esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Shoulder Line Extraction Method Fusing Edge Detection and Regional Growing Algorithm
## 2 Materials and Methods
### Regional Growing Algorithm
#### 2.4.2 Growth Criteria  
We assumed that the pixel with a gray value of 8 was the initial growth point, as shown in Figure 1, where the numbers in the figure indicate the gray levels of pixels. The growing criterion for the four neighborhoods of 8 was that the calculated result should be in the range between \(-1\) and \(1\) when the growing point was less than the neighbor point. If no difference could be found between the adjacent growing results, then the growing process stopped. Figure 3 illustrates four stages of the regional growing algorithm. Figure 3a depicts an original image, and the numbers are gray values. Figure 3b-d represents the growth process.  
The regional growing algorithm can avoid most misclassification areas. Still, for those regions that are connected to the correct area, it remains difficult to accurately identify them. This problem results in the inaccurate position of the shoulder line, and it is serious in the Loess Plateau area. To improve this concept, this study introduced slope gradient, and defined the following rules: if the slope gradient of the negative terrain was smaller than the given threshold and the negative terrain was adjacent to the positive terrain, then this negative terrain could be considered as positive terrain, as shown in Figure 4. The threshold of the slope gradient depended on the type of loess landform. This study tested different thresholds, compared the test results with the manual visual interpretation results, and chose \(7^{\circ}\) as the final slope threshold.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 3 Results
### Evaluation of Weighted Fusion Methodology
#### 3.3.2 Moderate Condition Analysis (June 2024)  
The forecasting performance for June 2024 and a period characterized by moderate weather conditions is illustrated in Figure 6. As shown in Figure 6, the weighted fusion methodology consistently outperforms direct aggregation, particularly during the second half of the month (13-29 June) where peak load forecasting becomes increasingly challenging. The weighted fusion approach successfully captures both the magnitude and temporal patterns of load fluctuations, demonstrating significantly lower prediction errors during high-load periods.  
Figure 7 elucidates hourly analysis of load forecasting performance during June. The top panel reveals that direct aggregation consistently underestimates load during peak hours (8:00-16:00), with an average underestimation of approximately 7.3%. In contrast, the weighted fusion approach reduces this underestimation to approximately 3.2%. The bottom panel quantifies this improvement through hourly MAPE values, with weighted fusion reducing peak-hour errors by 4.8-6.1 percentage points in contrast with direct aggregation.  
Figure 6: June 2024 forecast comparison (The yellow shadow indicates the peak area).  
The scatter plot analysis in Figure 8 further confirms these observations, with weighted fusion achieving superior coefficient of determination (\(R^{2}\)) values for both peak hours (0.9204 vs. 0.7183) and non-peak hours (0.9453 vs. 0.8349). This indicates that weighted fusion provides more consistent and reliable forecasts across all operational conditions.
# A Systematic Review on Design Thinking Integrated Learning in K-12 Education
## 3 Results
### How to Evaluate Students' Learning in DTIL?  
#### 3.3.1 Dependent Variables  
All the dependent variables assessed across all the studies are included in Table 3. The measured competences mainly covered four aspects of students' learning in the DTIL context: subject learning performance, design thinking, emotional/social aspect and skill. (1) Subject concepts like scientific knowledge (e.g., [34; 35; 36; 37]) were discussed. (2) The design thinking concept, such as the understanding of design thinking (e.g., [38; 39; 40; 35]), and design thinking practice, such as the design thinking process (e.g., [21; 26; 31; 41; 42; 43; 44; 45; 46]) or the design thinking work (e.g., [47; 48; 49; 31; 43; 45; 43; 45; 44; 31; 43; 46]), were assessed. (3) Emotional/social aspects, like attitude, desire, engagement, or collaboration, etc, were tested in some reviewed studies (e.g., [27; 49; 50; 51; 52]). (4) Other skills, such as creativity, productive thinking, problem solving, and critical thinking, were also evaluated (e.g., [23; 26; 29; 34; 53; 54]).  
Among these dependent variables assessed (see Figure 7), 31 (or 70.5%) studies tested the design thinking, 17 (or 38.6%) discussed the emotional/social aspect, nine evaluated the subject learning performance, and eight tested skills (or 18.2%).
## 2 Dechirp Influence on Squint Sliding-Spot Imaging Mode
### Extension of the Range-Doppler Model after Dechirp
These two items are the basis for the geometric design in the next section. It should be noted that each dechirped aperture of the dechirped data is eventually focused to the location with the closest range position and zero doppler frequency, the geometric positioning is also performed on this new image, so even if there is a non-ideal attitude error that causes the doppler frequency after dechirp to be non-zero, it will only cause the decrease in the image radiation focusing quality, and no position error will arise because of this. The image coordinates of the target on the slant range image are known, the process of solving its geographic coordinates is called geometric forward calculation, and this process is mainly used for the geometric positioning. The latitude and longitude coordinates of the target are known, the process of solving its image coordinates is called geometric inverse calculation, and this process is mainly used for geometric calibration. Based on the upper two characteristics of the new dechirped range image, combined with the range equation in (6), respectively, this paper constructs an accurate satellite-ground position relationship suitable for the squint sliding-spot imaging and proposes a geometric process including forward and inverse calculation. Figure 6: Squint sliding-spot imaging of target by the SAR satellite and the geometric forward process based on antenna pointing vector. Figure 7: (a) Curved range history of the target; (b) after dechirp; (c) after range crop along azimuth.
###### Abstract  
ZnO nano- and microstructures co-doped with Eu and Li with different nominal concentrations of Li were grown using a solid vapor method. Different morphologies were obtained depending on the initial Li content in the precursors, varying from hexagonal rods which grow on the pellet when no Li is added to ribbons to sword-like structures growing onto the alumina boat as the Li amount increases. The changes in the energy of the crystallographic planes leading to variations in the growth directions were responsible for these morphological differences, as Electron Backscattered Diffraction analysis shows. The crystalline quality of the structures was investigated by X-ray diffraction and Raman spectroscopy, showing that all the structures grow in the ZnO wurtzite phase. The luminescence properties were also studied by means of both Cathodoluminescence (CL) and Photoluminescence (PL). Although the typical ZnO luminescence bands centered at 3.2 and 2.4 eV could be observed in all cases, variations in their relative intensity and small shifts in the peak position were found in the different samples. Furthermore, emissions related to intrashell transitions of Eu\({}^{3+}\) ion were clearly visible. The good characteristics of the luminescent emissions and the high refraction index open the door to the fabrication of optical resonant cavities that allow the integration in optoelectronic devices. To study the optical cavity behavior of the grown structures, \(\mu\)-PL investigations were performed. We demonstrated that the structures not only act as waveguides but also that Fabry-Perot optical resonant modes are established inside. Quality factors around 1000 in the UV region were obtained, which indicates the possibility of using these structures in photonics applications.  
Luminescence; ZnO; rare earth doping +
Footnote †: journal: Applied Sciences
## 2 Overview of the Basic Concepts of Wind Farm Design and Optimization
### Adverse Factors and Conflicting Objectives
#### 2.2.3 Environmental Impacts  
Although the World Health Organization (WHO) recognizes wind energy as the power conversion technology with the lowest impacts (during construction and operation) on human health and the local environment [92, 93, 94, 95, 96] precautions must be taken during the wind farm design process. Environmental impacts can be defined as all the potential impacts the wind farm will produce in the environment of the site during its construction and while operating. Environmental Impact Assessments (EIAs), which must be in compliance with existing guidelines proposed by national Environmental Affairs Agencies (EAA), should in general include the description and study of the impacts on the air, water and soil. In addition, EIAs should study the effect of possible pollution emissions and their distributions in space and time, noise generation, shadow flicker issues, visual impact and the impacts on wildlife and biodiversity. Examples of EIA guidelines are the United Nations Environment Programme (UNEP) guidelines [97] and the International Finance Corporation (IFC) performance standards on environmental and social sustainability [98]. The environmental factors that most affect the micro-siting process are briefly described next:Wildlife and Biodiversity  
Wind farms may produce side effects during their construction and while operating on: (1) the local vegetation, by inducing local micro-climate changes (e.g., a spatial and temporal turbulence-induced impact on the heat flux, temperature and moist concentrations of the atmospheric surface layer) [99,100] and (2) the local wildlife, with special emphasis on the short and long-term loss, degradation and fragmentation of bird, bat and marine life (e.g., for offshore wind farms) habitats [101, 102, 103, 104, 105, 106, 107]. Most of the potential impacts of wind farms on migrating birds/bats can be reduced to acceptable levels through a smart micro-siting design (e.g., bird electrocution fatalities can be dramatically reduced by burying internal wind farm power lines underground while bird-WT collision fatalities can be reduced by properly separating WTs from sensitive zones such as migration corridors) and by performing site-specific mitigation processes. Identifying zones (e.g., natural protected areas) of possible concern should be done as early as possible in the wind farm planning stage. In high-risk zones the ultimate mitigation solution could be to implement control strategies on selected WTs during bird or bat migration. Decision and assessment of such possible actions will require detailed site-specific studies.
## 3 Results and Discussion
### Selection of the Optimal Radial-Flow Type
The main channels for inlet and outer flow followed the momentum exchange control mode, and therefore, the static pressure in the inlet channel increases along the flow direction while the static pressure of the outer flow channel decreases along the flow direction. When the CP-\(\pi\) flow with opposite direction of the bypass flow and interflow is used, the static pressure drop decreases and gives better gas distribution [14]. Figure 3: Pressure changes in the outer flow channels of radial-flow adsorbers in a stabilized cycle. In summary, for current radial-flow PSA oxygen generation, CP-\(\pi\) flow is shown to be the optimal type based on the above flow characteristics, and is therefore selected to be further explored for its advantageous oxygen generation properties in the following sections, and similar results are obtained by simulation [17] and experiment [21].
## 4 Electron Transfer-Based Assays
### Ferric-Reducing Antioxidant Power  
#### 4.1.1 Working Principle of FRAP Assay  
The FRAP assay was developed in the 1990s by [PERSON] and [PERSON] [37]. In their 1996 publication, they introduced a novel method for measuring antioxidant capacity based on the reduction of Fe\({}^{3+}\) (ferric) to Fe\({}^{2+}\) (ferrous). This method was designed to be simple, rapid, and reproducible. The FRAP assay is based on electron transfer to an iron (Fe) complex with 2,4,6-tripyrid-s-triazine (TPTZ):  
\[\text{Fe}^{3+}+\text{TPTZ}\rightarrow[\text{Fe}^{2+}-\text{TPTZ}]^{+}\]  
The reduced form of the complex exhibits a dark blue color. Its intensity is proportional to the antioxidant concentration in the sample. The reaction is carried out at a low pH (<4), maintained by an acetate buffer at pH 3.6 [37]. The reduced complex reaches its maximum absorption at 593 nm. An increase in absorbance values corresponds directly to the content of antioxidants in the sample. The results are expressed as moles of Fe\({}^{2+}\) equivalents per unit mass of the sample [38; 39].  
The FRAP reagent consists of an acetate buffer, TPTZ, and FeCl\({}_{3}\) and is prepared ex tempore, immediately prior to analysis [40]. This solution was originally used to assess the antioxidant power of blood plasma based on its ability to reduce Fe\({}^{3+}\) ions to Fe\({}^{2+}\) ions [37]. Currently, the FRAP assay is used to evaluate ferric-reducing antioxidant power by measuring total antioxidant capacity in both biological fluids and food samples [41; 42].  
The FRAP assay is widely recognized as a simple and cost-effective method, making it suitable for use in various laboratory settings [43]. The reaction is highly reproducible, and the results directly correlate with the molar concentration of antioxidants present. Notably, no concentration-dependent stoichiometric variations have been observed, indicating that the Fe(TPTZ)\({}_{3}\)\({}^{+}\) concentration in the FRAP reagent does not influence the final results [37]. Moreover, no decrease in absorbance was observed for at least 30 min, indicating the absence of agents that inhibit Fe\({}^{3+}\) reduction or cause Fe\({}^{2+}\) reoxidation. However, these conclusions were based on studies analyzing plasma antioxidant activity [37]. Although Fe\({}^{3+}\) naturally occurs in plant-based foods [44], no research has specifically investigated its potential role as an interfering factor in the FRAP assay. However, the presence of such ions in extracts may affect the measured antioxidant potential [45]. Therefore, it remains unclear whether the iron content in plants affects the reaction mechanism. Additionally, it has been shown that the results of the FRAP assay may vary depending on the solvents used, potentially contributing to discrepancies in findings [46].
# Deep Learning and Embedding Based Latent Factor Model for Collaborative Recommender Systems
## 4 The Proposed Model
### Latent Factor Model
\[L_{DL}=\min_{\theta}\left[\sum_{i,j}(\hat{r}_{ij}-r_{ij})^{2}+\lambda W^{2}+ \alpha b^{2}\right]. \tag{9}\]  
We conclude that the proposed method learns the latent features in their corresponding deep neural network architecture by minimizing the two loss functions specified at Equations (8) and (9) at the same time using the Adaptive Moment Estimation (Adam) algorithm [33]. In contrast to many other models, such as [21; 25; 26], DELCR does not combine latent vectors to flow via the shared DNN. We constructed a dual deep learning model that learns users' and items' latent features separately for the proposed technique. Here, user and item input vectors are projected into the next connected layer until they reach the output layers. To extract non-linearity features in the data, we used the activation function tanh in each hidden-layer of the network and ReLu in the output layer of the network. To illustrate this concept, let \(p_{0}\) represent the initial user latent feature vector and \(q_{0}\) represent the initial item feature vectors. Then, for the user fully connected DNN layer, we apply Equation (10). \[ \begin{split}& x_{u1}=\sigma(w_{u1}^{T}p_{0}+b_{u1}),\\ & x_{u2}=\sigma(w_{u2}^{T}x_{u1}+b_{u2}),\\ &\vdots\\ & p_{uL}=\sigma(w_{uL}^{T}x_{uL-1}+b_{uL}),\end{split}  \tag{10}\]  
where \(w_{uL},w_{u2},\ldots,w_{uL}\) and \(b_{u1},b_{u2},\ldots,b_{uL}\) indicate weights and biases of users, respectively. In this case, \(\sigma\) stands for an activation function. From each layer we have \(x_{u1},x_{u2},\ldots,p_{uL}\) as an output. Likewise, completely connected item layers can be expressed using Equation (11). \[ \begin{split}& x_{v1}=\sigma(w_{v1}^{T}q_{0}+b_{v1}),\\ & x_{v2}=\sigma(w_{v2}^{T}x_{v1}+b_{v2}),\\ &\vdots\\ & q_{vL}=\sigma(w_{vL}^{T}x_{vL-1}+b_{vL}),\end{split}  \tag{11}\]  
where \(q_{vL}\) denotes the output latent factor from item layers. **prediction layer**: After obtaining the latent features, \(p_{uL}\) and \(q_{vL}\) of users and items, respectively, we apply Equation (12) to estimate the desired rating score, \(\hat{r}_{ij}\). \[\hat{r}_{ij}=p_{uL}\odot q_{vL}, \tag{12}\]  
where \(\odot\) represents the dot product.
## 5 Simulation and Analysis
### Analysis of Intra-Day Optimization Results
It is evident from the figure that, since MPC considers the system state across multi-time sections and can respond to system changes in advance, the GT output fluctuations and ramp rates are smaller under Method 1, effectively reducing the mechanical loss of GT. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **VPSSH1** & **VPSSH2** & **BES1** & **BES2** \\ \hline Method 1 & 0.001 & 0.011 & 0.015 & 0.071 \\ \hline Method 2 & 0.063 & 0.082 & 0.018 & 0.001 \\ \hline Method 3 & 0.254 & 0.286 & 0.257 & 0.212 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The imbalance value of VPSSH and BES capacity ratio at the end of the day under different methods. Figure 14: Intra-day reactive power of two SVGs under Method 1. Figure 15: The change of VPSSH and BES capacity ratio under different methods at intra-day stage: (**a**) VPSSH1; (**b**) VPSSH2; (**c**) BES1; (**d**) BES2. In summary, the proposed optimal dispatch method for the distribution network in this paper takes full advantage of the flexibility and voltage regulation capability of VPSSH under generating/pumping phase modulation conditions. Additionally, the use of MPC for intra-day rolling optimization of the distribution network effectively achieves daily operating capacity balance and smoothes the active power output of the units.
## 3 PSHA for Non-Ergodic GMMs
### Including the Spatial Correlation of the Non-Ergodic Terms  
In the previous section, the epistemic uncertainty in the \(\Delta\mu\) was assumed to be fully correlated between all sources; however, the \(\Delta\mu\) are not fully correlated. The \(\Delta\mu\) terms are modeled by a Gaussian process, with a covariance matrix that describes the variance and correlation length of the \(\Delta\mu\).  
To include the spatial correlation structure between non-ergodic medians across seismic source locations, we follow the methodology from [31] and use the Kahrunen-Loeve expansion of the Gaussian process \(\gamma\).  
First, the principal component analysis (PCA) of the spatial correlation matrix is computed, leading to a set \((\lambda_{k},f_{k})\) of eigenvalues and eigenvectors. The Gaussian process \(\gamma(lat,lon)\) is approximated by the following:\[\gamma(lat,lon)=\sum_{k=1}^{N_{RL}}\sqrt{\lambda_{k}}\;f_{k}(lat,lon)\;\xi_{k} \tag{22}\]  
in which the \(\xi_{k}\) are independent standard normal random variables. The most significant eigenvalues and eigenfunctions of the eigenvalue decomposition of the matrix of spatial correlation can be efficiently calculated and approximated using probabilistic algorithms given by [32].  
This procedure can be used to efficiently generate samples of \(\gamma(Lat,Lon)\) at the locations of the subsources that have the target correlation structure given by the non-ergodic GMM. These correlated samples are then used to approximate the hazard curve for each subsource location from their PC expansion which includes the spatial correlation of the \(\Delta\mu\) across different source locations.  
The PC expansion of a hazard curve at one subsource \(i\) with location \((Lat_{i},Lon_{i})\) including the K-L expansion representation of \(\gamma\) is now given by the following:  
\[Haz_{g}(IM>z) = \sum_{k=0}^{P}C_{k,i}(z)\;\Psi[\gamma(Lat_{i},Lon_{i})] \tag{23}\] \[= \sum_{k=0}^{P}C_{k,i}(z)\;\Psi[\sum_{k=1}^{N_{RL}}\sqrt{\lambda_{ k}}\;f_{k}(Lat,Lon)\;\xi_{k}]\]  
The random variable \(\xi\) from the fully correlated PC expansion in Equation (11) has been replaced by \(\gamma\) which is a function of \(N_{KL}\) independent random variables.  
Because the spatial correlation is only included via the K-L expansion in the Hermite polynomials, Steps 1 to 3 are unchanged from the fully correlated case. The revised steps (after Step 3) are described below:
## 4 Discussion
### Potential Applications  
Due to the high precision reconstruction effect, the CCTM model may also be applied to land surface phenology detection and monitoring, seasonal characteristics extraction, and trend analysis. For seasonal characteristic extraction, the CCTM model is specially designed to decompose the seasonal characteristics into four change parts and effectively extract the seasonal variation characteristics. For the trend analysis, because the CCTM model can decompose the time series into different variation types, the CCTM model can analyze trends of different variation types. The phenology extraction demands extracting the time series statistical features to obtain different transition states [52]. The CCTM model can represent the time series practically with high accuracy, improving the efficiency and accuracy of phenological extraction.
## 3 Results
### CNT Dispersion in Water Using SP and Ultrasound  
The obtained absorbance spectra and \(\zeta\)-potential results for the CNT/water/SP dispersions are presented in Figure 4. Absorbance spectra showed that all samples exhibit a characteristic peak around 250 nm, corresponding to the presence of individual CNTs. SPs A, B, and C showed smooth absorbance profiles with one pronounced peak corresponding to individual CNTs. SP D showed two pronounced peaks, one corresponding to CNTs and one corresponding to the SP naphthalene aromatic groups. \(\zeta\)-potential results showed that all SPs conferred a negative surface charge to the CNT. It is known that the higher the absolute value of \(\zeta\)-potential, the more stable a colloid would be [33], and that for cement-based systems absolute values above 25.0 mV yield stable suspensions [34]. It was found that only SP D presented a result above 25.0 mV in absolute value.  
Figure 3: (**a**) RAMAN spectrum and (**b**) transmission electron microscopy of pristine CNTs.  
Figure 2: FTIR spectra of superplasticizers A, B, C and D.  
Raman spectra were used to verify any damage induced on the CNTs by the sonication process for all SPs studied. The obtained \(\mathrm{I_{G}}/\mathrm{I_{D}}\) values, which describe the density of structural defects, are presented in Figure 5. All dispersions presented \(\mathrm{I_{G}}/\mathrm{I_{D}}\) values lower than the pristine CNTs, indicating sonication-induced damage in all samples, regardless of the SP type or CNT:SP ratio used. No clear trend could be identified regarding the type of SP and the amount of damage induced on the CNTs.  
After initial screening of the four SPs, clear differences in dispersing efficiency could be identified among them. To better understand the limitations of each type of SP, variations in CNT:SP concentrations were performed. Using the UV-Vis results, SPs were divided in two groups, B and D, as the best performing, and A and C, as the worse performing. The obtained results are presented in the following sections.  
Figure 4: (**a**) UV–Vis absorbance spectra and (**b**) \(\zeta\)-potential values of different CNT/SP/water dispersions with 0.25% of CNT and 1.00% of all studied SP.  
Figure 5: \(\mathrm{I_{G}}/\mathrm{I_{D}}\) ratios obtained from Raman spectroscopy results for all CNT dispersions studied.