esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Bidding Results
The deployed bidding strategies for WPPs and DR aggregators in the energy and regulation markets (Case S2, Case S4, and Case S6) are shown in Figure 7. Compared with the individual bidding strategies of WPPs and DRAs, the operation of the coalition dispatches wind energy or DR resources to track the regulation signal with the aim of maximizing the total profit. In the cooperation scheme, the up-regulation is provided by the DR resource while the down-regulation is provided by wind power. Figure 7a shows that the WPP provides less capacity in the up-regulation market. The reason is that the wind generator has to reduce its schedule to allow for the up-regulation service, which leads to a loss of revenue. To achieve maximum benefits, most of its capacity is submitted to the energy market. From Figure 7c, it can be seen that the coalition actively participates in the regulation market during 6-9 and 15-24, since during these hours, the regulation market price is higher compared to the other time periods. Comparing Figure 7a-c, more resources are allocated to the regulation market in Case S6 compared to Case S2 and Case S4. The reason is that the cooperation scheme can optimize the operation states of wind generator and DR resources to respond to regulation signals more economically. In this way, the operator can provide regulation capacity by dispatching wind power or DR resources at lower costs. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Player**} & \multirow{2}{*}{
\begin{tabular}{c} **Uncoordinated** \\ **Profits (USD)** \\ \end{tabular} } & \multicolumn{2}{c}{**Nucleolus-Based Method**} & \multicolumn{2}{c}{**Shapley-Value Method**} \\ \cline{3-6}  & & **Profit (USD)** & **Surplus** & **Profit (USD)** & **Profit (USD)** & **Surplus** \\ \hline WPP 1 & 55,534.5 & 57,563.95 & 2029.45 & 57,563.95 & 2029.45 \\ \hline DRA & 25,782 & 27,811.45 & 2029.45 & 27,811.45 & 2029.45 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of results of allocated profits based on the game theory methods. Figure 6: The expected profits from each market.
# Calibration of a Hybrid Machine Tool from the Point of View of Positioning Accuracy
## 2 Materials and Methods
### Kinematic Structure of the O-X Glide Mechanism
\begin{split} u_{1}=x_{p}-x_{R1}+p_{1}-\sqrt{l_{1}^{2}-\left(z_{p} -d\right)^{2}}\\ u_{2}=-x_{p}+x_{R2}+p_{2}-\sqrt{l_{2}^{2}-\left(z_{p}+d\right)^{2}}\\ u_{3}=y_{p}\end{split}  \tag{5}\]  
Upon closer examination, we find that the expressions we have derived are structurally identical, with the only difference being the sign in front of the square root. This square root is a key component, representing the basic relationship between the internal and external coordinates of the mechanism, which is essential for configuring the control system. The expression can generally describe the solution of direct kinematics:  
\[P(x_{p},y_{p},z_{p})=\text{f}(s_{1},s_{2},s_{3}) \tag{6}\]  
By transforming the previous expressions, it is determined on the basis of Equations (4) and (5) and the following expression is obtained:  
\[t_{3}x_{p}+t_{4}z_{p}+t_{5}=0 \tag{7}\]  
based on which, we derive xp as  
\[x_{p}=t_{6}+t_{7}\cdot z_{p} \tag{8}\]  
\(z\) is obtained by inserting expression xp into Equation (5):  
\[t_{8}z_{p}^{2}+t_{9}z_{p}+t_{10}=0 \tag{9}\]  
After the substitutions are introduced in the previous expressions, we derive  
\[ \begin{split} t_{1}=x_{R1}-p_{1}+u_{1},\\ t_{2}=x_{R2}+p_{2}-u_{2},\\ t_{3}=2t_{2}-2t_{1},\ t_{4}=-4d,\\ t_{5}=t_{1}^{2}-t_{2}^{2}-l_{1}^{2}+l_{2}^{2},\ t_{6}=-(t_{5}/t_{3}),\\ t_{7}=-(t_{4}/t_{3}),\ t_{8}=1+t_{7}^{2},\\ t_{9}=2(t_{6}-t_{1})\cdot t_{7}-2d,\\ t_{10}=(t_{6}-t_{1})^{2}+d^{2}-l_{1}^{2}.\end{split}  \tag{10}\]  
Based on the previous equations, the following expressions are obtained:  
\[ \begin{split} z_{p}=\frac{-t_{9}-\sqrt{t_{9}^{2}-4t_{8}t_{10}}}{2 t_{8}}\\ x_{p}=t_{6}+t_{7}\cdot z_{p}\end{split}  \tag{11}\]  
The obtained expressions were used to configure the control system of the machine tool prototype based on the O-X glide mechanism.
## 6 Application II: Fast Filtering of Time-Vertex Signals
### Spectrum-Adapted Approximation of Time-Vertex Filtering  
Due to the high complexity of the spectral decomposition required to compute \(\mathbf{U}_{G}\), approximation methods have been developed to accelerate joint time-vertex filtering, such as Chebyshev2D [64], ARMA2D [67], and the Fast Fourier Chebyshev algorithm [50].  
As outlined in Algorithm 3, we can use the methods described in Section 3 to efficiently approximate the filtering of time-vertex signals. The overall complexity of our method is \(\mathcal{O}(NT\log T+TKM)\), where \(M\) is the number of nonzero entries in \(\mathbf{L}_{G}\). The FFT of \(N\) discrete-time signals of length  
Figure 9: Two time-vertex filters defined for a random Erdös–Renyi graph with 500 vertices and edge probability 0.2. (**a**) An ideal lowpass filter; (**b**) a wave filter. The blue dots correspond to the positions of eigenvalue pairs.  
\(T\) takes \(\mathcal{O}(NT\log T)\). The loop computes \(T\) spectrum-adapted approximations to matrix functions with polynomials of degree \(K\), and thus has a complexity of \(\mathcal{O}(TKM)\) for sparse \(\mathbf{L}_{G}\).
```
1:Input weighted undirected graph \(G\) with \(N\) vertices, time-vertex signal \(\mathbf{X}\in\mathbb{R}^{N\times T}\), filter \(h\)
2:Output time-vertex filtered signal \(\mathbf{Y}=h(\mathbf{L}_{G},\mathbf{L}_{R})\mathbf{X}\in\mathbb{R}^{N\times T}\)
3:\(\tilde{\mathbf{X}}\leftarrow\) FFT of \(\mathbf{X}\), where the \(n\)th row of \(\tilde{\mathbf{X}}\) is the 1D FFT of the \(n\)th row of \(\mathbf{X}\), for \(n=1,\cdots,N\)
4:Estimate the spectral density of \(G\)
5:for\(t\) in \(1:T\)do
6:\(f_{t}(\lambda_{G})=h(\lambda_{G},\lambda_{R_{t}})\)
7: Find the best order \(K\) polynomial approximation \(p_{k}(\lambda_{G})\) to \(f_{t}(\lambda_{G})\) via interpolation on the warped Chebyshev points (described in Section 3.1), or weighted least squares regression with evenly spaced abscissae and weights from the estimated spectral PDF (described in Section 3.2)
8: Compute \(p_{k}(\mathbf{L}_{G})\tilde{\mathbf{x}}_{t}\), where \(\tilde{\mathbf{x}}_{t}\) is the \(tth\) column of \(\tilde{\mathbf{X}}\)
9:\(tth\) column of \(\tilde{\mathbf{Y}}\leftarrow\,p_{k}(\mathbf{L}_{G})\tilde{\mathbf{x}}_{t}\)
10:endfor
11:\(\mathbf{Y}\leftarrow\) inverse FFT of \(\tilde{\mathbf{Y}}\), where the \(n\)th row of \(\mathbf{Y}\) is the 1D inverse FFT of the \(n\)th row of \(\tilde{\mathbf{Y}}\)
12:return\(\mathbf{Y}\)
```  
**Algorithm 3** Spectrum-adapted approximate time-vertex filtering.
# Investigation of THz Absorptive Signatures in Opioids
## 3 Molecular Dynamics (MD) Simulation Results  
First, we performed MD simulations of one single molecule for: (a) fentanyl, (b) oxycodone, and (c) hydrocodone using Gaussian [18]. Hydrocodone is also an opioid used for pain relief, but is less potent than oxycodone. MD simulations of a supercell containing multiple molecules are postponed to future work. Our previous experience with lactose monohydrate indicated that single-molecule simulations may yield results very close to supercell ones.  
As shown in Figure 1, fentanyl has its lowest absorption line at 0.337 THz (337 GHz) and its strongest line at around 1.3 THz. The vibrational motion for the 0.337 THz of fentanyl viewed from various directions is shown in Figure 2.  
Oxycodone and hydrocodone have similar structures to fentanyl chemically but lack the strong signature around 0.337 THz according to the vibrational modes shown in Figure 3. However, they both display moderately strong signatures around 1.4 (more precisely 1.358 for oxycodone, 1.405 THz for hydrocodone) and stronger signatures around 1.6 THz (\(\sim\)1.595 for oxycodone and \(\sim\)1.625 THz for hydrocodone). The resemblance of the two spectra is explained with almost identical molecular structures of the two with one exception: hydrocodone has a hydroxyl group at the carbon-14 position of oxycodone [17]. This is evidenced by the vibrational motion for the 1.358 THz mode of oxycodone shown in Figure 4 and the 1.40 THz mode of hydrocodone shown in Figure 5.  
Figure 1: Absorption lines of fentanyl.  
Figure 2: Fentanyl, the lowest 0.337 THz vibration mode. Three views of atomic motions (blue arrows) and dipole moment derivative (brown arrow). Color legends: Grey—carbon, blue—nitrogen, red—oxygen, and white—hydrogen.
## Measurements Results
### Stability of the CR's Resistance Value  
As observed in Figure 8, the stability of the three types of CR with a wire strain that accomplishes the geometry of the CRs, are less than 0.03 \(\upmu\Omega/\Omega/\)day. The measurements were made using a MIL 6000B bridge, with an applied power of 10 mW and within a temperature-controlled environment of \(\pm\)0.02 \({}^{\circ}\)C. The measurement system gives the 1:1 or 10:1 ratio between the measured CR and a 10 k\(\Omega\) ESI standard resistor. Because the reference standard resistor has stability better than 0.1 \(\upmu\Omega/\Omega/\)year, the stability of the measured CR was not affected.
# BER Aided Energy and Spectral Efficiency Estimation in a Heterogeneous Network
## 4 Conclusions  
Instead of SINR, we proposed the simpler-to-measure BER as the key performance indicator, by abstracting the performance degradation due to various (generally non-AWGN) impairments, by the according AWGN ones, which have the same effect on BER as any specific distortion.  
It emerged that inserting small cells into HetNets of any distribution of BSs significantly improved both the energy and spectral efficiency. So, with smaller distances in between the BSs and UEs of contemporary networks--e.g., LTE and LTE-A, the trend is the rationalization and optimization of the signal coverage, by reinforcing it in the areas of increased traffic.  
Such a strategy seems to be appropriate in the tested exemplar environments, but it needs to be enhanced and fine-tuned with other sophisticated tests, taking into account other impairments, e.g., RF interference, traffic patterns, bandwidth and channel allocation, etc., with management that is aimed to enable the projected QoS level, complexity reduction, and fair distribution.  
This work was aimed at analyzing and verifying the simplifyied real-life HetNet performance testing, by focusing the BER rather than analog values such as SINR. This paves the way for the following R&D and field tests, while also taking into account various  
Figure 7: Simulation results for: (**a**) SE and (**b**) EE.  
design and deployment issues, by using sophisticated test hardware and dedicated software simulation tools.  
Conceptualization, J.M., V.L. and A.L.; methodology, J.M. and A.L.; software, J.M.; validation, A.L. and V.L.; formal analysis, J.M. and A.L.; investigation, A.L.; resources, J.M. and A.L.; data curation, A.L.; writing--original draft preparation, J.M. and A.L.; writing--review and editing, V.L.; visualization, J.M. and A.L.; supervision, V.L.; project administration, A.L.; funding acquisition, A.L. and V.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.
## 5 Conclusions  
With the continuous advancement of smart sensors, wireless communications, and IoT applications and services, wireless body area networks (WBANs) are becoming increasingly popular. However, several adversarial attacks and the attendant weaknesses make it challenging to develop secure IoHT applications from a security perspective. The widespread usage of anomaly detection with deep learning has been employed to construct appropriate models for detecting and mitigating adversarial attacks. Nonetheless, these attacks could deceive deep learning models into misclassifying the model results. This can lead to erroneous decisions, such as incorrect patient diagnoses and erroneous medicine administration. This paper addressed the effect of adversarial attacks on deep learning-based anomaly detection models and applied several safeguard techniques to defend networks against such adversarial attacks. The effectiveness of the suggested model  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Subject No.** & **Accuracy** & **Loss** & **Recall** & **Precision** & **F1-Score** \\ \hline
**Subject 1** & 98\% & 0.05\% & 98\% & 97\% & 97\% \\ \hline
**Subject 2** & 99.36\% & 0.005\% & 100\% & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Retraining the model as a defense method.  
Figure 7: The model after retraining as a defense method.  
was assessed in the face of adversarial attacks that were intended to give attackers control over the classification process used by the anomaly detection algorithm. The effectiveness of the suggested model was evaluated using the FGSM and BIM adversarial attack techniques. The suggested model showed its capacity to identify anomalies in the face of various adversarial attacks. More specifically, the evaluation results revealed that the model achieved an average F1 score of around 97% and an accuracy of 98% in the face of such attacks. However, as a future task, more scenarios regarding adversarial attacks need to be explored. In addition, more countermeasures and defense methods, such as reactive defense, should be investigated.  
Conceptualization, A.A. and M.A.R.; formal analysis, A.A. and M.A.R.; investigation, A.A. and M.A.R.; methodology, M.A.R.; software, A.A.; validation, A.A.; writing--original draft, A.A.; writing--review and editing, M.A.R. All authors have read and agreed to the published version of the manuscript.  
This research has not received funding.  
The authors gratefully acknowledge Qassim University, represented by the Deanship of Scientific Research, on the financial support for this research under the number (COC-2022-1-1-J-25671) during the academic year 1444 AH/2022 AD.  
The authors declare no conflict of interest.
## 3 Methodology
### PPG Signal Preprocessing  
#### 3.1.1 Gaussian Filter  
Signal cleaning can be completed using a variety of different filters. In our work, a Gaussian filter was used for high-frequency noise removal. A Gaussian filter [41] is a nonuniform lowpass filter, and consequently is a popular choice among the general-purpose filters for the removal of the high-frequency components of a PPG signal. This filter performs remarkably well as a bandpass filter in the frequency domain when it is tuned by the values of the mean center frequency and standard deviation. In our work, a Gaussian filter was used for noise removal, as shown in Equation (1) below:  
\[G(x)=e^{-\frac{(x-x)^{2}}{2\pi^{2}}} \tag{1}\]  
In Equation (1), the standard deviation can be adjusted by determining the degree of smoothing. The function performed the best for smoothing the PPG signal when the value of the center frequency was set to \(c=0.068\) and the standard deviation was set  
Figure 5: Proposed model for the quantitative analysis of glucose level from smartphone video.  
to \(w=0.0543\). A sample PPG signal with high-frequency noises which was cleaned by applying the Gaussian filter is given in Supplementary Material Figure S6.
## 2 Simulation Methodology
### Dynamic Rolling Analysis  
By carrying out transient simulations, the computing time required to arrive at the steady solution would increase in comparison to the steady-state procedure, since the behavior of a rolling tire is entirely transitory. As a result, a successful approach was developed to mimic rolling conditions. The dynamic rolling analysis was performed over two steps.  
Step 1: Loading Analysis  
The tire was simulated under different pressures and loadings with static-state mechanical simulations. The displacements were then obtained under a non-rotating state. Different loadings (1, 2, 3, 4, 5, 6 kN) were applied to the rim, and an inflation pressure of 32 psi was placed at the interior surfaces of the tire. The loading force was applied by a remote force to the inner rim surface with a scope to the center point of the rim. This was performed with the aim of simulating the effect of the shaft. The boundary conditions for the loading analysis are shown in Figure 3a.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Rubber** & **Body-Ply** & **Bead Wire** \\ \hline Density (kg/m\({}^{3}\)) & 1400 & 1200 & 6500 \\ Poisson’s ratio & - & 0.3 & 0.3 \\ Young’s modulus (Mpa) & - & 500 & 207 \\ Mooney–Rivlin constant (Mpa) & C10 = 8.061 & - & - \\  & C01 = 1.805 & - & - \\ Thermal conductivity & 0.293 & 0.293 & 60.5 \\  & Hysteresis & 0.1 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties used in the simulation [22].  
Figure 3: Boundary conditions: (**a**) loading analysis; (**b**) rolling analysis.  
Figure 2: Mesh for the simulation.  
The boundary conditions for the upward displacement of the road were established using the displacement data from Step 1. Different speeds were added to the road component to simulate the rolling effect, and this speed was applied constantly to the road. The rim was considered to have a constant displacement in any direction that was not free rolling around the \(x\)-axis. Following the investigation, the overall strain energy density of the tire was discovered. This step was performed via transient analysis, so it took longer than Step 1. The boundary conditions applied in the rolling analysis are demonstrated in Figure 3b. The simulation time was 1s for 10 time steps.  
Introducing two steps into the rolling analysis aims to speed up convergence. If loading analysis was utilized in the dynamic rolling analysis, the loading and rolling analyses should also be linked and used, in addition to the damping ratio. As a result, obtaining the converged solution would take longer. Although the displacement control approach does not produce \"real\" dynamic simulation results, the quantity of computer resources required for the simulation is substantially decreased; thus, these results are adequate for the present applications. When comparing the static and dynamic rolling models for the reference case, the achieved results showed that the average energy density varied at around approximately 4%. Hence, the simplified model is able to ensure a low computational cost with acceptable error.
## 3 Results  
### Model Performance  
In both phases, we conducted three approaches (RF, GRNN, and SVM) to reconstruct GPP values and applied five validations in each phase. In Phase 1 (1895-1937), tree-ring indices (TRI) in four plots were involved in our regression (Figure 4), and the rest of the three plots did not have available data from 1895 to 1937. GRNN obviously outperformed the other approaches of RMSE, MAPE, MAE, and Adjusted R2 of GRNN were the following: 559.45 g C m\({}^{-2}\) year\({}^{-1}\), 13.91%, 428.45 g C m\({}^{-2}\) year\({}^{-1}\), and 0.53, so we chose GRNN. The SVM was the worst whose RMSE (1203.08 g C m\({}^{-2}\) year\({}^{-1}\)) was more than twice as much as GRNN's. In Phase 2 (1938-1985), all seven plots were used in our research. The RF had the lowest RMSE (520.21 g C m\({}^{-2}\) year\({}^{-1}\)), the lowest MAPE (13.17%), and the lowest MAE (404.64 g C m\({}^{-2}\) year\({}^{-1}\)), even though SVM had the highest adjusted R square (0.69). The R square could not fit non-linear curves very well, and the rest of the metrics for SVM were the worst among the three. Then, we chose RF as our best model in Phase 2 and GRNN ranked second. For the same approach, the performance in Phase 2 using seven plots was always better than that in Phase 1 using four plots where RF's RMSE in Phase 1 and Phase 2 were 580.98 g C m\({}^{-2}\) year\({}^{-1}\) and 520.21 g C m\({}^{-2}\) year\({}^{-1}\), while GRNN RMSEs in Phase 1 and Phase 2 were 559.45 g C m\({}^{-2}\) year\({}^{-1}\) and 541.03 g C m\({}^{-2}\) year\({}^{-1}\), respectively. Even though the GRNN was the best model in Phase 1, its metrics were still worse than the RF's (the best model in Phase 2). More details about the 5-fold validations can be found in the Appendix A.
# PPG and Bioimpedance-Based Wearable Applications in Heart Rate Monitoring--A Comprehensive Review
## 4 Biological Signals for Wearable-Based HR
### Pulse wave transit time (PTT)  
By combining PPG sensors from the wrist and finger it is possible to calculate pulse wave transit time (PTT) or the time required for a pulse wave to traverse the distance between two arterial sites within the human body [37] as shown in Figure 7b. PTT can also be derived by acquiring PPG from a single location and ICG P-point [52].
## 2 Materials and Methods
### Methodology
#### 2.2.3 Phase 3: Data Import and Storage  
The extracted satellite data and the rain gauge data are imported into MariaDB. MariaDB is one of the open source relational databases. Although this database originated from the developers of MySQL, it has better performance in average import time [64]. The MariaDB Server code base is ensured to remain open for usage and contributions on technical merits ([[https://mariadb.org/](https://mariadb.org/)]([https://mariadb.org/](https://mariadb.org/)) (accessed on 12 June 2021)). The MariaDB is being hosted in CentOS Linux. As compared with Microsoft Excel, MariaDB can handle a huge amount of data and there is no limitation problem faced when pumping the data into it. Indexing is performed for the tables for the fastest retrieval and the joining of the tables. Research showed that MariaDB has improved compression performance for flash devices, storage efficiency, and CPU utilization [65]. All the extracted IMERG data are transformed from HDF5 files into datasets in the tables in the databases with Python program. CSV files retrieved from DID are imported into tables using the data importing tool. The data can be retrieved anytime by anyone with a proper connection setup.