esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical TCP Model for Millimeter-Wave 5G NR Systems in Dynamic Human Body Blockage Environment
## 5 Conclusions  
In this paper, we have developed a simple analytical approximation for TCP throughput achieved at 5G NR air interface in a dynamic human-body blockage environment. The model considers the random movement of blockers and represents the TCP throughput as a function of the environmental and propagation conditions and 5G NR system parameters.  
Our numerical results indicate that TCP performance in dynamic human-body blockage conditions is determined by an interplay between the RTT, the frequency of blocked/non-blocked state changes as well as their durations. We have shown that for a wide range of system parameters, TCP achieves its theoretical rate. However, TCP throughput degrades when the RTT is comparable to blocked/non-blocked durations while the frequency of state changes is high. Such conditions are not typical for dynamic human-body blockage environments, allowing TCP to benefit from high bandwidth of the 5G NR air interface.  
Among environmental parameters, the blockers' speed is shown to provide the highest impact on TCP throughput. High blocker speed decreases the durations of blockage and non-blockage intervals and simultaneously increases the frequency of UE state changes. Thus, one may expect significant TCP performance degradation in mobile environments.  
Figure 8: TCP throughput as a function of blocker’s density, \(v_{B}=1\).  
Figure 7: TCP throughput as a function of UE-BS distance.  
**Author Contributions:** Conceptualization, D.M. and A.O.; methodology, D.M.; software, D.M.; validation, P.K., O.E. and J.H.; formal analysis, A.O.; investigation, A.O.; resources, J.H.; data curation, Y.K.; writing--original draft preparation, A.O. and D.M.; writing--review and editing, P.K., O.E., J.H. and Y.K.; visualization, A.O.; supervision, D.M.; project administration, A.O.; funding acquisition, J.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** For the research, the infrastructure of the SIX Center at Brno University of Technology was used.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 8 The SCS Instrument  
### Scientific Scope and X-ray Techniques  
The Spectroscopy and Coherent Scattering (SCS) scientific instrument is located at the SASE3 FEL source and aims at time-resolved experiments to unravel the electronic, spin and structural properties of materials in their fundamental space-time dimensions. Scientific objectives include, but are not limited to the understanding and control of complex materials [81; 82; 83], the investigation of ultrafast magnetization processes on the nanoscale [84; 85], the real-time observation of chemical reactions at surfaces and in liquids [86; 87], and the exploration of nonlinear X-ray spectroscopic techniques that are cornerstones at optical wavelengths [17; 88].  
The SCS instrument operates in the soft to tender X-ray regime (250 eV-3000 eV) covering a wide range of core level resonances: K-edges of most 2p and 3p elements (starting from carbon), L\({}_{2,3}\)-edgesof 3d and 4d elements (transition metals) and M\({}_{4,5}\) edges of 4f elements (lanthanides). Time-resolved resonant spectroscopy offers element-, site-, orbital-, and spin-selective probing of complex material dynamics that is either directly related to or indirectly coupled to the valence electrons. Physical properties such as oxidation state, magnetism, local symmetries and ordering as well as elementary excitations can be investigated using X-ray Absorption Spectroscopy (XAS) [86; 89], X-ray Resonant Diffraction (XRD) [81; 82; 83] and Resonant Inelastic X-ray Scattering (RIXS) [87]. A particular aim of the SCS instrument is to combine these powerful spectroscopic techniques with X-ray diffraction and microscopy methods, which provide nanometer spatial- and femtosecond time resolutions. Such experiments open up a route to follow the dynamics in complex systems on their relevant length and time scales. The SCS instrument further implements Coherent Diffraction Imaging (CDI) techniques, i.e., X-ray holography [90; 91]. A time series of reconstructed CDI images can elucidate excited state dynamics in real space.
## 2 Materials and Methods
### Data Source  
The GIMMS NDVI3g dataset provided by NASA was used to estimate the EOS on the Qinghai-Tibetan Plateau. The dataset was available from 1982 to 2015 with an 8-km spatial resolution and 15-day temporal resolution [32]. Some previous processes (e.g., calibration, noise removal) were performed for this version to better detect the vegetation dynamics [32]. This dataset has been widely used to detect long-term vegetation dynamics [33; 34; 35]. Due to the normalized difference vegetation index (NDVI) data might be misrepresented by snow [36]; we used the average temperature of a sequence of five days less than 0 \({}^{\circ}\)C to screen out the pixels that might be covered by snow. Temperature, precipitation, and insolation data from 1982-2015 were extracted from the China meteorological forcing dataset (1979-2015) downloaded from the Big Earth Data Platform for Three Poles with a spatial resolution of 0.1\({}^{\circ}\) and temporal resolution of 3 h ([[http://poles.tpdc.ac.cn/](http://poles.tpdc.ac.cn/)]([http://poles.tpdc.ac.cn/](http://poles.tpdc.ac.cn/)), Accessed on 15 August 2021) [37].  
Human activities, including grazing density and economic development, were quantified using economic statistic data. For example, the grazing density were represented by the number of large animals (one large animal equal to five sheep unit) and sheep, and uniformly converted into sheep units. The economic development levels were quantified as the production of primary, secondary, and tertiary sectors. These data come from the statistical yearbooks of Qinghai and Tibet from 1982 to 2015.
# Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
## 4 Proposed Method  
This study presents the method AI-driven AMI network planning with DA-based information and a link-specific propagation model (AIDA) created for positioning gateways and routers in the smart grid communication network, considering the position of poles and DA equipment.  
We consider the average link received power (LRP) for connectivity analysis. The LRP is computed using a detailed propagation model, including diffraction loss. The diffraction loss takes into account the topographic profile between the geographic coordinates of the points in the link budget analysis. The Delta-Bullington method [37] is used to calculate the diffraction loss using the characteristics of the terrain profile between the coordinates. The method estimates the LRP based on the technical characteristics of the devices, including transmission power, receiver sensitivity, and antenna gains.  
Regarding AI techniques, the proposed method explores unsupervised machine learning algorithms (clustering algorithms) and graph-processing algorithms (e.g., minimum spanning tree). Furthermore, exhaustive search and greedy search algorithms are used.
## 3 Modeling and Validation
### CFD Validation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Thrust/kN** & **The Relative Error/\%** & **Torque/kN\(\cdot\)m** & **Relative Difference/\%** \\ \hline case1 & 664.77 & — & 2737.40 & — \\ case2 & 698.59 & 5.088 & 3594.54 & 31.312 \\ case3 & 714.68 & 2.304 & 3898.55 & 8.458 \\ case4 & 716.04 & 0.189 & 3935.65 & 0.952 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Grid size of wind turbine blade surface. Figure 5: Schematic diagram of grid division: (**a**) Boundary layer of blade section and nearby grid; (**b**) Blade surface mesh and flow field grid; (**c**) Grid of blade tips; (**d**) Grid of hub. To verify the accuracy of the numerical results, the uniform wind speeds were calculated for five conditions: 5 m/s, 7 m/s, 8 m/s, 10 m/s, 11 m/s and 11.4 m/s, the rotation speed of the NREL 5 MW wind turbine corresponding to different wind speeds is shown in Table 3.  
The thrust and torque results obtained from CFD calculations were compared with those obtained by NREL officials and some scholars [6,22,23], as shown in Figure 7. It can be seen from the figure that the thrust calculated by STAR CCM+ in this paper is lower than the data in the official NREL report, which has a high agreement with the results of FAST simulation, because the thrust calculated in the official NREL report includes the gravity classification perpendicular to the direction of the slurry disk. The results of this paper are in good agreement with those of [PERSON] [22], who also simulated by STAR CCM+. The calculation results of CFD method are generally low due to the absence of an actual blade model in the fluid domain by the Actuator Line Method. In terms of torque, the results in this paper are slightly higher than the official report when the wind speed is low, and the torque value is slightly smaller than the official report when the wind speed gradually increases. Secondly, in terms of torque, the results of this paper's method do not differ much from those obtained by other scholars using different methods, and are in the highest agreement with [PERSON]'s results, which are also simulated by STAR CCM+. The maximum error of the simulated horizontal thrust and torque compared with the official report is 3.65% and 6.73% at 11.4 m/s, and the deviation of the simulated results is within the acceptable range, which verifies the accuracy and validity of the model in terms of aerodynamic performance. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Wind Speed (m/s)**} & **Rotational Speed of the** & **Angular Velocity of Rotation** \\  & **Turbine (rpm)** & **(rad/s)** \\ \hline
5 & 7.39 & 0.774 \\
7 & 8.46 & 0.886 \\
8 & 9.16 & 0.959 \\
10 & 11.43 & 1.197 \\
11 & 11.87 & 1.243 \\
11.4 & 12.10 & 1.267 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Rotation speed of wind turbine at different wind speeds. Figure 6: Grid independence verification: (**a**) Thrust; (**b**) Torque.
# Angle-Resolved Fluorescence of a Dye Coupled to a Plasmonic Nanohole Array
## 2 Materials and Methods  
### GNA Nanofabrication  
The PEF platform is the GNA consisting of a hexagonal array of PMMA/air nanoholes embedded in an optically thick gold layer deposited on a glass substrate [31]. The nanofabrication protocol is based on a combination of colloidal lithography and plasma-based processes. Cleaned SiO\({}_{2}\) microscope slides were activated under glow discharge for 15 min, and then a silanization process was performed. Thus, the silanized SiO\({}_{2}\) was coated with a 180 nm thick layer of polymethyl methacrylate (PMMA) using a spin coater. Consequently, a monolayer of polystyrene nanoparticles (diameter = 500 nm) was assembled and transferred on top of the PMMA layer using the Langmuir Blodgett technique. This monolayer was then conditioned via O\({}_{2}\) plasma etching to nanostructure the underlying PMMA layer. Consequently, a gold layer was deposited by magnetron sputtering with the same nominal thickness as the PMMA layer. The residual of the nanosphere mask was lifted off, resorting to an isopropanol bath under ultrasonication. Finally, thermal treatment at 300 \({}^{\circ}\)C for about one hour was performed to improve the stability of the gold layer. Surfaces fabricated were characterized by resorting to the SEM technique. The gold ring surrounding the top of each hole was about 30 nm in height. A SEM image is reported in Figure 1, showing a very well-ordered nanohole array with a pitch around 460 nm.  
Figure 1: Scanning electron microscope image of the plasmonic metasurface.
## 5 Evaluation
### Comparison of Embedding Vector Distribution between Learning Methods  
The embedding vector distributions of the testing set extracted from the model trained with the SOTA learning method, the triplet loss learning method, the ArcFace learning method and the proposed learning method were compared. To compare the distributions, the mean and variance were calculated for the distance between the center of the object's embedding vectors and the embedding vector (\(D\)), as well as for the length of the embedding vector (\(L\)). In Figure 8, each point represents one object; in addition, the x-axis is \(D\), and the y-axis is \(L\). The mean and variance of \(D\) and \(L\) of the model trained by the SOTA learning method, the triplet loss learning method and the ArcFace learning method were much greater than the equivalent values for the model trained by the proposed learning method as shown in Figure 8. These results suggest that embeddings vectors extracted from models learned with the proposed learning method are well clustered among classes based on distance compared to embedding vectors extracted from other models. In addition, it means that learning of triplet loss using distances other than angles is possible.  
In stage 1, the variance of \(L\) was extremely small, and the mean remained at an almost similar level, as shown in Figure 9. Figure 9 shows that the variance of \(L\) of the model trained in stage 1 was extremely small, and the mean remained almost the same. This means that the length of the embedding vector is similar overall. Stage 1 of the proposed learning method is similar to the existing triplet loss learning method. Afterward, in stage 2, the variance of \(L\) increased but was extremely insignificant, and the variance of \(D\) decreased. The distribution of the mean of \(L\) was expanded, and the distribution of the mean of \(D\) was reduced. Therefore, the embedding vector was located in a wider area, and an aggregation of the vector for each object was conducted at the same time during the training. This result means that the model learned by stage 1 was learned based on the distance by performing stage 2 of the proposed learning method. As a result, we demonstrated that classification has been performed on vector spaces beyond the plane of a sphere with a radius of 1. In addition, the distribution of embedding vectors for each stage of the learning methods combined with the triplet loss and the ArcFace is similar, as shown in Figure 9. This means  
Figure 8: Mean and variance of \(D\), which is the distance between the center of the object’s embedding vectors and the embedding vector, and \(L\), which is the length of the embedding vector, of models trained with the state-of-the-art (SOTA) learning method, the triplet loss learning method, the ArcFace learning method and models trained with the proposed learning method. (**a**) mean; (**b**) variance.  
that the proposed learning method can be generalized even when combined with other loss functions of metric learning.
# Example usage calculate_urban_resilience('input_file_name')
#### 5.4.1 Earthquake Hazard  
In our study, the evaluation of earthquake risk considers both near-field and far-field seismic events for each municipality identified by the HASC_2 coding system. The earthquake risk score for each municipality is derived by averaging the values assigned to near-field (EC zoning 1) and far-field (EC zoning 2) earthquakes. This approach ensures a comprehensive assessment of seismic risk that considers the varied impacts of different types of earthquakes on the Portuguese municipalities.  
The variation in earthquake risk scores across different regions of Portugal, particularly increasing towards the south and southwest, emphasizes the necessity for tailored preparedness and infrastructure planning suited to the specific seismic activities of each location. In the detailed earthquake risk assessment conducted across Portugal, distinct regional variations become evident (Figure 4). In the Aveiro region, encompassing municipalities from Agueda to Vale de Cambra, the earthquake risk scores are generally uniform at 0.27, although some areas like Arouca and Castelo de Paiva register lower scores at 0.18. In contrast, the Azores region shows significantly higher risk levels, with many municipalities,such as Angra do Heroismo and Horta, scoring 1.0, indicating heightened risk. Some areas, like Corvo and Lajes das Flores in the Azores, however, have slightly lower scores at 0.73. Moving to Beja, the earthquake risk scores vary between 0.45 and 0.55, with Odemira exhibiting an elevated risk score of 0.64. The Braga region's scores are comparably stable, like Aveiro's, maintaining a score of 0.18.  
Figure 4: Map depicting Portugal’s 308 municipalities and their earthquake hazard status.
## 5 Experiments and Assumptions on Abnormal Behavior Detection and Noise Removal Performance
### Parameters Used in Ensemble Model for Attack Detection and Noise Reduction
#### 5.3.2 Random Forest  
In this experiment, the parameters of the internal models constituting the random forest are configured with the Gini index in the same way as the decision tree. Also, we confirmed that the dataset used in the experiment does not have significant decision boundaries for trees of depth 5. Thus we set the maximum depth of the random forest model to 4 for efficiency of training time.
## 3 Related Work
### Privacy-Preserving Federated Learning  
While existing research has explored various strategies for protecting privacy in federated learning and blockchain systems, the proposed solutions can be primarily grouped into three distinct approaches: 1. Differential Privacy (DP); 2. Homomorphic Encryption (HE); and 3. Secure Multi-Party Computation (MPC).  
1. Differential Privacy (DP): DP mechanisms safeguard blockchain-powered federated learning (BFL) systems through randomized data modification during information exchange. Learning Chain [25] implements such a mechanism by distorting local gradients through probabilistic noise injection based on exponential mechanisms prior to blockchain integration, disseminating these adjusted parameters throughout the BCFL framework. Blade-FL [26] adopts a dual-role architecture where participants simultaneously engage in computational validation and model refinement via decentralized peer-to-peer training, incorporating Gaussian-distributed perturbations during gradient preparation before cryptographic encapsulation. Biscotti [11] enhances BCFL security through a hybrid approach combining DP with multi-party computation techniques, effectively countering both adversarial manipulation and privacy inference attempts.
2. Homomorphic Encryption (HE): Homomorphic encryption (HE) serves as a cryptographic method to enhance secure learning by allowing computations on cipher-texts without requiring prior decryption. For example, SL\(+\)HE [15] implements partial HE to encrypt swarm learning updates, thereby enhancing security against inference attacks in blockchain-based federated learning (BCFL). Similarly, additive HE can be applied in distributed learning systems to protect model updates and maintain gradient confidentiality, as detailed in [12]. Meanwhile, PBFL [27] leverages cosine similarity metrics to detect malicious gradients while employing fully HE for secure aggregation processes.
3. Secure Multiparty Computation (SMC): This cryptographic technique safeguards the data of participants by producing randomized data points divergent from source information, which are then allocated across participating entities for decentralized processing. Within SMC frameworks, participant-held datasets remain indecipherable until aggregated computational outputs are synthesized through collaborative analysis. Illustrating this paradigm, the MPC-driven PPML framework [16] caters to single-server architectures, while SecureML [28] (alongside Securenn [29]) target distributed multi-server ecosystems requiring coordinated computation protocols.
# The Effect of Seed Removal and Extraction Time on the Phenolic Profile of Plavac Mali Wine
## 3 Results and Discussion
### Plavac Mali Wines' Phenolic Profile
#### 3.1.2 Phenolic Acids  
The main phenolic acidic groups present in wines are hydroxybenzoic and hydroxycinnamic acids in either the free or conjugated form. Among benzoic acids, a similar pattern, as already shown in the work by [37] was defined, namely with gallic acid as the predominant one followed by _trans_-caftaric acid whose concentrations were, as shown in Table 1, more influenced by seeds removal than maceration time. In the work published by [38], prolongation of the maceration time from 5 to 15 days resulted in higher levels of phenolic acids that can be, according to [39], ascribed to the hydrolysis of their esters, which was not so emphasized in this research. One of the reasons could be the further interaction of individual acids with other compounds, possibly a copignmentation reaction with anthocyanins as reported by [40; 41; 42]. Thus, longer maceration times may increase the extraction of seed phenolics, especially gallic acid whose higher concentrations could be connected with the richness of grape seeds in gallic acid [43] even though according to [16] longer maceration times did not always correspond to an increase in wine phenolic concentration. As evident in Table 1, the presented results pointed out a stronger influence of seed removal than maceration time on gallic as well as _trans_-caftaric acid concentrations being lower when compared to the variants where seeds were present. It can be related to their strong antioxidant function which was more pronounced in the variants without seeds, where the flavonoid antioxidant action was diminished [27].
## 3 Tests on the Optimal Mix Proportion of Composite-Cemented Soil
### Comparative Analysis  
The UCS of the FA-WG-composite-cemented soil, ordinary-cemented soil, FA-cemented soil, and WG-cemented soil was compared to test their properties. The mix proportions of these four types of cemented soil are listed in Table 7, and the comparison results are illustrated in Figure 4.  
It can be seen from Figure 4 that the growth rate of the strength of the four types of cemented soil shows a similar pattern, and the strength of the cemented soils after being cured for 28 d is substantially improved compared with that after 7 d; the strength still approximately linearly grows after 28 d, while the growth rate declines greatly compared with that before 28 d. After each curing period, the composite-cemented soil always shows the highest strength, which indicates that co-doping of FA and WG exerts a remarkable effect in improving the strength of cemented soil. The strength of WG-cemented soil after being cured for 28 d is slightly higher than that of the FA one. This implies that WG has a more obvious effect on improving the strength of the cemented soil in the early stage; afterward, the FA-cemented soil is obviously superior to the WG one in terms of strength, indicating that doping of FA has an obvious effect on improving the strength of the cemented soil in the middle and late stages.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**Factor A**} & \multirow{2}{*}{**Factor B**} & \multirow{2}{*}{**Factor C**} & \multicolumn{3}{c}{**UCS/MPa**} \\ \cline{5-6}  & & & & **7 d** & **28 d** & **60 d** \\ \hline
1 & A1 & B1 & C1 & 1.63 & 3.51 & 4.06 \\
2 & A1 & B2 & C3 & 1.50 & 3.95 & 5.12 \\
3 & A1 & B3 & C2 & 2.22 & 4.41 & 5.62 \\
4 & A2 & B1 & C3 & 2.56 & 4.32 & 5.51 \\
5 & A2 & B2 & C2 & 3.10 & 4.66 & 5.67 \\
6 & A2 & B3 & C1 & 3.12 & 4.76 & 6.25 \\
7 & A3 & B1 & C2 & 3.54 & 6.09 & 7.41 \\
8 & A3 & B2 & C1 & 3.67 & 6.02 & 7.61 \\
9 & A3 & B3 & C3 & 4.33 & 6.35 & 8.20 \\ \hline \hline \end{tabular} Notes: Factor A is mixing ratio of cement, A1—6%, A2—9%, A3—12%. Factor B is mixing ratio of FA, B1—4%, B2—8%, B3—12%. Factor C is mixing ratio of WG, C1—1%, C2—3%, C3—5%.  
\end{table}
Table 6: UCS of the FA-WG-composite-cemented soil (MPa).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Mixing Ratio of Cement** & **Mixing Ratio of FA** & **Mixing Ratio of WG** \\ \hline Ordinary-cemented soil & 9\% & 0\% & 0\% \\ FA-cemented soil & 9\% & 12\% & 0\% \\ WG-cemented soil & 9\% & 0\% & 3\% \\ Composite-cemented soil & 9\% & 12\% & 3\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Mix proportions of the four types of cemented soil.