esa-sceva/satcom-chunk-collection huggingface dataset:
# Responses of Water Temperature and Level to Large Earthquakes in Tibet
## 4 Discussion
### Characteristics of the Water Temperature and Water Level Variation and Their Correlation  
We collected and sorted all co-seismic data recorded by two sets of underground fluid instruments since the establishment of the station and compared and analyzed the observation data recorded following earthquakes of different magnitudes and of the same magnitudes. We concluded the following characteristics of the co-seismic responses of the water level and water temperature at Lhasa station. (1) The post-seismic response of the water level oscillation shows that the water level rise is greater than the water level fall depending on the intensity of the water level oscillation. Although the water level oscillation can take tens of minutes or even hundreds of minutes, once this oscillation stops, the water level returns to the pre-earthquake level, and the decline is not large. (2) Regarding the co-seismic water temperature response, a continuous step rise in water temperature spans several hours following an earthquake before returning to the preearthquake value. However, this change depends on the distance of the measurement site from the epicenter of the earthquake. The recovery time following this change is faster if the distance is shorter and slower if the distance is longer. (3) The magnitude and focal depth of an earthquake are closely related to the oscillation amplitudes of the water level and water temperature. If the magnitude is large and the earthquake depth is shallow, the oscillation amplitude of the water level will be relatively large. In view of this increased amplitude, the water temperature will also increase faster; that is, the larger the oscillation amplitude of the water level is, the higher the increase in water temperature will be. (4) In 2017, after the water level probe was damaged by a lightning strike and replaced, the water level recorded by the water temperature was not the same as that recorded in the well.  
The co-seismic change behavior of water temperature is closely related to the change in the water level. Previous studies suggested that the co-seismic change in the water level is the necessary condition for a co-seismic change in the water temperature (e.g., [55]). Water level co-seismic changes occur in all cases alongside water temperature co-seismic changes [56; 57; 58; 59; 60]. However, water level co-seismic changes do not necessitate water temperature co-seismic changes [61]. The data from the Lhasa station show that the water temperature but not the water level can record some cases of co-seismic changes (e.g., the M6.9 earthquake that occurred in Milin, Tibet on 18 November 2017, and the M6.3 earthquake that occurred in Metok in 2019) [62; 63]. Judging from the timing of these events, this result may have been related to issues with the probe. All of these instances occurred after the probe was replaced in 2016, but other possibilities are not excluded, and this issue needs to be further studied.
## 2 Oxygen Blow and Stirring Dynamics Model
### Grid-Independence Test  
Mesh quantity greatly affects the accuracy of flow field numerical results, wherein lots of meshes can obtain smaller discrete errors, but easily produce greater round-off errors. To verify the validity and stability of the numerical predictions, the fluid domain was divided using hexahedral grids. Four computational models with different mesh numbers (i.e., case I, case II, case III, and case IV) were tested. The mesh number of four different cases are 245,893 grids, 502,854 grids, 860,254 grids, and 1,035,908 grids. Figure 3 shows the axial velocity distribution at the center of the steel converter in the different cases. It can be found that numerical curves of four different mesh numbers had similar trends. As the mesh quantity reached a certain value, the numerical curves of the velocity took on uniform trends (case III and case IV). However, the variable values (case I) with less mesh quantity are quite a bit lower than that with the other mesh numbers, especially for the prediction on the gas-liquid interface. It was demonstrated that a lower mesh number cannot provide sufficiently accurate results. For the numerical result at the gas-liquid coupling interface, the mesh number had a major impact on the variable predictions. Only slight differences were observed in their peaks and troughs for the curves (case III and case IV), and the numerical error is less than 3.9% (when H = 3.49 m, the velocity of case III is 87.68 m/s, while case IV is 91.23 m/s), which satisfied the accuracy requirement. Therefore, to save computation time, the mesh size consistent with case III was adopted in the present study.  
Since the inlet boundary condition is the pressure inlet, the blown gas in the nozzle will accelerate purely in a short process, but the final blown velocity is approximate to the design Mach number. In the simulation of a conventional oxygen lance, the maximum jet velocity is 475 m/s, about Mach 1.40, and the design Mach number is 1.5. Because the boundary layer is not considered in the design work, the actual jet velocity is slightly lower than the theoretical value, which is similar to our simulation. Therefore, the efflux velocity error of the simulation is within about 5%. It is worth noting that axial  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Physical Property** & **Oxygen** & **Molten Steel** & **Unit** \\ \hline Density & compressible & 7200 & kg/m\({}^{3}\) \\ Temperature & 300 & 1900 & K \\ Dynamic viscosity & \(1.9\times 10^{-5}\) & \(6.5\times 10^{-3}\) & Pa-S \\ Specific heat capacity & 919 & 670 & J/(kg-K) \\ Heat conductivity & 0.025 & 15.000 & W/(m-K) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Material attributions of oxygen and molten steel.  
Figure 3: Velocity distribution for different mesh sizes.  
velocity at the center of the steel converter is well below the maximum jet velocity. It is caused by no central nozzle in the designed oxygen lance, and the working five nozzles on the circumference form a low-speed zone at the center.
## 6 Experimental Results
### Results  
Tables 7 and 8 show the statistical results obtained with the first set of benchmark functions as follows: the benchmark function (BenFun), the average value obtained with the benchmark function (Avg), the standard deviation (StdDev), the median (Med), the minimum value achieved (Min), the optimal value (Opt), the true percentage deviation (\(TPD\), %) or the difference between the minimum value achieved, and the optimal value published (\(DMO\)), the worst result obtained or maximum value reached (Max), the search subset (SeaSub), and the optimal location (OptLoc). Optimal locations found were rounded to the values indicated under the OptLoc column in Table 8. For this set of functions, a total number of 1000 movements or 1000 restarts as the stop condition were considered for all the experiments, except in the experiments with functions f3, f4, f5 and f6; with those last functions, we use a maximum of 9000, 3000, 1500 and 4000, respectively. Very good results were obtained in 100.0% of the benchmark functions. Optimization results obtained by VLE were compared with the corresponding results reported for PSO [24], GSA [2], DE [42], and WOA [34], in [29,34]. All results obtained by VLE were rounded to four decimals. The results published for PSO, GSA, DE and WOA, were rounded in Tables 7 and 9 to four decimals, using scientific notation, only for presentation purposes. However, all computations were realized using the reported decimals by their respective authors. All experiments performed with VLE consisted of at least 31 executions with a certain set of VLE parameters. VLE parameters were tuned until a best possible solution was found. The technique for tuning was the parametric sweep. After this, the truncated mean was calculated by removing all possible strong outliers one by one. This process was done at 6.5% (\(f_{4}\), \(f_{6}\), and \(f_{7}\)), at 13.0% (\(f_{1}\), \(f_{2}\), \(f_{10}\), \(f_{11}\), and \(f_{13}\)) and at 20.0% (\(f_{8}\), \(f_{9}\), and \(f_{12}\)) depending on the number of strong outliers found. There were no outliers found with functions \(f_{3}\), \(f_{5}\), \(f_{13}\), and \(f_{14}\). All statistics reported for VLE in this work are presented according to this methodology. The reason for removing the strong outliers was
# Metal Artifact Reduction in X-ray CT via Ray Profile Correction
## 2 Methods
### Seamless In-Painting  
Recall that sample profiles are only computed for rays that traverse the segmented metal in the metal-only CT volume. We integrate these rays and store them in a corrected sinogram. Figure 6a shows a portion of an uncorrected sinogram while Figure 6b shows the same region with the corrected profiles only. The final task is to replace the metal shadow regions of the uncorrected sinogram with these corrected regions. However, a direct replacement of the data can lead to undesired discontinuities around the boundary of the metal shadow, resulting in the generation of new artifacts [22]. Therefore, it is important to seamlessly combine the new data with the existing ones at the boundary while internally keeping the relative contrast and the details of the data. To achieve such a seamless in-painting, we solve the following minimization problem [35]:  
\[\hat{P}^{new}=\min_{p^{new}}\sum_{i\in R}\left(\sum_{j\in N_{i}^{\prime}\cap R }(\
abla P_{j}^{new}-\
abla P_{j}^{corr})^{2}+\sum_{j\in N_{i}^{\prime}\cap R }(P_{j}^{new}-P_{j}^{orig})^{2}\right)\,, \tag{7}\]  
where \(P\) is projection data and its superscripts \(new\), \(corr\), and \(orig\) represent the in-painted, corrected, and original projection data, respectively. \(R\) denotes the metal shadow region and \(N_{i}\) is the eight-connected neighborhood of a pixel, \(i\). In this equation, the first term aims to preserve the gradients of the original (uncorrected) projection data in the metal shadow regions while lowering the intensities to non-metal values. Preserving the original gradients ensures that the detail and contrast in the projection data are maintained for the subsequent reconstruction. The second term in the equation affects the metal shadow region's boundary only and ensures a smooth transition to the outside regions. Figure 6c shows a result of this seamless in-painting process using the same region as in panel Figure 6a,b with Gauss-Seidel iteration and successive over-relaxation.  
Figure 5: Ray profile correction: (**a**) ray profiles extracted from noisy, prior, metal-only, and corrected CT volumes and interpolation weights in (**b**) metal region and (**c**) non-metal region.
# Development of Multimodal Physical and Virtual Traffic Reality Simulation System
## 4 Integration of Co-Simulation Components
### Design Challenges and Solutions of PTV Vissim--CARLA Integration
#### 4.1.4 Developed Traffic Signal Synchronization Module  
To fully integrate CARLA and PTV Vissim, a traffic signal synchronization module was developed in Python. This module retrieves signal states from PTV Vissim and applies them to the corresponding signals in CARLA. The synchronization process is distributed across three key scripts: _vissim_simulation.py_ for PTV Vissim operations, _carla_simulation.py_ for CARLA operations, and _run_synchronization.py_ to coordinate the synchronization between the two systems.  
The module first extracts traffic signal data from PTV Vissim (Task 1 in Figure 4), storing information such as controller IDs, signal group IDs, and signal states. These data  
Figure 3: The 3D traffic network creation.  
are then cross-referenced with traffic signal IDs assigned by CARLA (Task 2 in Figure 4), ensuring proper alignment between the two simulations. Once the traffic signal states are updated in CARLA (Task 3 in Figure 4), they dynamically reflect the real-time changes in PTV Vissim, allowing for an accurate representation of signalized intersections and adaptive control strategies.
###### Abstract  
Direct Position Estimation (DPE) is an alternative GNSS positioning method that models received satellite signals as a function of the receiver's navigation state, allowing for the direct estimation of position, velocity, and time within the navigation domain. However, existing DPE algorithms face significant challenges due to the non-convex nature of the optimization problem and the large solution space, resulting in high computational complexity. To address these challenges, this paper introduces a framework for searching for navigation solutions in DPE through swarm intelligence algorithms, combined with a low-complexity correlation approach. Furthermore, an adaptive Dung Beete Optimization (ADBO) algorithm is developed. By leveraging insights from fitness landscape analysis, the ADBO algorithm dynamically adjusts subpopulation proportions and the convergence factor while incorporating hybrid mutation strategies for effective adaptation to various types of optimization problems. Benchmark function tests demonstrate that the ADBO algorithm achieves superior convergence performance compared with other popular swarm intelligence algorithms. Both extensive simulations and real GNSS data experiments further validate that the proposed framework, incorporating the ADBO algorithm, achieves improved positioning accuracy compared to traditional positioning methods while outperforming traditional search algorithms and other swarm intelligence algorithms in both accuracy and computational efficiency.  
Global Navigation Satellite System (GNSS); Direct Position Estimation (DPE); Dung Beete Optimization (DBO); Fitness Landscape Analysis (FLA)
###### Abstract  
In space laser communication, the wide divergence angle of beacon light leads to substantial spatial losses, compounded by background light and detector noise; this results in compromised precision in the detection of the beacon light position. To solve this problem, a high-precision detection technique and communication composite technology employing a four-quadrant detector (QD) with beacon spread-spectrum modulation are proposed. Pseudo-random sequences (PRNs) are employed to spread the beacon communication spectrum, with the spread-spectrum signal utilized to modulate the intensity of the transmitted beacon light at the transmitter end. At the receiver, QD photocurrent signals are cross-correlated with an identical PRN that is used for modulation. The strong auto-correlation properties of PRNs, which are uncorrelated with noise, enhance the output signal-to-noise ratio (SNR), enabling precise position detection and beacon communication under high-SNR conditions. Theoretical analysis is used to explore the effects of spreading gain on the sensitivity of system detection and the precision of position detection. The experimental results demonstrate that the beacon spread-spectrum modulation scheme effectively detects the position of the light spot. At a received optical power of \(-37\) dBm and spreading sequence PRN depths of 1023, 127, and 31, the root-mean-square error (RMSE) values are 0.983 \(\upmu\)m, 2.876 \(\upmu\)m, and 7.275 \(\upmu\)m, respectively. This corresponds to improvements of 14.96 dB, 10.29, dB, and 6.26 dB compared to direct detection precision (30.811 \(\upmu\)m). Additionally, under an identical signal bandwidth, the sensitivity improves by 14.6 dB, 10.1 dB, and 6.4 dB, respectively. The proposed beacon spread-spectrum scheme mitigates the limitations of hardware reception sensitivity and position-detection precision, demonstrating its potential application in high-precision detection in long-distance interstellar laser communication.  
Keywords: space laser communication; four-quadrant detector; position-detection precision; detection and communication multiplexing; spread-spectrum gain +
Footnote †: journal: _Sciences_
## 1 Introduction
Respective cases contradict typical WSN platform paradigm, which effectively dictates the transition of power-hungry hardware to low-power states for substantial periods of time. Driven by such conditions, this paper proposes a holistic solution that allows mitigating the respective side effects based on a highly efficient and resource conservative data compression hardware accelerator. Specifically, the critical contribution of this work is the design, development and system level evaluation of the potential benefits of the proposed hardware component taking into consideration realistic, demanding biomedical datasets as well as real performance characteristics of state-of-the-art IoT platform. As it will be presented, the developed compression algorithm and its hardware design is able to compress the targeted modalities on-the-fly. Additionally, it is able to yield a considerable compression rate in all cases, thus enhancing the transmission capabilities of an IoT platform. Furthermore, the proposed hardware accelerator leads to drastic power conservation due to the data volume reduction, assuming that IoT nodes are able to deactivate radio interface when not transmitting. To the best of the authors' knowledge, this is the first attempt to offer a practical, efficient and feasible complete IoT compression component going beyond proposing an isolated software compression approach. On the contrary, the proposed solution has been implemented in hardware and evaluated taking into account realistic wireless transmission delays and power consumption statistics of prominent IoT wireless communication interfaces. The rest of the paper is structured as follows: In Section 2 critical information is provided highlighting the application scenarios, relative literature and the data characteristics of the specific problem. Section 3 presents the design and evaluation of the proposed compression algorithm yielding the required characteristics so as to optimally address the targeted problem. Section 4 comprises the cornerstone of this work and presents the actual hardware module design, implementation and evaluation. Section 5 offers a critical system level performance evaluation of the proposed hardware accelerator assuming that it has been integrated in prominent real IoT platforms and two different wireless communication interfaces. Finally, Section 6 offers a summarizing discussion on performance evaluation and module's implementation capabilities while Section 7 highlights the main points of this work and offers significant future extensions based on the presented work.
## 3 Results and Discussion  
### Grain Yield  
Significant differences in grain yield were found depending on the barley variety. At the same time, no such effect was observed depending on the dose of nitrogen applied to the crop in the case of three of the four varieties tested. Only in the case of the Hobbit variety, it was shown that higher doses of nitrogen applied to the crop stimulated an increase in yield. Similar grain yields for this variety were obtained by applying 120 and 150 kg of nitrogen per hectare. A comparison of mean yields for individual varieties showed significantly higher yields for Hobbit and Zoom than for the Galation and Sandra varieties (Table 2).  
Zoom, Hobbit, and Galation are hybrid varieties that have higher yielding potential than conventional genotypes (population varieties) such as Sandra. This is because hybrids obtained by crossing different genetic lines, thanks to heterosis, are characterized by greater vitality and greater development than conventional genotypes [37; 38; 39]. The results obtained in our study correspond to the results of other authors [38; 40; 41]. [PERSON] et al. showed that increased nitrogen levels considerably influenced the yields, which were highest when 120 kg N ha\({}^{-1}\) was applied [41].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**N-Dose/Variety**} & \multicolumn{4}{c}{**Barley Grain Yields (Tonnes Per Hectare)**} \\ \cline{2-5}  & **Galation** & **Hobbit** & **Sandra** & **Zoom** \\ \hline
60 kg N/ha & 9.03 \(\pm\) 0.69 a & 10.44 \(\pm\) 0.36 a & 9.52 \(\pm\) 0.56 a & 10.38 \(\pm\) 1.15 a \\
90 kg N/ha & 8.81 \(\pm\) 0.90 a & 9.91 \(\pm\) 0.36 a & 9.02 \(\pm\) 0.71 a & 9.81 \(\pm\) 1.16 a \\
120 kg N/ha & 10.03 \(\pm\) 0.22 a & 11.32 \(\pm\) 0.19 b & 9.53 \(\pm\) 0.77 a & 11.07 \(\pm\) 0.33 a \\
150 kg N/ha & 9.60 \(\pm\) 0.61 a & 11.16 \(\pm\) 0.32 b & 10.15 \(\pm\) 0.37 a & 10.83 \(\pm\) 0.27 a \\ \hline ANOVA & & & & \\ \(p\)-values & & & & \\ \hline N-dose & ns & 0.0019 & ns & ns \\ \hline Mean for Variety & 9.36 \(\pm\) 0.75 A & 9.71 \(\pm\) 0.65 B & 9.56 \(\pm\) 0.68 A & 10.52 \(\pm\) 0.88 B \\ \hline ANOVA \(p\)-value \textless{} 0.0001 & & & & \\ \hline Data are presented as means \(\pm\) standard deviations (SD); mean values followed by different letters within columns (a,b) and mean values followed by different letters in the row “Mean for Variety” (A,B) are significantly different at \(p<0.05\); ns—not statistically significant. & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Total grain yields (tonnes per hectare) of four barley varieties in relation to different nitrogen fertilization levels (N-dose) in the controlled field trial in Poland.
## 5 Conclusions  
From the problem of frequency deviation between the measured signal and reference signal in the digital lock-in amplifier, an accurate DFT-based signal frequency estimation method is studied. This frequency estimation method can automatically measure the frequency value of the measured signal. Then, the reference signal will be generated according to this frequency estimation value without prior frequency information. Moreover, for the more general low SNR measured signal with multiple frequency components, an iterative software implementation DLIA method integrated with the frequency estimation method is designed. Under the different SNR conditions and the different number of frequency components, the relative errors of the amplitude estimation are less than 1.7%. This proposed software DLIA method has relatively higher precision and is convenient to integrate with other signal processing systems in different applications.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Local SNR**} & \multicolumn{4}{c}{**Frequency Estimation**} & \multicolumn{4}{c}{**Amplitude Estimation**} \\ \cline{3-8}  & & **Source** & **Calculation** & **Relative** & **Source** & **Calculation** & **Relative** \\  & & **(Hz)** & **(Hz)** & **Error (\%)** & **Source** & **Calculation** & **Error (\%)** \\ \hline \multirow{4}{*}{3} & \(-1.07\) & 50 & 49.939 & 0.122 & 0.5 & 0.507 & 1.40 \\ \cline{2-8}  & 8.4  
**Author Contributions:** Conceptualization, K.C.; methodology, Y.W. and Y.C.; software, Y.W. and L.W.; formal analysis, H.W.; resources, H.W.; writing--original draft preparation, Y.W.; writing--review and editing, K.C.; validation H.W.; supervision, K.C.; project administration, K.C.; funding acquisition, Y.C. and K.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Key R&D Program of China (No. 2020 YFB1711000).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.