esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical and Experimental Investigation of Guided Wave Propagation in a Multi-Wire Cable
###### Abstract  
Ultrasonic guided waves (UGWs) have attracted attention in the nondestructive testing and structural health monitoring (SHM) of multi-wire cables. They offer such advantages as a single measurement, wide coverage of the acoustic field, and long-range propagation ability. However, the mechanical coupling of multi-wire structures complicates the propagation behaviors of guided waves and signal interpretation. In this paper, UGW propagation in these waveguides is investigated theoretically, numerically, and experimentally from the perspective of dispersion and wave structure, contact acoustic nonlinearity (CAN), and wave energy transfer. Although the performance of all possible propagating wave modes in a multi-wire cable at different frequencies could be obtained by dispersion analysis, it is ineffective to analyze the frequency behaviors of the wave signals of a certain mode, which could be analyzed using the CAN effect. The CAN phenomenon of two mechanically coupled wires in contact was observed, which was demonstrated by numerical guided wave simulation and experiments. Additionally, the measured guided wave energy of wires located in different layers of an aluminum conductor steel-reinforced cable accords with the theoretical prediction. The model of wave energy distribution in different layers of a cable also could be used to optimize the excitation power of transducers and determine the effective monitoring range of SHM.  
guidelines, multi-wire cable; wave structure; contact acoustic nonlinearity; energy transfer
## 2 Materials and Methods
### Data Sources  
#### 2.2.1 Data Sources and Processing  
The basic information required for the study are land-use data, lithology data, and DEM (Digital Elevation Model) data. The 1985-2020 land-use data are from the China Land Cover Dataset (CLCD) produced by Prof. [PERSON]'s team in the School of Remote Sensing and Information Engineering, Wuhan University, and it is the first Google Earth Engine (GEE) platform to produce Landsat-derived high-resolution Chinese land cover data (30 m), which has an overall accuracy of 79.31%. Third-party tests have shown that the overall accuracy of CLCD is higher than datasets such as FROM_GLC and GlobeLand30, which are freely available via the Internet ([[https://doi.org/10.5281/zenodo.5210928](https://doi.org/10.5281/zenodo.5210928)]([https://doi.org/10.5281/zenodo.5210928](https://doi.org/10.5281/zenodo.5210928)) (accessed on 10 March 2022) [45]. The dataset divides the surface into nine categories: cropland, forest, shrubland, grassland, water, snow/ice, barren land, impervious land, and wetland. We obtained the land-use data of Guizhou Province from 1985 to 2020 using administrative boundary cropping. We found that there was no snow/ice category among them, and the wetland area was small and could be merged into water. The lithological data were obtained by scanning, correcting, and digitizing 1:200,000 maps of Guizhou Province. DEM data from the Japanese Earth observation ALOS satellite with a resolution of 12.5 m and a resampling of 30 m can be extracted using the ArcGIS spatial analysis tool (download available at [[https://search.asf.alaska.edu/](https://search.asf.alaska.edu/)]([https://search.asf.alaska.edu/](https://search.asf.alaska.edu/)) (accessed on 20 February 2021). All of the previously mentioned layers were defined as the WGS_1984 geographic coordinate system and Albers_Conic_Equal_Area projection coordinate system.
## 4 Numerical Simulation of Slope Stability under Different Vegetation
### Vegetated Slope Stability Analysis
#### 4.3.2 Displacement  
To illustrate the effect of the root-soil composites of C.d, M.m, and G.s on the slope displacement, the maximum horizontal and vertical displacements were analyzed. The simulation results are shown in Table 5 and Figure 6. The results show that the vertical displacements of both the bare and vegetated slopes were significantly greater than the horizontal displacements. According to the statistical results, the vertical displacement was 13.9 to 22 times that of the horizontal displacement. Additionally, the horizontal and vertical displacements of the C.d, M.m and G.s mixture slopes were significantly less than the bare slope. The displacement could be effectively reduced under the action of the root system in the four different growth periods of C.d, M.m and G.s mixture. As the growth period increased, the maximum horizontal and vertical displacements further decreased. This suggests that plant roots play an important role in the soil-shearing process. Through the anchoring effect of deep root systems and the reinforcement effect of shallow root systems, the horizontal and vertical displacements are affected, thus improving the shear strength. The shear strength mainly affects the horizontal shear force of the soil; hence, the reduction in the horizontal displacement is greater than in the vertical displacement. According to the decreasing degree of horizontal and vertical displacement, this shows that the slope protection effect of the G.s mixture is better.
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 6 Current Challenges and Future Research Directions
### Application Challenges
#### 6.2.3 Investment Return Assessment Complexity  
ETC system construction requires substantial infrastructure investment and continuous operational maintenance input. How to scientifically assess economic and social benefits to provide support for investment decisions represents a complex, systematic engineering challenge.  
The quantitative assessment of social benefits faces methodological challenges. ETC system applications not only generate direct economic benefits but also produce significant social benefits, including environmental benefits, safety benefits, and public convenience benefits. Research by [PERSON] et al. demonstrated that ETC technology can significantly reduce particulate pollutant emissions compared to manual toll collection [60]. However, these social benefits are often difficult to accurately quantify in monetary terms, creating challenges for comprehensive benefit assessment. How to establish scientific social benefit assessment systems and accurately quantify the comprehensive value of ETC systems represents the key to improving the scientific validity of investment decisions.  
Trade-offs between long-term benefits and short-term costs increase assessment difficulties. ETC system construction investments are primarily concentrated in early phases, while benefit realization often requires extended timeframes. Factors such as technology upgrades, equipment updates, and standard changes may also affect system lifecycles and investment returns. This temporal mismatch makes traditional investment return analysis methods insufficient for accurately reflecting the true value of ETC systems, necessitating the development of more suitable assessment methods and tools.
## 4 Results and Comparison
### PNR-TDNN Experiments
#### 4.2.1 Discussion of Baselines  
To facilitate a fair comparison, the experiments in this study utilize a subset of the data referred to in Section 3.1. These data are further annotated and include instances of noise interference as well as combined noise and reverberation interference. Specifically, the sub-datasets from N-zhvoice/NR-zhvoice for speaker verification are presented in Table 3. The baseline model comprises TDNN, Resnet-SE, Res2 net, Eres2 net, ECAPA- TDNN (512 channels), CAM++ (32 channels), along with other state-of-the-art methods within the last two years (as shown in Table 4, such as MFA-Conformer,Wespeaker, RedimNet, Gemini). All baseline models exhibit a satisfactory fit within 30 epochs, which is why the training is limited to this number of epochs.  
For dataset A, as presented in Table 4, CAM++ demonstrates superior performance compared to other methods with respect to both EER and MinDCF. RedimNet achieves the second lowest EER. In the case of datasets B and C, CAM++ achieves a notable lead in EER, whereas ECAPA-TDNN and Resnet-SE yield the optimal MinDCF results. Additionally, the results following the addition of reverberation interference are worse than those with noise alone, indicating the detrimental effect of reverberation on the speaker recognition task. This study argues that the trailingphenomenon in the frequency domain impacts the network's capability to process information effectively. The computational complexity of the model is measured in terms of floating-point operations (FLOPs) and is assessed using a CPU (Intel(R) Xeon(R) Gold 5218R [EMAIL_ADDRESS] GHz, the batch size for these evaluations is fixed at 12). The real-time factor (RTF) is a metric used to gauge the efficiency of the model's processing time, defined as the ratio of the time taken by the model to process a single audio to the duration of the audio itself.  
Figure 10: Convergence of HuBERT/WavLM.
# A Systematic Literature Review on Military Software Defined Networks
## 4 Identified Significant Research Contributions
### Tools
[49] presented a hierarchical self-organizing SDN architecture for mobile tactical networks, where the network is dynamically self organized and partitioned into multiple temporary domains, while each domain is assigned with a node that operates as the local SDN controller. A corresponding protocol is proposed by the authors, including (i) a neighborhood discovery mechanism, (ii) a distributed network partition algorithm, and (iii) an abstraction of dynamics. The proposed mechanisms provide strong incentives towards future research and development, although extended verification and proof of concept experiments are recommended.
# FL-LoRaMAC: A Novel Framework for Enabling On-Device Learning for LoRa-Based IoT Applications
## 3 Problem Statement and Design Considerations
### Challenges Faced by ML  
The key challenges faced by the IoT ecosystem for the implementation of typical machine learning techniques with respect to sending data to the central server include:  
* _High data volume:_ A sensor node may capture data once every few minutes but the gateway is connected to multiple such sensor nodes. All the data are collected by the gateway and sent to the central server. This can lead to a significant amount of traffic in the network that can result in transmission losses and high latency issues in a real-time environment.
* _Data privacy:_ Typically, the machine learning algorithms run on third-party cloud servers. During both the training phase as well as the testing phase, the data need to be sent to the server where the model resides. These data can be private to the individuals. These data can be exploited by third-party service providers; hence, regulatory organizations like California Consumer Privacy Act (CCPA) in the US impose many restrictions in terms of the privacy of sensitive customer data. These may lead to regulatory problems in many applications, such as healthcare.  
In IoT applications that involve private user data, the need to send data to a central server for machine learning purposes can be a challenge. This challenge can be addressed by using federated learning, which is an artificial intelligence technique that allows learning to be performed without sending data to the central server.
## 2 Modeling of Tethered UAV Dynamics
### Dynamics Modeling of UAV Platforms  
#### 2.1.1 Coordinate System Selection and Parameter Description  
As shown in Figure 3, to describe the rotation and movement of an aerial UAV platform, it is necessary to use the airframe coordinate system \(\rm{O_{B}\text{-}X_{B}\text{-}Y_{B}\text{-}Z_{B}\text{.}}\) To determine the position of the UAV, it is essential to choose the ground coordinate system \(\rm{O_{E}\text{-}X_{E}\text{-}Y_{E}\text{-}Z_{E}\text{.}}\) In the body coordinate system, the linear displacement along the axial direction of OX, OY, and OZ is represented by \(x\), \(y\), and \(z\), respectively, and the linear velocity is represented by \(u\), \(v\), and \(w\), respectively. The attitude angles around the OX, OY, and OZ axes are defined by \(\varphi\), \(\theta\), and \(\psi\), respectively, and the angular velocities are expressed by \(p\), \(q\), and \(r\), respectively. The moment of inertia around the OX, OY, and OZ axes is represented by \(I_{x}\), \(I_{y}\), and \(I_{z}\), respectively, and \(I_{xy}\), \(I_{yz}\), and \(I_{xz}\), respectively, define the inertia products of the XOY, YOZ, and XOZ planes. The lift and moment generated by the rotor of the UAV are expressed by \(F_{\rm{i}}\) and \(M_{\rm{i}}\) (i = 1,2,3,4), respectively.  
Figure 2: Hardware components of a tethered UAV.
# State of the Art Review about Bio-Inspired Design and Applications: An Aerospace Perspective
## 4 Applications of Bio-Inspiration in the Aerospace Industry
### Sensors
A bio-inspired anti-impact manipulator (BAM), based on kangaroos' spring-like movements, was developed and tested for in-orbit capture of this debris [97]. The spring-like motion of the bio-inspired structure helped reduce the impact velocity of the incoming waste. The scales embedded on the skin of a teleost fish provide stiffening and stabilizing mechanisms for protection and robustness [98]. The scales provide strain stiffening, which promotes the development of morphing structures for aerospace vehicles.
## 1 Introduction  
The use of de-icing salts during low-temperature periods in structures either without a waterproof treatment or a deficient one might result in the entrance of chloride ions through the upper face of their decks. Furthermore, this phenomenon may be aggravated by the presence of other previous relevant concrete pathologies such as pores, hollows or cracks inside the concrete which may ease the circulation of aggressive chemicals until steel reinforcements bars.  
This article aims to show the role of de-icing salts in the corroding process of steel rebars and their effects on post-tensioned elements. Consequently, this article pays special attention to the brittle rupture phenomenon of transversal rebars for being extraordinary, as well as highlights the most likely mechanisms that gave rise to this pathology and emphasizing the important use of waterproofing treatments, not only to reduce brittle rupture failures of the steel rebars but also to prevent alkali-aggregate reactions of concrete. The article focuses on a representative case where this process has been observed. The Nudo de Colmenar Bridge, a motorway junction located in the north of Madrid Region with winter road maintenance activities based on the spreading of de-icing salts with concentrations varying from 10 to 15 g/m\({}^{2}\).  
The science and engineering in corrosion of reinforced and post-tensioned concrete structures have been developed for more than half a century. The processes of study are slow and need decades to manifest and this means that continuous inspections are needed in order to detect the appearance of durability issues on the structure. Some maintenance tasks, such as the use of de-icing salts to avoid ice and snow in road structures, can be cheap and effective in the short term although they can be disastrous in the mid-term as shown in this study. In social terms, the tremendous financial burden caused by corrosion receives little attention in comparison with health or climate change even though they can be of similar or even greater impact in terms of costs [1]. Additionally, some educational challenges appear in this aspect, showing that civil engineers are generally not well trained to address these challenges This study shows a case study on the effects of the continuous use of de-icing salts in post-tensioned slabs and structures and the reduction of the life span, showing that the study of new alternatives to de-icing salts should be addressed for road maintenance tasks.
# A Novel Reconstruction Method for Irregularly Sampled Observation Sequences for Digital Twin
## 4 Simulation Experiments and Analysis
### Discussion  
For a well-trained sequence reconstruction model, the parameters related to the latent feature weight, including \(r_{1}\), \(r_{2}\), \(q_{1}\) and \(q_{2}\), will decide the reconstruction results. For the simulation experiments in this paper, the value of \(r_{1}\) is set based on the largest measurement noise variance. While guaranteeing the noise in a reasonable range, the features of the real observation data are more utilized for sequence reconstruction. If the noise level is smaller than \(r_{1}\), there is no need to increase the weight of the reference observation feature and the model will primarily utilize the actual observation features to realize sequence reconstruction. While if the noise level is larger than \(r_{1}\) due to electromagnetic interference or sensor failure, the weight of the reference feature should be increased so that the negative impact of noise on reconstruction accuracy could be reduced. In addition, in order to guarantee that the fused feature always contains some feature information of the actual data, the maximum reference feature weight is set to \(q_{2}<1\).  
In conclusion, for the reconstruction of noisy, irregular multivariate sequences in controlled systems, the PRN-NCDE model outperforms existing methods. Simulation results confirm its effectiveness in improving reconstruction accuracy under sparse measurements and high noise levels, making it suitable for digital twin systems in complex coal-fired power plant environments.  
Figure 6: Reconstruction results of furnace pressure and gas oxygen content under high noise levels.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sequence Reconstruction Model** & \(\mathbf{RMSE}_{\mathbf{R}^{2}}\) & \(\mathbf{RMSE}_{\mathbf{R}^{2}}\) & \(\mathbf{RMSE}_{\mathbf{R}^{3}}\) \\ \hline PRN-NCDE & 0.0158 & 0.0155 & 0.0182 \\ RNN-NCDE & 0.0599 & 0.0581 & 0.0771 \\ RNN-NODE & 0.0471 & 0.0479 & 0.0529 \\ \hline \hline \end{tabular}
\end{table}
Table 5: RMSE results of different models under high noise levels.
###### Abstract  
Vehicular Ad-Hoc Network (VANET), a vital component of Intelligent Transportation Systems (ITS) technology, relies on communication between dynamically connected vehicles and static Road Side Units (RSU) to offer various applications (e.g., collision avoidance alerts, steep-curve warnings and infotainment). VANET has a massive potential to improve traffic efficiency, and road safety by exchanging critical information between nodes (vehicles and RSU), thus reducing the likelihood of traffic accidents. However, this communication between nodes is subject to a variety of attacks, such as Man-In-The-Middle (MITM) attacks which represent a major risk in VANET. It happens when a malicious node intercepts or tampers with messages exchanged between legitimate nodes. In this paper, we studied the impact on network performance of different strategies which attackers can adopt to launch MITM attacks in VANET, such as fleet or random strategies. In particular, we focus on three goals of MITM attacks--message delayed, message dropped and message tampered. The simulation results indicate that these attacks have a severe influence on the legitimate nodes in VANET as the network experience high number of compromised messages, high end-to-end delays and preeminent packet losses.  
Vehicular Ad-Hoc network; security; Man-In-The-Middle Attack; smart cities; simulation; Intelligent Transportation System; Internet-of-Things +
Footnote †: journal: Sensors
## 3 Problem Formulation
### Insight of the Studied Problem
Reconfiguration of different regions can only be performed serially, and only one region can be reconfigured each time. \[\left(Rs_{i},Re_{i}\right)\cap\left(Rs_{j},Re_{j}\right)=\emptyset \tag{6}\]  
For any reconfiguration node \(\mathbf{RN_{i}}\), its reconfiguration process from reconfiguration start time \(Rs_{i}\) to reconfiguration end time \(Re_{i}\) cannot overlap with any other reconfiguration node \(\mathbf{RN_{j}}\) in time. Constraint 4. The task can only be executed on a region after this region has been reconfigured. \[Es_{i}\geq Re_{i} \tag{7}\]  
The physical structure of the FPGA determines each reconfiguration node can only start its execution phase after reconfiguration in the region where it is located. Hence, for each node \(\mathbf{RN_{i}}\), its execution start time \(Es_{i}\) must be larger than its reconfiguration end time \(Re_{i}\). Constraint 5. The execution of a certain task can starts only when the dependent data arrives. \[Es_{i}\leq E_{pn_{i}},\forall pn_{i}\in\mathbf{PN} \tag{8}\]  
Since there is a data dependency relationship between tasks, task \(n_{i}\) can only start execution after having obtained the dependent data from all its parent tasks. Therefore, the execution start time of task \(n_{i}\) should be greater than or equal to the execution end time of all parent tasks. In our partitioning and scheduling model, the objective function is the scheduling length (SL). For an application, the smaller the SL means the higher the computing efficiency of the system [27]. In order to quantify the solution of the partitioning and scheduling problem, we define the objective function as follows:  
\[\text{SL}=\max_{0\leq i\leq\left|V\right|}\left(E\epsilon_{i}\right)-\min_{0 \leq j\leq\left|V\right|}\left(Rs_{j}\right) \tag{9}\]  
where \(\max(E\epsilon)\) is the execution end time of the last reconfiguration node, and \(\min(Rs)\) is the first reconfiguration start time of a reconfiguration node. Which represents the time span of the entire application running from the beginning to the end on the reconfigurable device. This article uses SL as the main indicator for measuring the solution quality.
## 3 Design of Hardware and Software for the Data Monitoring System
### Design of Software for the Data Monitoring
#### 3.2.2 Communication Protocols  
For a better understanding of the communications, Table 1 shows a glossary of all symbols used in the flowcharts and communication examples.