esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
LOSO validation (with and without personalization) was applied, resulting in the customized ResNet model, which achieved the lowest mean absolute error, with values of 9.43 mmHg for systole and 6.88 mmHg for diastole. [PERSON] et al. [2] developed a U-net deep learning architecture that utilizes fingertip PPG signals for non-invasive estimation of the Arterial Blood Pressure (ABP) waveform. The authors collected data about 100 subjects from MIMIC-III database. Using their deep learning model, the tool converted the PPG signal from milliVolts (mV) to ABP measured in mmHg. The mean absolute error metrics achieved for SBP were 3.68 \(\pm\) 4.42 mmHg, and for DBP were 1.97 \(\pm\) 2.92 mmHg. [PERSON] et al. [32] proposed a deep learning model to learn how to perform signal-to-signal translation from PPG to Arterial Blood Pressure (ABP). The authors obtained a translated ABP signal, which extracted the SBP and DBP values accordingly to ease the comparative evaluation. With this approach, they achieved an average absolute error below 5 mmHg, with 70% confidence for SBP and 95% confidence for DBP without complex feature engineering. [PERSON] et al. [33] developed a convolution-based deep autoencoder (DAE) model with the application of multi-domain adversarial training to learn cross-domain features. This approach enabled the authors to achieve a mean absolute error (MAE) of 7.945 and 4.114 for uncalibrated DAE in systolic BP (SBP) and diastolic BP (DBP), respectively. After using 80 s of data for calibration, the MAE of DAE was reduced to 5.424 and 3.144 accordingly. [PERSON] et al. [34] created PPG2 ABP for non-invasively estimating the continuous Arterial Blood Pressure (ABP) waveform by utilizing Photoplethysmography (PPG) signals. The authors used a two-stage cascaded deep learning-based method to estimate the continuous ABP. To validate the developed tool, the authors applied the mean absolute error metric and obtained values for SBP and DBP of 5.727 \(\pm\) 9.162 mmHg and 3.449 \(\pm\) 6.147 mmHg, respectively. In contrast to the aforementioned approaches, the aim of this study is to develop a neural network-based learning model that takes only the SpO2 signal as input. Furthermore, it is not guaranteed that the training subset is free from leakage of patient data contained in the test and validation subsets. For the proposed method, the dataset used in the experimental protocol was completely partitioned based on patient data, isolating the training, test, and validation subsets.
## 5 Numerical Investigation on DOP
### Effect of Striking Velocity  
Figure 18 shows the DOPs of UHPC targets with 2% steel fiber volume content at striking velocities of 340 m/s, 400m/s, 450 m/s, 500m/s, 550 m/s, 600 m/s, 650m/s, 700 m/s, 750 m/s, and 800 m/s. The numerical results manifest that for the UHPC targets of 100 MPa, 120 MPa, 140 MPa, 160 MPa, and 180 MPa, the DOP increases with the increasing striking velocity. Taking the scenario of the 140 MPa UHPC target as an example, the DOPs at striking velocities of 400 m/s, 500 m/s, 600 m/s, 700 m/s, and 800 m/s are 38.8 cm, 53.4 cm, 71.7 cm, 95.0 cm, and 131.6 cm, respectively. The increments between adjacent striking velocities are 14.6 cm, 18.3 cm, 24.3 cm, and 39.0 cm, respectively, and present an exponentially growing trend. For UHPC targets with other compression strengths, the growing trend is similar. The above findings show an exponential relationship between the DOP and striking velocity for UHPC.
## 3 Results and Discussion  
### Density Measurements  
The density measurements described in the previous section yielded the experimental data presented in Figure 5. The collected data show that an increase in printing temperature corresponded to an increase in the material's density, while an increase in layer height, on the contrary, demonstrated an inverse dependence. At the same time, the nozzle speed fluctuations, according to the experimental data, had an insignificant effect on the density of the material. Fluctuations in the specimens' density can be caused by various factors, such as fabrication defects, such as pores, delamination, etc.; as a consequence of selected printing parameters and their combinations; as well as inconsistency in the printing process itself. The observed peak in density at 201 \({}^{\circ}\)C, despite the general decreasing trend in density for specimens manufactured between 203 and 210 \({}^{\circ}\)C, could be attributed to a specific combination of parameters at this temperature that temporarily enhanced the material's compaction or reduced the occurrence of defects.
# Performance Evaluation of UAV-Enabled LoRa Networks for Disaster Management Applications
## 3 Proposed UAV Topology Control Algorithm
### Connection Recovery and Maintenance (CRM)
Instead, all the UAVs would trigger the NRM almost at the same time and all would lose the connection with the covered GNs. In order to avoid this kind of situation, UAVs that are covering a set of GNs determine, immediately after losing the connection to the BS, the neighboring UAV with which they share the highest percentage of GNs. If this percentage is above a certain threshold of \(p_{shared}\), one of the two UAVs must be redundant. Every UAV has a fixed ID that has been assigned before the operation. The UAV with the lowest ID among the two proceed to cut by half its _persist_ intervals, while the other UAV double their _persist_ intervals. During the next time steps, one of the redundant UAVs, the one with the lowest value of _persist_, triggers the NRM before the others and reposition itself closer to the BS. This return, in some cases, helps to re-establish a connection between partitioned UAVs and the BS.
# Scalable Fabrication of Si-Graphene Composite as Anode for Li-ion Batteries
## 3 Results and Discussions
It is noticed that the columbic efficiencies exceeded 100% at several points, which could be attributed to the uncontrolled temperature during the half-coin cell test. A relatively higher room temperature during the discharge process would deliver a discharge capacity that is higher than the charge capacity, resulting in the columbic efficiency going beyond 100%. Conclusively, the improved capacity retention of the Si-graphene anode is attributed to the uniform distribution of Si nanoparticles and  
Figure 5: (**a**) Specific charge/discharge capacity of Si-graphene and Si anodes at 0.05C; (**b**) specific charge/discharge capacity of Si-graphene anode at 0.05C, 0.1C, and 0.5C, respectively; (**c**) specific charge/discharge cycling performance of Si-graphene and Si anodes at a current rate of 0.1C in the first 100 cycles. graphene nanosheets, although the initial specific capacity is decreased due to the intrinsic low capacity of graphene. To further understand the improved performance of the Si-graphene electrode compared to pure Si anode, electrochemical impedance spectroscopy (EIS) was performed, and the Nyquist plots for both electrodes are shown in Figure 6. It is seen from Figure 6 that the Si-based coin cell showed a higher electrochemical resistance than the Si-graphene composite coin cell. An equivalent circuit (see insert of Figure 6a) was used to fit the EIS plots, where R\({}_{0}\) is the bulk resistance, CPE is constant phase element, CPE\({}_{1}\mid\) R\({}_{1}\) is assigned to interfacial electrode/electrolyte charge transfer impedance, CPE\({}_{2}\mid\) R\({}_{2}\) is assigned to SEI layer at the lithium anode surface, and W represents a Warburg (mass transfer) impedance at low frequency. The EIS parameters after fitting the EIS plots to the equivalent circuit are summarized in Table 1. The charge transfer resistance R\({}_{1}\) of the Si-Graphene based cell (224 \(\Omega\)) is 40% lower than that of the Si-based cell (355 \(\Omega\)), as shown in Table 1, which indicates faster electrochemical conversion reaction kinetics of the Li/Si-graphene cell as compared to the Li/Si cell. This is because of the excellent electrical conductivity of graphene nanosheets. The bulk resistance R\({}_{0}\) and the SEI resistance R\({}_{2}\) of the Si-graphene based cell are lower than those of the Si-based cell, as shown in Table 1, suggesting the higher electrode conductivity and the denser SEI layer of the Si-graphene based cell than the Si-based cell. In addition, the reduced Warburg resistance of the Si-graphene composite coin cell indicates the enhanced mass transfer activity of Li ions, which promotes the electrochemical performance. The electrochemical impedance measurements further confirm that the improved performance of the Si-graphene anode is attributed to the graphene nanosheets that tolerate the volume change of Si nanoparticles and increase the electrical conductivity.
# The Impact of Stimulation Intensity on Spatial Discrimination with Multi-Pad Finger Electrode
## 3 Results
### Spatial Discrimination  
The results of the spatial discrimination test of six electrode pads are presented in Figure 4a for each stimulation amplitude and phalange (distal phalanx--pink, proximal phalanx--brown), while the average success rates per phalange (regardless of the amplitude) and amplitude level (regardless of the phalange) are shown in Figure 4b,c, respectively.  
The spatial discrimination was significantly better on the distal phalanx (\(p<0.05\), Figure 4b). The success rates (medians/IQRs) were 73.3/33.3% for the distal and 60/30% for the proximal phalanx.  
The Friedman test showed that there were no statistically significant differences in spatial discrimination across the current amplitude levels (Figure 4c). Median success rate values were in the range from 53.3% (110% ST) to 70.0% (130, 170, 190, and 230% ST) with an overall average median value of 65.4% (5.3%) and interquartile range of 22.8% (6.1%). The trends in Figure 4a follow the main effect analysis with success rate for proximal phalanx being consistently higher compared to that of the distal phalanx.  
Figure 4: (**a**) Success rate in spatial discrimination obtained for different current amplitudes for the distal phalanx (pink) and proximal phalanx (brown); (**b**) Main effect of phalanx—the success rate for each phalange averaged across current amplitude levels; (**c**) Main effect of current amplitude—success rate for each current amplitude level averaged across the two phalanges. The red line, box, whiskers and red mark indicate the median, interquartile range upper/lower boundaries and outlier, respectively. Asterisks indicate significant differences (*: \(p<0.05\)).  
Figure 3: The localization (LT), smearing (SMT) and discomfort (DT) threshold as the function of sensation threshold: (**a**) The estimated probability density functions. Shaded parts of the estimated density curves are confidence intervals (95% of the data); (**b**) Boxplot representation of the threshold distribution. The red line, box, whiskers and red mark indicate the median, interquartile range upper/lower boundaries and outlier, respectively.
# The Influence of Motion Data Low-Pass Filtering Methods in Machine-Learning Models
## 3 Results
In contrast, for models using RFC, a filter with a higher order and cutoff (i.e., sixth-order with cutoff of 10-12 Hz) results in the optimal performance (Table 3). Figure 1: The step length calculated from gait data filtered using Butterworth low-pass filter with different filter cutoff frequencies (4-20) and orders (2, 4, 6) in (**a**) regular gait, (**b**) gait-slip, and (**c**) gait-trip trials. Figure 2: ICC results between the standard filtering method (fourth-order Butterworth with cutoff of 6 Hz) and other Butterworth filters for all calculated gait features, including: (**a**) step length, (**b**) step width, (**c**) toe clearance, (**d**) gait speed, (**e**) foot angle, (**f**) shank angle, (**g**) thigh angle.
## 2 Secure Network Encryption Protocol
### Data Transmission with Strong Freshness  
For strong freshness, two messages are being exchanged, which are shown in Figure 2. In this protocol, \(A\) first sends a fresh nonce \(N_{A}\) to \(B\) with a request message \(R_{A}\). When \(B\) returns its response (_i.e._, the actual data are being transmitted here), it includes \(N_{A}\) while calculating the MAC. The inclusion of the nonce ensures that \(B\) has sent this message in response to \(A\)'s request. This ensures a total order through the request to response pair and is referred to as strong freshness in SNEP. Moreover, this completes the challenge to response exchanges and authenticates the identity of \(B\) to \(A\).  
\begin{table}
\begin{tabular}{l l} \hline
**Notation** & **Meaning** \\ \hline \(A,B\) & Principals, such as communicating nodes. \\ \hline \(N_{A}\) & Nonce generated by \(A\). \\ \hline \(C_{A}\) & Counter generated and updated by \(A\). \\ \hline \(\chi_{AB}\) & Master secret key shared between \(A\) and \(B\). \\ \hline \(K_{AB}\) & Symmetric key between \(A\) and \(B\) used for encryption of \\  & messages. This is derived from \(\chi_{AB}\). \\ \hline \(K^{\prime}_{AB}\) & MAC key between \(A\) and \(B\) used for MAC calculation. \\  & This is derived from \(\chi_{AB}\). \\ \hline \(\{M\}_{K_{AB}}\) & Encryption of message \(M\) with the encryption key \(K_{AB}\). \\ \hline \(\{M\}_{\langle K_{AB},IV\rangle}\) & Encryption of message \(M\) with the encryption key \(K_{AB}\) \\  & and the initialization vector \(IV\). \\ \hline \(MAC(K^{\prime}_{AB},M)\) & Computation of the message authentication code (MAC) \\  & of message \(M\), with the key \(K^{\prime}_{AB}\). \\ \hline \end{tabular}
\end{table}
Table 1: Notations being used in SNEP.  
Figure 1: Data transmission protocol with weak freshness.
## 4 Effects of Porosity Change on the Radionuclides Transport through the Buffer Material  
To prove how the effect of temperature on the porosity of bentonite significantly impacts the results of the safety assessment, we used a test case to prove this. Assuming that the canister will fail after the closure of the disposal repository, the failure time is divided into three periods: early, medium, and late. Early failure assumes that the canister will fail within 1-1000 years after closure, mid-term failure assumes that the canister will fail within 10-100,000 years after closure of the disposal facility [46]. Herein, we present the early failure scenarios. The early failure case assumes that the failure time of the canister is one year after disposal repository closure. Figure 8 shows the case where the simulation time is 20,000 years and the minimum transport distance and concentration penetration path between the fracture and the paths of the canister are called Q1, Q2, and Q3 where Q1 is the path at the vertical intersection of the canister and fracture, Q2 is the path at the Excavation Disturbed Zone (EDZ) under the disposal tunnel, and Q3 is the path at the junction of EDZ and the disposal tunnel top [47].  
This simulation only evaluated the radionuclides transport to Q1 in near-field. Then, the influence of porosity change on the transport processes in buffer material was also evaluated. We found that the effect of temperature change on the porosity is the most obvious near Q1. Radionuclides of I-129, Ni-59, Sr-90, and Cs-137 were selected for simulation analysis in early failure case. The model domain is 0.35 m.  
The calculated saturated hydraulic conductivity for compacted bentonite is 1.9 \(\times\) 10\({}^{-13}\) m/s based on the computation model [48]. The experimental value of the hydraulic conductivity is 6.4 \(\times\) 10\({}^{-14}\) m/s for the compacted FEBEX bentonite at dry density of 1650 kg/m\({}^{3}\) and is subject to granitic water [49]. Thus, the hydraulic conductivity of the compacted bentonite is very low. Therefore, only the diffusion transport was considered in this study. For the radionuclide release model, the degradation rate (DR) and instant release coefficient (IRF) were considered [46]. Table 5 lists the model parameters.
# Efficient Parallel FDTD Method Based on Non-Uniform Conformal Mesh
## 4 Discussion and Conclusions  
The FDTD algorithm is a commonly used numerical method in electromagnetic fields. It enjoys advantages of easy understanding and simple implementation, wide application, high precision under reasonable grid resolutions, and good stability under CFL conditions. However, when it comes to complex structure and large size problems, the number of grids will become very large, and the traditional FDTD algorithm obviously cannot bear such a huge computational burden.  
In order to solve the above problems, a parallel FDTD method based on non-uniform conformal mesh is introduced. We use virtual mesh to exchange magnetic field information in adjacent subregions, and the electric field update is divided into inner and outer parts; moreover, the magnetic field exchange and internal electric field update are carried out at the same time. In this method, the whole iteration process is interrupted only once, which can be easily extended to the CPML absorption boundary and can realize the unification of internal electromagnetic field update formulas and PML update formulas; thus, it is very suitable for parallelization and avoids unnecessary data transmission. We test the parallel effects of a single node and multi-node on the supercomputing platform. It is shown that when the number of processors is small, especially with the single node, the parallel efficiency is higher than 90% and the acceleration ratio is close to linearity; however, with the increase in the number of processors, the cross-node communication time involved will also rise, and the parallel efficiency will decrease, but the overall parallel effect is good, which can effectively solve large-scale computing problems.
## 5 Verification by Field Monitoring Data  
The stratum displacements induced by various effect factors and total displacement are shown in Table 3. The effect ratio of ground loss is over 95%, which plays a decisive role. The ratios to total of frictional force \(f_{d}\) and \(f_{s}\) are much less than 1%; they are too small to be considered. The ratios of additional thrust \(q\) and synchronous grouting pressure \(p\) are a little larger, but are no more than 3%. As shown in Figures 8 and 16, the variations of these two factors make some small quantitative changes, but no qualitative differences. It shows that this conclusion has good universality for different working conditions. The total ground settlement is compared with field monitoring data in Figure 17. From this, we can see that there are some differences between them. For example, the calculating settlement maximum is 10.25 mm, while monitoring data is 9.7 mm. The difference rate is 5.3%, which belongs to acceptable error range. What is more, they have the same trend and the difference is stable. This is because that the elastic model is adopted in this paper, but in practice with the redistribution of stress, a pressure arch will be formed around the tunnel. The soil out of the arch is prevented from entering the loosening zone, which cause the field monitoring data to be slightly smaller than theoretical calculation result.
## 3 Results
### Selection of Characteristic Wavelength  
There are often multiple correlations between different bands of the spectrum. These correlated band signals have redundancy, which increases the amount of calculation but hardly provides more effective information. At the same time, it may introduce noise signals or cause overfitting of the model. In spectral analysis, characteristic wavelengths are often extracted to reduce redundant variables. In this research, the CARS method was used to select characteristic wavelengths for the prediction of N, P\({}_{2}\)O\({}_{5}\), and K\({}_{2}\)O content. The final results are shown in Table 2.  
Figure 4: Spectral curves of fertilizers after preprocess (each color of the curve in the figure represents the spectrum of a sample).  
Figure 3: Original spectral curves of fertilizers (each color of the curve in the figure represents a sample).  
After the characteristic wavelengths were selected, the spectral variables were reduced to about one-tenth of the original, which greatly reduces invalid information. The characteristic wavelengths selected for N, P\({}_{2}\)O\({}_{5}\), and K\({}_{2}\)O were all concentrated in 1050-1640 nm, indicating that the near-infrared band had a greater contribution to detection than the visible light band.
## 5 Conclusions  
Ni-Ti-Ag shape memory alloys have been successfully synthesized by the casting technique using a VAR furnace. Ag, as an additive material, was incorporated into Ni-Ti based alloys at different weight percentages (1.5, 3 and 4.5 wt.%). The result of the FESEM analysis showed that Ag homogeneously dispersed into the Ni-Ti-based alloy, creating two main phases (austenite and martensite). While XRD analysis demonstrated the emergence of the main phases: Ni, Ti and Ag, as evidenced in the XRD peaks. In addition, the increase in weight percentage of Ag led to an increase in Ag peaks. DSC examination demonstrated that the austenite transformation starting temperature at (\(-\)1.6 \({}^{\circ}\)C) and transformation temperatures with increasing Ag wt.% are 19.7 \({}^{\circ}\)C, 12.7 \({}^{\circ}\)C and 12.3 \({}^{\circ}\)C, respectively. The addition of Ag significantly affected the mechanical properties of the Ni-Ti-based alloy in regard to its micro-hardness and compressive strength. The maximum value of compressive strength was obtained in the alloy with 3 wt.% Ag, while the results of micro-hardness testing showed a slight decrease with the increase in weight percentage of Ag.  
Data curation, K.D.S. and W.A.K.A.-M.; Formal analysis, F.A.; Investigation, K.D.S. and W.A.K.A.-M.; Methodology, K.D.S. and W.A.K.A.-M.; Resources, W.A.K.A.-M.; Supervision, B.E.; Validation, W.A.K.A.-M.; Visualization, F.A.; Writing--original draft, K.D.S.; Writing--review & editing, W.A.K.A.-M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
We acknowledge support by the Deutsche Forschungsgemeinschaft (DFG) German Research Foundation and the Open Access Publishing Fund of the Technical University of Darmstadt. The authors would like to also thank the University of Technology- Iraq.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test**Sample & **0\% Ag** & **1.5\% Ag** & **3\% Ag** & **4.5\% Ag** \\ \hline Compression stress (MPa) & 1055 & 1155 & 1507 & 1000 \\ \hline Hardness (HV) & 508 & 468 & 465 & 463 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Compressive stress and microhardness results.  
Figure 5: Micro-hardness (Hv) of Ti-Ni-Ag alloys.