esa-sceva/satcom-chunk-collection huggingface dataset:
# Fourier Neural Operator Network for Fast Photoacoustic Wave Simulations
## 3 Results
### Hyperparameter Optimization  
A study was conducted to investigate the impact of hyperparameter selection on the FNO network's accuracy. All FNO networks were trained on the breast vasculature dataset for 200 epochs, which was sufficient for all networks to converge to a minimum loss. The number of Fourier modes had the largest impact on the FNO network because it directly affected the truncation error in the Fourier layers. FNO networks with fewer modes typically produced simulations with a blurred appearance (Figure 7). This was due to the loss of high-frequency information necessary for accurately simulating the sharp transitions of the acoustic wavefront. For a computational grid of 64 \(\times\) 64, the FNO network with a maximum number of 64 modes produced the highest quality photoacoustic simulations.  
Increasing the number of channels improved the FNO network's accuracy with diminishing returns (Table 1). Results show that there was little benefit in having an FNO network with more than five channels. Parameterizing the FNO network with a higher number of modes or channels results in a more complex model that requires more GPU memory. Interestingly, the time for a trained network to complete a simulation remained approximately the same. There was a small increase in computation time for the larger FNO networks with 64 modes and a higher number of channels.  
Figure 6: Comparison between FNO Network and k-Wave simulations for initial pressure sources using the (**a**) Shepp–Logan, (**b**) synthetic vasculature, (**c**) tumor and (**d**) Mason-M phantoms at t = [1, 10, 20] time steps.  
Figure 8: Simulations shown are for a 128 \(\times\) 128 computation grid. (**Top**) Comparison between FNO and k-Wave forward simulations at t = [1, 40, 80, 120, 160] time steps. (**Bottom**) Comparison between FNO and k-Wave adjoint simulation t = [200, 220, 240, 260, 280] time steps. The input to both adjoint simulations was sensor data sampled with a linear array from the k-Wave forward simulation.  
Figure 9: RMSE and standard deviation bands between the k-Wave and FNO forward simulations on the AWA2 test dataset. Distribution of pressures (25 th, 50 th and 75 th percentiles) are provided as a frame of reference.
## 2 Preliminaries
### Machine Learning Perspective
#### 2.2.3 Gated Recurrent Unit  
A Gated Recurrent Unit (GRU) is a type of Recurrent Neural Network (RNN) that was introduced by [PERSON] et al. in 2014 [25]. It is similar to a Long Short-Term Memory (LSTM) network but has a simpler structure with fewer gates and parameters.  
GRUs have two gates: the update gate and the reset gate. The update gate controls the flow of information from the previous state to the current state, and the reset gate controls the flow of information from the input to the current state. By using these gates to selectively allow or block the flow of information, GRUs are able to effectively capture long-term dependencies in sequential data.  
GRUs have a number of advantages over the more commonly used LSTMs:  
* Simpler structure: GRUs have a simpler structure with just two gates (reset and update gates), whereas LSTMs have three gates: input, output, and forget gates. This makes GRUs easier to implement and compute.
* Faster training: Due to their simpler structure, GRUs typically train faster.
* Less prone to overfitting: This simplified structure also means fewer parameters, which reduces the risk of overfitting.
* Require less memory: GRUs require less memory since they have fewer parameters to store during training. This enables building larger networks.  
* Easier gradient flow: The simpler structure of GRUs allows gradients to flow more easily during backpropagation, reducing the vanishing gradient problem.  
The empirical confirmation that GRUs can outperform LSTMs in stock price prediction is presented in [26]. Considering the aforementioned factors, it was decided to use GRUs instead of LSTM units as a building block of the proposed neural network. In this work, GRU cells are combined with the earlier described CNN layers in the complex architecture applied for stock price movement prediction.
## 2 Materials and Methods
### Interpretation of Regional and Local Dataset Acquired with A-DInSAR  
For an adequate interpretation of the dataset acquired with A-DInSAR, at a local and a regional scale, different information layers have been analyzed together with the geocoded PS in a GIS environment (ArcGIS 10.3). The geo-information layers used are the following (Figure 2):  
* Aerial orthophotos acquired in 2017 (OrtoPNOA 2017 CC-BY 4.0 scne.es) with a pixel resolution of 0.25 m.
* Topographic maps of the Principality of Asturias at scale 1:25,000.
* Digital Terrain Models: Elevation, Slope, and Orientation with pixel resolution of \(5\times 5\) m.
* Soil and geological maps at 1:25,000 and 1:50.000 scales, respectively.
* Land use map of 2014 at scale 1:25,000 by SIOSE plan (_Sistema de Informacion de Ocupacion del Suelo en Espana_- Land Occupation Information System in Spain).
## 3 Main Results
### Stability Analysis
\begin{split}\dot{V}&\leqslant-\left(k_{1}-\frac{1}{ 2}\right)x_{e}^{2}-\left(\frac{U_{r}}{\sqrt{y_{e}^{2}+\Delta^{2}}}-\frac{1}{2k_ {1}}\right)y_{e}^{2}-k_{2}\dot{V}^{2}-\left(k_{3}-1\right)\dot{r}^{2}-\left(k_ {4}-1\right)\ddot{u}_{r}^{2}\\ &\quad-\sum\limits_{i=1}^{2}\frac{k_{ei}^{2}}{2}\sigma_{i}^{2}- \sum\limits_{i=1}^{2}\frac{\iota_{i}}{2}\dot{\mathbf{W}}_{i}^{T}\dot{\mathbf{W }}_{i}-\sum\limits_{i=1}^{2}\frac{\lambda_{i}}{2}\delta_{i}^{2}+\sum\limits_{i =1}^{2}\frac{1}{2}\Big{(}\delta_{i}-\delta_{i}^{0}\Big{)}^{2}+\sum\limits_{i =1}^{2}\frac{\iota_{i}}{2}W_{iM}{}^{2}\\ &\quad+0.2785\left(\chi_{r}\delta_{1}+\chi_{u}\delta_{2}\right)+ \sum\limits_{i=1}^{2}k_{ei}^{2}\mu_{i}^{2}+\frac{1}{2}\Delta\tau_{r}^{2}+\frac {1}{2}\Delta\tau_{u}^{2}+\frac{1+k_{1}}{2}\theta^{2}\\ &\leqslant-\kappa_{2}V+\vartheta_{2}\end{split}  \tag{42}\]  
where \(\kappa_{2}=\min\left\{2k_{1}-1,\frac{2U_{r}}{\sqrt{y_{e}^{2}+\Delta^{2}}}- \frac{1}{k_{1}},2k_{2},\frac{2k_{3}-2}{m_{3}},\frac{2k_{4}-2}{m_{1}},\min \limits_{i=1,2}\left(k_{ei}^{2}\right),\min\limits_{i=1,2}\left(\iota_{i} \lambda_{\min}(\Gamma_{i})\right),\min\limits_{i=1,2}\left(\lambda_{i}\vec{ \xi}_{i}\right)\right\}\), \(k_{1}=\max\left\{\frac{1}{2},\frac{\sqrt{y_{e}^{2}+\Delta^{2}}}{2U_{r}}\right\}\); \(\vartheta_{2}=\sum\limits_{i=1}^{2}\frac{1}{2}\big{(}\delta_{i}-\delta_{i}^{0} \big{)}^{2}+\sum\limits_{i=1}^{2}\frac{\iota_{i}}{2}W_{iM}{}^{2}+0.2785\left( \chi_{r}\delta_{1}+\chi_{u}\delta_{2}\right)+\sum\limits_{i=1}^{2}k_{ei}^{2} \mu_{i}^{2}+\frac{1}{2}\Delta\tau_{r}^{2}+\frac{1}{2}\Delta\tau_{u}^{2}+ \frac{1}{2}\Delta\tau_{u}^{2}+\frac{1+k_{1}}{2}\bar{\theta}^{2}\). Synthesizing Equations (41) and (42), we have  
\[\dot{V}\leqslant-\kappa V+\vartheta \tag{43}\]  
where \(\kappa=\min\{\kappa_{1},\kappa_{2}\}\) and \(\vartheta=\max\{\vartheta_{1},\vartheta_{2}\}\) with the design parameters satisfying the conditions: \(k_{1}>\max\left(\frac{1}{2},\frac{\sqrt{y_{e}^{2}+\Delta^{2}}}{2U_{r}}\right), k_{2}>0,k_{3}>1,k_{4}>1,k_{51}>0,k_{52}>0,k_{\sigma 1}>\frac{1}{2}k_{51}^{2}+\frac{1}{2},k_{\sigma 2}> \frac{1}{2}k_{52}^{2}+\frac{1}{2}\). Then, the following inequality can be obtained  
\[0\leqslant V\leqslant\left(V(0)-\frac{\vartheta}{\kappa}\right)e^{-\kappa t}+ \frac{\vartheta}{\kappa} \tag{44}\]  
In conclusion, it follows the definition of \(V\) that the tracking error signals \(\eta_{e}=[x_{e},y_{e},\vec{y},\vec{r},\vec{u}_{r}]^{T}\) are locally UUB, which ultimately converges to the compact sets \(\Omega_{1}=\left\{\eta_{e}\in\mathbb{R}^{5}\right.\|\left.\|\eta_{e}\right\| \leqslant\sqrt{2\left(V(0)-\frac{\vartheta}{\kappa}\right)e^{-\kappa t}+ \frac{2\theta}{\kappa}}\right\}\). The ultimate compact set can be easily tuned by adjusting the design parameters. Meanwhile, the parameters estimation errors \(\tilde{\mathbf{W}}_{1},\tilde{\mathbf{W}}_{2},\tilde{\delta}_{1}\), and \(\tilde{\delta}_{2}\) are locally UUB. Theorem 2 is thus proved.
## 2 Materials and Methods
### Measurement
#### 2.2.2 Multi-View RGB Senor-Based Motion-Analysis System (4 DEYE)  
Utilizing a multi-view RGB sensor-based motion-analysis system (4 DEYE, SYM Healthcare Inc., Seoul, Republic of Korea), which consists of five cameras, the subject's motion was photographed in 12 Hz areas in five different directions. In addition, a custom OpenCV- [23] and OpenPose [24]-based analysis program was used to analyze hip and knee joint angles from images of movements in various directions. In detail, the OpenPose software ([[https://cmu-perceptual-computing-lab.github.io/openpose/web/html/doc/md_doc_00_index.html](https://cmu-perceptual-computing-lab.github.io/openpose/web/html/doc/md_doc_00_index.html)]([https://cmu-perceptual-computing-lab.github.io/openpose/web/html/doc/md_doc_00_index.html](https://cmu-perceptual-computing-lab.github.io/openpose/web/html/doc/md_doc_00_index.html)) accessed on 20 November 2023) can acquire five images simultaneously using five cameras, i.e., estimating the 2D position of seven physical key points: neck, left shoulder, right shoulder, right hip, knee, and ankle. Then, based on information on the relative position and direction of the camera, the 3D positions of each key point were re-implemented in four 2D positions corresponding to the key point using OpenPose software. The bending and stretching of the hip joint were analyzed indirectly as the angle of the thigh axis for the entire body, and the bending or stretching of the knee was analyzed indirectly as the angle between the thigh and the tibia axis. Body coordinates are analyzed by defining the z-axis as a vector from the joint to the neck, and the x-axis as a vector perpendicular to a plane formed by the left shoulder and right shoulder.  
In addition, x- and y-axes were defined as vectors orthogonal to the x- and z-axes, and the femur and tibia-axis vectors were defined as vectors pointing from the right hip to the knee and from knee to the ankle, respectively. The hip-flexion angle was calculated as the angle between the negative z-axis and thigh-axis vectors of the body coordinates, and hip flexion was quantified in the 3-dimensional motion regardless of the sagittal plane. The hip-flexion angle to lift the leg increased from 0\({}^{\circ}\) (i.e., anatomically neutral position) to 120\({}^{\circ}\). Finally, the analyzed joint angle was calibrated to match the length with the data collected in the 100 Hz area using the VICON motion-tracking system. Detailed equipment specifications for VICON(r) and 4 DEYE(r) are attached in Table 1.
###### Abstract  
As vital comments on landslide early warning systems, accurate and reliable displacement prediction is essential and of significant importance for landslide mitigation. However, obtaining the desired prediction accuracy remains highly difficult and challenging due to the complex nonlinear characteristics of landslide monitoring data. Based on the principle of \"decomposition and ensemble\", a three-step decomposition-ensemble learning model integrating ensemble empirical mode decomposition (EEMD) and a recurrent neural network (RNN) was proposed for landslide displacement prediction. EEMD and kurtosis criteria were first applied for data decomposition and construction of trend and periodic components. Second, a polynomial regression model and RNN with maximal information coefficient (MIC)-based input variable selection were implemented for individual prediction of trend and periodic components independently. Finally, the predictions of trend and periodic components were aggregated into a final ensemble prediction. The experimental results from the Muyubao landslide demonstrate that the proposed EEMD-RNN decomposition-ensemble learning model is capable of increasing prediction accuracy and outperforms the traditional decomposition-ensemble learning models (including EEMD-support vector machine, and EEMD-extreme learning machine). Moreover, compared with standard RNN, the gated recurrent unit (GRU)-and long short-term memory (LSTM)-based models perform better in predicting accuracy. The EEMD-RNN decomposition-ensemble learning model is promising for landslide displacement prediction.  
landslide displacement prediction; decomposition-ensemble model; recurrent neural network (RNN); ensemble empirical mode decomposition (EEMD); maximal information coefficient (MIC) 2021114684. [[https://doi.org/10.3390/app11104684](https://doi.org/10.3390/app11104684)]([https://doi.org/10.3390/app11104684](https://doi.org/10.3390/app11104684))  
1  
A Novel Decomposition-Ensemble Learning Model Based on Ensemble Empirical Mode Decomposition and Recurrent Neural Network for Landslide Displacement Prediction
# Multi-Granularity Dilated Transformer for Lung Nodule Classification via Local Focus Scheme
## 5 Discussion
### Effectiveness of the Deformable Dilated Transformer  
In this part, we analyze the effectiveness of deformable attention (DA) and dilated layer in Deformable Dilated Transformer (DDFormer) for an extensive ablation study. As shown in Figure 3, the key parts of DDFormer are Deformable Attention (DA) and Dilated Layer. First, we only replace the deformable attention from the DDFormer with the regular multi-head attention (RA) to examine the effectiveness of deformable attention mechanism. Next, we choose the dilated layer as the feedforward layer (the original one is a multi-layer perceptron (MLP)). Finally, we jointly use deformable attention and dilated layer to construct the network. We compared the above four settings and tabulated the results in Table 2. From the results, we can observe that using the dilated layer combined with the RA and DA both can improve the performance across all metrics, as expected. This is due to the fact that the dilated layer can increase spatial receptive fields and without introducing extra computational cost. Moreover, we can see that Deformable Attention has a clear performance gain (1%) compared with Regular Attention, which indicates that DA indeed improves the overfitting problem and obtains a more discriminative global representation. Notably, the combination of DA and Dilated Layer (i.e., DDFormer) outperforms all the other settings over multiple criteria.  
Moreover, we conduct an ablation study to examine the optimal number of DDFormer blocks as our ultimate structure. Specifically, we use the different number of blocks to verify the corresponding AUC and accuracy results, which are listed in Table 3. We can observe that the accuracy obtains the optimal performance when we use four base DDFormer blocks, and the performance starts to decline as we set the number to exceed 4. We believe  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **AUC** & **Accuracy** & **Precision** & **Sensitivity** & **F1-Score** \\ \hline HSCNN [38] & 85.6 & 84.2 & - & 70.5 & - \\ Multi-Crop [39] & 93.0 & 87.14 & - & 77.0 & - \\ Local-Global [40] & 95.6 & 88.4 & 87.3 & 88.6 & 88.3 \\ Gated-Dilated [5] & 95.1 & 92.5 & 91.8 & 92.2 & 92.6 \\ Swarm [41] & - & 93.7 & 93.5 & 92.9 & - \\
3D DPN [12] & - & 90.2 & - & 92.0 & 90.4 \\ MV-KBC [42] & 95.7 & 91.6 & 87.7 & 86.5 & 87.1 \\ MSCS-DeepLN [6] & 94.0 & 92.6 & 90.3 & 85.5 & 87.9 \\ MK-SSAC [43] & 95.8 & 92.5 & - & 84.9 & - \\ ProCAN [15] & 97.1 & 94.1 & 94.5 & 93.1 & 93.8 \\ \hline
**MGDFormer** & **98.5** & **96.1** & **95.9** & **94.4** & **95.2** \\
**(ours)** & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons of state-of-the-art methods on the LIDC-IDRI dataset.  
the main reason is that a small number of modules cannot have sufficient feature extraction capability, but excessive ones will lead to the overfitting problem, and the two phenomena above can both lead to a decrease in performance.
# HFDF-EffNetV2: A Lightweight, Noise-Robust Model for Fault Diagnosis in Rolling Bearings
## 5 Conclusions  
This study addresses the challenge of achieving real-time and high-accuracy FD on platforms with limited computational resources by proposing a lightweight model called HFDF-EffNetV2.  
In noise-interference experiments on the CWRU bearing dataset and a self-constructed bearing dataset, the average accuracy of the proposed model increased by 1.51%, 5.07%, 2.38%, 18.39%, 2.37%, and 11.23% and by 1.3%, 5.5%, 2.45%, 21.25%, 3.55%, and 25.71%, compared to EfficientNetV2-S, ResNet-18, RepViT, SqueezeNet, MPNet, and MobileNetV3, respectively. With small sample sizes, the proposed model outperformed these six models in average accuracy by 2.68%, 0.14%, 6.06%, 10.89%, 2.72%, and 7.74% and by 2.76%, 0.5%, 5.96%, 8.95%, 3.26%, and 10.69%, respectively. These results show that the Fused-MBPyConv and BSMBConv-MLCA proposed in this study achieved deeply coupled resolution of cross-scale fault features while maintaining light weight through feature fusion via staged channel-compression transformations. The HFDF strategy enables global integrated FD with cross-level fault feature relationships through multi-stage information fusion.  
Although the proposed model achieved excellent diagnostic results in preliminary experiments and has 1.85 M parameters and faster diagnosis time compared with other lightweight models, which verifies its potential for real-time and highly accurate deployment on edge devices, the model may still be limited by the hardware configurations on resource-constrained edge devices. Indeed, in actual equipment operation, rolling bearings may experience fault types never before encountered by the models. Therefore, future research will combine signal processing with threshold-independent open-set identification strategies to enhance the diagnostic capabilities of the proposed model for unknown faults and evaluate its performance across different edge devices and real-world environments.  
Conceptualization, J.P. and D.Z.; methodology, D.Z.; software, D.Z.; validation, D.Z. and J.P.; formal analysis, D.Z. and T.H.; investigation, J.P.; resources, J.P.; data curation, F.H.; writing--original draft preparation, D.Z.; writing--review and editing, J.P., T.H. and J.N.; visualization, D.Z.; supervision, J.P.; project administration, F.H.; funding acquisition, F.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Guangxi Key Research and Development Program Project Grant No. AB24010202.  
Not applicable.  
Figure 19: Experimental results for different ablation models under different SNRs in Case 1.
# Intra-Reliability of a Wearable Near-Infrared Sensor for Monitoring the Intensity of Exercise
###### Abstract  
The Q-LAC analyzer, a portable device employing near-infrared spectroscopy (NIRS), was designed to measure muscle oxygen saturation (SmO\({}_{2}\)) during physical exercise. This study aimed to assess the reliability of the Q-LAC analyzer in determining SmO\({}_{2}\) levels during incremental cycling exercise. Thirteen physically active males (age 28.1 \(\pm\) 5.3 y; height 181.2 \(\pm\) 5.7 cm; body mass 79.9 \(\pm\) 11.1 kg; BMI 24.2 \(\pm\) 2.4 kg/m\({}^{2}\)) participated in this study. A submaximal incremental exercise test (SIET) on an electromagnetic cycle ergometer with a seven-day interval was performed twice. SmO\({}_{2}\) levels in the vastus lateralis (VL) muscle of the dominant leg were simultaneously recorded using the Q-LAC device during both tests. The study calculated the intraclass correlation coefficient (ICC), the coefficient of variation (CV), the standard error of measurement (SEM), the smallest worthwhile change (SWC), and mean detectable change (MDC). A within-within-subjects ANOVA revealed no statistically significant effects for session (F\({}_{1,12}\) = 0.97, \(p\) = 0.34, \(\eta^{2}\) = 0.07) and the interaction between session and workload (F\({}_{4,48}\) = 0.19, \(p\) = 0.94, \(\eta^{2}\) = 0.02). ICC values ranged from 0.72 to 0.91. Furthermore, the analysis of CV, SEM, and SWC indicated that SmO\({}_{2}\) measurements obtained with the Q-LAC device exhibit good reliability but marginal sensitivity. In conclusion, the portable Q-LAC analyzer provides consistent measurements of muscle oxygen saturation during low-to-moderate-intensity exercise on a cycle ergometer.  
muscle oxygen saturation; near-infrared spectroscopy; portable device; exercise intensity Article
## 3 Results and Discussion
### Socio-Economic Analysis of Hydrogen Production Pathways  
To complement the environmental findings, a socio-economic analysis was conducted to provide a fuller picture of sustainability, comparing hydrogen production from biogas reforming and agricultural residue gasification. Biogas reforming, typically applied in urban contexts with established waste management systems, offers socio-economic benefits, such as improved sanitation, waste valorization, and employment opportunities in system operation and maintenance. However, its economic viability is hindered by high capital costs and feedstock variability, which require supportive policies and investments. In contrast, agricultural residue gasification promotes decentralized energy systems and rural economic development by creating jobs in biomass collection and plant operations while also mitigating environmental issues, like open biomass burning. Despite its higher technical and infrastructural requirements, gasification offers long-term potential when supported by stable biomass supply chains and capacity-building efforts. From a policy standpoint, both technologies align with circular economy and greenhouse gas reduction goals, and their widespread adoption can be facilitated through targeted subsidies, carbon credits, and development programs.  
In addition to environmental and socio-economic aspects, a techno-economic assessment provides further insight into the feasibility of biogas reforming and biomass gasification. This includes capital expenditure (CAPEX), operational expenditure (OPEX), and the levelized cost of hydrogen (LCOH), defined as the average cost per unit of hydrogen produced over the lifetime of the system.