esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discussion
### Evaluation of Antioxidant Capacity  
In the analysis of variance applied to the samples, the EC\({}_{50}\) values obtained for each measurement of antioxidant capacity in each partition met the assumptions (with \(p\) = 0.05). At a 5% significance level, the analysis indicated significant differences in the EC\({}_{50}\) behavior among the different partitions used in the study. The Tukey test was used for multiple comparisons.  
The values for the crude extract and the ethyl acetate and n-butanol (the most polar) fractions obtained from the leaf extract of _B. laeifolia_[24] and _B. argyrophylla_[25] were compatible, with EC\({}_{50}\) values of 4.5 \(\pm\) 0.9 and 4.3 \(\pm\) 0.8 ug.mL\({}^{-1}\) for the crude extract, respectively. Similarly, the previously mentioned fractions also had the lowest EC\({}_{50}\) values, being 4.1 \(\pm\) 0.5 and 8.4 \(\pm\) 1.0 \(\upmu\)g.mL\({}^{-1}\) for _B. laeifolia_ and 4.1 \(\pm\) 0.1 and 4.8 \(\pm\) 0.1 ug.mL\({}^{-1}\) for _B. argyrophylla_. For _Banisteriopsis oxyclada_, the value of the crude extract was 25.90 \(\pm\) 1.10 ug.mL\({}^{-1}\), and the best relative values for the fractions corresponded to the ethyl acetate fraction (EC\({}_{50}\) = 6.50 \(\pm\) 0.40 ug.mL\({}^{-1}\)) and the n-butanol fraction (EC\({}_{50}\) = 6.80 \(\pm\) 0.80 ug.mL\({}^{-1}\)) [29].  
The calculated antioxidant activity index (AAI) values for the gallic acid standard (19.05), the CE (4.07), and the EtOAc (6.23) and MeOH (2.13) fractions were the best, indicating a very strong antioxidant activity they are greater than 2.0 [40; 41]. The AAI values for the DCM (0.76) and Hex (0.48) fractions suggested moderate to poor antioxidant activity.  
The AAI relates the 2,2-diphenyl-1-picrylhydrazyl (DPPH) concentration used in the experiment with the EC\({}_{50}\) of the sample, the result is a constant for each analyzed material that if obtained under the same conditions, that is, same extraction procedures or collection sites, should yield similar or very close results [41]. In this study, it was observed that the fractions with the highest AAI values were the most polar ones, in which the most active and major compounds are phenolics, such as flavonoids. This highlights their ability to donate hydrogen to a free radical to mitigate potential damage [26]. The EC\({}_{50}\) results, similar to the investigations of extracts and fractions of the leaves of the aforementioned _Banisteriopsis_, suggest that the best EC\({}_{50}\) values are related to the clear discrepancies in the polarities of the fractions.
## 3 Research Design
### Research Methods
#### 3.2.1 Network Analysis  
This research constructed physician network models using social network analysis software Gephi 0.10.1 [35], where network nodes represented individual physicians, and network edges indicated physicians belonging to the same hospital. Utilizing Gephi's \"filter\" function, physicians were categorized into four groups based on professional titles: \"Chief Physician\", \"Associate Chief Physician\", \"Attending Physician\", and \"Resident Physician\", constructing corresponding subnetworks. Key network indicators for each subnetwork were analyzed, including average degree, graph density, number of connected components, and modularity index, with comparative analyses conducted across different professional titles. Additionally, physicians were filtered by disease type (depression, leukemia, diabetes) to construct corresponding disease subnetworks, comparing their network indicators to reveal distribution characteristics and structural differences across different classifications. Through these methods, the aim was to comprehensively understand the network distribution of physicians across the professional title and disease-type dimensions and their inherent patterns.
# The Current State and Future Potential of Microbial Control of Scarab Pests
## 5 Fungi
### Metarhizium Species  
_Metarhizium anisopliae_ was first discovered in the cereal cdafer (_Anisoplia austrica_) by [PERSON] in 1879 and was also the first putative fungi to be used as a biocontrol for insects in the 1880s [117]. It produces green muscularine disease and is documented to infect more than 200 insect species in agricultural and forest environments [118]. Many different species and/or strains of _Metarhizium_ exist and exhibit insecticidal activity. _M. flavoviride_ and _M. album_ are two other species known to be broadly entomophogenic [119; 120; 121; 122]. For scarabs, _M. brunneum_ was recently shown to infect Japanese beetle [123] and _M. anisopliae_ var. _majus_ is a strain associated with the rhinoceros beetle in the South Pacific [101].  
Like _B. bassiana_, _Metarhizium_ is easily cultured and is a popular microbial option for pest control, comprising a comparable share of the global mycoinsecticidal market, at about 34% [91]. In fact, _Metarhizium_ is the most commonly introduced entomopathogen used for insect biocontrol [124]. There are currently two strains registered with the U.S. EPA, ESF1 and F52, which are available across seven registered products but many others are available globally [36; 84]. For scarab species, studies show that _M. anisopliae_ is broadly effective on several species [95; 96; 97; 98; 101; 125; 126; 127; 128]. [PERSON] and [PERSON] [95] reported 82-97% mortality of second and third instar June beetles after a soil application of _M. anisopliae._ [PERSON] et al. [129] reported 100% mortality of _O. rhinoceros_ and [PERSON] et al. [98] 100% of first-instar _A. corpulenta_ larvae after treatment with _M. anisopliae._ [PERSON] et al. [126] reported 96% mortality of _P. crinita_ and _A. flavipennis_ 10 days after exposure, and [PERSON] et al. [127] reported mortalities of greater than 70% for _A. majale_, _M. catanea_, and _A. orientalis_. _M. anisopliae_ and _M. brunneum_ is also highly effective on Japanese beetle grubs and adults [92; 123; 130].  
Only a couple epizootics of _Metarhizium_ have been documented, one for the large brown cicada (_Graptopsaltria nigrofuscata_) in Japan and one in the Chinese burrower bug (_Schiodtella formosuna_) in China [131; 132]. No _Metarhizium_ epizootics have been reported in any scarab populations. Despite this, _Metarhizium_ has been shown to persist in the soil, particularly in the rhizosphere, for years after application [133; 134; 135; 136; 137], bolstering its potential as an effective biocontrol agent.
###### Abstract  
The aim is to evaluate whether apigenin K and melatonin M5250 were able to stimulate bone formation after tooth extraction at one, two, and three months follow-up. Six male beagle dogs were used. Apigenin K and melatonin M5250 immersed in hemostatic collagen sponges were placed in the third and fourth premolar and the first molar extracted sockets; the second premolar was used as control. At one, two, and three months, bone core biopsies were performed, and picosrius-hematoxylin was used for the staining process. In the first month, a higher amount of calcified bone tissue was observed in the melatonin (\(77.87\%\pm 1.2\%\)) and apigenin K (\(69.81\%\pm 1.8\%\)) groups than the control group (\(57.27\%\pm 0.54\%\)), with apparent discrepancies in values between the three groups (\(p<0.04\)). In the second month, there was a considerable improvement in the results in the areas with melatonin (\(79.81\%\pm 0.11\%\)) than in those of apigenin K (\(71.65\%\pm 0.52\%\)) and control (\(64.77\%\pm 0.44\%\)) (\(p<0.04\)). In the third month, the number of mature bone was similar to all the groups. The creation of new bone was significant in the melatonin group (\(82.78\%\pm 0.87\%\)), followed by the apigenin K group (\(78.76\%\pm 0.43\%\)) and the control group (\(57.27\%\pm 0.11\%\)). From this experimental study in dogs, it can be concluded that melatonin and apigenin K can accelerate the process of mineralization of the bone matrix, and thus the creation of laminae in the early stages of healing (1 month). Less reabsorption of the post-extraction sockets can be expected with the topical application of melatoninin and apigenin K. It seems that the stimulatory effects of bone healing induced by the topical application of melatonin and apigenin K are defect-size-dependent, being more evident in small defects compared to larger defects.  
Keywords:apigenin K; melatonin M5250; bone formation; cavity preservation +
Footnote †: journal: Journal of the American Statistical Association
## 3 Methodology
### Proposed ML Models
#### 3.7.7 Probit Regression  
Probit regression is a statistical method used for binary dependent variables similar to logistic regression. It predicts event probabilities by using a normal cumulative distribution function (CDF) instead of a logistic CDF. This model links input features to the probability of an outcome assuming a normal distribution of the latent variable. Probit regression is useful when the assumption of normally distributed errors is correct. It is often applied in fields such as econometrics and biometrics as an alternative to logistic regression [37].
# Combined CFD-Stochastic Analysis of an Active Fluid Injection System for Jet Noise Reduction
###### Abstract  
In the framework of DANTE project (Development of Aero-Vibroacoustics Numerical and Technical Expertise), funded under the Italian Aerospace Research Program (PRORA), the prediction and reduction of noise from subsonic jets through the reconstruction of turbulent fields from Reynolds Averaged Navier Stokes (RANS) calculations are addressed. This approach, known as Stochastic Noise Generation and Radiation (SNGR), reconstructs the turbulent velocity fluctuations by RANS fields and calculates the source terms of Vortex Sound acoustic analogy. In the first part of this work, numerical and experimental jet-noise test cases have been reproduced by means RANS simulations and with different turbulence models in order to validate the approach for its subsequent use as a design tool. The noise spectra, predicted with SNGR, are in good agreement with both the experimental data and the results of Large-Eddy Simulations (LES). In the last part of this work, an active fluid injection technique, based on extractions from turbine and injections of high-pressure gas into the main stream of exhausts, has been proposed and finally assessed with the aim of reducing the jet-noise through the mixing and breaking of the turbulent eddies. Some tests have been carried out in order to set the best design parameters in terms of mass flow rate and injection velocity and to design the system functionalities. The SNGR method is, therefore, suitable to be used for the early design phase of jet-noise reduction technologies and a right combination of the fluid injection design parameters allows for a reduction of the jet-noise to 3.5 dB, as compared to the baseline case without injections.  
jet-noise; stochastic noise generation and radiation; Reynolds Averaged Navier Stokes; Large Eddy Simulations +
Footnote †: journal: Applied Physics Letters
## 3 Multi-Agent Stage Feedback Carbon Trading Mode
### Carbon Trading Cost Model  
Based on the traditional carbon trading model, a multi-agent stage feedback carbon trading model is proposed. The carbon trading cost is calculated in stages, and the feedback factor is introduced. The cost feedback is based on the actual carbon emissions of the CIES to reduce carbon emissions indirectly. The specific carbon trading cost model is as follows:  
\[C_{\text{tc}}=\left\{\begin{array}{l}-p_{\text{co}_{2}}(1+2\delta_{\text{a} })(E_{\text{co}_{2}}-s-E_{\text{q}}),E_{\text{q}}\leq E_{\text{co}_{2}}-s\\ -p_{\text{co}_{2}}s(1+2\delta_{\text{a}})-p_{\text{co}_{2}}(1+\delta_{\text{a} })(E_{\text{co}_{2}}-E_{\text{q}}),E_{\text{co}_{2}}-s<E_{\text{q}}\leq E_{ \text{co}_{2}}\\ p_{\text{co}_{2}}(E_{\text{co}_{2}}-E_{\text{q}}),E_{\text{co}_{2}}<E_{\text{q }}\leq E_{\text{co}_{2}}+s\\ p_{\text{co}_{2}}s+p_{\text{co}_{2}}(1+\delta_{\text{p}})(E_{\text{q}}-s-E_{ \text{co}_{2}}),E_{\text{co}_{2}}+s<E_{\text{q}}\leq E_{\text{co}_{2}}+2s\\ p_{\text{co}_{2}}s(2+\delta_{\text{p}})+p_{\text{co}_{2}}(1+2\delta_{\text{p}}) (E_{\text{q}}-2s-E_{\text{co}_{2}}),E_{\text{co}_{2}}+2s<E_{\text{q}}\leq E_{ \text{co}_{2}}+3s\\ p_{\text{co}_{2}}s(3+\delta_{\text{p}})+p_{\text{co}_{2}}(1+3\delta_{\text{p}}) (E_{\text{q}}-3s-E_{\text{co}_{2}}),E_{\text{co}_{2}}+3s<E_{\text{q}}\end{array}\right. \tag{7}\]  
where \(C_{\text{tc}}\) represents the total cost of carbon trading, \(p_{\text{co}_{2}}\) represents the transaction price of carbon emissions, \(\delta_{\text{a}}\) and \(\delta_{\text{p}}\) represent positive feedback factors and negative feedback factors, respectively, and \(s\) represents the range of carbon emissions.  
The proposed carbon emission trading model divides the actual carbon emissions into multiple interval scales, and the unit carbon trading price increases with the increase in actual carbon emissions. Meanwhile, reward and penalty factors are introduced. When the actual carbon emissions of the trading agent exceed the set threshold, the penalty fee is levied on it; on the contrary, when the carbon emissions of the trading entity are lower than the set threshold, a reward is given. This avoids the problem of the transaction cost of the excessive emission agent being the same as that of the compliance agent and ensures the fairness of the multi-transaction agent.
## 2 Materials and Methods
### Convection--Diffusion Equation of Chemoattractant  
Based on the oil-water two-phase flow model, it is necessary to establish the convection-diffusion equation of chemical attractants to simulate the diffusion of pollutants (toluene) in oil phase to water phase.  
Convection-diffusion equations for chemo-attractants describe the dissolution of bacterial chemotaxis (e.g., toluene) into water [28]. The concentration distribution and the concentration gradient of chemical attractants can be obtained by solving this equation.  
The two-dimensional plane chemoattractant convection-diffusion equation used in this simulation is:  
\[\frac{\partial a}{\partial t}=D_{e,a}\frac{\partial^{2}a}{\partial x^{2}}+D_{e,a}\frac{\partial^{2}a}{\partial y^{2}}-\frac{v_{x}\partial a}{\partial x}- \frac{v_{y}\partial a}{\partial y}, \tag{12}\]  
\(a\)--Chemoattractant concentration, mol/m\({}^{3}\);  
\(t\)--Time, s;  
\(D_{e,a}\)--Chemical attractant effective diffusion coefficient, m\({}^{2}\)/s;  
\(v_{x}\)--Two-dimensional phase field model to solve the fluid transverse velocity, m\(/\)s;  
\(v_{y}\)--The longitudinal velocity of the fluid solved by the two-dimensional phase field model, m\(/\)s.  
The effective coefficient of chemical attractants \(D_{e,a}\) is expressed as:  
\[D_{e,a}=\epsilon_{p}D_{a}, \tag{13}\]  
\(D_{a}\)--Diffusion coefficient of chemical attractant, m\({}^{2}\)/s;  
\(\epsilon_{p}\)--Porosity, dimensionless.  
In this simulation, the chemoattractant was toluene in the oil phase.
## 3 Introduction to the Dataset
### Real-World Testing  
After completing the debugging of the algorithm model, the model was deployed to the server and traveled to the field to test the model's effectiveness. Under the guidance of experts from the Qinghai Grassland General Station, the author's team visited areas where plateau age-grouse are active, photographed them with a drone, uploaded the results to the server, and tested the model. Comparing the two figures, there is not much difference in the number of age-grouse holes present in the same height shot, but most of the holes in Figure 10 are ungnawed sage-grouse holes, whereas in Figure 10 there are more gnawed sage-grouse holes and a larger area of gnawed sage-grouse holes, accounting for more than 35% of the area, which is a severely eroded grassland. For the corresponding area in Figure 11, prevention and reasonable guidance are needed, while for the corresponding area in Figure 11, measures such as grassland restoration and rodent eradication are needed.  
Figure 10: Low hazard meadow scene segmentation.  
Figure 9: Comparison of algorithm predictions.  
To evaluate the model's generalizability and robustness, we also conducted cross-dataset validation, testing the model on multiple datasets to verify its applicability in various scenarios. These datasets include a private dataset containing three plant species: Thermopsis lanceolata, jic, and zmc. Although the dataset is currently confidential, we have provided sample images in Figures 12-14 to demonstrate the model's detection performance across different plant types. By testing the model on these datasets with varying environmental conditions and object characteristics, we validated the model's generalizability across diverse real-world scenarios. The results show that the model effectively handles different plant species and demonstrates strong robustness in diverse conditions, further enhancing our confidence in its broad applicability in practical use.  
Figure 11: High hazard meadow scene segmentation.  
Figure 12: Plant detection on Thermopsis lanceolata.
# Perspective on Double Pulsed Gas Metal Arc Welding
## 2 Process and Weld Characteristic of DP-GMAW
### Weld Pool and Bead Characteristic
The solidification growth rate decreases with time from 0 s to 0.1 s, because the weld pool expands from 0 s to 0.1 s with its trailing edge moving in a direction opposite to the welding direction. Despite the constant welding speed, the trailing edge comes to a standstill with a decrease in the solidification growth rate from 0 mm/s at 0.04 s, which indicates that the expansion speed of the weld pool at its trailing edge is same as the welding speed. The growth rate is negative between 0.04 s and 0.1 s, because the expansion speed of the weld pool is much greater than the welding speed, remelting occurs, and solidification stops near the trailing edge of the molten pool between 0.04 s and 0.1 s. Due to the remelting phenomenon, the effective length of the solidified metal of DP-GMAW is longer than that of CP-GMAW, whereas the solidification time of DP-GMAW is shorter than that of CP-GMAW. Therefore, the mean value of the solidification growth rate \(R\) is 10.8 mm s\({}^{-1}\) for the DP-GMAW weld. This value is greater than the constant \(R\) of SP-GMAW, which is 8.0 mm s\({}^{-1}\). Figure 4: Solidification growth rate versus time of CP-GMAW and DP-GMAW (reproduced with permission from [29], Copyright Elsevier, 2017). Figure 3: Weld pool boundary of 6061 aluminum alloy by DP-GMAW during thermal pulse phase. The cooling rate (\(GR\)) is defined as the product of the temperature gradient (\(G\)) and the solidification growth rate (\(R\)). The increase in \(R\) produces a greater \(GR\), and the mean cooling rate of DP-GMAW is higher at the same heat input. Note that the value of the solidification structure is significantly affected by the cooling rate. From previous studies, the relation between the primary dendrite arm spacing (\(\lambda_{1}\)) [32] and secondary dendrite arm spacing (\(\lambda_{2}\)) [32; 33], and the cooling rate (\(GR\)), is given as  
\[\lambda_{1}=a(G\times GR)^{-1/4} \tag{4}\]  
\[\lambda_{2}=b(GR)^{-n} \tag{5}\]  
where \(n=0.33\) to \(0.5\), \(a\) and \(b\) are coefficient; their values are dependent on the alloy system. Therefore, both \(\lambda_{1}\) and \(\lambda_{2}\) decrease with an increase in the solidification growth rate and cooling rate. Therefore, DP-GMAW will produce refined grains and dendrites as increased cooling rate. The increased cooling rate is identified as the second mechanism of microstructural refinement during DP-GMAW.
# Ephemeral Node Identifiers for Enhanced Flow Privacy
## 3 Method and Implementation
### Communication Across IPv6 Networks
#### 3.3.2 Global IPv6 Network  
The test was performed at a public venue, the Hackathon event of the Internet Engineering Task Force (IETF), IETF118 in Prague, Czechia (CZ). The test involved communication between a node acting as a client system in Prague, and a similar node in St Andrews (UK) acting as a server system. The network addressing and topology is shown in Figure 5.  
To distinguish all ILNP packets from normal IPv6 packets at end systems, an IPv6 Destination Option (as in Section 4.6 of RFC8200(S) [29]) is used, which we refer to as the _Nonce_[30]. This places a randomly generated value in a specially-defined IPv6 Destination  
Figure 4: (**a**) shows the real world threat model these defences contest with: an eavesdropping on-path attacker listens to traffic between some client machines and some servers, and tries to correlate flows. (**b**) is the Testbed setup used to demonstrate this attack. Three separate client machines (A, B, and C) were connected to a server over a network of three routers, one of which was malicious and sought to perform a privacy-compromising attack.  
Figure 5: IPv6 connectivity test for use of eNIDs, demonstrating operation across IPv6 Global Connectivity, as well as use of unmodified IPv6 applications—rsync, ping6, and ssh. Short flows for each application were set-up between client and server and all the packets captured. Although originating from the same machine within approximately 60 s of each other, each flow had a different NID value—the lower 64 bits of the IP address—so each would look like a different client system to an observer.  
Option header which both identifies the packet as an ILNP packet (for correct processing at the destination) and provides some protection against off-path packet spoofing.  
Application flows were generated sequentially and separately with rsync (TCP), ping6 (ICMP), and ssh (TCP). The traffic between server and client was captured at the server, i.e., the packets that had successfully traversed the IPv6 Global Connectivity.