esa-sceva/satcom-chunk-collection huggingface dataset:
# The Role of PIXE and XRF in Heritage Science:
## 8 The MACHINA Project  
The strong demand of scientific analysis for conservation and preservation of works of art led to the project of an instrument with the same performance as the standard IBA techniques (effectiveness and sensitivity, non-invasiveness and non-destructiveness), with the additional features of transportability, lightness, and low power consumption. To answer the query, the INFN-CHNet group, in collaboration with the Conseil Europeen pour la Recherce Nucleaire (CERN) and the Opificio delle Pietre Dure (OPD) in Florence, a world-renowned conservation institution, has started the MACHINA project [62], an accelerator of about 600 kg weight, with a footprint of about 2.5 m \(\times\) 1.6 m, services included, and a power consumption of a few kW. It will produce 2 MeV proton beams thanks to a RadioFrequency source and an high-frequency RFQ accelerating part. The RF source produces 20 keV protons that will be delivered into the Low Energy Beam Transport (LEBT) line, which consists of a focusing stage and a diagnostic station. The beam will then be injected in the HF-RFQ, where it is focused and accelerated to the final energy of 2 MeV. A permanent magnet quadrupole doublet focusing system and a second diagnostic station will be installed downstream before the beam extraction in air. Moreover, it will be possible to insert two beam energy degraders to lower the proton beam energy.  
Figure 9: Visible and elemental maps of the two areas of the parchment scanned.  
The detection system of MACHINA has been designed to hold two SDD detectors for PIXE analysis, each optimised for the acquisition of different X-ray ranges, and one CdTe detector for Particle Induced Gamma-ray emission (PIGE) analysis. Another SDD detector will monitor the beam current detecting the X-rays emitted by the exit window. As underlined in the article [27], with the expertise of the INFN-CHNet group with detector systems, other techniques can be added to the ones already planned.
## 1 Introduction
In addition, the most interesting areas for harnessing the wave resource power are the Galician coast, as it represents the area with the greatest potential in Spain, with maximum values above 35 kW/m, and the Cantabrian coast, with an average of maximum values around 30 kW/m [4,5]. Figure 1: Wave power distribution, in kW/m (Reproduced with permission from [6,7]). Given the previously described situation, the construction of a wave facility in the north coast of Spain is proposed, taking advantage of the wave energy converters that are currently available on the market and best addresses the coastal needs and characteristics. On the other hand, it is possible to take advantage of existing maritime structures to install devices for harnessing renewable energies. The north coastal facade in Spain has some of the great breakwaters built in the country [9]. The research included in the present paper analyzes the way of using existing breakwaters not only for the shelter of the vessels in their typically port function, but also as the main structure to incorporate wave energy converters. Figure 2: Annual average wave power, in kW/m: (**a**) In the Iberian Peninsula and (**b**) Canary Island coast (Reproduced with permission from [8]). The analysis is focused on one of the biggest important breakwaters in that coastal facade: Punta Langosteira (Outer Port of A Coruna), Dique Torres (Port el Musel, Gijon), Dique Norte (Port el Musel, Gijon) and Punta Lucero (Port of Bilbao). In this way, it is possible to strengthen the image of the \"Green Port\" in the Spanish port system. To make the decision of the most suitable location, different aspects are considered, such as the point provided with the highest energy potential, the constructive viability, the fact of not being an obstacle for the maneuverability of vessels approaching the port and being built in a temporary window of one year. Therefore, the objective of the research included in the present manuscript is the location analysis for the installation of a wave power plant in the notorious dikes on the north coast of Spain, studying aspects as significant as the energy potential of location, the feasibility of the construction process and the economic viability of the construction project. To achieve that, the content of the paper is as follows: (a) Methods, (b) Alternatives description, (c) Design basis, (d) Hydraulic and energetic behavior, (e) Structural behavior, (f) Constructive viability, (g) Evaluation of alternatives, (h) Economic viability and (i) Conclusions.
# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 6 Exemplar Workflow: Partially Automating Object Detection with MLOps Tools
### Fully Automating the MLOps Workflow  
In the workflow depicted in Figure 3, rolling back to the previous version of models is done manually. Furthermore, as the final product is a single model and not a pipeline, the model's continuous monitoring and continual learning are not performed. Furthermore, there are no options for model governance nor the implementation of a network layer or an orchestrator.  
Additionally, a monitoring system is required to ensure a fast feedback loop for solving bugs and other problems that may be encountered in production. Some of the primary metrics for monitoring are listed in Tables 2-4. Once a fault is found, then an action policy is implemented. The action policy decides what changes should occur based on the alerts generated by the monitoring system. For example, when drift is detected, then the system has to calculate the ideal time of deploying a new model. All of these decisions are made based on the thresholds set by the domain expert, which should be highly related to the monitoring platform's alerting mechanism.  
As an organization gets more comfortable with data science, more focus is given on containerization and full automation of ML workflows in addition to automation of CI/CD pipelines. This is also referred to as the final level of automation (MLOps Level 2) [33]. With this step, multiple pipelines can be deployed as a single entity rather than an individual model. These pipelines can be versioned. With containerization, container orchestrators like Kubernetes come into play. These all are important for managing the evolving workflow. For scalability and complete automation of MLOps workflow containers, orchestrators and resource managers form together an essential toolchain. With these tools, continuous and scalable ML pipelines are achieved.
## 3 Results and Discussion
### Crushing Strength  
Figure 2 shows the average measurements taken for different samples of each carrier (fresh, reduced, oxidized). The materials OXMN009 and FEMA011 have a lower crushing strength, greater than 2N in all cases. This is the limit value, according to the literature [55], for use as an oxygen carrier in a CLC system without having to undergo a calcination process to improve its mechanical properties. Additionally, when comparing the crushing strength of the oxidized and reduced phases, no significant differences were observed for any of the residues.  
An increase in the resistance of materials FEMA004, ILME007, and CRSI003, with respect to fresh material, was observed, which can be associated with the effect of the operating temperatures (950-1000 \({}^{\circ}\)C) in the batch fluidized bed reactor. This is close to the calcination temperature (1100 \({}^{\circ}\)C) proposed by the literature [55]. Although all materials were subjected to the same operating temperatures, the same behavior was not observed for all residues, since the crushing strength of the OXMN009 material remained more or less constant, while the FEMA011 material was the only one that showed a decrease of that property.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Waste from OC** & **Reducing Gas** & **Operation Time (h)** & **Attrition Rate (\%/h)** \\ \hline CRSI003 & H\({}_{2}\) & 8.3 & 0.0027 \\ FEMA004 & H\({}_{2}\) & 7.8 & 0.0074 \\ FEMA011 & H\({}_{2}\) & 7.3 & 0.2007 \\ FEMA011 & CH\({}_{4}\) & 3.7 & 0.4156 \\ ILME007 & H\({}_{2}\) & 6.5 & 0.0008 \\ OXMN009 & H\({}_{2}\) & 4.1 & 0.0653 \\ OXMN010A & H\({}_{2}\) & 9.1 & 0.0059 \\ OXMN010A & CH\({}_{4}\) & 4.5 & 0.1506 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Discontinuous batch fluidized bed (bFB) operating times to obtain waste and attrition.
## 5 Conclusions  
To tackle the EUA problem in a variety of environments, we propose an attention-based multiagent reinforcement learning (AMARL) algorithm, which can well adapt to the dynamic environment and learn in the unknown environment, and jointly consider user allocation and bitrate selection in the reward function, to achieve the long-term QoE guarantee of users, and we applied the attention mechanism to the critic to reflect the influence of different agents. Then, we conducted experiments through scenario simulations, and our experiments showed that our approach significantly outperformed the Pensieve-based random and greedy policies. The algorithm could analyze the optimal allocation scheme for edge servers in real time in complex and variable environments to maximize the QoE for users. Our future direction is to realistically simulate the process of video transmission from the edge server and to specify the edge server resources beyond the simple number of connections. At the same time, we would also consider more influencing factors, such as security and network dropouts.  
**Author Contributions:** Conceptualization, X.T. and F.C.; methodology, X.T.; validation, Y.H.; writing--original draft preparation, X.T. and Y.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was funded by National Natural Science Foundation of China (61602214).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare that there are no conflict of interest regarding the publication of this paper.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Number of Agents** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline AMARL & 3 & 3 & 4 & 4 & 5 & 5 \\ Greedy strategy & 3 & 4 & 5 & 6 & 6 & 7 \\ Random strategy & 4 & 5 & 6 & 7 & 7 & 8 \\ Step by step & 3 & 4 & 4 & 5 & 5 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Number of servers used.  
Figure 12: Parameter variation trend as the number of users changes: (**a**) QoE, (**b**) bitrate, and (**c**) smoothness.
## 3 Results and Discussion
### Volatile Compound Profile  
The volatile compounds presented in _E. billardieri_ and _E. planum_ are shown in Table 5. The predominant volatile compounds found in EBF and EBT were sesquicineole (24.32% and 25.03%), spatelenol (20.69% and 14.37%), trans-chrysanthenyl acetate (11.95% and 11.84%), mesitylene (2.44% and 4.95%), \(\beta\)-elemene (0.62% and 1.88%), cyanophyllene (0.43% and 1.58%), and (-)-carvone (1.55% and 0.76%). The results indicated that the contents of sesquicineole, mesitylene, \(\beta\)-elemene, and carvophyllene in EBF were higher than those of EBT. \begin{table}
\begin{tabular}{l c c c c} \hline  & \multicolumn{2}{c}{_E. billardieri_} & \multicolumn{2}{c}{_E. planum_} \\ \cline{2-5}
**Mineral** & \multicolumn{2}{c}{**EBT**} & **EBF** & **EPT** & **EPF** \\ \hline \multicolumn{5}{c}{Macro (\%)} \\ Ca & 0.579 \(\pm\) 0.017 b & 1.727 \(\pm\) 0.071 a & 0.835 \(\pm\) 0.040 b & 1.983 \(\pm\) 0.190 a \\ K & 1.386 \(\pm\) 0.073 d & 2.009 \(\pm\) 0.037 c & 3.229 \(\pm\) 0.010 a & 2.918 \(\pm\) 0.020 b \\ Mg & 0.238 \(\pm\) 0.012 d & 0.389 \(\pm\) 0.002 a & 0.318 \(\pm\) 0.010 b & 0.278 \(\pm\) 0.020 c \\ Na & 0.003 \(\pm\) 0.001 b & 0.005 \(\pm\) 0.001 b & 0.012 \(\pm\) 0.010 a & 0.010 \(\pm\) 0.001 a \\ P & 0.135 \(\pm\) 0.014 d & 0.249 \(\pm\) 0.005 c & 0.459 \(\pm\) 0.001 a & 0.382 \(\pm\) 0.001 b \\ \multicolumn{5}{c}{Micro (ppm)} \\ As & 0.14 \(\pm\) 0.01 c & 0.23 \(\pm\) 0.01 b & 0.17 \(\pm\) 0.20 c & 0.30 \(\pm\) 0.01 a \\ B & 10.95 \(\pm\) 1.58 c & 16.94 \(\pm\) 0.20 b & 37.25 \(\pm\) 1.00 a & 36.05 \(\pm\) 1.11 a \\ Cd & 0.01 \(\pm\) 0.01 a & 0.03 \(\pm\) 0.01 a & 0.061 \(\pm\) 0.001 a & 0.03 \(\pm\) 0.01 a \\ Cu & 3.32 \(\pm\) 0.25 c & 4.54 \(\pm\) 0.18 b & 9.37 \(\pm\) 0.44 a & 9.99 \(\pm\) 0.47 a \\ Fe & 32.35 \(\pm\) 2.03 d & 178.15 \(\pm\) 3.64 b & 95.52 \(\pm\) 3.21 c & 363.57 \(\pm\) 6.16 a \\ Mn & 34.49 \(\pm\) 2.06 c & 46.97 \(\pm\) 0.68 b & 142.47 \(\pm\) 5.01 a & 148.89 \(\pm\) 3.23 a \\ Pb & 0.32 \(\pm\) 0.04 c & 0.56 \(\pm\) 0.01 b & 0.55 \(\pm\) 0.14 b & 0.97 \(\pm\) 0.02 a \\ Se & 0.41 \(\pm\) 0.02 a & 0.63 \(\pm\) 0.18 a & 0.69 \(\pm\) 0.20 a & 0.51 \(\pm\) 0.05 a \\ Zn & 10.27 \(\pm\) 1.06 c & 18.87 \(\pm\) 0.26 b & 26.75 \(\pm\) 0.73 a & 28.51 \(\pm\) 2.49 a \\ \hline \end{tabular}  
* Values are mean \(\pm\) standard deviation (n = 3). Different lowercase letters in the same row are significantly different (\(p<\) 0.05); EBF: _E. billardieri_ flowers; EBT: _E. billardieri_ thorps; EFP: _E. planum_ thorps.  
\end{table} 
Table 4: Elemental analysis of the two studied plants *.
## 4 Model Results
### Performance Analysis
Additionally, the R and R2 values for both datasets suggest that the algorithm ability to capture the variability of the data is good. When considering the entire dataset, the M5P algorithm exhibited a total MAE of 48.0491, RMSE of 76.1038, R of 0.9849, and R2 of 0.9700. These values are within an acceptable range, indicating that the developed algorithm performs well in predicting the values of interest. To gain a deeper understanding of the M5P model accuracy and effectiveness, Figure 6 provides a comparison between predicted and experimental punching shear strength values. This comprehensive visual representation includes data from both the training and testing datasets, allowing for a more thorough evaluation of the model performance from other alternative methods. Also, the cross-validation method was applied on our model to evaluate its robustness across different subsets of the data. The data were divided into five folds, with each fold serving as a test set once while the remaining folds served as the training set. The performance was measured using three key metrics: R2, RMSE, and MAE. The results can be seen in Table 3.  
Our model demonstrated consistent performance across all folds with average R2, RMSE, and MAE values of 0.9517, 90.85, and 55.32, respectively. The R2 values range from 0.9410 to 0.9697, indicating that our model explains between 94.1% and 96.97% of the variance in the dependent variable, which is a strong indication of the model's  
Figure 6: Comparison of the predictions generated using the M5P-tree-based model with the experimental measurements of punching shear strength for both training and testing data. predictive power. The average R\({}^{2}\) of 0.9517 suggests that the model, on average, explains approximately 95.17% of the variance in the dependent variable across the folds. RMSE and MAE are measures of prediction error. In our cross-validation, the RMSE varied between 56.00 and 125.16, with an average of 90.85. The MAE ranged between 38.75 and 66.28 with an average of 55.32. The standard deviations (SD) of R\({}^{2}\), RMSE, and MAE across folds were 0.0121, 25.20, and 10.84, respectively, indicating a reasonable consistency in performance across folds. In conclusion, regarding the consistency of performance metrics across cross-validation folds and the comparable results from the train, the test split indicates that our model is robust and generalizable across different data subsets.
# Efficient Key-Value Data Placement for ZNS SSD
## 5 Implementation  
The proposed method was implemented on the RocksDB file system plugin, ZenFS. When modifying the zone allocation method of ZenFS, the proposed method was developed based on the lifetime of each zonefile. Furthermore, the original erase method did not fully utilize the ZNS space. Therefore, by modifying the original erase scheme, we added the garbage collection logic for the key-value store in ZNS SSD to utilize the ZNS space more efficiently. Additionally, the garbage collection thread was implemented to execute separately from the main thread.  
As stated by the NVMe 1.4 standard [24], the ZNS interface restricts the number of active and open zones. These values are defined by the vendors. In ZenFS, active zones indicate zones where write is being processed in the zonefile. Open zones indicate all the zones that do not have a closed state. For this reason, all active zones must be open. Therefore, the developer must take care of those values.  
If the garbage collection thread timer is too short, it produces unnecessary overhead by checking the garbage collection routine. Otherwise, if the garbage collection thread timer is too long, empty zones may be exhausted too quickly before garbage is collected. Therefore, the developer must set the thread wake timer to the appropriate value. The proposed method uses the 1000 ms defined in the host-level FTL [25] for open-channel SSD to wake the garbage collection thread.  
To copy the valid data, we require the information about all the zonefiles contained in the victim zone. Therefore, it is required that each zone has information about all the zonefiles contained in it. However, finding the extent associated with the specific zone in the zonefile incurs overhead. Therefore, the proposed method adds the zonefile and extent mapping table to a zone and can find the extent in the zonefile faster. It was found that the space overhead for adding zonefile and extent mapping table to each zone was negligible. For example, our implementations showed that the space overhead, in the worst-case scenario, used 768 bytes and, in the average case, used 118 bytes. Assuming that the disk has a number of zones of 512, to maintain the whole zone, the mapping table needs an average space of around 0.5 KiB and, in the worst-case scenario, it needs to be around 384 KiB. This is a negligible size in modern memory.
## 4 Experiments
Additionally, the main lobe of the STC was significantly narrower than the no-MF and approximate to that of the MTC for both the three-section and five-section STCs; as the number of sections in STC increases, the main lobe becomes much narrower. Although the proposed STC can realize MF by narrowing the main lobe, the overall magnetic field intensity is reduced because the N-sections excite the magnetic field in the opposite direction. In other words, the STC is a tradeoff that sacrifices magnetic field intensity for a narrower main lobe. To clarify the tradeoff relationship, we can use the peak value of the main lobe \(p_{\text{m}}\) to characterize the total magnetic field intensity. Figure 9 compares the maximum FoMs that can be achieved by STC- and MTC-based probes and their corresponding \(w_{r}\) values. The maximum current of the transmitting coil was set to 1 A, and the total number of turns was 300 for all probes. The three- and five-section STCs were designed according to the optimal parameters given in Figures 4 and 6, respectively. From Figure 9, it can be observed that increasing \(p_{\text{m}}\) clearly decreased the MF effect with respect to FoM and \(w_{\text{r}}\). Note that \(p_{\text{m}}\) was a constant value to ensure that the eddy current field was sufficiently large enough to meet detection range and SNR requirements. Setting \(p_{\text{m}}\) higher is not required because the casing is not very large, and setting \(p_{\text{m}}\) lower would not result in an acceptable SNR. Specifically, when \(p_{\text{m}}\) = 0, the STC- and MTC-based probes achieved their maximum FoM values regardless of the magnetic field intensity. The FoM and \(w_{\text{r}}\) values of both STCs nearly reached that of the MTC. In addition, the FoM and \(w_{\text{r}}\) values of both the three- and five-section STCs remained unchanged, with increasing \(p_{\text{m}}\) until \(p_{\text{m}}\) = 0.289 \(\times\) 10\({}^{-3}\) T and 1.592 \(\times\) 10\({}^{-3}\) T, respectively. The five-section STC had a higher FoM than the three-section STC at the cost of a reduced magnetic field intensity. However, when \(p_{\text{m}}\) > 1.592 \(\times\) 10\({}^{-3}\) T, the two STCs had very similar FoMs because most of the coil turns were allocated to the middle section. In most cases shown in Figure 9, the FoMs of the STC nearly reached that of the MTC-based probe. At \(p_{\text{m}}\) = 2.75 \(\times\) 10\({}^{-3}\) T,  
Figure 8: Experimental and simulation results for MF with the five-section STC. the FoMs of the STCs and MTC-based probes were equal because the N-section had zero turns to maximize \(p_{\text{m}}\). In general, the MF performance of STC with a single transmitter and a simplified single electrical circuit was close to that of the MTC, which can reach the needed sensitivity for the detection of submillimeter defects, and is more suitable for harsh downhole environments with limited space.
# A Dyna-Q-Based Solution for UAV Networks Against Smart Jamming Attacks
## 6 Discussion and Conclusions  
In this paper, we propose a dual-controller cooperative SDN-based UAV network wireless communication scheme and design a Dyna-Q-based reinforcement learning algorithm using power allocation and track planning collaborative optimization against smart jamming. The proposed Dyna-Q algorithm has faster convergence speed and more stable performance than other three algorithms. Researchers have applied many algorithms, such as WoLF-PHC, Q-learning, DQN, and fast-DQN, to study the interactions between smart attackers and smart defenders. The DQN and fast-DQN algorithms belong to deep reinforcement learning algorithms, which need a large amount of calculation resources and have high energy consumption. Although these kinds of algorithms can reach higher performance, it is neither economical nor realistic to deploy such a large amount of calculation resources on the UAV platform. WoLF-PHC is a practically decentralized learning algorithm. It is a simple and practical algorithm for mixed-strategies learning. It does not need to know the recent behaviors of the agent and the current strategy of the opponent. However, the algorithm does not prove that it can converge to the Nash equilibrium strategy, and the stability of the algorithm is insufficient. The Q-learning algorithm is the most basic model-free reinforcement learning algorithm. The temporal-difference (TD) learning idea is the basis of many reinforcement learning algorithms. However, the UAV network has too much action space. The Q-learning algorithm can only update one Q value in an episode, and the learning efficiency is low. The method is not based on any model, which makes the SDN-based UAV network unable to use its cooperative sensing ability. The Dyna-Q algorithm is often overlooked because its environment model is difficult to build. The SDN-based UAV network has a large number of sensors and its state information can quickly converge to the network controller, which can be used to build an environment model. Therefore, the Dyna-Q-based reinforcement learning algorithm is more suitable to solve the smart defense problem of UAV networks.  
Writing--original draft preparation, Z.L.; supervision, Y.L. ([PERSON]); data curation, Y.S.; validation, Z.W., W.Q., Y.L. ([PERSON]).  
This research received no external funding.  
The authors declare no conflict of interest.
# Ambient LED Light Noise Reduction Using Adaptive Differential Equalization in Li-Fi Wireless Link
## 2 Ambient Light Noise Model and Adaptive Differential Equalization  
It is important to implement mathematical modeling of both the ambient light noise and the proposed technique to theoretically verify that the proposed technique compensates for the deterioration of the transmission performance caused by the interference noise generated by the ambient light source. Section 2.1 describes the time-varying equations of the interference noise generated by the two light sources (fluorescent lamp and dimmable LED) mentioned in the introduction, and the proposed technique explains the noise-reduction process mathematically in Section 2.2.