esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results  
### DPPH Radical Scavenging Assay  
The 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assay was performed to determine the antioxidant activity of extracts and essential oils obtained from the various parts (bark, needle, and pollen) of _P. eldarica_ (Figure 1). In the DPPH assay, a higher DPPH radical scavenging value indicates a higher antioxidant activity. The DPPH radical scavenging activity of methanolic extracts in all groups were significantly higher than that of other extracts and essential oils (\(p<0.01\)). Among the active methanolic extracts, the methanolic bark extract (mean value 93.9%) demonstrated significantly higher antioxidant activity (_p_ < 0.01) than the methanolic pollen extract (mean value 77.57%) and no significant difference with methanolic needle extract (mean value 83.90%).  
In Vitro Evaluation of Cytotoxic Effects of P. edlarica Extracts and Essential Oils on A549 Lung Cancer Cells  
Cytotoxicity of _P. edlarica_ extracts and essential oils on A549 lung cancer cells was measured using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazololim bromide (MTT) assay. We observed >80% cell death in A549 cells treated with 100 mg/mL of all hexane extracts, the methanolic needle extract, and needle essential oil (Figure 2). The half-maximal inhibitory concentration (IC\({}_{50}\)) of the methanolic needle extract at 0.3 mg/mL revealed its high toxicity, comparable with that of doxorubicin (DOX; a positive control). Among pollen fractions, the hexane extract with an IC\({}_{50}\) value of 31.7 mg/mL was the most toxic; among bark fractions, the essential oil was the most toxic, with an IC\({}_{50}\) value of 17.9 mg/mL (Table 1). Based on these findings, the methanolic needle extract, hexane pollen extract, and bark essential oil were selected for further investigation.  
Figure 1: DPPH radical scavenging (%) of the four different extracts (hexane, methanolic, aqueous extracts, and essential oils (each 1 mg/mL)) prepared from the _Pinus ellarica_ bark, needles, and pollen. Data are expressed as mean \(\pm\) standard deviation (SD), representative of three independent experiments.
# Development of Patient Specific Conformal 3D-Printed Devices for Dose Verification in Radiotherapy
## 3 Results and Discussion
### Conformity of 3D-Printed Bolus  
It is well known that the presence of air gaps between the bolus and patient's surface indicate a lack of conformity. In addition, it is not possible to anticipate the size of the air gap in the treatment planning system, which results in inhomogeneity of the dose distribution. [PERSON] et al. reported that, while using a 6 MV photon beam, a 4 mm air gap between patient's surface and bolus leads to approximately 0-4% surface dose reduction while up to 10% reduction resulted from a 10 mm air gap, depending on different parameters such  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Criteria** & **Value** \\ \hline Sorensen–Dice Coefficient (DICE) & 0.957 \\ Jaccard Coefficient (JAC) & 0.917 \\ Proportional Agreement (PA) & 0.998 \\ Cohen’s Kappa (KAP) & 0.956 \\ Goodman \& Kruskal’s Gamma (GKG) & 0.913 \\ Rogot–Goldberg Agreement (RGA) & 0.978 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Similarity coefficients calculated using the DSCImageCalc software.  
Figure 4: CT scans of the objects of interest: (**A**) CT scan of real HNC patient, with indicated HU values for different organs; (**B**) image of phantom–phantom–bolus assembly; (**C**) CT scan of the PMMA head phantom with attached 3D-printed phantom and 3D-printed bolus on it, with indicated HU values.  
as field size and angle of incidence [11]. In order to evaluate the conformity of the 3D-printed boluses to the patient surface, each bolus was attached to the 3D phantom, which was mounted on the surface of the PMMA head phantom and the constructed phantom-phantom-bolus assemblies were CT-scanned. The 3D-printed PLA bolus provided a good fit to the patient phantom; however, some small air gaps were detected in CT images between the bolus and the patient-specific phantom surface (Figure 6). The size of the air gaps was evaluated by slicing through the CT scans. The largest air gaps were detected in the cheeks area. They varied from 0.4 to 2.1 mm for 0.5 cm thick bolus; while for the 1.0 cm bolus, it varied from 0.6 to 2.2 mm. The gaps measured in the nose region varied between 0.7 and 1.3 mm for the 0.5 cm thick bolus and was equal 1.4 mm for the 1.0 cm bolus. Observed air gaps were smaller as compared to those indicated by [PERSON] et al. [11]. This indicates the potential of 3D-printed boluses in achieving higher conformity (small air gaps) to the irregular surfaces as compared with commercially available flat boluses and thus may improve dose distribution to the target. This good skin-bolus contact is of high importance especially in the case of highly conformal radiotherapy (e.g., IMT, VMAT), which delivers higher radiation doses and requires accurate tumor targeting.
# Dynamic Analysis of Cavitation Tip Vortex of Pump-Jet Propeller Based on DES
## 2 Numerical Method and Computational Setup
### Three-Dimensional Model and Mesh
#### 2.5.3 Grid Independence Analysis  
In order to eliminate the influence of insufficient number of grids on the computational accuracy, meanwhile avoid the waste of computing resources caused by excessive grids, it is mandatory to conduct the grid-independence analysis of the computational domain beforehand. Table 2 shows the comparison of different grid numbers for grid independence analysis. By increasing the number of boundary layer nodes, six sets of grids including 1.76 million, 3.36 million, 4.87 million, 6.37 million, 8.81 million, and 10.37 million are obtained. By comparing the thrust coefficient, torque coefficient, and propulsion efficiency of a different number of computational domain grids under design conditions, the most appropriate number of grids is concluded.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parts** & **Y Plus** \\ \hline rotor blades & 12 \\ rotor wall & 20.5 \\ stator blades & 2.9 \\ stator wall & 16.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average Y+ of rotor and stator components.  
Figure 5: Pump-jet propeller mesh and rotor local mesh: (**a**) mesh of pump-jet propeller (**b**) local boundary layers near the blades (**c**) local boundary layers in the tip clearance.  
It can be seen from the table that as the number of grids increases, the margin between the propulsion efficiencies becomes smaller and smaller. Therefore, in order to obtain the flow field information near the wall more accurately and reduce computational resource consumption, the case of 8.81 million grid schemes is selected for numerical study in this paper.
# Key Issues on Integrating 5G into Industrial Systems
## 5 Integration of 5G with Industrial Systems
### Practical Cases
#### 5.2.2 Extension of the 5G Terminal as a Gateway  
By adopting a star-topology structure, a 5G network allows 5G base stations to communicate directly with terminals. However, when the communication path between a base station and a terminal is blocked by obstacles, blind spots in 5G communication will be generated, causing failed communication between the two. To address this problem, the concept of 5G terminals can be evolved from a \"point\" to an \"area\". Specifically, the original 5G terminal can be expanded into a wireless communication LAN of field bus quality, known as the ProudLink self-organizing wireless mesh network, as shown in Figure 7. In this network, link communication nodes are directly wired to sensors, field devices, and PLC systems, thereby achieving smooth data conversion between the wireless LAN and the field. In addition, 5G gateways, as a special type of 5G terminal equipment, are used to play the data communication functions between the LAN and 5G base stations. We have demonstrated this approach in a real-world case. The OpenRAN split architecture can be considered with a front-haul connected to radio units, a mid-haul connected to a 5G gateway, and a backhaul to a 5G base station.
## 2 Traditional Eigenvalue-based Cooperative Spectrum Sensing
The tradeoff is usually determined with the aid of receiver operating characteristic (ROC) curves that show the variation of \(P_{\text{fa}}\) versus \(P_{\text{d}}\) with changes in the threshold value.
## 2 Proposed Solution
### Mask Detection  
This section describes the model used to detect the presence/absence of masks. The framework's mask recognition stage entails identifying and tracking the presence (or absence) of masks. The model used for this purpose computes the bounding box of the face as well as the degree of confidence in the presence of a mask. As with prior object detection models, this model outputs a time-varying vector representing the spatial localization information for faces as \(J_{m}(t)=\{bb_{m1}(t),bb_{m2}(t),\ldots,bb_{mk}(t),\ldots,bb_{mn}(t)\}\), where \(n\) is the number of face bounding boxes at time \(t\) and \(bb_{mk}(t)=(u,v,r,h,cm)\) is a five-tuple representation of the bounding box encompassing a detected face at time \(t\). The variables \(u,v,r\), and \(h\) have the same definitions as those in Section 2.1. The confidence measure \(c_{m}=(c_{mask},c_{nomask})\) is a two-tuple in which \(c_{mask}\in[0,1]\) indicates the probability of the presence of a mask and \(c_{nomask}\in[0,1]\) indicates the absence. Similar to Section 2.1, the tracking model returns a vector of the same size \(\overline{J_{m}}(t)\) containing tracked bounding boxes for each \(t\).  
Similar to Section 2.1, the YOLO network was utilized for mask detection and the DeepSORT algorithm was utilized for tracking the masks across frames. The YOLO model was first initialized with the pre-trained COCO weights and then fine-tuned using the images from the Moxa3k dataset [35] as well as the UT and UOP datasets, which were labeled for mask detection. The DeepSORT model used the weights trained using the MOT dataset [54] for initialization. The DeepSORT algorithm handles minor occlusions of masks. However, another layer of interpolation (such as for people detection) was not implemented because the algorithm is supposed to detect when people remove masks (i.e., people cannot disappear while masks can).
# Automated Evaluation of C-ITS Message Content for Enhanced Compliance and Reliability
## 2 Materials and Methods  
To evaluate the effectiveness and compliance of C-ITSs, a series of controlled laboratory tests were conducted. This section outlines the specific types of C-ITS messages that were the focus of the testing, the relevant standards that guided the evaluation process, the tools developed and employed for testing, and the methodology used to ensure rigorous and reliable results. By following this structured approach, the study aims to provide a clear and replicable framework for assessing the accuracy and compliance of C-ITS message content.
## 2 Transmission Cross-Sections (TCSs) through a Subwavelength Slit for a Lossless Conducting Plate
The guide wavelength \(\lambda_{g}\) for each MIM waveguide in Figure 1 is listed in Table 1. The values of \(\lambda_{g}\) for cases of low permittivity are relatively great, while the \(\lambda_{g}\) values for high permittivity cases are smaller. Meanwhile, the intervals of TCSs peaks were obtained as \(0.5\lambda_{0}\) and \(0.1650\lambda_{0}\) for the PEC and the metallic permittivity of \(-5\), respectively, as found in Figure 2. These intervals, computed by the MMT, are exactly the same as \(1/(2\lambda_{g})\), as listed in the last column of Table 1. Therefore, resonant transmission phenomena were systematically solved as a function of metallic permittivity, and their periodicity can be easily understood. Figure 2: Transmission cross-sections (TCSs) for various permittivities: (**a**) metal which is a perfect electric conductor (PEC); (**b**) \(\varepsilon_{r}=-2000\); (**c**) \(\varepsilon_{r}=-100\); and (**d**) \(\varepsilon_{r}=-5\). The transmittance for the PEC case is shown in the inset of (**a**).
## 2 Distributed Optical Fibre Sensors
### Rayleigh-Based Distributed Sensing
#### 2.1.1 Optical Time Domain Reflectometry  
Historically speaking, OTDR was developed almost simultaneously by two independent research groups: [PERSON] and [PERSON] at Hughes Research Laboratory [16] and [PERSON] at Bell Telephone Laboratories [17]. In particular, [PERSON] applied the technique directly to installed links and firstly provided an expression for the received signal levels. The first ever proposed DOFS was indeed based on a polarisation-sensitive OTDR scheme (POTDR) [18; 19], while the original setup of [PERSON], [PERSON], and [PERSON] was proposed as a distributed sensor only in 1983 by [PERSON] et al. [20].  
Figure 1 shows the working principle of conventional OTDRs. In that implementation, a broadband source is used to generate light pulses; a circulator (or a coupler) separates the forward path from the backward one; a photodetector measures the light intensity; and dedicated electronics drive the devices, process the data, and record the measurements.  
In standard OTDR, the best spatial resolution that can be obtained is half the length of the pulse, but in real applications, other factors (e.g., low signal-to-noise ratio, SNR) may impair the performance and make the resolution worse. An overall reduction of SNR is indeed observed for reduced pulse lengths, as less energy is injected in the fibre: overall, in conventional OTDR, the SNR is roughly proportional to the square of the spatial resolution [9]. To circumvent this limit, other time-domain techniques were proposed over the years, exploiting correlation [21; 22], pulse coding [23; 24], or photon-counting (\(\
u\)-OTDR) [25; 26; 27].  
Another time-domain approach uses the coherent nature of Rayleigh backscattering originated by a narrowband light source: as mentioned above, the backscattered signal is the coherent vectorial sum of fields generated at scattering centres and encodes the information about their position. Like in a distributed multi-path interferometer, the received backscattered signal experiences speckle noise known as Rayleigh fading [28; 29]. Despite the randomness distribution of centres' positions and corresponding relative optical phases, the resulting \"interference pattern\" represents a snapshot of scattering centres' positions along the fibre. If the fibre is perturbed (e.g., strained or heated/cooled), the relative positions of scattering centres change (i.e., the relative optical phase difference), causing the interference pattern to change. This change can be tracked, and the mechanism is exploited--also commercially--to implement DOPSs for dynamic strain and vibration detection (distributed vibration sensor, DVS or distributed acoustic sensor, DAS) with spatial resolution comparable to that of conventional OTDRs. This time-domain solution is called phase-OTDR (or \(\phi\)-OTDR) [30; 31; 32; 33; 34].
# A Compact Bat Algorithm for Unequal Clustering in Wireless Sensor Networks
## 3 Compact Bat Algorithm
### Compact Bat Algorithm
With respect to \(\delta\) values, the update rule of each element is given as:  
\[\delta_{i}^{t+1}=\sqrt{\left(\delta_{i}^{t}\right)^{2}+\left(\mu_{i}^{t}\right) ^{2}-\left(\mu_{i}^{t+1}\right)^{2}+\frac{1}{N_{p}}\left(winner_{i}^{2}- \mathit{loser}_{i}^{2}\right)} \tag{17}\]  
\[[winner,\mathit{loser}]=\mathit{compete}\left(x_{\mathit{best}},\ x^{t+1}\right) \tag{18}\]  
In general, a probabilistic model for compact BA is employed to represent the bat solution set where neither the location nor the velocities are stored, however, a newly generated candidate is stored. Thus, modest memory space is required, and it is well suited for the limited hardware resources in WSNs. A parameter \(\omega\) is used as a weight to control the probability of sampling of \(\mu_{i}\) in \(PDF\) as Equation (15) between left [-1, \(\mu_{i}\)] that is \(P_{L}(\mathrm{x})\), for \(-1\leq x\leq\mu_{i}\), and right [\(\mu_{i}\), 1] that is \(P_{R}(\mathrm{x})\), for \(\mu_{i}\leq x\leq 1\). The extended version Equation (15) of PDF for sampling approach is applied in this paper. The generating new candidates of the bats are employed by sampling from \(PV\) such as when \(r<\omega\) then it generates the coefficient \(x_{i}\in[1,0]\) for \(P_{L}(\mathrm{x})\), otherwise \(x_{i}\in[1,0]\) for \(P_{R}(\mathrm{x})\). The details for compact BA are as follows:  
**Step 1** Initialization: \(\mu\) and \(\delta\) of the probabilistic model vector \(PV\). **Step 2** Initialize parameters: the pulse rate \(r_{i}\), the loudness \(A_{i}\), and \(\beta\) are set to random; search range definition of pulse frequency \(f_{min}\), and \(f_{max}\). **Step 3** Generate the global best solution \(x_{\mathit{best}}\) by means of perturbation vector \(PV\); assign \(F_{min}\) to fitness (\(x_{\mathit{best}}\)). **Step 4** Generate the local best solution \(x^{t}\) from \(PV\). **Step 5** Update velocities \(v^{t}\) and locations \(x^{t}\) according to updating rule of the standard BA algorithm, Equations (8)-(10); if (\(\beta>r_{i}\)), select a local solution \(x^{t+1}\) around selected best solution as Equation (11); assign the function value \(F_{new}\) to fitness \(\left(x^{t+1}\right)\). **Step 6** Compare \(x^{t+1}\) and \(x_{\mathit{best}}\), let one be the \(winner\), and the other one is the _loser_ according to Equation (16). **Step 7** Update \(PV\) according to Equations (17) and (18). **Step 8** A new solution is accepted if the solution improves (\(F_{new}\) less than \(F_{min}\)), and not too loud (\(\beta\) less than \(A\)) then update global best \(x_{\mathit{best}}\) to \(x^{t+1}\) and assign function \(F_{min}\) to \(F_{new}\). **Step 9** If the termination condition is not met, go to step 4. **Step 10** Output the global best solution \(x_{\mathit{best}}\).
## 5 Simulation
### Simulation Setup  
#### 5.1.1 Configuration of Clients  
We assume that there are 50 clients that are uniformly distributed in a \(2\) km \(\times\) 2 km area, which is covered by a BS located at the center of the area. All the clients upload their local models to the FL server via the BS. The pathloss between the BS and client \(i\) is calculated based on \(128.1+37.6\times d_{i}\), where \(d_{i}\) is the distance in kilometer between the BS and client \(i\). Then, the channel gain is calculated based on \(g_{i}=10^{-(128.1+37.6\times d_{i})/10}\)[35]. Meanwhile, the transmission power \(p_{i}\) is set to be 1 Watt for all the clients, and the amount of available bandwidth for each client in uploading its local model is 30 kHz. In addition, each client has around \(|\mathbf{\mathcal{D}}_{i}|=1000\) data samples (i.e., 50,000 combined for all clients) to train its local model. The number of CPU cycles required for training one data sample (i.e., \(C_{i}\)) among clients is randomly selected from a uniform distribution, i.e., \(C_{i}\sim\mathcal{U}(3,5)\times 10^{5}\) CPU cycles/sample. The CPU frequency of a client \(f_{i}\) is also randomly selected from a uniform distribution, i.e., \(f_{i}\sim\mathcal{U}(0.8,3)\) GHz. Other simulation parameters are listed in Table 1.  
Figure 3 shows the probability density function of the latency (i.e., \(t_{i}\)) among all the clients in a specific time instance. The median value in the clients' latency is around 10 s, so we initially set up the deadline \(\tau\) of a global iteration to be 10 s, and we will change the value of \(\tau\), later, to see how it affects the performance of LESSON.
## 2 Data and Methods
### Comparative Data (SFM, Seismic Data)  
Unmanned aircraft systems (UAS) are used for acquisition of near-field photogrammetric data that allow for affordable DEM generation [15; 16; 41; 46; 47]. In this study, we used a DJI Phantom 4 RTK drone (Figure S6) to collect image data on 15 January 2022, covering the entire post-eruptive main crater area. We used the Structure-from-Motion (SfM) approach implemented in the Agisoft Metashape software (vs. 1.8.0) to generate a high-quality DEM and orthophoto with a resolution of 0.10 and 0.05 m, respectively. Due to the RTK module, the geolocation accuracy was significantly improved, here achieving ~1.3 cm accuracy, although without additional ground control points, a slightly higher vertical error was expected [48]. Our data are very similar to a previous UAS survey in [35], but provide us with an even higher resolution.  
We used the UAS data to validate our SAR amplitude image analysis and to explore satellite artifacts, such as geometric distortions of the satellite radar data, and camera visibility limitations that can lead to uncertainties in identifying surface structures, especially those associated with steep slopes and changing topography.  
The Cumbre Vieja eruption was preceded by an increase in seismic activity recorded two years before, which motivated maintaining a dense seismic network that was still operating when the eruption started in 2021. Two sets of new seismic swarms were interpreted as indicators of sill and alike intrusions that eventually led to the opening of the first eruptive vents on 19 September 2021 [49]. According to [PERSON] et al. (2022) [49] and [PERSON] et al. (2022) [50], the syn-eruptive seismic activity clustered at two depths located at a shallow level of 10-14 km depth and at a deeper level of 33-39 km depth. The seismic catalog, which we use here for comparison, was produced and provided by [PERSON] and colleagues of the National Geographic Institute (IGN) of Spain, and it contains some stations operated by us [51]. The IGN catalogue is used to count the number of daily events at different depths from September to December 2021. To be consistent with our kymograph analysis, we filtered the data and selected only seismic events that occurred during the night from 20h00 to 08h00 UTC. For our analysis, the data were plotted according to the number of events per day and classified into the following depth ranges: greater than 20 km, between 9 and 20 km, and less than 9 km. We compared the seismic data, main crater changes in SAR data, and the lava fountain activity extracted from the kymographs as a track of intensity peaks.