esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
Location-Based Opportunistic Routing Protocols (LBORPs) are recognized and identified to perform better using knowledge for dynamic selection of forwarder devices through their location and route message packets to the receiver [7]. The functioning of location-focused routing protocols is studied to perform well in IoUTs atmospheres. Hence, these location-based protocols decrease performance in IoUTs networks due to communication ejects or holes (sometimes called void holes and region) [5]. The researchers discussed the working of LBORPs communication voids in IoUTs along with their problems and disadvantages. The first LBORP is VBF and the second one is HH-VBF, which are analyzed where VBF establishes a simulated vector pipe for routing among source and receiver sink nodes, only those in the virtual vector. Whereas, in the HH-VBF protocol, each node uses different virtual pipes and during each time of transmission the direction of the virtual pipe changes. A comparison of both VBF and HH VBF protocols was performed to analyze the performance of both protocols, and the networks consequently received better performance without communication void holes. Routing issues and challenges related to the IoUTs were identified to assist the scholars in developing and deploying efficient IoUTs routing protocols with multiple communication holes. Further the estimated distance based approaches are discussed [20; 21] in which the routing path and its calculation from sink. Another routing technique called TANVEER, with its three sub-sections, LBA-TANVEER, DPD-TANVEER, and BIN-TANVEER, experimented with IoUTs [22]. These schemes are based on triangular calculations to bypass empty regions and follow the smooth route towards destinations. The author used a straightforward approach to mitigate the blank nodes inside the routing path. The experimental results suggested that the TANVEER and BIN-TANVEER secured satisfied marks against a fraction of empty regions.
# Application of Mathematical Models for Blood Flow in Aorta and Right Coronary Artery
## 2 Materials and Methods
### Simulation Details  
In this study, COMSOL Multiphysics(r) 6.3 was used to simulate blood flow dynamics in the aorta and RCA. To discretize the computational domain, tetrahedral elements were employed, automatically generated by the software's default settings. Mesh quality was visually inspected to ensure the absence of distorted cells, which could compromise solution reliability. Both for the aorta and for the RCA, the mesh consisted of approximately 450k elements. Refinement was increased near walls, outlets, and geometric transitions to improve accuracy.  
For the viscoelastic FENE-P model in the aorta, additional refinement was applied below the renal artery to address issues in this critical area, using the \"Size\" tool to enhance mesh density. The final simulations used a physics-controlled \"fine\" mesh size, balancing  
Figure 2: RCA - 3D geometric model.  
accuracy and computational efficiency. The solvers employed were GMRES for the aorta and both PARDISO and GMRES for the RCA, with convergence achieved at a residual error tolerance of \(10^{-6}\). The computational times required for solving the Navier-Stokes equations varied across models: aorta simulations took at least twice as long as RCA simulations and were even more computationally intensive for viscoelastic models.  
To solve the Navier-Stokes equations, the following choices were made: the Pardiso solver with a pivoting perturbation of \(1.0\times 10^{-13}\), the P1-P1 finite element method for spatial discretization with linear elements for both velocity components and the pressure field, and the P1-P2 finite element method for viscoelastic simulations to ensure greater accuracy and stability in capturing complex flow behaviors.
## 2 System Model
### Direct Adaptive Multi-Resampling Turbo Equalizer
#### 2.2.1 Adaptive Multi-Resampling Equalizer  
Firstly, the received signal \(r(t)\) is resampled by using the resampling factor \(u_{1}\), \(u_{2}\), \(\ldots\), \(u_{M}\), and \(M\) resampled baseband signal branches are generated. Each resampling factor  
Figure 2: The structure of DAM-TEQ.  
correspondingly represents an estimated Doppler factor \(\mu_{m}\). The \(m\)th resampling branch is denoted as  
\[r_{m}(t)=r\bigg{(}\frac{t}{1+u_{m}}\bigg{)} \tag{5}\]  
Let the initial sampling rate be \(f_{s}\), and the branch signal received at time \(n\) can be discretely expressed as  
\[r_{m,n}=r_{m}[n]\] \[\approx\sum_{l=0}^{L-1}h_{m,l,n}x_{n-l}\ \ +w_{m,n} \tag{6}\]  
\[h_{m,l,n}=h_{l}\bigg{(}\frac{n}{(1+u_{m})f_{s}}\bigg{)},w_{m,n}=w\bigg{(}\frac {n}{(1+u_{m})f_{s}}\bigg{)} \tag{7}\]  
where \(x_{n-l}\) is the transmitted signal at time \((n-l),h_{m,l,n}\) is the \(l\)th coefficient of the channel impulse response, and \(L\) is the total length of the channel impulse response. The resampled branches are fed into the DFE bit by bit, which consists of the m parallel feedforward filter (FFF), phase lock loop (PLL), and feedback filter (FBF). The equalizer coefficients include the FFF coefficient \(\mathbf{c_{m}}\), the phase correction coefficient \(\theta_{n}\), and the FBF coefficient \(\mathbf{b}\). The branch signals are processed sequentially, module by module.  
Suppose the length of the FFF is \((N_{1}+N_{2}+1)\), where \(N_{1}\), \(N_{2}\) are the length of the non-causal and causal parts of the filter. Therefore, the \(m\)th FFF coefficient is expressed as  
\[\mathbf{c}^{\prime}_{m}=\left[c^{m}_{-N_{1}},\ldots,c^{m}_{0},\ldots,c^{m}_{N_{2}}\right] \tag{8}\]  
The input \(\mathbf{r_{m,n}}\) and output \(p_{m,n}\) of the \(m\)th FFF are represented as  
\[\mathbf{r}^{\prime}_{m,n}=\left[\mathbf{r}_{m,n-N_{1}},\ldots,\mathbf{r}_{m,n},\ldots,\mathbf{ r}_{m,n+N_{2}}\right] \tag{9}\]  
\[p_{m,n}=\mathbf{c}^{H}_{m,n}\mathbf{r}_{m,n} \tag{10}\]  
PLL corrects the phase to obtain the correction coefficient \(\theta_{n}\).  
Assuming the FBF length is \(N_{3}\), the FBF coefficient is expressed as  
\[\mathbf{b}^{\prime}=\left[b_{1},\ldots,b_{N_{3}}\right] \tag{11}\]  
The input \(\widetilde{\mathbf{x}}_{\mathbf{n}}\) and output \(q_{n}\) of the FBF are represented as  
\[\widetilde{\mathbf{x}}^{\prime}_{n}=\left[\widetilde{x}_{n-1},\ldots,\widetilde{ x}_{n-N_{3}}\right] \tag{12}\]  
\[q_{n}=\mathbf{b}^{H}\widetilde{\mathbf{x}}_{n} \tag{13}\]  
Therefore, the output of this equalizer is  
\[\hat{x}_{n}=\sum_{m=1}^{M}p_{m,n}e^{-j\theta_{n}}-q_{n} \tag{14}\]  
The estimation error \(e_{n}=x_{n}-\hat{x}_{n}\), and then recursive least squares (RLS) is used to solve, and the equalizer coefficients \(\mathbf{c_{m}},\theta_{n}\), and \(\mathbf{b}\) are iteratively updated bit by bit according to the estimation error [33]. The resampling rate and the branches are updated by detecting the convergent equalizer coefficient, described in the next section.
## 5 Conclusions  
To the best of our knowledge, this study represents the first characterization of the volatile signature of onions from Spanish variety cultivated in the different geographical areas of Madeira Island. HS-SPME/GC-qMS methodology combined with multivariate statistical analysis revealed a useful approach to discriminate the geographical origin of Spanish onions crops. The volatile composition of the Spanish onions is affected by the geographic region of harvest, both qualitative and quantitative expression. VOMs belonging to sulfur compounds (dipropyl disulfide (26), 3-(acetylthio)-2-methylfuran (59)), furanic compounds (dimethylmethoxyfuranone (50), ethyl furanone (65) and acetyloxy-dimethylfuranone (74)), and lactones (whiskey lactone isomer (72)), could be applied as authenticity markers of Spanish onions cultivated in Madeira Island, contributing within regulatory framework to assessment of the samples with claims such as \"protected geographical indication\" or \"product of island farming.\" However, authenticity markers validation in the food science field is a slow process and further investigations in this sense (with multi-year assay) are required.  
Conceptualization, S.F., R.P., and S.M.; methodology, A.G., F.M., and R.P.; software, F.M. and A.G.; validation, S.F. and R.P.; formal analysis, S.M.; investigation, R.P. and J.S.C.; resources, J.S.C.; data curation, R.P. and S.M.; writing--original draft preparation, S.F. and S.M.; writing--review and editing, R.P., S.M. and J.S.C.; visualization, R.P.; supervision, S.M. and J.S.C.; project administration, J.S.C. funding acquisition, J.S.C. All authors have read and agreed to the published version of the manuscript.  
We gratefully acknowledge financial support from FCT-Fundacao para a Ciencia e a Tecnologia (project PEst-OE/QUI/UI0674/2013, CQM, Portuguese Government funds), Madeira 14-20 Program (project PROEQUIPRAM - Reforo do Investimento em Equipamentos e Infraestruturas Cientificas na RAM - M1420-01-0145-FEDER-000008) and by ARDITI-Agencia Regional para a Desenvolvimento da Investigacao Tecnologia a Invacao through the project M1420-01-0145-FEDER-000005 - Centro de Quimica da Madeira - CQM+ (Madeira 14-20). The authors also acknowledge Post-Doc grants, SFRH/BPD/97387/2013 and ARDITI-CQM/2017/008-PDG to R.P. and S.M., respectively.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Towards Explainable Quantum Machine Learning for Mobile Malware Detection and Classification +
## 2 Background
### Image-Based Malware Detection  
In recent years, a popular method to classify malware consists of converting malicious software into images and then applying deep learning models based on images to perform classification tasks [20,21]. This process starts from the executable file, which is transformed into an array of values by grouping the bits in blocks and casting the bytes to unsigned integers. Then, the array of values is scaled into a 2D matrix and converted to a grayscale (or RGB) image, by casting each value to a pixel [1].  
Most of the malware is a variant of previous malware samples, with some differences in the source code to mislead signature-based antimalware: this is the reason why malware variants of the same original malicious sample are grouped into malware families. Assuming that the malware of the same family shares part of the code, also its corresponding images will have patterns in common. Similar to the classical classification of objects within images, a deep learning model trained on a large number of input samples will be able to recognize the pattern that characterizes one malware family rather than another.  
Figure 1 reports two samples of two Android malware converted to images, belonging to two different families (i.e., _Mecor_ and _Airpush_, respectively). The images may look like random noise, but the information coming from the input executables is preserved.
# A Resource Oriented Route Selection Framework Using Contextual Information Based on Fuzzy Logic
## 7 Conclusions  
In this paper, we propose an objective function that enhances the route selection process for IoT based smart city network while using the contextual information from the requirements of the application. The proposed context-oriented objective function can comprehensively assess the proposed routing metrics ETX, QFI, and REI and combine them using a fuzzy logic technique. QFI takes the status of the parent chain into account before choosing the preferred parent of a node. Each application may have diverse requirements that simultaneously need up to some extent and using contextual information through three routing metrics the proposed fuzzy system classify the routes that are based on the defined fuzzy rules to perform the specific application objective. Meantime, a simple conceptual context-aware system architecture indicates the characteristic of the metric parameters. This way, we tried to balance the network resources to be used by IoT based smart city applications. In general, to meet the application requirements, COOF recognizes the network conditions by taking different node and link characteristics into consideration in dynamic traffic scenarios.  
Extensive simulation testing is performed to verify the performance supremacy. The simulation results verify that the proposed approach improves the packet receiving ratio, packet loss ratio, end-to-end delay, and average energy consumption, as compared to others. In our proposed approach, the explicit benefit of handling traffic dynamicity makes COOF capable of prolonging the network lifetime and maintaining lower delay, even at high traffic flows. Moreover, in large scale networks, the COOF improves significantly in terms of the packet loss ratio, packet receiving ratio, end-to-end delay, and average throughput, as compared to other OFs. Additionally, two different design parameters in COOF significantly contributed to the performance of an IoT based smart city network. Overall, COOF is able to find the optimum path and along with ensuring the maximum network resource utilization, regardless of network dynamicity.  
[PERSON] conceived the proposed protocol, performed the simulation tests, and wrote the manuscript and [PERSON] supervised the work. This research received no external funding. The authors declare no conflict of interest.
# A Hierarchical Security Event Correlation Model for Real-Time Threat Detection and Response
## 2 Proposed Approach
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Data Source** & **Technique Used** & **Approach** \\ \hline [PERSON] et al. [8] & Network alerts & (1) Splunk is used to aggregate alerts (2) Text analysis of the created hyperalerts & Splunk is used to perform the first level of the correlation to aggregate IDS alerts. The aggregated alerts are then further clustered using text analysis. \\ \hline [PERSON] et al. [7] & Network alerts & (1) Attribute similarity (2) Prerequisite and consequences & IDS alerts are initially correlated using attribute similarity to create hyperalerts. The resulting hyperalerts are then correlated using prerequisites and consequences. \\ \hline [PERSON] and [PERSON] [6] & Network alerts & Pre–post conditions & IDS alerts are correlated using pre–post conditions to discover and generate hyperalerts and discover local attacks. The same technique is applied again on the hyperalerts to discover global attacks. \\ \hline [PERSON] et al. [33] & Network alerts & (1) Attribute similarity (2) Prerequisite and consequences & IDS alerts are initially correlated using attribute similarity to create hyperalerts. The resulting hyperalerts are then correlated using prerequisites and consequences. \\ \hline [PERSON] et al. [9] & IDS alerts & NLP using NER and semantic similarity & Attributes of aggregated alerts are extracted using NER and then correlated based on semantic similarity. \\ \hline Proposed Model & Network events & (1) Attribute similarity and (2) graph correlation based on time and association rules & Raw event records are initially correlated based on attribute similarity to achieve aggregation and create hyperalerts. The hyperalerts are then correlated using both temporal features and association rules to establish global traffic patterns that may flag suspicious flows. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of related works.
## 4 Inertia Identification of Wind Farms Based on Analytical Optimization
### Specific Steps for Identification Control Parameter and Structure  
The steps for estimating the equivalent inertia of the wind farm are outlined as follows:  
Step 1: Acquire and preprocess system disturbance time-series data to establish an optimized dataset ensuring analytical accuracy;  
Step 2: By integrating the dynamic equations corresponding to each module, a suitable library of feature functions is chosen to develop the sparse identification model. Subsequently, the parameters of the model are determined utilizing the algorithm described in Step 3;  
Step 3: Based on the SR3 algorithm in Section 4.1, the identification process involves splitting the optimization problem into data fitting and sparse regularization. Ridge regression is used for initial sparse matrix estimation, followed by the application of iterative thresholding to the relaxation variables, ultimately yielding the frequency domain expression for the wind farm's inertia time constant;  
Step 4: Compare the identified control parameters and structure with those set in the actual simulation to evaluate the model fitting accuracy and the reliability of the identification. The specific procedure is illustrated in Figure 3.  
Figure 3: Identification and validation path.
# A _uv_-Cellular Network: Design, Implementation and Experiments
## 6 Conclusions  
In this article, we proposed a large-scale UWSN with the main purpose of underwater monitoring called _uw_-Cellular network. It is a hydroacoustic network with a hierarchical architecture. Different types of network nodes, whether fixed or mobile, can be flexibly added to or removed from the network according to different needs. We also give the design principles of the protocols for the _uw_-Cellular network.  
As a simulation study, we implement the CLA protocol and evaluate its performance in the Aqua-Net Mate. The extensive simulation results show that CLA outperforms than VBF and MFLOOD protocol in terms of the Packet Delivery Fraction, Packet Latency and Effective Receive-to-Send Ratio (ERSR). As an outfield case study, an _uw_-Cellular network with twenty underwater network nodes and one onshore node has been implemented. Not only CLA but also other layer protocols and functions have been implemented and applied to verify that the protocol we proposed is feasible for the scenarios we targeted. We conducted the experiments in the real environment, and the results of Network Packet Delivery Rate, Data Rate and Throughput show that the _uw_-Cellular network not only can run stably in the real environment, but also achieve good performance.  
We will continue to optimize the large-scale _uw_-Cellular network architecture. Hundreds of network nodes, including more fixed and mobile network nodes, will be deployed in real underwater environments. Meanwhile, we are working to identify new needs from the real scenarios and trying to provide better support for the latest technique and application advances. _uw_-Cellular network will evolve as the underwater network research proceeds. And we consider it can be applied in underwater environments and can solve real problems.  
Conceptualization, J.Z.; methodology, J.Z. and X.P.; software, J.Z., X.P. and M.L.; validation, J.Z., X.P. and M.L.; formal analysis, J.Z., X.P. and M.L.; investigation, J.Z., X.P. and J.G.; resources, J.Z. and X.P.; data curation, J.Z., X.P., J.G., T.Z. and Y.G.; writing--original draft preparation, J.Z.; writing--review and editing, J.-H.C. and Z.P.; visualization, J.Z.; supervision, J.-H.C. and Z.P.; project administration, J.-H.C.; funding acquisition, J.-H.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the National Natural Science Foundation of China under Grant 61971206, Grant 62101211, and Grant U1813217; in part by the National Key R&D Program of China under Grant 2018 YFC1405800 and Grant 2021 YFC2803000; in part by Overseas Top Talents Program of Shenzhen under Grant KQTD20180411184955957, and Grant LHTD20190004.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this paper are available after contacting the corresponding author.  
The authors would like to thank the anonymous Reviewers for their careful reading and valuable comments.  
The authors declare no conflict of interest.
# Indistinguishability Operators Applied to Task Allocation Problems in Multi-Agent Systems
## 7 Generalization of the Results  
Firstly, this section provides a recapitulation of the main results of the paper, together with a generalization of them to other kinds of missions. Moreover, it provides some realistic application where the proposed task allocation approach can be applied.  
As a recapitulation of the paper, it must be recalled that, as defined in Section 3, an indistinguishability operator provides the degree of equivalence, in fuzzy logic, between the elements of a set \(X\). Thus, an indistinguishability operator \(E\) gives a numerical value between 0 and 1 (\(E:X\times X\rightarrow[0,1]\)) that must be interpreted as the measure of similarity between two elements. In our case, the set \(X\) can be considered as the set of tasks to be carried out. We have demonstrated that two of the most used response functions, Original Possibility Response Function (OPRF) and Exponential Possibility Response Function (EPRF) (see Equations (10) and (11)), are indistinguishability operators when both depend on the distance between the robot and the task to execute. Moreover, a max-min algebra has been used to compose the fuzzy transition matrices (see Equation (9)). The fuzzy Markov chains obtained from the indistinguishability operators converge to a stationary state in, at most, \(m-1\) steps, where \(m\) is the number of tasks (see Section 5.1). In contrast, the probabilistic Makov chain used by the classical RTM methods, in general, converge asymptotically. The aforementioned points could be considered as a methodology where firstly, the system's designer should choose one algebra and one indistinguishability operator and then model the behaviour of each robot as a fuzzy Markov chain.  
In order to validate our approach, Sections 5.2 and 6 provide an example of a foraging-like mission. This kind of mission, where the robots must gather or execute a set of tasks placed in an environment, is one of the most common mission used to evaluate multi-robot systems (see [3; 14; 23]). Despite its  
Figure 5: Number of iteration required to converge with probabilistic Markov chain with different values of \(n\) power (\(n=1,2\)), \(nTH=2\), several number of tasks and two clusters of tasks. 500 iterations means no convergence.  
simplicity, many more complex and realistic missions are similar to it. For example, consider a scenario where the robots must search and rescue the victims of an earthquake. A human operator can mark on a map the regions of interest--where is it more plausible to find victims. Each task, or cluster of tasks, can be considered a region of interest where a robot, after reaching one of them, starts the rescue operation. Each robot would implement the aforementioned possibilistic Markov chain and decide autonomously the next task to carry out. In order to take this decision, the next task to execute will be randomly selected from the fuzzy state vector \(x(\tau)\) (see Equation (9)).