esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This integrated study of core, large-section imaging, and FE-SEM analyses reveals critical linkages between sedimentary facies evolution and the Guangxi Movement in the Wufeng Formation, with direct implications for shale reservoir characterization. Key findings include:  
1. The complete Stow sequence (T0-T8) in fine-grained turbidites documents systematic flow deceleration from high-velocity (>25 cm/s) traction-dominated regimes to low-energy nepheloid settling. Hemipelagic deposits, dominated by sub-16 \(\upmu\)m grains with graded planar lamination, reflect seasonal terrigenous influx modulated by hypopycnal plumes and volcanic inputs. Shallow shoal facies, enriched in angular biogenic carbonates, record in situ accumulation under quiescent conditions. These facies transitions are tightly coupled to boundary layer shear dynamics and hydrodynamic sorting mechanisms, as evidenced by cyclical silt-mud couplets and volcanic ash thickness variations.
2. The vertical progression from turbidite-dominated (WF1-WF2) to hemipelagic (WF3) and shallow shoal (WF4) facies correlates with three stages of the Guangxi Movement: Stage 1 (WF1-WF2): Intense tectonic compression and volcanic activity generated high sediment flux, driving turbidity current deposition; Stage 2 (WF3): Waning tectonism reduced terrestrial input, favoring hemipelagic sedimentation via mixed vertical settling and weak advection; (3) Stage 3 (WF4): Stabilized subsidence enabled carbonate production in shallow waters, with volcanic ash (<0.3 cm) acting as a nutrient source for biogenic productivity.
3. Hemipelagic deposits exhibit superior reservoir potential (porosity: 3.34-4.15%; TOC: 2.91-4.10%) due to biogenic quartz enrichment and minimal allochthonous dilution. In contrast, fine-grained turbidites show degraded properties (porosity: 1.58-3.81%; TOC: 0.15-2.6%) from dense terrigenous packing. This contrast highlights the critical role of depositional energy in organic matter preservation and pore network development.  
**Author Contributions:** Conceptualization, Z.S.; methodology, Z.S.; software, T.Z.; validation, L.Q.; formal analysis, Z.S.; investigation, P.W.; resources, L.Q.; data curation, M.Z.; writing--original draft preparation, Z.S.; writing--review and editing, Z.S. and G.F.; visualization, T.Z.; supervision, G.F.; project administration, Q.Z.; funding acquisition, Q.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the 14 th Five-Year Plan of the Ministry of Science and Technology of PetroChina (Grant No. 2021 DJ1901).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** Author [PERSON] was employed by the company PetroChina. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Preliminaries: Network Model and Problem Definition
### Task Model  
Let \(x_{t}^{i}\) denote the generated task at the time \(t\) of the mobile device \(ud_{i}\), and its deadline is \(\tau_{i,t}\). The 0-1 variable \(D_{i,t}^{k}(0\leq k\leq n+2)\) indicates the offloading status of the task \(x_{t}^{i}\), and we have the following.  
\[D_{i,t}^{k}=\left\{\begin{array}{l}1\,,\,\text{For }x_{t}^{i},\,\,\text{the offloading goal is }en_{k}\\ 0\,,\,\text{the offloading goal is not }en_{k}\end{array}\right.,1\leq k\leq n \tag{1}\]  
Here, if \(D_{i,t}^{0}\) is 1, the task \(x_{t}^{i}\) will be performed in local server. If \(D_{i,t}^{n+1}\) is 1, \(x_{t}^{i}\) will be offloaded at edge sever. If \(D_{i,t}^{n+2}\) is 1, \(x_{t}^{i}\) will be discarded by the user. The offloading decision vector \(\mathbf{D}_{i,t}=\left(D_{i,t}^{0},D_{i,t}^{1},\cdots D_{i,t}^{n+2}\right)\) can uniquely determine the offloading direction of the task \(x_{t}^{i}\). When the task \(x_{t}^{i}\) is offloaded to a node \(en_{j}\), the weight of CPU computing resources allocated to the task \(x_{t}^{i}\) is expressed as \(ff_{i,t}^{j}\). When the server is running, it dynamically allocates its computing resources to each task according to its weight. Specifically, the computing power \(ff_{i,t}^{j}\) that can be occupied by the task \(x_{t}^{i}\) executed on nodes can be expressed as follows:  
\[f_{i,t}^{j}=\frac{ff_{i,t}^{j}}{ff_{\text{sum}}^{j}}\cdot f^{\text{edge}} \tag{2}\]  
where \(f^{\text{edge}}\) represents the computing power owned by a single edge node and \(ff_{\text{sum}}^{j}\) represents the total computing power weight of all tasks performing computing in the node \(en_{j}\).  
This paper categorizes computing tasks into four categories based on their requirements for security and delay: high-priority tasks, critical tasks, low-priority tasks, and regular tasks. The specific classification description is as follows:  
1. High-priority tasks have high requirements for delay, which are related to security and have hard indicators for task completion rate.
2. The key task is a computing task that requires extremely high security and can appropriately lower delay requirements but cannot be discarded.
3. Low-priority tasks are not related to security but are in scenarios that require energy savings, or tasks with excessive data volume and computation time.
4. Except for the above three types of computing tasks, all other computing tasks are routine tasks.
## 5 Ignition Process and Mechanisms
### Mechanical Mixing  
In addition to buoyant mixing, mechanical mixing as another important way of mixing, has not been clearly defined in the previous literature. In this paper, the mixing caused by the high flow rate of the reactants, nozzle structure, flow shearing, and diffusion is collectively regarded as mechanical mixing. Due to the aforementioned advantages of the volumetric reactor that are in contrast to those of the tubular reactor, the influence of the structure of a volumetric reactor on the hydrothermal flame is of paramount concern. In the existing volumetric reactors from ETH and NASA, reactants are injected into the chamber through a free jet. Compared with the flow in the tube, the interactions between the wall and jet are weak, and the shear layers between jet and fluid in the reactor and the presence of the nozzle greatly enhance the mixing, thereby affecting the entire ignition process. A high flow rate facilitates the transition from laminar flow to turbulent flow. By controlling the flow rate of the oxidant, [PERSON] et al. identified the difference in reaction paths at different mechanical mixing intensities by means of luminescence spectroscopy [58; 59]. And they found by measuring the results that at the wavelength of 310 nm corresponding to OH radicals, the flame B with a low airflow rate was lack of OH radicals, while there was a peak on the curve of flame A with a high airflow rate.  
Generally speaking, the generation of the flame is the result of the joint effort of physical and chemical factors. The ignition mechanism of hydrothermal flame can be attributed to a certain degree of mixture of reactants undergoing intense oxidation reaction after a certain residence time (ignition delay time), and the reactant temperature determines the ignition delay time. The buoyancy mixing of the reactants (especially the water near the critical point) and the mechanical mixing caused by the nozzle structure, coupled with the self-diffusion, synergistically realize a proper mixing degree of the reactants. At the same time, the heats from the oxidation reaction or external heating device are transferred rapidly through heat conduction and convective heat transfer among the reactants with high thermal conductivity and thermal diffusion coefficient, resulting in the rise of local temperature and the appearance of flame. Through the division of the ignition process, it can be found that when the reactants with appropriate mixing degree reach the appropriate temperature, hot spots will be randomly produced, and then gradually develop into a continuous flame surface, and finally achieve stable flames.
## 3 Experimental Results and Analysis
### Experimental Result and Comparative Analysis  
#### 3.4.1 Comparative Experiments  
Figure 8 shows the training results of the proposed model \(mAP50\), from which it can be seen that the training model gradually converges after 220 epochs, and the map value is 97.87% at the 300 th epoch. And Figure 9 shows the \(PR\) curve, from which it can be seen that among the recognized objects. The recognition accuracy is slightly lower in comparison with the garbage bags, kites, and balloons, due to the existence of the phenomenon of occlusion in some of the bird's nests.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Object Category** & **Object Number** & **Object Ratio** \\ \hline bird nest & 2289 & 44.68\% \\ kite & 1168 & 22.79\% \\ balloon & 607 & 11.84\% \\ trash bag & 1020 & 19.91\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of foreign object categories.  
This paper conducts eight comparative experiments, benchmarking our proposed method against classical models: YOLOV5, YOLOV6, YOLOV7, YOLOV8, FCOS, SSD, RetinaNet, and Faster R-CNN. The comparative analysis, detailed in Table 2 below, demonstrates that our method outperforms these models in \(mAP50\), \(mAP50\)-95, Recall, and Precision metrics. Our model's distinctive advantage lies in the adoption of the RevCol framework, which overcomes the traditional feature extraction network's tendency to lose some features. We have enhanced the RevCol detection capability by integrating a feature fusion part using the C2f module. Additionally, for targets with extreme aspect ratios, we replaced the BottleNeck in the C2f structure with the DySnakeConv module. The detection head incorporates a decoupled detection head and an auxiliary training head, contributing to the model's superior performance. Experimental results validate that richer feature information significantly improves the detection of foreign objects on transmission tower lines.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Comparison of Results of Common Models**} \\ \hline
**Models** & \(mAP50\) & \(mAP50\)**-95** & **Recall** & **Precision** \\ \hline YOLOv5 & 0.9545 & 0.6836 & 0.8978 & 0.9636 \\ YOLOV6 & 0.9107 & 0.6397 & 0.8804 & 0.9110 \\ YOLOV7 & 0.9471 & 0.6457 & 0.8896 & 0.9287 \\ YOLOV8 & 0.9686 & 0.6923 & 0.9310 & 0.9580 \\ FCOS & 0.9576 & 0.6533 & 0.8897 & 0.9297 \\ SSD & 0.7570 & 0.5673 & 0.6571 & 0.7632 \\ Faster R-CNN & 0.9528 & 0.6523 & 0.8923 & 0.9312 \\ RetinaNet & 0.9253 & 0.6399 & 0.8838 & 0.9245 \\ Ours model & 0.9798 & 0.7211 & 0.9516 & 0.9815 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of results of common models.  
Figure 8: Experimental results of RCDAM-Net model.  
Figure 9: Example image of RCDAM-Net model detection.
## 3 Applications of IL-Based Gels
### Al-Ion Batteries  
Al-ion batteries are a low-cost and rechargeable high-energy density battery option considering their abundance, high resistance to oxygen and moisture, and the four times higher volumetric capacity of Al compared to Li [94]. [PERSON] and [PERSON] [95] have summarized a number of proposed electrochemical reactions for various Al-ion cells, while [PERSON] et al. [96] reported their prospective related views on Al-battery technology. The disadvantages of Al-ion batteries, such as relatively short shelf life [97], are overcome by electrodeposition of Al in the form of a polymer-based IL-based gel of 35 mol-% 1-ethyl-3-methylimidazolium chloride 65 mol-% Al chloride (EMIMCl-AlCl\({}_{3}\)) with polymer gelification of polyethylene oxide (PEO), which was reported by [PERSON] et al. [98] who focused on Al-conductive polymer battery (ACP). Theoretically, they used a Lewis acid (at the anode) and a neutral IL electrolyte (at the cathode) with a Lewis neutral IL-based gel at both electrodes. For the first time, the deposition of Al reached a potential stability window of 5 V without any dendritic growth, with 60% coulombic efficiency potentially able to be further increased.
# Application of Graph Theory for Blockchain Technologies
## 5 Graph Theory Application in Blockchain Systems
### Simulating Blockchain Networks
#### 5.4.2 Stress Testing  
Simulating sudden surges in peak transaction loads on scaled versions of existing graphs examines system robustness. Mining inequality metrics on resultant graphs quantify capacity margins [88]. Figure 20 simulation demonstrates how scaling the blockchain network and introducing surges in transactions can affect network characteristics, such as mining power distribution, indicated by changes in average degree centrality. The increase in average degree centrality from the baseline to the scaled network under peak load suggests a shift in the network's mining power distribution, potentially pointing towards emerging bottlenecks or capacity margins. Orange Nodes represent nodes in the scaled blockchain network. Edges indicate transactions between nodes, with an increased number of transactions to simulate a surge and examine network robustness under stress.  
**Assumption 19**.:
* _The blockchain network is modeled as a transaction graph_ \(G(V,E)\)_._
* _Network robustness is evaluated under simulated peak traffic._  
**Application Statement 19**.:
* _Subjecting scaled blockchain transaction graphs to simulated peak traffic loads reveals system capacity margins and bottlenecks via quantitative mining inequality metrics._  
**Proof.**  
1. Model baseline blockchain transaction network as graph \(G(V,E)\). Obtain baseline mining power distribution.
2. Create scaled synthetic network with amplified transaction and mining traffic based on \(G\)  
Figure 20: Stress testing.  
3. Simulate surges and analyze emergent mining inequality and centralization metrics on the stressed graph. \(\square\)  
This examines the robustness of the margins relative to baseline network inequality. Graph-based blockchain simulations under peak projected loads determine capacity headrooms and reliability weaknesses before real-world deployments.
# Study and Analysis of Interference Signals of the LTE System of the GNSS Receiver
## 4 Simulation Studies
### Noise in the GNSS Receiver--Identification
The obtained results for the GPS signal  
Figure 11: Signal processing in the GPS receiver working on the L1 frequency; on the left side, the signal waveform in color without the noise suppression system applied; on the right side, the working system operating in the LTE system signal elimination for the reception of signals from five satellites. Figure 12: Signal processing in the GPS receiver working on the L1 frequency; on the left side, the signal waveform in color without the noise suppression system applied; on the right side, the working system operating in the LTE system signal elimination for the reception of signals from six satellites. translate into an improvement in the accuracy of determining the position of a user within 1 to 2 m in terms of the coordinates \(x\) and \(y\). It can be seen that for height, this improvement is about 0.5 m.  
Similar simulations were carried out for the Galileo system, as shown in the figures below (Figures 13-15). Figure 14: Signal processing in the Galileo receiver operating on the L1 frequency; on the left side, the signal waveform in color without the noise suppression system applied; on the right side, the working system operating in the LTE system signal elimination for the reception of signals from five satellites. Figure 13: Signal processing in the Galileo receiver operating on the L1 frequency; on the left side, the signal waveform in color without the noise suppression system applied; on the right side, the working system operating in the LTE system signal elimination for the reception of signals from four satellites. The results for the Galileo system are similar to those for GPS signals. The position of a user with a Galileo receiver has improved by 1.5 to 2 m. However, the difference between the two different simulations is not due to the signal of the LTE system; the reason is that the GPS receiver selects different sets of satellites in the computation performed in the correlator. The operation of interference signals from the LTE system makes the position of a user less precise by \"hiding\" one visible satellite. More research has been done on what happens when the signal power of the LTE system becomes low and high. Studies have shown that when the power is 10 times higher than the power considered previously, the GPS receiver cannot acquire enough satellite signals (not at least four satellites but only two) to obtain position information. In the case of receiving the signal provided by the LTE system at the input of the GNSS system receiver, this signal may be treated as an interfering signal. The obtained results from computer simulations show that when the signal from the LTE system has a power 10 times lower than the adopted standards, it does not result in a deterioration of the accuracy in the process of determining the position by the GNSS system.
## 2 Experimental Methods
### Degradation Properties Assays  
The potentiodynamic polarization curves were performed using an electrochemical workstation (Gamry 2000+, Gamry, Warminster, PA, USA) with a standard three-electrode configuration. To obtain a stable open circuit potential, the samples were immersed in the simulated body fluid (SBF) solution for 5 min. Then, the measurement was conducted in a potential ranging from \(-\)300 mV to 300 mV compared to the open circuit potential. The potentiodynamic scanning rate for the test was 1 mV/s. The corrosion current density (\(i_{\mathrm{corr}}\)) and corrosion potential (\(E_{\mathrm{corr}}\)) were obtained through Tafel extrapolation method.  
Figure 1: Particle size distributions of powders: **(a)** ZK60 and **(b)** La; **(c)** schematic of selective laser melting system; **(d)** the surface of the selective laser melted sample.  
Immersion tests were carried out in SBF solution at 37 \(\pm\) 0.5 \({}^{\circ}\)C. The ratio of the exposed area to the volume of SBF solution was 1.25 cm\({}^{2}\)/mL. Each specimen was immersed in SBF solution for 14 days, then removed from the solution and subsequently washed with distilled water 3 times. The morphology and the composition of the degradation product film on sample surface were assessed by SEM and EDS. After immersion for 3 days, 7 days, and 14 days, each specimen was removed from the solution. The degradation products were removed by immersing the samples in chromic acid solution for 20 min for the weight loss test. The weight loss rate \(\Delta W\) (mg/cm\({}^{2}\)/day) was calculated by the formula given in Reference [37]. The degradation rate (\(P_{\mathrm{w}}\), mm/year) was calculated based on the weight loss rate by the equation: \(P_{\mathrm{w}}\) = 2.10\(\Delta W\), as in Reference [38].
## 4 Numerical Validation
### Numerical Validation of Hilbert Transform  
The effects of the Hilbert transform and the STFT on frequency modulated signals are compared using the following signals.  
\[q(t)=\cos(103\pi t+0.2\sin(101\pi t)+0.2\sin(89\pi t)+0.2\sin(79\pi t)) \tag{29}\]  
in which fundamental frequency is 51.5 Hz; modulation frequencies of IF are 50.5 Hz, 44.5 Hz and 39.5 Hz; oscillating amplitudes are 10.1, 8.9, and 7.9; the instantaneous frequency (IF) is  
\[51.5+10.1\cos(101\pi\,\mathrm{t})+8.9\cos(89\,\pi\,\mathrm{t})+7.9\cos(79\, \pi\,\mathrm{t}) \tag{30}\]  
The sampling frequency is 5120 Hz, the sampling point is 10,240. The waveform of ideal IF of \(q(t)\) is shown in Figure 5. To enlarge the waveform for easy viewing, only the results from 0 s to 0.6 s are displayed here. The results obtained by STFT, and Hilbert transform are shown in Figure 6. By comparing Figures 5 and 6a, it is found that their waveforms are the same, except that the value at the left end of Figure 6a is slightly smaller than the ideal value. The short-time Fourier transform (STFT) is used for \(q(t)\), when the length of the window is 128. Additionally, the result of STFT is shown in Figure 6b. By comparing Figures 5 and 6b, it is found that the instantaneous frequency fluctuation range of the STFT results is larger than that of the ideal instantaneous frequency. Additionally, Figure 6b has a lower frequency resolution. To make the frequency resolution of STFT higher, the length of the window was adjusted to 1280. By comparing Figures 5 and 6c, it can be found that Figure 6c cannot show the characteristic of rapid change of instantaneous frequency. Additionally, the time-frequency diagram obtained by using STFT cannot guarantee good frequency resolution and the fast-changing instantaneous frequency can be displayed at the same time, no matter how we adjust the length of the window. In fact, the time-frequency diagram obtained by using STFT cannot guarantee good time resolution and good frequency resolution at the same time [29].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**The Modulation** & **The Oscillating** & **The Oscillating** & **The Oscillating** & **The Absolute Error** \\
**Frequency of IF** & **Amplitude of Ideal IF** & **Amplitude of Purified IF** & **The Absolute Error** \\ \hline
39.5 Hz & 7.77 Hz & 7.80 Hz & 0.03 Hz \\
44.5 Hz & 8.79 Hz & 8.53 Hz & 0.26 Hz \\
50.5 Hz & 9.98 Hz & 9.57 Hz & 0.41 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: The absolute error of oscillating amplitude of purified IF.  
Figure 4: Fourier spectrum of IF for simulated signal (red: ideal; blue: purified).
## 4 Numerical Validation  
The validity of the two-layer SVD is verified by numerical simulation signals in Section 4.1. The effects of the Hilbert transform and the short-time Fourier transform (STFT) on frequency modulated signals are compared using numerical simulation signals in Section 4.2.
## 3 Results
### Accuracy  
To assess how close the values measured are from the true concentrations, accuracy at 16.7 \(\upmu\)g/mL was calculated as percent recovery. The results indicated in Table 3 demonstrate a good accuracy of the proposed method at the evaluated concentration.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Analyte** & **CBDV** & **CBDA** & **CBGA** & **CBG** & **CBD** & **CBN** & **\(\Updelta\)9-THC** & **\(\Updelta\)8-THC** & **CBC** & **THCA** \\ \hline Rt (min) & 1.87 & 2.36 & 2.46 & 2.59 & 2.71 & 3.53 & 4.05 & 4.15 & 4.54 & 4.65 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Retention times (Rt) of cannabinoids identified in the standard mix.  
Figure 1: Chromatogram of 10 cannabinoids standard mix (16.7 \(\upmu\)g/mL).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Analyte** & **S1** & **S2** & **S3** & **S4** & **S5** & **S6** & **S7** & **S8** & **S9** \\ \hline CBDV & 1.29 & 1.02 & N/A & 1.62 & 1.26 & N/A & 4.2 & 1.82 & 1.96 \\ \hline CBDA & 1.09 & 1.45 & N/A & 1.36 & 1.48 & 1.68 & 1.33 & 1.57 & N/A \\ \hline CBGA & 2.07 & N/A & N/A & N/A & 1.46 & 1.52 & 1.67 & N/A & N/A \\ \hline CBG & 1.23 & 1.05 & N/A & N/A & 1.09 & N/A & 1.41 & N/A & N/A \\ \hline CBD & 1.29 & 1.06 & 1.12 & 2.21 & 1.13 & 1.09 & 1.29 & 1.33 & 1.07 \\ \hline CBN & N/A & 1.68 & 1.42 & 3.37 & N/A & N/A & 1.19 & N/A & N/A \\ \hline \(\Updelta\)9-THC & 1.29 & 2.06 & 1.19 & N/A & 1.54 & 1.15 & 1.40 & N/A & N/A \\ \hline A8-THC & N/A & N/A & N/A & 1.27 & N/A & N/A & N/A & N/A & N/A \\ \hline CBC & 1.14 & 7.23 & 2.15 & 1.39 & 1.05 & 1.05 & N/A & N/A & N/A \\ \hline THCA & 1.42 & 1.19 & N/A & N/A & 1.15 & 1.21 & N/A & N/A & N/A \\ \hline \hline \end{tabular}
\end{table}
Table 2: Peak purity indexes of each peak from sample solutions (300 fold).