esa-sceva/satcom-chunk-collection huggingface dataset:
# Stable Throughput Analysis of Heterogeneous Hybrid FSO/RF Networks with Cognitive Radio Capability
## 4 Stable Throughput of Cooperative System (COG)
### A Hybrid Combination of SMT and SAS  
In this subsection, we analyze the stability region of a hybrid cooperative system in which the RO system operates as SMT and HY system as SAS. In this case, during a single time slot, RO transmits up to two packets at the same time, and HY transmits a single packet. Then, we have \(v_{j,n,0}\) and \(v_{j,n,1}\) for (A15)-(A18) and (A19) and (A20) in Appendix A.3; then, we have the service rate of \(\mu_{j,1}\) which is given in (8).  
In the first dominant system, the service rates for \(Q_{\mathtt{HY}}\) and \(Q_{\mathtt{RO}}\) are then given by  
\[\mu^{(1)}_{\mathtt{HY}} = v_{\mathtt{HY},1,1}, \tag{36}\] \[\mu^{(1)}_{\mathtt{RO},l\in\{1,2\}} = \left(1-\frac{\lambda^{(1)}_{\mathtt{HY}}}{\mu^{(1)}_{\mathtt{HY} }}\right)v_{\mathtt{RO},l,0}+\frac{\lambda^{(1)}_{\mathtt{HY}}}{\mu^{(1)}_{ \mathtt{HY}}}v_{\mathtt{RO},l,1}. \tag{37}\]  
The stable region for the first dominant system \(R^{(1)}_{\mathtt{COG},\mathtt{VS}}\) (hereafter, the subscript VS is used to denote the hybrid cooperative system considered in this subsection) can thus be expressed by  
\[R^{(1)}_{\mathtt{COG},\mathtt{VS}}=\{\lambda^{(1)}_{\mathtt{HY}},\lambda^{(1 )}_{\mathtt{RD}}|\lambda^{(1)}_{\mathtt{HY}}<\mu^{(1)}_{\mathtt{HY}},\lambda^{(1 )}_{\mathtt{RO}}<\mu^{(1)}_{\mathtt{RO},1}+\mu^{(1)}_{\mathtt{RO},2}+\psi^{(1) }_{\mathtt{RO}}\}, \tag{38}\]  
where \(\psi^{(1)}_{\mathtt{RD}}\) is obtained by replacing \(\mu^{(1)}_{\mathtt{HY},1}\), \(\mu^{(1)}_{\mathtt{HY},2}\), and \(\lambda^{(1)}_{\mathtt{HY}}\) for \(\psi\) given in Equation (16) with \(\mu_{\mathtt{RO},1}\), \(\mu_{\mathtt{RO},2}\), and \(\lambda_{\mathtt{RO}}\), respectively.  
In the second dominant system, the service rates \(Q_{\texttt{R0}}\) and \(Q_{\texttt{HY}}\) are given by  
\[\mu^{(2)}_{\texttt{R0},J\in\{1,2\}} = v_{\texttt{R0},J,1}, \tag{39}\] \[\mu^{(2)}_{\texttt{HY}} = \left(1-\frac{\lambda^{(2)}_{\texttt{R0}}-\psi^{(2)}_{\texttt{R0} }}{\mu^{(2)}_{\texttt{R0},1}+\mu^{(2)}_{\texttt{R0},2}}\right)v_{\texttt{HY},1,0}+\frac{\lambda^{(2)}_{\texttt{R0}}-\psi^{(2)}_{\texttt{R0}}}{\mu^{(2)}_{ \texttt{R0},1}+\mu^{(2)}_{\texttt{R0},2}}v_{\texttt{HY},1,1}, \tag{40}\]  
respectively. The stable region for the second dominant system \(R^{(2)}_{COCO,\text{VS}}\) can now be obtained by  
\[R^{(2)}_{\text{CO},\text{VS}} = \{\lambda^{(2)}_{\texttt{HY}},\lambda^{(2)}_{\texttt{R0}}|\lambda ^{(2)}_{\texttt{HY}}<\mu^{(2)}_{\texttt{HY},1}+\mu^{(2)}_{\texttt{HY},2}+\mu^ {(2)}_{\texttt{HY},3}+\psi^{(2)}_{\texttt{HY}}, \tag{41}\] \[\lambda^{(2)}_{\texttt{R0}}<\mu^{(2)}_{\texttt{R0},1}+\mu^{(2)}_{ \texttt{R0},2}+\psi^{(2)}_{\texttt{R0}}\}.\]  
Finally, the stable region for the hybrid cooperative system is obtained by  
\[R_{\text{CO},\text{VS}}=\{R^{(1)}_{\text{CO},\text{VS}}\cup R^{(2)}_{\text{ CO},\text{VS}}\}. \tag{42}\]
## 3 Methods
### Canopy Cover Estimation
#### 3.1.2 Estimating Canopy Cover from Digital Aerial Photogrammetry  
The canopy cover estimation from digital imagery was based on the DAP point clouds, although the digital orthomosaic can also be used to determine canopy cover through image segmentation techniques. As the tree canopy and understory show high spectral similarity on the pixel level and texture similarity on the object level, image segmentation methods usually introduce relatively large uncertainty in the separation of canopy and below-canopy pixels. In this study, we used the normalized CHM\({}_{\text{DAP}}\) to extract canopy by a height threshold of 2.0 m. Note that the empty cells in CHM\({}_{\text{DAP}}\) were filled with the median of the neighboring cells. In addition, the outlier cells were removed based on the minimum and standard deviation of the eight neighboring pixels introduced in [2].
# Multi-Sensor Precipitation Estimation from Space: Data Sources, Methods and Validation
###### Abstract  
Satellite remote sensing complements rain gauges and ground radars as the primary sources of precipitation data. While significant advancements have been made in spaceborne precipitation estimation since the 1960s, the emergence of multi-sensor precipitation estimation (MPE) in the early 1990s revolutionized global precipitation data generation by integrating infrared and microwave observations. Among others, Global Precipitation Measurement (GPM) plays a crucial role in providing invaluable data sources for MPE by utilizing passive microwave sensors and geostationary infrared sensors. MPE represents the current state-of-the-art approach for generating high-quality, high-resolution global satellite precipitation products (SPPs), employing various methods such as cloud motion analysis, probability matching, adjustment ratios, regression techniques, neural networks, and weighted averaging. International collaborations, such as the International Precipitation Working Group and the Precipitation Virtual Constellation, have significantly contributed to enhancing our understanding of the uncertainties associated with MPEs and their corresponding SPPs. It has been observed that SPPs exhibit higher reliability over tropical oceans compared to mid- and high-latitudes, particularly during cold seasons or in regions with complex terrains. To further advance MPE research, future efforts should focus on improving accuracy for extremely low- and high-precipitation events, solid precipitation measurements, as well as orographic precipitation estimation.  
multi-sensor precipitation estimation; satellite precipitation product; validation; global precipitation measurement 2024  
6
## 5 Result and Discussion
### Truck Travel Pattern Predictability  
Three models--KNN, BP neural network, and LSTM--predicted truck driver locations in four datasets. Figure 6 depicts the cumulative probability distribution. The results highlight C13 as the most predictable truck group, with over 95% achieving over 80% accuracy in predicting the next location. C10 follows closely in predictability, with C11 also exhibiting high predictability, especially in the BP neural network and LSTM models. While C12 shows a prediction accuracy of over 80% in only 50% of trucks, it maintains a high activity range and operating intensity. Model performance is comparable in C10, C11, and C13, but KNN significantly lags in the more complex C12 group. Conventional and deep learning models perform similarly when predicting easy truck locations; however, the conventional model falters in complex scenarios.  
The study reveals that truck groups with comparable stability exhibit limited spatial activity ranges and lower operating intensity, yet boast the highest predictability for their next location. Conversely, the group with the most routes, average operating miles, and  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Cluster Centroid** & **C10** & **C11** & **C12** & **C13** \\ \hline Turning radius & 84 & 163 & 133 & 77 \\ Number of route cities & 1.2 & 1.5 & 3.13 & 1.09 \\ Average operating hours & 5.3 & 6.58 & 9.86 & 4.14 \\ Average operating mileage & 133 & 224 & 461 & 94 \\ Operating days & 194 & 111 & 158 & 71 \\ Average number of consecutive operating days & 4.39 & 2.39 & 3.62 & 1.74 \\ \hline \end{tabular}
\end{table}
Table 3: Description of travel characteristics clustering centroids.  
Figure 6: Cumulative prediction accuracy distribution of the different travel patterns. (**a**) Cumulative dis-tribution of KNN prediction; (**b**) Cumulative distribution of BP Neural accuracy; (**c**) Cumulative distribution of LSTM prediction accuracy.  
operating hours shows the lowest predictability. Surprisingly, the truck group with the highest average consecutive operating days demonstrates the lowest predictability, despite similarities in other indicators.
## 4 Implementation and Experiments
### Discussion  
This section will investigate the experimental results, analyzing the system's performance and practical application potential. Firstly, the experimental results show that NutritionCLIP excels in food image classification, demonstrating its accuracy and reliability on the Food 101, CNFOOD-241, and Taiwanese Food 101 datasets. This indicates that our fine-tuning method effectively enhances the model's performance in food classification, enabling it to achieve high classification accuracy across different food image datasets.  
Regarding volume estimation, the MVS-based method demonstrated good accuracy but varied performance across different food categories. Specifically, the meal category had higher errors, indicating that predicting the volume of foods with diverse shapes, sizes, and types remains challenging and requires further optimization in future work.  
In evaluating language model generation capabilities, NutriLLM performed excellently, especially in generating nutrition-related text, demonstrating its potential practical value. This suggests that our model is instrumental in analyzing users' dietary habits and providing recommendations.  
In summary, these results highlight the effectiveness of our approach across multiple tasks and point to directions for further improvements to enhance the system's overall performance and application scope.
# The Improved A* Algorithm for Quadrotor UAVs under Forest Obstacle Avoidance Path Planning
## 3 Results  
### Segmented Evaluation Function Parameter Determination Results  
Experimental designs were conducted to assign different values to parameters \(a\) and \(b\) in order to obtain the relevant index results. Figure 5 presents the number of nodes traversed and the search time of the algorithm when the weight factors \(a\) and \(b\) take various values. It can be observed that when factor \(a\) was set to five or six, the number of nodes traversed and the path search time of the algorithm was found to be the highest. This is primarily due to the fact that, at this stage, the algorithm is more inclined toward the Dijkstra algorithm, resulting in weaker heuristic information.  
The optimal parameter combination obtained in the initial experiment was adopted for the second test, where different obstacle maps were utilized, and the starting and ending positions were varied. The relative lengths of the resulting paths are presented in Figure 6.  
Figure 5: Results of evaluation indexes with different parameters: (**a**) path search time with different parameters; and (**b**) number of traversal nodes with different parameters.  
The results depicted in Figure 6 indicate that when factor \(a\) is set to two, the path length obtained is the shortest among all tested values, with an even shorter path length observed when \(b\) is in the range of [1; 3]. Additionally, Table 1 shows the corresponding path search time and the number of traversed nodes within this interval.  
Based on the results shown in Table 1, the differences in path search time and the number of traversed nodes are found to be relatively small when factor \(a\) takes the value of two, and factor \(b\) falls in the range of [1; 3]. Figure 6 illustrates that when \(a\) is set to two and \(b\) is one, the shortest paths are obtained in different obstacle maps, and the optimal results are achieved when compared. Therefore, this study ultimately determines the values of factors \(a\) and \(b\) to be two and one, respectively, to obtain the final evaluation function:  
\[f(n)=\left\{\begin{array}{ll}2g(n)+\Big{(}1+\frac{d}{L}\Big{)}h(n),&h(n)\geq \frac{L}{3}\\ g(n)+\Big{(}\frac{d}{L}\Big{)}h(n),&h(n)<\frac{L}{3}\end{array}\right. \tag{8}\]
## 2 Modeling and Controller Design
### Controller Design of MMC-HVDC System
#### 2.4.1 High-Level Control  
High-level control generates the reference current from the HVDC voltage, reactive power and standalone AC voltage controller as presented in Figure 4. VDC controller comprises a PI controller that processes the error generated from the difference between the reference and actual HVDC voltage. The feedforward controller (information of DC link current) is added with the PI controller's output for improved dynamic performance. Any current change in the HVDC link is reflected in the controller output signal that adjusts the real current command of MMC1. MMC1 could inject fixed reactive current or provide proportional reactive power with the PCC1 voltage. MMC2 creates an isolated AC voltage for renewable energy integration. It receives renewable energy while holding the AC voltage and injects it into the HVDC link.  
AC voltage controller has a PI controller and a feedforward controller. PI controller adjusts its output based on the difference between the reference and actual AC side voltage of MMC2.  
In contrast, the feedforward controller provides a signal based on any change occurring in the AC current signal beforehand. Altogether, it keeps AC voltage constant while receiving renewable energy. In addition, the AC voltage control strategy of MMC2 controller slightly increases the AC side reference voltage (V\({}_{\text{sd-ref}}\)) with the HVDC link voltage during the fault in the point of common coupling of MMC1, which proportionally increases the PCC voltage. PV and battery controller take action to counteract the PCC voltage rise. If the HVDC link voltage surpasses 1.05 pu, the d-component of reference AC voltage (V\({}_{\text{sd-ref}}\)) is increased proportionally, whereas it remains constant if HVDC link voltage is below 1.05 pu. The q-component of reference AC voltage (V\({}_{\text{sq-ref}}\)) is regulated to zero.  
Figure 3: MMC equivalent circuit.
# Innovative Vaporization of Wood Panel Waste into Activated Biochar for Efficient Phenol Adsorption
## 2 Materials and Methods
### Physical and Chemical Characterization of Biochar and Activated Biochar  
Pore structure was characterized by nitrogen (N\({}_{2}\)) and krypton (Kr) adsorption at \(-\)196 \({}^{\circ}\)C and carbon dioxide (CO\({}_{2}\)) adsorption at 0 \({}^{\circ}\)C using a Micromeritics ASAP 2460 surface area and porosimetry analyzer (Norcross, GA, USA). Prior to adsorption experiments, WPW, biochar, and activated biochar were degassed under vacuum conditions for 48 h at 80, 105, and 250 \({}^{\circ}\)C, respectively. Kr absorption was measured to assess materials with low surface area. Ultra microporosity was determined by CO\({}_{2}\) adsorption, and micro- and mesoporous were determined by N\({}_{2}\) adsorption.  
The adsorption isotherms for N\({}_{2}\) and CO\({}_{2}\) were processed mathematically to obtain key structural parameters. Specific surface area (SBET, m\({}^{2}\)/g) was determined using Brunauer-Emmett-Teller (BET) analysis applied within the appropriate relative pressure range (P/P\({}_{0}\) = 0.01-0.05) [35]. Micropore volume (V\(\mu\), cm\({}^{3}\)/g) was estimated with the Dubinin-Radushkevich (DR) equation [36]. Total pore volume (Vt, cm\({}^{3}\)/g) was calculated based on the quantity of nitrogen adsorbed at a relative pressure of 0.97 [37], and mesopore volume (Vm, cm\({}^{3}\)/g) was determined as the difference between Vt and V\(\mu\).  
The elemental composition (C, H, N, S, O) of all materials was analyzed with a Perkin Elmer 2400 CHNS/O organic elemental analyzer (Waltham, MA, USA) using combustion in a pure oxygen stream. Oxygen content was determined by difference, using Equation (1):  
\[\text{O}\%=100-\text{CHNS}\% \tag{1}\]
# Minimum Frequency and Voltage Stability Constrained Unit Commitment for AC/DC Transmission Systems
###### Abstract  
An increased use of the high-voltage direct current (HVDC) technologies can have important effects on frequency performance and voltage stability of the receiving-end grid during normal operation as well as during blocking failure. The main reasons are the inherent characteristics of the HVDC such as its much larger capacity than thermal plants and lack of voltage supporting ability to the alternating current (AC) grid. These has led to new challenges for AC/direct current (DC) power grid operators in terms of ensuring power system security. To address these challenges, a unit commitment (UC) of the receiving-end in the AC/DC hybrid grid is presented in this paper. In the proposed model, primary frequency modulation constraints are added to provide sufficient capacity for HVDC blocking. Besides, grid security constraint after secondary frequency regulation is also considered because HVDC blocking failure would cause large range power transfer and transmission lines overload. Meanwhile, voltage stability constraints are employed to guarantee enough voltage supporting capacity from thermal plants at the HVDC feed-in area. Based on the characteristics of the model, Benders decomposition and mixed integer programming algorithm are used to get the optimal transmission power of the HVDC and schedule of thermal units. The study is done by considering the IEEE-39 and Jiangsu power grid in eastern China, containing two HVDC transmission projections respectively. The results are also validated by simulation of different HVDC blocking failure scenarios.  
hybrid AC/DC power grid; unit commitment; high voltage direct current (HVDC) blocking; frequency deviation; voltage stability; Benders decomposition; mixed integer programming (MIP) Article
## 3 Results and Discussion
### Preliminary Studies for Phenolic Extraction  
There are four major phenolic classes of metabolites in olive fruits, including phenolic acids, flavonoids, lignans, and secoriridoids. Secoriridoids, e.g., oleupopein, ligstroside, and simple phenolics, are the main phenolics of olive fruits [56]. Due to their antioxidant and antimicrobial activities, they are considered an abundant source of bioactive ingredients in the food industry. So, the recovery of phenolic extracts using alternative approaches to the classical maceration method can promote sustainable and eco-friendly practices, often leading to energy cost savings and developing value-added products. Extraction is the most critical step to isolating bioactive extracts with high yields, if designed properly through optimization studies. The extraction parameters, including solvent type, extraction time, and temperature, can affect the extraction yield and the functionality of the final extract [57].  
Hence, one of the main goals of this current study was to study the effects of various extraction conditions, including solvent type, extraction time, and temperature on  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Chemical Composition** & **Value (mg/kg dw of Olive Fruit)** \\ \hline Chlorophyls & Chlorophyll a & 86.61 \(\pm\) 0.99 \\  & Chlorophyll b & 61.80 \(\pm\) 2.34 \\  & Total Chlorophyls & 148.41 \(\pm\) 3.33 \\ Carotenoids & Total carotenoids & 85.90 \(\pm\) 1.22 \\ Tocopherols & \(\delta\)-tocopherol & 0.99 \(\pm\) 0.01 \\  & (\(\beta\)+\(\gamma\))-tocopherol & 1.68 \(\pm\) 0.12 \\  & \(\alpha\)-tocopherol & 104.39 \(\pm\) 1.26 \\  & Total tocopherols & 107.05 \(\pm\) 1.38 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pigments and tocopherol composition (mg/kg) of Greek WOF sample.  
the phenolic composition and antioxidant activity of WOF extracts, by using eco-friendly extraction processes like UAE, MAE, and ASE. Firstly, the efficiency of different extraction solvents, including hexane, ethyl acetate, ethanol/water (70:30), methanol/water (70:30), and acetone/water (70:30) was studied by using UAE for determining the TPC of WOF extracts. UAE was selected as it is one of the simplest and inexpensive extraction systems for phenolic compounds in common laboratory equipment [58]. As illustrated in Figure 1, the highest TPC value was detected in both ethanol/water (70:30, \(v/v\)) and methanol/water (70:30, \(v/v\)) extracts (63.98 and 63.07 mg GAE/g dw, respectively), followed by acetone/water (70:30, \(v/v\)) and ethyl acetate extracts (55.80 and 13.57 mg GAE/g dw). Hexane extract (1.75 mg GAE/g dw) was the least efficient solvent to extract phenolic compounds from WOF, which is in agreement with the values (2.12 mg GAE/g dw) reported by [PERSON] et al. [17], who isolated phenolic compounds from oleaster fruit by maceration. According to our preliminary results, ethanol/water (70:30, \(v/v\)) was selected for optimization studies as it is recommended as a \"green solvent\" due to its low toxicity and its potential enhancement of environmental sustainability and social well-being.
# Topology Optimization of Multi-Materials Compliant Mechanisms
## 2 The Topology Optimization Model for Multi-Material Compliant Mechanisms  
### Generalized SIMP  
In topology optimization, SIMP method drives the intermediate density values of design variables to 0/1 by penalty factor power, which leads to less design variables and a simple calculation. In this paper, the multi-material problem is decomposed into a series of single material subproblems, and the generalized SIMP interpolation model is established to optimize the topology of multi-material compliant mechanisms. For example, the elastic modulus interpolation model of three-phase materials can be expressed as:  
\[E(x_{1},x_{2})=x_{1}^{p}x_{2}^{p}E_{2}+x_{1}^{p}(1-x_{2}^{p})E_{1}+(1-x_{1}^{ p})E_{0} \tag{1}\]  
where \(x_{1}\) represents the presence or absence of element material, \(x_{2}\) represents which phase material the element material belongs to, \(E_{1}\) and \(E_{2}\) are the elastic modulus of material 1 and solid material 2 respectively. It is worth noting that the void region is considered a phase material with a very small Young's modulus \(E_{0}\). The penalization factor is indicated by \(p\) (e.g., \(p\) = 3). Figure 1 shows a random distribution of multi-material in the design domain.  
The equivalent element stiffness matrix can be obtained from Equation (2), where \(M\) is the number of elements, \(k\) is the stiffness matrix of each element:  
\[\begin{split} k\big{(}x_{1}^{e},x_{2}^{e},\dots,x_{M}^{e}\big{)} =\sum\limits_{i=1}^{M}\big{(}x_{i}^{e}\big{)}^{p}k_{i}^{e}\\ i=1,2,\dots,M\,e=1,2,\dots,N\end{split} \tag{2}\]