esa-sceva/satcom-chunk-collection huggingface dataset:
# Safety of Nuclear Power Plants with Respect to the Fault Displacement Hazard
## 4 Baseline Information for Plant Response to Permanent Ground Displacement
### Case Studies for Existing Designs  
Basic investigations of nuclear power plants for PGD have been published in the JANSI-FDE-03 document [22]. Here, case studies have been performed for a \"design basis\" \(\delta a=0.3\) m and a \"beyond design basis\" \(\delta b\) displacement below the center of typical PWR and BWR (Boiling Water Reactor) reactor buildings, assuming a soft foundation (Vs = 500 m/s) and a hard rock foundation (Vs = 1500 m/s). The study covers the analysis of the response to PGD for base mat, building structure, piping and mechanical components of typical PWR and BWR reactor types. The loads due to displacement of building have been combined by normal operating and seismic loads of the same origin as for the ground displacement. The evaluation procedures and allowances comply with nuclear standards of the design basis for each of the failure modes while sufficient margin should be demonstrated. Criterion for the beyond design basis was set that buildings and structures should maintain the shape as the whole structure without leading to total fracture.  
In case of components and piping systems, compliance with nuclear codes has been required as for the design basis accidents. The normal operational and seismic loads have been combined with the loads from declination and deformation of supporting structures.  
Three important statements can be extracted from the JANSI-FDE-03 [22] study:  
* The available analytical tools are capable of simulating the fault displacement-plant interaction and for the evaluation of the response of the plant SSCs to the PGD.
* The nuclear codes and standards are applicable for the evaluation of the integrity and functionality of structures, systems and components for the load case of PGD.
* For the PWRs and BWRs specifically not designed for PGD, the analyses demonstrated that the safety functions of the structures, systems and components remain even in case of significant ground movement.  
Moreover, the fragility analyses published in [23; 24] demonstrated a high HCLPF (High Confidence for Low Probability of Failure) values of typical existing reactor design with respect to PGD failure modes for both foundation and containment.  
The findings of the studies referenced above will not be presented and discussed but they are implicitly used to support the discussion on the safety issues due to PGD.
## 2 Materials and Methods
### In Vivo Evaluation of Anticancer Effects
#### 2.3.2. Dosing and Definitions of the Study Groups  
In this experiment, the gefitinib dose was set as 120 mg/kg, which has previously shown anticancer effects on mice with xenografts [21], while sasam-Kyeongokgo doses of 400, 200, and 100 mg/kg and an administration interval of 5 min between gefitinib and sasam-Kyeongokgo were set on the basis of the results of previous animal experiments [20].  
The animals were placed in the following seven groups (eight animals per group):  
1. [label=(0)]
2. Intact control: animals that received the normal vehicle (sterile distilled water: 10 mL/kg) alone.
3. Tumor-bearing control: animals that received sterile distilled water after NCI-H520 tumor cell transplantation.
4. G120 group: animals that received gefitinib (120 mg/kg) alone after tumor cell transplantation.
5. SKOG400: animals that received sasam-Kyeongokgo (400 mg/kg) alone after tumor cell transplantation.
6. GSK200: animals that received gefitinib (120 mg/kg) and sasam-Kyeongokgo (200 mg/kg) after tumor cell transplantation.
7. GSK100: animals that received gefitinib (120 mg/kg) and sasam-Kyeongokgo (100 mg/kg) after tumor cell transplantation.  
For the tumor cell transplantation, NCI-H520 (ATCC, American Type Culture Collection Center, Manassas, VA, USA) cells were cultured in RPMI 1640 (Gibco, Grand Island, NY, USA) medium supplemented with 10% fetal bovine serum (FBS) and maintained by subculture in an incubator (Model 311; Thermo Forma, Marietta, OH, USA) at 37 \({}^{\circ}\)C in 5% CO\({}_{2}\). A tumor cell suspension was prepared at a concentration of 1.0 \(\times\) 10\({}^{8}\) cells/mL, and 0.2 mL (2 \(\times\) 10\({}^{7}\) cells/mouse) of the suspension was transplanted subcutaneously into the right dorsal buttock of each mouse to induce the formation of a solid tumor mass.  
Fifteen days after the transplantation of the NCI-H520 lung cancer cells into the nude mice, sasam-Kyeongokgo was suspended in sterile distilled water, and concomitant oral administration of the suspension was performed for 35 days at doses of 400, 200, and 100 mg/kg of BW once daily within 5 min after the administration of gefitinib, which was orally administered at a dose of 120 mg/kg.  
In addition, the same dose of sterile DW was provided, instead of the second agent, to groups receiving the sasam-Kyeongokgo and gefitinib single administration. For the normal and tumor transplantation medium control groups, sterile distilled water alone was administered twice at intervals of 5 min.
## 5 Mechanical Properties  
MDF can effectively improve the mechanical properties of Mg alloys through grain refinement strengthening, solid solution strengthening and precipitation strengthening mechanisms. It was reported that the ultimate tensile strength can be improved up to 27% of Mg-Li alloys [75], 84% of AZ91 alloys [69], 100% of AZ31 alloys [76] and 128% of Mg-RE-Zn alloys [77]. Table 3 summarizes the ultimate tensile strength (UTS), tensile yield strength (TYS) and elongation (El) of Mg alloys processed by MDF. It can be seen that there are significant differences in the impact of various elements on the mechanical properties, which can be attributed to the contribution of second phases, precipitation and texture to performance during MDF process.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Material** & **Processing History** & **UTS (MPa)** & **YS (MPa)** & **El (\%)** & **Ref.** \\ \hline Mg-8 Al & MDF with accumulative strain of 0.72 & 486 & 384 & 8.1 & [65] \\ \hline Mg-8 Al & MDF with accumulative strain of 1.20 & 396 & 308 & 11.1 & [20] \\ \hline Mg-3 Al-1 Zn & MDF with accumulative strain of 2.70 & 215 & 153 & - & [47] \\ \hline Mg-2.70 Zn-1.34Y-0.37 Ca-0.02 Zr &
\begin{tabular}{c} MDF with accumulative strain of 1.73 \\ + hot rolling \\ \end{tabular} & 260.51 & 192.29 & 17.83 & [78] \\ \hline Mg-6.5 Gd-1.2Y-0.49 Zr & MDF with accumulative strain of 2.64 & 297.1 & 247.7 & 26 & [73] \\ \hline Mg-7 Gd–2Y–1 Zn–0.5 Zr & MDF with accumulative strain of 435.3 & 395.2 & 17.6 & [42] \\ \hline Mg-4.9 Gd-3.2Y-1.1 Zn-0.5 Zr & MDF with accumulative strain of 1.35 & 359 & 331 & 9.1 & [79] \\ \hline Mg-8.8 Al-0.47 Zn-0.21 Mn-0.17 Ag & MDF with accumulative strain of 0.90 & 352 & 216 & 17 & [33] \\ \hline \end{tabular}
\end{table}
Table 3: The mechanical properties of MDFed Mg alloys.  
Figure 7: (0001), (10\(\overline{1}\)0) and (11\(\overline{2}\)0) pole figures of Mg-6.5 Gd-1.2Y-0.49 Zr alloy processed by MDF: (a) 2 passes, (b) 3 passes and (c) 4 passes [73].
## 3 Methods  
In this section, we investigate the performance of two state-of-the-art multilingual models, XLM-Roberta and mBART, on the challenging cross-lingual natural language inference (XNLI) task [14]. We evaluate these models using the XNLI dataset, which is a large and diverse collection of natural language inference examples in multiple languages. This dataset, built on the MultiNLI corpus, includes 14 additional languages and serves as a valuable resource for assessing the performance of cross-lingual natural language inference (NLI) models. By using XNLI, we aim to determine how well these models handle cross-lingual sentence representations and achieve state-of-the-art results in this domain. The ability to handle data across multiple languages is a crucial requirement for modern natural language processing (NLP) systems, enabling them to cater to diverse global audiences and facilitate cross-lingual communication.  
Table 1 shows a subset of English data from the XNLI dataset; we have 3 columns: hypothesis, premise, and class label (target) 0, 1, and 2. The distribution of these class labels is 33.33 percent per class in our data.  
To assess the models' robustness in handling translated data, we employed two popular translation services, Google Translate, to generate translated versions of the test data. By evaluating the models on both the original and translated test sets, we aimed to gain insights into their ability to maintain consistent performance when dealing with potential translation errors and linguistic divergences.  
We used Google Translate because of its advanced techniques that significantly improve translation accuracy, particularly for low-resource languages. These improvements include enhanced model architecture, better noise handling in datasets, and effective multi-lingual transfer learning through M4 modeling, as well as leveraging monolingual data. These strategies ensure that Google Translate can deliver high-quality translations across a wide range of languages, making it a reliable tool for generating realistic multilingual test data.
# Optimised Tuning of a PID-Based Flight Controller for a Medium-Scale Rotorcraft
## 5 Conclusions  
A mathematical model of a rotorcraft was developed. Assumptions were made to simplify the calculations of thrust and torque generated by the rotors of the rotorcraft, as well as to limit the effects of surface drag on the system. Two trim conditions were investigated: hover and 10 m/s forward flight. The paper presented the use of computational intelligence optimisation techniques for tuning the PID controllers' gains that were developed for a rotorcraft system.  
It was observed through numerical simulation that the optimised PID controllers are effective around the trim point for which they were developed, with ACO-PID performing better for hover and GA-PID for forward flight. However, this conclusion is not universal and is only valid for the hover and forward flight cases investigated in this paper. The controllers are able to tolerate some deviation from this operating point, such as an increase in forward speed and when subjected to gust winds. However, they cannot be employed to control the rotorcraft through its entire flight envelope as they started to lose stability. Since the PID controller can only affect one input for every reference input, the cross-coupling effects can be noticeable when translating, for example. These effects become more pronounced as the rotorcraft moves away from the designed trim condition. Hence, different controllers need to be designed for different operating regions and gain scheduling employed to transition from one controller to the next as the region changes. However, the ACO-optimised controller seems to outperform the other optimisation algorithms both in holding the trim state and recovering from external disturbance.  
A follow-up to the proposed optimised PID controller for the rotorcraft is to employ a robust nonlinear controller that not only operates in the entire flight envelope of the aircraft but is able to handle disturbance and is also tolerant to bounded uncertainties and actuator loss of effectiveness, such as a sliding mode controller.  
Conceptualization, L.J.M. and J.O.P.; Data curation, L.J.M.; Formal analysis, L.J.M. and J.O.P.; Methodology, L.J.M.; Software, L.J.M.; Supervision, J.O.P.; Validation, J.O.P.; Writing--original draft, L.J.M.; Writing--review & editing, J.O.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 16: The position of the rotorcraft in response to starboard gust disturbance at 10  m/s.
# Joint Optimization of Relay Communication Rates in Clustered Drones under Interference Conditions
## 4 Intelligent Channel Selection Scheme
### Bayesian Optimization Algorithm
The Discrete Flexible Actor-Critic algorithm includes the complexity of network forward and backward propagation \(O(N^{n})\), where \(N^{n}\) denotes the number of network layers; the complexity of experience replay \(O(batch\_size)\); and the complexity of policy and value updates \(O(kN^{n})\), with \(k\) being the number of network updates. Hence, its computational overhead is \(O(batch\_size\cdot N^{n})\). The second part involves the Bayesian optimization process, which includes the complexity of Gaussian process training \(O(u^{3})\), prediction complexity \(O(u)\), and acquisition function optimization complexity \(O(u^{r})\), with \(u^{r}\) representing the number of evaluated candidate points. Therefore, the overall computational complexity of the proposed algorithm in this section is \(O(u^{3}+u\cdot u^{r}+u^{r}+batch\_size\cdot N^{n})\), primarily dominated by \(O(u^{3})\).
# Research on Solidification Methods and Stabilization Mechanisms of Sulfate Saline Soils
## 4 Results and Analysis
### Microstructure Analysis of Stabilized Soils
#### 4.3.5 XPS Analysis  
Figure 13a shows the XPS complete spectra of FA, FAS, and FAS1. Peaks for Ca 2p, Si 2p, Al 2p, Fe 2p, Na 1s, Mg 1s, and O 1s can be seen, suggesting the presence of elements, like Ca, Si, O, Al, Na, and Mg, in the samples. Each sample spectrum has peaks for the same elements, with noteworthy variations in width and height, meaning that the elements in the samples are the same, but different types of compounds are present.  
Figure 13b shows that there are variances in the binding energies of oxygen in the samples, indicating differences in the chemicals bound to oxygen in the three samples [55].  
Figure 13c shows that the Ca 2p binding energies in FA and FAS1 are 346.61 eV and 350.27 eV, respectively [56; 57]. The consistent Ca binding energies show that both have similar chemical stability, implying the presence of molecules such as CaCO\({}_{3}\), CaSO\({}_{4}\bullet\)2H\({}_{2}\)O, and C\(-\)S\(-\)H. The decreased binding energy of Ca in FAS indicates that Ca remains more active, making it more susceptible to substance binding, perhaps involving CaCO\({}_{3}\) and C\(-\)S\(-\)H.  
Figure 13: XPS spectra of O, Ca, S, Si, and Al in FAS, FAS1 hydration 28 d, and FA: (**a**) wide range; (**b**) O 1s; (**c**) Ca 2p; (**d**) S 2p; (**e**) Si 2p; (**f**) Al 2p.  
Figure 13d shows that the binding energies in the S 2p fitting spectra of FA, FAS, and FAS1 were 167.76 eV, 167.84 eV, and 168.10 eV, respectively [46; 58]. The chemicals for FAS and FAS1 are gypsum and AFt. The greater binding energy of S molecules in FAS1 as compared to FA and FAS suggests that FA and FAS can continue to react with Na\({}_{2}\)SO\({}_{4}\). The Si 2p fitting spectra of FA, FAS, and FAS1 show binding energies of 100.83 eV, 101.06 eV, and 101.26 eV, respectively [46; 58]. The comparable chemicals include SiO\({}_{2}\), C\(-\)S\(-\)H, and AFt. FA contains active SiO\({}_{2}\), which hydrates to provide increased binding energy C\(-\)S\(-\)H, resulting in more stable AFt in the presence of sulfate.  
Furthermore, the Al 2p fitting spectra of FA, FAS, and FAS1 show binding energies of 72.78 eV, 73.20 eV, and 73.30 eV, respectively [58]. The analogous chemicals may be Al\({}_{2}\)O\({}_{3}\) and C\(-\)A\(-\)H. Al molecules in FA have a lower binding energy than FAS and FAS1, meaning that they remain more reactive.  
Compared to FA, the binding energies of O 1s, Ca 2p, Si 2p, and Al 2p in FAS and FAS1 are lower. From the results of XRD, SEM, FTIR and TG\(-\)DSC, it is evident that the high reactivity of FA leads to the formation of C\(-\)S\(-\)H and C\(-\)A\(-\)H gel materials through hydration reactions and the solidification of sulfate compounds.
## 5 Conclusions
### Nomenclature  
\begin{tabular}{l l} A\({}_{r}\) (m\({}^{3}\)) & Reactor cross sectional area \\ a (m\({}^{2}\) m\({}^{-3}\)) & Effective gas-liquid interfacial area per unit bed volume \\ C\({}_{i}\) (mol/m\({}^{3}\)) & Concentration of component i \\ C\({}_{\rm tot}\) (mol/m\({}^{3}\)) & Total concentration of gaseous components \\ C\({}_{\rm tot}^{\rm L}\) (mol/m\({}^{3}\)) & Total concentration of liquid components \\ D\({}_{\rm ax,L}\) (m\({}^{2}\)/s) & Liquid phase axial dispersion coefficient \\ D\({}_{\rm g}\) (m\({}^{2}\)/s) & Gas phase axial dispersion coefficient \\ D\({}_{\rm t}\) (m) & Reactor diameter \\ d\({}_{0}\) (m) & Gas distributor diameter \\ d\({}_{\rm B}\) (m) & Bubble diameter \\ g (m/s\({}^{2}\)) & Acceleration due to gravity \\ H (m) & Reactor height \\ H\({}_{i}\) (m\({}_{\rm G}^{3}\) m\({}_{\rm L}^{-3}\)) & [PERSON]’s solubility constant of gaseous component i \\ H\({}_{i}^{\rm t}\) (MPa. m\({}^{3}\)/kmol) & Coefficient in [PERSON]'s law for gaseous component i \\ \(-\Delta\)H\({}_{\rm s,i}\) (kJ/kmol) & Heat of solution for gaseous component i \\ K\({}_{\rm l}\) a (s\({}^{-1}\)) & Volumetric Mass transfer coefficient \\ MW\({}_{\rm ave,L}\) (kg mol\({}^{-1}\)) & Average molecular weight of liquid components \\ P (Pa or bar) & Pressure \\ Pi,sat (bar) & Vapor pressure of component i \\ R (J mol\({}^{-1}\) K\({}^{-1}\)) & Gas constant \\ R\({}_{\rm co}\) (mol kg\({}_{\rm cat}^{-1}\) s\({}^{-1}\)) & Carbon monoxide consumption rate \\ r\({}_{\rm i}\) (mol kg\({}_{\rm cat}^{-1}\) s\({}^{-1}\)) & Chemical reaction rate of component i \\ T (K) & Temperature \\ t (s) & Time \\ U\({}_{\rm g}\) (m/s) & Superficial gas velocity \\ U\({}_{\rm l}\) (m/s) & Liquid velocity \\ V\({}_{\rm cat}\) (m\({}^{3}\)) & Volume of catalyst in column \\ V\({}_{\rm f}\) (m\({}^{3}\)/h) & Volumetric gas flow rate \\ V\({}_{\rm gas}\) (m\({}^{3}\)) & Volume of gas in column \\ V\({}_{\rm liquid}\) (m\({}^{3}\)) & Volume of liquid in column \\ V\({}_{\rm L}\)(0) (m/s) & Center-line liquid velocity \\ z (m) & Axial coordinate \\ Z & Compressibility factor, dimensionless \\
**Greek Letters** & Chain growth probability factor, dimensionless \\ \(\epsilon_{\rm g}\) (m\({}_{\rm G}^{3}\) m\({}_{\rm R}^{-3}\)) & Gas holdup, dimensionless \\ \(\epsilon_{\rm 1}\) (m\({}_{\rm M}^{3}\) m\({}_{\rm R}^{-3}\)) & Liquid holdup, dimensionless \\ \(\epsilon_{\rm cat}\) (m\({}_{\rm cat}^{3}\) m\({}_{\rm L}^{-3}\)) & Catalyst volume fraction \\ \(\tau_{\rm 1}\) & Activity coefficient for component i, dimensionless \\ \(\rho_{\rm cat}\) (kg/m\({}^{3}\)) & Catalyst density \\ \(\rho_{\rm SL}\) (kg/m\({}^{3}\)) & Slurry density \\ \(\mu_{\rm t}\) (Pa.s) & Liquid viscosity \\ \(\sigma\) (N/m) & Surface tension \\ \(\rho_{\rm G}\) (kg/m\({}^{3}\)) & Gas density \\ \(\
u\) (m\({}^{2}\)/s) & Kinematic viscosity of phase \\ \(\tau\) & Dimensionless time coordinate \\ \(\rho_{\rm G}\) (kg/m\({}^{3}\)) &
# Application of Blockchain Technology in Emergency Management Systems: A Bibliometric Analysis
## 5 Discussion
### Theoretical Insights and Practical Applications
#### 5.1.4 Smart Cities and Urban Emergency Resilience  
Cluster #7 illustrates the integration of blockchains with smart city infrastructure to support data sharing, civic engagement, and emergency services. Theoretical models emphasize the role of blockchains in enabling secure, tamper-proof, and transparent urban data ecosystems [94; 107; 108]. Foundational works explore how blockchain technology can support system interoperability, automation, and smart governance in civil infrastructure and public health.  
On the practical side, applications include blockchain-based systems for enhancing safety through participatory IoT [47; 108] and managing smart infrastructure with improved data security and responsiveness [107]. The immutability and auditability of blockchains can improve trust between citizens, service providers, and government bodies during urban emergencies.
## 3 Software Receiver Implementation
### Back-End
#### 3.2.1 Constellation Demapper  
For the implementation of the constellation demapper of the receiver, soft demapping has been considered due to its known superior performance with respect to hard demapping [29]. The input of the demapper module is the set of symbols of a PL frame (8100 symbols \(\times\) 16 codewords), previously detected at front end and provided as a set of couples of in-phase (I) and quadrature (Q) elements. Also, the demapper gets as input the ModCod and SNR estimated at the front end, and processes each of the 16 parts of the PL frame (8100 symbols) to produce 16 Codewords to be given as input to the SCCC decoder.  
The Demapper computes \(n\) Euclidean distances between each I and Q couple and the constellation points related to the used modulation, i.e., QPSK (ACM 1-6), 8 PSK (ACM 7-12), 16 APSK (AMC 13-17), 32 APSK (ACM 18-22), 64 APSK (ACM 23-27).  
For instance, for a QPSK, there are four points, so the demapper would compute four Euclidean distances between each input point, with coordinates I and Q, and the four points of the constellation. Each computed distance is then multiplied by the estimated SNR.  
Considering that a CW contains 8100 symbols, for ACM 27, the demapper has to compute more than 3 million (\(384\times 8100\)) distances and multiplications. These would be calculated in a serial process by using two nested iterative cycles, with a subsequent high computational cost. Instead, on a GPU, a single thread can compute a single Euclidean distance, with a number of threads executed in parallel that is equal to the number of distances to be computed. In this way, all the computations can be resolved in just one instruction, eliminating the iterative execution. After the computation of the distances, the demapper finds a set of \(m\) LLR values for each symbol, with \(m\) that is the modulation cardinality, proportional to the ACM. To do so, it computes the Jacobian logarithm [30], referred to as max\({}^{\star}\) (1).  
\[max^{\star}(a,b)\overset{\Delta}{=}ln(e^{a}+e^{b})=max(a,b)+ln(1+e^{-|a-b|}) \tag{1}\]  
Specifically, the max\({}^{\star}\) is computed on \(m\) sub-sets of distances, reducing the initial values to the final \(m\) values, for each I, Q couple. At the end of the demapper, 16 codewords with a size equal to \(m\times 8100\) will be produced.  
Thanks to the use of GPU, the Euclidean distances computation is processed in a few microseconds, while the max\({}^{\star}\) function, because of its nature (i.e., the efficiency of the logarithm and exponential functions) and because of the large number of max\({}^{\star}\) operations to be computed, represents the most computation-intensive part of the demapper.  
Figure 5: Implemented Back\(-\)End Architecture.  
The design of the demapper itself shows how an efficient use of GPU parallelization can increase the performance of the implemented receiver. These design choices will be further investigated in Section 4, where the performance of the receiver will be evaluated in comparison with the state of the art.