esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis of Selected Design Variants of the Considered Structure
### Solution IV: H-Beam Bracing  
Solution IV involves adding horizontal H-shaped steel sections to the upper and lower edges of the side panels, as depicted in Figure 25d. The material selected is ST12, with the assumption that the box-type substation design incorporates four support beams to share the load, with each beam bearing approximately 30825 N of load. Due to structural constraints within the box, the beam width is limited to a maximum of 48 mm. Calculations yield the dimensions of the H-shaped steel as follows: length 1500 mm (equivalent to the length of the side panels), width 48 mm, and height 10.9 mm.  
The simulation results after adjusting the structure are shown in Table 13, leading to the following conclusions:  
1. Deformation Analysis: The deformation is greatest in the 1.5 mm thickness, indicating that the support from the H-shaped steel is slightly less effective for thinner walls. The deformation in the X and Y directions is relatively consistent across all thicknesses, demonstrating that the H-shaped steel provides adequate lateral support, maintaining the overall rigidity of the structure.
2. Stress Analysis: In all thicknesses, the stress in the Z direction increases as the wall thickness decreases, with the 1.5 mm thickness showing the highest stress, indicating that the structural load-bearing capacity decreases with a reduced wall thickness. The  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Thicknesses** & **Response** & **X** & **Y** & **Z** & **XZ** & **ZY** \\ \hline \multirow{3}{*}{2.5 mm} & Von Mises Stress (MPa) & 0.39249 & 0.3623 & 3.5288 & 3.5351 & 3.5288 \\  & Total Deformation (mm) & 0.053931 & 0.067546 & 1.1509 & 1.1522 & 1.1509 \\  & Von Mises Stress (MPa) & 0.33654 & 0.36237 & 3.8212 & 3.8276 & 3.8212 \\  & Total Deformation (mm) & 0.05526 & 0.067625 & 1.1515 & 1.1529 & 1.1515 \\  & Von Mises Stress (MPa) & 0.24349 & 0.36263 & 3.9997 & 4.0073 & 3.9997 \\  & Total Deformation (mm) & 0.054887 & 0.067747 & 1.1441 & 1.1454 & 1.1441 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Combination simulation results of three thicknesses under Solution III.  
2.5 mm thickness exhibits higher stress in the X direction, while in comparison, the stress significantly decreases in the 2.0 mm thickness (0.23713 MPa), demonstrating that the H-shaped steel reinforcement is more effective for medium wall thicknesses.  
The simulation contour plot, as depicted in Figure 25e illustrates that the addition of H-shaped steel reinforcement components along the upper and lower edges of the side panels has enhanced the rigidity and stability of the box-type substation structure, reducing the deformation caused by external forces.
## 4 Protocol Design
### Switching Authentication Stage  
When a user leaves the service area of the currently connected satellite, the user needs to find the next satellite to continue using the satellite service. If the service requested by the user in initial authentication stage has not expired, the user can obtain the service by providing transaction information. The user address in the transaction within the blockchain is used as the trusted credential to facilitate data authentication, as shown in Figure 6, which can be divided into three steps.  
Figure 5: Initial authentication stage.  
Figure 6: Switching authentication stage.  
The user sends the public key, user address, authentication center address, service type, service duration, timestamp, and signature to the satellite. The calculation method is shown in (5). \[\begin{array}{c}m5=Pk,From,To,Server,Duration,Timestamp,\\ Sign(Pk,From,To,Server,Duration,Timestamp)\end{array}\] (5)
2. After receiving the information, the satellite node verifies the correctness of the public key, address, service type, service duration, timestamp and signature. According to the data on the blockchain, the satellite node checks whether the address exists corresponding to the user's public key and whether the service time has unevpired. If the authentication succeeds, the new session key, service type, service duration, and timestamp encrypted with the user's public key are returned to the user. Otherwise, the request is rejected. The calculation method is shown in (6). \[\begin{array}{c}m6=Enc_{pk}(K,Server,Duration,Timestamp),\\ Sign(Server,Duration,Timestamp)\end{array}\] (6)
3. The user receives the returned information, decrypts it with the private key, obtains the new session key, and uses the new session key to obtain services from the satellite.
# Optimization Procedure for Computing Sampling Time for Induction Machine Parameter Estimation
## 2 Parameter Estimation Problem and System Poles
### Determination of the Linear System Poles
With determined discrete poles \(z_{k},k=1\ldots n\), the procedure from classical Prony method found in [21] is used to find damped exponentials \(R_{ij}e^{j\phi_{ij}}\). In this paper entire complex exponential approximation is used for evaluating the fitness of the exponential series to the measured data, but only the eigenvalues are used in computing the optimal sampling time.
## 3 Results  
### Cyclic Voltammetry  
As a first step in the examination of the interaction of the immobilised _M_tLPMO9H with the electrode surface via direct electron transfer, cyclic voltammetry was performed. _M_tLPMO9H was immobilised on a Co-f-MWCNTs modified GC electrode and the potential was scanned between 0.6 and \(-\)0.1 V at a scan rate of 0.1 V s\({}^{-1}\). The voltammogram is presented in Figure 1 (black curve). Two rather faint peaks appear, one during the cathodic scan appearing at 0.252 V and one during the anodic scan at 0.281 V. Capacitance currents are evident in this voltammogram. The peak separation is estimated at 0.029 V, indicating a quasi-reversible reaction. The cyclic voltammetry of the Co-f-MWCNTs modified GC electrode in the absence of the enzyme is also present in Figure 1 (red curve). In this case, no redox peaks are detected, indicating that the redox peaks can indeed be attributed to the redox reaction of the enzyme and not any interference of the matrix. In order to extract the kinetic constant \(k^{0}\) and the transfer coefficient \(a\) from a cyclic voltammogram, assuming Butler-Volmer kinetics [24], one would take advantage of the correlation of the anodic and cathodic peak shift by varying the scan rate. However, these equations do not take the capacitance current and how it might affect the dependence of the peak shift into account. Moreover, at the higher scan rates which would be necessary in such a case, the capacitance would be more dominant, thus rendering the analysis even more difficult. One option would be to subtract the capacitance current from the total current in order to have only the Faradaic component and base the analysis on that alone. However, uncertainty rises for the precision of this approach in cases of non-ideal capacitance. The same uncertainty arises in case one would attempt to extract other magnitudes from the cyclic voltammogram, such as the surface coverage of the enzyme immobilised on the electrode surface. Nonetheless, an interesting first approach to making an estimation of the value of the \(k^{0}\) would be to solve the equations for the immobilised species numerically in the case of cyclic voltammetry for different standard electron transfer kinetic constants. In order to do so, the problem has to be formulated, by considering an electrochemical reaction having formal electrode potential \(E^{0\prime}\), where both oxidised (E\({}_{\text{Ox}}\)) and reduced (E\({}_{\text{Red}}\)) form of the enzyme are immobilised on the electrode surface  
\[\text{E}_{\text{Ox}}+\text{e}\xrightleftharpoons[k_{\text{a}}]{k_{\text{c}}} \text{E}_{\text{Red}} \tag{1}\]  
where \(k_{\text{c}}\) the cathodic kinetic constant and \(k_{\text{a}}\) the anodic one. The units for both kinetic constants are 1/s.
## 2 Related Work and Theory
### Attention Mechanism  
The attention mechanism dynamically adjusts weights to focus on important features. Channel attention highlights channels with rain streak features, while spatial attention prioritizes regions with rain, preserving background details. Combining both improves deraining by focusing on key features and enhancing image quality.
## 2 Materials and Methods  
### Study Site Description  
The Liuxihe (LXH) reservoir basin is selected as the study area. This headwater catchment is located in Guangdong Province of China within the latitudinal range of 23.67-23.96\({}^{\circ}\)N and the longitudinal range of 114.03-113.75\({}^{\circ}\)E (Figure 1). The climate is dominated by a typical subtropical monsoon, plenty of sunshine, and abundant precipitation. The annual mean temperature is about 20.3 \({}^{\circ}\)C. The mean annual precipitation is nearly 2100 mm and the rainfall is concentrated in March to September. Between these two rainy seasons are the dry season spanning from October to February [45]. The total area is about 456.7 km\({}^{2}\) and the elevation is between 154.3-1135.1 m (Figure 1). As one of the first ten national forest parks in China, this area provides the essential source of drinking water and key ecological service for the Pearl River Delta (PRD) which includes the big cities of Guangzhou, Hong Kong, Macau, Shenzhen, etc. [45]. Broad-leaved and needle-leaved evergreen forests are the main vegetation types and account for more than 80% (365.4 km\({}^{2}\)) of the total area (Figure 1). We conducted 10 independent measurements of the FCO\({}_{2}\) at Chenhe Dong (CHD) nature reserve and the Guoyuan (GY) within the LXH National Forest Park (Figure 1). The information on the environmental characteristics for these two areas is shown in Table 1.  
Figure 1: Location of the Guo Yuan (GY) and Chenhe Dong (CHD) within the LXH national forest park. The purple points in the Google Earth images represent the independent measurement sites of the FCO\({}_{2}\). The grey background represents the topography, which is obtained from the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM, 30 m) ([[http://www.gscloud.cn](http://www.gscloud.cn)]([http://www.gscloud.cn](http://www.gscloud.cn)) (accessed on 21 October 2020)).
# LIMUNet: A Lightweight Neural Network for Human Activity Recognition Using Smartwatches
## 3 Materials and Methods
### Data Preprocessing
#### 3.1.1 Data Splitting  
In this paper, whether it is the PAMAP2 open-source dataset or the LIMU dataset developed in this research, the data are collected by users wearing sensing devices for a certain period while performing a series of repetitive actions. The characteristics of the data contained in these datasets include significant length inconsistency, where different segments of data may correspond to different behavior patterns performed by the user, and these patterns may overlap and intertwine within the data. It is precisely because of this peculiarity of the data that they are not directly suitable as input data for neural network models without appropriate processing and transformation. This is because neural networks typically require input data to have a certain format and structure in order to ensure that the model can effectively learn and extract features. However, due to the aforementioned length variability in the data within the dataset and the presence of complex overlapping behavior patterns, directly using these data for training and predicting with neural networks may lead to poor model performance or even prevent the model from correctly learning and recognizing different behavior patterns. Therefore, in the face of such special datasets, specific data preprocessing and feature extraction methods need to be adopted to make the data more suitable for the requirements of the neural network model, thereby improving the model's learning efficiency and prediction accuracy.  
In order to obtain a data format suitable for input into the neural network, this paper uses a sliding window to segment the data. This method is based on an initial fixed-size window, which slides in one direction with a fixed step size, and at each slide, a segment of the data of the window size is captured.  
In the PAMAP2 dataset, the IMU has a sampling rate of 100 Hz, meaning 100 data points are sampled per second. To ensure a relatively short inference time, this paper sets the sliding window to 171 data points (1.71 s), while the sliding distance is set to 85 data points (0.85 s). The configuration of the sliding window allows this paper to capture important features of behaviors without excessively increasing the computational burden. When all data points within the sliding window correspond to the same type of behavior, the window is considered a data example of that specific behavior and is added to the sample set S. This strategy helps control the size of the dataset without sacrificing the diversity of the data. If the window contains different types of behavior data, this window is discarded to ensure that the model's training is not confused. This helps to improve the accuracy and reliability of the model, as only data of the same type are used to train and test the model.
## 6 Discussion
Larger and more diverse datasets would be beneficial for further validation and generalization of the model. Furthermore, while using SigLIP 2 in a zero-shot manner demonstrates its strong generalization capabilities, fine-tuning the model on the CLEFT dataset might further improve performance. The computational cost is another limitation; feature extraction with SigLIP 2 is computationally more expensive than with BiomedCLIP. While the classification time remains fast, the increased feature extraction time may be a consideration in resource-constrained settings. Finally, this study is limited to a single dataset; evaluation on other CL/P datasets would strengthen the generalizability claims. The improved accuracy achieved with SigLIP 2 has significant practical implications for CL/P classification in clinical settings. More accurate classification can lead to earlier and more precise diagnosis, enabling timely intervention and potentially improving treatment outcomes. The ability to distinguish between different CL/P types with higher confidence can inform more personalized treatment plans, tailoring interventions to the specific needs of each patient. The AI-powered model can assist clinicians in the diagnostic process, potentially reducing their workload and improving efficiency. The model could also be integrated into telemedicine platforms, allowing for remote assessment of CL/P, particularly in areas with limited access to specialized care. While the increased computational cost of feature extraction with SigLIP 2 is a consideration, the classification itself remains fast. With appropriate hardware (e.g., a GPU-equipped workstation), the model can provide near real-time classification, making it suitable for integration into clinical workflows. Further optimization, such as model quantization or the use of efficient inference engines, could further reduce the computational burden. The ease of implementation, leveraging readily available pre-trained models from Hugging Face [21], also contributes to its practical applicability.
## 2 Apparatus
### Design
The spectrometer is mounted horizontally, and the electron and ion sides are separable between the repeller electrode and the ion extractor electrode to facilitate mounting of the spectrometer on the two opposite CF200 flanges of the experimental chamber. The mounting flanges are CF200 to CF160 zero-length reducers, allowing for convenient mounting of the detectors after the spectrometer has been installed. In order to allow for floating voltages on the flight tubes, ceramic spacers are used between these and the supporting grounded mount tubes, which are made of stainless steel. While the ion flight tube is made of stainless steel, the electron flight tube is made of \(\mu\)-metal to provide shielding from external magnetic fields. In addition, a sliding \(\mu\)-metal cylinder with holes for the laser beam and the molecular beam, visible on the far left in Figure 1c, can be moved in to shield also the electrode assembly. The flight tubes have a front brim, which is supported by four rods via ceramic spacers (white parts in Panel (b)) and bushings (yellow parts in Panel (b)). These brims also hold the electrodes, which can be conveniently reached and exchanged from the top of the chamber without unmounting the flight tube assemblies. The electrodes are mounted with ceramic bushings (yellow parts in Panel (b)) on rods made out of Vespel\({}^{\circledR}\), separated by ceramic spacers (white parts in Panel (b)). No electrode or grounded parts are closer than 10 mm apart to allow for large voltage differences without the risk of arcing. The final design offers a large versatility by allowing for voltages of \(\pm 10\) kV to be applied to any of the spectrometer components (electrodes or flight tubes), and the choice of a mesh in the repeller electrode allows for independent tuning of the ion sides without affecting the electron imaging. The physical design makes it easy to mount and dismount the whole or parts of the spectrometer, and while being dimensioned for use in the existing experimental chamber, the assembly can be easily transferred also to chambers with other geometries.
# A Robust and Efficient Method for Effective Facial Keypoint Detection
## 4 Experimental Results
### Main Results
#### 4.2.1 Comparison with Existing Methods  
We first compared the mobile-distill-prune model and ResNet-50 with state-of-the-art face landmark detection methods on a 300W dataset using a regression combined with a heatmap approach. The results are shown in Table 1. To comprehensively evaluate model performance, we report two versions of the mobile-distill-prune model: mobile-distill-prune-0.25X and mobile-distill-prune-1X. Mobile-distill-prune-0.25X compresses the model by setting the pruning rate to 75%, while mobile-distill-prune-1X is the full model without pruning. Both models were trained using only the 300W training data. Additionally, we include the performance of ResNet-50 for comparison. Although the performance of mobile-distill-prune-0.25X is slightly lower than that of mobile-distill-prune-1X, it still outperforms other competitors in many aspects, demonstrating excellent detection capabilities. This comparison shows that mobile-distill-prune-0.25X finds a good balance between model compression and detection accuracy. Despite sig nificant reduction in model size through pruning, the detection accuracy does not drop substantially, making it an ideal compromise for practical applications. ResNet-50 consistently shows the best overall performance among all the models tested, especially in handling challenging facial data. This indicates that ResNet-50 has strong feature extraction capabilities and robustness. However, this also highlights areas for improvement in the mobile-distill-prune model. By further optimizing the balance between pruning and distillation, the performance of the mobile-distill-prune model may be enhanced, bringing it closer to ResNet-50's level of detection accuracy. We further evaluated the performance differences of various methods on the AFLW dataset. The AFLW value results obtained by these methods are reported in Table 2. As can be seen from the table, methods including TSR, CPM, SAN, PFLD, and our mobile-distill-prune series, as well as ResNet-50, significantly outperform other competing methods. Among these outstanding methods, our ResNet-50 achieved the best accuracy (NME 1.84), followed by our mobile-distill-prune-1X (NME 1.86). PFLD 1X ranked third with an NME value of 1.88.
# LSTGINet: Local Attention Spatio-Temporal Graph Inference Network for Age Prediction
## 2 Related Works
Although the attention mechanism can effectively suppress this noise, its modeling ability for semantic information is limited. It cannot fully capture the deep discriminative features in brain-age medical images, so the prediction performance and model efficiency must be improved. [PERSON] et al. (2018) proposed a novel graph-structured brain age prediction model, which combines a multi-hop graph attention (MGA) module with a convolutional neural network to fully capture the local and global features in the brain image and form a dependency relationship between the local and global features. At the same time, the MGA module is embedded after the convolution layer, and a distance scoring strategy is used to convert the acquired local feature map into graph-structured data to predict brain age more accurately. [PERSON] et al. (2018) proposed a dual-graph attention-based disentangled multi-instance learning (DGA-DMIL) framework to improve age estimation. The network MRI data are input to a 2D convolutional neural network to capture the unique aging patterns in MRI and a dual-graph attention aggregator is used to learn backbone features by leveraging the intra-instance and inter-instance relationships. At the same time, a disentanglement branch is introduced to combine age-related features with health-related features. Irrelevant structural representations are separated to improve the interference of redundant information on age prediction. Considering the significant changes in fetal brain morphology, [PERSON] et al. (2018) investigated the ability of graph convolutional networks (GCNs) to predict age in premature infants by considering morphometry and surface mesh topology measured on the cortical surface as sparse graphs. [PERSON] et al. (2018) developed a GNN model for predicting the age of AD patients in rs-fMRI data. They compared the performance of the proposed GNN model with traditional machine learning models. At the same time, the model was also used to identify key brain regions in AD, and the final experimental results show that the GNN model can predict the brain age of normal controls using rs-fMRI data from the ADNI database. Although these graph methods can predict and classify unstructured brain age information, they all use a single feature extraction method in the feature acquisition stage. When the feature representation is insufficient, the prediction performance will seriously decline. In addition, these simple graph prediction methods are prone to produce a large amount of redundancy when nodes aggregate and transmit information. At the same time, they fail to fully capture the hidden correlations between nodes.