esa-sceva/satcom-chunk-collection huggingface dataset:
# Coherent Chaotic Communication Using Generalized Runge-Kutta Method
## 2 Materials and Methods
### Analysis of the Gokyildirim Discrete Model
#### 2.2.2 Bifurcation and Lyapunov Spectrum Analysis  
For further experiments, the integration step \(h\) was chosen to be 0.01 s. Figure 4 shows the bifurcation diagram and Lyapunov spectrum illustrating the various dynamics  
Figure 3: A 2D bifurcation diagram showing the dynamics of the system at various integration steps \(h\) while varying the value of \(\alpha\). Corresponding 1D bifurcation diagrams for \(\alpha\) are given for different values of integration step \(h\).  
of the discrete Gokyildirim et al. system for different \(\alpha\) values taken from a range between \(\alpha=-400\) and \(\alpha=1000\).  
Figure 5 shows a 2D bifurcation diagram with varying parameters \(a\) and \(\alpha\). The bifurcation diagram indicates the presence of both chaotic and periodic behavior. Additionally, Figure 6 presents phase portraits for various values of \(\alpha\) and \(a\).  
Figure 7 shows a 2D bifurcation diagram for \(\alpha\) and parameter \(b\).  
Figure 8 displays 2D bifurcation diagrams for different values of \(b\) for \(\alpha\) and parameter \(a\). It is evident that as the parameter \(b\) increases, the range of \(\alpha\) values decreases.  
Figure 4: Bifurcation diagram and Lyapunov spectrum with respect to \(\alpha\). The phase portraits for different values of \(\alpha\) are shown above.  
Figure 5: A 2D bifurcation diagram of \(\alpha\) and parameter _a._  
Figure 6: Phase portraits for different values of \(\alpha\) and \(a\).  
The features discovered in the dynamics of the discrete Gokyildirim et al. system during its analysis indicate that a coherent chaotic communication system can be constructed on its basis. Let us describe it by introducing a novel chaotic signal modulation technique.  
Figure 7: The 2D bifurcation diagrams for parameters \(\alpha\) and \(b\).
## 3 Results and Discussion
### Fatty Acids
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Fatty Acids**} & \multirow{2}{*}{**Time**} & \multicolumn{4}{c}{**Treatment**} \\ \cline{3-6}  & & **C** & **P** & **O1** & **O2** \\ \hline \multirow{3}{*}{SFA (\%)} & 0 & 41.23 \(\pm\) 0.12 \({}^{\rm{a}}\) & 40.07 \(\pm\) 0.06 \({}^{\rm{a}}\) & 40.87 \(\pm\) 0.09 \({}^{\rm{a}}\) & 41.00 \(\pm\) 0.00 \({}^{\rm{a}}\) \\  & 1 & 41.07 \(\pm\) 0.17 \({}^{\rm{a}}\) & 40.30 \(\pm\) 0.14 \({}^{\rm{a}}\) & 41.00 \(\pm\) 0.22 \({}^{\rm{a}}\) & 41.13 \(\pm\) 0.17 \({}^{\rm{a}}\) \\ \hline \multirow{3}{*}{MUFA (\%)} & 0 & 49.27 \(\pm\) 0.05 \({}^{\rm{a}}\) & 50.93 \(\pm\) 0.06 \({}^{\rm{a}}\) & 49.37 \(\pm\) 0.12 \({}^{\rm{a}}\) & 49.23 \(\pm\) 0.05 \({}^{\rm{a}}\) \\  & 1 & 49.30 \(\pm\) 0.08 \({}^{\rm{a}}\) & 50.87 \(\pm\) 0.17 \({}^{\rm{a}}\) & 49.17 \(\pm\) 0.05 \({}^{\rm{a}}\) & 48.97 \(\pm\) 0.17 \({}^{\rm{a}}\) \\ \hline \multirow{3}{*}{PUFA (\%)} & 0 & 9.47 \(\pm\) 0.09 \({}^{\rm{a}}\) & 8.80 \(\pm\) 0.00 \({}^{\rm{a}}\) & 9.63 \(\pm\) 0.05 \({}^{\rm{a}}\) & 9.67 \(\pm\) 0.05 \({}^{\rm{a}}\) \\  & 1 & 9.43 \(\pm\) 0.17 \({}^{\rm{a}}\) & 8.80 \(\pm\) 0.08 \({}^{\rm{a}}\) & 9.60 \(\pm\) 0.16 \({}^{\rm{a}}\) & 9.87 \(\pm\) 0.24 \({}^{\rm{a}}\) \\ \hline \multirow{3}{*}{TFA (\%)} & 0 & 0.20 \(\pm\) 0.00 \({}^{\rm{a}}\) & 0.20 \(\pm\) 0.00 \({}^{\rm{a}}\) & 0.20 \(\pm\) 0.00 \({}^{\rm{a}}\) & 0.20 \(\pm\) 0.00 \({}^{\rm{a}}\) \\  & 1 & 0.20 \(\pm\) 0.00 \({}^{\rm{a}}\) & 0.20 \(\pm\) 0.00 \({}^{\rm{a}}\) & 0.27 \(\pm\) 0.09 \({}^{\rm{a}}\) & 0.23 \(\pm\) 0.05 \({}^{\rm{a}}\) \\ \hline \multirow{3}{*}{PUFA/SFA ratio} & 0 & 0.23 \(\pm\) 0.00 \({}^{\rm{a}}\) & 0.22 \(\pm\) 0.00 \({}^{\rm{a}}\) & 0.24 \(\pm\) 0.00 \({}^{\rm{a}}\) & 0.24 \(\pm\) 0.00 \({}^{\rm{a}}\) \\  & 1 & 0.23 \(\pm\) 0.00 \({}^{\rm{a}}\) & 0.22 \(\pm\) 0.00 \({}^{\rm{a}}\) & 0.23 \(\pm\) 0.00 \({}^{\rm{a}}\) & 0.24 \(\pm\) 0.00 \({}^{\rm{a}}\) \\ \hline \hline \end{tabular}  
* SFA—the sum of saturated fatty acids; MUFA—the sum of monounsaturated fatty acids; PUFA—the sum of polyunsaturated fatty acids; TFA—the sum of trans fatty acids; the values are expressed as means \(\pm\) standard deviations; \({}^{\rm{a}}\) means marked with lowercase letters differ significantly between times (\(p<0.05\)); \({}^{\rm{a}}\) means marked with uppercase letters differ significantly between treatments (\(p<0.05\)). \({}^{\rm{a}}\) means marked with uppercase letters differ significantly between treatments (\(p<0.05\)).  
\end{table} 
Table 5: Fatty acid content in the tested sausages during storage.
# Experimental Study on Rock-Like Specimens with Single Flaw under Hydro-Mechanical Coupling
## 4 Discussion  
X-ray CT scanning overcame the difficulties in observations within nontransparent materials, and the spatial geometry was determined in a nondestructive way. Through microscopic observation based on SEM, the microscopic traces left during the initiation and propagation of the crack surface were obtained, which provide a basis for judging the tensile or shear properties of the cracks.  
Figure 16 shows various crack types initiated from the pre-existing flaws identified by scholars. They used sandstone, molded gypsum and Carrara marble as materials for uniaxial compression tests; high-speed camera observed the damage process and obtained different crack types. Compared with the crack types summarized in this paper, the difference of tensile cracks is not obvious, but shear cracks and mixed cracks are more common in this experiment. In their uniaxial compression tests, the brittle rock specimens with a single flaw usually show tensile failure, while mixed failure and shear failure occur only in a few cases. However, under conventional and hydraulic coupled triaxial tests, the specimens are more prone to mixed failure and shear failure. Therefore, the shear crack types and mixed crack types in Figure 16 are different from those in Figure 12. Triaxial compression tests have the cases that the failure plane is not coplanar with the pre-existing flaw plane, which would not happen in uniaxial compression tests.  
Macro observation, including surface and internal cracks, and micro observation based on SEM, provide evidence to determine the geometry and mechanism. Although the test results are derived from post-test observation rather than direct observation of the fracture process, the results also provide a useful reference for mechanical properties and cracking behavior under hydro-mechanical coupling.  
Figure 16: Various crack types obtained by scholars. (**a**) Brittle sandstone specimens by [PERSON] and [PERSON] [10]; (**b**) molded gypsum and Carrara marble specimens by [PERSON] and [PERSON] [8; 9].  
The results obtained in this laboratory study can be conceptually extended to the field scale since they indicate that the maximum bearable deviator stresses and the failure mode of jointed rock mass are significantly affected by the in-situ crustal stress as well as their coupled confined water pressure.
## 3 Methodology
### Geostatistical Methods
#### 3.2.2 Kriging with an External Drift (KED)  
In geostatistics, the kriging with an external drift (KED) method allows the use of auxiliary information, available at all locations, which has an effect on the local spatial trend of the dependent variable. So, the predictions are made as with kriging, with the difference that the covariance matrix of residuals is extended with the auxiliary predictors.  
The secondary variable's spatial behaviour is similar to an indicator of the general trend typical of predictions' representation. In this study, the KED method was applied because it allows incorporation of the elevation and geographic coordinates as the secondary variables. This choice is in accordance with the rainfall observation analysed, which have a global elevation-controlled trend in the south-western to north-east direction.  
Let \(z(x)\) be the external deterministic variable. The model for the random function is:  
\[Y(x)=a+b.z(x)+Y_{R}(x),\]where \(Y_{R}(x)\) is a residual stationary random field, \(z(x)\) is known everywhere and \(a\) and \(b\) are coefficients to be estimated if \(Y_{R}(x)\) is needed. Kriging of \(Y\) at a location \(x_{0}\) where it is unknown is a linear combination of the data:  
\[Y^{*}(x_{0})=\sum_{i=1}^{n}\lambda_{i}(x_{i}), \tag{4}\]  
where the \(Y(x_{i})\)'s are the values at the n measured sites and the \(\lambda_{i}\)'s solve the following kriging system:  
\[\left\{\begin{array}{ll}\sum\limits_{j=1}^{n}\lambda_{j}\text{C}\big{(}x_{i} -x_{j}\big{)}-\mu_{1}-\mu_{2}z(x_{i})=\text{C}(x_{i}-x_{0})&for\ i=1,\ldots,n \\ \sum\limits_{i=1}^{n}\lambda_{i}=1&\\ \sum\limits_{j=1}^{n}\lambda_{i}z(x_{i})=z(x_{0})\end{array}\right., \tag{5}\]  
The KED requires a less demanding semivariogram analysis compared to OK which requires a semivariogram for each of the covariates.  
The last constraint is added so that the linear predictor \(Y^{*}(x_{0})\) filters the effect of the external drift, \(\mu_{1}\) and \(\mu_{2}\) are the Lagrange multipliers that account for the unbiasedness constraints with the minimal prediction variance.
# FedCO: Communication-Efficient Federated Learning via Clustering Optimization +
###### Abstract  
Federated Learning (FL) provides a promising solution for preserving privacy in learning shared models on distributed devices without sharing local data on a central server. However, most existing work shows that FL incurs high communication costs. To address this challenge, we propose a clustering-based federated solution, entitled Federated Learning via Clustering Optimization (FedCO), which optimizes model aggregation and reduces communication costs. In order to reduce the communication costs, we first divide the participating workers into groups based on the similarity of their model parameters and then select only one representative, the best performing worker, from each group to communicate with the central server. Then, in each successive round, we apply the Silhouette validation technique to check whether each representative is still made tight with its current cluster. If not, the representative is either moved into a more appropriate cluster or forms a cluster singleton. Finally, we use split optimization to update and improve the whole clustering solution. The updated clustering is used to select new cluster representatives. In that way, the proposed FedCO approach updates clusters by repeatedly evaluating and splitting clusters if doing so is necessary to improve the workers' partitioning. The potential of the proposed method is demonstrated on publicly available datasets and LEAF datasets under the IID and Non-IID data distribution settings. The experimental results indicate that our proposed FedCO approach is superior to the state-of-the-art FL approaches, i.e., FedAvg, FedProx, and CMFL, in reducing communication costs and achieving a better accuracy in both the IID and Non-IID cases.  
federated learning; Internet of Things; clustering; communication efficiency; convolutional neural network Article
## 2 Materials and Methods
### Data Sources
\begin{table}
\begin{tabular}{c c c c} \hline
**Data** & **Year** & **Original Resolution** & **Data Sources** \\ \hline GPP & 2000–2020 & 500 m & [[https://lpdaac.usgs.gov/products/mod17a2](https://lpdaac.usgs.gov/products/mod17a2) hv006]([https://lpdaac.usgs.gov/products/mod17a2](https://lpdaac.usgs.gov/products/mod17a2) hv006) (accessed on 14 December 2024) \\ Grassland range & 2021 & 500 m & mcd12q1v006 (accessed on 14 December 2024) \\ LAI & 2000–2020 & 0.05\({}^{\circ}\) & \begin{tabular}{c} [[http://globalchange.bnu.edu.cn/research/laiv6](http://globalchange.bnu.edu.cn/research/laiv6)]([http://globalchange.bnu.edu.cn/research/laiv6](http://globalchange.bnu.edu.cn/research/laiv6)) (accessed on 21 June 2025) \\ \end{tabular} \\ DEM & 2020 & 90 m & \begin{tabular}{c} [[https://zenodo.org/records/4700264](https://zenodo.org/records/4700264)]([https://zenodo.org/records/4700264](https://zenodo.org/records/4700264)) (accessed on 14 December 2024) \\ \end{tabular} \\ TEM & 2000–2020 & 0.50\({}^{\circ}\) & \begin{tabular}{c} [[https://cruta.uaa.eea.uk/cru/data/](https://cruta.uaa.eea.uk/cru/data/)]([https://cruta.uaa.eea.uk/cru/data/](https://cruta.uaa.eea.uk/cru/data/)) \\ \end{tabular} \\ PRE & 2000–2020 & 4000 m & \begin{tabular}{c} [[https://www.climatologylab.org/](https://www.climatologylab.org/)]([https://www.climatologylab.org/](https://www.climatologylab.org/)) \\ terracimate.html (accessed on 14 December 2024) \\ \end{tabular} \\ SSR & 2000–2020 & 4000 m & \
\begin{tabular}{c} [[https://www.climatologylab.org/](https://www.climatologylab.org/)]([https://www.climatologylab.org/](https://www.climatologylab.org/)) \\ terracimate.html (accessed on 14 December 2024) \\ \end{tabular} \\ VPD & 2000–2020 & 4000 m &
\begin{tabular}{c} [[https://www.climatologylab.org/](https://www.climatologylab.org/)]([https://www.climatologylab.org/](https://www.climatologylab.org/)) \\ terracimate.html (accessed on 14 December 2024) \\ \end{tabular} \\ \hline \end{tabular} Note: Variable names and corresponding abbreviations. Gross primary productivity (GPP), leaf area index (LAI), digital elevation model (DEM), temperature (TEM), precipitation (PRE), downward surface shortwave radiation (SSR), and vapor pressure deficit (VPD).  
\end{table} 
Table 1: Description of datasets used in this study.
# E-MQTT: End-to-End Synchronous and Asynchronous Communication Mechanisms in MQTT Protocol
## 2 Related Works
### Comparison between MQTT and Other Protocols  
There have been also research works measuring the performance of MQTT compared to other protocols [23; 24; 25]. Recent comparisons have also emerged within specific contextual settings. [PERSON] et al. [27] assess the efficacy of AMQP and MQTT in the context of an internet radio system. [PERSON] et al. [29] briefly analyze advantages and disadvantages of MQTT, CoAP, AMQP, and HTTP across various aspects, including architecture, message size, cache support, quality of service, and more. [PERSON] et al. [30] conducted a comparative analysis of two protocols, MQTT-SN and CoAP, in the publish-subscribe version as outlined in a recent IETF draft. The experiments indicate that CoAP offers a viable alternative to MQTT-SN in publish-subscribe scenarios, with CoAP being preferred for highly dynamic networks. [PERSON] et al. [26] undertake a comparison of the performance of MQTT, CoAP, and OPC UA within information-centric networks and industrial IoT environments. [PERSON] et al. [28] compare AMQP, MQTT, and HTTP by leveraging real-time public smart city data.
# Modelling and Experimental Testing of Passive Magnetic Bearings for Power Loss Reduction
## 5 Conclusions  
In this research work, a thrust-radial PMB configuration is presented. This architecture also allows for the use of radial PMBs for partial rotor weight compensation, thus reducing the power losses due to friction in the mechanical thrust bearing used for axial stabilisation. A numerical model is presented to evaluate the bearing forces and stiffness and the possible axial offset range of the thrust-radial configuration.  
An experimental test rig is developed to analyse the thrust-radial PMBs with different offset configurations, measuring the magnetic axial forces by means of static tests and the rotor power losses with angular speed sweep down tests.  
Figure 9: (**a**) Total power loss in A, B, and C configurations; (**b**) power loss percentage reduction due to the partial weight compensation in B and C configurations.  
The experimental model comparison between the axial forces measured with the static tests and the numerical values shows compliant results, validating the PMB force model.  
The reduction in power losses is shown by the increased rotation time before the system stops, which is proportional to the weight compensation achieved in the three PMB configurations.  
The percentage reduction in total power losses, achieved in the PMB configuration with higher rotor weight compensation, varies from 84.9% to 47.7% in the range of \(0\div 200\) rad/s, and it would increase in the case of operation in low pressure environments, such as FESSs.  
Conceptualization, A.V. and S.P.C.; methodology, A.V. and S.P.C.; software, S.P.C. and S.V.; validation, A.V., S.P.C. and S.V.; formal analysis, S.P.C., A.V. and S.V.; investigation, S.P.C.; resources, A.V.; data curation, S.P.C.; writing--original draft preparation, S.P.C.; writing--review & editing, A.V., S.V. and S.P.C.; visualization, S.P.C.; supervision, A.V and S.V.; project administration, A.V.; funding acquisition, A.V. All authors have read and agreed to the published version of the manuscript.  
This publication is part of the project PNRR-NGEU which has received funding from the MUR-DM 351/2022. This research was developed within the \"Network 4 Energy Sustainable Transition-NEST\" project. The \"NEST\" project is funded under the Italian National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.3--Call for tender No. 1561 of 11 October 2022 of Ministero dell'Universita e della Ricerca (MUR) and the European Union--NextGenerationEU consortium.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Other Datasets  
The normalized difference vegetation index (NDVI) data used in this study were obtained from the NOAA climate data record (CDR) of AVHRR surface reflectance ([[https://www.ncei.noaa.gov/products/climate-data-records/](https://www.ncei.noaa.gov/products/climate-data-records/)]([https://www.ncei.noaa.gov/products/climate-data-records/](https://www.ncei.noaa.gov/products/climate-data-records/)), accessed on 8 April 2023). This dataset has a spatial resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) and daily temporal frequency, covering the period 1981-2019. Daily composite NDVI observations were temporally aggregated into annual means, and subsequently spatially resampled to a 5000 m resolution using the Google Earth Engine platform.  
Vapor pressure deficit (VPD) was derived from the ERA-Interim reanalysis dataset ([[http://apps.ecmwf.int/datasets/](http://apps.ecmwf.int/datasets/)]([http://apps.ecmwf.int/datasets/](http://apps.ecmwf.int/datasets/)), accessed on 8 April 2023) [41]. This dataset offers monthly values at a 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) spatial resolution from 1986 to 2016. The monthly VPD data were aggregated into annual means for this period.  
Terrestrial water storage (TWS) was sourced from the GRACE TWS Reconstruction dataset (GRACE_REC_v03), which provides reconstructed TWS anomalies at 0.5\({}^{\circ}\) spatial resolution and monthly intervals, covering 1979-2016 ([[https://doi.org/10.6084/m9.figshare.7670849.v3](https://doi.org/10.6084/m9.figshare.7670849.v3)]([https://doi.org/10.6084/m9.figshare.7670849.v3](https://doi.org/10.6084/m9.figshare.7670849.v3)), accessed on 8 April 2023) [42]. The monthly TWS data were aggregated into annual means for the period 1986-2016.
## 3 Materials and Methods
### ChIP Analysis  
To confirm the binding of ANAC074 to NRS1, NRS2, and MybSt1 actually occurs in plants, ChIP analysis was performed. The coding regions of ANAC074 without the termination codon were ligated in frame to the N-terminal of GFP driven by 35 SCAMV promoter to generate the ANAC074::GFP fusion gene, and transformed into _Arabidopsis thaliana_ in the Columbia ecotype background. The 3-week-old transgenic plants were used for ChIP assay following the procedures of [PERSON] et al. [47]. Briefly, protein and DNA were cross-linked by 3% formaldehyde. The purified cross-linked nuclei were sonicated to shear the chromatin into 0.5-0.7 kb fragments. We saved 1/10 of the volume as input control. The remaining sonicated chromatin was divided into two aliquots. These two aliquots were respectively incubated with GFP antibody (ChIP+) or a rabbit anti-hemagglutinin (HA, Beyotime, Shanghai, China) antibody as a negative control (ChIP-). The antibody-bound complex was precipitated with protein A Agarose beads. The DNA fragments were released from the immunoprecipitated complexes by reversing the cross-linking at 65 \({}^{\circ}\)C for 3 h. Immunoprecipitated DNA was purified by chloroform extraction. PCR was performed and visualized by gel electrophoresis. The primers used for PCR are shown in Supplemental Table S1. PCR was performed as follows: 94 \({}^{\circ}\)C for 2 min; 40 cycles of 94 \({}^{\circ}\)C for 12 s, 58 \({}^{\circ}\)C for 30 s, and 72 \({}^{\circ}\)C for 30 s; and 72 \({}^{\circ}\)C for 5 min.
## 5 Discussion  
This paper presents results based on an AE example to illustrate the method and demonstrate the steps for risk identification using HAZOP. The results presented represent models that consolidate the best practices in diagnosing and treating blood flow obstruction. Although the results are promising, they represent only one AE case; therefore, further studies should be conducted to complement the modules outlined for the SCVAD.  
According to the IEC 31010 standard, another 31 techniques can be combined, and depending on the scenario, this is a factor that should be considered [50]. As previously mentioned, ISO 14971 provides guidance on the specification of techniques for the regulation of medical devices, particularly for VADs [49]. Therefore, a risk identification study can be conducted for each AE using techniques that meet the specific objectives and inherent characteristics of each AE.  
Although the data collected from [PERSON] et al. [25] provided valuable information for obtaining important results in this work, this aspect can be improved, as the contribution of the medical team is fundamental and indispensable. They hold the specialized knowledge required to establish criteria that guide best practices for the diagnosis and treatment of AEs.  
Another important point is that the method presents a formalism for obtaining logical models in PNs, which are validated through computational simulation and reflect the actions recommended by the experts. In this way, it can be ensured that the models used are a faithful and consistent representation of the actions recommended by the medical team, facilitating their application in a real-world context.  
PNs also facilitate the transcription of these models into event-driven computable algorithms compatible with event-driven architecture (EDA). This allows interactions between the entities to be efficient and dynamic. In this way, the integration between the medical team and the other system entities can be established smoothly, ensuring that the necessary information for decision-making is always accessible and up to date.  
In this context, APIs can be specified to allow the conversion of data from the physical environment, such as patient vital signs or monitoring sensor data, into a digital format. These APIs play a crucial role in making data available, which is essential for ensuring that medical decisions are based on up-to-date information.  
Once made available, this data can be shared through an event broker using a publish-subscribe service. This mechanism facilitates instant communication between the entities involved in the process, enabling effective coordination in managing AEs, monitoring the patient's health, and executing mitigation actions when necessary.