esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring
## 8 Acoustic Emission Technique  
Acoustic non-destructive measurement technique uses transient elastic sound waves that are emitted as bursts of energy into a material under test. The Acoustic Emission (AE) monitoring process requires a material deformation, which is used as the source, and transducers to capture the stress waves that are generated by the source. Sensors located at the surface of the material (Figure 11a) are used to detect these waves. AE sensors are the heart of the AE system as they transform waves into electrical signals. In the case of non-integral sensors, the signal is amplified before transmitted to the AE instrument, whereas for integral sensors with an embedded amplifier, the wave is directly transferred to the AE instrument. The signal waveform is affected by the path from source to the sensor, the sensor characteristics and the system measurement. The signal is then transferred to the AE data acquisition system. The main AE features are shown in Figure 11b and include: amplitude, energy, duration, hit, rise time (=delay between the onset and highest peak of waveform) and count. Subsequent data analysis can include signal strength versus location, amplitude versus location, intensity analysis and average frequency (=number of counts/duration) versus RA (=rise time/peak amplitude) and can be performed via different algorithms. The technique is very sensitive and, therefore, able to real-time monitor microscopic events inside an object by separating background noise from AE. The generated elastic waves by the release of energy within a material can be utilized to detect/locate flaws as well as to evaluate their growth rate as a function of the applied stress. The method is characterized as burst/distinct pulses and continuous emissions for crack propagation and determination of fatigue crack activities in structures. Accumulation of AE hits can be used in order to measure crack initiation and growth and classify damage severity in structures. One of the AE advantages is that damage sources can also be localised by using multiple sensors.  
The use of acoustic techniques for structural health monitoring dates back to the 70s [80]; this section focusses on recent advancements in concrete, steel, composite, masonry, timber and additively manufactured structures, as discussed below.
# Unsupervised Machine Learning Techniques for Detecting PLC Process Control Anomalies
## 5 Results and Discussions
### Limitations  
While this work makes significant contributions to the scientific body of ICS anomaly detection, there are some limitations to the proposed approaches. The proposed histogram-based visualization approach is limited to anomaly-detection algorithms with signed output results. A detection algorithm's output must be positive and negative real numbers representing normal and anomalous points or vice-versa in order for the proposed visualization approach to be effective. The histogram-based visualization plots reveal that OCSVM and OCNN make less-confident predictions. OCSVM and OCNN have similar decision scores distributions across all five test sets because they are both formulated from a similar optimization problem. Observing OCSVM and OCNN performance limitations may not have been possible without the proposed visualization approach. Furthermore, comparing anomaly-detection algorithms performances based on decision scores instead of traditional binary predictions requires decision scores normalization. Since different anomaly detectors might produce decision scores on different scales, decision scores normalization is required for fair comparison.  
In addition, there are some limitations associated with the proposed techniques. OCSVM is sensitive to the choice of kernels, and \(\
u\) parameters [45], hence OCSVM is not robust in ICS applications without a deeper understanding of the ICS. OCSVM limitation can partially be solved by using variable subsampling [65] during model training in the context of ICS with unpredictable behavior. The feed-forward nature of the neural network in OCNN makes the algorithm sensitive to noise [45]. Therefore, clean ICS data should be used for OCNN training to avoid model overfitting. Finally, IF algorithm's recursive data partitioning could lead to lower performance in high-dimensional ICS data due to the masking effect of locally noisy and irrelevant features. As a result, feature-selection techniques [66,67] should be employed in high-dimensional ICS data before IF model training.  
Although IF has a stellar performance on key evaluation metrics on test sets 1-3, it achieved lower recall values on test sets 4-5, which contain timing bits error. Some anomalous data points are detected by either OCSVM or OCNN, but IF fails to detect them. This shows that the proposed algorithms have strengths and weaknesses on different subsets of the data, and hence, a single detection algorithm may not be able to generalize to an arbitrary ICS setup. However, aggregating the predictions from individual anomaly detection models could potentially result in a robust model capable of detecting anomalies in an arbitrary ICS setup.
# Experimental and Simulation Identification of Xanthohumol as an Inhibitor and Substrate of ABCB1
## 1 Introduction  
ATP-binding cassette subfamily B member 1 (ABCB1; also known as p-glycoprotein [Pgp] or multidrug resistance protein 1 [MDR1]) belongs to the family of ABC membrane transporters. The most studied biological role of ABCB1 is exporting chemically unrelated substrates; therefore, it is usually involved in MDR to cancer chemotherapy, and the modulation of drug pharmacokinetics and interactions [1, 2]. ABCB1 has different kinds of substrates, ranging in size from 330 Da up to4000 Da [1]. Inhibitors of ABCB1, as well as its non-toxic substrates, might be used to overcome drug resistance and understand the intestinal absorption of drugs. Much effort has been devoted to screening for natural phytochemicals, which can inhibit ABCB1-mediated transport, and thus have the potential application of reversing drug resistance [3].  
Xanthohumol (XN, Figure 1) is a well-known prenylated flavonoid that exists uniquely in hops (_Humulus lupulus_ L.), and its general dietary source is beer. As we have reviewed previously, XN has multiple health benefits, mainly including metabolic syndrome attenuating effects, hepatic protection effects, antimicrobial activity, anti-parasite effects, cancer chemo-preventive effects, cancer therapeutic activity [4], and also inhibitory activities in cancer angiogenesis [5; 6]. Interestingly, recent studies have shown that XN could sensitize cancer cells to certain anticancer drugs [7; 8]. Moreover, XN was considered to be an inhibitor of the breast cancer resistance protein (ABCG2) transporter [9]. Considering that both ABCB1 and ABCG2 play a vital role in MDR and share some common substrates, we investigated whether XN could also be a substrate of ABCB1, which might account for its drug resistance reversing effects.  
Here, for the first time, we identified XN as a new substrate of ABCB1 that competitively inhibits ABCB1 transport functions, increasing the efficiency of the chemotherapy agent substrates of ABCB1, e.g., doxorubicin (DOX) and colchicine (COL). Our molecular docking studies further revealed that XN could bind to ABCB1 with the same binding site of DOX.
## 4 BPSK FDA System Design
### Link Budget  
When \(E_{b}/N_{0}\) is increased, the overlaid scatter points in Figure 4 begin to shift clockwise on the BER curve. We desire that the Barker code region achieves a minimum BER of \(10^{-5}\), requiring \(E_{b}/N_{0}\approx 9.6\) dB. From [44],  
\[\begin{split} E_{b}&=\frac{P_{avg}}{R_{b}}\\ N_{0}&=\frac{P_{n}}{B_{n}},\end{split} \tag{10}\]  
Figure 3: Simulated baseband response for a BPSK FDA with \(N=2\), \(f_{0}=720\) MHz, and \(\Delta f=10\) kHz located at the far-field point \((R,\theta)=(2\) m, \(-25^{\circ})\).  
where \(P_{avg}\) is the average receive power, \(R_{b}\) is the bit rate, \(P_{n}\) is the noise power, and \(B_{n}\) is the noise bandwidth. From [44; 45],  
\[P_{n,dBm}=10\cdot\log_{10}(kTBF\cdot 1000), \tag{11}\]  
where \(k=1.38\cdot 10^{-23}\) J/K is Boltzmann's constant, \(T=290\) K at room temperature and the noise factor \(F=10^{NF/10}\). The noise figure (NF) of the TwinRX daughterboard with input frequency of 720 MHz is at most \(NF=5\)[46]. We use \(B_{n}=2\) MHz (the first null to first null bandwidth) instead of the sampling bandwidth \(B=10\) MHz as a buffer in the power calculation. Using Equations (10) and (11), the necessary receive power is found to be \(P_{avg}=-89.1\) dBm.  
The link between transmitter and receiver, in dB form, is, ref. [44]  
\[P_{r}=P_{t}+G_{t}+G_{r}-\text{FSL}, \tag{12}\]  
where \(P_{t}\) is the power transmitted, \(G_{t}\) is the gain of the transmit antenna, \(G_{r}\) is the gain of the receive antenna, and \(\text{FSL}=(4\pi R/\lambda_{0})^{2}\) is the free-space loss for the link. We measure \(P_{t}=0\) dBm at the transmit antenna terminals, and the log-periodic antenna gains are assumed to be \(G_{t}=G_{r}=6.5\) dBi. For our close-range testing at \(R=2\) m, the power at the receiver is found to be \(P_{r}=-31.5\) dBm, which is sufficient for the desired BER.
## 2 Materials and Methods
### Design Conditions for Overtopping Prevention Structures  
For overtopping prevention structures, the design conditions must address the complex displacement behavior and potential defects arising from the differential settlements caused by adding superstructures to dams. For concrete structures in particular, exceeding  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & \(G_{s}\) & \(kv\) **(m/s)** & \(W_{opt}\) **(\%)** & \(\gamma_{dmax}\) & \(c\) **(kPa)** & \(\Theta\) **(\({}^{\circ}\))** & **USCS** \\ \hline Fill zone & 2.65 & 2.32 \(\times\) 10\({}^{-6}\) & 14.0 & 17.3 & 16.7 & 24.0 & SC \\ Core & 2.69 & 3.11 \(\times\) 10\({}^{-8}\) & 23.0 & 15.8 & 34.3 & 9.0 & CL \\ Filter & 2.62 & 7.82 \(\times\) 10\({}^{-5}\) & - & 19.6 & - & 40.0 & SP \\ Crushed stone & 2.65 & 2.55 \(\times\) 10\({}^{-4}\) & - & 22.8 & - & - & SP \\ \hline \hline \end{tabular} \(G_{s}\) = specific gravity; \(kv\) = vertical coefficient of permeability; \(\gamma_{dmax}\) = maximum dry density; \(c\) = cohesion; \(\Theta\) = internal friction angle; USCS = unified soil classification system; SC = clayey sand; CL = clay with low plasticity; SP = poorly graded sand.  
\end{table}
Table 1: Geotechnical properties of the tested materials.  
Figure 2: Soil particle distribution.  
the design storage capacity of the dam can result in water leakage and overtopping failure, thus emphasizing the critical nature of displacement behavior analyses [30].  
Considering that constituent materials and water levels affect vulnerable areas when the overtopping water level is reached, this study proposes a design of an overtopping prevention structure wherein a core is installed at the center of the dam crest to prevent water leakage-induced cracks (see Figure 3).
## 3 Results  
The results are divided into three primary subsections. The first section investigated the impact of data length (\(m=2\), 4, 6, 8, 10) on SampEn values calculated for six types of perturbations (Table 1) by definition in the AP, ML, and vert directions. The values of the tolerance window \(r\) were always equal to 0.2 \(\times\) SD, where SD was the standard deviation of the studied time series. This approach facilitated the identification of the specific \(m\) value associated with the highest and lowest regularity across the six analyzed perturbation scenarios in each direction.  
The subsequent section presents a comprehensive analysis, comparing regularities for time series calculated for six perturbations possibilities (Table 1)--separately, within each \(m\) and each direction (AP, ML, vert).  
The final chapter addresses an issue of which direction (AP, ML, or vert) exhibits the highest/lowest regularity within each of the six perturbations studied. As in earlier sections, the results are displayed for each \(m\).  
As demonstrated in the first subsection, the complexity of the analysis increased due to the impossibility of selecting a single optimal \(m\). Nonetheless, this approach ensured a comprehensive examination of the data.  
The Impact of Vector Length (\(m\)) on Sample Entropy Values across Six Perturbation Possibilities  
After conducting Friedman's ANOVA with Dunn-Bonferroni post hoc tests, it was shown that for the ML direction (Figure 3A), for each of the six perturbation possibilities, significantly higher (\(p<0.0001\)) SampEn values were noted for \(m=2\) than for those recorded for \(m=8\), 10. Additionally, significantly higher (\(p<0.0001\)) SampEn values were found for \(m=4\) than for those calculated for \(m=6\), 8, and 10, as well as for \(m=6\) in comparison to those noted for \(m=10\).  
In the vertical direction (Figure 3B), the trend of stacking SampEn values was consistent with that described for the ML direction, with two exceptions. For \(m=2\), SampEn values were significantly higher than those calculated for \(m=6\), a difference not observed in the ML direction. For \(m=4\), SampEn values were not significantly higher than those reported for \(m=6\), which was noted in the ML direction.  
In the AP direction, the trend of SampEn values did not follow a pattern from the previous directions (Figure 3C), illustrating remarkable diversity. For \(m=2\), SampEn values were significantly lower than those recorded for \(m=4\), 6, and 8. Moreover, for \(m=6\), SampEn values were significantly higher than those associated with \(m=10\). These consistent trends extended across three perturbation possibilities (Acc-ToeOff, Acc-Mid,and Dec-Mid). In the case of Acc-Initial, CampEn values at \(m=2\) were notably lower than those derived for \(m=6\), \(8\), and \(10\). The identical relationship existed for Dec-ToeOff, except for relations involving \(m=10\). In contrast, for Dec-Initial, CampEn values at \(m=2\) were significantly lower when compared to all other CampEn counting possibilities.
# Performance Evaluation of Asphalt and Asphalt Mixtures Modified by Fuel-Resistant Admixture
## 3 Results and Discussion
### Performance of FRMA
#### 3.1.2 DSR Test  
The results of the DSR test are presented in Figure 6. The parallel experimental results meet the error tolerance requirements specified for repetitive tests in AASHTO T 315 [20].  
The complex modulus is primarily used to assess the shear resistance characteristics of asphalt, as shown in Figure 6a. With increasing temperature, the complex modulus of FRMA with different dosages of FRM significantly decreased, and the differences among various compositions gradually diminished, indicating a substantial influence of temperature on the complex modulus of asphalt. At higher temperatures, the asphalt became softer, leading to a significant reduction in its ability to resist shear deformation. Additionally, under the same temperature conditions, the introduction of the modifier resulted in a significant increase in the complex modulus, indicating that adding the modifier effectively enhanced the asphalt's resistance to shear deformation.  
The phase angle reflects the variation in the asphalt's viscoelasticity ratio, with a larger phase angle indicating more significant viscosity. The results of the phase angle in Figure 6b show that the phase angle of all asphalt samples increased with increasing test temperature. Increased temperature prompts asphalt to transition to a non-Newtonian viscous fluid state, with an increased proportion of viscous components, making the viscosity more pronounced at high temperatures. Under the same temperature conditions, the addition of FRM reduces the phase angle of the asphalt, and with an increasing dosage, the phase angle  
Figure 5: Variations in penetration, softening point, and ductility with varying FRM dosages.  
gradually decreases. This suggests that adding FRM increases the stiffness and elasticity of the asphalt.  
The Superpave design method employs the high-temperature rutting factor (\(\left|\text{G}^{\ast}\right|/\text{sin}\delta\)) to evaluate asphalt's resistance to rutting, with a stipulated requirement for the original asphalt: \(\left|\text{G}^{\ast}\right|/\text{sin}\delta\geq 1.0\) kPa. A higher rutting factor value indicates better resistance to rutting. As shown in Figure 6c, at the same temperature, the addition of FRM enhanced the asphalt's resistance to rutting, indicating that FRM can effectively improve the high-temperature stability of asphalt. With increased FRM dosage, the rutting factor of FRMA decreased significantly at elevated temperatures, signifying considerable changes in the asphalt's viscoelasticity, a decrease in the proportion of elasticity, and reduced deformation recovery capability. As the temperature increased, the differences in the rutting factor for FRMA with different FRM dosages diminished, indicating an increased influence of temperature on the high-temperature performance of asphalt with increasing FRM dosage. The high-temperature performance of FRMA became more sensitive to temperature changes.
## 2 Study Goal and Site Description
### Satellite Data Acquisition and Processing
To estimate the LST from the observed IR brightness temperature \(T_{b}\), we used the approach below [25]:  
\[\mathrm{LST}=\frac{\mathrm{T_{b}}}{1+\left(\frac{\lambda\,\mathrm{T_{b}}}{ \mathrm{d}}\right)\ln(\mathrm{e})}\, \tag{1}\]  
where \(\mathrm{T_{b}}\) is the blackbody temperature; \(\lambda\) is the wavelength of the emitted radiance; \(d\) is defined by \(d=ch/k_{B}\), where the velocity of light (\(c=3\times 10^{8}\) m/s) is multiplied by [PERSON]'s constant (\(h=6.26\times 10^{-34}\) J.s) and divided by [PERSON]'s constant (\(k_{B}=1.38\times 10^{-23}\) J/K); and \(e\) is the land surface emissivity. The emissivity is calculated using Equation (2) [26]:  
\[\mathrm{e}=0.004+\ 0.986\ P_{V}, \tag{2}\]  
where \(P_{V}\) is the proportion of vegetation, which is sometimes referred to as the fractional vegetation cover and calculated using Equation (3): \begin{table}
\begin{tabular}{c c c} \hline \hline
**Constant** & **K1** & **K2** \\ \hline Units & W/ (sr m\({}^{2}\) um) & kelvin \\ L5 TM & 607.76 & 1260.56 \\ L8 TIR & 774.89 & 1321.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Brightness temperature constant values for use with Equation (1). \[P_{V}=\left[\frac{\mathrm{NDVI-NDVI_{min}}}{\mathrm{NDVI_{max}-NDVI_{min}}}\right]^{ 2}. \tag{3}\]  
In this equation, NDVI is the normalized difference vegetation index. To calculate the NDVI of the surface, we used Equation (4) [27]:  
\[\mathrm{NDVI}=\left[\frac{\mathrm{NIR\ - RED}}{\mathrm{NIR+RED}}\right], \tag{4}\]  
where NIR represents the near-IR band reflectance and RED is the visible red band reflectance. \(\mathrm{NDVI_{max}}\) and \(\mathrm{NDVI_{min}}\) in Equation (3) are the maximum and minimum NDVI indices in the image, respectively, for which \(\mathrm{NDVI_{max}}\) = 0.5 for vegetation and \(\mathrm{NDVI_{min}}\) = 0.2 for soil can be used [28]. Atmospheric scattering and absorption may also affect the estimation of land surface emissivity from NDVI [29]. In this study, the effects of scattering and absorption on the NDVI--particularly the atmospheric absorption by carbon dioxide and water vapor--were not taken into consideration. This could result in an overall underestimation of the absolute LST by 1-3 K. However, it would not affect the contrasts and gradients of the observed surface temperature because (1) there is no reason to expect meaningful variations in the atmospheric composition over the area of a landfill and (2) such uncertainties relative to the nominal absolute temperatures, which were subsequently considered, were not found to meaningfully impact hot spot detection.
# Yeast Starter Culture Identification to Produce of Red Wines with Enhanced Antioxidant Content
## 3 Results
In evaluating the fermentation performance of these starter candidate strains, secondary fermentation products were detected and quantified in the nine wines by SPME-GC-MS analysis (Table 5). Figure 1: Comparison of total anthocyanin content in wines obtained by the nine-selected yeast and the commercial strain (CM). Data are mean \(\pm\) S.D. and are representative of three different assays performed. OE = Oenin equivalent. Data were submitted to one-way analysis of variance (ANOVA), [PERSON]’s post hoc method was applied to establish differences between each yeast strain versus control. * indicates statistically significant differences (\(p<0.05\)) between each yeast strain versus control. A total of 23 volatile substances belonging to the classes of alcohols, esters, aldehydes, phenols, and volatile acids were identified. Within the alcohol class, all samples presented higher values, ranging from 26.86 mg/L (ITEM 9530) to 51.57 mg/L (ITEM 9508). Notably, 2+3-methyl-1-butanol and phenylethanol emerged as the most abundant molecules among alcohols. The second class of molecules that quantitatively affect the complete volatile profile of the different samples is that of esters associated with fruity notes. The highest values were detected in ITEM 9507 (7.42 mg/L), ITEM 9500 (7.15 mg/L) ITEM 9501 (6.04 mg/L), ITEM 8766 (5.37 mg/L), and finally ITEM 6920 (5.03 mg/L). The presence of acetic acid was not detected, while an acidic component was identified, in quantities varying from 2.05 mg/L (CM) to 4.64 mg/L (ITEM 9508), due to the presence of butanoic, hexanoic, octanoic, and decanoic which contribute positively with notes of freshness. Furthermore, in order to identify the yeast strains that produce wines with the best volatile profiles, principal component analysis (PCA) was performed on the Pearson correlation matrix, on the concentrations of molecules detected by GC-MS (Figure 3). The two bi-plots PC1 vs. PC2 showed the projection of the variables considered on the plane defined by the first and second principal components that explain 49.76% of the total variance. Along the positive component of PC1 (29.15%), the strains ITEM 9500, ITEM 9501, ITEM 9507, and ITEM 9508 cluster in the same group for a positive correlation with n-decanoic acid, isoamyl acetate, monoethyl succinate, ethyl lactate, ethyl hexanoate, 3-hexen-1-ol (E), methyonol. On the other hand, along the positive component of PC2 (20.61), strain ITEM 6920 differs from the others for a greater content of phenylethanol, ethyl butanoate, octanoic acid, 2+3-methyl-1-butanol, ethyl octanoate and 3-hexen-1-ol (Z). All other strains were characterized by low concentrations of all identified volatiles. Figure 2: Principal Component Analysis (PCA) performed employing the data obtained by the HPLC analysis of the wines obtained using the nine selected strains.