esa-sceva/satcom-chunk-collection huggingface dataset:
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 3 Mobility with UAV Technology
### Mobility in Three Dimension (3D)
#### 3.3.2 Speed Limitation in 3D  
Today, WiFi, WiMAX, and cellular technologies are now available networks. Smart devices rely on mobility-based network services, thereby increasing their demand. Con  
Figure 6: UAV–based station coverage according to altitude. Equations (1) and (2) describe how to produce the \(A\) and \(R\), as shown in the illustration.  
sumers expect an internet connection at all times and from any location. It is important to remember that when the UAVs are traveling faster than the UE, HO may frequently occur. HO depletes energy and causes connection delays. To solve this issue, the speed of the UAV must be limited by using the following equation:  
\[Speed\ limit(\alpha)=S\Big{(}\frac{w}{\delta}\Big{)} \tag{4}\]  
where \(w\) signifies the RSS value. As the distance between the BS and the terminal increases, the received signal intensity decreases. The scenario then moves to the execution phase when the RSS falls below the threshold [49; 51; 52].
###### Abstract  
Aim: The aim of this study was to evaluate the correlation between malocclusions and visual defects. This is a case-control study evaluating the prevalence of visual defects in patients with different types of malocclusions. Methods: One-hundred and sixty patients aged from 5 to 14 were evaluated using the ROMA index to detect malocclusion; the ones with the lowest scores were used as the control group. They were also submitted to visual-capacity inspection for motility and refractive disorders. Results: Our work showed an enhanced prevalence of refractive defects or fusionalvergence defects and alteration of eye movements (especially the saccades) in almost all dental malocclusions. Statistics: The Kappa test values for ROMA index were between 0.643 and 1.00 for the intraoperator agreement (\(0.00<p<0.002\)) and between 0.773 and 1.00 for the agreement between operators (\(p=0<0.001\)). The statistically significance level for the correlation malocclusion/visual defects was set at \(p<0.05\). Statistical analyses were performed with the STATA software (version 15.0, Stata Corp LP, College Station, TX, USA). Conclusion: Considering the high level of the statistical analysis and the accuracy of the methodology used, these data allows the establishment of a huge correlation between sagittal, transversal and vertical malocclusions with ocular disorders (myopia, hyperopia, astigmatism and ocular motility defects).  
Keywords: dental malocclusions; visual disturbances; pediatric patients; refractive defects; alteration eye movements; ROMA index +
Footnote †: journal: Applied Sciences
# Neural Network-Based Reference Block Quality Enhancement for Motion Compensation Prediction
## 4 Experimental Results  
### Experiment Settings  
The proposed method was implemented based on H.266/VVC reference software VTM-9.3. The proposed RBENN was used to improve the quality of all luma CUs above \(16\times 16\) before MC. The LDP configuration was tested in the experiment under the H.266/VVC common test conditions. We encoded class D video sequences during the test, where the first 32 frames of each video sequence were encoded and decoded. There was no overlap between the training video sequences and the tested video sequences. The QPs used in our experiments varied among 22, 27, 32, and 37. The BD rate, which represents the bit rate increment at the same reconstruction quality, was used to measure the rate-distortion (RD) performance. The negative BD rate (BD rate reduction) means there is a positive coding gain, while a positive BD rate means there is a negative gain. To evaluate the reconstruction quality, PSNR and SSIM were used. Therefore, there are two kinds of BD rates: a PSNR-based BD rate and an SSIM-based BD rate.
## 3 Testbed Description
### Experimental Settings and Parameters  
The parameters for the outdoor and indoor experiments, presented in Table 1, were both established and conducted on the territory of Kazakh National University (KazNU). Data were collected for three metrics: bandwidth, PDR, and jitter.  
The data were collected using Iperf, a network performance testing tool originally developed by NLANR/DAST. Iperf is designed to measure TCP and UDP bandwidth and allows users to adjust various parameters and UDP settings for performance evaluation. Our experiment utilized one iperf3 client and one iperf3 server.
# Design of Multi-Chain Traceability Model for Pepper Products Based on Traceability Code
## 4 Construction of Multi-Chain Traceability Model for Pepper Products
### Multi-Chain Model Performance Tests  
#### 4.5.1 Enquiry Efficiency Performance Test  
This experiment is a simulation test in a virtual machine with an environment based on Centos 7.9, Docker 18.09, and Fabric 1.4. The system runs on 4 GB of RAM, 20 GB of ROM, and 10 Mb/s of bandwidth. The Fabric federation network consists of 4 peer nodes and 3 orderer nodes.  
In order to compare the query efficiency of the multi-chain model with that of the single-chain model, a validation data query time-consuming experiment is designed. Sets consisting of 1000, 4000, 6000, 8000, 10,000 and 12,000 datapoints were prepared to test the time required to query 1, 50, 150 and 200 pieces of traceability data, respectively. The results of the experimental data comparison are shown in Figure 4.  
Figure 4: Query time comparison chart.  
As shown in the figure, when querying the traceability data, the single-chain query method shows a linear increase in query time as the total quantity of data increases. The query time of the multichain model follows the increase of the number of data entries of the query's traceability records being less affected by changes in total data volume. In the actual pepper industry chain application, the pepper product batch record is about 200, and the total quantity of data in the blockchain system increases with the increase of nodes and time, so the multi-chain model is more suitable for the actual application scenario.
## 4 Results and Discussion
### Burr formation  
Figure 9 details the top burr height results formed by both cutting fluid supply systems. Top burr height generated by the Cut-list was substantially smaller than that produced by the conventional cooling system, by up to 31.70%. As one of the main reasons for burr formation is tool wear, the reduction of burr size is not surprising due to the reduction of tool wear discussed in the previous section. This can be also attributed to the ability of the new system to direct the high momentum impinging jet into the tool-workpiece interface that helped the jet to hit the insert edge effectively and led to maintaining the edge of the cutting tool in a sharper condition with less of a metal tearing effect. In addition, the results showed a similar trend in decreasing burr height with increased feed rate regardless of the cooling system used.
# Using Graph Partitioning for Scalable Distributed Quantum Molecular Dynamics
## 3 Algorithms for Graph Partitioning Considered in Our Study
### Edge Cut Graph Partitioning
#### 3.1.3 Hypergraph Partitioning  
A hypergraph formulation is an alternative approach to the classical interpretation of the density matrix as an adjacency matrix (where each nonzero interaction in the density matrix is an undirected and unweighted edge).  
The set of all neighbors of a vertex form a single hyperedge in the hypergraph formulation. The main advantage of using hyperedges consists of the fact that minimizing the edge cut with respect to hyperedges results in either all or zero vertices being included in a block: if a hyperedge is not a cut edge, then this automatically implies that all the neighbors of that core vertex (i.e., its halo) are included in the same block.  
We use _hMETIS_ of [19] to compute hypergraph partitionings. _hMETIS_ is the hypergraph analog of _METIS_.
# Research on Performance Prediction Model of Impeller-Type Breather
## 2 Numerical Method
### Two-Phase Flow Calculation Method
#### 2.1.1 Governing Equations for Gas Phase  
The gas phase governing equation was established based on the two-way coupling method, with consideration of the effect of droplet motion on airflow. The governing equation of gas phase per unit volume is as follows:  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot(\rho\mathbf{u})=S_{m}, \tag{1}\]  
where \(\rho\) is the density of the gas; \(\mathbf{u}\) is the velocity of the gas; \(S_{m}\) is the source term. In the oil and gas two-phase flow in the breather, the evaporation of oil droplets will cause the source term to change if operating in a high heat environment.  
\[\frac{\partial(\rho\mathbf{u})}{\partial t}+\
abla\cdot(\rho\mathbf{u}\mathbf{u})=-\
abla p +\
abla\cdot\mathbf{\tau}+S_{u}, \tag{2}\]where \(p\) is static pressure;\(\tau\) is stress tensor; \(S_{u}\) is the source term. The influence of oil droplets on airflow momentum includes the momentum carried by oil droplet evaporation and the momentum exchange caused by oil droplet movement.
# Effect of Seawater Irrigation on the Sugars, Organic Acids, and Volatiles in 'Reliance' Grape
## 5 Conclusions  
Sewater irrigation significantly increased the sugar content but decreased the organic acid level in grape berries compared to the controls. S1 treatment significantly increased the concentrations of C6 esters and the terpene levels (citronellol, \(\beta\)-myrcene, \(\alpha\)-terpineol, and trans-rose oxide) in berries. Berries irrigated with 10% seawater had better fruit quality during the pea-size stage (S1) than S2, S3, and the control groups. RT-qPCR analysis revealed that _VvLOXA_ is the key gene involved in volatile biosynthesis when grapes are treated with 10% seawater. Combined with the 4-year 10% seawater irrigation experiment results obtained by our team, irrigation with 10% seawater since the pea-size period is a feasible measure in viticulture without causing soil salinization. Further, it is worthwhile to explore the effects of seawater irrigation on volatiles, phenolic acids, and anthocyanin compounds in areas with annual rainfall greater than 638 mm.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/horticulturalae8030248/s1](https://www.mdpi.com/article/10.3390/horticulturalae8030248/s1)]([https://www.mdpi.com/article/10.3390/horticulturalae8030248/s1](https://www.mdpi.com/article/10.3390/horticulturalae8030248/s1)), Table S1: Meteorological data for the vineyard in 2018; Table S2: Calibration curves for the quantification of volatile compounds; Table S3: Primer sequence for RT-qPCR; Table S4: Effect of seawater treatment on the concentrations and varieties of volatiles in 'Reliance' grape at EL 31; Table S5: Effect of seawater treatment on the concentrations and varieties of volatiles in 'Reliance' grape at EL 33; Table S6: Effect of seawater treatment on the concentrations and varieties of volatiles in 'Reliance' grape at EL 35; Table S7: Effect of seawater treatment on the concentrations and varieties of volatiles in 'Reliance' grape at EL 36; Table S8: Effect of seawater treatment on the concentrations and varieties of volatiles in 'Reliance' grape at EL 37.  
Conceptualization, H.Z.; Data curation, M.L.; Formal analysis, M.Y.; Funding acquisition, Y.D.; Project administration, M.L. and H.Z.; Resources, M.T.; Software, M.Y.; Visualization, Y.Y. and Y.D.; Writing--original draft, M.L.; Writing--review and editing, Z.G. All authors have read and agreed to the published version of the manuscript.  
This research was supported by China Agriculture Research System of Ministry of Finance (MOF) and Ministry of Agriculture and Rural Affairs (MARA) of the People's Republic of China (Funding Number: CARS-29).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Experiments, Results and Discussion
### Summary of Results  
A wide range of experiments under different conditions were carried out. Several solutions were obtained for each test survey, although only some of the solutions for each case have been presented. Other solutions and cases not presented here demonstrated the same tendencies.  
In general, basic positioning using code pseudoranges only tend to cause major position errors beyond 100 m under adverse conditions. The application of Doppler observations in addition to code-based observations and proper processing procedure can drastically improve the accuracy of the results achieved. The Doppler-only solution is clearly deficient, as can be seen in Figure 8. The smoothing of code-based observations with Doppler observations using the Hatch filter does not perform effectively under such conditions either, as Figure 8 illustrates. Although the use of the elevation angle weighting and the SNR weighting has the potential to improve the quality of the results (see Figure 8), the use of additional weighting proved risky, since it can also produce poor results.  
The best overall solution and also the sturdiest positioning technique is the custom procedure using both types of observables, code and Doppler. As can be seen from Figures 7 to 11, the quality of the calculated positions is considerably higher in comparison to the basic positioning. The accuracy of the positions falls within a reasonable value range, _i.e._, 10-20 metres, even under particularly adverse conditions. It should be noted that the positioning mode is still autonomous. The results achieved proved to be better than the results of the internally derived positions from the receiver (see Figures 6, 8 and 10).  
The ionosphere and the troposphere both affect the propagation of all signals, irrespective of the conditions experienced by the receiver. Therefore, the ionosphere and the troposphere models should be used in any case to enhance the accuracy of the results. Proof of this is indicated by the case studies presented.  
The conditional DGPS is applicable in a mixed-condition environment of a kinematic survey. If the conditions for the reception of the signals are particularly bad, then the cDGPS cannot be used. Conditional DGPS can be used however under conditions which experience periods of minor attenuation and multipath level. Results of the cDGPS in two cases can be seen in Figure7 and Figure 9.The results are noticeably improved following recalculation of the positions.
# Weed Detection and Classification with Computer Vision Using a Limited Image Dataset
## 3 Results
### Comparison of the Six Classifiers  
A comparison was performed using all features except HOG because this feature set yielded the best results. The Table 23 illustrates that both KNN and ANN yielded the poorest results, failing to achieve even a 60% classification accuracy despite the availability of 160 images per weed. SVM and NB performed slightly better, with SVM outperforming NB, yet still falling short of practical usability, with SVM achieving only 78.75% accuracy. In contrast, RF and GBM demonstrated promising results in our study. RF yielded a 75% accuracy even with just 5 images while achieving a remarkable 94% accuracy with 160 images per weed. Two algorithms (RF and GBM) proved to be effective for weed detection and classification purposes.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Features** & **Classifier** & **5** & **10** & **20** & **40** & **80** & **160** \\ \hline All exp. Hu, HOG & \multicolumn{1}{c}{RF} & 76.86 & 84.33 & 86.53 & 90.45 & 92.72 & **93.77** \\ All exp. Hu, HOG & \multicolumn{1}{c}{GBM} & 66.57 & 74.58 & 81.99 & 90.30 & 92.75 & **94.35** \\ All exp. HOG & \multicolumn{1}{c}{GBM} & 66.22 & 74.03 & 83.00 & 90.46 & 92.81 & **94.56** \\ \hline \hline \end{tabular} The bold column is the best precision values.  
\end{table}
Table 22: The 3 best results with 160 training images.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Features** & **Classifier** & **5** & **10** & **20** & **40** & **80** & **160** \\ \hline All exp. Hu, HOG & \multicolumn{1}{c}{GBM} & 66.57 & 74.58 & 81.99 & 90.30 & **92.75** & 94.35 \\ All exp. HOG & \multicolumn{1}{c}{RF} & 75.43 & 83.04 & 86.16 & 91.20 & **92.85** & 93.71 \\ All exp. HOG & \multicolumn{1}{c}{RF} & 66.22 & 74.03 & 83.00 & 90.46 & **92.81** & 94.56 \\ \hline \hline \end{tabular} The bold column is the best precision values.  
\end{table}
Table 21: The 3 best results with 80 training images.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Features** & **Classifier** & **5** & **10** & **20** & **40** & **80** & **160** \\ \hline All exp. HUG & \multicolumn{1}{c}{GBM} & 66.57 & 74.58 & 81.99 & 90.30 & **92.75** & 94.35 \\ All exp. HOG & \multicolumn{1}{c}{RF} & 75.43 & 83.04 & 86.16 & 91.20 & **92.85** & 93.71 \\ All exp. HOG & \multicolumn{1}{c}{RF} & 66.22 & 74.03 & 83.00 & 90.46 & **92.81** & 94.56 \\ \hline \hline \end{tabular} The bold column is the best precision values.  
\end{table}
Table 23: The comparison of the 6 classifiers’ results.  
Figure 11 illustrates that the RF classifier performed best with a small number of images, while the GBM slightly outperformed it with a larger dataset. Conversely, the ANN model struggled to learn the data structure even with 160 images, and with fewer than 40 images, it appeared to make random guesses.
## 2 Materials and Methods
### Data  
#### 2.2.1 Land Surface Data  
The hydrologic model, which is necessary for flood simulations, is typically constructed based on land surface data (geo-spatial information) such as a Digital Elevation Model (DEM), land cover, and soil data. In this study, a 1-arcsecond DEM with a resolution of approximately 30 m was obtained from the National Geographic Information Institute's Land Information Platform [43]. For land cover and soil data, the 1:25,000 scale subdivision land cover map and the detailed soil map provided by the Ministry of Environment's Environmental Geographic Information Service [44] and the National Institute of Agricultural Sciences' Korean Soil Information System [45] were utilized, respectively. Figure 2 illustrates the secondary datasets of land cover classification, Hydrologic Soil Groups (HSGs), and Curve Numbers processed from the original land surface data for the hydrologic model development. It can be observed that the Yongdam Dam watershed is predominantly composed of mountainous areas and agricultural areas, accounting for 70.6% and 25.9%, respectively.  
Figure 1: Location of the study area (Yongdam Dam watershed).  
Figure 2: Land use, hydrological soil groups, and Curve Number of the study area.
###### Abstract  
The increasing environmental challenges posed by the widespread use of fossil fuels and the fluctuating nature of renewable energy have driven the need for more efficient and sustainable energy solutions. Current research is actively exploring hybrid energy systems as a means to address these issues. One such area of focus is the integration of Organic Rankine Cycles (ORCs) with gas and steam turbines, utilizing both natural gas (NG) and solar parabolic trough collectors (PTCs) as energy sources. This study examines the performance of a hybrid system implemented in Kirkuk, Iraq, a region known for its substantial solar radiation. Previous research has shown that hybrid systems can effectively enhance energy conversion efficiency and reduce environmental impacts, but there is still a need to assess the specific benefits of such systems in different geographical and operational contexts. The analysis reveals a thermal efficiency of 59.32% and an exergy efficiency of 57.28%. The exergoeconomic analysis highlights the optimal energy cost at USD 71.93/MWh when the compressor pressure ratio is set to 8 bar. The environmental assessment demonstrates a significant reduction in CO\({}_{2}\)/emissions, with a carbon footprint of 316.3 kg CO\({}_{2}\)/MWh at higher compressor pressure ratios. These results suggest that integrating solar energy with natural gas can substantially improve electricity generation while being both cost-effective and environmentally sustainable.  
renewable energy integration; Kirkuk power plant; exergoeconomic analysis; carbon dioxide emissions; hybrid power system +
Footnote †: journal: applied sciences
# Pulmonary Fissure Detection in 3D CT Images Using a Multiple Section Model
## 3 Pulmonary Fissure Detection  
### Improved Orientation Partition Scheme  
[PERSON] et al. (2014) presented an orientation partition scheme for pulmonary fissure segmentation, but the approach may cause part of the fissures to remain undetected. To solve the problem and suppress clutters, we first used a threshold \(T\) to modulate the vector field:  
\[\overrightarrow{V_{2}}=\left\{\begin{array}{cc}\overrightarrow{V_{1}},&if \ F^{3D}>T\\ 0,&else\end{array}\right. \tag{10}\]  
Then, we divided the stick kernel orientation \(\theta\) range from 0 to \(\pi\) into \(n\) non-overlapped partition. The \(n\) sub-regions were marked with \(R_{1}\), \(R_{2}\),..., \(R_{\text{n}}\) and are shown in Figure 3. The mathematical expression is as follows:  
\[F_{i}=\left\{\begin{array}{cc}1,&if\quad\theta\in R_{i}\ and\ \overrightarrow{V_{2}}\
eq 0 \qquad 1\leq i\leq n\\ 0,&else\end{array}\right. \tag{11}\]  
In each partition \(F_{i}\), we considered objects with a volume smaller than a threshold \(V\)th to be clutters and marked them with \(N_{\text{i}}\). The reason is that clutters appear as smaller objects in the partition \(F_{i}\) and usually have a lower volume. The purified results can then be represented as  
\[F=\sum_{i=1}^{n}F_{i}-\sum_{i=1}^{n}N_{i} \tag{12}\]  
The purified results in the axial plane, sagittal plane and coronal plane are described with \(F^{A}\), \(F^{S}\) and \(F^{C}\).  
Figure 3: Improved orientation partition scheme.  
Figure 2: The orientation response of an oriented derivative of stick (ODoS) filter: (**a**) original image, (**b**) vector filed, (**c**) zoomed image of the red rectangle region in (**b**).  
To illustrate the effect of the proposed method, we marked the clutters \(N_{\mathrm{i}}\) with different colors, as shown in Figure 4a. The purified results \(F^{\mathrm{A}}\), \(F^{\mathrm{S}}\) and \(F^{\mathrm{C}}\) are shown in Figure 4b-d, respectively. It can be seen that a number of clutters have been largely removed.