esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Evaluation
### Model Evaluation
#### 3.3.4 Threat Level Assessment (End-to-End System)  
The threat level quantification algorithm was tested on the three datasets mentioned earlier. Since there is no publicly available ground truth for videos for this parameter, the results of the algorithm were evaluated by comparison with expert human input. For this purpose, 462 samples of frame pairs from video sequences were chosen. The system was then evaluated by observing the increment/decrement of the inferred threat level \(T(t)\) and comparing the results with the expert human input. The performance of the full system is evaluated using accuracy, precision, and recall.  
The expert responses were obtained by showing a pair of frames and asking if the threat of COVID-19 spread has increased or decreased from the first frame to the second. Since a high disparity in identifying the impact of COVID-19 spread can exist amongst human experts in certain instances, ground truth cannot be established for such pairs of frames. To identify such instances, a thresholding minimum majority required to establish ground truth was set as 70%, and all frame pairs with a higher disparity (i.e., less than 70% majority) for any given choice were removed. In the evaluation conducted, five such frame pairs were identified and removed. One such frame pair is shown in Figure 5 to conclude this factor. As it can be observed, it is difficult to assess the change in threat for COVID-19 spread across these two frames.
## 3 Results and Discussion
### Practical Prospects
#### 3.4.3 The Sampling Line Set-up  
In the field tests, the measuring devices were taking air samples from either an independent or a shared gas tube. The sampling process can be an important source of errors, and it was possible that the NH\({}_{3}\) concentration levels were inherently different between tubes due to adsorption and absorption of NH\({}_{3}\) molecules, air leakage, and spatial variability of the gas concentrations [27]. To ensure identical gas compositions and avoid sampling errors confounding the comparisons, ideally the air samples were transported to all devices via one shared sampling tube. Such a set-up was established among the Rosemount, Axetris, and acid traps in the pig barn test. Setting up a shared tube between the acid traps and the INNOVA, Picarro, and Gasmet analysers was however not accomplished, since the respective pump capacity of these gas analysers could not be smoothly tuned, risking unbalanced pressure changes in the sampling tube. This could sabotage the impinger sampling due to backflow or violent splashing of the acidic solutions. Therefore, a second sampling tube was established. Another solution would have been adding a control valve (made of low absorption materials) at each analyser inlet. Using an independent tube per device, as in the poultry barn test, would, on the other hand, ensure  
Figure 4: The scatter plot (**left**) and relative bias (**right**) between the generated reference concentrations (diluted from a certified 52.9 ppm\({}_{\rm v}\) ammonia cylinder with nitrogen) and three gas analyser (\(\circ\) INNOVA, \(+\) Rosemount, \(\times\) Axetris) measurements on ammonia (NH\({}_{3}\)). The dashed line denotes 1:1 regression.  
representative test results to the actual work condition, in particular the air pressure and flow rate. This would be necessary in case that the accuracy of an analyser was pressure or flow rate dependent. For all field tests in this study, measures were taken to prevent sampling errors, including using PTFE-based materials for the sampling line set-up, ensuring no air leakage, and placing the sampling tube inlets closely together. In future work, it could be worthwhile to investigate the uncertainty caused by sampling from separate tubes and account for the effect accordingly in the VERA protocol.
# SDR-Based 28 GHz mmWave Channel Modeling of Railway Marshaling Yard
## 3 Software-Defined Radio-Based Channel Modeling
### Corresponding Algorithms
#### 3.3.1. ZC Sequence Selection  
In our measurement system, the sample rate of the ZC waveform is 200 MHz, the bandwidth is 160 MHz, and the length is 2048 points. The expression of the selected ZC sequence is:  
\[x[n]=e^{\frac{imu^{2}}{N}},0\leq n\leq N-1,N=2048 \tag{1}\]  
where \(u\) is the root index of the ZC sequence.
# A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2
## 4 Experiments
### Experimental Analysis
#### 4.2.2 Evaluations on GTSDB  
We also evaluated our Model-B on GTSDB and compared it with other methods. Table 4 illustrates the numerical values of precision and recall for our method. With the increasing threshold, the precision rate was higher and the recall rate was lower. When \(t\) = 0.4 or \(t\) = 0.5, the whole performance of Model-B was more efficient and robust.  
Figure 6: The precision-recall curves of the three super categories.  
Figure 7: Examples that are difficult to identify.  
Table 5 shows the comparison between Model-B and other methods. The other studied methods are based on CNNs. It should be stressed that results of other methods are referenced directly. From Table 5, it can be seen that Model-B was faster than other CNN-based methods, and the average running time was found to be the fastest at 0.017 s. The precision rate was the second highest, whether \(t=0.4\) or \(t=0.5\). The recall rate was inferior to other methods.  
The AUC values for the three super categories of traffic sign and running times are shown in Table 6. The methods of References [6; 9; 11; 12] are based on the traditional detection algorithms. As shown in Table 6, although our method was only better than the method in Reference [9], the running time proved to be fastest. It is worth mentioning that the method in Reference [11] performed best on prohibitory signs and the method in Reference [12] performed best on prohibitory signs and mandatory signs in the GTSDB competition hosted by IJCNN 2013. However, the methods based on CNNs have stronger robustness in complex traffic scenes and better generalization abilities for other subclasses. The GTSDB is mainly focused on the small size and illumination conditions, lacking other challenges. The strength of CNN-based methods is not fully shown. In addition, because the codes are undisclosed, the above methods could not be evaluated according to the CCTSDB, and thus we could not further analyze and compare their performance.
## 4 Grid Planning and Design
### Alternative Development Strategies as regards the Grid of the Future  
The dramatic increase in demand will require the development and research of new strategies for the development of grid infrastructures. Potential subareas will be explained:  
* A line designed for alternating current, but carrying direct current can evacuate more energy.
* A possible and optimal solution will be to increase the voltage levels in the lines. This requires an adaptation and development of the current equipment.  
After the review of the works, the author has considered grouping them in the following subsections: Direct Current and Extra High Voltage.
## 4 Results and Discussion
### Algorithm Performance Evaluation
For example, in the first and second rows, the improved YOLOv8 model shows fewer cluttered frames and achieves more accurate fine-grained category differentiation. In the third and fourth rows, the effectiveness of the proposed algorithm in handling complex scenes is clearly demonstrated. However, certain limitations remain, such as low confidence levels in some complex scenarios and occasional instances of omission or misrecognition. These issues are likely due to two main factors. The first factor is the small size of tuna in some images and high image noise. These challenging images were intentionally included to ensure robustness under real-world conditions on fishing vessels with limited resources. Second, the dataset size per scene is limited, with 1179 images across 54 scenes, averaging around 20 images per scene. Additionally, variations in conditions, such as day and night, reduce the number of relevant images even further, constraining potential performance improvements.
# Prediction of Air Pollution Concentration Based on mRMR and Echo State Network
## 2 Preliminaries  
### Feature Selection Method  
Time series that affect air pollution are high-dimensional data, which not only contains rich information, but also has irrelevant or redundant factors. These irrelevant and redundant factors reduce the prediction accuracy and efficiency of the model. Thus, analyzing the relationship between variables and selecting valuable input variables are important for prediction.  
Feature selection is the most typical data preprocessing method [21]. It consists of four parts: generation process, evaluation function, stop criterion and verification process. Currently, Common feature selection algorithms include random forest (RF), correlation feature selection (CFS), fast correlation-based filter (FCBF), mutual information (MI) [22], information gain (IG) [23], regularization models, relief-based algorithms, and genetic algorithm. However, these feature selection algorithms usually ignore the redundancy relationship between features. Random forests have over-fitting on noise regression problems and do not give continuous output. The CFS and FCBF feature selection methods are slow to calculate and cannot handle large-scale data efficiently. MI and IG do not require the type of data distribution but have high computational complexity for high-dimensional data. The regularization model has a better selection effect on high-dimensional data with a size much larger than the number of samples, but it does not perform satisfactorily in low-dimensional data selection. For the prediction of PM\({}_{2.5}\) concentration with high noise, how to select appropriate feature selection method is very important. In this paper, the minimum redundancy maximum relevance (mRMR) [20] is used for feature selection. After this, a set of purest features can be obtained, and the redundancy features are removed while guaranteeing the maximum relevance.
# Performance Study and Efficiency Improvement of Ice Slurry Production by Scraped-Surface Method
## 2 Experimental
### Experimental Procedure  
All the experiments were performed following the same procedure. A brine solution or nanofluid was prepared with certain concentrations and then poured into the ice slurry storage tank. Then, the volume flow rate of solution and blade scraping speed were set to certain values. The cooling unit was switched on. The solution in the ice storage tank was continuously cooled, and the system power consumption was recorded after the temperature of the solution reached 5 \({}^{\circ}\)C. Then, ice began to grow on the inner wall and was scraped. Ice slurry sample was taken every 10 minutes and the ice packing fraction was measured. The coefficient of performance (COP) is defined as the ratio of available cooling by the refrigeration cycle, \(Q\) and system power consumption, \(W\), which is measured by digital watt-hour meter:  
\[\text{COP}=Q/W, \tag{1}\]  
\[Q=m_{\text{IS}}\times c_{\text{p}}(T)\times(T_{2}-T_{1})+m_{\text{IS}}\times \alpha\times L, \tag{2}\]  
where \(m_{\text{IS}}\) is the total mass of ice slurry, \(c_{\text{p}}(T)\) is the average specific heat capacity of the solution, \(T_{1}\) and \(T_{2}\) are the initial and final temperatures of the solution, \(\alpha\) is the ice packing factor (IPF) of the ice slurry, and \(L\) the melting latent heat of ice.  
The measurement of IPF was based on calorimetry method and the energy conservation principle. It was assumed that there was negligible heat loss during the measurement process. The value of IPF was calculated by measuring the initial and final temperatures and masses of the sample after mixing with 200 g of hot water at 40 \({}^{\circ}\)C. The room temperature was controlled at 25 \({}^{\circ}\)C by an air conditioner and three adjusting heaters. The fluctuation of the ambient temperature was \(\pm 0.5\)\({}^{\circ}\)C. The IPF of each ice slurry sample for different ice-making time was measured three times and the average value was used for analysis.  
In order to verify the reliability of this method for IPF measurement, the solid crushed ice with masses of 10, 20, 30, 40 and 50 g was weighed and added to 150 g of liquid water at 0 \({}^{\circ}\)C. The ice slurries with theoretical IPF of 6.25%, 11.76%, 16.67%, 21.05% and 25.0% were obtained, and then the IPF of each ice slurry sample was measured according to the calorimetry method. The calculated results were 6.12%, 11.64%, 16.83%, 21.19% and 24.73%, respectively. Comparing the above data, it can be seen that the error between the experimental value and the theoretical value is small. Therefore, it is considered that the IPF obtained by the calorimetry method is reliable.
## 3 Results and Discussion
### Physical Properties
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Bulk Density**} & \multicolumn{3}{c}{**Open Porosity**} \\
**Mortar Identification** & & \multicolumn{3}{c}{**(kg/m\({}^{3}\))**} & \multicolumn{3}{c}{**(\%)**} \\ \cline{3-8}  & & **28d** & **90d** & **180d** & **28d** & **90d** & **180d** \\ \hline slaked air lime & A & 1679 (3) & 1704 (5) & 1712 (2) & 28 (0.1) & 28 (0.2) & 29 (0.1) \\ \hline  & A+Q10\% & 1714 (4) & 1736 (11) & 1748 (5) & 29 (0.5) & 29 (0.2) & 29 (0.2) \\ slaked air lime & A+Q30\%:M\({}_{1}\) (30\%) & 1715 (5) & 1742 (5) & 1760 (2) & 27 (0.3) & 27 (0.3) & 27 (0.1) \\ + quicklime & A+Q30\%:M\({}_{1}\) (30\%) & 1926 (27) & 1826 (13) & 1856 (10) & 27 (0.2) & 26 (0.2) & 26 (0.2) \\  & A+Q30\%:M\({}_{2}\) (10\%) & 1696 (4) & 1735 (3) & 1739 (5) & 28 (0.2) & 27 (0.1) & 28 (0.2) \\  & A+Q30\%:M\({}_{3}\) (5\%) & 1697 (2) & 1739 (6) & 1745 (9) & 28 (0.2) & 27 (0.2) & 27 (0.1) \\ \hline slaked air lime & A+NS3\% & 1623 (5) & 1619 (2) & 1635 (5) & 34 (0.8) & 35 (0.6) & 35 (1.3) \\ + nano-SiO\({}_{2}\) & A+NS5\% & 1622 (5) & 1564 (2) & 1569 (2) & 36 (0.1) & 35 (1.0) & 35 (0.3) \\ \hline  & C:A & 1746 (7) & 1766 (3) & 1777 (5) & 28 (0.2) & 28 (0.1) & 30 (0.5) \\ slaked air lime & C:A:M\({}_{1}\) (30\%) & 1877 (7) & 1893 (6) & 1893 (7) & 26 (1.2) & 26 (0.1) & 26 (0.2) \\ + white cement & C:A:M\({}_{2}\) (10\%) & 1755 (9) & 1773 (7) & 1776 (9) & 28 (0.2) & 28 (0.3) & 28 (0.3) \\  & C:A:M\({}_{3}\) (5\%) & 1756 (5) & 1774 (7) & 1771 (14) & 28 (0.6) & 28 (0.2) & 28 (0.2) \\ \hline Natural hydraulic lime & NHL & 1757 (2) & 1767 (8) & 1775 (7) & 26 (0.1) & 26 (0.2) & 27 (0.2) \\ \hline \hline \end{tabular} (i) standard deviation reported in brackets.  
\end{table} 
Table 4: Physical mortar properties.
## 6 Conclusions  
This study was conducted to identify the applicability of NTF derived from nacelle LIDAR measurements for wind turbine power performance testing. \(\text{PC}_{\text{NTF, NL}}\) was compared with \(\text{PC}_{\text{Cup}}\), \(\text{PC}_{\text{NTF, Cup}}\) and \(\text{PC}_{\text{NL}}\). Next, the uncertainties of the power curves and the AEPs were discussed in detail. The results can be summarized as follows:  
1. \(\text{NTF}_{\text{NL}}\) was estimated using the table method given in IEC 61400-12-2, and a strong relationship was found between the nacelle LIDAR and the nacelle anemometer wind speeds, which was close to a slope of one.
2. \(\text{PC}_{\text{NTF, NL}}\) had a high correlation (based on the slope and \(\text{R}^{2}\)) with the reference power curves based on the IEC standards, which led to the conclusion that \(\text{NTF}_{\text{NL}}\) can be used for power performance testing of other wind turbines.
3. The combined standard uncertainty of \(\text{PC}_{\text{NTF, NL}}\), was similar to that of \(\text{PC}_{\text{NTF, Cup}}\) based on IEC 61400-12-2. Additionally, the uncertainty in NTF mostly accounted for the combined standard uncertainty of the power curve derived from NTF.
4. Because \(\text{AEP}_{\text{NTF, NL}}\) and \(\text{u}_{\text{AEP, NTF, NL}}\) were very similar to \(\text{AEP}_{\text{NTF, Cup}}\) and \(\text{u}_{\text{AEP, NTF, Cup}}\) by means of the IEC 61400-12-2 in this work, there is potential for application of the NTF from the nacelle LIDAR measurements for wind turbine power performance testing without a met mast.  
A major part of the work was done by [PERSON] He analyzed the data and wrote a manuscript. [PERSON] contributed the format of a paper and thoroughly reviewed the manuscript.  
This work was supported by Jeju Energy Corporation (JEC) and Korea Testing Laboratory (KTL) with funding provided by the Human Resources Program in Energy Technology (No. 20164030201230) of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), with financial resources provided by the Ministry of Trade, Industry, and Energy, Republic of Korea.  
The authors declare no conflict of interest.
## 4 Non-Destructive Investigation
### Structural Investigation
#### 4.2.8 Dynamic Investigation  
Dynamic identification tests are useful to obtain the natural frequencies and mode shapes of the structure by measuring structural vibrations due to ambient forces. Accelerometers were located at 14 locations in the middle section of the different walls of the tower (Figure 12), mostly intending to capture expected out-of-plane modes. Three accelerometers were deployed along the height of wall EW3.  
Four natural modes of vibration of the structure and associated frequencies were identified (Figure 15). It is acknowledged that the modes captured may include the effects of soil-structure interaction. Past studies measured accelerations at the bottom of the structure as a way of filtering the effects from the soil on the structure's response [18; 19]. However, it is noted that no accelerometer could be placed at the base of the wall because the wall extends several meters below the ground on the interior side and the exterior part is inaccessible. To gain confidence that the modes measured capture only the behaviour of the tower, the mode shapes and frequencies were estimated using two different techniques: Enhanced Frequency Domain Decomposition (EFDD) [20] and Stochastic Subspace Identification  
Figure 14: Horizontal GPR scans carried out at different locations in one of the tower walls.  
(SSI) [21], implemented in the ARTeMIS software [22]. The four modes were identified using both methods and the mode shapes were compared on the basis of the Modal Assurance Criterion (MAC). MAC is a statistical indicator that ranges between 0 and 1, with 1 indicating fully consistent mode shapes [23; 24]. The MAC values for the mode shapes obtained using the two techniques ranged between 0.88 and 0.98, confirming that the four measured modes are independent of the estimation method used to obtain them.  
The first mode corresponds to the out-of-plane movement of the eastern wall EW3. The second mode involves the movement of the corners between NW1-EW3 and EW2-EW1, with a north-to-south out-of-plane movement. Mode-3 corresponds to the out-of-plane movement of the intersecting corner between NW1 and NW2. Finally, Mode-4 corresponds to the out-of-plane movement of NW2 and its extension to the west. The modes obtained reflect the influence of existing cracks in the structural behaviour of the tower, since all of them are local modes involving different parts of the masonry. Although the cracks facilitate the out-of-plane movement of the different walls of the tower, the results indicate that the disconnection between the walls caused by their presence is not total.
## 3 Theoretical Models and Verifications
### Blade Element Momentum (BEM) Code Validation  
In this work, a commercial wind turbine for Class III with blade length 59.8 m and 2.1 MW of rated power is used as a reference. The aerodynamic performance of the blade is calculated by considering a tip speed ratio of 10.6 with a range of wind velocities between 3 m/s and 20 m/s. Air density is set to 1.225 kg/m\({}^{3}\) and the cone and tilt angle of the wind turbine are set to zero degrees. The RFoil is used to calculate the aerodynamic characteristics of Delft University (DU) series airfoils for a specific Reynolds number according to their locations. The power coefficient and the power curves calculated by BEM code are implemented in MATLAB software (MATLAB 2014b; The MathWorks Inc.: Natick, MA, USA, 2014). In order to validate the BEM code, the aerodynamic performance data from the wind turbine manufacturer, based on simulations by WINDnovation Engineering Solutions GmbH, are compared with the BEM code. The numerical simulations and the manufacturer's data show an excellent agreement as shown in Figure 2. The power curve calculated by BEM code shows that the rated power is achieved with wind velocity of 9 m/s. Therefore, the aerodynamic performance prediction of the wind turbine blade calculated by BEM code for this study can be considered acceptable.
## 3 Experimental Results and Analysis
### Validation of the Effectiveness of the Rail Surface Condition Identification Algorithm
#### 3.3.2 Validation of the Effectiveness of Knowledge Distillation  
To assess whether knowledge distillation enhances the identification accuracy of the lightweight model and improves overall model performance, a Group 2 ablation experiment was conducted. In this experiment, the GoogLeNet-Rail model was utilized as the teacher model to guide the MobileNet-Rail model (student model), transferring learned feature knowledge through the distillation process. A critical parameter in knowledge distillation is the distillation temperature (\(T\)), which significantly influences the student model's learning effectiveness. To investigate its impact, experiments were conducted with different temperature values, and the results are presented in Table 4. The highest identification accuracy of 97.11% was achieved at a distillation temperature of 10. Furthermore, the performance comparison of the student model with and without knowledge distillation, specifically at \(T=10\), is illustrated in Figure 10. According to the experimental results, it is evident that the loss values of MobileNet-Rail are lower and exhibit reduced fluctuation after knowledge distillation. Moreover, the accuracy, precision, recall, and F1 score are all enhanced, while the inference time is slightly reduced. The optimal performance is achieved when the distillation temperature is set to 10. In summary, the knowledge distillation effectively enhances the identification capability of the student model, improving its  
Figure 9: Impact of transfer learning on the GoogLeNet model vs. MobileNet model: (**a**) loss vs. accuracy change curves of the GoogLeNet model before and after transfer learning; (**b**) loss vs. accuracy change curves of the MobileNet model before and after transfer learning.  
classification performance. Notably, at an appropriate distillation temperature, the student model's accuracy can match or even surpass that of the teacher model.
# VENuS-Derived NDVI and REIP at Different View Azimuth Angles
## 2 Materials and Methods
### Pixel Subtraction  
The VIs' value change was calculated as measured by the VEN\(\upmu\)S satellite from two different view azimuth angles. First, the indices were calculated for all pixels in the overlap zone for each of the two tiles, and later, the former tile value (W08) was subtracted from the later one (S01):  
\[\Delta\mathrm{VI}=\mathrm{VI}_{\mathrm{S01}}-\mathrm{VI}_{\mathrm{W08}} \tag{4}\]  
where VI is the vegetation index (Equations (1) and (2)), and subscript S01 and W08 indicate the relevant tile.