esa-sceva/satcom-chunk-collection huggingface dataset:
# Unobtrusive Vital Sign Monitoring in Automotive Environments--A Review
## 5 Optical Methods
### Photoplethysmography  
Photoplethysmography (PPG) is a well-known technique allowing measurement of blood volume changes in the skin or in a small part of the body [79]. In classical (local) PPG, the light source typically is a light-emitting diode (LED, either in the VIS and/or NIR frequency range) while the sensor is a photodetector (PD). In _transmissive_ PPG (tPPG), the light transmitted through the tissue is detected by a photodetector opposite the light source, while in _reflective_ PPG (rPPG), the photodetector detects light that is scattered and/or reflected from the skin (see Figure 9).  
Note that, for tPPG, transmission length is limited to a few mm up to cm (allowing typical locations like earlobe or finger), while, in rPPG, penetration depth typically is up to 3 or 4 mm. Reflective PPG relies on sufficient ambient or actively injected light which is then modulated by blood flow in the upper and lower plexus of the skin capillaries. As a result, on their (typically banana-shaped) way through the skin, about 1 out of 20,000 photons reaching the skin from the light source will finally follow a path that allows them to exit from the skin and fly back to the detector collecting this flow of photons.  
In 2010, the integration of reflective PPG monitoring into the steering wheel of a BMW 650i test car was demonstrated by [PERSON] and colleagues [26]. The authors used a reflective pulse oximetry sensor connected to an OEM III module manufactured by Nonin Medical (Nonin Medical Inc., 13700 1 st Avenue North, Plymouth, MI, 55441-5443 USA). In the same year, [PERSON] et al. demonstrated SpO\({}_{2}\) sensing in the steering wheel of a Mercedes S-Class demonstrator cockpit (DAIMLER AG, Mercedesstrasse 137, D-70327 Stuttgart, Germany) [25], next to ECG monitoring in the steering wheel and capacitive ECG monitoring in the seat. These authors employed a Nonin 8000R reflective probe. Last but not least, also in 2010, [PERSON] and colleagues [23] reported on a steering wheel design featuring conductive ECG fabrics and reflective PPG using a single frequency (infrared light at 940 nm).  
Figure 8: Proper locations for cameras. Locations can be differentiated by the angle of attack \(\rho\).  
However, a general drawback of this approach is that 100% sensor coverage can not be guaranteed. When integrating reflective PPG sensors into the wheel, the driver has to voluntarily touch/cover the hidden PPG sensor with perfused skin. Hence, this technique may be especially useful for on-demand applications.
## 3 Results and Discussion
### Analysis of the Alloy Phase Diagram and Initial Microstructures
#### 3.1.2 DTA Thermal Analysis Curve and Characteristic Temperatures  
In the eutectic equilibrium state, the eutectic composition of the compound changes gradually along with the atomic number of the RE element, and the solubility of the RE in the solid Mg decreases gradually as the atomic radius of the RE element increases. The first derivative of the cooling curve is \(dT_{c}/dt\), which is related to the solidification reactions for the different phases, which can be determined to determine the critical solidification characteristics of as-cast ZM6 alloys. The heating temperature was 780 \({}^{\circ}\)C, and the heating speed was 40 \({}^{\circ}\)C/min. Figure 3 shows the differential thermal analysis (DTA) curves of the as-cast and as-compressed ZM6 alloys.  
From Figure 3a, when the temperature reaches at 636.54 \({}^{\circ}\)C, the elements in as-cast ZM6 alloy are all dissolved, and the eutectic transition occurs between 567.91 \({}^{\circ}\)C and 586.90 \({}^{\circ}\)C. It can be observed from Figure 3b that two defined peaks are present in the as-compressed ZM6 alloy. The ZM6 alloy consists of dissolved Zn and RE in \(\alpha\)-Mg matrix and a small eutectic Mg-Zn phase, Mg-RE phase, and Mg-Zn-RE phase. Due to the addition of RE elements, a certain number of eutectic compounds are formed. When the temperature is cooled to the primary \(\alpha\)-Mg phase formation reaction at 644.56 \({}^{\circ}\)C (point A), the non-equilibrium eutectic reaction occurs at 560 \({}^{\circ}\)C [15] and ends at 527.89 \({}^{\circ}\)C, resulting in  
Figure 3: DTA curve: (**a**) DTA curve of the as-cast ZM6 alloy; (**b**) DTA curve of the as-compressed ZM6 alloy.  
Mg\({}_{12}\)Nd compounds being obtained. The temperature of the whole eutectic reaction is within the range of 567.91 \(\pm\) 10 \({}^{\circ}\)C.
# Self-Supervised Spatio-Temporal Graph Learning for Point-of-Interest Recommendation
## 4 Experiments
### Experimental Setup
#### 4.1.3 Evaluation Metrics  
To evaluate the model's performance, we adopted two general metrics, Recall@N and mean average precision (MAP@N), where N means the top-N POIs recommended by the model. In our experiments, N was set to 5, 10, 20, and 50 for a comprehensive comparison.
## 3 Target State Estimation and Observer Trajectory Quality Metric
### Observer Trajectory Quality Metric
If the measurement packet can be transmitted from \(A_{i}\) to the base station successfully, the information gain from \(A_{i}\) is \(\mathbf{\hat{I}}_{i,k}=\mathbf{\hat{H}}_{i,k}^{\mathrm{T}}\mathbf{\hat{R}}_{i,k}^{-1}\mathbf{\hat{H}}_{i,k}\). For planning purposes, we need the value of the information gain in the future times (\(k+l\)), thus the expectation must be used. At future times (\(k+l\)), the prediction of the information gain matrix \(\hat{\mathbf{I}}_{k+l|k}\) is defined as  
\[\hat{\mathbf{I}}_{k+l|k}=\sum_{i=1}^{N}\beta_{i,k+l}\cdot\hat{\mathbf{\hat{H} }}_{i,k+l}^{\mathrm{T}}\mathbf{\hat{R}}_{i,k+l}^{-1}\hat{\mathbf{\hat{H}}}_{i, k+l} \tag{29}\]  
\[\mathbf{\hat{H}}_{i,k+l}=\
abla_{\hat{\mathbf{\hat{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{ \mathbf{     }}}}}}}}}}}}}}}\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbfmathbfmathbfmathbf{ \mathbfmathbf {       }}}}}}}}}}}}}}}}}} \mathbf{}}} \mathbf{\hat{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbfmathbf{ \mathbf{ \mathbf{         }}}}}}}}}}}}}}}}} \hatAccording to Equation (32), the observer-trajectory quality metric combines the sensing and communication channels into a utility function. This quantity metric is used to optimize the trajectories of the UAVs, so that the UAVs intelligently move to the locations that maximize both the information gains and the successful transmission probabilities in communication links with the base station.
## 2 The Forecasting Model
### Least Squares Support Vector Machine (LSSVM)  
Least Squares Support Vector Machine is an improved technique for Support Vector Machine (SVM) proposed by [PERSON] et al. [40] that transforms the nonlinear problem into the linear estimation problem in high-dimensional feature space through least squares value functions and equality constraints to improve the computation speed and generalization ability [16; 17; 18].  
Figure 1: The process of Ensemble Empirical Mode Decomposition (EEMD). EMD, Empirical Mode Decomposition; IMF, Intrinsic Mode Function.  
Supposing that the training set is \((x_{i},y_{i}),i=1,2,\cdots,N\), where \(N\) is the total number of training samples, \(x_{i}\in R^{m}\) are input samples and \(y_{i}\) are output samples, the linear function of LSSVM in high-dimensional feature space is:  
\[f(X)=\omega^{T}\varphi(X)+b, \tag{3}\]  
where \(\omega\) is the weight vector; \(b\) is the bias constant. Considering the complexity of the function and the error of fitting in the regression problem, it can be expressed as an equality constraint optimization problem:  
\[\underset{\omega,b,e}{\text{min}}(\omega,e)=\frac{1}{2}\omega^{T}\omega+ \frac{\xi}{2}\sum\limits_{i=1}^{N}e_{i}^{2} \tag{4}\] \[s.t.\ y_{i}=\omega^{T}\varphi(X)+b+e_{i},\,i=1,2,\cdots,N\,\,.\]  
Setting \(\alpha_{i}\) as the Lagrange multipliers, through establishing Lagrange equation, Equation (4) can be transformed into unconstrained optimization problem, as shown in Equation (5):  
\[L(\omega,b,e,\alpha)=J(\omega,e)-\sum\limits_{i=1}^{N}\alpha_{i}(\omega^{T} \varphi(x_{i})+b+e_{i}-y_{i}). \tag{5}\]  
According to the Karush-Kuhn-Tucker condition of non-linear optimal programming, the parameters \(\alpha_{i}\) and \(b\) are obtained, and substituted them into Equation (3) to get:  
\[f(X)=\sum\limits_{i=1}^{N}\alpha_{i}\varphi(x_{i})^{T}\varphi(x_{j})+b. \tag{6}\]  
For LSSVM, the commonly used kernel functions include linear kernel function, polynomial kernel function, Sigmoid kernel functions, and RBF (Radial Basis Function) kernel function. Linear kernel function is a special case of RBF kernel function, and compared with polynomial kernel function and Sigmoid kernel function, RBF kernel function has fewer parameters to be determined. As long as the value of kernel parameters is determined, the performance of kernel function can be played at the minimum cost. So, Radial Basis Function (RBF) is selected as the kernel function of LSSVM to achieve the prediction function of LSSVM in this article. The Radial Basis Function is a scalar function along the radial symmetry. Because the function is similar to the Gaussian distribution, it is also called a Gaussian kernel function:  
\[f(X)=\sum\limits_{i=1}^{N}\alpha_{i}\exp\left(\frac{-\left\|x_{i}-x_{j} \right\|^{2}}{2\sigma^{2}}\right)+b. \tag{7}\]
## 2 Data and Method
### Data Processing
#### 2.4.3 Geometrical Correction of Image  
The geometric correction of a remote-sensing image is to correct all kinds of distortion in the process of imaging and obtain a consistent image [47]. A new image required by a map projection or graphic expression is an important link in remote-sensing image processing and the most complex and dense part of calculation [48]. In this paper, the geometric correction module built in ENVI is used for correction.
# Radiation Hardened Read-Stability and Speed Enhanced SRAM for Space Applications
## 2 Proposed RHRSE-20T SRAM
### Write Operation  
Assume the cell is in a state where logic '1' is stored (Q = S1 = S3 = 1, QB = S0 = S2 = 0). To perform a write operation for '0', BL is set to '0' and BLB is set to '1'. Both WL and WWL are driven HIGH, activating all three pairs of access transistors during the write operation. As WL and WWL transition from '0' to '1', the following occurs: node Q switches from '1' to '0' through transistor N9, node S3 switches from '1' to '0' through transistor N14, and node S1 switches from '1' to '0' through transistor N12. Simultaneously, node QB switches from '0' to '1' through transistor N10, node S0 switches from '0' to '1' through transistor N11, and node S2 switches from '0' to '1' through transistor N13. This completes the write operation of '0' in the SRAM.
## 5 Analysis
### Performance of Design Methods  
All capacities presented in this study are unfactored (i.e., Characteristic) capacities. Calculated capacities (\(Q_{c}\)) using the four chosen design methods were plotted versus the interpreted capacities (\(Q_{m}\)) obtained from the NYU criterion for all possible plugging conditions in Figure 2. The 1:1, 1:2, and 1:0.5 lines were also plotted to underline the ideal relation between \(Q_{c}\) and \(Q_{m}\) and its boundaries. The 1:1 line represents an ideal scenario where \(Q_{c}\) is equal to \(Q_{m}\), while the 1:2 line and the 1:0.5 line indicate over or underestimation by a factor of 2, respectively. Additionally, key statistics are listed for each design method, such as the mean, standard deviation, and the coefficient of determination (R\({}^{2}\)). The mean and standard deviation are the descriptive statistics describing the data distribution with the mean representing the central value, and the standard deviation representing the variation in the data. R\({}^{2}\) is used to describe how well the fitted line, the 1:1 line, in this case, captures the behavior of the real data. Note that R\({}^{2}\) can have a negative value when the model selected does not follow the trend of the data. This can be clearly noticed in Figures 2 and 3 for the FHWA design method in the unplugged condition, where it is clear that the 1:1 line (chosen model) does not follow the trend of the data (red dots).  
Figure 2: Calculated (\(Q_{c}\)) vs. interpreted (aka measured, \(Q_{m}\)) capacities for all plugging conditions for each design method. Statics of \(Q_{c}/Q_{m}\) are shown in the inset tables.  
A uniform datum of comparison between the performance of the various methods was established by normalizing the calculated capacities by the interpreted capacities (\(Q_{c}\)/\(Q_{m}\)), for each test, which also helps to better visualize the target value of 1. Values higher than 1 indicate that the design method is over-estimating the pile capacity, while values less than 1 indicate that the design method is conservative in its capacity estimation.
# Frequency-Shifted Optical Feedback Measurement Technologies Using a Solid-State Microchip Laser
## 3 Applications of the Laser Feedback Interferometer
### Refractive Index Measurement
BS, beam splitter; PD, photo detector; AOM\({}_{1}\) and AOM\({}_{2}\), acousto-optic modulators; L, lens; Mr, reference mirror; S, sample; ME, optical wedge (measurement mirror); RF\({}_{1}\) and RF\({}_{2}\), radio frequency signal generators; FM, frequency mixer. (Figure reproduced from Ref. [73]). [PERSON] [74] also reported a novel method to measure the liquid refractive index based on the double-beam laser frequency-shift feedback. The system is shown in Figure 14. Two parallel beams are adjusted to each monitor the displacement of the liquid surface and the M\({}_{\text{R}}\) at the bottom of the tank. When the liquid level increases by \(\Delta h\), the change of the two external cavity lengths are:  
\[ \begin{array}{c}\Delta L_{1}=-n_{0}\times\Delta h\\ \Delta L_{2}=(n-n_{0})\times\Delta h\end{array}  \tag{12}\]  
where \(n_{0}\) is the air refractive index, and \(n\) is the liquid refractive index to be measured, which can be expressed as:  
\[n=n_{0}\times\left(1-\frac{\Delta L_{2}}{\Delta L_{1}}\right) \tag{13}\]  
Since the liquid refractive index depends entirely on the displacement measurement of the two beams, the refractive index measurement is also traceable. Thus, the results can be used as a reference for calibration of the liquid refractive index. Five different liquid samples, including the distilled water, ethanol, cyclohexane, silicone oil, engine oil, and NaCl solution with various concentrations, are measured in the experiment, which proves that the repeatability is better than 0.00005.
## 3 Experimental Setup
### Underwater Channel Characterization
In this study, we traced \(2.7\times 10^{9}\) photons (each photon has an energy of \(4.42\times 10^{-19}\) J, thus, the total energy of the traced photons is 1.2 nJ) from the light source to a receiver with an MC simulator based on the Jerlov II water parameters shown in Table 1 and the parameters described in in the experimental setup of transmitter and receiver. The water quality parameters include the attenuation coefficient \(c\) and the albedo \(\omega\). The parameters of the transmitter include light wavelength, initial divergence angle, and laser spot radius. The parameters of the receiver include field-of-view (FOV) and aperture diameter. The simulation results are presented in Figure 6b, which is the curve of power loss and 3 dB underwater channel bandwidths as a function of transmission distance. The simulation results show that link loss was \(-85.73\) dB when the transmission distance was 37.88 m.  
In UOWC, the communication bandwidth limits the maximum communication rate. The communication bandwidth is determined by the channel bandwidth, the transmitter, and the receiver bandwidth. We measured the transmitter bandwidth at the LD and transmitter, which was about 1 GHz. For underwater channels, the 3 dB bandwidth is determined by the multipath effect generated by scattering [46; 47]. On the one hand, for a given transmitter, receiver, and water quality parameters, the multipath effect increases with the transmission distance due to the increased scattering events. On the other hand, an appropriate optical system for the transmitter and receiver can limit the multipath effect, and a reduced light divergence angle, FOV, and receiving aperture size can reduce the influence of the multipath effect and increase the 3 dB bandwidth of the communication system [48]. In this study, we simulated the 3 dB channel bandwidth under Jerlov II-type water by setting the initial divergence angle as 286 \(\upmu\)rad, the FOV as 100 mrad, and the aperture diameter as 3 cm. The simulation results shown in Figure 6b suggest that the 3 dB bandwidth of the tested beam was less than 384.62 MHz at a transmission distance of less than 37.88 m.  
In this part, we tested the air-bubble-induced turbulence in the experimental water tank. As mentioned above, the interaction between the laser beam and turbulent medium causes amplitude variations (scintillation) in the optical beam carrying the information, which results in fading of the received optical power. Based on the log-normal turbulence model described by Equation (7), as shown in Figure 7a, we measured the received optical power over 2 min (red curve) and calculated the normalized received optical power fluctuation (blue curve), and the scintillation index (S.I.) was calculated. Figure 7b shows the fit of the log-normal with the measured data histogram.
## 4 Conclusions  
The goal of a less than 1% synchronization error between parallel converters PWM signals can be achieved using both methods presented in this paper. The difference is that the CAN peripheral interrupt method is strongly related to the CAN peripheral behavior; using, for example, a 550 ns TQ results in a synchronization uncertainty that is more than the required 1%. It is clear that the results in Figure 6 represent the standard deviation of the delay, but some bad cases have been recorded during tests, where the delay was higher than 1 us so that, with a 100 us PWM period, the specification was not met.  
The GPIO interrupt method is intrinsically CAN peripheral independent and tests have shown a less than 0.1% synchronization error, one order of magnitude better than the requirements, even in the worst case.  
This work paves the way for the second phase (fine synchronization) that is required to parallelize converters without any reactive filters.  
Conceptualization, A.S. and C.C.; methodology, A.S.; software, G.L.R.; validation, A.S., C.C. and M.B.; formal analysis, A.S. and G.L.R.; investigation, A.S., C.C. and G.L.R.; writing--original draft preparation, G.L.R.; writing--review and editing, C.C., A.S. and M.B.; visualization, G.L.R. and M.B.; supervision, A.S. and C.C.; project administration, A.S.; funding acquisition, A.S. All authors have read and agreed to the published version of the manuscript.  
This research has financially been supported by the Programme \"FIL-Quota Incentivante\" of University of Parma and co-sponsored by Fondazione Cariparma, under the project REBASED (REduced-BAllast distributed power converters for Safety, Efficiency and power Density).  
Not applicable.  
The authors declare no conflict of interest.
## 6 Conclusions  
In this paper, a tool wear prediction method named STAGCN is proposed. The model can capture intricate spatial-temporal dependencies and enhance feature expressiveness. By leveraging the Bi-Mamba model, the proposed method captures temporal dependencies in both forward and backward directions, enhancing the ability to understand long-term patterns in tool wear degradation. The AGCN effectively captures the dynamic relationships between sensor nodes, adjusting node embeddings based on wear stage evolution over time. Additionally, MTLA enables the model to focus on critical features at different temporal, spatial, and channel scales, improving robustness and interpretability.  
The experimental results demonstrate that the STAGCN outperforms traditional approaches in both accuracy and generalization, making it a valuable tool for predictive maintenance by potentially reducing machine downtime and extending tool lifespan. However, challenges remain in severe wear stages, where increased sensor noise, nonlinear wear progression, and extrapolation limitations reduce prediction reliability. Beyond performance, real-world deployment feasibility is a key consideration. Implementing the STAGCN on industrial machines and edge-computing environments requires balancing computational efficiency with predictive accuracy. Future research can address these issues by integrating advanced noise reduction techniques, developing domain adaptation or transfer learning strategies to enhance model robustness in extreme wear conditions, and exploring uncertainty quantification methods to improve reliability. Additionally, incorporating contextual factors such as tool material properties and cutting conditions could further enhance prediction accuracy. Finally, optimizing computational efficiency remains crucial for real-time deployment in industrial settings.  
Conceptualization, Y.X. and H.L.; methodology, Y.X.; software, Y.X. and Y.L.; validation, Y.X., G.Z. and Y.L.; formal analysis, Y.X. and G.Z.; investigation, Y.X. and G.Z.; resources, Y.X., G.Z. and Y.L.; data curation, Y.X.; writing--original draft preparation, Y.X.; writing--review and editing, Y.X., Y.L. and H.L.; visualization, Y.X.; supervision, H.L.; project administration, Y.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 17: Impact of different optimizers on metrics: (a) MAE; (b) RMSE.  
Data Availability Statement: The original data presented in the study are openly available at [[https://phmsoicety.org/phm_competition/2010-phm-society-conference-data-challenge](https://phmsoicety.org/phm_competition/2010-phm-society-conference-data-challenge)]([https://phmsoicety.org/phm_competition/2010-phm-society-conference-data-challenge](https://phmsoicety.org/phm_competition/2010-phm-society-conference-data-challenge)), accessed on 15 February 2024.  
The authors declare no conflicts of interest.