esa-sceva/satcom-chunk-collection huggingface dataset:
# Qualitative Analysis of Sleeper Supporting Condition for Railway Ballasted Tracks Using Modal Test
## 3 Natural Frequency Analysis of Ballasted Track Sleepers
### Numerical Analysis Results
#### 3.2.2 Natural Frequency Based on Changes in Spring Stiffness  
Natural frequencies were analyzed according to changes in the vertical spring stiffness of the gravel ballast, with the spring stiffness set to vary from 200 to 0.1 kN/mm. According to the KR C-14010 [29], the stiffness of a normal sleeper should be 200 kN/mm; therefore, the spring stiffness was used as a variable to represent the reduced stiffness of a damaged sleeper. The mode shapes were also analyzed based on the changes in the spring stiffness of the ballast. As the spring stiffness decreased, changes in the first bending mode shape were observed, as shown in Figure 12.  
Specifically, with a spring stiffness of 50 kN/mm, an upward bending deformation occurred at the center of the sleeper, as highlighted in Figure 12a. When the spring stiffness was further reduced to 40 kN/mm, a downward bending deformation emerged at the center, as shown in Figure 12b.  
The results of the natural frequency analysis, based on variations in ballast spring stiffness, are presented in Figure 13.  
Under a spring stiffness of 200 kN/mm, the natural frequency was approximately 247.73 Hz. When this stiffness was halved to 100 kN/mm, the natural frequency decreased to approximately 198.82 Hz. Furthermore, a drastic reduction in spring stiffness by 90% to 10 kN/mm resulted in a natural frequency of 136.39 Hz.  
Figure 11: Bending mode shape analysis results for sleepers in normal gravel ballast conditions. (**a**) First mode (247.73 Hz); (**b**) second mode (319.71 Hz); (**c**) third mode (450.3 Hz).
## 3 Coupling Relationship between Charging Stations and Travel Route
### Impact of Charging Stations on Travel Route  
We assume that when the daily driving distance exceeds the EV maximum travel range along with a need of fast charging, the location of the charging stations will influence the EV user's travel route. Therefore, the principle of the shortest distance or the shortest travel time may not work in simulating the travel route.  
A five-node traffic network is in Figure 5 where the number indicates the distance. The red nodes b, d, and e indicate the location of fast charging stations. We assume that there is a closed-loop trip chain which has two destinations in the traffic network with the starting point O (node a) and two destinations D\({}_{1}\) and D\({}_{2}\) (nodes b and d). Through [PERSON]'s algorithm, the shortest path of the trip chain is a-b-d-e-a with a distance of 31. We assume that the EV maximum travel range is 13 which could not sustain b-d. In order to complete the trip, the user needs to charge at nodes b and e to reach D\({}_{2}\) before getting back to a. In this case, the travel route would be a-b-e-d-e-a with a distance of 33. Fast charging demands occur at node b and node e. We find that the distribution of charging stations would affect the user' travel route. When the shortest path cannot satisfy the travel demand, the user will look for other paths. When an EV user passes by a charging station, the necessity of charge is also considered in this paper.
# A Study on a Modified Model for the Hold Time of a Clean Extinguishing Agent
## 1 Introduction
This analysis method predicts the outflow volumetric flow using the leakage characteristics obtained through the EIT, and the hold time is predicted using this value. That is, the interpretation of advection proposed by [PERSON] and [PERSON] is based on the prediction of the outflow volumetric flow out of the enclosure. The outflow volumetric flow through the leakage area of the enclosure is derived from the general orifice theory. In this case, \(n\) is the leakage exponent. It is simply expressed as an exponent of \(1/2\) in the derivation of [PERSON]'s equation, but it has a value that varies from 0.5 (turbulent flow) to 1 (laminar flow), depending on the flow of the fluid [15]. The analysis of the outflow volumetric flow formed by the density and pressure difference involves the analysis of the height of the neutral plane, which is the height at which the pressure inside and outside the enclosure equalizes. The analysis of the height of the neutral plane can be derived through the conservation of mass inside the enclosure [16; 17]. In other words, it is analyzed while assuming that the inflow and outflow mass flow rate are the same. However, [PERSON] and [PERSON] derived an equation for the outflow volumetric flow while assuming that the volume flow rate is the same rather than the mass flow rate. There is no significant difference when the leakage exponent \(n\) has an approximate value of 0.5 or the difference in density inside and outside the enclosure is small. However, when the leakage exponent \(n\) increases or the density difference increases, the difference in the predicted hold time according to the two assumptions increases. In the study by [PERSON] and [PERSON], the leakage exponent \(n\) was estimated to be approximately 0.5, but they also noted that \(n\) can vary from 0.5 depending on the actual flow characteristics [14]. Early theoretical models used a fixed leakage exponent \(n\) of 0.5, so this difference could not be seen. However, a variable leakage exponent \(n\) has been adopted since the 2007 edition of NFPA 2001 and the 2006 edition of ISO 14520-1. Specifically, in NFPA 2001, \(n\) is suggested as a value between 0.48 and 0.85 [8]. Therefore, in this study, we propose a modified theoretical model for hold time that is interpreted as the mass flow, which we attempted to verify through theory and experiment. The experimentswere conducted using _IG-01_ and _HFC-125_. By comparing the predicted hold time with the observed hold time, the proposed theory was validated.
## 4 Algorithm Tuning and Experimental Evaluation
### Rating Prediction Accuracy Improvement Achieved by the Proposed Algorithm
The combination of the CF\({}_{\text{EPC}}\) algorithm with the proposed algorithm resulted in a relative improvement of 15%, on average in relation to the gains obtained when using the plain version of the CF\({}_{\text{EPC}}\) (from 6.7% to 7.8%, in absolute figures), considering the MAE error metric. Similarly, the relative improvement, considering the RMSE error metric has been found to be equal to 19%, on average (from 5.8% to 6.9%, in absolute figures). The experiment demonstrates that the performance gains of the CF\({}_{\text{EPC}}\) algorithm is further enhanced by approximately 50% of the performance gains achieved when the proposed algorithm is independently applied on sparse datasets (i.e., the Amazon datasets), while for the dense dataset (Movielens Latest 100K dataset) the performance enhancement of the CF\({}_{\text{EPC}}\) algorithm is approximately equal to the 25% of the gains achieved by the proposed algorithm on the same dataset. Figure 5 illustrates the improvement in the MAE achieved by the inclusion/combination of the presented algorithm to the CF\({}_{\text{EPC}}\) algorithm, when using the CS as the similarity metric and again taking the performance of the plain CF algorithm as a yardstick. The combination of the CF\({}_{\text{EPC}}\) algorithm with the presented algorithm resulted in a relative improvement of 14%, on average in relation to the gains obtained when using the plain version of the CF\({}_{\text{EPC}}\) (from 6.7% to 7.6%, in absolute figures), considering the MAE error metric. Similarly, the relative improvement, considering the RMSE error metric has been found to be equal to 15%, on average (from 6.1% to 6.9%, in absolute figures). The experiment demonstrates that the performance gains of the CF\({}_{\text{EPC}}\) algorithm is further enhanced by approximately 50% of the performance gains achieved when the proposed algorithm is independently applied on sparse datasets (i.e., the Amazon datasets), while for the dense dataset (Movielens Latest 100K dataset) the performance enhancement of the CF\({}_{\text{EPC}}\) algorithm is approximately equal to the 30% of the gains achieved by the proposed algorithm on the same dataset.
## 3 Strain Gauges and Distributed Optic Fiber Strain Measurements
### Berea Sandstone--Increasing Confining Pressure  
#### 3.1.1 Strain Gauge Readings  
Out of the eight channels mounted on the sample, four appeared to be damaged during the test. Figure 4 presents the strain readings (negative for compressive strain) from the four working channels (the others are damaged/malfunctioned during the test) with increasing confinement, including two (Ch5 and Ch7) aligned with the optic fiber direction (measuring predominantly the hoop strain) and two (Ch2 and Ch8) in the near-vertical strain. It is noted that the vertical strains (Ch2 and Ch8) are markedly greater than the hoop strains (Ch5 and Ch7). For example, the hoop strain measured by gauge Ch8 reaches \(\sim\)1100 \(\mu\varepsilon\), compared to \(\sim\)650 \(\mu\varepsilon\) by Ch7, when the confining pressure is increased to 12 MPa. Moreover, there is also notable discrepancy between the channels orientated in the same direction. It is further observed that the strain readings increased with the confining pressure in a bi-linear manner, indicating a change (increase) in the sandstone bulk modulus when the confining pressure is increased beyond 6-8 MPa.
# Color Point Cloud Registration Algorithm Based on [PERSON]
## 5 Conclusions  
A color point cloud registration algorithm based on hue is presented to solve the problem of different lighting conditions. Our algorithm uses hue as the color information to form the error function to obtain the optimal solution. Experiments show that using hue instead of gray can effectively improve the registration success rate and is more robust to light conditions. Using the dataset, the recall of registration is improved by 8.63% compared with the current state-of-art algorithm, and the RMSE of registration is reduced by 14.3%. From the experimental results, the algorithm has good adaptability to light conditions and is more applicable to the actual situation. In addition, real-world scene scans are performed using existing laboratory equipment, and these point clouds are used to further verify the feasibility of the algorithm. At present, because the algorithm searches for corresponding pairs by nearest point matching, the noise of three-dimensional point cloud may lead to inaccurate correspondence, so the algorithm has poor robustness to noise. In the future, our work will focus on this, increasing the robustness of the algorithm to the noise of point clouds.  
Conceptualization, S.R.; methodology, S.R. and Y.W.; software, S.R.; validation, S.R., X.C. and H.C.; data curation, S.R., H.L. ([PERSON]) and H.L. ([PERSON]); writing--Original draft preparation, S.R. and Y.W.; writing--Review and editing, X.C., S.R. and H.C.; supervision, X.C.; project administration, X.C. and Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Major Project of Scientific and Technical Supporting Programs of China during the 13 th Five-year Plan Period, grant number 2017 YFC0109901 and 2018 YFC0116202.  
**Institutional Review Board Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 7: The results of reconstruction of the office. (**a**) The whole scene of the office. (**b,c**) Different parts of the office.
# A Self-Organizing Area Coverage Method for Swarm Robots Based on Gradient and Grouping
## 4 The Coverage Mechanism Based on Grouping
### Grouping-Based Coverage Algorithm  
It is important to note that during the coverage based on grouping, \(T_{r}\) needs to be initialized and \(\alpha.\mathbf{x}(t)\) is recorded when the robot switches from the Detection status to the Coverage status. The coverage algorithm based on grouping is shown in Algorithm 7.
```
Input: the robot \(\alpha\) in Stop status Output: update the status of the robot \(\alpha\)
1. while \(T_{s}\
eq 0\) do
2. if \(\forall\alpha^{\prime}\in A\wedge\|\alpha.\mathbf{x}-\alpha^{\prime}.\mathbf{x} \|>d_{\mathit{cover}}\) then
3. if \(\dagger\exists\alpha^{\prime\prime}\in A\wedge\alpha^{\prime\prime}.\mathbf{v} \leftarrow\alpha^{\prime\prime}.\mathbf{v}-(\alpha^{\prime\prime}.\mathbf{x}- \alpha.\mathbf{x})^{0}\) then
4.\(\alpha.\mathit{status}=\) Deploy;
5. end if
6. end if
7.\(T_{s}\gets T_{s}-1\);
8. end while
```
**Algorithm 6** Deadlock release algorithm.
```
Input: the robot \(\alpha\) without gradient Output: the robot \(\alpha\) covered the target area
1. Initialization: all robots are in Initial status, and gradient is initialized to infinity
2. while (\(\alpha.\mathit{status}\)!= Arrive) do
3. if \(\alpha.\mathit{status}\) == Initial then
4. Algorithm I: Gradient generation algorithm ;
5. end if
6. if \(\alpha.\mathit{status}\) == Detection then
7. move\({}_{\mathit{allowed}}\) = true;
8. if \(\exists\alpha^{\prime}\in A\wedge\|\alpha.\mathbf{x}-\alpha^{\prime}.\mathbf{x} \|\leq d_{\mathit{cover}}\) then
9. if \((\alpha^{\prime}.\mathit{grad}>\alpha.\mathit{grad})\vee(\alpha^{\prime}. \mathit{grad}=\alpha.\mathit{grad}\wedge\alpha^{\prime}.\mathrm{ID}>\alpha. \mathrm{ID})\) then
10. move\({}_{\mathit{allowed}}\) = false;
11. end if
12. end if
13. if move\({}_{\mathit{allowed}}\) == true then
14. if \(\alpha.\mathit{status}=\) Deploy;
15. end if
16. end if
17. if \(\alpha.\mathit{status}\) == Deploy then
18. Algorithm IV: Self-organizing grouping algorithm;
19. Algorithm V: Deadlock detection algorithm;
20. end if
21. if \(\alpha.\mathit{status}\) == Stop then
22. Algorithm VI: Deadlock release algorithm;
23. end if
24. end while
```  
**Algorithm 7** Coverage algorithm based on grouping.
# Low Delay Inter-Packet Coding in Vehicular Networks
## 1 Introduction
### Organization of the Paper  
In Section 2, we formulate the coding problem, and setup a performance measure (Equation (1)) appropriate for short sessions of quasi real-time packet transfers, especially when the channel conditions vary in an unknown deterministic way within the session. Two classes of convolutional codes are described and analyzed in Section 3. Simulation results are presented and discussed in Section 4. This section also includes the results of calculations based on packet loss measurements from the Asta-Zero test site. Finally, in Section 5, we indicate future lines of work that naturally emanate from our findings.
# University Student Dropout Prediction Using Pretrained Language Models
## 6 Conclusions  
In this study, we introduced a novel framework for predicting university student dropout by combining individual demographic and academic information. We formulated each feature into a natural language format and utilized a PLM to capture the relationships between different types of data. We further recast the student dropout prediction task from a binary classification task to an NLI task to maximize the effectiveness of the language model. Experimental results demonstrated that the proposed framework STUD significantly outperformed several baseline methods. Notably, our model achieved a remarkable improvement of 9.00% on the F1-Score compared to the current state-of-the-art model. We showed that demographic and academic information was meaningful for predicting student dropout and that the recasting task was successful. However, STUD required many tokens (on average, 123 tokens) for formulating structured data into unstructured data and recasting the task as an NLI task. In future work, we aim to minimize the number of tokens for formulating and recasting the task to accommodate a wider variety of information (e.g., counseling records).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Mode**} & \multicolumn{4}{c}{**Dropout Prediction**} \\ \cline{3-6}  & & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{4}{*}{\(\text{STUD}_{SimCSE-BERT}\)} & _Classification_ & 0.880 & 0.397 & 0.351 & 0.372 \\  & \(\text{NLI}_{Discrete}\) & 0.881 & 0.409 & 0.377 & 0.392 \\  & \(\text{NLI}_{Continuous}\) & 0.872 & 0.384 & **0.429** & 0.405 \\ \cline{2-6}  & _Ours_ & **0.901** & **0.516** & 0.416 & **0.460** \\ \hline \hline \end{tabular}
\end{table}
Table 6: The comparison of dropout prediction performance for various settings.  
Figure 3: The difference in both numerical and categorical features between students who dropped out and those who did not drop out.  
**Author Contributions:** Conceptualization, K.-M.K.; methodology, H.-S.W. and K.-M.K.; software, H.-S.W. and K.-M.K.; validation, H.-S.W.; formal analysis, K.-M.K. and M.-J.K.; investigation, H.-S.W. and K.-M.K.; resources, K.-M.K.; data curation, M.-J.K.; writing--original draft preparation, D.K. and H.-S.K.; writing--review and editing, H.-S.W., M.-J.K. and K.-M.K.; visualization, H.-S.W.; supervision, K.-M.K.; project administration, K.-M.K.; funding acquisition, K.-M.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Basic Research Program through the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (no. 2022R1C1C1010317) and the Research Fund, 2021 of The Catholic University of Korea.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing not applicable. The code is available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Mathematical Model of Stepless Capacity Control System
### Mathematical Model of Actuator
\tag{3}\]  
Figure 2: Displacement diagram of valve plate and actuator. where, \(k_{unloader}\) represents the spring stiffness of the actuator, \(x_{0}\) represents the precompression of the spring, \(x_{max}\) represents the maximum compression of the spring, \(\alpha_{5}\) represents the start angle of withdraws, and \(\alpha_{6}\) represents the end angle of withdraws. (3) The initial condition of ejection or withdraw:  
\[\left\{ \begin{array}{l}v(0)=0;\\ x(0)=0;\\ \end{array} \right. \begin{array}{l}status3-ejection\\ \\ \end{array} \]  
\[\left\{ \begin{array}{l}v(0)=0;\\ x(0)=h_{2};\\ \end{array} \right. \begin{array}{l}status5-withdrawal\\ \\ \end{array} \]  
\[\left\{ \begin{array}{l}v(0)=\gamma v_{1};\\ x(0)=L_{unloader};\\ \end{array} \right. \begin{array}{l}status3-rebound\\ \\ \end{array} \]  
\[\left\{ \begin{array}{l}v(0)=\gamma v_{2};\\ x(0)=L_{unloader};\\ \end{array} \right. \begin{array}{l}status5-rebound\\ \\ \end{array} \right.\]  
where, due to the existence of backflow clearance in the backflow process, the initial displacement of the retraction process is \(h_{2}\), and \(L_{unloader}\) represents the actuator stroke. (4) Constraint condition  
\[\left\{ \begin{array}{l}h_{2}\leq(h_{2})_{max},\alpha_{2}\leq\theta\leq \alpha_{3}\\ k_{unloader}x_{0}+F_{i}+F_{cy}-mg\cos\alpha-f\geq 0,\alpha_{5}\leq\theta\leq \alpha_{6}\\ \end{array} \right. \tag{4}\]  
Constraint condition 1 is to ensure that the clearance at the balance position of the actuator in the process of ejection and backflow is less than or equal to the maximum allowable backflow clearance. Constraint condition 2 requires that the upper limit should be withdrawn so that the valve can be closed normally to ensure the adjustment effect. The motion equation of ejection and withdrawal can be obtained from the differential equation and constraint conditions of the actuator:  
\[x=\left\{\begin{array}{l}ejection\left\{ \begin{array}{l}C_{1}(1-cos\sqrt{ \frac{k}{m}t}),\alpha_{2}\leq\theta\leq\alpha_{3}\\ (L_{unloader}-C_{2})cos\sqrt{\frac{k}{m}t}+\frac{\gamma v_{1}}{\sqrt{\frac{k_ {unloader}}{m}}}+C_{2},\mbox{First rebound}\\ \\ withdrawal\left\{\begin{array}{l}(h_{2}-C_{3})cos\sqrt{\frac{k}{m}t}+C_{3}, \alpha_{5}\leq\theta\leq\alpha_{6}\\ (L_{unloader}-C_{4})cos\sqrt{\frac{k}{m}t}+\frac{\gamma v_{2}}{\sqrt{\frac{k_ {unloader}}{m}}}+C_{4},\mbox{First rebound}\\ \end{array}\right.\end{array} \right. \tag{5}\]  
where,  
\[C_{1}=\frac{P_{h}-F_{i}+mg\cos\alpha-f-k_{unloader}x_{0}}{k_{unloader}}\]  
\[C_{2}=\frac{P_{h}-F_{i}+mg\cos\alpha+f-k_{unloader}x_{0}}{k_{unloader}}\]  
\[C_{3}=\frac{k_{unloader}x_{max}+F_{i}+F_{cy}-f-mg\cos\alpha}{k_{unloader}}\]  
\[C_{4}=\frac{k_{unloader}x_{max}+F_{i}+F_{cy}+f-mg\cos\alpha}{k_{unloader}}\]
# Precise Orbit Solution for Swarm Using Space-Borne GPS Data and Optimized Pseudo-Stochastic Pulses
## 4 Experiments
### Comparison with PSO Produced by ESA  
Swarm PSOs are released by ESA, which are of a high accuracy, and are widely applied throughout the world [3]. Swarm ZD reduced-dynamic POD is compared with Swarm PSOs produced by ESA. The related results are as follows.  
Figure 8a shows the RMS values of orbit differences for Swarm-A in R, T, N and 3D directions during DOY 274-298, 2014. The figure indicates that RMS values of orbit differences for Swarm-A in R, T and N directions are in the range of 2-5 cm, and 3D direction is in the range of 4-8 cm.  
Figure 8b shows the RMS values of orbit differences for Swarm-B in R, T, N and 3D directions during DOY 274-298, 2014. The figure indicates that the RMS values of orbit differences for Swarm-B in R, T and N directions are within 1.5-3 cm, and 3D direction is within 3-4.5 cm.  
Figure 8c shows the RMS values of orbit differences for Swarm-C in R, T, N and 3D directions during DOY 274-298, 2014. The figure indicates that RMS values of orbit differences for Swarm-C in R, T and N directions are in the range of 2-5 cm, and 3D direction is within 4-8 cm.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Satellite** & **Mean RMS Values of GPS Phase Residuals (mm)** \\ \hline Swarm-A & 10.6 \\ Swarm-B & 9.4 \\ Swarm-C & 10.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean RMS values of GPS phase residuals for Swarm-A, Swarm-B and Swarm-C during DOY 274–298, 2014.  
Figure 7: RMS values of GPS phase residuals for Swarm-A, Swarm-B and Swarm-C during day of year (DOY) 274–298, 2014.  
Table 3 shows the RMS values of orbit differences for Swarm-A, Swarm-B and Swarm-C during DOY 274-298, 2014. The table indicates that the mean RMS values of orbit differences for all Swarm satellites are within 2-3 cm in R and T directions, within 2-4 cm in N directions, and in the range of 3-6 cm in the 3D direction.  
Figure 8 and Table 3 indicate that orbit accuracy of Swarm-B is better than that of Swarm-A and Swarm-C in each direction, and orbit accuracy of Swarm-A is basically equal to that of Swarm-C. These results are consistent with the conclusion of Swarm GPS phase residuals. The precise orbits for all three Swarm satellites also have high and stable orbit quality on the basis of the Swarm orbit strategy in the article under high ionospheric activity.  
orbit differences for Swarm-A, Swarm-B and Swarm-C in the R, T and N directions on the DOY 296, 2014. Moreover, no significant systematic differences are found between the orbital results and PSOs produced by ESA. The orbit differences mainly come from the random error of Swarm orbital period, which agrees with the general law of reduced-dynamic OD. In addition, during the period of high ionospheric activity, space-borne GPS observation quality and force deficiencies are worse than normal. These factors lead to orbit differences a bit larger than usual. In conclusion, the results show that the Swarm orbit strategy is effective and offers good orbit quality under the highest ionospheric activity.