esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Dune Sand on Drying Shrinkage Cracking of Fly Ash Concrete
## 2 Experimental Plan and Method
### Materials and Mixture Proportions  
The cement used was ordinary Portland cement that had a density of 3.15 g/cm\({}^{3}\) and fineness of 3440 cm\({}^{2}\)/g. The density and fineness of the fly ash used were 2.31 g/cm\({}^{3}\) and 3135 cm\({}^{2}\)/g, respectively. The chemical analysis data of the cement and fly ash are shown in Table 1.  
As the fine aggregate, a binary sand with replacements of DS was used, and the morphologies of CS and DS are shown in Figures 1 and 2, respectively. CS is composed of relatively large particles with angular shapes. However, DS is composed of relatively small particles with spherical shapes. Table 2 shows the physical properties of DS, CS, and coarse aggregate (CA). The maximum size of CA was 20 mm. Table 3 shows the chemical composition of DS, and it mainly consists of SiO\({}_{2}\) and CaO.  
Figure 3 shows the particle size distribution of the binary sand according to the DS/FA ratios. Neither CS nor DS was within the standard particle size range recommended by KS F 2527 [18] or ASTM C 33 [19].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multicolumn{6}{c}{**Chemical Composition (\%)**} \\ \cline{2-9}  & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **CaO** & **MgO** & **SO\({}_{3}\)** & **K\({}_{2}\)O** & **LOI** \\ \hline Cement & 21.49 & 5.66 & 3.25 & 61.45 & 2.29 & 2.38 & 1.00 & 2.33 \\ Fly ash & 57.10 & 18.76 & 6.96 & 10.27 & 1.56 & 0.61 & 0.89 & 3.84 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of cement and fly ash.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Aggreate**} & \multicolumn{3}{c}{**Physical Properties**} \\ \cline{2-5}  & **Density (g/cm\({}^{3}\))** & **Absorption Ratio (\%)** & **Fineness Modulus** \\ \hline DS & 2.61 & 1.19 & 0.7 \\ CS & 2.61 & 1.53 & 3.6 \\ CA & 2.70 & 0.77 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical properties of aggregates.  
Figure 1: CS appearance.  
Figure 2: DS appearance.  
The mixture proportions of concrete used in this study are shown in Table 4. The sand-to-aggregate (S/a) ratio and chemical admixture (AD) content were adjusted through trial mixing test. In the case of a mixture using 100% CS, it was not possible to achieve the target slump despite the increase in AD content. The mixtures with 10% or higher DS/FA ratios ensured that the target slump was reached in this study.
# Edge-Guided Hierarchical Network for Building Change Detection in Remote Sensing Images
## 3 Results
### Case Analysis  
To demonstrate the advantage of our proposed approach, we select the post-disaster reconstruction after the 6.3 magnitude earthquake in Christchurch, New Zealand, as a case. The bi-temporal images are obtained in the same area in April 2012 and 2016, respectively. The number of buildings in the area increased significantly during the four years. We chose two areas of size 256 \(\times\) 256 for building change analysis. Figure 9 shows the addition of some new buildings to the original building stock in the area. Our EGHNet performs well in detecting the increase in buildings in this area. Specifically, the change map detected by EGHNet is almost the same as the ground truth.
## 4 Conclusions  
1. In the study area, the reservoir porosity of the Chang 9 oil layer formation is between 3 and 12%, and the permeability is between 0 and 1.5 \(\times\) 10\({}^{-3}\) um\({}^{2}\), classifying it as an ultra-low-porosity and ultra-low-permeability reservoir; Chang 9 sandstone is composed of feldspar sandstone and lithtic feldspar sandstone. Among them, the average content of quartz is low, at less than 31%; the average content of feldspar is high, amounting to more than 34%; and the average content of rock debris is distributed between 10% and 20%. Therefore, the compositional maturity of the Chang 9 sand body is generally low. The particle size parameters and particle size distribution are complex and changeable, and the particle size distribution shows positive skewness, indicating that the sediments in the sand body are mainly coarse components; the kurtosis of the particle size frequency curve is flat to very sharp.
2. By analyzing the plane distribution characteristics of sedimentary facies in the Chang 9 period of the Chang 9 oil layer formation in the Ordos Basin, it has been determined that the sedimentary pattern of the Chang 9\({}_{1}\) period basically inherited the distribution characteristics of sedimentary facies of the Chang 9\({}_{2}\) period. However, due to the basin subsidence and large lake transgression, the lake shoreline moved landward, the lake area expanded, the lake water deepened and a semi deep lake-deep lake facies belt appeared in the southeast part of the basin.
3. The vertical structure of the sand body of Chang 9 oil layer formation in the Ordos Basin can be roughly summarized as a box-shaped structure with upward thinning, a bell-shaped structure with upward thinning and a funnel-shaped structure with upward thickening. The sedimentary microfacies evolution of the Chang 9 oil layer formation has certain continuity, and the reservoir of the Chang 9 oil layer formation in the Chang 9 period belongs to the slow rising period of the lake basin. In the early stage of Chang 9, the lake basin rose, the shallow lake area became larger and the delta area gradually decreased. In the middle to late stage of Chang 9, the delta front area expanded, the source supply was sufficient and the sand body developed well.  
Figure 12: Sedimentary model of Chang 9 oil layer formation in Yanchang Formation.  
**Author Contributions:** Writing--Original Draft, X.Q. and Y.W.; Analysis, Validation, Data Processing, Graphical Processing, X.Q.; Investigation, Conceptualization, Supervision, Review, Y.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data and materials are available on request from the corresponding author. The data are not publicly available due to ongoing research using a part of the data.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
The objective of this work is the application of an optimization method based on PSO to minimize the costs associated with the investment and operation of an SAPV system for a guaranteed level of reliability. The proposed method implements the following improvements over previous contributions:  
* Simultaneous consideration of uncertainty in PV generation and domestic demand and modeling the occurrence of random faults affecting the system. * Calculation of the total cost of the installation based on the direct investment costs, as well as the costs of energy to be provided in hours when an auxiliary fuel generation system must operate. * Introduction of the reliability evaluation method based on Monte Carlo sequential simulation in a cost optimization process using PSO. * The use of real-world demand and irradiance data from PVGIS [41] and datasheets from commercial photovoltaic modules. What sets this study apart is the utilization of real-world data spanning an entire year, as opposed to relying solely on typical values. This real data integration adds a crucial layer of authenticity to this research, ensuring that the findings are rooted in practical, real-world scenarios. Moreover, this research goes beyond conventional analyses by offering a comprehensive discussion section. Thorough comparisons are conducted, enabling a deeper understanding of the design, costs, and reliability aspects of SAPV systems. This holistic approach allows us to take the analysis one step further and provide optimal results. Section 2 of this manuscript presents the energy balance in an SAPV system, a reliability evaluation method performing sequential Monte Carlo simulation, and the optimization method to minimize SAPV investment and operation costs subjected to the desired reliability level of the electric supply. In Section 3, the results of the proposed method are validated against an iterative algorithm, and a particular case study is evaluated. In Section 4, a discussion of the results takes place, as well as some additional considerations and findings obtained from the sensitivity analysis performed. Finally, in Section 5, some conclusions about the advantages of the proposed method for SAPV design are drawn.
## 4 Food Industry Application
### Antimicrobial Effect  
Numerous studies have reported the potential application of artichoke as a functional ingredient in processed foods, showing its high antimicrobial effect. They have demonstrated the ability of artichoke extracts to inhibit the growth of bacteria [193; 194; 195; 66; 12] and fungi [196].  
Meat and meat products are exposed to many potential hazards in the post-slaughter production process, such as foodborne pathogenic microorganisms and environmental contamination of equipment [184], so the use of artichoke as a meat preservative may be of interest. Both Gram-negative and Gram-positive bacteria can be inhibited by artichoke heads, leaves or stems, especially common pathogens such as _Staphylococcus aureus_, _Pseudomonas aeruginosa_, _Escherichia coli_ and _Salmonella_ spp. [197; 114]. In addition, there is a growing disapproval of the use of synthetic additives in the food industry. Therefore, the replacement of these additives by artichoke extracts may be of interest in the search for natural alternatives to the management of microbial contamination of perishable foods.  
The reason for this potent antimicrobial activity is probably based on the high content of phenolic compounds in the different parts of the artichoke. Polyphenols can lower the intracellular pH, chelate some metals that are necessary for the survival of microorganisms or change the permeability of the bacterial cell membrane and disrupt the transport of substrates [198]. Flavonoids present in artichoke leaves can also interfere with microbial enzyme activity [199], and different caffeoylquinic acid derivatives can disrupt bacterial cell walls [200].
## 3 Application Examples
### Collection and Analysis of Root Exudates of Rose (Rosa canina L.) by HPLC-DAD  
Rose plants (_Rosa canina L._) were grown in medium with 2% sucrose for the collection of root exudates as shown in Figure 4. After four weeks, plants were removed and medium was filtered and stored at -20\({}^{\circ}\)C. For the chemical analysis of root exudates, exclusion of microbial contamination is crucial because microorganisms use the exudates as nutrients and contaminate the medium with their own metabolites. In 11 experiments with 3 to 6 parallel cultures each, contamination occurred in less than 5% cultures. A total of 13.2 L medium were collected, concentrated in vacuum and extracted with ethyl acetate (see Section 2.5.) for the analysis and purification of selected components of root exudates. Figure 5 shows a typical result of the analysis of root exudates of rose plants collected in bioreactors by HPLC coupled to a UV detector (HPLC-DAD).
# Machine Learning-Based Security Pattern Recognition Techniques for Code Developers
## 1 Introduction
### Main Contributions  
We propose a programming language agnostic solution for CWE patterns identification in source code. The concept is based on source code split in two vectorial representations: control flow and data flow. Control flow contains the possible reserved keywords, methods, functions, methods which are usually found in source code and the data flow contains all the other words which are not part of the control flow (e.g., identifiers). The control flow section is processed in two ways: by word embeddings (e.g., Word2 Vec) to preserve the semantics of the code, and by clustering (e.g., K-Means) of the resulting word embedding vectors (e.g., semantically represented tokens) which lose the dependency of the lexical syntax of the programming language.  
We experimented with a suite of machine learning algorithm on the resulting representation of source code to identify how CWE pattern in both C/C++ and Java can be detected in this abstract representation, with good results in detection with low false positives rates, which makes this approach a good candidate to contribute in a holistic software security assurance process. Our approach is feasible mostly when source code is developed within communities of software developers with similar programming behavior (e.g., software hubs).  
In our experiments we used the NIST Software Assurance Reference datasets for vulnerable and not-vulnerable code written in C/C++ and Java. These datasets are provided by NIST as a reference set to test the capabilities of source code static analysis tools. The code samples have a high level of abstraction but also preserve some similarities in the way the programs are written, close to what strongly connected communities of programmers in software hub have in common.  
The next sections of this document are structured as follows:  
* Chapter 2--The Proposed Architecture: describes the functional architecture of the proposed solution, which transforms the source code into an Intermediate Representation which preserves the CWE patterns with low dependency of the lexical structure of programing language.
* Chapter 3--Experimental Results: describes our tests on the source code dataset provided by NIST, including the dataset processing, the word embedding and clustering mechanism, the control flow and data flow representation and result obtained using well-known machine learning-based algorithms.
* Chapter 4--Discussion and related work: position our approach in the overall context of machine learning-based security weaknesses detection in source code.
* Chapter 5--Conclusions: presents our conclusions and next steps related to the multi-language CWE detection approach.
# UWB Localization with Battery-Powered Wireless Backbone for Drone-Based Inventory Management
## 3 UWB Localization System Design
### Further Optimizations
#### 3.6.1 Optimization I: Single Final UWB Message  
The first optimization reduces the number of messages transmitted by using only one single ranging-final message (Figure 5a). This reduces the number of slots from \(2+3n\) to \(3+2n\). Because the range is calculated on all anchors at the same time, the reporting is done at the end of the superframe.
# Predictive Maintenance for Remanufacturing Based on Hybrid-Driven Remaining Useful Life Prediction
## 3 Experiment and Result
### Results and Analysis
#### 3.3.2 Prognositc Approach Analysis  
Following the proposed framework, the analysis of the prognostic approach should be conducted once the degradation performance has been evaluated. As shown in Figure 9, the green line represents the learned reliability function, the blue line is the historical states, and the red line is the actual future state. It is obvious that the green line is close to the red line with the increase of the historical data (25%, 50%, and 75%). This means that the accuracy of the reliability increases as time progresses  
For quantitative comparison of prognostic outcomes, two metrics including root mean square error (RMSE) and R2 are selected, which are defined as follows:  
\[\text{RMSE}=\frac{1}{N}\sum_{i=1}^{N}(\hat{r}_{i}-r_{i})^{2} \tag{9}\]  
\[\text{R2}=1-\frac{\sum_{i=1}^{N}(\hat{r}_{i}-r_{i})^{2}}{\sum_{i=1}^{N}(\hat{ r}_{i}-\bar{r})^{2}} \tag{10}\]  
As shown in Figure 10, the RMSE is very large and the R2 is close to zero when the machine starts working after a while. In this period, the machine is operating under normal conditions, and the reliability should be close to 1. As degradation continues, the RMSE will decrease to zero, and the R2 will increase to 1. The results show that the reliability function will be highly accurate after accumulating a certain period of historical data. Based on the analysis of the results, it is obvious that the reliability function is dynamically changing where model parameters will be quickly updated once new data becomes available.  
Figure 8: Degradation performance assessment of _Bearing1\(3\).  
Figure 10: The quantitative comparison results of learned reliability function under different historical data percentages: (**a**) _Bearing1\(3\); (**b**) _Bearing2\(6\).  
Figure 9: Learned reliability function from the historical data of 25%, 50%, and 75% during the whole degradation period: (**a**) _Bearing1\(3\); (**b**) _Bearing2\(6\).  
When the reliability function has been learned, the RUL could be predicted according to Equation (8). The prediction error \(Er_{t}\) at each time step \(t\) is designed as:  
\[Er_{t}=\frac{ActRUL_{t}-PredRUL_{t}}{ActRUL_{t}} \tag{11}\]  
where \(ActRUL_{t}\) and \(PredRUL_{t}\) are actual and predicted RUL value, respectively. As shown in Table 2, the RUL prediction values at different time steps are calculated. It is obvious that the errors ranging from 50% to 80% are closer to zero which means the RUL prediction results have a higher level of credibility.  
To further analyze the proposed hybrid-based RUL module, we compared it with the two effective methods [36; 38] experiment on the PRONOSTIA platform. As shown in Table 3, the results indicate that the proposed method performs better than other methods for \(Bearing1\_3\), while the performance under \(Bearing2\_6\) is not as good as other methods.
## Appendix A Specific Measures of the Radar Data Quality
### Beam Blockage by the Terrain  
The percentage of the transmitted energy blocked by the terrain was computed according to the energy distribution along across the radar beam. The 10-m resolution ASTERGDEM (Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model) digital terrain model was used in the computation. The resulting QI is then:  
\[qi_{3,i}=\begin{cases}1-B;&B\leq B_{max}\\ 0;&B>B_{max}\end{cases} \tag{11}\]  
where \(B\) is the actual blockage and \(B_{max}\) is a user-defined threshold of blocking, both from the interval 0 to 1. The software offers the possibility to correct the blockage by dividing the measured energy by the blockage.  
The issue of blocking the radar beam is rather complex, as there are a number of objects (beyond clouds and hydrometeors, there is the relief, birds, insects, etc.) that interact with the radar signal in different ways. Therefore, different authors presented different approaches to the mathematical quantification of these effects. For instance, [PERSON] et al. [19] defined a single empirical form for the effect of beam shielding that incorporated the ground clutter. On the other hand, both [PERSON] et al. [18] and [PERSON] et al. [16] treated the beam blocking and the ground clutter as two independent effects, defining the latter one in a binary way: 0 if ground clutter was present, and 1 otherwise. The weakening character of the radar signal along the radar beam was further described, for instance, by the attenuation by the hydrometeors [19], the occultation rate [18], and so-called path integrated attenuation [17].
## 3 Results and Discussion
### Validation of Lifetime Data Sets
#### 3.1.2 Calculation and Verification of Lifetime  
Adjust the boundary conditions in the finite element simulation and perform the calculations again from Equations (7)-(12). The 12 groups of simulation boundaries and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Equivalent Stress** & **Temperature** & **Equivalent Strain** \\ \hline Stationary blade & & & \\
1 & 1481.51 MPa & 1033.34 K & 0.006926 \\
2 & 1300.34 MPa & 1198.93 K & 0.006033 \\
3 & 800.83 MPa & 900.29 K & 0.005503 \\ Stationary blade & & & \\
4 & 1308.44 MPa & 1220.02 K & 0.009332 \\
5 & 1376.43 MPa & 1393.42 K & 0.009482 \\
6 & 1385.02 MPa & 1300.23 K & 0.009744 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Calculations of checkpoints under full load conditions. Figure 9: Equivalent stress and equivalent strain of the blades. EOH results are presented in Table 3, and these results have been normalized. Moreover, the boundary conditions in the table were taken from the actual operation data. The data calculated using the Control and Engine Health Management (CEHM) system developed by the Liburdi company were introduced to verify the EOH evaluation results [39]. CEHM is a state-based management system that collects and stores on-site data to analyze and monitor the performance of gas turbine units. The simulation boundary conditions were modified and synchronized to the settings of the CEHM system, and the prediction effect was judged by comparing the increment of EOH. The differences between the two methods are displayed in Table 4. The relative error of the blade EOH was less than 1%, demonstrating the reliability of the dataset obtained through the life prediction method proposed in this paper. By utilizing the data from a certain operational period as boundary conditions, which included load variations, the EOH growth under the actual load variations could be determined. The operation data of the actual gas turbine unit within 137 h were calculated using the simulation model, and a data set containing 137 groups of EOH data increments was obtained. As shown in Figure 10, this illustrates an upward trend in EOH during the unit start-stop process. The gas turbine started up after 8 h of actual operation, and continued running at a high load for the first 30 h. The increase in EOH per hour during this period was greater than 1. The unit was then shut down for 40 h and underloaded for 30 h. After running for 110 h, the growth of EOH experienced fluctuations. This was attributed to the rapid load fluctuations in the unit, thereby affecting the boundary conditions of the simulation model. \begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Relative Error** & **No.** & **Relative Error** \\ \hline
1 & 0.687\% & 7 & 0.887\% \\
2 & 0.468\% & 8 & 0.746\% \\
3 & 0.446\% & 9 & 0.676\% \\
4 & 0.424\% & 10 & 0.636\% \\
5 & 0.986\% & 11 & 0.543\% \\
6 & 0.894\% & 12 & 0.541\% \\ \hline \end{tabular}
\end{table} 
Table 4: Relative error of the EOH values at different sampling points.
# Compact Quad-Port MIMO Antenna with Ultra-Wideband and High Isolation
## 4 Result and Discussion
### Diversity Properties of MIMO Antennas
#### 4.3.2 Diversity Gain  
Another important parameter describing the diversity characteristics of the MIMO system is the Diversity Gain (DG), which can be calculated by ECC [24].  
\[DG=10\times\sqrt{1-\left|ECC\right|^{2}} \tag{5}\]  
Figure 16 shows the diversity gain of the proposed antenna at ports 1 and 2 is greater than 9.99 dB, which indicates that the MIMO antennas have good diversity properties.