esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Spectral Extraction  
During image acquisition, some random spatial noises were generated due to light heat and ambient conditions. A 3 \(\times\) 3 window median filter was applied on each band image to reduce the noise. Later, the region of interest (ROI) was manually selected using the 662 nm band image and spectral information was extracted from the respective beef samples' HSI images [18]. The mean of the collected spectral data was saved according to the sample's number, resulting in 30 mean spectra from each class of samples (a total of 90 from the 3 classes). From individual classes, 70% of the spectral data were used for calibration, and 30% were used for validation during model development.
# Volatiles in Berries: Biosynthesis, Composition, Bioavailability, and Health Benefits
## 2 Biosynthesis of Volatiles in Plants
### MVA and MEP Pathways  
The two precursors of all terpenes, IPP and DMAPP, are generated from two different pathways in different subcellular compartments: the mevalonic acid (MVA) pathway in the cytosol and the 2-methylerthiritol 4-phosphate (MEP) pathway (1-deoxy-xylulose-5-phosphate (DOXP) pathway) in plastids (Figure 1) [37]. The MVA pathway generates IPP from acetyl-CoA, while the MEP pathway produces IPP and DMAPP from pyruvate and glyceraldehyde-3-phosphate (GA-3P) [38; 39]. In the MVA pathway, there are six enzymatic reactions to generate IPP. Three acetyl-CoA are sequentially condensed, reduced to mevalonate (MVA), then form IPP through two phosphorylations and decarboxylation by mevalonate kinase, phosphomevalonate kinase, and mevalonate diphosphate decarboxylase, respectively [40; 41]. Produced IPP in the MVA pathway further forms its allylic isomer, DMAPP, by isopentenyl diphosphate isomerase [42]. In the MEP pathway, seven enzymatic actions are involved in generating IPP and DMAPP [43]. Condensation of pyruvate and GA-3P generates DOXP, and DOXP synthesizes MEP by DOXP reductoisomerase (DXR). Further transformations form 1-hydroxy-2-methyl-2-(E)-butenyl 4- diphosphate (HMBPP) and (E)-4-hydroxy-3-methlbut-2-enyl diphosphate reductase (HDR) catalyzes the conversion of HMBPP to IPP and DMAPP [44]. MVA pathway mainly synthesizes sesquiterpenes, which account for about 28% of total flower terpen, while the MEP pathway synthesizes more monoterenes and diterpenes, accounting for about 53% and 1% of total flower terpen, respectively [45; 46]. However, metabolic crosstalk exists between the MVA and MEP pathways, especially from plastids to cytosol [47; 48; 49].
# Ventricular Fibrillation Prediction and Detection: A Comprehensive Review of Modern Techniques
## 2 Modern ECG Signal Processing Techniques for Detection and Prediction of Ventricular Fibrillation
### Neural Networks and Deep Learning Algorithms
#### 2.2.3 Convolutional-Recurrent Neural Networks (CRNNs)
Thus, the authors use a sequence-to-sequence RNN model together with a convolutional neural network (CNN) to perform a heartbeat detection task. In [33], the authors propose a recording-level, not heartbeat-level, ECG classification method by combining a 1D deep convolutional neural network (CNN) and a long-term memory network (LSTM). The proposed method [33] uses CNN to automatically extract features of ECG patches without complex data preprocessing or artificial feature schemes and then uses two LSTM layers to model the underlying temporal correlation relationship between ECG patches. For the vanishing gradient problem and to improve feature representation power, the authors construct a residual attention module by combining residual connectivity and attention mechanisms.
## 5 Conclusions  
The beam dynamics design and structural design of a 162.5 MHz 10 mA CW proton SC RFQ have been successfully completed. In the dynamics design phase, we carefully selected various input parameters to achieve efficient transmission and acceleration. The results demonstrated that this SC RFQ can effectively accelerate proton beams from 30 keV to 2.5 MeV over a length of 2.03 m, achieving a remarkable 100.0% transmission efficiency. In the EM field simulation, the cavity's operating frequency was calculated to be 162.53 MHz, consistent with the design specifications. The peak surface electric field and magnetic field were optimized to 56.82 MV/m and 27.46 mT, respectively, both under the safety limit to mitigate the risk of quenching.  
Prior to constructing the full-length cavity, we planned to construct a 30-centimeter-long cavity for vertical tests under SC conditions to verify the performance of the SC cavity after surface treatments, cooling, and vacuum extraction. Simulation results for the 30 cm model cavity indicated a total frequency change of approximately 1.77 MHz due to post-treatment processes. Consequently, setting the initial frequency of the 30 cm cavity to 160.73 MHz is advised, with additional stiffeners in areas experiencing substantial  
Figure 23: The change of cavity frequency with the distance of the end plates moving outward.  
Figure 24: The off-axis electric field distribution of the cavity corresponding to the different squeezing amounts of two end plates of the SCRFQ.  
deformation to enhance cavity stability. Finally, the tuning study of the short cavity revealed that squeezing the end plates to change the distance between them and the vane tips is an effective tuning method. The frequency sensitivity was about 331 kHz/mm, providing enough capability for correcting frequency deviations caused by machining errors.  
The next phase involves initiating the construction of the 30 cm testing cavity for vertical tests to validate the SC cavity's ability to achieve the desired performance.  
Formal analysis, Y.X.; Investigation, Y.X. and Z.W.; Methodology, Y.X.; Project administration, Z.W.; Resources, F.Z.; Software, Y.X.; Supervision, Z.W., Y.L., M.H. and T.W.; Writing--original draft, Y.X.; Writing--review and editing, Y.X. and Z.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are available upon request. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
# Underwater Sensor Network Redeployment Algorithm Based on Wolf Search
## 3 System Model
### Underwater Energy Consumption Model  
The RAWS algorithm should not only consider the coverage problem in the node deployment process, but also consider the energy consumption problem in the mobile process of nodes. Because once some nodes run out of energy consumption, the underwater nodes are not convenient for replacing batteries. Then, the coverage effect will decrease. So we can adjust timely the nodes according to the energy consumption situation. For the underwater deployment algorithm, the choice of the energy consumption model is crucial [28].  
Underwater sensor networks adopt special underwater acoustic communication, and the energy consumption model of an underwater network is based on the sound wave. This study used this model to consider the energy consumption of the underwater network. \(E_{tx}(d)\) expresses the energy that nodes send data as follows:  
\[E_{tx}(d)=P_{r}\times T_{p}\times A(d), \tag{8}\]  
where \(d\) is the data transmission distance, \(T_{p}\) is the data transmission time, and \(P_{r}\) are the minimum power packets that can be received. When the transmission distance of \(A(d)\) is \(d\), the underwater acoustic signal attenuation model \(A(d)\)is provided by  
\[A(d)=d^{\lambda}\alpha^{d}, \tag{9}\]  
where \(\lambda\) is the energy diffusion factor (spherical diffusion to 2) and parameter \(\alpha=10^{a(f)/10}\), as determined by absorption coefficient \(a(f)\)[29]. The formulation is as follows:  
\[a(f)=0.11\frac{10^{-3}f^{2}}{1+f^{2}}+44\frac{10^{-3}f^{2}}{4100+f^{2}}+2.75 \times 10^{-7}f^{2}+3\times 10^{-6}, \tag{10}\]  
where \(f\) is the carrier frequency (unit: kHz). The unit of the absorption coefficient is \(\mathrm{dB/m}\).
## 3 Experiments and Analysis
### Experimental Results and Analysis
#### 3.4.2 Effectiveness of Multi-Network Integration  
The experiment is used to verify the effectiveness of multiple network integration of VGG-16-7. The size of the image block extracted on the DRIVE dataset is 23 \(\times\) 23, and the size of the image block extracted on the CHASE_DB1 dataset is 31 \(\times\) 31. In the case of the same network structure, a single network is used to compare with triple networks integration, the results are shown in Tables 4 and 5.  
According to the data in above tables, the integration of Acc and SE of triple networks is significantly better than in the single network.  
In Table 4, the Acc and SE of multi-networks integration are 96.64% and 81.16%, respectively. Compared with a single network, it has increased by 0.06% and 1.81%, respectively.  
In Table 5, Acc and SE of triple networks integration increased by 0.01% and 0.27% compared with the single network.  
Compared with a single network, multiple networks can extract the features of image blocks more comprehensively and reduce the omission of vascular information. The experimental results on the DRIVE dataset and the CHASE_DB1 dataset confirm the effectiveness of multi-network fusion.
###### Abstract  
This paper deals with the problem regarding the optimal placement and sizing of distribution static compensators (D-STATCOMs) in radial and meshed distribution networks. These grids consider industrial, residential, and commercial loads within a daily operation scenario. The optimal reactive power flow compensation problem is formulated through a mixed-integer non-linear programming (MINLP) model. The objective function is associated with the minimization of the expected energy losses costs for a year of operation by considering the investment costs of D-STATCOMs. To solve the MINLP model, the application of a master-slave optimization approach is proposed, which combines the salp swarm algorithm (SSA) in the master stage and the matricial backward/forward power flow method in the slave stage. The master stage is entrusted with defining the optimal nodal location and sizes of the D-STATCOMs, while the slave stage deals with the power flow solution to determine the expected annual energy losses costs for each combination of nodes and sizes for the D-STATCOMs as provided by the SSA. To validate the effectiveness of the proposed master-slave optimizer, the IEEE 33-bus grid was selected as a test feeder. Numerical comparisons were made against the exact solution of the MINLP model with different solvers in the general algebraic modeling system (GAMS) software. All the simulations of the master-slave approach were implemented in the MATLAB programming environment (version 2021b). Numerical results showed that the SSA can provide multiple possible solutions for the studied problem, with small variations in the final objective function, which makes the proposed approach an efficient tool for decision-making in distribution companies.  
distribution static compensators; salp swarm optimization algorithm; daily load curves; annual operating costs minimization; radial and meshed distribution networks +
Footnote †: journal:  
0000-0001-6978-2227][PERSON]
## 5 Simulation Results
### Testing System after Energy Storage Stations' Allocation in a Day under Normal Operation Conditions  
After allocating the energy storage stations in the power system, the change in hourly demand and wind speeds in a day was assumed to be as shown in Table 3. The total power losses for each hour were calculated using the four optimization alternatives as shown in Figure 4, showing that NPM drove the power system to 8% lower power losses than that of WPM. The results also showed that WPM was better than PSO by 13%. GA was the worst scenario with 32% more losses than that of PSO. The voltage deviation index for each hour was simulated using the four optimization alternatives as shown in Figure 5, showing that NPM drove the power system to a 2% lower voltage deviation index than that of WPM. The results also showed that WPM was better than PSO by 3.5%, while GA was the worst scenario with 4.2% more deviation than that of PSO.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Number of**} & \multicolumn{2}{c}{**GA**} & \multicolumn{2}{c}{**PSO**} & \multicolumn{2}{c}{**WPM**} & \multicolumn{2}{c}{**NPM**} \\ \cline{2-9}  & **Cs (USD)** & **Time (s)** & **Cs (USD)** & **Time (s)** & **Cs (USD)** & **Time (s)** & **Cs (USD)** & **Time (s)** \\ \hline
300 & 2566 & 1245 & 2434 & 1177 & 2355 & 1003 & 2311 & 993 \\
600 & 2304 & 2570 & 2316 & 2314 & 2122 & 2118 & 2056 & 2008 \\
900 & 2110 & 3616 & 2098 & 3418 & 2087 & 3256 & 2037 & 3078 \\
1200 & 2090 & 4022 & 2066 & 4001 & 2006 & 3992 & 2003 & 3962 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Difference between the four alternatives in terms of total costs due to losses and time taken to apply optimization.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Time (h)** & **Total Load (MW)** & **Wind Speed (m/s)** \\ \hline
0 & 29,184 & 5.5 \\
1 & 28,799 & 5.1 \\
2 & 27,904 & 4.6 \\
3 & 27,396 & 4 \\
4 & 26,728 & 4.2 \\
5 & 25,949 & 4.3 \\
6 & 25,208 & 4.8 \\
7 & 25,329 & 4.4 \\
8 &
## 5 Generation-Reserve Co-Optimization Model
### Objective Function  
The objective is to minimize the total operation cost, which involves the generation and reserve cost. The function of the generation cost of SGs is typically expressed as a quadratic function, which can be expressed as  
\[f_{C_{i}}(P_{i,t}^{G})=c_{2,i}(P_{i,t}^{G})^{2}+c_{1,i}P_{i,t}^{G}+c_{0,i} \tag{16}\]  
where \(P_{i,t}^{G}\) is the load of generator \(i\) at period \(t\); and \(c_{2},c_{1}\), and \(c_{0}\) are cost coefficients. The total generation cost \(C_{1,t}\) is the accumulation of the generation cost of all the generators at period \(t\), which can be expressed as  
\[C_{1,t}=\sum\limits_{i=1}^{N}{f_{C_{i}}(P_{i,t}^{G})} \tag{17}\]  
For RG units, since they do not consume fossil fuels, the cost coefficients are assumed to be zero. The reserve cost function for SGs can be expressed as a linear equation  
\[f(r_{i,t}^{u},r_{i,t}^{d})=c_{i}^{u}r_{i,t}^{u}+c_{i}^{d}r_{i,t}^{d} \tag{18}\]  
where \(r_{i,t}^{u}\) and \(r_{i,t}^{d}\) are the upward and downward reserves provided by generator \(i\) at period \(t\), and \(c_{i}^{u}\) and \(c_{i}^{d}\) are cost coefficients, correspondingly. The total reserve cost \(C_{2,t}\) is the accumulation of the reserve cost of all the generators at period \(t\), which can be expressed as  
\[C_{2,t}=\sum\limits_{i=1}^{N}{f_{C}(r_{i,t}^{u},r_{i,t}^{d})} \tag{19}\]  
For the VSG-RG units, since the reserves are based on the deloading strategy, which incurs opportunity cost in normal operation, the coefficients are assumed to be close to those of traditional SGs.  
By the combination of generation and reserve cost, the objective function can be expressed as:  
\[\min\sum_{t=1}^{T}\left(C_{1,t}+C_{2,t}\right) \tag{20}\]  
where \(T\) is the number of dispatch periods.
# A Modified Bearing Capacity Model for Inclined Shallow Anchor Cable with Experimental Verification
###### Abstract  
Most theoretical models of shallow anchor cables do not take the effect of anchor inclination into consideration, which is an important factor influencing load distribution, stress concentration, and failure mechanisms. In this paper, a modified bearing capacity was developed for a single anchor cable, taking the anchor inclination into consideration, based on the principle of limit equilibrium. Then, a series of indoor pull-out tests of single anchors with different inclinations were performed, where the effects of the anchor inclination on the bearing capacity and failure mechanisms were carefully analyzed. The experimental bearing capacities were compared to the predicted data of the proposed modified model, as well as other existing experimental results, aiming to verify the applicability and accuracy. The results show that the bearing capacity increases with decreasing anchor inclination because the vertical component of the force acting on the anchor cable increases. The failure models of the anchor cables, pulled out at different angles, exhibit an asymmetric \"inverted trumpet\" shape, which is caused by the varying stress distributions around the anchor cable during pull-out. In addition, the bearing capacities of the theory differ very little from the experimental and previous results, with a max error of nearly 10%. This study confirms that the proposed model reliably captures the effects of anchor inclination, providing valuable insights for designing inclined anchors in engineering practice.  
bearing capacity; anchor inclination; inverted conical; shallow anchor cable; physical model test +
Footnote †: journal: applied sciences
## 2 Materials and Methods
### Methods
#### 2.2.3 Validation Methods  
In order to assess the accuracy of the CNN-STK model, the common metrics, including the coefficient of determination (\(R^{2}\)), root mean square error (\(RMSE\)), and mean absolute error (\(MAE\)), were computed using the independent validation dataset. The formulas of the metrics are as follows:  
\[R^{2}\ =\ 1-\frac{\sum_{i\ =\ 1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i\ =\ 1}^{n}(y_{i}-\overline{y})^{2}} \tag{6}\]  
\[RMSE\ =\ \sqrt{\frac{\sum_{i\ =\ 1}^{n}(\hat{y}_{i}-y_{i})^{2}}{n}} \tag{7}\]  
\[MAE\ =\ \frac{\sum_{i\ =\ 1}^{n}|\hat{y}_{i}-y_{i}|}{n} \tag{8}\]  
where \(n\) represents the number of sample points; \(y_{i}\) is the observation of the \(i\)th point; \(\hat{y}_{i}\) is the estimation of the \(i\)th point; \(\overline{y}\) is the mean of the observations. The larger the \(R^{2}\), the lower the \(RMSE\) and \(MAE\), and the higher the accuracy of model estimations.  
In addition, we used the same variables and samples to construct a CNN model, an RF model, and an RF-STK model (a hybrid method combining RF and spatiotemporal Kriging with the same strategy as CNN-STK) for comparison. The accuracies of these models were evaluated using the same metrics, enabling a comparative assessment of the effectiveness of the CNN-STK model. Then, we compared the CNN-STK interpolation results with a model simulation dataset (CAMS XCO2) and ground-based measurements (TCCON XCO2) to assess the reliability of the model predictions.  
Last but not least, we separately calculated the relative errors (\(RE\)s) (Equation (9)) between the CNN-STK estimations and observations to discuss the spatial heterogeneity of accuracy. The smaller the \(RE\), the higher the precision of the interpolation.  
\[RE\ =\ \left|\frac{\hat{y}_{i}-y_{i}}{y_{i}}\right|\times 100\% \tag{9}\]
## 3 Results and Discussion
### Evaluation of Cytotoxicityicity and Photo-Protective Effect in Fibroblast Cell Line HS-68  
From a toxicological point of view, safety issues arise from nanomaterials with at least one dimension smaller than 100 nm; therefore, SLNs are generally regarded as safe nanocarriers as they commonly show particle sizes bigger than 100 nm and consist of well-tolerated ingredients [2; 38]. As far as SLN topical application is concerned, [PERSON] et al. [39] reported that SLNs with positive surface charge or containing stearic acid as a solid lipid showed significant cytotoxicity on mouse fibroblast cells, HaCaT keratinocyte cells, or J774 macrophage cells used as model systems to evaluate skin toxicity. On the contrary, [PERSON] et al. [40] did not observe any cytotoxicity in fibroblasts and keratinocytes cells after treatment with SLN containing different lipid matrices.  
In this work, we assessed the cytotoxic effects of the lipid nanocarriers under investigation containing different percentages of IDE on human skin HS68 fibroblast cells.  
Figure 5 shows the response of fibroblast cells exposed to increasing concentrations of different IDE-loaded SLN and NLC. Results are expressed as a percentage of viable cells compared to the control. Cell viability evaluations on sample concentrations in the range 10-100 \(\mathrm{\SIUnitSymbolMicro m}\) revealed that the highest non-toxic concentration was 25 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, and for this reason, the experiment of induction of oxidative stress was carried out with non-toxic concentrations of the samples. Figure 6 shows that the exposure of cells to UV radiation, which were able to induce toxicity in this cell line in these experimental conditions [26], determined a significant decrease of vitality, as calculated by the MTT test (\(p<0.05\)). On the contrary, treating the cells with 25 \(\mathrm{\SIUnitSymbolMicro g}\)/mL of different IDE nanocarriers before the exposure to UV light resulted in a notable photoprotective effect, as cell viability increased in a dose-dependent manner compared to unprotected cells.  
Figure 5: Effect of increased doses of idebenone (IDE)-loaded solid lipid nanoparticles (SLN) and nanostructured lipid carriers (NLC) on the percentage of vitality of HS68 fibroblast cells, determined by MTT (each data is presented as mean \(\pm\) SD; n=12). Statistical analysis for the comparison: * \(p<0.05\) vs. Co. Co = control (no treatment), NLC = unloaded nanostructured lipid carrier, SLN = unloaded solid lipid nanoparticles, NLC-IDE 1 = NLC loaded with IDE 1% w/w; SLN-IDE 1 = SLN loaded with IDE 1% w/w; NLC-IDE 1.5 = NLC loaded with IDE 1.5% w/w, SLN-IDE 1.5 = SLN loaded with IDE 1.5% w/w.
# A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
## 4 Congestion Control Schemes for RPL
### Schemes Exploiting Path Diversity
#### H-Rpl  
[PERSON] et al. propose a resource aware hierarchical RPL(H-RPL) for heterogeneous wireless IoT networks consisting of nodes with different resources and capabilities [41]. To support multiple tasks, H-RPL can use different routing metrics and OFs at different tiers of an H-DODAG topology. H-RPL also enables each node to determine its routing type, such as upward routing, nonstoring downward routing, or storing downward routing based on the availability and requirement of resources. To indicate the routing type of a node, H-RPL uses a modified MOP (mode of operation) mechanism. In the original RPL, all nodes that join a DODAG as routers use the same MOP as the root, and nodes with different MOPs join only as leaves. In contrast, H-RPL allows mixed MOPs in a DODAG. Also, H-RPL allows the MOP of a node to be adaptively changed depending on available resources (e.g., expected routing lifetime), required resources (e.g., required routing memory), and neighbor nodes' MOPs. Since a higher MOP requires more memory to support more routing functions, H-RPL shifts routing workload from nodes with lower MOPs to nodes with higher MOPs. To avoid congestion by upward data packets, H-RPL distributes upward packets among parent nodes based on the queue occupancy index (QOI) which is defined as the average queue utilization. A node ranks its parents according to their QOIs. The higher the QOI, the lower the ranking. Upward packets are distributed between parents in proportion to the ranking. Performance of H-RPL was compared to that of the standard RPL using NS-2 simulator with IEEE 802.15.4 MAC and PHY. Nonstoring nodes were battery-powered and able to buffer five packets, whereas storing nodes were mains-powered and able to buffer ten packets. Since the simulated network consisted of storing nodes and nonstoring nodes, H-RPL operated in the mixed MOP = 1 and 2, and the standard RPL operated in MOP = 1. Simulation results show that H-RPL outperformed the standard RPL in terms of upward data PDR, downward data PDR, and network lifetime.