esa-sceva/satcom-chunk-collection huggingface dataset:
# A Real-Time Remote Respiration Measurement Method with Improved Robustness Based on a CNN Model
## 4 Experimental Results
### Results of Respiration Estimation in a Restricted Movement Environment
Figure 12 illustrates this problem by comparing the estimated signals. Because the reference signal used in the experiment was acquired through a belt-type respiratory measurement sensor, the detailed shape of the waveform may vary depending on the degree of looseness of the belt and the position in which it was worn. Therefore, in this study, the figure of correlation, which can confirm the similarity between the estimated signal and the reference, is not used as an absolute performance indicator, but rather as data to check whether the estimated signal represents a breathing pattern well. From a similarity perspective, we can see that intensity-based methods do not express respiratory signals well with extremely low values, and that the proposed method and optical flow-based methods showed a high degree of agreement of 0.9 or more in all situations. In fact, it may be confirmed from Figure 12 that the signal estimated through the corresponding methods was almost similar to the reference. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scene \#** & **Method** & **MAE (ppm)** & **MOD (ppm)** & **LOA (ppm)** & **ICC** & **Mean of** \\ \hline \#1 & Ours & 0.08 \(\pm\) 0.14 & \(-\)0.00 & \(\pm\)0.38 & 0.999 & 0.93 \(\pm\) 0.04 \\ \#1 & Optical flow [62] & 0.09 \(\pm\) 0.16 & \(-\)0.01 & \(\pm\)0.36 & 0.999 & 0.95 \(\pm\) 0.03 \\ \#1 & Intensity [57] & 3.33 \(\pm\) 3.30 & 0.95 & \(\pm\)11.06 & 0.326 & 0.49 \(\pm\) 0.26 \\ \#2 & Ours & 0.16 \(\pm\) 0.16 & 0.04 & \(\pm\)0.56 & 0.999 & 0.93 \(\pm\) 0.03 \\ \#2 & Optical flow [62] & 0.12 \(\pm\) 0.15 & 0.05 & \(\pm\)0.71 & 0.999 & 0.94 \(\pm\) 0.04 \\ \#2 & Intensity [57] & 2.16 \(\pm\) 2.69 & 1.00 & \(\pm\)6.40 & 0.518 & 0.55 \(\pm\) 0.27 \\ Total & Ours & 0.12 \(\pm\) 0.15 & 0.02 & \(\pm\)0.48 & 0.999 & 0.93 \(\pm\) 0.04 \\ Total & Optical flow [62] & 0.11 \(\pm\) 0.15 & 0.02 & \(\pm\)0.57 & 0.999 & 0.95 \(\pm\) 0.04 \\ Total & Intensity [57] & 2.75 \(\pm\) 3.03 & 0.97 & \(\pm\)9.03 & 0.429 & 0.52 \(\pm\) 0.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Respiration measurements in motion-controlled environments. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Scene \#** & **Method** & \(p\)**-Value** \\ \hline \#1 & Ours–optical flow [62] & 0.599 \\ \#1 & Ours–intensity [57] & 0.000 \\ \#2 & Ours–optical flow [62] & 0.531 \\ \#2 & Ours–intensity [57] & 0.000 \\ Total & Ours–optical flow [62] & 0.928 \\ Total & Ours–intensity [57] & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Analysis of error statistics in a motion-controlled environment. Figure 11: Bland–Altman plot of three breathing estimation methods for scene #1 and #2: **(a)** ours, **(b)** optical flow-based, and **(c)** intensity-based. Figure 12: Comparison analysis of reference and inference respiration signal.
## 2 Mathematical Model of VTOL Platform with PI Control  
The diagram of the 1-DOF Quanser QNET 2.0 VTOL model is shown in Figure 1.  
The VTOL system is a complete experimental setup used to investigate the dynamics of the aircraft along one axis (1 DOF), i.e., pitch. The propeller assembly acts as an actuator operated by a DC motor that provides uplift to the platform. The total length is divided into three segments, as shown in Figure 1, as \(l_{1}\), \(l_{2}\), and \(l_{3}\). The \(\theta\) shows the angle of the platform and the horizontal axis will vary from \(\pm 0^{\"}\). The various physical parameters of the VTOL system are described in Table 1.  
Figure 1: Single DoF model of QNET VTOL system.  
The VTOL system is an electromechanical system, as shown in Figure 1. The thrust torque acting on the body, \(\tau_{t}\), is described in Equation (1) as  
\[\tau_{t}=J\ddot{\theta}+B\dot{\theta}+K\theta \tag{1}\]  
In Equation (1), \(\theta\) is the pitch angle, and \(J\) is the moment of inertia, which can be calculated as in Equation (2).  
\[J=\sum_{i=1}^{3}mr_{i}^{2} \tag{2}\]  
Expanding Equation (2) to find the moment of inertia \(J\) for the VTOL system for \(r=l\) in Figure 1 yields Equation (3) as  
\[J=m_{1}l_{1}^{2}+m_{2}l_{2}^{2}+m_{3}l_{3}^{2} \tag{3}\]  
Placing the values of various parameters of the VTOL system into Equation (3) from Table 1 yields \(J=0.0045\) kgm\({}^{2}\). The DC motor that operates the propeller assembly is a well-known rotational mechanical machine, and the torque of the DC motor is a significant parameter, expressed as \(\tau_{t}=K_{t}I_{m}\), where \(K_{t}\) is the thrust current torque constant measured in Nm/A. At the equilibrium position of the VTOL body along the horizontal axis, \(K_{t}I_{eq}=g(m_{1}l_{1}-m_{2}l_{2}+m_{3}l_{3})\) for \(\theta=0^{\circ}\). Thus, \(K_{t}=\frac{g(m_{1}l_{1}-m_{2}l_{2}+m_{3}l_{3})}{I_{eq}}\). At equilibrium, the value of \(I_{eq}=0.20A\). By simplifying and placing the values of various parameters in Equation (1), we have  
\[P(s)=\frac{\theta(s)}{I_{m}(s)}=\frac{34.711}{(s^{2}+0.44s+4.706)} \tag{4}\]  
The general model of the second-order system is described as  
\[G(s)=\frac{K\omega_{n}^{2}}{s^{2}+2\zeta\omega_{n}s+\omega_{n}^{2}} \tag{5}\]
# Genesis and Magmatic Evolution of the Gejiu Complex in Southeastern Yunnan, China
## 5 Discussion
### Influence of Magmatic Sources and Partial Melting Conditions on Initial Tin Enrichment  
The heat from the upwelling asthenosphere triggered extensive melting of the lithospheric mantle and lower crust, leading to crust-mantle interactions. This process not  
Figure 11: Y-Ce-Nb diagram [68].  
Figure 12: Integrated model of Geju magmatic evolution.  
only provided the thermal energy necessary for crustal partial melting but also introduced minor mantle-derived materials into the granitic melts, forming a bimodal magmatic association [74]. In addition to granites, coeval igneous rocks such as gabbro, diorite, monzonite, and alkaline rocks are widely distributed in the western Cathaysia Block [14]. These Late Cretaceous mafic, intermediate, and alkaline rocks are aligned with extensional structures, forming a volcanic rock assemblage controlled by lithospheric extension and asthenospheric upwelling, together with the granitic bodies [75].  
Quantitative partial melting models by [PERSON] et al. [50] demonstrate that magmas generated by biotite dehydration-melting under high temperatures (>800 \({}^{\circ}\)C) can significantly enrich Sn, as seen in the Sn-bearing granites in the South China tungsten-tin metallogenic province and other global W-Sn metallogenic belts. As shown in Figure 9b, the primary magmas of the Gejiu granites were likely produced by biotite decomposition reactions, which are closely associated with biotite breakdown, as observed in Sn-bearing granites worldwide. The input of mantle-derived heat promoted biotite decomposition, releasing Sn into the magmas and resulting in the high enrichment of Sn in the Gejiu melts, laying the foundation for tin mineralization. Therefore, high-temperature melting and biotite decomposition reactions are critical for the formation of initial Sn-enriched granitic melts.  
Furthermore, the delayed crystallization of biotite may further enhance tin enrichment. The final product of fractional crystallization in the Gejiu granites is equigranular biotite granite, indicating that biotite crystallized last. Biotite crystallization tends to incorporate Sn, significantly reducing the Sn content in the residual melt and weakening the ore-forming potential of the magma. Thus, the delayed crystallization of biotite prevented the premature sequestration of Sn, providing favorable conditions for hydrothermal tin mineralization and ultimately forming world-class tin deposits [76].
## Results and Discussion  
### Synthesis of 6a-j  
The synthetic pathway of the compounds (**6**) is illustrated in Scheme 1. N-benzylisatin (**3**) was prepared using our previously reported method [31] by heating same equivalent of isatin (**1**) and benzyl bromide (**2**) at 80 \({}^{\circ}\)C in dimethylformamide (DMF) for 12 h. _N_-benzylisatin monohydrazone (**4**) also was prepared using previously reported hydrazone formation method [27; 32; 33; 37] using 1.2 equivalent of hydrazine hydrate and _N_-benzylisatin (**3**) at refluxing condition for 3-4 h in alcoholic solution (MeOH/EtOH). Designed _N_-benzylisatin-aryl hydrazones (**6a-j**) were synthesized by reacting compound **4** with substituted aryl aldehydes (**5a-j**) in absolute ethanol in presence of catalytic amount of glacial acetic acid [27]. The choice of the substituents and its positions on the aromatic ring of benzaldehyde is to offer various electron withdrawal and donation on the aromatic ring which will, in turn, give different degrees of electron density on the biologically active parts of isatin molecule.
## 3 Results and Discussion
### EPR Spectroscopy Investigation  
EPR measurements clearly confirm the presence of unpaired electrons in all the investigated systems, according to previous studies [43; 22]. The X-band EPR spectra of the bituminous signals consist of two non-overlapping EPR signals, one centered at about 3480 G due to vanadyl ions (spin 7/2), and the second in a range between 3510 and 3530 G associated to organic radicals (see Figure 5 for the asphaltene fractions).  
A complete set of spectra acquired for both the asphaltene and maltene fractions is illustrated in Figure S1 of Supplementary Material. Free radicals associated with non-localized \(\pi\) system, stabilized by resonance in polyaromatic centers, can be studied by EPR spectroscopy owing to the influence of the environment on the radical magnetic properties. Therefore, various parameters can be derived from the analysis of spectra, furnishing different pieces of information, sometimes complementary, sometimes partially overlapping.  
Figure 4: Comparison of \(\Delta T=T_{\mathrm{TR}}\) (aged) \(-\)\(T_{\mathrm{TR}}\) (unaged) for the series of investigated samples.  
Figure 5: X-band EPR spectra of unaged asphaltene samples mixed with 2% \(w/w\) of antioxidants registered at room temperature. CTRL indicates the control sample with no antioxidant added.
## 5 Proposed Taxonomy of the Attacks, DDoS Attacks, and DDoS Detection Approaches on CC
### Taxonomy of DDoS Attacks on CC
#### 5.2.4 ICMP Flood Attack  
ICMP messages are used to locate hosts on a network, map network structure, and determine the OS in use. It can also be used as a vehicle for various DDoS attacks on CC. For example, an adversary could crash the targeted host with ICMP Echo Request (ping) packets by broadcasting them quickly without waiting for replies, similar to the UDP flood attack principle. The targeted VM's resources would deplete rapidly, affecting the VM's availability. All Internet protocols permit specific data packets. In this attack, the destination CC resources or VM receives more data packets than the protocol allows, forcing the TCP/IP stack to fragment all data packets on the sender side and assemble them on the receiving side. When large amounts of fragmented data must be reassembled, the destination system's performance will suffer. In other words, adversaries flooded the victim machine by sending a huge number of ICMP echo requests. When the infected machine tries to respond, the maximum bandwidth used will be near the maximum amount. As a result, legitimate users could not connect to the CC network. When the CCC tries to send the reply, adversaries send an ICMP echo request packet. The bandwidth utilization will reach the maximum, and new users cannot connect to the network during this time [54]. Furthermore, the adversary could leverage a compromised CC device as an intermediary to send ICMP echo requests to flood the local network, resulting in an insider attack. Figure 7 depicts the ICMP flood attack.  
Figure 6: Ping of death attack.
## 2 Discovery of PGIP and Its Variety and Diversity  
In 1971, [PERSON] and [PERSON] [8] discovered PGIP in protein extracts from cell cultures of pea, bean, tomato, and _Acer platanoides_, and presented evidence that PGIP is located in the plant cell wall. In 1993, [PERSON] et al. [9] purified the 38.5 kDa molecular weight PGIP from raspberry fruit. In 1994, [PERSON] et al. [10] separated and purified PGIP from germinated soybean seeds and successfully cloned the soybean PGIP gene for the first time. More recently, scientists have identified PGIP in paddy rice, European rape, _Arabidopsis thaliana_, alfalfa, mung bean, citrus, and sisal [11; 12; 13; 14; 15; 16; 17]. The NCBI database has published 400 full or partial sequences of PGIP from both monocotyledons and dicotyledons, but there have been few investigations into the function of these proteins to inhibit PG protein. Plants can have multiple PGIP genes. For example, _Brassica napus_ L. has the most PGIP's found in a plant, with 17 BnPGiPs on different chromosomes [13]. _Phaseolus vulgaris_ has at least five PGiPs on chromosome 10. _Arabidopsis thaliana_ has two PGiPs, AtPGIP1 and AtPGIP2, on chromosome 5, encoding proteins that are 76.1% similar in amino acid sequence [11].  
The expression level of PGIP varies among different plant species, different organs, and different stages of development. The expression level of PGIP protein is proportional to the plant's anti-disease ability. [PERSON] et al. [18] used Western blotting to show a higher PGIP level in a wheat variety with good disease resistance, SW89-2598, than the level in a variety with poor resistance, 3626. Similarly, [PERSON] et al. [19] showed via qRT-PCR that good resistant wheat variety YSBR1 has higher PGIP expression than a variety with poor resistance. Tobacco genetically integrated with the PGIP gene from _Hypericum perforatum_ exhibited increased anti-disease ability [20]. A study of fungal resistance on two varieties of wheat found PGIP expression level proportional to the anti-disease ability [21]. For the same variety of wheat, different organs have different levels of PGIP, with the maximum in stems, a moderate amount in roots, and the lowest levels in leaves [22]. In the late period of plant development, the expression level and activity of PGIP in fruits are negatively correlated with maturity [23].
## 3 Experimental Procedures
### Experimental Design
#### 3.1.2 Meter Placement Design  
The positions of displacement meters and strain gauges are given in Figure 3. A YHD-50 displacement meter was fixed at the top of the specimen to record the deformation of the sand mat, which was used to eliminate the impact of the uneven loading plate. Four YHD-50 displacement meters were fixed on the actuator of the loading device at equal angle intervals to record the longitudinal displacement of the specimen. At the half height of the specimen, four 100 mm concrete strain gauges were fixed on the outer surface of the sleeve along the circumferential and longitudinal directions to record the circumferential and longitudinal strains of the sleeve, respectively. At the same time, at the half height of the self-stressed reinforced specimens, four 80 mm concrete strain gauges were fixed on the inner surface of the sleeve along the circumferential and longitudinal directions to record the circumferential and longitudinal strains of the filled concrete, respectively. Both the displacement and strain time histories were recorded using a TDS-530 static data collector with an acquisition frequency of 0.5 Hz, as shown in Figure 4.  
Figure 4: TDS-530 static data collector.  
Figure 3: The placement of displacement meters and strain gauges. (**a**) Design positions of meters; (**b**) Actual positions of meters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multirow{2}{*}{**Group**} & \multirow{2}{*}{\
\begin{tabular}{c} **D \(\times\) H =1** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **ACSC \(\star^{2}\) of** \\ **Unreinforced Specim** \\ **(MPa)** \\ \end{tabular} } & \multicolumn{3}{c}{**Filled Concrete**} & \multicolumn{1}{c}{**ACSC of**} \\ \cline{5-7}  & & & **ACSC** & **Thickness** & **Self-Stress** & **LCSS** \\  & & & **(MPa)** & **(MPa)** & **(mm)** & **(MPa)** \\ \hline P1 & \multirow{2}{*}{UG} & 250 \(\times\) 750 & 20.1 & 21.7 & 0 & 0 & 20.9 \\ P2 & & 200 \(\times\) 750 & 20.1 & 21.7 & 0 & 0 & 20.9 \\ S1-A & ORG & 250 \(\times\) 750 & 20.1 & 19.8 & 40 & 0 & 20.9 \\ S1-S & & 250 \(\times\) 750 & 20.1 & 21.7 & 40 & 1 & 20.9 \\ S1-SS & SRG & 250 \(\times\) 750 & 20.1 & 21.7 & 40 & 0.8 & 13.6 \\ S2-S & & 200 \(\times\) 750 & 20.1 & 21.7 & 65 & 0.6 & 20.9 \\ \hline \hline \end{tabular} Note: *1: D \(\times\) H = the diameter and height of the unreinforced specimen. *2: ACSC = axial compressive strength of concrete.  
\end{table}
Table 3: Parameters and grouping of specimens.
# Successful Tests on Detecting Pre-Earthquake Magnetic Field Signals from Space
## 4 Conclusions
Publicly available datasets were analyzed in this study. These data can be found in the USGS Seismic Catalogue and ESA Swarm data portals. We would like to extend our sincere gratitude to the European Space Agency (ESA) for providing the invaluable data collected by the Swarm constellation mission for Earth Observation, and to USGS for providing the earthquake catalog. The authors declare that they have no competing interests.
## 2 Materials and Methods
### GC-MS/MS Analysis of Phenolic Acids in Cassava Root Flour
#### 2.4.2 Chromatographic Separation  
Phenolic acids were separated using a gas chromatograph (Trace 1300, Thermo Scientific, Waltham, MA, USA) coupled to a triple quadrupole mass spectrometer (TSQ 8000, Thermo Scientific, Waltham, MA, USA). The separation was performed on a non-polar Rxi-5 Sil MS capillary column (30 m \(\times\) 0.25 mm ID, 0.25 \(\mu\)m film thickness), with helium as the carrier gas at a constant flow rate of 1 mL/min. The injector temperature was set to 250 \({}^{\circ}\)C, and the injection was performed in splitless mode. The oven temperature program was as follows: initial temperature of 100 \({}^{\circ}\)C held for 4 min, ramped to 180 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min and held for 2 min, then increased to 320 \({}^{\circ}\)C at 20 \({}^{\circ}\)C/min and held for 5 min. The mass spectrometer operated in selected reaction monitoring (SRM) mode. The ion source and quadrupole temperatures were maintained at 250 \({}^{\circ}\)C and 150 \({}^{\circ}\)C, respectively, with the transfer line temperature also set to 250 \({}^{\circ}\)C. Phenolic compounds are identified by analyzing their unique fragmentation patterns and retention times. The resulting mass spectra are compared with known standards to identify the compounds.
## Appendix A Background Definitions  
The spectral decomposition of a symmetric positive definite matrix \(\mathbf{A}\) is given by \(\mathbf{A}=\mathbf{V}\Lambda\mathbf{V}^{\intercal}\) where \(\mathbf{V}\) is an orthonormal matrix and \(\Lambda\) is diagonal matrix of eigenvalues as follows:  
\[\mathbf{V}=\begin{bmatrix}\mathbf{v}_{1}&\dots&\mathbf{v}_{g}\end{bmatrix}\] (A1)  
\[\Lambda=\begin{bmatrix}\lambda_{1}&0&\dots&0\\ 0&\lambda_{2}&\dots&0\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&\dots&\lambda_{g}\end{bmatrix}\] (A2)  
where \(\lambda_{i}\geq 0\), for \(i\in\{1,\dots,g\}\). Then, \(\mathbf{A}\) to the power \(k\), where \(k\in\mathbb{R}\), is obtained by:  
\[\mathbf{A}^{k}=\mathbf{V}\Lambda^{k}\mathbf{V}^{\intercal}.\] (A3)  
The exponential of \(\mathbf{A}\) can be obtained by:  
\[\exp(\mathbf{A})=\mathbf{V}\exp(\Lambda)\mathbf{V}^{\intercal}.\] (A4)  
The logarithm of \(\mathbf{A}\) is given by:  
\[\log(\mathbf{A})=\mathbf{V}\log(\Lambda)\mathbf{V}^{\intercal}\] (A5)  
where \(\Lambda\) entries are strictly positive eigen values.  
Cholesky decomposition (factorization) of the positive semi-definite matrix \(\mathbf{A}\) is given by \(\mathbf{A}=\mathbf{L}\mathbf{L}^{T}\) where \(\mathbf{L}\) is a lower triangular matrix with nonnegative diagonal elements as follows:  
\[\mathbf{L}=\text{chol}(\mathbf{A})=\begin{bmatrix}a_{11}&0&\cdots&0\\ a_{21}&a_{22}&\cdots&0\\ \vdots&\vdots&\ddots&0\\ a_{g1}&a_{g2}&\cdots&a_{gg}\end{bmatrix} \tag{10}\]  
where \(a_{ii}\geq 0\), for \(i=1\ldots g\). Cholesky factorization is unique for positive definite matrices with \(a_{ii}>0\).
## 4 Discussion  
### Spatial Differences Between VHIopt and VHIori  
VHIori assumes that both VCI and TCI contribute equally to VHI, which diminishes the accuracy of VHI in monitoring agricultural drought [16; 22]. In contrast, VHIopt distinctly assesses the contributions of VCI and TCI to VHI, and its drought monitoring performance  
Figure 10: Drought change trends in the vegetation-covered areas of the YRB from 2001 to 2021 ((a) Theil–Sen Median trend analysis and Mann–Kendall test, (b) drought change characteristics, (c,d) percentage of area).  
is better than VHIori in different vegetation types, which makes up for the deficiency of VHIori. Compared to VHIopt, VHIori assigns a higher contribution of VCI to VHI, resulting in an underestimation of drought conditions in regions with dense vegetation cover and an overestimation in areas with sparse vegetation cover [22; 86].  
Figure 14 illustrates the spatial distribution of differences between VHIori and VHIopt in 2001. Positive differences, where VHIori exceeds VHIopt, indicate that VHIori underestimates the severity of agricultural drought. Conversely, negative differences, where VHIopt exceeds VHIori, suggest that VHIori overestimates drought conditions. In a typical drought year, VHIori tends to underestimate agricultural drought in the YRB, mainly in areas with high vegetation density, resulting in an overly optimistic assessment of vegetation health [6; 32]. For example, VHIori underestimates agricultural drought in the Ordos Plateau and Guanzhong Plain in May; it underestimates agricultural drought in the Hetao Plain in June. Conversely, VHIori overestimates drought conditions in the YRB primarily in areas with low vegetation density. For instance, VHIori overestimates agricultural drought in the headwaters of the YRB in May; it overestimates agricultural drought in the Loess Plateau in September. Therefore, VHIopt is more sensitive for monitoring agricultural drought than VHIori and provides a more accurate assessment of vegetation stress, enhancing the application prospects of VHIopt.