esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Membrane Characterizations  
SEM Analysis of the Membranes  
The surface and morphological properties of the pristine membrane CA and the photocatalytic membranes (ratios of the different membranes are given in Table 1) were investigated using SEM and the results are shown in Figure 4. Figure 4a shows a rough surface of CA consisting of pores that average 625 nm as shown in Figure 4b. These pores serve as passages for water to pass through and they are ideal for the immobilization of the photocatalyst particles [47]. The surface inspection of CTG-100, CTG-300, and CTG-500  
Figure 3: FTIR of CA membrane, CTG—500 membrane and 1% CuO/TiO\({}_{2}\)@GCN (1:9) powder.  
was found to be similar to that of neat CA except that the photocatalytic membranes exhibited sponge-like structures with significantly diminished pore sizes. The average pore sizes of CTG-100, CTG-300, and CTG-500 were 381, 302 and 324 nm, respectively. It was observed that the pore sizes decreased with increasing concentration of 1% CuO/TiO\({}_{2}\)@GCN (1:9) and thus, forming more sponge-like structures.  
The formation of the sponge-like morphology was due to the strong affinity of 1% CuO/TiO\({}_{2}\)@GCN (1:9) (0.5 wt%) to the CA membrane. This was a result of the rapid interchange between the solvent and non-solvent during the phase inversion fabrication  
Figure 4: SEM micrographs of (**a**) neat CA, (**c**) CTG—100, (**e**) CTG—300 and (**g**) CTG—500 and their respective pore size distribution displayed in (**b**,**d**,**f**,**h**).  
process [48]. The porous 3D interconnected structures extend within the membranes with almost visible circular pores for allowing water to pass through. Furthermore, this is beneficial for our cause as the 3D interconnected structure increases the absorption of light through the precise multireflection of the incoming rays [47].  
Surface Hydrophilicity  
The wettability evaluation of the membrane is an essential parameter to predict the interaction of the membrane films with water. The contact angle measurements were performed using the sessile drop method using water as a solvent for analyzing the hydrophobicity membranes. The results are shown in Figure 5 water droplets (20 \(\mu\)L) were placed onto the surface of the membrane for 20 s to obtain uniform dispersion.  
Figure 5a shows unmodified CA film exhibiting hydrophilic characteristics with an average contact angle of 86.2\({}^{\circ}\). The modified CA films containing 0.1, 0.3, and 0.5 wt% CuO/TiO\(\varnothing\)GCN (CTG-100, CTG-300, and CTG-500) as displayed in Figure 5b-d, showed improved hydrophilic properties with a reduced contact angle of 64.5, 56.6 and 47.3\({}^{\circ}\), respectively. As the weight loading of CuO/TiO\(\varnothing\)GCN was increased the hydrophilic nature of modified CA films increased. The co-existence of highly hydrophilic CuO/TiO\(\varnothing\)GCN surface properties contributed to an increase in the hydrophilic properties of the modified CA films [3,49].
## 3 Experimental and Simulation Setup
### Simulation Setup
#### 3.2.2 Simulation Parameters  
The 5G network was deployed in non-standalone (NSA) mode, indicating its reliance on a 4G Evolved Packet Core (EPC) network, mirroring practical scenarios where both 4G and 5G technologies coexist.  
For the user equipment (UE) configuration, a multi-connectivity approach was adopted, enabling simultaneous links with both the LTE eNB and the NR gNB. The LTE eNB served as a local traffic anchor within the core network, with data packets transmitted to and from the mmWave gNB using split bearers. This configuration, detailed in [44], ensures efficient flow management.  
The simulation scenario involved initiating an end-to-end data flow from the UE directed towards the gNB while sustaining a constant bitrate of 100 Mbit/s to analyze energy consumption comprehensively under varying distances and realistic operational conditions. Parameters defining the simulation setup included a bandwidth of 1 GHz for mmWave eNBs operating at a carrier frequency of 28 GHz with a transmission power of 30 dBm; for LTE eNBs the bandwidth was set to 20 MHz, operating at a carrier frequency of 2.1 GHz with a downlink (DL) transmission power of 30 dBm and an uplink (UL) transmission power of 25 dBm. This system accounts for a noise figure of 5 dB and a minimum Signal Interference Noise Ratio (SINR) threshold of \(-\)5 dB. Both eNB and UE utilized Uniform Planar Array (UPA) Multiple-Input Multiple-Output (MIMO) arrays, with sizes of 8 \(\times\) 8 and 4 \(\times\) 4, respectively. The scanning directions for eNB and UE were each set to 1. The Simulation Reference Signal (SRS) duration was 10 \(\upmu\)s, with an overhead of 5% and a period of 200 \(\upmu\)s between SRS transmissions. The UE speed was maintained at 0 m/s, and the RLC buffer size was set to 10 MB. The one-way delay on X2 links was configured as 1 ms, while the one-way MME delay was set to 10 ms. The UDP payload size for data transmission was specified as 1024 bytes (see Table 1).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Bandwidth of mmWave eNBs & 1 GHz \\ mmWave carrier frequency & 28 GHz \\ mmWave transmission power & 30 dBm \\ Bandwidth of the LTE eNB & 20 MHz \\ LTE carrier frequency & 2.1 GHz \\ LTE DL transmission power & 30 dBm \\ LTE UL transmission power & 25 dBm \\ Noise figure & 5 dB \\ Minimum SINR threshold & \(-\)5 dB \\ eNB UPA MIMO array size & 8 \(\times\) 8 \\ UE UPA MIMO array size & 4 \(\times\) 4 \\ eNB scanning directions & 1 \\ UE scanning directions & 1 \\ SRS duration & 10 \(\upmu\)s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
The simulation was meticulously designed to capture the nuances of edge node performance in a 5G deployment while taking into account distance variations. The primary focus was on assessing energy consumption patterns and efficiency in the specified network configuration, which provide valuable insights for evaluating system performance in practical 5G deployment scenarios.
## 1 Introduction
This drawback is more significant when we perform simulations with random packings, since a random packing has lots of tiny voids where surface tension has a great effect. Volume of fluid (VOF) model is one of the best implicit free-surface reconstruction methods. The VOF model is a surface-tracking technique applied to a fixed Eulerian mesh when the locus of the interface between two or more immiscible fluids is of interest. The main hurdles of using VOF rest on preserving a sharp boundary between the immiscible fluids and the computation of surface tension [14]. The velocity field and bubble profile in a vertical gas-liquid slug flow inside the capillaries has been modeled with a VOF technique, and the result was found to be in good agreement with published experimental measurements [15]. In the past two decades, the VOF model has been predominately practiced in the field of CFD simulation of TBRs [16; 13; 17; 18; 19; 20]. Many researchers are devoted to conducting simulation research about TBRs. [PERSON] et al. performed Eulerian multi-fluid simulations to study the effect of bed characteristics of local liquid spreading in a TBR [10]. [PERSON] et al. developed a new porous media model for CFD simulations of the gas-liquid two-phase flow in rotating packed beds [11]. However, these papers either modeled solid particles as a continuum phase or regarded the whole simulation region as a porous media. They only calculated the fluid-solid interactions through specific methods, without considering the influence of the actual particle shape on the fluid flow. Several authors made efforts to perform particle-resolved TBR simulations. [PERSON] et al. proposed a high-resolution CFD approach using discrete particles and designed a TBR with regular positioned catalyst particles for the Euler-Euler [21] and Eulerian VOF simulations [13], and studied the catalytic wet oxidation of phenolic wastewaters in their later studies [22; 23]. [PERSON] et al. performed CFD simulations on regular particles arrangement within a rectangular domain to investigate the local wetting behavior in a TBR [6; 16]. Simulations performed by both groups modeled particles with regular configuration and fixed distance between each other, which is difficult to achieve in a real reactor where catalyst particles are packed randomly, and the particle's shape can be complex such as a cylinder or hollow cylinder. Besides, their grid resolution are not high enough to give clear descriptions of the liquid flow configuration on the discrete particle surfaces. In this paper, detailed packing geometries with different particle shapes by discrete element method (DEM). In addition, the effects of factors such as surface tension and contact angle are discussed, which are hardly shown in empirical formulas. The CFD simulation was employed with the volume of fluid (VOF) method to investigate the factors affecting liquid holdup and wetting efficiency in a trickle bed reactor. This research can give guidance in trickle bed reactor design and operation in the future.
## 3 Results  
### Measuring Mutation Rate with Fluctuation Assay  
The crude extract, fractions, sub-fractions and epifriedelanol demonstrated a minimum inhibitory concentration of 3-6 ug/mL against _S. aureus_ strains ATCC25923, LA-MRSA and CA-MRSA. However, as fluctuation assays are dependent on scoring of growth of bacteria on solid media, knowledge of the concentrations of extract, fractions and epifriedeland needed to inhibit growth on MHA was required. Growth was observed at 375 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, while no growth occurred at 750 \(\mathrm{\SIUnitSymbolMicro g}\)/mL. Thus, fluctuation assay was carried out with 750 \(\mathrm{\SIUnitSymbolMicro g}\)/mL of extracts, fractions, sub-fractions and epifriedeland.  
Mutation rates in _S. aureus_ ATCC25923, LA-MRSA and CA-MRSA were then estimated using Luria and Delbruck fluctuation assay. The estimated mutation rate ranges from \(4.50\times 10^{-10}\) to \(6.14\times 10^{-9}\) per generation for _S. aureus_ ATCC25923 (Figure 1A), \(5.44\times 10^{-10}\) to \(3.28\times 10^{-9}\) per generation for LA-MRSA CC398 (Figure 1B) and \(2.65\times 10^{-10}\) to \(3.58\times 10^{-9}\) per generation for CA-MRSA (Figure 1C). Generally, mutation rates were observed to increase from crude extract to pure compound against all the strains tested except for the ethyl acetate fraction in _S. aureus_ ATCC25923 and sub-fraction of ethyl acetate in LA-MRSA CC398, where there was no significant difference with the extract. While mutation rates thus increased with purification, statistical analysis only showed significant differences to crude extracts, while no significant difference in the mutation rate of epifriedeland was observed compared to the methanol fraction, methanol subfraction, ethyl acetate fraction, and ethyl acetate subfraction against _S. aureus_ strains ATCC25923, LA-MRSA and CA-MRSA (_p_ > 0.05), except for the ethyl acetate subfraction, which exhibited a significant difference against _S. aureus_ ATCC25923 (_p_ < 0.05). Colonies from plates with bacteria growing in the presence of extracts at 750 \(\mathrm{\SIUnitSymbolMicro g}\)/mL were subjected to MIC determination. Generally, the MIC only increased moderately in the range of 2-4-fold. Numbers of colonies observed with the different strains and MIC of selected mutants can be seen in Supplementary File S2, Tables S1-S4.  
Figure 1: Estimation of mutation rates in _S. aureus_ ATCC25923 (**A**), LA-MRSA (**B**) and CA-MRSA (**C**) towards crude methanol extract, fractions and sub-fractions of this extract and epifriedeland obtained from the medicinal plant _Synadenium gluacescens_. Error bars represent 95% confidence intervals calculated as explained in the protocol. Statistical significance is shown as * \(p\) < 0.05, **_p_ < 0.01; ns: not significant with reference to the crude methanol extract.
###### Abstract
Once they were received on a computer, they were ordered for processing by means of a free software application that allows different models to be developed and data predictions (forecast) made. In this document, these predictions will be used to represent the traffic in the channel, given the temporal dependence that the data bursts present in a transmission. Likewise, it is known that for network services based on wireless transmissions, packets directly affect the bandwidth used and the quality of service (QoS) in the channel. Therefore, the representation of data traffic measured in kbps (kilobits per second) through time series and its modeling by means of ARIMA and SARIMA was proposed. For this, a formal analysis of the bandwidth used during a data transmission in the sensor network was carried out. To obtain the models, use was made of the GRETL free software package [7], and the Box-Jenkins methodology [8] was followed. Thanks to the obtained models, it was possible to discover the behavior of the main performance parameters that a medium-scale WSN system would have for the monitoring of a crop. Thanks to this analysis, the technical characteristics that a sensor deployment should have, in a specific area and for a type of crop, were determined. The main parameters that the system allowed us to determine were  
Figure 1: Assembly diagram of the Wireless Sensor Network (WSN). energy consumption, range, and coverage; supplied bandwidth; error rate; and data loss. In this way, deployment over large areas of land was more efficient and economical. In accordance with the foregoing, in the following sections a brief exposition of the basic concepts for the analysis of the time series and the ARIMA and SARIMA processes will be made. Then the Box-Jenkins methodology will be used to adjust the models exposed in a sample of the data traffic in the channel and predictions of the series will be made with the parameters obtained. Additionally, the results will be compared to determine the suitability of each model and its ability to represent the behavior of the original series. Finally, the best results are presented as a formal description of the traffic used during the transmission.
## 4 Results and Analysis
### Influence of Cracks Characteristics on the Wellbore Stability
#### 4.2.3 Influence of Crack Width on the Wellbore Stability  
Figures 17 and 18 illustrate the variations in stress and displacement around the wellbore concerning various crack widths (\(L_{\text{f}}\)). Notably, both the stress and the displacement at different locations around the wellbore increase gradually as \(L_{\text{f}}\) increases. For example, when \(L_{\text{f}}\) is 8 mm, the stress and displacement values at point A are 108.23 MPa and 1.27 mm, respectively. However, when \(L_{\text{f}}\) is 30 mm, the stress and displacement values at point A rise to 146.32 MPa and 1.52 mm, respectively. This represents a substantial increase of 35.13% in stress and 19.68% in displacement compared to the previous values. This trend underscores the fact that an expansion in the crack width leads to a reduction in the stability of the surrounding rock, consequently resulting in elevated stress and displacement levels at various points around the wellbore.  
Figure 16: Curve between the equivalent drilling fluid densities of collapse pressure and crack dip angles.  
Figure 14: Stress nephogram around the wellbore for different crack dip angles: (**a**) \(\theta=0^{\circ}\); (**b**) \(\theta=30^{\circ}\); (**c**) \(\theta=60^{\circ}\).  
Figure 19 presents the relationship between the equivalent drilling fluid densities of collapse pressure (\(\rho_{\rm c}\)) and crack widths (\(L_{\rm f}\)). It is evident from the figure that \(\rho_{\rm c}\) exhibits a linear increase with the increase in \(L_{\rm f}\). For example, when \(L_{\rm f}\) is 8 mm, \(\rho_{\rm c}\) is measured as 1.06 g/cm\({}^{3}\), while for \(L_{\rm f}\) = 30 mm, \(\rho_{\rm c}\) increases to 1.33 g/cm\({}^{3}\), representing a 25.47% increment compared to the previous value. This observed increase can be attributed to the reduced stability of the rock surrounding the wellbore as \(L_{\rm f}\) increases, resulting from stress concentration near the cracks, as demonstrated in Figure 17. Consequently, \(\rho_{\rm c}\) exhibits an upward trend with the growth of \(L_{\rm f}\).  
Figure 17: Stress nephogram around the wellbore for different crack widths: (**a**) \(L_{\rm f}\) = 8 mm; (**b**) \(L_{\rm f}\) = 12 mm; (**c**) \(L_{\rm f}\) = 16 mm; (**d**) \(L_{\rm f}\) = 25 mm; (**e**) \(L_{\rm f}\) = 30 mm.  
Figure 18: Displacement nephogram around the wellbore for different crack widths: (**a**) \(L_{\rm f}\) = 8 mm; (**b**) \(L_{\rm f}\) = 12 mm; (**c**) \(L_{\rm f}\) = 16 mm; (**d**) \(L_{\rm f}\) = 25 mm; (**e**) \(L_{\rm f}\) = 30 mm.
## 3 Simulation Results
### Isotropic Bandwidth  
The isotropic bandwidth of the antenna can be defined as the band of frequencies, for which the gain variation remains \(\leq\)3 dB. The proposed antenna was simulated for total gain variation vs frequency. Figure 13 shows the obtained results, and it can be observed that, at the optimal frequency of 1 GHz, the total gain variation is minimum (0.5 dB), while it increases almost symmetrically on both sides. The 3 dB isotropic bandwidth of the antenna is 225 MHz i.e., 22.5% (885 MHz-1110 MHz).  
The azimuth and elevation gain plots at f\({}_{1}\) = 885 MHz and f\({}_{2}\) = 1110 MHz are shown in Figures 14 and 15, respectively. It can be seen in Figure 14 that the maximum gain for f\({}_{1}\) (885 MHz) frequency is 0.9 dB, while the minimum gain is \(-\)2.08 dB, which gives an overall difference in total gain of 3.07 dB. Similarly, the plots for f\({}_{2}\) (1110 MHz) are shown in Figure 15, which, again, shows that the overall difference in total gain is close to 3 dB. It is to be noticed that the gain plots shown in Figures 14 and 15 are complementary to each other.  
Figure 12: Total gain vs azimuth angle (\(\phi\)) at different elevation angles \(\theta\).  
Figure 13: Total gain variation vs frequency of the proposed antenna.
## 4 Discussion
Since the setting time of AHPS is reduced by heat, it has been shown that heating may disintegrate the amine group, thus leading to incomplete polymerization of the sealer [38]. [PERSON] et al. [39] suggested that most of the unpolymerized components of the sealer may be released and act as irritants to the surrounding tissues. The severity of the inflammatory reaction decreased over time, and after 90 days a dense fibrous connective tissue containing only a few remaining inflammatory cells were found in contact with the sealer. Although at the end of the experiment AHPS appeared to be well tolerated by the surrounding bone tissues, reparative NHBF was not observed in any of the observation periods. Our findings agree with [PERSON] et al. [20] who demonstrated that AHPS does not possess osteogenic potential. The initial inflammatory reaction caused by freshly implanted ESHF may be due to the strong alkaline pH and the greater calcium and hydroxyl ion release from calcium silicate-based sealers [40]. After 30 days, this early reaction was significantly reduced, while an incipient NHBF was observed. At the end of the study, no inflammatory cells were seen in the areas in contact with ESHF. The contact area was completely replaced by healthy reparative bone formation, which was the result of prolonged release of bioactive molecules from the sealer [25]. However, our results differ from [PERSON] et al. [37] who reported that heating of calcium silicate-based sealers may affect their biomineralization property. In our study, heating of ESHF to 100 \({}^{\circ}\)C did not impede the reparative NHBF induction property of the sealer. This may be explained by the tricalcium and dicalcium of ESHF remaining unaltered during heating, even to 100 \({}^{\circ}\)C [41]. According to previous reports [41; 42; 43], this desirable mineralization action derived from the persistence of alkalinity and from the release of bone morphogenetic protein 2, together with alkaline phosphatase, are the two key factors that contribute to the mineralization activity of the sealer. Moreover, as is the case with other calcium silicate-based sealers, ESHF has a Ca/P ratio almost like human bone, which is considered a benefit to its biocompatibility/bioactivity [44]. It should be emphasized, however, that the findings reported here should not be interpreted as being equivalent to clinical outcomes. Only in vivo experiments in humans can confirm or reject our findings.
## 3 Saline-Based One Lung Filling (OLF)  
### Methodology  
Intillation of the lung with physiological saline is used for the removal of pathologic catabolite products [24]. However, such lung lavage does not necessarily generate acoustic access in a quality required for sonographic imaging. In contrast to PFC, saline does not absorb gas and therefore filling can only be performed in one lung requiring the ventilation of the contralateral side (Figure 3). Such OLF (One Lung Filling) requires lung separation and one lung ventilation (OLV) using double lumen tubes (DLT) as commonly performed in thoracic surgery.  
Acoustic impedance of saline is almost identical to tissue with low attenuation, avoiding impedance mismatch and scattering of ultrasound waves when passing the flooded lung parenchyma (Table 1). Initially, [PERSON] et al. [25] used physiological saline for these reasons resulting in an unimpaired sonographic visualization of central pulmonary structures in a porcine model. The work was intended for sonographic guiding during thoracoscopic or video-thoracoscopic surgery (VATS) where atelectasis was induced by a chest incision followed by the filling of the lung [26]. Motivated by these results,  
Figure 3: One lung flooding (OLF) scheme where one lung wing is filled with saline, while the ventilated contralateral lung is connected to a ventilation system with transcutaneous/endobronchial sonographic imaging. (Adapted with permission from [PERSON] et al. [25]).  
non-invasive strategies proofing OLF without chest incision was investigated. A slower lung filling time of 20 min was documented, but imaging results were unimpaired similar to filling after atelectasis.  
OLF is reversible and the liquid is drained out partially followed by immediate re-ventilation using the same settings for both lungs. The remaining saline is being absorbed completely by perfusion within approximately 30 min [27]. The safety of OLF was well investigated and seen as not causing deficiencies of oxygenation and pulmonary haemodynamic [28]. In addition, inflammatory response [29] and surfactant loss [30] were in the physiological range.
## 4 Results
### Class-Specific Performance Analysis
#### 4.3.2 Misclassification Patterns  
Three primary misclassification mechanisms are identified as follows:  
1. Semantic Overlap: Natural_hazard and Infrastructure_damage share 8.7% cross-class errors, as both involve disaster-related visuals (e.g., flood images vs. flooded road images).
2. Modality Noise: Health_related tweets with blurry images show 8.7% misclassifications, where the gating mechanism underweights visual features but struggles with ambiguous text (e.g., \"illness\" vs. \"injury\").
3. Class Imbalance: Hazardous_materials_release (minority class, 4.1% of dataset) has 15% more errors than the majority classes, highlighting the need for category-specific augmentation.  
To supplement quantitative findings, Figure 9 presents qualitative text-image classification examples. Correctly classified cases demonstrate CLIP-BCA-Gated's ability to align cross-modal semantics. Edge scenarios (e.g., hurricane satellite imagery \(\rightarrow\) other_relevant_information) and a critical false negative (cat post \(\rightarrow\) not_humanitarian, failing to detect sarcastic \"aid\") highlight fine-grained alignment strengths and limitations in interpreting ambiguous text, consistent with confusion matrix patterns.  
Figure 9: Qualitative classification outcomes.