esa-sceva/satcom-chunk-collection huggingface dataset:
# Polyhydroxyalkanoates (PHAs): Biopolymers for Biofuel and Biorefineies
## 2 Sources of PHAs
### Microbial Production of PHA Block Copolymers
The authors demonstrated the formation of block copolymers when butyrate (C4) and heptanoate (C7) were introduced into the growth media. Keeping this in view, [PERSON] et al., [118] adopted the same strategy and microbially linked scl-P3 HB and ml-HHx to synthesize P3 HB-_b_-PHHx diblock copolymers in a \(\beta\)-oxidation deficient recombinant _P. putida_ KT2442. The \(\beta\)-oxidation cycle was deleted to its maximum and glycerol was used as a carbon source. Recently, [PERSON] et al., [113] were able to synthesize poly(2-hydroxybutyrate) (P2 HB)-_b_-P3 HB polymers in _E. coli_ using glucose as carbon source and hydrophobic amino acids as supplementation. The authors demonstrated that block copolymers successfully accumulated in genetically engineered _E. coli_ by expressing the chimeric PHA synthase (PhaC\({}_{\text{AR}}\)) gene. It was demonstrated that PhaC\({}_{\text{AR}}\) exhibits strict substrate specificity and that it can synthesize P2 HB-_b_-P3 HB in engineered _E. coli_ from exogenous 2 HB and 3 HB. Moreover, 2-hydroxyalkanoate (2 HA) units were incorporated using PhaC\({}_{\text{AR}}\), the lactate dehydrogenase (LdhA), and CoA transferase (HadA). Four hydrophobic amino acids, leucine, Val, Ile, and phenylalanine, were introduced into the culture as supplementation and the required block copolymer was produced. Figure 6 shows the biosynthetic pathway of P2 HB-_b_-P3 HB in _E. coli_ from biomass-derived carbon sources. Figure 6: Poly(2-hydroxybutyrate-b-3-hydroxybutyrate) (P2 HB-b-P3 HB) biosynthesis pathways in engineered E. coli with amino acid supplementation. Amino acids are also supplied through the endogenous pathways. Reproduced with permission from [113]. Copyright 2020 Elsevier Ltd.
## 2 Materials and Methods
### Experimental Design and Progressive Deployment Strategy  
To ensure a comprehensive verification of system stability and performance across diverse aquaculture environments, this research adopted a progressive deployment strategy, transitioning gradually from controlled environments to open waters. This methodological approach enabled the systematic validation of the integrated multiple remote control and mobile communication technologies under varying operational conditions. The experimental design comprised five distinct testing phases, each targeting specific environmental conditions and system capabilities, as detailed in Table 3.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Test Environment** & **Test Location** & **Environmental Conditions** & **Test Parameters/Monitoring Items** & **Communication Conditions** & **Test Items/ Operation Mode** \\ \hline  & & & & Motor speed calibration, basic motion control (forward/backward movement, turning, ascending/descending); & Motor speed calibration, basic motion control (forward/backward movement, turning, ascending/descending); & Indoor & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & & \\ \hline  & & & & Water quality parameters & Control method: Multi-platform remote control system \\  & & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline  & & & & Water quality parameters & & Control method: Multi-platform remote control system \\  & & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline  & & & & Water quality parameters & & Control method: Multi-platform remote control system \\  & & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline  & & & & & \\  & & & & & \\  & & & & & \\ \hline  & & & & & \\  & & & & & \\  & & & & & \\ \hline  & & & & & \\  & & & & & \\  & & & & & \\ \hline  & & & & & \\  & & & & & \\  & & & & \\ \hline  & & & & & \\  & & & & & \\  & & & & & \\ \hline  & & & & & \\  & & & & & \\To ensure consistency and reliability throughout all testing phases, the following standardized conditions were maintained: all tests utilized identical ROV hardware configurations and sensor specifications, and a unified data collection frequency and transmission protocol were employed. This progressive deployment methodology ensured comprehensive validation of the system's capabilities while upholding scientific rigor and reproducibility across diverse aquaculture environments.
## 4 Theoretical Analysis
### Impact of the Stretch Factor  
#### 4.2.1 Routing Path Stretch  
In BSMH, packets are forwarded along the Euclidean routing path which is the image of the base path through the homothetic transformations. As described in Section 3.3.2, the scale factor of the homothetic transformation is defined by:  
\[\alpha_{1}=\left\{\begin{array}{l}\frac{1+\epsilon}{\theta_{1}} \sin\frac{(n-2)\pi}{2n},\,\text{if $s$ is a $t$-aware node}\\ Max\left\{1,\frac{\sin\frac{(n-2)\pi}{2n}\left\{\frac{(1+\epsilon)I_{0}}{\theta_ {2}}-\frac{P_{C(t)}}{2}\right\}}{\theta_{1}\left(L_{0}+\frac{\epsilon_{C(t)}}{ 2}\right)}\right\},\,\text{otherwise}\end{array}\right\},\,\text{otherwise}\]  
Obviously, \(\alpha_{1}\) is proportional with \(\epsilon\). It means that the greater the \(\epsilon\), the longer the Euclidean routing path. Therefore, when \(\epsilon\) increases, the routing path stretch will be increased too.
# Vehicle-in-the-Loop in Global Coordinates for Advanced Driver Assistance System
###### Abstract  
Most vehicle controllers are developed and verified with V-model. There are several traditional methods in the automotive industry called \"X-in-the-Loop (XIL)\". However, the validation of advanced driver assistance system (ADAS) controllers is more complicated and needs more environmental resources because the controller interacts with the external environment of the vehicle. Vehicle-in-the-Loop (VIL) is a recently being developed approach for simulating ADAS vehicles that ensures the safety of critical test scenarios in real-world testing using virtual environments. This new test method needs both properties of traditional computer simulations and real-world vehicle tests. This paper presents a Vehicle-in-the-Loop topology for execution in global Coordinates system. Also, it has a modular structure with four parts: synchronization module, virtual environment, sensor emulator and visualizer, so each part can be developed and modified separately in combination with other parts. This structure of VIL is expected to save maintenance time and cost. This paper shows its acceptability by testing ADAS on both a real and the VIL system.  
advanced driver assistance system; autonomous driving; model-based development; vehicle in the loop; vehicle validation +
Footnote †: journal: Applied
# Effect of Nano Hydrolcite on the Aging Resistance of a High Binder Content Stone Mastic Asphalt
## Materials and Methods  
### Materials  
The asphalt mixture selected for the study was a stone mastic asphalt with maximum size of aggregate 8 mm--SMA 8--designed according to the EN 13108-5 [39]. Figure 1 shows the particle size distribution of the aggregate mixture. Three aggregate fractions, obtained from crushed gabbro rock, were used to achieve the final mixture of aggregates: 0/6, 2/6, and 6/12 (the numbers d/D represent the minimum and maximum nominal sizes of the aggregate fraction). The dosages by mass of aggregates were 7%, 19%, 54%, and 20% for the fractions of filler 0/6, 2/6, and 6/12, respectively.  
The asphalt mixture can be considered binder rich, as the selected binder content was 7.5% (by mass of asphalt mixture). The binder was a polymer-modified bitumen, designated as PMB 45/80-65. In addition, 0.5% of natural cellulose fibers (by mass of asphalt mixture) were added to prevent the occurrence of binder drainage. The main properties of the control asphalt mixture are presented in Table 1.  
Two asphalt mixtures were produced: one designated as control (using PMB 45/80-65 binder as supplied), and other designated as modified, where the PMB 45/80-65 binder was modified with 3% (by mass of final binder) of nano hydrotalcite. The hydrotalcite, also designated as magnesium aluminum hydroxycarbonate, has the following chemical formula: Mg\({}_{6}\)Al\({}_{2}\)CO\({}_{3}\)(OH)\({}_{16}\) 4H\({}_{2}\)O. The modification was done by heating the original binder at 160 \({}^{\circ}\)C, adding the nano hydotalcite powder and applying high-speed stirring at 2000 rpm during 60 min. More details regarding the modification process can be found elsewhere [20]. Table 2 presents the physical properties of the control and modified binders.  
Figure 1: Particle size distribution.
## 4 Experimental Results
### Ablation Experiments  
To validate the effectiveness of each module improvement in enhancing the model performance, this study conducted ablation experiments on VGG16, the DHAM, and the RGSM individually and in combination. The evaluation criteria included the mIoU, mPA, and number of model parameters, as shown in Table 5. Module A represents the VGG16 encoder, Module B represents the DHAM, and Module C represents the RGSM.  
Firstly, Modules A, B, and C each showed improvements in the mIoU and mPA compared to the original UNet model. Module A, by enhancing the extraction of deep features, exhibited significant improvements in both the mIoU and mPA, but also led to an increase in the number of model parameters. Module B combined the different attention perspectives of the channels and spatial domains and adaptively adjusted the weights of the convolutional kernels in the spatial aspect to enhance the feature expression. With only a minor increase in parameters, the mIoU reached 77.54% and the mPA rose to 83.76%. Module C employed effective feature fusion operations to restore the features effectively, improving the model's ability to express feature details and enhancing the segmentation capabilities. The mIoU increased to 79.33%, the mPA rose to 84.24%, and the number of model parameters was reduced. The combination of any two modules outperformed their individual usage, indicating that the advantages of each module were not masked when used together but they rather complemented each other to enhance the overall segmentation capabilities of the model. The same trend was observed for the combination of three modules. Although Module A increased the number of model parameters, the lightweight nature of Module B and the parameter-reducing effect of Module C resulted in a final model with only a 2.54M increase in the number of model parameters compared to the original model. However, this model demonstrated improved overall segmentation performance, with an mIoU of 81.74% and mPA of 87.33%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attention Module** & **mIoU/\%** & **mPA/\%** & **Parameters/M** \\ \hline LSKA & 76.27 & 83.55 & 25.49 \\ CCA & 80.32 & 86.50 & 25.62 \\ ECA & 80.07 & 86.05 & 24.89 \\ BAM & 80.25 & 86.24 & 25.00 \\ CBAM & 80.45 & 86.69 & 24.94 \\ DHAM & 80.50 & 86.67 & 24.96 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different attention modules.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **mIoU/\%** & **mPA/\%** & **Parameters/M** \\ \hline UNet & 77.40 & 83.11 & 19.59 \\ UNet + A & 80.10 & 86.10 & 24.89 \\ UNet + B & 77.54 & 83.76 & 19.73 \\ UNet + C & 79.33 & 84.24 & 16.76 \\ UNet + A + B & 80.50 & 86.67 & 24.96 \\ UNet + A + C & 81.61 & 87.07 & 22.06 \\ UNet + B + C & 79.86 & 85.60 & 16.90 \\ UNet + A + B + C & 81.74 & 87.33 & 22.13 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of ablation experiments.
## 3 Experimental Setup and Output Characterization
### SFG and SHG Characterization
This phenomenon can be understood from the QPM theory that the relative intensities of nonlinear optical processes are dependent on the proximity of each process to perfect quasi-phase matching condition. The generation of blue laser is due to second-order SHG given that QPM even-order processes are possible since the average duty ratio may not be accurately 50:50 due to fabrication errors [27]. When the temperature was increased beyond 110 \({}^{\circ}\)C, the intensity of both SHG and SFG decreased significantly as shown in Figure 3. This means that the device should be designed to operate below 110 \({}^{\circ}\)C in order to generate the blue and orange lasers within the same crystal. The inset in Figure 4d is a photo of the generated SHG and SFG beams. Figure 4: Normalized measured SFG and SHG output spectra at (**a**) 25 \({}^{\circ}\)C; (**b**) 60 \({}^{\circ}\)C; (**c**) 105 \({}^{\circ}\)C; and (**d**) 110 \({}^{\circ}\)C. Inset: photograph of the generated SHG and SFG beams. Figure 3: Intensity of SFG and SHG light as a function of temperature. We also investigated the temperature dependence of the central wavelength of SHG and SFG. Figure 5 shows the SHG and SFG spectra at 40 \({}^{\circ}\)C (black line), 60 \({}^{\circ}\)C (red line) and 110 \({}^{\circ}\)C (blue line) with a spectral resolution of \(\sim\)0.7 nm. Whereas the central wavelength of SHG is not sensitive to the temperature variation (Figure 6), the central wavelength of SFG apparently changes by about 3 nm (597.4-600.3 nm) from 25 \({}^{\circ}\)C to 110 \({}^{\circ}\)C.  
The shift in the central wavelength of SFG is due to the broadband nature of ASE source and the change of SFG quasi-phase-matching condition. The latter comes explicitly from the temperature dependent refractive index of MgO:PPLN according to the Sellmeier equation. In particular, the phase-matching condition shifts to longer wavelengths with an increase in temperature. According to Figure 6, the experimental data for SFG are consistent with the theoretical values calculated by using the Sellmeier equation for the same temperature range [18], demonstrating that the MgO:PPLN crystal phase-matches over the entire 50 mm length. Figure 5: Measured SHG and SFG spectra at 40 \({}^{\circ}\)C (black), 60 \({}^{\circ}\)C (red), and 110 \({}^{\circ}\)C (blue). Figure 6: Dependence of central wavelengths of SHG (blue triangles) and SFG (black squares) on temperature. The red circles are calculated with the Sellmeier equation.
## 3 Optimization Empowerment Combined Model Based on Sliding Window
### Entropy Weighting Method  
The entropy weighting method can determine the weights of models based on the degree of variation of errors from individual prediction models, combined with information entropy. Due to differences in the methods of weighting based on entropy in information theory and statistics, a clear definition is provided when determining the weights for the nonoptimal positive-weighted combination forecasting model: the entropy value of an indicator is inversely related to its variability. If an individual model has a higher degree of variation in prediction errors, its corresponding weight in the combined forecast should be smaller. When a method has significant fluctuations in prediction error, its predictive performance is relatively poor, so it should be assigned a smaller weight.  
When using the entropy method to calculate the weights of various models, it is first necessary to normalize each model:  
\[p_{rt_{i}}=\frac{|e_{rt_{i}}|}{\sum\limits_{i=1}^{N}e_{rt_{i}}} \tag{13}\]  
where \(p_{rt_{i}}\) is the weight of the absolute prediction error of the \(r\)-th prediction method at time \(t_{i}\), \(e_{rt_{i}}=W(t_{i})-\hat{W}_{rt_{i}}\).  
The entropy value \(h_{r}\) and the coefficient of variation \(d_{r}\) corresponding to the sequence of absolute errors in the prediction of the \(r\)-th individual model are:  
\[h_{r}=-\frac{1}{\ln N}{\sum\limits_{i=1}^{N}p_{rt_{i}}\ln p_{rt_{i}}} \tag{14}\]  
\[d_{r}=1-h_{r} \tag{15}\]  
The weights corresponding to the \(r\)-th individual prediction model are:  
\[Q_{r}=\frac{1}{l-1}(1-\frac{d_{r}}{\sum\limits_{r=1}^{l}d_{r}}) \tag{16}\]  
This method takes into account the information provided by each data indicator, allowing for a more accurate reflection of the contribution of each indicator. It also exhibits a certain degree of robustness when dealing with different data distributions and levels of dispersion.
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 2 Fair Editions of Naive Bayes
### Calders and Verver Fair Variances of Naive Bayes  
In [3] three methods were proposed to modify the Naive Bayes classifier to ensure fairness. The first one adjusts the probability of the decision being positive so that it does not favor any sensitive group. More analytically, instead of modeling \(P(S\mid C)\) (where \(S\) is the protected attribute and \(C\) the result class) as in standard Naive Bayes, the classifier is altered to model \(P(C\mid S)\), then iteratively modifies probability distributions (e.g., adding probability mass to disadvantaged groups and subtracting from advantaged groups) until discrimination is minimized while keeping the total number of positive classifications roughly the same to avoid global distribution shifts.  
The second one trains separate Naive Bayes models for each value of the sensitive attribute and balances their predictions. For example, in the case of two groups--one favored and the other unfavored--two Naive Bayes models are trained. To remove discrimination, both models are balanced following the algorithmic logic of the first variant.  
The third approach incorporates a latent variable \(L\) under the assumptions that \(L\) is independent of \(S\) and that \(C\) is determined by applying discrimination to the \(L\) labels based on \(S\). The latent variable is estimated using the Expectation Maximization (EM) algorithm. According to this algorithm, during the Expectation Step, the probability of the latent variable \(L\) is estimated given the current classifier parameters. Subsequently, during the Maximization Step, the model parameters are optimized to maximize the likelihood.  
The application of these Naive Bayes variants to the benchmark Census Income dataset demonstrated that the two-model approach (training separate Naive Bayes models for different sensitive groups) was the most effective in achieving a balance between fairness and accuracy. In contrast, while the third approach was more mathematically rigorous, it exhibited instability, limiting its practical reliability.
## 1 Introduction  
Superhydrophobic coatings are increasingly attractive to the industry and academia due to their unique self-cleaning properties as a result of their water repelling characteristics [1]. Fabrication of superhydrophobic coating is an inspiration from nature as water is seen to repel on many natural surfaces such as those of the lotus leaves, butterfly wings, water striders' legs, and so on [2; 3; 4; 5]. This behavior of water drops rolling off their surfaces is due mainly to the presence of a combination of rough micro-nanostructure and low surface energy waxy materials on their surfaces. This concept has been well elaborated by [PERSON] _et al._[4] on the surface of the lotus leaves which has been the classic example is the field of superhydrophobicity for researchers around the globe emphasizing the importance of the geometry and the chemistry of the surface. Inspired by this phenomenon, we have recently transformed aluminum surfaces, copper and silicon surfaces superhydrophobic by first creating surface roughness using methods such as chemical bath deposition, electrochemical methods and chemical etching methods and then modifying those surfaces by either passivating using low surface energy molecules such as stearic acid or fluoroalkyl-silane (FAS-17) or by coating with rf-sputtered Teflon thin films [6; 7; 8; 9; 10; 11]. The multilayer deposition process of organic and inorganic materials [12; 13] such as nanoparticles as rough hydrophobic material [14; 15] are also techniques to obtain superhydrophobic surfaces.  
In all these cases, the surfaces exhibited water roll-off properties providing water contact angle values greater than 150\({}^{\circ}\), which is due to the air entrapment in the gaps of the rough structure produced in the first step, resulting in a composite structure of air and solid in combination with the presence of low surface energy components on the roughened surface which reduces the affinity of water to the surface. The effect of surface roughness on superhydrophobic properties has been elucidated by [PERSON] and [PERSON] in their mathematical models formulated in 1940s [16; 17].  
Superhydrophobic surfaces find tremendous importance in the technological world where they can be applied in domains such as anti-corrosion, anti-bifouling, electrowetting and even in drag reductions [7; 18; 19; 20; 21; 22; 23]. However, the most important challenge faced by industries is the feasibility of large-scaling in an effective and economical manner. Therefore, in this study, we present a simple sol-gel technique where silica nanoparticles are synthesized via a Stober process. In order to eliminate a second step of passivation or coating to obtain low surface energy, these silica nanoparticles are functionalized in the suspension using FAS-17 molecules before coating on aluminum substrates. The functionalized fluorinated silica nanoparticles suspended in a solution are then deposited via a spin-coating technique on to the aluminum substrates to obtain superhydrophobic coatings demonstrating large-scale feasibility.
## 3 Results and Discussion
### Comparison of Results  
Validation experiments were carried out to verify the predicted output parameters as FDM-processed parts are again fabricated using these parameter settings, and all tensile, impact, and flexural test were carried out to validate the obtained results, as depicted in Table 3.  
Figure 7: (**a**) Tensile strength between nonlinear regression and experimental, (**b**) flexural strength between nonlinear regression and experimental, (**c**) impact strength between nonlinear regression and experimental.  
The desirability approach, nonlinear regression, as well as experimental values are much closer to each other, which validates the desirability approach, and the process parameter settings obtained with nonlinear regression will achieve the optimum values of flexural strength, tensile strength, and impact strength.
## 3 Results and Discussion
### Effect of Aw- AgNPs against Breast Cancer Cell Line HCC712  
The cytotoxicity of Aw- AgNPs was evaluated against breast cancer cell line HCC712 in the range of 25 to 500 \(\mathrm{\SIUnitSymbolMicro g}\)/mL. Aw- AgNPs exhibited a dose-dependent response against HCC712 cells. Our findings showed that the percentage of cell viability decreased with increasing Aw- AgNPs concentration and significant cytotoxicity was observed at 100 and 250 \(\mathrm{\SIUnitSymbolMicro g}\)/mL (Figures 8 and 9). [PERSON] et al. [27] reported the cytotoxic effect of AgNPs prepared from _Buchanania axillaris_ extracts against human breast cancer cells (MCF-7). They found that, as the concentration of AgNPs increased, cytotoxicity increased, which is similar to our results. We earlier demonstrated the cytotoxic effect of green synthesized AgNPs from _Terfezia claveryi_ against MCF-7, and this is also supportive of the cytotoxicity results against the HCC712 cell line observed in this study [28]. Significant cytotoxicity against human breast cancer cell line HCC712 by using _Nigella sativa_ AgNPs was reported by [PERSON] et al. [5]. A recent study has also reported the cytotoxic activity on a human squamous cell carcinoma cell line (HSC-2) by using Ajwa date pit extract [34], in which they reported that the 0.63 mg/mL concentration significantly decreased the percentage of cell viability, which correlates with the findings of our present study.