esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions  
Kinematic reliability is an important index to evaluate repetitive positioning accuracy of mechanisms. Considering the randomness of input errors, kinematic reliability essentially reflects the performance of the mechanisms. This paper defines an index to evaluate the kinematic reliability of the improved Delta parallel mechanism and gives the corresponding calculation procedure. This provides a theoretical basis on kinematic reliability estimation of other parallel mechanisms.  
The response surface method is used to calculate the kinematic reliability of the improved Delta parallel mechanism in the combined direction, \(R=0.9994\). Reliability sensitivity analysis indicates that:  
1. The kinematic reliability of the improved Delta parallel mechanism has the strongest sensitivity to the mean of \(\Delta a_{2}\), followed by \(\Delta r_{32}\), \(\Delta a_{1}\) and \(\Delta a_{3}\); while the mean sensitivities of other errors are comparatively small.
2. The mean increase in each input error component does not wholly decrease the reliability of the mechanism, which indicates that the output positioning errors of the end-effector of the parallel mechanisms are caused by the coupling errors of each parallel branch. The increase in the variance of all the random variables reduces the kinematic reliability.
3. Among all the input errors, the variance sensitivity to the length errors of the driving arms are the largest, which indicates that the increase in variance mostly decreases kinematic reliability of the improved Delta mechanism.  
[PERSON] was in charge of the whole research and wrote the initial manuscript; [PERSON] assisted with sensitivity analysis on reliability of the mechanism; [PERSON] assisted with error modeling of the mechanism; [PERSON] was in charge of reliability analysis. [PERSON] revised the final manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 51575091,51205052, and funded by Aeronautical Science Foundation of China, grant number 20170250001, and funded by the Basic Science and Research Project of Chinese National University, grant number N160304008.  
Not applicable.  
Data Availability Statement: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors would like to thank [PERSON] and [PERSON] of Department of Mechanical & Aerospace Engineering of UD for helpful guidance and discussions on kinematic analysis of parallel mechanisms related to this work.  
The authors declare no conflict of interest.
## 3 Concept of an Automaton for Data Validation
### Example Runs  
To visualize our algorithms, we use three example data sets and present the runs through our automaton in Figures 9-11. The graphics each represent a time-dependent snapshot of the automaton.  
Figure 9 shows the automaton in case of clean data. As one can see in the Figure, our automaton runs step by step through the _potentially consistent_ states, starting with state \(S\) (timestep \(t=0\)). In each step, a check for category 3 inconsistencies is performed. After the last message has arrived, the data set remains in state _SMT_ until the last timer expires (timesteps \(t=3\) and \(t=4\)). The automaton switches into state _cat4_, where the whole data set is checked for category 4. As the data set is consistent, the automaton switches into the final state _con_. For the last two states, we do neither need to start a new timer nor calculate the new rank.  
Figure 11: Example run for a detected overtake.  
Figure 10: Example run for an expired timer.  
Figure 9: Example run for a consistent data set with messages arriving in order.  
Figure 10 illustrates the run of a data set with a potentially missing message. Again, we start in state \(S\), then change into \(SM\). At this point, no more messages arrive. When the timer expires (time step \(t=4\)), we switch into state \(cat4\). Since \(SM\) is a _potentially consistent_ state and we did not yet recognize any inconsistent content, we assume that the process has been stopped after step M. Our data are thus consistent and can be processed further.  
Figure 11 exemplifies a detected overtake in \(t=3\). The data set with \(id=1\) has rank \(r=1\) in state \(MT\). Data with \(id=2\) enter state \(MT\) and receive the rank \(r=2\). In \(t=5\), data with \(id=2\) switch into state \(SMT\). At this point, our automaton detects an overtake due to the higher rank of \(r=2\). According to our algorithm, each data set with smaller rank has to leave the current state. Since the overtake takes place in a normal state, the overtaken data set (\(id=1\)) is moved into the trash state.
## 5 Simulation
### Network Simulation  
The simulation is conducted using Omnet++ network simulator [27] along with the Vehicle simulation framework Veins [28], and the SUMO traffic simulator. During the simulation, three programs are running in parallel: OMNeT++ manages the network simulation, SUMO runs the road traffic simulation and sumo-launched acts as a proxy between the two. SUMO dynamically changes the values through the Traci interface. Veins's TraciManager class is responsible for connecting Sumo and Omnet++ as shown in Figure 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Description** \\ \hline Network simulator & Omnet++ 5 \\ Traffic simulator & SUMO 0.27.1 \\ Map source & Open street map \\ Simulated location & Part of Beijing city, China \\ Simulated area & 10 km \(\times\) 10 km \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
V2V Communication Parameters  
A detailed description of the wireless communication model used in the simulation is described in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline PHY model & 802.11 p \\ Channel frequency & 5.890 \(\times\) 10\({}^{9}\) Hz \\ Propagation model & Two ray \\ MAC model & EDCA \\ Propagation distance & 450 m \\ Maximum hop & 15 \\ Fading model & Jakes model rayleigh fading \\ Shadowing model & LogNormal \\ Antenna model & Omnidirectional \\ Transmission power & 20 mW \\ \hline \hline \end{tabular}
\end{table}
Table 4: Wireless communication parameter.  
Figure 4: Simulation flow.
## 4 Conclusions  
Due to convection and diffusion, the shape as well as the thickness of a fluid interface cannot be maintained for numerical simulations of multiphase flows. Furthermore, mass conservation cannot be guaranteed in the DI method because of physical and numerical diffusion when solving the C-H equation. In order to resolve these problems, an interface-corrected DI method with an improved MLBFS for solving incompressible multiphase flows was proposed. An interface correction term was introduced into the DI model by applying the Hamilton-Jacobi equation. This term only took effect around the interfaces between different phases. The fluxes were reconstructed by applying the particle distribution functions both before and after collisions. A local switch function was also introduced to automatically adjust the contributions of these distribution functions to the fluxes. A mass correction term was also introduced into the DI model to maintain mass conservation.  
The accuracy and capability of the proposed DI method were validated by simulating the Laplace law, the evolution of a square bubble, two bubbles merging, the Rayleigh-Taylor instability, and a droplet splashing on a thin film at a density ratio of 1000 with Reynolds  
Figure 24: Development of the interface with \(\mathrm{Re}=800\) and \(\mathrm{H}=15\) with the present method: (a) We \(\mathrm{=10}\), (b) We \(\mathrm{=50}\).  
numbers in a range from 100 to 2000. Then, the droplet splashing problem, along with other conditions, was explored on the previous basis and good results were obtained. By adding the correction terms to the interface zone, the mass conservation property of the fluids was found to be pretty good and the results for the interface shape were better than before. The interface shape and thickness with the proposed method were maintained better compared with the results from the original DI method. Moreover, fluxes could be evaluated according to the characteristics of the local multiphase flows. On the basis of the present results, the proposed method has high potential for effectively solving complex multiphase flows by maintaining the interface shape and mass conservation simultaneously.  
Conceptualization, Y.W.; methodology, Y.W. and Y.G.; numerical simulation, Y.G.; formal analysis, Y.G. and Q.H.; data curation, Q.H.; writing--original draft preparation, Y.G.; writing--review and editing, Y.W. and T.W.; supervision, Y.W.; project administration, Y.G.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of China, grant number 11902153, the Natural Science Foundation of Jiangsu Province, grant number BK20190378, and the Research Fund of State Key Laboratory of Mechanics and Control of Mechanical Structures, grant number MCMS-I-0122G01.  
Not applicable.  
All relevant data are within the manuscript.  
The authors acknowledge the support of the Key Laboratory of Computational Aerodynamics, AVIC Aerodynamics Research Institute.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### MMC PHA-Accumulation Performance  
The PHA-accumulation performance of the selected cultures was assessed through fed-batch tests carried out without nutrients supplementation, with continuous aeration and mixing. These tests were performed in a 1 L working volume reactor. When the SBR operation was stable at each period, a sample of the enriched culture was collected, and the fCW was fed, with no nitrogen supplementation, until the cells reached its maximum PHA-accumulating capacity. Air was supplied through a ceramic diffuser and mixing was provided by magnetic stirring (300 rpm). Air flow rate was controlled at 1 L-min\({}^{-1}\) (1 vvm), by a mass flow controller (GFC17A, Aalborg). For PHA accumulation tests, a biomass purge (0.4 L) was collected from the SBR at the end of the famine phase (ca. 12 h from cycle started). The fCW pH was adjusted to 8 by adding 4 M NaOH. The accumulation tests were then carried out without pH control, at room temperature (23-25 \({}^{\circ}\)C). DO was continuously acquired. The PHA accumulation experiments were performed by feeding the fCW pulse-wise, controlled by DO. When DO increased, a new pulse of fCW was injected. This procedure was repeated until no DO response was observed.
## 2 Data and Methods  
### Data  
Three databases were used, including The Gridded Population of the World version 4 (GPW-v4) population count grids database, the Global Human Settlement (GHS) Built-Up grids database, and the Inter-Sectoral Impact Model Intercomparison Project phase 2a (ISMIP2a) database.  
These different databases are inconsistent in resolution. The resolutions of the GPW and GHS datasets are 30 arc-seconds (\(\sim\)1 km), and that of the ISMIIP2a database is 0.5 degrees. For this study, we selected an intermediate resolution of 0.25 degrees. We used the Zonal Statics tool of ArcGIS/ArcToolbox to aggregate the first two databases and used the Resample tool to resample the third database (ISMIP2a database). The purpose of unifying the resolution of the different databases was to obtain sufficient data accuracy and data samples (2537 cells for Mexico and 1090 cells for Ethiopia) to analyze the spatial distribution characteristics of the two countries. A detailed description of potential factors influencing population redistribution and their data sources is given below (Table 1).
# QB4 MobOLAP: A Vocabulary Extension for Mobility OLAP on the Semantic Web
## 4 Results and Discussion  
### QB4 MobOLAP Vocabulary  
In this section, the mobility data cubes in the RDF representation are defined. The definition uses the FFB Transportation DW as an example, the logical schema of which is given in Figure 6. The QB4 MobOLAP Vocabulary allows the defining of cube schemas and cube instances as RDF triples. QB4 MobOLAP extends the QB4 OLAP Vocabulary with mobility concepts to support Mobility OLAP operations directly over RDF data with SPARQL queries. Figure 6 shows the proposed QB4 MobOLAP Vocabulary. The gray backgrounded rectangle is an extension vocabulary class, subclass, and object property from QB and QB4 OLAP. These classes could be used to represent the mobility cube class. QB4 MobOLAP extends the built-in function QB4 OLAP, which is represented with the classes qb4 mob:MobilityTemporalAggrFunction and qb4 mob:MobilitySpatialAggrFunction in the vocabulary. The qb4 mob:MobilityTemporalAgggrFunction class has the instances qb4 mob:TAvg, qb4 mob:TCount, qb4 mob:TMin, qb4 mob:TMax, and qb4 mob:TSum. The qb4 mob:MobilitySpatialAgggrFunction class has the instances qb4 mob:TCentroid, qb4 mob:TUnion, qb4 mob:TConvexHull, qb4 mob:TIntersect, and qb4 mob:TMBR. The QB4 MobOLAP also introduced the class qb4 mob:MobilityObject to represent mobility data types. The qb4 mob:MobilityObject has the subclasses qb4 mob:MobilityBase to store temporal types and qb4 mob:MobilityGeometry to store temporal-spatial types, as described in Section 3. The qb4 mob:MobilityBase has the subclasses qb4 mob:MobilityInteger and qb4 mob:MobilityDecimal, and the qb4 mob:MobilityGeometry has the subclasses qb4 mob:MobilityPoint, qb4 mob:MobilityCurve, and qb4 mob:MobilitySurface.  
Figure 5: The logical model of the FFB Data Warehouse.
## 2 Non-Autonomous Dynamic Systems
### Dynamic Response of the Steel Pylon  
Following the transposition of the attached mass inertia terms to the equation of motion, the eigenvalue problem for the pylon is now defined at time \(t=0\), where a fixed mass \(m_{L}=m_{0}\) has been placed at the top end \(x=L\). The characteristic equation for the pylon modeled as a waveguide under axial vibrations with the boundary condition \(m_{L}\ddot{u}(L,t)=-EAu^{\prime}(L,t)\) is:  
\[\begin{array}{c}\cos(k_{i}L)-R(k_{i}L)\sin(k_{i}L)=1-R(k_{i}L)\tan(k_{i}L)=0 \\ i=1,2,\ldots,\ \infty\end{array} \tag{4}\]  
In the above, the mass ratio is defined as \(R=m_{0}/m_{P}\), with \(m_{P}=\rho AL\) the mass of the stand-alone pylon and \(k_{i}\) is the corresponding wave number. Equation (2) can be solved numerically to recover the eigenfrequencies in two stages, starting with the bisection method to extract the roots, and followed by Newton-Raphson for better convergence [14].  
Given the formulation in Equation (1), separation of variables is possible and therefore the following expansion is obtained  
\[u(x,t)=\Phi_{i}(x)q_{i}(t),\ i=1,2,\ldots,\ \infty \tag{5}\]  
where \(\Phi_{i}(x)\) is the eigenfunction and \(q_{i}(t)\) is the generalized coordinate. Note that the summation convention is implied for repeated indices. For the stand-alone, cantilevered pylon, the eigenfunctions are given [10] as \(\Phi_{i}(x)=\sqrt{2/m_{P}}\sin(k_{i}x),\ k_{i}=(2i-1)(\pi/2L)\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Properties** & **Symbol** & **Value** & **Units** \\ \hline Modulus of Elasticity & \(E\) & 200 & GPa \\ Mass density & \(\rho\) & 7.85 & tn/m\({}^{3}\) \\ Cross-section mean radius & \(r\) & 0.3375 & m \\ Cross-section thickness & \(d\) & 0.0875 & m \\ Cross-section area & \(A\) & 0.1855 & m\({}^{2}\) \\ pylon clear height & \(L\) & 10.0 & m \\ pylon mass & \(m_{P}\) & 14.56 & tn \\ Attached mass & \(m_{L}\) & 2.91 & tn \\ Rate of attached mass change & \(\mu\) & \(\pm 1.0\) & tn/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensions and properties of steel pylon used as the numerical example.
## 2 Design of Distance-Rendering Methods
### Intensity-Based Model  
This simplified model extrapolates the BRIRs corresponding to different distances by applying a global gain to the initial reference BRIR measured at 1 m. The gain used to extrapolate the BRIR for a given distance was tuned so that the loudness of the resulting stimulus corresponded to the loudness of the stimulus generated with the BRIR measured at the same distance. The loudness criterion used here is EBU R128. This model is used in contrast to the envelope-based model and the rendering method based on measurements, as no modification of the reverberation-related cues is present when distance increases.  
This second model gives insights into the participants' auditory cues weighting strategies, as well as a potential influence of the environment on these strategies. Participants that primarily rely on intensity to infer a distance judgment were expected to show similarities in distance reports for stimuli created with this model and for stimuli generated with the reference measurements. Participants who primarily rely on reverberation-related cues would exhibit significant differences between distance reports associated with both types of stimuli.
# Multi-Templates Based Robust Tracking for Robot Person-Following Tasks
## 3 Method
### Score Fusion  
Equation (5) describes the score fusion process, where \(N_{pos}\) and \(N_{neg}\) are the number of collected positive templates and negative templates, respectively; \(f_{pos}\) and \(f_{neg}\) are the extracted features using the backbone network; and \(\varphi(\cdot)\) is the cross-correlation procedure.  
\[Score=\varphi(f_{gt})+\sum_{i}^{N_{pos}}\varphi(f_{pos}^{i})-\sum_{j}^{N_{neg}} \varphi(f_{neg}^{j}) \tag{5}\]  
The idea of score fusion is to enhance the responses of target-tracking using positive templates and to suppress false responses using negative templates. Figure 1 presents examples of the outcome of our score fusion procedure. Each column is a tracking frame. The top to the bottom rows illustrate the score map of fusion, ground truth, and two negative templates, respectively. We see that the ground truth score map not only responds to the tracked target but also increases responses in many other areas where the tracker is easily misled. After implementing our fusion, the unrelated responses are sufficiently suppressed.
# Cyberattacks and Security of Cloud Computing: A Complete Guideline
## 8 Future Directions
### Homomorphic Encryption  
Homomorphic encryption has the potential to revolutionize data privacy in cloud computing. It allows operations to be performed on encrypted data without decryption, preserving the confidentiality of sensitive information. Future research should focus on enhancing the efficiency and scalability of homomorphic encryption schemes for practical cloud applications.
# TrustShare: Secure and Trusted Blockchain Framework for Threat Intelligence Sharing
## 1 Introduction
### Contributions  
The key contributions of this paper are as follows:  
* **Design of TrustShare:** A novel regulation-compliant data sharing framework built on Hyperledger Fabric, supporting secure, privacy-preserving, and policy-driven cyber threat intelligence (CTI) exchange among mutually distrusting entities.
* **Fine-Grained Cryptographic Access Control:** Integration of Ciphertext-Policy Attribute-Based Encryption (CP-ABE) with advanced constraints, such as temporal validity, controlled revelation, and geolocation interlocking--enabling dynamic and sovereign data access control.
* **Hybrid Trust Enforcement via Smart Contracts:** Implementation of a programmable trust-aware access control logic using Hyperledger Fabric chaincode, combining direct (interaction-based) and indirect (reputation-based) trust metrics to automate sharing decisions.
* **Decentralized Storage Integration:** Coupling the IPFS with blockchain-anchored identifiers and CP-ABE-encrypted data to achieve scalable, efficient, and tamper-proof off-chain storage with verifiable access policies.
* **Compliance with Privacy Regulations:** Embedding GDPR-aligned features--including data minimization, revocation via certificate authorities, and auditability--into the design, ensuring the enforceability of the right to be forgotten (RtbF) and lawful data processing.
* **Practical Evaluation:** Demonstration of low-latency, high-throughput, and resource-efficient performance across distributed deployment environments, highlighting TrustShare's scalability and operational viability for real-world CTI alliances.  
To clarify our scientific contribution, we frame this work as the first to integrate a hybrid trust model (combining interaction-based and reputation-based scoring) with fine-grained access control (CP-ABE) and GDPR-compliant mechanisms in a blockchain-based CTI sharing platform. Our contributions go beyond assembling technologies: we introduce a modular framework that supports sovereign, secure, and audit-ready CTI collaboration across distrusting organizational boundaries and various technological solutions. The system is validated through a full-stack prototype with performance evaluations under realistic conditions.  
Our proposed infrastructure is particularly applicable to Security Operation Centers (SOCs) in high-risk sectors such as healthcare, finance, and government. It supports collaborative defense by enabling trusted intelligence exchange while adhering to modern regulatory and operational demands.
## 3 Results and Discussion
### Proposing New Formulas for FOS and TAC Estimation
\[\left[\mathrm{NaHCO}_{3}\right]_{\mathrm{corr}}=1.820\times\mathrm{TAC}_{ \mathrm{Nordmann}}-0.606\times\mathrm{FOS}_{\mathrm{Nordmann}}-37.24 \tag{10}\]  
\[\mathrm{TAC}_{\mathrm{corr}}=0.595\times\left[\mathrm{NaHCO}_{3}\right]_{ \mathrm{corr}}=1.083\times\mathrm{TAC}_{\mathrm{Nordmann}}-0.360\times \mathrm{FOS}_{\mathrm{Nordmann}}-22.16 \tag{11}\]  
\[\mathrm{FOS}_{\mathrm{corr}}=-0.126\times\mathrm{TAC}_{\mathrm{Nordmann}}+1.13 3\times\mathrm{FOS}_{\mathrm{Nordmann}}+62.95 \tag{12}\]  
In Table 2, it is shown that very low MAPEs were obtained by correcting the Nordmann FOS and TAC values using our new formulas. Knowing that the simulation with Scilab allowed a good prediction of the equivalent volumes of the two pH points and, thus, of the FOS and TAC values, it was possible to use the same program to estimate the equivalent volumes at the other pH points without doing the experiments (not shown). The developed Scilab code allowed to directly perform the multiple linear regression by finding the relations between [NaHCO\({}_{3}\)] or [HAc] and the equivalent volumes of the titrant. Thanks to the numerical tool, identifying the proper pH equivalent points would no longer be a main factor impacting the accuracy of the FOS and TAC determination.