esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing the Impact of an EV Battery Swapping Station on the Reliability of Distribution Systems
## 3 Assessment of Available Generation Capacity (AGC)
### EV Users Swap Behavior Modeling  
The uncertainty in EV users' behaviors should be fully considered and investigated; otherwise, it may cause the erroneous calculation results [20; 21]. EV users arrive at the BSS at different times, replacing empty batteries with different SOCs and leaving with fully charged batteries. Therefore, the number of EVs arriving at the BSS and the SOC of the replaced batteries at each moment are uncertain variables for the BSS operator. In order to allow for these uncertainties, we first determine the probability distribution of two uncertainty variable, and then generate a sequence of vehicle flow at the BSS and the initial SOC of the empty batteries through a sampling method.  
Considering a small quantity of EVs arriving in the early morning and different traffic flow at different times, it is hard to obtain a uniform form of a probability density function (PDF) to perform sampling. From another point of view, the average number of visits to the BSS per day is roughly fixed, and the moment of each visit roughly obeys a certain probability distribution. We can sample the time of each visit and finally aggregate the data to get the time series of traffic flow. In this paper, we adopt the PDF of the arrival time of each EV provided in [22] as the PDF of the occurrence of each BSS visit. In [22], it is assumed that there is no visit from 0:00 to 7:00. However, considering that the BSS has full-time service capabilities and diverse users, not single local users, this assumption is no longer suitable. So we modified the PDF, slightly increasing the probability of arriving in the early morning. It is assumed that there are about 510 times visits in a typical day. We simulated each visit to obtain the arrival time of each visit and further formed the EV flow of a whole year. Through using a modified PDF, the EV flow in a day-scale can be generated. The modified PDF of each visit's arriving time and the corresponding EV flow in a typical day are depicted in Figure 4. It can be seen from Figure 4 that most of the EV power exchange demand is concentrated in the time period 7:00-20:00, and the demand for power exchange in the early morning hours is very small. As analyzed later, the time difference of the power exchange demand makes the V2G of the BSS also have time difference characteristics.  
Figure 3: Management strategy of batteries with different charge levels.  
The other uncertainty of EV user behavior is the SOC of replaced batteries. The general processing method is assuming that the user behavior obeys a certain probability distribution according to local user preference. Here, we adopt normal distribution [23], which described as:  
\[SOC_{i}^{initial}=normal(\mu=0.50,\sigma=0.15). \tag{4}\]
## 5 Evaluation
### Experience Results
#### 5.2.3 Performance for Different Networking Modes  
The aforementioned evaluation focuses on the networking modes involving two inter-frequency carriers. In this part, we verify the performance of the proposed method with three inter-frequency carriers, including TDD 3.5 G, TDD 3.5 G, and FDD 2.1 G. The performance gains involving different loads and different numbers of antennae are demonstrated in Table 9. Our method owns 20-35% gain compared with the traditional method. Additionally, the average experience rates for different methods and scenes are shown in Figure 18. Our approach has obvious advantages.  
Figure 16: Average throughput for different cells.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scene** & **Cell1** & **Cell2** & **Load** & **Antenna** & **User** & **Gain** \\ \hline Scene 2.1 & TDD @40 M & TDD @40 M & Mid-Low & 64 TRX & 80 & 23\% \\ Scene 2.2 & TDD @40 M & TDD @40 M & Mid-Low & 32 TRX & 60 & 18\% \\ Scene 2.3 & TDD @40 M & TDD @40 M & Mid-Heavy & 64 TRX & 180 & 31\% \\ Scene 2.4 & TDD @40 M & TDD @40 M & Mid-Heavy & 32 TRX & 150 & 25\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance Gain for Networking TDD@40 M+TDD@40 M.  
In conclusion, our method improves system-level performance in a multi-user, multi-carrier scenario. The time-frequency-space resources can be allocated cooperatively. Compared with the classical method, the proposed method achieves better network-level user experience.
## 3 Methodology
### Federated Machine Learning-Based Algorithm for Edges  
Fundamentally, this algorithm distributes and enables edges to perform collaborative learning, and it avoids sending the sample data to the cloud server for its upgradation. This process reduces the massive computational complexity and resolves the privacy issues (a critical issue) when using a cloud-based server. The deployed model (global model) is first trained on a server using some initial data. Each edge (smart dermoscopy or mobile) then improves the model using data available on the device (samples of diseases which were correctly tested), i.e., federated data from the device. The edge is trained using the newly observed data and updates the local model's latest gradient weights. The changes made to the local model are summarized (as an update) and sent to the global model for global upgradation. However, to ensure faster transmission and avoid latency issues, random compression and quantization techniques are used. The process is done after several iterations (until a high-quality global model) is obtained on the cloud server. The edges send their trained models to the global model, which are averaged to obtain a unified cloud service model. TensorFlow federated and federated core application programming interfaces (APIs) are utilized for experimental purposes. A gradient guarantees convergence, whereas the model average cannot. The detailed steps of this algorithm are shown in Algorithm 2.  
```
Input: Edge receives the computed gradient (model M), \(\sum\)W, and computes the new gradient \(\Delta\)W. Initialization: The edge model downloads the global model
1: Receive the sample data to perform classification//Initial model is received from the server
2: If sample data belong to existing classes, then
3: Perform the classification//regular operation
4: Perform training within the edge device//to compute the updated gradients
5: Update gradient weight to update the global model
6: Send the global updates to all local models
7: If sample data do not belong to existing classes, then
8: Create and train and update the new instance//using Algorithm 1 [36]
9: Update gradient weight to update the global model
10: Send the global updates to all local models Output: The edges send the updated \(\Delta\)W to the cloud model.
```  
**Algorithm 2**Edge-Based Adaptive Ensemble CNN
## 3 Approach and Analysis: Architecture and Methodology
### Methodology: Machine Learning-Based Approach
#### 3.3.4 Feature Learning Layers  
From the input sound acquired, sound features were extracted, and its features were analyzed for classification and predictions. Learning algorithms from reinforcement machine learning techniques were used for learning the features of the sound acquired.
# Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks
## 3 System Model
### Network Model  
In this paper, we deploy \(N\) sensors in a circular field with radius \(R\). The \(N\) sensors are denoted as: {_N1_, _N2_,..., _Nn_}. We arrange the mobile sink within the sensor field and it can move freely in the sensor field. The whole sensor area is evenly divided into several sectors and each sensor belongs to a sector based on its geographical position, as is shown in Figure 1.
## 3 Results
### Performance of the Regionalized Algorithm versus Ocx  
While the Ocx algorithm aligns well with the region (Figure 2), refining it to better match the area's unique characteristics can enhance its accuracy. A polynomial fit was thus applied to model the functional relationship between F and the in situ Chl-_a_ IMECOCAL data. Polynomial functions were tried because any function can be reduced to a polynomial through Taylor series. After evaluating polynomials from the first to the fifth order, the following fourth-order function was selected based on quantitative metrics (\(R^{2},R^{2}_{a}\), SSE, RMSLE, and AIC).  
\[\log_{10}\left(\mathrm{mChl}-a\right)= 0.1746-1.9952\mathrm{F}+1.9992\mathrm{F}^{2}-4.1958\mathrm{F}^{3 }+3.3837\mathrm{F}^{4} \tag{7}\]  
Figure 3 shows the contrast between the in situ Chl-_a_ data and the mChl-_a_ derived from the OCx (global) and the regional algorithm (Equation (7)). The regional algorithm mChl-_a_ values closely mirror those of the global algorithm within the \(1.5<\mathfrak{R}<4.5\) (\(0.18<\mathrm{F}<0.65\)) range of blue/green reflectance ratios. For values of \(\mathfrak{R}>4.5\) (\(\mathrm{F}>0.65\)), the regional algorithm estimates are approximately 1.8 mg m\({}^{-3}\) higher than the global algorithm, which is even larger than the CI retrievals (Figure 2). Conversely, for ratios \(\mathfrak{R}<1.5\) (\(\mathrm{F}<0.18\)), the regional algorithm yields lower mChl-_a_. Quantitatively, the regional algorithm consistently outperforms OCx, as evidenced by the SSE values for different F ranges. For the \(\mathrm{F}<0.18\) area, the OCx sum of square error, SSE, is \(24.4881\left(\mathrm{mg\ m^{-3}}\right)^{2}\), while  
Figure 2: Results from applying OCx (blue squares), CI (yellow circles), and CalCOFI2 (pink triangles) algorithms to remote sensing reflectance data (\(\mathrm{F}=\log_{10}[\mathfrak{R}]\), Equation (2)) off the coast of Baja California compared to the log10 of in situ Chl-_a_ data (gray dots).  
for the regional algorithm, it is \(13.7582\big{(}\text{mg m}^{-3}\big{)}^{2}\). For the F \(>\) 0.6 region of the chart, the SSE for OCx is \(632.2217\big{(}\text{mg m}^{-3}\big{)}^{2}\) and for the regional algorithm it is \(500.5084\big{(}\text{mg m}^{-3}\big{)}^{2}\). This indicates that the regional algorithm yields better results than the OCx.
# A Fault Diagnosis Method of Mine Hoist Disc Brake System Based on Machine Learning
## 7 Conclusions  
* A fault diagnosis method of mine hoist brake systems based on machine learning is proposed, and the corresponding fault diagnosis model is established. This method is based on data and can meet the needs of fault diagnosis in a big data environment.
* A dynamic decision-making model based on C4.5 decision tree is established. The model takes information gain rate as the basis of attribute partition and can deal with training sets with small sample subsets, and discrete and continuous data. It can also deal with training samples with missing attributes. In addition to these, the model considers the correlation between attributes and introduces Kendall concordance coefficient to the process of establishing a decision tree. More accurate classification rules can be obtained.
* Through the fault simulation and comparison test, the validity and diagnostic accuracy of the diagnostic method are verified. The improved C4.5 decision tree classification algorithm can effectively improve the diagnostic efficiency and reliability.
* This study focuses on the application of decision tree algorithm in the fault diagnosis of hoist brake system, and the specific application of other machine learning algorithms in fault diagnosis can be further studied, especially the improvement of the algorithm needs to be further explored to make it more in line with the data characteristics of the hoist, which will become the focus of the author's next research.  
All authors ([PERSON];[PERSON]; [PERSON]; [PERSON]) designed the project and participated in the experiments and the interpretation of the results. Conceptualization, [PERSON]; methodology, J.L.; software, J.X. and S.J.; writing--original draft preparation, [PERSON]; writing--review and editing, J.L. and M.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Shanxi Province Nature Science Fund (201901D111056), the Shanxi province graduate education reform research topic (2019G047), China Postdoctoral Science Foundation (2019M651081).  
The authors declare no conflicts of interest.
# A New Method of Angle Measurement Error Analysis of Rotary Encoders
## 3 Analysis of Angle Measurement Error
### Angle Measurement Error Model Caused by Installation Eccentricity and Radial Error Motions  
When the grating disk works in actual condition, the installation eccentricity of the grating disk and the error motions of the rotating shaft exist. As shown in Figure 6, \(\delta_{\rm Y}\) is the radial error motion along the direction of the reading head, and \(\delta_{\rm X}\) is the radial error motion that is perpendicular to \(\delta_{\rm Y}\). \(O_{2}\) represents the geometric center of the grating disk, and \(O_{\rm O}C_{2}\) is the actual radial optical radius when the grating disk rotates to position \(O_{2}\). According to geometrical relationship, \(O_{\rm O}C=\delta_{\rm Y}(\theta)+e\cos\varphi+r\cos\beta\), and \(\beta=\arcsin\frac{\delta_{\rm X}(\theta)+e\sin\varphi}{r}\), so \(O_{\rm O}C=\delta_{\rm Y}(\theta)+e\cos\varphi+r\cos\biggl{(}\arcsin\frac{ \delta_{\rm X}(\theta)+e\sin\varphi}{r}\biggr{)}\), \(r\) is normally larger than 20 mm, \(e\) can be controlled within 0.01 mm, and \(\delta_{\rm X}\) can be controlled within 0.005 mm, so \(\frac{\delta_{\rm X}(\theta)+e\sin\varphi}{r}\) is no more than 0.001, so \(\arcsin\frac{\delta_{\rm X}(\theta)+e\sin\varphi}{r}\approx 0\). Then \(O_{\rm O}C=\delta_{\rm Y}(\theta)+e\cos\varphi+r\), the arc length which the grating disk passes through the reading head is expressed as Equation (4):  
\[l=\int_{0}^{\theta}O_{\rm O}Cd\theta=\int_{0}^{\theta}[\delta_{\rm Y}(\theta )+e\cos(\theta-\theta_{0})+r]d\theta, \tag{4}\]  
Figure 5: Rotary encoder works in condition with radial error motion.  
so the angle measurement error caused by the installation eccentricity and the radial error motion is calculated by Equation (5):  
\[\Delta\theta=\frac{\Delta l}{r}=\frac{l-r\theta}{r}=\frac{\int_{0}^{\theta}[\delta _{\mathrm{Y}}(\theta)+e\cos(\theta-\theta_{0})+r]d\theta-r\theta}{r}=\frac{e \sin(\theta-\theta_{0})+\int_{0}^{\theta}\delta_{\mathrm{Y}}(\theta)d\theta}{r}. \tag{5}\]
## 2 Materials and Methods
### Multi-Angle Spectral Observations
#### 2.3.2 Spectra Preprocessing  
The irradiance and radiance can only be used after the calibration of both dark current (DC) and the sensitivity of two channels to light. Unispec-DC lacks an internal shutter mechanism to correct for DC automatically. However, it has a thermometer that could track the dependency of DC on temperature. The measurements at midnight were used to fit the correlations between DC and temperature. Daytime DC for each band was estimated using these fitted midnight correlations. A cross-calibration approach by measuring the reflectance of a standardized reference target was used to correct differences in light sensitivity of two channels [63]. Then, canopy reflectance (\(\rho\)) was calculated as:  
\[\rho\ =\ ((\rho_{r}-\rho_{rDC})\times(\rho^{\prime}_{i}-\rho^{\prime}_{iDC}))/(( \rho_{i}-\rho_{iDC})\times(\rho^{\prime}_{r}-\rho^{\prime}_{rDC})) \tag{5}\]  
where \(\rho_{i}\) and \(\rho_{r}\) are the irradiance and radiance of the canopy measured using Unispec-DC, respectively; \(\rho_{iDC}\) and \(\rho_{rDC}\) are the irradiance and radiance produced by DC, respectively; the single quote mark means data measured from the reference target.  
Data measured with view azimuth angles ranging from 45\({}^{\circ}\) to 325\({}^{\circ}\) view zenith angles less than 63\({}^{\circ}\) (true view zenith angles approximately less than 75\({}^{\circ}\) owing to IFOV), and the solar zenith angle less than 75\({}^{\circ}\) was used. Due to failures of instruments, only data from day 101 to 275 in 2013 were used in this study.
###### Abstract  
The Anton Paar MCR 52 intelligent high-speed rheometer was utilized in this paper to conduct rheological tests on the Jiangjiagou debris flow slurry via linear loading. The relationship curves between shear rate and shear stress at different solid volume concentrations were obtained, and predictive formulas for yield stress and viscosity coefficient variations with the shear rate are proposed. Furthermore, the evolution mechanisms of yield stress and viscosity coefficient with changes in solid volume concentration of the debris flow were thoroughly analyzed. The results indicated that the rheological curves of the Jiangjiagou debris flow conformed to the Bingham fluid model. An increase in solid volume concentration continuously promoted the upward trend of the rheological curves, with significant increments observed only at high volume concentrations. A predictive model for debris flow rheological parameters was established based on the linear relationship between the rheological parameters and the solid volume concentration. The rise in solid volume concentration inhibited turbulence in the debris flow, while clay minerals enhanced the debris flow slurry's ability to capture pore water. Significant shear-thinning effects were observed within the debris flow, which were particularly more pronounced at lower shear rates. The study outcomes hold crucial engineering significance for a better understanding of debris flow rheological properties, the calculation of debris flow dynamic parameters, and disaster prevention and control.  
debris flow; rheological properties; solid volume concentration; rheological parameters; evolution mechanism +
Footnote †: journal: Applied Sciences
## 2 Methodology
### Study Cases
#### 2.5.1 System Overview  
The electrical network analyzed corresponds to the IEEE 33-bus radial distribution system. Due to its realistic configuration and widespread use, this testbed is a reference model for evaluating advanced monitoring, control, and diagnostic methodologies.  
Its key features include the following:  
* 33 buses and 32 distribution lines.
* Nominal voltage of 12.66 kV.
* Inclusion of various types of loads and line impedances.
* A radial structure prone to disturbances such as sags and interruptions.  
Table 2 summarizes the voltage range and power values across the buses.
# Characterization of Inter-System Biases in GPS + BDS Precise Point Positioning
## 3 Processing Strategy  
The final precise orbit (5 min interval for CODE and GFZ, 15 min interval for WHU) and clock products (30 s interval) from Wuhan University (WUM), center for orbit determination (CODE) and Deutsches GeoForschungsZentrum (GFZ) were employed. The absolute antenna phase-center correction models (igs14.atx) were applied to correct the phase-center offset and variation, which for BDS satellites obtained from European Space Agency (ESA). In order to analyze the characteristics of ISB, the ISB is modeled as white noise. The UNB3 model was used to correct the tropospheric dry delay and the tropospheric zenith wet delay is estimated as parameter, with Neil as the mapping function. The float phase ambiguities were estimated as constant for each continuous satellite arc. The traditional methods--turbo-edit detection--namely the geometry-free and MW observations were applied in the detection of cycle slip [27]. The elevation cutoff angle was set to 7\({}^{\circ}\) and an elevation-dependent weighting scheme (\(\sin^{2}(elevation)\)) for the observation was applied [28]. The weight ratio of GPS and BDS is set at 1:1.5 [29]. Due to the BDS GEO satellite orbit and clock accuracy, the weight of BDS GEO satellite observations is reduced by 10 times [30]. In order to strengthen the model, we estimated the ISBs with static PPP processing. The detail processing strategies are shown in Table 1. The distribution of the selected stations is shown in Figure 1.  
\begin{table}
\begin{tabular}{c c} \hline
**Options** & **Processing Strategies** \\ \hline System & GPS/BDS \\ Observation interval & 30 s \\ Estimation principal & Kalman filter \\ Frequency & L1,L2/B1,B2 \\ Elevation cutoff angle & 7\({}^{\circ}\) \\ Receiver clock & White noise \\ Inter-system bias (ISB) & White noise \\ Ionospheric delay & White noise \\ Tropospheric delay & Random walk (\(10^{-8}\)m\({}^{2}\)/s) \\ Receiver coordinates & Static \\ Ambiguities & Constant over time \\ \hline \end{tabular}
\end{table}
Table 1: Adopted models and strategies.
## 2 Materials and Methods
### Pipelines Setup
#### 2.2.1 Data Scaling and Feature Extraction  
Effective data preprocessing involves handling variations in data scales and units. This is usually performed through data standardization and normalization. Our study  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Pipelines Index** & **Pipelines Setup** & **Pipelines Abbreviation** \\ \hline
1 st & StandardScaler + PCA + XGBost & PS-XGB \\
2 nd & StandardScaler + PCA + Random Forest & PS-RF \\
3 rd & StandardScaler + PCA + neural network & PS-NN \\
4 th & StandardScaler + PolynomialFeatures + XGBost & PFS-XGB \\
5 th & StandardScaler+ PolynomialFeatures + Random Forest & PFS-RF \\
6 th & StandardScaler + PolynomialFeatures + neural network & PFS-NN \\
7 th & RobustScaler + PCA + XGBost & PR-XGB \\
8 th & RobustScaler + PCA + Random Forest & PR-RF \\
9 th & RobustScaler + PCA + neural network & PR-NN \\
10 th & RobustScaler + PolynomialFeatures + XGBost & PFR-XGB \\
11 th & RobustScaler + PolynomialFeatures + Random Forest & PFR-RF \\
12 th & RobustScaler + PolynomialFeatures + neural network & PFR-NN \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pipelines setup and abbreviation list.  
Figure 2: Flowchart depicting the process, including data scaling, feature extraction, and machine learning algorithm, for pipelines setup.  
incorporates two scaling techniques: StandardScaler, which normalizes data to have a mean of zero and a standard deviation of one, and RobustScaler, which uses the median and interquartile range, making it less prone to outliers.  
For the StandardScaler, it follows the following function [44]:  
\[z=(x-\mu)/\sigma \tag{1}\]  
where \(x\) is the original dataset, \(\mu\) represents the mean of the dataset, \(\sigma\) denotes the standard deviation of the dataset, and \(z\) is the processed dataset.  
For the RobustScaler, it follows the following function [45]:  
\[v^{\prime}=(v-median)/IQR \tag{2}\]  
where \(v^{\prime}\) is the processed value, \(v\) is a value from the original dataset, _median_ represents the median of the dataset, and \(IQR\) denotes the interquartile range of the dataset.  
We employed principal component analysis (PCA) for feature extraction to reduce the data's dimensionality while preserving critical information. PolynomialFeatures was also used to generate complex polynomial combinations of features. We comparatively analyzed four preprocessing combinations (StandardScaler with PCA, RobustScaler with PCA, StandardScaler with PolynomialFeatures, and RobustScaler with PolynomialFeatures), aiming to discern the optimal preprocessing strategy for our dataset.