esa-sceva/satcom-chunk-collection huggingface dataset:
# Decreasing the Rail Potential of High-Speed Railways Using Ground Wires
## 4 Electric Parameter Calculation
### Calculation of Impedance of Overhead Conductor-Earth Circuit  
The calculation of the external impedance of the overhead conductor-earth circuit (see Figure 2) can be carried out using the Carson-Pollaczek formula [24;25]. The calculation formulas for the external impedance of a single conductor-earth circuit and the mutual impedance of two single conductor-earth circuits are  
\[Z_{s}=\frac{j\omega\mu_{0}}{2\pi}\left(\ln\frac{2h_{i}}{r_{i}}+\int\limits_{0 }^{\infty}\frac{2e^{-2\lambda h_{i}}}{\lambda+\sqrt{\lambda^{2}+\frac{i\omega \mu_{0}}{\rho}}}d\lambda\right) \tag{9}\]  
\[Z_{m}=\frac{j\omega\mu_{0}}{2\pi}\left[\ln\frac{D_{ik}}{d_{ik}}+\int\limits_{0 }^{\infty}\frac{2e^{-\lambda(h_{i}+h_{k})}\cos\lambda x_{ik}}{\lambda+\sqrt{ \lambda^{2}+\frac{i\omega\mu_{0}}{\rho}}}d\lambda\right] \tag{10}\]  
The integral terms of Equations (9) and (10) reflect the influence of finite earth conductivity on impedance, commonly referred to as Carson correction terms, which can be represented by the second type of Bessel function and Struve function. After considering the impedance inside the wire, [PERSON] organized the above formula as [26]  
\[Z_{ii}=(R_{i,\mathrm{int}}+\Delta R_{ii})+j(\frac{\omega\mu_{0}}{2\pi}\ln\frac {2h_{i}}{r_{i}}+X_{i,\mathrm{int}}+\Delta X_{ii}) \tag{11}\]  
\[Z_{ik}=\Delta R_{ik}+j(\frac{\omega\mu_{0}}{2\pi}\ln\frac{D_{ik}}{d_{ik}}+ \Delta X_{ik}) \tag{12}\]  
where \(Z_{i,\mathrm{int}}\), \(R_{i,\mathrm{int}}\), \(X_{i,\mathrm{int}}\) and \(r_{i}\) are the internal impedance, ac resistance, internal reactance and radius of the conductor \(I\); \(h_{i}\) and \(h_{j}\) are the average heights from the ground of conductors \(i\) and \(j\), respectively; \(x_{ik}\), \(d_{ik}=\sqrt{\left(h_{i}-h_{j}\right)^{2}+x_{ik}^{2}}\) and \(D_{ik}=\sqrt{\left(h_{i}+h_{j}\right)^{2}+x_{ik}^{2}}\) represent the horizontal distance, distance and mirror distance between conductors \(i\) and \(k\), respectively; \(\Delta R\) and \(\Delta X\) are the Carson corrections considering the earth return effect.  
Figure 2: Overhead conductors.  
For the fundamental frequency, by simplifying \(\Delta R\) and \(\Delta X\), generally used simplified Carson formulas are obtained  
\[Z_{ii}=Z_{i,\text{int}}+\frac{\omega\mu_{0}}{8}+j\frac{\omega\mu_{0}}{2\pi}\ln \frac{D_{g}}{r_{i}} \tag{13}\]  
\[Z_{ik}=\frac{\omega\mu_{0}}{8}+j\frac{\omega\mu_{0}}{2\pi}\ln\frac{D_{g}}{d_{ik}} \tag{14}\]  
where \(D_{g}=659\sqrt{\frac{\rho}{f}}\) is the equivalent depth of the earth contour loop.
# FGM: Noisy Label Learning via Fine-Grained Confidence Modeling
## 4 Experiments  
### Experiment Settings  
#### 4.1.1 Datasets  
We validate our method on four commonly used benchmark datasets, including CIFAR-10, CIFAR-100 [32] with noisy synthetic labels and clothing1M [22], WebVision 1.0 [33] with real-world noise. CIFAR-10 and CIFAR-100 are accurately labeled with 50K training images and 10K test images. Clothing1M is a large-scale real-world dataset with a noise rate of approximately 38.5% [34]. It consists of one million training images collected from the web and 10,526 accurately labeled test images. WebVision contains 2.4 million training images with an estimated noise level of 20%. Following previous methods [15; 19], we use a mini version of WebVision which consists of the first 50 classes from Google subset for training. The WebVision validation set and the ImageNet ILSVRC12 validation set are used for evaluating. To be consistent with previous works, we evaluated two kinds of noisy synthetic labels on CIFAR-10 and CIFAR-100. In symmetric noise (Sym) settings, every category has the same noise rate, and noisy labels are evenly distributed among all the classes. As for asymmetric noise (Asym), a certain percent of labels of each class are relabeled into a similar category (e.g., dog \(\leftarrow\) cat).
## 5 Rotor Temperature Modeling and Prediction  
### Rotor Temperature Prediction Experiment Under Medium-Sized Samples  
Rotor temperature is a critical indicator of PMSMs' operating conditions. The accurate prediction of rotor temperature helps to optimize motor performance, extend equipment lifespan, and prevent failures. This section conducts experiments using the sample from Group A. The input features are multiple easily monitored parameters during PMSM operation (current, voltage, speed, etc. ), and the target variable is the rotor temperature. The rotor temperature ranges from a minimum of 21.93 C to a maximum of 113.61 C, covering a wide spectrum from low to high temperatures. Testing model performance across a wide temperature range allows for a more comprehensive evaluation of its adaptability. For instance, it assesses whether the model performs well in low-, medium-, and high-temperature intervals. In practical applications, motor rotor temperatures are likely to fluctuate significantly. Therefore, medium-scale samples can simulate some typical real-world scenarios. To avoid randomness in the test results, RIME-XGBoost and the three comparison models are trained and predicted sequentially for 20 consecutive times. The accuracy, prediction bias, generalization ability, and computational efficiency of the hybrid methods are evaluated using RMSE, MSE, MAE, MBE, R-squared (R\({}^{2}\)), and Runtime (the duration of one round of training and testing). Subsequently, the population and iterations of each hybrid model are set as shown in Table 4. The hardware configuration of the computer used in the experiment is as follows: Intel Core i7-11800H processor (Intel, Santa Clara, CA, USA), Samsung DDR4 3200 MHz 16 GB RAM (Samsung, Suwon, Republic of Korea), and NVIDIA GeForce RTX 3060 6 GB GPU (Nvidia, Santa Clara, CA, USA). The training and testing results are presented in Table 5.  
First, the performance of each model is analyzed. RIME-XGBoost performs exceptionally well on the training set, with an RMSE of 0.044, MSE of 0.0019, MAE of 0.032, and R-squared close to 1 (0.9999). It demonstrates an extremely high degree of fit to the training data. Additionally, the MBE is close to 0, indicating minimal prediction bias. On the test set, R-squared remains high (0.9972), while RMSE and MSE show slight increases. Although the variability of the predictions increases, the generalization ability remains strong. The \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **RIME-XGBoost** & **SMA-RF** & **SO-BiGRU** & **EO-SVR** \\ \hline Population & 20 & 20 & 10 & 30 \\ Iteration & 20 & 30 & 5 & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Algorithm parameters of hybrid models. average Runtime is 52.95 s, significantly lower than that of SO-BiGRU, indicating moderate computational complexity. SMA-RF shows lower errors on the training set, with an RMSE of 1.7814. The R-squared value suggests a high degree of fit to the training data. The MBE is 0.0456, indicating a small positive prediction bias. On the test set, RMSE and MSE increase slightly, while R-squared remains at 0.9909. Its average computation time is 28.37 s, demonstrating
## 3 Monitoring System and Optimized Building Operation
### Data Preprocessing and Analysis  
The data from project \"Vordere Viehweide\" is loaded as a CSV file into the program structure. This is a data preprocessing program. The time imported as UNIX time is used to generate characteristics such as month of the year (MoOY), day of the week (DOW), hour of the day (HOD), minute of the hour (MOH), and minute of the year (MOY). The other data that serve as characteristics are the ambient temperature (Tamb) and the global radiation (I_Rad) as well as the demand for the household electricity (E_dem) that serves as the target value. In addition, the data (without E_dem) is analyzed by a clustering method (KMeans) to classify the data. The dataset consists of data every minute for three quarters of a year. The ideal number of nine clusters was determined using a grid search. In a scatterplot (see Figures 13 and 14), the training dataset (85% of the total dataset) of the respective characteristics (x-axis) is plotted with the target value (y-axis). This shows that the power requirement varies between the months and days of the week. In addition, the power requirement always increases only in the evening of the day. The temperature shows that a higher power requirement tends to be the case on colder days.  
The clusters of the k-means will represent a classification of different commonalities that have the remaining characteristics. The clusters in which the power consumption is low and the clusters in which the power demand is high can be identified. Figure 14 shows as a heatmap the correlations (Pearson) between the characteristics. It can be seen that HOD and MOD have the highest Pearson correlation with 0.165. Then, we follow MoOY and MOD with 0.10-0.11. In addition, the clustering priority can be read from the representation, namely that it is strongly oriented to the hour of the day (HOD) and the minute of the day (MOD). The application is applied in Python and the scikit-learn library [41].  
Figure 14: Heatmap: Characteristics dependent on the electricity demand and Pearson correlation between the datasets.  
Figure 13: Scatterplot: Characteristics dependent on the electricity demand (minutely values) and Pearson correlation between the datasets.
## 2 Materials and Methods
### Quantitative Real-Time PCR Analysis  
Primers were designed using the Primer3 website ([[http://bioinfo.ut.ee/primer3-0.4.0/primer3/](http://bioinfo.ut.ee/primer3-0.4.0/primer3/)]([http://bioinfo.ut.ee/primer3-0.4.0/primer3/](http://bioinfo.ut.ee/primer3-0.4.0/primer3/))) [26] based on the sequence of genes involved in the wheat phenylpropanol pathway (Table 1). Each qRT-PCR reaction mixture (20 \(\upmu\)L) contained 5 \(\upmu\)L cDNA diluted 20-fold, 10 \(\upmu\)L 2\(\times\) SYBR Green buffer, 3 \(\upmu\)L DNase-free water, and 2 \(\upmu\)L primers (10 ppM). All qRT-PCR reactions were performed thrice using a Bio-Rad CFX machine and Bio-Rad CFX Manager software (version 2.0, Bio-Rad Laboratories, Inc., Hercules, CA, USA). PCR reactions were performed under the following thermal cycling conditions: 95 \({}^{\circ}\)C (5 min), 38 cycles of 95 \({}^{\circ}\)C (20 s), 56 \({}^{\circ}\)C (20 s), 72 \({}^{\circ}\)C (20 s), and 72 \({}^{\circ}\)C (8 min). The expression of actin (_TaActin_, accession number: GQ339780) was used as a reference to quantify the expression of genes involved in phenylpropanol biosynthesis in wheat sprout  
Figure 1: Proposed phenylpropanol and flavonoid biosynthetic pathway in wheat sprout. Blue color denotes the phenylpropanols and flavonoids assessed in this study via HPLC analysis. Red color indicates the enzymes whose gene expression was assessed via qRT-PCR. PAL, phenylalanine ammonia-lyase; C4H, cinnate 4-hydroxylase; C3H, 4-hydroxycinnamate 3-hydroxylase; COMT, caffe acid 3-O-methyltransferase; 4 CL, 4-coumaroyl CoA ligase; CHS, chalcone synthase; CHI, chalcone isomerase; F3H, flavanone 3-hydroxylase; F3’H, flavonoid 3’-hydroxylase; FLS, flavonol synthase; DFR, dihydroflavonol-4 reductase; 3 GT, flavonoid 3-o-glucosyltransferase.
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 5 Results  
### Quiet Eye Characteristics Across Skill Levels  
#### 5.1.1 Quiet Eye Duration  
One-way ANOVA revealed significant differences in Quiet Eye (QE) duration across skill levels (F(2,27) = 15.32, \(p\) < 0.001). Post-hoc Tukey tests showed that:  
* Elite players (M = 289.5 ms, SD = 34.2) had significantly longer QE durations compared to both intermediate (M = 213.7 ms, SD = 41.5, \(p\) < 0.001) and novice players (M = 168.3 ms, SD = 38.9, \(p\) < 0.001).
* Intermediate players had significantly longer QE durations than novice players (\(p\) = 0.012).
## 7 Cross-Layer Design Schemes
### Network and MAC Layer Schemes
Any protocol operating at the network layer needs to resolve the inherent conflict between energy efficiency and throughput. Sleep Collect and Send Protocol (SCSP) [90] dynamically calculates the node sleep and data receive (collect) periods depending on the amount of incoming traffic. The network layer uses a modified version of ZigBee routing that is more tolerant to node failures. The MAC layer provides the list of neighbor nodes to the network layer, which in turn provides multiple forwarding choices to it. Therefore, the MAC protocol has the option to change to the next hop router during transmission if the message is not successfully transmitted after \(n\) number tries. SCSP saves energy consumption by switching between active and sleep periods by dynamically adapting them depending on the amount of received traffic. The protocol uses a simple but efficient routing protocol that does not need route maintenance or discovery and works jointly with the MAC layer to enhance its fault tolerance properties. Simulations show that SCSP extends the network lifetime and connectivity in comparison with 802.15.4. As the channel quality can vary due to the changing characteristics inherent in the physical layer of a wireless network, resource at the MAC layer is therefore, variable [80]. These variations can result in fluctuations of link capacity and transmission since transmission rate is related to many factors, not just link quality. Joint optimization for rate control coupled with resource allocation and routing in the context of multichannel operation is a complex challenge for network/MAC designers.
## 2 Materials and Methods
### Registration Software Implementation  
All processing and registration steps described in paragraph 2.3 were incorporated in a Python-based ([[http://www.python.org](http://www.python.org)]([http://www.python.org](http://www.python.org))) software (v3.7.0, Python Software Foundation, Beaverton, OR 97008, USA) and graphical user interface (GUI). A screenshot of the main window is shown in Figure 3.  
The registration process and all registration-related functions were implemented using SimpleITK package [37], a simplified interface to the C++-based ITK library, whose design allows an easy wrapping for most interpreted languages, such as Python and R.  
Miscellaneous functions for image processing and manipulation were implemented with SciPy, NumPy, Scikit-image, Matplotlib, Nibabel, and Nilearn. Support functions include accuracies indexes calculation as described in paragraph 2.5, header restoration, and evaluation of the running median using the \"binapprox\" method [38], running mean, and standard deviation [39]. The software allows to apply the estimated transformation to other images, and the GUI embeds a simple visualization tool to check the registration results.  
The software developed in this study is freely available and can be downloaded at the following link: 10.6084/m9.figshare.13019579.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Rigid Registration** & **Elastic Registration** \\ \hline
**Transformation** & Euler transform (6 parameters) & FFD based on cubic B-Splines \\
**Similarity metric** & Mattes Mutual Information & Mattes Mutual Information \\
**Sampling strategy for** & 50\% of voxels randomly chosen & 50\% of voxels randomly chosen \\
**metric evaluation** & & \\
**Optimizer** & Gradient descent & Gradient descent \\
**Number of iterations** & 5000 & 2000 \\
**Minimum convergence value** & \(10^{-7}\) & \(10^{-7}\) \\
**Number of multi-resolution steps** & 3 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters chosen for the rigid and elastic image registration.
# Soil Erosion Assessment and Prediction in Urban Landscapes: A New G2 Model Approach
## 3 Results
### G2 Model Results
The average value for the investigated area is 3.63 t\(\cdot\)ha\({}^{-1}\cdot\)y\({}^{-1}\), and the values range from 0 to 193.11 t\(\cdot\)ha\({}^{-1}\cdot\)y\({}^{-1}\) (Figure 8b). In the case of both periods, the investigated area belongs to the category of medium erosion, although there was a slight decrease in the average value of soil loss of 11.68% in 2019. According to the spatial layout shown in Figure 8a,b, and according to Table 6, all forms of the category of destructiveness are present in the research area. In addition to the analysis of the shares of different areas, a comparison was performed in relation to the land cover as one of the most dynamic parameters. According to Figure 9, the average values of loss are shown in relation to areas with a high degree of porous surfaces. Water bodies and urbanized artificial surfaces with a high degree of non-porous surfaces are excluded from the analysis of soil loss and comparison. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Soil Loss Class**} & \multicolumn{2}{c}{**2001**} & \multicolumn{2}{c}{**2019**} & \multicolumn{2}{c}{**2001–2019**} \\ \cline{2-7}
**t\(\cdot\)ha\({}^{-1}\cdot\)y\({}^{-1}\)** & **km\({}^{2}\)** & **\%** & **km\({}^{2}\)** & **\%** & **Variation (km\({}^{2}\))** & **Change (\%)** \\ \hline Very Low & \textless{}0.05 & 331.15 & 62.90 & 299.73 & 62.26 & \(-\)31.42 & \(-\)9.49 \\ Low & 0.05–1 & 7.59 & 1.44 & 7.40 & 1.54 & \(-\)0.20 & \(-\)2.62 \\ Low Medium & 1–2 & 12.80 & 2.43 & 14.21 & 2.95 & 1.40 & 10.95 \\ Medium & 2–5 & 28.33 & 5.38 & 38.75 & 8.05 & 10.42 & 36.78 \\ High Medium & 5–10 & 63.86 & 12.13 & 58.73 & 12.20 & \(-\)5.13 & \(-\)8.04 \\ High & 10–20 & 57.92 & 11.00 & 45.22 & 9.39 & \(-\)12.69 & \(-\)21.92 \\ Very High & \textgreater{}20 & 24.81 & 4.71 & 17.36 & 3.61 & \(-\)7.45 & \(-\)30.02 \\
**Total** & & 526.46 & 100.00 & 481.39 & 100.00 & \(-\)45.07 & \(-\)8.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Soil loss in the study area for 2001 and 2019 by applying the G2 model. Figure 7: Spatial distribution of coefficient L for 2001 (**a**) and 2019 (**b**).
# A Multi-Scale Feature Fusion Hybrid Convolution Attention Model for Birdsong Recognition
## 2 Research Method
### Construction of Time-Frequency Spectrum Features
#### 2.1.2 Log-Mel Spectrogram Features  
Log spectrogram is one of the most widely used input features in many speech signal processing applications [15]. The Mel spectrogram represents the energy distribution of a signal on the Mel frequency axis, which is obtained by converting linear spectral data to the Mel scale using a filter bank. Compared with linear spectrograms, the log-Mel spectrogram captures the frequency components of birdsong in a way that emphasizes perceptually and structurally important frequency bands, making it more effective for distinguishing subtle acoustic patterns.  
The Mel frequency scale is a nonlinear frequency scale that approximates how frequency components are spaced in real-world sounds. It allocates more resolution to lower frequencies and compresses higher frequencies, which helps emphasize the parts of the signal that carry more discriminative information.  
In this study, first, we apply the Short Time Fourier Transform (STFT), as described by [PERSON] and [PERSON], to obtain spectrograms [13]. Then, we use a Mel filter bank with 40 frequency bands to project the spectrogram onto the Mel scale, producing the log-Mel spectrogram. The extracted birdsongs are shown in Figure 1b. The formula is shown in (2), where _Mel bank_ represents the Mel filter bank. The relevant description is in Equation (2).  
\[Log-MelSpectrum(t,f)=\log\Bigl{(}Mel\ bank\Bigl{(}\bigl{|}STFT(y)\,{}^{1}{}^{2} \Bigr{)}+\epsilon\Bigr{)} \tag{2}\]
## 4 Autonomous Orbit Control  
The relative orbital elements are used by an autonomous feedback orbit control law derived in [31]. This control law guarantees a bound control acceleration expressed in the Radial, Transverse, Normal (RTN) frame. Figure 4 shows the simulation results for two followers after a reconfiguration maneuver starting with a pure along track (unsafe) condition. Figure 7a shows the eccentric vector \(\delta\epsilon\) and the inclination vector \(\delta i\) components,  
Figure 6: Example of relative eccentricity \(\delta_{b}\) (in red) and relative inclination \(\delta_{i}\) (in green) vectors of two followers \(i=1,2\).  
which achieves a final state close to the pattern defined in Figure 6. Figure 7b shows the evaluation of the radial/normal minimum distance metric \(\delta r_{m}^{min}\). Figure 7c shows the along-track separation, which can be defined dynamically for each of the followers. As there is a small helix component added to the along-track formation, there will also be an oscillation on the along-track distance, whose amplitude doubles the amplitude of the radial/normal component. The proposed geometry only sketches the idea of the relative geometry, while the definition of the parameters should be given by the application and can be changed dynamically using autonomous orbit control. The reconfiguration between the unsafe along-track formation and the safe one proposed in the previous section is examined in this section.  
Figure 7: **(A)** Simulation of a Robust Multibaseline AT-InSAR formation for follower 1. **(a)** Shows the components of \(\delta\xi\) and \(\delta\dot{\xi}_{l}\) **(b)** shows the \(\delta r_{m}^{min}\) and **(c)** shows the along track separation between follower 1 and the chief. **(B)** Simulation of a Robust Multibaseline AT-InSAR formation for follower 2. **(a)** Shows the components of \(\delta\xi\) and \(\delta\dot{\xi}_{l}\) **(b)** shows the \(\delta r_{m}^{min}\) and **(c)** shows the along track separation between follower 2 and the chief.  
Figure 8 shows the result for follower 1 with an orbit control period of 20 s, and assuming here ideal orbit navigation. For a satellite mass of 100 kg, the simulated thrust bound would be 1 mN in all directions. Figure 9 shows the coordinates given by the difference between relative perigee angle \(\varphi\) and relative ascending node angle \(\theta\), with respect to the norms of the relative eccentricity \(\delta\varepsilon\) and relative inclination \(\delta\dot{\varepsilon}\) vectors, scaled by the chief's semi-major _axis_\(a_{c}\)[32]. Notice that the beginning of the trajectory is at the origin of \(\delta\varepsilon\) and \(\delta\dot{\varepsilon}\), and thus it is not under a safe condition, while at the end, the difference \(\phi-\theta\) is nearly zero (i.e., the relative eccentricity and inclination vectors are collinear) while the stationary relative eccentricity and inclination norms are approximately 40 m when scaled by the semi-major _axis_. Therefore, the reconfiguration achieves the desired baseline and the required safe condition.
## 4 Evaluation of Test Results
### Dynamic Deformation Data  
In the static load tests, a total of 24 dynamic loading cases were performed, with 8 cases for each of spans S1, S2, and S3. During the dynamic loading, a 5 cm-high obstacle was placed at the center of the respective bridge spans to enable vehicle passage. Acceleration data were recorded using accelerometer sensors during vehicle crossings. These data were processed to determine the acceleration, velocity, and displacement values at the relevant points caused by the dynamic loading. Based on the ratio of the dynamic displacement at the specific point to the maximum displacement observed in the static condition, the Dynamic Amplification Factor (DAF) was calculated. The values obtained for the calculation of the Dynamic Amplification Factor from static and dynamic displacement results are presented in Table 10.  
In evaluating the Dynamic Amplification Factor, to minimize errors caused by measurement uncertainties, displacement values 3 mm or less obtained under static loading (marked in bold and italic in Table 10) were excluded from consideration. Out of the 144 measurement points, 52 points with displacements less than 3 mm were excluded, and evaluations were performed based on the remaining 92 points.  
According to the AASHTO LRFD 2017 specifications, the permissible DAF value is limited to 33%. As seen in Table 10, four measurement points exceeded this allowable limit. A general evaluation of Table 10 reveals that 100 (92 \(-\) 4)/92 = 95.6% of the measurement points remained within the allowable limits, and this ratio was deemed acceptable for the operational use of the bridge.