esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Source and Methods
### Methods
#### 3.2.3 Integrated Methane Enhancement (IME) Method  
We calculated the emission flux rate of methane point sources using the Integrated Methane Enhancement (IME) method. Since the instantaneous methane plume from a single point source is relatively small, many sources do not conform to Gaussian behavior, leading to significant errors in flux rate inversion using Gaussian plume modeling. The IME method links the total plume mass to the point source emission rate [39], and this approach depends on the effective wind speed and the characteristic size of the plume, resulting in lower errors. The methane emission flux rate Q (kg/h) is computed based on the IME model and the methane concentration enhancement within the plume. The specific calculation process is as follows:  
\[IME=\mathrm{k}\sum_{i=1}^{n_{p}}\hat{\alpha}\left(i\right) \tag{4}\]  
In the formula, \(IME\) stands for the total mass of the methane plume; k is calculated by considering [PERSON]'s law (1 mol CH\({}_{4}\) = 0.01604 kg), the spatial resolution of the GF5-02-AHSI data (30 m), and an assumed atmospheric layer thickness (8 km); np denotes the total number of pixels within the plume; and \(\alpha\)(\(i\)) represents the \(\Delta\)XCH\({}_{4}\) value of the \(i\)-th pixel. Based on the research by [PERSON] et al. [29], we relate the total methane emission (IME) to the emission rate \(Q\).  
\[Q=\frac{IME\times U_{eff}}{L} \tag{5}\]In the formula, \(L\) represents the length scale of the methane plume, which is the square root of the plume's area, and \(U_{eff}\) is the effective wind speed, derived from the measurable 10 m wind speed \(U_{10}\):  
\[U_{eff}=a\times U_{10}+b \tag{6}\]  
The relationship between \(U_{eff}\) and \(U_{10}\) is established based on a set of mesoscale meteorological models (Weather Research and Forecasting model, WRF) coupled with large eddy simulations [40], with data sourced from the Global Forecast System-Fifth Generation (GEOS-FP) dataset [41]. Large-scale background wind speed is provided, where a and b are empirical coefficients determined by combining the simulation results from the WRF model and the EOS-FP dataset. The WRF model is a mesoscale meteorological model used to simulate meteorological conditions in specific regions. Using the \(U_{10}\) data provided by EOS-FP as input, the WRF model is run to generate high-resolution wind field data. The relationship between \(U_{eff}\) and \(U_{10}\) is determined through statistical methods (such as regression analysis) to establish the coefficients \(a\) and \(b\) in the formula, thus obtaining the linear relationship between \(U_{eff}\) and \(U_{10}\). The existing relationship is utilized to directly calculate \(U_{eff}\) using the average relationship shown in Figure 3, unless new coefficients need to be calibrated for new regions or new meteorological conditions. The relationship between the 10 m wind speed and the effective wind speed in Changzhi, Shanxi, China, and the Permian Basin in the United States is specifically shown in Figure 3.
# Optimal Meshing Degree Performance Analysis in a mmWave FWA 5G Network Deployment
## 5 Experimental Simulation and Results
### Comparative Performance Analysis of Topologies in Terms of Latency  
Figure 8 presents a boxplot of the results in terms of latency. As we can observe in Table 1 and Figure 8, Topology 3 has the lowest median network latency (i.e., expected negative gain), followed by Topology 2 and Topology 1. Specifically, the median latency in Topology 3 is around 1% lower than that in Topology 2, and the median latency in Topology 2 is 16% lower than that in Topology 1. Furthermore, the \(CV\)s of Topology 1, Topology 2, and Topology 3 are 74%, 79%, and 23%, respectively, which means that Topology 2 has the highest risk in terms of the volatility of latency and Topology 3 has the lowest  
better than Topology 3 in terms of PER since the reduction in expected gain (about 20%) is lower than the reduction in risk (30%). Topology 3 is better than Topology 2 since it offers an expected gain of 50% with less risk (6%).
## 2 Related Work
### Proposed Method  
In summary, the primary issues with the above methods are model overfitting, leading to local optima, and the need for improvement in decoding algorithms. This paper proposes the following enhancements:* **Model Architecture:** The study adopts the advanced Conformer architecture and conducts preliminary experiments with three variant structures. A model weight fusion method is proposed, where during the convergence phase of training, the models' weights are organically fused based on loss values. This approach addresses two key aspects: first, smoothing and reducing the sensitivity of specific training iterations to noise in the data, thereby preventing overfitting during training; second, combining the different strengths of the three networks to improve overall recognition accuracy.
* **Decoding Algorithm:** A new decoding algorithm, \"Dynamic Adaptive Decoding\", is introduced. This method connects CTC and attention mechanisms using nonlinear functions to form a hybrid decoding approach. It optimizes the output by dynamically evaluating and selecting the superior results from both decoding methods, leading to more accurate speech recognition. Additionally, a penalty mechanism is introduced to regulate the reliance on a single decoding method by adjusting penalty coefficients, thereby enhancing the robustness of the decoding process.  
Finally, experiments will be conducted on the LibriSpeech dataset. Due to its large scale (approximately 1000 h), the results will ensure generalizability, avoiding issues related to specialized domains or small dataset sizes, thereby validating the reasonableness and feasibility of the proposed methods.
## 4 Experiment
### Experimental Results
#### 4.2.4 Experiment 4: Calculation and Analysis of Correlation Coefficient  
To demonstrate that confidence, as an inherent attribute of the classifier, can significantly enhance the predictive capability of the regression model when used as a sample feature, we calculated and recorded the correlation coefficients between the confidence-based feature vectors of 15 base classifiers and the label vectors on each dataset. These were then compared with vectors composed of other sample features.  
As shown in Figure 3, \(X_{ij}\) represents the \(j\)-th feature of the \(i\)-th sample in the dataset, \(C_{i}^{k}\) represents the classification confidence of the \(k\)-th classifier for the \(i\)-th sample, and \(y_{i}^{k}\) is the regression value of the \(k\)-th classifier for the \(i\)-th sample. \(\mathbf{X_{ij}}(i=1,2,3\ldots\ldots.n)\) is the vector composed of the first feature, \(\mathbf{C_{i}^{k}}(i=1,2,3\ldots\ldots,n)\) is the vector composed of confidence values, and \(\mathbf{y_{i}^{k}}(i=1,2,3\ldots\ldots,n)\) is the vector of regression values. The correlation coefficient is calculated as follows:  
Let the two vectors be defined as follows:  
\[\mathbf{x} =[x_{1},x_{2},\ldots,x_{n}]\] \[\mathbf{y} =[y_{1},y_{2},\ldots,y_{n}]\] \[\overline{x} =\frac{\sum_{i=1}^{n}x_{i}}{n}\] \[\overline{y} =\frac{\sum_{i=1}^{n}y_{i}}{n}\]  
The formula for the correlation coefficient \(\rho\) is as follows:  
\[\rho=\frac{\sum_{i=1}^{n}\left(x_{i}-\overline{x}\right)(y_{i}-\overline{y}) }{\sqrt{\sum_{i=1}^{n}(x_{i}-\overline{x})^{2}}\times\sqrt{\sum_{i=1}^{n}(y_ {i}-\overline{y})^{2}}} \tag{24}\]  
The specific experimental results are shown in Figure 4. As we are only concerned with the strength of the correlation between vectors, the ranking here considers the absolute values of the correlation coefficients between the two vectors. It can be seen that the confidence feature vector generally has the highest correlation coefficient with the regression values among all feature vectors, significantly enhancing the predictive capability of the regression model and providing support for prior and posterior probability calculations and classification accuracy.  
Figure 3: Illustration for correlation coefficient computing.  
Readers may notice that the confidence vector of the SVM is often negatively correlated with its regression value vector. This indicates that as the confidence of the SVM increases, the confidence vector of the SVM increases, the confidence vector of the SVM increases.  
Figure 4: The result of \(\rho\) on different datasets. Note: The mark named R above the bars means the rank of confidence vector between all the feature vectors.  
the consistency ratio of its peer predictions decreases. This situation aligns precisely with the decision rule in our proposed DTS algorithm when \(P<Q\): namely, to heed the opinions of experts who are highly confident in their decision and believe that most others are unaware of the correct answer. This further reinforces the decision-making concept of CTS-SR when \(P<Q\) in our proposed method.
# AI-Assisted Security Alert Data Analysis with Imbalanced Learning Methods
## 4 Experiments
### Performance Evaluation  
A security alert data analysis system should be evaluated based on its recall and FPR to ensure that it can effectively detect positive alert samples associated with critical events, ensuring that no malicious activity goes undetected while minimizing FPs [73]. A high recall is essential to guarantee the effectiveness of the model, whereas a low FPR is necessary to prevent alert fatigue.  
The classifier algorithms mentioned in Section 3.5 were used for the performance evaluation. The experimental results for the evaluation metrics presented in this section were obtained using a stratified 10-fold cross-validation to validate the performance of the algorithms. The data were split into ten equally sized subsets, each containing an approximately equal proportion of the target class. Subsequently, the model was trained and tested on each of the ten subsets, and each subset was used as a testing set once and as a training set nine times. This process was repeated until all ten subsets were used as the testing set.  
Next, the performance of the proposed system was evaluated. First, using the enterprise dataset, the baseline performance of the algorithms is presented using imbalanced data, followed by the results obtained with oversampled data. The results obtained using the ensemble resampled data are then provided. To validate the performance of the proposed approach, we present the evaluation results of the algorithms on the benchmark dataset for imbalanced, oversampled, and ensemble resampled data. In addition, the PR curves and their mean PR-AUC were calculated to assess the robustness of the algorithms. Finally, a performance comparison with existing state-of-the-art security data analysis approaches was conducted, the results of which are discussed in detail.
# Injury Patterns and Frequency in Swimming: A Systematic Review
## 4 Discussion
### Shoulder Injuries  
This review confirms that the shoulder is the joint most frequently affected in swimmers, accounting for 39.04 \(\pm\) 1.95% of injuries across the analyzed studies [3; 7]. This aligns with findings by [PERSON] et al. [7], where 91% of elite swimmers reported shoulder pain, and 69% of those undergoing imaging exhibited supraspinatus tendinopathy. The shoulder's biomechanical role in swimming, contributing to approximately 90% of forward propulsion [25], predisposes it to overuse injuries such as rotator cuff impingement, tendinitis, and instability [2; 7]. Repetitive overhead motions in swimming, combined with high training volumes, increase the injury risk [25; 26].  
Moreover, factors such as muscular imbalances, excessive joint laxity, and poor technique further exacerbate the risk of shoulder injuries [17; 20]. Specific styles of swimming, such as freestyle and butterfly, involve extensive internal rotation and adduction of the shoulder, which increase strain on the rotator cuff [25]. Differences in stroke mechanics, such as unilateral versus bilateral breathing, may create asymmetrical loading, increasing injury risk [17; 20].  
These findings underscore the necessity of tailored preventive strategies. Effective interventions should focus on managing training loads, correcting stroke mechanics, and implementing shoulder-specific strength and mobility exercises. Such approaches are critical for minimizing overuse injuries and optimizing performance in competitive swimmers.
## 5 Measurements
### Application in Imaging Diagnosis System  
The proposed 105 GHz circular-loop FSS has been implemented and used as a component in the ECEI (Electron Cyclotron Emission Imaging) system, for protecting the imaging antenna-detector array from a high-power stray EM signal. To provide the best performing FSS filters, several filters of the same design were fabricated and tested under same test environment. Figure 11 shows the measured frequency responses of eight circular-loop FSS filters for the KSTAR ECEI system, including  
Figure 10: Measured angular performance: (**a**) Square-loop FSS Filter, (**b**) Circular-loop FSS filter.  
Figure 9: Measurement setup: (**a**) Block diagram, (**b**) DUT with oblique angle incidence of \(14^{\circ}\).  
the performance deviations with arithmetic mean and standard deviation (STDEV). The measured results of the fabricated filters have negligible differences of \(\sim\)1 dB in notch-rejection and shift of \(\sim\)0.25 GHz in resonant frequency due to fabrication tolerances. The proposed circular-loop FSS filter is practical and adequate for the mm-wave imaging diagnostic system applications.
## 4 Experimental Results and Discussion
Curve (i) is the normalized SNL, and curve (ii) is the calculated normalized noise power of the intensity difference between the signal and idler using Equation (10) with the experimental parameters mentioned above and a different pump parameter \(\sigma\). Blue diamonds are experimental data. The experimental results were recorded by an SA with an RBW of 100 kHz, a VBW of 50 Hz, and a sweep time of 500 ms. When \(\sigma\) was 1.04, the output power of the signal and idler was 10 mW and 2 mW, respectively, and a maximum noise reduction can be observed. Clearly, the quantum correlation between the signal and idler was stronger when the pump power was closer to the pump threshold of SRO. Increasing the pump power, the observed quantum correlation between the twin beams degraded. When \(\sigma\) was 1.11, the output power of the signal and idler was 25 mW and 6 mW, respectively, and a quantum correlation of 1.3 dB between the  
Figure 4: Normalized noise power spectrum of the intensity difference between two down-conversion beams. Curves (i): normalized SNL, curve (ii): theoretical prediction, blue diamonds: experimental data. The inserted figure is the normalized intensity noise power spectrum of the pump. twin beams was observed. The noise power of the intensity difference between the signal and idler was not measured when \(\sigma\) was larger than 1.11, because photodetectors will be saturated with the larger incident powers of the signal and idler.
## 5 Performance Evaluation
### Simulation Results
#### 5.2.2 Changes in Network Density vs. Number of Forwarders  
The basic performance of the protocols with varying network densities and duty cycles for a single-packet transmission from the source to the sink is shown in Figure 7. The average number of forwarders per hop with increasing node density is shown in the two graphs in Figure 7. As the number of nodes increased, the number of nodes forwarding the packet also increased;  
Figure 5: Comparison of the average energy consumption per node with varying non-uniform densities under different traffic loads.  
Figure 6: Comparison of the average energy consumption per node with varying non-uniform densities and duty cycles (for a single packet transmission).  
however, the increase in the FCM-OR protocol was slower than those in the existing ORW and ORR protocols. The forward count was also lower for the FCM-OR protocol. Although the wake-up period increased, the proposed protocol outperformed the existing protocols.
## 5 Properties of Shape Groups
### Occurrence and Properties
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Shape Groups (1-15)**} & \multirow{2}{*}{\#} & \multicolumn{3}{c}{\(D_{\text{max}}/\text{\mu m}\)} & \multicolumn{3}{c}{\(A/10^{-9}\)\(\text{m}^{2}\)} & \multicolumn{3}{c}{\(A_{\rm r}\)} \\ \cline{3-11}  & & **Median** & **15.9\%** & **84.1\%** & **Median** & **15.9\%** & **84.1\%** & **Median** & **15.9\%** & **84.1\%** \\ \hline _(1) Needles/long columns_ & 350 & 956 & 555 & 1485 & 142 & 81.3 & 232 & 0.195 & 0.119 & 0.363 \\ _(2) Crossed needles/col._ & 72 & 1267 & 910 & 1737 & 233 & 148 & 357 & 0.181 & 0.124 & 0.282 \\ _(3) Thick columns_ & 124 & 473 & 283 & 659 & 69.0 & 34.1 & 105 & 0.411 & 0.281 & 0.654 \\ _(4) Capped columns/full._ & 221 & 662 & 476 & 1084 & 175 & 89.1 & 393 & 0.498 & 0.332 & 0.666 \\ _(5) Plates_ & 213 & 504 & 336 & 856 & 143 & 65.7 & 387 & 0.748 & 0.596 & 0.823 \\ _(6) Stellar crystals_ & 57 & 1450 & 864 & 1876 & 634 & 235 & 1260 & 0.443 & 0.315 & 0.561 \\ _(7) Bullet rosettes_ & 55 & 1040 & 724 & 1305 & 348 & 194 & 570 & 0.430 & 0.326 & 0.561 \\ _(8) Branches_ & 629 & 1078 & 714 & 1589 & 360 & 170 & 748 & 0.415 & 0.313 & 0.526 \\ _(9) Side planes_ & 428 & 1105 & 856 & 1470 & 432 & 255 & 773 & 0.462 & 0.368 & 0.573 \\ _(10) Spatial plates_ & 51 & 699 & 500 & 978 & 239 & 131 & 422 & 0.659 & 0.544 & 0.758 \\ _(11) Spatial stellar cryst._ & 235 & 1032 & 621 & 1683 & 420 & 168 & 1040 & 0.521 & 0.431 & 0.599 \\ _(12) Graupel_ & 74 & 571 & 390 & 912 & 156 & 75.6 & 435 & 0.646 & 0.597 & 0.719 \\ _(13) Je Jeonllting particles_ & 96 & 279 & 212 & 509 & 40.2 & 21.2 & 95.5 & 0.596 & 0.384 & 0.765 \\ _(14) Irregulars/aggregates_ & 518 & 821 & 503 & 1452 & 247 & 97.6 & 619 & 0.488 & 0.337 & 0.618 \\ _(15) Spherical particles_ & 42 & 181 & 108 & 236 & 24.8 & 8.60 & 48.7 & 0.961 & 0.924 & 0.976 \\ \hline \multicolumn{11}{c}{**All shape groups together**} \\ \multicolumn{11}{c}{**(regardless of shape)**} & 3165 & 899 & 489 & 1454 & 244 & 89.7 & 614 & 0.461 & 0.286 & 0.640 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The total number of particles, #; particle size, \(D_{\text{max}}\); cross-sectional area, \(A\); and area ratio, \(A_{\rm r}\), for the shape groups. The median and percentiles 15.9% and 84.1% are displayed. For comparison, results for all the data, i.e., all the shape groups together regardless of shape, are also shown. Figure 4: Microphysical properties of snow crystals for each shape group are shown in solid lines. The median and the spread corresponding to (**a**) particle size, \(D_{\text{max}}\); (**b**) cross-sectional area, \(A\); and (**c**) area ratio, \(A_{\rm r}\), are shown. Values of the median are represented as points. Lower and upper ends of the vertical bars are given by the percentiles 15.9% and 84.1%, respectively, representing the data spread. For comparison, median and the spread corresponding to all data (regardless of shape) are also shown in black dashed lines.
###### Abstract  
In this paper, we propose a deep learning-based perception method in autonomous driving systems using a Light Detection and Ranging(LiDAR) point cloud data, which is called a simultaneous segmentation and detection network (SSADNet). SSADNet can be used to recognize both drivable areas and obstacles, which is necessary for autonomous driving. Unlike the previous methods, where separate networks were needed for segmentation and detection, SSADNet can perform segmentation and detection simultaneously based on a single neural network. The proposed method uses point cloud data obtained from a 3D LiDAR for network input to generate a top view image consisting of three channels of distance, height, and reflection intensity. The structure of the proposed network includes a branch for segmentation and a branch for detection as well as a bridge connecting the two parts. The KITTI dataset, which is often used for experiments on autonomous driving, was used for training. The experimental results show that segmentation and detection can be performed simultaneously for drivable areas and vehicles at a quick inference speed, which is appropriate for autonomous driving systems.  
Keywords:autonomous driving; perception; deep learning; segmentation; detection; LiDAR; point cloud +
Footnote †: journal: Applied Sciences
# Study on the Control of Torque Distribution of 4 WD Corn Harvester Operation Drive
## 3 Simulation and Result Analysis
### Simulation When Only the Front Left Wheel Does Not Slip  
The initial state of the simulation was a state where only the front left wheel did not slip after inter-axle torque control. The adjustment control process was to apply a certain degree of braking to the corresponding slipping wheels based on the inter-axle differential clutch combination so that the part of the driving torque was transferred to the left front drive wheel; if it slipped again, further torque control was carried out, and if necessary, engine output torque control was implemented to finally improve the traction. The traction force of the whole machine was finally improved. The simulation results are shown in Figure 11a,b.  
The simulation results showed, when no control was applied to the harvester, within 1.6 s, the average speed of the slip wheels first rose sharply to the maximum value due to slippage and then fell sharply to below 3 km/h due to the increase in attachment, and the speed of the harvester showed a decreasing trend due to wheels slippage during the process. After 1.6 s, the speed increased with the average speed of the slip wheels and finally reached the uniform speed at the same time. The harvester speed and the speed of the slip wheel fluctuated greatly during the whole process, and the speed of the slip wheel was overshoot, the maximum overshoot was 17.14%; after the control was applied, as shown in the solid line in Figure 11, the speed of the harvester and the average speed of the slip wheels increased steadily during the whole adjustment process, and the adjustment time to reach the uniform speed was 36.6% shorter than that when the control was not applied. From the speed change curve, it could be qualitatively analyzed that in the process of adjusting the harvester drive wheel slippage, the tractive force of both the whole harvester and the well-attached drive wheels increased after the torque control was applied compared with that without the torque control, which was able to make the adjustment time shorter.  
In summary, active torque control distribution could effectively eliminate three-wheel slip, and improve the passing ability as well as driving stability of the harvester during operation.