esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Definition and Implementation of Fast Fourier Transform  
The FFT algorithm is widely used to analyze the frequency content of power system signals and detect the presence of power oscillations and their frequencies, including their oscillatory modes [31,32]. While FFT may be less accurate than other oscillation detection methods, such as the Matrix Pencil Method or Prony Analysis, it offers advantages in terms of computational efficiency, as detailed in [45]. This efficiency, coupled with its simpler implementation, makes FFT suitable for inter-area oscillation detection, where computational efficiency is prioritized over the results. The FFT transforms a signal in the time domain into its representation in the frequency domain, providing information about the frequencies and amplitudes of this signal. This facilitates the analysis of the frequency components within the signal. Applying the FFT to the measured signal in inter-area oscillation detection allows the oscillation frequencies of interest (between 0.1 and 1 Hz [4]) to be analyzed and identified within a moving window, which is continuously updated as the simulation runs.  
The FFT is introduced in DSPF as a function programmed in C, encapsulated in a.dll file. The output of the block is the trigger signal for the POD-P controller, Enable_POD. The FFT value in the area of interest (0.1-1 Hz) must exceed a threshold to trigger this signal. This threshold is set at 15 mHz, the trigger frequency specified in the POD Controllers Implementation Guide [16].  
Exceeding this FFT threshold signifies the presence of power oscillations within the specified frequency range, subsequently activating the POD-P controller. The FFT calculation uses 10,000 samples with a time step of 0.5 ms, ensuring a detailed frequency analysis. Additionally, the calculation is performed continuously throughout the simulation. During the RMS simulation, the FFT operates over a floating window of 5 s, with recalculations occurring every 0.5 s. The FFT value obtained in the current window is compared with the value from the previous window to determine whether to activate or deactivate the POD-P, providing a double confirmation mechanism to enhance reliability. The POD-P controller activates or deactivates only when the FFT results from two consecutive time windows yield the same result (both 1 for activation or both 0 for deactivation). A 5 s delay is also implemented before deactivation to better replicate real-world operational procedures.  
This approach improves upon previous FFT-based POD solutions by integrating a robust activation and deactivation mechanism that minimizes false positives through double confirmation and a time delay. Furthermore, the function is encapsulated in a.dll file within DSPF, enabling inter-area oscillation detection during simulation runs entirely within the software environment. These enhancements ensure reliable activation through real-time simulation with varying network conditions, providing a balance between computational efficiency and robust oscillation damping.
## 3 Results
### Difference in Trends of GPP Derived from NIRv  
The estimation of GPP is influenced by land cover changes and related products, and thereby, so is the estimated trend of GPP changes. GPP is the linear product of NIRv based on land cover types. Therefore, the estimation of GPP not only directly depends on land cover types, but it also depends on NIRv. Figure 6a shows the changes in NIRv. The average value of NIRv trends in the YRB was 1.3 \(\times\) 10\({}^{-4}\)/yr. The overall trend in the YRB was greening. Figure 6b shows the staMOD GPP trends, with an average increase of 2.13 gCm\({}^{-2}\)yr\({}^{-1}\). The trend of GPP was highly consistent with the trend of NIRv. For example, the increased regions of GPP were consistent with the increase in NIRv. Figure 6c shows the dynMOD GPP trends, with an average increase of 2.14 gCm\({}^{-2}\)yr\({}^{-1}\). Figure 6d shows the dynCCI GPP trends, with an average increase of 1.70 gCm\({}^{-2}\)yr\({}^{-1}\). The value was slightly lower than that of MODIS-based GPP trends.  
The statistics for the multi-year average trends of GPP using the different LC schemes and their differences by the sub-basins are shown in Figure 7. Figure 7a shows the NIRv trends, with the highest increase of 2.3 \(\times\) 10\({}^{-4}\)/yr in the LSG. This basin was located in the Loess Plateau, and the effects of GFG were significant. In contrast, ALG had the lowest NIRv trends, with an average of 0.2 \(\times\) 10\({}^{-4}\)/yr. This basin was located in the Qinghai-Tibet Plateau, and the primary land cover was GRA with barely any changes. The trends of GPP were highly consistent with that of NIRv, with a similar distributi  
Figure 6: Changes in (**a**) NIRv and (**b–d**) GPP using the different land cover (LC) schemes, using the Theil-Sen estimator, over the Yellow River basin from 2001 to 2018. The LC schemes include (**b**) the static MODIS LC data in 2001, (**c**) dynamic MODIS LC data, and (**d**) dynamic CCI LC data. The significance of the Theil–Sen slope was estimated by the Mann–Kendall trend test. Shaded regions indicate where changes were significant at \(p\) < 0.05. Frequency histograms displaying the areal proportions (%) of corresponding changes are inset.  
plots. Figure 7b shows that there is little difference in the trends of dynamic and static GPP, and the impact of multisource products' discrepancies on GPP trends is relatively obvious. The differences caused by the discrepancies in multisource products seemed to be larger than those caused by land cover dynamic changes.
# Nutritional and Functional Properties of Gluten-Free Flours
## 3 Results
### Mineral Composition of GF Flours  
The mineral composition of the flours is presented in Table 2. In general, calcium, magnesium, and iron are scarce in the gluten-free diet ([PERSON] et al., 2014). The authors underlined the importance of calcium intake and bone metabolism in people with coeliac disease. From the studied flours, calcium levels were high in amaranth (189.7 mg/100 g), left (166.7 mg/100 g), and gram (150.1 mg/100 g), compared with the other flours with much lower values ranging from 2.2 to 64.3 mg/100 g. The lowest content was for maize and rice flours. Moreover, calcium content in brown rice was six-fold higher than in rice because dehulling process decreases the calcium levels as stated previously ([PERSON] et al., 2011). Gram contained 2.3 times more calcium than chickpea. Similar to the calcium content, magnesium levels were high in amaranth m and teff (226.5 mg/100 g), as well as quinoa flours (229.5 mg/100 g), followed by buckwheat (221.8 mg/100 g) and gram (181.1 mg/100 g). High content of iron was found in teff (8.7 mg/100 g) and amaranth (7.4 mg/100 g) followed by chickpea, gram, and quinoa (5.6-6 mg/100 g). On the other side, tiger nut, rice, and brown rice flours were deficient in iron (<1 mg/100 g). The teff and gram flours were a good source of zinc (around 4 mg/100 g). From a nutritional point of view, zinc and iron are essential elements in human nutrition, especially for diabetic patients, enhancing insulin production ([PERSON] et al., 2011; [PERSON] et al., 2014). Potassium content ranged from 113.5 mg/100 g (rice flour) to 1175.4 mg/100 g (plantain flour), being the highest element. Other flours with high potassium content were the chickpea flours between 1127.7-1144.1 mg/100 g (with higher value for gram than chickpea) and tiger nut (717.6 mg/100 g). Sodium content was between 0.52 mg/100 g (maize flour) and 16.1 mg/100 g (teff flour). Gram had a lower sodium content than chickpea. It is widely known that a diet low in sodium and high in potassium helps in lowering the blood pressure and reducing the risk of cardiovascular disease ([PERSON] et al., 2014). The phosphorus content (\(p<0.05\)) decreased in the following order: gram > amaranth > quinoa > chickpea > teff > brown rice > buckwheat > millet > oat > tiger nut > rice > maize > plantain. According to [PERSON] et al.
## 3 Results and Discussion
### Film Morphology

Table 1: Quantitative atomic force microscopy (AFM) roughness data for fast and slow-filtered films formed from 400 \(\upmu\)L aliquots of arc-discharge SWCNT suspension. Figure 4: AFM images for fast (**top row**) and slow (**bottom row**) filtered films formed from 400 \(\upmu\)L aliquots of arc-discharge SWCNT suspension. known to produce non-homogeneous films it cannot be assumed that both fast and slow filtration setups will produce films of equal transmittance. The slow-filtered film had a transmittance of 78.5% whilst the fast-filtered film had a much lower value of 51.2%. This is a significant observation as a difference in transmittance of this size would be expected to give a large PCE difference. It is thus apparent that there is a difference in the light transmittance vs. film conductivity for SWCNT films produced via slow filtration. To further demonstrate this difference, the sheet resistance for both sets of films was measured and plotted here as Figure 5.
# Enhancement of In-Plane Seismic Full Waveform Inversion with CPU and GPU Parallelization
## 2 Mathematical Model
### Numerical Implementation in FDM  
For the numerical implementation of Elastodynamic Wave Equations (1)-(5), they are discretized in finite time and space over a two-dimensional grid. The finite-difference operators are based on simple differences over a small interval of space and time as given in Equations (6) and (7).  
\[\frac{\partial P^{+}}{\partial x}\approx\frac{P[i+1]-P[i]}{dh}\;\;\text{ Forward operator}. \tag{6}\]  
\[\frac{\partial P^{-}}{\partial x}\approx\frac{P[i]-P[i-1]}{dh}\;\;\text{ Backward operator} \tag{7}\]  
\(P\) is the given function, \(i\) is the grid index, and \(dh\) is a small increment in the space or time variable \(x\). A combination of forward and backward operators is also possible.  
A standard staggered grid (SSG), suggested by [32; 33], is widely implemented by other researcher for the computation of two-dimensional seismic wave propagation problems. The standard staggered grid, as implemented in [12] and shown in Figure 3 is used for numerical implementation in the paper. An arithmetic mean is used to obtain Density \(\rho\), and a harmonic mean is used to obtain Lamis' Constant parameter \(\mu\) at the half grid points in the staggered grid. A detail of Finite Difference Discretization of Seismic FWI Problem and computational implementation can be found in [12; 31].  
Perfectly Matched Layers (PML), as implemented in [31], is used as a boundary condition. The perfectly matched layer frame is added to the finite difference grid, where the wave equation is solved in the frequency domain with coordinate stretching in this region (see Figure 4). The arrangement absorbs the outgoing waves and creates a reflectionless boundary frame (see [24]) and the Sommerfeld's radiation condition is numerically implemented.  
For FWI, a start model has to be initialised in the first step. A start model could be informed guesswork with the information available about the ground. The mathematical model is solved for the given seismic source, and the output signals are recorded at the receiver locations. This part is called the forward model, and the modelled output at the receivers is called modelled data (\(u^{\text{mod}}\)). The seismic recordings that we have from the field recordings are referred to as observed data (\(u^{\text{obs}}\)). The difference between them is referred to as residual data and can be measured using vector norm. For this case, we use \(L_{2}\) norm, given by Equation (8). The Gradient-based method given in [12] is implemented in parallel FWI computation in the paper.  
Figure 3: A staggered grid configuration for use in seismic finite difference modelling [12; 31; 32; 33].  
\[\mid L\mid_{2}=\left(\sum_{i=1}^{n}\mid u^{mod}-u^{obs}\mid^{2}\right)^{1/2} \tag{8}\]  
where \(n\) is the number of receivers. \(L_{2}\) norm is related to energy, which is used to calculate the energy gradients, and with proper step length, we gradually minimise the misfit with every iteration; see [31].
# Detection and Evaluation of Construction Cracks through Image Analysis Using Computer Vision
## 1 Introduction  
In recent years, the construction industry has sought to develop new methodologies to improve its processes, where innovation plays a substantial role supported by new equipment and the permanent and rapid development of technologies. Innovative methodologies improve the construction processes and guide the automation of the monitoring and control of construction works, achieving a better-quality final product.  
In line with the construction process automated monitoring, relevant information can be collected by installing conventional and thermographic cameras (sensitive to heat) and generating images and videos that can be used to detect cracks, microcracks, and other construction problems related to concrete [1], which is one of the most used materials in construction [2]. Although there have been previous studies using the YOLO family to detect and classify cracks [3], they are limited and do not consider the analysis of the detected cracks in concrete to measure the criticality of the failure. Based on this, it is proposed in this work to detect and evaluate construction cracks through image analysis using computer vision.
# Low-Power LoRa Signal-Based Outdoor Positioning Using Fingerprint Algorithm
## 3 Proposed Fingerprint Algorithm  
This study aims to implement LPWA IoT services in which moving objects can be tracked in specific areas where LoRa networks can be built. Elderly people, children, pets, and livestock can be tracked and managed safely in service areas such as amusement parks, ski resorts, and pastures.  
For realizing this scenario, the positioning accuracy for end-devices should be under 30 m; end-device size should be smaller than 3 cm (W) \(\times\) 5 cm (H) \(\times\) 2 cm (D); end-device weight should not exceed 50 g; and batteries should last for at least one month without recharge for stable service.
# Numerical Modeling of Scholte Wave in Acoustic-Elastic Coupled TTI Anisotropic Media
###### Abstract  
Numerical modeling of acoustic-elastic media is helpful for seismic exploration in the deepwater environment. We propose an algorithm based on the staggered grid finite difference to simulate wave propagation in the interface between fluid and transversely isotropic media, where the interface does not need to consider the boundary condition. We also derive the stability conditions of the proposed method. Scholte waves, which are generated at the seafloor, exhibit distinctly different propagation behaviors than body waves in ocean-bottom seismograms. Numerical examples are used to characterize the wavefield of Scholte waves and discuss the relationship between travel time and the Thomsen parameters. Thomsen parameters are assigned clear physical meanings, and the magnitude of their values directly indicates the strength of the anisotropy in the media. Numerical results show that the velocity of the Scholte wave is positively correlated with \(\varepsilon\) and negatively correlated with \(\delta\). And the curve of the arrival time of the Scholte wave as a whole is sinusoidal and has no symmetry in inclination. The velocity of the Scholte wave in azimuth is positively related to the polar angle. The energy of the Scholte wave is negatively correlated with the distance from the source to the fluid-solid interface. The above results provide a basis for studying oceanic Scholte waves and are beneficial for utilizing the information provided by Scholte waves.  
acoustic-elastic coupled equation; anisotropic; Scholte wave; staggered-grid finite-difference method +
Footnote †: journal: Applied  
**Citation:** [PERSON]; [PERSON].  
Numerical Modeling of Scholte Wave in Acoustic-Elastic Coupled TTI Anisotropic Media. _Appl. Sci._**2024**, 14, 8302. [[https://doi.org/10.3390/app14188302](https://doi.org/10.3390/app14188302)]([https://doi.org/10.3390/app14188302](https://doi.org/10.3390/app14188302))
## 4 Results
### Navigation Risk Analysis  
According to the results of the navigation risk assessment in the four seasons given in Figure 11, winter has the greatest navigation risk in the study area throughout the year. In winter, accompanied by large winds and waves, sea fog weather also occurs occasionally, and the navigation risk in the sea area is high. The descending order of risk levels in the other seasons is spring, summer, and autumn.  
Figure 12: Sensitivity analysis results.  
In winter, the navigation risk in the southern part of the study area is greater than in the northern part. In spring, the high-risk navigation area is concentrated in the central part of Bohai Sea, extending from south to north. The high-risk area in spring has a trend of developing to the outer side of Bohai Sea. In summer, the high-navigation risk area retreats to outside of the Bohai Strait, and the risk value in the northern Yellow Sea increases. In autumn, the risk value in the sea area decreases to a minimum, although the western part of Bohai Sea has a relatively high value.  
For further analysis, the evaluation results were calculated for every shipping region, as shown in Figure 13. In winter and spring, high-risk shipping regions are in the south, where the risks are high on the routes to Laizhou Port, Yantai Port, Harbor Port, Shidao Port, and Qingdao Port. In summer and autumn, high-risk shipping regions are in the north, and the risks are higher on the routes to Yinkou Port, Dalian Port, and Dandong Port. Other high-risk routes are from Qingdao Port to Yantai Port in spring and summer, and from Tianjin Port to Yantai Port and Shidao Port in spring and summer.  
Figure 13: Risk distribution in the waterway areas in (**a**) spring; (**b**) summer; (**c**) autumn; (**d**) winter.
# Coordinated Dispatch of Multi-Energy Microgrids and Distribution Network with a Flexible Structure
## 5 Simulation Result  
### Case Set Up  
For the aim of verifying the validity of the proposed model, this paper established a test system including IEEE 33-bus distribution network (voltage reference value is 12.66 kV, 37 remote-controlled switches(K1-K37) with three wind farms, three photovoltaic power stations, and three multi-energy MGs, the upper limit of control actions is set to 24), as shown in Figure 5. The operation data of distributed generators and MGs is based on history. The test system was set up on Thinkpad T440p, 2.90 GHz with 8 cores and developed using MATLAB 2017b with toolbox CPLEX 12.9.  
The voltage is subjected to the constraint of 0.93 and 1.07. Three wind farms are connected to node 8, 10, 30. Three photovoltaic power stations are connected to node 13, 16, 18 (forecast 24-h power generation is shown in Figure 6). Three microgrids are connected to the 22, 25, 33, respectively, the load flow of MGs is not considered in this paper [29].  
MG1 is CCHP-based MG, while MG2 and MG3 are CHP-based MG and CCP-based MG, respectively. The parameters of devices in MGs are given in Table 2. The price of natural gas per cubic meter is V2.2. The heat value of natural gas is 9.69 kWh/m\({}^{3}\). The prices of power interaction between DN and MGs are shown in Table 3, which can show that the peak-to-valley price method is adopted.  
Figure 5: IEEE33-node distribution network.  
Figure 6: Forecast output power of photovoltaic (PV) array and wind turbine (WT).  
To protect the benefit of electric power enterprises, the price of electricity traded between MGs cannot be higher than that of trading between microgrid and DN.  
To verify the rationality of the proposed method, there are three comparative cases to show the impact of the proposed method, given in Table 4.  
* **Case 1:** Optimal dispatch of MGs. MGs purchase or sell power to DN and buy natural gas from the gas network without consideration of the DN [15].
* **Case 2:** Optimal dispatch of MGs and operation of DN (considering power loss) without consideration of the DN reconfiguration [29].
* **Case 3:** The method proposed in this paper. The bi-level optimal dispatch considering operations of MGs and DN considering reconfiguration, through controlling the switch on each tie line in DN, operating condition of devices and power interaction of the PCC (point of common coupling, the connection point between the MG and the DN).
## 4 Conclusions  
This study was conducted in the Uttarakhand Himalaya of India to evaluate the eco-environmental risk and health hazard (carcinogenic or noncarcinogenic risk) to humans using sixteen different soil elements. The elemental concentrations of Cr and Cu were found above their background concentration and threshold limits. The contamination indices (Igeo, EF, and CF) evaluated showed that Cu, Cr, and to some extent, Ni and Rb have a considerable impact on soil contamination. The evaluation of DC and PLI obtained values of 36.66 and 1.26, respectively, suggesting that soil of the studied Himalaya catchment is polluted with heavy metals, whereas the calculated PERI was 75.56, indicating low ecological risk. The overall results show no significant ecological risk associated with the heavy metal contents in the soils. Observed elemental contaminations can be ascribed to both geogenic anthropogenic causes and the studied catchment. The health risk assessments for selected heavy metals, whose content was above the Indian, Canadian, and Dutch guidelines, suggest that no major carcinogenic risks for adults were evaluated, due to soil intake, but Cr and Mn concentrations indicate potential carcinogenic risk for children. Therefore, regular monitoring of the reported metals in soils, food products, and vegetables are obligatory to prevent accumulation of metals in the food chain. These key findings will be highly useful for the water resource planners, managers, and environmentalists to make strategic planning in advance to take care of the ecology and human health for people living in the vicinity of the Himalaya catchment.  
Conceptualization and data collection, A.K.1; methodology, A.K.1 and M.C.-P.; formal analysis, A.K.1 and M.C.-P.; investigation, A.K.1, M.C.-P. and P.A.D.; resources, A.K.1 and M.C.-P.; writing--original draft preparation, A.K.1 and M.C.-P.; writing--review and editing, A.K.1, M.C.-P., M.K., A.K.3 and P.A.D.; supervision, A.K.1; project administration, A.K.1; funding acquisition, A.K.1 and M.C.-P. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the Project UID/GEO/04035/2019 (GeoBioTec Research Centre) financed by FCT--Fundacapora a Ciencia e Tecnologia.  
All the authors are highly grateful to the Principal Chief Conservator of Forests (PCCF), Uttarakhand, for permission to carry out work in the forest divisions. A.K.1 is highly thankful to Prof. [PERSON] (Prof and Ex. Head, AHEC, IIT Roorkee) for encouragement and support for new findings and guidance from time to time. The authors thank anonymous reviewers.