esa-sceva/satcom-chunk-collection huggingface dataset:
# Research of Modal Analysis for Impeller of Reactor Coolant Pump
## 4 Discussion
### Theoretical Expression of Natural Frequencies of the Impeller  
Equation (2) is the motion differential equation of a static impeller in the air [24]. While the underwater rotating impeller is subject to the damping of water, centrifugal force and hydraulic load. Therefore, modified from Equation (2), Equation (3) stands for the motion differential equation of the rotating impeller in water. In general, the velocity damping term can be neglected, and the system is approximately undamped [25]. Then after solving Equation (3), the \(i\)-th order main vibration expression of the impeller is Equation (4); therefore, natural frequencies of the wet modal rotating impeller are represented by Equation (5). Similarly, natural frequencies of the dry modal static impeller are expressed as Equation (6).  
\[M\ddot{\textbf{R}}+\textbf{C}\dot{\textbf{R}}+\textbf{K}\textbf{R}=\textbf{0}, \tag{2}\]  
\[(\textbf{M}+\textbf{M}_{\textbf{a}})\ddot{\textbf{R}}+(\textbf{C}+\textbf{G}) \dot{\textbf{R}}+(\textbf{K}+\textbf{D}+\textbf{B})\textbf{R}=\textbf{0}, \tag{3}\]  
where \(\textbf{M}\), \(\textbf{M}_{\textbf{a}}\), \(\textbf{C}\), \(\textbf{G}\), \(\textbf{K}\), \(\textbf{D}\) and \(\textbf{B}\) respectively represent the structural mass matrix, the added mass matrix, the damping matrix, the gyro matrix, the stiffness matrix, the matrix and the rotational damping matrix. \(\ddot{\textbf{R}}\), \(\textbf{R}\), and \(\textbf{R}\) respectively represent the acceleration vector, the velocity vector and the displacement vector.  
\[\textbf{R}^{(i)}=\alpha_{i}\dot{\textbf{\phi}}^{(i)}\cdot\sin(\omega_{i}t+ \varphi_{i})\qquad\qquad(1,2,\cdots,n), \tag{4}\]  
where \(\alpha_{i}\) and \(\varphi_{i}\) are related to the initial state of motion. The array \(\textbf{\Phi}^{(i)}\), which is called the \(i\)-th model, represents the relative ratio of vibration amplitudes per coordinate when the structure is in the \(i\)-th order main vibration.  
\[f_{i}=\frac{1}{2\pi}\sqrt{\frac{(K+D+B)_{pi}}{(M+M_{a})_{pi}}}, \tag{5}\]where the subscript pi represents the \(i\)-th order. \(M_{pi}=\mathbf{\Phi}^{(i)\intercal}\mathbf{M}\mathbf{\Phi}^{(0)}\), others are similar to \(M_{pi}\),  
\[{f^{\prime}}_{i}=\frac{1}{2\pi}\sqrt{\frac{K_{pi}}{M_{pi}}}. \tag{6}\]  
From Equation (3), it is found that natural frequencies of the impeller are related to added mass, centrifugal force and hydraulic load. The effect of water damping is added to the mass matrix item, and the effect of centrifugal force and hydraulic load is added to the stiffness matrix item. Comparing Equation (4) with Equation (5), it is found that added mass increases natural frequencies, while the centrifugal effect and hydraulic load decrease natural frequencies. The analysis of our research in Section 4.1 follows the theoretical analysis result. However, since the effect of centrifugal force is small, it can be ignored in the natural frequencies study of the impeller.
## 5 Results and Discussion
### UHIs Detected
These results show that most of the urban areas of the city of Naples identified as UHIs can be considered UHIs with a high level of reliability. This imply that the proposed method provides a good reliability level for the UHIs detected. To test the accuracy of the obtained results, the detected UHIs were compared with those obtained by applying the UHI detection method in [29], where a sampling interval of 10 m was used and the number of classes was fixed to seven. The sum of the UHI areas detected by employing this UHI detection method [29] was 42.97 km\({}^{2}\), representing 36.30% of the area of the city. About 95.75% of the UHIs detected using the proposed method were covered by UHIs detected using the method [29]. Table 2 shows the percentage of UHIs with _High_, _Medium_, and _Low_ reliability that are covered by UHIs detected using the method of [29]. The results in Table 2 show that over 97% of UHIs classified as High reliability are covered by UHIs detected using the method of [29]. This highlights that the proposed UHI detection method provides accurate results and, unlike other clustering-based UHI detection algorithms, provides an effective estimate of their reliability. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reliability Class** & **Area [km\({}^{2}\)]** &
\begin{tabular}{c} **Percentage of Area Covered by UHIs** \\ **Obtained Using [29]** \\ \end{tabular} \\ \hline High & 43.87 & 97.02\% \\ Medium & 2.62 & 75.16\% \\ Low & 0.05 & 57.68\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Percentage of UHIs covered by UHIs detected executing the algorithm of [29]. Figure 9: Thematic map of UHI detection by reliability analysis.
# Toughening Behavior of Carbon/Epoxy Laminates Interleaved by PSF/PVDF Composite Nanofibers
## 5 Conclusions  
In this study, two thermoplastic polymers, i.e., PVDF and PSF, were applied individually and at the same time, in form on nanofibers into CFRP, to study their influence on GIC and GIIC. The investigation produced the following results:  
1. Interleaving reference laminates with nanofibers made of both polymers lead to increased Mode-I and Mode-II energy-release rates;
2. PVDF nanofibers offer better results than PSF nanofibers: the first improved GIC and GIIC by 57% and 66% against 9% and 51%, respectively, of the latter;
3. Mixed PVDF/PSF nanofibrous mat had a synergistic influence, increasing Mode-I and Mode-II energy-release rate by 75% and 78%, respectively. These results are similar to the results presented in Reference [39], in which PCL film/nylon nanofibers were applied.
4. SEM micrographs showed that PSF started to engage in phase separation from the epoxy and changed to spherical particles during curing process. Hence, when the crack tip reached these particles, it deflected from its main path and absorbed energy.
5. When PVDF nanofibers are interleaved, they mix with epoxy and melt during cure, creating a plastic zone in front of the crack tip, requiring higher energy for it to propagate. The melting is a critical factor; otherwise, the PVDF cannot affectively toughen the laminates.  
Conceptualization, H.S. and R.P.; methodology, H.S., A.Z., and H.H.; investigation, H.S. and R.P.; data curation, T.M.B.; writing--original draft preparation, H.S.; writing--review and editing, R.P.; visualization, T.M.B.; supervision, G.M. All authors have read and agreed to the published version of the manuscript.  
Figure 7: Morphology of fractured surface in (**a**) reference, (**b**) PSF-, (**c**) PVDF-, and (**d**) PVDF/PSF-modified laminates.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Decentralized Identity Authentication with Auditability and Privacy
## 4 Baseline Protocol
### Registration  
In the registration phase, the client first generates its keypair \(\langle vk,sk\rangle\leftarrow\mathit{KeyGen}(1^{\lambda})\), then derives its identity \(\mathit{did}\gets H(vk)\), and initializes its authentication counter \(cnt\gets 0\). With these values, the client creates a registration request  
\[\mathit{regReq}\leftarrow\langle vk,info,\mathit{Sig}_{sk}(vk\|info)\rangle\]  
which includes \(info\), other human-readable information (e.g., a name or a comment), and the signature computed over the content of the request. The request is sent to the blockchain, where the \(registerDID\) smart contract method handles it. The method implements basic sanity checks, including signature validation and the insertion of the verification key and other information into the _identity map_ (\(\mathit{didMap}\)), which is a mapping between the \(\mathit{did}\) (key) and a \(\langle vk,info,cnt\rangle\) tuple (value), whereupon the registration \(cnt\) is set to 0.  
Upon successful execution of the code, blockchain nodes create an inclusion proof \(\beta^{\mathit{did}}\), which states that the identity was successfully registered and is part of the blockchain. The registration process is complete, and the client is ready to use its \(\mathit{did}\) for authentication.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Auditability** & **Privacy** & **Performance** \\ \hline Existing protocols & ✗ & ✗ & ✓ \\ Baseline & ✓ & ✗ & ✓ \\ Privacy-preserving & ✓ & ✓ & ✗ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of our protocols with existing DID authentication protocols.
# Quantitative Assessment and Grading of Hardware Trojan Threat Based on Rough Set Theory
## 3 Assessment of Grade Division and Grade Quantity
### Determination of Grade Quantity Based on BWP  
Due to the uncertain number of clusters, here, we use BWP to evaluate the clustering results and determine the optimal number of clusters. In general, a good clustering result should reflect the internal structure of the dataset as much as possible in order to ensure that the objects within the same cluster are as similar as possible and the objects in various clusters are as different as possible. From the perspective of distance measure, it is the optimal clustering result that minimizes the intra-cluster distance and maximizes the inter-cluster distance. BWP is used to reflect the intra-cluster tightness and inter-cluster separation of the clustering result [22].  
Assume a dataset with \(n\) objects, \(S\)= {x\({}_{1}\),x\({}_{2}\), \(\cdots\)\(\times\)\({}_{n}\)}, and suppose that \(n\) objects are divided into \(k\) clusters; we define the inter-cluster distance, \(b(j,i)\), of an object \(i\) in cluster \(j\) as the minimum average distance between object \(i\) and the object of each other cluster. Its calculation is as follows:  
\[b(j,i)=\min_{1\leq c\leq k,\
eq i}\left(\frac{1}{n_{c}}\right)\sum_{p=1}^{n_{c }}\left\|x_{p}^{c}-x_{i}^{j}\right\|^{2} \tag{15}\]  
where \(c\) and \(j\) are the relevant cluster numbers, \(n_{c}\) represents the number of objects in cluster \(C\), \(x_{p}^{c}\) represents the \(p^{th}\) object in cluster \(C\), and \(\left\|\cdot\right\|^{2}\) is the square of the Euclidean distance.  
Similarly, we can define the intra-cluster distance, \(w(j,i)\), of an object \(i\) in cluster \(j\) as the average distance from this object to other objects in cluster \(j\). Its calculation is as follows:  
\[w(j,i)=\left(\frac{1}{n_{j}-1}\right)\sum_{p=1,p\
eq i}^{n_{j}}\left\|x_{p}^{ j}-x_{i}^{j}\right\|^{2} \tag{16}\]  
The cluster effectiveness of object \(i\) in cluster \(j\) is defined as \(BWP(j,i)\). The effectiveness of the clustering result is the sum of \(BWP(j,i)\), the calculation method for which is as follows:  
\[BWP=\sum\text{BWP}(j,i)=\sum\frac{b(j,i)-w(j,i)}{b(j,i)+w(j,i)} \tag{17}\]  
Theoretically, the larger \(b(j,i)\) is, the higher the separation between clusters, and the smaller \(w(j,i)\) is, the higher the tightness within the cluster. The clustering result with the largest \(BWP\) is the most effective, and its number of clusters is defined as the number of HT threat grades.
## 4 Results
### Soil Salinity Prediction and Accuracy Assessment
#### 4.4.1 Soil Salinity Prediction and Accuracy Assessment Using the IDW Method  
The spatial distribution maps of soil salinity prediction of HH and VV modes using IDW with a power parameter of 2 are displayed in Figure 9. Essential statistical data of soil salinity prediction with ECe data using IDW of two modes by sub-district are reported in Table 8.  
Figure 9: Spatial distribution of soil salinity prediction using IDW of two modes: (**a**) HH and (**b**) VV.  
In Figure 9, the patterns of soil salinity distribution from two modes at the sub-district levels are slightly different. Under the HH mode, at a depth of 0-0.75 m, the top three sub-districts, Non Thai, Ban Wang, and Khang Phlu, show the highest mean values of soil salinity, with a value from about 2654 to 5627 mS/m. On the contrary, three sub-districts, Banlang, Sai O, and Makha, display the lowest mean value of soil salinity, from about 587 to 989 mS/m. At a depth of 0-1.5 m of the VV mode, the top three sub-districts, Kampang, Ban Wang, and Khang Phlu, show the highest mean values of soil salinity, with a value from about 2697 to 4752 mS/m. On the contrary, three sub-districts, Banlang, Sai O, and Makha, show the highest mean values of soil salinity, with a value from about 2697 to 4752 mS/m. On the contrary, three sub-districts, Banlang, Sai O, and Makha, show the lowest mean values of soil salinity, with a value from about 2697 to 4752 mS/m. On the contrary, three sub-districts, Banlang, Sai O, and Makha, show the lowest mean value of soil salinity, from about 1083 to 1442 mS/m. Besides, the coefficient of variation (CV) value of the two modes, which reflects the variability of predicted ECe, displays the lowest at Thanon Pho, with 13.76% and 9.50%, respectively. In comparison, the CV value of the two modes exhibits the highest at Non Thai, with 98.42% and 78.81%, respectively.  
At the district level, the mean ECe value of topsoil (HH) is 2276.31 mS/m, with an SD of 2001.08 mS/m and a CV of 87.91%. Meanwhile, the mean ECe value of subsoil (VV) is 2395.08 mS/m, with an SD of 1502.69 mS/m and a CV of 62.74%. These findings indicate a higher variation of soil salinity at the topsoil level than at the subsoil level.  
In addition, the result of the accuracy assessment of the IDW method with the testing dataset was reported in Table 9. As a result, the ME and RMSE values of the HH mode are about 663.18 and 1308.51 mS/m, and those of the VV mode are about \(-\)80.63 and 803.92 mS/m. The R\({}^{2}\) of the HH mode is 0.8422, and that of the VV mode is 0.8354. The PBIAS of the HH mode is about \(-\)43.67%, and that of the VV mode is about 3.23%.  
When ME values are compared between two modes, the HH mode delivers a higher error in the mean. Likewise, the RMSE value of the HH mode is higher than the VV mode. Moreover, the PBIAS value of HH mode is higher than VV mode. In the meantime, the R\({}^{2}\) values of HH and VV modes are indifferent, providing a very strong correlation between measured and predicted values.
# Wedge-Splitting Test on Carbon-Containing Refracories at High Temperatures
## 4 Results and Discussion
### Inverse Evaluation with the Consideration of Creep  
In this chapter, the tensile creep behavior was considered in the inverse evaluation. The creep behavior of this material was already investigated by the authors [13]. It is clear that creep has an influence on the service behavior and furthermore on the results of the high-temperature wedge splitting test. The aim of these simulations is to calculate the specific fracture energy, which is caused by crack propagation only. The contribution of creep depends on the loading rate. For low loading rates, the influence of creep is comparatively high and vice versa for high loading rates. The Norton-Bailey creep equation was applied (Equation (5)).  
\[\dot{\varepsilon}_{\sigma}=K(T)\sigma^{n}\;\varepsilon_{\sigma}^{a} \tag{5}\]  
Here, \(\sigma\) is the applied load, \(\dot{\varepsilon}_{\sigma}\) is the creep strain rate, \(\varepsilon_{\sigma}\) is the creep strain, \(K(T)\) is a temperature-dependent function and the parameters \(n\) and \(a\) are the stress and strain exponents, respectively.  
These parameters were implemented in the simulation of the wedge-splitting test and the creep behavior was considered in the whole specimen volume. The diagrams (Figures 9-11) in Section 4.2 shows the results in detail. The left diagrams (a) at each temperature show the experimental curve and the fits, both, with and without consideration of creep. The strain-softening behavior, which is plotted on the right side in Figures 9b, 10b and 11b, shows the change of the energy and the tensile strength of the material at each temperature.  
Table 3 shows the change of the parameter with respect to temperature when creep is considered. The inverse calculated parameter \(G_{f}\) shows an increase of the specific fracture energy with increasing temperature. The ratio between the specific fracture energy and the tensile strength that is related to the brittleness shows, for both cases (with and without consideration of creep), an increase with increasing temperature. The difference between \(G_{f}\) with consideration of creep and \(G_{f}\) without consideration of creep (Table 2), \(\Delta G_{f}\), shows a maximum temperature of 1370 \({}^{\circ}\)C and is between 15% and 47% for the high-temperature tests.
# Synthesis of Sources of Common Randomness Based on Keystream Generators with Shared Secret Keys
## 8 Experimental Verification  
To confirm the theoretical analysis presented in Sections 4-6, we conducted 1000 repeated simulations of the proposed CR-based SKD system operating in perfect secrecy mode with a basic data block length of \(n_{IR0}=2000\) bits per run.  
Figure 13 shows the empirical spread of the measured all four system parameter curves (yellow, pink, red, green), displaying the one-sigma confidence regions around each curve. The results show excellent agreement between the simulated and theoretical predictions from Figure 8, confirming the accuracy of the analytical model under perfect secrecy constraints.  
Figure 14 presents the histogram of the generated perfectly secret key lengths across all simulation runs (solid line), with the vertical dashed line indicating the theoretically expected value calculated using Equations (106) and (107). Due to the low variance observed in the simulations, we conclude that the experimental and theoretical results for the perfect secret key rate exhibit exceptional agreement. The expected theoretical value for the distilled secret key rate was computed as: \(\left|\widetilde{K}_{s}\right|_{max}=0.5\cdot\left(C_{2}-C_{1}-\frac{t}{\mu_{IR0 }}\right)=0.1051\), confirming the system's ability to consistently generate secret keys at the predicted rate while maintaining perfect secrecy.  
The yellow, purple, magenta, and green curves represent different system parameter configurations. The excellent agreement with theoretical predictions from Figure 8 validates the analytical model.  
Figure 13: Empirical one-sigma spread of the measured secret key rates under perfect secrecy mode across 1000 simulation runs of the proposed CR-based SKD system with \(n_{IR0}=2000\) bits. The yellow lines \(\Psi_{L}\) represent the lower bound on [PERSON]’s equivocation. The green lines \(\mu_{IR}\) are the remaining data efficiency of the IR protocol. The upper bound reduced by security margin \(\widetilde{t}\) is shown by the purple lines. The input innovative entropy \(IE\) is represented by magenta lines.
## 3 Results  
### Worker Groups and Average Doses  
In this study a total of 42 personal inhalable dust samples was performed. Sampling times ranged from 120 min up to 425 min based on the duration of the work shift.  
From the information provided by personnel on work record sheets, it was possible to calculate the average time dedicated by each worker category to specific activities. Laboratory animal attendants worked for about 167 min (average time) of their shift in animal rooms, where they were mostly changing cages; the remaining time they spent in the wash and storage areas.  
Researchers dedicated most of their work shift to laboratory (average time 164 min) and office activities (average time 83 min), while technicians carried out different activities equally distributed between the animal rooms, laboratory and office.  
Following the strategy described in the statistical method paragraph, an average dose for each bio-contaminant and an average exposure time were attributed to the four work categories. The results are summarized in Table 1.  
As regards the individual dose, endotoxin concentrations exceeding the recommended occupational exposure limit proposed by the Dutch Expert Committee on Occupational Safety (DECOS) [33] of 9 ng/m3 were found in two cases in the animal attendant category. This occurrence deserves great attention and prevention measures for risk containment.  
No exposure to Can f 1 and Equ c 4 allergens was found.  
As it can be appreciated by Table 1, the laboratory animal attendants are largely more exposed to endotoxins and allergens than the other categories. Furthermore, technicians have higher levels of mouse allergens than researchers. This fact can be visually evaluated by looking at Figure 2. Fel d 1 was detected in three researchers (average concentration: 0.11 ng/m3).  
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline  & \multicolumn{5}{c}{**Bio-Contaminant Doses**} \\ \hline \multirow{2}{*}{**Work Category**} & \multicolumn{2}{c}{**Endotoxin**} & **Mus m 1** & **Rat n 1** & **Fel d 1** & **Exposure Time** \\  & & **(ng/m3)** & **(ng/m3)** & **(ng/m3)** & **(ng/m3)** & **(min per Day)** \\ \hline Researcher & average & 0.57 & 2.73 & 0.00 & 0.11 & 303.4 \\  & dev.st & & & & & 79.0 \\  & max error & 0.91 & 6.37 & 0.00 & 0.27 & \\ \hline Researcher & average & 0.56 & 0.08 & 0.00 & 0.00 & 351.0 \\  & dev.st & & & & & 35.7 \\  & max error & 0.59 & 0.13 & 0.00 & 0.00 & \\ \hline Laboratory & & & & & & \\ animal & average & 4.59 & 15.85 & 0.61 & 0.00 & 330.7 \\ attendant & & & & & & 50.4 \\  & dev.st & & & & & 50.4 \\  & max error & 4.70 & 12.25 & 1.59 & 0.00 & \\ \hline Technician & average & 0.37 & 10.67 & 0.00 & 0.00 & 292.5 \\  & dev.st & & & & & 17.7 \\  & max error & 0.216 & 11.51 & 0.00 & 0.00 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average dose of bio-contaminants by worker category.