esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model Description  
### Neural Network Description  
The MLP network, which is composed of several neurons, is a basic artificial neural network [17]. MLP is a fully connected neural network with three or more layers (an input layer, an output layer, and one or more hidden layers) of nonlinearly activating nodes. The nonlinear relationship between the input and output can be obtained by using a nonlinear activation function. In this paper, the nonlinear relationship between shale gas production and reservoir properties and fracturing parameters is obtained using MLP. However, the MLP cannot deal with sequential data because it lacks memory functions.  
A recurrent neural network (RNN) allows information to be transferred from one step to the next [18]. In RNN, a neural network unit can be regarded as the superposition of multiple units, and each neural network transmits a message to the next neural network. This chain-like structure enables the RNN to connect the previously stored information with the current information. Thus, RNN can infer subsequent events from previous events, which is suitable for solving time series data problems. However, because of gradient disappearance and gradient explosion, RNN cannot solve the problem of time series forecasting [19].  
The LSTM network is a special kind of RNN, which was proposed by [PERSON] and [PERSON] [20]. LSTM's design was inspired by the logic gates of a computer. LSTM introduces a memory cell (or cell for short) that has the same shape as the hidden state (some papers consider the memory cell as a special type of the hidden state), engineered to record additional information. To control the memory cell, we need a number of gates. One gate is needed to read out the entries from the cell. We will refer to this as the output gate. A second gate is needed to decide when to read data into the cell. We refer to this as the input gate. Lastly, we need a mechanism to reset the content of the cell, governed by a forget gate. This is more practical than the ordinary recurrent neural network because of its ability to process the sequential data [10].  
The forget gate is expressed as:  
\[f_{t}=\sigma\Big{(}w_{fh}h_{t-1}+w_{fx}x_{t}+b_{f}\Big{)} \tag{1}\]  
where \(f_{t}\) is the sigmoid gate, \(\sigma\) is sigmoid activation function, \(h_{t-1}\) is the hidden state from the previous time step \(t\)-1, \(x_{t}\) is the input at the time step \(t\), w represents the weight, and \(b\) is the bias.  
The input gate is calculated by:  
\[i_{t}=\sigma(w_{ih}h_{t-1}+w_{ix}x_{t}+b_{i}) \tag{2}\]  
\[C_{t}^{*}=\tanh(w_{Ch}h_{t-1}+w_{Cx}x_{t}+b_{C}) \tag{3}\]  
where \(i_{t}\) is the sigmoid layer, \(C_{t}{}^{*}\) is the tanh layer, and tanh is the tanh activation function.  
\[C_{t}=f_{t}\times C_{t-1}+i_{t}\times C_{t}^{*} \tag{4}\]  
The output gate is written as:  
\[o_{t}=\sigma(w_{oh}h_{t-1}+w_{ox}x_{t}+b_{o}) \tag{5}\]  
\[h_{t}=o_{t}\times\tanh(C_{t}) \tag{6}\]  
where \(o_{t}\) is the sigmoid layer and \(h_{t}\) is the tanh layer.
## 2 Experimental Section
### Measuring Device
#### 2.1.3 Software Design  
The image acquisition software interface is shown in Figure S1 which was developed with C# and Camera_NET library in VS2012 platform. \"Camera selection\" was used to select the infrared camera. \"Camera settings\" were used to set the brightness, white balance, hue, saturation, and exposure of the camera. The software can automatically save the set parameters. \"Snapshot the frame\" obtained a frame image and took the average RGB value of the region of interest (ROI). In this way, the RGB value corresponding to the VC solution was obtained. Then, the corresponding grayscale and log-grayscale values were calculated and the corresponding Commission Internationale de L'Eclairage (CIE) Lab (CIE L*a*b*, L* for the lightness from black (0) to white (100), a* from green (-) to red (+), and b* from blue (-) to yellow (+)) values were obtained by converting the RGB color space to the CIE Lab color space. \"Determine\" opened the measurement part, which included zero calibration, span calibration, and measurement functions.  
Grayscale used black tones to represent objects, and quantified the gray values from 0 to 255. \"L\" represents the brightness or the luminance of the light through suspension, \"a\" represents the range from red to green, and \"b\" represents the range from yellow to blue. L ranges from 0 to 100, and a and b range from +127 to -128.  
Figure 3: Camera printed circuit board (PCB) details.  
Equation (1) was used to compute the grayscale and was calculated by a logarithmic grayscale:  
\[grayscale=R\times 0.299+G\times 0.587+B\times 0.114. \tag{1}\]  
An approximate conversion algorithm was used to transform the RGB color space to the CIE Lab color space as follows [20].  
First, the RGB color space was converted to CIE XYZ color space (X, Y, and Z are extrapolations of RGB created mathematically to avoid negative numbers. Y means luminance, Z is somewhat equal to blue, and X is a mix of cone response curves chosen to be orthogonal to luminance and non-negative):  
\[\begin{bmatrix}X\\ Y\\ Z\end{bmatrix}=\begin{bmatrix}0.412453&0.357580&0.180423\\ 0.213671&0.715160&0.072169\\ 0.019334&0.119193&0.950227\end{bmatrix}\begin{bmatrix}R\\ G\\ B\end{bmatrix} \tag{2}\]  
\[\left\{\begin{array}{ccc}X&=&\frac{X}{2555\times 0.950456}\\ Y&=&\frac{X}{255}\\ Z&=&\frac{Z}{255\times 1.088754}\end{array}\right.. \tag{3}\]  
Then, the XYZ color space was converted to the CIE Lab color space,  
\[\left\{\begin{array}{ccc}L&=&116f(Y)-16\\ a&=&500[f(X)-f(Y)]\\ b&=&200[f(Y)-f(Z)]\end{array}\right. \tag{4}\]  
\[f(t)=\left\{\begin{array}{ccc}t^{\frac{1}{3}}&if\ t>(\frac{6}{29})^{3}\\ \frac{1}{3}(\frac{29}{6})^{2}t+\frac{4}{29}&\text{otherwise}\end{array}\right.. \tag{5}\]
## 3 Case Study B  
In this section, the practicality of the proposed method is validated using a point cloud case publicly available in the Rockbench repository [48] (Figure 10). The case is located along Highway 15 near Kingston, ON, Canada, and was acquired using a Leica HDS6000 laser scanner. It contains 2,167,515 data points along with their color information.
# Blockchain Integration in the Era of Industrial Metaverse
## 5 Blockchain Integration in Metaverse  
The developments and technological advancements presented in the previous paragraphs in the field of blockchain technology have led many businesses to experiment with the design and development of proprietary blockchain platforms (Figure 13). The objective of the metaverse is the provision of a scalable, safe, and user-friendly blockchain platform. Furthermore, the metaverse also focuses on the creation of digital assets and smart contracts. Within the metaverse environment, users are provided with the necessary tools required for the creation and release of digital assets using what is also known as the metaverse Digital Asset System. Smart contracts, which pose a major theme in blockchain, can be utilized within the metaverse in order to enable users to easily, safely, and legally transfer digital assets. The decentralized nature of blockchain aligns well with the metaverse, enabling the creation and operation of a virtual economy. Especially with the use of various kinds of utility tokens and NFTs (non-fungible tokens). With the use of NFTs, a user's digital assets can be shielded from copying and hacking. However, in order to achieve successful integration of blockchain in the metaverse, the issue of double spending remains an open challenge. The metaverse originated as a medium for the co-existence of a virtual and physical world.  
Figure 12: Keyword network for “blockchain” and “Society 5.0.”  
Therefore, one challenge emerging is the ability to trade and invest irreplaceable tokens for real/physical money. Advantages to utilizing the metaverse include the following:  
* Investing in financial products and services now has a lower barrier to entry.
* Assisting individuals in monetizing their work and experiences (such as gaming) in order to compensate for income losses due to inflation and the pandemic.
* Reducing costs and increasing transaction efficiency by eliminating middlemen in procedures such as money transfers.
* Creating interest from a diverse range of demographic groups outside of the traditional financial sector and presenting novel concepts and opportunities.
* Managing community-involved and connected projects.  
In the research work of [PERSON] et al., published in 2021 [58], the authors have performed a comprehensive literature review on the integration of blockchain technology in the upcoming metaverse. Furthermore, a plethora of applications are provisioned in order to setup an economic ecosystem within the metaverse, such as applications for office work, social networking applications, non-fungible token (NFT) marketplaces, etc. Another aspect that must be further elaborated in the near future is the development of suitable security and privacy frameworks in the metaverse, which could minimize users' privacy violations, identity theft, and/or any other kind of fraudulent activity. Furthermore, another requirement for future development is the design and development of metaverse-oriented cryptography mechanisms for privacy preservation.
## 2 Background
### Dynamic Adaptive Streaming over HTTP  
Dynamic adaptive streaming over HTTP (DASH) is an MPEG standard defining a multimedia format and description. DASH contains media files divided into small chunks of 2 to 10 s called segments. Each media file segment is encoded into multiple versions of bit-rate streams or quality levels. The quality level comprises frame rate (temporal), bit-rate (signal-to-noise) and resolution (spatial). Details related to different quality levels of media are stored in XML manifest, known as Media Presentation Description (MPD). One or more _Periods_ are defined in each MPD. These _Periods_ contain various parts of media, such as video with different viewing angles, various codes used, audio for different languages, captions, and subtitles. These media parts have some features that do not change for one _Period_, (such as sound channels, bit rate, and frame rate) and are arranged in _Adaptation Sets_. One or more _Adaptation Sets_ are included in each _Period_. This allows the clustering of logically similar multimedia parts. The _Adaptation Set_ comprises a set of representations that contains interchangeable versions of each media content, such as different resolutions, bit-rates. For example, the _Adaptation Set_ may contain media parts encoded with the same codec, resolution, language, and audio channel formats (e.g., stereo or 5.1 channel). The _Adaptation Set_ enables the client to eliminate a range of multimedia parts that do not meet their requirements. Although a single representation is sufficient for a playable stream, multiple _Representation Sets_ enable the client to adapt the media stream to their current requirements for network bandwidth. Over time, the client can adapt to the resolutions, bit-rates, codecs, and so forth, available in a _Period_. Figure 1 shows the detailed logical structure of MPD. The DASH client first obtains the MPD through HTTP standard complaint GET requests. By parsing the information provided by the MPD, the DASH client chooses the appropriate encoded alternative based on network conditions and starts content streaming by selecting segments. MPEG-DASH specification for server defines only MPD and segment formats, while on the client-side it defines the parsers. The transmission of MPD, media encoding formats comprising segments, customer behavior for obtaining the segments, adapting heuristics, and playing content are beyond the MPEG-DASH scope. The size of the segment and the selected encoding technique define the granularity of the streaming adaptation.  
Figure 1: Structure of Media Presentation Description (MPD).
## 5 Simulation Results and Discussion
### Experimental Results and Analysis  
As shown in Figure 5 and Table 2, our proposed method achieves the best results with a labeled sample proportion of 1-4%. This indicates that semi-supervised learning methods based on pseudo-labeling and consistency regularization perform well in the AMC domain. We find that both semi-supervised learning methods (pi-model, VAT, proposed method) and machine learning methods (decision tree, support vector machine) have a higher classification accuracy than supervised learning methods when the number of labeled samples is small (1-5% of labeled samples). These supervised learning methods only use a backbone network for classification. This suggests that deep-learning-based methods are driven by data, and their classification accuracy is greatly affected in the case  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Dimension of the original signal & 4096 \(\times\) 2 \\ \hline Input data dimension & 64 \(\times\) 64 \(\times\) 3 \\ \hline The number of training samples & 16,000 \\ \hline The number of testing samples & 4000 \\ \hline The proportion of labeled samples & 1%, 2%, 3%, 4%, 5% \\ \hline Arch & WideResnet \\ \hline Optimizer & SGD \\ \hline Initial learning rate & 0.3 \\ \hline Threshold & 0.95 \\ \hline Unlabeled loss weight \(\lambda_{u}\) & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental setup.  
of insufficient data samples. The machine-learning-based method is not affected much by the number of data samples. However, it requires hand-designed features, which limits the improvement to the recognition accuracy. We found that the classification accuracy of both semi-supervised learning methods (VAT and Pi-model) is not good when the sample size is extremely small (1% of labeled samples). However, at this point, the recognition accuracy of our proposed semi-supervised method exceeds 94%. This suggests that our proposed method is suitable for scenarios where the number of labeled samples is extremely low. We find that the recognition accuracy of all methods increases as the sample size increases. This is because our proposed method learns the distribution of data not only in unlabelled data but also in labeled data. The greater the amount of labeled data, the more prior knowledge is gained. The recognition performance of the model is improved.
# A Networked Sensor System for the Analysis of Plot-Scale Hydrology
## 2 Materials and Methods
### Transpiration Calculations from Sap Flow Measurements  
In this study, the xylem sap flow (i.e., the velocity of the water being transported through the active sapwood of the tree) is calculated using an empirical equation based on daily temperature differences between a pair of heated and reference temperature probes [41; 58]:  
\[Q_{s}=0.000119\times\left(\frac{\Delta T_{0}-\Delta T}{\Delta T}\right)^{1.231}, \tag{4}\]  
where \(\Delta T\) is the temperature difference between the upper and lower probes (\({}^{\circ}\)C); \(\Delta T_{0}\) is the maximum daily value of \(\Delta T\) (i.e., zero sap flow) (\({}^{\circ}\)C); and \(Q_{s}\) is the sap flux density (m\({}^{3}\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)). Calculated quantities of \(Q_{s}\) are converted to transpiration based on the following equation [59; 60; 61; 62]:  
\[\tau=Q_{s}\left(\frac{A_{s}}{A_{G}}\right), \tag{5}\]  
where \(\tau\) is the rate of transpiration (m\(\cdot\)s\({}^{-1}\)); \(A_{s}\) is the tree sapwood area (m\({}^{2}\)); and \(A_{G}\) is the ground area (m\({}^{2}\)). The ratio \(A_{s}/A_{G}\) depends on the study site and is indicative of the tree density and the predominant tree species. It has been shown that this ratio can be as low as 1 m\({}^{2}\cdot\)ha\({}^{-1}\)[63] and reach values as high as 25 m\({}^{2}\cdot\)ha\({}^{-1}\)[61] or 40 m\({}^{2}\cdot\)ha\({}^{-1}\)[64]. Estimates of \(A_{s}\) may be determined empirically, such as by the following allometric equation [65]:  
\[A_{s}=B_{0}\cdot d^{B_{1}}, \tag{6}\]  
where \(d\) is the measured tree diameter at breast height (cm) and \(B_{0}\) and \(B_{1}\) are species-specific coefficients determined by regression techniques.  
In a 2010 sap flow study at the ASWP site [66], 22 trees were surveyed to identify their species, take measurements of \(d\), and estimate \(A_{s}\) from three core samples taken at breast height. Based on the results of that study, only the silver map (_Acer saccharium_) trees, predominantly in site 2, produced measurable sap flow quantities.  
In 2016, another survey was conducted to estimate \(A_{s}/A_{G}\) for the silver maples in site 2. Figure 6 shows the surveyed area in site 2 for the \(A_{s}/A_{G}\) ratio estimation (approximately 1300 m\({}^{2}\)) where three sap flow nodes (2014, 2084 and 2134) are located (pink circles in Figure 5). To establish the boundary of the surveyed area, first, a preliminary perimeter was defined using trees located around the sap flow nodes. Then, this perimeter was displaced 5.33 m, which is the approximate mean distance between neighboring trees. The area of influence for each sap flow node was established using the Thiessen polygons criterion (i.e., the colored regions in Figure 6). These regions were used to calculate one \(A_{s}/A_{G}\) ratio for each node according to the number and diameter of trees within their area of influence. The diameters at breast height of 25 trees, including the three trees with sap flow sensors, were measured and the sapwood areas, calculated using Equation (6), are compared with the measurements made in 2010.
## 2 Materials and Methods
### Data Collection  
In Mexico, the National Water Commission (CONAGUA) is a federal agency responsible for monitoring, surveillance, and management of aquifers [21]. The analysis of the groundwater samples was carried out by an accredited laboratory [22] which applied a methodology based on the standard methods (APHA) [23]. The data used in this study were obtained by this federal agency. For economic and strategic reasons, four sampling wells were monitored, which were in sites with intense anthropogenic activity. These wells are used for water consumption. The extraction of groundwater for consumption purposes is carried out using pumping systems.  
The hydrochemical and water quality parameters considered in this study were: chaperones (HCO3\({}^{-}\)), fecal coliforms (FC), total organic carbon (TOC), ammonium (NH3), nitrites (NO2\({}^{-}\)), nitrates (NO3\({}^{-}\)), organic nitrogen (ON), total nitrogen (TN), total phosphorus (TP), total dissolved solids (TDS), electrical conductivity (EC), pH, chlorides (Cl\({}^{-}\)), fluorides (F), silicon oxides (SiO2), potassium (K\({}^{+}\)), manganese (Mn), sodium (Na\({}^{+}\)), sulfates (SO4\({}^{-}\)), calcium (Ca\({}^{2+}\)), magnesium (Mg\({}^{2+}\)), total hardness (TH), water temperature (WT).  
Quality control (QC) and quality assurance (QA). The sampling wells were monitored by CONAGUA. An accredited laboratory carried out the analysis of 23 parameters sampled every six months in the 2012-2019 period. The water sampling was carried out according to Mexican regulations. Based on these regulations, chemical products of analytical grade were required for the preparation of the standard solutions and reagents. In addition, replicates were performed to ensure the reliability of the results and comply with quality control required by the General Directorate of Standards and the Federal Law on Metrology and Normalization.
## 5 Conclusions  
A new structural design of the first-story isolation system in RC frame buildings was proposed and developed in this study. The numerical results were verified by the testing results regarding the seismic responses. The following conclusions were achieved:  
* The first-story isolation system with the coupled beam-column connections increases the seismic capacity of the first-story isolated RC frame building under strong earthquake excitations compared to a non-isolated frame building.
* The isolation system significantly decreases the story accelerations and inter-story displacements of the superstructure in the first-story isolation model, indicating a good damping effect of the isolation system that can intensively absorb the seismic energy. For the substructure, its story acceleration increases slightly, and there is almost no damping effect as it is rigidly connected with the base.
* The displacement angels of the substructure in the first-story isolation RC building are small and corresponding column horizontal deformation is in the elastic stage. The safety capacity of the substructure is higher than that of the superstructure.
* The coupled beam-column connections could be potentially used in the practical first-story isolation system to avoid the requirements of large column stiffness and large column size on the first floor.  
**Author Contributions:** Conceptualization, Y.W. and A.Q.; methodology, Y.W. and N.L.; software, N.L.; validation, Y.W., N.L., and A.Q.; formal analysis, Y.W. and N.L.; investigation, N.L.; resources, A.Q.; data curation, Y.W. and N.L.; writing--original draft preparation, Y.W.; writing--review and editing, A.Q.; visualization, N.L.; supervision, A.Q.; project administration, A.Q.; funding acquisition, Y.W. and A.Q.  
**Funding:** The authors disclosed receipt of the following financial support for the research, authorship, and publication of this article: The research work was supported by the National Natural Science Foundation of China (Grant No. 51778149).  
**Conflicts of Interest:** The authors declared no potential conflicts of interest concerning the research, authorship, and the publication of this article.
## 2 Materials and Methods
### Cross-Bubble Transition Network (CBTN)
1  performing phase space reconstruction on \(x(t)\), \(y(t)\) to get \(X_{t}=[x_{t},x_{t+\tau},x_{t+2\tau},\ldots,x_{t+(d-1)\tau}]\) and \(Y_{t}=[y_{t},y_{t+\tau},y_{t+2\tau},\ldots,y_{t+(d-1)\tau}]\), \(t\in[1,2,\ldots,N-(d-1)\tau]\) for \(t=1\) to \(N-(d-1)\tau\) performing ascending sort on \(X_{t}\) to get its position index \(\pi_{X_{t}}\), \(Y_{t}\) is rearranged according to \(\pi_{X_{t}}\) to get \(G_{t}=Y_{t}(\pi_{X_{t}})\), \(S_{i}\in[0,1,\ldots,d(d-1)/2]\). //\(d(d-1)/2\) is the maximum swaps number \(6\) Using \(S_{i}\) as network nodes, by temporal adjacency relationship of \(S_{i}\) to construct a directed weighted complex network \(W\). for \(i=0\) to \(D\) // \(D=d(d-1)/2\) \(S_{W_{i}}=-\sum\limits_{j=0;j\
eq i}^{D}w_{ij}\log 2(w_{ij})\), // \(\sum\limits_{j=0}^{D}w_{ij}=1\) \(9\) normalizing \(S_{W_{i}}\) to get \(H_{W_{i}}=S_{W_{i}}/S_{i,\max}\), // \(S_{i,\max}=\log 2(D+1)\) \(10\)\(H_{NOTE}=0\). // \(D=d(d-1)/2\) \(11\) for \(i=0\) to \(D\) \(H_{NOTE}=H_{NOTE}+p_{i}H_{W_{i}}\). // \(p_{i}\) is the probability distribution of \(S_{i}\). \(13\) return \(H_{NOTE}\). ```  
**Algorithm 1**Cross-bubble transition entropy  
To demonstrate the performance of the NOTE to track the deterministic dynamical variation in time series, the values with the NOTE and the original CPE were obtained separately for the symbolized sets A = [2 2 4 3 5 1 2 ] and B = [1 2 2 5 3 2 4 ]. The probability distributions of the elements in the sets A and B were the same. The probability of individual elements sorted in an ascending order were [0.143, 0.428, 0.143, 0.143, 0.143]. The original CPE method would yield an entropy value of 2.128 for both sets. The directional weighted adjacency matrices constructed for the elements in sets A and B according to their temporal adjacency relationship are shown in Figure 1a,b, respectively. The two adjacency matrices exhibited distinct differences. The NOTE value of these two adjacency matrices was 0.1846 and 0.2925, respectively, which shows the different dynamical variations contained in the sets A and B.