esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Formulation
### Design Problem of Structured Block-Sparse Feedback Controllers  
First, we formulate the design problem of structured block-sparse feedback controllers.  
In SANs, actuators and sensors are located in a distributed way. Some actuators and sensors may be shared a communication channel. In reduction of the number of times of communications, we focus on communication channels. For example, in Figure 1, communication channels are expressed as routers, and the number of communication channels is three. It is desirable that the number of communication channels used in control is smaller.  
We consider using the block-sparse reconstruction problem for matrices.  
We suppose that the numbers of actuators and sensors are \(m\) (the number of elements in the control input) and \(p\) (the number of elements in the measured output), respectively. Let \(\mathcal{I}^{a}=\{1,2,\ldots,m\}\) and \(\mathcal{I}^{s}=\{1,2,\ldots,p\}\) denote the sets of actuators and sensors, respectively. The sets of actuators and sensors assigned to the communication channel \(i\in\{1,2,\ldots,q\}\) are denoted by \(\mathcal{I}^{a}_{i}\) and \(\mathcal{I}^{s}_{i}\), respectively, where \(\mathcal{I}^{a}_{i}\subseteq\mathcal{I}^{a}\), \(\mathcal{I}^{a}_{i}\cap\mathcal{I}^{a}_{j}=\emptyset\), \(\mathcal{I}^{s}_{i}\subseteq\mathcal{I}^{s}\), and \(\mathcal{I}^{s}_{i}\cap\mathcal{I}^{s}_{j}=\emptyset\).  
In the case of Figure 1, \(q\) is given by \(q=3\). For the communication channel 1 (router 1), we set \(\mathcal{I}^{a}_{1}=\{1,2\}\) and \(\mathcal{I}^{s}_{1}=\{1,2\}\). For the communication channel 2 (router 2), we set \(\mathcal{I}^{a}_{2}=\{3\}\) and \(\mathcal{I}^{s}_{2}=\{3,4\}\). For the communication channel 3 (router 3), we set \(\mathcal{I}^{a}_{3}=\{4\}\) and \(\mathcal{I}^{s}_{3}=\{5,6\}\).  
Under the above preparations, consider the following problem.  
**Problem 2**.: _For System (9), find a feedback controller (10) such that the closed-loop system is asymptotically stable, and the number of communication channels is as few as possible._
# Uncertainty Analysis and Improvement of Propellant Gauging System Applied in Space
## 2 Theoretical Model of the PGS Method  
The basic process of the PGS method is to inject gas into the propellant tank, which will improve the inner gas pressure. Then, the initial gas volume is calculated by comparing the start and end pressure in the propellant tank before and after the gas injection. As the total volume is assumed to be unchanged, the propellant volume can be obtained by taking away the gas volume.  
Figure 1 shows the process of the PGS method. \(P\), \(V\), and \(T\) represent the pressure, volume, and temperature and the subscript \(P\) and \(T\) represents the gas in the injection room and in the tank.  
The gas is regarded as ideal, and then we can obtain:  
\[PV=nRT \tag{1}\]  
According to the mass conversion, the gas mass injected into the tank is equal to the gas from the bottle:  
\[n_{1i}+n_{2i}=n_{1f}+n_{2f} \tag{2}\]  
where the subscript 1, 2, \(i\), and \(f\) represent the injection room, tank, before injection, and after injection.  
Combining Equations (1) and (2), one can obtain:  
\[\frac{P_{1i}V_{1i}}{T_{1i}}+\frac{P_{2i}V_{2i}}{T_{2i}}=\frac{P_{1f}V_{1f}}{T _{1f}}+\frac{P_{2f}V_{2f}}{T_{2f}} \tag{3}\]  
The volume of the injection room and tank are suspected to be solid before and after gas injection and the propellant in the tank is non-compressible, so one can obtain:  
\[V_{2i}=V_{2f}=V_{2} \tag{4}\]  
\[V_{1i}=V_{1f}=V_{1} \tag{5}\]  
Then comes:  
\[V_{2}=V_{1}\Bigg{(}\frac{P_{1i}}{T_{1i}}-\frac{P_{1f}}{T_{1f}}\Bigg{)}/\left( \frac{P_{2f}}{T_{2f}}-\frac{P_{2i}}{T_{2i}}\right) \tag{6}\]  
\[V_{L}=V_{0}-V_{2} \tag{7}\]  
where \(V_{0}\) represents the total volume of the tank and remains unchanged before and after gas injection.  
Consequently, the residual propellant mass in the tank can be calculated as:  
\[V_{L}=V_{0}-V_{2}=V_{0}-V_{1}\Bigg{(}\frac{P_{1i}}{T_{1i}}-\frac{P_{1f}}{T_{1f }}\Bigg{)}/\left(\frac{P_{2f}}{T_{2f}}-\frac{P_{2i}}{T_{2i}}\right) \tag{8}\]
## 2 The Calculation for the Correlation of Far-Field Patterns of an MAA  
In the context of compressed sensing, the measurement modes can be selected as the radiated far-field patterns of the MAA in a given frequency range. Consider the measurement matrix \(H\), which corresponds to the frequency-diverse far-field patterns. \(H\) is an \(M\times N\) matrix. \(M\) represents the quantity of the measurement modes, which is equal to the number of the sampled frequency points. \(N\) denotes the far-field pattern pixels at one frequency point, which is the product of the azimuth angles and elevation angles. The matrix reconstruction metric is the average mutual coherence \(\mu_{g}\), which is defined as follows [13].  
Firstly, with the measurement matrix, the Gram matrix can be obtained [17; 18]:  
\[G={H_{n}}^{\dagger}H_{n} \tag{1}\]  
where the superscript \(\dagger\) represents the conjugate transpose of the matrix, and \(H_{n}\) is the measurement matrix \(H\) with normalized columns. The average mutual coherence is  
\[\mu_{g}=\frac{\sum\limits_{i\
eq j}\left|G_{ij}\right|}{N(N-1)} \tag{2}\]  
Figure 1: A metamaterial aperture illuminating a human target scene.  
As pointed out by [PERSON] et al. (2013), an increase in the \(Q\)-factor of the element on the metamaterial aperture causes \(\mu_{g}\) to decrease, which indicates that less correlation between measurements represents better orthogonality of the far-field patterns. Here, the frequency quality factor \(Q\) can be defined as  
\[Q=\frac{f_{0}}{2\Delta f} \tag{3}\]  
where \(f_{0}\) is the center operating frequency, and \(2\Delta f\) represents the frequency range, which the amplitude response decreases by 3 dB. Usually, when the operating frequency band is given, the center frequency \(f_{0}\) is fixed and the frequency interval \(\Delta f\) could be changed by selecting different resonant elements with varying frequency agility features. For the strongly resonant element with a high \(Q\)-value, its frequency selectivity is good, which means the \(\Delta f\) or the bandwidth is relatively small. By using this kind of element on the MAA, frequency-agile field patterns with good orthogonality can be obtained. As a result, the target scene can be perfectly reconstructed by utilizing a simple metamaterial aperture antenna instead of mechanical scanning or antenna arrays.  
Figure 2 shows the flow chart for calculation procedure of \(\mu_{g}\). Firstly, an MAA with randomly distributed elements is designed. At each frequency point, the phase and amplitude responses of the elements on the MAA can be obtained by numerical simulations. Next, the far-field patterns across the frequency band can be calculated using array synthesis technology. Finally, far-field patterns form the measurement matrix and the average mutual coherence \(\mu_{g}\) can be obtained. Here we will focus on the theoretical analysis for the variation of \(\mu_{g}\) when the \(Q\)-factor of the resonant elements of the MAA changes.
## 4 Experiments
### Model Complexity Analysis  
In previous experiments, the proposed method has demonstrated its superior ability in both quantitative and qualitative FSR performance. In this section, we compare its model performance, size, and execution time with other state-of-the-art methods, whose results are shown in Figure 12. According to the figure, our method achieves the best quantitative results while maintaining comparable model size and execution time. Hence, the proposed approach strikes a better balance between model performance, size, and execution time compared to other state-of-the-art methods.
## 3 Results
### Incubation with SA-MIPs Did Not Result in Increased Cytokine Secretion  
THP-1 cells with functional inflammosomes were achieved by pre-stimulation with 1 \(\upmu\)g/mL LPS. After 4 h, cells were left with the medium alone or subsequently incubated with three different concentrations of SA-MIPs, Alhydrogel or PLGA, respectively, for 24 h. As a control, cells not pre-stimulated with LPS were incubated with the corresponding concentrations of the particles. After incubation with SA-MIPs or PLGA, limited secretion of IL-1\(\upmu\) could be determined, whereas Alhydrogel induced high levels of secreted IL-1\(\upmu\) from the THP-1 cells (Figure 3, left). Without pre-incubation with LPS, no activation of the inflammosomes took place (Figure 3, right) resulting in the absence of secreted IL-1\(\upmu\). SA-MIPs induced low levels of secreted TNF-\(\upalpha\) and IL-6 in the mouse RAW 264.7 cells. The levels of excreted TNF-\(\upalpha\) and IL-6 were in the same range as for the two reference particles Alhydrogel and PLGA (Figure 4).  
Figure 3: Secretion of IL-1\(\upmu\) after incubation with SA-MIPs, Alhydrogel and PLGA. THP-1 cells were either primed with LPS 1 \(\upmu\)g/mL for 4 h at 37 \({}^{\circ}\)C or incubated with cell culture medium alone before the cells were harvested and incubated with 0 \(\upmu\)g/mL, 25 \(\upmu\)g/mL, 50 \(\upmu\)g/mL or 100 \(\upmu\)g/mL SA-MIPs, Alhydrogel or PLGA. Media from the incubations were collected after 24 h and the content of IL-1\(\upmu\) was analyzed using ELISA. The left part of the graph shows THP-1 cells primed with LPS, and the right part is the unprimed control. The results represent the mean of three independent experiments \(\pm\)SD.  
Figure 2: Fluorescence microscopy images of the SA-MIPs’ potential intracellular location in RAW 264.7 cells. Prior to fixation, the cells were incubated for 24 h with culture medium (control) or 100 \(\upmu\)g/mL SA-MIPs. Cells were stained for nuclei (DAPI, blue), actin filaments (rhodamine-phalloidin, red) and SA-MIPs (NBD, green). Scale bar at 10 \(\upmu\)m. Arrows mark binding or internalization of SA-MIP. One representative experiment out of at least three independent experiments is shown.
# Hunting Molecules in Complex Matrices with SPME Arrows: A Review
## 5 SPME Arrow Applications
### Environmental Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Compound**} & \multicolumn{2}{c}{**PAL SPME Arrow**} & \multicolumn{2}{c}{**SPME ([PERSON] et al.) [(50)]**} & \multicolumn{2}{c}{**SSBSE ([PERSON] et al.) [(51)]**} \\ \cline{2-7}  & **MDL (log L-1)** & **RSD (\%) (\(\pm\) 10 mg L-1)** & **LOD (SD \(\times\) 3)** & **RSD (conc. at S/N = 3\(\times\)3)** & **LOD (conc. at S/N = 3\(\times\)3)** & **RSD (\%) (at S0 mg L-1)** \\ \hline Naphthalene & 0.3 & 5.7 & 2.7 & 9 & / & / \\ \hline Acrapapillaryline & 0.2 & 6 & 1.8 & 6 & 0.1 & / \\ \hline Acrapapillaryline & 0.1 & 7.1 & 0.9 & 3 & / & / \\ \hline Fluorene & 0.2 & 5.6 & 3 & 10 & 0.1 & 8.3 \\ \hline Phenanthorene & 0.2 & 5.5 & 2.1 & 7 & 0.1 & 1.1 \\ \hline Anthracene & 0.3 & 7.6 & 2.1 & 7 & 0.2 & 2.1 \\ \hline Pyrene & 0.2 & 6.4 & 3.6 & 12 & 0.2 & / \\ \hline Fluoromethane & 0.2 & 6.2 & 2.1 & 7 & 0.2 & / \\ \hline
1,2-Beamanthracene & 0.1 & 6.2 & 2.1 & 7 & 0.2 & 6 \\ \hline Chrysene & 0.1 & 11 & 1.5 & 5 & 0.2 & 10.6 \\ \hline Benzo(b)fluoranthene & 0.2 & 10.5 & 2.7 & 9 & 0.1 & / \\ \hline Benzo(d)fluoranthene & 0.2 & 8.6 & 1.8 & 6 & 0.1 & / \\ \hline Benzol(a)hyperne & 0.3 & 7.2 & 3.6 & 12 & 0.1 & / \\ \hline Indeno(l,2,3) & 0.8 & 9.2 & 3.6 & 12 & 0.3 & / \\ \hline Diben(a)anthracene & 0.6 & 11.3 & / & / & 0.3 & / \\ \hline Benzo(g)hyperylene & 0.8 & 11.9 & 1.8 & 6 & 0.3 & / \\ \hline MDL values & & & & & & \\ \hline confidence & & & & & & \\ interval(hot & & & & & \\ determined & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of SPME, SPME Arrow and stir bar sortive extraction (SBSE) for determination of polycyclic aromatic hydrocarbons (PAHs) in water via direct immersion. MDL and RSD results were obtained with PAL SPME Arrow (250 \(\mu\)m \(\times\) 20 mm, 10.2 \(\mu\)L) for PAHs in water in comparison with literature data for classical SPME fibers and SBSE bars. Table reprinted from Reference [(48)] (open access).
# Numerical Analysis of the Bottom Thickness of Closed Rectangular Tanks Used as Pontoons
## 2 Materials and Methods
### Verification of Pontoon Buoyancy and Stability  
Rectangular tanks used as pontoons in the construction of floating platforms, besides the appropriate strength of their walls, must meet the requirements of buoyancy and stability. Pontoon buoyancy is determined by calculating its immersion depth and by controlling its freeboard, i.e., the vertical distance from the top edge of the pontoon (deck) to the surface of the water. Pontoon stability is determined by the location of the metacentric point and the tilt angle of the pontoon. The metacentre is the theoretical intersection point of the buoyant vector of the tilted floating vessel and its plane of symmetry. The distance from the centre of gravity of the floating vessel (e.g., pontoon), the metacentric height, is a measure of its stability [6]. In floating vessels, gravity and buoyancy forces are applied to different points. When the floating vessel is not tilted, both forces are in one straight line, perpendicular to the level of water. When the floating vessel tilts due to acting loads, the point at which the buoyancy force is applied is shifted in the direction to which the tilt occurs. A vertical line that is perpendicular to the water level and passes through the buoyancy force application point at the point of intersection with the axis of symmetry of the floating vessel marks the point known as the metacentre.  
If the metacentre is above the centre of gravity, i.e., the metacentric height is positive, then the balance of the floating vessel is stable. If the metacentric distance is negative, it indicates unstable equilibrium.  
According to recommendations adopted in Poland, in compliance with [29] when designing platforms to be loaded with a crowd, it should be assumed that the load is 3.0 kPa, and the tilt should be verified by acting on half the platform width with a load of 1.0 kPa. The freeboard of floating elements should be at least 0.05 m, with the tilt angle not exceeding 6\({}^{\circ}\) (according to [8]) and 10\({}^{\circ}\) (according to [29]). Simplified calculations can be performed in compliance with [30] for the tilt angle not exceeding \(15^{\circ}\). When verifying stability, buoyancy and metacentric height, characteristic loads should be included in calculations (i.e., load factor \(\gamma_{\text{F}}=1\)) by analogy with the verification of serviceability limit states provided in, for instance, Eurocode 2 [31]. PN-EN 14504 [8] states that when verifying the stability of floating platforms, the partial safety factor should be taken as equal to 1.0. The results of stability and metacentric height calculations presented later in this paper were obtained on the basis of the procedures provided for in the Australian Standard AS 3962-2001 [30]. Figure 1a shows the schematic drawing of the pontoon (cross-section) used to verify buoyancy, and Figure 1b shown the schematic used to verify stability.
# Characterization of HVSR and VRSR in the Loess Plateau of China Based on Strong-Motion Data
## 4 Results and Discussion
### Characterization of the Fourier Amplitude Spectrum
#### 4.1.1 Characterization of the Fourier Amplitude Spectra at Different Magnitudes  
The selected strong-motion records were categorized into five magnitude (\(M_{\text{wg}}\)) ranges: 3.1-4.0, 4.1-5.0, 5.1-6.0, 6.1-7.0, and 7.1-8.0. For each range, the Fourier amplitude spectra of the EW, NS, and UD components were computed, and the average Fourier amplitude spectrum for each component was determined. The mean Fourier amplitude spectral curves are shown in Figure 7, and the maximum value of the mean curve (\(F_{max}\)) and the predominant period (\(T_{f}\)) for each group are presented in Table 1.  
As shown in Figure 7 and Table 1, the Fourier amplitude spectra of the three components are notably influenced by the earthquake magnitude. Overall, this influence becomes more pronounced as the magnitude increases. With an increase in magnitude, both \(F_{max}\) and \(T_{f}\) exhibit a clear upward trend. Additionally, within the same magnitude range, the \(F_{max}\) of the horizontal component is substantially higher than that of the vertical (UD direction), and the \(T_{f}\) for the horizontal component is also larger.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{\(M_{wg}\)} & \multicolumn{2}{c}{**EW**} & \multicolumn{2}{c}{**NS**} & \multicolumn{2}{c}{**UD**} \\ \cline{2-7}  & \(F_{max}\) **(gal)** & \(T_{f}\) **(s)** & \(F_{max}\) **(gal)** & \(T_{f}\) **(s)** & \(F_{max}\) **(gal)** & \(T_{f}\) **(s)** \\ \hline
3.1–4 & 2.02 & 0.10 & 2.21 & 0.10 & 1.22 & 0.10 \\
4.1–5 & 4.55 & 0.18 & 4.81 & 0.15 & 2.27 & 0.12 \\
5.1–6 & 6.07 & 0.18 & 4.54 & 0.17 & 2.45 & 0.12 \\
6.1–7 & 7.78 & 0.77 & 6.99 & 0.66 & 3.89 & 0.88 \\
7.1–8 & 32.35 & 3.00 & 32.44 & 3.00 & 16.06 & 2.78 \\ \hline \end{tabular}
\end{table}
Table 1: List of maximum and exceptional periods of the mean Fourier amplitude spectrum for different magnitude subgroups.  
Figure 6: Schematic diagram of the data processing and calculation flows for seismic data analysis in the Loess Plateau region.
# Automated Rain Detection by Dual-Polarization Sentinel-1 Data
## 3 Co-Analysis of Rain Rate and C-Band SAR Backscattering
### Statistical Analysis
At VV channel, the NRCSratio values are likely to be negative for strong wind speeds, leading to apparent darker features at VV than at VH with respect to the background NRCS. This is in line with the third case study presented in Figure 2. The sensitivity to rain is also found to be incidence angle dependent in VV as opposed to VH. This suggests less impact of rain at low incidence angles in VV. In addition to assessing the mean behavior for the NRCS during rain events, our study also confirms a significant variability around the averaged trend (see vertical bars in Figure 7). The two main reasons are the possible temporal mismatch between the satellite and the ground-based radar, as well as the extreme variability of the rain features observed, which might include adjacent dark and bright areas(areas of increased as well as of decreased NRCS values relative to the background) as illustrated in Figure 2. For VV polarization, at low to moderate winds (<16 m/s), the NRCSratio shows an increasing trend with rain rate at all incidence angles. Overall, these results are coincident with previous studies of [PERSON] et al. (2019) and [PERSON] et al. (2020). The mean NRCS\({}_{\text{ratio}}\) values are found to be positive excepted for high winds.
## 5 Simulation Experiment
### Analysis of FAR  
Figure 5 shows that FAR will yield a lower value for the smaller legal training sample \(\rho\) in a scenario where the legitimate user's channel gain ratio \(k\) is the same as the attacker's  
Figure 4: When \(k=-3\), the change in the receiver gain is tested with the increase in the number of experiments at \(\rho=20\) and \(\rho=10\), respectively, and the experiment is tested \(1000\) times.  
channel gain. The reason for this is that as the SINR decreases, the channel estimation error (CER) decreases. The CER has a direct impact on the signals' quantization quality, which seriously influences the subsequent calculation of the FAR. It can be observed from the two curves in Figure 5 that with different values of \(\rho\), the lowest value is present at \(x=9\). This is due to the fact that the Q-learning algorithm slowly optimizes the threshold in order to find the optimal threshold. As the threshold value changes from \(x=1\) to 9, the FAR changes accordingly. The slope of the FAR progressively becomes smaller when the threshold approaches the optimal threshold. However, the slope of the FAR tends to stabilize after 400 training sessions. For \(\rho=20\), the FAR remains stable at about 0.225%, which is 13% greater than \(\rho=10\).  
Similarly, the FAR is analyzed between the SARSA-based detection and Q-learning method. Figure 6 depicts that the threshold changes constantly with the increase in the number of training. Initially, the receiver has no information about the system environment; therefore, the FAR decreases according to Equation (16). Nevertheless, during the training phase, the receiver employs past experience detecting impersonation attacks to choose the optimal threshold by continuously testing the environment. Thus, the optimal threshold gradually stabilizes after reaching a number of experiments. Moreover, the dynamic wireless channel between mobile users and edge nodes causes signal fading, resulting in some differences in the channel vector. As a result, the FAR slightly varies but remains stable at 20.0-20.6%. Mostly, the FAR of Q-learning algorithms is higher than that of the SARSA algorithm. This is due to the SARSA conservative nature that finds a suitable threshold via a continuous trial-and-error process. Hence, when detecting an impersonation attack, the SARSA algorithm has a lower chance of misinterpreting a legal signal as an illegal signal, resulting in a lower rejection. As a result, it accepts more legal signals at the receiving side and improves the SKG between the communicating parties.
# A Framework for Fall Detection Based on OpenPose Skeleton and LSTM/GRU Models
## 2 System Architecture and Method  
The proposed framework uses an existing UR Fall detection dataset [10] as input and preprocesses the data to retrieve skeleton joint points. The joint points data are further processed to recover missing points. They are then divided into training and testing datasets. Figure 1 show the block diagram of the procedure of the data processing, training and testing. The framework first divides the movies from the dataset into continuous image sequences. It uses OpenPose to extract the skeleton from the picture sequence and obtain the positional data of 25 human body joint points. To preprocess the joint point data, we proposed a minimum and maximum normalization method. The processed joint point data were divided into a test set and a training set and input to the LSTM model for training and testing.