esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion  
### Synthesis of Polylaticide-Based Polymer Containing Multiple Tetraphenylethane Groups  
A PLA-based polymer (polyester-urethane, PLA-PU) was synthesized by coupling PLA-diol with a low molecular weight diol containing TPE (TPE-diET) using hexamethyldiisocyanante (HDI). First, polylactide diol was prepared via the cationic ring-opening polymerization of L-lactide using a difunctional initiator [21]. TPE-diET diol bearing primary -OH groups was synthesized according to the procedure described in our earlier article [13]. As a result of the preliminary experiments, DMF was chosen as a solvent that was able to solubilize all the substrates and products of polyurethane synthesis, and DBTDL was used as a catalyst for the formation of urethane linkages. It was important that the prepared PLA-based polymer had the assumed structure with TPE units covalently  
Figure 1: Tracking the formation of PLA-based polyurethane (PLA-PU) containing TPE groups by \({}^{1}\)H NMR analysis (DMSO-d\({}_{b}\)). Red arrows indicate changes in the position of the terminal -CHOH (from PLA: (**a**,**b**)) or -CH\({}_{2}\)CH\({}_{2}\)OH (from TPE-diET: (**c**,**d**)).  
linked to the PLA chain. Thus, the synthesis was performed in two steps--first, PLA-diol was reacted with a two-fold excess of HDI in the presence of a catalyst at 40 \({}^{\circ}\)C, and next, after about 1.5 h, the TPE-diET was added to a small amount of solvent. The reaction was continued up to 24 h, and the product was analyzed at each reaction step by \({}^{1}\)H NMR of the reaction mixture. Finally, the product was precipitated two times before final analyses and further use. The success of coupling and the incorporation of TPE units in the polymer structure was confirmed by \({}^{1}\)H NMR and FTIR analyses. Corresponding spectra are shown in Figures 1 and S1.  
In the \({}^{1}\)H NMR spectra, signals corresponding to all expected units from polylactide, TPE-diET, and HDI are present. Additionally the signal of the formed -NH- groups from urethane at 7.43 ppm appears after the addition of HDI and at 5.72 ppm after the addition of TPE-diET. Signals corresponding to -CHOH PLA end groups and to -CH\({}_{2}\)CH\({}_{2}\)OH groups in TPE-diET were shifted to new positions with higher chemical shift values after the reaction of -OH groups with diisocyanate (Figure 1).  
In the IR spectra, the signals corresponding to -NH- groups at 3330 and 3400 cm\({}^{-1}\), to C=O groups from urethane at \(\sim\)1730 cm\({}^{-1}\), and to -C-NH- at \(\sim\)1630 cm\({}^{-1}\)[22] are visible in addition to signals characteristic of PLA and TPE-diET (Figure S1). The obtained PLA-based polyurethane (PLA-PU) analyzed by SEC chromatography had a moderate molecular weight (its GPC curve is shown in Figure S1).
# Maximization of Average Achievable Rate for NOMA-UAV Dual-User Communication System Assisted by RIS
## 1 Introduction
The effects of RIS phase, UAV power allocation, and UAV trajectory optimization on system performance are further examined. The core objective of this study is to optimize the RIS phase, UAV power allocation, and trajectory design in the RIS-assisted NOMA-UAV system to maximize the system's average reachability. We model the RIS phase, UAV power allocation, and trajectory, and propose an optimization scheme to enhance the system's overall performance. The principal outcomes of this research are presented below. * We design a RIS-assisted NOMA-UAV communication system. Unlike the scheme in reference [17], which optimizes the UAV's hovering position, our algorithm addresses the system performance optimization problem by optimizing the UAV's trajectory. Optimizing the drone's position improves communication coverage, but this method has spatial limitations. In contrast, the trajectory optimization algorithm is more flexible, enabling a wider coverage area and more stable signal transmission. However, UAV trajectory optimization increases the complexity of the modeling, making the optimization problem more challenging to solve. * We divide the problem into three manageable sub-problems--RIS phase optimization, UAV trajectory optimization, and power optimization and solve it using alternating iterative optimization. We compare our proposed scheme with four benchmark algorithms. As the number of RIS array elements increases, the optimization performance of our proposed scheme (OT&OP&OW) improves slightly. When the UAV transmission power increases, OT&OP&OW improves significantly. The results of all optimization algorithms outperform the random phase curve (T&OP), and our proposed schemes outperform the other benchmark algorithms. Notation: Some symbols are given here. \(\|\cdot\|\) represents Euclidean norm. \(|\cdot|\) denotes amplitude, \((\cdot)^{H}\) denotes complex conjugate matrix, and\((\cdot)^{T}\) denotes taking matrix transpose. \(\mathbb{CN}(*,*^{2})\) represents the circular symmetric complex Gaussian distribution with mean \(*\) and variance \(*^{2}\). And \(\widetilde{\cdot}\) indicates that this is a random component. Angle\((\cdot)\) denotes taking phase.
## 4 Experiments and Results
### Experimental Setting and Datasets  
To show the ability of the proposed method to jointly model inter-target correlation and nonlinear input-output relationships. We provide the evaluation of real-world data.  
The 18 real-world datasets are widely-used benchmarks for MTR in Mulan [37], which is a Java Library for Multi-Label Learning, and the statistics of these datasets are summarized in Table 1. Specifically, as is shown in Table 1, we use 2-fold cross-validation for SCM1d/SCM20d, 5-fold cross-validation for RF1/RF2, and 10-fold cross-validation for the rest of the datasets.  
We also compare with existing state-of-the-art MTR models including multi-output support vector regression (mSVR) [38], multi-layer multi-target regression (MMR) [27], stacked single-target (SST) [25], random linear target combinations (RLC) [26], ensemble of regressor chains (ERC) [25], output kernel learning (OKL) [29] and multi-task feature learning (MTFL) [39]. Two evaluation metrics including average relative root mean squared error (aRRMSE) and mean absolute percentage error (MAPE) are employed to evaluate the performance of the above-mentioned multi-target regression methods. The formula for aRRMSE is given as follows  
\[aRRMSE=\frac{1}{Q}\sum\limits_{i=1}^{Q}\sqrt{\frac{\sum\limits_{j=1}^{N_{test}} \left(y_{i}^{(j)}-\hat{y}_{i}^{(j)}\right)^{2}}{\sum\limits_{j=1}^{N_{test}} \left(y_{i}^{(j)}-\mathbf{y}_{i}\right)^{2}}} \tag{22}\]  
where \(\mathbf{y}_{i}\) is the average output value of the samples in training set on the \(i\)-th target. The aRRMSE can effectively reflect the stability of an algorithm. The smaller the aRRMSE is, the better the stability of the algorithm. Likewise, the MAPE is defined as follows:  
\[MAPE=\frac{1}{Q}\sum\limits_{i=1}^{Q}\left|\frac{y_{ij}-\hat{y}_{ij}}{y_{ij}} \right|\times 100\% \tag{23}\]  
where \(Q\) is the target number, \(y_{ij}\) and \(\hat{y}_{ij}\) are the real and predicted values of the test sample \(j\) on the target \(i\), respectively. A low MAPE indicates better performance of model. The parameters \(\lambda_{1}\), \(\lambda_{2}\), \(\lambda_{3}\) are chosen by cross validation from a search grid of \(10^{\{-5\leq 1:3\}}\) on the training set by tuning one with the others fixed. We use the radial basis function (RBF) kernel for nonlinear regression, and set the kernel parameter varied in \(\{2^{-3},\cdots,2^{3}\}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Samples** & **Input (_d_)** & **Target (Q)** & **\#-fold** \\ \hline ANDRO & 49 & 30 & 6 & 10 \\ ATP1D & 337 & 411 & 6 & 10 \\ ATP7D & 296 & 411 & 6 & 10 \\ EDM & 154 & 16 & 2 & 10 \\ ENB & 768 & 8 & 2 & 10 \\ JURA & 359 & 15 & 3 & 10 \\ OES10 & 403 & 298 & 16 & 10 \\ OES97 & 334 & 263 & 16 & 10 \\ OSALES & 639 & 413 & 12 & 10 \\ RF1 & 9125 & 64 & 8 & 5 \\ RF2 & 9125 & 576 & 8 & 5 \\ SCM1D & 9803 & 280 & 16 & 2 \\ SCM20D & 8965 & 61 & 16 & 2 \\ SCPF & 1137 & 23 & 3 & 10 \\ SF1 & 323 & 10 & 3 & 10 \\ SF2 & 1066 & 10 & 3 & 10 \\ SLUMP & 103 & 7 & 3 & 10 \\ WQ & 1066 & 16 & 14 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The statistics of the 18 datasets.
# Covert Channel Based on Quasi-Orthogonal Coding
## 4 Simulation Tests
### Simulation No. 7  
Sorting was carried out for the parameters as for case no. 6 (sorting a block of data equal to 64, and a covert symbol with a length of 16 samples). The modulation for covert data is 8 PSK. The results are presented in Figure 12.  
As expected, sorting yields a lower BER. However, comparing Figure 7 with Figure 12 graphs, it is clear that for the given bit rates and power levels of the covert signal, better results are obtained (regardless of the sorting process) for 8 FSK modulation. 8 PSK modulation relative to 8 FSK requires more energy per bit.  
All simulations presented above aimed to show that, for the given waveform of the covert channel, it is possible to find an optimal sorting pattern to minimize imperfect SIC operation in the covert signal demodulator. This seems to be easier for FSK modulation and longer frames. However, it is necessary to keep in mind that the longer the frame, the lower the probability that channel gain is constant, which is the main assumption of this method.  
Figure 9: Use of 2-PSK modulation for covert transmission (**a**) Averaged FFT spectrum of unsorted cover and 2 PSK (**b**) Averaged FFT spectrum of sorted cover and 2 PSK (**c**) Averaged cross-correlation value of unsorted cover and 2 PSK (**d**) Histogram of the cross-correlation value of the unsorted cover and 2 PSK (**f**) Histogram of the cross-correlation value of the sorted cover and 2 PSK (**f**) Reception of covert information (2 PSK) for the unsorted and sorted cover (non-ideal SIC).  
Figure 10: Use of 8-PSK modulation for covert transmission (**a**) Averaged FFT spectrum of unsorted cover and 8 PSK (**b**) Averaged FFT spectrum of sorted cover and 8 PSK (**c**) Averaged cross-correlation value of unsorted cover and 8 PSK (**d**) Histogram of the cross-correlation value of the unsorted cover and 8 PSK (**e**) Histogram of the cross-correlation value of the sorted cover and 8 PSK (**f**) Reception of covert information (8 PSK) for the unsorted and sorted cover (non-ideal SIC).  
Figure 11: Use of 4-PSK modulation for covert transmission (**a**) Averaged FFT spectrum of unsorted cover and 8 PSK (**b**) Averaged FFT spectrum of sorted cover and 4 PSK (**c**) Averaged cross-correlation value of unsorted cover and 4 PSK (**d**) Histogram of the cross-correlation value of the unsorted cover and 4 PSK (**e**) Histogram of the cross-correlation value of the sorted cover and 4 PSK (**f**) Reception of covert information (4 PSK) for the unsorted and sorted cover (non-ideal SIC).
# An Equilibrium Strategy-Based Routing Optimization Algorithm for Wireless Sensor Networks
## 1 Introduction
### Contributions  
Based on the above analysis, the major contributions of this paper are summarized as follows:  
* We defined the link cost for the sake of energy saving and less delay, by considering the residual energy of the node, the transmission energy consumption and the forward energy consumption of the next hop.
* We generated the minimum routing graph based on the link cost. Here, in order to obtain this graph, the shortest path set of each node is calculated from the source node to the sink node according to the improved Dijkstra algorithm [28].
* We proposed an \"edge-cutting\" strategy to balance the load in the minimum routing graph, so that the network structure can be adjusted in real time to optimize the route by dynamically sensing the node load. Simulation results show that our algorithm can decrease the average network energy, balance the node load, extend network lifetime, and also reduce transmission delay and packet loss rate, showing a good network performance.  
The rest of this paper is organized as follows: in Section 2, we describe the network model and related definitions. Section 3 introduces the proposed ESRA algorithm in detail. The performance of our algorithm is analyzed and discussed in Section 4 according to relevant simulation results. Finally, the conclusions drawn are summarized in Section 5.
# Topology and Control Strategy of PV MVDC Grid-Connected Converter with LVRT Capability
## 2 Principle of the Converter
### LC Filter in the Converter
#### 2.4.2 Design of the Output Inductor  
The transfer function of LC filter \(G_{LC}(s)\) is described as:  
\[G_{LC}(s)=\frac{i_{o}(s)}{i_{2}(s)}\frac{1}{L_{o}C_{oc}}\frac{1}{s^{2}+{\omega_{r }}^{2}} \tag{16}\]  
\(\omega_{r}\) is the resonance angular frequency. \(C_{oc}\) is the equivalent output capacitance of the converter. It is equal to \(N_{m}C_{o}\). \(N_{m}\) is the number of modules in the converter.  
\[\omega_{r}=\sqrt{\frac{1}{L_{o}C_{oc}}} \tag{17}\]  
The main harmonic frequency of \(i_{2}\) is defined as \(\omega_{h}\), then  
\[\frac{|i_{o}(j\omega_{h})|}{|i_{2}(j\omega_{h})|}=\frac{1}{\left|L_{o}C_{oc}( j\omega_{h})^{2}+1\right|} \tag{18}\]  
The equation is sorted out to be:  
\[L_{o}=\frac{1}{C_{oc}\omega_{h}^{2}}\bigg{(}\frac{|i_{2}(j\omega_{h})|}{|i_{o }(j\omega_{h})|}+1\bigg{)} \tag{19}\]  
\(\alpha_{h}\) is defined as the proportion of harmonics current with frequency \(\omega_{h}\) to the rated output current. In the dc-dc converter, \(\omega_{h}\) is equal to \(4\pi f_{s}\). Then the minimum vale of output inductor \(L_{o1}\) can be described as:  
\[L_{o1}=\frac{1}{C_{oc}\omega_{h}^{2}}\bigg{(}\frac{|i_{2}(j4\pi f_{s})|}{I_{ o}\alpha_{h}}+1\bigg{)} \tag{20}\]  
During the process of LVRT, the discharge of output capacitor \(C_{oc}\) will lead to a surge current. The output inductor \(L_{o}\) should be designed to suppress the surge current.  
\[L_{o}\frac{di_{Lo}}{dt}=\eta V_{g} \tag{21}\]  
where \(\eta\) is the voltage sag depth of \(V_{g}\). \(V_{g}\) is the voltage of MVDC grid. Equation (21) is sorted out as:  
\[\Delta i_{Lo}=\frac{\eta V_{g}T_{LVRT}}{L_{o}} \tag{22}\]  
\(T_{LVRT}\) is the voltage falling time. \(\Delta i_{Lo}\) is the increment current during LVRT.  
\[\Delta i_{Lo}=\alpha_{i}I_{o} \tag{23}\]  
\(\alpha_{i}\) is the allowable overcurrent ratio. The inductance is obtained as:  
\[L_{o2}=\frac{\eta V_{g}T_{LVRT}}{\alpha_{i}I_{o}} \tag{24}\]  
The value of the output inductor should be designed as the maximum one between \(L_{o1}\) and \(L_{o2}\).
Due to their boiling point being higher than that of SF\({}_{6}\), as shown in Table 2, dry air has been used as a carrier gas. Because of that, the dielectric strength of the gas mixture is also reduced, so a balance must be found between getting the boiling point low enough, while maintaining the dielectric properties. The boiling point of PFK5 is higher than the one of HFO3E, which means that it should be more diluted. Moreover, this favors the fact that HFO3E has lower dielectric strength. The understanding of the mixing process of these new gas mixtures is critical to define the physical and chemical properties that affect the electrical insulation and arc quenching properties. Therefore, the goal of this paper is the study of the diffusional behavior of the binary and ternary mixtures made with PFK5, HFO3E, and dry air, as possible alternatives for SF\({}_{6}\) for medium voltage switchgear, to anticipate the oncoming change of regulations. A chamber has been designed and built to study the diffusion of these binary and ternary mixtures, using UV-Vis spectroscopy to measure the concentration in the chamber during the mixing process. This technique was already used by some authors to measure these kinds of gases [46; 47]. A diffusion process model is proposed to describe the mixture process; the model is fitted to the experimental concentration values in an algorithm developed in Scilab, which numerically calculates the diffusion parameters of the mixtures. In this work, a novel experimental equipment capable of measuring and collecting the online concentration of a gas mixture has been designed and tested with fluorinated gas mixtures, candidates to replace SF\({}_{6}\) in medium voltage switchgear. Although this study has been focused on the analysis of a specific case of new fluorinated gas mixtures, this non-destructive technology would be of great interest for any application in which it is necessary to determine the physicochemical properties of a gas mixture through its  
Figure 1: Molecular structures of (**a**) C\({}_{5}\)F\({}_{10}\)O (PFK5) and (**b**) C\({}_{3}\)F\({}_{4}\)H\({}_{2}\) (HFO3E). composition. Some examples include the design of gas mixtures that require specific characteristics, the monitoring of reactive mixtures, or the online malfunction detection and maintenance without the need to take samples that could compromise the integrity of the gas mixtures. Moreover, a mathematical model that simulates the diffusion process of a component in a binary or ternary gas mixture has been proposed and implemented. The mixing process of the new insulating gas mixtures and the time it takes for them to reach stability have been determined. This article intends to lay the groundwork for future experiments to study the effect of different perturbations, such as temperature, humidity, and concentration changes, among others, during the filling, mixing, and post-mixing process of the new insulating gas mixtures.
###### Abstract  
Objectives: To conduct a systematic review assessing quantitative enamel loss occurring after orthodontic debonding and clean-up procedures. Materials and Methods: A systematic search following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was performed on different databases (Embase, Medline, Scopus, Web of Science) for papers investigating volumetric enamel loss due to bracket and clear aligner attachment debonding and/or clean-up procedures. Studies investigating in vivo and in vitro articles published in the English language until 16 July 2022 were included. The study selection was then performed by two authors who screened the abstracts independently. Results: Of 421 screened abstracts, 41 articles were selected for full-text analysis. Finally, nine studies were included in this review. No in vivo papers were retrieved. In vitro papers investigated volumetric loss caused by the removal of metal brackets (n = 7), ceramic brackets (n = 1), and both (n = 1). The clean-up procedure varied among all investigations. Impressions at baseline and after debonding/clean-up were superimposed, and the volumes were subtracted using different 3D digital analysis software. Among all included studies, the volumetric loss of enamel ranged from 0.02 \(\pm\) 0.01 mm\({}^{3}\) to 0.61 \(\pm\) 0.51 mm\({}^{3}\) per tooth. Conclusions: Debonding and clean-up procedures produce enamel loss. The debonding/clean-up procedure that is able to cause the least enamel volume loss has yet to be identified.  
debonding; clean-up; enamel loss; debracketing; volumetric loss; enamel damage +
Footnote †: journal: _Sciences_
## 5 Discussion  
The developed blockchain-MEC V2X system achieved higher reliability than existing V2X models. The optimized blockchain-MEC V2X reduced the percentage of blocked tasks by an average of 41% compared to traditional V2X systems. This is due to introducing resources at the edge in an optimized way. Another performance improvement achieved by the developed blockchain-MEC V2X system is latency efficiency. The optimized model of the developed system achieved an improvement of average latency efficiency of 39% compared to traditional systems. The introduction of MEC servers reduced communication latency and, with the optimized offloading scheme, the resources were utilized in a higher-efficient way that achieved this latency performance improvement. Furthermore, the optimized model of the developed V2X system achieved higher energy efficiency than  
Figure 8: Energy performance of the developed systems compared with the traditional systems, for different values of deployed vehicles.  
Figure 6: Average percentage of blocked tasks for different values of deployed vehicles.  
Figure 7: Latency performance of the developed systems compared with the traditional systems, for different values of deployed vehicles.  
traditional systems, by an average of 36%. This is due to task offloading, which was performed in an optimized way that achieved the minimum energy consumption in handling each task. The developed system is limited to MEC resources; however, increasing such resources affects the overall cost of the network, including both OPEX and CAPEX.
## 6 Conclusions
In addition, beside complex cryptography at the network layer, physical layer security (PLS) is emerging as a new paradigm to robust cooperative systems security level against eavesdroppers by exploiting characteristics of wireless channels and noise [30; 31]. The advantage of PLS is that it can operate independent of higher layers, so that it can be used to augment existing security schemes. PLS quantifies a system secrecy by secrecy capacity, which basically is the maximum between 'zero' and 'the difference between capacity obtained at a legitimate destination and capacity obtained at eavesdroppers'. Since the secrecy capacity is usually in complex form, investigating the effect of the channel correlations on the secrecy performance of cooperative systems is a research-worthy, yet challenging, problem. This research was funded by the Ministry of Oceans and Fisheries, Korea (in part for 'Development of an Integrated Control System of Eel Farms Based on Short-Range Wireless Communication' and in part for 'Domestic Product Development of Marine Survey and Ocean Exploration Equipment'). [PERSON] proposed the idea of analyzing the effect of the correlation between two channels of each relay on the performance of a multi-AnF-relay system with the BRS scheme, derived analysis, and performed Monte-Carlo simulations. [PERSON] supported and supervised the research. The authors declare no conflict of interest.
###### Abstract  
The quality of grouting in pre-stressed pipelines plays a critical role in ensuring the safety and durability of pre-stressed concrete bridges. In this study, the transmission wave method was proposed as a means to assess the quality of grouting in pre-stressed pipelines. The ABAQUS finite element simulation (FE simulation) method was used to study the propagation of hammer stress waves in pre-stressed pipes. A full-scale test was conducted to verify the numerical simulation using the AGI-BWG instrument system developed to detect the quality of grouting. The results show that the propagation speed of transmitted waves increases and the frequency shifts towards higher frequencies with an increase in void length within pre-stressed pipelines. This research suggests that the propagation velocity of elastic waves in pre-stressed pipelines serves as a key indicator of grouting quality. The transmission wave method, based on hammer signals, proves to be an effective tool for detecting the quality of grouting in pre-stressed pipelines.  
pre-stressed pipes; transmission wave method; grouting quality; finite element simulation; full-scale test +
Footnote †: journal: Applied Sciences