esa-sceva/satcom-chunk-collection huggingface dataset:
# Microalgal Growth in Aquaculture Effluent: Coupling Biomass Valorisation with Nutrients Removal
## 1 Introduction
Moreover, there is great potential in replacing food and fish oils with microalgae in aquaculture. Microalgal biomass has fair amounts of the proteins, lipids, vitamins, and pigments necessary for aquaculture feed. Additionally, using microalgae as a supplement to animal feed enhances growth and, at the same time, decreases feed consumption. Furthermore, this diet boosts the immunological response and resistance to disease by increasing antibacterial and antiviral activity [19]. [PERSON] et al. [20] used a combination of _Nannochloropsisulata_ and _Schizcndytrium_ sp. to feed Nile tilapia fish and found that the microalgal-based feed increased the nutritional value and fish growth indicators. Additionally, [PERSON] et al. [21] studied _Isochrysis_ sp. and _Nannochloropsis_ sp. as substitutes for fish oil and fish meals in a rainbow trout diet. The authors found that _Isochrysis_ sp. enhances the digestibility of proteins and lipids, when compared to traditional fish meal and oil. However, the feed for aquaculture has to be easy to digest and have a good nutritional diet. Thus, current studies only incorporate microalgal biomass in the cultivation feed and do not use microalgal biomass as a substitute, since the amount that best benefits growth and survival rates has not yet been optimised [22]. It is still necessary to investigate the possible consequences of using the biomass of microalgae grown in wastewater and find effective and achievable solutions in the context of mass production of feed and/or supplements for aquaculture. It is possible to apply the circular economy concept to the use of microalgae in aquaculture. In other words, it is a way of making greener one of the economic activities that contribute most to water pollution. The aquaculture wastewaters can be treated by microalgae, whose biomass can be later used as an additive or supplement to the aquaculture feed, thus avoiding the addition of extra nutrients and fish oils, which are traditionally used and are one of the main causes of excess nutrients in the aquaculture wastewater. Therefore, this study aimed to assess the growth of _C. vulgaris_ in aquaculture wastewater from a trout farm aquaculture facility and valorise the produced biomass through the analysis of its biochemical composition. Nitrogen and phosphorus removal efficiencies were assessed, and the biomass biochemical composition was analysed in terms of lipids, carbohydrates, proteins, and pigments.
## 6 Strategies for Improving Protein Digestibility
### Soaking and Germination  
Soaking and germination are traditional methods often used for legumes and grains. These processes activate endogenous enzymes that degrade antinutritional factors and partially hydrolyze proteins. Germination also increases the bioavailability of essential nutrients, including amino acids, while enhancing the overall digestibility of proteins. Some studies have shown that soaking increases nutritional properties like protein digestibility and decreases antinutrients such as trypsin inhibitors, oligosaccharides, and phytic acid [163]. [PERSON] et al. [164] indicated that soaking is a food processing method that improves legumes' protein digestibility by reducing antinutritional factors, such as phytates, which inhibit protein breakdown. By hydrating the legumes, soaking helps soften cell walls, making proteins more accessible to digestive enzymes and, thereby, enhancing bioavailability [165]. While soaking generally enhances protein digestibility, the effectiveness can vary depending on the legume type and other processing methods.  
Germination improves legume protein digestibility by reducing antinutritional factors like protease inhibitors, activating proteases, and breaking down storage proteins, including allergens [166]. This process increases free amino acids, particularly asparagine. The extent of digestibility improvement varies by species and strain, and further research is needed to identify the key factors. In addition, [PERSON] et al. [167] indicated that germination can improve sesame protein's structural and functional properties. Therefore, germination is an effective approach to improve the protein digestibility of plant seed protein.
## 4 Discussion and Conclusions  
### Discussion  
#### 4.1.1 Accuracy Analysis  
Overall, the Luojia1-01 data provided finer spatial details and more accurate impervious surface extraction results due to spatial resolution. The extraction results obtained with the three nighttime light images showed similar spatial patterns: (1) The impervious surface of the remaining landform types extracted using the Luojia1-01 data exhibited a higher degree of overlap with the actual impervious surface. (2) The impervious surface patches extracted using the NPP-VIIRS showed a high degree of separation. (3) The extraction of impervious surface patches using the Flint data demonstrated a complete and high degree of aggregation. However, the extracted impervious surfaces contained blurred boundaries. The results of the extraction of impervious surface densities in the flat topography and economically developed areas were less sensitive to image resolution.  
In terms of the spatial accuracy of the extraction results, the Luojia1-01 data were more accurate than the NPP-VIIRS and Flint data. However, the accuracy of impervious surface extraction was poor with all three data types. Furthermore, the extraction of impervious surfaces using only nighttime light data was not effective for obtaining impervious surface information in areas with large scale topographic fragmentation. The results with the Luojia1-01 data were underestimated in the Karst plateau and overestimated in the rest of  
Figure 7: Average relative error of urban impervious surface extraction results for different landform types.  
the density range, while the results with both NPP-VIIRS and Flint data were overestimated in all landscape types. This behavior was mainly caused by the low resolution of the NPP-VIIRS and Flint data, and the overestimation of impervious surface due to the overflow of values from the data itself.
## 4 Full-Scale PCS Response Analysis and Optimization Under Traffic Load
### Load-Bearing Capacity of Optimized Structures  
The incorporation of prestressed rebars and the application of prestressing have a substantial effect on the load-bearing capacity of cantilever structures [57; 58; 59]. To fully assess the performance of the optimized cantilever structure, further simulation analysis of its load-bearing capacity was conducted. As illustrated in Figure 19, a displacement control method was employed to apply a downward vertical displacement at the most unfavorable position of the entire structure. This method facilitates the investigation of the load-bearing capacity and overall stability of the structure under various optimization scenarios.  
Figure 18: Concrete stress distribution under full load.  
Figure 20 presents the displacement-load curves and final damage distribution for both the original and optimized cantilever structures. As shown in Figure 20a, during the initial loading phase, the original model exhibits the smallest slope, reflecting its lowest initial stiffness. With the addition of prestressed rebars, the initial stiffness of Cases 1, 2, and 3 gradually increases. Notably, Cases 2 and 3 demonstrate significantly higher initial stiffness compared to both the original model and Case 1, emphasizing the positive effect of prestressed rebars on the overall stiffness of the structure. Regarding peak load, the original model has the lowest capacity, while the optimized models show progressive improvements, with Case 3 achieving the highest peak load, indicating a substantial enhancement in load-bearing capacity. Finally, the analysis of the descending segment reveals that the original model's load-bearing capacity declines the fastest, while the optimized models exhibit a more gradual reduction. Cases 2 and 3, which have the lowest descending segments, suggest that the optimized structures offer superior ductility. The damage distribution shown in Figure 20b reveals that, in the original model, damage is primarily concentrated at the upper part of the inner beam and the beam-column joint. In Case 1, the prestressed rebars placed at the upper inner side of the beam significantly alleviate damage at the beam-column joint, demonstrating the effectiveness of prestressed rebars in mitigating stress concentration and enhancing the overall structural integrity. In Cases 2 and 3, a further reduction in the damage of the beam-column joint is observed, indicating that placing prestressed rebars on both the inner and outer sides of the beam effectively reduces damage at this critical junction, improves stress distribution, and prevents the propagation of damage. In summary, the analysis demonstrates that placing prestressed rebars at the top of the cantilever beam significantly enhances the overall structural stiffness, peak load capacity, and ductility, while also delaying the onset of structural damage. This optimization leads to a more balanced stress distribution, thereby playing a vital role in improving the stability and safety of the structure.  
Figure 19: Schematic diagram of the loading method.
###### Abstract  
Background: Change of direction (COD) and sprint abilities are essential in soccer, while reactive agility involves decision-making and perceptual/cognitive skills. This study aimed to examine the relationships between sprint speed, COD, and reactive agility in U12 soccer players. Methods: Thirty-three male players (age 10.40 \(\pm\) 0.32 years) completed sprint tests (5 m, 10 m, 15 m), the Modified Agility T-Test (MAT), and the Y-Shaped Reactive Agility (Y-RSA) test, across two sessions. Results: The sprint, COD, and reactive agility tests showed high reliability. The Y-RSA test demonstrated strong reliability in regard to the initial sprint, COD-phase time, and total time. Significant correlations were found between sprint, COD, and reactive agility metrics. The sprint times were positively associated with the MAT and reactive agility times (e.g., sprint 15 m vs. total Y-RSA time: r = 0.740, \(p\) < 0.01). The smallest worthwhile changes (SWCs) indicated that these tests effectively detect performance improvements. Conclusions: This study demonstrated significant correlations between sprint speed, change of direction, and reactive agility in U12 soccer players, with the 15 m sprint showing the strongest association with reactive agility. These findings support the combined use of physical and perceptual/cognitive assessments to better inform soccer-specific training and evaluate the performance of youth players.  
correlation; performance assessment; linear sprint; change of direction; performance assessment; visual stimulus; Y-shaped reactive agility; U12 soccer players +
Footnote †: journal: Applied Sciences  
Article
# Innovative Process for Dried Caper (_Capparis spinosa_ L.) Powder Production
## Materials and Methods
### Antioxidant Capacity  
The Antioxidant Capacity (AC) of the methanolic extracts obtained in Section 2.6 was measured by a 2,2-diphenyl-1-picrylhydrazyl radical (DPPH) assay via a commercially available assay kit (Bockit, KF-01-007) according to the manufacturer's protocol. Briefly, 50 \(\mu\)L of sample or trolox (positive control) were appropriately diluted in dimethyl sulfoxide (DMSO) and mixed with 1450 \(\mu\)L of DPPH radical solution (0.06 mM) provided by the kit. After 30 min of incubation at room temperature in the dark, the total Antioxidant Capacity (AC) was determined by measuring absorbance at 517 nm through a spectrophotometer (Multiskan(tm) FC Microplate Photometer, Thermo Scientific(tm) against pure methanol and calculating the corresponding percentage of the inhibition of the radical DPPH, as reported in the kit's instructions, by using the following equation:  
\[\text{AC (\%)}=\text{(AbsControl}-\text{AbsSample)}/\text{AbsControl}\times 100 \tag{5}\]  
AbsControl: absorbance of sample-free DPPH solution; AbsSample: absorbance of DPPH solution containing the sample [17].
## 1 Introduction  
Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, classified into non-alcoholic fatty liver (NAFL) and non-alcoholic steatohepatitisitis(NASH) [1; 2]. The former hardly progresses; however, the latter is progressive and may lead to cirrhosis or hepatocellular carcinoma (HCC) [3]. Most cases of HCC are caused by viral hepatitis; however, recently, the incidence has gradually decreased due to the direct-acting antiviral drug therapy for hepatitis C and nucleic acid analog therapy for hepatitis B [4; 5]. Conversely, the incidence of NASH-related HCC (NASH-HCC) is increasing annually as metabolic syndrome factors, such as obesity and diabetes, increase [6]. The multiple parallel hit theory has been hypothesized to cause NASH [7]. However, the pathogenic mechanism of NASH-HCC has not been elucidated.  
Periodonal disease is an independent risk factor for NAFLD [8] and is significantly associated with liver fibrosis in NAFLD [9]. [PERSON] et al. [10] analyzed the intestinal microbiota of patients with cirrhosis. They reported that oral cavity-derived bacteria were detected at a high frequency in correlation with the severity of the disease, leading to the later mouth-gut-liver axis concept [11]. The detection rate of _Porphyromonas gingivalis_--a major causative bacterium of periodontitis --in the saliva of patients with NAFLD was significantly higher than that of control participants and increased with the aggravation of NAFL and NASH disease [12]. Furthermore, [PERSON] et al. [13] recently demonstrated that the positive rate of salivary _P. gingivalis_ (>0.01%) in patients with NAFLD was significantly correlated with liver cirrhosis. Additionally, [PERSON] et al. [14] reported the presence of _P. gingivalis_ in the immunohistochemical staining of liver tissues from patients with NASH. It was presumed that this occurred because _P. gingivalis_ entered the blood from the periodontitis lesion and reached the liver.  
Epidemiological studies have reported that periodontal bacteria increase the risk of developing various cancers and cancer-related deaths [15]. Notably, _P. gingivalis_ level is high in the esophageal flora of patients with esophageal cancer [16] and correlates with malignancy [17]. In addition, _Fusobacterium nucleatum_--a periodontal pathogen--has been detected in digestive organs, including the esophagus, stomach, and large intestine, and in breast cancer tissues [18; 19; 20; 21]. Recently, [PERSON] et al. [22] revealed that _F. nucleatum_ strain in colorectal cancer tissue is similar to that of saliva and originates in the oral cavity. However, few studies exist regarding HCC; one reported high blood levels of reactive oxygen species in HCC patients with periodontitis [23]. Moreover, little is known about the relationship between NASH-HCC and periodontopathic bacteria.  
We hypothesized that _P. gingivalis_ and _F. nucleatum_ are associated with the pathogenesis of NASH-HCC. This study aimed to analyze the oral microbiota data of patients with NASH and NASH-HCC and examine the association of periodontopathic bacteria in NASH-HCC.
# An Efficient Framework Model for Optimizing Routing Performance in VANETs
## 4 Optimization-Based Multiple Objective
### Performance Index Model
Therefore, we construct the function of the communication cost as follows, according to the criticality of each metric:  
\[Communication\ cost=f(W_{T}\frac{V_{T,i}}{U_{T}},W_{P}\frac{V_{P,i}}{U_{P}},W_{D} \frac{L_{D,i}}{V_{D}},W_{O}\frac{L_{O,i}}{V_{O}}) \tag{3}\]  
Figure 1: Statistical Function of Performance. where \(i\) refers to the interact response when there is any change in one of the varying factors - \(T,~{}P,~{}D\), or \(O\). Therefore, the arithmetic average of summation can be formulated as a weighted ratio of the response metrics according to the respective upper or lower bound. Where \(f\) represents the fitness cost of communication that quantifies overall system performance at each iteration. To define the upper or lower bounds between 0 and 1, the equation of cost function \(f\) can be formulated as follows:  
\[\textit{communication cost}=\sqrt{(W_{T}\frac{V_{T,i}}{U_{T}}+W_{P}\frac{V_{P,i }}{U_{P}}+W_{D}\frac{L_{D,i}}{V_{D}}+W_{O}\frac{L_{O,i}}{V_{O}})}. \tag{4}\]  
The objective of the cost function \(f\) is paramount to maintain the quality of solution. The ratios of \(V_{T,i}/U_{T}\), \(V_{P,i}/U_{P}\), \(L_{D,i}/V_{D}\), and \(L_{O,i}/V_{O}\) represent the values of relevant metrics and influencing factors. For example, the ratio \(V_{T,i}/U_{T}\) represents the measurement of how much \(V_{T}\) value of response metric \(i\) deviates from its respective upper bound \(U_{T}\). Accordingly, the \(V_{P,i}/U_{T}\) measures the deviation value of response metric \(i\) from its respective upper bound \(U_{P}\). Similarly, the ratios \(L_{D,i}/V_{D}\) and \(L_{O,i}/V_{O}\) measure the deviations of \(V_{D}\) and \(V_{O}\) from its lower bound \(L_{D,i}\) and \(L_{O,i}\), respectively. The key advantage of the performance index is to enable us to quantify the main and interactive factor values on the performance response, which characterizes the functional relationship between a set of performance metrics.
## 2 Materials and Methods
### System Model
#### 2.1.3 Computation Model  
1. UAV Computation Model  
The task is transmitted through the channel between the mobile user device and the UAV, and the transmission delay is as shown in Formula (13). The transmission energy consumption between the mobile device m and the UAV \(n\) is as shown in Formula (14).  
\[T_{mn}^{\Omega 2A}(t)=\frac{D_{m}}{R_{mn}(t)} \tag{13}\]  
\[E_{mn}^{\Omega 2A}(t)=P_{n}^{r}T_{mn}^{\Omega 2A}(t)=\frac{D_{m}P_{n}^{r}}{R_{mn }(t)} \tag{14}\]  
Here, \(P_{n}^{r}\) refers to the receiving power of the UAV.  
Upon receiving the complete data of a task from the user device, the UAV makes a task offloading decision and obtains the proportion of tasks calculated locally on the UAV. The task computing delay is as shown in Formula (15).  
\[T_{mn}^{UAV}(t)=\frac{\gamma_{m0}^{n}(t)D_{m}C_{m}}{f_{mn}(t)} \tag{15}\]  
Here, \(f_{mn}(t)\) represents the computing resources allocated by UAV \(n\) to user \(m\). Assuming that UAV \(n\) evenly allocates computing resources to each user, i.e., \(f_{mn}(t)=\frac{F_{n}}{M_{n}(t)}\)\(\gamma_{m0}^{n}(t)\) is the proportion of tasks generated by user device \(m\) during time slot \(t\) executed on UAV \(n\). If \(\gamma_{m0}^{n}(t)=0\), it means the UAV offloads all tasks to the edge server, while if \(\gamma_{m0}^{n}(t)=1\), it means all tasks are executed locally on the UAV. The energy consumption of UAV \(n\) processing user device \(m\) is as shown in Formula (16). \[E_{mn}^{UAV}(t)=\kappa[f_{mn}(t)]^{3}T_{mn}^{UAV}(t)\] (16) Here, \(\kappa\) represents the effective switching capacitance.
2. EC Computation Model If the \(\gamma_{m0}^{n}(t)\) in the previous stage is not 1, it indicates that the UAV will offload part of the tasks to the edge server. The transmission delay in this process is as shown in Formula (17). The energy consumption generated by the UAV uploading task data to the edge server is as shown in Formula (18). \[T_{mnk}^{A2G}(t)=\frac{\gamma_{mk}^{n}(t)D_{m}}{R_{nk}(t)}\] (17) \[E_{mnk}^{A2G}(t)=P_{n}^{t}T_{mnk}^{A2G}(t)=\frac{\gamma_{mk}^{n}(t)D_{m}P_{n} ^{t}}{R_{nk}(t)}\] (18)  
Here, \(P_{n}^{t}\) is the transmission power of UAV \(n\). The task calculation delay on the edge server is as shown in Formula (19).  
\[T_{mnk}^{EC}(t)=\frac{\gamma_{mk}^{n}(t)D_{m}C_{m}}{f_{mk}(t)} \tag{19}\]  
Here, \(f_{mk}(t)\) represents the computing resources allocated by the edge server \(k\) to user \(m\). Similar to the UAV calculation model, this study assumes that edge server \(k\) evenly allocates computing resources to each user, i.e., \(f_{mk}(t)=\frac{F_{k}^{t}}{M}\). In summary, when all tasks within the coverage area are completed, the total energy consumption and total delay of the UAV \(n\) are as shown in Formulas (20) and (21), respectively.  
\[E_{n}(t)=\sum_{m=1}^{M}\rho_{m}^{n}(t)\lambda_{m}[E_{mn}^{G2A}(t)+E_{mn}^{UAV }(t)+E_{mnk}^{A2G}(t)] \tag{20}\]  
\[T_{n}(t)=\sum_{m=1}^{M}\rho_{m}^{n}(t)[T_{mn}^{G2A}(t)+\underset{k}{max}\{T_ {mn}^{UAV}(t),T_{mnk}^{A2G}(t)+T_{mnk}^{EC}(t)\}] \tag{21}\]
## 3 Results and Discussion
### Evaluation of Daily Precipitation  
All precipitation products exhibited low accuracies with respect to daily precipitation, as shown by significant differences between observations and estimations at \(p<0.05\) (Table 3). Estimations by CMORPH and TRMM exhibited, however, the best linear correlations with rain gauge data (R\({}^{2}\) = 0.16 and 0.15, respectively) and were followed by APHRODITE (R\({}^{2}\) = 0.12), PERSIANN-CDR (R\({}^{2}\) = 0.11), and GPCP-1 DD (R\({}^{2}\) = 0.09). Besides exhibiting a relatively low R\({}^{2}\) value, the APHRODITE presented (with 16.55 mm/day) the lowest RMSE, followed by the PERSIANN-CDR (16.68 mm/day), CMORPH (16.77 mm/day), GPCP-1 DD (17.25 mm/day), 3B42 RT (18.11 mm/day), and 3B42V7 (18.35 mm/day). By contrast, GPCP-1 DD had the lowest R\({}^{2}\) and the largest RMSE value. The poor performance of GPCP-1 DD was expected, as this had previously been reported in other studies. For example, [PERSON] [56], using 76 rain gauges over Greece, found that the poorest performance occurred when using GPCP-1 DD, with a bias and RMSE of 28.7% and 52.2%, respectively. Along the heavy-rainfall regions over India, [PERSON] _et al._[57] found the average bias using GPCP-1 DD to be -8.29 mm/day, which was bigger than when using TMPA (-8.03 mm/day). Moreover, [PERSON] and [PERSON] [58] reported that GPCP-1 DD performed well over most of the contiguous United States, except for the humid northwest region with its average precipitation of over 3.47 mm/day. These authors concluded that GPCP-1 DD had a poor ability to estimate heavy precipitation, which is common in tropical regions such as Malaysia.  
Figure 3: Two-dimensional histogram of mean monthly precipitation between precipitation products with the rain gauges for the period 2003–2007.  
With positive RB and ME values, 3B42 RT, 3B42V7, and PERSIANN-CDR systematically overestimated observed daily precipitation. By contrast, the other three precipitation products systematically underestimated the observed daily precipitation, as indicated by negative RB and ME; for example, CMORPH underestimated daily precipitation by 13.2%. Similar results were reported by [PERSON] and [PERSON] [13], where CMORPH showed significant precipitation underestimation over the western Black Sea region of Turkey compared to other SPPs. The origin of such underestimates might come partially from the complex topography in the study region, with the presence of orographic rain and the elevation ranging from 0 to more than 2000 m and up to 4046 m.a.s.l. over relatively short distances (Figure 1). The most likely hypothesis for explaining the estimation drifts is the inability of the IR sensor to properly consider warm cloud top temperatures and of the PMW sensor to detect warm orographic clouds without ice particles [59]. In addition, the low-orbit satellites are unable to monitor the rain intensities of a storm continuously [60]; this fact could also contribute to underestimation of the CMORPH.