esa-sceva/satcom-chunk-collection huggingface dataset:
# Robustness, Stability, and Fidelity of Explanations for a Deep Skin Cancer Classification Model
## 4 Discussion and Conclusions  
In this paper, we compared two currently popular XAI/IML explanation techniques applied on top of a well-performing deep CNN classification model classifying seven types of skin lesion. Both XAI/IML techniques showed a hundred percent fidelity to the original CNN model. However, integrated gradients was clearly better with regard to the other two quantitative metrics (stability and robustness). In comparison, LIME explanations were not stable (each run produced a different explanation) and less robust than the integrated gradients explanation, but the qualitative visualization seemed to use more features and were somewhat more intuitive. Moreover, in contrast to the integrated gradients, which  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Integrated Gradients**} \\ \hline  & **akice** & **bcc** & **bkl** & **df** & **nv** & **mel** & **vasc** \\ \hline Lipschitz Robustness & 0.0012 & 0.001 & 0.0014 & 0.0008 & 0.0018 & 0.0014 & 0.0012 \\ mean (\(\pm\) std) & \(\pm\)0.0003 & \(\pm\)0.0005 & \(\pm\)0.0004 & \(\pm\)0.0005 & \(\pm\)0.0006 & \(\pm\)0.0004 & \(\pm\)0.0005 \\ \hline Stability \% & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline Local Fidelity \% & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline  & & & & & & & \\ \hline Lipschitz Robustness & 0.0004 & 0.0004 & 0.0007 & 0.0005 & 0.0009 & 0.0004 & 0.0002 \\ mean (\(\pm\) std) & \(\pm\)0.0002 & \(\pm\)0.0001 & \(\pm\)0.0002 & \(\pm\)0.0003 & \(\pm\)0.0003 & \(\pm\)0.0001 & \(\pm\)0.0001 \\ \hline Stability \% & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline Local Fidelity \% & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative quality indicators for the different explanation techniques.  
Figure 6: Lipschitz robustness estimate for LIME and integrated gradient explanations for test instances of the basal cell carcinoma (bcc) class. The explanations of the integrated gradient technique are clearly robuster than the LIME explanations.  
depend on the model internals' gradient, the LIME explainer is model-agnostic, and thus more portable and applicable also when the classification model is changed.
# Modelica-Based Energy Management of PEMFC Hybrid Power System of Vehicle
## 1 Introduction
Recently, [PERSON] et al. [17] investigated the impact of velocity prediction on the performance of MPC-based EMS for FCEVs. Four MPC prediction settings were tested: prescient MPC, frozen time MPC, exponentially decreasing MPC, and MPC with a Markov chain model. This research compared the results across different driving cycles to explore how prediction horizon and accuracy affect hydrogen consumption and battery charge sustainability. [PERSON] et al. [18] proposed an energy management strategy based on a variable-step multi-step prediction MPC aimed at reducing energy losses in a fuel cell and supercapacitor hybrid energy storage system. This strategy ensured that the battery current and the supercapacitor's SOC remained within an appropriate range. MPC-based EMS holds great promise for achieving efficient power conversion in various hybrid electric vehicles. Therefore, MPC is considered a promising control algorithm for unlocking the energy-saving potential of FCEVs and can be deployed in onboard controllers. This study aims to design a PEMFC hybrid power system for a 100 kW truck. The Modelica-based model is built to investigate the multi-energy flow coupling characteristics, including thermal, electrical, and hydrogen energy, of the hybrid system. Then, a hybrid method combining the proposed state machine and MPC is also used for the EMS of the 100 kW truck. The main contributions of this study are listed as follows:1. A hybrid power system of 100 kW rated power is designed. The mathematical models of the PEMFC system, lithium-ion battery system, and other subsystems will be presented in detail. Subsequently, one-dimensional modeling of the fuel cell and zero-dimensional modeling of the lithium-ion battery will be performed using Modelica through the Dymola 2023X platform. 2. The vehicle's power demand is divided into different stages, and a hybrid method combining the proposed state machine and MPC (SM-MPC) is proposed for the vehicle's EMS. 3. The performance of the proposed SM-MPC EMS is validated using test data from two driving cycles, and it is compared with SM and MPC methods. The rest of this study is organized as follows: Section 2 introduces the modeling process of the PEMFC stack and other components. In Section 3, the proposed energy management strategy will be described briefly. The simulation and evaluation analysis are discussed in Section 4. Finally, Section 5 gives the conclusion and prospectives of this study.
# Secure On-Off Transmission in UAV Relay-Assisted mmWave Networks
## 4 Simulation Results
Figure 6 illustrates the effect of the eavesdropper density \(\lambda_{E}\) on the SOP with different beam alignment error \(\sigma_{k}\) and in both the on-off and non-on-off transmission schemes. Obviously, with the increasing of \(\lambda_{E}\), no matter \(\sigma_{k}\) equals to \(0^{\circ},8^{\circ}\) or \(10^{\circ}\), the SOP increases in both two transmission schemes. Besides, the SOP becomes large as \(\sigma_{k}\) deteriorates. The reason is that the channel capacities of legitimate links decrease due to the lower channel gain when \(\sigma_{k}\) increases. Nevertheless, the SOP in  
on-off transmission scheme outperforms the SOP in non-on-off transmission scheme. It demonstrates that the secrecy performance in the considered networks can be improved by using the on-off transmission scheme. Figure 7 shows the SOP versus the beam alignment error \(\sigma_{k}\) with different \(N_{R_{x}}\) in both the on-off and non-on-off transmission schemes. It can be observed that the SOP increases as \(\sigma_{k}\) becomes large. However, we can find that the increase of SOP is not remarkable when \(\sigma_{k}\) is relatively small. This is because the target nodes still can fall in the main lobe of the transmit antenna when \(\sigma_{k}\) is small. Moreover, it is noteworthy that the SOP can be improved by adopting the on-off transmission scheme, but the improvement is slighter when \(N_{R_{x}}=16\). It can be explained that the channel capacity of \(R_{x}\to D\) link always exceeds \(\mu=5\) due to the high antenna gain with \(N_{R_{x}}=16\). In addition, when \(\sigma_{k}\) is not too large, the better SOP can be obtained by adopting more antenna elements due to the high antenna gain. However, in the high \(\sigma_{k}\) regime, the antenna array with lesser elements will provide better SOP due to its larger beamwidth of the main lobe.
## 5 Application of eNO\({}_{3}\)R to Brackish Groundwater: A Case Study
### Cathode/Anode Passivation  
The presence of calcium, magnesium, and carbonates within brackish groundwater constitutes a significant impediment to the development of a large-scale process. In high pH environments, cations like Ca\({}^{2+}\) and Mg\({}^{2+}\) can contribute to the formation of a passivating surface layer on the cathode duo to electrophoretic migration phenomena. Furthermore, calcium and magnesium readily react with hydroxides formed during electrolysis and carbonates to produce insoluble compounds that form a layer that deposits on the cathode surface, thereby reducing the efficiency of nitrate reduction to nitrogen and ammonium [212]. Indeed, among the strategies proposed to reduce cathode passivation is using pulsed current instead of direct current [213]. So far, there have been few studies aimed at investigating the effect of pulsed current such as [PERSON] et al. [101] who demonstrated that the use of pulsed electrolysis (PE) effectively inhibited the HER across all PE experiments compared to constant potential electrolysis (CE), accompanied by lower energy consumption. Additionally, Cu single-atom-modified gels (Cu SAGs) used in the study also exhibited good stability after ten successive catalytic cycles, with a slight performance decrease noted after the seventh cycle. Therefore, it is crucial to focus on this topic in future research concerning this issue, while also emphasizing the stability of the cathode [101]. Now the question arises: Is this strategy advantageous for the anode? The clearest aspect is that pulsed currents can momentarily disrupt the accumulation of ions at the electrode surface, which contributes to delaying the decay of the anode potential. However, the question of whether the application of dual-pulsed or multi-pulsed electro-oxidation is more beneficial has already been addressed in the research by [PERSON] et al. [214] in which they showed that Multi-Pulse Electro-Oxidation has the highest total nitrogen removal compared to Direct Current Electro-Oxidation and Dual-Pulse Electro-Oxidation. This mode of MP-EO perfectly improves the mass transfer and specifically during the T\({}_{\text{off}}\) period between pulses, the oxidants and intermediates produced can efficiently transfer into other areas, thereby improving the overall efficiency of the process. However, the dual-pulsed approach, which was expected to yield favorable results, showed the opposite in this study. This outcome is attributed to the type of anode used, specifically stainless steel. It is evident that this material is not designed for the specific chlorine evolution reactions required [214]. Other specific types of materials, such as Ti/Ru-Ir, have not yet been tested in this context and for the same specific conditions to determine whether the dual-pulsed effect on ammonium removal efficiency is truly superior to that of direct current.
# Breast Cancer Image Classification Using Phase Features and Deep Ensemble Models
## 4 Experimental Results
### Experimental Details and Evaluation  
The proposed method was implemented in Python 3.9.12 using OpenCV 4.8.0, TensorFlow 2.13.0, scikit-learn 1.1.2, and the Phasepack library ( Phasepack library available at: [[https://bit.ly/47](https://bit.ly/47) wDCou]([https://bit.ly/47](https://bit.ly/47) wDCou), accessed on 4 Octuber 2024. The implementation was carried out on an HP personal computer with an Intel Core i5 processor, 12 GB of RAM, and a Windows 10 operating system. Additionally, the Google Colab Pro service with an NVIDIA A100 GPU was used. For feature extraction, the proposed methodology described in Section 3.5 was applied, considering a training/testing split of 70/30. All experiments employed the ensemble classifiers voting, stacking, bagging, and boosting. It is noteworthy that these ensemble classifiers perform effectively for unbalanced datasets and reduction of bias and overfitting effects. For evaluation, four widely used performance metrics are considered: accuracy (_Acc_), precision (_Pre_), recall (_Rec_), and F1-score (_F_1). These metrics are computed from true positives (_TP_s), true negatives (_TN_s), false positives (_FP_s), and false negatives (_FN_s).  
Accuracy (_Acc_) measures the ratio of correct predictions to the total number of predictions:  
\[Acc=\frac{TP+TN}{TP+TN+FP+FN}. \tag{16}\]  
Precision (_Pre_) quantifies the ratio of true positives to all positive predictions:  
\[Pre=\frac{TP}{TP+FP}. \tag{17}\]  
Recall (_Rec_), or sensitivity, evaluates the model's ability to correctly identify positive cases:  
\[Rec=\frac{TP}{TP+FN}. \tag{18}\]  
Finally, the F1-score (_F_1) is the harmonic mean of precision and recall:  
\[F1=2\times\frac{Pre\times Rec}{Pre+Rec}. \tag{19}\]
# 3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors
## 4 Experimental Setup
### Considered Methods in the Experimental Configuration  
We considered as classifiers the Support Vector Machine (SVM), the k-Nearest Neighbours (kNN) and the Random Forest (RF) techniques. The parameters used are detailed in the Appendix A.  
The considered texture feature extraction techniques are: the 3D GLCM reviewed in Section 2.4, the IGLCM_3D described in Section 3.2, the LBP_2D operator [8], the 3D version of the BM3 DELBP operator described in Section 3.1, a typical Convolutional Neural Network (CNN) and the proposed method.  
LBP_2D is the uniform rotation invariant Local Binary Pattern operator defined in (1). This descriptor was used for 3D images by computing the LBP codes for each slice of the XY plane. The final feature vector represents the concatenation of the histograms corresponding to each plane. The same approach was used in [8] to compute the LBP_2D for the same database.  
We also considered a deep-learning technique to compare our proposed method to. We built a typical CNN, which is used as an end-to-end method, acting both as feature descriptor and classifier. The network architecture was constructed by trial and error in order to obtain the best classification performance in a time efficient manner, being detailed in Appendix A. The complexity of the network can be increased by adding more convolutional layers and filters, but this would come with a major disadvantage: increased processing times.
# Research on Short-Term Wind Power Forecasting by Data Mining on Historical Wind Resource
## 5 Numerical Case of Interval Prediction  
At present, we learn that most of WPF methods focus on deterministic prediction. There is a drawback of deterministic prediction, in which the error between the predicted and the measured still exists, so that it is important to study the interval prediction of wind power by which the instability of wind power caused by wind uncertainty can be better described.  
In the study of the interval prediction method, reference [16] explored a forecasting method combined empirical mode decomposition and extreme machine learning. Additionally, the forecasting results show that the interval prediction effect is good. Reference [17] proposed an interval prediction model based on the wavelet neural network, optimized by a multi-objective intelligent algorithm. Compared with traditional multi-objective methods, this method has better performance. However, most of the existing forecasting models only output point or interval forecasting separately; it does not provide more detailed information for related staff, which is helpless to the development demand of the energy, Internet and power market [18]. Researchers begin to apply fuzzy information granulation to power prediction, as the maximum and minimum values of the interval prediction can be obtained by such methods. The deterministic and interval prediction results of complex nonlinear time series can be obtained at the same time, which provides a technical support for dispatching [19].  
Major existing wind power interval prediction methods have disadvantages such as complex calculation, slow convergence speed and low efficiency. For example, the choice of the Hessian matrix and derivative operations will increase the computational complexity of the model. The Elman neural network belongs to the feedforward neural network, which can approximate any nonlinear function under the given kernel function condition. The estimation of the upper and lower limits of the neural network interval is based on the parameters of the optimized neural network, and does not need prior knowledge and hypothesis estimation, i.e., the lower-upper bound estimation method (LUBE). This paper establishes the Elman neural network wind power interval prediction model based on continuous time similarity clustering.
## 2 Materials and Methods
### Instruments  
#### 2.2.1 UBUMonitor Tool  
UBUMonitor [35] is a desktop application executed on the client, implemented with Java, and with a graphic interface developed in JavaFX. The application connects to the selected Moodle server, through web services and the REST API provided by the server. In the absence of web services to some specific data recovery, web scraping techniques are additionally used. All communication between the Moodle server and the UBUMonitor client is encrypted by HTTPS protocol for security reasons. As a result of these queries, the data are obtained in JSON and CSV format and are processed and transformed in the client into Java objects. For the visualisation of the collected data, the hybrid solution of applying Java and embedding web pages with different graphic JavaScript libraries, within the desktop application, is used. The data can be saved on the client to optimise access times in subsequent queries and offline access to the data, using the serialisation mechanism available in Java. The serialised files with the subject data are stored encrypted with the Blowfish algorithm [41]. A diagram of the operation of the tool can be found in Figure 2. This application is open source and free of charge and includes four modules: (1) visualisation module (allows an analysis of the access frequencies in components, events, sections, or course seen in Moodle) with options to analyse logs in different graphics (boxplot, etc.). All the visualisation options allow the export in graphic format and in.csv format, for the elaboration of reports and their subsequent analysis with other tools; (2) the comparison module analyses the student logs in the components, events, sections or course seen in Moodle, grades and completion of activities, giving information about the frequencies from a visual comparison in ranking and in evolution analysis of the selected  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Students Gender**} \\ \cline{2-9}
**(Students)** & \multicolumn{4}{c}{**Women**} & \multicolumn{4}{c}{**Men**} \\ \cline{2-9}  & \(n\) & \% & **Mage** & **SDage** & \(n\) & **\%** & **Mage** & **SDage** \\ \hline
49 & 41 & 83.67 & 22.37 & 2.19 & 8 & 16.32 & 21.63 & 1.77 \\ \hline \hline \end{tabular} Note. Mage, mean age; SDage, standard deviation age; \(n\), number of participants in each group; \(n\) (students), total number of participants.  
\end{table}
Table 1: Characteristics of the sample.  
students); (3) risk of dropping out module (gives information by intervals (0-3 days, 3-7 days, 7-14 and more than 7 days) about students' access to the subject and about access to the Moodle platform; (4) clustering module allows finding the clusters from different algorithms (\(k\)-means ++, fuzzy \(k\)-means, DBSCAN, MutiMeans++, etc.) and from different distances (Euclidean, Manhattan, etc.) that are processed from two Java libraries [42, 43].
# Rosehip Seed Oil-Incorporated Chitosan Films for Potential Fruit Packaging Applications
###### Abstract  
The concept of food packaging plays a crucial role in ensuring consumer satisfaction and extending the shelf life of food products. The rising trend of introducing innovative materials for food packaging has become prominent in recent years. The present study aims to investigate the impact of rosehip seed oil (RSO) on the physical, physicochemical, antioxidant, and antimicrobial properties of edible films based on chitosan for potential fruit packaging applications. Scanning electron microscopy revealed a uniform distribution of the incorporated emulsion throughout the edible film. The addition of RSO increased the deformation at break in both tensile and puncture test, thereby improving the elastic properties of the films. The resulting films exhibited a light-yellow color with high opacity. The immobilization of RSO led to a decrease in water content by almost two times and an increase in water vapor permeability of the films. The films showed enhanced antioxidant activity and retained good protective properties against the yeast _S. cerevisiae_. Consequently, these newly formulated multicomponent films are found to be suitable for applications in the development of active food packaging because of their physical, antioxidant, and antimicrobial properties.  
chitosan; edible film; hydrophilic hydrophobic balance; rosehip seed oil +
Footnote †: journal: Applied sciences
###### Abstract  
The rolling bearings in moment wheel assemblies (MWAs) or control moment gyros (CMGs) are not only the core components in spacecrafts but also prone to failure. Therefore, a high reliability is the critical characteristic for spacecraft bearings, and long-life testing on the ground is one of the main means for bearing reliability assessment. In practical applications, a convenient and reliable method is required for monitoring the health status of abnormal bearings in MWAs during the long-life test. In this paper, a monitoring method based on the clustering fusion of normal operation acoustic parameters is proposed for the identification of abnormal bearings. Firstly, the characteristics of MWA's acoustic signal and its feasibility as a monitoring medium are clarified based on tests and modal analysis. Then statistical parameters and sound quality parameters are introduced to characterize the changes caused by bearing faults, and the root mean square (RMS), kurtosis, and sharpness parameters are selected to construct the feature vectors. The K-medoids clustering technology is used to fuse the characteristic parameters, and the safety distance for normal bearing operation can be obtained by a suitable method. Finally, the abnormal index is presented based on the excess rate and excess distance to judge the abnormal states of several types of bearings through tests. The research results indicate that the presented monitoring method based on the clustering of the normal operation acoustic parameters can not only identify various faults of the spacecraft bearing (ball defects, outer ring defects, cage instability, etc.) effectively but also give a quantitative evaluation of the severity of the abnormality.  
spacecraft bearing; modal analysis; characteristic parameter; health monitoring; fault +
Footnote †: journal: Applied Sciences
## 3 Results  
### Membrane Characterization  
The thickness (\(\delta\)), porosity (\(\varepsilon\)) andmean pore size (\(d_{\text{mean}}\)) of the membranes used in this study is given in Table 1. These properties mainly determine the absolute flux [33]. The maximum pore size (\(d_{\text{max}}\)) and water contact angle (\(\theta_{\text{water}}\)) are mostly important for the wetting resistance, which is sufficiently high for pure water. The water contact angle for the PTFE membrane is the highest (138\({}^{\circ}\)), followed by the PES membrane (132\({}^{\circ}\)) and the PE membrane (120\({}^{\circ}\)). The hexadecane contact angle (\(\theta_{\text{hexadecane}}\)) is used to test the oleophobicity of the membranes. As expected, PTFE and PE do not show any resistance to wetting of hexadecane, whereas the oleophobic PES membrane shows a contact angle of 92\({}^{\circ}\) with hexadecane.