esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Prospects  
This paper describes a method that combines the idea of \"forgetting factor weighting\" with eigenvector recursive PCA. The proposed eigenvector recursive PCA with a forgetting factor enables innovative operational modal parameter identification for LTV structures. Because the eigenvalues and eigenvectors of the autocorrelation matrix destroy the PCA model, a rank-1 matrix is used to correct the errors. The new PCs and eigenvectors are updated according to the eigenvalues and eigenvectors from the previous step, allowing ERPCAWF-based OMA to identify the transient modal parameters for LTV structures. This paper also describes the steps to control vibration using modal parameters. Simulation results demonstrate that the ERPCAWF-based OMA method has the advantages of faster run time, lower memory space requirements, higher identification accuracy, and greater stability than PCAWF- and ARPCAWF-based OMA. Our approach can identify modal parameters quickly and accurately, and further apply them to solving vibration control problems.  
However, this research also has some limitations. The value of forgetting factor has a great influence on the accuracy of recognition results. If no forgetting factor is added, the recognition accuracy gradually decreases. Therefore, how to set the self-adaptive selection of forgetting factors based on the time-varying speed characteristics of vibration signals is our future research direction. Before being applied to engineering applications, ERPCAWF-based OMA should be verified under different types of ambient excitation, time-varying speed especially fast linear time-varying, more complex three-dimensional engineering structures, online health monitoring, and fault diagnosis. Identifying the damping ratio of LTV structures will be addressed in subsequent studies.  
Conceptualization, C.W. and X.L.; Data curation, C.W.; Formal analysis, C.W., H.H. and X.L.; Funding acquisition, C.W. and H.H.; Investigation, C.W., H.H. and X.L.; Methodology, C.W. and H.H.; Project administration, C.W. and J.C.; Resources, C.W. and X.L.; Supervision, C.W., X.L., and J.C.; Validation, C.W., H.H., and J.C.; Visualization, C.W. and H.H.; Writing--original draft, C.W., H.H., and X.L.; and Writing--review and editing, C.W., H.H., and J.C. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (Nos. 51305142, 51305143, 18 BTJ031 and 71571056), Postgraduate Scientific Research Innovation Ability Training Plan Funding Projects of Huaqiao University (No. 17013083018), the Quanzhou Science and the Technology Plan (No. 2018C110R) and the National Science Foundation (No. DMS-0907710).  
The authors declare no conflict of interest.
## 2 Experimental Assessment of Curtain Wall Seismic Performance
### Static Behavior of Curtain Walls: Description, Experimental Setup, and Results  
In the static seismic performance test, as shown in Figure 1, the upper and lower rigid beams were firmly fixed to the curtain wall frame, and static displacement was applied in the horizontal direction using a 20-ton hydraulic cylinder and pump located on the middle rigid beam. Figure 2 shows the specimen used for the static seismic performance evaluation. The specimen outline is shown in Table 2.  
Figure 1: Test methods in the AAMA 501.4 standard.  
Figure 2: Cont.  
The horizontal displacement was divided into four stages according to the seismic grade, and the displacement corresponding to each grade was repeated in the application of three cycles. Figure 3 shows the state of the curtain wall to which displacement by seismic grade was applied.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Profile**} & \multicolumn{2}{c}{**Aluminum Curtain Wall/SC-ECW-ST-01**} \\ \hline \multicolumn{2}{c}{Dimension} & \multicolumn{2}{c}{3600 mm (W) \(\times\) 7600 mm (H)} \\ \hline \multirow{4}{*}{Aluminum frame} & Mullion & (60 mm \(\times\) 150 mm) Extruded bar \\ \cline{2-3}  & Transom & (60 mm \(\times\) 150 mm) Extruded bar \\ \cline{2-3}  & Thermal break & Polyamide (Mullion and transom) \\ \hline \multirow{4}{*}{Glass} & \multicolumn{2}{c}{42 mm Low-E triple glass (6 mm Semi-reinforced} \\  & glass + 12 mm Air + 6 mm Plain glass + 12 mm Air + 6 mm Semi-reinforced glass)} \\ \hline \multicolumn{2}{c}{AL. sleeve of expansion joint} & 250 mm \\ \hline \multirow{2}{*}{Anchor part} & Steel anchor & 2L-110 \(\times\) 100 \(\times\) 9T \(\times\) 120 LG \\ \cline{2-3}  & Spacing & Refer to Figure 2 \\ \hline \multirow{2}{*}{Finish} & AL. Bar & PVDF Coat \\ \cline{2-3}  & Steel anchor & Hot-dip galvanizing \\ \hline \hline \end{tabular}
\end{table}
Table 2: Designed member specifications of specimen components.  
Figure 3: Applied displacement based on seismic grade: (**a**) Special level \(\pm\)36 mm; (**b**) I level \(\pm\)54 mm; (**c**) II level \(\pm\)72 mm; (**d**) AAAA 501.6 level \(\pm\) 150 mm.  
Figure 2: Conventional stick curtain wall system: (**a**) front view; (**b**) cross-section.  
The examination results of the behavior of the curtain wall when applying the four levels of static displacement are as follows. When a displacement of 54 mm was applied, part of the weather sealant installed inside and outside of the curtain wall began to break, and the broken area and its range increased when a displacement of 72 mm was applied. It was confirmed that when the target displacement of this study, i.e., 150 mm, was applied, the glass was broken and fell due to residual deformation after the weather sealant was broken. Based on the test results, the main vulnerabilities of the curtain wall are summarized in Figure 4 and Table 3.
## 3 Results and Discussion
### Precipitation Estimates
#### 3.1.2 Spatial Variability  
The spatial distributions of the total cumulative gridded precipitation from ground- and space-borne radar for eight flood events, extracted using the aforementioned data processing methods, are depicted in Figure 7.  
Figure 7a displays the 1 km \(\times\) 1 km grid-based RAR precipitation total for each individual flood event. The varying precipitation values across different grids indicate that even within the same watershed, there can be regional differences in the rainfall amounts for the same event. While several factors may contribute to this, the similarity in distribution among six out of the eight events suggests that it is primarily influenced by the topographic characteristics of mountainous areas. Therefore, if the areal average precipitation data from rain gauges are used for flood simulations, the lack of deliberation for this spatial distribution may lead to inaccurate analysis results or difficulties in model calibration. On the other hand, Figure 7b represents the spatial distribution of IMERG precipitation data at an approximate resolution of 10 km (0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) grid). Although there are differences in the total precipitation across grids, the spatial resolution of this IMERG dataset is less dense compared to the RAR data, which may pose some limitations for related analyses. Nevertheless, it is deemed to be highly useful for flood analysis in ungauged or global river basins where rainfall data are unavailable.  
Figure 6: Scatter plots comparing the areal-averaged hourly RAR and IMERG products with gauged data, respectively.
###### Abstract  
Root--associated microbial communities play important roles in the process of adaptation of plant hosts to environment stressors, and in this perspective, the microbiome of haplotypes represents a valuable model for understanding the contribution of microorganisms to plant tolerance to salt. Although considered as the most promising haplotype candidate to crop cultivation, _Salicornia ramosissima_ is one of the least-studied species in terms of microbiome composition and the effect of sediment properties on the diversity of plant-growth promoting bacteria associated with the roots. In this work, we aimed at isolating and characterizing halotolerant bacteria associated with the rhizosphere and root tissues of _S. ramosissima_, envisaging their application in saline agriculture. Endophytic and rhizosphere bacteria were isolated from wild and crop cultivated plants, growing in different estuarine conditions. Isolates were identified based on 165 rRNA sequences and screened for plant-growth promotion traits. The subsets of isolates from different sampling sites were very different in terms of composition but consistent in terms of the plant-growth promoting traits represented. _Bacillus_ was the most represented genus and expressed the wider range of extracellular enzymatic activities. Halotolerant strains of _Salinicola_, _Pseudomonas_, _Oceanobacillus_, _Halomonas_, _Providencia_, _Bacillus_, _Psychroducer_ and _Brevibacterium_ also exhibited several plant-growth promotion traits (e.g., 3-indole acetic acid (IAA), 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase, siderophores, phosphate solubilization). Considering the taxonomic diversity and the plant-growth promotion potential of the isolates, the collection represents a valuable resource that can be used to optimize the crop cultivation of _Salicornia_ under different environmental conditions and for the attenuation of salt stress in non-halophytes, considering the global threat of arable soil salinization.  
halophytes; phytobione; rhizosphere; endophytic bacteria; saline stress +
Footnote †: journal: Applied Sciences
## 5 Applications of BC-based Photocatalysts
### g-C\({}_{3}\)N\({}_{4}\)-BCPs  
The graphitic carbon nitride (g-C\({}_{3}\)N\({}_{4}\)) has elicited ripples in recent years since [PERSON] et al. [103] discovered the hydrogen production from water over g-C\({}_{3}\)N\({}_{4}\) in 2009. g-C\({}_{3}\)N\({}_{4}\) processes high physicochemical stability, non-toxic, and unique graphene-like two-dimensional structure. During recent decades, lots of research on BC-g-C\({}_{3}\)N\({}_{4}\) conjunctions has been reported.  
[PERSON] et al. [104] summarized the g-C\({}_{3}\)N\({}_{4}\)-based photocatalysts. The Schottky-junction between g-C\({}_{3}\)N\({}_{4}\) and the highly conductive carbon materials enhanced charge separation. [PERSON] and his co-workers [37; 57; 105] synthesized a series of BC-based g-C\({}_{3}\)N\({}_{4}\) photocatalysts for wastewater treatment. For example, BC supported g-C\({}_{3}\)N\({}_{4}\)/FeVO\({}_{4}\) were prepared for methylparaben and 2-chlorophenol photodegradation under solar light [37]. The BCPs exhibited excellent photocatalytic activity with the removal rate of 98.4% (methylparaben) and 90.7% (2-chlorophenol), due to the expended solar light response and enhanced charge separation. Recently, [PERSON] et al. [45] synthesized BC-based g-C\({}_{3}\)N\({}_{4}\)/Bi\({}_{2}\)O\({}_{2}\)CO\({}_{3}\)/CoFe\({}_{2}\)O\({}_{4}\) heterojunction. The high pesticide paraquat removal rate of 99.3% was reached under visible radiation, compared with that of 92.1% under solar light. Due to the greater absorption of the visible spectrum, BC elevated the quantum efficiency, charge separation, and generation of free radicals. [PERSON] et al. [72] synthesized BC-based g-C\({}_{3}\)N\({}_{4}\) via one-pot pyrolysis with different mass ratio. g-C\({}_{3}\)N\({}_{4}\) modification can expand biochar's capacity. BC100 (100 (urea): 1 (biomass)) showed 100% removal rate of RR120 through both adsorption and photocatalytic degradation under natural conditions. [PERSON] et al. [60] fabricated BC coupled g-C\({}_{3}\)N\({}_{4}\) nanosheets composites through two-step calcination strategy to remove the Cr (VI) from wastewater. Graphitized BCPs could completely degrade the Cr(VI) and total Cr, owing to the photogenerated electrons, high thermal stability, and large specific surface area. [PERSON] et al. [106] synthesized BC modified graphitic carbon nitride via pyrolysis of kapok fiber. The BCPs presented a narrow band gap and extended light absorption. The BC facilitated the electron transfer and improved the photocatalytic activity. It played a center role as the carbon source and substrate in the epitaxial growth of graphitic carbon nitride.
# Lightweight End-to-End Speech Enhancement Generative Adversarial Network Using Sinc Convolutions
## 5 Sinc-SEGAN Architecture
The first layer of the discriminator is also substituted with the Sinc convolution layer with 64 filters of length 251 and stride = 1. In summary, in the substitution architecture, the generator consists of 10 layers and the discriminator consists of five layers.
## 4 Experimental Results and Analysis
### Analysis of Generalization Performance Experiment Results
#### 4.4.2 Analysis of Experimental Results of Different Models
The unit of the recognition accuracy of the six foreign objects is %, the unit of the parameter is M, and the unit of FLOPs is G.  
Figure 15: Comparison of mainstream convolutional networks. (**a**) Parameter quantity results; (**b**) mAP50 and model calculation quantity. same hardware conditions. However, inference speed was influenced by factors such as network computing power and bandwidth, and an optimal balance was not achieved between recognition speed and accuracy. In the third experiment group, we vertically compared the improved network's detection data under various hardware conditions. The results indicate that the improved method does not impose strict requirements on hardware in terms of detection and inference speed. In other words, even when using devices with relatively weaker computing capabilities, our method can still achieve relatively good detection results. This provides a direction and possibility for applications in edge devices with limited computational resources.
## 3 Results
### Integrated Pollution Index  
#### 3.3.1 Integrated Pollution Index for Soils  
On the basis of the single pollution index (_PI_) mentioned above, the integrated pollution index (\(PI_{n}\)) to describe the pollution risk of heavy metals for soil of agricultural land is proposed by [PERSON] et al. [42] as  
\[PI_{n}=\Sigma PI \tag{3}\]  
where _PI_ are the single pollution indices of heavy metals mentioned above, and \(n\) is the count of heavy metals.  
According to Equations (1) and (3), the theoretical values of integrated pollution index (\(PI_{5}\)) for five heavy metals of Hg, Pb, Cd, As, and Cr include 0, 1-5, 10-14, 20-23, 30-32, 40-41, and 50. When \(PI_{5}\) values are 0, it means that these samples belong to the background level with the contents of the five heavy metals being lower than risk screening values in GB15618-2018. When \(PI_{5}\) values range from 1 to 5, it means that these samples belong to the screening level with the contents of the five heavy metals being lower than risk intervention values, while there is at least one heavy metal with contents greater than risk screening values. When \(PI_{5}\) values range from 10 to 50, the tens digit (n) of the \(PI_{5}\) values indicate the number of heavy metals with contents equal to or greater than risk intervention values.  
The actual \(PI_{5}\) values in 3312 surface soil samples from the study area range from 0 to 21. There are 2057 samples with \(PI_{5}\) values of 0 belonging to the background level, which is about 62.1% in total; 1107 samples with \(PI_{5}\) values varying between 1 and 5 belonging to the screening level, which is about 33.4%; 143 samples with \(PI_{5}\) values ranging from 10 to 14; and 5 samples with \(PI_{5}\) values varying between 20 and 21, which is about 4.5% in all soil samples. The geochemical map of \(PI_{5}\) in the Xinping area is shown in Figure 4, which can visualize the spatial distribution of heavy metals to trace integrated anomalies like the mineralization similarity of metallogenes presented recently [47-49].
# Joint Trajectory and Communication Design for Buffer-Aided Multi-UAV Relaying Networks
## 3 The Proposed Algorithm
### Overall Algorithm and Convergence
Similarly, the details of obtaining the optimal solution of (32) in Step 4 can show that:  
\[R\left(A^{l+1},P^{l+1},Q^{l}\right)\overset{(a)}{=}R_{Q}^{lb,l}\left(A^{l+1},P^ {l+1},Q^{l}\right)\overset{(b)}{\leq}R_{Q}^{lb,l}\left(A^{l+1},P^{l+1},Q^{l+1} \right)\overset{(c)}{\leq}R\left(A^{l+1},P^{l+1},Q^{l+1}\right) \tag{34}\]  
Combining (33) and (34), it can be concluded that:  
\[R\left(A^{l},P^{l},Q^{l}\right)\leq R\left(A^{l+1},P^{l+1},Q^{l+1}\right) \tag{35}\]  
Based on the analysis above, we can conclude that the objective value of Problem (18) is non-decreasing over the iteration \(l\), and the objective value of Problem (18) is upper bounded by a finite value. Therefore, our optimization problem is non-negative, non-decreasing, and bounded, i.e., the proposed algorithm converges. Next, we will discuss the complexity of the proposed algorithm. As shown in Algorithm 1, the key idea of the proposed algorithm is to optimize alternately the relay selection, UAV transmit power control, and UAV trajectory. To make the proposed problem more tractable, we decompose the original problem into the following two sub-problems iteratively. In each iteration, the main complexity of the proposed algorithm lies in Steps 3 and 4, which require solving a series of convex optimization problems. The computational costs of Steps 3 and 4 are about \(O\left(L\left(KN+2N\right)^{3}\right)\) and \(O\left(L\left(2N\right)^{3}\right)\), respectively, where \(K\) is the number of UAVs, \(N\) is the number of time slots, and \(L\) is the iteration numbers of the proposed algorithms [27; 28].
# Water Pipeline Leakage Detection Based on Coherent \(\varphi\)-OTDR and Deep Learning Technology
## 4 Deep Learning Training
### ResNet18 with Mel Spectrograms as Input  
In traditional neural networks, increasing the depth of the network is a common strategy to enhance recognition accuracy by extracting higher-level features from images. However, when the network depth reaches a certain point, the issue of gradient vanishing arises. Deep networks may perform worse than shallow ones due to this phenomenon. To address this challenge, in 2015, [PERSON] et al. proposed the ResNet18 neural network, which  
Figure 8: Structure of the CNN model. Abbreviations—FC: fully connected layer; GAP: global average pooling.  
Figure 9: Training results of the CNN model: (**a**) training curves; (**b**) confusion matrix.  
introduced residual blocks with shortcut connections to mitigate gradient vanishing. In conventional deep neural networks, during backpropagation, gradients are multiplied layer by layer with weight matrices, leading to gradual reduction and the eventual disappearance of gradients. In the ResNet model, the shortcut connections directly transmit gradients from the input to subsequent layers, effectively preventing gradient vanishing. A simple residual block structure is illustrated in Figure 10, where input data \(\text{x}_{i}\) is transformed via weighted operations into f(x) and the final output \(\text{x}_{i+1}\) is obtained by adding x and f(x). The specific model architecture is shown in Figure 11.  
Specifically, ResNet18 consists of multiple residual blocks, each containing two 3 \(\times\) 3 convolutional layers and a skip connection that adds the original input to the output. These skip connections facilitate the easier training of deep networks. The ResNet18 architecture comprises eighteen layers, including sixteen convolutional layers and two fully connected layers. Convolutional layers are organized into several residual blocks, each incorporating  
Figure 11: Structure of the ResNet18 model.  
Figure 10: Residual block structure diagram.  
multiple convolutional layers, batch normalization, and ReLU activation. Compared to deeper variants like ResNet50 and ResNet101, ResNet18 strikes a balance between training speed and network depth, with lower complexity and computational resource requirements. In our study, we constructed and trained the ResNet18 model using PyTorch-GPU 0.0.1 and Python 3.8. We adjusted the output dimension of the fully connected layer to 2 for binary classification. Input data consisted of Mel spectrograms derived from raw data. During training, we employed the Adam optimizer with a learning rate of 0.01 and conducted 200 epochs. The corresponding training curve is depicted in Figure 11(a). Using the model, we identified the test dataset and obtained a confusion matrix as shown in Figure 11(b). The achieved accuracy was 99.5%.
## 2 Experimental Apparatus and Procedure  
The GO-Al2O3 hybrid nanofluids were created by the two-step method [54] using sodium dodecyl sulfonate (SDS) as a surfactant. Different masses of the graphene oxide nanoplatelet (purity \(\geq\)99 wt %, number of layers <10, from Shenzhen Turing Evolution Technology Co., Ltd.) and nano-Al2O3 (content \(\geq\) 99.99%, average particle size (30 \(\pm\) 5 nm)) were fully dispersed in deionized water (DW). High-powered ultrasonication was used to disperse nanoparticles. Sodium hydroxide solution was used to adjust the pH.  
The hybrid nanomaterials were analyzed and characterized by an X-ray diffractometer (Model: XRD Ultima IV). WGZ-2000 turbidity meter (indication error of \(\pm\)3% F.S and repeatability \(\leq\)0.5%, from Shanghai Xinrui Instrument Co., Ltd. China) was used to test the absorbance of nanofluids with time. It can measure the degree of scattering or attenuation of light generated by insoluble particulate matter suspended in water or a transparent liquid, and quantitatively characterize the content of these suspended particulate matter with NTU (Nephelometric Turbidity Unit) as the turbidity unit.  
The transient hot wire method has been regarded as the most accurate method as well as the primary method for determination of thermal conductivity of a fluid and solid. The method has become a standard method in many countries [55; 56; 57]. Thermal conductivity is measured by a TC3000E transient hot wire instrument (Xiaxi Electronic Technology Co., Ltd. Xi'an, China), which has been employed widely by many researchers [58; 59; 60]. The instrument works according to transient hot-wire method that complies with standards of ASTM C1113, ASTM D5930, GB/T 10297, and GB/T 11205. A water bath with an accuracy of \(\pm\) 0.05 \({}^{\circ}\)C was employed to control temperature.  
X-Ray Diffraction (XRD) is very useful to show the crystal structure [61], the hybrid nanomaterials are characterized by XRD as indicated in Figure 1. It can be seen that the diffraction peak at 20 = 11.06\({}^{\circ}\) was the characteristic peak of graphene oxide (001) [62]; the diffraction peak at 20 = 25.57\({}^{\circ}\), 35.15\({}^{\circ}\), 37.77\({}^{\circ}\), 43.35\({}^{\circ}\), 52.55\({}^{\circ}\), 57.51\({}^{\circ}\), 68.21\({}^{\circ}\) and 77.2\({}^{\circ}\) is the crystal phase of \(\alpha\)- Al2O3 (012), (104), (110), (113), (024), (116), (300) and (119), respectively, which is consistent with the standard \(\alpha\)-Al2O3 spectrum [63].  
Figures 2 and 3 are scanning electron microscope (SEM) for the hybrid nanoparticles dispersion in the base solution for 120 min and 30 min of ultrasonication, respectively. The results showed that Al2O3 nanoparticles of about 40 nm distribute homogeneously on the graphene oxide nanosheets  
Figure 1: XRD of hybrid nanomaterials.  
when vibrated for 120 min using ultrasonic. However, a large number of nanoparticles aggregate together under 30 min of ultrasonication, which indicates poor dispersion of the nanoparticles.