esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Training and Evaluation  
To achieve consistency of experiments, we compared all classification performances using a 5-fold cross-validation method, which divides the entire dataset into 80% of the training set and 20% of the evaluation set. Because our dataset consists of an unbalanced number of benign, malignant, and normal cases, the random selection of data for training and evaluation sets from an entire dataset cannot guarantee the fair training of each class. In our simulation, the same distribution ratio of each class was maintained to select the training and evaluation sets, and the classification performances were compared. Since the main purpose of this work is the benefits of multiple time-domain feature maps in CNN-based deep learning, no artificial manipulation was applied to compensate the dataset imbalance and classification performances were compared under the same conditions for the traditional CNN and the proposed FC-CNN methods.  
As an augmentation method in the training phase, random numbers from a normal distribution were added to each batch with the same size as the input data, and a few blends of transformations were also applied. The learning process was conducted over 30 epochs when the highest accuracy of validation dataset occurs, and the batch size was set to 8. The CrossEntropyLoss function, which combines the LogSoftmax function and the negative log-likelihood loss function, was used as a loss function, and the Adam optimizer [34] with a learning rate of 1e-4 was also used. Since the proposed feature-channel CNN architecture utilizes three feature maps independently for training each channel backbone network, all weights of the individual CNN model were determined when the highest accuracy was achieved in the training phase. For the experimental environments, PyTorch (v.1.8.0) backend in Python 3.6 was used to implement the networks, and a personal computer outfitted with the Intel Xeon Gold 5120 CPU and NVIDIA Tesla-V100-PCIE graphics processing unit (GPU) was used to conduct all the experiments.  
The performance metrics used in this study were accuracy, recall, precision, F1 score, and area under the ROC curve (AUC) [35]. In the following equations, TP, TN, FP, FN, and f(x) represent the number of true positives, true negatives, false positives, false negatives, and the receiver operating characteristic (ROC) curve, respectively.  
\[\begin{array}{c} Accuracy(\%)\ =\ \frac{TP\ +\ TN}{TP\ +\ TN\ +\ FN\ +\ FP}\ \times\ 100\\ \
## 3 Simulation Analysis
### Solid \(\beta\) with Variable \(r_{0}\)  
In this part, the PDF of the CE is discussed at the condition of variable \(r_{0}\) when \(D\) and \(\beta\) stay the same. The parameters are all listed in Table 3.  
The atmospheric coherence length \(r_{0}\), also known as [PERSON]'s length, reflects the intensity of the atmospheric turbulence. With the growth of the turbulence, \(r_{0}\) gets smaller. Figure 3a-d show us the differences of the PDF of the CE when \(r_{0}\) varies from 0.1 m to 1 m. When the \(r_{0}\) equals to 0.1 m, shown by Figure 3a, the atmosp  
Figure 2: Hierarchical map of simulation progress.  
The PDF becomes nearly negatively exponentially distributed. This means that the mean value of the CE is quite small, so that the communication link is barely maintained. As the turbulence gets weaker, \(r_{0}\) becomes larger, from 0.2 m to 1 m. As presented in Figure 3b-d, a peak comes out at each curve of the PDF, and the distribution approximates the Rayleigh distribution. The average CE is larger, in which condition the communication is stabilized. Figure 4 gives a clear trend of the PDF of the CE.  
Figure 4: PDF of coupling efficiency as \(r_{0}\) varies from 0.1 m to 0.9 m.  
Figure 3: PDF of coupling efficiency with solid \(\beta\) and variable \(r_{0}\). (a) \(r_{0}=0.1\) m; (b) \(r_{0}=0.5\) m; (c) \(r_{0}=0.7\) m; (d) \(r_{0}=1\) m.  
\begin{table}
\begin{tabular}{c c} \hline
**Items** & **Value** \\ \hline Wavelength (\(\lambda\)) & 1550 nm \\ Pupil aperture (\(D\)) & 1 m \\ Focal length of coupling system (\(F\)) & 4.17 m \\ MFD of single-mode fiber (\(d\)) & 5.5 \(\upmu\)m \\ Atmospheric coherence length (\(r_{0}\)) & 0.1–1 m \\ \(\beta\) & 11/3 \\ \hline \end{tabular}
\end{table}
Table 3: Analysis parameters at condition of solid \(\beta\) with variable \(r_{0}\).  
The simulation shows that the modified Rician distribution describes the PDF of the CE well under the circumstance of both weak and strong turbulence, as shown by Figures 3 and 4. The model is extendable to the free-space optical communication system for further study.
# A Hierarchical Fusion SAR Image Change-Detection Method Based on HF-CRF Model
## 4 Conclusions  
To address the problem of semantic collapse caused by disparity computation, we proposed a hierarchical fusion SAR image change-detection model based on HF-CRF. This model adopts a hierarchical structure to compensate for the lost semantics, uses a D-DRUNet neural network to realize the fused semantic segmentation of multimodal disparity maps, constructs the first-order potential function of CRF, and uses local boundary entropy to realize the fused second-order potential function, which accurately reflects the dynamic semantic changes of multi-modal images. The CRF model is driven to converge with the change boundary accurately by minimizing the energy function. To verify the effectiveness of the method, we conduct experiments on the publicly available SAR dataset. The experimental results show that the HF-CRF model proposed in this paper achieves superior results on the test dataset compared with both traditional methods, as well as deep learning methods. In future work, we will combine self-supervised learning and the Siamese network structure to directly locate remote sensing change-detection regions in the spatiotemporal domain, to address the semantic loss caused by discrepant operations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Params** & **FLOPs** \\ \hline DD-CNN & 11.2 M & 73,279.01 M \\ TransUNet & 93.19 M & 64,357.53 M \\ Our method & 63.27 M & 85,928.02 M \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of module parameters and computation amount.  
**Author Contributions:** Conceptualization, J.Z. and Y.L.; methodology, J.Z. and Y.L.; software, Y.L.; validation, J.Z., Y.L., B.W. and C.C.; formal analysis, J.Z.; investigation, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, B.W.; supervision, C.C.; funding acquisition, J.Z. and C.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Key Research and Development Program of Shaanxi (No. 2023-ZDLGY-54, 2023-GHZD-44, 2021 ZDLGY02-09, 2019 ZDLGY13-07, 2019 ZDDLGY13-04), the National Natural Science Foundation of China (62072360, 61902292, 61971331, 62001357, 62072359, 62172438), the Natural Science Foundation of Guangdong Province of China (2022A1515010988), the Xi'an Science and Technology Plan (20 RGZN0005) and the Key Project on Artificial Intelligence of Xi'an Science and Technology Plan (2022 JH-RGZN-0003, 2022 JH-RGZN-0103, 2022 JH-CLCJ-0053).  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We are grateful to those who participated in the data processing and manuscript revisions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Conclusions  
Out of the seven FMS tasks, only one asymmetrical (trunk rotary stability) and one symmetrical (deep squat) task were moderately related to the newly developed HLLMS tool contributing a moderate relationship between the FMS total score and the HLLMS total score. Other FMS tasks were weakly related or unrelated with the HLLMS. This suggests that the two screening tools assess different aspects of movement quality and performance in healthy youth football players. The purpose of the HLLMS is to use the movement quality assessment outcome to prescribe targeted motor control exercises. Practically, it could be used in a clinical setting and on the field for primary prevention to protect healthy people, secondary prevention to prevent re-injury or overuse, and tertiary prevention to guide management of OA and reduce its impact on function and joint longevity, delaying or preventing joint surgery and improving the quality of life.  
Several potential applications of the HLLMS should now be investigated in various cohorts of different ages, physical activity, sporting groups, and genders to examine the utility of the screen for assessing movement quality and informing exercise interventions to improve movement control. It is also worth considering whether the HLLMS can be automated to avoid bias without negative effects on the nature of this screening tool.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11199298/s1](https://www.mdpi.com/article/10.3390/app11199298/s1)]([https://www.mdpi.com/article/10.3390/app11199298/s1](https://www.mdpi.com/article/10.3390/app11199298/s1)), Figure S1: Asymmetrical tasks of the Hip and Lower Limb Movement Score in relation to asymmetrical tasks of the Functional Movement Screen, Figure S2: Symmetrical tasks of the Hip and Lower Limb Movement Score in relation to symmetrical tasks of the Functional Movement Screen.  
Conceptualization, P.L., M.S. and N.B.; methodology, P.L. and N.B.; validation, P.E.M.; formal analysis, P.L. and M.S.; investigation, P.L. and D.S.; resources, D.S.; data curation, P.L. and D.S.; writing--original draft preparation, P.L.; writing--review and editing, P.E.M., D.S., N.B. and M.S.; visualization, D.S. and P.L.; supervision, P.L. and M.S.; project administration, P.L. and D.S.; funding acquisition, D.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was designed in accordance with the Declaration of Helsinki and approved by the local medical ethics committee (Ethics Approval number: 4/2017).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author.  
The authors thank the participants for their time, and the coaches and staff at their club for helping with the recruitment of players and logistics in carrying out the project.  
There are no conflict of interest in the present study and the study was not funded by any external bodies.
# Methane Detection Based on Improved Chicken Algorithm Optimization Support Vector Machine
###### Abstract  
Methane, known as a flammable and explosion hazard gas, is the main component of marsh gas, firedamp, and rock gas. Therefore, it is important to be able to detect methane concentration safely and effectively. At present, many models have been proposed to enhance the performance of methane predictions. However, the traditional models displayed inevitable shortcomings in parameter optimization in our experiment, which resulted in their having poor prediction performance. Accordingly, the improved chicken swarm algorithm optimized support vector machine (ICSO-SVM) was proposed to predict the concentration of methane precisely. The traditional chicken swarm optimization algorithm (CSO) easily falls into a local optimum due to its characteristics, so the ICSO algorithm was developed. The formula for position updating of the chicks of the ICSO is not only about the rooster of the same subgroup, but also about the roosters of other subgroups. Therefore, the ICSO algorithm more easily avoids falling into the local extremum. In this paper, the following work has been done. The sample data were obtained by using the methane detection system designed by us; In order to verify the validity of the ICSO algorithm, the ICSO, CSO, genetic algorithm (GA), and particle swarm optimization algorithm (PSO) algorithms were tested, and the four models were applied for methane concentration prediction. The results showed that he ICSO algorithm had the best convergence effect, relative error percentage, and average mean squared error, when the four models were applied to predict methane concentration. The results showed that the average mean squared error values of ICSO-SVM model were smaller than other three models, and that the ICSO-SVM model has better stability, and the average recovery rate of the ICSO-SVM is much closer to 100%. Therefore, the ICSO-SVM model can efficiently predict methane concentration.  
Keywords:methane detection; support vector machine; chicken swarm optimization; algorithm; concentration prediction +
Footnote †: journal: Applied Sciences
# A Display-Adaptive Pipeline for Dynamic Range Expansion of Standard Dynamic Range Video Content
## 3 Materials and Methods: A Pipeline for Dynamic Range Expansion of Video Sequences
### Artifact Suppression  
LDR content inherently contains noise and artifacts, a characteristic often subtly masked when displayed on devices with limited brightness and dynamic range [36; 37]. However, it is important to note that the presence of such artifacts, especially those originating from compression, is by design. Compression algorithms are optimized for efficiency, trading off imperceptible artifacts at lower dynamic ranges for reduced data sizes. Before expanding the dynamic range, it is crucial to filter the LDR input to reduce the visibility of these artifacts and noise, regardless of whether they stem from compression techniques or  
Figure 2: Flowchart of the proposed pipeline for inverse tone mapping. The dynamic range of an 8-bit LDR frame is expanded into a 16-bit HDR frame with a peak luminance of 6000 cd/m\({}^{2}\).  
the camera sensor. This pre-emptive measure prevents the exacerbation of artifacts during the dynamic range enhancement process. To achieve this, we employ a time-efficient implementation of the guided filter, specifically the \"fast guided-filter\" [38]. The guided filter, renowned for its edge-aware image filtering capabilities, has demonstrated good performance across various computer vision applications, including image denoising [39; 40]. It is able to remove/reduce noise and artifacts while preserving fine details and textures within the image [41; 42]. The fast-guided filter, an accelerated version of the original, maintains nearly identical performance while significantly enhancing processing speed, outpacing its predecessor by over ten times.  
We utilize the fast-guided filter's edge-preserving smoothing function to quickly and effectively remove artifacts from an input LDR frame. For this, the filter uses the same LDR input frame as the guidance image. The fast-guided filter has three parameters for artifact denoising control and processing speed. These parameters are as follows: the local window radius (\(r\)), the regularization parameter (\(\epsilon\)) to manage the degree of smoothness, and the subsampling ratio (\(s\)), which allows us to speed up the filtering process. The careful adjustment of the first two parameters enables precise management of the filter's intensity, tailoring the final appearance of the processed frame to our desired specifications.  
An outcome of this artifact denoising step is depicted in Figure 3, showcasing the results obtained from an input frame measuring \(1920\times 1080\) pixels using the following parameters: \(r=32\), \(\epsilon=0.1^{2}\), and \(s=4\). This figure visually demonstrates the performance of the fast-guided filter, effectively eliminating noticeable artifacts while preserving fine details and textures within the image.
## 3 Numerical Set-Up  
Two different 2D periodic structures of finite sizes are considered in this study, as shown in Figure 1. Both structures are composed of \(N\times N\) rigid scatterers arranged in a square lattice with a lattice constant \(a=0.085\) m, embedded in air, where \(N=21\). The SC shown in Figure 1a consists of cylindrical rigid scatterers with radius \(r=0.003\) m. The second periodic system is a metamaterial consisting of square scatterers with QWRs, as shown in Figure 1b. The geometric parameters of the resonator are its external side length, \(L=0.05\) m, and internal dimensions, \(d_{qw}=0.035\) m and \(l_{qw}=0.04\) m. See Refs. [24; 25] for specific details on the resonators. The host medium in both cases is air, with density and speed of sound \(\rho=1.213\) kg/m\({}^{3}\) and \(c=343\) m/s, respectively.  
Numerical experiments are carried out using FEM. A scattering problem in the frequency domain is solved for the two structures described previously. The excitation is an incident plane wave traveling along the positive \(x\)-axis. Perfectly matched layers are considered surrounding the numerical domain to approximate the Sommerfeld radiation condition. Following the spatial sampling criteria for periodic structures introduced in Section 2.2, the acoustic pressure is recorded at multiple positions separated by the lattice constant, \(\Delta x=a\), giving rise to a total of \(N\times N\) measurement points. Please note that the structures presented in Figure 1 are used to calculate the complex dispersion relation along the \(\Gamma\)X direction. To retrieve the dispersion relation along the \(\Gamma\)M direction both structures are rotated by 45 degrees while the incident plane wave is unchanged, i.e., remains traveling along the positive \(x\)-axis. Note also that the spatial sampling in this case is modified accordingly, \(\Delta x=\sqrt{2}a\).  
Figure 2: (**a**) Example of real and imaginary parts of the spatial pressure signal along the multilayer structure at the normalized frequency \(\omega/\Omega_{0}=0.25\). (**b**) Periodic multilayer structure composed of \(N_{uc}=50\) periodic elements, combining two artificial materials with different acoustic properties. (**c–f**) Double FT of the pressure field and dispersion relation of the multilayer structure considering different number of periodic elements and different values of the spatial sampling \(\Delta x\).
# Kalman Filter-Based Differential Privacy Federated Learning Method
## 2 Related Work  
Federated learning has been gradually applied to protect the data privacy of all parties in scenarios where multiple parties cooperate in training and need to exchange data, such as traffic monitoring [15], edge computing [14], and IoT [16], to ensure that the original data is not leaked. However, privacy analysis attacks mean that federated learning also faces the risk of privacy leakage. It is essential to solve the problems of federated learning, especially gradient privacy protection, where data privacy is a particular concern. There are currently two privacy-preserving methods in federated learning: (1) differential privacy and (2) homomorphic encryption.  
[PERSON] et al. [10] proposed a differential privacy deep learning method that combined machine learning or deep learning methods with differential privacy and proposed a new computational theory to obtain lower privacy consumption with smaller constraints, providing a basis for introducing differential privacy in federated learning. [PERSON] et al. [11] used a block coordinate descent to optimize the objective function and differential privacy, which could protect gradient privacy but performed poorly in non-convex models.  
[PERSON] et al. [12] used differential privacy federated learning for vehicle nets to train intelligent traffic management systems to protect vehicle location privacy but did not evaluate the accuracy under the influence of noise. Homomorphic encryption is a cryptographic method based on the computational complexity theory of mathematical puzzles, where the model parameters computed by each training node are aggregated and sent to the specified node after homomorphic encryption, and the model parameters obtained after decryption are the same as those obtained by direct aggregation without encryption, using this property to protect gradient privacy. [PERSON] et al. [13] used the chain aggregation method to protect gradient privacy, which improved the accuracy, but the chain aggregation approach introduced a large communication overhead. [PERSON] et al. [8] used the Pilliar algorithm combined with federated learning to protect gradient privacy, but the Pilliar algorithm requires complex power operations, which introduced a large time overhead. [PERSON] et al. [9] used the multi-key homomorphic encryption (MK-HE) to solve the collusion attack leakage problem brought by a single key, which improved privacy protection for federated learning. [PERSON] et al. [7] improved the encoding method during gradient transmission, reducing the computational overhead due to encryption but decreasing the accuracy.  
To balance accuracy and differential privacy, this paper uses a differential privacy approach to protect gradient privacy while using Kalman filtering to reduce the impact of the noise on the accuracy.
## 3 Results
### Taranto  
A dataset of ENVISAT images from the ascending orbit has been processed to retrieve the mean ground velocity of the Ionian coastal area of the Puglia region, around Taranto city (Figure 8). It is composed of 38 acquisitions, spanning a period from May 2003 to September 2010, just before the ENVISAT deorbiting operation was done extending the lifetime of the ESA mission. The dataset was processed by applying the IPTA-MB method of GAMMA software. In particular, we calculated the interferograms by applying a multi-look factor of 1 \(\times\) 5 in the range and azimuth directions, respectively, which leads to a ground pixel coverage of 20 m \(\times\) 20 m. Then, only coherent points (i.e., persistent scatterers) characterized by the high signal-to-noise-ratio of the interferometric phase were selected. The selection of the interferometric pairs was made by setting the maxima values of the spatial and temporal baselines. Such values were 280 m and 1300 days, respectively, which returned a stack of 107 point-wise interferograms used for the SVD inversion.  
Figure 8: InSAR analysis revealing general stability in the Taranto gulf with very slight subsidence detected only in the northern part of the city (P1 point).  
Figure 7: Augusta Bay subsidence revealed by C-band ENVISAT ascending (a) and descending (b) data.  
The area did not show ongoing deformation processes (Figure 8). There was no evidence of anthropogenic-induced displacement, such as subsidence due to water exploitation or building loading on soft soils, even though there were industrial activities in this region. Neither tectonic deformation nor significant sea-level rise changes seemed to be acting in this region [34].