esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Motivation  
In [28], the 5G-IPAKA protocol was proven secure in the mixed strand space model [29; 30; 31]. However, the above first shortcoming of the 5G AKA protocol has not been completely overcome. In the 5G-IPAKA protocol, whether \(SUCI\) is replayed can be found, but only the UE can find whether \(SUCI\) is replayed, while both the SN and the HN cannot find whether \(SUCI\) is replayed. This will lead to DoS attacks against both the SN and the HN, as shown in Figures 3 and 4.  
In Figure 3, the penetrator \(P\) replays a large amount of messages to the SEAF, which include \(SUCI\), \(SUCI^{\prime}\), etc., for different UEs. Then, the SEAF, AUSF, UDM, ARPF, and SIDF must respond if these UEs have not been authenticated, and the penetrator discards the response messages of the SEAF. As a result, DoS attacks against both the SN and the HN are formed, resulting in a great deal of energy of both the SN and the HN being consumed.  
In Figure 4, the penetrator \(P\) replays a large amount of messages to the AUSF, which include \(SUCI\), \(SUCI^{\prime}\), etc., for different UEs. Then, the AUSF, UDM, ARPF, and SIDF must respond if these UEs have not been authenticated, and the penetrator discards the response messages of the AUSF. As a result, DoS attacks against the HN are formed, resulting in a great deal of energy of the HN being consumed.  
Additionally, the 5G AKA protocol has large communication and computation overhead. As a result, whether the authentication is successful or failed, this will lead to a great deal of energy consumption. Compared with the 5G AKA protocol, the 5G-IPAKA protocol adds some calculations and fields, so it has larger communication and computation overhead than the 5G AKA protocol.  
Therefore, it is necessary to propose a novel 5G AKA protocol, which can completely overcome the above shortcomings of the 5G AKA protocol and has less communication and computation overhead than the 5G AKA protocol.
## 3 Results and Discussion
### Isothermal Crystallization and Kinetic Analysis
#### 3.3.2 Evolution of the Relative Crystallinity over Time  
To gain deeper insight into the crystallization kinetics of neat P3 HBHHx and its nucleated variants, the evolution of relative crystallinity over time was analyzed at various isothermal temperatures, as shown in Figure 12. The half-crystallization time (\(\tau_{1/2}\)), defined as the time required to reach 50% relative crystallinity, was extracted from each curve, and the crystallization rate (\(k\)) was calculated as the inverse. The corresponding values are listed in Table 6.  
Only data obtained under isothermal conditions, where full crystallization was achieved, were considered valid for the kinetic analysis. For neat P3 HBHHx (Figure 12a), valid data began at 80 \({}^{\circ}\)C, where \(\tau_{1/2}\) was 340 s, corresponding to a crystallization rate of 2.94 \(\times\) 10\({}^{-3}\) s\({}^{-1}\). As the isothermal temperature increased, the crystallization process became significantly slower, with \(\tau_{1/2}\) exceeding 1400 s at 95 \({}^{\circ}\)C.  
The incorporation of BN (Figure 12b) significantly reduced \(\tau_{1/2}\) to 99 s at 100 \({}^{\circ}\)C (\(k\) = 10.05 \(\times\) 10\({}^{-3}\) s\({}^{-1}\)), indicating a high nucleation efficiency and rapid crystal growth. The  
Figure 12: Evolution of the relative crystallinity over time for isothermal crystallization performed at the indicated temperatures with (**a**) P3 HBHHx, (**b**) PHH_05 BN, (**c**) PHH_05 PHB, and (**d**) PHH_2 Talc.  
crystallization curves were sharper and more symmetrical than those of the neat polymer, reflecting more uniform nucleation and a faster crystallization process.  
Similarly, PHB (PHH_05 PHB, Figure 12c) and talc (PHH_2 Talc, Figure 12d) improved the crystallization kinetics. At 90 \({}^{\circ}\)C, PHH_05 PHB had a \(\tau_{1/2}\) of 165 s (\(k\) = 6.06 \(\times\) 10\({}^{-3}\) s\({}^{-1}\)), while PHH_2 Talc crystallized even faster, with a \(\tau_{1/2}\) of 133 s (\(k\) = 7.53 \(\times\) 10\({}^{-3}\) s\({}^{-1}\)). The shapes of the curves for both samples remained relatively sharp, especially at intermediate temperatures (85-95 \({}^{\circ}\)C), indicating effective nucleation. However, the deduced rate constants were slightly lower than those of PHH_05 BN, suggesting that while PHB and talc efficiently promote nucleation, they provide fewer active nucleation sites than boron nitride.
## 4 Conclusions  
The present study investigated the use of ZnO nanoparticles as an adsorbent for the treatment of water contaminated with As(III) and As(V). The binding indicated no pH dependence from 2 to 4 and a slight decrease in the binding at pH 5 and pH 6. The adsorption experimental results indicated that arsenic removal was best accomplished for As(V). Batch adsorption studies performed showed that the adsorption of As(III) and As(V) was rapid. The maximum adsorption of both As(III) and As(V) was observed at pH 4.0 with percent As removals of 93% and 95% for As(III) and As(V), respectively. The isotherm studies were determined to fit the Langmuir isotherm model, indicating a monolayer adsorption of the ions. The data obtained from adsorption isotherms at different temperatures were used to calculate the thermodynamic parameters \(\Delta\)G, \(\Delta\)H, and \(\Delta\)S of adsorption. The enthalpy for the adsorption was observed for both As(III) and As(V) was negative and indicated an exothermic adsorption process. The values of \(\Delta\)G for As(III) and As(V) binding were found to be negative at the low and intermediate temperatures, indicating spontaneous binding. However, positive \(\Delta\)G were observed at the highest temperatures, indicating the reaction became nonspontaneous at higher temperatures. Furthermore, the \(\Delta\)G data confirmed the adsorption was exothermic. The thermodynamic data indicated that the reaction occurred through chemisorption for both As(V) and As(III) ions, based on the \(\Delta\)H values of \(-\)47.29 and \(-\)63.4 kJ/mol, respectively. Chemisorption is  
Figure 9: (**A**) Time dependency for the binding of As(III) to the ZnO synthesized nanomaterials. (**B**) Time dependency for the binding of As(V) to the ZnO synthesized nanomaterials.  
usually characterized by a formation of a bond between the nanomaterial and adsorbate indicating a very strong bond. The formation of a strong bond would reduce the ability of As release during its binding back into the solution. In addition, the binding was shown to occur within the first 60 min and remain constant thereafter.  
Conceptualization, J.G.P.; Investigation, H.M.M., G.T. and T.M.E.; Writing-original draft, H.M.M.; Writing-review & editing, A.M., M.A., E.P. and J.G.P.; Funding acquisition, M.A. and J.G.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the UTRGV Chemistry Departmental Welch Foundation Grant (Grant No. BX-0048), and the NSF PREM (DMR-2122178).  
Not applicable.  
The data presented in this study are available through request to the corresponding author.  
[PERSON] acknowledges and is grateful for the support provided by funding from the UTRGV Chemistry Departmental Welch Foundation Grant (Grant No. BX-0048), and [PERSON] acknowledges funding from the NSF PREM (DMR-2122178) Partnership for Fostering Innovation by Bridging Excellence in Research and Student Success.  
The authors declare no conflict of interest.
## 1 Introduction
[39] documented, with the help of the RAMS weather forecast model, how the GPM PMW radiometer constellation can be successfully exploited to analyze the life cycle (from development to mature phase) of TLC Numa. The authors exploited the GPM-CO DPR overpass, available during Numa's development phase, to provide insights into its microphysical and precipitation structure. In this context, the purpose of this work is to perform an analysis of the sensitivity of the WRF model to different microphysical parameterizations and IBCs for the case of the recent and powerful Medicare Ianos [40], focusing on its final mature stage. The GPM-CO conveniently made an overpass over the system in correspondence of this stage, just a few hours prior to making landfall in the western coast of Greece at its peak of intensity. Because of this, similarly to [PERSON] et al. [39], we employ the insightful observations from the spaceborne platform GPM-CO DPR for the validation of the three-dimensional structure of the simulated medicane during the Ianos TLC phase. In particular, the following characteristics of the TLC were considered from satellite data and compared with model simulations: reflectivity factor (horizontal and vertical distributions and contoured frequency by altitude diagram), rain rate, and integrated water column. To further validate the experiments, we also make use of the sea surface wind data from the Advanced Scatterometer (ASCAT) sensor onboard the MetOp-C satellite as reference data of the wind field. The remainder of the article is organized as follows: Section 2 describes the datasets and methodology used to carry out the experiments and analyses; Section 3.1 gives an overview of the synoptic situation along with some results regarding columnar hydrometeors, medicane tracking, and the cyclone phase space diagrams; Sections 3.2 and 3.3 present the validation of the simulations against the GPM-CO DPR and ASCAT datasets, respectively; lastly, Section 4 draws the final discussion and conclusions.
## 5 Conclusions
In conclusion, the comprehensive analysis suggests that the PPW criterion demonstrates the best predictive performance for layered rock strength, followed by the Pariseau criterion, and finally the SPW criterion. Future research should focus on developing unified multi-scale anisotropic models and multifield-coupled strength criteria for thermal-hydrological-chemical (THC) interactions, establishing dynamic constitutive relationships to reveal rate-dependent effects under cyclic loading, and creating machine learning-driven intelligent prediction systems. Efforts should integrate anisotropic criteria with engineering rock mass classification systems, validate practicality through in situ testing, and ultimately form an integrated workflow spanning geological modeling, strength prediction, and stability assessment. **Author Contributions**: Conceptualization, Y.L. ; methodology, Y.L. ; software, Y.L. ; validation, Y.L. and Z.Y. ; formal analysis, Z.Y. ; investigation, Z.Y. ; resources, J.W. ; data curation, Z.Y. and J.W. ; writing--original draft, J.W. ; writing--review and editing, Y.Y. and J.W. ; visualization, Y.Y. ; supervision, Y.Y. ; project administration, Y.Y. All authors have read and agreed to the published version of the manuscript. **Funding**: This research received no external funding. **Institutional Review Board Statement**: All research activities were conducted in accordance with the ethical guidelines and principles outlined by the Committee on Publication Ethics. **Informed Consent Statement**: All individuals involved in this study have provided their consent for the publication of the study findings. Any personal or identifying information that could potentially compromise privacy has been carefully removed or anonymized. **Data Availability Statement**: The data and materials used in this study are available upon request. **Conflicts of Interest**: Author [PERSON] was employed by the company Shenzhen Zhongjin Lingnan Non-ferrous metal Company Limited, and [PERSON], [PERSON], [PERSON] were employed by the company Cathay Safety Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 5 Input Capacitor Design  
In this section, a brief analysis of the input capacitor \(C_{in}\) is presented. In steady state and operating at rated power, the nominal duty cycle was approximately \(d_{nom}=0.25\), and the maximum duty cycle (limited by the switching period \(T_{s}/3\) since three PV panels were being used) was \(d_{max}=0.33\). In this work, PV panels rated at 340 W and with nominal voltage and current of 37 V and 9.18 A, respectively, were used.  
According to the current waveform in the input capacitor \(C_{in}\) (in parallel with the PV panel), the area under the \(\Delta Q\) curve in the charging cycle must be equal to the discharge area due to the energy balance in the capacitor.  
The area under the discharging curve (Figure 13) is formed by the turned-off time of the main switch, and the maximum amplitude of the current is \(\Delta I_{C_{in}}+I_{C_{inmin}}\), where \(I_{C_{inmin}}\) is the minimum capacitor current amplitude.  
Assuming an ideal operation, in one commutation period, this area is equal to the average current delivered by a PV panel and defined by \(I_{PV_{DC}}\). Thus, \(\Delta Q\) is defined by Equation (18), and \(C_{in}\) is defined by Equation (19), where \(\Delta V_{PV}\) is the PV panel voltage ripple desired:  
\[\Delta Q=(T_{s}-d_{nom}T_{s})I_{DC_{PV}}=C\Delta V_{PV} \tag{18}\]  
Figure 12: O-type ferrite core for each flyback transformer.  
Figure 13: Simulated input capacitor current.  
\[C_{in}=\frac{(1-d_{nom})I_{DC_{PV}}}{\Delta V_{PV}\cdot f_{s}} \tag{19}\]
## 3 Results and Discussion
### Analysis of the Impact of Process Parameters on Combustion Reaction
#### 3.3.1 Influence of Process Parameters on Bulk Reaction  
(1) Inlet velocity  
As shown in the Figure 13 and Table 4, with increasing inlet velocity, the methane consumption (combustion) ratio increases. The images of inlet velocities, 6 \(\times\) 10\({}^{-8}\) m/s and 1 \(\times\) 10\({}^{-7}\) m/s, coincide, indicating that when the flow rate is between them, it will not affect the methane-burning rate. When the flow rate exceeds 1 \(\times\) 10\({}^{-7}\) m/s, the change in methane consumption and the burning rate become particularly obvious.  
(2) Inlet concentration  
As shown in the Figure 14 and Table 5, the methane combustion ratio increases with the increasing inlet methane mass fraction. Furthermore, the combustion ratio and concentration are changed in multiples so that it can be inferred that they are linear. Notably, the burning rate of methane is constant at different concentrations; hence, the change in methane concentration in the bulk reaction does not cause a change in the burning rate.  
(3) Pore size  
As shown in the Figure 15 and Table 6, it can be seen that the methane combustion ratio changes linearly with the pore size. With the increasing pore size, the methane combustion ratio first increases and then decreases. Taking the pore depth of 0.00000024 m as the node, before that, the methane burning rate of different pore sizes fluctuates greatly; however, there is no violent fluctuation in the latter. Additionally, the smaller pore size at 60 nm has the largest change in the burning rate. When the pore size is 150 nm, the change is the most stable, and the overall value is also higher than the other two pore sizes. A finer delineation and testing of the aperture size has been made compared to the overall size of 300 nm in previous studies [5].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Inlet Velocity** & \begin{tabular}{c} **Inlet Methane** \\ **Mass Fraction** \\ \end{tabular} & \
\begin{tabular}{c} **Outlet Methane** \\ **Mass Fraction** \\ \end{tabular} &
\begin{tabular}{c} **Combustion** \\ **Ratio (\%)** \\ \end{tabular} \\ \hline
6 \(\times\) 10\({}^{-8}\) & 0.034 & 0.03391 & 0.27 \\ \hline
1 \(\times\) 10\({}^{-7}\) & 0.034 & 0.033997 & 0.0088 \\ \hline
13 \(\times\) 10\({}^{-8}\) & 0.034 & 0.0055258 & 83.7 \\ \hline \end{tabular}
\end{table}
Table 4: Effect of inlet velocity on combustion reaction.  
Figure 13: Effect of inlet velocity on combustion reaction.
# Parameter Calibration and System Design of Material Lifting System for Inclined Shaft Construction
## 3 System Design
### Monitoring System Design  
In order to ensure that operators can monitor the operating parameters of the hoisting system and the operating status of the mine car in real time during the operation of the hoisting system, a real-time monitoring system of the inclined shaft hoisting system is designed. The monitoring system consists of two parts: data center and monitoring equipment. The data center includes terminal monitoring system components such as optical terminal, video matrix, head and head decoder embedded in the main control system, and the monitoring equipment includes front-end monitoring equipment such as motor current monitor, mine car speed monitor and mine car driving distance monitor installed in the inclined shaft mine car. The display interface of the monitoring system is shown in Figure 5.  
Through the hoist monitoring system, the operator can realize the following operation functions:  
1. The dynamic and static screen generation of the hoisting system, such as the dynamic display of the mine car hoisting process, dynamic display of speed curve, dynamic display of armature current curve, dynamic display of hoisting vessel position, etc.
2. The fault self-test and self-inspection of the hoisting system, and can alarm the operator in time through the display interface of the monitoring system. The monitoring system can display the location, time and cause of the fault; monitor the important parameters of the system such as speed, current, voltage and operation status in real time; and store and process the important parameters such as speed graph and armature graph.
3. The monitoring system can monitor the power supply of each circuit of the hoisting system and realize real-time control of the speed of the speed control system by cooperating with the main control system to complete the operation of the hoist as fully automatic, semi-automatic, manual, maintenance, slow moving, emergency stop, low speed crawling, etc.
4. The data center of the monitoring system adopts modularized function and linearized structure design, which has good maintainability and expandability. The speed regulation and monitoring at the slope construction site can realize the ability of remote real-time control and diagnosis, which can assist users to analyze the operation status of the hoist in time and make corresponding adjustments.  
Figure 5: Display interface of hoist monitoring system.
## 4 Results
### Model Training and Evaluation
#### 4.1.1 RGCN Model Training Performance Featuring BERT-Based Features  
Table 7 shows the performance of RGCN (i.e., BERT-RGCN) when trained using the features based on BERT for SMILES. The RGCN model achieved high and comparable performance in terms of all performance metrics when trained using only drugs' features (i.e., \(AmD_{F}\)) and also when drugs' features were combined with antimalarial potential targets' features, as shown in Table 7. However, it revealed slightly higher performance achievement (i.e., accuracy: 0.9985, sensitivity: 0.9945, AUROC: 0.9993, and AUPRC: 0.9995) when trained using concatenated features of the antimalarial drugs and the monoacylglycerolglycerol Lipase target (i.e., \(AmD_{F}\) and \(PfMAGL_{F}\)) as shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Training result** & \(AmD_{F}\) & \(AmD_{F}\) & \(AmD_{F}\) & \(AmD_{F}\) and & \(AmD_{F}\) and & \(AmD_{F}\) and \(Tg_{F}\) \\ \hline BERT-RGCN & Accuracy & 0.9937 & 0.9943 & 0.9914 & 0.9958 & 0.9925 \\  & Sensitivity & 0.9917 & 0.9903 & 0.9828 & 0.9945 & 0.9855 \\  & Specificity & 0.9953 & 0.9974 & 0.9979 & 0.9969 & 0.9979 \\  & MCC & 0.9873 & 0.9885 & 0.9826 & 0.9915 & 0.9850 \\  & AUROC & 0.9992 & 0.9989 & 0.9990 & 0.9993 & 0.9990 \\  & AUPRC & 0.9992 & 0.9990 & 0.9992 & 0.9995 & 0.9992 \\ \hline Mordred-RGCN & Accuracy & 0.9967 & 0.9952 & 0.9940 & 0.9973 & 0.9934 \\  & Sensitivity & 0.9959 & 0.9952 & 0.9952 & 0.9965 & 0.9876 \\  & Specificity & 0.9974 & 0.9953 & 0.9932 & 0.9979 & 0.9979 \\  & MC & 0.9933 & 0.9903 & 0.9880 & 0.9945 & 0.9868 \\  & AUROC & 0.9999 & 0.9999 & 0.9998 & 0.9998 & 0.9998 \\ \hline \hline \end{tabular} \(AmD_{F}\)–Antimalarial drug features. \(PfAcAS_{F}\)–Acetyl CoA Synthetaes features. \(GGPP_{F}\)–Geranylgeranyl Pyrophosphate Synthase features. \(PfMAGL_{F}\)–Monoacylglycerolcerol Lipase features. \(Tg_{F}\)–All targets’ features.  
\end{table}
Table 7: Performance comparison of the model (i.e., BERT-RGCN) on training dataset using BERT-based features.
# A Robust Wind Turbine Component Health Status Indicator
## 2 Materials and Methods
### The Gaussian Mixture Copula Model
The generative model for a Gaussian copula is defined through a Gaussian distribution on the latent CDF transformations: \(\mathbf{X}^{j}=F_{j}^{-1}(\mathbf{U}^{j})\); \(\mathbf{U}^{j}=\Phi_{j}(\mathbf{Y}^{j})\); \(\mathbf{Y}\sim\Phi(\mathbf{\mu},\mathbf{\Sigma})\), where \(\Phi_{j}\) is the \(j^{th}\) marginal CDF corresponding to the multivariate normal distribution with CDF \(\Phi\), mean \(\mathbf{\mu}\) and covariance matrix \(\mathbf{\Sigma}\). The Gaussian Mixture Copula Model is obtained using a GMM as the joint density function in Equation (3), which allows writing the GMCM copula density as:  
\[c_{f}(\mathbf{U},\mathbf{\theta})=\frac{f_{GMM}(\mathbf{\
abla}_{\theta}^{-1}(\mathbf{U}))}{\prod _{j=1}^{p}\psi_{j}(\mathbf{\
abla}_{\beta}^{-1}(\mathbf{U}^{j}))} \tag{4}\]  
where \(\Psi_{j\theta}\) and \(\psi_{j\theta}\) are the \(j^{th}\) marginal CDF and PDF of the GMM \(f_{GMM}\). The generative model of the GMCM can be written as:  
\[\mathbf{X}^{j}=F_{j}^{-1}(\mathbf{U}^{j});\quad\mathbf{U}^{j}=\Psi_{j}(\mathbf{Y}^{j});\quad \mathbf{Y}^{j}\sim\psi_{j\theta} \tag{5}\]  
The likelihood of n observations from GMCM can be expressed in terms of the realizations of latent variables \(\mathbf{Y}\):  
\[\mathcal{L}=\prod_{i=1}^{n}\frac{\sum_{k=1}^{k}\pi_{k}\phi(\mathbf{y}_{i}|\mathbf{\mu} _{k},\mathbf{\Sigma}_{k})}{\prod_{j=1}^{p}\Sigma_{k}\psi_{j}(y_{j}^{i}|n_{k^{ \prime}}^{i},\mathbf{\Sigma}_{kj})} \tag{6}\]  
As a summary of the generative process of applying the GMCM Copula, Figure 6 illustrates the transformation of variables in the real domain \(\mathbf{X}\) to the transformed field \(\mathbf{U}\) ([49]) followed by modeling interdependencies among the variables using the GMCM. Subsequently, a simulation is performed in the copula space, and then an inverse transformation, as described previously, is applied to obtain simulated data in the real domain. It highlights the workflow of the GMCM model and how the transformations between the copula domain and real variable domain are integrated, providing a clear visual representation of the key stages of the generative process. As a final insight, after the theoretical foundations are exposed, the GMCM model can capture the relationship between the different distributions and estimate a joint probability. This estimation is the key to the generation of the failure index proposed in this work. The strengths of the GMCM models support its selection [50] for the aims of this study. In general, copula models provide a flexible characterization of multivariate distributions. In this sense, GMCM models relax the assumption of working with normal distributions [51]. Therefore, these models allow flexible dependency modeling, especially of non-Gaussian data. GMCM models have been applied to deal with unsupervised pattern recognition, clustering and outlier recognition applications, and they can model many kinds of multi-modal dependencies, notably asymmetric and tail dependencies.