esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Mechanical Damage in Green-Making Process on Aroma of Rougui Tea
## 2 Materials and Methods  
### Sample Preparation and Collection  
The experimental tea plantation of this study was the Jiulongshan base in the Wuyi Mountain area of Fujian Province (117.95\({}^{\circ}\) E, 27.71\({}^{\circ}\) N), at an altitude of 260 m. First, 20 kg fresh leaves (cultivar Rougui) were collected, and the standard of picking was three leaves and one bud. Then, they were processed according to the traditional production method of RGT. Briefly, the freshly plucked tea leaf samples were exposed to sun withering for 30 min. Subsequently, they were averagely divided into two groups. One group was subjected to manual green-making, including seven shaking steps, and the number of turns during shaking was 10, 20, 40, 60, 80, 120 and 300, respectively. The interval between two successive shaking steps was 1 h, and the green-making process lasted about 8 h. At the same time, under the same environmental conditions, another group of fresh leaves was spread for 8 h without shaking; the temperature during green-making was 23-25 \({}^{\circ}\)C and the relative humidity was 60-70%. Subsequently, the tea leaves of the two groups were produced into finished tea after fixation, rolling and drying. The samples included sun-withered leaves (SY); the experimental group (LY group) (1 st shaking (LY1), 3 rd shaking (LY3), 5 th shaking (LY5), 7 th shaking (LY7)); the control group (CK group) (1 st spreading (CK1), 3 rd spreading (CK3), 5 th spreading (CK5), 7 th spreading (CK7)); the finished tea with green-making (ZM); and the finished tea without green-making (DM). Three biological replicates for each sample point were obtained, immediately frozen and fixed in liquid nitrogen, and then stored in a \(-\)80 \({}^{\circ}\)C refrigerator for study. Samples SY, LY7, CK7, ZM and DM were taken to detect and analyze the volatile compounds. Samples SY, LY1, LY3, LY5, LY7, CK1, CK3, CK5 and CK7 were taken for transcriptome analysis. Samples SY, LY7 and CK7 were taken for proteomic analysis.
# Path Planning Strategy for Vehicle Navigation Based on User Habits
Hence, to the best of our knowledge, this paper explores the possibility of providing a higher level of personalized path planning using trajectory information. Specifically, three major contributions have been made and structured as follows. First, a log C-means clustering algorithm (LFCM) is introduced in Section 2.1 to lead the characteristic mining and clustering process of a specific user who provides the log trajectories. Second, an individualized quantitative evaluation system of road performance is built in Section 2.2 to obtain specific performance value in the view of the owner of the log tracks. Third, adaptive ant colony algorithm is improved and utilized to validate the path planning based on the personalized road values in Section 2.3 to find the optimal individualized path.
# HSVNet: Reconstructing HDR Image from a Single Exposure LDR Image with CNN
## 5 Training  
### Dataset  
A major challenge for successful development is to gather a sufficiently large paired LDR-HDR dataset which has multiple exposures of LDR images. Even though many HDR datasets are available on the web, most of them do not provide corresponding LDR images. FHDR [24] and HDRCNN [8] generated LDR images from HDR images to obtain training data. However, our goal for the dataset is to create authentic LDR images in multiple exposures paired with each HDR image. To use genuine LDR and HDR paired images, we use Fairchild database [14] which provides 7 or 9 multiple-exposure LDR images for each HDR image. We also generated our own paired dataset. We captured 504 scenes  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Loss Function** & **L1** & **HDR-VDP-2.2** & **PU21-SSIM** & **PU21-PSNR** \\ \hline cone space & 0.0624 & 52.5526 & 0.7601 & 15.9850 \\ \(d_{h}+MAE_{s,p}\) & **0.0583** & 52.0549 & 0.7341 & 13.6357 \\ \(\sqrt{d_{h}}+MAE_{s,p}\) & 0.0585 & **52.7337** & 0.7635 & 16.0414 \\ unit circle\({}_{h}+MAE_{s,p}\) & 0.0760 & 52.0152 & 0.7603 & 16.2927 \\ \(Min(6d_{h},1)+MAE_{s,p}\) & 0.0672 & 52.5142 & **0.7761** & **16.6479** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The test results for different loss functions: The same mask parameters are applied (\(s^{\prime}=1.0\), \(c=1.0\), \(v_{min}=~{}0.7\)). The values in bold indicate the best scores for each metric.  
Figure 11: (**left**) Euclidean distance of two points on a HSV cone space. (**right**) Euclidean distance of two H points on a unit circle.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **L1** & **HDR-VDP-2.2** & **PU21-SSIM** & **PU21-PSNR** \\ \hline
30\({}^{\circ}\) & 0.0647 & 51.9500 & 0.7542 & 15.4394 \\
45\({}^{\circ}\) & **0.0583** & 52.0048 & 0.7280 & 15.2573 \\
60\({}^{\circ}\) & 0.0672 & 52.5142 & **0.7761** & **16.6479** \\
75\({}^{\circ}\) & 0.0713 & 47.7744 & 0.6498 & 12.5720 \\
90\({}^{\circ}\) & 0.0617 & **52.5844** & 0.7608 & 16.4168 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test results with setting the maximum penalty over different degrees in the \(H\) loss function. The \(H\) loss function with degree \(\theta\) is \(Min(\frac{360}{\theta}d_{h},1)\). The values in bold indicate the best scores for each metric.  
using a Canon Mark 3 in 5, 7, or 9 different exposures. The resolution of the images is \(5760\times 3840\) pixels. The dataset comprises various categories: landscapes, outdoor, indoor, buildings, objects, daytime, and nighttime. We merged these LDR images (8-bit) with HDR images (32-bit) using the HDR Pro process built-in Photoshop. The number of total HDR images is 608.
## 5 Test Experiments
### Calibration Uncertainty  
#### 5.4.1 Transit Offset Error Calibration Uncertainty  
The standard uncertainty evaluated as type A was obtained from a series of measurements of the offset (\(EF\) on the measurement repeatability. Based on a set of measurements with \(n=3\), the type A uncertainty \(u_{1}\) was calculated as \(u_{1}=0.0006/\sqrt{3}=0.0003\) mm. The type A uncertainty \(u_{2}\) was obtained from a series of measurements of the installation eccentricity error of test rod A. Based on a set of measurements with \(n=3\), the type A uncertainty \(u_{2}\) was calculated as \(u_{2}=0.001/\sqrt{3}=0.0005\) mm. The type A uncertainty \(u_{3}\) was obtained from a series of measurements of the installation eccentricity error of test rod B. Based on a set of measurements with \(n=3\), the type A uncertainty \(u_{3}\) was calculated as \(u_{3}=0.001/\sqrt{3}=0.0005\) mm. The type B uncertainty \(u_{4}\) is the error of the telecentric measurement system; the corresponding standard uncertainty was determined as \(u_{4}=0.001/\sqrt{3}=0.0006\) mm. The type B uncertainty \(u_{5}\) is the changes in the environmental temperature; the error caused by environmental temperature was found to be \(\pm 1\%\), which can  
Figure 12: The trajectories of the central axis offsets of the test rod A and B.  
be considered as uniformly distributed. The corresponding standard uncertainty was determined as \(u_{5}=0.01/\sqrt{3}=0.006\) mm. Therefore, the combined standard uncertainty came out to \(u_{c}=\sqrt{u_{1}^{2}+u_{2}^{2}+u_{3}^{2}+u_{4}^{2}+u_{5}^{2}}=\sqrt{0.0003^{2}+0.0 005^{2}+0.0005^{2}+0.0006^{2}+0.006^{2}}=0.006\) mm. So, the uncertainty evaluation of the transit offset calibration method was 0.006 mm.
## 4 Feeder Selection with Reinforcement Learning
### Routing with SLA  
At each flow modification interval, the expected costs (or negative rewards) of the most recently selected feeder link is compared to that of the other links to update the routing policy. As a result, the selection probability of the most recently selected link either increases or decreases. The value of the reward or penalty is calculated through parameter \(\beta^{ft}\) as defined by the Equation (3)  
\[\beta^{ft}=\frac{a\_cost_{i}^{ft}-x_{1}^{ft}}{x_{2}^{ft}-x_{1}^{ft}}, \tag{3}\]  
where  
\(a\_cost_{i}^{ft}\) is the most recent average cost obtained for feeder link \(v\) at time \(ft\), \(x_{1}^{ft}\) is the minimum average cost, and \(x_{2}^{ft}\) is the maximum average cost.  
Parameter \(\beta^{ft}\) represents the normalized reward or penalty awarded to the selected feeder link with respect to the observed performance of the other links. The routing policy is updated using [17]:  
\[Pr[s_{v}]^{ft}\gets Pr[s_{v}]^{ft-1}+\left(1-\beta^{ft}\right)a\left(1-Pr[s_{ v}]^{ft-1}\right)-b\,\beta^{ft}Pr[s_{v}]^{ft-1} \tag{4}\]  
where \(v\) is the index of the most recent link. The probability of the other links are updated as follows:  
\[Pr[s_{j}]^{ft}\gets Pr[s_{j}]^{ft-1}-(1-\beta^{ft})a\;Pr[s_{j}]^{ft-1}+b\, \beta\left(\frac{1}{N-1}-Pr[s_{j}]^{ft-1}\right)\quad;\;j\
eq v \tag{5}\]  
where \(a\), \(b\) are the reward and penalty parameters. With ergodic assumptions, both parameters are identical.  
The feeder links are selected randomly according to the probability distribution \(Pr\) as described in Algorithm 3.  
\(Pr[s_{i}]=\frac{1}{N},\;i=1,...N,\;\text{where}\;N\;\text{is}\;\text{Number of links}\;ft>>mt>>dt\)  
```
1:Initialize:
2:\(0>a=b<1;\,0<\alpha<1,k=100\)
3:while true do\(\triangleright\) Repeat every \(ft\)
4:\(delays=[d_{1},\,d_{2},\,\dots,\,d_{N}]\)
5:\(pkt\_loss\_rate=[pkt\_loss\_rate_{1},\,pkt\_loss\_rate_{2},\,\dots,\,pkt\_ loss\_rate_{N}]\)
6:\(g=0\)
7:for\(i=1\) to \(N\)do\(\triangleright\) computing cost and its moving average
8:\(cost_{i}\gets k\;pkt\_loss\_rate_{i}+(1-pkt\_loss\_rate_{i})\;d_{i}\)
9:\(a\_cost_{i}+\alpha\;cost_{i}+(1-\alpha)\;a\_cost_{i}\)
10:endfor\(\triangleright\) Probability reinforcement
11:\(x_{1}\leftarrow\min\left\{a\_cost_{i}\right\}\forall\,i=1,\,2,\,\dots,\,N\)
12:\(x_{2}\leftarrow\max\left\{a\_cost_{i}\right\}\forall\,i=1,\,2,\,\dots,\,N\)
13:\(\beta\leftarrow\frac{a\_cost_{i}-x_{1}}{x_{2}-x_{1}}\) where \(v\) is the index of selected link
14:for\(i=1\) to \(N\)do\(Pr[s_{v}]\gets Pr[s_{v}]+(1-\beta)\,a\,(1-Pr[s_{v}])-b\beta Pr[s_{v}]\)
15:\(Pr[s_{i}]\gets Pr[s_{i}]-(1-\beta)aPr[s_{i}]+b\beta(\frac{1}{N-1}-Pr[s_{i }])\;\forall\,i\
eq v\),
16:endfor\(\triangleright\) Cumulative Probability computation
17:for\(i=1\) to \(N\)do\(g=g+Pr[i]\)
18:\(cum\_Pr[i]=g\)
19:endfor\(i=1\) to \(N\)do\(\triangleright\) Stochastic selection
20:if\(random.random()<cum\_Pr[s_{i}]\)then\(bestindex\gets i\)
21:endif
22:endfor return bestindex
23:endwhile
```  
**Algorithm 3** Latency and Packet-loss Optimized SLA Feeder Link Selection
## 7 Experimental Results
### Original BEE
#### 7.1.4. Longevity  
The number of sensors still alive sensors in the network is shown in Figure 6c. In the period from 0 to 450 round, more sensors died in BEE than in HEED and LEACH. After 600 round, BEE had more sensors left in the network. In long term, BEE has longer network lifetime than HEED and LEACH. In the next section, we will prove that the sensors died at the beginning have little impact on the coverage of the network.
## 2 Methods
### Method for Determining Early Warning Parameter Values
#### 2.2.2 Determination of the Time Window  
The time window is proposed to reduce the impacts of data fluctuations. A too-large time window will reduce the sensitivity of early warning, but under a too-small time window, the calculation of warning parameters will be greatly affected by fluctuations, and lead to large errors. In this study, the time window is determined by an empirical method as follows:  
For the monitoring data with obvious fluctuations, twice the fluctuation period is used as the time window.  
For the monitoring data with unobvious fluctuations, 15-30 times the data collection interval is used as the time window.
## 2 Theories and Methods
### Fundamentals of the CBP Model  
The CBP model is a typical conductive slug test theory whose purpose is to obtain the permeability coefficient by fitting a standard curve with a semi-log plot of the water level variation after being dimensionless in the test hole with time [4]. The groundwater continuity equation in the aquifer is the same as Equation (1).  
[PERSON] derived the water head variation relationship as follows:  
\[w=w_{0}F(\varphi,\tau) \tag{5}\]  
Among them, \(w_{D}=\frac{w}{w_{0}}\); \(F(\varphi,\tau)=\frac{8\varphi}{\pi^{2}}\int_{0}^{\infty}\frac{exp\left(-\beta \mu^{2}/\varphi\right)}{uf(u,\varphi)}du\); \(u=\frac{r^{2}S}{4T}\); \(f(u,\varphi)=\left[uI_{0}(u)-2\alpha I_{1}(u)\right]^{2}+[uK_{0}(u)-2 uK_{1}(u )]^{2}\).  
A set of \(w_{D}-\tau\) standard curves can be obtained, as shown in Figure 2.  
The aquifer permeability coefficient is calculated as follows:  
\[K=\frac{\tau r_{c}^{2}}{tB} \tag{6}\]  
The \(w^{\prime}-t\) measured curve can be plotted according to the slug test data. Keep the vertical coordinate axis of the measured curve and the standard curve aligned at the same horizontal height. Then, the measured curve is shifted along the horizontal axis to fit the standard curve. At the same time, select any matching point and record the corresponding coordinate values (\([\tau]\) and \([t]\)). Thus, the aquifer permeability coefficient can be obtained by Equation (6).  
Among them, \(\varphi\) is the dimensionless storage coefficient; \(I_{0}(u)\) and \(K_{0}(u)\) are the first- and second-type modified Bessel functions of the zero order; \(I_{1}(u)\) and \(K_{1}(u)\) are the first- and second-type modified Bessel functions of the order 1; the rest of the symbols have the same meaning as they do above.  
Figure 2: Slug test standard curves of the CBP model.
## 4 Our Approach
### Frame Skip Sampling for Data Augmentation  
The main difficulty of training neural networks with small datasets is that the trained models do not generalize well with data from the validation and the test sets. As the size of dataset is even smaller than the usual cases in our problem, we utilize the _random frame skip sampling_ that is commonly used to process video data such as video classification [38] for augmenting training data. The effectiveness if data augmentation has been proved in many tasks including image classification [39]. Here, we randomly extract multiple representative features of a video.  
Given a sign video \(S=(f_{1},f_{2},\ldots,f_{l})\) that contains \(l\) frames from \(f_{1}\) to \(f_{l}\), we randomly select a fixed number of frames, say \(n\). Then, we first compute the average length of gaps between frames as follows:  
\[z=\left\lfloor\frac{l}{n-1}\right\rfloor.\]  
We first extract a sequence of frames with indices from the following sequence \(Y=(y,y+z,y+2z\ldots,y+(n-1)z)\in\mathbb{N}^{n}\), where \(y=\lfloor\frac{l-z(n-1)}{z}\rfloor\) and call it a _baseline sequence_. Then, we generate a random integer sequence \(R=(r_{1},r_{2},\ldots,r_{n})\in[1,z]^{n}\) and compute the sum of the random sequence and the baseline sequence. Note that the value of the last index is clipped to the value in the range of \([1,l]\). We start from the baseline sequence instead of choosing any random sequence of length \(l\) to avoid generating random sequences of frames that are possibly not containing \"key\" moments of signs.
# GIS-Based Cluster and Suitability Analysis of Crop Residues: A Case Study in Yangon Region, Myanmar
## 2 Materials and Method
### Cluster Analysis
#### 2.3.2 Township-Based GIS Cluster Analysis of the Feedstock of the Crop Residue  
After calculating the crop residues in the 43 townships, the selected highest potential of crop residues generation data is prepared in ArcGIS to make the township-based cluster analysis with four steps. In step one, the Spatial Autocorrelation (Global Moran's I) tool simultaneously measures spatial autocorrelation based on selected crop residue generation amounts and township-based locations. It evaluates whether the pattern expressed is clustered, dispersed, or random using Moran's I Index value of both the z-score and \(p\)-value. The z-score and \(p\)-value indicate statistical significance. Step two is to determine the average distance of neighbor townships using the tool Calculate Distance Band from Neighbor Count. In step three, the tool of Incremental Spatial Autocorrelations creates a line graph and finds the distances where township-based spatial clustering patterns  
Figure 2: Research Framework.  
occurred with corresponding statistically significant peak z-scores. These peak distances are used as a radius parameter to ensure step four of Cluster and Outlier Analysis (Anselin Local Moran I). It distinguishes between a statistically significant township-based cluster of high values (High-High Cluster), clusters of low values (Low-Low Cluster), outliers in which a high value is surrounded primarily by low values (High-Low Cluster), and outliers in which a low value is surrounded primarily by high values (Low-High Cluster).
# An Efficient Single-Anchor Localization Method Using Ultra-Wide Bandwidth Systems
## 5 Experiment and Analysis
### Performance Evaluation
#### 5.2.1 Static test point
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Errors** & **Methods** & \(N_{\text{a}}=1\) & \(N_{\text{a}}=2\) & \(N_{\text{a}}=3\) \\ \hline \multirow{2}{*}{90\% error} & SAL-Normal & 20.4 cm & 20.8 cm & 20.6 cm \\  & Proposed SAL method & 21.3 cm & 23.0 cm & 23.4 cm \\ \hline \multirow{2}{*}{Median error} & SAL-Normal & 9.3 cm & 9.3 cm & 10.3 cm \\  & Proposed SAL method & 10.1 cm & 10.9 cm & 10.5 cm \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Localization error comparison in E1.
# A Nighttime and Daytime Single-Image Dehazing Method
## 4 Experimental Results and Discussion
### Qualitative Comparison  
#### 4.1.1 Nighttime Hazy Images  
We chose the dark channel prior method of [PERSON] et al. [9], the dehazing method based on deep learning of [PERSON] et al. [21] and the recent nighttime dehazing methods of [PERSON] et al. [26], [PERSON] et al. [24] and [PERSON] et al. [25] to carry out the nighttime dehazing experiments. The experimental images are shown in Figure 5. [PERSON] et al.'s [9] method incorrectly estimates the artificial light as the global ambient illumination, which causes the image to be too dark after dehazing. It proves that the nighttime ambient illumination should be a local variable rather than a simple fixed value. Because of the complex nighttime haze scene, the deep learning model of [PERSON] et al. [21] cannot be effectively applied. It deepens the color of dim nighttime images and introduces unreal colors (Figure 5N3). [PERSON] et al.'s [26] method completely changes the color of the image, diffuses the light source, and the color is unstable (Figure 5N6,N9). [PERSON] et al.'s [24] method diffuses the image light in the light source area (Figure 5N1) and produces noise in the sky and dark areas (Figure 5N2,N3,N8,N10). Although [PERSON] et al.'s [25] method has better image clarity, it also cannot avoid the problem of noise (Figure 5N2,N3,N9) and light source area diffusion (Figure 5N3,N4). The proposed MF Dehazer effectively solves the defects of the above-mentioned methods. On  
Figure 4: Comparison of regularizan.  
the basis of retaining the original color, both the dark and bright areas of the image are effectively dehazed, such as the reflection of the lake in Figure 5N1, the clearer and more realistic green space on the left of Figure 5N10, the details and levels of shrubs and leaves in Figure 5N1,N3,N5,N7, and the light source diffusion problem of each image is well solved.
## 1 Introduction
To demonstrate the major components in the SCO\({}_{2}\) power generation system, the SCO\({}_{2}\)'s heat exchanger and turbo-expander have been tested in a 1 MWe test loop [13; 14]. A tubular-type heat exchanger was selected as the primary heater, which was connected with a commercial natural gas-fired combustor [13]. The SunShot program is the first MW-scale SCO\({}_{2}\) power cycle demonstration in which the TIT is higher than 700 \({}^{\circ}\)C. A demonstration of the SCO\({}_{2}\) power cycle was performed by considering the unique characteristics of the CSP system [14]. In addition, the US Department of Energy (DOE) designated SCO\({}_{2}\) research as a cross-cutting technology and supercritical transformational electric power (STEP) program with a collaboration between fossil, nuclear, and renewable energy [15]. The program has focused on designing, constructing, commissioning, and operating a 10-MWe SCO\({}_{2}\) pilot plant test facility. The detailed design of the facility and equipment is now proceeding. Fabrication and construction of a pilot test facility with a simple-cycle test will be finished at the end of 2020, and facility operation and testing with the recompression cycle is scheduled until 2022 [16; 17]. In Korea, various kinds of experimental facilities have been developed and investigated for the SCO\({}_{2}\) power cycle [18; 19; 20; 21]. Recently, the development and testing of the SCO\({}_{2}\) power cycle for a waste heat recovery system started in the Korea Atomic Energy Research Institute (KAERI). The target of the project is to develop a prototype SCO\({}_{2}\) power generation system for a waste heat recovery system. The project aims at developing SCO\({}_{2}\) core technology, such as turbomachinery and heat exchangers. A simple recuperated cycle was selected. The major components of the prototype are an SCO\({}_{2}\) compressor and turbine, precooler, recuperator, and waste heat recovery heat exchanger. The target of the TIT is about 430 \({}^{\circ}\)C. To secure the TIT, it is important to manage the high-temperature and high-pressure heat exchange technologies under a SCO\({}_{2}\) environment. In this research, the SCO\({}_{2}\) heat exchanger that will be used in the SCO\({}_{2}\) power generation prototype is designed. The maximum target of the SCO\({}_{2}\) outlet temperature and pressure is 200 bar and 600 \({}^{\circ}\)C, respectively. Based on the design conditions of the SCO\({}_{2}\) heat exchanger, heat exchanger type selection, material selection, and tube specification based on the commercial availability were conducted. Preliminary performance analysis of the SCO\({}_{2}\) heat exchanger was conducted using an in-house heat exchanger code, and the flow and thermal stress analysis of the SCO\({}_{2}\) heat exchanger were performed using commercially available computational fluid dynamic (CFD) codes.