esa-sceva/satcom-chunk-collection huggingface dataset:
# Fine Activated Carbon from Rubber Fruit Shell Prepared by Using ZnCl\({}_{2}\) and KOH Activation
## 3 Results
### Characterization of RFSFACs
In particular, the sharp diffraction peaks at 31.74\({}^{\circ}\), 34.38\({}^{\circ}\), 36.22\({}^{\circ}\), and 47.48\({}^{\circ}\) correspond to ZnO (PDF card 76-704), while those at 25.46\({}^{\circ}\), 29.35\({}^{\circ}\), and 38.29\({}^{\circ}\), respectively, correspond to ZnCl2 (PDF card 74-517). Finally, the peaks at 26\({}^{\circ}\), 62\({}^{\circ}\), and 43.93\({}^{\circ}\) correspond to C (PDF card 26-1077) and indicate the existence of graphite in the activated RFSFAC products [48]. As shown in Figure 3a, the BET surface areas of RFSFACs 1-3 (prepared with KOH) are approximately 42, 141, and 160 m\({}^{2}\)/g, respectively. Similarly, the BET surface areas of RFSFACs 4-6 (prepared with ZnCl2) are 256, 400, and 456 m\({}^{2}\)/g, respectively (see also Table 3). For both sets of RFSFACs, the surface area increases with an increasing activator concentration due to the enhanced effect of the activation substance seeping into the RFS charcoal and opening the surface [49]. It can be observed that for a given impregnation ratio, the ZnCl2 activation agent results in a higher specific surface area than that obtained using KOH. In other words, of the two activating agents, ZnCl2 provides a more effective  
Figure 2: XRD patterns of RFSFAC products prepared using different impregnation ratios and activating agents. (**a**) KOH; (**b**) ZnCl2. activation performance. This may be because the RFS is lignocellulosic materials [50] with a greater content of oxygen and the ZnCl\({}_{2}\) is an acidic reagent capable of interacting with functional oxygen groups in RFS charcoal, thus catalyzing dehydration reactions, resulting in charring and creation of the pore structure [43]. Referring to Figure 4a, the total pore area of the KOH-activated RFSAC products increases from 11.88 to 26.17 m\({}^{2}\)/g as the impregnation ratio increases from 1:3 to 1:5 (see also Table 3). Similarly, for the ZnCl\({}_{2}\)-activated products, the total pore area increases from 20.47 m\({}^{2}\)/g to 40.61 m\({}^{2}\)/g. Furthermore, the proportion of mesopores (diameter between 2 and 50 nm) in the KOH products gradually decreases with an increasing impregnation ratio, while the proportion of micropores (diameter < 2 nm) gradually increases. By contrast, for the ZnCl\({}_{2}\) products, the proportion of mesopores increases, while the proportion of micropores decreases, as the impregnation ratio increases. It should be noted that these findings for the ZnCl\({}_{2}\) products are consistent with those reported in [30] for the production of activated carbon from cocoa pod husks. In general, all of the present RFSACs have around 60-80% micropores, 18-36% mesopores, and fewer than 4% macropores. However, of all the RFSACs, RFSAC6 has the highest pore area (40.61 m\({}^{2}\)/g) and percentage of mesopores (70.1%). Figure 3: (**a**) Brunauer–Emmett–Teller (BET) surface area; (**b**) average pore diameter of treated RFSAC, with different activating agents and impregnation ratio. (NCA, non-chemical activation).
## 1 Introduction
Previous studies have shown that the distribution and properties of the bedding structure affect the physical and mechanical properties of sandstone to varying degrees. [PERSON] et al. [18] and [PERSON] et al. [19] studied the anisotropic behavior of sandstones with different bedding angles through uniaxial compression, point load, and Brazilian tests. The test results showed that the anisotropy ratio of sandstone in the uniaxial compression test was lower than that in the point load test and Brazilian test. [PERSON] and [PERSON] [20] conducted uniaxial compression and Brazilian tests to study the mechanical response of sandstones with different bedding angles. The test results showed that the compressive strength and elastic modulus changed in a U-shape with increasing bedding angle, while the tensile strength decreased with increasing bedding angle. At present, there are few experimental research results on the influence of high temperature on the physical and mechanical properties of sandstone with different bedding angles, and the change rules of the physical and mechanical properties of sandstone after high temperature exposure reported by different scholars are also different. Therefore, through a series of physical and mechanical properties tests of sandstone with different bedding angles before and after high temperature exposure, (i) the change rule of its microstructure and the physical properties (color, mass, volume, density, wave velocity) of the sample after heat treatment were analyzed. (ii) We analyzed the mechanical properties (elastic modulus, compressive strength, cohesion, internal friction angle, and failure mode) of sandstone with different bedding angles after high temperature. (iii) The results reveal the control mechanism of temperature and bedding on the physical and mechanical properties of sandstone after high temperature exposure. The influence of high temperature on the anisotropy of the physical and mechanical properties of sandstone is discussed. As a typical sedimentary rock, sandstone usually has a bedding structure and obvious anisotropy. The directional mineral expansion in the bedding under high temperature has obvious directionality. The research results are of great significance for improving the damage rule of high temperature on the physical and mechanical properties of sandstone in the directions of different bedding angles and scientifically guiding the construction of high-temperature rock mechanics-related projects.
## 2 Related Work
### State of the Art
Complete evaluation-based techniques [3] need comprehensive evaluations, which are often computationally costly, to derive the performance for each configuration. Instead, partial evaluation-based approaches [21; 22; 33] allot each combination of various hyperparameters with incomplete training resources to acquire the evaluation result, hence conserving assessment resources. In order to identify Diabetic Maculopathy in retinal pictures, this research examines the effects of applying the Bayesian optimization (BO) technique on the classification results of deep neural networks. In this study, they present two major customized CNN models for detecting Diabetic Maculopathy in datasets from fundus retinography and optical coherence tomography (OCT), two different forms of retinal imaging. The best designs for the suggested CNNs are chosen, and associated hyperparameters are optimized, using the Bayesian optimization technique. The results of this work show that the efficiency of the suggested CNNs for the categorization of diabetic maculopathy in retina and OCT images may be improved by utilising Bayesian optimization to fine-tune the network hyperparameters [46]. Using the most recent developments in deep learning, techniques utilizing transfer learning have succeeded in solving this challenge. Hyperparameter settings, meanwhile, are a prevalent issue when using deep learning techniques. To identify COVID19 using X-ray pictures, we used a transfer learning-based classification technique that we modified and expanded in this study. We also used a variety of optimization strategies to handle the hyper-parameter configuration problem [47]. The momentum is often controlled by a fixed in conventional CNN optimization techniques. However, momentum hyperparameter optimization can be substantially challenging. They provide a unique adaptive momentum for quick and reliable settlement in this work. The use of adaptive momentum frequency reduces the necessity for momentum hyperparameter adjustment by proposing raising or reducing based on variations in each epoch's output [48]. Consequently, a strong technique for aiding doctors in taking medical decisions has appeared: deep learning-based human-centric scientific diagnostics. To automatically recognize Everything in blood pictures, various computer-aided diagnostic techniques have been created. For the purpose of detecting ALL in micro smear pictures, a novel Bayesian-based optimised CNN is presented in this paper. The design of the presented CNN as well as its hyperparameters are tailored to the raw data using the Bayesian optimization technique to improve classification efficiency. In order to find the number of network hyperparameters that minimizes an objective error measure, the Bayesian optimization approach employs an educated continuous search process [49].
## 3 Results and Discussion
### Ethanol Induces Cell Death  
To evaluate the protective effect of the Kakadu plum on alcohol-induced cell death the optimal concentration of ethanol was measured in the hepatocellular carcinoma cell line  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multicolumn{2}{c}{**DPPH**} & \multicolumn{2}{c}{**Reducing Power**} \\ \cline{2-5}  & **(mg GAE/g DW)** & **(mg AAE/g DW)** & **(mg GAE/g DW *)** & **(mg AAE/g DW **)** \\ \hline KKD-NT & 239.7 \(\pm\) 8.17 & 124.4 \(\pm\) 4.34 & 179.6 \(\pm\) 5.35 & 74.4 \(\pm\) 2.06 \\ KKD-WA & 215.9 \(\pm\) 16.2 & 111.1 \(\pm\) 8.60 & 140.6 \(\pm\) 7.80 & 60.5 \(\pm\) 3.08 \\ Pomegranate & 2.19 \(\pm\) 0.02 & 5.27 \(\pm\) 0.04 & 7.22 \(\pm\) 0.12 & 15.29 \(\pm\) 0.23 \\ Orange & 1.16 \(\pm\) 0.02 & 2.60 \(\pm\) 0.04 & 8.75 \(\pm\) 0.30 & 18.14 \(\pm\) 0.56 \\ Lemon & 1.66 \(\pm\) 0.08 & 2.47 \(\pm\) 0.05 & 6.65 \(\pm\) 0.44 & 14.08 \(\pm\) 0.83 \\ Blueberry & 2.84 \(\pm\) 0.01 & 6.86 \(\pm\) 0.03 & 8.94 \(\pm\) 0.33 & 18.86 \(\pm\) 0.61 \\ Raspberry & 3.29 \(\pm\) 0.03 & 8.13 \(\pm\) 0.07 & 11.76 \(\pm\) 0.20 & 23.79 \(\pm\) 0.37 \\ \hline \hline \end{tabular}  
* mg AAE/g DW: mg ascorbic acid equivalents (mg AAE)/ g dry weight (DW); ** mg GAE/g DW: mg gallic acid equivalents (mg GAE)/ g dry weight (DW) of fruits.  
\end{table}
Table 4: Potential antioxidant activities of KKD-NT and KKD-WA.  
Figure 1: DPPH scavenging activity of KKD-NT and KKD-WA.  
(HepG2). For that, we treated HepG2 cells with indicated concentrations of ethanol (0.25% to 8%) for 24 h, and then subjected them to MTT reagent to measure the cell survival rate. The HepG2 cell line is commonly used as a common model to assess the toxicity studies of various compounds against an oxidative stress inducer [52]. The viability of the cell in percentage showed a gradual decrease with the increasing concentration of ethanol in the HepG2 cells treated for 24 h. At a concentration of 4% ethanol, almost half of the cells were inhibited in comparison with the control group (0%) in HepG2 cells. Therefore, we selected 4% ethanol to induce cell inhibition in the HepG2 cells to examine the prevention of cell death by the extracts of Kakadu plum and to study the prevention of ethanol-induced ROS production by our samples.
# Seismic Damage Analysis of Box Metro Tunnels Accounting for Aspect Ratio and Shear Failure
## 2 Description of Tunnels and Site Conditions  
We used three types of rectangular cut-and-cover reinforced concrete box tunnels with various aspect ratios, as shown in Figure 1. They were single, double, and triple box tunnels designed and constructed for subway systems in South Korea. The baseline tunnel sections were developed based on 91 structural sections of subway tunnels in Korea. The height (H) was 6.0 m for all box tunnels, whereas the center-to-center width (B) varied from 6.0 m to 12.0 m. The aspect ratio (B/H) ranged from 1.0 to 2.0 for single and double box tunnels. For triple box tunnels, B/H = 2.0 was not modeled because such a section was not shown to be used. Instead, two baseline configurations were modeled where B/H of the left unit and the center unit was 1.5, termed B/H = 1.5 L and B/H = 1.5 C, respectively. The thickness of sidewalls, top slab and bottom slab was set to 1.0 m. The cross-sectional dimensions of the inner column of double and triple tunnels were \(0.4\times 1.0\) m, with a spacing of 1.0 m. The reinforcement ratio of structural members, determined based on the actual structural design, increased compatibly with an increment of the aspect ratios. The soil profile was assumed to be uniform and the depth of overburden was set to 7.0 m. The at-rest earth pressure coefficient (\(K_{0}\)) was set to 0.5.
## 2 Materials and Methods
### Data Sources
#### 2.2.3 Land Use/Land Cover Data  
A land use type map of China for the period 1985-to-2022 was provided by professors [PERSON] and [PERSON] from Wuhan University. It contained provincial annual land surface cover data ([[https://zenodo.org/records/4417809](https://zenodo.org/records/4417809)]([https://zenodo.org/records/4417809](https://zenodo.org/records/4417809)) (accessed on 17 September 2024)) [48]. This dataset encompasses all Landsat data accessible on the GEE platform, from which spatiotemporal features were derived and integrated with a random forest classifier to achieve classified maps, alongside a proposed post-processing technique involving spatiotemporal filtering and logical reasoning. The dataset was compared with the existing land cover products. There was a good agreement with the global forest change, global surface water, and impervious water time series datasets. The dataset classifies land use types into the following nine categories: cropland, forest, shrub, grassland, water, snow/ice, barren, impervious, and wetland. In this study, the six land use types of grassland, woodland, cultivated land, water body, bare land, and impervious areas were selected to study the response of vegetation to climatic drought. The meteorological drought and ecological drought values of different land use types were analyzed in ArcGIS software 10.8. The main data used in this study are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data Type** & **Data Name** & **Resolution** & \
\begin{tabular}{c} **Time and** \\ **Space Range** \\ \end{tabular} &
\begin{tabular}{c} **Time and Space** \\ **Range of Use** \\ \end{tabular} & **Bibliography** \\ \hline \multirow{4}{*}{GPP data} & The China Regional & & & & \\  & PML-V2 Land & & & & \\  & Evaptotranspiration and & 1 day, 500 m & 2000.02.26–2020.12.31 & 2001–2020, 1 km & [39; 40; 41; 42] \\  & Total Primary & & & & \\  & Productivity Dataset & & & & \\  & 1 km monthly mean & & & & \\ Temperature data & temperature dataset & 1 month, 1 km & 1901–2023 & 2001–2020, 1 km & [PERSON] et al. [43; 44; 45; 46; 47] \\  & for China & & & & \\  & 1 km monthly & & & & \\ Precipitation data & precipitation dataset & 1 month, 1 km & 1901–2023 & 2001–2020, 1 km & [PERSON] et al. [43; 44; 45; 46; 47] \\  & for China & & & & \\ Land use/land & Annual China Land & 1 year, 30 m & 1985–2022 & 2001–2020, 1 km & [PERSON] and [PERSON] et al. [48] \\  & cover data & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main data sources.  
Figure 3: Mean temperature and precipitation in the Shendong mining area from 2001 to 2020.
# Dynamic Spectrum Access for Internet of Things Service in Cognitive Radio-Enabled LPWANs
## 3 System Architecture  
If IoT devices are equipped with cognitive radio capabilities in LPWANs, they can work as a CR-based IoT system. In a CR-enabled LPWAN, wireless IoT devices collaboratively convey their event readings over idle spectrum channels. Some of the LPWANs share a single channel for communication, and an event sensing trigger causes the IoT devices to generate the burst traffic packets. Hence, many IoT nodes, within the specific event area in the densely deployed CR-LPWAN, seek to seize the same channel simultaneously. This increases the call blocking probability or packet losses, which cause transmission delay and excessive power consumption. Hence, it is necessary for CR-LPWAN to access a proper channel opportunistically among multiple channels to mitigate this latent challenge.
# Cell Response to Oxidative Stress in Antarctic Filamentous Fungi
## 8 Antioxidant Defence in Antarctic Fungi
### Non-Enzymatic Antioxidants  
In addition to the production of antioxidant enzymes, fungi have been shown to synthesize a wide variety of molecules with antioxidant and scavenging properties. For example, marine fungi produce phenolic compounds, anthraquinones, xanthones, carotenoids, indole derivatives, and alkaloids that participate in their cold stress response [200,232]. [PERSON] et al. [181] reported that the content of phenolic compounds increased under low-temperature conditions, thereby facilitating their ROS scavenging activity. Furthermore, the melanin present in the cells of Antarctic fungi demonstrated remarkable stability as well as significant antioxidant properties, which included a robust ability to scavenge DPPH free radicals [6]. It plays a critical role in protecting injured cells by reducing the presence of ROS. The function of melanin in protecting against cold stress has also been established in _Cryptococcus neoformans_[233] and _Cryomyces antarcticus_[201]. In addition, the elevated carotenoid biosynthesis exhibited a concomitant correlation with the adaptation to low temperatures in the Antarctic yeast _Rhodosporidium kratochvilowae_[166] and rock-inhibiting fungus _Knufia petricola_ A95 [202]. The cold adaptation in Antarctic yeasts, isolated from soils in King George Island, correlated with the production of freeze proteins and ergosterols [169].
## 5 Results and Analysis
### Interference Measurement Results and Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measurement Area** & **Figure** & **868.0–868.6 MHz** & **869.4–869.65 MHz** \\ \hline GCU & 2 & 97.3\% & 4.8\% \\ George Square & 3 & 13.2\% & 0\% \\ Kelvingrove Park & 4 & 64.9\% & 49.7\% \\ Buchanan Bus Station & 5 & 0\% & 0\% \\ Rottenrow Garden & 6 & 51\% & 54\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The probability of Signal Interference for LoRA Uplink and Downlink Mandatory Channels. Figure 11: Measurements at Kelvingrove Park. of mandatory LoRa channel experiencing interference, shown in Table 7 at this location is at 0%. The measurement at Rottenrow Garden was the final, and Figure 13. displays the results. The RF signal activity with intense power levels was noted in the audio subband, 863-865.0 MHz, unlike the previous locations. There were strong and continuous transmissions centred at 863.1 MHz, 863.6 MHz, and 864.4 MHz with power levels at \(-91.28\) dBm, \(-80.88\) dBm, and \(-106\) dBm, respectively. Additionally, weaker continuous and periodic signals were observed in the lower frequencies of 865.0-868.0 MHz sub-band and the upper part of this sub-band was occupied with strong periodic signal activity. These transmissions were centred at 867.3 MHz and 867.6 MHz with power levels at \(-51.3\) dBm and \(-49.9\) dBm, respectively. In the 868.0-868.6 MHz sub-band for mandatory LoRa uplink, diverse patterns of signal activity showed up. The strong transmissions centred at 868.1 MHz, 868.3 MHz, and 868.5 MHz were periodically observed. At the same time, a relatively weaker but more frequent signal activity was centred at 868.3 MHz with power levels at \(-99\) dBm. In this sub-band, an unusual continuous but weaker signal activity centred at 868.6 MHz with a power level at \(-107\) dBm was violating the DC regulations. The next sub-band with signal activity was 869.4-869.65 MHz for mandatory LoRa downlink channels. The activity of 4.082 s periodic signal transmissions centred at 869.5 MHz with power levels at \(-93\) dBm and another weaker signal activity centred at 869.5 MHz were observed at Rottenrow Garden. As shown in Table 7, the probability of LoRa signals experiencing interference in the 860.0-868.6 MHz and 869.4-869.65 MHz sub-bands for LoRa uplink and downlink in this location is at 51% and 54% respectively. Figure 12: Measurements at Buchanan Bus Station.
## 3 Experimental Results and Discussion
### Damage Model of Stress-Strain Relationship
#### Brief Review of BCM  
The BCM is developed based on the classical fiber bundle model (FBM) for characterizing the mechanical behaviors of materials. The model is considered as consisting of a bundle of parallel linearly elastic fibers assigned random fracture thresholds with a probability density of \(P_{d}(\varepsilon)\) and a chain of linked perfect plastic sliders with random yield thresholds and a probability density \(P_{r}(\varepsilon)\).  
After each failure event, the load carried by the intact fibers are redistributed (i.e., the equal-load sharing pattern), and the same plastic strain is produced by an individual slider after each sliding event once the yielding threshold is reached. Hence, the damage variable \(d(\varepsilon)\) (i.e., the accumulative distribution of the probability density) and the plastic variable (plastic ratio) \(r(\varepsilon)\) (i.e., the accumulative distribution of \(P_{r}(\varepsilon)\)) can be calculated based on the statistical method such as:  
\[d(\varepsilon)=\int_{0}^{\varepsilon}P_{d}(\varepsilon)d\varepsilon \tag{16}\]  
\[r(\varepsilon)=\int_{0}^{\varepsilon}P_{r}(\varepsilon)d\varepsilon \tag{17}\]  
Hence, the total damage variable coupling the effects of both the elastic damage and plasticity, named plastic-damage variable \(dr(\varepsilon)\), is defined as:  
\[dr(\varepsilon)=d(\varepsilon)+r(\varepsilon)=\int_{0}^{\varepsilon}P_{d}( \varepsilon)d\varepsilon+\int_{0}^{\varepsilon}P_{r}(\varepsilon)d\varepsilon \tag{18}\]  
Thus, the constitutive relationship for CA mortar can be modeled as follows:  
\[\sigma=[1-d(\varepsilon)]E_{0}\varepsilon \tag{19}\]  
It is assumed in the monotonic compressive case, the plastic-damage variable is not involved during the loading process in this work. Hence, the values of the total damage for a certain stress-strain curve, as well as mean and standard deviations of the damage, for the twelve CA mortar specimen can be calculated by following equations, the results of which are plotted in Figure 10.  
\[d_{i} =1-\frac{\sigma_{i}/\varepsilon_{i}}{E_{0}} \tag{20}\]  
\[d(\varepsilon)=\frac{1}{N}\sum_{j=1}^{N}d_{i}(\varepsilon) \tag{21}\]  
\[STD.(\varepsilon)=\sqrt{\frac{1}{N-1}\sum_{j=1}^{N}\left[d_{i}(\varepsilon)- \overline{d}(\varepsilon)\right]^{2}} \tag{22}\]  
where \(d_{i}\), \(\sigma_{i}\), \(\varepsilon_{i}\) are the \(i\)-th damage variables, stress and strain for a certain stress-strain curve, respectively. \(D(\varepsilon)\) and \(STD(\varepsilon)\) denote the mean damage and its standard deviations for the ten stress-strain curves, respectively. \(N\) denotes the numbers of the stress-strain curves.