esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Scaling Technological Foundations
#### 2.4.3 Scaling Mode (High-Volume Production)  
For larger user bases and production environments, the scaling mode is engineered to handle high-volume demands with enhanced scalability and reliability using vLLM and Ray:  
* **Distributed model serving** utilizes Ray to distribute model instances across multiple GPUs and nodes, enabling simultaneous handling of numerous requests. This distribution ensures high performance and low latency, even as concurrent user numbers increase.
* **Load balancing and horizontal scaling** leverages Ray's cluster orchestration capabilities to facilitate dynamic load balancing and horizontal scaling. As user demand grows, additional replicas can be spawned automatically to manage the increased load, maintaining consistent response times and system responsiveness.
* **Enhanced reliability** incorporates Ray's dynamic worker allocation and failover mechanisms to bolster deployment reliability. Comprehensive task scheduling ensures efficient resource utilization and minimizes downtime, even in the event of node failures.
# Dynamic Bending Behaviour of Sandwich Structures for Marine Applications
## 4 Results and Discussion
### Analytical Assessment of Impact Bending Performance  
#### 4.4.1 Failure Prediction Analysis  
Figure 10 depicts the normalised theoretical failure predictions of the FRPSS constituents before and after seawater exposure based on the governing equations discussed in Section 3.1. For the facesheet fracture failure (Figure 10a), moisture uptake led to greater degradation (18%) in performance in the Type A specimen, while Type B (GSV and GSH) experienced marginal changes at 3.7% and 2.4%, respectively.  
This is linked to the lack of additional stiffness in the through-thickness direction provided by the adhesive layers in the Type B specimen. For the indentation failure, GSP and GSH samples were reduced by 12.2% and 17.1%, respectively, while GSV, interestingly, increased by 11.4% (Figure 10b). This could be owing to the cross-linking behaviour of the vertical epoxy layer interaction with the adhesives of the top facesheet, which was absent in the other two configurations (GSP and GSH). It is worth noting that while this phenomenon took place only in the indentation failure mode of this configuration, it is not unusual to see an increase in some mechanical properties of adhesive layers after seawater exposure under certain conditions [31]. Generally, the shear core failure decreased by 18.6%, 16.2%, and 21.2% for GSP, GSV, and GSH, respectively, which was due to the degradation of the core shear strength by moisture uptake.
## 1 Introduction  
Aroma is one of the most important parameters to determine must and wine quality, also influences the grape flavor and contributes to the sensory character of the wine [1, 2, 3, 4]. The grape volatile compounds belong to several groups: terpenoids, C\({}_{13}\) norisoprenoides, benzenoid compounds, esters, C6 compounds, alcohols, thiols and methoxypyrazines [5, 6, 7]. The amount of these compounds depend on several factors such as grape variety, season, terroir, grape maturity, viticultural practices, etc. [1, 8, 9, 10]. Foliar fertilization is a technique that is increasingly used as allows a quick and efficient assimilation of the products applied to the plant, reducing soil contamination and costs [11, 12, 10].  
Foliar application of elicitors, molecules capable of activating the defensive systems of plants, can increase the synthesis of secondary metabolites [13, 14, 12]. Methyl jasmonate is a volatile organic compound derived from jasmonic acid [15, 16]. This elicitor has been mainly implicated as a mediator of plant responses triggered by wounding and insect feeding and is involved in the pathogen resistance [15, 17, 18]. Foliar application of methyl jasmonate has been shown to increase the synthesis of secondary metabolites such as amino acids [19], phenolic compounds [14, 18, 20], and volatile compounds [5, 18, 21, 20, 21]. Despite this, methyl jasmonate is a compound with a very high cost and with low chemical stability.  
In the last decade, nanotechnology has opened new horizons in several disciplines, including and agriculture [22]. Nanotechnology is providing very interesting results in agriculture by improving the efficiency of agrochemicals [23; 24]. Specifically in viticulture, there are few studies [25] in which nanocarriers have been applied to vineyards to improve the efficiency of fertilizers (i.e., urea) or elicitors (i.e., Mej) [26; 27; 28; 29; 30]. In this line, biomimetic calcium phosphate nanoparticles (ACP-NPs) have been proposed as promising Mej nanocarrier providing slow release kinetic and protection against thermal degradation [31]. ACP-NPs are non-toxic and biocompatible nanomaterials widely used in biomedicine for drug delivery, dental remineralization or bone tissue engineering [32]. But, the effect of the foliar application of this nanoelicitor on grape aromatic composition has not been studied so far, only in the wine volatile composition, i.e., fermentative aromas [33].  
Hence, this work aims at evaluating the influence of foliar application of nanoparticles doped with Mej (ACP-Mej) and foliar application of methyl jasmonate (Mej) in conventional form on volatile composition of _Vitis vinifera_ L. cv. Tempranillo grapes during two vintages.
# Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping
## 3 Materials and Methods
### Proposed Method
#### 3.2.2 Stacking Model  
(1) Classifiers  
XGB is a decision-tree-based ensemble machine-learning algorithm that uses a gradient boosting framework. It accurately predicts a target class by combining simple and weak models [21].  
LR is a machine-learning method that solves binary classification problems and predicts classification possibility. LR has advantages such as simple implementation and strong explanatory power, and it is widely used in the industry [22].  
Currently, DT is a mainstream prediction and classification technology. It is similar to human thinking. It adopts a recursive top-to-bottom method, carrying out internal node attribute comparisons and judging to split down from the node depending on attribute values, and concludes on the DT leaf [23].  
In probability and statistics, the Bayesian rule is based on prior knowledge of events probability. In machine learning, NBC is a probability statistical classifier based on Bayes theorem. The classifier uses conditional independence assumptions and chooses a very likely category as the sample final category. The algorithm is simple, easy to implement, and less sensitive to missing data, showing small error and stable performance [24].  
(2) Stacking  
The stacking model is a general method of using a higher-accuracy algorithm to combine lower-accuracy algorithms to achieve greater predictive accuracy. The best results are obtained when the higher-accuracy algorithm is in a first level and lower-accuracy  
Figure 2: Example of constructing a new feature by equidistance.  
algorithms are in a second level [25]. In this study, the stacking model consists of two layers as shown in Figure 4, level 1 and level 2. A higher accuracy model is used in the first layer (level 1), while lower-accuracy models are used in the second layer (level 2).  
XGB with a kind of ensemble function got the best accuracy, which is chosen as a first-level classifier. And LR, DT and NBC have primary and different characteristics, and they can complement XGB for the mismatched samples, which are chosen in a second level. The results of XGB and data are combined to generate data 1 for the second level classifiers.  
Figure 3: Cont.
## 2 Fundamental Concepts
### Wiretap Channel Models
#### 2.3.2 Broadcast Wiretap Channels  
In this channel model, Alice simultaneously transmits secret data content to multiple receivers in the presence of one or more unintended users, as shown in Figure 3. The common scenario for broadcast channels is a multi-user cellular network, in which the legitimate transmitter communicates with multiple intended receivers while being protected from eavesdroppers in the downlink. It is assumed that one source Alice is equipped with \(N_{A}\) antennae and the intended I users and J eavesdroppers have \(\mathbf{N_{Bi}}\) and \(\mathbf{N_{Ej}}\) antennae, respectively.  
In such scenarios, the signals that are received by other users may interfere with the desired signal due to the conditions under which each receiver obtains the mixed signals from all users. Furthermore, the susceptibility of eavesdroppers may also increase due to the opportunity for more information leakages in the downlink. Indeed, secrecy performance is not only affected by the eavesdropper and the mixed signals but also by the signals that are received by the other users [48]. Hence, a broadcast channel can be defined as a mixed multi-channel, which is described by [49; 50] as follows:  
\[y_{Bi}=\mathbf{H_{i}}x_{s}+\mathbf{n_{Bi}},for\,i=1,2,\ldots,\,I, \tag{3}\]  
\[y_{Ej}=\mathbf{G_{j}}x_{s}+\mathbf{n_{Ej}}\,for\,j=1,2,\ldots,\,J, \tag{4}\]  
where \(x_{s}\) is the \(n_{A}\times 1\) transmitted signal for the confidential information with a covariance matrix of \(Q_{X}=E\big{[}x_{s}x_{s}^{H}\big{]}\) for \(Q_{X}>0\). \(y_{Bi}\) and \(y_{Ej}\) are the signals that are received by the \(i^{th}\) intended user and \(j^{th}\) eavesdropper, respectively. \(\mathbf{n_{Bi}}\) and \(\mathbf{n_{Ej}}\) are the complex noise vectors at Bob and Eve, respectively. \(\mathbf{H_{i}}\in\mathbb{C}^{N_{Bi}\times N_{A}}\) and \(\mathbf{G_{j}}\in\mathbb{C}^{N_{Ej}\times N_{A}}\) are the matrices for channels  
Figure 2: A system model for a MIMO wiretap channel.  
\(\mathbf{H}\) and \(\mathbf{G}\) from Alice to the intended receiver \(i\) and unintended user \(j\), respectively. Compared to the traditional MIMO channel system, the design techniques for secure transmission over broadcast channels are somewhat complicated. This is because the information leakages to eavesdroppers and the inter-user interference need to be mitigated simultaneously.
# Dynamic Behavior Analysis of I-Shaped RC Beams under Combined Blast and Impact Loads
## 4 Numerical Results of I-Shaped RC Beams under Combined Loads
### Influences of Different Beam Depths  
Taking the beam depth as another structural parameter, the dynamic response and failure behavior of the structure under combined loads were studied under different beam depths. The explosion proportional distance was set to 0.32 m/kg\({}^{1/3}\) and the impact velocity to 6 m/s. When the depth of the I-shaped RC beam was 0.25 m, 0.3 m and 0.35 m, the damage states of the structure and mid-span displacements were numerically calculated. Figure 11 shows the damage states of beams under combined loads of different beam depths. From the numerical results, it can be seen that with the increase in the beam depth, the damage degree of the concrete in the direction of the beam depth decreased, and  
Figure 10: Time-history curves of mid-span displacement of beams under different beam spans.  
Figure 9: Damage states of beams under combined loads of different beam spans.  
the damage changes of the structure under the impact-blast load were more obvious. This is because when the beam depth is small, the distance between the concrete compression zone of the top plate and the concrete tension zone of the bottom plate decreases, and the overall bearing capacity of the structure decreases. However, the overall concrete damage range of the structure did not increase significantly. Figure 12 shows the time histories of the midspan displacement of the beam at different depths. It can be seen that as the beam depth increases, the maximum mid-span displacement decreases. When the structure was subjected to impact-blast loads, the influence of different beam depths on the maximum mid-span displacement was more sensitive than when the structure was subjected to blast-impact loads.
# A Novel and Adaptive Angle Diversity-Based Receiver for 6G Underground Mining VLC Systems
## 4 Results and Discussion
### Bit Error Rate  
An important metric that evaluates the complete UM-VLC system performance including the proposed solution is the BER. By using this metric, we can find the number of bits that have been altered either by the effects of the UM environment or by inefficiency in receiving solutions. This evaluation is carried out using Monte Carlo simulations and by considering the LED transmission based on the PHY-I mode with the On-Off Keying (OOK) modulation [7; 28].  
Figure 5 shows the BER curves obtained for the hemidodecahedral solution with adaptive orientation PDs and the ADRs of the state of the art in the proposed UM-VLC scenario. It is important to note that, like the other metrics previously analyzed, the curve that describes the BER of the solution presented in this article is the one that presents the best performance. Indeed, we can verify that when compared to the pyramidal ADR, its performance based on BER improves notably. This gain is due to the orientation and positioning efficiency of the PDs in the hemidodecahedral ADR due to the implemented methodology. This result would also allow us to deduce that the proposed solution mitigates to a certain extent the harmful effects of the UM-VLC environment, which drastically degraded the BER. Furthermore, given the automatic oriented granularity of the PDs in the ADR, the ICI produced is mitigated, since the interference caused by LEDs whose received power is not optimal is minimized.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**State-of-the-Art Solutions** & **Hemidodecahedral ADR with PDs with Adaptive Orientation** \\ \hline \hline Hemidodecahedral ADR with PDs fixed & 61.1\% \\ \hline Hemidodecahedral ADR with the ToA method & 50.4\% \\ \hline Pyramid ADR with the ToA method & 101.3\% \\ \hline Pyramid ADR with the PDs fixed & 160.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Percentage improvement in the user data rate of the proposed solution compared to state-of-the-art solutions.  
Figure 5: BER curves for the hemidodecahedral ADR with adaptive orientation PDs and the state-of-the-art ADRs architectures.
# Sensor-Based Solid Waste Handling Systems: A Survey
## 4 Wireless Sensor Networks Based Solid Waste Management Systems  
A Wireless Sensor Network (WSN) is a network of a large number of wireless sensors that are installed ad hoc to monitor the physical or environmental parameters of the system. Sensor nodes with an onboard CPU are used in WSN to monitor the environment in a specific area. The sensor nodes are connected to a central node called the coordinator node and all the coordinator nodes are connected to the base station, which serves as WSN's processing unit. The base station is connected to the internet to share data. WSN already showcased its applications in various domains such as home automation, environmental monitoring, agriculture, industry, waste management, health, and fitness monitoring. Among the aforementioned applications, WSN presents a key role in designing and deploying real-time solid waste management systems. Some of the main characteristics of WSN based monitoring systems are as follows:  
* Capacity for dealing with node failures;
* Optimal for nodes equipped with batteries;
* Nodes mobility and heterogeneity;
* Scalability to a vast distribution scale.
# Blockchain-Assisted Adaptive Reconfiguration Method for Trusted UAV Network
## 7 Conclusions  
This solution combines the operational process of the blockchain system with the dynamic update of the UAV network routing, where the evaluation of the data forwarding behavior of nodes to their respective neighboring nodes during intra-zone routing is combined with the list of neighboring nodes to construct node local state transactions. The periodically elected central node phase of the zone is used by the upper layer network to reach consensus on the local state of the network's prime nodes. The consensus results are used to perform a global trust assessment of the nodes, obtain new zone centers through clustering, generate new blocks, update the blockchain and then reconstruct the upper layer network. The tasking process of the UAV network is also a recording process of the blockchain system for node state migration and the continuous reconfiguration of the UAV network, with the aim of identifying and isolating untrustworthy nodes in a timely manner and maintaining the overall performance of the UAV network during the tasking process, which has also been proven to be effective in simulation experiments.  
Conceptualization, L.K.; Data curation, L.K.; Investigation, F.H.; Project administration, B.C.; Writing--original draft, L.K.; Writing--review & editing, F.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key Research and Development Program of China, under Grant 2019 YFB2102002; in part by the National Natural Science Foundation of China, under Grant 62176122, 62001217; in part by A3 Foresight Program of NSFC, under Grant No. 62061146002.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 8: Comparison of energy consumption in a consensus process with different numbers of malicious nodes.
## 2 Case Study: Turbine Models and Experiments
### Micro-Generation VAWTs  
The first class of VAWTs used in this work is composed by two prototypes representative of real-scale turbines for micro-generation (\(P_{max}<1\) kW) featuring the same cross-section and the same blade profile but different rotor shape, namely H-shape rotor and Troposkein. The straight H-shape VAWT consists of three unstaggered symmetric NACA0021 blades, each one connected to the hub through two spokes. Representation of the turbine configuration along with the coordinate system and triangle of attack notation are depicted in Figure 1a and Figure 1b. Troposkein rotor exhibits hub-to-hub blades, without supporting struts and hence offering a lower aerodynamic drag with respect to the H-shape design (at least for small-scale VAWTs). The main geometrical characteristics of the considered turbines are reported in Tables 1 and 2. The rotor solidity reported in Table 2 is calculated according to Sandia Laboratories definition [1], resulting higher than that of the H-shape rotor (even though the solidity evaluated locally at the equatorial plane is comparable to that of the H-shape rotor).  
The performance and wake shed by the turbines were experimentally characterized in the large-scale wind tunnel GVPM (Grande galleria del Vento del Politecnico di Milano [30]) in free-jet configuration, featured by a square cross sectional area of about 16 m\({}^{2}\) with the rotor placed in the center of the jet. The application of dedicated correlations [31] indicates a negligible free-blockage. The incoming flow features very low turbulence intensity (below 1%). Performance and load measurements were obtained by combining angular speed, torque and force measurements on the shaft of the turbine; velocity and turbulence measurements in the wake were also performed by traversing multiple hot wires, even though they are not used in this study due to the inherent limitations of BEM methods in reproducing the wake [18].  
\begin{table}
\begin{tabular}{l c} \hline Blades & 3 \\ Blade height & 1.460 m \\ Rotor diameter & 1.030 m \\ Solidity & 0.25 \\ Chord & 0.086 m \\ Blade airfoil & NACA0021 \\ Swept area & 1.5 m\({}^{2}\) \\ \hline \end{tabular}
\end{table}
Table 1: H-shape geometric parameters.  
\begin{table}
\begin{tabular}{l c} \hline Blades & 3 \\ Blade height & 1.510 m \\ Rotor diameter & 1.510 m \\ Solidity & 0.38 \\ Chord & 0.086 m \\ Blade airfoil & NACA0021 \\ Swept area & 1.5 m\({}^{2}\) \\ \hline \end{tabular}
\end{table}
Table 2: Troposkein geometric parameters.  
Full details on the geometrical features, on the installation in the GVPM, and on the performance/load data of the two turbines can be found in [12].
# Denoising Generalization Performance of Channel Estimation in Multipath Time-Varying OFDM Systems
## 1 Introduction
* Through extensive simulations, we verify the performance advantages of the proposed cascade algorithm over traditional algorithms and a single denoising algorithm. Then we evaluate the performance impact of the proposed algorithm trained on fixed SNR, channel model, and velocity, and tested on other SNR, channel model, and velocity points, to verify the adaptability of the proposed algorithm for real time-varying multipath channels. The remainder of this paper is organized as follows. The related work on DL-based OFDM channel estimation are presented in Section 2. The wireless OFDM communication system model, traditional channel estimation algorithm, and denoising network DnCNN are introduced in Section 3. The proposed denoising channel estimator is illustrated in Section 4. The simulation results are discussed in Section 5. Section 6 concludes this paper.
## 4 Spinning Experiment and Analysis
### Comparison Analysis  
To yield comparative data, the distribution of the sampling points on the workpiece is consistent with that of the post-processing of the finite element simulation software, and then the coordinate values of these marked sampling points were measured by a ZEISS MMZ-B203015 coordinate measuring machine (CMM). Its length measurement error is 4.5+L/300 (ISO10360-2:2009). The measuring process of CMM is illustrated in Figure 13. The straightness of the outer surface of the cone of 17 groups of experimental pieces was obtained by measurement and calculation. The data curve of the comparison between the experimental results and the simulation results is depicted in Figure 14.  
Figure 14: Comparison of experimental and simulation results of straightness tolerance.  
Figure 12: Experiment and simulation parts.  
It can be concluded from Figure 14 that the experimental results and simulation reuslts of the straightness tolerance of the cone outer surface possess the same change trend, with a small numerical error whereby the maximum error occurred in group 11. The experimental result of the straightness tolerance of the cone outer surface was 1.52 mm, and the simulation result was 1.37 mm, with an error within 10%; the minimum error occurred in group 17. The experimental result of the straightness tolerance of the cone outer surface was 0.155 mm, the simulation result was 0.15 mm, and the minimum error was 3%. The error of straightness tolerance between finite element simulation and spinning experiment was between 3-10%. The comparative analysis of simulation and experimental data demonstrates that the optimal combination of process parameters for the straightness of the cone outer surface has reference significance. The results of spinning experiments verify the reliability of the simulation of power spinning of variable-section conical spun parts.