esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Article Goals and Basic Findings  
The immense variability of seismograms prevents the effective organization of seismic databases to ease the retrieval of systematic knowledge about seismicity in general, known as data mining or metadata extraction [35].  
The issue of proper organization of a seismic database is a very complex matter, but that does not mean that one should not cope with it.  
The main idea supporting this research is not to use only the simple parametric attributes of seismograms for grouping (clustering) but to go deeper into the seismogram's structure and propose more complex characteristics suitable for effective clustering.  
Identifying the variability as the main factor in solving the problem, the authors will try to find methods of reducing it, thereby hoping to facilitate grouping. Therefore, the term 'potential' in the article's title, indicating conditional relation, means either more or less suitability of the approach.  
A few ideas defining the steps of seismogram processing are proposed, which can be assembled into one approach, yielding new characteristics of the seismogram with far less variability than the intrinsic ones found in the seismogram itself. These are the bigraph and its equivalent, better to say its complement, named the R-envelope. Both are based on parsing the seismogram into single random impulses, i.e., elementary/single fluctuations (EF/SF), and the subsequent ordering of SFs to give the said characteristics. The coherence between these two compelled the authors to qualify this approach as the transform.  
The presented results confirm the reduced variability. It is more prominent in R\({}_{\text{Q}}\). The variability range is narrow, reminiscent of an invariant. This finding deserves thorough further research.  
As for the bigraph, the range of variability possesses not only one degree of freedom, as in the case of R\({}_{\text{Q}}\) but a few more, residing in the system of bundles of edges, recognized in the mesh of edges in the bigraph. The structural information of the seismogram is, thus, preserved in the bigraph. Thus, the meaning of the article's title denotes the new type of bigraph as a transform of seismograms. On the other hand, it does not lessen the importance of R\({}_{\text{Q}}\), which found its place in the Graphical Abstract of this paper in the designation R\({}_{\text{Q}}\oplus\)BG.  
Because of its new characteristic(s), it was necessary to consider it from various aspects to better describe its properties and applicability.
## 3 Methodology
### Phase 2: Optimal Architecture Synthesis
#### 3.2.2 Architecture Design Principles  
The optimal architecture design incorporates empirically validated components through a principled synthesis approach. The enhanced multi-head attention mechanism, identified as the most critical component across all models, forms the architectural foundation. GELU activation functions, demonstrated to consistently outperform ReLU in RoBERTa experiments, replace standard activation functions throughout the model. Comprehensive layer normalization, showing consistent stabilization benefits, is strategically placed for optimal gradient flow. Simplified feedforward networks, inspired by DistilBERT's efficiency insights, reduce computational complexity while maintaining representational capacity. An enhanced classification head, based on DeBERTa's design improvements, provides sophisticated decision-making capabilities. Strategic dropout placement, validated through regularization analysis, prevents overfitting while maintaining model expressiveness.
## 5 Results and Discussion
### Proposed Method Limitations  
As a result of the incremental negative impedance effects of CPLs, and the nonlinearity and time dependency of converters' operation, classical linear control methods such as a conventional proportional integral PI controller have stability limitations around the operating points. Of course, the problem will be more complicated with the presence of highly penetrated CPLs. To ensure large signal stability, nonlinear stabilizing control methods must be applied [17]. Different compensation techniques need to be addressed and examined to enhance the MG dynamic stability in the presence of high penetrated CPLs. Additionally, our lab capability could not help us verify the obtained results experimentally and examine the effectiveness of the proposed controller.  
Figure 15: Microgrid response with a three-phase fault at CPL3 with different value of \(K_{ic\_AL}\). (**a**) Measured active powers of DGs. (**b**) DC voltages of CPLs.
## 3 Results and Discussion
### Texture
The same behavior was observed in the force needed to reach 80% strain, where in the single material treatments, NCD-WOC and NCD-SC, the values were 158.39 and 150.14 N, respectively, while for the multi-material treatments, NCD + CP-WOC and NCD + CP-SC, the values were 229.15 and 264.10 N, respectively. The following two key findings are presented in Table 2. First, factor F1 influenced both variables, force at 40% strain and force at 80% strain, where the multi-material foods (NCD + CP) were harder than the single-material foods (NCD). The presence of CP in the intermediate phase of the 3D-printed square pillars might have influenced these results because the carbohydrates and proteins of the CP might have formed special structures after cooking as a result of heat addition, such as protein gelation and starch gelatinization. Second, factor F2 influenced the force at 40% strain, where the steam-cooked 3D-printed square pillars were harder than the water oven-cooked ones. The presence of water in the steam-cooked 3D-printed square pillars may have led to a higher slope of the linear elastic region compared to the water oven-cooked 3D-printed square pillars, where the water inside their structure was replaced by air, which led to a lower linear elastic region. In contrast, factor F2 did not influence the force at 80% strain, and no significant difference was found between the hardness of the steam-cooked and water oven-cooked 3D-printed square pillars. This behavior might be due to the slope of the densification region of the strain-force curves of the water oven-cooked 3D-printed square pillars being higher than that of the steam-cooked pillars. Water oven-cooked 3D-printed square pillars might be prone to higher densification than steam-cooked pillars because of the absence of water. An earlier study reported that steamed rice starch 3D-printed foods exhibited a softer texture than roasted or microwaved foods [24], a finding that contrasts with the results of the present study, in which steam-cooked 3D-printed foods demonstrated a harder texture than water oven-cooked foods within the linear elastic region. Given the inherent complexity in comparing studies due to the differences in food materials and their compositions, cooking processes and their cooking times, as well as the methods of texture analysis applied, it becomes challenging to ascertain the underlying cause of this observed difference.
## 3 Methodology  
### Usecase Scenario  
In this section, we present our use-case scenario consisting of Patient A, who registers at Hospital X, which has been approved by the MoH, and asks Insurance Company II for access to their medical information. After the insurance provider has verified the request, Hospital X authorizes it by processing Patient [PERSON]'s Emirates ID. Lab tests are ordered during a consultation, and the additional requests are reviewed and approved by Insurance Company II. Prescriptions, lab data, and consultation reports are all stored on the blockchain by Hospital X. The validity and integrity of the saved data are then ensured by the MoH, which uses Patient A's Emirates ID as the key to validate the information. Following validation, MoH provides an NFT as a digitally authenticated certificate certifying Patient A's medical records. Authorized healthcare providers can securely access this NFT during the patient's  
\begin{table}
\begin{tabular}{c c c c c} \hline
**References** & **Blockchain Type** & **NFT** & **Network Performance Analysis** & **Security Analysis** \\ \hline
[26] & Hyperledger & \(\times\) & \(\times\) & \(\times\) \\ \hline
[27] & Ethereum & \(\times\) & ✓ & \(\times\) \\ \hline
[12] & Ethereum & \(\times\) & \(\times\) & \(\times\) \\ \hline
[28] & Komodo & \(\times\) & \(\times\) & \(\times\) \\ \hline
[29] & Hyperledger & ✓ & \(\times\) & \(\times\) \\ \hline
[30] & Ethereum & \(\times\) & \(\times\) & ✓ \\ \hline
[31] & Hyperledger & \(\times\) & \(\times\) & ✓ \\ \hline
[32] & Ethereum & \(\times\) & \(\times\) & ✓ \\ \hline
[33] & Quantum & \(\times\) & \(\times\) & \(\times\) \\ \hline
[34] & Ethereum & \(\times\) & \(\times\) & ✓ \\ \hline
[35] & Ethereum & \(\times\) & \(\times\) & ✓ \\ \hline
**PHR-NFT** & Hyperledger & ✓ & ✓ & ✓ \\ \hline \(\times\) means ”Not Included” and ✓ means ”Included”. & & & \\ \end{tabular}
\end{table}
Table 1: Comparison with existing works.  
visit and for a limited time afterward, ensuring patient-centric control over data access. With the approval of Patient A, the patient's information can also be safely accessed by other healthcare facilities across the globe that accept the MoH-approved NFT, ensuring data integrity, privacy, and interoperability.
# 3D Positioning of Drones through Images
## 4 Experimental Setup
### Experimental Results and Visualization of UAV Localization  
We conducted experiments on our newly adopted multi-height dataset, UL14 plus. The proposed MH-FPI model was compared with three backbone models: OS-FPI [6], WAMF-FPI [23], and FPI [6]. Figure 11 shows the accuracy rates for MA@3, MA@5, MA@10, MA@20, and MA@50 under the MA evaluation metric. MH-FPI demonstrated improved accuracy within 3 m, 5 m, 10 m, 20 m, and 50 m ranges. Notably, the 5 m and 10 m metrics showed improvements of 10.4% and 11.3%, respectively, compared to the OS-FPI model. Our model backbone, by comparing satellite images with two UAV images, achieved greater improvements in accuracy within the 5-20 m range. This result aligns with our hypothesis of first performing coarse localization using larger-scale UAV images, and then conducting fine localization using smaller-scale but more detailed UAV images within the area of interest. For images where the first coarse localization produces correct results, the second fine localization yields even more precise localization effects.  
Table 1 presents the RDS scores, parameter counts, computational operations, and select meter-level accuracies for each model. Admittedly, compared to the OS-FPI model, MH-FPI shows an increase in both parameter count and computational load, primarily due to the two localization operations in the backbone. However, our proposed backbone is more suitable for UAV localization problems in multi-height scenarios. The comprehensive RDS score improved by 7.7 compared to OS-FPI. The experimental results indicate that our proposed multiple attention structure helps geographical localization network models learn height changes and position offsets between images, overcoming challenges in localization tasks and improving the accuracy of localization results.  
Figure 12 displays the visualization of the MH-FPI network model's geographical localization effect. The first row shows input UAV aerial images of the query location. The second row presents corresponding satellite image maps of the query area. The third row visualizes the feature heat maps output by the network model. The fourth row shows the geographical localization results predicted using the feature heat maps, with green circles indicating true geographical locations and blue circles representing the predicted geographical locations based on the model's output feature heat maps. Observing these result images reveals that the network model has achieved high localization performance.  
Figure 11: Meter-level accuracy of different model parts.  
In some images, it is challenging to provide accurate localization results quickly through visual observation alone, whereas our network model can provide relatively precise localization results.
# Speed-Flow-Geometric Relationship for Urban Roads Network
## 6 Conclusions  
We determined the impact of various geometric and traffic parameters on the speed-flow-geometric relationship for Johor (Malaysia) roads segment for the first time. The multilinear regression analysis was used to develop speed-flow-geometric relationship models with the average travel speed (ATS) as a dependent variable and diverse geometric and traffic parameters as independent variables (traffic volume (veh/h and pcu/h), traffic calming speed density, access driveway density, right-turn density, and intersection density). In addition, various cross-sectional parameters (median, number of lanes and side friction) were considered. The impact of heterogeneous vehicles composition on speed-flow-geometric relationship was evaluated. The choice of average travel speed (ATS) enabled us to measure the vehicles run and delay time over the urban roads segment. The results obtained from the proposed models confirmed the impact of these new geometric and traffic parameters on the speed-flow-geometric relationship. Parameters such as traffic calming speed devices, access driveways, and intersection density displayed a significant impact on the developed speed-flow-geometric relationship models. In addition, the factors including the side-friction conditions, access driveways, medians, and traffic calming devices has influence on the urban road mobility and safety. The variability on the numerical values of longitude parameters over the five developed models showed that the speed-flow-geometric relationship was appreciably influenced by these parameters. The present study provided a fundamental insight into the better urban mobility design for the traffic management and operation based on the many combinations of the geometric and traffic parameters. The developed speed-flow-geometric relationship models can offer a gateway to highlight future directions for urban road safety in Malaysia. It is established that the proposed model can help the authority of the urban roads network providing better urban roads with high mobility, safe design and planning, leading to sustainable development.  
Conceptualization, T.M.A.-B. and S.A.H.; methodology, T.M.A.-B., S.A.H. and O.C.P.; software, T.M.A.-B.; formal analysis, T.M.A.-B.; resources, S.A.H.; data curation, T.M.A.-B.; writing--original draft preparation, T.M.A.-B.; writing--review and editing, S.A.H., T.M.A.-B. and N.M.; visualization, S.A.H. and T.M.A.-B.; supervision, S.A.H. and O.C.P.; project administration, S.A.H.; funding acquisition, S.A.H. and N.S.A.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Universiti Teknologi Malaysia, vote number Q.J130000.3651. 03M34 (PY/2021/00644).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Mathematical Model Description
### Thermal Model
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbols** & **Definition** \\ \hline \(K_{1}\) & \(A_{co}\cdot e_{c}\cdot\sigma\cdot 4\cdot T_{am}^{3}+A_{co}\cdot h_{out}\) \\ \(K_{2}\) & \(A_{ro}\cdot e_{r}^{*}\cdot\sigma\cdot\Big{[}1+\frac{4T_{am}^{2}\cdot A_{o} \cdot e_{r}^{*}\sigma}{K_{3}}\Big{]}^{-1}\) \\ \(K_{3}\) & \(\Big{[}\frac{1}{A_{ri}\cdot\dot{m}}+\frac{1}{2\cdot m\cdot C_{p}}\Big{]}^{-1}\) \\ \(K_{4}\) & \(\eta_{opt}\cdot\Big{[}1+\frac{4T_{am}^{2}\cdot K_{3}}{K_{3}}\Big{]}^{-1}\) \\ \(K_{5}\) & \(K_{2}\cdot\Big{[}1+\frac{4T_{am}^{2}\cdot K_{3}}{K_{3}}\Big{]}^{-1}\) \\ \(e_{r}^{*}\) & \(\Big{[}\frac{1}{t_{e}}+\frac{1-t_{e}}{t_{e}}\cdot\frac{A_{m}}{A_{ri}}\Big{]}^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Symbols and their definitions [42]. The dimensionless Nusselt number of the base fluid was obtained using the Dittus-Boelter in Equation (18) to cover the turbulent regime flow, while [PERSON]'s equation (Equation (19)) was used for the Nusselt number for the HNF [51]. The usage of Equation (19) is based on its ability to evaluate the Nusselt number a variety of aluminum-oxide-containing hybrid nanofluids in a mixture under a turbulent flow regime under a total volume fraction of up to 4%. \[Nu\ =\ 0.0074\cdot Re^{0.9}.\ p\
u^{0.67}.\varphi^{0.063} \tag{19}\]  
As seen in the literature, many researchers based their approaches on evaluating the Nusselt number of hybrid nanofluids using the experimental model proposed by [PERSON] in Equation (19), as was done by [PERSON] et al. [42], [PERSON] et al. [43], and [PERSON] et al. [52]. Moreover, in this research, the exergy efficiencies of hybrid and base fluids and their changes were defined based on the following equations. Equation (20) was used to demonstrate the output exergy [53] and Equation (21) represents the Petela formula that was used to measure the obtainable solar exergy [54]. These equations are used to define energetic efficiency, as shown in Equation (22), where this parameter is intended to determine the ability of the PTC to generate electricity [53]. \[E_{u}\ =\ Qu-\dot{m}\cdot Cp\cdot T_{am}\cdot ln\left(\frac{T_{out}}{T_{in}}\right) \tag{20}\]  
\[E_{s}\ =\ A_{a}\cdot G_{b}\cdot\left[1-\frac{4}{3}\cdot\left(\frac{T_{am}}{T_{ sum}}\right)+\frac{1}{3}\cdot\left(\frac{T_{am}}{T_{sun}}\right)^{4}\right] \tag{21}\]  
\[\eta_{ex}\ =\ \frac{Eu}{Es} \tag{22}\]  
Finally, a pressure drop assessment was also presented to compare the effect of using different hybrid nanofluid concentrations versus the base fluid. Accordingly, the Darcy friction factor obtained using the Blasius equation, and was used to evaluate the pressure drop values with temperatures for all thermal fluids, as explained using Equations (23) and (24), which cover the turbulent flow regimes [55]. \[f_{r,the}\ =\ 0.079/\left(R_{e}\right)^{0.25} \tag{23}\]  
\[\Delta P_{the}\ =\ f_{r,the}\cdot\frac{L}{d_{ri}}\cdot\left(\frac{1}{2}\cdot p \cdot u^{2}\right) \tag{24}\]
# On Fuzzy and Case-Based Dynamic Software Development Process Modeling and Simulation Approach
## 4 Materials and Methods--On the Fuzzy CMMN Model for SDP Simulation
### Fuzzy Inference in the SDP Model
This study used the following statistical tests: coefficient of determination (\(R^{2}\)), mean squared error (MSE), and root mean squared error (RMSE). The coefficient of determination (\(R^{2}\)) [46] is used to analyze how differences in one variable can be explained by differences in another variable (see Equation (5)). It ranges in the interval [0, 1]. \[R^{2}=1-\frac{SS_{res}}{SS_{tot}},\text{ where }SS_{tot}=\sum_{i}\left(f_{train_{i }}-\overline{f_{train}}\right)^{2}\text{ and }SS_{res}=\sum_{i}(f_{train_{i}}-f_{i})^{2}, \tag{5}\]  
where \(SS_{res}\) denotes the sum of the squares of residuals, and \(SS_{tot}\) the total sum of squares. The mean squared error (\(MSE\)) [41] (Equation (6)) is used to measure the average of the squares of errors between the output f and the training \(f_{train}\) values. \[MSE=1/n\sum_{i=1}^{n}(f_{i}-f_{train_{i}})^{2}, \tag{6}\]  
The predicted result is more precise when the value of \(MSE\) is closer to zero. The root mean squared error (\(RMSE\)) is a metric commonly used for comparing values predicted by a model or estimating in relation to values actually observed. \(RMSE\) is defined as the square root of the average squared distance between the actual score and the predicted score [46] (see Equation (7) [47]). \[RMSE=\sqrt{\frac{\sum_{i}(f_{i}-f_{train_{i}})^{2}}{n}}, \tag{7}\]  
\(RMSE\) is always non-negative and has a value of zero when the data fit perfectly. A lower \(RMSE\) is often preferable to a higher one [47]. The sum of squares is a statistical measure of deviation from the mean. The sum of squares due to error (\(SSE\)) measures the total deviation of the response values from the fit to the response values [48] (see Equation (8) [47]). \[SSE=\sum_{i=1}^{n}(f_{i}-f_{train_{i}})^{2}, \tag{8}\]  
The fit will be better for prediction if the \(SSE\) value is closer to 0, which means that the prediction model has a reduced random error component [47].
# Study of Partial Discharge Inception Voltage in Inverter Fed Electric Motor Insulation Systems
## 5 Discussion
### PDIV Estimation Models  
Analytical, FEM-based, and probability-based models are collected in this article. This resulted in a summary where the used variables, considered changing variables, and the pros and cons of each model, are gathered. Within the models, two considered rise time and pulse width (group 1), whereas six considered environmental conditions, whether pressure, temperature, or both (group 2). This summary can help with the selection of the best model that fits some specific requirements.  
In order to evaluate models from group 2 at high temperature, experiments were performed in a temperature range of 20-260 \({}^{\circ}\)C for two different enamel twisted pairs. To estimate the PDIV based on temperature evolution, the proposed new model offers the best compromise between accuracy and computational cost. The FEM-based models [38; 60; 62; 69] require FEM software and a large amount of computation time. In the Lusuardi model [26], it is necessary to measure the PDIV at least at a reference temperature. Moreover, the relative permittivity must not vary with the temperature. In the proposed new model, the changes in the relative permittivity with the temperature can be considered and FEM analysis and experiments could be skipped. However, it is true that the correct definition of \(\gamma\) value is a key factor and could be difficult to define. If \(\gamma\) for a specific material is not defined in the bibliography, the experimental measurements might be necessary for its definition. This issue is presented in all models using Paschen's law.  
It has to be mentioned that the models analysed in Figures 17 and 18 do not consider electrical waveform characteristics, such as overvoltage, pulse width, or rise time, which can influence the PDIV, as presented in Section 2.1. Despite their use for predicting the PDIV, when it is measured with a sinusoidal waveform, it is shown to be accurate (with different limitations regarding environmental conditions, depending on the model) and it is still not clear what their accuracy is in the estimation of PDIV in cases where waveforms with different characteristics are used. For instance, waveforms with overvoltage may be more difficult to predict. To consider these characteristics, the models proposed by [PERSON] et al. [43] and [PERSON] et al. [31] should be used. The former only considers the rise time and pulse width, whereas the latter considers overshoots as well. However, neither of them consider environmental conditions for twisted pair PDIV estimation. So, although the extended volume-time theory proposed by [PERSON] et al. [31] provides a good basis to consider most relevant electrical waveform characteristics, it is necessary to develop more studies regarding the models considering both environmental and electrical variables.