esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Sequential Microwave/Ultrasound-Assisted Extraction  
For the extraction process, a commercial microwave oven (Samsung, CQ4250, Seoul, Republic of Korea) with a frequency of 2.45 GHz and maximum input power of 1150 W was used. One gram of DOS was thoroughly mixed with a specific amount of the desired solvent in an Erlenmeyer flask. To prevent any possible thermal degradation of the sample  
Figure 1: Chemical structure of quercetin.  
and the loss of solvent during MAE, an intermittent heating-cooling process was used where after 10 s of irradiation, the flask was placed in an ice-bath and cooled down.  
This intermittent heating/cooling process was continued until the specified extraction time was passed. A digital temperature controller (Autonics, TCN4L-24R, Busan, Republic of Korea) was used to specify the actual temperature of the sample during the experiment. Then, the flask was closed with a rubber stopper and immersed in an ultrasonic bath (Elmasonic, P30H, Singen, Germany) and sonicated. For efficient sonication, the level of water in the bath was always above the level of solvent in the immersed flask. To prevent the excessive heating of the sample during the UAE process, the sample was cooled by adding ice cubes into the water bath, and the temperature was continuously monitored during the process. The manufacturer rated the apparatus with a peak power and effective power rating of 480 and 120 W, respectively; it had a proprietary algorithm for power adjusting based on system impedance. At a fixed bath volume (1.4 L) for 50, 60, 70, 80, 90, and 100% power settings, the peak powers were 171, 206, 240, 274, 309, and 343 W/cm\({}^{2}\), respectively, and the ultrasonic effective powers were 42, 51, 60, 69, 77, and 86 W/cm\({}^{2}\), respectively.  
After sonication, the sample was filtered through a Whatman filter paper (0.45 \(\upmu\)m). The sample was dried in an oven at 60 \({}^{\circ}\)C for 12 h till all the solvent evaporated. The dried sample was dissolved in 1 mL of methanol and placed in a microtube and centrifuged by a microcentrifuge (Labnet, C1301-P, Seoul, Republic of Korea) at 6000 rpm for 5 min. The samples were then analyzed to identify quercetin and total flavonoids concentration using a high-performance liquid chromatograph (HPLC). The optimal points at which the highest extraction yield was obtained were determined for the sequential extraction method (MUAE). The results were compared with those obtained by ultrasound-microwave-assisted extraction (UMAE) and the single extraction methods (MAE and UAE). The amounts of quercetin and total flavonoid content (TFC) were calculated based on a standard calibration curve developed using different concentrations of quercetin (1 to 100 mg/L) in methanol.  
The extraction yields were calculated with Equation (1):  
\[\text{Yield}(\%)=\frac{\text{Weight of extracted quercetin or total flavonoid (mg)}}{\text{Weight of DOS (g)}}\times 100 \tag{1}\]
## 3 Results and Discussion
### Emulsifying Capacity (EC) and Foaming Capacity (FC)  
Proteins are of utmost importance to food science, as they are used as stabilizers in food emulsions; this is of great concern because many foods are emulsions. [33]. The results of the EC and FC studies of SW and its treatment with ultrasonic pulses are shown in Table 2.  
The presence of ultrasonic pulses increased functional properties by 20 to 45%. This increase could be due to the denaturation suffered by the protein due to the ultrasonic pulses. Protein hydrolysates form hydrophilic and hydrophobic peptides, which is why they can be considered surfactant materials that help form oil-water emulsions. Emulsifying capacity is related to the size, type of protein, and type of peptide that is interacting at the interface [34]. When the formed emulsion is weak, it is due to the production of hydrophilic peptides that are located in the aqueous phase [35]. Therefore, protein concentration is related to the stability of the emulsions, since at low protein concentrations, the oil-water interface is controlled by diffusion. In contrast, at high protein concentrations, the activation energy does not allow for the migration of proteins in a diffusion-dependent manner, leading to aggregation of long-chain proteins and peptides [16; 36].  
The increase in foaming properties of proteins treated with ultrasonic pulses is related to solubility, pH, and the type of ion present in the protein solution [37; 38]. In sarcoplasmic proteins from rohu (_Laboe rolita_) [36], cod (_Gadus morhua_) [39], and sea bream (_Nemitperus hexodon_) an increase in foaming capacity has been found to be derived from an increase in pH on the primary side and the concentration of NaCl [38]. The sum of acid residues in fish proteins is greater than the sum of basic residues; therefore, minimum solubility is observed at an approximate pH of 4-5, and maximum solubility at alkaline pH [40; 41]. In this sense, the further a protein is from its isoelectric point, the better its foaming capacity. Similar results were reported for the devilfish and for giant squid [16; 41]. Extraction of proteins was carried out with different concentrations of NaCl, which directly impacts the formation of foam; when treating proteins with ultrasonic pulses, solubility increases due to denaturation. However, high NaCl concentrations decrease EC formation, possibly caused by aggregation and increased exposure to hydrophobic groups [42; 43].
###### Abstract
Some relevant studies for predicting the PPV are shown in Table 1.  
In light of the above, the good performance and adaptability of the ML models in predicting the PPV have been proven gradually, and hence a large number of studies of ML models for predicting the PPV emerged. Furthermore, the ML models with better \begin{table}
\begin{tabular}{l l l l} \hline
**Techniques** & **Input Variables** & **Number of Samples** & **Studies** \\ \hline RF, CART, CHAID, SVM, ANN & MC, HD, ST, PF, DI & 102 & [PERSON] et al. [1] \\ ANN & DI, MC & 100 & [PERSON] [45] \\ GEP, ANFIS, SCA-ANN & DI, MC, \(\rho\), SRH & 100 & [PERSON] et al. [47] \\ MPSO-ANN & DI, MC & 137 & [PERSON] et al. [48] \\ ANN, DT & TC, A, MC, NH, H, DI, HD, CPH & 137 & [PERSON] et al. [46] \\ ANFIS & DI, MC & 44 & [PERSON] et al. [49] \\ ANN & HD, ST, DI, MC & 182 & [PERSON] et al. [50] \\ FIS & B, S, ST, N, MC, DI & 120 & [PERSON] et al. [51] \\ ICA-ANN & BS, ST, MC, DI, V\({}_{\text{p}}\), E & 95 & [PERSON] et al. [52] \\ ICA & MC, DI & 73 & [PERSON] et al. [53] \\ ANN, FIS & MC, DI & 162 & [PERSON] [54] \\ \hline \end{tabular} Note: MC—maximum charge per delay; HD—hole depth; ST—stemming; PF—power factor; DI—distance from the measuring station to the blast face; \(\rho\)—rock density; SRH—Schmidt rebound hardness; TC—total charge; A—turned cross section area; NH—number of holes; H—hole diameter; CPH—charge per hole; B—burden; S—spacing; N—number of raw; BS—burden to spacing; E—Young modulus; V\({}_{\text{p}}\)—p-wave velocity; CART—classification and regression trees; CHAID—chi-squared automatic interaction detection; RF—random forest; ANN—artificial neural network; SVM—support vector machine; MPSO—modified particle swarm optimization; GEP—gene expression programming; ANFIS—adaptive neuro-fuzzy inference system; SCA—sine cosine algorithm; DT—decision tree; ICA—meperialist competitive algorithm; FIS—fuzzy logic.  
\end{table} 
Table 1: Research on blast-induced PPV prediction. adaptability and higher prediction accuracy used for the prediction of blast-induced PPV need to be further developed and improved. In the present study, we utilized a classic ML model termed the RF to conduct the PPV prediction task. After designing the regular RF model using the datasets collected from a quarry mine, we proposed five categories for modifying the regular RF model's weights and, as a result, making better predictions. The designed weighting frameworks include the improved weighted RF model based on the prediction accuracy of decision trees, as well as the optimized weights of decision trees obtained by three metaheuristic algorithms, i.e., whale optimization algorithm (WOA), gray wolf optimization (GWO), and tunicate swarm algorithm (TSA). Subsequently, four evaluation metrics were used to validate the performance of the developed models. Finally, a sensitivity analysis was conducted to identify the predominant factors for PPV prediction in this engineering case.
# Biomechanical Performance of the Cemented Hip Stem with Different Surface Finish
## 4 Analysis Results
### Interfacial Failure Analysis of the Cement-Stem Interface  
Table 2 presents the maximum tensile and shear stress induced at cement-stem interfaces, which were found to occur near the distal end of the stems, with a localized characteristic. The maximum interfacial tensile and shear stresses generated by a smooth stem were 10.6 and 6.27 MPa, respectively. For roughened stems, the maximum tensile and shear stresses were approximately 10.52 and 7.42 MPa, respectively. Since the integrity of the bonded interface was responsible for the long-term longevity of artificial hip replacement [2; 5], the extent of the failed interface was used to evaluate the performance of the cemented implants. The failure of the interface was quantified in the local region where the predicted stress exceeded the measured strength of the interface. As indicated in Table 2, for failure of the tensile mode, the percentage of interface failure induced by a roughened stem was 1.68%, while that induced by a smooth stem was about 0.96%. These findings indicated that the roughened stem tended to increase the interfacial separation rate when compared to the polished stem. Conversely, under the shearing failure mode, the percentage of failed interface around a smooth stem was 5.96%, which was higher than that around a roughened stem (3.47%). Additionally, the relative displacement of the cement-stem interface produced by polished and roughened stems was 65 and 46 \(\upmu\)m, respectively. Compared to stems with a smooth surface, stems with a roughened surface demonstrated superior ability to resist the relative micro-motion and shear failure along the cement-stem interface. Interface failure analysis indeed give some implications to the enhancement of bonding strength and reduction of stress level for maintaining the integrity of the cement-stem interface in cemented hip prosthesis.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Maximum Interfacial Stress (MPa)**} & \multirow{2}{*}{
\begin{tabular}{c} **Maximum** \\ **Interface** \\ **Micromotion** \\ \end{tabular} } \\ \cline{2-4}
**Surface** & \begin{tabular}{c} **At Proximal** \\ **Medial Region** \\ \end{tabular} & \begin{tabular}{c} **At Distal Lateral** \\ **Region** \\ \end{tabular} & \begin{tabular}{c} **Percentage of Failed Interfaces (\%)** \\ **Micromotion** \\ \end{tabular} \\ \cline{2-4}  & \begin{tabular}{c} **Tension/Shear** \\ \end{tabular} & \begin{tabular}{c} **Tension/Shear** \\ \end{tabular} & \
\begin{tabular}{c} **Tensile/Shear Failure** \\ \end{tabular} &
\begin{tabular}{c} **(\(\upmu\)m)** \\ \end{tabular} \\ \hline Smooth & 2.11/3.92 & 10.62/6.20 & 0.90/5.96 & 65 \\ Rough & 1.50/4.24 & 10.52/7.42 & 1.68/3.47 & 46 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Maximum stress induced within cemented femoral components under gait phase of 45%.  
Figure 8: Comparison of the stressed volume of cement mantle at different stress range produced by the stem with smooth and rough stem.
# Advanced Sensor Integration and AI Architectures for Next-Generation Traffic Navigation
## 1 Introduction  
Advanced traffic navigation systems are increasingly important in urban environments, where congestion presents significant mobility, safety, and environmental sustainability challenges. These systems use various technologies to solve traffic issues, including mobile crowd sensing (MCS), Internet of Things (IoT), and advanced data analytics [1].  
One of the primary approaches to managing urban traffic congestion is using MCS. This technology allows for collecting real-time vehicular data using smartphones and Global Positioning System (GPS)-enabled vehicles, eliminating the need for direct vehicle-to-vehicle communication. Such a model facilitates the monitoring of traffic conditions and aids in developing dynamic traffic management strategies that adapt to changing conditions, thereby reducing congestion and improving traffic flow. The integration of MCS into traffic management systems has been shown to provide cost-effective solutions for urban traffic challenges, enabling more responsive and optimized traffic control mechanisms [2].  
Real-time data analytics enable the estimation of traffic conditions on surrounding roads. By employing predictive models, traffic management systems proactively address congestion issues, thereby increasing the performance of urban transportation networks [3]. The use of big data and machine learning (ML) techniques, such as deep learning (DL), further enhances the accuracy of traffic predictions, allowing for timely interventions and improved route guidance for drivers [4; 5].  
The incorporation of IoT technologies into traffic management systems has also been transformative. IoT-based architectures facilitate real-time traffic updates and context-aware traffic monitoring with direct implications for urban traffic [6]. These systems integrate various data sources, including traffic signals, public transportation systems, and user-generated data, to comprehensively view traffic conditions [7; 8]. Such integration improves traffic flow and supports the development of smart mobility solutions that increase the overall quality of life in urban areas [9]. An important issue is ensuring the compatibility of the input-output devices [10].  
Furthermore, optimizing traffic signal systems using advanced algorithms, such as genetic algorithms, has been identified as a factor in reducing congestion during peak hours [11]. By dynamically adjusting traffic signals based on real-time traffic data, these systems decrease wait-times and improve traffic throughput [12]. Additionally, the implementation of intelligent traffic management systems (ITMS) has been shown to enhance road safety and reduce environmental impacts by minimizing fuel consumption and emissions associated with idling vehicles [13; 14; 15].
# The Impact of Additive Manufacturing on the Flexibility of a Manufacturing Supply Chain
## 3 Case Study
### Results  
Interesting insights into the performance of both technologies are obtained from comparing the estimation of the four supply chain flexibility metrics: (i) volume flexibility, (ii) mix flexibility, (iii) delivery flexibility and (iv) new product flexibility. As evident from Table 1, the supply chain employing IM shows greater volume and delivery flexibility with 65.68% and 92.8% compared to 58.70% and 75.35% for AM, respectively. This is primarily due to the existence of buffer inventory in the IM supply chain, which gives the ability to fulfil orders with volume as low as 200 and instant shipping of parts from the warehouse. This is unlike the AM route, which requires at least 82 h of production to process any order. AM, however, in this case study shows lower changeover time and cost of new product introduction to the system, which is indicated by the higher level of mix and new product introduction flexibility compared to IM (i.e., 0.33 h and 60 for AM compared to 4.91 h and 630,000 for IM, respectively).  
To analyse the relationship between volume flexibility and unit cost in this case study, the unit cost of different production volumes has been estimated for both manufacturing technologies. Then, the unit cost curve is plotted against the volume flexibility level at each production quantity scenario as in Figure 7A,B.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Flexibility Metric** & **Unit** & **AM** & **IM** \\ \hline Volume flexibility & \% & 58.70 & 65.68 \\ Mix flexibility & hr & 0.33 & 4.91 \\ Delivery flexibility & \% & 75.35 & 92.85 \\ New product flexibility & \(\mathbf{\hat{c}}\) & 0 & 30,000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the flexibility metrics.  
Figure 7: Relationship between volume flexibility and unit cost of the studied part for **(A)** IM and **(B)** AM.  
As can be seen in Figure 7, the unit cost of AM is not reduced with each increase in production quantity compared to IM (see also [PERSON] and [PERSON] [44]). In both supply chains, volume flexibility shows the same pattern of sharp increase at the beginning before it starts to plateau once reaching a certain production quantity. This is because the volume flexibility is estimated, as shown in Section 3.3.1, based on a minimum production quantity \(O_{min}\) and a maximum production quantity \(O_{max}\) in relation to the mean \(\overline{D}\) of the historical demand, which is 3240 parts a month in our study. Therefore, such diminishing of increase in the volume flexibility is expected since the increase in production quantity is rendered useless relative to the market demand. A supply chain that utilises IM, thus, would be more attractive when aiming at increasing volume flexibility due to the inverse proportion between the unit cost of its products and its volume flexibility.
# A Survey on Bias in Deep NLP
## 4 Discussion
### Association Tests
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Year** & **Ref.** & **Streetypei@i)** & **Model** & **Data** & **Lang.** & **Evaluation** & **Mitigation** & **Stage** & **Task** \\ \hline
2016 & [26] & Gender & Word2 Vec, GoVe & GoogleNews corpus (w2 xNEWS), & English & Analogies/Cosine & Vector Space Manipulation & After & - \\  & & Common Crawat & Common Crawat & Similarity & & & & & \\ \hline
2018 & [7] & Gender & GloVe [27] & OntoNotes 5,0, **Winbias**, & English & Prediction Accuracy & Data Augmentation & After & Conference \\  & & Occupation Data (BLS), B& RatL & & & & & & \\  & & & Occupation Data & (BLS) & RatL & & & & \\  & & & & & & & & & \\ \hline
2018 & [33] & Gender & GloVe [27], **CN-Cive**, & 207 English Wikipedia dump, & English & Prediction Accuracy, & Attribute Protection, Vector & Train (1), & Conference \\  & & & **Hard-GioVe** & **SemiBus (3)** & & Space Manipulation(1), & After (2) & resolution \\  & & & & & & & & \\ \hline
2019 & [8] & Ethnicity, Gender, & Word2 Vec & Reddit L2 corpus & English & PCA, WEAT, MAC, & Vector Space Manipulation & After & POS tagging, \\  & & Religion & & & & & & & POS churning, \\ \hline
2019 & [9] & Gender & Spanish fastText & Spanish Wikipedia, bilingual & English, & **CLAT**, WEAT & Vector Space Manipulation & After & - \\  & & & embeddings (MUSS) [40] & Spanish & & & & & \\ \hline
2019 & [41] & Gender & Transformer, GloVe, & United Nations [42], European [43], & English, & BLEU [44] & Vector Space Manipulation & Train, & Translation \\  & & Hand-Dubased GloVe, & nearest(2012, newestest2013), & Spanish & & (Hard-Dubies) & After & & \\  & & GN-Cubic & - Occupation data (BLS) & & & & & \\ \hline
2020 & [30] & General & CBOW, GloVe, & - & German, & WEAT, XWEAT, ECT, & Vector Space Manipulation, & After & - \\  & & FastFect, **DebiasNet** & - & Spanish, & BAT, Clustering & (**OxMeans**) (**BIAS**) & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\ \hline
2019 & [45] & Gender & BERT (base, uncassell), & - & English & Visualization, Text & - & - & - \\  & & & CPT-2 (small) & & & & & & \\  & & & & & & & & \\ \hline
2020 & [46] & Gender & RoBERTa/GloVe (1) & Common Crawat (1) & English & **WEAT\({}^{*}\)**SIRT** & Vector Space Manipulation, & Train & - \\  & & & & & & & & \\ \hline
2020 & [47] & Gender & BERT & Equity Evaluation Corpus, & English & EEC, Gender & Vector Space Manipulation & Train & - \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Previous work on bias detection and treatment in natural language processing (NLP): vector space manipulation.
# Optimizing CNN-LSTM for the Localization of False Data Injection Attacks in Power Systems
## 3 Methodology  
In this study, we formulated the problem of FDIA location detection as a multi-label classification issue, aiming to locate the measurements within the power system that were compromised by the attack. The CNN, a deep learning model widely utilized in image recognition and computer vision tasks [23], offers significant advantages in large-scale data classification tasks. By applying the CNN model to network attack detection, its powerful processing capabilities can analyze data from various network systems. LSTM, a special recurrent neural network capable of utilizing time series data for analysis [24], incorporates memory units to enable the modeling of long sequences. Additionally, LSTM can selectively update, forget, and output information through the design of gated units,addressing the challenge of gradient vanishing or exploding, thereby better capturing long-term dependencies.  
Due to the close temporal and spatial correlations in power systems measurement data, the failure of one node may lead to changes in the measurement values of multiple related nodes. Therefore, we utilized a CNN to extract the local correlation and spatial features from the measurement data of the power system, capturing local patterns in the data through the convolutional operations of its kernels. LSTM, leveraging its unique gating mechanism, effectively captures long-term dependencies within data, analyzing trends and periodicities in the power system's measurement data over time. By combining the strengths of CNN and LSTM, our model is capable of a comprehensive analysis of the spatiotemporal characteristics of a power system, accurately identifying differences between compromised and normal measurement values, thereby achieving a precise localization of the attacked positions.  
Selecting appropriate hyperparameters is crucial for constructing an effective deep learning classifier. Hyperparameter optimization aims to enhance the performance of deep neural networks on a given dataset. However, the manual selection of hyperparameters is often time-consuming and relies on experience and intuition, which may not lead to the optimal solution. The GA is a search algorithm that simulates the principles of natural selection and genetics and, compared to other algorithms, can efficiently search for the optimal solution in a complex parameter space. We employed a GA to assist in managing the cumulative increase in the number of parameters during model integration, ensuring the model's optimal performance.
## 2 Simulation Method of Three-Dimensional DFN Based on Monte-Carlo Technique
### Simulation of 3D DFN of Rock Mass  
#### 2.2.1 Monte-Carlo Technique  
The Monte-Carlo technique is the inverse process of sampling based on the theory of probability. It involves conducting statistical experiments with random numbers, and using the generated random numbers that conform to some random distribution function as the basis for application. The application of the Monte-Carlo technique to the simulation of 3D DFN is usually based on probability models of the geological parameters of rock fracture to generate a specified number of random numbers to represent the direction of dip, the dip angle, the side length, and coordinates of the center that conform to the form of the corresponding probability distribution. The coordinates of the vertices of each rectangular fracture are then obtained to generate a 3D DFN that conforms to the probability models of the geological parameters of rock fractures.