esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Examples
### The 47-Bar Planar Truss
The optimal design obtained by CSBO has a weight of 1881.795 lb after 8910 structural analyses, which is 0.9% heavier than the optimal design found by NCSBO. Moreover, CSBO required 1320 additional structural analyses to reach its result. These findings clearly demonstrate that NCSBO exhibits superior performance compared to standard CSBO. Figure 19 illustrates the optimized configurations of the 47-bar truss, accompanied by a color map illustrating the stress capacity ratio of each element under the most unfavorable loading condition. Figure 20 exhibits color map representations illustrating the stresses corresponding to each loading case. The maximum member stresses for the optimized design found by CSBO are 19.99994 ksi, 12.94114 ksi, and 19.99994 ksi for the first, second, and third loading conditions, respectively. The minimum stresses for these conditions are \(-14.99999\) ksi, \(-14.99995\) ksi, and \(-14.99998\) ksi. For the NCSBO design, the maximum member stresses are 19.99956 ksi, 12.940895 ksi, and 19.99956 ksi across the same loading conditions, with minimum stresses of \(-14.99987\) ksi, \(-14.99781\) ksi, and \(-14.99997\) ksi. These values indicate that the member stresses remain within the specified limits of [\(-15\), 40 ksi], as illustrated in Figure 21. Furthermore, Figure 21 illustrates the stress states relative to the  
boundary buckling values, which vary according to the length and cross-sectional area of each element. None of the elements surpass the stress limits, indicating the structural integrity and robustness of the truss's design under the considered loading conditions. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No. **} & \multirow{2}{*}{ \begin{tabular}{c} **Design** \\ **Variables** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **SCPSO** \\ **[**56**]** \\ \end{tabular}  } & \multirow{2}{*}{**BBM [**67**]**} & \multirow{2}{*}{**GA [**68**]} & \multirow{2}{*}{ \begin{tabular}{c} **D-ICDE** \\ **[**61**]** \\ \end{tabular}  } & \multi  
The convergence curve of NCSBO is compared with CSBO, as depicted in Figure 22. The optimum solutions are achieved after 7590 and 8910 analyses for NCSBO and CSBO, respectively. It was observed that NCSBO shows a faster convergence trend compared to CSBO in the early stages of the optimization process. However, in the later stages, the convergence behaviors of both methods become similar, with each eventually reaching the final optimum solution and remaining stable until the maximum number of analyses is reached. Figure 19: The optimized shapes of the 47-bar truss with a color map of stresses in members by (**a**) CSBO and (**b**) NCSBO. Figure 21: Comparison of member stresses in the optimized design of a 47-bar truss by **(a)** CSBO and **(b)** NCSBO. Figure 20: Color map of stresses in members of the 47-bar truss for three independent loading conditions optimized by **(a)** CSBO and **(b)** NCSBO.
# Analysis of MTF in TDI-CCD Subpixel Dynamic Super-Resolution Imaging by Beam Splitter
## 2 Sampling MTF of Subpixel Dynamic Super-Resolution Imaging
### MTF of Over-Sampling with Linear CCDs  
Assuming the linear CCD pixel size is \(p\) and its duty cycle is 100%, the over-sampling result of CCD pair in Figure 2 with the staggered offset \(a\) can be shown in Figure 4c.  
Figure 3: Partition results of the charge coupled device (CCD) sampling interval. (**a**) Partition result of traditional CCD; (**b**) Partition result of irregular CCD.  
We selected the center of each CCD pixel as the coordinate origin to establish a coordinate system. For the results from the new sampling, the Wigner-Seitz lattice is asymmetric. The intervals of Wigner-Seitz lattice are: \(-(p-a)/2\sim a/2;\;-a/2\sim(p-a)/2\).  
The MTF of the sampling results is (The derivation details can be found in Appendix A):  
\[MTF_{2}=\sin c(\pi fp)\{\frac{p-a}{p}\sin c[\pi f(p-a)]+\frac{a}{p}\sin c(\pi fa)\} \tag{2}\]  
For obtaining the condition of limitation, we set \(\frac{d(MTF_{2})}{da}=0\), the result is as below:  
\[\cos[\pi^{2}f(p-a)]=\cos(\pi^{2}fa) \tag{3}\]  
As Equation (3) can be applied to all types of \(f\), when \(p-a=a\) and \(a=p/2\), \(MTF_{2}\) may has the maximal value.  
According to the above analysis, assuming that there are \(n\) pair of linear CCDs participated in the subpixel imaging with the staggered offset values of \(a_{i}(i=1,2,\cdots,2n-1)\) respectively, the over-sampling MTF can reach its maximal value \(MTF_{mL}\) when \(a_{1}=a_{2}=\cdots=a_{2n-1}=p/2n\). (The detailed derivation is shown in Appendix A).
# A Miniaturized and Fast System for Thin Film Thickness Measurement
## 2 Measurement Scheme  
Based on the principle of thin-film interference and confocal spectral imaging, this paper built a low-cost and fast-speed thin-film thickness measurement system to study the thickness calculation algorithm of the thin film. As shown in Figure 1, a Light Emitting Diode (LED), a micro-spectrometer, and a self-designed confocal achromatic probe are used to realize the miniaturization of the measurement system.  
The light intensity changes periodically in a coherent region with destructive and constructive phenomena. For dielectric films, if the absorption of light by the film is not considered, the total interference intensity of the reflected light beam incident on the film can be approximately expressed as  
\[\begin{array}{l}I_{r}(z,\lambda)=I_{r1}+I_{r2}+2\sqrt{I_{r1}\cdot I_{r2}}\cdot \cos(2\pi z/\lambda+\phi_{0})\\ =I_{0}(\lambda)\cdot R_{01}+I_{0}(\lambda)\cdot T_{01}R_{1s}T_{10}+2I_{0}( \lambda)\sqrt{R_{01}T_{01}R_{1s}T_{10}}\cdot\cos(2\pi z/\lambda+\phi_{0})\end{array} \tag{1}\]  
where \(\lambda\) is the wavelength and \(\phi_{0}\) is the additional phase. \(R_{01}\) and \(T_{01}\), respectively, represent the reflectance and transmittance from the air to the film. \(R_{1s}\) and \(T_{10}\), respectively, represent the reflectance from the film to the substrate and the transmittance from the film to the air. For the case of normal incidence, the optical path difference \(z=2n_{1}\times d\) is determined by the film's refractive index \(n_{1}\) and thickness \(d\), where the refractive index is treated as a constant to improve computational efficiency.  
The envelope of the spectral interference signal measured by the spectrometer is approximately a Gaussian distribution. The phase term \(2\pi\varepsilon/\lambda+\phi_{0}\) caused by the optical path difference \(z\) determines the density of the interference fringes. As the core algorithm of the PPS analysis, Empirical Mode Decomposition (EMD) and Lomb-Scargle periodogram (LSP) process the high-frequency spectral interference information successively to extract the film thickness.
## 3 Results and Discussion
### Test Performance of the Control Parameter Self-Calibrating System  
The control model was based on the calibration relationship model between the mass flow rate and rotation speed. The calibration relationship model was established before the operation with no model parameters being updated during the process. The closer the calibration relationship model was to the actual relationship model, the higher the control accuracy. However, the actual relationship model was quite different from the original model because of the impact from working conditions. Hence, a dynamic calibration of model parameters was required during the process. In the first and second test stages, the parameters of the actual relationship model were detected based on the measured data from a certain period, but no update of the parameters was introduced. In the third test stage, the test procedure called for an update to the control model parameters to improve the control performance. To evaluate the quality of the operation of the seed-fertilized drill machine, an evaluation indicator \(E_{Q}\) was introduced, as shown in Equation (5).  
\[E_{Q}=\left|\frac{W_{real}}{W_{rotation}}-1\right|\times 100\%, \tag{5}\]  
where \(W_{real}\) is the weight of materials collected by the collection container and \(W_{rotation}\) is the cumulative application rate, calculated using the relationship model of the mass flow rate and rotational speed of the fluted rollers.  
The evaluation indicator of each test stage is shown in Figure 12, and related statistical results are listed in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Stage I**} & \multicolumn{4}{c}{**Stage II**} & \multicolumn{4}{c}{**Stage III**} \\ \cline{2-10}  & **avg** & **Std** & **max** & **avg** & **Std** & **max** & **avg** & **Std** & **max** \\ \hline Seed & 0.19 & 0.12 & 0.39 & 2.70 & 1.49 & 4.92 & 7.29 & 1.16 & 9.26 \\ Fertilizer & 1.21 & 0.82 & 1.97 & 3.56 & 0.21 & 3.82 & 7.86 & 3.06 & 11.65 \\ \hline \hline \end{tabular}
**Note:** avg is the average relative deviation of \(E_{k}\), Std is the standard deviation of the relative deviation of \(E_{k}\), and max is the maximum relative deviation of \(E_{k}\).  
\end{table}
Table 2: Statistic data of the relative deviation of model coefficient, \(E_{k}\) (%).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Stage I**} & \multicolumn{4}{c}{**Stage II**} & \multicolumn{4}{c}{**Stage III**} \\ \cline{2-10}  & **avg** & **Std** & **max** & **avg** & **Std** & **max** & **avg** & **Std** & **max** \\ \hline Seed & 0.59 & 0.34 & 1.00 & 3.39 & 1.16 & 4.73 & 0.97 & 0.69 & 1.61 \\ fertilizer & 1.94 & 0.62 & 2.68 & 4.88 & 0.39 & 5.25 & 1.22 * & 0.62 * & 2.00 * \\ \hline \hline \end{tabular}
**Note:** The results with * in the table are the calculation results after removing the singular point value, and if not, the corresponding results are 1.86, 1.50, and 5.00 from left to right in the table.  
\end{table}
Table 3: Statistical data of the relative deviation of cumulative application rate, \(E_{Q}\) (%).
# Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction
## 3 Results and Discussion
### Experimental Result and Analysis
#### 3.4.1 Results from the Indian Pines Dataset
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Class** & **Samples** & **SVM** & **EPF** & **SCMK** & **R2 MK** & **ASMGSSK** & **MsuperPCA** & **2 DSSA** & **2 DMSSA** & **SP-SSA** \\ \hline Alfalfa & 46 & 12.65 & 22.22 & 62.79 & 92.86 & **95** & 87.8 & 54.55 & 87.8 & 92.11 \\ Corn-notill & 1428 & 75.18 & 92.85 & 92.55 & 94.06 & 94.85 & 97.09 & 91.81 & 95.25 & **98.25** \\ Corn-mintill & 830 & 82.46 & 83.44 & 90 & 93.32 & 97.37 & 90.11 & 88.96 & 96.65 & **97.99** \\ Corn & 237 & 47.86 & 79.39 & 85.59 & 95.41 & 96.14 & **99.53** & 80.89 & 89.2 & 96.98 \\ Grass-pasture & 483 & 69.25 & 68.04 & 89.67 & 97.3 & **99.05** & 95.81 & 90.61 & 92.41 & 97.04 \\ Grass-trees & 730 & 80.19 & 96.72 & **99.56** & 98.36 & 99.21 & 98.61 & 93.23 & 99.54 & 99.51 \\ Grass-pasture-mowed & 28 & 88.89 & 88.46 & 69.23 & 80 & **100** & 96 & 92.59 & 92 & **100** \\ Hay-windrowed & 478 & 93.46 & **100** & 98 & 98.18 & 98.32 & 95.07 & 98.46 & 99.54 & 99 \\ Oats & 20 & 36.12 & 47.37 & **100** & 52.63 & 47.06 & 94.12 & 42.11 & 83.33 & **100** \\ Soybean-notill & 972 & 76.74 & 82.96 & 88.29 & 90.16 & 96.57 & 95.27 & 87.12 & **96.91** & 93.63 \\ Soybean-mintill & 2455 & 79.51 & 97.03 & 95.54 & 97.48 & 98.5 & 96.66 & 93.1 & 97.19 & **99.03** \\ Soybean-clean & 593 & 50 & 86.99 & 93.18 & 95.79 & 97.29 & 95.27 & 90.96 & 96.07 & **98.6** \\ Wheat & 205 & 99.01 & 96.94 & 92.23 & **100** & 97.21 & 98.9 & 99.48 & 98.91 & **100** \\ Woods & 1265 & 88.74 & 99.26 & 98.91 & 99.48 & 99.27 & 99.02 & 96.42 & **99.82** & 99.62 \\ Buildings-Grass-Trees-Drives & 386 & 65.18 & 87.87 & **99.45** & 94.37 & 96.73 & 88.95 & 90.16 & 94.24 & 98.46 \\ Stone-Steel-Towers & 93 & 41.32 & 97.75 & 72.73 & 92.94 & 97.5 & 93.9 & 97.75 & **98.81** & 89.74 \\ \hline
**OA:** & 76.42 & 91.25 & 93.82 & 95.98 & 97.54 & 96.04 & 91.95 & 96.83 & **98.15** \\
**AA:** & 67.91 & 82.96 & 89.23 & 92.02 & 94.38 & 95.13 & 86.76 & 94.86 & **97.5** \\
**K:** & 73.05 & 89.98 & 92.95 & 95.32 & 97.2 & 95.49 & 90.82 & 96.39 & **97.89** \\ \hline \end{tabular}
\end{table} 
Table 2: Classification results for the Indian Pines Dataset with 3% training for SVM, EPF, SCMK, R2 MK, ASMGSSK, MsuperPCA, 2D-SSA, 2D-MSSA, and SP-SSA algorithms.
# Enhancing Beamforming Efficiency Utilizing Taguchi Optimization and Neural Network Acceleration
## 4 Neural Networks for Synthesis and Optimization of Antenna Arrays
### Taguchi-Neural Network Architectures
This approach would enable more efficient spectrum utilization, improved quality of service, and greater reliability in wireless communications. Figure 12: SOM- topology and neighbor connections.
## 5 Proposed Optimization Problem
### Objective Functions  
The primary objective functions of the proposed multi-objective optimization problem are as follows. First, the minimization of operating costs aims to reduce the total expenses associated with energy consumption and the operation of backup storage systems. The cost function considers the expenses related to purchasing electricity from the grid, maintaining renewable energy systems, and running backup sources such as batteries and FCs. Second, the minimization of the LPSP focuses on ensuring a consistent and dependable power supply to fulfill the load requirements, thus minimizing the probability of power shortages. Third, the minimization of surplus power wasted in a dummy load seeks to optimize the use of generated power by reducing the amount of excess energy that cannot be stored or used effectively, thereby minimizing waste. The multi-objective problem is represented by these equations:  
\[\textit{Minimize}\ f(x)=\begin{bmatrix}f_{1}(x)\\ f_{2}(x)\\ \cdot\\ \cdot\\ f_{n}(x)\end{bmatrix},\ \textit{subjected}\ to\begin{pmatrix}G(x)\leq 0\\ H(x)=0\end{pmatrix} \tag{21}\]  
\[f_{1}=\text{Min}\ (C_{OM}) \tag{22}\]  
\[f_{2}=\ \text{Min}\ (\text{LPSP}) \tag{23}\]  
\[f_{3}=\text{Min}\ (P_{Sur}) \tag{24}\]  
where \(x=[x_{1},x_{2},.........,x_{m}]\) denotes control vectors, \(H(x)\)_and_\(G(x)\) denote the equality and inequality constraints, respectively, and \(f(x)=f_{1}(x),f_{2}(x),f_{3}(x),.....f_{m}(x)\) denote objective function values.  
The multi-objective optimization problem presented seeks to simultaneously minimize multiple objectives, including operation and maintenance costs (\(f_{1}\)), the loss of power supply probability (\(f_{2}\)), and surplus power (\(f_{3}\)), subject to certain constraints. Minimizing \(C_{OM}\) aims to reduce the costs associated with running and maintaining the system, while minimizing LPSP enhances system reliability by reducing the likelihood of power shortages. Additionally, minimizing \(P_{Sur}\) improves system efficiency by reducing excess power generation.
# Recent Progress in Fast Distributed Brillouin Optical Fiber Sensing
## 4 Developments of the Fast BOTDA Schemes
### Slope-Assisted Fast BOTDA  
Different from the classical BOTDA that needs curve-fitting of the whole BGS to get the sensing curve, an SA-BOTDA [39; 40] is implemented by selecting one of the two slopes of the BGS as the sensing curve. As shown in Figure 8, the central part of the left slope of the BGS can be simply regarded as a linear section. After fixing the probe wave's frequency in the middle of this linear section (i.e., \(N_{f}=1\)), the intensity of the Brillouin signal will be changed as the whole BGS as well as the BFS, which is shifted by the environmental strain or temperature. After injecting the one-shot pump pulse (i.e., \(N_{code}=1\)), the distributed BFS along the fiber can be easily demodulated by substituting the intensity of the Brillouin signal into the expression of the pre-fitted linear section. It should be noted that, based on Equation (6) and without averaging, the maximum sampling rate is also confined by the fiber length \(L\). However, the frequency variation of the SA-BOTDA is limited within 30-MHz corresponding to the linewidth of the BGS. To increase the dynamic range, recent researchers are concentrating on increasing the slope number and enlarging the available dynamic range of the single monotonous slope of the sensing curve.  
Figure 7: The fast BOTDA based on the dual modulation scheme [38]. (**a**) The dual modulation schematic diagram. The damped oscillation measurement is demonstrated as (**b**) the time-evolution of the BGS at the vibration point.
## 2 Theory and Formulation
#### Displacement Fields  
Based on HSDT, the displacement field can be stated as [71]:  
\[\begin{split} u(x,y,z,t)&=u_{0}(x,y,t)+z\phi_{x}(x,y, t)-c_{1}z^{3}\times\Big{[}\frac{\partial u_{0}(x,y,t)}{\partial x}+\phi_{x}(x,y,t) \Big{]}\\ v(x,y,z,t)&=v_{0}(x,y,t)+z\phi_{y}(x,y,t)-c_{1}z^{3} \times\Big{[}\frac{\partial u_{0}(x,y,t)}{\partial y}+\phi_{y}(x,y,t)\Big{]}\\ w(x,y,z,t)&=w_{0}(x,y,t)\end{split} \tag{5}\]  
In the Equation (5), \(u\), \(v\), and \(w\) show displacement components at any point in the structure. Furthermore, \(u_{0}\), \(v_{0}\), and \(w_{0}\) represent the displacement components at a point on the midplane of the plate around axial, circumferential, and thickness directions of the system, respectively. \(\phi_{x}\) and \(\phi_{y}\) are the rotation of the normal to the element middle plane in the circumferential and axial directions, respectively. In addition, \(c_{1}\) is expressed as 4/3h\({}^{2}\) in HSDT. The strain components can be given by:  
\[\left\{\begin{array}{l}\varepsilon_{xx}\\ \varepsilon_{yy}\\ \gamma_{xy}\\ \gamma_{xz}\\ \gamma_{yz}\end{array}\right\}=\left[\begin{array}{c}\frac{\partial u}{ \partial x}+\frac{w_{0}}{R_{1}}\\ \frac{\partial v}{\partial y}+\frac{w_{0}}{R_{2}}\\ \frac{\partial u}{\partial y}+\frac{\partial v}{\partial x}\\ \frac{\partial u}{\partial z}+\frac{\partial w}{\partial x}\\ \frac{\partial v}{\partial z}+\frac{\partial w}{\partial y}\end{array}\right.\] (6a) Finally, by replacing Equation (5) in Equation (6a), we have:  
\[\left\{\begin{array}{l}\varepsilon_{xx}\\ \varepsilon_{yy}\\ \gamma_{xy}\\ \gamma_{xz}\\ \gamma_{yz}\end{array}\right\}=\left[\begin{array}{c}\frac{\partial u_{0}} {\partial x}+z\frac{\partial\phi_{x}}{\partial x}-z^{3}c_{1}\Big{(}\frac{ \partial\phi_{x}}{\partial x}+\frac{\partial^{2}w_{0}}{\partial x^{2}}\Big{)} +\frac{w_{0}}{R_{1}}\\ \frac{\partial v_{0}}{\partial y}+z\frac{\partial\phi_{x}}{\partial y}-z^{3}c _{1}\Big{(}\frac{\partial\phi_{y}}{\partial y}+\frac{\partial^{2}w_{0}}{ \partial y^{2}}\Big{)}+\frac{w_{0}}{R_{2}}\\ \frac{\partial u_{0}}{\partial y}+\frac{\partial v_{0}}{\partial x}+z\Big{(} \frac{\partial\phi_{x}}{\partial y}+\frac{\partial\phi_{y}}{\partial x}\Big{)} -z^{3}c_{1}\Big{(}\frac{\partial\phi_{x}}{\partial y}+\frac{\partial\phi_{y}} {\partial x}+2\frac{\partial^{2}w_{0}}{\partial x\partial y}\Big{)}\\ (1-3z^{2}c_{1}\Big{)}\Big{(}\phi_{x}+\frac{\partial w_{0}}{\partial x}\Big{)} \\ (1-3z^{2}c_{1}\Big{)}\Big{(}\phi_{y}+\frac{\partial w_{0}}{\partial y}\Big{)} \end{array}\right] \tag{6b}\]  
It should be noted that our displacement field satisfies stress-free boundary conditions (\(\sigma_{xz}|_{x,y,zh/2}=\sigma_{yz}|_{x,y,zh/2}\)) and surface effects are not taken into account.
## 4 Results and Discussion
### Cross Analysis: RQ1 (CVs) vs. RQ2 (RE Activities)  
This section compares and contrasts the results obtained from RQ1 and RQ2. Figure 3 shows the support level provided by ontologies and taxonomies to each of the RE activities. Table 11 shows the coverage of primary papers for every combination of CV and RE activity.  
The cross analysis of RQ1 vs. RQ2 enables us to thoroughly assess the level of support given by CVs with respect to each stage of the RE lifecycle. The results of this comparative analysis depict convergence as Specification, Elicitation and Analysis of RE stages on average are still mostly supported by Ontologies and Taxonomies. Besides, the ratio of the support offered by the CVs to each RE stage is, in general, abundant.  
Some of the key insights that are evident from Table 11 are: (1) Elicitation, Specification and Analysis stages of RE are considerably more supported by both ontologies and taxonomies so it is worth investigating how these two types of CVs specifically can be enhanced to bring more support to the remaining stages of RE lifecycle (Validation and Change Management). Nonetheless, the remaining  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type of CV** & **Elicitation** & **Specification** & **Analysis** & **Validation** & \begin{tabular}{c} **Change** \\ **Management** \\ \end{tabular} & **Other** \\ \hline \multirow{8}{*}{**Ontology**} & S02, S08, S12, & \multirow{8}{*}{\begin{tabular}{c} S04, S05, S17, \\ S16, S27, S28, \\ S29, S32, S34, \\ S40, S55, S59, \\ S61, S62, S65, \\ S66, S70, S73, \\ S79, S82, S84, \\ S86, S87, S88 \\ \end{tabular} } & S04, S05, S17, & \multirow{8}{*}{\begin{tabular}{c} S05, S09, S15, \\ S25, S30, S33, \\ S34, S36, S40, \\ S41, S45, S48, \\ S58, S63, S64, S65, S77, S79, \\ S81, S75, S76, \\ S85, S75, S76, \\ S87, S88, S90 \\ \end{tabular} } & \multirow{8}{*}{\begin{tabular}{c} S01, S11, S19, \\ S35, S46, S51, \\ S54, S74, S79 \\ S81 \\ \end{tabular} } & S01, S03, S57, & S37, S53, S56, \\ \cline{1-1}  & S54, S74, S79 & S57, S67, & S57, S79, & S57, S79, \\ \cline{1-1}  & S65, S75, S76, & \multirow{8}{*}{\
\begin{tabular}{c} S81, S82, \\ S85, S90 \\ \end{tabular} } & \multirow{8}{*}{
\begin{tabular}{c} S06, S14, S20, \\ S26, S49 \\ \end{tabular} } \\ \cline{1-1}  & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 11: Coverage of RE activities in different CVs.  
Figure 3: Research question (RQ1) vs. RQ2.  
missing CVs (Folksonomies, Thesauri) also need to be investigated with the same aim to support the RE, and (2) investigate the suitability of combining CVs (hybrid approaches) and proposing automated tools to enhance the support level of these, less supported, RE tasks.
###### Abstract  
The performance of Day-1 Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) and its predecessor, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis 3B42 Version 7 (3B42V7), was cross-evaluated using data from the best-available hourly gauge network over the Tibetan Plateau (TP). Analyses of three-hourly rainfall estimates in the warm season of 2014 reveal that IMERG shows appreciably better correlations and lower errors than 3B42V7, though with very similar spatial patterns for all assessment indicators. IMERG also appears to detect light rainfall better than 3B42V7. However, IMERG shows slightly lower POD than 3B42V7 for elevations above 4200 m. Both IMERG and 3B42V7 successfully capture the northward dynamic life cycle of the Indian monsoon reasonably well over the TP. In particular, the relatively light rain from early and end Indian monsoon moisture surge events often fails to be captured by the sparsely-distributed gauges. In spite of limited snowfall field observations, IMERG shows the potential of detecting solid precipitation, which cannot be retrieved from the 3B42V7 products.  
satellite precipitation; similarity; error intercomparison; GPM; TRMM; Tibetan Plateau 10.390/rs8070569  
1
# On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning
## 2 Methods of On-Board Ship Detection
### Ship Detection Based on the YOLOv4-Tiny Network  
Traditional deep learning models have the disadvantages of involving many parameters and being very large. Applications in various fields have shown that YOLO is better at generalization than R-CNN. We constructed a YOLOv4-tiny-based [29] training convolutional neural network to obtain the final ship detection results in order to achieve real-time and efficient detection of ship targets, while, at the same time, considering model size and subsequent model injection through the satellite-earth data transmission system. The model was only 22.4 MB, which would satisfy the mission requirements for on-board real-time detection.