esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect Range of the Material Constraint in Different Strength Mismatched Laboratory Specimens
## 3 Results and Discussion
### The Contrast of Different Laboratory Specimens  
Compared with the results of the SENB specimen in the previous studies [24; 25] and the CT, SENT, and CCT specimens in this study, it can be found that the effect range of material constraint exists in all the specimens with different specimen geometries and loading configurations. Because these specimens are universal, we can arrive to a conclusion that the effect range of material constraint exists in all the strength mismatched specimens and structures. When a material located outside the effect range of material constraint, the fracture resistance curves, and crack propagation paths of the specimens and structures are no longer influenced by the mechanical properties of the material.  
In addition, the numerical value of the effect range is affected by the geometry constraint. The specimen with deep crack (high geometry constraint) has smaller effect range than the specimen with shallow crack (low geometry constraint); the SENB and CT specimens (high geometry constraint specimens) have smaller effect range than the SENT and CCT specimens (low geometry constraint specimens).  
Furthermore, different from the other specimens, for the specimens with high geometry and material constraints (CT-\"121\" specimen with deep crack, CT-\"1231\" specimen with deep crack, SENT-\"1231\" specimen with deep crack, CCT-\"1231\" specimen with deep crack), the fracture resistance curves decrease first with the changing of material constraint. The high geometry constraint and material constraint decrease the fracture resistance curve first.  
There are also some differences between different laboratory specimens in the crack propagation path. For all the \"121\" specimens with center crack, the cracks propagate straight and have not changed considerably under different material constraints. For the \"1231\" specimens with interface crack, the crack propagation path deviates to the side of material that has a lower strength in the process of crack growth under bending stress (CT-\"1231\" specimen with shallow crack, CT-\"1231\" specimen with deep crack). The crack propagation path deviates to the side of the material that has a higher strength in the process of crack growth under shearing stress (SENT-\"1231\" specimen with deep crack, CCT-\"1231\" specimen with deep crack). For the \"1231\" specimens with low geometry constraint (SENT-\"1231\" specimen with shallow crack, CCT-\"1231\" specimen with shallow crack), the crack propagation paths are insensitive to the changing of material constraint.  
In the future, the correlation of the effect range of material constraint with geometry constraint and strength mismatch should be established, and the effect range of material constraint of a structure can be obtained directly rather than by FEM simulations or experiments. Then, the results can provide scientific support for the structure integrity assessment and the design of strength mismatched structures.
# Efficient Online Engagement Analytics Algorithm Toolkit That Can Run on Edge
## 4 Experimental Results
### Blink Detection
#### 4.2.1 EAR with Constant Threshold  
Using the constant threshold of 0.2 as in [58], on the RT-BENE dataset, CEAR has an accuracy of 89.23%, an F1 of 24.64%, and an AUC-ROC of 80.84%. On the Eyeblink8 dataset, the model has an accuracy of 84.80%, an F1 of 10.58%, and an AUC-ROC of 66.65%. On the TalkingFace dataset, the model has an accuracy of 94.74%, an F1 of 54.26%, and an AUC-ROC of 97.28%.  
The model detected the most eye blinks out of all three datasets and has the highest recall and AUC-ROC scores. However, it performed poorly and was below average in terms of precision and F1 score. This indicates that the model has an imbalance between False Positives and False Negatives. The constant threshold of 0.2 has the highest trade-off between true positive rate and false positive rate, greedily detecting eye blinks. The detailed results on each dataset can be found in Table 2.
# Efficient Optimization-Based Trajectory Planning for Truck-Trailer Systems
## 3 Results and Discussion
### Challenging Examples, Edge Cases, and Limitations  
In addition to the single case study presented in this paper, the proposed approach was successfully tested on a modified case where reverse movement was considered instead of forward movement. To address the robustness and limitations of the proposed algorithm for trajectory planning of truck-trailer systems, additional more complex scenarios could also be considered. The proposed methodology can help in highly congested environments with high density of static obstacles, which would showcase its ability to find feasible paths under complex spatial constraints. Reverse parking with tight constraints poses another interesting challenge for a truck with multiple trailers in a scenario where spatial constraints and orientation precision are critical. Under real conditions, adverse initial alignments may occur, meaning that the truck-trailer system starts in a non-aligned or challenging initial orientations; such scenarios can help to evaluate convergence to a feasible solution. A typical use case would be a dense warehouse with multiple static obstacles such as shelves or parked vehicles, leaving only narrow paths for maneuvering. In such cases, the truck and trailers may start in a non-optimal initial orientation or with the trailers in a jackknifed configuration. This would help test the algorithm's capacity to recover from adverse initial conditions, and additional evaluation metrics such as time to reach a stable configuration, computational cost, and success rate could be included. Each phase in the proposed solution can be tested with increasing complexity to reveal how the algorithm builds robustness step-by-step. In addition, the results of the proposed algorithm can be compared to baseline solutions (e.g., RRT) in order to highlight differences in computational efficiency, solution optimality, and success rates under challenging scenarios.  
Regarding the limitations of the proposed solution, we point out the following main issues:  
* Scenarios with dynamic moving obstacles can be used to test algorithms' adaptability in the case of real-time trajectory adjustments. However, this is currently impossible with the proposed approach, as we focus only on parking assistance.
* The current methodology was not tested on scenarios that explicitly showcase areas where the algorithm might struggle, such as non-convex obstacle spaces.
* The tradeoff between trajectory smoothness and computational time represents an open topic for further analysis and validation tests that could demonstrate how the algorithm prioritizes these aspects.
## 2 Numerical Model and Simulations
### Governing Equations
#### 2.2.2 Mass, Momentum and Energy Conservation  
The governing equations of the conservations of mass, momentum and energy are presented in Lagrangian form as follows:  
The continuity equation of gas parcels is:  
\[\frac{D\rho}{Dt}+\rho\frac{\partial u}{\partial x}=0 \tag{1}\]  
The momentum equation is based on the Darcy-Brinkman-Forchheimer model:  
\[\rho\frac{Du}{Dt}=-\frac{\partial p}{\partial x}+S \tag{2}\]  
where \(S\) is the source term representing the force between fluid and wire meshes. It includes the osmotic force term and inertial force term by:  
\[S=-(C_{\mathrm{f}}\mu+\frac{1}{2}C_{\mathrm{j}}\mu)u \tag{3}\]  
where \(C_{\mathrm{f}}\) is the osmotic resistance coefficient and \(C_{\mathrm{j}}\) is the inertia coefficient. The two coefficients can be estimated according to the friction factor relations of the regenerator, which will be discussed in the following section.  
Energy equations of the gas parcels and solid matrix are respectively written based on the thermal non-equilibrium model. For the control volume, the heat transferred to gas by heat convection is equal to the sum of the enthalpy increment of flow and the internal energy increment. For the gas parcels in Lagrangian representation, the heat transferred to gas by heat convection equates to the increment of internal energy of the gas parcel itself. So the expression of energy conservation is given by:  
\[h\big{\{}T_{\mathrm{w}}(x,t)-T_{\mathrm{g}}(x,t)\big{\}}Pdx=\rho c_{\mathrm{p }}A_{\mathrm{x}}dx\bigg{\{}\frac{\partial T_{\mathrm{g}}(x,t)}{\partial t} \bigg{\}} \tag{4}\]  
For the solid matrix, the heat from the gas equates to the internal energy increment of the solid matrix, and the expression of energy conservation equation is as follows:  
\[h\big{\{}T_{\mathrm{g}}(x,t)-T_{\mathrm{w}}(x,t)\big{\}}Pdx=\rho_{\mathrm{w}} c_{\mathrm{w}}A_{\mathrm{x}}dx\frac{1-K}{K}\frac{\partial T_{\mathrm{w}}(x,t)}{ \partial t} \tag{5}\]
# Multipath Mitigation for BOC Signals Based on Prompt-Assisted-Offset Correlator
###### Abstract  
Tracking ambiguity and multipath are two serious threats in processing binary offset carrier (BOC) signals that are widely used in global navigation satellite systems (GNSS). Three-loop tracking methods, such as dual binary phase-shift keying tracking (DBT), use a delay lock loop (DLL), a phase lock loop (PLL), and a subcarrier PLL (SPLL) to track the code, carrier, and subcarrier, respectively, thus solving the tracking ambiguity problem. However, the multipath remains an important factor in the ranging performance deterioration of these tracking methods. Using the offset correlator (OC) technique in the SPLL can effectively mitigate subcarrier multipath errors, but it substantially raises thermal noise errors. To solve this contradiction, this paper proposes a prompt-assisted-offset correlator (PAOC) technique that combines the prompt and offset correlators in a complementary way to mitigate multipath for BOC signals. Compared with the original OC technique, the PAOC technique has less thermal noise performance loss. Moreover, this paper discovers and quantifies the interaction between carrier and subcarrier multipath errors by analyzing the coupling effect between SPLL and PLL. Most multipath mitigation methods for BOC signals ignore the carrier multipath in the PLL, so their subcarrier multipath performance is not optimal. Thus, this paper further proposes applying the PAOC technique in both PLL and SPLL to mitigate carrier and subcarrier multipath errors simultaneously. Theoretical analysis and experimental results demonstrate that the proposed method can significantly improve the multipath performance with small noise performance loss.  
GNSS receiver; BOC signal; multipath mitigation; dual BPSK tracking (DBT); offset correlator (OC); prompt-assisted-offset correlator (PAOC) 2023
## 2 Fundamentals of Non-Equally Proportional Discounting of Strength Parameters  
### Methods of Discounting Cohesion and Angle of Internal Friction  
In traditional finite element calculations, it is assumed that cohesion and the internal friction angle have the same discount factor in the process of strength discounting [19], as shown in Equation (1). In fact, the shear strength parameters \(c\) and \(\varphi\) of the soil mass on the potential slip surface play different roles. When the slope has a tendency to slide, if the friction force is sufficient to resist the downward sliding force, the internal connection bond of the soil mass may not be fractured, and the soil body does not rotate and orient itself along the shear surface. However, once the friction force is insufficient to resist the downward force, a small amount of soil sliding occurs on the slope. Further, the connecting bonds within the soil may be broken, and the cohesive force comes into play. Therefore, in the process of strength reduction, the reduction coefficients of \(c\) and \(\varphi\) differ, and the specific expressions are shown in (2) and (3) [20]:  
\[SRF=\frac{c_{0}}{c_{i}}=\frac{\tan\varphi_{0}}{\tan\varphi_{i}} \tag{1}\]  
\[SRF_{c}=\frac{c_{0}}{c_{i}} \tag{2}\]  
\[SRF_{\varphi}=\frac{\tan\varphi_{0}}{\tan\varphi_{i}} \tag{3}\]  
where \(SRF\) is the equal proportional discount factor of cohesion and internal friction angle, \(SRF_{c}\) and \(SRF_{\varphi}\) are the non-equal proportional discount factors, \(c_{0}\) and \(\varphi_{0}\) are the initial cohesion and internal friction angle, and \(c_{i}\) and \(\varphi_{i}\) are the discounted strength parameters.
## 3 Results and Discussion
### Nadir Points of the Moon-Based Observatory and Satellites on Earth
#### 3.2.2 Nadir Track Characteristics of Satellites and Moon  
Since the period of GEO and IGSO is one day, while the HEO is half a day, we can select the nadir track of satellites with variant orbital parameters for analysis in one day. To clearly illustrate the tracks of subsatellite points, we have included them in the Robinson projection map. For comparison, we have also included the tracks of sublunar points.  
Figure 9 shows the subsatellite points of GSO with an orbital inclination of 0\({}^{\circ}\) (GEO), 45\({}^{\circ}\) (IGSO), 75\({}^{\circ}\) (IGSO), and HEO. The density of the points reflects the duration of time that the satellite stays above this area.  
It shows that the longitude and latitude of the subsatellite points of the GEO satellite do not change in one day, and it is stationary above a certain point on the Earth. As for IGSO, the maximum latitude variation of the subsatellite is equal to that of the orbital inclination, and the period of longitude and latitude change is one day. The subsatellite point of the highly elliptical orbit has the longest duration in the middle and high latitudes of the northern hemisphere (also could be the southern hemisphere with appropriate orbital parameters), but the duration is very short in other areas.  
Figure 8: Latitude and longitude of sublunar points over one orbital period.  
Figure 9: Nadir track of satellites with variant orbital parameters and Moon-based observatory in one day.  
Here, we selected the subsatellite point of three common satellite orbits, GEO, IGSO, and HEO, for analysis. In fact, due to the high degree of freedom of the satellite orbital parameters, changes to them will lead to large changes in the nadir track. There are also some suitable orbits of satellites that can be discussed in the future.
# Research on 2D Laser Automatic Navigation Control for Standardized Orchard
## 3 Results and Discussion
### Feature Map and Navigation Parameter Acquisition Accuracy Test
The results show that the method of extracting the navigation path was accurate and reliable. This experiment puts the test platform in static simulation 2D laser point data at any position in the simulated orchard environment; the heading deviation and lateral deviation obtained by human measurement were taken as the true value. The result is a measured value. The detection range \(\theta\) is [\(-30^{\circ}\), \(30^{\circ}\)], with a sampling interval of \(5^{\circ}\). In order to avoid random errors caused by human operation, three tests were performed for each position to obtain an average value as the calculated heading deviation and calculation at that angle. The test results were shown in Table 4. The mean absolute deviation (MAD) and the standard deviation (SD) were used to evaluate and analyze experiment data. It can be seen from Table 4 that the maximum deviation of the heading angle is 0.95\({}^{\circ}\), the MAD of heading angle \(\theta\) was 0.682\({}^{\circ}\), the SD of heading angle \(\theta\) was 0.237\({}^{\circ}\). The lateral deviation \(d\) maximum error was 4.66 cm, the MAD was 2.119 cm and the SD was 1.010 cm. The method proposed was used to obtain the navigation path identification and navigation parameter acquisition, which can meet the accuracy requirements of the experimental platform in the orchard autonomous navigation operation.
## 2 Materials and Methods
### Methods
#### 2.2.6 Molecular Docking  
Molecular docking studies were carried out using AutoDock Vina v.1.2.0 [53; 54]. Prior to initiating docking studies, the PHF tau (PDB ID: 503L) was first downloaded from the RCSB Protein Databank web server as.pdb file and opened on AutoDockTools (1.5.6). To examine if the peptides also interacted with additional ligands of LRP1, docking studies were carried out with \(\beta\)-amyloid peptide (A\(\beta\) (1-42)), apolipoprotein E4, MMP-13 and ADAMTS-4 using PDB IDs 50 QV, 1 GS9, 1 AYO, 2 OW9 and 4 WQI, respectively [55; 56; 57; 58]. On the autodock tools interface, water molecules were deleted and polar hydrogen bonds and Kollman charges were added. The file was then saved as a.pdbqt file. The.pdb files of the each of the ligands mentioned above, including structures of fourteen different peptides generated by selection from the 23-amino-acid sequence of CCR2 LRP1, were generated on PyMOL (2.5.2) [59]. The ligand.pdb file was then opened on the AutoDockTools (1.5.6) workspace where the water molecules were deleted and were also saved as a.pdbqt file. The.pdbqt file for the receptor and ligand were then both opened on the AutoDockTools (1.5.6) workspace where a grid was created in the receptor indicative of the region where AutoDock Vina v.1.2.0 would perform the docking. For the fourteen peptides, two rounds of docking were conducted. The first round of blind docking conducted using Autodock Vina v.1.2.0 consisted of the peptides that were docked to PHF tau at an exhaustiveness of 8 with a grid box size of 126 A \(\times\) 126 A \(\times\) 100 A centered at 155.334, 125.441, 142.729 (\(\times\), y, z). The second round of docking (7 peptides) was carried out using a grid box of size 90 A \(\times\) 90 A \(\times\) 100 A and the exhaustiveness was increased to 16. At the end of those docking studies, a.txt file was generated with the binding affinities of the nine best conformations. The output structures displaying binding to the LRP1 pocket and with the highest binding affinities from Autodock Vina v.1.2.0 were used for further MD simulations. For docking with MMP-13, beta-amyloid peptide, ApoE4 and ADAMTS-4, a single round of docking was conducted with a grid box size of 90 A \(\times\) 90 A \(\times\) 90 A with exhaustiveness of 16. The docked structures obtained were then transferred to PyMOL and analyzed.
# Data-Based Stakeholder Identification in Technical Change Management
## 3 State of Research
### Shortcomings  
The review of the available preliminary work in the areas of data-based approaches in TCM and approaches for SI in TCM reveals several research gaps that are addressed by this contribution. Non-data-based approaches for SI, such as those presented by [PERSON] [4] and [PERSON] et al. [37], provide support for change-specific selection of relevant stakeholders based on the systematization of change characteristics. These approaches are characterized on the one hand by the high degree of abstraction, which does not allow the details of the change to be considered, and on the other hand by their manual character. As a result, the existing approaches only allow imprecise statements to be made with regard to affected stakeholders, which is also associated with a high level of manual effort for each identification. These approaches, therefore, do not provide the flexibility to efficiently account for the high variety of changes. Of the multitude of data-based approaches in change management (e.g., [9; 10; 39]), only [PERSON] [11] focuses on the change-specific prediction of relevant coordinating stakeholders for EC. However, this approach is limited to the utilization of change process data and therefore does not allow an in-depth analysis and exploitation of the contextual information in the change data. Furthermore, the impact on manufacturing stakeholders, such as assembly or logistics, is not considered. These data-based approaches for SI as well as the existing approaches for similarity assessment of EC [47; 48] are limited to the use of textual data and do not take into account potential additional data features collected during the change process (e.g., as the change costs) or the knowledge of affected stakeholders. However, according to [PERSON] et al. [48], the integration of relevant additional information offers the potential to increase model qualities. An efficient integration of the expertise of involved stakeholders to support data-based models has not been considered so far. The main objective of this contribution is therefore, the introduction of a procedure for data-based SI in TCM based on the approach of text classification, which includes all steps from the initial data selection to the improvement of model quality by the integration of expert knowledge. The approach is intended to serve manufacturing companies as a reference for the efficient introduction of classification models for change-specific prediction of relevant stakeholders. This should relieve coordinating employees in change management and increase the quality of SI. As a result, relevant stakeholders can be identified faster and automatically, allowing more time for planning and implementation. The intended approach should free employees completely from the task of SI. In addition, less relevant stakeholders are overseen, which reduces the risk of not considering relevant change impacts. This prevents possible negative consequences in change planning and implementation, such as additional costs, delays, or production stops.
## 3 Results and Discussion
### Simulation Validation
As shown in Figure 6, the multiscale method (MS) demonstrates a clear advantage over the traditional fully implicit method (FI) in terms of iterative solution speed, significantly improving the model's computational efficiency. The total runtime for FI and MS was measured, revealing that the runtime for MS was approximately 254 s, compared to about 408 s for FI. This indicates a 40% improvement in the computational efficiency of MS.  
Figure 4: Comparison of Cumulative Oil Production between Different Simulators for the One-Dimensional Validation Model. Figure 3: Relative permeability curves.
# Review of Advancement in Variable Valve Actuation of Internal Combustion Engines
## 2 Variable Valve Timing (VVT) System
### Mechanical VVT (MVVT)  
Porsche developed VarioCam, a mechanical VVT first used on the 1992 3.0 L engine in the Porsche 968 [(1,8)] It varies the timing of intake valves by adjusting the tension on the timing chain connecting the intake and exhaust camshafts. This mechanical design was not kept in a later version of VarioCam Plus, which uses a rotary vane hydraulic phaser as most HVVT systems do [(1,4)].