esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 The Effects of RF Exposure on Humans
### Diagnostic Benefits and Occupational RISK  
Among the numerous sections of the population that occupationally encounter RF exposure, workers using MRI equipment hold a special place. MRI is an imaging technique that employs strong static, gradient, and radiofrequency magnetic fields. It can image soft tissues--unobstructed by bone--with enhanced contrast. In a clinical MRI system operating at 1.5 T, because of its design, it is unlikely that radiological staff would be exposed to significant RF fields. The gradient field is pulsed rapidly in time and is a function of the imaging technique and design of the MRI system. Recently, the demand for increased spatial resolution and high signal-to-noise ratio from MRI instruments has prompted the development of systems using much higher static magnetic fields (greater than 11 T). This development has led to the use of higher RF frequencies for MRI, which, in principle, not only can augment the amount of RF power deposition inside the patient's body, but also increases the EMF exposure for workers using MRI in the hospital environment and workers employed for supporting, servicing, developing and manufacturing this equipment [5]. We report here a few examples of surveys to evaluate and confine these risks. In [PERSON] _et al_. [110], a thorough investigation identified the structure and the professional categories most likely to be involved in the use of MRI that include people using MRI for clinical diagnostics, MR-guided medical interventions on patients.  
[PERSON] _et al._[111] carried out a study by interviewing and evaluating self-assessing workers employed in production and development of MRI systems. To evaluate the level of RF exposure and generate a predictive algorithm universally applicable to model and predict the level of exposure and the effect people working with and around MRI systems might experience. This kind of information constitutes the basis for improvement in design, application and risk management associated with the use of MRI.
# Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks +
## 3 Proposed Statistical Dimensioning Model
### Average Delay and Choice of the Network Threshold  
In this subsection, we compute the average delay to connect the sensor device to the network independently of its transmitting rate. This average delay should not exceed a maximal delay \(\tau_{\max}\) that we assume proportional to the Time Transmission Interval (TTI). As long as the sensor is not accepted by the network due to the lack of resource, a new trial will be performed after TTI. We assume that the probabilities of being rejected after \(i\times\text{TTI}\) are independent and are equal to \(\left(\mathbb{P}_{\text{out},c}(N_{t})\right)^{i}\). The average delay to access to the network is hence,  
\[\tau=\left(\sum_{i=0}^{\infty}i\times\text{TTI}\times\left(\mathbb{P}_{\text{ out},c}(N_{t})\right)^{i}\right)\leq\left(\sum_{i=0}^{\infty}i\times\text{TTI} \times p_{th,n}^{i}\right). \tag{18}\]This expression can be simplified to,  
\[\bar{\tau}\leq\text{TTI}\frac{p_{th,n}}{(1-p_{th,n})^{2}}. \tag{19}\]  
By choosing \(p_{th,n}\) such that  
\[\text{TTI}\frac{p_{th,n}}{(1-p_{th,n})^{2}}\triangleq\tau_{\text{max}}, \tag{20}\]  
we make sure that the average delay in the network does not exceed \(\tau_{\text{max}}\). By letting \(\kappa=\tau_{\text{max}}/\text{TTI}\), the network threshold is,  
\[p_{th,n}=\frac{(2\kappa+1)-\sqrt{1+4\kappa}}{2\kappa}<1. \tag{21}\]
# ADAfinder Tool Applied to EGMS Data for the Structural Health Monitoring of Urban Settlements
## 2 Materials and Methods
### ADAfinder Tool  
The ADAfinder tool is one of the four modules, called ADAtools [35; 36], developed at the Research Unit of Geomatics of the Centre Tecnologic de Telecomunicacions de Catalunya (CTTC). The aim of the ADAtools is the semi-automatic extraction and preliminary analysis of ADA starting from the PSI-derived deformation maps. In particular, the ADAfinder tool has the main objective of identifying the ADA in the area of interest, starting from a dataset of persistent scatterers (PSs). An application that highlights its potential can be found in [PERSON] et al. [42].  
For a detailed description of the tools, see the user guide. In synthesis, the input is a file containing the dataset of PSs, in _ESRI shapefle_ or _csv_ format. The main information necessary for each PSs is its coordinates, its mean deformation velocity (expressed in mm/year), and its deformation time series in the period of interest.  
Initially, the \"isolated\" PSs and the \"stable\" PSs are removed. The first ones are defined as points with no other PSs around included within a defined distance, while the second ones are those PSs with a mean deformation velocity lower than a fixed \"stability threshold\", in function of the standard deviation (\(\sigma\)) of the velocities of all the PSs of the dataset (Vm). Then, the clusters of PSs are detected and, finally, the ADA are identified on the basis of a radius of influence and of the minimum number of PSs set by the user.  
The output of the ADAfinder tool consists of two _ESRI shapefles_: one containing the polygons representing the ADA's areas, and one containing the PSs included in the ADA. The statistics of the mean deformation velocity of each ADA are also provided.  
It is important to underline that the lack of ADA could be related not only to the absence of an active deformation, but also to the absence of PSs [43].
# Crust and Upper Mantle Structure of Mars Determined from Surface Wave Analysis
## 1 Introduction  
The knowledge of the Martian crust and upper mantle structure with depth is an important key to understanding its origin and evolution, but this structure remains among the least studied structures in geophysics and planetary science, due to the long distance of Mars from Earth, which has made the development and operation of scientific instruments in satellites and/or on land very difficult. However, in the last few years and with huge scientific and economic effort, the Interior Exploration Using Seismic Investigations, Geodesy, and Heat Transport (InSight) mission (which landed on Mars in Elsium Planitia) has provided the first seismic data recorded on the Martian surface. These invaluable seismic data have been analyzed to obtain the deep structure from P and S waves (e.g., [1]), the shallow structure from ambient-noise wavefield [2], and the crust and upper mantle structure from surface waves (e.g., [3]). The present study will complement the very scarce quantity of previous studies performed on the crust and upper mantle structure of Mars, with the dispersion analysis of the surface waves present in the record of the seismic event S1094b, and the inversion of this dispersion to calculate an S-velocity distribution with depth, for a depth range of 0 to 100 km, in which the seismic anisotropy present in the first 15 km of the crust will be considered. The seismic anisotropy in Mars has already been reported in previous studies [3, 4, 5], but only for the event S1222a. The seismic records obtained from InSight were analyzed by [PERSON] et al. to obtain surface group velocities [3, 6].  
Unfortunately, most of these records do not present surface waves, because they are affected by strong contamination with long-period wind noise and atmospheric pressure waves, as well as a strong crustal scattering; only the records of three seismic events are suitable for surface wave analysis: S1000a, S1094b, and S1222a. The largest marsquake recorded during all InSight missions was S1222a, providing the first clear signals of surface waves and their first overtones [3]. On the other hand, S1000a provided unclear surface waves and only Rayleigh wave dispersion was determined by [PERSON] et al. [6] due to its relatively small magnitude and large epicentral distance. The records of S1094b also were analyzed by [PERSON] et al. [6], and they only determined Rayleigh wave dispersion. In the present study, the seismic anisotropy is reported for the first time for the event S1094b, and is also related, for the first time, to the possible presence of sediments or lava flow layering for the first 5 km depth, and volcanic materials from a 5 to 15 km depth.
## 1 Introduction
The proliferation was similar in both groups, in opposition to phagocytosis, which was increased in the bevacizumab + RSV group vs. bevacizumab group [18]. It may give new insight for a concept using anti-VEGF agent with RSV supplementation in patients with wet AMD or proliferative diabetic retinopathy and which deserves further investigation. Moreover, RSV has been shown to suppress proliferation, migration, and fibronectin synthesis induced by transforming growth factor-\(\beta\)2, inhibiting epithelial to mesenchymal transition of retinal pigment epithelial cells and consequently the development of proliferative vitreoretinopathy, as the sirtuin1 activation effect [19]. [PERSON] et al. (2017) demonstrated RSV inhibition on the decrease of zona occludens-1 and the increase of mesenchymal marker vimentin expression by suppressing Smad2 and Smad3 phosphorylation [20]. The excessive proliferation and differentiation of endothelial cells are responsible for several eye diseases. Our previous studies showed that RSV inhibits proliferation, migration, and invasiveness of the endothelial cell line HECa10 [21]. The HECa10 cells are murine high endothelial cells (HEC) from the peripheral lymph nodes, established as a non-transformed cell line, displaying and maintaining the characteristic phenotype of HEC cells of the tissue of origin. Besides their characteristic ability to recognise and select lymphocytes and specifically adhering cells, HECa10 produce specific blood vessels proteins such as the E-selectin, angiotensin-converting enzyme, factor VIII-related antigen, von Willebrand factor and VE-cadherin [22; 23]. Based on previous results [21], here we asked if the effects of RSV are cell-type specific and how epithelial cells, here, eye-derived cell line ARPE-19, respond to RSV treatment. Therefore, we evaluated, in parallel, the effects of 1-100 uM RSV on these two cell lines' proliferation, death type: necrosis vs. apoptosis, cell cycle progression, and secretion of proangiogenic factors VEGF and bFGF.
# Detection of Forest Tree Losses in Cote d'Ivoire Using Drone Aerial Images
## 4 Discussion  
### Clearing Timing in Classified Forests and Data Acquisition Strategy  
Knowledge of the clearing process in classified forest was necessary for a good spatiotemporal distribution of the drone acquisition campaigns (Figure 16). Clearing is pro  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Tree Loss** & **StableForest** & **NonstableForest** & **Total** & **User’s Accuracy** & **CommissionError** \\ \hline Tree loss & 99 & 5 & 3 & 107 & 0.93 & 0.007 \\ Stable forest & 1 & 196 & 3 & 200 & 0.98 & 0.02 \\ Nonstable forest & 1 & 2 & 201 & 204 & 0.99 & 0.01 \\ Total & 101 & 203 & 207 & 511 & & \\ Producer’s accuracy & 0.98 & 0.97 & 0.97 & & & OA = 97\% \\ Omission error & 0.02 & 0.03 & 0.03 & & Kappa = 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrix and accuracies for the set of tree height distribution maps in April 2019 and tree loss over the period of November 2018–April 2019.  
Figure 15: Illustration of the identification of tree losses (the red line materializes the lost tree: new clearings or losses related to the burning of trees) on the orthomosaics from November 2018 and April 2019.  
gressive and discrete and is characterized by an anthropic cocoa farming process that can be subdivided into four (4) stages.  
From May to August: illegal farmers either broadcast cocoa beans into the forest understory or directly remove the forest understory before direct seeding of beans under the canopy. The first approach is becoming more and more prevalent in the Bossematie classified forest because it is almost impossible to notice the presence of cocoa crops at this stage, whereas in the second case, the ecoguards quickly identify the presence of illegal activities.  
From August to September: Once the seedlings appear (2 to 3 months later), they proceed to cut down the undergrowth, leaving the cocoa crop in sight. During this phase, which takes place during the short dry season, the seedlings are maintained. Until then, it is impossible to detect such a disturbance via optical remote sensing (this is the beginning of forest degradation).  
From September to October: As the rains pick up again, illegal farmers replace dead cocoa plants with new seedlings and increase the area of their fields.  
November to April: Illegal farmers set fire to the base of large trees to cause their death; this is the beginning of tree losses. During this phase which last all along the dry season, it becomes possible to detect such disturbances via optical remote sensing. The trees first lose their foliage, allowing a good amount of light to penetrate for crop growth, before falling (windfall) under the effect of the wind or being cut down with a chainsaw. The resulting cleared areas are usually considerable with a significant loss of biodiversity. It is therefore during this period that overflights must be intensified to detect the burning of the first standing trees and to alert the public to send deterrent patrols.
## 3 Results and Discussion
### In Vitro bioactivity Study
The diffraction peak at 31.7\({}^{\circ}\) overlaps with the characteristic peaks of HAp at the same area. However, due to peak broadening, the presence of HAp can be identified. Figure 6 shows the SEM images of samples before (a-e) and after (f-o) immersion in 2 xSBF solution for 2 weeks. Before immersion, the images reveal the 3D-printed structure of scaffolds which is characterized by the presence of clearly defined pores. Due to the lyophilization process, no shrinkage or other textural abnormalities were observed. Samples incubated in SBF solution (f-j) are covered with a dense layer of HAp. The SEM micrographs at higher magnifications (k-o) revealed that the HAp layer consisted of spherical aggregates. In another study, pure alginate 3D-printed scaffolds were soaked for 10 days in SBF, and no apatite formation was observed. However, the presence of 13-93 bioactive glass resulted in apatite mineralization [12]. Although pure alginate hydrogels did not show mineralization after incubation in SBF, the presence of other biopolymers in their network induced apatite formation. For example, cellulose nanofibril incorporation in alginate hydrogels results in apatite formation after immersion in SBF solution [40]. It has also been shown that alginate-starch aerogels are bioactive materials since the formation of HAp is induced even after one day of immersion in SBF. The precipitation is attributed to the parallel presence of calcium ions within the alginate network and the phosphate ions in SBF solution [41]. Figure 5: FTIR and XRD graphs of samples before (**a,c**) and after (**b,d**) immersion in 2 xSBF solution for 2 weeks. The FTIR spectrum of synthetic HAp and standard JCPDS card No 9-432 of HAp are also shown for comparison.
## 4 Analysis and Interpretation of Results
### Correlation Links Between Individual Production Sources and Residual Load
#### 4.3.6 Analysis of the Estonian Market  
In the case of Estonia, the correlation analysis shows interesting and, in some respects, different results compared to the other markets studied. The values of the Pearson correlation coefficient indicate weak to moderate relationships between the variables analysed.  
Considering the overall analysis over the entire period (2015-2025), it appears that wind generation (Wind) has a very weak negative correlation with solar generation (Solar) (\(-0.056\)) and with residual demand (residual load) (\(-0.139\)), reflecting the low degree of synchronisation of generation between these resources. The correlation between wind generation and electricity price is also low (\(-0.125\)), indicating that the impact of wind on pricing is relatively limited in Estonia.  
Solar generation (Solar) shows a very weak negative correlation with residual load (\(-0.031\)), while the relationship between solar generation and electricity price is only slightly positive (\(0.055\)). This result is contrary to expectations and is probably related to the low absolute share of solar in the Estonian energy mix, which minimises its ability to influence prices.  
On the other hand, residual load shows a stable and relatively strong positive correlation with electricity price (\(0.580\)), which is consistent with the assumption where higher residual load pushes up electricity prices.  
When analysed year-on-year, there is significant variability. In 2015-2019, correlations between RES generation and prices were generally low or insignificant. However, we observed some interesting changes in the period 2020-2025. In 2023 and 2024, the negative correlations between solar generation and residual load deepen significantly (up to \(-0.687\) in 2024), signalling that there is a significant decline in the need for traditional resources in periods of high solar generation.  
At the same time, the negative correlation between solar generation and price is increasing, although it remains relatively small. This may indicate an incipient influence of solar power on price dynamics, which is not yet dominant.  
Wind generation maintains a consistently weak relationship with the other variables, but the negative correlation between wind and electricity prices deepened in 2025 (\(-0.343\)), which may be due to increased wind capacity.  
Overall, the analysis confirms that in the Estonian electricity system RES plays only a minor role in pricing so far. The dominant factor remains the residual load, which has a stable positive correlation with price. The results thus show that despite the gradual development of RES, their ability to dampen or induce price volatility is still relatively small in Estonia during the period under review.
# Design Optimization of Truss Structures Using a Graph Neural Network-Based Surrogate Model
## 5 Summary, Conclusions, and Future Work  
In this study, a novel graph neural network (GNN)-based optimization technique is proposed for dealing with the size optimization of truss structures. The technique employs a particle swarm optimization algorithm to conduct an iterative search for the optimal solutions. A surrogate model based on a graph neural network is trained to approximate the nodal displacements of trusses with different sets of cross-sectional areas of the truss elements for the first 90% of iterations, while the remaining 10% utilizes FEM analysis to determine accurate nodal displacements. In this manner, this technique eliminates the need for finite element models to analyze truss structures, which in turn leads to computational time reduction. Moreover, three different truss optimization problems, a 10-bar truss, a 72-bar truss, and a 200-bar truss, are used to investigate the proposed GNN-based optimization technique against a conventional FEM-based technique. The results demonstrate the superiority of the GNN-based technique, as it arrives at the optimal design with no constraint violations and is faster in complex truss structures like the 200-bar problem.  
There are two avenues for future improvement in this research. Firstly, enhancing the surrogate model by incorporating a trained GNN to predict the stresses in the truss elements alongside the nodal displacements. Secondly, training a single GNN to predict the structural response of trusses of different topologies, paving the way to the simultaneous optimization of truss topology and size.  
Conceptualization, N.N., M.J. and M.E.-B.; methodology, N.N. and M.J.; software, N.N.; validation, N.N. and M.J.; data curation, N.N.; writing--original draft preparation, N.N.; writing--review and editing, M.E.-B. and M.J.; visualization, N.N.; supervision, M.E.-B.; project administration, M.E.-B.; funding acquisition, M.E.-B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Sciences and Engineering Research Council (NSERC), grant number 04683-2019.  
The generated and analyzed data are accessible from the corresponding author, M.E.-B., upon request.  
This research is also supported by the CSA Group Graduate Scholarship and the Alberta Motor Association Graduate Scholarship awarded to the first author, N.N. This support is gratefully acknowledged.  
The authors declare no conflict of interest.  
Figure 17: Optimization process time for the GNN-based and FEM-based techniques.
## 3 Example Analysis
### Using Analytic Hierarchy Process (AHP) to Calculate Subjective Weight
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**First-Level** & **Weight** & **Secondary Indicators** & \begin{tabular}{c} **The Weights of** \\ **Layer A** \\ \end{tabular} & **Subjective Weight** \\ \hline \multirow{4}{*}{\begin{tabular}{c} The reliability indicators (A1) \\ \end{tabular} } & \multirow{4}{*}{0.2804} & Average service availability index (B1) & 0.6667 & 0.1869 \\ \cline{3-4}  & & & System average interruption duration index (B2) & 0.3333 & 0.0935 \\ \hline \multirow{4}{*}{\begin{tabular}{c} The economic indicators (A2) \\ \end{tabular} } & \multirow{4}{*}{0.2804} & Initial investment cost (B3) & 0.5396 & 0.1513 \\ \cline{3-4}  & & Operation and maintenance cost (B4) & 0.2970 & 0.0833 \\ \cline{3-4}  & & Network loss cost (B5) & 0.1634 & 0.0458 \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} The power quality indicators (A3) \\ \end{tabular} } & \multirow{4}{*}{0.3120} & Voltage offset (B6) & 0.4286 & 0.1337 \\ \cline{3-4}  & & Frequency deviation (B7) & 0.4286 & 0.1337 \\ \cline{3-4}  & & Harmonic distortion rate (B8) & 0.1429 & 0.0446 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} The environmental protection indicators (A4) \\ \end{tabular} } & \multirow{4}{*}{0.1272} & Emissions of polluted gases (B9) & 0.6667 & 0.0848 \\ \cline{3-4}  & & Land occupation area (B10) & 0.3333 & 0.0424 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Subjective weight obtained by analytic hierarchy process.
## 3 Method
### Data Pre-Processing
#### 3.1.2 Data Preprocessing  
Using a rules-based entity extraction approach, we build a heterogeneous certificate graph containing certificate attribute nodes and certificate document nodes. The original dataset for constructing the heterogeneous graph uses benign and malicious certificates (both in pem format). The benign certificates come from the top one million website ranking files from Alexa [32]. The certificate fingerprints of the malicious certificates are obtained from abuse.ch [33]. We search for malicious certificates with SHA1 values corresponding to the fingerprints of previously identified malware certificates. Then, we decrypt the certificate using the openSSL toolkit to obtain X.509 standard structured certificate data. We put all the data into the corpus and tag each piece of data. The next step is data cleaning,  
Figure 1: X.509 certificate structure.  
and finally, feature values in the certificates are extracted from each certificate's data by a python script that stores the feature values in a new corpus.