esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of pH on Adsorption  
The pH of a solution greatly influences the behavior of adsorbents towards pollutants due to different interactions with them. The effect of pH on the removal of Cr(VI) and Ni(II) through Fe\({}_{3}\)O\({}_{4}\), PANI, and PANI/Fe\({}_{3}\)O\({}_{4}\) composites was determined by adjusting the pH of solutions in the range of 2-12 by adding some amount of 0.1 M HCl and 0.1 M NaOH as shown in Figure 6a. Concentration of each solution containing heavy metals was kept 50 mg\(\cdot\)L\({}^{-1}\). The graph shows that Cr(VI) adsorbed strongly in acidic media at pH less than 5 because at this pH chromium exists as an oxyanion (CrO\({}_{4}\)\({}^{-2}\), Cr\({}_{2}\)O\({}_{7}\)\({}^{-2}\), or HCrO\({}_{4}\)\({}^{-}\)) in aqueous solution. Moreover, at lower pH negatively charged sites like the oxygen of iron oxide and the nitrogen of amine groups in the chains of PANI and composites are protonated. These positively charged sites of adsorbent cause electrostatic interactions with negatively charged CrO\({}_{4}\)\({}^{2-}\), Cr\({}_{2}\)O\({}_{7}\)\({}^{2-}\), or HCrO\({}_{4}\)\({}^{-}\) ions, thus adsorption becomes enhanced [69]. However, in basic medium, deprotonation of these sites causes electrostatic repulsions between adsorbents and dichromate ions, hence causing adsorption to decrease in basic medium. [PERSON] et al. have studied the effect of pH on the removal of Cr(VI) by polypyrrole/calcium rectorite composite and reported that adsorption became decreased as the pH of the Cr(VI) ions solution increases [70]. The effect of pH on the removal of Ni(II) ions having 50 mg\(\cdot\)L\({}^{-1}\) concentrations was also studied from pH 2 to 12 at 30 \({}^{\circ}\)C shown in Figure 6b. The graph shows that adsorption of Ni(II) was less in acidic medium, because at lower pH, hydrogen ions may start to compete with Ni(II) ions for the surface active sites of adsorbents.  
But as the pH increases, the amine and imine groups in PANI as well as in composites become free due to un-doping, hence resulting in the enhancement of adsorption. The highest adsorption of Ni(II) occurred at pH 7.5, but as the pH further increased, adsorption once again decreased. Less adsorption of Ni(II) in strong acidic and strong basic environments may be due to the existence of nickel as Ni(OH)\({}^{+}\) and Ni(OH)\({}_{3}\)\({}^{-}\) respectively which may cause electrostatic repulsions with adsorbent materials. Early studies show that nickel exists in aqueous solutions as Ni(OH)\({}_{3}\)\({}^{-}\), Ni(OH)\({}^{+}\), Ni(OH)\({}_{2}\)\({}^{0}\), and Ni\({}^{2+}\) depending on the pH of solution; however, it exists predominantly in Ni\({}^{2+}\) form at pH 6-8 [71]. [PERSON] et al. have demonstrated that Ni(II) forms complex due to precipitation at pH higher than 8, thus causing pore blockage at the surface of the adsorbent materials [72].
## 4 Proposed Detection Scheme
### Performance Analysis  
The performance of the proposed joint estimation method is evaluated with respect to its failure probability, which can be defined as \(P_{f}=\text{Prob}\{(\hat{v},\hat{n})\
eq(v,i)\}\). In this section, consider the AWGN channel. Under the hypothesis \(H_{1}\) that the PSS signal is synchronized (i.e., \(m=v\) and \(n=i\)), it follows that the two terms on the RHS of Equation (15) are \(\sum_{k\in\mathcal{D}^{+}}\hat{Y}_{1}^{I/Q}(k+v)D_{n}^{I/Q}(k)\sim\mathcal{N}( \mu_{f1},\sigma_{f1}^{2})\) with mean \(\mu_{f1}=(N_{p}/2-1)E_{p}^{2}\) and variance \(\sigma_{f1}^{2}=(N_{p}/2-1)E_{p}^{2}(E_{p}\sigma_{W}^{2}+\sigma_{W}^{4}/2)\). Thus, the second term on the RHS of Equation (15) given by \(|\sum_{k\in\mathcal{D}^{+}}\hat{Y}_{l}^{Q}(k+v)D_{n}^{Q}(k)|\) follows a folded Gaussian distribution given by  
\[f(x)=\sqrt{\frac{2}{\pi\sigma_{f2}^{2}}}e^{-(x^{2}+\mu_{f2}^{2})/2\sigma_{f2} ^{2}}\cosh\left(\frac{x\mu_{f2}}{\sigma_{f2}^{2}}\right),\;x\geq 0 \tag{24}\]  
with mean  
\[\mu_{f2}=\sigma_{f1}\sqrt{\frac{2}{\pi}}e^{-\frac{\mu_{f1}^{2}}{2\sigma_{f1} ^{2}}}+\mu_{f1}\left[1-2Q\left(\frac{\mu_{f1}}{\sigma_{f1}}\right)\right] \tag{25}\]  
and variance \(\sigma_{f2}^{2}=\mu_{f1}^{2}+\sigma_{f1}^{2}-\mu_{f2}^{2}\), where \(\cosh(x)\) represents the hyperbolic cosine function and \(Q(x)\) represents the \(Q\)-function. For \(N_{p}\gg 1\), it is reasonable to assume that \(\Phi_{d}(m,n)\sim\mathcal{N}(\mu_{h1},\sigma_{h1}^{2})\) with \(\mu_{h1}=\mu_{f1}+\mu_{f2}\) and \(\sigma_{h1}^{2}=\sigma_{f1}^{2}+\sigma_{f2}^{2}\), whose probability density function (PDF) under the hypothesis \(H_{1}\) is denoted by \(f(x|H_{1})\). Under the null hypothesis \(H_{0}\) that the signal is not synchronized (i.e., \(m\
eq v\)), the sum of two terms on the RHS of Equation (15) is \(\sum_{k\in\mathcal{D}^{+}}\hat{Y}_{l}^{I/Q}(k+v)D_{n}^{I/Q}(k)\sim\mathcal{N}( 0,\sigma_{h0}^{2})\) with \(\sigma_{h0}^{2}=(N_{p}/2-1)E_{p}^{4}/2+(N_{p}/2-1)E_{p}^{2}(E_{p}\sigma_{W}^{ 2}+\sigma_{W}^{4}/2)\). As such, \(\Phi_{d}(m,n)\) under the hypothesis \(H_{0}\) is viewed as skew normal RV [21]  
\[f(x|H_{0})=\frac{1}{\sqrt{\pi\sigma_{h0}^{2}}}e^{-\frac{x^{2}}{4\sigma_{h0}^{ 2}}}\left[1-Q\left(\frac{x}{\sqrt{2}\sigma_{h0}}\right)\right]. \tag{26}\]For equally likely IFOs and SIDs, the conditional probability that the event \(A_{0}=\{\hat{v}=v,\hat{n}=0\}\) happens given that the PSS with ZC root index \(z_{0}\) was transmitted is expressed as  
\[P(A_{0}|z_{0})=\int_{-\infty}^{\infty}f(x|H_{1})\left[F(x|H_{0})\right]^{4M+1}dx, \tag{27}\]  
where \(F(x|H_{0})\) is the cumulative distribution function of \(f(x|H_{0})\).
# SMCSPO-Based Robust Control of AUV in Underwater Environments including Disturbances
## 3 Controller and Observer Design
### Spo
The perturbation of the entire sliding observer structure for monitoring the system states is given by  
\[\dot{\mathbf{X}}_{1}=\mathbf{\bar{X}}_{2}-\mathbf{k}_{1}sat\left(\mathbf{\bar{X}}_{1}\right) -\mathbf{\alpha}_{1}\mathbf{\bar{X}}_{1}, \tag{29}\]  
\[\dot{\mathbf{X}}_{2}=\mathbf{\alpha}_{3}\overline{\mathbf{U}}\mathbf{-}\mathbf{k}_{2}sat\left( \mathbf{\bar{X}}_{1}\right)-\mathbf{\alpha}_{2}\mathbf{\bar{X}}_{1}+\mathbf{\hat{\Psi}}, \tag{30}\]  
\[\dot{\mathbf{X}}_{3}\mathbf{=}\mathbf{a}_{3}^{2}\left(-\mathbf{\bar{X}}_{3}\mathbf{+}\mathbf{\alpha}_{ 3}\mathbf{\bar{X}}_{2}\mathbf{+}\overline{\mathbf{U}}\right). \tag{31}\]  
The perturbation (\(\mathbf{\hat{\Psi}}\)) can be calculated as  
\[\mathbf{\hat{\Psi}}\mathbf{=}\mathbf{\alpha}_{3}\left(-\mathbf{\bar{X}}_{3}\mathbf{+}\mathbf{\alpha}_{ 3}\mathbf{\bar{X}}_{2}\right), \tag{32}\]  
where \(\mathbf{k_{1}},\ \mathbf{k_{2}},\ \mathbf{\alpha_{1}},\ \mathbf{\alpha_{2}},\) and \(\mathbf{\alpha_{3}}\) are positive constants of the sliding observer, and \(\mathbf{\bar{X}}_{i}\mathbf{=}\mathbf{\hat{X}}_{i}-\mathbf{X}_{i}\) is the estimated error of the state variables. The saturation function in (29) and (30) is used to reduce chattering in the sliding surface. It is given by  
\[sat(\mathbf{\bar{x}}_{1})=\left\{ \begin{array}{cc}\mathbf{\bar{x}}_{1}/|\mathbf{\bar{x}} _{1}|,&if\ |\mathbf{\bar{x}}_{1}|\geq\epsilon_{0}\\ \mathbf{\bar{x}}_{1}/\epsilon_{0},&if\ |\mathbf{\bar{x}}_{1}|<\epsilon_{0}\end{array}  \right., \tag{33}\]  
where \(\epsilon_{0}\) is the SPO boundary layer.
## 5 Comparison of Different Topologies
### Topology Comparison Based on Scalability and Self-Organization
This scheme also allows simple routing protocols with small footprint that are scalable and occupy small memory space.
## 4 Opportunities and Open Research Areas in VANET Multimedia
### FM-Based Drivers' Image Notification in VANET  
In [54], the research investigates a driver alert system that utilizes visual notifications transmitted through the FM radio broadcasting network. The study investigates an approach to providing warning messages to drivers using images, aiming to enhance situational awareness and road safety. In this research, the authors propose a system where warning messages are conveyed to drivers through images rather than traditional text-based messages. The images are transmitted over the FM radio broadcasting infrastructure, which allows for broader coverage and reach to a wide range of vehicles equipped with FM receivers. The study explores the technical implementation of the system and evaluates its real-time effectiveness in delivering vital information to drivers. However, it is worth noting that the FM radio, while widely accessible, is not designed for disseminating VANET-related information. Its broad coverage area may inadvertently lead to the transmission of unsolicited messages.  
Unlike the dedicated nature of DSRC vehicular ad hoc networks (VANETs), FM radio lacks the tailored communication capabilities required to transmit critical notifications precisely to specific sections of the town, such as those facilitated by roadside units (RSUs) in VANETs. Secondly, the limited range of FM radio antennas may lead to inconsistent and uneven coverage, potentially resulting in some vehicles not receiving vital warnings in time, as expected by [54]. In contrast, the infrastructure of DSRC VANETs, with its RSUs strategically placed in proximity to roadways, ensures comprehensive coverage and efficient message dissemination. This targeted approach optimizes the delivery of audio-visual notifications, enhancing situational awareness for drivers within the VANET environment.
## 3 Problem Description and Methodology
### Formulation of The Problem  
We first introduce the problem of classification with class imbalance in spam detection on social networks and, then, extend it to cost-sensitive learning.  
Assume that given a dataset \(S=\{(x_{n},y_{n})\}(n=1,\ldots,N)\) with \(N\) data samples, \(x_{n}\) represents the \(n\)-th sample instance belonging to the input space, and \(y_{n}\) indicates the label of \(x_{n}\) and belongs to the label set \(Y=\{1,\ldots,K\}\). The goal of classification is to train a classifier \(f:X\to Y\) to minimize the expected error of the classifier on the training set.  
However, class imbalance is a common problem in many classification applications. Because a conventional classifier uses the same cost to classify all the considered classes, it is highly susceptible to skewed class distributions. In spam detection on social networks, [PERSON] et al. [11] found that the true positive rate (spam detection rate) of the positive class decreased significantly (by 33% on average) when the class imbalance rate (IR) rose from 2 to 20. In particular, when the class imbalance rate is 20, the average detection rate dropped to 34%, which means that spam detection misses more than 66% of spam.  
To solve the class imbalance problem, we use cost-sensitive learning in our ensemble learning framework. Cost-sensitive learning extends conventional classification techniques to the classification of imbalanced data by assigning different costs to each class, which punishes each class of errors differently based on the assigned costs. The training goal after applying the cost-sensitive learning is to find a classifier \(f:X\to Y\) that minimizes the expected risk.
## 2 The Analytic Network Process  
In the evolution that leads to accidents, the internal relationships within the same hierarchy are similar to a complex network. Different risk elements at the same level may be independent, interrelated, and self-relating or even have subordinate and dominant relationships [15]. Briefly, elements of the system influence or are influenced by other elements. LNG storage tank leakage accidents have typical characteristics described above.  
Currently commonly used MCDM methods include AHP, the Technique of Order of Preference by Similarity to the Ideal Solution (TOPSIS), and other decision-making methods [23]. However, none of the above methods can consider the interdependence between risk factors leading to accidents. AHP is the most common MCDM method in risk assessment, and most previous studies used AHP to evaluate risk factors. AHP only considers the linear association between different hierarchies but ignores the interdependence between indicators, which exists with certain limitations and deviations [24; 25].  
Analytic Network Process is a decision-making method based on the Analytic Hierarchy Process (AHP) that adapts to a non-independent recursive hierarchy. Compared with the AHP model, the ANP model exists not only a hierarchical structure [18; 19; 20] but also a network hierarchy of internal recycling dominance at the same level. ANP is able to consider the internal connections of the clusters, replace the linear hierarchy with a nonlinear structure, and add the feedback mechanism to consider the dominant role of low-level elements on high-level elements [23]. The model solves the limitation of independent internal elements within the same level of AHP and comprehensively considers the complex interrelationships between different criterion layers [24]. The calculations of ANP are more realistic. Moreover, ANP is an MCDA method capable of providing consistency verification, so it is also able to observe the interdependencies between all elements involved in the process [16]. The application of the ANP model is more accurate and comprehensive in the weighting process of the LNG storage tank leakage assessment model.
## 3 Computational Examples and Analysis
### Case Study II: Tri-Objective Optimization
#### 3.2.3 Results of Asphalt Mixture Proportion Optimization using GPR-AWMOPSO
Figure 10: The proportion optimization results for diatomic and basalt fibre modified asphalt mixture using GPR-AWMOPSO: (**a**) Pareto front showing three objectives (F\({}_{1}\), F\({}_{2}\) and F\({}_{3}\)) and (**b**) the primary objective function value (F\({}_{1}\) + F\({}_{2}\)) versus iteration. Figure 10 displays the swarm maximum value of the objective function (at the swarm best position) versus iteration for traditional MOPSO, MADS and GPR-AWMOPSO. After several numbers of iteration, it can be found that a significant increase in the maximum objective function value occurs, indicating that both traditional MOPSO, MADS and GPR-AWMOPSO algorithms are efficient and possess good precision in the proportion optimization of composite modified asphalt mixture. In terms of calculation speed, the convergence rates of GPR-AWMOPSO and MADS are similar. Moreover, it is observed that the optimization rate based on the proposed GPR-AWMOPSO has a faster convergence rate compared with the traditional MOPSO. (3) Performance Verification  
The high-temperature permanent deformation resistance, low-temperature crack resistance as well as moisture stability were also conducted to evaluate the comprehensive pavement performances of composite modified asphalt mixture designed by the orthogonal method and the proposed GPR-AWMOPSO algorithm. For the two groups of designed mixture proportions, three replicate specimens were prepared for the rutting experiment at 60 \({}^{\circ}\)C, splitting experiment at \(-10\)\({}^{\circ}\)C as well as immersion Marshall and freeze-thaw splitting experiments, and the corresponding results are shown in Figure 11. It can be observed that compared with the specimens designed by the orthogonal method, the dynamic stability of specimens designed by GPR-AWMOPSO increases by 6.03%, the tensile strain increases by 9.61%, and the residual Marshall stability and tensile strength ratio (TSR) increase by 1.02% and 1.18%, respectively. The comparison results verify that the proposed GPR-AWMOPSO algorithm is the best option for maximizing the performances of composite modified asphalt mixture.
## 2 Islanding Detection Methods
### Computational Intelligent Based Methods
#### 2.6.3 Decision Tree Method (DT)  
The decision tree approach is a hierarchical paradigm that divides a difficult decision-making challenge into a number of manageable choices. This technique uses a wavelet packet transformer or discrete wavelet transformer to extract the necessary features in order to detect islanding based on transmitted voltage or current signals. After the extraction of those features, the DT processes the given feature data set to detect the islanding [44]. The islanding relay's threshold setting can be optimized using this technique, allowing for the smallest detection area during islanding operation for various scenarios and configurations.
## 1 Introduction
LABs have been shown to exhibit strong antagonistic activity against both related and unrelated microorganisms, including marine pathogens from genera such as _Staphylococcus_, _Vibrio_, _Pseudomonas_, and _Escherichia coli_. This antagonistic effect is mainly due to a reduction in the pH, microbiological competition for nutrients, and the production of a range of inhibitory substances, such as lactic and acetic acids, acetoin, diacetyl, hydrogen peroxide, and bacteriocins [20]. Furthermore, live stocks can be exposed to multiple toxins from seawater, such as heavy metals, myxotoxins, and pesticides, among others, which may exert combined effects. In such instances, LAB play a crucial role through lactic acid fermentation. Probiotic LAB strains effectively eliminate toxins, while enhancing the nutritional value of the seafood [21]. Lactic acid bacteria (LAB), including some _Lactobacillus_ spp. (_Lactiplantibacillus plantarum_, _Lactiplantibacillus acidophilus_, _Lactiplantibacillus thermophilus_, _Lactobacillus delbrueckii_ subsp. _bulgaricus_, _Lactiplantibacillus casei_, etc. ), are commonly used as probiotics in fish and shellfish aquaculture nutrition [15; 18; 19; 22; 23; 24; 25]. Recent studies conducted with _Lpb. plantarum_ demonstrate encouraging outcomes in enhancing the survival [26; 27; 28; 29], growth, and physiological condition of shellfish [30]. While the exact functional role of _Lpb. plantarum_ in bivalves has not been established, numerous publications suggest their potential involvement in the nutrition or immunity of the host organisms [22; 23; 31]. The isolation of lactic acid bacteria from the marine environment holds the potential to significantly enhance their efficiency and adaptation. These highly beneficial strains are commonly derived from diverse sources, including water, soil, fish exudates, commercial products, and even the gastrointestinal tracts of the target animals [32]. For lactic acid bacteria to be effective, they must fulfill several fundamental criteria to ensure their survival in the specific habitat conditions where they are employed. Additionally, they need to exhibit desirable characteristics that are beneficial for aquaculture. The relationship between the host and the microbe is an aspect that requires more attention, as the properties of isolated strains are specific to their origin [33]. Therefore, the objectives of this study were: (1) to isolate and identify lactic acid bacteria from _A. opercularis_; (2) conduct supplemented feeding of queen scallop to investigate how the addition of lactic acid bacteria affects its health status; and (3) observe the influence of lactic acid bacteria on the growth, commercial quality, and biological indices of queen scallop.
# Optimization of CART Models Using Metaheuristics for Predicting Peach Firmness
## 5 Results
The _oneSE_ selection function defined in _care_ is used to prioritize simpler models during the tuning process. Therefore, the parameter values are automatically obtained to gain the best model fit using the repeated 10-fold cross-validation and _oneSE_ function, which chooses the simplest candidate model within one standard error of the best performance. By using 10-fold cross-validation repeated 10 times and selecting the simplest candidate model, the risk of overfitting is minimized. The tuned parameters for the ANN model are the number of units in the hidden layer and the weight decay. The result of the tuning process is the ANN model with 1 node in the hidden layer and 0.5 as the value of the weight decay parameter. The maximum number of boosting iterations in the tuned boosting model is 10, and the maximum depth of a tree is 1. For the random forest model, only the number of variables randomly sampled as candidates at each split is tuned, and its final value is 1. The tuned ANFIS model uses six linguistic terms, and the maximum number of iterations is limited to 17. The developed models made predictions on the 1000 different test datasets, and the mean RMSE value shown in Table 1 is calculated. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **RMSE** \\ \hline Default CART & 1.722285 \\ GA Optimized CART & 1.706319 \\ BA Optimized CART & 1.707731 \\ DE Optimized CART & 1.58699 \\ PSO Optimized CART & 1.617216 \\ DA Optimized CART & 1.574756 \\ GWO Optimized CART & 1.570924 \\ WOA Optimized CART & 1.574094 \\ BHO Optimized CART & 1.575638 \\ MLR & 1.654896 \\ ANFIS & 6.127994 \\ ANN & 1.612046 \\ Bagging & 1.684522 \\ Boosting & 1.576703 \\ Random Forest & 1.726419 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Test RMSE summary. The \(t\)-test is conducted to measure the statistical difference between the mean RMSE values (Table 1) of the default CART and its optimized variations. Table 2 shows the results of the conducted \(t\)-tests. Next, the power of the \(t\)-tests is calculated, and the results are given in Table 3.  
Since the conducted Welch \(t\)-test assumes the normal distributions of samples, Figure 5 shows the RMSE distributions for the default CART model and a few of its optimized variations.
###### Abstract  
Recently, the Third Generation Partnership Project (3 GPP) introduced new radio (NR) technology for vehicle-to-everything (V2X) communication to enable delay-sensitive and bandwidth-hungry applications in vehicular communication. The NR system is strategically crafted to complement the existing long-term evolution (LTE) cellular-vehicle-to-everything (C-V2X) infrastructure, particularly to support advanced services such as the operation of automated vehicles. It is widely anticipated that the fifth-generation (5G) NR system will surpass LTE C-V2X in terms of achieving superior performance in scenarios characterized by high throughput, low latency, and enhanced reliability, especially in the context of congested traffic conditions and a diverse range of vehicular applications. This article will provide a comprehensive literature review on vehicular communications from dedicated short-range communication (DSRC) to NR V2X. Subsequently, it delves into a detailed examination of the challenges and opportunities inherent in NR V2X technology. Finally, we proceed to elucidate the process of creating and analyzing an open-source 5G NR V2X module in network simulation-3 (ns-3) and then demonstrate the NR V2X performance in terms of different key performance indicators implemented through diverse operational scenarios.  
5G NR V2X; 3 GPP; C-V2X; connected vehicle; ns-3 simulation; V2X communication; performance analysis Article