esa-sceva/satcom-chunk-collection huggingface dataset:
# Validation of Drifting Buoy Data for Ocean Wave Observation
## 2 Data and Methods
### ERA5 Wave Data  
The ERA5 wave data can be obtained from [[https://cds.climate.copernicus.eu/](https://cds.climate.copernicus.eu/)]([https://cds.climate.copernicus.eu/](https://cds.climate.copernicus.eu/)) (accessed on 30 June 2021). The ERA5 wave height and period are defined as \(H_{e}\)= \(H_{0}\)\(\equiv\) 4\(M_{0}^{1/2}\) and \(T_{e}\) = \(T_{0}\)\(\equiv\)\(M_{-1}M_{0}^{-1}\), respectively, where  
\[M_{n}=\int_{0}^{\infty}f^{n}F(f)df. \tag{1}\]  
\(f\) is a wave frequency, and \(F(f)\) is a wave frequency spectrum [13].  
The system used to produce the ERA5 data has been described in Reference [14]. The wave model was based on the WAM cycle 4, e.g., References [15; 16]. The reanalysis data were produced using a coupled model system, which is called the Integrated Forecasting System (IFS). Observations from buoys and satellite scatterometers were assimilated in the ERA5 atmosphere data through a four-dimensional variational data assimilation scheme [17]. The wind input source function was from Reference [18]. The nonlinear interaction source function was from Reference [19]. The dissipation source function was from Reference [20]. The altimeter wave height data were assimilated in the ERA5 wave data through the optimal interpolation method [16; 21].  
The spatial resolution of the downloaded ERA5 wave data (\(H_{e}\) and \(T_{e}\)) was 0.5\({}^{\circ}\). The 3 h ERA5 wave data were spatially interpolated on the drifting buoy position.
## 5 Discussion  
According to the validity analysis of the HDE deceptive jamming algorithm in Section 3.2, the algorithm assumes beam center approximation and the decomposition of \(G(\xi,\eta,r_{P})\), which will lead to the residual RCM and quadratic phase error after imaging processing. However, the imaging quality of HDE deceptive jamming can be guaranteed by limiting the maximum trajectory deviation by Equation (43). Combining the constraints of Equations (43), (52) and (57), the following conclusions can be drawn for the HDE algorithm.  
* The range imaging quality of the fake point target is related to the fake target point's slant-range length \(r_{P}\). As \(r_{P}\) becomes longer, the range broadening of the target will become larger.
* The azimuth imaging quality of the fake target is related to the slant-range length \(r_{P}\) and trajectory deviation \(d_{max}\) of the fake target. As \(r_{P}\) becomes longer, the azimuth broadening of the fake target will become larger. As the trajectory deviation \(d_{max}\) becomes larger, the peak sidelobe ratio PSLR in the azimuth direction will become higher, and the sidelobes will be asymmetrical.  
These phenomena can be seen in Table 2. The range and azimuth broadening of \(P_{1}\)\(\sim\)\(P_{9}\) increase with the increase of the target slant range, which is caused by the residual RCM with the increase of the target slant range. At the same time, the residual RCM also causes the main lobe position of each point to shift slightly in the range direction and azimuth direction. Due to the influence of the quadratic sinusoidal phase error, there will be asymmetry of the side lobe and distortion of the main lobe at each point in the azimuth direction, which makes the deviation of the main lobe in the azimuth direction larger than that in the range direction. However, the maximum IRW broadening in Table 2 does not exceed 0.7%, the maximum main lobe offset does not exceed 0.5 range resolution cells, and has almost no effect on the fake scene imaging in Figures 13-15. In summary, the imaging quality of the deceptive jamming of the HDE algorithm can be guaranteed.  
According to the analysis in Section 4.3, the HDE algorithm has significant advantages over other deceptive jamming algorithms in terms of computational complexity. This is because the azimuth time-frequency hybrid domain modulation in the HDE algorithm significantly improves computational efficiency and flexibility, making nonlinear-trajectory deceptive jamming more feasible. It should be noted that the jamming target of the HDE algorithm is not limited to nonlinear-trajectory SAR, and it can also jam linear trajectory SAR. However, the proposed HDE algorithm still has certain problems. For example, the algorithm has yet to be extended to adapt to squint SAR, spaceborne SAR, and other SAR working modes. These are issues that we need to study further in the future.
## 2 Issue of Ultra-Narrow Spot in mmWave and Terahertz
Furthermore, this communication zone undergoes mobility when mounted on a moving platform, and the timeframe available for utilizing the communication zone is contingent upon the relative velocity. For example, when a communication device traverses a 1 m spot at a velocity of 20 km/h, the available time amounts to approximately 0.18 s. As the crossing speed increases, the duration correspondingly diminishes. Hence, mmWave or terahertz bands deployed on mobile platforms exhibit temporally and spatially restricted ultra-narrow communication zones, introducing novel and unique challenges. In conventional wireless standards, including the 60 GHz band, an association process that takes charge of the exchange of essential information for data transmission, such as identification, communication parameters, and encryption keys, demands time. This poses challenges in utilizing an ultra-narrow spot beyond the mmWave band. For instance, within a device compliant with IEEE 802.11 ad, the wireless LAN standard operating in the 60 GHz band, an average of 2.2 s has been reported as necessary to complete an association process [22], which leads to a difficulty for using ultra-narrow zones that exist for mere fractions of a second. In such scenarios, techniques like beamforming are employed to track the communication zone by scanning beams, with the aim of maximizing the available time for utilizing the communication zone [13]. Nonetheless, executing three-dimensional beam scanning and tracking in UAV communications demands intricate systems, particularly challenging to implement while adhering to stringent constraints on size, weight, and power consumption [23]. This tendency can become more critical as frequency increases, i.e., towards terahertz frequencies [12]. Figure 2: Half power (\(-3\) dB) area of mmWave or terahertz. (**a**) An illustration of a communication spot (red hatched area) and its geometric parameters. (**b**) Relationship between the cross-sectional spot size and frequency for different distances \(d\).
## 3 Results and Discussion
### Adsorption Kinetics  
Figure 3 describes the effect of contact time on NO3-N uptake by AEE-3 and A520E at 298 K, and all the data were simulated using two commonly used kinetic models [21; 22]:  
\[\text{Pseudo}-\text{first}-\text{order}:\ Q_{\text{t}}\ =\ Q_{\text{e}}(1-e^{-k_{ \text{t}}t}) \tag{3}\]  
\[\text{Pseudo}-\text{sec}\,\text{ond}-\text{order}:\ Q_{\text{t}}\ =\ \frac{k_{2}Q_{\text{e}}^{2}t}{1+k_{2}Q_{\text{e}}t} \tag{4}\]  
The initial adsorption rate was calculated by Equation (5):  
\[h_{0}\ =\ k_{2}Q_{\text{e}}^{2} \tag{5}\]  
where \(Q_{\text{e}}\) (mg/g) is the equilibrium adsorption capacity of NO3-N, and \(Q_{\text{t}}\) (mg/g) refers to the amounts of NO3-N in resins at time \(t\) (min). \(k_{1}\) (1/min) and \(k_{2}\) (g/(mg\(\cdot\)min)) represent the rate constants for the pseudo-first- and pseudo-second-order kinetic models, respectively. \(h_{0}\) is the initial adsorption rate (mg/(g\(\cdot\)min)). The kinetic parameters, together with the coefficient of determination (\(R^{2}\)), are listed in Table 2. It is evident that the adsorption behavior of NO3-N onto both resins was preferable to fit the pseudo-second-order based on the \(R^{2}\) values. Obviously, AEE-3 exhibited a greater capacity for NO3-N than A520E, illustrating that the introduction of alkyl chains would form quaternary ammonium groups which can easily interact with NO3-N via electrostatic attraction. Finally, a larger \(h_{0}\) value of AEE-3 was obtained, and the difference in the adsorption rate is related to the difference of the resin matrix.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Resin**} & \multicolumn{2}{c}{**Strong Base Ion-Exchange**} & \multicolumn{1}{c}{**Average Pore Diameter a**} & \multicolumn{1}{c}{**BET Surface Area**} & \multicolumn{1}{c}{**Pore Volume**} \\  & **Capacity (mmol/g Dry Resin)** & **(nm)** & **(m\({}^{2}\)/g)** & **(cm\({}^{3}\)/g)** \\ \hline D311 & 0 & 40.92 & 31.88 & 0.36 \\ AEE-3 & 3.42 & 47.44 & 23.46 & 0.20 \\ \hline \hline \end{tabular}  
* BJH Desorption average pore diameter.  
\end{table}
Table 1: Physicochemical properties of two resins.  
Figure 2: FT-IR spectra of D311 and AEE-3.
# Deep-Learning-Based Framework for PET Image Reconstruction from Sinogram Domain
###### Abstract  
High-quality and fast reconstructions are essential for the clinical application of positron emission tomography (PET) imaging. Herein, a deep-learning-based framework is proposed for PET image reconstruction directly from the sinogram domain to achieve high-quality and high-speed reconstruction at the same time. In this framework, conditional generative adversarial networks are constructed to learn a mapping from sinogram data to a reconstructed image and to generate a well-trained model. The network consists of a generator that utilizes the U-net structure and a whole-image strategy discriminator, which are alternately trained. Simulation experiments are conducted to validate the performance of the algorithm in terms of reconstruction accuracy, reconstruction efficiency, and robustness. Real patient data and Sprague Dawley rat data were used to verify the performance of the proposed method under complex conditions. The experimental results demonstrate the superior performance of the proposed method in terms of image quality, reconstruction speed, and robustness.  
PET image; direct reconstruction; cGANs +
Footnote †: journal: Applied Physics Letters
## 5 Discussion
### Limitations and Future Perspectives  
As shown in the red boxes in Figure 14a,b, due to the inherently dark tone of the shade house and the potential formation of shadows at its edges when exposed to sunlight under certain situations, the shade house and its shadows often overlap when viewed from a top-down remote sensing perspective. This results in a blurred distinction between the boundary of the shade house and the surrounding soil. In manually annotated datasets, the reliance on visual interpretation inherently introduces some level of error, which may accumulate as network training progresses, thus impacting the final semantic segmentation accuracy. This is particularly evident in the blurring of boundary regions, leading to insufficient precision in the extraction results. Therefore, future research should focus on incorporating boundary extraction methods to refine the delineation of shade houses' boundaries, which will further improve extraction accuracy and minimize the impact of boundary errors on both model training and final outcomes.
grandiflora_ that oxidize \(\beta\)-amyrin at the C-28 position to produce oleanolic acid [18; 23]. In addition to oxidizing \(\beta\)-amyrin at the C-28 position to produce oleanolic acid, some CYP716 catalyze triterpene oxidation at different positions, including C-6, C-2\(\alpha\), and C-12 [24; 25; 26]. For example, PgCYP716A47 catalyzes the C-12 hydroxylation of dammareendiol-II to form protopanaxadiol [27]. Subsequently, under the catalysis of CYP716A52v2, protopanaxadiol is oxidized to produce the downstream product, protopanaxatriol [28]. _P. grandiflora_ is an important traditional Chinese medicinal plant with a wide cultivation area [29]. Oleanolic acid, a pentacyclic oleanane-type triterpene, is the main active ingredient in _P. grandiflora_[30]. The CPY716 family plays a major role in catalyzing the oxidation of \(\beta\)-amyrin into oleanolic acid. However, a systematic understanding of the _CYP716_ gene family and its evolutionary history is lacking. In this study, a total of 22 _PgCYP716_ genes were identified, distributed among seven subfamilies. Analysis of the _cis_-acting elements of _PgCYP716_ genes revealed that they were mainly involved in plant hormone regulation and responses to abiotic stresses. The _PgCYP716A264, PgCYP716A391, PgCYP716A291_, and _PgCYP716 BWv3_ genes were cloned for subsequent experiments. The results of subcellular localization, yeast heterologous expression, and transgenic hairy root transformation indicated that all four enzymes were membrane proteins and oxidized \(\beta\)-amyrin to produce oleanolic acid. This study enhanced the understanding of the saponin synthesis pathway, expanding the gene pool for saponin biosynthesis and accelerating molecular breeding. Moreover, this study provided genetic resources for the cultivation of high-efficiency and high-quality varieties of _P. grandiflora_.
## 3 Results and Discussion  
### Dataset Preparation  
The dataset employed in this study was derived from the YUV Trace Dataset, which was made available by Arizona State University. It encompassed a multitude of video sequences in YUV format [16], offering a valuable resource for research in the domain of video processing and compression. The dataset comprised a diverse range of content, including people, scenery, buildings, creatures, and more common images of human landscapes. In this paper, eight video sequences--Akiyo, Bus, Bridge, Foreman, Flower, Container, Tempete, and Basketball--were selected for analysis. Each frame was extracted, and frames that were too similar to each other were removed to ensure the integrity of the dataset. A total of 1000 training images were selected, and the training set, validation set, and test set were divided according to a ratio of 8:1:1, resulting in 800 training sets, 100 validation sets, and 100 test sets, respectively. The training set was trained using the training method described in Section 2, and the test set was predicted based on the training results. The present study will initially compare the network improvement before and after the improvement. This will be followed by an analysis of the ablation experiment. Thereafter, the improvement effect under different QP values will be explored. Finally, this method will be compared with two other common network methods (ESRGAN and DRRN).
# Rotate Vector Reducer Fault Diagnosis Model Based on EEMD-MPA-KELM
## 4 Establishment of RV Reducer Fault Diagnosis Model  
### EEMD Preprocessing  
Empirical Mode Decomposition (EMD) is an empirical mode decomposition method that can reflect the instantaneous frequency of data, but since the input noise is generated randomly, it is easy to cause problems such as mode aliasing, end effect, and stop standard of screening iteration. To solve the above problems, [PERSON] [21] and others proposed Ensemble Em-pirical Mode Decomposition (EEMD). The method solves the mode aliasing phenomenon by adding white noise to fill the discontinuous signal segment. In the process of noise signal decomposition, the filtering characteristics of white noise signal are used to solve the IMF average value for many times to eliminate the interference of white noise on the original signal at discontinuous points [22].  
Data preprocessing steps are as follows [23]:  
Step 1: Divide the fault data into several sample data, and add normal white noise \(S_{w}(\omega)\) to each sample data \(x(t)\) to obtain a new overall \(x_{s}(t)\).  
\[x_{s}(t)=x(t)+S_{w}(\omega) \tag{23}\]  
The expression of normal white noise \(S_{w}\) (\(\omega\)) is as follows:  
\[S_{w}(\omega)=\sum_{t=-\infty}^{\infty}R_{w}(l)e^{-j\omega t}=\sigma^{2} \tag{24}\]  
In the formula, the expression of \(R_{w}\) (\(l\)) is as follows:  
\[R_{w}(l)=\sigma^{2}\delta_{l},\quad l=0,\pm 1,\pm 2\cdots \tag{25}\]  
In the formula, the expression of \(\delta_{l}\) is as follows:  
\[\delta_{l}=\left\{\begin{matrix}1,&l=0\\ 0,&l\
eq 0\end{matrix}\right. \tag{26}\]  
Step 2: Perform EMD decomposition on \(x_{s}\) (\(t\)), the sample data with white noise added, to obtain several IMF components. The flow chart is shown as follows:  
\[x_{s}(t)=\sum_{c=1}^{n}imf_{c}(t)+r_{n}(t) \tag{27}\]  
In the formula, \(imf_{c}(t)\) is the \(c\) IMF of EMD decomposition. \(r_{n}(t)\) is the participating weight after decomposing n IMFs.  
Step 3: Repeat the above steps, adding a new white noise sequence each time;  
Step 4:Take definite integral of IMF obtained each time and divide it by segment length. In order to avoid the influence of positive and negative signs of the value after decomposition, IMF absolute value is processed before.  
Its flow chart is shown in Figure 2:
## 4 Results and Discussion
### Soil Water--Groundwater Recharge Mode Analysis
#### 4.1.1 Soil Moisture Migration Stratification
The silty clay layer at the depths from 4.4 m to 5.7 m has a certain retardation effect on the cohesive soil water infiltration, which reduced the soil moisture content change in the cohesive soil layer above the depth of 5.0 m caused by the groundwater level change.
## 4 Multiple Adiabatic Passages of Magnetic Field through Zero Value  
What can happen if the Larmor frequency \(\omega(t)\) passes adiabatically through zero value several times, at instants \(t_{0},t_{1},t_{2},\ldots\)? We suppose that we know all coefficients in the transition rules through zero frequency at each instant \(t_{k}\):  
\[\left\{[\omega(t)]^{-1/2}e^{i\phi_{k}(t)}\right\}_{t\ll t_{k}}\to \left\{[\omega(t)]^{-1/2}\left[u_{+}^{(k)}e^{i\phi_{k}(t)}+u_{-}^{(k)}e^{-i \phi_{k}(t)}\right]\right\}_{t\gg t_{k}}, \tag{32}\] \[\phi_{k}(t)=\int_{t_{k}}^{t}|\omega(z)|dz. \tag{33}\]  
The resulting transformation after \(N\) zero-crossings can be written as  
\[\left\{[\omega(t)]^{-1/2}e^{i\phi_{0}(t)}\right\}_{t\ll t_{0}}\to \left\{[\omega(t)]^{-1/2}\left[U_{+}^{(N-1)}e^{i\phi_{0}(t)}+U_{-}^{(N-1)}e^{- i\phi_{0}(t)}\right]\right\}_{t\gg t_{N-1}}, \tag{34}\]  
assuming that \(U_{\pm}^{(0)}=u_{\pm}^{(0)}\). To establish the recurrence relations between the coefficients \(U_{\pm}^{(N-1)}\) and \(U_{\pm}^{(N)}\), we apply the rule (32) to the function arising after the \(N\)th zero, using the superposition principle and taking into account that the function \([\omega(t)]^{-1/2}e^{-i\phi_{k}(t)}\) transforms as \(\varepsilon^{*}(t)\) after the frequency passes through zero value:  
\[\left\{[\omega(t)]^{-1/2}e^{-i\phi_{k}(t)}\right\}_{t\ll t_{k}}\to \left\{[\omega(t)]^{-1/2}\left[u_{+}^{(k)*}e^{-i\phi_{k}(t)}+u_{-}^{(k)*}e^{i \phi_{k}(t)}\right]\right\}_{t\gg t_{k}}.\]  
Then, using the relations  
\[\phi_{0}(t)=\Phi_{k}+\phi_{k}(t),\qquad\Phi_{k}=\int_{t_{0}}^{t_{k}}|\omega(z )|dz, \tag{35}\]  
we can write the following equation for \(t\gg t_{N}\):  
\[U_{+}^{(N)}e^{i\phi_{0}(t)}+U_{-}^{(N)}e^{-i\phi_{0}(t)} = U_{+}^{(N-1)}e^{i\phi_{N}}\Big{[}u_{+}^{(N)}e^{i\phi_{N}(t)}+u_{- }^{(N)}e^{-i\phi_{N}(t)}\Big{]}\] \[+ U_{-}^{(N-1)}e^{-i\phi_{N}}\Big{[}u_{+}^{(N)*}e^{-i\phi_{N}(t)}+u _{-}^{(N)*}e^{i\phi_{N}(t)}\Big{]}.\]  
Equating the terms with the same time dependence \(e^{\pm i\phi_{N}(t)}\), we arrive at the following recurrence relations:  
\[U_{+}^{(N)}=U_{+}^{(N-1)}u_{+}^{(N)}+U_{-}^{(N-1)}u_{-}^{(N)*}e^{-2i\phi_{N}}, \tag{36}\]  
\[U_{-}^{(N)}=U_{-}^{(N-1)}u_{+}^{(N)*}+U_{+}^{(N-1)}u_{-}^{(N)}e^{2i\phi_{N}}. \tag{37}\]  
One can verify that the identity \(|U_{+}^{(N)}|^{2}-|U_{-}^{(N)}|^{2}=1\) is the consequence of identities  
\[|U_{+}^{(N-1)}|^{2}-|U_{-}^{(N-1)}|^{2}=1,\qquad|u_{+}^{(N)}|^{2}-|u_{-}^{(N) }|^{2}=1.\]We see a strong dependence of coefficients \(U^{(N)}_{\pm}\) on the phases \(\Phi_{N}\), i.e., on the time intervals between the passages through zero frequency. In the case of double passage through zero, the following inequalities hold:  
\[\Big{(}|u^{(0)}_{+}u^{(1)}_{-}|-|u^{(1)}_{+}u^{(0)}_{-}|\Big{)}^{2}\leq|U^{(1)}_ {-}|^{2}\leq\Big{(}|u^{(0)}_{+}u^{(1)}_{-}|+|u^{(1)}_{+}u^{(0)}_{-}|\Big{)}^{2}. \tag{38}\]  
In particular, two identical passages through zero frequency can result in the value \(U^{(1)}_{-}=0\), i.e., in reestablishing the standard adiabatic behavior, under the condition \(\text{Re}\Big{[}u^{(0)}_{+}e^{i\Phi_{1}}\Big{]}=0\).
## 4 Discussion
### Effectiveness of the Morning-to-Noon Ratio for Monitoring Drought Stress
Our results indicated that the diurnal variation in \(\Phi_{\text{P}}\) decreased with increasing light intensity, whereas \(\Phi_{\text{F}}\) first increased but then decreased, with its peak occurring in the morning (Figure 6b,d). This finding is consistent with those of previous studies. This suggests that \(\Phi_{\text{F}}\) remains relatively high in the morning. Although the morning peak shifted earlier under severe drought conditions, causing the morning SIFy values to be slightly lower, they were still less affected by water stress than were the midday values. Therefore, we propose that NMR\({}_{\text{SIFy}}\) is an effective approach for monitoring water stress changes. In this study, the correlations between the \(\Phi_{\text{Fanopy}}\) values decoupled by the two methods and the leaf-scale Fs values notably differed. Among them, \(\Phi_{\text{F}}^{\text{FCVI}}\) exhibited a greater correlation with Fs, indicating a positive relationship, but the correlations of both \(\Phi_{\text{F}}^{\text{FCVI}}\) and \(\Phi_{\text{F}}^{\text{NIRv}}\) with Fs were not high (Figure 14). This finding indicates that \(\Phi_{\text{F}}^{\text{FCVI}}\) is more accurate than is \(\Phi_{\text{F}}^{\text{NIRv}}\) in reflecting canopy physiological information. [PERSON] (2020) compared these two indices via a simulated dataset and reported that the FCVI yielded a smaller error in estimating the fluorescence emission efficiency than did NIRv [20]. The error of using the NIRv is generally greater for canopies with higher vegetation coverage. Changes in the diurnal variation in the LAI caused by water stress can lead to differences in the accuracy of \(\Phi_{\text{F}}^{\text{NIRv}}\) estimates at different times of the day. This may explain why the diurnal variation pattern of \(\Phi_{\text{F}}^{\text{NIRv}}\) was less sensitive to changes in water stress (Figure 8). This suggests that the uncertainties in the FCVI and NIRv approaches in estimating \(\Phi_{\text{Fanopy}}\) might also contribute to the inaccuracies in NMR\({}_{\Phi_{\text{F}}-\text{FCVI}}\) and NMR\({}_{\Phi_{\text{F}}-\text{NIRv}}\) in water stress monitoring. Therefore, although NMR\({}_{\Phi_{\text{F}}-\text{FCVI}}\) can represent changes in water stress, its stability and sensitivity are inferior to those of NMR\({}_{\text{SIFy}}\) (Figures 15 and 16, respectively). Since NMR\({}_{\text{SIFy}}\) can capture both structural and physiological changes in vegetation, it encompasses more aspects of the vegetation's response to water stress compared to other indices. Therefore, NMR\({}_{\text{SIFy}}\) demonstrates greater sensitivity in monitoring daily drought conditions and may be applicable to a wider range of vegetation types. We recommend the use of NMR\({}_{\text{SIFy}}\), that integrates both vegetation and physiological information, for drought monitoring.