esa-sceva/satcom-chunk-collection huggingface dataset:
# Cow Dung Ash in Mortar: An Experimental Study
## 3 Results and Discussion
### Effects of CDA on Workability  
The workability or fresh property of the plastic mortar was determined by measuring its consistency using the flow table method in accordance with ASTM C1437. The result  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Category** & **Properties** & **Test Standards** & **Examined Samples** & **Curing Ages** \\ \hline Fresh & Workability & ASTM C1437 & All & - \\ \hline \multirow{4}{*}{Hardened} & Water absorption & ASTM C1403 & \multirow{4}{*}{All} & \multirow{4}{*}{3, 7, 28 and 56 days} \\  & Bulk density & ASTM C642 & & \\ \cline{1-1}  & Compression strength & ASTM C109/C109M & & \\ \cline{1-1}  & Homogeneity & ASTM C597 & & \\ \cline{1-1}  & Sulfate attack resistance & ASTM C1012 & & \\ \hline \multirow{2}{*}{Microstructure} & Thermal decomposition & - & \multirow{2}{*}{CDA0, CDA10, CDA30} & 28 days \\ \cline{1-1}  & Mineralogical composition & - & & 7 and 28 days \\ \hline \hline \end{tabular}
\end{table}
Table 4: The fresh, hardened, and microstructural properties test results of the mortars.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Mix Code**} & \multicolumn{2}{c}{**OPC Content**} & \multicolumn{2}{c}{**CDA Content**} & \multicolumn{1}{c}{**Water**} & \multicolumn{1}{c}{**Sand**} \\ \cline{2-7}  & [\%] & [kg] & [\%] & [kg] & [kg] & [kg] \\ \hline CDA0 & 100 & 3.219 & - & 0 & 1.631 & 8.85 \\ CDA5 & 95 & 3.058 & 5 & 0.161 & 1.631 & 8.85 \\ CDA10 & 90 & 2.892 & 10 & 0.321 & 1.628 & 8.84 \\ CDA15 & 85 & 2.725 & 15 & 0.481 & 1.625 & 8.82 \\ CDA20 & 80 & 2.56 & 20 & 0.64 & 1.621 & 8.8 \\ CDA25 & 75 & 2.395 & 25 & 0.798 & 1.618 & 8.78 \\ CDA30 & 70 & 2.231 & 30 & 0.956 & 1.615 & 8.76 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Proportions and quantities of materials in mortar mixtures.  
of this test is shown in Figure 3. As can be observed from this figure, the slump of the mortar decreased significantly with increasing levels of CDA replacement. The greatest reductions in slump were observed for CDA replacement levels of 25% and 30%, with slumps decreasing by 68% and 90%, respectively, compared to the reference mix (CDA0). Similar findings were reported by [29; 30]. This phenomenon is primarily attributed to the higher porosity of CDA particles in comparison to cement. The porous nature of CDA particles leads to an increased absorption of mixing water within the mixture, especially as the proportion of CDA in the mixture increases. Consequently, compensating for the reduced workability resulting from CDA utilization necessitates a higher quantity of superplasticizer to maintain the desired workability of the mortar. According to ASTM C1437, the initial flow of the mortar should be 110 \(\pm\) 5%. Hence, only the fresh mortar containing 15% CDA met this requirement.
## 3 Results and Discussion
### SEM-EDS Analyses  
The SEM images of the cement samples are presented in Table 2. The spots were marked to obtain an SEM image of the exact location and ensure accuracy. The cement samples' SEM images were captured at 40, 100, 300, and 500 magnifications. The images presented are of 300 magnifications. The analyses of the samples highlighted no significant differences in microstructure before and after the exposure cycles.  
Energy dispersive X-ray spectroscopy (EDS) is often used with an SEM to convert characteristic X-rays to electrical voltages to qualitatively and semi-quantitatively describe the distribution. Using SEM-EDS, qualitative and semi-quantitative elemental information can be obtained at an individual point (point analyses) and across an area (elemental maps). An SEM-EDS analysis reports detected elements in weight or atomic percent (at %). The EDS analysis report is presented in Figures 5-7 in terms of at % for this study.  
The samples 1Y1, 1Y2, 18M1, and 18M2 showed an overall reduction in the at% of Oxygen and Calcium. The overall Oxygen decrease of 3.1%, 2.2%, 2.1%, and 3.4% was observed in the samples 1Y1, 1Y2, 18M1, and 18M2, respectively. The Oxygen content increased for samples LW1 and LW2 by 1.3% and 2%, respectively, and there was no significant change in the Calcium content. The at% of Carbon increased by 3.5-5% in samples 1Y1, 1Y2, 18M1, and 18M2. However, there was an overall reduction in the at% of Carbon for samples LW1 and LW2. This difference in change can be attributed to different  
Figure 4: Flowchart of methodology.  
additives in the LW1 and LW2 samples. The change in the elemental information indicates hydrogen-induced reactions leading to the dissolution of the compounds in the cement matrix. This equation can give a possible explanation.  
\[C-S-H+2H^{+}\leftrightarrow Ca^{2+}+SiO_{x}OH_{x} \tag{3}\]  
Due to mineral dissolution and solid particles breaking down into ions, the resulting ions are not visible in the mapping because the ions are smaller than microscopic or at deficient concentrations, not producing detectable signals for EDS mapping. Hence, a decrease in Calcium at% is observed in the EDS mapping.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Batch** & **Before Exposure** & **After 1 st Cycle** & **After 2 nd Cycle** & **After 3 rd Cycle** \\ \hline
1Y1 & & & & \\ \hline
1Y2 & & & & \\ \hline LW1 & & & & \\ \hline LW2 & & & & \\ \hline
18M1 & & & & \\ \hline
18M2 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: SEM Analyses of Cement Samples.  
Figure 5: EDS mapping of 1Y1 and 1Y2.  
Figure 6: EDS mapping of LW1 and LW2.  
Figure 7: EDS mapping of 18M1 and 18M2.
## 6 State of the Art and Figure of Merit  
A comparison between previous low-noise amplifiers available in open sources can be found in Table 6. The proposed LNA offers the highest available gain among others while meeting an NF of less than 1.6 dB over the entire two-GHz bandwidth, i.e., 13-15 GHz. The area-aware Figure of Merit (\(FoM_{Area-aware}\)), Equation (7) [24], is calculated to provide a comparative indicator of performance across different LNAs reported in the open literature using similar technology and operating frequency ranges.  
\[FoM_{Area-aware}=\frac{BW(\mathrm{GHz}).Gain(lin)}{(F-1).P_{DC}(\mathrm{mW}).A( \mathrm{mm^{2}})} \tag{7}\]  
where \(BW(\mathrm{GHz})\) is the bandwidth in \(\mathrm{GHz}\), \(Gain(lin)\) is the gain of the LNA on the linear scale, \(F\) is the noise measure, which can be found from the expression \(NF=10*log_{10}(F)\), \(P_{DC}(\mathrm{mW})\) is the total dissipated power in mW and \(A\) is the area of the realized LNA in \(\mathrm{mm^{2}}\). According to this FoM, the LNA proposed in this manuscript has the highest area-aware FoM at 0.933, making it the best performer in terms of balancing bandwidth, gain, noise, power, and area.  
The proposed LNA can withstand continuous-wave (CW) input powers of up to 18 dBm and pulse-wave inputs of up to 15 dBm before performance degradation occurs. To the best of our knowledge, this is the only GaAs-based LNA design reported to demonstrate robustness under input power overdrive conditions for CW and pulsed signals in the open literature.  
Figure 21: The square pulse-modulated sinusoidal signal of 50 nSec and 15% duty cycle at 15 GHz frequency and 10 dBm pulse power: (**a**) The input pulse, (**b**) Zoom of the input pulse.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Power (dBm)** & **Pulse Width (nS)** & **Duty Cycle (\%)** \\ \hline
10 & 50 & 15 \\
5 & 50 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Pulse values that will result in the safe operation of the LNA.
# IoT-Enabled Precision Agriculture: Developing an Ecosystem for Optimized Crop Management
## 3 Overview of Precision Agriculture
### The Proposed System Model
Itincludes the network protocols required to transfer digital information from the sensor to the application layer. The application layer comprises user applications, data analysis, and dashboards used to monitor and optimize precision operations. The artificial intelligence module hosts machine learning models to predict and anticipate anomalies and actions to support the automation process. Remote Sensing hosts applications to analyze images coming from satellites and drones. All these layers work together to enable farmers to monitor and optimize their crops and livestock, leading to more efficient and productive farming operations.
## 3 Results and Discussion
### Year by Year Identification  
We set the number of training sample for each year to be no less than one. The verification samples may not cover seven different years, but verification tests of 10 times cover all seven years. After running verification tests 10 times, each time, the obfuscation matrix was obtained as shown in Figure 3, and each year's label corresponds to the times of prediction errors in Table 1. These charts show that ginseng at age 5 is more likely to be mispredicted and ginseng at age 7 has the highest prediction accuracy. The age of 5-year-old ginseng can be easily overestimated, and most of them are predicted to be 6 years old. Generally, ginseng increases rapidly in 3-4 years, and the growth rate will slow down after 4-5 years, which may be one of the reasons why 5- and 6-year ginseng can easily be mispredicted [30].  
The 10-time validation test accuracy of year-by-year identification tests is shown in Table 2. The highest accuracy is 85.7%, but it fluctuates greatly. The lowest accuracy is only 42.9%, and its average accuracy is 60%. The change of different ginseng saponin contents is different with age, which may cause the results of each test to differ greatly [30].  
For each training and test, random forest can provide the band's importance. The year-by-year identification was repeated 10 times randomly, and their band importance is shown in Figure 4. By averaging the importance of 10 times, VNIR has 108 bands in total, accounting for 68.9% of the importance, especially between 900 nm and 1000 nm. SWIR has 288 bands in total, with only some contributions near 1940 nm and 2480 nm, accounting for a small proportion.  
Figure 3: The obfuscation matrix of 10 verification tests. The horizontal and vertical coordinates, respectively, represent the predicted and real years of ginseng. The values on the diagonal refer to the percentage of correct label predictions; other values that are not on the diagonal include the percentage of labels that are incorrectly predicted for the corresponding year label.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Year Label** & **Error Times** \\ \hline
1 & 4 \\
4 & 3 \\
5 & 9 \\
6 & 5 \\
7 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Each year’s label corresponds to the times of prediction errors.  
The year-by-year identification was divided into seven categories, and the samples were divided into verification data sets and test data sets. The number of training samples in some categories is 1 or 2. The number of samples prepared for the experiment is insufficient and the distribution in each year is uneven, which has a negative impact.
## 4 Parametric Studies
### Influence of the Parameter Related to Project Condition on the Applicability  
First, check the parameters related to the project condition: \(k_{b}\) is related to the building cost and \(k_{f}\) to the failure cost. Figure 10 compares the \(EC_{tot}\) of both the proposed and conventional methods.  
Figure 10: Influence of \(k_{b}\) and \(k_{f}\) on expected total costs (\(\text{COV}_{\text{RP}}=0.15\), \(k_{c}=1.0\)). \(k_{b}\) and \(k_{f}\) are parameters relating to building cost and failure cost, respectively.  
Figure 9: Constitution of expected total costs (\(k_{b}=1.5\), \(k_{f}=120\), \(k_{c}=1.0\), \(\text{COV}_{\text{RP}}=0.15\)).  
In the conventional method, the optimal SF and the optimal \(EC_{tot}\) increase with an increase in \(k_{\!f}\). Additionally, as \(k_{\!b}\) becomes higher, the optimal SF becomes smaller because the incentive to decrease \(C_{\!b}\) becomes more significant.  
In the case of the proposed method, the optimized \(EC_{\!tot}\) seems to change only slightly. This is because \(EC_{\!f}\) is a small part of the \(EC_{\!tot}\) (Figure 9). The optimized \(EC_{\!tot}\) of the proposed method becomes less than that of the conventional method when \(k_{\!b}\) and \(k_{\!f}\) are high; in other words, structures that are easily costly require a high performance and high reliability.
## 4 Numerical Results  
Prior to validating the proposed method through numerical studies, some implementation issues and the development environments are briefly discussed. All the algorithms mentioned above are developed with in-house code, written in the MATLAB 2017 Ra. In performing some numerical operations, the following MATLAB commands are used: _eigs_ for solving eigenvalue problems, _patternsearch_ for optimization in kriging, _fitcsvm_ for training support vector machine, and _ksdensity_ for estimating the probability density function (PDF). In stationary kriging, the Gaussian type correlation function is applied. Finally, the numerical simulations are carried out on a personal desktop operating Windows 10 with Intel Core [EMAIL_ADDRESS] GHz and 16 GB RAM.
## 1 Introduction
Simultaneously, the effect of transceiveraltitude, coverage radius, and beam azimuth rotation is estimated in order to enhance the coverage and capacity performance of the involved drone VLC systems correspondingly. The rest of this article is organized as follows: the drone VLC with distinct optical beam configurations is presented in Section 2. Numerical results are presented in Section 3. Finally, Section 4 concludes this paper.
## 2 Materials and Methods
### Surface Characterization  
#### 2.3.1 Topography  
The atomic force microscopy (AFM) analysis was conducted using a Veeco Metrology Multimode NanoScope IV (Veeco Instruments Inc., Oyster Bay, NY, USA) instrument. It operated in tapping mode within an air environment, utilizing a cantilever with a 0.06 N/m spring constant. Two arbitrary regions (20 \(\mu\)m \(\times\) 20 \(\mu\)m for SST and PVC, and 100 \(\mu\)m \(\times\) 100 \(\mu\)m for SIL) were examined from a pair of samples at room temperature (23 \(\pm\) 2 \({}^{\circ}\)C). The rate of scanning was fixed at 1 Hz.  
Surface roughness estimates, along with 2D and 3D (Figure S1 in Supplementary Material) images, were generated using Gwyddion 2.61 software (Czech Metrology Institute, Brno, Czech Republic). The average roughness (\(R_{a}\)) was evaluated, which computes the average of the absolute values of surface profile deviations from the mean line across a designated sampling distance [50].
# Acceptance Model for Mobile Building Information Modeling (BIM)
## 4 Proposed Mobile BIM Acceptance Model  
### Overview of the Proposed Model  
The relationships among the key factors presented in the literature review and factor analysis were established in the form of a hypothesis to present a model for mobile BIM acceptance. First, the external variables adopted were tool quality, personal efficacy, behavior control, organization innovativeness, and collective efficacy, as derived from the results of the factor analysis. Second, the internal variables adopted were the ease of use and usefulness and consensus on appropriation  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Component** & **Items** & **Factor Loading** & **Eigenvalue** & **Cumulative \%** & **Cronbach’s \(\mathbf{\alpha}\)** \\ \hline \multirow{8}{*}{Tool Quality} & SVQ2 & 0.885 & 10.906 & 37.606 & 0.958 \\  & IQ3 & 0.868 & & & \\  & SVQ4 & 0.863 & & & \\  & SVQ3 & 0.854 & & & \\  & IQ2 & 0.844 & & & \\  & SVQ5 & 0.819 & & & \\  & IQ1 & 0.807 & & & \\  & IQ4 & 0.785 & & & \\  & SVQ1 & 0.772 & & & \\  & SYSQ2 & 0.708 & & & \\ \hline \multirow{8}{*}{Behavior Control} & ES1 & 0.893 & 4.714 & 53.862 & 0.946 \\  & EP1 & 0.871 & & & \\  & EP2 & 0.861 & & & \\  & EP3 & 0.857 & & & \\  & IP1 & 0.808 & & & \\  & IS3 & 0.805 & & & \\  & IP2 & 0.796 & & & \\  & ES2 & 0.742 & & & \\ \hline \multirow{8}{*}{Personal Efficacy} & PE2 & 0.922 & 3.218 & 64.957 & 0.897 \\  & PE4 & 0.887 & & & \\  & PE1 & 0.869 & & & \\  & PE3 & 0.839 & & & \\  & PI2 & 0.561 & & & \\ \hline \multirow{2}{*}{Organization Innovativeness} & Ol3 & 0.905 & 2.654 & 74.109 & 0.907 \\  & Ol2 & 0.858 & & & \\ \cline{1-1}  & Ol1 & 0.806 & & & \\ \hline \multirow{2}{*}{Collective Efficacy} & CE4 & 0.790 & 1.055 & 77.746 & 0.932 \\ \cline{1-1}  & CE3 & 0.764 & & & \\ \hline \multicolumn{2}{c}{Kaiser–Meyer–Olkin Measure of Sampling Adequacy} & 0.870 \\ \hline \multicolumn{2}{c}{Approx. Chi-Square} & 3172.681 \\ \multicolumn{2}{c}{Bartlett’s Test of Sphericity} & df. & 406 \\ \multicolumn{2}{c}{Sig.} & 0.000 \\ \hline \hline \end{tabular}
\begin{tabular}{c} SYSQ: System Quality, IQ: Information Quality, SVQ: Service Quality, IS: Internal Support, IP: Internal Pressure, ES: External Support, EP: External Pressure, PI: Personal Innovativeness, PE: Personal/according to the IS success model and the characteristics of BIM. Finally, the intentions adopted were the individual intention and organization intention. Nine hypotheses on the relationships among the variables were established to suggest factors affecting mobile BIM acceptance (as shown in Table 4 and Figure 3).
## 3 Results and Discussion
### THg of Sediment from Other Rivers  
Collection of sediment samples were even extended to other rivers that flow through the abandoned mine area, to determine the trend of Hg concentration surrounding the study area. The measured THg concentrations across several rivers in the area ranged from 5.3-11 mg kg\({}^{-1}\). These values are more than 60 times higher than the global background, indicating Hg accumulation in the river caused by the erosion of the soil in the area with a naturally higher Hg background concentration. In comparison, sediments in Tagburos River show significantly higher Hg concentration than the other rivers since it traverses the abandoned mine site and the stockpiled calicines. Mercury may be moved along with the sediments and through water runoff, thereby increasing Hg values in the sediment deposited in Tagburos River.
# Evaluating the Utility of UV Lamps to Mitigate the Spread of Pathogens in the ICU
## 2 Materials and Methods
### Mosaic Virus Transfer  
Pathogen transfer analysis was performed using two genetically distinct mosaic virus DNA markers. These mosaic viruses have been studied extensively for modeling pathogen transmission in clinical environments [20]. Prior to installation of the UV lights, a standard quantity of cauliflower mosaic virus DNA was suspended in sterile saline. A standardized 1.15 mL volume was sprayed onto high-touch surfaces, including bed rails, touchscreen monitors, computer keyboards, and ventilators in 4 of the 33 ICU patient bays. Swabs from high-touch surfaces in the 29 non-inoculated bays were then obtained 24 h (n = 100 swabs), 48 h (n = 99 swabs), and 96 h (n = 78 swabs) post inoculation to demonstrate the baseline spread of this pathogen proxy throughout the ICU. A total of 277 baseline swabs, plus positive controls, were obtained and amplified via polymerase chain reaction (PCR). After deployment of the UV lights, the same protocol was followed with a second, genetically distinct, mosaic virus DNA marker to determine the impact on pathogen transfer to non-inoculated bays. A total of 261 swabs plus positive controls were collected post intervention at 24 h (n = 98 swabs), 48 h (n = 83 swabs), and 96 h (n = 80 swabs).
# On-Board Ship Detection in Micro-Nano Satellite Based on Deep Learning and COTS Component
## 2 On-Board Ship Detection Based on Deep Learning
### Image Cropping  
Due to the large size of remote sensing images and the limitation of computing resources, we divide the large-scale remote sensing images into 1024 \(\times\) 1024 image patches, which are used as the input images for training and testing. The longest existing ship is the Knock Nevis (458.45 m), which is an ultra large crude carrier from Singapore. In order to prevent the ship target from being cut off at the margin of patches, overlapping sampling is used during image cropping. The overlap step is set to 256 pixels in this paper.
# Deep Learning Based Android Anomaly Detection Using a Combination of Vulnerabilities Dataset
## 4 Experiments  
### Performance Indicators  
As it relates to the detection of malwares, we refer to True Positive (TP) as the number of malwares actually classified as such, True Negative (TN) as the number of benign apps classified as such, False Positive (FP) as the number of benign apps wrongly classified as malwares, and finally False Negative (FN) as the number of malwares wrongly classified as benign. More informative measures, widely used in malware detection analysis work, are derived from these simple measures, such as:  
* Precision: The ratio of actual malwares in the set of apps classified as such: TP/(TP+FP)
* Recall: The ratio of malwares that were detected as such: TP/(TP+FN)
* Accuracy: The percentage of applications that have been appropriately categorised: (TP+TN)/(TP+TN+FP+FN)
* F1-Measure: A performance indicator that takes into account both the precision and recall of the obtained classification: 2 \(\times\) (Recall \(\times\) Precision)/(Recall + Precision)
* Area under ROC Curve (AUC): A measure of the predictive power of the classifier that basically informs on how well the model can distinguish between classes (here, benign apps vs. malwares).  
For all these measures, the higher, the better, with 1 being the perfect value.
# Graphene-Enhanced CuW Composites for High-Voltage Circuit Breaker Electrical Contacts
## 2 Materials and Methods
### Mechanical and Physical Property Testing Methods  
The densities of the samples were determined, prior to and following the addition of graphene, through the application of the Archimedes principle with ultrapure water. Subsequently, the relative density of the samples were calculated using the theoretical density formula. The flexural strength was determined via a three-point bending test, utilizing an electronic universal testing machine, with the samples prepared as standard parts measuring 50 mm \(\times\) 10 mm \(\times\) 4 mm. The electrical conductivity of polished samples was evaluated by a digital metal conductivity meter at room temperature, with at least five measurements conducted for each specimen. A hardness test was conducted on the polished surface of the samples using a Brinell hardness tester, with a load of 750 kg and a dwell time of 30 s. All tests were rigorously conducted in accordance with the standard of ASTM B702 or GB/T 8320, ensuring that at least three samples of each composition underwent measurements to obtain an accurate average value.
# Low Power EEG Data Encoding for Brain Neurostimulation Implants +
## 2 Materials and Methods
### Materials
#### 2.1.2 Encoding Algorithm
It is worth noting that it allows recording signal changes over time rather than the absolute width of the measurements for each period, which saves storage space and energy for subsequent transmission [26; 27]. Since data transmission is performed using a serial protocol, the bit transitions are of interest. Because the system that emerges from this study must temporarily store data and then wirelessly forward it to another network node, the encoding must reduce computing, storage, and transfer consumption. For all of the reasons stated above, Delta encoding is deemed most appropriate for this implementation.