esa-sceva/satcom-chunk-collection huggingface dataset:
# High Performance Computing in Satellite SAR Interferometry: A Critical Perspective
## 1 Introduction
Different investigations have been conducted by employing different HPC to enhance the computational performance of many remote sensing applications. A review of recent developments of HPC applied to hyperspectral imaging is provided in [7]. [PERSON] et al. [8] implemented soil moisture estimation using a cluster, and other studies included implementations of image mosaicking based on MPI [9], data fusion [10], and band registration [11]. In addition, GPU-based approaches for SAR CFAR detection [12] and double-bounce SAR simulation [13] were also proposed. Large-scale InSAR processing over vast areas by using conventional InSAR techniques have faced incredible challenges because of the inherent large computational burden. Specifically, current InSAR SAR applications involve large-region coverage, multi-temporal, multi-band datasets processing to achieve accurate and up-to-date information about Earth's surface at the regional and global scale. As large-scale SAR data processing problems are concerned, it is then clear the crucial role that HPC has in achieving timely up-to-date information of interest without sacrificing accuracy. More specifically, new frontiers for solving large-scale InSAR processing problems have been opened by HPC. Specific parallel algorithms have been presented to cope with the burden of different elemental stages of the interferometric processing, and several prototype solutions have been developed to exploit the parallel processing capabilities of different high-performance infrastructures. Although there has been a flourishing of HPC applications in SAR interferometry in recent times, a systematic overview of the state of art in this field is currently lacking. In this paper, we present the state-of-the-art and the most recent developments in exploiting HPC methodologies and architectures in the context of interferometric SAR processing techniques. The existing implementation strategies are systematically analyzed and discussed, thus including solutions based on parallel and distributed platforms, multi-core environments, and specialized architectures such as graphics processing units (GPUs). How HPC has been used in the field of InSAR over the last 15 years is reviewed, thus identifying emerging trends and offering a perspective on the potential future opportunities and challenges. The paper is structured as follows. Section 2 presents SAR interferometry fundamentals and introduces the different InSAR processing schemes and stages, while Section 3 presents operative application scenarios. An overview of HPC fundamentals concepts and models is provided in Section 4. HPC approaches in InSAR fundamental functional stages, and entire SAR interferometric techniques using parallel computing, are addressed in Sections 5 and 6, respectively. Section 7 draws some conclusions.
## 4 Experiment Study
### Comparative Experiment Results and Analysis
For the prediction scale of 3 h, the values of the evaluation metrics RMSE, MAE, SMAPE, MAPE, and R of the proposed ELFNet model are 0.0417, 0.0333, 0.2813, 0.4118, and 0.9892, respectively. ELFNet outperforms other deep learning techniques according to the SMAPE evaluation criteria, with ResNet-18 coming in last, with a result of 0.4996. Figure 13c illustrates that the prediction scale is 3 h.  
ELFNet still maintains a high prediction accuracy, while the prediction errors of the other three methods are the highest for ResNeXt-50. This also reflects that, under the assumption of some evaluation indicators, the depth of the network cannot further improve the prediction accuracy. When the ResNet-18 error fluctuates the most, the increase becomes progressively bigger. This convincingly demonstrates that the ELFNet model put forward in this research has great prediction stability and continues to produce reliable forecasts at various prediction scales. A histogram of prediction deviations (Figure 15) was created to show the estimated error margin and distribution properties for all electricity prediction horizons. The normal distribution, along with the mean and variance, is shown by the black dashed line. In general, the position of the normal distribution curve is determined by the mean value, while the shape of the normal distribution curve is determined by the variance. The closer the mean value is to 0, the smaller the variance is and the more accurate the prediction accuracy. In this experiment, for the 1 h prediction horizon, the mean is 21.4271 and the variance is 133.7763; for the 2 h prediction horizon, the mean is \(-\)14.9310 and the variance is 140.0519; and for the 3 h prediction horizon, the mean is 33.3051 and the variance is 171.8637. These data all indicate the accuracy of ELFNet's predictions. Figure 12: Forecasting results of ELFNet and deep learning methods for 2 h ahead. (**a**) is the prediction result, (**b**) is the local enlargement, and (**c**) is the correlation diagram between the true value and the predicted value. Figure 14: Evaluation metrics for 3 h ahead. Figure 13: Forecasting results of ELFNet and deep learning methods for 3 h ahead. (**a**) is the prediction result, (**b**) is the local enlargement, and (**c**) is the correlation diagram between the true value and the predicted value. Figure 15: Error distributions of ELFNet and other methods.
# GNSS Signal Compression Acquisition Algorithm Based on Sensing Matrix Optimization
###### Abstract  
Due to the sparsity of GNSS signal in the correlation domain, compressed sensing theory is considered to be a promising technology for GNSS signal acquisition. However, the detection probability of the traditional compression acquisition algorithm is low under low signal-to-noise ratio (SNR) conditions. This paper proposes a GNSS compression acquisition algorithm based on sensing matrix optimization. The Frobenius norm of the difference between Gram matrix and an approximate equiangular tight frame (ETF) matrix is taken as the objective function, and the modified conjugate gradient method is adopted to reduce the mutual coherence between the measurement matrix and the sparse basis. Theoretical analysis and simulation results show that the proposed algorithm can significantly improve the detection probability compared with the existing compression acquisition algorithms under the same SNR.  
GNSS signal; acquisition; ETF; conjugate gradient; compressed sensing; SNR
# State-of-the-Art Research on Loess Microstructure Based on X-ray Computer Tomography
## 4 The Application of CT in Loess Microstructure  
As CT technology and equipment continue to evolve and their costs decrease, they have been widely applied in the field of loess microstructure research. [PERSON] et al. [19] were the first to propose a method for quantitatively evaluating the structural properties of loess using CT technology. They conducted initial scans on loess compacted to different degrees and analyzed the initial structural changes in compacted loess for both CT values and CT images, paving the way for the application of CT in the field of loess structural research. [PERSON] et al. [103] proposed structural parameters for loess based on triaxial tests, offering a new quantitative approach to the study of the loess structure. Meanwhile, [PERSON] et al. [24] combined SEM images with CT images and conducted a supervised classification for the quantitative analysis of loess microstructure, laying the groundwork for many subsequent scholars to use CT technology to quantitatively analyze the microstructural characteristics of loess (e.g., the size, shape, contact relationships, and arrangement of particles; the morphology, distribution, and connectivity of pores, etc.) and to explore the quantitative relationship between micro-parameters and macroscopic collapse.
# Response of Global Terrestrial Carbon Fluxes to Drought from 1981 to 2016
## 4 Discussion
### Spatial Patterns of Impacts of Drought on Carbon Fluxes  
Previous studies have indicated that GPP responds to changes in temperature, precipitation, and/or radiation. The relative importance of these meteorological factors varies with regions [3; 77]. In this study, interannual variations of simulated annual GPP were generally positively correlated with SPEI, especially in regions south of 45\({}^{\circ}\)N. [PERSON] et al. [29] reported that vegetation activity and growth were significantly positively correlated with SPEI over 72% of global vegetated land areas. Based on flux observations in China, [PERSON] et al. [78] found that tower-based GPP increased with annual precipitation and mean annual temperature at low latitudes. In the BEPS model, NPP is calculated as the difference between GPP and autotrophic respiration, which comprises growth and maintenance respiration. The former was calculated as a proportion of GPP and the latter was calculated as a function of biomass, temperature, and reference respiratory rate. Consequently, GPP is the dominant determinant of NPP. Therefore, simulated GPP and NPP showed similar spatial patterns in response to drought.  
NEP is the balance between GPP and ER. In most regions of the globe, soil water content is seldom above field capacity and drought might cause both GPP and ER to decrease. As a consequence, GPP and NEP show similar sensitivity to drought, as has been reported by [PERSON] [79] and [PERSON] et al. [80]. At middle and high latitudes of the northern hemisphere, such as the Eurasia region, simulated NEP was less susceptible to drought than GPP and NPP (Figure 2). In this region, soil organic matter is rich and soil water content is often between field capacity and wilting point. Mild or moderate drought might significantly enhance heterotrophic respiration and limit GPP to a lesser extent. Therefore, NEP is less sensitive to drought in comparison with GPP and NPP in these regions.  
At high latitudes of the northern hemisphere, temperature and radiation play more important roles in regulating GPP and NPP [3]. The increase in precipitation is normally accompanied by decreases in temperature and radiation, and consequently causes GPP and NPP to decrease. The lower correlations between SPEI and simulated carbon fluxes in this study further confirm that abnormal wetness may induce the terrestrial carbon fluxes to decrease in this region.
# Steel Corrosion Behavior in Light Weight Fly-Ash Based Alkali Activated Mortars
## 2 Materials and Methods
### Sample Preparation  
Mortars were prepared by mixing fly ash with alkaline solutions, aggregates (silica sand or expanded perlite) and water for 5 min. The detailed procedure for sample preparation is reported in [24; 26]. Standard weight (named M) and light weight (named LWM) mortar samples were prepared using silica sand and expanded perlite as aggregates, respectively. In addition, when LWM samples were prepared, a 0.3 wt% of 30 vol% hydrogen peroxide solution was added to the slurry during the last minute of the mix as foaming agent [34]. Table 1 reports the composition of the investigated mortars. The obtained alkali activated mortars exhibited a targeted molar Si/ Al ratio of 2.9 and a total water content (considering the amount of water in the activating solutions and the extra water) of 10% and 22% for M and LWM samples, respectively.  
Specimens for physical and microstructural characterization were obtained by pouring the fresh mortars into cylindrical models (with a diameter of 35 mm). Samples simulating the coated steel structures were prepared by pouring the fresh mortars on the steel plate samples (50 mm \(\times\) 50 mm \(\times\) 1 mm) to produce 20 mm-thick coating, as sketched in Figure 1. All the samples were cured in sealed conditions at ambient temperature (T = 21 \(\pm\) 2 \({}^{\circ}\)C) and after 3 days samples were demolded and stored for a total curing time of 28 days.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Samples**} & **Fly Ash** & **8M NaOH** & **Sodium Silicate** & **Extra Water** & **Silica Sand** & **Expanded** & **H\({}_{2}\)O\({}_{2}\)** \\  & **(wt\%)** & **(wt\%)** & **(wt\%)** & **(wt\%)** & **(wt\%)** & **Perlite (wt\%)** & **(wt\%)** \\ \hline M & 28.3 & 2.1 & 10.7 & 2.2 & 56.5 & - & - \\ LWM & 54.5 & 4.1 & 20.5 & 7.7 & - & 13.0 & 0.3 \\ \hline \end{tabular}
\end{table}
Table 1: Mix design of the prepared mortar samples.  
Figure 1: Schematization of the laboratory samples simulating the coated steel structure by 20-mm thick alkali activated mortar (normal weight fly ash-based mortars (M) and light weight alkali activated mortars (LWM)) layers. These samples were used for electrochemical testing.
# RS-DETR: An Improved Remote Sensing Object Detection Model Based on RT-DETR
## 4 Results and Discussion
### Implementation Details  
In this study, our proposed method is implemented in the PyTorch framework with Python. The software environment includes Ubuntu 22.04, Python 3.8, PyTorch 1.13.1, and CUDA 11.7. The hardware setup comprises an Intel(R) Xeon(R) CPU E5-2696 v4 processor and an NVIDIA RTXA 3090 graphics card with 24 GB of VRAM. During training, the input image size is 640 \(\times\) 640 pixels, the training epochs are set to 100, the batch size is 8, the initial learning rate is 0.0001, the weight decay is 0.0001, and the optimizer is AdamW. All other training parameters align with those used in RT-DETR.
## 2 Materials and Methods
### Attention Mechanism Module  
SENet (Squeeze-and-Excitation Network) is a channel attention module that primarily uses Squeeze-and-Excitation to embed global information and perform adaptive weight allocation to obtain the importance of feature channel dimensions. Its structure is shown in Figure 3. F\({}_{\text{tr}}\) can be regarded as a conventional convolution operation, which is used to realize dimension transformation of feature graphs. The F\({}_{\text{sq}}\) model utilizes the global average pooling to condense the overall spatial information into a channel descriptor. This means that the two-dimensional channel features of the feature graph are condensed into a value with a global receptive field, resulting in final statistical data containing context information, effectively addressing the issue of channel dependency to some extent. To fully capture channel dependencies, F\({}_{\text{ex}}\) uses a simple gating mechanism with a sigmoid activation consisting of two fully connected layers to reduce model complexity and increase generalization. The first fully connected layer compresses the channels to reduce the calculation amount, and then enters the second fully connected layer to recover the number of channels through the ReLU activation layer, and then obtains the weight value of each channel through the Sigmoid activation layer. Finally, the F\({}_{\text{scale}}\) operation is used to multiply the calculated weight value of each channel with the two-dimensional matrix of the corresponding channel from the original feature map to obtain the final output feature map.  
For a feature map X of any dimension, after input to the SE module, C feature maps U of size H \(\times\) W are first obtained after F\({}_{\text{tr}}\) convolution operation. F\({}_{\text{sq}}\) performs global average pooling on the feature map of H \(\times\) W \(\times\) C and compresses it into a 1 \(\times\) 1 \(\times\) C feature vector z. All feature vectors are input to the first fully connected layer of F\({}_{\text{ex}}\) to obtain the feature vectors 1 \(\times\) 1 \(\times\) C \(\times\) R, where R is a scaling parameter and the value is 16, and the computation is reduced by the ReLU nonlinear activation layer. Afterwards, the output features are input to the second fully connected layer with C neurons, outputting a feature vector of size 1 \(\times\) 1 \(\times\) C. The vector values are then mapped between [0, 1] using the Sigmoid function to obtain the weights of each channel to the input feature layer, yielding the weight vectors. The weight vector s\({}_{\text{c}}\) of channel c is multiplied in the F\({}_{\text{scale}}\) part with the corresponding H \(\times\) W \(\times\) C feature vector output from F\({}_{\text{tr}}\) to obtain the weighted feature  
Figure 3: SE Attention Module.  
map \(\chi_{\text{c}}\), and finally, all the weighted feature maps are recombined to obtain the final output feature maps.
## 3 Experimental Analysis and Discussion
### Huazhong University of Science and Technology Bearing Experiment
#### 3.4.2 Comparison of Different Data Generation Methods  
Similar to previous comparisons, the method proposed in this paper was compared with BEGSMOTE, ADASYN, and GAN to verify its applicability in different experimental  
Figure 8: Diagnosis results of the proposed method on HUST datasets.  
Figure 9: Confusion matrix of the proposed method on HUST datasets.  
scenarios. The diagnostic results of each method on different datasets are shown in Table 10. As seen, when the degree of imbalance is low, such as in Dataset H, all methods perform well, with the GAN slightly outperforming BEGSMOTE and ADASYN. As the sample imbalance becomes more severe, the diagnostic performance of all methods decreases. However, the proposed method still achieves 92% accuracy in the extremely imbalanced Dataset E and attains the highest performance metrics on each dataset. This once again demonstrates its significant advantages and broad applicability in addressing issues across various imbalance ratio fluctuation ranges.
## 2 Materials and Methods
### Uncertainty Quantification Framework
[20] performed uncertainty analysis for fast reactors considering three input uncertainty sources, namely, basic delayed neutron data, energy dependence of delayed neutron yield, and fission cross-section uncertainties. The uncertainty in \(\beta\) was found to be about 4-5% [17; 20]. Similarly, [PERSON] and [PERSON] [21] estimated the uncertainties in \(\beta\) to be around 5% by accounting for uncertainties in delayed neutron yield and delayed fission spectra. [PERSON] et al. [22] employed first-order perturbation theory to estimate sensitivity coefficients and found that the total uncertainty in \(\beta\) for U-235 benchmarks was around 3%-7%. Based on these works, a 5% uncertainty is assumed for the neutron kinetic parameters. In this work, DAKOTA is used to perform uncertainty propagation and some preliminary correlation analysis. A simple interface was established between Gen-Foam and DAKOTA with a script written in the Python language. DAKOTA generates the input samples and uses this interface to propagate the uncertainties to the outputs of interest. Different outputs are studied from scalar quantities, such as the \(\mathrm{k_{eff}}\), and up to 3D fields, such as the temperature distribution. For all these outputs, preliminary qualitative sensitivity analysis is performed through the Spearman rank correlation estimates. The Spearman correlations account for non-monotonic behaviors but cannot capture more complex non-linear trends.
## 2 Methodology
### Step 1: Estimation of the Individual Modes  
#### 2.1.1 Free-Decay Form Estimation  
NExT method was proposed by [PERSON] et al. [36] to estimate and extract the response (a free-decay one) produced broadband dynamic excitations using the acquired response samples. It presents a slightly improved capability to deal better with noisy signals, which allows obtaining better free-decay responses unlike random decrement technique [37]. For this reason, NExT method has been employed in this work and in several applications, such as health monitoring [38; 39; 40]. This method assumes that any civil structure, subjected to broadband signals as excitation source, can be represented by the following motion equation:  
\[M\ddot{R}_{xy}(\tau)+C\dot{R}_{xy}(\tau)+KR_{xy}(\tau)=0 \tag{1}\]  
where \(M\), \(C\), and \(K\) are the mass, damping, and stiffness matrices, respectively; \(x\) and \(y\) are the measurement positions. Analyzing the aforementioned equation, it is seen that the cross-correlation function \(\ddot{R}_{xy}(\tau)\) fulfills the homogenous equation of motion; hence, it can be treated as the free-damped response of the structure. \(\ddot{R}_{xy}(\tau)\) is estimated by using the following equation:  
\[\ddot{R}_{xy}(\tau)=\frac{1}{N}\sum_{n=0}^{N}x[n]y[n-k] \tag{2}\]  
where \(x\) and \(y\) are the reference signal and the in-test signal, respectively, \(N\) is the length of the signals, and \(k\) is the variable index that goes from 0 to \(2N-1\).
## 3 Accelerated Corrosion Testing of the Reactor Concrete Block
### Results and Discussion
#### 3.2.2 Detection of Damage Using Acoustic Emission
This is shown in Figure 22. The magnitude of cumulative signal strength from the broadband sensors is lower in comparison to the resonant sensors, which is expected due to the lower sensitivity of the broadband sensors. Figure 21: Cumulative signal strength from resonant sensors on the aged concrete block specimen. Figure 20: AE data recorded from broadband sensors on the reactor concrete block specimen at the 0.25-inch concrete cover location. Figures 23 and 24 show the Intensity Analysis results calculated at each location. The estimation of initial damage for the aged concrete block specimen based on visual inspection and electrochemical results was located near the border between the 'no damage' region and the 'depassivation' region of the chart. For the control location, a lower initial damage state was used since no corrosion damage is expected in this specimen. AE activity from the resonant sensors at the 1.0-inch concrete cover location progressed from the initial state to the severe damage zone over the duration of the monitoring period. AE activity from the resonant sensors at the 0.125-inchconcrete cover location progressed from the initial state to the border of the cracking and severe damage zones. For the broadband sensors at the 0.25-inch concrete cover location, acoustic emission activity progressed from the initial state to the border of the cracking and severe damage zones. Figure 23: Intensity Analysis for resonant sensors on the reactor concrete block specimen. Figure 22: Cumulative signal strength versus time from broadband sensors on the aged concrete block specimen. The above results are indicative of cracking in the concrete matrix due to corrosion activity at all three locations. As with the electrochemical measurements, the acoustic emission activity indicated that the most severe damage occurred at the 1.0-inch concrete cover location. As mentioned above, this location is affected by cracking as noticed through leakage of the NaCl solution at this location. While many degradation models for reinforced concrete are based on diffusion and, therefore, do not directly address the presence of cracking in the matrix. The effect of cracking in the matrix may, nonetheless, be significant. Similarly, many models assume a homogeneous concrete matrix. The lack of homogeneity in the concrete matrix for actual structures such as the concrete test block may also play a significant role in the results.