esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Evaluation of the Object Tracking Algorithm  
Our goal is to verify the implementation of our own proposed algorithm using the _intersection of rectangles_ that represents the vehicle, i.e., the car. Unfortunately, it is challenging to determine the appropriate distance, mainly due to the loss of detection and thus the increasing distance. As the YOLOv4 algorithm reports stable detection with high accuracy, it is not suitable for the proposed evaluation. For this reason, we replace it with the Faster R-CNN MobileNetV3-Large 320 FPN algorithm, which is characterized by lower accuracy and higher loss of detection; for a detailed comparison, see the review by [PERSON] et al. [31].  
The measurement is performed on the same input video as in the performance evaluation. We set the limit distance of 25 pixels and the limit intersection of 25%. Table 2 demonstrates that higher detection loss causes the distance between two bounding boxes to be too large. It may be relatively challenging to set the appropriate distance. The results show that intersection is _key_ in the cases in which the distance is greater than determined. The detection loss exists in several frames of the video. Consequently, two vehicles are counted twice without the _use of an intersection_.  
Figure 11: Performance evaluation of the YOLOv4 on the GPU. (**a**) Performance of the full frame on the GPU; (**b**) performance of the part of the frame on the GPU.  
Figure 10: First frames of the Sample scenario #1. (**a**) Front view of the Sample scenario #1; (**b**) rear view of the Sample scenario #1. Source of the input video: [26].
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Specification** & [51] & [27] & [29] & [28] & [33] & [52] & **This Work** \\ \hline Input voltage & 300 mV & 300 mV & 300-720 mV & 300-600 mV & \begin{tabular}{c} 1.4 V (DC-DC converter input) \\ \end{tabular} & 300 mV & 330 mV \\ \hline Output voltage & 1 V & 1.8 V & 2.5 V & 2.5 V & 4.25 V & 3.3 V & 3.3 V \\ \hline \multirow{2}{*}{Inductor} & 326.7 uH & 2 uH & 1.5 mH & 1.5 mH & \begin{tabular}{c} Transformer 31.8 \\ mH (primary winding) \\ \end{tabular} & - & 22 uH \\ \hline Output capacitor & 8 uF & 42 uF & \begin{tabular}{c} 0.1 F \\ (supercapacitor) \\ \end{tabular} & 0.1 F & 68 mF & Multiple & 5 F \\ \hline \multirow{2}{*}{Maximum power extraction} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & Adaptable \begin{tabular}{c} Maximum \\ Power \\ Extraction \\ \end{tabular} & Yes & MPPT & - & MPPT \\ \hline Efficiency & 73\% & - & 58\% & 30\% & - & 35.02\% & 50.7\% \\ \hline \multirow{2}{*}{\begin{tabular}{c} Implementation \\ approach \\ \end{tabular} } & Discrete & Discrete & \begin{tabular}{c} Custom \\ integrated \\ circuit \\ \end{tabular} & \begin{tabular}{c} Custom \\ integrated \\ circuit \\ \end{tabular} & \begin{tabular}{c} Discrete \\ Components \\ \end{tabular} & \begin{tabular}{c} Discrete \\ Components \\ \end{tabular} & \begin{tabular}{c} Discrete \\ Components \\ \end{tabular} \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Multiple MFC \\ power extraction \\ \end{tabular} } & \multirow{2}{*}{No} & \multirow{2}{*}{No} & \multirow{2}{*}{No} & \multirow{2}{*}{No} & \multirow{2}{*}{Yes} & Multi mode &
\begin{tabular}{c} Yes (default \\ support for 9 \\ MPCs) \\ \end{tabular} \\ \hline \multicolumn{2}{c}{MFC health protection} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{No} & \multirow{2}{*}{No} & \multirow{2}{*}{Yes} \\ \end{tabular}
\end{table} 
Table 4: Comparison of MFC power management units. **Acknowledgments:** This work was supported by Consejo Nacional de Ciencia y Tecnologia (Mexico City, Mexico), Texas Instruments (Dallas, TX, USA), Silicon Labs (Austin, TX, USA) and Intel (Hillsboro, OR, USA). **Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Conclusions  
In this study, Fenton and ozone oxidation were used to pretreat petrochemical wastewater, and COD removal, B/C and intermediates in above two systems were compared. Under the conditions of pH = 3, 300 mg/L H\({}_{2}\)O\({}_{2}\), and 120 mg/L Fe\({}^{2+}\), the COD removal of the wastewater in Fenton oxidation system reached 89.8%, the B/C value reached 0.62, and the cost was about 1.78 $/kg\({}_{\text{COD}}\). In the ozone oxidation system, the COD removal of petrochemical wastewater reached 59.4% and the B/C value increased to 0.41 with a treatment cost of approximately 1.96 $/kg\({}_{\text{COD}}\) at an ozone concentration of 1 g/L and a  
Figure 5: Comparison of COD, B/C values, and treatment costs in different Fenton and ozone oxidation systems.  
flow rate of 80 mL/min. The GC-MS results suggested that Fenton oxidation, including the acidification process, can efficiently degrade the refractory substances in the wastewater and generate more biodegradable small molecular organic acids, which is more conducive to the improvement of biodegradability. Furthermore, the treatment cost of Fenton oxidation is also lower than that of ozone oxidation. In conclusion, Fenton oxidation is more suitable for the pretreatment of petrochemical wastewater.  
Conceptualization, G.R.; methodology, G.R. and S.C.; validation, G.R.; formal analysis, G.R., Z.W. ([PERSON]) and S.C.; investigation, S.C., X.R., R.L. and Z.W. ([PERSON]); resources, G.R.; data curation, G.R., [PERSON] ([PERSON]), S.C., T.R. and X.M.; writing--original draft preparation, G.R., Z.W. ([PERSON]) and S.C.; writing--review and editing, G.R., S.C., T.R., Z.W. ([PERSON]), R.L. and X.R.; supervision, G.R. and X.M.; project administration, G.R. and X.M.; funding acquisition, G.R. and X.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [National Natural Science Foundation of China] grant number [No. 22106036 and 21876042]; [Funding projects for the introduction of overseas students in Hebei Province] [C20210336]; [Natural Science Foundation of Hebei Province] [No. B2020202061 and B2019202200]; [the Ministry of Science and Technology of the People's Republic of China] [2020 YFC1808603]. The APC was funded by [National Natural Science Foundation of China] grant number [No. 22106036].  
Not applicable.  
The authors gratefully acknowledge financial support for this work from National Natural Science Foundation of China (No. 22106036 and 21876042), Funding projects for the introduction of overseas students in Hebei Province (C20210336), Natural Science Foundation of Hebei Province (No. B2020202061 and B2019202200), the Ministry of Science and Technology of the People's Republic of China (2020 YFC1808603). The authors declare no conflict of interest.
# PELM: A Deep Learning Model for Early Detection of Pneumonia in Chest Radiography
## 5 Discussion
### Strengths of Deep Learning Models  
One of the most prominent strengths demonstrated in this study is the high diagnostic accuracy achieved by the PELM ensemble model, which attained 96% accuracy, 99% precision, 91% recall, 95% F1-score, and 91% specificity on a large-scale curated dataset. These results surpass those reported by previous studies using single-model approaches or limited data, underscoring the value of combining multiple architectures to harness their complementary strengths. The fusion of convolutional and transformer-based features allowed the model to capture both local texture patterns and global contextual cues--critical for detecting pneumonia, which can vary significantly in appearance and distribution.  
The use of transfer learning, model fusion, and carefully designed preprocessing--including CLAHE-based contrast enhancement and clinically aware data augmentation--contributed significantly to performance and generalizability. These components helped mitigate the effects of dataset imbalance, image acquisition variability, and feature sparsity. Moreover, automation of pneumonia detection reduces dependency on subjective radiological interpretation, minimizes inter-observer variability, and lowers the potential for diagnostic errors in fast-paced clinical settings. Once deployed, these models are scalable, require minimal computational resources for inference, and can be integrated into existing picture archiving and communication systems (PACS) or electronic health record (EHR) platforms, facilitating broad clinical utility.
## 4 Discussion
### Finite Element Simulation  
In the present FEM, the long-term tooth movement could be simulated under various assumptions. Their validities have been discussed in the previous articles [17; 18; 19; 20; 21; 22]. The most basic assumption is that the tooth movement occurs in the same direction as the initial movement, which is caused by elastic deformation of the PDL. This assumption is consistent with clinical experiences. That is, when a force is applied to the crown, a tipping movement occurs. When a moment is applied together with the force, the tooth can move bodily. This change in movement pattern is observed in both initial and orthodontic tooth movements.  
The tooth moved by repeating the initial movement. The number of repetitions, \(N\), which should correspond to the elapsed time, could not be converted to actual time. This is because the relationship between the amount of initial movement and the rate of orthodontic movement has not been clarified in clinical settings. Hence, the treatment period cannot be predicted by the present FEM simulation.  
In the present FEM model, the bracket was excluded, and the archwire was fixed directly to the crown. This corresponds to the case where the archwire is firmly fixed into the bracket slot without a clearance gap. Under this reference condition, the efficacies of various springs were compared. In the actual case where there is a clearance gap between the archwire and the bracket slot, the archwire moves in the gap, and thereby the tooth tips [24; 25]. The amount of the tipping movement depends on the relative initial position of the bracket slot and archwire, which is unknown in clinical settings. At present, it is difficult to include the clearance gap in the FEM model.  
In clinical settings, forces act on the maxillary teeth not only from the orthodontic appliances but also from the cheeks, tongue, and mandibular teeth. These forces may affect tooth movement but were neglected in the present FEM. This is a limitation of the present study.
# Novel Approach in Vegetation Detection Using Multi-Scale Convolutional Neural Network
## 2 Materials and Methods
### Proposed Architecture
#### 2.1.4 Transposed Convolution  
Transposed convolution layers (also known as deconvolution layers) are employed to upsample the feature maps. This is crucial for segmentation tasks, where the output needs to match the original input dimensions. These layers help in reconstructing spatial information lost during downsampling.
# Antennas and Propagation: A Sensor Approach
### Antennas for 5G  
In Ref. [14], a multiband antenna for microwave and millimeter-wave applications is presented. The proposed antenna consists of a slotted, conical patch connected to a small triangular patch. It can be used for wireless local area network (WLAN) applications and for fifth-generation (5G) communication devices.  
In Ref. [15], the authors present a simple, compact and low-cost design method for low-profile, multi-band antennas. Such antennas can be employed in overcrowded, future generation networks in the K/Ka band. The proposed antenna structures consist of several monopoles, one for each operating frequency, along with a frequency selective feeding network. This concept leads to scalable structures suitable for 5G applications.  
In Ref. [16], a small antenna for sub-6 GHz 5G communications is proposed. The design consists of a wide-band antenna connected to a small multiplexer comprising three metamaterial channel filters. It can be used on channels within three frequency bands; channel selection is experimentally demonstrated so as to prove the validity of the presented design.
# A Study of Cavitation Erosion in Artificial Submerged Water Jets
## 2 Numerical Simulation  
### Turbulence Model  
We utilized the widely adopted commercial computational fluid dynamics (CFD) software, ANSYS Fluent 2020R2, for our simulation. ANSYS Fluent has a proven track record of success in cavitation analysis, including applications related to water jet pump tip leakage vortex and water jet phenomena [26; 27; 28]. The Stress-Blended Eddy Simulation (SBES) model employed in this study is chosen based on the SST \(k\)-\(\omega\) model, as it has demonstrated its capability to simulate the super-cavitation flow generated by high-speed water jets within the injection pressure range of 5-25 MPa [29]. The SBES turbulence model is specifically developed to capture the dynamic behavior of turbulence in distinct flow regions. It enables a seamless and efficient transition from Reynolds-averaged Navier-Stokes (RANS) to Large Eddy Simulation (LES) in the separated shear layer. The fundamental principle of the SBES model involves utilizing the RANS method to solve the flow field near the wall while employing the LES method to solve the flow region further away from the wall. This approach enhances computational accuracy and captures more intricate details of the flow field while mitigating the issue of excessive mesh requirements.  
The blended stress \(\tau_{SBES}\) between RANS and LES methods can be achieved by the following equation:  
\[\tau_{SBES}=f_{sh}\tau_{RANS}+(1-f_{sh})\tau_{LES}, \tag{1}\]  
The shielding function, denoted as \(f_{sh}\), plays a crucial role in determining the transition between the RANS and Large Eddy Simulation LES methods. It explicitly controls the switching mechanism between these two methods. The stress tensors, \(\tau_{RANS}\) and \(\tau_{LES}\), represent the modeled turbulent stresses in the RANS and LES frameworks, respectively. The turbulent viscosity \(\mu_{t}^{SBES}\) can be determined as follows:  
\[\mu_{t}^{SBES}=f_{sh}\mu_{t}^{RANS}+(1-f_{sh})\mu_{t}^{LES}, \tag{2}\]  
where \(\mu_{t}^{RANS}\) and \(\mu_{t}^{LES}\) were calculated by the SST \(k\)-\(\omega\) model and the Wall-Adapting Local Eddy-viscosity model (WALE), respectively.
## 3 Proposed System
### Macula Localization Module  
As mentioned before, the functionality of this module relies upon the outcomes generated by the OD Localization Module to conduct calculations pertinent to macula localization. Figure 4 illustrates the subprocesses inside the module.  
Due to the variations in color intensity and luminosity within fundus images, which consequently impact the visibility of the macula, we decided to employ two preprocessing methodologies aimed at enhancing the color representation of the macula region. First, we selected an image from the DRIVE dataset with suitable color and brightness for macula discrimination. Secondly, we applied the iterative robust homomorphic surface fitting (IRHSF) preprocessing [38] to the selected image to create a new one that serves as a reference to perform a histogram specification across the entire training dataset. The creation of the reference image was performed only once, and it is used to apply the histogram specification to any other retinal images. The details of the IRHSF preprocessing and histogram specification are described in the Appendix A.  
The initial images or their preprocessed counterparts, the histogram specification results, and coordinates corresponding to the OD were employed as inputs for this module to determine the macula area. To begin with, the calculations of the images, the OD center coordinates, and the DD were parsed, followed by the pre-processed images to split them into three channels: red, green, and blue.  
According to the literature the center of the macula, the fovea, is located at approximately 2.5 DD from the center of the OD with a little horizontal angle and is the darkest area of a fundus image. For this reason, a candidate area for its localization is defined as a rectangle of 2 DD length and 2.5 DD width as shown in Figure 5[39].  
Additionally, image processing techniques were used to highlight the exact location of the fovea within the designated rectangle. This entails employing contrast enhancement methods, such as CLAHE, in the green channel of the image to produce a new version. Subsequently, a binary image of the previous outcome was created using Otsu binarization to choose the optimal threshold value for each image. Finally, a median blur filter was applied. The Hough circles algorithm detected the circles and their centers within the result pre-processed designated area. In cases where multiple circles were identified, they were averaged to obtain a single result. This one was considered the center of the fovea. To end the process in the original fundus image the quadrants, OD, OD center, the macula area defined as a circle of 1 DD, and fovea were drawn.  
Figure 4: Macula Localization module subprocesses indicated by a red dot-line box.  
Figure 5: Simplified anatomical structure of human retinal image [39].
# Multi-Wavelength Digital-Phase-Shifting Moire Based on Moire Wavelength
###### Abstract
Since direct measurement of the optical system geometry parameters is difficult in practice [1], the approach in the current paper is to compute the moire wavelength (the distance between two consecutive bright or dark fringes of a moire pattern on a flat plate) for each phase map, by performing a moire system calibration over the full working depth, rather than by direct measurement of the optical system geometry. The method is the first to compute multiple moire wavelengths in calibrations over the working depth and employ them in multi-wavelength phase unwrapping in digital moire. Furthermore, the moire system calibration determines a moire-wavelength to height relationship that is used in pixelwise refinement of the moire wavelength and height during 3D reconstruction. This is the first time that this is applied in multi-wavelength digital moire. The method requires the projection and capture of only a single image to extract the phase map for each moire wavelength. A further advantage for fast measurement is that the projected grid is binary, which allows fast pattern projection. The method of multi-wavelength digital-phase-shifting moire based on moire wavelength has been demonstrated in measurement of an object with surface discontinuities and spatially isolated objects.
## 4 Application and Results
### Diagnostic Analysis
#### 4.4.1 Taylor Diagram  
A polar plot presented by [PERSON] (1967) was drawn for obtaining a visual understanding of model performance. It has the ability to highlight the goodness of model performance in comparison to observed values. The Taylor diagram depicts three statistics: (1) Correlation coefficient (the azimuth angle), (2) normalized standard deviation (radial distance from the origin), and (3) RMSE (distance from the reference observed point). A perfect matching of the predicted results is identified as a complete overlay by the reference point with the correlation coefficient equal to unity and the exact amplitude of variations compared with observations (1967; 1968; 1969; 1970).  
Figure 12(a) shows the Taylor diagram of the standalone models (MLP and CCNN). In the case of the best models, the diagram shows that the CCNN-III model had a lower RMSE than the MLP-III model. Although the correlation coefficients and standard deviations of the predicted data for both models were less than the observations, the node representing the CCNN-III model was closer to the observation node. Figure 12(b) shows the Taylor diagram of the hybrid models. The diagram shows  
Figure 12: Scatter plots of observed and predicted DO values using VMD-based soft computing models for the testing phase.  
that the DWT-MLP-III (S6) and VMD-MLP-III (\(K\) = 4 and \(\alpha\) = 10) models had a lower RMSE than the DWT-CCNN-III (S6) and VMD-CCNN-III (\(K\) = 4 and \(\alpha\) = 10) models. Although the correlation coefficients and standard deviations of the predicted data of the DWT-MLP-III (S6) and VMD-MLP-III (\(K\) = 4 and \(\alpha\) = 10) models were less than observations, the node representing the VMD-MLP-III (\(K\) = 4 and \(\alpha\) = 10) model was closer to the observation node.
###### Abstract  
The magnitude of pollution in Lake Hawassa has been exacerbated by population growth and economic development in the city of Hawassa, which is hydrologically closed and retains pollutants entering it. This study was therefore aimed at examining seasonal and spatial variations in the water quality of Lake Hawassa Watershed (LHW) and identifying possible sources of pollution using multivariate statistical techniques. Water and effluent samples from LHW were collected monthly for analysis of 19 physicochemical parameters during dry and wet seasons at 19 monitoring stations. Multivariate statistical techniques (MVST) were used to investigate the influences of an anthropogenic intervention on the physicochemical characteristics of water quality at monitoring stations. Through cluster analysis (CA), all 19 monitoring stations were spatially grouped into two statistically significant clusters for the dry and wet seasons based on pollution index, which were designated as moderately polluted (MP) and highly polluted (HP). According to the study results, rivers and Lake Hawassa were moderately polluted (MP), while point sources (industry, hospitals and hotels) were found to be highly polluted (HP). Discriminant analysis (DA) was used to identify the most critical parameters to study the spatial variations, and seven significant parameters were extracted (electrical conductivity (EC), dissolved oxygen (DO), chemical oxygen demand (COD), total nitrogen (TN), total phosphorous (TP), sodium ion (Na\({}^{+}\)), and potassium ion (K\({}^{+}\)) with the spatial variance to distinguish the pollution condition of the groups obtained using CA. Principal component analysis (PCA) was used to qualitatively determine the potential sources contributing to LHW pollution. In addition, three factors determining pollution levels during the dry and wet season were identified to explain 70.5% and 72.5% of the total variance, respectively. Various sources of pollution are prevalent in the LHW, including urban runoff, industrial discharges, diffused sources from agricultural land use, and livestock. A correlation matrix with seasonal variations was prepared for both seasons using physicochemical parameters. In conclusion, effective management of point and non-point source pollution is imperative to improve domestic, industrial, livestock, and agricultural runoff to reduce pollutants entering the Lake. In this regard, proper municipal and industrial wastewater treatment should be complemented, especially, by stringent management that requires a comprehensive application of technologies such as fertilizer management, ecological ditches, constructed wetlands, and buffer strips. Furthermore, application of indigenous aeration practices such as the use of drop structures at critical locations would help improve water quality in the lake watershed.  
monitoring; mitigations; spatial and temporal variabilities; principal component analysis; cluster analysis; discriminant analysis; water quality; pollution; correlation