esa-sceva/satcom-chunk-collection huggingface dataset:
# SOH Estimation of Lithium-Ion Battery Pack Based on Integrated State Information from Cells
## 2 Methodology
### Deterioration Information Integrate for SOH Prediction
#### 2.3.4 Model Training and Output  
Model training is the most critical link for determining the validity and accuracy of a neural network model. In order to obtain satisfying training results in the application scenario described in this paper, the following content needed to be well implemented:  
* Normalization: Good normalization was necessary for model training. In the application scenario of this paper, in order to efficiently produce the solution of the parameters, the time label and the ambient temperature are transformed into the same numerical magnitude and uniformly normalized.
* Dataset division: According to the scale of the data obtained from the experiment, the training dataset, validation dataset, and test dataset were divided with the ratios of 8:1:1 or 7:2:1. These division ratios were considered to provide sufficient training data for the optimization of the model parameters, and a validation dataset no smaller than the test set was used to ensure the training performance. It followed the general rules of dataset division of deep learning.
* Data ordering: according to the consistency of characteristics before and after, reordering the data was helpful for including various characteristics in the training data.  
After the input data preprocessing, the model was trained. During training, the specific model parameters were determined according to the performance of the training data. The output of model was set as the SOH of the battery pack.
# Design of a Compact Microstrip Decoupled Array
## 2 Materials and Methods
### Miniaturization Antenna
As shown in Figure 7, the position of the antenna feed point has a great influence on the impedance matching at the resonant frequency point, and the impedance matching can be better achieved by choosing the feed point position reasonably through formula calculation and simulation. When the feed-point position P = 11.2 mm, the \(S\) parameters of the antenna are optimized at the resonant frequency point. Figure 5: The slots to investigate the reflective characteristic. (**a**) Parameter v of U-shaped slots, (**b**) parameter v1 of rectangular slots. Figure 2:0 Effect of antenna size W and v on resonant point (unit: mm). (**a**) The antenna length parameter W, (**b**) parameter v1 of rectangular slots. The antenna has a frequency of 2.45 GHz. The miniaturized antenna unit was designed to be 23.4 \(\times\) 23.4 mm\({}^{2}\) (0.19 \(\lambda\)\(\times\) 0.19 \(\lambda\)), as shown in Figure 2:1 The \(S\) parameters and radiation patterns are shown in Figure 2:2  
The addition of slots greatly reduces the size of the antenna unit. The final parameters were simulated and determined using ANSYS HFSS 2020R2 software. The simulation results of the miniaturized microstrip antenna are shown in Figure 8a,b. The \(S_{11}\) of the  
Figure 2:3 The miniaturized antenna. Figure 2:4 Effect of feed point location on resonant frequency. Figure 2:5 Simulation results of miniaturization microstrip antenna unit. (**a**) S, (**b**) gain. antenna is greater than 21.6 dB, the antenna gain is 3.14 dBi, and the peak gain is at \(\theta\) = 0\({}^{\circ}\). The proposed antenna unit is approximately 59% smaller in size compared to previous versions. Compared with the two-element microstrip antenna (130 mm \(\times\) 130 mm), the size of the designed antenna is reduced by 64%. The overall size change of the antenna is shown in Figure 2:6
###### Abstract  
The Vectorized Earth Observation Retrieval (VEOR) algorithm is a novel algorithm suited to the efficient supervised classification of large Earth Observation (EO) datasets. VEOR addresses shortcomings in well-established machine learning methods with an emphasis on numerical performance. Its characteristics include (1) derivation of classification probability; (2) objective selection of classification features that maximize Cohen's kappa coefficient (\(x\)) derived from iterative \"leave-one-out\" cross-validation; (3) reduced sensitivity of the classification results to imbalanced classes; (4) smoothing of the classification probability field to reduce noise/mislabeling; (5) numerically efficient retrieval based on a pre-computed look-up vector (LUV); and (6) separate parametrization of the algorithm for each discrete feature class (e.g., land cover). Within this study, the performance of the VEOR classifier was compared to other commonly used machine learning algorithms: K-nearest neighbors, support vector machines, Gaussian process, decision trees, random forest, artificial neural networks, AdaBoost, Naive Bayes and Quadratic Discriminant Analysis. Firstly, the comparison was performed using synthetic 2D (two-dimensional) datasets featuring different sample sizes, levels of noise (i.e., mislabeling) and class imbalance. Secondly, the same experiments were repeated for 7D datasets consisting of informative, redundant and insignificant features. Ultimately, the benchmarking of the classifiers involved cloud discrimination using MODIS satellite spectral measurements and a reference cloud mask derived from combined CALICP lidar and CPR radar data. The results revealed that the proposed VEOR algorithm accurately discriminated cloud cover using MODIS data and accurately classified large synthetic datasets with low or moderate levels of noise and class imbalance. On the contrary, VEOR did not feature good classification skills for significantly distorted or for small datasets. Nevertheless, the comparisons performed proved that VEOR was within the 3-4 most accurate classifiers and that it can be applied to large Earth Observation datasets.  
Vectorized Earth Observation Retrieval (VEOR); machine learning; artificial intelligence; classification; support vector machines; Gaussian process; random forest; artificial neural networks; [PERSON] +
Footnote †: journal: 14 January 2022
# An Experience Transfer Approach for the Initial Data of Iterative Learning Control
###### Abstract  
Iterative learning control (ILC) requires that the operating conditions of the controlled system must remain unchanged in the repetitive learning process. If the parameters of system change, the former control experience of ILC would not be effective anymore. A new process of iterative learning has to restart, which will exhaust more time and resource. Compared with learning from zero experience, appropriate initial data for the first iteration could reduce the turns of iterations to achieve the target tracking accuracy. When the parameters of a linear system change, its structure and nature are still intrinsically related to the original system. So, if the experience obtained from original ILC could be correspondingly adjusted according to the difference of new and original system, and use the adjusted experience as the initial data in the new iterative learning process, it would reduce the time and save the resource in the new ILC. Based on the idea of experience inheritance and transform, an experience transfer approach for the initial data of ILC is proposed in reference to the relation between the new and original systems. In this paper, via the method of recombining, translational and amplitude adjusting, the experience of former ILC is transferred as the initial control data of new ILC. Simulation shows that the convergence iteration of ILC with experience transfer approach reduces 55-75%, which demonstrates the effectiveness and advantages of the approach proposed in this paper. Both the deviation of the first iteration in ILC and the turns of iterations for achieving desired accuracy are reduced greatly.  
iterative learning control (ILC); experience inheritance; experience transform; initial iterative control data +
Footnote †: journal: Applied Sciences  
1
## 3 Results
### Correlation Between LTPA and Domains  
To verify whether LTPA was associated with different domains of exercise-related attitudes, a correlation analysis was conducted between the LTPA score and the scores of the five domains considered (physical, psychological, motivational, sociality, and barrier). As shown in Table 4, people who were more active reported stronger beliefs in the physical benefits of exercise (\(p\) = 0.375, 95% CI [0.097, 0.603], \(p\) = 0.005). Secondly, higher activity levels were also tied to greater motivation to exercise (\(p\) = 0.413, 95% CI [0.139, 0.629], \(p\) = 0.002). By contrast, we found no significant connections between LTPA and the psychological, sociality, and barrier domains.
## 1 Introduction
[18] presented the automated RapidNAM concept that facilitates the design and manufacturing process of NAM appliances and that allows for the virtual modification and subsequent manufacture of the appliances in advance, with a growth prediction factor adapted to the patient's natural growth. [PERSON] et al. [19] presented a technique that combined intraoral molding with semi-automated plate generation and 3D printing. [PERSON] et al. [20] used computer vision to quantify the severity of nasal deformities in unilateral complete cleft lip and palate children and to compare the treatment of nasal deformities with optimal surgical outcomes. [PERSON] et al. [21] described in detail the use of dental scanners to obtain oral and maxillary models of cleft lip and palate children and the design and fabrication of PNAM orthodontics digitally using a combination of computer-aided design software and computer-aided machining. [PERSON] et al. [22] revealed an algorithm for the automated generation of patient individual NAM appliances for neonates with bilateral cleft lip and palate (BCLP), and presented results of software validation. [PERSON] et al. [23] designed a sectional PNAM appliance using 3D printing technology and used a nasal hook simultaneously for treatment. They found that the sectional PNAM appliance can help reduce the alveolar gap, improve the shape of the nasal arch, approximate the lip segment, and significantly improve the shape of the nose by correcting flattened nasal wings. However, although the existing methods can solve the cost and time problems of NAM equipment production, they have not formed a quantifiable, automatic, and batch method system, which can only be realized by virtual modification or manual modification. Therefore, this paper is designed to find an automatic and quick computer method that can effectively reduce the burden of doctors and provide a new computer method for the study of cleft lip and palate appliances. At present, most studies on the preoperative appliance for cleft lip and palate rely on the manual production of doctors or reverse-engineering software. The preoperative appliance for cleft lip and palate produced by these two methods shows the following shortcomings. (1) The production cycle of the appliance is long and requires the manufacturer to have certain medical experience. (2) The appliance has poor adaptability to different individuals and cannot form a quantifiable method for different individuals. The purpose of this study is to find out a quantitative and automatic design method for the preoperative appliance for unilateral complete cleft lip and palate, which is applicable to different individuals and does not depend on the experience of producers. This method relies on extracting the feature points in the cleft lip and palate model of patients to make adaptive appliances for different models. The structure of this experiment is shown in Figure 1.
# Explainable Machine Learning for Mapping Rainfall-Induced Landslide Thresholds in Italy
## 3 Results  
### Model Performance Comparison  
The evaluation metrics in this study were reported as average values from 10 repetitions of the complete leave-one-region-out cross-validation procedure, involving 4412 positive samples and 44,120 negative samples in the training and validation process. The results included 95% confidence intervals, and [PERSON]'s test [58] was employed to analyze whether significant differences existed between model outputs. All analyses were conducted using Python 3.11.12 with relevant machine learning libraries (detailed information in Supplementary Material S1). On this imbalanced dataset, the four models (XGBoost, RF, LightGBM, and LR) exhibited distinct performance characteristics (Figure 4). Table 5 presents the detailed numerical values of these evaluation metrics and the optimal hyperparameter configurations for machine learning models.  
The three tree-based methods (XGBoost, RF, and LightGBM) demonstrated consistently excellent predictive performance, achieving comparable AUC values of \(0.917\pm 0.026\), 0.916 \(\pm\) 0.026, and 0.917 \(\pm\) 0.026 respectively. In comparison, LR showed a relatively lower AUC of 0.813 \(\pm\) 0.016, whereas the empirical E-D curve baseline reached 0.903 \(\pm\) 0.032.  
The sensitivity-specificity trade-off analysis presented varying behaviors across models. Random Forest and LightGBM exhibited high specificity (0.906 \(\pm\) 0.043 and 0.930 \(\pm\) 0.035) but compromised sensitivity (0.696 \(\pm\) 0.134 and 0.670 \(\pm\) 0.111), which reflected a bias toward the majority class typical of imbalanced datasets and may result in the omission of some landslides that were difficult to predict. By contrast, since the E-D curve prioritizes identifying all potential landslides, it achieved perfect sensitivity (1.000 \(\pm\) 0.000) while greatly reducing specificity (0.219 \(\pm\) 0.034), causing a high false positive rate.  
Figure 4: (**a**) ROC curve comparison and (**b**) evaluation metric comparison.  
Superior and well-balanced performance was observed for XGBoost across all key metrics, with sensitivity of 0.792 \(\pm\) 0.075, specificity of 0.812 \(\pm\) 0.033, an F1 score of 0.731 \(\pm\) 0.037, and precision of 0.681 \(\pm\) 0.032. This performance profile indicated that the XGBoost model effectively balances the trade-off between sensitivity and specificity, enabling enhanced landslide identification while simultaneously reducing false positive rates. We conducted [PERSON]'s test to compare XGBoost with other machine learning models and found highly significant differences across all comparisons (\(p\) < 0.001). When compared to Random Forest, LightGBM, and Logistic Regression, the test statistics were \(\chi^{2}\) = 75.81 (\(p\) = 3.13 \(\times\) 10\({}^{-18}\)), \(\chi^{2}\) = 139.19 (\(p\) = 4.00 \(\times\) 10\({}^{-32}\)), and \(\chi^{2}\) = 478.66 (\(p\) = 4.18 \(\times\) 10\({}^{-106}\)), respectively. Consequently, all subsequent experimental analyses were conducted based on the trained XGBoost model.
## 2 Theoretical Background  
### Hierarchical Clustering Algorithm  
Hierarchical clustering is an efficient clustering algorithm [16] that constructs a dendrogram based on the similarity or distance between clusters. This algorithm can be categorized into two types: agglomerative and divisive methods. The agglomerative method, illustrated in Figure 1, follows a bottom-up approach. Initially, each object is treated as an individual cluster. Clusters are then successively merged based on a similarity or distance criterion until a stopping condition is met or all clusters are merged into a single group. The Pearson correlation coefficient [23], used to measure similarity, is calculated as shown in Equation (1). The distance between two feature vectors, x and y, is defined as \(1-\left|\rho_{xy}\right|\), where \(\rho_{xy}\) is the Pearson correlation coefficient. In the agglomerative clustering process, the Pearson correlation matrix is computed and used as the initial distance matrix for the clustering algorithm.  
\[\rho_{xy}=\frac{\text{Cov}(x,y)}{\sqrt{D(x)}\cdot\sqrt{D(y)}} \tag{1}\]where \(\text{Cov}(x,y)\) is the covariance of \(x,\ y\) and \(D(x),\ D(y)\) is the variance of the eigenvectors.
## 3 GROW Solution Overview
### Communications Solution  
Long-range underwater wireless communications rely on narrowband acoustic communications [5; 17], which are unsuitable for uploading large amounts of data from an AUV. Although other technologies such as optical and RF are able to provide higher throughputs [24; 28], they are affected by turbidity and strong attenuation, respectively, limiting their practical usage to short-range communications.  
The GROW solution, illustrated in Figure 2, addresses this problem by employing AUVs that operate as data mules between a fixed or mobile SU that acquires and logs the data--e.g., deep sea lander and survey AUV--and a CSU such as buoy, vessel, and Autonomous Surface Vehicle (ASV). The CSU is assumed to be equipped with a permanent connection to an onshore station, reachable through the Internet. It is responsible for scheduling the available DMUs. The DMUs are small and agile AUVs that establish a virtual bi-directional communications link between the CSU and the SU by traveling back and forth between them. This will fill the gap shown in the top right corner of the plot of Figure 3.  
The GROW solution considers two different communications technologies: a broadband, short-range communications link (optical or RF) used for data download from the SU to the DMU and upload from the DMU to the CSU; and a narrowband, long-range acoustic communications link for controlling the DMUs. Due to the intermittent connectivity of the short-range communications link, protocols designed for delay/disruption tolerant wireless networks are used.  
Due to the short distance required between the DMU and the mobile SU for enabling high bitrate underwater communications, GROW addresses the challenges of the following: (1) homing to a mobile target with uncertain or possibly corrupted information on its future trajectory; and (2) precise positioning of an AUV with regard to a mobile target accommodating strong disturbances induced by the motion of the DMU with regard to the mobile target (the SU).  
The correct scheduling of the DMUs is a key factor for the GROW system performance. In [9], we have proposed the Underwater Data Muling Protocol (UDMP), a communications protocol that enables the control and scheduling of the DMUs within the GROW framework for a file transfer application. The UDMP communications stack is presented in Figure 4 and runs on every node of the network. The scheduler defines the number of DMUs deployed and their sequence. UDMP is then responsible for handling all the control messages over the acoustic network according to the scheduler commands. It is also responsible for handling the split and reconstruction of the data chunks sent over the DTN.
# Corrosion Fatigue Assessment of Bridge Cables Based on Equivalent Initial Flaw Size Model
## 3 Corrosion Fatigue Performance of Steel Wire  
Corrosion fatigue coupling tests were carried out on high-tensile steel wires using an innovative corrosion fatigue test method proposed by [PERSON] et al. [26]. The proposed lifespan model based on the EIFS theory was used to analyze the fatigue lifespan of corroded steel wires at various stages and the effect of corrosion-fatigue coupling on the lifespan.
## 4 Analysis and Comparison of Air-Gap Flux Density and Torque Performance
### Analysis of Torque Performance  
The torque reflects the output capacity of the motor, and the torque ripple demonstrates the stability of the output of the motor, which are two crucial indicators of electromagnetic performance. When the values of \(k_{arc}\) and \(k_{seg}\) are different, that is, the flux- barrier structure changes, the torque value of the motor with the corresponding rotor structure can be calculated according to the MEC of proposed motor established in Section 3.  
The analytical torque is compared with the result of FEA simulation, as show in Figure 10. The stator current are set with \(I_{0}=21.79\) A and \(T=20\) ms (the frequency is 50 Hz). It can be seen from the figure that the analytical torque is basically consistent with the simulation results, which verifies the validity of the model and can be used for torque prediction in the early stage of motor rotor structure design. The influence of \(k_{seg}\) and \(k_{arc}\) on average torque and torque ripple can also be seen in Figure 10. It can be found that when \(k_{arc}\) is fixed, the average torque first increases and then decreases with the increase of \(k_{seg}\), while the torque ripple shows an opposite trend. Furthermore, average torque is hardly changed when \(k_{arc}\) remains constant, and \(k_{seg}\) varies between 0.2 and 0.6. When the overall trend of air-gap flux-density is closer to sinusoidal, the torque ripple will be smaller. According to Equations (35)-(38), the energy in the magnetic circuit will also change correspondingly when the magnetic barrier structure changes, which is finally reflected in the change of torque.  
Based on the above analysis, the structural parameters of SynRM with cross-shaped flux-barriers rotor structure with relatively better torque performance can be obtained as \(k_{seg}=0.4\) and \(k_{arc}=0.2\). At this point, the average torque of the motor is 85.26 Nm, and the torque ripple is 11.79%.  
In order to verify the effectiveness of the method, the same optimization was carried out for motors with three layers and four layers flux-barrier structures, respectively, as shown in the Figure 11. It can be found that for a SynRM with three layers of flux barrier, the torque can be increased from 75.2 Nm to 82.15 Nm by using the proposed method, and the torque ripple is reduced by 27.69%. For a SynRM with four layers of flux barrier, it is necessary to make a trade-off between the two performance indexes of torque and torque ripple. However, no matter how to choose, the motor with better performance than the initial motor can be obtained. Therefore, it can be proved that the proposed method is not only suitable for the motor with five layers of flux barrier structure, but also has universality.
# Research on Optimization of Target Positioning Error Based on Unmanned Aerial Vehicle Platform
## 2 Definition of a Coordinate System
### Camera Coordinate System \((O_{c}-X_{c}Y_{c}Z_{c})\)  
The origin is located at the intersection of the camera's optical axis and lens plane; when the camera is in the initial position, the \(X\)-axis points toward the right wing of the UAV, the \(Y\)-axis points to the body of the UAV directly underneath, and the \(Z\)-axis is the optical axis of the camera. When the camera's attitude angle changes, the coordinates at a certain point are expressed as \((X_{c},Y_{c},Z_{c})\).
# Transmitter Diversity Gain Technique Aided Irregular Channel Coding for Mobile Video Transmission
###### Abstract
* Differential scheme has been incorporated in order to embed the information between successive symbols, in which we do not need to know the fade in estimation of the closest symbol. The detailed discussion regarding previous work is carried out in Section 2. Section 3 presents preliminaries and system design criteria, while Section 4 presents the detailed description about the different parts of the proposed scheme. Section 5 presents the design of the IRCC. Section 6 provides a detailed explanation of the obtained performance results. Finally, the research article is concluded in Section 7.