esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this paper, an experimental system for single droplet generation and impingement with low-boiling point refrigerant under atmospheric pressure has been constructed successfully to investigate the dynamic behavior of R1336 mzz(Z) droplet impinging on the solid surface of carbon steel 45. The effects of roughness (0.8 \(\upmu\)m, 1.6 \(\upmu\)m, 3.2 \(\upmu\)m, 6.3 \(\upmu\)m) and _We_ (50-1000) on droplet impinging dynamics are explored. The main conclusions are summarized as follows:  
1. The shape of the droplet changed from a truncated spherical shape to a thin liquid layer and then spread into a liquid film with a thicker rim during the impinging period. Moreover, boiling and \"finger disturbance\" were observed. Boiling was noticed to be more obvious under higher _We_.
2. _We_ had a significant effect on the dynamic behavior of droplet spreading. When 0 \(<\tau<\) 0.1, the \(\beta\)-\(\tau\) curves almost coincided. At \(\tau\) = 0.1, the curves of low _We_ had a lower slope and were separated from those of medium and high _We_. At about \(\tau\) = 1, the curves at medium _We_ separated from the curve at high _We_ number. With the increase in _We_, the curve slope became larger, and \(\tau\) to reach \(\beta_{\text{max}}\) became longer.
3. The droplet exhibited the Cassie state on the surface with low roughness, and \(\beta_{\text{max}}\) increased with the increase in roughness. At a certain threshold of _Ra_ between 1.6 \(\upmu\)m and 3.2 \(\upmu\)m, the droplet underwent a Cassie-Wenzel transition, and \(\beta_{\text{max}}\) was significantly reduced due to larger energy dissipation.
4. The empirical formula of \(\beta_{\text{max}}\) on the carbon steel walls was proposed based on the regression analysis. The formula fitted well with experimental data on solid surfaces with different roughness.  
Conceptualization, Z.-F.Z. and S.-Y.C.; methodology, D.-Q.Z.; software, H.-J.X.; validation, B.C. and Q.Z.; formal analysis, H.-J.X.; investigation, S.-Y.C. and Q.Z.; data curation, S.-Y.C.; writing--original draft preparation, S.-Y.C.; writing--review and editing, Z.-F.Z.; visualization, D.-Q.Z.; supervision, Z.-F.Z. and B.C.; project administration, Z.-F.Z.; funding acquisition, Z.-F.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (52176163), the Ministry of Science and Technology of China, with the Key Program for International Science and Technology Cooperation Project (2017 YFE0134200). This work was also supported by the open funding (NVHSKL-202116) of the State Key Laboratory of Automotive Noise Vibration and Safety Technology, the Fundamental Research Funds for the Central Universities and Cyrus Tang Foundation.  
Not applicable.  
Figure 10: Experimental data and model prediction of \(\beta_{\text{max}}\) by the new empirical formula of droplet impinging on four different roughness surfaces.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.
# Fractional-Order Sliding Mode with Active Disturbance Rejection Control for UAVs
## 4 Experiments and Results
### Analysis of Experimental Results in Case of Weak Interference  
The simulation effect of adding the weak interference, where the controller controls the attitude angle as well as the position information, is shown in Figure 5a-f.  
\begin{table}
\begin{tabular}{c c c c} \hline  & \(\mathbf{\phi}\) **(rad)** & \(\mathbf{\theta}\) **(rad)** & \(\mathbf{\psi}\) **(rad)** \\ \hline FOSM-ADRC & 0.00108 & 0.0205 & 0.0018 \\ FOSMC & 0.00141 & 0.02734 & 0.00615 \\ SMC & 0.00105 & 0.02747 & 0.0078 \\ \hline \end{tabular}
\end{table}
Table 4: Steady-state error  
Figure 4: Experimental results without interference.  
Figure 5: _Cont._  
Figure 5a-f show the attitude angle and position information results in the three controllers in the weak interference in the experimental situation, during which they can work normally, and in the absence of interference in the three controllers, the control effect is similar and did not occur in a large change. The next step should be to further increase the interference lightness to observe whether there is a large change in the difference in the control effect of the three controllers.
## 4 Application of ISSA in Node Location in HWSNs
### Network Model  
Figure 4a shows the network connection of 50 nodes in an area of 100 \(\times\) 100 m\({}^{2}\), in which the number of anchor nodes represented by red \(\Circle\) is 20, the number of unknown nodes represented by blue \(\triangle\) is 30, and the blue solid line represents the neighboring nodes can communicate with each other. In addition, the communication radius of each node is different. The node localization of HWSNs is to find out the coordinates of unknown nodes using the information of finite anchor nodes, such as the coordinates of each anchor point and the distance from each anchor point to the unknown node. The distance from each anchor node to the unknown node is shown in Figure 4b, in which blue dashed line indicates the distance from the anchor node to the unknown node.
## 4 Confirmation from Simulated and Practical Results
### Verification with Simulation Results  
The simulation platform is one of the favorable and simplest environments to test and analyze the characteristics of any switching converter prior to its practical realization. Here, it is possible to obtain or develop a model of any solid-state device or components and even any segment of the power converting circuit. There is no need to sense the polarity of the input voltage as it is mandatory for real-time implementation, as generated control inputs are inherently aligned with source voltage. The switching transistor was modeled by using an ideal block of a fully operating switch. Its internal parameters were modified in accordance with the values obtained from the datasheet of the transistor used in the circuit employed for practical verification. An inductor of 1 mH was connected in series with the load, and a 4.7 uF capacitor was connected across the 150 \(\Omega\) load, as shown in the proposed circuit in Figure 2. The voltage gain of the converter for the simulation result was set to 0.5, and an improvement in the power quality of the output was gained with a switching frequency of 25 kHz. The plots of Figure 6a,b validate that the proposed topology can produce non-inverting and inverting forms of the input voltage, and their value is governable with the adjustment of the duty cycle control. The plot of Figure 6c indicates that step variation in the load frequency can be made possible with the proper setting of the non-inverting and inverting forms of the input voltage. In the present form, there are two consecutive positive and two negative pulses that double the period of the output voltage with respect to the input. Increment in the output period lowers its frequency accordingly. An alternative way can be adopted to set some other values of the output frequencies.
# Characteristics of Summer Flash Drought and Its Effect on Maize Growth in Liaoning Province, China
## 3 Results
### Classification and Causes of Flash Drought in Typical Years
#### 3.3.2 Atmospheric Circulation Patterns Coincident with Flash Drought  
The atmospheric circulation patterns that coincided with the summer flash droughts in Liaoning in 1989, 1997, and 2018 were analyzed by combining the mean potential height field in the middle and upper troposphere (200 hpa and 500 hpa), the anomaly field, and the West Pacific subtropical high index.  
In 1989, the geopotential height over Liaoning was a weakly negative anomaly area, and the position of the western ridge of the subtropical high was situated abnormally eastward compared with that in a normal year. This was unfavorable for the northward movement of low-latitude systems, such as the equatorial convergence zone and typhoons, which blocked the water vapor source for precipitation. Concurrently, the position of the westerly belt was westward and northward, resulting in a decrease in the activity of cold  
Figure 19: Changes in drought severity category derived from the evaporative demand drought index (EDDI) in 2018: (**a**) 25 July; (**b**) 1 August; (**c**) 7 August; (**d**) 15 August. (Note: D0: no drought; D1: weak drought; D2: moderate drought; D3: severe drought; D4: extreme drought; D5: exceptional drought).  
in the following section.  
In 1997, the geopotential height over Liaoning was a positive anomaly area, which extended westward to the vicinity of Mongolia, showing a latitude-zonal distribution. In the study region, the abnormal high-pressure ridge was active in summer, and the sinking airflow was prevalent, which was not conducive to precipitation. The East Asian coast and the Western Pacific in the latitudinal range of 20-35\({}^{\circ}\) N showed a negative anomaly distribution. The negative anomaly center was located in the coastal areas of Central and Eastern China, which was situated on the northwestern side of the subtropical high. The  
Figure 20: Gross primary productivity (GPP) anomaly in the summer of 2018: (a) June; (b) July; (c) August.  
position of the subtropical high was to the south and east, and the intensity was weak, which was not conducive to the northward transport of water vapor (Figure 22b).  
Figure 21: Maize yield reduction per unit area anomaly in 2018.  
Figure 22: Cont.  
In 2018, a strong positive anomaly at the height of 500 hpa was distributed over mainland China, Japan, and the surrounding areas north of 30\({}^{\circ}\) N. In summer, the active abnormal high pressure and the prevalent sinking airflow in the region were unfavorable for precipitation. Meanwhile, the superimposed subtropical high and the South Asian high on each other formed a deep high-pressure system with the same phases at high and low altitudes. An anomalous anticyclonic circulation formed a deep quasi-positive pressure structure, which led to sinking airflow being prevalent and resulted in continuous rainlessness and high temperatures in Liaoning (Figure 22c).
# Computationally Efficient Soft Detection Schemes for Coded Massive MIMO Systems +
## 2 Related Works  
### Uplink System Model with JIDD  
In this paper, we consider a coded massive MIMO system equipped with \(N\) receive antennas at the BS for \(M\) transmit antennas (\(N>>M\)), as shown in Figure 1. At the transmitter, we encode the information vector \(\mathbf{u}\) to produce the codeword \(\mathbf{c}\). Then, \(M\times K\) codewords are interleaved by an interleaver and are modulated successively before going through the channel, where \(K\) denotes the number of bits per transmitted symbol. Every \(MK\) bits from the interleaver, i.e., \(\mathbf{x}=[x_{1,1},\cdots,x_{1,K},x_{2,1},\cdots,x_{m,k},\cdots,x_{M,K}]\) are grouped and modulated to the transmitted symbol vector \(\mathbf{s}=[s_{1},s_{2},\cdots,s_{m},\cdots,s_{M}]^{T}\), where \(x_{m,k}\) is the \(k\)th bit of the transmitted symbol from the \(m\)th antenna, \(s_{m}\), which is independently mapped from a complex constellation \(O\). The received symbol vector \(\mathbf{y}=[y_{1},y_{2},\cdots,y_{m},\cdots,y_{N}]^{T}\) can be represented as follows:  
\[\mathbf{y}=\mathbf{H}\mathbf{s}+\mathbf{n}, \tag{1}\]  
where \(\mathbf{H}\) is an \(N\times M\) complex-valued channel matrix whose entries are independent and identically distributed with zero mean and unit variance, and \(\mathbf{n}\) is Gaussian noise vector whose elements are independent zero-mean complex Gaussian random variables with variance \(\sigma^{2}\) per dimension.  
At the receiver with JIDD scheme, there are three loops working together to improve the overall performance. Loop 1 denotes the iterations inside the channel decoder, Loop 2 is used to exchange soft information between the detector and decoder, and Loop 3 is inside the MMSE-PIC detector. First, SBI \(L\) is calculated by the MMSE-PIC detector. A detailed process to estimate \(L\) can be found in [4], and it is briefly discussed in Section 2.2. Afterwards, \(L\) is subtracted by the _a priori_ information, \(L_{a}^{d}\) from the channel decoder. Then, the extrinsic information is produced; \(L_{e}^{c}=L-L_{a}^{d}\). \(L_{e}^{c}\) is passed through the de-interleaver and its de-interleaved version, \(L_{a}^{c}\) is used by the channel decoder as the _a priori_ information. After decoding, the soft output \(L_{o}^{c}\) is generated and the extrinsic information for MMSE-PIC is calculated by \(L_{e}^{d}=L_{o}^{c}-L_{a}^{c}\). In addition, its interleaved version, \(L_{a}^{d}\), is fed back to the MMSE-PIC detector as the _a priori_ information.
# Research on Supply Chain Network Resilience: Considering Risk Propagation and Node Type
## 4 Numerical Simulation  
### Simulation Setup  
The full parameters of the proposed SCN risk propagation model include \(N\), \(\alpha\), \(\beta\), \(T\), \(\varepsilon\), \(\theta\), \(S\), \(R\), \(M\), and \(D\). They are divided into fixed and variable parameters. The fixed parameters are the size of an SCN (\(N=1000\)), the infection rate (\(\alpha=0.35\)), the recovery rate (\(\beta=0.50\)), risk propagation steps after reaching a steady state (\(T=80\)), and the number of nodes with initial risk (\(\varepsilon=10\)). The effects of these fixed parameters have been studied extensively [24; 25; 53], and, hence, to avoid duplication we only consulted their parameter settings. The studied network characteristic parameters here include the node-type ratio \(S/R/M/D\), scalar index \(e\), and clustering coefficient \(q\). During the experiments, the fixed parameters were kept constant and only the network characteristics parameters were adjusted. To improve statistical stability, the experiments were repeated 100 times independently under each network characteristic parameter and the results of these experiments were averaged.  
When studying the effect of e on SCNR, the fixed parameters were set as \(q=0.2\), \(S/R/M/D=1\):1:1:1, and \(e\) was adjusted from two to three with a step size of 0.1. When studying the effect of \(q\) on SCNR, the fixed parameters were set as \(e=3\), \(S/R/M/D=2\):3:3:2, and \(q\) was adjusted from 0 to 0.45 with a step size of 0.05. When studying the effect of \(S/R/M/D\) on SCNR, the fixed parameters were set as \(e=3\), \(q=0.2\), and six different groups of \(S/R/M/D\) were randomly selected (i.e., 1:1:1., 7:1:1:1, 2:2:15:1, 2:2:1:20, 35:2:2:1, and 1:1:47:1). When studying the effect of the type of initial risk node on SCNR, the fixed parameters were set as \(e=3\), \(q=0.2\), and \(S/R/M/D=1\):1:1:1. We used two methods of risk propagation. The first way was to choose one category among \(S\), \(R\), \(M\), and \(D\) as the initial risk nodes. The second way was to randomly select nodes in the SCN for risk propagation.  
In summary, we will address the following four questions: (1) How do the scalar indices affect the SCNR? (2) How do the clustering coefficients affect SCNR? (3) How do the node-type ratios affect SCNR? (4) How do the types of initial risk nodes affect SCNR?
## 2 Study Area and Data Processing
### Satellite Precipitation Data
#### 2.3.2 IMERG Version 05B Products  
IMERG is the unified U.S. algorithm that provides multi-satellite precipitation products for the U.S. GPM team [26]. The latest IMERG version 05B (V05B) products were released to the public in January 2018. IMERG V05B uses the 2017 version of the Goddard Profiling Algorithm to retrieve precipitation estimates from the GPM constellation using various precipitation-relevant satellite PMW sensors. Then the precipitation estimates are gridded and inter-calibrated into the GPM combined instrument product, further interpolated, and re-calibrated by the CPC Morphing-Kalman Filter Lagrangian time interpolation and the PERSIANN-Cloud Classification System recalibration schemes. The IMERG system is run twice in near-real time and provides IMERG-E and IMERG-L with 4-h and 12-h latencies, respectively. Moreover, IMERG adopts the GPCC monthly gridded gauge-analysis precipitation data set to adjust the near-real-time products and provides IMERG-F with a latency of two and half months. These products have spatial and temporal resolutions of 0.1\({}^{\circ}\) and 30 min, respectively. In this study, IMERG-E, IMERG-L, and IMERG-F half-hourly SPPs from 1 April 2014 to 31 December 2016 were acquired from the PMM website [64]. Precipitation values in the six sequential half-hourly precipitation files were summed up, and the time difference between local time and UTC (+6.5 h) were considered to derive the IMERG precipitation estimates at 3-h intervals. The three IMERG products were aggregated from a 0.1\({}^{\circ}\) to 0.25\({}^{\circ}\) spatial resolution using the approach suggested by [PERSON] et al. [47] to facilitate flood simulations on a spatial resolution of 0.25\({}^{\circ}\).
# PBR Clutter Suppression Algorithm Based on Dilation Morphology of Non-Uniform Grid
## 3 Separate False Alarm Clutter from Data  
We utilize the location dependency of target track within consecutive frames to separate false alarm clutter from data. To reduce the computation load, the method in this section is proposed based on the grid constructed in Section 2. More specifically, in this section, we resort to morphology, usually applied in image processing, to achieve clutter separation.  
The process is divided into three steps. Firstly, mark the point on the polar grid according to its position measured. Secondly, separate false alarm clutter point from data based on morphology algorithm. Thirdly, iteratively complete the operation frame by frame.
## 2 Materials and Methods
### Materials Characterization  
The prepared products were characterized by scanning electron microscopy (SEM) using a JCM-6000 electron microscope (JEOL, Rueil Malmaison, France) to observe the surface morphology of AC and AC-OH. The chemical composition of AC before and after adsorption and AC-OH were characterized through Fourier transform IR spectroscopy (FTIR) using a Tensor 27 (Bruker, Wissembourg, France) spectrometer with a ZnSe ATR crystal device. For each spectrum, 20 scans were accumulated with a resolution of 4 cm\({}^{-1}\). The thermal stability of AC and AC-OH was analyzed by differential scanning calorimetry (DSC) using a DSC-92 (Setaram, Caluire et Cuire, France) device at a heating rate of 5 \({}^{\circ}\)C min\({}^{-1}\) from room temperature to 550 \({}^{\circ}\)C. The zeta potential of each sample dispersion was measured in phase analysis light scattering (PALS) mode using a Zeta sizernanoZS setup (Malvern, Palaiseau). For the zeta measurements, nanoparticle suspension was obtained by adding 100 mg of each sample to 10 mL of ultrapure water.
## 2 Related Work
### Channel Interference
#### 2.2.2 Channel Interference in 802.11 Networks  
Two types of interference in the 802.11 network have been proposed by [PERSON] et al. [13]: one is the co-channel interference, which is caused by the transmission on the same frequency channel, and the other is the adjacent channel interference, which is caused by the transmission on the adjacent channels or overlapped channels. [PERSON] et al. [33] analyzed the adverse effects of adjacent channel interference in 802.11 networks. [PERSON] et al. [34] evaluated the effects of adjacent channel interference through extensive experiments. Previous studies on channel interference in 802.11 networks were mainly focused on how to allocate channels for these WiFi nodes to avoid co-channel interference and how to prove adjacent channel interference to assist the radio resources of different management mechanisms. Unlike previous work, which considered channel interference unfavorable on transmission systems, this paper leverages channel interference to defeat the CSI-based attack.
# Enhancing Underwater Images via Color Correction and Multiscale Fusion
## 4 Results
### Quantitative Evaluation  
In the quantitative evaluation, we selected three full-reference indicators to assess the quality of the underwater image shown in the figure: edge intensity (AG) [3], information entropy (IE) [3], and patch-based contrast quality index (PCQI) [35]. Furthermore, we employed two non-reference indicators, namely the underwater image quality measurement (UIQM) [37] and the underwater color image quality-evaluation index (UCIQE) [38], to evaluate the quality of the underwater images.  
AG is mainly used to represent the clarity of images, while IE is used to describe the average information content of underwater images. PCQI is used to evaluate the local contrast of underwater images. The UIQM is a comprehensive evaluation indicator  
Figure 4: Enhancement effects of different methods, including ARC, Fusion, GDCP, IBLA, TS, UDCP, MLLE, ACCDO, and our method. Enhancement effects of different methods on blue images, including ARC, Fusion, GDCP, IBLA, TS, UDCP, MLLE, ACCDO, and our method.  
Figure 5: Enhancement effects of different methods, including ARC, Fusion, GDCP, IBLA, TS, UDCP, MLLE, ACCDO, and our method. Enhancement effects of different methods on green images, including ARC, Fusion, GDCP, IBLA, TS, UDCP, MLLE, ACCDO, and our method.  
that includes color, clarity, and contrast. The UCIQE is a linear combination of color concentration, saturation, and contrast. We strive to comprehensively evaluate the existing methods and the methods presented in this article through these indicators.  
To further demonstrate the effectiveness of our algorithm, we evaluated the entire UIEB dataset, including UIEBR and UIEBC. Tables 2 and 3 present the performance indicators of this method and other methods. Our algorithm achieves the highest scores in IE, the UIQM, and the UCIQE on UIEBR and UIEBC, while its AG and PCQI scores are lower than those of the MLLE algorithm. Nonetheless, our algorithm still demonstrates competitiveness, and compared to mainstream methods, the MLLE algorithm performs better in handling the details and local contrast of degraded images, which is an area for improvement in our future work.
## 4 Experimental Evaluation
### Effectiveness of Detecting Varroosis  
The classification model for detecting _Varroa destructor_ was also prepared with the AutoML Vision Edge Service. This time, the training set consisted of 10,743 images that included 5748 pictures of healthy bees and 4995 pictures of bees infected by _Varroa destructor_ (unhealthy ones). Since there was no sufficient amount of pictures with infected bees, we used image augmentation techniques to prepare the training set by randomly rotating, shifting, and locating the _V. destructor_ on chosen parts of the bee bodies. To assess the effectiveness, the experiment itself took under analysis 50 pictures of bees (the test set) that were not a part of the training set used during the learning phase. For this series of experiments, we obtained the following results--the precision (PPV) of the model reached nearly 0.7, sensitivity (TPR)--0.94, while the F1 score was at the level of 0.8.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Image No.** & **TP** & **FP** & **FN** & **TN** & **PPV** & **TPR** & **F1** \\ \hline
1 & 9 & 0 & 2 & 0 & 1 & 0.81 & 0.9 \\ \hline
2 & 8 & 0 & 2 & 0 & 1 & 0.80 & 0.89 \\ \hline
3 & 4 & 0 & 0 & 0 & 1 & 1 & 1 \\ \  
Additionally, for presenting the effectiveness of the varroosis detection model, we also calculated the specificity (True Negative rate, TNR):  
\[TNR=\frac{TN}{N}=\frac{TN}{TN+FP}. \tag{4}\]  
The specificity of the varroosis detection model was 0.92. Taking into account all the above rates, the outcome of this experiment is satisfactory.