esa-sceva/satcom-chunk-collection huggingface dataset:
# Verifiable Threshold Multi-Party Fully Homomorphic Encryption from Share Resharing
## 1 Introduction
Therefore, there is an urgent need for an efficient TMFHE scheme that simultaneously supports flexible threshold structures, robust security against adversaries, and verifiability of protocol execution. Although homomorphic encryption has found increasingly widespread use in multi-party computation, existing TMFHE schemes generally suffer from structural limitations, incomplete security models, and inadequate verifiability. For instance, some schemes only support joint computation by all parties (e.g., N-out-of-N structures), which makes them unsuitable for real-world scenarios involving offline or dynamically changing participants. Others lack rigorous constraints on malicious adversaries and fail to provide effective verification mechanisms. While some improvement directions have been proposed in the literature--such as by [PERSON] et al. [19], who designed a universal thresholdizer without a complete verification mechanism; [PERSON] et al. [20], who enhanced adversarial resistance, but did not support general threshold structures; and [PERSON] et al. [21], who optimized key generation without addressing verifiability--these approaches still leave important issues unresolved. In light of these limitations, this work aims to construct an efficient TMFHE scheme that supports flexible threshold access structures and incorporates robust security verification mechanisms. The verifiable threshold multi-party fully homomorphic encryption (VTMFHE) scheme proposed in this paper is specifically designed to address the aforementioned challenges. Distinct from existing works, our approach introduces improvements and innovations in two key aspects. First, we enhance share resharing techniques to allow their seamless integration into LWE-based homomorphic encryption schemes. By combining Shamir secret sharing and share resharing, our scheme enables additive sharing of secret key shares, thereby supporting arbitrary t-out-of-N threshold structures and accommodating offline or dynamically changing participants. This significantly improves the flexibility and practicality of the protocol. Second, we introduce commitment and non-interactive zero-knowledge (NIZK) techniques to design verifiable distributed key generation and threshold decryption protocols. These mechanisms ensure the structural validity and computational consistency of key shares and partial decryption results generated by each party, thereby strengthening the scheme's resilience against malicious adversaries. Compared with prior studies, our scheme achieves better generality in structure and functionality, while maintaining a balanced trade-off among security, verifiability, and efficiency. Therefore, this work holds both theoretical significance and practical value for advancing secure and efficient multi-party homomorphic computation protocols.
## 3 Results and Discussion
### Linear Dependence between the Obtained Series  
Table 5 summarises the linear correlation relationships of the instantaneous values of the RTK receivers expressed by the Pearson's correlation coefficient. The correlations of the deviation of the positioning on the speed \(r_{dav}\) or on the acceleration \(r_{da}\) from the period of all the measurements did not have a significant effect. The dependencies calculated the same way in the evaluation from the \"FIX\" period were even weaker in significance than from the whole measurement period. No statistically significant linear relationship  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**RTK Receiver**} & \multicolumn{4}{c}{**Whole Measurement**} & \multicolumn{4}{c}{**“FIX” State Epoch**} \\ \cline{2-10}  & \(n_{w}\) & \(n_{f}\) & \(SSR\) & \(\mu_{err}\) & \(s_{err}\) & \(RMS_{err}\) & \(\mu_{err}\) & \(s_{err}\) & \(RMS_{err}\) \\ \hline Receiver A & 95 961 & 95 900 & 99.94\% & 0.008 m & 0.067 m & 0.009 m & 0.008 m & 0.067 m & 0.009 m \\ Receiver B & 92 978 & 92 978 & 100.00\% & 0.011 m & 0.088 m & 0.014 m & 0.011 m & 0.088 m & 0.014 m \\ Receiver C & 93 018 & 93 018 & 100.00\% & 0.01 m & 0.076 m & 0.011 m & 0.01 m & 0.076 m & 0.011 m \\ Receiver D & 23 267 & 8 971 & 38.56\% & 0.068 m & 0.317 m & 0.121 m & 0.01 m & 0.072 m & 0.012 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Error indicators during the static measurements: the number of samples \(n_{w}\) for each RTK receiver from the whole measurement period to which a valid MRA reference coordinate could be assigned (-); the number of samples \(n_{f}\) marked as “FIX” in the NMEA message (-); \(SSR\) indicator indicating the RTK receiver’s ability to solve the ambiguity of the integer carrier phase during the whole measurement period (%) and the error indicators \(\mu_{err}\), \(\sigma_{err}\) and \(RMS_{err}\) separately from the whole measurement period and separately from the “FIX” period (m).  
was found between any of the examined series. The evaluation of the dependence of the current system state on the speed \(r_{sv}\) and the dependence of the current system state on the deviation \(r_{sd}\) at Receiver C showed a very weak negative dependence. However, due to the proximity of the coefficient values to zero, even these dependencies could not be considered statistically significant.
# A New Engine Fault Diagnosis Method Based on Multi-Sensor Data Fusion
## 4 Applications
### Iris Data Set Classification  
The Iris data set [75], which is perhaps the best known database to be found in pattern recognition literature, involves classification of three species of the Iris flowers, _Iris setosa_ (\(S\)), _Iris versicolour_ (\(E\)), and _Iris virginia_ (\(V\)) on the basis of four numeric attributes of the Iris flower: sepal length (\(SL\)) in cm, sepal width (\(SW\)) in cm, petal length (\(PL\)) in cm, and petal width (\(PW\)) in cm. The frame of discernment should be \(\Theta=\{S,E,V\}\).  
In the Iris data set, there are 50 instances for each of three species. The data are obtained from the UCI (University of California Irvine) Machine Learning Repository [76] of machine learning databases. Among 50 instances of each species, 20 instances are randomly selected as the training set; thus, there are a total of 12 training models (Table 1 and Figure 3). Ten instances are randomly selected in the rest of 30 instances under each attribute of \(E\) (_Iris versicolour_) as the test set; thus, there are a total of four test models (Table 1 and Figure 3). Each of the four attributes is regarded as an information source. The final identification result should be \(E\) (_Iris versicolour_).  
combination method mentioned in Section 2.3, the final BPA is obtained (Table 3). Finally, through the decision making rules mentioned in Section 3.4, the final identification result is \(E\) (_Iris versicolour_).  
Samples from \(S\), \(E\), and \(V\) were tested as test sets 10,000 times, respectively, and the accuracy of the method on Iris data set classification is presented in Table 4 using the decision method mentioned in Section 3.4. The overall recognition rate of the three categories is 99.98%.
# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 2 Research Motivation and Contribution  
In the future wireless cellular networks, such as B5G, several interference problems have existed due to mobility of users, deployment of SBS with RNs that have low power and random deployment, unpredicted channel variation, sharing the frequency spectrum, utilization of frequency reuse concept, and the effect of intra-cell and inter-cell interferences [37; 38]. Therefore, this interference represents the main challenge for the new frequency spectrum of B5G cellular networks.  
Interference management issues have become the concern of many researchers in recent years, therefore, different technologies have been discovered to reduce various kinds of interference either by eliminating or coordinating them. However, ICI has the highest effects on multi-tier cellular networks. ICI is classified into two types, static coordination such as inter-cell interference coordination (ICIC) or dynamic coordination such as enhanced ICIC (elCIC). To eliminate the ICI, ICIC permits the UEs at the cell edge in adjacent cells to utilize various ranges of frequency such as resource blocks (RBs) and sub-carriers [39]. Furthermore, elCIC permits UEs to utilize various subframes (time ranges) for the same objective. In other words, a macro-cell (MC) and small-cell (SC) sharing a co-channel can utilize radio resources by various subframes [40]. These interference issues become more severe in B5G, resulting in a significant drop in the total performance of a network. The following points summarize the major contributions of this work:  
1. In the research community, proper and robust techniques for canceling interference and then lowering the noise level are still required. Therefore, we contribute to the current literature on the interference mitigation techniques in B5G by providing new insights into the management of interference issues in future generation networks.
2. To the best of our knowledge, the interference in HetNets in the literature is focused on co-tier interference and cross-tier interference. Moreover, in the D2D, the authors usually focus on power allocation and spectrum allocation strategies. However, limited attention was paid to the hybrid interference in these environments. The current study adds the issues related to hybrid interference to the literature. This provides a new vision for researchers to mitigate the interference issue in B5G.
3. The interference in the UDNs in this study is extended to include the spatial domain. This helps the researchers in the investigation of techniques toward higher overall user performance.  
Figure 1: The organization of the Paper.  
The different types of interference from UAVs were discussed in detail including drone interference, which is rarely mentioned in the literature.
* 5. In this work, future research challenges and suggested methods to reduce interferences are also covered.
## 4 Test Results and Analysis
### Reset Performance of Isolation Layer  
When horizontal initial displacement is paired with dynamic data acquired by the displacement gauge, the isolation layer is promptly reset, with the reset amount equaling 62.86 mm, or 83.81% of the horizontal initial displacement value of 75 mm. With time, the isolation layer residual displacement is 12.14 mm, which gradually decreases to negligible after 48 h.
## 3 Design of CP-ABE
### System Model  
Figure 2 shows that the system model consists of four entities, which are Data Owner (DO), Cloud Service Provider (CSP), Attribute Authority (AA), and Data User (DU). The DO are legal users that are responsible for encrypting and outsourcing the data in the Cloud. The data owner defined the access policy and performed a policy hiding process. While in the pre-processing process, the redundant data in the raw message is eliminated before it is encrypted in the encryption process. Next, the data owner uploads the ciphertext with the hidden value of the access policy to the Cloud storage. Hence, the DU (or recipients) who satisfy the access policy will be able to decrypt the data. In this work, Cloud Service Providers (CSPs) are assumed to be solitary entities that do not interest their users/clients. It means acting as a platform where various data and users can reach, passing the messages, and storing the files. The access policy is handled on the user's side (DO). The Attribute Authority (AA) is an accountable entity that works as a key generation center. The users' attributes will be authenticated by AA before granting access privileges to the authorized users to interact with the system. The AA might receive authentication privileges from the DO or any other Cloud security mechanism agreements.
## 3 Application Examples
### Well Productivity Evaluation  
Comprehensive analysis has been conducted on wells D-DST2 and G using conventional logging, imaging logging and array acoustic logging data. The fracture development interval was processed, the fracture effectiveness index and fracture seepage characteristic index were projected onto the well productivity evaluation chart. These indexes are concentrated in the high and middle zones. Based on the collected data, the productivity indexes for D-DST2 and G wells are 124.5 and 24.1, correspondingly, in line with the results obtained from the productivity evaluation (Figure 8).
## 3 Method
### Statistical Separability Analysis  
Parametric and non-parametric statistical measures were used to quantify the unburned-burned class separability for the different MSI bands (Table 2) and for the different spectral indices (Table 3) at each of the five sites. As a control, the unburned-unburned class separability was also quantified. The MSI bands and spectral indices with higher unburned-burned separability were considered more suitable for burned area mapping, particularly if their separability was greater than the unburned-unburned class separability.
# Estimation of the Measurement Accuracy of Wireless Passive Resonance Sensors
## 2 Signal-to-Noise and Interference Ratio by a High-Q Resonator as Cooperative Target  
To calculate the maximum achievable measurement accuracy of a passive transponder system using high-Q resonant sensors, we need the signal-to-noise ratio of the received signal in the reader. In this section, we first calculate the signal power of the signal backscattered by the passive transponder, which is received and sampled in the reader, using the radar equation and Friis' law [44]. The noise power contained in the signal is then calculated as thermal noise. At the end of the section, the elimination of interference caused by waves backscattered elsewhere in the transmission channel is discussed.
# Supporting the Management of Rolling Stock Maintenance with an Ontology-Based Virtual Depot
## 7 Short-Term Scheduling
### Computer-Based Short-Term Scheduling--Without Optimisation  
The first method used for short-term scheduling was a normal computer-based method without optimisation. This method aims to mimic the manual scheduling process and serves as a reference to evaluate the proposed optimisation method. The planning team first schedules the maintenance interventions with higher earliness/tardiness costs. Hence, the computer-based method without optimisation uses a priority parameter to help order the maintenance interventions based on their importance (e.g., earliness/tardiness costs). The method uses the long-term schedule (due dates) and priorities to inform the development of the short-term schedule while following the resource restrictions, maintenance window, and fleet availability requirement. Within the specified scheduling horizon, the first available slot is allocated to the first maintenance intervention in the queue (sorted by priority). The time slots per day are computed based on resource availability and maintenance duration. No optimisation technique is used to generate the short-term schedules and no reasoning about costs is taken into account. This is also the case when schedules are produced manually when multiple choices are available. The main inputs are depot resource constraints related to the various maintenance interventions, the fleet availability constraint (expressed as the number of allowed out-of-service units per day), and the maintenance interventions' due dates of the fleet units. Scheme 2 is an example of the produced short-term schedule where only four days a week are available to receive units within a depot. The table shows the results of one maintenance intervention type scheduling where two units only can be received per day. The gap between the due and scheduled dates is the number of days before the exam. The negative value of the days before the exam field means the maintenance intervention is scheduled after the due date.
# A New CO\({}_{2}\)-EOR Methods Screening Model Based on Interdependency Parameters
###### Abstract  
The successful implementation of carbon dioxide-enhanced oil recovery (CO\({}_{2}\)-EOR) is crucial in increasing oil production and reducing carbon emissions. For this reason, screening criteria are needed for the initial characterization of a suitable CO\({}_{2}\)-EOR reservoir. The existing screening model treats the screening parameters independently. Therefore, each parameter has its criteria limit and does not relate to the others. However, in reality, several screening parameters are interdependent, so we need a method that treats the interdependent parameters simultaneously. This research develops a new simultaneous screening model using the interdependency of the parameters. Quantitative and actual data were collected from CO\({}_{2}\)-EOR field documentation worldwide with a comprehensive analysis. A statistical approach with a correlation analysis method was used to determine the interconnected screening parameters. The results were synchronized with the expert domain to match actual physical conditions. The limit of simultaneous screening criteria was acquired by multivariate quality control (MQC) based on the principal component analysis (PCA) method. The proposed screening model was compared with 13 actual projects, and demonstrated improvements to previous models. The results match actual operations and follow the expert domain rules. If the miscible CO\({}_{2}\)-EOR is met, then the immiscible should also be appropriate but not vice versa. Nevertheless, four different immiscible projects are predicted to be slightly optimistic as miscible or immiscible.  
screening; CO\({}_{2}\)-EOR; principal component analysis; multivariate quality control Article  
A New CO\({}_{2}\)-EOR Methods Screening Model Based on Interdependency Parameters
###### Abstract
In order to illustrate this, Figure 1 was constructed for this study using the data available in the IRENA database (2018). Many researchers have tried to investigate the effect of RE policies on RE development. However, most research studies have taken into account only some policies and overlooked the rest. Moreover, many countries (20 to 40) have been involved in the studies over long time spans. Therefore, the econometric treatment could not be trustworthy due to the following reasons:  
1. Some countries involved in the studies did not implement all the policies considered. 2. The proposed techniques dealt with the data package of involved countries as a whole, which caused multidimensional problems, such as cross-sectional dependency and heterogeneity. The present study aims at investigating the effect of different policies on the installed RE capacity of the top three countries (shown in Figure 1) over a period of 18 years (2000 to 2017). In addition, the individual effect of policies on the installed capacity of solar, wind, hydro, and bio energies has been  
Figure 1: Total installed renewable capacity (data source: (2018)). explored. Furthermore, the relationship between RE development and patents is evaluated. The main motivation behind this study is to understand if RE policies can be the main drivers of RE development, and, if so, which type of policies are more effective in supporting the development. The authors believe that analyzing the case of the leader countries in the world (in terms of installed RE capacity) would reveal the secret behind their success. Time series linear regression analyses are carried out (using the SPSS software) to establish econometric models for each country. To avoid multidimensional problems, the analyses are performed on the data of each country individually. The rest of this paper is organized as follows: Section 2 presents the related literature review and highlights the contribution of this paper. Section 3 introduces country reviews, including the fossil fuel reserves and electricity generation for China, the USA, and Brazil. In Section 4, RE policies are demonstrated. The methodology of this study is provided in Section 5. Section 6 presents the results of the study, while Section 7 outlines the concluding remarks.
# Contact Force Surrogate Model and Its Application in Pantograph-Catenary Parameter Optimization
## 3 Sensitivity Analysis of Pantograph-Catenary Parameters
### Sensitivity and Its Rating of Pantograph-Catenary Parameters
Changes in the low-pass filtering frequency of the contact force time-domain signal will only affect the sensitivities to certain pantograph-catenary parameters under specific operating conditions, which include \(T_{\mathrm{c}}\) at all speeds, \(A_{\mathrm{c}}\) and \(m_{1}\) at 250 km/h, five catenary parameters, \(m_{3}\) and \(c_{1}\) at 450 km/h (Figure 6a). In other words, when the operating speed falls within the abovementioned speed ranges, altering the relevant pantograph-catenary parameters will result in a significant increase in the SDCF in the frequency range of 20-40 Hz. In all other cases, the impact of changing pantograph-catenary parameters on the SDCF in the frequency range of 20-40 Hz can be ignored. Figure 5: First-order and total sensitivities of SDCF to pantograph–catenary parameters. At speeds of 400 km/h and above, there is a significant difference between the first-order and total sensitivities of the catenary parameters, indicating that at higher speeds, there is an additional increase in the coupling effects between the catenary parameters, which affects the SDCF (Figure 6b). In addition, for catenary parameters at 350 km/h and below, as well as for pantograph parameters at all speeds, sensitivity definitions have very little impact on sensitivities. In other words, the impact of coupling effects between parameters on the SDCF can be ignored. Figure 7 presents the second-order sensitivities of the catenary parameters. At 350 km/h and below, the second-order sensitivities to all catenary parameters are approximately zero. As the speed increases, the second-order sensitivities of (\(T_{\text{c}}\), \(A_{\text{c}}\)) at 400 and 450 km/h and the second-order sensitivities of (\(T_{\text{c}}\), \(A_{\text{m}}\)), (\(A_{\text{c}}\), \(A_{\text{m}}\)) and (\(A_{\text{m}}\), \(L\)) at 400 km/h are relatively high, while the second-order sensitivities in other scenarios are close to zero. This indicates that at medium and high speeds, the interaction effects between any two catenary parameters on the SDCF can be ignored, but at higher speeds, it is necessary to consider the interaction effects between \(T_{\text{c}}\) and \(A_{\text{c}}\) or \(A_{\text{m}}\), \(A_{\text{m}}\), and \(A_{\text{c}}\) or \(L\) on the SDCF. Figure 6: Effect of low-pass filtering frequency (**a**) and sensitivity definition (**b**) on sensitivities. Figure 7: Second-order sensitivities of SDCF to catenary parameters.