esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposal of the New Protection Definition
### The Methodology and the Setup Used for the Laboratory Tests

Table 1: 5G-RANGE main parameters. \(1\)H, with 36 dB gain and a channel filter, were employed for each interfering signal generator to represent the real operating conditions of these systems in the field. A variable attenuator was also used to facilitate the proper adjustment of the power level at the input of the DTV receiver during the tests. Table 2 presents the BW and power configurations of the generated interfering signals, along with their operating mode. The power value shown refers to the maximum level delivered to the input of STB. Additionally, Figure 2 presents the measurement of the generated and characterized 5G-RANGE signal for laboratory tests. The ACLR values on channels \(n-1\) and \(n+1\) are observed to be \(-50.62\) dB and \(-49.55\) dB, and the ACLR on channels \(n-2\) and \(n+2\) are \(-72.86\) dB and \(-71.44\) dB, respectively. ACLR is the ratio, in dB, between the transmitted power in the assigned channel and the transmitted power in the adjacent channel. Given that, in the laboratory tests, the DTV modulator was configured to transmit on the UHF channel \(n=48\), the interfering signal was alternately allocated in neighboring UHF channels 46, 47, 49, and 50, referred to as interference on channels \(n-2\), \(n-1\), \(n+1\), and \(n+2\), respectively. The outputs of the variable attenuators of the DTV and interfering signal generation are combined to occupy the same medium and transmission spectrum. They are then divided by an RF splitter to allow simultaneous analysis of the combined signals. The first output of the RF splitter is connected to the input of the DTV reception station, where there is a 50 \(\Omega\)-to-75 \(\Omega\) impedance adapter, commercial DTV receivers (STBs), and the display for video visualization. STBs from three different manufacturers were used, employing \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Interference Signal** & **Settings** & **BW (MHz)** & **Input Receiver Power (dBm)** \\ \hline
5G-RANGE & GFDM and OFDM modulations & 6 & 13 \\ \hline
5G NR & Test mode: NR-FR1-TM1.—ETSI TS 138 141-1 [4] & 5 & 9 \\ \hline
4G/LTE & Test mode: E-TM1.—ETSI TS 136 141 [3] & 5 & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Settings for the generation of interfering signals. Figure 2: Spectrum of the 5G-RANGE using GFDM during the laboratory tests. Silicon Tuner technology, including two commercial ones and one fabricated and donated to families participating in the Brazilian government program called \"_Bolsa Familia_\". The second output of the RF splitter is connected to the spectrum analyzer, model MXA N9020A signal analyzer, manufactured by Agilent. The power level measurements of the DTV signal and the interfering signal were performed using the mentioned spectrum analyzer in adjacent channel power (ACP) mode, with a resolution bandwidth (RBW) of 10 kHz, a video bandwidth (VBW) of 300 Hz, and a span of 30 MHz, while the center frequency depended on the channel being analyzed.
## 1 Introduction
The physical and mechanical properties of white sandstone subjected to different chemical corrosion and freeze-thaw weathering under uniaxial and tri-axial compression tests have been investigated [29; 30]. A stereoscopic microscope was used to observe the fracture surface of white sandstone. Consequently, it was summarized that the elastic modulus and peak stress of white sandstone decrease after the combined effect of chemical corrosion and freeze-thaw cycles. The experiments on limestone under several different chemical solutions and freeze-thaw cycles have been carried out to study the physical damage characteristics [31; 32; 33; 34; 35]. The results revealed that the abundance of condensation nuclei and the pH value of water solution are the most important factors affecting the degree of damage to limestone under coupled chemical corrosion and freeze-thaw processes. The uniaxial compression tests have been to investigate the behaviour of yellow sandstone subjected to the combined effects of chemical corrosion and freeze-thaw cycles [29]. It was concluded that rocks exhibiting higher porosities are more prone to damage. Moreover, intercrystalline cracks can be more easily generated in yellow sandstone that has been subjected to chemical corrosion. Most of the studies on the combined effect of chemical corrosion and freeze-thaw weathering have focused on the physical and mechanical changes with different numbers of freeze-thaw cycles, however, the effect of minimum freezing and thawing temperatures has been ignored. It is possible that chemical reactions are affected by different low temperatures. In addition, no uniform method has been suggested to study the combined effects of rock type, chemical solution, number of freeze-thaw cycles and freezing time. This paper presents a study on the relationship between macro and micro mechanical properties of rocks and minerals subjected to the combined effect of chemical corrosion and freeze-thaw weathering. Biotite tonalite from He'nan province, China, has been selected for this study. The tonalite specimens were soaked in nitric acid solutions. Cyclic freeze-thaw experiments were then carried out, followed by uniaxial compression. SEM was used to observe the failure plane, and to investigate the deterioration mechanism for tonalite that had been subjected to the combined effect of chemical corrosion and freeze-thaw weathering. The results of this study would play an important role in decision-making for engineers in a practical engineering setting.
## 5 Discussion  
A problem with applying lidar to small scale meteorological phenomena is the spatial probe length averaging effect. In addition, the MuLiWEA performs spatial and temporal averaging of the measurements. These effects combined hinder capturing large velocity gradients that commonly occur on the boundaries of wakes. However, the algorithm resolves the wake with sufficient accuracy to determine e.g., its deficit and width. Because of the limitation on time resolution, it is not possible to investigate wake or boundary layer dynamics with the scanning speed of the currently available lidars using asynchronous PPI scans. This limitation could be overcome by synchronising the lidars or improving their temporal resolution. In the current situation, the target application of the algorithm is steady wake model validation on a 10 min time scale.  
The rotation of the near wake induces unsteady lateral and vertical wind speed components. This increases the uncertainty of a reconstructed wind field in case PPI scans are not executed horizontally at hub height but with varying altitude offsets. In particular, a significant deviation was observed in the wind direction for measurements taken at a distance from hub height. However, the effect on the absolute horizontal wind speed is negligible. Because the rotation of the wake is a deterministic phenomenon, it could be accounted for by the algorithm, in a similar way as the continuity adjustment was implemented.  
Because the simulated lidar measurements were modeled to a perfect accuracy, most of the errors in the investigated dual-Doppler lidar scenarios are indirectly a result of the irregular sample distribution over the height, which does not perfectly resemble the hub height at all locations on the horizontal plane.  
Although it was argued and proven by the error analysis that neglecting the vertical velocity component does not significantly increase the uncertainty of the reconstructed wind components, a valuable extension to MuLiWEA could be the inclusion of the vertical component. For this purpose, additional lidars are needed that measure with relatively high elevation angles.  
Figure 8: Reconstructed 10 min wind field on 20 February 2014 at 06:40:00 in the wind farm “alpha ventus” with the wake of turbine AV10 (alpha ventus, number ten): (**a**) mean absolute velocity and vector field of the 2D velocity; and (**b**) calculated absolute error on the mean horizontal velocity.  
Only a single 10 min reconstructed wind field was provided in the real measurement application section. This was mainly meant to illustrate the difference with respect to a simulated case and serve as an example. Further analysis of actual measurements, preferably with reference anemometry, could assist with evaluating the algorithm properly.
## 3 Results and Discussion
### Compressive Strength  
In Figure 8, the compressive strength of various concrete cubes mixtures containing powder glass and PA fibers is presented. It can be observed that the incorporation of 10% powder glass fibers slightly increases the compressive strength, while the addition of 0.5%, 1%, and 1.5% PA fibers causes a decrease in compressive strength. This enhancement is attributed to the pozzolanic reaction of powder glass, which can improve the strength and  
Figure 6: Density for cubes with powder glass and PA fibers (6 cubes/mix).  
durability of the concrete through the formation of additional cementitious compounds, such as calcium silicate hydrate (C-S-H) [39]. As shown, the compressive strength at 28 days decreases by 9.77%, 9.8%, and 13.8%, respectively, for the different levels of PA fibers as compared to the control mix. [PERSON] et al. [71] conducted a study that revealed a similar trend in which the compressive strength of concrete increased when 0.5% of jute fiber was used. This was also found to be the case of concrete with nylon and jute fibers [72], once the percentage of nylon and jute fibers in the concrete mixture exceeded 1%, the strength decreased due to increased air spaces and porosity resulting from poor compaction of a large number of fibers in the mixture. Moreover, [PERSON] et al. [73] found that the compressive strength of concrete increased up to 1% when the amount of human hair was increased, after which it began to decrease. This reduction is due to the lower interlocking strength between the PA fibers and cement, as well as the lack of bonding and sliding action between the fibers at high friction. Therefore, the selection of the appropriate proportion of PA fibers and any pretreatment used in creating mixtures of PA fibers and concrete is crucial [74]. It is noted that the relationship between these results and the UPV values is significant, as a decrease in UPV due to voids correlates with a reduction in compressive strength (Figure 9). Therefore, there is a direct correlation between the UPV values and the compressive strength of the material, whereby a decrease in UPV indicates the presence of voids and a decrease in compressive strength. The correlation between compressive strength and ultrasonic pulse velocity (UPV) has been extensively studied. It has been found that this correlation follows a normal exponential relationship with a high correlation coefficient, as reported in previous literature by [PERSON] et al. [39; 47]. For example, [PERSON] et al. [39] found that the coefficient of determination for pastes and mortars was reported to be 0.91 and 0.93, respectively.
## 3 Experimental Results
### Macroscopic Weathering Indicators
#### 3.1.2 Mass Loss  
Figure 7 illustrates the cumulative mass loss and loss rate curve of sandstone and limestone. The mass change rate was calculated using Equation (1). The results reveal the following facts.  
1. In the initial stage of the experiment, the mass of sandstone increased due to two main factors. Firstly, the fresh sandstone had a relatively large initial porosity, allowing the crystallites formed by salt weathering to accumulate in the surface pores and fractures of the rock. This resulted in only minor damage within five cycles. Secondly, the crystallites generated during the cycling process block the throats of the pores and fractures, creating liquid-filled voids. This led to an increase in the water content inside the rock sample [25,26].
2. On the other hand, limestone has a high initial density and small pores. As a result, even a small amount of salt crystallization can cause a reduction in mass.
3. In the later stage of salt weathering, after 25 cycles within the experiment, the destructive effects of salt weathering gradually intensified, resulting in a continuous decrease in the mass of the rock samples as the number of cycles increased.  
Firstly, the characteristics of the quality change of sandstone are analyzed. It should be noted that the experimental data of fresh sandstone are not included because the quality change of sandstone during 0-5 cycles is greatly affected by other factors. It can be seen that the loss rate of sandstone mass in the cycle presents a linear upward trend, and the  
Figure 5: Apparent morphological characteristics of limestone samples. (**a**) After 10 cycles; (**b**) after 15 cycles; (**c**) after 20 cycles; (**d**) after 25 cycles.  
Figure 6: Typical damage morphology of rock artifacts under sulfate erosion.  
loss rate is as high as 9.5% when the cycle reaches 25 times, and the cumulative mass loss is about 29.45 g. The change rate of limestone mass generally shows a linear increase. After the experiment, the final mass loss rate was only 0.26%, with a cumulative mass loss of approximately 1.2 g. This is 1/40 th of the mass loss rate observed in sandstone.  
\[R_{s}=\frac{M_{o}-M_{n}}{M_{o}}\times 100\% \tag{3}\]  
where \(R_{s}\) represents the mass loss over the exposure time, \(M_{o}\) represents the initial dry weight of the rock sample in grams, and \(M_{n}\) represents the dry weight of the rock sample in grams after \(n\) cycles of salt weathering.
## 3 Dataset Construction of Landslides in Yiyang City
### Extraction of Landslide Predisposing Factors  
#### 3.2.1 Geological Factors  
Geological factors have significant influence on landslide occurrence, as diverse geological structures exhibit varying mechanical and chemical properties, structural components, and rainfall infiltration rates. Based on the 1:200,000 Digital Geological Map Spatial Database, the extraction of rock structures (YSD), texture of rocks (YSC), stratigraphic types (DSN), and rock types (YSEB) is crucial in understanding their impacts on slope stability, as is illustrated in the following topographic illustrations, Figure 4a-e, respectively. Simultaneously, data of fault lines were extracted, and the distances to the faults were calculated as one of the influencing factors (shown in Figure 4f).  
\begin{table}
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**Order No.** & **Data Name** & **Data Sources** & **Spatial Resolution** & **Data Types** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{1} & Historical landslide points in Yiyang & Global Disaster Data Platform ([[https://www.gddat.cn/newGlobalWeb/](https://www.gddat.cn/newGlobalWeb/)]([https://www.gddat.cn/newGlobalWeb/](https://www.gddat.cn/newGlobalWeb/))\#/home) (accessed on 10 January 2024) & \multirow{4}{*}{Vector} & \multirow{4}{*}{document} \\ \cline{3-4} \cline{5-5}  & & European Space Agency’s & & \\  & & Copernicus & & \\ \cline{3-4} \cline{5-5}  & Digital Elevation Model & ([[https://portal.opentography](https://portal.opentography)]([https://portal.opentography](https://portal.opentography)). org/datasetMetadata? & 30 \(\times\) 30 m & Raster Grid \\ \cline{3-4} \cline{5-5}  & & & otCollectionID=OT.032021.4326.1) & & \\ \cline{3-4} \cline{5-5}  & & (accessed on 10 January 2024) & & \\ \hline \multirow{4}{*}{3} & Normalized Difference Vegetation Index (NDVI) for Land Cover in Yiyang & Landsat 8 OLI Image provided by NASA \& United States Geological Survey (USGS) Application-GloVis (usgs.gov) & \multirow{4}{*}{30 \(\times\) 30 m} & \multirow{4}{*}{Raster Grid} \\ \cline{3-4} \cline{5-5}  & \multicolumn{1}{p{113.8 pt}}{} & \multicolumn{1}{p{113.8 pt}}{} & & \\ \cline{3-4} \cline{5-5}  & \multicolumn{1}{p{113.8 pt}}{} & \multicolumn{1}{p{113.8 pt}}{} & & \\ \cline{3-4} \cline{5-5}  & \multicolumn{1}{p{113.
## 3 Database Used  
The effectiveness of the model is dependent on the variables used and the amount of data. The variables used to predict the bearing capacity were gathered from the literature [12], resulting in 245 experimental tests. All the test procedures and methods were established and described in a previous paper by the same authors [45]. To construct the models, related literature was utilized to justify the following predictors: the ratio of the sandy bed thickness to the foundation diameter (\(t/D\)), the ratio of the geogrid layer diameter to the foundation diameter (\(d/D\)), the ratio of the stone column length to its diameter (\(L/d_{sc}\)), the ratio of the length of the encasement to the diameter of the stone column (\(h/d_{sc}\)), and the shear strength of unreinforced soft clay (\(q_{u}\)). The models aim to predict the bearing capacity of the improved soft clay soil (\(q_{n}\)). These input variables have also been used by [PERSON] and [PERSON] for modeling this bearing capacity [12]. The data was then split into a training set and a testing set with an 8:2 ratio, including 197 observations for training and 48 for testing. A stratified sampling approach was used to ensure that the data had a comparable outcome distribution. Figure 2 shows the overall distribution of the variables, and Table 1 presents the statistics of the input variables for the training and testing sets.  
Figure 2: Relative frequency distribution of input and output variables.  
To demonstrate the variability of the data, examples include the ratio of the sandy bed thickness to the foundation diameter, which can range from 0 to 0.4; the ratio of the geogrid layer diameter to the foundation diameter, ranging from 0 to 4; the ratio of the stone column length to its diameter, ranging from 2 to 8; the ratio of the length of the encasement to the diameter of the stone column, ranging from 0 to 8; and the shear strength of unreinforced clay, varying between 3.86 and 53.82 kPa. Moreover, the measured bearing capacity of the composite soil also demonstrated a wide range of values, falling between 14.11 and 467.01 kPa. The wide range of potential values for both input and output variables in the dataset highlights the diversity of the samples included and the ability of the models constructed with this data to be applicable to a wide range of scenarios.
# Biological Properties of Sandalwood Oil and Microbial Synthesis of Its Major Sesquietrepenoids
###### Abstract  
Sandalwood essential oil is extracted from the heartwood part of mature sandalwood and is known for its pleasant fragrance and exceptional medicinal activities, including antimicrobial, antitumor, and anti-inflammatory properties. The (Z)-\(\alpha\)-santalol and (Z)-\(\beta\)-santalol are the most vital ingredients contributing to sandalwood oil's bioactivities and unique woody odor characteristics. Metabolic engineering strategies have shown promise in transforming microorganisms such as yeast and bacteria into effective cell factories for enhancing the production of vital sesquiterpenes (santalene and santalol) found in sandalwood oil. This review aims to summarize sources of sandalwood oil, its components/ingredients, and its applications. It also highlights the biosynthesis of santalene and santalol and the various metabolic engineering strategies employed to reconstruct and enhance santalene and santalol biosynthesis pathways in heterologous hosts.  
santalwood oil; sesquiterpenes; santalene; santalol; biosynthesis; metabolic engineering 2020 IEEEexample: accepted: 12721 processes and offering significant economic and environmental advantages. Driven by technological progress in metabolic engineering, process biotechnology, and synthetic biology, terpene-based bioactive compounds such as medicines, food ingredients, and perfumes produced by microbial fermentation have become viable and commercially competitive options [10; 11; 12]. Metabolic pathway design and enzyme engineering reform are key points for improving cell factory performance [13; 14].  
This review provides a general understanding of sandalwood essential oil and the microbial synthesis of its major terpenoids, specifically focusing on recent progress in metabolic engineering strategies to increase the santalene and santalol yield in engineered microbial cells.
## 5 Results and Discussion
### Feedforward Artificial Neural Network Parameter Selection and Result Analysis
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**No.**} & \multicolumn{1}{c}{**Variable**} & \multicolumn{1}{c}{**Indicator**} \\ \hline
1 & Month & 1, 2, 3..., 12 \\
2 & Day of the Month & 1, 2, 3,..., 30, 31 \\
3 & Week & Sunday-1, Monday-2... Saturday-7 \\
4 & Hour of Day & 0, 1, 2, 3..., 23 \\
5 & Weekday, Weekend & 1, 0, 0 \\
6 & Previous Day’s Load & L1(t-24), L2(t-25)... L1(t-47) \\
7 & Same Day of the Previous Week & L1(t-24 \(\times\) 7)... L24(t-47 \(\times\) 7) \\
8 & Temperature & T(t) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: ANN input variables. Unlike the time series models, the ANN considered all the parameters so that a higher number of parameters did not affect the forecasting; it tended to ignore the unnecessary terms as it progressed. Comparing the ANN model with the time series forecasting models, the MAPE on the weekdays was improved by 0.34%, and for the weekends, it improved by 8.04%. The time series models were unable to track the weekend demand pattern, and the magnitude of the load demand was way off course during the daytime from 8:00 a.m. onward. On the other hand, the FF-ANN model performed much better in terms of tracking the load for both types of days. As can be seen in Figure 11, in terms of the pattern as well as the magnitude of the electricity demand, the FF-ANN was able to follow both load profiles. When comparing the predicted data with the load forecasting using time series, especially on the weekend, we can observe a high variation in load amplitudes in Figure 10b. With the ANN implemented, a huge improvement can be observed for the weekend from Figure 12b.
###### Abstract  
The sweetness is an important parameter for the quality of Cuixiang kiwifruit. The quick and accurate assessment of sweetness is necessary for farmers to make timely orchard management and for consumers to make purchasing choices. The objective of the study was to propose an effective physical method for determining the sweetness of fresh kiwifruit based on fruit hyperspectral reflectance in 400-2500 nm. In this study, the visible and near-infrared spectral (Vis/NIR) reflectance and sweetness values of kiwifruit were measured at different time periods after the fruit matured in 2021 and 2022. The multiplicative scatter correction (MSC) and standard normal variable (SNV) transformation were used for spectral denoising. The successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) methods were employed to select the most effective features for sweetness, and then the features were used as the inputs of partial least squares (PLS), least squares support vector machine (LSSVM), back propagation neural network (BP), and multiple linear regression (MLR) models to explore the best way of sweetness predicting. The study indicated that the most sensitive features were in the blue and red regions and the 970, 1200, and 1400 nm. The sweetness estimation model constructed by using the data of the whole harvest period from August to October performed better than the models constructed by each harvest period. Overall results indicated that hyperspectral reflectance incorporated with MSC-SPA-LSSVM could explain up to 79% of the variability in kiwifruit sweetness, which could be applied as an alternative fast and accurate method for the non-destructive determination of the sweetness of kiwifruit. This research could partially provide a theoretical basis for the development of nondestructive instrumentation for the detection of kiwifruit sweetness.  
n _Applied Sci._**2024**, _4145_.