esa-sceva/satcom-chunk-collection huggingface dataset:
# Traffic Simulation of Future Intelligent Vehicles in Duisburg City Inner Ring
## 5 Conclusions and Discussions  
In this work, a traffic scenario of Duisburg city inner ring has been built, and traffic demand of two different data sources has been generated and compared. In the four selected verification points, the traffic volume generated from induction loop data has a better reproduction (\(\sigma=\)37.43, compared to real data) than the OD matrix data (\(\sigma=\)129.20, compared to real data). Vehicles with different automation levels (Level 0/no automation, Level 2/partial automation, and Level 5/fully autonomous) are simulated continuously in the Duisburg city inner ring scenario for 24 h of a day. Based on the real traffic volume collected in 2019, the vehicles with automation Level 2 and Level 5 have positive effects on traffic. For more congested traffic status, the positive effects of vehicles with higher automation levels are more obvious.  
This article uses a real scenario, the inner ring area of the city of Duisburg, as the traffic simulation scenario. Two kinds of data sources are provided for this article, and traffic demand generated from the two kinds of data sources are used separately. In the selected simulation scenario, induction loops data show better accuracy in reproducing the traffic. In this article, vehicles with different degrees of automation are simulated in the scenario. With the same traffic demand, highly automated vehicles can effectively increase average speed. In the case of increased road congestion (150% traffic volume), the increased average speed of automated vehicles is more obvious.  
X.M.: conceptualization, data curation, formal analysis, methodology, software, writing and editing. X.H.: conceptualization, methodology, software, validation, writing and editing, T.W. & D.S.: conceptualization, project administration and supervision, result validation and critical revision. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
Figure 9: Comparison of the average speed of vehicles with different automation levels in heavy traffic volume (150% traffic volume).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Restrictions apply to the availability of these data. Data was obtained from Duisburg city officials and the WBD and are available from the authors with the permission of Duisburg city officials and the WBD.  
**Acknowledgments:** We acknowledge support by the Open Access Publication Fund of the University of Duisburg-Essen.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Review of Structural Health Monitoring Techniques in Pipeline and Wind Turbine Industries
## 2 Health Monitoring Techniques in Pipeline Industry
### In Pipeline Industry
#### 2.1.4 Acoustic Method  
Acoustic emission originally occurred naturally within materials, and the term AE is used to define the transient elastic waves that resulted from a sudden strain energy release within a material due to the occurrence of microstructural changes. When enough energy was released, audible sounds were produced. The cracking in timber subjected to loads near the yield point produces audible noises, which are indicators of impending failure of wooden structures, in the same way that the drying of tin, the cracking of rocks, and the breaking of bones are familiar to nearly everyone's ear. With a predefined threshold of noise, any extra generated vibrations that are produced by deformations of the pipelines will provide a location of the source of such a phenomenon. The magnitude of changes in frequency and intensity can be compared to different types and extents of the deformation and damage to the parts in question. Acoustic emission in the proper sense covers the audible frequencies up into the high ultrasonic range. Measurement today is carried out in the range between 50 kHz and 2 MHz. At higher frequencies, the acoustic emission is not intense enough in most cases, and the material absorbs large parts of the signal. In general, at lower frequencies, background noise disturbs the measurement e.g., vibrations from vehicles and noise from pumps or from flowing medium. Acoustic emission is especially used in places where leak size estimation is not as necessary as the accuracy [20; 31; 32].
# Uniform Circle Formation for Fully, Semi-, and Asynchronous Opaque Robots with Lights
## 2 Preliminaries
### Cycle  
Each robot \(r\) operates in _look-compute-move_ atomic cycles. Each cycle at time \(t\) consists of these steps:  
* **Look**: the robot \(r\) takes the instantaneous snapshot \(snapshot^{t}_{r}\) according to its coordinate system \(\omega_{r}\).
* **Compute**: \(r\) runs the deterministic algorithm \(\mathbb{A}\) which, by having \(snapshot^{t}_{r}\) as sole input, computes the destination point of \(r\) and the (possibly) new color for the light of \(r\). Formally, \(\mathbb{A}(snapshot^{t}_{r})=\phi^{t}_{\omega_{r}}(r)\), where \(t^{\prime}>t\) will be the time the current cycle ends.
* **Move**: \(r\) sets its new color and, then, moves straight towards the destination point computed above, without being stopped (rigidity assumption). If \(r\) stays still, it performs a _null movement_.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 2 Background and Preliminaries
### Smart-Grid-Based Cybersecurity Standards
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Standard** & **Purpose and Focus** & **Integration in Smart Grids** & **Security Domains in Smart Grids** & **Advantages** & **Limitations** & **Key Challenges** & **Suggested Improvements** \\ \hline \multirow{4}{*}{NERC CIP [37]} & Cybersecurity enforcement for Bulk Systems (BESs), central systems & Applied to centralized control systems, limited DER coverage & Policies, access control, system configuration, perimeter security & Regulatory mandate, detailed protection directives & Limited edge-device or DER support & Adapting to dynamic and distributed architectures & Expand scope to include DECs and demand-response systems \\ \hline \multirow{4}{*}{IEEE 2003 [38]} & Layered cybersecurity architecture, modular deployment, and interoperability & Supports microgrids, and energy storage systems & Cyber-physical coordination, multi-layer defense & Promotes standardization and modular security design & Complex for SMEs or small-scale deployment & Deployment models and promote open API environments & Simplify architecture models and promote open API & Simplify architecture and promote open API \\ \hline \multirow{4}{*}{NIST SP 800-82 [39]} & Security guidelines [39] & Covers ICS/SCADA with smart grid infrastructure & Atsel identification, with smart grid infrastructure & Adaptable to AI/ML integration, decision anomaly detection & Requires interpretation, not always & Operationalizing knowledge & Develop plug-and-play \\ \cline{1-1}  & ISMS framework bridging IT and OT security & Used for power generation, automation systems, and distributed field operations & Data classification, access control, information flow, application handling & Globally recognized framework, high-level, not tailored to energy application & Graph implementation & Customing ISMS and tools for smart training & Graph-specific grid-specific grid-specific scenarios \\ \hline \multirow{4}{*}{IEC 62351 [41]} & Secure communication networks for power systems, particularly SCADA & Secures communication in exchanging systems, particularly SCADA and substations & Authentication, encryption, key management, BRAC, secure logging & Established protocols for SCADA and communication & Limited TLS/IPSec support, slow to update & Legacy system & Update encryption schemes, enable backward & Secure retrofitting options \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Cybersecurity standards for smart grids.
# PaperNet: A Dataset and Benchmark for Fine-Grained Paper Classification
## 5 Experiment
### Main Results
#### 5.3.3 Multi-Modal Classification  
Since the single-modal classification methods can not achieve satisfactory results, we consider using multi-modal classification methods. Some popular VQA and Image caption methods such as VisualBERT or UNITER are not suitable for the multi-modal fine-grained paper classification task. We used the model proposed by [25] for multi-modal paper classification. Additionally, in order to make the model more suitable for paper classification tasks, we improved the model and propose our method introduced in Section 3. As shown in Table 3, the proposed method achieves the highest accuracy in three datasets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Modality** & **Algorithm** & **PaperNet\_2** & **PaperNet\_20** & **PaperNet\_CV** & **PaperNet\_NLP** \\ \hline \multirow{2}{*}{Image} & ResNet50 & 83.94 \(\pm\) 0.55 & 50.30 \(\pm\) 0.66 & 60.23 \(\pm\) 0.61 & 45.33 \(\pm\) 0.50 \\  & DenseNet121 & 82.34 \(\pm\) 0.96 & 45.16 \(\pm\) 0.46 & 58.80 \(\pm\) 0.40 & 46.21 \(\pm\) 0.46 \\  & MobileNetV3 & 83.66 \(\pm\) 0.86 & 48.38 \(\pm\) 0.56 & 56.45 \(\pm\) 0.40 & 40.08 \(\pm\) 0.54 \\ \hline \multirow{2}{*}{Text} & Umfit & 96.26 \(\pm\) 0.44 & 71.30 \(\pm\) 1.12 & **75.30 \(\pm\) 1.06** & 73.33 \(\pm\) 0.18 \\  & Albert & 96.31 \(\pm\) 0.09 & 73.32 \(\pm\) 0.24 & 73.18 \(\pm\) 0.12 & 74.23 \(\pm\) 0.36 \\ \hline \multirow{2}{*}{Multi-modal} & Concat & 96.27 \(\pm\) 0.11 & 73.45 \(\pm\) 0.69 & 72.43 \(\pm\) 0.27 & 75.36 \(\pm\) 0.51 \\  & Our method & **97.05 \(\pm\) 0.05** & **73.85 \(\pm\) 0.39** & 74.26 \(\pm\) 0.32 & **79.27 \(\pm\) 0.37** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of accuracy.  
Figure 5: Examples of the PaperNet_CV dataset figures.  
Figure 6: Examples of the PaperNet_NLP dataset figures.
## 1 Introduction
The subcarrier spacing was varying from 78.13 Hz to 1250 Hz, and the OFDM symbol duration was from 0.8 ms to 12.82 ms. The minimum BER achieved was equal to 0.004. The adaptation of the OFDM modulation scheme parameters, such as symbol duration and subcarrier spacing, to transmission properties of the UAC channel, are well described in literature on wireless communications [8]. However, the detailed procedures of determining the UAC transmission parameters of the channel, such as the delay spread, Doppler spread, coherence bandwidth, and coherence time, are rarely presented. It applies in particular to the threshold levels of relevant transmission characteristics based on which the parameters are determined. The performed underwater acoustic OFDM communication experiment has shown that the choice of the criteria for determining transmission parameters, based on which OFDM modulation scheme is designed, has an impact on the achieved data transmission rate and reliability. The organization of the paper is as follows. Section 2 describes hardware of the UAC system used during the inland-water experiment as well as a physical layer of the OFDM data transmission. Section 3 presents the results of measurement of an underwater channel and estimation of its basic transmission parameters. In this section the setup of the experiment is described. In Section 4 the results of the OFDM transmission tests are described, which are commented in Section 5.
## 3 Tapped Delay Line Model
### Representation of Channel Impulse Response  
The received signal is consists of a certain number of replications of transmitted signal propagated via different propagation paths. The baseband impulse response of a multipath channel can be expressed as [25]  
\[h(t,\tau)=\sum_{i=0}^{N(t)-1}a_{i}(t)e^{\phi_{i}}\mathrm{e}^{i2\pi f_{i}(t)t} \delta(\tau-\tau_{i}(t)), \tag{1}\]  
where \(t\) and \(\tau\) are time and delay, respectively. The terms \(a_{i}(t)\), \(\tau_{i}(t)\), and \(f_{i}(t)\) are the magnitude, delay, and Doppler shift of the \(i\)th MPC at time \(t\). \(\phi_{i}\) is the initial phase of the \(i\)th path. \(N(t)\) is the total number of MPCs at time \(t\). \(\delta(\cdot)\) represents the is the Dirac-delta function, which describes the specific time of arrival \(\tau_{i}\) of individual MPC at time \(t\). \(f_{i}(t)\) represents the Doppler shift that is mainly caused by the movement of transmit and/or receive antennas. The Doppler shift frequency depends on relative moving speed \(\Delta v(t)\) of antennas, the wavelength, and the angle of arrival \(\gamma_{i}(t)\) of the microwave, and can be expressed as [25]  
\[f(t)=-\frac{\Delta v(t)}{\lambda}\cdot\cos(\gamma_{i}(t)), \tag{2}\]  
where \(\Delta v(t)\) represents the relative speed, \(\lambda\) is the wavelength calculated according to the carrier frequency \(f_{c}\) and the speed of light \(c\). Therefore, the maximum Doppler frequency value can be calculated based on geometric relationship when \(cos(\gamma(t))=1\). In addition, \(\gamma_{i}(t)\) includes the arrival direction and the movement direction, which determines the power spectral density or Doppler spectrum of \(\gamma_{i}(t,\tau)\)[26].  
In A2G communications, the received signal at the ground is distorted due to multipath propagation and blockage by ground objects. Considering the fact that the UAV was moving at a constant speed, we assume that the fading process is stationary over a relatively short period of time, i.e., in which the WSSUS assumption is valid. Thus, the scatters at different time delays are uncorrelated, and only depend on frequency separation, and different Doppler shifts are uncorrelated depending only on time differences [24]. A reasonable time window shall be selected, where a large window could cause non-stationary characteristics and an \"averaged\" Doppler spectrum, whereas a small window could result in low resolution in Doppler spectrum. As reported by [27; 28], the stationary time can be calculated as \(t_{s}=d_{s}/\Delta v\), where \(d_{s}\) represents the stationary distance in the range between \(20\lambda\) to \(40\lambda\) and \(\Delta v\) is the relative speed between transmitter and receiver. In this paper, we determine individual stationary time window lengths for different scenarios at various flight stages of the UAV to ensure a reliable analysis of the data.
# Efficient FPGA Implementation of an ANN-Based Demapper Using Cross-Layer Analysis
## 4 Hardware Implementation  
In this work, we design an efficient FPGA architecture for the bitwise AE described in Section 3 with focus on low-latency and low-power. To reduce the bandwidth and eliminate the communication overhead of a feedback channel from receiver to transmitter, we fix the constellations of the transmitter after training in software and implement only the receiver as trainable ANN on FPGA. The justification of such a system in terms of communication performance is given in Section 4.1. In summary, the AE system is jointly trained in software but can be fine-tuned and adapted to real channel conditions by re-training the demapper in hardware.
## 4 Verification and Accuracy Analysis of Single-Frequency Occultation Processing
### Accuracy Analysis of Refractive Index Products
Because the Abel transform suppresses high-frequency noise to a certain extent [42], the fractional error of the refractive index is often several times smaller than the corresponding fractional error of bending angle, so in the troposphere, the residual ionospheric noise (not filtered out below 15 km) is not expected to have a significant impact on the error in the retrieved refractive index profile. However, the root mean square error of partial single-frequency and dual-frequency inversion of refractive index above 25 km may come from the smoothed residual ionospheric noise and the prior climatologic situation near the transition layer, which have a certain impact. However, on the whole, the refractive index trend of single/dual-frequency inversion is basically the same, with good consistency. To sum up, the standard deviation of the single-frequency inversion of refractive index is slightly lower than that of dual-frequency inversion over 25 km, but the system deviation of the single-frequency reconstruction and that of the dual-frequency observation inversion of the refractive index are basically the same. Through the correctness verification of the single-frequency relTEC change rate, reconstruction excess phase Doppler and the accuracy analysis of the refractive index products, the single-frequency occultation inversion algorithm based on the BDS BII signal in this paper is shown to be correct and feasible.
# FMT: Unsupervised RGB-D Point Cloud Registration via Fusion Feature Matching with Transformer
## 2 Related Work
### RGB-D Fusion  
RGB images provide high-resolution color and texture details, while depth images effectively complement the geometric structural information of the scene. The combination of both modalities overcomes the limitations of individual modalities in terms of illumination variations and semantic deficiencies.  
With the continuous advancement of sensor technology and the increasing availability of computational resources, the cost of acquiring RGB-D data has steadily decreased, further promoting its widespread adoption and development in practical applications. To date, this fusion approach has been applied to various downstream tasks, such as 3D object detection [30; 31], segmentation [32], depth enhancement [33], image enhancement [34], style transfer [35], and more.  
Many studies [30; 36; 37] focus on extracting information from a single modality, followed by feature fusion. PVN3D [36] utilizes CNN and PointNet++ [38] to extract features from RGB images and point clouds, respectively, followed by DenseFusion [39] for feature integration. While this approach improves the accuracy of 6D pose estimation to some extent, it fails to deeply explore the complementary information inherent in RGB-D data. Some studies focus on leveraging one modality to guide the feature extraction process of another modality, as exemplified by methods such as BYOC [22], LLT [23], and GMF [40]. BYOC [22] introduces a novel self-supervised method that leverages pseudolabels of sampled correspondences generated by an initially random feature encoder to learn point-wise features for point cloud registration. LLT [23] proposes a geometry-aware visual feature extractor based on a linear Transformer, which incorporates geometric information to guide the extraction of visual features, thereby facilitating more reliable correspondence estimation. GMF [40] proposes a universal multimodal fusion module that integrates the structural and texture information of RGB to reject anomalous depth feature matching. Additionally, some studies employ bidirectional symmetric fusion of RGB and depth information, as seen in BPNet [41], FFB6D [42], and PointMBF [24]. BPNet [41] introduces a bidirectional projection network that facilitates the interaction of 2D and 3D information across multiple levels of the architecture, thereby better leveraging the advantages of both visual domains. PointMBF [24] introduces a bidirectional multi-scale fusion framework that focuses on effectively integrating the complementary modalities of RGB and depth information. By thoroughly leveraging the inter-modal relationships and complementarities, it enhances bidirectional information exchange and propagation, resulting in the generation of more distinctive features that establish a solid foundation for point cloud registration tasks.
## 3 Results and Discussion
### Color Kinetics of the NR Sheets
In this experimental case, the amino and carbonyl compounds in the rubber sheets were changed to the colored melanoidins after receiving heat. Much research [58; 59; 60] has concluded that the decrease in the amino and carbonyl compound levels before drying starts has a strong influence on decreasing the brownness color value on the product's surface area after drying. Therefore, the increased value of the brownness color on the dried NR sheet surface area showed that the NR sheet had a high value of the amino and carbonyl compounds remaining before the drying process started. For this reason, this indicated that a part of the amino and carbonyl compounds was removed from the NR latex in the coagulating process. The results present that the dried NR sheet samples using para rubber wood vinegar and acetic acid as the coagulating material had lower brownness color values on the surface area than the samples using bamboo and coconut shell wood vinegar. Thus, this indicates that the para rubber wood vinegar had a high capability for decreasing the amino and carbonyl compound values to the same level as acetic acid and a better level than bamboo and coconut shell wood vinegar. In other words, amino and carbonyl compounds were removed from the NR latex with acid substances, especially acetic acid. This may be due to the para rubber wood vinegar having a higher value of acetic acid than the bamboo and coconut shell wood vinegar. In addition, the results found that the Maillard interaction rate was higher with the increasing change in the water activity of the rubber sheets and the drying temperature. Enzymatic browning occurs when the polyphenols in an NR sheet are oxidized by the catalytic. This reaction reacts with other compounds in the sheet to create the colored polymeric compounds. The colors of each experimental sheet sample and the referent are presented in Figure 8. The results show that the NR sheet samples using para rubber wood vinegar as the coagulating material had a color value at the same level as the acetic acid and the referent. Figure 8: The color of each experimental sheet sample and the referent for (**a**) para rubber wood vinegar; (**b**) acetic acid; (**c**) bamboo wood vinegar; (**d**) coconut shell wood vinegar; and (**e**) the referent.
## 3 Dataset 1: Austrian Federal Forests
### Predictive Models
#### 3.3.1 Regression Task
_Random Forest_  
The random forest was trained following a similar approach to the decision tree regarding preprocessing, dataset splitting, and grid search for the best parameters. With 200 estimators, the model achieved acceptable prediction performance. For the four features, the MSE was found to be 121,154.73, with an RRMSE of 1.84. Similarly, for all seven features, the MSE decreased to 114,203.96, with a corresponding RRMSE of 1.71 [4]. The feature importance values derived from random forests are typically more robust than those from individual decision trees [33]. However, unlike decision trees, random forests do not provide explicit rules for interpretation. Despite this lack of interpretability, random forests generally outperform individual decision trees regarding predictive performance. _Fully Connected Neural Network_  
A fully connected neural network [37] was implemented to address the regression problem. Utilizing four layers with 50 neurons each, the neural network achieved an MSE of 93,215.32 and an RRMSE of 35.39 with the four basic features. When all seven features were used, the MSE increased to 155,141.41, with a corresponding RRMSE of 37.97 [4]. The optimizer employed was \"RMSprop,\" and the activation function utilized was the Rectified Linear Unit (ReLU). Figure 4 depicts the prediction results for the four features. One method for computing the importance of features is through the SHAP (Shapley values) explainable AI method [38; 39; 40]. Figure 5 illustrates the feature importance values obtained using SHAP values for four features (and refer to Table 1). Figure 4: The outcomes of the neural network model on the test set, utilizing 4 input features; reprinted with permission from [4]. [PERSON] 2022.