esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Nonionic Surfactant Brij35 on Metribuzin Residues in Soil
At the 0.3 m depth, the effect of surfactant treatments contrasted with the 0.1 m depth, since metribuzin concentrations were higher in the presence of surfactant compared to the tap water (Figure 6). This indicates that Brij35 influenced the downward movement of metribuzin. Given that metribuzin is a non-ionic compound, belonging to the weakly basic triazines, its sorption to soil is dependent on the amount of organic matter, clay content and pH. It was reported that metribuzin showed a great potential for leaching in soil [51,70]. At the 0.6 m depth, low concentrations of metribuzin (33.46-2.87 ug kg\({}^{-1}\)) were detected from day 15 to day 60, in the Brij35 treatments, but remained below detection limits for the tap water treatment. This highlights the role of surfactants in enhancing metribuzin mobility in the soil profile (Figure 7). The decrease in metribuzin concentrations over time and depth was reflected by the significant effects (\(p\leq 0.05\)) of treatment, depth, time and their interactions. Results for tap water concurred with the results of columns studies conducted by [PERSON] _et al._ (2004) and [PERSON] _et al._ (2009) [51,67]. They indicated the absence of metribuzin in soil samples from soil depths exceeding 0.30 m. Moreover, as the solubility of the herbicide increased, its ability to compete with organic surfaces decreased. This accounts for why, in certain conditions, the migration of metribuzin downward through the soil profile was improved [72]. Generally, surfactants play an important role in enhancing the mobility of pesticides and this fact has been confirmed by [PERSON] _et al._ (2007) and [PERSON] _et al._ (2011) [43,73]. Our study also showed a greater downward movement of metribuzin through a sandy soil in the presence (_vs._ absence) of the non-ionic surfactant Brij35. Generally, surfactants can increase the solubility of pesticides through their solubilization in the micelles, which formed above the critical micelle concentration (CMC) of the surfactant. Depending on this concentration, pesticide properties, the degree and the location of solubilization into the micelle, surfactant effects can vary. Since metribuzin is a polar herbicide, it is expected to be solubilized by the surfactant once CMC is exceeded. Furthermore, depending on their hydrophilicity, non-ionic surfactants have the ability to solubilize polar compounds [74]. This hydrophilicity is known to be governed by the number of the ethylene oxide groups (EO) in the surfactant molecule, _i.e._, the greater the number of EO groups, the more hydrophilic the surfactant [75]. Since Brij35 has twenty-three EO groups, it is expected to exhibit a high capacity to solubilise polar compounds, such as metribuzin in the environment. Figure 7: Metribuzin concentrations at 0.6 m depth.
## 2 Materials and Methods
### Validation of Monte Carlo Method  
To validate the Monte Carlo method used in this study, the results of this study were compared to those of [PERSON] et al. [30] and [PERSON] et al. [31] under the same conditions. The schematic diagram of the numerical analysis model is shown in Figure 3. The physical properties of the tumor tissue existing from the surface were uniformly mixed according to the concentration of GNPs, and the temperature distribution between the tumor tissue and the surrounding normal tissue was evaluated. Here, \(x_{1}\ =\ 5\) mm, \(x_{2}\ =\ 20\) mm, \(z_{1}\ =\ 5\) mm, \(z_{1}\ =\ 10\) mm, the intensity of the irradiated laser is \(0.5\) W/cm\({}^{2}\), and the radius of the laser is the same as that of the tumor. In addition, natural convection occurs on the skin surface, and the initial temperature of normal tissues and tumor tissues is assumed to be normal human body temperature, 37 \({}^{\circ}\)C. The thermal/optical properties used in the calculation are shown in Table 2.  
Comparison with relevant studies is listed in Figure 4. Figure 4a shows the temperature change over time at the center of the tumor (point A) when the intensity of the irradiated laser is \(0.5\) W/cm\({}^{2}\); Figure 4b shows the change in the \(x\) direction of temperature with respect to the depth of the tumor tissue at \(z\ =\ 0\) mm, \(2.5\) mm, \(5\) mm. Under the same study conditions, the results of this study showed an error within \(1.7\%\) when compared  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Temperature Range (\({}^{\circ}\)C)** & **Biological Effect** & **Weight,\(w_{j,t}\) (In Tumor Tissue)** & **Weight, \(w_{j,n}\) (In Normal Tissue)** \\ \hline
37 & Normal & 1 & 1 \\
37 \(<\) T \(<\) 43 & Biostimulation & 1 & 1 \\
43 \(\leq\) T \(<\) 45 & Hyperthermia & 1 & 2 \\
45 \(\leq\) T \(<\) 50 & Reduction in enzyme activity & 1 & 2 \\  & Protein denaturation & 2 & 3 \\
50 \(\leq\) T \(<\) 70 & (coagulation) & 3 & 4 \\
70 \(\leq\) T \(<\) 80 & Welding & 3 & 4 \\  & Permeabilization of cell & 4 & 5 \\
100 \(\leq\) T \(<\) 150 & Vaporization & 5 & 6 \\
150 \(\leq\) T \(<\) 300 & Carbonization & 6 & 7 \\ T \(>\) 300 & Rapid cutting and ablation & 7 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Laser-induced thermal effects [28; 29].  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Normal Tissue** & **Tumor Tissue and GNPs** \\ \hline Absorption coefficient [cm\({}^{-1}\)] & 0.02 & 121 \\ Reduced scattering coefficient [cm\({}^{-1}\)] & 6.5 & 0.5 \\ Density [kg/m\({}^{3}\)] & 1000 & 1100 \\ Thermal conductivity [W/mK] & 0.5 & 0.55 \\ Specific heat []/kgK] & 4200 & 4200 \\ Blood perfusion [s\({}^{-1}\)] & 0.001 & 0.00091 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Thermal and optical properties used in validation numerical analysis model [31; 32].  
with those of existing studies. This confirmed that the numerical analysis model using the Monte Carlo method used in this study is in good agreement with the previous studies.
# Experimental Study on LTE Mobile Network Performance Parameters for Controlled Drone Flights
## 1 Introduction
Therefore, aerial nodes of various types can be developed and adjusted for the necessary performance improvements, flexibility, and agility of the 5G and other next-generation mobile networks [32]. Moreover, two main factors [32] have to be taken into account regarding the UAVs and their connection with the wireless networks. One of them is related to the wireless networks' advancement to support the professional or private use of UAVs. Another one is how UAVs could support the performance of and improvement in the wireless networks themselves. For instance, to increase the capacity and availability of wireless networks when it is necessary by enlarging the operational coverage range, enhancing agility and availability using UAVs as aerial nodes.
## 2 Problem Background
### Need for Routing Fairness in OppNets
#### 2.1.1 Fair Routing as a Complement to Congestion Control  
In literature, ensuring routing fairness in SCF-based networks is often confused with, and at times may be analogous to, congestion control. Generally, congestion occurs when resource demands exceed available capacity, and may be detected by network capacity (i.e., rate of message generation and delivery), buffer occupancy on nodes, and message drop rates [25]. However, since traditional networks do not consider energy a scarce resource, applying such congestion control mechanisms may not always achieve fairness in OppNets. While congestion control attempts to prevent overloading (or excessive buffer occupancy), solutions aimed at ensuring fairness focus on achieving a balance between user satisfaction (i.e., a fairly even distribution of burden, in terms of resource utilization, among nodes) and throughput. In order words, a fair routing technique focuses on achieving acceptable network performance with a fairly equal rate of resource consumption among nodes, irrespective of the level of congestion (in terms of network capacity, buffer occupancy, or message drop rate). Hence, most fair routing solutions combine a forwarding utility that allows a wider range of relay nodes with a congestion control mechanism.
###### Abstract  
We examine the optical absorption in a hybrid structure composed of a metal nanoshell and a semiconductor quantum dot, while interacting with a linearly polarized probe electromagnetic field. First, we derive the equations of motion, in the rotating wave approximation. Then we proceed to the derivation of analytical expressions for the linear susceptibility of the metal nanoshell and the semiconductor quantum dot. The imaginary part of the susceptibility expresses the absorption coefficient. We find that by properly engineering the thickness of the metal nanoshell, the material of the dielectric core and the interparticle distance, we may achieve an optimum response. We identify the emergence of two distinct types of hybrid exciton states. One of them emerges in the strong exciton-plasmon coupling regime for low values of the dielectric constant and the radius of the dielectric core. This type of hybrid exciton exhibits an amplified gain without population inversion and a quenched absorption resonance accompanied by a suppressed exciton lifetime. The second type of hybrid exciton emerges in the weak exciton-plasmon coupling regime and presents the opposite spectral characteristics. Here, the exciton lifetime presents a substantial increase, especially for small interparticle distances, in which case the semiconductor quantum dot and the metal nanoshell are strongly coupled with one another.  
absorption spectrum; exciton lifetime; gain without inversion; hybrid nanostructure; metal nanoshell; semiconductor quantum dot +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Electron Beam Radiolysis Process  
EB irradiation of a solution is a form of \"high-energy oxidation\" based on the use of highly energy-charged electrons capable of generating at the same time strong oxidizing and reducing radicals by splitting water molecules, including: superoxides (a.k.a. hyperoxides, O\({}_{2}\)-), hydroxyl (-OH) and hydrogen (-H) radicals, solvated electrons (e\({}^{-}\)\({}_{\text{aq}}\)), and others. These species are very reactive (much stronger oxidation potentials than O\({}_{3}\), as shown in Table 1[4], and short-lived (half-lives are in the order of 10 us at 10\({}^{-4}\) M concentration), and are hence capable of carrying out rapid and extended molecular degradation, potentially to complete mineralization (depending on the absorbed dose and irradiation intensity). For these specific characteristics, radiolysis-based processes have been classified by researchers as Advanced Oxidation-Reduction Processes (AORPs), a subset of AOPs [16]. It should be noted that other irradiation technologies (i.e., gamma irradiation) will have similar final pollutant degradation effects [17], however, their reaction rates are much lower, due to lower achievable irradiation dose-rates (by one to three orders of magnitude), so for application purposes EB technology is currently preferable (Table 2).  
Table 3 summarizes overall possible EB radiation effects in a solution after radiolysis-induced generation of radicals. Such radical-generating reactions occur within a 10\({}^{-6}\)-10\({}^{-12}\) sec time span, and the degradation of organic molecules by the radicals occurs in that time frame. It should be noted that if radicals find no suitable molecules to react with (e.g., in pure water), an equally fast, reverse reaction takes place, and water molecules are recombined with no residues left in solution.
# Bangla Character Detection Using Enhanced YOLOv11 Models: A Deep Learning Approach
## 3 Methodology of the Proposed Bangla Alphabet Detection Algorithm  
The Bangla script presents significant challenges for visual recognition tasks due to its intricate character structures, including ornate curves and distinctive diacritical marks such as the _matra_, a horizontal line that frequently appears atop many characters. These stylistic complexities often result in visual similarities between characters, making accurate recognition difficult, even for human observers. To address these challenges, this study introduces an enhanced version of the YOLOv11 object detection framework, which incorporates a ResNet50 backbone and an attention mechanism to improve feature extraction and recognition accuracy.
# How Reputation Systems Change the Psychological Antecedents of Fairness in Virtual Environments
## 3 Results
### Effects of Socio-Demographic and Psychological Characteristics on Fairness
To statistically test the differences among treatments, thus having a more comprehensive understanding of our results, we carried out a new GLMM procedure that included both conditions (i.e., Rep ON and Rep OFF) at the same time. As we can gather from Table 7, reputation, age, sense of community, and self-efficacy directly affected people's fairness, which means that their effect is mostly independent of the reputation system. Except for self-efficacy that appeared to diminish fairness, the other parameters increased it. Instead, both Closeness and Neuroticism effects seem to be condition-dependent (i.e., we have interaction effects). In other words, we appreciated significant differences between the two conditions for both these psychological antecedents. When reputation was active in the game, Closeness increased fairness and Neuroticism decreased it, while their effects were not noticeable in the Reputation OFF condition. \begin{table}
\begin{tabular}{l l l l} \hline \hline \multicolumn{4}{c}{**Predictors of Fairness**} \\ \hline
**Best Model** & **Akaike** & **F** & **df-1 (2)** \\ \hline Fairness Rep OFF & 10953.659 & 11.968 *** & 3 (1557) \\ Fairness Rep ON & 11252.259 & 11.501 *** & 4 (1595) \\ \hline \hline \multicolumn{4}{c}{_Fixed coefficients for Rep OFF_} \\ \hline
**Factors** & **Coefficient (\(\beta\))** & **Student t** & \(p\).** \\ \hline Sense of Community & 0.196 & 4.33 & 0.001 \\ Self-Efficacy & \(-\)0.143 & \(-\)2.62 & 0.009 \\ Age & 0.169 & 3.74 & 0.001 \\ \hline \hline \multicolumn{4}{c}{_Fixed coefficients for Rep ON_} \\ \hline
**Factors** & **Coefficient (\(\beta\))** & **Student t** & \(p\).** \\ \hline Sense of Community & 0.084 & 1.99 & 0.047 \\ Neuroticism & \(-\)0.135 & \(-\)2.16 & 0.031 \\ Closeness & 0.130 & 2.27 & 0.023 \\ Age & 0.374 & 4.59 & 0.001 \\ \hline \hline \multicolumn{4}{c}{*** = \(p\) \(<\) 0.001; df = degrees of freedom.} \\ \end{tabular}
\end{table} 
Table 6: GLMM 3. Socio-demographic and Psychological predictors of fair behavior in each condition.
## 2 Necessity and Principles of ESC
### Principles  
Five principles of ESC are proposed in this work based on the above mentioned five aspects of necessity and other previous studies related to coordination of ESs or ES with renewable energies [24; 51; 52; 53; 54; 55].  
* Meet the total demand of stored energy  
When allocating the capacity of ES, it is a prerequisite to meet the total demand of stored energy. Only in this way can the balance of supply and demand be reached, and the stable operation of the EI system be guaranteed.
* Meet power grade demand  
The requirements for power grade are diverse due to the degree of economic development. For instance, industrial cities need a higher power grade, while areas with backward development and low per capita distribution density have fewer power grade requirements. Accordingly, the ESC should meet the demand of power grade.  
Figure 1: Various energy storages (ESs) of energy Internet (EI).  
Guarantee the reasonable storage and release speed  
Different places have different performance requirements for ES. For example, some places with a high penetration rate of renewable energies are prone to power fluctuation, which requires ESs with fast storage and release speed. As a result, it is necessary to take the speed of ES release into account while coordinating ESs.
## 4 Results and Discussions
### Analysis of the Fatigue Life Distributions in Interior-Induced Fracture Mode  
Although distribution characteristics of the fatigue strength \(\sigma_{w}^{*}\) at the given number of stress cycles were analyzed in the previous section, the fatigue life distributions could also be analyzed based on Equation (17), by giving any value of the stress amplitude \(\sigma_{w}^{*}\). Applying this method, fatigue life distributions were calculated at \(\sigma_{w}^{*}=1400\) MPa, \(1200\) MPa, \(1000\) MPa, and \(900\) MPa, respectively. The probability density functions thus obtained were indicated at the respective stress levels in Figure 12. Since the fatigue life increased logarithmically with decrease of the stress level, the probability density at the peak of the density function tended to decrease such that the integrated value of the density function maintained unity. Values of the peak density at the respective stress levels are indicated on the right-hand side, in Figure 12. As shown in this table, the peak density varied drastically, depending on the stress level, due to the above described reason, but the distribution curves were drowned when they had the same height for the sake of convenience.  
In order to distinguish the fatigue strength distribution and the fatigue life distribution, the notations of \(f(\sigma_{w})\) and \(F(\sigma_{w})\) are used for the fatigue strength distribution, whereas other notations of \(g(\sigma_{w})\) and \(G(\sigma_{w})\) are accepted for the fatigue life distribution. Similar to Figure 11, the percentile points of \(G=1\%\), \(10\%\), \(50\%\), \(90\%\), and \(99\%\) for the fatigue life are indicated by marks of \(\Diamond\)'s along the horizontal lines, at the respective stress levels. The respective thin dotted lines passing through the percentile points corresponding to  
Figure 11: Fatigue strength distributions and \(P\)-\(S\)-\(N\) curves in very high cycle regime.  
the same probability give the _P-S-N_ curves of this steel. All experimental data in the interior-induced fracture appear within the range of 1-99% in Figure 12. Thus, it was finally noted that the statistical fatigue property in the interior-induced fracture of this steel could be well explained from either one of the fatigue strength distribution and the fatigue life distribution, through the probabilistic model proposed in this study.
## 5 Simulation Setting and Results  
Simulation of EERD and CoEERD schemes and their comparison with the CoDBR and ODBR is the subject of this section. In each simulation mechanism, the nodes deployment is random and 1000 number of rounds are taken. By following the conventional method of multiple sinks model, four sink nodes with a separation difference of 100 meters are positioned at the ocean surface. After 50 rounds, the CP is broadcasted to keep the nodes updated about the deviated circumstances inside the network. To reduce the computation time, we average the plots on 10 J energy. If we increase the energy form 10 J, it increases the computation time. The fluctuation is also stable on 10 J energy, so we assume that the plots would be same if nodes are energized beyond 10 J. The communication range of each node is considered 100 around it. The total 400 sensor nodes are deployed, while the transmission rate is 10 Kbps, the information packet size is 1000 bits while the CP size is 48 bits and the total available bandwidth is 30 kHz. The comparative results are taken by using the MATLAB. Table 2 presents the simulation parameters.
## 1 Introduction
Many experimental studies involving double-torsion tests have been conducted on subcritical crack growth to determine the stress corrosion index [1; 2; 7; 19; 20; 21]. [PERSON] [1; 6; 7] and [PERSON] [19] characterised the subcritical crack growth according to the relationship between the stress intensity factor and crack velocity. [PERSON] et al. [20] investigated the effect of relative humidity on subcritical crack growth in sandstone using the double-torsion testing method. They reported that subcritical crack growth in sandstone was significantly affected by relative humidity. Although double-torsion tests have been used in many studies to examine subcritical crack growth, they have limitations [22; 23]. First, the double-torsion tests require a significantly thin plate with a thickness of 3-5 mm, and it is challenging to produce the specimens. Second, the heterogeneity of the rock results in poor repeatability of the relationship between stress intensity factor and crack velocity. Third, the loading equipment and method are sophisticated. Finally, only mode I tension loading is applicable. [PERSON]\({}_{\rm o}\) and [PERSON] [22] conducted several experimental tests to verify the constant stress-rate test to determine the stress corrosion index in rocks. They reported that the results of the constant stress-rate test agreed well with those of a double-torsion test. The effects of loading rate on the rock strength have long been studied, but there have been few studies on the relationship between stress corrosion and the degradation of rock strength with applied loading rate. Moreover, the effects of loading conditions, such as tensile or shear loading, and the fracture modes on the degradation of strength with loading rate have not been studied in detail. The objective of this study was to investigate the relationship between stress corrosion, which is the major cause of time-dependent cracking and the degradation of rock strength with loading rate under different loading conditions and fracture modes. The constant stress-rate test was used to determine the stress corrosion index. The constant stress-rate tests including Brazilian tension and three-point flexural tests for the tensile strength, short-beam compression and single-shear tests for the in-plane shear strength, and a torsion test of rectangular section specimens and a circumferentially notched cylindrical specimen test for the out-of-plane shear strength were conducted at different loading rates from 0.01 to 10 MPa/s using Coconino sandstone. The degradation of rock strength on the loading rate was analysed with different loading conditions and fracturing modes. The values of stress corrosion index (\(n\)) obtained from tensile, in-plane, and out-of-plane shear strength tests were compared. Additionally, the values of \(n\) were obtained from published data for different rock types with different loading conditions and compared with the results of the laboratory tests.