esa-sceva/satcom-chunk-collection huggingface dataset:
# Risk Prediction Model for Tailings Ponds Based on EEMD-DA-LSTM Model
## 4 Empirical Analysis  
### Data Preprocessing  
The safety monitoring data, such as the dip line, rainfall, reservoir level, and displacement, of valley-type tailings pond WKB-1 from September 2020 to October 2020, totalling 997 data points, were selected as the dataset. Compared with valley-type tailings impoundments, the initial dams of evening hill-type tailings impoundments are relatively long and have smaller reservoir capacities. In order to further verify the applicability of the EEMD-DA-LSTM-based tailings impoundment dam failure risk prediction method proposed in this paper, according to the different types of tailings impoundments and the variability of the impact of dam failure influencing factors on tailings impoundment dam deformation, the safety monitoring data of Pongsan-type tailings impoundment WKB-2 for the period of 4 March 2021-18 April 2021, including the displacement, reservoir level, and dry beach length, were selected, with a total of 620 data points as the dataset. In order to improve the reliability of the data and the continuity of the time scale, isolation forest (IF) was used for outlier detection, and the outliers and nulls in the original data were interpolated as missing values. The min-max normalisation method was used to scale the original data to eliminate the differences between features of different dimensions.
###### Abstract  
This paper studied the changing pattern of knee cartilage using 3D knee magnetic resonance (MR) images over a 12-month period. As a pilot study, we focused on the medial tibia compartment of the knee joint. To quantify the thickness of cartilage in this compartment, we utilized two methods: one was measurement through manual segmentation of cartilage on each slice of the 3D MR sequence; the other was measurement through cartilage damage index (CDI), which quantified the thickness on a few informative locations on cartilage. We employed the artificial neural networks (ANNs) to model the changing pattern of cartilage thickness. The input feature space was composed of the thickness information at a cartilage location as well as its neighborhood from baseline year data. The output categories were 'changed' and 'no-change', based on the thickness difference at the same location between the baseline year and the 12-month follow-up data. Different ANN models were trained by using CDI features and manual segmentation features. Further, for each type of feature, individual models were trained at different subregions of the medial tibia compartment, i.e., the bottom part, the middle part, the upper part, and the whole. Based on the experiment results, we found that CDI features generated better prediction performance than manual segmentation, on both whole medial tibia compartment and any subregion. For CDI, the best performance in term of AUC was obtained using the central CDI locations (AUC = 0.766), while the best performance for manual segmentation was obtained using all slices of the 3D MR sequence (AUC = 0.656). As experiment results showed, the CDI method demonstrated a stronger pattern of cartilage change than the manual segmentation method, which required up to 6-hour manual delineation of all MRI slices. The result should be further validated by extending the experiment to other compartments.  
artificial neural network; knee osteoarthritis; cartilage thickness; cartilage change prediction; cartilage damage index
# Research Progress of Visual Inspection Technology of Steel Products--A Review
## 4 Detection Methods
### Statistics
#### 4.1.1 Mathematical Morphology  
Mathematical morphology is a subject of image analysis based on lattice theory and topology. The basic operations include: corrosion and expansion, open and closed operations, skeleton extraction, limit corrosion, hit-and-miss transformation, morphological gradient, top-hat transformation, particle analysis, and watershed transformation. The cost matrix theory based on mathematical morphology, combined with the K-nearest neighbor (KNN) classifier, has been shown to detect eight defects of flat steel products in Dopont [18].  
In [PERSON] [19], [PERSON] [23], [PERSON] [55], a genetic algorithm combined with mathematical morphology was adopted to realize the defect detection of steel products. In particular, [PERSON] [23] studied an enhancement operator based on mathematical morphology (EOBMM), combined with a binarization method based on genetic algorithm (BMBGA), which can effectively overcome the effects of non-uniform illumination and enhance the detailed information of the image. A method using mathematical morphology in combination with filtering methods was described in [PERSON] [24]. There are other, similar combinations; for example, mathematical morphology can be combined with the curvelet transform or the Gabor transform. Mathematical morphology in conjunction with the curvelet transform has been used for the detection of metallic surfaces [PERSON] [56]. A morphological operation combined with an optimized Gabor filter method was derived to address the problem of detection performance decreasing due to billet shape, multiple defects, and scales [PERSON] [57]. There are also studies on morphological methods [PERSON] [53], [PERSON] [54]..
## 2 Materials and Methods
### Statistical Analyses  
Alterations in body weight, body composition, BP, and vascular endothelial function were assessed at baseline between HPM\({}_{2.5}\) and LPM\({}_{2.5}\) condition visits with the Wilcoxon signed rank test and paired _t_-test. The difference in outdoor and indoor PM\({}_{2.5}\) concentrations between HPM\({}_{2.5}\) and LPM\({}_{2.5}\) condition visits was confirmed with the Mann-Whitney U test. A 2 \(\times\) 2 analysis of variance with repeated measures was performed to demonstrate the difference in the acute effect of moderate-intensity aerobic exercise on vascular endothelial function between HPM\({}_{2.5}\) and LPM\({}_{2.5}\) conditions. A generalized estimating equation (GEE) was used to control the influence of baseline diameter and SR on FMD data [20; 22]. Using natural log-transforming baseline diameter (InD\({}_{\text{base}}\)), peak diameter (InD\({}_{\text{peak}}\)), and SR\({}_{\text{AUC}}\) (InSR\({}_{\text{AUC}}\)), the GEE was employed with diameter difference (InD\({}_{\text{peak}}\)-InD\({}_{\text{base}}\)) as the dependent variable and InD\({}_{\text{base}}\) and InSR\({}_{\text{AUC}}\) as covariates. Estimated means and estimated standard errors were back-transformed to scaled mean FMD and SE FMD with the following equations: scaled FMD mean = (e\({}^{\text{EM}}\) - 1) \(\times\) 100; scaled FMD SE = (e\({}^{\text{ESE}}\) - 1) \(\times\) 100. SR\({}_{\text{AUC}}\), FMD/SR\({}_{\text{AUC}}\), and scaled FMD were analyzed with data from eight participants excluding one outlier. Post hoc pairwise multiple comparisons were adjusted by Bonferroni correction. Data were presented as mean \(\pm\) SE, and all statistical analyses were performed using IBM Statistical Package for the Social Sciences Statistics (version 27, USA). All statistical significance was set as \(p\) \(<\) 0.05.
# Integrated Communication and Measurement System with BOC-Assisted OFDM
## 4 Simulation Results and Performance Analysis
### Demodulation Performance  
To evaluate the demodulation performance for the composite signal proposed in this paper, the composite signal is simulated under different Doppler frequency offsets (\(f_{d}=50\) Hz, \(100\) Hz, \(300\) Hz, \(500\) Hz, \(700\) Hz, \(900\) Hz), different power ratios (\(\alpha\):\(\beta\) = 0.5:0.5, 0.4:0.6, 0.3:0.7, 0.2:0.8, 0.1:0.9), and different SNRs. In addition, the BERs of the composite  
Figure 14: Doppler frequency measurement error under different conditions.  
Figure 15: PAPR performance comparison between the original OFDM system and the proposed OFDM+BOC system.  
signal with carrier Doppler compensation and with channel compensation are compared. The simulation results are shown in Figure 16.  
As seen from Figure 16a,b, channel compensation is still effective at low speeds, and a significant effect is observed for the composite signal with a power ratio of 0.1:0.9. Thus, we take the composite signal with a power ratio of 0.1:0.9 as an example for analysis.  
As shown in Table 2, the BER of the composite signal with carrier Doppler compensation is almost two orders of magnitude lower than that with channel compensation. Therefore, if high-precision Doppler frequency offset auxiliary demodulation is adopted for carrier Doppler compensation, then the BER is significantly decreased.  
Figure 16: BER of the composite signal under different circumstances.  
As seen from Figure 16c-f and Table 2, as the speed of the UAVs gradually increases, the BER with channel compensation becomes approximately equal to 0.5. However, the BER with carrier Doppler compensation remains below \(10^{-4}\). This means that channel compensation is no longer effective for the demodulation of the composite signal in a highly mobile environment. It also proves that carrier Doppler compensation can reduce the influence of the Doppler frequency offset on the composite signal and improve the communication performance.  
Furthermore, the BERs under different power ratios are compared in this paper. Our graphs demonstrate that with a decrease in the power ratio, the BER is reduced and the communication performance is improved. The reason is that the proportion of the OFDM signal increases and the impact of noise interference decreases.
# Underwater Sensor Network Redeployment Algorithm Based on Wolf Search
###### Abstract  
This study addresses the optimization of node redeployment coverage in underwater wireless sensor networks. Given that nodes could easily become invalid under a poor environment and the large scale of underwater wireless sensor networks, an underwater sensor network redeployment algorithm was developed based on wolf search. This study is to apply the wolf search algorithm combined with crowded degree control in the deployment of underwater wireless sensor networks. The proposed algorithm uses nodes to ensure coverage of the events, and it avoids the prematurity of the nodes. The algorithm has good coverage effects. In addition, considering that obstacles exist in the underwater environment, nodes are prevented from being invalid by imitating the mechanism of avoiding predators. Thus, the energy consumption of the network is reduced. Comparative analysis shows that the algorithm is simple and effective in wireless sensor network deployment. Compared with the optimized artificial fish swarm algorithm, the proposed algorithm exhibits advantages in network coverage, energy conservation, and obstacle avoidance.  
underwater sensor networks; 3D node deployment; coverage; wolf search Article
# Remote Sensing for International Peace and Security: Its Role and Implications
## 5 Future Perspectives  
### Technology Development Perspective  
Advances in remote sensing technology rely on the development of improved sensors, imaging devices, and communication networks, among others. Some future perspectives in these areas are listed below.
# Evaluation of the Long-Term Performances of SMA-13 Containing Different Fibers
## 4 Results and Discussion
### Cracking Resistance  
#### 4.2.1 Cracking Resistance at Low Temperature  
The results of the three-point bending test are presented in Figure 6.  
As is shown in Figure 6, the maximum bending tensile strain decreases when the aging degree increases. In the aging process, the light component of the asphalt binder volatilizes, resulting in the hardening and brittleness of the asphalt binder and the decrease  
Figure 6: Maximum bending tensile strain.  
of deformation resistance of the asphalt material [39]. It can be also observed from Figure 6 is that the maximum bending tensile strain of BFSMA-13 is greater than that of the LFSMA-13. The maximum bending tensile strain of the short-term aged and long-term aged BFSMA-13 is 8.04% and 13.83% lower than those of the unaged BFSMA-13. The corresponding decreasing percentage of the short-term aged and long-term aged LFSMA-13 is 13.45% and 22.41% respectively. Greater bending strain means asphalt mixtures are more capable of deformation and less prone to cracking at low temperatures.  
As is shown in Table 4, the optimal asphalt-aggregate ratio of BFSMA-13 and LFSMA-13 is 5.8% and 6.0% respectively. Normally, when the asphalt content is lower, the low-temperature performance of asphalt mixtures is poorer. Yet, the low-temperature anti-deformation ability of BFSMA-13 is better than that of LFSMA-13, which further proves that basalt fiber can better enhance the low-temperature anti-cracking ability of SMA-13 than lignin fiber.
# Fault Diagnosis of a Solenoid Valve Based on Multi-Feature Fusion
## 2 Analysis of Solenoid Valve Current Characteristics  
In this paper, the research group performed a simulation experiment for the electrification and closing process of an electromagnetic reversing valve and analyzed the corresponding results. To simplify its working process, the research group decomposed it into a magnetic circuit equation, circuit equation and motion equation [8]. When the solenoid valve was energized, the force on the valve core was as it is shown in Figure 1. Among them, \(F_{E}\) is the resultant force, \(F_{f}\) is the friction force, \(F_{s}\) is the hydraulic force and \(F_{t}\) is the spring force.
## 5 Applications in Food Products and Nutraceuticals
### Food Industry
#### 5.1.2 Functional Foods  
In the design of healthy foods as functional components, _Citrus_ seeds could bring great results due to their composition rich in oil and unsaturated fatty acids, proteins, vitamins, tocopherols, phytosterols, carotenoids, and phenolics compounds, minerals, as well as flavonoids, and dietary fiber in the form of non-digestible carbohydrates [1; 4; 94; 95]. _Citrus_ seeds are also outstanding for their content of tetracyclic triterpenol limonoids (limonin, nomilin, obacunone, and nomilinic acid) [92; 96]. [PERSON] et al. used dietary fibers extracted from orange and grapefruit seeds in the production of cookies. Evaluation of the incorporation effects of the fibers showed beneficial impacts on dietary fiber contents and antioxidant and phenolic capacity mainly due to their composition in bioactive components. The highest content of flavonoids and phenolic acids reached 12.93 mg/g. Moreover, the samples used as controls showed no flavonoid content. So, these new cookies could be assigned as functional products with high dietary fiber content [93].  
Furthermore, _Citrus_ wastes are also characterized in terms of their content of complex polysaccharides and coloring material, which are used extensively by the soft drink industries as clouding agents [97]. But also, _Citrus_ seeds have been used commercially to produce oil rich in unsaturated fatty acids (linolenic, palmitic, oleic, and stearic), so it is considered a healthy oil [94]. [PERSON] et al., 2019, screened the chemical composition and nutritional and biological attributes of SFE-CO\({}_{2}\)-extracted oils extracted from waste mandarin, lemon, and grapefruit seeds. The results qualified the evaluated seed oils as a potential resource due to properties such as high content of essential fatty acids, radical scavenging activity, and significant inhibitory effects on fungal tyrosinase and on melanoma cell growth [23].  
Polyphenols, carotenoids, and essential oils are recognized as the biologically most active compounds in _Citrus_ wastes and are linked to the greatest health benefits, mainly attributed to their antioxidant activity [92]. Several studies have found that _Citrus_ seed and leaf extracts contain high amounts of phenolic compounds, such as flavonoids [98]. For example, sour orange seeds have been found to contain limonoids and flavonoids as their main bioactive components. These flavonoids include flavonones such as hesperidin and naringin, compounds with direct health benefits due to their antioxidant, anticarcinogenic, anti-inflammatory, and cardiovascular-protective activities [99]. The phytochemical profile of _Citrus_ fruits has led them to be considered worldwide as potent functional foods [100].
## 3 Results
### Satellite Inversion 10 m Wind Field
#### 3.3.2 Comparison of HY-2C and CFOSAT Inversion 10 m Wind Field  
Figure 9 compares the 10 m wind fields over the Yellow Sea derived from HY-2C and CFOSAT retrievals during similar periods. The following analysis results were based on  
Figure 8: Comparison of HY-2C and CFOSAT inversion data with WRF simulation data. The values in the figure represent the difference between the satellite-retrieved wind speeds and WRF simulation data. (**a1**) represents the difference between HY-2C’s retrieved sea surface wind speed and WRF simulation data during low wind speeds, (**a2**) represents the difference during moderate wind speeds, and (**a3**) represents the difference during high wind speeds. Similarly, (**b1–b3**) represent the differences for CFOSAT’s retrieved sea surface wind speed compared to WRF simulation data during low, moderate, and high wind speed conditions, respectively.  
the evaluation metrics obtained from comparing the 10 m wind fields between HY-2C and CFOSAT.  
The correlation coefficient of 0.615 indicated a certain level of linear relationship between the 10 m wind fields derived from the HY-2C and CFOSAT data. This implies that the wind field data from the two satellites varied in conjunction with each other to some extent, indicating comparable detection capabilities for the 10 m wind fields over the Yellow Sea. The root-mean-square error of 1.28 and the mean bias ratio of 0.17 indicated a close agreement in the detection capabilities of the two satellites for the 10 m wind fields over the Yellow Sea. The linear regression equation, y = 1.15 x - 1.12, revealed the linear relationship between the 10 m wind fields of HY-2C and CFOSAT data. This equation suggests that the dependent variable increased proportionally with the increase in the independent variable. However, it is important to note that there was no direct mapping relationship between the x-axis and y-axis. Thus, this regression equation describes the overall trend between the two variables. Furthermore, some points may deviate significantly due to the slight mismatch in transit times between the two satellites.
## 3 Results
### Regression Learner Predicted vs. Actual Response
GPR exponential and GPR Matern have greater statistical algorithms than other regression models, according to the results. The Gaussian Process Regression (GPR) models are difficult to understand, according to the findings. Decision trees are non-parametric, interpretable, and fast, but they are locally optimized and prone to overfitting. Imbalanced classes also possess significant concern for decision trees. Although SVM is useful in high dimension space and acquires little memory, it is not suitable for large data sets, and target sets are closed to overlap. In stepwise regression models, predictive variables are carried out by removing the weakest correlated variable. Therefore, for case-sensitive datasets mostly, it is not suggested. The algorithm of Exponential GPR is indistinguishable from the Squared Exponential GPR. Aside from that, the Euclidean distance is not squared. Exponential GPR handles smooth capacities well with minor mistakes. However, it does not deal well with discontinuities. The Matern 5/2 kernel takes the fixed portion's spectral densities and makes Fourier changes to the Radial Basis function kernel. In contrast, it does not measure the data for high-dimensional spaces. Results show that GPR Exponential and GPR Matern have better predictive algorithms than other regression models. The Gaussian Process Regression (GPR) models have difficult interpretability. Still, they possess higher accuracy and are non-parametric Kernel-based probabilistic models, with multivariate distribution of the predetermined collection of random variables.
# Transmission Angle Analysis of a Type of Parallel Manipulators
## 2 Kinematic Analysis
#### Jacobian Analysis  
The derivation of Equation (1) with respect to time yields:  
\[\mathbf{v}=\dot{b}_{i}\mathbf{e}+l_{1i}\mathbf{\omega}_{1i}\times\mathbf{u}_{i}+l_{2i}\mathbf{ \omega}_{2i}\times\mathbf{w}_{i} \tag{3}\]  
where \(\mathbf{v}\), \(\mathbf{\omega}_{1i}\) and \(\mathbf{\omega}_{2i}\) are the linear speed of the moving platform, the angular speed of link \(A_{i}B_{i}\) and the angular speed of link \(B_{i}C_{i}\), respectively.  
Figure 1: Kinematic chains of parallel manipulators. (**a**) Actuated by revolute joint; (**b**) actuated by prismatic joint.  
Figure 2: Schematic diagram of a kinematic chain.  
When joint \(A_{i}\) is a revolute joint in the study, Equation (3) can be written as:  
\[\mathbf{v}=l_{1i}\dot{\theta}_{1i}(\mathbf{v}_{i}\times\mathbf{u}_{i})+l_{2i}\mathbf{\omega}_{2i }\times\mathbf{w}_{i} \tag{4}\]  
where \(\mathbf{v}_{i}\) denotes the unit vector in the direction of the angular speed of \(A_{i}B_{i}\). When joint \(A_{i}\) is a prismatic joint, Equation (3) is given as:  
\[\mathbf{v}=\dot{b}_{i}\mathbf{e}+l_{2i}\mathbf{\omega}_{2i}\times\mathbf{w}_{i} \tag{5}\]  
Taking the dot product on both sides of Equations (4) and (5) with \(\mathbf{w}_{i}\), we can obtain:  
\[l_{1i}\dot{\theta}_{1i}\mathbf{w}_{i}^{\text{T}}(\mathbf{v}_{i}\times\mathbf{u}_{i})=\mathbf{w }_{i}^{\text{T}}\mathbf{v} \tag{6}\]  
\[\dot{b}_{i}\mathbf{w}_{i}^{\text{T}}\mathbf{e}=\mathbf{w}_{i}^{\text{T}}\mathbf{v} \tag{7}\]  
Rewriting Equations (6) and (7) in the following matrix form yields:  
\[\mathbf{J}_{qr}\dot{\mathbf{q}}=\mathbf{J}_{xx}\dot{\mathbf{x}} \tag{8}\]  
\[\mathbf{J}_{qp}\dot{\mathbf{b}}=\mathbf{J}_{xp}\dot{\mathbf{x}} \tag{9}\]  
where \(\mathbf{J}_{qr}=diag[l_{11}\mathbf{w}_{1}^{\text{T}}(\mathbf{v}_{1}\times\mathbf{u}_{1})\quad l _{12}\mathbf{w}_{2}^{\text{T}}(\mathbf{v}_{2}\times\mathbf{u}_{2})\quad\cdots\quad l_{1n} \mathbf{w}_{n}^{\text{T}}(\mathbf{v}_{n}\times\mathbf{u}_{n})],\dot{q}=[\dot{\theta}_{11} \quad\dot{\theta}_{12}\quad\cdots\quad\dot{\theta}_{1n}],\mathbf{J}_{xt}=[\mathbf{w}_ {1}\quad\mathbf{w}_{2}\quad\cdots\quad\mathbf{w}_{n}]^{\text{T}},\mathbf{J}_{qp}=diag[ \mathbf{w}_{1}^{\text{T}}\mathbf{e}\quad\mathbf{w}_{2}^{\text{T}}\mathbf{e}\quad\cdots\quad \mathbf{w}_{n}^{\text{T}}\mathbf{e}],\mathbf{J}_{xp}=\mathbf{J}_{xt}=[\mathbf{w}_{1}\quad\mathbf{w}_{2 }\quad\cdots\quad\mathbf{w}_{n}]^{\text{T}}\text{ and }\dot{\mathbf{b}}=[\dot{b}_{1}\quad\dot{b}_{2}\quad\cdots\quad\dot{b}_{n}]\).