esa-sceva/satcom-chunk-collection huggingface dataset:
# A Genomic Survey of Signalling in the Myxococcaceae
## 3 Results
### TCS Proteins are More Enriched in Myxobacterial Core Genomes than Other Regulatory Proteins  
To investigate the relative distribution of proteins between the core and accessory genome, we categorised the proteins in each family as 'core' or 'accessory' for each group of genomes. For this purpose, we defined the number of core proteins as simply the mean number of family members, minus one standard deviation (rounded to the closest integer), with the remainder of the proteins being categorised as members of the accessory genome. The results of such an approach are provided within Table 3 for the illustrative protein families therein, for the ten _M. xanthus_ genomes. The results of this simple categorisation agree well with an intuitive assessment of core vs. accessory genome membership (Table 3).  
Taking this approach, and summing the results for each protein family, we were able to compare the tendencies of RRs and TFs/OCSs to be found in the core or the accessory genome of each group of genomes (Figure 2). As expected, the percentage of proteins in the core of the pan-genome is less for _Myxococcus_ spp. than for _M. xanthus_ strains, as the former have more diverse genomes (similarly when comparing _Corallococcus_ spp. with _C. exigius_ strains), and the _Myxococcus_ spp. genomes had a smaller core than _Corallococcus_ spp., reflecting their greater diversity and lower percentage core genome, as described previously [7]. Similarly, a greater proportion of _C. exigius_ regulators were found to be accessory, compared with those of _M. xanthus_, which agrees with the greater variability in the numbers of regulators in their genomes, as seen in Table 1.  
In all four groups of genomes, TCS proteins were assigned to the core to a greater extent than TFs/OCSs (Figure 2), suggesting that accessory TCS proteins acquired by'recent' horizontal gene transfer are purged from the genome faster than accessory TFs/OCSs. Possibly because recently acquired TCS proteins have the potential to disrupt pre-existing core TCS networks, while the expression of recently acquired TFs/OCS might be less likely to affect the functioning of core TFs/OCSs.
## 4 Results
### Models Evaluation and Mapping of AGB  
#### 4.3.1 Models Assessment by Evaluation Indices  
Table 4 presents the accuracies of four models for estimating the AGB of 56 forest quadrats. All four models overestimated the AGB. The ANN model resulted in an ME of 0.84 Mg\(\cdot\)ha\({}^{-1}\), and had the highest tendency for overestimation; the SVR model with an ME of 0.004 Mg\(\cdot\)ha\({}^{-1}\) showed the lowest tendency for overestimation. The MAE, indicating the extent to which the process leads to error, was lower with SVR (0.07 Mg\(\cdot\)ha\({}^{-1}\)), and higher with the other methods, ranging from 1.21 (ANN) to 4.01 Mg\(\cdot\)ha\({}^{-1}\) (GWR). The values of the RMSE suggested that ML methods, whose RMSE ranged from 0.08 (SVR) to 4.43 (RF) Mg\(\cdot\)ha\({}^{-1}\), produced less error than GWR. A better consistency between the measured AGB and the estimated one was discovered by the \(r\) values from the ML models (0.999 of RF to 1 of SVR and ANN) compared to the GWR model (0.995). The SVR model gave rise to the lowest RMSE and the closest-to-zero ME and MAE values, as well as the highest \(r\) value. Hence, in this study, the SVR model was the most accurate model for estimating AGB. Besides, the accuracy ranking of the four methods from high to low was SVR, ANN, RF, and GWR. To further analyze the modeling accuracy, the estimated values of AGB were plotted against the measured values (Figure 5). An estimation from the SVR model showed the best agreement along the 1:1 line, followed by those from ANN, GWR, and RF.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation Index** & **GWR** & **ANN** & **SVR** & **RF** \\ \hline ME & 0.04 & 0.84 & 4 \(\times\) 10\({}^{-3}\) & 0.55 \\ MAE & 4.01 & 1.21 & 0.07 & 3.48 \\ RMSE & 5.26 & 1.73 & 0.08 & 4.43 \\ \(r\) & 0.995 & 1 & 1 & 0.999 \\ \hline \hline \end{tabular} ME, MAE, RMSE, and r are the abbreviations of mean error, mean absolute error, root mean squared error, and correlation coefficient, respectively. The \(p\)-values of r were all below 0.01.  
\end{table}
Table 4: Performance evaluation of the GWR, ANN, SVR, and RF models.  
Figure 4: Training errors associated with a given number of neurons in the hidden layer.
## 1 Introduction
In an educational context, based on SPSS (v.29) and Python (v.3.12.0), there are two statistical studies comparing air pollution in Turkey (Adana), Slovakia (Banska Bystrica) and Romania (Craiova), identifying PMs pollution episodes and finding the correlation coefficients between some meteorological parameters and PMs concentrations [39; 40]. A few studies use approaches based on machine learning (ML). For example, in [41], five different hybrid feature selection-ML models (Artificial Neural Network, Support Vector Machine, Decision Tree, Gaussian process regression, and Linear Regression) for predicting and modelling the air quality index (AQI) and PMs concentrations in Craiova. In [42], other hybrid models (Input Variable Selection, ML, and regression method) are used to predict, model, and forecast the daily concentrations of PMs (PM\({}_{1}\), PM\({}_{2.5}\), PM\({}_{10}\)) and AQI. The limitations in the existing literature for Craiova in terms of present understanding and PMs predictions are as follows:  
* _Short-term forecasting_: the models used offer restricted future predictions for each air pollutant. * _Certain variables are missing_: an extensive study on air pollution prediction and forecasting, including all essential factors is needed. 3. _Models' generalizability_ is limited due to geographical and dataset constraints. 4. _Model selection_: some used models were chosen without proper assessment or rationale. 5. _Statistical evaluation_: some papers used limited statistical metrics and indices, which might impact model accuracy ratings. This work seeks to address these limits by investigating the application of several hybrid AI approaches for predicting, modelling, and forecasting hourly PMs concentrations. The dataset consists of 20 measured variables related to meteorological parameters and air pollutants. MATLAB 2023a is used for programming and visualisation purposes. This study is structured as follows: Section 2 presents Craiova's features and climate. This section also presents the dataset, data pre-processing, correlations, and trends. Section 3 presents the AI-based models employed, evaluation criteria and statistical indices, and study framework. Section 4 focuses on the best-performing models for PM\({}_{2.5}\) and PM\({}_{10}\) predicting and forecasting. The conclusions, challenges, and perspectives of this work are presented in the last section.
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## 2 Materials and Methods
### Methods
#### 2.3.4 Correlation Analysis  
GRACE-derived GWS time series (expressed in mm or \(\text{hm}^{3}\)) were compared to annual groundwater level declines recorded in wells from each aquifer. As part of the correlation analysis, in situ data were converted to equivalent units (mm/year) based on aquifer area to ensure consistency. The Pearson correlation coefficient (r) was calculated in two stages using Equation (2): first, to assess the relationship between GRACE GWS trends and well-based decline rates; and second, to evaluate the correlation between annual extraction volumes (\(\text{hm}^{3}\)/year) and annual water level decline (m/year), using the data presented in Table 1. For this second case, average values were obtained as \(\overline{\kappa}\) = 53.08 (average extraction) and \(\overline{\gamma}\) = 1.16 (average decline).  
\[\text{r}=\frac{\sum(\text{x}_{\text{i}}-\overline{\kappa})(\text{y}_{\text{i}} -\overline{\gamma})}{\sqrt{\sum(\text{x}_{\text{i}}-\overline{\kappa})^{2} \Sigma(\text{y}_{\text{i}}-\overline{\gamma})^{2}}} \tag{4}\]  
**Data**  
x (Extraction): [17.5; 11; 252; 25; 5; 8]  
y (Decline): [1.0, 0.75, 2.0, 1.5, 1.0, 0.7]  
\(\overline{\kappa}\) = 53.08 (average extraction)  
\(\overline{\gamma}\) = 1.16 (average decline).  
**r \(\approx\) 0.95**  
**Result**: r = 0.95, indicating a very strong positive correlation between extraction and decline suggests GRACE effectively captures depletion trends observed in situ, with high statistical significance. This validates GRACE's utility for regional monitoring.
# Design of CubeSat-Based Multi-Regional Positioning Navigation and Timing System in Low Earth Orbit
## 3 Results
### Thermal Analysis
#### 3.7.5 Spacecraft's Thermodynamical Equilibrium Equation  
By having knowledge about the thermal loads that apply on the satellite and its thermodynamical equilibrium equation, it is possible to have complete knowledge of its thermal state. The CubeSat is considered a compact object that has a uniform thermal capacity and temperature throughout its whole body. The equation is as follows:  
\[Q_{in}=Q_{out}+mc\cdot dT/dt\]  
where  
\(Q_{in}\): the thermal power absorbed by the satellite (W);  
\(Q_{out}\): the thermal power radiated from the satellite (W);\(m\cdot c\): the thermal capacity of the satellite \(\left(\mathrm{JK^{-1}}\right)\), with \(m\) being the satellite's mass (kg) and \(c\) being the specific heat capacity \(\left(\mathrm{JK^{-1}kg^{-1}}\right)\).  
The thermal power absorbed by the satellite \(Q_{in}\) consists of the thermal power from the Sun \(Q_{solar}\), the thermal power due to albedo \(Q_{albedo}\), the thermal power from the celestial body CubeSat that orbits \(Q_{planetary-IR}\), and the internal heat dissipation from the CubeSat \(Q_{internal}\). While in eclipse, \(Q_{solar}\) and \(Q_{albedo}\) are considered to be zero.  
\(Q_{in}=Q_{solar}+Q_{albedo}+Q_{planetary-IR}+Q_{internal}\)  
The thermal power radiated from the satellite \(Q_{out}\) is given by the following equation:  
\(Q_{out}=\varepsilon\sigma A_{total}T^{4}\)  
where  
\(\varepsilon\): the dimensionless constant of the surface emissivity of the CubeSat;  
\(\sigma\): the Stefan-Boltzmann constant (5.67 \(\times\) 10\({}^{-8}\) Wm\({}^{-2}\)K\({}^{-4}\));  
\(A_{total}\): the total surface of the satellite (m\({}^{2}\)).  
The differential is approached by the derivative's definition. While having defined beforehand the repetition K, the time step \(\Delta t\), and the corresponding temperatures, the following is assumed:  
\(dT/dt=\Delta T/\Delta t=(T[K]-T[K-1])/\Delta t\)  
It is also assumed that the S/C has not deployed its solar arrays, that it weighs 11.3 kg, and that its body is homogenous and has the specific heat capacity of aluminum Al 6061-T6.
###### Abstract  
For efficient radio network planning, empirical path loss (PL) prediction models are utilized to predict signal attenuation in different environments. Alternatively, machine learning (ML) models are proposed to predict path loss. While empirical models are transparent and require less computational capacity, their predictions are not able to generate accurate forecasting in complex environments. While ML models are precise and can cope with complex terrains, their opaque nature hampers building trust and relying assertively on their predictions. To fill the gap between transparency and accuracy, in this paper, we utilize glass box ML using Microsoft research's explainable boosting machines (EBM) together with the PL data measured for a university campus environment. Moreover, polar coordinate transformation is applied in our paper, which unravels the superior explanation capacity of the feature transmitting angle beyond the feature distance. PL predictions of glass box ML are compared with predictions of black box ML models as well as those generated by empirical models. The glass box EBM exhibits the highest performance. The glass box ML, furthermore, sheds light on the important explanatory features and the magnitude of their effects on signal attenuation in the underlying propagation environment.  
Keywords:path loss; radio propagation; wireless channel; comparative analysis of machine learning methods; explainable artificial intelligence +
Footnote †: journal:
# Real-Time Semantic Segmentation of Point Clouds Based on an Attention Mechanism and a Sparse Tensor
## 1 Introduction
The main contributions of this work are as follows:  
* A lightweight full convolutional network based on an attention mechanism and a sparse tensor is proposed to solve the real-time point cloud semantic segmentation problem for unmanned vehicle systems;
* We propose a global feature-learning module and a multi-scale feature fusion module. The former is used to learn global contextual information, and the latter is used to achieve the effective fusion of feature information at different scales;
* As demonstrated by the experimental results for the SemanticKITTI and NuScenes datasets, our model improves the mIoU metric by 6.4% and 5%, respectively, over existing models that can be applied in real time. The comparative efficiency analysis shows that our model is able to meet the real-time requirements. The rest of this paper is organized as follows. Section 2 summarizes the existing semantic segmentation methods. Section 3 describes the structure of LFNet and the two modules in detail. Section 4 gives the experimental results of LFNet on different datasets. Section 5 provides a summary of the work.
## 3 Results
### Effects of Different DO Concentration on Pollutant Removal and Electricity Generation  
With an increase in the aeration rate, the DO concentration of the system gradually stabilized at 60 days. When the aeration rates were 0 L\(\cdot\)min\({}^{-1}\), 2 L\(\cdot\)min\({}^{-1}\), 4 L\(\cdot\)min\({}^{-1}\), 6 L\(\cdot\)min\({}^{-1}\), and 8 L\(\cdot\)min\({}^{-1}\), the average cathode DO concentrations were 3.0 mg\(\cdot\)L\({}^{-1}\), 3.5 mg\(\cdot\)L\({}^{-1}\), 4.0 mg\(\cdot\)L\({}^{-1}\), 4.5 mg\(\cdot\)L\({}^{-1}\), and 5.0 mg\(\cdot\)L\({}^{-1}\), respectively (Figure 3a). The subsequent research analysis will be based on these DO concentrations. Figure 3b-d shows the relationship of pollutant removal and electricity generation with the DO concentration in the _L. hexandra_ CW-MFC system. The pollutant removal rate and power generation gradually stabilized after 45 d in the _L. hexandra_ CW-MFC system (Figure 3). The COD removal rate increased from 81.56% to 93.73%, and the Cr (VI) removal rate increased from 84.82% to 97.77% when the DO concentration increased from 3.0 to 4.5 mg\(\cdot\)L\({}^{-1}\). The maximum voltage of the system was 520 mV at 4.5 mg\(\cdot\)L\({}^{-1}\). The pollutant removal rate and electricity production decreased when the DO concentration increased to 5.0 mg\(\cdot\)L\({}^{-1}\).  
Figure 2: Surface morphology and microstructure of activated carbon: (**a,b**) pristine activated carbon; (**c,d**) _L. hexandra_ CW-MFC anode biofilm; and (**e,f**) _L. hexandra_ CW-MFC cathode biofilm.
# Development of Rockburst Research: A Comprehensive Review
## 3 Technologies and Methods for Rockburst Research
### Theoretical Research  
At the moment, theoretical research refers to analyzing the mechanisms and categories of rockburst via different aspects, such as the strength theory, the stiffness theory, the energy theory, the inclusion theory, the fractal theory, the defect theory, the energy disturbance theory, the blasting reliability theory, the instability theory, the catastrophe theory, the bifurcation theory, the dissipative structure theory and the theory of chaos [31; 32; 33; 34; 35; 36], which are used to study the deformation and stability of rock mechanics systems and the rockburst mechanism. Among them, research based on the strength theory, the energy theory and the stiffness theory is dominant.  
The strength theory is based on the concept of material strength in traditional mechanics, which only provides the necessary conditions for rockburst; it does not point out under what conditions rockburst will occur.  
The energy theory explains the failure mechanism of rockburst from an energy point of view, but it does not explain the failure conditions of an equilibrium state.  
According to the stiffness theory, the necessary condition for rockburst is the stiffness of the mine structure (ore body) being greater than that of the mine load (surrounding rock). However, it does not give a clear concept of the division and stiffness of the mine structure and mine load systems.  
Since rockburst is a process of rock mass moving from static equilibrium to dynamic instability, it is hard to comprehensively explain the mechanism and severity of rockburst through a single theory. Therefore, the existing technologies and methods examining rockburst mostly focus on numerical simulation, physical model tests and in situ monitoring.
# A Simulated Annealing Algorithm for the Generalized Quadratic Assignment Problem
## 3 Methods
### Simulated Annealing Algorithm
Replacing \(T\) with \(T_{0}\) and manipulating Equation (11), the following equation is obtained for the initial temperature, where \(ln\) is the natural logarithm function. \[T_{0}=-\Delta TC/ln(p^{\prime}) \tag{13}\]  
If the probability of accepting a neighboring solution \(\mathbf{S}\) with a cost of \(y\%\) above the cost of the initial solution \(\mathbf{S_{0}}\) is \(p^{\prime}\), then \(\Delta TC=yTC(\mathbf{S_{0}})\) and the following equation is obtained. \[T_{0}=-yTC(\mathbf{S_{0}})/ln(p^{\prime}) \tag{14}\]Figure 3: Flowchart of the simulated annealing algorithm. Recall, \(TC(\textbf{S}_{0})\) is the total cost of the initial solution \(\textbf{S}_{0}\) obtained from the construction algorithm presented in Algorithm 1. For setting the initial temperature \(T_{o}\), it was decided to accept solutions with a cost of 10% above the cost of the initial solution \(\textbf{S}_{0}\) (i.e., set \(y=0.10\)) and \(p^{\prime}\) was considered to take on the values 0.6, 0.7, 0.8, and 0.9. Based on preliminary experiments, it was decided to set \(p^{\prime}=0.9\). Based on \(y=0.10\) and \(p^{\prime}=0.90\), there is a 90% chance of selecting a solution \(\textbf{S}\), at the initial temperature \(T_{0}\), that is 10% above the cost of the initial solution \(\textbf{S}_{0}\). Now, the number of iterations to be performed at each temperature \(T,N_{T}\), is based on the number of possible solutions in the Shift and Swap neighborhoods, which are \(M(N-1)\) and \(M(M-1)/2\), respectively, as discussed earlier. Therefore, it was decided to set \(N_{T}\) according to the following equation where \(q\) is a percent of the total number of possible solutions in the neighborhoods of \(\textbf{S}_{0}\). \[NT=\lceil q[M(N-1)+M(M-1)/2]\rceil \tag{15}\]  
In other words, \(N_{T}\) is 4% of the total number of possible solutions in the neighborhoods of \(\textbf{S}_{0}\), rounded above to the nearest integer. It was decided to consider \(q=40\%\), 50%, 60%, 70%, 80%, 90%. After preliminary experiments, it was decided to set \(q=50\%\). It is important to note, that the preliminary experiments used to experimentally determine the SA algorithm parameters are based on solving several different types of GQAP instances that are loosely, medium, and tightly constrained as discussed in Section 3.1. These GQAP instances belong to a set of test problems taken from the literature. Next, the performance of the proposed SA algorithm on these set of test problems is presented and compared against the state-of-the-art heuristic algorithms available in the literature for the GQAP.
# A Review of Fault Diagnosing Methods in Power Transmission Systems
## 3 Fault Detection (FD)  
Typically, fault detection is done prior to classification and location estimation. Fault detection is performed based on the extracted features. Whenever a self-governing technique is used for fault detection, the classifier and the locator are activated after a fault is definitely detected. Furthermore, there is no need to devise fault detection algorithms when classifiers and locators are proficient to distinguish between healthy and abnormal conditions. However, some fault detection methods are discussed here.  
[PERSON] et al. used negative sequence components for fault detection [46]. To minimize the chances of false fault detection, the convolution of partial differential with respect to (w.r.t.) time of negative sequence components with a triangular wave is used to obtain a joint fault indicator (JFI). This JFI based fault detection is robust in amplitude variation and frequency deviation cases.  
The wavelet-based method is proposed for real-time fault recognition in TLs [47]. This technique is not exaggerated by the choice of mother-wavelet and has no time delay for fault detection for both long and compact wavelets.  
Numerous researches have been conducted for the detection of high impedance faults (HIF) [48; 49; 50] as conventional algorithms may fail to detect HIF. [PERSON] et al. extracted high-frequency data by using DWT with quadratic spline mother wavelet for HIF detection [48]. Wavelet coefficient from DWT and converted scale coefficients used to detect HIF in [49]. In [50], the mean of DWT coefficients is obtained via PCA to reduce the dimensionality of the features at different frequency bands.  
Normally, the fault detection time does not significantly affect the overall protection system performance which includes fault detection, classification and location mechanisms. Typically, fault detection is achieved in 2-10 ms as compared to 30 ms for fault-type classification. So, more details on fault-type classification and location techniques are presented in this paper. However, a comparison of different fault detection methods is given in Table 1 considering the algorithm used in those methods, complexity level, employed system, inputs, features, and results. Where, complexity is defined by considering the number of inputs and rules involved in algorithm development as simple, medium and complex. Further, the selection of complexity level is achieved based on feature training and testing time, accuracy, convergence, and variance along with data required.