esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Explanation
### Ablation Study
#### 4.3.1 Our Model vs. Baseline  
We compare the performance on each of the three well-known public datasets. In addition to VeRi-776, the other two dataset, as we know, comprise small, medium, and large datasets. Both in VeRi-776 and other datasets, as shown in Table 2, its performance is superior. Specifically, even if only LBT modules are available, R1 and MAP exceed the baseline by 1.0% and 3.9%, respectively, on VeRi-776. Moreover, we have performed experiments on VehicleID in three groups. The R1 increases 1.3% on the smallVehicleID, 2.7% on the mediumVehicleID, and 0.7% on the largeVehicleID. MAP increased 0.8% on the smallVehicleID, 2.9% on the mediumVehicleID, and 0.4% on the largeVehicleID, respectively.  
Even though the performance of those baselines was already excellent before adding TAM, our method is still superior. If we focus more on the average metric, it can be found that LBT and LBT\(+\)TAM, respectively, improve the MAP by 3.9% and 5.0% on VeRi-776, 0.8% and 0.9% on smallVehicleID, 2.8% and 3.0% on mediumVehicleID, and 0.4% and 0.6% on largeVehicleID. Note that the MAP has significant enhancements on mediumVehicleID.  
As shown in Table 2, LBT and LBT + TAM improved distinctly on VERI-Wild. Note that only when all modules are combined together can the framework's performance be maximized.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**VeRi-776**} & \multicolumn{6}{c}{**VehicleID**} & \multicolumn{6}{c}{**VERI-Wild**} \\ \cline{2-13}  & & \multicolumn{3}{c}{**Small**} & \multicolumn{3}{c}{**Medium**} & \multicolumn{3}{c}{**Large**} & \multicolumn{3}{c}{**Small**} & \multicolumn{3}{c}{**Medium**} & \multicolumn{3}{c}{**Large**} \\ \cline{2-13}  & **R1** & **MAP** & **R1** & **MAP** & **R1** & **MAP** & **R1** & **MAP** & **R1** & **MAP** & **R1** & **MAP** & **R1** & **MAP** \\ \hline baseline & 95.7 & 76.6 & 83.0 & 77.0 & 80.7 & 75.0 & 79.2 & 74.0 & 93.1 & 72.6 & 90.5 & 66.5 & 86.4 & 58.5 \\ \hline LBT (Our) & 96.7 & 80.5 & 84.3 & 77.8 & 83.4 & 77.9 & 79.9 & 74.4 & 91.8 & 72.7 & 90.3 & 66.7 & 87.6 & 58.8 \\ \hline LBT + TAM (Our) & 96.9 & 81.6 & 84.3 & 77.9 & 83.5 & 78.0 & 79.9 & 74.6 & 93.4 & 72.6 & 91.1 & 66.7 & 87.7 & 58.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results on the three datasets (%).  
Figure 7: Overview of Augmentation Results. **Column 1:** original samples. **Column 2:** augmented samples created by LBT. **Column 3:** augmented samples created by LBT + TAM.
## 4 Results and Discussion
### Partial Discharges Measurement  
The partial discharges (PDs) are one of the most occurring phenomena in composite insulation systems, especially in rotating machines. From this reason, the PD inception level and charge Qice has been investigated. The obtained results are shown in Table 4.  
The character of the discharge activity at comparative level of 18.5 kV/mm is shown in the form of Phase Resolved Partial Discharges (PRPD) diagrams (Figure 14). From these diagrams, it is evident that their character corresponds with the internal partial discharges. There are also very well visible the movements of the \"rabbit ear\" [70] during the changes of the applied voltage, which are indicators  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **PD Inception** & **Charge Qice 1** & **Charge Qice 2** \\  & **(kV/mm)** & **(pC)** & **(pC)** \\ \hline A—Nanostructured micaless microcomposite & 18.5 & 10 & 10 \\ B—PET reinforced composite & 16.6 & 20 & 37 \\ C—Glass reinforced composite & 10 & 25 & 117 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Charge Qice and PDs inception levels for investigated composites.  
Figure 13: Real and imaginary parts of complex permittivity of investigated composites.  
for void [71] partial discharges. The difference in discharge activity could be attributed to the strong inhomogeneity of the internal structure or imperfect adhesion between the various parts of commonly used composite materials. Here, small, gas-filled areas near fibres, film or mica paper could occur during the process of producing the insulating system [72; 73; 74].
###### Abstract  
Network flow task management involves the efficient allocation and scheduling of data flow tasks within dynamic satellite networks, aiming to effectively address frequent changes in network topology and dynamic traffic fluctuations. Existing research primarily emphasizes traffic prediction and scheduling using spatiotemporal models and machine learning. However, these approaches often depend on extensive historical data for training, making real-time adaptation to rapidly changing network topologies and traffic patterns challenging in dynamic satellite environments. Additionally, their high computational complexity and slow convergence rates hinder their efficiency in large-scale networks. To address these issues, this paper proposes a collaborative optimization framework based on Coding Multi-Path Theory (CMPT). The framework utilizes a Nash bargaining game model to simulate resource competition among the different participants, ensuring fair resource distribution and load balancing. It also integrates real-time network state monitoring with optimization algorithms, within a multi-path scheduling strategy, enabling the dynamic selection of optimal transmission paths to accommodate frequent network topology changes and traffic variations. Experimental results indicate that the proposed method reduced resource allocation task execution time by at least 18.03% compared to traditional methods and enhanced task scheduling efficiency by at least 14.01%. Although CMPT exhibited a slightly higher task latency on certain small-scale datasets compared to some baseline algorithms, its performance remains exceptional in large-scale and high-dimensional scenarios.  
network flow; task scheduling; multi-path transmission; network coding; game theory +
Footnote †: journal: _Mathematics_  
0000-0001-5000-5000-5000][PERSON]  
1 School of Computer Science and Engineering, Central South University, Changsha 410083, China; [EMAIL_ADDRESS] (S.W.); [EMAIL_ADDRESS] (C.Z.); [EMAIL_ADDRESS] (Y.W.); [EMAIL_ADDRESS] (L.L.) 12 Big Data Institute, Central South University, Changsha 410083, China 2
## 4 Materials and Methods  
### Bacterial Strains and Culture Conditions  
The _Pseudomonas aeruginosa_ strains used in this study, namely, PaCF1 (non-mucoid) and PaCF4 (mucoid), were obtained from sputum samples of chronically infected cystic fibrosis patients. Identification of such bacterial strains was conducted using a MALDI-TOF MS (Bruker Daltonics, Bremen, Germany) Microflex LT Mass Spectrometer with MALDI Biotyper 3.1 software (Bruker Daltonics, Bremen, Germany), following the manufacturer's protocol. A score \(\geq\) 2.00 allowed identification at the species level, as previously described [18]. For determining colony forming units (CFUs), _P. aeruginosa_ was plated on Tryptone Soy Agar (TSA, Oxoid, Basingstoke, Hampshire, UK). _L. rhamnus_ was isolated from a commercial product available on the market, as described in our previous work [18]; briefly, probiotic capsules were dissolved in water and streaked on the De Man-Rogosa-Sharpe agar (Oxoid, Basingstoke, Hampshire, UK) to isolate single colonies. Colonies were picked up and identified at the species level by MALDI-TOF MS, as described earlier. The _L. rhamnus_ strain was further confirmed as GG through whole genome sequencing and subsequent data analysis by [PERSON] (Beijing, China) [18]. Bacterial strains were grown in Luria-Bertani broth (_P. aeruginosa_) (LB, Sigma-Aldrich, St. Louis, MO, USA) or in De Man-Rogosa-Sharpe broth (_L. rhamnus_) (MRSB, Oxoid, Basingstoke, Hampshire, UK). For stock culture preparation, cultures at late-log phase were subdivided in aliquots and kept frozen at \(-80\)\({}^{\circ}\)C until use.
# Exploring the Benefits of Photovoltaic Non-Optimal Orientations in Buildings
## 2 Materials and Methods
### Case Study #2: Industrial PVSCS  
The second case under study is an industrial PVSCS for a meat-processing plant in Guijuelo (40\({}^{\circ}\)34\({}^{\prime}\) N, 5\({}^{\circ}\)40\({}^{\prime}\) W, province of Salamanca). The annual electricity consumption for the factory was 665 MWh before the expansion and was estimated to be more than 1000 MWh when the factory is fully operational, so the owners wanted to generate the most annual electricity as possible. As the new building was projected with a flat roof for easing the operation and maintenance of the PVSCS, several layouts for the PVSCS were analyzed.  
The Canadian Solar CS3W-440 MS PERC 440 W modules were proposed for installation, coupled with SolarEdge P950 power optimizers [49] and connected to 82.5-kW three-phase SolarEdge inverters [50]. During the design stage of this plant, several layouts were considered. The PVSYST simulations used the PVGIS database due the temperature data being more accurate for this location compared to the Meteorom 7.3 database. The models for the PV modules, power optimizers, and the inverters were provided with version 7.1 of PVSYST. Modules and strings allocation and orientation in the simulations were as close as projected as is allowed by the software. The oversizing of DC PV power over inverter nominal power was small for the three options, because the SolarEdge 82.5 kWinverters were composed of three 27.5-kW units. The detailed electrical model option was not available for mixed orientations connected to one inverter. As there were few shadings, PV modules are half-cell type, and power optimizers were used, the linear shading option was selected for the simulations. The error for this shading option was about 3% for all configurations with an error less than 1% between them in the comparison.  
The results from the simulations will be presented in Section 3 and there will be explained the selected and built layout, shown in Figure 5, together with preliminary results of the first year of operation.
## 6 Conclusions and Future Developments
Another direction may include the application of more sophisticated neural networks algorithms like a Transformer architecture for improved accuracy or feed-forward neural networks for higher computing efficiency. Taking in consideration the excellent performance of the ERT in this study, similar algorithms like Rotation Forest or Deep Forest could also be used. Finally, this study was mostly based on the use of Carrier-to-Noise Ratio information (e.g., \(C/N_{0}\)) but other relevant information provided by the GNSS receiver could also be used like carrier phase, pseudorange features, Horizontal Dilution Of Precision (HDOP) or Vertical Dilution Of Precision (VDOP). Conceptualization, G.B. ; Methodology, G.B. ; Software, G.B. and F.B. ; Resources, G.B. and F.B. ; Data Curation, G.B. and F.B. ; Writing--Original Draft Preparation, G.B. ; Writing--Review and Editing, G.B. and F.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to lack of approval of the data set publication by the JRC. The data set will be submitted to the internal JRC approval process for data publication after the acceptance of the publication. Author [PERSON] was employed by the company Piksel. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} Adam & Adaptive moment estimation \\ AGC & Automatic Gain Control \\ AWGN & Additive White Gaussian Noise \\ CNN & Convolutional Neural Network \\ CNR & Carrier-to-Noise Ratio \\ DC & Direct Current \\ DL & Deep Learning \\ ERT & Extremely Randomized Tree \\ FPGA & Field-Programmable Gate Array \\ GPS & Global Positioning System \\ GNSS & Global Navigation Satellite System \\ HDOP & Horizontal Dilution Of Precision \\ IF & Intermediate Frequency \\ IQ & in-phase (I) and quadrature (Q) \\ JRC & Joint Research Centre \\ LNA & Low Noise Amplifier \\ ML & Machine Learning \\ RFI & Radio Frequency Interference \\ SVM & Support Vector Machine \\ TF & Time Frequency \\ UHD & USRP Hardware Driver \\ USRP & Universal Software Radio Peripheral \\ \end{tabular}
## 4 Results and Discussion
### Benefits and Features  
Integrating pre-trained models with the cloud-hosted Streamlit application ensures instantaneous RUL predictions. Cloud deployment allows the application to handle multiple users simultaneously, adapting to varying loads. With minimal computational requirements at the user's end, the application is accessible to a broader audience. Users can explore battery health metrics through intuitive graphs, enhancing decision-making processes. The modular nature of the application allows for the easy incorporation of additional features, such as comparison across battery chemistry or integration with IoT sensors for real-time data input.  
The web application also incorporates Smart Battery Health Classifications, Usage guidelines and Optimization Strategies.  
For instance, Excellent Battery Health: RUL between 1112 and 741. Batteries in this range are expected to have a long remaining life and are performing optimally, e.g., Regular Monitoring, Optimal Charging Practices.  
Average Battery Health: RUL between 740 and 371. Batteries in this range are functioning, but they are past their peak performance and may require monitoring or eventual replacement, e.g., Conditioning Cycles, Reduced Load.  
Low or Poor Battery Health: RUL between 370 and 0. Batteries in this range are nearing the end of their useful life and likely need immediate replacement or significant attention, e.g., Load Reduction, Refurbishment.
# Coronary Centerline Extraction from CCTA Using 3D-UNet
## 2 Related Research  
Vessel centerlines can be extracted by either a segmentation and thinning pipeline [8] or by direct tracking [8]. The extracted centerlines can serve as input for tracking algorithms for segmenting the required vessel tree. In [9], the authors proposed Bayesian tracking combined with sphere fitting for segmentation. Vessel branching is performed using statistical models with three categories: locally disconnected vessel (disappearance in some of the slices in the cardiac computed tomography angiography CCTA), locally disconnected branch, and branch occurrence (domain-specific knowledge about coronary vessels). An example of sphere fitting to obtain vessel segmentation starting from centerlines can be seen in Figure 11 from [9]. In [10], the lumen segmentation was performed by tracking, using a minimal cost path based on a cost function with prior model information on three measurable parameters: vesselness measure, intensity similarity, and directional information.  
Tree-structured segmentation was also achieved in [11] using the centerline extracted using a deep learning approach. A tree-structured convolutional gated recurrent unit network was trained for tracking segmentation and achieves good results, especially at vessel bifurcations.  
Another method for vessel segmentation using a centerline constrained level set method was proposed in [12]. It improves on two active contour models, Chan-Vase and curve evolution for vessel segmentation, by integrating the centerline into the set evolution, as shown in Figure 13 from [10]. There are two major categories of vessel and centerline segmentation methods--rule based and machine learning based.
## 4 Ruthenium
### Cyclopropanation  
[PERSON] and co-workers have investigated the use of polymer-bound ruthenium-pybox complexes in the cyclopropanation of styrene with diazacetate (Scheme 9) [28].  
**Scheme 9.** Asymmetric cyclopropanation using polymer bound Ru-pybox (**11**) and Ru-salen (**12**) complexes.  
The catalysts were prepared via polymerization of vinylic monomers of the ligands, with and without an aromatic spacer, inside monolithic minireactors in stainless steel columns, which were subsequently treated with a solution of dichlororuthenium(II) \(p\)-cymene. In the case of catalyst **11a**, different degrees of cross-linking were also investigated. Minireactors were used to protect the air-sensitive complex from oxygen and to exploit the other advantages of flow reactors such as a large surface area and controlled reaction conditions. Cyclopropanation using **11a** with dichloromethane as the solvent afforded the desired products **13** in up to 50% yield, with a 4:1 _trans:cis_ ratio and moderate enantioselectivities, with dimerization of the ethyl diazacetate being the main side reaction and accounting for the moderate yield. The stereoselectivity was comparable to that obtained for a homogeneously catalyzed reaction using a pybox-Ru complex. The polymer-supported catalyst could be re-used a number of times, without any marked deterioration of the catalytic activity. Solventless conditions afforded up to 72% yield with **11a**, and the use of supercritical carbon dioxide as solvent in conjunction with complex **11b**, also gave good results, showing that the procedure could be carried out in a more environmentally friendly fashion. Likewise, [PERSON] and colleagues have studied the same reaction but using a polymer bound salen-Ru catalyst [29]. Here also two different spacers were used between the complex and the catalyst, a more rigid aromatic linker for **12a** and a flexible ethylene glycyl linker in the case of **12b**. Both catalysts performed better than a silica-supported counterpart, and the yields using the polymer bound catalysts were comparable to the corresponding reactions using a soluble catalyst. The more rigid polymeric catalyst **12a** gave somewhat inferior results in terms of stereoselectivity as compared to **12b**, although the catalytic activity was similar. For catalyst **12b**, diastereoselectivities and enantioselectivities were high (_trans/cis_ ratio: 10.9, _trans_: 95% _ee_, _cis_: 92% _ee_) and the catalyst could be recycled up to three times without any major loss of selectivity or yield, although the reaction times needed to attain the same conversion (95%) were markedly longer for the third run. Treating the catalyst with pyridine in between cycles in order to stabilize the Ru(II)-salen bis-pyridine complex was found to minimize catalyst deactivation and shorten the reaction times for the following runs.
## 5 Results Analysis  
### Benchmark Datasets Analysis  
The performance comparison with existing approaches (Table 6) showed our model performance and that of other recent state-of-the-art approaches. Our proposed model achieved the best accuracy of 98.62 and 98.95 among the UCSD ped1 and UCSD ped2 datasets, respectively, compared with the existing CLBP, MSCNN, densenet121 and Efficientnetv2 methods. The total number of training frames was 8040 and the total number of testing frames was 5360; among those testing frames, there were 66 and 73 misclassified samples, respectively. The resultant confusion matrix is shown in Figures 9 and 10. The proposed model achieved a good precision and recall rate compared with the other approaches. The proposed model trained with PETS2009 (737 training samples and 492 testing samples) achieved an accuracy of 96.97 with a total of 24 misclassified samples. The confusion matrix of the PETS2009 dataset with the proposed model is shown in Figure 11. The model achieved a better recall and precision than the other methods. This dataset had fewer samples compared with the other tested datasets with a high occlusion rate. Finally, the proposed model was tested on the UMN dataset and achieved an accuracy of 99.10 and 98.38 with a total of 17 and 17 incorrectly classified samples for UMN-Plaza1 and UMN-Plaza2, respectively. The relevant confusion matrix tables are shown in Figures 12 and 13. Figure 11: Confusion matrix heat map of PETS2009 for (**a**) MCMS-BCNN-Attention+densenet121 and (**b**) MCMS-BCNN-Attention+Efficientnetv2. Figure 12: Confusion Matrix Heatmap of UMN Plaza 1 for (**a**) MCMS-BCNN-Attention+densenet121 and (**b**) MCMS-BCNN-Attention+Efficientnetv2. Figure 10: Confusion matrix heat map of UCSD Ped2 for (**a**) MCMS-BCNN-Attention+densenet121 and (**b**) MCMS-BCNN-Attention+Efficientnetv2. The comparison of the performance of the models in Table 6 clearly shows that the proposed method provided better results compared to the available models from the literature. In this approach, we selected different state-of-art models along with our multicolumn multistage approach. The comparison results clearly show that our work achieved the best performance either with densenet121 or Efficientnetv2 compared to existing methods. The proposed model achieved the best average kappa performance value over the five datasets with 0.9583.
# Adaptive Recursive Sliding Mode Control (ARSMC)-Based UAV Control for Future Smart Cities
## 2 Mathematical Model
The controllability property of the system, coupling within the state, and the input involves the system matrices. The \(C_{c}\) matrix is a linear system said to be in controllable form if it is possible to find some input, and this input will transform the state's x(to) to the origin at a finite time. If there exists some input which gives \(x(t)=0\), admitted for all initial times and states, then it is verified for the controller [38]. The full rank of the controllability matrix further validates that the system is indeed controllable. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable Notation** & **Description** & **Units and Values of Parameters** \\ \hline \(I_{1}\) & Main rotor inertia & 6.8 \(\times 10^{-2}\) kgm\({}^{2}\) \\ \(I_{2}\) & Tail rotor inertia & \(2\times 10^{-2}\) kgm \({}^{2}\) \\ \(a_{1}\) & Constant & 0.0135 \\ \(b_{1}\) & Constant & 0.0924 \\ \(a_{2}\) & Constant & 0.02 \\ \(b_{2}\) & Constant & 0.9 \\ \(M_{g}\) & Gravitational momentum & 0.32 Nm \\ \(B_{1g}\) & Frictional parameter & \(6\times 10^{-3}\) Nms\({}^{2}\) rad\({}^{2}\) \\ \(B_{2g}\) & Frictional parameter & \(1\times 10^{-3}\) Nms\({}^{2}\)/rad\({}^{2}\) \\ \(B_{1g}\) & Frictional parameter & \(1\times 10^{-1}\) Nms/rad \\ \(B_{2g}\) & Frictional parameter & \(1\times 10^{-2}\) Nms\({}^{2}\)/rad \\ \(k_{gy}\) & Gyroscopic parameter & 0.05 rad/s \\ \(k_{1}\) & Gain of main motor & 1.1 \\ \(k_{2}\) & Gain of tail motor & 0.8 \\ \(T_{11}\) & Denominator of motor & 1.1 \\ \(T_{10}\) & Numerator of motor & 1 \\ \(T_{21}\) & Denominator of motor & 1 \\ \(T_{20}\) & Numerator of motor & 1 \\ \(k_{c}\) & Coupling reaction for gain & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of TRMS (UAV). \[C_{c}=\left[ \begin{array}{ccccccccc}0&0&0&0&0.0014&0&-0.0014&0&-0.0052&0\\ 0&0&0.0014&0&-0.0014&0&-0.0052&0&0.0058&0\\ 0&0&0&0&0.0016&0.0036&-0.0096&-0.0216&0.0491&0.1116\\ 0&0&-0.0016&0.0036&0.0096&0.0216&0.0491&0.1116&0.2468&0.5616\\ 1&0&0.0009&0&0.0008&0&0.0008&0&0.0007&0\\ 0&0.8&0&0.0008&0&0.0008&0&0.0008&0&0.0008\end{array} \right] \tag{13}\]  
The full rank property of the system is validated by the matrix shown above, which is a positive sign of controllability. The observability matrix \(O_{o}\) has been calculated and is provided below. The full rank property of the matrix confirms the observability of the system. \[O_{o}=\left[ \begin{array}{ccccc}1&0&0&0&0&0\\ 0&0&1&0&0&0\\ 0&1&0&0&0&0\\ 0&0&0&1&0&0\\ -4.7059&-0.0882&0&1.3588&0&0\\ 0&0&0&-5&1.6170&4.5\\ 0.4151&-4.6534&0&0&-1.3543&0\\ 0&0&0&25&-9.5532&-27\\ 22.1089&0.8294&0&0&-8.6574&0\\ 0&0&0&-125&49.1258&139\\ -3.9032&22.0356&0&0&5.8103&0\\ 0&0&0&663&-246&-702\end{array} \right] \tag{14}\]