esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cloud-Based Framework for Large-Scale Log Mining through Apache Spark and Elasticsearch
## 7 Discussion and Conclusions  
Log mining is the process of reconstructing sessions from raw web logs and discovering meaningful usage patterns and implicit linkages. The mining result is valuable in improving the data discovery experience. We proposed a cloud-based framework for large-scale log mining by integrating Elasticsearch and Spark. A customized partitioner named LogPartitioner solves the skewed data issue in the log parallelism process. The cloud platform serves as the framework with virtual resources.  
Figure 16: Statistical information for a selected month.  
Figure 15: An example of the session tree structure [4].  
Experiments demonstrated that the processing time of one month of logs decreased from 60+ minutes to ~5 min with the framework. To share the framework with the public, the research results were published as a GitHub open-source project ([[https://github.com/apache/incubator-sdap-mudrod](https://github.com/apache/incubator-sdap-mudrod)]([https://github.com/apache/incubator-sdap-mudrod](https://github.com/apache/incubator-sdap-mudrod))).  
Several aspects are worth further investigation. Firstly, since the framework cannot analyze real-time logs, a real-time data streaming tool, e.g., Apache Spark Streaming, can be introduced into the framework for near real-time analysis and tracking [29]. Secondly, the network transportation cost in the cloud-based framework should be experimented for the mining process when both the Elasticsearch and Spark cluster are integrated as a whole entity. Thirdly, although the framework reported in the article focused on log data mining, one can accelerate the analysis process with a customized partitioner for other types of data, e.g., geo-tagged tweets. We will continue this research to improve the framework with more specific performance-improving techniques.  
[PERSON] and [PERSON] conceptualized the study; [PERSON], [PERSON], and [PERSON] designed and developed the study; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] conducted the experiments; [PERSON], [PERSON], [PERSON], and [PERSON] performed the analysis; [PERSON] and [PERSON] supervised the research; [PERSON] wrote a draft of the manuscript, which was iteratively improved by all other authors of the manuscript.  
This project was funded by NASA AIST (NNX15 AM85G) and NSF (IIP-1338925). The research was partially carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.  
The authors declare no conflicts of interest.
## 3 Results
### Comparisons of Accuracy Among Different Implementations
As shown in Figure 7, the required time to complete the motion tracking as the increase of search range. However, the trend of time reduction between the standard NCC method and the Lou-Konofagou method remained the same. In contrast, with the increase of the search range, time needed for [PERSON] method to complete the required tracking remains relatively steady (Figure 7). Figure 6: Plots comparing computational efficiency under two different environments: (**A**) CPU and (**B**) GPU. In (**A**), the computing time is displayed in log\({}_{10}\) scale. Computing time was estimated based on the completion of 3-D motion tracking for a single slice of ultrasound data. Figure 7: Plot showing computing time when the search range changed under the (**A**,**C**) CPU and (**B**,**D**) GPU environments. In all plots, the computing time is displayed in log\({}_{10}\) scale. Computing time was estimated based on the completion of 3-D motion tracking for a single slice of ultrasound data. As shown in Table 1, the standard deviation values among 30 slices were low as compared to the mean value. This indicated that the time required to calculate NCC values was stable regardless of the implementation used.
## 1 Introduction
The calibration step is efficiently performed, and we discuss the feasibility of the proposed algorithm from practical implementation and computational efficiency perspectives. The remainder of this paper is organized as follows: We first discuss the related work in Section 2, and the signal model, including ICM for airborne passive radar, is introduced in Section 3. Subsequently, we review the existing optimization problems in SRA and LSA, and then the proposed joint optimization algorithm for MP clutter suppression is derived in Section 4. Section 5 presents some simulated experiments demonstrating the performance and validity of the proposed approach. Finally, Section 6 gives the relevant conclusions. _Notation_: In this paper, the Kronecker product and Hadamard product are expressed by the symbols \(\otimes\) and \(\odot\), respectively. The symbol \(E[\cdot]\) indicates the mathematical expectation, and \(\mathrm{Re}\{\cdot\}\) represents the real part of the argument. \((\cdot)^{T},(\cdot)^{*}\) and \((\cdot)^{\mathrm{H}}\) denotes the transposition, complex conjugation, and conjugate transposition, respectively. \(diag(\cdot)\) denotes the diagonal matrix, \(\mathbf{I}_{A}\) is the \(A\times A\) identity matrix, \(\mathrm{Toeplitz}(\cdot)\) is the Toeplitz matrix, and \(\mathbf{I}_{B\times\mathrm{C}}\) is the \(B\times C\) matrix with all elements being 1. In addition, \(\left\|\cdot\right\|_{F}\), \(\left\|\cdot\right\|_{2}\) and \(\left\|\cdot\right\|_{1}\) represent the Frobenius-norm, \(l_{2}\)-norm, and \(l_{1}\)-norm operations, respectively.
## 3 Results and Discussion
### Kinetic Analysis  
Figure 5 displays the \(p(u)/p(u_{0.5})\) curves for the tri-fuel with SC:HTB:OS of 40%:45%:15% at three heating rates. The deviation between the test curve and the other two curves is about \(0.5<\alpha<0.7\). The divergence has no bearing on the validity of the mechanism function; thus, it is feasible to select a single heating rate to study the combustion reaction. In this study, the experimental curve of combustion at 20.0 \({}^{\circ}\)C min\({}^{-1}\) is selected and compared with the standard curves drawn by 20 common kinetic models. The standard and experimentation curves are shown in Figure 6. When the conversion rate \(\alpha<0.5\), the most probable function \(f(\alpha)\) of tri-fuel is \([-\ln(1-\alpha)]^{2}\). With increasing temperature and the reaction proceeding, the reaction mechanism exhibits some differences. When the conversion rate is greater than 0.5, the \(f(\alpha)\) becomes \([-\ln(1-\alpha)]^{3}\). The \(f(\alpha)\) of each blended sample is listed in Table 3.  
Table 3 lists the \(E\) and \(A\) of the tri-fuels at the different mixing ratios. When a high proportion of HTB is present in the mixture, \(E\) is approximately 20% to 30% less than SC. The reactant particles can undergo conversion into active particles without the need for additional energy absorption, thereby the tri-fuel of high HTB proportion facilitates the reaction. The HTB fixed carbon occurs in a form similar to SC, leading to mixed combustion where more molecules are simultaneously participating in a reaction and the same particles smash into one another. This can explain the facilitating effect of HTB on the tri-fuel combustion. The \(A\) gradually decreases with the increase in HTB content, which has an inhibitory effect on the combustion. Overall, the promoting effect of HTB for tri-fuel combustion dominates. When the OS content is 10%, \(E\) decreases to 35.87 and 49.04 kJ mol\({}^{-1}\). Meanwhile, the increase is significant at \(0.5<\alpha<0.8\) when SC:HTB:OS = 40%:50%:10%. The pre-exponential factor of the three fuels increased as the OS content decreased. This phenomenon can be attributed to the limited spatial response that OS provides for HTB and SC, as mentioned in the previous section. A suitable mixing ratio results in a weakening of the combustion hindering effect of noncombustible particles in OS and thus an increase in the kinetic index of tri-fuel. Figure 5: Experimental curves of combustion of tri-fuel (SC:HTB:OS = 40%:45%:15%) at different heating rates. Figure 6: Comparison between the standard curves and the experimental curve.
## 3 Results  
The results of the rotation phantom experiment are shown in Figures 11-13. As can be observed in Figure 11, all the HRI estimates exhibit some degree of aliasing, as evidenced by the large errors at high velocities, with the three-angle single transmit sequence (sTx3 HRI) exhibiting the widest usable velocity range of all processing schemes using traditional processing. This result is predicted by the traditional Doppler slow-time frequency analysis, because with only three transmit events, the compounded dataset has the highest slow-time frame rate of the group, and thus the highest velocity range. Note that in each case, the aliasing error begins at lower velocities than predicted in Table 2. This is due to the broadband nature of the excitation. Because the excitation possesses frequency content above the center frequency, aliasing begins before the prediction, because the high-frequency components alias before the center frequency reaches its Nyquist rate. More conservative estimates could have been done by considering the entire band of the excitation, but such estimation schemes deviate from established conventions and were thus avoided to facilitate easy comparison. Comparing the traditional lag-one estimates of the LRI processing schemes, one can observe that the sTx3 dataset is characterized by large RMSE and standard deviations across the velocity range. This would suggest low correlations in the individual slow-time lines. It is possible that in this case the sidelobe motion might have been fast enough to corrupt the estimate due to the high angular step, even despite the aggressive clutter filtering employed. This could possibly explain why the performance is recovered in the sTx3 HRI scheme. For the seven-angle dataset (sTx7), the slow-time signal error resonates at around 200 mm/s. This error is anticipated due to the clutter filter stop band at that particular velocity range (see Figure 4). The effect also manifests in the power Doppler image in this dataset (Figure 14), because the signal is attenuated in the corresponding spatial region. This error is also exacerbated due to the sidelobe motion being present at those frequencies. While one could argue that such estimates would be suppressed by power Doppler thresholding, the fact remains that there is a weak spot in the velocity range that cannot be overcome using a conventional transmit sequence. Similarly, the velocity estimate is also extremely poor near the stationary regime, but because the signal-to-noise ratio (SNR) is low here, this is to be expected. This secondary error resonance at the compounded frame rate is not present using the traditional double-transmit method (dTx6). Even though the signal power is lower at these velocities, the fact that the sidelobes move at a slower rate allows for a more robust estimate across a larger range than the sTx7 dataset, despite having comparable channel frame rates. The error in the near stationary regime is also markedly better than both the sTx LRI processing schemes. Both pairwise estimation schemes had only minor improvements in the dTx dataset, suggesting that the dominant improvement is the
## 2 Test Setup and Simulation Environment
### Simulation Scenarios
These simulation variables values (Table 2) have remained constant within a single simulation run. Each combination of the simulation variables has been simulated 10 times using a different random seed for each simulation run, resulting in a total of 2100 simulation runs per technology and infrastructure density deployment. So, in total, 16,800 simulation runs were made. We have recorded E2E latency of each communication packet and Packet Delivery Ratio (PDR) for each simulation run. It should be noted here that each simulation run generated tens of thousands of data points for calculation of the evaluation metrics, i.e., E2E latency and PDR.
# Summarization of Remaining Life Prediction Methods for Special Power Plants
## 3 Future Research Directions
### RUL Prediction Method Driven by Data-Model Fusion
The sum, difference, and product of the two follow a normal distribution. Finally, the upper and lower boundaries of the confidence interval of the prediction result 100(\(1-\alpha\))% can be obtained. 3. Research on the real-time prediction method of RUL for a complex task profile  
A complex task profile essentially constitutes the conditions of special power plants, such as the change in gear transmission speed, the barrel launcher firing rate, and the changes in conditions which can lead to dynamic profile changes. Changes in dynamic data segmentation, thinning, and the amount of data are characterized by the small sample size and disequilibrium of the data. These aspects can also reveal random parameter drift in the random process. Therefore, the real-time prediction of RUL for special power plants with a complex task profile must be based on the historical measurement data set, which can be implemented using the following technical route:  
1. The real-time dynamic measurement data of a complex task profile can be extracted and preprocessed. The actual dynamic data of special power plants can be obtained, and the dynamic section can be constructed. 2. One extracts the random parameters based on the dynamic profile and the parametric model constructed in (2). 3. Based on Bayesian theory, the random parameters of the data-model-fusion-parameterized model are updated to obtain a real-time RUL prediction model for special power plants.
# Structural Monitoring of a Drawbridge in Operation: Signal Analysis
## 4 Results
After detecting abnormal situations, the main challenge is identifying the root cause. In this case, the two main possibilities are that either a hydraulic issue caused the oscillations or the structural behavior caused them. Any such event will then be submitted to further analyses to try to obtain a conclusive response to this question.
# Calculation of the Dangerous Failure Rate of the Safety Function
## 4 Rate of Dangerous Failure of a Safety Function for Dual Architecture with Two Absorption States
### Practical Application of the Equation for the Dangerous Failure Rate of the SF  
Let SF be implemented by a dual architecture based on complex safety in case of comparison failure, as shown in Figure 6. Let:  
* \(\lambda=\lambda_{L}=\lambda_{R}=1.10^{-6}\) h\({}^{-1}\) (hardware identical channels);
* the fault detection and negation mechanism is characterized by the coefficient of diagnostic coverage of faults \(c=0.99\) and the fault detection time \(t_{d}=2\); the fault negation time \(t_{N}\) is negligible with respect to the value \(t_{d}(t_{N}\ll t_{d})\);
* the considered time interval in which the rate of a dangerous SF disorder is calculated is 20 years (standard considered useful life ESRCS).  
The calculations are performed in the software environment Wolfram Mathematica 11.  
In Figure 9, the time dependence \(p_{D}(t)\) calculated according to Equation (23) is shown. It is a function that is increasing, but it holds that \(p_{D}(t\to\infty)<1\), i.e., does not meet the requirements for the distribution function. This trend can also be observed in the data presented in Table 2. In Figure 10, the time dependence \(\lambda_{D}(t)\) calculated according to (23) for \(\delta=0.5\) h\({}^{-1}\) and, in Figure 11, the time dependence \(\lambda_{D}(t)\) calculated according to (23) is shown, but assuming that \(\delta=0\) h\({}^{-1}\). From a comparison of Figures 10 and 11, it is possible to judge the influence of the mechanism of negation of the fault on the value of the rate of the dangerous failure SF. This fact ultimately has a positive effect on prolonging the time interval between the proof tests. The effect of online diagnostics in combination with a proof test on SI-SF is described in paper [11].  
Figure 8: CTMC with two states.
## 3 Result and Discussion
Figure 4: The liquid fraction distribution around the electro-thermal drilling probe (ETDP) for the case of heated probe tip temperature of \(T_{tip}\) = 368 K at time (**a**) \(t\) = 0 s, (**b**) \(t\) = 30 s, and (**c**) \(t\) = 60 s. The enlarged fields of (**d**) liquid fraction within the green dotted box. Figure 6a-c shows the evolution of heating power, \(P(t)\), over time for the different \(T_{tip}\). The evolution of heating power, \(P(t)\), shows the fluctuation behavior, but its time-averaged values converge to (\(P_{avg}=229.4\) W, \(\sigma=23.1\) for \(T_{tip}=348\) K), (\(P_{avg}=264.2\) W, \(\sigma=25.7\) for \(T_{tip}=358\) K), and (\(P_{avg}=298.6\) W, \(\sigma=28.4\) for \(T_{tip}=368\) K) where \(\sigma\) is the standard deviation. The fluctuation behavior is from the results, the time-averaged heating power, \(P_{avg}\), increases with the temperature of the heated probe tip. This is because the temperature difference between the mushy zone (273.15 K) and heated probe tip, \(\Delta T=T_{tip}-273.15\) K, becomes higher with the increment of \(T_{tip}\). The temperature differences are 75, 85 and 95 K.  
Therefore, the ROP increases with the temperature of the heated probe tip, \(T_{tip}\), due to the higher heating power. Figure 7a-c represents the variations of ROP over time, predicted based on Equation (10). As a result, the time-averaged ROP, \(\sigma_{avg}\), converges to (0.38 mm/s, \(\sigma=0.02\) for \(T_{tip}=348\) K), (0.43 mm/s, \(\sigma=0.02\) for \(T_{tip}=358\) K), and (0.49 mm/s, \(\sigma=0.03\) for \(T_{tip}=368\) K), respectively. The convergence of time-averaged ROP can be explained by that the time scale of the ROP of \(10^{-3}\)\(\sim\)\(10^{-4}\) is to with the time scale for thermal penetration, \(k/(\rho c_{p}\delta)\), of \(10^{-3}\) to \(10^{-4}\), where \(\delta\) is liquid layer thickness having an order between \(10^{-4}\) to \(10^{-3}\) (m) [11]. Figure 5: The penetration depth, \(\Delta z\), for different heated probe tip temperatures, \(T_{tip}\). Figure 6: Comparison of heating power for different heated probe tip temperature of (**a**) \(T_{tip}=348\) K, (**b**) \(T_{tip}=358\) K and (c) \(T_{tip}=368\) K.  
Figure 8 shows the evolution of liquid layer thickness for the different \(T_{tip}\) over time. As above-mentioned, the initial thickness of the liquid layer is assumed to be 1 mm based on previous literature [11]. It continuously decreases to 0.618 mm for the \(T_{tip}\) = 348 K, 0.623 mm for the \(T_{tip}\) = 358 K, and 0.629 mm for the \(T_{tip}\) = 368 K, at 60 s. Although the ice directly contacts with the heated probe tip, the thickness of the liquid layer does not become thicker; rather, it becomes thinner. This is because the molten liquid layer is squeezed by the ETDP and pushed radially outward. In addition, the time scale of the ROP of \(10^{-3}\)-\(10^{-4}\) has a similar order with the time scale of thermal penetration, \(k/(\rho c_{p}\delta)\), of \(10^{-3}\) to \(10^{-4}\). Therefore, the liquid layer maintains its thickness within 1 mm during the drilling process, as shown in Figure 8.
# A Novel Machine-Learning Approach to Predict Stress-Responsive Genes in Arabidopsis
## 3 Results and Discussion
### Identification of Differentially Expressed Genes  
Based on empirical Bayes statistics for differential expression and adjusted \(p\)-value \(\leq 0.01\), 439 genes were found to be DEGs between control and stress conditions. The ranking of the top 20 DEGs using the absolute value of log fold change is presented in Supplementary Table S1. Only three genes, including AT3G20810, AT1G31680, and AT1G30250, were down-regulated; the remaining genes were up-regulated. The top three up-regulated genes are AT4G27310, AT4G36010, and AT4G25480.
## 5 Interoperability, Standardization and Security
### Cybersecurity Considerations for Integrated Automated Irrigation Systems  
The interconnected nature of IoT-based automated irrigation systems presents unique security challenges and vulnerabilities that require a comprehensive cybersecurity approach.
## 6 Sedimentary Evolution Control Factors
### Control of Provenance Supply on Sand Body Distribution  
Samples were taken from 12 core wells in the research area, with an interval of 10 cm between each sample. Among them, there were a total of four samples from the Nen5 section, 22 samples from the Nen4 section, and 30 samples from the Nen3 section. Using a microscope to observe the samples and determine the type of heavy mineral, the point counting method was used to calculate the content of each mineral. Due to the small number of samples in the fifth section of the Nen section in the study area, Q-type or R-type clustering analysis cannot be used for more accurate quantitative research. However, the variation pattern of the heavy mineral composition can still be observed based on its combination. The ZTR index was first proposed by [PERSON] [45] and refers to the proportion of zircon, tourmaline, and rutile in transparent minerals. It is mainly used to distinguish the maturity of clastic rock components. The larger the ZTR index, the higher its maturity. The maturity of minerals is influenced by various factors such as transport distance, diagenesis, and ancient structures. Systematic research can be used to indicate sedimentary transport distance and source direction [46].  
In response to the material source problem of the Nenjiang Formation in the southern part of the Songliao Basin, an analysis was conducted on the types and contents of heavy mineral combinations in some core wells in the study area (Figure 10). After sorting and analyzing the heavy mineral data in the study area, it was found that the ZTR values gradually increased along the NE-SW direction, indicating that the source direction is northeast. The heavy mineral combination transitions from a combination of garnet (37%), zircon (33%), tourmaline (16%), and perovskite (9%) in the Nen 3 member (Figure 10A) to a combination of tourmaline (43%), zircon (33%), and garnet (24%) in the Nen 4 member (Figure 10B), and finally to tourmaline (45%), garnet (13%), perovskite (31%), and zircon (10%) in the Nen 5 member (Figure 10C). The content of stable heavy minerals in the longitudinal direction gradually increased from 89% in the third segment to 95% in the fifth segment (Figure 10). Based on the sand ratio map and sedimentary facies map of the Nen 3 to Nen 5 sections, it can be concluded that from the Nen 3 section onwards, due to the continuous supply of northern sediment sources, the delta front sediment gradually dominated (Figure 10D). However, with the further strengthening of river channel energy, the previously formed estuarine bar sand body was washed away, forming large areas of thin layered sand bodies on both sides and at the end of the river channel (Figure 10E). This continued until the Nen 5 section, and the estuarine bar sand body no longer developed in the study area (Figure 10F).  
Figure 9: (**A**) Relative Lake Level Changes and Mudstone Characteristics. (**B**) TOC content variation chart of the T16 well.