esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effects on APPJ Electrical Parameters
[PERSON] et al [28] observed a delay in the active current when applying a sinusoidal HV to produce a N\({}_{2}\)-O\({}_{2}\) DBD plasma in a closed environment. However, in that work the maximum O\({}_{2}\) concentration in the working gas was 1200 ppm (0.12%). Therefore, the results presented in Figure 3 provide more detailed information about the physical phenomena resulting from the addition of O\({}_{2}\) to the plasma. Figure 2: Curves of \(P_{dis}\) as a function of the He flow rate (**a**) and distance between plasma outlet and target (**b**), and curves of \(P_{dis}\) (**c**) and \(i_{RMS}\) (**d**) as a function of the O\({}_{2}\) percentage added to the working gas. **d** = 7 mm in (**a,c,d**). O\({}_{2}\) content is zero in (**a,b**). Moreover, by inspecting the \(i\) waveforms measured for different O\({}_{2}\) concentrations, it is possible to observe the appearance of narrow peaks in the current signal when there is more O\({}_{2}\) in the plasma. This finding suggests that as more O\({}_{2}\) is added the plasma undergoes a transition from a diffuse to a filamentary regime, just as it happened in [28] using a N\({}_{2}\)-O\({}_{2}\) gas admixture. Part of the results presented in Figures 2c,d and 3, especially the reduction of the \(i_{RMS}\) values at higher in O\({}_{2}\) content, can be explained by the high electronegativity of the O atoms that tend to capture electrons produced in the plasma. This in turn probably reduces the number of free electrons that are responsible for establishing the electrical current in the plasma jet. However, following an argument given in [28], another possibility is that as more O\({}_{2}\) is introduced into the plasma, most He atoms in metastable states (He\({}^{\text{M}}\)) first collide with the O\({}_{2}\) molecules, which reduces the population of secondary electrons that come into the plasma due to the collision of He\({}^{\text{M}}\) atoms with the surfaces of the system. Regarding the delay in the discharge ignition for higher O\({}_{2}\) concentrations, a possible explanation has also a relationship with the oxygen electronegativity, since the electrons accelerated by the variation of the electric field in the very first voltage oscillations of the burst signal may have been absorbed by the exceeding O\({}_{2}\) molecules and O atoms, making the discharge ignition more difficult until finally there are enough energetic electrons left to start the discharge.
###### Abstract  
In order to improve the accuracy of bearing fault diagnosis under a small sample, variable load, and noise conditions, a new fault diagnosis method based on an image information fusion and Vision Transformer (ViT) transfer learning model is proposed in this paper. Firstly, the method applies continuous wavelet transform (CWT), Gramian angular summation field (GASF), and Gramian angular difference field (GADF) to the time series data, and generates three grayscale images. Then, the generated three grayscale images are merged into an information fusion image (IFI) using image processing techniques. Finally, the obtained IFIS are fed into the advanced ViT model and trained based on transfer learning. In order to verify the effectiveness and superiority of the proposed method, the rolling bearing dataset from Case Western Reserve University (CWRU) is used to carry out experimental studies under different working conditions. Experimental results show that the method proposed in this paper is superior to other traditional methods in terms of accuracy, and the effect of ViT model based on transfer learning (TLViT) training is better than that of the Resnet50 model based on transfer learning training (TLResNet50) under variable loads and small sample conditions. In addition, the experimental results also prove that the IFI with multiple image information has better anti-noise ability than the single information image. Therefore, the method proposed in this paper can improve the accuracy of bearing fault diagnosis under small sample, variable load and noise conditions, and provide a new method for bearing fault diagnosis.  
fault diagnosis; continuous wavelet transform; Gramian angular field; vision transformer; transfer learning +
Footnote †: journal: Medical and Equipment Engineering
## 2 Materials and Methods
### Vegetation Ecophysiology Assessment Using the Normalized Burn Ratio (NBR) Model  
The normalized burn ratio (_NBR_) is a standard vegetation index widely used to highlight burned areas in areas where fire often occurs. The _NBR_ is considered the standard for fire severity assessment [34]. The combination of near-infrared (_NIR_) and mid-infrared (MIR) reflectance allows the _NBR_ index to be considered an operational tool [34; 35] for assessing the vigor and moisture of vegetation on a national scale. However, the _NBR_ formula is too similar to that of the NDVI [17]. To take advantage of the magnitude of the spectral difference provided by remote sensing data, the _NBR_ equation uses the ratio between _NIR_ (near-infrared) and _SWIR_ (shortwave-infrared) bands.  
The _NBR_ formula used in this study combines the use of both _NIR_ (_B8A_) and _SWIR_ (_B12_) wavelengths (Table 1) and is defined as:  
\[NBR=\frac{NIR(B8A)-SWIR(B12)}{NIR(B8A)+SWIR(B12)} \tag{1}\]  
A high _NBR_ value generally indicates healthy vegetation conditions. A low value suggests bare ground and/or recently burned areas. Values close to zero indicate unburnt areas [36].  
The burn severity rating (_dNBR_) was calculated from the _NBR_ products of the current study to express the dynamics of vegetation health. The difference between the NBRs obtained from the images for years one and two was used to calculate the delta _NBR_. This could then be used to estimate the burn severity rating. The _dNBR_ formula is shown below:  
\[dNBR\;or\;\Delta NBR=PrefineNBR-PostfireNBR \tag{2}\]  
_dNBR_ values vary widely from case to case, so interpretation in specific cases should also be carried out by ELD assessment so that the best results may be obtained. A higher value of _dNBR_ indicates more severe damage, but areas with negative _dNBR_ values may be experiencing regrowth. The interpretation of fire severity (Table 2) promoted by the United States Geological Survey (USGS) [35] was adopted for the purposes of this study.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Sentinel-2B** & **Center Wavelength (nm)** & **Spatial Resolution (m)** \\ \hline Band 1—Coastal aerosol & 442.2 & 60 \\ Band 2—Blue & 492.1 & 10 \\ Band 3—Green & 559.0 & 10 \\ Band 4—Red & 664.9 & 10 \\ Band 5—Vegetation red edge & 703.8 & 20 \\ Band 6—Vegetation red edge & 739.1 & 20 \\ Band 7—Vegetation red edge & 779.7 & 20 \\ Band 8—NIR & 832.9 & 10 \\ Band 8A—Narrow NIR & 864.0 & 20 \\ Band 9—Water vapor & 943.2 & 60 \\ Band 10—SWIR—Cirrus & 1376.9 & 60 \\ Band 11—SWIR & 1610.4 & 20 \\ Band 12—SWIR & 2185.7 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nature of Sentinel-2B images [28].
## 3 Materials and Method
### Dataset Collection
#### 3.1.1 Image Data  
During the collection of medical imaging data, various types of images were selected, including X-rays, CT scans, and MRI scans, with all data sourced from Radiopaedia [44] and the OpenI [45] medical image repositories. The number of images for each type was determined based on its clinical relevance, frequency of use in diagnostic procedures, and importance in addressing diverse medical scenarios. X-rays were prioritized due to their widespread use in diagnosing common conditions like chest diseases, while CT and MRI scans were included to represent more complex cases requiring advanced imaging techniques. Special attention was given to selecting images that reflect a variety of common diseases and complex cases. To ensure an adequate representation of each modality, the dataset was balanced with 5912 X-rays, 9649 CT scans, and 7345 MRI scans, which provided sufficient data for deep learning model training and evaluation without introducing significant biases, as shown in Table 1. Each image was accompanied by complete case descriptions and diagnostic results, ensuring both the richness and practical utility of the dataset. Additionally, rigorous data quality checks were applied to exclude low-quality or incomplete images, further enhancing the dataset's robustness and reliability.
## 4 Discussion  
The results of the proposed methodology presented in the article confirm the correctness of the methodology based on the ANN for prediction of the temperatures on the surface of the turbine blades at specific nodes representing the points defined in the coordinate system. The main goal of the article was to verify the proposed methodology in real practice. Therefore, the ANN was at first used for the prediction of temperatures on the surface of the turbine blade and consequently applied and verified on the experimental iSTC-21v jet engine. In addition, the goal of the research was to develop the methodology that is able to predict temperatures on the turbine blade immediately with significantly eliminated computational and time demands and at the same time the methodology that could be accordingly applied on any other engines or their parts.  
The proposed article involves not only the description of the methodology but also its direct practical application, also showing partial results that lead to the fulfillment of the main goal of the research. For the purposes of the methodology verification, the 3D model of the turbine and nozzle section for the CFD studies based on the iSTC-21v engine was created. The CFD analysis results are presented in Figure 6 shows one of the 10 performed CFD analyses. Using the CFD ANSYS software, the temperature fields were estimated for the iSTC-21v jet engine, and subsequently, the analyses were performed for 10 specified operation regimes of the engine in order to obtain inputs for the neural network.  
The most valuable contribution of the proposed methodology is presented in Figure 9, where the dependency between the data computed using the CFD and ANN method can be clearly seen. The figure shows the temperatures predicted using the ANN for the chosen 10 operational regimes of the jet engine in the specific nodes representing the points on the surface of the turbine blade.  
The new methodology is applied to other regimes (Figure 10), where are predicted temperatures for 1800 regimes. It is necessary to point out again that the relevant results presented in Figure 10 are emphasized with the dashed black line due to the character of the training data. This is caused due to the character of the training data obtained  
Figure 10: Estimated temperatures for one whole engine run using the new methodology based on the ANN.  
from the CFD analyses. The training inputs estimated by the CFD method involved just 10 operational regimes of the jet engine summarized in Table 1. However, there are not regimes included for the initial state during the no run of the engine; thus, in order to create a more accurate method, it is necessary to include some other regimes, for example, for ambient temperature and some other lower regimes. These regimes will be included in the future, and it will be the scope for further research.
## 3 Results  
### Catalytic Activity Measurements  
1.1 Effect of Preparation Method of CeO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) and Y-CeO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) on CBO Activity of Gold-Based Catalysts  
The benzene conversion as a function of the reaction temperature over gold catalysts, supported on prepared by mechanical mixing and impregnation CeO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) or Y-doped CeO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\), is presented in Figure 1. The reported data refer to complete oxidation, because intermediate products of partial oxidation were not detected.  
The degree of benzene conversion over Au/CeAl was below 10% at 150 \({}^{\circ}\)C. However, it sharply increased after rising the reaction temperature and attained almost 80% at 220 \({}^{\circ}\)C. In contrast,  
Figure 1: Temperature dependence of complete benzene oxidation (CBO) over gold-based catalysts.  
the benzene conversion over gold catalyst, supported on prepared by impregnation CeO2/Al2O3, i.e., Au/CeAlIM, showed gradual enhancement with increase of reaction temperature. Finally, this catalyst demonstrated benzene conversion below 80% at 300 \({}^{\circ}\)C. Additionally, results presented in Figure 1 indicated beneficial role of Y-doping of CeO2/Al2O3 on activity of gold catalysts. This effect is well visible over gold catalyst on prepared by MM support. It exhibited 63% conversion at 200 \({}^{\circ}\)C, while 28% conversion was measured in case of gold catalyst on undoped support.  
The comparison reveals the favorable effect of mechanical mixing as method for support preparation on CBO activity of gold-based catalysts. This finding directed our next research step and we used only supports prepared by MM for synthesis of Pd-based and bimetallic Pd-Au samples. For comparative purpose temperature dependences of CBO over gold catalysts on bare ceria and alumina are also shown in Figure 1. In accordance with many published results, Au/Ce exhibited superior performance, while Au/Al was inactive under our reaction conditions. The advantage of the Au/Ce sample was not only the high benzene oxidation activity, but also long-term stability within 24 h at 300 \({}^{\circ}\)C.
## 5 Conclusions and Future Work  
In conclusion, our proposed fusion of Deep SORT and YOLOv5 presents a highly effective solution for real-time vehicle detection and tracking in traffic management systems. This integration addresses key challenges such as vehicle detection, vehicle tracking, occlusions, varying illumination conditions, and the accurate tracking of unique vehicle IDs. This paper presents vehicle detection and tracking methods to achieve high accuracy and better performance in terms of precision, recall, and mAP than existing algorithms in real-time. The Deep SORT algorithm is integrated with YOLOv5 to reduce the false detection and missed detections due to occlusions, illumination, and counting unique IDs for tracking vehicles and other external factors. This proposed model improves the detection accuracy rate in videos. The results of our study demonstrate superior performance, with a mean average precision (mAP) of 92.18% and precision and recall rates of 91.25% and 93.52%, respectively, surpassing our existing methods.  
As we move forward, our focus will be on advancing the capabilities of our proposed system. We plan to explore advanced combinations of deep learning methodologies to further refine the accuracy of vehicle detection and tracking. Individual optimizations for YOLOv5 and Deep SORT will be undertaken to independently enhance their performance. Moreover, we aim to integrate the sustainability aspects into the system, aligning these with environmental considerations and energy efficiency. The continuous refinement of our approach will contribute to the development of intelligent transportation systems that are not only effective in real-time traffic management but also sustainable in the broader context. Through ongoing research and development, we anticipate making significant strides in the intersection of cutting-edge technology and sustainable traffic solutions.  
Writing--review and editing, S.K. and S.K.S.; Writing--original draft, S.K. and S.K.S.; Methodology, S.K., S.K.S. and S.V.; Implementation, S.K.; Validation, S.K. and S.V.; Resources, S.S., P.K. and S.K.S.; Visualization, S.K. and S.S.; Formal analysis, S.S. and S.K.S.; Supervision, S.K.S., S.S., P.K., B.-G.K. and I.-H.R.; Project administration, S.K., S.K.S., S.S., P.K., B.-G.K. and I.-H.R.; Funding acquisition, S.S. and I.-H.R. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Research Foundation of Korea (NRF) Grant from the Korean Government through the Ministry of Science and ICT (MSIT) under Grant 2021R1A2C2014333. The work is also supported by the Woosong University Academic Research Fund in 2023. This research was also supported by the Research Seed Grant funded by the Marwadi University, Rajkot, Gujrat (MU/R&D/22-23/MRP/FT13).  
Not applicable.  
Data are contained within the article.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 2 System Design Overview  
The three-phase inverter used during investigations is shown in Figure 1. Comparison of the presented solutions is made using both MATLAB simulation and experimental verification. The switching frequency \(f_{c}\) was set to 12.8 kHz--due to utilized hardware components, such as Matlab dSPACE device boards: dSPACE controller, DS5101 and DS2004 A/D, and inverter Danfoss 131F3040, used in empirical tests. The important fact that should be highlighted is that the PBC controller was working with the constant switching frequency and used the \(f_{c}\) as one of its input values in the control law. On the other hand, the MPC algorithm was using an average switching frequency calculated by the cost function \(J\) in the currently used operating sector vector depending on the sampling time [14; 15]. In the MPC controller, the PWM modulator and the control functions calculations are merged into one state, whereas in the PBC controller the modulator is located at the output of the control function. This fact leads to the assumption that operating frequency was set to 12.8 kHz in order to have a fair comparison between feedback PBC and predictive MPC [16; 17]. Typical load types for standalone systems are mainly static-resistive \(R\), dynamic \(R\), and nonlinear rectified \(RC\) load. Additionally, the initial values of \(L_{f}=3\) mH and \(C_{f}=60\)\(\mu\)F were taken into calculation during simulation. The paper presents a methodology of designing, implementation, and verification of the two types of MISO controllers--MPC and PBC--with their improved modifications of the control function. Thus, the calculations are based on \(\alpha\beta\) transformation. Both of the controllers used the same input variables, output filter values, and were working in the same stress condition--constant input DC voltage--600 V and changeable \(L_{f}\) and \(C_{f}\) values. The installation configuration is presented in Figure 1, and basic parameters of the studied system are listed in Table 1.
###### Abstract  
Co\({}_{1-x}\)Mn\({}_{x}\)Fe\({}_{2}\)O\({}_{4}\) nanoparticles (\(0\leq x\leq 1\)) have been prepared via the hydrothermal method. The prepared samples were studied using X-ray diffraction measurements (XRD), transmission electron microscopy (TEM), Raman spectroscopy, and magnetic measurements. All studied samples were found to be single phases and to have a cubic _Fd-3m_ structure. The average crystalline sizes are between 7.8 and 15 nm. EDS analysis confirmed the presence of cobalt, manganese, iron, and oxygen in all prepared samples. It was found by Raman spectroscopy that Fe\({}^{3+}\) would be placed on octahedral sites while Fe\({}^{2+}\) would, in turn, be displaced to tetrahedral sites while Mn ions will be placed on both sites. Both Mn\({}^{2+}\) and Mn\({}^{4+}\) are present in studied ferrites. The experimental saturation magnetizations for doped samples are much higher when compared with previous reports, reaching values between 3.71 and 6.7 \(\upmu_{\rm B}\)/f.u. The doping with Mn in nanocrystalline cobalt ferrite enhanced the magnetic properties due to changes in the cation distribution between the two sublattices. The higher magnetic moments are explained by the presence of Mn\({}^{4+}\) ions located preferentially on tetrahedral sites while Mn\({}^{2+}\) prefer octahedral sites, and by the high quality and crystallinity of our samples the nanoparticles being almost monodomain. Large values of the coercive field were found at 4.2 K while the hysteresis is almost absent in all investigated samples at room temperature.  
1
## 4 Experimental Results and Discussion
\[VAR=STD^{2}=\frac{1}{n-1}\sum_{i=1}^{N}\left(x_{i}-\overline{x}\right)^{2} \tag{17}\]  
For the experimental results, we generally considered the best case wherein rotation and flipping were applied after the color perturbation with RGB and CIE L*a*b* color spaces by using the inverse PSNR. In addition, the results obtained using the proposed method applied to Table 7, which showed higher accuracy, tended to be similar to or lower than those of the proposed method applied to Table 6. In the case of applying the inverse PSNR method in terms of the RGB and CIE L*a*b* color spaces and two geometric transformations, the variance of the classification accuracy was 14.3. This meant that the classification accuracy was heavily influenced by the randomly extracted training data. In addition, the figure below shows that, as mentioned earlier, a sufficient amount of training dataset increased the classification accuracy. In Figure 8, we compare the cases of using the traditional methods and the case of applying geometric transformation based on RGB PSNR + Lab PSNR training data, which was mostly the best case among the training data generated by the proposed method. The classification accuracy for Object #3 gradually increased in sequence. From Figure 8a,b, it was evident that the classification results were mostly misclassified to be of some output class. In the case of Figure 8c, Object #3 was mostly classified as Object #4. Their shapes were similar to a rectangle. By applying geometric transformations, the amount of the training data and the classification accuracy increased. Finally, Object #3 could be classified 100% using the re-trained classifier. In addition, Object #6 was mostly classified as Object #5, as depicted in Figure 8c. As depicted in Figure 9, Objects #4, #5, and #6 were significantly similar, especially when they were laid down in a standing position. However, using the proposed method, the classification accuracy for Object #6 could be improved. Because Object #8 was significantly similar to Object #7, as depicted in Figure 9, Object #8 was classified as Object #7, as depicted in Figure 8c. However, Object #8 could be classified 100.0% using the proposed method. Similarly, Object #9 was misclassified as Object #10, which was similar to Object #9. However, the proposed method could classify Object #9 with 100.0% accuracy. Figure 8: Comparison of confusion matrices: **(a)** Traditional Method #1; **(b)** Traditional Method #2; **(c)** RGB PSNR + Lab PSNR with no geometric transformation; **(d)** RGB PSNR + Lab PSNR with flipping; **(e)** RGB PSNR + Lab PSNR with rotation; and **(f)** RGB PSNR + Lab PSNR with rotation and flipping. Figure 9: Target objects for image classification.