esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results Analysis
### Effect of Load Magnitude  
The transducer probe used in NDT was a composite structure of piezoelectric ceramic, which used vibrations to generate a small force on the surface of test specimens to excite ultrasonic waves. The magnitude of this force was 0.1 MPa and was measured in a simulation process to define the amplitude on the surface of the model. To analyze the effects of varying loading forces on ultrasonic wave velocity, five different loading forces (0.02 MPa, 0.06 MPa, 0.1 MPa, 0.2 MPa, and 0.3 MPa) were  
Figure 6: Sensitivity of wave velocity to (**a**) dynamic modulus and (**b**) specimen density.  
Figure 7: (**a**) Schematic diagram of wave propagation at different depths. (**b**) Effects of thickness on wave propagation.  
used in the simulation process. The simulated results of ultrasonic wave velocity are presented in Figure 8.  
It can be seen that the simulated ultrasonic wave velocities at different loading forces remained the same. This can be explained by the fact that when ultrasonic waves propagated through the models, a group of damping waves was generated by the influence of density and the phenomena of wave reflection. This group of damping waves had certain velocities, which influenced ultrasonic wave propagation. With the rise of loading forces, attenuation of ultrasonic wave propagation also increased, which contributed to the higher velocity of the damping waves and the overall effect on ultrasonic wave propagation remained the same.
## 1 Introduction
In this study, acoustic emission is utilized to monitor the effects of ASR on large-scale, reinforced concrete structures while considering stress induced boundary conditions in completely plane-confined and partially plane-confined specimens. There are several differences between damage detection of small-scale and large scale structures using acoustic emission. The main challenge with monitoring of small-scale structures using AE is reflections. An example of an ASR study using AE on small-scale specimens was conducted by [PERSON] et al. [11]. For large-scale structures, attenuation and dispersion of waves are of primary concern. Many sensors with an appropriate layout are needed for damage detection and source localization. The test specimens were assembled, cured, and monitored at the University of Tennessee, Knoxville, and are part of a test program sponsored by the US DOE Light Water Reactor Sustainability Program. The confined specimen has a complete confinement provided by a rigid steel frame and steel reinforcement meshes. The unconfined specimen has partial confinement provided by steel reinforcement meshes. The specimens without transverse reinforcement resemble construction reminiscent of nuclear power plant containments. An unsupervised pattern recognition algorithm was employed to classify the AE signals based on frequency-energy based features. Different damage mechanisms for the confined and unconfined specimens were identified using AE data.
## 2 Related Work  
Language models based on a pre-training and fine-tuning paradigm achieve high performance on many NLU tasks [8; 9; 10], including GLUE tasks. Bidirectional Encoder Representations from Transformers (BERT) [11], a bidirectional model that consists of attention layers, in contrast to the existing recurrent neural network (RNN) based models, has made great strides in Natural Language Processing (NLP). XLNet [12] is another pre-trained language model, combining the advantages of auto-encoding and autoregressive pre-training methods. Along with this paradigm, several other models were introduced that improved the pre-training method of BERT or changed the model structure. StructureBERT [13] extends BERT by including language structure learning to predict the order of tokens and sentences during unsupervised learning. By adding this method, it can capture word and sentence structure bidirectionally[PERSON] RoBERTa [14] optimizes BERT by altering pre-training strategies and using more data to improve downstream task performance. DeBERTa [15] obtained the second-highest performance for the glue benchmark using a disentangled attention mechanism and an enhanced mask decoder.  
In contrast to these approaches, MTL is a learning method that aims to improve the overall performance of several tasks by simultaneously training related tasks on one model [16; 17; 18]. MTL has two distinct advantages[PERSON] First, a task with a relatively large amount of labeled data can assist with the learning of another task that lacks labeled data. Second, it can prevent models from overfitting to a specific task [19]. MT-DNN is an MTL model based on the pre-trained BERT, and it demonstrated impressive performance in general domain NLP tasks. ERNIE 2.0 [20], which uses continual pretraining and MTL methods, obtained state-of-the-art result for the GLUE benchmark. However, MTL can confuse a model and interfere with the learning of the central task [21]. For addressing this problem, HMTL [22] proposed a hierarchical MTL model that trained a low-complexity task first, followed by high-complexity tasks. [PERSON] et al. [23] proposed a method of quantifying similarities between datasets, and they conduct MTL using part of the GLUE dataset.  
In the medical computer vision domain, a successful method has been suggested to solve model confusion in MTL. [PERSON] et al. [6], using four auxiliary tasks to improve lung cancer prediction, introduced the Periodic Focusing Learning Policy (PFLP) and Internal-Transfer Weighting (ITW). However, the format of data used in this paper differs from NLP.  
In NLP, there are different input data formats and labels for each task. Furthermore, in the medical image MTL, one image has various annotations. Inspired by this approach, we present an SIWLM scheme that can be applied to an MTL model in NLP. With this scheme, our model can concentrate on all tasks sequentially while focusing on the central task. Figure 1 depicts an overview of MTDNN-SIWLM. Each component is covered in more detail in the next section.
## 4 Results and Discussion
### Summer Maize Recognition in Huanghuaihai Plain
#### 4.3.4 Extraction Results in Different Years  
The area extraction algorithm in the present research was employed to map the summer maize cultivated area in 2013 over Huanghuaihai Plain. The spatial distribution characteristics of summer maize in 2013 were basically consistent with those in 2012. It can be seen from Table 7 that the summer maize acreage from MODIS images in Beijing, Tianjin, Hebei, Henan, and Shandong were \(105.1\times 10^{3}\), \(178.2\times 10^{3}\), \(2630.4\times 10^{3}\), \(3104.3\times 10^{3}\), and \(2707.0\times 10^{3}\) ha, respectively. Compared to the statistical area, the precision of summer maize extraction results in five provinces was all above 85%. Meanwhile, the extraction results of the summer maize area from 43 cities and 245 countries of the study area in 2013 were compared to the statistical data, which showed that R\({}^{2}\) was 0.81 with an RMSE of \(132.6\times 10^{3}\) ha at the city level and R\({}^{2}\) was 0.68 with RMSE of \(17.3\times 10^{3}\) ha at the county level (Figure 13). It can be found that the precision of extraction results in different years had a high accuracy by using the standard summer maize EVI time series to map the summer maize, indicating that the method has a certain universality over a large scale.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Region**} & **Statistical** & **Extracted** & **Extracted** & **Public** & **Proportions** & **Proportions** \\  & **Data/10\({}^{3}\)** & **Area of** & **Area of** & **Public** & **of the Public** & **of the Public** & **of the Public** \\  & **ha** & **MODIS/10\({}^{3}\)** & **Landsat/10\({}^{3}\)** & **Area/10\({}^{3}\)** & **to the** & **to the Results** & **to the Results** \\  & **ha** & **ha** & **ha** & **Statistical** & **Based on** & **Based on** \\  & & & & & **Data/\%** & **MODIS/\%** & **Landsat/\%** \\ \hline Changyua-n & 29.4 & 29.7 & 29.3 & 20.1 & 68.4 & 67.7 & 68.6 \\ Gaotang & 37.7 & 34.0 & 34.4 & 28.3 & 75.1 & 83.2 & 82.3 \\ Linzhang & 36.6 & 43.9 & 37.5 & 32.9 & 89.9 & 74.9 & 87.7 \\ Lushan & 24.7 & 25.9 & 37.6 & 23.5 & 95.1 & 90.7 & 62.5 \\ Yongnian & 33.9 & 35.0 & 25.3 & 22.3 & 65.8 & 63.7 & 88.1 \\ Xinji & 43.2 & 60.1 & 51.2 & 45.9 & 99.4 & 76.4 & 89.6 \\ average & — & — & — & — & 82.3 & 76.1 & 79.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of summer maize extraction results based on MODIS and Landsat images in 2012.  
Figure 12: Comparison of spatial distribution of summer maize extraction results based on MODIS and Landsat images in 2012 (Left: MODIS, Right: Landsat).
## 5 Harmonic Compensation Principle
In the steady-state operation, once the system has fully compensated for the harmonic distortions, the harmonic voltage at the PCC stabilizes to a new value of \(\Delta\varphi_{\text{gh}}\angle\varphi\), where \(\Delta\varphi_{\text{gh}}\) represents the compensated harmonic voltage and \(\varphi\) is the phase angle shift caused by the compensation process. This steady-state compensation ensures that the grid voltage at the PCC is free from significant harmonic distortions, as the GFMC APF continues to provide dynamic compensation in response to any changes in the nonlinear load. By maintaining this harmonic compensation, the GFMC APF ensures that the voltage quality at the PCC remains high, facilitating stable grid operation and improving the overall power quality for systems operating under nonlinear load conditions.
# A Critical Review on Obesity: Herbal Approach, Bioactive Compounds, and Their Mechanism
## 4 Herbs That Control Obesity
### Panax ginseng  
The other name of the herb is ginseng; it belongs to the Araliaceae family. It is a herbal treatment used for thousands of years in East Asia (Korea, China, and Japan) to improve health, vitality, and longevity [93]. It has been demonstrated that _Panax ginseng_ has many physiological and pharmacological benefits such as anti-diabetic, anti-cancer, anti-inflammatory, anti-obesity, neuroregulation, antimicrobial, wound healing, etc. In addition to claimed benefits in chronic fatigue, these include advantageous effects against cancer, diabetes, hypertension, nociception, and stroke [94]. The main active components of this herb are ginseng saponins and polysaccharides [95]. The primary active constituent responsible for its anti-obesity effect is ginsengides. Ginseng is reported to have an impact on the levels of hormones such as leptin, ghrelin, and adiponectin, as well as appetite. It reduces the chronic inflammation of the hypothalamus brought on by HFD, enhancing leptin resistance and decreasing neuropeptide Y release. Additionally, it is suggested that ginseng inhibits pancreatic lipase activity that prevents the digestion and absorption of fat and carbohydrates, lowers blood glucose, and increases fecal weight. By controlling PAR-\(\gamma\)/C/EBP-\(\alpha\), PPAR-\(\alpha\), and AMPK, ginseng may also have an antiadipogenic impact and improve fat oxidation and energy expenditure [94].
## Materials and Methods
### Analytics
#### 2.3.3 Mean Pore Diameter  
The mean pore diameter was calculated as a function of the PSD represented by the water retention curve. According to the capillary theory, the pressure at which a pore empties (or fills) corresponds to the pore opening size (respectively the pore diameter) and is calculated according to [PERSON]'s law [51]:  
\[d=\frac{4\times\sigma}{h},\]  
where:  
\(d\) = pore diameter [m]  
\(\sigma\) = surface tension between water and air (0.0729 N/m)  
\(h\) = soil water potential (Pa)  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Substance**} & \multirow{2}{*}{**Formula**} & \multirow{2}{*}{**CAS-No.**} & \multicolumn{6}{c}{**Nutrient Solution**} \\ \cline{3-10}  & & & **A** & **B** & **C** & **D** & **E** & **F** \\ \hline D-Glucose & C\({}_{6}\)H\({}_{12}\)O\({}_{6}\) & 50-99-7 & 1 & 1 & 1 & 1 & - & - \\ Pepton & n.a. & n.a. & 0.5 & - & - & - & - & - \\ Starch & C\({}_{6}\)H\({}_{10}\)O\({}_{5}\) & 9005-25-8 & 0.5 & - & - & - & - & - \\ Yeast extract & n.a. & n.a. & 0.5 & - & - & - & - & - \\ Casamino acid & n.a. & n.a. & 0.5 & - & - & - & - & - \\ Pyrruvic acid & C\({}_{3}\)H\({}_{4}\)O\({}_{3}\) & 127-17-3 & 0.3 & - & - & - & - & - \\ Sodium acetate & C\({}_{2}\)H\({}_{3}\)NaO\({}_{2}\) & 127-09-3 & 0.5 & 1 & - & - & - & - \\ Oxalic acid & C\({}_{2}\)H\({}_{2}\)O\({}_{4}\) & 144-62-7 & - & - & 0.5 & - & - & 1 \\ Salicylic acid & C\({}_{7}\)H\({}_{6}\)O\({}_{3}\) & 69-72-7 & - & - & 0.5 & - & 1 & - \\ Kaliumdihydro-prognbat & KH\({}_{2}\)PO\({}_{4}\) & 7778-77-0 & 0.3 & 2.65 & 2.65 & 2.65 & 2.65 & 2.65 \\ Magnesiumusulfat & MgSO\({}_{4}\times\)\(\gamma\)H\({}_{2}\)O & 10034-99-8 & 0.05 & 0.2 & 0.2 & 0.2 & 0.2 & 0.2 \\ Dinatriumhydrogen-phosphate & Na\({}_{2}\)HPO\({}_{4}\) & 7558-79-4 & - & 4.33 & 4.33 & 4.33 & 4.33 & 4.33 \\ Ammoniumsulfat & (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) & 7783-20-2 & - & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 \\ Calciumchlorid & CaCl \(\times\)\(2\)H\({}_{2}\)O & 10035-04-8 & - & 0.05 & 0.05 & 0.05 & 0.05 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of culture media (in g/L).
## 4 Energy Consumption Model and Power Control  
### Energy Consumption Model  
The energy effect of the PEAFC-CL algorithm is closely related to the channel fading model; the energy consumption in the channel is given in Figure 5.  
The energy consumed for data transmission depends on the data volume sent and the sending power; thus the energy consumed in sending \(z\)-bit data is:  
\[E_{T}=z\left(\frac{P_{APP_{T}}}{R_{b}/R_{c}}+\frac{P_{T}}{\eta_{amp}R_{b}/R_{c}}\right) \tag{12}\]  
where \(P_{APP_{T}}\) refers to the power consumption of the transmission circuit, \(P_{T}/\eta_{amp}\) is the power consumption of the transmission amplifier, \(\eta_{amp}\) refers to the efficiency of the transmission amplifier (typically \(\eta_{amp}\leq 1\)), and \(R_{b}/R_{c}\) is the bit code rate. The energy consumed during data reception is:  
\[E_{R}=z\frac{P_{APP_{R}}}{R_{b}/R_{c}} \tag{13}\]  
where \(P_{APP_{R}}\) is the power consumed in data reception. The energy consumed by the sensing channel is \(E_{Sens}=P_{APP_{S}}T_{Sens}\), where \(P_{APP_{S}}\) is the power used in the sensing channel and \(T_{Sens}\) is the length of the sensing channel's window.
# Safe Path Planning of Mobile Robot Based on Improved A* Algorithm in Complex Terrains
## 3 Improvement of A* Algorithm
### Improved Evaluation Function  
Evaluation function is the core of A* algorithm. An appropriate evaluation function not only ensures the algorithm to search out the optimal path but also reduces the number of traversal states and improves the efficiency of the algorithm. Nevertheless, in the conventional A* algorithm only the distance cost of the robot has been considered, meaning that the shorter the distance it is, the better the path will be. As a result, the algorithm has only searched the shortest path, which is not necessarily a safe path. In order to obtain a safe path, it is necessary to introduce the safety cost into the evaluation function after the improvement of environment model proposed in Section 3.1. Thus, the actual path cost from state \(X\) to \(S\) can be expressed as  
\[\mathrm{g}(X)=\sum_{i=1}^{n}(\omega_{1}l_{i}+\omega_{2}s_{i}), \tag{2}\]  
where, \(n\) indicates the number of sections of the path from \(S\) to \(X\), \(\omega_{1}\) and \(\omega_{2}\) indicate the weighted coefficients of different parameters, and \(\omega_{1}+\omega_{2}=1\); \(l_{i}\) indicates the length of the \(i\)-th section of the path, and \(s_{i}\) indicates the safety cost of the \(i\)-th section of the path. Since the \(i\)-th section of the path passes through two adjacent states and the safety cost of the path section is not only related to the distance from obstacles but also the length of the path section, we define the safety cost \(s_{i}\) as  
\[s_{i}=d_{i1}l_{i1}+d_{i2}l_{i2}, \tag{3}\]  
where \(d_{i1}\) and \(d_{i2}\) respectively indicate the danger coefficients of the two states the path section passes through and \(l_{i1}\) and \(l_{i2}\) respectively indicate the lengths of the path section in the two adjacent grids and \(l_{i1}+l_{i2}=l_{i}\).  
As the robot can move along the diagonal direction in the grid map and the safety cost of the path from \(X\) to the goal state \(G\) is unable to be computed, the heuristic function \(h(X)\) is defined to be proportional to the diagonal distance from \(X\) to \(G\). Assuming \(X\)'s coordinates are \((x_{1},y_{1})\) and \(G\)'s coordinates are \((x_{G},y_{G})\) and \(x=|x_{1}-x_{G}|\), \(y=|y_{1}-y_{G}|\), then the diagonal distance \(l(X)\) from \(X\) to \(G\) can be expressed as \(l(X)=|x-y|+\min\{x,y\}\times\sqrt{2}\). In order to match \(g(X)\), the estimated path length should be multiplied by the same weight \(\omega_{1}\), thus  
\[h(X)=\omega_{1}l(X)=\omega_{1}\Big{(}|x-y|+\min\{x,y\}\times\sqrt{2}\Big{)}. \tag{4}\]  
Figure 3: Improved environment model.  
According to Equations (1)-(4), the evaluation function \(f(X)\) of the improved A* algorithm can be expressed as  
\[f(X)=\sum_{i=1}^{n}[\omega_{1}l_{i}+\omega_{2}(d_{i1}l_{i1}+d_{i2}l_{i2})]+ \omega_{1}\Big{(}|x-y|+\min\{x,y\}\times\sqrt{2}\Big{)}. \tag{5}\]
# Effect of Dynamic Loading Conditions on Maximizing Energy Dissipation of Metallic Dampers
## 2 Design Concept and Optimization Approaches  
An ideal damper should be reliable in performance, dissipate sufficient energy, and exhibit stable hysteretic behavior against the seismic load. However, conventional studies primarily considered the static loading condition to optimize and evaluate metallic dampers. To address this problem by comparing two different approaches, static and dynamic, a simple prismatic metallic damper with a hypothetical installation concept is proposed. Based on this concept, a basic process of the analysis and optimization is presented in the following sections of this study.
# Unsupervised Vehicle Re-Identification Method Based on Source-Free Knowledge Transfer
## 3 Experiment  
### Experimental Environment Settings  
Currently commonly used open-source deep learning frameworks include Caffe, Tensorflow, PyTorch, etc. The method proposed in this paper is trained in the PyTorch framework, including the ablation experiment, which is also conducted in PyTorch. Compared with other frameworks, Python, the development language of PyTorch, has the biggest advantage of supporting dynamic neural networks [26], and the framework is more intuitive and concise as a whole. All models were trained on the NVIDIA GeForce RTX 3060 graphics card, the initial learning rate was set to 0.0005, and the weight decay was set to 0.0005. The total number of iterations was set to 50 and optimized using the Adam optimizer. In order to facilitate the experiment, the model training framework proposed in [6] was used here to obtain the initial source domain model and target domain model. The source domain data were accessed here only to provide the initial source domain model,and the source domain data were no longer accessed in subsequent experiments. The main architecture of the model adopts ResNet50 [27]. Due to memory issues, the generator in the source-free knowledge transfer module was built using a modified CycleGAN architecture with three residual blocks. The source and target domain models were fixed during training. During the training process, \"source-like samples\" generated by the source-free knowledge transfer module were used for training in various ratios with target domain data. The ratios between \"source-like samples\" and target domain data were set as 1:5, 1:4, 1:3, 1:2, and 1:1. The clustering method employed is DBSCAN.
## 2 Materials and Methods
### Processing of Measurement Data
The high-frequent tail around \(1\,\mathrm{k}\mathrm{H}\mathrm{z}\) has an inductive imaginary part and originates from the connections of the battery. At the point where the spectrum crosses the \(x\)-axis, the impedance has only a real part and approximately describes the ohmic resistance. The first half-circle after the tail toward decreasing frequency is the charge-transfer process at the negative electrode [9]. The second half-circle is explicitly visible, when the spectrum of the negativeelectrode is measured separately from the whole battery. The origin of this half-circle is unknown, but an adsorption process on the surface of the electrode is assumed [9; 22]. The charge-transfer process at the positive electrode generates the third half-circle at frequencies below 100 mHz. This charge-transfer process is impaired by diffusion, when the half-circle turns into a tail [22]. The RC-element contains the charge-transfer resistance \(R_{ct}\) and the double-layer capacitance \(C_{dl}\). The double-layer capacitance is modeled as constant-phase-element (_CPE_). This EEC describes only the part of impedance spectrum, which corresponds to the charge-transfer process of the negative electrode together with the ohmic resistance and inductance of the whole cell and was described by [PERSON] [23]. The remaining impedance spectrum toward lower frequencies describes the unknown process on the negative electrode and the charge-transfer process on the positive electrode. This part is not further analyzed here, as most of the measurement points do not describe a LTI system, what is shown in the results section of this paper. Therefore, and because sulfation occurs mostly on the negative electrode, these points were ignored during the fitting processes. The fitting of an EEC allows a more accurate determination of \(R_{olmic}\). With the determination of \(R_{ct}\), the change of the charge-transfer process--not only a single impedance value of an unknown electro-chemical process--can be analyzed. The impedance spectra recorded on the whole cell and not on the negative electrode were fitted, as the spectra of the negative electrode do not contain information of the ohmic resistance of the whole cell. An example of a fitted impedance spectrum and the ignored part of the spectrum with the EEC are shown in Figure 3.  
As a fitting tool, a graphical user interface (_GUI_) was used, which was developed at our institute. It is implemented in Matlab to automatically fit impedance spectra and pulses to different EEC. The GUI is available as open source: [[https://github.com/[PERSON]/FittingGUI](https://github.com/[PERSON]/FittingGUI)]([https://github.com/[PERSON]/FittingGUI](https://github.com/[PERSON]/FittingGUI)).