esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Dual-Polarization Synthetic Aperture Radar
## 3 Hybrid Dual-Polarization
### Hybrid Compact Polarimetry Goes to the Moon  
The origin of an HCP SAR was motivated in large part by the requirements on two imaging radars that were being planned to orbit the Moon [24]. Their science requirements included measurement of the circular-polarization ratio, maximized potential to distinguish between backscatter types, and robustness to randomly oriented dihedral backscatter distributions. The implementation requirements included minimal mass and power. The hybrid dual-polarized architecture emerged as the optimum [27]. Circular polarization was transmitted, while the receivers were linearly polarized, nominally H and V. The baseline data product was stipulated to be the four Stokes parameters. This radar concept as implemented became the Mini-SAR on India's Chandraayan-1 and the Mini-RF on NASA's LRO, each independently launched in 2008, and successfully inserted into near-polar low-altitude lunar orbits.  
These two SARs were the first HCP orbital systems [27], providing the same class of polarimetric imaging radar information from lunar orbit as Earth-based radar astronomy [28]. All data were collected in the hybrid-polarimetric mode, over 400 h in the case of LRO. The instruments each had mass less than 15 kg, antenna areas of about 1 m\({}^{2}\), and modest power and spacecraft accommodation requirements.
## 3 The Three-Segment Optimization Architecture in LEO
These include more general aspects of the physical layers specific to different segments, such as the signal and antenna/beamforming-related optimizations, resource-management for individual devices within the network, such as satellites or vehicles, channel coding aspects, and multiple-access optimization. Likewise, some problems require handling from multiple segments, such as channel-based optimization, Multiple Access Channel (MAC) design, handovers, and security. More details about the segment-by-segment optimization problems are presented in the next sections.
# Review of Automatic Estimation of Emotions in Speech
## 7 Neural (Machine Learning) Techniques for Emotion Classification of Speech  
Applications in many fields (other than SER) that process data now primarily use ANNs, as they are powerful and can exploit an extensive range of data [93]. Nonlinear processing permits ANNs to internalize patterns that are very complex, but typically needs huge network models and much computation, which often means devices with few resources (e.g., cellular phones) cannot be used. ANNs transform data or recognize patterns, using automatic training based on many examples. An ANN maps an input sequence (e.g., speech) and uses a nonlinearity to form an output sequence (e.g., for SER, a set of likelihoods for the possible target emotions). The last decade has seen a major research shift toward neural SER [94], whether using spectral input [95] or direct use of speech waveforms.
# FF-PCA-LDA: Intelligent Feature Fusion Based PCA-LDA Classification System for Plant Leaf Diseases
## 3 Material and Methods
### Handcrafted Feature Extraction
#### 3.3.4. Gray Level Co-Occurrence Matrix  
The GLCM are statistical features to explore spatial image relationships. Different types of relationship (e.g., horizontal, diagonal, and off-diagonal) exist in images which need to be explored [25; 30] the frequency of spatial adjacency. A total of 22 GLCM features are extracted from the image using the following expressions:  
\[\text{contrast}=\sum_{n=0}^{N_{x}-1}n^{2}\Bigg{\{}\sum_{i=1|i-j|=n}^{N_{x}}\sum_ {j=1}^{N_{x}}p(i,j)\Bigg{\}} \tag{13}\]  
\[\text{Correlation}=\frac{\sum_{i}\sum_{j}p(i,j)-u_{x}u_{y}}{\sigma_{X}\sigma_ {Y}} \tag{14}\]  
\[\text{Sum of squares}=\sum_{i}\sum_{j}(i-\mu)^{2}p(i,j) \tag{15}\]  
\[\text{Inverse Difference}=\sum_{i}\sum_{j}\frac{1}{1+(i-j)^{2}}p(i,j) \tag{16}\]  
\[\text{Sum Average}=\sum_{i=2}^{2N_{x}}i\cdot p_{x+y}(i) \tag{17}\]  
\[\text{Sum Variance}=\sum_{i=2}^{2N_{x}}(i-f_{g})^{2}p_{x+y}(i) \tag{18}\]  
\[\text{Sum Entropy}=\sum_{i=2}^{2N_{x}}(i)\text{log}_{2}\{p_{x+y}(i)\} \tag{19}\]  
\[\text{Sum Entropy}=-\sum_{i}\sum_{j}p(i,j)\text{log}_{2}(p(i,j)) \tag{20}\]  
\[\text{Difference Variance}=\text{variance of}\quad P_{x-y} \tag{21}\]  
\[\text{Difference Entropy}=-\sum_{i=0}^{N_{x}-1}p_{x-y}(i)\text{log}_{2}(p_{x-y }(i)) \tag{22}\]  
Information Measure of Correlation (IMC):  
\[\text{HMC1}=\frac{\text{HXY}-\text{HXY1}}{(max\{\text{HX},\text{HY}\})}\]  
\[\text{IMC2}=(1-\exp[-2.0(\text{HXY2}-\text{HXY})])^{0.5}\]  
\[\text{HXY}=-\sum_{i}\sum_{j}p(i,j)\text{log}_{2}(p(i,j)) \tag{23}\]  
where, HX and HY are entropies of \(p_{x}\) and \(p_{y}\), and  
\[\text{HXY1}=-\sum_{i}\sum_{j}p_{x}(i,j)\text{log}_{2}(p_{x}(i)p_{y}(j))\]  
\[\text{HXY2}=-\sum_{i}\sum_{j}p_{x}(i)p_{y}(j)\text{log}_{2}(p_{x}(i)p_{y}(j))\]  
\[\text{Homogeneity}=\sum_{i=0}^{N-1}\sum_{j=0}^{N-1}\frac{p_{ij}}{(i-j)^{2}} \tag{24}\]  
\[\text{Dissimilarity}=\sum_{i=0}^{N-1}\sum_{j=0}^{N-1}p_{ij}|i-j| \tag{25}\]  
Additionally, auto-correlation, maximum probability, and cluster prominence are extracted.  
In this part, four handcrafted extracted features of MGH, LBP, HOG, and GLCM are concatenated to form a hybrid feature vector of size 437. These combined handcraftedfeatures are later fused with the deep features used for the development of the classification model.
# Adaptive Thermal Control of Cell Groups to Extend Cycle Life of Lithium-Ion Battery Packs
###### Abstract  
We present a novel approach for a battery management system in which adaptive thermal control is employed to balance the capacities of individual groups of cells within a lithium-ion battery pack. Maintaining capacity balance within the battery pack in this manner can significantly extend its cycle life. We explore the physical implementation of this concept and demonstrate that it is a viable way to extend the life of battery packs. The experimental setup consists of three pairs of cells connected electrically in series and supplied with coolant flow from a chiller. All cells are initially in capacity balance and are cooled uniformly for the first 50 fast charge/discharge cycles. Subsequently, cooling is halted to specific cell pairs to deliberately unbalance their capacities. Finally, cooling is selectively restored to correct the capacity imbalance between the cell groups by the end of 100 charge/discharge cycles. These results suggest that adaptive thermal control can be used effectively to maintain capacity balance within the battery pack.  
lithium-ion batteries; battery management system; thermal management system; battery degradation; grid storage; electric vehicles +
Footnote †: journal: Applied
## 3 Stratum-Foundation Pit-Station-Track Interaction Model
### Establishment of the Finite Element model
#### 3.1.1 Simulation of Foundation Pit Support System  
The foundation pit support system includes a foundation pit enclosure pile, reinforced concrete internal support, prestressed anchor rod, steel lattice column and concrete column structure. Due to the large number of elements and nodes in the three-dimensional simulation model, to better simulate the actual working conditions and simplify the calculation difficulty of the model, the foundation pit enclosure pile is simulated by a 2D plate element structure using the principle of equivalent stiffness; the prestressed anchor rod, concrete internal support, steel lattice column and concrete column are simulated by a 1D embedded beam element structure; and the corresponding prestressed constraints are applied. The main reason is that the beam element is an isotropic and nonyielding linear elastic material; thus, it can simulate well the characteristics of the structure, and the embedded beam element does not need to consider coupling with the soil structure grid, which simplifies the calculation process and greatly shortens the calculation time of the model. The simulation of the foundation pit support system is shown in Figure 20.
# Roll Motion Prediction of Unmanned Surface Vehicle Based on Coupled CNN and LSTM
## 3 Experiments
### Data Pre-Processing  
First, it is necessary to clean the data because the raw sensor data may contain noisy information. Reducing noise from raw data can minimize its effects on further modeling. The noise source can be either internal or external. In this paper, the major noise source is external because the sensor noise is caused by unavoidable external factors such as gradients and nonhomogeneous media. Statistical estimation was used to remove the internal noise, and the external noise was eliminated by a median filtering technique. Then, the seven pairs of status time-series data were divided into input \(x\) and output \(y\) by the lag time method. The input \(x\) can be expressed as \(\{S_{t-D},S_{t-D+1},\cdots,S_{t}\}\), and the output \(y\) can be expressed as \(r_{t+1}\). Finally, because these data are distributed differently, we normalized them.
# Analysis and Experimental Validation of Efficient Coded OFDM for an Impulsive Noise Environment
## 2 System Overview
### Implementation in GNU Radio
#### 2.2.2 The Receiver  
The receiver part of the IEEE 802.11 physical layer is designed to receive and demodulate OFDM signals. As described in [21], the OFDM receiver consists of a frame detection part, which exploits the short cyclic preamble repeated 10 times at the start of a frame. The frame is then aligned, equalized and decoded into the MAC information needed for proper delivery.  
NB: Since we evaluate performance over the air, the transmitter and the receiver used are interfaced with USRP radios. At the receiver side, recall that the last block was the decoding of the frame. However, this decoding is only performed by the Viterbi decoder. After that, we use Wireshark connector to save the information and performs MATLAB processing for the haul decoding of the rank metric.  
Figure 2: RC Encoder + WiFi Mapper block adding in _gr-ieee_ 802.11.
# Experimental Evaluation of Thermal Performance and Durability of Thermally-Enhanced Concretes
###### Abstract  
The thermal performance and durability of the thermally-enhanced concrete with various insulating materials were evaluated through a series of tests. Three types of insulating materials--diatomite powder, hollow micro-spheres, and a micro-foam agent--were used for both normalweight aggregate concrete (NWAC) and lightweight aggregate concrete (LWAC). The thermal conductivity was measured by two different test methods: quick thermal conductivity meter (QTM) and guarded hot wire (GHW) methods. Then, the results were compared with each other. All insulating materials used in this study proved their ability to reduce the thermal conductivity. Additionally, it can be found that the trend of a decrease in air-dry density is similar to that of thermal conductivity of thermally-enhanced concrete. Additional thermal transmission tests with seven large-scale specimens were conducted by using the calibrated hot box (CHB). However, from this tests, it was seen that thermal transmission reduction for tested specimens were not large compared to the thermal conductivity reduction measured by QTM and GHW, due to multiple heat transfer. To examine the durability of thermally-enhanced concretes, accelerated carbonation and freeze-thaw cycle tests were conducted. From the results, it can be found that the thermally-enhanced concrete shows good freeze-thaw resistance. However, the carbonation rates of the concretes increased rapidly and additional methods to improve the carbonation resistance should be considered.  
Keywords:thermal insulation materials; thermal conductivity; thermal transmission; freeze-thaw test; carbonation rate test; lightweight aggregate concrete +
Footnote †: journal: Applied Physics Letters
# Transformer Fault Diagnosis Method Based on Incomplete Data and TPE-XGBoost
## 1 Introduction
The synthetic minority oversampling technique (SMOTE) and recursive feature elimination (RFE) are used to deal with the data unbalancing problem of the DGA dataset in [18], and the performance of transformer fault diagnosis model based on kNN, SVM or XGBoost methods will improve significantly. All of these methods can achieve the purpose of assessing the transformer's operating condition. However, there are still many problems to be solved in practical applications. First, the interconnection between different components of dissolved gas in oil is not usually considered. Second, diagnostic models based on a single algorithm have low accuracy and weak model generalization performance. Integrated algorithms have a large parameter space, and the effectiveness of fault diagnosis depends on the appropriate combination of parameters. This paper proposes a TPE-XGBoost-based transformer fault diagnosis algorithm for incomplete datasets, which can handle transformer fault diagnosis in the presence of missing data. Firstly, the random forest regression method is adopted to fill in missing values of DGA data, and the correlation between different gases is used for feature derivation processing to obtain more feature information. Then, a tree-structured probability density estimator (TPE) is introduced to obtain the optimal parameter space of XGBoost. Finally, the effects of different data-filling and fault diagnosis algorithms are analyzed and discussed. This paper consists of five sections: Section 2 illustrates the principles of random forest regression and TPE-XGBoost methods. Section 3 describes the structure and application process of the fault diagnosis model. Section 4 presents the application effect of power transformers fault diagnosis and discusses the experimental results. Finally, Section 5 provides some conclusions that we have drawn from this study.
## 2 Materials and Methods
### Methods
#### 2.3.2 Data Use and Processing  
Google Earth Engine (GEE) is a cloud-based platform with vast databases for geospatial analysis on a global scale [69]. GEE has incomparable advantages in the processing of large-scale remote sensing data over a large period. The RTE algorithm, the simple cloud-mask algorithm, the MaxEnt classification, and the ROC test used in this paper are all programmed by the GEE platform. All datasets used can be obtained from GEE for free or processed based on free open datasets.
## 4 Results and Discussion
Figure 11: VT-measured observer estimation test using wrong initial values; (**a**) SOC, (**b**) service temperature changing, (**c**) SOH, (**d**) terminal voltage, (**e**) core temperature changing. Figure 12: VT-measured observer estimation test using wrong parameters; (**a**) SOC, (**b**) service temperature changing, (**c**) SOH, (**d**) terminal voltage, (**e**) core temperature changing. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**The Observers** & **SOC\%** & \(\mathbf{V_{1}}\) & \(\mathbf{V_{2}}\) & \(\mathbf{T_{S}}\) & \(\mathbf{T_{C}}\) & **SOH\%** \\ \hline Temperature-measured observer & No & Yes & Yes & Yes & No \\ Voltage-measured observer & Yes & Yes & Yes & No & No \\ VT-measured observer & Yes & Yes & Yes & Yes & No \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Observers’ observability for different states. Analyzing the previous results, SOC and SOH are unobservable for temperature observers, while \(T_{C}\), \(T_{S}\), and SOH are unobservable for voltage observers. Nevertheless, the resistance of the circuit could affect the temperature. On the other hand, the temperature would affect the resistance as well. Therefore, \(T_{C}\) and \(T_{S}\) could be observed by an indirect approach with the voltage observer since the convergence of the temperature after a long time. That could lead to the idea that both voltage and temperature observers might be combined together as a VT-observer. Based on the results, there is an advantage for the VT-measured observer as most of the states could be estimated because it combines the two observers. It should be indicated that the SOH could not be estimated by any observer. However, the SOH values could be estimated well by an open-loop observer, provided that the model of the battery and the initial values are accurate. Additionally, based on the results, a positive linear relationship between SOC and the charging time could be noticed, while SOH has a negative one. It requires about two hours for the SOC to replenish from 0% to 90% in the simulation, with an associated SOH decay of 0.005%. The reason for running the simulation with an upper limit of 90% is to keep the health of the battery because this test will be applied many times. The lowest SOH is 99.995%, and the highest SOC is 87.4% compared to the original status. That indicates that the battery health and charging time is a tradeoff. This led to a rise in the balance between efficiency and safety. For higher efficiency, the charging time should be minimized; For the highest safety, the aging condition should be minimized. Through comparing these results with the proposed model in [29], in real-life cases, a balanced point should be found in different scenarios, which leads to a new topic--optimal control for battery charging. Since the importance of batteries as an energy storage device, the proposed observer would definitely advance sustainability. Therefore, the importance of the estimation of the states in energy systems could be noticed.
# Neuro-Fuzzy Model Evaluation for Enhanced Prediction of Mechanical Properties in AM Specimens
## 4 Methodological Framework for Enhancing ANFIS Model  
The ANFIS model utilized in this study employs a five-layer architecture that combines neural network and fuzzy logic principles (Figure 2). The structure includes an input layer where membership functions calculate the degree of membership for each input variable, a rule layer that applies fuzzy rules, a normalization layer to standardize firing strengths, a defuzzification layer that computes weighted outputs, and an output layer that aggregates these results into a final prediction. This architecture enables ANFIS to effectively capture complex nonlinear relationships in the data while adaptively tuning fuzzy rules and membership functions during training.  
The methodology for developing and improving the ANFIS model includes several structures, designed to ensure the model's accuracy and effectiveness in predicting the mechanical properties of additive manufacturing (AM) parts.  
The initial phase of this research comprised an extensive literature review, focusing on two key areas: AM and neural-fuzzy systems, specifically, ANFIS. The research on AM is focused on the factors that influence the mechanical properties of printed parts, such as the printing parameters, material, and AM technology. Concurrently, the exploration of neural networks and fuzzy systems aimed at understanding predictive modeling techniques and how fuzzy model structures can enhance prediction accuracy and reliability.
# Influence of Body Weight Support Systems on the Abnormal Gait Kinematic
## 3 Results
### The Stride Length, Step Width, and Step Length  
Table 1 represents the comparison of the standardized values of the stride length, the step width, the step length of the left and right sides. When comparing the stride length, the statistical analysis indicated that the abnormal walking condition significantly differed from the normal walking, walking under the ABWS system at all unloading force levels, and the walking under the CW system at 70% (\(p<0.001\)). When the comparison between the walking under ABWS and CW systems, the significant  
Figure 9: The comparison of the ROS of the hip, knee and ankle joint on the right side. (**a**) represents the comparison of the hip, knee and ankle joint movement between normal walking and abnormal walking. (**b–d**) represent the comparison of ROM of the abnormal walking under the ABWS and CW system at 30%, 50% and 70%, respectively. differences were also observed (\(p<0.01\) at 30% and 70% unloading levels, \(p<0.001\) at 50% unloading level). The stride lengths of the walking under the ABWS system 50% and 70% unloading levels were not significantly different from the normal walking case (\(p=0.958\), and \(p=0.975\) at 50% and 70% unloading levels respectively). In contrast, the stride lengths while walking under the CW system at 30% and 50% unloading levels did not significantly differ from the abnormal walking case (\(p=0.814\), and \(p=0.094\) at 30% and 50% unloading levels respectively). From Table 1, the statistical analysis indicated that the step width of the abnormal walking condition significantly differed from all step widths of the other walking conditions (\(p=0.027\) when compared to normal walking, less than 0.001 when comparing with others). The significant differences were also found when comparing the normal walking case with walking both under the ABWS and CW system at all weight support levels (\(p<0.001\)). Furthermore, significant differences were also seen in the comparison of the step width between walking under the ABWS and CW system at all unloading levels. In terms of the step length of the left side (the normal side), there was no significant difference from the abnormal walking and normal walking (\(p=0.77\)). The step length of the left side in case of abnormal walking was significantly smaller than the ABWS and CW systems (\(p<0.001\)) excepted at the 30% unloading level of the ABWS system. When comparing the step length of the right side (the restricted side), the abnormal walking showed the significant difference from the normal walking case (\(p<0.001\))and all other conditions (\(p<0.001\) for ABWS system at all unloading levels, and \(p<0.05\) for CW system). Comparison of the right step length between the ABWS and CW system, the significant differences were found at 30% and 70% of unloading force (\(p<0.05\)).
## 2 WSNs Data Acquisition Model
### Compressive Sensing Theory  
Consider a grid WSN, consisting of \(N\) (\(N=I\times J\) ) sensors located on a two-dimensional plane for monitoring an environment with \(I\) and \(J\) sensors in the \(x\)- and \(y\)-directions, respectively. Each sensor in the network grid independently acquires a measurement and transmits this data towards the sink along the routing path. We firstly transform the two-dimensional data matrix \(\mathbf{U}\) of the original signal into one-dimensional data vector \(\mathbf{X}\) by Equation (1):  
\[\mathbf{X}=Vec(\mathbf{U})=\left[u_{11}...u_{I1}...u_{I2}...u_{I2}...u_{I \
u}...u_{I\
u}\right]^{T},\mathbf{X}\in R^{N} \tag{1}\]  
Taking into account the sparsity of natural phenomena (e.g., the temporal correlation and spatial correlation) in the monitored environment [1,2], based on classic compressive sensing theory, we can create \(M\) random projections of \(\mathbf{X}\), that is, \(\mathbf{Y}\in R^{M}\), and obtain the following mathematical model:  
\[\begin{cases}\mathbf{\chi}=\mathbf{\Psi}^{-1}\mathbf{X},\mathbf{X}=\mathbf{\Psi}\mathbf{ \chi},\left\|\mathbf{\chi}\right\|_{0}=K,K<<N\\ \mathbf{Y}=\mathbf{\Phi}\mathbf{X}+\mathbf{Z}=\mathbf{\Phi}\mathbf{\Psi}\mathbf{\chi}+\mathbf{Z},\\ \mathbf{\Phi}=\left\{\phi_{1},\phi_{2},...,\phi_{M}\right\}^{T},\phi_{i}\in R^{N} \end{cases} \tag{2}\]  
where \(\mathbf{\Phi}\) is the measurement matrix, which is referred to as the routing matrix since it indicates the way in which our sensor data is acquired and transmitted to the sink; \(\mathbf{\Psi}\) is an invertible transformation \(N\times N\) matrix; \(N>M\geq C_{{}_{K}}\cdot K\cdot\log N\) with a constant \(C_{{}_{K}}\); original signal \(\mathbf{X}\) is called \(K\)-sparse if it has only \(K\) significant components; \(\mathbf{\chi}\) is the sparse representation of \(\mathbf{X}\); and \(\mathbf{Z}\in R^{M}\) is a vector denoting a noisy signal from \(M\) sensor nodes after the random measurement. Considering the computation capability of WSNs, we let sink node with no energy constraints and high computing capability to complete the complex matrix computation while let each sensing node only to sensing the physical signal.