esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Vehicle Modeling for State Estimation
### Tire Force Calculation  
Thanks to the advantages of the DDEV, the longitudinal tire force can be calculated based on the wheel dynamic in Equation (2).  
Therefore, in this study, the well-known semi-empirical \"Pacejka 2002\" tire model [32] is only employed for lateral tire force calculation, which is helpful to reduce the computational effort. Lateral tire force is formulated by \"Pacejka 2002\" in two steps. Firstly, for the pure slip condition [32]:  
\[F_{s0}=D_{y}sin\big{\{}C_{y}arctan\big{\{}B_{y}\alpha-E_{y}\big{[}B_{y} \alpha-arctan\big{(}B_{y}\alpha\big{)}\big{]}\big{\}}\big{\}}+S_{Vy}. \tag{15}\]  
Subsequently, for the combined slip condition [32]:  
\[F_{s}=G_{y}F_{s0}+S_{Vyk}, \tag{16}\]where \(G_{y}\) is the weighting function, which always has a value between 0 and 1. The lateral and longitudinal slip ratio of each tire are given as  
\[\alpha_{ij}=\delta_{T\_ij}-arctan\left(\frac{v+ar}{u\pm\frac{1}{2}Tr}\right),\ ij=fl,\ fr \tag{17}\]  
\[\alpha_{ij}=-arctan\left(\frac{v-br}{u\pm\frac{1}{2}Tr}\right),\ ij=rl,\ rr \tag{18}\]  
\[\lambda_{ij}=-\frac{u_{w\_ij}-R\cdot\omega_{ij}}{u_{w\_ij}},\ ij=fl,\ fr,\ rl,\ rr. \tag{19}\]  
The wheel center speed \(u_{w\_ij}\) is given by  
\[u_{w\_ij}=\left(u\pm\frac{1}{2}Tr\right)cos\ \delta_{T\_ij}+(v+ar)sin\delta_{T\_ ij},\ ij=fl,\ fr \tag{20}\]  
\[u_{w\_ij}=u\pm\frac{1}{2}Tr,\ ij=rl,\ rr. \tag{21}\]  
For the sake of simplicity, the wheel camber is neglected as a low-effect parameter.
## 3 Results
### Effect of Stabilizer Contents on Compaction Characteristics
#### 3.1.2 Influence of Lime and Cement Content  
The optimum moisture content and maximum dry density of the mixed soil with lime and cement were demonstrated in Figure 7. As the stabilizer content increased, the maximum dry density also increased, with a maximum of 1.65 g/cm\({}^{3}\) at the addition of 8%, but the increment of dry density decreased at this content. The optimum moisture content of 2% lime-cement-stabilized soil is the largest at 19.66%, and then it decreases with the larger stabilizer content, which is consistent with lime-stabilized soil.  
Under the same dosage of stabilizer, the maximum dry density of lime-cement-stabilized soil is basically the same as that of lime-stabilized soil when the additional content is less than 6%. However, it is significantly lower than that of stabilized soil with a stabilizer content greater than or equal to 6%, indicating that lime-stabilized soil could obtain greater dry density. Similar results are reported in the literature for cement-treated clayey sediments dredged [18] and expansive over-consolidated clay collected from an urban site in Algeria [42].
# Analysis of Scintillation Effects on Free Space Optical Communication Links in South Africa
## 7 Average Bit Error Rate (BER) Analysis  
The average BER of an IM/DD single-input single-output (SISO) FSOC link based on a specific modulation scheme during periods of atmospheric turbulence is defined as [31; 56; 58]:  
\[P_{BER}=\int\limits_{0}^{\infty}P(e|\gamma)f_{\gamma}(\gamma)d\gamma \tag{56}\]  
where \(P(e|\gamma)\) is the conditional BER of the FSO link based on a specific modulation scheme and \(f_{\gamma}(\gamma)\) represent the PDFs of the Lognormal and Gamma-gamma turbulence models while considering the effects of pointing errors in Equations (51) and (52), respectively.
## 4 Results and Discussion
### Effect of Crossflow in the Three Dimension  
As the height of diffuser is short in the \(z\)-direction, the only observation at the \(xy\) plane is not regarded as proper because the flow must be three-dimensional in the narrow channel. Figure 15a,b represents the magnitude of vorticity from Figure 14a,b along the transverse section at the position of leading edge, center, and trailing edge of obstacles, respectively. The three components of vorticity are denoted as \(\omega_{x},\ \omega_{y},\ \omega_{z}\), marked in Figure 16.  
Figure 14: Vorticity contour around the obstacles: (**a**) Case 2 and (**b**) Case 8.  
Figure 15: Vorticity magnitude for (**a**) Case 2 and (**b**) Case 8.  
The \(\omega_{z}\) effects obviously on the two-dimensional uniformity mentioned in the previous section, but the narrow gap between upper and lower plates of diffuser results in \(\omega_{x}\), which is almost independent on the existence of obstacles. However, the crossflow or three-dimensional effect can be found in Figure 15a,b where it is denoted as \(\omega_{y}\), consisting of a horse sue figure of vortex filament with \(\omega_{x}\) in Figure 16. The strength of them is compared in Figure 17a-f where \(\omega_{y}\) is only marked significant at the leading edge position, but is diffused at the downstream: see Figure 17b,e. In Figure 17a-f, the magnitudes of \(\omega_{x}\) and \(\omega_{z}\) seems to be similar orders. Overall, the magnitudes are compared as \(\omega_{x}\approx\omega_{z}>\omega_{y}\), and, therefore, the vorticity components \(\omega_{z}\) (major) and \(\omega_{y}\) (minor) contributes to the mixing with the installation of obstacles inside the diffuser.  
Figure 16: Schematic of the three-dimensional vortices around obstacle.  
Figure 18 is the comparison of \(\omega_{\rm z}\) at the trailing edge of obstacles for case 2 (circle) and 8 (ellipse). The difference of vorticity components shows a remarkable difference between two cases: the amplitude of \(\omega_{\rm z}\) in the ellipse shape is approximately half of that in the circle shape. Thus, this elucidates the better characteristics of flow uniformity when we used 2:1 elliptic cylinders: see Figure 12.
# Study on Relay Contact Bounce Based on the Adaptive Weight Rotation Template Matching Algorithm
## 3 Adaptive Weight Rotation Template Matching Algorithm  
### Basic Flow of Rotating Template Matching Algorithm  
To achieve accurate identification of the stationary contact, this paper proposes a rotation template matching algorithm with adaptive weight. The basic process of the algorithm is illustrated in Figure 4. Firstly, capture a template image of a stationary contact point. Set the minimum angle for template rotation, \(\theta_{min}\), and maximum angle, \(\theta_{max}\), as well as the angle step size for each rotation, \(\theta_{step}\). Rotate the template from the minimum angle, \(\theta_{min}\), and when it starts rotating, the rotation angle increases each time, \(\theta_{step}\), until the rotation angle increases to \(\theta_{max}\). This generates a series of rotation templates at different angles. Then, match these templates individually with the relay image. The position and edges of the stationary contact in the image correspond to the highest matching position of the template, and the rotation angle of this template indicates the inclination angle of the stationary contact at that moment.
## 5 Discussion
### Resource Utilization Fluctuation (Mx)  
Resource utilization fluctuation (_Mx_) is an effective improvement in the process. It can have either positive or negative impacts on both cost and time factors. Pareto front displays optimum results retrieved from BIM-MOGA. This is useful for further analysis, such as human resource risk management.  
Table 3 displays an example of a comparison between the original plan and an optimal plan with a set of selected data from the Pareto front. The optimal plan is selected based on the original cost with the maximum time without delay in order to avoid blacklisting for a following governmental project. When considering the cost and time of both plans, which are equal, it shows that the optimized _Mx_ objective is clearly better than the non-optimized _Mx_ objective. This means we can reduce worker density on the construction site, leading to reduced risk-related worker factors. Furthermore, there are many optimal plans for utilizing that are better than one original plan.
## 2 Electrical Modeling of BTS in Free Space
### Electromagnetic Coupling Model
\(Q\) represents the quality factor of the resonant circuit, \(\omega\) denotes the angular frequency, and the formula for calculating \(U\) is as follows:  
\[U=-j\omega n\varphi Q=-j\omega nIQM(a,b,h,\mu,x,y) \tag{6}\]  
According to these relationships, the voltage of the OAU is related to the number of turns \(n\), the mutual inductance \(M\), and the current \(I\) of the balise. The mutual inductance \(M\) is further influenced by the dimensions, relative positions, and transmission medium characteristics of the OAU and the balise.
# Cross-Regional Pavement Temperature Prediction Using Transfer Learning and Random Forest
###### Abstract  
Significant regional environmental differences result in varied patterns of pavement temperature changes. To enhance the cross-regional adaptability of temperature prediction models, transfer learning (TL) was introduced into the random forest (RF) model to improve its generalization capability. Firstly, meteorological data on air temperature, solar radiation, relative humidity, and wind speed were collected from different regions. Pavement temperatures at various depths were also monitored over a long period. Secondly, prediction models were constructed using the RF method. The prediction performance of the models was evaluated. Thirdly, the RF model was optimized using TL with a feature enhancement strategy. Finally, the optimized model was validated using data from other regions not included in the initial training set. The results indicated that although the RF model achieved good prediction accuracy within individual regions, its performance declined when applied across different regions. After optimization through TL with feature enhancement, the model's prediction accuracy in target regions was significantly improved. Specifically, the mean squared error was reduced from 44.91 to 14.88, the mean absolute error from 4.91 to 2.78, and the coefficient of determination increased from 0.75 to 0.92. Further validation revealed that the determination coefficient exceeded 0.94 and the mean absolute error remained below 2.3 \({}^{\circ}\)C at all depths. In summary, the transfer learning approach based on the random forest model demonstrates strong adaptability to different regions. It effectively addresses the issue of reduced prediction accuracy caused by regional differences and provides a reliable method for accurate pavement temperature prediction across multiple regions.  
pavement temperature prediction; ensemble learning; random forest; transfer learning; cross-regional modeling 2025  
1
## 4 Discussion
### General Safety Culture Traits of SNPPs  
To ensure error-free operation, the theory of HRO considers that it is absolutely necessary to prioritize and focus on safety at all organizational levels [31; 32]. In this regard, high scores obtained in the safety and planning scales and in the attention to safety and formalization BARS are relevant aspects of our study. These results reveal four key defining characteristics of safety culture in SNPPs: (a) the perception of safety as an essential condition to successfully operate the plant (safety scale); (b) the assessment of solid organizational preparation and proactiveness to address potential problems (scale of resilience planning); (c) the belief that the organization prioritizes safety against production aspects (BARS attention to safety); and (d) the perception of a high level of activity standardization and documentation (BARS formalization). These four attributes are features of high reliability organizations [26].  
Our results also show a certain level of ambivalence concerning the capacity to manage organizational variability while maintaining system stability, in line with the postulates of [PERSON] et al. [35] and [PERSON] [33]. This characteristic is postulated from the theory of HRO and the engineering of resilience as a requirement for safe and reliable plant operation [37]. In this regard, workers in nuclear power plants perceive stations as organizations using robust preparatory practices to effectively and proactively manage future crises (planning scale). On the contrary, the perception of their strength to manage crisis is not as high (adaptive capacity). Thus, considering the paradigm of resilience and uncertainty management [16; 71], SNPPs are satisfactorily perceived as organizations that promote planning activities. Having stated that, the perception of their capacity to adapt to uncertainty and the unexpected is not as satisfactory.  
One last critical aspect of the SNPPs safety culture identified in our results is the organizational process relating to the establishment of objectives and resources allocation (BARS resources allocation). This is the study's range scale with the lowest average score and the one showing the highest level of polarization, that is, of opposite opinions confronted. Considering the dilemmatic aspect of this scale, it is worth noting that a considerable percentage of respondents perceive a scarcity of resources (including people, equipment, time, and budget) and deficiencies in the way the organization establishes and communicates its objectives.
## 5 Discussion
### Remaining Limitations of Open Access Satellite Data
For instance, while satellite-based rainfall estimates using TAMSAT can function well in specific locations, they cannot do so in locations with specific climatologic or terrain profiles. Despite the growing availability of satellite data for free and open use, issues of accessibility and usability remain. These issues tend to stem from data complexity, non-user-friendly interfaces, and limited capabilities for data handling and analysis among end-users [102]. One of the key problems is the non-interoperability of data across different data sources and types. For instance, climate data are frequently saved as different types and projections, resulting in challenges for users in combining and analyzing them in an optimal manner [101]. Moreover, the volume of satellite data can overwhelm ordinary programming in some cases, especially for those who lack technical knowledge or resources [103]. In an effort to overcome such challenges, platforms such as the Marble climate informatics platform and ENACTS have been created to offer interfaces and tools for data access, data discovery, and data analysis in an easy-to-use manner [104]. Such platforms seek to reduce entry barriers among users, especially in developing countries, through the provision of preprocessed data products and interactive visualization interfaces.
# Dynamic and Static Investigation of Ground Heat Exchangers Equipped with Internal and External Fins
## 6 Conclusions  
The influence of using fins in inner and outer surfaces of U-tube GHEs was investigated under the cooling mode. Internal fins increase the heat transfer rate more effectively. A GHE equipped with internal fins experiences enhanced fluid temperature changes along the borehole. For more details, under steady conditions (long timescale responses), when the fluid outlet temperature difference between a simple U-tube and a U-tube with external fins was 0.14 \({}^{\circ}\)C, the temperature difference between a simple U-tube and a U-tube with internal fins reached 0.6 \({}^{\circ}\)C, showing more than four times increase. Increasing the inlet fluid velocity can make the differences more noticeable; by changing the inlet fluid velocity in the range of 0.02-0.06 m/s, the fluid temperature change in the internally finned U-tube is increased by 5-11.3%, compared to the simple U-tube, while the fluid temperature change is just increased by 0.5-2.9% when the external fins are used. For the dynamic behavior, the priority of the inner fins is obvious too; for the short timescale response, with the inlet velocity of 0.053 m/s, the temperature change of the fluid in the outer and inner finned U-tube was increased up to 2.9% and 11% compared to the simple pipe. Short timescale responses by applying sinusoidal function boundary condition to the inlet fluid temperature show that the thermal responses of the smooth and finned GHEs are almost simultaneous by a considerable time shift. Simple and external finned pipes react up to two minutes faster than the pipe with internal fins. The fast, thermal response of the BHE helps to better fluid and ground temperature recovery which affects the performance of the ground source heat pump system.  
Using fins has a positive effect on the fluid temperature changes inside the pipe and heat transfer rate between fluid and borehole wall. By using fins, in particular, internal fins, it is possible to increase the GSHP system's efficiency, decrease the length of the tubes and reduce the initial costs of the system.  
Investigation, A.M.Z.; methodology, A.M.Z.; project administration, K.G.; supervision, K.G. and J.N.; writing--review and editing, A.A.-H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 11: Temperature contours for smooth (**left**) and finned U-tube (**right**) at different velocities; (**a**) inlet pipe; (**b**) outlet pipe.
# The Impact of GoLo Technology on the Quality Properties of Dealcoholised Wines
## 2 Materials and Methods
### Determination of Volatile Compounds by SBSE-GC-MS  
Wine volatile compounds were analysed in triplicate using stir bar sorptive extraction with a polydimethylsiloxane coating (10 mm length; 0.5 mm film thickness) and gas chromatography-mass spectrometry analysis [30]. The extraction process was conducted with stirring at 500 rpm for 60 min at room temperature (21 \(\pm\) 3 \({}^{\circ}\)C). The analysis was performed using an automated thermal desorption unit (TDU, Gerstel, Mulheim an der Ruhr, Germany) connected to an Agilent 7890A gas chromatograph coupled with a quadrupole Agilent 5975C electron ionisation mass spectrometer (MS, Agilent Technologies, Palo Alto, CA, USA). The GC system was equipped with a fused silica capillary column (BP21 stationary phase; 30 m length; 0.25 mm I.D.; and 0.25 \(\upmu\)m film thickness) sourced from SGE (Ringwood, Australia). Helium served as the carrier gas, maintaining a constant column pressure of 20.75 psi. Mass spectrometry (MS) analysis was conducted using scan acquisition in the _m/z_ range of 27 to 300, with an ionisation energy set to 70 eV. The temperature of the MS transfer line was kept at 230 \({}^{\circ}\)C. Data acquisition was performed in positive scan mode to minimise matrix interferences. For confirmation, the MS operated in single ion monitoring mode, utilising the characteristic _m/z_ values of each compound. Identification was carried out using the NIST library. Quantification was achieved using pure standards for each compound, along with the internal standard, 3-methyl-1-pentanol (Sigma-Aldrich, Steinheim, Germany).
## 5 Adaptive Quadrature Near Immersed Boundaries
### Particulate System with Spherical Fillers  
In the third example, a more complex geometry is analyzed to show the efficiency of kd-tree over oct-tree cell division. The number of quadrature points used in the example is on the order of \(1\times 10^{7}\). A particulate system was generated by random packing of 84 spherical fillers in matrix to achieve a volume loading of 43%, as shown in Figure 27. The net volume of solid spherical fillers was then evaluated separately using adaptive quadrature based on kd-tree as well as oct-tree cell division. The estimated volume listed in Table 10 indicates that kd-tree cell division achieves the same degree of accuracy as oct-tree subdivision after two levels of refinement. kd-tree results in a savings of \(5.53\times 10^{6}\) quadrature points relative to oct-tree subdivision, which amounts to 26% savings in the number of integration points for the same accuracy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Tree Type** & \(\mathbf{N_{qp}}\) & **Ratio of \(\mathbf{N_{qp}}\)** & \(\mathbf{V_{act}}\) & \(\mathbf{V_{approx}}\) & **Error** \\ \hline
3D tree & 15,753,744 & & 0.4256 & 0.4256 & \(3.3451\times 10^{-5}\) \\ Oct-tree & 21,287,043 & & & 0.4256 & \(1.9108\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of kd-tree and oct-tree cell division for quadrature cell refinement in a particulate system with 84 spherical fillers.  
Figure 26: Quadrature points generated for the example in Figure 24b using a kd-tree data structure with two sub-levels.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tree Type** & \(\mathbf{N_{qp}}\) & **Ratio of \(\mathbf{N_{qp}}\)** & \(\mathbf{V_{act}}\) & \(\mathbf{V_{approx}}\) & **Error** \\ \hline
2D tree & 3276 & 0.771 & 0.7173 & 0.7160 & \(1.7000\times 10^{-3}\) \\ Quad-tree & 4032 & & & 0.7160 & \(1.7000\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of kd-tree and quad-tree cell division for quadrature cell refinement in the presence of multiple inclusions with elliptical boundary (shown in Figure 24a).
## 3 Network Protocol
### Routing Protocol  
In this layer, network segmentation is defined, playing a crucial role in device interconnection. For the network proposed, the use of the Open Shortest Path First (OSPF) routing protocol is considered, which bases its costs on link bandwidth.  
All routers in the network are programmed with the same protocol. This enables them to share information among themselves and learn routes from the entire network topology, not just from their neighbors. Figure 3 illustrates the structure of an OSPF area in the network, i.e., the subnet covering a celestial body.  
Within this subnet, nanosatellites orbiting the celestial body act as devices. Satellites marked with a red dot represent those located at Lagrange points (LSR), which serve as routers within the subnet. The larger, higher-ranking satellite in the OSPF protocol is an Area Border Router (ABR), facilitating data intercommunication with other areas and providing information for routing decisions.  
As the network topology is dynamic, both LSR and the ABR routers must react and select the best available route based on metrics associated with each end-to-end route interface's cost.
# A Deep Learning Approach to Teeth Segmentation and Orientation from Panoramic X-Rays
## 4 Results and Discussion
While a more rigorous assessment would benefit from clinical validation by dental professionals, such an analysis was beyond the current scope of this study. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Label** & **Precision** & **Recall** & **DSC** & **IoU** & **RIoU** \\ \hline Upper incisors & 88.25 & 94.45 & 90.95 & 84.66 & 86.39 \\ Lower incisors & 84.84 & 93.76 & 88.77 & 81.11 & 83.35 \\ Upper canine & 85.31 & 92.54 & 88.36 & 81.73 & 83.47 \\ Lower canine & 87.27 & 95.64 & 90.96 & 84.44 & 86.74 \\ Upper premolars & 83.43 & 88.58 & 85.29 & 77.30 & 79.09 \\ Lower premolars & 87.65 & 95.69 & 91.19 & 84.90 & 87.84 \\ Upper molars & 84.04 & 89.67 & 86.05 & 79.19 & 80.08 \\ Lower molars & 82.10 & 88.81 & 84.61 & 78.42 & 80.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Categorical evaluation of teeth. Figure 12: Problems in the dataset: (**a**) Overlaps between premolars, and (**b**) overlaps between premolar and molar. However, in the ground truth, there is no instance overlap. (**c**) Presence of braces and fillings, (**d**) fuzzy molar tooth roots, and (**e**) poor annotations with sharp edges and cones. Furthermore, we calculate the number of false positives and false negatives in OBBs. This is performed to detect missing teeth. There is a total of 3382 teeth in the 111 test images. We train and evaluate our model five times. As shown in Table 3, we do not observe any false positives in any of the runs, and the number of false negatives ranges from three to six. This indicates that the model can be used effectively to detect missing teeth. The average inference time of the proposed model is approximately 1.2 s per image on Google Colab Pro+, with \(\sim\)66M model parameters, indicating its suitability for practical clinical use where immediate response is not critical.