esa-sceva/satcom-chunk-collection huggingface dataset:
# Explainable AI Assisted IoMT Security in Future 6G Networks
## 5 Experiments and Results
### Model Selection for XAI Technique Application  
Given its strong performance, the Random Forest model was prioritized, and XAI techniques--SHAP, LIME, and DiCE--were used to interpret its decision-making. While Random Forest provides some level of interpretability through built-in feature importance, it still behaves like black box models in high-dimensional scenarios. XAI techniques help to uncover complex feature interactions and offer instance-level explanations, thereby improving transparency in healthcare security. Figure 7 presents the feature importance plot generated by the Random Forest model using the scikit-learn Python library [36]. Among the features,'scptimes_idle' holds the highest importance, followed by'scptimes_user'  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class Types** & \begin{tabular}{c} **Total Count** \\ **(Before** \\ **SMOTE**) \\ \end{tabular} & \begin{tabular}{c} **Class Instance** \\ **(Before** \\ **SMOTE**) \\ \end{tabular} & \
\begin{tabular}{c} **Total Count** \\ **(After** \\ **SMOTE**) \\ \end{tabular} &
\begin{tabular}{c} **Class Instance** \\ **(After** \\ **SMOTE**) \\ \end{tabular} \\ \hline Normal & 132,884 & 132,884 & 4000 & 4000 \\ DOS & 9971 & & 1000 & \\ MiTM & 1672 & 12,339 & 1000 & \\ Ransomware & 528 & & 1000 & 4000 \\ Buffer\_Overflow & 68 & & 1000 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset categorization.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline
**Logistic Regression** & 98.37\% & 0.984 & 0.983 & 0.983 \\ \hline
**K-Nearest Neighbor** & 98.87\% & 0.988 & 0.988 & 0.988 \\ \hline
**Random Forest** & 99.85\% & 0.998 & 0.998 & 0.998 \\ \hline
**CNN** & 75.25\% & 0.752 & 0.752 & 0.752 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance evaluation metrics.  
and'scpustats_interrupts'. Features that were assigned to zero importance have been excluded from the plot, as they do not contribute to the model's predictive performance.
## 2 Experimental  
The AlSi5 Cu1 Mg-x (Pr+Ce) (x = 0, 0.3 wt.%, 0.6 wt.% and 0.9 wt.%) alloys were prepared from AlSi5 Cu1 Mg alloy and Al-5 Pr-5 Ce ternary master alloy. Table 1 shows the chemical compositions of the tested alloys were analyzed by an inductively coupled plasma-atomic emission spectroscopy. The prepared alloy was placed into preheated graphite crucible and melted completely in an electric resistance furnace at 720 \({}^{\circ}\)C. The Al-5 Pr-5 Ce master alloy was then added to the liquid to fabricate the AlSi5 Cu1 Mg-x (Pr+Ce) (x = 0, 0.3 wt.%, 0.6 wt.%, and 0.9 wt.%) alloy. The liquid was isothermally maintained at 720 \({}^{\circ}\)C for about 60 minutes to ensure that the Al-5 Pr-5 Ce master alloy was completely melted. The liquid was poured into a steel mold, which was preheated to about 180 \({}^{\circ}\)C.  
The metallographic specimens were etched with a 0.5 vol.% Hydrofluoric acid water solution. An optical microscope (OM, Nican M200 microscope) and a scanning electron microscope (SEM, FEI Quanta 200F) equipped with energy disperse spectroscopy (EDS, JSM-6701F) were used to characterize the microstructural evolution of the alloys. The grain size of Si was measured on Image-Pro Plus (IPP) software, the average length and aspect ratio according to the following Equations (1) and (2) [15]:  
\[\text{{ML}}=\frac{1}{m}\sum_{j=1}^{m}\left[\frac{1}{n}\sum_{i=1}^{n}L_{i} \right]_{j} \tag{1}\]  
\[\text{{AR}}=\frac{1}{m}\sum_{j=1}^{m}\left[\frac{1}{n}\sum_{i=1}^{n}\left( \frac{l_{i}}{l_{s}}\right)\right] \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Number** & **Materials** & **Si** & **Cu** & **Mg** & **Fe** & **Pr** & **Ce** & **Al** \\ \hline
1 & AlSi5 Cu1 Mg & 5.41 & 1.05 & 0.50 & 0.63 & 0 & 0 & 92.41 \\
2 & 0.3 wt.\% (Pr+Ce)/AlSi5 Cu1 Mg & 5.23 & 1.03 & 0.50 & 0.61 & 0.14 & 0.16 & 92.33 \\
3 & 0.6 wt.\% (Pr+Ce)/AlSi5 Cu1 Mg & 5.21 & 1.01 & 0.50 & 0.60 & 0.32 & 0.28 & 92.08 \\
4 & 0.9 wt.\% (Pr+Ce)/AlSi5 Cu1 Mg & 5.26 & 0.99 & 0.50 & 0.62 & 0.44 & 0.46 & 91.73 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of AlSi5 Cu1 Mg-x(Pr+Ce) composites (mass fraction %).  
_ML_: average length; _AR_: aspect ratio; _l/l\({}_{s}\)_: ratio of length and width of the particle; _Li_: the length of a particle; _n_: the number of particles of a single field; and _m_: the number of the fields. Three test bars were fabricated for each alloy composition. The casting test bars were processed according to standard of GB/T 6397-86. The tensile test was implemented at 0.02 mm/min on an UTM5150 machine at the room temperature to measure the UTS and breaking elongation values. The fractured surfaces of the tensile test samples were examined via SEM. Vickers hardness of the casting samples were obtained by using a Vickers-hardness 1000 A hardness tester device (Laizhou Huayin Testing Instrument Co., Ltd., Laizhou, China) and at least six independent measurements were obtained.
## 3 Methodology and Simulation Framework
### Proposed Pricing Schemes and Korean Electricity Market Overview
Finally, although uniform pricing can be implemented in various forms, this study focused on evaluating the transition from the current pricing scheme to zonal and nodal pricing. Therefore, we considered only the Uniform (SMP) scheme currently adopted in the Korean electricity market as the \"As-Is\" scenario. This reflected the unique characteristics of the Korean electricity market. * In contrast to typical uniform pricing, the Korean market incorporates a transmission flow model by using ATC for the capital (KY) and non-capital (NKY) regions. Figure 2: Proposed bidding zone configurations for zonal pricing in the Korean power system: (**a**) two bidding zones and (**b**) five bidding zones. * Due to the cost-based pool system, SMP mechanisms are applied to compensate the generators for no-load and start-up costs. * The market has a unique structure wherein a single transmission company (Transco) functions as a monopolistic retailer. These proposed pricing schemes facilitated the assessment of the effects of transitioning from existing to more granular pricing schemes. In Section 4, we present the quantitative results of the transition to zonal and nodal pricing schemes and examine their impact on potential conflicts of interest for the Transco, considering the unique aspects of the Korean electricity market.
## 3 Results and Discussion
### Optimization of IL-UMASDE  
#### 3.4.1 RSM and Model Fit  
Based on the results of the single-factor experiments, the interactions among the liquid-solid ratio, extraction time, and microwave power were studied using CCD combined with RSM. The experimental conditions and yields of CPT, HCPT, VCS-LT, and EOs for each experiment are shown in Table 1. The contour plots for interactions among liquid-solid ratio, extraction time, and microwave power are shown in Figure 6.  
The statistical significance of the model and factors were evaluated using ANOVA _F_-test at 99% confidence. The ANOVA results for the quadratic model are shown in Table 2.  
The data in Table 2 show that, according to the software analysis, acceptable determination coefficients (\(R^{2}\) = 0.9852, 0.9762, 0.9787, and 0.9811 for CPT, HCPT, VCS-LT, and EOs, respectively), a high significance level for the model (\(p\) < 0.01) and an insignificant lack of fit (\(p\) > 0.01) were obtained. These results indicate that the model was precise and applicable to the yields of CPT, HCPT, VCS-LT and EOs using IL-UMASDE. The larger _F_-value and smaller _p_-value show that the corresponding model terms were more significant. The results from Table 2 also show the significance of the linear terms, quadratic terms, and interaction terms. A second-order polynomial model was used to express the yields of CPT, HCPT, VCS-LT, and EOs using the following equations:  
\[\begin{array}{\[\begin{array}{c}Y_{\text{EOs}}=-2.251+0.159X_{1}+6.620\times 10^{-2}X_{2}+3.504\times 10^{-3}X_{3}+4.054\times 10^{-4}X_{1}X_{2}+\\ 3.654\times 10^{-5}X_{1}X_{3}+2.130\times 10^{-5}X_{2}X_{3}-7.415\times 10^{-3}X_{1} ^{2}-1.306\times 10^{-3}X_{2}^{2}-4.167\times 10^{-6}X_{3}{}^{2}\end{array} \tag{5}\]  
where \(Y_{\text{CPT}}\), \(Y_{\text{HCPT}}\), \(Y_{\text{VVCS-LT}}\), and \(Y_{\text{EOs}}\) are yields of CPT, HCPT, VCS-LT, and EOs, respectively; \(X_{1}\) is the liquid-solid ratio (mL/g), \(X_{2}\) is the extraction time (min) and \(X_{3}\) is the microwave power (W).  
Figure 6: Contour plots for extraction of CPT, HCPT, VCS-LT, and EOs using IL-UMASDE method: effect of liquid–solid ratio and extraction time on yields of CPT **(a-1)**; HCPT **(a-2)**; VCS-LT **(a-3)**; and EOs **(a-4)**; effect of liquid–solid ratio and microwave power on yields of CPT **(b-1)**; HCPT **(b-2)**; VCS-LT **(b-3)**; and EOs **(b-4)**; and effect of extraction time and microwave power on yields of CPT **(c-1)**; HCPT **(c-2)**; VCS-LT **(c-3)**; and EOs **(c-4)**.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Source\({}^{*}\)**} & **DFT** & \multicolumn{4}{c}{**CPT**} & \multicolumn{4}{c}{**HCPT**} & \multicolumn{4}{c}{**VCS-LT**} & \multicolumn{4}{c}{**EO**} \\ \cline{2-13}  &
## 6 Experiments
### Experiments with 4 Robots  
To demonstrate the advantages of the proposed approach when dealing with a greater number of scene agents, we conducted experiments with four robots; the results are presented in Table 6. Representative trajectories are visualized in Figure 10. Here, we can observe a larger increase in collisions for YA(n)A and TD3 methods. However, there is one additional collision also observed in the case of the YANA method in Experiment 3. Nonetheless, this figure remains notably lower compared to the collision occurrences in the other methods, particularly in a relatively intricate scenario involving robot exchanges. As the number of agents increases, making determinations concerning distance and step information becomes more challenging, since the other approaches struggle to achieve collision-free navigation in most scenarios. Despite this, we can still draw similar conclusions to those derived from the Experiments with two and three robots.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Av.** & **Std.** & \multirow{2}{*}{**Av. Steps**} & **Std. Steps** & **Av.** & **Std.** \\  & **Distance** & **Distance** & & & **Collisions** & **Collisions** \\ \hline
**Exp. 1**: & & & & & & \\ YANA & 31.593 & 1.966 & 257.6 & 45.221 & 0 & 0 \\ YA(n)A & 11.731 & 1.889 & 88.05 & 33.174 & 1.889 & 0.489 \\ TD3 & 23.863 & 2.37 & 145.5 & 4.334 & 0.45 & 0.686 \\ \hline
**Exp. 2**: & & & & & & \\ YANA & 17.333 & 0.237 & 124.3 & 9.154 & 0 & 0 \\ YA(n)A & 16.853 & 0.213 & 100.3 & 2.364 & 0 & 0 \\ TD3 & 8.909 & 1.686 & 57.1 & 16.045 & 3.5 & 0.888 \\ \hline
**Exp. 3**: & & & & & & \\ YANA & 20.204 & 2.677 & 196.6 & 42.668 & 0.05 & 0.223 \\ YA(n)A & 12.475 & 3.508 & 138.85 & 57.579 & 2.2 & 1.472 \\ TD3 & 12.431 & 1.744 & 127.9 & 11.693 & 1.95 & 0.759 \\ \hline
**Exp. 4**: & & & & & & \\ YANA & 22.678 & 0.869 & 270.7 & 15.39 & 0 & 0 \\ YA(n)A & 14.604 & 2.289 & 176.4 & 32.934 & 1.75 & 0.638 \\ TD3 & 13.11 & 0.587 & 203.35 & 6.318 & 1.95 & 0.223 \\ \hline
**Exp. 5**: & & & & & & \\ YANA & 13.981 & 0.978 & 186.65 & 46.646 & 0.05 & 0.223 \\ YA(n)A & 9.559 & 1.745 & 162.5 & 114.672 & 2.7 & 0.923 \\ TD3 & 12.385 & 1.323 & 103.95 & 5.735 & 0.55 & 0.998 \\ \hline
**Exp. 6**: & & & & & & \\ YANA & 8.303 & 0.227 & 48.75 & 2.51 & 0 & 0 \\ YA(n)A & 7.173 & 0.129 & 44.45 & 2.35 & 0 & 0 \\ TD3 & 7.053 & 0.099 & 39.9 & 0.967 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experiments with 4 robots.  
Figure 10: Experimental results with 4 robots in 6 scenarios. (**–f**) Representative recorded trajectorieswith YANA policy. (**g**-**l**) Representative recorded trajectories with YA(n)A policy. (**m**-**s**) Representative recorded trajectories with TD3 policy. Robots are represented with a large circle. Their trajectories and goal positions are represented with a line and a smaller circle in their respective colors. Robots not at their goal position, represent the point of collision.
## 2 Experimental Section
### HPLC-ESI-qTOFMS Analysis  
The HPLC system including a diode array detector (DAD) was coupled to an Agilent (Santa Clara, CA, USA) 6530 quadrupole--time-of-flight mass spectrometer (q-TOFMS), which was equipped with a dual spray ESI source. The q-TOFMS was used with the following operation parameters: capillary voltage, \(\pm\)4000 V; nebulizer pressure, 40 psig; drying gas flow rate, 13 L/min; gas temperature, 350 \({}^{\circ}\)C. During these experiments, fragmentor voltage was triggered automatically between 160 V and 210 V in positive mode and 140 and 240 V in negative mode. The lower value represents mild conditions in order to minimize in-source fragmentation, while the higher one is to foster in-source fragmentation. Full-scan mass spectra in the range of _m/z_ 50-1100 were recorded at 1.5 spectra/s scanning speed at all times during the chromatographic run. The instrument performed the internal mass calibration automatically, using an automated calibrant delivery system, which introduces the flow from the outlet of the chromatograph together with a low-flow (approximately 10 \(\upmu\)L/min) of a calibrating solution. The solution contains the internal reference masses of HP-921 (hexakis-(1H,1H,3H-tetrafluoro-pentoxy)-phosphazene) and purine. Protonated molecules of purine ([C\({}_{5}\)H\({}_{5}\)N\({}_{4}\)]\({}^{+}\) at _m/z_ 121.0509) and HP-0921 ([C\({}_{18}\)H\({}_{19}\)O\({}_{6}\)N\({}_{3}\)P\({}_{3}\)F\({}_{24}\)]\({}^{+}\) at _m/z_ 922.0098) were used as reference masses in positive ion mode, while deprotonated purine at _m/z_ 119.0363 and the formic acid adduct of HP-0921 ([C\({}_{19}\)H\({}_{19}\)O\({}_{8}\)N\({}_{3}\)P\({}_{3}\)F\({}_{24}\)]\({}^{-}\) at _m/z_ 966.000725) were used for the same purpose in negative ion mode. The DAD was acquiring data in the range of 200-800 nm in 2 nm steps at 0.5 spectra/s acquisition speed. Quantification of identified compounds was carried out based on high-resolution MS data using reference standards and the standard addition calibration technique. Caftaric acid was quantified in negative ion mode using the [M - H]\({}^{-}\) ion, whereas all remaining compounds were quantified in positive ion mode based on their [M + H]\({}^{+}\) ions. For this purpose, the MS was used in TOF only mode and was set into the extended dynamic range instrument state. Daidzein, which was added to all standards at equal quantities as a surrogate standard, was used for quality control purposes (e.g., checking chromatographic retention time reproducibility and sensitivity of the MS instrument in each run).
# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 3 Workflow of DevOps and MLOps
### ML and ML Pipeline  
An ML system is also a software system, but the development of an individual ML model is essentially experimental. A typical ML workflow starts by gathering more insights about the data and the problem at hand. The data need to be analyzed (Exploratory data analysis (EDA)), cleaned, and preprocessed (feature engineering and selection) such that essential features are extracted from the raw data (with the support of Data Stewards). This is followed by dataset formation for training, testing, and validation.  
Training, validating, and testing ML algorithms is not a straightforward method. The iterative process involves fitting the hyperparameters of an ML algorithm on the training data set while using the validation set to check the performance on the data that the model has not seen before at every iteration. Finally, the test dataset is used to assess the final, unbiased performance of the model. The validation dataset is unique concerning the test dataset because it is not kept hidden from the preparation of the model, however it is instead used to give an adequate performance of the ability of the last tuned model.  
At the very beginning only, these three sets of data are separated and should not be mixed at any point in time. Once the dataset is ready for training, the next step is algorithm selection and, finally, training. This step is iterative, where multiple algorithms are tried to obtain a trained model. For each algorithm, one has to optimize its hyperparameters on the training set and validate the model on the validation set, which is a time-consuming task (and implemented by a data scientist). Multiple iterations are required for acquiring the best model, and keeping track of all iterations becomes a hectic process (often, this is manually done in excel sheets). To regenerate the best results, one must use the precise configuration (hyperparameters, model architecture, dataset, etc.). Next, this trained model is tested using specific predefined criteria on the test set, and if the performance is acceptable, the model is ready for deployment. Once the model is ready, it is handed over to the operations team, which handles the deployment and monitoring process (usually implemented by an Operation engineer) such that model inference can be done.  
In the above process, all steps are incremental (similar to the waterfall software development style). These steps together form an ML pipeline as shown in Figure 1c. AnML pipeline is a script-based, manual process formed of a combination of tasks such as data analysis, data preparation, model training, model testing, and deployment. There is no automatic feedback loop and transitioning from one process to another is also manually done [29].
# Ensemble Learning for Skeleton-Based Body Mass Index Classification
## 1 Introduction
However, to the best of our knowledge, there has only been one study [28] to date in which a method for classifying BMIs using 3D human motion sequences has been investigated. According to the results presented in [28], classification accuracy was improved up to only 40%. Motivated by these results, in this study, we investigate a method that facilitates the improvement of the classification accuracy of BMI. A block diagram of the proposed BMI classification method is presented in Figure 1.  
The main contributions of this study can be summarized as follows. * We determine why the BMI classification accuracy in [28] was poor. Because the dataset considered had a class imbalance problem, the authors of [28] used the undersampling technique to balance classes. During our investigation, it was determined that the reason for performance degradation stemmed from the use of this technique. Therefore, we found that it is better to use an oversampling technique rather than an undersampling technique in terms of performance. * We demonstrate the usefulness of anthropometric features in the BMI classification problem. During this study, we observe that feature values calculated for each frame are not consistent over time, as a result of the inaccuracy in pose estimation. Furthermore, for some motion sequences, a large variance in feature values was observed over specific periods. Therefore, instead of using traditional anthropometric features, we employ anthropometric features measured over several different time periods (i.e., long/mid/short-term periods). The use of these features contributes to our ensemble model being able to obtain robust and accurate classification results. * We propose an ensemble model capable of classifying BMIs from 3D human motion sequences. In the proposed model, multiple \(k\)-nearest neighbor (\(k\)-NN) classifiers are trained separately using anthropometric features calculated from different time windows. Generalized results are then obtained by integrating the classification results obtained from the \(k\)-nearest neighbor (\(k\)-NN) classifiers. The evaluation results for the dataset considered in [28] demonstrate that the proposed model achieves state-of-the-art performance. The remainder of this paper is organized as follows. Section 2 presents a review of related work. The motivation for our study is discussed in Section 3. Our methodology for classifying BMIs from 3D human motion sequences is described in Section 4. The experimental setup is described in Section 5. The results and discussion are presented in Sections 6 and 7, respectively. Conclusions are presented in Section 8.
# Dynamic Compensation of a Fluxgate Magnetometer Based on a Hybrid Optimizing Algorithm
## 2 Materials and Methods
### Construction of the Dynamic Compensator for the Fluxgate Magnetometer
#### 2.1.2 Principle and Modeling of Dynamic Compensation
The purpose of the dynamic compensator is to continuously optimize \(u(k)\) through an optimization algorithm and gradually approximate the dynamic test signal \(x(k)\) after discrete sampling. Finally, the output data after compensation is infinitely close to the input dynamic test signal of the magnetometer to realize the non-distortion reproduction of the input signal of the magnetometer to the maximum extent. This requires the minimum difference between the output \(u(k)\) of the dynamic compensator and the input signal \(x(k)\) of the magnetometer, that is, the minimum value of the objective function shown in Equation (5). \[f=\frac{\sum\limits_{i=1}^{N}\sqrt{\left(u(k)-x(k)\right)^{2}}}{N} \tag{5}\]  
where \(N\) is the number of signals in the dynamic test signal sequence. Thus, the design of the dynamic compensator of the magnetometer is transformed into an optimization problem of the objective function. Parameter A in Equation (4) is solved by an optimization algorithm to minimize the objective function shown in Equation (5), that is, to complete the design of the dynamic compensator of the magnetometer.
# Robust Estimation of Vehicle Motion States Utilizing an Extended Set-Membership Filter
###### Abstract  
Reliable vehicle motion states are critical for the precise control performed by vehicle active safety systems. This paper investigates a robust estimation strategy for vehicle motion states by feat of the application of the extended set-membership filter (ESMF). In this strategy, a system noise source is only limited as unknown but bounded, rather than the Gaussian white noise claimed in the stochastic filtering algorithms, such as the unscented Kalman filter (UKF). Moreover, as one part of this strategy, a calculation scheme with simple structure is proposed to acquire the longitudinal and lateral tire forces with acceptable accuracy. Numerical tests are carried out to verify the performance of the proposed strategy. The results indicate that as compared with the UKF-based one, it not only has higher accuracy, but also can provide a 100% hard boundary which contains the real values of the vehicle states, including the vehicle's longitudinal velocity, lateral velocity, and sideslip angle. Therefore, the ESMF-based strategy can proffer a more guaranteed estimation with robustness for practical vehicle active safety control.  
Keywords:robust estimation; dynamic model; unknown but bounded noise; extended set-membership filter +
Footnote †: journal: Applied Sciences