esa-sceva/satcom-chunk-collection huggingface dataset:
### Material Generation Algorithm (MGA) and Major Contributions  
Material Generation Algorithm (MGA) is a novel comprehensive optimization framework inspired by the principles of material chemistry [40]. The purpose of the MGA is to increase the effectiveness of exploration and exploitation in the search space by simulating the creation and stabilization of chemical compounds to explore and optimize the parameter space. The algorithm mimics the formation of ionic and covalent bonds to generate new candidate solutions and assesses their stability to ensure convergence to optimal parameters. MGA is applied to estimate parameters for RTC France and Kyocera KC200 GT PV modules, considering their manufacturing technologies and solar cell models. The root-mean-square error between the experimental values and the partially estimated model current is considered as the fitness function.  
The key findings of this study are as follows:  
* A proposed MGA is introduced for the first time to solve the PV parameter estimation issue.
* The efficacy of the algorithm is evaluated by the SD and DD models, demonstrating an outstanding consistency between the simulated and actual data.
* The suggested MGA exhibits noteworthy benefits and resilience in comparison to previous findings documented for both PV modules.
* MGA improves the electrical characteristics of PV systems by precisely determining the PV parameters under various temperature and irradiance operating circumstances.
## 2 System Model and Problem Formulation
### Aerial Channel Model
In this regard, \(\mathcal{K}_{u}\) can be modeled in dB units using an exponential function of \(\theta_{u}\)[11; 13; 32], as follows:  
\[\mathcal{K}_{u}^{\text{dB}}=a_{3}\exp\big{(}b_{3}\theta_{u}\big{)} \tag{11}\]  
where \(a_{3}\) and \(b_{3}\) are environment- and frequency-dependent constant parameters that are given by [11; 13; 32]  
\[\mathbf{a}_{u}=\big{[}1,e^{-2\pi\frac{d_{u}}{\mathcal{K}_{c}}\sin(\phi_{u})}, \ldots,e^{-2\pi(N-1)\frac{d_{u}}{\mathcal{K}_{c}}\sin(\phi_{u})}\big{]}^{T} \tag{4}\]0  
In (12), \(\mathcal{K}_{0}^{\text{dB}}\) and \(\mathcal{K}_{\frac{\pi}{2}}^{\text{dB}}\) are the Rician factors in dB units when the elevation angles are \(0\) rad and \(\frac{\pi}{2}\) rad, respectively, which are obtained from measurements [32]. In addition, with respect to \(\theta_{u}\), \(\mathcal{K}_{u}^{\text{dB}}\) is monotonic function increasing from \(\mathcal{K}_{0}^{\text{dB}}\) to \(\mathcal{K}_{\frac{\pi}{2}}^{\text{dB}}\).
## 3 Meshless Approach Overview
### Methodology
#### 3.1.2 Shape Parameter  
For the precise implementation of the framework, the shape parameter evaluation is important [34]. Therefore, careful attention should be given for determining shape parameter value. Detailed discussions regarding values for the shape parameter can be found in [PERSON] [35], [PERSON] [36], and [PERSON] [37]. Most formulations for shape parameters depend on the distance, d, and node numbers, N. Other formulations display a changing shape parameter depending on the location of the node. In general, the shape parameter depends upon several variables such as the data point distribution, the basis function, and a computer precision (see [38]). The effect of the rbf Gaussian shaping parameter \(\alpha\) mentioned above is shown in Figure 3.
# Detectron2 for Lesion Detection in Diabetic Retinopathy
## 4 Results
### Analysis of Baseline Models in Object Detection and Instance Segmentation
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multicolumn{6}{l}{**Backbone: Mask R-CNN(R50-FPN)**} \\ \hline \multicolumn{6}{l}{**Object Detection**} \\ \hline
**IoU** & **Area** & **Maxdets** & **AP** & **IoU** & **Area** & **Maxdets** & **AR** \\ \hline
0.50:0.95 & all & 100 & 0.475 & 0.50:0.95 & all & 1 & 0.031 \\
0.50 & all & 100 & 0.584 & 0.50:0.95 & all & 10 & 0.303 \\
0.75 & all & 100 & 0.470 & 0.50:0.95 & all & 100 & 0.514 \\ \hline
0.50:0.95 & small & 100 & 0.205 & 0.50:0.95 & small & 100 & 0.204 \\
0.50:0.95 & medium & 100 & 0.612 & 0.50:0.95 & medium & 100 & 0.677 \\
0.50:0.95 & large & 100 & 0.734 & 0.50:0.95 & large & 100 & 0.747 \\ \hline \multicolumn{6}{l}{Instance Segmentation} \\ \hline
0.50:0.95 & all & 100 & 0.424 & 0.50:0.95 & all & 1 & 0.030 \\
0.50 & all & 100 & 0.545 & 0.50:0.95 & all & 10 & 0.274 \\
0.75 & all & 100 & 0.441 & 0.50:0.95 & all & 100 & 0.459 \\ \hline
0.50:0.95 & small & 100 & 0.160 & 0.50:0.95 & small & 100 & 0.171 \\
0.50:0.95 & medium & 100 & 0.543 & 0.50:0.95 & medium & 100 & 0.603 \\
0.50:0.95 & large & 100 & 0.686 & 0.50:0.95 & large & 100 & 0.700 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: COCO Evaluation Metrics for object detection and instance segmentation using Mask R-CNN (R50-FPN) baseline. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multicolumn{6}{l}{**Backbone: Faster R-CNN(R50-FPN)**} \\ \hline
**IoU** & **Area** & **Maxdets** & **AP** & **IoU** & **Area** & **Maxdets** & **AR** \\ \hline
0.50:0.95 & all & 100 & 0.477 & 0.50:0.95 & all & 1 & 0.032 \\
0.50 & all & 100 & 0.590 & 0.50:0.95 & all & 10 & 0.317 \\
0.75 & all & 100 & 0.478 & 0.50:0.95 & all & 100 & 0.514 \\ \hline
0.50:0.95 & small & 100 & 0.180 & 0.50:0.95 & small & 100 & 0.182 \\
0.50:0.95 & medium & 100 & 0.608 & 0.50:0.95 & medium & 100 & 0.662 \\
0.50:0.95 & large & 100 & 0.812 & 0.50:0.95 & large & 100 & 0.830 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: COCO evaluation metrics for object detection using Faster R-CNN (R50-FPN) baseline. The metric maxdets specifies the maximum number of detections per image for calculating the average precision (AP) and average recall (AR). \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Backbone** & **AP** & **AP50** & **AP75** & **APs** & **APm** & **API** \\ \hline Faster rcnn: BB & 47.720 & 58.988 & 47.782 & 17.975 & 60.761 & 81.220 \\ Mask rcnn: BB & 47.492 & 58.421 & 47.032 & 20.525 & 61.198 & 73.415 \\ Mask rcnn: IS & 42.378 & 54.517 & 44.141 & 16.031 & 54.304 & 68.574 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of object detection performance reported as Average Precision (AP) at different IoU thresholds, using different backbones.”BB” denotes the bounding box-based approach, while “IS” denotes the instance segmentation-based approach.
# FFNN-TabNet: An Enhanced Stellar Age Determination Method Based on TabNet
## 5 Experiments and Results
### Ablation Experiment and Analysis  
This study details an ablation study performed on the model to scrutinize each module's contribution within the FFNN-TabNet framework for determining the age of red giants and to ascertain its impact on improving determination accuracy. The study entailed disassembling the modules for individual determination and assessing their accuracy. To neutralize parameter-induced variances, identical parameters were employed across all model iterations. The model underwent segmentation into five distinct configurations in Figure 9:  
1. **TabNet 1:** The model excluding both PReLU activation function and FFNN module, termed TabNet without PReLU and FFNN;
2. **TabNet 2:** The model excluding only the FFNN module, termed TabNet without FFNN;
3. **TabNet 3:** The model excluding only the PReLU activation function, termed TabNet without PReLU;
4. **FFNN-TabNet:** The FFNN-TabNet model proposed in this study.  
As indicated in Table 3, the experimental outcomes, integrated with model analysis, suggest that the exclusion of the FFNN module leads to a notable increase in the determi  
Figure 8: Comparison of the actual ages of red giants and the determination results by the FFNN-TabNet model. This experiment utilized the test dataset.  
Figure 9: Comparison of Ablation Experiment Results by (**a**) Tabnet 1; (**b**) TabNet 2; (**c**) TabNet 3 models. The red solid line denotes the optimal linear fit for the data points. This experiment utilized the test dataset.  
nation error. This reveals the FFNN module's vital role in enhancing the TabNet model's performance and its ability to intricately capture the nonlinear relationships present in red giant data. Furthermore, the removal of the PReLU activation function results in a modest reduction in determination accuracy, highlighting the function's contribution to the model's nonlinear extraction capabilities. Most tellingly, the simultaneous removal of both the PReLU activation function and the FFNN module results in a drastic reduction in determination accuracy, with the R\({}^{2}\) escalating from 0.882 to 0.946. This indicates that the incorporation of the PReLU activation function and FFNN module is crucial for significantly improving the model's overall determination efficacy.
# Quantum Key Distribution in the Presence of the Intercept-Resend with Faked States Attack
## 4 Detecting the \(\mathit{IRFS}\) Attack
### The \(\mathit{IRFS}\) Attack with Blinding Pulses and Quantum Channel Substitution  
In the presence of the \(\mathit{IRFS}\) attack with blinding pulses, [PERSON] is in the middle of the quantum channel using an optical detection system similar to [PERSON]'s station. The challenge for [PERSON] is to reproduce the gains of single and double detection events at [PERSON]'s side to pass unnoticed in the quantum channel. However, the gain of the single detection events decreases linearly with the channel efficiency, but the double detection gain drops quadratically. In Appendix B, we show that, for practical parameters of the quantum channel, the eavesdropper cannot adjust the two gains simultaneously. [PERSON] cannot control the two gains because:  
1. The eavesdropper can adjust the transmittance of the channel to a unique value, either to adjust the single or the double detection gain.
2. [PERSON]'s optical pulses arrive at [PERSON]'s station sequentially. Thus, once the eavesdropper station has detected a pulse, she cannot know whether the next pulse will be also detected or lost. That is, [PERSON] does not know when a single or a double detection event will occur.  
[PERSON] could adjust the efficiency of the quantum channel to the gain of the double detection events. Therefore, in order to remove the excess of the single detection gain, [PERSON] could eliminate pulses according to some probability (e.g., 0.5). However, due to the second point stated before, the eavesdropper looses double detection pulses (a quarter in this example). [PERSON] could be more selective removing only single detection events where the detection occurs in the second pulse. Using this strategy, [PERSON] keeps the double detection gain unaltered. However, since [PERSON] announces publicly the number of single detections inside the biqubit, first or second (see Table 1), the presence of [PERSON] becomes noticeable.  
[PERSON] could combine the two strategies increasing the efficiency of the channel to produce an excess of the double, but also the single detection gain. The problem for [PERSON] is that once she chooses a strategy to remove pulses, it affects equally the single and the double detection gains. As discussed in A, such gains obey different rates: while the first decreases linearly, the second varies quadratically with the transmittance of the channel. In addition, at the receiver station, the single and double detection events are registered as random interleaved events.  
In B.1, we discuss a convenient method to compute the photon gain deviation caused by the \(IRFS\) attack at a practical level.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Renewable Energy Sources
#### 4.6.7 Remarks on Projects for RES--Integration and Monitoring  
It can be observed that there are two projects that mainly focus on the integration of renewable energy sources for houses/end users (ECO-LIFE, PV-NET), whereas the Ashton Hayes Smart Village project mainly focuses on the integration of RES on the LV network. On the other hand, projects E-harbours and Stockholm royal Seaport aimed at the improvement of industrial areas, like harbors and how energy efficiency can be achieved. Figure 5 shows an example of renewable energy sources and their integration in the grid. Table 8 sums up the characteristics of the projects that focus on renewable energy sources, their integration and monitoring.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Project** & **Aim** & **Characteristics** \\ \hline ECO-LIFE & Balance energy demand and consumption—RES integration & Building of low-energy houses \\ \hline PV-NET & RES usage & Installation of PV panels, Calculation of NET consumed energy \\ \hline i-EM & Monitoring of RES & Design and construction of renewable energy plants, monitoring of PV systems \\ \hline E-harbours & Integration of RES & Focus on harbors: maximize RES integration, EV as storage units \\ \hline Ashton Hayes Smart Village & Integrate RES, reduce CO\({}_{2}\) emissions & PV panels and wind turbines inclusion in the LV network \\ \hline Stockholm royal Seaport & Optimising resource consumption & Focus on industrial port area: recycle solutions, sustainable transport \\ \hline \hline \end{tabular}
\end{table}
Table 8: Summary of projects for Renewable Energy Sources—Integration and Monitoring.  
Figure 5: Integration of RES in the grid.
## 3 Model Validation  
The mathematical model (1) was validated against experimental data [20; 21]. To perform the model validation, the presented nonlinear optimization task (3) was solved. The absolute percentage error (APE) was used to measure model-experiment fitting accuracy:  
\[\text{APE}=\frac{|\text{F}_{\text{DDE}}-\text{F}_{\text{Dm}}\text{M}|}{\text{F}_ {\text{DDE}}}\cdot 100\% \tag{5}\]  
where F\({}_{\text{DDE}}\)--experimental PAN fiber diameter and F\({}_{\text{Dm}}\)--modeled PAN fiber diameter.  
The obtained results are presented in Table 1. The applied designations, e.g., S-05.1-13, should be understood as a sample of 5.1 wt% and applied voltage of 13 kV. The appeared discrepancies were very small, mainly in terms of the range \(1.00\times 10^{-8}-1.00\times 10^{-4}\); the mean value of APE was 3.46%. Larger disproportions were seen in the case of extreme concentrations (low: 5.1 wt% or high 20.3 wt%). Therefore, it can be observed that the model results showed a satisfactory agreement with the experimental data. Moreover, the lower the polymer concentration, the higher the value of the \(\chi\) parameter.
# Intelligence-Based Strategies with Vehicle-to-Everything Network: A Review
## 5 AI-Powered VANET Solutions
### Safety and Traffic Applications  
Safety Management vehicular ad hoc network apps are utilized to mitigate car collisions by distributing relevant data regarding potential hazards and obstacles. The utilization of Random Forest algorithms [125] aids in the prediction of crashes. Motorists must consider variables such as traffic signals, pedestrians, and GPS navigation. SVM and CNNs [126] detect highway conditions and forecast drivers' behaviors. Safety applications are employed to manage safe lane changes, facilitate navigation, and implement automatic emergency braking [127]. PCA approaches are used to obtain important data entries [128], while NB and DT-based solutions are employed to make appropriate judgments and decrease traffic congestion [129].  
Figure 6: AI and ML approaches for vehicular ad hoc network.  
Traffic management: The fundamental goal of traffic control and monitoring is to improve traffic flows, reduce transmission times, and give consumers real-time data regarding road circumstances [130]. RL methods can be employed to manage digital signal components and traffic lights efficiently [131]. GRU, a variant of RNN, can forecast local traffic flow volume using less complex gating mechanisms and requiring fewer processing resources [132]. The use of LSTM [133] can enhance the accuracy of traffic forecasts and optimize data fitting. The utilization of ACO swarm intelligence can effectively direct traffic flows to minimize congestion [134]. The CNN- or ANN-based methods provide superior prediction skills with a greater level of accuracy [133; 135]. The KNN algorithm can forecast the speed and density of vehicles at various periods throughout the day [136].  
Infotainment applications: VANETs are widely used for providing different amenities related to convenience, entertainment, and highway security applications. According to research, infotainment services, such as the exchange of multimedia content and movies, are widely favored [137]. Seamless Internet connectivity is crucial for users of VANET [138; 139], and the inclusion of entertainment features such as advertisements and parking availability improves the impression [140]. CNNs are highly suitable for the analysis of video and image information [141]. On the other hand, deep auto-encoders can improve file compression to adaptive compression of multimedia files [142]. The PSO technique can manage trip path direction by generating near-optimal paths using routes [143].
## 5 Numerical Simulation
Based on Equation (3), the 3-dB MAPDs of impedance signatures acquired in the numerical model for different grouting compactness were also calculated and are shown in Figure 12. It can be found that the 3-dB MAPD values of P1, P2, and P3 have a sudden increase when the grouting compactness reaches 25%, 50%, and 100% respectively, which is consistent with the experimental results and can be used to determine the grouting compactness level. When the structure is excited by PZT patches at three different frequencies--P1, P2, and P3, respectively--the displacement diagrams of the numerical model at empty, 25% grouting compactness, 50% grouting compactness, 75% grouting compactness, and 100% grouting compactness are shown in Figures 13-15. As shown in Figure 13, the increase of grouting compactness changes the deformation of the model near PZT1, and then it changes the vibration state of PZT1. Specifically, the change of the deformation of the model around PZT1 is 0.15\(\times\)10\({}^{-6}\) mm in the process of grouting compactness from empty to 25%. The change of the deformation is 0.02\(\times\)10\({}^{-6}\) mm, 0.01\(\times\)10\({}^{-6}\) mm, and 0.01\(\times\)10\({}^{-6}\) mm during the rest of the grouting process. Therefore, the change of the deformation of the model around PZT1 from empty to 25% is much larger than that of the other grouting processes, which explains why the resistance of P1 changes significantly in the process of grouting compactness from empty to 25%. According to Figure 14, as the grouting compactness increases, the deformation of the model around  
Figure 11: Simulated impedance signatures for different grouting compactness. Figure 12: 3-dB MAPD of simulated impedance signatures for different grouting compactness levels. PZT2 changes, thus changing the vibration state of PZT2. Figure 14b-d show that the deformation of the model near PZT2 is 0.19\(\times\)10\({}^{-6}\) mm in the process of grouting compactness from 25% to 75% and obviously greater than the deformation variation of other grouting processes, which means that the impedance signature of PZT2 is sensitive to the physical characteristics of this region. According to Figure 15, as the grouting compactness increases, the deformation of the model around PZT3 changes, thus changing the vibration state of PZT3. Figure 15d,e show that the grouting compactness from 75% to 100% has a particularly significant influence on the deformation of the model around PZT3. According to the above analysis, it can be seen that the displacement diagram corresponding to different peak frequency excitations is basically consistent with the change of impedance at each peak frequency, indicating the basic principle of the multi-sensing EMI method to monitor the grouting compactness. Figure 13: The structural deformation excited by PZT patches at frequency P1 (unit: mm). (**a**) Empty; (**b**) 25%; (**c**) 50%; (**d**) 75%; (**e**) 100%.
# LSTM Network-Assisted Binocular Visual-Inertial Person Localization Method under a Moving Base
## 3 Results
### Experiment and Analysis of Visual-Inertial Personnel Positioning  
In order to verify the reliability of the improved algorithm, indoor and outdoor experimental scenarios are selected, and the underground space is simulated with indoor-outdoor experiments and tested several times. The localization device is placed on the wearable undershirt, the camera device is used, as shown in Figure 9. The experiments are conducted by pedestrians wearing the undershirt on outdoor roads and indoor hallways. The experimental comparison results and analysis are as follows:  
(1) Outdoor playground experiment. The experimental scene was chosen as a school playground, and the experimenter walked around the playground wearing localization equipment. To verify the effectiveness of the algorithm, the VINS-fusion algorithm and the improved localization method were used for comparison. The localization trajectories of the two methods are shown in Figures 10 and 11. The total length of the trajectory is 400 m, and the accuracy is calculated by the offset distance and the total distance traveled. The endpoint of the improved positioning method is very close to the starting point, the offset is about 1.997 m, and the positioning accuracy error is 0.50%. As for the results of the VINS-fusion algorithm run, the positioning was relatively accurate for a short distance after the start, but the drift was more serious after that. After one lap, the endpoint of the positioning trajectory seriously deviates from the starting point. The offset is about 25.67 m and the positioning accuracy error is 6.41%. Positioning error is reduced by 92.20%. The comparison shows that the method proposed improves positioning accuracy.  
Figure 10: VINS-fusion positioning trajectory map.  
Figure 9: Testers’ experimental wear chart.  
(2) Outdoor road experiment. The total length of the trajectory is 725 m, the offset is about 3.674 m, and the positioning accuracy error is 0.51%. The experimental trajectory is shown in Figure 12.  
(3) Building experiment. Figure 13 shows the scene of the building. Experimenters wear positioning equipment along the building's pre-determined trajectory from the starting point, back to the starting point after turning back into the building, that is, the starting point and the endpoint coincide, during which they have to pass up and down the stairs, the total length of the trajectory is 210 m, and the schematic diagram of the building experiment is shown in Figure 14. Figure 15 shows the positioning trajectory of the experimental run; from the running trajectory, the starting point and the starting point nearly coincide, the offset is about 0.983 m, and the positioning accuracy error is 0.47%.  
Figure 11: Our algorithm positioning the trajectory map.  
Figure 12: Outdoor road positioning track map.  
Figure 13: Building environment point feature extraction.  
The positioning accuracy of the algorithm under the moving base was verified by indoor and outdoor experiments.
## 3 Materials and Methods
### Volatile Fraction Characterization  
The chemical composition of the pomegranate juice sample volatile fraction was performed by extracting analytes by means of the headspace solid-phase micro-extraction technique (HS-SPME) and analyzing them on a chromatography-mass spectrometer (GC-MS). In particular, a tri-phasic fiber coated with 50/30 \(\upmu\)m of Divinylbenzene-Carboxen-Polydimethylsiloxane (DVB/Carboxen/PDMS; Supelco, Bellefonte, PA, USA) was used, and the analyses were performed on a gas chromatograph (Thermo Scientific Trace 1300, Waltham, MA, USA) and a single quadrupole mass spectrometer (Thermo Scientific ISQ), with an electronic impact (EI) source. The instrument was equipped with a SUPERLOWAX 10 capillary column (Supelco, Bellefonte, PA, USA; 30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m). All the parameters applied for the analyte extraction, separation, detection, and identification were the same as those described in [PERSON] et al. [44]. All the compounds of interest were semi-quantified on the basis of the use of a reference (Toluene).