esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient User Pairing for Downlink NOMA in Massive MIMO Networks
## Abbreviations  
List of Acronyms EE Energy Efficiency NOMA Non-Orthogonal Multiple Access SCMA Sparse Code Multiple Access PDMA Pattern Division Multiple Access MIMO Multiple Input Multiple Output NLoS Non-Line of Sight SIC Successive Interference Cancellation SCP Sequential Convex Programming SE Spectral Efficiency IOT Internet of Things CSI Channel State Information 5G Fifth Generation of Cellular Communications BS Base Station MS Mobile Station D2D Device-to-Device KKT Karush-Kuhn-Tucker MCMC-NOMA Multi-Cell Multi-Carrier Non-Orthogonal Multiple Access GRASP Greedy Randomized Adaptive Search SSD Stochastic Sample Greedy DPCA Dynamic Power and Channel Allocation WAR Weighted Achievable Rate AmBC Ambient Backscatter Communication FTPC Fractional Transmitting Power Control DC Difference of Convex SCA Successive Convex Approximation SDMA Spatial Division Multiple Access JT-CoMP Joint Transmission Coordinated Multi-Point
# End-of-Life Scenarios for Mass Timber: Assumptions, Limitations and Potentials--A Literature Review
## 3 Results
### End-of-Life Scenarios: Reuse, Recycling, Energy Recovery and Landfill
#### 3.5.4 Landfill  
Landfill as an EoL scenario is discussed by 17 scholars. When timber is landfilled, a portion of the carbon in the timber can be stored longer, contributing to overall carbon sequestration. However, the environmental impact of landfilling timber is complex and involves several factors. As timber decomposes, landfill gas (LFG) is released, primarily consisting of methane (around 50%), which poses a significant environmental concern due to its strong greenhouse gas effects [49]. Reinforced by [PERSON] et al. [52], the decomposition of waste wood materials in landfills is recognized as a major contributor to methane emissions. This makes landfill the least preferred option as an EoL scenario for mass timber from an environmental perspective.  
The environmental impact of landfilling timber is influenced by the rate and extent of its decay, which are again challenging to predict accurately. According to [PERSON] and [PERSON] [31], these factors are affected by variations in landfill conditions, including climate, management practices, and the specific landfill design. Additionally, there is a lack of comprehensive, long-term studies on timber degradation in situ, which makes it difficult to assess how timber decomposes over several decades. Several research studies [53; 54] and the last IPCC guidelines [55], however, have highlighted that the degradation potential of wood in anaerobic environments, such as landfills, has been overstated, thus offering a potential reassessment of the negative effects. Thus, the variability in these conditions can significantly alter the environmental impact of landfilling timber, highlighting the need for more detailed research and improved landfill management practices to mitigate potential negative effects.
## 2 Rainfall Prediction Model Construction  
### Modeling Principle  
Deep learning is a concept introduced in recent years, which mainly refers to neural network models with more layers and neurons, including feedforward networks, feedback networks and self-organizing network models [23]. As one of the most popular variants of RNN, LSTM improves the problem of gradient disappearance in RNN through gate structure. LSTM cells contain three types of gates: input gates, which can be adaptively proportioned to the unit memory; forget gates, which mainly characterize the forgetting rate of the unit memory for a given input; and output gates, which mainly control the unit memory to influence the node output [24]. The LSTM structure is shown in Figure 1.  
The output structure of LSTM outputs a value of 0 or 1 to the number in \(c^{(t-1)}\) by reading \(h^{(t-1)}\) and \(x^{(t)}\), where \"1\" means keeping and \"0\" means discarding, as follows:  
\[f^{(t)}=\sigma\Big{(}W^{(f)}\cdot\Big{[}h^{(t-1)},x^{(t)}\Big{]}+b^{(f)}\Big{)} \tag{1}\]  
where \(h^{(t-1)}\) denotes the cell output at the previous moment and \(b\) denotes the corresponding bias term.  
The input gate determines how much information is allowed to be added to the cell, and the cell state is updated through the information decision of the sigmoid layer in conjunction with the information generation of the \(tanh\) layer [25]. The output gate determines which part of the information about the current cell state is output, still done through the sigmoid layer and the \(tanh\) layer. The input gate steps are shown in Equations (2)-(4), and the output gate steps are shown in Equations (5) and (6):  
\[i^{(t)}=\sigma\Big{(}W^{(i)}\cdot\Big{[}h^{(t-1)},x^{(t)}\Big{]}+b^{(i)}\Big{)} \tag{2}\]  
\[\tilde{c}^{(t)}=tanh\Big{(}W^{(i)}\cdot\Big{[}h^{(t-1)},x^{(t)}\Big{]}+b^{(c)} \Big{)} \tag{3}\]  
\[c^{(t)}=f^{(t)}\cdot c^{(t-1)}+i^{(t)}\cdot\tilde{c}^{(t)} \tag{4}\]  
\[o^{(t)}=\sigma\Big{(}W^{(o)}\cdot\Big{[}h^{(t-1)},x^{(t)}\Big{]}+b^{(o)}\Big{)} \tag{5}\]  
\[h^{(t)}=o^{(t)}\cdot tanh\Big{(}c^{(t)}\Big{)} \tag{6}\]  
where \(f\), \(i\), \(c\) and \(o\) denote the forget gate, input gate, cell state and output gate, respectively; \(h^{(t)}\) denotes the output at the current moment; and \(\sigma\) and \(tanh\) denote the activation function sigmoid and hyperbolic tangent function, respectively [26].
# Operator-Based Adaptive Tracking Capacity Control in Complex Manufacturing Processes
###### Abstract
* To effectively decrease the overshoot and excessive adjusting time in the face of large deviations, integral separated PI-type tracking controllers based on the RRCF (ISPI-RRCF) for the decomposed SISO systems were designed to improve the transient performance. * To adaptively adjust the parameters of the ISPI-RRCF tracking controllers and enhance the performance in real time, a single neuron associated with the supervised Hebb learning algorithm was adopted, where the related weight coefficients were constantly updated in the presence of disturbances and delays. The remainder of this paper is structured as follows: Firstly, a mathematical model concerning job shop systems with RMTs is introduced in Section 2. Thereafter, Section 3 introduces the adaptive capacity controller design process based on operator theory. Later on, Section 4 discusses the implementation of the proposed adaptive tracking capacity control method in a simulation, and then the results are analyzed and compared with those of a previous method. Finally, this paper is concluded in Section 5.
## 6 Future Projects of the Production of Green Hydrogen on an Industrial Scale
### GH2 Projects--Future Facilities (2025-2050)
#### 6.1.10. Cabeza del Mar Project--Magallanes Region  
The Cabeza del Mar on-shore wind farm is a facility that will be located in the north of Punta Arenas, in the Magallanes region. It will have 467 wind turbines, the owner of which will be Acciona and Nordex. Furthermore, the height of the towers from the ground level to the hub will be in the range of 110 m. Each wind turbine will have an installed capacity of 6.0 MW, and considering all the wind turbines, the wind farm will have a total installed capacity equivalent to 2800 MW, with an estimated capacity factor of 0.55. Figure 19 shows a panoramic image of the landscape of the project site, and Table 12 provides some key technical data for this GH2 production facility.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Units** \\ \hline Location & San Gregorio Commune, North of Punta Arenas, Magallanes Region & - \\ \hline Concession Owner & Acciona and Nordex & - \\ \hline Phase of the Project & Industrial Plant & - \\ \hline Type of Wind Farm & On-shore & - \\ \hline Number of Turbines & 467 & - \\ \hline Diameter of Turbines & 145 & m \\ \hline Hub Height & 110 & M \\ \hline Installed Capacity per Turbine & 6.0 & MW \\ \hline Total Installed Capacity & 2800 & MW \\ \hline Capacity Factor & 0.55 & - \\ \hline Equivalent Operating Hours (EOHs) & 5000 & Hours \\ \hline Source of Water & Sea Water—Desalination Plant & - \\ \hline Water Supply & 2,925,000 & m\({}^{3}\)/year \\ \hline Electrolyzer Capacity & 2250 & MW \\ \hline GH2 Production & 258,750 & Tons/year \\ \hline NH\({}_{3}\) Production & 1,451,250 & Tons/year \\ \hline CO\({}_{2}\) Avoided & 2,587,500 & Tons/year \\ \hline \hline \end{tabular}
\end{table}
Table 12: Cabeza de Mar GH2 plant main specifications.  
Figure 19: Panoramic view of the landscape in the Cabeza de Mar project.  
Considering Figure 19, it is evident that the Cabeza del Mar on-shore wind farm is situated near the desert part of Patagonia, where no contiguous human settlements are present.  
According to Table 12, this GH2 production plant will use seawater from a desalination plant, considering a water supply of 2,925,000 m\({}^{3}\)/year. Also, the plant will have an electrolyzer with 2250 MW of installed capacity. In this sense, the GH2 production plant will generate 258,750 tons/year of GH2, and 1,451,250 tons/year of NH\({}_{3}\). Finally, this facility will save 2,587,500 tons/year of CO\({}_{2}\) from being emitted into the atmosphere.
## 5 Experiment
### Quantitative Results  
Tables 2 and 3 show the quantitative results of trustworthiness and Shepard goodness on each dataset, respectively. We first evaluate these results by separating them into graph-based methods (LE, t-SNE, and PHATE) and PCA-based methods (PCA and BGP-LVM). LE, t-SNE, and PHATE exhibit high values on trustworthiness (local metric) but low values on Shepard goodness (global metric). They embedded a neighborhood graph, the local estimation of given data, and thus over-reflected the local structures in low-dimensional representations. This drawback resulted in misleading visualizations due to the lack of global preservation, such as the shape of clusters on DBPedia by t-SNE and PHATE in Figure 4. In contrast, PCA and BGP-LVM, especially PCA, performed a reliable reduction in global preservation with a high Shepard goodness value, but they did not show good local preservation, resulting in a non-visible representation. Our method exhibited high values for both trustworthiness and Shepard goodness, indicating that our method balances global and local structure preservation, i.e., ensuring the reliability and visibility of low-dimensional embedding. Our method is based on GP-LVMs and regularizes latent variables with a neighborhood graph. This approach contributed to retaining local and global structures and achieving the high reliability and visibility of low-dimensional representations. From the above, we confirm the effectiveness of our method quantitatively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **PCA** & **LE** & **t-SNE** & **BGP-LVM** & **PHATE** & **PM** \\ \hline MNIST & 0.738 & 0.756 & **0.994** & 0.823 & 0.871 & 0.881 \\ COIL-20 & 0.898 & 0.882 & **0.997** & 0.984 & 0.931 & 0.974 \\ DBPedia & 0.883 & 0.956 & **0.998** & 0.989 & 0.986 & 0.990 \\ FMNIST & 0.912 & 0.927 & **0.994** & 0.909 & 0.958 & 0.953 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative results of trustworthiness. We boldface the best results and underline the second-best results on each dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **PCA** & **LE** & **t-SNE** & **BGP-LVM** & **PHATE** & **PM** \\ \hline MNIST & 0.503 & 0.431 & 0.349 & 0.464 & 0.368 & **0.512** \\ COIL-20 & **0.818** & 0.633 & 0.611 & 0.525 & 0.355 & 0.687 \\ DBPedia & **0.778** & 0.490 & 0.339 & 0.594 & 0.361 & 0.694 \\ FMNIST & 0.876 & 0.692 & 0.579 & **0.883** & 0.615 & 0.862 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative results of Shepard goodness. We boldface the best results and underline the second-best results on each dataset.
## 2 Materials and Methods
### Features Selection Based on LDA  
Feature selection strategies were applied in order to select a limited set of optimal features able to improve the accuracy of the staining pattern classifier. The main idea of feature subset selection is to remove redundant or irrelevant features from the dataset, as they can negatively influence the classification accuracy and lead to an unnecessary increase of computational cost.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**First Function** & **Second Function** & **Classification Category** \\ \hline Er & Ch & homogenous \\ Di & Cn & speckled \\ Er & Ch & Nucleolar \\ Di & Cn & Centromere \\ Fs & Nt & nuclear dots \\ Fs & Nt & nucleolar membrane \\ \hline \hline \end{tabular}
\end{table}
Table 2: The best preprocessing for each pattern.  
There are many methods proposed in the literature for the decrease in dimensionality, used in supervised or unsupervised classification problems, such as sequential forward search [31; 48], random forest algorithm [49; 50], and ANOVA feature selection [51].  
Discriminant analysis algorithms have been used for dimensionality reduction and feature extraction in many applications of computer vision [52; 53]. In most discriminant analysis algorithms, the transformation matrix is found by maximizing the Fisher-Rao's criterion [54].  
Linear Discriminant Analysis (LDA) is probably the most well-known discriminant analysis technique. This method assumes that the \(C\) classes to which the data belong, are homoscedastic, that is, their underlying distributions are Gaussian with common variance and different means. The LDA method provides the (\(C\)-1)-dimensional subspace that maximizes the between-class variance and minimizes the within-class variance, in any particular data set. In other words, it guarantees maximal class separability and, possibly, optimizes the accuracy in later classifications. In this work, the LDA method was used. For each classifier, the set of features with the highest classifying power has been identified. The LDA method allowed a reduction of complexity, on average, of about a factor of eight.
## 3 Experimental Results and Analysis
### Hydrodynamic Pressure
#### 3.3.3 Amplitude Characteristics  
The maximum fluctuating pressure is further studied here as a key characteristic of jet flow. The probability density is an important statistical parameter describing fluctuating pressure and representing its overall amplitude. When the upstream water head was 1.90 m, the maximum pressure on the slab of the plunge pool was 1.20 m from the wall of the upstream tank. Figure 10 depicts the probability density of the maximum fluctuating pressure for different concentrations of sediments at an upstream water head of 1.90 m. In this figure, the probability densities of the maximum pressure at different concentrations basically satisfy a Gaussian distribution and are positively skewed, i.e., the positive fluctuations tend to be larger than the negative ones. The peaks of the probability densities decrease, and the weights of the probability densities increase, with increasing sediment concentration. [PERSON] and [PERSON] [37] conducted experiments studying the turbulence structure of open channel flow and suggested that the turbulence in sediment-laden flow is less intense than that in clear water flow. This further demonstrates that the turbulence intensity decreases, and the fluctuation of turbulence increases, with increasing sediment concentration.  
Figure 8: Relationship between fluctuating pressure and upstream water head for different concentrations of sediments: (**a**) Upstream water head of 1.90 m; (**b**) upstream water head of 1.85 m; (**c**) upstream water head of 1.80 m; (**d**) comparison of pressures measured at different upstream water heads for fresh water and sediment-laden flow of 250 kg/m\({}^{3}\) in concentration.
## 4 Convergence Analysis
### Application in Integral Equations  
The Chandrasekhar-\(H\) equation arising in Radiative Heat Transfer theory (see [15]) is a nonlinear integral equation which gives a full nonlinear system of equations if discretized. The Chandrasekhar-\(H\) equation is given as  
\[\mathcal{J}(H,c)=0,\quad H:[0,1]\rightarrow\mathbb{R} \tag{28}\]  
with parameter \(c\) and the operator \(\mathcal{J}\) as  
\[\mathcal{J}(H,c)(U)=H(U)-\left(1-\frac{c}{2}\int_{0}^{1}\frac{U\;H(v)}{U+v}dv \right)^{-1} \tag{29}\]  
If we discretize the integral given in Equation (29) using the Mid-point Integration Rule with \(n\) grid points  
\[\int_{0}^{1}f(t)\;dt=\frac{1}{n}\sum_{j=1}^{n}f(t_{j}),\quad t_{j}=(j-0.5)h, \quad h=\frac{1}{n},\quad 1\leq j\leq n \tag{30}\]  
we obtain the resulting system of non-linear equations as follows:  
\[\mathbf{F}_{\mathrm{i}}(\mathbf{U},c)=\mathbf{U}_{\mathrm{i}}-\left(1-\frac{c }{2n}\sum_{j=1}^{n}\frac{t_{\mathrm{i}}\mathbf{U}_{\mathrm{i}}}{t_{\mathrm{i}} +t_{j}}\right)^{-1},\quad 1\leq\mathrm{i}\leq n \tag{31}\]  
When starting with \((1,1,\ldots,1)^{T}\) vector, the system (31) has a solution for all \(c\in(0,1)\). The \(c\) are equally spaced with \(\Delta c=0.01\) in the interval \(c\in(0,1)\) and we choose \(n=100\). The approximate solutions are correct to 14 digits. We note that in this case the Jacobian is a full matrix. The stopping criterion is when, \(\|\mathbf{U}^{(k+1)}-\mathbf{U}^{(k)}\|_{2}+\|\mathbf{F}(\mathbf{U}^{(k+1)})\| _{2}<1(-11).\) Table 2 shows the result for the Chandrasekhar \(H\)-equation by implementation of our codes in double precision arithmetic.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Methods** & \multicolumn{2}{c}{**TP1**} & \multicolumn{3}{c}{**TP2**} & \multicolumn{3}{c}{**TP3**} \\ \hline  & \(\mathbf{Iter_{max}}\) & \(\mathbf{err_{min}}\) & \(\mathbf{p_{c}}\) & \(\mathbf{Iter_{max}}\) & \(\mathbf{err_{min}}\) & \(\mathbf{p_{c}}\) & \(\mathbf{Iter_{max}}\) & \(\mathbf{err_{min}}\) & \(\mathbf{p_{c}}\) \\ \hline \(2^{nd}NM\) & 7 & 8.8e\(-\)113 & 2 & 9 & 1.7e\(-\)107 & 2 & 8 & 1.7e\(-\)144 & 2.02 \\ \(3^{rd}CON\) & 5 & 3.2e\(-\)143 & 3.02 & 7 & 7.3e\(-\)285 & 3 & 6 & 6.3e\(-\)276 & 3.04 \\ \(4^{th}CON\) & 4 & 5.0e\(-\)104 & 4.02 & 6 & 2.7e\(-\)322 & 4 & 5 & 5.0e\(-\)246 & 4.14 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different methods for systems of nonlinear equations.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Methods** & \(\mathbf{Iter_{Total}}\) & \(\mathbf{Iter_{mean}}\) & **CPU Time (s)** \\ \hline \(2^{nd}NM\) & 414 & 4.18 & 23.28 \\ \(3^{rd}CON\) & 361 & 3.65 & 27.08 \\ \(4^{th}CON\) & 293 & 2.99 & 23.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Key results for the Chandrasekhar \(H\)-equation.
## 4 Circuit Description
### Circuit Module Design  
#### 4.2.1 RF Front-End  
The analog RF front-end consists of a high pass filter (HPF), one low noise amplifier (LNA) and a variable attenuator. With the HPF as off-board components, the system stays highly flexible. We choose SHP-100A+ as HPF to remove some low-frequency noise, which can just meet the demand of specifications in Table 1. Figure 5 depicts its actual shape and typical frequency response.  
The signal from the HPF is then fed to the input of LNA. LNA is one of the essential building blocks of RF receivers. We choose a high performance broadband LNA MMIC BGB741L7 ESD from Infineon to be the first stage amplifier. The noise figure is lower than 2 dB and the gain is more than 16 dB. Before designing the circuit, we make several simulations through the Advanced Design System (ADS) to select the appropriate parameters. The final circuit and frequency characteristic are shown in Figure 6. We can see that the amplifier gain is stable around 16.5 dB, and the passband fluctuation is  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Choice** \\ \hline signal model & \(N=4\), \(B=15\) MHz, \(f_{nyq}=2\) GHz \\ number of period \(m\) & \(16\) \\ alteration rate & \(2\) GHz \\ sign pattern length \(M\) & \(127\) \\ period \(f_{p}\) & \(2/127=15.7\) MHz \\ filter cut-pff & \(33\) MHz \\ sampling rate \(f_{s}\) & \(70\) MHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Prototype specifications.  
Figure 5: The frequency response (**a**) and a photograph (**b**) of the high pass filter (HPF).  
Figure 6: The circuit diagram (**a**) and frequency characteristic (**b**) of low noise amplifier (LNA).  
less than 3 dB. In addition, if the amplitude of input signal is very high, the LNA can be bypassed and directly fed to the variable attenuator.  
In order to increase the dynamic range, we use a digital step attenuator DAT-31R5A-PP+ from Mini-Circuits to prevent saturation of the back-end devices. It provides adjustable attenuation from 0 to 31.5 dB in 0.5 dB steps. The control is a 6-bit parallel interface, operating on single supply voltage. We connect the control logic interface and FPGA logic I/O. Then the desired attenuation state can be selected through man-machine interactive system. The schematic of the RF attenuator is attached in Figure 7a.
## 1 Introduction
Until now, most acoustic studies that analyzed elderly voices using deep learning methods and acoustic parameters have evaluated the sustained phonation of single vowels [13; 19; 20; 21; 22; 23; 24; 25], and only a few studies have examined the recognition of elderly voices [11; 12; 13; 14; 15; 16; 17; 18]. Furthermore, in terms of methodology, even the previous studies [10] that analyzed elderly voices using deep learning approaches merely identified the differences between elderly voices and hypokinetic dysarthria based on acoustic-phonetic analysis using both the cepstrum and spectrum by collecting the voice data of subjects. On the other hand, deep learning methods, such as the feedforward neural network (FNN) and convolutional neural network (CNN), have been widely used as data mining algorithms for developing disease prediction models in recent years [1; 28]. No study has explored the classification of elderly versus young voices using deep learning algorithms applied to the Saarbruecken voice database (SVD). Therefore, the ultimate goal of this research was to develop deep learning classifiers and various parameters that provide an accurate and objective system for classifying elderly and young voices. This work focused on deep learning methods, such as FNN and CNN, for the detection of elderly voice signals using mel-frequency cepstral coefficients (MFCCs) and linear prediction cepstrum coefficients (LPCCs), skewness, and kurtosis parameters. Voice data were obtained from the publicly available SVD. Finally, this work investigated voice performance using deep learning methods with various combinations of parameters. Therefore, the originality of this work can be found in its proposal of a new parameter and a novel deep learning method that combines HOSs, MFCCs, and LPCCs in the /a/ vowel voice signals of elderly and young individuals. The contribution of this paper can be summarized with the following points:  
* This paper introduces an intelligent elderly voice classification system based on deep learning classifiers and various parameters. * The suggested combinations of various parameters and deep learning methods can effectively distinguish young from elderly voices. * A lot of experimental tests are performed to confirm the effectiveness of the elderly voice classification system using the Saarbruecken voice database. * The experimental results emphasize the superiority of the proposed elderly voice classification system integrating deep learning methods and various parameters to monitor an elderly voice for an effective and reliable system.