esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Identification Discuss of Core Neutron Dynamic Module  
#### 5.2.1 Influence of the Tested Data  
For core neutron dynamic module, the identification results under two kinds of situations were further analyzed.  
(a) In the case of tested data with random noise  
Assuming that the tested data contained random noise with the maximum noise value reached 3% of its steady value, the parameters of the core neutron dynamic module without temperature feedback were identified. \(\beta\) and \(l\) could be well identified, whereas when \(\lambda\) was slightly different from the given value, the identification result was acceptable (see Figure 15).  
Figure 14: Sensitivity analysis of parameter change with one-loop temperature feedback.  
2. Input signal related to \(\beta\)  
For the core neutron dynamic module without temperature feedback, the input signal was 0.01 multiplied by \(\beta\). In this case, \(\lambda\) and the parameter \(\beta\) divided by \(l\) could be identified well. However, \(\beta\) and \(l\) were difficult to obtain since the parameter \(\beta\) divided by \(l\) could be simplified as one parameter from the differential equation of the core neutron dynamic module in Section 2. The correlation between \(\beta\) and \(l\) did not affect the identification of other parameters such as \(\lambda\) that were not associated with them.
# Evaluation of Ride Comfort under Vortex-Induced Vibration of Long-Span Bridge
## 2 Modelling of Vehicle and Excitation under VIV  
### Modelling of Two-Axle Vehicle  
Four typical vehicles, including a sedan car, a light truck, a minivan, and a motor bus, crossing a long-span bridge, are considered in this study. The above four vehicles are simplified and modeled as five rigid bodies connected by a series of dampers and springs, as shown in Figure 1.  
This study primarily investigates the ride comfort of a single vehicle exposed to vertical bending vortex-induced vibrations on a long-span bridge. Considering that vortex-induced vibration usually occurs at low wind speeds, this paper does not consider the influence of crosswind on vehicles. Therefore, the two-axle vehicle model with five rigid bodies has a total of 15 degrees of freedom: the vertical, rolling, and pitching motion of the car body as well as the vertical motions of the four wheels. According to the structural dynamics principle, the dynamic equations of each vehicle are established, and then based on the literature [15; 25; 27], the appropriate parameters are selected. The equations of motion and vehicle parameters can be found in Appendix A. The method used to solve the equations is the unconditionally stable Newmark-beta method, where beta \(\beta\) is set to 0.5, and \(\alpha\) is set to 0.25. The vehicle travels in a straight-line motion at a constant speed along the centerline of the bridge. The vehicle's gravity and dynamic response have a minimal impact on the long-span bridge when compared. Consequently, we have disregarded the interaction between the vehicle and the bridge, focusing solely on the one-way effect of the bridge on the vehicle.
## 5 Results Analysis
### Tall Bridge Pier Under Multidimensional Ground Motions
In contrast, the accelerations in the X and Y directions have negligible influences on both the Z-direction displacement and the YZ section shear stress. This suggests that seismic design for tall bridge pier should prioritize considerations related to Z-direction accelerations for accurate performance evaluation of the tall bridge. To gain further insight into the effect of multidimensional ground motion on the dynamic behaviour of tall bridge pier, the dynamic behaviour of such structures under different dimensions of pulse ground motion is studied. This is achieved through one-dimensional (Z direction), two-dimensional (X and Z directions), and three-dimensional (XYZ directions) analyses. When considering tall bridge pier with different damping ratios, the displacement of the X-direction section at a height of 20 m and the shear stress of the XY direction section at identical points are selected as the objects of examination. To illustrate, the time history curves of the tall bridge pier were generated using a damping ratio of 5%. Figure 12 presents the displacement and shear stress responses of a tall bridge pier's X section and XY section under multidimensional pulse ground motions, and a 5% damping ratio was selected. The peak displacements for the X section subjected to 1D, 2D, and 3D ground motions are 2.4497 \(\times\) 10\({}^{-12}\) m, 0.0416 m, and 0.0416 m, respectively. The corresponding peak shear stresses at the XY section are 1.5259 \(\times\) 10\({}^{-11}\) MPa, 0.2470 MPa, and 0.2468 MPa. These results highlight the significant increase in both peak displacement and shear stress in the XY section under multidimensional ground motions, and emphasize the need to consider multidimensional seismic effects in the service and analysis of tall bridge pier. Figures 13 and 14 show the variation trends of the X section displacement peak and XY section shear stress peak of the tall bridge pier structure with different damping ratios under multidimensional pulse ground motions, respectively. Figure 12: Time history curves of the displacement of the X section and shear stress dynamic response of the XY section of the tall bridge pier under multidimensional pulse ground motion: (**a**) displacement and (**b**) shear stress. Table 4 gives the X section peak displacement values of tall bridge pier structures with different damping ratios under multidimensional pulse ground motions. Table 5 shows the XY section peak shear stress of a tall bridge pier structure with different damping ratios under multidimensional pulse ground motions. Figure 13 and Table 4 show that the peak displacement at the X section of tall bridge pier increases with pier height under three-dimensional ground motion. In addition, the displacement under two- and three-dimensional seismic action is significantly greater than that under one-dimensional motion action, emphasizing the need to consider multidirectional pulse ground motion.
## 5 Incipient Fault Model
### Model Validation  
In Simulink, the experimental main circuit is constructed as an equivalent circuit, as shown in Figure 20. The power supply in this experiment provides an output voltage of 220 V at 50 Hz, with a maximum output capacity of 3 kVA. The equivalent resistance \(R_{\mathrm{s}}\) of the power supply is set to 3.2 \(\Omega\), and the equivalent reactance \(L_{\mathrm{s}}\) is 0.07 uH. The cables themselves contribute negligible resistance and reactance due to their short length, and their impedance is not explicitly modeled in this circuit. The current-limiting resistor \(R_{\mathrm{limit}}\) is set to 200 \(\Omega\), and the grounding resistor \(R_{\mathrm{g}}\) is set to 30 \(\Omega\). The compensation capacitor of the medium-voltage probe \(C_{\mathrm{probe}}\) is set to 2.5 pF. A controllable nonlinear time-varying resistance module is used to represent the fault resistance, with its resistance value determined by the output of the fault conductance control function.  
The comparison between the simulated and experimental waveforms of the sub-cycle arc is shown in Figure 21a,b. The waveform analysis indicates that the dynamic conductance modeled using the proposed sinusoidal approximation method successfully reproduces key characteristics, such as the voltage drop near the peak and the voltage stabilization during arc burning. The simulated waveform aligns well with the experimental waveform, with an average simulation error of 5.8%, demonstrating the effectiveness of the arc conductance modeling approach. The comparison between the simulated and experimental waveforms of the periodic current is presented in Figure 21c. The simulated waveform exhibits strong consistency with the experimental waveform in terms of overall variation trends. The average simulation error for the current is 13.2%, primarily attributed to fluctuations in the experimental waveform's amplitude across cycles, which differ from the idealized periodic variation assumed in the simulation. Overall, the simulated waveform accurately captures the characteristic current variation within half a cycle, confirming the effectiveness of controlling periodic conductance variation to regulate periodic current behavior.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Control Function** & **Value Range** \\ \hline \(g_{\mathrm{max}}/\mathrm{mS}\) & Maximum conductance & 0.3\(\sim\)1.5 \\ \(g_{\mathrm{min}}/\mathrm{mS}\) & Minimum conductance & 0.06\(\sim\)0.5 \\ \(t_{\mathrm{st}}/\mathrm{ms}\) & Current drop initiation moment & 2.5\(\sim\)5 \\ \(t_{\mathrm{0}}/\mathrm{ms}\) & Current drop duration & 0\(\sim\)6.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model parameters for periodic disturbance.  
Figure 20: Equivalent simulation circuit of the experimental circuit in Simulink.
## 3 Results and Discussion
### Color of Mayonnaises  
The enrichment with carotenoids extracted in oil from dried tomato by-products led to significant (\(p<0.05\)) color changes in the mayonnaise, which was expected considering that after extraction the oils had an orange color with increasing intensity depending on the extracted amount of dried tomato by-products. At the time of production, the lightness of the mayonnaise (L* values) dropped significantly (\(p<0.05\)) with the percentage of dried tomato by-products extracted in the oil, yellowness (b* values) increased, and a* values changed from the green (a* = \(-1.92\)) to the red domains (a* = \(9.91\) for MDTB2.5 and a* = \(13.43\) for MDTB5). These values of the color parameters may be attributed to the carotenoids extracted in the oils from dried tomato by-products. A significant lightness decrease and yellowness increase was also reported by [PERSON] et al. [3] after the incorporation of encapsulated sea buckthorn extract in the composition of mayonnaise. However, they found a decrease in redness after the enrichment of mayonnaise with carotenoids from sea buckthorn. The increase in redness (a* value) after the extraction of dried tomato by-products may be due to the red lycopene, which is reported as the dominant pigment in tomato peel [50].  
After processing, BHT addition determined the significant increase in L* and b* values as compared with the control, while a* values did not undergo significant changes. Higher a* and b* values were observed for the MDTB5B sample as compared with the similar sample without basil oil (MDTB5).  
The yellow color of traditional mayonnaise originates from the egg yolk and may be further influenced by the oil and by the addition of other ingredients with coloring effects, such as mustard or spices [1]. The vegan mayonnaise made with soy milk as an emulsifier had, generally, a paler color than the traditional mayonnaise due to the lower carotenoid pigments content of the soy milk compared with the egg yolk. As a result, the increment of yellowness of the soy milk mayonnaise makes it look more like the traditional egg yolk mayonnaise, and this may increase the consumers' willingness to purchase or taste it.  
L* and a* values were quite stable in the control mayonnaise during storage and only b* values decreased. The redness and yellowness (a* and b* values) significantly (\(p<0.05\)) decreased during storage in the mayonnaise samples enriched with carotenoids, which was previously related to the isomerization and to the oxidative degradation of carotenoids [51]. On the contrary, a* values increased and the b* values remained relatively constant in the samples with BHT addition.
## 4 Experiments and Results
### Evaluation Experiments Using Representations Extracted from a Fine-Tuned Image Encoder
#### 4.4.3 Example of Comparison on Image-to-Image Retrieval  
We compared the performance of our model and the existing pre-trained models based on the similarity calculation and ranking calculation of image embedding similarity. Table 12 shows part of the image-image search results.  
It can be seen that the image features extracted by the model after fine-tuning not only are similar in color, but also have better structural similarity than the other models. Compared with the results of simply using HOG features, our model shows improved performance in color information processing.
## 3 The Proposed Decoupling Uplink and Downlink Access Scheme
### Performance Analysis  
The association performance analysis of the proposed DUDA scheme is conducted based on stochastic geometry. Initially, the specific number of satellites located within a finite region \(\mathcal{R}\) of the PPP with density \(\rho_{k}\) is given by  
\[\Pr(N=n)=\frac{1}{n!}(\rho_{k}|\mathcal{R}|)^{n}\exp(-\rho|\mathcal{R}|), \tag{23}\]  
where \(|\mathcal{R}|=2\pi(r_{\mathrm{E}}+r_{k})^{2}(1-\cos\theta)\) is the area of the spherical dome. Let \(n=0\) and substitute (3) into (23), then the cumulative distribution function (CDF) of the geocentric angle \(\theta_{0}\) can be approximated as [20]  
\[F_{\theta_{0}}(\hat{\theta})\approx 1-\exp\biggl{(}-\frac{N_{k}}{2}(1-\cos \theta)\biggr{)}. \tag{24}\]  
Then, the PDF of the geocentric angle is obtained as  
\[f_{\theta_{0}}(\hat{\theta})=\frac{N_{k}}{2}\sin\theta\exp[1-\cos\theta]. \tag{25}\]  
Given the UT position as the reference point, \(\hat{\theta}\) also follows the above PDF. Assume that \(P_{H}>P_{L}\); then, the association probability for these cases are calculated as  
\[\Pr(\text{Case1})=\Pr\biggl{(}Z_{u_{0},L}(\hat{\vartheta}_{L_{0}})>\frac{P_{H }}{P_{L}}Z_{u_{0},H}(\hat{\vartheta}_{H_{0}})\biggr{)}=\int\biggl{(}1-F_{Z_{u_ {0},H}}\biggl{(}\frac{P_{H}}{P_{L}}z_{L}\biggr{)}\biggr{)}f_{Z_{u_{0},L}}(z_{L })\mathrm{d}x_{L}, \tag{26}\]  
\[\Pr(\text{Case2})=\Pr\bigl{(}Z_{u_{0},L}(\hat{\vartheta}_{L_{0}})<Z_{u_{0},H}( \hat{\vartheta}_{H_{0}})\bigr{)}=\int F_{Z_{u_{0},H}}(z_{L})f_{Z_{u_{0},L}}(z_{ L})\mathrm{d}x_{L}, \tag{27}\]  
Figure 3: The DUDA protocol for centralized network systems.  
\[\text{Pr}(\text{Case3})= \text{Pr}\bigg{(}Z_{u_{0},H}(\hat{\vartheta}_{H_{0}})<Z_{u_{0},L}( \hat{\vartheta}_{L_{0}})<\frac{P_{H}}{P_{L}}Z_{u_{0},H}(\hat{\vartheta}_{H_{0}}) \bigg{)}= \tag{28}\] \[\int F_{Z_{u_{0},H}}\bigg{(}\frac{P_{H}}{P_{L}}z_{L}\bigg{)}\Big{(} 1-F_{Z_{u_{0},H}}(z_{L})\Big{)}f_{Z_{u_{0},L}}(z_{L})\text{d}x_{L},\] \[\text{Pr}(\text{Case4})=\text{Pr}\bigg{(}\frac{P_{H}}{P_{L}}Z_{u _{0},H}(\hat{\vartheta}_{H_{0}})<Z_{u_{0},L}(\hat{\vartheta}_{L_{0}})<Z_{u_{0 },H}(\hat{\vartheta}_{H_{0}})\bigg{)}=0, \tag{29}\]  
where \(F_{Z_{u_{0},H}}(\cdot)\) and \(f_{Z_{u_{0},L}}(\cdot)\) are CDF and PDF of (16). We utilize the Monte Carlo method to obtain them due to the inverse function in (16) is not closed-form. We further appraise the transmission rate as a benchmark of performance, which is obtained by  
\[R^{l}=B\text{log}_{2}\Big{(}1+SINR^{l}\Big{)}, \tag{30}\]  
where \(l\in\{U,D\}\). Then, the throughput capacity of the NGEO system is obtained by  
\[\begin{array}{l}R_{\text{total}}\\ =\sum\limits_{u_{p}\in\Phi_{E}}B\log\Big{(}1+SINR_{u_{p}}^{D}\Big{)}+\sum \limits_{k_{q}\in\Phi_{k}}B\log\Big{(}1+\mathbf{1}\Big{(}SINR_{k_{q}}^{U}\Big{)} \Big{)},\end{array} \tag{31}\]  
and  
\[\mathbf{1}\Big{(}SINR_{k_{q}}^{U}\Big{)}=\left\{\begin{array}{l}SINR_{k_{q} }^{U},\text{ UT access k}_{q}\text{ in UL}\\ 0,\qquad\text{others}\end{array}\right..\]
# Three-Dimensional Modeling of the Xichang Crust in Sichuan, China by Machine Learning
## 6 Crustal Medium Structure  
In order to couple the above structural models into the prior comprehensive geological model, an appropriate modeling method was developed by integrating multi-source datasets such as topographic and geomorphic data, velocity structure dataset, fault plane data, seismic structure data, and Moho data. The topological relationship of crustal medium structure is established through the model calculation, the unified crustal medium structure model is finally generated, and the 3D format required for numerical simulation is derived, which can be used in the later finite element modeling. In addition, with the improvement of observation accuracy and the maturity of inversion methods, the result data used in modeling will be updated, and the corresponding model will be optimized accordingly, so that the model has a good inheritance. The model data can also be complex and diverse for the regions with different deep studies, making the model more adaptable.  
By the modeling of crustal medium structure, the distribution characteristics of sedimentary cover, fold basement, and crystalline basement in the Xichang area are revealed (Figure 6). The complex fault plane cuts the base and forms the corresponding fracture zone. Because of its low mechanical strength, it is broken in the extrusion process and forms earthquakes. Moreover, there are low-velocity anomalies under Mt. Gongga with high terrestrial heat flow value, indicating that the region's lithologic medium and physical state has changed significantly, resulting in the decline of its mechanical strength and a certain viscosity and ductility. There are also a lot of earthquakes around the lower part of Mt. Gongga, and the concentrated earthquake area further reveals the morphological characteristics of the focal region. The seismological velocity structure and fault model depend on and constrain each other and form the crustal medium structure model of the Xichang region together. Our model reveals better the relationship between different structures in the Xichang area, and the large-scale structure has good consistency with previous studies; the model provides more detailed features. It has obvious advantages in describing regional seismicity and can meet seismic scientific research and seismic engineering application modeling needs. Our model is significant for understanding the distribution and physical property differences of sub-blocks, structural characteristics and geodynamics problems, inversion, and other scientific problems.  
Figure 6: Modeling results of crustal medium structure.
# An Uplink Channel Estimator Using a Dedicated Instruction Set for 5G Small Cells
## 4 Design and Acceleration of Instruction Set  
### Vector Processor Instruction Set Architecture  
According to 5G micro-base stations, the typical feature of the baseband algorithm is its predictable small code and data volume. The team in our laboratory has developed a set of very long instruction word (VLIW) vector processing for baseband processing MCU + load/store + single instruction multiple data (SIMD). The specific architecture is described in [25]. The processor platform is based on an SIMD kernel design with variable data-level parallelism. For the convenience of scientific research, the native data width of the SIMD core is a \(16\text{b}+16\text{b}\) complex variable. Its framework includes basic time domain and frequency domain vector, matrix, transform, filtering and power and noise estimation instructions for baseband symbol processing. However, this vector processor still lacks commonly used acceleration instructions for specific operations, such as Weiner filtering, linear interpolation and matrix inversion. The work of this paper is based on the Wiener interpolation-oriented instruction set acceleration of the VP.
## 2 Research on the Collaborative Search Efficiency Evaluation Model of Multi-Sonar System
### Target Motion Model  
Under the assumption that, in the collaborative search task, the target disappears and escapes from a certain point, its motion model is as follows: after disappearance, the first step of motion direction is randomly selected within the range of 0\(\sim\)360\({}^{\circ}\). Starting from the second step of the motion direction, due to the limitation of the target maneuverability, the next step of the motion direction can only be randomly selected within a range of 90 degrees on both sides based on the previous step of the motion direction to continue moving forward. A schematic diagram of the target motion model is shown in Figure 1.  
Assuming that the target motion speed \(v\) remains constant and moves forward \(d\) meters within the time interval \(\Delta t\), the expression for the target motion trajectory is as follows:  
\[\begin{cases}x(i+1)=x(i)+\Delta t\cdot v\cdot\cos(\theta(i))=x(i)+d\cdot\cos( \theta(i))\\ y(i+1)=y(i)+\Delta t\cdot v\cdot\sin(\theta(i))=y(i)+d\cdot\sin(\theta(i))\\ \end{cases} \tag{1}\]
# Positioning-Based Uplink Synchronization Method for NB-IoT in LEO Satellite Networks
## 3 Position-Aided Uplink Synchronization Method
### NPSS Detection  
To tackle the significant Doppler shift in satellite channels, this paper proposes a time delay estimation method for NPSS named Multi-Section Correlation Algorithm (MSA), which combines coarse and fine estimation stages. Building upon this, a hybrid time-frequency domain approach is used to estimate the Doppler shift observation value, improving the accuracy of time delay and Doppler estimation under high-dynamic LEO satellite channels. The overall estimation process, shown in Figure 3, includes three main parts: coarse estimation of time delay, fine estimation of time delay, and Doppler shift observation estimation. First, for each sampling point, coarse synchronization detection is performed on a signal with a length of one NPSS by dividing it into 11 equal segments and applying segment-wise autocorrelation to obtain a coarse synchronization point. Next, a more accurate calculation is carried out in the frequency domain around the coarse synchronization point by correlating the signal with 11 locally stored ZC sequences, yielding the fine synchronization point. Finally, Doppler frequency offset estimation and compensation are performed on the time-synchronized signal.
# Radio Frequency Fingerprinting for Frequency Hopping Emitter Identification
## 4 Baseline Algorithms for RF Fingerprinting Method
### Baseline 3: Spectrogram-Based RF Fingerprinting  
The third baseline aims to reflect the recent approach in [8], which is based on the SF spectrogram. As described in [8], the author trained the Hilbert spectrum of the received hop signal in a residual unit-based deep learning classifier. To reflect this approach in baseline 3, the algorithm was designed to train an SF spectrogram directly in the residual-based deep learning classifier.  
The SF extraction and feature extraction processes were the same as those of the proposed method described in Sections 3.1 and 3.2.  
For classification, the classifier structure was set to the residual-based deep learning classifier described in [8]. After training the classifier, classification was performed using Equation (18).