esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Dimensional Identification for Unbalanced Mass of Rotor Systems in Operation
## 5 Conclusions  
In this work, a non-contact videometric measurement method is developed for identifying unbalanced mass of rotor bearing systems in operation. The trajectories of the feature point in 3D space are reconstructed when the rotor system is operating at 2400 rpm and it's R-square value is about 0.9981, which indicates that the reconstruction accuracy is high enough. The direction and the weight of the unbalanced mass can be detected more easily by analyzing 3D trajectories compared with the traditional ellipse analysis in 2D plane. In comparison with sensors methods, the relative error of the proposed method decreases from 30.6% and 41.3% to 2.2%. Moreover, by processing the out-plane displacement components with Fourier transform, the videometric measurement method studies out-plane vibration performance for the rotating speed at 3000 rpm. It has been found that there is an increasing additional high-frequency harmonic component with the increase of the unbalanced mass. Overall, this paper proposed a non-contact method, which provides a solution for three-dimensional vibration detection for rotor machinery in operation and has the potential to be used for fault diagnosis for rotor machinery.  
The authors appreciate the support of the National Natural Science Foundation of China (Nos. 51575156, 51675156, 51775164 and 51705122), and the Fundamental Research Funds for the Central Universities (No. JZ2017 HGPA0165, PA2017 GDQT0024). [PERSON], [PERSON], [PERSON] and [PERSON] conceived the idea and conducted the experiments. All the authors including [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] contributed to the discussion of the paper and approved the manuscript. [PERSON] directed the scientific research of this work.  
The authors declare no conflict of interest.  
Figure 14: The frequency spectrum at rotation speed of 3000 rpm: (**a**) x direction; (**b**) y direction; and (**c**) z direction.  
Figure 13: The frequency spectrum of out-plane displacement at different rotation speed: (**a**) 1200 rpm; (**b**) 1800 rpm; and (**c**) 2400 rpm.
## 2 Channel Estimation in OFDM
### Channel Modelling  
The most common models for solving the problem of UWA communication are propagation models. Wave equation, derived from the equations of state, continuity, and motion, is the theoretical base for all mathematical models of acoustic propagation. The simplified wave equation is as follows,  
\[\
abla^{2}\Phi{=}\frac{1}{c^{2}}\frac{\partial^{2}\Phi}{\partial t^{2}} \tag{9}\]  
where \(\
abla^{2}\) is the Laplacian operator. \(\phi\) is the potential function. c is the speed of sound. t is the time.  
Furthermore, there are five canonical models for solving wave equation [9]: ray theory, normal mode, multipath expansion, fast field, and parabolic equation model. Among the five models above, ray theory model is both applicable and practical in high frequency, and is therefore widely used in UWA channel simulation. Based on the theory of Gaussian beams [30], BELLHOP is one of the most effective implementations of ray model for solving the ray equations with cylindrical symmetry [31],  
\[\begin{split}\frac{dr}{ds}&=c\xi(s),\quad\frac{d \xi}{ds}=-\frac{1}{c^{2}}\frac{\partial c}{\partial r},\\ \frac{dz}{ds}&=c\xi(s),\quad\frac{d\zeta}{ds}=-\frac {1}{c^{2}}\frac{\partial c}{\partial z},\end{split} \tag{10}\]  
where \(r(s)\) and \(z(s)\) are the ray coordinates in cylindrical coordinates and \(c(s)[\xi(s),\zeta(s)]\) is the tangent versor along the ray. With the initial conditions (\(r(0)=r_{s}\), \(z(0)=z_{s}\), \(\xi(0)=\frac{\cos\theta_{s}}{c_{s}}\), and \(\zeta(0)=\frac{\sin\theta_{s}}{c_{s}}\). \(\theta_{s}\) is the launching angle. \([r_{s},z_{s}]\) is the source position. \(c_{s}\) is the sound speed at the source position), the coordinates of ray can be obtained by  
\[\tau=\int\limits_{\Gamma}\frac{ds}{c(s)} \tag{11}\]  
The BELLHOP model is designed to simulate two-dimensional acoustic ray tracing for a specific SSP in waveguides with flat or variable absorbing boundaries [18; 19; 32]. The program of BELLHOP offers various output options, including ray coordinates, eigenray coordinates, acoustic pressure, travel time, and amplitudes. In order to generate a valid output of UWA channels, the parameters for  
Figure 3: Flow chart of using simulation platform.  
modelling should be set correctly in BELLHOP files (environment file, altimetry file, bathymetry file, etc.) according to the user's guide [32].
# Study of the Dynamic Adaptive Calculation Method for River Water Environmental Capacity
## 3 Methodology
### Dynamic Adaptive Calculation Method for Water Environmental Capacity  
#### 3.2.1 Overall Framework  
The overall framework of the dynamic adaptive calculation for WEC is shown in Figure 2. This method starts from the dynamic nature of WEC [38], adaptively selects the calculation method of WEC according to different river attributes and external conditions, and studies the establishment of a dynamic adaptive calculation method for WEC, which provides a scientific basis and technical support for the calculation of river WEC in a dynamic and changing environment. The specific steps are as follows: (1) Summarize the applicable conditions, focusing directions, consideration factors, advantages and disadvantages, the data requirements of the existing WEC calculation methods, analyses and classify the WEC calculation methods [39; 40], so as to guide the selection of methods suitable for different research areas. According to the various attributes and actual conditions of the study area, the calculation method of WEC is selected adaptively. (2) Based on an in-depth analysis of the influence mechanism of changing factors, such as environment, demand, and conditions, on the traditional calculation model of WEC [41], a dynamic calculation method for WEC is established to cope with the changing environmental conditions. (3) The dynamics of time scale, model, guarantee rate, and parameter provide four ways of realizing the dynamic calculation for river WEC, based on an integrated platform for a WEC dynamic calculation simulation system to realize the dynamic application. The combination of calculation and application dynamics finally forms a dynamic calculation model for WEC. (4) Based on the dynamic calculation method for WEC, the results obtained under various changing environmental conditions are integrated into an interval, and an interval expression for WEC is proposed. A dynamic adaptive mechanism for WEC is established to adapt to dynamic environment changes, resolve risks caused by changes, and improve the quality of decision-making services.  
Figure 2: The overall framework of dynamic adaptive calculation method of water environmental capacity.
## 4 Experiments and Results: Performance Analysis
### An Overview of Network Simulator ns-3 Validation Suite  
Network Simulator ns-3 [83; 84; 85; 86], an open-source, event-driven simulator designed specifically for research in computer communication networks, offers a suite of validation tests to verify the accuracy and reliability of its simulation components. These tests are run daily on the ns-3 snapshot to ensure consistent performance and quality.  
The validation suite covers the most stable core of ns-3, which includes a variety of protocols and modules. Some of these protocols include application-level protocols such as HTTP, web caching, and TCPApp, as well as transport protocols such as UDP, TCP, RTP, SRM, routing protocols, router mechanisms, link-layer mechanisms, and others. Each protocol is tested using various test suite scripts that provide a comprehensive overview of the protocol's functionality.  
Figure 10: Data flow in a blockchain-based VANET.  
While the validation suite extensively covers many protocols, there are some protocols within the standard ns-3 distribution that are not covered by the validation tests. These nonvalidated protocols are maintained to the best of the team's abilities, and users are encouraged to report any issues.  
Furthermore, ns-3, being developed in C++, offers a feature known as \"Python bindings\". This allows developers to write simulation scripts in Python instead of C++, thereby increasing productivity and reducing programming errors. This is achieved using a tool called PyBindGen, which automatically generates C++ module extensions for Python. The Python bindings also facilitate the manipulation and visualization of the simulation results, as Python offers a wide range of libraries for data analysis and visualization, like NumPy, Pandas, and Matplotlib, among others.
## 4 Discussion and Conclusions  
In summary, we developed an image reconstruction method for PAT. This method combines a correlation matrix filter and a time reversal operator. We applied this method to reconstruct PA images inside a scattering layer. Both numerical simulation and phantom experiments were used to examine the performance of the proposed method. The simulation quantitively validated that the proposed imaging method provides higher imaging quality in complex scattering media compared with the classic beamforming method. The advantages are reflected in better a SNR and FWHM.  
Though demonstrating potential in preclinical and clinical applications, PAT is still in its early age of development. Among the several limitations preventing PAT's application, the main one is the amplitude and phase distortion of the received signals caused by the randomly distributed scatterers. In this research, we proposed an image reconstruction method to reduce the negative effect of the scattering waves by separating the direct waves from the received signals. With the filtered signals, PA images can be optimized, especially the resolution and the contrast between the targets and the background. This work might be valuable for understanding the physics of the interaction of sound in the complex media. The proposed method may potentially be used for acquiring high-quality images of the brain through the skull.  
In this study, the experiments were performed by a point-by-point scanning with a single ultrasound transducer. Point-by-point scanning is not fast enough to meet the imaging requirements in pre-clinical or clinical scenarios. This problem can be solved by using a circular ultrasound array, which is what we are preparing to study in our next work. Additionally, as future work, we will evaluate the proposed method in an in vivo study on a small animal.  
[PERSON] designed the simulations, employed the experiments, analyzed the results, and wrote the paper. [PERSON] assisted in both the experiments employment and writing of the paper.  
This research was funded by the National Key R&D Program of China (2016 YFC0102300), National Natural Science Foundation of China (NSFC) (11834008, 11874217), Fundamental Research Funds for the Central Universities and Natural Science Foundation of Jiangsu Province (No. BK 20181077).  
The authors declare no conflict of interest.
## 2 System Components
### Puc Mli
One DC voltage source, two auxiliary capacitors, and eight power semiconductor switching elements are used to design a fifteen-level PUC (PUC15) MLI topology, as shown in Figure 7a. Accordingly, to obtain fifteen levels at the output, the voltages of the capacitors C\({}_{1}\) and C\({}_{2}\) should be chosen as V\({}_{\rm c1}=(3V_{\rm dc})/7\) and V\({}_{\rm c2}=V_{\rm dc}/7\), respectively [54]. Using the same circuit, without adding any additional components, nine levels can also be obtained at the output. In this case, the capacitor voltages should be set as V\({}_{\rm c1}=V_{\rm dc}/2\) and V\({}_{\rm c2}=V_{\rm dc}\), respectively [55]. Another form of the PUC MLI topology that can be obtained by increasing the U-shaped circuit structure is the thirty-one level PUC (PUC31) MLI, as shown in Figure 7b [56]. To achieve thirty-one levels at the output voltage, one DC voltage source, three auxiliary capacitors, and ten power semiconductor switching elements are required. The ratios between the voltage source and the voltages of the auxiliary capacitors are given by V\({}_{\rm c1}=(7V_{\rm dc})/15\), V\({}_{\rm c2}=(3V_{\rm dc})/15\), V\({}_{\rm c3}=V_{\rm dc}/15\)[57]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{8}{c}{**Switching States and Output Voltage Values of PUC**} \\ \hline \multicolumn{8}{c}{**Seven-Level PUC MLI**} & \multicolumn{8}{c}{**Five-Level PUC MLI**} \\ \hline
**Case** & \(\mathbf{V_{out}}\) & **S1** & **S2** & **S3** & \(\mathbf{V_{out}}\) & **S1** & **S2** & **S3** \\ \hline
1 & Vdc & 1 & 0 & 0 & Vdc & 1 & 0 & 0 \\
2 & \(2\times\) Vdc/3 & 1 & 0 & 1 & Vdc/2 & 1 & 0 & 1 \\
3 & Vdc/3 & 1 & 1 & 0 & Vdc/2 & 1 & 1 & 0 \\
4 & 0 & 1 & 1 & 1 & 0 & 1 & 1 \\
4\({}^{\prime}\) & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
5 & \(-\)Vdc/3 & 0 & 0 & 1 & \(-\)Vdc/2 & 0 & 0 & 1 \\
6 & \(-2\times\) & 0 & 1 & 0 & \(-\)Vdc/2 & 0 & 1 & 0 \\  & Vdc/3 & & & & & & & \\
7 & \(-\)Vdc & 0 & 1 & 1 & \(-\)Vdc & 0 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Switching cases and output voltage values of PUC. Figure 6: The circulating current for each case [51]. When the five-level CHB inverter and five-level PUC MLI are compared, the PUC5 MLI will have only a single DC source, whereas the CHB inverter topology needs two DC sources for proper switching. Another feature is that the CHB topology is more complex in terms of the control scheme of the power semiconductors [58; 59; 60]. Table 3 compares the circuit components utilized in the PUC topology and other MLI topologies.
## 1 Introduction  
Geomechanical and thermohydromechanical modeling is often used in the development of oil fields and the drilling, construction, and completion of wells [1; 2; 3; 4]. For example, 1D geomechanical models have been widely applied in order to optimize the wellbore trajectory in drilling. Such models also allow us to calculate drilling the mud density window for preventing accidents during well construction in complex mining and geological conditions [5; 6; 7]. Based on 1D models, 3D geomechanical models are essential for determining the stress distribution throughout the oil field, including the overlying stratum.  
Besides, these models can be applied to analyze the influence of variable effective stresses on the filtration capacitive properties of reservoirs when reducing reservoir pressure and in the process of oil field development as a whole [8; 9; 10; 11].  
In order to predict the effectiveness of hydraulic crack creation in reservoirs and determine their geometric characteristics, geomechanical modeling is extensively applied [12; 13; 14], as well as to prevent sand removal from poorly cemented reservoirs [15; 16; 17]. For a more detailed analysis of the stress state near the well, numerical models of near-well zones are examined, considering cumulative and slotted perforations [18; 19].  
Currently, the issues of determining the stress-strain state of the near-well formation zone remain relevant. A number of models are based on an accurate analytical solution of the Lame problem of elasticity theory for a thick-walled cylinder loaded with external and internal pressures, but despite their widespread use, they do not take into account the influence of pore pressure and its gradient on the stress-strain state, which makes calculations very approximate [20].  
The present research discusses the features and results of modeling the near-well zone on the example of a numerical finite element model, which includes casing, cement stone, and oil-saturated rocks and takes into account the geometry of perforation channels.  
There are a large number of wells in the Perm Region, and their number is growing every year. An increase in the flow rate of these wells by 3-5% is of great importance for oil production in the region. This model will ensure that there are reliable calculations of the stress distribution in the considered elements of the well and rocks, which in turn, will allow the user to assess the stability of the casing, cement stone, and rocks. Moreover, the model will estimate changes in the rock permeability of the near-well zone and in the well productivity index.
## 3 AS01 SAR Satellite Data Experiment and Analysis
### Performance Evaluation of Enhancement Processing  
The evaluation of SAR image enhancement for ship wake can refer to SAR image quality evaluation methods, which are divided into two main categories: subjective evaluation methods and objective evaluation methods. Subjective evaluation is conducted by image interpreters through the visual comparison of images before and after processing, based on certain criteria to provide scoring evaluations. The results are heavily influenced by the interpreters' knowledge and work experience and usually involve a comprehensive evaluation of scores from multiple interpreters to improve objectivity. Objective evaluation requires establishing parameter indicators for judgment. In this section, the quality of SAR marine images containing ship wakes and sea surface textures is evaluated using grayscale statistical characteristics and information content before and after processing, with evaluations based on equivalent number and information entropy indicators. These information-based image quality indicators are theoretically robust and generally aligned with the visual effects of subjective evaluations. The equivalent number measures the relative intensity of speckle noise in SAR images. It quantifies the image's ability to identify different backscatter feature regions; a higher equivalent number indicates weaker speckle noise. Information entropy is a measure of uncertainty in random variables or processes used to evaluate the focusing quality of SAR images. Better focusing results in clearer images, leading to a reduction in image entropy [29]. The image quality evaluation results before and after processing are shown in Table 2.  
From the image processing results and the objective evaluation results in the table above, it can be concluded that the tail enhancement processing results based on OSMI images clearly reveal the ship's wake. Even small wakes in the upper left part and wave textures in the lower right part can be clearly observed. Additionally, the decrease in information entropy after enhancement indicates an improvement in image focus, with effective details being better reflected and the features of the wake in the image becoming more prominent. The decrease in the equivalent number of looks indicates the effective suppression of random speckle noise in the ocean background, which is consistent with the actual visual effect of the image.
# Deep Transfer Learning Approach for Identifying Slope Surface Cracks
## 4 Discussion
### Advantages and Applicability  
By exploiting the idea of transfer learning, a large sample dataset of concrete cracks (a total of 40,000 images) is combined with a small sample of surface cracks in soil and rock masses dataset (a total of 400 images), which we collected, sorted, and annotated. Pretrained deep learning models are employed for the identification of surface cracks in soil and rock masses. A high accuracy rate has been achieved. The limitation of using a smaller dataset of slope surface cracks in soil and rock masses is overcome. The massive amount of data required for deep learning model training is also reduced greatly. Therefore, we hope to build a refined deep learning model that can be deployed on UAVs for UAV aerial surveys to identify slope surface cracks and be applied to geological disaster monitoring and early warning.  
The utilization of the strategy of transfer learning is demonstrated to be effective. Taking LeNet5 as an example, before transfer learning, when only the small sample dataset is used, the highest accuracy rate can only reach 75%. After transfer learning, the large sample dataset and the small sample dataset were combined, and the highest accuracy rate of the identification of slope cracks was significantly improved, reaching 98.094%, an increase of more than 23%. The massive amount of data required for the identification of slope surface cracks is significantly reduced.  
The flexibility of the model is related to the depth of the model. The more layers of the model, the deeper and stronger the flexibility of the model. However, the more layers of the model, the more potential features of the dataset are extracted. When faced with a small-scale dataset, models with many layers are prone to overfitting. Deep learning models with few layers and a shallow depth can achieve better learning effects. In this paper, the dataset is small. The combined large and small sample datasets only have 40,400 images. Therefore, the AlexNet model and LeNet model with a small number of layers have a higher accuracy rate, and the accuracy of ResNet18 and Vision Transformer models with more layers is lower.
## 4 Pairwise Clock Synchronization under Exponential Delays
### Clock Offset Estimation under Exponential Delays
#### 4.1.4 Comparison of Estimators  
A comparison of MLE, BLUE and MVUE under both symmetric and asymmetric exponential delays is shown in Table 1. The criteria adopted in Table 1 include the estimator formula in terms of observations, bias, variance and MSE. It is observed that the BLUE and MVUE coincide in the presence of both symmetric and asymmetric distributions. Furthermore, all three estimators admit the same expression when the random delays are symmetrically exponential distributed. Under asymmetric exponential delays, albeit unbiased, the MLE can still outperform the MVUE when:  
\[\frac{\lambda_{1}^{2}+\lambda_{2}^{2}-\lambda_{1}\lambda_{2}}{2N^{2}}<\frac{ \lambda_{1}^{2}+\lambda_{2}^{2}}{4N(N-1)}\]  
or equivalently:  
\[N<\frac{2\lambda_{1}\lambda_{2}}{(\lambda_{1}-\lambda_{2})^{2}}+2 \tag{20}\]  
It can be seen that the MLE achieves a better performance when the means of the exponential delays are close to each other. In this way, the right-hand side of Equation (20) resumes being a large number, and the inequality Equation (20) generally holds with a reasonable selection of \(N\).
## 1 Introduction
The results indicated the effectiveness of the ML models in predicting the seismic response and classifying the damage state of URM buildings. The findings from the study also demonstrated the capability of ML models for future relevant studies in the field of structural engineering. The novelty of the present study lies in creating a comprehensive database and implementing various ML models to analyse the seismic response of URM buildings for the first time. The database developed specifically for this study focuses on the seismic analyses of entire buildings, as opposed to individual masonry structural components, an approach which has not, to this point, been documented in the existing literature. Further, a complete investigation of the performance of ML models in the seismic response prediction and damage state classification of URM buildings is still being explored. All of these features represent the main highlights of the present study, which is focused mainly on developing a wide range of ML models for the seismic damage assessment of URM buildings. The paper is organized as follows: Section 2 describes the methodology of the study. The development of the database through the selection of building archetype, followed by FE modelling, is discussed in Section 3. Then, the optimized parameters of the ML algorithms used for regression and classification are detailed in Section 4. Finally, Section 5 presents the results of the developed ML framework and the evaluation of the performance of the various algorithms in predicting the structural response and identifying the damage level associated with the URM building.