esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Characterization of Aloe Vena Mucilage  
The AVM was screened for phenols and hydroxyls by means of a GC-MS equipment Shimadzu QP2010 GC-MS. A total of six compounds (with peak areas of 80.74% for 1-hexanol, 2-ethyl., 4.74% diethyl phthalate, 4.27% hydroperoxide, 1-ethylbutyl, 4.12% ethylbenzene, 3.61% styrene, and 2.82% hydroperoxide, 1-methylpentyl,) were identified as outlined in peaks on Figure 1 and also in Table 6. Compounds such as hydroperoxide are very stable organic peroxides often used as radical initiators, as they perform homolysis at temperatures above 100 \({}^{\circ}\)C [36].  
The compound with the highest percentage peak height, 1-hexanol, 2-ethyl (also known as 2-ethylhexanol), exhibited a peak height of 84.33%. This compound is poorly soluble in water due to its large alkyl group but is soluble in various organic compounds [37]. It is characterized as a long-chain hydrocarbon with methyl sides and one hydroxyl group per molecule [38]. Notably, 1-hexanol, 2-ethyl easily biodegrades in water [39]. Used in cosmetics and cleaning detergents as a fragrance compound, it also plays a role in the manufacture of polyvinyl chloride plasticizers due to its non-crystallizing property and high boiling point [40; 41]. Additionally, it reacts with phthalate anhydride to form di-(2 ethylhexyl) phthalate, a widely used plasticizer in PVC production [42]. As reported by Ataman Chemicals [43], 2-ethylhexanol serves as an intermediate plasticizer, breaking the chain during the synthesis of condensation polymers.  
Diethyl phthalate with a peak height of 3.21% was also detected. According to [PERSON] et al. [44], diethyl phthalate is a short branched low molecular phthalate. Phthalates are plasticizers synthesized from phthalate acid and are often used to soften and increase the durability of plastics [45]. They can also be used in hair products, pharmaceuticals, and medical devices.  
These properties listed above highlight the potential of AVM to be used as a super-plasticizer in (SCC) concrete.
# Evaluation of English-Slovak Neural and Statistical Machine Translation
###### Abstract  
This study is focused on the comparison of phrase-based statistical machine translation (SMT) systems and neural machine translation (NMT) systems using automatic metrics for translation quality evaluation for the language pair of English and Slovak. As the statistical approach is the predecessor of neural machine translation, it was assumed that the neural network approach would generate results with a better quality. An experiment was performed using residuals to compare the scores of automatic metrics of the accuracy (BLEU_n) of the statistical machine translation with those of the neural machine translation. The results showed that the assumption of better neural machine translation quality regardless of the system used was confirmed. There were statistically significant differences between the SMT and NMT in favor of the NMT based on all BLEU_n scores. The neural machine translation achieved a better quality of translation of journalistic texts from English into Slovak, regardless of if it was a system trained on general texts, such as Google Translate, or specific ones, such as the European Commission's (EC's) tool, which was trained on a specific-domain.  
neural machine translation; statistical machine translation; text analysis; automatic evaluation; Slovak language; English language
# Progress in Phase Calibration for Liquid Crystal Spatial Light Modulators
## 4 Diffractive Phase Calibration Method
### Optical Elements Generated Method  
The intensity distribution of the light traveling through the optical elements could be affected by the phase distribution inside the elements. In addition, SLM phase modulation could be measured by generating the virtual elements on the SLM. However, not all of the principles of phase optical elements could be referenced to calibration. The phase distribution of the elements should be binary for variable control.  
A simple and robust method for determining the calibration function with binary phase Fresnel lenses is proposed [45]. As shown in Figure 8, a Fresnel lens is designed with binary phase level distribution: \(\phi_{1}\) and \(\phi_{2}\). The pattern of designed lens is uploaded on the SLM (PLUTO, Holoeye Photonics AG, Germany), which is illuminated by a collimated polarized light. The \(\eta\) is the ratio of the irradiance of the first foci and the illumination intensity on the SLM. It can be described as: \(\eta=\left(2/\pi\right)^{2}\sin^{2}((\phi_{2}-\phi_{1})/2)\), and the maximum \(\eta\) can be achieved while the \(\phi_{2}-\phi_{1}=\pi\). For the calibration procedure, the \(\phi_{1}\) region can be set to 0 grayscale while the \(\phi_{2}\) can be changed from 0 to 255. The shifted phase on \(\phi_{1}\) regions is zero, and the shifted phase on \(\phi_{2}\) regions changes from zero to the max. As the illumination intensity on the SLM is set to be constant, the irradiance change on the first foci is proportional with \(\eta\). By measuring the irradiance on the first foci while changing \(\phi_{2}\) with the fixed \(\phi_{1}\), the grayscale-phase relation can be derived.  
Figure 8: Schematic of an optical elements features analyzed method. BS: beam splitter; SLM: spatial light modulator; CAM: camera.  
Figure 7: Pattern of single \(8\times 8\)-pixel-grating unit uploaded for spatial nonuniformity measurement.  
Using this kind of optical element feature-based calibration method means that a global grayscale-phase LUT could be acquired. Because of the limitation on optical element size for optical feature replication, the spatial nonuniformity measurement is limited.
###### Abstract  
This study was initiated to evaluate the performance of asphalt binders and mixtures incorporating linear low-density polyethylene- calcium carbonate (LLDPE-CaCO\({}_{3}\)) pellet, either with or without titanate coupling agent. The detailed manufacturing process of modifier pellets was displayed. The coupling agent was used to enhance the cross-linking between materials by means of winding up covalent bonds or molecule chains, thus improving the performance of composites. In the preparation of modified bitumen, the preheated asphalt binder was mixed with the modifiers using a high shear mixer at 5000 rpm rotational speed for 45 min. Experimental works were conducted to evaluate the performance of asphalt binders in terms of volatile loss, viscosity, rutting potential, and low temperature cracking. Meanwhile, the asphalt mixtures were tested using the flow number test and tensile strength ratio (TSR) test. The addition of LLDPE-CaCO\({}_{3}\) modifiers and coupling agent does not significantly affect the volatile loss of modified asphalt binders. The addition of modifiers and coupling agent has significantly improved the resistance to permanent deformation of asphalt binders. Even though, the addition of LLDPE-CaCO\({}_{3}\) modifier and coupling agent remarkably increased the mixture stiffness that contributed to lower rutting potential, the resistance to low temperature cracking of asphalt binder was not adversely affected. The combination of 1% coupling agent with 3% PECC is optimum dosage for asphalt binder to have satisfactory performance in resistance to moisture damage and rutting.  
Keywords: asphalt modification; coupling agent; rheological behavior; plastic; calcium carbonate powder +
Footnote †: journal: Applied Sciences
## 2 Conventional MIMU/GNSS UTC Integration Architecture  
The receiver radio frequency (RF) front end converts the analog RF signal into a digital intermediate frequency (DIF) signal, which is input to the baseband signal processing module. The outputs of correlators are accumulated during the predetection integration interval to obtain coherent integration values. The coherent integration values can be expressed as  
\[\begin{array}{l}I_{\alpha}(n)=AD(n)R(\tau_{\alpha})\text{sinc}(\pi f_{e}T_{ coh})\cos(\phi_{e})+w_{\Omega,n}\\ Q_{\alpha}(n)=AD(n)R(\tau_{\alpha})\text{sinc}(\pi f_{e}T_{coh})\sin(\phi_{e})+w _{\Omega,n}\end{array} \tag{1}\]  
where \(I\) and \(Q\) are the in-phase and quadrature coherent integration values, respectively; \(\alpha=E,P,L\), where \(E,P,\) and \(L\) represent the early, prompt, and late correlators, respectively; \(A\) is the amplitude; \(R(\cdot)\) is the code correlation function; \(\tau_{\alpha}\) is the code phase offset; \(\text{sinc}(x)=\sin(x)/x\) is known as the frequency attenuation factor; \(T_{coh}\) is the coherent integration time; \(f_{e}\) is the carrier frequency error; \(\phi_{e}\) is the phase error; and \(w_{\Omega,n}\) and \(w_{\Omega,n}\) denote zero-mean noise terms with standard deviation \(\sigma_{n}\).  
For UTC navigation systems, the processing methods of the coherent integration values are divided into two categories. The first method is to directly send \(I\) and \(Q\) signals to the integration Kalman filter as filter measurements [16; 17]. The main problems of this scheme are the high complexity and the high processor load. The second solution is to configure a prefilter for each tracking channel. The code phase and carrier frequency errors of the prefilter output are converted into pseudo-range (PR) and pseudo-range rate (PRR) residuals, which are used as integration filter measurements [7; 18; 19].  
The second scheme is adopted in this paper. However, it is worth noting that the large frequency error mitigation method that we propose is not limited by UTC architectures. Figure 1 depicts a typical embodiment of MIMU/GNSS UTC integration architectures. In the rest of the paper, this architecture is called the conventional UTC (CUTC) architecture.
# Hybrid Trust Model for Node-Centric Misbehavior Detection in Dynamic Behavior-Homogeneous Clusters
## 7 Collaborative Node-Centric Misbehavior Detection Based on Hybrid Trust Values
### Node Layer Misbehavior Detection  
In the process of determining anomalous vehicles at the vehicle node layer based on the total trust value, a consistency parameter \(Av(v_{i})\) is introduced. The calculation of \(v_{i}\) involves the ratio of direct to indirect trust values of a vehicle, expressed as follows:  
\[Av(v_{i})=\frac{T_{d}(v_{i})}{T_{r}(v_{i})} \tag{22}\]  
Figure 8: Communication simulation scenario for vehicle trust value assessment.  
Figure 7: Traffic simulation scenario in OSM simulation map.  
The trust threshold for each vehicle at the vehicle node layer is also calculated as:  
\[T_{thresh}^{\circ}(v_{i})=\frac{\sum_{j=1}^{n}T_{tot}(v_{i})}{n} \tag{23}\]  
When \(Av(v_{i})=1\) is within a certain range, it indicates that the direct trust value a vehicle holds about another vehicle is similar to the indirect trust value received from neighboring vehicles. The determination process is as follows:  
\[\left\{\begin{array}{ll}T_{d}(v_{i})>T_{thresh}^{\circ}(v_{i}),\;\;v_{i}\; \textit{is trusted vehicle}\\ T_{d}(v_{i})\leq T_{thresh}^{\circ}(v_{i}),\;\;v_{i}\;\textit{is misbehavior vehicle}\end{array}\right. \tag{24}\]  
When \(Av(v_{i})>1\) falls outside a certain range, it indicates that the judgment of vehicle \(v_{j}\) about vehicle \(v_{i}\) differs from the indirect trust value received from neighboring vehicles about \(v_{i}\). In comparison to the trust values fed back by other vehicles, the direct assessment by \(v_{j}\) based on behavior of \(v_{i}\) considers \(v_{i}\) more trustworthy. This situation could be due to an increase in trustworthiness of \(v_{i}\) over time, hence the direct trust value being higher than historical feedback values. Therefore:  
\[\left\{\begin{array}{ll}T_{d}(v_{i})>T_{thresh}^{\circ}(v_{i}),\;\;v_{i}\; \textit{is trusted vehicle}\\ T_{d}(v_{i})\leq T_{thresh}^{\circ}(v_{i}),\;\;v_{i}\;\textit{is neutral vehicle}\end{array}\right. \tag{25}\]  
When \(Av(v_{i})<1\) is below a certain range, it suggests that the judgment of vehicle \(v_{j}\) about vehicle \(v_{i}\) differs from the indirect trust value received about \(v_{i}\) from neighboring vehicles. In this case, the trust value of \(v_{i}\) fed back by other vehicles is higher than the direct observational value. This could be due to a decrease in trustworthiness of \(v_{i}\) over time, hence the historical feedback values being higher than the direct trust value. Therefore:  
\[\left\{\begin{array}{ll}T_{d}(v_{i})>T_{thresh}^{\circ}(v_{i}),\;\;\;\;\;\; \textit{v_{i}\;\textit{is neutral vehicle}}\\ T_{d}(v_{i})\leq T_{thresh}^{\circ}(v_{i}),\;\;\;\;\;\textit{v_{i}\;\textit{is misbehavior vehicle}}\end{array}\right. \tag{26}\]
# Bio-Inspired Sleep Control for Improving the Energy Efficiency of a MEC System
## 4 Bio-Inspired MECS Sleep Control Method
### Sleep Decision by a MECS
The task transfer process is presented in Algorithm 2. As reported in [32], unnecessary task transfers ensue after a load of a server is transferred to the least-loaded neighbor because this neighbor may become overloaded by accommodating too much load from its neighbors. To avoid such an adverse situation, the tasks in MECS \(i\) must be distributed to each neighboring MECS that decides to be active, and the number of tasks transferred from MECS \(i\) to MECS \(j\) should be determined to be inversely proportional to the utilization of MECS \(j\). Among the neighboring MECSs of MECS \(i\), we denote the set of MECSs that are active during time slot \(t\) as \(N_{i}^{ON}(t)\). MECS \(i\) determines the proportion of tasks that will be transferred to MECS \(j\in N_{i}^{ON}(t)\) as follows:  
\[\eta_{i,j}(t)=\frac{1/\mu_{j}(t)}{\sum_{k\in N_{i}^{ON}(t)}1/\mu_{k}(t)}. \tag{9}\]  
Subsequently, MECS \(i\) transfers \(\eta_{i,j}(t)Q_{i}(t)\) number of tasks to MECS \(j\). When MECS \(j\) receives tasks from MECS \(i\), the length of the queue increases from \(Q_{j}(t)\) to \(Q_{j}(t)+\eta_{i,j}(t)Q_{i}(t)\). If \(Q_{j}(t)+\eta_{i,j}(t)Q_{i}(t)\geq q_{ih}\), the optimization constraints are violated. Therefore, in this case, MECS \(j\) informs MECS \(i\) of the situation by sending a negative notification to \(i\) to force MECS \(i\) to remain active during time slot \(t\). Our sleep decision algorithm converges the sleep-control vector to a stable point. This is attributed to the fact that this algorithm can regulate the number of sleeping MECSs even though it operates in a distributed manner. More specifically, MECS \(j\) (deciding to be active) prevents MECS \(i\), which decides to sleep from going to sleep when the length of the queue of MECS \(j\) becomes larger than \(q_{th}\) (if it accommodates the tasks from MECS \(i\)).
# Modernization Data Analysis and Visualization for Food Safety Research Outcomes
## 4 Type of Food Safety Experiments
### Methodology Comparison
#### 4.1.1 Data Visualization and Data Analysis
In this case, there is enough evidence to conclude that Method 1 and 2 are the same, while Method 3 is statistically different from Method 1 and 2 (Table 2). An alternative test is the test for the difference between two proportions. This can be implemented when comparing the proportion of positive (or negative) results between methods. A significant \(p\)-value serves as evidence to conclude that the methods are different when detecting positive (or negative) results. Note that this test is based on the central limit theorem, and in order to be valid, samples from each group must be reasonably large (at least 10 positive and 10 negative results in each sample) [34]. Finally, other measures such as sensitivity, specificity, positive predictive value, negative predictive value, and accuracy can also be estimated to evaluate the performance of the new method when compared with the gold standard. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Coefficient**} & \multirow{2}{*}{**Estimate**} & \multirow{2}{*}{**Standard Error**} & \multirow{2}{*}{**\(p\)-Value**} & \multicolumn{2}{c}{**95\% Confidence Intervals**} \\ \cline{5-7}  & & & & & **Lower (2.5\%)** & **Upper (97.5\%)** \\ \hline \multirow{2}{*}{Method 2} & Intercept & \(-\)0.028 & 0.035 & 0.426 & \(-\)0.099 & 0.042 \\  & Slope & 1.018 & 0.007 & \(<\)0.001 & 1.004 & 1.032 \\ \multirow{2}{*}{Method 3} & Intercept & \(-\)0.037 & 0.081 & 0.653 & 0.085 & 0.837 \\  & Slope & 1.023 & 0.016 & \(<\)0.001 & 0.991 & 1.056 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary table of linear model using least square regression method predicting bacterial counts using Method 2 and Method 3 when compared with Method 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Detection Methods**} & \multicolumn{3}{c}{**Detection Methods**} \\ \cline{2-4}  & **Method 1** & **Method 2** & **Method 3** \\ \hline Method 1 & - & 0.305 & 0.005 \\ Method 2 & 0.305 & - & 0.002 \\ Method 3 & 0.005 & 0.002 & - \\ \hline \hline \end{tabular} \(p\)-values \(<\) 0.05 show statistically significant difference.  
\end{table} 
Table 2: [PERSON]’s chi-square \(p\)-values of the different methods to detect a certain microorganism.
# A Cryptography-Powered Infrastructure to Ensure the Integrity of Robot Workflows
## 4 Application and Evaluation in a Real Robot System
### Results
#### 4.3.1 Data Overhead  
For measuring the data overhead caused by the additional communication, we captured the individual requests and responses with a network analyser. First, we analysed the traffic produced  
Figure 9: Client implementation in Node-RED.  
by calling the GRPC signing service and transmitting six signing requests differing in the size of the workflow queue. Table 2 shows the result of our measurements.  
The first column represents the size of the queue. Since the length of a workflow description and the amount of parameters can differ significantly between different workflows we had to define a base unit for our measurements. Referring to our use-case described in Table 1 we took a MoveBase operation (Wf No. 2) followed by a pick-and-place sequence (Wf No. 3-7) as a basis. Consequently, our use-case has a size of two base units (2 times a MoveBase followed by a Pick-Place sequence). For the measurements we omitted the initialization workflow, since it is only added once regardless of the queue size. The second and the third column contain the amount of application data transferred with the signing request and the corresponding response respectively. While the amount of data transmitted with the request is variable and grows linearly with the size of the workflow queue, the length of the response--containing the signature--depends on the length of the private key used for signature generation exclusively. As shown in Figure 4 apart from the additionally transported signature the verification request contains the same data as the signing request, which means that its length grows linearly with the workflow queue size as well. Regarding the data overhead produced by the execution request sent from the client to the workflow manager, we only have to consider the signature header. As already described before, the signature length does not depend on the workflow queue size. The Merkle tree itself is not transmitted but just its fixed-length root signature and thus causes a constant amount of data overhead. Note, that in terms of time however, the queue size has a linear impact due to increased effort of re-construction the tree (as discussed in the next section).
# Osteoarthritis--The Role of Mesenchymal Stem Cells in Cartilage Regeneration
## 4 MSC-Based Therapy for OA
### Sources of MSCs  
Different sources of MSCs exhibit distinct properties, and each has its own set of benefits and drawbacks.  
In terms of their ability to increase, umbilical cord (UC) and amniotic fluid-derived MSCs surpass fat and bone marrow-derived MSCs (BM-MSCs) [184; 185].  
MSCs acquired from the UC, amnion, and adipose tissue have a better immunomodulatory ability than MSCs generated from BM-MSCs; nevertheless, placental MSCs have the lowest immunomodulatory capacity [184].  
Both adipose tissue-derived MSCs (AD-MSCs) and BM-MSCs have a morphology similar to that of fibroblasts [186]. These cells express CD29, CD44, CD73, CD90, and CD105; however, they do not express CD14, CD31, CD34, CD45, and CD106; in addition, they do not express HLA-DR or c-kit [186; 187]. AD-MSCs displayed a more substantial capacity for adipogenic differentiation, while BM-MSCs demonstrated a greater ability for osteogenic development when both types of cells were developed in vitro [186; 187].  
The ability of BM-MSCs, synovial membrane-derived MSCs (SM-MSCs), and ADM-MSCs to differentiate was investigated by [PERSON] et al. [188; 189]. When compared to other types of cells, they discovered that SM-MSCs contained approximately 3000 nucleated cells per mg and were the sort of cell that was most capable of chondrogenesis. In addition, they found that SM-MSCs and infrapatellar fat pad-MSCs (IPFP-MSCs) had a higher proliferative capacity than AD-MSCs and that the pellets created by SM-MSCs and IPFP-MSCs were able to produce more cartilage matrix than the pellets made by AD-MSCs [190].
## 4 Simulation Results
### Evaluation of Engineering Design Problems  
In this section, the following three traditional engineering optimization problems are addressed by IMGO: the design of WBD, REBD, and PVD problems. The standard MGO and the proposed IMGO algorithms are performed in comparison with the reported results taken from several recent optimization algorithms. For a fair comparison, identical conditions are applied across all algorithms as follows: each simulation uses a population of 30 individuals, runs for 300 iterations, and is repeated over 50 independent trials. The simulations are conducted in MATLAB R2017b on a system equipped with an Intel(r) Core(tm) i7-7200U processor (2.5 GHz) and 8 GB of RAM (Intel, Santa Clara, CA, USA).