esa-sceva/satcom-chunk-collection huggingface dataset:
# Drying Behaviour of Western Hemlock with Schedules Developed for Norway Spruce and Scots Pine
## 4 Conclusions  
The purpose of this study was to determine the causes of the unique drying behaviour and the well-known longer drying time of western hemlock compared to Norway spruce and Scots pine. Due to a higher initial moisture content and the presence of wet pockets, particularly in the heartwood, hemlock fails to achieve the target moisture content when subjected to the same drying schedule as Norway spruce and Scots pine. Different drying behaviours were observed for hemlock sapwood, i.e., the evaporation front receded faster into the specimen compared with pine sapwood in the first 5 h, and after that, this receding slowed down. Analysing MC gradients of hemlock heartwood indicated regions with abnormally high MC, known as wet pockets. Average MC did not provide comprehensive information on drying behaviour and necessitated the utilisation of moisture content gradient analysis.  
A drying schedule adapted for timber prone to create wet pockets may result in overdrying for species not prone to the presence of wet pockets, if mixed timber species are dried together in the same kiln. It is also recommended to sort hemlock timbers before drying into high-content sapwood and high-content heartwood, respectively, and adapt the drying schedule to the type of wood. This approach aims to mitigate the challenge of non-uniformity in MC. Additionally, for heartwood stacks, suitable conditioning measures need to be implemented.  
Further research can be implemented by providing a drying schedule suited for hemlock heartwood and sapwood on a lab scale and applying the related schedules with MC gradient measurements on an industrial scale, which was the limitation of this study due to the lack of hemlock specimens.  
Conceptualisation, E.H. and J.C.; methodology, E.H., J.C., L.H. and D.S.; software, L.H. and E.H.; validation, E.H., J.C., L.H. and D.S.; formal analysis, E.H.; investigation, E.H.; writing--original draft preparation, E.H.; writing--review and editing, E.H., J.C., L.H. and D.S.; visualisation, E.H.; supervision, D.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 8: MC gradients in cross section of (**a**) spruce heartwood and (**b**) hemlock heartwood.
# Explainable Artificial Intelligence for State of Charge Estimation of Lithium-Ion Batteries
## 3 Methodology  
### Outline  
In this work, we aim to approach SoC estimation of a deployed EV battery in the following manner:  
* **Data** : Obtain data from EVs under real working conditions and utilize the battery signals in a data-driven approach.
* **NN Modeling**: Build NNs that can provide robust and resilient estimation of SoC and compare their performance.
* **Feature Analysis--Fusion of Static and Dynamic Features**: Engineer an approach for the fusion of features, specifically in terms of static and dynamic features, in a data-driven manner to estimate SoC.
* **Feature Analysis--Significance and Redundancy**: Use xAI and importance estimate methods to evaluate the data features used for the estimation of SoC.
# A Numerical Study of the Vibration Characteristics of a Haptic Actuator for a Dial Gear Shifter
## 3 Results and Discussion
### Vibration Responses and Sensitivity Analysis  
The vibration response of the dial gear shifter was calculated at the surface of the knob using the average acceleration value, as shown in Figure 9. Vibration was generated in the form of a harmonic force using a haptic actuator integrated within the dial gear shifter (Figure 5). For the reference model (i.e., experiment number 1 in Table 3), the acceleration levels at the knob were 3000 mm/s\({}^{2}\) on average and 2500 mm/s\({}^{2}\) at minimum. To confirm the performance of the baseline model, we conducted FEA for the other test cases. Table 4 lists the vibration responses measured at the knob grip and a comparison of the SNR. Figure 10 shows the sensitivities of each design parameter on the dB scale computed using the SNRs in Table 4. For parameter (A) (i.e., the radius of the knob grip), the condition of 31 mm exhibited the greatest SNR. Regarding the longitudinal length of the knob grip (i.e., (B)), a relatively short length was preferred. According to the sensitivity analysis of parameter (C), it is desirable to remove the side supports to increase the vibration output. Finally, as shown in (D), the knob grip must be attached by bonding rather than by applying screws.
## 5 Results
### Classifier Performance Comparison (RQ1)
By iteratively refining weak learners and focusing on hard-to-classify instances, CatBoost and XGBoost capture non-linear interactions and subtle patterns in patient trajectories that a single Decision Tree often overlooks. Second, the frequent usage of categorical features likely contributes to CatBoost's competitive edge, as it includes specialized strategies for dealing with categorical inputs and typically requires less extensive feature engineering. Third, while Random Forest does offer improvements over a single Decision Tree by aggregating multiple trees, it still lags behind gradient boosting, as indicated by both the average performance metrics and the post-hoc significance tests. Notably, Decision Tree yields the lowest overall scores, a result likely due to insufficient model complexity in capturing the intricacies of sepsis readmission pathways. Still, its interpretability may appeal to clinicians wanting an easily understandable framework, although, in high-stakes applications, maximizing predictive performance often remains a priority. Random Forest strikes a balance by maintaining some level of interpretability via feature importance analysis, but its predictive power is outmatched by boosting methods. In contrast, CatBoost manages to achieve superior accuracy and interpretability trade-offs, particularly because game-theoretic approaches (e.g., SHAP values) can provide post-hoc explanations for boosting predictions. The strong performance of CatBoost implies that advanced boosting algorithms can significantly enhance the early detection of potential readmissions, giving healthcare professionals additional lead time to intervene. By maximizing both AUPRC (for the minority class) and MCC (for balanced predictive quality), CatBoost reduces the risk of missing critical cases or triggering unwarranted alerts. These findings underline the value of ensemble-based approaches in healthcare analytics, especially in scenarios where patient-level events have complex temporal and categorical interdependencies. In summary, CatBoost exhibits statistically significant benefits in separating sepsis admission outcomes over XGBoost, Random Forest, and Decision Tree. Its higher AUPRC and MCC highlight a promising capacity for correctly identifying high-risk patients without an excessive false-alarm rate. As a result, CatBoost emerges as the leading candidate for the subsequent stages of calibration, UQ, and explainability within the proposed predictive monitoring framework.
## 2 Proposed EPP-Based Beam Scanning Approaches
### Test case (2): \(R=0.875\)  
In this case, the cosine wave half period is chosen to be greater than the array length, \(H>L_{h}\) and equals \(H=5.64\ \lambda\), hence \(R=4.935\ \lambda/5.64\ \lambda=0.875\). Accordingly, after applying the EPPs of the synthesized array elements for \(A_{c}\) changing from positive and the cosine wave amplitude is changing from positive to negative and its magnitude is changing from \(A_{c}=0\) to \(A_{c}=1.25\ \lambda\), the array main beam tilted from a broadside direction of \(\theta^{\circ}=90^{\circ}\) to \(\theta^{\circ}=59.27^{\circ}\) with minimal changes in the HPBW that decreased by \(0.34^{\circ}\) at \(\theta^{\circ}=59.27^{\circ}\) compared to the broadside direction, while the SLL increased from \(-29.79\) dB to \(-10.83\) dB as the main beam titled away from the broadside direction to \(\theta^{\circ}=59.27^{\circ}\). The achieved maximum scanning range is \(-30.73^{\circ}\leq\theta_{r}\leq 30.73^{\circ}\) around the broadside direction. The relation between the amplitude changes of the cosine waveform and the scanning angle, HPBW, SLL, and maximum scanning range for the eight-element ULAA at a compression ratio \(R=0.875\) are listed in Table 10. The polar plot of the synthesized radiation pattern using the SLL/EPP-BSA approach at compression ratio \(R=0.875\) and \(A_{c}=1.25\ \lambda\) is shown in Figure 31.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cosine Wave Amplitude \(A_{c}\)** & **Main Beam Direction \(\theta_{\omega}\) in \({}^{\circ}\)** & **SLL (dB)** & **HPBW in \({}^{\circ}\)** & **Maximum Scanning Range** \\ \hline \(0\) & \(90^{\circ}\) & \(-29.79\) dB & \(12.8^{\circ}\) & \\ \hline \(0.25\,\lambda\) & \(83.5^{\circ}\) & \(-25.55\) dB & \(12.76^{\circ}\) & \\ \hline \(0.5\,\lambda\) & \(76.8^{\circ}\) & \(-18.78\) dB & \(12.77^{\circ}\) & \\ \hline \(0.75\,\lambda\) & \(70.61^{\circ}\) & \(-14.96\) dB & \(12.75^{\circ}\) & \\ \hline \(\lambda\) & \(64.94^{\circ}\) & \(-12.5\) dB & \(12.67^{\circ}\) & \\ \hline \(1.25\,\lambda\) & \(59.27^{\circ}\) & \(-10.83\) dB & \(12.46^{\circ}\) & \\ \hline \(1.5\,\lambda\) & & & \\ \hline \(1.75\,\lambda\) & & & \\ \hline \(2\,\lambda\) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: The relation between the amplitude changes of the cosine waveform and the scanning angle, HPBW, SLL, and maximum scanning range \(\theta_{r}\) for 8-element ULAA at compression ratio \(R=0.875\).  
Figure 30: Polar plot of the synthesized radiation pattern using the SLL/EPP-BSA approach at compression ratio \(R=1\) and \(A_{c}=\lambda\).
## 3 Results
### Results of Numerical Analyses
The influence of cutting conditions on the form of the chip created is also visible. Figure 11: Diagram showing the location of the analyzed node No. 53170 (red point) before and after machining for \(\mathrm{a_{p}}=25\;[\mathrm{\upmu m}]\). Figure 12: Graph of the influence of the depth \(\mathrm{a_{p}}\) and rake angles \(\gamma\) on the type of treatment. For example, the case for \(\mathrm{a_{p}}=25\) [\(\mathrm{\upmu m}\)] and \(\gamma=-70\) [\({}^{\circ}\)] is described in detail. Figure 13 shows stress and strain maps for \(\gamma=-70\) [\({}^{\circ}\)]. For this wedge rake angle, we observe the phenomenon of flash formation but without chip formation. This process may be compared to smooth burnishing. The maximum values of HMH stresses at the selected time step prevail in the contact area of the wedge with the workpiece and are \(\mathrm{\sigma_{e,max}}=3670\) [MPa] (Figure 13a). The largest deformations were at the point of contact of the rake face of the wedge with the workpiece, and they amounted to \(\mathrm{\epsilon_{e,max}}=1.99\) [-] (Figure 13b). From the mesh applied (Figure 14a) it can be read that the height of the flash created was ca. 5 [\(\mathrm{\upmu m}\)]. The resulting burr due to the large rake angle had a size of approx. 20 [\(\mathrm{\upmu m}\)]. For this configuration of the wedge, the stresses propagate into the material. Figure 14b shows a diagram of the displacement of the node lying under the contact surface at a distance of 5 \(\mathrm{\upmu m}\) along the Y axis. It shows that in the initial phase of the process, up to ca. 8 [ms], the node was slowly displaced due to the approach of the tool. At 10 [ms], the tool was located exactly above the selected node, hence its rapid displacement to the value of \(-2.5\)\(\mathrm{\upmu m}\). After the tool had passed through, the elastic return of the material node to the value of \(-1.0\)\(\mathrm{\upmu m}\) took place. Compared to the initial value, this result shows a WW reduction of 1.5 \(\mathrm{\upmu m}\). Figure 14: A \(5\times 5\) [\(\mathrm{\upmu m}\)] mesh (**a**) and a diagram of the displacement of the node along the Y axis (**b**). Figure 13: HMH stress (**a**) and HMH (**b**) strain maps for \(\gamma=-70\) [\({}^{\circ}\)]. The exemplary results presented of numerical analyses allow, for a given tool geometry and the required burnishing depth, to determine the boundary rake angles for which the process of smoothing surface irregularities (burnishing) takes place.
## 4 Discussion
### Limitations  
In this study, we proposed high-resolution UAV-image-based models for field-scale \(V^{25}_{cmax,0}\) and GPP estimation and demonstrated enhancement through texture information. However, some limitations remain. Firstly, the field-scale \(V^{25}_{cmax,0}\) observation was retrieved from the BEPS-DE framework. The accuracy of \(V^{25}_{cmax,0}\) observation was completely determined by the EC-based GPP observation and the structure of the BEPS model. We note that there are several different methods for conducting \(V^{25}_{cmax,0}\) observation, including the data assimilation approach, sunlit GPP-based inverting method [60], and optimality photosynthesis hypothesis-based approach [15]. In addition, the BEPS model is highly parameterized, with many parameters predefined. The uncertainties in these predefined parameters will ultimately influence the retrieval of \(V^{25}_{cmax,0}\). For example, [PERSON] et al. [61] revealed that the variation in the nitrogen distribution coefficient within the canopy (\(k_{n}\)) resulted in a large gap between retrieved \(V^{25}_{cmax,0}\) values. Therefore, it is necessary to comprehensively evaluate the different \(V^{25}_{cmax,0}\) retrieval methods. This study evaluates four typical spatial information extraction methods, including \(\sigma_{ref}\), GLCM texture, LBPH texture, and CNN. Some newly proposed texture features, such as learning-based approaches and the entropy-based approaches, have garnered significant attention [28]. It is necessary to explore the capabilities of these new features for GPP estimation. Lastly, this study faced some challenges in sample size. The overwhelming advantage of CNN models in the training dataset demonstrates its strong ability to extract valuable spatial information. The performance of CNN algorithms primarily depends on big training data. The CNN-based \(V^{25}_{cmax,0}\) models were trained and validated only using 281 daily RBG and five-band reflectance maps. Compared with the complex structure of the CNN, the sample size in this study is relatively insufficient, resulting in a degree of overfitting. It is urgent to expand our dataset to fully harness the potential of deep learning.
###### Abstract  
In order to investigate the lightning damage behavior of an aircraft carbon fiber/epoxy composite-reinforced panel and its protection structures, four types of panels were selected to carry out a lightning experiment. Panels were without protection, with a full-sprayed aluminum coating, a local-sprayed aluminum coating, and a full-embedded copper mesh filling, respectively. Their surface and internal damage was detected via ultrasonic C-scanning. Results showed delamination damage for the protected and unprotected specimens due to substantial lightning Joule heat, thermal shock, and internal explosion. The aluminum coatings and the copper mesh had good shielding performance against anti-lightning strike damage. The protection method with a full-sprayed aluminum coating is more effective compared with the other two methods. This study is valuable to investigate the protection effectiveness of metal covers when aircraft composite structures are struck by lightning.  
lightning strikes; composite panel; metal protection; delamination damage; ultrasonic C-scanning Article
## 4 Output Power Modelling for the Switching Process of the DFVSPS Unit
### Generating Mode
#### 4.1.3. Orderly Shutdown Stage  
In the shutdown stage of pump mode, after the unit is disconnected from the grid, the output power to the grid is also reduced to zero. The power at this stage can be expressed by Equation (23):  
\[P_{S2}=0,\ \tau_{2}+N_{GS}\leq t<\tau_{3}, \tag{23}\]  
where \(\tau_{3}\) is the end time of shutdown stage, and the duration of this stage can be calculated by Formula (24):  
\[\left\{\begin{array}{l}f\frac{du_{m}}{dt}=T_{\rm e}-B_{m}\omega_{m}\\ T_{e}=-\frac{3p_{m}U_{s}L_{m}}{2L_{s}\omega_{s}}id_{r}\end{array}\right.. \tag{24}\]  
At this time, the stator side of the DFIM is short-circuited by a small resistor. Therefore, the value of the voltage \(U_{s}\) is different from that of the on-grid operation stage. The electromagnetic torque opposite to the rotation direction can be generated by applying the corresponding command current  
Figure 21: Comparison of active power waveform before and after equivalent during stable operation stage.  
Figure 20: Comparison of active power waveform before and after equivalent. (**a**) load ramping stage; (**b**) load rejection stage.  
to the \(d\)-axis of rotor side converter to accelerate the shutdown process of the unit. Referring to the parameters selected in the simulation of this paper, the duration of the shutdown stage is 60.4 s.
## 3 Analysis of the Load-Bearing Characteristics of a Single Pile
### Load-Sharing of Pile Body  
In the steel tube concrete mixing composite pile, the steel tube, concrete, and cement soil collectively bore the axial load transmitted from the upper structure. According to the principle of force equilibrium, the total axial load was equal to the sum of the axial forces borne by the steel tube, concrete, and cement soil. Let the total axial load be \(p_{1}\), the axial force borne by the steel tube be \(p_{2}\), and the axial force borne by the concrete be \(p_{3}\). Thus, the load-sharing ratio of the steel tube concrete was \(k_{1}=\frac{p_{1}}{p}\), the load-sharing ratio of the concrete was \(k_{2}=\frac{p_{2}}{p}\), and the load-sharing ratio of the cement soil mixing pile was \(k_{3}=\frac{p_{3}}{p}\) (where \(p=p_{1}+p_{2}\ +\ p_{3}\)). The load-sharing ratios of the three different materials are shown in Figure 10.  
Figure 8: Axial force of soil cement.  
Figure 9: Axial force of concrete.  
As shown in Figure 10, when the load on Pile No. 1 was less than 2800 kN, the load shared by the steel tube and concrete increased gradually with the increase in the load, while the load shared by the cement soil decreased gradually. When the load reached 2800 kN, the load-sharing ratio of the cement soil was 18.6%. After the load reached 3500 kN, with the increase in the load, the load-sharing ratio of the steel tube continued to increase, the load-sharing ratio of the cement soil tended to be relatively stable, and the load-sharing ratio of the concrete gradually decreased. When the load reached 7000 kN, the load-sharing ratios were approximately 53% for concrete, 28% for steel tube, and 19% for cement soil. At this time, the design load capacity (characteristic value) was \(7000/2=3500\) kN. When the pile met the design requirements, the combined load shared by the steel tube and concrete was four times that shared by the cement soil. The load-sharing results of the pile body indicated that in this pile-type structural system, the steel tube concrete rigid core pile, due to its material properties, bore the high-intensity load and became the key load-bearing component of the entire pile foundation system, thus effectively supporting the various loads applied by the upper structure.
# Performance Analysis of FSO-UWOC Mixed Dual-Hop Relay System with Decode-and-Forward Protocol
## 4 System Performance Analysis
### Average Channel Capacity
\bigg{]} \tag{46}\]Similarly, \(I_{C_{2}}\) can be expressed as:  
\[I_{C_{2}}=\frac{\xi_{2}^{2}}{2c\ln 2\Gamma(a)}H_{3,4}^{4,1}\left[\frac{1}{ \left(bA_{0}\right)^{\prime}\rho_{r}\tau}\right]\left(\frac{(0,1);(1,1),\left( \frac{\xi_{2}^{2}}{c}+1,\frac{r}{c}\right)}{\left(\frac{\xi_{2}^{2}}{c},\frac{ r}{c}\right)},\left(a,\frac{r}{c}\right),\left(0,1\right),\left(0,1\right)\right] \tag{47}\]  
By using [30] (Equation 9.301), [34] (Equation 07.34.21.0009.01) and [41] (Equation 1.1), \(I_{C_{3}}\) can be expressed as:  
\[ \begin{array}{ll}I_{C_{3}}&=\frac{\xi_{1}^{2}\xi_{2}^{2}}{2 rc\ln 2\Gamma(a) \Gamma(\bar{\rho})}\\ &\cdot H_{4,0;1,3;2,3}^{0,3;0,2,1}\left|\begin{matrix}\chi_{1}\left(1+\xi_{1}^ {2},1\right)\\ -\end{matrix}\right|\xi_{1}\left|\begin{matrix}\psi_{1}\\ \psi_{1}\end{matrix}\right|\psi_{1}\eta_{1}a\beta\left(\frac{1}{\mu_{T}} \right)^{\frac{1}{2}},\frac{1}{\left(bA_{0}\right)^{\prime}}\left(\frac{1}{ \rho_{r}\tau}\right)^{\frac{1}{2}}\Bigg{]}\end{array}  \tag{48}\]  
where \(\chi_{1}=\left\{\left(0,\frac{1}{r},\frac{r}{r}\right),\left(1,-\frac{1}{r},- \frac{r}{r}\right),\left(1,-\frac{1}{r},-\frac{r}{r}\right);\left(1,\frac{1}{r },\frac{1}{r}\right)\right\}\), \(\xi_{1}=\left\{\left(\xi_{1}^{2},1\right),\left(a,1\right),\left(\beta,1 \right)\right\}\), \(\psi_{1}=\left\{\left(1,1\right);\left(\frac{\xi_{2}^{2}}{c}+1,1\right) \right\}\), \(\psi_{2}=\left\{\left(\frac{\xi_{2}^{2}}{c},1\right),\left(a,1\right);\left(0,1 \right)\right\}\). Similarly, \(I_{C_{4}}\) can be expressed as:  
\[ \begin{array}{ll}I_{C_{4}}&=\frac{\xi_{1}^{2}\xi_{2}^{2}}{2r\ln 2\Gamma(a) \Gamma(\bar{\rho})}\\ &\cdot H_{4,0;2,4;1,2}^{0,3;3,1;2,0}\left[\begin{matrix}\chi_{1}\left|\xi_{2} \\ -\end{matrix}\right|\xi_{3}\left|\begin{matrix}\left(\frac{\xi_{2}^{2}}{c}+1,1 \right)\\ \psi_{3}\end{matrix}\right]\eta_{1}a\beta\left(\frac{1}{\mu_{T}}\right)^{\frac{1 }{2}},\frac{1}{\left(bA_{0}\right)^{\prime}}\left(\frac{1}{\rho_{r}\tau} \right)^{\frac{5}{2}}\Bigg{]}\end{array}  \tag{49}\]  
where \(\xi_{2}=\left\{\left(1,1\right);\left(1+\xi_{1}^{2},1\right)\right\}\), \(\xi_{3}=\left\{\left(\xi_{1}^{2},1\right),\left(a,1\right),\left(\beta,1 \right);\left(0,1\right)\right\}\), \(\psi_{3}=\left\{\left(\frac{\xi_{2}^{2}}{c},1\right),\left(a,1\right)\right\}\). By substituting Equations (46)-(49) into Equation (45), the specific closed-form expression for the system's end-to-end average channel capacity \(C\) can be obtained.
# Energy-Aware 3D Path Planning by Autonomous Ground Vehicle in Wireless Sensor Networks
## 6 Numerical Results
### A 100-Node Scenario
#### 6.2.3 GWO-Based Approach  
This subsection evaluates performance of a GWO-based solution. Figure 9 demonstrates the GWO's achieved path planning solution in 1000 iterations in the scenario in Figure 7.  
In Figure 10, the GWO-based approach achieves a path length of 17,794 m in 1000 iterations as a metaheuristic which approaches the solution iteratively.