esa-sceva/satcom-chunk-collection huggingface dataset:
# A Roadside and Cloud-Based Vehicular Communications Framework for the Provision of C-ITS Services
## 3 Roadside and Cloud Architecture
### Cloud Architecture
#### 3.2.3 PKI's Certificate Authorities (CAs)  
The PKI plays a key role in providing security and trust between the road users composing the local vehicular network. ITS-Ss, such as the vehicles' OBUs and RSUs, employ temporary and rotatory certificates--the Authorization Tickets (ATs)--which grant them both privacy and authentication in the vehicular network. Trusted authorities, namely, the certificate authorities (CAs), are responsible for providing the root of trust in the vehicular system, issuing ATs to requesting ITS-Ss.  
Requesting certificates from CAs is typically an automatic process, where new certificates are requested when currently owned certificates start to reach the end of their validity period. In the designed architecture, the road manager can also manually upload  
Figure 5: MOBICS dashboard interface, version 1.9.0 (current). Features include, the monitoring of RSUs statuses (**top left**), generation of events (**top right**), and traffic and history overview (**bottom**).  
CA-issued certificates to a deployed RSU through the available SSH connection. Other changes to the PKI, such as changes to the CA certificates themselves, can also be uploaded to the RSUs via this connection.
# Planetary-Gearbox Fault Classification by Convolutional Neural Network and Recurrence Plot
## 6 Conclusions  
In this paper, we proposed a combination of a CNN with recurrence-plot analysis in the angular-domain method for fault classification in planetary gearboxes. The combination of the CNN with the RP was compared with angular domain RP, CWT (cmor4-4), and IMF plots. Even though the CWT could also obtain high valuation accuracy, it needed more epochs. Moreover, due to the speed fluctuations and limitation of handcrafted features, the valuation accuracy of the two other methods was lower than that of the CWT and the proposed method. Finally, experiment results showed that the proposed method is superior to conventional methods on the fault classification of planetary gearboxes.  
Methodology, D.-F.W.; writing--review and editing, X.W., J.N., and G.L.; project administration, Y.G.; funding acquisition, Y.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51675251.  
The authors declare no conflict of interest.  
Figure 6: Confusion matrix of planetary-gearbox fault classification. (**a**) Continuous wavelet transform (CWT); (**b**) angular domain RP; (**c**) intrinsic mode function (IMF); (**d**) time domain RP (NO = normal, PG = planet gear, SG = sun gear, PC = planet carrier).
## 4 Test Results
### RR Performance Predictions for Individual Sections and End-to-End
Therefore, the network operator has to decide whether to lock modulation order in accordance with the norms for the fixed transmission rate, which automatically precludes maximal performance (i.e., maximal Ethernet throughput during favorable RR propagation conditions: no rain, no fading, etc. ), or configure the system onto adaptive modulation and coding, and thus a variable transmission rate and throughput, which requires adopting QoS parameters for traffic differentiation, shaping, and prioritization. In this regard, the system under test adopts the second option (i.e., operation with maximal possible throughput). Figure 7:0 Prediction for the fifth RR section; 256 QAM, 368 Mb/s.
# Task-Driven Real-World Super-Resolution of Document Scans
## 2 Related Work
### Task-Specific SR  
Although most of the efforts are concerned with purpose-agnostic enhancement, the validation of emerging SR techniques in the context of specific computer vision tasks has received growing research attention [12]. This also includes research focused on text detection and recognition. [PERSON] et al. used GAN-based SR to boost scene-text recognition performance [28], and [PERSON] et al. leveraged multi-task transformers for scene-text SR [29]. These methods typically report improvements in the recognition accuracy, but rely almost entirely on synthetically blurred images for training and evaluation. Hence, their effectiveness under real, uncontrolled degradations remains unclear. In the document analysis domain, the ICDAR Robust Reading Challenges have driven the development of SR applied to scanned receipts and book pages [30]. This has provided the first large sets of real LR documents, but without paired HR ground truth, which makes systematic training and evaluation of SR methods difficult. Inspired by these findings, our approach explicitly incorporates OCR-relevant feature losses to guide SR toward text-clean reconstructions, while monitoring and mitigating potential hallucinations.  
There were some attempts to train the networks for SR in a task-oriented manner by making the loss function focus on these specific tasks, thus guiding the training process accordingly. [PERSON] et al. applied an object detection loss for training an SISR network [31]. Although the task-driven training leads to worse PSNR scores than relying on the L1 loss, it was demonstrated that the super-resolved images constitute a more valuable source for object detection. Similar task-driven loss functions were also defined for semantic image segmentation [32; 33]. The losses based on segmentation masks raise mean IoU, but softens the textures. Importantly, all of these techniques were applied only to simulated LR images, and they were not tested in real-world scenarios. Task-driven OCR has been explored by [PERSON] et al. and [PERSON] et al. [28; 34], who report lower character-related error rates, yet their GAN-based networks occasionally hallucinate glyphs on unseen degradations. This is also a concern of our recent work on task-driven SR [35], in which we trained several architectures for SISR in a task-oriented way. In the work reported here, we adapt that technique for real-world images, and we incorporate multiple semantic losses that are dynamically balanced during training.
# Satellite Measured Ionospheric Magnetic Field Variations over Natural Hazards Sites
## 3 Lithospheric Atmospheric Ionospheric Coupling
### Earth-Ionosphere Waveguide  
The Earth-Ionosphere Waveguide extends from the surface (lower boundary) up to the lower ionosphere (upper boundary), which is the D-layer during daytime and the E-layer during night. The wave propagation between narrowband very low frequency (VLF; f = 3-30 kHz) and low frequency (LF; f = 30-300 kHz) transmitter and receiver stations is confined in this cavity, e.g., [19]. In a network configuration with several VLF/LF propagation paths it is possible to sample larger areas, e.g., over Southern Europe [20]. The upper waveguide boundary could be externally driven, e.g., through solar X-ray flares [21], but significant internal drivers can be acoustic waves (AW) and atmospheric gravity waves (AGW) [22]. A thorough and systematic understanding of phenomena up to the lower ionosphere is a basic requirement for investigations of the upper atmosphere (thermosphere) with decisive in-situ measurements. An important task is to systematically quantify uncertainties in the datasets and finally validate and verify the processes in accurate and predictive ways.
In brief, the existing studies only selected the factors of topography, meteorology, hydrology, and geological environment as evaluation factors without considering the InSAR deformation information for landslide susceptibility evaluation model construction, which affects its evaluation accuracy to some extent. For this reason, we took the Gengma seismic zone as the study area, employed the InSAR deformation rate and other conventional factors as the evaluation factors simultaneously, and adopted the Pearson correlation coefficient to exclude the covariance factor. Subsequently, we adopted the BO algorithm for hyperparameter optimization based on the CNN model to establish a landslide susceptibility evaluation model based on Bayesian optimization of a convolutional neural network (BO-CNN), which can improve the prediction accuracy of the model. Lastly, we used the BO-RF and PSO-SVM models for comparative analysis to verify the accuracy of the constructed model.
## 5 Experimental Evaluation
### Execution Times and Suitability Metric
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{**NB**} \\ \hline
**Feature Pattern** & **Feature Extraction Time** & **Model Building Time** & **Total Training Time** & **Testing Time** \\ \hline
**U** & 0.0028 & 0.3626 & 0.3654 & 0.1572 \\
**U,B** & 0.0162 & 1.2932 & 1.3094 & 0.2905 \\
**U,B,T** & 0.0299 & 2.3124 & 2.3423 & 0.4124 \\
**U,B,T,Q** & 0.0434 & 3.2504 & 3.2938 & 0.4971 \\ \hline \hline \multicolumn{4}{c}{**kNN**} \\ \hline
**Feature Pattern** & **Feature Extraction Time** & **Model Building Time** & **Total Training Time** & **Testing Time** \\ \hline
**U** & 0.0028 & 0.3339 & 0.3367 & 5.1004 \\
**U,B** & 0.0162 & 1.2770 & 1.2932 & 5.0536 \\
**U,B,T** & 0.0299 & 2.2337 & 2.2636 & 5.7653 \\
**U,B,T,Q** & 0.0434 & 3.2577 & 3.3011 & 5.3663 \\ \hline \hline \multicolumn{4}{c}{**SVM**} \\ \hline
**Feature Pattern** & **Feature Extraction Time** & **Model Building Time** & **Total Training Time** & **Testing Time** \\ \hline
**U** & 0.0028 & 137.9433 & 137.9461 & 30.2422 \\
**U,B** & 0.0162 & 201.2421 & 201.2583 & 45.3217 \\
**U,B,T** & 0.0299 & 247.0215 & 247.0514 & 57.4804 \\
**U,B,T,Q** & 0.0434 & 281.5109 & 281.5543 & 62.1898 \\ \hline \hline \multicolumn{4}{c}{**RF**} \\ \hline
**Feature Pattern** & **Feature Extraction Time** & **Model Building Time** & **Total Training Time** & **Testing Time** \\ \hline
**U** & 0.0028 & 56.5771 & 56.5799 & 1.4356 \\
**U,B** & 0.0162 & 219.9926 & 220.0088 & 2.0941 \\
**U,B,T** & 0.0299 & 399.2106 & 399.2405 & 2.4720 \\
**U,B,T,Q** & 0.0434 & 617.9247 & 617.9681 & 2.6590 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Execution times (in seconds) for the most promising feature patterns. Training Suitability and Testing Suitability ranked experiments by keeping the two phases (training and testing) separated. In Formula 5, we propose a unified _Suitability_ metric. \[\mathit{Suitability}(e_{i})=\alpha\times\mathit{TrainingSuitability}(e_{i})+(1- \alpha)\times\mathit{TestingSuitability}(e_{i}) \tag{5}\]  
i.e., the unified suitability is the weighted average of _Training Suitability_ and _Testing Suitability_, where \(\alpha\in[0,1]\) balances the two contributions. Table 9 reports the values of training suitability, testing suitability and unified suitability for the same experiments considered in Table 8. Figure 8 depicts the results, by using the same convention as in Figure 4, by using the unified suitability. The reader can see that the Naive Bayes classifier had the highest suitability values, due to its ability to combine high accuracy and very low execution times. Surprisingly, the kNN classifier obtained the second position; in fact, in spite of the fact that it obtained the worst accuracy values, it obtained the lowest execution times. Finally, the SVM classifier and the Random-Forest classifier were strongly penalized by their execution times. Consequently, on the basis of Table 9 and Figure 8, we can clearly say that the Naive Bayes classifier applied to the feature pattern of uni-grams clearly emerged as the most suitable solution for our application
# A Frequency-Domain Estimation Scheme for Frequency Offset with Large Range in OFDM Systems
## 4 Simulation Systems and Results
### Simulation Results
#### 4.2.1 The MSE Performance with Different Doppler Shifts and \(M\)  
Figure 5 shows the MSE of the normalized frequency offset with different \(\epsilon\) and \(M\). The number of configured RBs in the PUSCH is \(N_{rb}=40\). As shown in Figure 5, the MSE of \(M=20\) is near to that of \(M=1024\) when \(|\epsilon|\) is small (\(\epsilon=0.1\)), while the MSE of \(M=60\) is near to that of \(M=1024\) when \(|\epsilon|\) is large (\(\epsilon=-0.3\)). Since the larger \(|\epsilon|\) leads to the larger inter-carrier interference (ICI) [9], the larger \(M\) is needed to keep the estimation accuracy. The simulation results also show that the low-complexity implementation of Algorithm 1 could efficiently estimate the frequency offset, and the smaller \(M\) could be used to further reduce the calculation complexity, especially when \(|\epsilon|\) is small.  
Figure 4: An NR subframe with twelve data OFDM symbols and two DMRS OFDM symbols.  
Figure 5: The MSE performance with different \(\epsilon\) and \(M\).
## 2 Materials and Methods
### Data Collection and Preprocessing
#### 2.2.5 Coastal Cultural Heritage  
The final dataset refers to the location of the coastal cultural heritage assets in the Mediterranean countries of EU-27. Two distinct data sources were used, i.e., UNESCO's World Heritage Sites (WHSs) point locations ([[https://whc.unesco.org/en/list/](https://whc.unesco.org/en/list/)]([https://whc.unesco.org/en/list/](https://whc.unesco.org/en/list/)), accessed on 15 May 2024) and data extracted from OpenStreetMap (OSM) using Python scripts and keywords to identify place markers (i.e., \"heritage\", \"archaeology\"). The OSM dataset returned point, line, and polygon findings. Both datasets were reprojected from WGS84 to ETRS89-LAEA and through a spatial selection limited to the area of interest. In the case of point geometry heritage assets, a buffer zone of 50 m was applied to ensure that the monuments would not be omitted from the endangered assets due to Sea Level Rise.  
Twenty-eight UNESCO WHSs are located in the area of interest (e.g., Delos in Greece, the old city of Dubrovnik in Croatia, the City of Valetta in Malta, etc.). Furthermore, 3512 heritage point locations were retrieved from OpenStreetMap, presenting a very dense distribution in the coastal areas of Greece, Italy, France, and Spain (Figure 5).  
The final pre-processing algorithm can be seen in Figure 6.  
Figure 5: Density of Cultural Heritage Sites in the Mediterranean Region (Low: 1-High: 70).
## 2 Materials and Methods
### GNSS-Measured Motions and Stress-Deformation Cycle in Subduction Regions
Upper panel shows velocity vectors measured by the Kuril GNSS stations during time intervals from May 2007 to May 2011 (**a**) and from May 2011 to May 2015 (**b**). (1) Sources of strongest earthquakes with M \(\geq\) 8; (2) main shocks of earthquakes in 2006 and 2007; (3) subduction velocity equal to 80 mm/yr. The velocities indicated here are relative to the North American lithospheric plate. Lower panel shows northern (**c**) and eastern (**d**) components of the displacements of the Kuril network observation points. Dashed vertical lines denote the moments: (1) Simushir earthquake in 2006; (2) Simushir earthquake in 2007; (3) Eruption of Sarychev peak in 2009; (4) deep earthquake of the Sea of Okhotski in 2013. Figure 5: GNSS-observed motions in Chile subduction zone before (**a**) and after (**b,d**) large 2010 Maule earthquake modified from [17]. Map plots show the velocity vectors estimated from the Chilean and Argentinean regional satellite geodesy networks data for the time intervals of 27 February 2009–26 February 2010 (**a**); 28 February 2010–27 February 2011 (**b**); 28 February 2013–27 February 2014 (**c**); and 28 February 2016–27 February 2017 (**d**). Lower panel shows eastward displacement timeseries measured at CONT (**e**) and PCLM (**f**) GNSS stations. Displacements are relative to the South American lithospheric plate. Figure 6: Surface motions in Japan before (**a**), during (**b**) and after (**c**–**f**) 2011 Tohoku earthquake estimated from GEONET GNSS data, modified from [18]. Red arrows show velocity distributions for time intervals as following: 11 March 2010–10 March 2011 (**a**); 11 March 2011 (**b**); 12 March 2011–10 March 2012 (**c**); 11 March 2012–10 March 2013 (**d**); 11 March 2013–10 March 2014 (**e**), and 11 March 2014–10 March 2015 (**f**). Velocities are relative to the South American lithospheric plate.
## 7 Experimental Results and Analysis
### Resource Platform System Requirements  
The resource platform system contains a large number of resources and stores user login credentials. The back-end utilizes Spring Boot for data calls and a MySQL database for data storage. User identification and rights management are implemented using Spring Security. The chunking algorithm is packaged as a jar file and deployed in the back-end, where it can be invoked through the Maven management tool. This approach enables the easy and efficient invocation of the chunking algorithm on a highly scalable platform. The entire platform is deployed on an Ngin's server, and the environment is monitored using the Pagoda O&M management tool, which enhances the reliability of the resource platform. Ultimately, the server provides an interface to the front-end for relevant invocations. The deployment of the chunking algorithm in the resource platform system dramatically enhances the security of the data transmitted through the platform. The use of jar packages allows the chunking algorithm to be easily and quickly integrated into the system, thus increasing the scalability of the system. In addition, the chunked disorder algorithm can support data encryption scenarios for most functions of the resource platform system, including the encrypted transmission of data such as usernames, passwords, and text resources, further improving the system's security. Ultimately, the architecture design of the system and the deployment method of the chunked chaotic algorithm fully consider the scalability and security requirements of the system, providing users with a more secure and reliable resource platform service.  
Figure 21: Encryption and decryption times for different lengths of sharing codes.
_Applied_  
_sciences_  
Research on Dynamic Mechanical Properties and Constitutive Model of Basalt Fiber Reinforced Concrete after Exposure to Elevated Temperatures under Impact Loading  
**[PERSON] 1, [PERSON] 1,*(), [PERSON] 2,*, [PERSON] 2, [PERSON] 1 and [PERSON] 2**  
1 School of Civil Engineering, Chang'an University, Xi'an 710061, China; [EMAIL_ADDRESS] (W.L.); [EMAIL_ADDRESS] (Z.W.)  
2 School of Geology Engineering and Geomatics, Chang'an University, Xi'an 710054, China; [EMAIL_ADDRESS] (Y.Z.); [EMAIL_ADDRESS] (Y.Y.)  
* Correspondence: [EMAIL_ADDRESS] (J.Z.); [EMAIL_ADDRESS] (Y.L.); Tel.: +86-137-0926-5196 (J.Z.); +86-151-1489-4186 (Y.L.)  
Received: 16 September 2020; Accepted: 26 October 2020; Published: 30 October 2020  
**Abstract:** The dynamic mechanical properties of basalt fiber reinforced concrete (BFRC) with different fiber contents (0.0%, 0.1%, 0.2%, 0.3%, 0.4%), confining pressures (0 MPa, 5 MPa, 10 MPa, 15 MPa) and exposed to different temperatures (20 \({}^{\circ}\)C, 200 \({}^{\circ}\)C, 400 \({}^{\circ}\)C, 600 \({}^{\circ}\)C, 800 \({}^{\circ}\)C) were investigated by using a 50 mm split Hopkinson pressure bar (SHPB) apparatus, and the factors such as fiber content, temperature and confining pressure effect on the dynamic mechanical properties were analyzed. The results show that the dynamic peak stress increases first and then decreases with the increase of fiber content. At different temperatures, the peak stress and its corresponding strain correspond to different fiber content, and the optimal fiber content is between 0.1% and 0.3%. When the temperature was from 20 \({}^{\circ}\)C to 400 \({}^{\circ}\)C, the dynamic peak stress decreased less, while when the temperature reached 600 \({}^{\circ}\)C and 800 \({}^{\circ}\)C, the dynamic peak stress decreased greatly. The confining pressure can significantly increase the dynamic peak stress and change the crushing morphology of specimens. The damage variable was built based on the Weibull distribution. A dynamic damage constitutive model combining statistical damage and viscoelastic model was established based on component combination model. The fitting curve of this model fitted well with test curve by identifying fewer undetermined parameters compared with Zhu-Wang-Tang (ZWT) model; therefore, this model can well describe the dynamic properties of BFRC under impact load.  
**Keywords:** BFRC; SHPB; elevated temperatures; impact velocity; fiber content; confining pressure; constitutive model
## 2 Methods
### Image Deblurring via DeblurGANv2  
DeblurGANv2 is a deblurring method based on deep learning. DeblurGANv2 uses a general feature pyramid network as the backbone feature extraction network, allowing users to select Inception-ResNet-v2 [34], MobileNetv2 [30], and MobileNetDSC in the backbone, which enables users to optimize the accuracy, speed, or both. The running times of MobileNetDSC and MobileNet are less than 0.06 s, which makes real-time deblurring possible. The DeblurGANv2 model with a MobileNetv2 backbone feature extraction network was used to deblur images. MobileNetV2 was used as our backbone feature extraction network because it has a rapid runtime and maintains almost the same accuracy as the Inception-ResNet-v2 backbone.  
Not all the pictures collected by the camera are blurred. If all the collected pictures are deblurred, it not only wastes resources, but also causes serious information loss and reduces the accuracy of the obstacle detection process. Therefore, this study used the blur-judgment mechanism proposed by [PERSON] et al. [35] to perform a blur judgment of the collected pictures. The motion-blur image-processing flow is shown in Figure 1, in which the image blur value can be calculated by the Laplace operator.  
Through the Laplace operator, we could determine whether there was more or less high-frequency information in the picture. If the picture has more high-frequency information, the picture is considered clear; on the contrary, it is considered that the picture is relatively blurred. The Laplace operator is defined in Equation (1):  
\[\Delta f=\
abla^{2}f=\
abla\cdot\
abla f \tag{1}\]  
where f is a second-order differentiable real function. The template e operator is presented in Figure 2.  
\(D(f)\) is defined in Equation (2):  
\[D(f)=\sum_{y}\sum_{x}|G(x,y)|G(x,y)>T \tag{2}\]  
where \(G\) (\(x,y\)) is the convolution of the Laplace operator at the pixel point (\(x,y\)). \(T\) is the given threshold.