esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
For samples where a high \(\alpha\)-amylase content leads to a lower final viscosity of the paste, the stirrer slows down, resulting in higher FN readings, i.e., an underestimation of the \(\alpha\)-amylase activity [29; 30]. The smallest absolute value of bias was calculated when 0.2 g of malt flour (\(-\)0.65 CU/g) and 3.0 mL of malt extract (\(-\)0.91 CU/g) were used. The standard error of bias and 95% confidence interval of bias were the smallest for the method using 0.1 g of malt flour (\(SE_{B}\) = 7.64; CI [\(-\)1.07, 5.89]) and when using 1 mL of malt extract (\(SE_{B}\) = 9.35; CI [\(-\)1.07, 5.89]). The calculated upper limit of quantification (ULOQ), which was defined as the maximum \(\alpha\)-amylase activity that can be measured by the method, gradually decreased with increasing amounts of added malt flour and malt extract. The smallest ULOQ was determined for the method with 0.3 g of added malt flour (325.2 CU/g). It can be concluded that, using this amount of malt flour for analysis, the \(\alpha\)-amylase activity cannot be accurately estimated in malts with higher activity. The repeatability of the \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Equation 1** & \(r\) & **Bias (CU/g)** & **\(SE_{B}\) (CU/g)** & **CI\({}_{B}\) (95\%)** & **ULOQ** (CU/g)** & **Repeatability (CU/g)** \\ \hline \multicolumn{2}{c}{Malit flour (g)} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\
0.05 & AA = \(3645.0\times\exp(-0.019\times\) FN) & 0.973 & 0.86 & 11.07 & \(-\)4.18–5.90 & 1167.5 & 11.31 \\
0.1 & AA = \(1114.4\times\exp(-0.016\times\) FN) & 0.987 & 1.24 & 7.64 & \(-\)2.24–4.72 & 425.4 & 6.70 \\
0.2 & AA = \(546.5\times\exp(-0.015\times\) FN) & 0.987 & 3.10 & 7.44 & \(-\)0.28–6.49 & 222.6 & 3.86 \\
0.3 & AA = \(401.4\times\exp(-0.016\times\) FN) & 0.989 & 0.68 & 7.07 & \(-\)2.53–3.90 & 155.5 & 2.46 \\ \hline \multicolumn{2}{c}{Malit extract (mL)} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\
0.5 & AA = \(6089.6\times\exp(-0.019\times\) FN) & 0.974 & 1.20 & 10.97 & \(-\)3.79–6.20 & 1961.8 & 8.61 \\
1.0 & AA = \(2335.6\times\exp(-0.017\times\) FN) & 0.979 & 1.18 & 9.76 & \(-\)3.26–5.62 & 839.5 & 6.16 \\
2.0 & AA = \(962.9\times\exp(-0.015\times\) FN) & 0.978 & 0.93 & 9.99 & \(-\)3.61–5.48 & 377.8 & 4.23 \\
3.0 & AA = \(536.3\times\exp(-0.014\times\) FN) & 0.981 & 1.11 & 9.37 & \(-\)3.15–5.37 & 235.0 & 3.92 \\
4.0 & AA = \(442.9\times\exp(-0.014\times\) FN) & 0.978 & 0.68 & 9.99 & \(-\)3.87–5.22 & 187.1 & 2.19 \\
5.0 & AA = \(391.7\times\exp(-0.015\times\) FN) & 0.973 & 0.59 & 11.17 & \(-\)4.50–5.67 & 160.3 & 1.81 \\ \hline \end{tabular}
\end{table} 
Table 4: Agreement between standard Ceralpha method and modified Falling Number method (nonlinear relationship between Falling Number and \(\alpha\)-amylase activity).
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 2 Literature Overview in VBDA
### Problem Taxonomy and Applications of Video Processing in VBDA
#### 2.4.2 Activity and Behavior Analysis  
Human activity recognition plays a pivotal role in contextual video understanding, particularly in applications such as fall detection, violence recognition, retail analytics, and interactive media [5; 66]. The field has evolved from using handcrafted features to adopting deep learning architectures, including 3D CNNs [67], LSTMs [68], and Transformer-based models. Skeleton-based approaches [69] provide fine-grained motion analysis and are particularly effective in healthcare and smart home environments [70].
# Multi-Band Array Antenna Sharing a Common Aperture with Heterogeneous Array Elements
## 4 Conclusions  
We proposed a multi-band array antenna that shared a common aperture with heterogeneous array elements. The proposed array comprised one printed dipole antenna for S-band and nine E-shaped patch antennas for X-band. The current directions of the different band elements were orthogonal to each other, which properly decreased the mutual coupling interference. Moreover, to lower the mutual coupling interference among the X-band components, we installed cavities using multiple vias surrounding the X-band components. To validate the usefulness of the proposed design, the unit-cell was expanded to the 12 \(\times\) 12 X-band array configuration, and then the beam steering properties were observed in the X-band. The proposed antenna's bandwidth and average gain were 890 MHz and 5.2 dBi in the S-band and 1.51 GHz and 5.2 dBi in the X-band. The bore-sight gain of the extended array configuration after mounting on the ship mast was 35.6 dBi. The results confirmed that the proposed design was applicable for MFR applications even in a shared aperture.  
Conceptualization, S.W., H.K. and H.C.; methodology, S.W., H.K. and D.K.; software, S.W. and H.K.; validation, S.W., H.K., H.C. and D.K.; formal analysis, S.W.; investigation, S.W.; resources, S.W. and H.K.; data curation, H.K. and S.W.; writing--original draft preparation, S.W.; writing--review and editing, S.W. and H.C.; visualization, S.W. and H.K.; supervision, H.C.; project administration, D.K.; funding acquisition, D.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 9: Simulated radiation patterns before and after mounting on the ship mast: (**a**) 3D radiation patterns after mounting on the ship mast in the X-band; (**b**) radiation patterns before and after mounting on the ship mast in the X-band; (**c**) 3D radiation patterns after mounting on the ship mast in the S-band; (**d**) radiation patterns before and after mounting on the ship mast in the S-band.
###### Abstract
As the world's largest hydropower project, the Three Gorges Project (TGP) provided protection for flood control, water supply, shipping, and power safety for the contemporary era [24]. TGP was officially launched in 1994 and initially impounded in June 2003. It started storing water step-by-step from 135 m in late 2003, to 156 m and 172 m above sea level in late 2006 and late 2008, respectively. After one year of experimental water impoundment since the end of 2008, TGP was officially operated at full 175 m capacity and then lowered to 145 m during flood season [25]. The impacts of TGP have been discussed by governments and scientists since the 1950s, with ecological impact being one of the most controversial academic issues [26; 27; 28; 29]. The land use/land cover (LUCC) [30] and regional climate [31] changed significantly in the Three Gorges Reservoir Area (TGRA) since the construction of TGP. ET and its components, linking the energy change and water cycle in the soil-vegetation-atmosphere system, were greatly impacted by LUCC and climate changes. Thus, ET in TGRA were extensively investigated during the past five years [32; 33; 34; 35; 36; 37]. Most studies focused on reference evapotranspiration [34] or potential evapotranspiration [32]. A few works conducted the estimation of ET [36; 37] and its response to LUCC and climate changes [35; 36]. However, the spatiotemporal variations of ET components and how they contribute to ET changes during different impoundment periods have not been systematically investigated and are not fully understood. Therefore, this study was carried out to fill the knowledge gap; the main objectives were: (1) to characterize the spatial and temporal variations of ET and its components in TGRA from 2000 to 2020; (2) to quantify the contributions of climate change to the variations of ET and its components. The work can help researchers study the impact of TGP on regional energy change and water cycles; it can also improve our understanding of the regional ecological and climatic impact of TGP.
## 4 Plant-Derived Flavonoids That Induce AMPK Activation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Flavonoids**} & **Treatment** & **Model** & \begin{tabular}{c} **Change in** \\ **ADP/AMP** \\ **Ratio** \\ \end{tabular} & **Results** & **Ref.** \\  & **Concentration** & **(Cell or Animal)** & **Ratio** & & \\ \hline \multirow{3}{*}{Dihydromyricetin} & \multirow{3}{*}{50 mg/kg/day} & \multirow{3}{*}{\begin{tabular}{c} In vivo (skeletal \\ muscle of mice) \\ \end{tabular} } & \multirow{3}{*}{N.T} & \multirow{3}{*}{\begin{tabular}{c} Increased insulin sensitivity, glucose \\ uptake, and autophagy in skeletal \\ muscle via the activation of the \\ AMPA-PGC-1o-Sirt3 signaling pathway. \\ \end{tabular} } \\  & & & & \\ \hline \multirow{3}{*}{Bavachin} & \multirow{3}{*}{Up to 10 uM} & \multirow{3}{*}{3T3-L1 adipocytes} & \multirow{3}{*}{N.T} & \multirow{3}{*}{\begin{tabular}{c} Increased glucose uptake via GLUT4 \\ translocation, activated the Akt and \\ AMPK pathways, and enhanced insulin \\ signaling. \\ \end{tabular} } \\  & & & & \\ \hline \multirow{3}{*}{7-O-MA} & \multirow{3}{*}{Up to 10 uM} & \multirow{3}{*}{\
\begin{tabular}{c} HepG2 cells, 3T3-L1 \\ adipocytes \\ \end{tabular} } & \multirow{3}{*}{N.T} & \multirow{3}{*}{
\begin{tabular}{c} Enhanced glucose uptake, activated the \\ AMPK and PI3K pathways, and \\ improved insulin resistance. \\ \end{tabular} } \\  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of flavonoid-induced AMPK activation in T2 DM models. Table 1 provides a detailed summary of the effects of various flavonoids on AMPK activation in different experimental models related to T2 DM. “N.T” denotes cases where the change in the ADP/AMP ratio was not tested.
# DPMF: Decentralized Probabilistic Matrix Factorization for Privacy-Preserving Recommendation
## 4 Evaluation
### Setting
#### 4.1.5 Baseline Methods  
We compare SecSVD++ with three existing works as follows.  
* DMF [18] is a decentralized model based on MF. It is mainly designed for point-of-interest recommendation, therefore we simplify the setting and make it practical for handling the same rating prediction problem as DPMF.
* SVD++ [25] is an improved version for MF version that takes users' historical interactions into consideration.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & _\#Rating_ & _\#User_ & _\#Item_ & _\#Trust_ \\ \hline _FilmTrust_ & 35,497 & 1508 & 2071 & 1853 \\ \hline _Epinions_ & 30,000 & 1336 & 6584 & 7465 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset Analysis.  
* WRMF [12] is a typical centralized matrix factorization method for implicit feedback.
* PMF is the probabilistic model that we introduced in Section 2.2.
## 5 Simulation and Discussion
### Intraday MPC Rolling Optimization Simulation Analysis
#### 5.3.2 Intraday Scheduling Case and Reference Index Setup  
Four cases are set for comparative analysis in the intraday scheduling phase to explore the role of CHB in MPC-based intraday scheduling and the effectiveness of the THB under intraday MPC rolling optimization.  
Case A: Apply the day-ahead DHB strategy and further adjust the hydrogen blending ratio intraday, followed by MPC rolling optimization.  
Case B: Apply the day-ahead DHB strategy and further adjust the hydrogen blending ratio intraday, followed by MPC rolling optimization.  
Figure 8: Simulation forecast data.  
Case C: Maintain the THB hydrogen blending ratio consistent with day-ahead and perform MPC rolling optimization.  
Case D: The system is tested using actual intraday forecast data and corresponding forecast errors from a real-world IES demonstration project; the hydrogen blending scheme is consistent with that of Case C.  
To conduct a comparative study, six indicators were selected: average solving time, upper-layer tracking error, lower-layer tracking error, upper-layer adjustment cost, lower-layer adjustment cost, and total adjustment cost. The specific definitions are as follows:  
Tracking Error:Defined as the difference between the rolling optimization results and the tracking targets, integrated over time throughout the day to quantify the tracking effectiveness of the MPC.  
Adjustment Cost: Based on the tracking error, an adjustment cost coefficient is added to quantify the economic performance of the MPC results. The expression is  
\[\epsilon=\int_{0}^{T}\sum_{i=1}^{N}\lvert x_{i}(t)-x_{\text{ref},i}(t)\rvert dt \tag{44}\]  
\[f_{\text{adj}}=\int_{0}^{T}\sum_{i=1}^{N}\mu_{i}\lvert x_{i}(t)-x_{\text{ref}, i}(t)\rvert dt \tag{45}\]  
where, \(i\) represents the \(i\)-th element in the vector \(x(t)\), \(\mu_{i}\) is the adjustment cost coefficient, \(\epsilon\) is the MPC tracking error, and \(f_{\text{adj}}\) is the adjustment cost.The adjustment cost coefficient for \(P_{\text{grid}}(t)\) and \(G_{\text{gas}}(t)\) is associated with their corresponding \(p_{\text{grid}}(t)\) and \(p_{\text{gas}}(t)\), while the adjustment cost coefficients for other related equipment are equal to their respective operation cost coefficients \(c_{i}\).
# Software-Defined Vehicular Cloud Networks: Architecture, Applications and Virtual Machine Migration
## 3 The Architecture of Software-Defined Vehicular Cloud
### SDN Controller Assistance for Cloud-Based Vehicular Applications
#### 3.2.2 Cloud Vehicular Application Based on Storage Resource Sharing  
Storage resource sharing is an application where many data are stored on independent MEC storage resources. For example, video surveillance on a public transport bus uses high definition cameras to record real-time surveillance inside the public transport bus. This means the requirement of installing higher storage units. However, the cost of higher capacity units is not a suitable option. Cooperative storage is a valuable solution. The public transport bus accesses multiple MEC clouds toward its direction and stores the recorded real-time videos to its nearby MEC micro-data center's server. The recorded videos are deleted either when the public transit bus finishes uploading them or when it drives towards the next MEC micro-data center. The SDN communication infrastructure controller provides the location of the next MEC micro-data center to record (store) recorded videos. SDN communication infrastructure controller determines the next location based on a set of information such as speed limit, available size of storage resource at the MEC micro-data center, and the access radio of the MEC micro-data center. With this set of information, the active VM controller would migrate the storage as service only to a MEC micro-data center that has enough storage space. The SDN OpenFlow controller updates routing path of MEC micro-data center that has in-vehicle video surveillance files. Content-based routing modules at the application layer would suggest the SDN OpenFlow controller to define routing rules so that the road operators would easily permanently retrieve at a given time a different slice of video stored on different MEC micro-data center. At the time of an accident, the road authorities would request the active VM controller to initiate routing rules to allow the transportation bus to upload the recent video not yet stored on the MEC micro-data center.
# Securing Relay Networks with Artificial Noise: An Error Performance-Based Approach +
## 3 AN Maximizing Relay's SER Performance
### SER Expression for a Given \(z\)
Now, inserting Equation (7) into Equation (5), the SER expression can be rewritten as a function of \(\theta\) and \(|z|\) as,  
\[\text{SER}\left(\theta,|z|\right)= c\bigg{[}Q\left(\frac{|h|a}{\sigma}-\frac{|g||z|}{\sigma}\cos\theta \right)+Q\left(\frac{|h|a}{\sigma}+\frac{|g||z|}{\sigma}\cos\theta\right)\] Figure 2.0 Figure 2.1 Figure 2.2  
From Equation (8), we can observe that the channel gains \(|h|\) and \(|g|\) play an important role in the SER performance, which we summarize in the following propositions. **Proposition 1** (SER decreases in \(|h|\)). : _The SER in Equation (8) is a monotonically decreasing function of \(|h|\)._  
**Proposition 2** (SER increases in \(|g|\)). : _The SER in Equation (8) is a monotonically increasing function of \(|g|\)._  
To prove these propositions, we denote,  
Figure 2.3  
and,  
Figure 2.4  
We first focus on Proposition 1. Since \(\xi(|h|,|g|)\) is a decreasing function of \(|h|\), the first derivative of \(\xi(|h|,|g|)\) with respect to \(|h|\) satisfies \(\frac{\text{d}\xi(|h|,|g|)}{\text{d}|h|}\leq 0\), and for a given \(|g|\), \(\xi(|h|,|g|)\leq\xi(0,|g|)=1\). Similarly, \(\frac{\text{d}\eta(|h|,|g|)}{\text{d}|h|}\leq 0\) and for a given \(|g|\), \(\eta(|h|,|g|)\leq 1\). In addition, since \(0<c<1\), the first derivative of the SER expression of Equation (8) with respect to \(|h|\) can be written as,  
Figure 2.5  
which proves Proposition 1. This proposition agrees with the intuition that a stronger source-to-relay link improves the decoding performance at the relay. Regarding Proposition 2, for a given \(|h|\), we use the first derivative of the Gaussian \(Q\)-function  
Figure 2.6  
to obtain,  
Figure 2.7 Figure 2.8  
which indicates that \(\xi(|h|,|g|)\) is an increasing function of \(|g|\). Thus, we have,  
Figure 2.9  
Therefore, since \(0<c<1\), the first derivative of the SER expression of Equation (8) with respect to \(|g|\) can be derived as,  
\[E_{m}=\frac{2}{3}a^{2}T_{m}\left(M-1\right) \tag{3}\]0  
which proves Proposition 2. This proposition suggests that a stronger relay-to-destination link deteriorates the SER performance at the relay. A careful observation of Equation (8) shows that the AN design can be decoupled into the optimal design of the phase and the amplitude, respectively. In the following subsection, we first derive the optimal rotation angle \(\theta\) that maximizes the SER given by Equation (8). The value of \(\theta_{z}\), which denotes the corresponding phase of the AN defined following Equation (7), is then determined accordingly.
## 2 Materials and Methods
### Total Plate Counts, Microbial Isolation and Identification  
Total plate counts (TPCs) and microbial isolates were obtained from 10 to 25 g samples diluted 10-fold in sterile phosphate-buffered saline (PBS) solution and mixed for 1 min in a BagMixer 400 (Interscience, Saint Nom la Breteche, France). A volume of 50 mL of filtered liquid was aliquoted and subjected to serial dilution in PBS, obtaining dilution ratios of \(10^{-1}\), \(10^{-2}\), \(10^{-3}\), and \(10^{-4}\). A volume of 100 \(\mu\)L of diluted liquid was spread-plated onto various agar types (Table 2; Sigma-Aldrich, Burlington, MA, USA) and incubated for 72 h under the conditions for each respective agar medium. TPCs were obtained from duplicate colony counts on non-selective plate count agar (PCA) after 24 h for bacteria and 120 h for fungi, enumerating morphologically distinct filamentous fungi, following ISO 4833-2 [35]. Streak plating was performed on selective agar to isolate morphologically distinct colonies, and a single colony was suspended in 20 \(\mu\)L of sterile water and subjected to polymerase chain reaction (PCR; Platinum Taq DNA Polymerase, Invitrogen, Waltham, MA, USA) using the primers and conditions described in Table 2. Gel electrophoresis was performed on the PCR product with loading dye (DNA Gel Loading Dye, Thermo Scientific, Waltham, MA, USA) and a DNA ladder (GeneRuler 1 kb DNA Ladder, Thermo Scientific, Waltham, MA, USA). On confirmation of positive reaction, the PCR product was cleaned up using E.Z.N.A Cycle Pure Kit (Omega Bio-tek, Norcross, GA USA), and the DNA yield and purity were assessed by 260/280 nm and 260/230 nm absorbance ratios with a NanoDrop(tm) 2000 Spectrophotometer (Thermo Scientific, Waltham, MA, USA). Sanger sequencing (Bio Basic Asia Pacific, Singapore) of PCR samples was conducted via capillary electrophoresis of fluorescent-labeled DNA fragments, with read lengths of up to 800 bp (typically 500-600 bp). The sequence of each isolate was reviewed, and low-quality bases were trimmed. Subsequently, trimmed sequences were subjected to a Basic Local Alignment Search Tool--Nucleotide (Blastn) search against the NCBI nucleotide database using highly similar sequences (megablast) to ascertain the identity of each isolate, with a threshold of at least 99% percent identity for identification.
## 2 Materials and Methods  
### Plant Material  
A total of 8 cherry tomatoes (_Solanum lycopeneum_ var. _cerasiforme_) cultivars were analyzed. Based on the shape, 4 cultivars belong to pear or teardrop shape (Seychelles, Angelle, Santylum, and Dolcetini), with the other four belonging to round shapes (Katalina, Karelya, C-95, and Sweet star). Fruits of the 8 cherry tomato cultivars were collected (commercial ripening stage) from a commercial plantation, located at Los Arejos, Aguilas, Murcia, Spain (37\({}^{\circ}\)27\({}^{\prime}\)17.2\({}^{\prime\prime}\)\" N 1\({}^{\circ}\)40\({}^{\prime}\)31.8\({}^{\prime\prime}\) W and 285 m above sea level). These cultivars were kindly provided by Looije S.L. The commercial plantation is cultivated under homogeneous conditions. Fruit harvest was conducted on the 19 th of June 2019 and 2020 (the factor \"year\" did not significantly affect any of the main quality traits reported in Table 1, thus, results are reported as the average of the two years). Ten kilograms of each variety were selected and picked according to the optimal ripening stage and avoiding injured fruits to prevent skewing of results. After picking, fruits were immediately transported to the laboratory. All analyses were conducted within the 4 days after harvesting, except volatile compositions that were analyzed as soon as possible but in frozen material stored at \(-18\)\({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \multirow{2}{*}{**Attributes**} & \multirow{2}{*}{**ANOVA**\({}^{\dagger}\)} & \multicolumn{6}{c}{**Pern Cherry Tomatoes**} & \multirow{2}{*}{**ANOVA**\({}^{\dagger}\)} & \multicolumn{6}{c}{**Round Cherry Tomatoes**} \\ \cline{3-10}  & & **Angelle** & **Seychelles** & **Santyplum** & **Doleciani** & **ANOVA**\({}^{\dagger}\) & **Katalina** & **Sweet Star** & **C-95** & **Karelya** \\ \hline Eq. diameter (mm) & *** & 27.2 \(\pm\) 1 & 22.2 \(\pm\) 2.5 & 22.5 \(\pm\) & 22.7 \(\pm\) & 28.7 & 28.5 & 29.2 & 30.0 \\ Long. diameter (mm) & * & 37.9 \(\pm\) & 35.8 b & 37.3 b \(\pm\) & 37.6 b & * & 27.4 b & 28.5 ab & 28.4 b & 29.2 a \\ Shape index (mm) & *** & 1.39 \(\pm\) & 1.58 b & 1.66 a & 1.29 d & NS & 0.95 & 1.00 & 0.97 & 0.97 \\ Weight (