esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Model  
Most of the existing forecasting models in wind speed use the whole historic data in prediction, which are subject to be affected by seasonality. This study proposes a wind speed forecasting model for short-term based on the Dirichlet mixture model, dynamic time warping, and deep learning containing three stages. Figure 4 presents the flowchart of the proposed model.  
In the first stage, an LSTM deep learning model is constructed using whole data, which is referred to as the global model in the rest of the paper. The output of the trained LSTM layer is used for features that represent the temporal relationship of the time-series data. In the second stage, a Dirichlet mixture model and dynamic time-warping approaches are applied to features that are extracted at LSTM layer separately to cluster time series data. In the clustering process, the number of clusters is determined by Dirichlet mixture modeling.  
In the third stage, an LSTM deep learning model (local model) is created for each cluster. Particularly, constructing deep learning models using clustered data will reveal local patterns, while global models reveal global patterns. In the prediction stage, features of the test data are extracted from the LSTM layer of the global model. Thereafter, the test data are assigned to the corresponding cluster using the applied clustering model. Finally, the forecast is made by utilizing the assigned cluster's local LSTM model.  
We also construct an ensemble model which exploits the advantages of both the global and local LSTM models. In this model, both local and global LSTM models are ensembled as depicted in Figure 4.
## 4 Experimental Validation and Analysis
### Training and Optimization of RUL Prediction Model  
To effectively avoid overfitting in the MLP model, L2 regularization [32] was introduced to reduce overfitting. In this study, the optimization strategies included LBFGS, Sgd, and Adam [33], which were combined with three different activation functions. The training error evaluation indicators of the model were compared using \(R^{2}\) and the mean absolute error (MAE). Table 3 shows the comparative results of the prediction models.  
Based on the results presented in Table 3, we found that the optimal choice of optimization strategy and activation function was LBFGS and ReLU, respectively, as these corresponded to coefficient of determination values closer to 1 and minimized the average absolute error. To identify the optimal structure of the MLP model, we systematically varied the number of hidden layers between 2 and 8 while setting the number of hidden nodes to 15, 25, 35, and 45, with LBFGS and ReLU [33] used as the weight optimizer and activation function, respectively. The experimental results are presented in Table 4.  
Figure 8: The smoothing effect of different smoothing coefficients on sensor parameters.  
Figure 10 shows the validation set RMSE [34] of the MLP training model under different regularization parameters and maximum iteration numbers. From the figure, it can be observed that the RMSE is minimized when the maximum number of iterations of the MLP algorithm is set to 150 and alpha is set to 0.0001, and the model has the best training effect.  
The Bayesian parameter updating algorithm was used to optimize the parameters in MLP, and the results of hyperparameter selection are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Performance** & **Number of** & **Number of** & **Number of** & **Number of** & **Number of** \\
**Indicator** & **Layers** & **Nodes (15)** & **Nodes (25)** & **Nodes (35)** & **Nodes (45)** \\ \hline RMSE & 2 & 17.88 & 18.99 & 19.19 & 18.39 \\ Score & 2 & 571.54 & 762.14 & 752.13 & 642.38 \\ RMSE & 4 & 19.16 & 17.70 & 17.29 & 19.04 \\ Score & 4 & 784.26 & 577 & 510.80 & 824.43 \\ RMSE & 6 & 19.19 & 17.85 & 19.44 & 18.41 \\ Score & 6 & 724.27 & 554.06 & 929.44 & 629.32 \\ RMSE & 8 & 17.92 & 18.16 & 18.61 & 19.54 \\ Score & 8 & 611.64 & 626.56 & 720.00 & 953.93 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation of MLP hidden layer performance.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Identity** & **Identity** & **Logistic** & **Logistic** & **Tanh** & **Tanh** & **ReLU** & **ReLU** \\ \hline – & \(R^{2}\) & MAE & \(R^{2}\) & MAE & \(R^{2}\) & MAE & \(R^{2}\) & MAE \\ LBFGS & 0.7 & 17.93 & 0.79 & 13.94 & 0.8 & 13.62 & 0.81 & 12.98 \\ Sgd & 0.7 & 17.84 & 0.79 & 13.57 & 0.8 & 13.65 & 0.80 & 13.94 \\ Adam & 0.7 & 17.87 & 0.76 & 14.42 & 0.79 & 13.52 & 0.78 & 14.23 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation of MLP optimization strategies and activation function performance.  
Figure 9: The trend of normalized monitoring parameters.
# Applicability of Semi-Quantitative Evaluation of the Intercondylar Notch
## 3 Results  
A total of 330 patients were evaluated (for demographic information, see Table 1). The NWp measured at an average of 18.45 mm (11.60-33.50 mm) and NWj at an average of 17.21 mm (8.30-28.30 mm). Overall, the NWj was significantly smaller than NWp (\(p<\) 0.0001), and a significant difference between male and female NW was found in both the NWp (19.7 \(\pm\) 2.94 mm vs. 17.17 \(\pm\) 2.10 mm) and the NWj (18.75 \(\pm\) 3.05 mm vs. 15.63 \(\pm\) 2.42 mm) (\(p<\) 0.0001). No significant difference was found between the left and right knees at either measurement level (\(p\) = 0.121 and 0.439).  
The computed evaluation of the shape of the IN showed 43 (13.0%) with an A-shaped notch, 100 (30.3%) with an inverse U-shaped notch and 187 (56.7%) with an \(\Omega\)-shaped notch. Significant differences were found in the distributions across sex (\(p\) = 0.01) and age groups (\(p<\) 0.0001; see Figures 2 and 3).  
Figure 1: Demonstration of the application of a visual guide (green box) in the assisted semi-quantitative evaluation of the IN. (**a**) A-shaped notch of a 6-year-old patient. Note the difference between NWp and NWj (NWp \(<\) NWj). (**b**) Inverse U-shaped notch of a 30-year-old patient. Note the similar width of NWp and NWj (NWp \(=\) NWj). (**c**) \(\Omega\)-shaped notch of a 68-year-old patient. Note the difference between NWp and NWj (NWp \(>\) NWj).  
Figure 3: Distribution of the three shapes with a significant difference between the different age groups (\(p<0.0001\)).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Age Group** **(Years)**} & \multicolumn{2}{c}{**Side**} & \multirow{2}{*}{**Total**} & \multicolumn{2}{c}{**Notch Width (mm)**} & \multicolumn{2}{c}{**Shape**} \\ \cline{2-9}  & & & & & **NWp** & **NWj** & **A-Shape** & **
\begin{tabular}{c} **Inverse** \\ **U-Shape** \\ \end{tabular} & \multirow{2}{*}{**\(\Omega\)-Shape**} \\ \hline \(<\)11 & 19 (52.8\%) & 17 (47.2\%) & 36 & 16.13 \(\pm\) 2.68 & 17.70 \(\pm\) 3.19 & 24 (66.7\%) & 8 (22.2\%) & 4 (11.1\%) \\ \hline
11–20 & 24 (42.9\%) & 32 (57.1\%) & 56 & 18.01 \(\pm\) 2.55 & 17.17 \(\pm\) 3.16 & 8 (14.3\%) & 22 (41.1\%) & 25 (44.6\%) \\ \hline
21–30 & 29 (48.3\%) & 31 (51.7\%) & 60 & 18.63 \(\pm\) 2.69 & 17.43 \(\pm\) 3.27 & 6 (10.0\%) & 19 (31.7\%) & 35 (58.3\%) \\ \hline
31–45 & 31 (51.7\%) & 29 (48.3\%) & 60 & 18.96 \(\pm\) 3.10 & 17.15 \(\pm\) 2.98 & 3 (5.0\%) & 20 (33.3\%) & 37 (61.7\%) \\ \hline
46–60 & 33 (56.9\%) & 25 (43.1\%) & 58 & 19.38 \(\pm\) 2.90 & 17.36 \(\pm\) 3.37 & 1 (1.7\%) & 16 (27.6\%) & 41 (70.7\%) \\ \hline \(>\)60 & 30 (50.0\%) & 30 (50.0\%) & 60 & 18.60 \(\pm\) 2.36 & 16.61 \(\pm\) 3.08 & 1 (1.7\%) & 14 (23.3\%) & 45 (75.0\%) \\ \hline Total & 166 & 164 & 330 & 18.45 \(\pm\) 2.85 & 17.21 \(\pm\) 3.17 & 43 (13.0\%) & 100 (30.3\%) & 187 (56.7\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Demographic information on study population.  
Figure 2: Distribution of the three shapes with a significant sex difference between male and female patients (\(p=0.01\)).
###### Abstract  
This review aims to gain insight into the major causes of hair graying (canities) and how plant-derived extracts and phytochemicals could alleviate this symptom. Research articles on human hair graying were searched and selected using the PubMed, Web of Science, and Google Scholar databases. We first examined the intrinsic and extrinsic factors associated with hair graying, such as the reduced capacity of melanin synthesis and transfer, exhaustion of melanocyte stem cells (MSCs) and melanocytes, genetics and epigenetics, race, gender, family history, aging, oxidative stress, stress hormones, systematic disorders, nutrition, smoking, alcohol consumption, lifestyle, medications, and environmental factors. We also examined various plants and phytochemicals that have shown a potential to interfere with the onset or progression of human hair graying at different levels from in vitro studies to clinical studies: the extract of _Polygonum multiform_ and its major components, 2,3,5,4-tetrahydrooxystilbene-2-O-\(\beta\)-D-glucose and condition; the extract of _Eriadicon angustiifolium_ and its major flavonoid compounds, hydroxygenkvanian, sterubin, and luteolin; the extracts of Adzuku beans (_Vigna angularis_), Fuzhuan brick tea (_Camelia sinensis_), and _Gynostemma pentaphyllum_; bixin, a carotenoid compound found in _Bixa orellan_; and rhythmophylline, an alkaloid compound found in certain _Uncaria_ species. Experimental evidence supports the notion that certain plant extracts and phytochemicals could alleviate hair graying by enhancing MSC maintenance or melanocyte function, reducing oxidative stress due to physiological and environmental influences, and managing the secretion and action of stress hormones to an appropriate level. It is suggested that hair graying may be reversible through the following tactical approaches: selective targeting of the p38 mitogen-activated protein kinase (MAPK)-microphthalmia-associated transcription factor (MITF) axis, nuclear factor erythroid 2-related factor 2 (NRF2), or the norepinephrine-\(\beta\)2 adrenergic receptor (\(\beta\)2 AR)-protein kinase A (PKA) signaling pathway.  
hair graying; gray hair; canities; _Polygonum multiform_; _Eriadicon angustiifolium_; melanocyte stem cells (MSCs); microphthalmia-associated transcription factor (MITF); p38 mitogen-activated protein kinase (MAPK); oxidative stress; nuclear factor erythroid 2-related factor 2 (NRF2); norepinephrine; \(\beta\)2 adrenergic receptor (\(\beta\)2 AR)
## 3 Materials and Methods
### Machine Learning Classifiers
#### 3.6.2 K-Nearest Neighbors  
The K-Nearest Neighbors algorithm determines the class of an instance by comparing it to a nearby instance from the training dataset [12]. The distance measure is utilized to calculate the neighbors, which can be chosen from a variety of possibilities. Euclidean distance is the most widely used distance metric for numeric variables [51]. Giving each neighbor a weight of 1/d, where d is the distance between them, is a popular weighing approach [54]. The parameter to be optimized for the KNN classifier are the number of nearest neighbors and the distance weighting.
# Evaluation Model of Holographic Communication Experience in the 6G Era Based on Light Field Display
## 3 Method
### Model Structure  
Obtaining a large number of image data with different distortion types and levels in different scenes, due to the subjective disturbance and high cost of manual scoring, we decided to use a semi-supervised learning strategy to complete the deep learning model rather than fully supervised learning. At the same time, since there is currently no mature pre-trained model that fully matches our problem, we did not consider using transfer learning.  
In the early stage, we tried the current mainstream semi-supervised image-classification algorithms, but they all had problems of not being able to adapt perfectly or having poor results. Therefore, based on the existing research, we propose improvement ideas from the two perspectives of feature engineering and training strategies.  
In terms of feature engineering, for a light field image, CNN is used to obtain its content features, which can avoid the subjective impact of the image content when users score it. At the same time, the objective index extraction module is used to extract distortion features that are a combination of image attributes, two-dimensional distortion features, and light-field-distortion features, and the two are spliced together as a new feature access classifier. After verification, the model achieved better classification results, as shown in Figure 5.  
In the objective indicator-extraction module, we captured the following features of the image: two-dimensional features and light field features. The two-dimensional features include two evaluation results, peak signal-to-noise ratio and structural similarity, while the light field features refer to the practice of [PERSON] [35]. The four features they proposed were reproduced: air domain features, angle domain features, space-angle coupling relationship features, and projection domain features. The above six features are spliced into an objective indicator feature vector.  
In terms of the training strategy, this article proposes a way to assign constraints to pseudo-labels. The probability given by the softmax classification is higher than a certain threshold and is assigned a pseudo-label. As the training process proceeds, the threshold can be appropriately lowered to increase the convergence speed and avoid overfitting, as shown in Figure 6.  
Figure 5: Feature engineering improvement.  
The threshold filtering function is determined by the proportion of the marked data out of the overall data. When the marking data account for 10% of the overall data, it remains at 90%. Subsequently, as the proportion increases, the threshold decreases accordingly until it reaches 60% and no longer decreases. The change in the threshold is shown in the following formula, where t represents the ratio of labeled data to the overall data set.  
\[\alpha(t)=\begin{cases}90\%,&t<10\%\\ 1-t,&10\%<t<40\%\\ 60\%,&others\end{cases} \tag{1}\]
## 3 SAR Datasets and Methodology  
### SAR Dataset  
The radar images analyzed were acquired during the Map Italy Project [37], developed by the Italian Spatial Agency (ASI) and the Department of Civil Protection. The project aims to create an interferometric mapping of the entire Italian territory. It uses the StripMap HIMAGE mode (HH polarization) of the COSMO-SkyMed constellation in both Ascending and Descending modes, and it is characterized by a high image resolution (3 \(\times\) 3 m). HH polarization further improves the quality of the collected data, providing excellent detailing of surface deformations. This satellite system is specifically designed for Earth observation and is equipped with cutting-edge sensors that operate in the X-band frequency.  
Using both geometries provides a comprehensive view of the kinematics of the gravitational deformations in the study area [38]. Preliminarily, available archive image tracks were identified (Figure 3) in the area of interest, in ascending and descending geometry, characterized by the same acquisition incidence angle, a necessary condition for proper processing. Analyzing these datasets allows researchers to account for potential biases or discrepancies in the image acquisition geometry. This approach ensures that the study's results are more reliable and robust, as potential errors or inconsistencies can be identified and addressed by comparing the two datasets. Additionally, using two separate datasets, combined with an in situ survey, ensures that the study provides a comprehensive and accurate analysis of surface deformations in the area under investigation [39; 40].  
In detail, the image datasets processed in this study are:  
* Ninety-seven images were acquired in ascending geometry from 6 January 2017 to 12 November 2022.
* Eighty-one images were acquired in descending geometry from 28 January 2017 to 29 October 2022.  
Specifically, the images recorded in ascending orbit have an incidence angle of 29.2\({}^{\circ}\), while those acquired in descending orbit have an inclination of 31.9\({}^{\circ}\) relative to the vertical.  
Figure 3: Tracks of SAR images used in the area of interest (indicated in the black square) available in the ASI archive: ascending mode (**left**), descending mode (**right**).
## 3 Materials and Methods  
### Process Flow  
The advent of AIS technology has enabled the collection of extensive vessel data, providing opportunities for analyzing and visualizing maritime traffic patterns. Additionally, active research has been conducted on the processing, analysis, and visualization of large datasets. AIS is an effective method for collecting maritime data. AIS automatically transmits and receives various ship information, such as date and time, ship name, call sign, location, speed, and course, in real time between the ship and shore-based stations [25]. This technology has revolutionized ship movement data collection in real time and enabled the tracking and management of ship routes. Considerable data can be obtained from AIS, rendering data preprocessing a crucial step in determining analysis accuracy. The information obtained through AIS could be incomplete because of equipment malfunctions, signal interference, or intentional deactivation of the equipment. In particular, missing dynamic data of the vessel can negatively affect the quality of the analysis results. To address this problem, this study used linear interpolation to fill in the missing data and thereby improve the quality of the data and enhance analysis accuracy [26].  
Continuous accumulation of data in a database and systematization in a manner that allows users to filter only the information they need are necessary to process such a vast amount of data in real time. In this study, AIS data were used to integrate and visualize three key elements, namely maritime traffic density, traffic direction, and traffic flow, by coding the logic (Figure 1) in a MATLAB program, which was performed in an offline environment.
## 2 SWS Design and Discussion
### S-Parameters
In the frequency range of 87.5-95 GHz, the reflection loss \(S_{11}\) does not exceed \(-\)15 dB (black line), and the transmission loss \(S_{21}\) varies from \(-\)5.5 dB to \(-\)8 dB (red line). Due to the influence of high-frequency loss, the value of \(S_{21}\) decreases significantly with frequency (the value of \(S_{21}\) is negative while the absolute value of \(S_{21}\) increases). During the actual processing, the meander line SWS with the dielectric support or substrate needs to consider the loss of the intermediate seed layer connecting the dielectric layer and the metal layer, which causes a serious negative impact on wave transmission [19; 20]. Clearly, the transmission loss of SV-SL SWS with the seed layer removed is much lower. Figure 6: Electric field distribution of the transmission model of the proposed SWS. Figure 7: (**a**) Transmission characteristics of the 50-period SWS with input/output couplers. (**b**) \(S_{11}\) at the interaction of the wave with an electron beam in the manuscript. The \(S_{11}\) at the interaction of the wave with the electron beam is calculated through the input and reflected power at different frequencies, and the simulation results are shown in Figure 7b, which are less than \(-15\) dB within the frequency 88-95 GHz and are in good agreement with the results in Figure 7a.
###### Abstract  
Single-image super-resolution (SISR) plays a critical role in enhancing visual quality for real-world applications, including industrial inspection and embedded vision systems. While deep learning-based approaches have made significant progress in SR, existing lightweight SR models often fail to accurately reconstruct high-frequency textures, especially under complex degradation scenarios, resulting in blurry edges and structural artifacts. To address this challenge, we propose a Dense Residual Fused Attention Network (DRFAN), a novel lightweight hybrid architecture designed to enhance high-frequency texture recovery in challenging degradation conditions. Moreover, by coupling convolutional layers and attention mechanisms through gated interaction modules, the DRFAN enhances local details and global dependencies with linear computational complexity, enabling the efficient utilization of multi-level spatial information while effectively alleviating the loss of high-frequency texture details. To evaluate its effectiveness, we conducted \(\times\)4 super-resolution experiments on five public benchmarks. The DRFAN achieves the best performance among all compared lightweight models. Visual comparisons show that the DRFAN restores more accurate geometric structures, with up to +1.2 dB/+0.0281 SSIM gain over SwinIR-S on Urban100 samples. Additionally, on a domain-specific rice grain dataset, the DRFAN outperforms SwinIR-S by +0.19 dB in PSNR and +0.0015 in SSIM, restoring clearer textures and grain boundaries essential for industrial quality inspection. The proposed method provides a compelling balance between model complexity and image reconstruction fidelity, making it well-suited for deployment in resource-constrained visual systems and industrial applications.  
image super-resolution; hybrid attention mechanism; dense residual structure; industrial visual inspection 2025 IEEEexample:BSTcontrol  
1
# An Asymmetric Encryption-Based Key Distribution Method for Wireless Sensor Networks
## 3 The Key Management Scheme for Cluster-Based WSNs
### Network Model and Assumptions  
At present, wireless sensor networks commonly used in the industry mainly include two kinds of architectures, namely hierarchical structure and flat structure. A hierarchical architecture is usually used for large-scale WSNs due to its good scalability. A clustered hierarchical network is composed of base stations (BS), a large number of sensor nodes, and a small number of cluster heads (CH). BS is not limited by resources. The base station is responsible for managing all nodes of the network and receiving the service data collected via the sensor nodes. It is assumed that the cluster head has a higher configuration than the sensors, including battery capacity, memory size, communication, and computing capacity. Like the gateway, the cluster head assists in data transmission between the sensors and the base station. In the hierarchical architecture, sensors are divided into non-overlapping clusters, which collect data from the surrounding environment and send the original data to the base station. In this article, we focus on hierarchical architecture of WSNs.  
In our scheme, asymmetric encryption is used to realize the authentication between the base station, the CHs, and the sensor nodes. The public key is pre-loaded into each sensor before network deployment. With the public-key system, the proposed scheme not only realizes end-to-end identity authentication, but also provides security for subsequent key distribution processes.  
In our hierarchical WSN model, we make the following few assumptions:  
* The base station has more energy power for calculations and communications than sensors.
* The base station owns a pair of keys (a public key and a private key).
* The network is divided into several cluster regions. In each cluster, there is only one cluster head node, and its location remains unchanged. Each cluster head can be recognized as the gateway of its cluster.  
In terms of security and ease of management, each cluster generates different session keys for dialogs between sensor nodes and cluster heads.
* Both asymmetric and symmetric cryptography are used for each sensor. The former method provides mutual authentication and key distribution, and the latter method preserves the confidentiality of traffic transmitted.
* As an optional technology in our scheme, MAC (message authentication code) provides data integrity.
* The public key is pre-loaded into each sensor and the cluster head via an off-line dealer.
* Each sensor can store at least one public key and several session keys in its memory.
* Each sensor can randomly move among different clusters at a low speed.