esa-sceva/satcom-chunk-collection huggingface dataset:
# Electrochemical Ion Pumping Device for Blue Energy Recovery: Mixing Entropy Battery
## 5 Intercalation Materials as Cathode Electrode in MEB
### Na\({}_{2}\)Mn\({}_{5}\)O\({}_{10}\)  
Manganese dioxide-based materials are used as supercapacitor and are studied for their good electrochemical behavior, high specific capacitance, environmental-friendliness and low cost. Among the large quantities of manganese oxides reported in literature are Li-Mn-O, Na-Mn-O or Mg-Mn-O systems, which received extensive attention due to their tunnel or layered crystal structures, facilitating the cation intercalation/deintercalation [49] as shown in Figure 6. A \(Mn^{4+}\)/\(Mn^{3+}\) redox system involving a single-electron transfer is responsible for the \(MnO_{2}\) pseudocapacitive behavior. The energy storage mechanism is generally related to the accumulation of ionic charges in the double layer at the electrode/electrolyte interface. In aqueous electrolytes the charge/discharge is described by the following equation [5; 50]:  
\[MnO_{2}+M^{+}+e^{-}\leftrightarrow MnOOM \tag{46}\]  
\[5 MnO_{2}+2 Na^{+}+2e^{-}\leftrightarrow Na_{2}Mn_{5}O_{10} \tag{47}\]  
where \(M^{+}\) represents metal cations as \(Na^{+}\), \(Li^{+}\) and \(K^{+}\). The reaction mechanism implies an adsorption/desorption process of cations at the material surface and/or an insertion/extraction process of cations into the bulk material. The charge storage consists of a complete reduction of \(Mn^{4+}\)/\(Mn^{3+}\) energy extracted from \(MnO_{2}\), depending strongly on the quality of the material collector interface of the supercapacitor [50].  
Figure 5: Crystal structure of olivine \(LiFePO_{4}\) in projection [001] (ICSD no. 56291).  
Figure 6: Schematic illustration of the crystal structure of \(Na_{2}Mn_{5}O_{10}\). This structure consists of purple octahedral units \(MnO_{6}\) shared by corners and/or edges, where \(Na^{+}\) occupies positions within channels formed by octahedrals units (JCPDS 27-0749).
# Bearing Fault Diagnosis Based on Vibration Envelope Spectral Characteristics
## 3 Method
### Feature Extraction  
Feature selection is an essential task in any classification problem because the performance of a classification model depends on the appropriate selection of features. Impact vibrations caused by faults typically form characteristic peaks on the envelope spectrum, and the intensity and corresponding frequencies of these peaks reflect the fault type and severity. The intensity of a feature peak refers to the peak value of the envelope spectrum, while the frequency of a feature peak corresponds to the frequency at which the peak value occurs in the envelope spectrum. Both the intensity and frequency of feature peaks can be obtained simultaneously.  
Compared with widely used feature indicators in mechanical fault diagnosis, including mean value (mea), variance (var), kurtosis (kur), skewness (ske), and root mean square (rms). Using the CWRU and MFPT datasets of bearing faults, we evaluate fault diagnosis with different indicators as fault features using the Naive Bayes classifier.  
Figure 4 presents the accuracy of fault diagnosis using different indicators as fault features across two datasets. In the CWRU dataset, the accuracy of the var, kur, rms, and fai feature indicators can all reach 90%, with fai achieving the highest accuracy at 99.6%. In the MFPT dataset, only the kur and fai feature indicators achieve 90% accuracy, with both reaching a high accuracy of 100.0%. Considering the diagnostic accuracy across both datasets, fai emerges as the most effective fault feature indicator.  
In this study, traversing the global envelope spectrum plot to identify multiple characteristic peaks would significantly increase the computational workload. Through calculation, the characteristic frequencies of faults in the ball, outer race, and inner race were determined to be 70.58, 107.36, and 162.19 Hz, respectively. Therefore, this study chose the envelope spectrum plot up to 200 Hz as the target for searching for fault characteristics. Four peak values and their corresponding frequencies extracted from the envelope spectrum are considered fault features, as shown in Figure 5a-d, arranged in ascending order of frequency. This feature extraction approach has a much lower algorithmic complexity than standard methods such as wavelet packet transform, principal component analysis, singular value decomposition, etc.  
Figure 4: Classification accuracy of different feature indicators.
The Utilization of Vegetable Powders for Bread Enrichment--The Effect on the Content of Selected Minerals, Total Phenolic and Flavonoid Content, and the Coverage of Daily Requirements in the Human Diet  
[PERSON]  
1 Department of Commodity Science and Food Analysis, Faculty of Food Sciences, University of Warmia and Mazuruy in Oslstryn, Plac Cieszynski 1, 10-718 Olszyn, Poland; [PERSON]SON]  
2 Department of Nutrition and Dietetics, Faculty of Health Sciences, Mersin University, 33000 Mersin, Turkey; [EMAIL_ADDRESS]  
[PERSON]  
1 Department of Commodity Science and Food Analysis, Faculty of Food Sciences, University of Warmia and Mazuruy in Oslstryn, Plac Cieszynski 1, 10-718 Olszyn, Poland; [PERSON]## Abstract  
The aim of this study was to evaluate the content of selected minerals and total phenolic and flavonoid content of wheat bread and bread enriched with varying amounts of carrot powder (CP) and pumpkin powder (PP). In addition, the coverage of daily requirements of selected minerals was evaluated after the consumption of 100 g of each type of bread. The research included seven types of bread: wheat bread (WB) and bread enriched with 10%, 20%, and 30% CP and PP. The vegetable powders were obtained by freeze-drying. The concentrations of minerals--sodium (Na), potassium (K), calcium (Ca), magnesium (Mg), iron (Fe), zinc (Zn), copper (Cu), and manganese (Mn)--were determined by atomic absorption spectroscopy (AAS) method. Coverage of daily requirements after consumption of a serving of bread was determined based on Recommended Daily Allowance (RDA) and Adequate Intake (AI) ratios for minerals, according to the nutritional standards for the Polish population. The addition of 10% PP led to a greater increase in Na, K, Mg, and Cu in the bread compared to CP, while for Ca, Fe, Zn, and Mn a greater increase was observed with the addition of 10% CP than PP. Among macronutrients, the greatest changes in content were recorded for Ca--the addition of 10 and 20% CP and PP resulted in increases of 66, 113, 51 and 59%. Among micronutrients, the addition of CP and PP to wheat bread caused the largest changes in Cu (46-150% increase) and Mn (25-99% increase) content. Additionally, there was a tenfold increase in total phenolic content (TFC) when 30% CP was added to the bread. Consuming 100 g of bread with CP and PP provided the greatest coverage of K (41-60%), Cu (8-17.5%), and Mn (6-17%). These findings suggest that fortification of bread with vegetable powders has the potential to be a useful method of enriching the mineral composition and increasing the proportion of selected minerals in the human diet.  
food fortification; bread; carrot powder; pumpkin powder; minerals; mineral requirements Article
## 3 Results and Discussion
### Flavor Compounds in Coffee Beans Grown at Various Altitudes  
#### 3.3.1 Volatile Compounds as Analyzed by GC-MS  
The volatile aroma of coffee is one of the most complex sensory traits. It is formed by many volatile compounds with different aroma qualities, strengths, and concentrations [35]. A total of 112 volatile compounds were detected in the roasted coffee at different growing elevations (Supplementary Materials Table S1). Their contents were 33.00 mg/g, 29.47 mg/g, 29.57 mg/g, 26.49 mg/g, and 26.47 mg/g, respectively. The volatile composition of the coffee gradually decreased with increasing growing altitudes.  
The variables with a VIP (variable importance in the projection) greater than 1 were considered important factors contributing to group discrimination, and a higher VIP score indicated better discrimination [36]. VIP was used to screen for components whose abundance significantly changed with increasing altitudes, and the results are shown in Table 2. Eleven compounds were significantly affected by the growing altitude (VIP > 1), nine of which tended to decrease with increasing elevation and two of which tended to increase. The contents of these 11 compounds accounted for 58.54%, 54.41%, 53.92%, 48.71%, and 47.46% of the total content of the volatile components, respectively.  
Pyrazine was the compound most affected by the growing altitude, with seven of the nine compounds showing a decreasing trend. Pyrazine was the main volatile component in Pu'er coffee, and a total of 24 species were detected, which provided a nutty and roasted aroma for Pu'er coffee, increasing the complexity and depth of the aroma. The other two compounds that significantly decreased in content with increasing growth altitudes were 2-furanmethanol and pyridine. 2-furanmethanol was the largest alcohol compound in Pu'er coffee and is a milder compound that was once considered the main source of the sweet caramel-flavored aroma [37]. Pyridine is a pungent and diffuse compound. The reduction in pyridine in high-altitude coffee had a positive effect on the coffee flavor.  
The two components that increased with increasing growth altitudes were all aldehydes, namely, furfural and 5-methyl-2-furancarboxaldehyde. Furfural has been described as pungent but sweet, bread-like, caramellic, and cinnamon-almond-like. Many reportsindicated that these compounds had a relatively high content profile, and both had a positive influence on the flavor of coffee [38]. In addition to the 11 components with significant elevation effects, there were many nonsignificant components. For example, ketones, furans, phenols, and other components contributed sweet, caramel, and burnt aromas, as well as fruit aromas, to Pu'er coffee. These flavors also constituted the main flavors of Pu' er coffee. The significantly different flavor components decreased the nutty and roasted flavors of the Pu'er coffee while increasing the sweet and caramel aromas [39].
## 3 Results and Discussion  
### Simulation Analysis  
#### 3.1.1 Response Speed Simulation Analysis  
In this study, Matlab was utilized to simulate the position servo system, aiming to validate the effectiveness of both the recursive least squares (RLS) observer and the variable-parameter sliding mode control (VSMC). To showcase the efficiency of the designed controllers, a step position of 15 radians was initiated, with the motor operating under no-load conditions. Figure 7 illustrates the position tracking curves for the three control methods, along with an enlarged view spanning from 0.05 to 0.10 s. Upon closer examination, it is evident that the time required for the Proportional-Integral (PI) control to achieve the desired position was approximately 0.14 s, while for SMC, it was 0.12 s, and for VSMC, it was 0.07 s.  
Figure 6: The relationship surface between \(c\), \(T_{\rm L}\), and \(\theta_{\rm ini}\).  
While the VSMC strategy demonstrated a superior response speed and shorter settling time compared to the other control methods, it is important to note that the simulation was conducted under no-load conditions. In real applications, external disturbances and varying load conditions can affect the performance of the VSMC strategy. This limitation should be considered, as the control system might need further tuning or compensation mechanisms when applied in real-world scenarios. The impact of load variations on the response speed and accuracy of VSMC in practical applications is an area for future research.  
Furthermore, the stability and robustness of the VSMC strategy were examined in this study. The VSMC controller, due to its sliding mode nature, offers robustness against parameter variations and external disturbances. This is because the sliding mode control inherently drives the system to the sliding surface, where the system's behavior is less sensitive to changes in the system's parameters. However, while the simulation results demonstrated a good robustness under ideal conditions, the real-world implementation of VSMC may require further validation, particularly under conditions with more significant parameter uncertainties and external disturbances.
# Improved Ensemble-Learning Algorithm for Predictive Maintenance in the Manufacturing Process
## 4 Methodology
### Data Preprocessing  
The original dataset needs to be preprocessed for various objectives such as for handling the missing values. The data obtained from the manufacturing industry and blister packing machines need to be processed before importing into the machine-learning model. In the manufacturing field, obtaining the anomaly data is challenging because the probability of the occurrence of an anomaly situation is lesser than that of a normal situation. Therefore, the manufacturing data may face the data imbalance problem. Data imbalance refers to a sample size of data in which one class outnumbers the others by a significant proportion. However, most of the real datasets in the manufacturing field suffer from an imbalanced ratio. The data imbalance problem may cause failure of classification accuracy. In both cases, the data should be preprocessed for data balance. This study used the synthetic minority oversampling technique (SMOTE) to balance the proportion of each class datum. SMOTE, proposed by [PERSON] et al. [40], is an oversampling method based on the k-nearest neighbor (KNN) algorithm. KNN is judged based on the Euclidean distance between data points in a solution space. The procedure of SMOTE is summarized as follows.  
* Step 1: exploring the minority class input data point.
* Step 2: finding the KNNs of the explored input data point.
* Step 3: select one of these neighbors' point, and place a new point on the path connecting the point under consideration and its chosen neighbor.
* Step 4: Repeat Steps 1 and 2 until the termination condition is met (i.e., until the data are balanced).  
After balancing the data proportion, the features were selected from the preprocessed data via data balance and mutual information (MI) estimations. MI is a score that measures the mutual dependencies among variables. It is useful in feature selection as it maximizes the MI between the target variables and joint distribution in multidimensional datasets.  
\[\text{MI}(X;Y)=\sum_{y\in Y}\sum_{x\in X}p(x,y)\log(\frac{prob(x,y)}{prob_{1}( x)prob_{2}(y)}) \tag{1}\]  
where MI (\(X\); \(Y\)) represents the mutual information between variables \(X\) and \(Y\). The variables \(prob(x)\) and \(prob(y)\) refer to the probability distribution of \(x\) and \(y\), respectively, and \(prob(x,y)\) represents the joint probability distribution of \(X\) and \(Y\).
## 4 Results and Discussions
RF has demonstrated more robustness than SVM and MLP, as evidenced by its ability to handle cases like no failure, which maintains a high precision of 0.98 and recall of 1.00. However, it is important to emphasize that the fact that the data is imbalanced indeed constitutes one of the reasons for the models to fail in classifying random and tool wear failures. With respect to attribute selection, although the works of [PERSON] et al. [10], [PERSON] et al. [11], and [PERSON] et al. [19] have addressed industrial applications, none of them address the application of attribute selection techniques to prioritize the selection of the most relevant sensors for data collection. Research like this can assist in selecting industrial sensors, as some may be acquired and used without significantly contributing to detecting or preventing machine failures. This is because not all sensors are equally relevant for monitoring or predicting machine failures. In the example addressed in this research, a company could only use sensors that measure air and process temperature, rotation speed, and torque while optionally monitoring tool wear due to this variable not significantly contributing to failure prediction. Thus, the framework presented here could result in a cost reduction associated with sensor acquisition. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**RF**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**MLP**} \\ \cline{2-7}
**Failure Type** & **Precision** & **Recall** & **Precision** & **Recall** & **Precision** & **Recall** \\ \hline Heat Dissipation Failure & 0.85 & 0.87 & 0.73 & 0.35 & 1.00 & 0.57 \\ No Failure & 0.98 & 1.00 & 0.98 & 1.00 & 0.98 & 1.00 \\ Overstrain Failure & 1.00 & 0.05 & 0.00 & 0.00 & 0.00 & 0.00 \\ Power Failure & 0.86 & 0.81 & 0.96 & 0.84 & 0.96 & 0.77 \\ Random Failure & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\ Toolwear & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Models’ performance for each failure type.
## 3 Results and Discussion
### Analysis of Model Interactions  
Response surface analysis was performed on the significant interaction effects of the experimental factors to investigate the impact of interaction terms on the two experimental outcomes.  
Figure 3 illustrates the influence of the interaction levels of various factors on potato germination rate. The potato germination rate varies with changes in the factors. Among them, the interaction between treatment voltage (A) and gas flow rate (B) is the most significant, followed by the interactions between gas flow rate (B) and treatment time (C) and between voltage (A) and treatment time (C). Therefore, it can be concluded that, within a specific range, appropriately increasing treatment voltage and gas flow rate helps to reduce the potato germination rate.  
Figure 4 shows the impact of the interaction levels of various factors on potato germination rats. As shown in Figure 4a, it can be observed that the potato weight loss rate initially decreases and then increases with increasing treatment voltage and gas flow rate.  
Figure 3: The interaction of various factors on the sprouting rate of potatoes. Relationship between treatment voltage and gas flow rate on potato germination (**a**); Relationship between treatment voltage and treatment time on potato germination (**b**); Relationship between gas flow rate and treatment time on potato germination (**c**).  
As depicted in Figure 4b, the potato weight loss rate increases with increasing treatment time and initially decreases before increasing with the rise in treatment voltage. As depicted in Figure 4c, the potato weight loss rate increases with increasing treatment time and initially decreases before increasing with the gas flow rate. Among them, the interaction between treatment voltage (A) and gas flow rate (B) is the most significant, followed by the interactions between treatment voltage (A) and, treatment time (C) and gas flow rate (B). Therefore, it can be concluded that, within a certain range, appropriately increasing treatment voltage and gas flow rate contributes to a reduction in the potato weight loss rate.
## 5 Discussion of Explainable AI Approaches
### Improving AI Models through XAI  
XAI methods help explain the decisions made by AI models and can help enhance them. The reflective neural network Reflective-Net introduced in [83] uses a reflection process to improve its accuracy. In this reflection process, first a classifier makes a prediction based on an input \(I\), and it generates an explanation \(E\). Then, the input \(I\) and explanation \(E\) are given to a reflective network that refines the prediction. Training the reflective network with correct and incorrect explanations helps increase its accuracy.  
XAI techniques have been found to be highly effective in improving deep learning model performance as described in [84]. MobilNet, a ConvNet to detect metal surface defects, was used in [84] on a dataset of images containing images with super imposed text and company logos. Through the use of LRP, it was demonstrated that the performance of the model of mode was consistently negatively affected by that unwanted information. The LRP analysis showed that the model learned to identify patterns in the text and logos rather than the actual surface defects. The performance of the model was greatly improved by removing the superimposed text and logos from the images before training.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Techniques** & **Study** & **Post-hoc** & **Ante-hoc** & **Global** & **Local** \\ \hline Class Activation Mapping and its variants & [2; 5; 33; 34; 35; 36; 37; 38; 39; 40; 41; 46; 47; 50] & ✓ & - & - & ✓ \\ \hline Attention-based Explanations & [7; 8; 26; 51; 52; 53; 54; 55; 56] & - & ✓ & - & ✓ \\ \hline Local Interpretable Model-Agnostic Explanations (LIME) & [57; 58; 59; 60; 61; 62; 63] & ✓ & - & ✓ & - \\ \hline Layer-wise Relevance Propagation (LRP) & [47; 64; 65; 66; 67; 68; 69; 70] & ✓ & - & - & ✓ \\ \hline Image Captioning with Visual Explanations & [74; 75; 76] & - & ✓ & - & ✓ \\ \hline Concept Activation Vectors & [77; 78; 79; 81] & ✓ & - & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of explainable AI techniques used for medical image analysis and report generation. Checkmarks in the last 4 columns indicate whether each explainable AI technique is post hoc or ante hoc and if it generates global and local explanation.
## 2 Materials and Methods  
### Subjects and Study Design  
Celiac disease patients on a remission state (on a GFD at least 1 year), subjects with non-celiac gluten sensitivity (self-reported symptoms occurring after consuming a gluten-containing diet and adherence to a GFD for least 1 year) and healthy controls were recruited to the study. We decided not to test our NCGS subjects according to the Salerno criteria involving a separate gluten challenge trial for reasons discussed in later chapters [15]. The total number of the subjects recruited was 74, of which 49 completed the whole study period. After analyzing the food diary data, 6 subjects from NCGS group were excluded due to the consumption of gluten-containing food products. Thus, samples from celiac disease (CeD) patients (\(n=19\)), non-celiac gluten sensitive subjects (\(n=10\)) and healthy subjects (\(n=14\)) were available for the further analyses. Based on food frequency questionnaire (FFQ) and 4d food diaries, all study subjects reported consumption of oat products daily. Demographic characteristics of study groups are presented in Table 1. Study subjects were recruited to the study from Turku region, Finland during the period August 2017-April 2018. Exclusion criteria were BMI below 18 or above 30, antibiotic treatment within the previous 6 months, use of any medication with gastrointestinal effects (e.g., laxatives or proton pump inhibitors) and blood donation or participation to another clinical study within a month. Before the study entry, the volunteers were interviewed to assess the eligibility of the study. The subjects were ascertained to be in good health by means of self-reporting and normal results in screening blood tests (total blood count, fasting glucose and liver, kidney and thyroid functions, wheatspecific immunoglobulin E (IgE), total immunoglobulin A (IgA) and IgA antibodies to tissue transglutaminase (tTGAbA)). After the screening tests, the study subjects were enrolled in the study and were instructed to keep gut symptom diaries for 30 days. Study subjects consumed their habitual diet throughout the study period and were asked to fulfill food diaries during the last four days of the study. In addition, volunteers were asked to fulfill an FFQ of their dietary habits. Based on the FFQ, The Index of Diet Quality was calculated as explained in detail by [PERSON] et al. [16] to assess the adherence to a health-promoting diet. Fecal samples for the microbiota, SCFAs, \(\beta\)-glucuronidase and ammoniacal nitrogen analyses were collected on the last day of the study period. The study protocol was approved by the Ethics Committee of the Hospital District of Southwest Finland (Identifier: ETMK:42/1801/2016) and subjects were enrolled in the study after written informed consent was obtained. The study was registered at ClinicalTrials.gov (Identifier: NCT02761785).