esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of Lead Rubber Bearings for Vibration Reduction in High-Tech Factories
## 5 Design of LRBs Concerning the Micro Vibration  
The LRBs should possess large stiffness for frequent small or moderate earthquakes but small stiffness for extreme earthquakes. If the micro vibration is the major concern for the high-tech factory, the investigation of Section 4 indicates that the selection of LRBs should first consider the problem of the large micro vibration induced by the wind load. Thus, the LRB with a large initial stiffness (\(K_{e}\)) and a small ratio of the final stiffness over the initial stiffness (\(\alpha\)) should be used, where the large \(K_{e}\) can resist wind loads and the small \(\alpha\) can reduce seismic loads. However, this situation may cause difficulties in finding a suitable LRB, so we will first select LRBs with large \(K_{e}\), where 5E5 kN/m (\(F_{y}\) = 300 kN and \(K_{v}\) = 8E7 kN/m) and 3E5 kN/m (\(F_{y}\) = 200 kN and \(K_{v}\) = 5E7 kN/m) are used at the bottom of the big and small columns, respectively. Then, \(\alpha\) is set to 1%, 2.5%, 5%, and 7% for three cases. The artificial earthquake is set using the PGA of 0.32 g and \(T_{s}\) of 0.9 s (Figure 5), and the average speed of the turbulent wind load is set to 25 m/s. The finite element results are shown in Figures 14 and 15, where Figure 14 shows the velocity dB changing with frequencies for the wind load, in which the results are not dependent on \(\alpha\) because the yield of LRBs is not obvious under the average wind speed of 25 m/s, and Figure 15 shows the base shear ratio (\(R=Smax_{LRB}/Smax_{NO-LRB}\)) changing with \(\alpha\). The two figures indicate the following features:  
(1) Figure 14 shows that the slab vibrations induced by the wind load are similar between the factories with and without LRBs, where the vibrations of the LRB factory are slightly large about 2 to 3 dB greater than those without LRBs. This improvement is significant compared to the result in Figure 11, because the large initial stiffness of the LRB resists the wind loads. Moreover, most of the LRBs are still not yielded, so the slab vibrations are independent of the LRB parameter \(\alpha\).  
(2) Figure 15 shows that the \(\alpha\) should be smaller at higher LRB initial stiffness to reduce the seismic load of high-tech factories. This situation may make it difficult to obtain a suitable LRB, for example, \(\alpha\) in Figure 15 is less than 2%. Nevertheless, using the large initial stiffness and small \(\alpha\) may reduce seismic responses but not increase the micro vibration for high-tech factories  
Figure 14: Velocity vibration dB changing with frequencies at the first to third floors under the wind load with the average speed of 25 m/s for the factory with or without LRBs (The dB values with LRBs due to \(\alpha\) from 0.01 to 0.07 are almost identical.).
## 3 Materials and Methods
### Simple DPS: Scheme and Algorithms
#### 3.3.3 Parameter Selection: Density Level \(\alpha\)  
The selection of level \(\alpha\) greatly affects the result of the DPS algorithm. A convenient means for selecting the level \(\alpha\) is fuzzy comparisons [8]. They allow us to effectively construct the limitation level so that the DPS results are really dense against the general background, that is, they are empirical clusters.  
The fuzzy comparison \(n(a,b)\) of two non-negative numbers \(a\) and \(b\) is a measure of the superiority of number \(b\) over number \(a\), expressed as a scale of segment \([-1,1]\):  
\[n(a,b)=\text{mes}(a<b)\in[-1,1]\]  
A fuzzy comparison of a number \(a\) and a finite set \(B\)\((a\in\mathbb{R}^{+},B\subset\mathbb{R}^{+})\) can be defined as the mean of fuzzy comparisons \(a\) with all numbers from \(B\):  
\[n(a,B)=\frac{\sum_{b\in B}n(a,b)}{|B|},\quad n(B,a)=\frac{\sum_{b\in B}n(b,a)} {|B|}\]  
Figure 4: Block diagram of the DPS.  
and understood as a measure of minimality \(\operatorname{mes\,min}_{B}a\) and a measure of maximality \(\operatorname{mes\,max}_{B}a\) of the number \(a\) against the background \(B\):  
\[\operatorname{mes\,min}_{B}a=n(a,B),\quad\operatorname{mes\,max}_{B}a=n(B,a).\]  
The measure of maximality \(\operatorname{mes\,max}_{B}a\) enables formulating the necessary requirement for the DPS algorithm results: its density at each of its points must be significant (maximum enough) against the background \(X\).  
To do this, it is necessary first to calculate the density of the entire space \(X\) at all its points  
\[P_{X}(X)=\{P_{X}(x):x\in X\}.\]  
This is a background of \(X\). If \(\beta\in[-1,1]\) is the required level of density extremeness \(P\) against the background of \(X\), then the immediate level \(\alpha=\alpha(\beta)\) for \(P\) is uniquely determined by \(\beta\) from equation  
\[n(P_{X}(X),\alpha)=\beta, \tag{6}\]  
since the relation \(\alpha\to n(P_{X}(X),\alpha)\) is of continuous and monotone kind. Equation (6) can be solved by dividing the segment at halves.  
Therefore, the DPS algorithm must find a subset \(X(\beta)\) in \(X\), that is \(\beta\)-extremely \(P\)-dense against the general background \(X\) at each of its points:  
\[x\in X(\beta)\leftrightarrow n(P_{X}(X),P_{X(\beta)}(x))\geq\beta \leftrightarrow P_{X(\beta)}(x)\geq\alpha(\beta)\]  
and split it by the components of the \(r\)-connectivity for \(r\) from (5).
## 6 Conclusions  
In this study, multi-temporal HJ-1 CCD images were used to generate an uncropped to total arable land ratio (UALR). This ratio was applied to time series MODIS reflectance imagery to retrieve an adjusted NDVI for a cropped arable land endmember. This UALR-adjusted NDVI and MODIS NDVI were then used for a crop condition assessment based on an inter-annual comparison of spatial variability and seasonal dynamics. Crop conditions derived from the two different NDVI datasets were compared, analyzed and evaluated for a study area in the North China Plain. The results indicate that the UALR-adjusted NDVI generates more accurate crop condition estimates, since it eliminates the influence of inter-annual variability in arable land utilization, adjusting for the differences in parts of a pixel that may not be cropped in one year, but may be cropped in another year. The proposed method yields more accurate monitoring results, simply by removing the variance in NDVI that is caused by the change in crop area, not the crop condition. Incorporating information on uncropped arable land can play a significant role in reducing the uncertainty from inter-annual variation in the cropping area for crop condition monitoring. Since it is even more important to capture accurate crop conditions in the early growing stage than acquiring the exact production after harvest, the suggested method could be an important addition to existing crop monitoring systems, following further evaluation in other major crop producing regions.  
This paper was funded by National High Technology Research and Development Program of China (863 program) (Grant No. 2012 AA12A307 and 2013 AA12A302) and the Program of International Science and Technology Cooperation (Grant No. 2011 DFG72280). The HJ-1 CCD data was provided by CRESDA. The authors would like to thank [PERSON] for his support in the crop condition monitoring methodology and his contributions to the crop condition assessment. Thanks go to [PERSON] and the anonymous reviewers for reviewing the manuscript and providing comments to improve the manuscript.  
[PERSON], the corresponding author, brings us the concept of the proposed method and mainly contributes to the discussion section. [PERSON] writes up introduction, data, method and conclusion sections. [PERSON], together with [PERSON], [PERSON] and [PERSON] write up the method and results sections[PERSON] Data processing and results analysis are done together by all the listed authors.  
The authors declare no conflict of interest.
## 3 Line-Scan Hyperspectral Imaging Instruments
### Line-Scan Hyperspectral Systems and Calibrations
In the RCI mode, a one-axis motorized positioning table moves the samples to collect hyperspectral Raman data using multiple line scans. In the SORS mode, a single line scan is performed to acquire a Raman scattering image using one CCD exposure for subsurface  
Figure 4: Line-scan hyperspectral imaging systems working in (**a**) reflectance/fluorescence mode in visible and near-infrared (VNIR) region; and (**b**) reflectance mode in short-wavelength infrared (SWIR) region. Reproduced with permission from [13], Copyright Springer, 2011.  
inspection applications. In-house software developed using LabVIEW (National Instruments, Austin, TX, USA) is used to perform parameterization and data-transfer functions. Spectral and spatial calibrations are needed for line-scan hyperspectral imaging systems to acquire meaningful data. The spectral calibrations map the pixel indices along the spectral dimension of the image sensor to the wavelengths or wavenumbers. Spectral calibration lamps are common tools used in spectral calibrations. For hyperspectral Raman systems, the spectral calibrations use relative wavenumbers as references instead of wavelengths used in absolute spectral calibrations. A single-wavelength laser and chemicals with known relative wavenumber shifts are usually used to calibrate the Raman systems. The relationship between pixel positions and corresponding wavelengths or wavenumbers can be established using linear or nonlinear regression models. On the other hand, the spatial calibrations are useful to adjust the field of view and estimate the spatial detection limit. The spatial resolution along the direction of sample movement depends on the step size of the movement, and the spatial range is determined by the step size and the number of scans. For the direction parallel to the scanning line, the length of instantaneous field of view (IFOV) determines the maximum sample width that can be covered, and it can be changed by adjusting the focal length of the lens and the lens-to-sample working distance. Figure 6 shows a spatial calibration example for a line-scan hyperspectral imaging system using a piece of paper printed with parallel lines spaced 2.5 mm apart. Under a setting using a lens with a 35 mm focal length and a working distance of 200 mm, the spatial resolution is 0.07 mm/pixel, and the IFOV length is 71.68 mm (Figure 6a). When a 23 mm lens and a 355 mm working distance are used, the spatial resolution is reduced to 0.20 mm/pixel, and the IFOV length is extended to 204.80 mm (Figure 6b). Hence the system can be arranged for either high spatial resolution or large field of view to accommodate different resolution and sample size requirements. Figure 5: Line-scan hyperspectral Raman system working in (**a**) line-laser Raman chemical imaging mode for surface evaluation; and (**b**) point-laser spatially offset Raman spectroscopy mode for subsurface evaluation. Reproduced with permission from [14], Copyright Elsevier, 2017.
## 4 Experimental Results
### Evaluation Metrics  
In the experimental component, we used two evaluation metrics, mIoU (mean Intersection on Union) and Dice (Dice Similarity Coefficient), to measure the similarity between predictions and labels. We also used three evaluation metrics--recall, specificity, and precision--to measure the classification accuracy of the prediction graph. The formula is as follows:  
\[MIoU=\frac{1}{N}\sum_{i=1}^{N}IoU_{i}, \tag{18}\]  
\[Error\ Rate=\frac{FP+FN}{TP+FP+TN+FN}, \tag{19}\]  
\[Recall=\frac{TP}{TP+FN}, \tag{20}\]  
\[Precision=\frac{TP}{FP+TP}, \tag{21}\]  
where _TP_ is the true positive, i.e., the foreground region that is correctly predicted in the prediction map. _TN_ is the true negative, i.e., the background region that is correctly predicted in the prediction map. _FP_ is the false positive, i.e., the portion of the prediction map that is judged as the foreground region but the portion of the labeling map that is judged as the background region. _FN_ is the false negative, i.e., the portion of the prediction map that is judged as the background region and the portion of the labeling map that is judged as the foreground region.
## 4 Numerical Results and Discussion  
### Experiment Validation  
Figure 9 shows comparisons between the above experimental results of the relationship between the water pressure gradient and the flow rate with that of the numerical simulation. The results indicate high consistency for all cases. This shows that the nonlinear and hydraulic coupling effect of the fluid flow proved by the above experiments can be realized by numerical simulation.  
A two crossed fractures model was established, and the intersection angle of the two fractures was 60\({}^{\circ}\), as shown in Figure 10. The fracture aperture was set up to 1 mm when generating the models. These simulations include three cases of setting only one outlet and three cases of setting different combinations of two outlets. A comparison of the simulation results with the test results [74] is shown in Figure 11, where \"E\" represents the test results, \"N\" represents the numerical results, and the figure represents the number of the selected outlet. The results indicate high consistency for all cases.  
Figure 9: Comparison of the experimental results with the numerical code results: (**a**) unfilled condition and (**b**) filled conditions of G3.  
A multiple crossed fractures model is shown in Figure 12. The length and width of this model are 20 cm and 10 cm, respectively. It is composed of five fracture sections, two inlets, and two outlets. Different fracture sections have different apertures, dip angles, and lengths, as shown in Table 3. Because the model has two inlets and two outlets, different combinations can be carried out, and the four working conditions shown in Table 4 are simulated.  
Figure 11: Comparison of the experimental results with the numerical code results of Model 1: **(a)** one inlet and one outlet; **(b)** one inlet and two outlets.  
Figure 10: Two crossed fractures model.  
As shown in Figure 13, \"E\" represents the test results and \"N\" represents the numerical results. Figure 13 shows a comparison between the numerical results and the corresponding test results [75], which again shows high consistency for all cases.
## 4 Results
### Implementation Details  
We train the two streams separately. For the slow pathway's training, we sample clips of 8 frames at a frame rate of 1/32 from RGB videos. We first re-scale the original frame to short-side 320 pixels, then randomly crop 224 \(\times\) 224 pixels from the reshaped frames at training. We perform the center cropping in validation and three spatial cropping (top-left, center, bottom-right) of the same temporal index in test. The tubelet size is \(16\times 16\times 2\), and we use the central frame method to initialize the tubelet embeddings. For the fast pathway's training, we sample clips of 32 heatmaps at the frame interval of 10. Different from the slow pathway, we generate the pseudo heatmap to 224 \(\times\) 224 pixels and feed them to the Transformer blocks directly without scaling, resizing, and cropping. Importantly, since skeleton heatmaps are mid-level features, the number of Transformer layers for the fast pathway is reduced to 10, less than the slow pathway where the block is repeated 12 times. However, the layer amount of temporal Transformer encoder is the same, which is four for all experiments.  
Both pathways use the ViT model pretrained on ImageNet-21K [35] and SGD optimizer [36]. For Kinetics400, we train our mode for 15 epochs with an initial learning rate of 0.005/8, which is divided by 10 at epochs 5 and 10. For the other three datasets, we train our model for 10 epochs with an initial learning rate of 0.005/8, which is divided by 10 at epoch 5. Following TimeSformer [19], we average the scores of these three crops to obtain the final prediction result in the test. We use the Transformer implemented in MMAction2 [37].
# The Modification of pBFT Algorithm to Increase Network Operations Efficiency in Private Blockchains
## 3 Modification of the pBFT Algorithm
### The Performance of the Consensus Algorithm
#### 3.1.2 The Network Communication Cost within a Consensus Process  
While processing pBFT, all nodes are broadcasted to in the prepare phase. Another broadcast is given out to all the nodes in the commit phase. With the total number of nodes \(N\), the network connection cost within these processes can be calculated as 2 \(N^{2}\).
# Deep Reinforcement Learning for the Agile Earth Observation Satellite Scheduling Problem
## 4 Experiments
### Experimental Setting for AEOSSP
#### 4.1.2 Competitors  
To verify the validity of the proposed method, we use some construction heuristics as baselines of the AEOSSP. Specifically, we design the following four heuristics: the start time of observational time window ascending (STWA), the profit of task descending (PTD), the ratio of profit and image time descending (RPID), and the conflict degree of task descending (CDTD). The construction heuristics solving framework is shown in Algorithm 3. Each heuristic sorts the candidate tasks by the construction rule and inserts the tasks that satisfy the constraints into the solution sequence in turn until there is no task to insert. In CDTD, the conflict degree \(Cd_{i}\) means the number of VTW overlaps between \(task_{i}\) and other tasks, defined as Equations (30) and (31). We constitute the GDNN-DQN by training the GDNN with the DQN.  
\[Cd_{i}=\sum_{k=1}^{n_{\text{task}}}o_{ik} \tag{30}\]  
\[o_{ij}=\left\{\begin{array}{ll}1,&\text{if }wb_{i}<we_{j}\wedge wb_{j}<we_{i} \\ 0,&\text{otherwise}\end{array}\right. \tag{31}\]  
```
0: Task sequence \(Tsk\), sequence rule \(pt\), ascending sign \(l_{\text{rank}}\)
0: Scheduling solution \(Sln\)
1:\(Sln=\emptyset\)
2:\(Sln_{\text{temp}}=\emptyset\)
3:if\(1==l_{\text{rank}}\)then
4:\(Tsk=\text{RankAscendingBy}(Tsk,pt)\)
5:else
6:\(Tsk=\text{RankDescendingBy}(Tsk,pt)\)
7:endif
8:for\(i=0\) to \(|Tsk|\)do
9:\(Sln_{\text{temp}}=Sln\)
10:\(Sln_{\text{temp}}.\text{InsertTask}(Tsk_{i})\)
11:\(isFeasible=\text{ConstraintCheck}\big{(}Sln_{\text{temp}}\big{)}\)
12:if\(isFeasible==\text{TRUE}\)then
13:\(Sln=Sln_{\text{temp}}\)
14:endif
15:endfor
16:return\(Sln\)
```  
**Algorithm 3** General framework of construction heuristics  
In addition, to validate the efficiency of GDNN-PPO, some high-quality solution algorithms are required for comparison. As mentioned in Section 1, the existing exact methods simplify the time-dependent transition time, and the solution time of CPLEX is unacceptable when the number of VTWs exceeds 27; thus, we do not consider the exact algorithms. We compare the GDNN-PPO algorithm with three high-quality competitors: (1) GRILS [10], which is a state-of-the-art heuristic method for the AEOSSP; (2) self-adaptation differential evolution (SDE) [36], which is an algorithm that has been shown to be effective at solving the AEOSSP; and (3) the self-adaptation genetic algorithm (SGA) [14], which is an improved genetic algorithm designed to solve the AEOSSP. The decoding method of the SGA is the same as Algorithm 3. The crossover and mutation parameters are updated according to the proportion of entering the next generation. The parameters of the high-quality competitors are shown in Table 4.
# Fast Optimization of the Installation Position of 5G-R Antenna on the Train Roof
## 3 A Fast Prediction Model for the Coupling Coefficient
### Prediction Model of Coupling Coefficient Based on MMNN  
As mentioned above, this paper uses the MEA-BPNN algorithm and the PSO-ELM algorithm to construct sub-prediction models. Next, the prediction performance of the two prediction models before and after optimization is analyzed, and the prediction model based on the MMNN is finally established, as shown in Figure 7.  
Figure 6: The specific flow block diagram of the PSO-ELM.  
As shown in Figure 7, in the prediction model proposed in this paper, X = 14 m is first taken as the dividing line. When X \(\leq\) 14 m, module neural network 1 (MMN1) is used for prediction; when X > 14 m, MNN2 is used for prediction. Then, in MNN1, with X = 3 m as the dividing line, a prediction model is constructed based on the BPNN algorithm and the MEA-BPNN algorithm, respectively, at X \(\leq\) 3 m and X > 3 m, as sub-neural network 1 and sub-neural network 2, and then MNN1 is constructed by integrating strategies. Similarly, in MNN2, Y = 0.8 m is taken as the dividing line. When Y \(\geq\) 0.8 m and Y < 0.8 m, prediction models based on the ELM algorithm and the PSO-ELM algorithm are constructed, respectively, as sub-neural networks 3 and sub-neural networks 4, and then MNN2 is constructed by integrating strategies. Finally, by integrating the predicted results of MNN1 and MNN2, the final predicted value is obtained.  
In Figure 7, S represents the expected value of the coupling coefficient, and \(S_{1}^{\prime}\) and \(S_{2}^{\prime}\) represent the predicted values obtained from MNN1 and MNN2, respectively. In this paper, the \(MSE\) is used, as shown in Equation (1):  
\[MSE=\frac{\sum_{i=1}^{N}\left(S_{i}^{\prime}-S_{i}\right)^{2}}{N} \tag{1}\]  
where \(N\) represents the number of coupling coefficients, and \(S_{i}^{\prime}\) and \(S_{i}\) represent the predicted values and expected values of the coupling coefficients corresponding to the roof antenna located at the i-th position, respectively. The \(MSE\) value is used to determine whether the established prediction model meets the preset accuracy requirements, which represents the accuracy of the prediction results. The smaller the value is, the more accurate the prediction results are, and the default target value is 0. When the MSE value is small enough, the accuracy requirements of the prediction model will be considered, and the training will stop, thus completing the construction of the prediction model [12].
# New Product Short-Term Demands Forecasting with Boxplot-Based Fractional Grey Prediction Model
###### Abstract  
The cost of investing in new product development (NPD) is high, and it is a feasible way to use demand forecasts for customer or end-users as a decisive reference. However, this short-term time-series data has difficulties in learning because there is no past performance on which to base the estimates. In the past, it has been proven that the cumulative method of the fractional grey prediction model (FGM) is better than the traditional integer cumulative method of the grey model (GM) model. There are many studies using different optimal algorithms to determine the moderate score order. How to set the coefficient of \(\alpha\) in FGM is also worth exploring. Therefore, this research reveals a new fractional grey prediction model which uses box-and-whisker plots to estimate the trends of data, known as the boxplot-based fractional scale prediction model (boxplot-based FGM, BP-FGM) to improve the accuracy of predictors by setting the coefficient sets of \(\alpha\). In the experiment, the examined dataset was collected from a well-known equipment manufacturer as the research object. For modeling, the mean absolute percentage error (MAPE) was established as the objective function of the optimization model, the results from three datasets verified the effect through the commodity attributes and public test data of its production, and the experimental results show that BP-FGM has better prediction results than FGM.  
Keywords:demand forecasting; short-term time series; grey models; fractional grey model; box-and-whisker plots +
Footnote †: journal: Applied