esa-sceva/satcom-chunk-collection huggingface dataset:
# Soft Sets Extensions: Innovating Healthcare Claims Analysis
## 4 Soft Sets Extensions
### Soft Set
#### 4.1.2 Example  
Let us define the universe of discourse \(U\) as a set of patients.  
\(U=\{\text{Patient1, Patient2, Patient3, Patient4}\}\) and a subset included in \(U\) representing patients with specific conditions:  
\[M=\{\text{Patient1, Patient3, Patient4}\}.\]  
Now, let us consider an attribute related to medical conditions:  
\[a=\text{condition},\]  
with attribute values representing different medical conditions:  
\[\text{Condition}=A_{1}=\{\text{diabetes, hypertension, asthma}\}.\]  
We define a function: \(F\): \(A_{1}\to P(U)\),  
where \(P(U)\) represents the power set of \(U\).  
Then, for example,  
\[F(\text{asthma})=\{\text{Patient2, Patient3}\},\]  
This means that both Patient2 and Patient3 have been diagnosed with asthma.  
This representation (Figure 1) allows us to capture complex relationships between patients and their conditions. It is particularly useful in healthcare claims data analysis because  
It can handle uncertainty: if a patient's diagnosis is uncertain, we could represent it by associating the attribute with multiple patients or using fuzzy sets within the mapping.  
Figure 1: Soft set representation of patient conditions.  
It accommodates missing data: if we do not know whether a patient has a particular condition, we simply would not include them in the corresponding subset.  
It facilitates pattern recognition: by looking at the mappings, we can easily see patterns like comorbidities (e.g., Patient 2 has both hypertension and asthma).  
This soft set representation provides a flexible framework for analyzing healthcare claims data, allowing us to capture and manipulate uncertain or imprecise information effectively.
## 2 Theoretical Background
### Design of Experiments (DoE) and Response Surface Methodology (RSM)  
Design of Experiments (DoE) is a statistical framework used to systematically explore the relationships between input parameters and system responses. By carefully selecting a reduced set of simulations, DoE maximizes the information extracted while minimizing computational expense. Common DoE techniques include Full Factorial (FF), Central Composite Design (CCD), and Latin Hypercube Sampling (LHS), each offering specific advantages depending on the complexity and dimensionality of the design space.  
Response Surface Methodology (RSM) complements DoE by constructing surrogate models--continuous approximations of the input-output relationship--based on the sampled data. These response surfaces enable rapid evaluation of system performance at arbitrary parameter combinations, facilitating fast optimization. Various techniques can be applied to construct response surfaces, including second-order polynomial regression, Kriging, non-parametric regression, and sparse grid interpolation.  
In this work, a Genetic Aggregation Response Surface (GARS) technique is applied. This method combines multiple individual response surfaces into a single composite model using a Multi-Objective Genetic Algorithm (MOGA). The aggregated response surface \(G(x)\) is computed as:  
\[G(x)=\sum_{i=1}^{n}\omega_{i}R_{i}(x) \tag{3}\]  
where \(R_{i}(x)\) denotes the individual response surfaces, and \(\omega_{i}\) are the weights optimized through MOGA to minimize prediction error and maximize fidelity to the training data.  
The optimization workflow integrates DoE for initial sampling, RSM for surrogate modeling, and genetic algorithms for multi-objective optimization, creating a highly efficient and flexible design framework. This combination of advanced techniques allows for comprehensive exploration of the design space and robust identification of optimal design configurations, particularly when dealing with complex multiphysics systems such as the CUSP payload.
# Sustainable Power Consumption for Variance-Based Integration Model in Cellular 6G-IoT System
## 5 Conclusions  
This study introduced a variance-based integrating model for the 6G-IoT approach that considered ECC. Initially, the BS charged huge IoT devices concurrently utilizing WPT in the downlink. After that, IoT devices with the gathered energy performed the communication task and the computation task in the uplink in a similar spectrum. To develop the overall ECC performance, a JBDA model was proposed for IoT devices and BS. From the results, for N = 32, when SNR = 17.5, the NCE of IACO work was less than 0.000, while the NCE of ZFBF was 0.110, the NCE of Fixed MMSE was 0.050, the NCE of UFBF was 0.060 and the NCE of JBDA was 0.055. Gradually, the NCE dropped down and when SNR = 2.5, the values came to a stable range until SNR = 17.5. The MSE of IACO work was 0.011, while ZFBF, Fixed MMSE, UFBF, JBDA, ACO and IACO-C obtained relatively high errors of 0.021, 0.028, 0.015 and 0.012. The MAE of IACO work was 0.086, while ZFBF, Fixed MMSE, UFBF, JBDA, ACO and IACO-C obtained relatively high errors of 0.091, 0.093, 0.093 and 0.090. The numerical findings demonstrate that the proposed IACO optimizes energy efficiency and reduces the power consumption by deploying only a subset of the activated RRUs in the integrated IoT models to enhance the energy efficiency. In the future, this research will be further extended by analyzing other optimization algorithms to consider the accuracy and other convergence factors.  
**Author Contributions**: The paper investigation, resources, data curation, writing--original draft preparation, writing--review and editing, and visualization were done by P.R. The paper conceptualization and software were conducted by S.S. The validation and formal analysis, methodology, supervision, project administration, and funding acquisition of the version to be published were conducted by L.D.N. and G.C.C. All authors have read and agreed to the published version of the manuscript.  
**Funding**: This research received no external funding.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.  
**Conflicts of Interest**: The authors declare no conflict of interest.  
**Nomenclature**  
Abbreviation  
Al  
Artificial Intelligence  
ANN  
Artificial Neural Network  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Performance** & **D2-QDPO3**[34] & **JEERA**[35] & **Proposed IACO** \\ \hline EE (Mbit/Joule) & 11.2 & 4.8 & 14.3 \\ \hline Throughput (Kbps/Hz/J) & - & 10 & 13.6 \\ \hline Latency (s) & 19.1 & - & 12.7 \\ \hline Power Consumption (dBm) & - & 48 & 34 \\ \hline Average Response Time (ms) & - & 147 & 98 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative Analysis.
## 3 FTN Signal Processing Based on the UEP Algorithm
### Simulation Results
The simulation result shows that the UEP-based FTN transmission method, with the applied priority algorithm, demonstrated a better performance than the conventional FTN transmission method. Figure 10: The performance of the FTN method based on the UEP algorithm (\(\tau^{\prime}=40\%\)). Figure 9: The performance of the FTN method based on the UEP algorithm (\(\tau^{\prime}=30\%\)).
## 3 Materials and Methods
### Empirical Formulae
In his model, bedload transport rate is computed from the volume of particles in motion (per unit streambed area) and their mean velocity. The Meyer-Peter-Muller formula is normally restricted to 0.4 to 28.6 mm uniform sediments or mixtures in steep rivers with high rates of bedload transport [4; 30]. Despite the fact that the data upon which the formula was based were obtained in flows with little or no suspended load, the MPM equation is commonly employed to predict bedload transport in a wide range of fluvial systems, even for flows with appreciable suspended loads. For [PERSON] [17], bedload transport reflects the random nature of particle paths and exchanges between the bed and stream. In his approach, the main driver of bedload transport is the number of sediment particles entrained. Although he used probabilistic concepts, his bedload equation is deterministic (and in good agreement with the MPM equation). The concepts of his work are in use today, e.g., bedload transport as an intermittent process, dimensional analysis, probabilities of entrainment and deposition, influence of turbulence on incipient motion [31; 32]. The Einstein-Brown formula is a modification developed by [PERSON], [PERSON] and [PERSON] of a formula by [PERSON] [33], presented in [PERSON] [18]. The equation was based on flume data with well-sorted sediments (d = 0.3-28.6 mm). The [PERSON] and the Einstein-Brown formulae are based on the concept that the fluid forces on the bed sediment fluctuate randomly due to the turbulence of the flow. They consider that a small amount of sediment moves randomly in jumps or steps even when the mean hydraulic forces at the bed are very small, resulting in continuous values of bedload at low transport rates [34]. The [PERSON] [21] formula was developed for particle sizes ranging between 0.2 and 2 mm. The bedload transport model for steady flow proposed by the author is a parameterization of a detailed grain saltation model representing the basic forces acting on a bedload particle. The Van Rijn model is appropriate for rivers transporting sandy sediments in conditions of subcritical flow.
## 3 Results and Discussion
### Comparisons between NIRExpNet and State-of-the-Art Methods  
The NIRExpNet and several state-of-the-art methods were compared in the Oulu-CASIA NIR facial expression database under dark illumination. All the methods employ the 10-fold cross-validation, and the parameters of the methods were set according to the original works.  
It is observed that LBP-TOP outperforms 3D HOG, which are both hand-crafted features, in terms of achieving higher recognition rates. This may suggest LBP-TOP is more suitable for NIR FER.  
NIRExpNet (78.42%) outperforms the LBP-TOP (72.33%) and 3D HOG (60.00%) that use hand-crafted features. This result verifies that NIRExpNet can automatically extract features which help to improve the recognition rate.  
It is also seen that NIRExpNet (78.42%) outperforms other CNN-based methods, such as DTAGN (66.67%) [38] and 3D CNN DAP (72.12%) [31]. The DTAGN and 3D CNN DAP both use temporal features. The higher recognition rate achieved by NIRExpNet could be because global and local features were fused in our design, which provide more information of facial expression. The results may also imply that the structure of NIRExpNet is more suitable for NIR FER.  
LBP-TOP can achieve the second highest recognition rate according to Table 4. In Table 5, we list the recognition rates of NIRExpNet and LBP-TOP on every type of expression for comparison. It is seen that NIRExpNet can achieve higher recognition rate nearly on every expression. One exception is the fear expression: LBP-TOP has higher recognition rate (68.18%) than that of NIRExpNet (62.44%). This may be due to the limited size of training data. If large database is available for training, a deeper network can be designed, which may improve the recognition rate of recognizing fear expression.  
Figure 5: Confusion matrix of NIRExpNet and global network for NIR FER (Numbers inside brackets represent the results of global network).  
We also compare the performance of other CNN-based methods in Table 5. It is seen that 3D CNN DAP can achieve higher recognition rate on every type of expression than DTAGN. Furthermore, NIRExpNet can achieve the highest recognition rate on every type of expression in all three CNN-based methods. This may be because NIRExpNet is well designed and suitable for solving the NIR FER problems. The 3D CNN DAP has very similar recognition performance with LBP-TOP. However, it is observed that 3D CNN DAP has much higher recognition rate on the disgust expression, and also much lower recognition rate on the fear expression. This may again indicate that the size of training data is not sufficient, in that for some expressions, the feature extracted by 3D CNN DAP is not general enough. A larger database for NIR FER research is needed.
## 2 Materials and Methods
### Regression Techniques  
In the analysis of our experimental data, regression techniques were implemented using MATLAB, a high-level programming and numeric computing environment. Linear regression was utilized to assess the strength and nature of the relationship between independent variables and a continuous dependent variable. The fitlm function in MATLAB facilitated the estimation of the coefficients by minimizing the sum of the squares of the residuals, providing a best-fit line to the observed data. The adequacy of the model fit was evaluated using the coefficient of determination, R2, and adjusted R2 values, along with F-statistics derived from an analysis of variance for the regression model. Multiple regression analysis was conducted when more than one independent variable was considered. The fitlm function was again employed, allowing for the assessment of each predictor's unique contribution to the dependent variable, while controlling for the effects of other predictors. The significance of each predictor was examined through \(t\)-tests, and multicollinearity was assessed using variance inflation factors. For binary outcome variables, logistic regression was applied using the fitglm function, specifying a binomial distribution. This method provided probabilities of the outcome as a logistic function of the independent variables. The results included odds ratios with corresponding confidence intervals, and the model's predictive accuracy was quantified by the area under the receiver operating characteristic curve. Non-linear relationships were modeled using the fitnlm function, which allowed for fitting arbitrary non-linear functions to the data. This method was particularly useful in cases where theoretical considerations suggested non-linear dynamics between variables. The iterative process adjusted parameters to minimize the residual sum of squares, and model fit was evaluated based on the residual analysis and goodness-of-fit measures such as Akaike's Information Criterion. Polynomial regression was carried out to capture non-linear trends by fitting polynomial equations to the data. This was accomplished using the polyfit and polyval functions, which provided a polynomial model of a specified degree based on the least squares estimation.
## 5 Variants of EOICHD Protocol  
In [27], authors compared EOICHD protocol with well-known cluster-based routing schemes, namely LEACH and its variant LEACH-C based on network lifetime, energy consumption, etc. The observations implied that the performance of EOICHD is far better compared to LEACH and LEACH-C. It was also understood that every CH node needs to be \(d_{opt}\) distance apart from one another so that the network's energy could be prolonged. However, the behavior of EOICHD for different values of \(d_{opt}\) is not understood well. To have a better understanding of the EOICHD protocol, in this paper, we extend the work presented in [27] by considering the three variants of EOICHD. In this paper, we have considered a network of randomly dispersed nodes over a network area of dimension \(100\times 100\) m\({}^{2}\). The variants of EOICHD are determined by dividing the network into zones of different sizes. We are proposing three variants of EOICHD protocol by dividing the network area into \((5\times 5)\), \((4\times 4)\), and \((3\times 3)\) zones. The variations in the number of zones result in distinctions in the value of \(d_{opt}\). The variants of EOICHD and estimations of \(d_{opt}\) values of the corresponding EOICHD variant are described further below.
# A Review on Deep-Learning-Based Cyberbullying Detection
## 9 Challenges, Open Issues, and Future Trends
### Future Trends  
Challenges and issues of technology may unveil the opportunity to conduct further research. There are many avenues to extend the above issues for deploying concrete research. We mainly discuss a few possible aspects as future trends.  
* Multilingual and multimedia content: In current times, social media and other virtual platforms are widely used among different levels of users in terms of age group, culture, language, taste, education, etc. Since social media is a vital platform for propagating cyber harassment, users may use multilingual and multimedia content; therefore, we may put more attention on building efficient cyberbullying detection systems for multilingual and multimedia content.
* Cyberbullying detection-specific word embedding: In recent times, researchers are introducing different domain specific word-embedding techniques, because these platforms produce accurate results for relevant sets of vocabularies. For example, Med-BERT is used for health-domain-based BERT-aware embedding systems. In this connection, researchers may propose a specialized word-embedding system for cyberbullying detection problems.
* Cyberbullying detection in SMS and email: Users are concerned with combating cyberbullying problems, which largely propagate through social media platforms. However, future researchers may put more attention on investigating Short Message Service (SMS)- and email-based cyberbullying detection methods.
* Cyberbullying impact on mental health: Cyberbullying may leave a long-term impact on the mental status of an individual. Some may take a life-threatening step or commit self-injury to curb the severity of the harassment and take death for granted. Therefore, mental health researchers can consider this issue as a timely topic and introduce different methods to fight against cyber harassment.
* Use of cutting-edge deep learning: With the advancement of deep-learning-based methods, we may introduce more subtle and delicate techniques to detect cyberbullying problems. For example, stacked and multi-channel CNN or Bi-LSTM-based cyberbullying-based frameworks or their advanced version or hybridization of these models may produce more sophisticated solutions to counter the problems.
## 5 Discussion
### Potential Human Health and Ecological Risks  
The presence of anthropogenic PTEs at concentrations above certain thresholds can pose an unacceptable risk to human health and surrounding ecosystems. For screening purposes, the median concentration of each PTE was compared to its generic reference level (Figure 10), which is statutorily defined as the concentration of a contaminant that does not result in a level of risk higher than the maximum acceptable limits for human health or ecosystems [14].  
Figure 10: Median concentrations of trace elements in the soil cores collected in each sampling site of the north transect (**a**) and south transect (**b**). The red dashed vertical lines indicate the regulatory guidance values for soils of southern Spain [46], above which adverse effects on human health may occur.  
Figure 9: Pollution load index (_PLI_) values of the soil core samples.  
The results show that the concentrations of As, Tl, and Pb in soil throughout the transects are well above the regulatory levels established for wetland use, reaching values above which adverse health effects may potentially occur. The Tl content in the sample 2b from the southern transect exceeded 100 times the guidance value. Consequently, the soil should be declared as polluted according to the Spanish regulatory framework, which was implemented following the guideline of the European Directive 2008/98/EC. The maximum allowable concentrations of Cu, Sb and Co were also exceeded in some samples. By contrast, the level of risk associated with exposure to the other anthropogenic PTEs (Zn, Cd and Se) is expected to be acceptable because their concentrations fell within the regulatory threshold values in all sampling locations.  
The ERI values were highly variable across the study area (Figure 11), ranging from 1185-1274 (very high ecological risk) in the vicinity of the sulphide heaps to 80-88 (low ecological risk) in the ochre zone. Interestingly, the surface soil collected in the sampling site 5a (ochre zone) of the transect north had an ERI value as high as 884, indicating very high ecological risk for the wetland, because the tidal channels drain the jarositic yellow zone and spread PTEs to distal areas.  
Apart from the concerns arising from exposure to soil contaminants, the efflorescent minerals are vulnerable to wind erosion and may contaminate the environment and affect human health [47]. By dissolving in the humid environment of the respiratory system, the readily soluble salts may enter directly into the bloodstream and cause potential health problems to nearby residential receptors [48]. It is important to note, therefore, that the concentrations of PTEs, notably Cu and Zn, detected in the efflorescent sulphates make these transient minerals potentially toxic through airborne respirable particles.
# Measurements and Analysis of Sound Reflections from Selected Building Facades
## 2 Materials and Methods
### Measurement Procedure and Equipment  
Measurement of sounds at the facade of buildings was carried out using two microphones (M1 and M2 or M1F and M2F) 1 m and 3 m away from the facade, respectively. During measurements at both heights of 2 m (M1, M2) and 6 m (M1F, M2F), 10 one-minute samples were taken each. This activity, at both heights, was repeated twice at each location. The result was a record of the history of sound pressure changes with time-weighted fast. The measurement set-up is illustrated in Figure 4. The position of microphone M3 allowed additional measurement in the free sound field as defined by Appendix B [20]. The ground was the only reflecting surface for this case. In addition, it was ensured that there was no reflecting surface behind the microphone (at least at a distance \(d\geq 2\cdot a^{\prime}\)) and in the direction normal to the direction of incidence of the sound wave (at least at a distance \(b\geq 4\cdot d^{\prime}\)). Distances were measured using a Leica DISTO A5 laser meter (Leica, Wetzlar, Germany) with a measuring range of 200 m and an accuracy of \(\pm 3\) mm. Recordings of measured  
Figure 8: Geometry of microphone location near reflecting surface.  
sounds were made using two SVANTEK noise analyzers (SVANTEK, Warsaw, Poland) with Class 1 windshields: the SV 977A (M3) and SV 958A (M1 and M2). Three GRAS 40 AZ microphones (GRAS, Copenhagen, Denmark) were used, with a range of measured sound frequencies from 0.5 Hz to 20 kHz. The microphones were mounted at a height of 4 m using a tripod (M3) and at elevation (M1 and M2) using a portable measuring stand [44]. The measuring instruments and the reference sound source used during the tests had valid calibration certificates. During the tests, periodic calibration of the measurement paths of the microphones used was carried out using an external standard SV03 class 1 sound source. During the calibration of the measurement paths, no deviation greater than 0.1 dB in relation to the reference sound was observed. During the data acquisition process, the environmental conditions were monitored using an SV 205 weather station (SVANTEK, Warsaw, Poland) positioned on a tripod at a height of 3 m from the ground under the M3 microphone. Tests at selected building facades were conducted on different days with similar meteorological conditions:  
* Temperature 17 \(\div\) 21 \({}^{\circ}\)C;
* Humidity 55 \(\div\) 70%;
* Average wind speed 0 \(\div\) 2 m/s.