esa-sceva/satcom-chunk-collection huggingface dataset:
# Roll Motion Prediction of Unmanned Surface Vehicle Based on Coupled CNN and LSTM
## 4 Experimental Results and Discussion
### Case Study 2: \"JingHai-III\" USV  
As in Case Study 1, the 20,000 time-step time-series data were selected as the training set from the dataset of \"JingHai-III\". Several representative data were selected as test sets. These test sets were named \"JingHai-III\" test set-1, \"JingHai-III\" test set-2, and \"JingHai-III\" test set-3, respectively. Test set-1 is the data recorded by \"JingHai-III\" as it moves in a straight line. Test set-2 is the data recorded by \"JingHai-III\" doing the turning motion. Test set-3 is the data recorded under the curve movement of \"JingHai-III\". They were 470, 1400, and 600 time steps in these sets, respectively. Figure 7a-c shows the trajectory history of \"JingHai-III\" test set-1, \"JingHai-III\" test set-2, and \"JingHai-III\" test set-3, respectively. These three trajectories are the most likely to occur when \"JingHai-III\" conducts a mission.  
The final RMSE and MAE values of each test set are listed in Table 6. Figure 8 shows that the predicted values fit the actual measured values well. Figure 8a-c shows the final prediction results of the proposed model for \"JingHai-III\" test set-1, \"JingHai-III\" test set-2, and \"JingHai-III\" test set-3, respectively.  
The results of these models on three test sets are listed in Tables 7-9, respectively. It can be seen that our proposed model had the best performance. The decrease percentages \(M\)(2/1) in RMSE of the proposed model compared to the other models are shown in Table 10. Slightly different from the  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **RMSE (*)** & **MAE (*)** \\ \hline ARIMA & 0.14 & 0.10 \\ DNN & 0.13 & 0.09 \\ Univariate LSTM & 0.12 & 0.08 \\ Multivariate LSTM & 0.09 & 0.07 \\
**CNN+LSTM (proposed)** & **0.08** & **0.06** \\ \hline \hline \end{tabular}
\end{table}
Table 3: RMSEs and MAEs of each model at \"JingHai-VI\" test set-2.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Test Set** & **CNN+LSTM/ARIMA** & **CNN+LSTM/DNN** & **CNN+LSTM/Univariate LSTM** & **CNN+LSTM/Multivariate LSTM** \\ \hline Test set-1 & 36\% & 18\% & 13\% & 7\% \\ Test set-2 & 43\% & 38\% & 33\% & 11\% \\ Test set-3 & 32\% & 17\% & 6\% & 17\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Decrease percentage in RMSE of the proposed model compared to other models in each test set of “JingHai-VI”.  
Figure 7: The trajectory history of “JingHai-III” test sets: (**a**) test set-1; (**b**) test set-2; and (**c**) test set-3.
# Using Drones to Estimate and Reduce the Risk of Wildfire Propagation in Wildland-Urban Interfaces
###### Abstract  
Forest fires have become one of the most destructive natural disasters worldwide, causing catastrophic losses, sometimes with the loss of lives. Therefore, some countries have created legislation to enforce mandatory fuel management within buffer zones in the vicinity of buildings and roads. The purpose of this study is to investigate whether inexpensive off-the-shelf drones equipped with standard RGB cameras could be used to detect the excess of trees and vegetation within those buffer zones. The methodology used in this study was the development and evaluation of a complete system, which uses AI to detect the contours of buildings and the services provided by the CHAMELON bundles to detect trees and vegetation within buffer zones. The developed AI model is effective at detecting the building contours, with a mAP50 of 0.888. The article analyses the results obtained from two use cases: a road surrounded by dense forest and an isolated building with dense vegetation nearby. The main conclusion of this study is that off-the-shelf drones equipped with standard RGB cameras can be effective at detecting non-compliant vegetation and trees within buffer zones. This can be used to manage biomass within buffer zones, thus helping to reduce the risk of wildfire propagation in wildland-urban interfaces.  
drones; UAV; wildfire; forest fire; CHAMELON; fire prevention; fire risk; buffer zones; wildland-urban interface 2025
## 3 Methodology
### Result Analysis: SHAP Explainable and MK Test Method  
#### 3.4.1 SHAP Explainable  
The SHAP model interpretation tool was initially proposed by economist [PERSON] and originated from a game theory [58]. With [PERSON] [59] introducing this concept into the field of ML, SHAP helps reveal the results of black-box models and improves model transparency and user's trust in ML models. Furthermore, [PERSON]'s initial use of SHAP in landslide susceptibility modeling has gradually led to an increased focus on model interpretability in LSM [60].  
The SHAP value serves to quantify the contribution of each feature to the model, thereby facilitating a deeper understanding of the role that these features play within the model. The SHAP is computed as follows:  
\[\phi_{i}=\sum_{S\subseteq\{x_{1},\ldots,x_{p}\}\setminus\{x_{i}\}}\frac{|S|! (p-|S|-1)!}{p!}[f_{x}(S\cup\{x_{i}\})-f_{x}(S)], \tag{3}\]  
where \(\phi_{i}\) denotes the contribution of the \(i\) th feature, \(S\) denotes the collection of all features excluding feature \(x_{i}\), and \(p\) indicates the overall number of features. The \(f_{x}(S\cup\{x_{i}\})\) indicates the model predicted value when features are added on top of the subset of features \(S\), and \(f_{x}(S)\) represents the model predicted value when only the subset of features \(S\) is used.  
The interpretation of SHAP includes global and local interpretations, with Shapley values serving as an additive feature attribution method. Each observation model displays a Shapely value, resulting in each characteristic displaying a particular shape. For a specific input feature \(z\), local accuracy requires that the interpreted model's output matches the output of the reduced input \(z^{\prime}\), as given by the following equation:  
\[f(z)=g(z^{\prime})=\phi_{0}+\sum_{i=1}^{M}\phi_{i}z^{\prime}_{i}, \tag{4}\]  
where \(g\) denotes the explanatory model, \(z^{\prime}\in\{0,1\}^{M}\) denotes the simplified \(z\) input features, \(M\) signifies the overall number of input features, and \(\phi_{i}\) is as derived from Equation (3). \(\phi_{0}\) denotes the model output in the absence of all features. For a global interpretation, the average impact of each feature was obtained by averaging the SHAP values for all samples.  
In this study, we used SHAP to easily estimate the influence of various features on DL models [61; 62]. First, we create a DL SHAP \"Explainer\" object that uses training samples as background data. Then, the characteristics of the test samples are interpreted and analyzed. Finally, we generate a summary plot that shows the importance ranking of each feature. This methodology not only offers a comprehensive analysis of each feature's contribution to the predicted outcomes but also enhances the accuracy and dependability of model interpretations.
## 7 Conclusions  
1. To address the significant impact of the penalty factor and the number of decomposition layers on the results during VMD decomposition, we introduced the MVO-VMD decomposition method and further combined MPE and WDT to propose the MVO-VMD-MPE-WDT joint denoising method. This method uses MVO to optimize the decomposition layers (K) and penalty factor (\(\alpha\)) in VMD decomposition. The original signal is decomposed into multiple IMFs, and the MPE of each IMF is calculated. IMFs are classified into noisy and pure signals using a threshold of 0.6. The noisy signals are denoised using WTD, and the pure signals are then superimposed to complete the denoising process. The effectiveness of this method has been demonstrated using synthetic simulation signals. The denoised artificial signals achieved using this method closely match the original signals in terms of waveform, and both frequency-domain and time-domain SNRs are higher than those obtained by other methods. Additionally, the mean squared error is as low as 0.58293, showing significant advantages over Wavelet Threshold Denoising and non-optimized VMD denoising methods.
2. A flow-induced vibration model test is conducted on a hydraulic engineering project in Southwest China, and the proposed method is applied to denoise the measured vibration signals from the model test. The results indicate that the noise in the gate vibration signals is effectively removed using the proposed method. The denoising error is minimized to 1.14, which is significantly lower than that of other denoising methods, such as WTD. Additionally, the use of the MVO optimization method to optimize the VMD hyperparameters results in the shortest computation time compared to other optimization methods, demonstrating a strong advantage in computational efficiency.
3. In terms of preserving signal characteristic information, a finite element model of the flow field is established to compute and compare the frequencies from the numerical simulation with those obtained from the denoised measured signals in the model test. The first- and second-order frequencies of the measured signals are 8.2 Hz and 18.31 Hz, respectively, while the corresponding frequencies from the numerical simulation are 8.27 Hz and 18.20 Hz. The overall error between the measured and simulated frequencies is less than 1%, further confirming that the proposed joint denoising method successfully retains essential information in the vibration signals while effectively removing noise. This method is applicable for denoising measured gate vibration signals.
## 1 Introduction
As an alternative to K\({}_{2.2\cdot 2}\)/K\({}_{2}\)CO\({}_{3}\)(KHCO\({}_{3}\)), tetrabutyl ammonium bicarbonate (Bu\({}_{4}\)NHCO\({}_{3}\)) was introduced at about the same time [13] for solubilization of [18F]fluoride, and, due to its lower basicity, was found to be effective in a number of nucleophilic fluorizations. Since the presence of water severely reduces the reactivity of the fluoride ion in nucleophilic substitution reactions, trace water from the eluent mixture need to be carefully removed. Customarily this is achieved through a multi-step azeotropic drying process with the addition of several aliquots of acetonitrile. Although this procedure was implemented in most of the currently available commercial synthesis modules, the efficiency of drying depends on the reactor vessel design, heat transfer, vacuum/drying gas flow, and time. Incomplete drying strongly (and negatively) affects the reactivity of the [18F]fluoride, drastically reducing yields. As a whole, the azeotropic drying step is time-consuming (6-15 min) and is usually associated with significant losses of radioactivity due to decay and absorption of the radionuclide on the inner surfaces of the reaction vessel. Another limitation of this approach is the use of the substantial amounts of the base to facilitate elution of [18F]fluoride retained on the standard large-size anion exchange cartridges (Sep-Pak Plus QMA Light, PSHCO\({}_{3}\)). A typical kryptofix-based eluent contains 2.0-3.5 mg (15-25 \(\upmu\)mol) of K\({}_{2}\)CO\({}_{3}\), which is an unacceptably large amount when dealing with base-sensitive precursors. Resulting basic reaction conditions often lead to the formation of undesired radiolabeled by-products and the generation of chemical impurities resulting from the decomposition of the precursor. In order to address these issues, considerable efforts have been expended to develop shortened [18F]F\({}^{-}\) adsorbtion/elution protocols omitting the azeotropic drying procedure and, more importantly, providing less basic and milder 18F-fluorination conditions for base-sensitive precursors. A number of innovative methods and techniques were introduced by different groups through optimization of the base and phase-transfer agent used, eluting solvent composition, cartridge conditioning solvent (anion), and other parameters. As a result, a number of fine-tuned protocols for the preparation of particular radiotracers have been developed and published. In this review, our focus is on the improvement of \"classical\" methods for the activation of [18F]fluoride facilitating their implementation into the routine automated production of radiotracers, as well as emerging methodologies and techniques. One needs to acknowledge that in recent years a number of microfluidic and microscale approaches have been introduced for small-scale production. However, they, as well as electrochemical methods used for reprocessing of [18F]fluoride, lie outside of the scope of the current review.
## 5 Discussion
Additionally, the computational efficiency of the models was not assessed in this study, highlighting the need for the future analysis of scalability, particularly for large-scale healthcare applications. Lastly, interpretability remains a challenge for clinical adoption, and techniques like SHAP values or LIME are recommended to improve transparency in predictions. These limitations suggest valuable directions for further research, moving toward more robust, interpretable, and scalable methods for real-world clinical diagnostics. Finally, we identify three primary areas for further research: data imbalance, interpretability, and computational efficiency. Although we utilized sampling methods like SMOTE and random oversampling to mitigate data imbalance, future work could explore cost-sensitive and ensemble methods to address overfitting and improve model robustness. Interpretability is another critical area; incorporating explainability techniques, such as SHAP values or LIME, could increase model transparency, aiding clinical adoption by clarifying how specific features influence predictions. Additionally, computational efficiency was not fully evaluated in this study, highlighting the need for scalability assessments to ensure model effectiveness in large-scale applications like the EPIVIGILA system. Addressing these gaps would significantly enhance the clinical applicability and reliability of machine learning models in healthcare diagnostics.
## 3 Network Model  
To simplify the network model, we adopt a few reasonable assumptions as follows.  
(1) There are \(N\) sensor nodes that are distributed in an \(M\times M\) square field.  
(2) The BS and all of the nodes are stationary after deployment.  
(3) All of the sensor nodes can be heterogeneous.  
(4) All of the sensor nodes are location-unaware.  
(5) All of the nodes can use power control to adjust the amount of transmit power.  
(6) The BS is out of the sensor field. It has enough energy, and its location is known by each node.  
(7) Each node has a unique identity \(id\).  
To transmit \(l\)-bit data to a distance \(d\), the radio expends energy as:  
\[E_{T_{x}}(l,d)=\left\{\begin{array}{ll}l\times E_{elec}+l\times\varepsilon_{fs }\times d^{2},&d<d_{0}\\ l\times E_{elec}+l\times\varepsilon_{mp}\times d^{4},&d\geq d_{0}\end{array}\right. \tag{1}\]  
where \(d\) is the transmission distance. \(E_{elec}\), \(\varepsilon_{fs}\) and \(\varepsilon_{mp}\) are the parameters of the transmission/reception circuit. According to the distance between the transmitter and receiver, the free space (\(\varepsilon_{fs}\)) or multi-path fading (\(\varepsilon_{mp}\)) channel model is used.  
While receiving \(l\)-bit data, the radio expends energy as:  
\[E_{R_{x}}(l)=l\times E_{elec}. \tag{2}\]
## 3 Results
### Raman Spectroscopy  
We then focused strictly on the RDP-ice interface. For the 0.5 g samples, each RDP grain reacted independently. RDP grains are polyhedral-shaped, resulting in suboptimal contact with the ice. Therefore, the RDP grains were roughly crushed and molded into a semi-spherical measurement spoon to obtain 0.05 mL of RDP, representing a single grain. Raman spectroscopy specifically targeted the interface between the 0.05 mL of RDP and the ice surface. We collected 62 spectra. Each spectrum was smoothed to facilitate analysis, and the baseline was corrected to obtain spectra similar to those depicted in Figure 6. The corresponding peaks are indicated in the figure for informational purposes only.  
Figure 5: Minimum temperature of the sodium formate (NaFo) at the ice surface for the 0.5 g (**top**) and 5 g tests (**bottom**) and the corresponding thermal photographs of the ice surface at \(-10\)\({}^{\circ}\)C during the ice melting test at 0, 1, and 10 min, as well as at the min.  
These spectra show five significant peaks representing O-C=O in-plane deformation, C-C stretch, pyridine or isothiocyanate salt, CH\({}_{3}\) symmetric deformation in CH\({}_{3}\)CO\({}^{-}\), carboxylate ions, and CH\({}_{3}\) stretch. The most prominent peak is observed at 930 cm\({}^{-1}\), which likely corresponds to C-C stretch, typically found in NaFo [35]. It could also correspond to pyridine or isothiamate, used to prevent salt aggregation. Monitoring this peak would be useful during analysis, as the peak intensity relates to the concentration of the associated chemical bond. Continuous monitoring of this peak provides insight into the kinetics of ice melting and its variations. A weaker peak intensity indicates greater RDP dilution and increased ice occurrence. (Note that the omission of detailed peak frequency indexed to RDP chemical bonds is to maintain conciseness and clarity in the text.)  
Thermal video (Figure 7) recorded how RDP temperatures evolved. The Raman probe and the aluminum foil are also visible in these images.  
Figure 6: Raman spectra of the runway de-icing product obtained after 125, 293, 608, and 1196 s (laser at 785 nm, integration time of 20 s, average of four spectra), with the identification of the main Raman peaks.  
Figure 7: Thermal pictures during Raman spectroscopy of the RDP (sodium formate, NaFo) with a Raman probe at **(A)** 125 s, **(B)** 293 s, **(C)** 608 s, and **(D)** 1196 s.  
The RDP begins to dissolve and melt the ice, resulting in a high temperature (Figure 7A). The top of the RDP dome is not yet in solution, indicating a highly effective product, as it gradually dissolves, leading to a strong melting reaction (Figure 7B). When fully dissolved, the product remains effective (Figure 7C). Finally, after almost 20 min (Figure 7D), the reaction between RDP and ice (or between brine and ice) is no longer thermally visible.
## 4 Experiments  
In order to evaluate the merits of the two modified algorithms (TROS and QROS), a set of benchmark test problems is selected from the literature [15; 16; 17] and shown in Table 1. The benchmark problems include unimodal and highly multimodal functions, convex and non-convex functions, and separable and non-separable functions. TROS and QROS were compared with the original PROS algorithm and three well-known and widely used EC algorithms for optimization: genetic algorithm (GA) [18], particle swarm optimization (PSO) [19] and differential evolution (DE) [20]. PROS, TROS and QROS were implemented in Java by the authors. GA, PSO and DE were run using pymoo, an open source framework for multi-objective optimization in Python, version 0.6.0 [21]. The code for the benchmark problems running on pymoo were revised by the authors (for experiments of shifted search space). The hyper-parameters of GA, PSO and DE are selected using the default values from the pymoo package (they are the typical values suggested in the literature).  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Function** & **Formulation and Global Optimum** & **Search Space** \\ \hline \(f_{1}\) & Sphere & \(\sum_{i=1}^{D}x_{i}^{2}\) & \([-10,10]^{D}\) \\  & & \(\mathbf{x}^{*}=(0,0,\ldots,0),f(\mathbf{x}^{*})=0\) & \\ \hline \(f_{2}\) & Ellipsoid & \(\sum_{i=1}^{D}ix_{i}^{2}\) & \([-10,10]^{D}\) \\  & & \(\mathbf{x}^{*}=(0,0,\ldots,0),f(\mathbf{x}^{*})=0\) & \\ \hline \(f_{3}\) & Schwefel 1.2 & \(\sum_{i=1}^{D}(\sum_{j=1}^{i}x_{j})^{2}\) & \([-5.12,5.12]^{D}\) \\  & & \(\mathbf{x}^{*}=(0,0,\ldots,0),f(\mathbf{x}^{*})=0\) & \\ \hline \(f_{4}\) & Rosenbrock & \(\sum_{i=1}^{D-1}[100(x_{i+1}-x_{j}^{2})^{2}+(x_{i}-1)^{2}]\) & \([-2.048,2.048]^{D}\) \\  & & \(\mathbf{x}^{*}=(1,1,\ldots,1),f(\mathbf{x}^{*})=0\) & \\ \hline \(f_{5}\) & [PERSON] & \(\sum_{i=1}^{D}x_{i}^{2}+(\frac{1}{2}\sum_{i=1}^{D}ix_{i})^{2}+(\frac{1}{2}\sum_ {i=1}^{D}ix_{i})^{4}\) & \([-10,10]^{D}\) \\  & & \(\mathbf{x}^{*}=(0,0,\ldots,0),f(\mathbf{x}^{*})=0\) & \\ \hline \end{tabular}
\end{table}
Table 1: Benchmark test problems. (Unimodal functions: \(f_{1}\)–\(f_{5}\); multimodal functions: \(f_{6}\)–\(f_{12}\); convex functions: \(f_{1}\)–\(f_{2}\), \(f_{5}\); non-convex functions: \(f_{3}\)–\(f_{4}\), \(f_{6}\)–\(f_{12}\); separable functions: \(f_{1}\)–\(f_{2}\), \(f_{6}\)–\(f_{7}\), \(f_{12}\); non-separable functions: \(f_{3}\)–\(f_{5}\), \(f_{8}\)–\(f_{11}\))The experiments were carried out on a 3.20 GHz computer with 16 GB RAM under a Windows 10 platform. Multiple runs were conducted for each problem by each algorithm with a different seed for the generation of random numbers for each run. For fair comparison, all algorithms end with the same maximum number of objective function evaluations. We follow the experimental settings of the PROS paper [15] for the population size and the maximum number of objective function evaluations. In order to increase the reliability of the statistical results, the number of runs was increased to 100, 100 and 30 for 5D, 10D, and 50D problems, respectively (instead of 10 for all dimensions in [15]). The settings are summarized in Table 2.
## 2 Materials and Methods
### MTS Test  
For the measurement of cell viability or metabolic activity after exposure to tested substances, the MTS test (CellTiter 96(r) Aqueous One Solution Cell Proliferation Assay, Promega, Madison, WI, USA) was performed. This assay has been conducted at the same time as the RTCA, using the cells from the same passage. The cells were seeded into a 96-well plate (Greiner-Bio-One, Kremsmunster, Austria) with a density of 8.6 \(\times\) 10\({}^{3}\) cells/well in a final volume of a 100 \(\upmu\)L medium. After 20 h, tested samples were added to the wells to reach the final concentration of 125-1000 \(\upmu\)g mL\({}^{-1}\) and placed into an incubator for 48 h. Finally, 20 \(\upmu\)L of MTS solution was pipetted into each well and after 4 h of incubation, the absorbance (A) was measured at 490 nm (microplate reader Synergy HT; Biotek, Winooski, VT, USA). The cells without treatment were considered as the control with 100% metabolic activity (MA). Change in MA was expressed in % using the Formula (4):  
\[\%\text{MA}=\frac{\text{CI sample}}{\text{CI control}}\times 100 \tag{4}\]
## 3 Results
### General Approach Proposed
#### 3.8.3 Stability of the System-n-State Variables  
From the transfer function of the system, the poles and zeros of the system can be identified; the poles of the system correspond to the eigenvalues of the system. We can define a system as stable when its output is bounded; that is, its output is not \(\pm\infty\) but a concrete value. We could also define as stable that it evolves in a similar way to the input variable. The calculation of the stability of the system is equal to zero and the characteristic equations of the numbering and denominator (the eigenvalues) are taken out and plotted on a real and imaginary plane. Where the poles are in the left half-plane, it is a stable process, and if not, the process would be unstable.