esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
It is worth noting that the major advantages of the proposed approach and software code compared are (i) the use of few input data to build the numerical model--essentially related to the geometry of the construction and few properties of the material; (ii) the possibility of reproducing specific features of the construction, as for any kind of discrete model, e.g., the bonding condition of orthogonal walls, the recesses in the walls, and others; (iii) the automatic detection of collapse mechanisms that are not defined a priori, but rather are the results of numerical analysis; (iv) the possibility of identifying very complex mechanisms, which simultaneously may involve in-plane and out-of-plane collapses, triggering the failure of large parts of the construction; (v) the computational efficiency of the software that, conversely to similar rigid-block-based models, has a low computational demand, which is recognized to be the main limitation for the large-scale employment of RBLA [22]. Considering that the focus of the present study was also the quantitative evaluation of the effectiveness of interventions, a novelty introduced in the paper is the development of a specific module to calculate the participating mass ratio associated with the failure mechanisms and collapse load multiplier. All this considered, the paper is arranged as follows. In Section 2, the knowledge acquisition path for the castle is described. Historical analysis, an in situ survey, and the results of visual inspections and laboratory tests are reported. The transformations carried out through the centuries were clearly identified, together with the past and current damage due to seismic events that hit the castle, including recently. The information gained in this previous step was then used to set up a consistent and realistic numerical model of the structure under investigation. Section 3 is devoted to an explanation of the implemented analysis techniques, with a detailed description of the potential that the LiABlock_3D software was able to offer, while Section 4 presents the numerical model of a castle wing. Section 5 describes the initial analysis that was carried out of the mechanisms involving the simplest macro-elements by implementing both the LiABlock_3D software and some hand-made calculations. Verification and sensitivity to the rigid block size were also confirmed. The software was then used, as described in Section 6, to analyze mechanisms with a higher level of complexity and, based on the obtained results, the effectiveness of three possible retrofitting interventions was evaluated in terms of seismic safety indices.
# Design of Multifunctional Mesosphere-Ionosphere Sounding System and Preliminary Results
## 4 Typical Experimental Results
### Ionospheric Sounding
According to the QPS model, Figure 17 shows the electron density profile obtained from the inversion of the ionogram of Figure 16. The electron density profile above the peak height is fitted by Chapman model [42]. As can be seen from Figure 17, the profile of the inversion layer contains the E, Fl and F2 layer with obvious turning points between the adjacent layers. The boundary between the E layer and the F1 layer is at the true height of 113.5 km. For the F1 and F2 layers, the boundary is at 185.5 km. The true peak height of the critical frequency is 247.1 km, with an electron density of \(4.722\times 10^{5}\) el/cm\({}^{3}\). Since the Es layer does not exist conventionally, the inversion for the Es layer is included in the program. According to the above experimental results, the design concept and engineering value of the MAI system are verified. On the same hardware platform, the MAI system combines the function of the MF radar for the sounding of the lower ionosphere and the ionospheric sounding function of the ionosonde. Through the inversions of the sounding data, the wind field of the mesosphere, the profiles of the electron density in the lower ionosphere, and the electron density above the conventional ionosphere E layer can be obtained. If these results are used scientifically in future research, it will be very promising to study the physical characteristics of the lower ionosphere and ionosphere.
# Effect of Track Irregularities on the Response of Two-Way Railway Tracks
## 4 Numerical Results and Discussions
### Track Dynamic Response
#### 4.3.2 Case 02  
The second case considered that the trains were moving on both tracks in the opposite direction. Figure 8 presents the plots of the vertical response at the center of both tracks which is calculated by assuming the different levels of track irregularities on the left and right tracks at different speeds. The maximum displacements at the center of both tracks for case 02 are shown in Figure 9. The maximum displacements in case 01 and case 02 were compared. The maximum displacement at the center of tracks was 8% higher than that of case 01 at low speed. However, the difference increased up to 30% at high trains speed.  
The results for the two-way track show that the moving train on one track can influence the response of the adjacent track. However, this influence significantly increases with the speed of the trains and is highly affected by the track's conditions. The ground vibrations radiating from one track causes an increase in ground response of nearby track. These vibrations in nearby track acts as the increase in the unevenness of the track and vice versa. This increase in track unevenness leads to an increase in the dynamic impact forces due to the interaction between the wheel and track, which causes an increase in the track vertical response when the train is moving on both tracks.
## 3 Method  
### Identification and Scheduling of the Bottleneck Operations  
#### 3.1.1 Identification the Bottleneck Operations  
TOC technology is laid stress on confirming the bottleneck to make non-bottleneck operations fully cooperate with the whole production system [39], so that the whole production system can have a maximum producing capacity without changing the production flow time, and finally it can improve the practical effect of scheduling. Uncertainty on the critical path affects the improvement of the construction schedule. The bottleneck operation in PERT can be regarded as the most influential operation, resulting to the difference between actual and planning construction duration [36; 40; 41]. The greater the uncertainty or variance of process operation duration is given, the higher possibility that this operation can be a bottleneck in the schedule. On the other hand, the critical path can largely affect the uncertainty, which is also considered as the key chain and affected the reliability of project completion time. Consequently, there are four principles as follow to recognize the bottlenecks:  
1. Project duration is influenced by the major critical path of which the total time difference is zero or minimum. If duration on the critical path does not match the schedule, it makes the start time of subsequent activities and project completion time change.
2. When the DBR schedule management technique is applied in the process of compiling a project schedule, since the bottleneck operations must limit operation, which reduces the certainty of project completion time, the bottleneck operation must exist  
Figure 1: Drum–buffer–rope.  
in the critical path. The degree of uncertain operational time can be used as the selection standard of bottleneck operations. In other words, the greater the standard deviation of the operation time that is given, the higher possibility that it may be the bottleneck operation.
3. Sometimes the critical path is not only one, and each critical path may have a chance to have an effect on distribution of construction, in addition to considering the standard deviation of operation duration, selecting the bottleneck operations also needs to take into account the standard deviation of critical path completion duration at the same time.
4. When there are several bottlenecks in a project network, the impact of the operation near the convergence point is higher than other operations, and the operation closest to the convergence point should be chosen.
# Ionospheric TEC Prediction in China Based on the Multiple-Attention LSTM Model
###### Abstract  
The prediction of the total electron content (TEC) in the ionosphere is of great significance for satellite communication, navigation and positioning. This paper presents a multiple-attention mechanism-based LSTM (multiple-attention Long Short-Term Memory, MA-LSTM) TEC prediction model. The main achievements of this paper are as follows: (1) adding an L1 constraint to the LSTM-based TEC prediction model--an L1 constraint prevents excessive attention to the input sequence during modelling and prevents overfitting; (2) adding multiple-attention mechanism modules to the TEC prediction model. By adding three parallel attention modules, respectively, we calculated the attention value of the output vector from the LSTM layer, and calculated its attention distribution through the softmax function. Then, the vector output by each LSTM layer was weighted and summed with the corresponding attention distribution so as to highlight and focus on important features. To verify our model's performance, eight regions located in China were selected in the European Orbit Determination Center (CODE) TEC grid dataset. In these selected areas, comparative experiments were carried out with LSTM, GRU and Att-BiGRU. The results show that our proposed MA-LSTM model is obviously superior to the comparison models. This paper also discusses the prediction effect of the model in different months. The results show that the prediction effect of the model is best in July, August and September, with the R-square reaching above 0.99. In March, April and May, the R-square is slightly low, but even at the worst time, the fitting degree between the predicted value and the real value still reaches 0.965. We also discussed the influence of a magnetic quiet period and a magnetic storm period on the prediction performance. The results show that in the magnetic quiet period, our model fit very well. In the magnetic storm period, the R-square is lower than that of the magnetic quiet period, but it can also reach 0.989. The research in this paper provides a reliable method for the short-term prediction of ionospheric TEC.  
attention mechanism; multiple-attention LSTM (MA-LSTM); ionosphere; total electron content +
Footnote †: journal:
# Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network
## 4 Experimental Comparison  
Objective: The experiment aims to validate the optimization effect of integrating CNN with LSTM to assist Kalman filtering for fused navigation. Therefore, during the experimental process, scenarios are set up for both undisturbed and disturbed conditions. In the undisturbed scenario, the effectiveness of loosely coupled and tightly coupled Kalman filtering methods is primarily compared. In the disturbed scenario, the focus is on comparing the errors between using a conventional CNN and a neural network combining a CNN and LSTM, as well as the differences in navigation effectiveness before and after using neural network assistance.  
Experimental Equipment: Ubox M8N for receiving GNSS signals. WHEELTEC N100N as the inertial navigation module. UM980 as RTK ground truth.  
Data Collection: Base station coordinates (x, y, z). Measurement distance. Signal strength of the first path for transmitted signals. Signal strength of the first path for received signals.
## 4 Hardware Implementation
### Client Device and Additional Hardware  
#### 4.2.1 Client Device  
We implemented this design on a low-power client device. The Nucleo-1444 H743 ZI2 development board was employed because of its low power consumption and quick development time. This development kit has 112 general input/output (GPIO) pins, 2 10-16 bit analog-to-digital converters (ADCs), 3.3 V and 5 V power supplies, and 2 MB of flash memory. This kit's microcontroller unit (MCU) is the STM32H743 ZI2, which operates at 3.3 V and 480 MHz.
# Data-Driven Personalized Learning Path Planning Based on Cognitive Diagnostic Assessments in MOOCs
## 3 Method
### Knowledge Difficulty Model  
Based on the overall performance of students in MOOC learning scenarios, this section constructs a scoring model to measure the difficulty of specific knowledge points. The input parameters of the knowledge point-based difficulty model are the average exercise test scores of all students who have studied the knowledge point. The output of the knowledge difficulty model is the difficulty level of knowledge.  
\[diff(j)=w_{1}[1-\overline{sco}(j)]+w_{2}\overline{rep}(j)+w_{3}\overline{com}(j) \tag{1}\]  
In Equation (1), \(w_{1}\), \(w_{2}\) and \(w_{3}\) are weights of the input parameters. \(\overline{sco}(j)\), \(\overline{rep}(j)\) and \(\overline{com}(j)\) are the average test score, average number of repeated watching of the video and average number of comments of the \(j\)-th knowledge point, respectively. The larger the \(diff_{j}\) value, the more difficult the \(j\)-th knowledge is to master.  
\[\overline{sco}(j)=\frac{\sum_{i=1}^{N^{\text{history}}_{i=1}}{sco_{\text{ij }}}}{N^{\text{history}}_{\text{j}}} \tag{2}\]  
Figure 1: Content hierarchy of course resources.  
\[\overline{rep}(j)=\frac{\sum_{i=1}^{N_{\text{j}}^{\text{history}}}rep_{\text{ij}}}{N _{\text{j}}^{\text{history}}} \tag{3}\]  
\[\overline{com}(j)=\frac{\sum_{i=1}^{N_{\text{j}}^{\text{history}}}com_{\text{ ij}}}{N_{\text{j}}^{\text{history}}} \tag{4}\]  
In Equations (2)-(4), \(N_{\text{j}}^{\text{history}}\) is the total number of students who have learned the \(j\)-th knowledge point. \(sco_{\text{ij}}\), \(rep_{\text{ij}}\) and \(com_{\text{ij}}\) are the test score, number of repeated watching instances of the video and the number of comments of the \(i\)-th student for the \(j\)-th knowledge point, respectively.  
For these three input parameters, i.e., \(\overline{sco}(j)\), \(\overline{rep}(j)\) and \(\overline{com}(j)\), the Analytic Hierarchy Process (AHP) [34] was used to determine the weights of the score model by professors majoring in education and working in universities in China to quantify the difficulty of knowledge mastery.  
Table 1 shows the weights of the knowledge difficulty model generated by AHP. After calculation, the Consistency Ratio (CR) is equal to 0.033, which is less than 0.1. As a consequence, the result passed the consistency test.  
To validate the proposed model, data originating from the MOOCCubeX dataset [33] were adopted. Student exercise tests scores, student video watching behavior and student comment data were extracted from this dataset for further testing.
# An Efficient and Reliable Algorithm for Wireless Sensor Network
## 8 Conclusions  
In this paper, the proposed EERASCA protocol is presented based on efficiency and reliability of the sensor network.  
In this paper, unicasting and multicasting are used for data forwarding from small to large network instead of flooding to improve throughput and overhead. The proposed protocol gives promising results in low and medium mobility. However, in high mobility, PUMA and ERASCA performance is better than the proposed protocol. In the proposed protocol, as the nodes increases then throughput, PDF and overhead is improving as  
Figure 11: Comparison of Covered Area with PDF, Overhead and Throughput.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Protocol** & **Mean Interval** & **Confidence Interval** \\ \hline EERASCA PDF & 65.18\% & 93.23–40.12\% \\ \hline ERASCA PDF & 62.70\% & 88.54–36.86\% \\ \hline PUMA PDF & 60.62\% & 86.78–34.45\% \\ \hline EERASCA Overhead & 4.93 byte/s & 8.36–1.50 bit/s \\ \hline ERASCA Overhead & 4.98 byte/s & 8.51–1.45 bit/s \\ \hline PUMA Overhead & 5.175 byte/s & 8.90–1.45 bit/s \\ \hline EERASCA Throughput & 82.39\% & 89.23–75.55\% \\ \hline ERASCA Throughput & 80.44\% & 88.54–72.34\% \\ \hline PUMA Throughput & 78.10 & 85.55–70.65\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confidence Interval for PDF, Overhead and Throughput.  
compared to PUMA and ERASCA. Similarly, by increasing the coverage area, the number of entry and exit of the node increases. Hence, frequent flooding and reconfiguration occur in PUMA and ERASCA, which further decreases the performance in packet drop, congestion, overhead, PDF and throughput as compared to EERASCA. The protocol is tested in real-time as well as in NS-2 environment, and it outperforms the PUMA and ERASCA in term of throughput, PDF and overhead.  
The future work in this paper shows that its performance is not satisfactory, similar to PUMA and ERASCA during high mobility in term of throughput and PDF, which can be improved by improving its clustering mechanism.  
F.K. have collected data from different resources and performed investigation, methodology, original draft preparation and simulation. S.A., H.G., G.C. and C.-G.K. contributed resources, formal analysis and editing. T.W.: writing--review & editing, visualization. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The simulation files/data used to support the findings of this study are available from the corresponding author upon request.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-0-01846) supervised by the IITP (Institute of Information and Communications Technology Planning and Evaluation).  
The authors declare no conflict of interest.
# Vulnerability Analysis of the China Railway Express Network Under Emergency Scenarios
## 5 Vulnerability Analysis of CRN Under Emergency
### Simultaneous Failure of Nodes and Edges  
This section investigates the specific impact on the network when both nodes and edges fail simultaneously due to sudden incidents, primarily analyzing the effects of deliberate attacks on the main sites and edges in Tables 1 and 2.  
As shown in Figure 18, when intentionally attacking nodes and edges in the network, the points and edges that have a significant impact on the network (Xi'an and Chengdu-Deyang) are targeted. Compared to removing only the nodes with high influence in the network (Xi'an), although it accelerates the propagation process of cascading failures, it reduces the range of cascading failure propagation. In contrast, compared to removing only the edges with high influence in the network (Chengdu-Deyang), it speeds up the propagation process of cascading failures. When intentionally attacking key nodes and edges, the redundancy of the network may play a certain buffering role. Although it quickly triggers cascading failures, due to the existence of other alternative paths and redundant connections in the network, these alternative paths will limit the propagation range of cascading failures to some extent. When attacking sites with high influence (Xi'an) and  
Figure 16: The cascading failure propagation process on edges in CRN under different \(\epsilon_{2}\): (a) deliberate attack based on edge weight; and (b) deliberate attack based on edge betweenness.  
edges with low influence (Wuwei-Urumqi) in the network, it accelerates the propagation of cascading failures in the network. The failure of key nodes will lead to a large number of directly connected nodes failing, and these failures further spread through edges. Even if these edges are not significantly affected when removed individually, they will accelerate the propagation process of failures when key nodes fail, thereby overall accelerating the diffusion speed of cascading failures in the network. The study also found that, when intentionally attacking nodes and edges with low influence in the network, even though the removed ones are ranked highly in terms of main indicators in the network, due to the redundancy of the network, the local impact not being transmitted, the limitations in indicator selection, and the combined effects of key nodes and edges, as well as the overall stability of the network, these factors together do not lead to further propagation of cascading failures in the network.  
Figure 17: Cascading failure propagation process in CRN under deliberate attacks on edges with different \(\gamma\): (**a**) deliberate attack based on edge weight; and (**b**) deliberate attack based on edge betweenness.  
Figure 18: The cascading failure propagation process in CRN with simultaneous deliberate attacks on nodes and edges.
# Experimental Study on the Bearing Capacity of Gas Oil-Contaminated Coastal Sand
###### Abstract
[PERSON] and [PERSON] [19] assessed the strength properties of hydrocarbon-contaminated soil samples. They conducted tests on three soil samples, each contaminated with a different type of hydrocarbon (see Table 1). Their findings revealed that the CBR value and shear strength of both sand and laterite samples initially increased with the contamination content up to a certain point, after which they started to decrease. [PERSON] and [PERSON] [23] studied the short- and long-term effects of light crude oil contamination on the maximum shear modulus of Firoozkoh and Ottawa sand using bender element testing. Their findings revealed that the introduction of 4% crude oil into both soil types significantly increased the small-strain shear modulus. However, further contamination led to a notable decrease in the maximum shear modulus (\(G_{Max}\)). In light of the literature, it can be noticed that changes in soil strength due to hydrocarbon contamination depend significantly on the specific properties of both hydrocarbons and the soil. In the majority of the cited studies, very limited focus has been allocated to the unsaturated behavior of hydrocarbon-contaminated soils, which can be greatly influential on the strength properties and bearing capacity of soils. This approach is also commonly adopted in the estimation of the bearing capacity and settlement of shallow foundations rested atop unsaturated soils [24,25]. Such approaches, which are based on a worst-case scenario, may not represent the realistic condition for many shallow foundations and consequently can lead to an uneconomical foundation design [26, 27, 28, 29]. Figure 1: (**a**) Yeonggwang wind power farm; (**b**) HD Hyundai Oilbank’s Daesan plant. Considering the literature outlined in Table 1, it becomes evident that only a limited number of studies have addressed the bearing capacity of hydrocarbon-contaminated soils. These studies often concentrated on coarse-grained sand with a single relative density and a limited range of degree of saturation. In contrast, our present study seeks to investigate the bearing capacity and strength characteristics of fine-grained coastal sand contaminated with hydrocarbons under different relative densities and contamination levels. Fine-grained natural sand not only mirrors the type of sand commonly found in coastal areas where major industries are located but also facilitates the exploration of prominent unsaturated behaviors, such as soil suction. The combined influence of the degree of saturation and relative density is known to greatly affect the failure mode of the foundation's subgrade [26]. Therefore, our research aims to provide insight into the transition in the failure modes influenced by unsaturated conditions and the relative density. Our approach utilizes a physical model to determine the foundation bearing capacity across different subgrade conditions and corroborates these results with direct shear tests. The study ultimately compares the predicted bearing capacity with the measured values based on these experiments.