esa-sceva/satcom-chunk-collection huggingface dataset:
# Atmospheric Density Inversion Based on Swarm-C Satellite Accelerometer
## 2 Materials and Methods
### Data Preprocessing and Correction
#### 2.1.1 Preprocessing of Swarm-C Accelerometer Data  
This step processes the spike signal caused by the propellant and the data anomaly caused by the error detection and correction (EDAC) fault event in the accelerometer data. These two data anomalies are also unique to the Swarm-C satellite. Errors in the accelerometer's RAM code memory occur approximately once a month and per satellite. The error usually occurs in the South Atlantic Anomaly and polar regions. The cause of the  
Figure 1: The main workflow of this study.  
malfunction may have originated from cosmic radiation. Since the EDAC implementation sometimes fails, the easiest way to recover from the error is to restart the accelerometer, i.e., turn the instrument off and on again.  
During the operation of the Swarm-C satellite in orbit, when the control torque from magnetic torque is not enough to maintain the satellite's attitude, the attitude propellant will be activated. Since the thrusters have a short start-up time and are of the switch (Boolean) type, they produce a transient acceleration peak lasting for several seconds that can be detected by the accelerometer, which produces an acceleration spike signal. Although the accelerometer will record the acceleration spike signal caused by the propellant, this part of the data needs to be eliminated when calculating the aerodynamic acceleration. The resulting data gap needs to be filled by interpolation. The accelerometer spike signal caused by the propellant and the processed results are shown in Figure 2a.  
In the accelerometer data of the Swarm-C satellite, the accelerometer data abnormality caused by the EDAC fault event also needs preprocessing. For example, Figure 2b records an EDAC failure event of the Swarm-C satellite on 17 May 2015. The curve of accelerometer data shows a massive step after experiencing the EDAC failure event. The blue signal represents the data before correction, and the red curve represents the accelerometer data after correction.  
In case of an EDAC fault event during accelerometer data processing of the Swarm-C satellite, manual displacement correction can be performed on the step signal in the data.
###### Abstract  
A study was conducted to determine the effect of dielectric barrier discharge non-thermal plasma (DBD-NTP) on _Salmonella typhimurium_ and _Staphylococcus aureus_ populations on solid surfaces and in liquid suspensions. Our results showed that inactivation kinetics of _S. typhimurium_ and _S. aureus_ by DBD-NTP treatments can be well predicted with mathematical models. The survival curves of both _S. typhimurium_ and _S. aureus_ showed a log-linear phase followed by tailing behaviors on solid surfaces, and shoulder behaviors followed by a log-linear phase in liquid suspensions. The \(D\) values (decimal reduction time) for _S. typhimurium_ and _S. aureus_ in suspension were higher than those on solid surfaces (_p_ < 0.05). Additionally, the maxima of sublethal injury values under low NaCl concentration and neutral pH condition were higher than those under high NaCl and low pH condition. In addition, mathematical modeling was evaluated to predict the final inactivation result for potential industrial applications. This study indicates that different microbial supporting matrices significantly influence the inactivation effect of DBD-NTP; it also provides useful information for future applications of NTP in enhancing food shelf life and safety.  
DBD plasma; _Salmonella typhimurium; Staphylococcus aureus_; solid media; liquid media; inactivation kinetics model +
Footnote †: journal: Applied Sciences
## 3 Results  
### Experimental and Estimated Natural Carbonation Rates  
All carbonation rates obtained represent the typical outspoken linear relation between the carbonation depth and the square-root-value of the exposure time. R\({}^{2}\) values approach 1.00, with values equaling 0.92 or higher (Table 4). The mortar mix with HVFA additions seems most susceptible to carbonation. Given its Portland cement content of only 50%, hence lower availability of portlandite as a main carbonatable material, the CO\({}_{2}\) buffering capacity of this HVFA mortar will probably be on the low side. Thus, an experimental natural carbonation rate A\({}_{\text{nat}}\) of 15.29 mm/\(\surd\)years does not really come as a surprise for this mix design. When comparing this value with the natural carbonation rates estimated from the slightly/highly accelerated carbonation tests using Equation (1), it is immediately clear that this approach results in a significant underestimation of the actual natural carbonation rate A\({}_{\text{nat}}\). When the estimation is based on the output of a highly accelerated carbonation experiment at 10% CO\({}_{2}\) (A\({}_{\text{nat}}\): 6.35 mm/\(\surd\)years), this implies an underestimation of the actual value with no less than 59%. Given the fact that the conversion formula proposed by [PERSON] and [PERSON] [15] (Equation (1)) can normally be used for elevated CO\({}_{2}\) levels up to 3%, this severe underestimation was perhaps to be expected. Note that for this reason, carbonation at 10% CO\({}_{2}\) was no longer considered for the other two mortar mixes. However, when using the result of the slightly accelerated carbonation experiment at 1% as input for calculating A\({}_{\text{nat}}\) (10.89 mm/\(\surd\)years), the actual natural carbonation rate is still underestimated with 29%. Apparently, Equation (1) seems not very suitable to be applied on accelerated carbonation test results of the studied HVFA mortar.  
The CEM II/B-V 42.5 N mix and the CEM I 42.5 N mix are characterized by experimental natural carbonation rates of only 3.10 mm/\(\surd\)y and 2.31 mm/\(\surd\)y, respectively. The composite cement has a reduced Portland clinker content of 65-79% according to NBN EN 197-1 [22]. This seemed to have had consequences for the reduction of the CO\({}_{2}\) buffering capacity and the related carbonation rate. The A\({}_{\text{nat}}\) value of the CEM II/B-V 42.5 N mix turned out 1.34 times higher than for the CEM I 42.5 N mix. When comparing the estimated natural carbonation rate with the experimental one, it can be noticed that the underestimation of the former value is much less pronounced. For the CEM II/B-V 42.5 N and CEM I 42.5 N based mortar mixes, the actual A\({}_{\text{nat}}\) value is underestimated by only 6.0% and 7.5%, respectively. For mortar consisting of the two commercial cements much less harm seems to come from the conversion of carbonation test results obtained at 1% CO\({}_{2}\) using Equation (1).
## 4 Discussion and Conclusions
### Implications for Future Research  
Based on our results, we proposed several implications for future research to address the limitation of current literature. First, the relatively small body of publications identified in this review suggests the need for more empirical investigations in this area. In addition to impact studies, a variety of research methodologies should be utilized to provide answers to more practical questions such as why AR works, in what contexts, for which population, and how. Second, the scope of research on AR-supported professional training should be further expanded. Other training contexts other than engineering or health and medicine deserve further investigation, such as the service sector, manufacturing industry, and teacher development. Replicate studies in different contexts can further boost the credibility and generalizability of the research findings. Third, the complete summary statistics required by meta-analysis should be properly reported in future research studies to allow the calculation of AR's aggregated effect on professional training. Several experimental studies reviewed in this study were missing key statistics such as means and standard deviation, which highlights the urgency to address this research limitation. Lastly, we encourage conducting a cost analysis of AR-supported professional training in addition to determining its effect size. As argued by [PERSON] (2020), effect size alone does not reflect the cost of a program, and thus cannot justify its value for large-scale implementation [61]. Therefore, it is necessary to examine the cost-effectiveness of AR programs in order to make rational decisions regarding their adoption in professional training contexts. **Author Contributions:** Conceptualization, X.H. and H.L. ; methodology, Q.F. and H.L. ; validation, Q.F. and H.L. ; formal analysis, X.H. and Y.C. ; writing--original draft preparation, X.H. ; writing--review and editing, Y.C., Q.F. and H.L. ; visualization, X.H. ; supervision, H.L. ; project administration, H.L. ; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the National Natural Science Foundation of China grant number 62177021. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are openly available in Mendeley Data at [[https://doi.org/10.17632/sd89r4](https://doi.org/10.17632/sd89r4) zg56.2]([https://doi.org/10.17632/sd89r4](https://doi.org/10.17632/sd89r4) zg56.2) (accessed on 5 January 2022). **Conflicts of Interest:** The authors declare no conflict of interest. **Appendix A**: **Research Articles Selected for the Systematic Review**
# Fast Sound Source Localization Based on SRP-PHAT Using Density Peaks Clustering
## 5 Simulations
### Computational Load Analysis  
As shown in Section 4.2, when SRP-PHAT was used for analysis, assuming that the search space was the sectoral area in Figure 3, the azimuth angle range was 75\({}^{\circ}\)-195\({}^{\circ}\), the elevation angle was 20\({}^{\circ}\)-70\({}^{\circ}\), and the search step was 1\({}^{\circ}\), so \(N_{\theta}\) was 121, \(N_{\varphi}\) was 51, the power values of 121 \(\times\) 51 = 6171 grid nodes had to be calculated for each frame. In contrast, ODB-SRP-PHAT only needed to calculate the power values at eight nodes in the ODB. Its online computational load was only 0.1% of SRP-PHAT.
### Second-Generation (2G) Technology  
A commercial introduction of second-generation (2G) mobile systems based on GSM standards was made in Finland by Radiolinja in 1991, at the end of the 1980s. Both the conventional speech service and low-bit-rate data services were supported. In 1947, plans for the first cellular network were originally discussed. It was created with the intention of being utilized for military operations to provide more sophisticated communication methods to the troops. Many new types of broadcasting technology evolved between 1947 and 1979, roughly. In contrast to Europe, which was establishing its own communication systems, the United States started to build the AMPS (Advanced Mobile Phone Service) network. However, 2G cellular networks had several drawbacks. The data transfer rates were relatively low, limiting the types of services that could be offered. Further, the coverage and signal quality were inconsistent, especially in rural and less densely populated areas. Security features were also relatively basic, making these networks more vulnerable to eavesdropping and fraud. As technology advanced, the need for faster and more reliable networks led to the development of subsequent generations of mobile technology [4].
## 2 Related Works
### Existing Publisher-Subscriber Approach
#### 2.1.3 Quality Issue  
The data quality can be measured from various points of view in the IoT [32], but the most fundamental of those is the _intrinsic data quality_, which measures the accuracy and trustworthiness [33]. Our proposed smart broker measures data quality by considering only the incoming publisher data as a big data set. This incorporates various aspects of ISO 8000 specifications, such as _timeliness_ and _reducing redundancies_[34]. However, it also ignores the subscriber's characteristics and desires. This is due to the basic nature of publisher-subscriber architecture, which only focuses on the fast distribution of incoming publisher data.
## 4 Materials and Methods
### Cloning of SmMTPSLs Full-length cDNA and Expression in E. coli  
The total RNA was extracted from the leaves of _S. moellendorffii_ using Plant RNA Isolation Kit (BEI-BEI Biotech, Zhengzhou, China) and cDNA was synthesized by PCR using the HIScript II Q RT SuperMix (Vazyme Biotech Co., Ltd., Nanjing, China) according to the manufacturer's instructions. Then PCR products were cloned into the protein expression vector pCold-TF and were fully sequenced. All primers used were listed in Table S3. The constructs and insert-free pCold-TF (negative control) were used to transform _E. coli_ strain BL21 (DE3) [44].  
_E. coli_ BL21 (DE3) containing target plasmid was preincubated overnight at 37 \({}^{\circ}\)C in Luria-Bertani (LB) media supplemented with 50 \(\upmu\)g/mL chloramphenicol and 50 \(\upmu\)g/mL ampicillin. The culture was transferred into 200 mL LB media and grown at 37 \({}^{\circ}\)C with shaking (200 rpm) until an optical density at 600 nm (OD600) of 0.5-0.7 [14]. Protein expression was then induced with 1 mM isopropyl-\(\beta\)-D-hiogalactopyranoside (IPTG) and cells were grown with shaking (200 rpm) at 15 \({}^{\circ}\)C for 24 h. Then the cells were harvested with centrifugation (5000 rpm, 5 min), resuspended in PBS buffer, and homogenized by sonication on ice. The recombinant proteins were purified by a Ni-NTA Sefinose His-bind column according to the manufacturer's protocol. Protein concentrations were detected using the Bradford reagent (Amresco, Shanghai, China) with bovine serum albumin (BSA) as standard [45]. SDS-PAGE analysis was used to detect the purity of recombinant protein.
## 3 Results
### Field Campaign Support  
In addition to processing data from static stations, FRM4 DOAS was also used in support of a number of international field campaigns. A list of these campaigns, that took place between 2016 and 2022, is given in Table 10. They generally involved both MAX-DOAS and PANDORA systems as well as airborne imaging DOAS instruments, and their focus was on air quality characterisation using remote-sensing techniques as well as satellite validation.  
Figure 16 illustrates the results from the application of the FRM4 DOAS system to ground-based measurements during the GMAP campaign. This campaign took place in the Republic of South Korea in October and November 2021 and aimed to validate the GEMS (Geostationary Environmental Monitoring Spectrometer) instrument on board the GEO-KOMPSAT-2B platform that was launched a few months earlier. In early October, several instruments were collocated in Incheon city before being deployed inside and  
Figure 15: Overview of the FRM4 DOAS total ozone demonstrational data processing covering the period from January 2018 until late September 2024.  
around the Seoul metropolitan area. The figure shows results from the intercomparison of NO2 and HCHO VCD measurements from three collocated instruments in Incheon: the IUP Bremen research grade MAX-DOAS, an Airyx Skyspec operated by BIRA and a PANDORA operated by the Pukyong National University (PKNU). All measurements were filtered according to the approach described in Section 2.3.7.  
From inspection of Figure 16, one concludes that all three systems agree well for NO2 VCD (upper panels) with a slope and a Pearson correlation coefficient both close to unity. The level of agreement slightly degrades for HCHO, but it remains acceptable considering the weaker sensitivity of the measurement technique to this molecule. Overall, this demonstrates the good level of consistency achievable in a campaign between different instruments (of different designs) when these are centrally processed using FRM4 DOAS.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Campaign** & \multirow{2}{*}{**Campaign Objective**} & \multirow{2}{*}{**Location**} & \multirow{2}{*}{**Date**} & \multicolumn{2}{c}{**Number of Instruments**} & \multirow{2}{*}{**Reference**} \\ \cline{3-3}
**CINDI-2** & & Intercalibration of UV–Visible trace gas remote sensing instruments & Cabauw, Netherlands & September 2016 & 15 & [50] \\ \hline \multirow{3}{*}{TROLIX} & Geophysical validation of Sentinel-5p/TROPOMI data products using the Dutch Ruisdael Observatory & Cabauw, Netherlands & September–October 2019 & 6 & [52] \\ \cline{1-1} \cline{2-6} \cline{5
# Optimisation of Synchronous Grouting Mix Ratio for Shield Tunnels
## 2 Materials and Methods
### Performance Testing  
(1) Fluidity, bleeding, and segregation rates  
Referring to the cementitious grout (JC/T 986-2018) [29], grouting materials for pre-stressed structure in highway engineering (JT/T 946-2022) [30], and technical specification for application of self-compacting concrete (JGJT 283-2012) [31] standards, we conducted truncated cone fluidity, cement slurry free-bleeding rate, and a mixture of anti-segregation jumping table tests to analyse the fluidity, free-bleeding rate, and segregation rate of the mortar, respectively.  
(2) Rheological properties test  
An R/SP-SST soft solid testing rheometer (AMETEK Brookfield, Inc., Middleboro, MA, USA) was used with a V40-20 rotor to simulate the shear flow state, test the rheological parameters of the shield tunnel synchronous grouting slurry, and establish a rheological curve. RHEO2000 was used to collect and analyse the experimental data.  
The test followed the following process: (1) pre-shearing process: the slurry was stirred at a shear rate of 100 s\({}^{-1}\) for 60 s, dropped to 0 s\({}^{-1}\), and then stopped for 30 s; (2) data collection process: the shear rate increased from 0 to 100 s\({}^{-1}\), and a total of 60 data points were measured. A high shear rate was applied at the beginning of the pre-shearing to break the static structure of the freshly mixed slurry and then left to stand for 30 s to dissipate the residual stress caused by the pre-shearing process [32].
# Patch-Font: Enhancing Few-Shot Font Generation with Patch-Based Attention and Multitask Encoding
## 3 Methodology
### Multitask Encoder for Style Disentanglement
#### 3.2.2 Patch-Based Multi-Headed Attention Mechanism  
Recent works, such as VQ-Font [3] and CF-FONT [26], leverage self-attention mechanisms for font generation by introducing attention blocks or applying pixel-level attention across entire images. While effective, this pixel-wise attention incurs significant computational overhead, leading to longer generalization times.  
To mitigate this, we adopt a patch-based attention mechanism that segments each font image into 25 overlapping patches of size 32 \(\times\) 32 pixels. This patch-based strategy reduces complexity by focusing on localized style patterns rather than processing the entire image at once. Each patch is encoded independently, and its relevance is determined through self-attention. Similarity scores (computed using dot product attention) guide the weighting of patches. This allows the model to prioritize stylistically significant patches while preserving critical local features such as stroke width, serifs, and curvature.  
By aggregating patch-level features, the model learns to capture both global and fine-grained style attributes, enhancing the fidelity of the generated fonts without the computational burden of exhaustive pixel-level processing. We extract patches from font images during data loading time. The attention scores are computed as follows:  
\[\text{attention\_scores}_{\text{patch}}=\text{softmax}\Bigg{(}\frac{q_{\text{ patch}}\cdot k_{\text{patch}}^{T}}{\sqrt{\text{head\_dim}}}\Bigg{)}v_{\text{patch}} \tag{2}\]  
where  
* \(q_{\text{patch}}\cdot k_{\text{patch}}^{T}\) and \(v_{\text{patch}}\) represent the query, key, and value vectors for each patch, respectively.
* \(k^{T}\) represents the transpose of the Key \(k\).
* \(\sqrt{\text{head\_dim}}\) is a scaling factor to normalize the dot product.
# A Simulation of Tire Hydroplaning Based on Laser Scanning of Road Surfaces
## 3 Test Methods
### Simulation Model
#### 3.2.2 Road Surface Modeling
To achieve accurate surface reconstruction from laser-scanned point cloud data, the NURBS (Non-Uniform Rational B-Spline) method was employed in this study. NURBS offers strong geometric flexibility and smoothness, making it particularly suitable for modeling complex and irregular pavement textures. Compared with simple polygonal or spline fitting techniques, NURBS provides better continuity and control over local surface features, which is essential for preserving the macroscopic texture relevant to  
Figure 10: Road surface model processing flow: (**a**) CAD processing point cloud data; (**b**) road point cloud data; (**c**) polygonal tessellation of point cloud data; (**d**) surface model. hydroplaning analysis. The mathematical expressions for NURBS curves and surfaces are given in Equations (8) and (9), respectively:  
\[\mathrm{p}(\mathrm{u})=\frac{\sum_{i=0}^{n}\omega_{i}\mathrm{N}_{i,\mathrm{k}}( \mathrm{u})\mathrm{p}_{i}}{\sum_{i=0}^{n}\omega_{i}\mathrm{N}_{i,\mathrm{k}}( \mathrm{u})},0\leq\mathrm{u}\leq 1 \tag{8}\]  
In the formula, \(\omega\mathrm{i}\) (I = 0, 1. \(\ldots\), n) is the weight coefficient; Ni and K(\(\mathrm{\mu}\)) are B-spline basis functions; Pi is the control point. By increasing the number of iterations, NURBS curves can be extended to NURBS surfaces, resulting in the expression of NURBS surfaces [35; 37]:  
\[\mathrm{p}(\mathrm{u})=\frac{\sum_{i=0}^{n}\sum_{j=0}^{n}\mathrm{N}_{i,\mathrm{ k}}(\mathrm{u})\mathrm{N}_{j,\mathrm{l}}(\mathrm{v})\omega_{\mathrm{i}} \mathrm{p}_{i}}{\sum_{i=0}^{n}\mathrm{N}_{j,\mathrm{k}}(\mathrm{u})\mathrm{N} _{j,\mathrm{l}}(\mathrm{v})\omega_{\mathrm{i}}\mathrm{p}_{i}},\mathrm{u}, \mathrm{v}\in[0,1] \tag{9}\]  
The NURBS fitting can obtain an accurate surface model with good geometric continuity and realize high-fidelity road reconstruction, as shown in Figure 10d. 3. The meshing of the pavement model (Figure 11). In Geomagic Studio software, the reverse reconstructed pavement model is exported as an.iges file and imported into Hypermesh software for meshing. During the meshing process, considering that the quality of the mesh significantly affects the accuracy and convergence of simulation results, the pavement surface should be divided into quadrilateral elements as much as possible, with triangular elements used only at the sharp corners of the surface. A too dense mesh increases computational time costs, while a too sparse mesh affects result accuracy. Therefore, the side length of the pavement mesh is generally within the range of 2 mm to 4 mm.