esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
It is important to stress here that both types of application led to a substantial increase in plant height relative to the control combination (spraying with tap water). Research corroborating the positive effects of extracts from various seaweeds on soybean growth spans more than a dozen years. Multiple studies have proven that the application of seaweed extracts increases plant height by a dozen to several dozen percent, mainly as a function of concentration [34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46]. The two-year study presented here found that biostimulant application led to an increase in pod count regardless of the nozzle used. Conversely, 1000 seed weight was found to differ between biostimulant applications using a standard nozzle and an air-injector nozzle (Table 2). These differences could be attributed to the larger quantity of pods and, consequently, seeds. Studies by [PERSON] et al. [47] have demonstrated that while a higher pod and seed count causes 1000 seed weight to decrease, it nevertheless has a beneficial effect on yield. The present study has corroborated this--the yield was significantly higher after biostimulant application despite a lower 1000 seed weight. Protein concentration in soybean seeds was higher after biostimulant application using standard nozzles (Table 3). Biostimulant application with air-injector nozzles did not have a significant effect on protein content in soybean seeds. No significant differences were found in fat content between soybean seeds from plants treated with the biostimulant and plants treated with tap water (Table 3). [PERSON] et al. [48] and [PERSON] and [PERSON] [49] have noted that this effect could be due to the modification of plant physiological processes by biostimulants. The application of a seaweed-extract biostimulant had a positive effect on economic viability (Figure 5). The average annual increase in income from soybean sales, resulting from biostimulant application using standard nozzles, in comparison to air-injector nozzle application, was 34% higher. It should be noted that the application with both types of nozzles led to an increase in income from soybean sales in each year of the study. The economic aspects of using biostimulants were addressed by [PERSON] et al. [20], who concluded that the increased yield and the relatively low cost of biostimulant application were the critical advantages of this solution. [PERSON] et al. [50] claimed that the use of biostimulants would allow farmers to profit more from higher yields. Previous research by the authors of this study has also proven that biostimulant application in soybean and bean cultivation is an economically viable solution [10; 17]. Literature on the economic effects of biostimulant application is scarce. This is probably due to the focus being more on yield quality as a factor offsetting expenditures from the use of biostimulants. Some elements of economic aspects are discussed in [PERSON] et al. [51] and [PERSON] [52]. Figure 5: The economic effects of biostimulant application using selected sprayers.
# On Multi-Hop Decode-and-Forward Cooperative Relaying for Industrial Wireless Sensor Networks
## 4 Channel Capacity Performance Analysis
### The End-to-End Average Capacity  
The end-to-end average channel capacity \(C_{avg}\) of the relay network can be obtained from  
\[C_{avg}=\int_{0}^{\infty}C(\gamma_{e})\cdot f_{\gamma_{e}}(\gamma_{e})\,d \gamma_{e}, \tag{27}\]  
where \(C(\gamma_{e})\) is the instantaneous capacity in (23) and \(f_{\gamma_{e}}(\cdot)\) is the PDF of the equivalent SNR \(\gamma_{e}\) given in (24). The closed-form expression for the average capacity is given below.  
**Proposition 4**.: _The end-to-end average capacity of the \(L\)-hop relay network under the balanced power allocation scheme can be expressed as follows:_  
\[C_{avg}=\sum\limits_{n=0}^{\infty}\sum\limits_{q=1}^{\infty}\sum\limits_{k=0}^ {2q-1}\frac{\exp(-A)\cdot A^{n}}{\ln(2)\,n!}\cdot\frac{2\cdot(-1)^{k}}{2q-1} \cdot\binom{2q-1}{k}\cdot\mathrm{E}\Big{[}\Big{(}\frac{2\beta_{n}}{\gamma_{e }+2\beta_{n}}\Big{)}^{k}\Big{]}. \tag{28}\]  
_where the \(k\)-th moments \(\mathrm{E}\Big{[}\Big{(}\frac{2\beta_{n}}{\gamma_{e}+2\beta_{n}}\Big{)}^{k} \Big{]}\) of \(\frac{2\beta_{n}}{\gamma_{e}+2\beta_{n}}\) can be expressed as follows_  
\[\mathrm{E}\Big{[}\Big{(}\frac{2\beta_{n}}{\gamma_{e}+2\beta_{n}}\Big{)}^{k} \Big{]}=\sum\limits_{\
u=0}^{(L-1)(m-1)}\frac{\omega_{\
u}\cdot\Gamma(m+v)}{ \Gamma(m)\cdot L^{\frac{m+v-k-1}{2}}}\cdot\Big{(}\frac{2\beta_{n}mN_{0}}{ \Omega E_{b}P_{Rx,\ell+1}}\Big{)}^{\frac{m+v+k-1}{2}}\cdot\exp\Big{(}\frac{ \beta_{n}mLN_{0}}{\Omega E_{b}P_{Rx,\ell+1}}\Big{)}, \tag{29}\]  
_with \(\omega_{0}=1\), \(\omega_{1}=L-1\), \(\omega_{(L-1)(m-1)}=\frac{1}{[(m-1)!]^{L-1}}\), and \(\omega_{\
u}=\frac{1}{v}\sum\limits_{\lambda=1}^{\Lambda}\frac{\lambda L-v}{ \lambda!}\omega_{\
u-\lambda},\
u=2,3,\ldots,(L-1)(m-1)-1\) are computed recursively with \(\Lambda=\min\{\
u,m-1\}\)._  
Proof.: See Appendix B.1.
## 4 Experimental Proof of Concept
### Results  
We present the scattering parameters that we measured in the lab in Figure 12. Although the return losses (S11) are higher than the figure that we obtained by simulation (15.33 dB), that value still fulfils the design requirement specified in Section 2 (10 dB). The isolation between ports in Figure 12 is also poorer than in the simulated results, but still acceptable considering the requirements (40 dB minimum).  
Figures 13-16 show a notable agreement between the measured and simulated patterns, with small differences in the side and back lobes attributed to the mast used to fix the prototype during the measurements. Additionally, the slight discrepancy in the depth of the null in the \(\Delta_{\varphi}\) pattern is due to the sensitivity of the measurement process. Lastly, we measured a gain of 11.8 dBi, with the difference from the design target (13 dBi) easily compensable through radar processing techniques not accounted for in Equation (1), such as signal integration.  
Figure 11: Measurement setup in the anechoic chamber.  
Figure 10: Antenna prototype.  
Figure 13 and 14 illustrate the comparison between the measured (red) and simulated (blue) \(\Sigma\) and \(\Delta_{\theta}\) radiation patterns on the horizontal plane, while the corresponding results for the \(\Sigma\) and \(\Delta_{\theta}\) patterns on the vertical plane are shown in Figures 15 and 16.  
Figure 14: Comparison of the simulated and measured \(\Delta_{\theta}\) diagrams on the horizontal plane.  
Figure 12: Antenna S-parameters measured in the Anechoic Chamber.  
Figure 13: Comparison of the simulated and measured \(\Sigma\) diagrams on the horizontal plane.
## 3 Reconstruction Results  
To demonstrate the effectiveness of the layer-based shifted Fresnel diffraction for 3D reconstruction, optical experiments are implemented. Figure 4 illustrates the optical diagram of the holographic reconstruction experiment. In the experimental work, the PLUTO phase-only SLM was used for optical reconstructions. The pixel number of the SLM was 1920 \(\times\) 1080. The pixel pitch was 8 \(\upmu\)m, and the SLM was addressed with 8-bit grayscale levels. The wavelength of the laser used in our experiment was 633 nm. A 4-f system was used in the reconstruction path, for filtering out the zero-order interruption and unwanted orders [28; 29; 30].  
Figure 3: Diagram of the layer-based shifted Fresnel diffraction for 3D computer-generated holograms (CGHs) calculations.  
Figure 2: Shifted Fresnel diffraction with different scale factors: (**a**) \(\Delta u=6\)\(\upmu\)m, (**b**) \(\Delta u=8\)\(\upmu\)m, (**c**) \(\Delta u=10\)\(\upmu\)m.  
When generating CGHs by using a layer-based algorithm with a shifted Fresnel diffraction, the source planes are the partitioned layers of the 3D scene, and the observation plane is the hologram plane. The original 3D scene used for generating the CGH was a dragon model located 200 mm behind the hologram plane. The dragon model was partitioned into 50 layers, according to its depth information. Since shifted Fresnel diffraction is used in the CGH calculation, the sampling intervals of the object layers can be adjusted. In order to scale the reconstructed 3D scene, the lateral sampling distances of the object layers were set to 6 \(\upmu\)m, 8 \(\upmu\)m, and 10 \(\upmu\)m, respectively. Figure 5a,c,e presents the numerical reconstruction results when focusing on the head of the model. Figure 5b,d,f presents the numerical reconstruction results when focusing on the tail of the model.  
Figure 6 demonstrates the optical reconstruction results of CGHs with different sampling rates, which are consistent with the numerical results shown in Figure 5. According to the numerical and optical demonstrations, we can see that the depth information of the CGHs with different scale factors could be reconstructed with high quality, which demonstrates that the reconstructed images can be scaled with precise control of the sampling rates. Since the scaled sampling was addressed on the object layers of the original 3D object, and because the reconstruction processes of different CGHs are based on the unified sampling rules, smaller sampling interval would lead to a smaller size for the reconstruction image.  
Figure 4: Optical diagram of the holographic reconstruction experiment.  
Figure 5: Numerical reconstructions of CGHs with different sampling rates. (**a**,**c**,**e**) focus on the head; (**b**,**d**,**f**) focus on the tail.
# Total Polypphenols and Antioxidant Properties of Selected Fresh and Dried Herbs and Spices
## 4 Discussion
### Antioxidant Activity Measured by the DPPH Method  
In powdered ginger of the analyzed commercial products of Company 1 and Company 2, antioxidant activity amounted to 65.38 \(\pm\) 0.36% RSA and 71.30 \(\pm\) 0.50% RSA, respectively. Other authors showed comparable antioxidant activity in dried ginger [44]. Research by [PERSON] and [PERSON] [40] showed much lower values in the products of the same companies. Similarly, the lower scavenging capacity of free radicals contained in 80% ginger methanol extract (28.4% RSA) as measured by DPPH was demonstrated by [PERSON] et al. [11]. The authors also achieved lower AA in 80% acetone (32.7%). In black pepper, antioxidant activity of commercial products was achieved at the level of 37.37 \(\pm\) 1.32% RSA and 67.22 \(\pm\) 2.18% RSA, respectively, for Companies 1 and 2. Newerli-Guz [38] presents higher antioxidant activity values in the commercial products of the same companies (amounting to approx. 85% RSA).  
In this study, both companies' commercial marjoram was characterized by a low antioxidant activity (3.85 \(\pm\) 0.12% RSA and 5.88 \(\pm\) 0.32% RSA), compared to the results of Newerli-Guz [39], who obtained values of 88.78% RSA for this spice.  
According to [PERSON] et al. [44], the antioxidant activity of dried sage was 88% RSA. In the same study, antioxidant activity measured by the DPPH method of oregano was found at the level of 70% RSA, which significantly exceeds the results obtained. For fresh ginger, [PERSON] and [PERSON] [40] obtained antioxidant activity at the level of 40.88% RSA, which also exceeds the results presented in this study. Similarly, the antioxidant activity of marjoram in the surveyed trading companies was significantly lower compared to the results of Newerli-Guz [39], who had the value of 88.78% RSA.  
The influence of the drying process on the content of biologically active compounds is not clear. Most often, higher temperature and longer process time cause greater degradation of active substances; however, these changes may depend on the species of herb. Therefore, it is extremely important to select the appropriate methods and parameters of the drying process, which condition the high retention of active compounds [50].
# Risk-Based Virtual Power Plant Implementation Strategy for Smart Energy Communities
## 5 Discussion  
### Effectiveness of the Risk-Based VPP Implementation  
Based on the VPP implementation results shown in Tables 1 and 2, the effectiveness of the risk-based VPP implementation can be discussed. From the values of **P1** and **P2**, it can be seen that a higher capacity was obtained as the relaxation multiplier \(\gamma\) increased. The energy balance was considered a risk factor in **P1** and **P2**, and the relaxation multiplier imposed the priority of the risk. This indicates that when the priority of the risk was increased, more resources were required. It can also be confirmed that of the VPP implementation results, **P0** had the largest resource capacity. This is because the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Cost** & **P0** & **P1** & **P1** & **P1** & **P2** & **P2** & **P2** \\  & & **(\(\gamma\)=0.1)** & **(\(\gamma\)=0.5)** & **(\(\gamma\)=1)** & **(\(\gamma\)=0.1)** & **(\(\gamma\)=0.5)** & **(\(\gamma\)=1)** \\ \hline \multicolumn{8}{c}{Commercial SEC} \\ \hline VPP & 11,304 & 7934 & 9723 & 10,092 & 5779 & 7876 & 8240 \\ Grid & 15,736 & 18,984 & 16,621 & 16,257 & 23,143 & 19,105 & 18,624 \\ Total & 27,041 & 26,918 & 26,344 & 26,349 & 28,922 & 26,981 & 26,864 \\ Unit price & 0.060 & 0.060 & 0.059 & 0.059 & 0.064 & 0.060 & 0.060 \\ \hline \multicolumn{8}{c}{Residential SEC} \\ \hline VPP & 2353 & 1930 & 2255 & 2311 & 1693 & 1823 & 1849 \\ Grid & 5225 & 5746 & 5311 & 5264 & 6126 & 5816 & 5769 \\ Total & 7578 & 7675 & 7566 & 7575 & 7818 & 7639 & 7618 \\ Unit price & 0.043 & 0.043 & 0.043 & 0.043 & 0.044 & 0.043 & 0.043 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Monthly costs according to VPP service ($/month).  
Figure 5: Demand purchasing ratios from the VPPs and the utility grid.  
energy balance was included as a constraint in the problem of **P0**. This shows that risk-based VPP implementation is effective. Moreover, the VPP was configured using the values of **P0**, **P1** and **P2** while selecting the same resources. This shows that a risk factor considering temporal correlation between the demand and resources acts as a dominant factor in VPP implementation. This further proves that the proposed, risk-based approach is well-designed.
## 2 Related Work
### Deep Learning Methods
However, this method still exhibits localized distortion and limited generalizability under extreme conditions. Additionally, [PERSON] et al. [35] introduced a nighttime dehazing method that integrates grayscale and color modules, achieving dehazing under complex illumination by frequency domain decomposition and module collaboration, although its performance is limited under extremely low illumination and strong interference lighting. [PERSON] et al. [36] proposed a user-guided edge-aware cascaded network for single-image reflection removal, which uses interactive user hints to guide the decomposition of reflections and background, enabling fast and effective reflection removal. However, the method's reliance on user interaction limits its applicability in fully automated scenarios. In summary, deep learning methods have significantly advanced the development of low-light image enhancement and glare removal, demonstrating strong capabilities in detail restoration and adaptability. Nevertheless, these methods still face challenges, including reliance on paired data, limited generalization under extreme conditions, high computational complexity, and constrained adaptability in specialized scenarios, necessitating further research to enhance their practicality and robustness.
# A Comprehensive Review of Milk Components: Recent Developments on Extraction and Analysis Methods
## 7 Micro-Constituents' Analysis
### Atomic Absorption Spectroscopy (AAS) and Inductively Coupled Plasma (ICP)  
AAS and ICP are powerful tools for the analysis of minerals and trace elements in milk, such as calcium, magnesium, zinc and iron. AAS has been mainly used for calcium determination in milk. To address chemical interferences, particularly from phosphates, lanthanum is typically added to the sample as a releasing agent (matrix modifier). In flame atomic absorption spectroscopy (FAAS), liquid samples are easily introduced into the analyzer as aerosols following a preliminary treatment, which can involve conventional ashing [157] or wet digestion [158]. Although AAS is fast and less expensive than ICP techniques, it allows for the detection of one analyte at a time and does not provide high sensitivity. Instead, inductively coupled plasma optical emission spectroscopy (ICP-OES) [159] and inductively coupled plasma mass spectrometry (ICP-MS) [160] are both highly sensitive and versatile techniques used for the analysis of trace elements in milk. ICP-OES is more robust and can be used for routine analysis ensuring good LOD and LOQ values. However, without derivatization, it is not possible to analyze mercury and arsenic. ICP-MS allows trace analysis of those metals that require derivatization in ICP-OES. On the other hand, high-cost instrumentations are required. Table 5 compares the performances of ICP-OES and ICP-MS in terms of LOD, LOQ and linearity for the analysis of metals in milk.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multicolumn{2}{c}{**LOD ICP-OES (mg/kg)**} & \multicolumn{2}{c}{**LOQ ICP-OES (mg/kg)**} & \multicolumn{2}{c}{**Upper Limit of Range ICP-OES (mg/kg)**} \\ \cline{2-4}  & **LOD ICP-MS (mg/kg)** & **LOQ ICP-MS (mg/kg)** & **Upper Limit of Range ICP-MS (mg/kg)** \\ \hline \multirow{2}{*}{Al} & 1.08 & 0.56 & 125 \\ \cline{2-4}  & 0.03 & 0.1 & 100 \\ \hline \multirow{2}{*}{Ca} & 0.56 & 1.68 & 125 \\ \cline{2-4}  & 0.89 & 2.67 & 1000 \\ \hline \multirow{2}{*}{Cd} & 0.18 & 0.55 & 125 \\ \cline{2-4}  & 0.0001 & 0.0003 & 2 \\ \hline \multirow{2}{*}{Cr} & 0.06 & 0.17 & 125 \\ \cline{2-4}  & 0.001 & 0.003 & 50 \\ \hline \multirow{2}{*}{Cu} & 0.05 & 0.15 & 125 \\ \cline{2-4}  & 0.012 & 0.036 & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the performances of ICP-OES and ICP-MS in terms of LOD, LOQ and upper limit of calibration range for the analysis of milk trace elements [161; 162].
###### Abstract  
In many tropical regions, national forests plantation programs have been promoted. Those plantations frequently contribute to habitat changes. However, the associated effects of forest plantations on habitat fragmentation and landscape connectivity are unclear. From 2008 to 2018, we examined land use changes, plantations, and deforestation of the Manabi province (Ecuador) provided by the Ecuadorian Ministry of the Environment. Four scenarios were created: (i) land uses in 2008, (ii) land uses in 2018, (iii) land uses in 2018 without deforestation, and iv) land uses in 2018 including reforestation. Fragmentation and connectivity metrics were analyzed using ArcGisPro and Graphad 2.6 software, respectively. _Puma yogouaroundi_ was selected as the reference species. At regional scale, forest plantations had a significant effect on land uses changes and fragmentation during the study period. Forests decreased from 33.7% to 32.4% between 2008 and 2018, although other natural land uses, mostly those involving shrubs, increased by almost double (from 2.4% to 4.6%). Most of the deforestation affected native forests during this period, and most reforested areas in 2018 covered former agricultural land. Fragmentation decreased in the number of patches and increased in the average patch size. When considering reforestation, deforestation was higher than the reforested area (58 km\({}^{2}\) of difference), increasing the number of patches but with smaller size. Reforestation increased connectivity with a higher number of links and distance, particularly in central and extreme northeast areas of Manabi province. The scenario without deforestation also increased connectivity for _Puma yogouaroundi_ in the west part of the Manabi province. Our findings suggest that forest plantations contribute to forest conservation by increasing the connectivity between fragmented patches.  
afforestation; edge effects; landscape management; dry tropical forests; connectivity
## Abbreviations  
**Nomenclature** **Description**  
\(M_{af}\) [kg/s] & Mass air flow  
\(\rho_{s}\) [kg/m\({}^{3}\)] & Density  
\(V\) [m/s] & Velocity  
\(A_{i}\) [m\({}^{2}\)] & Cross-section for station \"\(i\)\"  
\(R_{a,g}\) [k/kg \(\cdot\) K] & Specific gas constant for air, gases  
\(k_{a,g}\) [\(\cdot\)] & Adiabatic exponent for air, gases  
\(p_{0}\) [bar a] & Test cell air pressure  
\(DP\) [mbar a] & Differential pressure  
\(T_{0}\) [K] & Test cell air temperature  
\(D\) [m] & Section diameter  
\(p_{i,t}\) [bar a] & Total pressure in station \"\(i\)\"  
\(T_{i,t}\) [K] & Total temperature in station \"\(i\)\"  
\(p_{is}\) [bar a] & Static pressure in station \"\(i\)\"  
\(T_{i,s}\) [K] & Static temperature in station \"\(i\)\"  
\(I_{C,t}\) [k/kg] & Compressor total specific actual work  
\(I_{C,id}\) [kJ/kg] & Compressor total specific ideal work  
\(\eta_{C,t}\) [\(\cdot\)] & Compressor total adiabatic efficiency  
\(h_{i,t,dt}\) [kJ/kg] & Total specific ideal enthalpy for station \"\(i\)\"  
\(h_{i,t}\) [kJ/kg] & Total specific enthalpy for station \"\(i\)\"  
\(\eta_{T,t}\) [\(\cdot\)] & Turbine total adiabatic efficiency  
\(I_{T,t}\) [kJ/kg] & Turbine total specific actual work  
\(I_{T,t,id}\) [kJ/kg] & Turbine total specific ideal work  
\(F\) [kg] & Engine thrust  
\(G_{f}\) [kg/s] & Gas flow  
\(C_{5}\) [m/s] & Nozzle outlet velocity  
\(F_{f}\) [kg/s] & Fuel flow  
\(i_{a}\) [kJ/kg] & Air enthalpy polynomial function
### Multi-Path Routing
#### 1.2.2 Disadvantages  
The following are some of the drawbacks of multi-path routing:  
1. Route coupling: WSN communicates over a shared wireless channel. As a result, if a sending node keeps the shared wireless channel busy, other nodes are unable to receive data. While interference can degrade nearby transmission quality, several metrics for estimating self-interference between distinct channels have previously been suggested. Route coupling may be stated as follows: Transmission may interfere with multi-path routing because some nodes are in the transmission range of others, even though the routes are node-disjoint pathways. The use of route coupling demonstrates that node-disjoint routes are insufficient to increase performance.
2. Increasing end-to-end delay and network load: Due to duplicated data packets, control packets, or transmitting packets at the same time in a multi-path routing network, traffic is disproportionately loaded. The end-to-end latency of sent packets rises as a result of the heavy traffic.  
There are two types of multi-path routing protocols. Protocols in the first category use distinct pathways to get packets to their destination. On the other hand, protocols in the second category send numerous copies of the same packet to the same destination through several pathways to increase network resilience. Although this method increases network stability and packet prioritizing, it may cause network conflict and congestion.  
It is also true that no routing protocol can cover all of the obstacles and concerns. Instead, several routing protocols approach and resolve various complex difficulties. Some routing methods, for example, provide energy balancing among all nodes in the network, extending the network's lifetime and preventing void holes. Furthermore, several alternative routing methods disregard energy balance. As a result, all routing protocols may be classified into numerous categories based on their aspects.