esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Lubrication Performance in SSCs of Different Machine Sizes
#### 3.3.1 Total Torque T\({}_{\rm t}\) in a Whole Period  
In this calculation, \(\delta\) is set 0 throughout. The calculation result is shown in Figure 16.  
It can be observed that total torque \(T_{\rm t}\) are always negative for SSLT and SCT. For MCT, \(T_{\rm t}\) are negative in 3 and 6 m\({}^{3}\)\(\cdot\)min\({}^{-1}\) machines. However, in the 12 m\({}^{3}\)\(\cdot\)min\({}^{-1}\) machine, \(T_{\rm t}\) presents positive in the whole period.  
It can be easily found that for any definite MPP, total torque \(T_{\rm t}\) increases with the machine size in the studied range. Taking the SCT as an example, the peak value of \(T_{\rm t}\) increases from \(-\)1.93 N\(\cdot\)m to \(-\)1.13 N\(\cdot\)m when the discharge capacity raises from 3 m\({}^{3}\)\(\cdot\)min\({}^{-1}\) to 12 m\({}^{3}\)\(\cdot\)min\({}^{-1}\). It proves that \(T_{\rm a}\) increases more quickly than \(T_{\rm b}\) as the machine size raises. For MCT, the \(T_{\rm t}\) curve rises much more rapidly than the other two MPPs as the machine size is enlarged. This is mainly because the MCT has the largest wedge at leading side.  
It can also be found that in the machines of the same discharge capacity, the MCT often has the maximum \(T_{\rm t}\) and the SCT has the minimum \(T_{\rm t}\). In the 12 m\({}^{3}\)\(\cdot\)min\({}^{-1}\) machines, the peak value of \(-\)1.13 N\(\cdot\)m, \(-\)0.195 N\(\cdot\)m and 4.37 N\(\cdot\)m for the SCT, SSLT and MCT, respectively. In MCT, \(T_{\rm t}\) is positive and the load \(T_{\rm t}\) will be borne by the water films at trailing sides. Although 4.37 N\(\cdot\)m\(>\) \(|\)\(-\)1.13\(|\) N\(\cdot\)m, the water film stiffness of the trailing side is often higher than that in the leading side. Therefore, it is still difficult to judge which MPP has the best performance.
## 4 Results and Discussion  
The relative positioning experiments for smartphones were conducted in both open and occluded environments. The experimental setup is depicted in Figure 6. The reference station, equipped with a geodetic receiver, was situated in an open environment on a rooftop, as illustrated in Figure 6a. The smartphones used for this experiment were the Huawei Mate40 and Huawei P40. In the open environment, both smartphones were placed on the roof of a high-rise building, ensuring open and unobstructed surroundings, as shown in Figure 6b. They formed an ultra-short baseline of nearly 5 m with the reference station. Conversely, in the occluded environment, the smartphones were positioned adjacent to a high-rise building. This setup resulted in a short baseline of over ten meters with the reference station. In this setting, the satellite signals for the smartphones were prone to blockages and interference, leading to multipath errors and NLOSs, as depicted in Figure 6c. Static GNSS observation data were gathered for approximately 2 h in each environment, with a sampling interval set at 1 s. Subsequently, the baseline reference values were derived using high-precision static data processing software.
## 5 Numerical Illustrations
### Validation and Convergence Studies of the Present Formulations
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**B.C.**} & \multirow{2}{*}{**Mode**} & \multicolumn{2}{c}{**H/L = 1/15**} & \multicolumn{2}{c}{**H/L = 1/7**} \\ \cline{3-6}  & & **Semi-Analytical** & **Ref. [34]** & **Semi-Analytical** & **Ref. [34]** \\ \hline C-C & 1 & 0.02922 & 0.02922 & 0.12638 & 0.12638 \\  & 2 & 0.07864 & 0.07864 & 0.31839 & 0.31839 \\  & 3 & 0.14951 & 0.14951 & 0.47191 & 0.47191 \\  & 4 & 0.22054 & 0.22054 & 0.56747 & 0.56747 \\  & 5 & 0.23843 & 0.23843 & 0.85145 & 0.85145 \\ C-F & 1 & 0.00467 & 0.00467 & 0.02132 & 0.02132 \\  & 2 & 0.02883 & 0.02883 & 0.12527 & 0.12527 \\  & 3 & 0.07896 & 0.07896 & 0.23576 & 0.23576 \\  & 4 & 0.10990 & 0.10990 & 0.32336 & 0.32336 \\  & 5 & 0.15015 & 0.15015 & 0.57597 & 0.57597 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Non-dimensional frequencies \(\Omega\) for C-C and C-F FGM straight beams (\(\alpha=1\)). The convergences of the dimensionless fundamental frequencies for FG-GPLRC circular arches with \(\lambda=1/10\) and \(\theta_{0}=\pi/3\) are demonstrated in Table 4, where the results are given against numbers of layers \(p\) and discrete points \(N\). It can be seen that the present method shows a fast rate of convergence and numeric stability. The discrete point number \(N=20\) and \(p=50\), which yields an excellent convergence, will be adopted hereafter.
###### Abstract  
This study aims to examine the applicability of nuclear inelastic scattering (NIS) and conventional Mossbauer spectroscopy for calibration of the frequency scale of ab initio calculated phonon density of states (PDOS) of iron ternary chalcogenides. NIS measurements are carried out on the quasi-one-dimensional ternary chalcogenide RbFeSe\({}_{2}\) to obtain the partial PDOS of the iron atoms in the compound. We compare the experimental PDOS with our previous results on vibrational properties of RbFeSe\({}_{2}\) obtained with density functional theory (DFT) ab initio calculations, conventional Mossbauer, and infra-red spectroscopies. The experimental PDOS measured by NIS is collated with the ab initio calculated one. The frequency correction factor for the ab initio results is determined as 1.077, in good agreement with value of 1.08 obtained previously from the temperature dependence of the Lamb-Mossbauer factor of the iron atoms in RbFeSe\({}_{2}\). We conclude that nuclear inelastic scattering and temperature dependence of the Lamb-Mossbauer factor in conventional Mossbauer spectroscopy can be equally applied for evaluation of the frequency correction factor for ab initio calculated phonon density of iron of ternary chalcogenides.  
nuclear inelastic scattering; phonon density of states; ab initio DFT theory; DFT phonon frequency correction factor +
Footnote †: journal: Applied Physics
# Fundamental Spacetime Representations of Quantum Antenna Systems
## 3 The General Theory of Quantum Antenna Systems  
### Preliminary Considerations  
According to Definition 2, a q-antenna system consists of a _spacetime source region_\(D_{\rm s}\) together with a current source function defined on this region. The _receiving_ q-antenna is located in the compact spacetime region \(D_{\rm r}\). The overall spacetime configuration is illustrated in Figure 1. To allow information transmission from the source to the receiver regions, the two spacetime domains \(D_{\rm s}\) and \(D_{\rm r}\) must have timelike separation [53].3 For simplicity, \(D_{\rm s}\) and \(D_{\rm r}\) as spacetime regions are assumed to be factorizable into the forms  
Footnote 3: The source region is a _spacetime_ region.  
\[D_{\rm s}=S_{\rm s}\times T_{\rm s},\quad D_{\rm r}=S_{\rm r}\times T_{\rm r}, \tag{3}\]where \(S_{\rm s}\) and \(S_{\rm r}\) are the three-dimensional spatial subdomain components of the original four-dimensional regions \(D_{\rm s,r}\) satisfying  
\[\mathbb{M}^{4}\supset D_{\rm s,r}\supset S_{\rm s,r}\subset\mathbb{R}^{3}, \tag{4}\]  
while \(T_{\rm s,r}\subset\mathbb{R}\) is the \"temporal component\" (timelike slice) of the source/receive regions \(D_{\rm s,r}\), respectively. Here, both \(D_{\rm s,r}\) and \(T_{\rm s,r}\) are assumed to be _compact_, i.e., \(D_{\rm s,r}\) and \(T_{\rm s,r}\) are closed and bounded sets in the standard Euclidean metric space \(\mathbb{R}^{3}\).4  
Throughout this section, the quantum field \(\phi(x)\) is allowed to refer to a general field with possible spins \(0,1/2,1,2\), etc., without worrying much about indices (in the next sections, we work only with scalar fields for simplicity). Moreover, any particular QFT under consideration may itself be of the free (non-interacting) or interacting types. Interacting field theories often require the use of perturbation theory in order to obtain practical results. However, all information about the interacting system is conveniently encoded in the _propagator_, which will be used extensively in our theory below.5 Therefore, for generality, in this paper, we utilize the propagator concept as the fundamental mathematical carrier of physical information about the generic quantum antenna system in spacetime.
# Improved Least Squares Phase Unwrapping Method Based on Chebyshev Filter
## 2 Methods  
### The Least Squares Phase Unwrapping Method  
In INSAR processing, the wrapped phase is the principal value of the original absolute phase, wrapping between \(-\pi\) and \(\pi\)[10]. Phase unwrapping is the process of recovering the original phase from the wrapped phase. The phase obtained by phase unwrapping is called the unwrapped phase. The LS phase unwrapping method treats phase unwrapping as an optimization problem, specifically, finding the minimum value of the unwrapped phase and the wrapped phase gradient. Mathematically, it can be represented as follows [16]:  
\[Min=\sum_{i=0}^{M-2}\sum_{j=0}^{N-1}\left|\phi_{i+1,j}-\phi_{i,j}-\Delta_{i,j} ^{x}\right|^{2}+\sum_{i=0}^{M-1}\sum_{j=0}^{N-2}\left|\phi_{i,j+1}-\phi_{i,j} -\Delta_{i,j}^{y}\right|^{2}, \tag{1}\]  
where \(M,N\) represent the horizontal and vertical size of the phase matrix, \(\phi_{i,j}\) represents the unwrapped phase, and \(\Delta_{i,j}^{x},\Delta_{i,j}^{y}\) represent the horizontal phase gradient and vertical phase gradient, respectively. The progress can be expressed as follows [16]:  
\[\begin{array}{c}\Delta_{i,j}^{x}=W\left(\phi_{i+1,j}-\phi_{i,j}\right)\,(i= 0,1\ldots M-2;j=0,1\ldots,N-1)\\ \Delta_{i,j}^{x}=0\hskip 28.452756 ptotherwise\\ \Delta_{i,j}^{y}=W\left(\phi_{i,j+1}-\phi_{i,j}\right)\,(i=0,1\ldots M-1;j=0,1 \ldots,N-2)\\ \Delta_{i,j}^{y}=0\hskip 28.452756 ptotherwise,\end{array} \tag{2}\]  
where \(W\) represents the wrapped symbol, and \(\phi_{i,j}\) represents the wrapped phase.  
From Equation (1), we can derive the following [16]:  
\[\left(\phi_{i+1,j}-2\phi_{i,j}+\phi_{i-1,j}\right)+\left(\phi_{i,j+1}-2\phi_{ i,j}+\phi_{i,j-1}\right)=\rho_{i,j}, \tag{3}\]  
where [16]  
\[\rho_{i,j}=\Delta_{i,j}^{x}-\Delta_{i-1,j}^{x}+\Delta_{i,j}^{y}-\Delta_{i,j-1 }^{y}. \tag{4}\]We can obtain the unwrapped phase by using discrete cosine transform [16]:  
\[\phi_{i,j}=DCT^{-1}\Bigg{\{}\frac{DCT\{\rho_{i,j}\}}{2\Big{(}\cos\Big{(}\pi\frac{ i}{M}\Big{)}+cos\Big{(}\pi\frac{j}{N}\Big{)}-2\Big{)}\Bigg{\}}, \tag{5}\]  
where \(DCT\) and \(DCT^{-1}\), respectively, represent discrete cosine transform and discrete inverse cosine transform [16].
# SIFT-Based Depth Estimation for Accurate 3D Reconstruction in Cultural Heritage Preservation
## 2 Materials and Methods
### Experimental Setup
The process began by carefully capturing multiple high-resolution photographs of the artifact from various angles. Ensuring significant overlap between images was crucial since this allowed the software to identify corresponding features across the photos. To enhance stability and minimize shadows that can disrupt depth estimation, a tripod was utilized, and consistent lighting was ensured throughout the capture process. Next, specialized photogrammetry software (AgiSoft) was employed. This software meticulously analyzed the overlapping areas within the images, pinpointing common features such as corners and edges. Using these identified points, the software estimated the 3D position of the camera for each image. This data then fueled the creation of a dense 3D point cloud, accurately representing the surface of the artifact. Finally, the depth map from this point cloud was derived. The software assigned X, Y, and Z coordinates (distance from the camera) to each point, subsequently translating them into a grayscale image where brightness corresponded to relative proximity to the camera. The pseudo-code of the proposed method is shown below. Figure 5: Global-local path networks for monocular depth estimation [43]. function merge2 graphs(GraphA, GraphB):  
Find common frames (images) between the two graphs commonFrames = intersect(GraphA.frames, GraphB.frames)
# Screening of Scaffolds for the Design of G-Quadruplex Ligands
## 3 Results and Discussion
### FRET Melting Studies  
The ability of the scaffolds to bind and stabilize G4 structures was evaluated by a high-throughput FRET melting assay [25]. This method is commonly used for first screening of G4 binding and stabilization efficiency of a small molecule [19]. The scaffolds were tested against four G4 DNA sequences (c-MYC, KRAS21 and VEGF promoters, and 21G human telomeric repeats) one G4 RNA (pre-MIR150) [26] and a double-stranded DNA control. The ligands were screened at 5 \(\upmu\)M, except acridine derivatives, which were tested at 1 \(\upmu\)M. The melting temperatures are summarized in Table 2.  
Overall, FRET-melting data showed that screened scaffolds are poor G4 stabilizers, with the notable exception of two acridine precursors, C\({}_{8}\)NH\({}_{2}\) and C\({}_{5}\)NH\({}_{2}\). This evidence suggests that the alkylamino chain is also important for G4 binding and stabilization beyond the aromatic ring. C\({}_{8}\)NH\({}_{2}\) proved to be the most effective binder for all G4 sequences (\(\Delta T_{\rm m}=17.4\)\({}^{\circ}\)C for FmycT, 26.8 \({}^{\circ}\)C for F21T, 7.2 \({}^{\circ}\)C for FkrasT, 4.5 \({}^{\circ}\)C for FvegfT, and 4.6 \({}^{\circ}\)C for Fpre-MIR150T at 1 \(\upmu\)M concentration), followed by C\({}_{5}\)NH\({}_{2}\) (\(\Delta T_{\rm m}=16.3\)\({}^{\circ}\)C for FmycT, 15.6 \({}^{\circ}\)C for F21T, 18.0 \({}^{\circ}\)C for FkrasT, 2.5 \({}^{\circ}\)C for FvegfT, and 0.9 \({}^{\circ}\)C for Fpre-MIR150T at 1 \(\upmu\)M concentration). The FRET-melting studies corroborate the other series of acridine derivatives, in which the nature of the acridine scaffold and the alkylamide chain influence the G4 stabilization. Previous studies have shown that the acridine moiety interacts with the G4 surface by end stacking interactions and the alkylamide interacts by groove binding [27]. In addition, acridine derivatives have stronger G4 stabilizing efficiency on pre-miRNA structure [28]. The phenyl and quinoline scaffolds displayed null stabilization of the DNA/RNA G4 structures studied, when compared with phenanthroline derivatives. The phenanthroline analogue Phen-2, described previously, which has a \(\uppi\) system extension, showed an increase in melting temperature of FmycT and F21T, when compared with its precursor, Phen-1. Compound Phen-1 exhibited a minimal increase in \(T_{\rm m}\) of 2.5 \({}^{\circ}\)C to F21T and no stabilization of FmycT even at 5 \(\upmu\)M, while the Phen-2 showed a moderate increase in \(T_{\rm m}\) of 8.6 \({}^{\circ}\)C for F21T and 11.3 \({}^{\circ}\)C for FmycT at the same concentration [15]. The functionalization with ester group in the phenanthroline moiety, when compared with oxazole functionalization, improved the G4 binding capacity against F21T and FkrasT sequences. On the contrary, the introduction of an additional pyrazine ring in phenanthroline moiety reduced stabi
## 1 Introduction  
Emerging eVTOL aircraft is becoming a hot topic for aerospace engineers due to the concept of urban air mobility (UAM) [1].  
As the battery energy density increases and the battery technology continues to optimize and develop [2, 3], the improvements in rechargeable batteries make various electric UAM aircraft designs possible. According to calculation by [PERSON] et al. [4], different UAM aircraft consume 130 Wh to 1200 Wh per passenger per mile, depending on the design and utilization, while terrestrial electric vehicles and combustion engine vehicles are expected to consume more than 220 Wh and 1000 Wh per passenger per mile, respectively. They also found that, based on the specific power and energy of lithium-ion batteries, various UAM aircraft designs are close to technological feasibility.  
As populations become concentrated in cities, traffic congestions worsen, and UAM is considered as a potential solution. Therefore, many institutions and companies around the world are developing UAM vehicles, building infrastructure, and researching flight operating systems [5]. At the same time, the flight experience of passengers also becomes an important aspect of the design and operation of eVTOL aircraft. For example, a study by [PERSON] et al. [6] shows that perceived safety, noise and vibration, availability and access,passenger well-being, concern for the environment, and vehicle motion are the six aspects that passengers care about the most, and they need to be highlighted and addressed well in the design.  
Meanwhile, compound aircrafts are attracting a lot of attention in both academic and industrial fields because of the performance constraints of conventional helicopters and planes. A compound-wing UAV is a VTOL aircraft that combines fixed wings and rotary wings, also known as fixed-wing hybrid UAVs [7]. A compound-wing configuration eVTOL aircraft uses completely independent thrusters (non-vector thrust) for cruise and lift, relies on the wing to provide cruise lift [8], and uses innovative powerplants and aerodynamic designs to achieve the VTOL capability of rotors and the speed/altitude performance of planes [9]. Fixed-wing UAVs have advantages such as large flight speed, high flight altitude, long flight duration, and high fuel efficiency. Rotary-wing UAVs have features such as vertical takeoff and landing, hover capability, and flexibility, making them applicable in various scenarios. Compound-wing UAVs boast the advantages of fixed-wing UAVs and rotary-wing UAVs, show the characteristics of safety, cost-effectiveness, environmental friendliness, flexibility, diversity, etc. [10], and are suitable for a wide range of applications such as urban, suburban, and long-distance tasks [11]. The characteristics of the compound-wing UAVs determine that this type of aircraft has a wide range of applications, such as being highly utilized in a wide range of services such as photography, path planning, search and rescue, power line inspection and civil construction [12].
# The Effect of Slow-Release Fertilizer on the Growth of Garlic Sports and the Soil Environment
## 3 Results and Analysis
### The Effects of Different Treatment Groups on the Chlorophyll Content of the Garlic Seedlings  
As shown in Figure 4, the SPAD values of the garlic seedling leaves in the four different treatment groups exhibited a consistent pattern throughout the growth stages. In the early growth stage, the SPAD values were relatively low due to insufficient chlorophyll synthesis. As the garlic seedlings developed rapidly, the SPAD values increased [53], attributed to leaf maturation and enhanced photosynthetic activity. In the middle and late stages, the SPAD values fluctuated within a certain range, likely due to peak chlorophyll accumulation during the vigorous growth period and environmental influences such as alternating sunshine and shade [54]. The relative chlorophyll contents in the T1, T2, and T3 treatment groups were significantly higher than those in the CK group, with increases of 4.47%, 7.69%, and 8.23%, respectively. These results suggest that the application of slow-release fertilizers in place of conventional chemical fertilizers can significantly enhance the relative chlorophyll content in garlic sprouts. This is because magnesium is a key component of chlorophyll, playing a vital role in its formation and in facilitating photosynthesis. Compared with the T1 group, the application of the slow-release fertilizers in the T2 and T3 groups could continuously provide a balanced nutrient supply to the crops, thereby increasing the relative chlorophyll content in the garlic seedlings.  
Figure 3: (**a**) The effect of different treatment groups on the height of garlic sprouts; (**b**) The effect of different treatment groups on the stem thickness of garlic sprouts. The different superscripted letters represent a significant difference between different treatments (\(p<0.05\)). Error bars represent \(\pm\)SD.  
Chlorophyll, the primary pigment involved in photosynthesis, directly reflects the intensity of crops' photosynthetic activity and serves as an accurate indicator of the photosynthetic rate. Measurement of the SPAD values is based on the absorption characteristics of chlorophyll, particularly its peak absorbance in the blue and red light regions. By comparing the absorption and transmittance of specific light wavelengths, the SPAD values can be calculated, which indirectly reflect the relative chlorophyll content in the leaves.  
The results of this study demonstrate that substituting conventional fertilizers with slow-release fertilizers significantly increases the chlorophyll content in garlic seedling leaves, thereby enhancing the efficiency of the conversion of light energy into chemical energy. These findings are consistent with those of [PERSON] [55], who reported that applying 130 kg N h\({}^{-1}\) of biologically active sulfur-coated urea significantly increased chlorophyll content, and [PERSON] et al. [56], who observed elevated SPAD values in maize following the application of slow-release fertilizer.
# Motor On-Line Fault Diagnosis Method Research Based on 1D-CNN and Multi-Sensor Information
## 4 Feature Layer Fusion of Multi-Source Heterogeneous Sensors Based on 1D-CNN
### Interpretation of Result
#### 4.5.2 Accuracy Rate and Loss Function Value  
The total number of training samples of the model is 800, the number of mini-batch training samples is 20, then the training times of each iteration are 40. Since the total iteration times of the model are 20 times, its total training times are 800 times. Figure 8 shows the change in accuracy rate and loss function value (i.e., root mean square error) in the training process of the model aimed at the training set and the test set.  
Figure 8: Change of model’s accuracy rate and loss function value.  
In the model's training process, the training set's accuracy rate increased steadily and converged to 99.3% with small fluctuations. The loss function value gradually decreased and approached 0. The fluctuation of the accuracy rate of the test set was obviously better than that of the training set, and the speeds of rising and convergence were faster. The loss function value was also closer to 0 than the training set. Therefore, this model had a significant effect on feature mining, fusion, classification, and diagnosis of the motor fault data.