esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Effect of Solvent Ratio in Budesonide Cyclodextrin Inclusion Complexes
[27] concerning CD inclusion complexes containing a flavonoid, crsiliol, confirming the successful formation of the complexes. Figure 4: \({}^{1}\)H-NMR spectra of CD and CD–BUD complex containing 10 wt% BUD. The peak at 4.79 ppm is attributed to D2O. Figure 5: SEM images of CD–BUD inclusion complexes in H\({}_{2}\)O/EtOH ratios (**a**) 50/50, (**b**) 60/40, (**c**) 70/30, (**d**) 80/20, (**e**) 90/10. In a subsequent stage, through the X-ray diffraction analysis, the physical state of the inclusion complexes was assessed (Figure 6) and the degree of crystallinity was calculated according to Equation (1). CD is a crystalline oligosaccharide with many characteristic peaks in its diffractogram in 12.66\({}^{\circ}\), 15.96\({}^{\circ}\), 16.12\({}^{\circ}\), 18.24\({}^{\circ}\), 19.00\({}^{\circ}\), 21.14\({}^{\circ}\), 23.00\({}^{\circ}\), 24.22\({}^{\circ}\), 25.7\({}^{\circ}\) and 27.10\({}^{\circ}\)[28]. BUD is a crystalline pharmaceutical compound with a main sharp peak at 15.8\({}^{\circ}\), secondary peaks at 12.2\({}^{\circ}\), 23.2\({}^{\circ}\), and many smaller peaks as well [29]. Concerning the inclusion complexes, the assessment of their crystalline structure is hard to be observed in the diffractograms in Figure 4. The degree of crystallinity is calculated through Equation (1) (Table 1). The degree of crystallinity of neat BUD is equal to 70.4%, while for neat CD, it is equal to 72.2%. All the CD-BUD samples reveal lower crystallinity values owed to the complexation between the oligosaccharide and the drug molecules. Maximum complexation is conducted in sample CD-BUD-10 with the H\({}_{2}\)O/EtOH 80-20 ratio, since the lower degree of crystallinity is calculated, i.e., at 45.5%. The ratio of the organic solvent has an impact on the complexation of BUD and CD. The increasing ratio of EtOH proportionally affects the crystallinity of the CD-BUD samples while the ability for the successful formation of inclusion complexes decreases. This is according to the literature since the molecules of the organic solvent compete with the active compound's molecules for inclusion in the interior cavity of CD [15]. However, concerning the H\({}_{2}\)O/EtOH 90/10 ratio, the crystallinity of the sample is increasing again, attributed to the highly hydrophobic nature of BUD. In the aforementioned ratio, the amount of organic solvent is insufficient for solubilizing the drug, which remains insoluble. \begin{table}
\begin{tabular}{c c} \hline \hline
**Sample** & **\% of Crystallinity** \\ \hline Budesonide & 70.4 \\ Cyclodextrin & 72.2 \\ CD–BUD-10\_50-50 & 68.6 \\ CD–BUD-10\_60-40 & 64.8 \\ CD–BUD-10\_70-30 & 59.1 \\ CD–BUD-10\_80-20 & 45.4 \\ CD–BUD-10-90-10 & 68.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Degree of crystallinity of BUD, CD and CD–BUD inclusion complexes in various H\({}_{2}\)O/EtOH ratios. Figure 6: XRD spectra of BUD, CD and CD–BUD inclusion complexes at different H\({}_{2}\)O/EtOH ratios (50/50, 60/40, 70/30, 80/20 and 90/10 \(\
icefrac{{v}}{{v}}\)).
## 4 Experimentation and Results
### Experimental Setup
#### 4.1.4 Experimental Details  
The codes were implemented on Python 3.10 with PyTorch 1.13.1 deep learning library, and all experiments were conducted on a Lenovo graphics workstation with the following specifications:  
* CPU: 13 th Gen Intel(R) Core(TM) i7-13700F 2.10 GHz;
* GPU: NVIDIA GeForce RTX 4090;
* RAM: 64 GB;
* OS: Windows11.  
Since the original dataset is NetCDF files, the netCDF4 library was utilized to read these data files, while Pandas, Numpy, and Matplotlib python packages were utilized for data processing and analysis.
# Pavement Defect Segmentation in Orthoframes with a Pipeline of Three Convolutional Neural Networks
## 3 System Configuration  
The aim of this research is to develop a ConvNet based pavement distress detection system (PDDS) that is able to automatically generate a mask of defective road areas for a given orthoframe. However, we must first note that the orthoframe is an RGB image of resolution 4096 \(\times\) 4096 pixels, which due to GPU memory limitations is too large to feed to a single ConvNet as an input, especially considering that better results are achieved by training the ConvNet with a batch size of 8 to 64 [24]. To tackle this issue, the proposed system operates in three stages, using three separate ConvNets:  
1. Road Segmentation Network (RSN) is a U-Net inspired ConvNet that generates a road area mask for the orthoframe so that the parts of the orthoframe where pavement defects cannot exist are explicitly eliminated. The network operates on orthoframes cut and resized to 512 \(\times\) 512 pixels. The masks are restored to the original size, after the prediction is made by the network.
2. Defect detection network (DDN), a classification ConvNet that assigns a binary classification (defective/non-defective) to each of 224 \(\times\) 224-pixel patches sampled from the area of the orthoframe belonging to the predicted road area mask.
3. Defect Segmentation Network (DSN), another U-Net based ConvNet, is applied to generate pixel level segmentation for all the patches belonging to the defective category. The 4096 \(\times\) 4096-pixel defect mask is constructed by arranging the patches back together.  
The workflow is illustrated in Figure 3. In addition to the defect segmentation mask, the system computes the real-world coordinates of the patches categorized as defective using the corresponding VRT file accompanying each orthoframe in the dataset.
# Laser Transmission Characteristics of Seawater for Underwater Wireless Optical Communication
## 3 Results and Discussion
### Average AOA
#### 3.1.1 AOA Distribution on the Whole Receiving Plane  
Figure 3 shows the variations in the average AOA on the whole receiving plane for the different waters with the attenuation length. Generally, the average AOA of the various water types increases with increasing attenuation length. For example, when the attenuation length is 15, the average AOAs are 9.6\({}^{\circ}\), 15.3\({}^{\circ}\), and 26\({}^{\circ}\) in clear water, coastal water, and turbid harbor water, respectively. Compared to clear water, the variations in the average AOA are much higher in coastal and turbid harbor waters. For instance, when the attenuation length ranges from 1 to 25, the average AOA of clear water increases from 6.7\({}^{\circ}\) to 10.9\({}^{\circ}\) while it increases from 5.5\({}^{\circ}\) to 18\({}^{\circ}\) and from 7.7\({}^{\circ}\) to 28.1\({}^{\circ}\) in coastal and turbid harbor waters, respectively. As shown in Table 2, the single scattering albedos \(\omega_{0}\) of the various waters significantly differ, with the minimum value obtained in clear water (\(\omega_{0}=\)0.25) and the highest value obtained in turbid harbor water (\(\omega_{0}=\)0.83), indicating that photons in  
Figure 2: Receiving and transmitting schematic diagram.  
turbid harbor water are scattered more than those in clear water at the same attenuation length, leading to the higher average AOA in turbid harbor water.  
Notably, the average AOA in coastal water is lower than that in clear water at an attenuation length smaller than 3. According to the radiative transfer equation, the scattering angle is influenced by the \(\omega_{0}\) and SPF. Specifically, a higher \(\omega_{0}\) corresponds to a higher probability of scattering events, and a higher SPF suggests a greater possibility of specific scattering angles. To gain deeper insights into the effects of \(\omega_{0}\) and SPF on the average AOA, we undertook a thorough quantitative analysis using parameter values from these three water types (detailed results are omitted). The findings show that while the impact of SPF diminishes as the attenuation length increases, the influence of \(\omega_{0}\) remains robust. Therefore, the effect of scattering at smaller angles (less than 1\({}^{\circ}\)) may reach equilibrium with the \(\omega_{0}\) effect at a certain attenuation length, as evidenced by the intersection of the black and red lines in Figure 3. Furthermore, turbid harbor water consistently demonstrates the highest average AOA among the three water types, primarily due to its significantly elevated \(\omega_{0}\) value.
## 4 Experimental Setup
### Experiments Results  
In this section, our algorithm SA-MPCMOEA is compared with the following state-of-the-art algorithms: The proposed SA-MPCMOEA was empirically compared with six state-of-the-art CMOEAs: CTAEA [29], RVEAa [30], ABSAEA, CCMO, and NSGA-II. to examine its performance.  
All compared algorithms were implemented in MATLAB (R2023a) within the PlatEMO platform [31] and executed on a computer equipped with a 13 th Gen Intel Core i5-13400 2.50 GHz CPU and the Microsoft Windows 10 operating system.  
1. CTAEA balances convergence, diversity, and feasibility by maintaining two collaborative archives: the convergence-oriented archive (CA) and the diversity-oriented archive (DA). By adaptively selecting individuals from these archives based on their distinct evolutionary states to generate offspring solutions, the algorithm achieves superior performance on constrained multi-objective optimization problems.
2. RVEAa employs a reference vector regeneration strategy to address constrained many-objective optimization problems with irregular PFs (Pareto Fronts) and utilizes the angle-penalized distance to maintain a balance between convergence and diversity.
3. ABSAEA employs an adaptive Bayesian surrogate-assisted evolutionary algorithm to address expensive multi-objective optimization problems, where the hyperparameters of the acquisition function are dynamically tuned to select candidate solutions for real function evaluations, thereby reducing the computational cost of evaluations.
4. CMOEAD ensures the diversity of solutions by performing niching operations on individuals within the population. Through the adaptive updating of reference points, the CMOEAD algorithm can simultaneously search for multiple Pareto-optimal points in a single run to achieve efficient exploration.
5. NSGA-II improves computational efficiency by utilizing fast non-dominated sorting and an enhanced dominance mechanism. Owing to its selection operator, which constructs the mating pool based on fitness and distribution characteristics, the algorithm achieves well-distributed solution sets across most optimization problems while demonstrating superior convergence performance near the true Pareto-optimal front in practical scenarios.
## 2 Materials and Methods  
### Model Geometry and Mesh  
3D model geometry is presented in Figure 1. Cross section in \(x\)-\(z\) plane is presented in Figure 2 and gives a detailed representation of antenna geometry. Position numbers (1-6) in both figures correspond to the same elements. Antenna had a square \(x\)-\(y\) cross-section 1 \(\times\) 1 mm\({}^{2}\). We used free tetrahedral user-controlled mesh, as the correlation between maximum mesh element size and wavelength must be controlled. Maximum element size was set equal to 40 \(\upmu\)m.
## 2 Background and Related Work
### Write Amplification Factor (WAF)  
Another reason to use a data protection policy that is differentiated according to the characteristics of each cluster is related to the performance of the SSD. If our goal is simply to lower the error rate, we can apply the maximum data protection strength to all clusters. However, doing so will increase WAF because of indiscriminate parity data writing and adversely affect performance.  
WAF stands for write amplification factor. WAF is the ratio of writes generated by an actual device (i.e., flash memories) to user write. In addition to simply writing data, the operation of finding and writing a new location in the flash memory for reading, updating, and rewriting data takes place. Consequently, even if the user attempts to write \(n\) bytes, \(n+a\) bytes are written inside the flash memory. WAF has a significant impact on flash memory storage I/O (Input and Output) performance; that is, as WAF increases, I/O performance decreases [12]. Parity data writes are another factor that increases WAF, which increases the number of P/E cycles, resulting in faster wear [12]. Rather than adding parity data to all clusters to reduce error rates, performance and reliability should provide differentiated data protection strength by selectively adding parity data based on the number of errors in each cluster.
## 4 Results
### Result of Verification and Validation Phase
#### 4.4.2 Findings from Study 1  
Based on the AHP analysis conducted which was used to determine the ranking of features and ranking of the alternative sizes involved. From the first calculation of AHP, it can be deduced that size feature has the highest priority compared with the other two features, followed by the label feature and colour feature. This could be because the users require a feature to distinguish the signifiers during large displays. From the user's perspective, the ranking obtained shows the priority of the feature to be integrated into the signifiers used. Analysis of the AHP results showed that the size feature affords a huge need to improve the visibility of the signifiers. In addition, the need for text as a label on the icon button is also able to help users in their navigation. This label can provide a cue to comprehend well the signifiers they have seen. This is because there might be a possibility for them to come across an unfamiliar signifier.  
Meanwhile, for the second calculation of AHP analysis, it can be deduced that the medium size (100%) is found to be the best for a signifier, followed by large size (200%) and small size (50%). The medium size obtained is suitable for the screen size used and is seen to distinguish the signifier well and help it appear obvious. Participants involved agreed that the signifier need to be large but become a distraction to the user, thus medium size was chosen. These findings will be applied to the high-fidelity prototype. It will be further tested by the test participants.
# Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization
### Contribution  
The contribution of our work is threefold:  
* Improving the localization accuracy of trilateration technique: We develop high accuracy fingerprint based indoor localization scheme which is based on sparse representation and matrix completion theories. As said before, trilateration technique is based on pairwise distances between the target node and the anchor nodes. Due to the fact that propagation conditions are not optimal in an indoor environment, several pairwise distances cannot be measured. So, our main contribution is to enhance the localization's accuracy of trilateration by estimating all pairwise distances.
* Matrix completion resolution: We formulate the indoor localization scheme as a simple optimization problem which enables efficient and reliable algorithm implementation.
* Solving the proposed optimization problem: We develop closed-form algorithms, which can be reproduced by simple implementation, to solve the indoor localization problem. Specifically, we adopt recent methods like Nesterov accelerated gradient (Nesterov), adaptative Moment Estimation (Adam), Adadelta, Root Mean Square Propagation (RMSProp) and Adaptative gradient (Adagrad).  
The remainder of this paper is organized as follows: In Section 2, we present the system model. In Section 3, details of our contribution and different algorithms are given. Obtained simulation results are presented and discussed in Section 4. The method verification using real measurements is detailed in Section 5. Finally, we conclude the paper in Section 6.  
Notations: The following notations are used throughout our paper. \((.)^{T}\) is the transpose operation. \(||.||_{2}\), \(||.||_{F}\) and \(||.||_{*}\) denote the \(l_{2}\) norm, the Frobenius norm and the nuclear norm, respectively. \(diag(\mathbf{A})\) returns a column vector of the diagonal elements of the matrix \(\mathbf{A}\). \(\
abla(f(.))\) is the gradient of function \(f\). \(\mathbf{A}^{(t)}\) is the matrix obtained at iteration \(t\). \(\mathbf{A}\cdot\mathbf{B}\) is the product of matrices. \(\mathbf{A}\odot\mathbf{B}\) is the Hadamard product of \(\mathbf{A}\) and \(\mathbf{B}\). Matrices are presented by non italic bold capital letters. Furthermore, variables are presented by italic lowercase characters.
## 2 Voltage Differencing Buffered Amplifier (VDBA)  
The VDBA simply is a cascade connection of a voltage buffer and an operational transconductance amplifier (OTA). The voltage-current relationship of the VDBA is presented in Equation (1) and the functional block representation is given in Figure 1a.  
\[\left[\begin{array}{c}I_{\text{P}}\\ I_{\text{N}}\\ I_{\text{Z}}\\ I_{\text{ZC}+}\\ I_{\text{ZC}-}\\ V_{\text{W}}\end{array}\right]=\left[\begin{array}{ccc}0&0&0\\ 0&0&0\\ g_{\text{m}}&-g_{\text{m}}&0\\ g_{\text{m}}&-g_{\text{m}}&0\\ -g_{\text{m}}&g_{\text{m}}&0\\ 0&0&1\end{array}\right]\left[\begin{array}{c}V_{\text{P}}\\ V_{\text{N}}\\ V_{\text{Z}}\end{array}\right]. \tag{1}\]  
The VDBA is a six-terminal device, of which CMOS implementation is shown in Figure 1b. The first transconductance amplifier stage is built by the transistors M1-M14. The output current of the transconductance amplifier depends on the difference in voltage between P and N terminals. If operation in saturation region is assumed and \(W/L\) ratio for transistors M1 and M2 are made identical then the output current \(I_{\text{Z}}\) of the OTA is given by Equation (2): The voltage follower forms the second stage of the VDBA designed using transistors M15-M21.  
\[I_{\text{Z}}=g_{\text{m}}(V_{\text{P}}-V_{\text{N}})=\left(\sqrt{2I_{\text{ Bias}}K_{i}}\right)(V_{\text{P}}-V_{\text{N}}), \tag{2}\]  
where the transconductance parameter \(K_{i}=\mu C_{\text{ox}}W/2L\) (\(i=1,2\)), \(W\) is the effective channel width, \(L\) is the effective length of the channel, \(C_{\text{ox}}\) is the gate oxide capacitance per unit area, and \(\mu\) is the carrier mobility.
## 1 Introduction
### Organization  
The paper is organized as follows: the system model for the IRS-assisted D2D underlay cellular network is described and the optimization problem is constructed in Section 2. The proposed RL-based solution schemes are presented in Section 3. The performance analysis and simulation outcomes of the proposed RL-based solution schemes are provided in Section 4. Finally, Section 5 concludes the paper.
## 4 Experiments
### Reconstruction with Real-World Images  
To evaluate the reconstruction performance of various models in real-world scenes, we curated an LDR dataset. The dataset comprises LDR images [70; 71; 72; 73] from online platforms (Getty Images, Unsplash, iStockPhoto, and [PERSON]) and images taken by the authors. In the realistic scene depicted in Figure 10, the results generated by existing methods are very poor in terms of brightness saturation and color consistency, as perceived by the  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**ROR** & **DQ** & **RLM** & **CAAM** & **TFM** & **PSNR-N \(\uparrow\)** & **PSNR-\(\mu\)\(\uparrow\)** & **HDR-VDP-2 \(\uparrow\)** \\ \hline ✓ & ✓ & ✓ & ✓ & ✗ & 43.84 & 33.78 & 57.48 \\ ✓ & ✓ & ✓ & ✗ & ✓ & 36.65 & 31.92 & 53.46 \\ ✓ & ✓ & ✗ & ✓ & ✓ & 45.24 & 33.85 & 51.47 \\ ✓ & ✗ & ✓ & ✓ & ✓ & 43.61 & 33.67 & 56.92 \\ ✗ & ✓ & ✓ & ✓ & ✓ & 38.78 & 32.99 & 55.92 \\ ✓ & ✓ & ✓ & ✓ & ✓ & **45.80** & **34.16** & **57.57** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation analysis on our modules. Here, \(\uparrow\) indicates that a higher score represents better quality, while \(\downarrow\) indicates that a lower score corresponds to better reconstruction.  
Figure 9: Visual presentation of ablation studies on different modules.  
human eye. HDRUNet and KUNet exhibit excessive brightness saturation in the living room and floor scenes, with colors appearing overly whitened. Moreover, for the detail restoration of overexposed areas, the detail information generated by some methods shows obvious content distortions, such as DeepHDR and CEVR, which have almost no effect on repairing the information of the trees outside the window. SingleHDR can repair the overall contour information of the overexposed area but still suffers from content loss in detail restoration. In contrast, our method performs well in maintaining the consistency of brightness and color (e.g., the brightness of the living room and the color of the objects) and in restoring the details of overexposed areas (e.g., the trees outside the window in the figure). The TFM block, which provides luminance details to the ROR module, enhances the restoration of overexposed areas, resulting in sharper contours and richer details in our results. Additionally, by reconstructing the linearized mapping, the RLUNet model generates results that are closer to the human eye's visual perception of real-scene tones.
## 5 Experimental Evaluation
We also looked at the average number of cars in the platoons, as an indicator of how much the join maneuvers were facilitated. The results are presented in Tables 1-3 for low, medium and high traffic densities, respectively. To facilitate their interpretation, we directly present the average values of travel times and fuel consumption from configurations 1, 2 and 3 normalized with regard to the best average values from configuration 0 (that is, choosing the most favorable case for traditional traffic lights from the results achieved by FT, MP or AMP policies). \begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{3}{c}{**All Cars**} & \multicolumn{3}{c}{**Platoon Members**} \\ \cline{2-6}  & **Travel** & **Fuel** & **Travel** & **Fuel** & **Avg. Platoon** \\  & **Time** & **Consum.** & **Time** & **Consum.** & **Length** \\ \hline Configuration 1 (One-hop VTLs) & 0.961 & 0.914 & N/A & N/A & N/A \\ \hline Configuration 2 (Traditional traffic lights + platoons) & VNIBR-R & 0.996 & 0.993 & 0.931 & 0.947 & 2.18 \\  & VNIBR-P & 0.997 & 0.994 & 0.939 & 0.945 & 2.11 \\  & VNIBR-E & 0.996 & 0.994 & 0.933 & 0.949 & 2.13 \\  & VNAODV & 0.999 & 0.997 & 0.941 & 0.951 & 2.04 \\ \hline Configuration 3 (VTLs + platoons) & VNIBR-R & 0.933 & 0.878 & 0.364 & 0.401 & 3.14 \\  & VNIBR-P & 0.954 & 0.886 & 0.445 & 0.496 & 3.22 \\  & VNIBR-E & 0.941 & 0.882 & 0.397 & 0.427 & 3.11 \\  & VNAODV & 0.968 & 0.913 & 0.686 & 0.740 & 2.64 \\ \hline \end{tabular}
\end{table} 
Table 1: Results for low traffic density (time and fuel normalized regarding best average values from configuration 0). \begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{3}{c}{**All Cars**} & \multicolumn{3}{c}{**Platoon Members**} \\ \cline{2-6}  & **Travel** & **Fuel** & **Travel** & **Fuel** & **Avg. Platoon** \\  & **Time** & **Consum.** & **Time** & **Consum.** & **Length** \\ \hline Configuration 1 (One-hop VTLs) & 0.947 & 0.904 & N/A & N/A & N/A \\ \hline Configuration 2 (Traditional traffic lights + platoons) & VNIBR-R & 0.998 & 0.997 & 0.954 & 0.960 & 2.21 \\  & VNIBR-P & 0.997 & 0.996 & 0.947 & 0.971 & 2.28 \\  & VNIBR-E & 0.998 & 0.997 & 0.956 & 0.962 & 2.18 \\  & VNAODV & 0.999 & 0.998 & 0.976 & 0.981 & 2.09 \\ \hline Configuration 3 (VTLs + platoons) & VNIBR-R & 0.918 & 0.861 & 0.357 & 0.402 & 4.15 \\  & VNIBR-P & 0.909 & 0.870 & 0.257 & 0.286 & 4.88 \\  & VNIBR-E & 0.914 & 0.867 & 0.313 & 0.252 & 4.45 \\  & VNAODV & 0.932 & 0.889 & 0.485 & 0.506 & 3.25 \\ \hline \end{tabular}
\end{table} 
Table 2: Results for medium traffic density (time and fuel normalized regarding best average values from configuration 0).