esa-sceva/satcom-chunk-collection huggingface dataset:
These studies demonstrate that type II TA systems may be involved in some way with the stress response of bacterial pathogens; consequently, they can often survive in diverse niches by sensing and adapting to changes in environmental cues and altering their gene expression patterns [17]. These challenging environmental conditions may be encountered by hybrid _E. coli_ strains, which are pathogenic strains with the potential to cause distinct types of infection in intestinal and extraintestinal niches, due to the presence of virulence factors of diarrheagenic _E. coli_ (DEC) and extraintestinal _E. coli_ (ExPEC). Hybrid strains are considered to be more virulent than other pathogenic _E. coli_ due to their genetic machinery that enables the colonization and multiplication in such different conditions as the intestine, blood, meninges, and urinary tract [18]. For instance, the gastrointestinal tract presents low oxygen concentration, differences in nutrient accessibility, and pH levels that vary from acid in the stomach to alkaline in the intestine [19]. On the other hand, the extraintestinal milieu is characterized by expressive alteration in osmolarity, oxygen radicals, and nutrient availability [20; 21]. In a previous study, the _E. coli_ strain BA1250 isolated from diarrhea was classified as an atypical enteropathogenic _E. coli_ (aEPEC) through the presence of _eae_, the absence of _bfpA_, and the capacity to induce the attaching and effacing (A/E) lesion [22]. Subsequently, after the whole genome sequence analyses, assembly, and annotation of its genome, carried out using the SPAdes and Prokka pipelines, BA1250 was characterized as a hybrid pathogenic aEPEC/ExPEC [23]. Phylogenetic analysis showed that this strain is more related to _E. coli_ strains with the potential to cause urinary tract infections [24] than to other aEPEC strains. Recently, we also described that BA1250 was able to infect multiple niches in a zebrafish in vivo model, showing its capacity to colonize and persist in the intestine, bloodstream, and pericardial cavity of the host [25]. Therefore, herein, we investigated the activation of chromosomally expressed type II TA systems present in BA1250 (CcdB-CcdA, MazF-MazE, PasT-PasI, YhaV-PrIF, and YoeB-YefM), and their association to a possible enhancement of hybrid _E. coli_ fitness under diverse stress conditions.
# An Algorithm for the Fisher Information Matrix of a VARMAX Process
## 1 Introduction
This is often the case in the literature, although [29], for example, consider a VARMA model where the dimension of the unobserved errors is smaller than that of the observed output. Except when we will mention the tensor Sylvester matrices, the dimensions of the observed input \(x_{t}\) and the observed output \(y_{t}\) need not be the same, like [7; 18] but contrary to [19]. We store the VARMAX \((p,r,q)\) coefficients in an \(l=(n^{2}(p+q)+nmr)\times 1\) vector \(\vartheta\) defined as follows:  
\[\vartheta=\text{vec}\{A_{1},A_{2},\ldots,A_{p},C_{0},\ldots,C_{r-1},B_{1},B_{2 },\ldots B_{q}\}.\]  
The vec operator transforms a matrix into a vector by stacking the columns of the matrix one underneath the other, according to vec \(X=\text{col}(\text{col}(X_{ij})_{i=1}^{n})_{j=1}^{n}\), see, e.g., [30]. The observed input variable \(x_{t}\) is assumed to be a stationary \(m\)-dimensional Gaussian VARMA process with white noise process \(\eta_{t}\) satisfying \(\mathbb{E}\{\eta_{t}\eta_{t}^{T}\}=\Omega\succ 0\) and  
\[a(z)x_{t}=b(z)\eta_{t},\]  
where \(a(z)\) and \(b(z)\) are, respectively, the autoregressive and moving average matrix polynomials, such that \(a(0)=b(0)=I_{m}\) and \(\det(a(z))\
eq 0\) far all \(|z|\leq 1\) and \(\det(b(z))\
eq 0\) for all \(|z|\leq 1\). The spectral density of process \(x_{t}\) is defined as, see, e.g., [26]  
\[R_{x}(e^{if})=a^{-1}(e^{if})b(e^{if})\Omega b^{*}(e^{if})a^{*-1}(e^{if}), \tag{4}\]  
where \(i\) is the standard imaginary unit, \(f\) is the frequency, the spectral density matrix \(R_{x}(e^{if})\) is Hermitian, and we further have, \(R_{x}(e^{if})\succ 0\) and \(\int_{-\pi}^{\pi}R_{x}(e^{if})df<\infty\). Therefore, there is at least one solution of (1) which is stationary.
## 3 GLRT Based Detection Scheme  
In this section, we adopt the GLRT for the detection problem introduced in Equation (6) since it is asymptotically an optimal detector and it is well-suited to the presence of unknown parameters. Later on, we will use it as a benchmark for the proposed techniques. For the detection problem (6), the GLRT statistic becomes:  
\[\Lambda_{\mathrm{T}}\left(\mathbf{X}_{N}\right)=\frac{\underset{\mathbf{ \Sigma}_{0}}{\text{max}}f_{\mathbf{x}}\left(\mathbf{X}_{N};\mathbf{\Sigma}_{0} \right)}{\underset{\mathbf{\Sigma}_{1}}{\text{max}}f_{\mathbf{x}}\left( \mathbf{X}_{N};\mathbf{\Sigma}_{1}\right)}\gtrless_{\mathcal{H}_{1}}^{ \mathcal{H}_{0}}\gamma, \tag{7}\]  
Figure 1: Multi-sensor with Multi-antenna (MAMS) methodology.  
where \(\gamma\) is the threshold, \(f_{\mathbf{x}}\left(\mathbf{X}_{N},\mathbf{\Sigma}_{0}\right)\) and \(f_{\mathbf{x}}\left(\mathbf{X}_{N};\mathbf{\Sigma}_{1}\right)\) are likelihood functions under hypothesis \(\mathcal{H}_{0}\) and \(\mathcal{H}_{1}\), respectively. Similarly, the (\(KL\times 1\)) matrix \(\mathbf{X}_{N}=\left[\begin{array}{cccc}\mathbf{x}\left(1\right)&\mathbf{x }\left(2\right),&\cdots,&\mathbf{x}\left(N\right)\end{array}\right]\) contains all of the available samples of \(\mathbf{x}(n)\), \(n=1,\ldots,N\). Solving Equation (7) we get the final expression of the GLRT as:  
\[\Lambda_{\mathrm{T}}\left(\mathbf{X}_{N}\right)=\frac{\left|\mathbf{\hat{ \Sigma}}_{1}\right|}{\left|\mathbf{\hat{\Sigma}}_{0}\right|}\gtrless_{ \mathcal{H}_{1}}^{\mathcal{H}_{0}}\gamma, \tag{8}\]  
where \(\mathbf{\hat{\Sigma}}_{1}=\frac{1}{N}\sum_{n=1}^{N}\mathbf{x}(n)\mathbf{x}^{ H}(n)\) is the maximum likelihood estimator (MLE) of \(\mathbf{\Sigma}_{1}\). Similarly, under the alternate hypothesis, we assume that only noise is present and \(\mathbf{\Sigma}_{0}\) is diagonal matrix, then \(\mathbf{\hat{\Sigma}}_{0}=\text{diag}\left(\frac{1}{N}\sum_{n=1}^{N}\mathbf{x }(n)\mathbf{x}^{H}(n)\right)\). In practice, the GLRT is used based on the assumption that the sample size \(N\) is large while the sample dimensions \(\{K,L\}\) are small. However, when the sample support is limited (in particular, when \(N\leq KL\)), the GLRT degenerates due to the ill-conditioning of the estimated covariance matrix [22]. A way to reduce these limitations will be presented next.
## 2 Materials and Methods
### Explanation of the Features  
To interpret the results of our machine learning models and identify the most influential predictors, we utilized SHAP (SHapley Additive exPlanations), a state-of-the-art algorithm for explainable artificial intelligence (XAI). SHAP provides a unified framework for quantifying the contribution of each feature to the model's predictions, offering both global and local interpretability.  
The SHAP method is based on cooperative game theory, where each feature is treated as a \"player\" in a predictive game, and its contribution to the final prediction is assessed in terms of Shapley values. These values are calculated by considering the marginal contribution of a feature across all possible subsets of features, ensuring fairness and consistency. Unlike traditional feature importance methods, SHAP assigns feature contributions in a manner that accounts for interactions and dependencies among predictors:  
\[\Phi_{j}(x)=\sum_{F\subseteq S-\{j\}}\frac{|F|!(|S|-|F|-1)!}{|S|!}[f_{x}(F \cup j)-f_{x}(F)] \tag{4}\]  
where \(x\) is an instance, the sum is over all the subsets S of features that include the feature j, \(\frac{|F|!(|S|-|F|-1)!}{|S|!}\) is a weight parameter that multiplies all of the permutations of S! by the potential permutations of the remaining class that does not belong to S, and \(f_{x}(F\cup j)\) and \(f_{x}(F)\) denote the regression score obtained by including and removing, respectively, feature j.  
In this study, we used SHAP to generate explanations for the predictions of our ensemble models. Specifically, SHAP summary plots were employed to visualize the overall importance of each feature across the dataset. This analysis provided valuable insights into how different variables, such as pollution levels, socio-economic indicators, and mortality data, influenced the model's predictions.
## 5 Implementation and Results  
In this section, the implementation of the proposed model and the results are presented. The problem of the first and the second stages is modeled as a MILP problem. The proposed model is solved by the CPLEX solver in the GAMS software [23]. It should be mentioned that the main advantage of the GAMS platform in comparison with other heuristic algorithms is finding the most optimal solution, while heuristic algorithms do not guarantee finding the optimal solution. Furthermore, all the reported results in this section are the most optimal ones, and the \"Absolut Gap\" of the solutions is obtained as equal to \"zero\", which shows the preferability of the GAMS solver in comparison with heuristic solvers. The solver is used to find the solution until the \"Absolut Gap\" is obtained as equal to zero and lasts for a few minutes. It should be mentioned that a computer (Intel Core I5 2.8 GHz, 6 GB RAM) was utilized to solve the proposed framework.  
In the following, the implementation of the proposed framework in the IEEE 34 bus test system and an actual Iranian power distribution feeder, which has 66 buses, are presented.
# Wind Loads on a PV Array
## 1 Introduction  
The use of solar energy has increased, due to public concerns about climate change and environmental pollution. The total respective capacity for solar photovoltaic (PV) systems was 303 GW and 402 GW in 2016 and 2017 ([PERSON], 2017). A PV system consists of inclined panels, which are usually mounted on the rooftops of residential or commercial buildings. Floating PV on reservoirs, ponds, or lakes are another emergent solar energy system ([PERSON], 2017; [PERSON], 2017; [PERSON], 2017; [PERSON], 2017). In nearshore areas, a PV system floats in the ocean using a buoyancy system ([PERSON], 2017).  
To harness solar energy, PV panels for roof-top or ground systems are installed at an optimal tilt angle that allows the sunlight to fall perpendicular to the panels' surface. Wind loads depend on the tilt angle, the angle of incidence of the wind, and the spacing and sheltering of the arrays ([PERSON] et al., 2019; [PERSON] et al., 2019; [PERSON] et al., 2019; [PERSON] et al., 2019; [PERSON] et al., 2019). For an inclined panel with a length-to-width ratio, \(L\)/\(W\), of 2 that faces a uniform flow, [PERSON] et al. (2019) showed that there is a decrease in the sectional lift coefficient as the angle of tilt increases (\(\alpha=15^{\circ}\)-\(25^{\circ}\)). Corner vortices are also observed. For an inclined panel (\(L\)/\(W=0.22\)) on flat roofs, an increase in \(\alpha\) (\(=20^{\circ}\)-\(45^{\circ}\)) produces greater suction ([PERSON] et al., 2019). [PERSON] et al. (2019) noted that wind-induced loads on an inclined panel are due primarily to pressure equalization at large angles of tilt and turbulence at small angles of tilt. The effect of the angle of incidence of wind, \(\beta\), was investigated by [PERSON] et al. (2019). There is greater suction on the upper surface near the windward corner for \(\beta=15^{\circ}\)-\(60^{\circ}\). A study by [PERSON] and [PERSON] (1999) obtained similar results.  
For a PV system, wind loads are significantly reduced by the presence of neighboring upwind arrays (sheltering effect) ([PERSON] et al., 2019; [PERSON] et al., 2019). Therefore, this study only determines wind loads for a stand-alone PV array (nine panels, \(L\)/\(W=0.19\)). In addition, the wind loads on the PV panels in a sea environment are not the same as those for PV panels that are located on land. The motion of a pontoon results in thevariation in \(\alpha\) during a wave cycle. The safety of PV panels in environments that feature large waves is a practical issue for the system design. To ensure that the system functions properly, the effect of \(\alpha\) (up to 80\({}^{\circ}\)) and \(\beta\) is determined.
## 2 Materials and Methods: Ten-Koh Mission Description
### Satellite Concept of Operations
* D The mission experiment is executed and the housekeeping and missions data are stored in different memories on-board the satellite to be downlinked during the next passes over the MGS. The process in Steps A-D is repeated for every new mission experiment. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Phase** & **Mission Phase** & **Duration** & **Description** \\
**Number** & & & \\ \hline
1 & LEOP: Launch & 2–3 weeks & * & Starts after separation from the launch vehicle. \\  & & & * & Subsystems and instruments are powered on. \\  & & & * & Comprehensive satellite subsystem checkout. \\  & & & * & Recording of Satellite Telemetry Data (STD). \\  & & & * & Downloading and analysis of scientific and technical data by ground control station. \\  & & & * & Checking the operation of payload instruments (by real time mode commands). \\  & & & * & Transition to normal operation. \\
2 & Normal & 1 year starting after & * & Science data collection. \\  & Operation & 3 weeks from the launch & * & Long and short term planning of payload instruments. \\  & & & * & Communication sessions with ground stations in charge of controlling the satellite. \\
3 & Extended & 1 year, starting after & * & Variations in science operations. \\  & Operation & Normal Operation & * & Orbit decay monitoring. \\
4 & End of mission & \textgreater{}3 years & * & Planning and execution of the end of life operation. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Satellite mission operation phases.
# Automatic False Alarm Detection Based on XAI and Reliability Analysis
## 3 Materials and Methods
### Overview  
We can learn the features that are important for the AI model to predict, and based on this, we can measure the reliability of the AI model prediction. The reliability of the AI model has two aspects--the analyst and AI--for calculating the two indicators. A composition diagram of the proposed method is as shown in Figure 1. After we train the AI model with the training dataset, we make a summary plot for each label using the SHAP explainer. The summary plot of each label can be easily compared and identified for each feature by the degree of that SHAP value's contribution to prediction with the corresponding label. We then select important features that substantially contribute to the prediction with each label. After that, the sum of the SHAP value of the selected feature (SSSF) is used as the first indicator. Here, using the outlier algorithm, the reliability score that additionally considers the outliers based on the feature values is measured and used as the second indicator. We used k-nearest neighbors (k-NN) as an outlier algorithm in the proposed method. SSSF is an indicator of the degree to which the data are biased toward the label based on important features from the human point of view, and the reliability score is an indicator that adds the outlier score, which is a minute difference in the data that the model sees from the AI point of view. Through these two indicators, we propose a method for screening important alerts for subsequent analysis in a real security environment where many threats occur.  
Figure 1: Overall structure of the proposed method.
## 2 Powder Bed Fusion
### Thermodynamical Phenomena  
The PBF process involves complicated thermodynamical phenomena and physio-chemical behavior, as powder particles are melted using a laser beam with high energy. Thermodynamic monitoring is important to control the performance of the process, but it is difficult as the molten pool is small and moves quickly. Recently, numerical modelling has been developed to study the physical mechanism deeply. Metal SLM involves principles for multiscale coordinate control, and they include deformation and stress (macroscale), melting behavior and laser absorption (mesoscale), and the development of microstructure (microscale) [15].  
The limited energy on the powder bed and low operating temperature resulting from low laser power value in SLM lead to generating residual pores between the neighboring small molten pools. The low temperature decreased the liquid surface tension, leading to the melt flow [15]. These conditions cause a remarkable reduction of the melt pool convection, and simultaneously lead to a weakening of the melt migration between the current and solidified neighboring tracks. As a result, the porosity is shown obviously on the top surface and cross-section of the SLM part. Conversely, increasing the laser power results in a larger molten pool size with a longer lifetime of the liquid. The considerable laser energy input leads to an intensified convection within the melt pool with sufficient melt migrations between the high laser power tracks, which produce high-quality SLM parts without distinct defects on both the cross-section and top surface. In addition, cellular morphology is presented with the microstructure surrounding the tracks with no obvious defects. Reference [15] presented the following Figures 4 and 5 for the top surface morphology of the produced Inconel 718 part with two different laser powers, 90 W and 120 W.  
Therefore, with a low laser power value of 90 W, irregularly shaped porosity was shown distinctively in the tracks where heat transfer and limited mass occurred. Increasing the laser power value to 120 W decreased the porosity. Therefore, producing high-quality Inconel 718 parts are achievable with optimal laser power that can be specified using mesoscale simulation and analysis [15].  
Figure 4: Top surface morphology with laser power of 90 W [15].
# Shape Effect and Accuracy Analysis of Rock Tensile Strength Test
## 3 Comparison and Analysis of the Accuracy Between Direct Tension and Brazilian Splitting Tests
### Test Scheme
#### 3.1.2 Parameter Selection  
Based on different specimen shapes, specimens with various diameters were established to study the effects of shape and size on the tensile strength of rocks. The selected test material has a compressive strength of 50 MPa and a tensile strength of 5 MPa. The elastic modulus of the experimental material is E = 10 GPa, the Poisson's ratio is \(\
u\) = 0.18, and the density is \(\rho\) = 2.3 g/cm\({}^{3}\). In practical research, numerical simulations often use homogeneous material models, while the rock materials in actual experiments are heterogeneous. To more accurately reflect the experimental results, we adopted model microparameters in the numerical simulation that closely resemble the mechanical properties of the experimental materials. Additionally, parameter calibration was performed to ensure the consistency of the simulation results with experimental data. By controlling parameters, we aimed to make the simulated test conditions as close as possible to the actual experimental conditions, thereby ensuring the accuracy of the numerical simulation results. The calibrated model microparameters are shown in Table 3. The mesh was divided using tetrahedral elements, with a unit size of 2 mm. The elastic modulus of the model is assumed to be the same in tension and compression, and the simulated material is defined as a linear elastic material. In field tests, whether drilling rock cores or processing dumbbell-shaped specimens, the diameter of the specimens will have some error. To simulate real-world conditions, the experimental plan is arranged as shown in Table 4.  
For the three types of direct tensile specimens, the \(Z\)-axis is the centreline of the model. The boundary conditions are as follows: one end face has no displacement along the \(Z\)-axis, and the centre point of this end face has no displacement in the X, Y, or \(Z\) directions. A force load is applied to the opposite end face.
## 4 Numerical Analyses
### Sharing Mechanism  
The shear stress on the upper surface of the debris flow can be estimated as follows [33]:  
\[\tau=\frac{1}{2}C_{\text{D}}\rho v^{2} \tag{12}\]  
where \(\tau\) indicates the shear stress, \(C_{\text{D}}\) denotes the drag coefficient, \(\rho\) represents the density of the mixture, and \(v\) denotes the velocity of the debris flow. As shown in Figure 6, at 6 s, \(\Delta v\), representing the variance between the velocity of the debris flow and that of the ambient water, is approximately 0.4 m/s. The drag coefficient \(C_{\text{D}}\), suggested by [PERSON] et al. [57] and [PERSON] [58] to be within the range of 0.0015 to 0.007, is chosen as 0.0015 in this study. The density of the mixture is considered to be 1500 kg/m\({}^{3}\). Using Equation (12), the shear stress on the upper surface is calculated to be approximately 0.18 Pa.  
On the other hand, the shear stress on the upper surface of the debris flow can be determined from the simulation results. The shear strain rate at 6 s, with the mixture model, increases with the mixing of the slurry and ambient water, as shown in Figure 10a. Therefore, the interface shear strain rate can be better illustrated by the counterpart results from the VOF model, as shown in Figure 10b, in which the maximum shear strain rate at the interface between the slurry and the water is approximately 100 s\({}^{-1}\). Then, the shear stress can be calculated by multiplying the shear strain rate with the viscosity of water, as \(\tau=100\times 0.0013=0.13\) Pa, which is very close to that calculated from the drag force. Therefore, the shearing mechanism of the transformation from debris flows into turbidity currents is validated. Below, Equation (12) is adopted to quantify the interface shear stresses between the soil and water during shearing.  
Figure 10: Shear strain rate contour at 6 s. (**a**) The mixture model. (**b**) The VOF model.  
Figure 9: Vorticity contour at 15 s.
# System Proposal for Mass Transit Service Quality Control Based on GPS Data
## 5 System Overview
### The SQCM  
Based on specification of the part of the transport network to be analysed--i.e., a stop, a route, a set of line services, etc.--and the temporal aspects of the analysis, this module obtains the data and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Field** & **Description (Size)** \\ \hline \hline \(\text{RAVL}_{\text{Veh}}\) & Vehicle identifier. (2 bytes) \\ \hline \(\text{RAVL}_{\text{T}}\) & GPS Time of day expressed in Coordinated Universal Time (UTC). (4 bytes) \\ \hline \(\text{RAVL}_{\text{Lat}}\) & Latitude of the vehicle expressed in degrees. (4 bytes) \\ \hline \(\text{RAVL}_{\text{Long}}\) & Longitude of the vehicle expressed in degrees. (4 bytes) \\ \hline \(\text{RAVL}_{\text{Alt}}\) & Altitude of the vehicle position expressed in feet. (4 bytes) \\ \hline \(\text{RAVL}_{\text{Rel}}\) & Horizontal speed of the vehicle expressed in miles per hour. (2 bytes) \\ \hline \multirow{6}{*}{\(\text{RAVL}_{\text{Age}}\)} & Age of the GPS data: \\  & 0: data not available \\ \cline{1-1}  & 1: \textgreater{}10 s \\ \cline{1-1}  & 2: \textless{}10 s \\ \cline{1-1}  & (1 byte) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of the RAVL data structure.  
parameters required for quality control. The SQCM, as shown in Figure 3, is executed in four steps: the first step, tracking, consists of obtaining the initial set of position data that will be used for quality control; the second step, filtering, ensures that quality control will be carried out using an integral data set, selecting reliable position data to that end; the aim of the third step is to obtain the passing times for each of the stops on the line services analysed; and finally, the fourth step consists of calculating the metrics used for quality control.