esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Machine Structure Operating Principle and Design of the Flux-Controllable NI HTS FSM  
### Machine Structure of the Flux-Controllable NI HTS FSM  
Figure 3 shows the topology of the flux-controllable FSM proposed in this paper. It is composed of a rotor core, armature windings, NI HTS field coils, and additional field windings. All windings, including the field windings, are located in the stator core. Accordingly, the machine has a robust rotor structure. A ferromagnetic material is used for the stator and the rotor core. Three-phase armature windings are considered, and the armature windings are placed in each stator slot, with each winding connected in series. The NI HTS field coils generate the main magnetic flux during the steady-state operation, and additional field windings are used for flux regulation. Unlike conventional rotor excitation machines, the FSM has a doubly salient structure, with the field coils placed in the U-shaped iron core of the stator. In this machine, the rotor core has a simple laminated iron core which has salient poles. The flux direction of each field coil is opposite between the adjacent field coils.
# SDebrisNet: A Spatial-Temporal Saliency Network for Space Debris Detection
## 5 Experimental Results and Analysis
### Test on Real Video Sequences  
To further investigate the applicability of the proposed method, it was also tested on two real video sequences. The first video was acquired by a Newtonian telescope. It included two space debris named Iridium-33 and Cosmos-2251. The second video was obtained by the TJO telescope at the Observatorio Astronomico del Montsec (OAdM). Both videos were taken with the sidereal tracking mode. The tracks of the space objects are shown in Figure 9. The details of the observed space objects and optical sensors are shown in Table 9. In a practical circumstance, there is significantly more noise, intensity variations, and imaging artifacts in the real video, which increases the difficulty of the problem. Subframes were selected from the original frame size to show the above challenges, as shown in Figure 10. Since there are no known ground truth pixel coordinates of the space objects, only the qualitative test results are presented in this section. Part of the centroid coordinate detection results are shown in Figure 10. We can see that the intensity of Iridium-33 varies significantly in the continuous 3 frames (frame 340-frame 342). However, the space debris could still be detected by SDebrisNet. The second space debris Cosmos-33 is undetected in frame 435 due to the extremely low intensity caused by the tumbling characteristics. Frame 74 contains a striped white bright line called the smear-tailing phenomenon. It forms as the CCD is capturing a high-brightness point light source, which may affect the accuracy of detecting space debris in video images. Frame 320 has a lower SNR than frame 74, while frame 545 contains even more serious stray light noise compared to frame 320. The space object shows as a streak in frame 695. It is because the observation platform jitters in the horizon direction. Nevertheless, our method could work well in all of the difficulties in video 2.  
\begin{table}
\begin{tabular}{l c c c} \hline
**SNR** & \(CE\downarrow\) & \(P_{d}\uparrow\) & \(P_{f}\downarrow\) \\ \hline
10 & 0.312 & 1.000 & 0.000 \\
5 & 0.312 & 1.000 & 0.000 \\
1 & 0.359 & 1.000 & 0.009 \\
0.8 & 0.361 & 1.000 & 0.011 \\
0.6 & 0.371 & 1.000 & 0.015 \\
0.4 & 0.477 & 0.991 & 0.022 \\
0.2 & 0.749 & 0.982 & 0.027 \\
0.1 & 0.896 & 0.969 & 0.036 \\ \hline \end{tabular}
\end{table}
Table 8: Detection results of SDebrisNet on the SDD dataset with different SNRs.
# Optimal Waveform of the Partial-Respond Signal Based on Minimum Out-of-Band Radiation Criterion
## 2 Optimized Mathematical Modeling of the Optimized Waveform  
### Minimum Out-of-Band Radiation Criterion  
In order to optimize the baseband symbol signals with higher spectral roll-off rate, an optimized mathematical function is established which minimizes the out-of-band radiation criterion [9] as  
\[J=\frac{1}{2\pi}\int\limits_{-\infty}^{+\infty}g(\omega)|S(\omega)|^{2}d\omega \tag{1}\]  
where \(a(t)\) is the symbol signal waveform. It is an even function in the symbol period \(\left[-T/2,T/2\right]\). \(\left|S\left(\omega\right)\right|^{2}\) is the power spectrum of \(a(t)\), and \(g(\omega)\) is an increasing function that determines the roll-off rate of the spectral density function \(S(\omega)\). The value of \(n\) determines the degree of suppressed out-of-band radiation. To achieve a convergent and integrable \(J\), the roll-off rate of \(\left|S\left(\omega\right)\right|^{2}\) should be greater than the rate of increase in \(g(\omega)\) to limit the roll-off rate of the power spectrum \(\left|S\left(\omega\right)\right|^{2}\) of the signal \(a(t)\). Figure 1 plots the increasing function, \(g(\omega)=\omega^{2n}(\omega>0)\) versus \(n\).  
Following a series of mathematical manipulations (including Taylor series expansion [10]), Equation (1) may be written as  
\[J=(-1)^{n}\int\limits_{-T/2}^{T/2}a(t)a^{(2n)}(t)dt \tag{2}\]  
where \(a^{(2n)}(t)\) represents the \(2n\)-order derivative of the symbol signal. Equation (2) reveals that the criterion for minimum out-of-band radiation may be transformed into the problem of unraveling the symbol function \(a(t)\) when minimizing the Pan-functional \(J\).
# A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR
## 4 Datasets and Performance Evaluation
### Image Retrieval Performance While Using Oliva and Torralba (OT-Scene) Dataset  
To demonstrate the effectiveness of the proposed research, experiments are performed on the challenging OT image dataset. The best performance for the proposed research is obtained for a vocabulary size of 600. As the proposed approach has been designed on a classification-based framework, Figure 16 provides a class-wise comparison of the classification accuracy of the proposed approach with the recent state-of-the-art classification approaches [46; 47]. [PERSON] et al. [46] propose a fusion of color, texture and edge descriptors to enhance the performance of image classification and report an accuracy of 86.4%. Our proposed approach outperforms SPM by 3.85% [48] and yields 1.06% higher accuracy compared to [46]. [PERSON] et al. [47] use the Object Bank (OB) approach to construct powerful image descriptors and boost the performance of OB-based scene image classification. The best mean classification accuracy for the proposed RCSIR is 87.46%, while the accuracy reported by [PERSON] et al. [47] is 86.5%. The proposed  
Figure 14: Result of image retrieval for the semantic class “Lizards”.  
Figure 15: Result of image retrieval for the semantic class “Antique Furniture”.  
approach provides 0.96% higher accuracy compared to their work. It is observed that the performance of the proposed approach is low for the natural coast and the open country category due to high variability in these classes. The proposed approach based on spatial features provides better performance compared to the state-of-the-art retrieval approaches.  
The comparison of the proposed research with existing research [8] in terms of precision is presented in Table 5. The proposed approach provides 13.17% higher accuracy compared to the second best method in comparison. The experimental results validate the efficacy of the proposed approach for content based image retrieval.
###### Abstract  
Subtropical forests can sequester a larger amount of atmospheric carbon dioxide (CO\({}_{2}\)) relative to other terrestrial ecosystems through photosynthetic activity and act as an important role in mitigating global climate warming. Compared with the model-based gross primary production (GPP) products, satellite-derived solar-induced fluorescence (SIF) opens a new window for quantification. Here, we used the remotely sensed SIF retrievals, two satellite-driven GPP products including MODIS (GPP\({}_{\text{MOD}}\)) and BESS (GPP\({}_{\text{BESS}}\)), and tower-based GPP measurements at two contrasting subtropical forests to provide a systematic analysis. Our results revealed that GPP and the associated environmental factors exhibited distinct seasonal patterns. However, the peak GPP values had large differences, with stronger GPP in the evergreen needleleaf forest site (8.76 \(\pm\) 0.71 g C m\({}^{-2}\) d\({}^{-1}\)) than that in the evergreen broadleaf forest site (5.71 \(\pm\) 0.31 g C m\({}^{-2}\) d\({}^{-1}\)). The satellite-derived SIF retrievals showed great potential in quantifying the variability in GPP, especially for the evergreen needleleaf forest with \(r\) reaching up to 0.909 (\(p<\) 0.01). GPP\({}_{\text{MOD}}\) and GPP\({}_{\text{BESS}}\) showed distinctly different performances for the two subtropical forests, whereas the GPP estimates by exclusive use of satellite-based SIF data promised well to the tower-based GPP observations. Multi-year evaluation again confirmed the good performance of the SIF-based GPP estimates. These findings will provide an alternative framework for quantifying the magnitude of forest GPP and advance our understanding of the carbon sequestration capacity of subtropical forest ecosystems.  
forest GPP; solar-induced fluorescence; MODIS GPP; BESS GPP
# Security Architecture for Swarms of Autonomous Vehicles in Smart Farming
## 1 Introduction
### Challenges of Autonomous Vehicles  
Autonomy in addition to efficiency and productivity can also bring drawbacks and a sense of unreliability. Farmers are sometimes reluctant to adopt these new technologies in fear of vehicle accidents or cyber security attacks [6; 7]. How do we elude malicious use of autonomous vehicles? How can we guarantee that unauthorised users do not connect to our network and send commands to a drone or ground vehicle that can put our system in danger? How can we avoid alterations in the content of the messages? It is critical to analyse the vulnerabilities of the system and to provide a security design architecture that considers confidentiality and integrity of the data and messages, authentication and authorisation of the users or systems, and non-repudiation of data [8].  
Most of the vulnerabilities mentioned above can be avoided by using a secure communication system which, in turn, depends on the technology selected for data exchange. In the case of autonomous vehicles, additional aspects need to be considered when deciding upon the data transmission technology to be used, such as multi-node data exchange, real-time constraints, reliability, dynamic discovery, message durability, fast algorithms or energy management. In this paper, the authors present DDS (Data Distribution Service) as the most appropriate communication technology for distributed critical systems.  
Additionally, the maximum potential in use cases involving fleets of vehicles can be obtained when combining vehicles of different typology (aerial and terrestrial), with different capabilities and equipment (e.g., advanced sensors) and from different vendors. For example, remote sensing (information gathered by means of UAVs) and ground sensing (information collected from UGVs-Unmanned Ground Vehicles-or tractors), wide maps of environment provided by UAVs, can be used to plan precise missions for UGVs to reduce trip costs and the mission's duration. In these cases, semantic interoperability challenges need to also be addressed. Moreover, vendor-specific security solutions are no longer valid when heterogeneous ecosystems are deployed, and a global security solution is needed.  
This paper addresses the research challenge of implementing a global and economical solution that covers all five areas of security (i.e., confidentiality and integrity of the data and messages, authentication and authorisation of the users or systems, and non-repudiation of data) in a multi-node communication system that guarantees the constraints of heterogeneous autonomous robots in terms of real-time, reliability, energy and QoS management.
# Hybrid Oversampling and Undersampling Method (HOUM) via Safe-Level SMOTE and Support Vector Machine
## 5 Computational Results
### Comparing HOUM with W-SIMO and RusAda  
In this subsection, the best G-mean values of the HOUM techniques are compared with the G-mean results of W-SIMO [1] and RusAda [22] methodologies, which are proposed to balance the dataset. Table 5 demonstrates the performance of the HOUM, W-SIMO, and RusAda methods. The * sign in some cells indicates the absence of certain results due to the focus on different datasets in various studies. Please consider the following points when comparing the algorithms.  
* The G-mean values in the first column of Table 5: All the datasets were balanced using the HOUM (HOUM-R was used for Liver and Ionosphere, and HOUM-L was used for Haberman and Transfusion). Then, the Diabetes, Liver, Haberman, and Transfusion datasets were classified using an ANN, while the Ionosphere dataset was classified using the RF method.
* The G-mean values in the second column of Table 5: All the datasets were balanced using W-SIMO. Then, all datasets were classified using an SVM.
* The G-mean values in the third column of Table 5: All the datasets were balanced using RusAda. Then, all datasets were classified using a decision tree.  
The utilized classifier might be an important indicator for this comparison. Here, the compatibility of the classifier with the balancing method can be considered a significant factor. The developed HOUM algorithm has demonstrated high performance with an ANN, W-SIMO shows good compatibility with SVMs, and RusAda can be effectively employed with decision trees.  
\begin{table}
\begin{tabular}{l l c c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**HOUM-R**} & \multicolumn{3}{c}{**HOUM-L**} & \multicolumn{3}{c}{**SMOTE**} & \multicolumn{3}{c}{**SMOTEENN**} & \multicolumn{3}{c}{**SMOTETonck**} & \multicolumn{3}{c}{**Original Dataset**} \\ \cline{3-14}
**Dataset** & **Classifier** & \begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **G-Mean** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **G-Mean** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **G-Mean** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **G-Mean** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **G-Mean** \\ **(\%)** \\ \end{tabular} \\ \hline Climate & KNN & 82.00 & 50.20 & 81.00 & 53.50 & 76.00 & 65.60 & 67.59 & **72.84** & 75.93 & 56.42 & **92.00** & 33.30 \\  & RF & 91.00 & 33.20 & 91.00 & 0.00 & 93.00  
According to Table 5, the developed HOUM algorithm obtained better performance results in four of five datasets. However, the conclusive comparison results were obtained after classifying the datasets using the ANN and RF algorithms after balancing W-SIMO and RusAda.
# Cerebrovascular Reactivity Assessment during Carbon Dioxide Inhalation Using SPECT
## 3 Results  
The demographics and experimental results for all subjects are shown in Table 1. The MAP of all subjects ranged from 65-99.33 mmHg at pre-scan and 70-105.33 mmHg at post-scan. The MAP differed significantly from pre-scan to post-scan (\(p=0.011\)), probably due to the hypercapnic stimulation via CO\({}_{2}\) inhalation; however, the HR did not (\(p=0.092\)). The maximum ratio of radiation counts was 3.69 and the minimum was 2.93 (Table 1). The mean (SD) end-tidal CO\({}_{2}\) (Et-CO\({}_{2}\)) values were 26.96 (2.56) mmHg and 43.63 (2.48) mmHg before and during CO\({}_{2}\) inhalation (N = 7), respectively.  
The pre-processed first and second SPECT images of a representative subject are shown in Figure 3. In the second image, there was an increase in signal intensity in the frontal, temporal, and occipital cortices and the cerebellum compared to the first image. However, there were no specific values demonstrating increases in target ROIs between the first and second images. As shown in Figure 3c, there was a quantitative difference between the two SPECT images in a total of 116 ROIs on the voxel-based CVR map. The mean CVR value in the cerebellum was \(-2.28\%\), which was used for the relative CVR values in subgroup ROIs.  
As shown in Table 2 and Figure 4, the CVR values in the following eight subgroup ROIs were significant: ROI #02 (right temporal cortex), ROI #03 (vermis, medial), ROI #04 (left insula/cingulate), ROI #05 (right insula/cingulate), ROI #06 (left frontal cortex), ROI #07 (right frontal cortex), ROI #10 (left parietal cortex), and ROI #12 (left central cortex) (\(p<0.05\)). Specifically, the high relative mean CVR values were 3.337% in ROI #03 (vermis) and 3.148% in ROI #05 (right insula/cingulate). The low relative CVR values were 0.586% in ROI #08 (left occipital cortex) and 0.597% in ROI #09 (right occipital cortex), but they were not statistically significant.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ROI** & **Region Name** & **Hemisphere** & **\# of ROIs** & **Relative Mean Voxel Based CVR Value (\%)** & \(p\)**-Value** \\ \hline ROI \#01 & Temporal & L & 10 & 0.756 & 0.285 \\ ROI \#02 & Temporal & R & 10 & 1.849 & 0.047 * \\ ROI \#03 & Vermis & M & 8 & 3.337 & 0.007 * \\ ROI \#04 & Insula, Cingulate & L & 4 & 2.745 & 0.005 * \\ ROI \#05 & Insula, Cingulate & R & 4 & 3.148 & 0.007 * \\ ROI \#06 & Frontal & L & 15 & 1.862 & 0.005 * \\ ROI \#07 & Frontal & R & 15 & 2.127 & 0.007 * \\ ROI \#08 & Occipital & L & 6 & 0.586 & 0.508 \\ ROI \#09 & Occipital & R & 6 & 0.597 & 0.285 \\ ROI \#10 & Parietal & L & 6 & 2.274 & 0.017 * \\ ROI \#11 & Parietal & R & 6 & 1.231 & 0.059 \\ ROI \#12 & Central & L & 4 & 2.515 & 0.007 * \\ ROI \#13 & Central & R & 4 & 1.463 & 0.285 \\ \hline \hline \end{tabular} Abbreviations: L, left; R, right; M, medial; ROI, region of interest. * \(p<0.05\).  
\end{table}
Table 2: The relative mean voxel-based CVR values and statistical \(p\)-values for non-parametric one-sample Wilcoxon t-test with a mean reference CVR value of \(-2.28\%\) in 13 subgroup ROIs.
# Transmission Efficiency of Cycloid-Pinion System Considering the Assembly Dimensional Chain
## 2 RV Reducer Structure  
RV reducers have two reduction processes: planetary reduction and cycloidal-pin reduction. The principal characteristic is the simultaneous engagement of several teeth of the cycloid, which makes the reducer have a smoother force transmission. The RV reducer assembly and transmission mechanism are shown in Figure 2. In particular, input shaft 1 begins to run under the engine drive. The force is then transmitted to crankshaft three via planetary gear 2. When the first deceleration phase is completed, the cycloid gear begins to engage with the pin teeth driven by the crankshaft. Then, the pin tooth pushes the pin tooth case to start to rotate, completing the second stage of deceleration. Finally, by connecting with different parts of the RV reducer, different reduction ratios can be reached.  
Figure 1: Thesis research framework.  
Figure 2: RV reducer structure.
## 4 Experimental Results
### The Reconstruction of CSNGAN-ASPP
#### 4.3.4 Comparison with DCGAN  
The reconstruction performances were also compared between CSNGAN-ASPP and the DCGAN on different TIs. The DCGAN network parameters were set according to [PERSON] et al. [44]. After finishing training, the comparison between the CSNGAN-ASPP and DCGAN reconstructed models is shown in Figure 25. In Figure 25a, for the 2D delta model,  
Figure 23: The scatter and box plots of the facies ratios for the 50 realizations and their references.  
Figure 24: The MDS scatter plots for the relationship between the 50 realizations and their references.  
compared to the DCGAN results, CSNGAN-ASPP effectively recovers and reproduces the nonstationary properties, demonstrating superior channel connectivity and reduced noise and artifacts. This also proves that CSNGAN-ASPP's effective utilization of sparse conditioning data, without introducing additional noise or affecting the quality of the generated results. Figure 25b further illustrates these differences. As shown in Figure 25b,c, given the cross-section constraints, the structural features of fold and aquifer models can be effectively reconstructed by CSNGAN-ASPP, while DCGAN exhibits poor channel continuity and more pixel noise. The quantitative evaluation results further indicate that the spatial variability curves of the CSNGAN-ASPP simulation results closely match the reference model, whereas the spatial variability of the DCGAN simulation results is relatively less accurate. This demonstrates that CSNGAN-ASPP has a superior simulation performance across various reservoir and geological types.
## 3 Results
### Validation of the Results
Figure 8: Velocity values of a rockfall, shown in Figure 7, obtained by the GPP model. The continuous black line corresponds to the landslide boundary, and the black point to the release reference point according to the IFFI inventory. Figure 9: Other examples of the application of the proposed method and GPP model. (**a**) Velocity values of a shallow landslide obtained by a Matlab code based on the Heim method; (**b**) velocity values of the same shallow landslide shown in (**a**) obtained by the GPP model; (**c**) velocity values of a rockfall obtained by a Matlab code based on Heim method; (**d**) velocity values of the same rockfall shown in (**c**) obtained by the GPP model. In all the figures, the continuous black line corresponds to the landslide boundary, and the black point to the release reference point according to the IFFI inventory. The comparison between the maximum velocity values obtained with the two methods showed a good correspondence. In particular, a good positive correlation was found, as shown in the following graphs (Figures 10 and 11), and Pearson correlation coefficients of 0.95 and 0.99 were obtained respectively for rockfalls and shallow landslides. Non-parametric Kendall and Spearman tests, which measure the strength of the relationship between two variables, were also carried out, leading to the following results: a Kendall coefficient of 0.73 and a Spearman coefficient of 0.89 were achieved for rockfalls; regarding shallow landslides, these coefficients were 0.87 and 0.96, respectively. Moreover, not only the maximum velocity values were compared, but also the velocity distribution within each analysed landslide was investigated. To achieve this goal, boxplots of the velocity values of each landslide have been made both for the proposed approach and for the GPP model. Figure 11: Graph of correlation between the proposed method and GPP model maximum velocity values for ten selected shallow landslides. Each point shown in the graph represents the maximum velocity value of a shallow landslide computed with the two methods. Figure 10: Graph of correlation between the proposed method and GPP model maximum velocity values for ten selected rockfalls. Each point shown in the graph represents the maximum velocity value of a rockfall computed with the two methods. In Figures 12 and 13, the boxplots relating to rockfalls and shallow landslides are shown. These graphs highlight a good correlation between the velocity values distributions obtained with the proposed method and the GPP model.