esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Analysis
### Visualized Comparative Analysis of Experimental Results
#### 4.4.1 Visual Analysis of Ship-1 Trajectory Prediction  
It can be seen from Figure 6 that ship-1 has a tendency of greater steering amplitude and continuous maneuvering. Firstly, the AIS data of ship-1, LOG, LAT, SOG and COG, are taken as the input samples of the GA-LSTM network model. The position of ship-1, LON and LAT, are taken as the output samples of the model, and the experimental results are shown in Figure 7.  
The collected AIS data of ships are divided into training and test sets according to the above method. Figure 7 shows the LON and LAT of the ship predicted by the GA-LSTM model proposed in this paper. It can be seen that the LON and LAT predicted by this model are basically consistent with the actual LON and LAT of ship-1, which can accurately predict the position of ships in inland rivers.  
Figure 6: The original trajectories of experimental ships.  
Figure 7: Prediction results of LON and LAT of ship-1.  
In order to further prove the feasibility and effectiveness of the proposed model, this paper conducts a comparative analysis of the LSTM model and the SVM model. The experimental results are shown in Figure 8. It can be seen that: (1) For the location prediction of ship-1, the SVM model performs the worst; even if the predicted ship trajectory has the same general trend, the position information has a larger deviation. This is due to the weak generalization ability of the SVM model and its ease of falling into local extreme values. (2) The prediction effect of the LSTM method is slightly better than that of the SVM method; however, the two methods are not as good as the method proposed in this paper. This is because the network hyperparameters of the LSTM method have difficulty manually obtaining the optimal solution, which makes the model prediction performance lower than that of GA-LSTM method. (3) In order to better compare the pros and cons of the three methods, zoom in and analyze the position of ship-1\({}^{\prime}\)s navigation status: the LSTM and GA-LSTM methods have comparable predictive performance in ship-1\({}^{\prime}\)s direct navigation state (see 1 in Figure 8). The GA-LSTM model shows better performance when performing large-scale steering or continuous steering (Figure 8 at 2). In general, the GA-LSTM model performs best and can predict ship-1\({}^{\prime}\)sailing position more accurately. The reason is that the GA obtains a better combination of LSTM network parameters, which makes the method proposed in this paper better than the ship trajectory predicted based on the SVM and LSTM models.
# Self-Assembled MoS2/ssDNA Nanostructures for the Capacitive Aptasensing of Acetamiprid Insecticide
## 3 Results and Discussions
### Analytical Performances
#### 3.4.2 Comparison with Reported Acetamiprid Aptasensors  
Regarding the large literature of acetamiprid detection, the analytical performances presented by SPCE/LA-MoS\({}_{2}\) are solely compared with those of published on aptasensing (see Table 1). These studies showed the use of conventional electrochemical techniques such as CV, DPV, and EIS while in this work ECS was used for the first time to achieve sensitive detection of acetamiprid and lead to obtain signal on detection. In terms of LOD the developed analytical device showed the lowest value of 14 fM where the other biosensors are in pM range [15; 16; 42; 43]. The biosensors achieving LOD in the femtomolar range are formed with various nanomaterials including metallic nanoparticles [17; 44] which can have negative impact for environment. Moreover, the SPCE/LA-MoS\({}_{2}\) aptasensor presents an easy fabrication process obtained by two steps where patterning through electrodeposition of LA-MoS\({}_{2}\) on SPCE presents an advantage for biosensor conception and chemical attachment of aptamer ensures it to have a longer shelf life. This approach taking advantage of adsorption/desorption process of ssDNA and aptamer complex from MoS\({}_{2}\) surface and the variation of capacitance readout demonstrated excellent balance between high performance, simplicity, and cost-effectiveness compared to others electrochemical biosensors.
# An Integrated Adaptive Kalman Filter for High-Speed UAVs
## 3 Verification and Analysis
### Practical Data Based Verification
#### 3.2.3 Hyper-Parameter and Initial Error  
The update rate of inertial sensor's data was 200 Hz. The update rate of GNSS was 25 Hz. The update rate of SINS was 150 Hz. The initial error of UAV is given in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Attitude \(\theta\), \(\varphi\), \(\gamma\)/\({}^{\prime}\)** & \begin{tabular}{c} **Velocity \(V_{E}\), \(V_{N}\),** \\ **V\({}_{\text{ll}}\)/m/s** \\ \end{tabular} & \
\begin{tabular}{c} **Horizontal** \\ **Position \(\lambda\), \(L\)/m** \\ \end{tabular} &
\begin{tabular}{c} **Vertical Position** \\ **H/m** \\ \end{tabular} \\ \hline SHAKF & 13.6, 5.50, 43.9 & 0.58, 0.66, 0.22 & 28.7, 28.8 & 10.5 \\ STF & 17.1, 6.59, 45.6 & 0.70, 0.80, 0.45 & 31.8, 41.9 & 10.7 \\ H\({}_{\infty}\) & 12.7, 3.81, 49.7 & 0.88, 1.79, 0.59 & 22.1, 32.4 & 4.46 \\ SAKF & 6.14, 1.59, 41.7 & 0.65, 0.48, 0.54 & 10.4, 13.1 & 8.65 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The RMS obtained by the output of four algorithms in scene 2 (100 s–150 s).  
The filtering parameters are showed as follows, \(b_{g}=0.95\), \(b_{\alpha}=0.9\), \(\theta=0.01\), \(\alpha=diag[1,1,1,1,\cdots 1]\), \(\rho=0.9\), \(\mathrm{R_{max}}=diag(0.5,0.5,1,10/\mathrm{R_{e}},10/\mathrm{R_{e}},20)^{2}\). Because the real condition was slightly different from the simulated one, we changed the value of filtering parameters to reach the best performance.
# A Stackelberg Game Approach to Model Reference Adaptive Control for Spacecraft Pursuit-Evasion
## 4 Simulation Experiment
This result stems from the adaptive control mechanism of MSE, which continuously updates its parameters, while SSE relies on predictive optimization. MSE demonstrates stronger robustness, maintaining high performance under noise interference and signal loss, whereas NE and SSE exhibit significant performance degradation. Figure 10b analyzes the position error with standard deviation. MSE consistently satisfies the 20% safety threshold across all interference scenarios, including noise and signal loss. In contrast, NE and SSE show higher error variability, particularly under time delays. These results confirm the reliability of MSE in disturbance-prone environments.
## 1 Introduction
With the development and integration between disciplines, carbon flux estimation methods based on machine learning, satellite remote sensing data, and reanalysis data have been developed. Simultaneously, large-scale land ecosystem model estimations have benefited from the development of global flux sites, such as Ameri Flux, Euro Flux, and China Flux. These flux towers have also promoted the study of carbon cycling in terrestrial ecosystems [24]. In recent years, the development of data fusion techniques combining global flux site data, reanalysis data, and remote sensing data has provided support for the fine-scale representation of carbon flux on remote sensing scales [25,26]. Although research using EC systems and remote sensing data to study the carbon flux and carbon cycling processes in grasslands is increasing, research on carbon flux in the alpine grassland ecosystem of the Tien Shan Mountains is currently limited. In previous studies, most research focused on either using eddy covariance data or remote sensing data separately to study carbon flux [27,28]; there is a lack of research that combines both approaches. Therefore, the objectives of our study are (1) to investigate the temporal variations in carbon flux in the alpine grassland ecosystem of the Tien Shan Mountains, (2) to explore the main influencing factors of carbon flux in relation to meteorological factors, and (3) to present a remote sensing spatial distribution map of carbon flux in the Tien Shan region and analyze the spatial distribution pattern of carbon flux in the alpine grassland ecosystem of the Tien Shan Mountains. The hypotheses of our study are as follows: (1) the alpine grassland ecosystem of the Tien Shan Mountains has a strong carbon assimilation capacity during the daytime and peak growing season, (2) temperature has a significant influence on carbon flux absorption and emission, and (3) from the perspective of remote sensing spatial distribution, the alpine grassland region of the Tien Shan Mountains acts as a carbon sink during the growing season. This study focuses on revealing the spatial and temporal patterns of carbon flux in the Central Tian Shan grassland ecosystem and the influencing mechanisms. It provides a reference for the study of carbon flux in grassland ecosystems in other regions and theoretical support for the study of carbon balance and carbon mechanisms in other terrestrial ecosystems.
## 4 Results
### Performance Analysis with Combined Signals  
When comparing the results obtained by combining all signals from the Mini-BESTest tasks to classify fallers and non-fallers with the individual task results, we observed several key differences that indicate improved model performance when aggregating all signals.  
* Accuracy: The model achieved an accuracy of 0.8855, which was higher than most of the individual tasks, except for a few tasks such as \"Walking over obstacles\" and \"One-step back correction\", which also demonstrated high accuracy. Combining all the signals improved the model's ability to generalize between fallers and non-fallers, resulting in better overall performance.
* Precision: An accuracy of 0.9014 was remarkably high, which indicated that the model was very effective in correctly predicting positive cases (fallers) while minimizing false positives when all signals were used together.
* Recall: The recall of 0.8793 was also high, suggesting that the model was effective in identifying most positive cases (fallers), which is crucial in fall prevention contexts.
* F1 Score: With an F1 score of 0.8902, the model demonstrated a good balance between precision and recall, which is critical in classification tasks like fall risk, where both correct detection and reduction of false positives are essential.
* Specificity: The specificity of 0.8924 indicated that the model was also very effective at correctly identifying negative cases (non-fallers), reducing the number of false positives.  
Balanced Accuracy: With a balanced accuracy of 0.8859, the overall model performance was significantly improved compared to individual tasks. This suggested that combining all signals provided a more complete and robust assessment of fall risk by integrating various aspects of postural control, sensory orientation, and dynamic gait.
## 1 Introduction  
The ongoing quest to identify more efficient and environmentally friendly materials to be employed in solar cell devices has shifted the focus in recent years towards quaternary chalcogenides and hybrid halide perovskites. While hybrid halide perovskites face stability issues due to their admixed organic molecules, chalcogenide materials exhibit intrinsic structural properties, which have prevented their large-scale application in solar cell devices so far.  
Concerning the latter, Cu\({}_{2}\)ZnSnSn\({}_{4}\) (CZTS) and Cu\({}_{2}\)ZnSnSe\({}_{4}\) (CZTSe) are prominent examples, and with band gaps of about 1.0 eV (CZTSe) [1,2] and 1.53 eV \(\ldots\) 1.67 eV (CZTS) [3], their solid solution Cu\({}_{2}\)ZnSn(S\({}_{x}\)Se\({}_{1-x}\))\({}_{4}\) (CZTSSe) includes the optimal band gap region of about 1.34 eV for single junction solar cell devices according to the Shockley-Queisser limit [4]. Ultimately, actual devices based on CZTSSe already demonstrated power conversion efficiencies of about 13% [5]. However, there are limitations to the actual device performances, originating from several intrinsic structural properties, e.g., a large defect concentration of Cu-Zn antisite defects [6] and Cu\({}_{2}\)Zn-ZnCu defect complexes due to Cu-Zn disorder [7]. Ultimately, these defects and Cu-Zn disorder lead to a large deficit in the open-circuit voltage V\({}_{\rm oc}\)[8].  
The importance of the Cu-Zn disorder in kesterite materials and its implications on the structural, electronic, and optical properties have led to increased research efforts into their atomistic origins. Experimental techniques include solid-state NMR and Raman spectroscopy [9], near-resonant Raman scattering [10], neutron diffraction [11; 12], and complementary neutron and anomalous X-ray diffraction [13; 14], respectively. The experimental findings are supplemented by theoretical investigations, mostly employing a combination of first-principles calculations based on density functional theory (DFT) and Monte-Carlo calculations based on cluster expansion methods [15; 16; 17; 18].  
A recently devised technique to speed up hybrid functional calculations for materials science investigations [19] provided the motivation for the current work, namely the re-evaluation of Cu-Zn disorder in CZTSe employing first-principles calculations based on DFT and _special quasirandom structures_ to model the structural disorder.  
The paper is organised as follows. Section 2 shortly presents the necessary theoretical background, introduces the recently devised method to speed up hybrid functional calculations [19], and provides the necessary computational details. In Section 3 the results on the structural (Sections 3.1 and 3.2), and electronic and optical (Section 3.3) properties are presented and discussed alongside experimental data and other theoretical investigations. Section 4 provides a short summary and outlook.
## 3 Results and Discussion  
### Contents and Compositions of Volatile Compounds from Seeds and Cones of Greek Fir  
The below-described experiments are the continuation of the research on EOs hydrodistilled from seeds and cones (cone scales) from different confierous trees [18; 24; 25; 26]. Many of these plant materials are unknown, probably due to difficulties during the collection of cones with ripe seeds. The essential oil (EO) isolated from _Abies cephalonica_ seeds displayed a high yield, nearly 30 times greater than that obtained from the cone scales (0.58-0.66%; averaging 0.62%). In fact, it reached an impressive level of 18.22%, with a range between 17.46% and 19.13%. The seed essential oil possessed an alluring, forest-fresh, citrus-like aroma, in contrast to the less appealing, resinous, earthly scent with a hint of lemon notes found in the cone scale EO. This difference in aroma is attributed to the seed oil's lower limonene content and the higher concentration of high-boiling volatile compounds, which were detected in the cone scale EO. Notably, the yield of _Abies cephalonica_ seed EO (18.2%) surpassed those of previously reported _Abies concolor_ seed EO (5.4%) and _Abies koreana_ seed EO (4.9%) [18; 24]. It also outperformed the most aromatic material to date:_Abies alba_ seed EO (7.4-14.3%) [25]. Through chromatographic and spectroscopic techniques, we successfully identified a total of 101 compounds within the EOs isolated from the Greek fir. These compounds constituted 99.4% of the total seed EO composition and 98.3% of the total cone scale EO composition. Chromatograms of both EOs are shown in Figure 1. Additionally, the structures of 13 volatiles in the Greek fir seed EO were confirmed using \({}^{1}\)H-NMR spectroscopy. The specific compounds identified in both the seed and cone scale EOs are listed in Table 1. In both the seed and cone EOs, the predominant group of terpen detected were monoterpene hydrocarbons, constituting 95.6% of the total seed EO and 88.6% of the total cone scale EO. This composition aligns with those of previously published _Abies cephalonica_ needle or bark EOs, in which the concentration of this terpene group typically fell within the range of 80-90% [1; 12; 13; 14; 15]. Monoterpene hydrocarbons were similarly the primary chemical group found in the EOs obtained from the seeds and cones of all the previously explored fir species: _Abies concolor_[18], _Abies koreana_[24], and _Abies alba_[25]. Figure 1: The gas chromatograms of (**A**) seed and (**B**) cone essential oils (numbers refer to compounds listed in Table 1).
# Efficient Knowledge Distillation for Brain Tumor Segmentation
## 5 Ablation Study
### Ablation Experiments  
As the final part of the experiment, to better verify the effectiveness of CD, we performed ablation experiments on the BraTs2018 dataset.  
Table 4 lists the performance of the student with and without KD and CD when segmented. The results showed that each method brought positive effects to the student, especially when the two methods were combined; they enabled the student network to obtain better segmentation results. In addition, in Table 5, we also demonstrated the sensitivity of our method to hyperparameters, given random weights \(\alpha\), \(\beta_{1}\), \(\beta_{2}\) of Equation (6). Within a certain range, each experiment was trained by 20 epochs with different combinations of hyperparameters, and we finally chose 0.9, 0.1 and 1.8 to complete all our experiments.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Models** & **KD** & **CD** & \multicolumn{3}{c}{**Dice Score (\%)**} & \multicolumn{3}{c}{**Hausdorff Distance**} \\ \hline  & & & **ET** & **WT** & **TC** & **ET** & **WT** & **TC** \\ \hline T: UNet & \(\times\) & \(\times\) & 78.83 & 85.69 & 85.81 & 2.77 & 2.57 & 1.66 \\ S: DeepResNetNet & \(\times\) & \(\times\) & 78.44 & 85.19 & 85.36 & 2.79 & 2.60 & 1.68 \\ \hline  & ✓ & \(\times\) & 79.03 & 85.73 & 87.68 & 2.76 & 2.55 & 1.56 \\ S+ours & \(\times\) & ✓ & 78.83 & 85.44 & 87.34 & 2.78 & 2.60 & 1.55 \\  & ✓ & ✓ & **79.69** & **85.91** & **88.20** & **2.71** & **2.53** & **1.52** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiments of our method on the BraTS2018 dataset. The teacher was UNet and the student was DeepResUNet.  
Figure 3: The segmentation results were obtained by applying our distillation method in four cases (**a–d**) of the BraTS validation dataset. ET—yellow, TC—yellow+red, WT—yellow+red+green. The teacher was UNet and the student was DeepResUNet.
# Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data
## 4 Results  
### Evaluation of Paddy Rice Classification  
Tables 4 and 5 show the accuracy assessment results of each approach for both multi-class and binary classifications. This experiment is repeated for 10 times, and the highest overall accuracy and kappa coefficient are shown in Tables 4 and 5. In scheme 1, summer season data showed high accuracy in both sites because of the greenness of the paddy rice. While schemes 2 and 6 showed the highest accuracy in site A (100% and 1.00) and site B (99.33% and 0.99). It is not surprising that the accuracy is high when using time series data since they provide the phenology information of paddy rice. The use of SAR data only (i.e., scheme 3) did not work well for paddy rice classification, resulting in relatively low accuracy (~91%). The McNemar test results showed that there was a significant difference between scheme 3 and the other schemes for both sites (Tables 6 and 7). It is because scheme 3 uses SAR data of all dates only for classification and showed worse accuracy than the other schemes, which indicates that optical sensor data are crucial for paddy rice classification. In site B, scheme 5 showed a significantly higher accuracy than scheme 4, which implies that data from the growing season would be more contributing than data from the no growing season (from November to March) for paddy rice classification.  
There is no big difference in classification accuracy by classification model. The McNemar test also showed that there was no significant difference between RF and SVM classifications (Tables 6 and 7). However, the accuracy of SVM was slightly higher (~1-3%) than that of RF at both of the sites. The producer's accuracy (PA) and user's accuracy (UA) of SVM were also higher than RF (Supplementary Tables S1 and S2). The classification accuracies for site A (95% (0.93) in multiclass classification, 100% (1.00) in binary classification) were higher than those for site B (88.67% (0.86) in multiclass classification, 99.33% (0.98) in binary classification). The PA and UA in site A were also higher than those in site B (Supplementary Tables S1 and S2). In comparison with site A, site B is very cloudy during summer due to the Asian Monsoon, so there were not enough Landsat data for site B during summer which is important to classify land cover and paddy rice.
# Microstructure and Properties of AA6061/SiCp Composites Sintered under Ultra High-Pressure
## 3 Results and Discussion
### Evolution of Hardness  
Figure 12 shows the hardness variation of the AA6061/N-SiCp composite. Figure 12a shows the hardness variation curve at 4.5 GPa and 550 \({}^{\circ}\)C. It can be seen that the hardness gradually increases with the addition of the N-SiCp. The hardness of the AA6061/15 vol.% N-SiCp reached up to 48.3 HRA, which was 52.8% higher than that of pure AA6061. For the AA6061/N-SiCp composite, the higher content of N-SiCp can lead to better deformation resistance of the SiC. When the indenter contacts the surface, the higher hardness of the composite could be found. When the volume fraction is high, despite there being many agglomerations in the material interior, the distance among the nanoparticles is very small, therefore the agglomeration has the same bearing capacity for the indenter load. It can be seen from Figure 12b that the hardness of the AA6061/5 vol.% N-SiCp increases with the augment of the temperature at 4.5 GPa. With the temperature increasing, small-sized Al particles crowd more tightly in the microstructure and the interfacial area between N-SiCp and Al matrix increases. It can improve the dispersion uniformity of SiC, dispersion strengthening effect, and the hardness value.  
Figure 11: The curve of the relative density of AA6061/N-SiCp composite: Relationship between relative density and volume fraction for AA6061/5 vol.% N-SiCp sample (**a**). Relationship between relative density and sintering temperature at 4.5 GPa for AA6061/5 vol.% N-SiCp sample (**b**).  
However, the hardness is slightly decreased at 650 \({}^{\circ}\)C. According to the microstructure analysis in Section 3.1, the SiC can react with the Al substrate at 650 \({}^{\circ}\)C according to the following reaction:  
\[4 Al+3 SiC\to Al_{4}C_{3}+3 Si \tag{4}\]  
The reaction is controlled by a dissolution-diffusion mechanism [27]. The Al\({}_{4}\)C\({}_{3}\) phase is an excessive brittle phase which reduces the interfacial bonding strength between SiC reinforcement and Al matrix. Correspondingly, the mechanical properties are degraded. When the indenter is pressed into the surface, the ability to transfer the load at the interface is lowered, thereby lowering the hardness.