esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward a Detailed Evaluation of Wireless Industrial Data Distribution Approaches +
## 6 The Test Cases
### Scalability Performance  
The manufacturing experimentation facility gives us an important ability to test the methods on real conditions and derive useful indications. However, at the same time, it does not allow us to perform larger-scale, or variable experiments, easily and fast. For this reason, and in order to test the scalability of smart data distribution in industrial environments, we developed a simulation model based on the manufacturing experimentation facility layouts. Figure 9 displays the results on the average data access latency for two different alternative approaches, for different numbers of nodes in the network. Specifically, the first approach is the entirely centralized approach C1 which delivers the data to the nodes requesting them through the CNC. The second approach employs our smart distributed data management  
Figure 8: Second test case set-up.  
D2 and uses proxies so as to distributively allocate the data across the network. The red points represent the values for the maximum latency threshold (100 ms) provided by the industrial operator. We can see that the efficient management of proxies provided by the smart data distribution process results in a better performance compared to the entirely centralized alternative. The average latency achieved by smart data distribution respects the typical Industry 4.0 constraints and always remains lower than the data access latency threshold (red line in Figure 9). This simulative result can be explained also intuitively: the dynamic proxy selection process is able to adapt to the underlying latency requirements and arrange the data distribution process in such a way that the corresponding hard constraints are respected.
# Nitrogen Oxide Removal by Coal-Based Activated Carbon for a Marine Diesel Engine
## 3 Results and Discussion
### Effect of AC on NO + NH\({}_{3}\)  
When the temperature is lower than 300 \({}^{\circ}\)C, the denitration ability of AC as the reductant was poor. To further improve the denitration ability of AC, NH\({}_{3}\) is generally added as the reductant [25; 26]. As shown in Figure 3, the denitration ability of AC at different temperatures is investigated in this paper when the inlet gas is 1000 \(\upmu\)L/L NO + 1000 \(\upmu\)L/L NH\({}_{3}\), i.e., slow SCR reaction Equation (1). There are two explanations for the catalytic denitration mechanism of AC when NH\({}_{3}\) is present. One is the Eley-Rideal mechanism (E-R), in which the adsorbed NH\({}_{3}\) reacts with the gas phase NO [27], and the other is the Langmuir-Hinshelwood mechanism (L-H), in which the adsorbed NH\({}_{3}\) reacts with adsorbed NO [28]. Both mechanisms suggest that the NH\({}_{3}\) adsorption process on the surface of AC is the rate-limiting step of Equation (1). Therefore, more acidic oxygen-containing functional groups on the surface of AC can contribute to the adsorption of NH\({}_{3}\), which increases the reaction rate of  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Species** & **Carboxyl** & **Lactone** & \begin{tabular}{c} **Phenolic** \\ **Hydroxyl** \\ \end{tabular} & \begin{tabular}{c} **Acidic** \\ **Groups** \\ \end{tabular} & \
\begin{tabular}{c} **Basic** \\ **Groups** \\ \end{tabular} &
\begin{tabular}{c} **Total** \\ **Contents** \\ \end{tabular} \\ \hline Value & 0.150 & 0.045 & 0.233 & 0.428 & 0.278 & 0.705 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Surface chemical characteristics of activated carbon (Unit: mmol/g).  
denitration. Figure 4, which is supported by [28], shows the L-H mechanism of NO and NH\({}_{3}\) reacting on the surface of AC, and NH\({}_{3}\) is mainly adsorbed on the phenolic hydroxyl group. The coal-based AC used in this paper is acidic and the phenolic hydroxyl-containing functional group is relatively high, which promotes denitration. It can be seen from the comparison between Figures 2 and 3 that the trend and concentration of CO after adding NH\({}_{3}\) are basically the same as without NH\({}_{3}\), indicating that NH\({}_{3}\) has no effect on the decomposition of the oxygen-containing functional groups. After adding NH\({}_{3}\), NO can be converted to N\({}_{2}\) by slow SCR reaction Equation (1), and the denitration rate of AC is slightly increased. The near-stable denitration efficiencies of AC at 15 \({}^{\circ}\)C, 100 \({}^{\circ}\)C, 200 \({}^{\circ}\)C, and 300 \({}^{\circ}\)C are 10.5%, 9.1%, 10%, and 12%, respectively, which are slightly higher than without NH\({}_{3}\). However, the catalytic effect of AC on the Equation (1) is still small.
## 3 Results and Discussion  
### Seismic Fragility Assessment  
#### 3.1.1 Derivation of Fragility Function Parameters  
According to the above considerations, two parameters, i.e., \(IM_{mi}\) and \(\beta_{tot}\), are required to derive the corresponding fragility functions. The value of \(IM_{mi}\) for \(PGA\) or \(PGV\) concerning the various \(DS\)s is computed using a regression analysis for the nonlinear numerical results. Particularly in this study, a traditional linear regression analysis is adopted using the natural logarithm of the \(DI\) and \(IM\) as the variables. As shown in Figure 9a,b, the different data points (360 in total) stand for the corresponding damage indexes under increasing levels of \(PGA\), while the solid blue line indicates the regression fit curve for these Ln(\(DI\))-Ln(\(IM\)) datasets. It is noted that \(IM_{mi}\) is computed based on the regression fit equation with the different thresholds of \(DS\). \(\beta_{D}\) is calculated as the dispersion of the different data points with regard to the curve of regression fit [26], and the total lognormal standard deviation \(\beta_{tot}\) is calculated using Equation (2). The detailed fragility function parameters in terms of \(IM_{mi}\) and \(\beta_{tot}\) are also presented in Figure 9.
## 3 Result and Discussion
### Detection of New Red Spiked in Water Using Positively Charged Au NPs-Embedded MIHs as a Substrate  
Figure 6 demonstrates a series of SERS spectra of New Red when the positively charged Au NPs-embedded MIH substrate was incubated with a series of New Red aqueous solution at different concentrations. A progressive increment in signal intensity was observed when the New Red concentration increased from \(1.64\times 10^{-7}\) to \(1.64\times 10^{-4}\) M. A linear plot of the logarithmic intensity of the signal at 1572 cm\({}^{-1}\) versus the logarithm of the New Red concentration indicates that the detection is linear within the concentration range from \(1.64\times 10^{-6}\) to \(1.64\times 10^{-4}\) M with the LOD of New Red at \(1.64\times 10^{-7}\) M in water. The linear equation was \(\text{lgI}_{1572}=5.66+0.46\text{lgC}_{\text{New Red}}\) (\(\text{lgI}_{1572}\) refers to the  
Figure 5: The SERS spectra acquired using the positively charged Au NPs-embedded MIHs as substrates after the MIHs were respectively incubated with the solutions of New Red **(a)**, Erythrosine B sodium (b), Amaranth **(c)**, and Allure Red **(d)** for 2 h at the concentration of \(1.64\times 10^{-5}\) M.  
logarithmic intensity at 1572 cm\({}^{-1}\); lgC refers to the logarithmic concentration of New Red) and the coefficient of this regression was determined to be R\({}^{2}\) = 0.99949.  
Table 1 summarizes the recovery and the relative standard deviation (RSD) of the detection of New Red. It can be seen that the recoveries were found to be 86.3% to 100.6% and the values of RSD were in the range of 2.3% to 7.7%. The high recovery and low RSD values achieved in this study illustrated good precision and accuracy of the detection of New Red based on SERS using the MIHs as substrates.
# A Doppler Frequency-Offset Estimation Method Based on the Beam Pointing of LEO Satellites
## 6 Conclusions  
Building on the development of 5G-A NTN technology and the communication requirements of mobile terminals directly connected to satellites, we investigated a novel Doppler frequency-offset estimation method that operates without GNSS guidance and is tailored for the high dynamics of low-Earth-orbit (LEO) satellite communications. We analyzed the principles underlying satellite-to-ground Doppler frequency offsets, derived the relationship between satellite beam pointing and satellite velocities, and proposed a Doppler frequency-offset estimation method based on satellite beam pointing. Furthermore, we conducted a detailed study on how parameters such as the Earth's latitude and the satellite's orbital inclination affect this method. Under varying conditions of minimum satellite elevation angles and terminal geographical locations, we compared the Doppler frequency-offset estimation results using the satellite beam-pointing method to those from STK's SGP4 model. The simulation results show that when a satellite's minimum elevation angle is \(0^{\circ}\), the frequency-offset estimation errors of both methods lie within \(\pm 200\) Hz, and the RMSE of the frequency-offset estimation does not exceed 100 Hz. When the minimum elevation angle is greater than \(0^{\circ}\), the error range and the RMSE of the frequency-offset estimation increase to approximately \(\pm 2000\) Hz.  
Conceptualization, Y.S., J.X. and C.S.; Methodology, Y.S. and J.X.; Software, Y.S. and X.L.; Validation, Y.S., X.L. and S.A.; Resources, C.S.; Data curation, S.A.; Writing--review & editing, Y.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 2 Principles of Grounding Systems
### Regulatory Standards and Guidelines
#### 2.2.3. National Electrical Code (NEC) Standards  
The National Electrical Code (NEC), particularly Article 250, provides detailed requirements for grounding systems in the United States. It defines acceptable grounding electrode systems, the minimum size of grounding conductors, and the methods of connecting electrical systems to earth [10]. It requires the grounding system to possess adequate capacity to safely carry fault currents and reduce the risk of electric shock. Table 2 shows the National Electrical Code (NEC) Standards for Grounding in the USA.
## 3 Numerical Analysis  
### Finite Element Model for a Concrete Gravity Dam  
To verify the validity of the proposed SSF in Equation (3) and compare it with the other SSF formulations in Equations (1) and (2), the seismic analysis of a 70 m concrete gravity dam is performed as depicted in Figure 4. The dam is modeled by a finite element program, Abaqus. The model consists of 685 eight-node isoparametric plane stress elements. The concrete properties are the elastic modulus \(E_{c}=28.1\) GPa, Poisson's ratio \(\upsilon=0.12\), and the density \(\rho_{c}=2300\) kg/m\({}^{3}\). The cohesive force of the concrete is suggested to be 10 percent of its compressive strength. The compressive strength of the concrete is from 20.7 MPa to 34.5 MPa [24]. In this study, the cohesive force of the concrete dam-foundation interface is assumed as 2.07 MPa, which is 10 percent of the minimum compressive strength. The friction coefficients \(\tan\phi_{i}\) and \(\tan\phi_{f}\) are derived from USACE [25] and ACI [26]. According to USACE [25], the coefficient of the internal friction \(\tan\phi_{i}\) is set as 0.7. In ACI [26], the coulomb friction coefficient \(\tan\phi_{f}\) is prescribed as 0.6.  
The hydrostatic and hydrodynamic pressures are applied, and the uplift pressure is ignored in this numerical analysis to avoid unnecessary complexity. The hydrodynamic pressure is applied according to the simplified added-mass formulation of [PERSON] [27], defined as:  
\[m=\frac{7}{8}\rho_{w}\sqrt{h_{w}(h_{w}-y)}\,A \tag{11}\]  
where \(m\) is the added mass acting as the hydrodynamic pressure, \(\rho_{w}\) is the density of water, \(h_{w}\) is the depth of the upstream reservoir, \(y\) is the vertical height from the surface of water, and \(A\) is the area per vertical height.
## 5 Results and Discussion
### Summary
According to [PERSON] et al., advanced SSB structures and their integration with beamforming mechanisms improve the signal-to-noise ratio during initial access, directly reducing BER and retransmission rates, which is especially important in high-interference and multipath environments [58]. In the measurements with the less demanding sensor configuration, another very important parameter was the Scheduling Request Period. This defines the time interval after which a physical layer signaling message is sent by the User Equipment (UE) to inform the network that it has data ready for uplink transmission. Additionally, the number of PDCCH candidates at Aggregation Level 2 (AL2) was increased from 4 to 8. This change provides the UE with more decoding opportunities, improving the reliability of control message detection, particularly in degraded radio conditions. Although it slightly increases the complexity of blind decoding, it helps maintain low latency and stable uplink performance.
# Elemental Mercury Removal over CeO\({}_{2}\)/TiO\({}_{2}\) Catalyst Prepared by Sol-Gel Method
## 3 Results and Discussion
### The Effects of flue Gas Components on Catalysts Performance
#### 3.2.3 The Effects of NO and NH\({}_{3}\) on Catalytic Performance  
The effects of NO and NH\({}_{3}\) concentrations on the elemental mercury removal efficiency of the catalysts was investigated in this section. The ratio of NH\({}_{3}\) to NO was controlled at 1:1. From Figure 4c, it can be seen that the highest efficiency of pure TiO\({}_{2}\) catalyst was only 30.6%, which is much lower than that of other catalysts. For CeO\({}_{2}\)/TiO\({}_{2}\) catalysts, relatively higher Hg\({}^{0}\) removal efficiency was found when the concentrations of NO and NH\({}_{3}\) were 200 \(\upmu\)L\(\cdot\)L\({}^{-1}\) and 400 \(\upmu\)L\(\cdot\)L\({}^{-1}\). For instance, the value of \(\eta\) for CeO.2 Ti was about 96.3% under either set of working conditions. Actually, NO could react with O\({}_{2}\) in the flue gas and produce NO\({}_{2}\), which has strong oxidation that would improve the Hg\({}^{0}\) removal performance of the catalysts [24]. As a result, the Hg\({}^{0}\) removal efficiency increased when the concentrations of NO and NH\({}_{3}\) rose from 0 to 200 \(\upmu\)L\(\cdot\)L\({}^{-1}\). However, the value of \(\eta\) stopped rising, and even begin decreasing if more NO and NH\({}_{3}\) was introduced. That should be explained by the adverse effect of NH\({}_{3}\) on the oxidation of elemental mercury. The active site on the catalyst surface would be partly occupied because of the competitive adsorption between NH\({}_{3}\) and HCl, which would bring about an inhibitory effect on the oxidizing reaction between HCl and elemental mercury [25]. In addition, NH\({}_{3}\) could react with O\({}_{2}\)[26]. Considering the important role of O\({}_{2}\) in Hg\({}^{0}\) oxidation process, high concentration HN\({}_{3}\) was definitely unfavorable for elemental mercury removal. In general, the efficiency of CeO.2 Ti is the highest. If the catalyst was placed in the middle or rare part of the SCR equipment, the concentrations of NO and NH\({}_{3}\) were moderate for elemental mercury removal, since the SCR reaction was carried out. Relatively high Hg\({}^{0}\) removal efficiency was achieved.
# Execution Model to Reduce the Interference of Shared Memory in ARINC 653 Compliant Multicore RTOS
## 4 Evaluations
### Benchmark  
We experimented with the TDMA, AER, and multi-TDMA models applied the micro-benchmarks proposed in the previous study [15]. The benchmark followed the phase structure of the AER model described in Section 3.2.2, implemented with one main function and each phase function. Each phase function was called from the main function. The detailed benchmark operation is shown in Figure 12. The A phase read the array stored in shared memory and stored it in the local variable. Then, to minimize L1 cache misses, the array was accessed by 32 KB, the size of L1 on the target board. The E phase carries out a simple addition operation using local variables stored during the A phase. During the R phase, the value calculated in the E phase was updated in the main memory.  
Before experimenting with the execution model, we measured the number of cache misses that occurred at each phase of the benchmark. The measured values for each phase are shown in Table 2. In the E phase, the L1 misses were approximately 15 times fewer than in the A phase, and 31 and 55 times less for the L2 misses and BIU requests, respectively. The cache misses that occurred in the E phase were due to the context switch caused by the calling of each phase function in the main function. The low number of L2 misses and BIU requests in the R phase is because the data is already stored in the cache during the A phase. These results show that the cache misses and BIU request counts in the E phase are acceptable.  
Figure 11: Architecture of T2080 QDS.
# Cycle Consistent Generative Motion Artifact Correction in Coronary Computed Tomography Angiography
## 4 Results
### Clinical Data  
#### 4.2.1 Datasets  
The 4D CT data utilized in this study were retrospectively collected using a dual-source CT scanner (SOMATOM Definition Flash, Siemens Healthineers, Forchheim, Germany) at Severance Hospital, South Korea, with the acquisition dates ranging from December 2015 to March 2016. The dataset comprises scans from 140 patients, systematically reconstructed at 10% intervals throughout the cardiac cycle. The training data comprised patches from 20 points in eight phases in 100 4D CT volumes. The number of phases \(K=8\) was determined by the number of temporal-phase quantizations, 10, minus the 40% and 70% phases designated as the targets for cross-phase style transfer. Several 4D CT volumes had \(K\) values of less than eight, in which phases with extreme motion artifacts were excluded because manual annotation of the coronary artery was impossible. The final dataset comprised a total of 5868 pairs of mid-RCA patches, which were then augmented to 35,208 using vertical and horizontal flips and rotations. Each patch sampled from the 3D volume was constructed to be of size 64 \(\times\) 64 pixels. Validation and test sets, comprising 2152 patches from 30 4D CT volumes and 734 patches from 10 4D CT volumes, were similarly constructed.
# Clustering Moving Object Trajectories: Integration in CROSS-CPP Analytic Toolbox
## 2 Related Work  
As mentioned above, the proposed approach adapts the algorithm presented in [13] for clustering and performs anomaly detection on that basis. Consequently, in what follows, we review previous approaches for trajectory clustering and detection of anomalies.
# Predicting Activities of Daily Living with Spatio-Temporal Information
## 6 Results and Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Test Sets**} & \multicolumn{3}{c}{**Recognition Accuracy (\%)**} \\ \cline{2-4}  & **MIT PlaceLab Dataset** & **van Kasteren Dataset** & **Aruba Dataset** \\ \hline
1 st Set & 63 & 91.16 & 96.20 \\
2 nd Set & 82.22 & 68.96 & 72.62 \\
3 rd Set & 91.55 & 97.19 & 76.17 \\
4 th Set & 79.75 & 89.66 & 79.22 \\
5 th Set & 81.56 & 92.56 & – \\
6 th Set & 94.02 & 89.30 & – \\
7 th Set & 81.38 & – & – \\
8 th Set & 80.45 & – & – \\ \hline
**Average** & **81.74** & **88.14** & **81.05** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Recognition performance in predicting activity based on time and location on each test set. Figure 4: Recognition performance on different training-test splits. In this study, the temporal information is processed based on a 30 min interval. This time interval is a preliminary choice to validate our approach. However, a more adequate interval could be determined by preprocessing the data and finding the most suitable interval for each activity. Methods that could potentially be applied include rough and fuzzy sets [20]. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**(a) Training Time (In Minutes)**} \\ \hline
**Training Sets** & **MIT PlaceLab Dataset** & **van Kasteren Dataset** & **Aruba Dataset** \\ \hline
1 st Set & 11.34 (1478) & 5.68 (1137) & 53.88 (2728) \\
2 nd Set & 14.38 (1625) & 3.90 (983) & 30.14 (2192) \\
3 rd Set & 13.77 (1592) & 5.84 (1140) & 31.17 (2218) \\
4 th Set & 12.95 (1563) & 5.21 (1086) & 38.95 (2419) \\
5 th Set & 13.05 (1561) & 6.63 (1197) & – \\
6 th Set & 16.08 (1688) & 4.80 (1047) & – \\
7 th Set & 11.31 (1456) & – & – \\
8 th Set & 15.36 (1672) & – & – \\ \hline
**Average** & **13.53 (1579)** & **5.34 (1098)** & **38.54 (2389)** \\ \hline \multicolumn{4}{c}{**(b) Prediction Time (In Seconds)**} \\ \hline
**Test Sets** & **MIT PlaceLab Dataset** & **van Kasteren Dataset** & **Aruba Dataset** \\ \hline
1 st Set & 0.0275 (327) & 0.0146 (181) & 0.0674 (841) \\
2 nd Set & 0.0154 (180) & 0.0248 (335) & 0.1216 (1377) \\
3 rd Set & 0.0172 (213) & 0.0152 (178) & 0.1191 (1351) \\
4 th Set & 0.0234 (242) & 0.0197 (232) & 0.0842 (1150) \\
5 th Set & 0.0212 (244) & 0.0098 (121) & – \\
6 th Set & 0.0080 (117) & 0.0216 (271) & – \\
7 th Set & 0.0406 (349) & – & – \\
8 th Set & 0.0100 (133) & – & – \\ \hline
**Average** & **0.0204 (226)** & **0.0176 (220)** & **0.0981 (1180)** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Computational time: (a) Training time (in minutes) and (b) Prediction time (in seconds). The value in parentheses represents the number of user-interaction instances in each respective training/test set.