esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[52] was also limited to precisely detect points moving with velocities below 3.5 cm/year in the LOS by PS-MTI technique. In this study, we focus on PS-MTI technique since SBAS did not provide any reliable deformation map due to the high inherent noise observed through the interferograms and caused by spatial and temporal decorrelation induced by vegetation and snow. We report on the recent and ongoing monitoring activities on the Corvara landslide having velocity rates between dozens of centimeters to meters per year with results based on X-band COSMO-SkyMed (CSK) imagery from 2013 to 2015, as well as the validation of these using Differential solutions retrieved from Global Position System (DGPS) acquisitions. For the SAR monitoring, artificial CRs designed for the application in high-alpine conditions have been installed. Although CSK data were acquired on quite regular basis (with some exceptions during winter periods), the study area was particularly challenging due to its mountainous characteristics including the absence of natural scatterers, due to the presence of vegetation and changing atmospheric conditions (e.g., high precipitation rates and the presence of snow). Moreover, the entity of movements encompasses a wide range of velocities where the interferometric techniques are sometimes at limit conditions. It is beyond the scope of the paper to provide a detailed geomorphological interpretation of the landslide phenomena and an analysis of movement triggering conditions; these will be put forward in a separate paper. Instead, we focus on the methodologies applied as well as their opportunities and limitations for monitoring landslide deformations. Moreover, we give some outlook on future research directions and the expected landslide monitoring capacities related to the new Sentinel-1 satellite constellation [53].
### Existing Solutions and Research Gaps  
#### 1.1.1 Existing Solutions  
Automatic modulation classification research has driven the development of several notable solutions aimed at improving the recognition of modulation schemes in wireless communication systems. Below, we summarize some of the key contributions:  
* Traditional Techniques: Early approaches like maximum likelihood (ML) methods, decision-theoretic approaches, and feature-based classifiers have been extensively studied. These methods utilize signal characteristics such as amplitude, phase, and frequency to distinguish modulation schemes with varying degrees of success.
* Signal Processing-Based Methods: Advanced signal processing techniques, such as wavelet transforms, cyclostationary feature detection, and higher-order cumulants, have been applied to improve the accuracy and robustness of AMC in diverse wireless environments.  
* Machine Learning Approaches: Recent developments have focused on leveraging supervised learning algorithms, such as support vector machines (SVM) and k-nearest neighbors (k-NN), to classify modulation schemes. These models often rely on pre-extracted features and have shown promise in controlled scenarios.
* Deep Learning Solutions: The integration of deep learning models, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), has revolutionized AMC by enabling end-to-end learning. These models process raw I/Q data directly, eliminating the need for manual feature extraction, and have demonstrated superior performance in complex environments.
# Early-Age Cracking in Concrete: Causes, Consequences, Remedial Measures, and Recommendations
## 4 Factors Affecting Early-Age Cracking
### Environmental Conditions  
Evaporation of water is always a challenging issue during concrete construction. Therefore, many researchers [(70; 23; 12)] studied this issue and identified initial temperature, ambient temperature, humidity, wind, and solar radiation as the major factors for the evaporation of water. [PERSON] and [PERSON] [(12)] found out that humidity, air temperature, and wind velocity have a direct relationship with the evaporation rate. Also, [PERSON] et al. [(70)] observed that solar radiation has a significant effect on the temperature variation of concrete after the effect of hydration heat disappears. They also noticed that dry ambient air increases the initial temperature of concrete elements, which is further increased by the hydration heat, because the initial temperature of concrete at placement becomes higher than the ambient temperature. Consequently, it may lead to inadequate concrete properties due to incomplete hydration that increase the potential of cracking. Furthermore, dry ambient air decreases the internal humidity of concrete and induces the large tensile stresses. Thus, the probability of cracking may be increased.  
[PERSON] et al. [(71)] investigated the effect of external environment on sensitivity to the formation of early-age cracks by self-restrained strains and identified that external temperature has a significant effect to reach the maximum temperature and the wind velocity mainly influences the temperature difference between the core and surface of concrete elements. Moreover, [PERSON] and [PERSON] [(9)] found out that the surrounding environment affects the surface temperature of adjoining structure which may contribute to cracking.  
Rainfall is another challenging issue during the construction process. Rainfall increases the relative humidity of the environment. A higher relative humidity could reduce the shrinkage because it provides the water continuously for the hydration process [(72)]. However, it may alter the water-to-cement ratio, especially during the first-day casting. A high water-to-cement ratio can alter the mechanical properties of concrete and increase the likelihood of the early-age cracking in concrete.  
Cool weather is also not favorable for concrete placement in cold regions. The constructors face certain challenges to place concrete during winter, especially for \"thin\" concrete structures, mainly due to the temperature and hydration progress in the entire structure. The thermo-activation of the hydration rate induces a negative effect on the structural stability at formwork removal [(39)].
# A Novel Evolutionary Algorithm for Designing Robust Analog Filters
## 5 Conclusions  
We proposed a GPRD, a novel algorithm for designing robust dynamic systems based on genetic programming and bond graph modeling, and applied it to the synthesis of analog filters. Comparative studies of evolving robustness by GAs (parameter tuning) and by GP (simultaneous topology and parameter search) showed that traditional parameter-tuning-based robust design puts severe limits on the possible robustness achievable through parameter-tuning. We also found that our GPRD robust design algorithm enabled us to find analog filters with much higher robustness (Type-I, Type-II, or both) with respect to the variations in its parameters compared to the parameter-tuning-type evolutionary algorithms (G3 PCX-GA) for robust design of fixed functional structures.  
There are several issues related to GP-based robust design. One is the computational complexity, since GP is notorious for its high computational demand. Unfortunately, the multiple samplings used to evaluate the robustness of candidate solutions add additional computational load to the evolutionary robust design process. Improved Monte Carlo sampling strategies are needed. The second issue is the need to develop algorithms that evolve robust systems with respect to topology perturbation or component failures. Another related question is how the perturbation pattern affects structures. For example, how does the evolution system respond differentially to component removal as an absolute number or as a percentage of components. Trade-offs between improving robustness by parameter search and by topological innovation will be studied and methods to control it will also be developed. We also observed that, while the proposed methodology is effective for the case study on analog filter design, it may require more refinement; for example, topology constraints may need to be added to the evolved topology to achieve a practical, realistic design.
## 4 Experiments Results
### Experimental Setup  
Google Colab with GPU was used to run the experiments. DeepCNN, hybrid CNN-LSTM, and hybrid CNN-GRU models were implemented using Keras and were optimized using the KerasTuner. The Scikit-learn was used to create ML models, which were then optimized via grid search. Each dataset was divided into an 80% training set and a 20% testing set. Models were optimized and trained on a training set before being tested on a testing set. The results for every model for the testing set were recorded.  
Several experiments were carried out to discover the best parameters for DeepCNN, CNN-LSTM, and CNN-GRU models using KerasTuner. The datasets were divided into two parts: 80% training and 20% testing. Models were optimized using the training set. To extract features and generate feature matrices for DL models, CBOW and SkipGram word embedding were utilized.
# Model Based Open-Loop Wind Farm Control Using Active Power for Power Increase and Load Reduction
###### Abstract  
A new wind farm control algorithm that adjusts the power output of the most upstream wind turbine in a wind farm for power increase and load reduction was developed in this study. The algorithm finds power commands to individual wind turbines to maximize the total power output from the wind farm when the power command from the transmission system operator is larger than the total available power from the wind farm. To validate this wind farm control algorithm, a relatively high fidelity wind farm simulation tool developed in the previous study was modified to include a wind farm controller which consists of a wind speed estimator, a power command calculator and a simplified wind farm model. In addition, the wind turbine controller in the simulation tool was modified to include a demanded power tracking algorithm. For a virtual wind farm with three 5 MW wind turbines aligned with the wind, simulations were performed with various ambient turbulent intensities, turbine spacing, and control frequencies. It was found from the dynamic simulation using turbulent winds that the proposed wind farm control algorithm can increase the power output and decrease the tower load of the most upstream wind turbine compared with the results with the conventional wind farm control.  
wind farm control; wind farm model; function minimization; power increase Article
# A Dual-Branch Self-Boosting Network Based on Noise2 Noise for Unsupervised Image Denoising
## 4 Experiments
### Overall Performance Comparison
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Methods** & **BM3D** & **DnCNN** & **FFDNet** & **Restormer** & **IRCNN** & **SwinIR** & **FEUNet** & **N2N** & **Ours** \\ \hline Cameraman & 26.19 & 27.26 & 27.03 & **27.88** & 27.16 & 27.79 & 27.88 & 27.34 & 27.00 \\ House & 29.57 & 29.91 & 30.43 & **31.56** & 29.91 & 31.11 & 31.56 & 30.73 & 30.98 \\ Peppers & 26.67 & 27.35 & 27.43 & 28.04 & 27.33 & 27.91 & 28.04 & 27.71 & **28.23** \\ Starfish & 24.90 & 25.60 & 25.77 & 26.66 & 25.48 & 26.55 & 26.66 & 25.87 & **27.16** \\ Monarch & 25.71 & 26.84 & 26.88 & 27.39 & 26.66 & 27.31 & 27.39 & 27.01 & **28.55** \\ Airplane & 25.17 & 25.82 & 25.90 & 26.17 & 25.78 & 26.14 & 26.17 & 26.01 & **27.40** \\ Eagle & 25.87 & 26.48 & 26.58 & **26.92** & 26.48 & 26.91 & 26.92 & 26.67 & 26.65 \\ Lena & 29.02 & 29.34 & 29.68 & 30.19 & 29.36 & 30.11 & 30.19 & 29.81 & **30.49** \\ Barbara & 27.21 & 26.32 & 26.48 & 28.31 & 26.17 & **28.41** & 28.31 & 27.09 & 27.29 \\ Boat & 26.73 & 27.18 & 27.32 & 27.83 & 27.17 & 27.70 & 27.83 & 27.47 & **28.36** \\ Man & 26.79 & 27.17 & 27.30 & 27.51 & 27.14 & 27.45 & 27.51 & 27.28 & **28.60** \\ Couple & 26.47 & 26.87 & 27.07 & 27.61 & 26.86 & 27.53 & 27.61 & 27.24 & **28.02** \\ Avg. & 26.70 & 27.18 & 27.32 & 28.01 & 27.12 & 27.91 & 28.01 & 27.52 & **28.37** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: PSNR results of various competing methods on individual images from Set12 dataset corrupted with Gaussian noise (\(\sigma=50\)). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Methods** & **BM3D** & **DnCNN** & **FFDNet** & **Restormer** & **IRCNN** & **SwinIR** & **FEUNet** & **N2N** & **Ours** \\ \hline Cameraman & 29.38 & 30.03 & 30.06 & 30.72 & 30.12 & 30.69 & 30.15 & 31.44 & **31.46** \\ House & 32.86 & 33.04 & 33.27 & 34.18 & 33.02 & 33.91 & 33.37 & 34.08 & **34.21** \\ Peppers & 30.20 & 30.73 & 30.79 & 31.31 & 30.81 & 31.28 & 31.01 & 31.84 & **32.33** \\ Starfish & 28.52 & 29.24 & 29.33 & 30.01 & 29.21 & 29.96 & 29.47 & 30.69 & **31.52** \\ Monarch & 29.30 & 30.37 & 30.14 & 31.02 & 30.20 & 30.95 & 30.30 & 32.02 & **32.57** \\ Airplane & 28.42 & 29.06 & 29.05 & 29.47 & 29.05 & 29.44 & 29.18 & 31.22 & **31.45** \\ Eagle & 28.94 & 29.35 & 29.43 & 29.75 & 29.47 & 29.75 & 29.64 & 30.94 & **30.95** \\ Lena & 32.07 & 32.40 & 32.59 & 33.00 & 32.40 & 32.98 & 32.73 & 32.97 & **33.90** \\ Barbara & 30.66 & 29.67 & 29.98 & 31.39 & 29.93 & **31.69** & 30.29 & 30.89 & 31.27 \\ Boat & 29.87 & 30.19 & 30.23 & 30.73 & 30.17 & 30.64 & 30.31 & 31.18 & **31.89** \\ Man & 29.59 & 30.06 & 30.10 & 30.33 & 30.02 & 30.32 & 30.12 & 31.90 & **32.19** \\ Couple & 29.70 & 30.05 & 30.18 & 30.60 & 30.05 & 30.57 & 30.25 & 31.28 & **31.74** \\ Avg. & 29.96 & 30.35 & 30.43 & 31.04 & 30.37 & 31.01 & 30.57 & 31.70 & **32.12** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: PSNR results of various competing methods on individual images from Set12 dataset corrupted with Gaussian noise (\(\sigma=25\)).
# A Trusted Lightweight Communication Strategy for Flying Named Data Networking
## 5 Performance Evaluation
### Results and Discussion  
Figure 8(a) represents the ratio of the detected unauthenticated data packets for both, our proposed scheme and NDN authentication strategies in respect to the time for respectively 10 and 30 UAVs. Resulted curves show that for low density both strategies show less detection efficiency as compared to the high density, i.e., always in favor of the vanilla NDN strategy. However, after a short period of time (required to establish trust among UAVs); our proposal proposed scheme shows almost the same results as NDN strategy.  
Additionally, Figure 8(b) represents the generated false positive and it shows that, except for the beginning of the experiments where inter-UAVs trust did not reach yet its stable values, the generated false positive is almost the same for both NDN and our proposal. From both the figures, it is clear that the trust-based data authentication strategy of our proposed scheme sustained almost the same performance of the vanilla NDN strategy with less delay and energy consumption for our proposal thanks to the distributed monitoring technique and the trust-based data authentication.  
On the other hand, Figure 9(a) presents the average end-to-end delay of data packets and it shows that our proposed scheme reduces the delay by more than 300% as compared to vanilla NDN for 30 UAVs density. Whereas, this enhancement is around 80% for low density cases (i.e, 10 UAVs). Thus, the results advocate that our proposal outperforms the vanilla NDN and makes our proposal more suitable for real-time applications as well as the different FANET applications.  
Figure 9(b) shows the packet delivery ratio for both our proposal and vanilla NDN forwarding strategies. It depicts that for both low and high density scenarios, our proposal trust-aware forwarding strategy outperforms vanilla NDN forwarding strategies, showing almost optimal results for high density case.  
Figure 10(a) analyzes the energy consumption of our proposal. We analyzed the scenario with 20% of malicious UAVs also for both cases with and without the distributed monitoring process in respect of the network density. Resulted curves show that while sustaining the detection performances, our distributed monitoring technique have reduced the average energy consumption by more than two times for high density cases, for instance in the case of 50 UAVs the average consumed energy is about 14,000 W.s for the continuous promiscuous mode. Whereas using our solution the average consumed energy becomes about 4400 W.s which is a significant optimization.  
Finally, for the introduced communication overhead with the piggybacked recommendations, Figure 10(b) depicts that our strategy of piggybacking the trust opinion to the data messages introduces increased values with the increasing number of UAVs and this is due to the consideration of all trusted  
Figure 11: Generated overhead and energy efficiency our proposal.  
nodes opinions to strengthen the detection process, and theses trusted UAVs are majority in the normal case.
## 4 Influence of Vehicle-Bridge Coupling Vibration on Anti-Disturbance Performance of Concrete
### Influence on Concrete Strength  
The normal concrete (NC-1 and NC-2) and the ultra-high-performance concrete (UHPC-1 and UHPC-2) were vertically disturbed under the different random vehicle-bridge coupling simulation vibration circumstances. The physical and mechanical properties of the adopted concrete material were tested according to Chinese standard GB/T 50,081 [21]. Different types of test result are compared as shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Serial Number of** & \multicolumn{2}{c}{**Vibration Parameter**} & **Rate of Aggregate** \\
**Specimen Group** & **Amplitude (mm)** & **Frequency (Hz)** & **Segregation (\%)** \\ \hline NC1-0-0 & 0 & 0 & 1.6 \\ NC1-2-6 & 2 & 6 & 1.7 \\ NC1-3-3 & 3 & 3 & 1.7 \\ NC1-3-6 & 3 & 6 & 1.8 \\ NC1-3-9 & 3 & 9 & 1.7 \\ NC1-4-6 & 4 & 6 & 2.0 \\ \hline NC2-0-0 & 0 & 0 & 2.2 \\ NC2-3-6 & 3 & 6 & 3.2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Aggregate segregation rate of NC specimens.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Serial Number** & **Compressive** & **Flexural Tensile** & **Splitting Tensile** & **Elastic Modulus Under** \\  & **Strength** & **Strength** & **Static Compressive Stress** \\ \hline NC1-0-0 & 52.7 & 6.1 & 5.17 & 58.64 \\ NC1-2-6 & 57.7 & 6.0 & 5.30 & 47.43 \\ NC1-3-3 & 59.9 & 4.8 & 4.71 & 49.57 \\ NC1-3-6 & 61.7 & 5.9 & 4.08 & 51.11 \\ NC1-3-9 & 61.1 & 4.9 & 4.74 & 46.16 \\ NC1-4-6 & 62.0 & 4.6 &  
disturbance. It is mainly because these parameters are sensitive to the characteristics of the interface transition zone between coarse aggregate and cement paste. Existing studies have indicated that the vibration from the initial setting to final setting will lead to significantly. It can be concluded that the shrinkage property of NC material is sensitive to the amount optimization of coarse aggregate.  
It can be learnt from Figure 9b that the vehicle-bridge coupling vibration is not significantly affected by the shrinkage performance of UHPC material under the vibration condition of 3 mm and 6 Hz. In particular, the shrinkage ratio of UHPC-1 is larger than that of UHPC-2 under both static and vibration disturbance conditions. For UHPC-1 material, the shrinkage value of specimens poured under vibration disturbance condition is less than that of specimens poured under static state (UHPC1-0-0) within 45 days curing time. However, it exceeds the specimens poured under static condition (UHPC1-0-0) after the 60 days, while for the shrinkage value of UHPC-2 material, specimens poured under vibration disturbance condition is always less than that of specimens poured under static state.
# A Lyapunov-Optimized Dynamic Task Offloading Strategy for Satellite Edge Computing
## 1 Introduction
Each mission can be offloaded to any other satellite in the constellation for collaborative computing via an ISL. This problem has not been extensively studied by scholars. * A strategy for offloading using the Lyapunov and Hungarian algorithms is proposed. The method is straightforward in principle, easy to implement, and has demonstrated superior performance compared with other benchmark algorithms. The remainder of this paper is organized as follows. Section 2 summarizes the related work. In Section 3, we describe the system model and problem formulation. Additionally, Section 4 presents the proposed algorithm. Simulation results and analysis are presented in Section 5. Section 6 summarizes the paper.
## 4 Experimental Results and Discussion
### Performance Analysis on Satellite Image Pair
Although the acquisition interval of image pair 9 and image pair 10 is more than 300 days, the satellite images are all collected in winter, so the land surface does not change much. Therefore, even if the acquisition interval reaches 310 days, as long as the surface changes little, the sun angle difference, and the intersection angle of the image pair are not large, the proposed method can still achieve good generation results. For the DSMs generated by the S2P method, most of the CP values are poor, while the values generated by the traditional BA method are better. However, in general, the CP value of the proposed method is 0.6%-3.0% higher than that of the traditional BA method in most cases. We also compare the ME of the DSM altitude, which reflects the accuracy of the 50% generated 3D points in DSM. In Table 3, the ME value of the DSM generated by the proposed method is from 0.71 m to 1.93 m, which is 0.4-6.44 m lower than that of the S2P method and 0.01-0.06 m lower than that of the traditional BA method in most cases. Therefore, for most image pairs, the proposed method is better and more robust than the S2P method and the traditional BA method. From Tables 2 and 3, we easily see that a large acquisition interval brings more uncertainty about surface buildings and vegetation, and it is difficult to generate a good DSM for a large sun angle difference or large intersection angle. According to Table 3, the CP of the DSMs generated by the proposed method for the first three image pairs is more than 60%, and the ME is less than 1.27 m. Therefore, the proposed method can obtain better-generated results for image pairs with small differences in acquisition intervals, sun angle difference, and intersection angle. In general, when the acquisition interval is less than 15 days, the sun angle difference is less than 20\({}^{\circ}\), and the intersection angle is 3\({}^{\circ}-15^{\circ}\), the proposed method can generate a good DSM.
## 3 Samples and Methods
### TEM Observation  
After the pretreatment, the samples to be tested were sent to the Beijing Physical and Chemical Analysis Center to analyze the TEM grid by transmission electron microscopy to observe the particle characteristics (such as size, shape, and composition) of the enriched nanoparticles. The transmission electron microscope (TEM) used for detection has a spot resolution of 0.20 nm, a lattice resolution of 0.1 nm, and a STEM-HAADF resolution of 0.17 nm, of which the minimum spot diameter is 0.8 nm. The transmission electron microscope is equipped with an X-ray energy dispersive spectrometer (EDS) with an accelerating voltage of 300 kV, and the samples observed under the microscope obtained in this study were all carried out in this environment. In the study, the composition of the particles was determined using EDS in the absence of reference substances; therefore, we cannot report the exact mass percentage of each chemical composition. When observing, we set the spot radius to \(<\)0.2 \(\upmu\)m. The relative content of a specific element or component in the particle is expressed in mass percent. Two points were selected to study the background composition of the Al grid. The results show that the aluminum mesh is mainly composed of C, O, Al, and Si. One contains C (94.21%), O (3.55%), Al (1.61%), and Si (0.63%); the other contains C (92.68%), O (3.76%), Al (2.85%), and Si (0.71%) [18].