esa-sceva/satcom-chunk-collection huggingface dataset:
# Encapsulation of Fennel Essential Oil in Calcium Alginate Microbeads via Electrostatic Extrusion
## 3 Results and Discussion
### Swelling Kinetics
As can be observed, at 1 and 1.5% alginate concentrations, microbeads without whey protein absorbed 60% of the total water within the first 20 min, indicating an initial burst, which occurs due to the increased porosity of the device and rapidly increased diffusion coefficient [36]. According to the value of \(n\), which was lower than 0.5, the swelling behavior of these microbeads followed Fickian diffusion. With the addition of 0.75% whey protein, the absorption time was prolonged, with 60% of the water absorbed after 40 and 60 min of 1 and 1.5% alginate, respectively. In the case of adding 1.5% whey, swelling occurred significantly slower, with 40% of the water released by 120 (1% alginate) and 140 min (1.5% alginate), reaching equilibrium after 160 min. These results indicate that the interaction of whey proteins and alginate resulted in the reduction in hydrogel's porosity, which was also observed in freeze-dried oregano EO microbeads [13]. At 1% alginate, neither of the microbeads containing whey protein swelled according to Fickian diffusion (\(n>0.5\)), while at 1.5% alginate, only the microbeads with the highest amount of whey protein exhibited anomalous behavior, which likely included an interplay of swelling and erosion of the polymeric matrix [38]. At 2% alginate concentration, trends were somewhat different. Microbeads without whey protein absorbed 60% of the total water after 120 min, with an increase observed up to 160 min before reaching equilibrium. These results suggest that the porosity of 2% alginate hydrogel was lower than those with less alginate, likely due to a denser polymer network and stronger interactions between polymer chains within the hydrogel, reducing the space available for pore formation, which also resulted in anomalous swelling, which did not follow Fickian diffusion [39]. With the addition of 0.75% whey protein, 60% of the water was absorbed within the first 20 min, indicating an initial burst, which might have been caused by the formation of protein aggregates within the alginate matrix, which created additional void spaces or channels within the hydrogel structure, thereby increasing its overall porosity and resulting in a swelling behavior consistent with Fickian diffusion. In the case of adding 1.5% whey protein, similar to other alginate concentrations, swelling occurred significantly slower, with the maximum water uptake occurring between 140 and 160 min, suggesting a different release mechanism, which was also supported by the values of \(n>1\). The radical difference between adding 0.75 and 1.5% of whey protein could have been caused by the change in the interaction of alginate and whey protein due to different concentrations [9], where instead of promoting porosity, these interactions resulted in stronger binding between the protein and polymer molecules, thus forming tighter hydrogel structure with lower porosity.
# Development of Multimodal Physical and Virtual Traffic Reality Simulation System
## 5 Case Studies
### Case Study II
#### 5.2.4 Results Analysis  
The analysis focused on two primary measures to evaluate pedestrian responses to approaching vehicles: gap acceptance and heart rate change. Gap acceptance was defined as the moment when the pedestrian perceived it was unsafe to cross, recorded through a controller input. This measure serves as an indicator of perceived risk in relation to vehicle speed. Heart rate change was assessed by comparing the average heart rate of pedestrians during the 5 s before and after the critical moment, defined as the point at which the vehicle reached its minimum distance from the pedestrian. Paired \(t\)-tests were conducted to determine statistical significance, with \(p<0.05\) as the threshold.  
The results showed that the mean gap acceptance time was 3.18 s (SD = 0.19) for the 30 mph trials and 3.70 s (SD = 0.20) for the 50 mph trials. A paired \(t\)-test revealed a statistically significant difference between these conditions (\(t=10.06,p<0.00001\)), indicating that pedestrians accepted significantly larger gaps when faced with faster-approaching vehicles. This supports the first hypothesis that higher vehicle speeds result in more conservative gap acceptance behavior, reflecting an increased perception of risk.  
Similarly, heart rate analysis revealed a significant increase in both conditions. For the 30 mph trials, heart rate rose from the pre-event average to a post-event average with a mean increase of 1.91 ppm, which was statistically significant (\(t=15.05,p<0.00001\)). For the 50 mph condition, the heart rate increase was even more pronounced, with an average change of 3.54 ppm (\(t=37.49\), \(p<0.00001\)). These results support the second hypothesis, indicating that faster vehicle speeds trigger stronger physiological stress responses in pedestrians.  
Together, these findings demonstrate that pedestrians not only adjust their behavioral responses (by accepting larger gaps) but also exhibit measurable physiological reactions (through elevated heart rate) in response to approaching vehicles at higher speeds. Table 2 summarizes the mean values, standard deviations, and statistical test results for both gap acceptance and heart rate change. The consistent and statistically significant effects across both behavioral and physiological measures validate the capability of our simulation system to generate realistic scenarios for pedestrian safety research.
# Phishing Node Detection in Ethereum Transaction Network Using Graph Convolutional Networks
###### Abstract  
As the use of digital currencies, such as cryptocurrencies, increases in popularity, phishing scams and other cybercriminal activities on blockchain platforms (e.g., Ethereum) have also risen. Current methods of detecting phishing in Ethereum focus mainly on the transaction features and local network structure. However, these methods fail to account for the complexity of interactions between edges and the handling of large graphs. Additionally, these methods face significant issues due to the limited number of positive labels available. Given this, we propose a scheme that we refer to as the Bagging Multiedge Graph Convolutional Network to detect phishing scams on Ethereum. First, we extract the features from transactions and transform the complex Ethereum transaction network into three simple inter-node graphs. Then, we use graph convolution to generate node embeddings that leverage the global structural information of the inter-node graphs. Further, we apply the bagging strategy to overcome the issues of data imbalance and the Positive Unlabeled (PU) problem in transaction data. Finally, to evaluate our approach's effectiveness, we conduct experiments using actual transaction data. The results demonstrate that our Bagging Multiedge Graph Convolutional Network (0.877 AUC) outperforms all of the baseline classification methods in detecting phishing scams on Ethereum.  
phishing node detection; Ethereum; graph convolutional network; node classification, transaction network +
Footnote †: journal: Applied sciences
## 2 Results and Discussion
### In Vitro Biological Evaluation
#### 2.2.2 Antibioticfilm Activity
Figure 3: The compounds that exhibit the most favorable binding energies in molecular docking calculations in their interaction with (p)ppGpp synthetases/hydrolases: (**a**) _E. coli_ RelA, (**b**) _S. aureus_ Rel, (**c**) _M. singematis_ Rel; with FtsZ proteins: (**d**) _E. coli_ FtsZ, (**e**) _S. aureus_ FtsZ, (**f**) _M. singematis_ FtsZ. The pyruvate kinase (PK) enzyme is present in both bacterial and fungal cells. Targeting this enzyme could explain the observed antibacterial and antifungal properties of the investigated compounds. We evaluated the possibility of an interaction of benzimidazole derivatives **3** with PK according to the mechanism described earlier [20]. Crystal structures of _E. coli_ PK1 (PDB 1 PKY) and _S. aureus_ PK (PDB 3 TOT) were employed for molecular docking. _M. singematis_ PK was obtained using structural alignment of AlphaFold-predicted PK homomer with _M. tuberculosis_ PK. Similarly, _C. albicans_ PK was obtained by aligning with _S. aureus_ PK (PDB 3 TOT). The strongest binders turned out to be _C. albicans_ PK--**3 az** (\(-6.224\) kcal/mol); _E. coli_ PK--**3 ap** (\(-8.114\) kcal/mol); _S. aureus_ PK--**3u** (\(-8.628\) kcal/mol); and _M. singematis_ PK--**3d** (\(-5.982\) kcal/mol) (Figure 4). \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & \multicolumn{3}{c}{**Pyruvate Kinase**} & \multicolumn{3}{c}{**FsZ**} \\ \hline \multirow{3}{*}{**Compounds**} & \multirow{3}{*}{_C. albicans_ (AF)**} & \multirow{3}{*}{_E. coli_ (PDB 1 PKY)_} & \multirow{3}{*}{_S. aureus_ (PDB 3 TOT)_} & \multirow{3}{*}{_E. coli_ (AF)_} & \multirow{3}{*}{_S. aureus_ (AF)} & \multirow{3}{*}{_M. singematis_ (AF)} \\  & & & & & & & \\ \hline  
q** &Target identification via molecular docking provided three plausible models of interaction for the investigated compounds. (**p**)ppGpp synthetases/hydrolases, FtsZ proteins, or pyruvate kinases could be involved in the mechanism of antibacterial action of the studied compounds. It is worth noting that the compounds that frequently attained favorable binding energies when interacting with the investigated targets were **3d**, **3o**, **3t**, **3u**, **3x**, **3 aa**, **3 af**, and **3 ag**. With the exception of **3d**, these compounds exhibited notable antibacterial activities. However, differences in the predicted binding energies of different structures were not always fully consistent with the observed antibacterial activity of the compounds. Molecular docking produced favorable binding energies for the compounds to interact with _E. coli_ target proteins, but _E. coli_ cells were found to be susceptible to only several compounds. This could be attributed to the poor permeability of _E. coli_ cells wall for hydrophobic compounds. Further experimental efforts for target identification are required.
## 3 Factors Affecting Accuracy and Accuracy-Improvement Methods
### Effect of the Position Offset of the QD  
Although the position offset \(\Delta z\) can reduce the measurement error caused by the elliptical laser spot of the LD, the defocus will also affect the angular error measurement accuracy. If the stage has no angular errors, \(\Delta z\) will not influence the QD output, as shown in Figure 5a. However, it can be seen from Figure 5b that \(\Delta z\) will induce measurement errors (\(\Delta\theta x_{1}\) and \(\Delta\theta y_{1}\)) when the stage has angular errors (\(\theta x\) and \(\theta y\)). \(\Delta\theta x_{1}\) and \(\Delta\theta y_{1}\) can be derived by:  
\[\Delta\theta x_{1}=\frac{\Delta z\cdot\mathrm{s}}{f^{2}}\theta x\text{ and } \Delta\theta y_{1}=\frac{\Delta z\cdot\mathrm{s}}{f^{2}}\theta y, \tag{9}\]  
where \(s\) is the random measurement position, which is the distance between the detector and the sensor head.  
In addition, the incident position of the laser beam on the FL will be varied by the straightness errors (\(\delta x\) and \(\delta y\)), as shown in Figure 5c. In this case, the existing of the position offset \(\Delta z\) will cause a QD output \(\Delta x_{2}\) and further induce measurement errors (\(\Delta\theta x_{2}\) and \(\Delta\theta y_{2}\)). \(\Delta\theta x_{2}\) and \(\Delta\theta y_{2}\) can be derived by:  
\[\Delta\theta x_{2}=\frac{f+\Delta z}{f}\delta x\text{ and }\Delta\theta y_{2}= \frac{f+\Delta z}{f}\delta y, \tag{10}\]
# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
## 5 Measurement Results and Discussion
### Power Measurements and Path Loss Modeling
#### 5.1.3 Zigbee at 868 MHz  
Following the same procedure, the RSSI values of the Zigbee measurements, path loss models, their dispersion and CDF are shown in Figure 9, and the parameters of the propagation models are summarized in Table 3. Using this technology, it has only been possible to make measurements in the NLOS-1 zone, resulting in a CI slope of \(n=1.98\) and FI slope of \(\beta=1.69\), due to the fact that the signal power has been attenuated enough to not overcome the diffraction at the R1 point.
## 3 Observation Models in Single or Multi-GNSS  
### Uncombined Observation Model with Triple-Frequency Multi-GNSS  
There are IFB and ISB in triple-frequency multi-system, where the observation equations can be illustrated by the following formula.  
\[P_{1}^{sys}=\delta\rho^{sys}+\delta I^{sys}+ISB_{sys-ref}+\varepsilon_{P_{1}^ {sys}} \tag{26}\]  
\[P_{2}^{sys}=\delta\rho^{sys}+\gamma_{2}^{sys}\delta I^{sys}+ISB_{sys-ref}+ \varepsilon_{P_{2}^{sys}} \tag{27}\]  
\[P_{3}^{sys}=\delta\rho^{sys}+\gamma_{3}^{sys}\delta I^{sys}+IFB_{sys}+ISB_{ sys-ref}+\varepsilon_{P_{3}^{sys}} \tag{28}\]  
\[L_{1}^{sys}=\delta\rho^{sys}-\delta I^{sys}-\lambda_{1}^{sys}\delta N_{1}^{sys }+ISB_{sys-ref}+\varepsilon_{L_{1}^{sys}} \tag{29}\]  
\[L_{2}^{sys}=\delta\rho^{sys}-\gamma_{2}^{sys}\delta I^{sys}-\lambda_{2}^{sys} \delta N_{2}^{sys}+ISB_{sys-ref}+\varepsilon_{L_{2}^{sys}} \tag{30}\]\[L_{3}^{sys}=\delta\rho^{sys}-\gamma_{3}^{sys}\delta I^{sys}+IFB_{sys}+ISB_{sys- ref}-\lambda_{3}^{sys}\delta N_{3}^{sys}+\varepsilon_{L_{3}^{sys}} \tag{31}\]  
According to the above formulae, the hardware delays (\(bias_{L_{1}^{sys}}^{r}\), \(bias_{L_{1}^{sys}}^{s}\), \(bias_{L_{1}^{sys}}^{r}\), and \(bias_{L_{1}^{sys}}^{s}\)) are absorbed by ionospheric delay \(I_{1}^{sys}\) and ambiguity \(N_{i}^{sys}\). The estimated ionospheric delay \(\delta I^{sys}\) combines ionospheric delay and some hardware delays. The estimated ionospheric delay can be also eliminated by ionosphere-free models. The estimated ambiguities \(\delta N_{i}^{sys}\) are the combination of float ambiguities and some hardware delays. These hardware delays can influence the integer feature of ambiguities. In ambiguity-fixed PPP, FCB or uncalibrated phase delay (UPD), provided by the Continuously Operating Reference Stations (CORS) network, corrects the non-integer estimated ambiguities \(\delta N_{i}^{sys}\) to integer estimated ambiguities. It means the observation equation in the third frequency carrier phase \(L_{3}\) must contain IFB, namely, it must follow Formula (16).
## 2 A Review of Spaceborne GNSS-R Missions
On 2 September 2020, the FSSCat satellites were launched successfully into a 535 km Sun-synchronous orbit. **DoT-1:** After the decommission of the UK DMC and TDS-1 satellites, SCTL has successfully demonstrated GNSS-R from its 18 kg DoT-1 satellite since September 2020. The latest GNSS-R payload on-board the DoT-1 satellite is incorporated within the new small form factor Core Avionics module integral to all SCTL's future satellite platforms. This innovation paves the way for any SSTL satellite that can accommodate a nadir pointing antenna to become part of a GNSS-R small-sat constellation [20]. **FengYun-3E:** On 5 July 2021, China's FengYun-3 E (FY-3E) meteorological satellite was launched with the GNOS II [54] GNSS remote sensing instrument, which is capable of making measurements of both GNSS RO and GNSS-R. The primary objective of the GNOS II GNSS-R data is to measure the near sea surface wind speed [21]. In addition to the standard DDM product, GNOS II instrument is also scheduled to collect GNSS-R raw IF data sets from specific targets over ocean, land and sea ice. In addition to above-mentioned GNSS-R missions in orbit, some other spaceborne missions have been proposed or under study to use reflected GNSS signals and other signals of opportunity for different geophysical applications, such as PARIS in-orbit demonstrator mission [55], PRETTY mission [56], Cookie concept [57], 'CAT-2 mission [58], G-TERN mission [22], HydroGNSS mission [23], TRITON mission [59], and SNOOPI mission [60], which are not explained in detail in this paper.
## 4 Conclusions  
SMPUs with high molecular weight polyol PEG-6000 were successfully synthesized with various combinations of diisocyanates as a hard segment, and the results were compared with SMPUs with low molecular weight PCL-2000 polyol. The thermal stability, thermal mechanical properties, crystallinity and shape memory effects of the SMPUs were investigated. The conclusions can be summarized as follows;  
* The PEG-SMPUs show a slightly better thermal stability than PCL-SMPUs. Both sample series have transition temperatures between 40\(\sim\)50 \({}^{\circ}\)C, suitable for medical device applications.
* The PEG-SMPUs have a higher crystallinity and lower phase compatibility than those of PCL-SMPU samples. With the increase of IPDI concentration, the crystallinity increases, leading to deteriorated phase compatibility and shape memory effect for both the sample series.
* The PCL-SMPUs have larger elongations at break and recovery stresses than those of the PEG-SMPUs, _i.e._, high mechanical strength at room temperature. Longer molecular chains, lower cross-link density and higher crystallinity are believed to be responsible for the weaker thermal mechanical properties of the PEG-SMPUs.
* The PEG-SMPUs have a higher recovery ratio, a higher frailty and a much shorter shape recovery time, and demonstrate superior SME effect as compared to the PCL-SMPUs. SMPUs with high molecular weight polyol can be excellent candidates for applications in engineering and as microactuators, in particular when fast actuation is required.  
Figure 11: Shape recovery of the extended PCL-S3 sample at 60 \({}^{\circ}\)C. The shape recovery takes place slowly and is completed up to \(\sim\)80% after 50 s; a much slower rate than that recorded for PEG-SMPUs.
## 3 Experimental Results and Discussion
### Drone Image Identification and Tracing Test in Different Weather Environments  
This paper uses the DJI Mavic Pro four-axis drone to achieve tracing tests of drone flight in all kinds of weather. The results of tracing tests on sunny days and at flight altitudes of about 70-105 m  
Figure 11: The inner hardware structure of the dual-axis tracing device.  
Figure 12: 3D simulation and physical circuit picture of the main control unit and motor control unit.  
are shown in Figure 13. At more than 102 m, drone movement cannot be determined (restricted by the resolution of the full-color camera). The test results of various weather conditions---sunny, cloudy, and rainy--- are shown in Figures 14-16. The detailed test description is as follows:  
(1) Sunny environment: As shown in Figure 14, the acquisition effects of the drone's image on sunny days are different based on different brightness and sunshade levels due to different conditions. In the case of large changes in ambient brightness, the effect of light on the lens and tracing is quite large. In addition, the size of the drone is small, which can cause the phenomenon that the target drone is lost while in image processing, and then recovered.  
(2) Cloudy environment: Figure 15 shows the acquisition effects of the drone's image on cloudy days. The cloudy background is clean and free of other interference. In addition, the difference in grayscale presented by the sky is not obvious, making the tracing target more significant. The tracing effect was good, and the target drone was not easy to lose.  
Figure 14: Results of the tracing test on sunny days (drone flight altitude is 105 m).  
Figure 13: Tests of drone flight altitudes (on a sunny day).  
(3) Rainy environment: Figure 16 shows the acquisition effects of the drone's image on rainy days. In general, no drone appears on rainy days (a normal drone is usually not waterproof); drone players usually choose to take off in clear weather. In the case of rain, the lens is susceptible to raindrops. Coupled with poor vision, it is impossible to clearly present the tracing image, and it is difficult to judge the target, which leads to a poor tracing effect.
## Materials and Methods
### Determination of As(III)  
The sample (10 mL) was placed into the measuring vessel and 2 mL of the supporting electrolyte was added. The concentration of As(III) was quantified by two standard additions of As(III) standard solution. As(III) determination was carried out using anodic stripping voltammetry. The determination was carried out with the following parameters: a conditioning step, consisting of five cyclic scans from \(-\)0.2 to +1.0 V at a scan rate of 100 mVs\({}^{-1}\) in the supporting electrolyte to remove any deposited As or other elements. The deposition potential of \(-\)0.6 V was held for 60 s to reduce As(III) to As(0), then followed by a waiting period of 10 s. During the stripping step, the deposited As(0) is reoxidized to As(III) giving the analytical signal. The anodic stripping was performed by scanning from \(-\)0.3 V to +0.4 V with a potential step of 0.01 V and frequency 100 Hz. For the determination of As(III), the deposition potential should not be more negative than \(-\)0.6 V, otherwise the As(V) would also be partly reduced and deposited. The arsenite peak is located at approximately +0.0 V. No signal was detectable in the blank solution (containing only supporting electrolyte and deionised water instead of sample). In these analytical measurement procedures, two standard additions were performed and the corresponding currents (\(\mu\)A) were measured after subtraction of the background current. The signal current was then plotted against concentration. The concentration of arsenic was calculated from the slope of the regression curve, drawn through the points. Two measurements were done per analysis, from which the average peak current was computed.