esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modeling of the Grid-Connected Systems of Doubly Fed Wind Turbines Through Series Compensation  
### Mathematical Model of Doubly Fed Asynchronous Wind Turbines  
Doubly fed induction generators (DFIGs) are not only the core components of variable-speed constant-frequency wind turbines, but are also a key component in the process of promoting the localization of wind turbines. The design and application of DFIGs are of great significance for improving the efficiency and reliability of wind power systems.  
The \"doubly fed\" in DFIG's name comes from its unique structure: the stator is directly connected to the grid, the rotor is connected to the grid through a set of back-to-back converters, and both the stator and rotor are involved in feeding power to the grid. The doubly fed wind turbine mainly includes a wind turbine, transmission system, doubly fed induction motor, back-to-back power converter, and control system, and its complete topology is shown in Figure 1. These components work together to ensure that the wind turbines can efficiently and consistently convert wind energy into electricity. The back-to-back power converters of the DFIG are composed of rotor-side converters (RSCs) and grid-side converters (GSCs) to achieve stable energy flow. XT is the reactance of the step-up transformer, and RL and XL are the equivalent resistance and reactance of the transmission line, respectively. XC is the reactance of the series compensation capacitor of the transmission line.  
Figure 1: The topology of the doubly fed wind turbine is connected to the grid through series compensation with the SDC connected to both the rotor side and grid side.  
This paper proposes an optimized additional damping method for both the rotor- and grid-side controllers, which can achieve efficient suppression of sub-synchronous oscillations.
###### Abstract  
Orthogonal transformations, proper decomposition, and the Moore-Penrose inverse are traditional methods of obtaining the output layer weights for an extreme learning machine autoencoder. However, an increase in the number of hidden neurons causes higher convergence times and computational complexity, whereas the generalization capability is low when the number of neurons is small. One way to address this issue is to use the fast iterative shrinkage-thresholding algorithm (FISTA) to minimize the output weights of the extreme learning machine. In this work, we aim to improve the convergence speed of FISTA by using two fast algorithms of the shrinkage-thresholding class, called greedy FISTA (G-FISTA) and linearly convergent FISTA (LC-FISTA). Our method is an exciting proposal for decision-making involving the resolution of many application problems, especially those requiring longer computational times. In our experiments, we adopt six public datasets that are frequently used in machine learning: MNIST, NORB, CIFAR10, UMist, Caltech256, and Stanford Cars. We apply several metrics to evaluate the performance of our method, and the object of comparison is the FISTA algorithm due to its popularity for neural network training. The experimental results show that G-FISTA and LC-FISTA achieve higher convergence speeds in the autoencoder training process; for example, in the Stanford Cars dataset, G-FISTA and LC-FISTA are faster than FISTA by 48.42% and 47.32%, respectively. Overall, all three algorithms maintain good values of the performance metrics on all databases.  
autoencoder; feature extraction; extreme learning machine; shrinkage-thresholding algorithms +
Footnote †: journal: Applied Sciences
## 4 Numerical Results and Discussion
### Temperature Dependence of the Magnetization of CBCO  
Initially, we undertake an analysis of the temperature-dependent behavior of the magnetization, \(M\), along the \(b\)-axis, utilizing Equation (5) from the CBCO framework. The resulting depiction, as illustrated in Figure 2, is found to be consistent with empirical observations from various experimental sources [3; 6; 9; 17; 18]. Notably, a pronounced reduction is observed around the critical temperature (\(T_{\text{C}}\)) = 70 K, aligning with theoretical expectations (denoted as curve 1). Furthermore, the magnetization values at lower temperatures provide compelling evidence supporting the presence of charge ordering among the Co ions. Upon the application of an external magnetic field, \(h\), both the magnetization, \(M\), and the critical temperature (\(T_{\text{C}}\)) demonstrate an increase (denoted as curve 2), thereby elucidating the influence of external perturbations on the magnetic properties of the system. The magnetic field, \(h\), tries to arrange the spins in its direction. Thus, it increases those that are parallel to its direction or rotates those that are in the opposite direction. The final effect is an increase in the magnetization, \(M\). When the external magnetic field, \(h\), is absent, there is a second-order phase transition in the system, and the magnetization, \(M\), vanishes at the critical magnetic phase transition temperature because there is a discontinuity in the magnetic susceptibility vs. temperature \(T\) (not shown here). When the external magnetic field, \(h\), is non-zero, the magnetic susceptibility vs. \(T\) is now continuous. The phase transition is broader.
# Structural-Missing Tensor Completion for Robust DOA Estimation with Sensor Failure +
## 3 Structural-Missing Tensor Completion  
In this section, we propose a structural-missing tensor completion method, facilitating the effective DOA estimation robust to sensor failures. The structural-missing tensor completion method includes spatio-temporal dimension augmentation and augmented tensor nuclear norm minimization optimization. The details in implementing the Hankel tensor formulation and solving the Hankel tensor completion problem are both provided.
# Modeling and Analysis of Contactless Solar Evaporation for Scalable Application
## 3 Result and Discussion
### Parametric Analysis of the Scalable SCE
#### 3.3.3 Air Layer Thickness  
Besides the above two parameters, the distance between the bulk water surface and the emitter surface (referred as \"air layer thickness\"), which is also an important geometrical parameter for scalable SCE evaporation. This can be explained by the both variations of heat and mass transfer, due to the change in the resistance of heat transfer and the vapor diffusion, respectively.  
Figure 10a shows the absorber-emitter temperature \(T_{\text{ae}}\), water surface temperature \(T_{\text{wat+t}}\) and evaporation rate under different air layer thicknesses, with the same hole diameter (\(d\) = 1.1 mm) and hole area ratio (\(\varphi\) = 14%). When the air layer thickness increases, the evaporation rate decreases, due to the increased thermal conductivity resistance and weakened vapor diffusion. It is worth noting that the temperature difference between \(T_{\text{ae}}\) and \(T_{\text{wat+t}}\) increases firstly and then shows dynamic stability after the inflection point of 35.9 \({}^{\circ}\)C is achieved under the air layer thickness of 10 mm, as shown in Figure 10b. It can be explained that the temperature difference mainly depends on both the heat conduction flux and heat resistance in the air layer, when the air layer increases, the heat conduction resistance of air layer increases, while the proportion of the heat conduction flux decreases.  
Figure 8: \(T_{\text{ae}}\), \(T_{\text{wat+t}}\) and evaporation rate under different hole area ratios, with the same hole diameter (\(d\) = 1.1 mm) and air layer thickness (\(\delta\) = 2.0 mm).  
Hence, when they tend to be in dynamic equilibrium, the temperature difference between the two remains constant at last. It can be highlighted that a thinner air layer above the water surface can improve evaporation performance because it reduces the distance that the evaporated molecules must travel before they are released into the atmosphere. This can lead to more efficient evaporation and a higher rate of mass transfer from the liquid to the gas phase.
# Chemical and Sensorial Characterization of Scented and Non-Scented Alstroemeria Hybrids
## 4 Discussion
### Ocimene Isomers  
Three out of the four ocimene isomers detected ((Z)-Ocimene, (E)-Ocimene and o-Cymene) did not show significant differences, in terms of absolute areas, between the scented (14E07 and 13M07) and the non-scented (13B01) hybrids. Monoterpenes, particularly ocimene isomers, have been strongly associated with multiple interactions between plants and other organisms [33]. In this study, (E)-Ocimene was detected in all the hybrids evaluated. A previous study also reported this monoterpene in alstroemeria hybrids as the major VOC detected [25]. (E)-\(\beta\)-ocimene is a very common VOC present in the floral scent of a wide range of species [14]. In a more recent review, (E)-\(\beta\)-ocimene was identified in the floral scents of 47.5% of the 291 plant species and in 75.0% of the 63 plant families analysed [30]. Furthermore, this compound has been reported to be emitted at a high concentration from insect-damaged plants of Medicago truncatula [34] and Lotus japonicus [35]. Its abundance has been linked to its role in the attraction of different pollinators, including beetles [36], butterflies [37] and bees [38]. Therefore, the presence of these monoterpenes in the floral scent of a non-scented alstroemeria hybrid, which are perceived by insects but not by our human olfactory system, can be attributed to their ecological role, even though these compounds are normally described as showing a very low odour detection threshold (ODT) [39].
# Anomaly Detection of Remote Sensing Images Based on the Channel Attention Mechanism and LRX
## 3 Hyperspectral Anomaly Detection Based on the Channel Attention Mechanism and LRX
### Auto-Encoder Network  
The auto-coder network is illustrated in the right part of Figure 1 and comprises two components: the encoder and the decoder. The encoder consists of three Dense layers with a neuron configuration of [128; 64; 32], along with an intermediate hidden layer containing 16 neurons. Similarly, the decoder consists of three Dense layers with [32; 64; 128] neurons. The specific network structure is shown in Table 1, where \(N=H\times W\).  
First, the channel attention matrix \(M_{c}\) is element-wise multiplied with the input image \(F\), resulting in \(Input=M_{c}\odot F\). Reshaping this resulting matrix to dimensions \(N\times D\) yields the input matrix \(X\), where \(N=H\times W\). Second, the input matrix \(X\) is sequentially passed through each Dense layer and the intermediate hidden layer of the encoder. The outputs are of size \((N,128)\), \((N,64)\), \((N,32)\), and \((N,16)\), respectively. Third, the output \(Z\) of the intermediate hidden layer with a size of \((N,16)\) can be calculated by Equation (6), where \(w\) and \(b\) represent the weights and biases of the encoder layers. Fourth, \(Z\) is fed through each Dense layer of the decoder. The corresponding outputs are of size \((N,32)\), \((N,64)\), and \((N,128)\), respectively. Finally, the number of neurons in the last Dense layer matches the number of bands \(D\), resulting in the reconstructed feature matrix \(X^{\prime}\) with the size of \(N\times D\). \(X^{\prime}\) can be calculated by Equation (7), where \(\hat{w}\) and \(\tilde{b}\) represent the weights and biases of the decoder layers. Reshaping \(X^{\prime}\) to dimensions \(H\times W\times D\) yield the reconstructed image \(\hat{F}\). The auto-encoder performs non-linear dimensionality reduction by learning a mapping function that extracts implicit features from the input image. This process effectively reduces noises in the input image and generates the reconstructed image with less noise.  
\[Z=f(X,w,b), \tag{6}\]  
\[X^{\prime}=f(Z,\hat{w},\tilde{b}), \tag{7}\]  
The loss function employed is the root mean square error to jointly train the channel attention network and the auto-encoder network. The initial learning rate is set to 0.0004, and the Adam optimizer is utilized for training. The number of training epochs is set between 10 and 300 depending on the specific datasets. The proposed channel attention network and the auto-encoder are implemented using the PyTorch framework. To conduct the comparison experiments, a PC equipped with an Intel(R) Core(TM) i7-7700 HQ CPU running at 2.80 GHz and the Windows 10 operating system is utilized.
## 3 Main Theoretical Results
### Monotone-Iterative Technique
\begin{split}&\varsigma^{(0)}(0)-\frac{1}{\mu}\Delta(\varsigma^{(0)} (0),\varsigma^{(0)}(a))-\varsigma^{(1)}(0)+\frac{1}{\mu}\Delta(\varsigma^{(1)}( 0),\varsigma^{(1)}(a))\\ &\leq\varsigma^{(0)}(0)-\varsigma^{(1)}(0)+(\varsigma^{(1)}(0)- \varsigma^{(0)}(0))+\frac{\delta}{\mu}(\varsigma^{(0)}(a)-\varsigma^{(1)}(a)) \leq 0.\end{split}  \tag{10}\]  
Applying the inequalities (9) and (10) we obtain \(\varsigma^{(1)}(\sigma)\leq\varsigma^{(2)}(\sigma),\ \sigma\in[0,a]\). Inductively we prove the inequalities  
\[\varsigma^{(n)}(\sigma)\leq\varsigma^{(n+1)}(\sigma),\ \ \sigma\in[0,a],\ \ n=0,1,2,\ldots.\]  
Similarly, by induction we prove the monotonicity of the sequence \(\{\varpi^{(k)}(t)\}_{k=0}^{\infty}\), i.e.,  
\[\varpi^{(n)}(\sigma)\geq\varpi^{(n+1)}(\sigma),\ \ \sigma\in[0,a],\ \ n=0,1,2,\ldots.\]  
We use condition 1 and obtain \(\varsigma^{(0)}(\sigma)\leq\varpi^{(0)}(\sigma)\), for \(\sigma\in[0,a]\). Therefore, from conditions 1, 2 we obtain inequalities  
\[ \begin{split}&\varsigma^{(1)}(\sigma)-\varpi^{(1)}(\sigma)\\ &=\Big{(}\varsigma^{(0)}(0)-\varpi^{(0)}(0)-\frac{1}{\mu}\big{(} \Delta(\varsigma^{(0)}(0),\varsigma^{(0)}(a))+g(\varpi^{(0)}(0),\varpi^{(0)}( a))\big{)}\Big{)}\\ &\qquad\times\epsilon^{(\xi-1)\frac{\sigma}{\xi}}E_{\gamma}(- \lambda\big{(}\frac{\sigma}{\xi}\big{)}^{\gamma})\\ &\quad+\frac{1}{\xi^{\gamma}\Gamma(\gamma)}\int_{0}^{\sigma}( \sigma-\tau)^{\gamma-1}e^{(\xi-1)(\frac{\sigma-\tau}{\xi})}E_{\gamma,\gamma} \big{(}-\lambda\big{(}\frac{\sigma-\tau}{\xi}\big{)}^{\gamma}\big{)}\Big{(} \theta(\tau,\varsigma^{(0)}(\tau))-\theta(\tau,\varpi^{(0)}(\tau))\\ &\qquad+\lambda\big{(}\varsigma^{(0)}(\tau)-\varpi^{(0)}(\tau) \big{)}\Big{)}ds\\ &\leq\Big{(}\varsigma^{(0)}(0)-\varpi^{(0)}(0)+\frac{1}{\mu} \big{(}\mu(\varpi^{(0)}(0)-\varsigma^{(0)}(0))+\delta(\varsigma^{(0)}(a)- \varpi^{(0)}(a))\big{)}\Big{)}\\ &\qquad\times\epsilon^{(\xi-1)\frac{\sigma}{\xi}}E_{\gamma}(- \lambda\big{(}\frac{\sigma}{\xi}\big{)}^{\gamma})\\ &\quad+\frac{1}{\xi^{\gamma}\Gamma(\gamma)}\int_{0}^{\sigma}( \sigma-\tau)^{\gamma-1}e^{(\xi-1)(\frac{\sigma-\tau}{\xi})}E_{\gamma,\gamma}( -\lambda\big{(}\frac{\sigma-\tau}{\xi}\big{)}^{\gamma})\Big{(}\lambda(\varpi ^{(0)}(\tau))-\varsigma^{(0)}(\tau))\\ &\qquad+\lambda\big{(}\varsigma^{(0)}(\tau)-\varpi^{(0)}(\tau) \big{)}\Big{)}d\tau\leq 0,\ \ \sigma\in[0,a].\end{split}  \tag{11}\]  
Inductively we prove the inequalities  
\[\varsigma^{(n)}(\sigma)\leq\varpi^{(n)}(\sigma),\ \ \sigma\in[0,a],\ \ n=0,1,2,\ldots.\]  
Therefore, the sequence \(\{\varsigma^{(k)}(\cdot)\}_{k=0}^{\infty}\) is increasing, bounded by \(\varsigma^{(0)}(\cdot)\) and \(\varpi^{(0)}(\cdot)\) and equicontinuous on \([0,a]\). Thus, it is convergent uniformly on \([0,a]\). Denote \(\chi(t)=\lim_{n\to\infty}\varsigma^{(n)}(t),\ \ t\in[0,a]\). According to what was proved above, the inequalities  
\[\varsigma^{(m)}(\sigma)\leq\chi(\sigma),\ \sigma\in[0,a],\ \ \ m=0,1,2,\ldots, \tag{12}\]  
hold. We take the limit as \(m\to\infty\) in the definition of functions \(\varsigma^{(m)}(.
# Application of Sensitivity Analysis to Progressive Collapse Resistance of Planar Truss Structures
## 3 Judgment of Critical Elements Based on Sensitivity Analysis
### Modeling Approach  
FE models of the two trusses were modeled in the commercial FE software ABAQUS. The elements were simulated using the B31 unit. The steel material is modeled using bilinear isotropic hardening constitutive, and the modulus of elasticity, E = 2.06 \(\times\) 10\({}^{5}\) N/mm\({}^{2}\)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Cross-Section** & **Yield Strength (MPa)** & **Ultimate Strength (MPa)** & **Elongation** \\ \hline UC & \(\Phi\)25 \(\times\) 1.5 & 300 & 409 & 26\% \\ LC & \(\Phi\)20 \(\times\) 1.0 & 305 & 418 & 26\% \\ W & \(\Phi\)14 \(\times\) 1.0 & 278 & 415 & 35\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometric dimensions and material properties of members.  
Figure 1: The geometry of truss-PJ (unit: mm).  
Figure 2: Geometry and number of the roof truss (unit: m).  
[PERSON]'s ratio \(\
u=0.3\), and the modulus of elasticity \(E_{t}\) of the strengthening stage are taken as 0.01 E, as shown in Figure 3. Creating of hinge-type connection in the interaction module simulates the hinge between chords and web members. The rigid connection between the chords is achieved by sharing a common joint at the junction of elements. Implicit dynamic analysis is used to simulate the dynamic effect of the structure upon the element failure. Figures 4 and 5 are the finite element models of the two trusses.  
The balanced configuration of the remaining structure and several critical structural responses after initial local failure in the test are compared with the FE results. Figures 6 and 7 show the equilibrium configuration of the structure and the vertical displacement of the typical position LJ2 after the failure of the web member W2, respectively. ABAQUS simulation results and the experimental results have good consistency. All of them show that the remaining structure regains stability through the anti-progressive collapse mechanism after the failure of W2. The vertical displacement of LJ2 continues increasing and tends to be flat at approximately 0.3 s.  
Figure 4: Test truss finite element model.  
Figure 5: Steel roof truss finite element model.  
Figure 3: Bilinear isotropic hardening constitutive model.  
Figure 6: Balanced configuration after W2 failure: (**a**) Test truss, (**b**) FE model.  
Better consistency is also reflected in the strain variation of elements. Figures 8 and 9 show that ABAQUS aptly simulates the lower chord's catenary action under tension. In conclusion, the modeling approach adopted in this paper can accurately simulate the progressive collapse behavior of planar trusses and can be used for subsequent finite element analysis.
# Biomolecules from Microalgae and Cyanobacteria: Applications and Market Survey
## 2 Microalgae
### Compositions and Technologies for Photoautotrophic Production of Microalgae and Cyanobacteria
#### 2.3.4 Carbohydrates  
Microalgae have been suggested as a promising source of carbohydrates, which are located not only inside the chloroplast but also in the cytosol and extracellular compartments. These mono-, oligo-, and polysaccharides are produced during photosynthesis, and their contents can reach 50% of the dry weight of some microalgae, depending on the species and the cultivation and environmental conditions [39; 78]. Cyanobacteria and red microalgae store, respectively, glycogen (\(\alpha\)-(1,4-1,6)-D-glucan) and floridean starch (hybrid between starch and glycogen), while green microalgae accumulate amylopectin-like polysaccharides [39; 78] (Table 3).  
Carbohydrates are classified as simple or complex compounds. The former contains just one or two sugars and are called monosaccharides, while the latter has two or more sugars, called di-, oligo-, and polysaccharides. The latter are polymers of carbohydrates (glycans), and they are extracted from many species and genera of microalgae, mainly from _Chlorella_ sp., _Tetraselmis_ sp. (Chlorophyta), _Isochrysis_ sp. (Haptophyta), _Porphyridium purpureum_, and _Dixoniella grisea_ (Rhodophyta) [65; 78; 79]. Some microalgae also produce, depending on species, structural polysaccharides qualified as fibrillary, which participate in the formation of the cell wall and could be used in pharmaceutical, cosmetic, and food industries. They also have the potential of applications such as thickening or gelling agents as well as bioactive compounds, citing antiviral, anti-inflammatory, anticoagulant and antithrombotic, antitumor, and immunomodulatory activities [80]. The second class is known as energy reserve polysaccharides and exocellular polysaccharides (EPSs), mainly used for biofuel production. Finally, polysaccharides can also be involved in cell communication [40]. Carbohydrates such as Nostoflan, cyclic depispeptides, microvirin, ichthyopeptins A and B, and many others compound extracts from Cyanobacteria also show strong antiviral properties [13; 76; 77].
# GIS-Based Environmental Monitoring and Analysis
###### Abstract  
The environment is an important aspect of sustainable development. Environmental degradation, global warming, forest degradation, agricultural crop production shortfalls, and glacier coverage changes are severe problems that require us to take immediate action. Timely and effective monitoring of the environment enables us to further our knowledge regarding the current status of these conditions, and GIS is a powerful tool that provides vital support for the achievement of sustainable development goals.  
Geographic Information Systems (GIS) have extensive applications in environmental monitoring, data analysis, and advanced geosciences. They facilitate effective land use management, soil monitoring, digital soil mapping, and the analysis of erosion, landslides, terrain slopes, and vegetation. In forestry, GIS supports forest monitoring, fire management, the creation of digital elevation models, and the assessment of flammability indices. This technology is also invaluable in managing natural resources and analyzing natural disasters such as floods, droughs, and earthquakes, contributing to disaster risk reduction. The integration of technologies like LIDAR, remote sensing, 2D and 3D analyses, GeoAI, smart city applications, GIS software and devices, spatiotemporal patterns, WebGIS, and mobile and distributed GIS enables advanced data processing and analysis. In precision agriculture and offshore aquaculture, GIS optimizes production processes. For water and ocean monitoring--including oceanography, sea-level rise, land-ocean interactions, coastal environment monitoring, coastal changes, erosion, and water quality--GIS provides essential analytical tools. Additionally, these systems are employed in monitoring climate change, biodiversity loss, air quality, and particulate matter (PM) levels. GIS-related image processing techniques, big data management, data mining, multi-temporal analysis, decision support, and visualization techniques play a crucial role in effective environmental management.