esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Foundry Wastes in Eco-Efficient Construction Materials: A Review
## 5 Valorization of Foundry Waste in Construction Materials
### Foundry Waste Mixtures
It was concluded that properties such as compressive strength, flexural strength,and tensile strength were improved with the replacement by up to 15% GGBS and up to 30% WFS in place of natural sand. [PERSON] et al. [112] investigated the effects of using WFS as a partial substitute for fine aggregates, as well as the use of GGBFS and SF as partial substitutes for cement in mortars. The addition of 20% WFS, 15% SF, and 15% GGBFS resulted in an increase in compressive, tensile, and flexural strength in the mortar mixtures. The results of the mechanical properties tests with 20% WFS substitution were substantially better compared to other mixtures. [PERSON] et al. [113] conducted a study to determine the most effective ratio of WFS, FA, and electric arc furnace slag (EAFS) for the production of geopolymer bricks. The properties analyzed included compressive strength, microscopic structure, mineralogical phases, and infrared spectra. The bricks with the highest compressive strength were subjected to heavy metal leaching tests, and their environmental impact was assessed through a life cycle analysis (LCA). The research revealed that the geopolymer bricks with the highest compressive strength were made with 40% WFS, 30% FA, and 30% EAFS. These bricks exceeded the prescribed strength requirements for paving. Geopolymer bricks can be classified as non-hazardous according to the acceptable levels of heavy metal leaching. The LCA results indicated that the production of geopolymer bricks had a much lower environmental impact compared to concrete production in all dimensions. Another interesting study was presented by [PERSON] et al. [114]. Four different industrial wastes were used in the investigation--WFS, CMS, nickel slag, and refractory brick--to create chemically bonded ceramics (cement). The formulations with nickel slag and CMS showed better results due to the plate-type crystals in the microstructure and the higher relative density. These results indicated that it is feasible to use industrial waste in the acid-base cement technique to create bonded ceramics that can be incorporated into construction materials for non-structural uses. The use of different foundry wastes in construction materials has improved performance by leveraging the specific qualities of each type of waste. The studies presented demonstrated the feasibility and advantages of using a mixture of foundry waste, such as WFS, slags, and other industrial byproducts, in concrete and mortar formulations. Important conclusions include the potential for partial replacement of natural aggregates with foundry waste, improving mechanical properties and promoting environmental sustainability.
# Visual Enhancement Capsule Network for Aspect-Based Multimodal Sentiment Analysis
## 2 Related Work
### Single-Modal Sentiment Analysis  
Single-modal sentiment analysis is mainly divided into text sentiment analysis and image sentiment analysis. Text sentiment analysis is to extract and recognize the implicit sentiment or emotion in text, which has been widely studied in the field of natural language processing in recent years.With the advent of deep learning, deep network models have been extensively used in text sentiment analysis for automatically learning the feature embedding representations of text. [PERSON] et al. proposed an AdaRNN model based on a recursive neural network (RNN) to transmit the emotional information of text through a syntactic dependency tree [13]. [PERSON] introduced a convolutional neural network (CNN) for the first time to extract the fine-grained sentiments of text [14]. [PERSON] et al. built a novel sentiment classification model using the sentiment supplementary information of negation and inversion in text [15]. [PERSON] et al. used a directional graph convolutional network (GCN) for joint aspect extraction and sentiment analysis [16]. Furthermore, the long short-term memory network (LSTM) has been widely applied to the tasks of text sentiment classification for capturing the context dependency of affective entities [17; 18; 19; 20].  
At the same time, great progress has been made in image sentiment analysis based on deep neural networks. [PERSON] et al. analyzed image sentiment using a CNN for the first time [21]. [PERSON] et al. introduced a multilevel visual attention mechanism into a CNN sentiment classification [22]. [PERSON] et al. used a weakly supervised interaction discovery network to capture the cross-spacial abstract sentiment relations [23]. [PERSON] et al. employed deep metric networks for image sentiment analysis based on cross-domain semi-supervised learning and via heterogeneous semantics, respectively, in [24] and [25]. Text sentiment analysis and image sentiment analysis have been found to have good effects using different methods. However, the above methods are only for single-modal data, whereas most user-generated data is multimodal.
# Numerical Simulation of Cavitation Erosion Aggressiveness Induced by Unsteady Cloud Cavitation
## 3 Results
### Free Stream Velocity Effects
Note that \(P_{tot\_load}\) and \(P_{eff\_load}\) are the accumulated potential power and the accumulated effective power for one complete shedding cycle, respectively, and \(P_{agg}\) is the accumulated effective power per unit time. The maximum \(P_{tot\_load}\) along the chord, which is found at 40% of the chord for each \(U_{inf}\) because of no threshold has been considered, has also been selected to quantify the flow velocity effects. As observed in Figure 15a, \(P_{tot\_load}\) increases approximately as \(U_{inf}\) to the third power for one shedding cycle, which is in accordance with the numerical results obtained by other researchers like [PERSON] et al., [23], [PERSON] et al., [24] and [PERSON] et al., [27], although they did not take into account the collapse efficiency. As observed in Figure 15b, \(P_{eff\_load}\) increases approximately as \(U_{inf}\) to the fourth power when considering the efficiency which is in accordance with the numerical results obtained by [PERSON] et al. [39]. Finally, because the shedding frequency increases with the flow velocity, \(P_{agg}\) increases approximately as \(U_{inf}\) to the fifth power as shown in Figure 15c but this curve might rise faster, i.e., present a higher exponent, because the shedding frequency has been slightly overestimated for the lowest \(U_{inf}\) and underestimated for the highest \(U_{inf}\) in the present calculations. In addition, the experimental cavitation numbers are higher for the higher velocities than those in the numerical model, which implies higher environment pressures and higher collapse efficiencies for higher velocities. Consequently, the erosion rate may increase with an exponent higher than 5, which would be in agreement with the experimental investigations by [PERSON] et al., [40; 41] who found that the erosion damage followed a power law with \(n\) values from 5 to 8. Figure 15: Predicted cavitation erosion intensity indicators at 40% of the chord as a function of flow velocity.
## 2 Related Work
### Adversarial Attacks on Graph Contrastive Learning  
Due to the remarkable achievements of contrastive learning in computer vision, researchers have begun to explore its application in graphs, achieving competitive results. Contrastive learning models are typically divided into three main components: data augmentation, contrastive modes, and contrastive optimization objectives. In node-level tasks, common data augmentation strategies include feature-based augmentation, structure-based augmentation, and sampling-based augmentation. After data augmentation, the model treats views obtained from the same subgraph as positive pairs, and views from different subgraphs as negative pairs. The optimization of the model is then guided by maximizing the mutual information between positive pairs and minimizing the mutual information between negative pairs. [PERSON] et al. [24] define augmentation operations such as edge deletion, node deletion, edge insertion, and node insertion to generate different views of the graph. [PERSON] et al. [25] enhance the consistency between graph representations by introducing uniform perturbations such as node deleting, edge perturbation, and featuremasking. [PERSON] et al. [16] generate different views by removing edges and the masking node features. Based on GRACE, [PERSON] et al. [26] introduced innovative augmentation strategies designed to preserve important edges and features, while introducing perturbations to less significant edges and features.  
Researchers have already focused on adversarial security in graph contrastive learning. [PERSON] et al. [27] employed discrete optimization to reformulate the attack problem as an optimization problem. This approach effectively directed the victim model to map the target node to the specific embedding. [PERSON] et al. [28] worked on the adversarial security of graph contrastive models. By calculating the gradients of the adjacency matrices of two views, they flipped the edges to maximize the contrastive loss. [PERSON] et al. [29] performed poisoning attacks on graph contrastive models through node injection. They computed the gradients of the contrastive loss on the adjacency matrix in an unsupervised manner, and flipped the edges with the greatest gradients, thereby significantly degrading the accuracy of downstream tasks.  
These works concentrated on the adversarial attack targets of graph contrastive models. In contrast, the proposed GCIA method aims to perform evasion attacks on black-box GNN models. Within the GCIA method, the graph contrastive model is used for extracting node embeddings from unlabeled data. By maximizing the change in target node embedding, GCIA can achieve a high success rate in the first query.
# Modeling and Analyzing Preemption-Based Service Prioritization in 5G Networks Slicing Framework
## 5 Conclusions  
This paper proposes a Pre-emption-based Prioritization (PP) scheme \"merging\" the classical Resource Reservation (RR) and Service Prioritization schemes. A Queueing System (QS) model analyzing the operation of multiple Network Slice Instances (NSIs) at common 5G BSs is provided to evaluate or estimate the proposed PP-scheme efficiency. Expressions are given to calculate system Key Performance Indicators (KPIs): the mean numbers of servicing requests, the admission and blocking probabilities for arriving requests, and the average utilization of the total and guaranteed network capacities. A qualitative numerical analysis comparing the proposed PP-scheme with the classical RR-scheme is given. Thus, the operation of three NSIs instantiated for 4K Live Video, 4K 360-degree VR Panoramic Video and 8K FOV VR Video requests is considered. The key results of the conducted comparative analysis are the following. Given some baseline, the proposed PP-scheme can provide:  
* Up to more than 60% gain in terms of admission probability of arriving 4K 360-degree VR Panoramic Video requests at the 2 nd NSI;
* Up to 100% gain in terms of blocking probabilities of arriving requests;
* Up to 15% in terms of average utilization of the guaranteed network capacity of the 2 nd NSI.  
As next stage of this research topic, the PP-scheme efficiency can be evaluated or estimated given a QS model supporting also data applications using Transmission Control Protocol (TCP), e.g., Web Browsing, File Download, Social Networking, etc. Future research can investigate the optimal capacity settings for all NSIs at common 5G BSs given the PP-scheme.  
Conceptualization, Y.A., E.M. and Y.G.; Data curation, Y.A.; Formal analysis, Y.A.; Funding acquisition, E.M.; Investigation, Y.A.; Methodology, Y.A., E.M. and Y.G.; Project administration, E.M. and Y.G.; Resources, Y.A.; Software, Y.A.; Supervision, E.M. and Y.G.; Validation,  
Figure 11: The average utilization of the total network capacities vs. the offered load.  
Y.A.; Visualization, Y.A. and E.M.; Writing--original draft, Y.A.; Writing--review and editing, Y.A., E.M. and Y.G. All authors have read and agreed to the published version of the manuscript.
* [1]The research was funded by the Russian Science Foundation, project no.22-79-10053, ([[https://rscf.ru/en/project/22-79-10053/](https://rscf.ru/en/project/22-79-10053/)]([https://rscf.ru/en/project/22-79-10053/](https://rscf.ru/en/project/22-79-10053/)), accessed 1 September 2022).
* [2]**Institutional Review Board Statement:** Not applicable.
* [3]**Informed Consent Statement:** Not applicable.
* [4]**Data Availability Statement:** Not applicable.
* [5]**Conflicts of Interest:** The authors declare no conflicts of interest.
###### Abstract  
This paper investigated the fluid-structure interaction vibration response of an aircraft liquid-filled pipeline under external random vibration and internal pulsating pressure. First, the fluid-structure interaction solution is theoretically analyzed, and the advantages and disadvantages of the direct coupling method and the separation coupling method are compared, with the latter chosen as the simulation analysis method in this study. Second, taking the U-shaped oil pipeline of an aircraft engine as an example, simulation modeling was performed to compare and analyze the fluid-structure interaction vibration response of aircraft liquid-filled pipelines under different working conditions, obtaining the vibration response characteristics of stress danger points under various conditions. Finally, a test bench for an aircraft liquid-filling pipeline was built to explore the influence of external random vibrations with different kurtoses, different pipe wall thicknesses and different working conditions on the vibration response danger points of aircraft liquid-filling pipelines, verifying the simulation conclusions and providing a basis for aircraft liquid-filling pipeline design.  
fluid-structure interaction; vibration characteristics; aircraft liquid-filled pipeline
# Instability Characteristics of and Control Techniques for Mudstone-Clay Composite Roof Roadways
## 3 Microscopic Experimental Study on Mudstone-Clay  
### Experimental Methods  
Experimental samples were collected through interval drilling at various locations along the material roadway of the 18,511 th working face in Xiegou Coal Mine, Shanxi Xishan Jinxing Energy Co., Ltd. The samples were dried before testing. The experimental research system consists of a loading system, an X-ray diffractometer, and a SEM, as shown in Figure 2. For XRD analysis, dry rock samples were ground into powder (<30 mg diameter, >10 mg mass) and tested using an X-ray diffractometer (DX-2700B model) (Dandong Haoyuan Instrument Co., Ltd., Dandong City, Liaoning Province, China) to determine the material hydrophilic properties. The diffractometer was operated at 40 kV and 40 mA, with the measurement range of 20 from 0\({}^{\circ}\) to 80\({}^{\circ}\) and a step size of 0.02\({}^{\circ}\). The resulting diffraction patterns were analyzed using the X'Pert HighScore Plus 4.0 software and compared with the International Centre for Diffraction Data (ICDD) database to identify the mineral phases present in the samples. Microstructural features were observed using a high-resolution field emission SEM (Gemini SEM model). SEM analysis was conducted to investigate the microstructure and morphology of the mudstone and clay samples. Small pieces of the samples were mounted on aluminum stubs using carbon tape and coated with a thin layer of gold to enhance conductivity. Using the Hitachi S-4800 field emission scanning electron microscope produced by Suzhou Science Instrument Co., Ltd., Suzhou, China, the sample was examined under an accelerating voltage of 15 kV. High-resolution images were obtained at various magnifications to observe the surface features, pore structure, and particle arrangement of the samples. Macroscopic failure characteristics were analyzed through loading experiments using an electro-hydraulic servo-controlled rock mechanics test system (MTSC64.106), with displacement-controlled loading at 0.5 mm/min and 10 Hz collection frequency. The stress-strain curves, peak strength, and failure patterns were recorded and analyzed.
## 3 Background
### Classification of the DRL Algorithms  
Learning in DRL is based on actor or action learning, where policy learning is done to perform the best action at each state. The policy is obtained from data, and this learning continues with actions based on the learned policy. The agent will be trained in reinforcement learning based on critic-only, actor-only, and critic-actor approaches. RL algorithms are classified based on these three approaches [33].  
In the critic-only approach, the algorithm will learn to estimate the value function by using a method known as generalized policy iteration (GPI). GPI involves the steps of policy evaluation, i.e., determining how good a given policy is and the next step of policy improvement. Here, the policy is improved by selecting greedy actions in relation to value functions obtained from the evaluation step. In this manner, the optimal policy is achieved [34].  
The actor-only approach estimates the gradient of the objective by maximizing rewards with respect to the policy parameters based on an estimate. The actor-only approach is also known as the policy gradient method. Here, the policy function parameter will take the state and action as input to return the probability of the action in the state [35]. Suppose \(\theta\) is the policy parameter, \(G_{t}\) is the expected reward at time \(t\), and the estimate for maximizing rewards is given in Equation (3), where \(\pi\) represents the policy and \(a_{t}\) and \(s_{t}\) represent the action and state, respectively, at time \(t\).  
\[\theta_{t+1}=\theta_{t}+\alpha\
abla ln\pi(a_{t}|s_{t},\theta_{t})G_{t} \tag{4}\]  
The actor-critic approach will form the policy as the actor will select actions, and the critic will evaluate the chosen actions. Hence, in this approach, the policy parameters \(\theta\) will be adjusted for the actor to maximize the reward predicted by the critic. Here, the value function estimate for the current state is summed as a baseline to accelerate learning. The policy parameter \(\theta\) of the actor is adjusted to maximize the total future reward. Policy learning is done by maximizing the value function [36].  
DRL is an action-critic-based value learning function that compromises current and future rewards [37]. The stock prediction problem can be formulated by describing the state space, action space, and reward function. Here, the state space is the environment designed to support single or multiple stock trading by considering the number of assets to trade in the market. The state space will show a linear increase with increasing assets. The state space has two components: the position state and the market signals. The position state will provide the cash balance and shares owned in each asset, and the market signals will contain all necessary market features for the asset as tuples [38]. The information is provided to the agent to make predictions of market movement. Here, the information is a hypothesis based on technical analysis and of the future behavior of the financial market based on its past trends. The information will also be used by economic and industry conditions, media, and news releases.
## 3 Malfunction Probability Models for Communication Delays and Bit Errors
### False Trip Probability Model for Inconsistent Delays
#### 3.1.1 Relationship between Inconsistent Delay and Protection Action Criterion  
1. The action criterion of the current differential protection Currently, the following two criteria are commonly used for the activation of transmission line phase current differential protection [5,22]: Start criterion: \(I_{cd}\geq I_{op_{s}}\), \(I_{cd}\) is the differential current; \(I_{op}\) is a constant determined offline. The threshold of the relay protection is \(I_{\max}\): \[I_{\max}=I_{op}\] (3) Braking ratio criterion: \(I_{cd}\geq kI_{res}\); \(k\) is the braking coefficient; \(I_{res}\) is the amount of braking current. Two typical forms of \(I_{res}\) are: 1. \(I_{res}=\left|\dot{I}_{n}-\dot{I}_{n}\right|\). The relationship between the currents of two ends and measured current is depicted in Figure 2. According to the braking ratio criterion, the threshold for this criterion can be expressed as: \[I_{\max}=kI_{res}=2 kI_{L}\cos(\frac{I_{cd}\omega_{0}}{4})\] (4) where \(I_{L}\) is load current, \(\omega\) is synchronous angular velocity. 2. \(I_{res}=\left|\dot{I}_{n}\right|+\left|\dot{I}_{n}\right|\). The threshold of the relay protection using this criterion is: \[I_{\max}=kI_{res}=2 kI_{L}\] (5)
2. The relationship between the differential current and inconsistent delay The inconsistent delay \(t_{cd}\) causes the sampling time error (\(t_{cd}/2\)) between the two ends [23]. This produces a differential current \(I_{cd}\) when there no fault is happening or the fault is out of protection area. From Figure 2a, the angle difference between \(-I_{n}\) and \(I_{m}\) can be obtained: \[\theta=t_{cd}\times\omega/2\] (6) Therefore, the differential current is:\[I_{cd}=\left|\dot{I}_{m}+\dot{I}_{n}\right|=2I_{L}\sin(\frac{I_{cd}\omega_{0}}{4}) \tag{7}\]  
Equation (7) shows the relationship between the differential current and the inconsistent delay.
# Traceability and Spectral Analysis of Transmission Error in Rotary Vector Planetary Gear Reducers
###### Abstract  
The rotary vector planetary gear reducer is a critical joint reducer widely employed in various robotic systems. Due to numerous components and complex motion, the machining and assembly of its components significantly impact its motion accuracy. This paper utilizes transmission error (TE) traceability analysis and Fourier analysis to identify and characterize the TE components in rotary vector planetary reducers. By analyzing the motion characteristics of individual components, this study identifies the frequency characteristics of each component. Fourier frequency domain discretization, based on experimental TE measurements, verifies these characteristics, identifying the main factors affecting TE and providing theoretical support for enhancing transmission accuracy.  
rotary vector planetary gear reducer; transmission error; Fourier analysis; error traceability 2020 IEEEexample:BSTcontrol