esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Antibiotic Degradation and Fate Kinetics in Surface Water
### Hydrolysis  
Hydrolysis involves the oxidation of a compound by enzymes and the reaction of a compound with either the hydrogen or the hydroxyl component of the water molecule producing hydrolyzed compound. The molecular structure of some antibiotics contains hydrolyzable functional groups (amide, carbamate, ester, and halogens), rendering antibiotics susceptible to hydrolytic degradation [50]. These hydrolyzable functional groups also appear frequently in many classes of antibiotic molecules (penicillins, cephalosporions, and macrolides). Hydrophilic contaminates containing polar ions will have a higher tendency for hydrolysis. Hydrolysis is the primary removal mechanism in most polar or hydrophilic antibiotics, especially for amid and ester-containing substances, such as macrolides and \(\beta\)-lactams.  
The rate of hydrolysis is controlled by the structure of specific antibiotics or reactants, the pH of the water, and temperature [51; 52]. For instance, amoxicillin, one of the \(\beta\)-lactams antibiotics, easily degrades into amoxicillin penicilloic acid, amoxicillin 2\({}^{\prime}\)5\({}^{\prime}\)-diketpiperazine, amoxicillin penilloic acid, and 3-(4-hydroxyphenyl) pyrazinol due to the hydrolysis instability of its \(\beta\)-lactam ring [53]. Penicillin is hydrolyzed to penicillin acid by the activity of \(\beta\)-lactamase enzyme [44]. Conversely, fluoroquinoline and sulfonamide antibiotics hardly break down via hydrolysis under normal environmental conditions. These antibiotics lack structural features that are easily hydrolyzed under environmental circumstances.  
At a high temperature, sulfonamides are ideal for hydrolysis and hydrolyzed to sulfanilic acid, attributed to cationic forms of sulfonamides in acidic solution, which are more sensitive to hydrolysis than the neutral and anionic forms of the compound. The hydrolysis of fluoroquinolones requires higher temperatures and higher concentrations of bases in the environment, which rarely occurs. Further, temperature variation in the same compound results in a variable hydrolysis rate constant. In an ambient environmental condition (pH 7 and 25 \({}^{\circ}\)C), the hydrolysis half-lives of cefalotin, cefoxitin, and ampicillin were 5.3, 9.3, and 27 d, respectively. However, with an increase in temperature (pH 7 and 60 \({}^{\circ}\)C), the hydrolysis half-lives of cefalotin, cefoxitin, and ampicillin were 0.067, 0.11, and 1.1 d [51]. A natural river sample with 100 \(\upmu\)g/L amoxicillin solution lost 90% of amoxicillin in 8 weeks at 4 \({}^{\circ}\)C, while becoming completely lost at 20 \({}^{\circ}\)C in the observed time [52]. The hydrolysis of cefalotin, cefoxitin, and ampicillin at pH 4 was negligible, while the rate of their hydrolysis was found to be rapid in alkaline conditions (the half-lives at pH 9 and 25 \({}^{\circ}\)C were 1.4, 6.6, and 6.7 d, respectively) [51].
## 4 Simulations and Discussion
### Comparison and Discussion  
To verify the superiority of the BA-ELM compensation method, comparison studies were conducted with other methods, namely, the BP method, AC method, and LSSVR compensation method. In addition, to further illustrate the better prediction accuracy and generalization abilities of the BA-ELM, the performance of the ELM method in eliminating the systematic error was also evaluated in the comparison experiment. The results on the compensation accuracy of these methods are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **IX** & **XBC** & **EXBC** & **XAC** & **EXAC** \\ \hline
1 & 51.2439 & 51.2978 & 0.0539 & 51.2439 & \(1.2326\times 10^{-8}\) \\
2 & 72.7366 & 72.6829 & 0.0537 & 72.7366 & \(1.1070\times 10^{-7}\) \\
3 & 361.2308 & 361.2483 & 0.0535 & 361.2308 & \(2.1059\times 10^{-8}\) \\
4 & 55.8758 & 55.8377 & 0.0382 & 55.8758 & \(7.8417\times 10^{-8}\) \\
5 & 199.6277 & 199.5891 & 0.0386 & 199.6277 & \(1.8986\times 10^{-7}\) \\
6 & 66.2154 & 66.2681 & 0.0527 & 66.2154 & \(3.8834\times 10^{-8}\) \\
7 & 69.2903 & 69.3424 & 0.0521 & 69.2903 & \(4.5690\times 10^{-9}\) \\
8 & 430.3110 & 430.3609 & 0.0499 & 430.3110 & \(2.9145\times 10^{-8}\) \\
9 & 108.1923 & 108.2428 & 0.0505 & 108.1923 & \(1.5801\times 10^{-9}\) \\
10 & 43.2980 & 43.3493 & 0.0514 & 43.2980 & \(1.9520\times 10^{-8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The systematic error before and after compensation of the simulated star image with \(\sigma_{PSF}=0.3\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **IX** & **XBC** & **EXBC** & **XAC** & **EXAC** \\ \hline
1 & 387.9959 & 387.8897 & 0.1062 & 387.9959 & \(8.5532\times 10^{-8}\) \\
2 & 85.2216 & 85.2741 & 0.0525 & 85.2216 & \(1.0517\times 10^{-7}\) \\
3 & 55.9882 & 55.8842 & 0.1040 & 55.9882 & \(3.4377\times 10^{-8}\) \\
4 & 486.1995 & 486.2568 & 0.0072 & 486.1995 & \(3.4220\times 10^{-8}\) \\
5 & 464.0505 & 464.1439 & 0.0933 & 464.0505 & \(2.9850\times 10^{-9}\) \\
6 & 378.8036 & 378.7457 & 0.0579 & 378.8036 & \(2.3147\times 10^{-8}\) \\
7 & 507.9022 & 507.8212 & 0.0811 & 507.9022 & \(3.9890\times 10^{-9}\) \\
8 & 307.8156 & 307.7550 & 0.0606 & 307.8156 & \(2.3306\times 10^{-8}\) \\
9 & 240.8466 & 240.7790 & 0.0676 & 240.8466 & \(2.2531\times 10^{-7}\) \\
10 & 89.8935 & 89.8146 & 0.0789 & 89.8935 & \(3.7056\times 10^{-8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The systematic error before and after compensation of the simulated star image with \(\sigma_{PSF}=0.671\).  
From Table 4, we can see that the prediction error of our method was much smaller than those of the other compensation methods--the BP method (\(2.0\times 10^{-3}\) pixels), the AC method (\(5.0\times 10^{-4}\) pixels), the LSSVR compensation method (\(6.0\times 10^{-5}\) pixels), and the ELM method (\(4.0\times 10^{-5}\) pixels). Our method needs longer training time because of the parameter optimization, but the compensation model training is an off-line process.
# Processing of AZ91D Magnesium Alloy by Laser Powder Bed Fusion
## 3 Results and Discussion
### Microstructural Analysis  
Two metallographic sections were made of the thread heads of sample 7-1. Initially, the first section was performed in the longitudinal direction (\(L\)) and the second in the transverse direction (\(T\)) in relation to the central (macro) axis of the sample. In the following step, the relative areal density was measured in these samples. The longitudinal sample has a porosity of 0.6% and the transverse sample of 0.3%. In both cases, the pores were mostly small and of irregular shape.  
The microstructure can be seen in both sections in the etched state in Figure 12. Figure 12 shows solidified melt pools. Individual pools overlap each other, indicating good metallurgical bonding between adjacent layers. In the cross-section (Figure 12b), individual laser trajectories are visible. The trajectories are not continuous, which is due to a change in orientation during the scanning of the individual layers. Similar results are obtained in other studies [1; 23; 26]. The irregular shape of the pores in the material is visible in the metallographic images. The cavities are predominantly very small (approximately 10 \(\upmu\)m) and distributed over the entire volume of the sample.  
For a detailed assessment of the microstructure, an analysis using a scanning electron microscope (SEM) was carried out. Figure 13 shows the very fine microstructure of the material, consisting of the \(\alpha\)-Mg matrix, divorced \(\beta\)-Mg\({}_{17}\)Al\({}_{12}\) phase at the grain boundaries and very small, irregularly arranged, Mn-rich precipitates [27; 28]. The image shows the difference in grain size in the area of the center of the solidified melt pool and at its edge (fusion boundary), where the microstructure is much finer. To determine the actual chemical composition of individual phases, energy dispersive spectroscopy (EDS) was performed (Table 8).  
Figure 11: The fracture surface of sample 7-1: (**a**) spherical pores with cracks; (**b**) fine dimple morphology with hints of cleavage facets.  
Figure 12: Sample 7-1: (**a**) longitudinal-L section; (**b**) transverse-T section.
Tremendous efforts have been directed in the past decades to potentially address the aforementioned issues. Recent pioneer studies have demonstrated the effective approach to reduce noble metal usage via scaling down the catalyst size to clusters or single atoms [12], incorporating the noble metals with cheap transition metals in the form of alloys [13], and epitaxial or core-shell nanostructures [14; 15]. In addition, several studies have highlighted noble metal-free catalysts for ORR and HER including transition metal chalcogenides [16], nitrides [17], phosphides [18], carbides [19], and borides [20]. Moreover, the development of effective metal-free ORR and HER catalysts are also underway [21]. Despite great endeavors, noble metal-free catalysts are still in their infant stage and are not competitive to Pt-based catalysts. Keeping all this in view, herein, we present a mini-review of ORR and HER electrocatalysts, mainly emphasizing noble metal-based heterogeneous nanocatalysts (NCs).
## 3 Method
### The Proposed FastDARTSDet  
Based on DARTS [22], we build a supernet containing all candidate operations and connections in the search space. Nodes of supernet represents feature maps and edges denotes operations. We utilize \(e_{ij}\) to denote the edge from node \(v_{i}\) to \(v_{j}\), and utilize \(\{e^{o}_{ij},o\in\mathcal{O}\}\) to denote candidate operations on edge \(e_{ij}\), where \(\mathcal{O}\) is an operation candidate set for each edge. Similar to prior works [21; 22], we design \(\mathcal{O}=\{\)zero, identity, max pooling, average pooling, \(3\times 3\) convolution, \(5\times 5\) convolution, \(3\times 3\) dilated convolution, and \(5\times 5\) dilated convolution\(\}\). To search architectures via gradient-based method, we follow DARTS [22] and define the architecture parameters \(\hat{\mathbf{\alpha}}^{o}_{ij}\) to represent the importance of  
Figure 1: Macro architecture of the backbone supernet. Each cell is independently searched. \(N\) is the number of repeated normal cells, which affects the model size.  
different candidate operations on each edge. The output of operations \(e_{ij}^{o}\) are averaged with weight \(\alpha_{ij}^{o}\) to obtain the output of edge \(e_{ij}\), represented as \(o_{j}(x_{i})\):  
\[o_{j}(x_{i})=\sum_{o\in O}\alpha_{ij}^{o}e_{ij}^{o}(x_{i}),\quad\alpha_{i,j}^{o }=\frac{\exp(\hat{\alpha}_{i,j}^{o})}{\sum_{o^{\prime}\in\mathcal{O}}\exp(\hat{ \alpha}_{i,j}^{o^{\prime}})}\in[0,1] \tag{1}\]  
where \(\mathbf{\alpha}\) denotes the normalized architecture parameters, \(x_{i}\) denotes the output of \(v_{i}\), and, thus, the output of \(v_{j}\) can be computed as follows:  
\[x_{j}=\sum_{i<j}o_{j}(x_{i}) \tag{2}\]  
Similar to DARTS [22], we address NAS as a bi-level optimization problem and solve it by SGD algorithm. The optimization problem is formalized as follows:  
\[\min_{\mathbf{\alpha},\gamma} \mathcal{L}_{val}(\mathbf{W}^{*}(\mathbf{\alpha},\mathbf{\alpha},\mathbf{\gamma}))\] (3) s.t. \[\mathbf{W}^{*}(\mathbf{\alpha},\gamma)=\operatorname*{arg\,min}_{\mathbf{W}} \mathcal{L}_{train}(\mathbf{W},\mathbf{\alpha},\mathbf{\gamma})\]  
where \(\mathbf{W}\) denotes the supernet weights, \(\mathbf{\alpha}\) denotes normalized architecture parameters for backbone following Equation (1), \(\gamma\) denotes normalized architecture parameters for FPN module.  
Unlike DARTS that search on a rather simple classification task, we propose to search on a much more complex computer vision task, object detection. The major differences lie in two aspects: (1) the detection datasets are larger than classification datasets, and (2) the detection models are more complex than the classification datasets. Specifically, detection models usually contain multiple parts, including backbone, feature pyramid network, and detector head. In this work, by defining the joint search space of backbone and FPN in the Section 3.1, we adopt the differentiable-based neural architecture search algorithm to search CNN architectures for object detection task.
## 3 Analysis and Discussion
### Modeling
#### 3.3.3 Soil Moisture Modeling: Relationship to Soil, Vegetation, and Climate  
The assessment of SM is intrinsically linked to the interaction between soil properties, vegetation, and climate. Remote sensing technologies, such as satellite imagery and UAV data collection, predominantly focus on surface soil moisture (SSM), capturing data from the uppermost soil layer. These measurements are significantly influenced by factors like soil texture, surface roughness, and vegetation cover, all of which are crucial for determining the accuracy and reliability of the moisture data obtained.  
Evapotranspiration (ET), a variable that integrates the effects of soil, vegetation, and climate, adds further complexity to this relationship. High-resolution UAV data can capture detailed variations in ET at the plot or field level (small areas), reflecting microtopographic features, vegetation density, and soil structure. This data is vital for understanding the dynamic processes of SM in heterogeneous landscapes, where small-scale variations in vegetation and soil properties are pivotal to the overall moisture balance. Conversely, satellite-based measurements, such as MODIS satellite products provide ET estimates with a spatial resolution of 500 m to 1 km and daily temporal resolution; while effective for  
Figure 6: Process for soil moisture products derived from different instruments, covariates, machine learning.  
regional and global assessments, this low resolution may overlook the fine-scale interactions between soil, vegetation, and climate that are essential for site-specific SM management and ecological studies. Table 4 provides a descriptive summary of how soil moisture modeling techniques relate to soil and vegetation characteristics.
###### Abstract  
Structural health monitoring is an important aspect of maintenance for bridge columns in areas of high seismic activity. In this project, recently developed piezoceramic-based transducers, known as smart aggregates (SA), were utilized to perform structural health monitoring of a reinforced concrete (RC) bridge column subjected to pseudo-dynamic loading. The SA-based approach has been previously verified for static and dynamic loading but never for pseudo-dynamic loading. Based on the developed SAs, an active-sensing approach was developed to perform real-time health status evaluation of the RC column during the loading procedure. The existence of cracks attenuated the stress wave transmission energy during the loading procedure and reduced the amplitudes of the signal received by SA sensors. To detect the crack evolution and evaluate the damage severity, a wavelet packet-based structural damage index was developed. Experimental results verified the effectiveness of the SAs in structural health monitoring of the RC column under pseudo-dynamic loading. In addition to monitoring the general severity of the damage, the local structural damage indices show potential to report the cyclic crack open-close phenomenon subjected to the pseudo-dynamic loading.  
cyclic crack monitoring; smart aggregate; active sensing; wavelet packet-based damage index +
Footnote †: journal: Applied Sciences  
1
## 2 Definition of Smart Grid  
Smart simply means intelligent, productive, clean, neat, or automatic running stuff, and grid means a network for supplying electricity to consumers. As of now, there is no single definition for a smart grid. Therefore, it can be defined in both simple and complex terms. A smart grid can be described in simple words as electricity with a brain. However, the National Institute of Standard and Technology (NIST), USA, defines a smart grid as follows [9]:  
_\"A modernized electric grid that enables bidirectional energy flows and uses bidirectional communication and control capabilities that will lead to a range of new functionalities and applications\"._ The IEEE definition of a smart grid is [10]  
_\"A revolutionary endeavor-with new communications and control capabilities, energy sources, generation models, and compliance with cross-jurisdictional regulatory structures\"._ The IEC definition of a smart grid is [11] the following:  
_\"It is a power grid that can intelligently integrate the actions of all connected users-generators, consumers, and those who do both-to efficiently provide a sustainable, economical, and secure power supply\"._  
According to the U.S. Department of Energy, a smart grid is defined as  
_\"A smart grid uses digital technologies to improve the reliability, security, and efficiency (both economic and energy) of the electric system-from large-scale generation to utility systems to electricity consumers and a growing number of distributed generation and storage resources\"._  
Based on the above definitions, the smart grid can be characterized as a bidirectional flow of energy and information that enables the utility to efficiently manage energy transmission and distribution while allowing customers to influence energy decisions. The smart grid takes advantage of real-time communications and information technology data to enable a smooth balance between energy demand and electricity supply. The main difference between the smart grid and the conventional power grid is the bidirectional energy exchange and two-way knowledge sharing between utilities and consumers. The smart grid improves reliability, infrastructure efficiency, economic development, and protection against cyber-attacks and power interruptions.  
Therefore, the working definition of the smart grid can be formulated as follows: \"A two-way digital communication in the electric power flow with the ability of self-healing,interoperability and prediction under various uncertainty conditions, equipped with cyber security measures against malicious attacks\".  
Thus, a smart power grid means smarter generation, transmission, distribution, and integration of customers, operations, markets, and service providers. The main objective of a smart grid is to enable intelligent or efficient use of available resources. In [5; 6], the authors evaluated conventional power grids and smart power grids. The difference between conventional power grids and smart power grids is shown in Table 1.
# Multivariate Time Series Deep Spatiotemporal Forecasting with Graph Neural Network
## 4 Experiment Analysis
### Ablation Study  
A model combining an attention mechanism and time series segmentation is proposed in this paper. In this section, an ablation experiment is performed on this model, and the effectiveness of the above method will be verified. Among them, the models containing different modules are named as follows:  
* w/o A: The attention mechanism part is removed from the information selection process of the graph convolution module. The output of the information selection step is passed directly to the next section.
* w/o S: Remove the down-sampling convolution module from the model and replace that part with a fully connected layer.  
The effectiveness of the module is verified by removing the attention method and the down-sampling convolution module. The results of the ablation experiments on the test set are shown in Table 4. Ablation experiments verify the effectiveness of the modules in four datasets. The best results are shown in bold black. The overall model outperforms the ablation model by comparison. In Table 4, the attention method has advantages on Electricity and Traffic, and the down-sampling convolution method is more effective on Exchange Rate and Solar Energy. The effectiveness of integrating these two parts into the model is verified in Table 4. Among them, the data are filtered by the attention method, so that the data retain more important information and enhance the robustness. Down-sampling convolution captures the deep spatiotemporal information of the sequence and enhances the predictive power of the data on cyclical trends. Overall, the accuracy of model predictions is improved. To verify the effectiveness of the module, we plot the one-dimensional time series (one variable) of the Electricity test set in Figure 13, where the green curve is the real data and the yellow dashed line is the predicted value. We can see that the full model captures the spikes in the data better, and the w/o S model does not capture this change.  
Figure 12: Solar Energy (Horizon = 24) forecast results.  
In summary, time series forecasting involves temporal, spatial, and periodic information. Of course, introducing the modules proposed in this paper will incur additional computational costs. However, it is very helpful to consider the above information. The improvement of this information on the prediction task is demonstrated by the results of the ablation experiments.
## 2 Analytical Calculation Procedure  
In this section, the analytical development employed to estimate the behaviour of a surface-mounted PMSM is explained. First, the magnetic flux density that the permanent magnets create in the air gap is calculated. Next, the magnetic flux density is used to calculate the electromagnetic torque of the machine and the magnetic pressure on the stator teeth, which is then used to calculate the vibration on the external surface of the stator.  
The analytical procedure is limited to ideal machines, which means that the eccentricity, the manufacturing tolerances or other types of faults are not considered in the analysis. Due to these assumptions, the prediction of the model will differ from the real behaviour of the machine. However, that error can be assumed, as the aim of the model is to observe the trends of the behaviour of the machine with respect to several design parameters, rather than to obtain a highly accurate prediction.
## 4 Experimental Result of the Position Tracking System and Discussion  
Following the measurement testing at a fixed position, the experimental results of a moving object are explained in this chapter. This experiment was conducted in an indoor anechoic chamber and the actual working environment.
## 2 Network Virtualization
### NV Typical Architecture  
NV refers to the software abstraction of the traditional network resources. Moreover, NV can control multiple physical networks into virtual ones. It can also split a network into multiple VN disks. The typical architecture of the NV is depicted in Figure 1. A VN is composed of multiple virtual nodes connected through virtual links.  
Figure 1: Network virtualization architecture.  
NV emphasises separating the roles of the traditional Internet service providers (ISPs) into two independent entities [5, 6]: infrastructure providers (InPs), who are in charge of the physical infrastructure and service providers (SPs), who build VNs by combining resources from multiple InPs.