esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A barrier wall is a vertical engineered layer designed to block contaminated soil, thereby controlling pollution sources, preventing pollutant migration to groundwater, and limiting pollution spread. Cement-bentonite barrier walls, widely adopted for their seepage control capability, structural strength, and cost-effectiveness, face sustainability challenges due to high cement consumption. This study systematically investigates the coupled effects of steel slag substitution rate and bentonite dosage on the mechanical-permeability of barrier materials for the first time and proposes steel slag (containing dicalcium silicate (C\({}_{2}\)S) and tricalcium silicate (C\({}_{3}\)S) phases similar to cement clinker) as a partial cement substitute in steel slag-cement-bentonite barrier materials, aiming to reduce cement usage and utilize industrial waste. Through unconfined compressive strength tests, direct shear tests, and variable head permeability tests, the effects of steel slag substitution rates (0-50%) and bentonite dosages (46-54%) on material performance were systematically investigated. Key findings include (1) unconfined compressive strength decreases linearly with increasing steel slag substitution but grows exponentially with bentonite dosage; (2) cohesion exhibits a negative exponential relationship with steel slag substitution and a linear positive correlation with bentonite content--the unconfined compressive strength of the materials with bentonite dosage of 50% and 54% were 1.51 and 2.84 times higher than those with bentonite dosage of 46%, respectively; (3) cohesion and unconfined compressive strength conform to \(c\) = (0.23-0.39)\(q_{u}\); (4) permeability decreases with higher steel slag substitution and bentonite dosage, achieving controlled low permeability (\(<\)1 \(\times\) 10\({}^{-7}\) cm/s). This research provides a sustainable solution for barrier wall construction by integrating waste recycling and performance optimization.  
vertical engineered barrier; strength; hydraulic conductivity; sustainable construction materials 2025154544
# An Enhanced System Architecture for Optimized Demand Side Management in Smart Grid
## 1 Introduction  
World population and energy demand are increasing rapidly. The provision of reliable and sustainable energy services according to 21 st century requirements is becoming more challenging day by day [1; 2; 3]. Real-time monitoring, dynamic control and users' active participation in energy services are vital to ensure a sustainable future. The traditional grid is not supposed to provide such dynamic services because of its aged infrastructure. It is needed to convert the traditional grid into a smart grid in order to meet the energy management challenges [4; 5; 6]. The smart grid is envisioned to provide sustainable energy services dynamically and efficiently to fulfill the requirements of meeting the ever increasing demand and addressing the green concerns. Various applications of smart grid are mentioned in literature such as smooth integration of renewable resources, transmission and distribution automation, optimized energy management _etc._[7; 8].  
Demand Side Management (DSM) is one of the important aspects of optimal energy management in which consumers' behavior is influenced to manage the electricity consumption [9]. Home Energy Management Systems (HEMS) are found among important smart grid applications to ensureDSM [10; 11]. HEMS help in peak load reduction and consumers' total energy cost minimization by using embedded sensors and Advanced Metering Infrastructure (AMI). Consequently, use of peaker plants and carbon footprint are reduced, which are desirable and important for sustainable future [12].  
Although peak load reduction can be achieved in several different ways, appliance scheduling is one of the promising methods for this purpose. A novel energy management mechanism, namely Comprehensive Home Energy Management Architecture (CHEMA), is presented in this paper. CHEMA consists of a six-layer model with multiple scheduling options, and it has been implemented in Simulink with embedded MATLAB code. Important contributions of this work are listed as the following:  
* A unique HEMS architecture which covers more aspects of the residential energy management problem and multiple cost minimization options integrated at the second layer;
* Formulation of optimization problem which is unique with the concept of partial base line load and its inclusion in the optimization problem, discussed in detail in Section 3;
* Control of lighting and heating loads with the help of a Persons Presence Controller (PPC);
* Solution of the energy cost optimization problem with a single Knapsack technique with embedded Matlab code in Simulink.  
The remaining paper is arranged as follows. Section 2 presents related work and Section 3 is dedicated for the proposed architecture. The results are elaborated in Section 4 and conclusions are drawn in Section 5.
## 2 Materials and Methods
### Task and EEG Data  
In the experiment phase, the real driving route (about 254 km) was from Shenyang (41.78\({}^{\circ}\) N, 123.43\({}^{\circ}\) E) to Dandong (40.12\({}^{\circ}\) N, 124.38\({}^{\circ}\) E), Liaoning, China. The drivers drove for about 3 h along the whole drive, and almost all driving distances were chosen on a monotonous highway. To reduce artifacts in the physiological data recording, an automatic shifting car was selected, and the use of the radio was banned during the experiment [8]. The driving experiments started at about 13:00. To avoid the influence of circadian fluctuations, the experiments were scheduled at the same time each day.  
Each participant was asked to keep the motor vehicle on the track as accurately as possible to avoid other vehicles. EEG signals were continuously collected by EMOTIV EPOC (very convenient to deploy under real driving conditions, wireless device, and inexpensive compared with other EEG acquisition equipment) during driving. EEG data from 14 channels (AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4, see Figure 1), which were arranged according to the international 10-20 standard, and two reference electrodes, which were located at the bilateral posterior mastoid [20], were recorded during the real driving. The participants' EEG signals were captured by the neuroheadsets and then wirelessly transmitted to an external laptop via the USB receivers for further processing. The sampling frequency was kept at 128 Hz. Subsequently, the data of each participant for the first three minutes and the last three minutes out of those of 3 h were selected for further preprocessing. In the case of EEG signals, the undesired artifacts such as eye blinking were reduced by using independent component analysis (ICA) in the EEGLAB software. The subjective fatigue level of participants was investigated before and after the real driving experiments by the Stanford Sleepiness Scale (SSS) [21] (1-2 = conscious, 3-4 = slight fatigue, 5 = medium fatigue, 6-7 = severe fatigue). The facial video was recorded using a video camera installed in front of the participants.
# Diagnosis and Prediction of Large-For-Gestational-Age Fetus Using the Stacked Generalization Method
## 4 Experiment Results
### Results of GridSearch Based RFECV + IG Feature Selection Scheme for LGA Prediction  
To highlight the importance of proposed GridSearch-based RFECV feature selection scheme which is followed by IG feature selection scheme, we executed the initial experiments considering all featuresas per MFV created and the features selected by GridSearch + RFECV features selection scheme. Table 3 can be referred for the details of the results. From the results, it is foreseen that the GridSearch + RFECV features selection scheme improved the LGA classification prediction scores and best performed with SVM classifier (using linear and RBF kernel). Furthermore, the classification performance of all of the classifiers on GridSearch + RFECV features subsets are also improved compared to the results of MFV features subset. Based on foreseen improvement, considering the primary objective of this research, which is to identify principal features subset for a better LGA prognosis, we executed the first proposed experimental process. Figure 4 presents the results of the initial experiment process where an ensemble of feature selection scheme is created using GridSearch + RFECV and IG feature selection scheme. From the results, it is discerned that all of the proposed classifiers outperformed with principal ten feature subsets. SVM (with the linear kernel) outperformed among all with prediction precision, recall, accuracy, AUC, specificity, and F1 scores of 0.97, 0.61, 0.83, 0.87, 0.999, and 0.74 respectively, followed by SVM (with RBF kernel), LR, and DT. SVM (with RBF kernel) and LR classifiers remained almost similar by producing similar performance metrics scores whereas DT classifier remained weak in producing noticeable performance metrics scores.  
Figure 4: Comparative results of various machine learning classifiers on different features subset obtained as the result of applying GridSearch (for parameters tuning), RFECV feature selection scheme, and Information Gain (IG) feature selection scheme.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Scheme** & **Feature Subset** & **Metrics** & **SVM (Linear)** & **SVM (tbf)** & **Logistic Regression** & **Decision Tree** \\ \hline
**Master Feature Vector** & All & Precision & 0.8352 & 0.2025 & 0.8289 & 0.4970 \\  & All & AUC & 0.8447 & 0.2014 & 0.8281 & 0.9070 \\  & All & Recall & 0.6560 & 0.1198 & 0.6569 & 0.6991 \\  & All & F1-Score & 0.7166 & 0.1117 & 0.7236 & 0.5746 \\ \hline
**GridSearch + RFECV** & GridSearch + RFECV(All) & Precision & 0.9498 & 0.9691 & 0.9200 & 0.4961 \\  & GridSearch + RFECV(All) & AUC & 0.8690 & 0.8606 & 0.8699 & 0.5899 \\  & GridSearch + RFECV(All) & Recall & 0.6461 & 0.6059 & 0.6686 & 0.7008 \\  & GridSearch + RFECV(All) & F1-Score & 0.7663 & 0.7433 & 0.7716 & 0.5745 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of all features subsets selected by GridSearch-based RFECV features selection scheme using well known ML classifiers with 10-fold cross validation.
## 4 Experimental Results
### Discussion  
The objective of this research is to solve two main challenges of IR pedestrian detection. One is that IR images have some adverse properties, such as a noisy nature and low-resolution. The  
Figure 9: Visual comparisons of detection results on four example images among SSD, encoder-decoder module, and encoder-decoder + attention (AED-CNN). The green bounding boxes denote the ground truth. The red bounding boxes show the detection results. (**a**) SSD [16], (**b**) encoder-decoder module (**c**) encoder-decoder + attention (AED-CNN).  
other is that IR images are susceptible to weather. To overcome these two problems, we propose an attention-guided encoder-decoder convolutional neural network. Motivated by the recent success of CNNs in object detection, we design a novel encoder-decoder module based on the well-known SSD approach [16]. The proposed encoder-decoder module learns effective features from low-resolution IR images. Inspired by the attention mechanism applied in CNN [39], we further propose an attention module to highlight IR pedestrians even when thermal energy is indistinguishable between pedestrians and the background.
# Multi-Objective Robust Optimization Reconstruction Algorithm for Electrical Capacitance Tomography
## 7 Validation and Discussion
### Results and Discussion
#### 7.5.5 Consistency of Reconstruction Performance  
In this section, we systematically evaluate the stability of the MOROR algorithm by analyzing the variance in the image error and correlation coefficients, with the quantitative results shown in Figure 7. These indicators quantify the consistency of the algorithm across different reconstruction tasks. To further validate the analysis results, Figure 8 demonstrates the performance of different algorithms in terms of the image error and correlation coefficients under various reconstruction objects. These quantitative results lay a foundation for a thorough comparison between the performance of the MOROR algorithm and that of other methods, which supports the subsequent optimization of these algorithms.  
By analyzing the data visualization results in Figure 7, it can be seen that among all the algorithms evaluated, the MOROR algorithm exhibits significant advantages across multiple dimensions. In particular, the MOROR algorithm achieves the lowest values in the two critical performance indicators, i.e., the variances in image errors and correlation  
Figure 7: The variances in image errors (**a**) and correlation coefficients (**b**). (1. L\({}_{12}\)R, 2. L1R, 3. FL1R, 4. LRR, 5. PRPCG, 6. \(L_{1/2}\)R, 7. TL1R, 8. MOROR, 9. LTV, 10. L1 SOTV, 11. ELNR, 12. L11R, and 13. FOENR).  
coefficients, far surpassing other algorithms. This result fully demonstrates the exceptional stability and effectiveness of the MOROR algorithm in image reconstruction tasks, indicating its ability to consistently maintain high-quality reconstruction results while handling complex tasks with excellent robustness.  
To delve deeper into the specific performance of each algorithm across various reconstruction tasks, Figure 8 illustrates the image errors and correlation coefficients of different algorithms in a range of reconstruction objects. Despite minor variations in the image errors and correlation coefficients across different reconstruction targets, the MOROR algorithm demonstrates consistently stable performance, exhibiting a narrower range of performance fluctuations and maintaining consistently low error levels. This indicates that the performance of the MOROR algorithm is less related to task characteristics, exhibiting stronger adaptability and performance consistency.
# Task-Oriented Adversarial Attacks for Aspect-Based Sentiment Analysis Models
## 6 Experimental Results and Analysis
### Discussion  
The experimental results from the three defined attacks highlight the significance of designing adversarial examples particularized to the specific characteristics of the target  
Figure 5: Comparison of the results from the ABAA attack, baseline 1, and state-of-the-art methods on the Laptop dataset. The comparison includes the perturbation ratio, semantic similarity, and accuracy reduction achieved by the different attacks. task. We focus on aspect-based sentiment analysis and incorporate its unique features to develop a specialized adversarial example model and its associated attack strategies. The literature review reveals that most adversarial attacks on deep learning models for sentiment analysis adopt a generalized approach, treating the task as document-level analysis. The lack of task-specific approaches can lead to the modification of irrelevant terms, impacting the imperceptibility of the perturbations, and, in some cases, reducing its effectiveness in misleading the model. Table 3 provides an overview of the state-of-the-art adversarial attack methods, summarizing their principal characteristics. This summary highlights the lack of level-specific approaches and the need to particularize sentiment analysis attack methods to the unique characteristics of each analysis level. Unlike prior works, our proposed adversarial example model leverages the specific characteristics of aspect-based sentiment analysis to drive modifications that effectively degrade model accuracy. The main premise of our model approach is that focusing on task-specific attributes ensures that modifications target terms integral to the aspect's sentiment, disrupting the model's task understanding and causing incorrect classifications. As demonstrated in our experimental attacks, effective adversarial strategies for aspect-based analysis succeed in significantly misleading the models, generating adversarial examples that preserve the semantics of the inputs while maintaining the imperceptibility of the modifications. In the context of white-box attacks, document-level approaches fail to prioritize terms based on their relevance to the evaluated aspects, leading to unnecessary modifications of terms unrelated to the aspects. This compromises the imperceptibility of the modifications (refer to Table 2). Furthermore, as document-level attacks disregard the relationship between opinion terms and aspects, the semantic connection of aspect-related terms often remains intact, rendering the modifications inconsequential from the perspective of the target model. Designing adversarial examples based on task-specific characteristics reveals an unconsidered vulnerability. By altering the elements that support the task classification, the connection between these elements can be disrupted, thereby compromising the model's
###### Abstract  
As engineering systems become increasingly complex, performance requirements rise, and tolerance for design parameter variations becomes more crucial due to increased uncertainty. Tolerance to parameter variation can be measured by the volume of the solution space. A larger solution space implies a higher tolerance to parameter changes and thus greater robustness. The box-shaped solution space, represented by intervals with respect to each design parameter, has the advantage of showing which design parameters can be decoupled. Therefore, this paper proposes a new multi-objective optimization problem to optimize both the performance and volume of the box-shaped solution space simultaneously. Often, optimal performance and maximum volume are conflicting objectives, indicating a trade-off between performance and robustness. Furthermore, the DIRECT-NSGA-II approach is proposed for solving this multi-objective optimization problem. The DIRECT algorithm evaluates the minimum value of the performance function within the box-shaped solution space, while the NSGA-II algorithm identifies Pareto-optimal solution spaces. Finally, two case studies are implemented to illustrate the effectiveness of the DIRECT-NSGA-II method. We can conclude that (I) the proposed DIRECT-NSGA-II approach is suitable for black-box performance functions, (II) any point within the obtained solution space is a good design point, and (III) the proposed optimization problem considers both performance optimality and robustness, enabling the identification of a representative set of Pareto-optimal solution spaces that balance these two factors.  
Keywords:performance optimality; robustness; box-shaped solution space; DIRECT; NSGA-II +
Footnote †: journal: Applied Sciences
## 3 Optimization of SM-OGFC by Response Surface Methodology (RSM)
### Response Surface Interaction Analysis  
Response surface and Contour plot of regression model is shown in Figure 6A1-D2.  
Figure 6: \(Cont\).  
Figure 6: _Cont._  
As shown in the comparison between the four group diagrams, the influence of asphalt-aggregate ratio on \(-15\)\({}^{\circ}\)C splitting strength is the most significant, which is characterized by the steepness of the surface in Figure 6A\({}_{1}\),B\({}_{1}\).  
Figure 6B\({}_{1}\) shows a three-dimensional plot of \(-15\)\({}^{\circ}\)C splitting strength against asphalt-aggregate ratio and mixing temperature when silane coupling agent content is 2% and the compaction times is 60. As can be seen from Figure 6B\({}_{1}\), with the increase of the asphalt-aggregate, \(-15\)\({}^{\circ}\)C splitting strength continues to grow until it reaches a stable value. At the mixing temperature of 140 \({}^{\circ}\)C, \(-15\)\({}^{\circ}\)C splitting strength rises from 2.52 Mpa to 3.64 Mpa with the increase of asphalt-aggregate ratio from 4.3 to 5%. In addition, with the increasing of asphalt-aggregate ratio within the reasonable range, \(-15\)\({}^{\circ}\)C splitting strength is maintained at about 3.64 Mpa. This is because the OSW has higher specific surface area compared with stone, thus it can absorb more free asphalt. With the increase of asphalt-aggregate  
Figure 6: (**A\({}_{1}\)**) 3D surface graph of \(-15\)\({  
ratio, the structural asphalt adsorbed on the aggregate surface gradually increased. Therefore, the mechanical properties of SM-OGFC to resist low temperature load are improved.  
The shape of contour can reflect the strength of interaction. Oval indicates that the interaction of two factors is significant while circle is the opposite. It can be seen the contour line is oval from Figure 6A\({}_{2}\),B\({}_{2}\) and D\({}_{2}\)), so the interaction of asphalt-aggregate ratio and silane coupling agent content, asphalt-aggregate ratio and mixing temperature, compaction times and mixing temperature are significant. In comparison, the interaction between silane coupling agent content and mixing temperature is slightly weaker.
## 2 Numerical Simulations of Vibratory Sheet Pile Driving
### Simulation Model
This study adopted the driving force \(F_{p}(t)\) = 800\(sin(2\pi t_{0}t)\) + 100 kN, which was suggested in [10] for a standard hydraulic vibratory river ICE 44-30V operating at a frequency \(f_{0}\) = 20 Hz with an eccentric moment \(m_{e}\) = 50.7 kg\(\cdot\)m. To save computational time, the duration of driving force was defined as 0.5 s only, while the total duration of the analysis steps was defined as 1.2 s so that the generated vibrations could propagate across the whole soil domain. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Pile Type**} & \multicolumn{2}{c}{**U-Type Dimensions**} & \multirow{2}{*}{**Length (m)**} & \multirow{2}{*}{**Mass (kg/m)**} \\  & \(b\) (m)–\(h\) (m)–\(t\) (m) & & \\ \hline Steel sheet & 0.4–0.17–0.016 & 15 & 76.1 \\ \hline \end{tabular}
\end{table} 
Table 1: Properties of the simulated sheet pile. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Soil Type**} & **Density** & **Modulus of** & **Poisson’s** & **Friction Angle** & **Cohesion** \\  & **(kg/m\({}^{3}\))** & **Elasticity (MPa)** & **Ratio** & **(Degrees)** & **(kPa)** \\ \hline Sandy clay & 2000 & 80 & 0.4 & 25 & 15 \\ \hline \end{tabular}
\end{table} 
Table 2: Soil properties reported in the previous studies [10; 11; 12]. The Abaqus/Explicit dynamic analysis was used for the calculations since it is efficient in calculating large models with short dynamic response times. The explicit procedure adopts a conditionally-stable central-difference time integration rule. The time increment steps were automatically selected by ABAQUS during the analysis in this study.