic Loads of the Wind Turbine
## 2 Analytical Study
### Solving the Nacelle Attitude Angle Using Euler Angles  
Attitude solutions are relatively common in computer graphics and aerospace, and Euler angles [13] have been widely used in the field of attitude description due to their simplicity and ease of use. A set of Euler angles, which are the yaw, pitch, and roll angles obtained from the rotation of the nacelle about the X, Y, and \(Z\) coordinate axes, are used to describe the attitude of the HAWT nacelle, as shown in Figure 3.  
Assume that the nacelle transitions from one attitude to another in the sequence of transverse roll-pitch-yaw, wherein the transition matrix around the axis is:  
\[R_{1}(\varphi_{x})=\left[\begin{array}{ccc}1&0&0\\ 0&\cos\varphi_{x}&-\sin\varphi_{x}\\ 0&\sin\varphi_{x}&\cos\varphi_{x}\end{array}\right] \tag{19}\]  
\[R_{2}(\varphi_{y})=\left[\begin{array}{ccc}\cos\varphi_{y}&0&\sin\varphi_{y} \\ 0&1&0\\ -\sin\varphi_{y}&0&\cos\varphi_{y}\end{array}\right] \tag{20}\]  
\[R_{3}(\varphi_{z})=\left[\begin{array}{ccc}\cos\varphi_{z}&-\sin\varphi_{z} &0\\ \sin\varphi_{z}&\cos\varphi_{z}&0\\ 0&0&1\end{array}\right] \tag{21}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**\(K_{R}\) (GNm/rad)** & \(K_{L}\) (GN/m) & **1 st** & **2 nd** & **3 rd** \\ \hline
**10** & 0.5 & 0.20862 & 2.0824 & 6.8703 \\
10 & 1 & 0.20874 & 2.0991 & 7.1053 \\
10 & 5 & 0.20882 & 2.1124 & 7.2858 \\
20 & 0.5 & 0.25229 & 2.2495 & 6.9903 \\
20 & 1 & 0.25245 & 2.2725 & 7.2701 \\
20 & 5 & 0.25249 & 2.2909 & 7.4874 \\
50 & 0.5 & 0.29507 & 2.5064 & 7.2177 \\
50 & 1 & 0.29533 & 2.5409 & 7.5925 \\
50 & 5 & 0.29554 & 2.5683 & 7.8877 \\
100 & 0.5 & 0.31463 & 2.6753 & 7.4038 \\
100 & 1 & 0.31494 & 2.7180 & 7.8656 \\
100 & 5 & 0.31519 & 2.7519 & 8.2307 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The first three orders of the towers’ inherent frequency (unit: Hz).  
Figure 3: Nacelle attitude angle, (**a**) yaw, (**b**) pitch, (**c**) roll.  
The attitude matrix of the nacelle can be obtained as follows:  
\[R_{A}=R_{1}(\varphi_{x})R_{2}\big{(}\varphi_{y}\big{)}R_{3}(\varphi_{z}) \tag{22}\]
## 3 Results
### Data Acquisition
Higher sampling frequencies are particularly important for detecting rapid changes in pressure or flow, which are often indicative of faults or anomalies. Insufficient sampling rates may result in data that fail to accurately represent the dynamics of the system, potentially leading to undetected issues or delayed responses. * For pressurized water systems operating within a range of 8 to 12 bar, it is essential that the pressure signal be recorded with an accuracy of at least one-hundredth of a bar. This conclusion stems from tests conducted with data recorded at a lower precision, specifically with a resolution of one-tenth of a bar, which proved unsuitable for detecting subtle pressure variations. * The duration of data samples is another critical factor in the detection and diagnosis of faults. Time series shorter than 2 s have been shown to be inadequate for accurately identifying anomalies in system operation, as they do not provide enough information to characterize the behavior or trends in the data. Samples of at least 3 s yield significantly better results, enabling more reliable fault detection. Furthermore, extending the sample duration beyond 3 s enhances the accuracy and robustness of anomaly  
Figure 2: Time series of pressure drop data collected during fault mode (y-axis in bars). identification, as larger datasets allow for a more comprehensive analysis of system behavior. However, it is also important to balance sample size with real-time processing capabilities, as excessively long durations may increase computational demands and affect the responsiveness of the fault detection system.
# Transmission Tower Re-Identification Algorithm Based on Machine Vision
## 3 Experiment and Analysis
### Experiment on Transmission Tower Identity Matching  
#### 3.3.1 Model Training  
To enhance the performance of the trained model under the constraints of a limited dataset, this study employed transfer learning during model training. The backbone feature extraction networks of all architectures adopted parameters pretrained on the ImageNet dataset for model initialization. In terms of model construction, this research evaluated three distinct backbone network architectures: MobileNet [41], MobileViT [42], and Inception-ResNet-v1 [43]. The Adam optimizer was employed, with betas set to (0.9, 0.999). A cosine learning rate schedule was used, initializing the learning rate at 0.001 with a minimum rate of \(10^{-5}\). The model's batchsize was set to 27, with the triplet loss hyperparameter \(\alpha\) set to 0.15. The training results are illustrated in Figure 15. As shown in Figure 15, models employing an online triplet generation strategy demonstrate an accelerated curve convergence speed when the backbone network generates 128-dimensional feature vectors, in comparison to using basic triplet. It is noteworthy that in large networks such as Inception-ResNet-v1, minor variations during the initial training phase can lead to substantial differences in output. However, the introduction of online triplet generation has been observed to decrease fluctuations in the convergence curve. Furthermore, for the Inception-ResNet-v1 backbone network, using a 256-dimensional feature vector under the online triplet generation strategy results in more pronounced fluctuations in curve convergence compared to using a 128-dimensional feature vector.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **FLOPs/G** & \
\begin{tabular}{c} **Inference (Batchsize = 1** \\ **A4000 Ubuntu)/ms** \\ \end{tabular} &
\begin{tabular}{c} **Inference (Batchsize = 1** \\ **Windows)/ms** \\ \end{tabular} \\ \hline YOLOv5n-conv head (ours) & 0.974 & 0.791 & 3.6 & **8.1** & **20.5** \\ YOLOv5n-c2f head (ours) & 0.974 & 0.792 & 4.8 & 10.5 & 24.6 \\ YOLOv5n [15] (in 2022) & 0.977 & 0.792 & 4.3 & 9.8 & 23.8 \\ YOLOv5s [15] (in 2022) & 0.975 & 0.789 & 15.8 & 10 & 51.8 \\ YOLOv8n [17] (in 2023) & 0.977 & 0.819 & 8.2 & 10.1 & 35.2 \\ YOLOv8s [17] (in 2023) & **0.98** & **0.821** & 28.7 & 10.9 & 43.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance results of object detection algorithms on the test dataset.