esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In the first phase of this study, a reliable and cost-effective data monitoring system was developed to enable the remote and real-time monitoring of solar PV energy systems. The infrastructure of the IoT-based system consists of a data recording unit, a cloud system, a web interface, and a mobile application. Data from the solar PV system can be monitored independently of location through a mobile application that can be installed on smartphones or via the ThingSpeak platform. The distinctiveness of the proposed system at this point lies in making solar PV systems and the weather parameters affecting them accessible in a cost-effective manner through open-source software and mobile applications. In the second phase of the study, prediction models were developed to determine the expected data for comparison with the obtained real measurement data. This approach allows for the identification of potential system issues based on the difference between actual solar PV power and expected solar PV power derived from existing meteorological data. Machine learning methods including linear regression, SVM, decision trees, random forests, and KNN were employed to develop prediction models based on measurement data. The performance of these models was then numerically compared using performance metrics including MAE, MSE, RMSE, and R\({}^{2}\). Through the comparison conducted using these performance metrics, the random forests algorithm emerged as the most successful model across all criteria. In terms of accuracy performance on test data, the random forests algorithm achieved the highest accuracy rate of 87%. Other algorithms ranked in descending order of success were decision trees with 81%, KNN with 79%, SVM with 67%, and linear regression with 64% accuracy rates. In conclusion, this study successfully presents a cost-effective solar PV power monitoring system and a machine learning-based solar PV power predictor.  
In future work, increasing the number of parameters in the dataset, experimenting with different machine learning methods, and testing the developed system in large-scale solar fields are planned.  
This research was funded by Karabuk University within the scope of Scientific Research Projects, grant number KBU-21-DS-018.  
Not applicable.  
The author declares no conflict of interest.
## 4 NOAA-20 Algorithm Upgrades and Science Data Products
Following the lessons learned through S-NPP product maturity, adapting Cal/Val tools developed during S-NPP Cal/Val process to NOAA-20, and methodologies optimized in collecting the in situ measurements, the teams are expediting the NOAA-20 validated maturity.
## 4 Performance Comparison of Different Models
### Stationary Test and Model Building
Normally, the optimum epoch can be hardly determined automatically, and overfitting may be encountered. In such a case, the parameters of artificial neural network-based models are fixed after the training step is completed. However, for the ARIMA and SVR models, the parameters are updated in each prediction step.
###### Abstract  
Eco-friendly pervious concrete containing fly ash (FA) and blast furnace slag (BFS) were prepared in this study. The compressive strength and hydraulic behaviors were investigated to explore the effect of replacement content of FA and BFS. Rheological tests of cementitious pastes were first conducted and the results showed that FA could increase the apparent viscosity and BFS did not change the rheology performance. Compared to traditional concretes, FA and BFS both decreased the compressive strength of pervious concrete at 28 d, while pervious concrete incorporated with FA and/or BFS presented comparable strength at 60 d. Compared to the control concrete mix, at the same replacement rate, FA changed the compressive strength more obviously than BFS. FA and BFS both decreased the effective porosity and permeability coefficient of pervious concrete. However, when the replacement rate (30%) was the same, concretes with ternary blends presented obviously larger porosity than binary blends. The relationships between porosity and permeability, and strength were also established.  
pervious concrete; fly ash; blast furnace slag; hydraulic properties; compressive strength; porosity +
Footnote †: journal: Applied Sciences
## 3 Model Solution
### Selection of Regularization Parameters and Simulation
Particularly for \(s_{1}(t_{i}^{(1)})\), the reconstruction result obtained through cubic spline interpolation is very poor due to spectral aliasing effect, while the proposed method can get accurately reconstruction results, with solution errors in the order of \(10^{-10}\). Figure 3: (**a**) L-curve for \(s_{1}(t_{i}^{(1)})\). (**b**) The results of Tikhonov regularization for the spectrum reconstruction of \(s_{1}(t_{i}^{(1)})\). (**c**) L-curve for \(s_{2}(t_{i}^{(2)})\). (**d**) The results of Tikhonov regularization for the spectrum reconstruction of \(s_{2}(t_{i}^{(2)})\). In SAR signal processing, the azimuth echoes located in the same range direction can be approximated as LFM signals under certain conditions. Similar to the processing of SAR azimuth echoes, we multiply the reconstructed results of \(s_{2}(t_{i}^{(2)})\) obtained using the two methods by a phase function \(\exp(j\pi(m\Delta f)^{2}/K)\) in the frequency domain and perform the IFFT operation, where \(K\) is the chirp rate of \(s_{2}(t)\). Figure 5 shows the corresponding results and compares them with the pulse compression results of the original uniformly sampled signals. The results indicate that both cubic spline interpolation and the Tikhonov regularization method can correctly focus at \(t=0.05\) a after pulse compression. However, the result obtained using cubic spline interpolation exhibits higher sidelobes, while the Tikhonov regularization method is closer to the pulse compression result of original uniformly sampled signal. This also demonstrates the potential application of the proposed method in THz SAR azimuth non-uniform sampling problems. Further detailed discussions on this issue will be conducted in Section 4. Figure 4: Reconstruction result of Spline interpolation for (**a**) \(s_{1}(t_{i}^{(1)})\) and (**b**) \(s_{2}(t_{i}^{(2)})\). Reconstruction result of Tikhonov regularization for (**c**) \(s_{1}(t_{i}^{(1)})\) and (**d**) \(s_{2}(t_{i}^{(2)})\). Comparison between the solution error of Spline interpolation and Tikhonov regularization for (**e**) \(s_{1}(t_{i}^{(1)})\) and (**f**) \(s_{2}(t_{i}^{(2)})\). Figure 5: Comparison of pulse compression results between reconstructed signal from \(s_{2}(t_{i}^{(2)})\) and original uniformly sampled signal.
## 2 Research Area and Data Sources
### Indicator Selection
#### 2.3.2 Calculation of Landscape Pattern Index  
After preprocessing, the vectorization of land use/cover in the Inner Mongolia Autonomous Region was obtained, and it was divided into six categories: farmland, forest land, grassland, sandy land, water body, and construction land (Figure 1). The Kappa coefficient was used to evaluate and verify the accuracy of the classified images, and the overall accuracy reached 77%, which is higher than the minimum accuracy requirement. Then, the data were converted to TIFF raster format and imported into Fragstats 4.2 software to calculate the landscape pattern index.  
Based on the spatial distribution of vegetation in Inner Mongolia, we preselected indices that have a high impact on patches, including patch density (PD), edge density (ED), patch area ratio (PLAND), shape index (LSI), aggregation index (COHESION), fractal dimension index (PAFRAC), aggregation index (AI), sprawl index (CONTAG), dispersion and parallelism index (IJI), and splitting index (SPLIT). The calculation formulas and ecological significance are shown in Table 2. Due to the high spatial heterogeneity and scale dependence of the blue-green spatial pattern in urban areas [24], it is necessary to select an appropriate moving window scale. After comprehensively considering data availability, computational efficiency, and the geographic scale, this study determined that a grain size of 300 m and a moving window size of 900 m are optimally suited for landscape pattern analysis within the study area.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Indicators** & **Name** & **Formula** & **Implication** \\ \hline PD & Patch density & \(\frac{n_{i}}{\Lambda}\) & The larger the number of patches per unit area of a landscape, the higher the degree of landscape fragmentation \\ \hline ED & Edge density & \(\frac{E}{\Lambda}\) & The length of the edge of a landscape per unit area affects the edge effect and species composition. The larger the patch, the higher the degree of fragmentation \\ \hline \end{tabular}
\end{table}
Table 2: Landscape pattern indices and meanings (majority).  
Figure 1: Land use/cover types in Inner Mongolia.
## 1 Introduction
Furthermore, they presented experimental schemes to avoid the formation of crack and bubble nucleation when water is freezing [11]. To reconstruct the internal complex structure of such an ice-encased part, traditional focusing imaging methods are no longer suitable. Focusing imaging can precisely localize the defects but it is difficult to obtain the information of the shape and appearance. Moreover, these methods ignore multiple reflection signals and are unable to obtain the information of lower and side surfaces. [PERSON] et al. applied a diffraction mitigation technique to carry out ultrasonic imaging/tomography [10; 11]. However, the performance of diffraction tomography is limited due to its assumption of the first-order diffraction. It can achieve a better resolution than ray tomography but it is only suitable for low-contrast defects. Therefore, to quantitatively evaluate real defects with arbitrary forms in complex-shaped structures, a full wavefield containing all of the possible linear and higher-order diffraction effects should be considered in the tomography. To solve the inversion problem with a recorded full wavefield, full waveform inversion (FWI) tomography was firstly derived in geophysics for seismic wave imaging [12; 13; 14; 15]. The FWI is performed via an iterative minimization of the misfit between an experimentally measured wavefield and a numerically calculated wavefield generated with a forward model. Based on a certain optimization algorithm, the estimated model of the tested object is iteratively updated. The iteration process will end when the convergence criterion is satisfied; then, the final model is the approximation of the tested object. FWI automatically considers higher-order scattering effects; thus, it has been widely studied in medical applications for breast cancer screening and diagnosis [16], bone quantitative imaging [17] and delamination detection in civil infrastructures [18; 19]. Recently, FWI has also been applied in guided wave technology to quantify corrosion defects in plate-likestructures [20; 21]. In this article, a frequency-domain FWI is introduced as an imaging algorithm for the quantitative defect detection in a complex structure combined with ice-coupled ultrasonic testing. The rest of this paper is structured as follows. In Section 2, the fundamental theory of FWI including forward modelling and the successive-frequency FWI procedure will be briefly introduced. The numerical experiment setup and data calibration of the ice-coupled ultrasonic testing for turbine blades are then illustrated in Section 3. The reconstructed results for various defects are shown and discussed in Section 4. Finally, concluding remarks are drawn in Section 5.
# The Dynamic Response of an Arch Dam During a Record Low-Intensity Earthquake
###### Abstract  
The dynamic behavior of a concrete arch dam, the Baixo Sabor dam, built in Portugal, is investigated. A numerical model was developed to represent the dam, foundation, and reservoir system. This model was calibrated and validated through comparison with experimental data from forced vibration tests and ambient vibration monitoring. Recently, a low-intensity earthquake occurred in the region and the dam response was recorded by a seismic monitoring system. The previously calibrated numerical model was used to analyze the dynamic response of the dam under this seismic input, employing a dynamic boundary formulation that takes into account the wave propagation in the rock mass, preventing wave reflections. The results obtained show generally good agreement with the experimental measurements at the dam crest and abutments. The analysis methodology and the main issues involved in the modelling of arch dams under seismic action are discussed.  
Keywords:concrete arch dam; dynamic behavior; numerical model; experimental test; dynamic monitoring +
Footnote †: journal: Applied sciences
###### Abstract  
The issue of active attitude fault-tolerant stabilization control for spacecrafts subject to actuator faults, inertia uncertainty, and external disturbances is investigated in this paper. To robustly and accurately reconstruct actuator faults, a novel mixed learning observer (MLO) is explored by combining the iterative learning algorithm and the repetitive learning algorithm. Moreover, to guarantee robust spacecraft attitude fault-tolerant stabilization, by synthesizing the mixed learning algorithm with the sliding mode controller, a novel mixed learning sliding-mode controller (MLSMC) is designed based on the separation principle, in which the mixed learning algorithm is used to update composite disturbances online, including fault errors, inertia uncertainty, and external disturbances. Finally, a numerical example is provided to demonstrate the effectiveness and superiority of our proposed spacecraft attitude fault-tolerant stabilization control approach.  
spacecraft attitude stabilization; fault-tolerant control; mixed learning observer; learning sliding-mode control +
Footnote †: journal: Applied
## 3 Numerical Method and Verification
### Seismic Wave Input  
The time history analysis method is selected to analyze the seismic response characteristics of vertical double-layer overlapping pipe jacking. This method can accurately obtain the displacement, velocity, acceleration and internal force of each particle at different times under the action of seismic waves and reflect the direction, characteristics and continuous effect of ground motion. The surrounding rock of the tunnel site is moderately weathered limestone of grade IV, which belongs to a class II site in the code for seismic design of buildings [19]. The designed seismic group is group III, the site characteristic period is 0.45 s and the seismic identification intensity is 6 degrees. However, as an important urban lifeline project, the seismic fortification should be increased by one degree and the designed basic seismic acceleration value is not less than 0.1 g. Based on Code for seismic design of urban rail transit structures [20] and Seismic ground motion parameter zonation map of China [21], the selected seismic wave should be close to the natural period of the site where the structure is located to increase the seismic response of the structure. El Centro wave was selected as the seismic wave input to the bedrock; its characteristic period is close to the design value of the tunnel site characteristic period, which satisfies the wave selection requirements of seismic waves. According to the seismic fortification standard of the Jinan area, the peak acceleration of ground motion is adjusted to 0.1 g, 0.2 g and 0.4 g, respectively, for calculation. Nevertheless, the spectral characteristics of the time history curve are unchanged.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Thickness/m**} & **Volumetric** & **Elastic** & **Shear Wave** & **Poisson’s** & \multirow{2}{*}{**Cohesion/KPa**} & **Internal Friction** \\  & & **Weight/KN\(\cdot\)m\({}^{-3}\)** & **Modulus/MPa** & **Velocity/m\(\cdot\)s\({}^{-1}\)** & **Ratio** & **Angle/(\({}^{\circ}\))** \\ \hline Plain fill & 2.0 & 18.0 & 58.33 & 160 & 0.4 & 15 & 10 \\ Silty clay & 14.0 & 18.0 & 111.43 & 270 & 0.3 & 28 & 16 \\ Limestone & 14.0 & 23.0 & 13,138.50 & 620 & 0.22 & 800 & 45 \\ Pipe segment & - & 25.0 & 34,500.00 & - & 0.2 & - & - \\ (C50) & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical parameters of materials.  
The time history curve of the seismic wave band from 0 s to 20 s is selected for calculation. The damping ratio of all vibration modes is 0.05, the loading time is 20 s and the analysis time step is 0.02 s. A total of 1000 steps of calculation results are output. The seismic wave is inputted horizontally and transversely along the bedrock at the bottom; its acceleration time history and Fourier spectrum are shown in Figure 4.
###### Abstract  
This paper focuses on investigating the effect of the receiver's tilted angle on the channel capacity of an underwater wireless optical communication (UWOC) system, in which an avalanche photodiode (APD) detector is adopted as the receiver. Under the non-negativity, peak power, and average power constraints, the lower bounds on the capacity of UWOC are derived in detail according to different average-to-peak power ratios. With modeling achieving the maximum of the lower bounds of the capacity as an optimization object, we prove that the proposed optimization issue is in fact a simple convex optimization about the tilted angle of the APD receiver, and then present related theoretical solution for it. Both theoretical analysis and simulation results show that by appropriately tilting the receiver, we can significantly enhance the final capacity performance of the UWOC with APD receiver.  
UWOC; APD receiver; pointing error; capacity bound; convex optimization Article
## 2 Materials and Methods
### Sources and Processing of Data
#### 2.2.4 Data on Land Usage  
The land use data adopts the 30 m China Long Term Series Land Use Dataset (CLCD) released by the team of Professor [PERSON] and Professor [PERSON] from Wuhan University, with an overall accuracy of 79.3% and a cultivated land accuracy of over 80% in many years. The land use types in this study area were mainly cultivated land and forest land, with a relatively large proportion of cultivated land area. Therefore, these data have good applicability in this study area. This dataset contains annual land cover information in China from 1985 to 2020, showing a continuous coverage period of 30 years. The biggest advantage of the CLCD dataset is its high temporal resolution, providing annual land use classification results, which is very valuable for studying the dynamics and trends of land use change. This dataset divides land into 9 categories, namely farmland, forests, shrubs, grasslands, water bodies, ice and snow, bare land, impermeable surfaces, and wetlands. Using the \"Classification of Land Use Status GB-T21010-2007 [38]\" and regional land use characteristics, this study reclassified these into six groups in ArcGIS 10.8 software: landthat is farmed, land that is forested, land that is grassland, land that is near water, land that is being built on, and land that is not being used.
## 3 Proposed Method
### Analysis of Phase Transition
And the deterministic bipartite graph measurement matrix demonstrated a superior phase transition curve compared to the aforementioned random measurement matrices in the cases of Gaussian sparse signals, while exhibiting similar phase transition curves for binary uniform sparse signals. Furthermore, our proposed measurement matrix outperformed all other measurement matrices for both Gaussian sparse signals and binary uniform sparse signals. Figure 2: Phase transitions of several measurement matrices for (**a**) Gaussian sparse signals and (**b**) binary uniform sparse signals.
## 4 Test Experiments and Discussion of DMMAEO
### Impacts of Enhancement Mechanisms on EO
& \(2.437\times 10^{-15}\) & \(2.710\times 10^{-15}\) & \(2.294\times 10^{-15}\) & \(2.372\times 10^{-15}\) & \(2.710\times 10^{-15}\) \\  & TF20 & Ave. & \(-3.202\times 10^{0}\) & \(-3.322\times 10^{0}\) & \(-3.242\times 10^{0}\) & \(-3.243\times 10^{0}\) & \(-\mathbf{3.322\times 10^{0}}\) \\  & Std. & \(3.956\times 10^{-2}\) & \(1.424\times 10^{-15}\) & \(6.998\times 10^{-2}\) & \(5.715\times 10^{-2}\) & \(1.489\times 10^{-15}\) \\  & TF21 & Ave. & \(-8.465\times 10^{0}\) & \(-1.015\times 10^{1}\) & \(-1.015\times 10^{1}\) & \(-1.015\times 10^{1}\) & \(-\mathbf{1.015\times 10^{1}}\) \\  & Std. & \(2.681\times 10^{0}\) & \(1.603\times 10^{-4}\) & \(1.565\times 10^{-4}\)GMEO, and CMEO on 21, 19, 20, and 21 functions, respectively. These results mean that the DMMAEO significantly performs better than the canonical EO and other EO variants in most test functions. For the overall performance, the Friedman test results given by Table 5 show that the DMMAEO ranks first, followed by DMEO, CMEO, GMEO, and EO. These results mean that the proposed DMMAEO exhibits the most outstanding overall performance among the various EOs. To summarize, from the above experimental results obtained by the various EOs, all three enhancement mechanisms are helpful to improve the performance for the canonical EO, and the proposed DMMAEO performs best by incorporating all three enhancement mechanisms. Figure 2: Convergence curves for canonical EO and four EO variants across 9 test functions.