esa-sceva/satcom-chunk-collection huggingface dataset:
# A Feature-Based Cost Estimation Model for Wind Turbine Blade Spar Caps
## 4 Results and Discussion  
Based on assumptions and key parameters (see below), cost curves were derived from predicted costs for 35 to 75 m, from which GFRP retrofit costs and 3 m longer length CFRP retrofit costs were taken. Using model-calculated data for turbine investment costs, O and M costs, and GFRP and CFRP retrofit costs, LCOE for the three scenarios was calculated and compared.  
_Assumptions, Key Parameters and Definitions_  
* Tool Life \(n_{t}:1250\) parts [43].
* Capital cost amortised over 20 years, buildings and equipment 85% utilised.
* E-glass and carbon fiber composites spar caps: 35 to 75 m blade length.
* Assume \(M=10\),000 kg for upper molding mass limit. Glass = GFRP, Carbon = CFRP.
* Production Rate \(n_{t}\) 285 ppa for 35 m spar cap; 272 ppa for 75 m spar cap. PPA = parts per annum.
* Prices, 2018: Carbon fiber: \(\varepsilon\)5/kg, \(\varepsilon\)33/kg. Glass fiber: \(\varepsilon\)2/kg. Resin: \(\varepsilon\)3/kg.
* Capacity Factor: 0.2 for 20 years [1], 0.3 for 5 year extension [1].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Spar Cap Length** & **Rated Power** & **Wind Turbine** & **O and M Cost** \\
**(m)** & **(MW)** & **Installed Cost (\(\ell\)/kW)** & **(\(\ell\)/kW/year)** \\ \hline
35 & 1.59 & 1500 & \\
40 & 2.08 & 1450 & \\
45 & 2.63 & 1400 & \\
50 & 3.24 & 1350 & \\
55 & 3.93 & 1300 & 50 \\
60 & 4.68 & 1250 & \\
65 & 5.49 & 1200 & \\
70 & 6.36 & 1150 & \\
75 & 7.32 & 1100 & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Wind turbine capital costs, operational, and maintenance costs.
## 3 Cubature-Principle-Assisted IMM-Adaptive UKF Algorithm (CPIMM-NAUKF)
### Implementation Steps of the CPIMM-NAUKF Algorithm  
Based on Sections 3.1 and 3.2, the CPIMM-NAUKF structure is shown in Figure 3. The implementation steps of the CPIMM-NAUKF are as follows.  
Step 1: NAUKF filtering process.  
First, calculate the covariance by setting \(\mathbf{\hat{X}}_{0}\) and \(\mathbf{P}_{0}\) of the standard UKF, and compute \(\mathbf{P}_{k/k-1},\mathbf{P}_{(XZ)k/k-1},\mathbf{P}_{(ZZ)k/k-1}\). Next, select the new adaptive gene selection by setting up \(\mathbf{\Delta}_{k}\) through Equation (27), and recalculate \(\mathbf{P}_{(ZZ)k/k-1}\) with Equation (28)  
\[\overline{\mathbf{P}}_{(ZZ)k/k-1}=\sum\limits_{i=0}^{2n}W_{i}^{(c)}[\gamma_{k/k-1 }^{(i)}-\mathbf{\hat{Z}}_{k/k-1}][\gamma_{k/k-1}^{(i)}-\mathbf{\hat{Z}}_{k/k-1}]^{T}+ \mathbf{\Delta}_{k}\mathbf{R}_{k}. \tag{36}\]  
To determine the convergence strategy, judge the divergence according to Equation (32). If it is satisfied, go directly to the next step; otherwise, update \(\mathbf{P}_{k/k-1}\) by using Equations (33)-(35).  
Next, to obtain the NUKF filtering results, use the following:  
\[\mathbf{K}_{k}=\mathbf{P}_{(XZ)k/k-1}(\overline{\mathbf{P}}_{(ZZ)k/k-1})^{-1} \tag{37}\]  
Figure 3: The cubature principle assisted IMM-adaptive UKF algorithm (CPIMM-NAUKF) structure.  
\[\hat{\mathbf{X}}_{k} =\hat{\mathbf{X}}_{k/k-1}+\mathbf{K}_{k}(\mathbf{Z}_{k}-\hat{\mathbf{Z}}_{k/k-1}) \tag{38}\] \[\mathbf{P}_{k} =\overline{\mathbf{P}}_{k/k-1}-\mathbf{K}_{k}\overline{\mathbf{P}}_{(ZZ)k/k-1} \mathbf{K}_{k}{}^{T} \tag{39}\]  
From Equations (38) and (39), obtain the \(\hat{\mathbf{X}}_{1,k},\ \mathbf{P}_{1,k},\hat{\mathbf{X}}_{2,k},\ \mathbf{P}_{2,k},\ldots,\) and \(\hat{\mathbf{X}}_{m,k},\ \mathbf{P}_{m,k}\) for UKF filters 1 to \(m\) of IMM-UKF.  
Step 2: The CPIMM filtering process.  
First, compute the mixing probability. Calculate the mixing probability \(u_{ij,k-1}\) according to Equation (3). Next, determine the Interaction by using CP. After \(\hat{\mathbf{X}}_{1,k},\ \mathbf{P}_{1,k},\hat{\mathbf{X}}_{2,k},\ \mathbf{P}_{2,k},\ldots,\) and \(\hat{\mathbf{X}}_{m,k},\ \mathbf{P}_{m,k}\) are obtained based on Step 1, calculate \(\hat{\mathbf{X}}_{j,k-1}^{o}\) and \(\mathbf{P}_{j,k-1}^{o}\), respectively, with Equations (18) and (19). Then update the model probability by calculating the mixing probability \(u_{j,k-1}\) according to Equation (5). To complete the fusion, calculate resultant mean and covariance matrix with Equations (8) and (9).
# A Python Framework for Crop Yield Estimation Using Sentinel-2 Satellite Data
## 2 Materials and Methods
### Methods
#### 2.5.3 Yield Estimation  
Yield estimation was conducted using regression analysis to assess the relationship between the 'Area Under the Curve' (AUC) of vegetation index plots (mean index value) and the corresponding mean yield values across all fields. The AUC was calculated over the entire growing season as well as within a specific time period (3 March 2024 to 27 May 2024), corresponding to key phenological stages from Tillering to Stem Elongation and continuing through Grain Filling. The AUC, \(\int_{l_{1}}^{l_{2}}f(t)\cdot t\), was computed using the trapezoidal rule,  
Figure 3: Spatial distribution of measured yield per field: soft wheat (**Top**) and durum wheat (**Bottom**).  
implemented in Python. The analysis was conducted independently for durum and soft wheat crop fields.  
For this study, five (5) distinct vegetation indices were examined, as summarized in Table 6. These include the Normalized Difference Vegetation Index (NDVI) [53], Green Normalized Difference Vegetation Index (GNDVI) [54], Enhanced Vegetation Index (EVI) [55], and the Leaf Area Index (LAI) [56], which was derived from EVI [57]. Additionally, a novel vegetation index, RE-PAP, was developed utilizing the Red Edge (RE) bands of Sentinel-2. This new index is based on findings from the literature that highlight the value of the RE region in improving the performance of vegetation indices (VIs) [58]. The RE region is particularly effective in capturing variations in chlorophyll content. Healthy plants, which have higher chlorophyll levels, exhibit a red-edge shift toward longer wavelengths. Conversely, unhealthy plants with lower chlorophyll content display a red-edge shift toward shorter wavelengths [59]. The formulation of this index is grounded in the following rationale. The subtraction term (RE3 - RE1) effectively captures the physiological state of vegetation by isolating red-edge shifts, which serve as key indicators of chlorophyll content. The division by RE2 (B6) accounts for variations in light penetration and scattering influenced by the canopy structure, thereby functioning as a reference band to normalize reflectance variations and ensure that the index output accurately reflects structural and physiological changes in vegetation. Finally, the multiplication by the ratio (NIR/RED) enhances the sensitivity of the index to vegetation vigor by leveraging the pronounced contrast between the high near-infrared (NIR) reflectance and low red reflectance observed in healthy plants.
## 4 Survey Findings and Results from and Through Discussion
### Survey Sample Characteristics and Descriptive Statistics
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**First Experiment 1**} & \multicolumn{3}{c}{**Second Experiment 2**} & \multicolumn{3}{c}{**Third Experiment 3**} & \multicolumn{3}{c}{**Fourth Experiment 4**} & \multicolumn{3}{c}{**Fifth Experiment 5**} & \multicolumn{3}{c}{**Sixth Experiment 4**} \\ \cline{2-13}  & **f** & **\%** & **f** & **\%** & **f** & **\%** & **f** & **\%** & **f** & **\%** & **f** & **\%** & **f** & **\%** \\ \hline
**Male** & 8 & 30.8\% & **6\%** & 12 & 54.5\% & **9\%** & 10 & 71.4\% & **7.4\%** & 9 & 29\% & **6.7\%** & 3 & 30\% & **2.2\%** & 17 & 53.1\% & **12.6\%** \\
**Female** & 18 & 69.2\% & **13.3\%** & 10 & 45.5\% & **7.4\%** & 4 & 28.6\% & **3\%** & 22 & 71\% & **16.3\%** & 7 & 70\% & **5.2\%** & 15 & 46.9\% & **11.1\%** \\
**Subtotal** & 26 & 100\% & **19.3\%** & 22 & 100\% & **16.4\%** & 14 & 100\% & **10.4\%** & 31 & 100\% & **22\%** & 10 & 100\% & **7.2\%** & 32 & 100\% & **23.7\%** \\ \hline \hline \end{tabular}  
* Undergraduates students, AUTh’s SJMC 1. 2 Postgraduate students, AUTh’s SJMC 2. 3 Learners, IEK. 4 Undergraduates students, AUTh’s SJMC 3. 5 Postgraduate students, AUTh’s SJMC 4. 6 Undergraduates students, UWM’s DCM.  
\end{table} 
Table 3: The statistical distribution of the variable of gender per experiment. Figure 2: Budding journalists’ demographics per experiment. said that Greek Zoomers are not as open-minded as their international peers. Nevertheless, this may be an isolated survey result that requires further investigation, as a later and more recent different study conducted with the same survey sample does not appear to confirm it [179]. Furthermore, this survey finding can also be characterized as replicable, as it appears to be a common occurrence for adult woman media learners studying media studies to dominate as an overall survey sample in empirical studies, particularly in various empirical studies and surveys conducted in Greece [4; 6; 180]; however, this does not seem to hold true at each experiment of this survey (Table 3). In short, more details can be found in Tables 3-5 in order to have a comprehensive view of the demographics of the budding journalists who participated in this survey.
## 3 Control Methods Used in Agricultural Vehicle Path Tracking Control
### Agricultural Articulated Vehicle  
Articulated vehicles mainly consist of two parts, the front, and the rear, connected by a rigid hinge body in the middle. The structural characteristics of the articulated vehicle not only reduce the turning radius, but also ensure the adhesion between the tire and the ground in rough terrain, making them suitable for transportation operations in non-structural rough terrain. Agricultural wheeled articulated vehicles experience severe vibrations when driving on rough terrain such as farmland. Compared with the traditional front-axis steering structure, the steering structure of the articulated vehicle allows both the front and the rear car body to move relative to the ground during the steering process, which increases the difficulty of path tracking. [PERSON] et al. [91] proposed a sliding mode variable structure path tracking controller for articulated agricultural vehicles. To suppress chattering, the symbolic function was replaced by a continuous function, and the Lyapunov function was used to prove its stability. [PERSON] et al. [92] proposed an adaptive PID controller, which used the reinforcement learning algorithm to tune PID parameters adaptively. Reinforcement learning adaptive PID controllers have smaller overshot and vibrations than traditional ones so that the articulated vehicle can track the predetermined path more accurately. To improve the response speed of articulated agricultural vehicles, [PERSON] et al. [93] proposed a linear quadratic regulator-genetic algorithm (LQR-GA) path-tracking method based on preview information. Preview information can improve the system response speed, and the genetic algorithm can optimize the control effect.  
In order to keep the tractor and trailer on the same working trajectory, [PERSON] et al. [38] designed a nonlinear model predictive path-tracking controller for the tractor-trailer system. During the straight-line path tracking, the lateral error of the trailer is much less than 10 cm. To improve the trajectory tracking performance of autonomous tractor-trailer systems, [PERSON] et al. [94] combined fast-distributed nonlinear model predictive control with nonlinear movement level estimation for state and parameter estimation. The small agricultural tractor-trailer system is shown in Figure 12. The proposed method can improve the path-tracking accuracy of tractor-trailer systems and robustness to environmental disturbances. Apart from the development and improvement of control methods by the researchers mentioned above, commercial companies have proposed commercial solutions for autonomous tractor-trailer systems, such as John Deere's iGuide and iSteer and Trimble's TrueGuide. The difference between iGuide and iSteer, which is primarily used for straight-line path tracking, is that iGuide is a passive guidance solution where the implement does  
Figure 11: Test prototype and test environment.  
not have a steering system, while iSteer is an active guidance solution where both the tractor and the implement have a steering system.
## 3 Introduction to the Dataset
### Analysis of Experimental Results
In this work, three improvements were made to the YOLO v8 model, the network was trained in the order of improvement, the best weights obtained from the training were tested, and the experimental results are shown in Table 4.  
Figure 6: Training loss curve. Figure 7: Validation loss curve. As can be seen from Table 4, the three improvement strategies proposed in this paper led to a certain degree of improvement in the YOLO v8 model on the plateau mouse rabbit hole dataset. Among them, the CBAM attention mechanism achieved the most obvious improvement, and according to the experimental data demonstration, the CBAM module mainly improved the recognition rate of gnawed mouse holes, and the boundary depiction of gnawed mouse holes in the validation set was closer to the actual value. The DRConv module also improves the model, as it improves the representation of the convolution so that the recognition accuracy of all categories is improved accordingly. Using the three improvement strategies simultaneously, the best performance was achieved for all metrics, with the average accuracy rate increasing from 77.9% to 82.74%, the mAP50 increasing from 76.55% to 83.54%, and the average recall rate increasing from 71.53% to 81.39%. The mIoU increased from 67.65% to 72.69%. The change in mlou for each of these categories is shown in Table 5.  
In Table 5, the segmentation accuracy of each category has been improved to some extent, among which the improvement in the gnawed mouse holes is the most obvious, but the improvement in the stone category is not satisfactory. This paper mainly focuses on mouse holes; so, we will not investigate the reason why the stone category has no obvious improvement here. Figure 9 shows the comparison of the prediction effect of the algorithm before and after the improvement of Figure 5. From the figure, it can be seen that the accuracy of all \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Algorithm** & **mPrecision** & **mAP50** & **mRecall** & **mIoU** \\ \hline YOLO v8 & 77.9\% & 76.55\% & 71.53\% & 67.65\% \\ CoT & 78.18\% & 78.51\% & 73.80\% & 68.26\% \\ CBAM & 79.52\% & 79.83\% & 75.11\% & 70.19\% \\ DRCONV & 79.35\% & 79.61\% & 74.98\% & 70.34\% \\ ALL & 82.74\% & 83.54\% & 81.39\% & 72.69\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental data. Figure 8: Precision and recall curves. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Labels** & **Mousehole0** & **Mousehole1** & **Stone** & **Cow Dung** \\ \hline Original model & 60.74\% & 59.44\% & 66.93\% & 83.5\% \\ New Model & 65.65\% & 68.38\% & 67.41\% & 89.32\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Lifting effects by category. categories of segmentation has been improved, and the small stones that were not originally identified in the upper left corner are identified in the improved model. It can therefore be seen that the improved algorithm has a higher detection rate and has a much better overall segmentation performance.
## 3 Data Preparation  
### Geometric Data  
The Yongji test road is a 24 km long mountainous freeway with two lanes in each direction and numerous small-radius curves. A cross-sectional view of the road is illustrated in Figure 1. The road's blueprinted longitudinal grade varies from \(-6.0\%\) to \(+4.0\%\), and it has a cross-section width of 10.50 m (including a 0.5 m wide curb, 3.75 m wide lanes, and 2.50 m wide shoulders).  
The vertical curves' grades are represented by downslope and upslope lines, with sag and crest curves appearing when the grade direction changes within the horizontal curve. The test freeway has 70 combined curves, including 23 downslope, 21 upslope, 14 crest, and 12 sag curves. The statistical description of geometric design parameters for combined curves is presented in Table 1. Each horizontal curve was divided into individual segments, including approach transition, circular section, and departure transition. Table 2 presents the variations in geometric design parameters of the preceding 300 m segment of the combined curve relative to the combined curve, as well as the variations in geometric design parameters of the following 300 m segment of the combined curve relative to the combined curve. Figure 2 illustrates the layout of each type of combined curve.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multicolumn{6}{c}{**Continuous Variable**} \\ \hline
**Variables** & **Description** & **Mean** & **S.D.** & **Min** & **Max** \\ \hline \(G_{mean}(\%)\) & Mean slope of combined curve & \(-2.23\) & \(2.39\) & \(-5.25\) & \(5.25\) \\ \(\Delta G\) & Slope differential of maximum and minimum slope & \(2.86\) & \(1.93\) & \(0.00\) & \(8.10\) \\ \(L(m)\) & Length of combined curve & \(413.97\) & \(135.35\) & \(222.51\) & \(790.76\) \\ \hline \end{tabular}
\end{table}
Table 1: Geometric design parameters of combined curves.  
Figure 1: Details of simulated road cross-section.
## 4 Results and Discussion  
### Results of Hydrodynamic Coefficient Identification  
When identifying the hydrodynamic coefficients, the coefficient of determination \(R^{2}\) is commonly used to evaluate the goodness of fit. The expression for \(R^{2}\) is:  
\[R^{2}=(1-\frac{SS_{res}}{SS_{tot}})\times 100\% \tag{28}\]  
where \(SS_{res}=\sum\left(y_{i}-\hat{y_{i}}\right)^{2}\) represents the sum of squares of the residuals; \(SS_{tot}=\sum\left(y_{i}-\overline{y}\right)^{2}\) is the total sum of squares; and \(y_{i}\), \(\hat{y_{i}}\), and \(\overline{y}\) are the actual, estimated, and mean values, respectively. Table 4 reports the coefficient of determination \(R^{2}\) for the identified Bayesian ridge regression models, with all \(R^{2}\) values exceeding 0.964. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Force Direction** & \(\mathbf{R^{2}}\) & **Force Direction** & \(\mathbf{R^{2}}\) \\ \hline \(X\) & 0.985 & \(K\) & 0.989 \\ \(Y\) & 0.997 & \(M\) & 0.973 \\ \(Z\) & 0.987 & \(N\) & 0.964 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Coefficient of determination (\(R^{2}\)) for the identified Bayesian ridge regression models. Figure 15: The non-dimensional time history curves for Z and M in pure pitch motion. The hydrodynamic coefficients are obtained using the Bayesian ridge regression model identification. The sensitivity analysis of the hydrodynamic coefficients for the submarine's standard motion equations used the direct method proposed by [PERSON] (2016). For detailed information on this method, please refer to ([PERSON] et al., 2016). Figure 16 depicts the sensitivity analysis results for the typical coefficients, considering the maneuvering conditions of a 35\({}^{\circ}\) rudder angle turn and a 10\({}^{\circ}\)/10\({}^{\circ}\) zigzag motion. The submarine's standard motion equations are simplified by selecting sensitivity values (s) greater than 2%. Table 5 lists the hydrodynamic coefficients obtained with the Bayesian ridge regression model after simplifying the motion equations. Figure 16: Sensitivity distribution of hydrodynamic coefficients in various directions. The hydrodynamic coefficients in the (**a**) X direction; (**b**) Y direction; (**c**) Z direction; (**d**) K direction; (**e**) M direction; (**f**) N direction. Table 6 compares the hydrodynamic coefficients obtained through Bayesian ridge regression identification and through CFD simulation restraint model testing. Table 6 highlights that the hydrodynamic coefficients obtained using the Bayesian ridge regression model closely match those calculated from the CFD-constrained experiments. The Bayesian ridge regression method yielded hydrodynamic coefficients with most errors within 10%, while few exhibit larger errors, with a maximum error of \(-43.26\%\). Based on these findings, it can be concluded that identifying hydrodynamic coefficients through Bayesian ridge regression is feasible. Moreover, compared to traditional CFD methods, the identification method proposed here enables the acquiring of all hydrodynamic coefficients and offers a simple and efficient approach.
# Robust Registration of Medical Images in the Presence of Spatially-Varying Noise
## 3 Results
### Evaluation of the Proposed Algorithms on the MRIs from the BrainWeb Dataset
#### 3.1.3 Registration Performance in the Presence of Bias Field
Another observation that is worth noting is that, for the intensity-based benchmark algorithm, the RC similarity measure has at least \(\sim\)50% higher accuracy compared to any other similarity measures. This is consistent with observations in the previous studies on the robustness of RC to bias filed noise [22]. However, the running speed is at least \(\sim\)60% higher compared to other similarity measures. That is, there is a trade-off between accuracy and running speed for the RC similarity measure. Interestingly, this is not the case for the EMD-based algorithms when using MI similarity measures. Both LR-EMD and AFR-EMD achieve the highest accuracy and lowest running time compared to all other algorithms. We have also determined the registration performance in the presence of three and four Gaussian kernels and reported the T-RMSE, I-RMSE, and the running time in Appendix A.1 (Figures A1 and A2). For these two cases, the EMD-based methods have approximately similar performance to the intensity-based registration when SSD, CC, or RC are used as the similarity measure. When using MI as the similarity measure, EMD-based registrations achieve 40% to 65% higher accuracy both in T-RMSE and I-RMSE compared to the intensity-based registration. Overall, our results suggest that MI is the most suited similarity measure to the EMD-based registration techniques. A summary of the quantitative performance measures is presented in Table 1. T-RMSE, I-RMSE, and the convergence percentages reported in this table are the average values across all the bias field noise conditions (\(0\leq K\leq 4\)). Using MI as the similarity measure, AFR-EMD achieves 42% lower error rate in intensity and 52% lower error rate in transformation compared to intensity-based hierarchical registration. AFR-EMD with MI achieves 27% lower error rate in intensity and 21% lower error rate for transformation compared to intensity-based registration with RC similarity measure (which is robust to bias field noise). For LR-EMD, the error rate is 32% lower for intensity and 41% lower for transformation when using MI similarity measure. Figure 7: Registration performance in the presence of two Gaussian kernels. Mean and variance of T-RMSE and I-RMSE for images in BrainWeb dataset are shown in (**A**,**B**) panels. (**C**) panel compares the running time for all methods.