esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Trajectory Tracking Control of a Magnetorheological Prosthetic Knee Joint
## 3 Design of MR Damper Used in the MR Prosthetic Knee Joint
### Establishment of Reverse Mechanics Model of the MR Damper  
In order to make the MR damper accurately output the expected damping force, a reverse mechanical model of the MR damper is needed. Since the relationship between the current and the damping force cannot be directly inverted and since the BP neural network can realize the approximation of any nonlinear mapping relationship and has a strong generalization ability, the BP neural network is used to establish the inverse mechanical model of the MR damper.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Current /A**} & \multicolumn{5}{c}{**Parameter Value**} \\ \cline{2-6}  & \(F_{\text{m}}\) & \(a\) & \(k\) & \(C_{0}\) & \(f_{0}\) \\ \hline
0 & 1.63 & 0.30 & \(-\)0.42 & 0.32 & \(-\)0.13 \\
0.4 & 26.75 & 0.  
Due to the limited experimental data, the established adjustable sigmoid model data is used as the training and verification samples of the BP neural network. Training samples are obtained by changing the piston rod displacement, piston rod speed and output damping force in the adjustable sigmoid model. The sample data is randomized, and the BP neural network is initialized for training, and finally, the data is normalized.  
The established BP neural network is trained and verified by setting the training number to 100 times. The result is shown in Figure 13. It can be seen that the fitted value of the BP neural network is close to the sample value, showing a sinusoidal distribution. Figure 14 shows the error curve between the sample value and the fitted value, and the maximum training error of the BP neural network is only 0.026.  
Figure 12: Comparison of dynamic performance of proposed MR damper: (**a**) damping force-displacement, (**b**) damping force-speed.  
Figure 13: Fitting accuracy curve using BP neural network.
## 3 Thermal Analysis of the WCLL COB Segment
### Thermal Analysis of the Equatorial Radial-Toroidal Cell of Each of the 7 COB Poloidal Regions
#### 3.1.2 Loads and Boundary Conditions
A first attempt value of mass flow rate was selected for each group of DWTs and channel, and a related heat transfer coefficient, calculated using the Dittus & Boelter (D&B) correlation [13], was considered. In particular, an inlet temperature of 295 \({}^{\circ}\)C and a water temperature difference (\(\Delta\)T) between inlet and outlet equal to 33 \({}^{\circ}\)C were utilized for the calculation. So, the heat transfer coefficients (HTCs) and the mass flow rates values needed to ensure the selected \(\Delta\)T were calculated by means of an iterative procedure [14]. Lastly, adopting a \"frozen field\" calculation approach, it was possible to calculate the water bulk temperature along each channel/tube abscissa. In particular, the adopted iterative procedure consists of imposing the bulk temperature and mass flow rate (i.e., mass flow per unit area) at the inlet of each of the cooling tubes or channels together with the \"first attempt\" (i.e., calculated using the D&B correlation) HTC. The latter does not change during the current iteration. Hence, such an approach allows calculating the bulk temperature along the flow direction and using it for the imposition of the convective heat transfer condition. The thermal analysis has been iterated so as to obtain an HTC value able to produce a thermal rise between water inlet and outlet of -33 \({}^{\circ}\)C. In Table 2 the HTC values adopted for each poloidal region in the last iteration for the complex of cooling channels and DWTs, where HTC\({}_{\rm{CC}}\), HTC\({}_{\rm{DWTs}}\) and HTC\({}_{\rm{DWTs,rec}}\) indicate the HTC values used for the cooling channels, first group of DWTs and recirculation DWTs, respectively, are reported.
# Classification of Breast Cancer in Mammograms with Deep Learning Adding a Fifth Class
## 2 Related Work
2018 [20] used low energy (LE) and FEDM images from the INbreast database to recombine them and perform malignant or benign mass classifications with a shallow DCNN (SD-CNN) based on ResNet. They got an AUC of 0.84 for LE images, and when using LE plus recombined images, increased the AUC to 0.92 with an accuracy of 0.90, specificity of 0.94, and a recall of 0.83. Using the DDSM database, [PERSON] et al. [23] applied transfer learning using a CNN based on the VGG architecture. Following the region proposal convolution neural networks (R-CNN) methodology, they extended the work to a specific domain (DS-RCNN), adding SVM to classify architectural distortions, achieving more than 80% sensitivity and specificity, with 0.46 false positives per image with a true positive rate of 83%. Aiming to generate faster detections; [PERSON] et al. [38] yielded the INbreast and CBIS-DDSM databases [39], applying CLAHE to improve the quality of the images [40] and Otsu thresholding to remove its background and subtract initial weights to train their model with mammograms taken in Ethiopian hospitals. Creating their own Faster R-CNN based on VGG to perform classifications, they reached a detection precision of 91.86%, a sensitivity of 94.67%, and AUC-ROC of 92.2%. Table 1 summarizes the studied approaches and methodologies that use neural networks to classify breast cancer. It shows the databases used with their type and quantity of images, data augmentation, detectable classes, and models with different performance metrics. Among methods to improve the quality of the images, CLAHE stands out due to its ability to improve images through the histogram by limiting contrast, especially in homogeneous areas, avoiding noise amplification. The main advantages of CLAHE are its modest calculation requirements, ease of use, and excellent result in most images [39]. [PERSON] and [PERSON] [41] aimed to use filters such as the median, average, minmax, and wiener, combined with CLAHE; applying the method to 20 mammograms and concluding in a mammography enhancement where the wiener filter reduces noise while CLAHE enhances the image. When performing segmentation and classification with DL and neural networks, the literature usually reports classifications for malignant and benign masses or microcalcifications; nevertheless, few studies use both kinds of lesions. Furthermore, several ROIs segmented by the algorithms are false positives [20; 23], which reduce the rate of correct detections. Due to this, in this work, we train three CNNs and propose a fifth classification of the \"Normal\" type, corresponding to breast tissue without any lesions, intending to increase the detector's effectiveness by classifying and reducing the false positive rate  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
## 2 Methodology
### Evaluation Index  
In order to evaluate the effect of each model, the R\({}^{2}\), RMSE and fit value were adopted as the evaluation indexes of the identification model, and the IAE and ME were selected as the performance evaluation indexes of the controller. The IAE takes into account the long-term operational performance of the control system, as well as the amplitude and duration of the error. It evaluates the control effect by calculating the cumulative sum of absolute errors. The smaller the IAE value, the closer the output of the control system is to the set value, and the better the control performance. The IAE takes into account the accumulation of errors throughout the control process  
load conditions above 90%, which may reduce the thermal efficiency and increase safety risks--scenarios rarely encountered in practice. While neural networks typically exhibit limited extrapolation performance outside the training data distribution, the selected data window sufficiently captures most real-world operating demands, ensuring model reliability within these bounds. The explicit testing of extrapolated scenarios (e.g., loads 10% above or below this range) was not conducted, but the comprehensive coverage of typical conditions supports the model's practical applicability. To verify the accuracy of the identification model, an additional test dataset comprising 20,600 data points was used. The selected neural network architecture is a 3-9-18-9-3 structure, with input layer neurons corresponding to the throttle valve opening, coal feed rate, and water feed rate, and output layer neurons representing the predicted steam pressure, unit load, and header temperature. The transfer function model, in contrast, is a second-order system. The fit of both identification models against the test data was evaluated, with the results presented in Figure 4 and Table 1. Figure 4 demonstrates that both methods accurately capture trends in real field data. Table 1 highlights the neural network's superior performance for steam pressure and header temperature, reducing the RMSE by 30.6% and 27.4%, respectively, and yielding higher coefficients of determination and fit values, indicative of better accuracy. However, for the unit load, the performance of both methods is comparable, with evaluation metrics on the same order of magnitude, likely due to the relatively smooth variation in load, which both models effectively capture. These findings underscore the neural network's significant potential for unit parameter identification, offering enhanced capability to handle nonlinear systems and complex data compared to the traditional transfer function model, with a greater prediction accuracy and improved fit.
# Angle of Arrival for the Beam Detection Method of Spatially Distributed Sensor Array
## 1 Introduction
Subsequently, a detailed analysis is conducted to evaluate the influence of the structure on the field of view and angular resolution, leading to the optimization of the spatially distributed sensor array design. A multi-sensor data fusion algorithm is proposed to enhance measurement accuracy. Finally, the primary sources of measurement errors in the spatially distributed sensor array were discussed, and error calibration was implemented. The two-dimensional angle of arrival was also tested, and the test results verified the effectiveness of the proposed multi-sensor data fusion algorithm.
## 4 Time-Frequency Analysis for Feature Extraction
### Hilbert-Huang Transform (HHT)
#### 4.4.1 Empirical Mode Decomposition (EMD)  
This adaptive tool decomposes a signal into a finite series of IMFs, each representing simple oscillatory patterns that characterize the signal's local frequency traits.
## 2 Theoretical Analysis
### Analytical Model for Key Strata (KS)
\[f(x,y) = \frac{\sigma_{1}-\frac{1+\sin\varphi}{1-\sin\varphi}\sigma_{3}}{R_{ c}} \tag{11}\]  
where \(R_{c}\) and \(\varphi\) denote the uniaxial compression strength and frictional angle of the key stratum, respectively, and \(\sigma_{1}\) and \(\sigma_{3}\) indicate the maximum and minimum principal stresses, for which the following relationship holds:  
\[\frac{\sigma_{1}}{\sigma_{3}} = \frac{\sigma_{x}+\sigma_{y}}{2}\pm\sqrt{\left(\frac{\sigma_{x}- \sigma_{y}}{2}\right)^{2}+\tau_{xy}^{2}} \tag{12}\]  
The configuration parameters in the key strata models were empirically chosen and estimated according to field testing results and theoretical calculations (in Figure 5), as follows: \(S\) = 140 m, \(L\) = 100 m, \(h_{k}\) = 20 m, \(h_{a}\) = 16 m, \(h_{b}\) = 25 m, \(R_{c}\) = 40 MPa, \(\mu\) = 0.24 and \(E\) = 32 GPa. Then, by substituting the above parameters into Equations (8)-(11), the calculation of water inrush zoning on the key strata can be carried out, as shown in Figure 7, where a value \(\geq\) 1 indicates the destruction of the key strata and occurrence of water inrush, and a value \(<\) 1 indicates that water inrush does not occur. Obviously, there are three dominant areas of damage on the horizontal key layer, as shown in Figure 7a, one of which appears in the middle of one side of the coal wall (for example, H\({}_{1}\)), while the other two are symmetrically located on the side boundaries of the model, approximately 80 m away from the coalface (for example, H\({}_{2}\) and H\({}_{3}\)). According to the damage area and \(f\) (\(x\), \(y\)), the rankings of the dominant damage zones are as follows: area(H\({}_{2}\)) = area(H\({}_{3}\)) > area(H\({}_{1}\)), and \(f\)(H\({}_{2}\)) = \(f\)(H\({}_{3}\)) = 1.5 \(>\)\(f\)(H\({}_{1}\)) = 1, respectively. Hence, it can be inferred that the water-protruding risk of the horizontal key strata is R(H\({}_{2}\)) = R(H\({}_{3}\)) > R(H\({}_{1}\)). However, in comparison with the horizontal model, as shown in Figure 7b, more attention should be focused on the lower part (e.g., I\({}_{2}\)) of the inclined strata undergoing the most severe destruction, and the damage area and \(f\) (\(x\), \(y\)) could be ordered as follows: area(I\({}_{2}\)) > area(I\({}_{1}\)) > area(I\({}_{3}\)) and \(f\)(I\({}_{2}\)) = 2.5 \(>\)\(f\)(I\({}_{1}\)) = \(f\)(I\({}_{3}\)) = 1, respectively. Therefore, the risk of groundwater outburst is theoretically calculated to be R(I\({}_{2}\)) > R(I\({}_{1}\)) > R(I\({}_{3}\)). Additionally, the failure zone (for example, I\({}_{1}\)) in the inclined model shows a significant deviation from the up-dip to the down-dip. Therefore, effective countermeasures such as goat filling and floor grouting should be emphatically and purposefully implemented within these dangerous areas after coal excavation to ensure safe and efficient coal production [44; 45; 46].
# MPFM-VC: A Voice Conversion Algorithm Based on Multi-Dimensional Perception Flow Matching
## 2 Related Work
### Cascaded VC with Diffusion Models  
With the rapid advancement of diffusion models in the field of generative modeling, they are among the mainstream acoustic models within cascaded VC frameworks. DiffWave, introduced by Huawei in 2020 [30], pioneered the use of denoising diffusion probabilistic models (DDPMs) for waveform generation from Gaussian noise. Grad-TTS [31] extended that approach to TTS tasks by applying the diffusion process to spectrum generation, achieving notable improvements in naturalness, prosody control, and temporal variability.  
Building upon this foundation, Diff-VC [32] employed diffusion-based denoising to enhance speech naturalness and timbral accuracy, while DDDM-VC [33] further improved clarity and stylistic consistency. ComoSpeech [34] integrated conditional diffusion with a hybrid autoregressive/non-autoregressive decoding strategy, allowing for finer control over emotional expression, prosodic variation, and speaking rate. Models derived from ComoSpeech, including our improved variant, have reported subjective evaluation results approaching human-level performance.  
Despite these advancements, diffusion-based VC models still face several limitations compared with variational approaches. These include high computational complexity, slower inference speed, and reduced robustness under conditions of distribution shifts.
###### Abstract  
Previous studies have proposed various frameworks and algorithms to optimize routes to reduce total transportation cost, which accounts for over 29.4% of overall logistics costs. However, it is very hard to find cases in which mathematical models or algorithms are applied to practical business environment cases which require reusable packaging, especially daily operating logistics services like convenience store support systems. Most previous studies have considered developing an optimal algorithm which can solve the mathematical problem within a practical amount of time while satisfying all constraints, such as the capacity of delivery and pick-up, and hard or soft time windows. For daily delivery and pick-up services, like those supporting several convenience stores, it is required to consider the unit transporting the container, as well as the demand, capacity of trucks, travel distance, and traffic congestion. In particular, reusable transport containers and trays should be regarded as important assets of logistics centers. However, if the mathematical model focuses on only satisfying constraints related to delivery and not considering the cost of trays, it is often to leave the empty trays on the pick-up points when there is not enough space in the track. In this study, we propose a mathematical model for optimizing delivery and pick-up plans by extending the general vehicle routing problem of simultaneous delivery and pick-up with time windows, while considering left-over cost. With numerical experiments it has been proved that the proposed model may reduce the total delivery cost. Also, it seems possible to apply the proposed approach to the various logistics businesses which require reusable transport containers like shipping containers, refrigerating containers, trays, and pallets.  
vehicle routing problem; delivery and pick-up; time windows; left-over cost; reusable container
# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
## 3 Results
### Which Studies Provide Feedback for Participants Derived from Obtained Results? (RQ5)
#### 3.5.2 What Kind of Feedback Is Delivered?  
Most feedback systems in the reviewed literature rely on quantitative metrics to make classroom dynamics visible. These include measures such as talk-time, frequency of authentic questions, or discipline-specific vocabulary use, which serve as interpretablebaselines for reflection and instructional refinement. For example, one study provides real-time speaking ratios that help teachers and students rebalance participation mid-lesson [58]. Another tracks how often teachers pose authentic questions and examine the relationship between these frequencies and student engagement levels [101].  
Beyond raw counts, several platforms enhance quantitative feedback with interpretative guidance. They highlight specific moments where teachers used effective discourse strategies and offer actionable suggestions, such as rephrasing or elaboration moves, that may promote deeper student reasoning [41,100]. Dashboards frequently incorporate color-coded visualizations to identify zones of high or low engagement, helping educators focus attention where it is most needed [27,83].  
Notably, while quantitative feedback dominates, few studies attempt to provide qualitative or normative feedback, for example, judgments about whether a teacher's interaction style aligns with pedagogical best practices. This absence may reflect an implicit reluctance to define what constitutes 'good teaching.' Educational contexts vary widely, and there is little consensus on ideal instructional behavior. In practice, providing such evaluative guidance would require not only technical robustness but also normative frameworks capable of accounting for differences in subject matter, age group, and cultural setting. As a result, most systems remain focused on reporting metrics rather than interpreting them in light of pedagogical theory or instructional goals.
# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
## 6 Conclusions and Future Work
### Literature Analysis
This combination on the surface appears to be too complicated and does not have any obvious advantages. Possible implementations within the red area in Figure 10 could include digital twin models with embedded AI capabilities, and finite element method-based models that account for satellites' and UAVs' surface vibrations that use data-driven models for FDP. These methodologies have not been explored in the literature; therefore, their respective area is left empty in Figure 10.  
Figure 10: Compilation and classification of the methodologies utilized in the literature on satellite/UAV Fault Diagnosis and Prognosis. Furthermore, to obtain more insight into how often these methods are used in the current literature, pie charts of the number of works designed around each of the prominent method categories are demonstrated by Figures 11 and 12 for single-agent and multiagent literature, respectively. It is noticeable from Figure 11 that data-driven methods are being heavily employed in the area of single satellite and UAV FDP. Consequently, with these methods now composing a significant portion of the literature (54%), it is imperative to address some of the underlying issues that inhibit their real-life implementations such as real-time monitoring shortcomings, faulty dataset dependencies, and poor integration with other subsystems such as the fault-tolerant module. Model-based methods also constitute an extensive share of the research on single-agent FDP especially because of their added robustness to noise. Hybrid methods are shown to be on the rise, with only 7% of the works focused on them, underscoring the need to develop them further. Finally, other methods are rarely used on their own in this field only appearing in 4% of the works, but it is beneficial to be familiarized with their potential combinations with other method categories. On the other hand, the same pie chart depicted by Figure 12 for multiagent FDP shows a complete detachment from hybrid and other methods which could be due to the works on multiagent systems being fewer in numbers. It can also be seen that model-based methods are significantly more popular in this area appearing in 93% of the works. This signifies the potential for data-driven methods to become increasingly involved with multiagent FDP. Figure 11: Breakdown of the methodologies used in the single-agent FDP literature.