esa-sceva/satcom-chunk-collection huggingface dataset:
# Behavioural Realism and Its Impact on Virtual Reality Social Interactions Involving Self-Disclosure
## 3 Results
### Results for the Perceived Eye Contact Measure  
The ANOVA for the perceived eye contact duration measure showed a significant effect of avatar type (\(F\)(1,56) = 14.55, \(p<0.01\), \({\eta_{p}}^{2}\) = 0.21). The iClone UNREAL avatar (\(M\) = 66.90, \(SD\) = 24.51) had a higher perceived eye contact duration when compared to the Vive Sync avatar (\(M\) = 52.64, \(SD\) = 28.75). [PERSON]'s measure of effect size (\(d\) = 0.53) indicated a moderate to large effect (see Figures 9 and 10). There was no significant effect of disclosure source (\(F\)(1,56) = 0.31, \(p\) = 0.58, \({\eta_{p}}^{2}\) = 0.01). Disclosures from either the participant (\(M\) = 59.60, \(SD\) = 23.16) or the interviewer (\(M\) = 59.94, \(SD\) = 22.43) did not influence perceived eye contact duration (Figure 9). There was a significant effect of disclosure type (\(F\)(1,56) = 9.33, \(p<0.01\), \({\eta_{p}}^{2}\) = 0.14). Positive disclosures (\(M\) = 60.53, \(SD\) = 23.10) resulted in longer perceived eye contact duration when compared to negative disclosures (\(M\) = 59.01, \(SD\) = 22.40). [PERSON]'s measure of effect size (\(d\) = 0.07) indicated a small effect (Figure 10). Additionally, no significant interaction effect was observed between avatar type and disclosure source (\(F\)(1,56) = 0.02, \(p\) = 0.90, \({\eta_{p}}^{2}\) < 0.01), avatar type and disclosure type (\(F\)(1,56) = 0.20, \(p\) = 0.89, \({\eta_{p}}^{2}\) = 0.00), disclosure source and disclosure type (\(F\)(1,56) = 0.67, \(p\) = 0.42, \({\eta_{p}}^{2}\) = 0.01), or between all three (\(F\)(1,56) = 3.17, \(p\) = 0.08, \({\eta_{p}}^{2}\) = 0.05), suggesting that the effects of avatar type, disclosure source, and disclosure type on perceived enjoyment ratings were independent of each other. These results demonstrate that the iClone UNREAL avatar and positive disclosures led to higher perceived eye contact ratings.  
Figure 8: Enjoyment ratings for avatar types according to disclosure type. Error bars represent 95% confidence intervals.  
Figure 9: Perceived eye contact duration types according to disclosure source. Error bars represent 95% confidence intervals.
## 5 Review of Case Studies: Flying Vehicle Routing Optimization
The results of the studies indicate positive outcomes in terms of improving efficiency, reducing energy consumption, enhancing coverage, optimizing logistics, and achieving better solution quality. Many studies have demonstrated significant improvements compared to existing approaches or highlighted the potential of the proposed methods in real-world scenarios. However, it is important to note that the results may vary depending on the specific conditions and constraints of each application domain. While the studies provide valuable insights and contributions to their respective fields, there are a few limitations and areas for further improvement. Some studies acknowledge the need for future research to consider additional factors or challenges, such as energy consumption in realistic environments, or scalability [9; 27; 66]. These future research directions can enhance the practicality and applicability of the proposed optimization techniques [53; 70]. In terms of optimization techniques, it would be beneficial to explore the use of other metaheuristic algorithms or hybrid approaches to further improve solution quality and computational efficiency. Additionally, several studies focus on single-objective optimization, and future research could consider multi-objective optimization to address conflicting objectives and provide decision-makers with a range of trade-off solutions. Furthermore, while the studies highlight the benefits of using UAVs for delivery and logistics, there is limited discussion on the regulatory, safety, and ethical aspects associated with UAV operations [69; 70]. Considering these factors is crucial for the successful integration of UAVs into real-world scenarios. Overall, the studies reviewed provide valuable contributions to their respective domains and demonstrate the effectiveness of optimization techniques in addressing complex problems. However, further research is needed to address the identified limitations and to explore new avenues for optimization in the context of emerging technologies and evolving application domains. In summary, performance evaluation metrics such as the average travel time, energy consumption, message delivery reliability, and network congestion were utilized to assess the effectiveness of the GA-based approach for multi-objective message routing in EnFVs. The results obtained in various studies demonstrate the capability of GAs to provide efficient and effective routing solutions, improving the travel time, energy consumption, reliability, and overall system performance.
## 3 Problem Modeling
### Insertion Operators  
Once the destroy operator is performed, an insertion operator should be chosen to construct a complete solution. The customer insertion operators have to ensure energy feasibility with respect to the largest energy consumption for each EV on each arc.  
GI operator (Greedy Insertion). This operator tries to find the most feasible insertion position for each removed customer. If there is no feasible position for a removed customer, a new route will be created for it which contains this customer only.  
RI operator (Regret-k Insertion). This operator calculates the difference between the cost of the first and the kth best insertion of each removed customer and inserts them one by one to the position of their best insertion in descending sequence of the difference. Regret-2 and Regret-3 are adopted in our algorithm.  
Based on the existing work, two revised customer insertion operators are proposed in this paper, as defined below.  
TBI operator (Time-k-Based Insertion). This operator is designed based on the Time-Based Insertion operator of Keskin and Catay [24]. With this operator, the difference between the total route time with the first and the kth best insertion of each customer is calculated. The customer with the largest difference to its best insertion is first inserted at its best insertion, and then the process is repeated until all the removed customers are inserted. Time-2 and Time-3 are employed in this paper.  
EBI operator (Energy-k-Based Insertion). This operator is designed based on the Regret-k insertion operator. Similarly, the difference between the total energy consumption with the first and the kth best insertion of each customer is calculated, and the customer with the largest difference is the first to be inserted at its best insertion. Energy-2 and Energy-3 are considered here.  
After removing RSs from a route, the current feasible solution may become energy-infeasible. Thus, the RS insertion operators are needed to repair the solution.  
GIE operator (Greedy Insertion Energy). This operator identifies the first customer at which the SoC of the vehicle is negative, then inserts the RS which increases the least cost to the arc between the customer with negative SoC and its preceding neighbor. If this operator is infeasible (no available RS can be inserted), the previous arcs should be attempted.  
GIEC operator (Greedy Insertion Energy with Comparison). This operator calculates the increased cost after inserting the best RS at the position before the customer with negative SoC and the increased cost after inserting the best RS before the preceding customer. Then, compare the outcomes of two insertions and choose the one with the least increased cost.
## 3 Experimental Setup: Reflectometer and Matching Network Considerations
### Matching Network Design
For intended applications (e.g., voltage-sensitive scanning probe equipped with a SEBA sensor), a bandwidth on the order of 1 MHz is desirable and needs to be accurately evaluated. In higher-order circuits with distinct resonances, like the \(\Pi\)-network used here, the input impedance around a resonant frequency can be modeled as a simple RLC circuit. At resonance the power delivered to the load is at a relative maximum and the fractional bandwidth of the circuit is defined as the difference between the half-power frequencies above and below the resonant frequency \(f_{0}\)[30]. In the literature it is common to estimate the bandwidth available for gate modulation using \(-\)3 dB reduction in the magnitude of reflection coefficient (e.g., [31]) near the resonant frequency, however this definition is oversimplified because a 3 dB change in \(|\Gamma|\) does not always correspond to a true resonance (cancellation of positive and negative reactances). Moreover, a resonant match that is not critically coupled may not even have a 3 dB change in \(|\Gamma|\)(e.g., about \(-\)1 dB is was reported [26]). Therefore, to accurately evaluate the bandwidth of the experimental setup the response of the SEB to a modulating small signal (equivalent to 0.01e in magnitude) applied to the gate of SEB is simulated using Keysight ADS (the detailed  
Figure 5: Temperature dependence of the DC resistance of the 390 nH Coilcraft 0805 inductor (red) and 43 pF Johansson technology capacitor (black). description of the simulations will be published elsewhere) and it gives the result shown in the table within Figure 6b thus confirming the bandwidth requirement (>1 MHz) for fast SEBA sensor.
## Notations  
The following notations are used:  
\(|\bullet|\) number of elements in \(\bullet\)  
\(\mathrm{N_{var}}\) number of jobs used in the test problem  
\(\mathrm{N_{cpu}}\) number of processors contained in the given data center  
\(\mathrm{N_{run}}\) number of runs for the algorithms  
\(\mathrm{N_{gen}}\) number of generations in each run  
\(\mathrm{N_{sol}}\) number of solutions in each generation  
\(\mathrm{N_{non}}\) number of selected temporary nondominated solutions  
\(X_{i}\)\(i\)th solution  
\(x_{i,j}\)\(j\)th variable in \(X_{i}\)  
\(P_{i}\) the best solution among all solutions updated based on \(X_{i}\) in SSO  
\(p_{i,j}\)\(j\)th variable in \(P_{i}\)  
\(gBest\) index of the best solution among all solutions in SSO, i.e., \(F(P_{gBest})\) is better than or equal to \(F(p_{i})\) for \(i=1,2,\ldots,\mathrm{N_{sol}}\)  
\(\rho_{I}\) random number generated uniformly within interval \(I\)  
positive parameters used in SSO with \(c_{g}+c_{p}+c_{w}+c_{r}=1\)  
\(C_{g},C_{p},C_{w}\)\(C_{g}=c_{g},C_{p}=C_{g}+c_{p}\), and \(C_{w}=C_{p}+c_{w}\)  
\(F_{j}(\bullet)\)\(l\)th fitness function value of solution \(\bullet\)  
\(Max(\bullet)\) maximal value of \(\bullet\), i.e., \(Max(F_{l})\) is the maximal value of the \(l\)th objective function  
\(Min(\bullet)\) minimal value of \(\bullet\), i.e., \(Min(F_{l})\) is the minimal value of the \(l\)th objective function  
\(S_{t}\) set of selected solutions from \(\Pi_{t}\) to generate new solutions in the \((t+1)^{\text{th}}\) generation.  
Note that \(S_{1}=\Pi_{1}\) and \(|\)\(S_{t}\)\(|\)\(=\)\(\mathrm{N_{sol}}\) for \(i=1,2,\ldots,\mathrm{N_{gen}}\)  
size\({}_{i}\) size of the job \(i\) for \(i=1,2,\ldots,\mathrm{N_{var}}\)  
start\({}_{i}\) start time of the job \(i\) for \(i=1,2,\ldots,\mathrm{N_{var}}\)  
speed\({}_{j}\) execution speed of the processor \(j\) for \(j=1,2,\ldots,\mathrm{N_{cpu}}\)  
\(e_{j}\) energy consumption per unit time of the processor \(j\) for \(j=1,2,\ldots,\mathrm{N_{cpu}}\)  
Tub  
deadline constraint of job scheduling  
\(t_{i,j}\) processing time \(t_{i,j}\) where \(t_{i,j}=\begin{cases}size_{i}/speed_{j}&\text{if task $i$ is processed on processor $j$}\\ 0&\text{otherwise}\\ \end{cases}\) for \(i=1,2,\ldots,\mathrm{N_{var}}\) and \(j=1,2,\ldots,\mathrm{N_{cpu}}\)
###### Abstract  
This systematic literature review (SLR) provides a comprehensive application-wise analysis of machine learning (ML)-driven predictive maintenance (PdM) across industrial domains. Motivated by the digital transformation of industry 4.0, this study explores how ML techniques optimize maintenance by predicting faults, estimating remaining useful life (RUL), and reducing operational downtime. Sixty peer-reviewed articles published between 2020 and 2024 were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 guidelines, and were analyzed based on industrial sector, ML techniques, datasets, evaluation metrics, and implementation challenges. Results show that combining ML with diverse sensor data enhances predictive performance under varying operational conditions across manufacturing, energy, healthcare, and transportation. Frequently used open datasets include the commercial modular aero-propulsion system simulation (CMAPSS), the malfunctioning industrial machine investigation and inspection (MIMI), and the semiconductor manufacturing process (SECOM) datasets, though data heterogeneity and imbalance remain major barriers. Emerging paradigms such as hybrid modeling, digital twins, and physics-informed learning show promise but face issues like computational cost, interpretability, and limited scalability. The findings highlight future research needs in model generalizability, real-world validation, and explainable artificial intelligence (AI) to bridge gaps between ML innovations and industrial practice.  
machine learning; predictive maintenance; digital twins; Internet of Things; Industry 4.0; fault diagnosis; hybrid modeling 2020
# Multipoint Thermal Sensing System for Power Semiconductor Devices Utilizing Fiber Bragg Gratings
## 3 Sensors' Calibration and Installation
### FBG Sensors Installation  
In this setup, the FBG sensor was placed between the copper plate and the heatsink, unlike the method used in [31], where the sensors are placed directly on the IGBT chips. The direct method is exclusively meant for experimental purposes because of the necessity to break the ceramic casing embedding the IGBT chips, which is not suitable for commercial applications. For this approach, it is unnecessary to remove the housing of the IGBTs in order to place the sensor for junction temperature measurements. Instead, the expressions in (9) and (14) can be combined to estimate \(T_{j}\).  
The three FBG sensors (FBG1, FBG2, and FBG3) are connected in a series arrangement, as shown in Figure 6a, to form a single fiber. The inverter utilized in this experiment comprises four IGBT studs and a sole three-phase rectifier stud stacked on the heatsink. As depicted in Figure 6b, two of the IGBTs, together with the rectifier module, have been selected for this experiment. For the installation of the fiber sensor, the rectifier and IGBT studs were loosened off the heatsink, and the fiber sensor was laid out and examined for the two different configurations shown in Figure 6c,d.  
In the first arrangement (FIBER1), the fiber was laid beneath the module's plate across the two parallel IGBTs and diode chips, cutting through the plate vertically, as shown in Figure 6c. However, the second setup (FIBER2) involved positioning the fiber parallel to the plate and cutting through the module's plate horizontally, as illustrated in Figure 6d. To obtain precise measurement results, we positioned the sensor heads at the midpoint of the IGBT plate for both setups, following the location guidelines proposed in [22]. The IGBTs were then mounted to the heatsink, ensuring that the torque was maintained under an acceptable limit to avoid undue stress that may cause damage to the fiber. The experimental results for the two installations are discussed in the next section.
# An Ensemble Feature Selection Approach to Identify Relevant Features from EEG Signals
## 3 Results
### Selecting Relevant Features from the EEG Dataset
#### 3.2.1 \(N\)-Fold Cross-Validation  
Considering the best results for the classification were achieved with the SVM classifier, we used it in this stage. The results of the \(N\)-fold cross-validation calculated for different values of \(n\) can be seen in the following table. The value of \(n\) in Table 7 corresponds to the value used to determine the number of samples generated in the \(N\)-fold validation.  
Figure 5 describes the confusion matrix calculated for this evaluation for \(n=10\). The results show that the classifier SVM achieved a true positive rate of 96.43% and a true negative rate of 97.96%. Besides, the sensitivity was 96.78%, and the specificity was 97.95%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **DT** & **LR** & **RF** & **SVM** \\ \hline SelectKBest & 92.79\% & 94.59\% & 89.39\% & 93.43\% \\ RFE & 93.01\% & 85.09\% & 89.87\% & 93.43\% \\ Feature Importance & 94.56\% & 94.36\% & 89.64\% & 94.18\% \\ All Features & 92.89\% & 95.17\% & 89.62\% & 96.75\% \\ EFS & 96.05\% & 95.94\% & 89.79 & 97.46\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy results using different subsets of features.  
\begin{table}
\begin{tabular}{c c} \hline \hline \(n\) & **Accuracy (\%)** \\ \hline
1 & 97.39 \\
3 & 97.38 \(\pm\) 1.100 \\
5 & 97.45 \(\pm\) 1.210 \\
7 & 97.46 \(\pm\) 1.082 \\
10 & 97.46 \(\pm\) 1.080 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of \(N\)-fold cross-validation.  
Figure 6 describes the ROC curve with the results for this evaluation.  
Figure 7 describes the graph with the results of the recall versus precision.  
Figures 6 and 7 also present additional metrics: average weight precision, macro average precision, and micro average precision. These results are helpful when we have an unbalanced dataset. For this case, all lines are similar because we have a balanced dataset.  
Figure 5: Confusion matrix.  
Figure 6: Receiver operating characteristic (ROC) curve.
## 1 Introduction
On the other hand, the joint iterative optimization (JIO) technique reported in [16] outperforms the Krylov-based method with efficient adaptive implementations. However, the theoretical JIO dimensionality reduction transform matrix, Equation (63) in [16], is in fact rank-one; the column space of the JIO matrix is precisely the MVDR line. The rank selection scheme may fail to work; performance degradation is then expected. In order to address this problem, in this paper, we introduce a low-complexity robust data-dependent dimensionality reduction algorithm for reduced-rank beamforming and steering vector estimation. The proposed iterative optimization with steering vector perturbation (IOVP) design strategy jointly optimizes a projection matrix and a reduced-rank beamformer by introducing several independently-generated small perturbations of the assumed steering vector. With these vectors, the scheme updates a different column of the projection matrix in each recursion and concatenates these columns to ensure that the projection matrix has a desired rank. The contributions of this paper are summarized as follows:  
* A bank of perturbed steering vectors is proposed as candidate array steering vectors around the true steering vector. The candidate steering vectors are responsible for performing rank reduction, and the reduced-rank beamformer forms the beam in the direction of the signal of interest (SoI). * We devise efficient stochastic gradient (SG) and recursive least-squares (RLS) algorithms for implementing the proposed robust IOVP design. * We introduce an automatic rank selection scheme in order to obtain the optimal beamforming performance with low computational complexity. Simulation results show that the proposed IOVP algorithms outperform existing full-rank and reduced-rank algorithms with a comparable complexity. This paper is organized as follows. The system model is described in Section 2. The reduced-rank MVDR beamforming with IOVP is formulated in Section 3. A robust version of IOVP is investigated in Section 4, and simulations are discussed in Section 5.
## 2 Materials and Methods
### Greenhouse Experimental Design and Antiviral Activity Assay  
The ZnO NPs were suspended in sterile distilled water at 10 mg/mL, and then diluted with sterile distilled H\({}_{2}\)O to obtain a final concentration of 100 ug/mL. By using the half-leaf method [41; 42], the antiviral activity of ZnO NPs was primary tested on _Dutura strmonium_, as a local lesion host for TMV, according to the inhibition percentage towards the number of local lesions. Under insect-proof greenhouse conditions, tomato seeds were surface sterilized and cultivated in plastic pots (20 cm in diameter) filled with sterilized soil (clay: sand, 1:1). At 28 th days post seedlings, tomato seedlings were transplanted into new pots, and one week later, two upper true leaves of each tomato plant were dusted with carborundum and mechanically inoculated with 1 mL of semi-purified TMV as previously described [43; 44]. The experiment was carried out in five treatments, each treatment was comprised of five replicates, and each pot contained three tomato plants. The first treatment (T1) was mock-treated plants (control), in which tomato plants inoculated with viral inoculation buffer + foliar spraying of sterile distilled H\({}_{2}\)O. The second treatment (T2) was plants inoculated with TMV (infected). The third treatment (T3) included plants treated by foliar spraying of ZnO NPs 24 h before inoculation of TMV.  
The fourth treatment (T4) was plants treated by foliar spraying of ZnO NPs 24 h after the inoculation of TMV. The fifth treatment (T5) included tomato plants treated by twice foliar spraying of ZnO NPs, 24 h before inoculation of TMV and 24 h post-inoculation. All plants were kept under greenhouse conditions, 28 \({}^{\circ}\)C/16 \({}^{\circ}\)C (day/night) and 70% relative humidity, and daily observed for the recording of symptom development. Five independent biological replicates of tomato leaves of all treatments were collected at 21 dpi and subjected to RNA extraction.
# Review on Electrical Impedance Tomography: Artificial Intelligence Methods and its Applications
## 3 How EIT Works
### Forward Problem and Data Collection
#### 3.1.1 Types of Current Injection Patterns  
There are many types of configurations used for applying current and calculating the resulting voltages but most common is the pair drive configuration. The pair drive configuration consists of a single current source that is connected to the electrode pairs which is used to inject current into the medium and the resulting potential is calculated from the rest of the electrodes. After obtaining the first measurement, the AC source is moved to the other pair of electrodes and the process continues until all the measurements have been completed. The other configuration is the multiple drive configuration in which the current is applied to the medium by using more than two electrodes simultaneously. It is expensive and complex as compared to the pair drive configuration that uses only one AC source. Some of the current injection patterns that are used for the 2D configurations are as follows:  
* Adjacent Current Pattern
* Cross Current Pattern
* Opposite Current Pattern