esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Image Data Processing  
High resolution aerial imagery (1 m) from the U.S. National Agriculture Imagery Program (NAIP) was used as the primary data source. These data are being widely explored for their use in providing land-cover/land-use and change detection information [59, 60, 61, 62, 63]. Washington has complete state NAIP data for 2006, 2009, 2011, and 2013. Georectified NAIP image data were acquired from the WA Department of Natural Resources in 10 km \(\times\) 10 km tiles. Whole watershed mosaics were created for the two time periods each with 68,144 rows and 90,392 columns of pixels (Figure 3). The 2006 data had three visible spectral bands and was delivered at an 18-inch resolution. The 2006 data was resampled in ERDAS Imagine to one meter to match the resolution of the 2009 data. Additionally, the 2009 data included a fourth band covering the near infra-red spectrum. A three-band difference image was generated by subtracting the 2006 three-band data from the three visible bands of the 2009 data.  
For the 2009 data, the Normalized Difference Vegetation Index (NDVI) was calculated using the red and near infra-red bands according to [64]:  
\[\frac{NIR-red}{NIR+red} \tag{1}\]  
A 9 \(\times\) 9 pixel simple-variance layer was calculated for the infra-red band as a measure of texture [65]. These two layers were added to the four-band 2009 image to create a six-layer data stack for 2009.  
We created a land cover raster from the 2009 imagery to use the proportional area of different land cover types in each segment as input to the statistical model for predicting change. Only the 2009 imagery was used to generate land cover since the 2006 imagery lacked the NIR band and the ability to generate an NDVI image. The vegetation/no-vegetation mask created from the NDVI image was used to split the image into vegetated and non-vegetated images [65,66]. These two images were separately subjected to unsupervised classification. Twenty-five spectral classes were generated for the non-vegetation image and 50 for the vegetation image. The classes in these two resulting layers were aggregated separately and then added back together to create a 2009 land cover classification. The non-vegetation image was aggregated into the following classes: (1) shadow/water; (2) built/gray; (3) bare ground; and (4) uncertain. The vegetation fraction was classified as (5) herb/grass; (6) tree; and (7) shrub/indeterminate. Since the land cover classification was only used to inform the change model, a shadow class was used to recognize the inability to discern actual classes from limited reflected energy [67]. The shrub class was considered a semi-indeterminate class as the difference between dark grass, shrubs, and brightly lit trees can be very hard to distinguish [68, 69, 70].
## 3 Results and Discussion
### Biodistribution Studies
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **1 h** & & \\ \hline
**Organ** & **312.5 \(\upmu\)g/mL** & **312.5 \(\times\) 10\({}^{-1}\)\(\upmu\)g/mL** & **312.5 \(\times\) 10\({}^{-2}\)\(\upmu\)g/mL** & **312.5 \(\times\) 10\({}^{-3}\)\(\upmu\)g/mL** \\ \hline
**Blood** & 0.26 \(\pm\) 0.01 & 1.90 \(\pm\) 0.35 & 9.61 \(\pm\) 1.25 & 4.40 \(\pm\) 0.75 \\ \hline
**Liver** & 10.95 \(\pm\) 0.73 & 59.08 \(\pm\) 22.69 & 59.19 \(\pm\) 2.50 & 34.54 \(\pm\) 1.79 \\ \hline
**Heart** & 1.73 \(\pm\) 0.49 & 2.52 \(\pm\) 0.48 & 3.72 \(\pm\) 0.11 & 5.65 \(\pm\) 1.64 \\ \hline
**Kidney** & 8.96 \(\pm\) 0.82 & 7.29 \(\pm\) 0.88 & 16.33 \(\pm\) 1.96 & 7.40 \(\pm\) 1.23 \\ \hline
**Stomach** & 0.92 \(\pm\) 0.19 & 1.97 \(\pm\) 1.06 & 2.68 \(\pm\) 1.29 & 2.18 \(\pm\) 0.51 \\ \hline
**Intestine** & 0.85 \(\pm\) 0.22 & 10.48 \(\pm\) 4.39 & 25.49 \(\pm\) 14.79 & 4.13 \(\pm\) 3.41 \\ \hline
**Spleen** & 5.20 \(\pm\) 1.54 & 28.10 \(\pm\) 6.61 & 11.77 \(\pm\) 4.01 & 11.83 \(\pm\) 2.30 \\ \hline
**Muscle** & 0.24 \(\pm\) 0.07 & 0.47 \(\pm\) 0.22 & 0.84 \(\pm\) 0.11 & 0.76 \(\pm\) 0.21 \\ \hline
**Lung** & 153.83 \(\pm\) 49.40 & 86.32 \(\pm\) 17.33 & 47.31 \(\pm\) 8.36 & 105.21 \(\pm\) 32.37 \\ \hline
**Bone** & 0.26 \(\pm\) 0.09 & 0.95 \(\pm\) 0.23 & 1.81 \(\pm\) 0.63 & 0.97 \(\pm\) 0.27 \\ \hline
**Pancreas** & 0.45 \(\pm\) 0.11 & 0.63 \(\pm\) 0.15 & 1.63 \(\pm\) 0.24 & 1.24 \(\pm\) 0.27 \\ \hline
**Brain** & 0.21 \(\pm\) 0.03 & 0.23 \(\pm\) 0.09 & 0.09 \(\pm\) 0.06 & 0.44 \(\pm\) 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ex vivo biodistribution results for all tested concentrations at 1 h (\(n\) = 3 \(\pm\) SD). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **4 h** & & \\ \hline
**Organ** & **312.5 \(\upmu\)g/mL** & **312.5 \(\times\) 10\({}^{-1}\)\(\upmu\)g/mL** & **312.5 \(\times\) 10\({}^{-2}\)\(\upmu\)g/mL** & **312.5 \(\times\) 10\({}^{-3}\)\(\upmu\)g/mL** \\ \hline
**Blood** & 0.18 \(\pm\) 0.03 & 1.21 \(\pm\) 0.24 & 1.77 \(\pm\) 0.06 & 1.08 \(\pm\) 0.12 \\ \hline
**Liver** & 14.78 \(\pm\) 6.81 & 32.19 \(\pm\) 16.43 & 43.05 \(\pm\) 10.70 & 22.67 \(\pm\) 7.38 \\ \hline
**Heart** & 2.19 \(\pm\) 0.39 & 2.02 \(\pm\) 0.51 & 2.53 \(\pm\) 0.74 & 5.11 \(\pm\) 1.01 \\ \hline
**Kidney** & 7.35 \(\pm\) 1.51 & 4.82 \(\pm\) 0.42 & 7.85 \(\pm\) 0.38 & 7.32 \(\pm\) 0.62 \\ \hline
**Stomach** & 1.70 \(\pm\) 1.44 & 2.56 \(\pm\) 1.07 & 4.11 \(\pm\) 0.88 & 3.02 \(\pm\) 1.15 \\ \hline
**Intestine** & 2.74 \(\pm\) 1.07 & 16.99 \(\pm\) 6.29 & 32.66 \(\pm\) 1.27 & 17.98 \(\pm\) 4.99 \\ \hline
**Spleen** & 11.10 \(\pm\) 4.10 & 13.95 \(\pm\) 1.17 & 14.90 \(\pm\) 4.73 & 18.37 \(\pm\) 5.60 \\ \hline
**Muscle** & 0.41 \(\pm\) 0.08 & 0.28 \(\pm\) 0.12 & 0.84 \(\pm\) 0.48 & 0.57 \(\pm\) 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Ex vivo biodistribution results for all tested concentrations at 4 h (\(n\) = 3 \(\pm\) SD). Another point that needs to be highlighted is that the uptake in lungs for the most concentrated sample was very high (>50%) and significantly increased compared to all the other concentrations, as shown in Figure 3.
## 4 Experimental Results  
### Model Prediction  
The data for this study were sourced from the Taiwan Stock Exchange, focusing on ETF50 and its largest constituent stock, TSMC. The dataset spans from 30 June 2003 to 31 December 2020. Out of the total data, 90% was used for training and 10% for testing. After excluding null values from the calculation of technical indicators, the final dataset comprised 4297 records. The training data period is from 18 August 2003 to 8 April 2019, while the testing data period is from 9 April 2019 to 31 December 2020.  
The MSE of the predicted stock price by the original model is 11.5758, which decreases to 1.2687 after STBC. It successfully reduces the error by nearly 90%, effectively improving future stock price predictions. Figure 6 shows the daily closing prices of the predicted stock prices, while Figure 7 shows the daily closing prices after correction. In the figures, the grey dots represent the actual stock prices of the day and the black dots represent the predicted stock prices. It can be observed that there is a significant deviation between the actual and predicted stock prices during the period marked by the red box, which is from March 2020 to April 2020.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline Input Variables of the Genetic Algorithm & \(G_{1}\) & \(G_{2}\) & \(G_{3}\) & \(G_{4}\) & \(G_{5}\) & \(G_{6}\) & \(G_{7}\) & \(G_{8}\) & \(G_{9}\) & \(G_{10}\) & \(G_{11}\) & \(G_{12}\) \\ \hline Parameters of Membership Functions & \(KS_{L}\) & \(KH_{L}\) & \(KH_{H}\) & \(KB_{H}\) & \(DS_{L}\) & \(DH_{L}\) & \(DH_{H}\) & \(DB_{H}\) & \(RS_{L}\) & \(RH_{L}\) & \(RH_{H}\) & \(RB_{H}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: GA input variables.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value/Method** \\ \hline Evolution Rounds & 50 \\ Selection Method & Steady State Selection \\ Mutation Rate & 1\% \\ Crossover Method & Single-point Crossover \\ \hline \hline \end{tabular}
\end{table}
Table 7: Training parameters of the genetic fuzzy system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Indicator\(\backslash\)Function** & **BUY** & **HOLD** & **SELL** \\ \hline \(K\) & 0-0-20 & 20-50-80 & 80-100-100 \\ \(D\) & 0-0-20 & 20-50-80 & 80-100-100 \\ \(RSI\) & 0-0-30 & 30-50-70 & 70-100-100 \\ ACTION & 0-0-20 & 20-50-80 & 80-100-100 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Initial values of membership functions for technical indicators.
# Pilot-Based Adaptive Channel Estimation for Underwater Spatial Modulation Technologies
## 6 Simulation Results and Discussion
### Spectral Efficiency  
As mentioned above, the RLS can estimate the UWA-MIMO channel correctly, but the channel estimation is affected by the SMT spectral efficiency. The spectral efficiency of the UWA-SMT and the BER performance is a tread-off problem, where increasing the pilots improves the BER performance, but unfortunately, it reduces the spectral efficiency. The spectral efficiency of the SMTs compared with the pilot length is shown in Table 5. As the pilot length increases, the performance of RLS algorithms increases in terms of BER and MSE, but spectral efficiency is decreased. However, the total spectral efficiency of UWA-SMTs is higher than the conventional MIMO and such a pilot-length problem also exists in the conventional UWA-MIMO. In future work, we plan to improve the RLS to be used with a short pilot length in the UWA-SMTs and try different channel estimation techniques using a short pilot length to improve the UWA-SMT spectral efficiency.  
Figure 8: BER performance of the 4 \(\times\) 4 underwater FGSM-MIMO with Doppler-scaling factor.  
Figure 7: BER performance of the 4 \(\times\) 4 underwater FGSM-MIMO without Doppler-scaling factor.
# Intrinsic Interference Use for FBMC-IOTA Systems
## 4 Evaluation of IIU
### Complexity of Proposed Algorithm  
As explained earlier, we reduced the complexity of the IIU by eliminating some low valued weights. Let us define \(d_{c}\) as the effective number of weights, which corresponds to the effective number of neighboring symbols that interfere with each other on each parity symbol. Denote \(D\) as the total number of neighboring symbols (including those with low valued weights) that interfere on each parity symbol. In the proposed algorithm at each received parity symbol, each symbol has only \(d_{c}-1\) interferers, where \(d_{c}<D\). The complexity order of the proposed IIU algorithm is \(\mathcal{O}(|\mathbb{X}|^{d_{c}})\), which is much less than \(\mathcal{O}(|\mathbb{X}|^{D})\) (the optimal case). The complexity of IIU for FBMC-IOTA is shown in (25).  
\[\text{Complexity}=|\mathbb{X}|^{d_{c}}\times(\text{Iteration number})\times( \text{No. of parity symbols in error}) \tag{25}\]  
Please note that the highest possible value for the number of erroneous parity symbols is \(N\times L\). Apparently, there is a tradeoff between the accuracy of the algorithm and the complexity. Higher values of \(d_{c}\) will result in more accurate detection but the complexity will increase exponentially with regards to \(d_{c}\). We set \(d_{c}=6\), as it is an appropriate value to strike the balance between the accuracy of the algorithm and the complexity.
## 2 Theoretical Analysis  
SPs is an electromagnetic energy wave formed on the metal surface when the external light field is incident on the interface of the metal medium, and the free electrons in the metal are redistributed under the action of electromagnetic waves and collective oscillation occurs. Theoretical analysis shows that the SPs wave only has the TM polarization mode on the metal-medium surface, and its dispersion relation is as follows [33]:  
\[k_{\mathrm{sp}}=\frac{\omega}{c}\sqrt{\frac{\varepsilon_{\mathrm{m}}\varepsilon _{\mathrm{d}}}{\varepsilon_{\mathrm{m}}+\varepsilon_{\mathrm{d}}}} \tag{1}\]  
In the above formula, \(\varepsilon_{\mathrm{m}}\) and \(\varepsilon_{\mathrm{d}}\) are the dielectric constants of the metal and the medium, respectively, and \(\omega/c\) is the wave vector of the light in the air. The wave vector of the incident light field is always smaller than the propagating wave vector of SPs, which is the basic property of the SPs wave. By the phase shift method [34], the surface plasmons can be effectively excited only when the two vectors are matched.  
Due to the ohmic loss in the metal, the energy in the surface wave will be attenuated as the propagation distance increases. The propagation length of the SPs wave is calculated as:  
\[L_{\mathrm{spp}}=\lambda_{0}\frac{\left(\varepsilon_{\mathrm{m}}^{\prime} \right)^{2}}{2\pi\varepsilon^{\prime\prime}}\left(\frac{\varepsilon_{\mathrm{ m}}^{\prime}+\varepsilon_{\mathrm{d}}}{\varepsilon_{\mathrm{m}}^{\prime} \varepsilon_{\mathrm{d}}}\right)^{3/2} \tag{2}\]  
where \(\varepsilon_{\mathrm{m}}^{\prime}\) is the real part of the dielectric constant of the metal, and \(\varepsilon_{\mathrm{m}}\)\" is the imaginary part of the dielectric constant of the metal. It can be seen from the above formula that in order to obtain a large transmission length, the real part of the dielectric constant of the metal material should be larger, and the imaginary part should be smaller. Precious metal has a low loss and a low absorption coefficient and satisfies the above conditions; therefore, precious metals are generally selected. According to the references, gold and silver are generally used [35; 36], and silver is used as a metal in the design.
# Optimizing Faulting Prediction for Rigid Pavements Using a Hybrid SHAP-TPE-CatBoost Model
## 1 Introduction
Therefore, there is a need to develop novel prediction models with high accuracy and strong interpretability to assess their fault conditions. To achieve this goal, this study employed the Boruta algorithm to select the most important input variables. Subsequently, the RF, AdaBoost, GDBT, LightGBM, and CatBoost models were optimized using the TPE hyperparameter tuning method. As a result, the TPE-RF, TPE-AdaBoost, TPE-GDBT, TPE-LightGBM, and TPE-CatBoost fault prediction models were successfully developed. By comparing the performance of these models, the best one was determined. Finally, the best model was integrated with the SHAP interpretability framework to provide comprehensive and local explanations for interpreting model predictions. This study introduces several technical advancements, including the following: (1) Utilizing the TPE hyperparameter optimization algorithm to enhance the model structure and improve prediction performance, surpassing traditional machine learning methods and providing more accurate outcomes. (2) Employing the Boruta feature selection algorithm to identify the most informative and effective variables, thereby enhancing the efficiency of the prediction model. (3) Developing an interpretable model by integrating CatBoost with the SHAP framework, which facilitates comprehensive global and local explanations for interpreting model predictions.
# Forecasting Cost Risks of Corn and Soybean Crops through Monte Carlo Simulation
## 2 Materials and Methods
### Time Series Forecasting Based on Historical Data
The model predicted a trend of decreasing production costs for the crops in the 2023/24 and 2024/25 harvests if the costs of the most important inputs (i.e., fertilizers) decrease. On the other hand, the costs related to labor, soybean seed, and fungicides may show an upward trend, while the cost of dolomitic limestone corrective remains stable. Combined, these changes in the cost of inputs will increase the cost of crops and consequently their prices in the market. Furthermore, with the expected increase in labor costs, it is essential to invest in training for workers, as well as considering possible mechanization to mitigate financial impacts. Additionally, a careful analysis of soybean seed varieties, adoption of integrated pest management, and strategic use of dolomitic limestone are relevant to deal with constantly changing costs. Adequate financial planning and the analysis of the feasibility of redirecting some of the cultivation areas to soybeans, given their higher profitability compared to corn, is also recommended. However, continuous monitoring of the production and market conditions should be made to make informed and sustainable decisions. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Statistic DW**} & \multicolumn{3}{c}{**Theil’s U**} & \\ \hline  & **DTN-S** & **Arima (1, 1, 2)** & **DES** & **DTN-S** & **Arima (1, 1, 2)** & **DES** & **EM RMSE** \\ \hline NPKF (soybean) & 1.92 & 1.92 & 1.97 & 0.96 & 0.96 * & 0.97 & 9.9\% \\ \hline URF (soybean) & 1.91 & 2.0 & 1.64 & 0.94 & 0.92 * & 0.94 & \\ \hline DLLC (soybean) & DTN-S & DMA & DES & DTN-S & DMA & DES & 9.0\% \\ \hline  & 1.82 & 1.76 & 2.00 & 0.99 * & 0.96 & 0.94 & \\ \hline FUT (soybean) & DES & DTN-S & SES & DES & DTN-S & SES & 3.9\% \\ \hline  & 2.00 & 2.01 & 2.01 & 0.98 * & 0.98 & 0.98 & \\ \hline  & DES & DTN-S & SES & DES & DTN-S & SES & 3.6\% \\  & 1.99 & 1.99 & 1.99 & 0.96 * & 0.96 & 0.97 & \\ \hline TRAC (corn/soybean) & DTN-S & Arima (0, 2, 0) & DES & DTN-S & Arima (0, 2, 0) & DES & 6.0\% \\  & 1.70 & 2.00 & 1.70 & 0.99 & 0.99 * & 0.99 & \\ \hline Day (corn/soybean) & DTN-S & Arima (0, 2, 0) & DES & DTN-S & Arima (0, 2, 0) & DES & 2.0\% \\  & 1.58 & 1.82 & 1.58 & 0.99 & 0.99 * & 0.99 & \\ \hline CORB (corn/soybean) & DTN-S & Arima (1, 1, 1) & DES & DTN-S & Arima (1, 1, 1) & DES & 2.7\% \\  & 1.99 & 1.85 & 1.61 & 0.99 & 0.94 * & 0.99 & \\ \hline  & \multicolumn{3}{c}{Statistical DW: Durbin-Watson; DTN-S: Damped Trend Non-Seasonal; DES: Double Exponential Smoothing; DMA: Double Moving Average; SES: Single Exponential Smoothing; NPKF: NPK 05-25-25 fertilizer; URF: Fertilizer Urea; DLLC: dolomitic limestone corrective; FUT: fungicide trifluorstrobin tebuconazole; TRAC: tractor driver hours (h); DAI: daily worker hours (h); CORB: Corn bag. EM: Error Measure, RMSE: Root Mean Squared Error. * significant variable.} \\ \hline \hline  & & & & & & \\ \end{tabular}
\end{table} 
Table 4: Statistical analysis of the costs of corn/soybean and corn prices.
# ODGNet: Robotic Grasp Detection Network Based on Omni-Dimensional Dynamic Convolution
## 4 Experimental Validation  
In order to verify the performance of the ODGNet, in this section, firstly, the ODGNet is trained and tested on the Cornell dataset and compared with the results of the previous algorithms; secondly, in order to test the ability of the multi-object grasp detection, grasp detection experiments are conducted on the multi-object dataset; then, in order to test the contribution of each module to the algorithm, the relevant ablation experiments are conducted; and lastly, the grasping experiments are carried out in a simulation environment.
# Automatic 3D MRI-Ultrasound Registration for Image Guided Arthroscopy
## 3 Methods
### Segmentation
#### 3.1.1 Segmentation of Femoral Cartilage from US  
We adopt deep-learning based segmentation of femoral cartilage from the US volumes. Deep-learning algorithms provide non-iterative and rapid inference, which is beneficial since the US data are intra-operative and requires near-real-time processing for our target application. We trained a Mask R-CNN instance segmentation network [22] to simultaneously segment the medial and lateral femoral cartilage, also achieving identification along with region delineation [23]. The ground truth labels are marked interactively by the authors and validated by an expert clinician (refer Appendix A.1).
# A Unified Approach for the Control of Power Electronics Converters. Part II: Tracking
## 8 Conclusions  
This paper proposes a tracking control design method for a large class of converters, extending the results developed in the context of stabilization problems previously published by the authors. Under some assumptions, which can be seen as the expressions of a type of practical feasibility, simple tracking control laws are proposed. The difficulty is related to the determination of the state reference signal from the knowledge of the output reference, this difficulty being the consequence of the nonlinear and switching nature of the power energy converters and from the fact that, in order to apply the proposed techniques, the availability of the state is required. An extension is proposed to take into account possible perturbations. Two alternatives are considered: the case in which perturbations are unknown, and not measurable, and the case in which perturbations are known or can be measured with the help of simple and low-cost sensors. The interest in and potential of the proposed techniques are illustrated in terms of the very popular full-bridge inverter and a fourth-order boost inverter simulated with the PSIM software. In particular, it is discussed in terms of the delicate determination of the reference signals. The method based on the classical harmonic balance method can be used, but for the cases in which this method is not so easy to apply, an alternate technique is proposed. This technique is efficient when the dynamic of the output reference signal is slow compared to the dynamic of the converter.  
From the results of simulations, the proposed methods appear very promising and a crucial step is now to apply them to power converter prototypes. This will be carried  
Figure 7: Simulated waveform of the boost inverter feeding a resistive load and using a single control action.  
out in the near future. Several other points could be investigated to improve the method. Among them, the case in which only a measured output is available for control purposes is an important challenge and it can be addressed in terms of the observer-based controllers from which we obtained some partial results. Another interesting problem is to adapt the proposed method to the hybrid control framework in the spirit of the work proposed in [44].  
**Author Contributions:** Formal analysis, G.G.; Investigation, G.G., O.L.-S. and L.M.-S.; Methodology, G.G.; Validation, O.L.-S.; Writing-original draft, G.G., O.L.-S. and L.M.-S.; Writing-review and editing, G.G., O.L.-S. and L.M.-S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is being developed within the framework of the project entitled Hybrid self-adaptive multi-agent systems for microgrids--HISPALIS--with the support of the Agence nationale de la recherche (ANR-France).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.