esa-sceva/satcom-chunk-collection huggingface dataset:
# The Integration of AI and Metaverse in Education: A Systematic Literature Review
## 4 Results and Discussion
### Rq4: What Are the Challenges of Utilizing AI in the Metaverse for Education?
#### 4.4.2 Algorithmic Bias  
In an educational context, algorithmic bias can impact everything from personalized learning paths to student performance assessments, which leads to the unfair treatment of students. In addition, based on characteristics such as race and socioeconomic status, bias can result in an unfair advantage for certain groups of students. For example, content or assessments may be more aligned with the experiences of certain cultural groups, to the detriment of others who do not share the same background [106; 108]. This highlights the importance of the evaluation and the adjustment of AI algorithms to prevent discriminatory practices and ensure equitable educational opportunities.  
On the other hand, biases can manifest in the accessibility of educational technologies. If the design and implementation of these technologies do not take into account the diverse needs of users, it can lead to inequitable access, especially for students with disabilities or those from underrepresented communities [69]. Furthermore, biased feedback can undermine students' confidence and motivation, which may negatively impact their learning experience [106]. Therefore, establishing fair and accurate AI methods is important for the credibility of AI use in the metaverse for education [6; 106].
## Nomenclature  
\begin{tabular}{l l} CVaR & Conditional Value-at-Risk \\ SOC & state of charge \\ KDE & kernel density estimation \\ P2G & Power-to-Gas \\ CDF & cumulative distribution function \\ MILP & mixed-integer linear programming \\ TOU & time-of-use \\ CHP & combined heat and power \\ AGC & Automatic Generation Control \\ LiFePO\({}_{4}\) & lithium iron phosphate \\ \(\rho(t)\) & forecasted wind power at time \(t\) \\ \(p(t)\) & actual wind power at time \(t\) \\ \(e(t)\) & forecasting errors at time \(t\) \\ \(\mu(t)\) & sample mean \\ \(\sigma(t)\) & sample standard deviation \\ \(\dot{p}_{30}(t)\) & forecasted wind power on Day 30 \\ \(u^{s}(t)\) & standardized forecasting error at time \(t\) \\ \(e^{s}(t)\) & actual forecasting error at time \(t\) \\ \(p_{30}^{s}(t)\) & wind power output scenarios for Day 30 \\ \(k\) & index of the representative scenario or cluster \\ \(n_{k}\) & number of candidate scenarios in the \(k\)-th cluster \\ \(\mathrm{C_{k}}\) & total revenue of the wind farm \\ \(\mathrm{C^{sell}}(t)\) & electricity sales revenue \\ \(\mathrm{C^{c}_{k}}(t)\) & penalty cost for scheduling deviation \\ \(\mathrm{C^{b}}(t)\) & operation and maintenance cost of the energy storage system \\ \(\mathrm{C^{soc}}(t)\) & cost associated with the initial \(SOC\) \\ \(\mathrm{C^{fg}}(t)\) & arbitrage revenue from peak-valley electricity price differences \\ \(p_{sell}(t)\) & electricity selling price \\ \(p^{c}(t)\) & charging power of the energy storage system \\ \(p^{d}(t)\) & discharging power of the energy storage system \\ \(\mathrm{SOC_{0}}\) & initial SOC \\ \(w_{k}\) & weight of the \(k\)-th wind power output scenario \\ \(\mathrm{SOC_{max}}\) & maximum SOC of the energy storage system \\ \(\mathrm{SOC_{min}}\) & minimum \(SOC\) of the energy storage system \\ \(d\) & number of historical sample days \\ \(h\) & bandwidth parameter \\ \(\sigma\) & standard deviation of the forecasting error \\ \(\Phi_{\Sigma}\) & joint cumulative distribution function \\ \(u_{i}\) & marginal cumulative probability \\ \(S\) & number of initial scenarios \\ \(K\) & total number of clustered scenarios \\ \(\Delta t\) & time interval \\ \(C_{b}\) & cost coefficient of energy storage \\ \(E_{b}\) & rated capacity of the energy storage system \\ \(\gamma\) & arbitrage incentive coefficient \\ \(\alpha^{f}(t)\) & peak price indicators \\ \(\alpha^{g}(t)\) & valley price indicators \\ \(\zeta\) & baseline value representing the quantile \\ \(\eta_{c}\) & revenue deviation from baseline in scenario \(k\) \\ \(\eta_{c}\) & charging efficiency of the energy storage system \\ \(\eta_{d}\) & discharging efficiency of the energy storage system \\ \end{tabular}
## 3 Experimental Results
### Measurements of Mat Pressure Sensor
#### 3.1.1 First Experimental Test  
Initially, the measurements were performed on selected mat prototypes with the geometrical and technical details presented in Table 3.  
The resistance of the mat was measured using a technical method with a specific supply voltage, and the load on the mat was increased with a known weight. In the first test experiment, the measuring system was connected to the sensor mat and consisted of a  
Figure 4: Mat construction.  
\begin{table}
\begin{tabular}{l l l} \hline \hline Velostat Pressure Sensing Mat & n.1 & n.2 \\ \hline Transparent Plastic [cm] & 21.5 \(\times\) 17.5 & 16 \(\times\) 11 \\ \hline Copper Tape Conductive Adhesive [cm] & 0.5 \(\times\) 20 & 2.5 \(\times\) 34.5 \\ \hline Number of Copper Tapes & 14 & 8 \\ \hline Insulation Tape Adhesive [cm] & 0.4 \(\times\) 17 & 0.4 \(\times\) 30.5 \\ \hline Number of Insulation Tapes & 15 & 9 \\ \hline Velostat Conductive Sheet [cm] & 17 \(\times\) 17 & 30.5 \(\times\) 30.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Geometrical and technical specifications.  
power supply, two digital multimeters, and cables. The voltage and resistance measurements were conducted by using a resistor R of 250 \(\Omega\) connected in series to the mat, as displayed in Figure 3c. The supply voltage \(V_{g}\) is set to 5 V. The multimeters used to measure the electrical voltage and resistance were a PC510 Sanwa MULTIMETER, DIGITAL, with specifications of a voltage measuring range of AC 500 mV and 0.08% basic accuracy, and a Sanwa CD771, MULTIMETER, DIGITAL, with a measuring range of 400 mV and the best accuracy of (0.5% + 2) (Figure 5a). The DC power supply used is the TEKTRONIX PWS2323 BENCH POWER SUPPLY, 32 VDC, 3A. The electronic balance Lucznik KS-509 S is used to measure the weight of the materials and the foil.  
Four tests were conducted by using different contact elements to be applied on the mat, as shown in Figure 6. The pressure force was calculated on the mat using as contact element:  
1. a wooden disc with a radius of 48.3 mm and thickness of 4 mm (Test1)
2. a steel cylinder with a radius of 35.5 mm (Test2)
3. a small sponge (Test3)  
The variation in mat resistance (\(\Omega\)) in the function of pressure force expressed in Newtons for the different conducted tests is reported in Figure 6. As the force applied to the mat sensor increases, the mat exhibits a decrease in resistance.  
Figure 5: (**a**) Laboratory stand consists of the Velostat pressure sensor mat, force gauge, digital multimeter, and power supply; (**b**) Laboratory stand used for measurements conducted using the Arduino embedded system (board, cables etc.) and laptop compatibility with LabVIEW 2018 SP1.  
Figure 6: Resistance in function of pressure force applied on mat.
## 2 Study Area and Data  
### Study Area  
The study focused on two cases of forest fires that occurred in South Korea (Figure 1). Site A is Goseong, Gangwon-do and Site B is Andong, Gyeongsangbuk-do. As the frequency of dry days in winter and spring has grown, wildfires have become more common in these areas. The forest fire in Site A started at about 19:00 on 4 April 2019 and was put out around 18:00 on 5 April 2019. The damaged area was approximately 1227 ha. A forest fire in Site B started at about 15:00 on 24 April 2020 and was put out at 14:00 on 26 April 2020. The forest fire destroyed a forest area of around 1944 ha. While Site A has heterogeneous land-covers, including forest, cropland, built-up, waterbody, and snow with high-altitude mountainous areas, Site B consists of homogeneous land covers, such as forest and cropland.
# Mitigating Digital Ageism in Skin Lesion Detection with Adversarial Learning
## 3 Methods
### Fairness Criteria Definitions  
In the context of skin lesion detection, the fairness criteria proposed by [PERSON] et al. [14] are as follows.  
_Demographic Parity:_ This criterion ensures that the probability of positive predictions is approximately equal across different sensitive attribute groups. In other words, the model should not favor one group over another. The difference in the true positive rates for the sensitive (\(A=1\)) and non-sensitive (\(A=0\)) groups should be within a specified bound \(\epsilon\). This can be formulated as follows:  
\[|P(\hat{Y}=1|A=1)-P(\hat{Y}=1|A=0)|\leq\epsilon \tag{3}\]  
where \(\hat{Y}\) is the predicted class label, \(A\) is the sensitive attribute (e.g., race, gender), and \(\epsilon\) is the maximum allowed difference.  
_Equalized Odds_: This criterion ensures that the probability of false positives and true positives is similar across the sensitive and non-sensitive groups. The idea is to ensure that the model's error rates (false positives or negatives) are evenly distributed across the groups. The difference in the false positive rates for the sensitive and non-sensitive groups should be within a certain bound. This can be formulated as follows:  
\[|P(\hat{Y}=1|A=1,Y=0)-P(\hat{Y}=1|A=0,Y=0)|\leq\epsilon \tag{4}\]  
Figure 2: The process of learning a fair representation with MA-ADReL. An adversarial minimax game is established between a classifier \(g(Z)\) and adversary \(h(Z)\) based on the representation \(Z\) from an input image learned through a pretrained encoder \(f_{E}\). Together with a decoder \(f_{D}\), the architecture minimizes the classification, reconstruction, and adversarial losses.  
where \(Y\) is the true class label (e.g., \(Y=0\) indicates a benign lesion, \(Y=1\) indicates a malignant lesion). \(\hat{Y}\) is the predicted class label (e.g., \(\hat{Y}=1\) is a prediction of malignancy). \(A\) represents the sensitive attribute (e.g., gender, where \(A=1\) might represent male and \(A=0\) female). \(\epsilon\) again represents the acceptable difference between the false positive rates, ensuring equalized odds across groups.  
The fairness criteria defined in Equations (3) and (4) are straightforward to integrate for gender or racial bias mitigation by labeling them in discrete categories. However, dealing with continuously protected attributes, such as age, can be more challenging than with categorical attributes, because the fairness constraint needs to be defined over a continuous range of values. Manually classifying a continuous age distribution into different groups will also induce bias [1].
## 3 Mathematical Approach Using Homomorphic Encryption and Zero-Knowledge Proofs
### Results
#### 3.7.2 Timing Measurements for Generating and Verifying ZKP Proofs  
We analyzed the efficiency of generating and verifying ZKP proofs during each epoch to ensure computation integrity without exposing raw data. The average times for each dataset, shown in Table 3, provide insight into the feasibility of incorporating ZKP into Split Learning.  
ZKP generation and verification times were well within acceptable ranges for Split Learning, supporting effective data integrity without performance degradation. This illustrates the practicality of ZKP as a safeguard in collaborative training environments.
## 5 Limitations and Scope  
This study has certain limitations that must be addressed. At first, the surveillance study was restricted to a single tertiary care hospital instead of multiple institutions, which further limits the investigation, that may not reflect the antimicrobial resistance scenario in rural areas and other parts of Eastern India. Secondly, due to the exclusion of the information regarding the patients with prior antibiotic exposure and comorbidities, it potentially limits its application in real-world clinical scenarios. Thirdly, the seasonal trend analysis does not include any climatic factors, such as temperature and humidity details, which could have influenced the prevalence of pathogens. Fourthly, it does not include any molecular characterization of genes responsible for resistance, restricting its specific insights into the resistance mechanism. The scope of future studies must incorporate multicentric surveillance to obtain a geographical region-specific resistance pattern and explore temporal evolution. _K. pneumoniae_ was the most frequently isolated Gram-negative ESKAPE pathogen in our study, with the substantially highest proportion of isolates, leading to future research for understanding its clinical impact and resistance trend in bacteremia cases. Whole-genome sequencing of _K. pneumoniae_ isolates will further help in understanding the specific genes responsible for resistance and potential clonal dissemination [83]. The integration of artificial intelligence-based tools for real-time resistance prediction and empirical therapy guidance holds substantial promise, especially when trained on large-scale antibiogram datasets [84]. Finally, further extension of future scope to the surveillance of community-acquired infections and environmental sources that include effluents from hospitals might give a comprehensive overview of the regional AMR system supporting the One Health-driven policy interventions.
# Road Extraction Based on Improved Convolutional Neural Networks with Satellite Images
## 2 Methods
### The Construction of the D-Dense Block  
Three consecutive dilated convolution layers are plugged after the original dense block, and each layer has its expanding rate (2, 4 and 8) (Figure 4). Each dilated convolution is set as BN-ReLU-Conv (\(1\times 1\))-BN-ReLU-D_Conv. The computing process iterates n plus 3 times, then the D-Dense block generates feature maps by using channels \((n+3)\times k\).  
Each D-layer structure is shown in Figure 5. Here, \(r\) represents the expanding rate of dilated convolution.
## 2 Principle of Operation
### Parity Checker  
A parity checker plays a pivotal role in the reliability of digital communication systems by ensuring the precision of transmitted data through error detection. In its fundamental operation, this checker introduces an extra bit, commonly referred to as the parity bit, into the transmitted data stream. To implement parity checking in the circuit depicted in Figure 3, we enhance its configuration by incorporating an additional XOR gate. The even checker (Ce) is formed by launching the output Pe alongside \(\text{P}_{\text{e}}^{\prime}\), while the odd checker (Co) is established by launching the output Po alongside \(\text{P}_{\text{o}}^{\prime}\). The terms \(\text{P}_{\text{e}}^{\prime}\) and \(\text{P}_{\text{o}}^{\prime}\) correspondingly mirror Pe and Po generated during the parity generation process. The expressions for the even and parity checkers are \(\text{Ce}=\text{P}_{\text{e}}^{\prime}\oplus(\text{A}\oplus\text{B}\oplus \text{C}\oplus\text{D})\) and \(\text{Co}=\text{P}_{\text{o}}^{\prime}\oplus\overline{\left(\text{A}\oplus \text{B}\oplus\text{C}\oplus\text{D}\right)}\), respectively. If the parity checker yields a '1', it signifies the occurrence of an error during the data transmission from one location to another. Conversely, a '0' output from the parity checker indicates that no error was detected during the transmission process. This extension augments the circuit's capabilities, allowing it to effectively perform parity checking and contributing to the overall accuracy and reliability of digital data transmission in communication systems. In this context, the parity checker output Ce = '1' indicates an error has been detected, hence the \"Error\" notation in the truth table. The \"Error\" notation at Ce = '1' aligns with the principle that the parity checker is designed to flag discrepancies. Therefore, the \"Error\" label is used to emphasize the detection of an inconsistency or error during the parity checking process, rather than providing a direct logic state of the XOR operation. This label is intended to highlight that the system has detected an anomaly, prompting further investigation or corrective action. The truth table for 4-bit parity checking is shown in Table 1.  
Figure 3: Diagram and truth table for 4-bit parity generation using CR-SOAs-MZIs.
# A Charging and Discharging Data Privacy Protection Scheme for V2G Networks Based on Cloud-Fog-End
## 4 Correctness and Security Analysis
### Security Analysis
#### 4.2.2 Confidentiality
Challenge stage: Adversary \(A_{I}\) randomly selects two messages of equal length, \(m_{0}\) and \(m_{1}\), and randomly selects two identities, \(ID^{*}_{i}\) and \(ID^{*}_{r}\), where \(ID^{*}_{r}\) is the challenge identity. [PERSON] selects \(d\in(0,1)\) and performs a signcryption query for \(m_{d}\) to obtain an aggregation signcryption \(\varphi^{*}=(R^{*}_{i},C^{*}_{i},S^{*})\) and sends it to \(A_{I}\). After receiving \(\varphi^{*}\), \(A_{I}\) continues to initiate a series of polynomial bounded queries, but \(A_{I}\) cannot perform partial private key queries of \(ID^{*}_{r}\) and the unsigncryption queries of \(\varphi^{*}\). Guessing stage: Through the various queries in the first stage, adversary \(A_{I}\) outputs \(d^{*}\) as their own guess about \(d\). If the guess is correct, challenger C outputs \(d^{*}_{i}R^{*}_{i}=b(Y^{*}_{i}+h_{1i}P_{pub})=b[(1-h_{1i})P_{pub}+h_{1i}P_{pub} ]=bP_{pub}=abP\) as the solution to ECDHP, where \(R^{*}_{i}=bP\). From this, [PERSON] solves the ECDHP problem with \(A_{I}\). Theorem 2 (Confidentiality under Adversary \(A_{I}\)): _In the case of a stochastic prediction model and ECDHP difficulty, adversary \(A_{II}\) can win IND-CCA2 with a non-negligible advantage \(\epsilon_{12}\), there exists C who can solve the ECDHP difficulty problem with at least a non-negligible probability \(\left(1-\frac{q_{ab}}{2^{k}}\right)\left(1-\frac{q_{2}}{2^{k}}\right)\frac{ \epsilon_{12}}{en(q_{3}+q_{ab}+1)}\)._  
Proof of Theorem 2.: Challenger C is given a random ECDHP challenge instance \((P,aP,bP)\), where \(a,b\in Z^{*}_{q}\) and its values are all unknown, and [PERSON]'s goal is to calculate the value \(abP\) by the adversary \(A_{II}\). The proof process is similar to Theorem 1. In summary, challenger C has the ability to solve ECDHP, but this contradicts ECDHP, so the scheme satisfies confidentiality under the attack of adversaries \(A_{I}\) and \(A_{II}\).
## 2 Materials and Methods
### Measuring the Simulation Results  
A visualization of blush simulation is shown in Figure 3a. As an assumption, the shape of the defect was assumed, due to the visual similarity and to avoid adding unnecessary complexity to the problem, assumed to be similar to an ellipse. Therefore, the formula for calculating the area of the ellipse has been applied to determine the area of the defect. Figure 3b represents the measures needed to calculate of the defect area.  
Figure 2: (a) pvT data and (b) viscosity curves of the Benvic IR705 PVC material.  
The area of the ellipse can be calculated by Equation (1):  
\[Area=\pi\times\sideset{}{L}{\Big{/}}{2}\times\sideset{}{W}{2} \tag{1}\]  
where \(L\) and \(W\) represent the large and the small diameter of the ellipse, respectively. To calculate the area of the defect, a direct measurement of the value of \(W\), the value of \(L\) was calculated by measuring the length of the arc from top edge to bottom edge of the defect, as shown in Figure 3b, via Equations (2)-(7).  
\[\beta_{1}=\tan^{-1}(\sideset{}{h}{1}{\Big{/}}{g_{1}}) \tag{2}\]  
\[\alpha_{1}=180-(2\times\beta_{1}) \tag{3}\]  
Having Equations (2) and (3), the angle of the top arc of defect (\(\alpha_{1}\)) can be calculated. Additionally, Equations (3) and (4) calculate the same parameter (\(\alpha_{2}\)) for the bottom part of the defect.  
\[\beta_{2}=\tan^{-1}(\sideset{}{h}{2}{\Big{/}}{g_{2}}) \tag{4}\]  
\[\alpha_{2}=180-(2\times\beta_{2}) \tag{5}\]  
Equation (6) sums up the amounts of top and bottom angle of the defect's arc. Thus, \(\alpha_{T}\) represents the total angle of the defect's arc.  
\[\alpha_{T}=\alpha_{1}+\alpha_{2} \tag{6}\]  
\[L=\frac{\alpha_{T}}{360}\times 2\pi R \tag{7}\]  
According to Equation (7), the value of \(L\) can be calculated. Having the values of \(L\) and \(W\), the area of the defect was calculated in each case through Equation (1).
## 3 Results and Discussion
### Fresh Performance
#### 3.1.4 Inter-Test Relationships  
Figure 12 details the inter-relationships (linear simple regressions) between the results obtained in the different fresh tests. It can be appreciated that slump flows and \(t_{500}\) times obtained in the slump-flow and 16-bar J-ring tests showed proportional values, indicating that the free-flow results of SCC containing RCWTB exhibited the same trends with and without the presence of obstacles. However, the trend of the blocking ratios depended on whether SCC flowed in an imposed direction (3-bar L-box test) or whether it flowed under free-flow conditions (16-bar J-ring test). Therefore, a non-precise relationship between them was detected (Figure 12c). It is suggested that the cement paste was able to drag them more easily and successfully under free-flow conditions, which are generally the most favorable for SCC movement [42; 73].  
Figure 10: Experimental results of the 16-bar J-ring test: (**a**) slump flow; (**b**) \(t_{500}\); (**c**) blocking ratio.
# A Distributed Energy-Throughput Efficient Cross-Layer Framework Using Hybrid Optimization Algorithm
## 3 Energy-Throughput Efficient Cross-Layer Solution Using MPNMRA for WUSNs
### FEC Schemes  
For channel coding systems, forward error correction (FEC) offers an advantage over automatic repeat request (ARQ) in terms of improving the reliability of transmission lines without introducing additional retransmission or overhead costs. FEC codes add redundancy to the transmitted data, allowing the receiver to detect and correct errors without requesting retransmissions. In the context of energy-efficient coding for WSNs, multilevel cyclic BCH codes have been found to outperform convolutional codes and achieve approximately 15% better performance [6].  
Therefore, it is crucial to strike a balance between transmission quality and energy consumption in WSNs. The proposed approach aims to achieve this balance by optimizing the energy-throughput efficiency. By carefully selecting the appropriate coding scheme, such as FEC codes like multilevel cyclic BCH codes, the system can achieve reliable transmission while minimizing energy consumption and maximizing throughput. This cross-layer approach allows for a compromise between transmission quality and energy efficiency, ensuring maximum energy-throughput efficiency in WSNs.