esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Optimal Control Strategy of Four Drive Motors for an Electric Vehicle
## 2 Simulation Platform and Optimal Energy Management
### Electric Vehicle Simulation Platform
#### 2.1.5 Vehicle Dynamics Model  
The vehicle dynamics model needs to consider the impedance of various conditions while driving. The EV's propulsion is equal to the sum of its drive force and the reverse force in the direction of travel. The main loads during bus operation include air resistance \(R_{a}\), gradient resistance \(R_{g}\), and rolling resistance \(R_{r}\). Whenever a vehicle moves, it encounters resistance from the air, known as air resistance. This resistance increases proportionally to the square of the vehicle's velocity, shown in Formula (8):  
\[R_{a}=\frac{1}{2}\rho_{a}C_{d}A_{f}V_{vch}{}^{2} \tag{8}\]  
where \(R_{a}\) is the air resistance; \(C_{d}\) is the drag coefficient; \(A_{f}\) is the frontal area of the EV; \(\rho_{a}\) is the air density; \(V_{vch}\) is the vehicle velocity.  
Rolling resistance refers to the energy lost when a tire rolls. This energy loss is primarily due to the continuous deformation of the tire. Additionally, due to road surface irregularities, the tire experiences longitudinal forces that contribute to rolling resistance, as shown in Formula (9):  
\[R_{r}=\mu m_{vch}g\text{cos}(\theta) \tag{9}\]where \(R_{r}\) is the rolling resistance; \(m_{vph}\) is the total vehicle mass; \(\mu\) is the rolling resistance coefficient; \(g\) is the gravitational acceleration.  
The resistance a vehicle encounters when ascending a slope is referred to as gradient resistance, as indicated by Formula (10):  
\[R_{g}=m_{vph}g\sin(\theta) \tag{10}\]  
where \(R_{g}\) is the gradient resistance.  
Which has a significant impact while driving. Ultimately, the drive wheel speed and actual vehicle velocity will be calculated, and the results will be fed back to the driver model and used for overall vehicle torque control. The vehicle's acceleration force is equal to the drive force minus \(R_{a}\), \(R_{r}\), \(R_{g}\), and the braking force, as shown in Formula (11):  
\[m_{vch}\frac{dV_{vch}}{dt}=\frac{T_{f}\eta_{f}}{r_{w}}-R_{a}-R_{r}-R_{g}-F_{brk} \tag{11}\]  
where \(r_{w}\) is the tire radius; \(T_{f}\) is the output torque of the final drive; \(\eta_{f}\) is the overall efficiency of the final drive; \(F_{brk}\) is the braking force; \(\theta\) is the gradient angle.
###### Abstract  
Background: Several studies have investigated the potential role of the apparent diffusion coefficient (ADC) value of diffusion-weighted magnetic resonance imaging as a biomarker of high-grade and invasive bladder cancer. Methods: PubMed and the Cochrane Library were systematically searched in September 2021 to extract studies that evaluated the associations between ADC values, pathological T stage, and histological grade bladder cancers. The diagnostic performance of ADC values in detecting muscle-invasive bladder cancer (MIBC) and high-grade disease was systematically reviewed. Results: Six studies were included in this systematic review. MIBC showed significantly lower ADC values than non-muscle-invasive bladder cancer (NMIBC) in all six studies. The median (range) sensitivity, specificity, and area under the curve (AUC) of ADC values to detect MIBC among the four eligible studies were 73.5% (68.8-90.0%), 79.9% (66.7-84.4%), and 0.762 (0.730-0.884), respectively. Similarly, high-grade disease showed significantly lower ADC values than did low-grade disease in all four eligible studies. The median (range) sensitivity, specificity, and AUC of ADC values for detecting high-grade disease among the three eligible studies were 75.0% (73.0-76.5%), 95.8% (76.2-100%), and 0.902 (0.804-0.906), respectively. Conclusions: The ADC value is a non-invasive diagnostic biomarker for discriminating muscle-invasive and high-grade bladder cancer.  
apparent diffusion coefficient; bladder cancer; diffusion magnetic resonance imaging; neoplasm grading; neoplasm staging +
Footnote †: journal: Medical Imaging
# SMARTerra, a High-Resolution Decision Support System for Monitoring Plant Pests and Diseases
## 2 Methods
### Back-End
#### 2.2.6 Forecast of Locust Egg Hatching  
The model for forecasting the egg hatching date for the locusts is developed by LAORE based on the FAO eLocust model [25]. The procedure, illustrated in Algorithm 1, utilizes the following time series obtained from meteorological measurements: daily maximum temperature \(T_{\text{max}}\), daily average temperature \(T_{\text{mean}}\), and total daily precipitation \(R\). Forecast data predicted by the BOLAM model are not used.  
Currently, LAORE runs the model for a limited number of locations where the pest is most prevalent, e.g., the Ottana plain. SMARTerra can run the model simultaneously for the whole raster grid of Sardinia by using the interpolated measured data. The procedure identifies, for each pixel, the dates \(\text{day}_{1}\), \(\text{day}_{2}\), and \(\text{day}_{3}\) when the respective conditions are achieved, i.e., (i) the maximum daily temperature is lower than \(T_{\text{th}}=30.0\,^{\circ}\text{C}\) for \(n_{\text{days}}=5\) consecutive days, (ii) the rain accumulation since \(\text{day}_{1}\) is \(R_{\text{sum}}\geq R_{\text{th}}=100.0\,\text{mm}\), and (iii) the sum of growing degree days since \(\text{day}_{2}\) is \(D_{\text{sum}}\geq D_{\text{th}}=200.0\,^{\circ}\text{C}\).
## 6 Performance Evaluation
### Parameter Analysis
#### 6.3.3 Communication Range Analysis  
The communication radius 250 we used above is in line with Dedicated Short-Range Communications (DSRC) standards, which is a specific standard for vehicle-to-vehicle communications, and some existing research also uses this value in their experiments, such as [34; 35].  
In urban scenarios, even though the safe distance between cars is affected by the velocity, 50 m is a conservative value when the velocity is lower than 100 km/h. Therefore, with transmission radius 250 m, in one lane, there might be 10 vehicles in the communication range. Taking Beijing commercial center as an example, since the roads usually have 4-6 lanes, there may be more communicating vehicles, especially in the rush hours. Although the control and scheduling of several contacts is challenging, integrating our scheme with scheduling methods like [36] is a potential solution in the future.  
In order to further analyze the effects of communication radius, we conduct two groups of simulation experiments with the communication radiuses of vehicular nodes and roadside units ranging from 50 m to 250 m, respectively. The results are displayed in Figures 8 and 9.  
As shown in Figure 8, the overall performances of all the schemes differ a little with different radius of vehicular nodes because a majority of the requests are satisfied by the roadside units rather than the inter-vehicle communications. However, we see that the difference between DAS and DAS-RU increases when the radius rises because of more encounters between vehicles.  
Figure 8: Results with different communication radiuses of vehicular nodes. (**a**) access ratio; (**b**) average access delay.  
Figure 9: Results with different communication radiuses of roadside units. (**a**) access ratio; (**b**) average access delay.  
From Figure 9, when the roadside units have larger communication ranges, the delivery ratios of all the schemes reach plateaus after some growths. Meanwhile, the access latencies of DAS and DAS-RU drop a little and then remain stable, while Greedy and vDAS have shorter delays. A large communication radius of roadside units is helpful to communicate with more vehicles, and deliver the contents early. In addition, with a small transmission range, more roadside units require the V2V cooperation to forward data, and hence DAS and DAS-RU have a larger difference on their performances.
# Prediction of Cotton Yarn Quality Based on Attention-GRU
## 2 Cotton Yarn Quality Prediction Model  
Cotton spinning is the process of transforming cotton fibers into cotton yarn and thread. This spinning process is divided into two main processing routes: regular carding and fine carding, as illustrated in Figure 1. The processing stages encompass cotton carding, sliver drafting, coarse yarn spinning, and fine yarn spinning, among others [17].  
Based on the manufacturing characteristics of cotton spinning, it is evident that the outcomes of preceding processing stages will impact subsequent stages. Taking the relationship among the last drafting, coarse yarn spinning, and fine yarn spinning as an example, the unevenness in the weight of fine yarn and the CV value of sliver stiffness determine whether pronounced weft-wise or warp-wise streaks appear in the final cotton fabric [18]. Both of these factors are influenced by the internal fiber structure of coarse yarn. Similarly, enhancing the structural coherence of coarse yarn necessitates addressing the  
Figure 1: Cotton-spinning process flow.  
fiber straightness in the last drafting process. Furthermore, in the context of carding, the regular carding process involves directly supplying carded cotton sliver for drafting, while the fine carding process entails passing the carded sliver through a pre-drafting and coiling process before undergoing further combing [19; 20]. These two processes yield distinct differences in texture, durability, and uniformity in the resulting yarn.  
Consequently, when predicting yarn quality, it becomes imperative to consider the output of the preceding processing stage as input parameters for the subsequent stage. However, the predictive approach of the BP neural network involves feeding multiple spinning parameters into the network model simultaneously at a given moment [as depicted in Figure 2], thereby failing to unearth the temporal dependencies within the spinning process. To cater to the demand for analyzing temporal data within the spinning process, this study introduces recurrent neural networks (RNNs) to establish interconnections between input spinning parameters, enabling the model to capture the sequential nature of the spinning process.  
This paper focuses on the spinning production workshop of a company situated in Shijiazhuang, Hebei Province, China. The spinning process of this company is predominantly centered around cotton spinning and cotton blending.
## 4 Conclusions  
A single-shot 3D measuring method based on quadrature phase-shifting color composite grating projection is proposed. A corresponding physical model of 3D reconstruction is derived systematically. The existence of sensitivity differences and color crosstalk among the tricolor channels are effectively corrected. Many experimental results prove the practicality and validity of the proposed method. Compared with the traditional color PMP, the proposed method uses a set of complementary deformed patterns to extract a purer background. Therefore, the proposed method has the better measuring accuracy than the method of traditional single-shot color PMP. Owing to its single-shot feature, it also has a good application prospect in high speed and real-time measurement.  
Conceptualization, Y.C. and X.H.; methodology, Y.C.; validation, X.H., J.G. and Y.Z.; investigation, C.Y. and J.G.; resources, Y.C.; writing--original draft preparation, X.H.; writing--review and editing, Y.C. and X.H.; visualization, X.H.; and project administration, Y.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Special Grand National Project of China (under grant No. 2009 ZX02204-008).  
Not applicable.  
Data Availability Statement: Data is contained within the article or supplementary material. The data presented in this study are available in _A Single-Shot 3D Measuring Method Based on Quadrature Phase-Shifting Color Composite Grating Projection_.  
The authors declare no conflict of interest.  
Figure 9: Reconstructed results of real-time measuring at different states: (**a–c**) the deformed patterns in States A–C from the recorded video; (**d–f**) the deformed patterns of green component after correction; and (**g–i**) the corresponding reconstructed results of the gloved hand.
## 4 Results
### Implementation Details
#### 4.1.1 Post Processing  
To reduce the impact of stereo occlusion, we adopt a post-processing step similar to [15]. For the input image \(I\), we calculate the disparity map \(d^{l^{\prime}}\) of the image after its mirror flip \(I^{\prime}\). A disparity map \(d^{l\prime\prime}\) is obtained by turning this image \(d^{l^{\prime}}\) back. The final disparity map is generated by assigning the first 5% on the left from \(d^{l\prime\prime}\) and the last 5% on the right of the image using \(d^{l}\). The central part is the average of \(d^{l\prime\prime}\) and \(d^{l}\). This post-processing improves accuracy and reduces visual artifacts, which is called \(pp\).
## 3 Results and Discussion
### Correlation Analysis  
The analysis of the results from Sections 3.1 and 3.2 reveals that the vibration energy values obtained from the pre-embedded sensor method can reflect the grout filling quality within a certain range, while the density values measured by the impact-echo technique are highly correlated with the set grout filling degree.  
Under field construction conditions, since the sleeves are pre-embedded in the concrete members, it is not possible to directly use the impact-echo technique for grout filling degree detection, as the excitation point can only be placed on the surface of the concrete member, and not on the exposed reinforcement ends. Therefore, this section explores the correlation between the grout filling degree and the vibration energy values obtained from the pre-embedded sensor method, which can help directly obtain the grout filling degree in the field construction environment using the pre-embedded sensor method.  
To investigate the correlation between the vibration energy values obtained from the pre-embedded sensor method and the set grout filling degree, and to better reflect the relationship between the two, this study added three additional sets of grout filling degree experiments based on the pre-embedded sensor method test data, and the compiled test data are summarized in Table 2.  
The fitting analysis of the data in Table 2 shows that the vibration energy values obtained from the pre-embedded sensors and the fullness values obtained from the impact-echo technique follow an exponential relationship, and the function relationship can be expressed as:  
\[\text{y}^{\prime}=5978.5\times\exp(-x/22.4)+67.8 \tag{2}\]  
where \(x\) is the vibration energy value ranging from 70% to 100%, and when \(x<70\%\), \(\text{y}^{\prime}=255\).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Fullness Setting Value/\%** & **Fullness Detection Value/\%** & **Average Value/\%** & **Standard Deviation** & **Deviation/\%** \\ \hline \multirow{4}{*}{90} & 87 & & & \\  & 90 & & & \\  & 89 & 89 & 1.41 & 1.11 \\  & 88 & & & \\  & 91 & & & \\ \hline \multirow{4}{*}{100} & 95 & & & \\  & 97 & & & \\ \cline{1-1}  & 96 & 96.4 & 1.02 & 3.60 \\ \cline{1-1}  & 98 & & & \\ \cline{1-1}  & 96 & & & \\ \hline \end{tabular} Note: Deviation = ((Detection value – Setting value)/Setting value) \(\times\) 100%.  
\end{table}
Table 1: \(Cont.\)As shown in Figure 9, the fitting curve has an R\({}^{2}\) value of 0.995, indicating a high degree of fit, which can be used to express the relationship between the vibration energy value and the set grout filling degree.  
Furthermore, since the density values measured by the impact-echo technique are essentially the same as the grout filling degree within the sleeve, i.e., \(y^{\prime}=y\), the function relationship derived in this section between the vibration energy values from the pre-embedded sensor method and the fullness values from the impact-echo technique can be used to represent the relationship between the vibration energy values and the grout filling degree within the sleeve.
# Development of Korea Airport Pavement Condition Index for Panel Rating
## 4 Findings  
In this paper, the Korea airport pavement condition index was developed by defining the main distresses of the Korean airports using an engineering-based approach, and deriving the deduct value curve of the main distresses observed in Korea. Following are the main research results of this paper:  
Figure 11: Comparisons of PCI between ASTM D 5340 and KPCI; (**a**) asphalt pavement, (**b**) concrete pavement.  
Six types (alligator cracking, block cracking, joint reflection cracking, longitudinal and transverse cracking, patching and utility cut patching, and rutting) of asphalt pavement distresses were classified as the main distresses of the Korean airports. Similar distresses among 16 types of concrete pavement distresses were combined to yield 13 types of distresses. Among them, eight types (ASR, corner cracking, cracks, durability cracking, intersecting cracks, patching, scaling, and spalling) were classified as the main distresses of the Korean airports.
2. The DVC and CDV were derived by the pavement condition rating of the panel for the main distresses and multiple distresses for each pavement form. When the developed DVC and the DVC of ASTM D 5340 are compared, the DVCs were largely low or similar at the \"High\" and \"Medium\" severity levels and largely high at the \"Low\" severity level. The deduct value of the developed KPCI was higher as the amount of damage decreases in all three severity levels; however, it was lower than the existing PCI as the amount of damage increases. Moreover, the difference in the deduct values at each severity level was lower than the existing PCI. This is considered to be a result of incorporating the viewpoint of Korean airport pavement managers taking into consideration the importance of occurrences of distress even at low severity levels; and initial distress management.
3. As a result of comparing the KPCI and PCI derived in accordance with ASTM D5340, the developed criteria of the asphalt pavements and concrete pavements showed an overall trend lower than that obtained by ASTM D5340. Moreover, it was verified that this trend increases with worsening pavement condition. It appears that a better discriminating evaluation may be possible when determining pavement conditions by PCI results of the developed criteria. As a result, the KPCI can be considered more suitable to derive evaluation results of a wider range than ASTM D5340. The pavement condition index should be capable of being used for distinguishing between good and defective and for reasonable determination of superiority and inferiority even at an equal level from the aspect of criteria required for maintenance and repair determination. The KPCI has this capability, which is favorable for application in Korea.
4. It is considered that an existing problem where the PCI becomes lower after distress repair has been largely solved as the DVC is relatively lower than other distresses in the case of patching of asphalt pavements.
## 6 Case Study
### Comparison Results of Different Outage Strategies  
To verify the effectiveness of the proposed outage sequence optimization strategy, two more cases were designed for a comparative analysis:  
**Case 1:** Minimizing the importance level that corresponds to the distribution network outage (the proposed method).  
**Case 2:** Minimizing the number of load-shedding users (traditional method [26]).  
**Case 3:** CEES power system reliability assessment software V4.0 of Chongqing WLSL ElectricEnergy Star (Chongqing, China), whose method is average load shedding for each MV load (traditional method [27]).  
Figures 8-10 illustrate the load-shedding amount and locations from the 1 st to the 3 rd hour for Cases 1-3, respectively.  
Figure 7: Scores for different indicators.  
In order to distinguish between two adjacent medium-voltage users in the figure, we have used a yellow and blue format. Figures 8-10 show that the load-shedding amount in the 2 nd hour is less than that in the 1 st hour. This reduction is due to the increased output from the DG with PV and WT units in the 2 nd hour, which decreases the need for load shedding. In Case 1, the load-shedding amount for LP18 is minimal. According to the user importance results, LP18 has a relatively high importance level. Conversely, LP19 experiences the most load shedding because of its relatively low user importance. This result demonstrates that load shedding in the distribution network is performed according to the user importance, which confirms the effectiveness of the proposed outage sequence optimization strategy in minimizing the user importance loss in the distribution network.  
Table 1 compares comprehensive user importance loss \(S\) and number of load-shedding users \(N\) for the three cases.  
Table 1 shows that both the loss of user importance and the number of load-shedding users are greater in Case 3 than in Cases 1 and 2. This is due to the fact that Case 3 uses average curtailment and does not take relevant measures to reduce loss in the distribution network. And Case 2 has fewer load-shedding users but a higher comprehensive user importance loss than Case 1. By adopting the optimization strategy proposed by this paper,  
Figure 8: Outage sequence dynamic optimization results of Case 1.  
Figure 10: Outage sequence dynamic optimization results of Case 3.  
Figure 9: Outage sequence dynamic optimization results of Case 2.  
the user importance loss at \(t=1\), \(t=2\), and \(t=3\) can be reduced by 11.40%, 12.72%, and 48.3%, respectively. However, the number of load-shedding users increases by 6.8%, 5.6%, and 14.7% at \(t=1\), \(t=2\), and \(t=3\), respectively. Compared to traditional methods, the proposed outage optimization strategy can significantly reduce the economic losses of the power system and enhance the economic efficiency of the distribution network, all while minimizing the increase in the number of load-shedding users.
# AEA-RDCP: An Optimized Real-Time Algorithm for Sea Fog Intensity and Visibility Estimation
## 4 Experiments and Evaluations
### Dataset Affected by Atmospheric Light: AEA-RDCP Performance Evaluation
Table 3 compares the performance metrics of the RDCP and AEA-RDCP algorithms as shown in Figure 19, confirming that the AEA-RDCP algorithm achieves superior performance in both computation speed and fog detection accuracy. While removing only the sky region in the algorithm provides more accurate and stable DC percentage values for fog detection in original images, it is inefficient for images heavily influenced by external light sources like sunlight. The experimental results applying the proposed method to datasets affected by atmospheric light demonstrate that the threshold for atmospheric light pixels effectively estimates atmospheric light. Therefore, comparing with existing methods that only remove the sky region using two real-time raw image datasets, the AEA-RDCP algorithm consistently maintains accuracy under various conditions and improves fog density estimation accuracy by minimizing the impact of sunlight.