esa-sceva/satcom-chunk-collection huggingface dataset:
# Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
## 4 Discussion
### Comparative Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Year** & **XAI Framework** & **Output Format** & **Use Cases** & \
\begin{tabular}{c} **Programming** \\ **Language** \\ \end{tabular} & **ML/DL** & **Model Complexity** &
\begin{tabular}{c} **Open Source/** \\ **Commercial** \\ \end{tabular} & **Licence Type** \\ \hline
2024 & TNFRule [29] & \
\begin{tabular}{c} Textual Rules, \\ Visualizations \\ \end{tabular} &
\begin{tabular}{c} Optimization Tasks \\ of Cyber-physical \\ System \\ \end{tabular} & Python & Unsupervised ML & Flexible & Open Source & Apache 2.0 \\ \hline
2022 & CIAMPs [30] & \
\begin{tabular}{c} Human-readable \\ Rules, Visualization \\ \end{tabular} &
\begin{tabular}{c} unsupervised \\ problem to human \\ reachable rules \\ \end{tabular} & Python & Unsupervised ML, & Flexible & Open Source & Apache 2.0 \\ \hline
2021 & LOREs [31] & \
\begin{tabular}{c} Decision and \\ Counterfactual Rules \\ \end{tabular} &
\begin{tabular}{c} Understanding \\ Individual \\ Predictions \\ \end{tabular} & Python & Various & Simple to Complex & Open Source & MIT \\ \hline
2021 & DLIME [32] & \begin{tabular}{c} Text, Visualization \\ \end{tabular} & \
\begin{tabular}{c} Understanding \\ Model, behavior, \\ identified/ringing \\ debugging models \\ \end{tabular} & Python &
\begin{tabular}{c} Primarily \\ TensorFlow models \\ \end{tabular} & Flexible & Open Source & Apache 2.0 \\ \hline
2021 & OAKKAI [33] & \
\begin{tabular}{c} Text, factual \\ grounding highlights \\ \end{tabular} &
\begin{tabular}{c} Understanding \\ research behind \\ identified/ringing \\ influential features, \\ analyzing \\ model behavior \\ \end{tabular} & Python & Various & Simple to Complex & Open Source & N/A \\ \hline
2021 & TreeHAPs [34, 35] & \begin{tabular}{c} Text, Visualizations \\ (force Plots, \\ dependence Plots) \\ \end{tabular} & \
\begin{tabular}{c} Understanding \\ identifying \\ influential features, \\ analyzing \\ model behavior \\ \end{tabular} & Python &
\begin{tabular}{c} Decision trees and \\ Random Forests \\ \end{tabular} & Flexible & Open Source & MIT \\ \hline \hline \end{tabular}
\end{table} 
Table 4. Comparative analysis of XAI framework against several attributes.
# A Review of the Performance of Infilled RC Structures in Recent Earthquakes
## 4 Case Study
### Description of the Building  
The building has the plan dimensions of 20 \(\times\) 15 m\({}^{2}\), consisting of 4 \(\times\) 5 m\({}^{2}\) modules, with a storey height of 3 m. The building was designed by the Portuguese Laboratory of Earthquake and Civil Engineering (LNEC), according to the design code enforced in the 1980s in Portugal [42]. The layout considered for the infill walls is illustrated in Figure 24. The cross-sections of the columns are: 0.30 \(\times\) 0.60 m\({}^{2}\) (Storey 1 and 2); 0.30 \(\times\) 0.50 m\({}^{2}\) (Storey 3 and 4); 0.40 \(\times\) 0.30 m\({}^{2}\) (Storey 5 and 6) and 0.30 \(\times\) 0.30 m\({}^{2}\) (Storey 7 and 8). The columns' cross-sections are presented in Figure 25. The beams' cross-section is 30 \(\times\) 60 cm with different reinforcement detailing according to the structure's storey and layout. The beams cross-sections are presented in Figure 26. More details concerning the reinforcement detailing of all the structural elements can be found in [42]. Concerning the building's design, a global vertical load of 6.15 kN/m\({}^{2}\) plus a variable load of 2.5 kN/m\({}^{2}\) was considered. The selected concrete grade is C25/30, and the steel reinforcement grade is A400 [42]. A 3D model was generated in the software SeismoStruct [12], considering the disposition of the masonry infill walls along the structure's external perimeter and the building height, except at the ground floor. The layout considered for the infill walls distribution is illustrated in Figure 24.  
Figure 24: Case study: (**a**) plant disposition and (**b**) 3D bare frame model.  
Figure 25: Case study: columns’ cross-sections.  
Single leaf infill walls were selected for this study, representing the traditional unreinforced masonry panels constructed in Portugal, with complete contact with the surrounding RC frame and hollow clay weak bricks 150-mm thick. The relevant infill properties were selected based on the mechanical characterisation tests performed by [PERSON], et al. [43], and are described in Table 1.  
Here f\({}_{\text{c,h}}\) and f\({}_{\text{c,v}}\) are respectively the values of infill panels compression strength parallel and normal to bed joints, while f\({}_{\text{w,u}}\) stands for the sliding shear strength of the mortar joints and S\({}_{\text{s}}\) for the shear strength obtained from diagonal tensile strength test; E\({}_{\text{w,h}}\) and E\({}_{\text{w,v}}\) are the secant elastic modulus parallel and normal to bed joints, G is the shear modulus and W is the self-weight of the infill walls.
# Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping
## 2 Material and Methods
### Main Processing
#### 2.4.2 Disturbance Detection
The incidence angle and the tree height, shadows created by trees at the border between forest and non-forest areas, can be observed in high-resolution radar images, depending on the viewing direction (Figure 4). A sudden drop in backscatter in the radar time series characterizes new radar shadows. Thanks to the purely geometrical nature of the shadowing effects, this decrease of backscatter is expected to be persistent over time. New shadows should consequently remain visible for a long time and are easily detectable when dense time series of radar data, such as Sentinel-1 time series, are available. Ascending and descending orbit data are needed to detect new shadows on two sides of a forest disturbance patch. The entire disturbed area is then reconstructed from the newly detected shadows while using a convex envelope boundary operator. For every SAR image of the time series, new SAR shadows are identified and combined with previous/subsequent detections in the complementary orbit direction. If a disturbed area can be reconstructed, it is written to a disturbance detection file for this specific date and the area is removed from the forest area in the FNF mask. The single date detections are then summed over the time window of interest to a final SAR disturbance map (\"S1 Only\"). Combination of Optical and SAR  
SAR and optical data are combined by merging the final forest disturbance maps of both sensor types. For the chosen time window of interest, we processed and confirmed the forest disturbance detections for each sensor type separately (see workflow illustration Figure 3), and we then combined the sensor type specific forest disturbance map results with a simple \"Union\" process in a Geographic Information System. The union process joins all detections of the two forest disturbance maps, thus increasing the overall detected disturbed forest area.
## 4 Firmware Design
### Calibration  
The calibration process identifies the URSM parameters \(\alpha\) and \(\Gamma\) for each beacon. It is organized within subclass CalEar and consists of three steps:  
1. Measurement of the power spectrum density of the EM-field at a series of different positions, i.e., a grid.
2. Determination of RSS values for each beacon frequency by applying an FFT on the measured power spectrum density at each measurement position.
3. Fitting of the URSM for each beacon according to the collected data by using a non-linear least-squares algorithm.
## 3 Results and Discussion
### Content of TPC Using the Folin-Ciocalteu Reagent Method
#### 3.1.1 Extracts Obtained Using Shaking Solvent Extraction (SSE)
Among water extracts, the most phenolic compounds were those obtained from the leaves of the Ligol cultivar (42.0 mg GAE/1 g of DW), while the significantly lowest polyphenol content was observed in water extracts from the leaves of the Gloster cultivar (29.6 mg GAE/1 g of DW). In the study by [PERSON] et al. [41], similar results were obtained. Water extracts from apple leaves were characterised by significantly lower polyphenol content (21.7 mg GAE/100 mL) than water-alcohol extracts. In the extract containing 60% ethanol, a significantly higher concentration of phenolic compounds was obtained (127 mg GAE/100 mL) than with 30% ethanol (93.6 mg GAE/100 mL).
# A Review of Methods and Applications for a Heart Rate Variability Analysis
## 7 Applications of HRV Analysis
### Myocardial Infarction Detection  
Myocardial infarction (MI) is a widespread illness known as a heart attack. The prevalence of heart attacks in India is 64.37 cases per 1000 individuals. According to a recent assessment, around 1.5 million myocardial infarctions occur annually in the United States (US) [1]. Myocardial infarction takes place when a segment or segments of the heart muscle are deprived of oxygen. This happens when there is a restriction in the blood supply to the heart's muscles. The leading cause of MI is complete or partial artery blockage. Plaque deposits in coronary arteries can rupture and cause a blood clot. If the clot stops the arterial blood flow, the cardiac muscle will not receive enough blood, causing a heart attack. The risk factors for this cardiac issue have two aspects, i.e., genetic and acquired (from lifestyle, diseases, age, trauma, etc.) (Figure 15).  
The HRV features of patients who suffered from an acute myocardial infarction (AMI) are being analyzed extensively. According to most studies, patients with a decreased or abnormal HRV risk are dying within a few years of having an AMI. Many measures of HRV, including time-domain, spectral, and non-linear, have been employed in the risk stratification of post-AMI patients [126]. [PERSON] et al. (2019) proposed a model for accurately detecting heart failure utilizing decomposition-based attributes mined via HRV features. Three healthy volunteer datasets and two CHF datasets were used in the study. The Eigen Value Decomposition of the Hankel Matrix (EVDHM) technique was applied in this case for feature extraction. It employed least-square SVM with a radial basis function kernel as the classifier. The suggested approach produced an accuracy of 93.33%, a sensitivity of 91.41%, and a specificity of 94.90% utilizing 500 HRV samples [127]. Further, [PERSON] et al. (2021) presented an ML model for automated uncovering of myocardial infarction founded on ultra-short-term HRV [128]. They used artificial neural networks (ANN), random forest, KNN, and SVM for categorization purposes and the Relu Activation Function to select and reduce features for the model. This model yielded an accuracy of 99.01% and a sensitivity of 100%.
## 5 Results and Discussion
### Impact of Adsorption/Desorption  
When gas was extracted from the reservoir, the adsorbed gas content and the adsorption layer thickness decreased, which led to increases in effective porosity and effective pore diameter for bulk phase gas flow. Conversely, the existence of adsorbed gas occupied the available space for gas flow. The higher the adsorbed gas content, the smaller the effective pore diameter for bulk phase gas flow. The mass flux in the slip flow regime and Knudsen diffusion will have been underestimated if we had not considered the impact of adsorption/desorption (Figures 9a and 10a). Mass flux deviations (a ratio of the difference between actual mass flux and mass flux without considering the impact of adsorption/desorption to the mass flux without considering the impact of adsorption/desorption) in the slip flow regime and Knudsen diffusion induced by adsorption/desorption decreased and then increased with decreasing reservoir pressure, with maximum deviations of 1.56% and 2.54%, respectively (Figures 9a and 10a). Conversely, mass flux under surface diffusion would have been overestimated without considering the impact of adsorption/desorption (Figure 11b). Mass flux deviation under surface diffusion increased with decreasing reservoir pressures and was particularly significant at low pressures, with a maximum deviation of \(-75\%\) (Figure 11a). Adsorption/desorption had impacts on both bulk phase gas and adsorbed gas flow. The total mass flux would have been underestimated without considering the impact of adsorption/desorption (Figure 12b). The total mass flux deviation increased with decreasing reservoir pressure (Figure 12a) because there was a higher proportion of surface diffusion at low pressures.  
Figure 8: (**a**) Effect of pore size on gas flow of slip flow; (**b**) effect of pore size on gas flow of Knudsen diffusion.  
Figure 7: Effect of pore size on gas flow capacity.  
Figure 11: Effect of adsorption/desorption on surface diffusion. (**a**) Mass flux deviation with respect to pressure; (**b**) mass flux evolution with respect to pressure.  
Figure 10: Effect of adsorption/desorption on Knudsen diffusion. (**a**) Mass flux deviation with respect to pressure; (**b**) mass flux evolution with respect to pressure.  
Figure 9: Effect of adsorption/desorption on slip flow regime. (**a**) Mass flux deviation with respect to pressure; (**b**) mass flux evolution with respect to pressure.
# SepFree NMF: A Toolbox for Analyzing the Kinetics of Sequential Spectroscopic Data
## 2 Theoretical Foundations
### Interpreting \(H\) as a Clustering  
PCCA+ [19] is a clustering algorithm that projects the microstates with similar behavior to fewer dominant or macrostates (metastable states). Macrostates are collections of microstates grouped together by a similar feature.  
From a mathematical point of view, macrostates/metastable states correspond to conformations \(\tilde{U}\) that keep their structure upon application of the transition matrix \(\tilde{K}(\tau)\):  
\[\tilde{U}\approx\tilde{K}(\tau)\tilde{U}. \tag{4}\]  
This means that \(j\) metastable states are eigenvectors to the eigenvalues \(\ell_{j}\approx 1\), which are the dominant eigenvalues of \(\tilde{K}(\tau)\). Note that transition matrices have eigenvalues \(\ell_{i}\in[0,1],i=1,\ldots,n\) and that the first eigenvalue (Perron eigenvalue) \(\ell_{1}=1\). Now PCCA+ uses \(\tilde{U}\) is to assign to which degree a microstate belongs to/is a member of each one of the macrostates. The vectors in \(\tilde{U}\) in Equation (4) are not membership functions yet, so the problem now is to compute the membership functions from these dominant eigenvectors. Consider a transition matrix \(\tilde{K}(\tau)\in\mathbb{R}^{n\times n}\) with eigenvalues \(\ell_{i},i=1,\ldots,n\) and eigenvectors \(\mathcal{U}\in\mathbb{R}^{n\times n}\). Solving the eigenvalue problem, i.e., \(\tilde{K}(\tau)\mathcal{U}=\tilde{\Lambda}\mathcal{U}\), \(\tilde{\Lambda}=diag(\ell_{1},\ldots\ell_{n})\), \(\tau\) dominant eigenvalues are identified. Then the matrix of the dominant eigenvectors, \(\tilde{U}\in\mathbb{R}^{n\times r}\), is the input of the PCCA+. To find the membership functions \(\chi\), the algorithm has to project the matrix \(\tilde{U}\) such that the entries of \(\chi\) are not negative and form a partition of unity. That means finding a matrix \(\mathcal{A}\in\mathbb{R}^{r\times r}\) such that with  
\[\chi=\tilde{U}\mathcal{A} \tag{5}\]  
the membership functions matrix \(\chi\) satisfies  
\[\chi_{j}(i)\in[0,1],i\in 1,\ldots,n;\quad\sum_{j=1}^{r}\chi_{j}(i)=1. \tag{6}\]  
Equation (6) tells that \(\chi_{j}(i)\) gives information about how much the \(i\)-th microconformation belongs to the \(j\)-th macroconformation [19].  
With the membership functions in \(\chi\), the transition matrix of the macroconformations, \(K(\tau)\), can be computed with  
\[K(\tau)=\langle\chi,\chi\rangle_{\pi}^{-1}\langle\chi,\tilde{K}(\tau)\chi \rangle_{\pi}, \tag{7}\]  
with \(\pi\) being the density distribution (e.g., uniform, stationary distribution). We remark that \(\tilde{K}(\tau)\in\mathbb{R}^{n\times n}\) and \(K(\tau)\in\mathbb{R}^{r\times r}\) with \(r\ll n\).
## 3 Results and Discussion
### Modeling Time-Averaged Airflow Velocity Measured by the Tunnel Anemometers  
In this section, we again followed the evaluation presented in [18] to illustrate the significant improvement in the accuracy of the results presented here related to the accuracy of measurement of the value of the stable airflow velocity generated by the ventilation.  
Figure 11: Absolute differences ADs [m/s] between the experimental and simulated 5-point moving average of the airflow velocities determined by tunnel anemometers for the simulation presented in this paper (red curve) and the simulation in [18] (yellow curve).  
Figure 12: Relative differences RDs [%] between the experimental and simulated 5-point moving average of the airflow velocities determined by tunnel anemometers for the simulation presented in this paper (red curve) and the simulation in [18] (yellow curve).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**AD*** & **Mode 1** & **Mode 2** & **Mode 3** \\ \hline \multirow{3}{*}{Sim\({}_{\text{AG}}\)} & \(-0.08\) m/s & \(0.05\) m/s & \(\mathbf{0.24}\) m/s \\  & \(\mathbf{-0.41}\) m/s & \(\mathbf{-0.13}\) m/s & \(0.02\) m/s \\ \cline{1-1}  & \(\mathbf{0.77}\) m/s & \(-0.50\) m/s & \(-0.50\) m/s \\ \cline{1-1}  & \(0.50\) m/s & \(\mathbf{-0.59}\) m/s & \(\mathbf{-0.79}\) m/s \\ \hline \hline \end{tabular}
\end{table}
Table 8: Maximum and minimum difference AD* [m/s] between the experimental and simulated average airflow velocities determined by the tunnel anemometers using moving average.  
Figure 10: Five-point moving average of the airflow velocities v [m/s] measured by the tunnel anemometers AT1–3 in the considered time intervals in the simulation presented in this paper (red curve), in the experiment (black curve) and in the simulation in [18] (yellow curve).  
In Tables 9-11, the time-averaged airflow velocities measured by the tunnel anemometers AT1-3 in the considered time intervals in the experiment and in both simulations and their comparison are shown. The tables indicate that the time-averaged velocity obtained based on measurements by tunnel anemometers in the simulation reported in [18] differs from the time-averaged velocity obtained from experimental data from the tunnel anemometers by significantly more than the accuracy value of the grid anemometers (0.3 m/s), which points to the insufficient accuracy of the used representation of the measurement by tunnel anemometers. The results of the simulation presented in this paper indicate a significant increase in the accuracy of modeling the measurement by the tunnel anemometers, while the deviations from experimentally obtained values are less than the accuracy of the grid anemometers (0.3 m/s), but in the case of modes 1 and 3, slightly higher than the accuracy of the tunnel anemometers (0.1 m/s). However, such accuracy of modeling can be considered sufficient for practical applications of the model.
# Residual Stress Relaxation of DRWDs in OSDs under Constant/Variable Amplitude Cyclic Loading
## 4 Residual Stress Relaxation of DRWDs by External Cyclic Loading
### Welding Residual Stress Release of DRWDs under Constant Amplitude Cyclic Loading
#### 4.1.2 Stress Amplitude of External Cyclic Loading  
To study the influence of stress amplitude on residual stress relaxation, the TSA is increased from 10 MPa to 130 MPa. Figures 14 and 15 show the distribution of residual stress along the \(X\)-axis (perpendicular to the weld seam) and \(Z\)-axis (parallel to the weld seam), respectively, under TSA from 0 MPa to 130 MPa for R = 0.5. As the TSA increases from 10 MPa to 130 MPa, the peak values of \(\sigma_{x}\) and \(\sigma_{z}\) gradually decrease, the magnitude of stress relaxation increase, and the overall distribution characteristics of the residual stress remain unchanged.  
Figures 16 and 17 show that the welding residual stress relaxation coefficient A varies with the longitudinal and transverse stress amplitude as R increases from -0.5 to 0.5. First, it is observed that A decreases gradually with the increase in the loading stress amplitude, which indicates that the stress release increases gradually. The value of A remains 1 for TSA = 10 MPa, which indicates that the residual stress is not relaxed in such cases. Second, the value of A decreases rapidly in the first loading cycle and then gradually becomes stable as the number of loading cycles increases from 0 to 10. Third, the final value of A depends on the stress amplitude and stress ratio. For example, the value of A is stable at about 0.92 for TSA = 100 MPa and R = 0.5.  
To explain the conditions of residual stress relaxation, Table 2 shows the relationships among the residual stress relaxation coefficient A, the initial residual stress (\((\left(\sigma_{res}\right)_{ini}\)), the structure stress induced by the external loading (\(\sigma_{app}\)), and the yield strength of material (\(f_{y}\)) as TSA increases from 10 MPa to 130 MPa for R = 0.5. As illustrated in Table 2, the value of A = 0.99 for TSA = 10.00 MPa and LSA = 14.08 MPa. This shows that the residual stress hardly relaxes and the transverse stress is \(\mathbf{B}<1\); the longitudinal stress is \(\mathbf{B}>1\) and the Mises stress is \(\mathbf{B}<1\) in such cases. For the other four cases in Table 1, no matter how the longitudinal and transverse stress of \(\mathbf{B}\) changes, the Mises stress of \(\mathbf{B}\) and the value of \(\mathbf{A}\) are always less than 1, which reveals that stress relaxation occurs. Therefore, whether the stress relaxation occurs under the mechanical stress, which is induced by the external cyclic loading, depends on the sum of initial residual stress (\(\left(\sigma_{res}\right)_{ini}\)); and structure stress (\(\sigma_{app}\)) exceeds the yield strength of the material (\(f_{y}\)), rather than observing the stress variation of a certain uniaxial.  
Figure 17: The variation curve of residual stress relaxation coefficient A with LSA: (**a**) R = -0.5; (**b**) R = -0.25; (**c**) R = 0; (**d**) R = 0.25; (**e**) R = 0.5.
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 5 Fair Editions of Ensemble Models
### FairGBM  
In [25], FairGBM is proposed as an extension of Gradient Boosted Decision Trees (GBDTs) that explicitly incorporates convex fairness constraints--such as equal opportunity and equalized odds--directly into the learning objective via a Lagrangian dual optimization framework. This formulation enables FairGBM to balance predictive performance with fairness guarantees by treating fairness metrics as constrained optimization objectives rather than post hoc adjustments. Empirical results on benchmark datasets, including ACS Income, Adult, and Account Opening Fraud, indicate that FairGBM achieves substantial  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Key Idea** & **Fairness Metric(s)** & **Datasets** & **Remarks** \\ \hline \multirow{4}{*}{GAFairC [27]} & AdaBoost with group-aware & & & GAFairC achieves higher \\  & loss and fairness constraint & & & fairness \\  & components: penalty, intensity selection, post-pruning. & & Credit, Adult & maintaining/improving \\  & & & & accuracy \\ \hline \multirow{4}{*}{FAEM [28]} & Two-stage model: hybrid & Average Odds & & \\  & sampling (ADASYN), followed by two-layer & Difference, Equal & German Credit, & \\  & followed by two-layer & Opportunity & Adult, Bank, & \\  & stacking ensemble for & Difference, & COMPAS & fairness-aware models \\  & fairness-aware predictions & Disparate Impact & & \\ \hline \multirow{4}{*}{FairBoost [29]} & Based on & Demographic Parity, & Adult, Arrhythmia, & \\  & supports multiple sensitive & Equalized Odds & Drugs, & \\  & attributes via fairness-aware & Difference & German Credit & \\  & instance weighting & & & \\ \hline \multirow{4}{*}{Fair Voting Ensemble [30]} & Soft voting with separate & & & \\  & dynamic weights for & Counterfactual & & \\  & privileged/unprivileged & fairness, Equalized & & \\  & groups optimized via & odds (custom based) & & \\ \cline{1-1}  & RNGGA-II & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._reductions in group-based disparities while maintaining competitive accuracy, thereby demonstrating the feasibility of fairness-aware learning in tree-based models without significant trade-offs.