esa-sceva/satcom-chunk-collection huggingface dataset:
# Augmented Reality and the Digital Twin: State-of-the-Art and Perspectives for Cybersecurity
## 4 Perspectives for Cybersecurity
### Benefits for Cyber Security
#### 4.1.3 Integrating Domain Knowledge  
The last and most crucial benefit for cybersecurity when AR is applied together with a DT is the **integration of domain knowledge**. AR and the DT allow straightforward and efficient integration of experts and their domain knowledge into security processes. While the human factor has long been considered the weakest link in cybersecurity, experts' knowledge is indispensable to secure modern IT architectures and infrastructures [9]. Security experts can correlate the information they use for their security monitoring and analyses directly to the physical assets where the DT collects the respective data. Additionally, results, warnings, and alerts produced by the security analyses performed by the DT itself can be displayed in their context (see Section 4.1.1). This AR-based visualization of security-relevant data enables a far more in-depth security analysis combined with the physical context of the respective data. Security alerts can be displayed onto the asset they concern, and experts can be integrated directly into the analysis process. Furthermore, while operators or system engineers know how to interact with the physical system, security analysts most certainly lack this knowledge. AR combined with the DT can provide instructions on how to interact appropriately or which actions to take to prevent harm resulting from a cyber attack. In addition, functions that induce commands via the DT towards the physical system--without the human having to touch the physical system--might be provided by this solution.  
Besides the security domain knowledge of security experts, the domain knowledge of security novices is also highly relevant for incident detection, especially when dealing with CPS. Security experts might understand the cyber-aspect of CPS, but other experts, such as engineers, are needed to understand and evaluate the physical part of the system. Therefore, using an AR-powered DT approach, security novices can participate in security analyses. They can physically regard the system and add the AR-based device, which gives input on the security perspective. This input is coming from the digital-twin component and is properly visualized with AR. This benefits not only security users but also supports the actual operative staff with little focus on security. Although many security analytics processes are automated by now, it is not possible to entirely rely on automated systems to decide whether an anomaly is an actual incident or a benign activity. To make this decision, human domain knowledge (i.e., a clear and deep understanding of the system under consideration), the current situation, and contextual factors are necessary. When users are directly enabled to visualize security issues, and related data on the respective physical asset, this necessary integration of domain knowledge is strengthened.
## 3 Dynamic Reliability Framework
### Validation of the Maintenance Box
From Equation (6) it is possible to derive the instantaneous Weibull failure rate function:  
\[(t)=\frac{f(t)}{1-F(t)}=\beta/\gamma\cdot(t/\gamma)^{\beta-1} \tag{7}\]  
Figure 5 shows the Stochastic Activity Network models of the proposed benchmark AND gate designed to test the effectiveness of the SHyFTOO Maintenance Box. In particular, the Stochastic Activity Network of Figure 5a models the AND gate with the corrective maintenance (2C) policy, whereas Figure 5b can account for the preventive policy 2P. Figure 6 illustrates the comparison of the SHyFTOO Maintenance Box and the SAN model for the 2C and 2P maintenance policies. The results obtained confirm that the Maintenance Box model retrieve the correct results. These results confirm that policy (2P), as expected, is the most effective maintenance strategy. It can be seen that the unreliability of the system is similar but, as soon the first preventive maintenance occurs (at 2000 h) the two groups of trends start to diverge. This is because every 2000 h the preventive maintenance replaces the component as new, restarting the aging to 0. Figure 5: Stochastic Activity Network model of the AND Gate: (**a**) with corrective maintenance (2C) and (**b**) with preventive maintenance (2P). Figure 6: System unreliability for the AND benchmark comparing SAN (Stochastic Activity Networks) and SHyFTOO models.
In general, the method combines the WT for signal denoising, STFT for transient analysis, FT for steady-state analysis, the application of a comb filter to remove harmonic components during the transient regimen, two nonlinear measurements, i.e., energy (ENE) and total harmonic distortion (THD), for characterizing the damage severity, and two neural networks for the automatic diagnosis of SCTs under both regimes of operation using vibration signals. To evaluate the effectiveness of the proposed method, a modified transformer and its triaxial vibration signals under various operating conditions (i.e., 0-healthy, 5, 10, 15, 20, 25, 30, and 35 SCTs) are analyzed, considering both transient and steady states. The results demonstrate that the proposed method can accurately detect and characterize the damage severity in both regimes with a high degree of accuracy. The remaining of the paper is as follows. Section 2 describes the theoretical background of the proposed methodology. The proposed methodology and the experimental setup to test it are described in Section 3. Section 4 shows the obtained results and general discussions. Finally, conclusions are drawn in Section 5.
# SKVOS: Sketch-Based Video Object Segmentation with a Large-Scale Benchmark +
## 4 SKVOS Model
### SKVOS Task Definition  
Given a video sequence with \(T\) frames, let \(I_{t}\in\mathbb{R}^{H\times W\times 3}\) denote the RGB image at frame \(t\), where \(t\in[1,T]\), and a query sketch \(S^{i}\in\mathcal{S}\) corresponding to object \(O^{i}\in\mathcal{O}\). The objective of SKVOS is to generate the target masks \(\hat{\mathcal{M}}=\{\hat{M}^{i}_{t}\}_{t=1}^{T}\) over the video frames, where \(\mathcal{S}\) represents the set of all sketch references, and each video contains \(\mathcal{N}\) objects denoted as \(\mathcal{O}=\{O^{1},...,O^{i},...,O^{N}\}\). Each mask \(\hat{M}^{i}_{t}\in\mathbb{R}^{H\times W\times 1}\) represents the predicted segmentation mask of object \(O^{i}\) at time step \(t\). Since SKVOS is formulated as an instance-level task, it uses a sketch as prior reference, similar to other semi-supervised VOS tasks. Theoretically, sketches can be utilized not only for category-level localization but also uniquely provide fine-grained, instance-specific guidance. This is due to its detailed information, such as shape and pose, which enables precise object guidance within each video frame. Without any explicit category information, the sketch in SKVOS serves as a subtle indicator for matching specific instances, enhancing the system's ability to track individual objects.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Reference**} & \multicolumn{3}{c}{**Train Set**} & \multicolumn{3}{c}{**Validation Set**} \\  & & **Videos** & **Objects** & **\#A** & **Videos** & **Objects** & **\#A** \\ \hline \multirow{3}{*}{**DAVIS16**} & Mask & 30 & 30 & 30 & 20 & 20 & 20 \\  & Text-1 st & 30 & 30 & 60 & 20 & 20 & 40 \\  & Sketch (Ours) & 30 & 30 & 90 & 20 & 20 & 60 \\ \hline \multirow{3}{*}{**DAVIS17**} & Mask & 60 & 144 & 144 & 30 & 61 & 61 \\  & Text-full & 60 & 144 & 288 & 30 & 61 & 122 \\  & Text-1 st & 60 & 144 & 288 & 30 & 61 & 122 \\  & Sketch (Ours) & 60 & 144 & 432 & 30 & 61 & 183 \\ \hline \multirow{3}{*}{**YouTube-VOS**} & Mask & 3471 & 6459 & 6459 & 507 & 1063 & 1063 \\  & Text-full & 3471 & 6388 & 12,913 & 507 & 1063 & 2096 \\ \cline{1-1}  & Text-1 st & 3412 & 6006 & 10,897 & 507 & 1030 & 1993 \\ \cline{1-1}  & Sketch (Ours) & 3412 & 6006 & 18,018 & 507 & 1063 & 3189 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset statistics: provides the reference type, the number of videos, the number of objects, and the number of annotations (#A) for the DAVIS16/17 and YouTube-VOS datasets. For mask reference, only annotations in the first frame are taken into account. Text-1 st means the language expressions of the first frame. Text-full indicates the language expressions of the full video.
## 3 Results and Discussions
### Robocasting of Lattice, Dense and Bi-Layer Parts
#### 3.1.2 Porous Parts  
Figure 3 collects some representative digital photographs of a sintered 3D lattice sample from the perspective, top and side views. It can be seen that the sample kept its regular quadratic channel structure after post-printing processing such as drying, debinding and sintering, and the structures were, in general, highly defined and precise. The struts stretched without any noticeable deformation along X and Y directions and no macro-cracks were observed. The average dimensions of the dried and sintered samples were \(9.21\times 9.21\times 7.00\) and \(7.55\times 7.55\times 5.75\) mm\({}^{3}\) respectively, indicating 4.06% and 18.02% drying and sintering shrinkages, respectively.  
In Figure 4, a scanning electron micrograph (SEM) image shows the connection of two interpenetrating struts of a lattice scaffold. The traversing filaments (T) show homogeneous microstructure without noticeable borderline between them, highlighting a very well integration between successive layers through production steps. A deeper insight into the image shows a highly compact, well-sintered microstructure, with few residual pores.  
Figure 4: SEM image showing the contact area between interconnecting rods of adjacent layers.  
Figure 3: A representative lattice sample from different views; Perspective (**A**), top (**B**) and side (**C**) views.  
The physical characteristics of the sintered 3D scaffolds are listed in Table 2 (as average values of at least 10 samples). The relative density of the solid was calculated considering the final composition of samples as a 70/30 mixture of HA/\(\beta\)-TCP, as determined in a previous paper [48] (the densities of HA and \(\beta\)-TCP phases were considered as 3.15 and 3.07 g/cm\({}^{3}\), respectively). As the optimum pore size range for bone regeneration was proposed from 100 to 400 \(\upmu\)m [61], the lattice scaffolds with an average pore width of 275 \(\upmu\)m can be considered suitable for biomedical applications. Moreover, the relative density of the solid parts was 95%, showing an optimum density of the struts. Therefore, it can be said that the porous samples (i) being composed of a mixture of HA and \(\beta\)-TCP, which considered as the optimum composition for biological activities, (ii) having interconnected pores with optimum size for bone regeneration, and (iii) having the optimum solid density of 95% (theoretical density (TD), can be considered as suitable materials for bone replacement applications from the biological point of view.
## 4 Case Study Investigation
### Asphalt Mixture Paving Monitoring System  
In the asphalt mixture paving monitoring system, the GNSS board positioning and the carrier phase differential RTK technology were used to monitor the paving speed and paving mileage of the paving and collect the data, as well as the infrared temperature sensor to monitor the paving temperature in real time and draw a paving temperature change map for the staff to analyze. The monitored data, such as paving speed, paving temperature, paving mileage, and the working environment of the paver, were packaged and sent to the data management system through the 5G network transmission channels so that the real-time reception and storage of paving operation information could be completed. The construction work information could be dynamically displayed through the LED screen, which ensured the quality and efficiency of the paving. The on-site operation of the paver and the monitoring information of intelligent paving are shown in Figures 17 and 18.  
Figure 16: Transport vehicle electronic fence.  
Figure 15: Trajectory of transport vehicles.
# Vertical Force Monitoring of Racing Tires: A Novel Deep Neural Network-Based Estimation Method
## 2 Related Works
### Hybrid and Pure Neural Network Approaches in Vertical Tire Force Estimation
While the model maintains accuracy under moderate levels of Gaussian noise, its performance decreases with higher levels of data contamination. These findings suggest that the LSTM model's performance is influenced by the quality and diversity of the training data and its ability to handle deviations from the training parameters. [PERSON] et al. [22] estimate tire forces using a neural network. The study successfully reduces the computational complexity and storage requirements of the system by using an improved Levenberg-Marquardt (LM) learning algorithm and self-organizing neurons. They also increase the estimation accuracy by incorporating EKF and Moving Average (MA). Input parameters used to estimate vertical force include front wheel angle, yaw rate, longitudinal velocity, longitudinal acceleration, lateral acceleration, wheel angular velocity, and road adhesion coefficient.However, the reliance on onboard sensors, such as IMUs and wheel speed sensors, introduces limitations due to the indirect nature of the measurements. These sensors are affected by noise and vibrations from the vehicle chassis, which can reduce the accuracy of tire force estimation. [PERSON] et al. [23] introduce observer-based models for estimating vertical force. These models encompass an extended Kalman filter (EKF) scheme employing a linear tire model with stochastically adapted cornering stiffness, an EKF scheme employing a neural network (NN)-based linear tire model, a tire model-less Suboptimal-Second Order Sliding Mode (S-SOSM) scheme, and a Kinematic Model (KM) scheme integrated within an EKF.The analyzed methods, as presented in the study, exhibit various limitations that affect their performance and applicability. The extended Kalman filter (EKF) with a linear tire model struggles with capturing tire-road interactions under nonlinear conditions and suffers from observability issues during low dynamic scenarios. The EKF with a neural network-based tire model is highly dependent on the quality and diversity of the training data, limiting its generalization to unseen conditions. The Suboptimal Second-Order Sliding Mode (S-SOSM) method relies heavily on accurate parameter estimation and requires significant tuning, particularly for the EKF stage, while its assumption of a flat road reduces its robustness under varying road inclinations. Lastly, the kinematic model integrated with EKF fails to account for dynamic effects, depends on precise IMU placement, and is sensitive to observability issues during straight-line motion, leading to reduced accuracy. These limitations, as demonstrated in the analyses conducted within the study, underscore the challenges in achieving robust and adaptable estimation techniques.
# A 60 GHz CMOS I/Q Receiver for High-Speed Wireless Communication System
## 3 Measurement Results
All the control bits of LNA, S2D, and BBA are kept in the highest gain state (Gain code: 1111_111) to observe the I/Q baseband signal. Figure 15 shows the measured baseband output waveform at the highest gain state. The RF power is set at \(-\)30 dBm, and LO power is set to 5 dBm. The performance of the 60 GHz CMOS I/Q receiver is summarized in Table 1 along with the published design for comparison. A passive I/Q generator with a quadrature tandem coupler and Marchand balun is used with full EM simulation to generate an  
Figure 14: I/Q waveform measurement setup of 60 GHz CMOS I/Q receiver. Figure 9.0 I/Q waveform measurement results at high gain mode of 60 GHz CMOS I/Q receiver. accurate I/Q signal at 60 GHz. A transformer-based design technique is used in this work to generate accurate differential signals. As a result, the proposed 60 GHz I/Q receiver can also be implemented with compact chip size, improved conversion gain, and low power consumption at millimeter-wave frequency.
# Automatic Refactoring Approach for Asynchronous Mechanisms with CompletableFuture
## 6 Implementation and Evaluation
### Experimental Method  
We selected 9 real-world open-source programs to evaluate ReFuture. The chosen open-source programs are mature and well-known to demonstrate the actual refactoring effects of our method on large-scale programs. These applications include ActiveMQ [31], Hadoop [32], Elasticsearch [33], Jenkins [34], Tomcat [35], JGroups [36], Flume [37], ZooKeeper [38], and Tika [39]. These programs cover multiple domains: ActiveMQ is an open-source message broker, Hadoop is a framework for distributed data and computation, Elasticsearch is an open-source search and analytics engine, Jenkins is an open-source continuous integration (CI) tool, Tomcat is a free web server, JGroups is a group communication tool, Flume is a distributed, reliable, and available log system, ZooKeeper is an open-source distributed coordination service, and Tika is an Apache open-source content analysis toolkit. It is worth noting that Jenkins is a highly renowned and actively maintained open-source program, Elasticsearch is mentioned in the referenced literature [4], and JGroups is a well-known asynchronous program. The remaining open-source projects are from the Java project list on the Apache Software Foundation's website [40], which we randomly selected. Table 6 shows the version information, the number of asynchronous task submission statements containing ExecutorService constructs, the counts of execute and submit method invocations, and the total lines of source code. For RQ1 (applicability), we applied ReFuture to automatically refactor these projects and counted the number of occurrences of ExecutorService constructs for submitting asynchronous tasks, successful refactorings using the four patterns, as well as failed refactorings and reasons. For RQ2 (correctness), we assessed the correctness by running the project's test cases before and after refactoring to ensure no errors were introduced. For RQ3 (refactoring efficiency), we measured the changes in lines of code and refactoring time after applying ReFuture. For RQ4 (comparison), how does ReFuture compare to existing methods for refactoring traditional asynchronous constructs?
## 4 Experimental Results
To avoid excessive computation time, the domain is divided into 18 equal parts (_region of interest_) of size \(21\times 24\times 16\), as illustrated in Figure 14.  
In each of the eighteen domains, the operator matrix is thus of size \(201,600\times 8064\). Each of these domains is treated in a completely independent way. This division is possible because the heat sources are small and the time interval is short (2 s), thus limiting the distribution in the plane. The only problematic case in this division is when the thermal sources are at the boundary between two zones. Edge effects could possibly appear. In each of the eighteen domains, the solution computed by the program is a cube (3D matrix) of values close to zero at points that do not contain sources and high values (which correspond to the intensity of the source) whenever there is a heat source. The juxtaposition of all the domains enables estimating the totality of the studied volume. For this experiment, the inversion with a regularization \(L_{1}\) was computed on a dual processor with RAM = 128 GB: the duration of the algorithm was 5 h 45 min. Figure 15 illustrates the obtained results: view along the thickness (a) and along the \(x\) and \(y\) plane (b). It can be seen that shape of the heat sources are well estimated: being positioned and centred in the middle of the sample, on a plane situated on the middle plane along the \(z\) direction. Then, in a predictable manner, \"nodes\" can be located at the intersections of each ROI domain. These nodes are artefacts due to the division of the work space and because each domain is treated independently of the others. Figure 14: Schema of the ROI selection for the inverse processing.
# A Survey on Blockchain-Based Federated Learning
## 3 Blockchain-Based Federated Learning
### Functions of BFL
#### 3.2.2 Incentive Mechanism  
In order to solve the problem of a lack of incentive mechanism, BFL usually uses blockchain technology to construct incentive mechanisms to achieve the expected behaviors, or penalty mechanisms towards abnormal behaviors, to stimulate the enthusiasm of local users to contribute to the global model update [99; 100]. [PERSON] et al. [101] proposed the BlockFL framework, in which each device uploads its local model updates to related miners in the blockchain network. Miners are responsible for exchanging and verifying model updates, recording them in the blockchain, and providing corresponding rewards. [PERSON] et al. [102] introduced the concept of reputation as a measure of client trustworthiness, and used a multi-weighted subjective logic model to design a reputation-based trustworthy client selection scheme. At the same time, they used the immutability of blockchains to implement distributed reputation management and used contract theory to provide corresponding rewards by analyzing the computational power investment and model quality of the clients participating in model building. [PERSON] et al. [103] proposed the DeepChain scheme, distinguishing clients' performances in terms of activity and compatibility during the training process and urging clients to send correct and high-quality model updates. They also used blockchain technology to ensure model security and the audibility of the training process, achieving the objectives of confidentiality, audibility, and fairness. [PERSON] et al. [104] used blockchain technology to record all model updates comprehensively, and provided generous rewards to incentivize users to participate in FL. They proposed a weight-based client subset selection scheme, selecting clients for training based on the accuracy of each client's local model and the frequency of their participation in training, achieving high stability and faster convergence speed. [PERSON] et al. [105] designed an incentive mechanism based on deep reinforcement learning (DRL), applying traditional resource allocation strategies to the specialized distributed scenario of FL, in order to achieve optimal training strategies and pricing strategies for edge nodes.