esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Design and Methods
### Network Architecture Design
#### 3.1.2 Classification Network  
The transformed features from the feature projection networks are fed into the classification network, \(f_{c}(\cdot)\), which is responsible for predicting the corresponding labels. The classification network plays a crucial role in extracting meaningful patterns from the transformed features and making accurate predictions. It consists of several fully connected layers and a soft-max layer, with cross-entropy loss as the classification loss function. The classification loss is formulated as  
\[L_{c}[\mathbf{\hat{Y}},f_{c}(\mathbf{\hat{X}})]=\frac{1}{n_{s}+n_{l}}\mathcal{ L}(\mathbf{\hat{Y}},f_{c}(\mathbf{\hat{X}})), \tag{11}\]  
where \(\mathbf{\hat{X}}=[\mathbf{\tilde{X}}_{s};\mathbf{\tilde{X}}_{l}]\) consists of the transformed labeled data samples from two domains, \(\mathbf{\hat{Y}}=[\mathbf{Y}_{s};\mathbf{Y}_{l}]\) is the corresponding labels, \(f_{c}(\cdot)\) is the classification network, and \(\mathcal{L}(\cdot)\) is the cross-entropy loss function. By using the transformed features, rather than the original data, as input to the classification network, the model can benefit from the aligned feature representations and generalize better in the target domain with limited labeled data.
# Joint Uplink and Downlink Resource Allocation for D2D Communications System
## 3 Resource Allocation Algorithm
### Power Allocation
\(f\left(P_{m}^{c},P_{k}^{d}\right)\) is a convex function with respect to \(0\leq P_{k}^{d}\leq P_{\max}^{d}\) and the optimal power \(\left(P_{m}^{c*},P_{k}^{d*}\right)\) is found in the set of corner points: \(\Delta\Omega=\left\{\left(P_{\max}^{c},P_{L}^{u}\right)\left(P_{\max}^{c},P_{ H}^{u}\right)\right\}\). The optimal power allocation can be expressed as  
\[\gamma_{m,up}^{c}\geq\gamma_{\min}^{c},\gamma_{k,up}^{d}\geq\gamma_{\min}^{d} \tag{19}\]7  
Similarly, when D2D pairs reuse the downlink channel resources of CUs, the optimal power solution can be obtained by using the same method  
\[\gamma_{m,up}^{c}\geq\gamma_{\min}^{c},\gamma_{k,up}^{d}\geq\gamma_{\min}^{d} \tag{19}\]8  
\[\gamma_{m,up}^{c}\geq\gamma_{\min}^{c},\gamma_{k,up}^{d}\geq\gamma_{\min}^{d} \tag{19}\]9  
\[0\leq P_{m}^{c}\leq P_{\max}^{c},0\leq P_{k}^{d}\leq P_{\max}^{d} \tag{20}\]0  
\[0\leq P_{m}^{c}\leq P_{\max}^{c},0\leq P_{k}^{d}\leq P_{\max}^{d} \tag{20}\]1where \(\Delta\Psi\) is the optimal power set of \(P_{k}^{d}\), \(P_{L}^{d}\) and \(P_{H}^{u}\) are the lower and upper power values of \(P_{k}^{d}\), respectively.
## 4 Materials and Methods
### Material Characterization  
Instruments used for material characterization included: Axiomat microscope, Zeiss (Jena, Germany), in reflection mode, a confocal transmission microscope Leica SP8 X, UV-Vis Shimadzu UV--2401 PC spectrophotometer (Milan, Italy) and Fourier Transform Infrared-Attenuated Total Reflectance (FTIR-ATR) Perkin Elmer Spectrum Two Spectrophotometer (Rodgau, Germany). Raman spectra were collected using a LabRAM HR (Horiba-Jobin Yvon, Montpellier, France) spectrometer with 532 nm excitation laser under ambient conditions. Low laser power (<1 mW) was used to avoid heat-induced modification or degradation of the sample due to focused laser light during spectrum acquisition. The collection time was longer than 200 sec. The excitation laser beam was focused through a 50\(\times\) optical microscope (spot size 1 mm, and working distance 1 cm). The spectral resolution was 1 cm\({}^{-1}\). For AFM characterization, topographic and phase images were simultaneously acquired by the AutoProbe CP (Thermo-Microscope, Sunnyvale, CA, USA) microscope using a gold-coated Si tip with a radius of curvature < 10 nm and a resonant frequency of 80 kHz.in non-contact mode. Topographical images were used to determine the surface morphology and roughness (rms, root-mean-square roughness), while phase images highlighted the different chemisorption/packing of PDA on DE and PVP surfaces.  
Optical characterization of films of pure PVP-co-S alone and with PDA was performed by spectroscopic ellipsometry (SE). SE spectra of the pseudo complex refractive index \(\langle\)N\(\rangle\) = (n + ik) (where n is the real refractive index, and k is the extinction coefficient) and pseudoextinction coefficient were measured in the 1.5-6.5 eV (826-190 nm) range with a resolution of 0.05 eV at an incidence angle of 70\({}^{\circ}\) using a phase modulated ellipsometer (UVISEL, Jobin Yvon, Montpellier, France). To derive the thickness of PVP-co-S and its spectral dispersion of the refractive index, n, and the extinction coefficient, k, from the measured SE spectra of the pure PVP-co-S on glass, a simple substrate/homogenous film/air model fit analysis was used. Four Tauc-Lorentz oscillators described the main optical transitions of the PVP-co-S layer. From the optical modeling, a PVP-co-S thickness of 121 \(\pm\) 8 nm was derived that was consistent with the AFM measurements. Once the optical function of the PVP-co-S was determined, and the PDA optical properties were known, as shown in Ref. [30], the measured SE spectrum of PVP-co-S with PDA films on glass were fitted using Bruggeman effective medium approximation assuming a simple substrate/graded film/air model to derive the PDA incorporation (%volume fraction) along the film thickness. Voids were needed to estimate the density of the films.
# Optimizing CNT Loading in Antimicrobial Composites for Urinary Tract Application
## 2 Materials and Methods  
### Carbon Nanotubes  
For the preparation of samples with different CNT loadings and milling times, commercially available pristine multi-walled CNTs (CNT-O, Nanocyl(tm) NC3100, Sambreville, Belgium), produced by catalytic chemical vapor deposition with an average length and diameter of 1.5 um and 9.5 nm, respectively, were used. According to data from the manufacturer, these CNTs only contain residual traces of growth catalyst impurities [25].  
To produce CNT samples with different milling times, CNTs-O were physically modified by ball milling (BM) treatment. BM is a common technique used to adjust CNT length and open their closed ends, increasing their specific surface area [26]. Briefly, CNTs-O were submitted to BM treatment (Retsch MM200, Haan, Germany) at 15 vibrations s\({}^{-1}\) for 1, 2, 3 and 4 h.
## 3 Results and Discussion
### Optimization of Spectral Preprocessing Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Preprocessing Methods**} & \multirow{2}{*}{**LVs**} & \multicolumn{3}{c}{**LOO-CV**} & \multicolumn{3}{c}{**Prediction Set**} \\ \cline{3-6}  & & **R\({}^{2}\)cv** & **RMSECV\% (\(v/v\))** & **R\({}^{2}\)P** & **RMSEP\% (\(v/v\))** & **MREP\%** \\ \hline Raw & 7 & 0.9738 & 0.1912 & 0.9868 & 0.1654 & 9.51 \\ Nor & 8 & 0.9660 & 0.2175 & 0.9894 & 0.1590 & 8.93 \\ MSC & 8 & 0.9656 & 0.2254 & 0.9896 & 0.2380 & 9.96 \\ SNV & 8 & 0.9685 & 0.2172 & 0.9842 & 0.2549 & 10.76 \\ WT (k = 4, db3) & 10 & 0.9668 & 0.2150 & 0.9804 & 0.2055 & 14.71 \\ D2 st-17 & 6 & 0.9687 & 0.1968 & 0.9860 & 0.1815 & 10.00 \\ D1 st-9 & 6 & 0.9741 & 0.1661 & 0.9842 & 0.1931 & 11.07 \\ Nor-D1 st & 6 & 0.9696 & 0.1952 & 0.9923 & 0.1537 & 7.85 \\ D1 st-MSC & 5 & 0.9812 & 0.1558 & 0.9932 & 0.1413 & 7.10 \\ \hline \hline \end{tabular} Note: k means decomposition layers, db3 means wavelet function.  
\end{table} 
Table 2: Comparison of predictive performance of synergy interval partial least squares (PLS) calibration models based on different spectral preprocessing methods.
# Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning
## 1 Introduction  
### Pavement Management Systems  
Today, Road authorities worldwide are facing increasingly daunting challenges with regards to Pavement Maintenance and rehabilitation (M&R) programs. This is largely due to deficient budgets which have faced even further reductions over the last few years [1]. These reduced costs place significant stress on Pavement Management Systems (PMS), which try to balance budgets with the optimal road user conditions [2]. The PMS is highly data-dependent and the acquisition of this data can be costly and time-consuming. As a result of this, agencies are generally relegated to using conventional manual conditional surveys to detect and monitor the conditions of road networks [3]. This can lead to inefficient practices and strategies for interventions. As there is a direct relationship between accident rates and surface conditions [4], it is, therefore, vital to have effective systems in place to ensure the health of the road structure is kept in an optimal state. For this to be possible, authorities must have systems in place for the acquisition of road condition data, specifically: the identification of pavement distresses in the network, their location, classification and importantly their severity.
# Cyberbullying Analysis in Intercultural Educational Environments Using Binary Logistic Regressions
## 3 Methods
### Data Collection and Analysis Process  
The administration of the questionnaire in secondary schools was primarily authorized by the people in charge of the educative institutions. They were first informed about the aim of the study and then asked to complete an online form to participate.  
Secondly, an online authorization was requested from the legal guardians and parents of all participating students, insisting on the voluntarily nature of their participation and in the anonymity of the responses.  
Once consent was obtained, the application of the instrument began in the classrooms with the presence of the teachers. The completion of the questionnaire lasted between 12 and 20 min.  
Throughout this procedure, the ethical guidelines established in the Declaration of Helsinki and the protocol approved by the Ethics Committee of the University of Granada were followed.  
For the analysis of the data, the statistical software products SPSS and STATA version 25 were used. In order to verify the probability of students being cybervictims or cyberbullies from different ethnicities, cultures, or religions, the binary logistic regression analysis was used. This created a model that defines the relationship between the outcome or dependent variables (profile of cybervictims or cyberbullies) and the independent or explanatory variables (religion, ethnicity, and race) [56].  
In previous work, to do this, the dependent variables were converted to dichotomous, and were grouped in the following way: 1 = being a cybervictim and 0 = not being a cibervictim; 1 = being a cyberbully, 0 = not being a cyberbully. The analysis of one-hot encoding in R was used [57].  
\begin{table}
\begin{tabular}{c c c} \hline
**Province** & **N** & **\%** \\ \hline Granada & 59 & 7.8\% \\ Malaga & 70 & 9.3\% \\ Almería & 54 & 7.2\% \\ Jaen & 76 & 10.01\% \\ Cordoba & 102 & 13.5\% \\ Sevilla & 99 & 13.1\% \\ Cadiz & 62 & 8.2\% \\ Huelva & 47 & 6.2\% \\ Melilla & 108 & 14.3\% \\ Ceuta & 78 & 10.3\% \\ \hline Total & 755 & 100\% \\ \hline \end{tabular}
\end{table}
Table 1: Participants from the province of Andalusia, Ceuta, and Melilla.  
In the logistic analysis, the maximum likelihood method was used in order to choose the estimated value of the parameter, namely the one with the highest probability of occurring [58].  
In this analysis, the probabilities and the odds ratio are used to interpret the coefficients. The probability ratio \(\Omega\) is the correlation of the probability value calculated for x = 1, between the probability value calculated for x = 0 [59]. According to this, the odds ratio can be written as follows:  
\[\Omega\ (1,0)=(P\ (1)/[1-P\ (1)])/(P\ (0)/[1-P\ (0)]) \tag{1}\]  
In the binary logistic regression analysis, the resulting variable is coded dichotomously (0/1). The odds ratio is as follows:  
\[\Omega=e^{\beta 1} \tag{2}\]
## 1 Introduction
Wavelet packet transform contains powerful capacity to extract the features that are sensitive to the change of the system. In [13], wavelet packet transform was used to decompose the stator current signal and selected the coefficients as the feature to detect broken rotor bars, as well as air-gap eccentricity. Further research was performed in [14], in which the chosen depth and nodes to follow broken rotor bar are different from those in some other studies, like [15,16]. One of the important points which needs to be affirmed in wavelet packet-based techniques is the fact that frequency order is not the same as the node order due to down sampling [15]. To tackle this issue, the fine resolution advantage of wavelet packet decomposition (WPD) integrated with the spectral analysis of Fourier transform to extract the most appropriate sub-band including rotor fault feature frequency [17,18]. A further step toward an intelligent, reliable, and efficient diagnostic system may be provided by artificial intelligence (AI) techniques such as artificial neural networks (ANN), especially during the decision process. Wavelet analysis techniques and ANN were integrated for fault diagnosis in induction motors [14,19] and gearbox [20]. ANN, as one of the most popular classification methods for monitoring of equipment performance, uses the relationship between the type of fault and a set of patterns extracted from the collected signals without establishing explicit models. As can be seen in Table 1, these three researches also focus on rotor fault detection. However, there are some drawbacks in their studies, such as extracting only one feature to detect the fault or evaluating only one mother wavelet behavior impact on choosing the most appropriate frequency band. Almost all methods for fault classification are sophisticated. However, a trade-off exists so that increasing the complexity the fault detection capability is increased together with computational cost [23]. Therefore, the contribution of this paper consists of three points: In the signal processing step, exact localized fault frequency sub-bands are determined based on the combination of WPD and FFT named as wavelet packet signature analysis (WPSA) under arbitrary load conditions. In the feature extraction step, wavelet statistical parameters of are obtained from stator current and they are used as an input vector to the neural network (NN). In the classification step, a straightforward, small-sized, low-cost multi-layer perceptron neural network (MLP-NN) is used in order to have an intelligent, reliable, and noninvasive classifier.
## 4 Comparative Analysis  
### Clustering Methods Evaluation  
In our evaluation, we quantitatively compared the clustering performance of SVC, K-means, and GMMs using the following three widely recognized indices: the Silhouette Coefficient (SC), Calinski-Harabasz score (CHS), and Davies-Bouldin score (DBI). The Silhouette Coefficient measures how similar an object is to its own cluster compared to other clusters, defined for each sample as follows:  
\[SC=\frac{b-a}{\max(a,b)} \tag{11}\]  
where \(a\) is the average distance between a sample and all other points in the same cluster and \(b\) is the average distance between a sample and the points in the nearest cluster. Values closer to 1 indicate well-separated clusters. The Calinski-Harabasz score, calculated as the ratio of the between-cluster dispersion to the within-cluster dispersion, further validates the distinctness of clusters. The higher the CHS, the more distinct the clusters are [28]. In contrast, the Davies-Bouldin score, which evaluates the average similarity between each cluster and its most similar one, rewards lower values that imply better clustering performance [29].  
Figure 7 shows a bar chart comparing SC, CHS, and DBI for SVC, K-means, and GMMs. GMMs consistently outperforms the other two methods across all three metrics. The ability of GMMs to provide soft assignments helps it adapt to inherent variations in the battery data, allowing each cell to be assigned in a way that better balances the 15-cells-per-module requirement while accommodating natural differences in capacity, resistance, and RUL.  
In contrast, K-means, which strongly depends on the Euclidean distance to cluster centroids, can struggle with clusters that have non-spherical shapes or uneven data densities. SVC, particularly in its equal-number variant, does allow for more flexible boundaries compared to K-means, but the forced equal-cluster-size constraint means that if the natural distribution of battery parameters is strongly imbalanced, the clustering may not fully  
Figure 7: Comparison of evaluation indices for the clustering methods: (**a**) Silhouette Coefficient; (**b**) Davies–Bouldin score; and (**c**) Calinski–Harabasz score.  
capture all nuances of the data. As a result, both SVC and K-means can exhibit a slightly lower SC, a lower CHS, and a higher DBI than GMMs.
# AI-Driven Innovations in Software Engineering: A Review of Current Practices and Future Directions
## 6 Challenges and Considerations
### Ethical and Legal Issues
#### 6.2.2 Intellectual Property Concerns  
The use of AI in generating software artifacts raises complex intellectual property (IP) issues. Questions arise regarding the ownership of code or designs produced by AI systems: whether the rights belong to the creator of the AI, the user, or the AI itself [70].  
Moreover, AI models trained on proprietary or open-source code repositories may inadvertently replicate copyrighted material, leading to potential infringement disputes [71]. The legal frameworks governing AI-generated content are still evolving, creating uncertainty for organizations leveraging these technologies.  
Organizations must proactively address IP concerns by establishing clear policies and agreements that define ownership and usage rights. Consulting legal experts specialized in technology law can provide guidance. Additionally, implementing safeguards within AI systems to detect and prevent the unauthorized use of protected content can mitigate legal risks [72].
# Treatment of Aqueous Bromate by Superparamagnetic BiOCl-Mediated Advanced Reduction Process
## Appendix A  
As we discussed above, BrO\({}_{3}^{-}\) degradation in the UV/BiOCl-HAP\({}_{\text{SM}}\)/ formate system was mainly own to CO\({}_{2}^{\bullet-}\) induced reduction. Additionally, BrO\({}_{3}^{-}\) elimination by UV irradiation and adsorption can be ignored within a short reaction time (\(\leq\)5 min) (Figure 5). Considering CO\({}_{2}^{\bullet-}\) usually undergo one-electron reaction [52] and degradation intermediates are minor at the early stage of reaction (\(\leq\)5 min), the kinetic expression of BrO\({}_{3}^{-}\) degradation can be expressed as Equation (A1) and its integrated form as Equation (A2):  
\[-\frac{\text{d}\left[\text{BrO}_{3}^{-}\right]}{\text{dt}}=k_{\text{CO}_{2}^{ -},\text{BrO}_{3}^{-}}\left[\text{BrO}_{3}^{-}\right]\left[\text{CO}_{2}^{-} \right]\,,\] (A1)  
\[-\ln\frac{\left[\text{BrO}_{3}^{-}\right]}{\left[\text{BrO}_{3}^{-}\right]_{0 }}=k_{\text{CO}_{2}^{-},\text{BrO}_{3}^{-}}\int\left[\text{CO}_{2}^{-}\right] \text{dt}\,.\] (A2)  
Here, \(k_{\text{co}_{2}^{-},\text{ BrO}_{3}^{-}}\) is the second-order rate constant of BrO\({}_{3}^{-}\) with CO\({}_{2}^{\bullet-}\), [CO\({}_{2}^{\bullet-}\)] defined as the quasi-stationary concentrations of CO\({}_{2}^{\bullet-}\), and [BrO\({}_{3}^{-}\)]\({}_{0}\) and [BrO\({}_{3}^{-}\)] are the initial concentration of BrO\({}_{3}^{-}\) and concentration at time t, respectively.  
Because it is difficult to determine the absolute concentration of CO\({}_{2}^{\bullet-}\), the competitive kinetic method is applied. This approach is realized by introducing a reference compound (NB) of a known rate constant with CO\({}_{2}^{\bullet-}\)into the original reaction system. The reference compound (NB), which follows a similar kinetic equation to that for target compound BrO\({}_{3}^{-}\):  
\[-\ln\frac{\left[\text{NB}\right]}{\left[\text{NB}\right]_{0}}=k_{\text{CO}_{ 2}^{-},\text{NB}}\int\left[\text{CO}_{2}^{-}\right]\text{dt}\,.\] (A3)  
After dividing Equations (A2) with (A3), Equation (A4) is obtained:  
\[-\frac{\ln\left(\frac{\left[\text{BrO}_{3}^{-}\right]}{\left[\text{BrO}_{3}^{ -}\right]_{0}}\right)}{\ln\left(\frac{\left[\text{NB}\right]}{\left[\text{ NB}\right]_{0}}\right)}=\frac{k_{\text{CO}_{2}^{-},\text{BrO}_{3}^{-}}}{k_{\text{CO}_{2}^{-}, \text{NB}}}\,.\] (A4)  
The plot of \(\ln\left([\text{BrO}_{3}^{-}]/\ln[\text{BrO}_{3}^{-}]_{0}\right)\) versus \(\ln\left([\text{NB}]/\ln[\text{NB}]_{0}\right)\) yielded a straight line with a slope of \(k_{\text{co}_{2}^{-},\text{ BrO}_{3}^{-}}/k_{\text{co}_{2}^{-},\text{ NB}}=1.37\) as shown in Figure S2. The \(k_{\text{co}_{2}^{-},\text{ NB}}\) has been reported to be \(1.0\,\times\,10^{9}\) M\({}^{-1}\cdot\)s\({}^{-1}\) at pH 6-7 [52]. According to these values, \(k_{\text{co}_{2}^{-},\text{ BrO}_{3}^{-}}\) was found to be \(1.37\,\times\,10^{9}\) M\({}^{-1}\cdot\)s\({}^{-1}\). It is noteworthy that this determined value is corresponding to the initial attacking of CO\({}_{2}^{\bullet-}\) to BrO\({}_{3}^{-}\), which may generate an intermediate (not bromide).
# Mdpj _sciences_
## 1 Introduction
However, their flight model was two-dimensional rather than three-dimensional. [PERSON] et al. [26] proposed a maneuver decision-making method based on Monte Carlo tree search (MCTS), which uses MCTS to find the action with the maximum air combat advantage function value among seven basic maneuvers. This method verifies the feasibility of MCTS for maneuver decision-making, however, its action space is discrete as well. Although the above studies have made some progress, there are still some problems in current air combat maneuver decision-making. First, the action space of air combat maneuver decision-making is continuous, but existing methods usually adopt the method of continuous space discretization, which is a common drawback of existing methods. Second, existing decision-making methods determine whether it wins or not according to whether the target enters the missile attack area, namely, excluding the kinematic model and the dynamic model of the missile, which is another drawback. However, the target may not be shot down by the missile even if it enters the missile attack area. Therefore, it is necessary to determine whether it wins or not according to whether the missile hits the target. Third, RL algorithms often perform poorly in maneuver decision-making, so it is urgent to propose more effective and efficient RL algorithms address maneuver decision-making. These problems still require to be solved, and the goal of this article is to solve these problems. To solve the above problems of air combat, we propose a method based on the final reward estimation and proximal policy optimization (FRE-PPO). Our main contributions are as follows: (1) we used a deep RL method, namely proximal policy optimization, to handle maneuver decision-making in continuous action space. (2) We propose to use final reward estimation to replace the original advantage estimation function of the surrogate objective of proximal policy optimization to improve training performance and efficiency. (3) The proposed method is conducted in a self-play framework to generate air combat agents. (4) Ablation studies are provided to support our claims. Section 2 introduces the basic knowledge of RL. In Section 3, an air combat simulation environment based on an aircraft model and missile model is constructed, and an intermediate reward and final reward are designed. And the air combat agent training framework based on FRE-PPO is proposed, which can improve the maneuver decision-making ability of air combat agentsby means of self-play and FRE-PPO. In Section 4, ablation studies and simulation experiments are performed, and FRE-PPO is compared with the other four methods to verify its performance. Section 5 summarizes the experimental results and draws conclusions.