esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of Hydrolysis on the Antioxidant Activity of Olive Mill Waste
## 4 Discussion
### Total Phenolic Content, Total Flavonoid Content and Total Ortho-Diphenolic Content
#### 4.1.2 Correlation between TPC, TFC and TdOPC  
For both macerated and alkaline hydrolysed extracts, TPC had a strong significant positive correlation with TFC. In the literature, it was found that TPC is significantly correlated with TFC. In fact, a study conducted by [PERSON] et al. [4] on virgin olive oil stated that TPC was positively correlated with TFC (\(\rho\) = 0.90). Another study conducted by [PERSON] et al. [47] on phenolic content variability, along with antioxidant, antimicrobial and cytotoxic potential of selected traditional medicinal plants, stated that TPC and TFC shared a positive correlation. In this study, it was shown that flavonoids within methanolic extractshad the major constituents of TPC. On the contrary, no correlation with TFC was observed in acid hydrolysis.  
Similarly, TPC in macerated and alkaline hydrolys extracts was moderately correlated with TdoPC. This observation is in line with the study conducted by [PERSON] et al. [4], where the TPC in methanolic extracts was found to be correlated with TdoPC (\(\rho\) = 0.91). Figure 9 shows that heating is the reason why acid hydrolys extract has a lower correlation coefficient compared to that of alkaline and methanol extracts. This corroborates a study on grape canes conducted by [PERSON] et al. [48]. In this study, it was seen that extracts obtained after acid hydrolysis had a lower TdoPC when compared to extracts obtained after alkaline hydrolysis.  
In contrast to the above correlations in Table 4, the correlation between TFC and TdoPC was much higher. Little literature can be found on the correlation between flavonoids and ortho-diphenolic compounds. [PERSON] et al. [4] stated that TFC appeared to be correlated with TdoPC (\(\rho\) = 1.00). The catechol functional group found in some flavonoids may be the reason why TFC and TdoPC shared a high correlation; this is mainly attributed to the catechol functional group, which tends to be a more efficient antioxidant.
# Enhancing Security of Online Interfaces: Adversarial Handwritten Arabic CAPTCHAs Generation
## 2 Literature Review
### Adversarial CAPTCHAs
[44] combined adversarial noise with style transfer to enhance Latin CAPTCHA robustness. * Chinese Script: [PERSON] et al. [45] employed multi-target adversarial attacks on Chinese character CAPTCHAs, reducing solver accuracy to 12% while maintaining 88% human accuracy. * Cross-Script Analysis: Unlike Arabic, Latin and Chinese CAPTCHAs benefit from extensive datasets and standardized fonts. Arabic's cursive nature, contextual letter forms (e.g., isolated, initial, medial, final), and diacritics introduce unique challenges, making adversarial perturbations more effective against segmentation and recognition models. This work bridges a critical gap by tailoring adversarial techniques to Arabic's script-specific complexities.
## 4 Activation Methods of RP
### Other Activation Methods
They reported that the mortar activity indexes of dry carbonization, liquid carbonization treatment, and microbial mineralization treatment were 56.3%, 60.2%, and 76.4%, respectively, when subjected to the identical water demand and activation circumstances. The AI of the MM-RHCP was 42.8% more than that of the untreated RHCP mortar. The AI of MM-RHCP reached 70% after 7 d of mineralization treatment. Nevertheless, following the initial 7 d activation period, the AI gradually increased as the mineralization time was extended. They advised treating the RHCP to microbial mineralization for a duration of 7 d, at conditions of ambient temperature, atmospheric pressure, and 80% humidity. [PERSON] et al. [52] evaluated the effect of mechanical, thermal, and chemical activations on the AI of RCPs. For the chemical activation, they found that CaO (3%) is the most ideal, followed by CaSO\({}_{4}\) (1%) and Na\({}_{2}\)SO\({}_{4}\) (2%), while CH (4%) is the least optimal. They found that the most optimal method for combined activation involves using CH and CaSO\({}_{4}\) in a 1:1 ratio. They stated that, of all of the activations, thermal activation has the most significant effect, followed by chemical and mechanical activations. The authors claimed that the primary factors contributing to this improvement are the refinement of particle properties through mechanical activation, the stimulation of activity through chemical activation, and the promotion of nucleation in RCP after thermal activation, resulting in an increased number of nucleation points for hydration product formation. [PERSON] et al. [231] measured the SAI of RFP as per JG/T 573-2020 [292]. As a result of the chemical activation test, the SAI of the NI1-1% (test group with water glass as the chemical activator) was 68.3%, slightly exceeding that of NS1% (68.2%) (test group with Na\({}_{2}\)SO\({}_{4}\) as the chemical activator). They further studied the impact of combining chemical and thermal activation on the mechanical characteristics of mortar. The activation technique entailed heating the RFP to 700 \({}^{\circ}\)C and then adding the water-glass activator (with a modulus of one and a content of 1%) to produce the mortar. The SAI of the activated specimens reached a maximum of 69.0% and 57.5%, respectively, as compared to the unactivated specimens. When using a 30% dosage of NaOH, Na\({}_{2}\)SO\({}_{4}\), and water-glass chemical activators, there was only a slight improvement in the strength of RFP. This improvement was only observed in the mortar system during the first 3 d of its formation. An inverse relationship was detected between the amount of activator or the modulus of the water glass and the strength  
Figure 13: Activity index (%) of chemical, CO\({}_{2}\), and nano activation methods for RP. at later stages (28 d). They concluded that the impact of thermal activation on the RFP was most pronounced at a temperature of 700 \({}^{\circ}\)C, with a dosage of 30%.
# Intelligent Traffic Model for Unmanned Ground Vehicles Based on DSDV-AODV Protocol
## 2 Method
### Dsdv-Aodv
#### 2.3.1 Work Mechanism of the Protocol
[CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]. Energy-Saving Routing Protocols for Smart Cities. _Energies_**2022**, _15_, 7382. [CrossRef]
* [PERSON] et al. (2008) [PERSON]; [PERSON] [PERSON]; [PERSON] A new approach for integrating proactive and reactive routing in MANETs. In Proceedings of the 2008 5 th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, Atlanta, GA, USA, 29 September-2 October 2008; pp. 377-383. * [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Traffic Management in Ad-Hoc Network by Selection of Suitable Routing Protocol. _J. Physics Conf. Ser._**2022**, _2322_, 012096. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]. Analysis of routing protocol performance for V2X communication. In _Emerging Trends in Photonics, Signal Processing and Communication Engineering_; Springer: Berlin/Heidelberg, Germany, 2020; pp. 179-185. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON] A survey on QoS routing protocols in Vehicular Ad Hoc Network (VANET). _Telecommun. Syst._**2021**, _78_, 117-153. [CrossRef]
* [PERSON] and [PERSON] (2020) [PERSON] [PERSON]; [PERSON] [PERSON] QMM-VANET: An efficient clustering algorithm based on QoS and monitoring of malicious vehicles in vehicular ad hoc networks. _J. Syst. Softw._**2020**, _165_, 110561. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Low delay and seamless connectivity-based message propagation mechanism for VANET of VCPS. _Wirel. Pers. Commun._**2021**, _118_, 3385-3402. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON] An adaptive V2R communication strategy based on data delivery delay estimation in VANETs. _Veh. Commun._**2022**, _34_, 100444. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] An adaptive high-throughput multichannel MAC protocol for VANETs. _IEEE Internet Things J._**2020**, \(7\), 8249-8262. [CrossRef]
* [PERSON] and [PERSON] (2020) [PERSON]; [PERSON] An Enhanced Synchronized Multi-Channel MAC Scheme to improve Throughput in VANET. _Int. J. Commun. Netw. Inf. Secur._**2020**, _12_, 153-161. * [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON] Enhanced QoS Routing Protocol for an Unmanned Ground Vehicle, Based on the ACO Approach. _Sensors_**2023**, _23_, 1431. [CrossRef]

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## 3 Methods: Bayesian Modelling
### Bayesian Model: White Box  
From the previous subsection, it is known that BC contributes to both PM\({}_{2.5}\) and PN\({}_{0.3-0.025}\). From Figure 1, it is also known that the relationship between BC mass concentration with PM\({}_{2.5}\) and PN\({}_{0.3-0.025}\) on a logarithmic scale are linear and non-linear, respectively. Therefore, the proposed structure of BC proxy can then be written as:  
\[\log_{10}[\text{BC}]=\beta_{1}+\beta_{2}\,\log_{10}[\text{PM}_{2.5}]+\beta_{3} \,\text{e}^{\beta_{4}\,\log_{10}[\text{PN}_{0.3-0.025}]}+\varepsilon \tag{1}\]  
The mathematical description of the proposed proxy structure can be simplified to be:  
\[\mathbf{y}=\beta_{1}+\beta_{2}\,X_{1}+\beta_{3}\,\text{e}^{\beta_{4}\,X_{2}}+\varepsilon \tag{2}\]  
where \(\mathbf{y}\), \(X_{1}\), and \(X_{2}\) are \(\log_{10}[\text{BC}]\), \(\log_{10}[\text{PM}_{2.5}]\), and \(\log_{10}[\text{PN}_{0.3-0.025}]\), respectively. The notation \(\varepsilon\) is a random error term that follows a Gaussian distribution, \(\varepsilon\sim\mathcal{N}(0,\sigma^{2})\) and \(\sigma^{2}\) is a noise variance. Finally, the model coefficient is symbolized by \(\mathbf{\beta}=\{\beta_{1},\beta_{2},\beta_{3},\beta_{4}\}\). The proxy based on Equation (2)  
Figure 1: Scatter plots between black carbon (BC) mass concentrations and size-fractionated particulate matter (PM) mass/number concentrations measured throughout Jordan. Pearson (\(r_{p}\)) and Spearman (\(r_{s}\)) correlation coefficients and mutual information (MI) values are shown on the top of each subplot. The red data points represent the used features for the BC proxies’ inputs whereas the blue data points indicate the remaining unused features.  
can be considered as a WB model since the relationship between the inputs and output are visible and transparent.  
The aim in Bayesian modelling is not only to find single \"best\" values of model coefficients (\(\beta\)), but rather to explicitly account for the uncertainty of the coefficient estimate using the posterior distributions of model coefficients. [PERSON] rule [48] can be defined as:  
\[\text{posterior}\propto\text{likelihood}\times\text{prior} \tag{3}\]  
The following sub-subsections will discuss the setup of the prior distribution and likelihood function and then describe the Bayesian inference for obtaining the posterior and predictive distributions.
# Evaluation of the Long-Term Performances of SMA-13 Containing Different Fibers
## 4 Results and Discussion
### Cracking Resistance
#### 4.2.2 Cracking Resistance at Medium Temperature  
The results of SCB test is presented in Figures 7 and 8. The two figures show FI and K\({}_{\text{IC}}\) respectively.  
As is shown in Figure 7, the FI decreases with the aging degree of asphalt mixtures. This is because the aging process makes the asphalt more brittle, leading to the rapid propagation of micro cracks to macro cracks. On the other hand, the FI of BFSMA-13 is higher than that of LFSMA-13, indicating that the crack propagation rate of BFSMA-13 is lower than that of LFSMA-13 at different aging degrees. The FI of BFSMA-13 is 91.1%, 110.6% and 50.7% higher than that of LFSMA-13 in unaged, short-term aged and long-term aged stages, respectively. This is mainly because that the fracture strength of basalt fiber is bigger than that of lignin fiber and it can disperse some part of the stress [40]. Therefore, basalt fiber can better reinforce the anti-cracking ability than lignin fiber when it comes to the fracture energy and the crack propagation rate.  
From Figure 8, it can be seen that the \(\text{K}_{\text{IC}}\) of BFSMA-13 increases with aging process, while that of LFSMA-13 is just the opposite. Additionally, The \(\text{K}_{\text{IC}}\) of BFSMA-13 is greater than that of LFSMA-13. The \(\text{K}_{\text{IC}}\) of BFSMA-13 is 11.5%, 20.7%, and 32.4% greater than that of LFSMA-13 in the unaged, short-term aged, and long-term aged stages, respectively. According to the theory of fracture mechanics, \(\text{K}_{\text{IC}}\) is proportional to the cracking resistance of the material. Therefore, it can be argued that the cracking resistance of BFSMA-13 is better than that of LFSMA-13.
## 2 Background  
Agricultural robot technologies are nowadays one of the main catalysts for smart industry [2], but in most cases, the research and application projects focus on the development of individual specific-purpose machines [3] or delve deeply into the topic of advanced control and obstacle avoidance. Moreover, when required, recognition capabilities based on CV are often considered outside actual robotic integration.  
Among the very few solutions developed in recent years in viticulture contexts, one noteworthy example is introduced in [4] as a full-robotic monitoring system based on images taken from a camera mounted on a mobile robot, which localizes itself in the field using simultaneous localization and mapping (SLAM). Nevertheless, neither a full adoption nor a partial integration of such solutions for the present case study is feasible, since none of them have been framed in a perspective of _low-cost_ architecture development. In fact, although this aspect of applications of cobotic solutions in agriculture is of crucial importance for the diffusion of the mentioned means, especially in those contexts where it is essential from a social viewpoint [5], little consideration has been spent in the subject matter. In particular, in [6], the authors deal with aspects related to general architecture costs, while in [7], the authors discuss low-cost camera applications.  
For a CV-assisted cobotic solution to comply to economic constraints, three crucial problems must be addressed: (a) machine vision for fruit (in particular, grape clusters) recognition, (b) chemicals spraying and (c) coordination among humans and robots. Inwhat follows, a brief outline of the state of the art in the literature is reported for each devised problem.
# Technology Trends for Massive MIMO towards 6G
## 5 Cell-Free Massive MIMO
When access points are distributed in a large area, other low-cost fronthaul transport technologies such as millimeter wave and terahertz wireless can both provide huge bandwidth and avoid costly wired fiber links [38]. One other option is combined fiber-wireless fronthaul/midhaul transport to balance a trade-off between link quality and cost [39]. In the latter method, the short-distance fronthaul links can be deployed wirelessly between each access point and its respective edge cloud. On the other hand, the midhaul transport from the edge to the center can benefit from extra-reliable fiber connections. Mitigating hardware impairments that naturally appear as a result of low-cost transceivers deployed at the access points and wireless fronthaul nodes is another critical aspect of the cell-free massive MIMO deployment on the legacy network. * **Green and sustainable implementation:** In recent years, energy-saving techniques by mobile operators have gained more importance in reducing the environmental footprint and designing next-generation mobile communication systems in a green and sustainable way. Several works considered access point switching on/off methods in this research direction to save energy in a cell-free massive MIMO system. For example, in [40], the total downlink power consumption at the access points is minimized by turning off some of the access points. In addition, the virtualization  
Figure 3: The C-RAN architecture with cell-free massive MIMO functionality. and sharing of cloud and fronthaul/midhaul resources are crucial for minimizing total end-to-end energy consumption. In [35], the minimization of total downlink power consumption is considered for not only access points (radio side), but also the fronthaul and cloud computing resources. It has been shown that %14 power saving in comparison to the cellular small-cell system is possible in a small-scale system by not only benefiting from turning off access points but also turning off unused optical fronthaul resources and digital units. The results in [37] indicate that fully virtualized end-to-end resource orchestration is critical when it comes to fully benefiting from the energy-saving potential of O-RAN. At the end of the day, one should consider the limitations, energy consumption models, and the energy-saving mechanisms of digital units and processors in the edge and center cloud for the complete treatment of energy efficiency in a cell-free massive MIMO system. In summary, one should consider the scalability aspects, the network architecture, and the respective limitations, energy consumption models, and the energy-saving mechanisms of digital units and processors in the edge and center cloud for the complete treatment of energy efficiency in a cell-free massive MIMO system. The role of AI in this context becomes critical, as it can provide smart solutions for better energy management, among other things.
## 4 Performance Evaluation
### Source Type Classification Results in a Single Apartment Building  
Table 3 presents F1 scores of the source type classification results with \(t\) variation. The F1 scores with HH and HD set to the same category are placed in parentheses to distinguish them from the results of the original task. As exhibited by the differences between the F1 scores in two representations, \(f_{\mathbf{\Theta}^{j}_{\text{t,APT 1}}}(\cdot)\) and \(f_{\mathbf{\Theta}^{j}_{\text{t,APT 2}}}(\cdot)\) confuse HH and HD for all tasks. Figure 5 shows the time-frequency representations of HD and HH at the campus building (Figure 5a,d), APT I (Figure 5b,e), and APT II (Figure 5c,f). HD at the campus building shows repeated impact noise patterns (peaks) induced by bouncing of the hammer when it was dropped on terrazzo tile flooring. However, HD obtained from both apartment buildings was generated with a hammer on the slabs covered with vinyl floorings, which prevented the hammer from bouncing and mitigated the peak patterns. Hence, the source type classification results of assuming HH and HD as the same category need to be additionally considered. Figure 6a presents trend of the F1 scores of the source type classification results with \(t\) variation. This plot visualizes the results of the test tasks (\(\mathcal{T}_{\text{t,APT 1}^{\prime}\mid\text{APT 1}}\) and \(\mathcal{T}_{\text{t,APT 2}^{\prime}\mid\text{APT 2}}\)). The F1 scores of the source type predictive functions on inter-floor noise from the unlearned positions, are lower than those of the cross-validation tasks (\(\mathcal{T}_{\text{t,APT 1}}\) and \(\mathcal{T}_{\text{t,APT 2}}\)). SoundNet-based classifiers under-performed for source type classification tasks, as well as exhibited significantly low F1 scores for \(\mathbf{s}\) at \(t=0.152\) and \(0.501\) s. Additionally, \(t\) influences the similar variations of F1 scores related to the VGG16-based classification.  
In summary, the CNN-based approach demonstrated the feasibility and generalizability of the source type classification to inter-floor noise from actual reinforced concrete apartment buildings. The adapted two-dimensional CNN exhibited marginally better performance than that of the adapted one-dimensional CNN. This may be explained by the sharable low-level notions in the pre-trained knowledge.  
In addition, appropriate selection of \(t\) influences the performance with improved F1 scores of cross-validation, as well as test tasks against the inter-floor noise transmitted through the unlearned floor sections.
## 2 Materials and Methods
### Feature Extraction
#### 2.3.1 Convolutional Neural Network
The fine-tuning ability of a CNN refers to how easily and effectively it can be adapted to new tasks or datasets through this process. A CNN with high fine-tuning ability is one that has learned robust and general features in its pre-training and can therefore be quickly and accurately adapted to new tasks with a few training examples. In this study, two subjects were considered, one as the source and the other as the target. As described in Section 2.1.1, datasets consist of 85 training characters (15,300 samples), and we consider all these samples of the source dataset for the initial training (pre-training) of the convolutional neural network CNN1. However, based on this study's aim, we decided to only use 30 characters from the target dataset to retrain the model (CNN2). Thus, we used 5400 samples, which represents one-third of the total training samples. This decision was made to reduce the calibration time by minimizing the number of training iterations needed. A detailed description of the data utilized for pre-training, fine-tuning, and testing is provided in Table 1, which includes details on the specific sources and targets utilized in each dataset, as well as the number of trials and stimuli presented during the training and testing phases.
## 7 Factors Affecting WRF-Based Wastewater Treatment
### Fungal Strain Selection  
The selection of fungal strains certainly impacts the overall performance of WRF-based wastewater treatment systems because different fungal strains may be able to degrade different classes of pollutants and may contain different sets of enzymatic profiles. White Rot species belonging to Phanerochaete, Trametes, Pleurotus, and Ganoderma are well known for their wastewater treatment traits in general, as per the documented literature and research. However, the selection of certain fungal strains depends on the type of wastewater treatment system, type of pollutants, and objectives of wastewater treatment. The selection of an appropriate strain, or even an engineered strain, will enhance the WRF-based wastewater treatment systems' efficiency substantially [107].  
\begin{table}
\begin{tabular}{c c c c c} \hline
**WRF Species** & **Pollutant Type** & **Sub-Pollutant Type** & **Removal Efficiency \%** & **References** \\ \hline \multirow{2}{*}{_P. ostreatus_} & \multirow{2}{*}{EDC} & Bisphenol A & more than 90\% & [99] \\ \cline{3-5}  & & Nonylphenol & more than 90\% & \\ \hline \multirow{2}{*}{_P. chrysosporium_} & \multirow{2}{*}{EDC} & Bisphenol A & 60\% & \multirow{2}{*}{[100]} \\ \cline{3-5}  & & Nonylphenol & 65\% & \\ \hline \multirow{4}{*}{_Anthracophyllum discolor_} & \multirow{4}{*}{PAHs} & Phenanthrene & 22.60\% & \multirow{4}{*}{[101]} \\ \cline{3-5}  & & Anthracene & 7\% & \\ \cline{3-5}  & & Fluoranthene & 19.50\% & \\ \cline{3-5}  & & Pyrene & 8.50\% & \\ \hline \multirow{2}{*}{_Pleurotus pulnonarius_} & PCBs & Aroclor & 95.40\% & [102] \\ \hline \end{tabular}
\end{table}
Table 6: Various pollutants removal efficiencies by WRF.