esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Lightweight Model of Maize Seeding Object Detection Based on YOLOv7
## 3 Results and Analysis
### Comparative of Different Object Detection Models  
To verify the effectiveness of the LW-YOLOv7 model proposed in this paper, we trained and tested it on our dataset and compared it to other popular object-detection models. These models include Faster-RCNN, YOLOv3, YOLOv4, YOLOv5, and YOLOv8. The comparison results are shown in Table 5. Compared to LW-YOLOv7, the precision of the Faster-RCNN model decreased by 26.7%, the recall decreased by 27.4%, the mAP decreased by 37.6%, and the number of parameters increased by 45%, while the FPS is 14.3 f/s and decreased by 529.37%. Therefore, the detection accuracy of Faster-RCNN was significantly lower than YOLOv7 and did not meet the requirements of real-time detection.  
The precision of the YOLOv3 model decreased by 0.9%, the recall decreased by 3.3%, the mAP decreased by 1.9%, the number of parameters increased by 51.86%, and the value of FPS is 82 f/s. YOLOv3 can meet the requirement of real-time detection, but the accuracy is lower than LW-YOLOv7. Additionally, it also takes up more memory and disk space on mobile devices.  
The precision of the YOLOv4 model increased by 0.6%, the recall decreased by 2.5%, the mAP decreased by 1%, the number of parameters increased by 43.64%, and the value of FPS is 107 f/s. Therefore, YOLOv4 also requires more memory and disk space.  
The precision of the YOLOv5 model decreased by 0.1%, the recall decreased by 5.7%, the mAP decreased by 3.9%, the number of parameters increased by 35.01%, and the value of FPS is 114 f/s. Therefore, YOLOv5 not only has lower detection accuracy than LW-YOLOv7, but it also has a significant computation load.  
The precision of the YOLOv8 model increased by 1.9%, the recall decreased by 2.2%, the mAP has not changed, the number of parameters increased by 32.19%, and the value of FPS is 125 f/s. Compared with other popular algorithms, the comparison results show that the LW-YOLOv7 proposed in this paper has the highest mAP value, the model size is smaller than other popular object-detection algorithms, and the detection speed meets the requirements of real-time detection.
## 2 Two-Layer Optimization Framework for VPP
### Comparison of the Framework  
The comparison of the conventional framework and the two-layer framework proposed in this paper is shown in Figure 2.  
The two-layer framework proposed in this paper expands upon the conventional game-theoretic framework by incorporating household load management on the user side. The conventional game-theoretic framework computes unified scheduling for user collections, yielding results that do not offer practical guidance for individual VPP users. In contrast, the two-layer framework introduced in this paper considers variations in user electricity consumption scale and time-of-use preferences, establishing precise user mathematical models that interact with other VPP stakeholders. This framework formulates scheduling processes that better align with the needs of each stakeholder in the VPP while also devising appliance-level schemes on the user side to unlock their dispatch potential.
###### Abstract  
This paper presents an algorithm considering both power control and power management for a full direct current (DC) microgrid, which combines grid-connected and islanded operational modes, with real-time demand-side management optimization. The full microgrid is a hybrid dynamic system model consisting of two interacting parts: continuous-time dynamics and discrete-event dynamics. Such a full microgrid consists of photovoltaic sources, a DC load, battery storage systems, supercapacitor storage, a diesel generator, and a public grid connection, all connected on a DC common bus. This full microgrid is more reliable than a microgrid with only renewable sources or with only traditional energy sources, considering the power constraints imposed by the public grid as well as the sluggish dynamic of the diesel generator, self-discharging characteristic of the supercapacitor, and load shedding optimization. Meanwhile, this algorithm can automatically switch between grid-connected and islanded operational modes to optimize the power of the load shedding, take advantage of renewable energy, and keep the power balance in the full DC microgrid. The results under MATLAB/Simulink verify that the real-time control algorithm can maintain the power balance in real-time for the whole day and satisfy the power management strategy.  
microgrid; modeling; power control; power management; optimization; simulation +
Footnote †: journal: Applied Sciences
# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 2 Methods
### Feature Extraction from Quad-Polarimetric SAR Data
#### 2.2.5 Pedestal Height  
Normalized radar cross-section (NRCS) \(\sigma^{0}\) measures how detectable an object is per unit area on the ground. In the co-polarized signature of the scene, the \(\sigma^{0}\) is a function of both the tilting angle \(\Phi\) and the ellipticity \(\chi\) of the polarization ellipse. The pedestal height (PH) is defined as the lowest value of all the \(\sigma^{0}\), plotted in the co-polarized signature. The PH describes the unpolarized energy of the total scattering power and behaves as a pedestal on which the co-polarized signature is set [14; 33]. The normalized pedestal height (_NPH_) can be approximately calculated as the minimum eigenvalue divided by the maximum one:  
\[NPH=\frac{\min(\lambda_{1},\lambda_{2},\lambda_{3})}{\max(\lambda_{1},\lambda _{2},\lambda_{3})} \tag{12}\]  
For clean sea surfaces, the scattering mechanism is pure Bragg, so an _NPH_ value close to 0 is expected. For an oil-covered area, however, much higher _NPH_ can be expected due to the non-Bragg scattering that reflects a more diverted scattering mechanism.
## 5 Cost Analysis
### Cost Status of Oil Shale  
In 2020, the estimated value of the oil shale market was USD 2.8 billion, and it is projected to approximately double by 2030. However, it is important to acknowledge that oil shale is a low-grade energy commodity that becomes profitable only after mining and retorting processes. For instance, in the Green River Formation in Colorado, it takes about one ton of oil shale to produce 0.83 US barrels of oil. Only approximately one-fifth of the weight contains organic matter that can be utilized, while the remaining four-fifths must be mined and dealt with, posing environmental challenges. Addressing these environmental concerns is a significant aspect to consider (source: [[https://www.alliedmarketresearch.com/oil-](https://www.alliedmarketresearch.com/oil-)]([https://www.alliedmarketresearch.com/oil-](https://www.alliedmarketresearch.com/oil-)) shale-market accessed on 26 June 2023). Current technologies can support the development of mining and surface mining, but large-scale expansion still faces challenges. The cost in 2010 ranged from USD 70 to USD 95 per barrel. In situ methods have shown promise through small-scale tests in some areas, but environmental concerns may also be a cause for concern. Although the costs are expected to be lower than mining and surface mining, it is challenging to find relevant data in the public domain (source: [[https://scholar.law.colorado.edu/cgi/viewcontent.cgi?article=10](https://scholar.law.colorado.edu/cgi/viewcontent.cgi?article=10)]([https://scholar.law.colorado.edu/cgi/viewcontent.cgi?article=10](https://scholar.law.colorado.edu/cgi/viewcontent.cgi?article=10)) 03&context=promise-and-peril-of-oil- shale-development accessed on 26 June 2023).
## 3 Results and Discussion
### Feature Variable Screening  
The citrus leaves spectral data of Vis and NIR contains chemical composition and some information that is not relevant to this study of those leaves. In order to reduce the number of input variables and simplify the complexity of the model, the spectral feature variables were extracted, and then data fusion modeling, which is qualitative analysis based on spectral data feature level fusion. In the experiment, spectral feature variables were extracted through PCA and SPA. Firstly, the derivative spectra were compressed into a number of principal components between the two bands of 500-1000 and 1000-2500 nm before and after normalization. Secondly, the feature variables were fused under the premise of not losing the main spectral information, PCA compressed data to obtain new spectral variables that can replace lengthy spectral variables. The visible data were compressed into three principal component factors that contribute more than 99% in the range of 500-1000 nm, which represents the main information of spectra. The NIR data was compressed into eight principal component factors with a contribution rate over 92% in the range of 1000-2500 nm. Therefore, combining with the above, 11 principal component factors for feature level fusion model were created.  
According to the above data, the spectral variables were extracted by SPA, and then data feature level fusion was carried out. The feature variables were selected from 2220 variables before and after normalization. The minimum variables before running SPA were set to 10, while the maximum variables were set to 30. According to the number of information variables based on spectral matrix projection before and after normalization, spectral feature variables were selected. In both cases, a total of 55 and 60 feature variables were selected for feature level fusion model.
# Ct Value from RT-qPCR Can Predict SARS-CoV-2 Virus Assembly and Lineage Assignment Success
## 3 Results
### RT-qPCR and Assembly  
We examined the Ct values from RT-qPCR and whether these values could predict assembly success (statistics of Ct values according to COVID-19 severity are shown in Table 1). Samples with lower Ct values (E gene) were mostly assembled. The median Ct value for samples with a completely assembled genome was 24.24, while in the case of fragmented assembly, it was 28.76. The median of samples with failed genome assembly was 32.95. The correlation coefficient between Ct value and NG50 value of assembly was \(-\)0.81. In total, eight samples had undetermined Ct values and were left out from correlation statistics (seven with failed assembly and one with fragmented assembly). The relationship between RT-qPCR Ct value and NG50 assembly quality metrics is shown in Figure 1, where three Ct value zones of successful, unsuccessful, and unsure assembly can be seen. The grey zone of unsure assembly is between Ct values of 25 and 30, while everything that is lower was always successfully assembled and everything that is higher always failed. This visualization shows how the quality of metrics changes in relation to RT-qPCR Ct value. None of the negative samples were assembled into a complete genome, nor into the genome fragmented into more scaffolds.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Symptoms**} & **Min Ct** & **Max Ct** & **Mean** & **St.** & **Interquartile Range** \\  & **Value** & **Value** & **Mean** & **Deviation** & **Range** \\ \hline
**Severe** & 14 & 37.3 & 27.9 & 6.7 & \\
**Mild** & 15.4 & 33.6 & 24.8 & 5.3 & \\
**Asymptomatic** & 19.2 & 38.8 & 29 & 4.7 & \\
**All** & 14 & 38.8 & 28.42 & 7 & 10.93 \\ \hline \hline \end{tabular}
\end{table}
Table 1: RT-qPCR Ct value statistics for symptoms-derived group of patients and all patients.
## 3 Test Results and Analysis
### Flexural Toughness Analysis  
In this paper, ASTM-1609 [45] is used to evaluate the flexural toughness of high-performance fiber shotcrete. The standard takes the energy absorption value of the specimen to reach the specified deflection and the corresponding equivalent flexural strength ratio as the toughness index of the evaluated specimen. The energy absorption value of the specimen is characterized by the area of the load-deflection curve in the corresponding deflection range. The calculation of each evaluation index is shown in Equations (3) and (4).  
\[f_{600}^{\mathrm{D}}=\frac{p_{600}^{\mathrm{D}}l}{bd^{2}},\;f_{150}^{\mathrm{ D}}=\frac{p_{150}^{\mathrm{D}}l}{bd^{2}} \tag{3}\]  
In the formula, \(f_{600}^{\mathrm{D}}\) and \(f_{150}^{\mathrm{D}}\) represent the residual strength of the specimen when the mid-span deflection reaches \(l/600\) and \(l/150\), respectively.  
\[T_{600}^{\mathrm{D}}=\int_{0}^{l/600}p(\delta)d\delta,\;T_{150}^{\mathrm{D}}= \int_{0}^{l/150}p(\delta)d\delta \tag{4}\]  
In the formula, \(T_{600}^{\mathrm{D}}\) and \(T_{150}^{\mathrm{D}}\) represent the energy absorbed by the specimen when the mid-span deflection reaches \(l/600\) and \(l/150\), respectively.  
Since, in this test, the specimen has been damaged before the mid-span deflection reaches \(l/150\), which belongs to small bending damage, only the residual strength of the specimen at \(l/600\) deflection is calculated, and the energy absorption value is used to measure the ductility of the concrete specimen and the energy absorption capacity. The calculation results are shown in Figure 7.  
Figure 7: Flexural toughness.  
As can be seen in Figure 7, the \(f_{600}^{\rm D}\) and \(T_{600}^{\rm D}\) of the specimens with different thickness ratios show an increasing and then decreasing trend, reaching maximum values of 13.5 MPa and 19.9 N\(\cdot\)M at a thickness ratio of \(\lambda_{h}=2\). The \(f_{600}^{\rm D}\) of the specimens with different thickness ratios are 10.8 MPa, 11.7 MPa, 13.5 MPa, 12.4 MPa, 11.2 MPa, and 10.9 MPa, respectively, and the \(f_{600}^{\rm D}\) of the specimens with different thickness ratios are 8.3%, 25.0%, 14.8%, 3.7%, and 0.9%, respectively, as compared to the thickness ratio of zero. The \(T_{600}^{\rm D}\) of the specimens with different thickness ratios were 16.1N\(\cdot\)m, 17.7N\(\cdot\)m, 19.9N\(\cdot\)m, 18.5N\(\cdot\)m, 17.5N\(\cdot\)m, and 16.5N\(\cdot\)m, respectively, which were increased by 9.9%, 23.6%, 14.9%, 8.7%, and 2.5% compared to the thickness ratios of 0. The main reason for this is that the bond between the layers contributes to the toughness of the specimens under the layered concrete structure. The crack development passes through the layered position of the specimen, and the interlayer bond prevents crack development, which in turn improves the flexural toughness of the specimen. When the thickness ratio is too large, the stress at the delamination interface is higher under the same load, and the resulting stress concentration leads to more rapid interlayer damage, which in turn leads to a reduction in the flexural toughness of the specimen.
## 3 Application to Virtual Fit Analysis Method to Pilot Oxygen Mask Design
### Acquisition of Information for Virtual Fit Analysis  
#### 3.1.1 KAF Pilots' Facial Scan Images  
Three-dimensional (3D) scan images of 336 KAF pilots and cadets (278 males and 58 females) obtained in our previous study [1] were used to improve the existing oxygen mask design in the present study. The 3D scan images of pilots were collected using the 3D scanner Recxan 560 (Solutionix Co., Seoul, South Korea) and then eighteen facial dimensions were measured. Four mask size groups and pilots belonging to each size group (small narrow: 34, medium narrow: 121, medium wide: 76, and large wide: 105) were identified by applying a sizing system generation method [33; 34; 35] based on sellion-to-supramentale length and lip width (key facial dimensions of MBU-20/P) as shown in Figure 5a and four representative face models (RFMs) were found for each size group as shown in Figure 5b [35]. Of the KAF pilots and cadets, the profiles of 85 male pilots (small narrow: 21, medium narrow: 23, medium wide: 19, and large wide: 22) were photographed while wearing their current oxygen masks to examine the mask-wearing positions.
# AC Fault Ride-Through Strategy of a Transformerless Soft Normally Open Point
## 2 System Connection Form and Mathematical Model  
SNOP can be installed in several key locations of the distribution network. Figure 1 shows three typical ways [17]: (1) SNOP1: Connect two feeder lines in the same supply area; (2) SNOP2: Connect two feeder lines in different supply areas; (3) SNOP3: Connect multiple feed lines through multiple converters. This paper takes SNOP2 as an example.  
Figure 1: System connection form of SNOP.  
Figure 2 shows the equivalent circuit of MMC. \(2L\) is the arm inductance, and \(2R\) is the equivalent arm resistance. \(u_{a}\), \(u_{b}\) and \(u_{c}\) are the three-phase voltage. \(i_{a}\), \(i_{b}\) and \(i_{c}\) are the three-phase current. \(U_{d}\) and \(I_{d}\) are the pole-to-pole DC voltage and DC current, respectively. \(U_{\text{dp}}\) and \(U_{\text{dn}}\) are the positive and negative DC voltages. The point \"o\" is a virtual neutral point at the DC side. \(i_{jp}\) and \(i_{jn}\) are the upper and lower arm currents of phase j (j = a, b, c). \(u_{jp}\) and \(u_{jn}\) are the output voltage of the upper and lower arm in phase j. \(u_{o}\) is the voltage of point \"o\". Figure 2c shows the structure of the half-bridge SM (HBSM).  
According to [PERSON]'s voltage law, we can obtain  
\[u_{j}-2L\frac{di_{jp}}{dt}-2 Ri_{jp}+u_{jp}=\frac{U_{\text{d}}}{2}+u_{\text{o}} =U_{\text{dp}} \tag{1}\]  
\[u_{j}-2L\frac{di_{jn}}{dt}-2 Ri_{jn}-u_{jn}=-\frac{U_{\text{d}}}{2}+u_{\text{o} }=U_{\text{dn}} \tag{2}\]  
Plus Equations (1) and (2), a new equation is as follows  
\[u_{j}-\left(e_{j}+u_{\text{o}}\right)=Ri_{j}+L\frac{di_{j}}{dt} \tag{3}\]  
where  
\[e_{j}=\frac{u_{jn}-u_{jp}}{2} \tag{4}\]  
According to the symmetrical component method, the time-varying system can be decomposed into three positive-sequence, negative-sequence, and zero-sequence independent subsystems.  
\[R\text{i}_{abc}^{+}+L\frac{di_{abc}^{+}}{dt}=\textbf{u}_{abc}^{+}-\textbf{e}_ {abc}^{+} \tag{5}\]  
\[R\text{i}_{abc}^{-}+L\frac{di_{abc}^{-}}{dt}=\textbf{u}_{abc}^{-}-\textbf{e}_ {abc}^{-} \tag{6}\]  
\[R\text{i}^{0}+L\frac{d\text{i}^{0}}{dt}=u^{0}-e^{0}-u_{o} \tag{7}\]  
According to the Equations (5)-(7), there is no \(u_{o}\) in the positive and negative-sequence subsystems, and \(u_{o}\) is only determined by the zero-sequence subsystem. The positive and negative-sequence mathematical models obtained through the Park transform and the Laplace transform have been mature [11], so they will not be reiterated here.  
According to Equation (7), Figure 3 shows the zero-sequence model of MMC.  
Figure 2: Schematic diagram of MMC. (**a**) Equivalent circuit of MMC. (**b**) Diagram of bridge arm. (**c**) Diagram of sub-module.
# Influence of Fractal Disc Filter Flow Channel Parameters on Filtration Performance
## 2 Materials and Methods  
The design of fractal disc filter mainly consists of two parts [23; 24]: (1) With the analysis of flow channel characteristics and operating principles in the disc filter, the Minkowski curve was introduced. The fractal curve was applied along the central axis of the internal flow channels of the discs, with the fractal height being 0.30 mm. This design is shown in Figure 1a. (2) The buffer slot was designed for the middle area of the internal flow channels of the discs. The depth of the buffer slot and the flow channel in the entrance direction of the discs should be consistent, and the bottom edge should be straight. The width of the buffer slot is 0.40 mm. This design is shown in Figure 1b. The main difference between traditional disc filter and fractal disc filter is embodied in the fractal flow channel and the design of the buffer slot. This comparison is shown in Figure 1c.  
The filtration accuracy of a disc filter is controlled by the diameter of the flow passage internal circle, so the premise of flow passage optimization is that the internal circle diameter of the flow passage internal section (filtration accuracy) is unchanged. The optimization of the laminar flow passage structure is affected by many parameters, but these parameters are not completely independent of each other. Therefore, this paper intends to select parameters such as the tilt angle of the flow passage, the length and height of the bottom of the internal section triangle, the taper, the position, and the number of buffer slots for optimization.
## 4 Simulation Results
### Control Not Considering Fuel Consumption (Noiseless Environments)  
We present the MATLAB simulation results for our depth controllers based on Equations (5)-(7). This subsection considers noiseless environments. In other words, we do not add noise to control inputs in this subsection. The initial depth of the vehicle is 0.5 m. The target depth is 0.3 m. The depth control laws presented in this subsection do not consider fuel consumption. Hence, we do not update \(u_{0}\) as time goes on. We use \(u_{0}\) at time step 0 as our constant trim input and use it to derive (5)-(7).  
We planned to run the simulation for 50 s. However, the system diverges due to large planing force after the vehicle runs for 13.7 s.  
Figure 6 shows the depth of the vehicle with respect to time. Recall that the CG exists 2 m from the nose of the vehicle after the vehicle runs for 50 s. Since the CG moves, trim input must be adjusted according to the varying CG. However, we used \(u_{0}\) at time step 0 as our constant trim input and used it to derive (5)-(7). Initially, the depth of the vehicle reaches the target depth (0.3 m) after the vehicle runs for almost 3 s. However, the vehicle gradually leaves the target depth as time goes on. This occurs due to the fact that in the control laws presented in this subsection, we do not adjust the trim input considering the fuel consumption. The planing force appears from 13.3 s to 13.76 s. Initially, the planing force is not large. However, as time goes on, the planing force increases. We conjecture that the system diverges due to the large planing force.  
Figure 5: The planing force with respect to time (Proportional-Derivative (PD) depth controller).