esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Challenges and Future Research Directions
### Improved Modeling, Control and Aggregation of Virtual Inertia Systems  
Most research has focused on specific implementations of virtual inertia and the broader impacts of inertial response. Current literature, lacks accurate, mathematical models which represent the dynamics of the system. Such models are essential for parameter tuning and understanding the operational behavior when virtual inertia systems are interconnected to the power system. In [75; 111], a small-signal model for a virtual inertia system has been developed. The model was used to identifycritical operating modes through Eigenvalue analysis, and a technique to assess the sensitivity of the system to the parameter gains has been demonstrated. Similarly, a small-signal model of a synchronverter was developed in [32]. Such a model aids in improved tuning of the controller gains and provides granular control over how the overall system needs to be operated. An analytical approach to study the effect of microgrids with high RES penetration on the frequency stability has been described in [112]. Performance indices completely independent of the test system have also been proposed to better facilitate impact analysis. The behavior and coordination between virtual inertia systems and existing SGs are critical topics for further research. In the future, with numerous virtual inertia units, the coordinated and aggregated operation, and optimal placement will also be important research questions.
## 2 Materials and Methods
### Research Methodology
#### 2.5.3 Principal Component Analysis  
Principal component analysis (PCA) is a kind of analysis method to simplify the structure of data sets through the idea of dimensionality reduction, which plays an important role in the evaluation system of multiple indicators. Using PCA, the correlated cultural landscape factors of multiple villages and dwellings are transformed into a small number of uncorrelated 'principal component factors', which reflect the information contained in the original multiple factors, not only from the multi-indicator factor by reducing the dimensionality of the data, eliminating the differences in the scale [31], to increase the amount of information carried by cultural factors. In order to fully reflect the characteristics of the cultural landscape, the principal component factor is only based on the mathematical and scientific data. The principal component factors are only combined according to the weight relationship determined by mathematical statistics, which reduces the subjectivity of the traditional \"expert scoring method\" to determine the weight value. The extraction steps are as follows:Calculate the correlation coefficient matrix \(R\) for the normalized array \(Z\): \[R=\left[r_{ij}\right]_{P\times P}=\frac{Z^{T}Z}{n-1}\] (1) Calculate the eigenvalues of the correlation coefficient matrix \(R\): \[|R-\lambda I_{P}|=0\] Solve for \(P\) eigenvalues \(\lambda_{1}\geq\lambda_{2}\geq\ldots\geq\lambda_{P}\geq 0\). (2) Determine the number of principal components, m so that the extraction rate of principal component factor information reaches more than 80%: \[\frac{\sum_{j=1}^{m}\lambda_{j}}{\sum_{j=1}^{P}\lambda_{j}}\geq 0.8\]
# Vibration Control with Reinforcement Learning Based on Multi-Reward Lightweight Networks
## 4 Experimental Results and Discussion
### Relevant Comparative Experiments  
#### 4.6.1 Relevant Comparative Experiments for Lightweightness  
In Section 4.1, we emphasized the feasibility and advantages of the lightweighting approach. To gain further insights into the impact of lightweighting experiments on different  
Figure 12: Comparison between the DDPG and TD3 frameworks, confirming the overestimation phenomenon in DDPG. The TD3 framework leverages the minimum value from two networks, mitigating the overestimation issue inherent in the DDPG framework. (**a**) The Q-values and ground-truth values for DDPG. (**b**) The Q-values and ground-truth values for TD3.  
modules and parameter quantities, we conducted comparative experiments specifically focusing on the Actor, Critic, and reinforcement learning frameworks. The baseline model comprised Actor and Critic networks with fully connected layers of a 256 size (FC\({}_{256}\)), utilizing the DDPG reinforcement learning framework. In Table 3, the first column represents the Actor network structure, the second column denotes the Critic network structure, the third column indicates the reinforcement learning framework, the fourth column details the total parameter count, and the fifth column presents the reduction, where smaller values are preferred. The experimental findings reveal that the DRSN performs better than FC\({}_{256}\) in both the Actor and Critic networks. Additionally, despite the TD3 algorithm increasing the parameter count under the same network structure, there is a significant enhancement in the control effectiveness, and the increased parameter count remains within an acceptable range. Furthermore, we observed that improvements in the Critic network have a more pronounced impact on control effectiveness compared to improvements in the Actor network. The best vibration reduction can reach up to 20.240 dB.
## 5 Proposed Channel Model  
As discussed in previous sections, the path loss for LoS scenario is calculated using free space path loss (FSPL), and for NLoS scenarios, additional shadowing loss is included, formulated in terms of the normal distribution. The total path loss for the proposed channel model is  
\[L=L_{FSPL}+L_{A}+L_{ex} \tag{10}\]  
where \(L_{FSPL}\) is the average free space path loss, which is determined by the distance and frequency. \(L_{A}\) stands for the antenna loss. \(L_{ex}\) stands for the additional shadowing loss, which accounts for obstruction between transmitter and receiver as well as additional loss due to environmental effects.  
\begin{table}
\begin{tabular}{l l l} \hline
**Environment** & **s\({}_{1}\)** & **s\({}_{2}\)** \\ \hline _Suburban_ & 0.00112 & 4.944 \\ \hline _Urban_ & 0.00136 & 4.936 \\ \hline _Dense urban_ & 0.00141 & 4.931 \\ \hline _High-rise urban_ & 0.001591 & 4.927 \\ \hline \end{tabular}
\end{table}
Table 5: Standard deviations for vertical/horizontal polarization in different.
# A Fault Diagnosis Method of Mine Hoist Disc Brake System Based on Machine Learning
## 2 Architecture of Mine Hoist Fault Diagnosis System Based on Machine Learning  
According to the needs of the fault diagnosis of the hoist brake system, this study established the general fault diagnosis frame of a mine hoist brake system based on machine learning, which is shown in Figure 2.  
1. Data acquisition: The source of the data is mainly obtained from the monitoring system's tracking of the operating state of the hoist brake system and the historical diagnostic knowledge base of the brake system. These data are characterized by large quantities, large numbers of dimensions, and rich content, which make up massive and heterogeneous large data. The collected data is stored in the SQL Server database. Also, the filtering and sorting function of the database can meet the low-cost storage requirements of massive and diverse data. Moreover, the data suitable for analysis can be filtered and integrated.
2. Data processing: Process the collected hoist brake system fault data, extract the fault features, denoise the data, process the missing values and continuous values of the sample data, and implement the K-means clustering algorithm by SPSS software. Finally, discretize continuous values.
3. Model training: Use the decision tree algorithm C4.5 to classify the sample data, add the Kendall concordance coefficient to improve the algorithm and use the improved C4.5 decision tree algorithm to train the sample data. Therefore, the decision tree classification model is obtained.
4. Model application: The data is trained to obtain the algorithm model, which generates fault diagnosis rules and judges the state of the brake system by classifying the data. These data are then applied to the fault diagnosis of the hoist brake system.  
Figure 2: Fault diagnosis frame of mine hoist brake system based on machine learning.
## 6 Implementation of Structural Reliability Assessment on the OWT Support Structure
### Constraints and Design Criteria
#### 6.1.2 Fatigue Constraint  
The fatigue limit state is a crucial phenomenon in OWT support structures as they are subjected to significant cyclic loads. OWT support structures normally have a long service period that may exceed 20 years. This, in conjunction with the inspection intervals, affects the reliability requirement of the jacket structural design. According to the S-N curve method, the number of loading cycles to failure \(N\), can be obtained by  
\[logN=A-mlog\Delta S, \tag{23}\]  
where \(A\) and \(m\) are the intercept and the slope of the S-N curve on the log-log plot, respectively; \(\Delta S\) is the stress range. The two parameters in Equation (23), i.e., intercept \(A\) and slope \(m\), are generally given by design standards, e.g., DNVGL-ST-0126 [29]. The performance function of fatigue reliability assessment based on the S-N curve method can be expressed as follows:  
\[g_{f,\ SN}=logN-logN_{t}, \tag{24}\]  
where subscripts \(f\) and \(SN\) denote the fatigue limit state and S-N curve method, respectively; \(N\) is the number of loading cycles to failure given by Equation (23), and \(N_{t}\) is the number of loadingcycles expected during the given design life. The number of cycles during the design life \(N_{t}\) may be determined as a function of rated rotor speed \(n_{rated}\) and availability \(\eta_{a}\) (98.5%) on the location chosen [24,88]. Therefore, considering a design life of 20 years, the number of cycles can be expressed as follows:  
\[N_{t}=\eta_{a}.n_{rated}\times(20[year]\times 365[day/year]\times 24[hour/day] \times 60[min/hour]). \tag{25}\]  
The minimum fatigue safety ratio \(f_{sr,~{}min}\) must be greater than the allowable fatigue safety ratio \(f_{sr,allow}\), which is equivalent to the product of one and the material PSF (partial safety factor) \(\gamma_{m,f}\) for fatigue.  
\[f_{sr,min}\geq f_{sr,allow}. \tag{26}\]  
Since the material's PSF for fatigue limit state is 1.15 [29], \(f_{sr,allow}\) is equivalent to 1.15. Hence, the limit state function based on the fatigue safety ratio can be expressed as follows:  
\[g_{f,f_{sr}}~{}=f_{sr,allow}-f_{sr,min}. \tag{27}\]
## 4 Discussion
### Relationship between Internal and Interface Friction Angle of Xanthan Gum-Treated Sand  
In geotechnical engineering practice, it is common to express the interface friction angle (\(\delta\)) in terms of soil internal friction angle (\(\phi\)), where the aspect ratios of \(\delta\)/\(\phi\) and tan \(\delta\)/tan \(\phi\) are used to assess the interface shearing between sand and geotechnical interface materials (i.e., steel, concrete, and geosynthetic). For instance, tan \(\delta\) = (0.6-1.0) tan \(\phi\) for woven and non-woven geotextiles, tan \(\delta\) = (0.8-1.0) tan \(\phi\) for rough woven geotextiles, and tan \(\delta\) = 0.6 tan \(\phi\) for granular soil-solid polymer sheet [9; 44; 45; 46; 47].  
In this study, the interface friction angle of biopolymer-treated sand (\(D_{50}\) = 0.47 mm) and solid surfaces with a relative roughness (\(R_{n}\)) of 0.12 was obtained under various conditions. Figure 15 presents tan \(\delta\)/tan \(\phi\) aspect ratio of xanthan gum-treated sand obtained from this study. For the interface without asperity, the ratios at peak and post-peak are found to be 0.60 and 0.77, respectively, which is in accordant to the behavior of geosynthetic reinforced sand. For the interface including asperity, these ratios were 0.84 and 0.93, respectively, which are both higher than the typical range due to the predominant soil-soil failure inside the asperity. Thus, this study provides general trends of interface friction angle variation of xanthan gum-treated sand from plane surface attachment (interface I) to surface asperity embedded conditions (interfaces II-IV). However, further considerations are required to adopt this result for quantitative analysis since the actual field material has not been used in this study.  
Figure 13: Peak interfacial shear parameters of xanthan gum-treated sand (soil–structure interface): (**a,b**) peak interface friction angle (\(\delta_{\text{peak}}\)) and adhesion (\(c_{\text{apoilt}}\)) in initial state, (**c,d**) peak interface friction angle (\(\delta_{\text{peak}}\)) and adhesion (\(c_{\text{apoilt}}\)) in dried state, and (**e,f**) peak interface friction angle (\(\delta_{\text{peak}}\)) and adhesion (\(c_{\text{apoilt}}\)) in resubmerged state.  
Figure 14: Residual interfacial shear parameters of xanthan gum-treated sand (soil–structure interface): (**a,b**) residual interface friction angle (\(\delta_{residual}\)) and adhesion (\(c_{\textit{mresidual}}\)) in initial state, (**c,d**) residual interface friction angle (\(\delta_{\textit{residual}}\)) and adhesion (\(c_{\textit{mresidual}}\)) in dried state, and (**e,f**) residual interface friction angle (\(\delta_{\textit{residual}}\)) and adhesion (\(c_{\textit{mresidual}}\)) in resubmerged state.
# An Algorithm to Retrieve Precipitable Water Vapor from Sentinel-2 Data
## 3 Results
### Validation of Various PWV Products
#### 3.1.2 PWV Comparison of MOD05, ESA-L2A with S2-L2A  
To verify the performance of the proposed method of PWV prediction over a large area, on 1 August 2021 24 Sentinel-2 images that covered an area of 185,000 km\({}^{2}\) were selected. After calculating PWV for the whole region, the value was compared to the MOD05 and ESA-L2A products. Considering the scale effect among different products with different spatial resolutions, the spatial resolution of S2-L2A PWV was resampled to 1000 m to perform the comparison with MOD05. The scatterplots of S2-L2A PWV with MOD05 and ESA-L2A products are presented in Figure 3. The S2-L2A PWV shows high consistency with MOD05, reflected in its strong correlation (0.944), low RMSE (0.319 cm), and Bias (\(-\)0.280 cm). The Bias value was negative, meaning that the S2-L2A underestimated the PWV compared to the MOD05. The PWV of S2-L2A was higher than that of ESA-L2A, showing that the fitting line was much higher than the 1:1 line. Even so, the two products still showed good consistency, and the R, RMSE, and Bias values were 0.953, 0.426 cm, and 0.407 cm, respectively.  
The statistical results of the three PWV products are presented in Table 3. The PWV data retrieved by S2-L2A (0.282-3.600 cm) were distributed over a larger range than MOD05 (0.484-3.549 cm), while the products of ESA-L2A (0.161-3.005 cm) were generally lower. The standard deviation of MOD05 was the largest, followed by S2-L2A and ESA-L2A, indicating that most of the PWV values obtained from MOD05 were different from the mean value, and that their distribution was the most discrete.
## 7 5G Radio Propagation Mechanisms
### General 5G Models
The vegetation effects, which are based on the fact that mmWave signals can be attenuated and diffusely scattered by leaves and diffracted around the canopy of trees, in addition to gaseous absorption, were taken into consideration in this model. * IEEE 802.11 ay Channel Model: The IEEE 802.11 ad channel model, which was created for the 60 GHz range, is extended in IEEE 802.11 ay (57-68 GHz). The IEEE 802.11 ay model's multipath comprises D-rays, R-rays, and F-rays. It uses the Q-D channel modeling methodology inherited and extended from the MiWEBA model. The LOSray, ground-reflected ray, and other rays reflected from scenario-relevant objects are all examples of the relatively powerful D-rays. * MG5 GCM: Recently, a More General 5G Channel Model (MGCM) 3D non-stationary 5G channel model was proposed [172]. The model can cater to important 5G communication scenarios, such as massive MIMO, HST, V2V, and mmWave communication. The proposed model was created on the basis of the WINNER II and SV models. The array-time cluster evolution, i.e., the appearance and disappearance of clusters on the time and array axes, was modeled as a birth-death process. The delays, powers, and angles for newly generated clusters were generated at random on the basis of certain distributions at every time instant. Parameters for the survived clusters were updated on the basis of their geometrical relationships. To support massive MIMO communications, the spherical wavefront was calculated on the basis of the physical location of clusters. * NYUSIM Channel Model: The model was developed using millimeter wave (mmWave) field measurements. The NYUSIM was created using measurements from multiple mmWave frequencies ranging from 28 GHz to 73 GHz. The model is applicable for frequency ranges ranging from 0.5 to 100 GHz and RF bandwidths ranging from 0 to 800 MHz: \[PL^{NYU}(f,d)=FSPL(f,1\text{ m})+10 nlog_{10}(d)+AT+X_{\sigma}^{CI}\] (50)  
Note that \(AT[\text{dB}]=\alpha[\text{dB}/\text{m}]\times d[\text{m}]\) where \(\alpha\) is the frequency and AT is the atmospheric attenuation. In Table 11, a comparison of the general 5G channel models, with respect to their respective features, is given. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & \\ \hline \multicolumn{12}{c}{} \\ \multicolumn{12
# Single-Shot Temporal Contrast Enhancement Measurement of a Plasma Mirror by a Chirped Pulse
## 4 Conclusions  
In conclusion, we demonstrated the single-shot temporal contrast enhancement measurement of a plasma mirror by a spatiotemporally-overlapped chirped pulse of 2.5 ns duration. It relies on a single-shot reflective spectrum measurement of the chirped pulse  
Figure 4: Far-field pattern results of output igniting laser: (**a**) Without SOCP. (**b**) With SOCP.  
after the plasma mirror is induced by the igniting laser. Compared with the common delay-scan third-order cross-correlator, this single-shot method is direct and convenient, as it can avoid the demand for multiple accurate measurement on temporal contrast and sampling of the igniting pulse, and the temporal resolution can be controlled by adjusting the chirp of SOCP. Experiments were performed on a test platform in the frontend of an SG-II 5 PW laser system. Using the method, the temporal contrast enhancement \(10^{2}\) by a single plasma mirror was successfully measured, and was consistent with the theoretical value of 150. The effective temporal measuring range and measurement resolution are 2.5 ns and 3.925 ps, respectively. Moreover, we also monitored the efficiency and the far-field pattern of the igniting pulse to determine the influence on PM performance by the SOCP. The average PM efficiency maintained at about the 75% level, the equivalent to the original efficiency, and the far-field pattern showed no significant degradation, proving that an SOCP is suitable for the temporal contrast enhancement measurement of a plasma mirror. Finally, we hope that this work will contribute to the practical application of the temporal contrast enhancement measurement of plasma mirrors.  
[PERSON] and [PERSON] came up with the idea; [PERSON] and [PERSON] designed the experiment and the software program; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] performed the experiment; [PERSON] analyzed the data and wrote the original manuscript; [PERSON], [PERSON] and [PERSON] did the review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Chinese Academy of Sciences (XDA25020105, 181231 KYSB 20170022, CXJ)-2019S015); Science and Technology Commission of Shanghai Municipality (19560713700, 19 YF1453500, 20 ZR1464400, 20692114101); National Natural Science Foundation of China (11704392, 61705245, 12074399, 12004403).  
The authors declare no conflict of interest.
# Mobile Robot Tracking with Deep Learning Models under the Specific Environments
## 3 Method
### Data Sets  
Several data sets are created. Images are collected by an 8-megapixel camera (Leica, Wetzlar, Germany) with F/2.0 aperture and a fixed focal length. All images in our data sets are collected in two environments, an indoor environment and an outdoor environment. We designed the following data sets:  
(1) A training data set. A data set containing 2993 images is created. The images are 4160 \(\times\) 3120 pixels. All images have been carefully annotated and used as the training set. There is a whole target in the image. The target is captured from different distances, different angles and different circumstances. (2) Testing data set one. It contains 1718 images which are different from the training data set. All images have the same pixel as the training data set. (3) Testing data set two. The second testing data set with 661 images is also created. Images in testing data set two are chosen from the images which are not put in the target robot data set and testing data set one. The targets in these images are seen with different angles and are from different distances. Meanwhile, part of these targets is out of range. The target is missing about 10% to 30% of itself; some examples can be seen in Figure 2: Examples of testing data set two. (4) Testing data set three. A testing data set with 599 images is created by a random erasing method.  
There are some advantages to our designed data sets. First, the environments of the created data sets are different. To ensure the diversity of the environments, we take a series of actions which contain the exposure of the picture, use a random erasing method and so on. In addition, the target is captured from different distances, different angles Furthermore, some occlusion condition testing data sets are created. However, there exist some limitations of our designed data sets which should be improved in further studies. Second, the environments of the created data sets are not complicated enough. Moreover, the created data sets are relatively small. Besides, the images of the data sets keep the target from a bot close distance from the camera.