esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment
### Verification on Experimental Phase II of the SHM Benchmark Data
#### 3.2.2 Experiment Setup  
As previously stated, 12 accelerometers were employed in training. From [29], we can know that the acceleration measurements were captured at 200 Hz in all cases. The vibration response was measured for 300 s in Cases 1 through 5, 227.84 s in Case 6, and 900 s in the other cases.  
The data gathered for the whole structure (Case 1) and the data collected while the structure was severely damaged (Case 9) were used for training in this experiment. Following Section 2.1.4, using \(n_{s}=128\) samples as the frame length, each of Case 1's 12 undamaged signals was divided into \(n_{u}=468\) frames, while Case 9's 12 entirely damaged signals were separated into \(n_{d}=1404\) frames. Overall, 70% of the data should be used for training and 30% for testing, and 328 healthy samples and 983 damaged samples were used for training for each accelerometer. The model configuration of this experiment is the same as the model configuration in Section 3.1.2.  
To test the trained model and reflect the health status of the structure more intuitively, the data collected in 9 cases is decomposed into samples according to Section 2.1.4, then input processed data to the corresponding trained model. The model will output an anomaly score for the current sample, reflecting the likelihood of damage to the entire structure. Anomaly scores for all cases are shown in Table 3. All experiments were performed 5 times.  
Figure 11: The acceleration response of the 10 th accelerometer from Case \(1\) to \(4\). (**a–d**) represent Case \(1\) to \(4\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline ID & Location & Orientation & Case1 & Case2 & Case3 & Case4 & Case5 & Case6 & Case7 & Case8 & Case9 \\ \hline
**1** & 1F/West & N/S & 1.080 & 1.533 & 1.823 & 2.684 & 2.733 & 2.822 & 2.722 & 3.464 & 5.179 \\
**2** & 1F/Center & E/W & 0.108 & 0.106 & 0.104 & 0.096 & 0.970 & 0.980 & 0.111 & 0.113 & 0.113 \\
**3** & 1F/East & N/S & 1.07 & 1.151 & 1.392 & 1.695 & 1.878 & 1.967 & 2.168 & 3.313 & 4.601 \\
**4** & 2F/West & N/S & 1.080 & 1.217 & 1.727 & 1.789 & 1.842 & 1.878 & 2.558 & 3.288 & 5.438 \\
**5** & 2F/Center & E/W & \(-0.299\) & \(-0.289\) & \(-0.296\) & \(-0.306\) & \(-0.303\) & \(-0.302\) & \(-0.291\) & \(-0.295\) & \(-0.291\) \\
**6** & 2F/East & N/S & 1.132 & 1.820 & 1.847 & 1.858 & 1.892 & 1.911 & 2.984 & 3.782 & 5.475 \\
**7** & 3F/West & N/S & 1.135 & 1.411 & 1.572 & 1.882 & 2.008 & 2.414 & 2.469 & 3.147 & 4.407 \\
**8** & 3F/Center & E/W & 1.120 & 2.189 & 2.226 & 2.289 & 2.324 & 2.345 & 4.068 & 4.364 & 4.509 \\
**9** & 3F/East & N/S & 1.142 & 1.534 & 1.668 & 1.830 & 1.853 & 2.332 & 3.155 & 3.765 & 4.037 \\
**10** & 4F/West & N/S & 1.090 & 1.406 & 1.756 & 1.779 & 2.094 & 2.253 & 2.242 & 2.579 & 4.422 \\
**11** & 4F/Center & E/W & 0.185 & 0.174 & 0.167 & 0.1
## 4 Conclusions  
An improved analytical procedure has been developed and validated for the determination of trace amount of fluorine in biological samples using the molecular absorption of the GaF molecule. Modifiers and molecule-forming agents were applied in combined mode, both via thermal deposition in graphite furnace and addition to the sample. Thus, the calibration against aqueous standard solutions was possible. By using the complex matrix mixture, it was possible to enhance the pyrolysis temperature, which provides more efficient removal of matrix components. The commonly used ruthenium(III) nitrosyl nitrate was substituted by RuCl\({}_{3}\) in 20% HCl. The sensitivity and the limit of detection obtained are comparable with other reports that applied HR-CS GF MAS, including the one developed in our laboratory. The validated analytical protocol allows to determine low amount of fluorine directly after acid digestion of biological samples, which was impossible using other methods.  
Conceptualization, E.B.; methodology, A.G., A.T., A.R. and E.B., software, A.G. and A.T.; validation A.G., A.T. and A.R.; formal analysis, A.G. and A.T.; investigation, A.G., A.T., A.R. and E.B.; resources, A.T. and E.B.; data curation, A.G. and A.T.; writing--original draft preparation, A.G., A.T., A.R. and E.B.; writing--review and editing, A.G., A.T., A.R. and E.B.; visualization, A.G. and A.T. and supervision, E.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**CRM** & **Origin** & \begin{tabular}{c} **Certified/Added F** \\ **Content [\(\mu\)g/L]** \\ \end{tabular} & \
\begin{tabular}{c} **Found F** \\ **Content [\(\mu\)g/L]** \\ \end{tabular} &
\begin{tabular}{c} **Recovery** \\ **Factor [\%]** \\ \end{tabular} \\ \hline CRANBERRY-05 & lake water & \(70\pm 26\) & \(72\pm 2\) & 103 \\ ONTARIO-12 & lake water & \(114\pm 24\) & \(106\pm 1\) & 93 \\ NIST 1577c & bovine liver & \(25\)1  & 26.4 \(\pm\) 0.3 & 106 \\ NIST 1577c & bovine liver & \(50\)1  & 50.0 \(\pm\) 1.1 & 100 \\ MODAS-5 & cod tissue & \(25\)1  & 25.7 \(\pm\) 0.7 & 103 \\ MODAS-5 & cod tissue & \(50\)1  & 49.7 \(\pm\) 1.0 & 99 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Recovery factors for the determination of fluorine in certified reference material of waters and spiked experiment in selected biological certified reference material.
# Investigation of Impact Behavior of STS304L Steel Plate Under Cryogenic Temperature
## 3 Results and Discussion
### EBSD Analysis: Volume Fractions of Strain-Induced Martensite
In metastable austenitic stainless steels such as STS304L, the mechanical properties are governed by the austenite phase stability. If adiabatic heating suppresses strain-induced martensitic transformation, it can alter the material's mechanical response. A previous study concluded that at a strain rate of 200/s, adiabatic heating significantly reduced ductility by preventing strain-induced martensitic transformation [30]. Given that the strain rate in this study was higher than 200/s at 20 \({}^{\circ}\)C, the potential effects of adiabatic heating must be considered. When a high strain rate load (\(\sim\)1000/s) is applied to metastable stainless steel, martensite forms rapidly at low strain levels. However, at high strain levels, adiabatic heating suppresses martensitic transformation. Despite similar deformation patterns across all test temperatures, at 20 \({}^{\circ}\)C, the martensite fraction remained nearly constant across all regions (1 to 4). This suggests that adiabatic heating occurred at 20 \({}^{\circ}\)C, preventing further strain-induced martensitic transformation at a strain rate of 225/s. In this study, the lower impact energy absorption ability of STS304L at 20 \({}^{\circ}\)C can be attributed to the suppression of strain-induced martensitic transformation due to adiabatic heating. This means that the difference in the impact energy absorption ability across test temperatures was a result of reduced stored energy from phase transformation at 20 \({}^{\circ}\)C.
## 3 Results and Discussion
### PCA Results of the Aroma Compounds and Fatty Acids  
According to the PCA results of aroma compounds (Figure 1), both oil samples and encapsulated samples were in distinctly different groups during storage. For PCA, 33 variables were used for the NY oil (Figure 1) and 35 variables for the KY oil (Figure 2), and the two principal components explained 92.91% (F1, 83.21%; F2, 9.70%) and 93.54% (F1, 85.12%; F2, 8.41%) of the total variance for NY and KY, respectively. This situation demonstrates that PCA successfully reveals the differences between volatile compound profiles. Microcapsules obtained from the NY variety were characterized by \(p\)-cymene, 2-butyl octanol, \(y\)-terpinene, and benzaldehyde compounds, while microcapsules obtained from the KY variety were characterized by \(p\)-cymene, \(y\)-terpinene, and benzaldehyde compounds. The microcapsules obtained from both oil varieties were clearly separated from the oils. In both oil samples, the samples at 90 days were in different groups from the samples at 0 and 45 days.  
The 18 variables of fatty acids were selected for the PCA, and the explained variance was 71.38% (F1: 41.97%; F2: 29.42%). The application of the PCA algorithm showed four distinct groups (Figure 3). The microcapsules are significantly different from the oils from which they are obtained in terms of their fatty acid profiles. KY (1:4) is characterized by MUFAs and PUFAs, while NY (1:10) is characterized by lignoseric and docosanoic acid, KY (1:1) by Cis-10-heptadecenoik acid, and NY by linoleic acid and MUFAs.  
Figure 1: The scores and factor plane plots for aroma compounds of oil and microencapsules of NY.  
Figure 2: The scores and factor plane plots for aroma compounds of oil and microencapsules of KY.
## 2 Methodology
### Review of GRCFBS Methodology  
The GRCFBS technique provides a robust framework for coupling receptance matrices of subsystems, derived analytically or experimentally through substructuring methods in the frequency domain. Building on the Jettmundsen formulation [27], this method has been further refined and adapted to suit the specific case studies discussed in this paper. These refinements have introduced innovative equations to facilitate the generalized coupling of multiple subsystems, significantly improving the accuracy of dynamic response predictions. Notably, the sequence in which the FRF matrices are assembled is determined by the number of coordinates involved in the coupling, independent of each subsystem's DoFs. This method reduces the complexity of the equations needed to describe the entire reconfigurable dynamic system. Fundamentally, the relationship between force excitation (input) and vibration response (output) for a linear system is expressed as follows:  
\[\left\{u_{p}\right\}=\sum_{q=1}^{N}\left[H_{pq}\right]\left\{F_{q}\right\}. \tag{1}\]  
where \(\left\{u_{p}\right\}\) is the vector of the output response at the p-th generalized coordinate or measurement point, \(\left\{F_{q}\right\}\) is the vector of the input excitation force at the q-th generalized coordinate, and \(\left[H_{pq}\right]\
# PELM: A Deep Learning Model for Early Detection of Pneumonia in Chest Radiography
## 1 Introduction
### Motivation  
Traditional diagnostic approaches rely heavily on manual interpretation, which is prone to delay and variability, particularly in high-volume or low-resource settings. Although CXR remains a crucial tool for pneumonia diagnosis--where the disease typically appears as localized opacities--its effectiveness depends on radiologist experience and image clarity. Deep learning offers an opportunity to mitigate these limitations by learning visual patterns from labeled data, thereby standardizing and accelerating the diagnostic process.  
This study is motivated by the need to enhance the speed, accuracy, and consistency of pneumonia detection. We explore whether recent DL architectures, including hybrid and attention-based models, offer measurable performance gains over prior methods and assess their ability to generalize across a diverse clinical dataset.
# Deployment Protection for Interference of 5G Base Stations with Aeronautical Radio Altimeters
## 2 Methodology
### Integrated Interference Model
#### 2.1.1 5G Base Station Antenna  
Modern 5G base stations commonly utilize beamforming technology, which enhances the directional properties of the beams. There are two types of antennas, namely omnidirectional antennas and array antennas [29]. Array antennas come in various forms, such as 4 \(\times\) 4, 8 \(\times\) 8, and 16 \(\times\) 16. Omnidirectional antennas have equal gain in all directions and lack directional properties. Among the array antennas, a 16 \(\times\) 16 beamforming antenna has the strongest directional characteristics. Considering the practical interference scenarios, we will model and analyze both an omnidirectional antenna and a 16 \(\times\) 16 beamforming antenna.  
A beamforming antenna employs a rectangular antenna array configuration to achieve optimal performance. As shown in Figure 4, the array is composed of \(M_{g}\times N_{g}\) panels, with \(M_{g}\) denoting the number of panels in a column and \(N_{g}\) indicating the number of panels in a row. In an effort to maximize the antenna's efficiency, the antenna panels are uniformly spaced both horizontally and vertically, with spacings of \(d_{g,H}\) and \(d_{g,V}\), respectively. On each antenna panel, an array of antenna elements is strategically placed in both the vertical and horizontal directions to ensure the greatest possible signal gain. The number of columns is denoted by \(N\), while the number of antenna elements in each column with the same polarization is represented by \(M\). The antenna elements themselves are also uniformly spaced, with a horizontal spacing of \(d_{H}\) and a vertical spacing of \(d_{V}\).  
Figure 4: Beamforming antenna.  
Figure 5: Beamforming antenna. (**a**) 3D. (**b**) Elevation angle \(\theta=0^{\circ}\).
# BN-RA: A Hybrid Model for Risk Analysis of Overload-Induced Early Cable Fires
## 4 Numerical Analysis of the Fire Risk Assessment
### Simulation  
#### 4.3.1 Weight Determination of Fire Risk Factors  
To determine the weights of risk factors, a numerical dataset was generated using the Netica software based on the constructed BN with its probability relationships. In this study, 70 groups of cases were generated (Table 6).  
Using the entropy weight method to calculate the generated node data, the information entropy and weight vectors of three factors were obtained: \(H\) = [0.5962,0.6492,0.8673], \(w\) = [0.455,0.395,0.1495].
# Addressing Gaps in Small-Scale Fisheries: A Low-Cost Tracking System +
## 2 Materials and Methods  
### Monitored Vessel  
Currently, the tracking system is installed on an artisanal fishing vessel (10 m in length), operating in the port of Ancona (Italy). The vessel is licensed to use passive gears (gillnets, trammel nets, and traps) and adopts them according to the target species, the market demand, and/or the fishing season. The combination of the above factors inevitably influences the spatio-temporal behavior of the vessel. The use of passive gears includes two distinct operations: the setting and retrieval of the gears, after a certain time period (i.e., soaking time) and based on the above-mentioned factors and sea weather conditions. In addition, specific regulations related to the use of fishing gears are in force, imposing technical/numerical restrictions and regulating the fishing effort exerted at sea [39].
## 4 Optimization Model Solution
### SA-PSO Algorithm  
In order to improve the optimization ability of the PSO algorithm, in this paper, we introduced the SA algorithm into the CF-PSO algorithm. After updating the velocity and position of the particles each time, the Metropolis criterion of the SA algorithm is used to judge whether the PSO algorithm accepts the new solution or accepts the old solution with a certain probability so as to ensure that the PSO algorithm can continue to be globally optimal. We call it the SA-PSO algorithm.  
The specific steps for solving the reactive power compensation optimization model based on the SA-PSO algorithm are as follows:  
Step 1: The number of iterations n, number of variables D, number of populations \(N\), and learning factors \(c_{1}\) and \(c_{2}\) of the particle swarm are set, and the position and speed of all particles are randomly initialized.  
Step 2: The fitness of each particle is evaluated. The position and fitness of each particle are stored in the pbest of each particle. The position and fitness of the personal best in all pbests are stored in gbest.  
Step 3: The initial temperature of the SA algorithm is set according to (27).  
Step 4: According to (28), the probability of taking the old solution as the global optimal solution is calculated, and whether the new solution replaces the global optimal value is determined according to the Metropolis criterion.  
Step 5: According to the position update formulas in (25) and (26), the position and velocity of the particles are updated.  
Step 6: The new target value of each particle is calculated; that is, the compensated objective function value, the pbest value of each particle, and the gbest value of the group are updated.  
Step 7: A desuperheating operation is performed.  
Step 8: If the maximum number of iterations is not reached, starting from step 3, the process is performed again; otherwise, the result, that is, all compensation weight coefficients, is outputted.  
Each compensation weight coefficient is multiplied by the corresponding current component, which is the reference compensation current.
# A Compact Monopole Wideband Antenna Based on DGS
## 2 Antenna Design Methodology and Optimization Process
### Evolution of the Proposed Antenna Design  
The design evolution of the proposed antenna begins with a G-shaped radiator and a conventional rectangular ground plane on a 1.524 mm thick Taconic TYL substrate, referred to as antenna-1 (stage-1), as shown in Figure 4a. This configuration yields two notch bands: 8.79-8.93 GHz and 15.03-17.60 GHz, resulting in a total bandwidth of 2.71 GHz. In the second stage, antenna-2 (stage-2) introduces an L-slot with an area of 404.43 mm\({}^{2}\) at the center of the ground plane (Figure 4b). This modification enhances performance, producing four notch bands: 4.18-7.64 GHz, 8.12-8.37 GHz, 12.9-17.45 GHz, and 18.77-19.86 GHz, with a total bandwidth of 9.35 GHz, significantly improving upon antenna-1. The final design, antenna-3 (stage-3), features a semi-circular slot across the horizontal of the L-slot with a radius (R\({}_{\text{S}}\) = 2.6 mm), transforming the notch bands into a passband (Figure 4c). Note that for all three stages, the top patch is kept constant, while the lower or ground patch is modified at three different stages. This results in a wideband antenna with a bandwidth of 14.84 GHz (4.08-18.92 GHz) at S\({}_{11}\)\(<-\)10 dB, achieving maximum gains of 4.18 dBi at 16.90 GHz and a peak realized gain of 7.4 dB at 15.5 GHz. In summary, the evolution from stopband to passband involved iterative design improvements aimed at enhancing performance across a broader frequency range. The introduction of the L-slot and semi-circular slot significantly shaped the radiation pattern, minimized unwanted reflections, and increased bandwidth, ultimately optimizing the antenna's overall performance as depicted in Figure 5, resulting in a much better reflection coefficients observed than previous stages.  
Figure 4: Evolution of the compact monopole wideband antenna based on DGS.
## 4 Conclusions  
In this study, the content of PAHs in dried medicinal plants and their infusions were analysed. In dried plants, HMW compounds predominate, of which benzo[b]fluoranthene (BbF) and benzo[a]pyrene (BaP) were present in all samples.  
The average transfer rates of BaP and 4 PAHs as markers of PAH contamination varied between 8.59 and 28.82% and 9.56 and 29.78%, respectively, with the highest values being obtained for _Hypericum perforatum_. In the case of \(\sum\)16 PAH compounds, the transfer percentages varied from 7.25% (_Taraxacum officinal_) to 32.86% (_Calendula officinalis_).  
The statistical analysis revealed that out of 16\(\sum\)PAH considered, 9 reduced their content when transitioning from the plant to the infusion: Ace, Flu, Ph, An, Flu, Pyr, BaA, BbF, BkF and IP. The other differences are not statistically significant, in part due to the small amounts detected.  
Exposure to PAHs, evaluated by the MOE values obtained, were well above the reference value established by EFSA (2016), showing that the consumption of medicinal teas presents a low risk to health.  
However, taking into account the fact the daily intake of PAHs from food and other sources (smoking, harmful environment and drinking water) is also added to that from  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Plants**} & \multicolumn{2}{c}{**BaP**} & \multicolumn{4}{c}{**4 PAHs**} \\ \cline{2-7}  & **C** & **EDI** & **MOE** & **C** & **EDI** & **MOE** \\ \hline _Hypericum_ & & & & & & \\ _perforatum_ & & & & & & \\ _Artensiusi_ & - & - & - & - & - & - \\ _dismithum_ & & & & & & \\ _Tilia sp._ & 360.7 & 1.030 & 69,961 & 787.7 & 2.25 & 1,338,582 \\ _Matricaria_ & 22.18 & 0.063 & 1,111,110 & 88.92 & 0.254 & 1,343,873 \\ _chamonilla_ & & & & & & \\ _Achillea_ & 270 & 0.771 & 90,791 & 655.7 & 1.873 & 181,526 \\ _millefolium_ & & & & & & \\ _Taraxacum_ & & & & & & \\ _officiale_ & & & & & & \\ _Calendula_ & & 126.97 & 0.362 & 193,370 & 212.81 & 0.608 & 559,210 \\ _Grataegus_ & 436.21 & 1.246 & 56,179 & 794 & 2.268 & 149,911 \\ _monogyna_ & & & & & & \\ \hline \hline \end{tabular} \(C\)—concentration (ng/L); EDI—exposure daily intake (ng/kg.bw/day); MOE—Margin of Exposure.  
\end{table}
Table 6: Margin of Exposure (MOE) values for BaP and 4 PAHs in tea infusions.  
\"health products\", we believe that it is necessary to control plants and herbal medicinal preparations, in the same way as foodstuff.  
Conceptualisation, M.M.B. and S.B.; methodology, V.C.; software, N.A.D.; validation, D.A.D.; formal analysis, C.B.; investigation, N.A.D.; resources, V.C.; writing--original draft preparation, S.B.; writing--review and editing, M.M.B.; visualisation, E.R.; supervision, V.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, and further inquiries can be directed to the corresponding author.  
We acknowledge the National Institute for Marine Research and Development \"[PERSON]\".  
The authors declare no conflicts of interest.
# The Role of Multi-Agent Systems in Realizing Asset Administration Shell Type 3
## 2 Background
### Asset Administration Shell Type 3 Definition and Requirements
#### 2.2.1 Definition  
As summarized in Table 2, the definitions of AAS Type 3 presented in official documents, namely [3; 19; 20; 21], as well as in selected research works, are brief, generic, and remain limited in scope, evidencing the early stage of conceptual development. However, they consistently emphasize several common and core aspects: the ability to interact and collaborate through the I4.0 language (VDI/VDE 2193) and the integration of autonomy and intelligence to support decision-making processes. Additional requirements, as well as concrete guidelines for the design and implementation of such components, are still in the early stages of development, particularly in official documents, which only highlight the importance of adopting the I4.0 language for communication.  
Based on these definitions, this work proposes the following definition for AAS Type 3: _AAS Type 3 represents the next evolutionary step of the AAS concept, extending AAS Type 1 and Type 2 solutions. AAS Type 3 not only serves as a key enabler of interoperability in 14.0 environments but also introduces a higher degree of autonomy, intelligence, and collaborative capabilities. It acts as a decision-making entity capable of autonomously interacting with other AAS Type 3 instances to exchange information and collaborate, e.g., through negotiation. Furthermore, it possesses the intelligence to self-manage its associated asset, leveraging AI techniques to support the decision-making process and adaptive behavior throughout the asset's lifecycle._