esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Reduced Concept Lattice  
### Basic Notion of the Concept Lattice  
A concept lattice, also regarded as a formal concept analysis, is widely used in several fields due to its excellent properties in knowledge reduction and rule extraction [32; 33; 34]. Each node of the concept lattice is a formal concept composed of two parts: extension (or object) and intension (or attribute). Extension can directly reflect the generalization and specialization relationship among these concepts through the Hasse diagram.  
A formal context \(K=(E,G,I)\) consists of two sets: \(E\) (object set) and \(G\) (attribute set), and a relationship, \(I\), between the two. In the object set \(A\in P(E)\) of a formal context, the attribute set, \(B\in P(G)\), defines the mapping, \(f\) and \(g\):  
\[\left.\begin{array}{l}f(A)=\left\{\,g\in G|\forall e\in A,(e,g)\in I\, \right\}\\ g(B)=\left\{\,e\in E|\forall g\in B,(e,g)\in I\,\right\}\end{array}\right\} \tag{1}\]  
where \(g\) and \(e\) are the elements of the attribute set, \(G\), and the object set, \(E\), respectively. A pair \((A,B)\) from the formal context that satisfies the two mappings above is a concept; we denote \(A\) the extent and \(B\) the intent of the concept \((A,B)\).  
For concept \((A_{1},B_{1})\) and \((A_{2},B_{2})\), if \(A_{1}\subseteq A_{2}\) or \(B_{2}\subseteq B_{1}\), then we denote \((A_{1},B_{1})\) the son concept or sub-concept and \((A_{2},B_{2})\) the parent concept or hypernotion. Furthermore, lattices induced by all partial order relations of the hypernotion-sub-concept from the formal context represent the concept lattice.
# Comparing EEG-Based Epilepsy Diagnosis Using Neural Networks and Wavelet Transform
## 2 Related Studies
Out of 700 EEG signals, their method correctly classified 694 signals, resulting in a detection rate of 99.1%. [PERSON] et al. [25] developed a method to extract features based on the entropy of the EEG signal and optimized them using a selection method. They applied a linear regression model with an SVM classifier to the optimized features to distinguish between focal and non-focal signals and achieved a classification rate of 92.8% using an open-access EEG data set. [PERSON] et al. [26] used a wavelet and Shannon-entropy-based method to recognize epilepsy seizures and applied SVM, LDA, artificial neural networks, and k-nearest neighbor as classifiers for their proposed system. [PERSON] et al. [27] proposed a recurrence quantification analysis approach to classify EEG signals. The results of the study were visualized using principal component analysis and classified using an SVM classifier. [PERSON] et al. developed a machine-learning-based approach using wavelet transform to classify individuals with alcohol use disorder. The study used SVM and Naive Bayes methods [28]. [PERSON] et al. [29] proposed a method using decomposition of Hilbert-Huang epileptic vibration and used the minimum SVM classifier to distinguish between normal and epileptic EEG signals. [PERSON] et al. [30] used a wavelet Fourier analysis to detect sleep signals in EEG and applied a least-square SVM classifier. [PERSON] et al. [31] used discrete wavelet transform (DWT) and orthogonal matching pursuit (OMP) techniques to extract coefficients from EEG signals, then calculated nonlinear features and statistical features using these coefficients. They evaluated the performance of the proposed techniques using three commonly used EEG data sets. [PERSON] et al. [32] proposed the use of Mel frequency cepstral coefficients (MFCCs) and generalized regression neural networks to distinguish normal and seizure EEG recordings. [PERSON] et al. [33] used discrete wavelet transform (DWT) to divide EEG signals into sub-bands and extract features. They also used genetic algorithms to select effective features and applied artificial neural network (ANN) and vector support machine (SVM) classifiers to the data. The simulation results showed that the proposed method had higher accuracy in detecting epilepsy attacks compared to other similar approaches.
## 2 Materials and Methods
### Imputation Models
#### 2.3.4 Unconditional Mean Imputation (UMI)  
The method used in this study to perform the unconditional imputation of the mean is \"mean\" from the MICE package of R [45,46], whereby in the instances that are unknown, the algorithm completes them by introducing into all of them a single value obtained by calculating the mean of the other samples that are known [47]. In this way, the missing instances are imputed by values that lie in the centre of the distribution. This type of imputation usually has some undesirable problems such as an inadequate estimation of the variance and a deviation from the correlation between the features [48].
## 3 Results and Discussion  
### Volatile Compounds Identified by GC-IMS  
#### 3.1.1 Analysis of the Topographic Plots of Volatile Components in Teas by GC-IMS  
The GC-IMS technique, with high separation efficiency, fast response of ion mobility spectra, and high sensitivity, was used to analyze the volatile compounds of tea leaves after different treatments [10; 22]. The two-dimensional top view of GC-IMS (Figure 1A) can visually compare the composition of volatile substances among different samples. White indicates lower intensity, and red indicates higher intensity. Compared with the FTL, the number of flavor substances in BT and GT had increased, and the concentration of most flavor substances had also increased.  
Figure 1: Topographic plots of three tea samples. (**A**) 2D-topographic plot and (**B**) differentiation plot of volatile compounds. In BT and GT, red and blue dots indicated that the concentration of the compounds are higher and lower than the reference (FTL), respectively.  
A difference-comparison model was used in comparing the volatile varieties of the tea samples (Figure 1B). If the volatile compounds were consistent, the background after deduction was white, while red indicated that the concentration of the substance was higher than the reference, and blue indicated that the concentration of the substance was lower than the reference. The topographic plot of the fresh tea leaves (FTLs) was selected as a reference, and the topographic plot of the other samples was subtracted from the reference. Each point represents one compound. Compared with the number of FTLs, the number of volatile compounds in BT and GT increased, and the concentrations of most volatile compounds also increased [23]. The changes in BT are more obvious than those in GT.
# Effects of Reflectance of Backsheets and Spacing between Cells on Photovoltaic Modules
## 3 Results
### Comparison of Backsheets for MBB PV Modules  
Figure 3a,b show the short-circuit current (I\({}_{\text{sc}}\)) and maximum power (P\({}_{\text{max}}\)) of the MBB PV modules by various cell spaces, 0.5 (black line), 1.5 (red line), and 2.5 mm (blue line), respectively. In Figure 3a, I\({}_{\text{sc}}\) increased as the area of the white backsheet increased (B < B-W < W). The result indicates the reflected visible light affects I\({}_{\text{sc}}\) leading to an increase in PV module efficiency. The difference in I\({}_{\text{sc}}\) was about 0.1 A for all PV modules, regardless of the gap spaces. Interestingly, the PV module with the broadest gap space (2.5 mm), blue line in Figure 3a, exhibited the highest I\({}_{\text{sc}}\) among the three PV modules. It reveals the optical gain by white backsheet results in an increase in I\({}_{\text{sc}}\). For P\({}_{\text{max}}\) in Figure 3b, the same tendency was observed (B < B-W < W). The increase in Pmax using 2.5 mm gap space was about 1.4% compared to that with 0.5 mm gap space. However, the PV module with 2.5 mm with B and B-W backsheet did not show the highest P\({}_{\text{max}}\) because its open-circuit voltage (V\({}_{\text{oc}}\)) value was not as high as that of other values. It seems that it can be influenced by the variation of V\({}_{\text{oc}}\) because V\({}_{\text{oc}}\) variation does not show any trend as shown in Tables A1-A3 in Appendix A.  
Figure 2: The reflectance of white and black backsheets in the vis–near-infrared (IR) range.
# Combination of LiCs and EDLCs with Batteries:
## 4 Conclusions  
In this paper, three energy storage devices have been considered for hybridisation, a battery, an SC and an HC bank. Different possible combinations of these energy storage devices were analysed for both the performance and cost implications. It was deduced from the results of the analysis that the most effective use of energy would be obtained from an efficient combination of the three energy storages. This efficient combination was facilitated using a multiple input DC-DC converter. The effect of allowing trickle charging to take place in the charging of SCs and HCs was highlighted, as it has been unduly overlooked in previous research.  
In this research, it was proposed that the SC and HC banks be connected directly in parallel and hybridised with the battery pack through a DC-DC converter such that only a two-input converter is needed. This was instead of each energy storage having its own input port, which would result in the need for a three-input converter. The two- and three-input converter topologies were compared, and from the results obtained, the two-input converter was not only sufficient for the effective hybridisation of the three energy storage devices considered, but also amounted to less resource use and consequently cost savings. In the implementation of the multiple input converter, the efficiency of the converter was tested at different duty cycles and different load conditions, as well. The efficiency values in all cases were above 70%, which is within acceptable limits. In the hybridisation topology proposed in this research, a specific power of 13.53 kW/kg was attained, which is much higher than the 7.83 or 7.69 kW/kg that would have been obtained in a battery-SC or a battery-HC hybrid energy storage system.  
In conclusion, the results obtained in this experimental implementation prove categorically that:  
1. An effective combination of SC, battery, and HC through the use of a multiple input DC-DC converter is the most efficient hybridisation topology.
2. The combination of HC and SC in one energy storage system does not need to come with an increase in the input ports of the DC-DC converter. Since the energy can be utilised effectively from both of them while they are connected directly in parallel, there is no need for an increase in the resource requirement.  
Conceptualization and writing, original draft preparation, IJ. Writing, review and editing, and supervision, N.G. and R.G.  
This material is based on research/work supported wholly/in part by the National Research Foundation (NRF) of South Africa (Grant Numbers: 112236). The research findings are those of the authors and not of the NRF **Conflicts of Interest**: The authors declare no conflict of interest.
# Benchmarking Message Queues
## 3 Related Works
Our research is more extended since we considered including Redis and ActiveMQ Artemis and assessing the performance of these message queues under various workloads and scenarios. [PERSON] et al. [23] conducted an analysis of RabbitMQ and ActiveMQ, specifically focusing on their publishing and subscribing rates. Their study employed different-sized images as a real-world comparison instead of traditional byte string loads and considered both single and multiple publisher-consumer scenarios. While their research highlighted performance differences between RabbitMQ and ActiveMQ, our study extended the comparative analysis to include Redis and Apache Kafka. Additionally, we evaluated the latency and throughput of these message queues, presenting more detailed results, including percentile-based end-to-end latency metrics. [PERSON] et al. [24] conducted a study focusing on message queueing technologies for flow control and load balancing in the IoT scenario. They specifically evaluated RabbitMQ and Apache Kafka within a smart home system cloud, assessing their performance with different numbers of consumers. The results highlight that Kafka exhibited stable data buffering and a lower average CPU usage, with no instances of reaching maximum CPU usage during testing. In comparison, our work extended beyond their scope by examining additional message queue technologies, including Redis and ActiveMQ Artemis. Furthermore, we provided a comprehensive analysis of latency and throughput across a diverse range of workloads. Our study examined Redis, ActiveMQ Artemis, RabbitMQ, and Apache Kafka, shedding light on their respective performance characteristics. We assessed Redis's publish/subscribe operations and evaluated the enhanced ActiveMQ Artemis rather than the traditional version. Notably, our findings highlight ActiveMQ Artemis' advantageous latency performance in scenarios with low throughput, distinguishing it from RabbitMQ. Additionally, we provided comprehensive results featuring distinct graphs for throughput and latency, encompassing various percentiles. To ensure unbiased and consistent results, we utilized the OpenMessaging Benchmark tool from The Linux Foundation, a trusted and popular open-source solution. The comparison between our study versus the existing studies is shown in Table 1.
# Fault Diagnosis of Intelligent Production Line Based on Digital Twin and Improved Random Forest
## 4 Experiments and Analysis
### Results Analysis
The clustering results for 300 classification trees are shown in Figure 9. It can be seen that there are about 70 base classifiers in the eleventh cluster, which shows that these trees are very close to the results of the test set, and the similarity of these trees is very high. In order to improve the accuracy of the final classification results and the difference of each base classifier, we selected the best-performing tree in each cluster to form a new random forest, and the result of the classification was used as the weight of the weighted voting for online fault diagnosis. A confusion matrix was used to represent the results of different algorithms in order to explain the experiment better. Figure 10a shows the IRF + DT algorithm proposed in this paper. The model was pretrained through virtual data, and then the decision tree was rescreened through a hierarchical clustering algorithm, the parameters of the model were transferred to the new model through transfer learning, and the new model was trained through a training set of actual data. Finally, the accuracy of the model in the test set of actual data was as high as 97.8%. Figure 10b shows the random forest model that used the  
Figure 8: Accuracy of each base classifier. Figure 9: Clustering result for classification trees. data from the digital twin shop floor and the physical production line. The accuracy rate was 90.8%. These results show the effectiveness of the proposed IRF + DT algorithm in the automotive rear-axle assembly line. The IRF + DT algorithm more accurately distinguished the fault status of the equipment, which shows that data distribution and insufficient data sampling affect the performance of the IRF algorithm. It can be seen from the confusion matrix that the accuracy of the model can reach 100% in the case of normal state, conveyor belt fault, and four-wheel positioning device fault if there is a large volume of data generated by the digital twin. This is because there is a large amount of normal state data, and the characteristics of the conveyor belt and four-wheel positioning device are relatively simple. However, for the tightening robot, the loading robot and the unloading robot are still misclassified. The model cannot easily distinguish between the robots. This is caused by the similarity of the characteristics between the robots.
# Finite Element Analysis of a Novel Approach for Knee and Ankle Protection during Landing
## 2 Materials and Methods
### Data Analysis  
In this study, the von Mises stress was used as the standard to measure the stress level. With respect to the analysis of ankle protection, the stress distribution and peak von Mises values of the inferior articular surface of tibia, as well as the horizontal force on the talus, were evaluated with and without the brace at three jumping heights. Similarly, for the knee, the von Mises distribution, peak von Mises, and contact area of the menisci and articular cartilages were investigated.
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 5 Sillcom\({}_{3R}\): Basic Design with Three Online Rounds
### Eliminating RSS Reconstruction to Reduce One Round  
The communication in RSS distance computation comes from the share reconstruction performed by the three computing parties. After reconstruction, the RSS distance needs to be converted into the input for the secure shuffle algorithm. We observe that performing RSS share reconstruction is entirely redundant--i.e., the 3P-ASS shared distance values obtained after RSS computation can be directly transformed into the input of the secure shuffle algorithm with just one round of communication. Since \(P_{2}\) holds the complete random permutation \(\Pi\) and the three-party shares of the computed distances, it can apply the random permutation to its share and send the permuted share to \(P_{0}\) and \(P_{1}\) in a single communication round. We note that the one round of communication in the share conversion phase can be merged into the two rounds of communication in secure shuffling. We detail this process in Algorithm 3.  
```
0:\(P_{2}\)'s 3P-ASS shared distance vector \([d_{0}]_{2},\ldots,[d_{m-1}]_{2}\).
0:\(P_{0}\) and \(P_{1}\) each gets 2P-ASS share of shuffled \(\pi([d_{0}]_{2},\ldots,[d_{m-1}]_{2})\).
0:Offline preprocessing:
1:\(P_{2}\) samples \(m\) random values \((rp_{0},\ldots,rp_{m-1})\).
2:\(P_{2}\) samples random permutation \(\pi\in[m]\rightarrow[m]\). Online computing:
1:\(P_{2}\) perform \(\pi(rp_{0},\ldots,rp_{m-1})\) and sends to \(P_{0}\).
2:\(P_{2}\) perform \(\pi([d_{0}]_{2}-r_{0},\ldots,[d_{m-1}]_{2}-r_{m-1})\) and sends to \(P_{1}\).
```  
**Algorithm 3** Share conversion \(\Pi_{SC}\)
## 2 Materials and Methods
### Data Source and Type  
The study gathered both qualitative and quantitative data from primary and secondary sources. Primary data were collected directly from selected pastoralist households using structured and unstructured questionnaires. Secondary data came from various sources, including the Meteoblue database [41], published journals, books, online sources, academic databases, governmental and intergovernmental databases, and organizational databases, as explained below.  
For primary data, structured and unstructured questionnaires were administered to 350 pastoralist households across six kebeles, using both English and Afan Oromo to ensure accessibility and comprehension. Respondents remained anonymous to protect their privacy. This mix of quantitative data from meteorological sources and qualitative insights from household surveys provided a comprehensive perspective on climate variability and adaptation strategies in the region's pastoralist communities.
## 3 Results and Discussion
It may be attributed to the reaction kinetics, particularly [PERSON]'s principle [28], associated with unfavorable diffusion of OH\({}^{-}\) ions from cathode reservoir to anode reservoir in the SWE cell. That is, H\({}_{2}\) and NaOH are simultaneously generated during the SWE reaction (e.g., the related half reaction: 2H\({}_{2}\)O + 2e\({}^{-}\)\(\rightarrow\) H\({}_{2}\) + 2 OH\({}^{-}\)). When OH\({}^{-}\) ions are diffused to the anode reservoir across the membranes, the NaOH production is relatively reduced on the basis of the theoretical stoichiometric ratio of NaOH to H\({}_{2}\). This peculiar trend can be alleviated by increasing the thickness of the C layer up to 10 \(\upmu\)m. In contrast, NR212/AA-DVB membranes with the thickness higher than 10 \(\upmu\)m experience the reduction of their caustic current efficiency owing to the conflicting effect of Na\({}^{+}\) ion transport. Figure 9: Current-voltage polarization behavior of NR212/AA-DVB membranes at 90 \({}^{\circ}\)C.  
In addition to electrochemical single cell performance and efficiency, the other barometer needed for a desirable SWE membrane design is the electrochemical durability under practical SWE operation conditions. Figure 11 shows the changes of electrochemical single cell performances obtained when the constant current density of 0.6 A\(\cdot\)cm\({}^{-2}\) is continuously applied. In NR212 used for comparison, the cell voltage increases rapidly up to with high fluctuations within 2.5 h. After 9 h elapse, the cell voltage largely deviates from the nominal value of \(\sim\)3.2 V, and suddenly falls below the theoretical SWE cell voltage of 2.2 V. This pattern is associated with membrane failures including the formation of pinholes and defects, and membrane thickness reduction. After the incorporation of C layer, the rate at which the overpotential increases becomes slow, and the degree of the voltage fluctuation is also lessened. The C layer accounting for 17% of the total thickness in case of NR212/AA-DVB_10 contributes to improving the membrane lifetime by 2.5 time in comparison with that of NR212 composed of a single PFSA matrix. When comprehensively considering the SWE membrane characteristics, the optimum C layer thickness for the desirable double-layered membrane formation is 10 \(\upmu\)m.  
Figure 11: SWE durability test results of NR212/AA-DVB membranes obtained at the constant current density of 0.6 A\(\cdot\)cm\({}^{-2}\). Here, the SWE operation temperature is 90 \({}^{\circ}\)C.  
Figure 10: (**a**) Hydrogen and (**b**) caustic current efficiency of NR212/AA-DVB membranes as a function of C layer thickness at the constant current density of 0.6 A\(\cdot\)cm\({}^{-2}\). Here, the saline water electrolysis (SWE) operation temperature is 90 \({}^{\circ}\)C.
## 4 Discussion
### Rationale for the Exclusion of Artefacts from Aixplorer Elastogram Analysis  
All Aixplorer shear-wave elastograms obtained _in vivo_ contained artefacts that appeared to correspond with the position of the acoustic radiation force beams; they moved with the lateral translation of the transducer and their number varied with the lateral size of the elastogram box. Artefacts were also observed _in vitro_ and were most clearly visualised in the two stiffer inclusions. Across all volunteers, Aixplorer \(c_{s}\) estimates (including  
Figure 6: Anisotropy ratios (ratio of mean anti-radial \(c_{s}\) to mean radial \(c_{s}\) in individual pairs of elastograms obtained at the same location in the same breast quadrant on the same breast in the same individual) measured in skin, adipose and parenchyma for Aixplorer and S2000. *, ** of *** indicates where median \(c_{s}\) estimates for anti-radial and radial transducer orientations were significantly different with \(p\)-value < 0.05, < 0.01 or < 0.001, respectively.  
artefacts) in adipose tissues were significantly greater than \(c_{s}\) estimates in parenchyma (Figure 4b, which was contrary to what has been reported in the literature. When artefacts were excluded the findings of the current study agreed with three large (minimum number of women 89) clinical studies that measured \(c_{s}\) in normal breast tissue using S2000 2D-SWE and found that breast parenchyma had statistically greater \(c_{s}\) than adipose tissue [26; 27; 28]. In addition, [PERSON] et al. [33] found similar results using Aixplorer in 46 women.  
On visual examination of the elastograms, artefacts appeared most intense (greater \(c_{s}\)) below the gel-skin and skin-adipose interfaces. 'Artefactual vertical bands of stiffness' were observed by [PERSON] et al. [34] in their study of breast lesions using Aixplorer 2D-SWE. [PERSON] et al. [34] attributed these artefacts to tissue compression and noted they were difficult to avoid at superficial depths despite using large amounts of ultrasonic gel and a no-compression scanning protocol. We concluded that \(c_{s}\) estimates obtained in regions with artefacts were less reliable and only data from regions that were free from artefacts were used for subsequent analysis of anisotropy ratios.