esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Frequency Attribute Analysis of Channel 1 Data of Lunar Penetrating Radar
## 3 Results
### Data Interpretation  
As can be known from the above analysis, only the frequencies of the IMF2 and IMF3 components are in accordance with the radar's transmission frequency and are available. These two components are superimposed for further analytical interpretation. Figure 12a shows the data after IMF3 is superimposed with IMF4, and Figure 12b plots all the traces. From these two figures, especially in Figure 12b, it is found that the SNR of the data below 3000 ns is very low, making it difficult to pick up effective and convincing events. Therefore, we will focus on the shallow part.  
Figure 13 shows the information within the shallow part of 3000 ns, but the events below 300 ns are not continuous. It is necessary to be very careful to interpret such information. This paper aims to decompose and analyze the CH-1 data, and the deep information no longer needs deeper explanation. Instead, information within the first 200 ns corresponding to the CH-2 data is selected for analysis. According to the hierarchical information of [PERSON] et al. [6], [PERSON] et al. [10], [PERSON] et al. [11], and [PERSON] et al. [14], there are three well-recognized interfaces (Figure 14), namely:  
1. The interface between the ejected materials of the Chang'E-3 crater and the lunar regolith;
2. The interface between the lunar regolith and paleoregolith;
3. The bedrock interface.  
Figure 9: Hilbert spectrum of IMFs from N104.  
Figure 10: IMFs of LPR CH-1 overall data. (**a**) IMF1. (**b**) IMF2. (**c**) IMF3. (**d**) IMF4. (**e**) IMF5. (**f**) IMF6. (**g**) IMF7. (**h**) Residual.  
Figure 11: IMFs of stagnation points’ traces in Figure 7. (**a**) IMF1. (**b**) IMF2. (**c**) IMF3. (**d**) IMF4. (**e**) IMF5. (**f**) IMF6. (**g**) IMF7. (**h**) Residual.  
Figure 12: (**a**) Superimposed component of IMF3 and IMF4 and (**b**) all the traces.  
Figure 13: Superimposed component of IMF3 and IMF4. (**a**) 0-1000, (**b**) 1000–2000, and (**c**) 2000–3000 ns.  
Figure 15 shows the information within 200 ns of the CH-1 data. There are three distinct events at the same position corresponding to the CH-2 data. It proves the rationality of stratification at the shallow part.
## 4 Development of Machine Learning Models  
The objective of this data-driven analysis is to explore the applicability of the various ML algorithms for the identification of the structural response and subsequent classification of the damage states. The regression and classification of damage states of URM buildings using ML algorithms are highly advantageous in identifying the underlying relationships between the input (structural parameters, material properties, and exciting force) and output variables (structural response and damage level) with minimal engineering judgement. These approaches are computationally efficient and are highly effective in identifying the hidden patterns in the structural response. Further, the models can be easily updated with additional data from experimental and numerical investigations.  
The maximum IDR was considered the continuous quantitative output variable in the regression-based models. On the other hand, the categorical output considered in the classification-based approaches was the damage state, which was classified based on the structural response of the URM building to the earthquake's excitation. Further, both parametric and non-parametric approaches were used in the study, in both prediction and classification, to encompass a wider range of ML algorithms. In each model, the model parameters were optimized by reducing the five-fold cross-validation loss. The models were programmed and developed using MATLAB R2024b.  
Linear models such as LR, SLR, and RR were initially employed in prediction modelling due to their straightforward implementation and interpretative capabilities. To address the challenges associated with overfitting in linear models, techniques such as  
Figure 7: Distribution of the damage states in the developed database.  
cross-validation and regularization were implemented. Following the development of linear models, various non-linear models were explored to capture the complex relationships between predictor and response variables in seismic response prediction. Notably, DT, RF, and SVM were utilized for prediction and classification tasks, owing to their robustness against outliers and their flexibility in accommodating diverse data types. Additionally, the NN model was incorporated into the prediction and classification processes due to its adaptability and learning capabilities.  
As for the classification-based models, NB, DA, and AB were examined for their ease of implementation and interpretability, and may prove beneficial in future applications related to damage classification in URM buildings. The KNN model was selected to classify damage states due to its efficacy with small datasets and its straightforward application, which could be helpful for the present study. Moreover, the literature also suggests that the aforementioned models demonstrate superior performance across various structural-engineering applications [6; 9; 10; 11]. The model development and parameter selection for every ML model considered in the present study are discussed in this section.
## 3 FE Analysis for the Flange Segment
### Comparisons of Cumulative Fatigue Damage Caused by Flange Gaps  
This section quantifies the effect of various flange clearances on the cumulative fatigue damage of bolts. The bolts around 0 and 180 degrees are predominantly subject to pressure and tension; therefore, the latter are analyzed. The parameters of the three types of flange gaps, their corresponding computing method, and cumulative fatigue are summarized in Table 5 and plotted for clarity in Figure 18.  
Due to the high sensitivity of fatigue to the stress range, as in Equation (13), the relationship between load cycles (\(N\)) and stress range (\(\Delta\sigma\)) follows a power law. As a consequence of this sensitivity of fatigue damage to stress variations, the fatigue damage magnitude varies considerably between gap types.  
In summary, the following conclusions can be drawn from all results of this study:  
1. The FE-derived nonlinear curve representing the bolt force relative to the tower wall is asymmetrical. The Schmidt-Neuper algorithm yields conservative structural design results for engineering applications;
2. The bolt fatigue damage is significantly affected by flange clearance of types I and II but is insensitive to type III. Throughout the manufacturing, processing, and installa  
Figure 18: The nonlinear relationship between \(Z\) and \(F_{\text{VS}}\) and resultant cumulative fatigue damage under given conditions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method Description** & **Parameter** & **Cumulative Fatigue Damage** \\ \hline Schmidt-Neuper algorithm & — & 0.29 \\ No gap using the FE approach & — & \(5.61\times 10^{-6}\) \\ Type I using the FE approach & \(h_{\text{I}}=1\) mm, \(l_{\text{I}}=90\) mm & 3.03 \\ Type II using the FE approach & \(h_{\text{II}}=1\) mm, \(l_{\text{II}}=90\) mm & 2.07 \\ Type III using the FE approach & \(h_{\text{III}}=6\) mm & \(3.82\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results of cumulative fatigue damages derived from various cases.  
tion of tower flanges, it is imperative to minimize the occurrence of type I and type II structures.
###### Abstract  
This paper takes a tunnel through a landslide in Northwest China as an example, constructs a mechanical model of a tunnel-landslide orthogonal system, and explores the deformation mechanism of a tunnel-landslide system and the technology of early warnings of near-slips. Given the problem that it is difficult to accurately monitor the deformation and damage characteristics of the tunnel-landslide system using conventional methods, FLAC3D was used to analyze the deformation mechanism of the tunnel-landslide orthogonal system and numerical simulation of the NPR constant resistance large deformation anchors for the early warning of near-slips. Based on the strength reduction method, by reducing the mechanical parameters of the shear strength of the slip zone and simulating different degrees of landslides, we obtained the change rules of the displacement and the axial force of the NPR constant-resistance large deformation anchor cable in the tunnel-landslide orthogonal system, established the warning mode of the Newtonian force tunnel-landslide orthogonal system, and successfully issued a near-slip warning in actual engineering applications. The above research is of great significance to the stability monitoring and risk assessment of tunnel-landslide systems.  
FLAC3D; tunnel-landslide orthogonal system; NPR constant-resistance large deformation anchor cable; Newtonian force early sliding warning
## 3 Results  
### Characterization of HNT and DB15-HNT Surfaces  
Scanning Electron Microscopy and FTIR Spectrum  
Surface characterization of HNT and DB15-HNT was performed via SEM. Figure 2a,b shows the surface of HNT covered with the DB15 dye. A comparison of FTIR spectra of pristine HNT, DB15 dye, and DB15 dye adsorbed on HNT revealed the following: absence of bands between 2800-3000 cm\({}^{-1}\) was identified in pristine HNT (Figure 3), whereas a tiny band appeared at around 3750 cm\({}^{-1}\) was ascribed to traces of adsorbed water. The typical bands around 2830 and 2852 cm\({}^{-1}\) were because of C-H stretching asymmetric and symmetric vibrations, respectively. The vibration band at 1000-1080 cm\({}^{-1}\) was correlated to the vibrations of single bonds of Si-O, Al-O, and C-O molecules.  
FTIR spectra for DB15 dye exhibit the distinctive N-H and CH-aromatic stretching vibrations bands around 2800-3000 and 2900 cm\({}^{-1}\), respectively. The vibration around 1700 cm\({}^{-1}\) was accredited to S=O of the sulfites group in dyes structure. The triplet bands generally ascribed to C=C in aromatic rings attached with N=N of the azo cluster are seen around 1640, 1570, and 1460 cm\({}^{-1}\). The multiple bands attributed to N-H and CH bending vibrations that appear at around 1460-1300 cm\({}^{-1}\) were also realized.  
FTIR spectra of DB15 dye showed a significant band in the range 2800-3000 cm\({}^{-1}\). This band appears to be less intense and tends to vanish due to the interactions between the NH groups in dyes with Si/Al in HNC as (Si\({}^{4+}\cdots\cdot\cdot\cdot\)NH and Al\({}^{3+}\cdots\cdot\cdot\cdot\)NH) in the DB15-HNT FTIR spectra. Additionally, the disappearance of the N = N stretching band around 1598 cm\({}^{-1}\) confirms the adsorption of the DB15 dye on HNT. The interaction between components will affect the property of double bonds and increase the character of the single bond, which is reflected in a sharp expansion in the force of the band around 1100 cm\({}^{-1}\). Moreover, the disappearance of IR absorption frequencies established the adsorption of DB15 onto HNT. Determination of pH\({}_{\text{PZC}}\) confirms that HNT at pH 7.61 displays zero charges (Figure 4).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Factor** & **Name** & **Units** & **Minimum** & **Maximum** \\ \hline A & Time & min & 0 & 180 \\ B & Temp. & \({}^{\circ}\)C & 27 & 50 \\ C & Conc. & mg L\({}^{-1}\) & 25 & 500 \\ D & Adsorbent conc. & g L\({}^{-1}\) & 0.500 & 6.000 \\ E & pH & - & 2 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental span for influencing factors [1] (Reproduction with permission from the publisher).  
Figure 2: Surface characterization of halloysite nanotubes before and after adsorption studies.
# RGB-D Camera for 3D Laser Point Cloud Hole Repair in Mine Access Shaft Roadway
## 5 Repair of Point Cloud
### Point Cloud Patching of 3D-NDT Algorithm  
The normal distributions transform (NDT) algorithm is used to represent the three-dimensional point cloud data with a set of normal distribution, count the probability density of the point cloud data through the normal distribution value, and carry out point cloud registration. It is not necessary to calculate the characteristics of the target point cloud and the source point cloud and find the characteristic point pair. 3D-NDT can voxelize and divide all point clouds, then repeat the consistent process of the NDT algorithm. The RGB-D roadway point cloud and 3D laser scanning point cloud were repaired by the 3D-NDT algorithm; the RMSE value was 31.9413 mm and the repair results are shown in Figure 17a,b.  
Figure 15: (**a**) Point cloud patching diagram of ISS + ICP algorithm. (**a1**) Initial pose; (**a2**) Coarse registration; (**a3**) Fine registration side view; (**a4**) Fine registration top view. (**b**) Fine registration verification diagram.  
The main steps of point cloud visual patching are coarse registration and fine registration of two kinds of point cloud data. Therefore, the accuracy measurement of repair effect is the evaluation of point cloud registration accuracy. The repair effect is evaluated by the root mean square error (RMSE) and the registration verification diagram. The comparison  
Figure 16: (**a**) Point cloud patching diagram of SVD + ICP algorithm. (**a1**) Initial pose; (**a2**) Coarse registration; (**a3**) Fine registration side view; (**a4**) Fine registration top view. (**b**) Fine registration verification diagram.  
Figure 17: (**a**) Point cloud patching diagram of 3D-NDT algorithm. (**a1**) Initial pose; (**a2**) Fine registration side view; (**a3**) Fine registration top view. (**b**) Fine registration verification diagram.  
of repair accuracy based on different registration algorithms is shown in Table 1. The formula of RMSE is as follows:  
\[\textbf{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}||\mathbf{d_{i}}||^{2}} \tag{3}\]  
where: \(n\) is the quantity; \(d_{i}\) is the distance difference error between the source point cloud data and the matching corresponding point pair.  
From Table 1 and the registration verification diagram of each registration algorithm, it is evident that the registration algorithm based on ISS + ICP has advantages over others with high registration accuracy of the source point cloud and target point cloud. Therefore, the ISS + ICP registration algorithm is the best way to repair the point cloud.
# An Efficient Scalable Scheduling MAC Protocol for Underwater Sensor Networks +
## 1 Introduction
Therefore, both handshake-based and random access-based MAC protocols are not as efficient as expected [19; 20; 21]. In contrast, contention-free MAC protocols are able to achieve higher level of efficiency. Most of these MAC protocols utilise a centralised scheduling method which spends a long time to globally collect the topology specifications from all sensors because of long propagation delay. As a result, a distributed scheduling method is preferred. In this paper, we propose an Efficient Depth-based MAC protocol (ED-MAC) which takes the energy efficiency, throughput, fairness, and collision avoidance into consideration. ED-MAC schedules the transmissions and receptions of data packets at both the sender and receiver sides to not only achieve the above-mentioned objectives, but also to address the spatial-temporal uncertainty and hidden terminal problems. After scheduling the media access following a distributed manner, and the normal operational phase, each node is occasionally awake to either transmit or receive data according to its schedule. The nodes can sleep when there is no packet to transmit or receive, to save more energy. The remainder of the paper is organised as follows: Section 2 introduces the related works. Section 3 presents challenges and requirements. Section 4 illustrates problem definition. Section 5 describes ED-MAC protocol in detail as well as discusses the offered traffic upper-bound and then analyses the number of slots of our proposed protocol. Section 6 presents the performance of ED-MAC protocol and compares it with UWAN-MAC and T-Lohi protocols through simulations. Finally, Section 7 concludes the paper.
# Mobility Management Scheme with Mobility Prediction in Wireless Communication Networks
###### Abstract  
Many registration schemes have been proposed to reduce the signaling cost required for user's mobility management in wireless communication networks. Various results on mobility management schemes to minimize the total signaling cost have been reported. The objective of this study was to analyze a registration scheme that could deal with mobility prediction and corresponding flexible tracking area list (TAL) forming. In this scheme, based on mobility prediction and corresponding TAL forms, a new TAL was constructed such that the registration cost could be minimized. In addition, a semi-Markov process model was newly presented for the registration scheme considering mobility prediction and corresponding flexible TAL forming for two different environments: urban and rural. Simulation studies were also performed to validate the accuracy of the semi-Markov process model. Numerical results showed that analytical and simulation results were very close (average relative error of 1.4%). The registration cost decreased as the moving probability (\(q\)) to the predicted direction increased. The performance of the proposed scheme was superior to distance-based registration (DBR) or TAL-based scheme especially when \(q\) was high. When call-to-mobility ratio was less than or equal to 1 corresponding to current small cell configurations, the proposed scheme outperformed the DBR or TAL-based scheme.  
mobility management; location registration; paging; tracking area list (TAL) based registration; flexible TAL forming registration; mobility prediction
## 5 Implementation
### Adaptive I-PD Controller  
The adaptive I-PD controller consists of a modified PID controller with variable gains. As a result, the block receives as input the value of \(K_{p}\), \(K_{i}\), and \(K_{d}\) gains, obtained from the calculation of the gains block, and readjusts the PID in each sampling time.
## 4 Results and Discussion
### Linear Static Finite Element Analysis Results
Given the 110 m construction span of the OMSS in this project, these deformations are all below the L/350 limit. The largest deformation occurs at the furthest point of the front nose, which can be considered a cantilever beam. According to AS 4100:2020, the deflection limit for a cantilever beam is L/250 [7]. Therefore, the overall structural deformation meets the standard requirements. Linear static analyses provide a preliminary assessment of axial forces and bending moments in structural members by assuming minimum deformations and ignoring geometric nonlinear effects. The advantage of this analysis is that it is efficient and suitable for more rigid structures with small lateral displacements, such as simple members in the OMSS. However, in complex structures, the limitations of linear static analysis gradually become apparent, especially when there is a risk of deformation and instability, which does not accurately capture the true behaviour of the structure. Linear static analysis does not consider second-order effects, such as P-delta effects [102]. This limitation makes further linear buckling analysis and nonlinear buckling analysis necessary. Linear buckling analysis determines critical buckling loads and modes by solving eigenvalue problems, which provide fundamental data for identifying potential points of instability. However, nonlinear buckling analysis is important under high accuracy demands, especially with high axial forces. This analysis combines geometrical and material nonlinearities by iteratively updating the stiffness matrix to reflect the deformed geometry, thereby analysing the load-deflection relationship and stiffness changes in detail to ensure the stability and resistance to the instability of the OMSS under loading. The combination of these buckling analysis methods will provide a more accurate basis for the comprehensive stability assessment of OMSSs, which is essential for the safety of the structure, especially in complex bridge construction equipment. Thus, these factors form the limitations of the FEA aspect of this project, and linear and nonlinear buckling analyses must be conducted in the subsequent analysis.
## 3 Methodology
### Data Preprocessing
#### 3.2.4 2D Representation  
Network traffic data encompasses numerous features, often containing intricate relationships that may be difficult to capture in a one-dimensional data representation. Consequently, there arises a necessity to convert these one-dimensional features into a two-dimensional matrix. By leveraging 2D CNN, we can capture relationships and spatial dependencies that span across diverse feature combinations [40]. After completing the aforementioned data preprocessing steps, the NSL-KDD dataset consists of 121 numerical features that are reshaped into an 11 \(\times\) 11 \(\times\) 1 matrix. Likewise, the UNSW-NB15 dataset contains 196 numerical features that are reshaped into a 14 \(\times\) 14 \(\times\) 1 matrix. Lastly, the CIC-IDS2017 dataset encompasses 78 numerical features that are reshaped into a 13 \(\times\) 6 \(\times\) 1 matrix.
# Protograph LDPC Code Design for Asynchronous Random Access +
## 5 Conclusions  
This work presented protograph LDPC code designs for asynchronous RA with SIC. For the code design, we made use of a simplified surrogate channel model that assumed Gaussian interference and constant interfering power over the interfered part of a packet. Considering more realistic RA channels, we showed that the proposed designs for the surrogate channel performed close to theoretical limits. Gains of 17%, in the supported channel load at a packet loss rate of \(10^{-2}\), were observed w.r.t. an off-the-shelf code. In contrast to literature where the physical layer code performance is often abstracted by means of decoding thresholds or decoding regions, physical layer LDPC code simulations for short blocks were performed, yielding a more realistic estimate of the achievable supported channel load (for a fixed target packet error rate). We found that abstracting the physical layer overestimated the performance for our setup by approximately 10% in the supported channel load, at a packet loss rate of \(10^{-2}\).  
**Author Contributions:** Conceptualization, F.C., B.M. and S.J.J.; methodology, M.S. and S.J.J.; software, F.C., validation, F.C., B.M. and S.J.; formal analysis, F.C. and B.M.; investigation, F.C., B.M., S.J., M.S. and S.J.J.; writing-original draft preparation, F.C., B.M., S.J., M.S. and S.J.J.; writing-review and editing, F.C., B.M.; visualization, F.C.; supervision, F.C. and B.M.  
**Funding:** This research was partially funded by the German Academic Exchange Service (DAAD) PPP Australian 2017 grant number 57319586 and by the Universities Australia (UA) grant number GJRCS17 for the project _Ultra-Low Latency Communication Strategies for Machine-to-Machine Communications_.  
**Acknowledgments:** The authors would like to thank [PERSON] for her support in the design of the asynchronous medium access simulation environment during its early stages.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 9: Dashed: PLR vs. channel load \(\mathsf{G}\) for the asynchronous RA protocol with replicas protected by different \((960,480)\) LDPC codes, as a result of physical layer simulations. Solid: curves from Figure 8 assuming the abstracted physical layer. The 5G corresponds to the ensemble with base matrix \(\mathbf{B}_{5G}\). The 5G perm. corresponds to the ensemble with base matrix \(\mathbf{B}_{5G}^{\pi}\). Ad-hoc corresponds to the ensemble with base matrix \(\mathbf{B}_{\mathsf{A}}\).