esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Three-Dimensional Modeling of Structural Planes Based on B-Spline Interpolation
### B-Spline Surface Calculation  
The core of B-spline surface generation lies in the computation of basis functions and surface interpolation using the control point grid. This study used Python's SciPy library and the bisplrep function to generate the B-spline model tck with degree kx = ky = 3:  
\[tck={\rm bisplrep}(X,Y,Z,k_{x}=3,ky=3) \tag{7}\]  
Here, \(tck\) includes knot vectors and weight parameters for surface interpolation. Using the bisplev function, the surface height \(Z\) over a mesh grid is evaluated:  
\[Z_{grid}={\rm bisplev}(X_{grid},Y_{grid},tck) \tag{8}\]  
The grid was generated using numpy.meshgrid(). Structural plane 1 was interpolated at y = 0, 500, 1000, 1500, 2000; y = 0, 500, 1000, 1500, 2000; and y = 0, 500, 1000, 1500, 2000 to visualize the surface sections. The resulting surface \(Z_{grid}\) was visualized using Matplotlib, as shown in Figure 12.  
The smoothness and local control of a B-spline surface are influenced by the spline order k: k = 2 (quadratic): Ensures C\({}^{1}\) continuity; some sharp transitions may appear. k = 3 (cubic): Provides C\({}^{2}\) continuity; balances smoothness and control, suitable for geological surfaces. k \(\geq\) 4k: Higher continuity (C\({}^{3}\) or above), but prone to overfitting and deviation from actual geological morphology.  
Figure 11: Spatial distribution of control points extracted from multiple tunnel faces for B-spline surface construction.  
Although higher-order splines (e.g., \(\mathrm{k}\geq 4\)) offer improved continuity, they are more prone to oscillations and overfitting, especially in noisy or irregular data. In our preliminary tests, the cubic spline (\(\mathrm{k}=3\)) provided smoother and more geologically plausible surfaces. Therefore, we selected cubic B-splines as a balance between continuity and robustness.
# Definition and Determination of Fin Substitution Factors Accelerating Thermal Simulations
## Nomenclature  
\begin{tabular}{l l} \(A_{0}\) & area smooth surface \\ \(A_{\mu}\) & area finned array \\ \(A_{\mu\beta}\) & area fin base \\ \(A_{f}\) & area of all fins \\ \(A_{fcs}\) & cross-sectional area of one fine \\ \(A_{fs}\) & area of one fin \\ \(B\) & fin width \\ \(b\) & fin spacing \\ \(Bi\) & Biot number \\ \(c_{Ro}\) & Rohsenow parameter \\ \(C_{Ro}\) & Rohsenow parameter \\ CFD & Computational fluid dynamics \\ \(c_{p}\) & specific heat capacity \\ FEM & Finite-Element-Method \\ \(FSF\) & fin substitution factor \\ \(g\) & acceleration of gravity \\ \(H\) & fin height \\ \(h\) & heat transfer coefficient \\ \(h_{0}\) & heat transfer coefficient smooth surface \\ \(h_{f}\) & heat transfer coefficient finned array \\ \(k_{\theta\text{in}id}\) & conductivity fluid \\ \(k_{f}\) & conductivity fins \\ \(L\) & fin length \\ \(L_{spe}\) & specific length \\ \(n\) & number of fins \\ \(Nu\) & Nusselt number \\ \(P_{f}\) & perimeter of one fin \\ \(Pr\) & Prandtl number \\ \(Q_{0}\) & Heat flux smooth surface \\ \(Q_{CFD}\) & Heat flux flow simulation \\ \(Q_{f}\) & Heat flux finned array \\ \(Q_{FEM+FSF}\) & Heat flux solid-state simulation \\ \(Ra\) & [PERSON] number \\ \(s\) & base plate thickness \\ \(T_{\infty}\) & ambient temperature \\ \(T_{S}\) & surface temperature \\ \(T_{\beta}\) & fin base temperature \\ \(W\) & base plate width \\ \(y+\) & dimensionless wall distance \\ \(\alpha\) & thermal diffusivity \\ \(\beta\) & thermal expansion coefficient \\ \(\eta_{f}\) & fin efficiency \\ \(\mu\) & fin parameter \\ \(\
u\) & kinematic viscosity \\ \(\rho\) & density \\ \end{tabular}
## 3 Results and Discussion
### Total Polyphenol Content and Antioxidant Capacity  
Processing, especially thermal treatment, during the production of potato crigs, provokes significant alterations in their quality. In the current study, non-blanched crigs obtained from _cv_. Ismena had, on average, a 1.6-fold lower content of total polyphenols than products from _cv_. Provita (with purple flesh) (Table 5). This could be connected with a higher content of phenolic acids and anthocyanins than for boiled potato tubers; the content of polyphenols in purple flesh potatoes was 10 times higher when compared to those with creamy flesh [20]. A similar observation was also made by [PERSON] et al. [21] for snacks with the addition of dried purple potatoes. In case of non-blanched samples (controls), the temperature of frying had a significant impact of the content of total polyphenols: the higher temperature, the higher the content of TPC, regardless the cultivar used (Table 5), which might be connected with the different thermostability of dominant polyphenic compounds [3]. Crisps obtained from _cv_. Provita exhibited rather higher total polyphenol content comparable to such purple fleshed cultivars as Valfi and Salad Blue, used in previous studies [3]. On the other hand, the non-specificity of this spectrophotometric method, especially interference of other substances present in the extracts examined, including newly formed sugar-based compounds formed during the Maillard reaction, might influence the levels of TPC [22]. In general, the application of blanning before frying led to a 64% and 49% decrease in the content of polyphenols in potato crigs from _cv_. Ismena and _cv_. Provita products, respectively. This was in line with the study of [PERSON] et al. [5], where a significant decrease in the content of total polyphenols and anthocyanins was demonstrated in potato crigs with purple flesh that were blanched in water and in organic acid solutions. [PERSON] et al. [23] observed similar relationships in apple slices. It was explained that during blanning, to a large extent, polyphenols, including anthocyanins, were washed into the water, and prolonged blanning time intensified this process. The difference between the degradation of polyphenols in blanched crigs from the analyzed potato cultivars showed that the compounds present in _cv_. Provita were more stable than in _cv_. Ismena. When the products were. compared after blanning and frying, it was noted that _cv_. Provita had 2.2-fold higher content of total polyphenols than _cv_. Ismena. This was connected with an initial composition of raw materials, especially biologically active components that react with the Folin reagent. The higher blanning temperatures, above 85 \({}^{\circ}\)C, favored the preservation of polyphenic compounds in _cv_. Ismena, used for production of potato crigs. No such relation was observed in case of _cv_. Provita. When considering the influence of blanning time at 85 \({}^{\circ}\)C, the highest retention of total polyphenols was indicated for the shortest time (Table 5),
# Economic Superiority of PIP Slip Joint Compared to Conventional Tubular Joints
## 2 Methodology
### Advantages in Annualized Cost and Payback Period  
The bar chart in Figure 8, comparing annualized costs and payback period for grout joints, bolted flange joints, and PIP slip joints in offshore wind applications, underscores the financial superiority of the PIP slip joint, with the lowest annualized costs of EUR 2.402 M/year and the shortest payback period of 2.915 years. These figures represent a 6.68% reduction in annualized costs and a 5.72% shorter payback period compared to grout joints (EUR 2.574 M/year, 3.092 years), and a 5.10% and 5.34% improvement over bolted flange joints (EUR 2.531 M/year, 3.080 years), respectively, highlighting the PIP slip joint's enhanced affordability and reduced financial risk due to its lower initial investment (EUR 28 M). The marginal differences between grout joints and bolted flange joints (1.67% in annualized costs and 0.40% in payback period) indicate similar financial performance, with bolted flange joints being slightly more efficient. These findings, consistent with the PIP slip joint's leadership in LCOE, IRR, and NPV, position it as the optimal choice for offshore wind projects, offering a compelling balance of cost-efficiency, quick investment recovery, and long-term financial sustainability, thereby supporting informed decision-making for renewable energy investments.  
Figure 8: Annualized cost and payback for different joint types.  
Figure 7: Benefit-Cost Ratio (BCR) and return on investment (ROI) for different joint types.
## 3 Methodology
### Rule Base
Let us assume that we are given three non-fuzzy sets of X, Y and Z and a fuzzy relation \(\mathrm{R}\subseteq\mathrm{X}\times\mathrm{Y}\) with a membership function \(\mathrm{\mu_{R}(x,y)}\) and a fuzzy relation \(\mathrm{U}\subseteq\mathrm{Y}\times\mathrm{Z}\) with a membership function \(\mathrm{\mu_{U}(y,z)}\). If the set Y has a finite number of elements, then the compound of the fuzzy relations \(\mathrm{R}\subseteq\mathrm{X}\times\mathrm{Y}\) and \(\mathrm{U}\subseteq\mathrm{Y}\times\mathrm{Z}\) is called the fuzzy relation \(\mathrm{V}=\mathrm{R}\circ\mathrm{U}\) in \(\mathrm{X}\times\mathrm{Z}\) in the form of a two-dimensional fuzzy set with the membership function:  
\[\mu_{\mathrm{R}\circ\mathrm{U}}(x,z)=\max_{y\in\mathrm{Y}}\{\min\{\mu_{R}(x,y ),\mu_{U}(y,z)\}\} \tag{3}\]
# PM2.5 Concentration Prediction Based on CNN-BiLSTM and Attention Mechanism
## 3 Experiment
### Results and Discussion  
The CNN-BiLSTM-Attention model is applied to the preprocessed dataset mentioned above to predict the PM2.5 concentration. Additionally, the attention values on each variable are obtained. The result is shown in Figure 7, where 0-12 on the horizontal ordinate represent PM2.5, PM10, SO\({}_{2}\), NO\({}_{2}\), CO, O\({}_{3}\), TEMP, PRES, DEWP, RAIN, WDSP, year, and month, and longitudinal coordinate indicates the attention value, showing the importance of each variable in the process of predicting PM2.5 concentration. The weights assigned to each variable are 0.131, 0.101, 0.077, 0.088, 0.073, 0.066, 0.065, 0.05, 0.053, 0.051, 0.059, 0.083, and 0.095. Among them, PM2.5 and PM10 have gained relatively large weights because they have a greater impact on the PM2.5 concentration.
## 3 Results and Discussion
### Optimization of the Electrochemical Aptasensor
#### 3.2.1 Optimization of Aptamer Immobilization  
Aptamer immobilization is critical for the development of electrochemical aptasensors. The secondary structure of the GA binding aptamer used in this study, which was predicted by the Mfold web server using 137 mM sodium (Na) and 1 mM magnesium (Mg) at both RT and 37 \({}^{\circ}\)C, is described in Figure S2. The GA aptamer formed a secondary structure with a large hairpin loop that plays a role in GA binding. It has a dissociation constant (KDa) of 5.78 \(\upmu\)M [10]. The optimal folding temperature was predicted to be 37.3 \({}^{\circ}\)C. For the optimization of aptamer immobilization, different concentrations of aptamer (0.1 to 5 \(\upmu\)M) were tested. Very low concentrations of aptamer (0.1 and 0.5 \(\upmu\)M) could not provide an efficient electrochemical signal. Moreover, a high aptamer concentration, such as 5 \(\upmu\)M, resulted in dense immobilization, leading to decreased sensitivity of the aptasensor. An aptamer concentration of 1 \(\upmu\)M showed an appropriate signal and better sensitivity in the context of SWV current (Figure 4A). The reaction time for aptamer immobilization was optimized by ranging the incubation time from 15 to 60 min. The largest changes in SWV current were observed after 30 min of incubation for the detection of 10 \(\upmu\)g mL\({}^{-1}\) GA (Figure 4B). According to our SWV data, 30 min was used for immobilization of the aptamer. The shortened aptamer immobilization period facilitated the fabrication process and provided the great analytical performance of the aptasensor. TE buffer (1\(\times\), pH 8.2) was used to dilute the aptamer, and the experiments for aptamer immobilization were performed at room temperature.
# Synergistic Effects of Fuel Components on Aromatics Formation in Combustion: A Review
## 3 Synergistic Effects on Benzene Formation
### Synergistic Effects between Hydrocarbons  
[PERSON] et al. [112] and [PERSON] et al. [12] conducted studies on the synergistic effect observed during the co-pyrolysis of 1,3-butadiene and propyne, focusing on benzene formation. [PERSON] et al. [12] experimentally investigated the co-pyrolysis of 1,3-butadiene and propyne in an isothermal laminar flow reactor at 973 K to 1273 K and a residence time of 0.3 s. Subsequently, [PERSON] et al. [112] developed a kinetic model for the co-pyrolysis of 1,3-butadiene and propyne. Figure 13 illustrates the substantial increase in benzene yield as a function of temperature during propyne pyrolysis, 1,3-butadiene pyrolysis, and their co-pyrolysis. In Figure 13, the weighted sum denotes the expected yield from the 1,3-butadiene/propyne co-pyrolysis experiment if the two fuels behaved as a linear combination of their individual contributions. In contrast, the CO-PY denotes the actual concentrations observed in the 1,3-butadiene/propyne co-pyrolysis. As shown in Figure 13, the benzene concentration in the CO-PY is higher than the weighted sum, indicating synergistic effects between 1,3-butadiene and propyne. Kinetic analysis revealed the synergistic effect of propyne addition on 1,3-butadiene, leading to enhanced formation of intermediate species and ultimately promoting benzene formation. The primary sources of benzene during the 1,3-butadiene pyrolysis were attributed to acetylene addition to the butadienyl radical. Sensitivity analysis involves increasing and decreasing each reaction rate constant by a factor of two and calculating their effect on the simulation benzene concentrations. Sensitivity and ROP analysis of co-pyrolysis at 1173 K revealed that the isomerization reaction of 1,3-butadiene exhibited the highest sensitivity to the formation of propargyl radicals through 1,2-butadiene [112]. The reaction of propargyl radical with propyne (3) predominantly yielded benzene, accounting for 57% of the benzene production. In comparison, the reaction of propargyl radical with allene (4) contributed to approximately 17% of benzene formation during the CO-PY and played a crucial role in the synergistic effect observed in benzene formation. A synergistic effect has been elucidated in fuel blends comprising methane, ethane, propane, and propene with ethylene in both co-flow flame and counter-flow diffusion flame [103, 104, 114, 116, 117, 118]. The addition of methane to ethylene flames leads to an increase in methyl radicals, subsequently enhancing the formation of propargyl radicals (3, 4). Notably, propargyl radicals play a crucial role in benzene formation. Therefore, these studies have demonstrated that methane addition to ethylene co-flow flames, laminar diffusion flames, and counter-flow diffusion flames induces a synergistic effect on benzene formation [103, 114, 116]. Researchers have observed a synergistic effect resulting from the addition of ethane and propane to ethylene flames [103, 115, 118]. In ethane and propane,
# Intelligent Deep-Q-Network-Based Energy Management for an Isolated Microgrid
## 4 Results and Discussion  
### Site Description  
Based on the weather and load data collected from this area, an optimal configuration of HRES is calculated by HOMER software [43]. Then, its simulation model is defined in MATLAB/Simulink for the implementation of our DQN-based EMS. In this part, the introduction of Basco Island is presented. This island is about 190 km away from Taiwan and is located in the northern region of the Philippines, where the major economic sectors are farming and fishing. On the island, the current source for power generation are diesel generators and fossil fuels, which require high operational costs due to the constantly increasing fuel prices and logistic costs. The location of Basco Island is excellent for marine resource management and tourism. As the government supports developing a sustainable economy, the local governors took the opportunity to invest in a more environment-friendly power system for the local community. Thus, research plays an important role in the economic development plan in this area. It ensures the continuous power supply with low cost of energy and environmental friendliness.  
Figure 7 shows the diagram of our presented HRES in HOMER software (left), as well as the load profile through the year at Basco station (right) presented in HOMER software. A daily power consumption with an average demand of 700 kW every hour is shown in Figure 8. The weather data used for system simulation were taken from the database of the National Renewable Energy Lab (NREL), which can be generated by HOMER software. The average year around solar radiation is 4.44 kWh/m\({}^{2}\)/day, while that of the wind speed is 7.22 m/s. Following the data, the energy system should supply 18 MWh a day with a peak power of 1.4 MW.  
Following the analysis in HOMER software, the optimal configuration of HRES in this case study is obtained [43]. It is reliable, environmentally friendly, and cost-effective. The proposed design includes a 5483 kW PV system, 236 pieces of 10 kW wind turbines, a 20,948 kWh battery system (48 V DC, 4 modules, 5237 strings), a 750 kW diesel generator, a 500 kW Fuel Cell system, a 3000 kW electrolyzer, a 500 kg hydrogen tank, and a 1575 kW converter. The Net Present Cost (NPC) of the system means the present value of the costs of investment and operation of a system over its lifetime. In this study, it was about 72.5 million USD. The Cost of Energy (COE), as the average cost per kWh of useful electrical energy produced by the system, was about 0.696 USD/kW. Furthermore, it can be concluded that the combination of the FC and the battery as the storage system is the best option for the design of HRES with lowest cost of energy. In this kind of system, FC is  
Figure 7: The proposed HRES (**left**) and the load demand at Basco station (**right**) presented in HOMER software.  
for a long term, while the battery is for short-term usage. Following the load demand at the applied area, the system is practical and cost-effective.
# Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications
## 2 Remote Sensing for Agriculture
### Sentinel-2 Spatial and Temporal Resolutions Compared to Other Satellites
Additionally, the constellation combination also helps maintain relatively detailed spatial resolution, with SPOT spatial resolution at 6 m and the Sentinel-2 maximum resolution is 10 m. Specifically in the case of Sentinel-2 resolution ranges between 10 and 60 m, but the agricultural dedicated bands are either 10 or 20 m, depending on the band width, while the other bands at 60 m are for atmospheric aerosols and water vapor observations mainly for improved image calibration. Due to their shorter revisit time and more detailed spatial resolution (compared, for example, to LandSat at every 16 days and mostly 30 m pixel size), more precision in sub-field monitoring can be performed using these paired satellite constellations, hence covering from agroecosystem to field scales more precisely and thus gaining relevance for use in agricultural contexts and even more specifically smallholder farming systems in developing countries. Finally, GeoEye-1, Worldview-3, Planet, and Quickbird are each constellations of numerous commercial satellites allowing higher temporal resolution (as high as a 24 h revisit time) and have a more detailed spatial resolution with pixels smaller than 3 m, but in comparison to Sentinel-2 they do not provide free and open-access global coverage and may, in some cases, only capture data at controlled moments when contracted, which limits their potential use.
## 2 Experimental Procedures  
### Materials Synthesis  
In order to ensure a desirable Cu\({}_{2}\)SnSe\({}_{3}\) compound, elementary powders of Cu (powder, 99.99%), Sn (powder, 99.99%), and Se (shot, 99.99%, Sinopharm Chemical Reagent Co., Ltd., Shanghai, China) were weighted accurately to the atomic ratio of Cu\({}_{2}\)SnSe\({}_{3}\). Then, the powders were mixed in an agate mortar with ethyl alcohol and dried in vacuum drier at 350 K. Next, the powder mixture was loaded into a graphite crucible, which was sealed in a quartz ampoule under vacuum. After the above steps were completed, the quartz ampoule was put in a vertical furnace heated slowly to 1273 K and held for 12 h, followed by cooling with different rates (samples 1-3). Then, some thus-obtained samples were annealed at 923 K for three days (sample 4). Finally, the ingots (sample 1-4) were ground into fine powder in an agate mortar.  
Commercial nano-TiO\({}_{2}\) powder (average particle size: 7 nm, 99.8%, Shanghai Chemical Industry Park Co., Shanghai, China) was incorporated into the Cu\({}_{2}\)SnSe\({}_{3}\) powder by ball milling in an argon atmosphere at volume fractions of 0.5, 1.0, 1.4, and 1.8 vol %, respectively. The TiO\({}_{2}\)/Cu\({}_{2}\)SnSe\({}_{3}\) composite powder was obtained in a planetary mill at 300 rpm for 180 min. The weight ratio of balls to powders was about 10:1. The TiO\({}_{2}\)/Cu\({}_{2}\)SnSe\({}_{3}\) composite powders were densified by spark plasma sintering (SPS 2040) in graphite die (10 mm in diameter) at around 740 K for about 8 min under uniaxial pressure of 50 MPa in vacuum.
# An Intrusion Resistant SCADA Framework Based on Quantum and Post-Quantum Scheme
## 1 Introduction
[18] proposed an efficient collision search algorithm based on quantum logic gates, which reduces the _security level_ of post-quantum from \(2^{128}\) to \(2^{119.6}\). Thus, a quantum attack requires reduced number of queries, which is on the order of \(2^{119.6}\), to crack a post-quantum algorithm. Therefore, it is crucial to strengthen the security of post-quantum hash algorithms. Moreover, the emergence of quantum computing has facilitated the application of quantum physics in resource-constrained devices. For example, researchers at the University of Bristol have introduced the chip-based Quantum Key Distribution (QKD) in 2017, and the ID Quantique company has developed a true Quantum Random Number Generator (QRNG) Chip and QKD systems as well [19; 20].
## Materials and Methods
### Methods
#### 2.3.2 Extraction of Potential Rice Paddies  
For the extraction of potential rice extent, our proposed method was to identify the Sentinel-1 images' pixels that were within the maximum and minimum mean backscatter values of permanent water. With this threshold, the areas with ephemeral potential water information throughout the year were all extracted (including the permanent water areas).  
Then the SRTM DEM products were used to remove the areas where paddy rice was impossible to grow. Studies have shown that it is difficult for rice to grow at altitudes above 2500 m and slopes greater than 2\({}^{\circ}\)[3;14;35]. Therefore, we used these above thresholds to remove the non-rice areas, after which the permanent water was also removed, leaving potential rice paddies in the remaining areas. The advantage of this step was that it could distinguish between flooded rice and forests, shrubs, settlements, and infrastructure, which showed a different threshold interval and no backscattering characteristic associated with surface water. However, classification errors occur in this process for some other crops affected by precipitation, resulting in backscattering values just within the threshold. Therefore, we still needed additional steps to extract other phenological characteristics to identify rice paddies accurately.