esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Calculation Simulation and Analysis
### Performance Analysis
#### 3.1.2 Computational Efficiency  
Among the partial solution methods, for the explicit-explicit method, such as Equation (10), since \(\mathbf{M}_{\text{f}}\) and \(\mathbf{M}_{\text{s}}\) are diagonal matrices, solving the inverse matrices \(\mathbf{M}_{\text{f}}^{-1}\) and \(\mathbf{M}_{\text{s}}^{-1}\) only needs to take the inverse of the original matrix elements, which makes the iterative solution of the field variables of linear complexity, and thus this method is the fastest; for the implicit-implicit method, such as Equation (15), it is necessary to solve \(\mathbf{A}_{1}^{-1}\) and \(\mathbf{A}_{2}^{-1}\), which are non-diagonal matrices. Thus, solving the inverse matrix takes more time, plus the two equations of fluid and elastomer are solved separately and iteratively, so this method takes the longest time. For the explicit-implicit method, such as Equation (19), the iterative solution of the fluid acoustic pressure variable is of linear complexity, and the solution of the elastomer displacement field variable is of nonlinear complexity, so the solution speed of this method is located in the middle of the first two methods.  
In the global solution method, from Equation (21), it can be seen that the coefficient matrices of the field variables are all non-diagonal arrays, and the solution is of non-linear complexity either by explicit or implicit methods; then, from Equations (23) and (24), it can be seen that the two solution methods need to carry out the multiplication of large matrices once, and ignoring the computation of the matrix summation, the solution time of both of them is comparable, and the later case also proves this conclusion.
# MAC Protocols for mmWave Communication: A Comparative Survey +
## 6 Challenges & Future Research Directions
### Multi-Hop Connectivity  
To address the problems of mmWave propagation, wireless backhaul networks in the mmWave range require meshing the connection between nodes [76]. In mmWave mesh constructions, the main feature is relaying in multi-hop communications that ensure high range with LoS [10; 39; 77]. The LoS length is comparatively very low compared to microwave, and even though directional transmissions may overcome the increased path loss of mmWave bands, hard obstruction mediums may cause outages. To prevent outages, numerous APs must be deployed, which necessitates a wireless multi-hop architecture in situations where fiber is unavailable or extremely expensive [78]. Nonetheless, the majority of conventional state-of-the-art MAC protocols do not have the multi-hop feature.  
In [15], the authors have addressed that, instead of the comprehensive multi-hop communication capability introduced in IEEE 802.11 ay, existing mmWave standards offer only single or two-hop communications. The reason behind popularizing the single-hop approach might lie in the limitations of the current TDD cellular networks. This approach forces all network cells to transmit either downlink or uplink backhaul data, which is particularly inconvenient for relay nodes [79]. Such actions lead to performance bottlenecks because they are unable to utilize the two-hop link resources at their full potential. Hence, this challenge must be addressed in future research toward multi-hop compatible mmWave communication systems.
# Correspondence between Multilevel Graph Partitions and Tree Decompositions
## 4 Correspondence between Multilevel Graph Partitioning and Tree Decompositions
### Tree Decomposition to Multilevel Partition  
In this subsection, we describe an algorithm named TD2 MLP that converts a rooted tree decomposition with leaf property \(T\) into a multilevel partition \(P\). For every bag \(X\), the algorithm computes a cell \(C_{X}\). The cell \(C_{X_{r}}\) of the root bag \(X_{r}\) is by definition \(V\). TD2 MLP computes all other cells using the formula  
\[C_{X}=\left(\bigcup\mathcal{X}_{d}\right)\setminus\left(X\cap X_{p}\right)\]  
where \(\mathcal{X}_{d}\) is the set of descendant bags of \(X\), which includes \(X\), and \(X_{p}\) is the parent bag of \(X\). For every non-root bag \(X\) of \(T\) with parent \(X_{p}\), the algorithm creates a tree edge from \(C_{X}\) to \(C_{X_{p}}\).  
The constructed multilevel partition has useful properties. Lemma 11 shows that \(X\setminus X_{p}\) corresponds to the separator of \(C_{X}\), i.e.,  
\[S_{C_{X}}=X\setminus X_{p}\]  
Figure 4: Node-based multilevel partition of the running example. The colors correspond to levels. The depicted multilevel partition can be obtained when partitioning the partitions of example Figure 2b.  
Similarly, Lemma 10 shows that, if the tree decomposition additionally fulfills the small bag property, \(X\cap X_{p}\) corresponds to the boundary of \(C_{X}\), i.e.,  
\[B_{C_{X}}=X\cap X_{p}\]
# Fabrication of Eco-Friendly Graphene Nanoplatelet Electrode for Electropolishing and Its Properties
## Materials and Methods
### Characterization
#### 2.3.2 Electropolishing Characterization  
The roughness of each electrode was measured using a surface roughness tester (Bruker Korea Co., LTD., DektakXT, Seoul, Korea). The chemical composition of the GNP electrode, copper electrode, and pristine stainless steel was determined by X-ray Photoelectron Spectroscopy (XPS) (K-alpha plus, Thermo Fisher Scientific, Waltham, MA, USA). For comparison of the Cr/Fe ratios, each electrode was analyzed by Auger Electron Spectroscopy (AES) (PHI-700, ULVAC-PHI, Kanagawa, Japan). Analysis to determine the component content eluted from each electrode was performed by ICP (Thermo Fisher Scientific, iCAP 7000, Waltham, MA, USA) and TOC (Shimadzu, Total Organic Carbon Analyzer TOC-L, Kyoto, Japan). For the electropolishing process, 304 stainless steel (10 cm \(\times\) 10 cm) was connected to the positive electrode (anode). The negative electrode (cathode) was connected to a copper or GNP electrode. Then, a current density of 20 ASD was induced at a temperature of 45 \({}^{\circ}\)C for 10 min [8; 9; 10].
## 3 Result and Discussion
### Heat Flux Analysis  
The heat leakage of the MLI at the liquid hydrogen temperature is analyzed on the basis of the LBL model. Figure 9 presents the heat fluxes in the MLI for different vacuum conditions. As shown in Figure 9a, the total heat flux of the MLI is 0.46 W\(\cdot\)m\({}^{-2}\) if the MLI operates under the uniform ideal vacuum of 10\({}^{-3}\) Pa. As the reflector is approaching the cold boundary, the radiation heat flux decreases and the solid conduction heat flux increases. Due to the ideal vacuum environment, the gas conduction heat flux is kept at a very low level.  
Figure 9b shows the heat fluxes of the MLI in a non-ideal vacuum condition. The interlayer pressure distribution is shown in Figure 6 (the testing case 4). It can be seen in Figure 9b that gas conduction and solid conduction contribute to the heat leakage considerably. The gas conduction heat flux may increase as the reflectors approach the cold boundary due to the presence of gas molecules. On the other hand, the solid conduction heat flux decreases as the reflector temperature decreases. This may be induced by the variation of heat transfer resistance. The gas conduction becomes the dominant heat leakage mechanism as the reflector approaches the cold boundary. Moreover, it can be seen that the radiation heat flux decreases as the reflector temperature decreases for the two situations. If the interlayer pressure deviates from the ideal vacuum, the residual gas conduction will contribute a considerable amount of heat leakage, and the solid conduction heat flux varies in a different manner in comparison with the heat leakage of the MLI in an ideal vacuum environment. Therefore, illuminating the interlayer pressure of the MLI in practical engineering is very necessary for the prediction of the thermal insulation performance.
# Design and Performance Analysis of an In-Band Full-Duplex MAC Protocol for Ad Hoc Networks
## 4 Mathematical Analysis
### Probability Analysis
#### 4.1.1 Probability Equation for BFD Communications  
When a PT initiates the transmission by sending RTS to a PR, BFD will take place if the PR has data for the PT; the probabilities are given below:  
* The conditional probability that the PT (or MS) has a data packet for PR is \(\frac{m(n-1)\lambda}{\lambda_{tot}}\).
* The probability that the corresponding PR has at least one data packet for PT in time \(T_{1}=T_{RTS}+T_{S}+T_{WAIT}\) is \((1-e^{-\lambda T_{1}})\). Therefore, the probability for BFD communication is, \[P_{BFD}=\frac{m(n-1)\lambda}{\lambda_{tot}}\Big{(}1-e^{-\lambda\ T_{1}}\Big{)}.\] (2)
## 5 Methods
### Parameter Estimation Using the MCMC Algorithm  
Model parameters were estimated using the Bayesian framework by sampling the posterior parameter distribution via an affine-invariant MCMC algorithm using the emcee v3 toolkit [47; 48]. For data reliability, the data used to calibrate the model consisted of the 7-day backward moving average of newly confirmed cases in Tokyo. Considering the rapid increase in the spread of the coronavirus pandemic in Tokyo, we assumed that the count data of newly confirmed cases followed a Gaussian distribution with a mean given by \(\xi I\) and variance given by \(1/\tau\). \(E_{0}\) and \(I_{0}\), the number of exposed and infectious individuals on the first day during the period, respectively, were also estimated. For simplicity, the number of recovered individuals, or \(R_{0}\), was set at zero; thus, the number of susceptible individuals, or \(S_{0}\), was calculated as \(S_{0}=N_{t}-E_{0}-I_{0}\) where \(N_{t}\) represented the total population of Tokyo. Consequently, we estimated seven parameters for each period: \(\beta\), \(\epsilon\), \(\rho\), \(\xi\), \(E_{0}\), \(I_{0}\), and \(\tau\). We used the same uniform distributions for all periods as the prior distributions for each parameter, as described in Table S1. The statistics of the posterior distributions are also listed in Table S1. The convergence of the MCMC chains is judged based on the integrated autocorrelation time in accordance with a previous study [47]. When the number of samples satisfied the inequality given by,  
\[M\geq C\hat{\tau}_{f}(M),\]where \(\hat{\tau}_{f}(M)\) was the estimated integrated autocorrelation time, the relative error in the estimation of the posterior distribution was considered sufficiently small.
# Smart Home Technology Solutions for Cardiovascular Diseases: A Systematic Review
## 3 Results
### Available Smart Home Technologies for CVD Management
#### 3.1.2 Communication Systems  
The communication system comprises an internal network connecting the sensors, the gateway functioning as an interface between the internal and external networks, and an external network transferring the captured data from the gateway to applications [14]. The internal network consists of interconnected sensors using popular short-range wireless protocol communication technologies such as Zigbee, Wi-Fi, and Bluetooth [14]. The internal communication systems used in the studies differed. For example, studies had used devices interconnect through short-range wireless protocol communication technologies such as Wi-Fi [67, 70] and Bluetooth [67, 68, 69, 70, 71].  
A gateway is a significant component in an SHT, performing functionalities including network interconnection, network management, and application management; however, the gateway could be a dedicated device or a mobile device such as a smartphone, a tablet, or a local smart sensor node placed in the environment or mounted on a computer server [14]. The studies have used computers [67, 69] and dedicated devices [66, 68, 69] as the gateway to receive the sensor data through the internal communication system, perform the necessary management functions, and forward the information to the end-user application over the external network.  
The gateway relays data using different external network systems such as fixed telephony networks, Wi-Fi, cellular networks, satellite networks, and other technologies to healthcare applications [14]. The healthcare applications were installed at a computer running in external networks as a web application [67, 68, 69, 70, 71] and app [70], and internet communication (telephony networks, cellular networks) was established to transfer information from the gateway to healthcare applications running in an external network [67, 68, 69, 71].
## 6 Simulation and Results
### Packet Size Adaption Performance
Figure 18 shows the comparison of the throughput with the MAB policy, the random packet size, the fixed packet size policy and the EKF policy as the number of mobile nodes is increasing. The blue line represents the MAB adaptive packet size. When the number of mobile nodes increases, the throughput slowly increases. The red line represents the fixed packet sizing scheme. The purple line represents the random packet sizing scheme. The throughput sharply declines as the nodes increase in number. Due to the increasing number of mobile nodes, the probability of conflicts increases and the channel quality is poorer, therefore, the throughput experiences a sharp drop. The green line represents the EKF. When using the EKF, the throughput slowly decreases as the number of mobile nodes increases.The throughput of the MAB adaptive packet size is clearly better than the other three strategies as the number of mobile nodes is increasing. Figure 18: The comparison of the throughput among different packet-sizing schemes as the number of mobile nodes is increasing.
## 3 Results
### Baseline AAPM-132 Tolerance Levels Analysis  
The mean TRE errors (mean \(\pm\) SD) between the TG132-refDVF, provided by the Report, and the TG132-testDVF, generated by the Velocity AI, were 2.31 \(\pm\) 1.13 mm for the phantom external contour (body). The overall % of voxels with a TRE of less than or equal to 2 mm was 45% (rounded to nearest 1%). This percentage value was considered as the OTL for the registrations evaluated in this study. Since the AAPM Report did not provide any dose error tolerance level information, the OTL could only be considered in terms of the spatial accuracy.  
Figure 4 reports, in the correlation graph below, the percentage of voxels with a TRE error \(\leq\)2 mm versus the percentage of dose errors \(\leq\)the DET (5% of prescribed dose in this analysis). The Figure 4 x-axes show the percentage of voxels with a TRE within 2 mm (as suggested by the AAPM-TG132) across the 20 registrations for each OAR. Significant variability was observed for all the organs and registrations. The markers '+' indicate the cases for which the percentage of voxels with a TRE < the 2 mm OTL, represented as a vertical red line in the graph. The percentages higher than the OTL (indicated as a 'circle' marker) were found in 30% (body), 55% (left parotid), 40% (right parotid), 31% (larynx), 85% (oral cavity), 100% (mandibula), 25% (brainstem) and 80% (spinal cord) of all the cases (N = 20), for all registrations. The Figure 4 y-axes report the corresponding dose error percentage within 3.5 Gy (5% of prescribed dose), across every registration for each OAR. The percentages of 66.3 \(\pm\) 5.8%, 63.9 \(\pm\) 16.1%, 62.0 \(\pm\) 17.5%, 69.3 \(\pm\) 16.4%, 82.2 \(\pm\) 11.1%, 85.1 \(\pm\) 8.0%, 66.7 \(\pm\) 28.2% and 93.6 \(\pm\) 6.2% were found for the body, parotids left and right, larynx, oral cavity, mandible, brainstem and spinal cord, respectively. Overall, the correlation graphs showed a generally higher proportion of voxels involving a dose error of less than 5% rather than a TRE of less than 2 mm (visualised as markers above the diagonal bisector line of each panel). The relation between the TRE and dose error percentage is quite complex and depends on a single registration (see also Section 3.5). For almost all OARs, some registrations passed the spatial OTL analysis but showed a quite low dose error <5% percentage, and vice versa.  
Figure 4: Correlation graph of the percentage of the TRE within 2 mm and percentage of voxels within 5% of dose error (3.5 Gy), across the 20 registrations and for each OAR. The ‘+’ markers indicate the cases for which the percentage of voxels was less than the 45% OTL, indicated as vertical red line. The OTL value was derived using TG132 datasets, as described in Section 3.4.
## Case Study
### Network Determination and Training
#### 4.2.4 The Number of Discretized Bridge Elements  
As mentioned in Section 2.3, the dimension of input data for the LSTM model is contingent upon the number of divided elements \(N_{e}\) of the bridge structure. When \(N_{e}\) is too small, accurately reflecting the stress state of the bridge structure becomes challenging. Conversely, a large value of \(N_{e}\) may result in increased input data volume and significantly reduced computational efficiency. Hence, it is essential to ascertain the optimal value of \(N_{e}\) to strike a balance between prediction accuracy and computational efficiency.  
The evaluation metrics of predicted displacement and acceleration for \(N_{e}\), ranging from 50 to 250, are depicted in Figures 15 and 16. The results indicate a gradual increase in both the Pearson correlation coefficient and \(\mathrm{R}_{\mathrm{W}}^{2}\) with the increased \(N_{e}\), suggesting an enhanced correlation between predicted and theoretical values. Furthermore, APVE demonstrates a downward trend in error values as \(N_{e}\) increases. The dimensional evaluation metrics exhibit a declining trend as \(N_{e}\) increases from 50 to 150. However, the rate of decline becomes slightly more moderate when \(N_{e}\) increases from 150 to 250. Hence, the optimal \(N_{e}\) in this study is determined to be 150, with the corresponding length of the bridge element set at 0.21 m.  
Figure 15: The influence of the number of bridge structure elements on displacement prediction accuracy.
# Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array
## 5 Numerical Experiments, Results, and Discussion
### Two Wideband Signals
Secondly, the signals are coherent. Obtaining better and quality performance when \(\phi_{2}\leq 3^{\circ}\) would require more antenna elements. When \(d=0\), for \(\phi_{2}>3^{\circ}\), WSF and ML techniques clearly perform better than MUSIC or PWD and follow the CRLB. WSF and ML techniques share the same performance in the partially correlated cases, i.e., \(d=0.5\) and \(d=1\). WSF and ML techniques have slightly lower RMSE than MUSIC. This means that, in partially correlated cases, WSF and ML techniques have better performance than MUSIC. ML techniques, WSF, and MUSIC share the same performance in the almost-incoherent scenario with \(d=8\). The performance of PWD when \(d=0\) is identical to that when \(d=0.5\), \(d=1\), and \(d=8\). Low variance is assumed by CRLB [26]. Based on [31], RMSE and CRLB agree by approximation when the DoA theoretical standard deviation of the signals is less than the half-angle of separation. This also holds for the cases studied. When \(\phi_{2}=3^{\circ}\) and \(d=0\), the CRLB is \(2.1^{\circ}\) for the first signal and \(2.3^{\circ}\) for the second signal, which is higher than half of the separation angle \(3/2=1.5^{\circ}\). Figure 4: Monte Carlo samples against \(\phi_{2}\) when \(SNR=20\) dB and the second signal is delayed by d samples. (**a**) \(\hat{\phi}_{1}\), \(d=0\) (**b**) \(\hat{\phi}_{2}\), \(d=0\) (**c**) \(\hat{\phi}_{1}\)\(d=0.5\) (**d**) \(\hat{\phi}_{2}\), \(d=0.5\) (**e**) \(\hat{\phi}_{1}\), \(d=1\) (**f**) \(\hat{\phi}_{2}\), \(d=1\) (**g**) \(\hat{\phi}_{1}\), \(d=8\) (**h**) \(\hat{\phi}_{2}\), \(d=8\).
# Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories
## 3 Results and Discussion
### Application of NDVI and TBI to Wavelength and Water Status  
For leaf water status assessment, studies have been conducted to establish a regression model using different wavelength bands. In addition to the multiple-band models employed in some studies [14; 29; 30; 31], the two-band model has been the focus of many studies in applications with different mathematical expressions, where \(\lambda_{1}/\lambda_{2}\)[6; 14; 15; 16; 17; 32] and \((\lambda_{1}-\lambda_{2})/(\lambda_{1}+\lambda_{2})\)[6; 18; 19; 20; 33; 34] have been employed. In the present experiment, NDVI and TBI were adopted to establish the regression model. The results are shown in Figures 9 and 10, respectively.  
Figure 8: Autocorrelation coefficient of wavelengths. (**a**) RAW; (**b**) LOG; (**c**) DIFF.  
Figure 9: _Cont._Figure 10: Cont.  
Figure 9: Normalized difference vegetation index (NDVI) of tomato wavelength and leaf water status. (**a**) RAW and WC; (**b**) RAW and MC; (**c**) LOG and WC; (**d**) LOG and MC; (**e**) DIFF and WC; (**f**) DIFF and MC.
## 4 Conclusions  
Our MD-MTD simulations clearly demonstrate that the solvent plays a critical role in the cellulose hydrolysis reaction catalyzed by novel enzyme mimic polymeric PSSA/PIL catalysts. The PSSA and cellulose could form stronger hydrogen bonding interactions, thereby disrupting the hydrogen bonding network in the cellulose substrate. Moreover, the [EMIM]CI solvent strengthens the PSSA/cellulose interaction, making the hydrolysis reaction more effective. The PIL chain in the catalyst plays a vital role in enhancing the catalytic activity of our unique polymer catalyst. The PIL chain has much stronger solvation free energies in both water and [EMIM]CI solvents, which enhances the solubility of the PSSA chains, as well as the cellulose substrate.  
Figure 11: The 2D free energy surface contour plot for system G, involving DP 20 PSSA and DP 20 PIL in water. CV1 and CV2 represent the numbers of hydrogen bonds formed between them.  
**Author Contributions:** Conceptualization, X.Q.; Investigation, X.S. and X.Q.; Writing-Original Draft Preparation, X.S.; Writing-Review & Editing, X.Q.; Supervision, X.Q.; Project Administration, X.Q.; Funding Acquisition, X.Q.  
**Funding:** This research was funded by United States National Science Foundation CBET 1264896.  
**Acknowledgments:** Computations were performed at the High Performance Computing Center (HPCC) at the University of Arkansas.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.