esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Compounding, Processing, and Recycling of Bionanocomposites  
For the compounding stage of the bionanocomposites, 400 and 1500 g/mol poly(ethylene glycol) (PEG) were first blended with NFC at 120 \({}^{\circ}\)C with a DLAB MS-H280-PRO magnetic hot plate (Beijing, China) for 40 min. Subsequently, the PEG/NFC was combined with PLA in a torque rheometer Brabender model 815,653 (Duisburg, Germany) for 5 min at 180 \({}^{\circ}\)C and 50 rpm. For the plasticized PLA used as reference, PEG was directly combined with PLA in the torque rheometer at the abovementioned conditions. As for the proportions, 7.5 wt% plasticizer was added with respect to PLA in the blends, and for the bionanocomposites, NFC proportions of 1, 3, and 5 wt% were included, based on the sum of the mass of PLA and PEG. These NFC percentages were precisely selected according to previous literature studies that found agglomeration at nanofiller percentages above 5 wt% (Barcelona, 2018; Barcelona, 2018; Barcelona, 2018). Therefore, the actual proportions of all of the components in the resulting nanocomposites were calculated, as shown in Table 1. For the sake of clarity, the nanocomposite samples were labeled according to the entire numeral of NFC weight percentage.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Designation**} & **PLA** & **PEG-400** & **PEG-1500** & **NFC** \\  & **(wt\%)** & **(wt\%)** & **(wt\%)** & **(wt\%)** \\ \hline PLA & 100.00 & - & - & - \\ PLA-400 & 92.50 & 7.50 & - & - \\ PLA-400-NFC1 & 91.59 & 7.42 & - & 0.99 \\ PLA-400-NFC3 & 89.81 & 7.28 & - & 2.91 \\ PLA-400-NFC5 & 88.10 & 7.14 & - & 4.76 \\ PLA-1500 & 92.50 & - & 7.50 & - \\ PLA-1500-NFC1 & 91.59 & - & 7.42 & 0.99 \\ PLA-1500-NFC3 & 89.81 & - & 7.28 & 2.91 \\ PLA-1500-NFC5 & 88.10 & - & 7.14 & 4.76 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition and designation of the samples.  
Afterward, these compositions were processed through injection molding into Type IV ASTM D638 dog-bone specimens [62] in a mini-injector jet HAAKE Minilet II (Vreden, Germany) with a preheating stage of 2 min at 190 \({}^{\circ}\)C. The temperature of the molds was 55 \({}^{\circ}\)C, and the injection and post-pressure were 40 MPa and 30 MPa, respectively.  
Subsequently, the dog-bone specimens were cut by hand into \(\sim\)3 mm diameter pellets and reprocessed into flat rectangle sheets with a thickness of 0.5 \(\pm\) 0.1 mm through compression molding using a custom-made four-axis hot plate press. Preheating at 190 \({}^{\circ}\)C for 10 min was followed by a pressure of 4.9 MPa for 3 min, 7.4 MPa for 3 min, and 9.8 MPa for another 3 min. After compression, the samples were removed from the hot press and cooled at room temperature with pressure to avoid deformation. The processing sequence and the appearance of the obtained specimens are shown in Figure 1.
# Regular Physical Activity and Dental Erosion: A Systematic Review
## 2 Materials and Methods  
### Search Strategy and Data Extraction  
A systematic review was conducted up to 20 November 2021, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines [22], using the databases PubMed, Scopus, and Web of Science. The search formulas included:  
* For PubMed: ((dental erosion) OR (tooth erosion) OR (erosi* lesion)) AND ((sport* professional) OR (physical activity) OR (sport*) OR (athlet*) OR (runner) OR (football*) OR (player) OR (cyclist) OR (swimm*) OR (sport* drink) OR (isotonic*))
* For Scopus: TITLE-ABS-KEY((\"dental erosion\") OR (\"tooth erosion\") OR (\"erosi* lesion\")) AND ((\"sport* professional\") OR (\"physical activity\") OR (sport*) OR (athlet*) OR (runner) OR (football*) OR (player) OR (cyclist) OR (swimm*) OR (\"sport* drink) OR (\"isotonic*\"))
* For Web of Science: TS = (dental erosion OR tooth erosion OR erosi* lesion) AND TS = (sport* professional OR physical activity OR sport* OR athlete* OR runner OR football* OR player OR cyclist OR swimm* OR sport* drink OR isotonic*).  
Records were screened by title, abstract, and full text by 2 independent investigators. The studies included in this review matched all the predefined criteria according to PICOS (population, intervention, comparison, outcomes, and study design), as shown in Table 1. A detailed search flowchart is presented in the Results section.  
The results of the meta-analysis are presented in forest plots using MedCalc Statistical Software version 19.5.3 (MedCalc Software Ltd., Ostend, Belgium) and Statistica 13.3 (Statsoft, Cracow, Poland).
## 3 Results
### Machine Learning Analysis for Pressure Estimation
#### 3.2.3 Implications of Correlations for Machine Learning Model Performance  
The observed correlations between plantar pressure and temperature, described in Section 3.1, provide insights that may explain the challenges faced in the first set of experiments, where temperature data were used to predict the plantar pressure. The weak and inconsistent correlations, especially in regions like the 1 stM and 3 rdM, suggest that temperature alone may not be a strong predictor of pressure in certain anatomical points. The lack of substantial linear or monotonic relationships indicates that the temperature data do not capture the complexities and variations in the pressure distribution across the foot. These findings imply that machine learning models trained solely on temperature features are likely to perform poorly when predicting plantar pressure as the underlying relationship between these variables is weak. The moderate to strong correlations in the midfoot regions (LatM and MedMF) may offer some predictive potential, but, overall, the weak correlations in other areas suggest that additional features or more complex data representations will be required to enhance the performance of the predictive models. This emphasizes the importance of incorporating more diverse and relevant biomechanical features when developing machine learning algorithms for plantar pressure prediction.
# Antitumor Effects of _Cannabis sativa_ Bioactive Compounds on Colorectal Carcinogenesis
## 3 Bioactive Compounds in the Prevention and Treatment of Colorectal Cancer
### Effects of Cannabinoids in CRC-Associated Mechanisms
#### 3.1.1 Cbd
reported that CBD treatments (1 and 5 mg/kg) decreased VEGF expression (\(p<0.05\)) in a dose-dependent manner in tumor tissues of CRC-induced mice when compared to cancer control groups [58]. Figure 5: Effect of CBD on pro-apoptotic mechanisms in colorectal cancer. \(\blacklozenge\), increase; \(\blacklozenge\), decrease; ATF4, activating transcription factor 4; CHOP, C/EBP homologous protein; eIF\(2\alpha\), phosphorylated eukaryotic initiation factor \(2\alpha\); PARP, poly (ADP-ribose) polymerase; PI3K/Akt, phosphoinositide 3-kinase/protein kinase B; ROS, reactive oxygen species; TRPV1, transient receptor potential cation channel subfamily V member 1. The figure was partly generated using Servier Medical Art, provided by Servier, licensed under a Creative Commons Attribution 3.0 unported license. Regardless, few studies have been carried out regarding CBD and angiogenesis; thus, more research is needed to understand the effects of CBD on angiogenesis, an anticancer mechanism. As stated earlier, adhesion, migration, and invasion mechanisms are involved in metastasis and the spread of cancer to other tissues. In HCT116 CRC cells, CBD at 1 (_p_ < 0.05) and 2.5 \(\upmu\)M (_p_ < 0.001) caused a significant reduction in GPR55-dependent adhesion and migration to endothelial cells, suggesting that CBD's antagonistic activity in GPR55 plays a key role in the reduction in metastasis [62]. In addition, [PERSON] and coworkers reported that CBD could prevent the epithelial-mesenchymal transition in HCT116 cells, by potentiating E-cadherin and inhibiting _N_-cadherin, snail, vimentin, and hypoxia-inducible factor 1-\(\upalpha\). In HCT116, SW620, and DLD-1 CRC cells, the expression of adenomatous polyposis coli and casein kinase 1 was likewise suppressed, while Axin1 was increased. Overall, these data indicate that CBD has anti-invasion and antimetastatic potential through the Wnt/\(\beta\)-catenin signaling pathway [47]. All the aforementioned mechanisms could be responsible for the potential of CBD treatment to slow the growth of CRC tumors. Indeed, several CRC xenograft animal models (DLD-1, HCT116 and CT26), after treatment with CBD (1-20 mg/kg body weight), experienced a delay in tumor growth, as well as in volume and weight [46; 47; 51; 58]. Furthermore, treatment with CBD (1 and 5 mg/kg) suppressed the development of aberrant crypt foci, CRC precursor lesions, and polyps in AOM-induced CRC mice [54]. It was reported that the administration of CBD can have some side effects, including the inhibition of hepatic drug absorption, changes in vitro cell viability, and decreases in fertilization capability. Despite these potential side effects, research has indicated that controlled administration of CBD in humans and animals is generally safe [63]. Therefore, more studies should be conducted for a better understanding of the possible side effects of CBD administration in the treatment of CRC.
# Acoustic Emission Monitoring in Prestressed Concrete:
## 2 Acoustic Emission and Attenuation of Elastic Waves in Concrete Constructions
These analyses primarily revealed a linear relationship between attenuation coefficient and frequency within the attenuation spectra. The frequency dependency extends to other signal characteristics, such as the signal's energy [29; 30]. The interplay between the wavelength and the size of the scatterer is critical; attenuation is markedly reduced when the wavelength exceeds the dimensions of the concrete's components or scatterers. Consequently, it has been proposed that interpreting signals in concrete at frequencies above 200 kHz presents significant challenges [20]. At the structural level, the propagation of elastic waves is significantly influenced by the presence of reinforcement bars and prestressing tendons, primarily due to the superior signal transmission capabilities of steel and the anisotropic arrangement of these elements, which serve as waveguides within the concrete matrix [31; 32]. It has been particularly noted that attenuation along prestressing wires in grouted ducts is higher compared to that of exposed wires [33; 34], a phenomenon attributed to energy dissipation through emission across the interface into the grout and surrounding concrete. Moreover, the wave propagation characteristics are also affected by the prestressing of the concrete. To explain, it has been observed that prestressed concrete members demonstrate lower attenuation levels in comparison to their reinforced concrete counterparts [35], which can be attributed to the prestressing of the concrete, which tends to close existing cracks. A comparison of damaged and undamaged reinforced concrete structures, as shown in [36], supports the idea that damage increases attenuation. Simply put, higher attenuation was observed in damaged structures. It is suspected that especially in areas with disturbances, Rayleigh waves, which transport the highest energy, do not transmit effectively [37]. These principles are relevant to the interpretation of the data and are taken into account in their interpretation. Furthermore, the data preparation in Section 3.3 is based on these explanations.
# Probabilistic Model Checking GitHub Repositories for Software Project Analysis
## 4 Experimental Results
### Participant Analysis
#### 4.3.2 Relationship Analysis  
One of the key features of GitHub is its role in facilitating collaboration among software developers and teams. Therefore, it is essential to understand the relationships and dynamics among participants in a project to promote effective collaboration. In this section, we analyze the behavior patterns of participants over time to gain insights into their activity levels, contribution patterns, and collaboration dynamics.  
\[filter(avg,\ R\{\"r\_steps\"\}=?[F\!participant],\ participant) \tag{14}\]  
Query (14) calculates the average time steps it takes for one participant to encounter another. A lower average time step indicates that team members interact and contribute to the project more frequently, while a higher average time step suggests longer intervals between individual contributions. The results are shown in Figure 11. The most frequent range observed falls between 2.0 and 2.5 average time steps, with an overall average of 2.51. This means that, on average, a participant encounters another participant approximately after going through around three time steps.  
\[filter(avg,\ P=?[X\ \"p1\"\ \&\ \"merge\"],\ participant\ \&\ \"accepted\") \tag{15}\]  
Query (15) is a query designed to determine the probability of who handled the merge for p1's merged pull requests. In this query, p1 is fixed as the person who performed the merge. Therefore, it calculates the likelihood of p1 being responsible for merging pull requests submitted by team members. Table 6 presents the results obtained from model checking.  
In the case of Sherlogs, p1 managed to merge nearly all of their pull requests, including their own, with a 100% merge rate for their own pull requests. DeSolver's p1 merged with a probability of 50% or less for all participants' requests. For kafkaVision and Redline, p1 did not merge their own pull requests but rather allowed other participants to manage them. Hearth's p1 exhibited the highest probability of managing its own pull requests but did not handle any of p4's pull requests.  
Figure 11: Result of Query 14: Average time steps to encounter another participant.
## 3 Mechanical Parameters  
Figure 2a-e depict the uniaxial compression stress-strain curves of specimens containing unparallel joints. It can be observed that the shape of the stress-strain curve of the specimen is significantly influenced by the joint angle. During the loading period, the stress-strain curve can be divided into four stages: microporosity compaction stage, elastic deformation stage, progressive fracturing stage (crack propagation), and post-failure stage (post-peak). In the compaction stage, the axial stress-strain curve exhibits a concave-up shape as the strain rises. This is attributed to the closure of the original pores and fissures within the specimen, which enhances its strength. As the strain rises, the stress growth rate accelerates until it reaches a threshold value and then stabilizes. At this point, the flawed specimen enters the elastic deformation stage, where the axial stress increases linearly with the axial strain, and cracks gradually initiate and propagate. As the axial load continues to climb, the axial stress-strain curve enters the fourth stage. In the post-peak stage, the specimen undergoes accelerated failure and develops apparent macroscopic cracks. Currently, certain specimens undergo a sharp drop in stress, promptly leading to a loss of their load-bearing capacity, whereas others exhibit a decrement of stress before ultimately losing this capacity upon reaching their peak strain.  
Figure 1: Test equipment and specimen shape: (**a**) experimental set-up; (**b**) specimen geometry.  
Figure 2f summarizes the evolution patterns of peak strength for flawed specimens under uniaxial compression at various joint inclination angles. When the lower joint angle is certain, the peak strength attains its maximum when the upper joint inclination angle is 90\({}^{\circ}\). As the upper joint inclination angle ranges from 30\({}^{\circ}\) to 90\({}^{\circ}\), the peak strength monotonically rises; when the joint inclination angle surpasses 90\({}^{\circ}\), the peak strength initially diminishes, followed by a subsequent increase. Taking the S-30 group as an example, the peak strength increases from 18.29 MPa (S-30-30) to 32.27 MPa (S-30-90), then decreases to 19.7 MPa (S-30-120), and subsequently rises to 26.22 MPa (S-30-150). Generally, peak strength peaks when the angle of joint 2 is 90\({}^{\circ}\), regardless of joint 1 angle.
# Application of Reinforcement Learning in Decision Systems: Lift Control Case Study
## 2 Description of the Problem and Concept of the Solution
### Traditional Heuristic Algorithms  
The Longest Queue First (LQF) algorithm utilizes a queue to determine which call to service at a given moment. When a passenger summons the lift from a floor or from inside the cabin, their request is added to the end of the queue. Requests are serviced in order of the longest waiting time. On each floor, there is typically only one button, so passengers cannot specify whether they want to travel up or down. Additionally, requests generated by pressing buttons inside the cabin take precedence over those generated on floors. Thisprioriitization ensures that passengers already inside are serviced before the lift responds to a new passenger's call.  
The Collective Algorithm (CA) extends the Longest Queue First algorithm. It involves stopping at intermediate floors during the service of the longest-waiting request, provided that there are active cabin or floor requests for those intermediate floors. Moreover, the Last Mission (LM) variant of this algorithm only stops at a floor if the direction indicated by its request aligns with the current direction of lift movement. In this variation, each floor typically has two call buttons, allowing passengers to specify the direction of their travel.  
Those heuristic approaches, although they solve the problem, have their limitations. They often rely on simple rules, like servicing the longest waiting queue, which may not optimize passenger waiting times or energy usage. These methods struggle to adapt to changing traffic patterns. As a result, more advanced techniques capable of adapting to passenger traffic patterns are being explored to address these limitations.
## 5 Discussion
### Performance of VS and ER Models
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Statistics and Equation 1** & **Definition, Range, and Match** & \multicolumn{2}{c}{**Site 1**} & \multicolumn{2}{c}{**Site 2**} & \multicolumn{2}{c}{**Site 3**} \\ \cline{2-7} \cline{6-7}  & **VS** & **ER** & **ER** & **VS** & **ER** \\ \hline NSE: Nash-Sutellite efficiency coefficient & NSE indicates a perfect match between measured (M) and predicted (P) & 0.90 & 0.98 & 0.84 & 0.90 & 0.98 \\ \(=1-\frac{\sum_{i}(D_{i}-D_{i})^{2}}{\sum_{i}(D_{i}-D_{i})^{2}}\) & data. NSE ranges from \(\infty\) to 1. Match is satisfactory from \(>\)0.7. & & 0.88 & 0.97 & 0.84 & 0.88 & 0.98 \\ \hline InNSE: logarithmic form of NSE & & & & & & \\ \(=\frac{\sum_{i}(D_{i}-D_{i})^{2}}{\sum_{i}(D_{i}-D_{i})^{2}}\) & inNSE emphasizes low values, and NSE the high ones. Match is & & & & & \\ \(=\frac{\sum_{i}^{\infty}(D_{i}-D_{i})^{2}}{\sum_{i}(D_{i}-D_{i})^{2}}\) & satisfactory from \(>\)0.7. & & & & & \\ \hline \(R^{2}\): coefficient of determination & \(R^{2}\) indicates the degree of linear relationship between M and P data. \(R^{2}\) & 0.90 & 0.98 & 0.90 & 0.91 & 0.98 \\ \(=\left(\frac{\sum_{i}(D_{i}-D_{i})^{2}}{\sum_{i}(D_{i}-D_{i})^{2}}\sum_{i}(D_{i }-D_{i})^{2}\right)\) & \(R^{2}\) & ranges from 0 to 1. Match is satisfactory from \(>\)0.7. & & & & \\ \hline PBIAS: percent bias & PBIAS calculates the average tendency of the P data to be higher or lower & & & & & \\ \(=\frac{\sum_{i}^{\infty}(D_{i}-D_{i})^{2}}{\sum_{i}(D_{i}-D_{i})^{2}}\times 100\) & than their M counterparts. The optimal value is 0. Perfect match is 0. & & & & \\ Acceptable match is in the \(\pm 25\%\) range. & & & & & \\ \hline RMSE: root-mean-square error & RMSE calculates the precision of the P data. Perfect match is 0. Increasing & & & & \\ \(=\sqrt{\sum_{i=1}^{\infty}(D_{i}-D_{i})^{2}}\) & RMSE values indicate that matching worse, typically due to outliers. & & & & & \\ \hline RSS: RMSE relative to standard deviation of the measured & & & & & & \\ \hline \(\frac{\text{data}}{\text{SRT}D_{i}}=\frac{\sum_{i}^{\infty}(D_{i}-D_{i})^{2}}{ \sum_{i}^{\infty}(D_{i}-D_{i})^{2}}\) & & & & & & \\ \hline MAE: mean absolute error & & & & & \\ \(=\frac{1}{2}\sum_{i=1}^{\infty}(D_{i}-D_{i})\) & MAE is the absolute difference in the P and M data. Perfect match is 0. & & & & & \\ \(=\frac{1}{2}\sum_{i=1}^{\infty}(D_{i}-D_{i})\) & MAE is the absolute difference in the P and M data. Perfect match is 0. & & & & & \\ \hline MSE: mean relative error & & & & & & \\ \(=\frac{1}{2}\sum_{i=1}^{\infty}(D_{i}-D_{i})\) & MAE is the relative difference in the P and M data. Perfect match is 0. & 0.02 & 0.01 & 0.01 & 0.02 & 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Equations, ranges, optimal values, and results for VS and ER models performance statistics and normalized errors at sites 1 to 3, after [(66; 68; 69; 70)]. For ER, the time-lapse ERT from September 2014 was selected. \begin{table}
## 4 Results and Discussion
### Precision, Recall, and F-score  
The values of the precision metric demonstrate (Table 2) that all models are detecting malware or intrusion with a probability varying from 0.888 for 10 system calls up to 0.996 for 1000 system calls, i.e., correctness of models can be considered as very high.  
The recall metric is designed to take evaluate the classification accuracy of intrusions/malware types by classes. In our case, the recall values are higher than corresponding precision values (Table 3). This can be explained by the fact that, in the case of a precision metric, benign system call class has a smaller size and decrease the overall accuracy compared to the recall metric.  
The F-score metric is calculated from the precision and recall as the harmonic mean of both values. The distribution of this metric is also similar to the accuracy (Table 4)--the longest system calls sequences are producing the best results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Count**} & **AWSCTD-** & **AWSCTD-** & **AWSCTD-** & **AWSCTD-** & **FWSCTD-** & **FCN** & **LSTM-** & **GRU-** \\  & **CNN-S** & **CNN-D** & **CNN-LSTM** & **CNN-GRU** & **CNN** & **FCN** & **FCN** \\ \hline
10 & 0.897 & 0.906 & 0.896 & 0.  
The results of precision, recall, and F-score demonstrate that all of the tested models are performing almost the same, and advantage of more complex models cannot be observed. The sequence of 600 system calls remains as the threshold for obtaining noticeably better and practically applicable results.