esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Study on the Seasonal Variation Characteristics of CI  
#### 3.1.1 Typical Pixel Selection Rules  
Based on the MCD12Q1 and GlobeLand30 datasets, 90 typical pixels were selected in the mid-to-high latitudes of the Northern Hemisphere (23.5\({}^{\circ}\)N-60\({}^{\circ}\)N), where vegetation exhibits significant seasonal variation, remote sensing data quality is high, and the region has strong ecosystem representativeness, to validate whether CI exhibits seasonal variation characteristics. Six typical pixels were selected for each IGBP class, including annual and biannual croplands differentiated based on MCD12Q2 (Figure 1), to further explore the ability of the CI to capture the growth status of annual and biannual vegetation. Hypothesis testing on the sample size of the selected typical pixels revealed that the 90-pixel sample size in the study area is sufficient to meet the requirements of this study at the 95% confidence level. This study defines the typical pixel selection rules as follows: (1) the IGBP class in the MCD12Q1 product remains consistent from 2001 to 2020; and (2) for 500 m pixels, the percentage of GlobeLand30 land cover types corresponding to the same IGBP class in the MCD12Q1 product should be 70% or greater. These criteria ensure the representativeness of  
Figure 2: Seasonal variation analysis of the CI and flow chart of the MODIS time-share two-stage CI product.  
the selected typical pixels and help avoid the problem of heterogeneity, with mixed pixels identified at a high spatial resolution, which can obscure seasonal variations. Additionally, to minimize noise and reduce the uncertainty of the CI inversion results due to the nearly 20-year time span, this study calculates the mean values of the CI and NDVI values in a 3 \(\times\) 3 pixel window surrounding each typical pixel. The distribution of these typical pixels is shown in Figure 1.
## 5 Forecasting Results
A new population is formed by the fireflies, in search of neighborhoods of the chosen solutions based on their quality. As the number of fireflies is same as \(SN\), in each cycle, the fireflies carry out \(SN\) searches. Thus, when the maximum cycle number (\(MCN\)) is achieved, a total of \(SN\times MCN\) searches are performed. After that, the complexity of the algorithm is established using \(\frac{\big{(}\hat{T}_{2}-T_{1}\big{)}}{T_{0}}\) as shown in Table 5 which displays the increase of \(\hat{T}_{2}\) by less than a factor of increment dimension. As a result, it is noted that the FFA algorithm time complexity is not excessively dependent on the problem dimension and scales at O (\(n\)). Specifically, the DRBM-FFA totally outperforms the other methods based on aspects of solution quality and running time. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **D** & \(T_{0}\) & \(T_{1}\) & \(\hat{T}_{2}\) & \(\frac{\hat{T}_{2}-T_{1}}{T_{0}}\) \\ \hline \multirow{3}{*}{MLP-FFA} & 10 & 0.490 & 0.465 & 2.809 & 4.780 \\  & 50 & 0.491 & 0.643 & 2.911 & 4.610 \\  & 100 & 0.493 & 0.720 & 3.108 & 4.800 \\ \hline \multirow{3}{*}{ARIMA} & 10 & 0.389 & 0.509 & 3.142 & 6.722 \\  & 50 & 0.378 & 0.734 & 3.708 & 7.855 \\  & 100 & 0.398 & 0.821 & 3.698 & 7.212 \\ \hline \multirow{3}{*}{ARIMA-PSO} & 10 & 0.470 & 0.489 & 2.809 & 4.852 \\  & 50 & 0.489 & 0.631 & 2.902 & 4.637 \\  & 100 & 0.489 & 0.715 & 3.212 & 5.090 \\ \hline \multirow{3}{*}{DRBM-FFA} & 10 & 0.411 & 0.233 & 0.703 & 1.145 \\  & 50 & 0.412 & 0.474 & 1.336 & 2.046 \\ \cline{1-1}  & 100 & 0.412 & 0.732 & 1.994 & 2.857 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Time Complexity Comparison. As a result, it is noted that the DRBM-FFA time complexity is not excessively dependent on the problem dimension. Specifically, the DRBM-FFA totally outperforms the others based on aspects of solution quality and the running time.
# Accurate Extraction of Cableways Based on the LS-PCA Combination Analysis Method
## 4 Conclusions  
In order to locate and observe the cableway in the ski resort, this paper proposes an adaptive algorithm based on 3D point cloud data collected by airborne lidar to segment and extract the cableway with high precision. In this method, the process consists of two steps: preprocessing the ropeway data and segmenting and extracting the ropeway data. The cableway data preprocessing stage primarily focuses on denoising and pre-segmenting point cloud data based on spatial geometric distribution; the cableway data segmentation-extraction stage primarily focuses on completing the final snow track extraction based on the LS-PCA combination analysis method.  
During the experimental verification stage, the three samples selected were all taken from the Beijing Winter Olympics National Alpine Skiing Center. Despite the differences in complexity between the three groups of samples, the algorithm described in this paper is capable of accurately segmenting and extracting the ropeway point cloud from the complex point cloud. Moreover, it has been determined that this algorithm can achieve an effective extraction rate of 90.59% based on the comprehensive test results. Consequently, compared to other methods, the expected result was achieved and lays the foundation for future inspections and maintenance of the cable car ropeways. In the future, we will increase our research on the semantic annotation and reconstruction of cables.  
Conceptualization, W.W. and H.Z.; methodology, W.W. and C.Z.; software, W.W.; validation, W.W., C.Z. and H.Z.; formal analysis, W.W.; investigation, W.W.; resources, C.Z.; data curation, W.W.; writing--original draft preparation, W.W.; writing--review and editing, C.Z.; visualization, W.W.; supervision, C.Z. and H.Z.; project administration, C.Z.; funding acquisition, C.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, a special project of the \"Science and Technology Winter Olympics\" (2018 YFF0300802), and the National Natural Science Foundation of China (NSFC) (61378020, 61775018).  
Not applicable.  
We thank the anonymous reviewers and members of the editorial team for their comments and contributions.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
2D and 3D two-dimensional and three-dimensional  
UAV unmanned aerial vehicle  
LiDAR light detection and ranging  
PCL Point Cloud Library  
SVM support vector machine  
PCA principal component analysis  
LS least square
# Non-Local Means Hole Repair Algorithm Based on Adaptive Block
## 3 Experimental Results and Analysis
### Qualitative Evaluation
#### 3.1.1 Result on Middlebury Database  
The proposed algorithm is applied to the Middlebury dataset, which contains the depth images of 2014 and 2021 and their corresponding grayscale images for depth recovery. Five scenes from the Middlebury dataset, namely \"Art\", \"Teddy\", \"Moebius\", \"Recycle\", and \"Jadeplant\", are specifically selected to compare and evaluate the subjective quality of the restoration results.  
From Figure 4, it can be observed that the three algorithms perform well in repairing small areas and internal voids. However, the MECI algorithm exhibits errors when dealing with edge holes, as evident in the repair details of \"Teddy\" and \"Jadeplant\" shown in Figure 4c. Additionally, the NLM algorithm shows issues with edge distortion and blurring when repairing large voids, as demonstrated in the repair details of \"Recycle\" and \"Jadeplant\" in Figure 4d.  
Figure 4: Illustrates a comparative evaluation of hole-inpainting outcomes from the MECI, NLM, and AB-NLM algorithms using the Middlebury dataset.  
The AB-NLM algorithm proposed in this paper achieves adaptability to different sizes of hole blocks by automatically adjusting the search and repair block sizes. For larger hole blocks, larger repair blocks are selected, while smaller repair blocks are used for smaller hole blocks. This approach yields excellent results for hole blocks of various sizes. As shown in Figure 4e, the repaired images not only effectively restore the hole areas but also enhance the details of occluded objects, resulting in sharper edges.
# Design of Shadow Filter Using Low-Voltage Multiple-Input Operational Transconductance Amplifiers
## 2 Proposed Circuit
### Non-Ideal Analysis  
The non-ideal transconductance \(g_{mn}\) of the MI-OTA at a frequency near its cutoff can be expressed as follows [36]:  
\[g_{mn}(s)\cong g_{m}(1-\mu s) \tag{20}\]  
where \(\mu=1/\omega_{gm}\) and \(\omega_{gm}\) represents the first pole frequency of \(g_{m}\).  
Considering the non-idealities in (20) for \(g_{m1}\) and \(g_{m2}\) (while neglecting \(g_{m3}\) and \(g_{m4}\)), the non-idealities can be approximated as \(g_{mn1}(s)\cong g_{m1}(1-\mu_{1}s)\) and \(g_{mn2}(s)\cong g_{m2}(1-\mu_{2}s)\), and the denominator \(D(s)\) of transfer functions (6)-(8) can be then written as follows:  
\[\begin{array}{c}s^{2}C_{1}C_{2}(1+g_{m3}R_{1})\Big{\{}1-\frac{C_{2}g_{m1}H _{1}-g_{m1}S_{m2}H_{1}H_{2}(1+g_{m3}R_{1})}{C_{1}C_{2}(1+g_{m3}R_{1})}\Big{\}} +sC_{2}g_{m1}\Big{\{}1-\frac{g_{m1}S_{m2}(H_{1}+\mu_{2})(1+g_{m3}R_{1})}{C_{2 }g_{m1}}\Big{\}}\\ \hskip 14.226378 pt+g_{m1}S_{m2}(1+g_{m3}R_{1})\end{array} \tag{21}\]  
The denominator \(D(s)\) of transfer functions (14)-(17) can be written as follows:  
\[\begin{array}{c}s^{2}C_{1}C_{2}\Big{\{}1-\frac{C_{2}g_{m1}\mu_{1}(1-g_{m3}R _{1})-g_{m1}S_{m2}H_{1}H_{2}(1-g_{m3}R_{1})}{C_{1}C_{2}}\Big{\}}+sC_{2}g_{m1} \Big{\{}1-\frac{g_{m1}S_{m2}(H_{1}+\mu_{2})(1-g_{m4}R_{1})}{C_{2}g_{m1}}\Big{\}} \\ \hskip 14.226378 pt+g_{m1}S_{m2}(1-S_{m4}R_{1})\end{array} \tag{22}\]  
From Equations (21) and (22), the non-ideal effects of the transconductances \(g_{m1}\) and \(g_{m2}\) can be mitigated by satisfying the following conditions:  
\[\begin{array}{c}\frac{C_{2}g_{m1}\mu_{1}-g_{m1}S_{m2}\mu_{1}H_{2}(1+g_{m3}R_ {1})}{C_{1}C_{2}(1+g_{m3}R_{1})}\ll 1\\ \frac{g_{m1}S_{m2}(H_{1}+H_{2})(1+g_{m3}R_{1})}{C_{2}g_{m1}}\ll 1\end{array} \tag{23}\]  
\[\begin{array}{c}\frac{C_{2}g_{m1}\mu_These conditions can be achieved by appropriately selecting the values of \(g_{m1}\), \(g_{m2}\) and capacitances \(C_{1}\), \(C_{2}\) to create large time constants, which are significantly greater than the small time constants associated with non-ideal effects.
# A Simple and Robust Equalization Algorithm for Variable Modulation Systems
## 3 Proposed Equalization Algorithm for VM System
### VM-DDLMS Algorithm Parallel Structure  
To optimize the structure of the VM-DDLMS algorithm, the VM-DDLMS algorithm is parallelized and divided into four parts: the filter coefficient update module, equalization correction superposition output module, QPSK adjudicator module, and error calculation module. When the signal is the frame header or the pilot, the filter coefficient update module updates the filter coefficients \(w_{i}(k)\) according to the input signal \(x_{i}(k)\) and the error signal \(e(k)\), and the filter coefficient update sub-module is controlled by the _enable_ signal. The equalization correction superposition module will obtain the equalization output \(y(k)\) by parallel accumulation \(w_{1}(k)*x_{1}(k),w_{2}(k)*x_{2}(k),\cdots w_{N}(k)*x_{N}(k)\). The QPSK adjudicator makes a symbol decision on the equalization output \(y(k)\) of the training sequence. The error calculation module calculates the error signal \(e(k)\) based on the equalization output \(y(k)\) and the QPSK decision output \(\hat{y}(k)\). To further improve the operating frequency of the VM-DDLMS algorithm, a pipeline design was carried out according to the structure of Figure 4, and registers were inserted between the combinational logic, which was divided into several small combinational logics to further refine the operation process and reduce the delay. As shown in Figure 4, the VM-DDLMS algorithm parallel structure inserting registers for the pipeline design is very clear and less complex.  
Figure 3: Structure diagram of the VM-DDLMS algorithm.
# A Review of Sharp-Switching Band-Modulation Devices
## 1 Introduction
Furthermore, their reconfigurability enables implementing other popular sharp-switching devices, such as tunneling transistors (TFETs) and impact ionization (I-MOS) transistors, again without abandoning the FD-SOI process. Figure 1: A schematic diagram of the tradeoff between power supply voltage V\({}_{\text{DD}}\) and power consumption. Curve 1 represents an ideal transistor with SS \(\approx\) 60 mV/decade of I\({}_{\text{D}}\) at room temperature. Downscaling the threshold voltage is unavoidable for device miniaturization and power reduction, but this produces curve 2, with an unacceptable I\({}_{\text{OFF}}\). An ideal sharp-switching device (curve 3) would provide the same or higher \(\mathbf{I^{*}}_{\text{ON}}/\mathbf{I^{*}}_{\text{OFF}}\) ratio when operated at a lower V\({}^{*}_{\text{DD}}\).
## 3 The Theory of the Proposed OL-UPSGBO Algorithm
### Benchmarking of the OL-UPSGBO Algorithm  
To benchmark the performance of the proposed OL-UPSGBO algorithm, the IEEE CEC 2017 test suite is utilized [58,59]. The CEC 2017 benchmark objective function set is one of the most complex bound constraint benchmark functions. It contains 30 objective functions in which functions \([F_{1}-F_{2}]\) are unimodal, \([F_{3}-F_{9}]\) are multimodal, \([F_{11}-F_{20}]\) are hybrid, and \([F_{21}-F_{30}]\) are composite functions. The essential functions on which CEC 2017 test suite is built are shown in Table 1, as well as in [58,59]. Figure 4: The flowchart of the proposed OL-UPSGBO algorithm. Bearing in mind the explicit complexity of the CEC 2017 test suite and the nature of the hybrid and composite functions, the OL-UPSGBO is tested using a dimension of 30 search agents. According to the CEC recommendations, the overall number of evaluations is set at 200,000 evaluations. Each algorithm execution is conducted 20 times for each function. Using this framework, the proposed OL-UPSGBO is compared to five other MHs: the original the Gray Wolf Optimizer (GWO) [60], the Whale Optimization Algorithm (WOA) [61], the Multi-verse Optimizer (MVO) [62], the Sine Cosine Algorithm (SCA) [63], the GBO [53], the Arithmetic Optimization Algorithm (AOA) [64], the Equilibrium Optimizer (EO) [65], and the Aquilla Optimizer (AO) [66]. The stochastic parameters of each MH are set as the original recommended settings suggested by each paper, as in Table 2. The parametric statistical comparison is made using the mean, best, worst, and standard deviation (STD). All the outcomes of executing the algorithm mentioned above are presented in Table 3 for the unimodal and multi-modal, hybrid, and composite functions, respectively.
# Antisense-Mediated Down-Regulation of Factor V-Short Splicing in a Liver Cell Line Model
## 2 Materials and Methods
### F5 Transcript Analysis
#### 2.5.2 \(F5\) mRNA Analysis by PCR and Gel Electrophoresis  
Due to the extreme differences in size and relative abundance of the full-length FV and FV-short transcripts, the development of a PCR-based protocol for the simultaneous amplification and quantification of both transcripts required extensive optimisation (see Results section). The full-length FV and FV-short transcripts were initially amplified using a common forward primer located in exon 12 (5\({}^{\prime}\)-ATGCGTGAGAATCTGAC\(3^{\prime}\)) and transcript-specific reverse primers in exon 13. The reverse primer for the full-length FV transcript (5\({}^{\prime}\)-TCTGCAAGGTTATTGACAGTG-3\({}^{\prime}\)) was located in the portion of exon 13 that is spliced out in FV-short (443-bp amplicon), whereas, the FV-short-specific reverse primer (5\({}^{\prime}\)-CATCTGACCAAGGTTAAATTAC-3\({}^{\prime}\)) was designed on the junction site created by the alternative splicing event (429-bp amplicon). Since the primers for the full-length FV transcript turned out to co-amplify an unexpected alternatively spliced transcript (see Results section), a second set of primers spanning exons 13-14 was designed and used in all subsequent experiments. Forward primers specific for full-length FV (5\({}^{\prime}\)-TATCACCTCTCCAGACCTTG-3\({}^{\prime}\), located in the portion of exon 13 that is spliced out in FV-short) and FV-short (5\({}^{\prime}\)-TTCTTCCCCAAGTAATATTAACCTTG-3\({}^{\prime}\), located across the junction created by the FV-short splicing event), were combined with a common reverse primer in exon 14 (5\({}^{\prime}\)-TTCTGAAATCATCAGAGTCTTCA-3\({}^{\prime}\)), yielding PCR products of 473 bp and 397 bp, respectively.  
To avoid competition for PCR reagents between full-length FV and FV-short mRNA, the two transcripts were amplified in separate PCR reactions run in parallel using the same amplification conditions (denaturation: 30 s at 92 \({}^{\circ}\)C; annealing: 20 s at 59 \({}^{\circ}\)C; extension: 25 s at 72 \({}^{\circ}\)C; repeated for 32 cycles) on a T100 Thermal Cyler (Bio-Rad Laboratories, Veenendaal, The Netherlands). PCR products were analysed by agarose gel electrophoresis (2% agarose) and their identity was confirmed by Sanger sequencing.
## 6 Conclusions  
Through axial compression tests and finite element analysis on four RECFAT columns, the following conclusions were drawn:  
1. The failure process of each specimen was generally consistent. After reaching the ultimate load, the two planes of the circular-end aluminum tube exhibited local bulging and formed oblique shear lines. Specimens with aspect ratios less than 4 exhibited localized bulging and oblique shear failure, while specimens with aspect ratios greater than 4 experienced overall instability failure.
2. As the aspect ratio increased, the load enhancement coefficient and ductility coefficient of the specimens decreased. When the aspect ratio exceeded 2.5, the load enhancement coefficient was less than 1 and the critical aspect ratio was between 2.0 and 2.5. For specimens with the same aspect ratio, those with higher aluminum content had smaller enhancement coefficients but better ductility. The optimal aluminum content was recommended to be between 8.5% and 13.5%.
3. Analysis of transverse and longitudinal strains in the plane of the circular-end aluminum alloy tube revealed that, due to the constraint effect of the aluminum tube, the transverse strain developed more rapidly after entering the plastic stage. The best constraint effect of the circular-end aluminum alloy tube was observed at an aspect ratio of around 2.0.
4. The established refined finite element model matched well with the experimental results and captured the inclined shear bulging of the circular-end aluminum alloy tube and the \"V\"-shaped concrete compression failure during specimen failure.
5. Finite element stress analysis and experimental strain measurements indicated the presence of an \"arch effect\" in the rectangular region of the circular-end aluminum alloy tube at the peak point. Due to the lower elastic modulus of the aluminum alloy, bulging occurred first when there was significant axial displacement, rather than concrete crushing and squeezing the aluminum alloy tube. Improving the strength of aluminum alloy would be more conducive to improving the axial compression bearing capacity of RECFST than increasing the strength of concrete.
6. Based on the experimental and finite element analysis results, a composite confinement model for circular-end aluminum alloy tubes was proposed, considering the efficiency of concrete strength enhancement in both the strong confinement and confinement regions. The calculated results had an error of 0.4%.  
J.B.: conceptualization, formal analysis, writing--original draft. Q.L.: conceptualization, funding acquisition, writing--review and editing. Y.Y.: conceptualization, data curation, writing--review and editing. Q.Q.: software, supervision, and visualization. All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge the financial support provided by National Key R&D Program of China (2021 YFB2600600, 2021 YFB2600605).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Optimal Frame Generation Time Threshold Calculation
### Algorithm Design
#### 4.2.2 Mean Squared Error (MSE) of the State Value Function  
The mean squared error (MSE) of the state value function \(\kappa_{\text{critic}}(\theta)\) is defined as  
\[\kappa_{\text{critic}}(\theta)=\frac{1}{N}\overset{N}{\sum}(G_{t}-V_{\theta}(s _{t}))^{2} \tag{20}\]  
This formula shows the mean square error between the predicted state value function \(V_{\theta}(s_{t})\) and the actual return \(G_{t}\). \(V_{\theta}(s_{t})\) is the estimated state value of the state \(s_{i}\) evaluation network under the current policy, and \(G_{t}\) is the return estimated by the Monte Carlo method. The specific formula for the total return with the time step of \(t\) is given by Equation (21):  
\[G_{t}=R_{t}+\gamma R_{t+1}+\gamma^{2}R_{t+2}+... \tag{21}\]  
where \(\gamma\) is the discount factor, typically set between \([0,1]\), representing the weight of future rewards. The agent learns more accurate state value estimation by minimizing the mean square error (MSE).
## 3 Our Method
### Dehazing Branch  
The input image \(I_{\text{in}}\) first passes through a 3 \(\times\) 3 convolution for feature extraction. The encoder-decoder structure is a deep learning modeling framework widely used for image-dehazing tasks, as shown in Figure 3; its main goal is to restore haze-affected images to a clear state through layer-by-layer feature extraction and reconstruction. The main role of the encoder part is to extract and downsample the image features step by step. It consists of a convolution operation and four feature-extraction layers. The size of each layer's feature map output \(En_{\text{out}}^{i}\) is halved relative to the layer \(En_{\text{out}}^{i-1}\) which is before it, and the number of channels \(C_{n}\) is twice as large as the previous layer \(C_{n-1}\). The key process can be expressed as follows:  
\[En_{\text{out}}^{i}=Down\Big{(}Group\Big{(}En_{\text{out}}^{i-1}\Big{)}\Big{)}, \quad i=\{1,2,3,4\} \tag{1}\]  
where \(Down(\cdot)\) indicates downsampling.  
The decoder part is responsible for reconstructing the features and recovering the high-resolution information of the image. Each layer uses a Group extraction module to extract image detail features. Accordingly, the decoder consists of four feature-reconstruction layers and convolution operations. Each layer \(De_{\text{out}}^{i}\) produces a feature map twice the size  
Figure 2: The overall architecture of CCD-Net.  
of the previous layer \(De_{\text{out}}^{i+1}\), while the number of channels is reduced by half. The main process can be described as follows:  
\[De_{\text{out}}^{i}=Up\Big{(}Group\Big{(}De_{\text{out}}^{i+1}\Big{)}\Big{)}, \quad i=\{0,1,2,3\} \tag{2}\]  
where \(Up(\cdot)\) indicates downsampling.  
The fundamental unit of the model is the Block Module, consisting of two convolutional layers, a channel attention layer, and a pixel attention layer. Details of pixel attention and channel attention are shown in Figure 4. This module is designed to process image features at multiple levels, and by using residual connections, it fuses low-level features with higher-level ones, preventing the loss of information. The output from each Block module is further enhanced in subsequent layers, enabling the model to capture more complex feature representations and improving the dehazing result.  
\[F_{Block}=PA(CA(Conv(\delta(Conv(F_{in}))))) \tag{3}\]  
where \(F_{Block}\) indicates the output after the Block module.  
In the Group module, multiple Block modules are stacked together to form a more complex unit responsible for multi-level feature fusion. Within each Group module, features are processed through several layers to transform the input feature map into higher-level representations, allowing the model to capture information from different scales and further enhancing the dehazing effect.  
Figure 4: Details of pixel attention (**a**) and channel attention (**b**).  
Figure 3: The structure of the dehazing branch.