esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions  
In soil with limited width, it is valuable to explore the action mechanism and distribution characteristics of active earth pressure under the action of intermediate principal stress and earthquake. Based on previous studies, the default slip surface is a logarithmic spiral. This study was carried out under complex conditions based on the plane strain twin-shear unified strength theory formula and considering the effects of soil arching and tension cracks. Three conclusions are summarized as below:  
Due to the contribution of the intermediate principal stress, the lateral earth pressure coefficient, the horizontal active earth pressure, the resultant force, and the action point gradually decreased with the increase of the intermediate principal stress weight coefficient \(b\). When \(b=1\), the strength potential of the material could be increased by 22.96% at most under the same seismic conditions. Therefore, reasonable consideration of the influence of the intermediate principal stress can reduce the consumption of materials to a certain extent, which has considerable economic benefits in engineering.  
The effect of earthquake on earth pressure is very significant. As the horizontal seismic coefficient \(k_{h}\) increased, the active earth pressure \(\sigma_{x}\) and the resultant force \(E_{a}\) gradually increased, while the action point \(h_{a}\) gradually decreased. As the vertical seismic coefficient \(k_{v}\) increased, the lateral earth pressure coefficient \(K\) gradually decreased--when \(k_{v}=0.4\), the reduction of \(K\) was the most obvious. In addition, as the wall-soil friction angle \(\delta\) increased, the soil arching effects became increasingly obvious, \(E_{a}\) gradually decreased, and \(h_{a}\) gradually increased. It is worth noting that in the calculations in this paper, \(h_{a}~{}>~{}H/3\) when \(b=0\) and \(\delta/\varphi_{0}>0.55\). At this time, it is necessary to strengthen the anti-overturning stability when designing retaining structures.  
The pseudo-static method was used in this study to deal with the dynamic earth pressure under earthquake action when soil has a limited width, which is equivalent to a further improvement of the Mononobe-Okabe method. Through comparison with previous studies, we found that both methods presented a \"convex\" nonlinear distribution along the retaining structure, and the decreasing points were all near 0.8\(H\). This indicates that the method proposed in this paper is reasonable and can provide a new approach for the subsequent study of 3D seismic earth pressure considering the intermediate principal stress.  
Methodology, D.K. and H.L.; data curation, W.G. and B.W.; writing, H.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
# 5G Security Threat Assessment in Real Networks
## 2 Background
### 5G NSA Arthitecture
#### 2.2.1 5G NSA NAS Security Setup Procedure  
As shown in Figure 3, the NAS Security Setup process is a preparatory task to deliver signal messages safely between the terminal and the MME on a wireless link. It performs an integrity check and ciphering function for NAS signaling messages. While an integrity check is defined as mandatory in 3 GPP TS. 33.401, ciphering is defined as an option [14].  
When a terminal (UE) sends a network access request (attach request) message (non-integrity, non-ciphering) to a 5G network, the value set in the UE Network Capability field in that message represents the type of ciphering and integrity protection algorithm supported by the terminal. Among EPS encryption algorithm 0 (EEA0) (Null), 128-EEA1 (SNOW3G algorithm), 128-EEA2 (AES algorithm), and 128-EEA3 (ZUC algorithm), an algorithm that be supported by the UE is set for ciphering algorithm. As shown in Figure 3, among EPS integrity algorithm 0 (EIA0) (Null), 128-EIA1 (SNOW3G algorithm), 128-EIA2  
Figure 3: NAS security setup procedure of 5G NSA.  
Figure 2: NSA Attachment procedure of 5G NSA.  
(AES algorithm), and 128-EIA3 (ZUC algorithm), an algorithm that be supported by the UE is set for integrity protection algorithm [15].  
Based on the value set in the UE Network Capability field in the terminal request message, MME selects ciphering and integrity algorithms that will be applied to the NAS message. In response to the request from UE, MME sends NAS security mode command message (integrity, non-ciphering) that includes replayed UE security capability (a field set by copying the value as it is set in the UE network capability field in the Attach Request message of the UE), NAS ciphering algorithm and NAS integrity protection algorithm selected by the MME, and other key information [14].  
The UE recognizes the ciphering and integrity algorithm selected by the MME, and sends NAS security mode complete message (integrity, ciphering) to the MME through verification process.  
The MME verifies the integrity of NAS security mode complete message, decodes the message, and then sends an attach accept message that allows for the UE to access the network to the UE. Subsequently, access stratum (AS) security setting process is carried out between the UE and a base station [16].
###### Abstract  
Unmanned Aerial Vehicles (UAVs) have recently received notable attention because of their wide range of applications in urban civilian use and in warfare. With air traffic densities increasing, it is more and more important for UAVs to be able to predict and avoid collisions. The main goal of this research effort is to adjust real-time trajectories for cooperative UAVs to avoid collisions in three-dimensional airspace. To explore potential collisions, predictive state space is utilized to present the waypoints of UAVs in the upcoming situations, which makes the proposed method generate the initial collision-free trajectories satisfying the necessary constraints in a short time. Further, a rolling optimization algorithm (ROA) can improve the initial waypoints, minimizing its total distance. Several scenarios are illustrated to verify the proposed algorithm, and the results show that our algorithm can generate initial collision-free trajectories more efficiently than other methods in the common airspace.  
unmanned aerial vehicles; collision prediction; collision avoidance; rolling optimization algorithm; predictive state space +
Footnote †: journal: Applied
# Dynamics Analysis of Rotating Cantilever Beams with Free End Mass
## 5 Conclusions  
An improved modeling method is introduced to study the dynamics of a rotating cantilever beam with free end mass. Based on the Green strain theory, the effect of rotating motion is not only introduced into the chordwise motion but also into the axial motion. The proposed model of this study shows reasonable convergence in the dynamic response and frequency convergence characteristics test. The result shows that the proposed model is an effective dynamic stiffening model for rotating cantilever beams with free end mass. The free end mass increases the amplitude of vibration response but decreases the frequency of vibration at the free end, and the influence increases with the increasing angular speed. The results show the dynamic stiffening effect of the proposed model in axial motion, while it shows the dynamic softening effect of the previous models. The proposed model is consistent in chordwise and axial frequency analysis, while the previous model is not. The effects of various parameters, such as angular velocity, free end mass ratio and center body radius ratio, on the free vibrations and free end axial deformations of the rotating beams are also analyzed.  
Figure 11: The first three-order dimensionless frequencies of rotating cantilever beams with different center body radius ratios \(\delta=0\), \(0.5\), and \(1\): (**a**) Chordwise frequency; (**b**) Axial frequency.  
Figure 12: The ratio of free end axial deformation of rotating cantilever beams to beam length with different center body radius ratios \(\delta=0\), \(0.5\), and \(1\).  
**Author Contributions:** Conceptualization, X.D. and D.Z.; methodology, X.D.; software, X.D. and J.Z.; validation, X.D. and J.Z.; formal analysis, X.D. and J.Z.; investigation, X.D.; resources, D.Z., X.G. and L.L.; data curation, X.D. and J.Z.; writing--original draft preparation, X.D.; writing--review and editing, X.D., D.Z. and X.G.; visualization, X.D. and J.Z.; supervision, D.Z., X.G. and L.L.; project administration, D.Z., X.G. and L.L.; funding acquisition, D.Z. and X.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Grants from the National Natural Science Foundation of China (Project Nos. 11772158, 12072159, and 12102191) and the Fundamental Research Funds for Central Universities (Project Nos. 30917011103, 30922010314).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Prediction Model of Marine Geomagnetic Diurnal Variation Using Machine Learning
## 5 Experimental Result Analysis
### Model Applicability Analysis  
In the case study, the SVM-RF algorithm shows superiority over the other three algorithms in daily variation calculations across all station selection schemes. However, the degree of accuracy improvement differs across different stations. For example, designating BEL as the primary station results in a 46% decrease in MAE, whereas for the FUR station, the MAE reduction achieved by the SVM-RF model is just 13%. Hence, an analysis of the  
Figure 16: The distribution of calculation errors for each method, (**a**) a bar chart of the error distribution of different algorithms, (**b**) a radar chart of the error distribution of different algorithms.  
applicability of SVM-RF is conducted to investigate its performance discrepancies across various stations.  
When HLP and BEL are designated as the primary stations, Tables 2-6 show higher calculation accuracy and Pearson correlation coefficients. Conversely, with FUR as the main station, the improvement effect is not as pronounced compared to the other calculation methods. The geographical location of FUR, centrally among all stations as depicted in Figure 4, suggests the inferior performance of other algorithms in inward estimation. In contrast, the SVM-RF model shows less apparent advantages. At edge sites like BEL and HLP, other algorithms exhibit poorer estimation accuracy, while the SVM-RF algorithm displays greater accuracy improvement and advantages. This observation implies that the SVM-RF model has a broader applicability, imposing lower requirements on the network structure of selected stations. Ultimately, it maintains high accuracy in both inward and outward calculations.
# Improved Localization and Recognition of Handwritten Digits on MNIST Dataset with ConvGRU
## 3 Methodology
### Video Frame Feature Extraction  
Due to issues like gradient explosion or vanishing gradients in the original GANs model [38], it is often challenging to use directly in practical scenarios. The deep convolutional generative adversarial network (DCGAN) framework proposed by [PERSON] has demonstrated its versatility through extensive testing and is frequently referenced in practical applications. This network primarily employs convolutional operations in the generator and transposed convolution operations in the discriminator, with both components forming a mirror-symmetric structure [39]. The design principles of this network are as follows:  
* Replace the pooling layers in the generator with transposed strided convolutions, while the pooling layers in the discriminator are replaced with strided convolutions for spatial downsampling. Their computation formulas are as follows in Equations (2) and (3): \[X^{\prime} =(X-K+2P)/b+1\] (2) \[X =b\big{(}X^{\prime}-1\big{)}-2P+F\] (3) Let \(X\times X\) represent the size of the image. For the transposed convolution operation, the kernel size is \(F\times F\), while for the standard convolution operation, the kernel size is \(K\times K\). \(P\) denotes the padding, and \(b\) is the stride for both the convolution and transposed convolution operations.
* Replacing fully connected layers with average pooling layers can enhance model stability, as fully connected networks often have too many parameters, which can lead to overfitting. To improve the model in its convergence speed, we directly connect the generator's input with the features from the convolutional layers and link the discriminator's output with the feature maps from the convolutional layers.
* By normalizing the entire structure of the network model, we can better address the issue of excessive overall bias caused by having too many network layers.
* In the generator, the Leaky ReLU function is used for the intermediate hidden layers, while the output layer employs the sigmoid activation function. In the discriminator, we use the Maxout activation function for all layers.
## Materials and Methods
### Sequence Assembly, Taxonomic and Functional Annotation  
To improve the accuracy of subsequent analysis, the adapter sequences were trimmed off on the 3'- and 5'-ends using the SeqPrep software. Sickle software was used to remove low-quality reads shorter than 50 bp with a quality value below 20 or with ambiguous base calls. Clean reads were assembled into contigs using the MEGAHIT software and multiple k-mer lengths [27]. The contigs shorter than 300 bp were excluded. Putative open reading frames (ORFs) were predicted using MetaGene and were clustered using CD-HIT with a 95% sequence identity and 90% coverage [28]. A non-redundant gene catalog was compiled from each cluster. Sequences were aligned against a non-redundant gene catalog using SOAPaligner at a 95% sequence identity to assess the gene abundance [29], and the gene abundance in each sample was calculated and normalized via the Reads Per Kilobase of per Million mapped reads (RPKM) [30]. The sequence data were deposited in the NCBI Sequence Read Archive under the project accession number PRJNA630248.  
Taxonomic annotation was performed against the NCBI nr database using DIAMOND ([[http://www.diamondsearch.org/index.php](http://www.diamondsearch.org/index.php)]([http://www.diamondsearch.org/index.php](http://www.diamondsearch.org/index.php)), version 0.8.35) BLASTp with an e-value threshold of 1e\({}^{-5}\)[31]. The EggNOG (version 4.5) [32], CAZy (version 5.0) [33], and KEGG ([[https://www.genome.jp/kegg/](https://www.genome.jp/kegg/)]([https://www.genome.jp/kegg/](https://www.genome.jp/kegg/))) databases [34] were employed for functional annotation using the same threshold value. The KOBAS 2.0 software was used to conduct a KEGG pathway mapping analysis [35]. The functional genes (i.e., enzyme-encoding genes) associated with fermentation were annotated, and the relative abundance of functional microorganisms at different times during fermentation was calculated using the total relative abundance of certain functional genes at different times as 100%.
# Visual Navigation Systems for Maritime Smart Ships: A Survey
## 3 Visual Perception Navigation Technology
### Single Perception
#### 3.1.4 Visual Technology  
Visual Technology, as a high-tech means of simulating human visual perception, gives machines the ability to \"see\" through image capture, processing and analysis. This technology captures images of the surrounding environment through high-resolution cameras and utilizes advanced image processing and pattern recognition algorithms to provide real-time visual information to ships. It is capable of recognizing channel markers, obstacles and other vessels, thus assisting navigation decisions. The application of vision technology not only enhances the ability of ships to navigate in complex sea conditions, but also provides additional security for maritime safety. [PERSON] et al. (2019) designed an overlapping-sector laser-propagation navigation system that utilizes red and green lasers to form a sector-shaped safety zone for safe navigation, which is easy to operate and highly sensitive [102]. [PERSON] et al. (2020) proposed a fine-grained classification RMA (ResNet-Multi-Scale-Attention) model based on deep learning for analyzing subtle and local differences between different types of beacons for beacon recognition [103]. Electro-optical (EO) sensors such as video cameras are used to complement marine radar for accurate detection of objects on the sea surface, and [PERSON] et al. (2022) proposed an enhanced convolutional neural network, called VarifocalNet*, that improves the detection of objects in harsh marine environments [104]. There is still a lack of low-cost and reliable sensing devices. [PERSON] et al. (2023) designed a vision-based method to recognize ships and their microscopic features for navigational planning of ship collision avoidance, which can effectively and efficiently identify the navigation signals of the target ship [105]. There are many challengesin maritime navigation, such as narrow visibility for ship navigators, the limited view length angle of a single camera, complex maritime environments, etc. [PERSON] et al. (2024) proposed panoramic visual perception-assisted navigation technology, introduced an SSIM-based region-of-interest search method and an ellipsoid-weighted fusion method, and put forward the ship panoramic visual stitching algorithm SSIM-EW. This technology obviously improves the ability to detect small targets at sea, which can expand the mariner's field of view, identify the targets missed by AIS and radar systems, guarantee navigation safety, and improve the level of ship intelligence [16].
## 3 Generalisation of Robustly and Adversarially Trained Models
### The Broad Testing of Robust and Adversarial Training
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Attack** & **Norm** & **Epsilon** & **Steps** & **Robust acc.** & **Adv. acc.** \\ \hline No attack & - & - & - & 0.813 & 0.91 \\ FGSM & \(L_{1}\) & 0.5 & 1 & 0.81 & 0.91 \\ FGSM & \(L_{2}\) & 0.25 & 1 & 0.59 & 0.87 \\ FGSM & \(L_{\infty}\) & 0.25 & 1 & 0.1 & 0.13 \\ FGSM & \(L_{\infty}\) & 0.025 & 1 & 0.22 & 0.62 \\ \hline PGD & \(L_{1}\) & 0.5 & 100 & 0.81 & 0.91 \\ PGD & \(L_{2}\) & 0.25 & 1000 & 0.483 & 0.82 \\ PGD & \(L_{2}\) & 0.5 & 100 & 0.202 & 0.75 \\ PGD & \(L_{\infty}\) & 0.025 & 5 & 0.168 & 0.54 \\ PGD & \(L_{\infty}\) & 0.25 & 5 & 0.08 & 0.06 \\ \hline C-W & \(L_{2}\) & 0.25 & 10 & 0.219 & 0.86 \\ DeepFool & \(L_{2}\) & 0.25 & - & 0.124 & 0.127 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Robust ResNet50 trained on \(L_{2}\) data set and related adversarial model. The epsilon column stands for the budget of perturbation; the norm stands for the way of measurement for this budget; steps—maximum iterations for adversarial example creation, Robust acc.—accuracy of robustly trained model, Adv. acc.—accuracy of the corresponding adversarially trained model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Attack** & **Norm** & **Epsilon** & **Steps** & **Robust acc.** & **Adv. acc.** \\ \hline No attack & - & - & - & 0.73 & 0.87 \\ FGSM & \(L_{1}\) & 0.5 & 1 & 0.73 & 0.87 \\ FGSM & \(L_{2}\) & 0.25 & 1 & 0.504 & 0.826 \\ FGSM & \(L_{\infty}\) & 0.25 & 1 & 0.07 & 0.19 \\ FGSM & \(L_{\infty}\) & 0.025 & 1 & 0.2 & 0.724 \\ \hline PGD & \(L_{1}\) & 0.5 & 100 & 0.731 & 0.87 \\ PGD & \(L_{2}\) & 0.25 & 1000 & 0.414 & 0.813 \\ PGD & \(L_{2}\) & 0.5 & 100 & 0.195 & 0.663 \\ PGD & \(L_{\infty}\) & 0.025 & 5 & 0.155 & 0.683 \\ PGD & \(L_{\infty}\) & 0.25 & 5 & 0.11 & 0.052 \\ \hline C-W & \(L_{2}\) & 0.25 & 10 & 0.51 & 0.81 \\ DeepFool & \(L_{2}\) & 0.25 & - & 0.13 & 0.111 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Robust ResNet50 trained on \(L_{\infty}\) data set and related adversarial model.
## 3 Result and Discussion
### Correlation of Rock Core Physical Properties
Figure 16 presents the nonlinear surface fitting analysis of \(F_{rc}\), \(K_{a}\), and \(\Phi\) for both sandstone and shale samples. It is evident from the figure that the R-Squared is quite high for both types of rock cores, with sandstone exhibiting an R\({}^{2}\) value of 0.9267 and shale at 0.8711. Table 5 summarizes the nonlinear surface fitting formulas for various rock cores, revealing a significant correlation in both sandstone and shale between the permeability, porosity, and the volume of large pores controlled by the small pore radius \(r_{c}\). \(F_{rc}\) shows better fitting since it used all the information from MICP and NMR. Whereas the \(r_{35}\) method is a trial-and-error procedure, other metrics such as \(r_{45}\), \(r_{55}\), and \(r_{65}\) may also be used for a better fitting, depending on the requirements. This approach finds a special pore throat that controls fluid flow in a porous media. It provides a comprehensive method to cohesively use information from both MICP and NMR, thereby making rock quality analysis more physically meaningful and accurate. By thoroughly analyzing these relationships, a more accurate assessment and optimization of the development strategies for oil and gas reservoirs can be achieved. \begin{table}
\begin{tabular}{c c c} \hline
**Lithology** & **Tuha Sandstone** & **Hechuan Shale** \\ \hline A\({}_{1}\) & 2.2573 & 0.2795 \\ B\({}_{1}\) & 0.1727 & 0.6397 \\ C\({}_{1}\) & \(-\)2.3085 & \(-\)0.1437 \\ R\({}^{2}\) (r\({}_{35}\)) & 0.4571 & 0.8003 \\ R\({}^{2}_{\text{adj}}\) (r\({}_{35}\)) & 0.1857 & 0.7204 \\ A\({}_{2}\) & 1.2527 & 1.5502 \\ B\({}_{2}\) & 0.5548 & 0.2316 \\ C\({}_{2}\) & \(-\)0.6095 & \(-\)0.327 \\ R\({}^{2}\) (\(F_{rc}\)) & 0.9267 & 0.8711 \\ R\({}^{2}_{\text{adj}}\) (\(F_{rc}\)) & 0.8900 & 0.8195 \\ \hline \end{tabular}
\end{table} 
Table 5: The nonlinear surface fitting analysis.