esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[23] investigated the effects of uncertainties and correlations in pipe parameters on power and gas flow of an electricity-gas IES. [PERSON] et al. [24] studied probabilistic power and mass flow of combined electricity and heating networks considering uncertainties in photovoltaic power and electricity/heat loads. Although the studies above [20; 21; 22; 23; 24] applied probabilistic techniques to perform probabilistic energy flow (PEF) analysis of IES, they have the following two limitations:  
1. The existing studies are limited to the probabilistic analysis of electricity-gas IES [16; 17; 18; 19] and electricity-heat IES [20]. To the best of our knowledge, studies on the PEF of the electricity-gas-heat IES are still not available in the published literature. 2. Interactions among different energy networks in an uncertain environment have not been thoroughly investigated by the existing studies. In fact, compared with the electricity-gas IES or electricity-heat IES, electricity-gas-heat IES will have more complicated interactions due to the tri-energy interdependency, necessitating further studies on interactions and their potential influences on the operation of IES. Accordingly, this paper intends to study the probabilistic analysis of an electricity-gas-heat IES (EGH-IES) considering various operation modes and correlated uncertainties, and thus to disclose the effects of uncertainties and interactions on the operation of IES. The contributions of this paper can be described as follows:  
1. Four typical operation modes of an EGH-IES (i.e., \"electricity following heat\" mode, \"electricity/gas following heat\" mode, \"gas/heat following electricity\" mode and \"electricity following gas\" mode) are presented and discussed with a focus on the variation transmission among the three subnetworks. 2. A PEF framework for an EGH-IES is proposed to address uncertainties and correlations in wind speed, photovoltaic power and electricity/gas/heat loads. A MCS method based on Latin hypercube sampling (LHS) and Nataf transformation (MCS-LN), which is able to accurately address correlated random variables following arbitrary distributions and effectively alleviate the computational burden of MCS, is then developed to solve the PEF problem. Taking the operation mode \"electricity/gas following heat\" as an example, probabilistic interactions among the electricity/gas/heating networks and effects of uncertainties on the operation of the EGH-IES are investigated and discussed with numerical simulations. The rest of this paper is organized as follows. Section 2 describes the steady-state modeling of the EGH-IES. Section 3 presents four typical operation modes of the EGH-IES. Section 4 presents the deterministic analysis of the EGH-IES. The probabilistic energy flow problem of an EGH-IES is formulated and then solved by the MCS-LN method in Section 5. Numerical results from a sample EGH-IES are presented and discussed in Section 6, and conclusions are finally given in Section 7.
# Human-Centered AI for Migrant Integration Through LLM and RAG Optimization
## 4 RIM, TOPSIS, and VIKOR Methods in Multicriteria Decision Making  
In multicriteria decision making, the reference ideal method (RIM), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method are widely utilized for evaluating and selecting alternatives based on multiple criteria [64; 65; 66]. The RIM and the TOPSIS and VIKOR methods offer distinct approaches to multicriteria evaluation, each with unique strengths and preferred areas of application. The RIM is suitable for simpler, more straightforward analyses, TOPSIS is beneficial for clear comparisons with positive and negative ideal solutions, and VIKOR is particularly useful in situations requiring the balancing of trade-offs among criteria.  
The RIM involves comparing various alternatives against a reference ideal which represents the optimal performance across specified criteria. This approach evaluates alternatives by measuring the distance of each option from the reference ideal, thereby identifying the alternative which is closest to this ideal. In contrast, TOPSIS seeks to identify the alternative with the smallest distance from the positive ideal solution while maximizing the distance from the negative ideal solution. This method entails normalizing the data and calculating either the Euclidean or Manhattan distances to both ideal solutions. VIKOR aims to find a compromise solution which minimizes the regret of decision-makers across all criteria. It employs a compromise index which considers the distances to both the ideal and anti-ideal solutions, weighing these factors appropriately to produce a ranking of the alternatives. Collectively, these methodologies provide structured approaches to decision making by assessing alternatives in relation to established reference points [67; 68].
# A UAV-Based Visual Inspection Method for Rail Surface Defects
## 3 Experiment Results
### Performance Analysis
#### 3.2.2 Defect Segmentation  
On the basis of the LWLC, this paper compares GSME with traditional image segmentation methods including the maximum entropy (ME) algorithm, the proportion emphasized maximum entropy (PEME) algorithm, and the GSTA method. The ME [37] method can confirm one threshold which maximizes the total content of information provided by object distribution and background distribution. After enhancing images by MLC, PEME [30] is used to obtain an optimal segmentation threshold. PEME is an improved ME method which reduces the proportion of the background  
Figure 10: Examples of four enhancement methods for non-uniform illumination rail images. (**A**) Three examples of extracted rail images. (**B**) Three examples of enhancement image by HE method. (**C**) Three examples of enhancement image by LN method. (**D**) Three examples of enhancement image by MLC algorithm. (**E**) Three examples of enhancement image by LWLC algorithm. In Figure 10, the discrete defects on images have been marked by red rectangle to compare enhancement performance of LWLC algorithm with related methods.  
information and increases an exponent factor in original equation. The GSTA method uses wavelet transform and the Otsu algorithm to enhance image edges, then Otsu to extract the objection of the image. In the comparative experiment, the PEME method adopts MLC for image enhancement, and the other three adopt the same LWLC approach. All methods adopt the same evaluation criterion mentioned above, as shown in Figure 11. According to the Refs. [30; 38] and our experiment, the parameter \(\rho\) of GSTA method, the parameter \(\beta\) of MLC+PEME and the parameter \(\alpha\) of LWLC+GSME are set to 0.2, 2, and 0.3, respectively.  
As shown in Figure 11B, LWLC+ME can retain more detailed information of defects than the LWLC+GSTA and MLC+PEME methods, but its performance in terms of noise suppression, non-defect points removal, and defect details preservation is weaker than that of LWLC+GSME. Reasons for the inapplicability of the PEME approach are as follows: on one hand, UAV images have lower contrast, relatively obscure texture features, and more noise due to lighting based on natural light and higher distance between camera and rail; on the other, the variation in natural illumination can't be controlled by a human being, and it has an inevitable effect on defect extraction. If the image is captured as the aircraft flies above the rail at different distances, there is no uniform model to set the exponent factor \(\beta\) of the PEME model. In Figure 11C, it can be seen that defect details including shape and area information can't be effectively retained by the LWLC+GSTA method, and there are more noise and non-defect points than with the other methods. Figure 11D shows that MLC+PEME highlighted defect areas, but that noise and non-defect points in the image can't be restrained. As shown in Figure 11E, it can be seen that defects are remarkably segmented with the least noise and non-defect points based on LWLC+GSME.
# Efficient Inorganic Stabilization Materials for Chromium and Arsenic Pollution in Water and Soil
## 4 Factors Affecting the Stabilization Process of Chromium and Arsenic Stabilized Materials
### The Influence of Environmental pH on the Stabilization Process of Chromium and Arsenic  
The pH of soil or groundwater is an important factor affecting the stabilization process for treating chromium- and arsenic-contaminated soil. The acidity or alkalinity of the contaminated site determines the valence state of chromium or arsenic. Chromium mainly exists in the form of Cr3+ under acidic conditions, and in the form of CrO4- and Cr2O7- under alkaline conditions. Arsenic mainly exists in the form of H3 AsO3 under acidic conditions, and in the form of H2 AsO4-, H2 AsO4-, and AsO43- under alkaline conditions. Figure 11 shows the valence states of As and Cr at different pH values [96]. Therefore, the environment in which stabilization materials are used also affects the efficiency of stabilization. K\({}_{2}\)FeO\({}_{4}\)-FeCl\({}_{3}\) was prepared by [PERSON] et al. [97], and the adsorption mechanism of p-arsenic acid (p-ASA) was analyzed. As the pH increases, the removal rate of p-ASA gradually decreases, from 98.58% at a pH of 5 to 41.83% at a pH of 9 (Figure 12a). [PERSON] et al. [98] combined polyethylene oxide (PEI) with graphene oxide (GO) to prepare polyethyleneimine-coated graphene oxide composite material (GO@PEI). At a pH of 3, the pseudo-first-order removal rate constant of Cr(VI) is 3.7 \(\times\) 10\({}^{-3}\) min\({}^{-1}\). As the alkalinity increases, the removal rate constant decreases to 3.0 \(\times\) 10\({}^{-7}\) min\({}^{-1}\). Aluminum-containing waste (AWR) was used by [PERSON] et al. [99] to form aluminum arsenate (AlAsO\({}_{4}\)) and aluminum hydrogen arsenate (AlHAsO\({}_{4}\)+) to effectively remove arsenic. The surface of AWR has small irregular flocs, which provided good adsorption sites and promoted the fixation of arsenic. By adjusting the pH value, especially when the pH was greater than 7, the aluminum and calcium ions in AWR were precipitated in the form of hydroxide to improve the stability of solid arsenic.
## 4 Wire Damage Model
### Wire-Saddle Groove  
Figure 4 shows the fitting curves of the damage degree, \(D\), of the fatigue wire between wire and saddle groove under different contact loads and different fatigue loads. The corresponding equations of the fitting curve are shown in Table 3. The goodness of fit (R2) is above 0.99, which indicates that the damage degree of the steel wire has a good quadratic function relationship with the fatigue cycles. Under the same contact load, the damage degree, \(D\), of the fatigue wire increases continuously, and the increase rate becomes faster with increasing fatigue cycles, indicating that the wire damage is aggravated. When the contact load, \(F_{n}\), is 60 N, 80 N, and 100 N, the maximum damage degree of the wire is  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameters** & & \(\mathbf{N=1\times 10^{5}}\) & \(\mathbf{N=2\times 10^{5}}\) & \(\mathbf{N=3\times 10^{5}}\) & \(\mathbf{N=4\times 10^{5}}\) & \(\mathbf{N=5\times 10^{5}}\) \\ \hline Contact load & 60 & 0.0035 & 0.0059 & 0.0086 & 0.0118 & 0.0165 \\ (N) & 80 & 0.0049 & 0.0087 & 0.0136 & 0.0192 & 0.0283 \\ (N) & 100 & 0.0074 & 0.0114 & 0.0167 & 0.0233 & 0.0337 \\ \hline Fatigue load & 1100 & 0.0035 & 0.0059 & 0.0086 & 0.0118 & 0.0165 \\ peak (N) & 1200 & 0.0063 & 0.0105 & 0.0151 & 0.0205 & 0.0299 \\ \(1300\) & 1300 & 0.0076 & 0.0132 & 0.0199 & 0.0285 & 0.0407 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cross-sectional failure area of fatigue wire (pair: wire) under different contact and fatigue loads.  
Figure 3: Continuity diagram.  
0.0161, 0.0185, and 0.0225, respectively. It demonstrates that an increase in contact load can clearly promote steel wire damage. When the fatigue load peak, \(F_{max}\), is 1000 N, 1100 N, and 1200 N, the maximum damage degree of the steel wire is 0.0161, 0.0237, and 0.0257, respectively. It shows that increasing fatigue load promotes the damage of steel wire. By comparing the influence of different contact loads and fatigue loads on the damage degree of steel wire, it can be found that an increasing contact load is more damaging to steel wire than an increasing fatigue load.
# Hormonal Interactions Underlying Rootstock-Induced Vigor Control in Horticultural Crops
## 4 Effect of Multiple Plant Hormones in Rootstock-Induced Dwaring
### Gibberellins (GAs)  
Gibberellins are crucial for plant development and dwarfing traits [72; 73; 74]. Additionally, GAs are essential hormones that regulate apical meristem differentiation by lowering their levels during dormancy and upregulating at dormancy release or bud burst [75]. Previous studies have shown that GAs can affect grafted plants even when transported nonpolar over long distances [76]. Moreover, GA metabolism is linked with the dwarfing ability of plants [45]. Dwarfing induced by GA can be categorized into two groups: a responsive mutant related to GA signaling and a synthetic dwarf mutant linked with the GA anabolic pathway. Further, synthetic dwarf mutants have a GA deficit because of defective GA synthetase or other enzymes. The treatment of GA can potentially restore synthetic dwarf mutants to their standard size. In addition, [PERSON] et al. [77] found that the expression of _GA20 oxidase_ gene was downregulated in the apple scion cultivar, when grafted onto vigorous rootstocks. GAs and their precursors are produced in the root portion and transported to the shoot by the xylem, where they can be metabolized to produce bioactive GAs [78; 79]. Both monocot and dicot plants exhibit dwarf phenotypes due to mutations defective in GA biosynthesis; in contrast, GA treatments enhance plant growth [80]. GA20 ox is a multifunctional enzyme that converts GA12 or GA53 into GA9 or GA20 through three oxidation stages [81]. Suppression of _GA20 ox_ gene decreases GA levels and induces dwarfism in numerous crops [82; 83].  
Additionally, size-controlling apple rootstocks restrict root-produced GA to aerial parts [84; 85]. However, the application of GA restored the node numbers on the primary axis and secondary shoots of vigor reduction or dwarfing rootstock [86]. Gibberellinsensitive Dwarf1 (_GID1_), the GA receptor in the signal pathway, can interact with GA to degrade DELLA repressor proteins after forming a GA-dependent complex and then regulate various physiological and biochemical activities in plants [87; 88; 89; 90]. Recent studies have shown that bread fruit scion grafted onto marang rootstocks had significantly greater expression of the GA catabolic gene (_AaGA2 ox1_), and lower expression of the GA biosynthetic (_AaGA20 ox3_) gene at various time points. Additionally, it was shown that scions grafted onto marang rootstocks accumulated more DELLA proteins (GA-signaling repressors), suggesting increased repression of GA response in scions grafted on marang rootstocks. This may result from the downregulation of GA biosynthesis or the upregulation of the GA deactivation pathway [17]. Plants with an abundance of DELLA often exhibit a dwarf phenotype [91]. There is strong evidence that rootstock-induced dwarfing is caused by a disruption in GA metabolism.
# Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning
###### Abstract
After estimating the vibration signals, the classification algorithms can be selected for FPRCI. To classify the signal's state different techniques have been used that are divided into three main groups: (a) classical classifiers such as the sliding mode technique [10]; (b)machine learning-based classifiers including support vector machine (SVM) [36] and decision trees [37]; (c) deep learning-based classifiers including convolution neural networks [38] and autoencoders [39]. In this work, the SVM is recommended for classification of the faults and identification of the crack sizes. In this research, the combination of the hybrid-based signal modeling, an adaptive neural-fuzzy-based variable structure Kalman filter, and SVM is suggested for FPRCI. Thus, this technique has three stages: signal approximation and estimation using the intelligent DT technique, residual signal generation, and classification. The first stage (intelligent digital twin) has two parts: (a) signal approximation and (b) signal estimation. Therefore, first, the normal signal is approximated using a combination of the mathematical-based approach and machine learning-based regression. Next, the combination of a Kalman filter (KF), the high-order variable structure technique (HVS), and the adaptive neural-fuzzy method (ANF) are recommended in this research, which from now on is called the hybrid robust Kalman filter (HRKF) for signal estimation. In the next stage, the residual signal is generated using the difference between the original and the estimated vibration signals. In addition, in the third stage, an SVM is suggested for FPRCI. This work has the following contributions:  
* The vibration signal approximation using a combination of the mathematical-based approach and machine learning-based regression is the first contribution of this work. * The vibration bearing signal estimation using intelligent DT based on the combination of a Kalman filter, the high-order variable structure technique, and the adaptive neural-fuzzy method (ANF) that is approximated using the proposed approach. * The anomaly classification and crack size identification using intelligent DT integrated with machine learning approach is the third contribution. This research work has the following parts. The proposed digital twin integrated with the machine learning approach is proposed in Section 2. Section 3 provides experimental results for the proposed digital twin integrated with the machine learning approach as compared with the other approaches. The conclusion is explained in Section 4.
Currently, the models used in object detection methods based on deep learning can be subcategorized into detection models based on region proposals and detection models based on regression. Models for deep-learning-based object detection based on region proposals first extract candidate regions in the detection area in preparation for subsequent feature extraction and classification. Typical examples of such models include the R-CNN (Regions with CNN features) [17], SPP-net (Spatial Pyramid Pooling Networks) [18], Fast R-CNN [19], Faster R-CNN [20] and R-FCN (Region-based Fully Convolutional Networks) [21] models. By contrast, models for deep-learning-based object detection based on regression divide a feature map using certain rules and establish relationships among the predicted bounding boxes, the default bounding boxes and the ground truths for training. Typical examples of models based on regression include YOLO (You Only Look Once) [22] and SSD (Single Shot Multibox Detector) [23]. Compared with methods based on region proposals, regression-based methods usually achieve better real-time performance. Although the accuracy of regression-based method generally is poorer than that of region-proposals method, the accuracy gap between them is not always large. For example, the accuracy of SSD is comparable to that of Faster RCNN on VOC 2007 test dataset [23]. Therefore, if the accuracy and real-time performance are both taken into consideration, regression-based methods will be a good choice. Nevertheless, the detection performances of both YOLO and SSD on small objects are unsatisfactory [24; 25]. The reason is that small objects may not even have any information at the very top layers of the deep learning model and rich representations are difficult to learn from their poor-quality appearance and structure. Therefore, small object detection is a challenge in the object detection based on deep learning.  
This paper proposes a multi-view object detection approach based on deep learning, with the aim of improving the performance of regression-based deep learning models when detecting small objects. In the proposed multi-view approach, the object detection results for a set of divided views are merged to improve the object detection capability. Experimental results demonstrate that this design can substantially improve the detection capability for small objects, which is of great value for improving deep learning technology for further applications in object detection.
# Investigation of the Influence of Splitter Blades on the Resonance Conditions of Impellers
## 3 Numerical Analysis
### Tuned Case
#### 3.2.1 Resonance Identification by Conventional Resonance Conditions  
Modal analysis of the impeller is carried out. The simplified [PERSON]'s advanced frequency evaluation (SAFE) diagram [17] is shown in Figure 3. The first five main blade dominated mode families MBF1\(\sim\)MBF5 are connected by black solid lines and the first two splitter blade dominated mode families SBF1\(\sim\)SBF2 are connected by blue solid lines. The disk dominated mode families DF are connected by pink dashed lines. Some mixed modes are omitted for clarity.  
In the conventional resonance conditions, the frequency matching condition and the shape matching condition can be written as Equations (28) and (29), respectively, in which \(\omega_{e}\) is the excitation frequency, \(\omega_{n}\) is the natural frequency of the impeller, \(n\) is the engine order of excitation, which usually takes the number of vanes or struts, \(N\) is the number of sectors, \(n_{ND}\) is the nodal diameter index, and \(k\) could be any integer.  
\[\omega_{e}=\omega_{n}, \tag{28}\]  
\[n=|kN\pm n_{ND}| \tag{29}\]  
From Equation (29), we know that the excitation generated from 21 vanes is capable of arousing the resonances of the ND4 modes. Thus, some of the ND4 modes are marked in the red box as shown in Figure 3, which are likely to encounter resonances. The bottom and top of the box represent the lower and upper bound of the excitation frequency, which is limited by the operating range of engine speed. Because the excitation frequency corresponding to the idle engine speed is very close to the natural frequency of mode 94, mode 94 is chosen as the dangerous mode for further analysis. The mode shape is shown in Figure 4. The deformation mainly occurred in the outer regions around the disk, the axial component of which is prominent. Besides, 13 nodal lines are obvious from the back view of the mode shape.  
According to the conventional resonance condition (Equation (29)), the ND4 modes can only be aroused resonances by the excitations with \(n=|\pm 4+17k|=4,13,21\ldots\). Therefore, the engine order excitation caused by the 21-vaned diffuser can arouse the resonance of mode 94. In addition, from previous studies we know that for the excitations with \(n=4,13,21\ldots\), the resultant resonant amplit  
Figure 3: The simplified [PERSON]’s advanced frequency evaluation (SAFE) diagram.