esa-sceva/satcom-chunk-collection huggingface dataset:
# Location-Based Relay Selection in Full-Duplex Random Relay Networks
## 4 Location-Based Relay Selection in FDRRNs
### Location-Based Relay Selection Algorithm for FDRRNs  
In conventional relay systems, the _max-min_ relay selection algorithm is regarded as an optimal solution from a capacity perspective in terms of the DF relay protocol [38]. In this scheme, the relay node that maximizes the minimum relay link between the first and second relay hops is selected. Following this approach, we can adopt a similar approach for FDRRNs as follows:  
\[\mathbf{x_{s}}=\arg\max_{\mathbf{x_{r}}\in\Pi_{R}}\min\{\gamma_{1},\gamma_{2}\}. \tag{18}\]  
In addition, the optimal relay selection scheme based on the outage probability can be obtained by selecting the relay node that minimizes the outage probability of an FDRRN as follows:  
\[\mathbf{x_{s}}=\arg\min_{\mathbf{x_{r}}\in\Pi_{R}}p_{\mathrm{o}}(\mathbf{x_{r }}). \tag{19}\]  
Note that while (18) and (19) can achieve good performance, they require a substantial amount of information, including instantaneous channel state information (CSI) and signal-to-noise ratio (SNR) details. Acquiring this information in a real environment is challenging and can lead to a significant signaling overhead, which ultimately degrades spectral efficiency. In particular, for FDR systems, obtaining the instantaneous status of SIC is even more challenging. Therefore, we propose a low-complexity algorithm that utilizes the potential relay location determined in (8). Based on Lemma 1, we propose a location-based relay selection algorithm for FDRRNs, which selects the relay node that is closest to the potential relay location from among the candidate relay nodes, as follows:  
\[\mathbf{x_{s}}=\arg\min_{\mathbf{x_{r}}\in\Pi_{R}}D_{\mathbf{x_{r}^{\prime}} \mathbf{x_{r}}}. \tag{20}\]  
It is important to note that the proposed algorithm requires only location information, which can be obtained from network-level parameters. As a result, it achieves the desired performance with reduced system overhead.
## 1 Introduction
Both wrapper and hybrid methods make selections based on classifier that may or may not work well with any other classifier. That is because the optimal feature subset is built when the classifier is built, and the selection depends on the hypotheses which the classifier makes. In our work, we have focused mainly on the supervised filter-based FS method [20] approach, where the feasibility check for a feature subset is based on a popular statistical measure called Pearson's correlation coefficient (PCC) [21] and the minimal redundancy maximal relevance (mRMR) [22], which takes into the account of relation of features with other features and also with the classes. Our adopted technique applies a supervised filter harmony search algorithm (SFHSA) in order to perform FS. Keeping the fundamentals of the algorithm intact, we have made modifications to the pitch adjustment procedure of the algorithm where we have incorporated the concept of cosine similarity for adjusting values of the variables. The details of implementation were elucidated in Section 4. Some applications of HSA on FS was discussed in [23, 24, 25, 26, 27, 28, 29]. The proposed HSA-based FS was applied on a set of on five different state-of-the-art feature descriptors that include uniform local binary pattern (uLBP), horizontal-vertical neighborhood local binary pattern (hvnLBP), Gabor filters,histogram of oriented gradients (HOG) and pyramidal HOG (PHOG). The method is tested on two popular FER datasets, namely Japanese female facial expression (JAFFE) [30] and Radboud faces database (RaFD) [31]. The flow of the paper is arranged as follows: Section 2 describes motivation behind the present work along with some state-of-the-art methods related to FER. Section 3 presents the detailed description of the datasets used. It also explains the five feature descriptors briefly. The proposed optimal feature subset selection method, called SFHSA, was detailed in Section 4. The performance evaluation of the SFHSA is reported in Section 5. Section 6 finally concludes the paper. The supporting codes are uploaded in the Github link: [[https://github.com/[PERSON]/Cosine-Similarity-based-Harmony-Search-Algorithm](https://github.com/[PERSON]/Cosine-Similarity-based-Harmony-Search-Algorithm)]([https://github.com/[PERSON]/Cosine-Similarity-based-Harmony-Search-Algorithm](https://github.com/[PERSON]/Cosine-Similarity-based-Harmony-Search-Algorithm)).
# Analysis of Equivalent Flexural Stiffness of Steel-Concrete Composite Beams in Frame Structures
## 5 Simplified Design Method
### Simplified Design Formula for Calculating Vertical Deflection
Based on the theoretical analysis results of \(\alpha_{\Delta}\) shown in Figure 16, a piecewise function is proposed as the simplified design formula for calculating the vertical deflection correction factor, \(\alpha_{\Delta}\):  
\[1,\alpha_{cr}\leq 0.1\] \[\alpha_{\Delta}=\left\{ \begin{array}{c}1,\alpha_{cr}\frac{( \xi_{1}-1)\alpha_{cr}+(0.25-0.1\xi_{1})}{0.15},0.1<\alpha_{cr}\leq 0.25 \\ \frac{(\xi_{2}-\xi_{1})\alpha_{cr}+(0.43\xi_{1}-2.5\xi_{2})}{0.18},\alpha_{ cr}>0.25\end{array} \right., \tag{34}\]  
where \(\xi_{1}\) and \(\xi_{2}\) are the calculated values of the vertical deflection correction factor when \(\alpha_{\rm cr}=0.25\) and \(0.43\), respectively, which can be calibrated by the following equations:  
\[\xi_{1}=0.06\alpha+0.96,\ \alpha_{cr}=0.25, \tag{35}\]  
\[\xi_{2}=-0.03\alpha^{2}+0.466\alpha+0.61,\ \alpha_{cr}=0.43 \tag{36}\]  
As shown in Figure 16, there is a good agreement between the calculation results of the simplified design formula and the theoretical model. It is demonstrated that, by  
Figure 16: Variation in vertical deflection correction factor \(\alpha_{\Delta}\) with parameters \(\alpha\) and \(\alpha_{\rm cr}\). elaborately considering the rotation constraints at the beam ends and the cracking effect in the concrete slab, the proposed design formula can accurately describe the equivalent flexural stiffness of the composite beams. The vertical deflection correction factor, \(\alpha_{\Delta}\), is observed to increase with the increase in the negative moment region factor, \(\alpha_{\text{cr}}\), or the stiffness amplification coefficient, \(\alpha\), which is consistent with the theoretical analysis results as previously discussed. For the particular case of \(\alpha_{\text{cr}}=0\), correction factor \(\alpha_{\Delta}=1\), as calculated using Equation (34), indicates that the simply supported composite beam does not need to adjust the calculated vertical deflection of the elastic analysis. In summary, using the proposed design formula, as described in the following steps, the vertical deflection of composite frame beams can be easily calculated:  
1. Based on the elastic analysis results of a structural design software, the lengths of the negative moment regions, \(l_{\text{cr1}}\) and \(l_{\text{cr2}}\), can be obtained from the internal force distributions. Subsequently, the negative moment region factor, \(\alpha_{\text{cr}}\), can be calculated using Equation (25);
2. By substituting parameter \(\alpha_{\text{cr}}\) and stiffness amplification coefficient \(\alpha\) into Equation (34), the vertical deflection correction factor, \(\alpha_{\Delta}\), can be obtained;
3. Utilizing the deformation results of the elastic analysis, the design value of the vertical deflection of the composite frame beam is determined using the proposed formula in Equation (32).
# Hub-and-Spoke Network Design Considering Congestion and Flow-Based Cost Function
## 6 Experiments and Numerical Results  
We ran numerical experiments to illustrate the performance, as well as different aspects of the proposed formulations. Such analysis would reveal the effect of different parameters on the final solution, bring managerial insights from the mathematical formulation, and also help to achieve a better understanding on the tradeoff of the decisions involved in such decision procedure.  
The experiments were ran on a DELL Laptop, with an Intel Core i5-4210U CPU at 1.70 GHz and 16 GB of RAM. All formulations were coded in C# and solved using the CPLEX 12.6.3 library. In all experiments, the maximum computing time was set to 86,400 s (one day).  
We used the Turkish network dataset. As it did not have the cost of the spoke location, we assumed it to be \(D_{k}=\omega F_{k}\), where \(\omega=0.3\). We started the experiments using constant inter-hub transportation cost function and further extended it to include flow-based cost function in the following experiments. We also needed to use the perceived utility, which is not in the dataset. To generate the utility values, we implemented the following rules by calculating the attractiveness of a set of O-D:  
If the distance between the starting point (m) and the origin facility (i) is more than the sphere of influence of (i) [which we arbitrarily consider to be 500], or the distance between the final destination (n) and the destination facility (j) is more than the sphere of influence of (j), the perceived utility of \(U_{mnij}\) is zero (0). Otherwise, the perceived utility is calculated as  
\[U_{mnij}=\begin{cases}10,000,\ if\ m=i\ and\ n=j\\  
lost demand, the solution time is relatively shorter than the mid-values of lost demand: for high lost demand costs, the model tends to locate more facilities to collect more demand, while for the lowest lost demand costs, the model is generous in losing demand (due to the fairly low penalty); for the mid-values, there is a trade-off on whether to satisfy or to lose the demand, which impacts the number of spokes and the structure of the network and consequently requires extra computational effort to make such decision.  
In general, as the hub utilization preference increases (lower values of \(\alpha\)), the total cost decreases (improves). In the case of 6, 7, and 8 nodes, since there is only one hub facility and no hub links (with lost demand cost = 0.8), the objective functions remain the same. For larger networks that have hub links, an intuitive outcome is usually observed.  
The run times reported in Table 2 also indicate the extensive computational effort required to solve this model for larger instances. Even though we set our maximum run time to 86,400 s, we were not able to solve larger instances.
## 4 Experiments
### Comparison with State-of-Art Models  
To more extensively evaluate the superiority of our lightweight LC-YOLO model for small target detection tasks, we compared it with current state-of-the-art target detection network models; the results are shown in Table 5. It can be seen that our LC-YOLO model was the best among all the compared networks, surpassing the classical algorithms [4,21,22] that are widely used in current practical projects and even exceeding advanced rotating target detection algorithms [25,26,30]. It is undeniable that the network structure of YOLOv5 has tremendous advantages. The mAP of YOLOv5n with parameters of only 1.77M reaches 91.36%, and the mAP of YOLOv5m with parameters of 21.04M under the same frame reaches 94.24%. Tiny LC-YOLO can achieve detection performance of 94.17% mAP with only 1.83M parameters, and its detection performance for small targets is very close to that of YOLOv5m and YOLOv5l with more extensive parameters. The detection performance of LC-YOLO with 7.30M parameters exceeds YOLOv5m and YOLOv5l under the same framework, and the mAP reaches 94.96%.  
Furthermore, among the models listed in the table our LC-YOLO model exhibits the highest recall value in small target detection tasks, demonstrating the effectiveness of the proposed optimization scheme in significantly enhancing the model's target retrieval ability. In practical engineering applications, LC-YOLO can complete high-precision small target detection tasks that only the heavyweight networks have previously been able to complete while requiring extremely low computing and storage costs.
# Nanotechnology Solutions for Controlled Cytokine Delivery: An Applied Perspective
## 3 Nanoparticles as Cytokine Delivery Systems
### Types of Nanoparticles for Cytokine Delivery
#### 3.1.4 Silica Nanoparticles  
Silica nanoparticles have also been used as a cytokine delivery system, showing high colloidal stability, extensive surface functionalization and possibility to control both structure and pore size [61]. Silica nanoparticles present limitations for the delivery of cytokines, due to the low internalization efficiency for larger biomolecules. Nonetheless, [PERSON] and collaborators solved this limitation by developing mesoporous silica nanoparticles with extra-large pores that can be applied to cytokine delivery in vivo [35]. These nanoparticles were tested for the delivery of IL-4 to modulate immune responses in mice by inducing M2 macrophage polarization, with clinical applications in anti-inflammatory or tissue homeostasis therapies. The cytokine half-life was improved, while showing minimal toxicity, which was evaluated through ROS and pro-inflammatory cytokine production [35]. Moreover, in addition to the efficient loading and delivery of IL-4, a cytokine with proven ability to stimulate M2 macrophage polarization [62], the nanoparticles were taken up by phagocytic cells, such as macrophages, demonstrating the potential application for in vivo anti-inflammatory M2 macrophage polarization.
## 3 Results
### Identification of Volatile Compounds during Yeast-to-Mycelial Phases in B. bassiana
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Duration of Fungal Growth (h)**} \\ \hline
**Compounds** & **Ix** & **24** & **48** & **72** & **96** & **120** & **144** \\ \hline
3-Methylbutanol & 1186 & 70.41 \(\pm\) 8.06 ab & 59.86 \(\pm\) 5.15 b & 16.63 \(\pm\) 0.22 c & 28.86 \(\pm\) 3.22 c & 64.96 \(\pm\) 5.26 b & 79.07 \(\pm\) 1.75 ab \\ Total alcohols & 70.41 (8.06) ab & 59.86 (5.15 b) & 16.63 (0.22) c & 28.86 \(\pm\) 3.22 c & 64.96 \(\pm\) 5.26 b & 79.07 \(\pm\) 1.75 ab \\ \hline Aerotian & 1285 & 4.26 \(\pm\) 1.64 & 2.46 \(\pm\) 0.88 ab & 0.45 \(\pm\) 0.18 b & 0.61 \(\pm\) 0.22 b & 2.64 \(\pm\) 0.04 ab & 2.50 \(\pm\) 1.15 ab \\
2-NonGenome & 1389 & 0.47 \(\pm\) 0.08 c & 2.64 \(\pm\) 0.51 bc & 0.41 \(\pm\) 0.05 c & 20.81 \(\pm\) 4.44 a & 12.34 \(\pm\) 0.06 ab & \\
2-Dodecanone & 1599 & nd & nd & nd & 18.54 \(\pm\) 2.75 a & 9.93 \(\pm\) 2.70 ab & nd \\
6-I0-Dimethyl-5,9-undcacedian-2-one & 1850 & nd & nd & 0.51 \(\pm\) 0.05 ns & 1.76 \(\pm\) 1.04 ns & nd \\
6-I0-Dimethyl-5,9-undcacedian-2-one & 1857 & nd & 1.50 \(\pm\) 0.05 ns & 0.51 \(\pm\) 0.05 ns & nd \\ Total ketones & 1726 & nd & nd & 0.47 \(\pm\) 0.08 ns & 1.14 \(\pm\) 0.62 ns & nd \\
2-Dodecanone & 1752 & nd & nd & 0.69 \(\pm\) 0.12 b & 3.83 \(\pm\) 0.47 a & nd \\ vinylcyclohexane & 1757 & nd & 5.19 \(\pm\) 0.86 bc & 18.13 \(\pm\) 0.26 a & & 1.20 \(\pm\) 0.30 d & 1.09 \(\pm\) 0.40 d \\
3-Terpinolactone & 1923 & nd & nd & nd & 2.82 \(\pm\) 1.72 s & nd \\ Total terpen & 1083 (5.52) & 29.99 (3.07) bc & 81.83 \(\pm\) 0.57 a & 29.96 \(\pm\) (6.17) bc & 7.95 \(\pm\) 1.92 s & 18.42 \(\pm\) 2.62 \\ \hline \hline \end{tabular} Compounds were analyzed using SPME-GC-MS and tentatively identified based on NIST library searches and Kovats retention indices (Ix). The presence of 3-methylbutanol was confirmed by comparison with the standard. The mean values correspond to the sum of three independent determinations. Statistical analyses were performed for the individual compounds. Different letters indicate statistically significant differences according to one-way ANOVA and ANOVA and Tukey’s test (\(p\leq 0.05\)). Ix was calculated using a mixture of normal paraffin C\({}_{6}\)–C\({}_{20}\) on an HP-FFAP capillary column and was compared with that available in the Pherobase database [30]. RT: Retention time (min). nd: not detected. ns: not significant.  
\end{table} 
Table 1: Relative abundance (%) of volatiles identified in _B. bassiana_ AI2 inoculated on MacConkey agar medium.
## 5 Simulation and Validation
### Considering Road Excitation  
**Case1:** Level ISO-_A_ road excitation  
Based on the vehicle dynamics model presented in Section 4, performance of vehicle roll behavior was also computed considering level ISO-_A_ road excitation. Roll angle, roll rate, and yaw rate for roll behavior are shown in Figure 10. From the results, it can be seen that with road excitation, vehicle roll responses are different under level ISO-_A_ road excitation. With _E_-Class (SUV) level car model in CARSIM(r) as a benchmark, simulation data of 9-DOF full-car model was found to be consistent with CARSIM data, i.e., half-car roll model and full-car roll model are no more equivalent.  
**Case 2:** Level ISO-C road excitation  
For level ISO-C road excitation case, roll angle, roll rate, and yaw rate for roll behavior are shown in Figure 11. It can be seen from the results that vehicle response for different dynamics models have significant impact on roll behavior under level ISO-C road excitation, and influence of road excitation cannot be ignored in this situation [21; 22; 23; 24; 25; 26; 27].  
Figure 10: Results with road Level ISO-_A_ excitation (steering wheel angle was 10\({}^{\circ}\) at 80 km/h). (**a**) Roll angle; (**b**) Roll rate; (**c**) Yaw rate.  
To further illustrate difference between 6-DOF and 9-DOF models with road excitation and various steering wheel inputs, vehicle responses were statistically analyzed. Previous research divided statistical parameters into dimensional parameters and non-dimensional parameters [21]. Dimensional parameters such as mean value, variance, maximum, etc., are used to depict steady stochastic processes especially suitable for statistical process description. Non-dimensional parameters, including Kurtosis, Crest factor, Clearance factor, etc., are utilized to describe transient processes, especially suitable for non-statistical process description. Vehicle steering wheel input is a transient response; due to this non-dimensional parameters should be adopted to study vehicle responses. Five widely applied features that suitable for depicting these temporary characteristics as listed in Table 4[22; 23]. Parameter \(F_{1}\) and \(F_{4}\) may indicate distribution characteristics. Parameters \(F_{2}\)\(\sim\)\(F_{3}\), \(F_{5}\) represent influence of impact or impulse input.  
Figure 11: Results with road Level ISO-C excitation (steering wheel angle was 10\({}^{\circ}\) at 80 km/h). (**a**) Roll angle, (**b**) Roll rate, (**c**) Yaw rate.  
To illustrate error in different dynamics models, with E-Class (SUV) level car model in CARSIM(r) as a benchmark, accuracy of proposed models and comparison results are summarized in Tables 5 and 6.  
Tables 5 and 6 show that different dynamics models have significant impact on the calculation accuracy, and higher accuracy using 9-DOF model can be obtained when coupling relationships exists between road excitation and vehicle dynamics.
# Enhancement of Underwater Images with Retinex Transmission Map and Adaptive Color Correction
## 3 Proposed Underwater Image Enhancement Method
### Local Backscattered Light Estimation  
The underwater medium is rich in suspended particles. When light is incident on the particles, the light will be scattered. Therefore, these particles can be regarded as a weak light source. The scattered light of these particles reaches the observer and constitutes a backscattered component. Most underwater restoration methods assume that the medium and illumination are uniform to estimate the global backscattered light. However, common underwater scenes often have non-uniform illumination and medium. To estimate a global backscattered light is not suitable because the backscattered light is non-uniform [14]. Moreover, when the scene's depth is so deep that artificial lighting is needed, the estimation error of global backscattered light will become more prominent. Therefore, estimating a local backscattered light can better adapt to the underwater environment.  
We utilize the method of [25] to estimate the local backscattered light. Given an underwater image \(I^{c}(x)\), it is assumed that the distance between the local block around the image pixel \(x\) and the camera is uniform. The backscattered light is obtained by Gaussian low-pass filtering of the underwater observed image:  
\[B^{c}(x)=G_{\Omega}(x)\otimes I^{c}(x), \tag{10}\]  
Figure 1: (**a**) An underwater image; (**b**) transmission map estimated by UDCP; (**c**) transmission map estimated by LDCP; (**d**) transmission map estimated by the proposed algorithm.  
where \(G_{\Omega}(x)\) is the Gaussian function, \(\otimes\) is the convolution operation, and \(\Omega(x)\) is a partial window around the pixel \(x\). Utilizing local backscattered light has better detail recovery finally.
## 3 Results
### Correlation between Phenolic Compounds and Minerals  
There are several elements known for their free radical scavenging properties. In this study, a moderate and strong correlation was observed between mineral content and phenolic compounds (Table 2).  
A positive and high correlation was observed between Mg and total phenolic content (TPC) (r = 0.802, \(p\) = 0.05) and total flavonoid content (TFC) (r = 0.774, \(p\) = 0.05). Also, a strong and positive correlation was recorded between Na and Mg (r = 0.737, \(p\) = 0.05). A  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Cultivation** & **Initially** & **Na g/kg** & **Mg g/kg** & **K g/kg** & **Ca g/kg** \\
**Substrate** & **pH Values** & & & & & \\ \hline S1 & 7.8 \(\pm\) 0.017 & 72.166 \(\pm\) 0.405 \({}^{\mathrm{c}}\) & 6.874 \(\pm\) 0.119 \({}^{\mathrm{c}}\) & 19.038 \(\pm\) 0.133 \({}^{\mathrm{c}}\) & 36.359 \(\pm\) 0.526 \({}^{\mathrm{b}}\) \\ S2 & 8.81 \(\pm\) 0.03 & 156 \(\pm\) 0.479 \({}^{\mathrm{b}}\) & 11.734 \(\pm\) 0.016 \({}^{\mathrm{b}}\) & 15.400 \(\pm\) 0.061 \({}^{\mathrm{d}}\) & 8.057 \(\pm\) 0.307 \({}^{\mathrm{d}}\) \\ S3 & 7.45 \(\pm\) 0.03 & 54.487 \(\pm\) 0.302 \({}^{\mathrm{d}}\) & 7.388 \(\pm\) 0.052 \({}^{\mathrm{c}}\) & 23.389 \(\pm\) 0.089 \({}^{\mathrm{b}}\) & 46.207 \(\pm\) 0.461 \({}^{\mathrm{a}}\) \\ I & 8.81 \(\pm\) 0.06 & 170.336 \(\pm\) 1.566 \({}^{\mathrm{a}}\) & 15.039 \(\pm\) 0.133 \({}^{\mathrm{a}}\) & 7.685 \(\pm\) 0.022 \({}^{\mathrm{e}}\) & 3.943 \(\pm\) 0.438 \({}^{\mathrm{e}}\) \\ Peat and & 5.18 \(\pm\) 0.03 & 45.962 \(\pm\) 0.292 \({}^{\mathrm{e}}\) & 11.329 \(\pm\) 0.124 \({}^{\mathrm{b}}\) & 40.867 \(\pm\) 0.229 \({}^{\mathrm{a}}\) & 25.520 \(\pm\) 0.346 \({}^{\mathrm{c}}\) \\ perlite (R) & & & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Elemental analysis results for _Salicornia europaea_ L. grown in different conditions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **TPC** & **TFC** & **Na** & **Mg** & **K** & **Ca** \\ \hline TPC & 1 & & & & & \\ TFC & 0.969 & 1 & & & & \\ Na & 0.298 & 0.194 & 1 & & & \\ Mg & 0.802 & 0.774 & 0.737 & 1 & & \\ K & \(-\)0.019 & 0.162 & \(-\)0.816 & \(-\)0.306 & 1 & \\ Ca & \(-\)0.523 & \(-\)0.516 & \(-\)0.880 & \(-\)0.915 & 0.465 & 1 \\ \hline \end{tabular} \(p\) < 0.05.  
\end{table}
Table 2: [PERSON]’s correlation coefficients of the phenolic compounds and minerals.  
moderate correlation was found between Ca, TPC, and TFC, indicating that these minerals might be a part of the phenolic compounds and play important roles in the polyphenol accumulation in _Salicornia_ plants. This study also corresponds with the research conducted by [PERSON] et al. [56], as we found a positive correlation between metal ions.  
In the present study, a low and negative correlation was also observed between minerals such as K, TPC, and TFC. Low and negative correlation coefficients among mineral and polyphenol contents may be due to the influence of cultivation substrates.