esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation +
## 1 Introduction
Our method extracts a clean BVP signal from the input via a two-step wide and narrow band frequency filter to accurately time heart beats and estimate heart rate variability. An overview of our method is illustrated in Figure 1.  
State of datasets.As the field of remote photo-plethysmography receives advances and the accuracies of rPPG methods improve, the demand for thorough, challenging and realistic datasets arise. Datasets originally created for alternate uses (e.g., psychological studies) often get re-purposed for rPPG analysis, which has unintended drawback (video compression, occlusion, etc.) [11; 12]. A large proportion of research in this field end up using self-recorded private datasets [13], due to which the results cannot be directly compared with prior work. These factors hinder proper development, evaluation and benchmarking of rPPG methods. To help alleviate this, we introduce a new publicly available multi-modal video dataset specifically designed to aid the study of camera-based rPPG algorithms for HR and HRV analysis. Ground truth signals.Another significant but overlooked complication with existing rPPG datasets arises from their provided ground truth signals, typically photoplethysmogram (PPG) or electrocardiogram (ECG) waveform. These signals are often plagued by artefacts, for example in the form of large spikes caused by unwanted sensor movement and interference [1,14]. This causes false peak detections resulting in incorrect ground truth HR and HRV measures, thereby leading to unreliable evaluation. Additionally, directly utilizing raw PPG/ECG signals as ground truth leads to another issue in the evaluation process: evaluates are free to choose the method of obtaining peaks on them, and they could use the very same peak detection algorithm that is used by the method under evaluation on the rPPG signal. Since the estimated rPPG peaks are evaluated against ground truth peaks generated by the same algorithm, this can lead to a \"detector-bias\" in the computed error measures. For example, an algorithm that blindly detects a fixed number of heart-beats on all signals will incorrectly report zero error. To help solve these problems caused by noisy ground truth signals, we introduce CleanerPPG: a public collection of human-verified peak/heart-beat annotations on ground truth signals of existing publicly available rPPG datasets. This makes the ground truth heart-beats absolute and independent, and therefore offers more accurate, fairer and standardized evaluations and benchmarking. CleanerPPG is made publicly available (this dataset is available for research purposes, and can be requested via e-mail to the corresponding author(s)) and intended to develop into a continuously growing community-driven collection for all future datasets.
## 2 Algorithm Principle
### Error and Noise Model
#### 3.3.2 Noise Model  
In communication and navigation systems, especially when analyzing and calculating the anti-noise performance of the system, we usually assume that the channel noise is Gaussian white noise in the system of navigation, which reflects the noise situation in the actual channel and more truly represents the characteristics of the channel noise. The mathematical model of probability density as follows [33]:  
\[p(x)=\frac{1}{\sqrt{2\pi}\sigma}e^{-\frac{(x-y)^{2}}{2\sigma^{2}}} \tag{34}\]  
where \(\mu\) is the mathematical expectation of noise, and \(\sigma^{2}\) is the variance of noise.  
In general, we use the signal-to-noise ratio (SNR) to express the strength of the signal, due to the SNR values of different ranging codes [34; 35]. However, the LEO constellation is still in the verification and deployment stage, and the relevant technical details on the signal design of navigation enhancement have not been announced. Therefore, we use the signal strength model to describe the SNR, which avoids SNR parameter settings for specific signals, and indicates a unified conversion to signal strength. The signal strength model is currently the most commonly used random model based on SNR. In comparison with other SNR models, the structure of this model is relatively simple and does not require any prior parameters. It can be defined as [36]:  
\[SNR=\frac{Signal\ intensity}{Noise\ intensity} \tag{35}\]
## 3 Results
### Porosity Prediction  
#### 3.4.1 Well-1 Site Porosity Prediction  
Figure 11 illustrates the inversion of porosity at the Well one location. There are three anomalies in the well log, i.e., thick SC3 and thin SC3A gas sand and water sand, just below the SC3A. Those three anomalies are represented by three high values of porosity (Figure 11a). The far seismic trace (Figure 11c) shows a strong AVO anomaly compared to the near seismic trace (Figure 11b), especially for shallower thick reservoir SC3. Although small, the anomaly of the deeper thin reservoir SC3A is recognizable. Figure 11d shows the traditional inverted EEI porosity. It is shown that the traditional EEI porosity is negatively correlated with the porosity log, and the absolute value is not recovered yet. The three targeted porous sand are identified by the traditional inverted EEI porosity; however the resolution is poor.  
Figure 11e shows the proposed inverted EEI porosity (blue curve), resulting from applying stochastic process and GRNN to the traditionally inverted EEI porosity (Figure 11d), overlaid by the targeted porosity log (red curve). As shown in Figure 11e, the porosity prediction is significantly improved. The match between inverted EEI porosity and targeted porosity log is improved. The deeper thin SC3A reservoir sand and the water sand are captured. The porosity of all porous sands is estimated closely to targeted log data. The mismatch between predicted and targeted porosity at around 1100-1200 ms is suggested due to strong seismic amplitude data that is not supported by sufficient porosity data measured at well logging, therefore, making the inversion overestimate the porosity value at that zone.  
Figure 10: Cross-plot between accuracy R\({}^{2}\) (R-squared) and smoothing factor (sigma) of porosity, water saturation and volume of shale prediction for \(\sigma\) (sigma) value scanned between 0 and 2.
## 4 Slip Model Inversion
### Distribution Slip Inversion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Longitude\({}^{f^{o}}\)** & **Latitude\({}^{f^{o}}\)** & \multicolumn{4}{c}{**I**} & \multicolumn{4}{c}{**II**} & \multirow{2}{*}{**Depth/km**} \\ \cline{3-3} \cline{5-10}  & & & **Strike\({}^{f^{o}}\)** & & & **Dip\({}^{f^{o}}\)** & & **Slip\({}^{f^{o}}\)** & & \\ \hline USGS & 100.01 & 25.77 & 135 & 82 & \(-165\) & 43 & 75 & \(-9\) & 17.5 \\ GCMT & 100.02 & 25.61 & 46 & 78 & 4 & 315 & 86 & 168 & 15 \\ GFZ & 99.92 & 25.73 & 319 & 88 & \(-165\) & 229 & 75 & \(-1\) & 17 \\ [PERSON] et al. & 99.91 & 25.65 & 134.88 & 80 & \(-170\) & & & 4.92 \\ [PERSON] et al. & 99.934 & 25.644 & 139 & 81 & \(-170\) & & & 6 \\ Our study & 99.87 & 25.67 & 132.4 & 76.2 & \(-176.3\) & & 13 \\ \hline \hline \end{tabular} USGS: United States Geological Survey; GCMT: Harvard University Global Centroid Moment Tensor Solution; GFZ: German Center for Geosciences; [PERSON]: reference [36]; [PERSON]: reference [37].  
\end{table} 
Table 2: Fault inversion parameters for the Yangbi Ms 6.4 earthquake. Figure 4: Fault slip distribution 2D display. Figure 5: Coseismic Sentinel-1A observations and the model fitting of the distributed slip inversion: (**a**,**b**)oseismic Sentinel-1A observations of the ascending and descending; (**c**,**d**) simulation of the ascending and descending; (**e**,**f**) residual error of the ascending and descending.
# A Generic Framework for Enhancing Autonomous Driving Accuracy through Multimodal Data Fusion
## 3 Related Work  
An extensive survey is presented to explore different image, video, and audio data fusion techniques proposed by eminent scholars to identify their major contributions and existing gaps. The details gathered will be a stepping stone to progress this research further to reduce the existing gaps.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Advantages** & **Disadvantages** \\ \hline \multirow{3}{*}{Multiplicative [8]} & • Stretches the histogram of all MS bands & • Difficult to interpret and restore the \\  & • Decreases the standard deviation & original image color \\  & • Used to fuse small targets & \\ \hline \multirow{3}{*}{Brovey simple method [9]} & • Simple and fast & \\  & • Restoration of high-resolution multispectral & • Ignores high-quality synthesis of \\  & image & spectral information \\  & • Useful for visual interpretation & • More spectral distortion \\ \hline \multirow{3}{*}{Subtractive method [8]} & • Fast, user-friendly, and radiometrically & • The output fused image has a lighter \\  & accurate technique for fusing PAN and MS & tone than the input MS image. \\  & data. & • Output image has less clarity \\ \hline \multirow{3}{*}{Wavelength [10]} & • Improves spatial and preserves spectral & • Color not smoothly dispersed in spatial \\  & characteristics & features \\  & • Edges of small objects are lost \\ \hline \multirow{3}{*}{Intensity hue saturation (HIS) method [11]} & • Enhanced fusion used for desperate datasets & • Limited bands \\  & • Converts color images into its RGB of HIS & • Difficult to fuse multispectral bands \\  & • Refundancy of data is decreased & • Low interpretability of principal \\ \cline{1-1} \cline{2-2} \multirow{3}{*}{Principe component analysis [11]} & • Large amount of input information is & • Tradeoff between information loss and \\  & reduced without information loss & dimensionality reduction \\ \hline \multirow{3}{*}{High pass filter [12]} & • Smooth texture of output images & • Less sharpness \\  & • Low clarity of objects \\ \hline \multirow{3}{*}{Ehler method [13]} & • Provides all basic spatial information & • More computational time \\  & • High resolution output & • Decreases with variation in the \\ \cline{1-1} \cline{2-2}  & & resulting image \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of various popular image fusion models.
## 2 Method
### Case Design and Boundary Conditions
Figure 1: Geometric model of the aircraft cabin in the reference case (Case 1). As shown in Table 4, the airborne pollutant was assumed to release from the exhalation of the passengers seated in 4B and 4E in Case 1, Case 3, and Case 5. In Case 2 and Case 4, the pollutant was assumed only in 4B, considering the geometry in the cabin was almost symmetric. When releasing pollutants, the realistic breathing cycle through only the nose was considered. To reduce the computing load, only the manikins seated in the fourth row were assigned respiration. According to [29], two nostril openings each with an area of 0.65 cm\({}^{2}\) were set up, as shown in Figure 3. The breathing profile was close to a sinusoidal wave with a cycle period of 4.4 s and a peak inhalation and exhalation rate of 0.37 L/s as:  
\[Q=0.37\ \text{sin}\ (1.43\ t) \tag{1}\]  
where \(Q\) is the transient respiration rate, L/s. The \(\theta_{\text{m}}\) and \(\phi_{\text{m}}\) of the exhalation jets were 60\({}^{\circ}\) and 69\({}^{\circ}\), respectively, as shown in Figure 3, according to the recommendation in [29]. The exhaled air temperature was assumed to be 33 \({}^{\circ}\)C. The above breathing profile was adopted as the boundary condition at the nostril openings. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Seat Layout** & **Illustration** &
\begin{tabular}{c} **Assumed Pollutant** \\ **Source Position** \\ \end{tabular} \\ \hline
1 & Upright seats in symmetric layout & Figure 1 & 4B, 4E \\
2 & Only seats 4A to 4C inclined & Figure 2a & 4B \\
3 & Seats inclined in half a cabin & Figure 2b & 4B, 4E \\
4 & All seats inclined but in symmetric layout & Figure 2c & 4B \\
5 & Misaligned seat rows & Figure 2d & 4B, 4E \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Case design overview. Figure 2: Geometric models of the aircraft cabin with different seat arrangements: (**a**) seats 4A to 4C inclined, (**b**) seats A to C inclined, (**c**) all of the seats inclined, (**d**) planar view of misaligned seat rows. Table 5 lists the major thermo-flow boundary conditions. The aircraft cabin at the cruising stage was considered. The cabin air pressure was set to 85 kPa, which was lower than that at sea level. The cabin surfaces were assigned with a fixed temperature, while the seat was treated as adiabatic. The manikin skin surface temperature was assigned to 30.1 \({}^{\circ}\)C [26,30] to reproduce the metabolic heat release.
## 3 Quality Factor Analysis of Air-Core Inductors
### Winding Structure Optimization of Solenoid Inductors  
For a tightly winding solenoid inductor, the winding diameter, \(\phi\), coil turn, \(N\), and coil section diameter, \(d_{\text{Cu}}\), determine its profile and inductance value. When the wind  
Figure 8: Relationships between \(F\) and \(T\) at different winding structures of planar spiral inductors.  
Figure 9: Wide outside winding and narrow inside winding structures of planar spiral inductors.  
ing structure is cylinder, the inductance value of the solenoid inductor is given by [40] the following:  
\[L=\frac{k\mu_{0}\pi\phi^{2}N}{4d_{Cu}}, \tag{17}\]  
where [PERSON]'s coefficient, \(k\), depends on \(\phi/(Nd_{Cu})\), as given in Table 4.  
The length of the solenoid inductor is given by  
\[l=N\pi\phi. \tag{18}\]  
Combining (14), (17) and (18), the quality factor of the solenoid inductor is derived as  
\[Q_{solenoid}=\pi\sqrt{\frac{\pi\mu_{0}f_{s}\rho}{n}}\frac{Nd_{Cu}}{2}k\frac{ \phi}{Nd_{Cu}}. \tag{19}\]  
According to (19), the quality factor depends on \(N\), \(d_{Cu}\) and \(\phi\). For \(N\) and \(d_{Cu}\), the quality factor increases monotonically with them. For \(\phi\), the function -related \(\phi\) in \(Q\) is defined as  
\[G\left(\frac{\phi}{Nd_{Cu}}\right)=k\frac{\phi}{Nd_{Cu}}. \tag{20}\]  
Based on Table 4 and (20), the approximation relationship between \(G\) and \(\phi/(Nd_{Cu})\) is plotted in Figure 10. According to Figure 10, at the same \(Nd_{Cu}\), increasing \(\phi\) is beneficial for improving quality factor, \(Q\), which causes the large volume. Unlike in planar spiral inductors, there is no optimized structure to improve the quality factor.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \(\phi/(Nd_{Cu})\) & 0.1 & 0.2 & 0.3 & 0.4 & 0.6 & 0.8 & 1 \\ \hline \(k\) & 0.96 & 0.92 & 0.88 & 0.85 & 0.79 & 0.74 & 0.69 \\ \hline \(\phi/(Nd_{Cu})\) & 1.5 & 2 & 3 & 4 & 5 & 10 & 20 \\ \hline \(k\) & 0.6 & 0.52 & 0.43 & 0.37 & 0.32 & 0.2 & 0.12 \\ \hline \end{tabular}
\end{table}
Table 4: [PERSON]’s coefficients with \(\phi/(Nd_{Cu})\).  
Figure 10: Approximation relationship between \(G\) and \(\phi/(Nd_{Cu})\).
## 2 Methods
### Marking Strategies  
We use five different marking strategies, i.e., algorithms that decide which elements are refined in terms of the local residual defined in Section 2.2:  
* Next-step estimator: inspired by [38], it is based on the estimation of the error that an element would have when refined. * Quantile: here, we order the elements from highest to lowest residual and we refine a given percentage, as shown in [39]. * Maximum: we define a threshold relative to the maximum residual in the mesh (using a parameter \(\theta\in[0,1]\)) [40]. * Fixed-energy fraction: we order the elements by their residual from highest to lowest, and we refine the first elements which constitutes a given percentage of the energy of the residual in the whole problem [41]. * SER (Solve-Estimate-Refine): we combine here the fixed-energy fraction and maximum strategies [42]. We mark sets of elements (ordered from highest to lowest residuals) which are smaller and smaller (determined by a varying Heaviside step function) to achieve a given percentage of the energy of the whole problem. If the step function is forced to select only one element, we have the fixed-energy fraction strategy. All of these strategies have been tested with unstructured meshes, i.e., with triangles or tetrahedra. We need to introduce modifications to these techniques for them to be applied to semi-structured meshes. In particular, we use a process of _conformation_ to avoid the well-known issue of hanging nodes [6]. We have a hanging node when a direct correspondence of basis functions cannot be established between neighbor elements: e.g., when the edge of one element starts in the middle of the edge of the neighbor element. We could use special strategies to remedy this problem [6], but they are out of the scope of this work. This conformation process makes undetermined the number of prisms to be refined in the marking step, since the refinement has to be propagated horizontally and vertically We call \(\mathcal{P}\) the set of elements of the original domain \(\mathcal{P}\), whereas \(\mathcal{P}_{\text{R}}\) is the set of elements marked to be refined, with \(\mathcal{P}_{\text{R}}\subset\mathcal{P}\). In Algorithm 1, we suggest a variation (introducing an adaptive threshold to guarantee that, in each round, some elements are marked) of the algorithm proposed in [38], assuming that the residual follows asymptotically \(\mathcal{R}=ch^{\lambda}\) after a first uniform refinement.
## 2 Materials and Methods
### Synthesis and Characterization  
#### 2.2.1 5-(2-Ethlyhexyl)-thieno[3,4-c]pyrrole-4,6-dione  
In a 250 mL flame-dried round bottom flask equipped with a cooling condenser, 3.44 g (20 mmol) of thiophene dicarboxylic acid was mixed with 100 mL of acetic anhydride. The mixture was stirred vigorously at 140 \({}^{\circ}\)C for 16 h (overnight). The temperature was then decreased to 75 \({}^{\circ}\)C and unreacted acetic anhydride was distilled off under a reduced pressure. The remaining crude, dark-brown solid was carried through to the next synthetic step without further purification. Then, 100 mL of toluene was added to the flask and 3.88 g (30 mmol) of 2-ethyl-1-hexylamine was added dropwise. The mixture was stirred at 120 \({}^{\circ}\)C for 20 h (overnight). After being cooled down to room temperature, the solvent was removed using a rotary evaporator (EYELA, Tokyo, Japan). For the next step, 30 mL of thionyl chloride was added to the crude solid. The mixture was stirred at 75 \({}^{\circ}\)C for 3 h. After being cooled to room temperature, the solution was slowly pipetted into 300 mL of water, causing precipitation of a brown solid. The crude solid was recovered by filtration and washed with water. Next, the product was purified by silica gel column chromatography using a dichloromethane/hexane gradient. Finally, the product was collected and dried, resulting in a white solid. Overall yield: 4.4 g (16.58 mmol,83%). 1H-NMR (400 MHz, chloroform-d, _s_): 7.67 (s, 2H, CH), 3.51 (t, 2H, CH2), 1.81-1.77 (m, 1H, CH), 1.38-1.21 (br m, 8H, CH2), and 0.94-0.87 (br m, 6H, CH3). HRMS (EI) m/z, calculated for C14H19 NO2S (M+): 265; found: 265.
## 2 Materials and Methods
### Design of the Experiments
As the original architecture of these models was not modified in any way (except for the last layer), all models were directly deep-tuned. Starting with the pre-trained weights from ImageNet, we trained the entire model end-to-end for 50 epochs using the Adam optimizer, with a learning rate of \(10^{-5}\) and the categorical cross-entropy loss function. The value of 50 epochs was set empirically, as we observed in all tests the worsening behaviour of the models as training continued. Batch size was variable depending on what was allowed in each model: 32 for CaiT_XXS36, DeiT, ResMLP_12, CeiT, Swin Base, Swin Tiny, and Twins-PCPVT, 16 for CaiT_S24 and ConViT, and 4 for ResMLP_24. In the pre-processing step, we applied the pre-processing functions included in the library Torchvision of Pytorch [48]. To avoid overfitting, we applied data augmentation to the input samples, consisting of random brightness with a factor of \(\pm 0.5\), random horizontal flip, random rotation \(\pm 15^{\circ}\) with \"nearest\" fill mode, and normalisation. Also, for these models, the final weights for each one were chosen from the epoch that maximised the validation accuracy among the five folds. This results in 5 different models per ViT-based system architecture, or 55 models in total. All the trained DL models were tested with an independent set (test set) consisting of 75 samples from healthy subjects and 73 samples from glaucoma subjects. Subsequently, all these models were applied to other external retinography sets: the Drishti-GS1 database [45], the Papila dataset [46], and the REFUGE challenge database [44] to perform a more objective and comprehensive validation of their behaviour. The performance results corresponding to each network architecture are calculated in terms of sensitivity, specificity, accuracy, balanced accuracy, and F1 score [54].
# _remote sensing_
## 4 Satellite Architecture and System Budgets
### Propulsion System Operations and Propellant Budget  
The orbit maintenance will be performed by an electric thruster. A thrust of 0.35 mN and a specific impulse of 2000 s are assumed as typical reference values [58]. The largest force to counterbalance in the long term is atmospheric drag, which can be evaluated in the order of 1 \(\upmu\)N, when considering the average atmospheric density at 500 km altitude [47]  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Pixels per line & 2048 \\ Bits per pixels & 12 \\ Bits per pixel-line & 24,576 \\ Orbit altitude (km) & 500 \\ Orbital speed (km/s) & 7.616 \\ Ground speed (km/s) & 7.062 \\ Ground speed (pixel/s) & 48.04 \\ Required frame rate with 25\% margin (fps) & 60 \\ Payload reference duty cycle & 0.45 \\ Average generated data bit rate (Mbps) & 0.664 \\ Total data amount per day (Gbits) & 57.331 \\ Data rate of the transmission system (Mbps) & 300 \\ Daily transmission time per observation angle, per & 1 min 36 s \\ spectral band (50\% lossless data compression) & \\ \hline \end{tabular}
\end{table}
Table 13: Data amount generated in one single acquisition in one observation angle.  
and an exposed flat plane surface of 0.03 m\({}^{2}\), with a drag coefficient of 2.2. The resulting acceleration on the satellite, assuming 10 kg mass, is in the order of 0.1 \(\upmu\)m/s\({}^{2}\), corresponding to an average required \(\Delta\)V of about 3 m/s/year and a propellant consumption of about 1.4 g/year. Due to the possible thruster activation times, which can last for minutes or even hours, the orbit adjustment maneuvers could be performed once per week, exerting the required weekly \(\Delta\)V of 0.06 m/s. A typical electric propulsion system will be able to fulfil this \(\Delta\)V value in about one hour, including the necessary 30 min typical warm-up time. A typical on-board propellant storage of 0.1 kg could last for several decades, exceeding the satellite operational life with great margin and allowing for a suitable post mission disposal maneuver, in compliance with the guidelines for small satellites [59].