esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on the Features of Infrasonic Waves of Sandstone under Shear Load
## 4 Analysis of Experiment Results
### Impact of Sandstone Water Saturation on Infrasound Features in Shear Destruction  
From the perspective of mechanics, during shear destruction, with increased water saturation, the shear resistance strength of the sandstone samples show a downward trend generally (as shown in Figure 10). This is because the authigenic clay mineral is an important interstitial material in sandstone and an important indicator mineral for the division of sandstone diagenesis stages. Clay minerals in rock absorb water and swell up, bringing about the concentration of force at the tips of microfissures, the structure damage between clay minerals, and a decrease in cohesion. The interference of water molecules also changes the physical state of rock, weakening the contact between particle surfaces. The results of experiments in this study are somewhat different from the traditional patterns [33]. The main reason is that the sandstone samples selected in this experiment were relatively tight and did not change much in saturation before and after being soaked in water.  
Figure 9: Number of infrasound events and average excellent frequency of samples with different sizes.  
From the perspective of acoustics, with increased water saturation, the total energy (Figure 10) and the number of infrasound events (Figure 11) generated during the rock shear failure show little regularity. Since the rock samples in this study were tight sandstone ones and did not change much in water saturation before and after soaking, the total energy and number of infrasound events showed little regularity in variation. However, the average excellent frequency of infrasound events decreased linearly in general with increased water saturation. Affected by water saturation, the shear resistance strength of the rock decreased, and the shear failure of the rock became easier; therefore, the failure speed increased, the failure surface became flatter and flatter, and the corresponding excellent frequency of infrasound became smaller and smaller.
## 3 Proposed Methods
### PSGS and MDP Formulation
Let \(\Theta\) be the set of simulated trajectories starting at \(S_{v}\); we set \(h_{v+1}\in\mathcal{H}^{\prime}\),which has the highest average value of the NPV of \(\Theta\). We then update \(\mathcal{H}^{\prime}\) using the heuristics from \(\mathcal{H}\) that yielded the highest mean NPV of \(\Theta\). This process is further explained in PSGS algorithm, which uses an MDP as shown in Algorithm 1. ```
0:\(S_{v}\), \(A\), \(C\), \(t,|\Theta|\), \(\mathcal{H}^{\prime}\), \(\mathcal{H}\), \(\mathcal{Q}\), ADP (Boolean value), \(h\), priority rule
1. If ADP
2. Extract values of \(A\), \(C\), \(t\) and \(A_{t}\) from \(S_{v}\)
3. End If
4. Calculate \(Q_{t}\) using Equation (4d)
5. While\(|C|<|\{1,\dots,\}|\) or \(A\
eq\emptyset\)
6.\(E=\left\{j:\forall P_{j}\in C\text{, }q_{j}:\preceq Q_{t}\right\}\)
7. While E \(\
eq\emptyset\)
8. If ADP
9. For\(\forall h\in\mathcal{H}^{\prime}\)
10. For\({}_{-}\)in \(1:|\Theta|\)
11. Execute PSGS algorithm given in Algorithm 1 with \"\(\text{ADP}=\text{false}\)\". 12. End For
13. End For
14. Update state using Equations (4a)-(4k)
15. Update \(\mathcal{H}^{\prime}\) using Equation (7)
16. Else
17. If\(|\boldsymbol{C}|=|\boldsymbol{C}_{v}|+1\)
18. Select \(e\in E\) based on \(h\)
19. Else if\(|\boldsymbol{C}|=|\boldsymbol{C}_{v}|+2\)
20. Select \(e\in E\) based on \(h_{i}\in\mathcal{H}\)
21. Else
22. Select \(e\in E\) based on priority rule. 23. End If
24. Update \(\boldsymbol{A}:=\boldsymbol{A}\cup\left\{e\right\}\)
25. Update \(\boldsymbol{A}_{t}:=\left\{j\to t+d_{j}:j\in A\right\}\)
26. Update \(Q_{t}\) using step (4)
27. Update \(\boldsymbol{E}\) using Equation (6)
28. End While
29. If! ADP and \(A\
eq\emptyset\)
30. \(t:=\min(f\in\left\{f_{t}:j\to f_{t}\in\boldsymbol{A}_{t}\right\})\)
31. Update \(C:=C\cup\left\{j:f\leq t:f\in\left\{f_{t}:j\to f_{t}\in A_{t}\right\}\right\}\)
32. \(A:=\boldsymbol{A}\setminus\boldsymbol{C}\)
33. Update \(\boldsymbol{A}_{t}\) using step (26)
34. Update \(\boldsymbol{Q}_{t}\) using step (4)
35. End If
36. End While
37. Output: Schedule, NPV, \(h_{i}\)
```  
**Algorithm 1** PSGS algorithm with ADP  
For a project, we execute the algorithm in Figure 1 with the inputs: \(S_{0}\), \(\emptyset\), \(\emptyset\), \(|\Theta|\), \(\mathcal{H}\), \(\mathcal{H}\), \(\mathcal{Q}\), ADP (= true), \(h\) (= random(\(\mathcal{H}\))), priority rule. If we initialize with (ADP = false), the algorithm results in a schedule with the usual generic PSGS. The priority rule can be random, or it can be devised based on certain heuristic or meta-heuristic algorithms. In this study, we use three different priority rules as explained in Section 3.4. Because the MC simulations are independent, we take advantage of multi-core computers for parallel execution. That is, given \(|\Theta|\) the size of the MC and the number of cores (\(nc\)), we distribute the simulations \(|\Theta|\) to the \(nc\) cores as \(\frac{|\Theta|}{nc}\).
## 3 The CFD Settings
### Turbulence Model  
One of the challenges in CFD modeling of flow inside an ejector is determining the turbulence model. Although many researchers have tested various turbulence models to predict an ejector flow, the results have not been conclusive as to which turbulence model gives the best prediction. Many of them have found that the k-omega SST turbulence model is the best one [12; 14; 15; 16; 17]. However, some others have found that RNG k-epsilon [18; 19] or even the standard k-epsilon models are better [20; 21; 22]. Some other researchers [23; 24] have also found that the realizable k-epsilon model can predict an ejector flow well, and even better if used in conjunction with the enhanced wall treatment [25].  
[PERSON] and [PERSON] [19] have tested four turbulence models: standard k-epsilon, realizable k-epsilon, RNG k-epsilon, and k-omega SST models. They have found that the RNG k-epsilon model results matched the experimental data (the mass flow rate and the shock structure) better than the others while the k-omega SST model was the second best. [PERSON] et al. [26] have used RNG k-epsilon turbulence model in their work and found that the CFD results predicted the experimental entrainment ratios with an error of no greater than 20%, and the predicted values tended to be higher than those of the experiment. [PERSON] et al. [23] applied the realizable k-epsilon model in their CFD work and found a good agreement between the CFD results and the experimental data at critical flow conditions (choked secondary flow) but not as good in the sub-critical conditions (unchoked secondary flow).  
In the current research, we tested two turbulence models during the validation, i.e., the k-omega SST and the transition SST turbulence models with the default values of model constants given in ANSYS Fluent. The best between those two was then used for the parametric study. Transition SST turbulence model was chosen as one of the candidates because flow in an ejector may have boundary layer separation and circulation at one or more locations such as at the wall of the diverging section of the primary nozzle (before the outlet) [10; 11; 12] and at the converging mixing section wall [11; 25]. This may cause re-laminarization of the flow at a certain region which might be better predicted by using the transition SST model. It is worth pointing out that only a few researchers have used the transition SST model for an ejector flow as done by [PERSON] et al. [27].
## 4 Problem characterization and Abstraction
### User Survey on HMI Design
#### 4.2.2 Key Findings from the User Survey  
The survey provided valuable insights into the preferences and needs of users regarding HMI design. Several key aspects emerged from the analysis:  
* Critical information display: Users identified process parameters, alarms, system errors, and temperature readings as the most essential data points for their daily work. Additionally, speed indicators and timestamps were frequently mentioned as important elements to display.
* Preferred interaction type: Large buttons and dropdown menus were favored for their ease of use and ability to support quick and efficient navigation.
* Work-enhancing functions: Users highlighted the importance of clearly visible large buttons, color-coded indicators, and predefined text input fields to streamline workflows and reduce interaction time.
* Challenges and usability concerns: Commonly cited challenges included operability under difficult conditions (e.g., exposure to dirt or time constraints), which could impact the effectiveness of the HMI.  
These application-dependent results form a solid foundation for the next phase of HMI development, ensuring that the design aligns with user expectations while addressing usability concerns. By integrating the identified preferences into the design process, the system can be optimized for both efficiency and ease of use in real-world industrial applications.
## 4 Conclusions  
To improve the accuracy and reliability of PPP positioning, the STD-augmented PPP model was proposed and validated. Different from the traditional method where the STD is obtained by ZTD and horizontal gradients, the carrier phase residuals obtained from backward PPP filter is considered to improve the precision of STD. The precise STD model is then used to augment the PPPfloat and fixed solutions.  
First, the marginal effects of STD on PPP accuracy were investigated based on the observations from European GNSS stations. Results indicated that the ZTD and STD modeling based on ZTD + gradients mainly improved the vertical coordinate accuracy, whereas the consideration of the carrier phase residuals for STD modeling could improve the three-dimensional accuracy to 0.5 cm/1 cm/1.2 cm in the N/E/U component.  
Then, three parallel GNSS receivers were selected to investigate the impacts of external STD corrections on ambiguity-float and fixed PPP. Results indicated that external STD correction could improve the RMS of positioning errors in the N/E/U direction by 14%/28%/31% compared to ZTD augmentation. In comparison, 51%/51%/60% improvement could be observed when the same station STD corrections are used. Similar improvements were also observed for the PPP fixed solutions, and the ambiguity-fixing rate could be improved with the STD correction. In addition, BDS observations were used to validate the improvement of STD augmentation. Results indicated that the positioning accuracy of STD augmentation could be improved compared with the ZTD strategy, and the positioning series could be smoothed if the same station STD correction was used.  
Overall, experimental results indicated that the STD corrections from the same station and the external station could both improve the PPP accuracy, which validated the proposed method. Furthermore, it was the unmodeled tropospheric errors, rather than the multipath and observation noise in STD modeling, that improved PPP.  
Conceptualization, C.X.; Formal analysis, L.Y.; Software, L.Z.; Writing--original draft, C.X., L.Y. and L.Z.; Writing--review & editing, C.X.  
This research received no external funding.  
We appreciate the Curtin GNSS Research Centre and IGS for providing the observation data.  
The authors declare no conflict of interest.
## 4 Case Study
### Virtual Coupling Train Diagram Analysis
Additionally, 12 up short-turn route trains virtually couple with full-length route trains at intermediate stations. Two short-turn route trains coupling at station 15 form a large formation to station 1. At 7:57:50, an eight-group virtual coupling train departs from depot 1, while remaining trains operate independently in four-group units. Virtual coupling technology surpasses multi-marshaling methods by allowing depot dispatch of both four-group units and eight-group virtual coupling trains, facilitating uncoupling at intermediate stations to better meet varying passenger flow demands. The passenger flow demand outside the short-turn route area is calculated in 15 min intervals, as illustrated in Figure 11.  
The \(x\)-axis in the figure denotes passenger starting points, while the \(z\)-axis represents total passenger flow at terminal stations from stations 16 to 23. A notable surge in passenger flow at these stations is observed between 7:45 and 8:15. To accommodate these passengers, seven full-length route unit trains and one virtual coupling full-length route train were dispatched from the starting station during this timeframe. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Diagrams**} & \multirow{2}{*}{**Objective Function** **(yuan)**} & **Passenger Waiting** **Costs** **(yuan)** & **Rolling Stock** **Purchase Costs** **(yuan)** & **Train Travel** **Kilometer Costs** **(yuan)** \\ \hline single full-length route & 155,986.26 & 181,372.52 & 24,000.00 & 106,600.00 \\ Full-length and short-turn routes & 151,109.83 & 183,570.46 & 22,560.00 & 96,089.20 \\ Virtual coupling & 139,942.03 & 164,808.05 & 21,120.00 & 93,956.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Cost Comparison under Different Transportation Organization Modes.
###### Abstract  
Automatic Identification System (AIS) data offer essential insights into maritime traffic patterns; however, effective visualization tools for decision-making remain limited. This study presents an integrated visualization processing method to support ship operators by identifying maritime traffic behavior information, such as traffic density, direction, and flow in specific sea navigational areas. We analyzed AIS dynamic data from a specific sea area, calculated ship density distributions across a grid lattice, and obtained visualizations of traffic-dense areas as heat maps. Using the density-based spatial clustering of applications with a noise algorithm, we detected traffic direction at each grid point, which was visualized in the form of directional arrows, and clustered ship trajectories to identify representative traffic flows. The visualizations were integrated and overlaid onto an S-57-based electronic nautical map for Mokpo's entry and exit routes, revealing primary shipping lanes and critical inflection points within the target area. This integrated visualization method simultaneously displays traffic density, flow, and customary routes. It is adapted for the electronic nautical chart (S-101) under the next-generation hydrographic information standard (S-100), which can be used as a tool to support decision-making for ship operators.  
visualization; AIS data; marine traffic behaviors; marine traffic density; marine traffic direction; marine traffic stream; representative route +
Footnote †: journal: Applied sciences
# Social-Aware Peer Selection for Device-to-Device Communications in Dense Small-Cell Networks
## 1 Introduction and Contribution
Similarly, the social aspects of users were not considered, which led to the unwillingness of a user to perform D2D communications with unknown fellow users. [PERSON] et al. proposed a joint social-position relationship based cooperative scheme that exploited both social and location parameters [PERSON] et al. (2018). A partner-selection algorithm with low complexity was derived using a joint closeness metric, which determined a two-layer (i.e., social position) relationship between UEs in a unified manner. The closeness metric values were calculated for the available UEs and sorted to select the most suitable peer for D2D communications. However, the proposed peer selection method was distributed, which increased the energy consumption of UEs. Moreover, the social and physical parameters were manipulated by UEs, which led to privacy concerns. [PERSON] et al. proposed an application-aware peer discovery algorithm that exploited cloud-based social proximity [PERSON] et al. (2018). The algorithm was based on cloud-assistance for computational offloading, which ensured that the method was energy efficient. Moreover, the proposed method significantly improved the peer discovery process compared to traditional peer discovery schemes. However, the algorithm was not suitable for generic D2D scenarios as it assumed that users had similar interests and the same application subscriptions from the cloud. [PERSON] et al. recently proposed a dynamic peer selection method with novel social-aware spectrum-power trading for D2D communications [PERSON] et al. (2018). The authors aimed to maximize the energy efficiency by considering the quality of service (QoS) and the minimum rate constraints. They developed two communication modes for cooperative D2D communications between cellular edge users and D2D transmitters, i.e., the D2D link mode and idle mode. A framework was formulated based on their proposed D2D modes to optimize the peer and mode selection in cooperative D2D communications. The authors asserted the notion that as the amount of D2D transmit power was determined by the social relationship between DUEs, a D2D pair can likewise gain a certain level of spectrum resource from cellular UEs based on the strength of their social relationship. The results demonstrated the efficiency of their proposed scheme in terms of energy efficiency and privacy protection. However, their proposed method had higher complexity. Moreover, D2D peer selection was centralized, which increased the load on legacy-based stations.
# Optimized Design of Flexible Quick-Change System Based on Genetic Algorithm and Monte Carlo Method
## 3 Results and Discussion
### Functional Verification Experiment Analysis of the Flexible Quick-Change System  
The versatile quick-change process system is produced in kind after a series of processing steps. The physical diagram of the developed and produced flexible quick-change process system is shown in Figure 12.  
Figure 11: (a) The distributions of turning cutting force loads and deformation areas. (b) Error at each load point under extreme operating conditions.  
The manufacturing errors of the foundation plate and the two types of fixture plates A and B were measured with a Zeiss Accura CMM, which included the position errors of the two adjacent zero point clamping device mounting holes on the outer ring of the foundation plate, the position errors of the two adjacent positioning pin mounting holes on the outer ring of the fixture plate, and the flatness errors of the surfaces of the foundation plate and the fixture plate. The measurement results are shown in Table 2 below.  
Taking the position accuracy of the center hole of the fixture plate as the reference, the fixture plate was repeatedly changed in the same station several times to measure its repetitive positioning accuracy, and the repetitive positioning accuracy of the two types of fixture plates A and B in different stations was measured. The measured results are shown in Figure 13. It is observed that the clamping accuracy meets the required clamping positioning accuracy of 0.01 mm (\(\pm 5\)\(\upmu\)m), and its value lies in the prediction interval of the Monte Carlo method.  
The quick-change system's repetitive positioning precision differs between stations because the foundation plate and fixture plate were improperly machined. The repetitive positioning accuracy of fixture plate A in the same station is lower than that of fixture plate B. This is mostly explained by the larger size of fixture plate B, which has a larger position error and flatness error for each unit than fixture plate A. Moreover, fixture plate A deforms less under the load, which has less of an impact on positioning accuracy. It is  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Foundation Plate** & **Fixture Plate A** & **Fixture Plate B** \\ \hline Position error (mm) & 0.0026–0.0094 & 0.0012–0.0128 & 0.0023–0.0098 \\ Flatness error (mm) & 0.0114 & 0.0067 & 0.0079 \\ \hline \end{tabular}
\end{table}
Table 2: Physical error measurement results.  
Figure 12: Structure diagram of a flexible quick-change process system. (**a**) foundation plate and zero point clamping system. (**b**) fixture plate. (**c**) flexible quick-change process system-workpiece.  
Figure 13: Repetitive positioning accuracy measurement results.  
obvious that flexible quick-change process technology is better suited for large aerospace component machining.
# A Survey of Methods and Technologies for Congestion Estimation Based on Multisource Data Fusion
## 5 Representation of Methods Used in Data Fusion
### Statistic Methods
(2019) aimed to modify the extended generalized Treiber-Helbing filter (EGTF) to fuse GNSS data (probe vehicles) and traditional traffic data from loop detectors, to enhance more accurate estimations of traffic states (speed, travel time) and emissions on urban expressways. [PERSON] et al. [PERSON] et al. (2019) proposed an algorithm for fusing travel time focusing on loop detector data and GPS probe vehicle data. The algorithm and procedure involve voting technique, fuzzy regression, and Bayesian pooling method. The results showed that the fused travel time is superior to the pure arithmetic mean method. The method produces more accurate, reliable, and realistic travel time. To more accurately estimate the MFD, the authors fused loop detector data and GNSS-FCD ([PERSON] and Piantanakulchai, 2018; [PERSON] and [PERSON], 2018). In [PERSON] et al. (2019), the authors used a simulation of an abstract grid network to validate results. Seven multi-sensor DF-based estimation techniques were investigated in [PERSON] et al. (2018). All methods were implemented and compared in terms of their ability to fuse data from loop detectors and probe vehicles (Bluetooth and GNSS) to accurately estimate freeway traffic speeds. Results show that most DF techniques improve accuracy over single sensor approaches. The analysis shows that the improvement by DF depends on the technique, the number of probe vehicles, and the traffic conditions. [PERSON] and [PERSON] (2018) used spurious data and traffic conditions to estimate the travel time. To achieve this, they used the Bayesian DF approach, combined with the Gaussian mixture model, to fuse the travel time data: Data originate from different types of sensors to improve accuracy, precision, and completeness in terms of spatial and temporal distribution. The difference in traffic conditions classified using the Gaussian mixture model, and the bias estimation from individual sensor by introducing a non-zero mean Gaussian distribution learned from the training dataset were added to the model. To prove the concept, the authors used measured data as input to simulator and multiple types of simulated sensors: loop detector, GPS, and virtual trip line. Travel time was modeled using Gaussian mixture models with two or three components, depending on the state of traffic flow characteristic for a certain road. As a result of the presented work, authors reported an improvement in accuracy of at least 16.3% over mean absolute percentage error of the baseline model with reduced standard deviation up to 6.03%.