esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Integration of PCA to Address QFD Limitation: The Complexity of Matrices
#### 2.3.1 PCA and Its Role in QFD Context  
PCA, introduced in the early 1900s and developed further in the 1930s, is a statistical method that reduces the dimensionality of datasets while retaining most of the original variability. It simplifies complex datasets by transforming the original variables into a new set of uncorrelated variables called principal components, ordered by the variance they explain [31; 32].  
The primary advantage of PCA is its ability to reduce the complexity of large datasets [51; 52], making it easier to analyze and interpret compared to other dimensionality reduction techniques like independent component analysis (ICA) and factor analysis (FA) [48]. In the context of QFD, for PCA, the primary goal is often to simplify and prioritize customer requirements and product features while retaining the most significant variance in the data. PCA is particularly effective in this scenario.  
In contrast, the study on spectral-temporal data found ICA and FA to be more effective because these methods are designed to handle specific types of data distortions and preserve the integrity of important components in ways PCA cannot. The different nature of the data and the distinct goals of the analysis in that study explain why ICA and FA outperform PCA in that specific context [53]. Therefore, while ICA and FA might be superior for handling highly distorted data in some contexts, PCA's simplicity, efficiency, and alignment with the goals of QFD make it a preferred choice for dimensionality reduction in QFD applications.  
By identifying and focusing on the most important components, PCA helps streamline the QFD process, enhancing its efficiency and effectiveness. This is particularly useful in handling the complexity of QFD matrices, where multiple customer requirements and technical characteristics need to be considered simultaneously [48; 54].  
The integration of PCA with QFD has been applied to address the complexity of QFD matrices. For example, since 2007, PCA-QFD integration has been successfully utilized to handle sparse high-dimensional data, demonstrating PCA's effectiveness in managing complex datasets and improving decision-making in product development [55]. More recently, PCA was applied to optimize the voice of stakeholders in the QFD method, streamlining stakeholder inputs and enhancing decision-making in the development process [54].  
However, not all applications of PCA-QFD integration have been successful. For instance, an attempt to use PCA-QFD integration in selecting functional requirements in construction automation was found to be inappropriate. This was due to the specific requirements and constraints of the construction industry, which rendered the PCA approach less effective in this context [48].
## 3 Implementation of The Proposed System
### Data Storage
#### (1) File Metadata
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset Information** & **Optical mapping Information** & **MEA Information** &
\begin{tabular}{c} **Intracellular and** \\ **Extracellular Information** \\ \end{tabular} \\ \hline \hline Dataset name & File name & File name & File name \\ \hline Experiment & Experiment name & Experiment name & Experiment name \\ \hline Experimental date & Purpose & Purpose & Purpose \\ \hline \multirow{3}{*}{Purpose} & Sample & Sample & Sample \\ \cline{2-4}  & (name, growth & (name, growth & (name, growth \\  & period, environment) & period, environment) & period, environment) \\ \hline \multirow{3}{*}{Environment} & Environment & Environment & Environment \\ \cline{2-4}  & (light, temperature, & (light, temperature, & (light, temperature, \\  & humidity, pressure) & humidity, pressure) & humidity, pressure) \\ \hline Equipment & Equipment & Equipment & Equipment \\ \hline Record software & Software & Software & Software \\ \hline Stimulation method & Record position & Record position & Record position \\ \hline Signal type & Record area & Record area & The start time \\ \hline Description & The start time & The start time & Duration \\ \hline Approval status & Duration & Duration & Number of channels \\ \hline is open & Dye process & Stimulation type & Sampling rate \\ \hline \multirow{3}{*}{Frame rate} & Amplification & Stimulation method & Signal type \\ \cline{2-4}  & Frame rate & Signal type & Stimulation type \\ \hline \multirow{3}{*}{Simulation method} & Sampling rate & Stimulation method \\ \cline{2-4}  & Stimulation method & Adjacent electrode distance & Reference electrode preparation \\ \hline Signal type & Electrode size & Reference electrode position \\ \hline Image format & Number of channels & Recording electrode preparation \\ \hline Image resolution & File size & Record the electrode position \\ \hline The number of images & Experiment file/picture/video & Description of process \\ \hline Total size & is open & File format \\ \hline Experiment file/picture/video & File size \\ \hline is open & & Experiment file/picture/video \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The Information of Metadata (An example table). Here, we use MySQL to store the two levels of dataset and experiment metadata as well as annotation information. Enhanced entity relationship (EER) design diagram is shown in (Supplementary Material Figure S1). In web applications, users often read a small number of experiment information (often one row or several rows from MySQL table) in each operation. Hence, it is more suitable to store experiment metadata in MySQL than HBase. However, as the data scale increases, we will also consider integrating key features with raw data in HBase to support data analysis tasks at scale.
## 4 Results on the Generation of Hybrid Petal-Type Vector Beams
### Case with Input Homogeneous Linearly Polarized Gaussian Beam  
First, we consider an input \(LG_{0}^{0}\) beam with linear vertical polarization, i.e., \(\mathbf{V}_{in}=\frac{LG_{0}^{0}}{\sqrt{2}}\Big{(}\mathbf{e_{R}}+\mathbf{e_{L }}\Big{)}\). According to Equations (3) and (6), after combining terms, the output beam reads:  
\[\mathbf{V}_{out}=\frac{1}{2}\Big{[}\Big{(}LG_{0}^{0}-iLG_{0}^{1}\Big{)} \mathbf{e_{R}}+\Big{(}LG_{0}^{0}-iLG_{0}^{-1}\Big{)}\mathbf{e_{L}}\Big{]} \tag{13}\]  
Figure 4: Results for the QW q-plate illuminated with an RCP vortex beam with \(\ell=2\). (**a**) Results in the near field; (**b**) results in the far field. Simulated and experimental polarization maps, with equivalent color representation as in Figure 3. Experimental images captured for different orientations of the analyzer, indicated as white arrows (NA indicates no analyzer).  
This output beam is therefore generated by the superposition of the fundamental \(LG_{0}^{0}\) mode with a \(LG_{0}^{\pm 1}\) mode in each polarization component. Figure 5 shows the corresponding results in the NF and FF. Due to the azimuthal phase difference in each polarization component, this superposition results in a NF polarization map which is specularly symmetric, with a straight vertical line dividing two regions of opposite handedness. This is clearly seen in the RCP and LCP components at the NF, which show two specularly symmetric intensity patterns with one dark azimuthal lobe at the left or right part of the beam, respectively.  
Like in previous cases, the propagation to the FF is affected by the different Gouy phase of each LG component. However, now, each polarization component is already carrying two modes. The Gouy phase difference between modes \(LG_{0}^{0}\) and \(LG_{0}^{\pm 1}\), produces a \(\pm 90^{\circ}\) rotation of the intensity pattern of the circular polarization components from NF to FF. This is shown in the FF patterns in Figure 5b. As this rotation is in the opposite sense for RCP and LCP components, the two intensity patterns match in the FF, having a minimum at \(\theta=0\) (upper vertical direction) while being maximum at \(\theta=\pi\) (lower vertical direction). As a consequence, the FF intensity pattern without analyzer presents one lobe located in the lower part of the beam, the polarization becomes linear at every point, and an off-axis singularity appears in the upper part of the FF intensity pattern. Let us note that the projection of the output vector beam over a horizontal linear analyzer yields a horizontally polarized sum of LG modes with \(\ell=\pm 1\), with a relative phase of \(\pi\) between them. This results in a Hermite-Gauss mode \(HG_{01}\). The amplitude of this mode does not change with propagation, as can be seen comparing the NF and FF intensity patterns for the horizontal analyzer. As we will show next, this case is just a particular situation of a more general case of petal-type beams encoded onto the two circular polarizations.
# Research on a Novel Nanosecond Marx Generator and Its Efficiency Analysis
## 3 Energy Efficiency Analysis of the Proposed Nanosecond Marx Generator  
The energy loss can be divided into charging loss and discharging loss [16].
# Two-Way Transmission for Low-Latency and High-Reliability 5G Cellular V2X Communications
## 3 Analysis of Outage Probability and Throughput of NOMA-V2X System
### Outage probability of \(\mathrm{D}_{2}\)
\begin{split}\mathcal{P}_{2}^{SIC}&=1-\left\{\sum_{j=0}^{ K-1}\sum_{l=0}^{K}\bigcup_{l^{\prime}}\bigcup_{j^{\prime}}\binom{K}{l}\binom{ K-1}{j}\frac{K{{H_{{h_{{h_{{2}}}}}}}^{m_{{{h_{{2}}}}}}(-1)}^{j+l}{\mathcal{A}}_{j^{\prime}}{ \mathcal{B}}_{j^{\prime}}{\mathcal{A}}_{l^{\prime}}{\mathcal{B}}_{l^{\prime}} \chi^{l}\delta^{l}e^{-{{\mu_{{h_{{2}}}}}}\chi^{\delta}}}\right.\\ &\left.\left[\frac{\Gamma\left({{m_{{h_{{2}}}}}+\tilde{J}}\right)} {{{{\mu_{{h_{{2}}}}^{j}}}(1+j)}^{{{m_{{h_{{2}}}}}+\tilde{J}}}}-\sum_{i=0}^{N} \sum_{q=0}^{\tilde{J}}\bigcup_{i^{\prime}}\binom{\tilde{J}}{q}\binom{N}{i} \frac{(-1)^{i}\rho^{\tilde{J}-q}\delta^{i}{\mathcal{A}}_{\tilde{J}^{\prime}} \mathcal{B}_{\tilde{J}^{\prime}}\Gamma\left({{m_{{h_{{2}}}}}+\tilde{J}}+ \tilde{i}-q\right)e^{-{{\mu_{{h_{{1}}}}}}\tilde{i}}}{\left({{\mu_{{h_{{2}}}}} \left(1+j\right)+{{\mu_{{h_{{1}}}}}}i\delta\rho}\right)^{{{m_{{h_{{2}}}}}+ \tilde{J}}+\tilde{i}-q}}\right]\right\},\end{split}  \tag{13}\]  
where \(\gamma_{2}=2^{2{{R_{{2}}}}}-1,\delta=\frac{\gamma_{2}}{\rho}\) and \(\chi=\frac{1}{\left({{a_{1}}-{a_{{2}}}\gamma_{{{2}}}}\right)}\). The detailed derivation for (13) is given in Appendix A.
# Assessment of Six Machine Learning Methods for Predicting Gross Primary Productivity in Grassland
###### Abstract  
Grassland gross primary productivity (GPP) is an important part of global terrestrial carbon flux, and its accurate simulation and future prediction play an important role in understanding the ecosystem carbon cycle. Machine learning has potential in large-scale GPP prediction, but its application accuracy and impact factors still need further research. This paper takes the Mongolian Plateau as the research area. Six machine learning methods (multilayer perception, random forest, Adaboost, gradient boosting decision tree, XGBoost, LightGBM) were trained using remote sensing data (MODIS GPP) and 14 impact factor data and carried out the prediction of grassland GPP. Then, using flux observation data (positions of flux stations) and remote sensing data (positions of non-flux stations) as reference data, detailed accuracy evaluation and comprehensive trade-offs are carried out on the results, and key factors affecting prediction performance are further explored. The results show that: (1) The prediction results of the six methods are highly consistent with the change tendency of the reference data, demonstrating the applicability of machine learning in GPP prediction. (2) LightGBM has the best overall performance, with small absolute error (mean absolute error less than 1.3), low degree of deviation (root mean square error less than 3.2), strong model reliability (relative percentage difference more than 5.9), and a high degree of fit with reference data (regression determination coefficient more than 0.97), and the prediction results are closest to the reference data (mean bias is only \(-0.034\)). (3) Enhanced vegetation index, normalized difference vegetation index, precipitation, land use/land cover, maximum air temperature, potential evapotranspiration, and evapotranspiration are significantly higher than other factors as determining factors, and the total contribution ratio to the prediction accuracy exceeds 95%. They are the main factors influencing GPP prediction. This study can provide a reference for the application of machine learning in GPP prediction and also support the research of large-scale GPP prediction.  
machine learning; gross primary productivity; prediction; key factors; Mongolian Plateau 2023  
68 May 2023  
682 June 2023  
28 June 2023  
27 July 2023  
2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
# A Survey: Network Feature Measurement Based on Machine Learning
## 3 Network Measurement Based on ML Method  
According to the classification of ML methods, which is the subject of this study, the application of various ML approaches to network measurement is introduced in the second section. Understanding the key traits of the various ML algorithms is useful for researchers. This section of the article will provide the ML-based solution from the standpoint of a real-world network measurement application. To characterize and visualize network behavior and quantify numerous network indicators, network measurement's primary objective is to gather traffic data linked to network operation [60]. According to the classification standard for network measurement objects, network measurement can be further separated into active measurement, passive measurement, network performance measurement, and network traffic measurement. An ML technique based on a network measurement object is presented in this study. Measurements of latency, packet loss, path loss, throughput, and bandwidth are all examples of network performance metrics. As demonstrated in Table 3, this article provides a detailed introduction to various application situations utilizing the ML approach for network monitoring.
## 4 Application of Blockchain Technology in Knowledge-Based Networks
### Network Administration
#### 4.4.2 Mobility Administration
A framework known as Blockchain and Backscatter Aided Internet of Things (BBAIoT) collects sensor data and sends it to a blockchain network to verify, store, and process in a trusted manner, where an optimization problem is solved considering the dynamics of the primary channel for time scheduling of transmission time and backscatter time [295]. Offloading--Offloading involves diverting certain tasks or data traffic from mobile devices to other entities in mobility management. An Actor-Critic-based Deep Reinforcement Learning (ACDRL) policy is used to achieve task scheduling and offloading in a 5G-based massive internet of things KDN network, while a PoAu agreement-driven blockchain is implemented to verify operations and blocks [296]. Furthermore, in a two-layer distributed vehicular network KDN architecture, consortium blockchain is used to share the network topology among multiple controllers, while the goal of Service Offloading and Migration (SOM) optimization is used to reduce the utilization of power and increase system speed, including the blockchain. Additionally, a deep reinforcement learning strategy has been employed to tackle the aforementioned optimization concern [297]. Moreover, a Computation Offloading (CO) platform that scrutinizes the decentralized control architecture of KDN to offload computation irrespective of different service providers uses blockchain to ensure unbiased and fair scheduling and offloading, while deep reinforcement learning is utilized to strengthen the transactions [298]. Alternatively, distributed blockchain has been involved in Secure Consensus and Reliable Data Offloading (SCRDO), where a resource allocation algorithm optimizes offloading and resource allocation with the aid of deep reinforcement learning [299]. Additionally, in mobile edge-cloud knowledge-defined IoT networks, blockchain has been utilized for protection from illegal offloading actions using access control, where offloading has been achieved by optimization considering offloading and consensus scheme decisions, computational resources, and channel bandwidth to cut down the amount of time and energy used while the offloading problem is solved usinga deep Q-network [300]. Similarly, blockchain is incorporated into a mixed computing framework that combines edge technology and cloud services to achieve shared agreement and resource administration by offloading data, while Markov decision-making processes and deep reinforcement learning are applied for combined conversation, calculations, and consensus issues [301].
## 2 Materials and Methods
### Methods
#### 2.2.3 SNSCNN Model Construction and Training  
To address the issue of convolutional neural networks' inability to selectively focus on high-density information regions, the integration of an attention module with CNN architecture allows the network to prioritize and extract features from areas with higher information density. By emphasizing important features and suppressing irrelevant ones, the accuracy and efficiency of CNN can be improved.  
In this study, a CNN model is constructed, as illustrated in Figure 6. To capture the spatial non-smoothness of the driver data, attention layers are connected to the input data of the driver. This enables the model to effectively extract relevant spatial information and enhance its ability to identify important features.  
1. Each of the 15 driver layers is cut into subgraphs of size \(51\times 51\), which are enhanced and used as the input of the CNN using the attention mechanism.
2. Multi-scale features are obtained after several convolution and pooling operations. First, \(49\times 49\times 16\) data are obtained by \(2\times 2\) convolution of the first layer, and then a \(24\times 24\times 16\) intermediate layer is obtained by \(2\times 2\) pooling; second, a \(22\times 22\times 32\) intermediate layer is obtained by \(3\times 3\) convolution, and then a \(10\times 10\times 32\) intermediate layer is obtained by \(3\times 3\) pooling operation; then, a \(2\times 2\) convolution operation is used, while a \(20\%\) Dropout operation is applied to it to avoid overfitting.
3. A fully-connected layer of \(84\times 1\) is obtained by using a fully-connected operation. The final layer is obtained by using the fully-connected operation with \(2\times 1\) data, and the mapping relationship between the driver and the label is established using the SoftMax classification function, which is the final classification result.  
The SNSCNN model is trained using fifteen driver layers as input. These layers are cropped into multiple \(51\times 51\) matrices centered on the label coordinates, and they are used along with the labels for training. The model achieves a training set accuracy of 0.8894.
## 3 Self-Adaptive-Coefficient Double-Power Sliding Mode Control Algorithm
### Design of Compensation Item Based on Double-Power Reaching Law  
A sliding mode control allows a robot to maintain system stability under the conditions in which parameters change and environmental interference occurs through the switching of the self-adaptive control [35]. To reduce the influence of model error and environmental interference on the trajectory tracking of the lower limb rehabilitation exoskeleton robot, its trajectory tracking was achieved by combining the estimated control torque with the sliding mode control method based on a double-power reaching law.  
The sliding mode surface \(\mathbf{s}\in\mathbb{R}^{n}\) for the sliding mode control was designed as follows:  
\[\mathbf{s}=\dot{\mathbf{\theta}}_{r}-\dot{\mathbf{\theta}}\dot{\mathbf{=}}\dot{\mathbf{e}}+\mathbf{ \Lambda e} \tag{11}\]  
where the interference \(\mathbf{\tau}_{d}\) is bounded. Based on the double-power reaching law, the compensation term \(\mathbf{\tau}_{c}\) for the actual control torque was designed as follows:  
\[\mathbf{\tau}_{c}=\mathbf{k}_{\mathbf{\kappa}}|\mathbf{s}|^{\mathbf{\alpha}}\text{sgn}(\mathbf{s})+ \overline{\mathbf{k}}_{\mathbf{\beta}}|\mathbf{s}|^{\mathbf{\beta}}\text{sgn}(\mathbf{s}) \tag{12}\]  
where \(\overline{\mathbf{k}}_{\mathbf{\beta}}=\mathbf{k}_{\mathbf{\beta}}+\frac{f}{|\mathbf{s}|^{\mathbf{ \beta}}}\); \(\text{sgn}(\mathbf{s})\) is the symbolic function; and \(\mathbf{k}_{\mathbf{\alpha}}\geq 1\), \(\mathbf{k}_{\mathbf{\beta}}\geq 1\), \(\mathbf{\alpha}>1\), \(0<\mathbf{\beta}<1\).  
Because \(\mathbf{\alpha}>1\) and \(0<\mathbf{\beta}<1\), during the entire sliding phase (\(\mathbf{s}\to 0\)), when the system state of the lower limb rehabilitation exoskeleton robot is far from the equilibrium point (e.g., \(\mathbf{s}>1\)), the compensation term \(\mathbf{\tau}_{c}\) is determined by the higher-order power term \(\mathbf{k}_{\mathbf{\alpha}}|\mathbf{s}|^{\mathbf{\alpha}}\text{sgn}(\mathbf{s})\). At this time, \(\mathbf{\alpha}>1\) makes the high-order power term larger, which effectively increases the approach speed. However, when the system state of the robot is close to the sliding mode surface (e.g., \(\mathbf{s}<1\)), the compensation term \(\mathbf{\tau}_{c}\) is determined by the low-order power term \(\mathbf{k}_{\mathbf{\beta}}|\mathbf{s}|^{\mathbf{\beta}}\text{sgn}(\mathbf{s})\). At this point, \(0<\mathbf{\beta}<1\) makes the low-order power term smaller and reduces the reaching speed, which effectively avoids an excessive reaching speed and reduces chattering of the system state.
## 5 Results  
### Results of the Proof of Concept  
#### 5.1.1 Sensitivity to the Distance Weighting  
The variation of the distance weight shows sensitivity of the removal result to this parameter. Although the variation is very small, there is a significant trend. The increase of DW entails a loss of image restoration quality. The filled images tend to be slightly negatively biased, which indicates artificial spectral innovations. The negative values of the DV indicate a loss of image content since the variance of the filled image is lower than the variance of the original image. In addition, the STD-DI points to a loss of similarity between the original image and the filled image by increasing DW. Although the Correlation Coefficient varies only at the third digital place, the decrease with an increase of DW is obvious. SAM as a measure for the spectral fidelity of the image also behaves inversely proportional to the variation of DW. All evaluated statistical measures are varying in very small intervals from their ideal value, which could be regarded as an indicator of a very high performance of the algorithm despite the variation of the inverse distance weighting function. On the other hand, this strong similarity of the statistical cloud removal results provides the opportunity to evaluate the sufficiency of the statistical measures to capture the findings of the cloud removal process. Therefore it is expedient to include a visual comparison of the removal results into the assessment (Figure 5).