esa-sceva/satcom-chunk-collection huggingface dataset:
# Parameters Sensitivity Analysis of Position-Based Impedance Control for Bionic Legged Robots' [PERSON]
## 6 Sensitivity Quantitative Analysis
### Sensitivity Histograms in Different Working Conditions  
According to Equations (23)-(27), the two sensitivity histograms are shown in Figures 18-21, when the four control parameters increase 10% in four working conditions.  
Figure 18: Sensitivity histogram in the first working condition. (**a**) Sensitivity index \(S_{1}\); (**b**) Sensitivity index \(S_{2}\).  
Figure 19: Sensitivity histogram in the second working condition. (**a**) Sensitivity index \(S_{1}\); (**b**) Sensitivity index \(S_{2}\).  
Figure 20: Sensitivity histogram in the third working condition. (**a**) Sensitivity index \(S_{1}\); (**b**) Sensitivity index \(S_{2}\).  
As it can be seen in the above Figures 18-21:  
1. In all conditions, \(S_{1}\) is a positive value, so the increase of the four parameters will result in the reduction of the position amplitude attenuation. The \(S_{1}\) of desired stiffness \(K_{D}\) is much greater than the other parameters, which illustrates that the variation of \(K_{D}\) has a remarkable influence on output position amplitude attenuation, and more relation with disturbance force amplitude than the disturbance force frequency. Under the first working condition, amplitude attenuation reduced about 0.2 mm when \(K_{D}\) increased 10%. The \(S_{1}\) of proportional gain \(K_{p}\) is greater than that of \(C_{D}\) and \(K_{i}\). The \(S_{1}\) of \(K_{D}\) and \(K_{p}\) have some correlation with disturbance force amplitude, but have little correlation with disturbance force frequency.
2. The \(S_{2}\) of the four control parameters has positive or negative values in different conditions. An increase of the parameters has different influences on the phase angle delay of the output position. Specifically, having more relation with disturbance force frequency than the amplitude, the \(S_{2}\) of \(K_{D}\) and \(C_{D}\) have nearly the same absolute value, but an opposite sign. Their increase has totally different influences on phase angle delay. An approximate 10% increase of them generates about 0.18\({}^{\circ}\) of phase angle delay error. The \(S_{2}\) of \(K_{p}\) has correlation with both disturbance force amplitude and frequency. The larger the frequency, the more remarkable the influence on phase delay. The \(S_{2}\) of \(K_{p}\) is close to the \(S_{2}\) of \(K_{D}\). The \(S_{2}\) of \(K_{i}\) is about 0.001\({}^{\circ}\) on magnitude order, much less than the other control parameters.
## 3 Results and Discussion
### Thermogravimetric Analysis  
The thermal behavior of MIPs was evaluated by measuring the weight loss (TGA, Figure 3A) and the rate of weight loss (DTG, Figure 3B) after heating the sample at a constant rate.  
The obtained thermograms were similar for all MIPs prepared, presenting mainly three steps of weight loss by analysis of both TG and DTG thermograms. All of the MIPs lose water in the initial step, up to approximately 100 \({}^{\circ}\)C. After that, a second step was observed between 250 \({}^{\circ}\)C and 370 \({}^{\circ}\)C, which was due to the depolymerization of Poly(MAA) resulting in a small yield of monomer [31]. The third major step is initiated at \(\sim\)370 \({}^{\circ}\)C due to an elimination of the water molecule adsorbed between pairs of carboxylic groups [31]. A more detailed analysis of thermograms in the 425-525 \({}^{\circ}\)C range, suggested that MIPs with a higher ratio of template and functional monomer led to a higher amount of weight loss. Since a lower amount of template was used in both MIP 3 and MIP 6, the content of polymer backbone is higher per unit of mass of MIPs when compared with those prepared with higher quantities of 2C-B.  
The peaks assigned in DTG showed the inflection point of the TG thermograms, which allowed us to conclude the difference between materials prepared by their stability. By comparing the weight loss rate of the MIPs and NIPs prepared through MW-assisted synthesis, it was possible to verify that for the same template:functional monomer ratio used, the peaks appeared at similar temperatures. However, when comparing materials with different ratios, a shift of temperatures is observed for both MIP1 and NIP1, which could be due to a lower polymer backbone content, leading to a poor thermal stability.  
Figure 2: FTIR spectra of prepared MIPs using thermal and microwave-assisted polymerization (**A**) and comparison of the spectroscopy data between the MIP 1–3 and their related NIP 1–3 (**B**).
###### Abstract  
The expansion of operation technology (OT) use and its tight integration with classical information and communication technologies have led not only to additional and improved possibilities in monitoring physical/manufacturing processes and the emergency of Industry 4.0 but also to a number of new threats, both related to the security of processed data and the safety of people, affected by physical processes and controlled by OT. Understanding potential threats has caused an increased demand for scientific research in the field, which is still relatively new and lacks established terminology. In this review paper, we aim to identify emerging trends and technologies in OT incident response, attack detection, applications of machine and deep learning for attack recognition, and security of OT protocols. An examination of research patterns from the Web of Science repository is performed to comprehend the panorama of publications and the present state of research in the area of OT security. The analysis shows a notable rise in publications concerning OT security, reflecting an increasing research interest. Proceeding articles and research articles were the predominant types of publications that were analyzed. The analysis further emphasizes the collaborative connections between researchers, academic institutions, and nations. Additionally, co-occurrence and citation analyses are carried out to offer an understanding of the associations between various keywords and/or research subjects. The study is finalized by suggesting future research directions on OT security. The uniqueness of this review lies in its focus on OT rather than the more commonly explored SCADA/ICS topics, attempting to cover a wider range of research topics instead of concentrating on a narrow area/method.  
operational technology; incident response; attack detection; datasets; sensors; machine learning; deep learning; intrusion detection; anomaly detection; OT; attack detection/recognition; OT protocol security; OT security
# On the Performance of Underlay Device-to-Device Communications
## 3 System Model
### Power Allocation in D2D Networks
#### 3.2.2 Random Power Allocation  
Let us denote \(P_{D_{k}}^{ran}\) the transmit power of the \(k\)-th D2D transmitter under the random power allocation, its value is randomly assigned and given as [6]  
\[P_{D_{k}}^{ran}=\alpha_{k}P_{\text{tot}},\ \forall k,\ \alpha_{k}\in(0,1),\text{ and }\sum_{k=1}^{N}\alpha_{k}=1, \tag{3}\]  
where \(\alpha_{k}\) is a random number generated by a uniform distribution.
## 6 Conclusions and Outlook  
This work presented a new approach for the direct calculation of group velocity curves using the WFE scheme. The WFE scheme was used to exploit the periodicity of the structure to extract the group and phase velocity curves from a representative periodic section. A comprehensive algorithm for calculating the velocity profiles was also constructed. Since the technique is based on FE method, it contains all the uncertainty associated with numerical modelling. This includes the effect of mesh size, element formulation, and model description. In this regard, all the standard best practices of FE modelling should be followed. Moreover, the accuracy of the results will only be as accurate as the model. In case of complex composite structures that have inherent manufacturing flaws, the results will always have uncertainty due to the model definition of the periodic section. Nevertheless, all modelling approaches face similar challenges. The method was compared with existing dispersion curve calculation techniques by comparing the results for complex multi-layered structures. A number of advantages were highlighted with respect to the existing techniques, which are summarised here:  
* The technique is easy to implement using the standard FE method without the need for custom formulations.
* It is capable of calculating dispersion properties for arbitrary layered plate-like structures.
* The periodic section is modelled with very few elements, which makes it highly efficient and computationally cheap.  
Figure 9: Group velocity curves for \([+45/0/-45/90/+45/0/-45/90]_{s}\) laminate from experiments (o) and the WFE (–) method.  
* The theory of the WFE method enables the technique to be used for truly periodic structures, such as textile composites, which cannot be modelled with any existing technique. Considering the strengths of this approach, it can be used in all guided wave application areas such as material characterisation, non-destructive testing, SHM, etc. It is especially useful in the NDE of aerospace structures, which are fabricated with layered composites and textile composites. It provides a suitable alternative to existing techniques while having the potential to handle more complex structures such as textile composites.  
[PERSON] performed all numerical simulations and wrote the manuscript, [PERSON] led the methodology and conceptual work, and [PERSON] provided the experimental validation. All authors have read and agreed to the published version of the manuscript. This work was conducted as part of the SAFE-FLY project funded by the European Union Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement No. 721455.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ongoing research. The authors declare no conflict of interest.
###### Abstract  
Several plant extracts are being investigated to produce edible coatings, mainly due to their antioxidant and antimicrobial activities. In this study, licorice root extracts were produced by ultrasound-assisted extraction and were combined with chitosan to elaborate edible coatings. Different solvents and temperatures were used in the extraction process, and the antioxidant and antimicrobial activity of the extracts were assessed. The most bioactive extracts were selected for the development of the edible coatings. The rheological properties of the coatings were studied, and they were applied on strawberry to evaluate their physicochemical and microbiological properties. The addition of licorice extract to chitosan resulted in positive effects on the rheological properties of the coatings: the incorporation of phytochemicals to chitosan decreased the shear stress and improved the restructuring ability of the coating solutions. The films presented a reduction of the Burger model parameter, indicating a reduction of rigidity. Furthermore, the strawberry coated with chitosan and licorice extract maintained good quality parameters during storage and showed the best microbiological preservation in comparison with controls. Hence, the use of chitosan with licorice extract is a potential strategy to produce edible coating for improving the postharvest quality of fruits.  
licorice; chitosan; edible coating; strawberry shelf life; rheological properties; ultrasound-assisted extraction +
Footnote †: journal: Applied Sciences
# Icing Detection over East Asia from Geostationary Satellite Data Using Machine Learning Approaches
## 4 Results and Discussion  
### Variable Importance  
The relative variable importance identified by the RF and MLL models is shown in Figure 3. The mean decrease accuracy (MDA) values calculated when a variable is randomly permuted were used to identify significant variables in the RF model, while the absolute normalized coefficient values were used in the MLL model [62; 63]. It should be noted that these metrics suggest the relative importance of variables in estimating the target variable, not the absolute indicator of variable significance.  
Figure 3: Relative variable importance identified by machine learning models. Mean decrease accuracy is calculated using out-of-bag samples when a variable is perturbed by random forest (RF). Absolute normalized coefficients are considered as a variable importance in the multinomial log-linear (MLL) models. The larger the value of a variable, the more significant the variable. The most significant (top 20%) variables are displayed in orange.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Reference**} \\ \hline
**Model Classified** & **Non-Icing** & **Icing** \\ \hline Non-icing & Correct negative (N) & Miss (M) \\ Icing & False (F) & Hit (H) \\ \hline \hline \end{tabular}
\end{table}
Table 5: A contingency table used in this study to calculate performance metrics.  
In COMS models, while CTH was identified as being very significant for both the RF and MLL models, visible, IR2, COT, and CTT were given different relative importances from model to model (Figure 3a,b). According to the cloud product retrieval algorithm of COMS, CTH is produced using CTT and IR channels, and COT is retrieved from the visible channel [34]. Thus, the significant input variables in the COMS models can be grouped into two groups--a long-wave infrared (LWIR) group (i.e., CTH, CTT, and IR) and a visible (VIS) group (COT and visible)--which have been frequently used in icing studies [10; 15; 20]. While the LWIR and VIS groups are generally identified as important by RF, the LWIR group was only considered significant by the MLL models. This might be due to the skewed distribution of the variables from the VIS group. Visible-based variables are sensitive to thin clouds, but they tend to saturate with thicker clouds [34]. Thus, it is not necessary to have high coefficients for the normalized variables (i.e., sensitive to much smaller values in the range of 0-1) in the MLL model.  
The variable importance given by the Himawari-8 models is somewhat different to that of the COMS models. Near-infrared (NIR) channels (i.e., CH05, CH07, and CH08) identified as the most significant variables by the RF model, while the LWIR channels (i.e., CH11, CH13, and CH14) were considered very important in the MLL model. NIR channels are widely used as a source to produce CP and cloud particle size data [33; 34; 35; 64; 65; 66; 35], and LWIR channels are related to the cloud top temperature and the amount of water vapor, which are the important input variables of the FIT algorithm [15].
## 3 Results and Discussion
### The Impact of Different Parameters on the Efficency of IME Process
#### 3.2.1 The Impact of pH and PC on the Efficiency of IME Process
The IME process efficiency consists mainly of the efficiency of the ZVI and sorption processes. For higher doses of microelectrodes probably dominates sorption process which is less dependent on pH value That issue was discussed in more detail elsewhere [31; 34]. Moreover, the higher efficiency of the m-IME process at a lower pH is also probably related to the significant removal of humic substances in the PC process at a lower pH. Therefore, at a lower pH, the negative effect of HSs (h humic substances) on the sorption on GAC and the microelectrolysis process is very low--carbon pores and the surface of the microelectrodes are only slightly blocked by macromolecular particles and HS flocks after acidification [37]. Due to the higher acid consumption required to obtain pH 2 and slightly worse COD removal obtained at pH 3 compared to pH 2, pH 3 was considered to be the optimal pH in the m-IME process.
###### Abstract  
The industry is currently trying to manage the waste generated during juice pressing. Berry pomace is an especially rich source of many bioactive compounds. Blackcurrent pomace is particularly valuable because of the large amount of extractable anthocyanins remaining in the fruit skin. The aim of the study was to evaluate the effect of ultrasound-assisted extraction (UAE) parameters on the content of anthocyanins and color parameters of blackcurrent pomace extracts. The pomace used for the study was very rich in anthocyanins--an average content of 853.2 mg/100 g fresh weight. The effect of temperature (25, 35, 45 \({}^{\circ}\)C), time (15, 30, 60 min) and material/solvent ratio (1:20 or 1:7) on extraction yield was analyzed. The extracts were obtained using two solvents: water-ethanol acidified with HCl and water acidified with citric acid. Either solvent type, as well as the other parameters of the extraction process, have an impact on the level of anthocyanins in the extracts. The lowest range of extraction yield (63-68%) was obtained for a 1:7 ratio with water-citric acid solvent. The highest range of extraction yield (74-93%) was obtained for a 1:7 ratio with water-ethanol solvent. The most efficient method was extraction in a water-ethanol solvent (50/50 \(v/v\)) acidified with HCl, at a 1:7 material/solvent ratio, at 35 \({}^{\circ}\)C, for 15 min, providing 93% process efficiency. A strong relationship (r > 0.991) was also found between anthocyanin content and color saturation (C*) or hue (h\({}^{\circ}\)) in the obtained extracts. Based on the E1% coloring strength results, all the extracts obtained can be considered strong colorants, especially those obtained with the water-citric acid solvent. Ultrasonic extraction can be successfully applied to extract pigments from blackcurrent pomace and obtain, for example, food coloring.  
extraction solvent; ultrasonic extraction; food waste management; green chemistry 2024  
1
# A Simulation Study of FRP-PCM Reinforcement for Tunnel Liniings with Void Defects
## 3 Results and Analysis
### The Influence of the Creep Phenomenon
#### 3.2.1 Calculation of the Creep Process  
Obtaining the Material Creep Model Parameters  
Currently, Abaqus provides three types of creep models: the power-law model and the hyperbolic sine law model. The power-law model has two forms: the time-hardening form and the strain-hardening form. The time-hardening form is the simplest, and it is particularly suitable for simple creep processes (such as those where the stress variation is not significant during the creep process). Its differential form is given in Equation (7).  
The equation shown in the image is the differential form of the time-hardening form of the power-law model for creep:  
\[\dot{\epsilon}^{cr}=A(q^{*})^{n}t^{m} \tag{7}\]  
The variables in the above equation are defined below.  
\(\dot{\epsilon}^{cr}\) is the equivalent creep strain rate.  
\(q^{*}\) is the equivalent deviatoric stress.  
\(t\) is time.  
\(A\), \(n\), and \(m\) are constants, representing the material's creep properties.  
This equation is used to describe the creep behavior of materials, with constants \(A\), \(n\), and \(m\) being determined experimentally for a given material. The equivalent creep strain rate is dependent on both the equivalent deviatoric stress and time.  
Figure 11 represents the relationship between creep strain, time, and equivalent stress, for which Equation (7) must be integrated. The result of the integration is shown in Equation (8):  
\[\dot{\epsilon}^{cr}=\frac{A}{m+1}(q^{*})^{n}t^{m+1} \tag{8}\]This integrated form of the equation gives the total creep strain \(\dot{\epsilon}^{cr}\) as a function of time \(t\), the equivalent deviatoric stress \(q^{*}\), and the material constants \(A\), \(n\), and \(m\). It describes how the creep strain accumulates over time under a constant equivalent stress.  
In Figure 11, the \(x\)-axis represents time in seconds, and the graph spans up to around 40 million seconds, or approximately 463 days. Over this period, the creep strain increased significantly at higher stress levels, while the lower stress levels show relatively minimal increases. This behavior is crucial when considering the long-term performance of materials in structural applications, such as tunnel linings, where sustained loads and time-dependent deformation must be carefully accounted for to ensure stability over extended periods.
## 3 Results Analysis and Discussion
### Cross-Section Strength Analysis of the Pile  
To analyze the safety of the pile-body structure, Equation (3) was used to verify the calculation result of the cross-section normal stress [31].  
\[\sigma=M_{x}/(\gamma\cdot W_{x})\leq f, \tag{3}\]  
where  
\(\sigma\)--cross-sectional normal stress (MPa);  
\(M_{x}\)--bending moment at the cross section around the \(x\)-axis (kN\(\cdot\)m);  
\(\gamma\)--cross-sectional plastic development coefficient;  
\(W_{x}\)--net cross-sectional modulus of the \(x\)-axis (m\({}^{3}\));  
\(f\)--design value of steel flexural strength (MPa).  
The steel-pipe pile texture used in the test was Q235 steel. According to the design code for steel structures, the design value of steel flexural strength was 215 MPa. Moreover, the cross-sectional plastic development coefficient was 1.15. In the finite element analysis results, the maximum bending moment of the pile body was 129 kN\(\cdot\)m. In accordance with Equation (3), the maximum cross-sectional normal stresses could be calculated as 44.9 MPa, which was within the permissible range of the design value of steel flexural strength. Therefore, upon completing the foundation pit excavation, the internal force of the steel-pipe pile structure was small, and the structure was safe.
## 2 Materials and Methods
### Methods
#### 2.2.3 Evaluation Metrics  
As given in Table 1, \(P^{\prime}\&N^{\prime}\) refer to the prediction categories generated by the proposed channel R-CNN; \(P\&N\) are the actual categories provided by the annotation. Aiming at illustrating the indication performance of the proposed channel R-CNN framework, the mean Intersection Over Union (\(mIOU\)), Overall Pixel Accuracy (\(OPA\)), Standard Mean Accuracy (\(SMA\)), and Dice coefficient (\(DC\)) are used to evaluate the segmentation performance. The DNH channel achieves the classification of segmented masks based on the confusion matrix in Table 1.  
As for the segmentation performance, record the segmentation results as \(r\) and the annotation as \(a\). Define the pixel confusion matrix \(C(C_{1,1},C_{1,2},\ldots,C_{i,j},\ldots,C_{n,n})\); \(n\) is the total number of statistical pixels, and \(C_{i,j}\) is the number of pixels annotated as \(a_{i}\) while segmented as \(r_{j}\). The Jaccard coefficients \(J_{i}\) for classes \(a_{i}\) are calculated as follows:  
\[J_{i}=\frac{TP}{TP+FP+FN}=\frac{C_{i,j}}{T_{i}+P_{j}-C_{i,j}} \tag{5}\]  
where \(T_{i}\) represents the total number of pixels annotated with \(a_{i}\), and \(P_{j}\) refers to the number of pixels segmented as \(r_{j}\). Thus, \(mIOU\) is presented as follows:  
\[mIOU=\frac{1}{N}{\sum^{N}}J_{i} \tag{6}\]  
\(N\) is the number of categories. \(OPA\) is given as follows:  
\[OPA=\frac{\sum_{i}C_{i,i}}{\sum_{i}\sum_{j}C_{i,j}} \tag{7}\]  
\(SMA\) is calculated as follows:  
\[SMA=\frac{1}{N}\sum_{i}\frac{C_{i,i}}{\sum_{j}C_{i,j}} \tag{8}\]  
\(DC\) is presented as follows:  
\[DC=\frac{2 TP}{2 TP+FP+FN} \tag{9}\]