esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Insulation materials are fundamental for decreasing energy losses and guaranteeing thermal and acoustic comfort in buildings, which may significantly contribute to decreasing the energy consumption related with poor thermal building conditions. These insulation materials should have a low susceptibility to biological degradation agents to decrease the risks of degradation of other construction materials, as well as decrease possible health risks related with the development of noxious biological degradation agents regarding indoor air quality, for example, or decrease possible structural risks posed by those agents. The present study aimed at evaluating the susceptibility of several insulation materials to mold growth and subterranean termites' attack. Insulation materials, including expanded polystyrene (EPS), mineral wool (MW), and expanded cork agglomerate (ICB), were tested against mold development, using maritime pine as a control. Three types of inoculations were made: (1) natural indoor inoculation; (2) artificial inoculation using _Aspergillus niger_ and _Pencilillium funcitulosum_; and (3) artificial inoculation using _Auroobsidium pullulans_. The susceptibility of the insulation materials referred to, plus wood/glass fiber (WGF), was evaluated for two subterranean termite species: _Reticulitermes grassei_ and _Reticulitermes flavipes_. The expanded cork agglomerate showed a higher susceptibility to molds than the other insulation materials tested. The remaining materials revealed a good performance, showing no growth or traces of growth of molds. All the materials tested showed susceptibility to subterranean termites, with both species being able to cross them to obtain access to the wood. However, wood/glass fiber showed a negative effect, which translated into lower survival rates and attack degrees of the wood. Some tested materials showed a good resistance to the development of biological degradation agents, namely an organic material (ecount fiber), a composite of organic and inorganic materials (WGF), and an inorganic material (EPS). These results indicate that it is possible to pursue the development of innovative and effective insulation materials with a low susceptibility to biological degradation agents, regardless of their organic or inorganic origin.  
Keywords:insulation materials; biological degradation; molds; fungal growth; subterranean termites +
Footnote †: journal: Applied Sciences
# Current Trends on Green Wireless Sensor Networks
## 2 Energy Conservation
### Data Reduction
#### 2.2.3 Data Prediction  
An alternative approach to save energy is to predict part of the sensed data without any transmission. Many different prediction-based data reduction mechanisms are proposedin the literature, also using different prediction approaches such as regression, neural networks, or machine learning. In [84] we can find a useful survey that also contains a systematic procedure for selecting a suitable scheme to make predictions. This work classifies the mechanisms in two groups:  
1. In the Single Prediction Schemes (SPSs), predictions are made in a single point in the network, either in the cluster head (CH), or in a sensor node when it is more expensive (in energy terms) to obtain a sample than to predict one. This scheme has been applied in conjunction with adaptive sampling [85], topology control [86] and clustering [87; 88; 89].
2. In Dual Prediction Schemes (DPSs), where clustering is used, the predictions are simultaneously made in CHs and nodes. The idea is that nodes and CH obtain the same prediction, but the node can check the precision of the prediction by comparing this with the real measure, and only send the measure to CH if the prediction is poor. In other case, CH uses the prediction.  
DPSs are potential candidates to optimize the data transmissions because they avoid unnecessary transmissions without affecting the quality of their measurements. Simulation results in [90] show that the number of transmissions can be reduced by almost 98% in the nodes with the highest load.  
[PERSON] et al. [91] propose the combined use of Dual prediction (DP) and Data Compression (DC) schemes in clustered networks, where DP is used for data reduction between nodes and CH by exploiting the temporal correlation and DC is used between the CHs and the BS by exploiting the spatial correlation.  
This combination has received special attention in the literature. [PERSON] et al. [92] present a comparative analysis using combinations of different mechanisms of each type. For the DP scheme, the work focuses on neural networks to perform predictions, in comparison to popular LMS approaches. Regarding data compression, principal component analysis, non-negative matrix factorization, truncated-singular value decomposition, and discrete wavelet are discussed and compared. The results show significant reduction in the number of transmissions when using both schemes while preserving the QoS requirements. More recently, the work in [93] proposes a gradient-based adaptive model that also uses a combination of DP and DC schemes.
# Estimation of Rock Load of Multi-Arch Tunnel with Cracks Using Stress Variable Method
## 2 Estimation Methods for Rock Load
### Numerical Analysis Approach
#### 2.2.2 Criterion of Stress Variable  
Owing to the stress transfer effect around the tunnel, the minimum principal stress was observed to be zero on the excavation periphery, and it gradually increased as the distance from the excavation periphery increased. In contrast, the maximum principal stress was at its peak near the excavation periphery, and it gradually decreased as the distance from the excavation periphery increased, as illustrated in Figure 4.  
The stress variable [9] is defined in Equation (5) as the ratio of the difference between the maximum and minimum principal stresses to the maximum principal stress:  
\[\text{Stress variable}\left(\%\right)=\frac{\sigma_{\text{max}}-\sigma_{ \text{min}}}{\sigma_{\text{max}}}\times 100 \tag{5}\]  
where \(\sigma_{\text{max}}\) = maximum principal stress and \(\sigma_{\text{min}}\) = minimum principal stress.  
The rock load on the tunnel is defined as the area around the tunnel periphery, which starts from the peak principal stress and ends at the point where a stress variable of 10% is achieved [9; 15]. Based on the available literature, a stress variable of 10% was applied in this study.
## 4 Simulation and Results
### Scenario 2: Six-UAVs Case  
To validate the efficacy of our proposed algorithm for a more complicated scenario, six UAVs in a common airspace implementing tasks are simulated. The initial position and the destination are shown in Table 3. All the results are presented in Figure 7.  
\begin{table}
\begin{tabular}{c c c} \hline
**UAV Number** & **Initial Position** & **Destination Position** \\ \hline
1 & 0, 0, 100 & 200, 200, 100 \\
2 & 0, 0, 0 & 200, 200, 200 \\
3 & 200, 0, 0 & 0, 200, 200 \\
4 & 0, 0, 200 & 200, 200, 0 \\
5 & 0, 200, 0 & 200, 0, 200 \\
6 & 200, 0, 100 & 0, 200, 100 \\ \hline \end{tabular}
\end{table}
Table 3: The initial and destination position of UAVs in Scenario 2.  
Figure 7: The situation and results in Scenario 2. (**a**) The initial collision-free trajectory and total distance in Scenario 2; (**b**) The 5000 th collision-free trajectory and total distance in Scenario 2; (**c**) The distance changes with iteration number increasing in Scenario 2; (**d**) The relative distance of the best trajectory in Scenario 2.  
We set the number of random searches in one iteration \(SN\) as 10, and the number of iterations \(IN\) as 5000. The expected time to detect a collision is 2 \(\bigtriangleup t\). The initial collision-free trajectory and the 5000 th collision-free trajectory are shown in Figure 7a,b. The total distance is 2553.89 and 2215.71, respectively.  
Figure 7c demonstrates the change of total distance with the iteration number. We can find that the proposed algorithm can get rid of local optimization point effectively. The total distance tends to be stable after 4500 iterations.  
Relative distance in the best result with fixed intervals is illustrated in Figure 7d. As expected, the shortest distance between two UAVs is larger than the radius of the protected sphere, which is 10 in the scenario.
## 1 Introduction
[PERSON] used visible/near-infrared technology to detect fresh pork by constructing the detection system, and the establishment of the partial least squares regression model predicted the correlation coefficient of intramuscular fat, protein, and water content \(\geq\)0.81 [17]. In addition to chemical composition, sensory evaluation is also an important indicator of meat quality, including color, texture, flavor, and juiciness, etc. Due to the relationship between these indicators and the physical and chemical properties, sensory evaluation of meat products can be detected by near-infrared spectroscopy. [PERSON] studied the pH, color attributes, and hydraulic system of adult beef and juvenile beef by near-infrared spectroscopy (1100-2500 nm) [18]. It was found that the correlation coefficients of L* and b* in color characteristics were 0.869 and 0.901, respectively. [PERSON] using NIR (near infrared spectroscopy) assessed the sensory quality of meat sausages. The NIR spectra described the main color and texture variations among sausages with a relative prediction of 0.87 to 0.93 for color attributes and 0.83 to 0.91 for juiciness and greasiness [19]. Hyperspectral imaging spectroscopy can also be used to monitor changes in physicochemical, microbiological, and sensory attributes of the packaged dry-cured sausages, and the developed technique can be implemented in quality inspection of food products without additional laborious chemical analysis and sensory evaluation [20]. All of the above research methods average hyperspectral data of each sample, which represents the spectral information of one sample, and ignores the component differences in different parts of meat, so it does not make full use of hyperspectral image information. The purpose of this paper is to (1) identify the grade of Cantonese sausage by hyperspectral image information, (2) find the effective wavelengths of the sausage samples in NIR bands, (3) compare the discriminant effect of PLSR and SPA-MLR, and (4) analysis the correlation between sausage grade with lean meat or fat meat.
## 5 Results  
### Exergy Analysis  
A conventional exergetic analysis is conducted to evaluate the real inefficiencies within the processes. The bar chart in Figure 6 shows a comparison of the results of the exergy analysis for the three design cases. The process design in Case I has a low exergetic efficiency of 28.15% since only a limited amount of the exergy of fuel (methane as process gas) can be converted to the exergy of product. The major part of the exergy of fuel (methane as fuel) is used to drive the process by providing heat to the SMR unit. Exergy destructions are mainly caused by the combustion process within the SMR furnace and the heat transfer within several heat exchangers. In Case II all of natural gas and oxygen (exergy of fuel) can be used directly in the process line to produce methanol. Although the production capacity of Case II exceeds that of Case I, less exergy of fuel is required. The energy demand of the SMR unit is satisfied by recovering the heat from partial oxidation in the combustion zone of the ATR. As a consequence, the combustion of the purge gas from the synthesis unit is sufficient to cover the energy demand of the remaining units. The combustion of only a small amount of gas reduces the exergy destruction and eventually also the exergy losses. In summary, the second case is of high thermodynamic performance having an exergetic efficiency of 55.6%. Case III has the highest production capacity (exergy of product) of all processes. Both, the DMR and SMR are highly endothermic processes, which require the supply of additional exergy of fuel to drive the reactions. To reduce the amount of extra fuel (equal to Case I), a high amount of process gas is purged and recycled to the furnace R-06, causing a reduction in the exergy of product. As in Case I, the exergy losses are mainly assigned to the flue gas leaving the system with stream 16. Most of the exergy destruction occurs within the furnace R-06, the GHR R-04, the DMR R-05 and some heat exchangers in sub-system V. This process design has an exergetic efficiency of 41%.
# Rice Secondary Metabolites: Structures, Roles, Biosynthesis, and Metabolic Regulation
## 2 Structural Diversity and Roles of Rice Secondary Metabolites
### Terpenoids and Their Biological Functions
#### 2.3.4. Triterpenoids and Their Biological Functions  
Triterpenoids are usually distributed in rice bran. Eight hydroxylated triterpenae alcohol ferulates (**188**-**190**, **193**, **194**, **196**, **197**, **200**) were isolated from rice bran. They showed moderate cytotoxic activity [98; 99]. The seed coats (or bran) usually contain large amounts of bioactive metabolites. This was also observed for the seed coats of quinoa (_Chenopodium quinoa_), where there were various triterpenoids distributed. Quinoa triterpenoids showed antimicrobial and molluscicidal activities [100]. There are few reports about the physiological and ecological functions of rice triterpenoids. The aglycones of rice triterpenoids are citrostadienol (**185**), cycloartenol (**191**), cycloeucalenol (**198**), gramisterol (**201**), and lupeol (**205**). On the basis of the biosynthetic pathway, citrostadienol (**185**), cycloeucalenol (**198**), gramisterol (**201**), and their derivatives are considered mortriterpenoids. Rice triterpenoids and their biological activities are listed in Table 6. Their structures are shown in Figure 6.  
\(\gamma\)-Oryzanol is a mixture of triterpene and sterol ferulates extracted from rice bran [101]. In addition to its antioxidant activity, \(\gamma\)-oryzanol is often associated with cholesterol-lowering, anti-inflammatory, anti-cancer, and anti-diabetic properties [102]. The mixture of triterpene alcohols and sterols, with its components such as cycloartenol (**191**) and 24-methylene cycloartanol (**195**) from rice bran, can lower postprandial hyperglycemia in mice and humans [103].  
The main triterpene ferulates are cycloartenol ferulate (**193**/**194**) and 24-methylenecycloartanol ferulate (**196**/**197**). Both cycloeucalenol _trans_-ferulate (**194**) and 24-methylenecycloartanol _cis_-ferulate (**196**) showed anti-inflammatory activity in mice with inflammation induced by 12-O-tetradeanoylphorbol-13-acetate [104].  
Figure 5. Structures of the diterpenoids isolated from rice.  
Figure 6: \(Cont\).
## 4 Selection of FEC Codes for the Railway Application  
FEC describes a methodology whereby the sender of some data includes an ECC to the initial data, in order to permit the receiver to detect and correct a number of errors that appear in a noisy channel. The procedure is given in Figure 3. This procedure can be applied on occasions when there are multiple receivers. Here, we have a noisy channel whereby messages are difficult to be sent. We experience a drawback of the packet increase with the addition of an ECC; however, the ECC gives the advantage that the receiver will be able to correct certain errors that are caused by noise or interference. This gives the  
Figure 2: Telecommunication configuration.  
result that systems, which include ECC can suffer some extent of a bit-error-rate (BER) in a given signal-to-noise ratio (SNR).  
As we have seen earlier, the FEC is divided into two types: block code and convolutional code. Here, we will deal with the Reed-Solomon (RS) and POLAR schemes. The RS scheme is proposed since it is the most typical and efficient scheme among the FEC schemes. Moreover, we use the POLAR codes, since they are relatively new and adequate for 5G communication. Other codes that could be utilised are the turbo and low-density parity-check (LDPC) codes. The turbo codes are used in 3G/4G mobile communications, and they are the first practical codes that reach the maximum channel capacity, which is the theoretical maximum for the code rate that reliable communication is accomplished under the presence of noise. The LDPC is the competitor of the POLAR codes, which are both suggested in 5G communication [38; 39]. The point of our paper is to apply it to an AE telecommunication network and not to invent a new FEC scheme.  
For information regarding the RS scheme, the reader can see the [40] paper. For a thorough description of POLAR codes and LDPC, the reader is directed to the [26] and [41] papers, respectively. For a brief comparison between POLAR and LDPC codes, the LDPC codes have been in the research domain for a large period of time, while the POLAR codes were introduced in 2009. The LDPC codes encapsulate an iterative decoding process, with which every consecutive attempt at decoding the block of information informs the next, until convergence to a permissible codeword. On the other hand, POLAR codes choose the recovered information block from a list, which is retrieved from the associated parallel successive cancellation decoding processes. In this way, decoding each consecutive bit of information informs the decoding of the next. For both schemes, the decoder of the channel exhibits a higher complexity to a large extent than the encoder, since it uses iterative or parallel decoding processes, which are based on probabilistic representations of the bits that are encoded. This occurs to surpass the uncertainty, which is attributed to noise, interference, and fading [42]. In this paper, POLAR codes are the main focus, which in conjunction with RS codes are checked for efficiency. The POLAR codes description is given below.
# Mechanical Properties of White Metal on SCM440 Alloy Steel by Laser Cladding Treatment
## 3 Results and Discussion
### SEM Microstructure Analysis and EDS Analysis
As shown in Figure 12, the melting state of white metal varied, depending on the amount of laser energy in the laser cladding process. If the amount of laser energy was low, the white metal bonded in a state where it did not completely melt, and if the amount of rare energy was high, the base material was affected, and a high-hardness layer was formed. If the laser power was high, the Fe component in the base meter was mixed into the white metal, which induced high hardness in the study. Figure 8: SEM images of the laser cladding at powder supply rates of **(a)** 2.5 rpm and **(b)** 3.5 rpm. Figure 10: SEM images of the laser cladding at head speeds of **(a)** 10 mm/s and (b) 20 mm/s. Figure 9: Comparison of EDS results with powder supply rates at **(a)** the cladding layer and **(b)** the HAZ layer. When white metal is laminated on the surface using the laser cladding process, it is important to produce high-quality clad by preventing the generation of mixed layers and HAZs, which cause the hardness to increase. This study focused on practical research by cladding white metal with SCM440 alloy steel, which is used as a material for tilting pad bearings, using laser cladding equipment that is actually used for industrial purposes. In this paper, micro Vickers hardness measurement, SEM and EDS analysis were verified. We discovered the effect of the heat input generated according to the process parameters of laser cladding on the hardness and composition of the cladding and proved that laser cladding is an effective alternative process.
# Hybrid RFSVM: Hybridization of SVM and Random Forest Models for Detection of Fake News
## 3 Implementation and Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Category**} & **Avg.** & **Avg.** & **Avg.** & **Avg.** & **Avg.** & **Precision** \\  & **Precision** & **Precision** & **[RF]** & **[SVMs]** & **[RFSVM]** & **[RFSVM]** \\ \hline Agriculture & 0.90 & 0.92 & 0.94 & 0.98 & 0.98 & 0.98 \\ \hline Aviation & 0.51 & 0.53 & 0.55 & 0.63 & 0.70 & 0.71 \\ \hline Sports & 0.57 & 0.59 & 0.61 & 0.67 & 0.76 & 0.74 \\ \hline Roads & 0.67 & 0.69 & 0.71 & 0.74 & 0.81 & 0.82 \\ \hline Residential & 0.55 & 0.57 & 0.59 & 0.63 & 0.65 & 0.63 \\ \hline Forest & 0.59 & 0.61 & 0.63 & 0.63 & 0.65 & 0.67 \\ \hline Village & 0.43 & 0.45 & 0.47 & 0.55 & 0.56 & 0.59 \\ \hline Finance & 0.90 & 0.92 & 0.94 & 0.98 & 0.98 & 0.98 \\ \hline Politics & 0.90 & 0.92 & 0.94 & 0.94 & 0.96 & 0.94 \\ \hline Technology & 0.43 & 0.45 & 0.47 & 0.55 & 0.54 & 0.55 \\ \hline \end{tabular}
\end{table} 
Table 7: Precision values for different news categories with different ML algorithms. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Category**} & **Avg.** & **Avg.** & **Avg.** & **Avg.** & **Recall** \\  & **Recall** & **[XGBoost]** & **Recall [RF]** & **[SVMs]** & **[RFSVM]** & **Reference** \\ \hline Agriculture & 0.43 & 0.45 & 0.47 & 0.51 & 0.62 & 0.61 \\ \hline Aviation & 0.23 & 0.25 & 0.27 & 0.35 & 0.52 & 0.53 \\ \hline Sports & 0.51 & 0.53 & 0.55 & 0.61 & 0.78 & 0.78 \\ \hline Roads & 0.10 & 0.12 & 0.14 & 0.18 & 0.33 & 0.35 \\ \hline Residential & 0.25 & 0.27 & 0.29 & 0.33 & 0.41 & 0.43 \\ \hline Forest & 0.53 & 0.55 & 0.57 & 0.57 & 0.72 & 0.71 \\ \hline Village & 0.33 & 0.35 & 0.37 & 0.45 & 0.58 & 0.59 \\ \hline Finance & 0.53 & 0.55 & 0.57 & 0.61 & 0.69 & 0.71 \\ \hline Politics & 0.53 & 0.55 & 0.57 & 0.57 & 0.67 & 0.67 \\ \hline Technology & 0.38 & 0.40 & 0.42 & 0.50 & 0.58 & 0.60 \\ \hline \end{tabular}
\end{table} 
Table 8: Recall values for different news categories with different ML algorithms. Cross validation of RF and SVMs provides a significant better result in terms of interpretability, time, and accuracy. Due to the better process of the training and feature extraction, accompanied by the hybridization of RF and SVMs, various improvements in accuracy, precision, recall, and F1-score across different news categories can be achieved. Currently, the hybrid model has been applied on small datasets in order to understand the better visualization of the data across different categories. Applying the hybrid algorithm on small datasets already provides brief insights into how the results are better, for different types of news categories, in comparison to individual machine-learning models. In our approach, currently RF and SVMs have been applied for comparisons within the various classes.