esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application to Model Updating of an In-Service Bridge with Extra-Width
### Analysis of Dynamic Characteristics  
Both the theoretical and experimental analysis of dynamic characteristics of Hunan Road Bridge are conducted. The experiment mainly focused on the dynamic characteristic of main girder using ambient vibration test. From the FE model, the first five mode shapes of girders of Hunan Road Bridge are obtained and shown in Figure 5. Table 3 shows dynamic characteristics of main girder from theoretical analysis and experimental data. Furthermore, the values of MAC (Model assurance criteria) are shown in Table 3. Based on test data, the damping ration of mode shape No. 1 and No. 4 in Table 3 are 2.40% and 1.59%, respectively. Thus, in Rayleigh ratio, the coefficients of alpha and beta are set as 0.038 and 0.006, respectively.  
From the results of the main girder in Table 3, except the difference between theoretical and experimental frequencies of first order, transverse bending mode is 3.08%. Other differences are all above 25% in which the difference of second order of vertical bending frequency is even up to 38.6%. However, the MACs show that the experimental vibration modes have a relatively high agreement with the simulated ones, all of the MACs are over 0.84. This phenomenon indicates that a big difference exists in mode frequencies between the real bridge and the FE model which must be updated to reflect the real dynamic performance. Moreover, due to the extra-width, the torsion mode emerges earlier than ordinary bridges, which enhances the importance of yielding a precise FE model for further performance evaluation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mode** & \
\begin{tabular}{c} **Theoretical** \\ **Frequency/Hz** \\ \end{tabular} & **Modal Shape** &
\begin{tabular}{c} **Experimental** \\ **Frequency/Hz** \\ \end{tabular} & **Difference/\%** & **MAC 2** \\ \hline
1 & 0.7208 & First order of vertical bending & 0.9030 & 25.28 & 0.979 \\
2 & 1.0643 & First order of torsion & 1.3670 & 28.44 & 0.973 \\
3 & 1.3378 & First order of transverse bending & 1.3790 & 3.08 & 0.915 \\
4 & 1.3892 & Second order of vertical bending & 1.9290 & 38.60 & 0.894 \\
5 & 1.7504 & Third order of vertical bending & 2.4170 & 38.08 & 0.849 \\ \hline \hline \end{tabular}  
* \({}^{1}\) Difference = \(\frac{\text{Experimental Frequency}-\text{Theoretical Frequency}}{\text{Theoretical Frequency}}\) \(\times\) 100%; \({}^{2}\) MAC, Model assurance criteria.  
\end{table}
Table 3: Detail dynamic characteristics of main girder from theoretical and experimental analysis \({}^{1}\).
## 4 Influence of Advance Rate on Front Abtment Pressure  
### Theoretical Analysis of Front Abtment Pressure  
A mechanical model was established with the coal seam as an elastic body, as shown in Figure 10, to theoretically analyze the evolution law of advanced abtment pressure under different mining speeds. Referencing [PERSON]'s [23] theoretical research on stope front abtment pressure, the moving coordinate system xyo and fixed coordinate system \(\mathrm{x^{\prime}o^{\prime}y^{\prime}}\) were defined. The moving coordinate system xoy, along with the distributed load on the overlying strata of the main roof, moves relative to the fixed coordinate system \(\mathrm{x^{\prime}o^{\prime}y^{\prime}}\) at a constant velocity.  
Based on this theoretical derivation, the front abtment pressure acting on the coal seam is expressed as  
\[\sigma_{y}=ke^{-\beta_{1}x}(A\cos\beta_{2}x+B\sin\beta_{2}x)+q \tag{14}\]  
where  
\[A=\frac{\left|\begin{array}{c}-\frac{1}{2}\frac{1}{Er}ql^{2},-2\beta_{1}\beta _{2}\\ -\frac{1}{Er}ql,3\beta_{2}\beta_{1}^{2}-\beta_{2}^{3}\end{array}\right|}{\beta _{1}^{2}-\beta_{2}^{2},-2\beta_{1}\beta_{2}}\left|\begin{array}{c}\beta_{1} ^{2}-\beta_{2}^{2},-\frac{1}{Er}ql^{2}\\ 3\beta_{1}\beta_{2}^{2}-\beta_{1}^{3},-\frac{1}{Er}ql\end{array}\right|} \tag{15}\]  
Figure 10: Mechanical model for front abtment pressure calculation.  
\[\beta_{1}=\sqrt{\sqrt{\frac{k^{\prime}}{4 EI}-\frac{mv_{5}^{2}}{4 EI}}}\ \ \beta_{2}=\sqrt{\sqrt{\frac{k^{\prime}}{4 EI}+\frac{mv_{5}^{2}}{4 EI}}} \tag{16}\]  
In the equation, \(EI\) is the stiffness of the main roof beam (where \(E\) denotes the elastic modulus of the main roof, \(I\) represents the moment of inertia of the main roof, \(I=\frac{b\eta^{3}}{12}\), \(b\) is the rock beam width, and \(h\) is the thickness of the main roof), MN/m;  
where  
\(k^{\prime}\)--foundation stiffness (defined as the load per unit deformation applied to a unit beam), MN/m;  
\(q\)--uniform force on the base top, MN/m\({}^{2}\);  
\(m\)--mass per unit length of the main roof;  
\(x\)--distance ahead of the coal mining face within the stope, m;  
\(l\)--width of working face.  
Equation (14) demonstrates that, with other parameters held constant, the front abutment pressure on the coal seam is solely dependent on the velocity v. The computational results from [PERSON] (2018) reveal that as the advance rate increases, the peak front abutment pressure intensifies, and its peak location progressively shifts toward the front abutment zone of the working face.
# 100 Picosecond/Sub-10\({}^{-17}\) Level GPS Differential Precise Time and Frequency Transfer
## 5 DPT Time and Frequency Transfer Validations
### DPT Time Transfer Validations with Closure Baselines  
In order to further verify the accuracy of the DPT time difference, we determined the time transfer between each of the _NYA1_, _NYA2_, and _NYAL_ stations and performed daily re-initialization for 7 days. We further evaluated the self-consistency of the DPT using the three stations' closure in Figure 7. Theoretically, the three stations' baseline closure is given by:  
\[\textit{Closure}=(NYA1-NYA2)+(NYA2-NYAL)+(NYAL-NYA1)=0 \tag{14}\]  
As shown in Figure 7, the estimated closure solutions for the 7 days had STD values of 4 ps for the GPS L1 frequency and 3 ps for the GPS L2 frequency after the initialization. Meanwhile, Figure 8 shows the mean value of the baseline closure of the daily solutions. The two dashed lines in Figure 8 represent the average values over 7 days for GPS L1 and L2 frequencies, with the GPS L1 frequency at \(-\)14 ps and the GPS L2 frequency at 9 ps after initialization. Notably, the closure did not change over time, indicating that the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**MJD**} & \multicolumn{5}{c}{**CV Time Transfer**} & \multicolumn{5}{c}{**DPT Time Transfer**} \\ \cline{2-9}  & **Mean\_C1** & **Mean\_P2** & **STD\_C1** & **STD\_P2** & **Mean\_L1** & **Mean\_L2** & **STD\_L1** & **STD\_L2** \\ \hline
59,764 & 292 & 64 & 1009 & 99 & 257 & 65 & 12 & 4 \\
59,765 & 349 & 64 & 952 & 97 & 255 & 65 & 10 & 4 \\
59,766 & 317 & 64 & 875 & 96 & 254 & 65 & 10 & 3 \\
59,767 & 322 & 64 & 996 & 87 & 327 & 64 & 14 & 4 \\
59,768 & 264 & 62 & 976 & 89 & 323 & 64 & 12 & 4 \\
59,769 & 304 & 61 & 890 & 86 & 324 & 65 & 12 & 5 \\
59,770 & 321 & 63 & 899 & 91 & 321 & 64 & 12 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: STD and mean results of GPS DPT and CV time transfer daily solutions for USN7-USN8 (ps).  
Figure 6: DPT time transfer results for USN7-USN8 after subtracting total time delay between the two stations.  
DPT processing is self-consistent in terms of time transfer. This consistency reinforces the reliability of the DPT technique for accurate time transfer.
# Numerical Study on the Application of Near-Infrared Temperature Distribution Measurement of HIFU
## 2 Theoretical Models
### Basic Theory
#### 2.1.4 Thermal Viscosity Equation  
When focused ultrasound propagates at the interface between infrared glass and a tissue layer, thermal and viscous losses can cause acoustic attenuation. Specifically, losses occur in the acoustic, thermal, and viscous boundary layers of near-infrared glass. In order to establish a model that accurately matches the temperature measured by near-infrared spectroscopy, one must account for the thermal viscosity phenomenon induced by infrared glass and assess its influence on the temperature.  
The continuity equation used in the thermal viscosity model is given below.  
\[i\omega\
u+\rho(\
abla\
u)=0 \tag{8}\]  
In Equation (8), \(\
u\) represents the velocity field. The multiplication of \(\
u\) and \(i\omega\) represents the differentiation of time. The momentum equation can be expressed as follows:  
\[i\omega\rho\
u=\
abla\bigg{\{}-pI+\mu\Big{(}\
abla\
u+[\
abla\
u]^{\mathrm{T} }\Big{)}+\bigg{(}\mu_{\mathrm{B}}-\frac{2}{3}\mu\Big{)}(\
abla\
u)I\bigg{\}} \tag{9}\]  
Here, \(I\) represents momentum, \(\mu\) represents dynamic viscosity, and \(\mu_{\mathrm{B}}\) represents volumetric viscosity. The left side of the equation represents the mass of the conserved quantity, and the term on the right represents the divergence of the stress tensor.  
The energy conservation equation can be expressed in the following manner:  
\[i\omega(\rho C_{b}T-T_{0}\alpha_{0}p)=-\
abla(-k\
abla T)+Q. \tag{10}\]  
Here, \(k\) represents the thermal conductivity and \(\alpha_{0}\) represents the thermal expansion coefficient at a constant pressure.  
Combining Equations (8)-(10) yields a linearized equation of the state associated with changes in the pressure, temperature, and density, as presented below.  
\[\rho=\rho(\beta_{T}p-\alpha_{0}T) \tag{11}\]  
In this equation, \(\beta_{T}\) represents the isothermal compression rate. Assuming that the medium in the pipeline is static, uniform, continuous, and lossless, the ultrasound pressure undergoes a harmonic change with time. Furthermore, the coefficient representing the influence of time on the ultrasound pressure is denoted as \(e^{i\omega t}\). Under minor vibrational disturbances, the dependent variable in the ultrasound field can be represented as follows:  
\[p=p_{0}+p^{\prime}e^{i\omega t} \tag{12}\]  
\[v=v_{0}+v^{\prime}e^{i\omega t} \tag{13}\]  
\[T=T_{0}+T^{\prime}e^{i\omega t} \tag{14}\]  
In the given equations, variables with subscript 0 are related to the background flow, while variables with superscripts are related to acoustics. Inserting the control equation, Equation (4), into the above equations and retaining only the linear terms in the first-order variable gives the control equation for ultrasound wave propagation related to viscous and thermal losses [18].  
The symmetric boundary constraint conditions in the thermal viscosity model are given below.  
\[\overrightarrow{n}\cdot v=0 \tag{15}\]
# Continuous-Wave THz Imaging for Biomedical Samples
## 3 CW THz Full-Field Imaging
### CW THz Digital Holography
The laser beam was divided into two beams of comparable intensities: the so-called object beam, which was directed toward the sample, and the reference beam, which was directed toward the camera. The system enabled real-time imaging with human healthy skin tissue sections. The resolution of the imaging system was 0.2 mm at the distance of 20 mm. The epidermis and dermis skin tissues could be distinguished from the amplitude image, and the difference of the optical path length at different positions of the tissue could be obtained from the phase information. In 2018, [PERSON] et al. presented and investigated the off-axis CW TDH based on the 2.52 THz FIRL 100 and a Pyrocam III (240 \(\times\) 240 pixels) detector to image an asparagus fern [68]. The autofocus algorithm was used to find the amplitude reconstruction distance of 51.52 mm, and the phase reconstruction distance was 52.29 mm. The outline of the plant and the water content distribution could be clearly seen from the amplitude images. The profile characteristics and relative optical thickness distribution of the asparagus fern were obtained by unwrapped phase'images. Gabor in-line CW TDH suffers from the inherent twin-image superposition, and the off-axis holographic schemes are easily affected by the surrounding mechanical vibration  
Figure 8: Schematic layout of the in-line CW THz digital holography (CW TDH) scheme. Figure 9: Schematic layout of the setup of off-axis CW TDH. and air flow, which are usually based on the Mach-Zehnder interferometer. Therefore, compared with this traditional configuration, the common-path CW TDH has the advantages of simple and compact configuration as well as high stability. The common-path interferometric geometry offers two interfering beams generated after passing through the object, both of which then follow nearly the same optical path. In 2020, [PERSON] et al. realised a CW THz self-referencing full-field phase imaging approach based on [PERSON]'s mirrors showed in Figure 10[69]. The wavefront downstream from the object was split by the [PERSON]'s mirrors, which were composed of two independently tip-tilt controlled square mirrors. An off-axis hologram was recorded by interference between the wave scattered by the object and the unscattered reference beam. Cicada wings were measured to demonstrate its capability. The complex amplitude distribution was reconstructed through a spectrum filtering, angular spectrum integral, and phase unwrapping algorithm successively. The fifth meridian's R5 optical thickness of 9.2 \(\upmu\)m was calculated through the relationship formula between the phase and the optical path difference.
## 4 Control Strategy of the Balancing Actuator
### Control Program  
When the control unit forms control commands according to the unbalance vibration of the machine tool spindle, the power supply unit is instructed to form the corresponding voltage signals. According to the principle of step rotation, the actuator needs a magnetic field with alternating directions during the balancing operation, so the voltage signals supplied by the power supply unit must alternate between positive and negative voltage.  
To ensure normal operation of the power supply unit, four DC-DC solid state relays and an I/O module with four output channels are adopted to cooperate with the power supply unit, each state relay corresponding to an output channel of the I/O module and the four output channels control the opening and closing of the four state relays. Four output channels and corresponding state relays are divided into two groups and each group corresponds to a coil of the actuator. When commands are not required, all output channels are normally open, the control circuit is disconnected, the state relays are not working and the power supply unit has no voltage output; when the control unit requires a driving coil to operate, the two output channels corresponding to the driving coil and state relays begin to close alternately, producing positive and negative pulse voltage signals. The final voltage signal received by the driving coil is the composite voltage of the two pulse voltage signals, as shown in Figure 14. Due to the effects of coil anti-electromotive force, there is a back voltage behind each working voltage. Because the width of the back voltage is quite shorter than that of the working voltage, certain circuits are used to limit its maximum value but the effect is neglected in the control program.  
Figure 13: Three pulse signals from Hall sensors: (**a**) Schematic diagram of the signals; (**b**) Signal collection results.  
Based on the LabVIEW platform, a control program was developed and its codes are shown in Figure 14. Through debugging of the program, two conclusions can be drawn: (1) when the commands are sent by the I/O module, the corresponding state relays can be opened correctly and the voltage signals can be correctly generated; and (2) when the width of the pulse voltage signal increases from 30 ms to 200 ms by 10 ms steps, the program runs 1000 times at each step and the total cycle number is 18,000 times. The debugging results show that the control accuracy of the program is always within 1 ms, which can meet the control requirements of the balancing actuator.
## 6 Simulative Experiments and Analysis  
### Improved A* Algorithm Simulation Experiment  
To evaluate the performance and viability of the modified A* algorithm proposed in this paper, simulation experiments were executed within the MATLAB 2021b environment.  
Figure 6: Flowchart of the fusion algorithm.  
The experimental scenarios selected were 20 \(\times\) 20, 30 \(\times\) 30, and 50 \(\times\) 50 grid maps, and the effectiveness of the enhanced A* algorithm presented in this paper was compared to that of the classic A* algorithm and algorithms from the literature [25]. In the grid maps, each grid has a size of 1 m, where black represents obstacles and white indicates traversable areas. The triangle symbolizes the robot's initial position, and the circle represents the goal point. These map scenarios of diverse scales can effectively assess the path planning capabilities of each algorithm in different complex environments. The simulation results are presented in Figure 7.  
By examining Figures 7a-c, 8a-c and 9a-c and Table 1, it becomes evident that the traditional A* algorithm had more turning and redundant points compared to the improved A* algorithm, which generally had fewer points. In this paper, the search efficiency was enhanced by dynamically adjusting the weight of the heuristic function in the standard A* algorithm and by carefully selecting key points. This resulted in the ability to find shorter paths more effectively and complete the search in less time, thus significantly improving the overall performance. These improvements were demonstrated in maps of various sizes. The improved A* algorithm presented in this paper achieved a reduction in path length of 6.63% and 0.63%, respectively, relative to the conventional A* algorithm and the algorithm discussed in the literature [25], respectively, and it reduced the search time by an average of 56% and 23.8% compared with the traditional A* algorithm and the algorithm from the literature [25], respectively. Meanwhile, with the increasing complexity and size of the maps, the corresponding gap between the path lengths and the search times became increasingly larger.  
Figure 8: Comparison of simulation results for 30 \(\times\) 30 raster maps. (**a**) Traditional A* algorithm. (**b**) Algorithm from [25]. (**c**) Algorithm in this paper.  
Figure 7: Comparison of simulation results for 20 \(\times\) 20 raster maps. (**a**) Traditional A* algorithm. (**b**) Algorithm from [25]. (**c**) Algorithm in this paper.
## 2 Results  
### Transcriptome Analysis  
A total of 999,477,676 raw reads generated from RNA-seq were further processed with the adapters removed, yielding a total of 872,291,032 clean reads of 149.92 Gb of sequence data, including 24.02 Gb, 68.40 Gb, and 57.50 Gb derived from samples on day 0, day 2, and day 7, respectively (Table S1). Based on the de novo assembly, a total of 109,443 unigenes with the N50 value of 1193 bp were annotated based on the clean reads of the three groups of samples, ranging from 201 bp to 13,626 bp in size. The size distributions of these unigenes revealed that a total of 35,250 unigenes ranged between 200 and 300 bp, followed by a total of 16,338 unigenes ranging from 300 to 400 bp (Figure S2). In total, 21,504 (\(\sim\)19.65%) unigenes were annotated based on the SwissProt database, 27,965 (\(\sim\)25.55%) unigenes were identified in the non-redundant (nr) protein database, 26,248 (\(\sim\)23.98%) unigenes displayed significant similarities to known proteins in the Pfam database, and totals of 19,648 (\(\sim\)17.95), 24,798 (\(\sim\)22.66%), and 31,590 (\(\sim\)28.86%) unigenes were annotated in the Kyoto Encyclopedia of Genes and Genomes (KEGG), the Gene Ontology database (GO), and the EggNOG database, respectively. The top two species with the highest number of annotated unigenes were _Chlamydomonas eustigma_ (4558 unigenes of \(\sim\)16.3%) and _C. reinhardtii_ (2782 unigenes of \(\sim\)9.95%) (Figure S1). There were 24,798 unigenes annotated to 11,212 GO terms revealed in three categories of the GO database, i.e., molecular function, cellular component, and biological process. The top two GO terms in these three categories were the biological process and the oxidation-reduction process of the molecular function, cytoplasm, and nucleus of the cellular component category, and ATP binding and protein binding of the biological process category. A total of 19,648 unigenes were enriched in 6 and 19 KEGG metabolic pathways at level 1 and level 2, respectively. The results of enrichment analysis based on the KEGG database revealed that the top two categories of metabolic pathways with the largest numbers of unigenes annotated were the metabolism category (4440 unigenes) in the KEGG pathway at level 1 and the translation category (1717 unigenes) in the KEGG pathway at level 2 (Table S2).
## 2 Numerical Simulation
### Computation Scheme  
The software ANYSYS ICEM 14.0 was used to generate the meshes, as shown in Figure 2. The mesh was created with a hexagonal grid. The rule of local grid refinement was applied to the boundary layers and adaptive grid refinement was also conducted in this mesh model.  
The computational fluid dynamics (CFD) software package, Fluent 14.0 was used for the numerical simulation. All the aforementioned equations accompanied by the specified boundary conditions were evaluated with the finite-volume method. In the equations, the momentum, turbulent kinetic energy and turbulent dissipation rate were modeled using the second-order upwind scheme. The numerical solution procedure adopted the semi-implicit SIMPLE algorithm. The convergence criterion was set to the relative residuals of all variables, including mass, velocity components, temperature, turbulent kinetic energy, and a turbulent dissipation rate of less than \(10^{-4}\), except for the energy where a value of \(10^{-7}\) was used.  
\begin{table}
\begin{tabular}{c c c c} \hline \(D_{\mathbf{s}}\) (mm) & \(D_{\mathbf{b}}\) (mm) & \(BDR\) & \(S\) (mm) \\ \hline
8 & 16 & 0.5 & 40 \\
8 & 16 & 0.5 & 51.77 \\
8 & 16 & 0.5 & 62.5 \\
12 & 16 & 0.75 & 40 \\
12 & 16 & 0.75 & 51.77 \\
12 & 16 & 0.75 & 62.5 \\
16 & 16 & 1 & 40 \\
16 & 16 & 1 & 51.77 \\
16 & 16 & 1 & 62.5 \\ \hline \end{tabular}
\end{table}
Table 2: Geometric parameters of the BTs.
## 4 Applications  
UAV-assisted SAGINs form a comprehensive information transmission platform that effectively enhances network communication capabilities, response efficiency, and coverage [38]. Drones, as a crucial component of the aerial network, due to their unique mobility and flexibility, play a vital role in various practical scenarios, such as post-disaster reconnaissance and emergency rescue [142], broadband access and network coverage in remote areas [143], security inspection and target tracking [144], low-altitude traffic and logistics distribution [145], IoT task collection and offloading [103], static resource caching and distribution [146], and crop health monitoring and pesticide and fertilizer spraying [147].