esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Channel Capacity in the Presence of \(\eta\)-\(\mu\) Fading and CCI
### Analysis of Parameters' Influence on the Channel Capacity
For \(w=-10\) dB it was 15, for \(w=0\) dB it was between 14 and 16, and for \(w=10\) dB it further increased, and the series converged more slowly. Additionally, when \(L\) increased, the number of summing elements reached a maximum of 16 for \(w=-10\) dB, and 18 for \(w=0\) dB and \(w=10\) dB. In this scenario, the convergence was the slowest.
## 3 Experimental Results
### Mineral Characterization
#### 3.3.1 Fine Aggregate: Manufactured and Natural Sand  
SEM images of the manufactured and natural sands at 300 X (left) and 5000 X (right) magnifications are shown in Figure 7. As expected, the natural sand is smooth and round, while the manufactured sand has a rough surface with an angular and elongated shape. Therefore, improved workability is expected with concrete containing natural sand. As shown in Table 4, the major components of the manufactured sand were oxygen (O), iron (Fe), silicon (Si), and tantalum (Ta), which account for more  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multicolumn{2}{c}{**Coarse Aggregate**} & \multicolumn{2}{c}{**Fine Aggregate**} \\ \cline{2-5}  & **Dolomite** & **Granite** & **Manufactured Sand** & **Natural Sand** \\ \hline C & 15.27 & 4.6 & 5.33 & 8.35 \\ O & 47.28 & 27.91 & 25.32 & 26.97 \\ Na & - & 2.65 & 2.15 & - \\ Mg & 12.18 & 1.53 & 1.11 & 0.38 \\ Al & 0.07 & 4.71 & 7.09 & 14.54 \\ Si & 0.12 & 18.59 & 11.49 & 20.6 \\ K & - & 0.79 & 1.14 & 8.71 \\ Ca & 25.08 & 2.82 & 2.99 & 0.35 \\ Ti & - & 0.36 & 0.29 & 0.76 \\ V & - & 0.38 & 0.09 & - \\ Cr & - & 0.2 & - & - \\ Mn & - & 0.21 & - & 1.14 \\ Fe & - & 12.7 & 15 & 13.76 \\ Co. & - & 3.43 & 3.52 & - \\ Ni & - & 3.9 & 5.49 & 2.84 \\ Cu & - & 1.91 & 1.31 & 0.29 \\ Zn & - & 0.86 & 2.89 & 1.29 \\ Nb & - & 3.62 & 4.58 & - \\ Ta & - & 8.82 & 10.23 & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Chemical compositions of aggregates (% by weight).  
Figure 6: The results of the compressive strength and the modulus of the elasticity.  
than 60% of the composition by weight. For natural sand, the predominant element is Si, and more than 75% by weight of the natural sand is O, Si, aluminum (Al), and Fe.
## 3 Jacobian Matrix Derivation for MES
### Jacobian Matrix for DEN  
The state variable of the DEN is defined as \(\mathbf{x}_{\rm e}=[\mathbf{\theta};\ |\,\mathbf{V}\,|\,]\), where \(\mathbf{\theta}/\,|\,\mathbf{V}\,|\,\) is the voltage angle/magnitude vector, and the mismatch vector of the DEN \(\mathbf{F}_{\rm e}\) is expressed as  
\[\mathbf{F}_{\rm e}=\left[\begin{array}{c}\Delta\mathbf{P}=P_{\rm S}-P_{\rm L}-{\rm Real }\big{(}\mathbf{V}\cdot(\mathbf{Y}\mathbf{V})^{*}\big{)}\\ \Delta\mathbf{Q}=\mathbf{Q}_{\rm S}-\mathbf{Q}_{\rm L}-{\rm Imag}\big{\langle}\mathbf{V}\cdot( \mathbf{Y}\mathbf{V})^{*}\big{\rangle}\end{array}\right] \tag{44}\]  
where \(\Delta\mathbf{P}/\Delta\mathbf{Q}\) is the mismatch vector of nodal active/reactive power. The Jacobian matrix of the DEN \(\mathbf{J}_{\rm ee}\) equals the partial derivatives of \(\mathbf{F}_{\rm e}\) with respect to \(\mathbf{x}_{\rm e}\):  
\[\mathbf{J}_{\rm ee}=\left[\begin{array}{cc}\frac{\partial\Delta\mathbf{P}}{ \partial\mathbf{\theta}}&\frac{\partial\Delta\mathbf{P}}{\partial|\mathbf{V}|}\\ \frac{\partial\Delta\mathbf{Q}}{\partial\mathbf{\theta}}&\frac{\partial\Delta\mathbf{Q}}{ \partial|\mathbf{V}|}\end{array}\right] \tag{45}\]  
Note that each submatrix in \(\mathbf{J}_{\rm ee}\) can be formulated in matrix form. The detailed formulation of \(\mathbf{J}_{\rm ee}\) can be referenced in [7].
# Potential of Room Acoustic Solver with Plane-Wave Enriched Finite Element Method
## 3 Numerical Experiments
### Sound Propagation in a Single Room
#### 3.2.2 Results and Discussion
Figure 5: Changes in (**a**) the enriched plane waves number \(q\) and (**b**) DOF when using Mesh 2 with \(C=13\). magnitude of less than 1 dB with fewer DOF than the finer mesh. This result suggests that the use of coarse mesh enriched by many plane waves is more effective than that of fine mesh enriched with a few plane waves from the aspect of computational cost. Additionally, we can observe an important aspect in the Mesh 2 results at 1 kHz and 1.25 kHz. There exists a proper number of plane waves for enrichment, i.e., an increase of DOF does not always produce more accurate results. Similar results were obtained from an earlier study [33]. The earlier report described this as attributable to the ill-conditioning of the resulting linear system, showing an increase of the condition number when continuously increasing the attached plane wave numbers. In addition, the present paper showed the mesh size effect on the resulting accuracy in limited conditions. Therefore, comprehensive investigations on this topic will be shown in future works with well-organized numerical experiments. Figure 6: Comparison of RMS error for (**a**) Mesh 1 and (**b**) Mesh 2. An FEM result obtained using 0.01 m mesh is also shown for comparison. Figure 7: Relations between DOF and RMS error in PUFEM analysis at four higher frequency bands: (**a**) 1 kHz, (**b**) 1.25 kHz, (**c**) 1.6 kHz, and (**d**) 2 kHz. The DOF in the \(x\)-axis is the value at a center frequency within each band.
# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 4 Model Realization and Result Analysis
### Experimental Parameter Optimization and Training Process
#### 4.1.3 Training Outcome  
Training converged with:  
* Validation accuracy: 91.2%
* Validation loss: 0.214
* <2% divergence between training/validation accuracy
* Validation results demonstrate effective generalization capability (Figure 6).
# Smart Grid Data Management in a Heterogeneous Environment with a Hybrid Load Forecasting Model
## 3 Related Works  
A wide range of applications has been proposed or discussed over the past ten years. They have been categorized into three main types: namely application, approach, and area of interest. For application, examples are forecasting, predictions, clustering, control, data management, and monitoring, big data analytics, and other applications. The approach includes time-series, regression, descriptive statistics, neural networks, decisions tree, and many hybrid machine learning approaches) [22]. Meanwhile, for the area of interest, some examples are generation, transmission, distribution and consumption, and the trading sector. Additionally, another emerging classification is based on the scope of the network that these applications can operate in, such as Home Area Networks (HANs), Neighbor Area Networks (NANs), and Wide Area Networks (WANs) [23]. This wide and varied vision horizon leads to the emergence of applications in different fields that may share some general characteristics, but each case can be considered unique due to the diversity and the difference of data types or the purpose for which it is created. In this context, many studies have contributed to the discussion of the challenges facing energy sectors. In this section, we focused on the most related works to our case study. It is divided into two main categories: (i) existing and potential applications in power consumption for both data management and load forecasting and (ii) challenges of applications in power consumption.
# Production of Photovoltaic Electricity at Different Sites in Algeria
## 2 Materials and Methods
### Photovoltaic System
#### 2.3.1 Mathematical Model of PV Generator  
The current-voltage characteristics of the solar cell's electrical circuit are presented in Figure 1. They can be described by the following simplified equation [10]:  
\[I=I_{ph}-I_{d}-I_{p} \tag{4}\]  
where \(I\) is cell current (A), \(I_{ph}\) is photo-current (A), \(I_{D}\) is diode current (A), and \(I_{p}\) is shunt resistance current (A).  
Equation (4) becomes:  
\[I=I_{Ph}-I_{0}\bigg{[}\exp\bigg{(}\frac{V+I.R_{s}}{nVT}\bigg{)}-1\bigg{]}-\frac {V+I.R_{s}}{R_{p}} \tag{5}\]  
where \(V\) is cell voltage (V), \(I_{0}\) is saturation current (A), \(V\) = KT/e is thermic voltage, e is electron charge (1.602 \(\times\) 10\({}^{-19}\) C), K is Boltzmann constant (1381 \(\times\) 10\({}^{-23}\) J/K), and \(T\) is cell temperature (K).  
The electrical power produced by the PV system is:  
\[P_{el}=I.V \tag{6}\]  
Moreover, the maximum output power is given by:  
\[P_{max}=(I\,V)_{max}=V_{QC}.I_{SC},\,FF \tag{7}\]  
Figure 1: The equivalent electrical circuit of the PV module, showing the diode and ground leakage currents [11].  
where \(V_{OC}\) is open circuit voltage (V), \(I_{SC}\) is short circuit current (A), and \(FF\) is fill factor \(P_{max}=V_{mp}*I_{mp}\), corresponding to the maximum power point, MPP.  
The energy conversion efficiency, \(\eta\), is given by:  
\[\eta=V_{mp}\cdot I_{mp}/P_{in}=V_{oc}\cdot I_{sc}\cdot FF/P_{in} \tag{8}\]
## 2 Architecture of the IMU/GNSS Integrated Navigation System
### Error-State EKF Model  
Using the error-state IMU equations as the system propagation equation, the state vector includes the IMU attitude error, IMU velocity error, IMU position error, inertial sensor biases, and GNSS receiver clock offset and drift for the error-state EKF [22]. The EKF is described as a system propagation equation (Equation (1)) and a measurement update equation (Equation (2)) [6]. In the measurement updates step, the GNSS pseudo-range and pseudo-range rate measurements are used for the measurement update, and the difference between the GNSS pseudo-range, GNSS pseudo-range rate and the corresponding predicted IMU pseudo-range, IMU pseudo-range rate constitute the EKF innovation. There is another expression of the system propagation equation, the full-state inertial navigation equations based EKF (i.e., the full-state EKF). For either the full-state or error-state in EKF, the essence is to estimate the state error. However, there is a difference between the error-state EKF and the full-state EKF in the system propagation step, the control input is added to the full-state EKF (Appendix A):  
\[\hat{X}_{k}^{-}=\partial_{k}\hat{X}_{k-1}+\Gamma_{\textit{{ink}}}\overset{ \rightarrow}{w}_{k} \tag{1}\]  
\[\hat{X}_{k}=\hat{X}_{k}^{-}+K_{k}\underbrace{\left(\vec{Z}_{k}-H_{k}\hat{X}_{ k}\right)}_{\vec{r}_{k}} \tag{2}\]  
where \(\hat{X}_{k}^{-}\) is the predicted error-state vector at epoch k, \(\hat{X}_{k}\) is the measurement updated error-state vector at epoch k, \(\partial_{k}\) is the dynamic state transition matrix at epoch k, \(\Gamma_{\textit{{ink}}}\) is the process noise matrix at epoch k, \(\overset{\rightarrow}{w}_{k}\) is the process noise vector at epoch k, \(K_{k}\) is the Kalman gain at epoch k (Appendix B.1), \(\overset{\rightarrow}{Z}_{k}\) is the estimate of the measurement produced by the states estimated by the Kalman filter, \(H_{k}\) is the measurement matrix at epoch k (Appendix B.1), and \(r_{k}\) is the EKF innovation vector at epoch k.  
Figure 1: Tightly coupled inertial measurement unit (IMU)/global navigation satellite system (GNSS) integration architecture.
## 5 Results of QoE\({}_{i}\) Modeling and Discussion
### Predictive Models of \(QoE_{i}\)
#### 5.5.1 Multiple Linear Regression Model  
Linear regression models are the most commonly used statistical models. The basic property that allows them such a status is the simplicity of creation. However, they have a number of disadvantages, especially when applied to large databases. In the observed case, the multiple linear regression model was created in the statistical software Minitab and can be expressed by the following equation:  
\[QoE_{i}=0.343+0.0115\,\mathrm{X}_{1}+0.104\,\mathrm{X}_{2}\,-0.04 38\,\mathrm{X}_{3}\,-0.0081\,\mathrm{X}_{4}\,-0.0133\,\mathrm{X}_{5}+0.0271\, \mathrm{X}_{6}\,+ \tag{6}\] \[0.0210\,\mathrm{X}_{7}\,-0.0085\,\mathrm{X}_{8}\,-0.0007\, \mathrm{X}_{9}+0.0035\,\mathrm{X}_{10}\]  
As an accuracy performance indicator of user \(QoE_{i}\) prediction, it is observed that the mean square error (MSE) of the test in this case has a value of MSE = 0.625, while the coefficient of determination is R\({}^{2}\) = 2.5%. Therefore, it is a very low coefficient of determination, which confirms the shortcomings of the linear model.
###### Abstract  
The aim of the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) project is to make autonomous underwater vehicles (AUVs), remote operated vehicles (ROVs) and unmanned surface vehicles (USVs) more accessible and useful. To achieve cooperation and communication between different AUVs, these must be able to exchange messages, so an efficient and reliable communication network is necessary for SWARMs. In order to provide an efficient and reliable communication network for mission execution, one of the important and necessary issues is the topology control of the network of AUVs that are cooperating underwater. However, due to the specific properties of an underwater AUV cooperation network, such as the high mobility of AUVs, large transmission delays, low bandwidth, etc., the traditional topology control algorithms primarily designed for terrestrial wireless sensor networks cannot be used directly in the underwater environment. Moreover, these algorithms, in which the nodes adjust their transmission power once the current transmission power does not equal an optimal one, are costly in an underwater cooperating AUV network. Considering these facts, in this paper, we propose a Probabilistic Topology Control (PTC) algorithm for an underwater cooperating AUV network. In PTC, when the transmission power of an AUV is not equal to the optimal transmission power, then whether the transmission power needs to be adjusted or not will be determined based on the AUV's parameters. Each AUV determines their own transmission power adjustment probability based on the parameter deviations. The larger the deviation, the higher the transmission power adjustment probability is, and vice versa. For evaluating the performance of PTC, we combine the PTC algorithm with the Fuzzy logic Topology Control (FTC) algorithm and compare the performance of these two algorithms. The simulation results have demonstrated that the PTC is efficient at reducing the transmission power adjustment ratio while improving the network performance.  
topology control; underwater network; AUV; probabilistic; transmission power adjustment +
Footnote †: journal: Sensors
## Materials and Methods
### Landscape Patterns Analysis  
Spatial characteristics such as location, direction, and distance are the bases to analyze the structure of landscape patterns. Landscape metrics, which incorporate characteristics such as patch shape, size, number, and spatial combination, are frequently used to detect and quantify the spatiotemporal variations in landscape patterns. These metrics indicated complexity in the type and arrangement of landscape patches. However, most of the metrics are correlated, which can lead to redundant information and make interpretation difficult. Appropriate landscape indices should be selected based on their properties, purposes, and contents to achieve reliable and quantitative descriptions of the research objects [41].  
In the study, eight frequently-used landscape metrics by referencing relevant work were selected to evaluate the fragmentation, connectivity and diversity of landscape patterns (Table 1), including percent of landscape (PLAND), number of urban patches (NP), patch density (PD), landscape shape index (LSI), edge density (ED), landscape division index (DIVISION), Shannon's diversity index (SHDI), and Shannon's evenness index (SHEI). Subsequently, eight landscape metrics were divided into four classes based on their definitions and features, including expansion, shape complexity, compactness, and landscape heterogeneity. PLAND depicts the relative abundance of each land-use type. NP is used to evaluate the discrete urban areas in the landscape, as urban areas expand and merge into a continuous urban fabric, NP values may decline. PD is an effective indicator to characterize aggregation or fragmentation of the urban landscape. LSI and ED reflect the connectivity in patches and shape complexity of urban landscape patches [42]. DIVISION reflects the fragmentation of the landscape, and SHDI and SHEI are landscape-level metrics that show the landscape diversity and heterogeneity in the landscape. More detailed descriptions and specific mathematical equations of the seven metrics employed in the study are shown in Table 1. We calculated these landscape metrics using the public domain software Fragstats version 4.2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Landscape Metrics** & **Abbreviation** & **Equations** & **Description** \\ \hline Percent of landscape & PLAND/\% & \(PLAND=(\sum\limits_{j=1}^{n}a_{ij})/A\) & \(a_{j}=\text{area (m${}^{2}$) of the patch \emph{ij}}\) \\ Number of patches & NP & NP = \(n\) & \(n=\text{the number of patches of a class}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of landscape metrics used in the study for urban landscape analysis.
## 3 Comparison between Circular and Slab Diffusion Models
### Durability Design of the RC Circular Column against Chloride Degradation
#### 3.3.3 Chloride Diffusion Coefficient \(D_{28}\) and Concrete Cover Thickness \(x_{\text{d}}\)  
Given the design value of the chloride diffusion coefficient \(D_{28}\), the \(\beta_{\text{d}}\) is satisfied by adjusting the minimum concrete cover thickness \(x_{\text{d}}\). The preliminary durability design result of \(x_{\text{d}}\) corresponding to a series of \(D_{28}\) is shown in Figure 13. Clearly, for the RC circular column, the value of the minimum cover thickness designed by the original slab diffusion model is lower than that designed by the modified slab one. The \(\Delta x_{\text{d}}\) is labeled as the difference between the design values of the minimum cover concrete thickness. Based on Figure 13, the \(\Delta x_{\text{d}}\) as a function of \(D_{28}\) is displayed in Figure 14, with an evident linear relationship. Meanwhile, the reduction of the radius also increases the \(\Delta x_{\text{d}}\). For an expected lifetime of 50 years, the maximum value of \(\Delta x_{\text{d}}\) is 2 mm when the radius is 30 cm. This value increases to 4 mm when the lifetime is 100 years. These data illustrate that the RC circular  
Figure 12: The effect of the shape of circular section on the service life prediction.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Time (Years)** & \(R\) = 30 cm & \(R\) = 50 cm & \(R\) = 70 cm & **Slab Diffusion Model** \\ \hline Atmospheric zone & 39 & 42 & 45 & 46 \\ Splashing and tidal zone & 57 & 60 & 61 & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Service life for different radius of circular section.  
column shape minimally affects the design results. As a conservative choice, the minimum concrete cover thickness increases by 5 mm based on the design result of the original slab diffusion model.  
The circular RC column for the atmospheric zone assumes a \(t_{\text{d}}\) of 100 years, with a fixed 28-day diffusion coefficient of \(6\times 10^{-12}\) m\({}^{2}\)/a, and the relationship between \(\Delta x_{\text{d}}\) and \(\beta_{\text{d}}\) is shown in Figure 15. Obviously, the effect of \(\beta_{\text{d}}\) on \(\Delta x_{\text{d}}\) can be overlooked.
## 1 Introduction
Alkali and alkaline earth metals in biomass ash were found to enhance the reaction rate of the blends more than expected [17; 19; 20; 24], while Si, Al and P contents in coals were found to inhibit the process [24]. External catalysts, such as Ca(OH)\({}_{2}\), dolomite, olivine and Na-Fe, were also used [7; 8; 25] to increase the yield of obtained hydrogen and reduce tar. The co-gasification of low-rank coals with agricultural wastes for the production of syngas under a steam or carbon dioxide atmosphere has not been sufficiently studied. The co-processing of lignite with wheat straw [26] or sunflower seed cake [6] via steam gasification showed that the biomass material exhibited a positive synergetic effect on the blends, attributed to the volatile content of biomass and the inherent alkali/alkaline earth metals. Similar results were obtained by the carbon dioxide gasification of lignite with soybean stalk or orange peel [14]. On the other hand, the reaction rate of lignite/corn stalk mixtures [27] during steam gasification was inhibited in comparison to that of component fuels, due to the evolution of the chemical structure during the process. Lignite is a principal fuel in many countries worldwide, including Germany, Poland, the Czech Republic, Bulgaria and Greece in Europe. Having higher proximity with biomass materials than high-rank coals, its co-processing with them will increase the operating flexibility of power plants and thereby their economic sustainability. A comparative assessment of the gasification performance of lignite/barley straw mixtures under different gasifying agents has not been reported so far, to the best of our knowledge. Additionally, the fusibility behavior of ashes at the high temperatures prevailing in the reactor and their environmental impact, as well as the evaluation of liquid or gaseous by-products through quantitative analyses, have not been addressed. Based on the above discussion, the current study aimed to investigate and compare the gasification performance of lignite/barley straw mixtures for syngas production, through experiments under steam or carbon dioxide in a fixed-bed system and a thermogravimetric-mass spectrometry system. The thermal behavior, reactivity, conversion, product gas composition and interactions between fuels were determined and correlated with the structural characteristics and inherent minerals in ashes, which were analyzed via mineralogical, chemical and fusibility tests.