esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Characterization of Cellulose by 1D and 2D NMR Spectroscopy
#### 3.3.1 General Information Obtained by 1D Solid-State CPMAS \({}^{13}\)C NMR Spectroscopy
[39]. Thus, an interpretation as ethers is more appropriate. The signal at \(\delta\)C/\(\delta\)H 146 to 142 ppm/ 3.7 ppm in the spectrum obtained with t\({}_{\rm c}\) = 200 \(\upmu\)s (Figure 5b) points to cross linking of these \(O\)-alkyl C to furan C (Figure 3, structure 10). The presence of methoxyl groups bound to phenyl units giving rise to cross peaks around \(\delta\)C/\(\delta\)H 56 ppm, 160-140 ppm/7.5, 3.8 ppm were not identified. **Further information obtained from the 2D \({}^{1}\)H-\({}^{13}\)C HETCOR NMR spectrum obtained with t\({}_{\rm c}\) = 1000 \(\upmu\)s (ketones and carboxylic C):** The long-range coupling between \({}^{13}\)C and \({}^{1}\)H during a CT = 1000 \(\upmu\)s, leads to a highly complex 2D \({}^{1}\)H-\({}^{13}\)C HETCOR NMR cross plot (Figure 5c) and an attempt to identify structural features has to be done with caution to avoid overinterpretation. The broad signals obtained in the \({}^{13}\)C chemical shift region attributed to aromatic C (160 to 100 ppm) covers the total \({}^{1}\)H chemical shift region except that assigned to aldehyde H or outer rim H in PAHs. On the other hand, cross peaks between 200 and 210 ppm and between 190 to 160 ppm in the \({}^{13}\)C dimension are identified and can be attributed to ketones and carboxyl C. The fact that those signals need a t\({}_{\rm c}\) = 1000 \(\upmu\)s to appear in a spectrum indicates that most of them have no adjunct protonated C. Thus, the respective protons providing polarization have to be at least an equivalent of three bonds apart from the carbon. The signal at \(\delta\)C/\(\delta\)H 187 ppm/6.65 ppm may indicate carbonic acid anhydride bridging to aromatic/furan units or quinones (Figure 4, structure 18). At \(\delta\)C/\(\delta\)H 174 ppm/6.2 ppm appears a cross signal, suggesting a carboxyl C connected either to a vinyl or furan unit. The cross peaks at \(\delta\)C/\(\delta\)H 174 ppm/3.8 ppm and \(\delta\)C/\(\delta\)H 174 ppm/2.2 ppm suggest the presence of methyl esters (Figure 4, structure 4). The ketone signals at \(\delta\)C/\(\delta\)H 205 ppm/3.6 ppm and \(\delta\)C/\(\delta\)H 205 ppm/1.9 ppm propose their interactions with \(O\)-alkyl H and methyl H (Figure 4, structure 9). The signal at 211 ppm/1.9 ppm is explained with long range interactions between ketone and CH\({}_{2}\) units. No cross peak can be revealed between ketones and aromatic or furan C. Signals assignable to quinones weren't either detected (\(\delta\)C: 200 to 190 ppm). Thus, the \({}^{13}\)C signal between 200 and 215 ppm may be best explained with ketones which are an integrated part of the bridging alkyl. The \({}^{13}\)C signal at 164 ppm correlates with \(\delta\)H 6.18 and 5.3 ppm which would also allow an assignment to carboxyl groups interaction with vinyl or furan units rather than to phenyl C.
# Neighbor Discovery for IPv6 over BLE Mesh Networks
## 6 Conclusions  
Based on the BLE mesh network, this paper proposed a neighbor discovery protocol for 6 LoBLEmesh networks according to 6 LoWPAN ND. The protocol defines the network architecture of the 6 LoBLEmesh network, the neighbor discovery process based on BLE 5.0, and the IPv6 address auto-configuration and update mechanism. Performance analysis and simulation experiments were discussed to verify the effectiveness of the protocol. However, performance analysis of the protocol in this paper is only limited to the impact of the number of nodes, and the selection of protocol parameters has also an important impact on the performance of neighbor discovery, which will be our future research direction.  
Figure 8: The average energy respect to the advertiser numbers.  
**Author Contributions:** Conceptualization, B.L. and Z.S.; methodology, B.L.; validation, B.L., J.L., J.W. and H.Z.; formal analysis, B.L.; investigation, Y.P.; resources, Y.P.; data curation, A.A.; writing-original draft preparation, B.L.; writing-review and editing, B.L.; project administration, Y.P.; funding acquisition, B.L. and J.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partially supported by National Natural Science Foundation of China [No.61702281], [No.61672299], [61671091], [61971079]; Guangzhou Science and Technology Plan Project Funding [201907010020]; Natural Science Foundation of CQ CSTC [cstc2017 cyjBX0057].  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
This study examines the effectiveness of in-service training programs aimed at enhancing teachers' knowledge and self-efficacy in the context of learning disabilities (LD) in mathematics. Despite the increasing use of both interactive online learning and face-to-face training methods in professional development, limited research has compared their relative effectiveness in this specific field. Furthermore, existing studies have not adequately addressed whether improvements in teachers' knowledge and self-efficacy are sustained over time. To address this gap, the present study employed a quasi-experimental design with two experimental groups. The sample consists of 80 classroom teachers, with 40 participants in the interactive online learning education group and 40 in the face-to-face education group. The training program consists of 16 h of instruction over four weeks. Data were collected using a demographic questionnaire and the Mathematics Learning Difficulty Area Teacher Self-Efficacy Scale, and statistical analyses were conducted. The findings indicate that, prior to the intervention, teachers in the interactive online learning education group exhibited significantly higher levels of knowledge and self-efficacy. However, the post-intervention results revealed no statistically significant differences between the two groups. [PERSON]'s d analysis indicated a moderate effect size for interactive online learning education before the intervention, which diminished to a small effect size afterward. This study validates the efficiency of interactive online learning from smart learning environments for in-service training programs aimed at enhancing teachers' knowledge and self-efficacy about learning disabilities in mathematics. These results suggest that both training modalities effectively improve teachers' knowledge and self-efficacy, but neither demonstrate a clear long-term advantage. This study underscores the need for further research to determine optimal strategies for sustaining professional development in this domain.  
interactive online learning; mathematics learning disability; teacher education; self-efficacy; face-to-face education +
Footnote †: journal: Applied sciences  
Article
## 3 Proposed Method
### Weibull Cycle to Failure (N) Analysis
#### 4.2.2 Cycle Random Behavior  
In this section the random behavior of the cycles (\(N\)) values is determined. By using the Weibull cycle \(\eta_{t}\) parameter and the \(t_{oi}\) elements in Equation (34), the corresponding expected cycle to failure values for each one of the \(Y_{i}\) elements are determined (see the seventh column in Table 5). Additionally, by using \(t_{0 sy}=\ 0.364517\) in Equation (34) the \(N_{sy}\) value that corresponds to the \(S_{y}\) value is \(N_{sy}=453,250,454\).  
The practical interpretation of data in Table 5 is that data given in the \(\sigma_{2i}\) column represent the minimum expected material strength that will be required when the constant applied stress is \(\sigma_{eq}=168.188640\) (remember that this \(\sigma_{eq}\) is constant for all the rows in Table 5). For instance, in the first row of Table 5 by applying the \(\sigma_{eq}\) stress, the required material strength is 66.35155. Thus, from Equation (7) the expected reliability will be 0.96729 which corresponds to \(N_{i}=273,683,422.9\). Similarly, data of the \(\sigma_{1i}\) column represent the Weibull scale parameter that the element must present to withstand a constant stress value of \(\eta_{s}=\ 301.4555\). For instance, in Equation (7) if we use \(t=301.4555\) with \(\eta=1308.124\) the reliability is 0.96729.  
Therefore, from the FEA simulation of the flat spring with a yield strength value of \(S_{y}=827\) MPa we found the principal stresses that are acting on the element are \(\sigma_{1}=491.75\) MPa and \(\sigma_{2}=184.8\) MPa. And by applying the propose method as a complement of the FEA results we have that the expected cycle to failure of the flat spring is \(N=453,250,454\) cycles with a reliability index of \(R(t)=0.901768\).  
On the other hand, although the analysis was performed base on the \(\sigma_{1}\) and \(\sigma_{2}\) FEA results, because the input of the proposed method is only the \(\sigma_{1}\) and \(\sigma_{2}\) values, and due to they can also be determined by the conventional static analytical method, then let us now present how the \(\sigma_{1}\) and \(\sigma_{2}\) values determined by the analytical method are used in the proposed method.
## 4 Prediction Model of the Leakage Current and Example Analysis
### Verification Analysis and Result
#### 4.2.2 Case Study 2  
The case study 2 applied a 5-day period from 1 March 2019 to 5 March 2019 as input data. Figures 14-16 display the predicted leakage current through the LSTM, GBR, and SVR models alongside the actual measured leakage current. The blue line represents the predicted leakage current, while the red line represents the actual measured leakage current. Table 4 presents the prediction errors of different algorithms.  
In case study 2, this study examines the prediction of leakage current for a period of 5 days. The results indicate that the proposed LSTM exhibits a lower _MAE_ of 0.0857 and a _MSE_ of 0.0276, while GBR shows a higher _EVS_ of 0.4144. The LSTM demonstration reveals that _MSE_ and _MAE_ approaching zero indicate small errors in predictions. However, the _EVS_ result is 0.2116, which is relatively low. This indicates that the model has a limited ability to explain the variance in the target variable, suggesting that there might be some variability that the model cannot capture. While the results of _MSE_ and _MAE_ suggest good performance in prediction, it is essential to enhance the LSTM model's explanatory power for the target variable to further improve overall performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & _MAE_ & _MSE_ & _EVS_ \\ \hline LSTM & 0.0857 & 0.0276 & 0.2116 \\ \hline GRB & 0.1346 & 0.0306 & 0.4144 \\ \hline SVR & 0.2913 & 0.1177 & \(-\)2.0080 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Case 2: Prediction errors of different algorithms.  
Figure 13: Case 1: Predicted and measured results of the SVR algorithms.  
Figure 16: Case 2: Predicted and measured results of the SVR algorithms.  
Figure 14: Case 2: Predicted and measured results of the LSTM algorithms.  
Figure 15: Case 2: Predicted and measured results of the GBR algorithms.  
This study examines the prediction of leakage current for a period of 6 days. The results indicate that the proposed LSTM exhibits a lower _MAE_ of 0.0897, a _MSE_ of 0.011, and a relatively higher _EVS_ of 0.3025. Similarly, it demonstrates that LSTM yields lower MSE and _MAE_ values while evaluating performance, and the LSTM model exhibits very small prediction errors. However, with the relatively low _EVS_, there is room for improvement in explaining the variance of the target variable.
## 4 Experimental Results
### LR-DOCT with In Vitro Blood Flow
The reason for this effect might be the optical inhomogeneity of human blood caused by the higher refractive index inside the erythrocytes. The incident light passing a group of erythrocytes is randomly delayed within the B-scan, which causes random phase fluctuations beneath this area, as in our case at the capillary center. Multiple scattering causing Doppler shadows might worsen the effect. The mean Doppler phase shift and the corresponding standard deviation of the diluted human blood flow are quantified for both experiments, and the results are presented in Figure 10. As seen, both measurements offer similar characteristics, which show the reproducibility of LR-DOCT. Moreover, the calculated phase shift is slightly higher for the FL-scan than for the M-scan in the depth range of resonantly detected erythrocytes. Additionally, the standard deviation of the mean phase shift is reduced (asterisk in Figure 10b,d) at the radial position \(r\) near the center of the capillary. Since the erythrocytes are not completely resonantly detected over the detected 400 A-scans, \(\Delta\varphi\) and \(\sigma_{\Delta\varphi}\) include the phase information of parts with no significant backscattering signal, which unfortunately increases \(\sigma_{\Delta\varphi}\). For a more precise analysis, parts of the trace with a high signal in the resonant region are selected, as shown in Figure 11. The number of lateral resonantly detected A-scans (originally \(N=400\) Figure 11a) is systematically reduced to \(N=90\) (Figure 11b), \(N=40\) (Figure 11c), and \(N=22\) (Figure 11d), showing an increased correlated backscattering signal at the capillary center with a decreasing number \(N\) of selected A-scans. Considering the diagram in Figure 12a, the Doppler phase shift of the resonantly detected erythrocytes at the capillary center differ slightly for \(N=400\) (Figure 11a), \(N=90\) (Figure 11b), and \(N=22\) (Figure 11d). Comparing \(\Delta\varphi\) of the M-scan and the FL-scan with \(N=22\), one can identify a difference up to 0.4 rad and 10 mm/s, respectively, for the resonant detection (Figure 12b). The standard deviation of the mean phase shift is maximally reduced for \(N=22\) of the FL-scan and the resonantly scanned depths (cp. Figure 12c,d). Figure 11: Structural, Doppler flow, and Doppler noise images of the forward lateral scan (FL-scan) of the diluted blood flow through the 312 \(\upmu\)m glass capillary are presented for \(N=400\) (**a**), \(N=90\) (**b**), \(N=40\) (**c**), and \(N=22\) (**d**) analyzed A-scans. Figure 10: (**a,c**) Calculated mean Doppler phase shift \(\Delta\varphi\) and resulting flow profile of the diluted blood flow of experiment A and B are presented as a function of the radial position \(r\) for the detected M-scan, FL-scan and BL-scan. (**b,d**) Corresponding standard deviation \(\sigma_{\Delta\varphi}\) of the mean phase shift versus the radial position \(r\) of the capillary lumen. The asterisk marks the radial position with strongly reduced \(\sigma_{\Delta\varphi}\) obtained with the FL-scan near the center of the capillary.
## 3 Results and Discussion  
### Room Temperature Properties  
Figure 2 shows the observation of the initial microstructure of the AMS4928 bulk alloy obtained through a forging process. In all XY, YZ, and XZ planes, a uniform \(\alpha+\beta\) Widomastatten structure was observed throughout the specimen, and no differences in microstructures according to directionality were observed. During VAR, a number of spherical fine grains of 5-10 \(\upmu\)m were observed due to the grain growth after its recrystallization.  
Figure 2: Optical micrographs of the as-received Ti-6 Al-4V alloy; **(a)** XY Plane, **(b)** YZ Plane, **(c)** XZ Plane.  
For verifying the hardness difference according to the directionality of the AMS4928 alloy, the Vickers Hardness was measured in the direction crossing the specimen, and the result is shown in Figure 3. Based on the measured values where the average hardness of the XY Plane was 301.29 Hv, the standard deviation was 22.6 Hv, the average hardness of the YZ Plane was 289.76 Hv, the standard deviation was 9.58 Hv, the average hardness of the XZ Plane was 301.88 Hv, and the standard deviation was 13.75 Hv, the hardness values were uniform without significant differences according to directionality considering the standard deviation.  
In order to evaluate the internal and external strength properties of the AMS4928 bulk alloy in the as-received state, the room temperature tensile strengths in the circumferential and central directions of the specimen were measured, and the resulting stress-strain curve is shown in Figure 4. As a result of the tensile test, the average yield strength, tensile strength, and elongation in the circumferential direction (A\({}_{1}\), B\({}_{1}\)) were 933 MPa, 990 MPa, and 9.5%, respectively, and the average yield strength, tensile strength, and elongation in the central direction (A\({}_{2}\), B\({}_{2}\)) were 890 MPa, 945 MPa, and 5%, respectively. There were differences of the yield strength and tensile strength of about 50 MPa, respectively, and elongation of about 5% according to the direction. This result is because the microstructure of the external has a narrower lath spacing than the internal due to the difference in cooling rates between the internal and external after VAR processing of the \(\Phi\)500 bulk material.  
Figure 4: Tensile stress–strain curves circumferential and central directions of as-received Ti-6 Al-4V alloy.  
Figure 3: Average Vickers hardness of the as-received Ti-6 Al-4V alloy.
###### Abstract  
The development of cracks and solution cavities in carbonate reservoirs can notably reduce the rock's mechanical properties, leading to a severe wellbore collapse problem during drilling operations. To clarify the influence of the characteristics of cracks and solution cavities on the wellbore stability in the Denying Formation carbonate reservoir in the Gaoshiti-Moxi area of Sichuan, the mechanical properties of carbonate rock were analyzed. Then, the influences of the attitude and width of cracks, the size and quantity of solution cavities, and their connectivity on wellbore stability were studied using FLAC3D 6.00 numerical simulation software. Our results show the following: (1) The cracks and solution cavities in the Denying Formation carbonate rock cause significant differences in the rock's mechanical properties. (2) The equivalent drilling fluid density of collapse pressure (\(\rho_{\rm c}\)) considering the effects of cracks and solution cavities is 6.4% higher than without these effects, which is in good accordance with engineering practice. Additionally, cracks play a more significant role than solution cavities in affecting the wellbore stability. (3) When the orientation of a crack is closer to the direction of maximum horizontal stress, and the dip angle and width of the crack increase, the stress and deformation at the intersection of the crack and wellbore gradually increase, and correspondingly, \(\rho_{\rm c}\) also increases. (4) The stress and displacement of various points around the solution cavities gradually increase with the increases in diameter and quantity of solution cavities, and \(\rho_{\rm c}\) also increases. (5) Compared with the situation where cracks and solution cavities are not interconnected, the stress disturbance area around the wellbore is larger, and \(\rho_{\rm c}\) is greater when cracks and solution cavities are interconnected.  
wellbore stability; denying formation; carbonate rocks; cracks and solution cavities +
Footnote †: journal: applied sciences  
Article
# TransformingBPMN Processes toSBVR Process Rules with
## 3 Transformation of BPMN Processes to SBVR Process Rules
### Transformation Rules

Table 1: Cont. For conceptual clarity, we specified all transformation rules using a unified template, the main principles of which were taken from [58]. In each transformation rule, the \(\mathit{PP}_{i}\) is presented as a fragment of a conceptualized BPMN process diagram, and the \(\mathit{RP}_{i}\) as a formalized textual structure expressed in Extended Backus-Naur form (EBNF). In \(\mathit{PP}_{i}\), some element abbreviations are used; however, with basic knowledge of BPMN, those abbreviated elements are easily identified intuitively (e.g., _start_ev_ stands for _Start Event_ or _seq_flow_ for _Sequence Flow_). In the formalization of \(\mathit{RP}_{i}\), the \(\mathit{vc(a)}\) refers to a particular SBVR noun concept representing a particular BPMN concept; in its turn, the \(\mathit{vc(a,b)}\) refers to a particular SBVR verb concept constructed upon a pair of noun concepts representing particular concepts of the BPMN process model. Again, as was mentioned before, both noun concepts and verb concepts are formed during the execution of Stage 1 of the model transformation process (Figure 1), which is outside the scope of this paper. In addition, it is important to note that the presented set of transformation rules covers a subset of concepts of the BPMN process model relevant to the extraction of the _process_ rules, meaning that the BPMN concepts that are unrelated to the extraction of the process rules or are uninterpretable by the automated interpreters (e.g., _Complex Gateway_), are left out of the scope of the current version of the approach as well. The following subset of BPMN concepts was considered in this transformation approach: pool/lane, start/intermediate/end events, interrupting/non-interrupting boundary events, activity, exclusive/inclusive/event-based/parallel gateways, data object, data store, message, message flow, sequence flow, and sequence flow with a condition. In Table 1, each formalized rule \(T_{i}\) is followed by examples of SBVR process rules as the execution result of that specific transformation rule. For more examples of the actual instantiation of the specified transformation rules refer to Section 5. Full semantic expressiveness of the acquired process rules is reached when considering the underlying business vocabulary comprising noun concepts and verb concepts because those concepts hold all the relevant semantic information inherited from the BPMN process model from which they were acquired (e.g., SBVR general concepts being enriched with descriptions and meta-information about the types of BPMN concepts that those general concepts represent). Furthermore, this aspect becomes critical when it comes to reverse engineering BPMN process models from SBVR specifications if there would be such a need.
## 2 Experimental Procedure  
### Synthesis of Spinel MnCe\({}_{x}\)Fe\({}_{2-x}\)O\({}_{4}\) Nanocrystallites  
Spinel MnCe\({}_{x}\)Fe\({}_{2-x}\)O\({}_{4}\) nanocrystallites were synthesized using the corresponding nitrates of metals Mn(NO\({}_{3}\))\({}_{2}\)-6H\({}_{2}\)O (manganese nitrate), Fe(NO\({}_{3}\))\({}_{3}\)-9H\({}_{2}\)O (ferric nitrate), and Ce(NO\({}_{3}\))\({}_{3}\)-6H\({}_{2}\)O (cerium nitrate), and urea was used as the fuel. Here, urea acted as fuel, whereas the precursors of metal nitrates (manganese, ferric and cerium nitrates) functioned as oxidizers. The chemicals were brought from SD fine (India) and were of systematic grade and utilized as usual. For pure manganese ferrite, the precursors of manganese nitrate and ferric nitrate were mixed by a continuing molar ratio 1:2 and dissolved in double distilled water. Urea was added to the main achieved solutions and agitated for 1 h and the final obtained homogeneous substances were transported to a silica crucible and were placed in a microwave oven (SAMSUNG, Noida, India), utilized as the mechanism of treatment. The microwave oven power was set as 900 W for 10 min (2.54 GHz). Under the influence of microwave energy (ME), the solutions were subjected to the following procedures: steaming, vaporization, desiccation, and finally decomposition; this caused the progression of the reaction gas and an explosion took place, which ground the speedy flame feathery construction of pure MnFe\({}_{2}\)O\({}_{4}\). Similarly for the preparation of Ce\({}^{3+}\) doped MnFe\({}_{2}\)O\({}_{4}\) NCs, cerium nitrate was added by a stoichiometry ratio. Additionally, products were splashed by water and ethanol, then dried at 70 \({}^{\circ}\)C for 30 min. The obtained samples, viz., x = 0.0, 0.1, 0.3 and 0.5, were labeled as MnFe\({}_{2}\)O\({}_{4}\), MnCe\({}_{0.1}\)Fe\({}_{1.9}\)O\({}_{4}\), MnCe\({}_{0.3}\)Fe\({}_{1.7}\)O\({}_{4}\) and MnCe\({}_{0.5}\)Fe\({}_{1.5}\)O\({}_{4}\) NCs, respectively.