esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The following artificial intelligence based optimization methods have been successfully used to solve OPF problems: moth swarm algorithm, MSA [19]; modified particle swarm optimization, MPSO [20]; modified differential evolution, MDE [21]; moth-flame optimization, MFO [22]; flower pollination algorithm, FPA [23]; adaptive real coded biogeography-based optimization, ARCBO and real coded biogeography-based optimization, RCBBO [24]; grey wolf algorithm, GWO and differential evolution, DE [25]; modified Gaussian bare bones imperialist competitive algorithm, MGBICA and Gaussian bare bones imperialist competitive algorithm, GBICA [26]; artificial bee colony, ABC [27]; simulated annealing and hybrid shuffle frog leaping algorithm [28]; Levy mutation teaching-learning-based optimization, LTLBO [29]; teaching learning-based optimization, TLBO [30]; hybrid MPSO and shuffle frog leaping algorithms, HMPSOSFLA, and particle swarm optimization, PSO [31]; Gbest-guided artificial bee colony, GABC [32]; differential search algorithm, DSA [33]; efficient evolutionary algorithm, EEA and eclectic genetic algorithm, EGA [34]; particle swarm optimization with aging leader and challengers, ALCPSO [35]. The above optimization approaches have been developed to solve simple and multiobjective OPF problems. These algorithms performed better than traditional mathematical programming techniques in solving multiobjective optimization problems because they are less affected by the Pareto front shape, and are capable of finding the optimal solutions sets in one run [36]. The assessment of these metaheuristics is commonly based on experimental comparisons. The objective of this research is to develop an original metaheuristic called hybrid imperialist competitive and grey wolf algorithm (HIC-GWA) to solve twelve different cases of simple and multiobjective OPF problems for hybrid power systems that includes PV and WE sources, in order to find effective, faster, and better solutions. The potential and efficiency of the HIC-GWA are presented and evaluated on two standard test systems: IEEE 30 and IEEE 118 bus power systems. Simulation results are compared with the abovementioned optimization approaches. The proposed HIC-GWA is a combination of two algorithms: the imperialist competitive algorithm (ICA) and the grey wolf optimization (GWO). ICA is a sociopolitically inspired optimization strategy that has been proposed to handle tough optimization problems [37]. This approach exhibits good performance in terms of convergence rate and improved global optimum [38; 39]. The GWO algorithm is an original swarm intelligence technique stimulated by the leadership hierarchy and hunting structure of grey wolves. This robust algorithm has been used in different complex problems because of the reduced number of random parameters and a faster convergence due to continuous reduction of search space [40; 41]. Each optimization technique, ICA and GWO, possesses certain specific intelligence to search for the solution of a problem. Therefore, a collection of such abilities enhances the power of the proposed metaheuristic
## 4 Materials and Methods
### Analysis of Volatile Flavor Compounds by SPME-GC-MS  
To extract the flavor compounds, a solid-phase microextraction (SPME) fiber (50/30 \(\upmu\)m, DVB/CAR/PDMS, Agilent, Santa Clara, CA, USA) was used. The extraction process parameters were optimized. The procedure involved placing 10 g of the sample in a 40 mL headspace vial and adding 1 \(\upmu\)L of 2-methyl-3-heptanone as an internal standard with a mass concentration of 0.816 mg/mL. After equilibrating at 45 \({}^{\circ}\)C in a water bath for 30 min, the SPME fiber was used for headspace extraction for 30 min. Then, it was desorbed for 5 min in GC-MS. A 7890B gas chromatograph equipped with a 5977A mass selective detector (MSD; Agilent, Santa Clara, CA, USA) was used to analyze the volatile flavour compounds of the samples.  
GC: A DB-WAX column (60 m \(\times\) 0.25 mm, 0.25 \(\upmu\)m) was used. Helium gas was used as the carrier gas with a constant flow rate of 1.2 mL/min. The temperature program for the column started at 40 \({}^{\circ}\)C and was ramped up at a rate of 4 \({}^{\circ}\)C/min to 230 \({}^{\circ}\)C, which was then held for 2 min. The system was operated in split-less mode.  
MS: An electron ionization (EI) source was applied at a voltage of 70 eV. The inlet temperature was set to 250 \({}^{\circ}\)C, while the ion source and quadrupole temperatures were maintained at 230 \({}^{\circ}\)C and 150 \({}^{\circ}\)C, respectively. The mass scan range used was 35 to 350 \(m/z\), employing full scan mode.
###### Abstract  
Zinc oxide and zinc oxide doped with tin oxide (ZnO-SnO\({}_{2}\)) particles were synthesized and successfully incorporated into a polymeric matrix by the photopolymerization reaction in the presence of Irg819 as the photoinitiator. The obtained samples were investigated by means of XRD, ESEM/EDX, TEM, FTIR, and Raman spectroscopy. The ZnO particles were obtained in the form of rods agglomerated in flower (or star) structures with lengths of 2-4 \(\upmu\)m and widths between 30 and 100 nm, while ZnO-SnO\({}_{2}\) samples evolved in the form of cubes, with sides of 350 nm. The prepared composite films with ZnO and ZnO-SnO\({}_{2}\) particles were tested in the photocatalytic degradation of malachite green (MG) dye. While the ZnO-based composite film showed a fairly high photocatalytic activity, the hybrid film containing ZnO doped with SnO\({}_{2}\) displayed 100% photocatalytic activity after only 45 min of irradiation, being among the most efficient photocatalysts known for MG degradation. In addition, the recycling tests demonstrated that this film displayed high stability during the photocatalysis reaction since no decrease in the photocatalytic performance was noticed after the first three cycles, indicating its suitability for dyes removal and wastewater purification.  
ZnO; ZnO-SnO\({}_{2}\); photopolymerization; composite; malachite green; photocatalysis Article
# Mechanical Performance Prediction Model of Steel Bridge Deck Pavement System Based on XGBoost
## 5 Results and Discussion  
### Prediction Model for Maximum Transverse Tensile Stress on the Pavement Surface  
In the training set, a combination of the grid search method and the five-fold cross-validation method was used to optimize the parameters of the machine learning model. R\({}^{2}\) was chosen as the main evaluation index to select the optimal combination of parameters, and the final evaluation result was the mean value of the five-fold cross-validation results. The optimal combination of parameters is learning_rate = 0.21; max_depth = 7; min_child_weight = 4; and n_estimators = 129. The prediction performance of the model after parameter optimization was verified on the test set, and the MAE of the XGBoost model on the test set was 0.040 The relationship between the predicted and true values of the XGBoost model on the test set is shown in Figure 12, from which it can be seen that the predicted values of most samples in the model are very close to the true values.  
The XGBoost model was compared with other traditional machine learning models. The models were evaluated using three metrics, MAE, RMSE, and R\({}^{2}\), and a grid search combined with cross-validation was used for each model to optimize its parameters. Table 5 shows the prediction results of each traditional machine learning model on the test set, and it can be seen that the XGBoost model shows a good performance. The XGBoost model was compared with the KNN model, and the RMSE was reduced by 37%, the MAE was reduced by 38%, and the R\({}^{2}\) was improved by 33%. The XGBoost model was compared with the SVM model, and the RMSE was reduced by 47%, the MAE was reduced by 47%, and the R\({}^{2}\) was improved by 62%.  
The SHAP architecture was used to perform an interpretability analysis of the developed prediction model, and the importance of the input feature variables is shown in Figure 13, where the importance of the input feature is calculated based on the average absolute value of each feature SHAP value. It can be seen from  
Figure 12: Relationship between predicted and true values of XGBoost model on the test set. Line: predicted values, points: true values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MAE** & **RMSE** & **R\({}^{2}\)** \\ \hline XGBoost & 0.040 & 0.049 & 0.871 \\ KNN & 0.064 & 0.080 & 0.657 \\ SVM & 0.076 & 0.093 & 0.537 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison with other traditional machine learning models.  
important feature variables are the upper pavement modulus of elasticity \(E1\) and the upper pavement thickness \(H1\), and the relatively more important feature variables are the lower pavement thickness \(H2\) and the steel bridge panel thickness \(T\).
# Sensitivity Analysis of Multi-Temporal Sentinel-1 SAR Parameters to Crop Height and Canopy Coverage
## 2 Materials and Methods
### Site Description  
The Konya basin (38\({}^{\circ}\) 40\({}^{\prime}\) N, 32\({}^{\circ}\) 26\({}^{\prime}\) E) in central Turkey is selected for field measurements and satellite images collection (Figure 1). The terrain of the study area is partly flat with a gently sloping (2%-6%) and, the smallest field area of approximately 0.5 and the largest 18 ha. in size. According to the Ministry of Agriculture and Forestry of Turkey, the distribution of major soils in the study region is Reddish Brown and Brownish soils Figure 2. The soil texture in the study area consists of clayed loam and loamy (medium structure), slightly alkaline, salt-free, and low contents of organic matter (1.30-2.08%).  
The region has an arid to semi-arid predominant weather conditions. The land use type is mainly agricultural land in the study site (Figure 3). Therefore, the demand for water consumption for irrigation is increasing due to the extent and dense of agricultural activities. Maize, sunflower, and wheat are three investigated crops patterns as they have different structures. Maize and sunflower based on field campaign, generally are planted at the beginning of May, and harvested at the end of May.  
Figure 1: The location map of the study area; general overview (left) and Sentinel-2 RGB image of the study site (right).  
Figure 2: Soil map of the study area.  
of August or the beginning of September in this study area. Winter wheat is seeded in previous November and harvested at the end of July.
## 3 Main Theoretical Results
### Monotone-Iterative Technique  
As is mentioned in Remark 1 the PFD gives wider opportunity for more adequate modeling. At the same time, only a small part of differential equations with PFD could be solved in explicit form and its causes some difficulties in the application. It requires some approximate methods to be used for their solution. One of these methods is the main goal of this paper. We will recall the main idea of the monotone-iterative technique. Starting from a given arbitrary lower solution of the studied problem we construct recursively in an appropriate way, by the help of an integral operator, a sequence of lower solutions which is increasing and convergent to a solution of the given problem. Similarly, starting from a given upper solution we construct a monotone decreasing sequence of upper solution which is decreasing and convergent to a solution of the studied problem. Note that this method is applied to various types of differential equations as well as to some types of fractional differential equations. In the case of fractional differential equations it is necessary that there be defined mild solutions, mild lower and mild upper solutions which can be used in the application of the monotone-iterative technique. Specially for the generalized proportional fractional differential equations this method is applied in [16]. However, the used fractional derivative is the Riemann-Liouville type of generalized proportional fractional derivative. This derivative, unlike the Caputo type derivative, requires a special type of initial conditions and this leads to the necessity of a definition of a particular integral operator and particular types of lower and upper solutions. Neither the integral operator nor the mild solutions and mild lower/ upper solutions could be applied to the Caputo-type generalized proportional fractional derivative studied in this paper. Additionally, in [16] the initial condition is studied, which is different to the nonlinear boundary condition in this paper and it has a huge influence on the construction of the algorithm for the successive approximations. It determines the necessity of independent essential study of the application of the monotone-iterative method to the GPDE (4). To the best of our knowledge, it is the first paper suggesting an approximate method for the generalized proportional Caputo fractional differential equation. **Theorem 2**. : _Let the following conditions be fulfilled:_  
1. _The functions_ \(\xi^{(0)},\alpha^{(0)}\in C([0,a])\) _are a mild lower solution and a mild upper solution of the GPDE (_4_), respectively, and_ \(\xi^{(0)}(t)\leq\alpha^{(0)}(t)\) _for_ \(t\in(0,a]\)_The function \(\vartheta\in C([0,a]\times\mathbb{R},\mathbb{R})\) and_ \[\vartheta(\sigma,x)-\vartheta(\sigma,y)\leq\lambda(y-x),\quad\sigma\in[0,a],\ x,y\in\mathbb{R}:\ \ \varepsilon(t)\leq x\leq y\leq \varpi(t),\] _holds, where \(\lambda>0\)._ 3.
## 3 On-Body Communication and Connectivity  
Typically, the on-body communication architecture is composed of sensors (to obtain physiological data), actuators (to act on obtained observations) and a coordinator (to control and coordinate both on-body and beyond body networks). Sensors (i.e., biological sensors, environment sensors, location and position sensors, etc.) could be connected directly to the on-body coordinator, which is often considered as more powerful with longer lifetime batteries than deployed sensors. Successful connectivity between on-body nodes and coordinators are often constrained due to ultra-low power consumption, processing and storage, data throughput or packet reception ratio and latency. These fine-grain constraints are often impacted by coarse-grain factors such as dynamic and diverse mobility (e.g., for sports and fitness applications), radio-link and signal propagation (indoor, outdoor, underwater, during emergencies and disasters etc.), interference management (co-channel, inter-channel) and coexistence strategies (time-shared, channel-hopping, collaborative etc.), and data dissemination schemes, as well as routing approaches. Consequently, to successfully support diverse wearable applications, the vital impact of coarse-grain factors is extremely important to analyze. In this section such factors are explored and presented.
# A Brazilian Space Launch System for the Small Satellite Market
## 4 Conclusions  
Considering the current development of the S-50 solid rocket motor for the Brazilian space program, we investigated a set of three expendable launch vehicle concepts to deliver small satellites to low-Earth orbit from the Alcantara Launch Center, Brazil. We combined launch vehicle performance and cost analysis to help judge the most cost-effective solution for small satellite costumers. Performance analysis showed that the three concepts would be capable of sending payloads in the range of 350 to 750 kg for LEO missions, in a variety of orbit inclinations. Concept 3 can deliver a satellite up to 725 kg in low inclination orbit (500 km altitude). This launch system, based on the S-50 motor and a liquid upper stage would offer dedicated launch services for the minisatellite class at affordable prices, from 32,000 to 39,000 US$/kg). Cost analysis showed that vehicle development effort claimed 781 WYr, or less than 80 million dollars and vehicle fabrication accounts for 174 WYr representing less than 23 million dollars per unit. The implementation of the cost model along with the vehicle performance model will facilitate future investigation on new types of launch systems; in particular, those relying on clusters of small liquid rocket engines, similar to the Electron (Rocket Lab). The large amount of data generated in the current development of the enhanced VLM should be used to refine the TRANSCOST model for improved cost predictions of small launchers.  
Conceptualization, P.L.K.d.C. and C.A.G.V.; cubesat data analysis, R.L.; _DBallistic_ model validation, O.S.; writing original draft, P.L.K.d.C., C.A.G.V., O.S., and R.L.; writing review and editing, C.A.G.V.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 7: Market trends in specific transportation cost versus payload mass. Black crosses refers to rideshare charges [19]; red crosses [9]; blue triangles C-1/2/3 (@ 500 km/polar); green bullets C-1/2/3 (@ 500/equatorial).
## 3 Graph Theory and G-Pre Preconditioning Algorithm Design
### G-Pre Preconditioning Algorithm Design
``` Input: Non-symmetric matrix \(A\), right-hand side vector \(b\), initial guess \(x_{0}\), tolerance \(tol\), max iterations \(max\_iter\) Output: Approximate solution \(x\)  
```
1: Step 1: Graph theory preconditioning
2: function preprocess_with_METIS(\(A\)):
3:graph = extract_graph(\(A\))
4: n_partitions = calculate_optimal_partitions(\(A\))
5:partition_result = METIS_PartGraph(graph, n_partitions)
6:P = construct_permutation_matrix(partition_result)
7:A_permuted = P.T @ A @ P
8:preconditioners =[]
9:for\(i\)in\(0\) to \(n\_partitions-1\):
10:block = extract_block(A_permuted, i)
11:M_i = ILU(_block_)
12:preconditioners.append(M_i)
13:returnP,preconditioners
14: Step 2: Preconditioned GMRES solver
15: function METIS_preconditioned_GMRES(\(A\), \(b\), x0, \(tol\), \(max\_iter\)):
16:P,preconditioners = preprocess_with_GRF(\(A\))
17:b_permuted = P.T @ b
18:x = P.T @ x0
19:r = b_permuted - A_permuted @ x
20:beta = norm(r)
21:Q = [r/beta]
22:H = empty_matrix
23:forj in\(0\) to \(max\_iter-1\):
24:v = apply_preconditioner(Q[j],preconditioners)
25:w = A_permuted @ v
26:fori in\(0\) to \(j\):
27:\(H[i,j]=\text{dot}(Q[i],w)\)
28:w = H[i,j] *Q[i]
29:H[j + 1,j] = norm(w)
30:Q.append(w/H[j+ 1,j])
31:\(e1\) = [beta, 0,..., 0].T
32:y = solve_least_squares(H [0:\(j+2\), 0:\(j+1\)], \(e1\))
33:residual = norm(H [0:\(j+2\), 0:\(j+1\)] @ y - \(e1\))
34:ifresidual < \(tol\):
35:break
36:x_update = P @ (Q [0:\(j+1\)] @ y)
37:x = x0 + x_update
38:return x
39:function apply_preconditioner(v,preconditioners):
40:\(z\) = zeros_like(v)
41:for\(i\)in\(0\) to len(preconditioners) - \(1\):
42:block_indices = get_block_indices(i)
43:\(z[block\_indices]=preconditioners[i].solve(v[block_indices])\)
44:returnz
```  
**Algorithm 4**G-Pre code process.
## 3 Results
### Analysis of Conductivity Temperature Compensation  
The test results were processed using the compensation function described in Equation (7), and the resulting temperature compensation coefficients were averaged to yield (k = 0.0198). Post\(-\)compensation, the absolute errors ranged from \(-\)93.35 uS/cm to 92.57 uS/cm, and the relative errors ranged from \(-\)2.72% to 2.87%. Figure 13 illustrates the comparison of two soil sample groups with conductivities of 805.1 uS/cm and 3814.8 uS/cm  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Salinity (\%)** & **0.00** & **0.05** & **0.10** & **0.15** & **0.20** & **0.25** \\ \hline
2265 FS measured & 157.30 & 748.20 & 1363.30 & 1932.50 & 2575.90 & 3157.60 \\ Measured values of & 155.60 & 762.10 & 1341.20 & 1956.40 & 2551.00 & 3096.20 \\ prototype (uS/cm) & 1.70 & 13.90 & 22.10 & 23.90 & 24.90 & 61.40 \\ Absolute error & 1.08 & 1.86 & 1.62 & 1.24 & 0.97 & 1.94 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of soil electrical conductivity measurement between 2265 FS sensor and the prototype.  
Figure 12: Soil electrical conductivity comparison test results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Soil Electrical** & & \\  & **Conductivity** & **Spectrum 2265 FS** & **FOM2/mts** \\  & **Prototype** & & & \\ \hline Electricity & 5 V–12 V DC & 6 V DC & 3.7 V DC \\ Soil electrical & & & \\ conductivity & 0.00–15.00 mS/cm & 0.00–19.99 mS/cm & 0.00–10.00 ms/cm \\ measurement range & & & \\ Soil electrical & & & \\ conductivity & \(\pm\)2\% FS & \(\pm\)1\% FS & \(\pm\)10\% FS \\ measurement accuracy & & & \\ Prices & 1500 CNY & 4500 CNY & \(>\)10,000 CNY \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of main parameters between 2265 FS sensor and the prototype.  
before and after temperature compensation, showing a significant reduction in measurement error.
## 3 Results
### Chondrogenic Pellet Culture NP Cell Marker Expression  
Next, the four top hitters in qPCR analysis, as well as the Colombier factors [47] of GDF5/TGF-\(\beta\)1, were applied for MSC differentiation in a standard chondrogenic 3D-pellet culture, to assess their ability to produce an NP-like chondrogenic matrix. Following 2 weeks of culture, pellets (\(n\) = 3) were harvested, RNA was isolated, and prepared for qPCR analysis. Expression levels were compared to MSC cultured without growth factor stimulation. CD24 expression (Figure 3A) revealed enhanced expression for all cases, with a significant increase for GDF5/TGF-\(\beta\)1 (\(p\) < 0.0001), TGF-\(\beta\)1/Wnt3a (\(p\) = 0.0001), and GDF6/TGF-\(\beta\)1/Wnt3a (\(p\) < 0.0001). Comparing the different combinations, we found GDF5/TGF-\(\beta\)1, GDF6/TGF-\(\beta\)1/Wnt3a, and TGF-\(\beta\)1/Wnt3a statistically outperforming GDF6/Wnt3a and TGF-\(\beta\)2/Wnt3a. SOX9 expression (Figure 3B) was similarly enhanced in all conditions, but most prominently for GDF5/TGF-\(\beta\)1 (\(p\) = 0.0003). COL2A1 (Figure 3C) showed similar results as the SOX9 assessment, with GDF5/TGF-\(\beta\)1 demonstrating the most prominent increase (\(p\) < 0.0001) and a significantly higher expression than all other combinations. Additionally, TGF-\(\beta\)1/Wnt3a and GDF6/TGF-\(\beta\)1/Wnt3a outperformed GDF6/Wnt3a and TGF-\(\beta\)2/Wnt3a. ACAN expression (Figure 3D) showed a trend of reduced expression in all conditions except GDF5/TGF-\(\beta\)1 and GDF6/TGF-\(\beta\)1/Wnt3a. Specifically, GDF5/TGF-\(\beta\)1 was the only combination that demonstrated an increase compared to the negative control, although not significant (\(p\) = 0.0594).  
Figure 2: Gene expression assessment of NP cell markers in monolayer culture of MSC stimulated by different combinations of growth factors. **(A)** Relative expression levels of CD24 mRNA, **(B)** T (Brachyury) mRNA, **(C)** SOX9 mRNA, and **(D)** COL2A1 mRNA. Values are presented for individual donors (shapes) and as average values (bar), with error bars representing standard deviations. Statistical analysis was performed by one-way ANOVA followed by * [PERSON]’s multiple comparison test, which compared growth-factor-stimulated MSC to unstimulated MSC and * [PERSON]’s multiple comparisons test comparing stimulated conditions. * or * indicates a \(p\) < 0.05, and ** or * a \(p\) < 0.005. NC: negative control involving unstimulated MSC cultured in identical 2D culture conditions and media, but without growth factor supplementation, NP: natural human nucleus pulposus cells obtained from surgical waste tissue.