esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Instead, molecular sieves are mainly prepared based on silica, alumina, and some metal oxides, with a specific surface area of up to 1000 m\({}^{2}\)/g, a pore volume of up to 2.5 cm\({}^{3}\)/g, and an average aperture between 2 and 50 nm [32]. Based on the different porous structures, the molecular sieves can be classified according to spatial structure into one-dimensional pore systems (MCM-41, SBA-15, FSM-16), three-dimensional pore systems (MCM-48), and partially disordered structures (MCF, CNS) [33]. The advantages such as corrosion resistance, high-temperature tolerance, and superior mechanical strength have led to the wide use of molecular sieves in biomedicine, food processing, and fine chemicals industry areas [34; 35; 36]. Specifically, mesoporous molecular sieves with pore sizes ranging from 2 to 10 nm make them appropriate carriers for enzyme immobilization [37]. For instance, [PERSON] et al. [38] found that the immobilized acetylcholinesterase (AChE) exhibited 1.3 times higher catalytic activity than the free enzyme and retained 94% of its initial activity after 60 days of reuse. [PERSON] et al. [39] reported that immobilized narring based on the mesoporous molecular sieve of MCM-41 could facilitate 95% of debittering rates for grapefruit juice in 30 min. Thus, mesoporous molecular sieves have shown great potential in enzyme immobilization, which is applicable for the enzymatic refining of edible oils. This study prepared an efficient and robust immobilized lipase based on the hydrophobically modified mesoporous molecular sieve MCM-41, which was then used for the enzymatic deacidification of high-acid rapeseed oil. Here, the different mesoporous molecular sieve including MCM-21, MCM-41, MCM-49, and various lipases (i.e., CALB, AYS, PS, CSL, AY400 SD) were investigated and screened. Then, the lipase immobilization conditions and the enzymatic reaction conditions including temperature, substrate molar ratio, and adding an amount of CALB@MCM-41-C\({}_{8}\) 4 A molecular sieve were optimized. Moreover, the catalytic stability and usability of CALB@MCM-41-C\({}_{8}\) were also explored. The aroma characteristics and oil quality (i.e., the oxidation stability, and peroxide values)of rapeseed oils before and after enzymatic deacidification were compared. Overall, this paper provides an efficient route for rapeseed oil deacidification based on the self-made immobilized lipase CALB@MCM-41-C\({}_{8}\).
# Minimum Frequency and Voltage Stability Constrained Unit Commitment for AC/DC Transmission Systems
## 5 Results and Discussion
### Jiangsu Power Grid
#### 5.2.3 Analysis of Transmissions Power after HVDC Blocked  
Based on the optimization results, transmissions power results after Jinsu's HVDC blocked in all hours, are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Case 1**} & \multicolumn{2}{c}{**Case 2**} \\ \hline \hline  & **Active Load/MW** & \
\begin{tabular}{c} **Static Voltage** \\ **Stability Margin** \\ \end{tabular} & **Active Load/MW** &
\begin{tabular}{c} **Static Voltage** \\ **Stability Margin** \\ \end{tabular} \\ \hline Hour 12 & 32,000 & 7.6\% & 32,000 & 9.1\% \\ Hour 13 & 31,700 & 7.8\% & 31,700 & 9.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Static voltage stability margin of the Sunan grid with the JINSU HVDC fed-in.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Cost/\$** & \begin{tabular}{c} **Longzheng HVDC’s** \\ **Average Power/MW** \\ \end{tabular} & \begin{tabular}{c} **Jinsu HVDC’s** \\ **Average Power/MW** \\ \end{tabular} & \
\begin{tabular}{c} **Iteration** \\ **Times** \\ \end{tabular} &
\begin{tabular}{c} **Computing** \\ **Time/s** \\ \end{tabular} \\ \hline Case 0 & 9,695,351 & 3000 & 6539.4 & 11 & 70.7 \\ Case1 & 10,691,683 & 3000 & 5382.5 & 30 & 189.6 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Operating cost and HVDC power.  
In case 0, affected by the secondary frequency regulation of the power system, the Meli-Mudu double line is out of limit at some hours after Jinsu's HVDC blocked; in case 1, by reducing Jinsu's HVDC transmission power and optimizing the generators' schedule, it ensures that the Meli-Mudu double lines still operates within limit after HVDC blocked.
# Grassland Wildfires in the Southern Great Plains: Monitoring Ecological Impacts and Recovery
## 4 Discussion  
### Effect of Wildfire Impacts on Grassland Greenness As Described by Vegetation Indices  
Vegetation indices prior to the beginning of the fire were all similar to the three years prior indices in early spring. The NDVI, EVI, and LSWI indices (Figures 2-4) all indicated that recovery from the three major fires was rapid. In all cases, the fires occurred in spring (March or April) when the perennial, warm season vegetation was dormant. The native vegetation evolved with fire-disturbance and energy reserves are stored below-ground during dormant periods. The onset of spring growth was triggered soon after the fires as air temperatures increased. Wildfires occurring at other times of the year would show different recovery responses. However, spring time is the greatest period of risk for wildfire in the Southern Plains due to high windspeed, very low humidity, and dormant vegetation. Fire risks are particularly high if a spring drought follows wetter growing seasons which may produce substantial fuel loads. The LSWI provided a sharp decline at the time of the fires that was not exhibited by the NDVI and EVI indices. [PERSON] and [PERSON] (2019) also reported greater biomass within 3 months in burned compared to unburned regions in the Northern Great Plains using Landsat-based  
Figure 5: Three year average Gross Primary Productivity (GPP) values as calculated following [PERSON] et al. (2017) for the unburned plots (blue shaded) and burned plots (red shaded) of the Anderson Creek (top), Starbuck (middle), and Perryton (bottom) wildfires. Fire-year GPP averages for the unburned (blue) and burned (red) plots are shown as solid lines with error bars of one standard deviation. Time of fire is marked by grey shaded columns.  
NDVI. [PERSON] et al. (2020) applied the NDVI to monitor post-wildfire vegetation indicators in grasslands in the USA, Spain, and Israel.
# Dynamic Optimization Using Local Collocation Methods and Improved Multiresolution Technique
## 3 Improved Multiresolution Technique
### New Generalized Dyadic Meshes  
Generalized dyadic meshes have been proposed in [27], but the method of [27] must start with an initial mesh with a mesh size twice larger than that of the coarsest mesh, which is inconvenient to use. To avoid this shortcoming, new generalized dyadic meshes are described in this paper.  
A uniform generalized dyadic mesh contains a hierarchy of mesh points that is obtained by successively bisection of a uniform initial mesh with \(N+1\) mesh points; that is:  
\[V_{j,\ N}=\Big{\{}\tau_{j,\ k}\in[0,\ 1]:\ \tau_{j,\ k}=k/(2^{j}N),\ 0\leq k \leq 2^{j}N\Big{\}},\ -1\leq j\leq J_{\max}, \tag{11}\]  
where resolution level \(j\), the spatial location \(k\) and the maximum resolution level \(J_{\max}\) are all positive integers. Note that \(N\) must be an even number because the minimum value of \(j\) is \(-1\).  
Let \(W_{j,\ N}\) denotes the mesh points that belong to \(V_{j+1,N}V_{j,N}\),  
\[W_{j,N}=\Big{\{}\hat{\tau}_{j,\ k}\in[0,\ 1]:\hat{\tau}_{j,\ k}=(2k+1)/(2^{j+1}N),0\leq k\leq 2^{j}N-1\Big{\}},-1\leq j\leq J_{\max}-1. \tag{12}\]  
It is easy to know \(V_{j+1,\ N}=V_{j,\ k}\cup W_{j,\ k}\), because  
\[\tau_{j+1,\ k}=\left\{\begin{array}{ll}\tau_{j,\ k/2},&\text{$k$ is even,}\\ \hat{\tau}_{j,\ (k-1)/2},&\text{otherwise.}\end{array}\right. \tag{13}\]  
Figure 1 shows an example of a generalized dyadic mesh with \(N=6\) and \(J_{\max}=5\). It should be noted that the generalized dyadic meshes determined by Equations (11) and (12) is compatible with the dyadic mesh given in [25; 26], where \(N\) must be equal to \(2^{j}\). The generalized dyadic mesh of this paper avoids dyadic restriction and therefore is more convenient to use. In addition, as can be seen in Section 3.3, the improved multiresolution technique based on this new generalized dyadic mesh avoids the inconsistency between the initial mesh and the coarsest mesh.
# Microalgae as Potential Sources of Bioactive Compounds for Functional Foods and Pharmaceuticals
## 3 Results and Discussion
### Evaluation of Bioactivities of Microalgal Extracts
#### 3.1.3 Anti-Tumoral Activity
The DCM fraction did show significantly better cell toxicity values for both cancerous cell lines (IC\({}_{50}\) = 22.3 \(\pm\) 1.8 ug/mL and 27.5 \(\pm\) 1.6 ug/mL for THP-1 and HepG2, respectively) and higher (>4.54 for HepG2, >5.60 for THP-1 cells) selectivity than the crude extract, however, it was not as good as etoposide, which displayed better results for THP-1 cell line (selectivity index = 1.56 \(\pm\) 0.1 and IC\({}_{50}\) = 0.924 \(\pm\) 0.241 ug/mL). These findings suggest that this fraction has more cytotoxic potential for adherent cells (such as HepG2 and S17) than suspension cell lines as THP-1. To confirm this hypothesis, in vitro studies with more cell lines (both adherent and suspension) should be performed. Despite these results, _P. tricornutum_ ethanol extract and its dichloromethane fraction can still be considered promising candidates for an anti-cancer therapy due its prophylactic anti-tumorous activity on HepG2 cells, especially when considering that the tested extracts are a mixture of compounds, which reduces possible overall bioactivity.
# Optical Wireless Fronthaul-Enhanced High-Throughput FC-AE-1553 Space Networks
## 3 System Model of Optical Wireless Fronthaul-Enhanced Hybrid Space Network
### Network Performance Evaluation Metrics
The weight factors for bandwidth \(\alpha_{ij}\), latency \(\beta_{ij}\), and packet loss \(\gamma_{ij}\) originate from the unique requirements of each transmission, whereas the values for bandwidth \(b_{ij}\), latency \(c_{ij}\), and packet loss \(d_{ij}\) are accumulated from the current and previous network transmissions.
## 4 An Efficient Convolutional Denoising Autoencoder Method for BDS NLOS Detection
### Proposed Denoising Autoencoder Module  
#### 4.2.1 Autoencoder  
Autoencoder is a type of artificial neural network trained in an unsupervised manner, learning to reconstruct its original input. An autoencoder consists of two parts: an encoder and a decoder, as shown in Figure 6. The encoder compresses the input \(x\) into a low-dimensional potential space representation \(h\) using a nonlinear transformation. The decoder reconstructs the original input data by means of a potential space representation \(h\). This process can be expressed as follows:  
\[h=f(Wx+b) \tag{6}\]  
\[\hat{x}=g\big{(}W^{\prime}h+b^{\prime}\big{)} \tag{7}\]  
where \(\hat{x}\) denotes the decoder estimation vector and \(f\) and \(g\) denote the nonlinear activation functions of the encoder and decoder, respectively. \(W\) and \(W^{\prime}\) denote the weight matrices of the encoder and decoder, respectively. \(b\) and \(b^{\prime}\) denote the bias vectors of the encoder and decoder, respectively. The overall network is trained using backpropagation to make \(x\) as close to \(\hat{x}\) as possible, thus updating of parameters \(\theta=\{W,b,W^{\prime},b^{\prime}\}\).  
Autoencoders have shown superior effectiveness in dimensionality reduction, target detection, image classification, and image denoising. Variational autoencoders (VAEs) have been widely used as enhanced autoencoders in the field of image processing and text gener  
Figure 5: The detailed framework of the proposed CDAENet algorithm for NLOS detection.  
ation [37]. An overview of the performance of VAEs in source separation, finance, bio-signal applications, target detection, image classification, and image denoising is presented [38]. Ref. [39] explored the applicability of the VAE model in modern game design.  
Inspired by the above autoencoder and variational autoencoder, we developed a denoising autoencoder to recognize NLOS signals in an urban forest environment.
## 2 Materials and Methods
### Determination of Mycotoxins  
Selective methods are required for quantitative mycotoxins extraction from the original food matrix. The mycotoxins extraction from the sample is a critical step and some important parameters can be optimized, such as the nature of the extraction solvent, temperature, time, and purification steps. For multiple mycotoxin analysis, good recoveries are obtained with different solvents such as acetonitrile (AcN), or a mixture of AcN/methanol (MeOH), usually using acidic conditions. There is not to be expected an important extraction of mycotoxins with only water, due to their low solubility in this solvent. For instance, in this work, our purpose for using water was to extract the high-added-value compounds (protein and antioxidants) from sea bass side streams, but not the mycotoxins.  
In a previous work carried out in our laboratory, UAE resulted to be a good procedure for mycotoxins extraction, being an effective tool for emerging mycotoxins extraction after applying ultrasound (20 kHz, 100 W, 30 min, 30 \({}^{\circ}\)C) using AcN as an extraction solvent, obtaining mycotoxin recoveries ranging from 78 to 91% [21]. In the present work, water was tested as a solvent to extract mycotoxins from the sea bass side streams, in the same conditions of time and temperature detailed above. For this, recovery experiments were performed for 11 mycotoxins (AFB1, AFB2, AFG1, AFG2, OTA, ZEA, ENNA, ENNA1, ENNB, ENNB1, and BEA) comparing absolute peak areas of each analyte in a viscera blank sample spiked before the extraction and absolute peak areas of each analyte spiked after the procedure. However, in this case, the recovery percentages obtained after UAE treatment were lower than 25%, showing the low affinity of water to extract mycotoxins from the sea bass side streams.  
After UAE extraction, dispersive liquid-liquid microextraction (DLLME) was used to preconcentrate and purify mycotoxins in the sea bass side streams extracts before the determination.
## 3 System Model
### Communication Model  
When mobile device \(n\) transfers task \(k\) to the edge cloud, the uplink data transfer rate for mobile device \(n\) is shown below:  
\[r_{n}=Blog_{2}(1+\frac{P_{n}H_{n}}{\sigma+I_{n}}) \tag{1}\]  
where \(B\) is channel bandwidth, \(\sigma\) is the noise power, \(P_{n}\) is the transmitted power of mobile devices, and \(H_{n}\) denotes the channel gain between edge cloud and mobile device \(n\). \(I_{n}\) indicates the interference between edge cloud and mobile device \(n\).  
Similar to many studies [16; 17; 18; 26], we neglect the downlink transmission delay, because the output data of many applications is usually much smaller than the input data.
## 3 Numerical Examples
### Double Mach Reflection  
This case was proposed by [PERSON] and [PERSON] [50]. Features a shock wave with a Mach number of 10 and an incidence angle of 60\({}^{\circ}\). The initial conditions are:  
\[(\rho,u,v,p)=\begin{cases}(8,8.25\cos(\pi/6),-8.25\sin(\pi/6),116.5),&x<\frac{ 1}{6}+\frac{y}{\sqrt{3}},\\ (1.4,0,0,1),&x\geq\frac{1}{6}+\frac{y}{\sqrt{3}}.\end{cases} \tag{51}\]  
Generate a uniform grid with a resolution of 960 \(\times\) 240 in the computational domain of [0, 4] \(\times\) [0, 1]. The shock wave starts at \(x=1/6\), with a post-shock flow condition at the bottom boundary when \(x<1/6\), and reflecting a boundary condition when \(x>1/6\)  
Figure 12: Density distributions of the blast wave problem at t = 0.038 were obtained using the LF, ROE, and URLBFS schemes compared to the reference solution. The results illustrate the differences in accuracy and resolution between these schemes. Panels (**a**,**c**) show the results using the WENO5 and WENO7 reconstruction methods, respectively, while panels (**b**,**d**) provide partial zoom\(-\)ins of key areas to visualize the differences better.  
The top boundary applies a time-dependent condition to match the shock wave motion, the left boundary enforces a post-shock flow condition, and the right boundary applies a zero-gradient condition. The ultimate simulation time is set to \(t=0.2\) and a fixed-time step size of \(DT=1\times 10^{-4}\).  
Figure 13 shows the modeling consequence of LF, LBFS, and URLBFS schemes at a resolution of \(960\times 240\). The flow field produced by the WENO7 reconstruction method exhibits a significantly higher resolution than that of WENO5, enabling it to capture finer small-scale vortex structures under the same solver conditions. This increased resolution allows for better representation of complex flow features, including vortices and shock interactions. When comparing the reconstruction methods, the URLBFS scheme introduced in this study consistently outperforms the LF and LBFS schemes in capturing intricate flow structures, particularly near slip lines. Moreover, the URLBFS method demonstrates superior accuracy in resolving strong shock fronts, providing more precise shock-capturing capabilities.
## Materials and Methods
### Method Validation  
The developed and optimized HS-SPME method was properly validated in terms of linearity, detection and quantification limits, intraday and interday precision, accuracy, and matrix effects (ME), according to the Association of Official Analytical Chemists (AOAC) and International Union of Pure and Applied Chemistry (IUPAC) guidelines [25; 26]. Matrix-matched calibration curves were obtained for each compound to evaluate the linearity of the proposed method, using six concentration levels for TMA (2, 5, 8, 10, 15, and 20 \(\upmu\)g/mL) and seven for DMA (2, 5, 7, 8, 10, 15 and 20 \(\upmu\)g/mL). The calibration curves were constructed by plotting the peak area of TMA and DMA against the analyte concentration and were fitted by linear regression analysis. The ME was evaluated by comparing the slopes of the matrix-matched and solvent-based standard calibration curves. Intermediate precision and repeatability were calculated by the standard addition method at a low (3 \(\upmu\)g/mL, LL), medium (9 \(\upmu\)g/mL, ML), and high concentration level (15 \(\upmu\)g/mL, HL) through inter and intraday variation measurements. Recovery was also calculated by spiking a known concentration sample with the previous LL, ML, and HL concentrations. The method limit of detection (LOD) and quantification (LOQ) for each analyte was determined using the residual standard deviation (_Sy/x_) of the corresponding calibration curve. The LOD and LOQ were calculated as 3.3 \(\times\) ((Sy/x)/b) and 10 \(\times\) ((Sy/x)/b), respectively, where (_Sy/x_) represents the standard deviation of the ordinate at origin and \(b\) represents the slope of the regression line [27].
## 4 Numerical Examples
### Nonseparable Spectrum  
Consider a nonstationary earthquake acceleration field with a total of 50 evenly distributed simulation points. The distance between two adjacent simulation points is 15 m. It is assumed that each simulation point has the same autospectrum described by a nonseparable one-sided spectral model [18], which is given by  
\[S(f,t)=4\pi(\frac{f}{2.5})^{2}e^{-0.15t}t^{2}e^{-(\frac{f}{25})^{2}t} \tag{37}\]  
where \(f=\frac{\alpha^{2}}{2\pi}\). The coherence function adopts the following model [30,31], i.e.,  
\[Coh_{jk}^{0}(f)=\rho(v_{jk},f)\exp\left(-i2\pi f\frac{v_{jk}}{V_{jk}}\right) \tag{38}\]  
\[\rho(v_{jk},f)=\beta\exp\left[-\frac{2v_{jk}}{\alpha\theta(f)}(1-\beta+\alpha \beta)\right]+(1-\beta)\exp\left[-\frac{2v_{jk}}{\theta(f)}(1-\beta+\alpha \beta)\right] \tag{39}\]  
\[\theta(f)=\theta_{0}\left[1+\left(f/f_{0}\right)^{b}\right]^{-1/2} \tag{40}\]  
where \(v_{jk}\) depicts the distance between the \(j\)th and \(k\)th locations; \(V_{jk}\) is the wave propagation velocity in the direction of seismic wave propagation; the parameters are selected as \(v_{jk}=150\) m; \(V_{jk}=2000\) m/s; \(\alpha=0.147\); \(\beta=0.736\); \(\theta_{0}=5210\) m/s; \(b=2.78\); \(f_{0}=1.09\) Hz.  
The basic parameters used in the simulation are the total time \(T=32\) s, the cutoff frequency \(f_{u}=8\) Hz, the time step \(\Delta t=1/64\) s and the frequency step \(\Delta f=1/32\) Hz.
# Managing a Smart City Integrated Model through Smart Program Management
## 1 Introduction
So, project management focuses on the single project initiative while program management deals with the integrated management of multiple correlated projects, starting from the assumption that managing the integration requires specific processes and can generate additional value. In this scenario, starting from the proposal of a Smart city integrated model [13], the goal of this research is to investigate the need for program management in a Smart city context and thus the potential benefits derived from using it. It also evaluates the actual level of integration along the two dimensions of a Smart city and the interdependency between smart projects. To address this goal, the authors have carried out a retrospective analysis on a set of 378 [14] projects belonging to nine different Italian Smart cities. The results obtained show the lack of integration between smart projects and motivate the adoption of what we define as \"Smart program management\" (i.e., the integrated management of a program containing several smart projects in the context of a Smart city). The paper is organized as follows: Section 2 briefly presents a general overview of Smart cities worldwide; Section 3 discusses related works; in Section 4 the program management approach is briefly sketched; Section 5 presents the Smart city integrated model and Section 6 introduces the Smart program management approach (i.e., a specialization of the program management approach tailored to the characteristics of a Smart city; Section 7 presents the research goals and questions along with the retrospective analysis planned, while Section 8 presents the data analysis carried out. Finally, Sections 9-11 respectively discuss the results, limitations of the work, and draw conclusions.
# A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA
## 3 An Overview on IoT Communication Protocols and Frameworks
### Comparison of IoT Communication Approaches
#### 3.8.9 Applicability Area  
The different approaches have been developed considering different areas of application, from an end-to-end communication perspective. For instance, CoAP has been devised to support HTTP in communicating to resource constrained nodes and hence, it is usually applied in device to edge communication. From the studied approaches, DDS and NDN are the single proposals that contemplate also D2D communication support. Then, from a perspective of edge to edge communication, HTTP, DDS, NDN are the approaches that can, by design, easily be used to support edge to edge communication. Approaches such as MQTT, CoAP or even OPC UA can also be used to support edge to edge communication. However, this would imply integrating both a client and server (or broker) entity in each Edge node.